cryptomining.ipynb

{
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   "attachments": {},
   "cell_type": "markdown",
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   "source": [
    "# Cryptocurrency miners detection using Network Traffic Classification based on Single Flow Time Series Analysis \n",
    "\n",
    "Problem: binary detection of cryptocurrency miners (use only first 30 packets)\n",
    "\n",
    "Raw data from: CESNET-DeCrypto [1]\n",
    "\n",
    "-----------------------------------------------------------------------\n",
    "[1] Richard Pln ́y et al. Datasets of Cryptomining Communication. Zenodo, October 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/cryptomining_design.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "         DST_IP  SRC_IP  PACKETS  PACKETS_REV    BYTES  BYTES_REV  \\\n",
       "0           NaN     NaN        2            2      148        439   \n",
       "1           NaN     NaN       20           22     1877       6770   \n",
       "2           NaN     NaN        7            7     1245       2957   \n",
       "3           NaN     NaN        2            2      148        439   \n",
       "4           NaN     NaN        7            7     1245       2957   \n",
       "...         ...     ...      ...          ...      ...        ...   \n",
       "2024898     NaN     NaN       27           20    10075       5634   \n",
       "2024899     NaN     NaN       21           16     3004       6665   \n",
       "2024900     NaN     NaN     2935         3317  1308550    2056112   \n",
       "2024901     NaN     NaN        9            7     1102       4191   \n",
       "2024902     NaN     NaN       21           16     2268       6661   \n",
       "\n",
       "           TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  ...  \\\n",
       "0        1.639497e+09  1.639497e+09     14444     57104  ...   \n",
       "1        1.639497e+09  1.639497e+09      5555     53200  ...   \n",
       "2        1.639497e+09  1.639497e+09     14433     52519  ...   \n",
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       "4        1.639497e+09  1.639497e+09     14433     52571  ...   \n",
       "...               ...           ...       ...       ...  ...   \n",
       "2024898  1.644516e+09  1.644516e+09       443      2024  ...   \n",
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       "2024901  1.644516e+09  1.644516e+09       443     12169  ...   \n",
       "2024902  1.644516e+09  1.644516e+09       443     40074  ...   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0            -67484.490984          0.498963           0.500429   \n",
       "1             16624.248136          0.984107           0.503136   \n",
       "2                      NaN          0.006667                NaN   \n",
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       "4                      NaN          0.006667                NaN   \n",
       "...                    ...               ...                ...   \n",
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       "2024899      258381.765523          0.936228           0.527018   \n",
       "2024900     -261552.961524          0.887753           0.422524   \n",
       "2024901                NaN          0.006667                NaN   \n",
       "2024902      -93883.330649          0.737959           0.486872   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.284358             NaN       29.133376     179.824927   \n",
       "1               0.283232             NaN       16.475484      10.479260   \n",
       "2                    NaN             NaN             NaN            NaN   \n",
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       "4                    NaN             NaN             NaN            NaN   \n",
       "...                  ...             ...             ...            ...   \n",
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       "2024901              NaN             NaN             NaN            NaN   \n",
       "2024902         0.276150             NaN       53.255155     478.725102   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.497263          0.074001   True  \n",
       "1                 -0.000199          0.500405   True  \n",
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       "4                  0.000000               NaN   True  \n",
       "...                     ...               ...    ...  \n",
       "2024898           -0.000199          0.715044  False  \n",
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       "2024900           -0.000199          0.318399  False  \n",
       "2024901            0.000000               NaN  False  \n",
       "2024902            0.475608          0.066887  False  \n",
       "\n",
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    "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",
    "df[\"LABEL_code\"] = df[\"LABEL\"].cat.codes\n",
    "\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": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>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",
       "      <th>LABEL_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "      <td>439</td>\n",
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       "      <td>14444</td>\n",
       "      <td>57104</td>\n",
       "      <td>273</td>\n",
       "      <td>273.5</td>\n",
       "      <td>205.766858</td>\n",
       "      <td>42341</td>\n",
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       "      <td>200.75</td>\n",
       "      <td>346.25</td>\n",
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       "      <td>128</td>\n",
       "      <td>75.372476</td>\n",
       "      <td>145</td>\n",
       "      <td>53.113553</td>\n",
       "      <td>309.794287</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.704681</td>\n",
       "      <td>2.651059</td>\n",
       "      <td>-1.973593</td>\n",
       "      <td>5.739099e-08</td>\n",
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       "      <td>False</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>-1.0</td>\n",
       "      <td>9.216136e+06</td>\n",
       "      <td>-67484.490984</td>\n",
       "      <td>0.498963</td>\n",
       "      <td>0.500429</td>\n",
       "      <td>0.284358</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>29.133376</td>\n",
       "      <td>179.824927</td>\n",
       "      <td>-0.497263</td>\n",
       "      <td>True</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
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       "      <td>NaN</td>\n",
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       "      <td>22</td>\n",
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       "      <td>1.744409e-08</td>\n",
       "      <td>-5.000000</td>\n",
       "      <td>6.137370</td>\n",
       "      <td>2.419241</td>\n",
       "      <td>0.559760</td>\n",
       "      <td>0.624693</td>\n",
       "      <td>False</td>\n",
       "      <td>0.721787</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.327778</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.947368</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.473684</td>\n",
       "      <td>False</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6707</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.167948e+00</td>\n",
       "      <td>1.678669</td>\n",
       "      <td>0.984305</td>\n",
       "      <td>0.984107</td>\n",
       "      <td>0.101889</td>\n",
       "      <td>0.051009</td>\n",
       "      <td>0.12287</td>\n",
       "      <td>509.444579</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.353158e+06</td>\n",
       "      <td>16624.248136</td>\n",
       "      <td>0.984107</td>\n",
       "      <td>0.503136</td>\n",
       "      <td>0.283232</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>16.475484</td>\n",
       "      <td>10.479260</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1245</td>\n",
       "      <td>2957</td>\n",
       "      <td>1.639497e+09</td>\n",
       "      <td>1.639497e+09</td>\n",
       "      <td>14433</td>\n",
       "      <td>52519</td>\n",
       "      <td>576</td>\n",
       "      <td>353.0</td>\n",
       "      <td>440.507662</td>\n",
       "      <td>194048</td>\n",
       "      <td>-0.133292</td>\n",
       "      <td>326.50</td>\n",
       "      <td>598.50</td>\n",
       "      <td>315</td>\n",
       "      <td>1520</td>\n",
       "      <td>1205</td>\n",
       "      <td>315</td>\n",
       "      <td>76.477025</td>\n",
       "      <td>310</td>\n",
       "      <td>53.819444</td>\n",
       "      <td>706.463223</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.592498</td>\n",
       "      <td>3.121399</td>\n",
       "      <td>0.758392</td>\n",
       "      <td>1.002750e-08</td>\n",
       "      <td>1.299713e-08</td>\n",
       "      <td>-0.834559</td>\n",
       "      <td>3.071602</td>\n",
       "      <td>2.521641</td>\n",
       "      <td>0.898227</td>\n",
       "      <td>0.448785</td>\n",
       "      <td>False</td>\n",
       "      <td>0.796548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4038</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.022540e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.582204</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>148</td>\n",
       "      <td>439</td>\n",
       "      <td>1.639497e+09</td>\n",
       "      <td>1.639497e+09</td>\n",
       "      <td>14444</td>\n",
       "      <td>57104</td>\n",
       "      <td>273</td>\n",
       "      <td>273.5</td>\n",
       "      <td>205.766858</td>\n",
       "      <td>42341</td>\n",
       "      <td>-0.140430</td>\n",
       "      <td>200.75</td>\n",
       "      <td>346.25</td>\n",
       "      <td>128</td>\n",
       "      <td>419</td>\n",
       "      <td>291</td>\n",
       "      <td>128</td>\n",
       "      <td>75.372476</td>\n",
       "      <td>145</td>\n",
       "      <td>53.113553</td>\n",
       "      <td>309.794287</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.704681</td>\n",
       "      <td>2.651059</td>\n",
       "      <td>-1.973593</td>\n",
       "      <td>5.739099e-08</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.006873</td>\n",
       "      <td>0.250024</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.563329</td>\n",
       "      <td>False</td>\n",
       "      <td>0.477121</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.660714</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>547</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-4.466079e+00</td>\n",
       "      <td>12.185155</td>\n",
       "      <td>0.654916</td>\n",
       "      <td>0.509492</td>\n",
       "      <td>0.971659</td>\n",
       "      <td>0.219109</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>4858.295315</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>7.279583e+06</td>\n",
       "      <td>115694.896686</td>\n",
       "      <td>0.509492</td>\n",
       "      <td>0.488942</td>\n",
       "      <td>0.278945</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>12.540566</td>\n",
       "      <td>40.043158</td>\n",
       "      <td>-0.145424</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1245</td>\n",
       "      <td>2957</td>\n",
       "      <td>1.639497e+09</td>\n",
       "      <td>1.639497e+09</td>\n",
       "      <td>14433</td>\n",
       "      <td>52571</td>\n",
       "      <td>576</td>\n",
       "      <td>353.0</td>\n",
       "      <td>440.507662</td>\n",
       "      <td>194048</td>\n",
       "      <td>-0.133292</td>\n",
       "      <td>326.50</td>\n",
       "      <td>598.50</td>\n",
       "      <td>315</td>\n",
       "      <td>1520</td>\n",
       "      <td>1205</td>\n",
       "      <td>315</td>\n",
       "      <td>76.477025</td>\n",
       "      <td>310</td>\n",
       "      <td>53.819444</td>\n",
       "      <td>706.463223</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.592498</td>\n",
       "      <td>3.121399</td>\n",
       "      <td>0.758392</td>\n",
       "      <td>1.002750e-08</td>\n",
       "      <td>1.299713e-08</td>\n",
       "      <td>-0.834559</td>\n",
