edge_iiot_binary.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IoT malware detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of IoT malware\n",
    "\n",
    "Raw data from: Edge-IIoTset [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "import collections\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import precision_score\n",
    "from sklearn.metrics import recall_score\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "#Import Random Forest Model\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "#Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# ML algorithms\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.ensemble import VotingClassifier\n",
    "from sklearn.ensemble import StackingClassifier\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "import catboost as cb\n",
    "\n",
    "# Import scikit-learn metrics module for accuracy calculation\n",
    "from sklearn import metrics\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../cnsm_final_datasets/edge_iiot_binary.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "         Unnamed: 0          DST_IP         SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                 0     224.0.0.251  192.168.1.101       60            0   \n",
       "1                 1  192.168.1.128.  192.168.1.101        7            0   \n",
       "2                 2  192.168.1.101.  192.168.1.128        6            0   \n",
       "3                 3  192.168.1.128.  192.168.1.101        8            0   \n",
       "4                 4  192.168.1.101.  192.168.1.128        7            0   \n",
       "...             ...             ...            ...      ...          ...   \n",
       "1432220     1432220  192.168.0.128.  192.168.0.170        7            0   \n",
       "1432221     1432221  172.217.19.42.  192.168.0.128       29            0   \n",
       "1432222     1432222  192.168.0.128.  172.217.19.42       22            0   \n",
       "1432223     1432223   104.16.87.20.  192.168.0.128       21            0   \n",
       "1432224     1432224  192.168.0.128.   104.16.87.20       15            0   \n",
       "\n",
       "         BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  ...  \\\n",
       "0         2940          0  1.640646e+09  1.640646e+09      5353  ...   \n",
       "1           46          0  1.640646e+09  1.640646e+09      1883  ...   \n",
       "2            4          0  1.640646e+09  1.640646e+09     61823  ...   \n",
       "3           46          0  1.640646e+09  1.640646e+09      1883  ...   \n",
       "4            4          0  1.640646e+09  1.640646e+09     61824  ...   \n",
       "...        ...        ...           ...           ...       ...  ...   \n",
       "1432220    102          0  1.638212e+09  1.638212e+09        80  ...   \n",
       "1432221   2588          0  1.638208e+09  1.638208e+09       443  ...   \n",
       "1432222   2118          0  1.638208e+09  1.638208e+09     53024  ...   \n",
       "1432223   2552          0  1.638212e+09  1.638212e+09       443  ...   \n",
       "1432224   7010          0  1.638212e+09  1.638212e+09     35314  ...   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           -261597.321119          0.199572           0.208451   \n",
       "1           -313387.471075          0.797161           0.867292   \n",
       "2              7094.431505          0.851000           0.785438   \n",
       "3              9179.647412          0.864509           0.816404   \n",
       "4              2202.584636          0.745707           0.619200   \n",
       "...                    ...               ...                ...   \n",
       "1432220      268404.310201          0.619156           0.243761   \n",
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       "1432222        3899.614072          0.217055           0.371308   \n",
       "1432223        4690.827782          0.664055           0.585775   \n",
       "1432224       -5276.683787          0.006667           0.469103   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.054236             NaN      -70.042430  297845.871189   \n",
       "1                    NaN             NaN     -249.116969   67780.547523   \n",
       "2               0.200317             NaN        4.158316       8.379427   \n",
       "3               0.175113        1.055740        5.576106       9.812098   \n",
       "4               0.272827             NaN        1.684225       0.290936   \n",
       "...                  ...             ...             ...            ...   \n",
       "1432220              NaN             NaN     6895.394550    3992.122541   \n",
       "1432221         0.308518       -0.226912        3.135308      42.324922   \n",
       "1432222         0.259434       -0.553421        3.704308      31.696439   \n",
       "1432223         0.261460        0.070717        2.625166       1.385119   \n",
       "1432224         0.290925       -0.133063        1.199077       0.296332   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.000596          0.000037  False  \n",
       "1                  0.182773          0.000189  False  \n",
       "2                  0.884464          0.002473  False  \n",
       "3                  0.885259          0.002474  False  \n",
       "4                  0.883868          0.091092  False  \n",
       "...                     ...               ...    ...  \n",
       "1432220           -0.000199          0.002736   True  \n",
       "1432221            0.176019          0.086074   True  \n",
       "1432222           -0.415412          0.076778   True  \n",
       "1432223           -0.254293          0.017806   True  \n",
       "1432224           -0.649839          0.162950   True  \n",
       "\n",
       "[1432225 rows x 84 columns]"
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_faetures = [\n",
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    "    \"PACKETS_REV\",\n",
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    "    \"BYTES_REV\",\n",
    "]\n",
    "\n",
    "statistics_features = [\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "]\n",
    "distribution_features = [\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "]\n",
    "time_features = [\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    ]\n",
    "behavior_features = [\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "]\n",
    "frequency_features = [\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]\n",
    "\n",
    "features = [\n",
    "    \"PACKETS\",\n",
    "    \"PACKETS_REV\",\n",
    "    \"BYTES\",\n",
    "    \"BYTES_REV\",\n",
    "    \"MEAN\",\n",
    "    \"MEDIAN\",\n",
    "    \"STDEV\",\n",
    "    \"VAR\",\n",
    "    \"BURSTINESS\",\n",
    "    \"Q1\",\n",
    "    \"Q3\",\n",
    "    \"MIN\",\n",
    "    \"MAX\",\n",
    "    \"MIN_MINUS_MAX\",\n",
    "    \"MODE\",\n",
    "    \"COEFFICIENT_OF_VARIATION\",\n",
    "    \"AVERAGE_DISPERSION\",\n",
    "    \"PERCENT_DEVIATION\",\n",
    "    \"ROOT_MEAN_SQUARE\",\n",
    "    \"PERCENT_BELOW_MEAN\",\n",
    "    \"PERCENT_ABOVE_MEAN\",\n",
    "    \"PEARSON_SK1_SKEWNESS\",\n",
    "    \"PEARSON_SK2_SKEWNESS\",\n",
    "    \"FISHER_MI_3_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_g1_SKEWNESS\",\n",
    "    \"FISHER_PEARSON_G1_SKEWNESS\",\n",
    "    \"GALTON_SKEWNESS\",\n",
    "    \"KURTOSIS\",\n",
    "    \"ENTROPY\",\n",
    "    \"SCALED_ENTROPY\",\n",
    "    \"HURST_EXPONENT\",\n",
    "    \"BENFORD_LAW_PRESENTED\",\n",
    "    \"P_BENFORD\",\n",
    "    \"NORMAL_DISTRIBUTION\",\n",
    "    \"CNT_DISTRIBUTION\",\n",
    "    \"TIME_DISTRIBUTION\",\n",
    "    \"AREA_VALUES_DISTRIBUTION\",\n",
    "    \"MEAN_SCALED_TIME\",\n",
    "    \"MEDIAN_SCALED_TIME\",\n",
    "    \"Q1_SCALED_TIME\",\n",
    "    \"Q3_SCALED_TIME\",\n",
    "    \"DURATION\",\n",
    "    \"MEAN_DIFFTIMES\",\n",
    "    \"MEDIAN_DIFFTIMES\",\n",
    "    \"MIN_DIFFTIMES\",\n",
    "    \"MAX_DIFFTIMES\",\n",
    "    \"MEAN_SCALED_DIFFTIMES\",\n",
    "    \"SIG_SPACES\",\n",
    "    \"SWITCHING_METRIC\",\n",
    "    \"DIRECTIONS\",\n",
    "    \"TRANSIENTS\",\n",
    "    \"CNT_ZEROS\",\n",
    "    \"CNT_NZ_DISTRIBUTION\",\n",
    "    \"BIGGEST_CNT_1_SEC\",\n",
    "    \"PERIODICITY\",\n",
    "    \"VAL\",\n",
    "    \"TIME\",\n",
    "    \"MIN_POWER\",\n",
    "    \"MAX_POWER\",\n",
    "    \"MIN_POWER_FREQ\",\n",
    "    \"MAX_POWER_FREQ\",\n",
    "    \"POWER_MEAN\",\n",
    "    \"POWER_STD\",\n",
    "    \"POWER_MODE\",\n",
    "    \"SPECTRAL_ENERGY\",\n",
    "    \"SPECTRAL_ENTROPY\",\n",
    "    \"SPECTRAL_KURTOSIS\",\n",
    "    \"SPECTRAL_SKEWNESS\",\n",
    "    \"SPECTRAL_ROLLOFF\",\n",
    "    \"SPECTRAL_CENTROID\",\n",
    "    \"SPECTRAL_SPREAD\",\n",
    "    \"SPECTRAL_SLOPE\",\n",
    "    \"SPECTRAL_CREST\",\n",
    "    \"SPECTRAL_FLUX\",\n",
    "    \"SPECTRAL_BANDWIDTH\",\n",
    "    \"PERIODICITY_SCDF\",\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# df['LABEL'] = df.LABEL.astype('category')\n",
    "bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "for F in features:\n",
    "    if F in bool_features:\n",
    "        continue\n",
    "    if F in distribution_features:\n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        df.replace([np.inf], 0.5, inplace=True)\n",
    "        df.replace([-np.inf], 0.5, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0.5\n",
    "    elif F in frequency_features: \n",
    "        df.replace([np.inf], -1, inplace=True)\n",
    "        df.replace([-np.inf], -1, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = -1\n",
    "    else:\n",
    "        df.replace([np.inf], 0, inplace=True)\n",
    "        df.replace([-np.inf], 0, inplace=True)\n",
    "        df.loc[df[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "for F in bool_features:\n",
    "    df.loc[df[F].isnull(), F] = False\n",
    "    df[F] = df[F].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>DST_IP</th>\n",
       "      <th>SRC_IP</th>\n",
       "      <th>PACKETS</th>\n",
       "      <th>PACKETS_REV</th>\n",
       "      <th>BYTES</th>\n",
       "      <th>BYTES_REV</th>\n",
       "      <th>TIME_FIRST</th>\n",
       "      <th>TIME_LAST</th>\n",
       "      <th>DST_PORT</th>\n",
       "      <th>SRC_PORT</th>\n",
       "      <th>MEAN</th>\n",
       "      <th>MEDIAN</th>\n",
       "      <th>STDEV</th>\n",
       "      <th>VAR</th>\n",
       "      <th>BURSTINESS</th>\n",
       "      <th>Q1</th>\n",
       "      <th>Q3</th>\n",
       "      <th>MIN</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN_MINUS_MAX</th>\n",
       "      <th>MODE</th>\n",
       "      <th>COEFFICIENT_OF_VARIATION</th>\n",
       "      <th>AVERAGE_DISPERSION</th>\n",
       "      <th>PERCENT_DEVIATION</th>\n",
       "      <th>ROOT_MEAN_SQUARE</th>\n",
       "      <th>PERCENT_BELOW_MEAN</th>\n",
       "      <th>PERCENT_ABOVE_MEAN</th>\n",
       "      <th>PEARSON_SK1_SKEWNESS</th>\n",
       "      <th>PEARSON_SK2_SKEWNESS</th>\n",
       "      <th>FISHER_MI_3_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_g1_SKEWNESS</th>\n",
       "      <th>FISHER_PEARSON_G1_SKEWNESS</th>\n",
       "      <th>GALTON_SKEWNESS</th>\n",
       "      <th>KURTOSIS</th>\n",
       "      <th>ENTROPY</th>\n",
       "      <th>SCALED_ENTROPY</th>\n",
       "      <th>HURST_EXPONENT</th>\n",
       "      <th>BENFORD_LAW_PRESENTED</th>\n",
       "      <th>P_BENFORD</th>\n",
       "      <th>NORMAL_DISTRIBUTION</th>\n",
       "      <th>CNT_DISTRIBUTION</th>\n",
       "      <th>TIME_DISTRIBUTION</th>\n",
       "      <th>AREA_VALUES_DISTRIBUTION</th>\n",
       "      <th>MEAN_SCALED_TIME</th>\n",
       "      <th>MEDIAN_SCALED_TIME</th>\n",
       "      <th>Q1_SCALED_TIME</th>\n",
       "      <th>Q3_SCALED_TIME</th>\n",
       "      <th>DURATION</th>\n",
       "      <th>MEAN_DIFFTIMES</th>\n",
       "      <th>MEDIAN_DIFFTIMES</th>\n",
       "      <th>MIN_DIFFTIMES</th>\n",
       "      <th>MAX_DIFFTIMES</th>\n",
       "      <th>MEAN_SCALED_DIFFTIMES</th>\n",
       "      <th>SIG_SPACES</th>\n",
       "      <th>SWITCHING_METRIC</th>\n",
       "      <th>TRANSIENTS</th>\n",
       "      <th>CNT_ZEROS</th>\n",
       "      <th>CNT_NZ_DISTRIBUTION</th>\n",
       "      <th>BIGGEST_CNT_1_SEC</th>\n",
       "      <th>DIRECTIONS</th>\n",
       "      <th>PERIODICITY</th>\n",
       "      <th>VAL</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MIN_POWER</th>\n",
       "      <th>MAX_POWER</th>\n",
       "      <th>MIN_POWER_FREQ</th>\n",
       "      <th>MAX_POWER_FREQ</th>\n",
       "      <th>POWER_MEAN</th>\n",
       "      <th>POWER_STD</th>\n",
       "      <th>POWER_MODE</th>\n",
       "      <th>SPECTRAL_ENERGY</th>\n",
       "      <th>SPECTRAL_ENTROPY</th>\n",
       "      <th>SPECTRAL_KURTOSIS</th>\n",
       "      <th>SPECTRAL_SKEWNESS</th>\n",
       "      <th>SPECTRAL_ROLLOFF</th>\n",
       "      <th>SPECTRAL_CENTROID</th>\n",
       "      <th>SPECTRAL_SPREAD</th>\n",
       "      <th>SPECTRAL_SLOPE</th>\n",
       "      <th>SPECTRAL_CREST</th>\n",
       "      <th>SPECTRAL_FLUX</th>\n",
       "      <th>SPECTRAL_BANDWIDTH</th>\n",
       "      <th>PERIODICITY_SCDF</th>\n",
       "      <th>LABEL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>224.0.0.251</td>\n",
       "      <td>192.168.1.101</td>\n",
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       "      <td>109.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>2438.475406</td>\n",
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       "      <td>-34.814260</td>\n",
       "      <td>1633.287018</td>\n",
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       "      <td>-174071.302265</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.457744e+07</td>\n",
       "      <td>-261597.321119</td>\n",
       "      <td>0.199572</td>\n",
       "      <td>0.208451</td>\n",
       "      <td>0.054236</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-70.042430</td>\n",
       "      <td>297845.871189</td>\n",
       "      <td>-0.000596</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>192.168.1.128.</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
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       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1883</td>\n",
       "      <td>61823</td>\n",
       "      <td>66</td>\n",
       "      <td>60.0</td>\n",
       "      <td>11.489125</td>\n",
       "      <td>133</td>\n",
       "      <td>-0.703465</td>\n",
       "      <td>60.0</td>\n",
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       "      <td>17.407766</td>\n",
       "      <td>8</td>\n",
       "      <td>12.121212</td>\n",
       "      <td>67.421913</td>\n",
       "      <td>0.714286</td>\n",
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       "      <td>0.522233</td>\n",
       "      <td>12.011358</td>\n",
       "      <td>3.874170</td>\n",
       "      <td>3.767915e-04</td>\n",
       "      <td>4.883776e-04</td>\n",
       "      <td>-0.500000</td>\n",
       "      <td>2.798373</td>\n",
       "      <td>1.664498</td>\n",
       "      <td>0.592906</td>\n",
       "      <td>0.470985</td>\n",
       "      <td>False</td>\n",
       "      <td>0.665736</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.496637</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>0.010121</td>\n",
       "      <td>0.005157</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.020341</td>\n",
       "      <td>0.020379</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>0.000446</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.014798</td>\n",
       "      <td>0.229526</td>\n",
       "      <td>False</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>466</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>-24101.201830</td>\n",
       "      <td>1611.718130</td>\n",
       "      <td>0.797360</td>\n",
       "      <td>0.980133</td>\n",
       "      <td>-6.469724</td>\n",
       "      <td>357.132637</td>\n",
       "      <td>0.21654</td>\n",
       "      <td>-32348.621952</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>2.083880e+07</td>\n",
       "      <td>-313387.471075</td>\n",
       "      <td>0.797161</td>\n",
       "      <td>0.867292</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-249.116969</td>\n",
       "      <td>67780.547523</td>\n",
       "      <td>0.182773</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>192.168.1.101.</td>\n",
       "      <td>192.168.1.128</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>61823</td>\n",
       "      <td>1883</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.953945</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>2.357023</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.684979</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.852814</td>\n",
       "      <td>2591.782055</td>\n",
       "      <td>2.357023e-01</td>\n",
       "      <td>3.227486e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.666667</td>\n",
       "      <td>0.650022</td>\n",
       "      <td>0.251463</td>\n",
       "      <td>0.285759</td>\n",
       "      <td>False</td>\n",
       "      <td>0.467697</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.413082</td>\n",
       "      <td>0.825000</td>\n",
       "      <td>0.008261</td>\n",
       "      <td>0.004891</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.019974</td>\n",
       "      <td>0.019999</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.000546</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.015083</td>\n",
       "      <td>0.265189</td>\n",
       "      <td>False</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>364</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.215011</td>\n",
       "      <td>4.683203</td>\n",
       "      <td>0.007263</td>\n",
       "      <td>0.891727</td>\n",
       "      <td>1.126226</td>\n",
       "      <td>1.375427</td>\n",
       "      <td>0.17333</td>\n",
       "      <td>5631.128620</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.799377e+04</td>\n",
       "      <td>7094.431505</td>\n",
       "      <td>0.851000</td>\n",
       "      <td>0.785438</td>\n",
       "      <td>0.200317</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>4.158316</td>\n",
       "      <td>8.379427</td>\n",
       "      <td>0.884464</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>192.168.1.128.</td>\n",
       "      <td>192.168.1.101</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1883</td>\n",
       "      <td>61824</td>\n",
       "      <td>65</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10.908712</td>\n",
       "      <td>120</td>\n",
       "      <td>-0.712583</td>\n",
       "      <td>60.0</td>\n",
       "      <td>65.00</td>\n",
       "      <td>60</td>\n",
       "      <td>90</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>16.782634</td>\n",
       "      <td>7</td>\n",
       "      <td>10.769231</td>\n",
       "      <td>66.539462</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.458349</td>\n",
       "      <td>12.375430</td>\n",
       "      <td>6.731956</td>\n",
       "      <td>5.777512e-04</td>\n",
       "      <td>7.205823e-04</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>3.534284</td>\n",
       "      <td>1.548795</td>\n",
       "      <td>0.516265</td>\n",
       "      <td>0.442503</td>\n",
       "      <td>False</td>\n",
       "      <td>0.647879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.327577</td>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.010765</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.006289</td>\n",
       "      <td>0.012624</td>\n",
       "      <td>0.032861</td>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.002932</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>0.020237</td>\n",
       "      <td>0.231973</td>\n",
       "      <td>False</td>\n",
       "      <td>1.142857</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>526</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.046638</td>\n",
       "      <td>5.651375</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.892919</td>\n",
       "      <td>1.013498</td>\n",
       "      <td>1.510552</td>\n",
       "      <td>0.10699</td>\n",
       "      <td>5067.492460</td>\n",
       "      <td>-6120.037809</td>\n",
       "      <td>2.583131e+04</td>\n",
       "      <td>9179.647412</td>\n",
       "      <td>0.864509</td>\n",
       "      <td>0.816404</td>\n",
       "      <td>0.175113</td>\n",
       "      <td>1.055740</td>\n",
       "      <td>5.576106</td>\n",
       "      <td>9.812098</td>\n",
       "      <td>0.885259</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>192.168.1.101.</td>\n",
       "      <td>192.168.1.128</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>1.640646e+09</td>\n",
       "      <td>61824</td>\n",
       "      <td>1883</td>\n",
       "      <td>60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1.414214</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.953945</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>64</td>\n",
       "      <td>4</td>\n",
       "      <td>60</td>\n",
       "      <td>2.357023</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.587599</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>84.852814</td>\n",
       "      <td>2203.344730</td>\n",
       "      <td>2.020305e-01</td>\n",
       "      <td>2.618615e-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9.142857</td>\n",
       "      <td>0.591673</td>\n",
       "      <td>0.210758</td>\n",
       "      <td>0.321324</td>\n",
       "      <td>False</td>\n",
       "      <td>0.443887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.280033</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.009191</td>\n",
       "      <td>0.006278</td>\n",
       "      <td>0.002946</td>\n",
       "      <td>0.012311</td>\n",
       "      <td>0.032823</td>\n",
       "      <td>0.005470</td>\n",
       "      <td>0.002935</td>\n",
       "      <td>0.000352</td>\n",
       "      <td>0.020512</td>\n",
       "      <td>0.266698</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>424</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.003317</td>\n",
       "      <td>0.167746</td>\n",
       "      <td>0.007660</td>\n",
       "      <td>0.891528</td>\n",
       "      <td>0.099598</td>\n",
       "      <td>0.041911</td>\n",
       "      <td>0.16774</td>\n",
       "      <td>497.991393</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>8.029687e+03</td>\n",
       "      <td>2202.584636</td>\n",
       "      <td>0.745707</td>\n",
       "      <td>0.619200</td>\n",
       "      <td>0.272827</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.684225</td>\n",
       "      <td>0.290936</td>\n",
       "      <td>0.883868</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432220</th>\n",
       "      <td>1432220</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>192.168.0.170</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>80</td>\n",
       "      <td>33090</td>\n",
       "      <td>74</td>\n",
       "      <td>60.0</td>\n",
       "      <td>38.548671</td>\n",
       "      <td>1486</td>\n",
       "      <td>-0.314987</td>\n",
       "      <td>60.0</td>\n",
       "      <td>60.00</td>\n",
       "      <td>60</td>\n",
       "      <td>162</td>\n",
       "      <td>102</td>\n",
       "      <td>60</td>\n",
       "      <td>52.092798</td>\n",
       "      <td>24</td>\n",
       "      <td>32.432432</td>\n",
       "      <td>82.673195</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.363177</td>\n",
       "      <td>4.202480</td>\n",
       "      <td>1.001827</td>\n",
       "      <td>9.975479e-06</td>\n",
       "      <td>1.292970e-05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.894584</td>\n",
       "      <td>0.591673</td>\n",
       "      <td>0.210758</td>\n",
       "      <td>0.321324</td>\n",
       "      <td>False</td>\n",
       "      <td>0.443887</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.436614</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.013064</td>\n",
       "      <td>0.015008</td>\n",
       "      <td>0.004709</td>\n",
       "      <td>0.015576</td>\n",
       "      <td>0.029921</td>\n",
       "      <td>0.004987</td>\n",
       "      <td>0.004514</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0.014345</td>\n",
       "      <td>0.347638</td>\n",
       "      <td>False</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>522</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-450.052190</td>\n",
       "      <td>1546.197851</td>\n",
       "      <td>0.619355</td>\n",
       "      <td>0.619156</td>\n",
       "      <td>0.224236</td>\n",
       "      <td>23.806847</td>\n",
       "      <td>-0.43733</td>\n",
       "      <td>1121.181565</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.793952e+07</td>\n",
       "      <td>268404.310201</td>\n",
       "      <td>0.619156</td>\n",
       "      <td>0.243761</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>6895.394550</td>\n",
       "      <td>3992.122541</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432221</th>\n",
       "      <td>1432221</td>\n",
       "      <td>172.217.19.42.</td>\n",
       "      <td>192.168.0.128</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>2588</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>53024</td>\n",
       "      <td>149</td>\n",
       "      <td>60.0</td>\n",
       "      <td>199.727314</td>\n",
       "      <td>39891</td>\n",
       "      <td>0.145464</td>\n",
       "      <td>60.0</td>\n",
       "      <td>99.00</td>\n",
       "      <td>60</td>\n",
       "      <td>676</td>\n",
       "      <td>616</td>\n",
       "      <td>60</td>\n",
       "      <td>134.045177</td>\n",
       "      <td>133</td>\n",
       "      <td>89.261745</td>\n",
       "      <td>246.553345</td>\n",
       "      <td>0.827586</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>0.445608</td>\n",
       "      <td>1.937642</td>\n",
       "      <td>1.928618</td>\n",
       "      <td>3.029616e-08</td>\n",
       "      <td>3.197436e-08</td>\n",
       "      <td>-3.564103</td>\n",
       "      <td>5.253090</td>\n",
       "      <td>1.867960</td>\n",
       "      <td>0.384514</td>\n",
       "      <td>0.695716</td>\n",
       "      <td>False</td>\n",
       "      <td>0.645858</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.629863</td>\n",
       "      <td>0.207315</td>\n",
       "      <td>0.743421</td>\n",
       "      <td>50.200818</td>\n",
       "      <td>1.928644</td>\n",
       "      <td>0.583471</td>\n",
       "      <td>48.607712</td>\n",
       "      <td>242.147978</td>\n",
       "      <td>8.648142</td>\n",
       "      <td>0.086327</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>46.080018</td>\n",
       "      <td>0.187677</td>\n",
       "      <td>False</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.629863</td>\n",
       "      <td>3848</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.307237</td>\n",
       "      <td>0.648360</td>\n",
       "      <td>0.824379</td>\n",
       "      <td>0.097993</td>\n",
       "      <td>0.058425</td>\n",
       "      <td>0.11406</td>\n",
       "      <td>489.962718</td>\n",
       "      <td>1499.463717</td>\n",
       "      <td>1.357787e+04</td>\n",
       "      <td>1588.889464</td>\n",
       "      <td>0.779480</td>\n",
       "      <td>0.480554</td>\n",
       "      <td>0.308518</td>\n",
       "      <td>-0.226912</td>\n",
       "      <td>3.135308</td>\n",
       "      <td>42.324922</td>\n",
       "      <td>0.176019</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432222</th>\n",
       "      <td>1432222</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>172.217.19.42</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>2118</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>1.638208e+09</td>\n",
       "      <td>53024</td>\n",
       "      <td>443</td>\n",
       "      <td>156</td>\n",
       "      <td>60.0</td>\n",
       "      <td>173.651951</td>\n",
       "      <td>30155</td>\n",
       "      <td>0.053547</td>\n",
       "      <td>60.0</td>\n",
       "      <td>168.25</td>\n",
       "      <td>60</td>\n",
       "      <td>668</td>\n",
       "      <td>608</td>\n",
       "      <td>60</td>\n",
       "      <td>111.315353</td>\n",
       "      <td>126</td>\n",
       "      <td>80.769231</td>\n",
       "      <td>230.664258</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.552830</td>\n",
       "      <td>2.349527</td>\n",
       "      <td>1.652482</td>\n",
       "      <td>5.208223e-08</td>\n",
       "      <td>5.597325e-08</td>\n",
       "      <td>-0.773672</td>\n",
       "      <td>5.007316</td>\n",
       "      <td>2.181899</td>\n",
       "      <td>0.489277</td>\n",
       "      <td>0.645573</td>\n",
       "      <td>False</td>\n",
       "      <td>0.710121</td>\n",
       "      <td>0.001530</td>\n",
       "      <td>0.625969</td>\n",
       "      <td>0.227372</td>\n",
       "      <td>0.854167</td>\n",
       "      <td>55.040139</td>\n",
       "      <td>1.852389</td>\n",
       "      <td>1.185360</td>\n",
       "      <td>94.780779</td>\n",
       "      <td>242.071271</td>\n",
       "      <td>11.527203</td>\n",
       "      <td>0.452215</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>46.270100</td>\n",
       "      <td>0.249129</td>\n",
       "      <td>False</td>\n",
       "      <td>1.238095</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625969</td>\n",
       "      <td>2945</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>0.307517</td>\n",
       "      <td>0.740939</td>\n",
       "      <td>0.325527</td>\n",
       "      <td>0.083016</td>\n",
       "      <td>0.062044</td>\n",
       "      <td>0.08584</td>\n",
       "      <td>415.080357</td>\n",
       "      <td>1319.492035</td>\n",
       "      <td>1.540541e+04</td>\n",
       "      <td>3899.614072</td>\n",
       "      <td>0.217055</td>\n",
       "      <td>0.371308</td>\n",
       "      <td>0.259434</td>\n",
       "      <td>-0.553421</td>\n",
       "      <td>3.704308</td>\n",
       "      <td>31.696439</td>\n",
       "      <td>-0.415412</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432223</th>\n",
       "      <td>1432223</td>\n",
       "      <td>104.16.87.20.</td>\n",
       "      <td>192.168.0.128</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>2552</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>35314</td>\n",
       "      <td>181</td>\n",
       "      <td>60.0</td>\n",
       "      <td>206.000000</td>\n",
       "      <td>42436</td>\n",
       "      <td>0.064599</td>\n",
       "      <td>60.0</td>\n",
       "      <td>152.00</td>\n",
       "      <td>60</td>\n",
       "      <td>577</td>\n",
       "      <td>517</td>\n",
       "      <td>60</td>\n",
       "      <td>113.812155</td>\n",
       "      <td>167</td>\n",
       "      <td>92.265193</td>\n",
       "      <td>270.862291</td>\n",
       "      <td>0.761905</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>0.587379</td>\n",
       "      <td>2.344660</td>\n",
       "      <td>1.076383</td>\n",
       "      <td>5.991999e-08</td>\n",
       "      <td>6.463128e-08</td>\n",
       "      <td>-1.630435</td>\n",
       "      <td>2.540234</td>\n",
       "      <td>1.875146</td>\n",
       "      <td>0.426915</td>\n",
       "      <td>0.605187</td>\n",
       "      <td>False</td>\n",
       "      <td>0.681982</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.552930</td>\n",
       "      <td>0.721154</td>\n",
       "      <td>1.419733</td>\n",
       "      <td>1.116450</td>\n",
       "      <td>0.888133</td>\n",
       "      <td>2.421404</td>\n",
       "      <td>2.567657</td>\n",
       "      <td>0.128383</td>\n",
       "      <td>0.057662</td>\n",
       "      <td>0.000070</td>\n",
       "      <td>0.701278</td>\n",
       "      <td>0.183070</td>\n",
       "      <td>False</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3812</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009247</td>\n",
       "      <td>0.623643</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.745508</td>\n",
       "      <td>0.237563</td>\n",
       "      <td>0.186311</td>\n",
       "      <td>0.18379</td>\n",
       "      <td>1187.815753</td>\n",
       "      <td>1962.317406</td>\n",
       "      <td>1.280615e+04</td>\n",
       "      <td>4690.827782</td>\n",
       "      <td>0.664055</td>\n",
       "      <td>0.585775</td>\n",
       "      <td>0.261460</td>\n",
       "      <td>0.070717</td>\n",
       "      <td>2.625166</td>\n",
       "      <td>1.385119</td>\n",
       "      <td>-0.254293</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1432224</th>\n",
       "      <td>1432224</td>\n",
       "      <td>192.168.0.128.</td>\n",
       "      <td>104.16.87.20</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>7010</td>\n",
       "      <td>0</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>1.638212e+09</td>\n",
       "      <td>35314</td>\n",
       "      <td>443</td>\n",
       "      <td>527</td>\n",
       "      <td>91.0</td>\n",
       "      <td>555.999101</td>\n",
       "      <td>309135</td>\n",
       "      <td>0.026777</td>\n",
       "      <td>60.0</td>\n",
       "      <td>836.00</td>\n",
       "      <td>60</td>\n",
       "      <td>1420</td>\n",
       "      <td>1360</td>\n",
       "      <td>60</td>\n",
       "      <td>105.502676</td>\n",
       "      <td>486</td>\n",
       "      <td>92.220114</td>\n",
       "      <td>752.733153</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>0.839929</td>\n",
       "      <td>2.679860</td>\n",
       "      <td>0.378354</td>\n",
       "      <td>1.939351e-09</td>\n",
       "      <td>2.161836e-09</td>\n",
       "      <td>-0.203608</td>\n",
       "      <td>1.589497</td>\n",
       "      <td>2.365596</td>\n",
       "      <td>0.605493</td>\n",
       "      <td>0.582157</td>\n",
       "      <td>False</td>\n",
       "      <td>0.832303</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.635707</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>1.708593</td>\n",
       "      <td>2.319056</td>\n",
       "      <td>0.805496</td>\n",
       "      <td>2.478584</td>\n",
       "      <td>2.687704</td>\n",
       "      <td>0.191979</td>\n",
       "      <td>0.072780</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>0.773057</td>\n",
       "      <td>0.248337</td>\n",
       "      <td>False</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7910</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.088505</td>\n",
       "      <td>0.206090</td>\n",
       "      <td>0.656505</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.171874</td>\n",
       "      <td>0.034039</td>\n",
       "      <td>0.20608</td>\n",
       "      <td>859.370832</td>\n",
       "      <td>2156.663490</td>\n",
       "      <td>1.474349e+04</td>\n",
       "      <td>-5276.683787</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>0.469103</td>\n",
       "      <td>0.290925</td>\n",
       "      <td>-0.133063</td>\n",
       "      <td>1.199077</td>\n",
       "      <td>0.296332</td>\n",
       "      <td>-0.649839</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1432225 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0          DST_IP         SRC_IP  PACKETS  PACKETS_REV  \\\n",
       "0                 0     224.0.0.251  192.168.1.101       60            0   \n",
       "1                 1  192.168.1.128.  192.168.1.101        7            0   \n",
       "2                 2  192.168.1.101.  192.168.1.128        6            0   \n",
       "3                 3  192.168.1.128.  192.168.1.101        8            0   \n",
       "4                 4  192.168.1.101.  192.168.1.128        7            0   \n",
       "...             ...             ...            ...      ...          ...   \n",
       "1432220     1432220  192.168.0.128.  192.168.0.170        7            0   \n",
       "1432221     1432221  172.217.19.42.  192.168.0.128       29            0   \n",
       "1432222     1432222  192.168.0.128.  172.217.19.42       22            0   \n",
       "1432223     1432223   104.16.87.20.  192.168.0.128       21            0   \n",
       "1432224     1432224  192.168.0.128.   104.16.87.20       15            0   \n",
       "\n",
       "         BYTES  BYTES_REV    TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  \\\n",
       "0         2940          0  1.640646e+09  1.640646e+09      5353      5353   \n",
       "1           46          0  1.640646e+09  1.640646e+09      1883     61823   \n",
       "2            4          0  1.640646e+09  1.640646e+09     61823      1883   \n",
       "3           46          0  1.640646e+09  1.640646e+09      1883     61824   \n",
       "4            4          0  1.640646e+09  1.640646e+09     61824      1883   \n",
       "...        ...        ...           ...           ...       ...       ...   \n",
       "1432220    102          0  1.638212e+09  1.638212e+09        80     33090   \n",
       "1432221   2588          0  1.638208e+09  1.638208e+09       443     53024   \n",
       "1432222   2118          0  1.638208e+09  1.638208e+09     53024       443   \n",
       "1432223   2552          0  1.638212e+09  1.638212e+09       443     35314   \n",
       "1432224   7010          0  1.638212e+09  1.638212e+09     35314       443   \n",
       "\n",
       "         MEAN  MEDIAN       STDEV     VAR  BURSTINESS     Q1      Q3  MIN  \\\n",
       "0         109   109.0    0.000000       0   -1.000000  109.0  109.00  109   \n",
       "1          66    60.0   11.489125     133   -0.703465   60.0   68.00   60   \n",
       "2          60    60.0    1.414214       3   -0.953945   60.0   60.00   60   \n",
       "3          65    60.0   10.908712     120   -0.712583   60.0   65.00   60   \n",
       "4          60    60.0    1.414214       2   -0.953945   60.0   60.00   60   \n",
       "...       ...     ...         ...     ...         ...    ...     ...  ...   \n",
       "1432220    74    60.0   38.548671    1486   -0.314987   60.0   60.00   60   \n",
       "1432221   149    60.0  199.727314   39891    0.145464   60.0   99.00   60   \n",
       "1432222   156    60.0  173.651951   30155    0.053547   60.0  168.25   60   \n",
       "1432223   181    60.0  206.000000   42436    0.064599   60.0  152.00   60   \n",
       "1432224   527    91.0  555.999101  309135    0.026777   60.0  836.00   60   \n",
       "\n",
       "          MAX  MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  \\\n",
       "0         109              0   109                  0.000000   \n",
       "1          90             30    60                 17.407766   \n",
       "2          64              4    60                  2.357023   \n",
       "3          90             30    60                 16.782634   \n",
       "4          64              4    60                  2.357023   \n",
       "...       ...            ...   ...                       ...   \n",
       "1432220   162            102    60                 52.092798   \n",
       "1432221   676            616    60                134.045177   \n",
       "1432222   668            608    60                111.315353   \n",
       "1432223   577            517    60                113.812155   \n",
       "1432224  1420           1360    60                105.502676   \n",
       "\n",
       "         AVERAGE_DISPERSION  PERCENT_DEVIATION  ROOT_MEAN_SQUARE  \\\n",
       "0                         0           0.000000        109.000000   \n",
       "1                         8          12.121212         67.421913   \n",
       "2                         0           0.000000         60.684979   \n",
       "3                         7          10.769231         66.539462   \n",