       "      <td>3.071602</td>\n",
       "      <td>2.521641</td>\n",
       "      <td>0.898227</td>\n",
       "      <td>0.448785</td>\n",
       "      <td>False</td>\n",
       "      <td>0.796548</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1.666667</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4038</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.024938e-16</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.398437</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>1</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>2024898</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>10075</td>\n",
       "      <td>5634</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>2024</td>\n",
       "      <td>589</td>\n",
       "      <td>249.0</td>\n",
       "      <td>548.652896</td>\n",
       "      <td>301020</td>\n",
       "      <td>-0.035465</td>\n",
       "      <td>230.00</td>\n",
       "      <td>1428.00</td>\n",
       "      <td>98</td>\n",
       "      <td>1428</td>\n",
       "      <td>1330</td>\n",
       "      <td>1428</td>\n",
       "      <td>93.149897</td>\n",
       "      <td>479</td>\n",
       "      <td>81.324278</td>\n",
       "      <td>798.024686</td>\n",
       "      <td>0.680000</td>\n",
       "      <td>0.320000</td>\n",
       "      <td>-1.529200</td>\n",
       "      <td>2.766777</td>\n",
       "      <td>0.656574</td>\n",
       "      <td>4.843920e-09</td>\n",
       "      <td>5.158753e-09</td>\n",
       "      <td>0.400668</td>\n",
       "      <td>1.698800</td>\n",
       "      <td>3.697797</td>\n",
       "      <td>0.796277</td>\n",
       "      <td>0.535540</td>\n",
       "      <td>False</td>\n",
       "      <td>0.840000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.895833</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>False</td>\n",
       "      <td>1.833333</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14745</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.220261e-01</td>\n",
       "      <td>4.002708</td>\n",
       "      <td>0.654519</td>\n",
       "      <td>0.654320</td>\n",
       "      <td>0.033453</td>\n",
       "      <td>0.056516</td>\n",
       "      <td>0.03425</td>\n",
       "      <td>167.264340</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>2.433135e+07</td>\n",
       "      <td>346412.823509</td>\n",
       "      <td>0.654320</td>\n",
       "      <td>0.485402</td>\n",
       "      <td>0.273372</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>119.652170</td>\n",
       "      <td>8.479094</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024899</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>3004</td>\n",
       "      <td>6665</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>40076</td>\n",
       "      <td>704</td>\n",
       "      <td>487.0</td>\n",
       "      <td>609.817186</td>\n",
       "      <td>371877</td>\n",
       "      <td>-0.071686</td>\n",
       "      <td>116.25</td>\n",
       "      <td>1379.00</td>\n",
       "      <td>91</td>\n",
       "      <td>1406</td>\n",
       "      <td>1315</td>\n",
       "      <td>1406</td>\n",
       "      <td>86.621759</td>\n",
       "      <td>559</td>\n",
       "      <td>79.403409</td>\n",
       "      <td>914.669749</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>-1.151165</td>\n",
       "      <td>2.664733</td>\n",
       "      <td>-0.106512</td>\n",
       "      <td>3.674682e-10</td>\n",
       "      <td>4.221888e-10</td>\n",
       "      <td>0.069095</td>\n",
       "      <td>0.993277</td>\n",
       "      <td>3.022055</td>\n",
       "      <td>0.842981</td>\n",
       "      <td>0.541799</td>\n",
       "      <td>False</td>\n",
       "      <td>0.884363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.170765</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>10.416667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5.545455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.092424</td>\n",
       "      <td>False</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8267</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9.628126e+01</td>\n",
       "      <td>241.097368</td>\n",
       "      <td>0.953115</td>\n",
       "      <td>0.936228</td>\n",
       "      <td>0.287956</td>\n",
       "      <td>3.696385</td>\n",
       "      <td>0.27468</td>\n",
       "      <td>1439.779106</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.848036e+07</td>\n",
       "      <td>258381.765523</td>\n",
       "      <td>0.936228</td>\n",
       "      <td>0.527018</td>\n",
       "      <td>0.297782</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>837.272075</td>\n",
       "      <td>993.283403</td>\n",
       "      <td>-0.016887</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024900</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2935</td>\n",
       "      <td>3317</td>\n",
       "      <td>1308550</td>\n",
       "      <td>2056112</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>36262</td>\n",
       "      <td>815</td>\n",
       "      <td>596.0</td>\n",
       "      <td>432.278845</td>\n",
       "      <td>186865</td>\n",
       "      <td>-0.306845</td>\n",
       "      <td>596.00</td>\n",
       "      <td>1110.00</td>\n",
       "      <td>176</td>\n",
       "      <td>1508</td>\n",
       "      <td>1332</td>\n",
       "      <td>596</td>\n",
       "      <td>53.040349</td>\n",
       "      <td>375</td>\n",
       "      <td>46.012270</td>\n",
       "      <td>919.222570</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.506617</td>\n",
       "      <td>4.277332</td>\n",
       "      <td>0.286897</td>\n",
       "      <td>8.253094e-10</td>\n",
       "      <td>8.693975e-10</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>1.856038</td>\n",
       "      <td>1.982798</td>\n",
       "      <td>0.404084</td>\n",
       "      <td>0.510586</td>\n",
       "      <td>False</td>\n",
       "      <td>0.735393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.068966</td>\n",
       "      <td>False</td>\n",
       "      <td>1.310345</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24452</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.095875e+01</td>\n",
       "      <td>3.375484</td>\n",
       "      <td>0.887952</td>\n",
       "      <td>0.887753</td>\n",
       "      <td>0.018281</td>\n",
       "      <td>0.170307</td>\n",
       "      <td>0.02170</td>\n",
       "      <td>91.407434</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.744182e+07</td>\n",
       "      <td>-261552.961524</td>\n",
       "      <td>0.887753</td>\n",
       "      <td>0.422524</td>\n",
       "      <td>0.236865</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>184.639457</td>\n",
       "      <td>29.454925</td>\n",
       "      <td>-0.000199</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024901</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>1102</td>\n",
       "      <td>4191</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>12169</td>\n",
       "      <td>638</td>\n",
       "      <td>378.5</td>\n",
       "      <td>589.553221</td>\n",
       "      <td>347574</td>\n",
       "      <td>-0.039466</td>\n",
       "      <td>206.25</td>\n",
       "      <td>998.00</td>\n",
       "      <td>111</td>\n",
       "      <td>1520</td>\n",
       "      <td>1409</td>\n",
       "      <td>1520</td>\n",
       "      <td>92.406461</td>\n",
       "      <td>486</td>\n",
       "      <td>76.175549</td>\n",
       "      <td>843.782481</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>-1.496048</td>\n",
       "      <td>2.604515</td>\n",
       "      <td>0.204279</td>\n",
       "      <td>3.050078e-09</td>\n",
       "      <td>3.804116e-09</td>\n",
       "      <td>-0.090622</td>\n",
       "      <td>1.440551</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>0.916667</td>\n",
       "      <td>0.608320</td>\n",
       "      <td>False</td>\n",
       "      <td>0.800969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.291667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>1.714286</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5109</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000e+00</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024902</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21</td>\n",
       "      <td>16</td>\n",
       "      <td>2268</td>\n",
       "      <td>6661</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>1.644516e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>40074</td>\n",
       "      <td>642</td>\n",
       "      <td>473.5</td>\n",
       "      <td>582.058416</td>\n",
       "      <td>338792</td>\n",
       "      <td>-0.048970</td>\n",
       "      <td>116.25</td>\n",
       "      <td>1379.00</td>\n",
       "      <td>91</td>\n",
       "      <td>1406</td>\n",
       "      <td>1315</td>\n",
       "      <td>1406</td>\n",
       "      <td>90.663305</td>\n",
       "      <td>502</td>\n",
       "      <td>78.193146</td>\n",
       "      <td>850.446206</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-1.312583</td>\n",
       "      <td>2.495454</td>\n",
       "      <td>0.142817</td>\n",
       "      <td>2.112949e-09</td>\n",
       "      <td>2.427594e-09</td>\n",
       "      <td>0.167294</td>\n",
       "      <td>1.226539</td>\n",
       "      <td>3.022055</td>\n",
       "      <td>0.842981</td>\n",
       "      <td>0.626450</td>\n",
       "      <td>False</td>\n",
       "      <td>0.884363</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.169399</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>10.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>5.545455</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.092424</td>\n",
       "      <td>False</td>\n",
       "      <td>1.454545</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7527</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-2.914237e+01</td>\n",
       "      <td>18.307306</td>\n",
       "      <td>0.262351</td>\n",
       "      <td>0.737959</td>\n",
       "      <td>0.343766</td>\n",
       "      <td>0.679253</td>\n",
       "      <td>0.36399</td>\n",
       "      <td>1718.829486</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>4.963573e+06</td>\n",
       "      <td>-93883.330649</td>\n",
       "      <td>0.737959</td>\n",
       "      <td>0.486872</td>\n",
       "      <td>0.276150</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>53.255155</td>\n",
       "      <td>478.725102</td>\n",
       "      <td>0.475608</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2024903 rows × 84 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DST_IP  SRC_IP  PACKETS  PACKETS_REV    BYTES  BYTES_REV  \\\n",
       "0           NaN     NaN        2            2      148        439   \n",
       "1           NaN     NaN       20           22     1877       6770   \n",
       "2           NaN     NaN        7            7     1245       2957   \n",
       "3           NaN     NaN        2            2      148        439   \n",
       "4           NaN     NaN        7            7     1245       2957   \n",
       "...         ...     ...      ...          ...      ...        ...   \n",
       "2024898     NaN     NaN       27           20    10075       5634   \n",
       "2024899     NaN     NaN       21           16     3004       6665   \n",
       "2024900     NaN     NaN     2935         3317  1308550    2056112   \n",
       "2024901     NaN     NaN        9            7     1102       4191   \n",
       "2024902     NaN     NaN       21           16     2268       6661   \n",
       "\n",
       "           TIME_FIRST     TIME_LAST  DST_PORT  SRC_PORT  MEAN  MEDIAN  \\\n",
       "0        1.639497e+09  1.639497e+09     14444     57104   273   273.5   \n",
       "1        1.639497e+09  1.639497e+09      5555     53200   406   336.0   \n",
       "2        1.639497e+09  1.639497e+09     14433     52519   576   353.0   \n",
       "3        1.639497e+09  1.639497e+09     14444     57104   273   273.5   \n",
       "4        1.639497e+09  1.639497e+09     14433     52571   576   353.0   \n",
       "...               ...           ...       ...       ...   ...     ...   \n",
       "2024898  1.644516e+09  1.644516e+09       443      2024   589   249.0   \n",
       "2024899  1.644516e+09  1.644516e+09       443     40076   704   487.0   \n",
       "2024900  1.644516e+09  1.644516e+09       443     36262   815   596.0   \n",
       "2024901  1.644516e+09  1.644516e+09       443     12169   638   378.5   \n",
       "2024902  1.644516e+09  1.644516e+09       443     40074   642   473.5   \n",
       "\n",
       "              STDEV     VAR  BURSTINESS      Q1       Q3  MIN   MAX  \\\n",
       "0        205.766858   42341   -0.140430  200.75   346.25  128   419   \n",
       "1        359.758252  129427   -0.060387  301.00   336.00   84  1440   \n",
       "2        440.507662  194048   -0.133292  326.50   598.50  315  1520   \n",
       "3        205.766858   42341   -0.140430  200.75   346.25  128   419   \n",
       "4        440.507662  194048   -0.133292  326.50   598.50  315  1520   \n",
       "...             ...     ...         ...     ...      ...  ...   ...   \n",
       "2024898  548.652896  301020   -0.035465  230.00  1428.00   98  1428   \n",
       "2024899  609.817186  371877   -0.071686  116.25  1379.00   91  1406   \n",
       "2024900  432.278845  186865   -0.306845  596.00  1110.00  176  1508   \n",
       "2024901  589.553221  347574   -0.039466  206.25   998.00  111  1520   \n",
       "2024902  582.058416  338792   -0.048970  116.25  1379.00   91  1406   \n",
       "\n",
       "         MIN_MINUS_MAX  MODE  COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  \\\n",
       "0                  291   128                 75.372476                 145   \n",
       "1                 1356   336                 88.610407                 216   \n",
       "2                 1205   315                 76.477025                 310   \n",
       "3                  291   128                 75.372476                 145   \n",
       "4                 1205   315                 76.477025                 310   \n",
       "...                ...   ...                       ...                 ...   \n",
       "2024898           1330  1428                 93.149897                 479   \n",
       "2024899           1315  1406                 86.621759                 559   \n",
       "2024900           1332   596                 53.040349                 375   \n",
       "2024901           1409  1520                 92.406461                 486   \n",
       "2024902           1315  1406                 90.663305                 502   \n",
       "\n",
       "         PERCENT_DEVIATION  ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  \\\n",
       "0                53.113553        309.794287            0.500000   \n",
       "1                53.201970        537.029468            0.850000   \n",
       "2                53.819444        706.463223            0.714286   \n",
       "3                53.113553        309.794287            0.500000   \n",
       "4                53.819444        706.463223            0.714286   \n",
       "...                    ...               ...                 ...   \n",
       "2024898          81.324278        798.024686            0.680000   \n",
       "2024899          79.403409        914.669749            0.583333   \n",
       "2024900          46.012270        919.222570            0.633333   \n",
       "2024901          76.175549        843.782481            0.625000   \n",
       "2024902          78.193146        850.446206            0.666667   \n",
       "\n",
       "         PERCENT_ABOVE_MEAN  PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  \\\n",
       "0                  0.500000              0.704681              2.651059   \n",
       "1                  0.150000              0.194575              2.451646   \n",
       "2                  0.285714              0.592498              3.121399   \n",
       "3                  0.500000              0.704681              2.651059   \n",
       "4                  0.285714              0.592498              3.121399   \n",
       "...                     ...                   ...                   ...   \n",
       "2024898            0.320000             -1.529200              2.766777   \n",
       "2024899            0.416667             -1.151165              2.664733   \n",
       "2024900            0.366667              0.506617              4.277332   \n",
       "2024901            0.375000             -1.496048              2.604515   \n",
       "2024902            0.333333             -1.312583              2.495454   \n",
       "\n",
       "         FISHER_MI_3_SKEWNESS  FISHER_PEARSON_g1_SKEWNESS  \\\n",
       "0                   -1.973593                5.739099e-08   \n",
       "1                    1.892487                1.610753e-08   \n",
       "2                    0.758392                1.002750e-08   \n",
       "3                   -1.973593                5.739099e-08   \n",
       "4                    0.758392                1.002750e-08   \n",
       "...                       ...                         ...   \n",
       "2024898              0.656574                4.843920e-09   \n",
       "2024899             -0.106512                3.674682e-10   \n",
       "2024900              0.286897                8.253094e-10   \n",
       "2024901              0.204279                3.050078e-09   \n",
       "2024902              0.142817                2.112949e-09   \n",
       "\n",
       "         FISHER_PEARSON_G1_SKEWNESS  GALTON_SKEWNESS  KURTOSIS   ENTROPY  \\\n",
       "0                      0.000000e+00         0.006873  0.250024  1.000000   \n",
       "1                      1.744409e-08        -5.000000  6.137370  2.419241   \n",
       "2                      1.299713e-08        -0.834559  3.071602  2.521641   \n",
       "3                      0.000000e+00         0.006873  0.250024  1.000000   \n",
       "4                      1.299713e-08        -0.834559  3.071602  2.521641   \n",
       "...                             ...              ...       ...       ...   \n",
       "2024898                5.158753e-09         0.400668  1.698800  3.697797   \n",
       "2024899                4.221888e-10         0.069095  0.993277  3.022055   \n",
       "2024900                8.693975e-10         0.147860  1.856038  1.982798   \n",
       "2024901                3.804116e-09        -0.090622  1.440551  2.750000   \n",
       "2024902                2.427594e-09         0.167294  1.226539  3.022055   \n",
       "\n",
       "         SCALED_ENTROPY  HURST_EXPONENT  BENFORD_LAW_PRESENTED  P_BENFORD  \\\n",
       "0              1.000000        0.563329                  False   0.477121   \n",
       "1              0.559760        0.624693                  False   0.721787   \n",
       "2              0.898227        0.448785                  False   0.796548   \n",
       "3              1.000000        0.563329                  False   0.477121   \n",
       "4              0.898227        0.448785                  False   0.796548   \n",
       "...                 ...             ...                    ...        ...   \n",
       "2024898        0.796277        0.535540                  False   0.840000   \n",
       "2024899        0.842981        0.541799                  False   0.884363   \n",
       "2024900        0.404084        0.510586                  False   0.735393   \n",
       "2024901        0.916667        0.608320                  False   0.800969   \n",
       "2024902        0.842981        0.626450                  False   0.884363   \n",
       "\n",
       "         NORMAL_DISTRIBUTION  CNT_DISTRIBUTION  TIME_DISTRIBUTION  \\\n",
       "0                        0.0               0.0           0.500000   \n",
       "1                        0.0               1.0           0.327778   \n",
       "2                        0.0               0.0           0.000000   \n",
       "3                        0.0               0.0           0.500000   \n",
       "4                        0.0               0.0           0.000000   \n",
       "...                      ...               ...                ...   \n",
       "2024898                  0.0               0.0           0.800000   \n",
       "2024899                  0.0               1.0           0.170765   \n",
       "2024900                  0.0               0.0           0.550000   \n",
       "2024901                  0.0               0.0           0.000000   \n",
       "2024902                  0.0               1.0           0.169399   \n",
       "\n",
       "         AREA_VALUES_DISTRIBUTION  MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  \\\n",
       "0                        0.660714          1.000000                 2.0   \n",
       "1                        0.888889          5.900000                 3.0   \n",
       "2                        0.291667          0.000000                 0.0   \n",
       "3                        0.660714          0.500000                 1.0   \n",
       "4                        0.291667          0.000000                 0.0   \n",
       "...                           ...               ...                 ...   \n",
       "2024898                  0.895833          0.800000                 1.0   \n",
       "2024899                  0.750000         10.416667                 0.0   \n",
       "2024900                  0.625000          1.100000                 1.0   \n",
       "2024901                  0.291667          0.000000                 0.0   \n",
       "2024902                  0.750000         10.333333                 0.0   \n",
       "\n",
       "         Q1_SCALED_TIME  Q3_SCALED_TIME  DURATION  MEAN_DIFFTIMES  \\\n",
       "0                   0.0             2.0       2.0        2.000000   \n",
       "1                   0.0            12.0      18.0        0.947368   \n",
       "2                   0.0             0.0       0.0        0.000000   \n",
       "3                   0.0             1.0       1.0        1.000000   \n",
       "4                   0.0             0.0       0.0        0.000000   \n",
       "...                 ...             ...       ...             ...   \n",
       "2024898             1.0             1.0       1.0        0.041667   \n",
       "2024899             0.0             1.0      61.0        5.545455   \n",
       "2024900             1.0             2.0       2.0        0.068966   \n",
       "2024901             0.0             0.0       0.0        0.000000   \n",
       "2024902             0.0             1.0      61.0        5.545455   \n",
       "\n",
       "         MEDIAN_DIFFTIMES  MIN_DIFFTIMES  MAX_DIFFTIMES  \\\n",
       "0                     2.0            2.0            2.0   \n",
       "1                     1.0            0.0            2.0   \n",
       "2                     0.0            0.0            0.0   \n",
       "3                     1.0            1.0            1.0   \n",
       "4                     0.0            0.0            0.0   \n",
       "...                   ...            ...            ...   \n",
       "2024898               0.0            0.0            1.0   \n",
       "2024899               0.0            0.0           60.0   \n",
       "2024900               0.0            0.0            1.0   \n",
       "2024901               0.0            0.0            0.0   \n",
       "2024902               0.0            0.0           60.0   \n",
       "\n",
       "         MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  TRANSIENTS  \\\n",
       "0                     1.000000       False          2.000000       False   \n",
       "1                     0.473684       False          1.052632        True   \n",
       "2                     0.000000       False          1.666667        True   \n",
       "3                     1.000000       False          2.000000       False   \n",
       "4                     0.000000       False          1.666667        True   \n",
       "...                        ...         ...               ...         ...   \n",
       "2024898               0.041667       False          1.833333        True   \n",