       "4                         0           0.000000         60.587599   \n",
       "...                     ...                ...               ...   \n",
       "1432220                  24          32.432432         82.673195   \n",
       "1432221                 133          89.261745        246.553345   \n",
       "1432222                 126          80.769231        230.664258   \n",
       "1432223                 167          92.265193        270.862291   \n",
       "1432224                 486          92.220114        752.733153   \n",
       "\n",
       "         PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  \\\n",
       "0                  0.000000            0.000000              0.000000   \n",
       "1                  0.714286            0.285714              0.522233   \n",
       "2                  0.000000            0.166667              0.000000   \n",
       "3                  0.750000            0.250000              0.458349   \n",
       "4                  0.000000            0.142857              0.000000   \n",
       "...                     ...                 ...                   ...   \n",
       "1432220            0.857143            0.142857              0.363177   \n",
       "1432221            0.827586            0.172414              0.445608   \n",
       "1432222            0.727273            0.272727              0.552830   \n",
       "1432223            0.761905            0.238095              0.587379   \n",
       "1432224            0.533333            0.466667              0.839929   \n",
       "\n",
       "         PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                    0.000000              0.000000   \n",
       "1                   12.011358              3.874170   \n",
       "2                   84.852814           2591.782055   \n",
       "3                   12.375430              6.731956   \n",
       "4                   84.852814           2203.344730   \n",
       "...                       ...                   ...   \n",
       "1432220              4.202480              1.001827   \n",
       "1432221              1.937642              1.928618   \n",
       "1432222              2.349527              1.652482   \n",
       "1432223              2.344660              1.076383   \n",
       "1432224              2.679860              0.378354   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      0.000000e+00                0.000000e+00   \n",
       "1                      3.767915e-04                4.883776e-04   \n",
       "2                      2.357023e-01                3.227486e-01   \n",
       "3                      5.777512e-04                7.205823e-04   \n",
       "4                      2.020305e-01                2.618615e-01   \n",
       "...                             ...                         ...   \n",
       "1432220                9.975479e-06                1.292970e-05   \n",
       "1432221                3.029616e-08                3.197436e-08   \n",
       "1432222                5.208223e-08                5.597325e-08   \n",
       "1432223                5.991999e-08                6.463128e-08   \n",
       "1432224                1.939351e-09                2.161836e-09   \n",
       "\n",
       "         GALTON_SKEWNESS   KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0               0.000000   0.000000  0.000000        0.000000        1.000000   \n",
       "1              -0.500000   2.798373  1.664498        0.592906        0.470985   \n",
       "2               0.000000  10.666667  0.650022        0.251463        0.285759   \n",
       "3              -1.000000   3.534284  1.548795        0.516265        0.442503   \n",
       "4               0.000000   9.142857  0.591673        0.210758        0.321324   \n",
       "...                  ...        ...       ...             ...             ...   \n",
       "1432220         0.000000   3.894584  0.591673        0.210758        0.321324   \n",
       "1432221        -3.564103   5.253090  1.867960        0.384514        0.695716   \n",
       "1432222        -0.773672   5.007316  2.181899        0.489277        0.645573   \n",
       "1432223        -1.630435   2.540234  1.875146        0.426915        0.605187   \n",
       "1432224        -0.203608   1.589497  2.365596        0.605493        0.582157   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.301030             0.004354   \n",
       "1                        False   0.665736             0.000000   \n",
       "2                        False   0.467697             0.000000   \n",
       "3                        False   0.647879             0.000000   \n",
       "4                        False   0.443887             0.000000   \n",
       "...                        ...        ...                  ...   \n",
       "1432220                  False   0.443887             0.000000   \n",
       "1432221                  False   0.645858             0.001000   \n",
       "1432222                  False   0.710121             0.001530   \n",
       "1432223                  False   0.681982             0.000000   \n",
       "1432224                  False   0.832303             0.000000   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                0.666667           0.500009                  0.875000   \n",
       "1                0.000000           0.496637                  0.687500   \n",
       "2                0.000000           0.413082                  0.825000   \n",
       "3                0.000000           0.327577                  0.725000   \n",
       "4                0.000000           0.280033                  0.833333   \n",
       "...                   ...                ...                       ...   \n",
       "1432220          0.000000           0.436614                  0.833333   \n",
       "1432221          0.629863           0.207315                  0.743421   \n",
       "1432222          0.625969           0.227372                  0.854167   \n",
       "1432223          0.000000           0.552930                  0.721154   \n",
       "1432224          0.000000           0.635707                  0.375000   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0              147.520426          150.018016       75.009759      225.027113   \n",
       "1                0.010121            0.005157        0.000364        0.020341   \n",
       "2                0.008261            0.004891        0.000358        0.019974   \n",
       "3                0.010765            0.012346        0.006289        0.012624   \n",
       "4                0.009191            0.006278        0.002946        0.012311   \n",
       "...                   ...                 ...             ...             ...   \n",
       "1432220          0.013064            0.015008        0.004709        0.015576   \n",
       "1432221         50.200818            1.928644        0.583471       48.607712   \n",
       "1432222         55.040139            1.852389        1.185360       94.780779   \n",
       "1432223          1.419733            1.116450        0.888133        2.421404   \n",
       "1432224          1.708593            2.319056        0.805496        2.478584   \n",
       "\n",
       "           DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0        295.035825        5.000607          5.000450       4.916481   \n",
       "1          0.020379        0.003397          0.000446       0.000038   \n",
       "2          0.019999        0.004000          0.000546       0.000025   \n",
       "3          0.032861        0.004694          0.002932       0.000019   \n",
       "4          0.032823        0.005470          0.002935       0.000352   \n",
       "...             ...             ...               ...            ...   \n",
       "1432220    0.029921        0.004987          0.004514       0.000033   \n",
       "1432221  242.147978        8.648142          0.086327       0.000025   \n",
       "1432222  242.071271       11.527203          0.452215       0.000023   \n",
       "1432223    2.567657        0.128383          0.057662       0.000070   \n",
       "1432224    2.687704        0.191979          0.072780       0.000069   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0             5.085931               0.983224       False          0.000000   \n",
       "1             0.014798               0.229526       False          1.333333   \n",
       "2             0.015083               0.265189       False          0.800000   \n",
       "3             0.020237               0.231973       False          1.142857   \n",
       "4             0.020512               0.266698       False          0.666667   \n",
       "...                ...                    ...         ...               ...   \n",
       "1432220       0.014345               0.347638       False          0.666667   \n",
       "1432221      46.080018               0.187677       False          0.928571   \n",
       "1432222      46.270100               0.249129       False          1.238095   \n",
       "1432223       0.701278               0.183070       False          1.100000   \n",
       "1432224       0.773057               0.248337       False          1.571429   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0             0.666667               1308   \n",
       "1             False        0.0             0.000000                466   \n",
       "2             False        0.0             0.000000                364   \n",
       "3             False        0.0             0.000000                526   \n",
       "4             False        0.0             0.000000                424   \n",
       "...             ...        ...                  ...                ...   \n",
       "1432220        True        0.0             0.000000                522   \n",
       "1432221       False        0.0             0.629863               3848   \n",
       "1432222        True        0.0             0.625969               2945   \n",
       "1432223       False        0.0             0.000000               3812   \n",
       "1432224        True        0.0             0.000000               7910   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL      TIME     MIN_POWER    MAX_POWER  \\\n",
       "0               1.0         True  109  5.002997 -98298.111034  2438.475406   \n",
       "1               1.0        False    0  0.000000 -24101.201830  1611.718130   \n",
       "2               1.0        False    0  0.000000     -0.215011     4.683203   \n",
       "3               1.0        False    0  0.000000      0.046638     5.651375   \n",
       "4               1.0        False    0  0.000000     -0.003317     0.167746   \n",
       "...             ...          ...  ...       ...           ...          ...   \n",
       "1432220         1.0        False    0  0.000000   -450.052190  1546.197851   \n",
       "1432221         1.0        False    0  0.000000      0.000015     0.307237   \n",
       "1432222         1.0        False    0  0.000000      0.000031     0.307517   \n",
       "1432223         1.0        False    0  0.000000      0.009247     0.623643   \n",
       "1432224         1.0        False    0  0.000000      0.088505     0.206090   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN    POWER_STD  POWER_MODE  \\\n",
       "0              0.200168        0.199572  -34.814260  1633.287018     0.00000   \n",
       "1              0.797360        0.980133   -6.469724   357.132637     0.21654   \n",
       "2              0.007263        0.891727    1.126226     1.375427     0.17333   \n",
       "3              0.007660        0.892919    1.013498     1.510552     0.10699   \n",
       "4              0.007660        0.891528    0.099598     0.041911     0.16774   \n",
       "...                 ...             ...         ...          ...         ...   \n",
       "1432220        0.619355        0.619156    0.224236    23.806847    -0.43733   \n",
       "1432221        0.648360        0.824379    0.097993     0.058425     0.11406   \n",
       "1432222        0.740939        0.325527    0.083016     0.062044     0.08584   \n",
       "1432223        0.999801        0.745508    0.237563     0.186311     0.18379   \n",
       "1432224        0.656505        0.006667    0.171874     0.034039     0.20608   \n",
       "\n",
       "         SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0         -174071.302265         -1.000000       1.457744e+07   \n",
       "1          -32348.621952         -1.000000       2.083880e+07   \n",
       "2            5631.128620         -1.000000       1.799377e+04   \n",
       "3            5067.492460      -6120.037809       2.583131e+04   \n",
       "4             497.991393         -1.000000       8.029687e+03   \n",
       "...                  ...               ...                ...   \n",
       "1432220      1121.181565         -1.000000       1.793952e+07   \n",
       "1432221       489.962718       1499.463717       1.357787e+04   \n",
       "1432222       415.080357       1319.492035       1.540541e+04   \n",
       "1432223      1187.815753       1962.317406       1.280615e+04   \n",
       "1432224       859.370832       2156.663490       1.474349e+04   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0           -261597.321119          0.199572           0.208451   \n",
       "1           -313387.471075          0.797161           0.867292   \n",
       "2              7094.431505          0.851000           0.785438   \n",
       "3              9179.647412          0.864509           0.816404   \n",
       "4              2202.584636          0.745707           0.619200   \n",
       "...                    ...               ...                ...   \n",
       "1432220      268404.310201          0.619156           0.243761   \n",
       "1432221        1588.889464          0.779480           0.480554   \n",
       "1432222        3899.614072          0.217055           0.371308   \n",
       "1432223        4690.827782          0.664055           0.585775   \n",
       "1432224       -5276.683787          0.006667           0.469103   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.054236       -1.000000      -70.042430  297845.871189   \n",
       "1              -1.000000       -1.000000     -249.116969   67780.547523   \n",
       "2               0.200317       -1.000000        4.158316       8.379427   \n",
       "3               0.175113        1.055740        5.576106       9.812098   \n",
       "4               0.272827       -1.000000        1.684225       0.290936   \n",
       "...                  ...             ...             ...            ...   \n",
       "1432220        -1.000000       -1.000000     6895.394550    3992.122541   \n",
       "1432221         0.308518       -0.226912        3.135308      42.324922   \n",
       "1432222         0.259434       -0.553421        3.704308      31.696439   \n",
       "1432223         0.261460        0.070717        2.625166       1.385119   \n",
       "1432224         0.290925       -0.133063        1.199077       0.296332   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.000596              True  False  \n",
       "1                  0.182773              True  False  \n",
       "2                  0.884464              True  False  \n",
       "3                  0.885259              True  False  \n",
       "4                  0.883868              True  False  \n",
       "...                     ...               ...    ...  \n",
       "1432220           -0.000199              True   True  \n",
       "1432221            0.176019              True   True  \n",
       "1432222           -0.415412              True   True  \n",
       "1432223           -0.254293              True   True  \n",
       "1432224           -0.649839              True   True  \n",
       "\n",
       "[1432225 rows x 84 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botnet: 101030\tclear: 1331195\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[[\"LABEL\"] + features].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_dataset(df):\n",
    "    assert isinstance(df, pd.DataFrame), \"df needs to be a pd.DataFrame\"\n",
    "    df.dropna(inplace=True)\n",
    "    indices_to_keep = ~df.isin([np.nan, np.inf, -np.inf]).any(1)\n",
    "    return df[indices_to_keep].astype(np.float64)\n",
    "\n",
    "df = clean_dataset(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1432225, 77)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botnet: 101030\tclear: 1331195\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df[df['LABEL'] == True])}\\tclear: {len(df[df['LABEL'] == False])}\")\n",
    "# print(f\"botnet: {len(df[df['LABEL_code'] == 1])}\\tclear: {len(df[df['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = df.sample(n=250000)\n",
    "df_features =  df[~df.isin(df_validation)].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_confusion_matrix(y_test, y_pred):\n",
    "    # Get and reshape confusion matrix data\n",
    "    matrix = confusion_matrix(y_test, y_pred)\n",
    "    percentage_matrix = matrix.astype('float') / matrix.sum(axis=1)[:, np.newaxis]\n",
    "    return matrix, percentage_matrix\n",
    "\n",
    "def plot_confusion_matrix(matrix, percentage_matrix, model=\"\"):\n",
    "    # Build the plot\n",
    "    fig, ax = plt.subplots(1, 2,figsize=(15,5))\n",
    "    sns.set(font_scale=1.4)\n",
    "    sns.heatmap(matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[0])\n",
    "    sns.heatmap(percentage_matrix, annot=True, annot_kws={'size':10}, fmt='g',\n",
    "                cmap=plt.cm.Greens, linewidths=0.2, ax=ax[1])\n",
    "    # Add labels to the plot\n",
    "    plt.tick_params(axis='both', which='minor', labelsize=5)\n",
    "    # tick_marks = np.arange(len(classifications_array)) + 0.5\n",
    "    # plt.xticks(tick_marks, classifications_array, rotation=85)\n",
    "    # plt.yticks(tick_marks, classifications_array, rotation=0)\n",
    "    ax[0].set_xlabel('Predicted label')\n",
    "    ax[0].set_ylabel('True label')\n",
    "    ax[0].set_title(f'Absolute Confusion Matrix for {model}')\n",
    "    ax[1].set_xlabel('Predicted label')\n",
    "    ax[1].set_ylabel('True label')\n",
    "    ax[1].set_title(f'Relative Confusion Matrix for {model}')\n",
    "    # pyplot.savefig(\"ddos_timeseries_plugin_classification.eps\", format=\"eps\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import STATUS_OK, Trials, fmin, hp, tpe\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "def tunne_together_XGBoost_classification(df_tunne, _features, verbose=True):\n",
    "    X=df_tunne[_features]  # Features\n",
    "    y=df_tunne['LABEL']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, stratify=y)\n",
    "    \n",
    "    def objective(space):\n",
    "        clf = XGBClassifier(\n",
    "                        n_estimators = int(space['n_estimators']), max_depth = int(space['max_depth']), gamma = space['gamma'],\n",
    "                        reg_alpha = int(space['reg_alpha']),min_child_weight=int(space['min_child_weight']),\n",
    "                        colsample_bytree=int(space['colsample_bytree']), \n",
    "                        # subsample=int(space['subsample']),\n",
    "                        # eta=space['eta'],\n",
    "                            )\n",
    "\n",
    "        evaluation = [( X_train, y_train), ( X_test, y_test)]\n",
    "\n",
    "        clf.fit(X_train, y_train,\n",
    "                eval_set=evaluation, eval_metric=\"auc\",\n",
    "                early_stopping_rounds=10,verbose=False)\n",
    "\n",
    "        pred = clf.predict(X_test)\n",
    "        # accuracy = mean_absolute_error(y_test, pred)\n",
    "        # accuracy = accuracy_score(y_test, pred)\n",
    "        accuracy = f1_score(y_test, pred)\n",
    "        if verbose is True:\n",
    "            print (\"SCORE:\", accuracy)\n",
    "        return {'loss': -accuracy, 'status': STATUS_OK }\n",
    "    \n",
    "    space={'max_depth': hp.quniform(\"max_depth\", 5, 30, 2),\n",
    "        'gamma': hp.uniform ('gamma', 1,9),\n",
    "        'reg_alpha' : hp.quniform('reg_alpha', 0,180,1),\n",
    "        'reg_lambda' : hp.uniform('reg_lambda', 0,1),\n",
    "        'colsample_bytree' : hp.uniform('colsample_bytree', 0, 1),\n",
    "        'min_child_weight' : hp.quniform('min_child_weight', 0, 20, 1),\n",
    "        'n_estimators': hp.quniform('n_estimators', 80, 400, 20),\n",
    "        # 'subsample': hp.quniform('subsample', 3, 20, 1),\n",
    "        # 'eta': hp.quniform('eta', 0.005, 0.3, 0.005),\n",
    "        'seed': 0\n",
    "    }\n",
    "    \n",
    "    trials = Trials()\n",
    "    \n",
    "    best_hyperparams = fmin(fn = objective,\n",
    "                        space = space,\n",
    "                        algo = tpe.suggest,\n",
    "                        max_evals = 100,\n",
    "                        trials = trials)\n",
    "        \n",
    "    print(\"The best hyperparameters are : \",\"\\n\")\n",
    "    print(best_hyperparams)\n",
    "    return best_hyperparams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [30:54<00:00, 18.55s/trial, best loss: -0.9997403375611705]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8645997218528685, 'gamma': 4.315421825039889, 'max_depth': 18.0, 'min_child_weight': 1.0, 'n_estimators': 220.0, 'reg_alpha': 0.0, 'reg_lambda': 0.9924969332410927}\n"
     ]
    }
   ],
   "source": [
    "best_hyperparams = tunne_together_XGBoost_classification(df_features, features, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def XGBoost_classification_tunned(best_hyperparams):\n",
    "      X=df_features[features]  # Features\n",
    "      y=df_features['LABEL']  # Labels\n",
    "      #Split on train and test\n",
    "      X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "      X_train = X_train_a[features]\n",
    "      X_test = X_test_a[features] \n",
    "      model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "            # subsample=int(best_hyperparams['subsample']),\n",
    "            # eta=best_hyperparams['eta'],\n",
    "      )\n",
    "      model.fit(X_train, y_train)\n",
    "      y_pred = model.predict(X_test)\n",
    "      \n",
    "      accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "      precision = precision_score(y_test, y_pred)  * 100\n",
    "      recall = recall_score(y_test, y_pred)  * 100\n",
    "      F1 = f1_score(y_test, y_pred)  * 100\n",
    "      print(\"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1))\n",
    "      matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "      plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")\n",
    "      return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.00 & 99.98 & 99.97 & 99.97 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = XGBoost_classification_tunned(best_hyperparams)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def while_XGBoost_classification_tunned(best_hyperparams, cycles=100):\n",
    "    X=df_features[features]  # Features\n",
    "    y=df_features['LABEL']  # Labels\n",
    "    best_results = (0,\"\",None,None, None)\n",
    "    i = 0\n",
    "    print(f\"\\r{i}\", end=\"\")\n",
    "    for i in range(cycles):\n",
    "        print(f\"\\r{i}\", end=\"\", flush=True)\n",
    "        tmp_f1 = 0\n",
    "        tmp_str = \"\"\n",
    "        #Split on train and test\n",
    "        X_train_a, X_test_a, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test\n",
    "        for F, N in zip(\n",
    "                [ features], # statistics_features, time_features, distribution_features, frequency_features, behavior_features, \n",
    "                [ \"All\"], # \"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\", \n",
    "        ):\n",
    "                X_train = X_train_a[F]\n",
    "                X_test = X_test_a[F] \n",
    "                model = XGBClassifier(\n",
    "                        n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "                        reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "                        colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "                        # subsample=int(best_hyperparams['subsample']),\n",
    "                        # eta=best_hyperparams['eta'],\n",
    "                )\n",
    "                model.fit(X_train, y_train)\n",
    "                y_pred = model.predict(X_test)\n",
    "                accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "                precision = precision_score(y_test, y_pred)  * 100\n",
    "                recall = recall_score(y_test, y_pred)  * 100\n",
    "                F1 = f1_score(y_test, y_pred)  * 100\n",
    "                if N == \"All\":\n",
    "                    tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\hline\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_f1 = F1\n",
    "                elif N in [\"Statistical-based\", \"Distribution-based\", \"Behavior-based\"]:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "                else:\n",
    "                    tmp_str += \"& \\cellcolor{Gray} \\\\textbf{\"\n",
    "                    tmp_str += N\n",
    "                    tmp_str += \"} & \\\\rowcolor{LigthGray} \" \n",
    "                    tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "                    tmp_str += \"\\n\"\n",
    "                    tmp_str += \"\\cline{2-6}\"\n",
    "                    tmp_str += \"\\n\"\n",
    "        if best_results[0] < tmp_f1:\n",
    "            matrix, percentage_matrix = get_confusion_matrix(y_test, y_pred)\n",
    "            best_results = (tmp_f1, tmp_str, matrix, percentage_matrix, model)\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(\"\")\n",
    "    print(best_results[0])\n",
    "    print(best_results[1])\n",
    "    plot_confusion_matrix(best_results[2], best_results[3], model=\"XGBoost\")\n",
    "    return best_results[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "24\n",
      "\n",
      "\n",
      "99.9840230068701\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}100.00 & 99.98 & 99.99 & 99.98 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_model = while_XGBoost_classification_tunned(best_hyperparams, cycles=25)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Evaluation of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}100.00 & 99.97 & 99.98 & 99.97 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL']  # Labels\n",
    "    \n",
    "\n",
    "y_pred = best_model.predict(X_validation)\n",
    "accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "precision = precision_score(y_validation, y_pred)  * 100\n",
    "recall = recall_score(y_validation, y_pred)  * 100\n",
    "F1 = f1_score(y_validation, y_pred)  * 100\n",
    "                \n",
    "tmp_str = \"\"\n",
    "tmp_str += \"\\multirow{-6}{*}{\\\\rotatebox{90}{\\centering \\\\textbf{Our approach}}} & \\cellcolor{Gray} \\\\textbf{All} & \\\\rowcolor{LigthGray}\" \n",
    "tmp_str += \"{:.2f} & {:.2f} & {:.2f} & {:.2f} \\\\\\\\\".format(accuracy,precision,recall,F1)\n",
    "tmp_str += \"\\n\"\n",
    "tmp_str += \"\\hline\"\n",
    "tmp_str += \"\\n\"\n",
    "print(tmp_str)\n",
    "\n",
    "matrix, percentage_matrix = get_confusion_matrix(y_validation, y_pred)\n",
    "plot_confusion_matrix(matrix, percentage_matrix, model=\"XGBoost\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"IoT-egde_iiot-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    writer.writerow([problem, \"-\", accuracy, precision, recall, F1, 0, 0, 0])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feat_importances = pd.Series(best_model.feature_importances_, index = X_validation.columns).sort_values(ascending = True)\n",
    "\n",
    "f_sf = feat_importances[statistics_features].sum()\n",
    "f_tf = feat_importances[time_features].sum()\n",
    "f_df = feat_importances[distribution_features].sum()\n",
    "f_ff = feat_importances[frequency_features].sum()\n",
    "f_bf = feat_importances[behavior_features].sum()\n",
    "f_df = feat_importances[default_faetures].sum()\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(aspect=\"equal\"))\n",
    "data = [f_sf, f_tf, f_df, f_ff, f_bf, f_df]\n",
    "ingredients = [\"Statistical-based\", \"Time-based\", \"Distribution-based\",  \"Frequency-based\",  \"Behavior-based\",  \"Default\"]\n",
    "def func(pct, allvals):\n",
    "    return \"{:.1f}%\".format(pct)\n",
    "wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),\n",
    "                                  textprops=dict(color=\"w\"))\n",
    "ax.legend(wedges, ingredients,\n",
    "          loc=\"center\",\n",
    "          bbox_to_anchor=(0.5, 0, 0, -0.05),\n",
    "          prop={'size': 12},\n",
    "          ncol=2)\n",
    "plt.setp(autotexts, size=12, weight=\"bold\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.38412124, 0.06303264, 0.007968833, 0.047135133, 0.2103421, 0.007968833]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PACKETS                     0.000000\n",
       "GALTON_SKEWNESS             0.000000\n",
       "BENFORD_LAW_PRESENTED       0.000000\n",
       "P_BENFORD                   0.000000\n",
       "AREA_VALUES_DISTRIBUTION    0.000000\n",
       "                              ...   \n",
       "ENTROPY                     0.063193\n",
       "AVERAGE_DISPERSION          0.071226\n",
       "VAR                         0.102824\n",
       "TRANSIENTS                  0.172130\n",
       "CNT_DISTRIBUTION            0.227372\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZMiVKqp08eVJ//fWX/v77b61bt05ff/21ZsyYocWLFysgIEBTpkzRjh07NGbMGKeHyqR4Um9Zv369R9cWi8XiUaszT84F+/727NmjefPmafTo0YqOjk50rwwNDdXChQu1dOlS9e7dW48//riOHTumNm3aqHXr1tq/f7/L++u1a9e0fv16bdiwQVWqVNHgwYPVvn17hYWF6fnnn1eOHDkc63722WeKjIxUsWLFNHDgQEnS6NGjNX78eFWuXFnFihXzuP4YExOjGTNmaM2aNRo8eLDjt3fr1i1169ZNu3btcpSjQIECmjp1qp577jktWLBAY8eO1c2bNx3LGzVqpOHDhzsl63bu3KnPPvtMFy9elGEY2rZtW6J7mP2+tGnTpkSf05UrVxQaGqru3btrxIgRbrUuDg4O1ujRox11ODN1rNGjR2vTpk2m75cjRoxw6zft6r5XuHBh9erVy9S1ZeDAgapRo4Zu3LihH3/8UUuXLtX+/ft14MCBZBOCq1ev1rBhw/Tjjz+qffv2at++vT766CO3XjCGh4ebqrckZfv27ZoyZUqy9/uuXbuqS5cuia7Hy5Yt07Jly1wmDE6dOiWbzabhw4dr//79Gj58eLIvMYKCgjRu3DjFxMSoSJEiHp1Db775pnr27KmoqCilS5dOL7zwgvLly+eoc549e1YnT57U2rVrtXXrVk2aNMnxjObps5vZ7yHhy3+zzxuvv/56kp9fUmw2myIjI2WxWNSiRQvH82ZCzz77rDZt2qSZM2dqwYIF6t69u1avXq2MGTOqZcuWOnnypKMsW7du1VdffeXoPtmkSRMVKFDA8WIu4YsFs8y++PL0eTZDhgyaOnWqcubMqZYtW+qxxx7Tli1btH37dg0ePFizZ8/W33//rQ8++EBnzpyRdKc+3KFDB73xxhse3RdSc00iqYY07dSpUxo0aJC8vLzUokULR0U4OjpaAQEBku485A4dOlQWi0WjRo3SvHnztHr1ao0bN07nz583nYDYs2ePBg8erPj4eOXJk0flypVLdDP49ddf9fvvv6tt27YaNmyYmjZtqkWLFnkUl5Tly5dr+PDhioiIUMGCBTVy5Ei9+OKLkpRkc+zAwEAdO3bM5VuDZcuWqU6dOh59D4cPH9arr76qwYMHK1euXJLuPIRs2LBBFotFX375pSpXrixJqlmzpt58800FBQWpd+/eGjhwoJYsWSJJevPNNx03rIkTJ2rRokWSpCpVqshisWjXrl1q166dSpcurWXLlpmKWb58uXr37q3JkydrzJgx2rZtmwICAjxuHeSu2NhYffLJJ1q3bp0Mw1CGDBmUP39+5ciRQ5GRkTpz5oz+/vtvDRw4UD/99JOefvppGYahuLi4JFu7uWK/kbZp00affvqpo4L++++/a9++fbJYLJoyZYqKFi0qSSpbtqzq16+vb775Rjt27HC0RuzYsaPKly/v9Pbv2rVr+vnnnzVnzhz99ttvqboZm+1+Zk94SPIoeZuUVatWJZs8qlGjhkeVbzMP9v/884/+/vtvx3EmV96Etm7d6niTly5dOo+SOalpieIqdu/evZoyZYrLsrz66qv6888/FRkZqU6dOqljx47q0aOHU4Vk5cqVGjx4sCIjI/XUU09p7NixKl26tG7cuGH6ofnQoUMaPHhwkt2b7aKjozVp0iTHZ1mlShXt3LnTg09ETm+BzbDfj9KnT6/69evrscce0/bt23X8+HENHTpUkyZN0rVr19S5c2cdPHjQ8dm/8847Hu0vX758bq8bHR2tq1evOv5t9nu3J/kjIiJUo0YNPfnkk5LuJBLt98IqVapoxIgRkqRhw4YpODhYr7zyiipXrqwVK1YoICBACxcuVKtWrVJsDebpsRmGoXbt2ik0NFQvvfSSy2TSs88+qzlz5uibb77R1q1b9cEHH2jlypVq1qyZqlevrv79+2vnzp2OB4zkHiI8rbdUqFDB8RmmJCQkxHFtS3gtN8PM/Schi8WixYsXa+jQofL29pa/v7/q16+v5557znHfO378uNatW6dFixZp9OjRSp8+vUqVKqXg4GAdOXJETz/9tNq1a6cKFSoof/78jnrSmTNn9PPPP2vBggXauXOnWrVqpbCwMPXp08fpRdm+ffsUEhIib29vTZw40fGg/9VXX6lOnToKDg7We++959H3sHHjRi1cuFDbt2+XYRhO58jIkSO1c+dOpU+fXo0aNZLFYnEkYvr376/BgwdLuvNbfeyxxxQSEqIffvhBuXPn1scffyzDMDR69GhHS918+fLp4sWLLj/niIgIXbhwIdnvYv369Tp27JgmTpyYZAv98PBw9e/fX5s2bZJhGGrevLnpetnVq1e1fPlyRUVFmbpfdunSxe1EccL7Xs6cOdW1a1dFR0ebvrZUrFhR2bNnV+vWrdW6dWuFhIRo8eLFWrlyZZIJwWzZsmn06NGqV6+eBg0apFmzZmn37t0aM2ZMsi3y4uPjTddbDMPQrVu33PpM7pbUff38+fPat29fknEWi0VlypTR2rVrFRISokmTJiV6sXD9+nX169dPW7dulWEYevvtt/XWW2+padOmps6h4OBgbd68WfHx8erUqZM6dOjgMul75coVBQUFadasWfroo4+0atUqx/dm9tmtWLFi+vjjjz16+f/ll1+afkbx5Npp/+4GDx6sFi1aJLnek08+qcGDB+uJJ57QxIkT9d1330m6032+bdu2atKkiY4fP64hQ4aoU6dOMgxDc+fOVZkyZRzbWLlypeN64wlPX3wlJann2WbNmilTpkxavHix4xx/5513NGjQIC1atEi///67unTposuXL6tgwYIaMWKEypUrp8DAQI/vC55ek+wvQB9JBv4ThgwZYhQtWtTYt2+f09/Xrl1r+Pr6GmXLljUiIiIcf4+NjTVq1KhhNGjQwPD19TW++OILIy4uzrH80KFDhq+vr+Hn52ccOXLE8fdz584ZJUqUMHr16mUUL17cKFWqlLFw4ULDZrO5LFd0dLQxf/58o0yZMkbx4sWNjRs3ehR38uTJROtcvXrV6Ny5s+Hn52f4+voan3/+uXHr1i23Pq/Jkycbfn5+Lpf5+voakydPNhUzefJko0iRIkb16tWN6Ohop2XTp083fH19jTfeeCNRXPv27Y2GDRsa27dvN3x9fY1XXnnFWLdunXHz5k3DMAzjypUrxgsvvGD4+fkZc+bMccT9+OOPRpEiRYwiRYqYivH19TWCgoIMwzCMEydOGM2aNTN8fX2NChUqGGvWrEnuI/P4c7EvGzp0qOHr62vUqlXLWLVqVaLvKjo62li/fr1Rv359x2+vb9++yZbJ7uTJk45j8fX1NSpWrJjotzVp0iTD19fXeOuttxLFd+rUyahUqZLh6+trDB48OMnfZULDhw83/Pz8jCJFihi1atUyWrdubeq/t956yzh37lyK/x06dMjp2N5++23D19fX6Natm3H16lVHeU6cOOH43Hbt2uX4+9GjR41ixYoZRYoUMf39JeXo0aPGsGHDjIoVKzr2+c477xg//PBDot9/SpYtW2aUK1fO8PX1NWrXrp3oGuZKRESE8emnnzrO/R49ehi1atUy/Pz8HH9z9z9PPhf7+e7p+XDw4EGjZs2ahp+fn9GmTRvjypUrRlRUlNMx9erVy+maPXnyZMPX19f45ptvnLa5d+9ew9fX1yhevLhx5swZx9+vXbtmlC1b1ujfv3+yn+Xhw4eNBg0aGH5+fkbp0qWN4ODgZNe/W8Jzr0iRIkbbtm2TPG5X7MtatmxpFC9e3Pjjjz8cy+Lj440uXboYxYoVM06fPm28/vrrjuuV/XqWmmuSO/bs2WO8/PLLhq+vr1G+fHmP9tmiRQujSJEixurVq53+vmXLFsPX19coWbKk8c8//zj+HhUVZVSqVMno1auX0/o7d+40atWq5biOJfy+PXH3sfXo0cPw9fU1JkyY4Fb84MGDDT8/P2PmzJlOf1+4cKFRrlw5w8/Pz+jevbsRFhbmMt7TesuHH36YYtni4uKMyZMnG8WLF3d8Xn///bdbx2XXqlUrU7+VhOdC8eLFjTFjxhglS5Y0KlWq5FSPcuXYsWNGhQoVDD8/P2Pw4MGGr6+v0bVrVyMqKirZuNu3bxs9evQwihQpYtSoUSPR8jFjxhi+vr5Gu3btEi3r0aOHUadOHY+/h6ZNmzo+24Tn7blz54yiRYsafn5+xqpVqxx/37Vrl+Hr62vUrVvX8PPzM7777jvHsosXLxqvv/66Ubp0aePmzZtGr169HNfCgQMHGhERER6f776+vsbo0aMNPz8/o2TJksbChQsTrbdv3z6jZs2ajnNhzZo1HtXLEtax7O7V/dLVfc9er0rttSWhmJgYY+3atcb7779vFCtWzPDz8zMqVKhgjBgxwjhx4oRjvbCwMOPjjz82fH19jVKlSjl9n3dbuHCh6XpLkSJFjJYtWybaljvX8NTcG+Li4ozhw4c7jmvx4sWOdX7++WejevXqjnrmpk2bDMPw7FpWpkwZo0iRIsayZcuSPRa7VatWGb6+vsa4ceOMWbNmefTs1qZNG4/qjz179rwn50JS7q5HVK1a1a04w7hzra9Tp47RqFEjo169ekarVq2cln///feGr6+v0b17d5fx7777bpL1OXdFRkYan332mePaP3nyZCM+Pt7t+JSeZytUqODynD1+/Lij7uzr62v06dPH8d0YhmE0a9bMaNSoUaI4d+4L7nB1Tbp+/bpbsf9FtFT7j/j5559VuXLlRGNP2VsbVK9e3ekNiLe3t6pWraoffvhBhQoVUv/+/Z0y0vYxS0qVKuVo0SPdeTNQtWpVbd26VfHx8Zo2bVqyg4imT59eLVq0cLxtHT9+vEdx8+bNc7TAkO502Ro0aJCuXbumJ554QsOHD7+vg5kmpWrVqoma9+/du1cWi8VlVy9fX18dOnRIy5YtU6ZMmTRv3jynLP+mTZsUGxurvHnzOrXIaNiwoYYOHarIyEhTMbNmzdK6devUpk0bPfvss/r+++81c+ZMTZkyRR999JG2bt3q9rg9Zhw+fFjBwcEqVqyY5s6d63L76dOn1yuvvKIqVaqodu3aunHjRqJxrlyZO3euxo8fr9u3b6tIkSK6dOmSXnnllURvWOzfg6vfybPPPqstW7Yof/78ic6FpPTt29cxPs758+d1/vz5FGMSslgsKba2+OmnnzRo0CBdvXpVjz/+uL744gvNmTNHefLk0dixY51+a/auL0WKFHG0hpTuDIxasWJFj1seueLn56eBAweqb9+++umnn7RkyRLt3LkzxbfxCV27dk2ff/65Nm7cKMMw5O/vr08//TTFrr579+5Vv379dOHCBfn4+GjQoEFq0KCBbt++re7du2vbtm2qUaOG260IH0Qz9ZIlS2rZsmXq37+/fvrpJzVq1EhZs2bV33//rSxZsuizzz5L1DV969at8vPzS9Rde+vWrZLutAxM2N0uZ86cql69uvbu3euyDDabTVOnTtW0adMUFxenMmXKKCAgING4hsm5+9z7888/TY9/aPfXX3/p5ZdfVvHixR1/s1qt6tSpkzZu3KgePXro+PHjqlixokaPHq0nnnhCn3zyiUf7ckdMTIzGjx+vOXPmyGazqVq1aho+fLijy60Z9u4Y9evXd/q7ffKJypUrO13DM2bMqGrVqmn//v1O61epUkWrVq3S5MmT9c033+jVV191ub+UujkmdWydO3dWoUKF1KNHD7eOq2/fvtq4caM2bNigDh06OP7evHlz1axZUyNGjNCaNWu0a9cul+PyXLp0yaN6y/bt25Mt16lTp9S3b1/98ccf8vb2Vq9evdShQwdT47yZdfe5MHr0aG3cuFExMTGaNm2aUz3KlVu3bjnGTNuyZYty586tUaNGpdi6LkOGDBo1apTWrl3rcrm9dXbCe4JdwYIFtWXLFo/rjytWrFD27Nn19ddfOy3ftGmTbDabChUq5HQPr1y5ssqUKaNDhw6pbNmyTvWTvHnzqk+fPnr//fc1cOBArV69Wrlz5/b4nEvIYrHok08+UZkyZfTpp59q0KBB2r9/v4YMGaIMGTJoypQpjuvgiy++qLFjxypfvnzq3bu36XpZwjqW3b24XyZ132vUqJGjtX1CZq8tCaVLl05169ZV3bp19c8//2jVqlVau3at5syZo7lz5zquLdmzZ9eYMWP0+uuva+jQofriiy+0evVql/eQnTt3mq63SHK0YL+fvLy81L9/f5UtW1YDBgzQwIEDtX//fuXJk0dff/214uPjVblyZQUEBDgGqPfkHPLy8pK3t7fbk6K99tprmj59unbu3KlMmTJ59Oy2e/duj+qP27dvvyfngit3XztPnz6tV155xa3PRLrzfZUrV05r166VzWZLNJRJ7dq1NWjQoCSHI/D19dWOHTu0d+9eBQYGur1f6X89K7JkyaKhQ4eqXr16+uyzzxQYGOj2MAjuPM/aey7czb7t8+fPq2fPnurUqZPT8jNnzqhevXqJ4ty5L6QkqWvSo4yk2n9EaGioy2TBgQMHZLFYXA4mmjt3bsXGxqpChQqmExCbN29WuXLl3E5kVapUSaVLl9bhw4f14osvmo6zPxhGRkY6xnMwDENvvPGGPvvsM2XLls2t7f3b7p4BxjAM/fbbb5Lk8mHT29tbsbGx+vXXX1WzZs1EzWZ3797tSMjd/UAQHx8vLy8vUzEvvviiVq5c6fi31WrVBx98oNq1azuaL+/fv1/Dhg1zWTFKOFCyGQsXLpSXl5cmTpyYYsJu48aNCgsLk9Vq1YYNG5Icd+rixYvq16+f9uzZI4vFog4dOqh79+4qU6aMo+utXVxcnP744w9JcnQNTsgwDNlsNlWqVMmpGX1yvLy8VLlyZccU8/dymvnIyEgNHz5cy5cvl2EYeu211/T5558re/bsGjBggKpXr246eXsvk2p27la+77Zx40Z9/vnnunr1qvLkyaMvvvgixVndYmJiNHbsWH333Xey2Wx66aWX9MUXXzjOuYwZMyowMFCtWrXS9u3b1bFjR5ff9cMiR44cmjp1qrp166YNGzboypUreuyxx7RgwQKXlbB7WTk6efKk+vbtq8OHD8vb21u9e/dWhw4d3G7qn9S5V6JECbfiXYmIiHA5CLP9QTMkJET+/v4aPHjwv5ocke5MgtC3b1+dOnVKGTNmVN++fZPthpKSpK6b+/fvl8ViUaVKlRIty58/v1N3OrvTp0/rwIEDkpLu4pTU36Xkj+3vv/9WgwYN3P4dZMyYUZUrV3Y5lpX0vzFSIyIiXH4GhmG4TCSkVG+5fv16kmUKCgrShAkTdPv2bfn6+iogIMDUhDxmJXUupEuXTgMGDFCxYsVcnpt3mzRpkp544gnlzJlTISEhatCggdsvtzJmzCiLxaIbN244/T06OtpxDXZV/4iNjZXVavW4/hgdHa169eol6rL2888/J3kvKlmypH755RcVK1Ys0TJ7l6k1a9aoRIkSmjp1qh5//PGkD9ykOnXqaOnSperZs6d++OEHHT58WDly5ND+/fvl5eWlbt266cMPP3RcXzypl91dx0rIk/tlSve9Cxcu6I033kgU58m1xZVs2bIpb968yp8/v0JCQlx2769Ro4Zat26tUaNG6cCBA47rU0KGYahp06am6i2S53XOe6FevXoqUaKEevfureXLl0u6k8T+5JNP1K5dO6d1PTmHbt++7XJiguSUKVNGq1atkre3t+rUqWP62e2nn35S/fr1Tdcfd+3apfr169+zc0FK+tpZoUIFZcqUKcXPIiH7DJiPPfZYohfcjz32mJo1a+Zylkzpziyj0p26VFIvIe+WcPzZhMOVmHnxtXfvXrefZ+Pj410+m9jHlC1evHiihJp052XN3TPHuntfSEpK16RHGUm1/4i4uLhEJ1xYWJhOnTolyfX4RvYBYj1JQBiGYbqi+sILL+jXX3/1KG7ZsmXatWuXBgwYoIsXLypnzpwaOnSoqbcZ90NYWJjTv48eParIyEh5e3u7/Czt4yFcvXrVZasle+XE1Q05Ojra5Q05uZgsWbI4DQxsZx84OCgoSKNGjXKMp5CQYRgKCgpSv379Ei1LyS+//KIqVaq4NXD1119/rdKlS8vHx8dl5Uy6M2nEyJEjHQ/io0aNcoyTkD17dl27ds1p/UOHDikqKkrp06dX2bJlE23v9OnTkpTkTTcp9gr/k08+6fFg1nf7+eef1b9/f124cEHZs2fX559/7vS2PywszKPk7b/Nncp3ZGSkvvjiC/3www8yDEMNGjTQ559/nuLsUr///rv69u2rv/76S5kyZdKnn34qf3//ROulT59e48ePV8OGDfXFF184xht8GN24cUOfffaZNm7cKKvVqhw5cujq1av65JNPNHbs2ERvJe9V5Wj27Nn68ssvdfv2bRUtWlQBAQHJztB8t+TOvdSIi4tz2SrHXrl+5plnNGTIEI8GqHdXfHy8pk6dqunTpztarYwaNcqt61Zy4uLiEv3t5s2b+vPPPyW5vj/fvn3baRKG6OhoTZgwQd99953i4uJUtmxZBQQEuF02d44tLi7O7Zne7J544olExzd37lxNnDhRN2/eVMGCBTVkyBCXL2nq16/vUb3FVWvWhIN2218Wde3a1bF9sy0QJKXY+jilc+HChQsuE+F3i46O1vnz59WqVSuFhobq8OHDph9QMmXKpKioKKe//fLLL4qNjVWmTJlczph54sQJ5cqVS//884/H9UdXsxj/+uuvklxP8GA/x+++lklyPEzmyJFD33zzjenfojsKFiyohQsXqkePHo7Zgx977DF99dVXiT4jT+plSdWx7ubO/dKd+150dHSi88HstcWVn3/+WT/88IPWr1+vW7duyWq1qnr16mrevLnTevYJVX7//XelS5dOHTp0cHmuDxgwwHS9RZLppNO99ueff+rcuXOOFxXe3t4uPztPnsFiY2NNzSIt3Tk3bt26JYvF4tGzm+TZy3/DMO7puZDctTNv3ryORJe7zp49qzx58uiFF17QunXrtHv3bqeXGV988YXLuK1bt2rjxo2S7nxH96Ie786LL5vNpoYNG96z51n7ZBN3y5kzp65cueL0N3fvC664Wxd/VJFU+4944oknElUAd+3aJcMwlDt3bj333HOJYg4ePCgvLy+PExBmW4fZ1zcblylTJt28eVPvvfeeDMNQnTp1NHTo0CRP+gfp4MGDTv+2X6xLlSqV6G1uVFSUdu7cqRdeeEEHDx5MVKk6e/asLl++LIvF4vJGZ7FYEt2QU4q5evVqkhXVbdu2KTg4WNKdm+Hd650/f96tmbNcuXDhglutE6Ojo1WiRAlVr15df/zxR6Kk2rVr1zRw4EBt3rxZkvT222/rk08+cXqrVaxYsURvm9avXy/pzk3z7gpRWFiYY4YyszMJejrzoCsxMTEaPXq05s2bJ5vNppo1a2rYsGGJ3tRnzZrVo+Ttv8Xdyvfu3bvVv39/RyXi888/T/GBMz4+XlOmTNGMGTMUFxen8uXLa+TIkS6bwds99dRTatOmjaZPn67Nmzfr5ZdfvifHeS/t2bNHffr0UWhoqPLmzasxY8boueee08cff6ydO3eqSZMmGjJkiFMyNbWVo/Pnz+vTTz/V/v37ZbVa9eGHH6pLly5uJ1zdOff+TS+//PK/mlA7efKk+vTpoyNHjihdunT65JNP9O67796TfWbOnDlRi4u9e/cqPj5e2bNnd9lq58iRI47v7pdfflG/fv105swZpUuXzjHlvbtlc/fY8ubNm+Rg8Em5dOmS4xp19uxZ9evXz3HdbtGihfr06ZPkbyRv3rwe1VvuviYuWbJEI0eOVGRkpJ5++mkFBASoVKlSTusEBgY6TfaSkoQtEe7m7rlw+/Ztt+o7GTJk0Lp16xQXF6dJkyZJkunzKm/evDp58qTi4uIc5/SqVask3Xnguvs8v3jxovbs2aM6derojz/+8Oh7sFqtioyMdPr7yZMndf36dVmtVpd1kNDQ0BSPpX79+v9KQk26k8gYP368owWvt7e3rl27pm+//VZDhgxx2m+6dOlM18uSq2NJ7t0vzdz3cufOnegzNXNtSejkyZP64YcftGLFCl26dEmGYSh//vx699131axZM6fJrOLi4hQYGKivv/5acXFx8vPzU0BAgHx9fV0ed0BAgOl6i3Tn+7l7YgH7bzWpmXHvhejoaI0YMUILFy6UYRhq1qyZypUrp6FDh2ro0KHatm2bRo4c6ZgwyJNnMMMwTA+14u3tLZvNply5cnn07Ga1Wj2qP1oslntyLrhz7SxdurTWrl2rsLAwpwmZkhIeHq5t27apUqVK6tq1qzZt2qQOHTqoUqVKjlnC73b06FFNmDBBO3bscHymFSpU8GiyKzt3Xnzdvn1bo0eP1vz583Xx4sV79jybVA+bUqVKafv27R7dFxLypC7+KCKp9h9RsWJFrVy5UlevXnW0tlm8eLEsFkui/uXSnUr677//rrx583qcgEg47bk77OubjbPf1CwWi0qWLKkKFSok26Q4ofs5XpLFYtGxY8f