       "2024899               0.092424       False          1.454545        True   \n",
       "2024900               0.068966       False          1.310345       False   \n",
       "2024901               0.000000       False          1.714286        True   \n",
       "2024902               0.092424       False          1.454545        True   \n",
       "\n",
       "         CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  DIRECTIONS  \\\n",
       "0              0.0                  0.0                547    0.500000   \n",
       "1              0.0                  1.0               6707    0.200000   \n",
       "2              0.0                  0.0               4038    0.285714   \n",
       "3              0.0                  0.0                547    0.500000   \n",
       "4              0.0                  0.0               4038    0.285714   \n",
       "...            ...                  ...                ...         ...   \n",
       "2024898        0.0                  0.0              14745    0.640000   \n",
       "2024899        0.0                  1.0               8267    0.333333   \n",
       "2024900        0.0                  0.0              24452    0.500000   \n",
       "2024901        0.0                  0.0               5109    0.375000   \n",
       "2024902        0.0                  1.0               7527    0.333333   \n",
       "\n",
       "         PERIODICITY  VAL  TIME     MIN_POWER   MAX_POWER  MIN_POWER_FREQ  \\\n",
       "0              False    0   0.0 -3.547767e+01   29.119334        0.996225   \n",
       "1              False    0   0.0 -1.167948e+00    1.678669        0.984305   \n",
       "2              False    0   0.0 -1.022540e-16   -1.000000        0.582204   \n",
       "3              False    0   0.0 -4.466079e+00   12.185155        0.654916   \n",
       "4              False    0   0.0 -1.024938e-16   -1.000000        0.398437   \n",
       "...              ...  ...   ...           ...         ...             ...   \n",
       "2024898        False    0   0.0 -1.220261e-01    4.002708        0.654519   \n",
       "2024899        False    0   0.0 -9.628126e+01  241.097368        0.953115   \n",
       "2024900        False    0   0.0 -1.095875e+01    3.375484        0.887952   \n",
       "2024901        False    0   0.0 -0.000000e+00   -1.000000        0.006667   \n",
       "2024902        False    0   0.0 -2.914237e+01   18.307306        0.262351   \n",
       "\n",
       "         MAX_POWER_FREQ  POWER_MEAN  POWER_STD  POWER_MODE  SPECTRAL_ENERGY  \\\n",
       "0              0.498963    0.999518   0.716704     1.00000      4997.590154   \n",
       "1              0.984107    0.101889   0.051009     0.12287       509.444579   \n",
       "2             -1.000000   -1.000000  -1.000000     0.00000        -1.000000   \n",
       "3              0.509492    0.971659   0.219109     1.00000      4858.295315   \n",
       "4             -1.000000   -1.000000  -1.000000     0.00000        -1.000000   \n",
       "...                 ...         ...        ...         ...              ...   \n",
       "2024898        0.654320    0.033453   0.056516     0.03425       167.264340   \n",
       "2024899        0.936228    0.287956   3.696385     0.27468      1439.779106   \n",
       "2024900        0.887753    0.018281   0.170307     0.02170        91.407434   \n",
       "2024901       -1.000000   -1.000000  -1.000000     0.00000        -1.000000   \n",
       "2024902        0.737959    0.343766   0.679253     0.36399      1718.829486   \n",
       "\n",
       "         SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  SPECTRAL_SKEWNESS  \\\n",
       "0                    -1.0       9.216136e+06      -67484.490984   \n",
       "1                    -1.0       1.353158e+06       16624.248136   \n",
       "2                    -1.0      -1.000000e+00          -1.000000   \n",
       "3                    -1.0       7.279583e+06      115694.896686   \n",
       "4                    -1.0      -1.000000e+00          -1.000000   \n",
       "...                   ...                ...                ...   \n",
       "2024898              -1.0       2.433135e+07      346412.823509   \n",
       "2024899              -1.0       1.848036e+07      258381.765523   \n",
       "2024900              -1.0       1.744182e+07     -261552.961524   \n",
       "2024901              -1.0      -1.000000e+00          -1.000000   \n",
       "2024902              -1.0       4.963573e+06      -93883.330649   \n",
       "\n",
       "         SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  SPECTRAL_SPREAD  SPECTRAL_SLOPE  \\\n",
       "0                0.498963           0.500429         0.284358            -1.0   \n",
       "1                0.984107           0.503136         0.283232            -1.0   \n",
       "2                0.006667          -1.000000        -1.000000            -1.0   \n",
       "3                0.509492           0.488942         0.278945            -1.0   \n",
       "4                0.006667          -1.000000        -1.000000            -1.0   \n",
       "...                   ...                ...              ...             ...   \n",
       "2024898          0.654320           0.485402         0.273372            -1.0   \n",
       "2024899          0.936228           0.527018         0.297782            -1.0   \n",
       "2024900          0.887753           0.422524         0.236865            -1.0   \n",
       "2024901          0.006667          -1.000000        -1.000000            -1.0   \n",
       "2024902          0.737959           0.486872         0.276150            -1.0   \n",
       "\n",
       "         SPECTRAL_CREST  SPECTRAL_FLUX  SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  \\\n",
       "0             29.133376     179.824927           -0.497263              True   \n",
       "1             16.475484      10.479260           -0.000199              True   \n",
       "2             -1.000000      -1.000000            0.000000             False   \n",
       "3             12.540566      40.043158           -0.145424              True   \n",
       "4             -1.000000      -1.000000            0.000000             False   \n",
       "...                 ...            ...                 ...               ...   \n",
       "2024898      119.652170       8.479094           -0.000199              True   \n",
       "2024899      837.272075     993.283403           -0.016887              True   \n",
       "2024900      184.639457      29.454925           -0.000199              True   \n",
       "2024901       -1.000000      -1.000000            0.000000             False   \n",
       "2024902       53.255155     478.725102            0.475608              True   \n",
       "\n",
       "         LABEL  LABEL_code  \n",
       "0         True           1  \n",
       "1         True           1  \n",
       "2         True           1  \n",
       "3         True           1  \n",
       "4         True           1  \n",
       "...        ...         ...  \n",
       "2024898  False           0  \n",
       "2024899  False           0  \n",
       "2024900  False           0  \n",
       "2024901  False           0  \n",
       "2024902  False           0  \n",
       "\n",
       "[2024903 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: 695496\tclear: 1329407\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_code\"] + 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": [
       "(2024903, 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: 695496\tclear: 1329407\n"
     ]
    }
   ],
   "source": [
    "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_features = df"
   ]
  },
  {
   "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_code']  # Labels\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0, 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, 50, 5),\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', 50, 400, 50),\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 [2:02:24<00:00, 73.44s/trial, best loss: -0.9982250280637456] \n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.7684282791221851, 'gamma': 6.084800808382009, 'max_depth': 25.0, 'min_child_weight': 4.0, 'n_estimators': 300.0, 'reg_alpha': 17.0, 'reg_lambda': 0.7925240030959909}\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_code']  # 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",
    "      \n",
    "      return model, X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99.88 & 99.92 & 99.74 & 99.83 \\\\\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model, X_test, y_test = 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_code']  # 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",
      "99.841966805835\n",
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.89 & 99.91 & 99.77 & 99.84 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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//Y8GDRpw7tw5i/Jbtmyhbt26rF69GltbW6pUqYLBYODo0aN07dqV3r17x3nXz2TJkuHs7Gzufuvo6Iizs7NFULZy5UoaNWrE5s2bSZs2LVWqVCFTpkxs3boVV1dXc2LtdR4/fmxOBLm4uLy2fIUKFVi9ejUDBw60GPMe0+vk4+ND/fr12bhxI7lz56Z8+fL4+vqydOlS3NzcePbsGRAeHDk7O5MhQwYAihUrhrOzM+XKlXuj1xmZBQsW8NNPP3HkyBFy5sxJlSpVyJw5M3v27OH7779n2bJlr63j6dOntGzZkqFDh3LhwgWcnJz46quveP78OQsXLsTFxYXTp09HeuzUqVPp3r07/v7+VKhQgWzZsnHixAm6du1qHl78NnLkyEGxYsXw8fHh2LFjEfYHBQWxbds2c6AZGR8fH+rVq8eUKVO4c+cOxYsXp3LlyqROnRoPDw/69u3L5MmTzeWrVatmTqhmyJAh0t+JKYmbMmVKKlSogLW1NXnz5o1wbmtrayZMmIC9vT2nTp1i0aJF5n1Pnjwx30Ndu3aNNolr+vsxlUmXLh3Ozs44OzubvxzE9H5909cSGUdHRwYMGACET5q7Z88ehgwZAoQ/3c+TJ0+EY7y9vTl58iQArq6ub3SeChUqMHr06Ncm0iJz9uxZAEqUKBFh34IFC2jSpAlbtmwhXbp0VK1alRw5crBnzx5at25tcV/E9JhTp07h6urKpk2bSJ48OV999RUFChTgzJkzDBs2jN69e5vLvsm9JyIfr927dwPhD+ZefX+cMmUKrVu35s8//yRbtmxUqVKFFClSsGbNGurWrfvGIw4GDhxI586d2b9/P59//jlVq1Ylf/783Lhxg3nz5uHm5sbz58+B/+IQk+rVq1t8rrzK9IB306ZNkT6w2r59O8+fP6dcuXLmL66BgYG0b9+eQYMGcfHiRQoUKMCXX36Jn58fM2bMoEGDBty+ffuNXtumTZsICQkhV65cbzRP7IgRI5gzZw5ff/21eVtISAhdunShe/fuHDt2DAcHB6pUqUKiRIn43//+R926daPsabZ69Wratm3LzZs3KV26NPny5eP8+fMMGTLEImFYrVo1i+SW6fM6plN3BAUF0apVK6ZOncrdu3cpUaIE5cqV4+nTpyxevJj69eu/0eT2x44do3bt2ixevJjAwECqVKlCkSJFOHfuHIMHD+b777+PdHL1gIAA3NzcWLx4MVmyZKFSpUoEBQWxbt06GjVqFGXiIDqm+HzTpk2R7t+9ezf+/v7RPvhcu3Ytrq6urFmzhhQpUvDll19SsmRJ/P392bBhA40aNeLSpUtA+PQhzs7OpEuXDvgv5n71dzJx4kRWr15N6dKlcXBwIFeuXJGe28HBgZ49ewLw888/W1z/rVu38r///c/8EPHVkSuv1hPdd4HY3K9v+loi06VLF0qUKEFAQAB9+/Zl3rx57Nu3j2zZsjFx4sRIRy9s3rwZo9FIoUKF3nhxrF69etG9e3cyZ878xm0ziSq+i8n3mJgcM3r0aPr374+npycGg4EqVaqQMmVKNm/ejJubG3v37gXe/N6Tj4RRYuzUqVNGg8FgrFy5sjE0NNS8vUePHkaDwWAcPXp0hGO++uoro8FgMDo6Oho3b95s3h4YGGjs2bOn0WAwGOvWrWvefvLkSaPBYDDWqFHD6OfnZ94eGhpqHDJkiNFgMBh//PFH83YvLy9jwYIFjQaDwbh48WJjWFiYed/FixeNFSpUMBoMBuOMGTPM248cOWI0GAzGatWqWbTVYDAYDQaD8d9//43wOqI6pm/fvkaDwWCcPn16hGuVP39+Y9GiRY1//vmnxb69e/caCxcubCxYsKDx4sWLEc71KtM1KVCggDE4OPi15SMTm+tkMBiMzZo1M967d8+87+bNm8bSpUsbDQaD8Y8//rA4V6tWrYwGg8G4evVqi+1RXSsT071y7Ngxo9EYfo84OTkZ8+fPb7x06ZJF2Q0bNhgNBoOxdOnSxpCQEPP2yH6Hffr0MRoMBqOLi4vxzp075u1BQUHG0aNHGw0Gg7Fs2bLGJ0+emPc1b97caDAYjPnz5zeuW7fO4txjxowxGgwGY4UKFSJ9HZF5uV1LliwxGgwG4+DBgyOU2759u9FgMBjnzp1rvHXrlrkNLzP9vf3www/GoKAg8/awsDDjrFmzjAaDwVisWDGL67J69WqjwWAwtmrVyqKul3/HL7fH9Pcd1XGm61+kSBHjjRs3jEaj0di5c2ejwWAwtm3b1uL+is66deuMBoPB2Lx5c4vtsb1fI3stb6Nbt27mugwGg3HcuHFRljVdi/Lly7/1eV5luu/c3d0ttgcHBxsfPHhgXL9+vbFUqVJGJyenCH8Thw4dMhoMBmPBggWN27Zti7CvWLFiRoPBYNywYUOsjmnbtq3RYDAYly5dalH+6tWrxhIlShgNBoPxwoUL5u1R3UMiEj+ZPnsMBoPx1q1bEfa/ePHCePPmTeP8+fONTk5ORoPBYJw4caJFmS1bthgNBoOxXLlyxtOnT1vsW7lypdHBwcFYsmRJi7gishjhwIEDRoPBYCxZsqTx6tWrFvW8/J7zcnxpNEYdz70aZ4SFhRmrVq1qNBgMRg8Pjwiv1fSe/HL9w4YNMxoMBmOTJk2MPj4+5u2BgYHGgQMHGg0Gg9HV1TVCXZEZPHiw0WAwGPv16/dG5SMzZcoUc2x+5coV8/awsDDj3LlzjQaDwVi0aFHzZ7XR+N+1NhgMxtmzZ1t8Ti5cuNBoMBiMhQoVMvr7+5u3//vvv+ZjXvW28fPGjRuNBoPB6ObmZgwMDDRvDwwMNLZv395oMBiMo0aNMm+P7HPk8ePHxlKlShkNBoPx559/toiPb926Zaxdu7bRYDAYBw0aZLHd1NZvvvnG+M8//5j3PXz40FijRg2jwWAw/vLLL5Ff7Fe82q4GDRoYDQaD8eTJkxHK/vDDD8b8+fMb79+/b3R3dzcaDAbjgAEDzPt9fX3Nf0+bNm2yOPbJkyfGRo0aGQ0Gg3Ho0KEW+0z36Kuxqul37ODgYDxx4oR5u+l3HdlxYWFh5s/4Nm3aGI1Go/HOnTvm67xx48Y3ui5GY9TfBWJzv0b1Wt7Uv//+a/7uYrrHPT09oyzfpk2bSN/f3lZ076mBgYHGa9eumd9XWrZsaRG/G40x+x7ztsf4+PgYHRwcjKVLl47wdzxz5kxzXS+L6t6Tj4t6jsWCqddYgwYNLDLsTZo0AcK7Yj59+jTSY+vWrcs333xj/rednR2jRo0ibdq0XLhwwdzF3dQlO3369