09ddfq127djp+/LiCg4NlsVjUqFGjROuPGjVK4eHhevnll3Xt2jWdPHnSafm2bdsk3ZkuPamZOe++kKYUs2fPnkQXwatXr2rw4MGOsa0aNGigQYMGJXqT7Ofnp2LFiiV6479nzx5J0pQpUxJVcOzL3H1wz5Ahg2OWqkOHDjm1slm/fr0GDx6sa9euKW/evBo5cqTLbjWvv/66+vbtq2HDhundd9/V0aNHtWDBAlksFr355ptO68bFxWngwIGKior6Vx/YU3Lo0CH17dtXf//9tzJnzqx+/fqpWbNmLtd9/vnnPUreSq5nSbPPznTx4sUkK6j58+d3+reZynd0dLTGjBnjSBa+/PLL+uKLL1JsFXjixAn16dNHR48eVfr06fXxxx+rbdu2bn1PH330kWPWq4eJzWaTzWbTu+++q/j4eNWpU0fDhw93nGvffPONpkyZoilTpqh3797avXu3Bg4cqAwZMqSqcrRo0SKNGjVKN2/e1DPPPKOAgACXSbikuHvuSXfGWLn74SIkJESSXLZydXcW3KSSDPdif99++62+/PJLRUdHq1ixYho9erTLhyBP5c+fX8eOHVNUVJTjOJYvX+7oNnP3b/rEiRP65Zdf1KBBA40aNUpz585VfHy8ihUrpoCAAD3//PNu79vMsfn5+WnHjh26detWimMbSnfO7W3btql48eKOcXGioqKUN29ejRgxQlWqVEk23tN6i7276tWrVzVw4EBHgqRVq1b6+OOPXbZ4TM3DUkJmzoW8efOaGhPK29vb4zGkihYtqpMnT6pHjx5q27atjhw54viN3d11+caNG+rVq5diY2NVv359ZcmSxaPvIVeuXInOp59++knSnd9Szpw5nZbFxcW5NQzBv/XC9NSpU+rVq5eOHTumzJkz6/PPP1fJkiXVvXt3rVmzRgcPHtS4ceMcLWYKFChgul7mqo5l5n5p9r5XtmxZbd261fS1xd5l9Nq1a1q1apWWL1+uI0eOyDAMeXt765VXXlHz5s1VrVq1RNs4cuSIPv30Ux0/ftwx7mXXrl2Tred5Um+R7iQhXNXjDcNQ69atXe4rtXW5Y8eOqXfv3jp58qSyZs2qoUOHOsasK1mypHr06KHNmzerSZMmGjNmjMqVK+fRtUyS27MY383Tl8c+Pj4e1R8zZ86c6nPB3Wtn48aNtWzZMg0aNEgTJ05M8fscMGCAbt++rSZNmuiZZ57Rd999p08//VT//PNPkjEXL17Utm3blCtXLo0aNUodO3ZMdh8pcffFV6NGjRxjrJp5nvU0edy0aVNt2LDBo/uCXWrq4o8akmr/EW3atNGSJUvUokULNWrUSEeOHNGuXbvk7e2d6Ia0c+dO9e3bV1arVf7+/po0adJDnYCwv1UxDEOHDh1yTNubHHsSLqk3hvaKoH2chLsFBwcnumHZH9xc3eDty/z8/DRu3Dh9+eWXio+Pl2EYKlasmNNnGRwcrJUrV+q3337Tk08+KX9/f/3zzz+aOXOm9u/fr3LlyunWrVuOhNzdA9BK0qJFixQbG6u4uDhTMWfPnnUaSHLVqlUaNmyY443Q559/7jLWbu/evUmOOTB58mSnfyd80583b163H57tjh49qvz58ysiIkJDhw7VypUrZRiGGjdurIEDBybZau6NN97Qjz/+qODgYM2bN0/Snd9D1apVnVr+jB07VqtXr9aFCxdUrFixZAf2/rfc/ba3QoUKGjlyZLIVrXr16umLL74wnbyVpDlz5mjOnDkut1urVi2Xf7cPeu5J5fvgwYOOZGHWrFnVv39/NW3aNMXP5ZtvvtGkSZMUHR2t4sWLa/To0ckO4pxaniZmzF4HFy9eLOnOw/OAAQP09ttvJ1qnS5cuevHFF9W7d28tXrxYv/32m8aPH+9R5SgmJkanT5/WmjVrJElt27ZV79693e5yYvbck+58Nkmd60l1x03N/SQ1+zt37pz69evnGFOpS5cu+vDDD1N8CWC2vC+88IKOHTumtm3bqmXLljpy5IjWrVsnq9Wa6MHw1KlT6tatm2w2m/bs2aPLly+bKpudJ8fWqFEjrVu3TmPHjtWgQYNS3If92nLhwgWNGDHC7d+Inaf1lubNm2vt2rUaMmSIrl+/rvz582vEiBEux4+yS21SzZNz4YUXXtBPP/2k69evJ0owuXL9+nXHAPNmPfPMM5LuXMvsiS17ORO2Eu/du7e2bNmimzdvqmrVqnrllVf09NNPe/Q91K9fX/PmzdPq1av12muv6fLly4570euvv56ojNOnT0/U2taskJCQRPW25Opz9mXff/+9Ro0apaioKJUoUULjx493tCBZtGiRhgwZoqVLl6p169bq2rWrOnXqpJdeesl0vcxex/LkfunJfa9FixZauXKl6WtL/vz51alTJ+3YscNRVy1UqJCaNWumpk2bJvnS68svv3TUV8y8oPGk3iJJhQoVSvKlcnL27dun8+fPm2rldv78ecekSdHR0SpdurTGjRvnVB8rXLiwFi9erMGDB2vZsmVq27atPvzwQ7Vu3dr0OSTJaWIeMzx9eVynTh2tWLHCdP2xSpUq2r17t0fngtlrZ8WKFVWvXj2tXbtWrVq1Uvfu3VW+fHmnl+xxcXHau3evJk6c6Jj4xN666oUXXtDKlSt1+fLlJD+/kiVLKjAwUNWqVUtxMpjk2Cf9cffFl72nlyRTz7OSkp1IIallFotFTZo00bJly0zfF6T7XxdP6yzGv9V2FvedvWJgv0FaLBZ9/vnnTg9ctWvX1oULF2QYhj744AP17NlTHTp00M6dOx03eHsCImGzWVcJiG7dujkN0JiSwMBATZ48Wd27dzcV17p1a+3bt8+jSrG9y4crSXXtsI/hYPbByWKxaM+ePRo7dqw2btyo2NhYvfTSSxowYIDTm9caNWooNDRUhQoV0owZM1SoUCFdu3ZN9erV0+3bt1WqVCmdOXNGoaGhypUrl1avXu1oAh0SEqI1a9bom2++Ubp06eTl5aWYmBi3Y9KnT69169bJy8vLaebFl19+WcOGDUs0COndn6Wnbt68qTlz5uiHH35wqwXIn3/+qcaNG6tOnTo6ePCgLl++rMcee0xDhgxx+dbvbjExMZo9e7Y2bNig2NhY1ahRQ507d3Z6e1erVi1duHBB5cuX18SJE1WlShUVLVrU1Jh/R48eVUhIiDZt2mT6jePx48fVp08fhYSEKEOGDOrVq5dbLStjY2Pl7++vkJAQeXl5OSVvFy1a5GgJenfyNj4+3qPkRVRUlEqVKmW68j1hwgR98803io+PV4kSJTRs2DC3PqMOHTo43qSWKFFCHTp0cLt1a8LfhmEY2r9/v86cOaOrV68qW7Zsevrpp/Xiiy86JZV8fX09+lwMwzA9YL792rJy5coUWxtduXJFH330kfbt26dMmTLp119/Vb9+/bRs2TKna3Xjxo0dDyCSc+XIy8vLsc/ixYu7PXGDfaDjAQMGmDr3UjOGXb9+/VSnTp1E3Q4k6dNPP3W5zN7N0FVXl5TExsYqICBAt27d0rPPPquAgAC3HnD8/Pw8+r3YbDZZLBanLogffPCBU4vKt99+WwcPHlR8fLyjImwv2wsvvOD2vhYuXOjRsRmGobZt22rfvn167bXX1LNnT5djtp0+fVoTJkzQunXr9Oyzz+rEiROO34ir7y85ntRbzp07p9WrV0u6c43o06eP20MTmLm+b926VfPmzVPr1q1NnwvSna5+7dq106uvvuro1pkce9clwzBUoUIFU+P77NmzR/v379fYsWOd7nvNmzd3+r3Wrl1bly5d0htvvKHPP//c8UDpyffwwQcf6PXXX9fFixeVL18+Xb9+XVFRUXrqqae0cuVKx7Y3b96slStXavXq1cqZM6euXbumtm3bqm3btomOo1atWskuM1ufs48BbL9Wv/vuu/roo49cJpeXL1+uIUOG6Pbt26pUqZL69++vd955x1S9zN7t1d4F09375TvvvONovWT2vnfkyBFNmTLF9LVF+t+s6/7+/i7Hxbqb/frXunVr9e7d2+0hMDytt6xevdr0uGMJy+lKSr8VLy8vdezYUd26dUv2O1iyZImGDRum6OholStXTm+88Yapc8gwDGXPnt3UkDj2SV+OHDni0bPbggUL9NZbb5n+HoKDg9WwYUNT50L69Ok1bNgwjR492vS1MyIiQp07d3ZMxJQpUyYVLFhQmTNnVnh4uM6fP6/bt2/LMAyVKFFCM2fOdNlV1Ew9sF69epo4caLb34Uk1a1bV2fOnJGXl5c++OCDFF9cefocNXnyZFP1joSNGo4ePapVq1aZvi+k5prkznf8X0RS7T/m4sWL2rx5s2JjY1W9enUVLlzYabl9Zq/33nvPMVuhpwkITx8sPJ25zWxLJ+nOw5jZctqbDHvSHNidSvC8efOUK1cu1alTx+nie+TIEX388ceOgU3z5cunL7/80mlsmKpVq+ratWvy9vbWl19+qXz58pmOqV27tipVqqQbN24oS5YsbrceSo1Tp06pYcOGKly4sObMmZPsW/urV6+qdevWOn36tFMiKF++fC4HN3bFYrFo6dKlya6zadMm5cmTxzFjoaczxNlvWsmJiYnRxYsX9fjjjzu6VZUoUcIxwPfzzz/vcnDfpPY3fPhwj5K3nrB/Lp5Wvs2yJx8k84nto0ePymazacaMGfruu+9cznCWOXNmtW3bVh07dlRMTIxeeuklvfDCC4ne7qbEkwk77JUceyu4lNhsNk2cOFEzZsxw/MbMVI48TXLdPZ7UvT73XPHkIcjOk3tDwv1lypTJ7RZgN27c8Oh3bbFYtGDBAq1fv97x3d09uPCrr76qa9euqV27durcubPGjBmjjz76yPRDpafHZrFY9OOPP6pdu3b666+/ZLFY9PTTT6tw4cKOh5m///5bZ86ccXRhmzt3rgICAjR48GCPu+2ZrbckvFabfdBIqUXyjRs3tGTJEs2fP98xWHZqzoWnnnpKGzZsUNWqVdWvXz+Xb/mPHTumkSNHas+ePXr11Ve1bt06t4/Jvp+ED1DJ+eOPP/TEE0+4nFXTk/rjhQsX9NlnnznGjypdurRGjx7tdD+rXr26/vnnH2XPnl0zZ86Uv7+/xy8yPJlle+nSpXr88cc1evToFLskHz9+XD169NCpU6f02GOPaebMmabqWLGxsZI8u1/aeXLfO3jwoKlrS8OGDeXv769GjRq5NW6VXe3atTVq1CiXA++nJDw8/L7VW5LqGpqS3377TbNmzXL7+P788091795dp0+f1tGjR02dQ57UIaT/1Tk9eXbLlSuXx9+DJ88oXbp08fjauXjxYn377bdasGCBzp49m2gdX19fvfXWW2rZsmWic8ZsPbBatWqqXLmypk+f7lb57Pz8/Dx68WVWaho1uNMgxdV9IbXXpEcRSbVHjKdJrXuVgEjp4Sgp7lQW/wsMw9CpU6cUGxur559/PtFbgb59+ypLlixq3bq1o7uHJzF+fn6qVKmSRo4cqXz58t2XY5s6daomTZqkJ554Qp06dVKdOnWcLuChoaFav369pk+frqtXr6pHjx768ssvPdqXJ7+X1LSyadKkifbt26fVq1erZcuWTrMpTps2TTNmzFBUVJS8vLz0+uuv67PPPnO71dDd3D22pJK3ZjVo0MCjyndS3UlTcv36dbdmzHNlwIABjpZu9of+woULK0eOHIqMjNTx48cdg+6+8MILKly4sFauXKkff/zxoW7Svn37dlWvXt2tdRNWjlLbcswTnl6rPd2fJI0cOdJ0jKf3MEluJ0XNsr/NNzvu6N1Se2xRUVEaM2aMfvjhB5ezt2XPnl3NmjVT165d78tEFXfXWzx9YJbuzEzqyuHDhxUcHKzVq1crOjraMYj466+/ru+//96jfVksFu3bt0/vv/++fv31V1ksFhUqVEjPPfecMmfOrJs3b+rEiROOJGWZMmX09ddf69tvv/X4+O7V2HGuJFd/jIqKUlxcnMtWNxMmTJCPj4+aNWum7Nmze3xvkP43ZpsZHTt21KhRo9xO+kZFRemzzz7TqlWrdPToUVN1rK5du3p0v/TkBbCdO9e/e3VtuXnzpunB9c1IWG8ZM2aMqlev7tYkV/eKu4PjJ3Tr1i199tlnGjduXIrrJjyHUjOBlLs9I+5+dnNXUvVHs88b9+pl9cmTJ3XhwgWFh4crV65cKlSoUKKxfu0iIyPvWz0wICDAoxdf98vy5cvl5+fn0ffwb1+T/otIqv1H2LvO/NebXNpsNm3fvl1r167V2bNnde3aNeXKlUtPPfWU6tatqxo1ajhV+vbt26cnn3wyyYvvg/bXX3/p1KlT9/17++6779SqVStTMXcPcG9G/vz5ZRiGxo0bp1mzZjm6KGTLls3xcJFwFrH33ntPH3/88T2pdNydyI2NjdWaNWt08uRJ5cuXT6+99lqqZxoLDAzUlClTJEmTJk1yjEcwe/Zsp7FBpDtdp8qWLasePXp4fMO6F9N+S3feHF++fFkRERHKkiWLnnzyyX+1wvxv69Gjh9atW6dSpUqpf//+iWYAlO60CB05cqSjW0G7du0c45vgfzw59+zddl21gkmJJy0fpDsPQZMnT9amTZt07do15cuXTw0bNlTHjh0f2oquJ0JDQxUfH++4l23cuFGbNm1yWsdqtapv3773bObE27dva+/evY6HmZw5c+rpp59W2bJlEz1MpcXvITY2VqtXr9a8efN06NAhSXJ0hXrrrbfUsGFDZc6cOdX3IZvNppkzZ2ru3LkuxxTLnTu33nnnHXXs2DHVSQ/cO3PmzLmvk13dT5s2bVLhwoUdL1rdsXr1aq1evVqBgYFat26d41zPnz+/GjRo4FaLPDP8/PzUtWvXRInigwcP6uDBgw/Nd2PvJm62lZOn/vnnH4/usQ/C/Ugc3s2TemCtWrU87rFjf36LiYlx3CfduY6bfZ41a+vWrY5uzP/myxb8D0m1/4ikbj5m3Y8EhKdCQkLUu3dvnTp1yuVMKBaLRQULFtSECRMc04gXLVpUXbp0MfW5JDV5QUoGDx6srl27qkOHDonKHRISosaNGyeKsSdj3GnZ4aoL4d1iY2N16dIlx8DIefPmdTnVcps2bdS0aVOXZUqKp9357u5y88svvygoKEibN29WTEyM4+/p0qXTyy+/rDZt2ricntuMU6dOaebMmVq7dq0CAwMdXSGuXr2qdu3a6cSJE47fkI+PjwIDAz1OVB06dEj+/v7KkSOHPvjgAzVs2FC5c+dWRESEatasqVu3bqlDhw7q3bu3JGnGjBmaMGGChg8f/q91uz1w4IBOnTqlvHnzqmrVqk435piYGM2bN08//PCDjh075nQuWa1W+fn56c0335S/v7+8vb1TXfk2Iz4+Xhs2bPCopdq+ffvUunVrVatWTdOnT0+2UmPv9nnjxg0FBgY6KkXR0dHasWOH9uzZo9DQUEVGRipLliwqUKCAypUrp5deeilVD71jx47Vli1bkjw+q9WqbNmy6bHHHtOLL77oNEBzSEiIHn/88RRnTU1o+/bt2rFjR6pagZnh6TVC8qy7QFhYmPz9/XX27Fmn37HFYlGpUqU0Z86chyKhk5pzaOzYsRoxYoSWLFmiNm3aOBLAgYGBic4v+6QVn3/++T0tf0pS+z1ERkZq+/btKlu2rNNvft++fZo6daqj/vHWW295fM28ceOGFi9erPfee08XLlzQggULtHjxYl2/ft3RKu3WrVtq3Ljxv/aW3Waz6cCBAzp9+rSuXbumnDlzqlChQnrxxRedriuBgYGqWLGiqUSzpy/mLBaL2rRpo9KlS5uaEXjBggX6/vvvU9UaNrXCw8O1d+/eRNfq0qVLu93NzJVz585p/vz5WrJkiX7++edk1727XnY/75cnT55UcHCwW5OK3C2pZ4Zly5Zp2bJlLic0sl9zateurZ9++inRud6qVSsNGDDAdFmSK2ODBg0StQAzU2/+t7jqJn706NF/9Vq2b98+BQcHa9OmTYkGuP83n908ffkfExOjmJgYt8e7vBc8rQemphfVmjVrNHLkSMe4wxkzZlS9evX0ySefJNk61pPnWXfc/bu02Wwk1e4jZv+EwsPDNXLkSLcSEOPHj/c4AXH16lX9888/ppuh/v33344xC6Kjo1W4cGHVrVtXzz//vHLkyKGbN2/q+PHjWrdunY4dO6YWLVpo9uzZKlu2rEfTEHvS5NU+6Ojt27cTLdu4caOmTJniVgLLbBdCe6uiAwcOaM6cOdqyZYtTosrb21svv/yy3nnnHae3iHv37vU4iZQlS5ZEU8abUbZsWZUtW1YxMTEKDQ3V1atXlStXLuXNmzfVD8D79u3Td9995xiTxmq1On0egwcP1vHjx5U9e3a1b99ekhQUFKQuXbro22+/9WhMoG+++Ube3t6aN2+e0/gZP/30k27evKmcOXOqW7dujr937NhRS5Ys0apVq1xWquLj4x1dEJKqEMTExGjGjBlas2aNBg8e7Hj4unXrlrp166Zdu3Y51i1QoICmTp2q5557TufPn9d7772n06dPOyoROXPmVLp06RQZGambN2/q8OHDOnLkiObPn69p06Y5ktJmKt+nTp1K1IomOf/884++//57LVy4UFeuXPEoqbZo0SKlT59eo0ePTjHxNX/+fIWFhSldunRavXq1ateurSVLlmjChAm6evVqouuGxWLRrFmz9Pjjj6tPnz6Ome3i4uK0YsUK7dixQ+Hh4XrhhRfUqlUrl4mvjRs3aubMmZLuTFGelITXnpdffllDhgzR448/riZNmrh8QWBvseQqEXDw4EHNmTNH/fr1U3h4uFasWKF169Yleitar149vf7666l6EN23b5/jOpgtWzaVK1fOVALSk6ThkCFDdPr0aZUvX14ffPCB8ufPr7///luBgYE6ePCg5s+f73LA86RcuHBBZ86ccfps7r7WeZKgXLp0qYoVK5YoAeHOOdSjRw9t3bpV+fLlSzQ+j8Viccy6bLPZNGzYMC1atEidO3dO1JLBnWPzxNWrVzV+/HidOXNG5cqVM/097NixQ7169VJERISmTp3qeBDdsWOHOnXq5BhI+/Llyzp06JCOHDmigQMHul2+P/74Q8HBwVqzZo1u376tAwcOaOvWrY7xOsuVK6c333xTdevWVZkyZUwNvO7OC6yErFarypcvn2KyzJ5cMZNU87RFiMVi0YgRI9S1a9dESbWgoCDNmTPH5bX8ypUrOnLkiEcPawl/t+5K+GB/4sQJjR8/Xlu3bnVMxJKQl5eX6tSpo48++sjUmFzbtm3TvHnztH37dqfxXM3Uy5YvX/6v3i9tNps2bNig4OBg7du3TzabTfPnz3f7GO2Sqhu7mjHzbps2bVKuXLn05ptvOs71RYsW6bvvvlOFChUcLfUTqlChgjp27GjqpbNhGFq1apVb3Srvlpok88aNG5Ncnlw38X/jWnbr1i398MMPmjdvnuNZzP679OTZrWvXrqa/h1WrVmny5MmaMGGCXnvtNadlkZGRCg8Pd9kbaMaMGS6TnyldO1Mz9ERcXJxH9cA8efK4/UwUEhLiOKb06dOrZcuWunbtmmN5VFSUli1bpl9++UWLFy9O1CX+999/V6tWrUw/zyYnqd9lwh5AqXXy5EmtWbPGZau6hNfFRxlJtUdcfHy82rdvr8OHD5tKQOTNm1etWrXSW2+95bS9Cxcu6Pz58y4rg/Pnz9eUKVO0b98+zZ492+lBtGPHji5nhZw9e7a+/PJLRUVFydvbWwMGDFCrVq0SJb1eeeUVde7cWQsXLtTQoUP18ccfa+XKlan6bLJkyaJatWq5Pb5CUFBQqvaXsAthlSpVHBcp+2cg/a8L4fLly3XmzBkFBwdr/Pjx+uabbxyVy/Tp0zsuzjdv3tT69eu1YcMGtWvXTn369PG4JUm5cuV04MABRUZGymKx6I033lCDBg2UJ08ej7aXPn16FShQwOXscmbcXemwfw5169Z1Gjz75MmT2rBhgywWi7788ktVrlxZklSzZk29+eabevPNNz1qam2z2VS7du1ED7y7d++WJL300kuJkoWVKlVyqridOXNGCxYs0Lp163Tp0iXHuBtPPPGE6tatK39/f6c33127dtX27dtlGIbTIKwjR47Uzp07lT59ejVq1Mgx8Hj79u21dOlStWnTxnF+duzYUeXLl3eaTvzatWv6+eefNWfOHP3222967733kjxudyrfKdm/f7+Cg4O1YcMGpxmzPKkQX7p0SS+//LJbidG5c+eqWrVqypw5s/744w/HuWcYhkqVKqUKFSooX758SpcunW7evKkzZ85oz549OnnypD755BOdOXNG7du31/vvv68DBw44Kq47duxwPFw8/fTTku5UOgcPHqxVq1ZJknLkyJHsmFBRUVG6dOmS9u/fr59++kmXLl3S/Pnzk3wICgkJ0fLly5NtXbN161YNGDAgUcLw4sWLunjxovbv36/Jkydr5MiRevnll1P8/OzuPvfsIiMjdejQIb322mtq2LChy+4Xd0uqVXFyScM9e/ZIunONtI/78uyzz6pSpUqqW7euNm7c6FZSbcWKFQoKCtLhw4cTLStWrJhat27teNi4e/ZVd7lqVZHSOWQYhrZu3apatWpp/PjxTueqXcLZNm/fvq0+ffpo2bJljol2zBxbkyZN1KJFC9P39cWLF6tw4cKmv4fQ0FB17dpV0dHRqlGjhqO7T1xcnAYPHqz4+HhVqVJFI0aMkCQNGzZMwcHBeuWVV5LtahYTE+Po1mlv0WG/L/z0008qWLCgGjZsqMaNG5u+/6T0AqtVq1aOh7PIyEilT5/e1Msi+zhAZpl5iXG3pK63ERERKQ77kFwC4m72CRUMw9DXX39t+sF+ypQpmjZtmnr27KmoqCilS5dOL7zwguNaHRkZqbNnz+rkyZNau3attm7dqkmTJiU7HmV4eLijZUfClpYVKlRQixYtTNfLUpOsSs6VK1ccL54uX77slFy5nx2ODMNQjhw5tGzZMqeWWPaWV0uWLHGZVAsPD0/1S2cz7GNmmf1sXNWP3ekmHhERobp1696za9nJkyc1f/58LV++XDdv3pRhGMqYMaPq16+vFi1aePzsFhER4fH3YJ+gIKHZs2e73WrQ3WtnaiZYevrpp/XSSy+ZrgfaJ4tJTnx8vL766iutWLFCklS6dGkVLFhQP/74oxo0aKAuXbroySef1F9//aXx48dr+/btmjt3rjp37uzYRlRUlHr06KG4uDjTz7N391By53fpyczodwsNDVW/fv0czzR3t1CdOnWqypcvr4CAgPs2RvfDiqTaI27p0qU6fPiwXn31VVMJiD///NMxS+bd20vuAmsYhpo3b66///7bcWKeOnVKGzZsUFBQkONN6cWLF/Xxxx/rl19+cVSG+/Tpk+IAxf7+/oqNjdWwYcMcD7Fmde7cWatWrdLp06e1evVqVatWTW+88YZq166d7FtsV28g3XXo0CEFBgY6uhCWKVNG0p1KrX0qZVddCHv27Kl169Ypa9as6tixo+rXr+/0kHDp0iWtWbNGM2bM0OzZs5U1a1Z16dLFozJ+9913unTpklasWKGVK1cqICBAY8eOVYUKFdSwYUO9+uqrSTbz/je6RCRV6ciWLZvi4uI0duxYp4cZ+0NHkSJFHL9n6U4Xg4oVK2rPnj1OlUR3XbhwweXb8AMHDshisbisMOXIkUM3btyQdKcbTUBAgGN6cLv4+HhduHBB3377rYKDg9W3b1+98847Wr16tbZt26bSpUvrs88+U/HixSXdqUQuWbJEFotFo0aNcrxRrF+/vtq3b6++ffvq/PnzatmypQYNGuSy8pgrVy699tpreu211zRixAjNnTv3nlfYo6KiHImY48ePO7ZfunRptWjRQp9++qlHrS4Mw3CrVUJMTIyaNGmiMmXK6Oeff9aWLVs0ZcoU5cqVS2PHjnX6bdxt+/bt6t+/vyZPnuxIfOXMmVMtW7bUY489pi1btmj79u0aPHiwZs+erb///lsffPCBzpw5I0lq27atevXq5TI54up4JkyYoJkzZ2rJkiXufxAutvPhhx/KZrOpatWqql+/fqK3omvWrNHWrVvVuXNnp+6wSUmuwt+0aVNduXJFK1as0Pfff6/g4GAVKFBADRs2VMOGDR3JRlfldCW5pGFERIQkJZqII0uWLKpRo0aKiYa4uDj16dNHa9askWEYypAhg/Lnz+/4bE6fPq3Dhw+rX79+2rx5s8aMGeMoa9asWfXKK6+oRo0aKU4E0rVr10Rv+N1h38+IESPc+s28/vrrGjVqlHbv3q13333X9LEdPXrUo/u6JFWrVs309xAUFKTbt29rwoQJql+/vuPvO3fu1Llz55QhQwYFBAQod+7cku50n3755Ze1cOFCl9fVCxcuOJJ8YWFhMgxD6dKl06uvvqpVq1Ypc+bM6tKli9q2bevR5C3jxo0z9QKrfPny6tq1a6J77t69e7V3716Xrby2b99uulyS5+MPpYbFYnGr3hMdHa1JkyY5dVnz5MHeMAz17NlT0dHR6tSpkzp06OCyznHlyhUFBQVp1qxZ+uijj7Rq1apE9/YjR444WnbY770+Pj5q2rSp3nrrLT3zzDM6dOiQevXqZapeNm7cuHvaLXH//v2aN2+eNmzYoLi4OBmGody5c6tp06by9/f3uLVpaiYzqVu3bqLPs3DhwnrppZcc42o+aBkzZtTt27cd1+k33njD9OyMyXUTb9KkidM9KTAwMNXXMpvNpk2bNik4ONjxwsgwDOXLl0/vvvuuGjdu7Gj1tGjRIo+e3R7UiE9mrp0jRozw+OX/0KFD3ZoI5e56YErX3VOnTqlv3776448/5O3trV69eqlDhw5q0KCBihcv7tSa0s/PT1OnTlW9evW0ZcsWp6TaqlWrdOHCBfXr18/082zz5s0lmftdSp4n9MuXL68zZ87I399fYWFh8vHxUc2aNfXcc88pZ86cjvrjli1btHfvXjVp0kTBwcEP9aRf/zaSav8he/fuNT0mww8//KA8efKYTkDs3LnTozIahqG//vpLTz/9tN5//33lzp1bP/30kxYuXKghQ4ZoyZIl+vXXX/Xhhx8qLCxMGTNmVJ48eRQbG+v2jF9vv/22goKCHEk1d962JtSsWTM1a9ZMV65c0Y8//uh445k5c2a98soratiwoSpXrpyqASTvNn/+fNNdCO0tmwoUKKDvvvvOZUIob968at++vV577TW1adNGX331ldMN36y8efPq/fff1/vvv6/jx49rxYoVWrVqlfr3768hQ4aoZs2aeuONN/TSSy85Nee2P1iktkuEO5WO+vXrq2bNmolaB+zdu1cWi0UvvfRSon35+vrq0KFDHs0sVqZMmUQVlcuXL+vs2bOO7kV3u3HjhrJmzarly5dr8ODB8vb2VvPmzV0mPNauXatFixbpiy++kLe3t7Zt26bs2bPr66+/dnqg2LRpk2w2mwoVKuT0AF+5cmWVKVNGv/zyi/Lnz6/+/fu7VWHp27evo+XcvXDq1CnHWG6RkZFOybQhQ4bI19dXkucD1r/55puKi4tLcb306dM7HnK3bt0qwzDk5eWlGTNmOBKUSalevbpmzpyppk2bav369cqUKZMWL17seKB95513NGjQIC1atEi///67unTposuXL6tgwYIaMWKEqbECLRaLevXqpXXr1nn8gsDeDSB9+vQaP368y8pmsWLF1KhRI+3YsUPdu3fXwIEDVbZsWeXMmdNpPTMVfulOMjc8PFxr1qzRypUrNXXqVE2dOtWxv9dee83xgOGp5L7vPHnypNj1YfTo0Vq9erXy58+v3r17q1atWk6zWcbGxmrz5s0aP3681q9fr8cee0xBQUFasWKF1q9fr2XLlumnn35S/fr19frrryf7/d7dktVd1apVc7u1tNVqVZUqVbRnzx6Pji01khqvJ7nvYefOnSpbtmyie9KOHTsk3bl2JfyNZMyYUdWqVdP+/fsTrR8cHKxt27bJZrM5ri1Vq1bVmDFjlCtXLl2/fl27d+/W2LFjNXXqVFWtWlWNGjVyKykqSTNnztTMmTNNvcCyt8q62969ezVlypQ0P86NxWJJscvUkSNHNHToUJ08eVKZMmXSJ598oqFDh3q0P8MwFBUVpVGjRiXboiZ37tzq3bu3ihYtql69eik4OFi9evVyjDM1b948R4tA+4uvn3/+Wa+99po+/fRTx3Y8qZeNGzdOf/75p0fHZxcVFaUff/xRwcHBjhdP9vrmq6++qvHjx6dqRu/USqoV0DPPPKMtW7bc38IkYdeuXdq4caNWrlypFStWaPny5Xr66af1xhtv6PXXX0+2herOnTsVHBxsqpt4aq5lV69e1cKFC/X9998rNDRUhmEoc+bMql+/vpYsWaIaNWokev5Zu3btfX12Sw2z105XLyLcNWbMGI/qgck1mAgKCtKECRN0+/Zt+fr6KiAgwJGUvnTpklq0aJEoxtvbW1WrVtXatWud/r5ixQrlz5/fo+fZ/Pnzm/5dSnda0JodK9xisejQoUPq3r27wsLC1K5dO/Xs2dPly72YmBhNmTJF06dPV69evbR48eIUh0L4ryKp9h+yb98+7d271611EzbDb9q0qekERGouzNmzZ9f333/vGL+nRo0aypUrl6ZNm6Zt27apV69eioyMVJkyZRQQEKA2bdqoSpUqbiexLBaLKlWqpK1bt0q604LMbCsy++D6JUuWVP/+/f+Pve+MiiLrut7VCAqiSDCB6R11BtFRVDASTWQUxBxQx5wVE+o4ZgQjShAdAygGQECBJivBRDIHRgUVBEXJOXZ9P1hVH9CBrmpkwuNea9Ya6a6u6q669567zzl74/79+wgODkZkZCRu3rwJZWVlmJmZwdzcnLFVtSCkpKRAT0+PUQthx44dkZWVBWdn52YrrLp27QpnZ2dYW1vD398fQP3iy7QiiNJfAYD+/ftj48aN2LhxI1JSUhAcHIzw8HCEh4dDQUEBRkZGsLCwgLa2tsQtEUyCjsLCQr6WVJIk8eTJEwAQuPFt06YNampqmPwUNFRVVelKJApUIKWqqopevXrxHZOSkgIVFRXs2bMHioqKOHfuHJ8YqYKCArS0tKClpYVp06Zh0aJFOHToEF390TRD//DhQ6FjdvDgwXj8+DFGjRol9mInJSWF0aNHSyRETZIkTcQ8fPiQ3sRoa2tj6tSp2Lp1K9TV1WlCDWBfcdGjRw/GWXJqYzVy5MhmCTUK6urqGDFiBBISEmBqasp3vfPnz4ePjw82bNiAr1+/YvLkyfjjjz+Emos0h1GjRrEie4H/33J44MCBZrO3Ojo6OHjwINavX4+bN29iwYIFAJiNvabo2LEjZsyYgRkzZiAnJ4cm4Q8ePAhHR0eMGjUKFhYWAtuFxAGPxxNKEEtJSaGurk7osampqfDy8oKGhgYuXbok0PWWqnLS0dHBwoULcfXqVVhbW2P//v3YtWsXYmNjERQUhICAAFy/fp12vLSwsBAoZ8AGwqovu3TpIlDAWFVVFQUFBay+myRgcx+ys7NhaWnJ9/fk5GR6HW8KVVVV5OXloaSkhG7by8jIAEmSkJWVhbGxMaZOnYq5c+eiZ8+eNAFw/vx5ZGVlwdfXF/7+/nRlRKdOnehWUGHIyMjAiRMnGCewJAFVockUlpaWePHiBXr37t1IIzE9PR3nz59HWloaVFVVMX369BZ3a2wIHo8HNzc3nD59GrW1tXQ816tXL9akGlCfBBC3VdDU1BQeHh64c+cOCIKAn58f8vPzQZIkVFVVYW1tDSsrK6ipqQms3GITlwEQ6PAqDt6/f48rV64gMDCQTjypqanBysoKVlZWGD9+PJSUlP5WQg3grwymIC0tzTqOamnIycnB0tISlpaWyM/PR2hoKIKCguDs7IyTJ09iyJAhsLCwgKmpaaMEkpGRET2fMGkTZzOXEQSBL1++wMDAgP7dKJLE2NgYsrKyQqvU//rrL+jq6rbq3o0NSJJkNXeamJgwTkR9+/YNqqqqrOJAQXFndnY2tm3bhqSkJHA4HCxbtgyrV69uFENXVVUJje2UlZX5EkofP35ktZ/18/NDQkIC4+cSALp3784qro6MjERqaiqWLFlCV+MKgoyMDDZs2AAOh4PTp08jIiICZmZmjM/3X8APUu0/BG1tbcbi866urqwICElgaGjIJ4htZWUFd3d3bN26FWVlZVi1ahVWrVoFDoeDvLw8xvbRioqKKCwsBADIy8vzCUUyAYfDgY6ODnR0dLB3715ER0cjODgYV65cgZeXF3r16iVwMWWCb9++CWy5EtVC+O3bNxAEIbYzjLq6OrS1tWnCp6HYprhoSKo1xPDhwzF8+HDs3LkTd+/eRUhICEJCQuDr6ytRjz1JkrCzs0NERITYQYe8vDx97ylQrkxt2rTB8OHD+c6TlZUlVKS9OeOAMWPG4OrVq0hPT8dPP/2Euro6XL16FQRBYNKkSXzvj46Oxtu3b6GpqYnKykq6ekcUfvnlFxw7dgy2traoqqpCt27d+N7z+PFjABA4B7Rr1w4kSTKuRpHEtp0kSYwbNw5fvnyhNzFTpkyBtbU13bJCORk2BFvdmf79+yMwMBApKSkC73FTJCcn48mTJ2jTpg1jAuTnn3/GgwcPBLbeUEFOVlYW1q9fj+XLlzP67KZoOJcxBaVzJm6AY2xsjP79+yMiIgK//vorrly5wmjsiULXrl2xePFiLF68GGlpaQgODgaXy4W9vT327NnD/MtJiOvXr0NKSgrOzs4CSaeGkJOTw5EjR2Bqagp/f38MGjQIMjIymDhxIiZOnIjS0lJEREQgKCgIf/75J86cOYNffvmF1p2UBA21Zxpi+vTpmD59Ot/fy8rKICUlBZIkGX83Yef6XhC0ISkrK6MrfQRVrVZWVoLH40FPTw8VFRV0tp6qUhZFXqupqWH9+vVYu3YtYmNj4evri7i4OHh5eeHSpUsgCKKRCDMFHx8f8Hg8xgksSVqtoqOjGeukkSQJd3d3ZGRk4PTp09DX1wdQL4y9cOFCulX78ePH4HK52LRpk0jdTLZIS0vD1q1b8fLlS7Rp0wZ2dnZYvHgx65auhtDU1GT0/qFDh+Lq1at49+4d3d5paWkpFqHIJi4DBLe2NgcejwdTU1M6aTF58mRMmTJFIBkjCOK4TlJk6vdyHf8eIEmSLyagksHJyclCx1jDuUNJSQlz5szBnDlz8OnTJwQFBYHL5WLfvn1wcHDA2LFjaYmXjx8/smoTZzOX3bx5EwDQp08fmJiYwMLCQmyNR7bJ49YGSZKs5k5/f39s2rQJ1dXVKCkpaTZ2pSQi5s2bB1dXV8ZxYNPKuBs3bsDBwQGlpaXo06cPHB0dBWrDikrscTgcPiMVtvvZuro6tG/fnpV8gbW1Nauq6KVLl0JJSalRNa4orFy5Ejdu3KA15v4X8YNU+w9hxIgRjAfO5cuXWREQkkAQyUKRBIWFhTh06BAmT55Mv6agoCBQ50UUcnNz0blzZ3z69Am2trYt1mYhIyMDExMTmJiYoKSkBOHh4fDx8YGLi4vEWgVMWwiLi4sZu2VqaGjQG+Hx48ezdkgSBikpKfTr1w/9+/dHamoq3rx5g8+fP7P+PJIkweVy0a9fP7GDjv79+/Nlqigx5SFDhvBVeFVUVODevXuN9DaYGAcsXLgQPj4+mDVrFsaNG4e//voLr169gpycXCNx7pKSEoSGhsLR0RHS0tIoLy+HhoaGSA2vhhg5ciQ0NTXx7NkzvuxXWloaCgoKwOFwBD4rOTk5ACC2sx0Fpu9vCJIkUVRUhDlz5sDY2Fjs1sd58+ax2nxRulF2dna4ePGiUO0uoP732rBhA9q1a4eqqqpmiYemoJ4hQVV/1G82cOBAiQk1oH5OVFBQYFX9UFxczPiYESNGIDw8HHPmzAFBEIzGnrjIz89HYWEhXY3BZhMqKZKTkzFmzBixv1PPnj0xduxYgaSvvLw8rK2taT25kJAQBAcHw8nJCUeOHAFJknj16hWr6xTlFCsIqampIEmS1Xe7c+cOm0tkDRUVFXpuopCYmIi6ujooKCgITDa8evUKNTU1kJGRwdq1a2FtbS0wySAKHA4HhoaGMDQ0RG5uLvz9/eHn54eMjAzcv38fenp60NfXh7W1NQwMDHDv3j1oaWkxTmA9fPiQ0XU1/QwmulfV1dWIiIjAx48faQkBCr///jtKS0uhoaFBuw06OTnh2LFjYq8/4oIS8K+srMSAAQPg6OjYos5wwtqMhYH6HWRlZTFz5kyMGzeOz+VUFJjGZQBYu5jLyspi1apVmDt3LqO1l4nr5NOnT1nPRX8X5s+fz/c3kiSFVkhTnSaC0KNHD6xYsQIrVqxAamoqTbBt2rQJsrKyUFFRQV5eHuM2cTZzGXWtAwcORJ8+fRg922yTx38H2MydUVFRePr0KW0E1a1bN6xZs4aPEM7Ly8OuXbvoav6pU6fi7NmzjONASq8sLy8PO3fupNuY586di02bNomlaSoO2O5n27Zti4qKCtbyBWyQlpYmsBpSGKSlpTF27FhaHuR/ET9Itf9xsCUgJIGgah9q0I4YMaIRoQbUV+ncvXsXtbW1Yk0eNTU1iI+Px4ABA/Dp0yeJrlUYSktLER0djYiICLx69YoOvNhmYtm0ENbU1IjlcNMQUlJSdOZEXV0dVlZWrK63Kb5+/YrQ0FBwuVzahYbKtpqZmdFOdGwgKyvLKOgwNjbG/v378eeff2LBggV4+/YtvL29QRAE37MFAIcOHUJxcTHtesjUOEBVVRWnTp3C5s2b6VZJWVlZODk5NdrsTZw4kTYn2LVrF06dOgUjIyNGv8XgwYPx7NkzvgpDKqBQV1fn08Kqra39W3Q0gHp3yNu3b6O2thalpaXQ1dVt1uacQvv27aGlpSX2+4F6Md7ff/8d1tbWmD17NiZMmIB+/fpBTk6O1qiLiIjA9evXUVlZiX379mHnzp2M9RHFeT9lby8JSJLE/fv30bNnT1akGhuySl5eniZtmY49UXj69ClCQkIQFhaGb9++0cYSM2bMgJmZGevMpqBKBqD5aobs7Gzo6OgwOlffvn2braRUUVGBra0tbG1t8e7dO+zatQspKSmsW3iTkpLw9etXsVyWMzIy8OjRIzq5wQR9+/aViFQT1sov6j707t0bsbGxqKiooPXeAgMD6falpuvpu3fv8OjRIzop4eHhgcePH8PAwAAmJiYi18Nx48Zh2rRpmDp1aqPfUkVFBUuXLsXSpUuRkJAAHx8f2nH29u3bUFJSQm1tLeNqdA0NDYlItfHjxzNKBrq4uKCmpgZbtmzBokWL6L8nJSUhNTUVbdq0gbOzM020uru7Y8KECfD29mZ9jQ2RlZWFbdu2ITk5GRwOBytWrMCqVatafMPH9PPatGkDgiCgrKwMDw8PnDlzBoqKinT71IABA4QeyyYuA8C6M6K8vBxHjhyh2+onT57cLLHK1kEXEDxmRY1X6rWWqDgUB2pqasjKymKkRcoEFHE9ceJEODk54dGjR6ioqEBUVBTjNvFhw4Yxnss4HA6UlZVx8+ZN3Lx5E1JSUjAwMMCUKVNgYGAg8ln/O/ZubMHUFKN79+54+PAhPnz4QP/t8+fP2LFjBzgcDn0P7ty5A3t7exQVFYHD4WDJkiXo3r07du7cyTgO7NatG8LCwrBnzx4UFBRAVVUVBw8eFLtSVFyw3c+OHj2a1utlKl/AFl+/fmWcsOrSpQvy8vJa/Fr+LfhBqv2Pgy0B8b1AuSs1xKRJk3Dv3j2cO3cOy5Yta/YzPDw88O3bN9jb29O6ai2ByspK3L59GyEhIYiPj0dNTQ04HA5GjhwJMzMz7NixA56enrRmGQXKoU5QVRj1GpsWQkpDhgnevXuH7t274+3bt4yOE4SCggKEhYWBy+UiJSUFPB4PMjIymDBhAszMzGBoaChRpRNQH7wNGjSIUdAxffp0+Pn54ejRozhx4gSdpdXQ0MDUqVPp93l7eyM4OBhPnjyBmpoapk+fzso4YMaMGdDT08OdO3eQlJSEmpoaaGtr87WT/vLLL2jXrh0WLVqEkSNHwsHBgXHw3a5dO3A4HDx58gRcLhempqb4+vUrPWbNzc35jvHw8GCt8UKBTfBNEARWr14Nf39/XLt2DdevX4eioiLMzc1hbW0tNNCaPHkyoqKiUFpaimfPnsHU1BQWFhYCS+8Foba2Fo6Ojjh37hzOnTvH9zpV0bZr1y7Y2NjQlRstDUmFWnk8HhwdHZGZmYmZM2fi8ePHAk1XqPn48+fPfPehbdu2KC8vZ3Te7OxsdO3aFatXr4avry+rgJ/C69evERISgtDQUGRnZ4MkSXTp0gW2trYwMzNrET1KQHAlAyC8moEgCFaZZx6P1+z35vF4uHfvHrhcLqKjo+n706lTJ1ZjqKqqCrt27YKbm5tIMreurg7bt28HwK7Np2mbClOIEkMWVVVCkiRsbW0xa9YsvHr1CuHh4eBwOHzvT09Px5o1a8Dj8eDh4YG8vDz4+voiPj4ed+/ehYODA/T19WFlZQUDAwO+82RnZ+PkyZNwdXWFgYEBpk+fzqc7NHLkSIwcORLFxcUIDAyEn58f3r59i3bt2jFey5gkA1oCsbGxUFdXb0SoUX8H6tvPGlYuKioqQldXl9bhFaThRiVvBN3XhokdX19fHDp0CGVlZfi///s/ODo6MqoG+94gCAKRkZF48OABfH19ERUVBU9PT3h5eeGXX34RuiFlE5cB7MY6h8OBl5cXfHx8EBkZiQsXLuDixYv4+eefYW1tDQsLC4HXyNZBNz8/X+iYFTZeqet2cXERaYomiKik9FSZwNraGq6urrh06RKj48RBamoqvTZlZWXREhWUTirTNvGZM2ciODiY0VwG1N8PVVVV+Pr6IiAggCb0FRUVRTpGS7J3ay1SlALTuZNyCTYzM8P69euhoqKCO3fuYM+ePTh+/DgmT54Mb29vHDx4EDweD/3794eDgwPdbTJt2jTGcaCdnR24XC6A+uT1li1bIC8vj9TU1GavV5j+paD5s0uXLrh37x7s7Ozg7Ozc7Gc33M+ylS9gC3l5eRQUFDA6pqioqEXO/W/FD1LtfxxsCYjvVQEmaDNgbW2N8+fP4+TJk5CWloatra3AgLW2thYeHh5wc3PDzz//LDDoYYrq6mrExcUhJCQEMTExdPWSpqYmzMzMYGJiQgcrO3bsQHFxsVDSUVjpNUEQrFoIORwOcnJy8OXLF7GyCdnZ2bh37x5sbGxYk2olJSWIiIgAl8tFQkICamtraUF7MzMzTJo0SaDFPVsQBAEvLy9kZGSIHXRIS0vD09MTR44cQVRUFGpqaqCnp4cdO3Y0em7OnDmDnJwc9O7dG2fOnEFhYSEr4wB9fX1069YNcnJytIaNIHh6ejb6t4qKSqNMnDj4+PEjevTogerqatjZ2eHIkSMoKChARUUFevTogdmzZ9PvvXPnDq1bpaioiPz8fMbmFA2DAqbBN0WqrVq1Cnfv3oWvry9u375NBwD9+/cXGNw5OjqiqqoKt2/fRlBQEK5fvw5vb2/07NmTFoEXVdI/a9Ys6Ojo4OrVqwgPD8fnz59p3Yvu3bvDyMgIM2fObCQAz9QhmG1iQRx35pqaGuTm5iIxMRGfPn1Cnz59MHPmTDg5OYk0XRFkREARJeXl5WIZJZSXlyMuLg4jR46kf2smYw+obxkICQkBl8vFx48fQZIkFBQUMG3aNJiZmWHEiBFCg3qmpGHXrl2Rk5PDqpIhPz+fcRvU69evoaqqKvC1hIQEcLlcREREoLCwECRJol+/fli0aBFOnDiBoqIiRm1M1BiaNGkSwsPDMW/ePGzdulUgWfH8+XPs378fz549w7Rp05CSksLqu0kCttUkI0aMgKurK54/f07f3yVLljT6nrNnz8bTp09RV1cHKysrmgyzsrJCeno6Tf5S1WUKCgo0IUkhJCSE1nuhnuPu3bsLrF7r2LEj5s+fj/nz5+PZs2fYtGkT4zWTaduupMjIyICxsTHf35OSkkAQhMA2z169etFtTsI03EiShL29vcC/A8Dy5ctp4s7W1hZ2dnZitQux3dhLQgiMHj0ao0ePRmFhIQIDA3Hjxg2kpqbC0dERBEEgJSUFUVFRdNKATVwG1M+BbMa6trY2tLW1UVJSgps3b8LPzw+pqak4dOgQDh8+DIIgkJ+f36jKha3rZFhYGGN9OqC+pZGt1AnbpHNLIT09nV6bPnz4AJIkoaSkhFmzZsHc3BzDhg1r9H6mbeKrVq1iPJdRlVB2dnZYv3497ty5A19fX9y9exeXL18GQRCIi4vDxYsXYWlpSRMWkuzd2N4HQfEjRToJmiOoNYXp3JmVlQVpaWk4OTnRMbupqSkqKiqwc+dOnDt3DkePHgVBEFi8eDHWrVvHl8RkGgc2dFh//vy52O6clBmXMP3LpvMnZRAYFhaG8+fPs9rPMnkutbW1BWr/ioN+/frRpizigOqsEGau9L8AgpRUCOoH/hFwcXHByJEjBYphNofi4mKBBERDtllfX78RAWFkZAQ1NTU+R5GsrCxkZ2cLvI6srCx8+vQJa9asEdjWoK6ujtWrVwt87e3bt5gzZw5KSkrQpUsX6Orqol+/fpCXl0dlZSXS09MRHR2Nr1+/QkFBAb6+vujZsyfGjRuHBQsWCK1kEIS6ujrcvXuXrjSgxH379+8Pc3NzmJmZCZykJNErUFNTQ1xcHDZv3ky3CMrKyuLw4cOYMGEC/b5Ro0bRry9cuBDnz5/HyJEjcfbsWZGBbFVVFRYuXIjHjx8jMDAQkydPFvpbC8OKFStw79491NTU0MSiubk5TExMmhURFXZvXVxc4OrqKnBDJ+i1urq6RkEHZSvdvXt3zJ8/v1HQ0RyuXLkCJSUlTJgwAW3atIGbmxtOnTqF8+fPi6Uzk5CQAFtbW6xduxYrV64U65wNsXHjRsTExNAkRXPIz8/HhAkTMHHiRKxbtw6///477t+/T98LJyenRu0ourq6+PbtGxQUFHD27FmBoubigCRJxgYoFJpmmPPz8xEQEAA/Pz+8f/8eQD2RrqenB2traxgaGvIFGMXFxQgNDUVwcDCSk5MB1LdWTZ48Gaampo02DIJQW1uLgoICKCoqCiTt1dXVWW3UeDweRo4cKfC3cXFxwYgRI/heO3XqFKNzjR49GgcOHICqqmqz7p2CUF5ejoKCAsyfPx87duxo9v1//PEHfHx84OHhwVfFI87YozLmQP3mbdy4cTA3N4eurm6z1VNs7wPAjhDas2cPfHx8cPPmTbFaJd+8eYMpU6Zg4cKF2Lx5MwDgyZMndEtrbm4u7dZHtbNSrrbiBuiC4O7ujiVLluDx48cgCAJdunTBL7/8AgUFBRQWFuLDhw/49OkTSJKEtrY2zp49C0dHR1bfra6uDj169GC8rmdnZ0tEyj19+pQ2xNDX1+drnZ40aRLy8/OxYMECrFy5UmDFXm1tLd229eDBA3oD1bDSR0lJCTweDzExMfD390dMTAxNUAirXgPqN4yU+7e4CSwjIyNUV1dj7dq1fCLYza17p06dEhonCcOvv/6KhQsXYuPGjfTfqqqqoKWlhdraWnh7e/ORBo6Ojrh+/TomTZrEauwFBwejtrYWQL2GpDjC4EC97pqCggJfpXZJSQlKSkoEEtclJSUoKioSeJwoUJ8p7Pl8+vQprl+/jrCwMJSXl4MgCHTq1AmWlpawsrLC169fGcVlPXv2bFaQXRgEVWS9ePECPj4+4HK5KC0tpa/PwsIC1tbWmDdvHiwtLfH77783Os7KygqpqanYunUr7eRM4fjx47hw4QIt1dEaYNoCSIEgCPrekSSJ5ORkZGRk0NU4PXr0gLa2ttAq3szMTISGhiIkJARv3rwBSZJo3749Jk6cCHNzc4wePZpxVWnDNvGqqioQBAElJSXcu3evReYyoL6t18/PD/7+/nTVMlUtbm1tjXHjxrHeu7EBm0pD6jgZGRlGc6ehoSH69evXiOgC6o1DqFiiffv2OHHihNiakM3FgWzX6PT0dIFrhigUFxcjNjYWdXV1Yu9nbW1tBSaAGkLUc8kG58+fx+HDh7F3715ac04Url27hj179mDfvn2wsbFhdc5/O36Qaj8gFpoSEGwXSCqTIuj4gIAADBgwQOBrBEFgxYoV2LZtG1JSUui/NfxcoF7H6ODBg6yDGqBxgNRwg9SSYrvCUF5eLrKF0NbWtlELob29PQICAjBw4ECsX78eY8aMaRQgkCSJuLg4HDlyBO/evcPcuXOxY8cOkb+1MFCb3i5dusDY2JiRRfPBgwdhZWXFJzLq7++PwMBAXLp0iS/7Sb0mLBgWJ+hgAhsbG5AkycjRcObMmcjIyICTkxMjfaYzZ87g9OnTKC8vh4mJCY4fP97sMatXr0Z0dDQuX75Mb14qKipQW1srcJNx/PhxdOzYETY2NlBQUKD13tigpbT3GiI5ORm+vr6IiIigXfwUFRVhaWmJbdu2CTwmJycHQUFBCAkJwevXryElJYVRo0bBwsICEydOZGw4AAiu8BIH1DMnLgiCoDf6wjbLBEGgQ4cOUFJSwrBhw4RWRYkLkiQxffp0vHjxArNnz8batWsFutwWFhbCyckJAQEBGDFiBF9VZVMIG3s1NTUgCAJdu3aFvr6+2O3pBEEgIiKC1XcEwEqv7N27d7C0tET//v3h5eUl1P0XqBcvnjdvHjIyMnDy5EmkpKQgNDSUrp5TVlaGiYkJzMzMBEoYSAoej4c///wT3t7efGLYAKCsrIw5c+Zg+fLl4HA4rL8bRZAwRcON7/fA169foaysLPbmNzs7G35+fggICMDnz5/p59PQ0BCnTp2i35efn49bt27hxo0bePv2LU0SN928PH78GLNmzWKcwGK7EeXxeHBzc2NkJKSnpwcdHZ1G7twPHjzAwoULISsri6SkJL7N5JIlS/D+/Xtai4kpJIkD2W7Q2RwnzvNZXl6O4OBg+Pr60u1nHA4Hr169YhyXfQ9UVFSAy+XC19eXdngkCAIkSWLJkiWws7Oj31tWVoYRI0aAx+PBz88PAwcObPRZDg4O8Pf3Z+20zQaSJJ1VVVXh4eGBy5cvC9RrUlZWxsyZM7FixQp6jrh48SJCQkLw4sULutVPX18f5ubmMDAwYG0m0RBN28TFmQOZzmUAcP/+ffj4+CA6OpqWnRG3Ernp3o3tfQgPDxcr+dsUHz9+xOnTpxnNnSkpKZg1axZ2797d6PW6ujoMHDgQMjIy8PX1pZNW/0ZkZmYy2s9S+y9qjyMsAQQ0fi7fvHlDJ6OZgsPhYOLEiSgrK4Ojo6NIQjYwMBA7d+5Ely5dwOVyW8zY4d+GH6TafwReXl7Q1NRkpGNB6Ryx2WxTOhxMwaRirCEaBkXPnj1DXFwc3r17h5KSEigoKKBPnz4wNjZulvgqLi5GYmIicnJyUFpaivbt26Nnz57Q1NSkA6WG5BETzR+CIBoF7MKQkZEhVOCWKaqrq7FhwwZER0eDIAi0bdsWvXr1QocOHVBRUYGMjAy60s7U1BSHDx9mrfXSMIAWN7ClgmBRwbCw16i/ixOoSBJ0AMD79+8xbdo0WFhY4I8//hD7uIMHD8LT01NgVYGLiwvc3NwEXgdVqaCrq4v4+Hjo6+tjy5Yt+Omnn/jem5aWhgMHDuDBgwcwNjYWi4D7J6CyslKshbW0tBS3bt2Cr68vXr9+LfY9T0tLo9tbMzIy0K5dOzx+/LglLl0siNPKKQwt5UYsDnJycjB79mxkZ2ejXbt20NTURN++fdGhQwc6K5qYmIiKigr07t0bV69eZaSJ0XDsVVdXA2DeotVSpExxcTFu377NV8kwbty4Rm6IFI4dO4YzZ85AVVUVS5cuhaGhYaOETH5+PsLCwuDu7o7c3Fxs2LABx44do8nPCRMm0NUOraFTQ5IkHj9+jA8fPiA3NxeKioro2bMntLW1+eZ1Nt+NTTsYhYZVmUzvw/cClVTy9fVFTEwM6urqhD5nL1++hJ+fH7hcLoqKitCmTRvo6+vTmplsElhstaDYjIc1a9bgyZMnuHPnDk2e7dy5E35+fpgwYQLffPX582cYGRlhwoQJmDVrFtTU1BiT+GwTNQUFBawqZsSVuhAEJknAN2/ewMfHB0FBQa3mZCfuegnUr31UJWpBQQEmT54MJycn+vU7d+5gxYoVUFBQwMOHD/nmpnnz5uHr168IDw9v9PfU1FTk5OSgpKQE7du3R69evdC3b99mryc7O5tvrLNtOWuK8vJyLF68mCapu3fvjr59+9I6t+/evaPdWIcMGYIzZ86gY8eOjeJ4ExMTTJgwQWxpEjZk8bNnz767jmBBQQHdthwcHPxdz9WSYDp38ng8kR1NZmZmOHr0aGt+he8GcfezaWlptHxBbm6u0ARQU7DtAKAcdJOTk7Fo0SLU1NRg8ODBMDAw4KuqCw8Px/PnzyEjIwNvb29a2+5/ET9Itf8IhLXXUWKsgvq9RbUgiML79++Rnp7OKItK4e+qlnn37h2OHTuG2NhYgYLMUlJSmDBhAjZs2MC6PJoKhJ8+fYqTJ09i8eLFfKXJtbW10NbWRp8+fbBjx44WczXy8/PDuXPn6Ja6htDQ0MCiRYsaidizyVBSGgxMglMKkgSmTDYmBQUFMDAwgLy8PF/Jc2pqKlJTUwWKElMtN8uWLWvUPtMcjh07Bg8PD6GkWnOtrYmJiVi0aBGeP38OgiDQs2dPPsIjLS0NJEli2LBhOHfuHGNzCqC+GuXbt29iBYvi6m+JQlJSErZv347IyEhGx1EbW3GIzaSkJFrDKi8v77tXy/xdqKioQF1dnURahUVFRThw4ABCQkJQV1cH4P9XOQD1OoRWVlbYtm0b63tfUFCAffv2ITk5GRs2bGB8/MSJE1l/x/Lycjg4OODmzZuoqakB0Jisb9OmDczNzbFjx45G5yBJEgcOHKC1a4B612IqKdFQ2Pm3336DnZ0dHaS2a9eOkRkFQRCtajXP5rtJCqb3ITo6Gj/99BP+7//+T+xzcLlccLlcxqR2Xl4eAgICsHjxYpHvo3RUQ0NDaaL41atXrZrAosDj8fDixQv07t27UXVUeno6zp8/j7S0NKiqqmL69OkoLy/HihUrMH78eNja2uLVq1c4cuQI6urqcPbs2UaV1EVFRVi+fDmePHmCkydP0i2qrUn2tybYVmRVV1e3iItzc2C7XtbU1MDW1hZpaWmIiYmhY4N169YhIiIC5ubmOHz4cKNj3r17h8mTJ8PS0hIODg7Iz8+Hu7s7goKC6A6NhlBUVISVlRWWLl3KV6EXFBQET09PvHz5ku84DQ0NzJs3T2JXwo0bN4LL5WLw4MHYvn27QOL/5cuXcHBwQHJyMoyMjODs7CwxmcAUrTmXsYUkezdJwHTu5HK5QuVp1NXVsWrVKtrooaVQWloKGRkZRhWMT58+xdOnTxsVi6SlpSE0NLSRYUCPHj1gZGTUIt1OTOULmHRiVFVV0ZWgDePpJ0+eYNOmTfj06ZPQAoh+/frhyJEjrKuX/yv4YVTwH0dzAtxUW0nTQFMUARESEoJTp04xbk8Avk8bWXOIjY3F+vXrUVFRAWlpaQwaNAjdu3eHtLQ0SktLkZmZibS0NISFhSE2NhabNm1inXG6c+cO1q5di9raWmhpafGRapTBw+vXr7FgwQI4OTnB1NQUDg4O0NXVZdxCeO7cOSQkJMDGxgY2NjbIysriy3g0dPyiMG/evFYNOForWFdUVERVVRWWLFnC91pUVBRcXV1FBnlsjAMkQYcOHeDt7Q13d3dcvnwZGRkZyMjIaPQeZWVlzJ8/H0uWLAGHw4GVlRVmzpyJGTNmNHpfdnY2srKyBOoeXb16lSb4Pn36hDNnzsDGxobvOSdJEuPGjcPw4cOxadMmOkD8/PkzTp48ifj4eJSUlGDQoEFYvXo13/NdVVWFI0eOwNvbm5WYcf/+/UVu7p8+fUprWH379g0kSaJ3796YMWMGzMzMGJ9PENjotoiL2NhYXLlyBR4eHmIfs3TpUqSkpLAm4AmCgKenJ5ycnGBnZ4f79+/zzRHjx4+XuIJIUVERx44dY338oEGDoKmpCR0dHejq6vK1LAlDXl4eZs+ejYyMDBAEgWHDhqFfv36NKhlSUlIQEBCAlJQUXLp0ia7YIggCO3fuxPjx43Hu3DkkJiairKwMZWVlAOrJRkNDQ9ja2jbSiyJJEhUVFaioqBDrGqmxwHS9JAiCcWvehw8fYG9vj6tXr7L6bmzB5j5QRiZN14eAgAAEBAQINOWg9GbYQJz1NTc3Fx8/fkRWVlYjowMZGRm4uro2SmC9efOm0bGCElhs8fr1a6xfvx4ZGRk4ffo0bYLz/PlzLFy4kN6EPn78GFwuF5s2bYKVlRUCAgLodmiSJDFlypRG39vOzg4xMTEoKyvD2LFjMXHiRNbC82xhY2NDt6tqampKNK/W1NTgy5cvtF5St27d+MhutvEOSZL4888/GcdlZ8+exYQJE777eiktLY2NGzdi7ty5jB10J0+ejBcvXmDp0qUoKCgASZJQUVFpFBtnZWUhPz8f58+fx82bN+Hh4YGBAweitrYWW7ZsQWhoKN1aqaqqSo/1jx8/4uXLl7C3t8edO3dw+PBhnDlzhvH3y87ORkhICHR0dHDmzBmhJPXAgQPh5eWFlStXIiIiAomJiaz0pYH6LhxB7qXNQVh1lai5LCAgAPHx8YzOc+3aNfj4+ODTp09YunQp472bi4sLq3g8Ozub9f5NW1ub0dxJuXAKw/eoCtfW1qbXo4ZITExEYmKiwN8sPj4erq6umD9/PnJycmBvb08L+zcczwRBwM3NDdra2nB0dET37t1ZXyeHw8G4ceMwbty4RvIFwsx3xJXGSExMpGVXOnbs2EijUVNTE+Hh4QgPD0d8fDzevXuH4uJidOrUia6q09PTa3XH638ifpBqP4DKykq+v4lDQLx+/bpVMh6CXAfFxfDhw7F+/XpUVVVh+fLlWLx4scBqiNzcXHh6euL8+fM4ffo0QkJCGOuyZWZm0ht7Ozs7zJo1i+89ffr0wf3793H58mWcOHECO3bswJAhQ+Dp6YkOHTrwBW+iWgirq6v5XAgFmUc0xbdv31gHHK2Nc+fOQVtbmzHJee3aNVaBw927d+ngvDnk5+czDogEQUZGBuvWrcOKFSvw5MkTPsKjaWvX69ev8e3bN77P8ff3b7by9NmzZ/jtt99QWlqKXr168f2u6enpKCwsRHR0NB4+fAh3d3f07NkTNjY2yM/PpwOFlJQULF68GO7u7nRm7OXLl9i4cSMyMjJAkiS6du0KLpfL5xJZWlqK4uJiga1GZ86c4XveX79+jZCQEISGhiI7OxskSaJLly6wtbWFmZkZoxZtUSBJkrFuizgoKirCjRs3cPXqVdauySRJIjExkQ4mmWzAGgagXbt2FTswZisp4OPjw+csJg6kpaWRnJyM5ORkODs7Q1FRETo6OvR/glpSSZKEnZ0dPn78iEmTJmHHjh0C5+3c3FwcOnQIwcHB2LRpE7y8vBr9LpQrYHV1NTIzM1FcXAwFBQWoqamhbdu2jT6LcjtjAqpqgqmeDUEQKC0txcWLF3H37l0UFxdj0KBBWLp0qUADgosXL8LZ2bnRms7ku1EoLS1FfHw8hg0b1uj3TEpKgpubW6MKKWtra9b3QdhznJWVJbK6iMfj4fr166wTC01RVlaGsLAwBAYGIiUlBTweDx07dsTs2bP5BJqZJrCYoqCgAL///juSk5NRWFiI/v37NyK8f//9d5SWlkJDQwM7d+4EADg5OeHYsWPw9fWFjo4OIiMjaaH0ptf/5MkTVFZWwsrKipHUQXMoLi5GUFAQwsPD+ao0jI2NYW5uTlc6vXjxAi9fvoS7uzs6dOiA0aNHQ0dHB3p6emLHXSkpKfDy8kJMTAzddg7UV0MaGhpi7ty5fEYx7du3h5aWltjzd1RUFJ48ecIoLsvLy6M1jZislz169BDooNgcCIJg5Tr5888/w9TUFIWFhbCwsMCKFSv45CdIksTz589x8eJFcLlcLFu2DCEhIXB1dQWXy4Wqqirs7Owwbty4RhX0NTU1uHPnDo4dO4aIiAgoKyvjypUrjNcv6n3iVH1yOBwcOHAAEyZMQEBAAOvWa6rSRlpaWuj8KAjCHMFFzWVxcXECCSJRHUa5ubl4/fo1SJJktXcjSRKurq70/4sLkiRZdxpRcy6TuVMUcfY9SDXKlbMpEhMT4erqKnI/kZGRgenTp6OwsBAdO3akWyQVFRVRVlaGt2/fIiYmBomJiTA1NcWKFSuEtmuKQtN7qqSkhAULFmDBggWN5AucnZ3h6uraSL5AGKqrq3Hs2DF4eXmBx+NBR0cHBw4c4JuHpaSkYGpqKtT1/Qfq8YNU+4G/DeIGYdu2bWM9ic6bNw8VFRU4dOiQSIJQRUUFdnZ2GDBgADZu3Ahvb29GbYBA/YampqYGrq6uIktuZWVlsWTJEigpKWHHjh0CM1gNIc7CV11djZKSkmZdOAMDA+Hg4NCqbUhskZSUBCcnJ6xZs4ZvYy8q6CBJErm5uYzPRxAEysvLsXfvXrF0y3bt2kUL7LcEZGRkBDpGthQoQePS0lLMnDlTYGVX3759ER0djbNnz+LatWtYu3YtDAwMkJeXB21tbaxatQoqKiq4ffs2XFxccPDgQejp6SEqKgp2dnaoqqqCiooKdu/ejdWrVyM9PZ3vHBcvXhRJ/pEkibS0NISEhIDL5eLjx48gSRIKCgqYNm0azMzMMGLEiBYNrMTVbXF1dcXdu3dp3RZRePnyJby9vcHlclFVVUW7jklSxUKSJOTl5WnnsubGuyQ4ePAgVq9ezWjsvX//Hq9evRJZHS0MycnJePHiBRISEpCQkIDHjx/j1q1bCAoKAkEQUFdXp6tbhg0bBg6Hg/j4eDx8+BBTpkzBoUOHhH62iooKjhw5gnbt2uHGjRuIiYmBoaEh3/tkZGTE0hBiinbt2qGyspK+d5aWlmLpjhQVFWHatGn48OEDvQ6kp6cjMjISnp6e9L35/PkzNm3ahEePHoEkSYFVjeJ+t7t372Ljxo0oKSmBm5sbHVzfvXsXy5cvR11dHUiSxNevX/H06VO8evUKenp6rO6DJGCbWKDA4/Fw9+5d3Lx5E9HR0fQYHT58OKZNmwYTExORm2pxElhAveRDUFBQI1J07ty5AsduVFQUdu/eTa9fW7ZswaJFi+jXk5KSkJqaijZt2sDZ2ZneiLq7u2PChAnw9vbGgQMHRFbtOjs7o2vXrujcuXOz1y4uYmNjsWPHDuTl5TWKVz5//ozPnz8jOTkZp06dgoODAwwNDXH9+nUkJCQgMTERjx49Qnh4OG1U0q9fP7paVUtLS2A71tGjR3Hu3DlaykNGRoaeq8vKyhAREYHIyEgsWLAAW7ZsweTJkxEVFYXS0lI8e/YMpqamsLCwwJAhQ0R+L1GtTMLiMkoUXEtLi/F6ySbpQrVp6enpiXSdzM3NhaysLO066ezsjMLCQqxfvx7Lly8X+tmDBw/GsWPH8Ouvv8LR0REnT56Et7c3NDQ0cOnSJYHmQNLS0pg0aRJ0dHSwcOFCXL16lf7NqDlQnDYxDw8PKCsri63xqaysDF1dXdrIgQ1UVVXp9WvkyJGwtLQUy9igJdvemuswkhRM7wMA2gCFKTEtCOLMnZ6engKTc1TlvbDX2JqusAVJkli7di0KCwuxYMECrF+/XqA2YnV1NVxdXXH69GkcPXqUVXWuqD3swIEDMXDgQNjb2zeSL7hz547Q7qIXL15g69atSE9PR7t27bB161bMnDmT8XX9wP/HD1LtB/4WMAnCpkyZwnoD/eDBA2hoaIit62Bqaorjx48jIiICurq6jM51+/ZtaGpqit3DPnXqVLoCgS1IksS8efOQkpICkiTRrVs3rFmzhs9lMy8vD7t27WLlkkeBTUk88P9bRtm0EArD9wg6CIKArq4uwsLCUFFRIbZxQGhoaItex/cCSZIoKirC7t27RS6campq2L17N7p27QpnZ2fExMSga9euOHv2LB0s9OvXDzIyMnB0dKRdf2pra2FiYoLdu3eLdBtsDjwejyae2rVrB1NTU5ibm9NW6t8DO3fuxKNHj8TWbfn999/h7OzM956amhpwuVxcuXIFz549A/D/HY9nzJgBCwsL1pplkZGRtPNpQEAAgoKCMHr0aFhYWGDChAkCP5fSamEaaIqab0WNvYsXLwJg1+b46tUrDBkyBEOGDMHSpUtRV1fHR7K9evUKHh4ekJeXx6hRo5CVlQU5OTm6Yqc52NvbIzIyEoGBgQJJte+F+/fvIyoqCsHBwQgKCkJgYCD69OkDS0tLmJubC61wcnd3x/v379GnTx8sWbKE3qD7+Phgz549uHHjBh4/fowVK1agsLAQ7dq1w4YNG1gbAuXk5GD16tWoqqqCvr4+vfGpra3F7t27UVdXhzFjxtAbrH379sHb2xupqalo37494/tQWFjI6jolQWpqKgIDAxEcHEzHH4qKipg1axZsbGxalFStqKjAkiVL6PUZqCcnfX19cfnyZfTp0wdAfWXg7t27ERISApIkIS0tjb59+zYi1ID6uAmob1Vq+MwoKipCV1dXLAMpYWQu2zU1LS0NK1asAI/Hw9ixY2FiYkJX11FVGqGhoYiNjcXKlSvh4uKC8ePHixznFy5cwMWLF9GuXTuMGDGCJtn69OmDs2fP4uzZs5CXl8fSpUthYmLS6Lf48uULQkNDcebMGVy8eBHy8vJwdHREVVUVbt++jaCgIFy/fh3e3t7o2bMnLCwsYGFhQd8LSUFVozJdLx0cHCQ6LzV3CsPly5cbuU7Gx8fjp59+EkqoNcXChQtx7do1hIWFQUpKCs7Ozs26bcvJyeHIkSMwNTWlEyEJCQkIDAxEv3796N9eWDvciRMnRFbZCELv3r0l6iC4ffs2UlJSEBwcjLCwMERHR0NeXh5GRkawsLD4bu6urQWCIDB69GhG9wEAXr16xYqYZovi4mKh1X/CXmsNsyBBSE1N5XPgbQoZGRls2LABd+7cwV9//YXhw4e3SGVzUwiTL2iIuro6uLm5wcPDA7W1tRg+fDgOHTrEdz2SuAP/W7qhWho/SLUfaHXEx8ezCsLYYPjw4Zg8eTKjYyhNK6YbEx6Px/g6hw4dilu3bjE6hgLVjtdw4vv8+TN27NgBDodDE4l37tyBvb09ioqKwOFwBOqN1dXVCcw8JSYmQlNTEzIyMqx1V0iSxOfPn1m1ELY2jh49ikWLFiEmJgaxsbHNGgccPHiQNanG4/FYtXtI8rv88ssvYmeili5dCn9/f3z69AnW1tZ82TcjIyMcOnQIe/bsgZSUFPbs2YOpU6eyvram6NKlC/T19SErK4uHDx/i4cOHIt9PEAStCcEEKSkp4HK5GDt2LGPdFqqqMDs7G9euXYOfnx+tT9O+fXuUl5fDyspK4g0TAPTs2RMrV67EypUr8fr1a9y6dQuhoaHYsmULZGVlMW7cOFhaWkJHR4f+Di4uLo0MCZpDc069zaFDhw4oKSmRSDOEgpSUFB/J9vLlSyQmJiI5ORlJSUkoKCgAQRBiGxy0b98eurq6ePbsGes1pbi4GLa2toyPIwgCZ86cQX5+PkJDQxEUFARnZ2ecPHkSQ4YMgYWFBUxNTRu1nt+9excKCgq4fv06TVTr6+tDSUkJp0+fRlxcHDZu3IjS0lIMHToUjo6O6NWrF+vvVlRUhMrKShw/fhwmJib03+/du4dPnz6hbdu2cHR0hIqKCgDgyJEjMDQ0xKtXr2iDGHFA3YegoCBW18kGjx49Ao/Hg5WVFf2Mjxo1CtOmTcPEiRNFmk6wJSmzsrKQlZVFk3bKysqIiYlBfHw8du/ejYsXL+LDhw9YtmxZo7jj5s2bAjerSUlJ9Ka4KXr16oWYmBj630wFs728vJqtmhcEBQUFyMjI4NixYwITihoaGpg8eTLu3r2LtWvXYufOnRg2bBj9nAsa58+fP6fHeUpKCuLi4gDUt69/+/YNPXv2xOXLlwW2inbr1g0LFy6Eqakp5s+fD3d3d5iYmOCnn36CiYkJTExMUFxcjNDQUAQHB8PNzQ1ubm70dZqamtLPNxtQmoVM18vvrTfctN0sMzOz0RgXB9ra2rhx4wZ0dHTEJgR69uyJsWPHIisrC0FBQfj27RtCQkIQHByMY8eO4cSJExg+fDgsLCxgZGTUKCHXpk0b1NbWMrrG6upqyMrKsh6zVCXU8OHDsXPnTsTHx9PJrBs3bqBLly4wMzODpaXlv1KUnSAIXLhwgdF9AMCamGZzH4YMGQKCIP4VDp8kSUJZWVls44QbN25g/PjxaN++fYvEhQAz+YK0tDRs2bIFr169grS0NDZv3oxFixYJjPlaW3v7v4AfpNoPsEZiYiJj15rq6mr4+PhIFIQxQWVlZbNtWk2hqqqKz58/MxYGT0lJYVxJIy8vz7qMmmozMDMzw/r166GiooI7d+5gz549OH78OCZPngxvb28cPHgQPB4P/fv3h4ODQ6NMdXh4OJydnbF161ZaCJlCVVUVfvvtN7oFl42WEIU//viDdQtha4KNcYAkkMQNlw2GDRsm9nulpKSgpaWFjIwMdOvWje91qn2ouroaf/75p8CNHluQJImcnBz4+PiIfQxbUo2aj9jottTU1MDb2xuxsbGoq6sDQRDQ0tLC1KlTYWRkhKFDhzLSZREXAwYMwIABA7BlyxYkJiYiKCgIkZGR4HK56NSpE13dt2rVKtYEGRtHsgULFsDV1VWiilhhqK6uRmFhIYqLi1FXV0fPtUxJZjU1NURFRQnUoxEHlCYNG6Jy1apVUFJSwpw5czBnzhx8+vQJQUFB4HK52LdvHxwcHDB27FhYWlpi/Pjx+PLlC8aNG8e3wbGysoK7uzu2bt2KsrIyrFq1CqtWraLnI6babQ2/2/Dhw/k221Q19ejRoxsRDu3atYOOjg6CgoIYu0KzcZGWBJQbtLKyMqZOnQobGxuxiQFxKsAEgSRJyMnJwc/Pj/6+c+bMwa5du+Dr64vnz59j1apV+Pr1K3r16oWDBw9CS0sLV69e5bvnVVVV9EZFUGxSU1MDDofDWjBbXl4eHTp0YPT9SktLUVhYiKNHjzZboa+jo4ODBw9i/fr1uHnzJhYsWCDwfVJSUtDU1MTPP/8MDQ0NDB8+HMHBwXj79i0+f/4MDodDt7CKAlVpbW1tDX9/f2zatIl+rWPHjpgxYwZmzJiBnJwcmjQ5ePAgHB0dMWrUKFhYWDD6LShQrrdN8T3XSwo8Hg/x8fEICwsTSKbq6+vTc0RFRQVjcxplZWXweDyBeo6i0LdvXzr527lzZ1oH6sOHDwgKCkJwcDB27dqFffv2QV9fHxYWFjA0NISamhrjVs6nT59CTU2N9ZhtuF5KSUnBwMAABgYGqKioQGRkJIKDg+Hl5YULFy6gb9++sLS0bDGTpNYEk/tAtb62bduWMTEtyX1o7TWCLXR1dcV2DZWWlsbYsWMlluBhI19w4cIFnDhxAlVVVdDQ0ICTk5PIsfy/Wm0mCX6Qaj/AGklJSWJPmE2rJZgEYevWrcPmzZvx559/Mr7GhpsvcTF16lS4uroyFjq1sLBg7B6Znp6Orl27CtSeag5fvnwBUC9STJEBpqamqKiowM6dO3Hu3DkcPXoUBEFg8eLFWLduXaNs/PXr17F7926QJInU1FQ+Uu3r16/o2rUrPn36hM2bN8PT0xPLly9nVQWRmJjYqi2EkoCpcQAgmGCmFk1BG/CEhAQQBMEqU8Wmuo1CQzFhcUDp/ggiZqhxpaen16IbBLa/C1s8f/4cenp6jHVbqDY+kiTRq1cvWFhYYMqUKd+lpF8YCILAyJEjMXLkSPzxxx+IjY1FcHAwbty4gStXrkBNTQ0WFhZYt24d489mQ6q1NFJTUxEbG4u4uDg8e/YMtbW1IEmSFjePj4+HsbExo88sKytDhw4dEBISwuqaLl682GJzU48ePbBixQqsWLECqampNMG2adMmyMrKory8XGDVH0VyFxYW4tChQ3zV2GzdMS0tLQW2+ScnJ9OVXU2hqqoKkiRRXl7O6FxURU9roU+fPvj48SNiY2MZJ7LYVHABwIoVK2BgYMC3OZw/fz58fHywYcMGfP36FZMnT8Yff/xBt3ArKiry6YI+evQINTU1kJWVFWge8u7dO3Ts2BGTJ08WSzDbysoK3t7edLurra0t4yTWrFmzUFRUJDapYGxsjP79+yMiIoKPVCNJEk+ePEFcXBwePHiAFy9e0Pp9ioqKMDc3x6NHj6CmpgYNDQ2xzqeurg5tbW3cvXu3EanWEF27dsXixYuxePFipKWlITg4GFwuF/b29qyqwoVV+jJdL4uLi5GYmIicnByUlpaiffv26NmzJzQ1NQXOP6mpqbCzs0N6enqj605PT0dycjJu3ryJXr164fjx49DQ0EBtba3YRAAFUdWcosDj8QTG4X369MGaNWuwZs0aPHv2DMHBwQgNDUVUVBTk5eXRrVs3pKamIiUlRSyH4uTkZDx58gRr167F5s2bWV2rMMjKysLS0hKWlpYoKChAaGgoQkJCcPz4cRw/fvxv6axoKTR3Hygd0IbPrbjE9JkzZwRqjInCxo0bYWRk1NJf87tBUNJZFLp06SLQDIvC58+f8f79e3Tr1o1PgoaNfMGnT59gb2+P5ORkSElJYdWqVVixYkWze2O2Zh//y/hBqv2HQA22hqAEewU5aIoj5isK2trajEXV/fz8ICcnxygIA+oDxn86tLW14ePjg5ycHLEcrHJycnD37l1MnDiRFalGbWKabhD09PRAkiROnDiBjh074sSJE3xBXGpqKvbs2QN5eXns3btX4Ma0Z8+eiIqKApfLxY4dO/Ds2TPcu3ePFan27ds3GBkZtXoLoSRgYhxA2W4LwqlTpxr9u2HlCpuWD0lINaZCyJmZmc2+h63WnjCw/V3Y4vPnz4z1tXr37o2amhq0b98eq1atgq2trVjkPRuiStyqI2lpaUyYMAETJkzA+/fvsWPHDqSkpMDd3Z0VqcYGAQEBEm0uysrKcO/ePcTFxSE+Ph5fv36lx8qAAQOgq6sLXV1dDB06FFJSUpg+fTpevHjB6ByJiYno2bMn6yz4jh07WB3XHNTV1aGuro6JEyfCycmJNh0QRABRm+ERI0YIlDdg+91qamr4tPnKysrw5s0bAIIz15WVlZCSkmJckcC2goEtLCws4OrqyqoynK15TGVlJXr06MH3d4p4z8rKEigUP2TIEMTHx6O2tpaeVygSeOzYsXxzzefPn/Hw4UO0a9cOJSUlYglme3h4YOPGjfDz82P13YD69YTp3DlixAiEh4cDqHc6jY+PR2xsLO7du4eioiL6mR88eDA93gcNGkQnEJi23GloaIhtipGfn4/CwkKUlpZ+N5KkufXy3bt3OHbsGGJjY2kjhoaQkpLChAkTsGHDBvTu3RtAfWJo7ty5qKqqwk8//QQjIyM+SZXw8HD89ddfmDlzJq19yRZMW7tev34t0O27IQYPHozBgwfD3t4eDx8+xKlTp5CSkgIAdFJXVMIqLS0NGzZsgKysLKZNm9aiRhxNoaioCGtra3Tq1Al1dXUSGSP80yDoPgQEBODmzZtC77soYrpdu3Z4/Pgxo2vIzc1l1Zn0d6GgoIDR+4uKitCuXTssXbq0kRlUXV0ddu/ejRs3btDzj5aWFo4ePYqgoCDcvHkTb9++ZSRf4OPjA0dHR5SXl6Nv375wdHTEwIEDJfvCPyAUP0i1/xCio6OFuiEK2ohTg5Zte9CIESMYZzavX7/OuK0SqJ+E2ILp9/P09GQVUE2dOhVXrlzBxo0bce7cOZHZmaqqKmzYsAF1dXWwsbFhpctVU1Mj8LtRFTccDgdeXl745Zdf+N7j6ekJAPjzzz+bFRo1NTWFnJwcli1bhqdPnzK+TqB+Y9aaLYQkSfKRfyUlJQAEi6hTrzFFUlISOnbsyEo3IisrC9nZ2c0Gmy2J+Ph4FBYWitX2UVxcTGvZiIKkLbB/N9jqtkhLS6OiogJHjhyBm5sbxo4di8mTJ0NfX18owfY9Nc4KCgoQHh4OLpeLlJQU1NXVAQDjVh1J8OnTJ9bryfz58/Ho0aNG1SlmZmb0xlpQJaGenh5cXV0RFRWFCRMmNHuOyMhIvH37Ftu2bYOLiwtGjhzJuMUhOzsbHTt2FFs/TBykpqYiJCQEoaGhyMrKAkmSUFVVbZZQHTp0qMC/s/1uKioqyMnJafS3xMRE1NXVQUFBQWCF0KtXr9ChQwe8e/eO8X0A6ufBpoLI1PdOTk7mGytsW1slQWBgIE16MkFdXZ3AzQ7VljNw4ECBQvHW1taIjIzEunXrYGtri1evXiEwMBAEQfBpYhYVFWHjxo2oqalBTU2N2ILZHA4Hp0+fpl032aCoqIjx5ldeXh4FBQU0IU6SJEiSRNeuXWFtbQ1dXV2MHTtWYCtqdXU143Z6KSkpgeQUhadPnyIkJARhYWH49u0bSJJE7969MWPGDLi5uTE6lzgQtV7GxsZi/fr1qKiogLS0NAYNGoTu3btDWloapaWlyMzMRFpaGsLCwhAbG4uTJ09CS0sL69atQ21tLXbs2IG5c+fyzcETJ07EypUr4ePjg7179wqt2hMXycnJePfunVhry5s3b5CYmIiFCxc2+9709HRwuVyEhoYiPT0dBEGgffv2yM7OhrW1NWbOnElXX3bo0AEVFRVIT09HWFgYrly5gsrKSjg4OKBz586sx6wo1NTUIDY2FlwuF3fu3KElBLS0tJCUlMR4LpMU30uYv+l9ACC203hTYrqiogKpqan/Cv05SQzxxAVJkggKCkJpaSni4+MxdepUmlRzdnaGr68vAGDMmDEgCAL379/HggUL6PugoqLCSL5g165d9PfKzs4WaxwCoLVqbW1tWesT/i/iB6n2HwFbh0x/f3+B9sTfg4AA2AVhQH0wxRbC7JeFobi4mNVvOXDgQMydOxeXL1+GpaUlli9fDj09vUYaNDk5OYiLi4O7uzuys7MxadIk2vqcaQuhMFCZ+IkTJwok1IB6MmjUqFFiO/cYGBgAEK9ySRBaqiWCCYRtvoT9nSAIVg6lJEmy0n4bMGAA1NTUWB0bEBDAV+lBfS9BC2BWVhYIgkBFRQV27doFZ2fnZp/xHTt2oLKy8m9zVGotsNVtGTBgAE6cOAFfX1/4+/sjIiICkZGR6NSpE90K2hQtrRFYWlqKiIgIcLlcPHz4kK5s0dHRgbm5OTZv3sy4PfLvQmJiIqSkpGBmZoYFCxZg4MCBzT57c+bMwfnz57Fjxw507txZ5HyWkJAAe3t7KCgowNramiacmBJP48ePx+rVq7Fq1SpGxzVFeno6QkJCwOVy8eHDB5AkCSUlJcyaNQvm5uYYNmxYsxsRUeQtwPy7DRs2DLGxsaioqKBbxSkyR09Pj+9+vHv3Do8ePYKRkRFiYmIY34fCwkIEBgYKrKSnnK0F/b21sW3bNqxevbrFN4bU2t8UhoaGsLKyQkBAAK1PSJIkpkyZAh0dHfp9dnZ2iImJQVlZGTp16gQOhyO2YPbKlStx48YN1kZJANCpUyfGJGd2djZqa2vx8uVLjBw5knb2FGSe0BSdO3emyVhx8e7dO74W6tevX9MkdnZ2NkiSRJcuXWBrawszMzP8+uuvAAA3N7cWi8uaQ2ZmJtavX4+qqiosX74cixcvFkjc5+bmwtPTE+fPn8eGDRuwcuVKZGdnw97eXuB4aYjp06ejpqYG+/btAyA4jhAFKo4gSRJ2dnbw8vIS2Qqfl5eH9evXg8PhCDVI+vTpE7hcLrhcLv766y+QJAkFBQXY2NjAzMwMI0eOhKenJ44cOYI///xToAwMSZJo164d/vjjD3rdbakxW1dXh3v37oHL5SI6OpomizQ0NGBmZgYzMzN069YN6urqjOcy6jW2HUYtuXcT5z4IQ3PEdFRU1L+CVHNxcRHaTSCswpQgCHz69Am+vr58pgCCcODAAZSWlkJZWRm7d++m5/O8vDxcuHABBEFg+/bt9PMSFBSEzZs3o2/fvtiwYQMMDQ0ZV1tTcxRTiYaioiKhDqw/IBg/SLX/CA4dOsTquICAAJHWxS2dFWYThAGQqCpA1PdraWzZsgVFRUUICgqi24Tk5eUhJyeH4uJiOrNFkiQmTZoEJycn+limLYTNQZRFfE5ODiZOnNjsZzRFaWkp42PEgaiWCKZtzWPHjsX9+/cZ6wp9/fqVlUMpW0FjSYJxylVOEIQ9QwRBwMjICGFhYZg7dy7Wrl0LbW3tRpnz2tpaJCYmwtnZGc+ePcOwYcOQkpIiMvgW9hpJkoiOjua7TsrwQlD1rKQt6WwwevRoXLx4kZVui5qaGtavX4+1a9ciNjYWvr6+iIuLg5eXFy5dugSCIBoJRrcEqVZRUYHbt28jJCQEd+/epYWxtbS0YG5uDiMjI7oSUVJdGaZjTxLIycmhvLwcISEhSExMhK6uLvT09DBmzBihAuqKiopwcHDA+vXrMWfOHEyaNEloJUNkZCRIksSZM2cYC7I3BFVZwwaZmZm0Fs+bN29ol9jJkyfD3Nwco0ePZm1c0xKYOXMmgoODYWtri1mzZuHVq1cIDw8Hh8Ph2xSmp6djzZo14PF4mD59OoyMjBjfBw8PD1bXmZiYyCqx8E+DKI0qBwcH6OjoIDIyEjU1NdDX1+fbtD158gSVlZWwsrJCQkICtLW1WQlmq6qqMjZ0AoBBgwYhPj4e5eXlfG3DglBeXk5XP9fV1eHt27dQUlKCiooKVFRUmtW1pEwLvnz5IpaOUXZ2Nu7duwcbGxukpaXRJPbHjx9p0mDatGkwMzPDiBEjBMZVbOIykiSFVncIWy8/fvyIiooKHDp0SGBChoKKigrs7OwwYMAAbNy4EZcuXYKqqmqzhBqF2bNnw9PTEx8/fhQZRwgDpdF75swZWFlZYenSpTA0NGwkeZKfn4+wsDC4u7sjNzcXGzZsaFRVk5OTg9DQUHC5XDx//hwkSUJWVhampqZ0dXLDsbFgwQIYGBjgwoULiI+PR3Z2Nv1a9+7dYWxsjFmzZqFXr16MvosoPHz4EFwuF5GRkSgsLKSJovnz58Pc3Bz/93//1+j9bETdqedAWCeRsL9TzxmbvVvDZ5zpfWgIJsT0vwVs13UlJSUcOHAAHTt2FKkFFxgYiMuXL4MgCPj6+jaSaYiOjkZNTQ26deuGOXPm0H+3sLDA+fPnIScnJ1YVeFNIYjD3byBC/2n4Qar9R1BWVob27dszPu7o0aPQ1NRkfBwbXS2AXRAG1JM5TM9JEAQrwebx48fD1taW8XEAaBfB8ePH4+rVq0hMTERJSQmdIWrbti3Gjh2LGTNmNDIGYLPZPnXqlEitF1HEm5ycnFCXqubAVBdKHAJQVEsEm7ZmNs5BZ86cYeVQ2nChe//+PUpKStCvXz+hzzePx4O7uzvrBZytaDZQT17m5eUhKSkJCxYsgKysLHr16kWTvllZWaisrARJkvj111/h6uqKUaNGiQy+Rb32+vVroUSZMOfT1q6Mmz9/Pi5fvsxKt4UCh8OBoaEhDA0NkZubC39/f/j5+SEjIwP379+Hnp4e9PX1YW1tDQMDA1bESWRkJEJCQhAbG0vfIw0NDVhYWMDU1FQsHUemYDr2JEFCQgKSk5MRFxeH2NhY3LhxA/7+/rS+kr6+PnR1dflaEI2MjODs7Izt27fTLSuCrldFRQUHDhxoVOnTWrh48SJCQkLodre2bdti4sSJMDc3h4GBgUgiRBAx3dxrbOcWLS0trFq1Cq6urvQmCwCWLFnSSBx/9uzZePr0Kerq6mBlZUUbGDC9D2zvhbq6OuvEwr8JVCWMMFAumJ07d8avv/7KWjDb399foMC1ONd3+/ZtHD9+XCytQUdHRxQXF2Pbtm0oLi6mW+m4XG4j3UQ9PT0MHTqU735Nnz4dAQEB2Lp1K86ePSty3FRVVWHTpk2ora3F/fv3ce3aNQD1jrWUO7Kurq5ILUy2cRkg/BkU9tySJImBAweKJNQawtTUFB4eHkhLS4OlpaXYMgyUFlNJSQmcnZ3FOqYptLW1UV5ejsuXL2PPnj3Ys2cP5OTkaAKdInooAm7p0qU00cblcvHo0SPavEBfXx/m5uYYP368SCOlPn36YM+ePQDqtQqLi4vRqVMnxmYLzWH//v0IDw9Hbm4u3Za8YMECmJubi9SiYiPqLskayjZBV1RUhCtXrrC6D2yJaTZgWkUJ1D9vlLQNU0hCPiUnJ2PRokVYv349Bg8eTCeU5OXlUVlZifT0dISHh9Nr6tixY/n2Jw8ePKArwpuOZSqZ8AP/fPwg1f4jWL58Oc6dO8dogXFxcYG7uztevnzJ+HyrV68WWQ4sDGyCMKA+q8kmo8ZWsFmSSgag3mDB2NgYFRUV+Pz5M4qLi6GkpIRu3boJvEdsFkhKN0cYRC1uqqqqSEtLY3zOuro6sUk1JnpQwtCagvWSOJTGxsZi//79tBGAjIwMFi9ezNeKQ+k5MRX6bQi2otkUPD09ceHCBVy7dg2ZmZl8wcQvv/yCGTNmYNasWSAIgjWJFx8fz+dc9E9E9+7dsXXrVuzfv5+xbosgqKioYOnSpVi6dCkSEhLg4+ODqKgoREdHIyoqCnJycoyFewFgzZo1IAgCXbp0wYwZM2BmZtYoWy6sipQtec5m7Pn7+6Njx46MNwsEQeDgwYMYPXo0Ro8eja1btyIrKwsxMTGIi4tDYmIiHj16hBMnTtDuq5T2koKCAiZNmoRRo0bh2rVriI+PR1paGr3p6tOnD4yNjWFpacmqGqclcOjQIfremZiYYMKECXT1dXMmNaKIaVGvscWaNWugp6eHiIgIukKqaZtibm4uZGVlsWDBAqxcuZL+e2vdB0kSC60NQe2Dzb1GEIRY7cWDBg2i/5/SKqPg4uICNzc3kWtNUVERlJSUYGNjgy1btmDWrFnNnrMhTE1NcfHiRVy+fBk8Hg9r164V2A5YWFgIJycnBAQEYMSIEbSuz7p165Cbm0sT6Q8ePMDp06fh4eGBjh07YuzYsXR7aOfOnTF06FC6LXbWrFlYv349xowZ0yhJQZIk4uLicOTIEbx79w5z586lq4a7dOkCfX19yMrK4uHDh3j48KHI70cQBLZt28boN2G7Pi9dupRxgnvo0KFITU1lLMqvqKiI8vJyiWKJnTt3Yvz48Th37hwSExNRVlZGO/pKS0vD0NAQtra2dPW3rq4ueDweOBwOtLW16apqNnNBu3bthGoWV1RU0HqibEBVElFztZaWFgiCwJcvX/DlyxeRxzJN/LemyzmFSZMmsboPlpaWdOs1E2KaLdhWUf4d0NLSgpeXFzZt2oSnT5/i2bNnfO8hSRL9+vXDx48fBWqTUqYcgvZ07du3b1W37Ly8PHz79q3VzvdfAkH+m32Af4CGuro69PT04Obm1uwEl5+fj82bN+P+/fsAWrfliiRJWqB29uzZYgVhPB4PCxYsYCWWyIZUU1dXh5qaGuNj2WRJioqK4Ofnh99++43RcUD9dXbs2FEgAZidnY0OHToIfI0gCBgbG+P8+fMICgoSK0OdlpYGU1NTdO7cWag2hii4uLgI/U0TExOFvsbmN2W74Tpy5AgsLCxw4MCBRn+n3CFlZWXB4/Gwa9euRg6ljx8/xpw5c+iMX4cOHVBQUACCILB582YsWrQIAODt7Q1HR0dUV1fTi6StrS2rqsiWMjdIS0tDdnY2Tfr27t37uxgnpKamIjU1Vews/N+Bixcv4siRI0IDckq3Zdu2bYzHQHFxMQIDA3HgwAEQBMFHZF67dg3Xr18XWr0H1I93pkEjJdDN1EyCIAhWxC91jUzDCoIgRK5D1dXVSEhIQGxsLO7evYsPHz6AIAhwOBz8+uuvdBUK02ul3FKZYNu2bVizZg0jTTU29w6of+bYbLzs7e1ZfTcAYo3Rr1+/QllZWWDFZWlpKYqLi1vVgOV7Q11dHVZWVrC2tmZ0nLgteRQaJqKYxmXz5s3Dly9fEBkZCaB+zXV1dRX6OSRJwsjICKqqqrRGmL6+Pg4ePNhsG2ZD5OTkYPbs2cjOzka7du2gqamJvn37okOHDnSVRmJiIioqKtC7d29cvXpV6OfX1dXh8ePHiI2NRWxsLO06SxAEHeOuWrUKGzZsQHR0NAiCQNu2bdGrVy86AZKRkYGysjKQJAlTU1McPnyYtdsdm/vAFgMHDsSSJUuwfv16sdfLEydOwN3dHVOnTsXBgwfFPteOHTsQFBSEvXv3tsiaXF1djczMTBQXF0NBQQFqamp8hhLUHKisrMyIBCQIgpEm8rx585CSkgIejyfxmGU6Z7fWs/Lt2zfW7qZs78OrV6/A4XAaEdPi4OLFi7C2tmZ0H+bNmwddXV0sWbJE7GMoSJp0lgR1dXUIDw9HfHw83r17x5dQ0tPTw8iRI2FlZdWoqCQzMxMTJ04EQRCIiYnh6zrYuXMn7ty5g3v37jG+JisrK8ycORMzZsxo9Pfs7GxkZWUJbFum1g6SJFttL/xfwQ9S7T8CCwsLvHv3DpMmTcKJEyeELgZJSUmws7PD169f0a5dOxgYGGDYsGGMz/fmzRvGVuoUBg0axCgI+/jxI1avXs2qmouN7g/TzCQFJgHYixcv4O3tjdDQUFRVVfE5BokDNi6qQP11crlcWFhYoHfv3rh06ZLIIDo3Nxfz589HWloapk2bhv379zM+J9vefDZBrbgb2KZVdDweD6tXr+arLqutrcWgQYMgJSUl0KF07dq1iIiIwG+//YZ169ZBRkYGT58+xbp161BRUYF79+7h2LFjuHDhAl36vX//fhgaGrLOrDFtwaXAtm2bDRo+0/7+/ggICBC7RYKNPklL4MOHD99Vt0VdXR3Tpk2jhaIpNLcBBoBx48YxPh+V6WXznLFph6CIKjbPmbiVcaWlpYiOjsaFCxeQmprKeI7IzMxEWVkZa3MfKsvP9BiAXcDPpq2ILYkHSL4xFOdZpqCvr8+IwKHAdKMtKSQhRdm2aTE97vz58zh8+DD27t2LadOmNXsfrl27hj179mDfvn3o168ftm3bhg8fPtDtuQ3lKZpDUVERDhw4gJCQEDop0ZBcl5aWhpWVFbZt2yaW7AeFb9++IT4+Hl5eXnxj3c/PD+fOncP79+/5jtPQ0MCiRYtgbm4OQLjUQHOorKxkrZnKVAdYXV2djnPFHUMuLi44deoUunbtitu3b4tVMVRTU4Px48cjJycHa9asaXHzHGForRhw3rx5tNMm2zHLpko7OjqaVRKfmsuqq6tRUlLSrMtmYGAgHBwcWBvlUIk9ppDk92SzXgpKPn5PsK0aZFrNamVlBUVFRZw/f57+m7e3N/bt24e+ffsiJCSE75iJEydCSUkJ169fZ3x9DeeVhhA1xzQk1digNZMR/zT8aP/8j8DT0xNz585FREQEtm/fLnCCcHd3h6urK2pra6Guro6jR4/CzMwM4eHhzX6+IALixo0bjK+TqoDw9/eng7AHDx7Q/eQNg7Dp06dj27ZtrEg/Ctu2bWO8EFDXwGYzIwrV1dXgcrm4cuUKnj9/Tp/rp59+Yk0iEASBqKgoxsepqalh1apVcHZ2hpGREebPnw89PT389NNPkJOTQ1FRET58+IDY2Fh4e3vTrWVMNVsotGa7zqpVq8S+57GxsfS9AASTD805lD5//hwaGhqNROGHDBkCe3t7rFu3DocPH4anpydkZWX5Wmzk5eUZtxpnZWWxCoTFqT5KSUlBeno6unXrhrFjxzIOhhpi3rx5jYSbRYk3N8XftSC3hm4LW+0zygWQCYQFVN8TVFVPSyErKwspKSlITk7Go0ePkJ6eTj9P/fv3x5gxYxh93vbt2+lWC3V1dcabPX9/f1aVeEDLrymiwOa7SYrMzExaN+7BgwdCHZ0/f/6M7du348uXL8jJyRH78xvGCGyNMthW5nTv3p3Vprm1xt7MmTNx7tw5WjBbFAIDA7F//350794d5ubmaNeuHW7evInjx4/Dy8sLy5cvx6xZs7B161a+iiNBUFBQgJOTE+zs7HD//n28e/cOJSUlUFBQQJ8+fTB+/HjaPKU55OTkIDk5GSkpKXjy5AnevHmD2tpaulqNgo2NDWxsbJCVlcV3vqa6mGznowEDBmDv3r2Mj2Nb6csGBEHg69evOHfuHJYtW9bs+z08PPDt27cWaZVjoiHbpk0bsfYaLQm2Y5YNwaKurs7IEK3hXEZV15EkiW7dumHNmjV8lV15eXnYtWsXHQccPHiQVfKYrdZ0eHg4FBUVGR9nb2/P+D4kJia2eqUzk8qqpr87E1JNT08PZ8+exaZNm3DkyBGUl5fD29sbBEHAxMSE7/2+vr7IzMwUqa35PWFlZdWqMjz/dvwg1f4jUFJSoom1wMBAtG/fHjt37gTA3+45f/58bNq0CTIyMqwJiLZt24o9yFNTU+lNMqWD0JJBmCiwqUbw9/fHgAEDWqyMODs7G1evXoWfnx/tIiQtLY1JkyZh5syZ0NbWZlSFUlVVhby8PPrfbHXjli9fjsrKSpw9exZubm4CXXpIkoSUlBQWLlyICxcusDoPwL4kW5QrqCg0F9SWlpbiwIEDePHiBYB6DbywsDBW11JYWCiwapP6zl5eXujRowfc3d3Rv3//Ru+xtbVlvOliQhQ3HXvV1dU4c+YMQkNDsXv3bprMLS8vx5o1a+g5AgB69uwJNzc39OvXj9H1NUX79u2hoqKC9+/fs6q2+t5orkr048ePAv++YcMGkRtODoeDDh06QFlZGcOHD/9Ht74KApuxR5IkXFxcJCIS/vrrL6SkpND/UaQLJXJvZmaGsWPHYsyYMejSpQurc1CbmfHjxzO+1oCAgFYjKr28vKCpqdnIJKA5XLt2DSRJsvpukuDIkSO4cOEC6urqQJIkFi1aBG1tbbi7uzcyUfL19YWjoyNKS0shIyPT7LwL1Mcwe/fubZSMYqrbR20u2Y5Da2vrVv09mUJOTg7Ozs60YHaXLl1AkiQiIyMFCmbLyMjg5MmTdEzWtm1bbNu2DRMnTsT27dtx5coVJCQk4MiRI2LPBV27dmW8AUtLS6NJtOTkZHz+/BkAaJF4CwsL6OjoYPTo0QKrGtm0J4mL7t27i/3epnEZG7Alunr37o2TJ09CWloatra2Atuya2tr4eHhATc3N/z888/466+/WF8nWw3Z73WfhKE1x2xDosrT0xOXLl0SmPBuOJeRJIk2bdo0ikE+f/6MHTt2gMPh0HPVnTt3YG9vj6KiInA4HCxZsgRSUlKs9m4qKiqs7gMlY8IU9vb2jO+Duro647ZdSSEukUqSJK5evUr/nlRMn5iYiLt376K4uBgDBw6EpaWlwPhw5MiROHPmDIKCgpCTk4OMjAzk5ORASUkJc+fOpd+XmpqK0NBQnDt3DnJyco0cQVsTampqf2tL7b8NP0i1/xA6d+4MLy8vzJ07F97e3mjfvj10dHTodk9lZWU4ODhAT0+PPqbpIigIggiI3bt3N0t61dXVwd3dHUFBQQAATU1N2niAApsgjAkOHTrE+JiAgABWWjRNcffuXXh7eyMuLg48Ho/eyI0dOxaHDx9uFCCKW4WSmJhIZ0U6duyI33//ndE1VVdXo7q6GvLy8iAIAhs2bMD48eNx7do1xMbG0kEhQRBQVVXFuHHjYGNjg19++UUiUo0S022utL0pqI2QtLS0WBlzcZCYmAh7e3tkZ2ejY8eO2LVrF8zMzJqt6hBWtVVRUSEw2KfGh5ycHC5fvsy6yq8pxHmmhY291atXIz4+HiRJNtoAODg44N69e5CRkcHkyZNBEARu3bqFhQsXIjQ0lHErCwBMnjwZUVFRKC0tRW1tLYB6MeYhQ4Yw/qzviYYVdUxAtSiIg/j4eJw5c4bxOf5OsBl7xcXFrKv6li1bhsePH9NOyZSG3ZgxY2gS7X/N4v3gwYNYvXo1H6nm6ekJLy8vgRUHubm5rXV5NAIDA/Hnn3+CIAh0794d2dnZUFRURFJSEhwcHLB//35UVFRg27ZtiIiIoJ0ODx061OwG7/bt29i1axfy8vLQuXNn7N+/n67Iad++PcaNG9ciCbjvgREjRmDp0qVYvHhxq5yvoWB2ZmYmgHp5goagBLOPHDkicDwNHz4ct27dwsmTJ+Hp6Ynp06djyZIlQtve2ZCU586dQ0pKCh49eoSioiL6uuTk5KCvr0+PeTaOpC2J1orLKHh6esLf3x/FxcXg8XjNttGXlJSAIAi4uLhgzpw5dFW8rq4un/tgdHQ0vn79CgUFBbi4uGDixImsrvHx48dYsWIFn4asm5sb2rdvL1RDlq2syr8FDecxqlK06dzWdC776aefkJCQADMzM6xfvx4qKiq4c+cO9uzZg+PHj2Py5Mnw9vbGwYMHwePx0L9/fzg4ODQyKBEFYXu3H+CHOPvQL1++wN7eHi9evACHw8HChQuxatUqbNy4kXa7puKmCxcuwMvLCyoqKgDqY/KTJ0/i3LlztIQERaZ2794dJ06caLSO/fbbb8jPz0ebNm1w+PBh1hp6P9C6+EGq/cfQtWtXumLtzJkzOHv2LHg8HvT09ODg4MCY1BBGQDSH9PR0bN26FS9evECbNm2wceNGLF68GJcvX2aceY+OjmbtFlZaWsqKEGCLkpIS3LhxA1evXkVGRgZIkoSsrCyMjY0xdepUzJ07Fz179mSsI1NdXY1jx47By8sLPB4POjo6AMBYKPrMmTN8ffSDBw+m70d1dTWKioqgqKjIp8/B1vEVqF+wVq1axZetakjyCYKqqiqtbTVy5EhYWlrCwMCA1ca9uroaR44coZ3K9PT0sH//ftbVLs2BIlyMjY1bjFATB8LGXlhYGOLi4qCpqYnff/+dFm/OysrCjRs3QBAEDh06BFNTUwCAiYkJFi5ciEuXLmHFihWMr8PR0RFVVVW4ffs2nJ2d8f79e8ycORM9e/aEhYUFLCws0KdPn5b86hKhffv20NLSEjimBKGiooJPG60pKisr8fnzZyQkJMDb2xskSdKbSKZgWpkD1Ad40dHRrLL1bMaeuro6li5dyvhcQH02nWrxGjt2LMaOHYvhw4e3aOvt3wU2JjsAhLaZlpSUNNL8+7tx8+ZNEAQBd3d3vHjxAq6uroiKisKSJUtw8+ZNbNy4EevWrUNSUhKkpaWxatUqutpCGKgNYWBgIC06/8cff0BBQQErV65ESEgIPn78CC6XCx0dHVhaWmL8+PEtlnxpCRQXF6OyspLv7+IYk7CFpqYmwsPDsXHjRoSFhWHQoEECBbNF/fZt27bF5s2boaGhATs7O7i7uwt8H0mSdLUSE5w6daqR0QhFog0dOlSkLtj3qlxnC0Fx2YEDB1i3+BcXF6O4uJge9+K4HxIEgf79++PGjRvYtm0bUlJS4Ofn1yjh0zChe/DgQfr6oqOjGTssJiQkgMfjCdSQ9fDwwPz58wVqyCYkJLR6y/Y/BcLmMhsbG/Tp0wdOTk70eDQ1NUVFRQV27tyJc+fO4ejRoyAIAosXL8a6desgLS0t1jmF7d3Y3oOCggIYGRmxOpYpvLy8Wr2qsTlQRlMlJSXo1asXHBwcMHz4cHh5eYHL5UJGRgYmJiZQVlZGfHw83r59i7179+LkyZPIz8/HypUr8fTpU5AkiQkTJmD37t0oLi5GTU0N+vfvzzcf6+jooH379pg3b14jl/cf+GfjB6n2H4Sqqiq8vLxoN6hx48YJbO0TBUkICE9PTxw/fhyVlZX45Zdf4OjoSGdEhWXe379/j/T0dIGZOUkm11GjRkFTU5O2ZWfrAiUOdu7ciZCQEFRUVIAgCGhpaWHKlCkwMTFhJM7bFC9evMDWrVuRnp6Odu3aYevWrZg5cybU1dUFWrBfvHhRbKHoppCRkRGaEZGkjF7Y5lAQydcQt2/fRkpKCoKDgxEWFobo6GjIy8vDyMgIFhYWYpN8z58/x9atW/H+/XvIyspi27ZtmD59Ot/7AgICkJiYKPAzhL3WnL4SkxYSSSFq7IWEhEBBQQF//vlnIxIzOjoaPB4PvXv3pgk1ABg9ejSGDh2K6OhoVqQaUL85MzExQVpaGlxcXLB7924EBwfT7cYaGhqYPHkyTE1N6Yxea6NhRd2zZ89gamoKCwuLFquo69u3L3R0dDBp0iTY2NgItFsXB2w34Gz16diMPQcHB9Yb36NHj2L06NH4+vUrUlNThepx/RshbE75J6Gurg6RkZEwNjZmfOybN28wduxYGBgY0FURcnJy2LBhA+bOnYuNGzciKSkJffv2xdGjR5utOHz48CG2b9+O7OxsKCgo4I8//mg0N61duxZr167Fs2fPcOvWLYSFhSE2NhZycnKYOHEiLCwsMHr0aIk0Ib8ncnNzW1yEm9o8y8vLQ0pKCv3790dERAR8fX0Zf1ZFRQVOnTpFa6EOGzZMYKWav78/I0HrhjpSzs7OGD16NKOE6feoXGcLYXEZWzRtHxRWiSoMPXv2hLe3N549e4a4uDg+SRVjY2P8/PPPjY5pKA8hLqgqU6Yasmz0jSn8m0k1UXNZXl4ezM3N+QgVPT09kCSJEydOoGPHjjhx4oTY62Fzeze294HH48HJyYnxcWzE7keMGIHnz58z2vs1l6Bni/z8fPz++++4ffs2SJLEzJkzsXXrVtr9NCwsDG3atMHVq1fp/eWmTZuwdu1aREdHIyMjA6tWrcLbt2+hoKCAnTt30uYnoqrPmnZ1/cC/Az9Itf8oevToQVesxcTE4NatW7C0tBTrWHEJiKbIzs7Gtm3bkJSUBA6Hg2XLlmH16tViZVZCQkKEkivNVWg01C4aNmwYhg0bRi8a0tLSSE5ORnJyMpydnaGoqAgdHR36P0EVY2yzJH5+fpCTk8PatWvx008/4d27d5g6dSrjz6FQV1cHNzc3eHh4oLa2FsOHD8ehQ4f4RHj/6xg+fDiGDx+OnTt3Ij4+HkFBQQgJCcGNGzfQpUsXmJmZwdLSUuBGra6uDq6urjhz5gxqa2uhra0NBwcH9OjRQ+C5srKyhGZuRb3GBqqqqqwrMJtCnLH3/Plz6Ovr8wUdDx8+BEEQjdrCKQwePJh1ZrMpCILAjBkzMGPGDOTk5ND38eDBg3B0dMSoUaNgYWGBiRMnNtJg+t5oWFEXFBSE69evw9vbu8Ur6n799VcA9SRX0zmN2mALm+sIgmDtTiUJmI49SVr5qQroK1euwNXVFVOmTEFNTQ2+fPmCgoICKCoqolu3bmJn6puDlZUVawKQKdgatbCtcAPEr+r59u0brl+/Dh8fH+Tm5rIi1UpKSvj0IhteQ0JCAgwMDHD8+HF6MyII1dXVcHJywpUrV8Dj8WBgYIB9+/YJ3XxQVdbbt2/H/fv3ERwcjMjISNy8eRPKysowMzODubk5PfbYQltbW+iaIeg7cLlcujK1tUBp6LF1BaRw//597Ny5E58/f4asrCx+//13zJgxQ+B7VVVVWRMlbKpeWrJynS2+V1zWtH2QIAix4tCkpCRargNo3HnQHMaPH8/YqXnXrl0Ck7nNaciy0Tem9gNM3M6pGI3JmP0eIEkS+/fvFzmXlZeXCyxUoPYlHA4HXl5e+OWXX8Q6pzh7N7au12FhYayMCr59+8Z47qQqwpomHph24UiKqKgo7Nq1C/n5+ejatSsOHDhAdwlReP/+PQwNDRsVbHA4HCxfvhxRUVFYt24d3r59i5EjR8LJyYlVFWtBQQH27t2L48ePS/ydmOCfWDH4T8cPUu0/jF69etEaa9u3b4esrKxIHQWmBERD3LhxAw4ODigtLUWfPn3g6OjYYpUe4lZoUAvFgAEDcPjwYfTt2xfJycl48eIFEhISkJCQgMePH+PWrVsICgqiW4309PSgo6ODYcOGgcPhsBZllJeXR2lpKTw8PNC1a1dkZGRg9uzZjFs9gXrx3i1btuDVq1eQlpbG5s2bsWjRohZxbALAOJCiwNZttCUgJSUFAwMDGBgYoKKiApGRkQgODoaXlxcuXLiAvn37wtLSEmZmZlBTU8O7d++wZcsWvH79GjIyMti0aRNsbW2F/oaSbHxF3Rdhr7FxcxQEccdeQUGBwDbUx48fAxBsJtGuXTtUVFS0yHU2RNeuXbF48WIsXrwYaWlpCA4OBpfLhb29Pfbs2UNfU2uBqqgzMTFBcXExQkNDv1tFXVVVldA5TdjfCYLAwYMHJTqvJGA69iQFSZJYt24dYmJiUF1dTf+9TZs2MDQ0xNy5cyUWz2VLUrKpMGp6rQEBAQgICPhursgcDqfZOT45ORne3t6IjIykzQUUFBRYnY/STWoK6m/dunXDyZMnRZIfz549w9atW/HhwwfIycnB3t4eNjY2Yp2fw+HQibK9e/ciOjoawcHBuHLlCry8vNCrVy9YWlrCwsJCqDaYKIjj2pqVlYWrV6/ixo0btCFRa4JyxGWL8vJyHDp0CL6+viBJElpaWnBwcBBJFomjydsciouLERQUhPDwcGRmZiI/Px9KSkro0aMHjI2NYW5uTj+XLVW5zhbfOy5jiurqasyfP1+gtIY4YOPUbG9vz0pDlo2+sbq6OgiCEHvOpSohCYJoEadlFxcXuLm5MW4dzsnJAUmS8Pb2ZjyXAf9f1mXixIliEWpM9m5s7gPAfr0UB03nTkpzrCkk6cJhgtLSUuzbtw+3bt0CSZKwtLTE77//jg4dOvC9t6SkBL179+b7O6UHmZqaiunTp2P37t30d6qtrUVQUBBtbDBo0CDMnTtXoDxTVFQUdu/ejby8vFYn1X4YFDDHD1LtPwJxstJNBWuB/681wZSAoJCXl4edO3ciJiYGADB37lxs2rSJdpRqCYgzmVdUVODLly9ISEjA06dPsWzZMvj7+6Njx44YMmQIhgwZgqVLl6Kuro6PZHv16hU8PDwgLy+PUaNGQVdXF7q6uozb9u7evYvQ0FD4+voiJSUFQH0Zt76+PqysrGBgYCDW51y4cAEnTpxAVVUVNDQ04OTkJLEDY1Owrbj6u4LHppCVlYWlpSUsLS1RUFCA0NBQhISE4Pjx4zh+/DhUVVWRm5uLqqoqDBw4EE5OTs2KHkuygLi4uAjMplIiwsJek0TnhenYk5OTQ2lpaaO/paWloaCgABwOB1paWnzH5OTktFg1nTDk5+ejsLAQpaWlIElSoAZRa6Jjx47fraLOwMAA9+/fx969exlfV1JSEtTU1BhZzTdX6dS0ynfChAnNtqo3N/aGDRsGb29vsa+xKe7fvw+SJBEeHg6gviW9U6dOKCsrQ1lZGSIiIhAZGYkFCxagsLCQ8ee/f/8eAJrVJONwOJCXl2+RdpLAwECoq6vTFX1ZWVnNus5+D1RUVODmzZu4cuUK3r59S5MwmpqamDlzJkxMTL7LeY2MjIQSarW1tXBxccGff/6J2tpajBgxAg4ODqzJWUrbxsTEBCUlJQgPD4ePjw9cXFzg6ura4tpa8fHx8Pb2Rnx8PG1I1K1bN9jY2DCqsPk78eDBA+zYsQOfP3+GjIwM1q9fjwULFjS73jdsOWWD2NhY7NixA3l5eY0Iwc+fP+Pz589ITk7GqVOn4ODgQDtsS1K5Lgm+d1y2evVqmJqaNmpzBpqvzuHxeC12DZLge2jI/hPcdpkQ1dRc5ufnBwA0ucV2LhOnQp7t3u2fAGFz5+fPn7FgwYK/5Zru379Pz4WKiorYu3evyGKU2tpagTE3VZH9f//3f9izZw99PyoqKrBkyRKkpKTQz9bdu3fh6+uLy5cv0/e8tLQUu3fvRkhICEiSFEjciQtB0jXUHlBQjEi91tz61dThnq2W5H8JP0i1/wjYZihJksS5c+dw8uRJRgQEUF8OvGfPHhQUFEBVVRUHDx7EqFGjWF2HKDDNpF27dg27d++Gj48Pn+uWlJQUH8n28uVLJCYmIjk5GUlJSYiKimJFeLRr1w5WVlawsrLCvn37cPnyZXTs2BHR0dG4ffs2FBQUQBAEqqqqBB7/6dMn2NvbIzk5GVJSUli1ahVWrFghUriXLZhodfzToaioCGtra3Tq1Al1dXV4/PgxPn36BA6Hg8GDB2Px4sX48OEDPnz4IPJzTpw4gd9++42Vfgeb8decu1fTBWvKlCl0sMpm7PXp04cvw0dVy6mrq/OV9tfW1uLevXvfRST16dOnCAkJQVhYGL59+0YHDTNmzBDLCKW10NIVdXJycujSpQurNskBAwZg1apVWLBggdBNVlMw0fK6fv06unbtimPHjmHYsGFiHdN07D158gSPHj0S+5xNcfbsWTohsXHjRpiYmDSqlPny5QtCQ0Nx5swZXLx4kZEDa0MQBIFx48aJdWzbtm0xbNgwzJ49m7Ur9LZt27B69eq/zb00PT0dV65cwc2bN2nyGqgn0/bs2SN2exFbCCNd3r59iy1btiA1NRVt27bFli1bJGp5bYjS0lJER0cjIiICr169AkmSLdY6LMiQCAD69++PDRs2wMDAABwO519Dqi1cuBAAMGjQIDg6OortuilJy2l8fDztIjl27FiYmJigf//+NIH+9u1bhIaGIjY2FitXroSLi0uj9bK1qmdbKy6LiooSOD+0VnVOS0FYMnrAgAGMn5V/AqkmLhrOZRwOByRJSlyJ3Nz6xGbvxuY+APXki7W1tcT6duLMnRoaGq0qAQLUm0o5OTnh6tWrtJnA3r17WXUaNYShoWGj+3j+/HkkJydDUVERs2bNgrKyMmJiYhAfH4/du3fj4sWL+PDhA5YtW4aMjAwA9b/9xo0bWV+DKOkaYTEiVRDQUAdT2PsoGBoaYs+ePf/TTqU/SLX/CNiK3s6ZMwdHjhwBAEYExIULF+jNz+DBg7FlyxbIy8u3uPguG8ycORPXr19HWFhYs1b21dXVKCwsRHFxMerq6tCmTRuJ2yiA+s0mh8NBXFwcoqKi4OvriwcPHoAkSQQGBuLVq1ewtraGhYUFlJSU4OPjA0dHR5SXl6Nv375wdHT8rqYK/4U++ZqaGsTGxoLL5eLOnTt8FU4kSeL58+dYt26dWJ/H4/FYOZmxfebV1dXFrhiMj4/HmTNn4OjoiLCwMHC5XADMxt6vv/4Kb29vcLlcmJqa4uvXr/D29gZBEDA3N+d7v4eHB3JzczFv3jzmX04ASJLEkSNHEBoaiuzsbJAkiS5dusDW1hZmZmYS6x59b0haUZeTk4OYmBjo6+uzOj81JzHZZIkT1Des8g0PD8fq1asRGBgo0pRG2NjT0tJiTYpmZGTgxIkTUFBQQHFxsUAX0W7dumHhwoUwNTXF/PnzkZmZiXXr1rFy8D116lSz76msrERhYSHu37+PBw8eYPHixbCzs2N8rr8DlPOrt7c3Hj58SLdFaWtrY+rUqdi6dSvU1dW/O6EmCtbW1qitrQVQL1eRmJgoFhFMEITA+1dZWYnbt28jJCQE8fHxqKmpAYfDwciRI2FmZoZJkyZJdL2vX7+Gt7c3QkJCUFlZCZIk0bNnT1hZWeHkyZMYPnw4xo0bJ9E5JEFeXh79/0wqr9u0aYOVK1di2bJlYrsfA+xbTouLi7F582bIyMjg2LFjAn8zquX+7t27WLt2LXbu3Ilhw4YJ1HX6XtWzrR2X/ZfREnG1OKioqEBdXV2LC9Y3h4ZzmYKCAvLy8polBUmSBJfLFfo+UaTanDlz6AQWk70b2/uQmJgoUTfHP33unDx5Mk1iDR48GCNGjEBwcHCzxzX3WzbVEL1z5w5kZWXh5+dH78PmzJmDXbt2wdfXF8+fP8eqVavw9etX9OrVCwcPHhTYRSIuJCF2xVmLqfgxOTkZt2/fxpcvX3D16tW/3Ujm78IPUu1/HBQxBoAxAUFN+M+fPxd7491aOiPDhw+niYemSE1NRWxsLOLi4vDs2TPU1taCJEl06NABo0ePhq6urkDRdjZo06YNjI2NYWxsjOzsbPj5+SEgIAB//fUXDh06hMOHD8PQ0BCRkZH075mdnU1njkWBJEmcPXv2X5PNa4ly9Lq6Oty7dw9cLhfR0dE0waGhoQEzMzOYmZnhxIkTrM7l7+8v8fUxgTjVgpWVlfj8+TMSEhLg7e2NLVu2NNI4YzL2gPossp2dHY4cOYKCggJUVFSgR48emD17Nv2eO3fu0BVZioqKjV5jirS0NISEhNCi3X/++ScUFBQwbdo0mJmZYcSIEf/oNoWWqKjj8XhISkrC/v37UVlZKZGYP1MwCYJnzJiBmJgYLF++HFevXuVbC8QZe5K0/fj4+IDH48HExAQ+Pj4i39u1a1c4OzvD2toaJSUlWLZsGePziXsfqqqqkJiYiMOHD+PPP/+Enp4etLW1GZ+PLVJTU/nMQihCVZCJyOvXr0GSJMaNG4cvX76AJEmoqqpiypQpsLa2pnV2tm7d2qLXSbW0ZmVlgSTJRu2twtpda2pq6P9/8+YN3rx5I9a5Gs4Z1dXViIuLQ0hICGJiYugNm6amJszMzGBiYiKxDuKtW7dw5coVPH36FCRJ0iTO1KlT6TF28uRJgcdGR0fzJU/EMSahNBTfv3+PkpIS9OvXT2hrNtU6deXKFVy7dq3Ra66uriK/G0EQ8PPzg4aGhsj3lZWV4cOHD+jQoQMrXbqGCAgIQGFhIY4ePdrsRlpHRwcHDx7E+vXrcfPmzWbbwlqyenbXrl2M4zKg/jdNSEhgdU6AOfFRUlLSbDt7U2zZsgVjxoxp9n21tbV8VXlsNGRbC0uXLkVKSkqLt3k3h4ZzGUVui6M/nJaWJrBjgSAIeHp6CoxLCYJolABmsndrba1HSebO1sTHjx/p/3/27BmeP3/e7DFsfsvMzEyMGzeOr7Bh/vz58PHxwYYNG/D161dMnjwZf/zxR7NyHM1BEiKUybEkSeL48eM4e/Ysbty4IdG+4d+MH6TafxDl5eUgCEKgy9bNmzcxZswYujyTrRNMXFwcfvrpJ8bHJSYmCgwAiouLAdRraQibqJhoCVGmAUB9MHjv3j3ExcUhPj4eX79+pbP2AwYMoDXUhg4dyihLKwqU8HNdXR39maqqqli7di3WrFmDuLg4+Pr6IiYmhl54qe9dXl4u9nlaQn+qqqoKd+/eRUJCAnJyclBaWor27dujZ8+e0NLSgp6eXov8LoIChJKSEgDCjRMoY4SHDx+Cy+UiMjKSFoHu3bs35s+fD3Nz80YtimyFWMU1xBAFGi/OpwABAABJREFUJmNP3GrBvn37QkdHB5MmTcKMGTPQpUsX1voKjo6O+P3332ndKk1NTTg5OTXShNi1axe+ffsGBQUFnD59WuyMb9Pg29LSEm/fvgVQ3xo9fvx4TJs2Dbq6ut+lpbml8Pr1a4SEhDRbUSeO2UdNTQ0KCgpo4n7GjBmsK9VaAwYGBhg6dCju3LlDB+lMxp4kuHfvHrS0tMRuHVBXV4e2tjbu3r2LTZs2tcg1CELbtm2hq6sLDQ0NTJw4EdeuXWtVUi06OlogAU+SpEBShtqQFxUVYc6cOTA2NpYo0y0uAgMDG5F8Dds4m75Ggdo4MgWPx6MrJaOjo1FWVgaSJNG/f3+Ym5vDzMysRZ3/tmzZgrZt20JfXx8mJiaYOHGi2Jud169fC60qFWVMYmRkhP3799ObZxkZGSxevJjPHODt27fYtm0b/W+mGz2KGAfqiaPExETIyMhAX1+fbsH6888/4erqSscbP//8M/bt28foPA0RFhaGn376SezEhLGxMfr374+IiAihpNr3qJ4F2MVl4kAQWUVhwYIFsLa2FvuzvLy8GFekUBInmZmZCA8Ph5GRUaNW+6CgIJw6dQqZmZlQVlbGjBkzsHLlSgDsNGRbk8xpbeIIaFwRJK4RDTVHCutYKC4upvdGDUEQBOu9W2snjyWZO1sTbIsTTp06JTDxRaHpa8XFxSgrK0NgYGCjVlpq7GVlZWH9+vVYvnw5q+v5u0AQBDZu3Ijw8HCEhIT8INV+4L+B8+fP48yZM9i3bx+fuGJpaSm2b98OGRkZLF++HMuWLWNNQLCFurq6yABAWNaSqcZZbm4uFBUVMX/+fDx69IgmuRQVFWFmZkYTaZL2yzfFx48f4eDggNjYWNpBy9jYGJs3b6bPRRAE9PX1oa+vj7y8PAQEBDTbpioI6urqGDBgAN/GSlQWvGlwf+PGDRw/fpxPKJi6zvPnz6Nz587YsmWLwBZBJhAWIADCgwqSJKGrq4vc3FyQJImuXbtiwYIFMDc3F9qG4e3tDT09PYkt7pmC6dhjil9//RWjRo3C169fJXK2OnfuHCoqKlBbWyvQzcja2hodO3aEjY1NIzdApsH3mzdvQBAEunTpAn19fcjKyuLhw4d4+PChyOsjCKLRJrE1QFXUcblcfPz4kXZCFFVRJ27rroyMDLS1tWFjYwNLS0uJrpMioUmSZFSZwCQh8euvv+LmzZvYv38/wsPDGY09SZCdnc3499HQ0MCNGzdQWlqK+Ph4DBs2rJFYblJSEtzc3JCWlgZVVVVMnz6d0Wa1IZSVlaGnp4dnz56xOr5htZagai5B0NHRYaVP4u/vj/Lycty+fRu1tbUoLS2Frq5uiyWNmkISkpFNJn3UqFEoKioCUJ+cmDNnDszMzPDzzz+zvo7mUFdXh7KyMuTk5CArKwv9+/dv9hi2jnkfP36k9cbatGmDDh06oKCgAG5ubmjfvj0WLVoEoH6tc3R0pB1yx40bB3d3d1bnpJyO6+rqANS3sJ06dQpfvnyhJUKUlJRQXl6Ov/76i67aYrOx//TpE208IC5GjBhBm5dQ+N7Vs5LImbAhqyh06NBB4NosDPLy8ozeT+HGjRv4448/UFdXh//7v/+jr/PWrVvYunUrSJKEnJwcysrK4ObmRifJ2JJWf3cV2/dEw3ksMTERBEE0O7dJom3MVsIlICCg1e8Dm7mztcGWVHNxcRGa+AL4k2I8Hg8xMTGIjY1tRKpR7ZIDBw5sUUKNaQVrQzCJGymMGjWK1mr+X8QPUu0/hBMnTuD06dMA6iu+mqK0tBTDhg1DcnIyTpw4gYKCAvTs2bNVCQg2g5QpqqurERsbi759++Lhw4eQkpKCmZkZFixYgIEDB363BeXLly+YNWsW8vPz6aCjoqICAQEBePToEfz8/PgCH2VlZSxevBilpaWsNCDYZsEB0G5oJEliyJAhGDFiBLp37w5paWmUlZUhIyMDCQkJSEtLw+bNm5GRkcEX/IkLtsHD+PHjkZubiy5dusDExARaWlogCAJfvnzBly9fBB5DZdB79+4NXV1d6OjoYNSoUWI50gpq1WkOFHHEdOyxIY5++eUXPHr0CJWVlRI57AqqpKOwYcMGvr9RwXdtbS3i4+Pp6hJxgu+cnJxm2/kaorVJtaYVdaampjA3N2+2oq65Z5oymmhJbRcvLy+6EkmcSjmAeUJCVlYWlZWVuHz5Mv1sizv2KIh7bQ1RXV3NWIdDSkoK1dXVGDduHEpKSuDm5kaTanfv3sXy5cvphMrXr1/x9OlTvHr1Cjt37mR8fUC97ldsbCyrYxtWa1H3sDlRfrYOwevWrYOfnx/8/f1x7do1XL9+HYqKijA3N4e1tXWLGyZIQvKzQWFhIf1sqqurIy0tTawWImFabM3h6tWr8PHxQVhYGJKSknD8+HGoq6tjypQpsLS0FKjzBTA3WaKwdu1a8Hg8/Pbbb1i8eDEqKirw7ds3rFu3Dh4eHpg/fz6OHTuGCxcugCRJjB07Fvfu3WNNdkdGRuLkyZNQUVGBsbExpKSkEBYWhrVr16JTp05QU1ODi4sLBgwYgOrqapw5cwYuLi4gSVJoZZIo8Hg8ob+ZMDTsPmit6lm2cRlbsootbG1tGZMC6enpsLCwgJSUFGbOnEkT0lVVVXB0dARQ38myd+9eEASBQ4cO4cqVK3BxcWFl2KKuri60nVEUqG6FfzpsbGygp6cHHR0daGlpiWUEIIm2sSTJYzb3gdLoZBof6+vrQ0lJifHc+W8B04pBf39/uiBCEMaOHdtSlwYAYhsyNQXb2ENRUZGVK/t/BT9Itf8IkpKScPr0aXTv3h3Ozs4YPHgw33u6deuGS5cu4cmTJ1i1ahU8PT3pNshevXoxIiBKS0shIyMDGRkZsa/x6dOnWLBgQYu5ewlCWVkZduzYgdzcXKxevRrPnj1DeXk5QkJCkJiYSOuljRkzhlVmTxTOnj2L/Px8mJmZoVOnTvD29kZgYCCOHTuG+Ph4XLp0SSgpNWrUKGhqakJHRwe6urpiBcdss+AAkJycDFdXVygpKeHIkSMYPXq00PfGx8dj+/btOHXqFHR0dAQ+W81BkuCBImY8PT1x8eLFZhcIitD8+PEjPn78CG9vb0hLS0NLSws6OjrQ0dERWtGQmprK2mWL6dibNGmS2C6LFKSlpVFRUYGRI0di+PDh9Jj9npm/9PR07Nq1C1JSUvj1119prZjmgu+5c+f+K0Sd/00VddQGr7i4WKjLmqT48uULlJWV6dZXauwxadNjM4Y6d+6Mt2/fMjKsePnyJSorK1FVVQV9fX16nqmtrcXu3btRV1eHMWPG0PpU+/btg7e3NyZOnIiRI0cyvsaamhrWrcvdu3enr49y4/pebZndunWjHd7u3r0LX19f3L59G15eXrh06RL69+/fosmlpKQkqKmpMU6aNVepJwrUs5mTkyP2MWy/89ChQzF06FDs3LkTQUFB8PHxwatXr5CamorDhw9DV1eX1ecKw/Pnz6GhoYHNmzfTya/Xr1/D3t4e69atw+HDh+Hp6QlZWVls2bIFs2bNkogovX79OpSVlREUFERvcpctWwYzMzN8+PABp06dojeBMjIyWL16NSIjI5Gamsq6aonp5jw7Oxtt2rRhXLkuCdjEZQ3XSyZkVWvj8uXLIEkS58+fbzQPxcTEIC8vD+3bt8fOnTvpGN/e3h7R0dHw9/dn7YIsqltBGP7O6raKigq+57ugoADXrl2jq5+tra3Rp08fvHjxAi9fvoS7uzutz+zr6ws9Pb1G1dOCUFNTg+TkZBQUFKB79+4YMmQIOByOyGP27dvHau8GsLsPALv4mCAIvH79mvHcyVSLsiXdcXk8HuLj4xEWFobMzEzk5+dDSUkJPXr0gJGREfT19en7w7TbKyAgABMmTBBKgreUQzWF1ihkaYjCwsJGHS7/a/hBqv1HcOXKFbRp0wYXL15sVm9JU1MTbm5umDFjBnr16gUpKSm8f/+eEQGhra0t0JaZcu8SNGHEx8fD1dWVMakmTMy3IWpqapCbm4vnz5+jrKwMQ4YMgY2NDaytrZGcnIy4uDjExsbixo0b8Pf3h5SUFAYPHgx9fX1aL0dS3L9/HwMHDsTRo0dpK2J1dXW4ubnB2NgYMTExQkk1aWlpJCcnIzk5Gc7OzlBUVKR/fx0dHYFtqpIInm/atAlSUlI4c+ZMs4Girq4uzp49C2tra/j4+LAi1dii4XekqvLE/d52dnZITExEQkICEhMTcf/+fdy/fx+HDx9Gly5d6EBkzJgx6NixI4D6ChumVTZXrlzB69evGY+9q1evMibVMjMz0bZtW0hJSdGOhE5OTujatWuj79OShHHD4Pvhw4d48eIFgOaD7+zsbNYVQa2Nlqyoq6urQ0FBATp06CCw8urdu3fo3r07K8t4W1tbAPXi49+jxL60tBQxMTEYOnQoK9JJEgwfPhzBwcFiV5lkZ2fTrpbHjx+HiYkJ/dq9e/fw6dMntG3bFo6OjrRQ/ZEjR2BoaAgfHx9W3++vv/5ibRdvbW1Nr4sUUfK9K7wIgqClDvLz8xEQEAA/Pz/aEMDPzw9fv36FtbU1DA0NMWrUKFab2KKiIqxZs4Zxtcy8efNYZ9HZaLFJivbt22PmzJmYOXMmUlNT4ePjg+DgYNy5cwcAEBoaCmlpaVhbWwutRBAHhYWFAtsjqXYyLy8v9OjRA+7u7i2SUHn9+jX09PQaVY0oKSnB0NAQAQEBAtepsWPHIjU1FatXr2Z831euXIn4+HiUl5eLpa9UXl6OuLg4lJSUoKysrNWqZ9nEZWzJKkEV7t8TDx8+xOjRo/mI/Xv37gGoj/saVum1adMGY8eOhZ+fH1xdXcWqxGoKW1vb75pUbylkZ2dj27ZtfO6HHz9+xLx582jTIqBe9sPR0RHXr1+nY81Hjx4hPDwcERERAIB+/frRxKyWllajYoTw8HDs3r27UXVPz5494eDggOHDhwu9xunTpyMpKYnx3g1gdx+o2JjNOAKYzZ2AZF04kiA1NRV2dnZIT09vRKimp6cjOTkZN2/eRK9evXD8+HFoaGggMDAQ6urqLV793VJozVZMkiRx//79Vpfe+SfhB6n2H8Hjx4+hp6cntoD5kCFDMHLkSGRkZCAiIgK5ubmMCAhh7kSJiYlwdXVtUUdKJgLybdu2hY2NDbZu3QopKSlISUlh9OjRGD16NLZu3YqsrCzExMQgLi6OXvxOnDgBZWVlevMxduxYVkz7ly9fMHPmTL6/U8FIWFiY0GOTk5Px4sULJCQkICEhAY8fP8atW7cQFBREk3NUafmwYcPA4XCQnZ0NeXl5mhASB6mpqUhNTcWTJ08wcuRIsbO76urqGDFiRCO32NZAw2o8FxcX/PXXX4wq9ExNTWFqagoA9DP+8OFDJCUlwc/PD35+fmjTpg0GDRoEkiShrq7OmKx0dnZmNfaSk5MZnScnJwcxMTEYN24cjh49yve8+Pr6ws/Pj64oo54XJpU/gtAw+G5YvdVc8B0fHy/ReVsLklR8UsjIyMC1a9cQHh6OL1++gMfjAQBd/bZw4UKaLLKzs4OKigrOnTsn8XlbEpmZmdi+fTuKi4thbm4usYYiU0yfPh0BAQGIjIykfz9hqKqqwqZNm8Dj8aChodGIUAPqWz8BYPTo0Y2cH9u1awcdHR3GYw+od8p++PAhZs2axfhYtmDr3CXIfVBJSQm//fYbfvvtNyQnJ8PX1xcRERG4ffs27ty5A0VFRaioqOD9+/ctceliobW12BqCWgsb6towgbq6Onbt2oWtW7ciNDQUN27cQFJSEi5duoTLly/jl19+wdSpUxm5M1OoqKgQSNh06tQJACAnJ4fLly9LpBfWEEVFRQLJYupv1HkbQhKxcTMzM9y+fRvHjx/Hjh07mn2/o6MjiouLQRBEq1bPsonLJCGrWrM6JycnBzo6Onx/T0lJAUEQApMO1FzKtjqxQ4cOEnUttAYqKysxb948ZGVloW3btqiqqqJf27lzJ75+/QpVVVVaJsPFxQX29vYIDg7G0qVLsXTpUtTV1fE9NxcuXMDFixfRrl07jBgxgtbLtLOzQ11dHTp37ozu3bsjIyMDGRkZWLx4MQICAtCnTx+B17l3714AYLx3A9jfBzbxsbDPETV3duvWDVpaWi3eCtkcnj9/jrlz56Kqqgo//fQTjIyM0L9/f3Tq1AllZWV4+/YtwsPD8ddff2HmzJm4ePEitm3bhtWrVzMi1RITE4W2zAt7jSAIVkR2a4HH48HR0RGZmZkC98H/K/hBqv1HkJuby1hHgtJnAuoXS6YEBBvweDzGhBtVRSYsOCUIAh06dICSkhIGDhwosiWVEjWeM2cOqqurkZCQgNjYWNy9excBAQEIDAwEh8PBr7/+ymdN3xyqqqroQLMp6aisrEzrgQiClJQUhgwZgiFDhghdlF+9egUPDw/Iy8tj1KhRiIyMhJycHA4dOoRJkyaJdY1RUVFwdXWl3RiZ4Oeff8bTp08ZHfNPgIuLC9zc3PDq1atGz3heXh7CwsJw7do1PHnyhPXnSzr2mgOPx0NSUhL279+PyspKWFlZifW8PH78GCdPnkSnTp0wZswY6OnpYfLkyYy/H9vgu6CggPG5/g5IGiReuXIFTk5OqKqq4psXqeo3f39/2NnZoXPnzvjrr79azdlJnGw0VeWblZUFHo8HQ0PDFiPUGo695jB06FBYWVkhICAAv/zyC+Lj4zFmzJhG4vokSSIuLg5HjhzBu3fvICMjI7CKJjk5GQRBYNSoUXyvqaqqIi8vTywB35qaGnz79g0PHjzAhQsX0KZNG8yYMaPZ41oKbFp0gOYz9lpaWtDS0sLvv/+OW7duwdfXF69fv0ZBQQHWrl2LkydPwsDAgNaJbA5ss/StrcXWENRayJZUo9C2bVtMmTIFU6ZMwcePH+Hj44ObN28iNTUVBw8eZEWqCQN1X42NjfkINbai+nFxcaitrRUYN1GtSM21ojGFqakpLl68iMuXL4PH42Ht2rUCE5mFhYVwcnJCQEAARowY0eqtTGzisk+fPmHo0KF8nyUOWcWmOgcAo8QqhdraWr5Ws8LCQqSnpwMQTHiXlZUxPo8kYKrVBzBvK26Kq1evIisrC/PmzUOHDh1o44/nz58jKSkJBEHA1dWVrkQdNmwYTExM4OXlRVfmC3punj9/jsTERCQnJyMlJQVxcXF08mjv3r2YPn06CIIAj8eDh4cHnJ2d4eXlhV27dom83tbau30PCJs7v3z5gpCQENogpTVQUVGBdevWoba2Fjt27MDcuXP51tGJEydi5cqV8PHxwd69e1m7jlMdXeK8RiUSvjepJsiNWJzxR8WPiYmJ+PTpE/r06fODVPuBfz86duzIeMHj8XhCW5CaTtQtRUAAYCQ62nBCaYkMSUPIyMjQ1WmlpaWIjo7GhQsXkJqaKpA8qqioQElJCdq3by/wd+PxePQkvGbNGqxZs4Z+jcPhNFt90RCCFuWXL1/Si3JSUhJIkkRZWRnWrVuHxYsXY+PGjWKXP1dWVjJuP5OXl6dt6v9toAKJzMxMPHjwAA8ePEBSUhLteiotLU07qDEF27FXW1vbLLFZU1ODgoIC1NbWgiRJzJgxA/r6+nzvE/W83Lx5EyEhIQgNDWVFqrENvv+JtuktDT8/P+zduxdt2rTB9OnTYWJign79+qFTp04oLS2lM5u+vr5wcnKCjIwMNDU1+SqrvheEBW6CoKamBhsbGyxZsqRFzv3q1SvEx8ejrq4Oo0ePpufOXr16QUtLC1ZWVnztKXv27EFxcTGio6OxdOlStG3bFr169UKHDh1QUVGBjIwMlJWVgSRJmJqaIioqiu85Kysro9sbBT2blZWVaNu2LSMBX5Ik0bZtW+zdu7dFHCbV1NTE0lMTlyhJT0/H1q1b8fz5c7Rp06bR2iMK8vLymD17NmbPno2XL1/Cz88PK1euRGZmJgIDA+Hn5wcbGxuxPusH6s1xNm/ejA0bNuD27dvw8/P7LucRpadYVlaGx48fo7i4GAMHDhRaQV1aWoqDBw8y6gRoKRAEARcXF8yePRtXrlyBv78/NDU10bdvX3To0AGVlZVIT09HYmIiKioq0Lt3bxw/frzF3dqZQpy4rKqqCjdu3MCBAwfo48RdL5sjUISBTWwsJyfHR0Ddv38fJElCRUUF/fr14zumtZOqlIyKuERQw/0CW0RHR6N3797Yvn07kpKSaEI5JiYGQH2nQcPWbjU1NdooRBikpKSgqamJn3/+GRoaGrTUAbVONUzUcDgcrFixAjExMYzWbwrfc+/2PdFac6cwhISEIDs7G/b29s0mQqZPn46amhraFI0JWrKLiwmYuhFLSUkxHn+jR4/GgQMH/idif2H4Qar9R6CmpsY4U/nixQuRAprfg4AgCAJeXl7Nvq+qqgonT57E8+fPAQBjxoxhdT5hyMrKQkpKCpKTk/Ho0SO6f54kSfTv358+X1RUFAIDA5GYmIiSkhL6eEVFRWhpaWHq1KkCSY6WRnV19f9j7zqjojjb6F06CKJAVOyxEkssiChVrChWVKxBjYDGXmmigo1iV4qSqIACAtJUsAHSLDQRsUAQVBSsIFIEKTvfD87Mx7KFnVnAEu85OUl2Z3ZmmZ15n/e+97kXJSUlKC0tRV1dHceKgpSUFP755x88fvwYBw8e5Nmq0RgEQdBefW7u1erWQE1NDfLz88FmszFp0iTk5+cDqP/+3bp1w4IFC6Crq4tRo0bxXF0WBkzvPTabLdSqqpSUFDQ0NDBnzhxMnz5d4LZVVVVIS0vDnTt3kJqaiuzsbFRWVgIAunbtSuscSXTs2JFR8c3Ue+p7wdu3b7F79260b98ep0+f5vJQateuHTQ0NKChoQETExP88ccfKC0tZZyg27lzZ7Rt25aWeqmpZ62YmBjk5eWhpKSEDh06MDqvxqipqYGdnR0uXrxIFWOkavHTp0/IzMxEZmYmvL29MWfOHA5/ISkpKbi5ueHChQs4deoUnj17Rk08SAwYMAB//vknpk6dirFjx3IZ1ScnJ6Ourg6Kioo8vTIfP34MJSWlJn0HyfRWJSUlDBs2DNOmTeNLUGRlZeHt27ccxGHv3r2p9zU0NDjuv1mzZjXbItHZs2dx6NAhVFVVoV+/fnBxcWGkHBs4cCBlB7Bz507cvn0bR44cwdSpU4Uyvy4rKxNK/dcQCxcuxJ9//vld+CzRgYSEBMaMGYMHDx606nHPnz+PAwcOcCzyzJgxA3v37uVQfN66dQvbt29vdR+vhujYsSNCQkKwd+9eREREULVmw0mcpKQkTExMYG1t3WwTNTrq2abAry5rPAkVZrzs0qWLSM+Ely9f4vbt2ygtLcWgQYP4hk+9fv0atra2KC4uRlxcHIqKiqCsrAygfpGIxWLxXOy7d+8eVY+3VnhAcxIQWVlZSEpKwtu3b1FeXo42bdqgW7duGDFiBMdCSW5uLsaPHw8Wi4WRI0dSbebJyclgsVg8Vfu9e/fmGWxEEATu37+P+Ph43LlzBw8fPqTSqNu3bw8xMTG+84ehQ4ciKCiI0Xdtiblba0FCQgLt27eHubk5oyAbppYCly5dQufOnYVWFi9cuBDe3t548eIFreN8DVKNSRrxsWPHmjzXhl1iw4cPb3Ul8beIn6TaDwIDAwMcP34c6enpQpED6enpuH//PhYvXky9VlNTg+TkZMTFxSEuLq5FCAhyoBKEx48fY9euXcjNzYWsrCy2bt2KhQsXAqhX+DQmd54/fw53d3c8efIECgoKmDhxIhYtWsShrsnOzkZaWhr1DzkRIwsdIyMjaGtrQ0tLCx06dEBpaSn++OMPpKam8mTpi4uLcf36ddy4cQNaWlo4dOgQo7+HIGRlZSEuLg7x8fF48OABpVYik4WuX7+OZcuWYfLkyVi9ejVu3bqFOXPmwNXV9Zs1zWwNFBYWUr/hpKQkilR69+4d9PT0KHVi4wmyo6MjI3NppvferFmzBCpKyEl9Q/8VXnj+/Dn1O0lJSUFNTQ0IgoCsrCxGjhzJ9/sKC01NTVy+fBlFRUXUa8IU3z+6BDwoKAjV1dU4ceJEk7+bz58/U6T8vXv3oKenR/t4pOEsnZYYUX2nmIA0IW/Xrh169eqFe/fuISIiApKSkigvL0d+fj7u3r2Ly5cvIygoCO/eveNqM5wzZw7mzJmDgoICPH36FGVlZVBUVETPnj05VlmHDx+OuLg4VFZWQlZWFgAQFhYGFosFPT09rsnf06dPce/ePUyfPl1kL73i4mJ4eHjg0qVL+PTpE9f77du3x6xZs2BhYdEibY6vX7+GjY0NkpKSwGKxYGZmhnXr1kFSUhJhYWGMP3fmzJmQkZGBlZUV/P39kZ6eLjAdmoSPj49QC2YNwWazGbe4fg0kJycjMTGRUoFNnz6dZxjJw4cPYW1tjdzcXMYtQoKIC17vxcbGwt7eHkC9kk1ZWRlZWVkIDw+HiooKtmzZAoIg4OLiAi8vLxAEAVVVVezZswfLly9n5OclJiYm0iRRUVERLi4u2Lx5M27fvs11r48bN06oRUK6EKUFrqm6rKioCI8ePaJNVo0dO5ZxGm5sbCy8vLw4uiE0NDTg4eHB0ZEQFBQEZ2dnlJeXQ1JSEl++fMH8+fMxY8YMPH78GLdv34aEhAQXyX3r1i1YWVlRHRfe3t4ICQmhdY6dOnWignaERXMQEHfu3IGzszOys7MB/P/aN7yHBg0aBCsrK4wYMQJlZWVcisja2loqoIlXeEDD39PHjx+RkJCAuLg43Lp1C58+fQJBEFQ4GlmPDRo0CAMGDMCgQYN4njep2BQGdOZuxcXFjNqFmdbHTH7TJLHFhLxl6jP44sULaGlpCS0eIO0lXrx4gYKCglYlAOmAaRpxVFTUV1PVfc/4Sar9IDA2Nsbff/+NdevW4cyZMzxXw0hkZ2dj3bp1EBMTg4GBAfz9/SkCoqqqipqQCyIgWgJsNhvu7u44ceIEamtrMWzYMDg7O6N79+54+vQp9u3bh+TkZEhKSmLq1KmwsbHBmzdvsGDBAo5JTXp6Oq5fvw5vb2+sWbMG6enp1ISWIAjIyMhAS0uLItEaE1C1tbVYunQpHj9+jJ49e2Lp0qWUn0fjieH58+dx69YtWFhYAKgvuHhNaMgHPb/JzsyZM1FRUYFbt24hPj4eCQkJePfuHSVl/+2336hrMWzYMIiLi0NNTQ1t2rTB77//jtDQUGzYsAGpqamYP38+HBwcmmz1o6ss+F4mP2RbF0EQ6NOnD+Tl5XH//n0kJSUJ9NtjukrM9N5bvnw5I7PY6upq3L17F/Hx8YiPj8fLly+poq5Pnz7U76RxyhRTmJqaIjg4GPPnz0enTp3AZrOFKr7nzp0r8rG/Zdy8eRMDBgwQinA4duwYOnbsiPbt2yM+Ph4bNmwQ+jiVlZWoq6uDtbU1pkyZgvHjx3+zZs/h4eFISEjA8OHDcezYMZw/fx7p6ekcqq0BAwbA0NAQ69atw/r16xEXF4fr16/z9IXs0qWLwO86f/58XL58GUuWLMGCBQvw+PFjXLt2DWJiYlwrznl5eVi7di3YbDajNuiGePjwISwsLPDx40dqYUZVVZUaHwoKClBcXIzTp08jPDwc8vLyWLZsWbP5sYWEhMDR0RFlZWXo0aMHnJycOAh9a2trxmoS0mOsYQuRMJCXl6edOiyq/1Froba2FpaWlrhy5QoAUOPymTNn4OPjQ3li1dXV4dixYzh16hRqa2tFIoRcXV3h6upKKejJCS3ZOtmYXCcJlR07dmDRokUA6sOTzM3N4evri1WrVmH79u2IjIwEQRCYO3curKysqEUbUdP2CgsL0aZNG57eaCdOnICenh7flPWOHTs2u71Hc4FuXZaTk4MZM2bQJqvIwBC6aEgSqaurQ1lZGSkpKUhJSYGjoyP27NmDyspKWFtb4/r16yAIAgMHDoSTkxPu378PBwcHuLm5Ud/Jzs6O43k9btw4FBYWgiAIrFixAidPnkRpaSmjevDZs2etmh4fFBQEe3t71NXVoUOHDhgxYgTHc/rly5dIT09HZmYmlixZgt27d0NRURHFxcUcn/PgwQNUVlby9fB88eIFxMXFYWJigocPH1L3bMeOHWFsbEwFoDV+PjZF7gp6v/HisbBzt+joaAwdOpT2dfjtt98YdR8wSXgmv7e8vDxGjBjBobJtKRQVFdH+fmRaclhYmNALWeR3ExMTE1otGxkZicjISEYeg0zTiENCQjB+/Hjax/uv4yep9oOgU6dOsLW1xfbt2zFz5kxMnToVenp66NWrF+Tk5PDp0ydK0XL16lXU1tZi06ZNWL58OQcB0dwTcmGRm5sLKysrPHr0CBISEti8eTPMzMzAYrHw6tUrzJ8/H+Xl5ZCXl4eYmBiCgoKQn5+Pdu3aobS0FKamptDW1sbHjx9x+vRppKenIzAwEHFxcVRKk7a2NrS1taGuri7wuwUEBODx48eYMGEC9u/fz9X6oqioiMGDB2Pw4MEwNTWFlZUVrl27BqB+wIqOjub5uQRB8FzxBeonSffu3eOQhhsZGVHXoykvERUVFXh7e8PFxQXe3t6wtrZGZmYmbGxs+A5ITJQF3wvExMQwefJkzJo1C6mpqcjIyGjy9yzK34LJvde3b1+hPrukpARycnLU+Y8cOZIyxVdQUMCECROo30lzpcE1RN++feHg4AAHBwe8fPkSAIQqvoVNlv1eUVhYCENDwya3+/LlCwoKCrB48WK8ffsWly5donUcCwsLpKWlgc1mQ01NDVOmTOFYCEhISEBiYiLfZ0vD85WWlqbUE0C9of/hw4cple+ECROwdu1aRunHQH0LRdu2bXH8+HGO4/CCkpISjh07hnHjxuHixYsUqXbt2jVER0ejuLgYnTt3xtSpU/kq7kaMGIHVq1fDzc0NmZmZVMFqbm7OMWlYuHAhMjIyUFdXh1mzZvEMMOCF/Px8ZGVlQUFBgRo3iouLYWZmhpKSEkybNg1//fUXevXqxbEfQRDIzMyEl5cXIiMj8f79e7x69Yrr8+kWy8XFxbCzs6Mm3wsXLsTWrVsplR6JmTNntlqLFoklS5ZgzZo1KC8vR2lpqVCtIN+LotrPzw+RkZGQkpLC5MmToaysjISEBOTk5GDXrl04duwYiouLsWrVKmRkZIAgCIwbNw4ODg6Mj0n+lhv/uymQhBpQXxdaWlrC3NwcdnZ2iIyMhIqKCvbu3cvRdiaKapMgCOzZswcBAQFwdnaGkZERx/sfP37E0aNHcfToUcydOxfbt2+nugjevXuH+Ph46l7X19enTcy2JExNTWnXZQ3HS7pkFQC0adOGFpFw7949lJSUwMPDA2PGjAFQr4w2NzdHeHg4Nm3ahPXr1yMlJQWSkpJYvXo1zM3NIS4ujr59+0JHRwc3b95ETU0NdHV1uZ5lMjIy6N27N5YvX45Zs2bh5MmTWLJkCe2W7XHjxiE+Pp6LzHn27Bny8vKa9Jdls9lISEjA1atX8fLlSxQXF0NJSQldu3bFpEmToK+vz6EyysrKgoODAyQlJWFvb485c+bwfCZWV1cjJCQELi4u2LFjB4YMGcLlZXb9+nUA9cqixsrUkpIS3L59GxUVFXj06BE0NTWho6MDXV3dZvHf5IfGi8fCzt327duHNWvW0L4Os2bNwpo1a7jM8qurq1FdXc23o4KJGis3NxclJSUoLy/HgwcPMGXKFEybNg1Dhgyh/VnCQlFREe/fv6e1z4cPHwDUK4OFXehMTk7muVgYGhqK0NBQnvOQvLw8vvPKpsA0jTghIaHJzy4pKYG3tzdHl9iECRMYneePgp+k2g8EUhmyd+9ehIWFITw8nGsbgiDQpk0b2NjYYNGiRTh06BAHAaGhoSE0mdZcRbuXlxeOHDmCqqoq/Pbbb3B2duYYjE6ePInPnz/D2dkZ06dPB4vFwuXLl2FpaQmCILB69WoOmer48eMxbtw4XL16FQcPHsTo0aNpGdxevXoVKioqcHJyatJLRlpaGk5OTrh79y6kpKQYR0CHhoZCXFwcRkZGWLp0KQYOHEj77ysuLg4bGxsMGTIEdnZ28PX1RVZWFo4ePco1wf2Re99NTEyQkJCAy5cvIyIiAuLi4mCz2fD19YWuri66d+/Oc799+/YJ9TfnZYRLrvDTufdIVFZW4uHDh5CWluYodCIjI7F//368efMG4uLi0NXVha2tLaqqqiAuLo7Jkydj5cqVApVxzYW5c+dCR0cH9vb2iIuLQ0RERJPF94+OqqoqoSaA0tLSuHbtGmpra3Hs2DFGYR+CJtQZGRnw8fHhS6rdvXsX9vb2lPfH6NGj4eTkhNevX2P58uX48uULgPqJmK+vL27fvo2goCDaQSZAvRJTS0urSUKNRPv27aGjo0N5zaxbtw4xMTEc3zcoKAiLFy/Gtm3beH7G2rVroaenh+vXr6Ompgb6+vpcz+EPHz5AVlYWS5cu5fK0u3fvHm7fvg1paWkYGhpS7aUODg4ICAigzkVJSQm7du3CgwcPUFJSgg0bNvBNcWWxWPj9999x6NAhDB48GE5OTpQfUUPQKZavX78Oe3t7FBcXo1OnTnB0dOSrknRychLqM1sCXl5ecHNzY9yG8y3i6tWrkJCQgL+/P7VYsGXLFqxbtw7R0dHIz8/H6tWrkZOTA0VFRdjZ2WHatGmMj9fQo9PV1VWov+fQoUN5qoOHDh0KALhy5QoGDx4Md3d3LkWGKM9ra2trhIWF8fVrYrPZmDlzJhXYUlFRgYMHD8LLywsHDx5EbW0tta2CggJ27dol1GJFayA5OZlRXUaOl3TIqjdv3iAqKoo2kUAuFpOEGlAfQrBx40YsXrwYmzZtQkpKCnr37o2DBw9yEdmqqqqUvQovXLp0iaslTkFBodnU0hEREU3+vrOysrB582bK+5hEXl4eUlNTER4eju7du+Pw4cOUGtLX1xd1dXU4ceIETx80ElJSUpg/fz7VlSItLY0XL15g9+7d+PPPP/HkyROcP38eLBYLs2fP5ti3trYWdnZ2qKysBIvFQl1dHXJycqCkpAQVFRWoqKi0aLgG07kbLzR1HUj1XWN4enoK3I+p9cGXL18QExODS5cuISAgAL6+vujWrRumTZuGadOmoWfPnow+lx/69++PxMREnimYvFBTU0MRT8bGxkK3SqqpqfHcnmkLaVN4+/Ytz99/U2nEpA/u+/fvcezYMapGmj59OiwsLPD+/XssWLAAr1+/pn4Xly9fxqRJk3DkyJFm/x7fC36Saj8YyME8JCQEcXFxKCwsxKdPn6CkpIQePXpg7NixmDp1KtWu0JiAkJGRgaamJrXqwY+AAMCzBYGEML33BQUFsLa2RmpqKpV4s3r1aq4H2t27d6Grq8vRsjN16lRcvHgRCQkJXObt8vLyGD9+PG7evMm1akpCUJJnbm4utLW1hZ5UysjIQFtbG8nJyYxXfK9du4bPnz8jIiICycnJ0NXVhZ6eHrS0tGiv3E6ZMgX9+vXDmjVrkJqaCmNjYxw/fpyDsCH9mX5E7Nq1CwDw77//IjY2FufPn0dBQQF2794NFouF7t27U/J4TU1NauVx9erVQhOZcXFx1CSZvJfo3ntAfbvcvn37qFaKnj174tixY3j9+jW2bNlCtfTU1tbi5s2bePjwIUaOHIn79+9T92yXLl2gr69PeWYIYyrOBKqqqhg8eDDi4+O5JggA7+L7R0anTp3w/PlzobeXkJDA8+fPBYbDNDeysrJgZmaG2tpa9O3bFxISErhz5w4sLCyo1gUbGxvo6Ojg48eP8PDwwO3bt+Hr60u1tNNBaWmpwGRCXujatSt1n0ZHR0NJSQmzZ89G586d8fz5cwQFBeHcuXMYOXIk31VQMpGPH86dOwdlZWUu9ce2bdsobyCCIHD8+HE4OzujqKgI/v7+kJaWRr9+/fDx40e8evUK69evR/fu3dGrVy++hFpjLFu2DE5OTnj27JmQfxFOlJWVYdeuXbh8+TIIgsDMmTNhZ2fXpNfi94LQ0FDaKXcsFgve3t4tdEbcePbsGQwMDDjUt2JiYli5ciWioqKwfv165OTkQFNTEy4uLq16j5OoqqriqTAl64d27drh1KlTjPyU+CE6OhphYWEYMGAAPDw8eH5vZWVlODo6YtWqVVixYgUiIyPRq1cvHD9+HED9vUve60+ePMGWLVvQrVu3b0LpLCcnx7guo0tWOTs7MyISysrKeKreyRo8KSkJY8aMweHDh7kUrcLga4/pmZmZWLx4Mb58+YJevXph0qRJ6Nu3L9q1a4eKigoqYTs7Oxvz58+Hl5cXhg8fjpSUFKirqwsk1Bpi1KhRGDp0KN6/fw9tbW34+vrCz88PQP3YoK2tzdEOf+DAAURGRqKwsBADBgzA9u3bER8fj7i4OEqB3LBFWE9PD8OGDeOqMXl5GQKC/QxZLJZIc7fvAdLS0pg8eTImT56M0tJSXLlyBZcvX4a7uzvc3d0xYMAAzJgxA1OmTOGoqZli4sSJuHXrFk6dOoUVK1Y0uf3Jkyfx/v37VleE00VtbS2HvzggfBpxcXEx5s6dizdv3lDvHT16FDk5OZCTk0NhYSHGjRtHdYmdP38e165dQ3h4uMgWG98rfpJqPyBUVVWxevVqLpkuLzQmIMhWotjYWIEEBMDM7JV8AAUFBcHJyQkVFRX49ddf4ezszLfH/8OHDzx7u/v27YuEhASeLW8dO3bk8nwQNsmzvLycdhKeqqoqSkpKaO3TEElJSUhNTaUG5eDgYISEhFDmpiRpws+TpDH69OmDCxcuwMbGBjdu3MCiRYsYx7V/r+jXrx/69euH6upquLq64siRI4iLi0NiYiLOnj2Lc+fOUcmaenp6mDp1Kn799VeBn1leXo69e/dSprWGhoaUOTRA795LTk6GlZUVpKWloaOjA4IgkJaWhpUrV6J9+/ZQUFDA3r17KcLj2LFjCA0NhbGxMTw9PXHnzh3qniULQElJSWhoaFCFVcN2k+rqapFbuuXl5fmSJl+7+G5tDBo0CDExMfj48SNFUAnCx48fkZiY2GSbS3Pi77//hpiYGM6dO0fJ/9PS0rB06VLU1tZi27ZtHGE1J06cwLhx4xATE8OIVPvy5QtthZucnBxqamoQGRkJRUVFhIaGckzO582bB2NjYwQHBzNuLeD1PL9w4QKCg4OhpKSE4uJi2NraIiQkBLa2tmjbti1+++03eHp6Uqqe0NBQ2NjYID8/H8bGxrTPgcn4kJiYiG3btuHdu3dQVlaGg4NDi/1+nj9/DhsbG/j7+7fI5/NDQUEBbW+11p7IkN51jUE+X7OysmBiYgJ7e/uv+hwk25F4YfLkyc1KqAH1dZyMjAw8PT2bnNh269YNx48fx/Tp0+Hv7w8JCQkuFRGZRnfu3DmRg0SaA0zqMhsbG6pbQhB4/U6YEAnV1dU8n7nka506dcKxY8da1c6luVBZWYn169dzjFWN7/0JEyZg1apVCAwMxK5du7BlyxZcvnyZr0JHEAYNGoTQ0FAEBwfDy8sLN27coNTPjRXOJKGmoaGBo0ePUinR69evx4cPH6jfzJ07d3DixAmcPHkSbdu2hba2NtUeCgj2MgR4+xmyWCxqHyZzt9bE2LFjMXfuXMyePZtxwnjbtm0xb948zJs3j7LQiIiIwL59++Ds7IxRo0Zh2rRpmDBhAiOFPVCvNjt9+jSOHTsGSUlJLFmyhGcLdm1tLU6ePAl3d3f069ePCsD4VtGxY0eu8VWYNOJffvkFJ0+exJs3b7Bu3TosXLgQEhIS8PX1xeHDhyEuLo6FCxdyzCvnzJkDQ0NDXLx48Sep9hP/PcTFxcHPzw8nT56kCAgLCwuUl5cjMTFRIAFx5cqVJgkIXvjw4QNWrlyJuLg4APU+LJs3bxY44FdWVvKMUydX3XjtKyYmhrq6OgD16onVq1cLneRZXV1Ne0VPRkaGo42huLgYcnJyHKohksnPzc2FqqoqZs+eTa08SkpKYvTo0Rg9ejSsrKxQUFCA2NhYxMfHIzk5Gffu3cORI0egrKxMESZNQV5eHsePH4enpyeOHj2KHTt28F1BJwgCqampyM/P5/Cq0NDQ+GbIEn7yc2HAYrFgaGhItZU8efIECQkJ1N/21q1bcHJyEmgcmpycDBsbGxQWFqJt27bYsWMHXyWkMPDx8YG8vDyCg4Nx6dIluLm54erVq5g1axYKCwvh6OhIkcmqqqpwdHREWloaEhISsHHjRhgYGMDAwABAvboyNjaWMq29desWnJ2doaqqSq2SHjhwAIcPH6btY+Tj40MZ2y5duhRLly5tcp/z588jICCAr8H1j4DZs2fj0qVL2LlzJ44dO9bk9tu3b0dlZSXmzJnTCmdXj3v37mHMmDEcfhrq6uowMDDAjRs3MHbsWI7tJSUlMW7cOERGRrbaOZJ4+vQpJk2axPWM6tWrF/T09JCRkdGsxwsPD0eXLl0wffp0nDhxAqampjAxMYGhoSHevn2Lffv2cbTJzZo1C4GBgbh37x4jA3perXFNgfQVBerHOUHq8IZgsVjw8fGBl5cXlVY5aNAgWFhY8Cyivby8cPToUUatyUC9nQBTwmbWrFnffLt4bW0tTwUwWSf8+uuvcHBwaDayb8KECTAzM8O8efM4gkkKCwtRUFDAU11AEATOnz/PscjTEC3Rhvbw4UMYGBgIrRTp3bs3dHR0EB8fj0mTJnGRHtOnT0dwcDDu3bvX7OfKBEzqstDQUHTp0kVk8ltYIqEpTJo06bsk1ID6lsTCwkLY2NhwBc80homJCWpqarB7925ERETgy5cvtLs8FBQUqEACCwsLgQtL27ZtQ4cOHTB48GCu91RUVGBsbAxjY2PU1dUhPT2dChVoqGJTVVVF//79MWnSJFrn2RB0526tjcLCQhw7dgxubm4YM2YMTExMRDqPjh07wszMDGZmZsjNzcXly5cRGRkJGxsbODg4ID09ndHnSkpKwtXVFYsWLcL+/fvh7e0NXV1dKuysqqqKsmt49+4dFBUV4erq+s17iGlqauLy5cu004jnz5+PhIQEjBw5koNQXrFiBRITE5GamsphYwPUX5tx48bh7t27LfulvmH8JNX+Y/j06ROCg4Ph7+/P0zgZqCdjRCUg+GHq1KlUUicp7z948KDAfQiCwK1bt7B27Vrax2OS5CkKeVNYWAhra2ukpKTA09OTIr9evHiBP/74A+/fv6c++8yZMzyNfYH61LtFixZh0aJFqK6uRlJSEjVQhoaGIiwsDARBICQkpMlefgsLCwwePBibNm3CmzdvOAp/giBw8uRJnDt3DkVFRVz7KisrY/78+fjrr79aJYGnIWpqanD06FFs2bIFALB06VJGCpHGYLPZqK2thZycHJSUlKCsrEwZ8PNCdXU1Dhw4gHPnzoHNZkNPTw979uxhvOpGIiMjA2PGjOFQQPTo0QPjxo3D5cuXefrzjRkzhidR1bt3b8qfpaKiArdv30ZiYiKSk5MRGBiIwMBAsNlsmJiYYMOGDfjzzz+FPk9+xrbe3t7w8fHh6Qn14cMHDl+gHxGjRo3CxIkTcePGDZiZmcHGxoZDGUgiOzsbjo6OSEpKwsSJE3l6WLQUioqKKI+whiDJfF5pV0pKSigvL2/pU+NCRUUF34n/r7/+itjY2GY9Xk5ODiZOnMjxXJORkcGYMWMQEBDAc7I0YsQI3Lt3j9EklemYQu5XWFhIK6157ty5eP78ObV/Xl4ebty4AW9vb+peJtvM7927B4IguMyMhYUodgJdunThG0TxvcDAwEAgoVZRUYH09HSUlpZi4MCBfNPUy8vLsW/fPuTn51Om2WpqatRCSEhIyDflV1dSUoKuXbvS2qdXr16IjY3l+zcYMGAAHjx40Byn1+wQti4LCgoS2mNJGAgiEpp6rjR3m3h0dHSzKx754dKlS+jcuXOThBqJhQsXwtvbGxEREWCz2bRrVnFxcaGf08KSpuLi4hgxYgRGjBiBzZs34/3790hISICPjw+ysrLw5s0bnDhxgtZ58oMwczem4xBTREREIDg4GBcvXkRUVBSio6OhqqoqsnoNqBcvkIEGBEEwXhQi0bdvXwQHB8Pa2hppaWkU+USC/Ntpa2tj37596NixIzQ0NGg/A1sTpqamCA4Opp1GPHfuXFy8eJGngGPQoEFITU3l+b27detGBff9F/GTVPuP4NGjR/D19UVkZCSVHNimTRtMnTpV4H50CYiGqK6uxuvXr/HLL79QSrOGLTAPHz6k2uiawv3794XarjGYJHleuXKFESFAEAT++OMPFBQUQFFRkaOP3c7ODu/evUPnzp2xceNGAPWedDY2Nhg8eLBA/wMpKSlqFbS8vBzR0dE4c+YMsrKy8Pr1a6HObfTo0QgNDcWePXsoUvPz589YtmwZHjx4AIIgoKqqit69e1NeFU+fPsXLly/h5uaGxMREeHp6ilxMJScnU8qJgQMHYvr06Txl6Q8fPoS1tTVyc3MpUk1BQYFRMlhNTQ0IgoCrqyvS0tJw//59Kn5cRkYG6urqWLBgAbS0tLj2zczMhJWVFZ49ewZZWVlYW1vDxMSE/hfngY8fP/JsXSZDJHgRDG3btsXnz58Ffm6bNm2grq4OCQkJtG/fHjdv3kR2djZVHOzfvx/x8fFwdnYWyfunrKyM1iT/R8S+ffvw/v17JCYmYurUqejRowf69OkDOTk56h7Kz88HQRAYNmwY9u3b16rnx091S5JCjb02gHqVE+nlxwRZWVlUvPyTJ09AEITAuHmSIBBkECwpKYmamhrG58QLpaWlPNt2SRUar2dNa7fQME38OnnyJAIDA9GzZ0+Ym5tDRUUFMTExCAwMhIODA4KDg5Geno6//voLJSUlkJGRwcaNG2kn+n3PEGXxrDEEKdvPnz+PAwcOoKKignptxowZ2Lt3L8ek/9atW9i+fbvQYzovEATB95ks6HnNNLioffv2tNuayYkvr2cPUD9+iTo5bg3wq8uePHmCt2/ftthxGxMJrY3mCigQBi9evICWlpbQ3RIsFgujRo2iOmG+Fbx9+xapqalU/fnvv/+itrYWLBarxRKQRZm7NSd69+4NS0tLbNmyBbGxsQgJCUFsbCyOHj3KSL2WkZGBiIgIXL16lRIp9OjRA/PmzROpc4REt27d4OvriwcPHiA+Ph5Pnz5FWVkZFBUV0bNnTxgaGnIE6TENYmgtME0jHjhwID5//syznZacz/Oqh8TFxTm6tv5r+Emq/cAgfWr8/PyolT+CIDBgwADMmzcP06ZN42qrrKysxP3795GWliYUAZGSkoLIyEgsWLCA40Fz4sQJeHp6orKyEuLi4pg6dSq2b9/OyCfDxsaGMh5tCLJPnFdbJ/kekyTPK1euICUlRag2GxJJSUnUcf/44w9YW1tTBXNmZiZSUlLAYrHg5uZGGcgOHz4ckydPho+PD+zs7Hh+bkFBAdLS0pCamop79+5R6UcEQaBv3748iSB+6NSpE8d3srOzo8jKgIAAKiWsIR49egRHR0ekpqZi+/btOHr0qNDHa4ja2lpYWlriypUrAP6foHnmzBn4+PhQ7SN1dXU4duwYTp06hdraWkZtVsXFxbh06RJev36NtLQ0SlHp6upKFTFkYpa6ujpP1UldXR3c3Nzg6emJ2tpaaGhowNHRsVlXpPi1FJGTDX6rrLwIj5cvX1JFW2pqKpX0SBAEevbsiUWLFkFLSws9e/aEtbU17t69i+nTp8PBweGbSVr7HiEvLw9fX1/8/fffOHv2LJ4/f84VXqCiooJFixbBwsICHh4etI/R0A+jIWFFgiSl+BFXTBWDvr6+0NPT46l0E4To6GiKDCKfy/ySScltvobZL5vN5jmxJ4m95j4nkthvCHLcIIvdxmCxWEL5MzbG/fv3oaioiICAAMq8Xl9fH0pKSjhx4gTi4+OxadMmlJeXY9iwYXB2dhbJ2Lqh0qyqqgpsNlso9dnXIAVINJfyWRBiY2OpdkxVVVUoKysjKysL4eHhUFFRwZYtW0AQBFxcXODl5UUtbolCrPFS0JDtwD4+PjzfY9J1AADdu3enrSr7Vlo7RYGgugxAk4mddCGISHB3dxeYHCjoPV5txE2BqQKPIAiexC7pfdwwRZAEmdpMBw2JXibPcDabLVCFJiYmBgUFBSgrK0NdXR0zZ87kWhzNzc3lqMfI+5kgCHTs2BHTpk2Djo4ORo8eTSnDExISMHz4cI6FzpSUFLi7uyM3NxedO3eGiYkJ32eWsHM3FxcX2teBhChjopiYGMaOHYuxY8eiuLgYFy9eRHBwsFDqtSdPniAiIgJXrlyhiJ8OHTpgyZIlMDIy4qkqFxW///47X5/vhmCi6q+urkZ5eXmrhQ0xSSMmwYvQ/tbDGb4mfpJqPyAKCwtx/vx5XLhwAR8/fqRUaZ8/f8asWbM4iK3i4mLcu3ePGgCePHmCuro6aqIjiIAgo94BQEtLiyLVvLy8qEhdUuIfFhaG/Px8+Pr60r4hbWxskJ6eznMVnVSH8XqdxWIxSvIE6ttkhSXVWCwWVVD17NkTtra2HN+RbFsaMmQIRypqly5doK2tjVu3blGvZWdnU4NiWloateJJmkoaGRlBW1sbWlpaIsmm09LSEBkZie7du+Ply5c8CTWgvkXXx8cHq1atwvXr15GcnMyoVcfPzw+RkZGQkpLC5MmToaysjISEBOTk5GDXrl04duwYiouLsWrVKmRkZIAgCIwbNw4ODg7UZzx79gxlZWWUEogX2Gw2tLS0OCbqHTt2hJaWFkcRIwhPnz6FpaUlnjx5AikpKWzZsgVLliz5ZgYSgiDw5MkT6p5NS0ujDKoJgkC7du1gaGgILS0taGtrc6kQAgIC8Pfff8PNzQ0bN25EXFwc7OzsGBu8/tchJiaGFStWwNzcHGlpaXjx4gWKi4vRvn179OjRA+rq6hRBShK7wpIJ5Lbkb68hYdUQBEHwJK4IgkBUVBSj79UwLVdXVxc6OjpNpsuK4o31PfnvMUmsBMB3TCGTEEk0vO50STVSIT5u3DiuNMhZs2bBw8MDVlZWqKiooEJVRPXNbBgKRP62GwcFNSccHR2p36Sw8PT0xKlTpygik67ymReh3dR7Xl5elCqA9J958+YNzM3N4evri1WrVmH79u2IjIwEQRCYO3curKysGLXhKigooKysjHb6riiYMGECnJ2dERMTw+XPyAsxMTHfvLE3L9Cpy/T09CAtLU1rURbgJtCFJRLc3d0RFhbG97fJ7z2mZCrd8aTheMeP2AXA8/fDZrPh6+uL7du3C328Dx8+4JdffsGrV6+E9qBsDGGDUxISEuDp6QlnZ2fk5+cjLS0N9+7do7pBCIKAnJwc9PX1qXqssUVEYmIiNm3ahLKyMri7u1OkWmJiIlauXEnNx969e4eMjAw8fvwYdnZ2jOduLi4utK8D+V28vb2ptGwSZOgbPyKSxWJx/WaUlJQof95Hjx7hwoULiIyMpNRr+vr60NPTw9u3bxEZGYkXL16AIAgoKipi7ty5MDIywsiRI1ukJq+pqUFqaio+fvwIVVVVDBkyROD4OGLECFrnQVru/P333xzXQBiRiCigm0b8E8zwk1T7gXDr1i34+voiLi4OdXV1YLFYVKrlpEmTMGzYMC65JunbRN7EwhIQDx48gKurK9q1a4cVK1Zg2LBhAOofsMePHweLxYKZmRk2b94MoL6gPXz4MJVeSAdMVtNIZGRk0Cafhg8fjoyMDK60n6bwzz//8HzQJycng8Vi8ZwA9O7dG3fv3sWKFSuQnp5ODVDk6hI5EGtpaTWrTDwwMBBSUlKYOHEiTp8+LXBbMTEx7N27F+PHj0doaCgjUu3q1auQkJCAv78/5aW3ZcsWrFu3DtHR0cjPz8fq1auRk5MDRUVF2NnZUSa8cXFx2LNnD+UBKCUlBTMzMy6PvZycHFhbW1O/ZVtbW55FjCCcOnUKx44dw5cvXzBw4EC4uLjQ2r81QBAEdQ8RBEElfuro6EBLSwsDBw4UOMiTJNC4ceOwbds