Bbj4K2trenUqRODBg2yWBnTNLlq3bp1ad68ucU4cEdHR8aMGQOED0uMq8kv38SsWbMIDQ2lc+fOFl3RASpXrkzbtm0JDg5m3rx5r63LdE3SpEkT47m0Ynudhg8fbn4KBOE9oGrUqAGEd8V+FwICAnj27BmJEyeOMM+Es7MzgwcPZtiwYRbDuF519+5d1q9fj42NDe7u7hb12Nra0r9/f4oVK8bDhw/N9/fLatSoEeEpX9u2bYHwyYgjG0byOl9//TW2trZs3749wtAIU1tN3fMjkz59eipWrEjv3r2xtbU1b7eysqJZs2ZA+BOjt53voF27dub/f938X6anxi9evGDw4MGsWrWK7du3kyFDBn7++edYT8oc2/v1bV5LZEaMGGEePpk+fXrz09TImIZ/RDWp7dOnT+nVq1eUP5GZNm2axVLfBQsWpFy5cvTu3ZugoCDmzp0boWfh77//DkD79u2pWbOmxb6yZcuah7DOmDEjVseY3o8+//xzi/J58uRhzJgxjB8/3mIxAxH5eFWtWtXivcjBwYEiRYpQrVo1xo4di9FopHPnzhEmnza9ZwwePJiiRYta7GvUqBHOzs74+fm9tvf3s2fPqFmzJh07dozQMy1Pnjzm3igvD8d/G1ZWVtSvXx+IuEr0nTt3OHbsGKlTp6ZatWpA+DDIVatWYWdnx6+//mrxvm9nZ8fQoUPJnj07p0+fjnYou8nLMW9MBAUFsWDBAgDGjh1rMVzPysqKtm3bUrt2bZ4/f26eKuBlRYoUoV27dhafk02bNiVJkiQEBQWZJ+CPa6bXnTVrVoteSHZ2dvTt25ehQ4dafGeIzB9//MGTJ08oXbo0vXr1soiPs2fPzpQpU7C2tmb16tWR9gTr06ePRU+XtGnTUq9ePSDmca2p1+KrvccePXrEgQMHKF++fJS/63v37lG1alXq169P7dq1LfalTJnS3DPt1q1bb9WmEiVKULx4cfO/o4uJrKysGDduHGnSpOHgwYOsXr2aPn368OTJExo2bBhtbPomYnu/vs1riUzmzJnNcQ1As2bNKFy4cJTlTb3nouoFtm/fvihju8i+U0DE99TChQtTq1Ytli1bRvHixZkxY4bFqJ+YfI+J6TFGoxF7e/sIMVzr1q3p37//B1tYQd4tJcdiyN/fn61bt2JjY2MeUmlSqlQpcufOjb+/f4T5jkzq1q0bYVuSJEmoWLEiAEeOHAGgePHi2NnZcfz4cVxdXZk7dy6XLl3CaDSSIUMGWrRoYTEsx3Tcy93oX1axYkXSpUuHv7//W8/zFVNhYWHmdpUvXz7SMqbJOA8dOvTa+kwJkMjmmHhTsblOqVKlinQ4WcaMGQHMwyrjWtq0aTEYDDx79ow6deowefJkjh49ak6GNG/enK+//prEiRNHWcfRo0cxGo0UKVKEzz77LMJ+Kysrc8BhukYvi2xoYIYMGczJGtNQjrd9XRUqVMDX19di3jRfX1/27dtH2bJlzdc2Mv379+f333+3+J08ffqUs2fPsm7dOnPb3iYZbG9vH2GFntcZNmwY2bJl4+jRowwbNgxra2smTpwYJ4mR2NyvMXktr1q5cqU50f/gwQMWL14cZVnj/w/FMUYxh1xgYCAbN26M8icyBoPBnIB0dnbm22+/5auvviJHjhw8f/6c7777ziLwCg0NNc/9ENU1c3Z2xtraGi8vL+7duxejYyA8aQbhS6QPGTKEnTt34ufnB4Qnk+vWrfvOVr8SkferWrVq5vehGjVqkCpVKiD8y/qYMWM4ePAgnTp1sviS+uDBA3Ny4XUx0OsSSNWrV8fd3d1irqnQ0FBu3rzJtm3bzImC2Dz8rF+/PtbW1mzfvt2invXr12M0Gvn222/NCZyjR48SHBxM3rx5I32fs7W1NQ+Xf5PkmCmhE9P47uzZszx9+pQMGTLwxRdfRFrG9IAvsvZEFuMkSpTIPFwqJjHOmyhTpgxWVlZs3LiRNm3asHTpUvNcU7lz56Zp06bRTs0A/8UJUSVs8uTJQ6FChQgNDY10GouXkywmsY1ra9eujY2NDVu3brWY72zLli3mB35RyZcvHxMnTmTs2LEW2+/evctff/1l7kTwtvd6/vz536p8hgwZGDVqFABDhgzBw8ODfPnyMXjw4LeqJzKxvV/f9rW8KigoiKVLl5r//b///S/aZKMprotqap6rV69GGdtFNe/uy++pzs7O1KpVizJlymBvb8/Jkydp1KiRRVI6Jt9jYnKMwWAgXbp03Lp1i3r16vHbb79x5swZQkNDSZw4Ma1btzY/JJCEJX4uYfcRME3EnzRpUvr16xdhv+mDZOnSpebeKy+L6stq1qxZAcxfvDJlysSvv/7KwIED8fT0xNPTEwifk6hixYrUrVvX/OUMMD8Nim6cc44cOXj48GGM5hCICV9fX/P1iG5uAYD79+8THBxs0QPoVaYP6ydPnry2bFRic51SpkwZaXnTk42okgJxYcqUKXTt2pUrV64wc+ZMZs6cSdKkSSlTpgy1atXi22+/jfZ6mF5LdMkS077I7g/TF4GXWVtbY21tTWhoaIznsnN2dmbv3r1s2rSJKlWqAOFzOgQHB7/2ngH4559/WLFiBadOneLGjRvmHmxWVlavTdZEJrLX+TopUqRg9OjRtG7dmpCQEJo0aRJlsPO2YnO/xuS1vOzYsWP88ssvWFtb07BhQ1auXMmkSZMoXrx4hB4QgLlHpek97FVp06aN8BTax8cnQo/Sl9WoUSPKJ3S7d++mW7duDBkyhMyZM1OpUiV8fX3NAXNU93ry5MlJly4d9+/f5+7du9jY2Lz1MRkzZqRHjx74+Piwc+dO/vjjD/744w+sra0pUqQIVatWpVGjRqRJkybK1yYiH4/+/ftbrEz4/PlzBgwYwJYtW5g8eTJFihSJMLn4y3MVlSxZMtr632ReqefPn7N+/Xr27NmDl5cXPj4+5h7jpodBsYlDMmfOTPny5fnrr7/Yu3evuRetqSeZaV6yl9t74cKFKOcFfbVsdEzx3cOHD2PU9jf9rITIP6Oi+rw0xXdxPV+vSeHChRkxYgRjx47l0KFD5gfFpvm/GjRo8No52N70tXt6ekaIE6ytrSOdXD22cW369OkpW7YsBw4c4NChQ+YOABs2bCBFihRUrVo12uPDwsLYt28fGzdu5PLly3h7e5vnTItpj/zUqVO/9THVqlWjTp065r+B0aNHx8kKr7G9X2PyWl42cuRIzp8/j6OjI0mSJOH06dN069aN5cuXRzqPWsaMGblx4wb379+PtD7T5Psv++2335gyZUqUbXj1PdXk+fPnjBo1iv/973+0bduWrVu3kjhx4hh9j4nJMUmSJGH69On07NmTq1evMmXKFKZMmYK9vT3ly5endu3aVK9ePdYjQyT+UXIshky9FJ4/fx7t0zAvLy8OHjwY4WlhVG+qpg+glxMcVatWpXz58uzfv5+//voLDw8Pbty4wbp161i3bh2urq6MHDnS4vjomD7co+th9KZ1vImXnxZF1SPjZSEhIdEmePLly0fSpEl5/vw5p0+fplSpUq9t66RJkyhYsCAVKlQgZcqUsbpO7+uN8NVVhSD8CeKGDRs4fvw4f/75Jx4eHly4cIE///yTP//8k8WLF7No0aIol6J+m9cd2Qfju3rtVatWJXny5OzZs4dnz56RLFky1q9fT7JkySKs6PSqpUuXMmrUKMLCwsiQIQNOTk7kypULR0dHypQpw1dfffXWAW1Mhh4C/Pnnn+b/3717N127do2TnmOxuV9j+logPBjr3r07ISEhtG/fnp49e3L37l327dtH9+7dWbt2bYQvE6aE2f3797l+/fpbTRAbE6YE1NKlS1myZAmVKlV640D+5WsWk2MgfDGFadOm4eXlxe7duzly5AinTp3i9OnTnD59mrlz57Jo0aLXfnEUkY9P0qRJmTBhAj4+Ppw8eZK2bduydu1ai6FipveMZMmSvTYZEN3qjAA3btygVatW+Pj4kDhxYgoWLEj58uXJkycPxYoVY/ny5XGyQlqDBg3466+/WL9+PTVr1uTMmTNcv36d/PnzW0zEbXptOXLkwMnJKdo6CxUq9NrzFilShOXLl0e7uvPLPD09OXDgAKVKlXptLGhieq9/nzHOy6KKR1xdXfn666/Zu3cvBw4c4NixY9y5c4cVK1awYsUKunXrxo8//hhlvfE1rnVxceHAgQNs2rSJihUrcuPGDc6cOUODBg2iTTAFBgby/fff4+HhgZWVFQaDgRo1apA7d24KFSrEnTt3zAsEvY2YxEQBAQEWK8f/8ccfkT4cfBeiu19jE9+tWbOGlStXkjhxYsaPH0/SpEmpV68e586dY8KECQwaNCjCMUWLFsXDw8Pcy/5dSpo0KUOGDGHXrl3cvn2b3bt3880338Toe0xMv/sUK1aM7du3c/jwYfbv34+HhwdXrlxh27ZtbNu2jYoVKzJr1iyLYZ/y8VNyLAZOnjzJlStXsLe35+DBg1EmmXr27MmmTZtYvHhxhOTY3bt3I/3SbJon4tU5pZIkSUKNGjXM81rdvXuX1atXM3XqVFauXEmTJk0oWLAgmTJl4ubNm9y8eTPKJxGmVTRN3cSjYup1E9kcVk+ePIn22JelSZMGOzs7goKCGDx4cKx7siRNmpSKFSuyY8cONm/e/NqA6PDhw+a5hDZv3kzKlCnj9DrFlCkYiSwJBpiHZkV23MuBYEBAAHv37mXUqFGcP3+eVatW0aZNm0iPNQ17iK7btOl1x3TOj5gw3d9r165l9+7dODk5cerUKerVq0fSpEmjPO7OnTuMGTOGsLAwBg8eTLNmzSyCvICAgHf2pPdVhw4dYuHChaRIkYJSpUqxd+9eBg4caDE/VUx9iPs1JCSE7t27c//+fUqUKEHXrl0BGDduHC4uLty+fZv+/fvz22+/WRyXJ08eChYsyPnz5/njjz8i7Vkb10w9NUzvn6lTpyZx4sQEBgZy69atSBN0/v7+5nno0qdPT8qUKd/6mJflyZOHPHny0L59e0JCQjh27BgTJkzgwoULTJ48mZkzZ8bpaxaR+MHW1paJEyfi7OzMvXv36Nu3L3PnzjXvN33u2tjYxHoOypEjR+Lj40PZsmWZMmVKhHhq9uzZMa77ZVWrViV16tTs37/fvDIgWPYag/96epmGwMWWaZW+W7du4enp+dreUgsXLmTTpk0ULlyY//3vf+b2RLda/D///AO82xgnpvGzvb09Li4u5iFe//zzD4sXL2bx4sVMmzYNV1fXKD/jM2XKxLVr17h586bFiJKXveu4NjLVq1cnWbJk7Ny5kxEjRpjvpdeNCliwYAEeHh5kyZKFOXPmROiR+fLq4O/a8OHDuX37NhUqVOD8+fOsXr2aypUrm7+TxdSHul8vXrzI8OHDARgwYACOjo4A5hW2Fy9eTJkyZSI8nHZ2dmbOnDmcOnWKS5cumY97VxInTsxnn32Gr6+vOb6LyfeY2Hz3sbW15csvv+TLL78E4PHjx2zZsoXx48fz119/sXv37ljfBxK/aM6xGFi5ciXAa+d3Ms1Ftm/fvgh/kPv3749QPiAgwLzdNI/YwoULqVKlSoQvVpkyZeKnn37CYDAA/3VXNw3jimrp5P379+Pr60vq1KlfO1bd9AQzsu6zJ0+ejPSYyIK+RIkSmZfhjWo54nXr1vH1118zYMCAaNtkYpowdeXKlZw5cybKcs+ePTMHbBUqVCBv3rxA3F6nmDJNcB7Z9b106VKEOR5MS3S3b9/eYnuKFClwdnY2B1PRLZleunRprK2t8fT0jPTD2Gg0snnzZoAog6t3xdSrcPPmzebfy+uCp9OnTxMSEkKaNGkiTFQPln9nLz85iuunpI8fP6Zv374YjUb69evH+PHjyZAhA3v27GH58uWxrv9D3K8///wzx48fJ02aNEyePNk8F0zatGnNy3zv3r3bPJnsy/r164eNjQ0LFy6M9L3uVablumPKNDeLaVh6okSJzMOXorpmmzZtwmg0YjAYSJs2bYyO8ff3p2HDhlSoUIHAwEBzuUSJElG2bFk6duwIWA4nUhd8kYQnW7Zs5gcBBw4csOi9lSVLFnLmzIm/vz9Hjx6N9PgZM2bg7OzM5MmToz2Paa6o1q1bR0iMPX36lBMnTgCxH/5nZ2eHi4sLwcHB7Nq1i61bt2JnZxeh978ppvDw8IjygV6HDh1o0KABW7dufe15U6dObV7UasSIERbvq686evQo27dvBzA/ECxcuDDJkyfn/v37UV5r09yW7zLGedv4+eeff+bLL780J45MPvvsMwYNGoS9vT0hISHRTofyujjBy8uLCxcukChRIkqXLv3GryW2kiVLRpUqVXj69Cn79u1j06ZNZMuW7bVtMN3rX3/9dYTEGPwX373rB6CbN29mw4YNpE6dmvHjx5vnGhs8eHCsp6f5EPern58fnTt35sWLF3z77bfmvzcI7+VnWpBjwIABEb67Ojg40KhRIwB69OiBv79/tOcKDg7m0qVLMW5rSEiI+btKtmzZgJh9j4nJMVu2bKFmzZoReiemSZOGZs2amedSfJPh4vJxUXLsLfn5+Zk/4KObSBLCP6hy5sxJWFgYS5Yssdg3Y8YMiwkxnz9/Tp8+ffDz86NChQrmbuu5c+fm9u3bLFiwgL///tuijjNnznDt2jUSJUpEwYIFAWjVqhW2trasW7cuwqpHly9fNr+pN2/e/LVzdZnaMH/+fIsPn7/++osVK1ZEeowpWfhqkGRaLW/cuHEWQ88Azp07x7hx47h27Rq5c+eOtk0mRYoUoW3btoSGhtK6dWtWrVoVYVJOb29vfvjhBy5cuEDKlCkZOnSoeV9cXqeYMiUxtm/fbvEB9PDhw0i7ijs6OnLr1i32798f6co/pkAhuqENWbJkoXbt2oSGhtKtWzeLD/aQkBAmTJjAqVOnSJs2rTnZ9r6ULVuWDBkycODAAdauXUuWLFkoU6ZMtMeY5nJ6/PixRZd3AA8PD/NwY8AiyI7qPo2pwYMHc+/ePb788ksaNWpEqlSpGDFiBADjx4/n2rVrsar/fd+v27ZtY8GCBVhZWfHzzz9HmGj5iy++4IcffgBg4sSJ5rkQTUqXLk2fPn0ICwujQ4cOTJw4MdL5Mry8vOjXrx9dunQBYvY0++jRoxYrB5uY5r2YPXt2hKT80aNHmTRpEoBFL8u3Pcbe3h4bGxvu37/Pzz//bNFLIDg42BzUvvw3Gdf3nojED66urubPrPHjx1vMm2V6qNW/f/8IQwb379/PzJkzuXLlymt7YphGHOzevdvigc+jR4/o1q2buWfrq0kl0/C1t+n1b3o/dXd35+HDh+beZC/LmjUrtWvXJiAggE6dOplXXYTwpMX06dPZu3cvly9ffu2E8iY9evQgR44cnD17lqZNm3Lu3LkIZXbt2kXnzp0JDg6mSpUq5pUcEydOTIsWLYDwa+3l5WU+xmg0snDhQrZs2UKSJElwc3N742vxtt42fs6RIwd3795l2rRpERIuu3btwt/fn5QpU0Y7TYGrqyspU6bEw8ODyZMnW4xKuHPnDt26dcNoNOLs7Pxee44B5nhyypQp/PPPPzg7O7/2QZHpXj948KDFQgjBwcFMnjzZvIBTVPd6XHzG3rlzh2HDhgEwdOhQ0qdPT+3atalRowa+vr7069cvVvP7ve/71Wg00qdPH3PveFOc+rLBgweTO3du/Pz86N69e4TvVoMHD6ZIkSJ4eXnh7OzMtm3bIpQxJdUbNGhgTmC/7T0XEhLCuHHjePz4MWnTpqVy5cpAzL7HxOQYBwcH8xRGryYu//nnH3OS++XerXF578mHo2GVb2ndunW8ePGCnDlzmntDRcXKyooGDRowefJk1qxZYx6WBOGJkZYtW1K8eHHSpUvH8ePHefjwIblz57ZYmaVixYo0aNCA1atXU6dOHZycnEifPj3379/n9OnThIWF0bNnT3OPibx58zJmzBgGDBjA8OHDWbhwIY6Ojjx69IgTJ04QGhrKt99+y08//fTa19quXTtOnDjB9u3bqVGjBgUKFMDb25vz58/TsGFD/ve//0U4xpTcWr58Obdu3aJSpUq4urpSvnx5evXqxaRJk/jhhx/Ily8fuXLlMr8Oo9HI119/HeVwwMj07t2bJEmSMG3aNAYNGsTEiRPJnz8/qVOn5s6dO5w7d47Q0FCyZcvG9OnTLYajxeV1iqlvvvmGWbNmmQOFL774wrxiXsaMGSldurTFuH57e3tGjBhB37596dmzJ7NmzSJXrlw8ffqUkydP8uzZMypVqsTXX38d7XmHDRvGrVu3OH36NDVq1KBkyZIkT56cM2fO4OPjQ5o0aXB3d3/vwZO1tTW1a9dmwYIF/PPPP7Rv3/618ymULl2aokWLcubMGVq2bEnJkiVJnTo1169f58qVK6ROnZoMGTJw//59Hj58aO45aLpPz58/T+vWrTEYDG/ca/FVq1atYufOnaRMmdK8ohGEDw+pW7cu69atMy9jHdPE1fu8X69du2a+Fj/88IN5At1Xde7cGQ8PD06cOEG3bt1Yt26dxWIVrVu3JmfOnAwZMoQ5c+Ywd+5cHB0dyZo1K6Ghody4ccPc4ytx4sQ0bNiQbt26RXquHTt2mIcWmISEhPDPP/9w4cIFIHyFtZeXun/5Padjx47kz5+fzz//nH///ZfTp08D4ckw01PSmB4zYsQImjZtyuLFi9m5c6c56X3+/Hnu3btH9uzZ6dSpk7l8XN57IhK/jBw5EhcXF3x9fRk5ciS//vorAI0aNeLChQssW7YMNzc3ChQoQPbs2c0xFYQn3V/3+d2uXTtGjBjBypUrOXbsGAaDgSdPnnDy5EmCgoLImzcvV69eNSfJTHLnzs2FCxfo2LEjBQoUoFevXtFOAg7hD+RMQ+Qh4pBKE1NMcfToUWrVqkXBggVJkyYNFy5c4Pbt2yRKlIhffvmFzJkzv8klJHny5KxYsYKffvrJPDdV7ty5yZUrF1ZWVly8eNHcQ7527dqMGTPGItHSqVMn/v77b3bv3o2LiwvFixcnbdq0XLp0iRs3bpAsWTLGjh0b6arjceVt4+eGDRuyc+dODhw4QLVq1ShevDipUqUyH2Ntbc3gwYOjnWYibdq0uLu707FjR2bOnMn69espUqQIT58+5dixYwQGBvLFF1/EaJ6u2Cpfvjzp0qXj6tWrwOtHBQC0bNmSrVu3cvnyZapWrYqTkxOhoaGcOXOGx48fR3uv79+/H3d3dzw8PKhTp06MVhUMCwszd1qoVauWRXwxbNgwjh07xqFDh5g/fz5t27Z96/pN3uf9OnPmTPbu3UuSJEmYMmWKeQTLy5IlS8bkyZNp1KgRZ8+e5eeff2bgwIHm/YkTJ2bJkiX88ssvLFq0iK5du2Jvb0/+/PlJmzYtT5484cKFC+ZEfLZs2ejatWuUv/OxY8da3NdGo5Fnz57h6enJgwcPSJw4MRMmTLBoa0y+x7ztMXny5KFz585MnTqVli1bUrhwYbJmzYqvry8nT54kODiYxo0bW6zyGlf3nnxYSo69JdOQyjd5YweoV68e7u7u+Pn5sW7dOvP2X3/9leXLl7N+/XrOnDlD5syZadiwIe3bt48wmfrIkSMpVKgQGzZs4MqVK5w5c4bUqVPz1Vdf0bx5c/MQTBMXFxccHByYN28eR44cYffu3aRKlYoKFSrg6ur6xn+olSpVYuHChcyaNYszZ86wb98+8uTJw9ixY6ldu3akybHGjRtz6dIldu3axf79+0mePDmurq5AeLBQokQJFi5cyIkTJ9i7dy+pU6emdOnSNG7cmFq1ar31pIadO3ematWq/PHHH5w8eZIzZ84QGBiIvb09JUqUoEaNGjRq1CjSST/j6jrFVNKkSVm+fDm//fYbe/bs4cCBA6RPn55GjRrRqVMn83wAL6tbty7p0qVjyZIlnD17Fi8vL5InT06BAgVwcXGhYcOGr72GKVKkYPHixaxcuZKNGzdy8uRJwsLCyJo1K+3bt6dly5bmFQffNxcXF/MwvTf5G7OxsWHevHnMnTuX7du3c+bMGaysrMiaNStt2rShbdu2zJo1iyVLlrB3717zU31HR0f69evHwoULOX78ON7e3vTv3/+t23vjxg3GjBkDwMCBAyP0sBo4cCCHDh3i/Pnz/Prrr/Tu3futz2HyPu7XZ8+e0blzZ54+fUrp0qXNPboiY2Njw6RJk6hbt655/rHp06dblKlSpQpffPEFe/bsYfv27Vy9epXDhw8TEhJC2rRp+eqrr/jiiy9wcXGJduGCK1eucOXKFYttSZIkIX369NSqVYs6deqYVzl9Wbt27ShevLj5Pefq1aukSZOGmjVr0rx580iHdbztMQ4ODqxcuZI5c+bg4eHBgQMHSJQoETly5KBhw4a0adPGImkY1b2n4ZYiH7/PPvuMTp06MXHiRLZu3YqLi4v5vWno0KFUqlSJFStWcObMGa5cuUL69OmpVKkSzZo1i3bFXpNmzZqRKVMm5s+fj5eXF3v27CFNmjR8+eWXNG/eHHt7exo0aMCBAwcICgoyTy49evRohg4dyqVLl/D19eXatWuvTY5BeNLm/PnzZMmSJcLcuSammGLVqlVs3LiRS5cuERwcTJYsWahfvz5t2rQxTwHyptKnT8+yZcvYvHkz27Zt48KFC+be8RkzZuTbb7+lYcOGkQ41s7W1Zfr06axfv57Vq1dz6dIlXrx4QebMmWnevDktW7bks88+e6v2vK23jZ8TJUrE9OnTWbRoEdu2bcPT05Pg4GDSpk3Lt99+S+vWrSlcuPBrz1u2bFk2bNjA3Llz+euvv9izZw/JkyenaNGi1KtXj7p168ZqEveYSpQoEd988w2LFy+mcOHCbzRKpFChQqxatYrp06fj6enJvn37SJEiBfny5aNOnTq4uLhQsWJFbt++zeXLl82L3nTo0IE7d+5w8OBB9u/fT+7cuWMUH82ZM4djx46RLl06i5EnEN4LasiQIXTv3p1ffvmFcuXKxXj+rfd1vx4+fBh3d3cABg0aFO0iQaY4ZcSIESxatIgyZcpYXMPEiRPTv39/mjdvzpYtW9i3bx/e3t6cPn2axIkTkylTJmrUqEHlypWpUqVKtPfcq730ra2tSZ48OTly5MDZ2ZlmzZpFWGUyJt9jYnJMp06d+Pzzz1m1ahWXLl3iwoUL2NvbU6ZMGRo2bBjhYUZc3XvyYVkZY9MfVERERERERERE5COmOcdEREREREREROSTpeSYiIiIiIiIiIh8spQcExERERERERGRT5aSYyIiIiIiIiIi8slSckxERERERERERD5ZSo6JiIiIiIiIiMgnS8kxERERERERERH5ZCX60A0QERGR+MGqevY4q8u40zvO6hIRERGR2FGcFz0lxyIRlzeNyKfI9Gb5IvTZB26JyMctiU2yD90EkQRHcZ5I7CjOE4k7ivXiDyXHREREJJyV1YdugYiIiIi8C4rzoqXkmIiIiITTTKQiIiIiCZPivGjp8oiIiIiIiIiIyCdLPcdEREQknLrbi4iIiCRMivOipeSYiIiIhFPMJCIiIpIwKc6LloZVioiIiIiIiIjIJ0s9x0RERCScutuLiIiIJEyK86Kl5JiIiIiEU39yERERkYRJcV60dHlEREREREREROSTpZ5jIiIiEk7d7UVEREQSJsV50VJyTERERMIpZhIRERFJmBTnRUvDKkVERERERERE5JOlnmMiIiISzlqPFEVEREQSJMV50VJyTERERMIpZhIRERFJmBTnRUvDKkVERERERERE5JOlnmMiIiISTqsYiYiIiCRMivOipeSYiIiIhFPMJCIiIpIwKc6LloZVioiIiIiIiIjIJ0s9x0RERCScVjESERERSZgU50VLyTEREREJp5hJREREJGFSnBctDasUEREREREREZFPlnqOiYiISDitYiQiIiKSMCnOi5aSYyIiIhJOc1GIiIiIJEyK86KlYZUiIiIiIiIiIvLJUs8xERERCacHiiIiIiIJk+K8aCk5JiIiIuE0F4WIiIhIwqQ4L1oaVikiIiIiIiIiIp8s9RwTERGRcHqgKCIiIpIwKc6LlpJjIiIiEk6rGImIiIgkTIrzoqVhlSIiIiIiIiIi8slSzzEREREJpweKIiIiIgmT4rxoKTkmIiIi4bSKkYiIiEjCpDgvWhpWKSIiIiIiIiIinyz1HBMREZFwemQmIiIikjApzouWkmMiIiISTt3tRURERBImxXnRUu5QREREREREREQ+Weo5JiIiIuH0QFFEREQkYVKcFy0lx0RERCScutuLiIiIJEyK86Kl5JiIiIjEOy9evGDRokVs3LiRW7dukSxZMkqXLk2HDh1wdHS0KDtu3Djmz58fZV379u0jc