2hIaGIjU1Fbt37/7uY9i/JsTExKChoSHweSWKzw6TwiokJASqqqq01G3keyYmJkhJScGzZ8/w4sUL+Pr6QlJSEiNGjICOjg50dHQ41MkARErsCw0N/WaI66bANLGS7vWPiIhAZGQkpkyZwvF6eXk5SktLebbteXp6orS0lCe5QioqSkpK4OTk1GwJXQ3VkORiGy/fr7y8PFhZWSEzMxMSEhKYOXMmIyLW29sbCgoKXGOqq6sr3N3deZIF1dXVIhF9/AhtQe+x2WyMGDGCw9C5U6dOsLS0hLm5Oezs7BAZGQkVFRXs3bsX+vr6jM9v6dKlcHNzE8nfji5mzJgBDw8PbN26FUePHhVIcsbFxcHS0hJSUlKMQjtEQXZ2Nvr37w8AtOoXpnVZSkqK0KR7QwLd0NCQuueFJRJEqY+ZgB8Z0xhfvnzBsWPHkJmZCaC+E0TYQAsSHz58QE1NjUBrgIaoqalBQkICfvvtN7x69YqREpbFYjXZdl9VVYXXr18jKSkJvr6+sLS0RGVlJYD6OmDw4MHUb2TYsGF8z/3t27dYs2YNvnz5An19faq1tra2Fvb29qirq4OWlhZlG7F79274+vpiwoQJVGAU3bkb01bvgoIClJaW8n2G8hsPmxrTBw4ciIEDB8LGxgbx8fG4cuUKIiIiEBUVBTExMcjIyGDKlCmYOnUqdHV1hfodMMW1a9dgb2/P0dLerVs3ODo6Ql1dnec+dP6eX758oRbA2Ww2l1CkKZFIS4IfoUZX1diSi2nfA36Saj8IJk2aRHn3dO/eHdOmTcPMmTObbN2RkZGBpqYm31UUfiAj0f38/DhkpDExMaioqED79u05SA8LCwsEBwfj77//Rp8+fYSS1ZIwMjJinCSopqZGWz6ura2N+/fv054AnThxgmsgq62tpXzjeD2UyQdSXFyc0K2JzYHMzEzo6ekJ/bch062Yets9e/YMBgYGFKEG1D/EV65ciaioKKxfvx45OTnQ1NSEi4sLtVpH+v6w2WxISEhAQUEBHz9+hLu7O9q0aUMZ7vv6+sLZ2ZmKl7exscHcuXNpneOiRYuo1pTff/8dZmZmPNv5eIFp0ldycjJcXV2RnJxMtYcJagkj3+vTpw91z2poaND+jZOfcf78eXh7e8PJyQnLli3jud3XbNH6ntC48KmpqcGVK1eotN8pU6Y0q3m1MAgNDcWbN2/4qtj4vc5isbBr1y4A9ROb5ORkJCUlITk5Gbdv38bt27exf/9+dOjQgVIMaWlpiey52JSyoKHSl4Qo7WvkmNKwwCUnB7yU0QUFBVQrHRPQXZBwdXVFXl4e1+teXl5NGtbzah8nx5ORI0e2euT92bNncejQIVRVVaFfv35wcXERaqGIlzerILTE84qpAjM8PBwDBgzgep1Uhl+5cgWDBw+Gu7s7z9CQbx3t27fHvn37sHbtWpibm2PEiBFUa1GbNm3w6dMnPH/+HHFxcbh//z4IgoCzszOsrKz4qj0F3X8sFgve3t4AhFeue3h44MSJExS5M3LkSKHvQ6Z1mYaGBu173c/Pj/I4pkMktLankzDf6/Hjx9i1axdyc3MhKyuLrVu3ClTJ8ENAQAB27tyJU6dOYcWKFU1uf/LkSbx//x42NjY4efIk7ePRAZlkO3HiRMybNw99+/bFmjVrMHr0aKHHQW9vb1RVVeHw4cOYPHky9fqtW7fw6tUrSEtLw9nZmSIjDxw4AAMDAwQGBjKeuzEl3ZtDLSUIHz58wIsXL1BQUEA9wzt06AB9fX3Iysri7t27TaZKslgsWFtbMzp+RkYGNm3ahLq6Ovzyyy9QVVVFfn4+8vPzYWZmhtDQUCrgqSGE/XsmJydT5yYuLo6ePXtSaZwtCaa+rCSYqBr/y/hJqv0gePHiBeTk5LB69WosWbJEaDY/JSWFEfOflpYGPT09rr7sO3fuAAD09PS4Co9Ro0bh/PnziI+P/yGTBBUVFVFcXMzx2oMHD6iI7uHDh3Pt8+LFC7Rv3x579uwRqjWxufD69WsYGBjQ2qdHjx5ISEhgdLyysjKeaV9kIZCVlQUTExPY29tzrJicOXMGbDYby5cvx/r16yElJYWMjAysX78eJ0+ehKmpKQ4dOoQzZ86AIAhoa2vDxsaGtuIqIyMDqampFCmSmZmJ9evXC70/0zS25ORkilADONvDBLWEXbp0idHxGiM+Ph6+vr4A6g2ieRWDLV1Mfc8oLS2Fo6Mjrl69CldXV0r5W1RUhKVLl+Lp06fUtT106BBcXV1pT7bi4uLg5+fHaJIgSjsmCRUVFUyZMoVSS5Ek2927d5GSkoILFy7gwoULkJCQQO/evaGrq0uFiwiDhIQEJCYmAmBGiIhCojQs4BtP8PkpTVgs1nefVkkqy1sDr1+/ho2NDZKSkigF+7p16zj87Oh6s7Y2mCowQ0NDuVpwgf+HYLRr1w6nTp1qtTTFlsC4cePw999/Y+vWrUhJSUFqairXNgRBoHPnzti+fTsMDAxgZWXVpNqT1/3HYrFoK9eZEu5AfTI9k7ps5MiRtBdQSGuA1iQSmhtsNhvu7u44ceIEamtrRfZrNDY2xunTp3Hs2DFISkpiyZIlPBcLamtrcfLkSbi7u6Nfv36YOHGiqF9FaAwePBijRo3Cu3fvMGnSJFr73rp1i/JWbghyPBw9ejSHuk9GRgY6OjpITU1lPHdjipYIqKioqMDVq1cRFhaGtLQ0sNls6lnIYrHw9u1bBAYGCv15otwL5FzDwcEBJiYmVGDTyZMncfToUfj4+GDHjh20P7e6uhqHDh2Cj48P2Gw2dHV1sXfvXpFCwuhg9erVjFRuLBaLsarxv4yfpNoPAi0tLdy5cwcHDhyAu7s7tLW1MWPGDOjr6wt88FZVVUFKSoqWIiojIwOFhYU81TlpaWlgsVjQ1NTkek+Q6fy3lCRIms7SAemJ0rgQvH79OoD6lcvGrbclJSW4ffs2Ro0axSi1prq6GtXV1YzMLiUkJGgntPBLEhQG/Ez5yc/79ddf4eDgwPXwz8zMxIABA7B161bqtSFDhsDGxgbr16/H/v374e3tDVlZWVhaWmLBggX47bffsGbNGi4vIpLA4lXskmRhc5AQwqLheSQlJSElJaXFlEzPnj1DXl4edc8WFRXB3t4eUVFRIAgCRkZG2LFjB88JYEulU33vqKurw7Jly/Do0SOIiYlxtDTZ29tTrcykAtDb2xurV6/GlStXmmyD+fTpE4KDg+Hv709NHtesWcNBbgkDUdox+aExyVZUVISrV6/i/PnzyMrKQnZ2NhepRpoR8zqfjIwM+Pj4tPrCCbn6GhoaitDQUMbqM2Ewa9YszJ8/H/PmzWuxY9BBa03GQkJC4OjoSC2qODk5cRF6TLxZfxRMnjy52Qi10NBQni1FJBoarA8fPhzjx48XSvknDLS1tRETE4OrV68iLi4OhYWF+PTpE5SUlNCjRw+MGzeOY6GV6b2Wk5PDSLnOdKLNNE2QaXsrQRCtSiQ0J3Jzc2FlZYVHjx5BQkICmzdvhpmZGVXT+fj4YOjQobS6VCQlJeHq6opFixZRtZ6uri769OkDeXl5VFVVIS8vD9HR0Xj37h0UFRXh6urKN+SpKRQVFeH9+/e0a57+/fsjNTUVDx48oPX9nj9/zrOmJhd4R40axfVe586dUVRUxHju9s8//8Dc3JzWeQL1C9+//PKLyOoqNpuNxMREhIeHIzo6Gl++fAFBEFBXV8fcuXMxefJkREZGinQMJsjIyIC+vj7HGC0mJoa//voLsbGxjDxUHz58CCsrK+Tl5UFGRgZWVlaYP39+c552k5g5c6bQpFpWVhYlEJCRkWlVK4EfBT9JtR8Ep0+fRkFBAYKCghASEoLr16/jxo0baNeuHdUKygsaGhqMCIiamhou4undu3d4+fIl5eXWGKR5J100VSzyA/kgIVvshEVERATPh5CgyVdeXh6Ki4tRXFyM3bt3488//8STJ09w/vx5sFgszJ49m2P72tpa2NnZobKyEhMmTOA7YW7KO6epFiB+6NKlC+7fvw9dXV2h98nIyGixOHUDAwOef/OSkhKeijpSKeLj44OuXbvCw8MDffv2BQCOFK6GSE5OhpubG1/iSkxMrEVICH5ofB6pqalCkWojR46EtLQ0zM3NOV7PyspCVlYWz3s9IiKC+q1ERERg9+7dKCkpQbt27bBz506uldLGoJs6yVS59z0hJCQEjx49wsSJE2Fvb0+pGXJzc3Hjxg2wWCwcOXIEo0ePBgCMGTMGs2fPhre3N+U12RiPHj2Cr68vIiMjqWKzTZs2mDp1KgICAngW+6TSS1DCZnPj5cuXuHPnDu7cuYOUlBQUFRVR9xyvhFzy99PS95cg8q4xyGcI6XkprPrM0dER1dXVtEzyHz9+jH379nGRapGRkYiMjGRkpt0ScHV1haampkg+TePHj4eioiJWrVqFmzdvAgAWLlyIrVu3ck0gmXqz/igQpIDi1R4pqDWSJN+FmfwFBASgY8eOOHToEE8FPRPIyMhg5syZfGvNhmCq9Dx37hylXDc0NERubi5+/fVXgcr1PXv2MA5vYFKXEQQBT09PbNq0idaxmD4bXVxcRE6rFBUkAV5VVYXffvsNzs7OXH6b+/btw5o1a7jInMYLfo3Rt29fBAcHw9raGmlpabhw4QJHrUiOO9ra2ti3bx+lAOK3kFFYWIiCggKezzh/f39GNbWkpCS+fPlCuwunpqYGVVVVHK9VVFTg33//BcDbL6+qqgrS0tKM527Xr19H//79aV+HWbNmYfXq1VyfK+ziPlkDXL58maoX2rdvjwULFmDOnDkc7avNsbgt6DrzQlFRETWHaIyhQ4ciKChI6GPX1dXB3d0dJ0+eRG1tLdTV1eHk5MRhx/Tw4UNER0ejuLgYnTt3hqGhIc9uHlHh5OQk1Pl6eHhQHTBDhw6Fs7Nzs5/LfwE/SbUfCF26dMGGDRuwbt06xMXFISgoCPHx8fDx8cHZs2fBYrHw8uVLFBcXU8UcUwICANeKMSlZ7ty5M0+5d1paGqPvRadYbIiGpBod01h+EBRNTkJbWxu+vr7w8/MDAKqwa1iUHThwAJGRkSgsLMSAAQMwc+ZM2NjY8JwwC+OdwwSjR4+Gl5eX0D4MqampuH//PtatW9es50GCnwKusrKS58SDVD3Kycnh3LlzzV4kfssoLS3Fly9fuF6PioqCm5sb30kNQRBYu3YtpU4zMDDA7t27hTIPZpI6+b2YzjPF1atX0aFDBxw4cIBjtZj8O/Xr148i1IB6xZ+mpibi4uI4SLWamhpERkbCz88PDx48AFD/9xswYADmzZuHadOmQU5ODgEBATzPg1R6tSSpVlNTg+TkZMTFxSEuLo569hMEgW7dumHBggXQ1dXFX3/9JdSkuqXQGuQdE5N8ADzvWVJl0RKIjo7m217H7z0y2U0UUi0/Px8eHh4oLi5Gp06d4OjoyHEfNARTb1ZhfC6bE7y8/BqCxWJxEBfz5s3DoEGDRDqmoPZIQa3JglRglZWVePPmDZKSknDt2jWsWbMGYWFhIiWJtyYaKtcbBmIIUq6LgqioqFary5gSCTY2Nvj48aNQ2zZMq5wwYQKj4zVEQUEBrK2tkZqaSql6Vq9eTUsJ23DBjx+6desGX19fPHjwAPHx8Xj69CnKysqgqKiInj17wtDQkIvEe/LkCd6/f8/1WSEhIc1+7V6+fMn3PUFdOHJyclQIBonk5GTU1dVBUVGRpx/j48ePoaSkhLKyMsZzN15o6jrw695panH/1KlTCA8PR05ODlUXjho1CnPnzsWECRM4bACaE3Svc01NDVc3EQkFBQUu8pMfcnNzYWlpicePH0NSUhJbt27Fn3/+yVEP7927F+fOnQPw/1r5+PHj2LJlCxVA0Vogw4MePnwICQkJbNq0CWZmZj+TQBniJ6n2A0JMTAwGBgYwMDDAhw8fEBISggsXLiA/Px+3b9+Gnp4e9PX1aadwNkZ8fDzy8vLQq1cv1NXVwd/fHywWi6efQXR0NHJychgdx9jYmHF7DhPJ7vHjxxmtpLJYLHh4eMDLyws3btxATU0N9PX1uVJESUJNQ0MDR48e/Srkg6mpKc6dO4cbN2402eqam5uLjRs3QlZWlrb5f0uB/JsZGho2G6FGEASKiopoydu/hkqIKQiCQFRUFNq0aQNbW1uh73868vH/ErKzs6Grq8vVfkEqn/T09Lj26d+/P0WcFRYW4vz587hw4QI+fvxIqdI+f/6MWbNmNQsxxDRAg5yckyRaUlISqqqqQBAEZGVloaenB11dXejq6rbI6mpLorKyEnV1dVSxraqqCjMzsyYXTITBtxTq8eTJE74TCkHvMUVZWRl27dqFy5cvgyAIzJw5E3Z2dgIVDEy9WVubVGvquhIEgU+fPuHTp0/Iy8tDSEgIbG1tAQieVPN779ChQ7STEkkIU7vMmzcPsbGxWLlyJfz9/Wn5hzYEk5a+8+fPMw6eYqJcb22MHDmyWZ4lwkIYUp5XWmVYWJhIz+6goCA4OTmhoqICv/76K5ydnWm3FNLF77//3uLHoIu3b98iNjaW0b6dO3dGdnY2KisrqYXlsLAwqn5oXHc9ffoU9+7dw/Tp0/HixQtRT71VsH//fgD1thGzZ8/GnDlzmgzQ+xoQ5hnfFM6cOYMjR47gy5cvGDBgAFxcXNCnTx+ObS5duoSzZ89CSkoK48ePR+fOnfH8+XPExMTA2dkZv/32G0/7pJaAt7c3Dh8+jKqqKvTv3x/Ozs4ciwhM5zUsFotKrP2v4Sep9oOhpqYGqamp+PjxI1RVVTFkyBBYWFjAwsICSUlJCAwMpFpkYmJiGE8CWCwWxMXFsWDBAowdOxbZ2dl4/Pgx5OTksGTJEmq7srIyXLlyBc7OzpCUlERNTQ3tY3Xp0oWxOTSTfZiYiZOQkpKi/t78sG3bNnTo0AGDBw9mdIzmgKqqKqysrLB7924A9Ya8Y8aMQZ8+faCgoIDKykrk5eXh6tWr8PPzQ1VVFRwdHUVKKOPVQijMe019j+YCQRDw9/fnWuETxg+K7uBTWVnJ1XZDEESTighyO6bQ1NSEo6Mjrb+bMPLx/yJKSkq4FB4EQVAJubxa4CUkJFBVVYVVq1YhLi4OdXV1VLv87NmzMWnSJAwbNozviildMA2ZYLFYGDt2LBWO0adPH4pEGzFiRIulErcGLCwsKFPkhpOWf/75R6j9vyXijB9YLBYjUpZpEZ2YmIht27bh3bt3UFZWhoODg1CE7vv375vVm7WlIIzfX0Piwt3dnfr780tPI4lrfu+JYrAvDMaMGYNhw4bh5s2bjEk1fi19LRU89T0o1zU1NXmGNbQUhLXkaJxW6efnx+h+//DhA+zs7BAXFwcAWLJkCTZv3vzNjAklJSVwcHBo8eOw2WykpKRgz549qKqqYrTwOGjQIGRnZ2PJkiVYsGABHj9+jGvXrkFMTAx//PEHx7Z5eXlYu3Yt2Gw2ZsyYwYiU/hrQ19fH3LlzYWBgwNjr7lvHq1evYGNjg9TUVIiLi2P16tX466+/eCo2Q0JCICsri/Pnz6N///7U6ykpKVi2bBnOnz/f4qRaYWEhrK2tkZKSAjExMaxYsQJr1qzhUg0y/Y39JNV+4ofAtWvXYG9vj5KSEuq1bt26wdHREerq6tDU1ISmpiZKS0sRFhaGCxcuIDs7m9GxSLnq1q1bqRtPVlYWLi4uHAXNhAkTKC+1HTt2tMpgRyI7O5vjofWtQNBkQ1Ax2txYvHgxbt68icTERPzzzz88J5UEQUBGRgY7d+4Uua2LXwuhoPe+hQlsS7SUWVhYcE3qgZb9viwWC15eXrT3S0lJQZcuXX4mATWCvLw8x7MWqFcAlZeXQ0JCAurq6lz7+Pr6oqamBjExMejevTvld9lSK7eiPEfGjRsHMTExTJ48GbNmzYKGhsY3M3ESFQRBMG5xZKJ+bm2wWCxG7WRMSbWGhuRSUlJwdXUVyieOVD82hLDerLy8UpOSkgAAbm5uXJ9LvtdSkJGRwa+//opff/0VWlpaMDIygqysLNq3b0/7s4qLixEcHAwdHZ0WTYkbPHgwwsPDm/1zWzt4qiWU60yRkZFBexwXRb1HF2Ra5a1btxjtP3XqVOoeHDhwIID6RdmmwLS2qa2txaVLl5CYmIjS0lIMGjQIixcv5tlREBUVBXt7exQVFTE6FlBPljW1IFBTU4OPHz+itrYWBEFg3rx5fO0ZBIGsqR48eIDMzEzqb9Q4SGDhwoXIyMhAXV0dZs2axTPA4FsFk9Ty7wmBgYFwdnbG58+f0bt3bzg7O1P3BS9kZ2dj0qRJXHNTDQ0NaGtrIyMjo0XPNzg4GI6OjigvL0fPnj3h7OyMIUOG8Ny2NT2mfxT8JNV+EGRkZGDTpk2oq6vDL7/8AlVVVeTn5yM/Px9mZmYIDQ1Fz549AQBt27aFqakpTE1NRUr209PTw82bN5GSkoKamhpoaGhwpQf2798fMjIy+PPPP6GpqSkyqfbs2TOUlZWhT58+fJOr2Gw2PDw8cOLECVrmkg3x4cMHUU6TMVq7GB02bBhu3boFExMTJCQkcBxbVVUVhoaGWLBgAeNIdBJM/UJCQkIErgB+z22JYmJijFqwmN6zBEEgLCyMNjlqamrK06D2v46+fftyFUBRUVEA6hNqG7e9VVZWoqKiAuLi4ti8eTOWLFnS4imMogSLkM+Ey5cvIyIiAjIyMtDU1KQUa6I+E742zp49y2i/1k7D5eV/Rip9eJFgTJ4pnz9/RlhYGAiCYKwaJieEhYWFQo9hBEEw9mYV5JV6/Phxjv8nFZetNV50794dOjo6ePbsGa5evUp7fzU1NdjZ2QEA+vTpAx0dHejp6WHEiBHN6kEkKysrtFfQ94DmVK4zRXx8PM/fWUup95igf//+yMzMZLRvw4Wkhw8f4uHDh810VtyorKyEubk50tLSqOdLYmIigoKCcO7cOWpOU15eDnt7e0RERIAgCPTo0UOk9khhFN5SUlLQ0NDAnDlzMH36dEakGlBfB54/fx7Xr1+nLGO0tbU5tvnw4QNkZWWxdOlSLjuZbx2t3SIuCvh5jQoab0NCQij/scLCQirtnR8+ffqEyMhI3Lx5k2uhp1+/fi22+FNUVAQ7OzuqVXnx4sXYsmULZGRk+O7THIER/zX8JNV+EJw5cwZsNhsODg4wMTEBi8UCm83GyZMncfToUfj4+GDHjh3Nflw5OTno6+vzfd/b25vrNSZJggRBYMKECVRogZSUFMzMzDhMjIH6yHVra2uqdYKJHxRBEDh//jzs7e1p7dfaSE5OFlnZNH78eHTp0oV6eFZVVaG0tBTt2rVrVlUK0xWP0NBQvqoHFovF8z2CIODq6vqTBOIB8v6hg29BLfgtwtDQEHv27ME///yDpUuXIicnB76+vmCxWJgxYwbX9mQbLZvNxoEDB+Du7g5tbW3MmDED+vr6LU6w0UF8fDx27doFAPj3338RGxtL+QfGxsaCxWKhe/fulLeapqZms7Ws/gQnBPmf8Zt0CDvm5ebmws/PD+Hh4aioqAAgWFEsCEz2cXV1xaVLl2h7sw4dOpRWcvXXQK9evRirGg8dOoSkpCQkJycjJycHOTk58PLyoohtHR2dZvEzfPPmDS0P0Z9gjtZeMBUESUlJ1NbWMtqXaS3HRAV7+vRppKamUimRysrK1Fhkb28PLy8vPH/+HCtWrKDIeVNTU2zatAlDhw5ldJ4sFkvgs4xMU20q7ZIOhgwZwlctBNSn3iorK/NsnxSURPstoLVbxEVBU16j/MZbskb+/PmzUMf58uULqquruV6XlpbmGWgkKq5evQoHBwd8/PgRnTt3xr59+1pU7fj+/XuRrIK+Z3w7VfxPiISMjAzo6+tzxEeTaTyxsbHfVMsK3SRBNpsNoD5hR0JCAgoKCvj48SPc3d3Rpk0b/PnnnwDqW6ucnZ1RXV2NNm3awNraGhcvXhT6vN6/f08ZIH8PCqikpCSRz1NNTY1DeSEjIyNw5eJrgAmp8yMTQT/yd/ueYGJiggsXLuDgwYM4cuQI6urqqNTO2bNnU9v5+vri8uXLuH//Prp164Z//vkHYWFhCAkJwfXr13Hjxg20a9eOagUVBLoLEiQmTJiA9PR0lJaWYuDAgXwn4+Xl5di3bx9CQ0Opz+3Xrx/69esHCwsLlJeXIzExEXFxcUhMTMTZs2dx7tw5atWeIAi8evWKa/JYWloKAHj9+jXX75d873sAGSjBBCTZ3xCC2hWB+kUPpmET/MBmsxEVFQVfX1+qLiBTXPPz87nGBGHBRBW5du1aREZG0vZmdXFx+eYDMkh1HF08e/YM0tLSlKr/w4cPSE5Opki22NhYitju2rUrdHV1oaOjg9GjR/NN0eaF8vJyxMbGYtiwYbTP8WviR1WutyZevnzJ05tOGIiSUsqLWBQ0Nly/fh3S0tK4cOEC9XxZtGgRduzYgaCgIGRmZmL16tV49+4dunfvjn379vFsGacDFoslksK7JSAonZdJEi3d60CiOe+vb4lkBkRrdaRzT6ipqWHNmjWttui/efNmREZGAqgP+rC0tIS8vLxQpGXDOqC6uhplZWVNLsKQVjktbbfwreInqfaDoKioiG/a0dChQwW2QTJ5ULLZbCQmJkJHR0fofTw9PSEpKYmpU6fSOlZSUhIKCgqwfPlyrF+/HlJSUsjIyMD69etx8uRJmJqa4tChQzhz5gwIgoC2tjb27NkDVVVVodMqw8LCsHfvXur/dXV1uRKcSFlwamoq16DD1BD8v4KsrCz88ssvtJM1lyxZQnuFU01NDSwWi6/pvzBhAK2Jly9f4vbt25RfyOjRo3lu9/r1aypR7ufk4etDUlIS3t7eOHDgAKKiolBTUwM9PT1s27aNY0XZ09MTb9++RY8ePeDp6YkePXpgw4YNWLduHeLi4hAUFIT4+Hj4+Pjg7NmzYLFYePnyJYqLi7kmPnQXJMj39uzZQymRAGDGjBnYu3cvx3neunUL27dvx+vXr/l+Z3l5eRgaGsLQ0BBAPaGXkJCA5ORk3Lt3D0D9ai4/n6axY8fy/eyvASZ+PaKAn8+YoHZFYbzJBIFUMkhLSyMgIACBgYF4+/YtAEBcXBwGBgaYN28edHR0oKamhnHjxrVKwV9dXY22bdsy8mZNSUlBx44dv7kFoIbIzc1FZWUl/vnnH5iZmXG8l5WVhaysLJ4kekREBMckWUVFBVOmTMGUKVMA1JNsJMGWmpoKPz8/+Pv7Uz6Owvhmvnz5Era2tigtLaVdj31tkOp0giA4wn34KdfJ91o6+OF7AZlWKajDpKXAL5gD4D02kL6zjUkuU1NTBAYGYuPGjXj37h1mzJiBnTt38rWEaS0w6cIRBUyJe7rXAaivI7y9vRESEsLxellZGQD+XtHfyyJwa7Y6VlZWIisrq1WsJCIiIqj/zszM5ArA4Afymfn48WM4OjpSLdidOnXC2rVrYWxszLF9UVERduzYgZiYmGY9/+8NP0m1HwQ1NTV8228UFBQE+mYIMhUWREDcv3+fi1RzdXWFu7s7zwKmuroatbW1tFcEDAwMMHDgQGzdupV6bciQIbCxscH69euxf/9+eHt7Q1ZWFpaWlliwYIHQn11cXIydO3ciKioKBEHAxMQEgYGBSEhIQEJCAtf2BEHwfCg1h19LdHQ0CIIAm82mJsiCevlFAVNiiWlxOmvWLJ6+XC2RrAl8PwP5gQMHqNZtEhoaGvDw8ECbNm2o14KCguDs7Izy8nIAoF3ckO8xBdNVxW+1JaG50LZtW+zatYtqleSFFStWQElJCePHj+do8RQTE4OBgQEMDAzw4cMHhISE4MKFC8jPz8ft27ehp6cHfX19GBsbY8yYMYyKvtevX+POnTsoLy+HqqoqlJWVkZWVhfDwcKioqGDLli0gCAIuLi7w8vICQRBQVVXFnj17mvxsNpuN2tpayMnJQUlJCcrKyhRx9y14GzUFpn49AH2T/C5duqCwsPCrtKTv27cPwcHBkJKSooy1u3fvjpcvX2LOnDnNYnOwZs0aDuKHhKDWJE9PT4o8ouvNqqamBmdnZ0ybNg0mJiat7nPXFHJzc5GYmAg2m82z9oqKioKbmxuj8B8VFRUYGRnByMgIeXl5uH37NgICAvDvv//izp07MDU15btvTU0NPnz4gIKCArDZbBgYGHx3pBp5bzX+tzD7MAFdT8NvFY3TKpmSCEw9siQkJAQqrnihoKCAow4iQQb7FBQUYMOGDVi5ciXP/UNDQ7m6dMhryes+Ie8LJqolgiBoL3qJOm/gRS6TEFTjMxmfCwoKUFpayldZ/r0LC169eoWuXbvS2ocgCOjq6mLt2rUcXWJAvb9aQUEBz0CkzMxMnD59WmRSVRgwDWQCgKdPn2LRokWorKykXnv9+jW2bdsGMTExavy6efMmbGxs8OnTJ4iJicHc3FzU0/5u8ZNU+0HQVNEg6P3mJiCa+/NKSkpgYGDA9frIkSMB1A/yXbt2hYeHB1+1Hi9ERUVh586dKCoqQocOHbBnzx7o6enh2bNnzXbudEB6xwHcvfvNbdgpSgtTc+7XEsmat27dwvv372lPtFp7YkYQBP755x+wWCyoq6tDWVkZKSkpSElJgaOjI/bs2YPKykpYW1vj+vXrIAgCAwcOxKNHjxgXN/yMWJs6T0Erm/zwUx1Qj4ULF/J9LzQ0FKGhofDx8YGFhQUsLCyQlJSEwMBAinCOiYmBkpISo7S2lStXQkxMDHZ2dli0aBGAeh8lc3Nz+Pr6YtWqVdi+fTsiIyNBEATmzp0LKysrnn4xlZWVuH//PtLS0pCWlob79+9T6Y0yMjJQV1fHggULoKWl1Wr3UmNii/SZFKTuIn//TP16fHx8GJvktxapVlVVhYsXL8LX15dK+JaQkMDUqVNhbGyMESNGNKtiNyoqiuc1F7Y1ia43q6GhIWJiYiiV1uDBgzFv3jwYGRl9NfVabW0tPnz4gNu3b1Pt4M2tKH737h3i4+Nx584dJCUlUSmHpFE2IFw6bZcuXTBnzpzvbvLTsGXJ1dWVdtsbEzDxNGxNCNMeziutkqlSjalHVl1dHW0Vi5qaGk/jd1JAMHDgQL6EGlD/rOdX7wi6T3h5QfNC4/ubCUkuCkhCjfxvYSAmJsZITcSUNGtu+4KWgrGxMZycnIRW07958wabN2/G+/fv8f79e673Q0JC+D6f7t+/zzNlVtDCOIvFooKw6IBpIBMAWFpaorKyEkZGRtiwYQNUVFRw8+ZNODg44PDhw5gxYwZ8fX2xb98+sNls9O3bF46Ojhg0aBDjY37v+Emq/cfB1AyyJSdNd+7cQVZWFuTl5aGvr4/Kykqe/g/t2rUDUF+Qnzt3Tugo9fLycuzZswfh4eEgCAJGRkbYuXMn2rZtC0C0hxBTkKSSIOVWY9jY2GDAgAGMktq+hgkoE7DZbAQEBNBaBfL392dcbNP1migtLW1yEs8LBQUFIAgCYmJi8PDwwJgxYwDUG52am5sjPDwcmzZtwvr165GSkgJJSUmsXr0a5ubmePPmDe3vBdQP1FlZWYz+LvLy8lBQUGB03J/gj4KCAq42c01NTWhqaqK0tBRhYWG4cOEC/v33X0af/+jRIwwfPpwi1ACgU6dOsLS0hLm5Oezs7BAZGQkVFRXs3buXY6JVXFyMe/fuITU1FWlpaXjy5AnlG8disaCmpgZtbW1oa2tDXV29WYNNhIWrqytHCwz5b373Y0OC6+bNm5CVlaXt10OOE98ydHV1UV5eTrVOFRQUIDExkafq43tDTU0NunbtisTERFy6dAnBwcF48OABMjMz4ejoiOnTpze7eo0JAblhwwYcPnxYpOOy2Wykp6cjPj4ecXFxFEFKEAQ6duwIY2Nj6OrqQltbu8nnupiYGOTl5aGkpERbMSQIrd3y1ppgsthnY2PTqkSCsGRH47TK5sbX8MhqnJLZEHQXAUkkJCSgV69eTW5HEAT8/f2pFNV+/fo16+JwUyBr+NYil781n7nmRnV1NVavXo0///wTmzdv5lioaIyoqChs27aNsiSgCzKMgN+9y+v11rR8+fTpEy5cuID09HT07NkTLi4ulFXIlClTUFlZCTs7O5w6dQoHDx4Ei8WCmZkZ1q9f36zp1N8jfpJqP9HqIAgCly9fxu3btyEtLY1p06ZBXV0dNTU1WLNmDeLj46ltJSUlmzTNNDQ0FJpQu3PnDmxtbfH69Wu0b98eO3fupPyBviZIKX5BQQFiYmKEkubb2NjgyZMnjFoQWntFTRQwWQWii9zc3CYVWYK8JvgZjfNCQxJAW1ubItSAeoJ448aNWLx4MTZt2oSUlBT07t0bBw8epCaJohQ348aNo13w29jYYMmSJT/TVFsZbdu2hampKUxNTZGamsroM8rKyjBgwACu18lktCtXrmDw4MFwd3fnSmsiJyzkb7Vjx47Q0tKiTNGZGl03hq+vL/Lz8xk9x9asWYPa2lqqrTY9PR0ZGRlYunRpk/uePXsWY8eOpe3X8z3cB2VlZZCTk8PKlSvx8eNHeHl5CUWozZo166t4TiYnJ1O+dgMHDsT06dN52lk8fPgQ1tbWyM3NxZYtW7Bo0SIsWrQI2dnZCA4OxqVLl1pEvdbUs53FYkFBQQHt27fH8OHDMXv2bIwYMYIxqUYQBDZu3Eh5bRIEAUlJSWhqakJXVxe6urro168fxz6kcr+10dotbw0/R5gxt6ioiJFyHWDms2RjY9Oq91BTqbv80iq/fPny3Sc2C5rAM70fhNnvzZs3sLGxwcOHDyEmJoZly5YxVn0mJSVBU1OT0b5M8CNcd2Eg7POBREBAANatW4fTp08jIyMDhw4d4lp8qK6uhpOTE/z9/UEQBMaMGYPY2Fha5xUdHS1Q1fk18fDhQ/j6+uLKlSv48uULZGVlMXXqVK7UWT09PRAEgSNHjqBt27Y4cuQIXy/o/xp+kmo/EPi1dgnyf3j69KnAEANeIAkIJiAIAmZmZrh9+zb1GQEBAVQyUFxcHFRUVDBixAh8/PiRUnEIWgETxh/gy5cv2L9/P/z8/CgvkT179jRbnPyRI0eQm5vL1fojLHJzc9G7d2/a+ykrK0NXV5fRMZnA39+flmfd10BiYiKt7cPDw6mkNSZeE6WlpUJN4hvD3d2dZ7syWZAnJSVhzJgxOHz4MEeq29WrV2kTwUVFRSAIAmpqarQnCt+Tb8z3hOrqaqEi2MPDw7Fv3z7079+f9jEqKysRExODbdu2cbxOqg7btWuHU6dO8VRfycjIQFNTE1paWtDW1mb0fBIGV69eRWpqKlRVVWnfR+PHj8fmzZs5zHiFhYeHB08PFWH9euiAqXKFacvH3LlzERkZicOHD1OTCx8fH0ydOlUgGdqaSgugvh7YtGkTrly5Qv0/i8XCmTNn4OPjAxUVFQBAXV0djh07hlOnTqG2tpZSqZPo378/bG1tYWlpiZiYGISEhCAxMRF2dnbNol5rbWU3QRC4evUqunXrBiMjI+jq6mLUqFEC0z2Zjgtbt27F6dOnGZ1na5p7k8ebP38+5s2bh6VLl1Jm2S2lXGdal0VGRnI9W1pKvRcVFcWRlCsMcnNzsWHDBly6dInxcemCiaKfIAih2pmbCy9fvkRFRYXA5wQZalZWVobu3bvD0dER6urq+OOPP3Dq1Claim1XV1d4eHjg0aNHtM/V29ub0XX/448/BFpS8ENhYWGr3++iwNjYmBZZ2b9/fwQHB8Pa2hpRUVEwNjbG/v37KbIoNzcXmzZtwr///gtJSUlYWlpi8eLFtMeULl26oG3btt9Mymx1dTUiIyPh5+dHqS4JgkCvXr2Ql5fHU9VM1hBiYmLw8fFhVJv+qPhJqv1AEOT9APD2fyAIAsHBwZg9e7ZQx2hIQDDFrVu3oK2tjXnz5kFcXBznz5+Hi4sL2rRpA01NTXh6elIrKXfu3MHSpUupm50JMjIyYGVlhefPn0NeXh62trZcySWiIi0tjbGaBAD+/PNP+Pn5Cb19cXExCILAhw8fRJoI0U2evHv37jdPqrFYLLDZbFy5cgWTJ0/mu111dTUcHBwQEhICgiAwYsQI+Pr6ttp5urq68lSPkK916tQJx44d4yrStm7dijZt2ghNpiYnJ2Pz5s2in/BPNAsapimx2WwQBIGQkJAm05SYTi4EtQdNnjyZbztjSkoKR7BCS8PJyQn37t3Dvn37eHq6NYavry9cXFxQXV1N6zhxcXHw8/NDXV0dT6WDsH49dPDq1SsA9Fs4mCp6du/eDVtbW1y+fBmurq548+YNHB0d4eLiAn19fb5K5a9hCxAZGQkpKSlMnjwZysrKSEhIQE5ODnbt2oVjx46huLgYq1atQkZGBgiCwLhx4/jWIBISEpg4cSImTpyIDx8+IDQ0lEu9FhgY2CznXVpainfv3qGsrAxt2rRBly5dmrW9VkxMDO3bt4eCggIUFBSanKwzHRc+fPjA+ByZ1h5MPDqB+mcnqVwn/y6AYOW6KCDrMmEnv2Rdlpuby1el19zqPScnJ8jLy9Ou4Ruaj7cGmCj6gfrFRX5kHK/AGHLf1atX0z5HW1tbpKWl8fSCLS4uxvbt2xETEwOCIDB//nxYWVlRRHdKSgrWrFkDd3f3JsfN4uJibN26Fbdv36Z9jiTI6y4syOteUVHBqLOCIAhGHoIEQYjUIv727VvU1dVRYTekRU5DiImJwcrKiqOW6dKlC23SSl5eHq6urjh16hQOHz4MMzMz/PXXX+jYsSP27duHyspK9O3bFwcPHuRSCjcnqqur8fr1a/zyyy8tlmhbWFgIf39/XLhwASUlJZQaeuLEiZg/fz40NDT4Eoakcm3ChAk/CbVG+Emq/SBgWtxs27YNO3bsgJycHC0CgikIgkDfvn1x6tQp6jUDAwMYGRnh+fPnWL9+PYc0mSR5BPlICSpGDh8+jFOnTqGurg6///47du/ejS5dulApioJAZ8ASFW/fvsXSpUsxfvz4JrdNSUnBpk2bAIBLlksHTJInv4ZvEl0YGhoiMjISW7du5WuAnZeXhw0bNiAnJwdiYmL466+/sGrVqlY7x7i4uCbvo0mTJvH8e9fU1GDdunX4+++/MWLECIGf4e7uDnd3d9TW1op0vj/RPGicpkT+BoRJU9LS0qJ9PFNTU4Gry4JUS40nBqWlpYiJiUF+fj6Ki4uhpKSErl27YuzYsVzKISbQ0NDA9evXkZ2djaNHj/It6EpLS2Fra0ulJc+dO7fJz/706ROCg4Ph7+9PEVxNQZBfD10MGTIEDx48aNV2D1lZWcydOxdv376Fq6srFi9ejEuXLlHhFywWi/JYJNWxM2fOZDS5F0W5LikpCX9/fwwcOBAAsGXLFqxbtw7R0dHIz8/H6tWrkZOTA0VFRdjZ2WHatGlCfbaKigrMzc2xbNky+Pn54fDhwyItzgH1dZCfnx/Cw8ORnZ3N8b3FxMTQv39/zJkzByYmJiIR0iwWC0ZGRkhMTERGRgY8PT0hLy9PtV/r6emhY8eOHPswHRd69OjB+DyZYt++fUL9zpqrVZQpyLrM19e3SR+6xnXZjBkzWuMUIS4uzqiGV1dXb5XzI8GE6CI7PwSRag0Xmxp6ZjIh1QDez7KoqCjs2LEDxcXF6NixI/bu3QsdHR2Obfr27YuEhARs3rwZR44c4fu7TUlJwebNm/Hu3TvIyMjA2tqa0XmS172pOUPj6965c2ehCdiGCAkJYaSsSk5OZtQiDgA7duxAcHAwTE1NYWVlBaB+4acxucdisSAlJYWdO3fSPj9eWL58OTQ0NLB161a4ublRx1i8eDG2bt3aLO2zBEHAwcEBCxYs4CDoTpw4AU9PT1RWVkJcXBxTp07F9u3bm23BJjExEb6+voiPj6cWdYH6emf//v20bD3IhPSf+D9+kmo/CJjKchUUFLBhwwbaBARdGXdDNJboi4mJQVtbG8+fP+fL/peWlvL0qWCxWHB1deW7WsVms6nBLTMzU2gvsdZOLjQyMkJEREST6qrGJIkwE0peCAsLY5Q86eTkxOh4rYnevXtTxRU5yWjok3Hx4kXY29vj8+fP6Ny5Mw4cOIDhw4dzfMbnz5/BYrF4ttuEh4dDS0uLy4OqKdCd2PMjdbdv347du3dj5cqVOHPmDAYPHsy1TcOVUIIgMGvWLMZJZZ07d/4uzNm/B5DFEpmmdOHCBZw8eRJt27ZtsTQlUVsMPn/+DEdHR4SHh6OmpgYA52SXTJXctm2bSAsR3t7eOHjwIE6fPo158+bBzs6O6/mWmpqKrVu34vXr12jbti127dolsOXt0aNH8PX1RWRkJL58+QKCINCmTRtMnToVAQEBAs+nOQ13paWlv2q7B4vFwrZt27B161bcuHEDgYGBSE5Oxv3792FsbIz+/fvD2NgYQ4cOFfp7v3//Hs+fPxf53AwMDChCDaivB1auXImoqCisX78eOTk50NTUhIuLCxeRJAjJyckICwtDVFQUysrKIC4uLpKBfEFBAZYvX44XL15Qv//27dtDUlIS5eXlqKiowOPHj7F79274+/vjxIkTAJipDVksFlxcXEAQBDIzMxEXF4e4uDhcv34d165dA4vFQt++faGvrw8dHR2oq6szHhe2b9/O+G/SENXV1SgrK2vSUiMsLAxSUlKwsLAQ6nPj4uJEJkOZgqzL/vzzT5w9exbt27fnuV3DuqxPnz4cHqgtjSNHjjCq4VtzERFAq6YfNxfKy8uxe/duXLx4EQRBYPr06di+fTvP0CZvb28sXrwY169fh62tLU+hg4eHB9zc3FBbWws1NTUcPHiQsa0Ced3J+pwX+F13QSb8/MD02jG1D7l9+zYCAwOhqqrKFRzBYrEowpXNZmP37t0ICgrCqlWraNfl/CAhIQEpKSnqb9u+fXvo6uo2C6FGppSfP38eWlpa1LzXy8sLR44cAQBqsSMsLAz5+fnw9fVlvMBQVlZGzT3y8/NBEARkZWVhaGiI2bNnY/HixejWrRttn9yvueDxreInqfYfx/jx43HgwAFs3ryZFgHh6urKU3qdlJQEADzlxeR7vCbo5GuCJmV0V8PJVZlvAaTnR0OwWCxqRXn//v2oqanB9evX8fz5c5SXl3P8LRoXw/PmzUNAQADlOUMX4eHhYLFYtJMnRVHGMUnWZAoWiwVnZ2dYWlpi1apVOH36NNTU1LBr1y4EBweDIAhMmTIFu3bt4vrNnT59Gp6enti9ezcmTJjA8V55eTlsbW0hJSWFlStXYsWKFU2eC7+JvTBqSV5YtGgRampq4OTkBHNzc5w9e5bDm63hSqiCggIcHBwwZcoUjBw5kpGB8t9//91iflr/NTROU5KUlASLxYKlpWWLpSkJSmUT9F7nzp1RVFSEhQsXIj8/HywWC8OHD0efPn3Qrl07VFRU4OnTp0hLS0NoaCjS0tJw9uxZWsRHQ4iJiWHr1q0YNmwYrK2tsWPHDqSmpsLBwQHS0tJwc3PDiRMnUFtbC3V1dRw4cICnB2JNTQ3lEfLgwQMA9WPBgAEDMG/ePEybNg1ycnJNkmrfMxqPHySkpKRgZGQEIyMjvHz5EhcuXEBoaCiysrLg6OgISUlJ6m8mCKSvEAB07dpVaPUfL/BSSpHPm6ysLJiYmMDe3l6oieCzZ88QFhaGS5cuUWNKt27dYGZmBmNjY8bj5efPn2Fqakr5dllYWEBDQ4MjAKG4uBh3796Fj48P7t+/j+XLlwOon2yHhIRwfF5ZWRkA3l575HtA/Tj2+++/4/fff8fatWtRXFyM+Ph4xMfH49atW/j777/xzz//QE5ODqNHj4ahoSGuXr1Ka1wQFQ3b2QmCQKdOnbB27dom29mbmqSXl5dj7969ePjwIQBQ36010bAuW758OXx8fJqsyzZs2MCXfBMEpob1TGv47wHNQcKVl5ejtLSU9lzg9u3b2LZtGxVqtmvXLq56sCGUlJQoYi0sLAxt2rSBnZ0dAO52T1NTU2zZskWkzg/yum/cuJHynvsWrzuTLqrY2FiEhoZi7NixOHToEM+gmYYKvaqqKlhaWiI0NFRosl4QPD09cfz4cdTW1kJbWxvDhg2Dh4cHVq5ciUWLFsHS0pLxtXvw4AE1F7a0tMSwYcMA1D/3jx8/TtV+pGWLp6cnDh8+jNDQUEa2RXZ2doiIiEBlZSU155w5cyYmT54sVGupIOLsJ6nGjZ+k2k/A0NAQtbW1tAmIxtLrhmhs2N8w8ZDXJFFQwbx27dpWiYxuLty7dw9ubm7Q19eHqakpgP97fjSGk5MTZsyYATExMRw+fJhqe7GwsMDp06chIyPDUQy3a9cOe/bswfjx41FdXc04Zerff/9llDwpCpgka4qCqVOnora2Fra2tjA3N0eHDh2Qk5MDOTk5bN++nae688iRI5TC4PXr11zvl5eXY/jw4UhNTcWRI0fw8eNHnvJ9YSb2w4cPR0FBARXG0RiC3lu6dClqampw8OBByvelW7duHCuhw4cPx4EDB6hikqmadc6cObC0tPzmvfS+BxQVFTVbmlJWVhaSkpLw9u1blJeXo02bNujWrRtGjBjBofjld9+xWCyB7z169AibN2/GixcvMHHiRGzbto0nYfbhwwc4OTnh8uXL2LJlC3x8fEQqtsaPH4+QkBBs2LAB4eHhePToEdq1a4fU1FSIi4tj7dq1+Ouvv7jGjMLCQpw/fx4XLlzAx48fKfL68+fPmDVrFs/inp8nj6D3RGktAupDcxITE/leOz09PZEWLwBAV1cXRkZGMDEx4btNt27dsHHjRqxfvx6xsbEIDAxEQkKCwM8tLi7Gzp07ERUVBYIgYGJiAmtrawwfPpxnUJKgkCRyPOc1YSIVwr/++iscHBwE/p5KSkoQERGB8PBwZGZmUi2lkydPxty5c5sllezcuXMoKCjAggULsGPHDp7no6SkhClTpmDKlCnYt28fzp49C4IgUFpayneBiJ/fIb/vq6SkhJkzZ2LmzJlgs9nIzMxEUlISIiMjERUVBTExMWzevJnWuCAKGrezA/XjpjDt7IKQnJwMGxsbFBYWom3bttixYweMjIxaTf1Fgkld1tqG9QDzGp4pRPHIavw5b9++pXwJu3fv3uwLeF5eXrTmDwRBYNeuXVTC4/jx47Fr1y6hVDy//PILfHx8sHjxYvj6+qJNmzbQ0dGhfifKyspwdHSEnp6eqF8LwP+tTq5duybSdRflOlRWVlL7NVeL4sWLF9GmTRvs27dPqOTmqVOnwsnJCXfu3BGJVHv37h0sLS2RlJQEcXFxbN68GWZmZgAATU1NbNy4Eb6+vrh37x4OHTpEtT+GhoZyzYXJZzs5BySRl5cHoP4Zv2zZMur1mJgYVFRUoH379li7di31uoWFBYKDgxEREcGIVLtw4QLk5OSwbt06GBsbo1OnTrT257UoRJ6/oPeYBCz9CPhJqv0EAPoEBNMVJKbpmK2Juro63Lhxg3aSFgAEBgbC3t4ebDabS4ZMmiwD9ZLluLg4HDx4kJpki4uL48iRI1i7di1iY2OxevVqqKurU20Fo0aNgrOzMzWxFSWgoKysjFHyJFMwLeAFmawLg5kzZ6K2thbbt29HaWkpBgwYgCNHjqB79+5c26akpODEiRNQVVXF0aNH8fvvv3Nt06lTJ5w9exb379/H6tWr4e3tjYkTJ1IrgHQn9mFhYTyLUEHvka3J5ubmqK2txdGjR7FkyRL07NkTt2/fhri4ONasWcMl8+dH0DWFyspK7Nq1C3Fxcdi3bx9tifhP/B+fP38WOU3pzp07cHZ2RnZ2NoD/K3gbTsQHDRoEKysrkSbOCQkJuHv3LmbOnCmw7VtFRQUHDhyAjIwMgoODERsbCwMDA8bHBYDu3bsjMDAQ69evR3R0NFgsFpSVleHh4cF1X966dQu+vr6Ii4tDXV0dtRo7e/ZsTJo0CcOGDePbsiFoYagl/HqCg4Nx+PBhKo23IVgsFk6fPo1ffvkFlpaWmDp1KqNjAPVj2IULFxAcHAxlZWUQBMFXvSYmJoaxY8di7NixlAk8L0RFRWHnzp0oKipChw4dsGfPHo7JoaCgJKat5wYGBjwJppqaGty8eRPh4eGIj49HbW0tlVhmYmKCGTNmMFIL8UN0dDQ6d+4MW1tboQhjKysrXLt2DZ06dcKhQ4ea7TwAoLa2Fg8fPkRaWhoyMjLw4MEDynO2bdu2tMcFUdC4nV1FRQU3b96Eg4MDo3b26upqHDhwAOfOnQObzYaenh727NnTpJ9ZS4JuXdbahvUkmCwiMgUTjyzyvikuLoaHhwcuXbqET58+cW3bvn17zJo1CxYWFlBUVGy2cxYWBEHA398fAPD7779j5MiRuHz5slD7mpqaomPHjpRizdPTE3///Tf1W3Z0dGyyPbopVFZWoq6ujnqWr1q1Cm3btkVISAit6y7KdYiKikJYWBiSk5M5lLXt27enxl5ebchAfQdKcnIy14LS0KFDqePcv38fOjo6Qnu1iomJQUtLi1KAMUF0dDTs7Ozw8eNHdO/eHQcPHuRoodfQ0MDFixexadMm3L17F8bGxtixYweA+nkKv7lK4/qCV70G1Nd1QP0Ca2NCftSoUYxJKnl5eZSXl+PkyZNIT0/HmDFjMHnyZKHreEGLQvze+y8r2H6Saj9BgQ4BwZRUc3V1pX3DieLfRgfv379HQEAA/P39UVxczJNU4Yfq6mpqhatdu3bYt28fhwoMqH/QNFSr7d27F+fOncP169cpHzVJSUkcO3YMq1atQmJiIlUMkysmDf92oiS1VVdXM0qeZAqy5YMu1NTUaK0CNXyPxJw5c1BbWwsHBweUlJTw/U5+fn6QkJCAl5dXk+bNQ4cOhbu7O+bNmwd/f39UVlbSntg39hZkgr/++gu1tbVwc3PD69ev0bVrV7i4uPCU+f/xxx+M/X169OiB2NhYTJ8+HXv37uVbMP0EMwibphQUFAR7e3vU1dWhQ4cOGDFiBFRVVSlfp5cvXyI9PR2ZmZlYsmQJdu/ezTjpeN26dRwtLE3BxsYGQUFBWLlypciT9pqaGhw6dAixsbEA6v1NiouLcebMGTg4OFB2AZMmTaI8Qrp3745p06Zh5syZ6NatW5PHaG1/H4IgYGdnB4IgMGTIEIwcOZK6dhUVFcjPz0dSUhJyc3OxdetW5OfnM/Y9SkxMxKVLlxASEkK1zzVUr/Eb23j50ZSXl2PPnj0IDw8HQRAwMjLCzp07OWwcmC7wNOW3w29BR0dHB6WlpSAIAjIyMjAyMsLcuXNbzHz9+fPnGDdunNCt2OLi4hg9ejQSEhJE9tGrqKhAeno6lTCemZlJ2QhISkpi2LBhWLhwIbS0tChvOjrjgiho3M4OAFOmTEFlZSXtdvbMzExYWVnh2bNnkJWVhbW1tUClZWuCTl3W2ob1DUGnhmcKUci5hw8fwsLCglpwVFFR4Ri/CgoKUFxcjNOnTyM8PBwnT57k8FtsLZDEx4MHD4Ty8yNJQ7Ie7dy5M3x8fPDHH3/gzZs3GDt2LNzd3Zvl3CwsLDgSStXU1LBnzx4MHTpU6OvO9DqUlpZi9erVSE1N5WnHU1xcjOvXr+PGjRvQ0tLCoUOHKKLs6dOnOHToEOLi4jjC0UiIi4tj/Pjx2LhxIz58+MC3Bu/QoQMGDBjA9Xrnzp15koPCglwomzZtGuzt7XnOj5SUlHDmzBkcP34cJ06cgI2NDbS0tGilhJuZmWHcuHFcnR9paWlgsVg8W8DbtWvH+LslJibiypUrCAoKQkJCAhITE+Ho6Ah9fX3MmjWLa57aEK0ZrPSj4Cep9hMcEJaAEAV0PUZaOgEqNTUVvr6+uHHjBurq6qgHPh1TYzJyuq6uDq6urkIVr3/99Rf8/f0RHR3NEU4gJSUFd3d3rFixAnfu3IG2tjbPdgmmSW3C7MMveZIp+KkkhAGdVSASjb/j/PnzUVdXh927d2PJkiXw8/PjWjFMT0+Hnp6e0GloQ4YMgaamJiIiInD58mXaE/uzZ88KdZymsHbtWtTW1uLkyZNQUFDgqUAERCPx/vnnHxw+fBg+Pj5YuXIlFixYACsrq2Yxbf2J/0NQmlJWVhYcHBwgKSkJe3t7zJkzh+e9XF1djZCQELi4uGD79u3o27cvT9PyppCdnQ19fX2h79uGRShd/8uG3yMvLw+bNm1CdnY25OTksHPnTvz+++9Yt24drly5goyMDBw8eBDDhg3DixcvICcnh9WrV2PJkiW0EheZkmqC1KX88PLlSxAEAWVlZRw4cEBgS2JCQgJsbW1x/Phx6Ojo0FrcAeqVDGJiYli0aBEWLVqE69evIyAgAE+ePKHUa2pqajAxMcG0adMEXt87d+7A1taW8hXauXMnTwU304l2Q1KNjvfRp0+foKamhrlz52L69Ok8jcObExUVFbQVJr/88guKiopw7Ngx6OjoYOjQoUKTzVevXkVaWhrS0tKQnZ3NkdLWt29faGlpQVtbGxoaGnyJR2HHBVHQHO3sdXV1cHNzg6enJ2pra6GhoQFHR0d07dq12c9XFAhbl7W2YX1jtHQNz5RALy4uxpQpU1BSUoJp06bhr7/+4jKgJ4M5vLy8EBkZiRUrViAiIoKRYo18tjx58gRsNlsow/xnz541W6BC165dqd9CbGwsLl68iOnTp4v8uQDv8VXY615cXAwzMzPa1yE8PBzm5uZ4/PgxevbsiaVLl2LkyJHo3LkzRcbl5+fj7t27OH/+PG7dugULCwv4+fkhMTERGzZsQGVlJSQlJTFo0CCuxcDc3FxcvXoVcXFxkJCQQHV1Nc/zNzEx4Um2V1RUiDQOkLVGU6m9LBYL69atg7q6OrZs2YI7d+7g9OnTQh9HXFwcHTt25PC/e/fuHV6+fMnhs90Qnz59Yjx/kpGRwaxZszBr1izk5eUhKCgI4eHhVAq4oqIiWCwWvnz5wrXv1wpW+p7xk1T7CS4IQ0A0xLNnz1BWVoY+ffrwNT5ks9nw8PBg7DHS3KisrER4eDj8/PyQk5NDDVJDhw7F8+fPISsrS5uwevfuHQYNGiT0arCSkhI0NDR4roRJSUnBw8MD5ubmiI+Ph729Pezt7Tm2oUOSNExqE6awbw7fjYYYNWoUhg4dCh0dHejq6gq9+sjPg00Q/v77bwC8VRAqKirIz8/HwoULua7T27dvYWRkROtY/fv3x927d9GmTRvaE/uUlBR06dKlWbxtNm7ciJqaGpw+fRrm5uY4ffo0170oKolnbW2NCRMmwNbWFn5+fkhKSsKBAwcY+/r9RH3R0rCIEvTM8fX1RV1dHU6cOAEdHR2+20lJSWH+/Pno2bMnpVYLDAykfW5v3rzBxIkTae2zcuVKeHt74/79+7SPBwABAQFwcnJCZWUlBg8ejEOHDlHkdFBQEBwcHBASEoI//vgDa9asgZaWFu7cuYMDBw7A3d0d2tramDFjBvT19Zu8D11dXaGpqUmbbLa2tqY9NpALNZ6enk0++3R1dfH333/D2NgYgYGBtEm1xkqGiRMnYuLEiaitrcXNmzcRHByMxMRE7Nq1Cy4uLjAyMsK8efM4iNcvX75g//798PPzA5vNhoGBAfbs2SNy6xIvkP5MN27cQHR0NEerMS/vJqCeEG1NpWFtbS3tBQRpaWkQBAEPDw94eHhAQUEBo0ePho6ODvT09AQGemzYsIFqN1ZRUcHo0aOhra0NLS0tWq2QwowLokDUdvanT5/C0tIST548gZSUFLZs2YIlS5YIvL9EVa6LAmHqstY2rOcFujV8a8Db2xslJSXYsGEDZGVlYW5uzqWEIYM5Dh06hMGDB8PZ2Rn+/v60lEAkyJZzsrYXtgW9OVNKu3fvTnms2draQlZWVmDYgagQ5ro3vA78/q68rsO2bdvw+PFjTJgwAfv37+fyOlNUVMTgwYMxePBgmJqaUi3wZ86cgZubG758+YKVK1fCzMyM5/ziw4cP8Pb2xunTp8Fms6nxS1hkZWUxDkkC6hfL6Cg6tbW1ERYWRgUKCIvOnTsjPz+f47XExETqPV7nkJaWxjOUiS569eoFKysrbN68GVFRUQgKCsKdO3dAEATCwsLw+PFjGBsbY9q0aVBSUsK4ceOwZMkSns/Vn+CNn6TafxyCVm8EERAsFguTJk3Cnj17qOQvKSkpmJmZcZgsAkBOTg6sra3x+PFjsFgs2pJS8sZuDuTl5cHPzw/h4eEoLy/nINMcHBwEtl41BQ0NDQwZMoTne6SSrTH69u2L+/fv8/W7MjMzw7NnzxAQEAAJCQlMmjSJem/dunXUcQWhcVKbk5MTFi9eLNR3ai5ISkoiNTUVqampOHr0KNq3bw8dHR3qH379/Q1Xc4SFMAPAixcv8OLFC47XCIJARUUFrWOx2WxISEigsrKS9sTe1NQUq1evpl3A8WuHlpOTg4qKCjIyMvDnn39yES+iGqwDgLq6Oi5evIhjx47B29sbJiYmMDc351uMkEbVP/F/NJwwkiuIvN5rjJSUFKirqwsk1Bpi1KhRAMA4mVFGRgafP3+mtQ/T1eLMzEx4enpSviHLly/Hxo0bOe4faWlp7Nu3DyNHjoSDgwOOHj2KUaNGITAwENHR0QgJCaFaT9q1a0cpRvmBvI/okmpMFMLXrl1DmzZthF5MUFNTw8iRI5GWlkbrOCR4jTUSEhKYMGECJkyYgKKiIoSGhiIkJIRSr/Xv3x/z5s3DgAEDYGVlhefPn0NeXh62traMW4iFAenPRI6RDWsSft5NAKfSkCAIpKamIj8/H8XFxVBSUkLXrl2hoaHRbP5hTMBisbBx40YkJyfj3r17uHbtGq5fvw4A6NOnD7XINGLECA5iRVtbmyLRhDXn/5rjQmMI085+6tQpHDt2DF++fMHAgQPh4uIilFqrOZTrwoJJXUaitQzrRanh9+3bJ/LxhUVCQgJ69eqFlStXwtXVlW/qNIlly5bh/PnziI2NZUSqkYq6qKgoREdHi+RBLAyEWVwka/aGID1y6YLpdY+JiaGugzAgr0NKSgpUVFTg5OTUZHiAtLQ0nJyccPfuXfj5+aGyshJOTk4Cx2MVFRVs3rwZv/32GzZu3Ii0tDS8e/dOqEWE/Px83Lt3j8P4ny6YtEiT/nl0oKWlBX9/f+Tl5aFXr16oq6uDv78/WCwWz0XM6Oho5OTkYOnSpbTPjx8kJCRgaGgIQ0NDFBYWUing2dnZcHJywv79+2FgYICCggK+Apif4I2fpNp/HMKs3vAiIIB6soYkFRQUFPDx40e4u7ujTZs2+PPPPwHUqyucnZ0pDy9ra2tGktKG/i2kgbSwZARBEIiOjoavry/u3r1LtZNqaGhg9uzZsLKygpqamkiEWl1dHSorK/kONosWLeLZNiMpKQkWi9Wk3xVBEPD19YWvry/H64IGZH5JbeRKNdPkSSZthKmpqXj48CGSkpKQlJSE9PR0XLx4EZcuXQKLxYKamhr09PSgo6OD4cOHUxOh9evXU68LuwrFtHhyc3Oj7VP38OFD9OnTB+7u7ggKCqI1safbIkeC9CXkZXROvnb//n1KLcTPYL2uro5nymBycjKGDh3Kd/VcWloaW7duxYABA7B582Z4eHjwPdcfmVRj0vJGEATc3NwYpSkVFRUJTag1REMjYTro0aMH30kqPyQnJwvlZwbUt6lGRkbC19cXjx49ApvNhoqKClxcXKClpcV3v5kzZ2LgwIFYv3497ty5g5UrV+LWrVtYt24d4uLiEBQUhPj4ePj4+ODs2bNgsVh4+fIlRbaICkGhDfxw9epVTJkyhdY+/fr1Q0ZGBu1jCQNlZWWYmZnBzMwMT548QWRkJM6dOwcHBwdISEigrq4Ov//+O3bv3o0uXbqgvLy8yc9kom5ueA+RQQd0nhkEQeDkyZM4d+4cioqKuN5XVlbG/Pnz8ddff4mcqMoELBYLFhYWsLCwQF1dHdcYeObMGXh5eUFGRgYjR46kSLZTp07RPlZzjQvNCX7t7IsWLcK9e/cA1JvBm5mZ4fnz55Sanh/Wr1/fYr55vCBqXdaShvUkmNbwrU2qvXz5ksPmRBhoaGjgxo0bjI5HPlsKCgoQExPTrEENvMC0nmO6H9PrThAETxJYEDQ0NHDhwgWMHTtW6IRPGRkZaGtr48qVKxgwYIDQz/UpU6bg8OHDyM/Px44dO+Du7i5wYaSurg62trYAIFJ7bVZWFn755Rda9yXpUSZMazGJZcuWITAwEAsWLMDYsWORnZ2Nx48fQ05OjkM8UlZWhitXrsDZ2RmSkpJcHmzNhc6dO2PdunVYu3Yt4uPjERQUhNjY2P9seqeo+Emq/cfBlIDw9fXFw4cPsXz5cqxfvx5SUlLIyMjA+vXrcfLkSZiamuLQoUM4c+YMCIKAtrY29uzZw1jC2nCySbaPNjWosFgszJ49G4GBgXjz5g0IgkDnzp0xc+ZMGBsbU54dVlZWjM4J+H+4QWBgIGpqaqgUrsZQUlLiOaF79eoVOnTowNMgWhQ0ldQGiJY8SRfi4uIYMmQIhgwZwneC8fjxY5w8eRLy8vIYNWoUdHV1ea7s6+npYcSIEXwNj5kWT69fv8bx48eRnp6OYcOGNbl9eno67t+/j8WLF6NLly7YsGFDq0zsRW1NuHbtGo4ePQorKyuuwIEvX75g+fLlUFRUhLW1Nc8EwsrKShw/fpxqzR0+fHizGiF/L+CXdMgP5OSsrq6Or9JCUJoSi8VipAKrqamhvQ9Q74vk5uaGqKgojB8/vsntb9y4QamSBaGgoAD+/v4IDg5GSUkJCIKAuLg49PT04OTkJNT90bdvXwQHB2P79u2IiIgAUN9qZmBgAAMDA3z48IFSYeXn5+P27dvQ09ODvr4+jI2NBZrztgSqqqpoq87k5eVRVVXVQmdUj9TUVERGRiIqKgqVlZWULyhQrx4UdiLEdFxoWH+4uroiOztb6Jrk8+fPMDMzQ3p6OgiCgKqqKnr37o127dqhoqICT58+xcuXL+Hm5obExER4enpyLM7RRXR0NK1WwsbPB15jYGZmJpKTk5Gamoq0tDTEx8cDqFeUk2QCm81GQkICrl69yjGGdO3aFYaGhtDT04OYmFirB28IA36EVMN7ITMzE+vXrxf6M+k+d0WBqGFCLWlYT6KlFVjNhcrKSqHTHEkoKyvT7h5gis+fPyMsLAwODg6M1K1iYmKMnoFMwfS6b9u2jdF1IAiCdgqvqqoq6urqMHToUFr7aWtro7CwELGxsfjjjz9gZWXF0wYhMzMTe/bswYMHDzB37lyhVb28MGvWLJ6dI4KUjhkZGfDx8aFFqnXu3BnHjx/H1q1bqTmsrKwsXFxc0KlTJ2q7CRMmUOEEO3bsENrrmSlYLBb09fWhr69PKdkPHDjQosf8EfGTVPuPgykBcezYMQwYMABbt26lXhsyZAhsbGywfv167N+/H97e3pCVlYWlpaXILHvDySa5stNUgctisXD06FHIyclRSjFeJpBM0DjcgCAISEhI4M6dO2Cz2UINytXV1bh16xa0tbVx9OjRZjkvYZLagOZJnhQFvCYYjx49oiYYKSkp1EqJiYkJkpOTkZOTg5ycHGplX1NTk1rZb44Bx9jYGH///TfWrVuHM2fOoE+fPny3zc7Oxrp16yAmJoZ58+ZRr9OZ2DOFKJOngIAA2NvbgyAIZGVlcZFq7969Q8eOHfHq1Sts3boVHz584JCd3759G3Z2dnj9+jVkZWWxfft2ju//X4KwbezFxcXYtWsX5Z04depUbNq0ifbxxo0b16pqm0WLFuH06dPYtm0bfvnlF76t7QCQlJQEGxsbKCoq8v1tJyQkwNfXFwkJCZTxeqdOnTBnzhzMnTuXth+KrKwsDhw4wLPYVlFRoRRCSUlJCAwMpIrjmJiYZlGsNQU2m40bN27A19cXbDabSuEUFi3VtvjixQuEh4fj4sWLKCgoAEEQ+PXXX2FpaQlvb++vouhiAjs7O9y7dw+///47bG1teU7cHj16BEdHR6SmpmL79u0ijbOkko4OBKmcxMXFMXToUPTr1w8DBgyAuro6Ll++jJycHKplOysrC5s3b0ZeXh6HoiUvLw+pqakIDw9H9+7dcfjw4WYh1eiERZAQ9B35vcc0ZCkpKQnBwcG0lOuioDnChFrSsB4QLY2zNVFbW0vbO05SUpIi+VsKubm5lCUMSeAxUY8xVZwxhSjhMEyuA8A/jZkfyM4duosZ7dq1A0EQGDZsGNLS0jBv3jx06NAB/fv3h6KiIkpKSvD8+XO8evUKBEFAQ0ODUqsxBb/rR/p6Nid5raenh5s3byIlJQU1NTXQ0NDgCuPo378/ZGRk8Oeff/JMBG1JlJeX/0z+ZIifpNpPMEJJSQkMDAy4Xic9sHx8fNC1a1d4eHg0S+pUQ7NEb29v+Pj4CHXTjx8/Hp8/f0ZMTAxqa2tRXl4OXV1dRhMHQeEG8+fPR3V1NXbs2IGzZ88K5QF38uRJVFRU0DbH5wdhk9qA5kuebC5UV1ejpKQEpaWlqKurg4SEBPX3dXBwAFBvZJqcnIykpCQkJycjNjYWsbGxYLFY6Nq1K3R1daGjo4PRo0dDVlYW5eXlSEhIwPDhwzkK8JSUFLi7uyM3NxedO3eGiYkJjI2N0alTJ9ja2mL79u2YOXMmpk6dCj09PfTq1QtycnL49OkTnj9/jri4OFy9ehW1tbXYtGkT3993UxN7giBQVlbWpLcILzAJNyDTI+Xl5bFr1y6ev41u3bohKioKkZGR2LZtGw4cOABtbW106dIFTk5OCAoKAkEQGDFiBBwdHYVu9fsRIUwbe0xMDHbs2IGioiL88ssv2LNnDxeR2dIgCIJ2mASpPHJ0dMSGDRuwaNEiTJw4EWPGjEGfPn2goKCAyspK5OXl4erVq7hx4wYIgoCnpyeHmq6srAzBwcHw9/dHfn4+R4Lhxo0bMWbMGMbk0atXryjFmyAfRU1NTWhqaqK0tBRhYWG4cOEC/v33XwD8jfCbgiAF14cPHyj18rt376jv/DXvlU+fPiEyMhLh4eHIyMgAQRCQlpbGtGnTYGJiQi02kbYN3zrS0tIQGRkJbW1teHp68h3PBw4cCB8fH6xatQrXr19HcnIyI5/O5pxQEQSB+/fvIz4+Hnfu3MHDhw+phbn27dtT405mZiYWL16ML1++oFevXpg0aRL69u1LKfFycnJw7do1ZGdnY/78+fDy8hI6JIkfvLy84ObmRos8FNSyLug9Ju1FampqlOm/sMr1bwGtbVj/E4LBZrMRFRUFX19fyuKAIAh069YNc+fOhYWFRYsc9/Xr13j27Bk6deqEXr168fVBFITWCnNrTtBJ5Sa3J1ur//nnH/j6+uLt27d4+/Ytx3bKyspYtGgRVq5c+VW9M5lATk5OYC1I16tNEMrLy+Hl5YXExESUlpZi0KBBsLCw4Ckc8PLywtGjRymVPK9gmKZAPvv/i/hJqv0EANAmICorK3mu9pOyYjk5OZw7d45DzioKFBQUqEls27ZtwWKxhJrU3rx5ExcuXEBISAjOnz+PgIAAqnA1NjYWSi4sbLjB58+fceLECezfvx/S0tKYP38+3888e/YsTpw4gT59+vAkJ0mvExI1NTW4cuUKcnNzoaqqiilTplCrP62Z1NacyMrKQlxcHOLj4/HgwQPU1taCIAgqJU1XV5ejZVVFRQVTpkyhvIk+fPhAEWypqanw8/ODv78/JCQk0Lt3bxQWFqKsrAzu7u7UbzoxMRErV66kJjHv3r1DRkYGHj9+DDs7O8ydOxcAsHfvXoSFhSE8PJzrvAmCQJs2bWBjY4NFixYJ9V15Teyzs7Ph4+NDO+GUJDxI9UuPHj04Vrny8vJw+vRpjntWU1OTGuT++ecfgaojoN7XQk5ODitXrsSBAweQk5OD169fQ0pKChs2bMDSpUsZmz//F1BeXk79hgiCwJQpU7Bz504YGxuLlKbE9G9OdxWd3H7SpEk4evQobG1tERkZiStXrvDcVkVFBXv37qU83548eQJfX19ERESgqqqKmrDMmjULx44dg7q6OsaOHcvou8THx8PPzw8JCQmoq6sT+m/Stm1bmJqawtTUFKmpqVi8eLFAI3xB4EWqkc+gGzduUM8yFRUVGBsbw9PTE2PHjqVFoItqEFxbW4vY2FiEh4cjLi4ONTU1IAgC/fr1g4mJCaZPny5SO2RzoCHZSwYVNHyNbHlWVlaGuro65s2bh0GDBiEwMBBSUlLYv39/kwtkYmJi2Lt3L8aPH4/Q0FBGpJqoaqCPHz8iISEBcXFxuHXrFj59+kS1Pf/+++/Q1dWFrq4uBg0aBBaLhcrKShgZGaG2thbbtm3D4sWLuX7nEyZMwKpVqxAYGIhdu3Zhy5YtuHz5crOmewoDQWnugtrZmeDQoUPUmN/SynVeEKYuE0ZV0pyG9T8hPIqKiqgFD5KgERcXh4GBAebNm8fIs7QxwsLCEBkZiTVr1lAq6rq6Otjb2yM4OJgaW0eMGIGUlBSePoj80NAHkQTdudv3BDExMVhYWMDc3Bzp6el4/vw5Pnz4gPbt26Nbt27Q0ND4bpTVXwslJSVYsGABnj9/Tv3O8vLycOPGDXh7e1O/0devX2PLli24d+8eCIKAuro60tLSBAbD8MN/eW7wk1T7CSQmJmLTpk20CAh+IG8mQ0PDZiPUREGnTp2wZs0arF69GomJiQgKCkJMTAzlddW3b1+eDwAm4QZycnI4ePAgTE1N4eDggLNnz2L8+PFQU1NDu3bt8PHjRzx79gxXr17F06dPIS0tjf3791MrrKWlpXB0dMTVq1fh6uoKbW1tAPWFwNKlS/H06VPqoXjo0CG4urpCWlqaUVJbeXk5pKSkaMnAMzIykJGRwZgQqKiowK1btxAfH4+EhARKxcFisfDbb79RE4thw4YJNVCqqKjAyMgIRkZGyMvLw+3btxEQEIB///0XT548gZiYGPT19Snytba2Fvb29qirq4OWlhZl0rt79274+vpiwoQJ0NTUxNy5c6Gjo4OQkBDExcWhsLAQnz59gpKSEnr06IGxY8di6tSpUFFRof03aDixV1NTg7y8PCOfrCdPnmDDhg3Iz8/HiRMnqBWvzMxMLFu2DBUVFSAIAunp6YiMjMSWLVuQkpKCUaNGNUmokRgzZgyGDBlCqQEHDRoEZ2dnoRLa/su4e/cubG1tUVhYCEVFRezcuZMigkVNU3J1daW9us1isWgHcDTExIkTMWrUKJw/fx4JCQnIzc1FaWkp2rVrh549e8LQ0JAiaC5evAg/Pz9KDSUrK4vp06dj9uzZFJlx7Ngx2udQWlpKKd5evnxJPQdHjhxJLV5UV1ejrKysycWE8PBw6t5XU1MTyYelsrISFy9ehK+vL6VeJlfMJ06ciEOHDkFCQgKenp6MCHSCIGhf74KCArDZbOjo6FDkDXkdTExMeLbL8kJhYSHatGnD1ZYCACdOnICenh4GDBhA69wao+Fkkvzvxq99+vQJnz59Ql5eHkJCQmBra4vMzEzo6ekJ3carrKwMXV1dyqS/teDq6or4+Hg8fPiQIg07duwIY2Nj6OrqQltbm+fzPyIiAoWFhbCxscEff/wh8BgmJiaoqanB7t27ERERQS0MtQZau0Wo8aIaHeU6U9Cty1rbsP57Aql6IZ9RTdWSzaXKIggCmzdvxvXr16kFj+7du+Ply5eYM2cO7O3tm+U4dnZ2CA4OBgDMnj2betYePXoUQUFBAOrTH1ksFm7fvo127dphwYIFjIkhpnM3uuoj8jokJyfTGo+SkpIAiE6ysFgsDB8+XGQl7rcAfgFwwoCJbY+npyeePXuGnj17wtzcHCoqKoiJiUFgYCAcHBwQHByM9PR0/PXXXygpKYGMjAw2btwIU1NT/Pbbb1wJ9T8hGD9Jtf843r59izVr1uDLly+0CIimwDSQoKXAYrEo0qa4uBihoaEcbUAXLlzAu3fvYGxsjOzsbFy4cIFRuMGwYcMQFBSELVu24OnTp8jLy+PahvSwOXr0KPr16wegfiVr2bJlePToEcTExFBdXU1tb29vj5ycHCgqKlKR0d7e3li+fDnYbDbYbDbtpDYNDQ2KbGwIQcmqCQkJcHNzY0SqmZqa4t69exxtLkZGRtQ1oetx9O7dO6p9JikpiUp+ExMTQ4cOHfDu3TscOnSII23q1q1bePXqFaSlpeHs7EyRYgcOHICBgQECAwOpVWZVVVWsXr2adipacXExXr9+jYEDBwrcjvS8W7JkCW0vnE+fPmHSpEkoKSmh2oFIbN++HeXl5RgwYADVJuPi4oJDhw6BxWLRbjkZOHAg7t+/j3Xr1mHFihU/VwUFoLq6Gi4uLpRidMyYMdi9e3ezhpB8rYlX27ZtqVZmQbC0tIS0tDT09fUxefJkTJgwQSTlzOPHj+Hr64vIyEhK8da2bVsYGxtj3rx5+PXXX/H48WP88ccfSEtLo3za1q5dy7W4UFRUhB07diAmJoZ6bdy4cYy8qJ49ewY/Pz+EhYVR6uUuXbpQBei4ceOgpKREtb0wadcG6iczbm5utJUMQP0K9aBBg2BiYgIjIyOhU9sIgsCePXsQEBAAZ2dnLnuCjx8/4ujRozh69Cjmzp2L7du3M269a0j2urq68mw9rKqqwuvXr5GUlAR3d3c4OjpS6hI66NGjBxISEhidJ1O4urpCXFycQ0VFjvmCcOnSJXTu3LlJQo3EwoUL4e3t3eqkGpMkd6D+vmOChm2jdJTr6urq8PLyon08JnWZgoICrly5wmjR7UcHqXohn1HCEDt0CZnKykrKQuTixYu4fPkygHqiWlZWlupSGTFiBE9bBCZ2AADw77//4sKFC+jevTu2bNlCqd6Kiopw5swZsFgs2NraUvf0pUuXsHXrVrRv355RTS3K3I2J+gj4//xAGDQci/i1gfMD08Ty7wFNJQrzA1Mla2JiIhQVFREQEEAtkOnr60NJSQknTpxAfHw8Nm3ahPLycgwbNgzOzs4cwWNdunRhpO7+r+InqfYfh7e3N6qqqnD48GFaBERxcfHXOmWRoaSkhOXLl2P58uVITU1FUFAQrl+/jpiYGNy8eRNsNhuSkpKMww3U1NRw6dIl3LlzB1euXKEky+3atUP37t0xadIk6OvrcxAUISEhePToESZOnAh7e3uKZMrNzcWNGzfAYrFw5MgRjB49GkC9imjGjBlUKiDdpDZy1bwxkpOT4ebm1uxJYsnJyRAXF4eRkRGWLl2KgQMH0hpY2Gw20tPTER8fj7i4OGRnZwMAz5X/xYsXo1u3blzx7YmJiQCA0aNHcxS8MjIy0NHRQWpqKuPvR/pPpaSkUESssBN7ujh79ixKSkpgaWnJ4YGUkpKCrKwsSEhI4OjRo5SHk4eHB8aPH4/q6mraSZBSUlKQl5fHqlWrBG5XUVGB58+fQ0FB4T+ZBPrgwQNKMSonJwcbGxvMmTOnWY/BRG2mpqbW6qmAtbW1qKiowNu3b1FQUEDbU5NsqWqoeGOxWNDU1MTdu3cxZcoUKmH06dOnWLRoESorK6n9X79+jW3btkFMTIx6Jt68eRM2Njb49OkTxMTEYG5ujhMnTjD6fsuWLaPUy3JycpgxYwZmzpyJUaNG8d2H6f3OxHMHAGJjY7Fnzx5GKjwbGxuEhYVBQkICd+/e5SLV2Gw2Zs6ciWvXriEoKAgVFRU4ePAgo/MUBjIy/2PvrOOiSvv3fx06JMUAFnV1VdYCfQCVNLAIFbADcXUt7FjETjCwMcACFERaaQmlVErFAJRYxQVFAaV7zu8PfnO+DBPMDGHxfr2e17POnDPnDDNzzn1/7utzXWL4/fff8fvvv0NbWxvGxsZobGxEQ0MDT69TV1fXJsUSvzQ2NiIrKwvy8vJQUFCAgoJCq4tI7969g7a2Ntc+QQRBYMyYMYiJiWmPU+5w+FUgcRozsFOuZ2VlUYoZXuFnXGZhYQFXV1ds2bKFr2P+rDRX6fr7+8Pf359n5S43rFixAqmpqZCUlERFRQX125g+fTqmTJnS6uLC9u3beS560MfTEhIS8PDwYBhfRkVFob6+Hr1792awCzE1NcW1a9cQHh7OU1EtJiYGHh4eGDBgAF9zt8GDB7c6nmOFn58fJSrghXPnznFsEWcFfW7C69iFIAicO3eOp306m84OiaOnDrdUnJuZmeHixYuwsbFBZWUlJSL40bzpvje6imq/OAkJCRg1ahTPBYi7d+/ylfrED1FRUR3m/aKhoQENDQ3s3r0bd+/ehbe3N9LT01FfX9+mcAOCIKCtrQ1tbW2utg8LC0PPnj3h4ODA0JJJb68YNGgQNXADmibLoqKioNFoPMdcA+DLHL8tSEhIoKqqCsHBwUhKSqL80rS1tTm2PwYEBCAmJgYPHz5EWVkZSJKEsLAwRo8eTancWq78FxQUsEzXSklJoQZYLVFSUqLUbrzSfGJPHwxwM7Gn0Wh8HS8mJgaqqqpMpuL0CZWmpiaDKbqcnBz09PQQGRmJnJwcno71+vVrSmVTUFCApKQkiIiIwMDAgBqcXrlyBefPn6eMTQcNGoSDBw9y3Wb2I9PQ0ABHR0dcuXIFDQ0N0NLSgr29Pd8Kjo6GUzx8e6CqqorMzEy8fPkSycnJOHXqFFRVVTFz5kxMnz4dcnJybPf98OEDPD094ePjg5KSEqo4bW5uDjMzMygrKzMViZydnSn/qY0bN0JBQQH379/H/v37cerUKcyYMQPu7u6ws7MDjUbDwIEDYW9vj2HDhvFdVHv06BEkJCRgbW2NRYsWQVRUlK/X4YbmkwpeJqL8FlKjoqIQEBCAIUOGQEtLC66urjh48CDDNt27d4e9vT3WrFmDlStXIiQkBNOnT++UAI4+ffpAV1cX8fHxPLdypqWldfrv0tPTEzExMYiJiUFISAhCQkIY7A709fUxcuRIpvESPdiEF+Tk5PD169d2PPvW4VfVY2Njg8mTJ7fbeXBSro8cOZLBm5UX+BmXjR49GjExMdiyZQuqqqpAEATLYu6dO3egra3drkrm75nmapekpCQQBMGzAiYzMxOZmZmtLiDTQ6DovrBmZmY8/50lJSUxYcIEhk4ATvj4+MDAwIBJofjo0SMQBAF9fX2mggU98bc1SktLKfsDejLwx48f+Zq7paSk8KU8ar4Pt58DwOhHWVVVhfLy8laTe+lKVm7DTFh5zX2vdHZIXEVFBcvOMbo909evX3HkyBHMmDGjU8/rZ6WrqPaLw28BAmDv70MQBMfneJWwcjMQbpmqwyvdunXDggULsGDBAsTExODcuXMoKSnhO9yAV16/fg09PT0mjzP64IPVoHDhwoWURxyvdMR74ERiYiJSUlIopZmvry/8/Pwoo2YDAwPo6ekxefTQVw1VVFSodtExY8ZwVBzU1tYytZxVVlZSrb6sVopqamr4nhw3n9jLy8vjxo0bOHnyZKsTe36VTHl5eSyTO+mmt80H+XT69OkDkiSRmJiInJwcrnzRcnJykJiYiJkzZ+LChQu4cOECFW8vIyODc+fO4ePHj3BwcADQpACtqqrC69evsXTpUgQEBPzU6aBZWVn4559/kJmZCVFRUfzzzz9crTh/yzSljoiHb46UlBQIgkB8fDwCAwPh5eWF9PR0ZGZm4vjx49DT02O778SJExnaO6dPn96q6ffTp0/Rr18/HDt2jFr0MDIyQnV1NXbt2oWrV6/ixIkTIAgCy5cvx4YNG9qcEti3b1+8e/cODg4O8PX1hYGBAWbMmNHh19T8/Hyu/FjoSgYnJyeej+Ht7Q0xMTE4OzvD09OT47YqKio4d+4cpk+fTk0oO4P+/fsjLi4Oz58/R2pqKv73v/+1uk9KSgrVxt6ZqKurQ11dHRs2bEBRURF1/3v06BEuXboEJycnSEtLQ0dHh2oP7dGjB2RkZPD582eejlVUVNTpBRp+VD106C2T/MCLcp0fz1I6/IzLBg8ejOfPn+PatWtwdnbGwYMHmWwXKioqsGPHDoiIiGDVqlVYuXIl3+f4KxEZGYnz589zVcyZPXs2QkJCcOrUKZw6dQpDhgzBjBkzYGJiwlEpumbNGgQHB+Pdu3cICQmBrq4upk+fjokTJ3IcI96+fZvlXCU1NRUAWN7LJCUlUVlZyfY1X716Rdkf1NbWUiFZJiYm1GJGSzpq8bg5vHwOzf8m7Fr8W9Jy4SgjIwOZmZlM3l61tbU4e/YsXrx4AQBcixjYUV5eziQ4oCvsPnz4wNTd09Ywoc6ARqOxFITQr2laWlpdBbV2pKuo9ovDbwEC4M/fpy2eQPRUnearHOxSdU6cOMGTguvr16+QkJCgVDgGBgYgSZKncIO2qL++fv3KdL4kSVKr8axaUIWEhKh2vtZMpQ0MDFj6R3QWwsLCGDt2LMaOHQsbGxvk5+fjwYMHiI2NRVJSEp48eYLTp09ThtL0ATHQtNosJycHKSkpSElJtRquoKCgwBS9nZSUhMbGRsjIyLA0105PT+fZ141O84n9xYsXQRAEVxN7JSUlvhSYVVVVTJ9zbW0tVaxm9V2pr6+HsLAwGhoasH79ety4cYPj+y0qKsK6detAkiT69euHkydPQkFBAVOnToWgoCDCwsKwfv16yMrKQllZGY6Ojvjzzz9RV1cHZ2dnODo6wtXVlfJ1+xkxNzenWtD69OnDld8ISZL477//Oi1NqS0BBW1BUlIS8+bNw7x585CZmQkvLy8EBQXh/v37AIDQ0FAICwvD3Nycui7RaDRISEhg3rx5mDBhAldKx+LiYpiYmDANGvX19UGSJE6fPg1paWmGFi06ZmZmfF0Tw8PDkZycDC8vL0REROD69etwcXHBoEGDYG5uDlNTU55fs62wUjLww8uXLzF+/Hiu/aAGDBgAXV1dPH36lOdj0b2PunXrBgAwNDTkagGNIAgICQmBIAhs27YNrq6uHIv3OTk52LRpE8TFxTvVb6wl9CRYc3NzNDY24unTpyxVbKqqqhATE0N8fDwaGhooXz5O1NfXIy4urtPv8TNnzuTr2lRUVMSXYXdcXBzev3/Pk3K9LfA7LquqqsKxY8cANE3GW1JRUYFRo0YhJSUFp0+fxpcvX6iW9l8Bunq5Izl48CB27NiBoKAg+Pj4UEb9x44dg4GBAdvjr1+/HuvXr8fz589x9+5dhIWFISYmBhISEpg0aRJMTU0xduxYJtWZsLAwamtrGR57//49Pn36BIIgWH5XiouLmcaA9fX1CAkJgYeHB54/fw6g6Ts3ZMgQzJ07F6amppCQkIC/v3+nLh53Ji2VdElJSUyLgenp6Thw4ABycnIgLi6Obdu2YcGCBW06LqcwIX7Tyrmluroa5eXlkJSU5Nr/tD0YOXIk2+fc3Ny+266L75WuotovDr8FiH79+iE8PLyzTpMhVWfKlCnU4+xSdaysrBAQEEAVYKqrq/Hy5UuIiooyTNZCQkJw/PhxfPz4EYKCgtDT08OOHTugoqLCU7jB+PHjMWHCBL4NKKWlpZlaNzIyMlBRUUEZ7bYkPz8f0tLSOHjwYKeYSrcnysrKWLhwIRYuXIi6ujokJiYiJiYG8fHx8Pf3R0BAAAQEBNC3b1+oqakhPj4eaWlpcHZ2Rrdu3aCtrQ1dXV3o6+szSclHjRqFmJgYVFdXU4q2gIAAamW55WeUnZ2NJ0+esFz14wZ+J/b8+izJycmhqKiI4bEnT56gvr4e4uLiLIsR2dnZUFBQgIWFBc6cOYMpU6bA0tIS+vr66N+/PyQkJFBaWoq3b98iJiYG7u7uqKiowKJFi5CUlITu3bsjMDCQat9buXIljI2N8fbtW5w7d46azImIiGDt2rWIiIjg28fmR6G5P92bN2+o6wI3dISXDL+kpqYiNzcXvXv3ho6OTrt7aqiqqmLPnj2wsbFBaGgofH19kZycjBs3buDmzZsYPHgwLCwssHHjRvj6+sLJyQnOzs6Qk5ODqakpZs6cybZYUFVVxXLxhF4wFhAQgJubG0NKM522qPU0NTWhqamJ8vJy3LlzBz4+PsjMzMSRI0dw/PhxEASBkpISrosi/MJJycAPX79+5dk3p3///khISOD5WHTvI/piALdJrDk5OejVqxcWL16MQ4cOwdzcHPPmzcO4cePwxx9/QEpKCtXV1cjNzUVYWBg8PDxQU1MDe3v776bVTlBQkLKe2LJlCz5//oy4uDi4ubkxqDiuXr3KlYrJyckJnz9/hq2tbUeeNhNHjhzhaz9VVVWqTY0XaDQaBAQEeFKut4Vu3brxPC57/vw5GhsboaSkhDNnzrC8H/fu3Rs3btzAs2fPYG1tDVdXV0yePPmHTDcsKytDUlISCgsLUVFRAUlJSaioqEBdXZ3lIi/QVEA3NDTs8HOjF9Jnz56NrKwseHl5ITAwEFFRUYiOjqaSsTMyMpjuMSNGjMCIESOwY8cOPHz4EEFBQYiIiMCdO3fQvXt3GBsbw8TEBMOHDwfQpNxtaa8RGxsLoOkayarlMTExkbreFhQUUPYHX758oa7lVVVVMDMzY7pfdfbi8fcCjUbDhQsXcOnSJTQ0NLA01+cHfsOE2kJkZCQCAgKQlJTEEM4gJycHDQ0NWFhYdLgCnNP4hJs24bq6Onz48AE9evRoUyjVz0JXUe0Xp7MLEHTq6+uRkpKCL1++QFFREWpqamwnc/Hx8QypOvR4+tZSdTw9PWFpaYk7d+7Azs6Okur269cPZ8+exYcPH7B161bK26qhoQH379/Hy5cvcefOHYabT2vhBnJycmyVYtygrKyMtLQ0hsfofgJqamrUaj6d6upqJCQkQEREBO7u7hAWFmZIpqLT2abS/CAiIkIVLysqKhAVFYXr168jMzMTeXl5CA8PB0mSePHiBbWyf+/ePYSHh4MgCAwcOBAGBgbQ1dXF//73P8ybNw9BQUFYsmQJ5s+fj/T0dISHh0NAQIApTS03Nxfr1q0DjUbjWwLdlol9c1p6QtAN23NycqCoqAgjIyNIS0tDTU0NcXFxDBP24OBgAICOjg7TTZKenGdoaIhVq1ahpqYGly9fplo6WZ2HoKAgli5dim3btlHeP839sOTl5TF+/Hj4+/uznAjo6Oi02j72o8NPYczS0hJmZmadmqZEVw+GhoYyTPCqqqqwbt06PHz4kHpMRUUFFy5cwB9//NHu5yEqKoqZM2di5syZePfuHby8vHDnzh1kZmbCzs4OGRkZWLVqFR49egRvb29ERkbC1dWV+u3womygF7gnTZrU6u+ORqPh5cuX6Nu3L8P1Ozc3F9euXUNOTg6UlJQwZ84cphYeKSkpLFq0CIsWLcLLly/h5eWFkJAQVFRUICIiAnp6ejA1NW1X2wBulQz8wI8vV01NDde+Qy3hVbmek5OD+Ph4mJiYYNGiRWhoaICDgwOuXLmCK1eusHx9MTEx7N27t8OVMbxSWFiIlJQUpKam4tmzZ3jz5g0aGhpAEAQGDx6M6upqnD17FsLCwliyZAnLFp6GhgY4OTnhwoULGDRoULv6lPFLWVkZPn36RKkulJWV2SovJCUloaGhwbVfbWRkJM/K9bYwcOBAnsdlqampIAgCLi4u6Nu3L8fXV1dXx4ULFzB37lzcunXrhyqqZWdn4+TJk4iJiWHpDysoKAhDQ0Ns2rSp1b9DZzBw4EDs3LkT27ZtQ0REBLy8vJCUlIRnz57B3NwcgwcPppTGzcc6AgIC0NXVha6uLg4cOICoqCgEBQXBw8MDbm5u6NOnD6ZPnw51dXXcvn0bKSkp0NDQQFVVFdzd3UEQBJPvGdDUav/+/XuMGDECa9asQUxMDBobGylVm4WFBaZMmYKRI0eyVJd9q7nbtyQnJwc2NjZ49eoVhISEsGXLFixfvrxdvNTaEh7GK2VlZbC2tkZKSgrLe2BJSQnu3buHiIgIaGtr4+TJk3zPLduD5ORkhISEYP78+QxK4EuXLlH2N4KCgjAxMcHu3bs7VWn3vdFVVPvFaWsBgm4QW1JSAiUlJRgYGLTqYREeHo59+/YxDN5VVFRgb2/PcuXP398f4uLiVKrO5MmTsXnzZnh5ebWaqqOqqgobGxuIiorCwMAAQkJCSEhIwKpVq6iB2eHDh6Grq4svX77g7Nmz8Pf3h6urKzZt2sTy/FmFG2RkZIAgCMo/gVfc3d1x6NAhXLlyBVZWVsjKyqJuyKyKPUeOHEFpaSlIksTQoUNx8eJFlith7Eylvxfy8/ORmpqKlJQUPHnyBLm5uVSS0sCBAymPBIIgqJXDdevWoaSkBLGxsYiNjUVCQgIuX76MK1euQEJCAmPHjsW4cePw4MEDvHjxgrpp/f333wxFhQULFiAtLQ2NjY0wMzPjmN7HD9xM7MvKymBvb4+wsDA4OjpSLa/FxcWwsrJCdnY2df4nT56Eo6MjzM3NERERgQ0bNmDJkiVIT0+nBlPz5s1jeP3S0lJs3rwZ9fX1mDZtGgiCwKZNmzBx4kTKRJvusUEQBJSUlDBhwgTMmjWLOufS0lKWCg/6Y6wm1BISElSb+M8Kv4UxukFsZ7F27VrExcVRvyk69vb2VGGeniR89+5dLF26FKGhoUwTxvakb9++2LZtGzZt2oTo6Gj4+PhQz9HbxL9+/YqAgAD4+voiMzMTR48epa6xkZGRGDduXKvH6devH8fnMzIysHHjRuTl5VFt8gDw4sULLF26FJWVlSBJEk+fPkVISAi2bt2KZcuWsXytYcOGYdiwYbC1tUVISAi8vb3x7Nkzyjbgzz//hJ+fH9d/o5aUl5eDRqPBwMCAKyUDP/Tp04cq1HHLkydPWjWebgsNDQ0oKirCw4cPcfr0aTQ2NlJtnFZWVhg3bhyuX7+OuLg4BgsGRUVFTJ06FfPnz/8uEolzcnKoIlpKSgrVFkj3ATM1NYWuri7Gjh0LeXl5ZGVlYeHChTh+/DhcXV2hp6eHP/74A926dUNNTQ1yc3MRFRWFT58+QUZGBo6OjjyFKbUndXV18PDwwJ07d/D69WuGiaKAgAAGDx6MWbNmYc6cORASEsKMGTMQGRmJiooKPH/+HEZGRjA1NYWamhrH4wQHB1OKdm6V621h6tSpPI/L6uvrMWjQIK4LSWpqahg9enSb0sc7m5iYGGzcuBHV1dUQFhbGsGHDoKioCGFhYVRUVOD9+/fIycmhWifPnj3L0U+zMxEREaFSYt+/fw8fHx/4+/sjMzMT9vb2cHBwYHsNFBERwbRp0zBt2jSUl5cjPDwcXl5elH+0lJQU/vrrL6ipqSEvLw+FhYWQl5fHokWLqNfIzMxEaGgorl69CqCpW4YkSfTp04dSZnPjRdvZi8ffGpIkYWFhgZqaGvz55584evRou7Z6twfcBEE1NDTAysqK6viysrKClpYWlJSUqN9PXl4eHj9+DE9PTyQkJGDFihXw8PDg+/oeFRXF1nKE3XMEQcDOzo7ywQOausHof3MXFxecPn0aAKhrXUBAAPLy8qhr5K9IV1HtF0dDQwPW1tY4f/48zwUIFxcXnDhxgiHaXkpKCgcOHGBppA40JXBt3rwZjY2N6NGjBxQVFZGXl4e8vDwsX74c/v7+TBOhp0+fYty4cXyl6ri5uaFbt27w9fWlfvjv3r2DmZkZCgoKYG9vT8nQFRUVYW9vj9TUVMTFxbEtqtFpHm7w6tUrhokhJ1i1BM2ZMwc+Pj44ceIENXGgqw8sLCyo7dzd3REUFIRnz55BVFQUBEHA2dm5VQ+clqbSQPsmtHLL69evkZqaSv2PLl8nSRIKCgowNjaGjo4OtLW1OXriycvLU6oXGo2GFy9eIDExESEhIdRq9u3bt3Hv3j3U19fDwMCAKljRKSoqgri4OKysrPiKGOcWdhP7xsZGLF26FK9evYKAgACD0nDfvn3IysqCjIwMZejs6uoKa2trhIaGwszMDP7+/tTqGkmSmDlzJnR1danX2LJlCx48eIDKykro6OgwmCXTC5RA04SotLQUcnJyLKXgDQ0NLNUA9DZidgrTtvgn/oz8999/IEkS169fx4YNGzrlmCEhIYiNjYW6ujp2796N+/fvIzw8HPn5+fD19QVBEDhy5AiMjIwAANOmTcPSpUtx48YNrF69usPOKy8vD3369IGQkBAmT57MUmEjKysLKysrWFlZIS0tDbdv30ZYWBiysrKwbt06yMrKgiRJplbo5nC6xpWWlmLp0qX4+vUrBg4cyFAc3r17NyoqKjBkyBDKF/DYsWM4efIkxo4dy7K1ho64uDgsLCxgYWGBnJwcSpHXmjkzOxISEuDu7k750X358oUrJQM/TJo0CUePHuV61T46OhqvX7/m22idRqPx7AO2ceNGBkVPv379sH//fgBNqrmysjLIysp2qIKJW65evYrU1FQ8efIEpaWlAJquixISEjAwMIC2tjZ0dHRYBscMHDgQvr6+2L59O1JTU+Hj48PwfaZfX3V0dGBnZ9ehhU1O5OfnY9myZXj37h2ltpaTk6MmiJWVlUhPT8fBgwdx69YtXLp0CUePHkVtbS2io6MRGBiI27dvw93dHSoqKjA1NYWpqSnL+ya9GMKLcr0t7df8jMsAMI01WmPw4MF48uQJ3+fZmbx//x4bN25EbW0tVq1aheXLl7NcgCkqKoKrqyuuXbuGTZs2ITg4mOfvaMuws6SkJJAkyTIEjQ4vfqUqKirYtGkTNmzYgAcPHsDLywtxcXGt7kfvqLh37x7S09NBkiRERETg4uKCrVu3Un6BioqKOH36NMO9ZdmyZSgpKaG+l+Li4rC2tsaSJUt4+q62Ze72I5Gfnw8/Pz+QJIn6+nqsXr0a1tbWHWqrwC/cBEHdvn0b6enpmDRpEo4fPw4xMTGG52VkZDB8+HAMHz4clpaWsLGxQXh4OO7cuQNzc3O+zisjI4Pt+IPdc/RFekdHR8jKymLlypWU/1p5eTnOnTtH+URv2bIFQFNo26lTp+Dv78/3uf7ofH/fyi46nXXr1kFfX5+nAkRsbCzlp6GmpgYlJSW8ffsWGRkZ2Lp1K1RUVDB06FCmY12/fh00Gg379+/HnDlzQBAEaDQanJyccObMGbi5uWHPnj0M+xQXF/OdqpOWloZx48YxrBr27dsXEydORFBQEMvBz7hx4+Dv70/9u7GxkeUKQVJSEtTV1SEiIoKhQ4dS7/f9+/cIDw/HlClTGFacAgMDce7cObx//x7du3fH3LlzsWbNGggKCkJYWBiurq5wcHBAZGQk6uvroa+vj507dzIc29nZGYWFhejbty/KysowZswYvk2l2SW0Amh3w+OVK1fi6dOnlG8AvS2HPqnQ1tbmuT2qoaEBL1++RGpqKtLS0vD8+XN8/PgRAKg2SU4r3zdv3kT37t1ZfrYVFRUoKytrF58FdhN7Pz8/vHr1CpMnT8a+ffuodtGcnBxERESAIAgGH7Zx48bBwsICrq6usLe3h66uLiIiIqjfbEsT7mfPnqGmpgZmZmbYu3cv2/MTERH5bryGfkZiY2Ph4eFBDdY7U8EXHBwMGRkZXLlyBd26daMKM1FRUaDRaOjbty9VUAOaVGIjR45EVFQU8vLyeD7ev//+C6Bp8eTs2bNYvnw5U0BAQ0MDZsyYgX79+mHnzp0sDZxbQv8t79q1C0FBQfD29qYSvzw9PbFv3z6W+3Eqqt24cQNfv37FP//8g7/++ot6PDk5GZmZmRASEsKZM2eoa/jFixdhaGgId3d3HD58uNVzBpquuba2tti6dSvVNsYLU6ZMQV5eHpWKWlpainv37nVYqu6MGTNw8eJFbNu2DRMnTuS4bUxMDP755x+IiIi0KQCAU/GdIAhISUlBTk4Oo0aNgoWFBcfvi5iYGNMk5Vty/PhxAE0LD8OHD6fudyNHjuRqUqiiogJ3d3c8f/4csbGxyM7ORnl5OWRkZNCvXz9MnTq1TWoNfsMi6FRVVcHS0hL5+fnQ1NTEihUroKmpyfAZlJSU4PHjx3Bzc8OzZ8+wbNkyBAQEQExMjFL9lJWVITQ0FEFBQZQtAT2t0cjIiGmMw6tyXV9fH3PmzOH578PPuOzr1688X+NpNNoP0zJ148YNVFdX48iRIxxbqhUUFLBlyxb8+eef2Lx5M9zd3bF582aejuXo6AiCIKhrBP3/2Y1b6dvyumAsICCACRMmYMKECWxTd2tqahAdHY3g4GDExcWhvr4eAgICGD16NIyNjTF58mRISUkhODgYubm5qK+vx8CBA5nGl7q6upCUlMTixYtx8OBBPHr0CA4ODrhw4QJ0dHQwY8YMqqumNfiZu/1IeHt748iRI6ioqAAA3Lp1i6sAo++ZsLAwKCgo4MiRI63eq0RFRXHkyBE8fvwYoaGhfBWq2qJgv3XrFoSEhODh4YH+/ftTj0dHR6OyshJycnJYt24d9fiKFSvg6+uL4ODgrqJaF782vBYgPDw8ICQkhEuXLkFdXZ0qQNy9exc2Nja4efMmyx9zWloaDAwMMHfuXOoxAQEBrF69Gg8ePGCZnteWVJ0vX76wbLeiF0tYmXZKS0ujqqoK4eHhOHPmDGxsbJjMImtra7Fs2TLIyMhg+/btlDG0r68v9u7di8bGRvz+++/U5If+d6GvUldWVuLChQvIysrC2bNnqeMeOHAABw4cYDonOitXroS8vDwMDQ2hrq7eJlNpfpRE/KrbYmJiqGQzHR0d6Ojo4H//+x9PaoLKyko8ffqUap958eIFZc4tLCyMkSNHYsGCBdDW1mZZ0G0JJyWci4sLV9HfdDj9Xdg9FxYWhp49e8LBwYHh7xAVFQUAGDRoEENBQlVVFaNHj0ZMTAy2bNlCrdqz48yZM+jVq1dXwewbUFZWRqUxvn//nvqt/fnnn5g1a1annceLFy9gYGDApCR4/PgxpfJtyYgRIxAQEICXL1/yfVy635WGhgZTUY2eTpmRkQErKyscO3aMobDHCQkJCcyZMwdz5szBmzdvqKRNVgUggiDg6urKsuWSrqZRVVVlKKgBTdcqoCmMoHnxSk5ODnp6egz3qNZSl/X19TFkyBAICwuz9NVpjXfv3kFCQgLW1taorKzExYsXO6ygBjS9Rzs7O6xbtw53794FSZJwdnZG//79ISkpyRBm8uzZM5AkiaNHj/J8H6IjICDAt4KPE1+/foWrqysyMjIgJSWFyZMnMyh1O4s5c+ZAR0cHY8eO5Svpubn3rI6ODtasWdOuQSL8hkXQuXnzJvLz8zF//nzs2bOH5b1OXl4eRkZGMDIygp2dHW7cuAEvLy9YWlpS20hLS2Pu3LmYO3cuCgsLERgYiODgYNjZ2eHo0aMYM2YMTE1NMWnSJJbFp9aU69HR0XwV1ejnxsu4bP78+TynLr98+fKbKQ155dGjRxgyZAjXHoVGRkZwcnJCQkICz0W1tWvXMvw7MTERycnJTI9zgiRJBAcHQ01NjeW1c9myZdDX18f8+fOZFhjr6uoQGxuL4OBgPHjwADU1NSBJEurq6jA2Nsa0adNYFnxZKU/pHD16lPrva9euIT8/H97e3vDz88O9e/dw7949hpCe1vheFo/bk6KiIuzatYu6F6urqyMtLe2HL6gBTYvmOjo6XBfRxcTEoKOj02qyPDuSkpJgaGjY6iIZKy5evEiFmTXn0aNHAJrC2FrO4caMGcPXAuLPQldRrQuuaFmAePXqFQwNDaGrq0v1XGdkZGD69Onw9fVlK2UvLi5m8PVpjrq6OpXk2Zy2pOoUFxezXA2gt66x61FvbGzExo0bQZIkMjMzmYpqnz59Qq9evfDff/9h27ZtKCoqgr6+Pvbs2QNBQUHMmzePWkGura2lbqQzZ87EgQMHqLYrDw8PREZGcp2E1DwyuqWptL+/P/z9/TkaqNNNpflJ3moLJ06coLxieCEsLIxqFX39+jVoNBpVoKB7runo6EBTU7PDEsC4gT55LysrA41Go25gnCb2BQUFmDlzJtNNKSkpiW3BY/DgwVx7Hg0bNozh381vqmVlZSgrK+NqMkySJEvfBfrEgVXiXEdMlH8E0tPTqTRG+gBcWloa5ubmmDt3Ln7//XeG7blRwbYFdosKdLUqK184MTExVFdX87XCWVxcjFOnTgFoaj+eP38+0zb9+vXDw4cPcfPmTZw+fRo7d+6Empoagx8Wt9An8exafujfc1b7lZeXs7QpSE5OBkEQTMVAoMlz7MGDByBJEocOHerw1GVtbW1KySAkJAQajYaoqCiulQz8MHHiRFy+fBnW1taorq6mPs/mkCQJJSUl7N69G+PHj++Q86BTWVmJt2/fQkpKisEb7fPnzzh79iwePnwIUVFRTJ8+HStWrMDnz58xf/58fPjwgbpXBAUFYcqUKZQHTGfBqRDTGrx6z/JLW9r0o6KioKSkhB07dnC14EZvZYqMjGQoqjWnV69eWL58OZYvX46cnBwEBQUhJCQEtra22L9/P3XtaklryvWOpPm4bPz48Th37hyePn2KkSNHtjoue/r0KZ49e8bgu/U9U1BQwLMv18iRI6kwJV5gVTxLSUnhuqj25csX/PXXX8jMzMTevXuZPGffv3+PhIQEPHz4EL6+vrh8+TIUFBQQHx+PkJAQREVFUb6aAwcOhImJCYyNjfleRGCFsrIyNm7ciPXr12Pbtm0ICgpCeXk55cVJEATev3+PkpISvpI723PxuLMwMTGh2uXpC+QkSbY6JiEIAtu3b+/w82sLFRUVHD8TVigqKvIcIETH398fysrKfBXVPn/+zHI/ehgLqy4xWVlZ6rP7FekqqnXBF1+/fmVrxDpkyBC2E//6+nq2/i9SUlIsZfP6+vq4fPkyz6k6xsbGTMlN3PD582eQJMnRH05FRQWRkZEICQnBzp07KXNTkiRx7do1BvXcgwcPUFxcDElJSezatYuaKNva2iIqKgp+fn58xYu3NJXOz8+nvBzY0dJUuvlKuJKSEkaMGNGuK+F0WCmq6uvr8fHjR3z58gVycnLo3bs308Rz48aNlKRfQUEBY8eO5cpzrbOhT97pE5Tmk3x2E3uSJJneA0mSlC8LKwWmkJAQ6uvr+TrH5ufE6jw5wcmToXmrdHN+FaNSekqrh4cH0tLSqPaT0aNH4/HjxzAyMmIa6PGjguUHCQkJqnWCDkmS+PLlCwQEBFh+xwoLCyEtLQ0zMzOej3fw4EHQaDScO3cOEyZMYLuduLg4/v77b8jLy2Pnzp1wc3ODq6srX98ZgiD4WhmdOnUqk8KstraWUu2w+tvQW35sbW0REBDQ4anLzZUMbm5uqKurw9q1ayErK8u1koEfdHR0YGVlhUuXLmHq1KkoKChAaWkp5OXlKfuE5qvUnDyO2NH82lNQUICkpCSIiIjAwMCAWsW/cuUKzp8/T40LBg0ahIMHD+K3337D7NmzqaIJ0KTMzcrKgoSEBAoKCjBx4kTo6Ojgy5cv8PT0pHxpvpVRd1lZGQIDAxEeHs4wSf7tt98wdepUmJiYUN9HfrxnvwVv377FxIkTuS4YCwoKYuzYsVz5VgFNraNfv35FRUUFSJJkGB+2t3K9vTA3N8fly5exfv16XL9+neO47PXr11i/fj0EBAQYuje+Z2pqanguUsrKyqKqqqqDzoiRmJgYeHh44Ny5c1i6dCkyMzMxaNAgpgUtoKngZG9vDzc3N2RkZGDFihUoLCykigLKyspYuHAhjI2NO9wUX0BAAL///jsEBATw4MED+Pn5wcfHB3l5eXj48CH09fVhYGAAc3NzjBs37puFknQGzQtIL1++pMarrq6uHPf7EYpqdXV1PAsAxMTEGLzLO5OWiy6fPn3C+/fv2XaJlZaWdmjI1fdOV1GtC76or69nO5CSlJRk6ynR2qooq+eXLFmCW7du8ZSqIyEhgYULF+LSpUs8vKsm6IW4K1eutJpGZWRkBAkJCaxatQpxcXEYO3Ys04WG3m6pp6fHcLEREhKCjo4O1wPMljQ3leY0gaXT0lS6s1bCW5Kamgo3Nzc8ePCAYUIqJCSE8ePHY9GiRZSCht4qyo/nWmdQXV2NwMBASEhIAGiSdmdnZ2PKlCmt7mtmZsa0+pSRkYGKigoICQmx/Azy8/P5jtamt5UCTYMTNzc3hsfYER4ezhAx30UTHz58gKenJ3x8fFBSUkKpd8zNzWFmZgZlZWWW39nbt29j3759PKlgrays+DrHfv36sS2GqqqqMn2uDQ0NSEhIYDkB4YaHDx9CXV2dq+sRAFhYWMDFxYVBOSspKQkNDQ2eJg28+EDRkZOTYwo5ePLkCerr6yEuLs6y1SQ7Oxvi4uIICAjAkCFDeE5dbvlZcwNdySAoKIjz589j/PjxiI2NbTclAzvof39WSrWWtPQ+ao3m3kd0D63GxkYATUbN586dw8ePH+Hg4ACgqb2vqqoKr1+/xtKlSzFlyhR8/PgR69evx4IFCyAkJAR3d3ecOnUKgoKCWLBgAYM366xZszB16lTcvXv3mxTVYmJisHPnThQXFzP8jT58+IAPHz4gJSUF586dg729PcaPH8+X9+y3oLKyEt27d+dpnx49enBUMqSlpSE4OBhhYWHUAmffvn0xd+5cyMjI4PDhw9+1cr13797YsWMHdu/ejZkzZ2LAgAHUtV5CQoKhhTosLAwNDQ3YvHkz2w6O743GxkaeVbJ0lW1HUVpaStkt0O0FvLy8kJmZCTMzMxw+fJjlYrGoqCjMzMxgbGyMLVu2IDIykrou9ezZE6qqqsjJyaEsWjhBEATOnTvXLu9HQUEBK1aswIoVK5CYmAgvLy8qWTI6Ohry8vLUvOJnpKUijZtUzS7aHyUlJSZvXfpYTUlJiWWqdmpqKhQVFTvl/L5HuopqXXz3yMvL85yqc/z4ccobISkpiWklPTExEQBw/vx5polAbm4uALRaUKMzbtw4qKmpIS0tjaWXAieprIKCAr58+cLVcVrS3FT6zJkzHLdtaSr9rVbCT5w4gatXr1IDLBEREcjKyqKyshKVlZW4d+8eIiIiYGVlhX/++YeKHf9eaelJo6yszLJtkxWDBw9mUlLSFTdqampMqz3V1dVISEhgauvkluaFB2lpaRAEwVUxoqXnVBdNTJw4kaG9c/r06Sx/483JzMzE/v370a1bN55UsDo6OnxNuiZOnIhTp04hJCSE8i0TEBAASZIsFXBOTk4oKirC4sWL4ebmBnV1dZ58TN6/f89zm8LIkSOpYkdkZCQqKirw/PlzGBkZwdTUlON1eOLEiViyZAlPx6OjpqaGuLg4hjRmeouSjo4O08Txw4cPSExMhIyMDMTExPhKXeanqEbnt99+g6amJi5cuICioiKOSgZFRUX06tWL54JHc6ysrLg2G27ejsWL7xF9wqqgoICpU6dCUFAQYWFhWL9+PWRlZaGsrAxHR0f8+eefqKurg7OzMxwdHREZGQktLS0G4+2VK1ciPj4eKSkpWLhwIcNxevXqhYkTJ+Lx48dcvvv2Iy4uDqtXrwaNRoOOjg6mTZtGpc1WVlYiKysLoaGhiImJwZo1a+Do6MiX9+y3oKGhgefkWVFRUaqASicjIwPBwcEIDQ1FQUEBpeJesmQJjI2NMXz4cABNCwE/gnKdHtxx+PBhyiahpfKXJElISkrC1taW6fvaBWtIkmQYr7969YqyW6CrFCUlJWFiYoKQkBD06NED+/fvb7X7QkREBIcOHUJiYiJV8C0sLKTS6bmho5T5o0ePxujRo1FWVoaAgAD4+PjgzZs3HXIsbmn5ObQ3LX8r+fn5iI6O5ks93wX/aGtr49atW8jNzUX//v3R2NiIW7dugSAIlontUVFRyMrK4nsR+Gegq6jWxQ/BkCFDeErVaa60SEpKYjsIbbmyRF8R5pWhQ4fi2bNnTOq9r1+/UkU6TU1Npv0qKysplROvNDeV/vvvv6GoqAiSJBEZGdmqqbSDg0Onr4RfvnwZly9fRrdu3bBixQpMmzaNwTj248ePCA0NhbOzM1xcXNCtWzdYW1tTz9NoNMTFxSEsLIxl+4y+vn6HtK62RmNjI9zc3Nh6xLBj6tSpOHToEK5cuQIrKytkZWVRbc2s1BRHjhxBWVlZh3sYdcEdNBoNEhISmDdvHiZMmMBV8YnevsCrCvbGjRvo378/z4MVLS0tCAgIYMuWLXBwcMCXL19Ao9Hw22+/MfgA3b9/n/IukpOTw4IFC6ChoYG1a9cyvS9OKkeCIFBSUsLTOXbr1g2CgoI4evQoamtrER0djcDAQNy+fRvu7u5QUVGBqakpTE1NmQr9+fn5LFurucHc3BwRERHYsGEDlixZgvT0dAQEBFBR8s0pLS3F5s2bUV9fj5qaGowfP57v1GU6mZmZKCwsRHl5OSQlJdGnTx+OBtdmZmbUpKI1JQONRsO6deuYClt1dXWoq6vjqj1DSkoKUlJSXL3Hlsfh1vdo+fLl6N69OwIDAynV5MqVK2FsbIy3b9/i3LlzVBK1iIgI1q5di4iICLx584ZlQvWwYcOQkpLC0vNIRUUF4eHhXL2f9qKsrAzbtm2DiIgITp48yVLBSU+5jI+Px/r167Fr1y5UVlby7D37o5GTk4Pg4GCEhITg3bt3IEkSMjIymD17NoyNjaGlpcVUqPjelevNmT17NnR1dWFjY4PExER0796doYV6woQJMDEx4fo68j3xrawdrKysMH36dNy5cwceHh6U/QlJkhgyZAjmzp0LU1NTSEhIQEtLC4aGhlz7ksrIyEBXVxcPHjzgq8Olo5GWloalpSUsLS2RkpLyTc+FlwWX5vCaLkyno4t4nQ0roQcn6CKQzjoe0PQbX7p0Kby8vDB//nxMmDABr1+/Rnp6OiQkJBgWM8vLyxEaGoqjR49CWFiYpZfur0JXUa2LToeV6TnA2ficIAjY2dlxnapDh5eUIDqXL1/m2T9BREQEAgICTO/r4cOH1KrqH3/8wbRfWlpam9IZ6abS27Zto47dPOIYYG0q3dkr4Xl5eTh9+jRUVFRw8+ZNli1TvXv3xtKlS2FkZARLS0tcvHgR06ZNQ//+/ZGZmYktW7YgNzeX4eaam5uLlJQU3LlzB3369MGpU6cwZMiQdj13buBnYj9nzhz4+PjgxIkTOH36NBobG6nBoYWFBbWdu7s7goKC8OzZMygrK/OdYtachoYGkCQJOzs7FBYWoqKiApKSklBRUYGGhgb09fV/as+O9mDjxo3w9fWFk5MTnJ2dGRK7WE34gSYT/DFjxvCsgn38+DG8vLzw5MkT2NnZcVUUcXd3x7Fjx9DY2AgdHR3qWqSuro5jx44xBLjs2bMHnz9/hoyMDC5dusTx9cvLy9mGCkhJSaG4uJir90YnNzeXuh6Iiopi2rRpmDZtGsrKyhAaGoqgoCCqRZBegDAyMmrzZHT8+PEwMzODv78/oqOjATRdK2fOnAldXV1quy1btuDBgweorKyEjo4OFYLDC/TU5ZKSEly8eBGBgYEsW+Dk5ORgZmaGFStWcN3mzUrJ8Pr1a5bbOjs7fzfG1DExMXj8+DFMTU0Z2pDl5eUxfvx4+Pv7Y9SoUUz76ejoIDMzk2V6Gn2BipV6SlBQsNN9afz9/fH161ecOHGi1ZZoXV1d2NnZYePGjQBYvweAvffsjwSNRqOUsmJiYjAyMoKJiQn09PQ4thZ+78r1ligqKkJLSwvJycmdHg7VkbALX2JHeXk5SJLkGKLFCUtLSxQUFFB2C1++fKFUaVVVVTAzM2NqDayurub5HqGsrIz6+nqWAT68kJmZiczMTJ48L+vq6rjynbtz5w7s7Oz4LrQ0/wzoHrDcfC6FhYVU2jYdblJuV6xYgeXLl8PS0pLrdOGW4wsLCwtYWFhwFWb0vSWZsoKT0KMlza0S+CU5OZmv41lbW+PcuXPYtm0b5Z0sLi6OY8eOMQRgTZo0iRrP7Nmzh63f+q9AV1GtC77x9/dHUlIS8vPzQaPRKKUOvbjDSrlDkiRH03P667aEXlTjFX6KalFRUQyTSm5ip1+/fg0pKSnExMSguLiYarvx8fEBQRAsE1SePHmCFy9eMKkieEVHRwfR0dHYunUrIiIiMGLECI6m0gB/KaxtwcvLCzQaDWfOnGk1Or5Xr144c+YMzM3N4efnhylTpmDRokWora1F//79MWXKFKb2mfDwcLx+/Rrz5s2Di4sLy8nY94awsDBcXV3h4OCAyMhI1NfXQ19fHzt37mQoaDk7O6OwsBB9+/aFs7NzmxMhfX194ebmxnIwRRAErl27hh49euCff/5pk0n+z86qVauwatUqPHr0CN7e3oiMjKRUXIMHD2Y5oC4sLMSkSZN4Os7QoUPh4+MDTU1N3Lt3D69fv8aZM2fYDk7LysqwY8cOREVFgSRJzJ49GwcPHkR1dTUaGhpYqo/Mzc0hLS2NWbNm8e3ZBzQNaIuLi1FYWNjq7xxo+nvEx8ez/JtIS0tj7ty5mDt3LgoLCxEYGIjg4GDY2dnh6NGjGDNmDADwHdwBNHm36OrqIiIiAvX19TAwMKBat+g8e/YMNTU1MDMzw969ezFp0iSeW1xramqotiT6hFBBQQGKiooQFhZGRUUF8vPzUVJSgmvXruHOnTtwcnLiyWC9uZLhe1XxtPQ+otFoLBeV6I81t3egQy+csVIlf2/hKGFhYejfvz/LoB5WTJ06FQMHDmy1vYuVaqOtYRH8wm6RlB3Nx349e/aEgYEBxMXF8fjx41bbc5sbkX+vynV+oJvrOzk5fetT4Qp24Uuc4Ca9kb5d898xSZJ4/PgxYmJi0NjYSBmkW1hYYMqUKRg5ciTLAnTPnj0Zgky4oaioqE2L3HQiIyNx/vx5ropq6enpsLe3R2pqKuUR6Ofnx6QEKy4uxp49e6gFIH6xs7Oj/r50BRirz6Xl50Cj0XDt2jWej0ej0Xj+rkyYMIHv0CK6Fcv3Cj9z0raiqanJd6FYX18f9+/fR3JyMurr66Gpqck0Rhw8eDDExMTw119/tWqB8rPTVVTrgm/y8/ORn59PDfBaVsJZVcYJguDZbDI1NRUAEBAQwPM58pOOpqOjg2vXriEnJwcDBgxoNXY6JycHiYmJmDBhAqKjozFv3jzMmDED6enpePjwIYSEhJgKjAkJCbCxsYGAgADTJI4fxMTEoKqqiqioKNy+fbvV7flJYW0LCQkJ0NDQ4FpFpqqqCk1NTcTGxiIkJAQNDQ3YuXMnFi1axHSznTRpEtasWQMvLy8cOHAAW7duRVBQEN9ttZ2JtLQ0Dhw4gAMHDrDdZuXKlZCXl4ehoSHPBsEtcXR0xPnz56kWZ3rbsLCwMCorK5GXl4fExETk5ORg27ZtyMvLY/At6oKZsWPHYuzYsfj69SsCAgLg6+uLzMxMHD16FARBIDU1FZGRkRg3bhwkJCR4LgKJiIhATEwMrq6uOHHiBK5du4a5c+di165dTNeOlJQUbNu2DR8+fKC+W3TfNk7G3Zs2beL9jbNgyJAhePHiBTZv3oyrV68yqOFaUltbi02bNqGxsRGzZs3i+Lq9evXC8uXLsXz5cuTk5FCtqiRJ4urVq206f2NjY45FD/pCAH2y1TJ1mRuSkpJQXl4OGo0GU1NTrF69Gv3792fYhiRJvHjxAi4uLggJCcHKlSsRHBxMDWDr6upQXl7eqk/anTt3vrtWGXbeRxUVFSwXCeg2Cj9KUYQd//33H8/t+lpaWnx5JrUlLKIttLZIyg6SJFFYWAgvLy+u96EX1b535To3sDLX/xHgJtyIFS4uLlwv2MTExODFixfUZxsdHY0+ffpQSvDmtiHsGDhwIB4/fswxVK05DQ0NiI+P71SVTXZ2NhYuXIjq6moA/1cs37lzJwQEBKj5y/3792Fra4vS0lIICAjg77//5vuY1tbW1G+e7n/Z3GalOfTPAQCVqMsNnz9/xtu3b/k+xx9BbcYv7VFU40bo0RwtLa02HVdCQoKjH2xrqay/El1FtS74ornCxd/fH/7+/lxLu3mtmNva2oIgCPj6+vK0H8BfUY2eSLd+/XrcuHGD47ZFRUVYt24dSJLE8uXLYWBggP3791MBCARBYNeuXQxtqxMnTqTMeFeuXNmpce90+ElhbQsFBQWYPn06T/sMGTIEt27dQk1NDWxtbbF48WKO28+ZMwf19fU4ePAggoOD26VY+T3Q3PuqLaSkpOD8+fOQl5eHrq4u7t69iy1btrDcNi4uDjt27MC5c+egq6vLk1n9r4qsrCysrKxgZWWFtLQ03L59G2FhYcjKysK6desgKysLAQEBngsyr1+/Rq9evSAgIIBt27Zh5MiR2L59O/bs2YOUlBTs378foqKiOH/+PC5duoSGhgb873//g4ODQ6enMNHNwlNTUzF9+nSsWrUK+vr6DG04hYWFiI2NxcWLF1FQUIDJkydDR0eH62OUlJTg69evqKioANC0QMCrnyFBEFwPBFsGg/CTupydnQ2gqXi5atUqtuc0YsQInDx5EsOHD8fRo0dx69Yt6OvrU0oGkiTRu3dvrFu3rsOUDO1FfX09QkJCOHoftUVRzKoNma6I+PDhA9M9jF/vvbZQWlrKc3IyN63drPgWCgh+0/hSU1P5Thh/8eLFD61c52Su/yPAjycW0FQoao2KigocPnwYL1++pB6TkJDA2rVrsWTJEp4WFU1MTPDgwQNcuXIFq1evbnX7y5cvo6ioCCtXruT6GG3F2dkZ1dXVMDY2xsaNG+Hj4wMnJydIS0vj1KlTmDFjBtzd3WFnZwcajYaBAwfC3t6e77AqgNkehpX/ZcvPYerUqdi3bx9L5XBLAgICcPjwYQBN4Tr8FIy/l3sYr3Tr1q1TxlytCT26+HZ0FdW64IvmhbGkpCQQBMFzsYxb34GZM2d2alvH77//Dmtra5w5cwZTpkyBqqoqSJJEeXk5UyS6u7s7KioqsGjRIqirq0NdXR26urq4f/8+6uvroaenx6RIEBMTw4ABA7Bs2bJfJs2mrq6O55QwQUFB1NXVQUlJqdWCGp0FCxbA1dW104tq9FZoXuBlYt8eeHp6QlBQEM7Ozrh//z7HbfX09HD58mWYm5vDy8urq6jGI2pqalBTU8OuXbsQFBQEb29vauW9pKSEUsG2Bl0F2/waaWhoCD8/P2zcuBF37tzBq1evICsri5SUFAgKCmLdunVYvXo1BAQE+GoJA9rWRkcQBExNTREYGEhNpLp16wYJCQmUlZVRKliSJDF58mQcO3as1ddMS0tDcHAwwsLC8PnzZ5Akib59+6KoqAgEQfD12+OXlqnLzf3XWkJPXSYIAioqKmwLai1ZunQpPD09ERoaCicnJ0rJADQVjFpTMvATttNe0BV5BgYGXHkf8YubmxvbhTxuip2dgaysLM8tlvRCYVu8ZzsLfscv/O5HL0D8aMp1kiS5Mtf/WeEmICUpKQm2trYoKCiAtLQ09uzZA19fXzx69AgODg64cOECdHR0MGPGDBgYGLRaYJsyZQouX76Ms2fPorq6GitWrGB5/MrKSly4cAHXr19Hz549+VqI55enT5+iX79+OHbsGAQFBSEsLAyCIPDPP/9g165duHr1Kk6cOAGCILB8+XJs2LCBK9VdW2D1OXDTvl5SUoK9e/ciMjISJElizpw52L59+3dV0ObEn3/+ibVr17JV7XEDfVH1ZyE5OZnvfVkF8/0KdBXVuvhmcOs7cOTIkc45oWasWrUKNTU1uHz5MpKTk0GSJFPRkCRJCAoKYunSpdi2bRv1uKKiIkd1UWBg4A/f1sIrPXr0QFZWFk/7ZGdngyAIjBkzhuu/F337mJgYns+xeQEiKSkJJEm2WpSgT3rordC8QJIky4lRa/A7cXr27BlGjx6NoUOHtlpUA5pacLW0tKj26y54R0JCAnPmzMGcOXPw5s0bXLlyBXfu3KFUsPLy8mz3ba6CbRlO0adPH3h5eWHDhg2IiooCQRDo3r07Ll68yFAA/RYtYQRB4Pjx45g4cSJu3bpFtT6Wl5cDaDJg19HRwdy5czm2FGRkZCA4OBihoaGUsrdnz55YsmQJjI2NMXz4cKiqqjKkYvICK59Lbt9f89RlDQ0NavGEXeqymJgYxo4dy9NxNDU1ERAQgIaGBkrJoKCggPv372P//v0clQyttdN2BAkJCXB3d6euLV++fOHK+4hV8YhT4Yi+Ot/ZKkx+GDZsGOLi4lBVVcVV0aSqqgqxsbEAWm+rbA/v2R/Nyys4OBgFBQU/jHK9oKCACojZvn17hxWYO5O1a9fCyMgIRkZGDI9zaknjFJBSV1cHBwcH3Lx5EzQaDfr6+jh06BB69uwJY2Nj5Ofnw9vbG35+frh37x4iIiIgKytLtYKyQ1hYGCdPnsTixYtx+fJl3Lx5E6NGjUL//v2pBZ63b98iNTUVdXV1kJSUbDWkp70pLi6GiYkJUyiUvr4+SJLE6dOnIS0tjdOnT/N8/+AVTp9Da0RGRmLv3r0oLi5Gz549cejQIejr61PP/wiLzj9bwmh7sHjx4p/W266j6CqqddEFCwiCwKZNmzBx4kTs3bsX6enp1AWXIAgoKSlhwoQJmDVrFgYPHszTa38vBbXOXAn/3//+h6CgIHz8+JEhNYYdBQUFSEhIYGtkzQk5OTmejcSrq6tZFiA4FdWab8vPxN7S0pLlxKg1+P0MiouLeS4kDBo0CGlpaTwfqwtmBg0ahGPHjqFPnz5wdHTElClTYGlpCX19fWqgz0kF25z6+nqcPHkSDx48AAAICQmhpKQE169fx/79+yEtLQ2gbS1h/Krc6EydOhVTp05FdXU1Pnz4gLKyMsjLy6N3795swzZycnIQHByMkJAQvHv3DiRJQkZGBrNnz4axsTG0tLSYBnnKysp8mfDya9JOD56hpy4nJycjJSWFabvmqcv09l9e6N69O+rr6/H7779TSgYAMDIyQnV1datKhs5Ud0+ZMgV5eXkgSRLS0tIoLS3FvXv3uPI+4lQ8Ynd9FBAQ+CFahIyNjREdHY1Tp05x1f529OhRlJWVYenSpTyPK7jlR/XyApoWJH8E5Tq9wBwTE0MlznJTYP4RiIyMZBmEwk9L2osXL2BjY4N///0X4uLi2L59O9MCkrKyMjZu3Ij169cjJiYG3t7eiI2NhZubG27cuAGCIBiCKpozYMAA3LlzB7t27UJsbCzi4+MRHx/PMHYTFBTElClTYGtry1W4TntSVVXFULSiF3bo70NAQIAKPupIuPkcWFFRUYFDhw5RPp7GxsbYu3cvNf6gw+uiM31hr7N8tH9m1q5dy3d4wK+qNmsLXUW1LtqMoaEh3z4L/FJWVoZPnz6hvLwckpKSUFZWhqSkZLsfZ8SIEZg4cSIyMzORlpZGeaS01TC+I1BWVoaGhgbX23dmCuucOXPg7+8PGxsbXL58mWOCZW1tLbZu3YqGhgbIyMjg8+fPPB2LnwSnFStWAABf0u9z587xNbHv7FVqegIhwL2vYbdu3do9tOJnISoqCv3798fvv//O9T4hISHIzMzEypUrcfnyZVy4cAEXLlxg2o6dChZoMuPevHkzXr9+DQkJCezduxcjRozA+vXrERoairS0NJw4cQIjR45sc1EtMzOTaWBLv2awGvCyup6Ii4sztcCzYvr06ZSaVUxMDEZGRjAxMYGenl6HXG/5NdymQ09dDgsLQ0xMDAoKCtimLjc0NPCc2ksvjmlpafGlZHB1dYWfnx/DY3S1ILviOkEQiIyM5Ok8AeDdu3eQkJCAtbU1KisrcfHiRa4Kap15DWxsbERERAQV3NEZGBkZwcXFhVJ/rF+/nqVZ+9evX3Hs2DH4+/tDS0uLSrhsT350Ly+g6Xumra3dKcp1fmleYO7Tpw/++OMPfP78uVV/3l+JxsZGnD9/Hs7OzmhoaICmpibs7e3x22+/sd1HQEAA48ePx/jx41FUVAQ/Pz/4+PggLy8PDx8+hL6+PgwMDGBubo5x48ZR18wePXrAyckJBQUFTNfpfv36wcDAgGffw45i3bp1DJ5nkyZN6tCC2vjx45Geno758+dz/TnQefToEXbs2IEPHz5ATk4Oe/fuZXtt5XXRme6Ryk8nR1dRjZG2jAG7rlm88/1VBrroVOiqJH6gr1apqqqyXLlqb+rq6uDh4YE7d+7g9evXDIoiAQEBqKqqwsLCAnPmzOmQSZiIiEi7xG1z4suXL3j48CEqKysxePBgqKmpsd02LS0NaWlp1A2IlxtXZxd0Ro4cCTMzM/j7+2P+/PnYuHEjtLW1GSaLJEkiNjYWDg4OyM7OxqJFi5Cbm4v4+Hg0NDRw9ZnW19cjLi4Of/75J8/nSBAEXzcgfhU9ne2nR5IkNRnR0tLiqrD2vagqv0fo/hstvzOcgltyc3MRFRUFR0dHTJw4EZ6enoiJiUFxcTGA1lWwt2/fxpEjR1BdXY3hw4fj5MmTVPHC29sb+/fvh5+fHxYvXoy1a9dy7eHFjqioKJbFJ3atyy1bR1v+u76+HqGhocjJyYGioiKMjIyoVe03b96AIAj07NkTBgYGEBcXx+PHj/H48WO250eSJOLj4/n63bbHQpCYmBhmzpzZoQN5Vu033CgZysrK2Jrzs1MN8Ktu09bWpryPhISEQKPREBUV1ar3UWdcAz9//ozbt2/Dy8sLRUVFnVpUIwgCjo6OWLBgATw8PODn5wd1dXUMGDCAStnOzc1FUlISqqur0bdvX5w6dardjs9NWMSP5OVVXFzcKcr1ttC8wMyruf6vQHZ2Nv755x9kZGRAREQEW7duxZIlS3i69igoKGDFihVYsWIFEhMT4eXlhcjISERFRSE6Ohry8vJISEhg2EdJSQnz589v77fTofTr16/VbWg0GvLz8yEnJ0e1rjY0NODmzZuIjIxESUkJfv/9d8ybNw96enrUftnZ2di9ezfPn0NtbS2OHz8ODw8P0Gg0jB8/HocOHeKYSs3rojM7H+2MjAy8fv26w+63nanu7uLnpOtq/4vDbwhAZ/dM5+fnY9myZVRLEEEQkJOTg7CwMCoqKlBZWYlXr14hPT0dt27dwqVLl9pl0lRSUkLJ9+l8+fIFnp6eyMnJgZKSEszNzbm6+bWGv78/Dhw4wKAMGj58OI4cOcJS6REXF4fz58/znHwHdH5BBwD279+PsrIyREVFYcWKFRAVFUWfPn0gJSWF6upq5OXlobKyEiRJwsjICNu3b4ePjw8SEhJw9epVrlKZnJyc8PnzZ75WuPghJiamy4fhF4Xd556fn8+VweuIESMo/7O6urpWVbDr1q2jVETLli3Dpk2bGLYVFRWFnZ0dtLS0sH//fpw5cwaJiYk4duwYw0Q0MzMThYWFlMq3T58+LEMT+L1G1NXVwdbWFmFhYXB0dKSSPYuLi2FlZYXs7Gzqb3fy5Ek4OjpSA26SJFFYWAgvLy+uj/fs2TO+zvNHhr4YwU7J0FYVHq9cu3aN8j5yc3NDXV0d1q5dy5X3UUeRkpICd3d3REREoLGxkWol7mx69eoFPz8/HD58GMHBwXj06BEePXrE0IImLCxMGXu3R5GroKAAnp6e8PHx6dCwiM6ms5TrbaF5gZlXc/2fnatXr+Ls2bOora3F0KFDcezYMa4CezgxevRojB49GmVlZQgICICPjw/evHnD1b51dXX48OEDevTo8V0Wl1ubm8XExGDv3r0oLCyEoKAg/vrrL2zevBmbN29GREQEdX3Jzc1FdHQ0Vq9ejfXr1/P9OaSlpcHGxgZv375Ft27dsGPHDqYU6vaAnY+2o6MjXr9+3WHXL1bq7tbgV939I1NdXU2NHzuiQ+xH5te+wnfBU8/058+f8fbtWwCdq2CpqqqCpaUl8vPzoampiRUrVkBTUxNiYmLUNiUlJXj8+DHc3Nzw7NkzLFu2DAEBAQzb8EJBQQG2b9+O5ORkhvaId+/eYfHixVQCHdA0oTh69ChXCTnsSE5Oxs6dOyEqKgozMzOIioriwYMHeP78OebMmYOLFy/+8P3tIiIiOH/+PHx8fHD16lX8+++/TIOfIUOG4K+//qL+5ubm5rh27RrOnj0LYWFhLFmyhKkVCmhamXNycsKFCxcwaNAgTJ48ucPeR0d50pSVlSEpKQmFhYWoqKiApKQkVFRUoK6u3m6TwfLycipdjttz6qLj4UYFGxERAQUFBRw7dgza2tpst5s5cyaGDh2KDRs24NGjR5g5cyYCAwNx8eJFBAYGorS0lGkfOTk5mJmZYcWKFdR3jZ+Ba2NjI+bMmYNXr15BQEAAdXV11HP79u1DVlYWZGRksHTpUgBNg1hra2uEhobydbzc3Nx2ad2pra1FfHw8EhMTmX5/Ghoa0NfXZ7ruVFRUIC4uDqNGjWLw4klOTsaFCxeoRRe6Nw2vZs3ceNCwW8zhd0HJ1tYWhoaGfIU40L2PLCwsEBISgrS0NK69j9qL6upqKmUxKyuLukerq6tj3rx5mDZtWocctzVkZGRw7NgxbNmyBQ8fPkR2djbKy8shIyODfv36YeLEiTx77rGiuZdXY2MjCIL4aby8AGDw4MGdplznl+YFZl7N9X9maDQaHBwcADQtKC1fvhxv376l5hSc4OZ6JC0tDUtLS1haWjJ4XCYnJyMkJATz58/HoEGDqMcvXboEZ2dnVFdXQ1BQECYmJti9ezffRYLmCxm5ubkgSZKrxY1///2X7XOcimqZmZmUXYmqqiry8/Nx+fJlVFRU4N69e1BXV8fWrVuhoqKCFy9e4OjRo7h48SLu3buHnJwcALx9Dnfu3EF0dDQaGxsxYsQIHDx4EMrKyqioqGj1Pf4ocFJ3s+NXUbdFRkYiICCACp6iIycnR91jOAVP/Sp0FdV+cbjtmQ4ICMDhw4cBAL/99lunJnLevHkT+fn5mD9/Pvbs2cPyIiYvL08lEtnZ2eHGjRvw8vLiS8VVU1ODxYsXIz8/HzIyMgyTqV27duHTp09QUlLCpk2bADStntja2mL48OHo06cPX+/xypUrEBISwq1bt6hW2u3bt+PkyZNwdXXFypUr4eLiwpDs96Mya9YszJo1C/n5+UyTi5Y+PMLCwnB0dMTChQtx/PhxuLq6Qk9PD3/88Qfl90Vvqfv06RNkZGTg6OjIsvDWVth50mhpabVJ+ZednY2TJ08iJiYGNBqN6XlBQUEYGhpi06ZN6Nu3L9/HIkkSbm5uLNsSu+gcioqKeFY2xsXFIT4+Hvr6+jhy5AhXBYmBAwfC19cXu3fvRlBQEExMTCjFioKCAhQVFSmVb35+PkpKSnDt2jXcuXMHTk5OGDp0KF/vz8/PD69evcLkyZOxb98+6lxzcnIQEREBgiAY/L/GjRsHCwsLuLq6YsuWLXwdszmNjY0sf/tJSUlQV1dn6Wvm6+uLU6dOobi4mOmzIQgC165dQ48ePfDPP/9Qxf74+Hhs3rwZ5eXluHDhAlVUi4+Px6pVqyhl1KdPn5CWlgaSJPlKCG4NXgf0jY2N+Pr1K2RlZVn+nfz9/aGsrMx3MioAqKioUKpiXryP2kJubi5lDVFRUcFQTNu/f3+HG31zS69evTpMJd7Sy4tewOHG2+5HYfLkyZ2qXOdXEUMQBLZv386Xuf7PDP13+eLFC2zYsIHr/TIyMlBXV4fy8nKObYZAU/HHzs4OiYmJcHR0xPnz5wE0KQjpRTUXFxecPn0aANC3b18ATXOcvLw8uLu781Uosba2pvajBw60ZktAkiSCgoJYqq3p6ZeslFMEQWD48OEQEhKCu7s7hg4diurqavz111+4desWevfujatXr1IFwl69emH48OEwMTFBVlYWdZ68fA40Go0SU7x48YLr4vCP1MmxZMkSvuaMPzNlZSmw6/0AAP7ASURBVGWwtrZGSkoKy8+ypKSEWjjQ1tbGyZMnv4ki/Huhq6jWBUdKSkqwd+9eREZGgiTJdm1R4JaoqCgoKSlhx44dXN3sbGxsEB4ejsjISL4ukLdu3UJ+fj4WL16M7du34+LFiwCabiTJyckgCALnz5+nVj9HjRqFadOmwc3NDbt27eL5eEBTC9OkSZMYvOlERUVha2sLGRkZnD17FqtXr4anp+dPM0hWVlbmSlFBLxBs374dqamp8PHxYfJtApqMw+3s7No1wYkfTxpeJvYxMTHYuHEjqqurISwsjGHDhjEUPN6/f4+cnBzKDP3s2bMQFxfHkydPmJJKuUFRUZGPv0IX7YWnpyfLx+l+MKwmcWlpaXBzc+MpVQ1oCgjYsWMHoqKiUFJSAlNTU6xevZqplZwkSbx48QIuLi4ICQnBypUrERwczNXA6NGjR8jMzES3bt1gYGCAsLAw9OzZEw4ODgzfc/qK/aBBgxgM9VVVVTF69GjExMS0qagWHh6OM2fOwMbGhmm1tLa2FsuWLYOMjAy2b9/OoDymT7pIkoSamhq0tLSo319lZSXy8vKQmJiInJwcbNu2DXl5ebCwsMDatWtRW1sLAwMD6hrW0NCAffv2obGxEdra2lSgy8GDBxEZGQlbW1ueFTOWlpYc73nc3A/z8vLg6emJ8PBwfPz4kZoc9erVC1OmTMGcOXN4CtvgBX68j7iFrgRxd3fH48ePKVsITU1NWFhYwMbGBqqqqt9FQe3Tp0+IjY1FSUkJlJSUYGBgACkpqXY9xq/g5dXZyvWAgABKucLLvZZeVOPXXP9nhZ+C8tevX7F48WKkpqaCJEn07t0b69atY2o7LC4uxp49e6hU4OfPn8PR0RGysrJYuXIlRo4cCaBJrX/u3DkqMZl+33F2dsapU6fg7+/PV0vjlStXsGXLFpSVlaFHjx749OlTq4UnesGM3WILO+UUQRCorq7GtGnTqAUwcXFxbNq0CZaWljAwMGBS3PXq1QuGhoYIDAyEqakpz+8vLCyML1V4bW3tN7Gb4QcpKalOD937nmloaICVlRXS09PRr18/WFlZQUtLC0pKStQcJS8vD48fP4anpycSEhKwYsUKeHh4/PTXMnb8fHfdLtqNyMhI7N27F8XFxejZsycOHToEfX39Tj+Pt2/fYuLEiVQaWmsICgpi7NixiIuL4+t4UVFR6Nu3L1MR78GDBwAANTU1hsmRsrIydHR0+J4cAEBlZSV69+7N8rk1a9agpKQEN2/exMqVK3H79u12H5B3FuHh4dQkX0lJCSYmJlwZmKqoqMDd3R3Pnz9HbGwsk8Jt6tSpDNL+tsKPJw2vE/v3799j48aNqK2txapVq7B8+XLKaLY5RUVFcHV1xbVr17Bp0yYEBQXh/Pnz8Pb2xrhx47B7926u31fXgOHb0tK4nw49ZbM9vUL+++8/bNmyBVVVVdi0aRPb0AKCIDBixAicPHkSw4cPx9GjR3Hr1i2sWrWKWkl/+PAhREVFYWpqiv/973+or6/H2rVrERsbS72OsLAwhIWFMXnyZCZFWFJSEgiCYHn/GDx4MFWw5ofbt29j3759IEkSmZmZTL+9T58+oVevXvjvv/+wbds2FBUVwcrKCikpKTh//jzk5eXh4ODAlJ7ZnLi4OOzYsQPnzp3Dv//+i5qaGpw6dYqhnTAhIQH//fcfREVFcfToUSgoKAAAHBwcMH78eDx//hxLlizh+f1xUitwei4yMhKenp44evQoampqGIoCjY2NKCgowPXr1+Hu7g4bGxssXLiQ53PjhbZ4H7Xk4sWL8PLywsePH0GSJJSUlDBz5kyYm5tTyXU2Njbtefp84+LighMnTjB4s0pJSeHAgQPtGprwK3h5dbZyPSgoCKtWrUJ6ejp0dHR4Skqtr69HSkoKvnz5AkVFRaipqXVYgflHQEBAgOf7W3Z2NmbPno3q6mrqsQ8fPmDnzp0QEBCgilb379+Hra0tSktLISAggL///hu3bt2CkJAQPDw8GBaSoqOjUVlZCTk5OYakzRUrVsDX1xfBwcF8FdV0dXXh6uqKRYsWgUajISAgoNVFlLYkM5qYmDAVuYYPHw4AbJOmZWVlQRAEX+OMH9WHsQv+uX37NtLT0zFp0iQcP36cyU5JRkYGw4cPx/Dhw2FpaUkJWu7cudMhXns/Aj/HnbaLdqWiogKHDh3CnTt3QJIkjI2NsXfvXiqlrbOprKxsVfLdkh49erD0DuKG7OxsjBs3jpr8GhoaQllZGX5+fiAIArq6ukz7DBgwgGNKXWsoKChwnGDs2LED//77LxISErB27VpcuXKF72N9CxobG7F+/XpER0czTO68vb2xaNEi7Ny5k+P+zQeoOjo6WLNmDVtfP37SOOkrhfx60vAzsS8oKEB1dTWOHDnCcUVTQUEBW7ZswZ9//onNmzfDw8MDBw8exKdPnxATE4PZs2e3qV2ri5+L2NhYeHh4IC4ujprIc5sCunTpUnh6euLBgwdYtmwZVq1ahYcPH1K/2du3b8PW1hbl5eWIiYmBgoICNDQ08OXLFyQnJ6O2tpYp2IUkSaq9RUNDg+mYQkJCqK+v5+u9ZmZmYv/+/ejWrRvbIoWKigoiIyMREhKCnTt3wsHBATo6OvD09ISgoCCcnZ1bbXfV09PD5cuXYW5ujpiYGEqd3Jz4+HgAwNixY6mCGtCUEKqrq8vg88MLnHxeOCkZAgICsG/fPggJCWH27NmYNm0aBg4cCFlZWVRWViIrKwthYWHw9vbGoUOHOq3wws77iBfOnDkDCQkJLFy4EFOnTmX5vfoeiI2Npawy1NTUoKSkhLdv3yIjI4PyO+K31bolv4qXV2cq13v06AEXFxfMnj0bSUlJsLGx4WrxLjw8HPr6+gypoyoqKrC3t8f//vc/lgXmrKwsvs+zs4mKimJSV2VmZgIAy1ZbXlXWdOieZ8bGxti4cSMUFBRw//597N+/H6dOncKMGTPg7u4OOzs70Gg0DBw4EPb29hg2bBgmT54MfX19JmX2o0ePAAD6+vpMxacxY8YgMDCQb4sMS0tLHDhwAJs3b4a7uzsOHTrEcfu2LHKqqKggMTGR4TFxcXG4u7uzLCKTJImHDx/ydcycnBy4u7tjz549PO3X2NiINWvW8BXkRld7dvHtCAsLg4KCAo4cOdKqP7moqCiOHDmCx48fIzQ0tKuo1kUXQNMNZ8eOHfjw4QPk5OSwd+/eTo2gZ0VDQwPP5rqioqJobGzk63hfv35FQEAANRhWVVXFH3/8gQMHDgAA/ve//zHt01bfgLFjxyIgIICtNFtAQABnzpzB3LlzkZSUhNWrV7dL4mhn4enpiaioKMjLy8PCwoKaXHh7e+PmzZvQ0tLCpEmTWO4bHh6Offv2sR2gtsTR0ZGn1kj6tiRJYvny5Tx70vA7sVdSUsKQIUO4nuwYGRnByckJCQkJ2Lx5M44ePYopU6bg6NGjfLWOkCSJlJQU5OXlUd4uv/32GzQ1NTs1iKSLtlNWVkaFZ7x//5767gsKCvIUZQ80hddERETg5s2bSEhIgI6ODubOnQtBQUF4enri2LFjkJSUxOjRo+Hs7Exdmx89egQrKyukpaUxvF5GRgYqKiogJCTE8vdK967kB1dXVwBNrTdqamoctzUyMoKEhARWrVqFGzdu4NmzZxg9ejTXRQ1VVVVoaWkhMTGRpQIhJSUFBEFgzJgxTM+9ffuW59RCZ2dnSElJISAggKf9gKZWqCVLlkBOTg5Xr17FkCFDGJ6XkZGBhoYGNDQ0MHv2bPz111/U/S4pKYnnhQmCICjTbH68j3iFIAhUVVUhOjoaDQ0NqKiogJ6e3nfXcuLh4QEhISFcunSJYTHu7t27sLGxwc2bN9tVAUIPi/jZvbw6U7kuJSWF06dPY9asWTh06FCrBZe0tDRs3rwZjY2N6NGjBxQVFZGXl4e8vDwsX74c/v7+1NiteYG5LWrdziYjI4Ntoczf35/l4/z4lD19+hT9+vXDsWPHqN+2kZERqqursWvXLly9ehUnTpygWjk3bNhAdbR8/vyZ5WJjamoqCILA6NGjmZ6jLzhw85tkpTq3tLSEkZERfH194e/vj7///pvybGPFxIkT+fbxMjExwcmTJ7F9+3ZYW1tT41RW99iioiLY29vjzZs3XC+w0Wg0REREwN3dnUoz57ao9vnzZ9y+fRteXl4oLCzk+rNvuV1XUY01dKFHR5OTkwMdHR2uwzvExMSgo6PDUyDTz0ZXUa0LAE3tacePH4eHhwdoNBrGjx+PQ4cO8awQ4wV6IeN7g5Vy4vnz56iuroaIiAhGjRrFtM+7d+/alEK3Zs0ahIeH459//sH169cZEjDpdOvWDZcvX8bixYuRkJDwQ7UKhISEQEZGBv7+/gwrx3PnzoW5uTl8fX1ZFtV4GaDS4VdSf+7cOYiLi/PsScPvxL6goIBKBuSWkSNHIjg4GEDTAHDdunW4du0aEhISuG7NJkkSTk5OuHnzJoqLi5me7969O+bNm4fVq1d/d5PU7438/HxqwNn8MQBsjV3bk/T0dCo8g97iJy0tDXNzc8rzj9dwk+7du6OyshKhoaH4448/cPXqVeq58ePHw9jYGG/fvsWGDRsYFjvGjh0LGRkZpnRZety8mpoaU3tzdXU1EhISMGzYMF7fOoCmZLcxY8a0+rujM27cOKipqeHx48coLi7mWeE5aNAgPHr0iMlHsbKyklIas0ppfv78OcuFIUdHR1y4cAHp6elMz9XV1aGiooKvwfOdO3dQU1ODCxcuMBXUWjJ48GCcPHkSS5YsAUmSSE5O5npQTF+QIAgC48ePh729Pc/eR/xw//59+Pj4wM/PD56enrh9+zbk5ORgYmICc3NzBm/Sb8mrV69gaGjIpG6fPn06fH198eTJkw457s/u5cWLcr09UFVVxaxZs+Dp6Ynk5GSOSezXr18HjUbD/v37MWfOHBAEARqNBicnJ5w5cwZubm4sixM/SggVv0XgY8eO8Xy9zc/Ph4SEBNP3U19fHyRJ4vTp05CWlmYIv2lOy/vvp0+f8P79e6oDoSWlpaUQExPD33//zdX5xcTE4MWLFwDAoE7ev38/kpOTmVTbrN4fvwnrS5YsQUxMDAICAhAbG4uHDx+y3C4iIgIbNmwAjUbD4MGDsWLFCo6vW1RURBXEPn36RF3fdXR0Wj2nlJQUuLu7IyIiggrrkZCQ4KoYR5Ikbt26hRcvXlD3j5YLSvRCLqeFJn4VuVFRUZ3SjRUVFYX+/fvz5GMaEhKCkJAQarFLVVW1U+5xFRUV6NmzJ0/7KCoqMgggfjW6impdIC0tDTY2Nnj79i26deuGHTt2tCrdnDVrFvT19aGrqwt1dXW+BjRWVlbfpURUQkKCqXX03r17AJomTC0nR1+/fsXDhw9ZKhS4RUVFBW5ubti9ezfS09PZqhqUlJTg4eGBTZs2UWb1PwLZ2dmYMmUKUytG//79oa+vz6RuocPPAJXfotqTJ0/48qThd2L/7Nkznm/isrKyqKqqov69aNEiLFq0iOv9q6qqsHz5cjx9+hQkSUJRUREDBgygVmizs7Px/v17nD9/HvHx8XB2dv5mbd8/AgEBASwHeCRJYvHixR1yzPr6eoSGhsLDw4NKlqSvvD9+/BhGRkbUCm9DQwNbfxV2CAsLo7GxEe/evYORkRHDcwICAtDR0cHbt29ZKkGGDx+O+Ph4XLlyBVZWVsjKyqLS1GbMmMG0/ZEjR1BWVobx48fzdI50CgsL2Spc2TF06FD4+Pigvr6e6xVYOvSiYGFhIcPjSUlJaGxshIyMDNsilri4OMvHORVeaTQa3NzceFYyREdHY8iQIRx94pozevRoqKur4+nTp9DU1ORZ3VhSUoKFCxfy5X3ED71798batWthbW2N+Ph4eHt7Izo6mlJkDRw48Lu4N379+pWtUmXIkCGdok7qyLCIbwGvyvX2wtbWFn///Xerqtq0tDQYGBhg7ty51GMCAgJYvXo1Hjx48MOrOPg1nbe1tcWXL1942ockSYbxDh26ylJAQABubm4sw0iUlJSQl5fH8Bi9RV9JSQl9+vRh2ic1NRX9+/dvdQxZUVGBw4cP4+XLlwCAqVOnYt++fdTzv/32G+Xt2FGIiorCxcUFbm5uePfuHdvtpKSkICkpienTp2Pjxo1s70MpKSnw8PBAREQEGhoaqKRwc3NzzJkzh+37qa6uxp07d+Dh4YGsrCyG5OV58+Zh2rRprXYaffz4Eba2tnj58iUEBATQ2NiIT58+sWwlJkmSY5ovv0U1bhavPnz4gH///Re9e/dmaivmFvp9q+V3zN/fH/7+/iyVsHSfyM6mrq6O7feFHWJiYq0Wk39muopqvzinTp3C1atX0djYiBEjRuDgwYNQVlZGRUUFx/1evnyJV69e4eLFi5CSksLYsWOhq6sLfX19rj0spKSkuDbcZ+XhwAl+PRyAJqVGaWkpDh48iL/++gsZGRnw9PQEQRCwsLBg2LahoQG7du1CdXU1z5O7lgwbNgz+/v74999/Oaar9urVCx4eHrh37x6DUfj3TGVlJdt2k99//50KgWhJZw1QY2JiICwsjMjISJ49afid2D979oxnHyMhISHQaDSe9mnOrl278OTJE4wYMQI7duyAuro60zavXr2Cvb09UlJSsHv3bpw5c4bv4/3McFIrsCMpKQlKSkp8He/Dhw9UeEZJSQll0G5ubg4zMzMoKyu36+plWVkZy4Iq/TFWoRojRoxAfHw8Tpw4gdOnT1Or1UOGDGG4drq7uyMoKAjPnj2DsrIyz4pNOhISEjz7sYmIiEBMTAx1dXU8LwbRt4+JiUF1dTU14AwICKCCGFoWc7KzswHwn7zLj5KhoKAAU6ZM4WmfESNG4OnTp9DS0uJ5YeKff/7h2/uoLRAEAT09Pejp6aGkpAT+/v4MAQg+Pj749OkTzM3NMX78+E5XZNXX17MNWJKUlERNTU2nnk97hkV8C/hRrrcXoqKiXE28i4uLMXDgQJbPqaurw9vbu71P7bunuroagYGBHMe1rKC3R7aE/jueNGkS23RfbW1t3Lp1C7m5uejfvz8aGxtx69YtEATBMg02KioKWVlZsLKy4nhOSUlJsLW1RUFBAaSlpbFnzx4YGxvz9L7aC2FhYSxbtozjNlpaWkxqejq5ubnw9vZGQkICVRCj3+MmT56MkydPsh2j5ubmwsPDA3fu3EFFRQVDMW3//v1cpy4HBATg8OHDKC8vR58+fWBvbw9vb+9vtigSEBCAkJAQrF27llKPNjY2Yt++ffD19aXep4aGBk6cOMGzkovdIhqrrocufjy6imq/OE5OTtTF68WLF1xX+QmCwKZNm5CUlIQnT54gPDycUnP98ccf0NXVhZ6eHjQ0NHhWSrCCk4cDp3Pkh+7duyM3Nxfu7u7w8PAA0HQh1NHRYVBuODg4ICQkBAUFBTx5Y7Hi8+fP6NGjBwBwLQuePHkyy1W875GGhga2N2dhYWG2E+OOHKCWlpZSPlT//fcfAP48afid2PMLvy2Fp0+fRkhICHR0dODs7Mx2gjl06FC4ublhzZo1uHfvHpKSknhWrvwK3Lhxg+d9VFVV+VbnTpw4kaG9c/r06Sx9YdoLkiRZFgQ4FaIEBQVBEARmz56NyMhI1NfXQ19fHzt37mT4vjk7O6OwsBB9+/aFs7Mz378HJSUl5OTk8LTP69ev0atXL77bbgiCQGlpKZYsWYL58+cjPT0d4eHhEBAQYFIn5ubmUglzndmSWFFRwXNCdGtGxJxoi/dReyEvL49ly5Zh2bJlSElJgbe3N+7du4fo6Gjcv38fcnJymD59epdPD9onLOJbwG9rZWdSX1/PVpkjJSXV6YXU74EVK1YgNTWVZZt7a3C6jnEqoC5duhReXl6YP38+JkyYgNevXyM9PR0SEhIMhbry8nKEhobi6NGjEBYWxvz581m+Xl1dHRwcHHDz5k3QaDTo6+vj0KFDPBdVOhtW9+t///0XHh4euH37Nmpra0EQBJSVlWFmZgYzMzNMnDgR8vLyTGN2kiQRFRUFd3d3PH78mFLJa2pqwsLCAjY2NlBVVeWqoFZSUoLdu3dT4WXz5s2DjY0NxMXFO1Rxyoldu3bB19cXAGBhYUEV1c6cOUPNNbS1tUEQBB4+fAgrKysEBAS0yxy3i5+DrqLaLw6/qgkAVEtBY2MjXr58icTERCQmJuLp06e4fv06XFxcICYmBi0tLarIxs8qYmdHORMEAYIgsHnzZkRERKC+vh4GBgZYs2YNw3b0gpqmpibOnDnTppWVpUuXwt3dnWvD7traWuzbtw8BAQE/TaoXKzpigPrq1SvKh6q2thYkSUJSUpLBw44XTxpFRUW+JvYAf4VfkiRRWlrK83fFz88PoqKiOH78eKuKDQEBARw+fBiGhobw9/fvKqq1I+Xl5Uy+Y/QCz4cPH5iKpvTnaDQaJCQkMG/ePEyYMIFrDx5/f3+eFJ28KIJZQRAEDhw4QAW7sGLlypWQl5eHoaFhm1IndXR0cO3aNeTk5GDAgAGtbp+Tk4PExETMnDkTWVlZLD8LTpSVlYEgCKxZswbnz5+n/F8A4O+//2b4TBYsWIC0tDQqMKej24Gao6CggLdv3/K0D6cWotYoLi6GiYkJ395H7Q09hGH37t24e/cuvL29kZGRAVdX11+iqNYZYRHfgm/dWsnNtYIkSdTW1nJ8/leERqPxpcTh9DfnNH5SUlLCuXPnsG3bNio8QVxcHMeOHUPv3r2p7SZNmkTZvezZs4dlu/aLFy9gY2ODf//9F+Li4ti+fTtbdXVDQwPCwsKgqKhIFYdcXV2Z2vro10pe789A0/ume/nyytKlS6mCGP3e6+Li0qp9zcWLF+Hl5YWPHz9SKvmZM2fC3NycurfZ2NhwdQ6RkZHYs2cPSkpK0KtXLxw+fJjJd7KziY+Ph4+PD/r06YOtW7dS51NcXIzr16+DIAjs2LGDWjgLDAzEtm3b4OnpyVfQxI8Cr8FFP8q9pKPoKqr94rTFLJiOoKAg1NTUoKamRhXZXrx4gaSkJKSkpCA1NZVqU/ztt98QERHB0+vz6+HQnIKCAuTn53PdskUQBFU0ZMfOnTvRs2dPDB8+vM3nl52djWXLlsHFxYVlW1VzcnNzsWHDBmRlZf30KY2tDUC5HaDW19cjJCQEHh4elI8NvS2NbujOrjWhNU8aUVFRvHnzhueJPdA02PLz8+PqPQBNBRkAfH1XgKZJLrepb927d4eenh6ePXvG9fl10Tpubm5sE+QmTJjAdr+NGzfC19cXTk5OcHZ2hpycHNWWzCqNkk5+fj7PhTL6ZKWjWjAWLFjA9jlO3iItsbCwgIuLC9avX48bN25w/G4XFRVh3bp1IEkSc+bMgZ+fH8fPghPr1q2Dvr4+7t27Ry26tDRyLioqgri4OKysrHhO02wr6urqePDgAb58+cJVgE5JSQni4uL4Pl5VVRVLxQY33kcdSbdu3bBgwQIsWLAAr169go+PD/VcUlISkpKS+Pbg/B5JT0/vtLCIb8G3bq2cMGFCq9dEkiRx+fJleHh4QEdHB9u3b2/T4vXPAkmSPBcfSJKEn58f7OzsWD7f2mehr6+P+/fvIzk5GfX19dDU1GRajBw8eDDExMTw119/MSm/Gxsbcf78eTg7O6OhoQGampqwt7dnu0CSl5cHa2trZGdnY+bMmVRRrby8nOU9mCTJNt2f+YEetGNtbY3y8nI4OTlx5Qd95swZSEhIYOHChZg6dSrLoIfWqKiowMGDB3H37l2QJInp06dj9+7dHNWIT58+hbKyMsv7y549e6Cvrw9DQ0Oez6Ul/v7+EBcXh4eHB0PoRFRUFOrr69G7d28sXLiQetzU1BTXrl1DeHj4T19U4ye46Felq6jWRbsjKCgIdXV1DBo0CEOGDMH//vc/BAUFISsri2qz6yzokxleBtDc3uA4JRnRV6a4NZfU1NREcnIyVq1ahatXr7JVZ929exf79u1DVVUVunfv3ukqvrbwLS60BQUFlA/Vly9fKFVaVVUVzMzMeP77sfKkef36NYSFhXme2BMEgbKyMr7a0F6+fMnzd6W8vJxjvDsr+vbt26bJ9s8Mr0lmwP+1YjRfKeeWVatWYdWqVXj06BG8vb0RGRlJXWcGDx7MUrHKT8GIjqWlJcuiL72wy+r9059rC7x4i/z++++wtrbGmTNnMGXKFFhaWkJfXx/9+/enAmfevn2LmJgYuLu7o6KiAosWLYK6unqbJ7r0hSR23Lx5E927d4egoCBTUa2iooLr3z0/SoavX7+iqqoKBw4cwKlTp1rdfs+ePaiuroaFhUW7thRz433UWQwdOhRDhw6l/p2UlITz5893eFGN3edHH2ewmpDxo0TJzs7u1LCIb8G3bq20sLBAYmIi/vvvP0hJSWHw4MHo1asXKisr8eLFCypNW0FBAQICArh37x6ePn2KO3fucL2Y9bMjKSkJDQ0NrvwNIyMjISgoyPJeQ/+NsFqUJAiCSp2WkJCAgYEB22Ow+51lZ2dj/fr1yM3NhaioKLZu3YolS5awHcfW1dVh+fLlyMvLw+TJk5naSAmCoK4DjY2N+Pvvv/H8+XNMmDChVR+39qRv37549+4dHBwcICMjAxqNhszMzFbtCQiCQFVVFaKjo9HQ0ICKigro6elx7VP58OFD7Ny5Ex8+fICcnBwOHDjA0Yu4trYWGzZsQExMDOzs7JjEFYWFhfDy8oK3tze0tbVx+vRpni0PmvP06VOMGzeOoaAGNBUh6X6pLUUM9Lntz8rPtODUWXQV1bpggCRJpKSkIC8vj/KN+u2336CpqdmqKookSTx79gyxsbF49OgRXr58SRlV0+Pu9fX1O+mdNOHo6Ej5btD/zYn2qrTz2lbk7OxMecFYW1vj4sWLDJ4zdXV1OHDgAGWUqa+vD3t7+1bbO74nHB0dOf79Walt+G2VSEhIgLu7O2JiYtDY2EhFqFtYWGDKlCkYOXJkq4lEnGjpSZOcnMzzxH7p0qV8Hbumpga7d+/m+bsydepUnlN5+En/+VXIz8+nrhe8QBBEmxQiY8eOxdixY/H161cEBATA19cXmZmZOHr0KAiCQGpqKiIjIzFu3Lg2t+1yKvqyW3zo7OL5qlWrUFNTg8uXL+PChQu4cOEC0zYkSUJQUBBLly7Ftm3bALSPSpsTnLx2XFxccP78eVhbW7f6OvwqGfT19REWFobq6mr8888/LNPKcnJycPjwYTx69AhTp05lqwhpKx1lHv8j0Nrnx6rgxs9vyNnZ+ZuERXQm7aVc5xcTExP4+/tjzpw52LZtG8MkvrGxEadPn4azszMkJSUxatQovH37Fk+ePIGlpSWGDx+OzMxMAGCZXkgQRIf9/r4XJCQkUFFRgefPn8PIyAimpqYcFyZUVVXR2NjI9vfD7v7U1nvQ1atXcfbsWapI6+fn12oXgq+vL/Ly8rB27Vq2xYjm3xcbGxssXLgQX7586VR7jfDwcCQnJ8PLywshISEAmrqBBg0aBHNzc5iamrLc7/79+/Dx8YGfnx88PT1x+/Ztal5nbm7OtihXU1ODY8eO4datWyBJEoaGhjhw4ADHIjNJklizZg0SEhIgLy/PslgmLi6OtWvXwsfHBwkJCVi7di3fLbFAkwqWVRBJamoqALBcbJKUlERlZSXfx/zeaY+iGn0B8VdR63YV1boA0HQRc3Jyws2bN6nVtuZ0794d8+bNw+rVqxlWJr58+YK4uDjExMQgISEBpaWl1ARmxIgRVDLXsGHDvolSiX5RSExMRHJy8ndbeRcXF8fly5exbNkyJCQkYMuWLTh9+jQEBATw77//Ui18wsLC2LJlC8tEpO8dfge87JJf2Q1Q7927h4qKChAEgT59+lAtcioqKlwfk1dPmsePH/M8sW9L6y79uxIfH48ZM2YgKCio1e+KsrIyz62caWlpXCWe/YqIiYmhpqYG3bp1w6RJkzB9+vROnaDKysrCysoKVlZWSEtLw+3btxEWFoasrCysW7cOsrKymD59OszMzPgyyaerbHlV3XY29NCciRMnwtPTEzExMdQ9jCAIKCkpYcKECZg1a1a7q6V4aVVt7k2SlJQEkiSpNvDz588zXR/pz9HNo3nlzz//xF9//YUHDx4gJiYGKioqGDBgAKXoyc3NRU5ODkiSxKhRoxgm9BUVFYiLi8OoUaMY0ryTk5Nx4cIF5OTkQElJCXPmzOEqeONXbQdpi1KUV76HsIifnXPnzmHo0KEs/SIFBQWxZcsWODs74+3btwwehVlZWVQKMADK46s5P3tRTUBAAA8fPkR0dDQCAwNx+/ZtuLu7Q0VFBaampjA1NWUqvn+Le87ChQvx5MkTAECvXr3w6dMnvH37tlWPytu3b6NXr15YvXo1V8eht0+21cOUHzQ1NaGpqYnevXvj8uXLGDx4MDIzM3HkyBEcP34cBEGgpKSEIWCsd+/eWLt2LaytrREfHw9vb29ER0dTIV4DBw5keZ2fMWMG8vLyADQlTGtpabWq7nrx4gXi4+Ohp6eHc+fOsVzYlZaWxtq1a7Fo0SKsWbMGSUlJCA4O5juJVVhYmMkL8f379/j06RO1KN+S4uJilgnpXfwf9AVEXoMGf1S6impdoKqqCsuXL8fTp09BkiQUFRUxYMAAyMrKorKyEtnZ2Xj//j3Onz+P+Ph4ODs7w83NDbGxsXj58iVIkgRJkujVqxfMzc2hp6cHHR2dNklx24vmRbSUlJTvtqgGNK16XLlyBVZWVoiIiICtrS20tbWxf/9+VFVVYcCAAThx4kSnpsi1F/QCGK+oqqq2mvzacoBKn5xu27YNS5Ys4ckInV9Pms6e2NO/K9OmTUNOTg5X35WxY8fCxcUFqampXKUrpaSk4NmzZ1i/fn2bz/dn5OHDh4iMjERQUBACAwMREBCAfv36Yfr06TAxMeFYxP306RNiY2NRUlICJSUlGBgYtOl6SW9F3LVrF4KCguDt7Y0XL17A1dUVN27c4Ct1jV5MlZaWptLBeKUzfatGjBhBBQXU1dWhtLQUcnJybQpCaA1eWlWbe5PQr1H0fc+dO8ewbXMFpLKyMt9KBnd3d1y8eBE3b95EXl4eNbmh0717d1haWuLvv/+mivzx8fHYvHkzysvLceHCBaqoFh8fj1WrVlHq80+fPiEtLY36bnEqnP2qRbX2UKBkZmYiMzOz1UCi7y0s4mckIyMDixYt4riNnp4eEhMTqcJbSEgIHj9+zDG45VdBVFQU06ZNw7Rp01BWVobQ0FAEBQVRC5FDhgzBjBkzYGRkBAUFhW+yoEdXJgFN92mSJLm6f9FoNMyaNYvrdkg69JCEb4GoqCgIgkBAQABevnxJqdcqKioQEREBPT09mJqaMijRCIKgxBIlJSXw9/eHj48P3rx5AwDw8fHBp0+fYG5ujvHjxzMUl58/f44XL160el70zqLTp0+32ikhKyuLkydPYurUqQgICOC7qKaiosIUOEb3Au/fvz/D4hKdxMREvgKIWI0b6MXVlJQUpgW2b1F47YI/uopqXWDXrl148uQJRowYgR07dkBdXZ1pm1evXsHe3h4pKSnYvXs3wsPDISgoiNGjR1PJnoMGDer8k+8AYmJi4OHhAScnp04/drdu3XD9+nUsWbIEd+/epQw9582bB1tb2za1LP6INPc8e/XqFdLT0zF79myO+1y9ehXZ2dlwcHDAhQsXoKOjgxkzZsDAwIDjBLs9PGk6c2LfrVs3zJw5E87Ozlx9VywtLXHz5k1s27YNrq6uHIs+OTk52LRpE8TFxVv9e/+qSEhIYPr06Zg+fTpKSkoQGhqKwMBAnDlzBmfPnoWamhpMTU1hZGTEYBbv4uKCEydOMLTiSklJ4cCBA5g6dWqbz2nOnDmYM2cO3rx5Ay8vLwQGBrbpNdtCZ/lWtURERAQ9evTo1GNyouX751Y53bLYxisiIiLYsGEDVq9ejWfPniE7Oxvl5eWQkZFBv379oKmpyTAJLCwsxNq1a1FbWwsDAwNqUtvQ0IB9+/ahsbER2tralKLm4MGDcHd3B8A+dIVb76MuWBMZGYnz58+3WlT7XsMi2hteletA+6nAJCQkWk3JlZCQgLi4OKUuzczMxPPnz9slcOt7gJ/QFVafl7S0NObOnYu5c+eisLAQgYGBCA4Ohp2dHY4ePYoxY8YgPT0dy5Ytw/Lly9vj1Lli5syZ1CIAfUG3td8eAAQEBLC954wcOZKtb1p9fT2/p9quDBs2DMOGDYOtrS1CQkLg7e2NZ8+eUUq0P//8k+kaLi8vj2XLllHWNd7e3rh37x6io6Nx//59yMnJQU1NDXp6ejydy+XLl9G3b99Wg7jo9O7dG3p6ekhJSeHpOM3R19fH5cuXkZKSAg0NDVRVVcHd3R0EQWDatGlM23t7e+P9+/d8FfECAgIQEBDA9DhJklS6aMvHf9WFqR+NrqLaL05qaipCQkKgo6MDZ2dntqssQ4cOhZubG9asWYN79+4BaPKQyMrKgry8PBQUFKCgoPDDmrGWlpbC19cXt27d6vQwhZZISUnh+vXrsLS0xOvXr2FoaIh9+/Z903P6VjQfiObn5+Pp06fw8PBodZ/8/Hx4e3vDz88P9+7dQ0REBGRlZalWUFa0tydNZ0zs6SuNgwYNavW7oqioCBsbGxw6dAjm5uaYN28exo0bhz/++ANSUlKorq5Gbm4uwsLC4OHhgZqaGtjb239XxYnvFXl5eSxcuBALFy7Ef//9h8DAQISEhODgwYOwt7eHjo4Opk+fDjExMRw5cgRAk7pMSUkJb9++RUZGBrZu3QoVFRUGM/XmcKuIas6UKVMwfvz4Nr2375nMzEz06NGDJ2/JuLg4xMfHs5x8dxSsimfcKKfbKzVUREQEWlparSqnXF1dUVNTg1OnTqGmpgaHDh2Cm5sbEhIS8N9//0FUVBRHjx6lzJwdHBwwfvx4lJSUcPTf6yjvoy5a53sKi2gPeFWuA+1XVBs1ahSio6Px6NEjlmq/5ORkREVFUUnAJEni4cOHPFlPfO/QfYq5tfPgxqe4V69eWL58OZYvX46cnBwEBQUhJCQEJSUlOH36dKcW1ej3Z6Dpvb5+/ZqrQKu4uDhUVFSwfE5HR4cpHRoAxo0bh1evXvF/sh2AuLg4LCwsYGFhgZycHHh5eeHOnTuttu9paGhAQ0MDu3fvxt27d+Ht7Y2MjAx8+fIFt2/f5ukcLl26hHHjxvG0T58+fXD//n2e9mnOkiVLcOvWLfz1119QU1NDXl4eCgsLIS8vz6BOzczMRGhoKK5evUqlofKCpqYm3+fYxfdPV1HtF8fLywsiIiI4fvx4q7JlAQEBHD58GIaGhtDS0sKQIUMQExODkJAQhISEgCAI/Pnnn9DT04O+vj5Gjhz53Q+aX716BXd3d4SEhKC2tpZKhzQxMfmm5yUjIwMXFxcsXrwYUVFRcHZ2xooVK77pOf1IKCsrY+PGjVi/fj1iYmLg7e2N2NhYatWNIAi8f/+eCuMA+Pek+dYTe4IguP6uLFq0CA0NDXBwcMCVK1dw5coVpm1IkoSYmBj27t3L1QptF4z89ttvWL16NVavXo3MzEyqwLZ161YQBAGCIHD58mXo6upS+9y9exc2Nja4efMm2wH84sWL+bqeEgTBV/vnj4CZmRmsra2ZilORkZGIiopi+bdMS0uDm5tbpxbVfhQSEhIwatQoTJs2DY6OjlQhNz4+HkBTC3nzdDQxMTHo6uri8ePH8PT0/Cbn3AV3/AxhEd867dza2hpxcXFYtmwZpk2bhpEjR6JHjx6oqKjAkydPEBgYCAEBAaxduxY0Gg2WlpbIzs7G9u3bv+l5tycdrTouKSnB169fqQIVr+FK34revXsjLS2Np33y8vIYEtkbGxtZzsOSkpKgrq4OERGRNp8nLwwYMAC2trbYunUr14ribt26YcGCBViwYAFevXoFHx8fno/bo0cPfP78mad9ysvL2yTqkJeXh4uLC7Zu3Urd9xQVFXH69GnIyspS2y1btgwlJSUQEhLC8ePHeV50vnHjBt/n2MX3T1dR7RfnxYsX0NfX5/pi1L17d+jp6SErKwtOTk7YsGEDioqKEBsbi5iYGDx69AiXLl2Ck5MTpKWloaOjQ7WHfi+Kl/r6eoSEhMDDwwPPnz8H0FRIGDJkCObOnQtTU1NISEh06jmxSwq1s7PDmjVrcOrUKYiIiGDy5MlM2/wqqSr8ICAggPHjx2P8+PEoKiqCn58ffHx8kJeXh4cPH0JfXx8GBgYwNzfn25Omsyf2zb8r5eXlIEkS1dXVXH9XrKysMG7cOFy/fh1xcXEMr6eoqIipU6di/vz56NOnD8/n1gUjqqqqUFVVxaRJk3Ds2DHKr6V5QQ0Apk+fDl9fX8ogmROSkpLQ0NDg2bvlZ4SdWiIzMxMBAQHffBLeFlatWgVJSclObRsrKCjA9OnTmR5PSUkBQRAYM2YM03NKSkooLS3tCjT5zvneFzi54Vu3UKqqquLixYvYuXMngoODqfREoOla1LNnT9jZ2WHEiBH48OEDUlJSoK2tjfnz53/Ds25fOqKolpaWhuDgYISFheHz588gSRJ9+/ZFUVERz0qgb4Wenh4uXbqEly9fchVY9OjRI/z777+YNWsWwsPDcebMGdjY2MDAwIBhu9raWixbtgwyMjLYvn37N1nwFxYWZtkC2RoDBw6EhoYG/v33X/z+++9c79etWzeEhYXxdP9+/PgxFBUVeT7H5gwZMgTBwcHIzc1FfX09Bg4cyDTO0tXVhaSkJBYvXszTe+ri16CrqPaL8+HDB57bg/r27Yu4uDjq3woKCjA3N4e5uTkaGxvx9OlTxMTEMKnYVFVVoa+vj02bNrX32+AKkiRx8uRJ+Pj44MuXL5QqraqqCmZmZt90AjZhwgSOg16SJHH06FEcPXqU4fGfWYXS3igoKGDFihVYsWIFEhMT4eXlRRW+oqOjQaPR+PKk6eyJffPvCj0kZOLEiQzn09p3pV+/fti/fz+ApsjzsrIyyMrKdvpK6M9MZmYmgoODERoaivz8fOp7wsqzEmga0NGL/KyYMWMGIiMjUVFRgefPn8PIyAimpqZQU1PriNPv4htTWVmJqqoqhmJVRysZamtrmRaUKisrKRNqVq0rNTU1qKurg5ubGywtLdt0/C7aRldYRMczduxYhIWFISEhAc+ePcOXL18gKSkJNTU1TJgwgfoNSktLIygoCH/88cc3PuNvDyuf4oyMDOr+WFBQQBUllyxZAmNjYwwfPhyqqqoMfqSdQfOxVFlZGWg0GsNj7GhoaKASYN3c3Fga29Ohe/WKiYmBRqNh48aNIEkSmZmZTEW1T58+oVevXvjvv/+wbds2FBUVsfVn44bmC7kZGRmg0WhcLe4+fvwY27Ztg5GREcPjFRUVKCsrY7m47+zsjHPnzmHdunVMxVhOydnKysp48+YNvL29ufL0pfubWVhYtLptaxAEgQEDBrB9vuW4uj2h/y2bIyAggN69e3fYMbtoX7qKar84QkJCPMur6+rq2CayCAoKUr31W7ZswefPnxEXFwc3NzdkZGQgMzOz04tqCQkJCAoKAkmScHZ2puKRLSwsMGXKFIwcOfKbBwB0qc06l9GjR2P06NEoKytDQEAAfHx88Pr1a5bbfm+eNM2/K+Xl5SgrK2vTCp2YmBjExMTa49R+eXJzcykFw9u3b0GSJOTl5TF//nyYmJhgwYIFbE17JSUlUVNTw/a1jx49itraWkRHRyMwMBC3b9+Gu7s7VFRUYGpqClNT05+ixasLZjpLyaCgoIDCwkKGx5KSktDY2AgZGRkMGTKEaZ/09HSQJMnWT62LzqMrLKJzEBERoRTw7JCUlMQff/wBV1dXuLm5ISoqqhPP8NvDyqc4JyeHuj++e/cOJElCRkYGs2fPhrGxMbS0tJiKv/7+/lRqMrfQv+/80DxUgb4Qxk36IkEQWL9+Pc6cOYMZM2Zg2bJlmDx5MkN754cPHxASEgJnZ2eUlZVh2bJlOHXqFLp168Y2qEhFRQWRkZEICQnBzp074eDgAB0dHQwcOJCv99fSc5AgCJY+hC0hSRK5ublMj7u4uOD8+fOteq61hFNy9uDBgyk/YxqNhjlz5rBcFCBJErdu3YK9vT26deuGWbNm8XQObeXLly84cOAATp06xfO+79+/x+XLl/G///0PM2bMAPB/f8uWODk5QV9fv83n20XH01VU+8VRVlbGs2fPeNonLS2NY6tHYWEhUlJSkJqaimfPnuHNmzdoaGig1GqdyZQpU5CXl0dFNFtbW2PmzJnfnXFsdHT0tz6FXxJpaWlYWlrC0tKy1e/m91KwaP5dcXR0xPnz57u+P9+Q9+/fIzQ0FMHBwXjz5g2lgJ0xYwZMTEwwduzYdmvVFBUVxbRp0zBt2jSUlZUhNDQUQUFBuHDhAi5cuIAhQ4ZgxowZMDIyYvC+6uLH5fbt29i3b1+nKBlGjRqFmJgYhgTkgIAAEAQBfX19polNdnY2Vy3LXXQOXWER3x/l5eVs7T1+Rtj5FAsJCVEFfzExMRgZGcHExAR6enoc09Hz8/O5Kmo1py3f6ebFT14LokpKSqiqqsLVq1dx8uRJnDx5EmJiYpCWlkZpaSlqa2upbVetWoWPHz8CAK5cudKq4tzIyAgSEhJYtWoVbty4gVWrVvHx7oB//vmHL/VfZ3qQCgsLgyAICAoKYt++fXB2doauri769+8PCQkJlJWV4e3bt4iPj8fHjx8hKCiIM2fO8ORrzIqGhgYEBgYiPj4eZWVlGDZsGBYtWsTydSMjI7Fv3z4UFxfzXFR78OABNm3aRHWJ0ItqQFOhkD4XodFoePPmDezt7buKaj8IXUW1X5yxY8fCxcUFqamp+N///tfq9ikpKXj27BnWr19PPZaTk0MV0VJSUvDhwwcATReHXr16wdTUFLq6uhg7dmynp4O+e/cOEhISUFNTw8OHD7Fu3bpOPX4X35a6ujqUl5e3erO9c+dOq2lWP/rk488//+Rrv64WY9a4uLggODgYL1++BEmSEBUVxaRJk2BiYoJx48Z1eCuttLQ05s6di7lz56KwsBCBgYEIDg6GnZ0djh49ijFjxsDU1BSTJk2CpKRkh55Le6KsrAwNDY1vfRrfBSRJYv/+/Z2mZJg3bx6CgoKwZMkS9OjRAzQaDeHh4RAQEMDixYsZts3NzcW6detAo9F+yGsjvXX+Z+FXU0J18f3AjU/xqFGjQBAEevbsCQMDA4iLi+Px48d4/Pgx29clSRIDBw7Enj17OuutMAgGpKWlQRAET36RW7ZswcSJE3Hjxg1ER0ejurqaWqQQEhLC+PHjYWlpCU1NTRgaGmLMmDFcWziMGzcOampqePz4Mby8vDo1uKizg30IgoCXlxe2bt2KrKws3L59m+H90q/dw4cPx4EDB/ge39Kprq7G33//jdTUVOq14+Pj4e3tjZs3b1KL6hUVFdi3bx+Cg4Mp7z9eKC4uprq1du3axdSyShAEAgICqH9v27YNQUFBiI2N7Sqs/QB0FdV+cSwtLXHz5k1s27YNrq6uHBVcOTk52LRpE8TFxVFfX481a9bgyZMnKC0tBdB0kZOQkICBgQG0tbWho6PDsTe9M9DW1sajR4/w+PFjiIuLY/369ZgxYwYMDAw4ro61FVVV1XZPTkxNTUVubi569+4NHR0dCAgItOvr/0ykp6fD3t6eukH27t0b69atg7m5OcN2xcXF2LNnD6X0+lk8aVh9V/idQP5ME8/25MiRI9QkYdq0aTA0NES3bt0AgGWbBJ2ioqJ2P5devXph+fLlWL58OXJychAUFISQkBDY2tpi//79ePr0absfkx8+ffqEuLg4lJSU4Pfff4e+vj5T8dHMzOybG5J/L9B/e/woGQ4cOMDz8TQ0NGBtbc3UgvL3339jxIgR1L8XLFiAtLQ0NDY2wszMjKv2ofZk4sSJWLJkSZs83NatW/dTLbJ1BUV00dkUFBTA09OTa59ikiRRWFgILy8vro+RnZ0NLS2t9j51tjRvSfzvv/9Ao9HYtim2hO45qa6uDnV1dTQ0NODjx48oLi6GrKwsFBUVGe53hYWFmDRpEk/nN3ToUPj4+PwSljGDBw9GYGAgUlJS8ODBAxQUFKC0tBTy8vLo27cvJk6c2OZiGp1r164hJSUFcnJymD9/Prp3744HDx4gLi4O+/btg4uLC96+fYuVK1ciLy8PQNP8efPmzTwdx8PDA9XV1XB0dIShoWGr269fvx7BwcG4d+9eV1HtB6CrqPaLo6ioCBsbGxw6dAjm5uaYN28exo0bhz/++ANSUlKorq5Gbm4uwsLC4OHhgZqaGtjb21MR4QICAhg+fDhVRBs5cmSHFasqKioQFxeHUaNGMZiAJicn48KFC8jJyYGSkhLmzJlDFU+uXbuG/Px8eHt7w8/PD/fu3UNERARkZWVhamra7oUvOoaGhlxdMJtTV1cHZ2dnhIaGYt++fdQNuqqqCuvWrcPDhw+pbVVUVHDhwoUuE1wWZGdnY+HChQwtTHRjWAEBAeozv3//PmxtbVFaWgoBAQHQaDS+PGm+RdGJ/l1xd3dnOD6n70pmZmann+evwKdPn+Dq6sqVhwvdA4RV0Y3e4sKqUMCLR0xJSQm+fv2KiooKkCTJ0aeNG3hV9JSXl8PZ2ZlacKHvGxwcjJ07dzK0wCgpKeHs2bMYOnRom87xR8PQ0JDrIgi/SgZ+WbduHfT19XHkyBE8efIEV69ehY6ODsM2RUVFEBcXh5WVFdasWUOZTneW91F+fn6Xh1sL2qPQ2EUX3JCQkAB3d3fExMSgsbGRK59ifgKbbG1tuQoJaE8WL17MsIAqICDA9W+qpa+YkJAQfvvtN/z2228st5eQkEB9fT31b27aTUVERCAmJtZulh9lZWVISkpCYWEhKioqICkpCRUVFairq0NGRqZdjtFW6D7dHcn9+/chLi4OHx8f6t68cOFC7NmzB97e3njx4gWsra3x6dMn9OnTB3Z2dnydU2xsLAYPHsz1/FBFRQVqamrfzcJoF5zpKqp1gUWLFqGhoQEODg64cuUKrly5wrQNSZIQExPD3r17MXPmTDx58gQ6OjoYO3YspKWluTpOTk4O3N3d+ZJyx8fHY/PmzSgvL8eFCxeoolp8fDxWrVqFxsZGkCSJT58+IS0tDenp6di1axeAphXcjRs3Yv369YiJiYG3tzdiY2Ph5uaGGzdugCAIvH//HiUlJZ3entqctWvXIi4uDiRJori4mHrc3t4eCQkJEBERwYwZM0AQBO7evYulS5ciNDSUUsd00YSzszOqq6thbGyMjRs3QkFBgTI9PXXqFGbMmAF3d3fY2dmBRqNh4MCBsLe3x6xZs/jypPkW0L8rdK9AOl3flc6DVRJia9CLDpyKD6yea00lmZaWhuDgYISFheHz589UW8LcuXNhbGzM83k2x8rKiknhyY6qqirMnz8fOTk5DMW4xMREaiHGzMwMKioqePnyJaKiorBs2TIEBATwnXDFyrOI/jv98OEDU0Gwrb/h5sVpAQEBDBkypNWCNX0C1Fxd0LLNoyV0RSOv4Sh0JUNbUFNTg46ODp49e8ZUUAOAmzdvonv37gxegZ3tfdQFI12Fxi46A7pPMUmS6NOnD7U43ZpPMT/qY1tbW76VSKxSJbmBJEkQBAFJSUloaGi0mx8qK5SUlJCTk0P9mxv/vdevX3NMFuWW7OxsnDx5EjExMUzjSKApoMvQ0BCbNm3iucWxrfj7+3f6YvX79+8xYcIEpsUuS0tLeHl5YdOmTfj06RNmzJiBvXv3MqVkc0teXh5MTU152mfo0KEcxwodhaOjI0aPHs3XWJcOLwuIPwNdRbUuADRNnMaNG4fr168jLi6O4cKuqKiIqVOnYv78+ejTpw8AcN1aQqPREBERAXd3d0pCzWtRrbCwEGvXrkVtbS0MDAyoH2hDQwP27duHxsZGaGtrw87ODgBw8OBBuLu7Y9KkSRg9ejT1OgICAlRiU1FREfz8/ODj44O8vDw8fPgQ+vr6MDAwgLm5OcaNG9ehN9OWhISEIDY2Furq6ti9ezel3MjPz4evry8IgsCRI0eoOOtp06Zh6dKluHHjBlavXt1p5/kj8PTpU/Tr1w/Hjh2jPkMjIyNUV1dj165duHr1Kk6cOAGCILB8+XJs2LABwsLCfHvSTJw4ka+JPUmSXEeG0/H398eVK1eQk5PTad+VW7duYf78+Tzv97Nz48YNnvfhVcnDiYyMDAQHByM0NBQFBQUgSRI9e/bEkiVLYGxsjOHDh8Pe3h4fPnzgqQ3f2dkZV69eRWJiIgBASkoKUlJSXO175coVZGdnY8mSJTAzM4OjoyMiIiKwatUqkCSJGzduYOTIkdT2QUFB2Lp1K5ycnLB3717e/gD/Hzc3N7YTqAkTJvD1muw4ffo0Ll68yNR639qEkT5p4aVln37NaK5k4Aa6kqEj6dmzJ9NjXW27HcPP5v3WxY8N3afY2toaS5Ys6VAblbZgZ2fHVdGeXkRrjoTE/2PvzONqyv8//jpJWtBqy77NmBjL2LVJSDuhDAbzs44wtiQSNUhkibJkmSyF9lQ3UVGJtBhZIxGplErLTdru+f3R456v3Fvde6rbdp5/cZZ7PnVP53w+7+X1kgabzcaTJ0+gp6cHQ0NDgauFhUFVVRUXLlxAamqqQO/o1NRUPHz4sMEdNpGRkdi4cSNKS0vRsWNHjBgxAr169ULHjh3BZrORnp6O1NRU3Lx5E5GRkTh+/HiDricsHz9+FHnChc1m860o5AaLMzIysHHjRtoGEVxKS0tr1bg1MDDAiBEjeLZLSUmhsrKyQdelg7OzMwB6CWQuw4YNE7lBYXPSMp+GDM3CgAEDYGtrCwCUK4mcnBwtwe3c3Fxcv34dnp6eyMnJoV5c/LLe9XHx4kV8+/YNR48eha6uLrU9JiYGHz9+RKdOneDg4EC53Tk6OkJLSwuenp41gmrfo6SkhFWrVmHVqlV4+PAhPD09ERYWhvDwcISFhUFCQoLHZa0+CILAiRMnhP75gOrWKFlZWZw7d65GNVF4eDg4HA769+9PBUmAaoOJMWPGIDw8vE0H1crKyqg2gs6dO6NXr171npOXlwcDAwOeoKiGhgZIksSxY8fQtWtXHDt2DJMnT6b2NySbQmdhT5IkbGxscPfuXTg4OAhURZaRkYE3b95ATk6O9r2Snp6O+/fvU+5G3/8OvicrKws7duxAbGwsE1RrJH7Uhqmt3aOyspLvYiU1NRXBwcFgsVh4//49SJKErKws5s+fD319fUyYMKHGZPTixYvo0qUL1NTUanyOs7MzTp48yVewuLy8HEVFRbTayUJCQjB+/HhK1NjZ2Rmenp6wsbGh2oK+x8DAAH5+foiMjBT4Gt8jal2ZxMREABC47ePz589IS0sDUP1+EHYxFB4eXqOSQRAaUskQHh6OQYMGYeDAgQKfw2KxQJIkevfuLVLto/aCMJWiDAxNDVen2NHRESdPnoSqqmqT6RRfunSJ9rzM3Nxc4MBMZGQknj59CgDo1q0bwsPDERERgcDAQFy/fh3u7u7o27cvDA0NYWho2GhO8HPnzoWbmxs2bNhQb5IuNzcX69evB0mSMDU1pQIewlJUVAQvLy+UlZVhzZo1WLFiBd+5Z25uLi5evIgLFy5Qwvp0ycjI4NGl41Y1JyQktIikQVVVFTp27Miznbv+GD58eIMDakD12vP7TqTvGTBgAN9769OnT3wTWQwtDyao1s6pbeEkKSlJK9udkJAADw8P3L59G5WVlSBJEkpKSjAxMYGpqWmt2gJ1ERMTg99++61GQA2obv0EqoMG3IAad+xqampISEgQ6PMnTpyIiRMnoqioCP7+/jh48CDKy8sRHh4u1MO+IZmVp0+fQlNTk+flFhsbC4Ig+ApUjhw5sllKgung5uaGZcuWCXVOcnIytmzZguDgYADViwtBPuPr1698X0Dc1l4xMTFcunSJp62KriYN3YV9RkYGSJJEREQE5s+fD2dnZ4EriujeK46Ojvj3339rlPuPHz8ep06dqpE98/LygoODA9hsdpO7WLZH0tPTERoaiuzs7BoVjoGBgThx4gTS09OhqKgIMzMzrF27Fh06dICRkRFSUlIAVD/j9PT0YGBgAHV1dVqLmfqebXTayTIzM3k0cLS1tWFjY1Nre9DPP/9Mu4KvsXRlhEFMTEygKkV/f3/s27cPQHW2+8CBA0JrsDg6OtKqZJg5cybYbLbQ7d7r1q2Dubk51q1bV8OFlauZxi9xUJcpR1MiSg03utBtQQP+p60oTKUoA0NTI0qd4h+D9FVVVXw7SOLi4jB69OgacxVBTEjYbDb27duHZ8+eAQBmzZqFPXv2oFOnTtDV1YWuri6KiooQEhKCoKAgnDx5EidPnoSKigqMjY2hp6dXY+0hLAMHDoS5uTmcnJygo6ODYcOGgSRJFBcXQ1paGoWFhUhLS0NkZCTc3d3BZrOxePFijB49GgsWLKDWHMKsU7gFDgcOHKjzu1JSUsKWLVvwyy+/YPPmzSBJEuHh4Twt/lzpA37uoFyNOX9/f75rFZIkeVylWyp0CkL40bdvX6oLQBCqqqoQExNTwyiIoeXCBNXaOY2hw1FaWoobN27A3d0dKSkpIEmSanOZOXMmjhw50qAMVmZmJoyMjHi2JyQkgCAITJo0iWefsrJyrdmA2ujatSuWLFkCU1NTLF26FElJSdDU1MTy5ctpj11Qvnz5wldTiCtOya8CQFJSsoYYf0uGK3q9f/9+gRZ67u7uVHCzseBOxmbMmMFXp4ju3wLdhf2wYcOwePFiPHv2DI8fP4apqSns7e0xc+bMes+lc6+w2WycO3cOBEFg7NixUFRURHx8POLj42Fvb4+9e/eitLQU27dvx61bt0CSJIYPH44DBw7Q+vkY+OPj44Pdu3ejqqqqRqLgxo0b2LZtG4Dq9pOSkhKcPHkSKSkpOH78OF6/fk25jWpqakJKSgqxsbF1itKTJIl79+5h3bp1Tf5zAYCioiLPpFtRURHz5s2DoqIi33M+fvzYpoIG+fn52L17N8LCwqiqgu3btwutwZKamopPnz5BTEyMqmSoS/Pz+0qGmzdvYujQoTA3Nxfqmt8vzr5v5+RXadDctAYNt4a0oNExHGgNgUaG1o+odYpDQ0Ph5OQES0tLng6SsrIyLF++HLKysti+fTsMDAwE+sy4uDhYWVkhMzMTXbt2hY2NDV/90a5du8LMzAxmZmbIzs5GYGAggoODsX//fjg4OGDSpEkwNDTEjBkzam3rq4s1a9bg27dvOHv2LOLj40GSJM8cjiRJdOjQAX/++ScsLCxqbO/cuTNmzJghcIvd2bNn0b17d4GDn3p6ejhz5gySk5Px8uVLHjMGLnU5QAubTIqLi2tx7qb8qtjoMGPGDOzbtw9BQUEC3avXrl3Dly9fMGvWrEa5PkPTwgTVGGjz7t07eHh4wN/fn3Ka6927NzUZ19bWhoKCQoNLwsvKyngWJCUlJXj9+jUA/v3e375943EfKi8vR3Fxca2LOy6hoaF49+4dRo4ciejoaKxatQpjx45t0M9QH1wNh+9JTU3Fly9fICYmxvellJ2dLbBJRHMzfvx43Lp1C69evYKTk1OtE4CioiLs2LGDqhIURm9MUBqrdL8xkJOTw6VLl2BrawsfHx/8/fffWLlyJTZt2lTnYozOvUIQBAiCwKlTpzB16lQA1VV9K1euREBAADZv3oy///4b8fHx6NixI8zNzbFy5UqRagu2dd6+fQsbGxt06NChRqa5rKwMDg4OIAgCxsbGsLOzo7LJHh4eCAsLA1A9ic7Ozoanp6fA13z8+HFT/Ch8GTNmDEJDQ/HgwYMabcV79+7le3xkZCTCwsIwY8aMBl03JycHUVFRyM/Ph7KyMjQ1NZslUBcWFobdu3cjLy8P3bt3x969e/lWjtYGPw3SDRs2UJUMS5YsgYaGBgYNGlRnJcOVK1daREtNU9IaNNzotqDRrX5pDYFGhraDKHSKr1+/jj179oAkSSQnJ/ME1XJyctCjRw98/PgRFhYWyM3NrbOjoby8HI6Ojrhy5Qo4HA40NDSwd+9egdrrevTogRUrVmDFihVITU1FUFAQWCwWrKysYGtrS8uhkSAIbNq0Cdra2ti9ezdevHhBPbsJgoCysjKmTZuGefPm1UgGcwMzDx8+hL+/P4YMGUK1p9Ylk3L8+HGMHj1aqDGOGTMGaWlp2LNnj9A/HyC8ScWwYcMgKyvLt/qtLgiCoLS1WyoGBgY4ceIEbGxsICsrC3V19VqPjYiIwKFDh9CzZ0/o6OiIcJT/Izk5mVZHVGNWrLYmmKAaAy3+/PNPxMbGgiRJSEtLw9jYGLNnz+ZbNdZQlJSUkJ2dXWNbXFwcqqqqICsrCxUVFZ5zXrx4QWXIXrx4AXt7eyQmJoIkSfTs2RPr16/n0SnJy8uDjY0NVXl05MgRGBoaYu/evXVmYRqDAQMG8GSAuOMYNmwY5OXla+yrrKxETEyMUNo3zcnFixdx+PBhXLhwAWZmZrC2tuYJmCUkJMDCwgJZWVno2rUr7OzsmiQ701SLCLoLewkJCezbtw8jR47Evn37cPbsWTx//hyHDx+GnJwc33Po3CtAdQk7N6AGVAdzN23ahMWLF2Pz5s2Ij4/H4MGDcfjw4XYlLioquMGOCxcuYNy4cZQuyt27d5GXlwcZGRlYW1tTbSxWVlYIDw+Hr68v7O3thb6elZUVTztmU7Ju3TqEh4djxYoVmDRpEs6fP8/3uJcvX+Lo0aO4d+8exMXFG6QL6ebmhsOHD9cQ8u3SpUuTPT/4wWazsXfvXgQEBIAkSejr62P37t0CJz3q0iD9vpKB2370Iz9WMly5cqWxf8Q6IQhC5AGu1qDh1pAWNDq0hkAjQ9ukLp3iiIgIKCgoUPMQQUlOToatrS06d+5c6/O8b9++CAsLA4vFws6dO+Ho6AhVVVUMHTqU59inT5/C0tIS7969g5SUFLZv3w5TU1NaP29+fj4KCgqogoJv377R+hwuI0eOhLa2NpKTk5GUlITCwkLIy8vXWpQwd+5czJ07F58/f0ZwcDCCgoJw5MgRHDt2DGPHjoWhoSF0dHQgKytb47xv374JnYyXk5NDZWWlSJ8t3Mo4YWhoUC0uLq5Wrbra9hEEIVRFuJycHOzt7WFubo5Vq1ZBTU0NM2fOxM8//ww5OTkUFBTg7du3CAkJQVRUFJVclZKSov1zNYTw8HBaJm5MUI2BQQgePHhAOQAtXryYpyqsMfntt98QGRmJ0tJS6sHi7+9P6Uf9GCR58+YNHj16BCMjI7x58waLFi2q0SaZlZWFnTt3QkxMjPrDv3PnDqysrFBYWAgxMTGsXLkSffr0wZIlS3DmzBncuXMHWlpaTfYzamtr4+jRo2CxWNDT00NOTg7c3d1BEATfEuEzZ84gNze31egRiImJwcLCAmPGjMH27dthY2ODhIQE2NraolOnTnBxccHp06dRWVmJsWPHwtHRUSBTgtqoK3DWFEG1xljYm5mZ4ZdffsHff/+NmJgYzJs3D87OzjzBLYIg8PjxY6HvFTExMb4TTa5l/cOHDzF16lQcPXq02V7gbZ3Y2FhMnjyZp5qQu9hQV1ev0R4tLi4OVVVVREdH8w2m1IeVlRX1/dKBTjvZ4MGDUVZWhs+fP9d6TFZWFqKioqCgoIADBw7QDuBGRUVR7cmjRo2CsrIy0tLS8PLlS2zduhV9+/al3HGbigcPHmDHjh3IysqCvLw8du/eLfDfvKAapNxKhmvXriEyMpKSNqirkkGUEARRQ1BcGO2j9oygLWiC0hoCje2NYcOGtbsF5o86xd7e3pQeqDBw25LPnTtXr/umnp4epKWlsWbNGly+fBl2dnbUvqqqKri4uMDV1RWVlZUYP3487O3thdZ4TkpKQnBwMG7evInPnz+DJEn0798fZmZmDfq7/REJCQl069ZNoGO7detG6Q2npaUhMDAQQUFBsLGxwT///ANNTU0YGhpCS0sLEhISqKqqErp7SFxcHFVVVbQlALjdRJmZmZCRkeEJ9AHA6dOnoaGhQRVJaGtrizQhCFQ/j2ub7/y4jyAIKvklrMzCtGnTcObMGVhZWSE6OprSB/8ekiQhLy8PR0fHWg33REF7c+9sKExQjYHWwqlTp074+vUrHB0d4ePjA01NTRgbGzfJH9+CBQsQFBSEpUuX4vfff8eLFy8QGhoKMTExnqDS27dvsX79enA4HBgbG8PV1RWlpaXQ19fHxo0boaSkhDt37sDW1hZHjx6FsbEx3N3dsX//fnA4HAwdOhT29vaUrfGmTZsa7HwjCIsXL8a1a9ewZcsWODo64suXLygtLUWfPn2wcOFC6rg7d+5QJefy8vI19rUGpk+fDl9fX2zcuBEBAQF4/vw55OTkkJCQgA4dOmD9+vX466+/KE0+uly8eBG+vr4827n6Mfz2kSRJ62+hsLAQr169AtDwhf3IkSPh6+uLzZs348GDB1iwYAHs7Ox4NAV79eol9L2Sn5/PV/ODu61nz544fvw4s+BtQrKzs3mcOIFqV0mCIPhOnpSUlPDlyxdRDI8Huu1kL1++RE5OTq3HjBw5Es7OzlBTU6NliMPFw8MD4uLiOH36dI3f640bN2BpaYkrV67UWuH3vUGEoHyv8VhWVoZDhw7Bw8MDHA4HWlpa2Lt3b73yAnQ1SEeOHEmJFZeXl9dbydAcbX1NoX3UFmlICxpD62L69OmYPn16cw+jWeDqFC9ZsgRPnjwR+vz4+HhMmjSp3oAal6lTp2LUqFE1dEbfvHmDbdu24eXLl5CQkMDWrVuxdOlSgZ+PL1++RHBwMEJCQpCZmQmSJNG9e3csXboU+vr6+PXXX4X+ubgkJyejW7du9b4zvocbhOHXGjlgwACsX78e69evx5MnTxAUFISQkBCEhYVR2mt0IUmSls4jUN0ttHfvXly/fh0ODg48AcgvX77AyckJTk5OVAfLsGHDRFoZJyrdWS4aGhoIDQ0Fi8XCzZs3kZaWhtzcXMjJyaFfv37Q0dGBkZER3wCkKNHW1hb576Y1wwTVGGgvnC5fvgxPT0/cvn0b//77L9zc3PDTTz/BxMQEhoaGjTa+cePGwdzcHC4uLnj69CmlN7By5coajigLFy5EUlISqqqqMGfOHEyaNAm7du3CgAEDcPDgQSpzrqenh9LSUlhbW+P8+fM4fPgwCILAihUr8PfffzeaIKUwSEtL48qVK9i1axfu378PkiQxevRoHDx4sMai08bGBp8/f4asrCxOnz4ttLtbS6Bfv37w9PTE33//jfDwcBAEAUVFRZw6darRHG6KiopqNR2oax+dvwWSJNGxY0daC3t+yMvL4/z58zh69CjOnTsHS0tLPHnyhJpEEQRB616pr81BR0eHCag1MZWVlTzPF5IkKQdFfvqQJSUlQovcNxYNaSfr3r07SJJEQkICPnz4gLy8PHTp0gUDBgzA2LFjayw0r1y5AgUFBejp6Ql1jefPn2P69Ok8gUojIyP4+Pjg0aNHtZ47bdo0oQNP3Mx0UlISLC0tkZaWhs6dO2PHjh08cgI/0pgapIJUMjg7O9faylLXz0eXxtY+aqs0ZgsaQ9NCt+qPIAihHP5aK//99x969+7NNxhsY2MDDQ0N6jlPZ26XnZ0tdCBo+PDh8Pb2BgCcP38ex48fR1lZGYYPH46DBw8K7KIcHBwMFouF9+/fgyRJyMrKYv78+dDX18eECRMaJWkxZ84cym35e7hts/zmjUlJSbh06VK9emPcJIyVlRViY2Nx4sQJ+Pn58TVFEQYZGRmMGzdOKH08Kysr+Pv7o2PHjnzNxzgcDmbPno3Q0FB4eXk1ix5ocwSOOnfuDFNTU+b534ZggmoMtBdO48ePx/jx41FcXIyAgAB4e3sjOTkZBw4cwKFDh0AQBPLz81FZWdlgs4L169dDQ0MDt27dQkVFBTQ1NXksjnNzcyElJYVly5Zh7dq1AKp10gwMDHheABoaGiBJEseOHUPXrl1x7NixGsLa31PXwvD7IATdhSEXZWVlnD9/HqWlpaisrOSrx2ViYoKuXbti3rx5zZ7BoEtFRQWOHDmCu3fvAqguLc/Pz8e///4LW1vbBpsv0On/B6ozMnT+FjZs2IBJkybRWtjXhpiYGLZs2YJRo0bB0tIS7u7uePXqFX766ScATXOvtMYAbWujR48efIO23La/IUOG8OxLSkoSuBWEH/y0QLgLPhcXF54J7PeLQbrtZBwOB66urrhy5QpfF2ZpaWksXboUq1atQnl5OY4fP46ZM2cK/ewsKChA//79+e5TUVGpszqCrrtYYWEhFi1ahKqqKowcORL//PMPevfuzWMe8j1r166lnN2E0SClW8kg6kUJSZKNqn1UH5cuXarRbtoaaKwWNAbRQccNHGj75g9lZWX4+++/ERkZif379/PMmbhmOl5eXpgyZQqOHTtGyzhGWloaFRUVQp0jISEBSUlJLFq0iJp7jRw5EitWrEBaWhrS0tLqPH/fvn3IysoCUO2arqenBwMDA6irqzd4HfMjtT2nueLwdHRUv+ft27dgsVgICQmhEndA7Z0ctVFcXAzgf4ZqT548gZ6eHgwNDeutIgwPD4e5uTlUVFRw6tQp9OjRg+cYRUVF2NvbY+3atVi9ejVSU1Px/v17gcfX1omMjISHhwfOnDnT3ENhqAcmqMbQYB2OLl26YPHixVi8eDGePXsGT09PsFgssNls3L59G+rq6jA0NISJiUmD2kNHjRpV5wP8ypUrUFRUrBFA+/r1K98sGtfEQExMDJcuXeKrRSOqheGP1KVnVVcral3ZrZbC27dvsXnzZrx69QrS0tLYvXs3Ro4ciQ0bNiAkJARJSUk4fPgwxowZQ/saDVls0flbKCkpob2wr4/p06fDx8cH69evR3x8PBISEmrsb8v3Sltk4sSJCAoKQl5eHhUo4bb/8dMPefToEZ4+fYoFCxbQvmZdOiEnTpyo8f/vdULowmazsWLFCiQlJYEkSSgrK2PQoEGQk5MDm81GSkoKMjIycOrUKURHR2PQoEEoKSnBn3/+KfS1Kioqaq0slpGRqVM8mmvuISzDhg2jfj9Pnz4VSC+Jw+EAALZt2yaUBindSgagOvMubPZ92LBhyMjI4NHO4QaCExISeBaCGRkZ1LaGah8JSmvTDWtoC5ogtMZAY0snOTlZoOPevn0LS0tLPH36FOLi4gIZVLRWSJLE2rVrERMTAwUFBb7BMikpKaxbtw7e3t6IiYnBunXrKH00YVBWVkZqaqpQ57x69Qo9evRAYmIite3p06f4+++/BTqfw+FATEwM3bt3h6amJqSkpBAbG1ujpZQfBEFg+/btQo31RxpDf+/jx49gsVhgsVh49eoVVWU3b9486OvrY9myZXV2a9SGmJgY7t+/j4iICAQGBuL69etwd3dH3759KdfRAQMG8Jzn5eUFSUlJuLq61utq3LdvX5w4cQJ6enp4/vy5UONrKPXJQYiJiaFz584iSz4XFhbCx8cHV69excePH0VyTYaGwwTVGBqVESNGYMSIEbCysgKLxYKXlxceP36MS5cu4fLly/jll1+EypAIgzB6JNzA24wZM/gG1ES5MGwsGiu71VRcv34dBw4cQGlpKX799VccOXIEffv2BVD94rW1tYWvry/++OMPrFu3DmvWrKF1HW1tbSxdupS2/oOw0F3Yr1u3TiAB0gEDBsDT0xM7d+4Ei8Vq0EKMu2AODw+Hn59fjZY1fotpLvzaEhmEZ8mSJfDx8cGCBQtgbGyMoKAgANXPox/v15iYGFhaWkJMTIzHKVdQGtLSIGzrIJedO3fi8ePHGDVqFHbs2ME3yMJ1ZI6Pj8ezZ8+wbNkygdpympuqqirIy8sLbeSRnZ2NyspKoTVIm7qSgR/+/v7w9/fnOxZ+xjjcMTZU+0gY6P7cjbHwFRa6LWjC8mOgkTGLEA2XL1/GkSNH8O3bN/z00084ePBgmxb2DggIQExMDNTU1HDixAm+z8KuXbti3bp1WLx4MdauXYu4uDgEBwcLLeavqqqKCxcuIDU1VeC2zYcPH2L27NkYPnw4rbmSr68vSJKkqu0EpTGeLVz9PWHfvdnZ2QgJCQGLxaIkcqSkpKCnpwd9fX2oq6tTc1S6nRxAtZa2rq4udHV1UVRUhJCQEAQFBVGu1CoqKjA2Noaenh4VQHv27Bm0tLTqDahxGTx4MIYNG4bs7Gza46SDoHIQnTp1wm+//YaFCxc2iVbi8+fP4e7uDhaLhbKyMpAkCRkZmWbRIJ0zZ06DjK7aI0xQjaFJkJKSoiyfU1NT4enpiYCAAKEtkoHql8CgQYMwcOBAgc/hZmrqg19mBWjbC8PmYP369QgLCwMALF++HJs2bapRSt+pUyfs378fEyZMgK2tLZycnPDw4UMcPHhQ6Na3jIwM2m0bokSYgIeUlBSOHDkCAwMDFBYW0r4md8FMkiSP8Gxti2mCIPDixQva12T4H0OHDoWtrS1sbW3h4uJCVTBZW1vXeHZoa2tTosirV6+m7WAp6qBafHw8QkNDoaamhjNnztSqu6KiooLz589DQ0MDhYWFPG6oLY3Pnz/j+vXr8PT0RGFhIR48eCD0Z8THx4tEg7Qh0A2ex8fHC+08+r32kbAIU/Xy40JJlEE1ui1oAGg73zFmEaIhKysLVlZWePjwIaXJu2HDhmbR5BUlAQEBlGRKfckFOTk5HDlyBLNmzYK/v7/QQbW5c+fCzc0NGzZswOXLl6kOE37k5uZi/fr1IEkSpqamGD16tFDX4kK3CtbW1lboc0mSxNmzZ2m9p/Pz83Hz5k2wWCw8evQIHA4H4uLi0NTUhIGBAbS1tfl+P41Vzdq1a1eYmZnBzMwM2dnZCAwMRHBwMPbv3w8HBwdMmjQJhoaGKCgoELrFfcqUKbh8+XKjjFNQBJGD+PbtGwoKCnD//n08ePAAK1aswJYtWxp87YqKCrBYLHh4eFCdLSRJQkVFBWZmZjA0NGwWXd2WWqDRkmGCagxCU1paiqqqKoHLYAcPHgwrKyts3bqVCqwIw7p16/i2wPj5+cHPzw+XLl3iOeft27cCZWT4ZSba6sKwObl9+zaUlJRw8OBBTJkypdbjuBnGv//+Gw8ePMDs2bMRExMjwpG2bKZNm0b73O8XzFxDBuaepU98fDx69+4ttDbX/Pnzoaamhjt37uD58+coLCzkae+UlJTE4MGDsXz5cpE6YH0PnXYyLy8vSEhI1DCGqY2rV6+ioKAAHTt2BIvFoh1EaEoSEhLg7u6O27dvo6qqimqlEZaqqirk5eXh0KFDItEgpQvdhczEiRNpax/RQdDJPkmSuHr1Kp4+fQoAtPTbGgLdFjQAtBKQjFmEaPD19YW9vT2Ki4vRv39/HDhwoEGSFa2Jly9fYurUqQLP/3v27Al1dXUe6QpBGDhwIMzNzeHk5AQdHR0sWbIEGhoaGDRoEKSlpVFYWIi0tDRERkbC3d0dbDYbixcvph1QS01NxdOnT2FjYyP0uVZWVnXKDdRGWVmZ0OcAgLq6OtWqOn78eBgYGEBHR6fBmsT8KC8vR3l5ea3feY8ePbBixQqsWLECqamplOu8lZUVSJJEQUGBUNf78OEDpKWla+2cqAu6iSFB5SDKysoQFxeHQ4cO4dy5c9DQ0KB9zczMTFy7dg3e3t748uULVZX29etXzJkzp9mDWoK2v/OjLVfr1gUTVGvn0Fk4rVq1ComJiUJXr5w8eRIpKSnQ1dUV6rzaWmDqalf7nrpKevnta2sLw5aAhoYGDhw4UGemkcvQoUPh4+ODXbt2ITg4WASjq0bUmjSibmH6fsHs7OwMFxcXkWcD2xJLlizhG+wXhF69emHhwoW17g8MDISYmFhDhgegfp2QuqCTsX/69Ck0NDQE+ju/fPky1NTUIC0tjWfPntEZIoDq5Ao/zTiuDhi/NnCCIGqtdiotLUVAQAA8PDyQkpJCvX9Gjx6NBQsWCPX++r7CLTc3F7NmzRJKg7S10BDtIzoIEmj+9OkTrKys8OzZM4iJieHPP/8UKqjVGMyePVtkovXJyckiNYtoj+Tn58Pa2hp37twBUO04b2FhIXQ7eGuGzWajZ8+eQp3Tr18/6ncmLGvWrMG3b99w9uxZqs3wR0iSRIcOHfDnn3/CwsJCqM/ncDi4ffs23N3dqfUEnaAaHf09APWa1dRGVVUVCIKAvLw8ioqK4OHhAQ8Pj3rPS09Ph52dHY/2M5vNRlFREd8koaurK1xcXAQK9Ofn56OgoIByuCZJUmhdYW7xhbASLqLoqujUqRPU1dWhoqKCGTNm4Nq1a0IH1WJiYuDu7o7IyEjqexw3bhzmzp0LHR0djBkzRmDd1aaE7vurPXe3MEG1dg7dUmc67mIJCQm0slUNpTanG+7C6sd9nz59QseOHUW6MGzruLq6CnW8lJQUHB0dadmw06UhmjR0FvbcthFBaM4WJgb+NKXDYmME1ADBdUJ+hCAIvvpZ9cHNMNdHeXk55syZgzFjxiA2NhbR0dFCX4sLt+qyNvj9XfL7nbx9+xYeHh4ICAigFgRAdTDN1tZWqPZGQSvc6tMgpXuPiYmJNaj1lx/cRdeP1+nZs2eDtI+aAn9/f+zbtw/FxcXo168f7O3tMXbs2Ca5Vl0cOHBAZNfiBolFZRbR3rh16xb27NmD/Px89OzZE/b29rU6xrdlunXrhs+fPwt1TnFxsUDzaX4QBIFNmzZBW1sb165dQ2RkJGUcRhAElJWVMW3aNMybN0+oZ3Rubi6V8MjJyaEMelRVVWmNUxB+1N97/fp1g7oFSJJEbm4ucnNzhTrneydQLm5ubgIHzn4kKSkJwcHBuHnzJj5//gySJNG/f3+YmZmhoqIC586dQ0REhECdFtyKMWVl5RbtjKyoqAgNDQ2hA4Y6Ojr48OEDSJJEv379YGhoiNmzZ1P60i0JYYKFnz9/pmQNGmv+2hphgmoMbZ66nG747SNJEpWVlfV+bmMuDBlq8vHjR1y9ehU+Pj60DAdqC3LVBTfISkeThu7CXpBqtcZqYWKz2ZCQkBBKoDopKQlJSUkiM31gaBpkZGSEnqDS0a3icDgCJRckJCRgbm4OoNounm5Wll/rvzCQJInw8HC4u7sjNjaWWliNHz8ec+fOhaWlJYYNGybQYq0hFW61aZB++fKlQT8fXdLT03H27FmMHTsWxsbGAP636PqRM2fONEj7qDHJz8/Hrl27EBERAZIksWDBAlhaWraLSqL4+HiRmkW0F4qLi2FnZ4egoCCQJInZs2fD2tpaZC6ALY0BAwZQOoGCEhsbi169ejXouiNHjqSSrOXl5SgsLIS8vLzQrfIJCQnw8PDA7du3UVlZCZIkoaSkBBMTE5iamjZJIKc2/b1ff/0VxcXFPBXl3DVJVlYWT2KFu4+u4UBjddO8fPkSwcHBCAkJofRfu3fvjqVLl0JfXx+//vorAODLly/w9PSEhYUFnJycoKamVutnRkZGYtu2bejUqRMuX77cooNqQHUFZmRkpFDnvH//HtLS0jA3N8fSpUubTepBEATtZOEmsQCgT58+Ik0mtTRa7rfJwNAI0HnxzJ07V6AXT2MtDBn+R1RUFDw8PBAdHU2VRdOhviAXPwiCoKVJ05CFfX2Voo3ZwjR+/HhKn/B74uLiEBcXx7eyJTo6Gi4uLkxQrZUybtw4JCYmgs1mgyAIGBkZQV9fXyCnZD8/P4Gu8WPQV1idrKSkJNpt14JWWvPTKTt16hQ8PT3x6dMnyuF59uzZMDExoSbzlpaW9X52Y1a4ATU1SLkLE2HhcDi0tPgIgsCGDRuwadMmfPv2DUVFRVRQDaj+rrlaKRwOB69fv4a9vT1CQkJEqn3Ej7CwMNjY2CA/Px89evTAvn376lzAtTWys7MxY8YMoc5piFlEe+DevXvYuXMncnJyoKioCFtb23Yv8aGrq4vdu3fDy8tLIFdqLy8vpKenY+7cuY02BgkJCaEMrEpLS3Hjxg24u7tTCQ9uNc3MmTNx5MiRJgtu1Ke/d+nSpVrnkHVVdjWGVEldcz9+pKamIjg4GCwWC+/fv6cqsOfPnw99fX1MmDCBZ84uLy+P/fv3Y/369Vi5ciXGjRsHdXV1DBo0CDIyMjXeDY8fPwZJknBwcGjxATWg2mBA2PtmypQpePDgARwdHXHy5EmoqqrC2NgYmpqaLTrAxo/8/Hzs3r0bYWFhVJJs+/btzWKq0FJoXd8gA4OQ0Hnx9OnTR2h9mIYsDNs7RUVF8PHxwdWrV5Genk4tSidMmMAj4C4oc+bMEXpR+f79e+zevVtoTRo/Pz+BqseEFSBv7BYmrr7Fj8TFxcHFxaXR28XaA/yyzIIgrLkBXa5cuYJPnz4hMDAQQUFBcHBwgKOjIyZMmABDQ0PMnDmz1ooLOrpVo0aNwuPHj5GYmCjQvZqQkIDHjx/zBHqFJT09HaGhodDR0anRRhEYGIgTJ04gPT0dioqKMDMzw9q1a9GhQwc4OTlBWloaixYtwqxZs4Rqw2lIhVtmZiZkZGT4toOePn0aGhoaUFFRoVwE6VYyCNPGQxAE9TmbNm0CUO1I++NCmCCIGg7BFhYWCAoKQlRUVJNrH9UGm83GP//8gxs3boAkSRgZGWHXrl3o0qVLo3x+Q6AbgCEIQmhTJ2lpaZGaRbQHVqxYQQUJJCQk4OzsLJArMkEQfCVH2gIGBgY4ffo0bG1tweFwYGpqyjf5yU222Nvbo3Pnzpg3b57Q10pOTka3bt2gqKgo8DnR0dG4d+8erKys8O7dO3h4eMDf359KePTu3ZuaH2pra0NBQaFJghmC6O+Jah5QG8LM/TgcDtWZISkpCT09PRgYGEBdXb3e35+2tjbOnj0LCwsLxMfH85UB4ia2du3aBWtra9y/fx9qampQVVWl3Trc1Lx69Uqo4C4AXLhwARkZGfDy8oKvry9u3bqF27dvQ05OjmoFbQ2EhYVh9+7dyMvLQ/fu3bF3715oaGg097CaHSaoxtCm0dbWxtKlS4WqtJk6dSpcXFxEvjBsb7x48QLu7u5gsVj49u0bSJJE165dYWJiAjMzMwwcOJD2Z/fu3VtovUBuZQ5dTRo6C3t+tOcWptZGXVnm2hC1iGvPnj2xcuVKrFy5EikpKQgMDERwcDB27NgBW1tbTJ06FUZGRtDQ0KACOYLAL+irrKyMWbNmYcuWLXBzc8OAAQNqPT81NRWbNm2CpKSkQBUPteHj44Pdu3ejqqoKAwcOpP72bty4gW3btgGoDjiUlJRQZjnHjx8HQRD4+vUrIiIiUFlZCTabDXV19XrNaehWuJEkib179+L69etwcHCAvr5+jf1fvnyBk5MTnJycMH/+fOzatQsAvUoGgiAEqtLOz8+HnZ0dVWWooqKCFy9ewNnZGdOnT6/3/A0bNiA4OBi3bt2ChoZGk2gf1cX9+/exc+dOZGVlQV5eHnZ2dkJXazUlwlZLc6FToS1qs4j2AjfYnJmZKXACRVTmFM2BtLQ0Dh8+jGXLlmHPnj1wdXWFmpoaVZVaVFSEtLQ03Lt3D58+faISGMIExrjMmTOHrxlQWFgYwsPD+cpnJCUl4dKlS3j9+jWV8JCWloaxsTFmz55N2xhAGATV3xPUcbIl0b17d2hqakJKSgqxsbH1to9zjbVUVVURERGBmzdvIjIyEpmZmSgsLISCggL69+8PbW1taGhoQEJCAnl5eQgICMCNGzdAEAR++eUXqKurQ11dHWPGjGkRml2JiYmIjY3F77//LvS5vXv3xsaNG7FhwwZERkbCy8sLUVFRlJYqQRBIT09Hfn5+iwsostls7N27FwEBASBJEvr6+ti9e3eTuM62RpigGkObJiMjo1Y9tdqYO3cuzp49K9KFYXuhoqICISEh8PDwQFJSElXhMXHiRMTGxkJPT6/ZRPgboklDd2H/I+29ham10blz5xZRESMoQ4cOxebNm7F582YkJiYiKCgIoaGhCA0NhaysLHR0dGBoaFinQG19QV9ra2vs2rULJiYmWLhwIaZPn44hQ4ZQ939KSgpu3bqF69ev49u3b/jnn3+EdpPj8vbtW9jY2KBDhw5YsGABfvrpJwDV2ocODg4gCALGxsaws7MDQRA4cOAAPDw8EBYWhjt37sDb2xu+vr64du0arl+/Dnl5eRgYGMDExKRWS3i6FW5WVlbw9/dHx44dUV5ezrOfw+Fg9uzZCA0NhZeXF0pKShpUyVBf5XRERARsbGyQl5eHbt26Ye/evXB2dsbPP/8sUEANqK7cHTVqFP777z9qW2NpH9XFt2/fcPDgQVy9ehUkSWL69Omws7NrcQsQurpHdGhpZhFtAVF+f62JMWPGwNPTE1u3bkVKSgquX79eI5DIDUT++uuvsLOzwy+//ELrOrUZtSQnJ8Pf379OTdoHDx5Q2lWLFy8WiTxLW9ffI0kS2dnZ8PT0FPic793qJSUlMXv27HqfOffu3UNcXBwePnyIuLg4PH/+HM+fP8eZM2fQuXNnTJ48mQqy0Z07/IggAfOKigp8/vwZDx48wL///gtxcXGYmZnRvqaYmBi0tLSgpaWF3Nxc+Pr6wtvbGx8+fMD9+/ehoaEBTU1NmJiYYOrUqfUm/JqaBw8eYMeOHVQSa/fu3Xw7etozTFCtnSNIKfuPZGRkgCRJodue+C0khLkm1+76+21AdaXYjy9fuhliAOjVq5dIF4btgaysLFy7dg3e3t7Iz8+nKjxMTEwwZ84c9O7du9ZFrKigq0nj6elJe2HPXby25BYmhtpZunRpq22bHTt2LMaOHQtra2vcu3cPwcHBCA4OhpeXF3r16sU3iy5I0Hf+/PmorKyEg4MDzp8/j/Pnz/N8DkmS6NSpE2xsbGi1BXG5cuUKSJLEhQsXagS37t69i7y8PMjIyMDa2poy57CyskJ4eDh8fX0xffp0SmPw3r178PLyQkREBJUtHjp0KN+KEzoVbuHh4fD394eKigpOnTrFtzpIUVER9vb2WLt2LVavXg0Wi4XTp0/z6Do2FDabjX379sHf3x8kSUJPTw+7d++GrKwstm3bBkNDQ6E+b/jw4TVaQr9HWO0jQTE2NsaHDx8AVAfxJkyYgKCgIIHOFaU+pCglIVqKWURbgpH0qJ2ff/4ZgYGBSEhIwN27d/lWHtENpjUG/fv3x/v37+Ho6AgfHx9oamrC2Ni4yeaZDdHfy8nJQVRUFPLz86GsrAxNTc0WN/cT1FirMVBSUkJhYSFsbW0BVD+vvg+y3bp1C7du3QJBEBg8eDDU1NSgrq6O8ePHC2XE9T3COKVz5y92dnbUfL+hKCkpYdWqVVi1ahUePnwIT09PqiIzIiICCgoKiImJaZRrCUtZWRkOHToEDw8PcDgcaGlpYe/evbSqT9s6TFCtnePs7FxDT6U+uMeSJCm0Xgi3KokO/v7+fCfuJEnijz/+aNRrAaJdGDYWnTt3brDDUlOhra1do73TyMgIEydObO5h1YCuJg1BEOBwOLQX9s3RwtSS7xUG0dKhQwcMGTIEQ4cORXJyMl6/fo2srKwaxwgb9P3999+hpqaGq1evIjQ0FFlZWeBwOCAIAr169YKOjg4WLFiA/v37N2jssbGxmDx5Mk+1GHfyqa6uXqNKQFxcHKqqqjWcmgmCoLLe+fn58PPzg7e3N16/fg0A8Pb2Rk5ODkxMTKClpUWrws3LywuSkpJwdXWFkpJSnT9T3759ceLECRgZGcHb27tRg2qxsbHYsWMHMjMzISsri927d0NPT4/aX1paChkZGb7nGhgYYMSIETzbpaSkUF5ejry8PNraR8Ly/v176t9Pnjyh2lfrgjsnEGVQ7cdEoDDUVS3Kj4EDBza7WUR7oKKiAp8+fcKXL18gLy+Pnj17CtU239YYN24c32rdysrKZhjN/wgNDUV8fDw8PT1x+/Zt/Pvvv3Bzc8NPP/0EExMToZMH9UFXfy8/Px/5+fk1fl9dunSpVde3uSAIgpb5DZf09HTcv38fRUVFGDFiBN92WKA6Ab9jx44arZVKSkrQ09Oj3lXcIBtXn+3y5cu4ePEiJCUla1RNC0N9VeFiYmLo0qULFBQUMGbMGBgaGjZ4/lIbEydOxMSJE1FUVAR/f/8a8xFRk5SUBEtLS6SlpaFz587YsWMHTExMmmUsrQEmqNbOoVtl4ebmJrIeamEnl9/TkEmtqBaGjcWyZcuwbNmy5h4GXzgcDqSlpbFgwQJMmzaNag9qCi5dukQrw0xXkwYArYV9VFQU7OzsmqWFqSXfKwyiIScnByEhIWCxWHjy5AkAUNoz3+t90Q369u3bF9u2bcO2bdtQWVlJLUIbsw0wOzubb3t0YmIi1Vb+I0pKSvjy5Qvfz1NQUMDy5cuxfPlyJCQkwMvLC7du3UJERATu3LkDeXl5GBkZYfv27UJVuD179gxaWlr1BtS4cLPv3AVCQysZysvLcfDgQSrTPHXqVPzzzz88VWRKSkqUDtqPDBgwgK8UwqdPn1BWVoarV6/S0j6iE1RrLdWhf/zxB+3knjBGE1yayyyiPZCYmIhLly7h7t27NbouxMXFoaWlhcWLFwut49qaaSwN2aZk/PjxGD9+PIqLixEQEABvb28kJyfjwIEDOHToEAiCoAJajfFeElZ/j3s8QRAYNWoUlJWVkZaWhpcvX2Lr1q3o27cvhg8f3uBx/Uh4eDgyMjLw8uVLcDgcWFlZITk5GQD4Po/pPIu+x9HREf/++y84HA61bfz48Th16lSNJI6XlxccHBzAZrPrrDhTUlLCzz//jOLiYpAkCQ6Hg9TUVHz79o32GFuivl3Xrl2xZMkSLFmyhK+5Q1Nz9OhRnD9/HlVVVRg5ciT++ecf9O7dG2w2u95z20rLs7AwQbV2Dt3JqSgntZcvX6Z97rBhw+Dn54e4uDihziMIAhcvXhTJwhCg//skCAInTpxo1LE0BRs3boSPjw/OnDkDV1dXyMvLU043jd0i8OPEtqqqiu+ELi4uDqNHj6Ze3nQ1abgl6D9S38I+JycHV69eBSBcC5O3tzf69etX73E/Img1KkPb5cuXL7h58yZYLBYSExPB4XAgISGB6dOnQ19fH1paWpT2TGPqVomLizdJG2BlZSVPlUhBQQHevn0LgH9CpqSkRCDLd24Fxq5du3Djxg14eXnh5cuXuHjxIrZv3y5UhVtBQQFlYiAogwYNQkxMDNzc3HD48GHalQxPnjyhMs3S0tKwsrKqtbK6b9++ePjwocBjrKqqqrMlRRDtIzq0lqAaFxkZGYwbN67JAwsEQYjcLKI9cPjwYZw/f54KCkhISEBOTg4lJSUoKSmhHPyWLVuGbdu2tWmjAoC+OUxz0aVLFyxevBiLFy/Gs2fP4OnpCRaLBTabjdu3b0NdXR2GhoZ1amnWBx39vZ07dyIhIQGnT5+ukRy6ceMGLC0tceXKlSZpuXz58iVevnxJzQm5Jl0//vt7GtJldO7cORAEgbFjx0JRURHx8fGIj4+Hvb099u7di9LSUmzfvh23bt0CSZIYPnw4Dhw4UONzCgsLER0djaioKMTGxuLz58/Uvl9++QWrV69ude6T5eXlKC4urrfCOyAgAPv37xfq3dwYnDlzhvrenz59KrD+pqjNuFoSTFCNQWgiIyPh4eGBM2fONOhz/Pz84OfnJ7R7nrBkZGQIrbHG7wXSVAtDoDqjLwzcNtzWMnlbs2YN1qxZgwcPHsDLywthYWG4ePEiLl26hJ9//rlJxJJDQ0Ph5OQES0tLnhaqsrIyLF++HLKysti+fTsMDAxoa9J06NCB1sIe+F+gS5gWJpIkhSoF/769u7XcL60BZWXlVuF4VFxcjFu3boHFYuHhw4eorKxEhw4dMHnyZOjr62PmzJl8s4qtQbeqR48ePM/2+/fvgyRJKCkpYciQITznJCUlCfUc79y5MxYuXIiFCxfi+fPn8Pb25jmmvgo3kiRx9+5dbNmyReDrfvv2DdLS0tTiQthKhsrKSjg7O+PcuXOorKzEhAkTYG9vX2cV74wZM7Bv3z4EBQXBwMCg3jFeu3YNX758YZ4rtWBsbIywsDCw2Ww8efIEenp6MDQ0FNgMhy6iMItoL5w9exZnz55F586dsWrVKujq6taozPr06RNCQkLg6uoKNzc3dO7cuU07wTfEHEZQA5SmZMSIERgxYgSsrKzAYrHg5eWFx48fU5XGv/zyC3x9fYX+XDrdEampqZg+fTpPtbWRkRF8fHzw6NEjoT+zPr4P0tVVSdxYBAQEgCAInDp1ClOnTgUAfP36FStXrkRAQAA2b96Mv//+G/Hx8ejYsSPMzc2xcuVKdOjQAc+fP0dUVBQiIyPx9OlTcDgckCQJeXl56OvrU0mtxujuoDtn4RZgCMOLFy9gb2+PxMREkCSJnj17Yv369TxtlXl5ebCxsWm2KrqGGCW1V5g3LINAFBYWwsfHB1evXsXHjx8b5TP5mQ80BXPmzGmQFoAoEDSwWFZWhuPHj1MBmClTpjTlsBqdyZMnY/LkySgoKIC/vz98fHyQnJxMTcYSExMRFhaGqVOnNmgBcP36dezZswckSSI5OZknqJaTk4MePXrg48ePsLCwQG5uLpYtW0ZLkyYqKorWwl5BQQGLFi0S+mfLyMgQ6H7+8V4BUKfOR3OKCrdGWmK7wI/89ddfiImJQUVFBUiSxOjRo2FgYABdXd16s6OtQbdq4sSJCAoKqqHn5e3tDYIg+Gp+Pnr0CE+fPsWCBQtoXW/48OH1tuPwq3B78eKF0Joojx49AofDgbi4uNCVDCkpKdi2bRuSk5PRqVMnbNu2TaDvxMDAACdOnICNjQ1kZWWhrq5e67ERERE4dOgQevbsyaPB19SEh4dj0KBBGDhwoMDnsFgssFgsWuZMdHFwcEBZWRkiIiIQGBiI69evw93dHX379oWhoSEMDQ3rdBdvDJrKLKI98OHDBxw7dgx9+/bFlStX+BqM9OzZE3/++Sf09PSwZMkSnDp1Crq6uhg0aFAzjLjpaag5TEtBSkoKc+fOxdy5c5GamgpPT08EBAQ0uNXxe+rT3ysoKKhVPkZFRYWSZWhMvp87ZmRkICIioknXR69fv4aqqioVUAOqqxg3bdqExYsXY/PmzYiPj8fgwYNx+PBhDBs2DFZWVoiOjkZeXh6VuB45ciQVRBsxYkSjJ3Lq6maqS3dc2HG8efMGixYtQmlpKbUtKysLO3fuhJiYGFVgcOfOHVhZWaGwsBBiYmJYuXKlUNdpDFrDHLelwQTVGOrk+fPncHd3B4vFQllZGUiShIyMjEBZ7MaivsUAV0BSUVERv/32G6ZPn16jvad3794tXutCkPG9ePECdnZ2SE1NhZSUFCwsLLBw4UIRjK7xkZOTo3S9kpKScP36ddy8eRMpKSlYv3495OTkYGRkhDlz5ghdjp+cnAxbW1t07ty51hapvn37IiwsDCwWCzt37oSjoyNUVVVpadKUlZXRXtg3tI2JzWajqKiIJ6PE716xs7Oj1QLKVKEIRlFRESIiIvDhwwfk5+dDQUEBffr0wbRp0yAnJ9csY7pz5w4IgkD37t0xa9Ys9O7dGxwOB8HBwfWe2xpa7JYsWQIfHx8sWLAAxsbGePHiBe7fvw9xcXGe90ZMTAwsLS0hJiaG+fPn09LyAqr/Hvbv31/vcd9XuB04cABubm6IiIjAtGnT6j03IiICr169gpSUFK1KBhMTE6pdtF+/foiLixNIAoHr8GZubo5Vq1ZBTU0NM2fOxM8//ww5OTmqAjckJARRUVFUJcrSpUvr/ezGhOva+uM9Wlf1+9u3b2m1aDWUTp06QVdXF7q6uigqKkJISAiCgoKo94uKigqMjY2hp6cnsOYeP5KTk9GtWzeRmUW0Bzw9PcHhcODk5MQ3oPY9PXr0gJOTE0xMTODr64utW7eKaJSipTHMYYShuLiYR5+sqKgIQHUw4sc5DXefMAwePBhWVlbYunWr0F0j/BBUf6+ioqJWkwsZGZkGaYTVxveFDBkZGSBJUuDiBjr61sXFxRg6dCjPdm4S9+HDh5g6dSqOHj0KKSkpANXP8Q4dOmDWrFmYNWsWpkyZ0uRuqLUVNvj5+cHf37/ROqpcXV1RWloKfX19bNy4EUpKSrhz5w5sbW1x9OhRGBsbw93dHfv37weHw8HQoUNhb2/P1yRIlGRmZkJGRgaysrI8+7gu5UxingmqMfChoqICLBYLHh4eVKaEJEmoqKjAzMwMhoaGAmnSNBbC6KFdv34dPXr0wJEjR/Dbb7814ahEB4fDwcmTJ3H69GlUVlZizJgxcHBwoKWr1RIZNWoURo0aBWtrawQFBcHLywtPnz7FxYsXcfnyZaF787ml2OfOnau3zUZPTw/S0tJYs2YNLl++DDs7O6E1aRqysG8obm5ucHFxobKrdd0rrTUA29L5+vUr7O3tERAQQLnHft+aLS4uDgMDA+zcubNZxFtJkkR2drbAk0Lu2BszY99UDB06FLa2trC1tYWLiws1dmtr6xo6h9ra2sjMzARJkli9ejWGDx8OPz8/6jsSJtgsaFDte/7v//4Pvr6+sLCwgJOTE19zBS6RkZHYtm0bJCQkUFFRQauS4XsX49evXwtcJcfV6Dxz5gxVLXDv3j2e47gtOI6Ojs3i4lzb9yWq6ne6dO3aFWZmZjAzM0N2djYCAwMRHByM/fv3w8HBAZMmTYKhoSFmzJhRqwtrbcyZM4dvoLGpzCLaAzExMRg3bhxUVFQEOn7YsGEYP3487t2712aDao1tDlMfly5dqvXdJUiCQhg6duwIXV3dBn2GMPp7zcH35ilcORFBK8vpzAnKy8v5Psu423r27Injx4/zGBNUVVXh9u3byMrKQmpqKjQ0NJo0sFRbYQN3/dlYhRn//fcfBgwYgIMHD1Iam3p6eigtLYW1tTXOnz+Pw4cPgyAIrFixAn///XezuguTJIm9e/fi+vXrcHBwqGFiBVTr9Do5OcHJyQnz58/Hrl272rUbMhNUY6DIzMzEtWvX4O3tjS9fvlBVaV+/fsWcOXOatO++LgRZDJaWluLTp094+PAhQkNDsW7dOvj7+zf94JqY1NRUWFpa4vnz5xAXF8eWLVtqWHe3JaSlpWFqagpTU1O8fv0anp6eCAwMFPpz4uPjMWnSJIF1a6ZOnYpRo0YhNjaW2iaMJk1DFvaNiaD3SkVFBRISEvDlyxcoKytj5MiREBMTa9SxtBfy8vKwcOFCfPjwAQRB4LfffsOQIUOoSfSbN2+QmJgIPz8/JCYm4vLly/VWPDQmraHarKHMnz8fampquHPnDioqKqCurs7TeiUpKYnBgwdj+fLlNVpdSJJE586dMWPGDGhqagrUcu7q6goWiwU9Pb0a22urGgWqK16Ki4tBEARWrlyJcePGUeOUkZGp0Vr++PFjkCQJBwcHWFpa0qpkaGhWXUNDA6GhoWCxWLh58ybS0tKQm5sLOTk59OvXDzo6OjAyMuKbtWYQjB49emDFihVYsWIFUlNTERQUBBaLBSsrK9ja2lLOr4JSW6Cxqcwi2gOZmZkwMjIS6hwVFRX4+Pg00Yian6Y0h/mRhmg6NXUlMj+E1d9rTvMoGRkZKCoqIi0trdGDk8Kgo6PDE1CLjo5GZGQkoqKicP/+fTx+/BgnTpyAvLw81NTUoK6uDjU1NcjLyzfTqOmTl5cHAwMDHtMaDQ0NkCSJY8eOoWvXrjh27BgmT57cTKP8H1ZWVvD390fHjh1rVF1y4XA4mD17NkJDQ+Hl5YWSkhIcPny4GUbaMmCCagyIiYmBu7s7IiMjUVVVBYIgMG7cOMydOxc6OjoYM2YM5QjXHAiTITAzM8Pdu3exZs0aylmxteLm5oZjx47h27dv+OWXX+Dg4ECJwrY26FQP6OjoQEtLS+jzsrOzMWPGDKHOGT58OF8BckAwTZqGLOwbA0HvldDQUOzZswcFBQXUtr59+8Le3h5jx45t1DG1dUiSxJYtW/D+/XvMnDkTO3fu5Bswy83NxYEDBxAUFIStW7fi0qVLIguKNySo1lp0qwCgV69edVZiBgYG8gSOL168iMDAQNy6dQt+fn6IiIiArq4uDAwMeFqbvmfdunXUAvJ7fqwa5cfZs2dhYWGB+Ph4JCQk8OwnSRLKysrYtWsXtLS0YGlpWetn1UVjZNU7d+5MJTkYmpb8/HwUFBSAzWaDJMkmaftiEJ7y8nKh574dOnSgqpTaIqIwh+HSEE0nui6edINqdPT30tLSaFfw0aGgoADKysooLCwEm82GhIQE/vjjD5GYp9QGv+r9bt26Yd68eZg3bx4qKyvx6NEj3L17F9HR0bhx4wYCAwNBEARGjBgBdXV1aGhoYOTIka2i2ODr16/o3r07z3au2YKYmBhl4NbchIeHw9/fHyoqKjh16hTfe1pRURH29vZYu3YtVq9eDRaLBSMjIx4d6/YCE1Rr5+jo6ODDhw8gSRL9+vWDoaEhZs+eXSO70tqYOnUqxowZgzt37uDSpUu0XHmak4yMDGzfvh0JCQkQExPDX3/9BXNz81bt3PV9ybkw0LFmlpaWrtH+JAgSEhLo2LFjDV00Qfhek4bOwr6hFBUVgcPhwMHBod57JSkpCZs3b0ZVVRW6deuGXr164cOHD/jw4QNWrFgBPz+/JhfNbktER0cjNjYWs2fP5rF//x4lJSU4OjpCUlISPj4+uHv3Lq1gsahpTbpV9cHv727ixImYOHEibGxsEBkZicDAQPj5+eH69evo1asXJSTPb5FIF1VVVURERODmzZuIjIxEZmYmCgsLoaCggP79+0NbWxsaGho8mfvWgCi0j9oSSUlJCA4Oxs2bN/H582eQJIn+/fvDzMyMp8WGoXno1q0bUlJShDrnzZs36NWrVxONqPkRtTkMXURdmUlHf8/Y2BgBAQF87zFu4JJfaybXcbKyshKBgYG4d+8eioqKMGLECCxevJjvHDYsLAx79uxBXl4eHj9+3KzmKcIgLi6OCRMmYMKECdi2bRuysrIQGRmJyMhIPHz4EE+ePMHJkychKysLVVXVVlslxa1cmzFjRosIqAGAl5cXJCUl4erqWq/eZ9++fXHixAkYGRnB29ubCaoxtE/ev38PaWlpmJubY+nSpa06cPM9v/76KwICAngy9lVVVTxlt0B13/zo0aObfTHj5eWFAwcOoKSkBAMHDoSDgwPVhtgWkJGRwbhx4/h+B42FsrIyUlNThTrn1atXYLPZuHr1apNp0jR2QM3Ly4uqxhwwYEC998q///4LDocDW1tbmJqagiAIcDgcnDlzBk5OTrh06RJsbGwadYxtGW9vb8rpTBCsrKxw+/Zt+Pv7t4qgWmvVrRIWCQkJzJgxAzNmzACbzcatW7cQGBiIc+fOwdXVFT///DOMjIygr6/fKK27kpKSmD17NuXy1RTY29tTLTKC4urqivPnz+Phw4cAqqt0vn79Spls8DM7EBMTw/LlywGIVvuotfLy5UsEBwcjJCSEkgLo3r07li5dCn19ffz666/NPUSG7xg7diyCgoLw6dMn9OzZs97jMzMzERMTg3nz5olgdM1Dc2jI5uTkICoqCvn5+VBWVoampma9wvVN6WjJDzr6e0B1MqIu3Wh++wiCQGlpKVauXInExETqXX3v3j14eXnhypUrVGCMzWZjz549CA4OpgL3ojJP4VLXnKGuffxaiXv16oUFCxZgwYIFqKysBIvFwoULF5CcnAwWi9Vqg2pcWlJA89mzZ9DS0hL4Hhg8eDDU1NSEli5oS7SNCAoDbaZMmYIHDx7A0dERJ0+ehKqqKoyNjQXWl2mpSElJ1WihCA0NhZOTEywtLXki6GVlZVi+fDlkZWWxfft2kTqbcsnNzYW1tTUiIyMBAEuXLsWWLVuaPcjXWBgbGyMsLAxsNhtPnjyBnp5ek5Wcq6qq4sKFC0hNTa2haVYbqamp1EKSHy1Nk+b7e4U7mQoICKj3XklKSoKmpibMzMyobdzqtrt37wplCMJQHYjV1NQU2HxARkYG6urqtYrLNwV03ZhaQxtFU9C5c2eYmJjAxMQEubm5CA4ORlBQEA4ePAhHR8c620KbCj8/P75/m3VVMjx8+BBdunThCao5Ozvj5MmTfKt/y8vLqcrXs2fP4ty5c1i0aBE2btwIoHpx5+zsDIIgqOcOQRAoLy9vkPZRWyc1NRXBwcFgsVh4//49SJKErKws5s+fD319fUyYMKHd/r21dExNTeHn5wdLS0ucPXu2zndsWVkZtm7disrKSpFXZYkSUWvIurm54fDhw5SbMQB06dKlVmf35oKO/p6uri6ioqJw+vRpoa934cIFJCQkQF5eHr///jsUFRWpFsk9e/bAzc0NaWlpWL16NT58+ACg+l2xefPmGp/TlOYpXPz9/WvVuK5tH78ulYqKCjx58gSJiYlITExEUlISCgsLQZIkunTp0iymOY1NS3oXFBQUoE+fPkKdM2jQIMoJuD3SeqMmDI3ChQsXkJGRAS8vL/j6+lLONHJyclQraGvk06dPVAn09evXsWfPHpAkieTkZJ6gWk5ODnr06IGPHz/CwsICubm5InfmMTAwQGFhIQBQkw9BMi4EQWD79u1NOrbGwMHBAWVlZSIpOZ87dy7c3NywYcMGXL58mdIq4Edubi7Wr19fw62xpfP9vdKtWzfk5OQIdK9kZ2fXGjAePXo0vLy8GnWcbZ1Pnz5h5syZQp3Tu3dvhIWFNdGIeKErhNycAsotBSUlJSxduhRLly7FmzdvYGNjg7i4uAb9bthsNqKjo/Hbb7/VqHqLj4/HyZMnkZqaCmVlZZiamsLExARAdfDsRw2j7xE2GF7f+G1sbODj4wMpKSlISUnV2Pf9+4YkSbi4uODixYuIioqqt3KkPWJkZES1dklKSkJPTw8GBgZQV1dv1UnL9sKYMWMwZ84c+Pn54ffff8fGjRsxZcqUGpX2JEkiKioKjo6OePPmDRYvXtxi2reaClFpyEZFRVHSCqNGjYKysjLS0tLw8uVLbN26FX379hU4WFdUVIS4uDhkZ2eDzWZDRkYGffv2xejRoxvFcIWO/l6fPn0gJiZWrwZmZWUlz/Pi4MGDkJKSgre3NyVxs2jRItjY2MDLywtPnz6Fubk5cnJy0K9fP+zfv7/epFBjm6cA/KvNBIXNZlMBtMTERDx79gzl5eUgSRLi4uIYOXIkpkyZAjU1tVZluFXXWqMlrUPk5eVr6C8Lwrdv36jq9vYI81ZnQO/evbFx40Zs2LABkZGR8PLyQlRUFC5duoTLly+DIAikp6cjPz+/zgBFS4HNZuPu3bsYM2YMkpOTYWtri86dO9ea2erbty/CwsLAYrGwc+dOODo6QlVVFUOHDhXZmL9/cD179gzPnj0T6LzWElQDILKS84EDB8Lc3BxOTk7Q0dHBkiVLoKGhgUGDBkFaWrqG0567uzvYbDYWL16MK1euNOJP23R8f6/k5OQAqBZdrw+SJGud9HXp0oURxxYSSUlJfP36VahzSkpKRBp8SE5Opn0uXaHntgKHw0FMTAxYLBbCw8MbrAFGkiSmTZuG4uJinDx5kgqq3bt3D2vWrEFVVRVIkkROTg6SkpLw4sUL2i6e/KrXBB2jt7c3Ro8ejRMnTvAVF1+6dCn1744dO+Kff/5BQEAAFi9eTOuadOHXNsQNPiYkJPAED+sKTDYVr1+/BkEQ6N69OzQ1NSElJYXY2NgaTtP8aE3v9baOra0tioqKEB4ejlWrVqFTp07o168funTpgtLSUnz48AElJSUgSRJ6enrt5nsThYash4cHxMXFcfr06RpVtzdu3IClpSWuXLlSbwfBmzdvcOTIEURGRvI1kOjQoQOmT5+OTZs2oX///rTH2hD9vfT0dISGhkJHR6eGnnVgYCBOnDiB9PR0KCoqwszMDGvXrkWHDh2Qnp6OadOm8WhGL1myBJ6enti0aRNycnJgbGyM3bt3C+2+2ljmKZcvXxb6HDs7OyQmJmLChAkgSZJ6lg8YMACqqqqYMmUKJk6cKHCXgCDU9s6sqyIc+J++nTBcvHgRvr6+tX5WbftEmZAFgH79+gndWfHo0SOROty3NJigGgOFmJgYtLS0oKWlhdzcXPj6+sLb2xsfPnzA/fv3oaGhAU1NTZiYmGDq1KlNqotFl/T0dOzYsQNFRUUwMDCgHnbnzp2rt9VQT08P0tLSWLNmDS5fvgw7OztRDBmA6EVVm5umLjlfs2YNvn37hrNnz1JBux8hSRIdOnTAn3/+CQsLi1YTVPv+XqlL7+1H6tN9Y6qThKN///5CVwnFxcWJ1ATGysoK06dP5ysgzcCfhw8fgsVi4datWygoKABJkhgyZAj+7//+D/r6+kI7CwOgFibFxcXQ1NSkFkKVlZXYs2cPqqqqMGXKFMp17p9//oG7uztmzJghUEsLv0oGOpAkCUlJSRw7dkwgt7758+fj2LFjuHfvHhYvXkxL+4gutbUNkSSJP/74g+/25qgCIEkS2dnZ8PT0FPgcukE1xiyi8ZGQkICLiwu8vb1x/vx5vHv3Dq9fv65xjIqKCv7v//6vWaRDWiqNUTX0/PlzTJ8+naeN3cjICD4+Pnj06FGd50dGRmLjxo0oLS1Fx44dMWLECPTq1QsdO3YEm81Geno6UlNTKeOY48ePQ11dndZY6ervjRkzBrq6uqiqqsLAgQOp+cGNGzewbds2ANXmWyUlJTh58iRSUlJw/PhxsNlsvq153PMzMjKwceNGrFmzRuCfoaWYp3h4eAAA5OTkMHnyZEyZMgWqqqpNKjNQ31yutv103ilFRUW1Pntr29cc764ZM2bAwcEBERERAumiRkRE4NWrV1i9erUIRtcyYYJqDHxRUlLCqlWrsGrVKsTGxsLLy4tawEdEREBBQaHBfdO9e/cWSKNGkKx7RUUFcnNzkZGRAQ6HAy0tLRgYGODYsWOYNGmSwNpdU6dOxahRo+rNJDc2ohZVbUk0Rck5QRDYtGkTtLW1ce3aNURGRiIvL4/ap6ysjGnTpmHevHmtrlXj+3slIyMDERERAt0/gpgpMAiOhoYGXFxcEBYWhunTp9d7/O3bt5GSkiLSSgY/Pz/07t2bCarVw+PHj6nFRG5uLkiSRO/evSndq4Y+I5KSkgAAR44cga6uLrU9JiYGHz9+RKdOneDg4EBV6Do6OkJLSwuenp6YOHGi0JUMDWHy5MkCLQqB6oDD5MmT8eTJE5FqHzWkpUiUiDpZxphFNB3z5s3DvHnzkJGRgTdv3qC4uBiysrIYMGCASBMl7YmCgoJaq8dUVFTqrKJJT0/Hxo0bUVZWhjVr1mDFihV8K5tyc3Nx8eJFXLhwAZs2bUJwcDCtShs6+nsVFRVISEiAuLg4FixYgJ9++ona7+DgAIIgYGxsDDs7OxAEgQMHDsDDwwNhYWGoqqpCx44deT6b240wfPhwgQJqTW2ewmazISEhIZQ+9O+//w5JSUls27atzmBSYyWTANCuCheWluiOXhvGxsY4deoULCws4OTkVKfxUWRkJLZt2wYJCYkGmZK0dpigGkO9TJo0CZMmTUJRURH8/f3h7e3Nt8w5OTkZ2dnZKC4uhoyMDPr161enUPycOXMECgYIUw3Su3dvzJs3DytXrgRQrSMlbGXB8OHD4e3tLdQ5DI1DY5Wccxk5ciTliFleXo7CwkLIy8szejYMDWbRokW4cOECdu7ciW7dutUZuH/48CGsrKwgKytLaWUxNC8vXrygFhPcKh5FRUUsWrQI+vr6GDNmTK3nhoeH87QTcltt+QWvnz9/DgA1AmpAdesnUB3I+r7lXVJSEmpqakhISICPjw92794tVCVDQxgyZAjf7Z06deKrPdSvXz9EREQ0mvaRINBpKWoORJksY8wiREPv3r15Wu4YmoaKigq+gSOg2vinrvnh5cuXUVpaigMHDtSpDa2kpIQtW7bgl19+webNm+Hu7s4j5i8IdPT3fvrpJ7x58wYXLlyoUWBw9+5d5OXlUe7i3ICUlZUVwsPD+bYH/oiqqmqt+0RpnjJ+/HisW7cO5ubmNbZz3aTXrVvHc46ioiJcXFywcOFCodti6VKfrl1j0ZqeHfLy8ti/fz/Wr1+PlStXYty4cZR+ooyMTA0pncePH4MkSTg4OAhtbtCWYFaWDLWSmZnJs3BQUVHBjRs3qAxRfn4+Tp06hcDAQEo8/Xvk5eUxZ84crFq1irYYaH0ZBDExMXTu3BkKCgro3r17jX3S0tKoqKgQ6noSEhKQlJQUepyNRVFREQIDAxEaGlpDy65Pnz6YNWsWDAwMGkVYtaUgqpJzCQkJgVqaWhNlZWUgSRJLliwR6F7hFwwA6g4IEARBtaUx/A95eXnY29tj48aNWLRoEWbOnImpU6diyJAhlN7O27dvcfPmTdy+fRskScLV1bVVCbq3Bt0qupiYmIAgCHTp0gVz5syBgYEBJk+eLNBi4uXLl3j58iXffX5+fjzbamutTkhIAEEQmDRpEs8+ZWVlfP78GTY2NujQoYNQlQwNgZ/uEACsXLmSSlZ9D/cZ1FDtI4aGERER0dxDaPP82EJcUVGBkJAQpKamolevXtDT00PXrl2bcYQMXB48eAAVFRWBzdb09PRw5swZxMTE0AqqAcLr7718+RKTJ0/m6djhdgGpq6vXqK4TFxeHqqoqoqOj6x1LbcFIUZunfK+J9j1xcXFwcXHhG1TjnldbW6ylpSVIkuTbFtsWKS8vR3l5eaNqyAmKtrY2zp49CwsLC8THxyMhIYHnGJIkoaysjF27dkFLS0vkY2xJMEE1BgDV4oIuLi7Q1NSk2i19fX3h4uLCc+yBAwdgbGyMZ8+eYdWqVfjy5QtIkoSSklINvYKMjAzk5+fjwoULCAgIwJkzZ2hlqxuSQVBWVkZqaqpQ57x69arZhBYjIyOxc+dO5OXl1XgRZWVlISsrCwkJCThx4gTs7e1b9cOrKUvOk5OT0a1bN8r9VRCio6MpzSNRadI4Oztj4sSJtFuZuKYiQM1qzrrulbqCAQD/gAATVKsdHR0dODk5YceOHWCxWAgJCeE5hvts3LdvX53l8y2R1qJb1RAqKioQFhYmsAiwlJQUbGxshLqGtbU1z3dfUlJCaTPxewZwqzBIkhS6kiErK0uo8X3P+/fvhTr+7du3ANAg7aOGkJmZCRkZGb6JptOnT0NDQwMqKipNdn2Gtk1RURHs7e1x8+ZNODs7UxVAeXl5WLZsGd68eUPNC44cOQJnZ2eRVb0w1E5mZiaMjY2FOmfMmDEIDg6mfU1h9ffGjh3LV8MtMTERBEHw1dNUUlLCly9faI+xNZincNeUYmJifJNJADB79my+bbGCSHE0J+vWrYOenh709PRqbGez2SgqKuJbcezq6goXF5c65+5NiaqqKiIiIijtwczMTBQWFkJBQQH9+/eHtrY2NDQ0hGrxbaswQTUGeHp6Ys+ePeBwODyVPCRJUno8HA4HkZGROHz4MKZMmYIVK1agoKAAhoaG+Ouvv3gstUmSxNOnT+Hm5gYWi4XVq1cjODhYJFVWjx49wpcvX6CqqooLFy4gNTW1zlZULqmpqXj48KHA2a3GJDo6Gn/99Rc4HA5UVVWhq6uLoUOHQk5ODiUlJUhJSUFISAgiIyOxdu1aODs7tyqtJFGVnM+ZMwfm5uY8GbC6RP25mkei1KRxdnYGQE8fiM69wlSKNA0zZ87EpEmTcO3aNURHRyM1NRVFRUWQk5PDgAEDMGvWLBgZGTVbBUNcXBx1rwkKQRCtRreqIZAkidLSUpSWlgp1nrBtfSdOnOB578XFxaGqqgqysrJ8gz4vXrwAAFqVDN7e3ny/94cPHwIAXFxceBIE3H0PHjxAUVGRQPdrbm4uHj58CA6HQ1v7iC4kSWLv3r24fv06HBwceKqav3z5AicnJzg5OWH+/PnYtWtXrdUbbQlRmkW0daqqqvDnn3/i+fPnEBMTQ3l5ObVvz549SElJgaysLP78808A1a5+5ubmCAkJaZCDOUPD+fbtm9DvXDk5OaEdvfkhqP5eZWUlzzOpoKCASlTweweXlJRQLvZ1vdtr28d97ovKPIUO3Pk4nbbYlh5UCwsL4+us7ubm1qyBs/qQlJTE7Nmzm2Vt3JpggmrtnI8fP8LOzg5ycnLYv38/pk6dWmM/QRA1qtX27duHK1euwNbWFgUFBXW6yxAEgZEjR+LIkSP49ddf4eDggKtXrwrlRkOXo0ePIiEhASwWC25ubtiwYQMuX74MBQWFWs/Jzc3F+vXrQZIkTE1Nm3yM31NUVAQLCwtISEjgyJEjfAM4KioqMDY2xr1797BhwwZYW1vjt99+g7y8vEjHSgdRlpzX1mqVnJwMf3//OoNLvXr1atSxNAV07xUWi9Uq7pXWSNeuXSljl5ZGfHy8wLqUBEFQ1WYtdXLXWHBbnoUlPj6epyW2Pvr374/w8HCUlpZCSkoKQHUVIEEQ0NDQ4EkmvHnzhqru4pcMqq+SAfifZg0/Tpw4UeP/33/vX79+xYEDBwSqTrWzs0NVVRVIkqStfUQXKysr+Pv7o2PHjjWCHVw4HA5mz56N0NBQeHl5oaSkBIcPH270cbQkRGkW0R7w9fXF8+fPMXPmTOzZs4eaP6ampuL27dsgCALHjh3D5MmTAVQbXc2dOxcXL17Eli1bmnPobQY/Pz++zzGu1AA/IzOCIFBVVSX03FJcXLzW9nc61Ke/16NHDx7JhPv371PV7fz0LZOSktCtWzcqqFbbM/7Hfd8/41t6gvXjx48A0CRtsQwMTQkTVGvnuLu7o6qqCs7Ozvjtt9/qPf6vv/7C1atXERcXh0GDBgkcIPvzzz9x7do13L17VyRBNS4DBw6Eubk5nJycoKOjgyVLlkBDQwODBg2isj1coUV3d3ew2WwsXrwYo0ePFtkYgeqJQ0FBAQ4fPlxvRZSamhr279+PjRs3IiAgAMuWLRPNIBtAayg5FxMTaxW6NG39XmFoXMaPH8+0IzUif/zxh9AVtVxdmaVLl+L333/HixcvEBoaCjExMZ422rdv32L9+vXgcDgQFxenVckgKSnJV/9MkHHGxcXBz88P3759g6WlJV8phNzcXNjZ2eH27dvQ1tYWuHW2sQgPD4e/vz9UVFRw6tQpvmNUVFSEvb091q5di9WrV4PFYsHIyAiampoiHauoiIqKEqlZRHvg5s2b6N69OxwdHWu0NnEd/H766ScqoAYAw4YNw8SJExEZGckE1RqJjIyMOrU6+QWVWoIEgSD6exMnTkRQUBDy8vIouRJvb28QBMG3C+XRo0d4+vQpFixY0KAguSgNVOhQUlLCd3tTtsUyMDQGTFCtnXP//n2MGjVKoIAaACgoKGD8+PGIjY0V+qE+fvx43L59m84wG8SaNWvw7ds3nD17FidPnsTJkyd5jiFJEh06dMCff/4JCwsLkY/x5s2bGDRokMDC/LNmzcLQoUNx69atVhMoIUmyRZectxbaw73S2mjJ5iITJkyoVQxYUBjdqmrS0tKoxZKMjAzGjRsnsONYamoqnjx5gqdPn1LVtCtXrqTciQFg4cKFSEpKQlVVFebMmYPExERalQx9+/al/Z3n5ORg8eLFYLFYCA0NxciRIzFs2DDIysqioKAA7969Q2JiIiorKzFo0CD8888/Ig+qeXl5QVJSEq6urvW22fXt2xcnTpyAkZERvL2922xQzcPDgzGLaGRevXoFdXV1Hq2guLg4qsr0R37++ecmaXduj9RnUlYXS5YsEXlwTVj9ve3bt8PHxwcLFiyAsbExXrx4gfv370NcXJynAi8mJgaWlpYQExPD/Pnz23SAnF+1oKBtsQwMzQkTVGvnZGZmwsTEhO++2lxbhg4divv370NOTk6oaykqKtaagWhKCILApk2boK2tjWvXriEyMhJ5eXnUPmVlZUybNg3z5s3Dzz//LPLxAdXlzsIaD0yYMAGhoaFNNKLGpTVN5kWpScNtSRWGN2/eQFdXV6hzWtO90tpoy+YibV23is1mw83NDffu3UNRURFGjBiBVatW8Q1Wubm5wcnJCUC1qzSbzcaTJ0+gp6cHQ0NDjBo1qt7rJSUl4datW6ioqICmpia16OKSm5sLKSkpLFu2DGvXrsXu3btpVzLQpXv37vD394eDgwMCAgLw33//4b///qPah4BqZ7m5c+di586dzbKQefbsGbS0tATWrRo8eDDU1NTw33//NfHImo/nz583m1lEW6WgoIDHUZ4kSTx+/BgAb3saUN2KJqzjPAN/BK2wrqys5NvqefHiRfj6+gp8veLiYoGP/RE6+nv29vawsLDA4cOHKZ1LgiBgbW1do+1fW1ubMvRavXp1mw6oAdWSAYWFhTW2CdoWy8DQnDBBtXZOeXk5JCUl+e5btGgR32o07qJJWKePjh07oqqqSvhBNhIjR46kqgLKy8tRWFgIeXn5JrGRFhbuWIShc+fOYLPZTTSixqWll5tzEbUmTXh4ONVKIigcDqdN3yutibZuLtKWdasKCgrw+++/U9VnQHXr5e3bt3Hx4kXqXZGVlYWtW7fi0aNHIEkS48ePx/nz5xEREYHAwEBcv34d7u7u6Nu3LwwNDWFoaIgBAwbwveaoUaPqDL5duXIFioqKVPXbkiVLqEoGXV1dPHv2TOBKhoYgLS0NW1tbWFhY4M6dO0hLS0Nubi7k5OTQr18/TJs2jecZRFf76OLFi0KPr6CgAH369BHqnEGDBlGaPG2RgoICkZtFtHU6d+6MgoKCGttevnwJNpsNcXFxjB07luecjIyMZqtKbqukp6cjNDQUOjo6NYT+AwMDceLECaSnp0NRURFmZmZYu3Yt9fwsKioS2qGdbnUbXf29/Px83L59G3fu3EFFRQXU1dV5TN8kJSUxePBgLF++nJpLh4eHY9CgQRg4cKDAY2SxWGCxWEIbFzUUYX+nffr0QWFhoUiTSQwMjUHzRxMYmpXu3bvj06dPfPcpKCjwFfbniki2ZiQkJFpUVkNOTq5O3Qh+ZGZm8tWSYaAHSZIi16QZNmwYXyegumCxWMy90gJo6+YibV23ytXVFe/evcOAAQOwcuVKKCkpISIiAp6enrC1tYWPjw/+++8//PXXXygoKICkpCQ2bdpEtRXp6upCV1cXRUVFCAkJQVBQECUvwP3e9fT0hHIB/LEqZujQobC1tYWtrS1cXV1BkiTExMTqrWTYsGEDrd8JQRA1Wjk7d+4MQ0NDgc4VpfaRvLw8T7CjPr59+yZ0dX1roqKiQuRmEW2doUOHUk6EXLh/H6NGjaohlg4ApaWliImJwYgRI0Q2xraOj48Pdu/ejaqqKgwcOJAKqt24cQPbtm0DUJ0EKCkpwcmTJ5GSkoLjx48LnaxsKA3V31u4cGGtnx0YGAgxMbEa29atW8fX5d7Pzw9+fn58W2ffvn0r8t8LUO10X1sg75dffuHZxk1y0WmLZWBoTpigWjtn4MCBePDgATgcDs9Dmx/l5eWtKtubnJyMbt26UdkOQYiOjsa9e/dgZWXVhCOryYgRIxAdHY2vX78K1E7z9etXREVF8RXsZKAH18FOlJo02traQmsfFRYWMvdKC6A1GEasW7eO9vfe1nWr7t27B1lZWVy/fp2qLNHU1ISCggJOnz6NqKgobN68GWw2G2PGjIGDgwP69evH8zldu3aFmZkZzMzMkJ2djcDAQAQHB2P//v1wcHDApEmTYGhoiBkzZkBGRkbocc6fPx9qamrYs2cPIiMjERwcXG8lw5kzZ2j9TugGuRqifUSHfv36CV159ejRIyaxwCAUs2bNwt69e3Hu3DksW7YMKSkpcHd3B0EQMDY25jn+wIEDKCoqanVt/i2Vt2/fwsbGBh06dMCCBQvw008/AQDKysrg4OBAfQ92dnYgCAIHDhyAh4cHwsLCMH36dJGOtSn19/itzWpzuc/IyBDanbqpqW2sdUEQBLKyspi2WIZWBRNUa+fMmjULUVFRuHz5MpYuXVrv8WfOnEFJSQkIgqi13aM2hK2u4cKvbaQ+kpOTAVS3HfLL5oSFhSE8PJxvkCQpKQmXLl0SaVBNX18fEREROHr0KHbu3Fnv8Q4ODigqKsK8efNEMLrWR3FxMTIzM2ts47YBZGVl8bzkuftagyYNc6+0DFqDYURDDAraum7Vp0+fMG3aNJ5WrTlz5uDUqVOwtLRESUkJzM3NYW5uLlDSqUePHlixYgVWrFiB1NRUBAUFgcViwcrKCra2trR/N7169cKvv/6KqKgonoAawFvJQLca4datW0JrPHKZPXt2vcfUpn0kLDNmzICDgwMiIiLqDWgDQEREBF69eoXVq1c3+NoM7QdTU1N4e3vj8OHDOHbsGKqqqkCSJFRUVDB37lzqOHd3dwQFBeHx48fo3bs3TE1Nm3HUbYcrV66AJElcuHChhn7d3bt3kZeXBxkZGVhbW1OBLCsrK4SHh8PX15dWUK28vBzl5eU8FYiCwOjv8Ye7FqNDVlaW0G2xrYHw8HCe9TD398Rv3fny5UuRjIuh4TBBtXbOrFmz4OzsjEOHDqFTp0519qRfvnwZp0+fxpAhQ5CSklJvuwc/6GTChQnc/Xit2jIkXIH4liKgr6enBzc3N1y5cgUcDgcbNmzgq8tRUFCAgwcPws/PDxMmTOCb/WKorpyorXqirkVYa9CkYe6VlkFbNxdp67pVbDYbvXr14tnes2dPANU//4EDB/hWpAhCfn4+CgoKwGazQZJkk7bf/Rjw6927N63P4VZ/0GH27Nm0tY+ExdjYGKdOnYKFhQWcnJx4EiHfExkZiW3btkFCQoJpD2IQio4dO+LixYtwdHREWFgYKioqoKGhgZ07d9a4d11dXZGdnY3+/fvD1dVVaL1hBv7ExsZi8uTJPAEp7jtGXV29RgBMXFwcqqqqiI6Oxrp166Cnpwc9Pb0a57LZbBQVFUFZWZnneq6urnBxcaEVxGD09+qmoqICCQkJ+PLlC5SVlTFy5Mh6E1W9evUSui22NfDy5cta7zE/Pz++20XtZMtADyao1s6RlpbG4cOHsWTJEtja2uLy5cuYPn06hg0bBjk5OXz58gXv3r3DzZs38ebNG3Tq1AmHDh0SWvyzITTUVrs1QBAEnJ2dsXDhQnh4eMDX1xejR4/G4MGD0aVLF3z79g1v375FXFwcSktL0b9/fxw9erS5h90i4TdZEoSMjIxWoUnD3Cstg7ZuLtLWdas4HA7foA53QTxhwgShA2pJSUkIDg7GzZs38fnzZ5Akif79+8PMzEzgisbmZPbs2bQn73S1j+ggLy+P/fv3Y/369Vi5ciXGjRtHVTJwnePS0tIQGRmJx48fgyRJODg4CB0kbm2I0iyivdC1a1fY2dnBzs6u1mNWr14NBQUFTJ8+vUUYX7UVsrOz+QbMExMTQRAEX2kDJSUlfPnyBWFhYXz1at3c3GgHzuqC0d+rndDQUOzZs6fGfKJv376wt7fnG2wUlNYYUGsphRwMTQPz9GfAmDFj4OXlha1bt+LNmzd4+/YtzzEkSWLgwIFwcnKidA1EhaC22j/Cz62uJdOjRw/4+vpi3759CA4OxoMHD/DgwYMaFXcdO3aEqakptm/fLpCeVnskIiKC1nnCGgY0lDlz5vAVaRUE5l5pftq6uUh7160aM2aMQMe9fPkSwcHBCAkJofRdunfvjqVLl0JfXx+//vprE4+0dthsNtzd3aGlpcX3va2rqwt1dXWsWbMGCgoKlFGLsLx9+xaGhoYi1T7S1tbG2bNnYWFhgfj4eCQkJPAcQ5IklJWVsWvXrnahcyVKswiG//FjNc23b98gKSnZTKNpO1RWVvIkOgsKCqg1yvjx43nOKSkpgbS0NAoLC0UyRi6M/h5/kpKSsHnzZlRVVaFbt27o1asXPnz4gA8fPmDFihXw8/Or1S27LdKa2lQZhIcJqjEAqA4oBAYG4sGDBwgJCUFaWhpyc3MhJyeHfv36QUdHB5qamrTbNYDq8l8nJyds3bq1EUfOy8ePH3H16lX4+vo26XWaAllZWRw8eBBbtmzB/fv38ebNGxQXF0NWVhYDBgyAtrZ2q6kEYaibhmasmHuleWnr5iLtXbeqroqT1NRUBAcHg8Vi4f379yBJErKyspg/fz709fUxYcKEZg9WfPjwAX/88QdycnIgJSXFE1RLTU3Fu3fvkJaWhtDQUJw7dw5Dhw6t93NLSkqQlpaGLl26UMYNotY+4qKqqoqIiAjcvHkTkZGRyMzMRGFhIRQUFNC/f39oa2tDQ0OjXbTjidosgoE/8fHx2LFjB27fvt3cQ2n19OjRgydIfP/+fZAkCSUlJQwZMoTnnKSkJHTr1k3kQTVGf48///77LzgcDmxtbWFqagqCIMDhcHDmzBk4OTnh0qVLsLGxae5higwnJyeYmprylZ5gaP0wQTUGCoIgMGXKFEyZMkXgc+Li4nDv3j0UFRVh+PDhMDIyQqdOnXiOe/bsGbZv347U1NQmC6pFRUXBw8MD0dHRqKqqavZFTUPo0aMHk9FoIDk5OYiKikJ+fj6UlZWhqamJLl26NPewKBoi4Pp9VR1zrzQPbd0wgtGt4o+RkRFSUlIAVAsl6+npwcDAAOrq6i2m9YvNZmPZsmXIzs6GhoYG32rvwYMH49KlSzh//jwiIyOxevVqBAUFQVpaGpmZmYiLi4OEhAQ0NTUp19Jz587BxcWFaoX/6aef8M8//zRI+6ihSEpKYvbs2QIZJbRlBK3obyyziPZCVlYWjh8/jujoaBQXF2PEiBFYt24dJk+eXOO4srIyODo6wt3dnZbbIQMvEydORFBQEPLy8qCoqAgA8Pb2BkEQ0NbW5jn+0aNHePr0KRYsWIA3b96IdKyM/h5/kpKSoKmpCTMzM2qbmJgY/vrrL9y9e5e2ZnZr5dSpU3B1dYWamhpMTU2hpaXVKttYGfjDvFkZaFFZWYlt27YhJCQEACjL43///ReXLl2iHOOqqqpw/PhxnD9/HpWVlY1eOVNUVAQfHx9cvXoV6enp1GRmwoQJWLBgATZv3tyo12tOsrKy8O7dO/Ts2ZOvAxzD/3Bzc8Phw4dRWVlJbevSpQvs7Owwa9asWs8TpSYNXf0igiDw4sWLOo9h7pWmp60bRrQH3Sp+Llz17Xv16hXExMTQvXt3aGpqQkpKCrGxsYiNja3zWgRBYPv27Y0y7vq4cuUKMjMzsWbNGmzcuLHW4yZMmIAJEybA1tYW165dg4eHB8rLy3Hy5ElUVVUBqK6IPXHiBD59+gRHR0cAgIKCAr5+/YpXr17hzz//BIfDoa19JEquXr2K33//XaTXFDWiMotoD2RlZWHevHnIz8+n5paJiYlYsWIFTp06RT3Lnz9/js2bN+PDhw8gSRKGhobNOew2w5IlS+Dj44MFCxbA2NgYL168wP379yEuLs4zF4uJiYGlpSXExMQwf/58XLt2TeTjFbX+XkZGBuLj43m2AUBCQgJPcFdYuYrGIC8vr9YK6NGjR8PLy0vEI2peli9fjhs3biAyMhJRUVHo1q0b5s2bh3nz5tHWg2ZoOTBBNQZaeHh4gMViQUJCArq6ulBUVER0dDRSUlJgZ2eH48ePIz8/H2vXrkVSUhJIkoS2tjZsbW0b5fovXryAu7s7WCwWvn37BpIk0bVrV5iYmMDMzAwDBw4EgFYXVPP39weLxcK6deswcuRIANWByT179sDHx4d6SY4bNw6HDx/msfBmqK5Y5GoDjRo1CsrKykhLS8PLly+xdetW9O3bF8OHD+d7rig1afjpgdTG58+fkZaWBuB/4qzMvdK8tAfDiLauW1WXC1dd+0iSRHZ2Njw9PQW+liiDardv30b//v3x999/C3S8paUlwsLC4OXlhffv30NJSQmzZs1Chw4dcPPmTWzYsAFycnLo3bs3nJ2d8csvv6C8vByurq5wdnaGmJgYbe2jhpCeno779++jqKgII0aM4Kke4pKVlYUdO3YgNja2TQfVRGkW0R44ffo08vLyMH78eJibm0NJSQkRERFwdnbG/v37oaGhgbCwMGzZsgVlZWVQUlLCnj17GtTSzPA/hg4dCltbW9ja2sLFxYVK3ltbW2Pw4MHUcdra2pSe5erVq2ud37UEGlN/z9/fH/7+/jzbSZLEH3/8wXe7qDt4Kioq+HYvAaDmSe0JCwsLbNmyBVFRUfDx8cGdO3dw8uRJnDlzhqleawMwQTUGWty8eRPi4uK4evUq9QLbunUrNmzYgPDwcHz48AHm5uZISUmBrKwsrK2tG5y9q6ioQEhICDw8PKhAHTcLHhsbCz09Pb6LluLiYmRmZtbYxnUvzcrK4snmiNLZ9Husra3h4+MDAJg7dy4VKHFycqKyOVOmTAFBELh//z6WLVsGf3//Nl8+LiweHh4QFxfH6dOna1RP3LhxA5aWlrhy5QpfPTNRa9JcvnxZoOP8/f2xb98+AECfPn1w4MAB5l5pIbQHw4i2qltFV9MwMTGxQY5loiAtLQ36+voCL6AkJSUxefJkBAUFQVFREYGBgZSz7erVq6Gvr4+0tDScOHGCMleRkJDAunXrcPv2baSmptLWPqKLo6MjpdfDZfz48Th16hTVrgoAXl5ecHBwAJvNbnX3qDC8ffsWNjY2IjWLaOvExcWhR48eOHv2LBX4GDJkCCQkJODg4AB/f39YW1ujsrISurq62LNnD99qZQb6zJ8/H2pqarhz5w4qKiqoaunvkZSUxODBg7F8+fJWJYXREP09YZKyzUl9rdDtsVVaTEwMU6dOxdSpU/HlyxfcuHEDvr6+TPVaG4AJqjHQ4t27d9DS0qqRERITE8OaNWsQFhaGv//+GykpKZg4cSIOHjzYIEe4rKwsXLt2Dd7e3lQZvrKyMkxMTDBnzhz07t27TufGS5cu1RowEUSAWxTcu3cP3t7e6NevH7Zu3UoFg/Ly8vDvv/+CIAjs2LGDyj4FBgbCwsIC165d49uS2J55/vw5pk+fztOOZGRkBB8fHzx69IjveS1NkyY/Px+7d+9GWFgYSJKkAjOPHj1i7pUWRHswjGiLulV0F1+tYdFWWVmJrl27CnVOjx49UFVVBQ0NDSqgBlS3emppacHPzw+//fYbz3mqqqp48+YNIiMjaWkf0cHf3x/nzp0DQRAYO3YsFBUVER8fj/j4eNjb22Pv3r0oLS3F9u3bcevWLZAkieHDh9N2N20NNJdZRFvm8+fP0NHR4akk0tHRwYEDB2Bra4sOHTrA1ta2hhg9Q+PSq1cvngqv7wkMDGwRlT2i1N8TNCnL0LKRl5fH0qVLsXTpUrx48QI+Pj4ICgpiqtdaKUxQjYEWxcXF6N+/P892bkl2cnIyTE1NsWfPngY/CLS1tWu0dxoZGQnsoNdaovx+fn6QkpKCh4cHpUcHVOv6VFRUoGfPnli0aBG13dDQEBcuXEBoaCgTKPmBgoICvvcmAKioqODJkyd1nt8SNGnCwsKwe/du5OXloXv37ti7dy+l38LcKy0TQQwj0tLSYGVlhatXr4poVKKnPehWNQfTp09H7969BTq2Z8+eyMrKEurzP336BAB8q8e42/gFh6WlpcHhcFBWVkZL+4gOAQEBIAgCp06dwtSpUwFUu+uuXLkSAQEB2Lx5M/7++2/Ex8ejY8eOMDc3x8qVK9u0flhzmkW0VUpKStCzZ0+e7dy/h/Lycpw7d67WtmMG0VDbGoOfLibXIMrKyorn+Nra/QWB0d9jaCgqKipQUVGh5BgCAwMRExODqKgodO/eHZGRkc09RIZ6YIJqDLSorKzkqwMgJSUFABg4cCBsbW0bpX+fw+FAWloaCxYswLRp06hWN0GIiIho8PVFwX///YepU6fWCJIAoFrJNDQ0eCYOY8eORVBQkCiH2SqoqKjg0ffhIiMjU6eGQ3Nr0rDZbOzduxcBAQEgSRL6+vrYvXt3jaoT5l5pObDZbLi5uVEOyCNGjMDq1atr6L1wcXNzg5OTU6vTEGkPulVFRUUIDAxEaGgo0tPTkZ+fDwUFBfTp0wezZs2CgYFBg9q6GsPpd9iwYXVWZP94zr179/D161eBWo7LysoQFRUFAHxbJLnP07oSZKLUPnr9+jVUVVWpgBpQ/VzetGkTFi9ejM2bNyM+Ph6DBw/G4cOHBf69tWays7NbhVlEa6I2DSpupbqGhgYTUGvB1KWL6efnx3c73TULo79XO7WZ/tQV4CQIAvv372/ysbVEJCQkoKenh4kTJ8LPzw8uLi7Iyclp7mExCAATVGNoErS0tBpNEHPjxo3w8fHBmTNn4OrqCnl5eRgaGmL27NmUvktrJy8vj28VQmJiIgDwnRDLyMigpKSkycfWXmhuTZoHDx5gx44dyMrKgry8PHbv3s3XqZS5V1oGBQUF+P3335GWlkZlpt++fYvbt2/j4sWLVPA/KysLW7duxaNHj0CSJE8lSUumPehWRUZGYufOncjLy6vRipOVlYWsrCwkJCTgxIkTsLe3p23E0JROv/wwNjZGaGgoHB0dYWNjU+/xBw4cQFFRUYPe2aLUPiouLubrKMedDzx8+BBTp07F0aNHqURfW6eysrJZzCLaM21l/tkWoauZSRdR6+/R7Tqg61bfEOoKbgL8A5ztNaj29etXhIaGwt/fHwkJCeBwOJCVlYWpqWlzD41BAJigGkOT0JgT2TVr1mDNmjV48OABvLy8EBYWhosXL+LSpUv4+eefBdL5ycnJQVRUFPLz86GsrAxNTU106dKl0cbYUDp27IiysrIa29LT05GTkwOCIPguxPPy8oTWzWGonebSpCkrK8OhQ4fg4eEBDocDLS0t7N27l9Im+hHmXmkZuLq64t27dxgwYABWrlxJZaY9PT1ha2sLHx8f/Pfff/jrr79QUFAASUlJbNq0qdW04LYH3aro6Gj89ddf4HA4UFVVha6uLoYOHQo5OTmUlJQgJSUFISEhiIyMxNq1a+Hs7MxXI6w+Gur0KyxaWlqYMGECrl69isLCQmzcuLFGKzuX9+/f4+jRowgNDcXgwYPx5s0bWtfjIirto/Ly8hpBXS7cbT179sTx48dbXYC3IfTo0UPkZhHtHUbjqOUiau1LUevv8XOi5/K9URK/faJE1MHN1ghJkoiJiYG/vz/Cw8OpboYJEybA1NQUM2bMaFfvstYME1RjaDVMnjwZkydPRkFBAfz9/eHj44Pk5GSqiigxMRFhYWGYOnVqDSF5Nzc3HD58GJWVldS2Ll26wM7Ojm8lUHPQt29fpKam1tjGbccZNGgQX6OHhw8fok+fPiIZX3ugOTRpkpKSYGlpibS0NHTu3Bk7duyAiYlJnecw90rL4N69e5CVlcX169epjLOmpiYUFBRw+vRpREVFYfPmzWCz2RgzZgwcHBzQr1+/Zh614LR13aqioiJYWFhAQkICR44c4Wtao6KiAmNjY9y7dw8bNmyAtbU1fvvttxpC/oLQEKdfOhAEgUOHDmHZsmUIDg4Gi8XCgAEDMGjQIEhLS6OoqAhpaWmUro+ysjLOnDkDbW3tJtUhElUQQkdHp90tQiZOnIigoCCRmUUwMLRk4uPjaZ9Lx1lT1Pp7tZmv+fn5wd/fX+Ru9rXRGox9motXr17B398fQUFByM3NpRIgixcvxvz581vVfJGhGiaoxkCb5ORk+Pv7C72voQ5ycnJyWLZsGZYtW4akpCRcv34dN2/eREpKCtavXw85OTkYGRlhzpw5yMnJoRYmo0aNgrKyMtLS0vDy5Uts3boVffv2pa3r0phoaGjg7NmzSEhIwLhx4/D161e4u7uDIAjo6uryHO/l5YX09HTo6+s3w2hbPn5+fnwzedzFIr9qobS0NJFq0hw9ehTnz59HVVUVRo4ciX/++Qe9e/cGm82u87zJkyfDzc2NuVeamU+fPmHatGk8LRxz5szBqVOnYGlpiZKSEpibm8Pc3LzVVTW0dd0qPz8/FBQU4PDhw/W6QKupqWH//v3YuHEjAgICsGzZskYdS21Ovw1pzevRowd8fX1x6NAhBAQE4N27d3j37l2NY2RlZTFv3jysW7eOqi4XpQ5RU/F98qO9sGTJEvj4+IjMLKK9UNtcoq59zdFix1CTP/74g/ZziY5hgaj192pzq+fej4K62TOIltzcXAQGBiIgIACvXr0CSZIQExOr4fL5fVEIl8+fPzNVxa0AJqjGQJvw8HCEh4cLvU/YoNqHDx9qjdiPGjUKo0aNgrW1NYKCguDl5YWnT5/i4sWLuHz5MjQ0NCAuLo7Tp0/XCJjcuHEDlpaWuHLlSosoT166dCmuXr2K//u//8OoUaPw4cMHZGdnQ0FBAYsXL6aOS05ORkhICM6fPw9paekaLo8M/yMjI4OvMCoXfhNhkiRFqklz5swZahL29OlTof4uunTpwtwrzQybzUavXr14tnOz1QUFBThw4ACMjY1FPbRGoa3rVt28eRODBg0SONg8a9YsDB06FLdu3WrUoFpdTr8NRUpKCjY2Nti2bRvi4uKQmZmJoqIiyMvLY8CAAfjtt99qVBa2hHchAz2GDh0qUrOI9kJdc4na9rW0IHN7RkZGBuPGjWv2CmpGf48BAKZOnYqqqiqQJImePXti7ty5mDt3LpSVlfkeHx8fD3d3d4SHh+Pp06ciHi2DsDBBNQZaiLKkd9asWZg4cSLMzMwwffp0vlF8aWlpmJqawtTUFK9fv4anpycCAwPx/PlzTJ8+nacCycjICD4+Pnj06JGofow6UVBQgJubG7Zu3UqVrffq1QvHjh2DnJwcddzy5cuRn58PcXFxHDp0iMlc8IFu2fvWrVtFqklT20tUEJydnZl7pZnhcDh8J+rctrMJEya02oAa0PZ1qz5+/Ci08cCECRMQGhraKNcXxOm3sZCUlBQoUNeaWnUyMjJqbfGqax+d1q7WgijNItoDLaWFjkF4jI2NERYWBjabjSdPnkBPTw+GhoYYNWpUs4yntVWqMzQNJEli2rRpMDU1hbq6Ot/74uvXrwgICICHhwfevHlTaxUkQ8uDCaox0EKUGW1FRUU8ePAAsbGxUFBQwJw5c2Bqalpr9dpPP/0Ea2trbNu2DWPHjkX//v35HqeiooInT5405dCFQkVFBcHBwXj79i0qKiowdOhQnkW7mpoaZGRk8Mcff2DgwIHNNNKWjaBl75WVlTUCtJqamiLVpImIiKB1HhfmXmnZjBkzprmH0KS0dt2qwsJCobXROnfuXG97tiAI6vTLUDv+/v61SkzUto+um2prQlRmEe0Bui105eXljTwSBmFxcHBAWVkZIiIiEBgYiOvXr8Pd3R19+/aFoaEhDA0NMWDAgOYeJkM74+7du7UmuFNTU3H16lX4+/ujpKQEJElCUlISurq6jPZlK4EJqjG0eCIjIxEdHQ0fHx/cuXMH586dw4ULF+qtXpOQkEBFRQVPSx8XGRkZymWlpUAQRI1WjR9xcHAQ4WhaN+np6QgNDYWOjk4N57vAwECcOHEC6enpUFRUhJmZGdauXdusmjSZmZmQkZHha7F++vRpaGpq8rQPMPdKy4bfM6kt0dp1q+Tk5OpsEedHZmYmXyMQQRHW6Zcuzs7OtM4jCALm5uaNOpamoC1XmzU1TECt6fj48SOuXr0KX19fPHjwoLmH0+7p1KkTdHV1oauri6KiIoSEhCAoKAgnT57EyZMnKSMaPT09KCkpNfh6jP4eQ338GFDjcDgIDw+Hu7s7Hj58CKC6mq1Xr174v//7P8yePRtdunRpjqEy0KBtz/oZREZqaipCQkKQnp6O/Px8KCgooE+fPtDR0cFPP/3UoM8WExODpqYmNDU1UVBQgBs3blCTFkGr19oiX758gZ2dHY4ePdrcQ2lx+Pj4YPfu3aiqqsLAgQOpoNqNGzewbds2ANUtwyUlJTh58iRSUlJw/PhxkWvSkCSJvXv34vr163BwcODRd/ry5QucnJzg5OSE+fPnY9euXbUGieuCuVcYGGoyYsQIREdH4+vXrwLpIn79+hVRUVF8DUsEgY7TL12cnZ1BEARIkhToeO6xrSWoJqibKgODKIiKioKHhweio6NRVVXFtGq1QLp27QozMzOYmZkhOzsbgYGBCA4Oxv79++Hg4IBJkybB0NAQM2bM4Ct7IAiM/h6DoOTl5cHT0xPXr19HdnY2SJKEtLQ0dHV14ePjA01NTfzxxx/NPUwGIWGCagwNIjs7G1ZWVlRW7vtJPEEQOHnyJMaPHw8HBwe+ot7CIicnhyVLlmDJkiV4+fIlfHx8EBQUJHD1WkumsrISgYGBuHfvHoqKijBixAgsXryYbxVDWFgY9uzZg7y8PCZQ8gNv376FjY0NOnTogAULFlBB3bKyMjg4OIAgCBgbG8POzg4EQeDAgQPw8PBAWFiYyDVprKys4O/vj44dO/JtGeFwOJg9ezZCQ0Ph5eWFkpISHD58mLlXWgjh4eG1TqJr20cQBPbv39/UQ2sU2rJulb6+PiIiInD06FHs3Lmz3uMdHBxQVFSEefPmCX0tuk6/AL2KwHXr1gl9DgMDg+AUFRXBx8cHV69eRXp6OjX3nTBhAtOq1cLp0aMHVqxYgRUrViA1NRVBQUFgsViwsrKCra0t/vvvP6E/k9HfYxCExMREeHh44NatW6ioqAAAjBs3DnPnzsWsWbMgJSUFHx+fZh4lA10IUtBUJgPDD3z48AGmpqYoKChA165dMXXqVAwZMgTy8vIoKSlBSkoK7t69i7y8PMjJycHd3b3OdjW6VFRUICwsDL6+vrh//z44HA5VvXbu3DmsW7eO7yLD2dkZLi4utOyzG5vS0lKsXLkSiYmJ1OSMIAgoKiriypUrlPYDm83Gnj17EBwcDJIk0b9//0YTzm4r2NnZ4dq1a7h06RLGjRtHbQ8NDcXff/8NGRkZREZGUovVyspKTJ8+HSoqKjh58mS9n8/hcBqlhSY8PBzm5uZQUVHBqVOn6mwrS09Px+rVq/Hu3Ts4OTnh0qVLzL3SzAwbNozWeQRBtIhnTn0MGzas1ix6XcK5rUW3iiRJmJqa4tmzZ1i4cCE2bNjAt/26oKAABw8ehJ+fHyZMmECrVaeu32VdtJbfpahhs9mQkJAQStMvKSkJSUlJPK38DAzC8OLFC7i7u4PFYuHbt28gSRJdu3aFiYkJzMzMGP3SVkZ8fDxYLBZu3bqFvLw8kb+fy8vLaWmT1vYcy8jIQGZmZq2JLabdtHnhzgWGDBkCXV1dGBoa1pCn4R6zYMEC7Nmzp3kGyUCb1lXKw9BiqKysxIYNG1BQUIBly5Zh48aNkJSU5DmuvLwcLi4uOHPmDDZv3gxvb29a7Wt10bFjR0o34cuXL/Dx8YGLiwvOnz8PoHYtA24VCb+Xk6hfPBcuXEBCQgLk5eXx+++/Q1FREXfv3kV0dDT27NkDNzc3pKWlYfXq1fjw4QM17s2bN4tsjK2F2NhYTJ48uUZADajWQQMAdXX1GtUf4uLiUFVVRXR0tECf31iaNF5eXpCUlISrq2u9eh59+/bFiRMnYGRkhGPHjuHt27fMvdLMiNKspTloDdVmDYEgCDg7O2PhwoXw8PCAr68vRo8ejcGDB6NLly749u0b3r59i7i4OJSWlqJ///60Kz0b4vTLwMv48eOxbt06nlbVuLg4xMXF8U2iRUdHw8XFhQmqMQhNRUUFQkJC4OHhgaSkJCqpMHHiRMTGxkJPTw/bt29v7mEyCEhSUhKCg4Nx8+ZNfP78mUo6mpmZ8UhwNBUN1d+rTbutvv1Mu2nzIyUlheHDh2PAgAFN4vbN0HwwQTUGWty+fRvJyclYuXIltmzZUutxEhIS2LRpE8TExHD69GncunWrSV5apaWlCA0NRUhICB48eIDy8nIoKioiLy+vTp0DgP/LR9Qvnjt37kBKSgre3t7o3bs3AGDRokWwsbGBl5cXnj59CnNzc+Tk5KBfv37Yv38/T9CIoZrs7GyoqanxbE9MTKQmwj+ipKSEL1++iGJ4FM+ePYOWlpbAArmDBw+Gmpoa7t27x9wrLYCGtv62dNqDblWPHj3g6+uLffv2ITg4GA8ePMCDBw9q6JF17NgRpqam2L59u0Daa/xoqNOvsNjb20NdXZ3vc7AtQJIkX724uLg4uLi4MO2vDI1CVlYWrl27Bm9vb+Tn54MkSSgrK8PExARz5sxB7969aVcsM4iWly9fIjg4GCEhIZQmbvfu3bF06VLo6+vj119/Fck4Gkt/j2k3bZ0cOnQIXl5eCAgIQEBAADp06ICpU6di9uzZmDp1aquTLWKoCfPtMdDCz88PCgoKWL9+vUDHr127Fj4+Prhx40ajBtXu37+PgIAA3L59G6WlpQCAKVOmwNTUFNra2nj06FGjXaspSU9Px7Rp06ggCZclS5bA09MTmzZtQk5ODoyNjbF7927ai7v2QGVlJU81ZEFBAd6+fQuAfwVOSUmJyH+nBQUF6NOnj1DnDBo0CHfv3sWsWbOYe4WBoRGQlZXFwYMHsWXLFty/fx9v3rxBcXExZGVlMWDAAGhra0NOTk7k46qqqsLt27cxa9Ysoc+9ePEiunTpwhNUY1ogGRgER1tbu0Z7p5GREW2jEgbRk5qaiuDgYLBYLLx//x4kSUJWVhbz58+Hvr4+JkyYIJIEelPo702YMKExh8ggIgwNDWFoaIgPHz7Ay8sLfn5+CAsLQ3h4OOTl5aGnp9fcQ2RoAExQjYEWqampUFdXF1gLoGPHjlBVVaUsgxvCmzdv4O/vj8DAQOTk5IAkSfTo0QNLly7FvHnzagQbBH3xVFZWNmuGgM1m8w2wcHvtMzIysHHjRqxZs0bUQ2t19OjRg6cy8f79+yBJEkpKShgyZAjPOUlJSTxW102NvLw8CgoKhDrn27dvAMDcKwxNTnvTrerRo0eLqD78/Pkzrl+/Dk9PT+Tm5tIKqtUG0wLJwCA4HA4H0tLSWLBgAaZNm4aRI0c295AYBMTIyAgpKSkAqs2l9PT0YGBgAHV1dZHN9Rn9PYba6NevH7Zs2YKNGzfizp078PLywr1793DlyhUQBIGoqCi4ubnByMgICgoKzT1cBgFhgmoMtMjJyUHPnj2FOqd79+7Iy8ujdb38/HwEBgYiICAAL1++BEmSVNmsqakpNDU169S6Sk9PR2hoKHR0dGqIQgYGBuLEiRNIT0+HoqIizMzMsHbtWnTo0IHWOOlSVVXFV2uuU6dOAIDhw4czQRIBmThxIoKCgpCXl0e5YXp7e4MgCGhra/Mc/+jRIzx9+lTkjl39+vXDkydPhDqHW3nJ3CsMTU1b162i2yJIEAROnDjRyKMBEhIS4O7ujtu3b6OqqoqqqmBgYGgeNm7cCB8fH5w5cwaurq6Ql5eHoaEhZs+ejV9++aW5h8dQB69fvwZBEOjevTs0NTUhJSWF2NhYxMbG1nkeQRAN0scTtf4em81GdHQ0fvvttxpmV/Hx8Th58iRSU1OhrKwMU1NTmJiYNNp1GRqHDh06YPr06Zg+fTqys7Ph7e0NX19fZGRkwMHBAY6Ojpg6dSpMTEwwbdq05h4uQz0wQTUGWnTu3FloDarCwkJaEffVq1cjJiaGWmj07t0b8+bNg4mJSZ2OiVx8fHywe/duVFVVYeDAgVRQ7caNG9i2bRsAQFpaGiUlJTh58iRSUlJw/PhxocfZlKiqqjb3EFoNS5YsgY+PDxYsWABjY2O8ePEC9+/fh7i4OM9iPyYmBpaWlhATE8P8+fNFOs4ZM2bAwcEBERERAr0sIyIi8OrVq3qPY+4VhsagretWhYWFCXU8V2etMduFSktLERAQAA8PD6SkpFC/79GjR2PBggXQ1dVttGsxMDAIx5o1a7BmzRo8ePAAXl5eCAsLw8WLF3Hp0iX8/PPPmD17dnMPkaEOSJJEdnY2PD09BT6HblCtOfT37t27h82bN6O4uBgnT56k1kP37t3DmjVrqDVTTk4OkpKS8OLFC1hbWzfqGBgajx49esDc3Bzm5ua4f/8+PD09ER4ejrCwMERERDBO4K0AJqjGQIshQ4YI5VhDkiTu37+P/v37C32tyMhIiIuLY+bMmTA1NRUqaPD27VvY2NigQ4cOWLBgAX766ScAQFlZGRwcHEAQBIyNjWFnZweCIHDgwAF4eHggLCwM06dPF3qsTUVjO6a2ZYYOHQpbW1vY2trCxcWFWghbW1tj8ODB1HHa2tqUYO3q1asxfPhwkY7T2NgYp06dgoWFBZycnOoUFY+MjMS2bdsgISGB8vLyOj+XuVcYGOpHUKHnsrIyHD9+HE+fPgVQrdnZUN6+fQsPDw8EBASAzWbXCKbZ2tri559/bvA1GBgYGofJkydj8uTJKCgogL+/P3x8fJCcnEzNIRMTExEWFsYIjbcgRO3OLWr9vezsbKxbtw5lZWXQ1NSkZG8qKyuxZ88eVFVVYcqUKdi/fz8A4J9//oG7uztmzJjB6AK2AqZMmYIpU6bgy5cv1DOHoeXDPP0ZaKGlpUW5mAhS4XP9+nWkp6dj1apVQl/LwsICc+bMoVXlduXKFZAkCQsLCxQWFlJVanfv3kVeXh5kZGRgbW1N6QZZWVkhPDwcvr6+LSqoxiAc8+fPh5qaGu7cuYOKigqoq6tj0KBBNY6RlJTE4MGDsXz58mbRUpKXl8f+/fuxfv16rFy5EuPGjaPGKSMjg8LCQqSlpSEyMhKPHz8GSZJwcHCApaWlyMfKwNDWEERv88WLF7Czs0NqaiqkpKRgYWGBhQsX0roeSZIIDw+Hu7s7YmNjqWD/+PHjMXfuXFhaWmLYsGFMQI2BoYUiJyeHZcuWYdmyZUhKSsL169dx8+ZNpKSkYP369ZCTk4ORkRHmzJnDuII2M6Ke04laf+/ixYv49u0bjh49WqOiOSYmBh8/fkSnTp3g4OBAucs7OjpCS0sLnp6eTFCtFSEvL48///wTf/75Z3MPhUEAmKAaAy0WLFiA8+fPY9++fejatSt0dHRqPdbf3x979+5Fr169YGBgIPS1li9fTnucsbGxmDx5MgoLC2u0LMXExAAA1NXV0blzZ+p4cXFxqKqqIjo6mvY16RIXFwdnZ2eh9hEEwaN5xFBNr1696lwABwYG1qnDJwq0tbVx9uxZWFhYID4+HgkJCTzHcNsIdu3aBS0tLVhaWjL3CgNDE8LhcHDy5EmcPn0alZWVGDNmDBwcHNCvXz9an3fq1Cl4enri06dP1N/z7NmzYWJiQpmOMMFy4RCFax8DQ22MGjUKo0aNgrW1NYKCguDl5YWnT5/i4sWLuHz5MtOq1c4Qtf7e/7d372FV1fkexz8LDQXHC95IHC95KS9zyGOo5QU0c7wr2kktj5imOQlMnuxM+Zzy4FSaMzaTKTY5pxIUU0DBo5IYoCg6I16KMCGPOuYMzGheERVR2OcPhRHZKGw3e+0N79fzzPPM8/uttdcnnoXs/d2/9f3t2bNHPXv2LNciIC0tTdKt1ZUlBTXp1pfI/fv3t/oeE4B9UFSDTTw9PbV06VJNnz5dc+bMka+vrwYOHKhOnTrpJz/5iQoKCnTixAklJiYqMzNT7u7u+uijj1S/fn2H5jx9+rTVx+oOHjxY2jz0bs2bN69yvzh7KGkAXpm5O/v7UCixjdkFtRL9+vVTSkqKtm3bptTUVOXm5pb2H2zXrp0GDx4sf3//Mrswcq8A1eP48eN644039N1336lu3bqaO3euZsyY8UBFnKVLl8rT01OTJ0/WsGHD5OfnZ8fE1uXk5Gj//v3lxqRbmyJY65cn3dqgwhUsX768wi8WaCIPR/H09NSECRM0YcIEHT16VNHR0dq8ebPZseBgju6/l5ubqzFjxpQbP3DggAzD0JNPPlluzsfHx+bN4gDcH0U12MzPz0+RkZF6/fXXlZGRYXUnQ4vFok6dOmnJkiWmLIe/efNmuR5TFy9e1IkTJyRZ/wBx5coVeXp6OiRfCVdv+o0HU79+fQUGBlbqjRf3ClA9Vq1apQ8//FAFBQXq2rWrFi9eXNqH80EYhqGrV68qJSVFN2/eVH5+vgYMGFCtu0zHx8crPj6+3LjFYtGUKVMqzOkqK2wqKgreC6vbUJ0effRRvfXWW6UbYKH2cVT/vevXr5f7nHLlyhUdPXpUkvXPNgUFBaW7xAOwP4pqeCA9evRQYmKiEhMTtXv3bh07dkx5eXlq0qSJ2rdvr2HDhsnf379aPzzci7e3t3JyctSpU6fSsb1798pisah58+ZlxktkZGSoRYsWjoxJoQSVxr0C2FdOTo7efPNNHThwQG5ubnrllVcUHBxst6bjO3bsUGxsrDZu3Kh169Zp/fr18vLy0qhRozR+/Hi7f+HkKqvNbJWdnW12BNQCycnJ6tChgx555JFKn5OQkKAvv/xSy5Ytq8ZkcHbV3X+vefPmOn36dJmx9PR0FRUVqXHjxurWrVu5c44cOWJTb2oAlUNRDQ+sTp06GjFihEaMGGF2lHL69OmjLVu2yMfHp3QsNjZWhmFo8ODB5Y4/dOiQMjMzNWnSJEfGtElqaqrWrl2rTz75xOwocHLcK7BFbVjZExMTo/fff19XrlzRI488osWLF9u9yfTDDz+skJAQBQcHKy0tTTExMUpJSVFkZKRWr16tzp072/VnvXr1aru9ljO7ceOGDhw4oAsXLsjHx0e+vr5O81g/XF/J7+zdX2TFxcUpLi7O6g7CJ06cUFJSkqMiwgVUR/+9nj17KjU1VdeuXZOHh4ekW6uTDcOQv79/ub8nx44d06FDh6w+MgrAPiiqwSbZ2dlq0aKFmjVrZnaUewoKCtKGDRsUGxsri8Wi2bNna+/evapbt66CgoLKHLtnzx698cYbcnNzq9SOpma4dOmSNmzYoC+++EJ/+9vfzI4DJ8a9ggdVk/tWnT17Vm+99ZZSU1MlSVOnTtXcuXPL9C60N8MwNGDAAA0YMEDnz59XXFycYmNjSx/ZiY2N1ZkzZzR+/HgNGjTItBXeriAxMVFhYWG6ePFi6VibNm20aNEiPfHEE+YFQ41R0SPG1voVAvdjz/57kyZN0pYtWzR16lQ9//zzOnLkiBITE+Xm5lbu8f4TJ04oNDRUxcXFGjt2rD3+UwBYQVENNhk3bpzVb/AKCwtVWFhYZkdNM3Xu3FkLFizQ/PnzZbFYlJKSIsMw9NZbb6ljx46lxw0ePFi5ubmyWCyaNWuWunfvbmLq8r777jtFRUUpISFB169fl8ViUYMGDWzaTRU1G/cK7KUm960aNWqULl26JEml/95/8MEH9z3PMAy9+eabD3z9pk2b6qWXXtJLL72kAwcOKCYmRtu3b1dKSop27NghLy8vjRkzxqZrLVq0SAMGDLC6SU9NkJGRoddee01FRUVq0aKFWrVqpVOnTunUqVOaMWOG4uLi1L59e7NjAoBVD9p/z8/PT8HBwQoPD1dmZmbp3+qZM2eWWWn9wgsvKCMjQ0VFRRo3bpzVDQwA2AdFNdikog9bK1euVHh4uLKyshycqGLPPfecjh07poiICM2bN08DBgxQhw4dyhxTv359dezYUS+99JLGjRtnUtKybty4oYSEBK1du7Z0EwiLxaJu3bpp4sSJGj16tMM3VIBz4l6BvdX0vlV3rnA6fPiwDh8+XKnz7FVUu5Ofn5/8/Pz09ttv63//938VExOjrKwsRURE2HStiIgINWzYsFxRLSMjQxkZGeVWabuazz//XMXFxVqwYIEmTJggwzBUXFysTz75REuXLlVkZKTmz59vdkwAtYQZ/fdCQ0Pl7++v7du368aNGwoICFC/fv3KHHP27Fl5eHjoxRdf1OzZs226DoDKoaiGWqFhw4YyDENTp061Or9582an6cWSm5urdevWKTY2VhcuXChdaXT16lWNGzdOixYtMjsinAT3CqpbTe1b5Yy/Gz/5yU/0wgsv6IUXXtB3332n2NhYu77+7t27FR4e7vJFtYyMDAUEBGjixImlYyUbTOzcuVPp6ekmpgNQ25jVf6+kX1tF1qxZo2bNmtFKAHAAimqA5BQfEvfs2aOoqCilpqaqqKhIhmHIz89Pzz77rIYOHap//dd/ZTtsSOJegWPU5L5VzrIiuSKdO3fW3LlzzY7hlM6dO6fOnTtbnevRo4diYmIcnAhAbeas/fdatmxp2rWB2oaiGuAEhg4dqlOnTslisaht27YaPXq0AgMD1aZNG7Ojwclwr8AR6FtlPyEhIVZ3yM7Pz1deXl6Z3alLrFy5UitWrLBpZ7ia7saNGxV+adCwYUMVFBQ4OBEAAKjNKKqhxvrxxx/VokULSbe+RbKl6baj/PDDD/L09FRwcLCmTp2qunX51YR13CtwBPpW2U9SUpK6dOlSbnzVqlX37EHqzH+zzHS/nws/NwA1ma27bxuGwRc1QDXh0xhqnP379ysqKkrJycnKzMyUdKuhZ2hoqMnJKta3b1/96U9/0pIlS7RixQr169dPY8eOVUBAAEUTlMG9AkegbxWA2s7a43s5OTmSpAMHDpQr4JbMAdXJ1i8O+MIBqD58AoPNDMMwO0Kpq1evatOmTVq7dq2OHTsmi8XiVPnu57PPPlNOTo5iYmK0ceNGbd++XV999ZWaNGlS+ngfIHGvwDHoWwWgtouPj1d8fHy5cYvFoilTplgdd6X3nnBNNX13bsAVUVSDzSIiIrRx48YyY5cvX5YkDR482Oo5hmE88G43dzp+/Li++OILxcfH68qVK7JYLKpfv76GDx+uSZMm2e06jtC6dWvNmTNHv/zlL5WamqqYmBjt2rVLkZGRWr16tQzD0F//+ledP39eTZs2NTsuTMS9gupG3yrXVtUVNiV69epV7dnsITk52eqqoJIPm/PmzSs3ZxiGFi5cWO3ZUDO4yu8Cap/8/Hy5u7vL3d3d7CgAbqOoBpvl5eUpLy/P6lxFS+Dt8Q1ecXGxkpOTFRUVpX379km69e1gq1atNH36dAUGBqphw4YPfB2zuLm5adCgQRo0aJDOnj2rjRs3KjY2VqdOndLevXvl7++vgIAAjR8/XgMHDmSr7FqMewXVhb5Vrq2qK2wk1+q3k5WVVWEvOkmKi4srN0ZRDVWxevVqsyMAVvXq1UshISEKDg42OwqA2yiqwSbJyckOv+a5c+cUHR2t9evX6/Tp07JYLPL09NTw4cO1YcMGBQQEVPhhwVU1b95cL7/8sl5++WXt27dP0dHRSkpKUnJyslJSUtS0aVPt2bPH7JhwAtwrAKSav8Jm0aJFZkcAgDIc2X+vos3XVq5cqU8//bR0wQEAx6GoBpu0bt3aYdc6ePCg1q5dq+3bt+vGjRuSJD8/Pz377LMaNmyYPDw8tGHDBoflMUufPn3Up08f5eXlKT4+XrGxsTp69KjZseCEuFeA2qumr7AZN26c2RFQixw+fFjJyck6f/68fHx8NGzYMLVr187sWHAyztB/r7CwsMIniABUL4pqcHqTJ0+WYRjq1KmThg8frtGjR6tNmzZmxzJNo0aNFBQUpKCgIH377bdmx4ET417Bg6BvFYDa7L333tOaNWsk/bMIsmzZMr3++ut68cUXzQ0Hp1HTVwcDuD+KarBJZGSkzecGBQVV+RwPDw91795d7du3V6NGjWy+trOypenot99+q2+++Ua+vr7VmAzOhnsFjkLfKvuxVqC8V3HyXj/36vCXv/xFJ06cqHCTIaC22bx5s1avXi13d3c988wz8vHx0cmTJ5WSkqLFixera9eu6tOnj9kx4QRq+upgAPdHUQ02WbhwYaWWLVtb3lzVotpvf/tbxcTEaNOmTdq0aZPq1KmjgQMHKjAwUAMHDlTduq5/G1fUdDQ9PV3p6ekKCQkpd86uXbsUHh5uU5ESrot7BY5A3yr7uleB0lpxUrJ9Y5/evXvr5Zdf1owZM8qMZ2dnKzs7W4GBgeXO2bp1q8LDwx1ezAOc1caNG+Xh4aF169bpscceKx3fv3+/pk2bpnXr1lFUAwBIoqgGGwUHB1f6DX9qaqoyMzMl3WqmXlWjR4/W6NGjderUKcXExCguLq60AbuXl5dGjBhR5dd0NhU1HU1PT1d4eLjVQglqJ+4VOAJ9q+zH0QXKvLw8FRQUlBtPSkpSeHi41aIagLK+//57DR06tExBTbr1xVa/fv2UkZFhUjI4K/rvAbUXRTXYJDQ09L7H5Ofn67333tPhw4clScOGDVNYWJjN12zbtq3mzp2rOXPmaMeOHYqJiVFaWprWrFkjwzC0a9curVq1SmPGjFHTpk1tvg4AAPZCgRJwPXl5efL29rY69+ijj7LDIsqg/x5Qu1FUQ7VIT0/XvHnzlJubq0aNGmn+/PkaOXKkXV67Tp06euaZZ/TMM8/o9OnTio2N1caNG5WTk6PFixdryZIlGjhwoMaPH6+nn37aLtcEAMAW+/fvt/lcGmAD5rh586Yeeughq3P16tXT9evXHZwIzsqM/ntxcXFKT08vM1bSt7OiVh+GYSgiIsKuOQDcQlENdlVYWKglS5ZozZo1Ki4ulr+/v9599121bNmyWq7n7e2t4OBgBQcHa+/evYqOjlZycrKSkpKUkpKiI0eOVMt1AQCojClTptjcH40eZwDg3Mzov5eTk2N1d25J5YptJWz9OwTg/iiqwW4yMzP1xhtv6C9/+Ys8PDz05ptvasKECQ67ft++fdW3b19duHBB8fHx2rBhg8OuDQDAvTRo0EB+fn6qU6eO2VEAAHbi6P57kZGRdn09AA+OohoeWFFRkcLDw7Vy5UrdvHlTvXr10qJFi/TTn/7UlDxeXl6aNm2apk2bZsr1AQAoMXbsWCUlJSk/P1/ffvutRowYodGjR+vxxx83OxqAe2BlDyrD0f33evfubdfXA/DgKKrhgRw7dky/+tWvlJWVJXd3d73++uuaOnWqXd+IREZGqkePHvL19a30OevWrVN0dLQ2btxotxwAAFTV4sWLdf36daWkpGjz5s1av369oqKi1KZNm9Ldrdu3b292TAB3iYiIsPo+8vLly5KkwYMHl5szDENJSUnVng3Og/57ACiqwWaffvqpPvroI12/fl3du3fXb37zG3Xs2NHu11m4cKFCQkLKFdUiIiIUGRmp5OTkcuecPXvW5XrR8I0oKot7BXAt9erV0/DhwzV8+HDl5eXpyy+/1JYtW7RixQqtWLFC3bp109ixYzVixAg1b978ga/HvxHAg8vLy1NeXl6F89Z6WvG7BwC1D0U12GTy5Mk6dOiQJMnX11czZszQyZMndfLkyfuea+2bPVtcvnxZubm5dnktZ7B8+XItX77c6lzXrl0dnAbOjHsFcF2NGjXSxIkTNXHiRJ0+fVqbN2/W1q1btXDhQi1evFhPPvmkRo8erSFDhqhBgwY2XcPaCpt7ra4pmQNwi7UvbAEAsIaiGmxy8ODB0v+fmZmpV199tdLnutoKMkexWCxVPodvRGsn7hWgZvD29taMGTM0Y8YMHT9+XFu2bFFCQoLmzZunBQsW6Ouvv7bpde+1wqaiHeP4NwL4p9atWz/wa6Snpys9PV0hISF2SARnxr+fQO1GUQ02CQwM5A+IHWVnZ5sdAS6CewWomc6fP6+LFy8qPz9fFotFBQUFNr0OK2wA55Cenq7w8HCKarUA/feA2o2iGmzy/vvvmx2hRrpx44YOHDigCxcuyMfHR76+vnJzczM7FpwQ9wrg+jIyMrR161Zt27ZNP/74oywWi9q1a6eJEydq5MiRNr2mPVbYAAAqj/57QO1GUQ1wEomJiQoLC9PFixdLx9q0aaNFixbpiSeeMC8YnA73CuC6srKytHXrVn355ZfKzc2VxWJRy5YtNXXqVI0cOVL/8i//Ui3XLSoq0sWLF9WkSRPVqVOnWq4BALUNq4MBUFSDTbKzs9WiRQs1a9as0ufs3r1baWlpmjdvXjUmc00ZGRl67bXXVFRUpBYtWqhVq1Y6deqUTp06pRkzZiguLk7t27c3OyacAPcK4HqOHz+urVu3KiEhQT/88IMsFosaN26s5557TiNHjlTv3r2rZdXCqVOntG7dOiUmJuof//iHiouL5ebmJm9vbw0dOlQTJkzQI488YvfrAkBtQf89ADwrBJuMGzdOX3zxRbnxpKSkCotmGRkZioyMrO5oLunzzz9XcXGxFixYoF27dik6Olp79+7Vq6++qmvXrvFzQynuFcC1jBkzRqNGjdLHH3+s06dPa8SIEfr444+1Z88e/frXv1afPn2qpaC2bt06jR07Vp9//rlycnJUVFQki8WioqIi5ebm6vPPP9fYsWMVFRVl92sDACqvpP8eANfESjXYpKLdB7OzsxUfH69FixbZ9Xolr3unkl1E7x6/c85VZGRkKCAgQBMnTiwdc3Nz0yuvvKKdO3cqPT3dxHRwJtwrgGs5evSoDMNQy5YtFRAQIA8PD/35z3/Wn//853ueZxiG3nzzTZuuGR8fr7CwMNWtW1fPPfechg8frs6dO6tJkya6cuWK/u///k/btm1TTEyM3n33XdWtW7fMvykAAACoHIpqcAnJyclWexZYLBarK+MsFotLNQA9d+6cOnfubHWuR48eiomJcXAiOCvuFcD1WCwWnT59WtHR0ZU+x9ai2pkzZ7RgwQJ5eXnp008/Vbdu3crMN27cWH5+fvLz89Nzzz2n6dOn6/3331dAQIAefvjhKl8PAACgNqOoBqc3btw4syNUuxs3bqhevXpW5xo2bKiCggIHJ4Kz4l4BXIu9V27fT2xsrAoKCrRixYpyBbW7PfbYY/rd736nqVOnauPGjZo9e7aDUgIAANQMFNXg9Bz9gcQMFT1OW9l51B7cK4BrcfQXQykpKerWrZueeuqpSh3fp08f9ejRQykpKRTVAAAAqoiNClBj/fjjj2ZHAADAoXJzc+Xr61ulc3x9fZWTk1NNiQAAAGouimpwKYWFhTp37tx9j4uPj9eoUaMckAgAAOeRn5+vhg0bVumc+vXr8+g4AACADXj8Ey7hyJEjWrRokQ4ePCiLxaKHH35YoaGhGj9+fJnjzp07p/nz5yslJcWkpLZLTk62ulIgOztbkqxuyGAYhhYuXFjt2eBcuFcAVKR58+Y6efJklc754Ycf1KpVq+oJBNRSFouFlgwAUAtQVIPNLl++rNzc3DJjeXl5kqS///3v5d5IlMxV1bFjxzR58mRdu3atdOzvf/+7/uu//ktubm4KDAyUJO3YsUPz5s3TpUuX5ObmppkzZ9p0PbNkZWUpKyurwvm4uLhyYxRKaifuFQAV6dGjh3bu3KkLFy7Iy8vrvsefP39eu3fv1pAhQxyQDqi5Ll68qAULFuj3v/+9JCk0NFShoaEmpwIAVDeKarBZZGSkIiMjrc49/fTTdrvOypUrde3aNY0cOVJz5sxR8+bNtWPHjtI3LmPHjlVUVJQWLlyo4uJide7cWYsWLdLPfvYzu2WobrVhMwbYB/cKgHsZO3asEhIS9Otf/7r0w/29zJ8/X9euXdOECRMckA5wHTdv3tTmzZuVlpamvLw8/exnP9O///u/q1mzZuWOTUpKUlhYmM6dO1ep3zsAQM1BUQ028fHxcdi1vv76a7Vv316/+c1vVKdOHUnSiBEjdO3aNb311lv69NNP9cEHH8gwDM2YMUOvvvqqHnroIYflswdH7w4H18W9AuBeAgIC5O/vr23btunatWv61a9+pQ4dOpQ77vjx43rvvff0pz/9ScOGDdMTTzxhQlrAOV27dk0zZ84sbTsiSWlpaYqJidGaNWvUvn17Sbd6GIaFhWnr1q2yWCxq166diakBAGagqAabOLJn2blz5zRq1KjSgloJf39/WSwWffjhh2rUqJE+/PBDPfXUUw7LBQCAM/rggw80ffp07dy5U6mpqWrTpo06duyohg0bqqCgQCdOnNDx48dlsVjUs2dPHg0H7vLZZ5/pwIED8vLy0vPPP69mzZpp586d2r17t8LCwrRq1SqdPHlSs2bN0qlTpyRJQUFBeu2110xODldE/z3AtRkWfoNRTa5cuaKTJ0+qYcOGatu2rc2v06VLF4WEhCgkJKTMeFFRkbp37y53d3fFxMTosccee9DIAADUCIWFhfr444+1Zs0aXb58udx8s2bNFBQUpJkzZ8rNjc3ggTv927/9m44fP64tW7aodevWpePz589XTEyMoqOjFRwcrDNnzqht27ZauHCh/Pz8TEwMV3J3/z0Aro2Vanggubm5Sk9Pl7u7uwICAtSgQQNJ0v/8z/8oPDxcBQUFkqRHH31U77zzjnx9fe127ZKVa0OGDKGgBgDAHdzd3fXqq6/qlVde0TfffKNjx47p8uXLaty4sdq3b69evXqVWwEO4Ja//vWvevrpp8sU1KRbq9Gio6P1H//xHzpz5ozGjh2r//7v/5anp6dJSeEM6L8H1G4U1WCzFStWaMWKFSoqKpIkNW7cWMuWLdM//vEPLVmyRJLUtGlTXb16Vd9//72mTZum+Ph4tWnTxq45SvpaAACAf8rNzVWDBg3Uu3dv9e7du8zcH/7wBwUEBKhr164mpQOcV35+vn7605+WGy95D5uTk6M5c+boF7/4haOjwcnQfw8A6/1hk6+++kofffSRmjRposmTJysoKEj16tXTL3/5S61YsUKtW7dWXFyc9u7dq/T0dIWEhOjKlSuKiIiwexbDMOz+mgAAuCqLxaJ33nlHP//5z5WWllZu/sKFC1q6dKnGjx+v+fPn68aNGyakBJxXUVGR1U2v6tWrJ0nq3r07BTVI+mf/vSZNmmj27Nl6++231b9/f509e1ZhYWGSpJMnT+rZZ5/V1q1bJd1a8bhp0yYTUwOwJ1aqwSbr169Xs2bNtHnzZnl5eUmSZs2apZEjR+rkyZNatmxZ6bff7u7uCgkJ0VdffaV9+/bZdL17Fc4oqgEA8E/z5s1TfHy8HnroIRUWFpabLy4uVmBgoBITExUTE6MrV67ogw8+MCEp4Jr69etndgQ4iR07dsjDw0OxsbGljwtPnjy5tP9eZmYm/feAGo6iGmySlZUlf3//0oKadOtRz0GDBikuLk49e/Ysd06/fv20bt06m64XERGhjRs3lhs3DOOec0lJSTZdDwAAV5ScnKz4+Hh169ZNH3/8sby9vcsd06xZMy1atEizZ8/WrFmzlJCQoDFjxiggIMCExIDrsbaKDbUT/fcAUFSDTS5duqQWLVqUGy8Za9KkSbk5T0/P0o0LqiovL095eXlVmmMFGwCgtomJiVH9+vW1cuVKNW/e/J7HtmnTRsuWLdOYMWMUGxtLUQ0Aqoj+ewAoqsEmN2/elLu7e7nxkm/u3Nyst+sraeBZFcnJyVU+R5J+/PFHm84DAMBVHT58WIMGDbpvQa1Ex44d1b9/f3399dfVnAxwLenp6Vq+fHmV5gzDUHBwcHVHgxOh/x4Aimpwencvp76f/fv3KyoqSsnJycrMzKymVAAAOJ+LFy9aXTVxLx06dNCePXuqKRHgmtLT05Wenl6pOcMwZLFYKKqhHPrvATUfRTXUCFevXtWmTZu0du1aHTt2rPSNDQAAtYmXl5cuXrxYpXMKCgqstm0AaquQkBCzI6CGoP8eUPNRVIPNkpOTlZOTU2YsOztb0q2dx+6WlZVl9wzHjx/XF198ofj4eF25ckUWi0X169fX8OHDNWnSJLtfDwAAZ9a2bVt9++23VTrn0KFDVjc0AGorimoAgMqiqAabZWVlVVgoi4uLszpuj9VjxcXFSk5OVlRUlPbt2yfpVq+2Vq1aafr06QoMDFTDhg0f+DoAALiaIUOGaPHixUpJSdHTTz993+NTUlL0/fffa9asWQ5IBwA1D/33gNrNsNjSOR61XkVFs8oYN26cTeedO3dO0dHRWr9+vU6fPi2LxSJPT08NHz5cGzZs0KRJkxQWFmZzLgAAXN2FCxc0bNgw3bx5U0uXLlX//v0rPDY1NVVz585VYWGhEhISqtyLDaipkpOT1aFDBz3yyCOVPichIUEJCQkVFldQM3Xp0qVKx9/Zf686nuIB4HisVINNbC2M2eLgwYNau3attm/frhs3bkiS/Pz89Oyzz2rYsGHy8PDQhg0bHJYHAABn5eXlpYULFyo0NFQzZ86Un5+fBgwYoA4dOqhBgwa6dOmSTp48qdTUVH3zzTeyWCxavHgxBTXgDiEhIQoODi73GGhcXJzi4uIUGRlZ7pwTJ07YvGM9XBePCgOgqAanN3nyZBmGoU6dOmn48OEaPXq02rRpY3YsAACc0uDBg/XHP/5R//mf/6n9+/frwIED5Y6xWCzy8fHR22+/rUGDBpmQEnBeFT3Ik5OTo/379zs4DZwZRTUAFNXgEjw8PNS9e3e1b99ejRo1MjsOAABOrV+/fkpJSdG2bduUmpqq3NxcXbp0SU2bNlW7du00ePBg+fv7y93d3eyoAAAALouiGpzeb3/7W8XExGjTpk3atGmT6tSpo4EDByowMFADBw5U3brcxgAA3K1+/foKDAxUYGCg2VEAoEai/x4AN7MDAPczevRoRUZGKjExUTNmzFCTJk2UlJSk0NBQ+fv769133zU7IgAAAIBaJiQkRFu3bi03HhcXp6CgIKvn0H8PqFkoqsFltG3bVnPnzlVqaqqWL18uf39/Xbp0SWvWrJFhGNq1a5dWrVql8+fPmx0VAAAAQA1H/z0AFNXgcurUqaNnnnlGn3zyiVJSUhQaGiofHx/l5uZq8eLF8vf3V0hIiFJSUsyOCgAAAAAAaiiKanBp3t7eCg4OVnJysj777DMNHTpUhmEoKSmJ3XgAAAAAAEC1ocM7aoy+ffuqb9++unDhguLj47VhwwazIwEAAMAFWXt8LycnR5J04MCBco/9lcwBAGoXimqocby8vDRt2jRNmzbN7CgAAABwQfHx8YqPjy83brFYNGXKFKvjhmE4IBkAwJlQVAMAAACA23r16mV2BACAi6CoBgAAAAC3rV692uwIAAAXQVENAAAAAAAb0H8PqN0My92/5QAAAAAA4J66dOlSYS+9ivrslYxnZWVVdzwADsBKNQAAAAC4LSgoyKbzDMNQRESEndPAmdF/DwAr1QAAAADgti5dulQ4ZxhGucf57pxj9REA1C6sVAMAAACA2yIjI62Ox8XFKT4+vsJ5AEDtQ1ENAAAAAG7r3bu31fH09PR7zgMAah+KagAAAAAAVBH99wBQVAMAAAAAoIpKVi9ac7/+ewBqBopqAAAAAABUEf33AFBUAwAAAACgiui/B8DN7AAAAAAAAACAq6GoBgAAAAAAAFQRj38CAAAAwG0V7eiYk5Nzz3l2dASA2oeiGgAAAADcdq8dHe81z46OAFD7UFQDAAAAgNvYsREAUFkU1QAAAADgNnZsBABUlmGxWCxmhwAAAAAAwJXcq/9ebm6uevXqZXWe/ntAzUFRDQAAAADukp+fr927d6tnz57y9vYuHd+/f79WrFih48ePy8fHRxMmTND48eNNTAqzdOnSxabzDMNQVlaWndMAMAOPfwIAAADAHdLS0vTaa6/p8uXLWrFiRWlRLS0tTb/4xS9UVFQki8WiM2fOKCMjQ0eOHNFbb71lcmo4Gv33AFBUAwAAAIDbTp8+rZCQEF2/fl0BAQFq3bq1JOnmzZsKCwtTUVGR+vbtq4ULF0qS3nnnHUVFRWnIkCHq06ePmdHhYPTfA+BmdgAAAAAAcBYREREqKCjQ7373O/3hD3/Qo48+Kknas2eP/va3v8nd3V2LFy+Wt7e3vL29tWTJEjVp0kTR0dEmJwcAOBpFNQAAAAC4bc+ePerZs6eGDx9eZjwtLU2S9NRTT6l58+al4/Xr11f//v116NAhh+aE88jPz9eXX36p06dPlxnfv3+/pk2bJn9/f02aNEkbN240KSGA6sLjnwAAAABwW25ursaMGVNu/MCBAzIMQ08++WS5OR8fH507d84R8eBk6L8H1G6sVAMAAACA265fvy5PT88yY1euXNHRo0clSb169Sp3TkFBgerVq+eQfHAeJf33Ll++fM/+e6mpqUpNTdXgwYMVFRWlffv2mZwcgL1QVAMAAACA25o3b17uMb709HQVFRWpUaNG6tatW7lzjhw5oqZNmzoqIpwE/fcAUFQDAAAAgNt69uyp1NRUXbt2rXQsPj5ehmHI399fhmGUOf7YsWM6dOiQevbs6eioMBn99wBQVAMAAACA2yZNmqRLly5p6tSpiouL03vvvafExEQZhqEpU6aUOfbEiRMKDQ1VcXGxxo4da1JimCU3N1ddu3YtN07/PaD2YKMCAAAAALjNz89PwcHBCg8PV2ZmpiwWiyRp5syZ8vX1LT3uhRdeUEZGhoqKijRu3DirBRTUbPTfA0BRDQAAAADuEBoaKn9/f23fvl03btxQQECA+vXrV+aYs2fPysPDQy+++KJmz55tUlKY6V799xo3bkz/PaAWoKgGAAAAAHd5/PHH9fjjj1c4v2bNGjVr1kx16tRxYCo4kzv773l4eEiqXP+9MWPGmBEXQDWgqAYAAAAAVdSyZUuzI8BkkyZN0pYtWzR16lQ9//zzOnLkiBITE+Xm5kb/PaCWoKgGAAAAAEAV0X8PgGEp+c0HAAAAgFrO2m6OlWEYho4cOWLnNHAFGRkZ9+y/9/Of/1znz58v7b/n5uZmUlIA9kZRDQAAAABu69Kli83nZmdn2zEJaoozZ87Qfw+ooSiqAQAAAAAAAFXEulMAAAAAuC0/P1+FhYVmxwAAuABWqgEAAADAbV27dlVISIiCg4PNjgInR/89AKxUAwAAAIDbLBaLrK07WLlypfr06WNCIjirknulqv8rLi42OzoAO6lrdgAAAAAAcHaFhYXKy8szOwacCBtTAGClGgAAAAAAVUT/PQAU1QAAAAAAqKJevXrpj3/8o9kxAJiIohoAAAAAAFVE/z0AFNUAAAAAALAT+u8BtQdFNQAAAAAAAKCK2P0TAAAAAO4QFxen9PT0MmM5OTmSpKCgIKvnGIahiIiIas8GAHAeFNUAAAAA4A45OTmlRbS73V1sK2EYRnVGAgA4IYpqAAAAAHBbZGSk2REAAC6CohoAAAAA3Na7d2+zIwAAXIRhsbYHMAAAAAAAqFCXLl3UunVrtW7dusx4Tk6OcnNz1atXL6vn0X8PqDkoqgEAAAAAUEVdunSx6TzDMJSVlWXnNADMwOOfAAAAAABUEf33ALBSDQAAAAAAAKgiN7MDAAAAAAAAAK6GohoAAAAAAABQRRTVAAAAAAAAgCqiqAYAAAAAAABUEUU1AAAAAAAAoIooqgEAAAAAAABVRFENAAAAAAAAqCKKagAAAAAAAEAV/T+5jhBV3xmDwQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15,5))\n",
    "ax.bar(feat_importances.keys(), feat_importances.values)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PACKETS', 'GALTON_SKEWNESS', 'BENFORD_LAW_PRESENTED', 'P_BENFORD',\n",
       "       'AREA_VALUES_DISTRIBUTION', 'SPECTRAL_BANDWIDTH', 'Q1_SCALED_TIME',\n",
       "       'SIG_SPACES', 'PEARSON_SK2_SKEWNESS', 'CNT_ZEROS', 'PERIODICITY',\n",
       "       'TIME', 'MAX_POWER_FREQ', 'POWER_MEAN', 'SPECTRAL_SKEWNESS',\n",
       "       'SPECTRAL_CENTROID', 'SPECTRAL_CREST', 'CNT_NZ_DISTRIBUTION',\n",
       "       'PEARSON_SK1_SKEWNESS', 'PERIODICITY_SCDF', 'BYTES_REV', 'MEAN', 'MIN',\n",
       "       'Q1', 'MODE', 'ROOT_MEAN_SQUARE', 'SPECTRAL_FLUX', 'MAX_DIFFTIMES',\n",
       "       'TIME_DISTRIBUTION', 'PERCENT_BELOW_MEAN', 'Q3_SCALED_TIME',\n",
       "       'MAX_POWER', 'SPECTRAL_SLOPE', 'NORMAL_DISTRIBUTION',\n",
       "       'MEAN_SCALED_TIME', 'MEAN_SCALED_DIFFTIMES', 'SPECTRAL_ENERGY',\n",
       "       'COEFFICIENT_OF_VARIATION', 'BIGGEST_CNT_1_SEC', 'SPECTRAL_ENTROPY',\n",
       "       'BURSTINESS', 'PERCENT_ABOVE_MEAN', 'VAL', 'DURATION', 'MIN_POWER_FREQ',\n",
       "       'POWER_STD', 'SPECTRAL_KURTOSIS', 'BYTES', 'DIRECTIONS', 'MIN_POWER',\n",
       "       'POWER_MODE', 'MAX', 'FISHER_PEARSON_g1_SKEWNESS', 'PACKETS_REV',\n",
       "       'MEDIAN', 'KURTOSIS', 'STDEV', 'SCALED_ENTROPY', 'MIN_DIFFTIMES',\n",
       "       'SPECTRAL_ROLLOFF', 'MEDIAN_SCALED_TIME', 'MIN_MINUS_MAX',\n",
       "       'FISHER_PEARSON_G1_SKEWNESS', 'SPECTRAL_SPREAD', 'FISHER_MI_3_SKEWNESS',\n",
       "       'MEDIAN_DIFFTIMES', 'MEAN_DIFFTIMES', 'PERCENT_DEVIATION',\n",
       "       'SWITCHING_METRIC', 'Q3', 'HURST_EXPONENT', 'ENTROPY',\n",
       "       'AVERAGE_DISPERSION', 'VAR', 'TRANSIENTS', 'CNT_DISTRIBUTION'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PACKETS': 0.0,\n",
       " 'GALTON_SKEWNESS': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'P_BENFORD': 0.0,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'Q1_SCALED_TIME': 0.0,\n",
       " 'SIG_SPACES': 0.0,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0,\n",
       " 'CNT_ZEROS': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0,\n",
       " 'POWER_MEAN': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'SPECTRAL_CENTROID': 0.0,\n",
       " 'SPECTRAL_CREST': 0.0,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0,\n",
       " 'PERIODICITY_SCDF': 0.0,\n",
       " 'BYTES_REV': 0.0,\n",
       " 'MEAN': 0.0,\n",
       " 'MIN': 0.0,\n",
       " 'Q1': 0.0,\n",
       " 'MODE': 0.0,\n",
       " 'ROOT_MEAN_SQUARE': 0.00021279822976794094,\n",
       " 'SPECTRAL_FLUX': 0.00022829091176390648,\n",
       " 'MAX_DIFFTIMES': 0.00023673563555348665,\n",
       " 'TIME_DISTRIBUTION': 0.0002444555575493723,\n",
       " 'PERCENT_BELOW_MEAN': 0.00024551735259592533,\n",
       " 'Q3_SCALED_TIME': 0.0002591191150713712,\n",
       " 'MAX_POWER': 0.0002711098059080541,\n",
       " 'SPECTRAL_SLOPE': 0.00029952600016258657,\n",
       " 'NORMAL_DISTRIBUTION': 0.00032031963928602636,\n",
       " 'MEAN_SCALED_TIME': 0.000362104969099164,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0004713170346803963,\n",
       " 'SPECTRAL_ENERGY': 0.0006556716398335993,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0006752066547051072,\n",
       " 'BIGGEST_CNT_1_SEC': 0.0006952733383513987,\n",
       " 'SPECTRAL_ENTROPY': 0.0007610118482261896,\n",
       " 'BURSTINESS': 0.0008158992859534919,\n",
       " 'PERCENT_ABOVE_MEAN': 0.0008354356396012008,\n",
       " 'VAL': 0.0010352713288739324,\n",
       " 'DURATION': 0.0012356452643871307,\n",
       " 'MIN_POWER_FREQ': 0.0019786041229963303,\n",
       " 'POWER_STD': 0.0022175104822963476,\n",
       " 'SPECTRAL_KURTOSIS': 0.0023120346013456583,\n",
       " 'BYTES': 0.002499042311683297,\n",
       " 'DIRECTIONS': 0.0026297399308532476,\n",
       " 'MIN_POWER': 0.003918017726391554,\n",
       " 'POWER_MODE': 0.004696257878094912,\n",
       " 'MAX': 0.0047256494872272015,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.005072636995464563,\n",
       " 'PACKETS_REV': 0.005469790194183588,\n",
       " 'MEDIAN': 0.005496983416378498,\n",
       " 'KURTOSIS': 0.006110198795795441,\n",
       " 'STDEV': 0.00699829263612628,\n",
       " 'SCALED_ENTROPY': 0.007669941522181034,\n",
       " 'MIN_DIFFTIMES': 0.007944978773593903,\n",
       " 'SPECTRAL_ROLLOFF': 0.011335882358253002,\n",
       " 'MEDIAN_SCALED_TIME': 0.011752298101782799,\n",
       " 'MIN_MINUS_MAX': 0.011880723759531975,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.016051826998591423,\n",
       " 'SPECTRAL_SPREAD': 0.01846121996641159,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.018551858142018318,\n",
       " 'MEDIAN_DIFFTIMES': 0.020100044086575508,\n",
       " 'MEAN_DIFFTIMES': 0.020670397207140923,\n",
       " 'PERCENT_DEVIATION': 0.023357342928647995,\n",
       " 'SWITCHING_METRIC': 0.03385160118341446,\n",
       " 'Q3': 0.038178663700819016,\n",
       " 'HURST_EXPONENT': 0.05946389585733414,\n",
       " 'ENTROPY': 0.06319283694028854,\n",
       " 'AVERAGE_DISPERSION': 0.07122562825679779,\n",
       " 'VAR': 0.10282377898693085,\n",
       " 'TRANSIENTS': 0.17213019728660583,\n",
       " 'CNT_DISTRIBUTION': 0.22737151384353638}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=10):\n",
    "    if best_hyperparams is None:\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_features.sample(100000), selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL']  # Labels\n",
    "\n",
    "    best = 0\n",
    "    best_model = None\n",
    "    for i in range(0, cycles):    \n",
    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, stratify=y) # 70% training and 30% test   \n",
    "        model = XGBClassifier(\n",
    "            n_estimators = int(best_hyperparams['n_estimators']), max_depth = int(best_hyperparams['max_depth']), gamma = best_hyperparams['gamma'],\n",
    "            reg_alpha = int(best_hyperparams['reg_alpha']),min_child_weight=int(best_hyperparams['min_child_weight']),\n",
    "            colsample_bytree=int(best_hyperparams['colsample_bytree']),\n",
    "        )\n",
    "        model.fit(X_train, y_train)                \n",
    "        y_pred = model.predict(X_test)\n",
    "        accuracy = metrics.accuracy_score(y_test, y_pred)  * 100\n",
    "        precision = precision_score(y_test, y_pred)  * 100\n",
    "        recall = recall_score(y_test, y_pred)  * 100\n",
    "        F1 = f1_score(y_test, y_pred)  * 100\n",
    "        if F1 > best:   \n",
    "            best = F1\n",
    "            best_model = model\n",
    "            \n",
    "    \n",
    "    X_validation=df_validation[selected_features]  # Features\n",
    "    y_validation=df_validation['LABEL']  # Labels\n",
    "\n",
    "    y_pred = best_model.predict(X_validation)\n",
    "    accuracy = metrics.accuracy_score(y_validation, y_pred)  * 100\n",
    "    precision = precision_score(y_validation, y_pred)  * 100\n",
    "    recall = recall_score(y_validation, y_pred)  * 100\n",
    "    F1 = f1_score(y_validation, y_pred)  * 100\n",
    "    \n",
    "    return accuracy,precision,recall,F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [00:51<00:00,  1.94trial/s, best loss: -0.9988006716238906]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7054879105957464, 'gamma': 3.2847773103561275, 'max_depth': 20.0, 'min_child_weight': 5.0, 'n_estimators': 120.0, 'reg_alpha': 3.0, 'reg_lambda': 0.004980860922070951}\n",
      "100%|██████████| 100/100 [00:39<00:00,  2.55trial/s, best loss: -0.9967213114754099]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6104898231246171, 'gamma': 6.66447870837968, 'max_depth': 20.0, 'min_child_weight': 19.0, 'n_estimators': 100.0, 'reg_alpha': 119.0, 'reg_lambda': 0.594378403747425}\n",
      "100%|██████████| 100/100 [01:12<00:00,  1.39trial/s, best loss: -0.999059708509638]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.07566330377613362, 'gamma': 1.3893657710473164, 'max_depth': 24.0, 'min_child_weight': 7.0, 'n_estimators': 340.0, 'reg_alpha': 119.0, 'reg_lambda': 0.006070839015641427}\n",
      "100%|██████████| 100/100 [01:16<00:00,  1.31trial/s, best loss: -0.9988390991409334]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2485201181631722, 'gamma': 6.113389255757963, 'max_depth': 26.0, 'min_child_weight': 0.0, 'n_estimators': 260.0, 'reg_alpha': 3.0, 'reg_lambda': 0.4931808285143741}\n",
      "100%|██████████| 100/100 [01:33<00:00,  1.07trial/s, best loss: -0.9988086728615678]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7784082030959327, 'gamma': 3.278073830727031, 'max_depth': 20.0, 'min_child_weight': 4.0, 'n_estimators': 100.0, 'reg_alpha': 1.0, 'reg_lambda': 0.7075256711911848}\n",
      "100%|██████████| 100/100 [01:09<00:00,  1.43trial/s, best loss: -0.997164461247637]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7975931095860265, 'gamma': 5.590478272409001, 'max_depth': 22.0, 'min_child_weight': 9.0, 'n_estimators': 400.0, 'reg_alpha': 9.0, 'reg_lambda': 0.7214972824134197}\n",
      "100%|██████████| 100/100 [02:22<00:00,  1.43s/trial, best loss: -0.9992899408284024]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.890833710211757, 'gamma': 6.083050275164999, 'max_depth': 6.0, 'min_child_weight': 3.0, 'n_estimators': 80.0, 'reg_alpha': 0.0, 'reg_lambda': 0.47286412326099947}\n",
      "100%|██████████| 100/100 [00:32<00:00,  3.05trial/s, best loss: -0.9420754251910278]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.9582193444606281, 'gamma': 5.163518657847674, 'max_depth': 12.0, 'min_child_weight': 13.0, 'n_estimators': 320.0, 'reg_alpha': 16.0, 'reg_lambda': 0.5693825413343757}\n",
      "100%|██████████| 100/100 [00:27<00:00,  3.65trial/s, best loss: -0.9235474006116208]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.14638405776395116, 'gamma': 4.036825306405847, 'max_depth': 30.0, 'min_child_weight': 5.0, 'n_estimators': 160.0, 'reg_alpha': 1.0, 'reg_lambda': 0.9806511722426408}\n",
      "100%|██████████| 100/100 [01:21<00:00,  1.23trial/s, best loss: -0.9988176873965476]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6613334950161058, 'gamma': 1.3369861279233843, 'max_depth': 16.0, 'min_child_weight': 4.0, 'n_estimators': 240.0, 'reg_alpha': 60.0, 'reg_lambda': 0.05551978957557313}\n"
     ]
    }
   ],
   "source": [
    "import FEATURE_REDUCTED as FR\n",
    "REPORTS = []\n",
    "\n",
    "report = perform_val(FR.botnet_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.doh_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dos_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.dns_malware_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.tor_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.vpn_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.all_best_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.combo_2_features)\n",
    "REPORTS.append(report)\n",
    "report = perform_val(FR.crypto_features)\n",
    "REPORTS.append(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"IoT-egde_iiot-binary\"\n",
    "with open(\"feature_reduction_results.csv\", \"a\") as f:\n",
    "    writer = csv.writer(f)\n",
    "    for report, f_set in zip(REPORTS, FR.FEATURE_SETS):\n",
    "        writer.writerow([problem, f_set, report[0], report[1], report[2], report[3], 0, 0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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