+bM5n/7+fkxe/ZsduzYgY+PD+nSpaNmzZp07NgRe3v7CMffu3ePadOmceDAAe7fv0/WrFlxcXGhXbt22NnZRSh/48YNpk6dyrFjx3jy5Ak5c+akcePGNG3aFGvriDNanDt3jmnTpnH27FmePn1Kvnz5aNmyJc7Ozm9zyUREREQkhpQcExERkXDxZCbSoKAgvvvuO44fP07BggVxc3Pj0aNHbN26lZ07dzJt2jS++uorc/kLFy5gZWXFTz/9hFUkT0VTpEhh/v+AgABat27N+fPnqVixIjVr1sTT05P58+dz4MABVqxYYVH+7t27uLm58e+//1KjRg1y5MjB4cOHcXd3x8PDg99//x1bW1tz+StXrtC8eXOeP39O7dq1SZs2LXv27GHkyJF4enoyYcIEi7YdPnyY9u3bY2dnR+3atUmaNCnbtm2jV69eeHl50a1btzi8siIiIvLJiidxXnyl5JiIiIiEiyfd7RcvXszx48dxcXFhwoQJ5oRX8+bNcXNzY+jQoVSsWJFEicLDmEuXLpEzZ066dOny2rpnzpzJ+fPn6dy5M506dTJvnzx5MjNnzmTatGn069fPvH3cuHHcvn2b8ePHU7duXQDCwsLo27cvGzZsYMWKFbRo0cJcfvDgwfj5+bFo0SJKly4NQLdu3fj+++9Zv349tWrVokqVKkB4EnDAgAHY2NiwcuVK8uTJA0DHjh1xc3Nj1qxZ1KpVK0JPOREREZG3Fk/ivPhKuUMRERGJV27cuEHq1Knp3LmzRU+wwoULkzdvXu7evcvt27cB8Pb25smTJ+TPn/+19QYFBbFs2TJSpUpF+/btLfZ17NiRNGnS8L///Y+goCAAfHx82LZtG/ny5TMnxgCsra3p168fNjY2LF261Lz95MmTnD59mi+//NKcGAOws7Ojb9++ABblt2/fzp07d6hXr545MQaQMmVKunbtSlhYGMuWLXuTSyYiIiIisaDkmIiIiISzisOfWBg5ciRHjx4lZ86cFtufP3/O7du3SZQoEWnSpAHCh1QCb5Qc8/T05OnTp5QsWTLCXGF2dnaUKlUKf39/PD09AThy5AhhYWGULVs2Ql3p0qXD0dGR69ev4+PjA4QPkQQoV65chPIFCxYkderUeHh4EBoaalG+fPnyEcqb6jCVEREREYmVeBLnxVdKjomIiEg4a6u4+4lDz549w8PDg7Zt2+Ln50fr1q1JmTIl8F9y7OnTp3To0IFy5cpRtGhRXF1d2bx5s0U9169fB+Dzzz+P9Dw5cuSwKPem5a9du2ZRPleuXFGWDwoKwtvb+7X1p0iRgrRp0+Lt7W3uySYiIiISY/E0zosvNOeYiIiIxLmqVatGu3/37t1vVM/x48dp1qyZ+d9ubm706tXL/O+LFy8C8Pvvv1OxYkXq1avHv//+y+7du+nRowfnzp0zD2l8/PgxAKlTp470XKlSpQLCV7OMTXnT9tiWT5kyJY8ePSIgIIC0adNGWkZEREREYk/JMREREQkXDydqtbGxoUWLFgQFBfHnn3+yfPlyHj16xMSJE7Gzs8POzo5s2bIxatQoi+GMN2/exM3NjXnz5lGhQgXKly9PcHAwgMXqki8zDbUMDAwEICQkJE7Lm7a/bf3qOSYiIiKxFg/jvPhEyTEREREJF4cx05v2DHudYsWKUaxYMQACAgL47rvv2L59O05OTrRt25apU6dGepxp9cohQ4awdu1aypcvT5IkSQDMSbJXmZJQyZIlA3jj8smTJ3+j8qbtb1r+1faIiIiIxJhyY9HSnGMiIiLyUUiRIoV5SOWuXbteW75o0aJAeC8yiDis8VVPnjwBMM9n9qbl7e3t30l5Pz8/rKysSJEiRaT7RURERCRuKDkmIiIiAFhZWcXZT0yFhoZy+PBhduzYEel+0yT4jx494tmzZ5w5c8a8uuSrnj17BkDixIkByJMnD/BfsuxVt27dAiBv3ryxKm/aHln55MmTkyVLlteWDwgI4NGjR+TJkwdra4VrIiIiEjvxIc6LzxRtiYiICBA/giZra2s6d+5Mly5duHfvXoT9586dA8JXePTx8cHV1ZV27dqZ5+96mYeHBwBOTk4AFCxYEHt7e44fPx5hKGNQUBAeHh4kT56cAgUKAFCqVCmsra05fPhwhLofPnzIpUuXyJ07N+nSpQOgTJkyABw6dCjSdvv6+lK8eHFsbGxeW/7gwYMAlCxZMsI+ERERkbcVH+K8+EzJMREREYk3rKyscHFxwWg0Mm7cOMLCwsz77t69y/jx44HwVStz585NwYIF8fX1jTD32Llz55g9ezZJkyalSZMmQPgE93Xq1OHhw4fMmDHDovz06dPx9fXFzc2NRInCp2TNlCkTlSpV4uLFi6xevdpcNiwsjHHjxhEaGkqLFi3M252cnDAYDOzZs8ci4RUUFMSECRMALMpXqVKF9OnTs2rVKi5fvmze7ufnh7u7O1ZWVjRv3jxmF1JERERE3piV0Wg0fuhGxDdW1bN/6CaIfNSMO70BeBH67AO3ROTjlsTm/U7Enqh70TirK2TymRgf6+fnR7Nmzbhy5QoODg6UK1cOX19fdu3ahb+/Px06dKB79+4AXLp0iZYtW/LkyROKFy+Ok5MTt2/fZs+ePRiNRiZNmkStWrXMdT958gRXV1du3LhB2bJlKVy4MJ6enhw5coT8+fOzZMkSizm+bt26RePGjXn06BFVq1YlV65cHD58mHPnzlGhQgVmzpxpsdqkp6cnrVq1Ijg4mFq1apEpUyb27NnDtWvXqF+/PmPHjrV4rbt27aJLly4kTpyY2rVrkzx5crZt24aPjw+dO3emU6dOMb6OEjnFeSKxozhPJO68z1gvvsR58ZWSY5FQ0CQSOwqaROLG+06O2fVwirO6gn45Havjnz59yuzZs9m2bRu3b98mSZIkFClShFatWlGpUiWLsnfu3GHGjBns37+fBw8ekDJlSkqVKsUPP/xAwYIFI9T96NEjpk2bxu7du3n48CGZM2emevXqdOjQwTxJ/su8vb1xd3fnwIEDBAQEkC1bNlxcXGjTpo15xcmXXb58malTp+Lh4UFQUBA5c+akcePGNGnSxDyk8mXHjx/nt99+48yZMxiNRvLkyUOrVq349ttvY3EFJSqK80RiR3GeSNx5n7FefIrz4iMlxyKhoEkkdhQ0icSNTzk5JvKuKM4TiR3FeSJxR8mx+CPRh26AiIiIxA8JdYJVERERkU+d4rzoKTkmIiIigIImERERkYRKcV70tFqliIiIiIiIiIh8stRzTERERAA9URQRERFJqBTnRU/JMREREQFAMZOIiIhIwqQ4L3oaVikiIiIiIiIiIp8s9RwTERERQN3tRURERBIqxXnRU3JMREREAAVNIiIiIgmV4rzoaViliIiIiIiIiIh8stRzTERERACwQk8URURERBIixXnRU3JMREREAHW3FxEREUmoFOdFT8MqRURERERERETkk6WeYyIiIgKAHiiKiIiIJEyK86Kn5JiIiIgAYK2oSURERCRBUpwXPSXHRERERERERETkvdm6dSsLFizg6tWr2NjYUKxYMTp27EiRIkXe6PgXL14wa9YstmzZwu3bt0maNClOTk506NCBEiVKvHV7NOeYiIiIAOETtcbVj4iIiIjEH/EpzpsxYwbdunXjwYMHuLq6Ur16dY4ePYqbmxt//fXXa48PCQmhbdu2/Pbbb1hbW9OsWTMqVarE4cOHadGiBTt37nzrNqnnmIiIiABaxUhEREQkoYovcd7Vq1dxd3fHYDDwxx9/kCxZMgCaN2+Om5sbAwcOZMeOHSRJkiTKOjZs2MCJEycoXbo08+bNw9bWFoBGjRrRunVrhg8fTpUqVbCxsXnjdqnnmIiIiIiIiIiIvHMLFy4kLCyMn376yZwYA8ifPz8NGzbk7t277N69O9o6zpw5A0CDBg3MiTGAMmXKkC9fPu7fv8/Nmzffql1KjomIiAgQvopRXP2IiIiISPwRX+K8w4cPA1C+fPkI+8qVKwfAoUOHoq0jderUAHh7e1tsDw4O5tGjR1hbW5vLvCklx0RERASIX3NRiIiIiEjciQ9xXnBwMN7e3qRNm5aUKVNG2J8zZ04Arl27Fm09DRo0IHny5MydO5fNmzcTEBDA7du36du3L/fv38fV1ZU0adK8Vds055iIiIiIiIiIiLyRqlWrRrs/qmGRvr6+GI1GUqVKFel+U8LM398/2vpz5szJihUr6NevHz169LDY17lzZ3766adoj4+MkmMiIiICxJ+JWkVEREQkbsWHOC8kJATAYp6wl9nZ2QEQGBgYbT1+fn5MnjyZCxcuULRoUYoVK8ajR4/YvXs3c+fOJX369DRp0uSt2qbkmIiIiADxI2gSERERkbgXl3He6ybMj0rixImB8OGVkQkKCgKwmKg/Mn369GHv3r106tSJzp07m7ffuXOHZs2aMXToUD7//HO++OKLN26b5hwTEREREREREZF3yt7eHhsbmyiHTfr5+QFEOh+Zyd27d9m7dy9Zs2alY8eOFvuyZs1qHmb5xx9/vFXb1HNMREREAPUcExEREUmo4kOcZ2trS44cOfjnn394+vQpyZMnt9h/8+ZNAPLmzRtlHf/++y8AuXPnxto6Yn8vBwcHILwX2dtQzzEREREB4s8S3yIiIiISt+JLnFemTBmMRiOHDx+OsO/gwYMAlCpVKsrjM2TIAISvaGk0GiPsv379OgAZM2Z8q3YpOSYiIiIiIiIiIu9co0aNsLKyYsqUKRbDKy9dusTq1avJnDkz1apVi/L4bNmyUaxYMe7cucPMmTMt9j169IjJkycDUKdOnbdql4ZVioiICBA/utuLiIiISNyLL3Fe4cKFadOmDfPmzcPZ2ZlatWoREBDApk2bCAkJYcyYMeZVK/38/Fi4cCGAxcT748aNo3nz5vz666/8+eeflCxZkkePHrFnzx58fX1p1qxZtAm2yCg5JiIiIkD8CZpEREREJG7Fpzivb9++5M6dm2XLlrFs2TKSJ09O6dKl6dSpE0WKFDGX8/PzY9q0aYBlcuzzzz9n3bp1zJo1i71797Jw4ULs7OwoUKAATZs25ZtvvnnrNlkZIxuk+Ymzqp79QzdB5KNm3OkNwIvQZx+4JSIftyQ20S9jHdeyja4cZ3XdHvhnnNUlEpcU54nEjuI8kbjzPmM9xXnRU88xERERAcA6Hj1RFBEREZG4ozgvekqOiYiICKBVJkVEREQSKsV50VNyTEREROKdFy9esGjRIjZu3MitW7dIliwZpUuXpkOHDjg6OlqUDQsLY+XKlaxYsYJ//vmHxIkT88UXX9C1a1dy5coVoe7AwEAWLlzIunXruH37Nvb29lSuXJkuXbpEuuy3n58fs2fPZseOHfj4+JAuXTpq1qxJx44dsbe3j1D+3r17TJs2jQMHDnD//n2yZs2Ki4sL7dq1M08w+7IbN24wdepUjh07xpMnT8iZMyeNGzemadOmWFtrYXERERGRd00Rl4iIiADhE7XG1U9sBAUF8d133zFp0iRsbW1xc3OjYsWK7NmzhwYNGrB3716L8kOGDGHo0KGEhobStGlTypcvz86dO2nQoAGXLl2yKBsSEkKnTp2YNGkSqVKlomXLlhQpUoRVq1bRoEEDfHx8LMoHBATQunVr5syZQ86cOWnVqhU5c+Zk/vz5uLm5ERAQYFH+7t27NGnShFWrVlG4cGFatWpFihQpcHd3p127dgQHB1uUv3LlCq6uruzYsYNy5crRrFkzgoODGTlyJP369YvVdRQRERExiS9xXnyl5Ji8U3myfs62sUvw33CZf5YepVejDuZ9xfMV5tCU9fhvuMxh9w2UyV/c4tgfvm2O16KDPFl3ka1jlpArc06L/cNa9sRn5Skerj7HrG7jSWybOML5Uyazx3v5cVrVaBRp+wY07cz83r/EwSsViZ+CgoKo79KQYx7HzdtOHj9Jk4ZNKVOiLK71GnPk0JFIj50z83cGDxjyvpoq8YBVHP4XG4sXL+b48eO4uLiwevVq+vbty/jx41m6dClWVlYMHTqUkJAQAPbv38+qVauoUKECa9eupXfv3kyaNImZM2fy7NkzBgwYYFH3qlWr2L9/Pw0aNGD58uX07NmT3377jZEjR3Lv3j1Gjx5tUX7mzJmcP3+ezp078/vvv9OzZ08WLlxIhw4d+Pvvv80rKJmMGzeO27dvM3bsWKZMmUKvXr1YtWoVLi4uHDlyhBUrVliUHzx4MH5+fsydO5dx48bRp08fNmzYQJkyZVi/fj179uyJ1bUUiQuJbRPze4+JPF57njsrTtCjYfsoy1Yv8SWnZ+7Af8Nldo5fjiF7bov9PRv+wLVFh3i89jzzek0ieZL/JqNOniQZs7tP4N6qM9xadow+jX+yOLZb/e8x7vS2+Pm5/aAIbSjtWIyQbf/wWSbLxRfeJHYUeVuBgYEMHTSMCmUqUvXL6iycvyjKshcvXKJZ4xaUKV6Wpq7NuHD+gsX+rZu3UrumM2WKl6Vb5x48fvzYvM9oNPLrL1OoXP4rKn5RickTfyUsLCzCOUJCQnCt15gZ02ZabG9Uz5WiBYpZ/Pz991UAHj58RK9uvSlfuiJVKlZj8qQp5s9ZgL//vkrblt/xRYlyuHxTl62bt1rUvWjBYmpW+ZoyxcvSod1P/HPjHwBu374T4ZymnxPHT7zhFZa4El/ivPhKyTF5Z6ysrNg8aiH3fR9R7MdadJjSn0HNuuD2VV0ypE7H7gkrOHv9EiU7fsMff25g57hl5MiQFYAaJSsx4fuBdJk+hJIdv+Hpi2esHfa7ue6+jTvyk3NL3MZ0pNaA5lRxKs/QFt0jtGF8uwFkS5850vY1+aoOw1v2fDcvXiQeCAwMpG+v/nhd9TJve/jwEV06dqXW1zVZvW4VNWrVoGvn7tz1uWtx7NbNW5kxfearVYq8Fzdu3CB16tR07tzZ4ulk4cKFyZs3L3fv3uX27dsALFiwAICuXbuSKNF/s0VUrFiRypUrc/78eU6fPm3evmDBAqytrenRo4fFOV1dXTEYDOzatYu7d8P/HoKCgli2bBmpUqWifXvLZEDHjh1JkyYN//vf/wgKCgLAx8eHbdu2kS9fPurWrWsua21tTb9+/bCxsWHp0qXm7SdPnuT06dN8+eWXlC5d2rzdzs6Ovn37AliUF/lQfm4/iJKGIlTp3Zifpg5kaPPuNKhYO0K5Ap8Z2DxqIesP76DET19z8u9z7Pl5pTkB1r52M4a17MGA+eMp360e2dJnZtmA/xLMc3pMoFKRL6g77DvcxnTkx29b0L1BO4v6p29YQGbXYuaf4YsnW7QhkU0i5nSfgI2NjcX2N40dRd7WLz9P5sL5C8yZP5sBQ/oz67fZ7Ny+M0K5Z8+e06lDZ4qXKMbyVUsp6lSUTh268OzZcwDOep5j2OARdPipPYuXL8Tfz4/BA4aaj1+0YDFbN23jF/dfmDRlIps3bWHxgiURzrNw/iIuX75isS00NJR/btxk3qLf2b1vp/knV67PARjQZwD+AQEsWraAiZMnsG3zNhbMXQiEfxZ2+akr+QvkZ9XaP2jzXWsG9R/C+XPnAdi8cQuzZ8xm0NABrFyzgjSpU9OlYzeMRiOZM2eyON/ufTv5unYtChYuSJGiReLk+ovEFSXH5J3JlCYDp73O86N7f67evs5Wjz3sPnWQCoVK0bJaQx76PeZH9/5cvuXFr2t+58C5Y/zo3BKAb0pXYceJ/Ww+upu/b19n2KJfKJqnAOlSpgn/UtOgHb1mj2Tv6UMcu3yaoYsmUcJQ2OL85QuWomqxCvz70PJLv421Db91GcO8npPwuvPPe7seIu+T11UvWjRpifetWxbbT586jY1NIlp/14rsObLz/Q/fkdguMZ5nPIHwp42jho9m6KDhZM+RPbKqJQGLL93tR44cydGjR8mZ07LH8PPnz7l9+zaJEiUiTZo0hISEcOzYMVKlSkXhwoUj1FO+fHkADh06BMC///7LjRs3MBgMpE+fPtLyYWFhHDkS3pvS09OTp0+fUrJkyQhzhdnZ2VGqVCn8/f3x9Az/+zly5AhhYWGULVs2Qt3p0qXD0dGR69evm4duHj58GIBy5cpFKF+wYEFSp06Nh4cHoaGh0V8wkXcoWZKkfP+1G11/G8qpq+dYd3AbE1bOoFOd1hHK/ujckkMXTjB04USueF+j7++jefLUj2ZV6wHQuU4bJv1vNiv2rufCP1doNaE735aphiF7btKlTIPbV3X54dd+HDp/nAPnPOj7+xh6NfrBXH/+nHk543WRu4/vm38Cnj+1aEOfxj/i98zfYtubxo4ib+vZs+esXb2OPv37kL9AfqpWq0Lr71qxYtkfEcpu37qdxEkS06N3d3LnyU2f/r1JnjyZOZG2YtkKatSqjnMdZwwOBkaPG8WB/Qfw9g5/GLRs8XJ+6vwjxUsUo3SZUnTr0ZUVyyx7I9/85ybLlywndx7LHpu3vW8THBxMocKFSJ8hvfknUaJEBAUFkTZdOgYO7k+evHkoXrI41WpW5dTJUwB4Xb3Gndt36Nj5J3LkzEG9BnXJZ8jHcY/wnl8BAQF069mNipUq8tnnn9Hm+9bcuH6DR48eY2NjY3E+b29vdu3YzeixI7G1tY3z34dEL77EefFVvEyOBQUFsWXLFgYPHkyzZs1wdnamZs2a1KtXj5YtWzJy5Ei2bt1q0dVT4h+fR/doMvonc9BSrmBJvixchj/PHCZ3lpyc+PusRVdgz+sXKVugBAAP/R7zZeEyOOTIg421DS2rN+T6vzd5HPCEgp85kD5VWtYd3G4+dtmetdTs18z8bztbO+b0mEDHqQMJDA6yaFeKpMkpkjs/ZTo7c/iiuvNKwnTi+AlKlSnFomULLbanTp0KX19fdu3cjdFoZM+uvTx9+pR8hnxAeJD395W/WbJiEUWd9ETvUxNfg6Znz57h4eFB27Zt8fPzo3Xr1qRMmZLbt28TFBREzpw5Iz2nKbl27do1AK5fvw7A559/Hul5cuTIEaPypnIxrT+yRQNM5YOCgvD29o50/8dMsd7Ho2juAtgmSsShC/8Nzz9w7hhlHItF+LvLnSUnRy+esth29volc3yXO8tnHL30336fR/e4/+QhZQuUIHeWzwA4eumkeb/n9YtkTZfZPDwyf858XPG+FmVb82XLRUeXVvScNdJi+5vEjiIxceXyZUJCQnByKmreVqx4Mc56nosw5PGs51mKFXcy/91YWVnhVNyJM6fDH7B4njlL8RL/TTOTOUtmsmTJzNkznty7dw8fHx9KlPxvf7ESxbhz51/u379v3jZy2Gg6dOxA2rRpLM59zesamTNnInHiiEOJ7ezsGDthNDk/C//MvPq3F/v27Kdk6ZIApEqVEoA1q9cSFhbGmdNnuH79Oo75HQBo7OZKQ9cGAPj7+7Ni2Ury5M0ToQ0AU35xp36j+uTKHfnnnrxb8TXOiy/i3WqVBw8eZODAgdy9exej0RhpGQ8PD5YtW0aWLFkYPXp0pE9oJX65seQIn2XKzsbDO1l9YAv5c+ajaJ4CFmVyZMhK+pThb6JT182nWrEKXJq3j5DQEJ6+eEbF7g0ICwsjd5acPPL3pVzBkoxp25f0KdOy+sAW+v4+hqD/T4QNcOvMqavn2Xlif4S2PHnqR4Vu9d79ixb5gFybuEa6vXiJ4jRu2phe3XpjbW1NaGgoI0YP5/P/71afMqU9C5cueH8NFXmN48eP06zZf19g3dzc6NWrF4B5LpZUqVJFemzKlOEBvb+//xuVN21/tXzq1KmjLe/n5xer8q9rj6l8QqFY7+OSJW0mHjx5RHDIf4tJ3PW9T9LESUiXMg0Pnjz6b/vj+xGms8iRISuP/H3/25/uv/3JkiQlrX1q0qdMy93H4V/ws6XPwtXb183HAqRPlZbngS9IlzINrWs2YkHvX3ge9IK5W1cw6X+zzPXN7j6BYYsmm+syeZPYUSQmHtx/QOrUqbG1+68XVLp0aQkMDMTX15e0adOat9+//4A8eS17dKVNlw6v/5/368H9B2TMmCHC/rs+93hw/wEAGV7any5deN13fe6RIUMG1q1ZT1BQIA0a1Y8wJ9i1a9dJZGtLpx+7cOHcBT7P9Rnde3WncJFCFuXatvyOE8dPUqBgfhq7NQYga7asdOnWickTf+WXnycTGhrKjx07UKZsGYtj165ex7DBw7Gzs2PGnOkREiinTp7G88xZxk0c95qrKvJhxKvkmKenJz/88AO2trY0a9aM8uXLkzNnTlKmTImdnR1BQUH4+flx8+ZNDhw4wJo1a/jhhx9YtmwZhQoVev0J5INpMKI9mdNkYEaXsUzuMIzZW5YyuHlXvv+6KfO3/0HV4hWoU7YGtx+GDzXJmi4TSeyS0HRMJ67eucGgZl1Y0s+d0p2+JUXS5CRLnJRx3/Wn+8xh2FjbMLPrWGysbegyfTD5c+ajw7fNKdK++gd+1SLxz7Nnz7h9y5sOHX+gUuUv2b1zD+PHTKBI0cJ6iifE5YPAqlWrRrt/9+7db1SPjY0NLVq0ICgoiD///JPly5fz6NEjJk6caO5VFNXQDNNQyMDAQADzSpGvDpF8Xfk3rf9t2/O68qbtpvIJgWK9j0+yJEkj9MIP/P959l6d0P6PPzeyYcQ8lu9dx7Zjf9Ksaj1KORRl75nwoc1/7NtIf7eOHDjvwfV/b/HLD+HzKdnZ2nLz3m0OXzjBlJ+G03xcF+wS2TKsRfjcgHaJbHHMmReAu48f4Dy4DcXyFsS94whCw0L5dc3vfPe1G7aJEjFny9IIE/G/LnYUiannL15gZ2f5Hm56rw8Oslyd+MXzF9jZvjpE39Y8b+WLFy+wjTCE35ag4CBePH9hUffL/x8UFMTDh49wnzyVWfNmRtqr5/q1G/j7+VG/QT06dv6R1avW0L7tD6zduJrMWf5LWPcd0Ac/P3/GjR5Pv979cJ8+heDgYK5fv0FD1wbUrV+H48dOMG3KdEqUKkGp/+9dBvBF2TKs+N9y1q1ZR7dO3VmxegXZs2cz71+9ajVVqlUhU6aMr7mq8q4k0A5fcSZeJcd+++03bG1tWb58OY6OjpGWyZAhA3ny5OGrr76iUaNGuLm5MW3aNGbO1MTR8dmJK+HdhbvPHM7Sfu70mj2SdpP74P7TCGZ2Hctpr/P8tnERXzmFPxme2XUsqw9sYfnedQA0HdOJW8s8qFOuBiGhISRLkpQuvw1hv2f4vDA9Z41k+YDpdJsxlDk9JjBk4UTu+T74IK9VJD6bP3cBRqORDj+Fz+GSv0B+znqeZeniZQwaOvADt04komLFilGsWDEgfF6T7777ju3bt+Pk5ESpUqWA/5JYrzJ94UiWLHwy8CRJklhsf9Pyb1v/68onT578jcqbtpvKJwSK9T4+L4JekPiVL/SJ//9L+bPA5xbbtx//k+FLJrN6yGwS2SRi75lDLNq5mlTJ7QEYufRXcmfJyfk5ewgOCWbW5qWc9jqP37MAAFqM78r/hsziwf88efLUj/7zxlGuYEn8ngVw/sZl0tUvZO6Fdu7GJTKkTsePzi1Zvnc9o9v0oWqfJpG+huhix66/DYmyB6PI6yS2S0zQK0kw03u96T3eXDaxXYSeikFBwSRJGl7OLrEdwUGR7E+SBLv/Hw4ZFBRkHhppOk/SpEmYMHYCdeq5kC9f3kjbOXTEYF68eEGKFCkAGDhkAKdPnWHThs18/8N35nIOjuFDJUeMHkZT1+bcvn2Ho0eOcv7cBdZs+B9WVlbkL5Cfa17XmP/7fIvkWJasWciSNQuO+fty/NgJNq7byI+dOgDhD4P+3PMno8aNivZ6yruVUIdDxpV4lRw7deoU3377bZTB0qscHR359ttvtcx5PJUxdXrKFijB+kP/ze9w4Z8rJLZLTMpkKViwfSWLdv6PjKnT4/PoHuO/H8gNn/B5VUrkK8LoZVPNxz198Yy/b9/gs0zZzXNVXLp51bz/srcXSRMnIXv6LJQvWIqiuQsw6YchACRLnJSZXcfSuJIL3wxs8T5euki8dfH8RQyOBottjvkdufr31SiOkE9JXAZNb9oz7G2kSJGCXr160bx5c3bt2kX16uE9hE3DIF9lGo5oGl756rDJVz158iTS8lENa4xpeXt7+xiVTwgU6318bj/wIX2qtNhY2xAaFr44ROY0GXn24jm+AU8ilB+zbCoTV80iVXJ77vs+5I9BM7hxN3xxmGcvntN41I+kTGaPESP+zwK4u/I0N3zC93vduUGxDjXJkDodT576kyfLZ4SGhnLzXviE5KbEmMnFm1fJli4zNUtWIn3KtBxx3wCAFeHvZefn7GH0cncOng+fLy2y2DFDqnR6oCoxljFTBnx9fQkJCTGvmPzgwUOSJEmCfUr7V8pm5OGDhxbbHj54QPr04UMlM2bMyINI9mfIkJ6MmTKY686WLav5/wHSZ0jPti3bSZIkCcuXhk/QHxgYyOlTZ9ixfSdrN64mUaJE5sQYhH/e58r1Offu3SMgIIADfx2kRs3qWFuHT0lumtDf9/FjLp6/SD5DXosYwTG/I6dPngbA4+gxMmbMYJ6iw8rKity5c/HY19dc3vO0J8EhIZQt98VbXF2R9yteTcgfGhr61gFgihQpePr06esLynuXK0tO1gydQ9aX5pYoYSjCvccPKJwrP8sHTCcsLAyfR/cA+Lr0V+Zu93ce3qXAZ/nMx9nZ2pErcw6u+9zk1NVzBAYFWsxZlj9nPvye+nP7gQ95W1XAqUNN88+dh3cZsnAS3//S+z29cpH4K0PGDFzzspzM+Pr162R7qdu7fLriw0StoaGhHD58mB07dkS63zSp/aNHj8iWLRtJkiTh5s2bkZY1bc+bN/xJep48eSy2v+rW/6/u+r7L33plVdmXyydPnpwsWbJEuv9jpFjv43Pa6zzBISF8kf+/icArFCrFsStnIvS4avJVHSb/OIyg4CDu+z4kiV0Svipajr2nw+O78d8PpGX1hvg988f/WQAlDUVJldyeQxdOYGVlxfZxSyn0uSP3fR8SFBxE7TJVOXn1LP7PAvjuazcuzdtncT6nPAW4dOsqaw5sxaFtJXPs982g/1/9fFBLZm5aEm3s+NDv8bu6dPIJcHB0IFGiRHieOWvedurkKQoWKmBONJkULlKY06f++7sxGo2cPnmGIkXDV00tUrSweYVIAJ9/ffDxuUvhokXImDEjWbJk5tSJUxbnyZIlMxkyZGDj1vWsWvsHK9esYOWaFRQoWIBGjRsyfWZ4Z4PvWrdj5vT/5ucLCwvjypW/+TzX57x4/oK+Pftx1vO/13Dx/EVsbGz47PPP/j92vG7xWq5f+y92nD93AYsWLDHvCw0N5dKly+R+abqOs57nKFAgf6QLAsj7Ex/ivPgsXiXH8uTJw86dO994bo2AgAC2bdtG7ty5X19Y3rtjl09z4m9P5vWaSP6c+fi6dBV+bjeQ0cuncsX7Gs5fVKfDty3IlTkn0zuPJk2KVCzcsQqAOVuXMbBpF2qXqYohe25mdxuP//MANh7ehf+zAOZsXc7UjiMpk784X+QvzvjvB/D71uWEhoXideeGxU9IaAj3fB9w5//nMxP5lNVvWI8D+w+yeOESvG95s2TRUg7+dSjKCfxF3jdra2s6d+5Mly5duHfvXoT9586dA8JXhLS2tqZkyZI8fvyYS5cuRSh78OBBAPPwy4wZM5IrVy4uXbrEo0ePIi1vbW1NiRLhK+sVLFgQe3t7jh8/HmHoY1BQEB4eHiRPnpwCBQqYz2Ntbc3hw4cj1P3w4UMuXbpE7ty5SZcuHQBlyoRPZnzo0KFIX6evry/FixfHxsYmiqv18VGs9/F5HviChTtXMbPrWEoailKnXE16NfqBKWvnApApTQaS2IUPC7vifY0O3zanXoWvyZstF8v6T+PW/TtsPbYXgDsPfRjaojslDUUpnq8wS/q5M2PTYh77+2I0Gnn24jnjvu9P3my5qFOuJkNbdGfM8mkA7DyxnyxpMzLxh8Hkyfo5jSu70LfxT4z/4zcCnj+1iP3+uRs+EuGfu9489vd9bewoElNJkybFuc63jBo+mnNnz7Nn114WzV9M0xZNgfBJ9l+8CJ8vrHrNavj7+zNh7M94XfViwtifef78OTVq1QDAtUkjNm3YzJrVa7ly+QoD+w/my8oVzfN2NWrSiF9/mcIxj+Mc8zjOlF/czefJ+VlOi58kSRKTKlUqsv5/L7NKlb9kyaIl/LnnT25cv8HYUePw9/OnTj0X0mdIT9XqVRg7ajwXL1zi5PGTDBsyArdmTUiRIgXffPsNt27dYvKkKdy6eYuNGzax5n9rcWvuBkDjJo3YsG4DWzZt5cb1G4weMYbAwEBc6jibr9PVq1fNvdFE4qt4lRxr3bo1t27donHjxuzYsYOAgIBIyz1//pw9e/bQrFkzfHx8LFaxkvgjLCyMOkO/4+mL5xx2X8/vPSbgvm4e7mvncuehD66jOtClXlvOzt6FQ448VOvbhKcvngEwcdVMfl45E/eOI/CYtomMqdNTrY8bgcHhwXSPmcPZemwvW0YtYsvoRWw79if952nlE5HXKVK0CL9MmcjGdRtpWNeVTRs2M33mVPLmy/OhmybxQHx4omhlZYWLiwtGo5Fx48YRFhZm3nf37l3Gjx8PhK9aCeDqGp7YHT9+vMVcYn/99Rd//vknRYoUoWjRoubtrq6uhISEMGHCBIteLytXruTKlSvUrFmTjBnDJwu2s7OjTp06PHz4kBkzZli0c/r06fj6+uLm5mYeSpMpUyYqVarExYsXWb16tblsWFgY48aNIzQ0lBYt/hve7+TkhMFgYM+ePRYJsqCgICZMmABgUT4hUKz3ceoxczgn/j7L3okrmd55NEMXTmLtgfDV8HxWnqJx5fAvwSf/PsuP7gOY1H4wJ6ZvAaD2oFbmv7Wp6+ez4fBOto5ZzNbRi9l0dBe9Zo00n6fDlP6EhoVy8retTPphMJ2nD2bdwW0A3Lx3m28GtqRcgZJ4ztrJ2Lb96Pv7GFbt3/TGr0Gxo7wLvfr2pEDB/Hzfuh1jRo3lx04dqFY9fFGaqpWqs31reE/oFClSMPU3d06eOIVbo2Z4njnLtJlTSZYsKQBFnYoyeNggZk2fTcumrUmZMiUjRg83n6d121bU/LoG3bv0oHf33nzrUpsWrZq/URtbtGpO67atGTd6PI3qNcbrqhez5s00z2k5fNQwDA4GOnz/I9279KRipQp069EVgOzZszHr9xmcOnES1/pNmDdnPsNGDqV8hXIAVK5SmYFDBjBj+kxc6zfh5s1bzJzzG8mSJzOf/+GDh+YpCOTDiQ9xXnxmZYxnM1DOmDGDqVOnmj9E06ZNS6pUqbC1tSU4OBg/Pz8ePXpk3t+mTRv69OkTp22wqp799YVEJErGneFPbF+EPvvALRH5uCWxSfb6QnHIYXKtOKvrcvdtMT7Wz8+PZs2aceXKFRwcHChXrhy+vr7s2rULf39/OnToQPfu3c3lu3Tpwvbt28mdOzdVqlTh7t27bN26laRJk7JkyRKL+a2Cg4Np0aIFp06donDhwnzxxRdcv36dXbt2kSVLFlasWEHmzP9NB/DkyRNcXV25ceMGZcuWpXDhwnh6enLkyBHy58/PkiVLLOZxMSV+Hj16RNWqVcmVKxeHDx/m3LlzVKhQgZkzZ1qsTunp6UmrVq0IDg6mVq1aZMqUiT179nDt2jXq16/P2LFjY3wd46sPHespzhOJHcV5InHnfcZ68SXOi6/iXXIMwMvLiwULFnDkyBG8vb0tnuza2NiQI0cOypQpQ6NGjd7Jst4KmkRiR0GTSNz4VJNjAE+fPmX27Nls27aN27dvkyRJEooUKUKrVq2oVKmSRdmQkBAWLFjAmjVruHXrFqlSpaJkyZJ07tzZPK/Xy549e8asWbPYvHkzPj4+ZMiQgfLly9O5c2cyZcoUofyjR4+YNm0au3fv5uHDh2TOnJnq1avToUMH86T6L/P29sbd3Z0DBw4QEBBAtmzZcHFxoU2bNhFWLwO4fPkyU6dOxcPDg6CgIHLmzEnjxo1p0qRJghpS+bIPGespzhOJHcV5InFHybH4I14mx14WEhJiXgEkceLE2Nvbm4cvvCsKmkRiR0GTSNx438kxx1+/jrO6LnXbGmd1ScL2vmM9xXkisaM4TyTuvM9YT3Fe9N5tlikOJEqUiPTp03/oZoiIiCR4CXUOCYnfFOuJiIi8e4rzohevJuQXERERERERERF5n+J9zzERERF5P/REUURERCRhUpwXPSXHREREBADFTCIiIiIJk+K86GlYpYiIiIiIiIiIfLLUc0xEREQAdbcXERERSagU50VPyTEREREBFDSJiIiIJFSK86KnYZUiIiIiIiIiIvLJUs8xERERAfREUURERCShUpwXPSXHREREBNAqRiIiIiIJleK86GlYpYiIiIiIiIiIfLLUc0xEREQAdbcXERERSagU50VPyTEREREJp6BJREREJGFSnBctDasUEREREREREZFPlnqOiYiICKDu9iIiIiIJleK86Ck5JiIiIoB624uIiIgkVIrzoqdhlSIiIiIiIiIi8slSzzEREREB1N1eREREJKFSnBc9JcdEREQEUNAkIiIiklApzouehlWKiIiIiIiIiMgnSz3HREREBNATRREREZGESnFe9JQcExEREUCrGImIiIgkVIrzoqdhlSIiIiIiIiIi8slSzzEREREB1N1eREREJKFSnBc9JcdEREQEUNAkIiIiklApzouehlWKiIiIiIiIiMgnSz3HREREBNATRREREZGESnFe9JQcExEREUBBk4iIiEhCpTgvehpWKSIiIiIiIiIinyz1HBMREREA9EBRREREJGFSnBc9JcdEREQEUHd7ERERkYRKcV70NKxSREREREREREQ+Weo5JiIiIkD8eqIYEBDAnDlz2LFjB97e3iRKlIh8+fLRqFEjGjVqZFG2c+fO7NixI9J6bGxsuHDhgsW2e/fuMW3aNA4cOMD9+/fJmjUrLi4utGvXDjs7uwh13Lhxg6lTp3Ls2DGePHlCzpw5ady4MU2bNsXaOuJzxnPnzjFt2jTOnj3L06dPyZcvHy1btsTZ2TnSNh4+fJgZM2Zw+fJlgoODKViwIO3bt6dixYpverlEREREohWf4rz4SMkxERERAeJP0OTn50fTpk35+++/cXR0pEmTJrx48YLdu3czaNAgTp48ydixY83lL1y4QMqUKWnZsmWEul59TXfv3sXNzY1///2XGjVqkCNHDg4fPoy7uzseHh78/vvv2NramstfuXKF5s2b8/z5c2rXrk3atGnZs2cPI0eOxNPTkwkTJljUf/jwYdq3b4+dnR21a9cmadKkbNu2jV69euHl5UW3bt0syq9fv56+ffuSLl06nJ2dCQ0NZcuWLXz//feMGjUqQiJQREREJCbiS5wXX1kZjUbjh25EfGNVPfuHboLIR8240xuAF6HPPnBLRD5uSWySvdfzfbm8WZzVtd9taYyPHTt2LAsWLMDV1ZXhw4ebe2f5+fnh5ubG1atXmT17NpUqVcLPz49SpUpRrlw55s+f/9q6u3fvzpYtWxg/fjx169YFICwsjL59+7JhwwYGDRpEixYtzOUbN27MmTNnWLRoEaVLlwYgKCiI77//nqNHjzJjxgyqVKli3l6zZk0eP37M6tWryZMnj0W7r127xtq1a3F0dATg8ePHVKlSheTJk7N27VoyZMgAwJ07d3B1deXp06fs2LHDvF3ihuI8kdhRnCcSd95nrBdf4rz4SnOOiYiICBC+ilFc/cTG5s2bsbKyonfv3hbDFlOmTEm7du0A2LVrFwAXL14EIH/+/K+t18fHh23btpEvXz5zYgzA2tqafv36YWNjw9Kl/wV7J0+e5PTp03z55ZfmxBiAnZ0dffv2BbAov337du7cuUO9evXMiTFTu7t27UpYWBjLli0zb//jjz949uwZrVu3tkiAZc2ale+++45nz56xdu3a174uERERkdeJL3FefKXkmIiIiADh3e3j6iemQkNDad++PV27diVlypQR9pvmBHv69CmAeT6xN0mOHTlyhLCwMMqWLRthX7p06XB0dOT69ev4+PgA4UMkAcqVKxehfMGCBUmdOjUeHh6EhoZalC9fvnyE8qY6TGXetPyhQ4de+7pEREREXic+xHnxmZJjIiIiEm/Y2NjQsmVLfvzxx0j3b9u2DQAHBwfgv+TYv//+S8uWLSldujTFihWjZcuWHDx40OLY69evA/D5559HWneOHDkAuHbtmkX5XLlyRVk+KCgIb2/v19afIkUK0qZNi7e3N0FBQa8tnzNnTou2iIiIiMi7o+SYiIiIhIvn/e137drF9u3bSZYsGfXq1QP+G1bp7u5O6tSpadSoEeXKleP48eN89913LFmyxHz848ePAUidOnWk9adKlQoInyPs5fKm7bEtnzJlSsLCwggICDCXt7OzI2nSpBHKJk2aFFtbW/z9/SOtS0REROStxPM470N7o9UqBw8eHOMTWFlZMWLEiBgfLyIiIu9HXHaTr1q1arT7d+/e/Vb1HTx4kJ49ewIwdOhQMmbMSFhYGClSpOCzzz7D3d3dPNE9gKenJy1atGDMmDGULVuWPHnyEBISAmCxGuXLTEM2AwMDAV5b3rT9Tcub6jf1HAsJCYk0MfZy+RcvXkS5P64ozhMREUn4EupwyLjyRsmxVatWYWVlRUwWtlTQJCIiIrGxfv16Bg4cSHBwML169TJPpm9tbc2KFSsiPaZIkSK0atWKWbNmsWHDBrp3706SJEkACA4OjvQYU9IqefLkAK8tb9r+puVN9SdLlsxcPqqypvKmsu+S4jwRERH51L1RcqxTp07vuh0iIiLygVnH4QPFt+0ZFhmj0cgvv/zC7NmzsbGxYfjw4TRp0uSNjy9SpAgAN2/eBCIOg3zVkydPALC3t491+ZdXnzTx8/PDysqKFClSmMv/+++/vHjxwpxYM3n+/DnBwcFkzJjxTV5qrCjOExERSfjiMs5LiJQcExERESB+dbcPCgqiZ8+e7Nixg2TJkvHrr79SqVIlizJPnjzBy8uLZMmSWQypNHn+/DnwX4+uPHnyAP8ly15169YtAPLmzWtR3rQ9svLJkycnS5Ys5vLHjx/n1q1b5mNNAgICePToEXnz5sXa2tpc/t9//+XWrVvky5fPorypjaa2vEuK80RERBK++BTnxUdxNiF/aGioec4NERERkZgKCQmhY8eO7Nixg8yZM7N8+fIIiTEIn1fMzc2NPn36RFqPh4cHAEWLFgWgVKlSWFtbc/jw4QhlHz58yKVLl8idOzfp0qUDoEyZMgAcOnQoQvlz587h6+tL8eLFsbGxeW1508qZJUuWNG972/IfkuI8ERERiUtbt26lcePGlChRgtKlS/PDDz/g6en5VnXs2rWLFi1aUKJECUqUKEGDBg1Yu3YtYWFhb92eWCXHrly5Qr9+/fjyyy8pVKgQQ4YMAWDQoEHMnz8/RnNXiIiIyIdhbWUVZz+xMXXqVPbv30/mzJlZsWJFpL3CIDy5lCFDBi5fvsyqVass9u3bt4/Vq1eTIUMGvv32WwAyZcpEpUqVuHjxIqtXrzaXDQsLY9y4cYSGhtKiRQvzdicnJwwGA3v27LFIYAUFBTFhwgQAi/JVqlQhffr0rFq1isuXL5u3+/n54e7ujpWVFc2bNzdvd3FxIUmSJPz+++/4+PiYt//777/MmzePZMmS0bBhw7e6dnFJcZ6IiEjCEV/iPIAZM2bQrVs3Hjx4gKurK9WrV+fo0aO4ubnx119/vVEdkydPpmPHjly/fp06derg4uLC3bt36devHz///PNbt8nKGMPIZt26dQwePNhiIlkXFxcmTJjAN998w/Xr1/nmm2+YOHHiR9d9z6p69g/dBJGPmnGnNwAvQp994JaIfNyS2Lz7ydhfVnNtmzira3u9+TE67t69e1StWpWgoCC++uorChYsGGm53LlzU7t2bQ4dOkSHDh0IDAykYsWK5MuXj2vXrrFv3z6SJk3KnDlzLHpf3bp1i8aNG/Po0SOqVq1Krly5OHz4MOfOnaNChQrMnDnTYrVJT09PWrVqRXBwMLVq1SJTpkzs2bOHa9euUb9+fcaOHWvRrl27dtGlSxcSJ05M7dq1SZ48Odu2bcPHx4fOnTtHGMK4ZMkSRo4cSerUqalduzYAmzdv5smTJ4wZM4b69evH6DrGluI8EYmK4jyRuPM+Y734EOcBXL16FWdnZ/Lmzcsff/xhXnzo4sWLuLm5kTJlSnbs2BFhPtaXHTlyhFatWlGgQAHmz59P6tSpgfApN+rXr4+3tzc7duzgs88+e+N2vdGcY6+6cOECgwYNwsbGhh9//JHKlSvTuHFj8/727dszfvx4tmzZQrVq1fj6669jchoRERH5xBw+fNi8quPevXvZu3dvpOWqVq1K7dq1KVeuHKtXr2bmzJkcOXKEw4cPkyZNGurWrctPP/1Ezpw5LY7LkSMHK1euxN3dnQMHDvDXX3+RLVs2unXrRps2bSwSYxA+qf+KFSvMvdmCgoLImTMnQ4YMiXRxgGrVqrFo0SJ+++03tm7ditFoJE+ePPTu3dvcg+1lzZs3J3PmzMydO5c1a9ZgZ2eHg4MD7du3p2LFijG9jLGiOE9ERETelYULFxIWFsZPP/1ksSp3/vz5adiwIYsXL2b37t3mh4aRmTt3LgBjx441J8YgfLGjnj17cujQIfPCSW8qRsmxOXPmEBoayvTp0yOdA6Ru3brkzZuXRo0asXLlSgVNIiIiH4E4m4g0FurUqUOdOnXe6ph8+fIxadKkNy6fPXt287DIN+Hg4MC0adPeuHzJkiWZN2/eG5evVq0a1apVe+Py75riPBERkYQnPsR5gHnu1/Lly0fYV65cORYvXsyhQ4eiTI4FBgZy6NAh8ubNG+nUG9988w3ffPPNW7crRsmxY8eOUbBgwUgDJpNChQrh5OSEl5dXTE4hIiIi71lczCEhHz/FeSIiIglPfIjzgoOD8fb2Jm3atKRMmTLCflOP/2vXrkVZx99//01ISAgODg7cvn2bqVOn8tdff+Hv70+ePHlo3br1Wz9ohRgmx3x9fSlRosRry2XIkIFz587F5BQiIiIi8gEozhMREZHoVK1aNdr9u3fvjnS7r68vRqORVKlSRbrflDDz9/ePsu67d+8CcP/+ferVq0fq1KmpVasWAQEB7Nq1iz59+nDt2jW6d+/+Ji/FLEbJsTRp0vDPP/+8ttz169dJkyZNTE4hIiIi79nHNrG6vBuK80RERBKe+BDnhYSEAESY49XEzs4OCB86GZWnT58C4OHhQeXKlXF3dydx4sRA+MJLjRo1YubMmVSpUoWiRYu+cdtilBwrVaoUW7du5cCBA1SoUCHSMvv27ePvv//WPBQiIiIfifjQ3V4+PMV5IiIiCU9cxnlR9Qx7HVMS6+XVsF9mWpTp5Yn6X2VjY2P+/+HDh5vrhPCFl9q1a8eECRPYsGHDWyXHYjQn2/fff4+1tTWdO3dmwYIFXLlyxbzP39+ftWvX0qdPH6ytrWnVqlVMTiEiIiIiH4DiPBEREXkX7O3tsbGxiXLYpJ+fH0Ck85GZpEiRAoCMGTOSOXPmCPsLFSoE8Ea94F8Wo55jBQoUYOjQoQwfPpzx48cD4V30Nm7cyMaNGwEwGo307t0bJyenmJxCRERE3rP40N1ePjzFeSIiIglPfIjzbG1tyZEjB//88w9Pnz4lefLkFvtv3rwJQN68eaOsI3fu3MB/vcxeZRq6mTRp0rdqW4xX83R1dWXp0qVUrlyZpEmTYjQaMRqN2Nra8sUXXzBv3jy+++67mFYvIiIi75l1HP7Ix01xnoiISMISX+K8MmXKYDQaOXz4cIR9Bw8eBMKneIhKjhw5yJ49O76+vpEuDHTmzBkA8ufP/1btilHPMRMnJydmzJiB0Wjk8ePHhIWFkSZNGosxoCIiIiLy8VGcJyIiInGtUaNGrFy5kilTplCmTBns7e0BuHTpEqtXryZz5sxUq1Yt2jpatGjB2LFjGTVqFHPnzjX3QLt+/Trz588nSZIk1K1b963aFavkmImVlRVp06aNi6pERETkA9GE/BIZxXkiIiIfv/gS5xUuXJg2bdowb948nJ2dqVWrFgEBAWzatImQkBDGjBljXrXSz8+PhQsXAtC5c2dzHS1btuTYsWPs2rWL2rVrU716dQICAtixYwfPnj1jxIgRZM2a9a3aFavk2MWLF1m6dClHjx7l7t272NrakjVrVsqVK0fz5s3JkSNHbKoXERGR9yg+zEUh8YfiPBERkYQjPsV5ffv2JXfu3Cxbtoxly5aRPHlySpcuTadOnShSpIi5nJ+fH9OmTQMsk2PW1ta4u7uzatUq84+trS1FixalXbt2lC1b9q3bZGU0Go0xeTG///47kydPJiwsjMiqSJo0KePGjaNmzZoxqf6Dsqqe/UM3QeSjZtzpDcCL0GcfuCUiH7ckNlEvY/0uNNz8Q5zV9b/as+KsLnn/FOeJSFQU54nEnfcZ6ynOi16Meo4dOHCAiRMnkihRIpo1a0a1atXIli0bRqMRb29vNm3axNq1a+nVqxc5c+Z864nQRERE5P2LL93t5cNSnCciIpLwKM6LXoySY/Pnz8fKygp3d3eqVKlisS9nzpyUK1eO0qVL069fP2bMmIG7u3ucNFZERETeHYVMAorzREREEiLFedGL0Sqcnp6eODk5RQiYXla3bl0KFizI0aNHY9w4EREREXm/FOeJiIjIpyZGybGQkBAyZsz42nLZs2cnMDAwJqcQERGR98zayirOfuTjpThPREQk4VGcF70YDassXLgwp06dIigoyLzE5quMRiMXLlzA0dExVg0UERGR9yOhBjvydhTniYiIJDyK86IXo55jXbt25dGjR/Tr1w9/f/9Iy4wdO5bbt2/z008/xaqBIiIiIvL+KM4TERGRT80b9RwbPHhwhG3Zs2dn69atHDx4kCpVqpAjRw6SJEnC3bt3OXDgANeuXaNYsWJcu3aNL7/8Ms4bLiIiInHLSk8UP0mK80RERBI+xXnRszIajcbXFXJ0dMTKyorXFY2sjJWVFRcvXoxdK98zq+rZP3QTRD5qxp3eALwIffaBWyLycUtik+y9nq/ljk5xVteiGtPirC55txTnicjbUJwnEnfeZ6ynOC96b9RzrFOnuLuIIiIiIhJ/KM4TERGRT52SYyIiIgKAOtt/mhTniYiIJHyK86IXo9UqRUREJOHRKkYiIiIiCZPivOjFKjl29uxZbt26RVBQkMX2sLAwAgMDefjwIfv27WPVqlWxaqSIiIiIvF+K80RERORTEaPk2PPnz2nfvj3Hjx+PtpzRaNSKCCIiIh8JPVEUUJwnIiKSECnOi551TA6aN28ex44dw8bGhgIFCpA1a1asrKwoU6YM+fPnx8bGBqPRSK5cuZgyZUpct1lERETeASsrqzj7kY+X4jwREZGER3Fe9GKUHNu1axdWVlYsXLiQ1atX06VLF4xGIwMGDGDNmjXs3r2bQoUKcevWLbJn13LZIiIiIh8LxXkiIiLyqYlRcuzmzZsUKVKEEiVKAFCoUCGMRiOnTp0CIFOmTPz6668YjUYWLFgQZ40VERGRd8fayirOfuTjpThPREQk4VGcF70YzTkWGBhIlixZzP/+7LPPsLa25u+//zZvy549O05OTq+dr0JERETih4QZ6sjbUpwnIiKS8CjOi16Meo6lSpUKf39/878TJUpEpkyZuHr1qkW59OnT8+DBg9i1UERERETeG8V5IiIi8qmJUXLM0dGRkydP8ujRI/O2XLlyce7cOYvlvm/evEnSpElj30oRERF559TdXkBxnoiISEKkOC96MUqOffPNNzx//pzGjRuzZcsWAMqXL09AQACDBw/Gy8uLmTNncvHiRT777LM4bbCIiIi8GwqaBBTniYiIJESK86IXo+RY3bp1KV++PLdu3WLr1q0ANGrUCHt7ezZs2MC3337LlClTsLKyws3NLU4bLCIiIiLvjuI8ERER+dTEaEJ+Gxsb5syZw+rVq83bUqZMyZw5c+jXrx83btzAzs6O5s2bU69evThrrIiIiLw7Vgn0SaC8HcV5IiIiCY/ivOjFKDkGYG1tTaNGjSy2OTk5sW3bNh4+fEiKFClInDhxrBsoIiIi70eMupO/IwEBAcyZM4cdO3bg7e1NokSJyJcvH40aNYoQfwQGBrJw4ULWrVvH7du3sbe3p3LlynTp0oWMGTNGqNvPz4/Zs2ezY8cOfHx8SJcuHTVr1qRjx47Y29tHKH/v3j2mTZvGgQMHuH//PlmzZsXFxYV27dphZ2cXofyNGzeYOnUqx44d48mTJ+TMmZPGjRvTtGlTrK0jXuVz584xbdo0zp49y9OnT8mXLx8tW7bE2dk5FlcwdhTniYiIJCzxKc6Lj2KcHItOunTp3kW1IiIi8gnw8/OjadOm/P333zg6OtKkSRNevHjB7t27GTRoECdPnmTs2LEAhISE0KlTJ/bv30/x4sWpWrUqXl5erFq1in379rFq1SoyZ85srjsgIIDWrVtz/vx5KlasSM2aNfH09GT+/PkcOHCAFStWkCJFCnP5u3fv4ubmxr///kuNGjXIkSMHhw8fxt3dHQ8PD37//XdsbW3N5a9cuULz5s15/vw5tWvXJm3atOzZs4eRI0fi6enJhAkTLF7r4cOHad++PXZ2dtSuXZukSZOybds2evXqhZeXF926dXu3FzsGFOeJiIhIQvNGybGNGzfG6iQf8smniIiIvJn40t1++vTp/P3337i6ujJ8+HBzb6vevXvj5ubGmjVrqFWrFpUqVWLVqlXs37+fBg0aMGbMGHMdK1euZPDgwYwePZqpU6eat8+cOZPz58/TuXNnOnXqZN4+efJkZs6cybRp0+jXr595+7hx47h9+zbjx4+nbt26AISFhdG3b182bNjAihUraNGihbn84MGD8fPzY9GiRZQuXRqAbt268f3337N+/Xpq1apFlSpVAAgKCmLAgAHY2NiwcuVK8uTJA0DHjh1xc3Nj1qxZ1KpVC0dHxzi+wpYU54mIiCR88SXOi6+sjEaj8XWFHB0dY3UhL168GONjPwSr6tk/dBNEPmrGnd4AvAh99oFbIvJxS2KT7L2er/tfveOsrskVf47xsRUqVODBgwd4eHiQMmVKi33r1q2jb9++uLq6MnLkSGrWrMnNmzf/r707j4/xXP84/kkiEVmEJJKgUltDaOx7KLWUc1ra2rW1Ha2qrUoVdVSr9iqndrpRpUj90FYtFa01IaimYt9FBQlJRMg6vz/STI0sIkZMku/ba16v47mvuZ97xlFXrude2LlzJ+7u7iax7du359SpU/z22294enqSmJhIo0aNKFKkCLt27TJZEpmYmMgzzzxDcnIye/bswc7OjoiICJ599lkqVarETz/9ZNJ3VFQUzZo1w9vbm02bNgFw8OBBevToQfPmzVm8eLFJfFhYGB07dqRp06Z8+eWXQFpR6t133+WVV15h/PjxJvFbtmxhyJAhdOvWjQkTJuT6u8wJ5Xki8iCU54mYT17mepaS51mqHM0cq1+//qMeh4iIiAgpKSn079+fW7duZSiMAcaC1q1bt7h8+TLnzp2jatWqGQpjAP7+/pw4cYLg4GBefPFFQkNDuXXrFq1atcqwV5idnR3169dny5YthIaGUq9ePYKDg0lNTaVx48YZ+nZzc6Nq1aqEhYURERGBl5cXQUFBADRp0iRDfPXq1SlRogT79u0jJSUFGxsbY7y/v3+G+PQ+0mMeJeV5IiIiUtjlqDi2bNmyRz0OERERecysLWC6vY2NDb169cqyPX2WVpUqVTh79iwA5cuXzzS2XLlyAJw5cwYgx/Fnz56lXr16OYoPCwvjzJkzeHl5GeMrVKiQZfyff/5JeHg4Tz75ZLb9Ozk54erqSnh4OImJiZlu/G8uyvNEREQKPkvI8yzZI9mQX0RERPIfc+5F0apVq2zbAwMDH7jPrVu3snnzZhwcHHj55ZcJCQkBwMXFJdP49Os3b94E4MaNGwCUKFEi2/jY2NiHir/feHIaX7x4ca5fv05cXByurq6ZxoiIiIjkhPYcy56KY5lIX0cvIg8nr/dLEpGCa/fu3YwYMQKA8ePH4+HhQVJSEkCWs6rSryckJAAY4+8+XTK7+OTkZLPGp19/0P4TExMzbZfcUZ4nYh7K80SkIFFxTERERACwxnxPFHMzMywr69evZ+zYsSQlJfHuu+8aT420t7cHsi4epV93cHAwiU8vkpkr3tHRMUfx6ddzGn/veERERERyy5x5XkGk4lgmdPKKyMNJf5I4ePuIxzwSkfxtbvNP8/R+ljbd3mAwMHPmTBYvXoyNjQ0fffQR3bt3N7bfu2zyXjExMQDGjf3vXdZornhnZ+eHji9VqlSG+NjYWKysrHBycsq0P8kd5XkiDyc9z9PJryIPLy9nM1tanmdprB/3AERERETulZiYyNChQ1m8eDEODg4sWLDApDAGUKlSJQAuXLiQaR8XL14EoHLlynkan349s3hHR0dKly593/i4uDiuX79OpUqVsLZWuiYiIiLyKCnbEhERESDtFCNzvR5GcnIygwYNYsuWLXh5efHdd9/RvHnzDHEeHh5UqFCBY8eOcf369Qztu3fvxtramrp16wJQvXp1nJ2d2b9/f4aljImJiezbtw9HR0eqVasGQP369bG2tiYoKChD31FRURw7doyKFSvi5uYGQMOGDQHYs2dPhvjDhw8THR1NnTp1sLGxuW/87t27AahXr14W35KIiIhIzllKnmepVBwTERERAKzM+OthzJkzhx07duDl5cXKlSupWrVqlrFdu3YlOTmZ6dOnYzAYjNdXr17NiRMnaNu2LR4eHkDaBvcvvvgiUVFRLFiwwKSfefPmER0dTY8ePShSJG3XCU9PT5o3b87Ro0dZs2aNMTY1NZWpU6eSkpJCz549jddr1aqFj48P27ZtMyl4JSYmMn36dACT+JYtW+Lu7k5AQADHjx83Xo+NjWX27NlYWVnx2muvPdB3JyIiIpIZS8nzLJVZ9hw7f/48f/31Fy4uLlSrVo3Y2Fjjfh0iIiIiOXX16lW++uorAHx9ffn+++8zjatYsSLPP/88PXv2ZMuWLaxdu5ZTp07RqFEjzp49y9atWyldujSjR482ed/QoUPZtWsX8+bN4+DBg/j5+REaGkpwcDC+vr689dZbJvFjx44lNDSUsWPHsm3bNipUqEBQUBCHDx+madOmdOnSxSR+0qRJ9O7dm/79+9OuXTs8PT3Ztm0bZ86coWPHjiYz4IoVK8ZHH33E0KFD6d69O88//zyOjo5s2rSJiIgIhgwZwlNPPWWOr/WhKM8TERGRgs7KcPdj1ge0atUqFi1axOXLlwHo0KED06ZNo3fv3hQrVozJkyfj6upqtsHmFW3UKvJwtCG/iHnk9Yb8Y4P/a7a+JjWamKv3rV+/nvfee+++ca1atWL+/PkAxMfHs2jRIjZs2EBERASlSpXC39+fIUOG4OnpmeG9169fZ+7cuQQGBhIVFYWXlxdt2rRhwIABxk3y7xYeHs7s2bPZtWsXcXFxlC1blg4dOtC3b1/jiZN3O378OHPmzGHfvn0kJibi7e1Nt27d6N69u3FJ5d3279/P/Pnz+eOPPzAYDFSqVInevXvzwgsv5OQre2SU54lIZrQhv4j55OWG/JaQ51myXBfHPvjgAwICAjAYDBQrVozbt2/ToUMHpk+fTps2bQgPD6dy5cqsXLnSeGR5fqGkSeThqDgmYh55XRwbt3ec2fr6uOHHZutL8p7yPBHJiopjIuaTl8Ux5XnZy9WeY5s3b2b16tWUK1eOJUuWsH//fpP2xYsXU716dU6dOsWqVavMMlARERERefSU54mIiEhhk6vi2HfffYednR1ffPEFjRo1yrBEoEKFCixevBh7e3t+/vlnswxUREREHi0rrM32kvxLeZ6IiEjBozwve7nakP/IkSPUrVsXb2/vLGNcXV2pW7cuf/75Z64HJyIiInmnoB7NLQ9GeZ6IiEjBozwve7kq+d25cwcHB4f7xhUpUoQ7d+7k5hYiIiIi8hgozxMREZHCJlczx0qXLs3Ro0cxGAxYZVF9TE1N5dixY3h5eT3UAEVERCRvZPVvuhQuyvNEREQKHuV52cvVzLHmzZtz+fJlFi9enGXMl19+SUREBM2aNcv14ERERCTvWJnxl+RfyvNEREQKHuV52cvVzLE33niDH374gf/9738cOnQIf39/AKKioti6dSuBgYGsX78eJycn/vOf/5h1wCIiIiLy6CjPExERkcImV8WxUqVKsXjxYgYOHMivv/7Kb7/9hpWVFXv27GHPnj0YDAaKFy/OZ599RpkyZcw9ZhEREXkEtFGrgPI8ERGRgkh5XvZyVRwDqFGjBps2beL7779nz549/PXXX6SkpODp6UnDhg3p1q0brq6u5hyriIiIPELai0LSKc8TEREpWJTnZS/XxTEAJycn+vTpQ58+fcw0HBERERGxBMrzREREpLB4qOKYiIiIFBzWuTunR0REREQsnPK87OWqODZu3Lgcx1pZWTFhwoTc3EZERETykKbbCyjPExERKYiU52UvV8WxgIAArKysMBgMmbanf+kGg0FJk4iIiEg+ojxPRERECptcFccGDx6c6fWUlBRiY2M5ePAgR48epVOnTrRr1+6hBigiIiJ5Q08UBZTniYiIFETK87Jn1uLY3ebNm8f8+fPp0qVLbm4hIiIiecwaJU2iPE9ERKQgUp6XvUe2I9ugQYPw8vJi/vz5j+oWIiIiIvIYKM8TERGRguSRnlZZvXp1goODH+UtRERExEw03V4ehPI8ERGR/EN5XvYeaXEsIiKCpKSkR3kLERERMRNrJU3yAJTniYiI5B/K87L3SJZVpqamsmzZMkJDQ6lcufKjuIWIiIiIPAbK80RERKSgydXMsbZt22bZlpyczI0bN7h9+zZWVlZ07do114MTERGRvGOljVoF5XkiIiIFkfK87OWqOHb+/Pn7xhQtWpQ+ffroFCMREZF8wtrqkZ3TI/mI8jwREZGCR3le9nJVHPvmm2+ybLO2tsbBwYGKFStib2+f64GJiIiISN5TniciIiKFTa6KY4mJiTz99NOUKFHCzMMRERGRx0WnGAkozxMRESmIlOdlL1fz6saNG0fHjh3NPRYRERF5jKzM+EvyL+V5IiIiBY+l5XkbN26kW7du1K1blwYNGvDmm28SGhqa6/727duHr68vAwcOzNX7c1Uci4yMpHr16rm6oYiIiIhYLuV5IiIi8igtWLCAYcOGERkZSdeuXWnTpg179+6lR48e7Ny584H7u3nzJqNGjSI1NTXXY8rVssonn3ySM2fO5PqmIiIiYnmsNd1eUJ4nIiJSEFlKnnfq1Clmz56Nj48Pq1atwsHBAYDXXnuNHj16MHbsWLZs2fJAe5tOmDCBv/7666HGlauZYx9++CEREREMGDCAvXv3cvPmzYcahIiIiDx+ljbdXh4P5XkiIiIFj6XkeUuXLiU1NZWBAwcaC2MAvr6+dO7cmStXrhAYGJjj/n7++Wd++OEHWrdu/VDjytXMsRkzZuDs7Mz27dvZvn07kHZ6UVYbvB0+fDj3IxQRERGRPKM8T0RERB6VoKAgAPz9/TO0NWnShGXLlrFnzx6ef/75+/Z15coVPvroIxo2bEjPnj3ZunVrrseVq+LYoUOHMlxLSUnJ9SBERETk8bOU6fbyeCnPExERKXgsIc9LSkoiPDwcV1dXihcvnqHd29sbIEfbOxgMBsaMGUNycjJTp07l4sWLDzW2XBXHHmSKm4iIiOQPVla52m1BChjleSIiIgWPOfO8Vq1aZdueVS4RHR2NwWDAxcUl0/b0gllOtnRYtmwZu3fvZsqUKZQpUyZvimO9evXC39+fN998E4CyZcs+1E1FRERExDIozxMREZG8kJycDICtrW2m7XZ2dgAkJCRk28+pU6eYMWMGrVu3pmPHjmYZW46KY/v27cPLy8ssNxQRERHLpI30CyfleSIiIgWfOfO83M4yL1q0KJC2vDIziYmJACYb9d8rKSmJkSNH4ujoyIQJE3I1jszkalmliIiIFDyWsBdFVmbNmsXChQsJCQnJsEfFsmXLmDhxYpbvXb58OfXq1TP+PiEhgaVLl7Ju3TouXbqEs7MzLVq0YOjQoXh4eGR4f2xsLIsXL2bLli1ERETg5uZG27ZtGTRoEM7Ozhnir169yty5c9m1axfXrl2jTJkydOjQgTfeeMP4RPRu586dY86cOYSEhBATE4O3tzfdunXjlVdewdpaS11FRETk4VlCnufs7IyNjU2WyyZjY2MBMt2PLN3s2bM5cuQI8+bNw83NzWxjU3FMRERELNq6detYvHhxlu1HjhwBoHfv3pkWq8qUKWP838nJyQwePJgdO3ZQp04dWrVqxenTpwkICGD79u0EBASYzKKKi4ujT58+hIWF0axZM9q2bUtoaChff/01u3btYuXKlTg5ORnjr1y5Qo8ePbh8+TLPPfcc5cqVIygoiNmzZ7Nv3z6++OILk6UEJ06c4LXXXuP27ds8//zzuLq6sm3bNj7++GNCQ0OZPn36Q313IiIiIpbC1taWcuXKcf78eW7duoWjo6NJ+4ULFwCoXLlyln1s2LABgEGDBmXaHhgYSJUqVWjQoAHLli3L8dhUHBMREREArCzgieLdkpOTmT17NosXL8ZgMGQZd/ToUYoWLcqoUaOwsbHJts+AgAB27NhBp06dmDx5svH66tWrGTduHJMmTWLOnDnG6wsXLiQsLIwhQ4YwePBg4/X0mWxz585l9OjRxutTp07l0qVLTJs2jZdeegmA1NRURo0axQ8//MDKlSvp2bOnMX7cuHHExsbyzTff0KBBAwCGDRvG66+/zvr162nXrh0tW7bM2RcmIiIikgVLyfMaNmzIuXPnCAoKonXr1iZtu3fvBqB+/fpZvr9Xr16Zzjy7dOkSa9eupUKFCjz//PMPvIdqjotjUVFRhISEPFDn6bL7YCIiImIZrC1oz7GgoCAmTJjAmTNnqFGjBhcvXuTGjRsZ4hITEzl16hRVq1a9b2EMYMmSJVhbWzN8+HCT6127dmXZsmVs3bqVK1eu4OnpSWJiIitWrMDFxYX+/fubxA8aNIhVq1bx/fffM3z4cOzs7IiIiGDTpk089dRTxsIYgLW1NaNHj2bDhg0sX77cWBw7ePAghw4donnz5sbCGKRtRjtq1Cg6duzI8uXL86Q4pjxPRESkYLOUPK9Lly6sXr2azz77jIYNGxpn/R87dow1a9bg5eWVoWh2tz59+mR6fe/evaxdu5aKFSsyZMiQBx5Xjotje/bsYc+ePQ98AysrK+NyBxEREZGcWL9+PVevXmXEiBH07duXtm3bZlocO3nyJElJSfj6+t63z8uXL3Pu3DmqVq2Ku7t7hnZ/f39OnDhBcHAwL774IqGhody6dYtWrVpl2CvMzs6O+vXrs2XLFkJDQ6lXrx7BwcGkpqbSuHHjDH27ublRtWpVwsLCiIiIwMvLi6CgIACaNGmSIb569eqUKFGCffv2kZKSkqPC38NQniciIiJ5wc/Pj759+/LVV1/Rvn172rVrR1xcHD/99BPJyclMnjzZmHfFxsaydOlSgFwVvB5Ejnd5NRgMuXqlpqY+yvGLiIiImVhZWZnt9bA6d+5MYGAg/fv3z/K4b/hnvzErKyuGDx/OM888Q40aNejQoQPLly83yUPOnj0LQPny5TPtq1y5cgCcOXPmgeLT43Lbf4UKFbKMT0xMJDw8PNN2c1KeJyIiUrBZUp43atQoJk6cSMmSJVmxYgWBgYE0aNCAFStW4O/vb4yLjY1l7ty5zJ0796HveT85njnWoUMHbQorIiJSgFlZme9kxFatWmXbfr8jwO8+XTI7R48eBWDVqlU0aNCAF154gcjISLZv386ECRMICQlh1qxZWFlZGWeeubi4ZNpX+vX0fSzS40uUKJFtfPrJSrmNv9940uMfJeV5IiIiBZs58zxz6NKlC126dMk25oknnuD48eM56q9hw4Y5js2MNuQXERGRfMvKyooyZcrw9ttvm+zzFRkZSZ8+fdi4cSNNmjSha9euJCUlAWRYIpku/XpCQgKAMT6rmWv3xicnJ5s1Pv16eryIiIiIPBoqjomIiAhg3o1a7zczzFzGjRvHuHHjMlx3d3dn9OjR9OvXj7Vr19K1a1fs7e2BtE38M5N+3cHBAcAYn14kM1d8+rHl94tPv37vMeciIiIiD8pSNuS3VCqOiYiICGA5R3ybS82aNQG4cOECkHHZ5L1iYmIAKF68uEl8VssacxuffirTg8aLiIiI5FZBy/PMzbIWnYqIiIjkUFJSEqGhoYSEhGTaHh8fD0DRokUBqFSpEvBPsexeFy9eBKBy5cp5Gp9+PbN4R0dHSpcunWm7iIiIiJhHjopjgwcPpk2bNo96LCIiIvIYWZnxV15ISkqie/fu9OrVi+vXr2do37dvHwC1atUCwMPDgwoVKnDs2LFM43fv3o21tTV169YFoHr16jg7O7N///4MSx8TExPZt28fjo6OVKtWDYD69etjbW1NUFBQhr6joqI4duwYFStWxM3NDUjbOBZgz549GeIPHz5MdHQ0derUwcbGJqdfSa4ozxMRESn48luel9dUHBMRERHAso74zgkHBwdat25NamoqU6dOJTU11dh24cIFZsyYgbW1NX369DFe79q1K8nJyUyfPh2DwWC8vnr1ak6cOEHbtm3x8PAA0jbQf/HFF4mKimLBggUm9543bx7R0dH06NGDIkXSdqnw9PSkefPmHD16lDVr1hhj08eXkpJCz549jddr1aqFj48P27ZtMymQJSYmGk+OvDv+UVGeJyIiUvDltzwvr2nPMREREcm33n//fQ4fPsz69es5fvw4jRs3JjIyksDAQOLj4xkzZgw1atQwxvfs2ZMtW7awdu1aTp06RaNGjTh79ixbt26ldOnSjB492qT/oUOHsmvXLubNm8fBgwfx8/MjNDSU4OBgfH19eeutt0zix44dS2hoKGPHjmXbtm1UqFCBoKAgDh8+TNOmTTMcWT5p0iR69+5N//79adeuHZ6enmzbto0zZ87QsWNHmjdv/ui+PBEREREBwMpw92NTAeBOSvzjHoJIvmZvk3Zy2+DtIx7zSETyt7nNP83T+608tdRsfXWv3NtsfQG0bNmSS5cuERISYtwAP110dDQLFy5k69atRERE4ODgQI0aNejXrx+NGzfO0Fd8fDyLFi1iw4YNREREUKpUKfz9/RkyZAienp4Z4q9fv87cuXMJDAwkKioKLy8v2rRpw4ABA4yb6t8tPDyc2bNns2vXLuLi4ihbtiwdOnSgb9++xhMq73b8+HHmzJnDvn37SExMxNvbm27dutG9e/dHvqSyMFKeJ/Jw0vM8qzZPPOaRiOR/hl/C8+xelpznWQIVxzKhpEnk4ag4JmIeeV0cW3V6mdn66lbp0S8HFMkN5XkiD0fFMRHzycvimPK87Om0ShERERERERERKbS055iIiIgAFNjTh0REREQKO+V52VNxTERERAAK7OlDIiIiIoWd8rzsaVmliIiIiIiIiIgUWpo5JiIiIoCm24uIiIgUVMrzsqfimIiIiACabi8iIiJSUCnPy56WVYqIiIiIiIiISKGlmWMiIiICgLWm24uIiIgUSMrzsqfimIiIiACabi8iIiJSUCnPy56WVYqIiIiIiIiISKGlmWMiIiICgJWemYmIiIgUSMrzsqfimIiIiACabi8iIiJSUCnPy55KhyIiIiIiIiIiUmhp5piIiIgAYKVTjEREREQKJOV52VNxTERERACw1nR7ERERkQJJeV72tKxSREREREREREQKLc0cExEREUDT7UVEREQKKuV52VNxTERERACdYiQiIiJSUCnPy56WVYqIiIiIiIiISKGlmWMiIiICgJWemYmIiIgUSMrzsqfimIiIiACabi8iIiJSUCnPy55KhyIiIiIiIiIiUmhp5piIiIgAYK1TjEREREQKJOV52VNxTERERABNtxcREREpqJTnZU/LKkVEREREREREpNDSzDEREREBwErT7UVEREQKJOV52VNxTERERABNtxcREREpqJTnZU/LKkVEREREREREpNDSzDEREREBwErPzEREREQKJOV52VNxTERERACw1nR7ERERkQJJeV72VDoUEREREREREZFCSzPHREREBNApRiIiIiIFlfK87GnmmIiIiABppxiZ62Vus2bNokqVKsTGxmbavnHjRrp160bdunVp0KABb775JqGhoZnGpqamsnLlSl566SVq165No0aNGDZsGGfPns00PiEhgcWLF/Pvf/+bmjVr0rRpU/773/9y9erVTONjY2OZMWMGzz33HDVq1ODZZ59l6tSp3Lx5M9P4q1ev8sEHH9CyZUv8/Pxo27Yt8+bNIzExMQffjIiIiMj9WXKeZwlUHBOLNHjAEMa9/wEA/Xq/Ts1qtTO8Phj7IZD2Q8uUiVNp0bQlLZq2ZML4icTH336Moxcxvzs3bvPHwv38OmwzO977heOrw0hJSgHgdmQ8B2YGEzh4I3vG/0ZU2DWT917cfp5d729j29BNHPxsL/HXbmXo32AwcGBWMH/tuWi8dvqH4/zS/6cMr13vb8vw/st7w9k/Y4+ZP7VImnXr1rF48eIs2xcsWMCwYcOIjIyka9eutGnThr1799KjRw927tyZIf6DDz5g/PjxpKSk8Morr+Dv788vv/xCp06dOHbsmElscnIygwcP5tNPP8XFxYVevXpRo0YNAgIC6NSpExERESbxcXFx9OnTh88//xxvb2969+6Nt7c3X3/9NT169CAuLs4k/sqVK3Tv3p2AgAD8/Pzo3bs3Tk5OzJ49mzfeeIOkpKSH+OZEciYhIYHx//2Qpg2b0eqZNiz9+pssY48eOcar3XrSsE5jXun6KkfCjpi0b9ywkefbtqdhncYMGzKcGzduGNtiY2/y4biPeLZZK1r4P8u49z8gNvafovHZM2d58/W3aFK/Kf9q8zxfLPqS1NRUY/ue3UF0ebkrjeo2of9/3uTc2XM5vnf8rXg++mACLfyfpc2zbfnqi69N3ht2OIxer/SmYZ3GdPj3S/y4/scH+g5FcqqobVG+GD6DG2vD+GvlAYZ37p9lbJu6z3Bo4RZu/nCcX6Z9h88TFU3aR3R+kzPf7OHG2jC+evdTHO0dAGheozGGX8IzfZUrVYavR87MtC1w+ipj392ffZFTS3dx68eT/N/4L3ArXtLY5mjvwOJ3pnM14A8urgjhvW4DzfwtieQ9FcfE4mz8eRM7d+wy/n7mZ58SuP0X4+t/c2Zia2tLtx5dAFg4bxEH9h9k3sI5zF0wh98PHGTO/+Y8ruGLmJ3BYCB00QFSElOo/14T/N6ow7XQK5xefxyDwcCh+SHYuRSl4dimlG70BIcW7Od2VFqBODLsKifXHKVK9+o0HNsUGzsb/liw37T/VAPHV4Zx/WikyfUnn6vEM5+0Nr6afNQCW0dbvFuVN4m7fiySI8v+fJRfgeQRKzP+Mofk5GRmzpzJ6NGjTX5AvtupU6eYPXs2Pj4+/Pjjj4waNYpJkybx3XffYWtry9ixY7lz544xfseOHQQEBNC0aVPWrl3LyJEj+fTTT1m4cCHx8fG8//77Jv0HBASwY8cOOnXqxHfffceIESOYP38+H3/8MVevXmXSpEkm8QsXLiQsLIwhQ4bwxRdfMGLECJYuXcqAAQM4efIkc+fONYmfOnUqly5dYsqUKXz22We8++67BAQE0KFDB4KDg1m5cqVZvkuR7Mz8ZBZHwo7w+deLef+DMSyav5hfNv+SIS4+/jaDBwyhTt3afBewnJq1ajJ4wFDjQ8k/Qw/z4bgJDBjYn2XfLeVmbCzj3h9vfP/EjyZy4vgJ5i2cw4LP53Pm9FkmfDABgNu3bzNowBA8PDxYsfpb3v/vaL5dtpzVKwMAOHXyNEPeGkqLli1Y+f0KfKv58nrf/sTfis/RvT8aP4H9IQeYNWcW0z6ZQsDKAL5ZsgyAmzdvMujNwdSqXYs16wN4863+fDhuAr8fPPRIvm8p3D7p/1/q+dSg5chuDJwzlvGvvUOnZs9niKv2pA8bJi5lfdAW6g78FwdPHmbbJ6uNBbD+z7/Kh72G8/7X0/Af9jJl3b1Y8X7avzF7juzHq2ttk9eO0GDW7trExWt/8fa88SZtjYZ24E7iHWav+wqA+lVq8eXwGXy0bBaNhnagpLMLS0bOMo7t8+HTaV6jES992I8ekwfx1gs9eafTG3nw7cnDsLQ8z9KoOCYWJSY6hlkz/kd1v+rGay4lXHAv5Y57KXdKupZk9v/m0qdfb6o/nRaza+cuOnXpSPWnq/O0X3W6dO/C3uB9j+sjiJhdfMQtYs5EU71PLZzKOFPyKTcqdahCxL5L3Dgexe1r8VR7zQ+n0s5U+FdlSlQsyV+7LwAQ+edV3Kq5U6qGJ46eTlRs70Nc+E0Sb6Yt17pz4zYHZgZz7Y8rFHGwNblvEfsiFHWxN77Obz2DYxlnyrWsYIw5/eMJfp+9Dwd3h7z7QuSRsaTp9kFBQbRv355Fixbh5+dHyZIlM41bunQpqampDBw4EAeHf/5/6OvrS+fOnbly5QqBgYHG60uWLAHg7bffpkiRf7ZebdasGS1atCAsLIxDhw6ZxFtbWzN8+HCT+3bt2hUfHx+2bt3KlStXAEhMTGTFihW4uLjQv7/pTIBBgwZRsmRJvv/+e+NyyYiICDZt2sRTTz3FSy+9ZIy1trZm9OjR2NjYsHz58px/aSK5EB9/m7Vr1vHemPfwreZLq9Yt6dOvNytXrMoQu3njZoraF2X4yHeoWKki740ZiaOjg7GQtnLFSp5r14b2L7bHp4oPk6ZOZNeOXYSHXyI+/jZbtwQyeuxoqlWvhm81X94b8y7bAn8lISGBg/sPEhsTw7gPx1K+QnmaNW9Gz16vsnHDRgBWrwqgZq0aDBoykPIVyjNsxNs4Ozmx4aef73vvGzdusOnnzYz78L/UrlOLOvXq8PaIt/nm7xlyERFX8G/mzzvvDuOJck/wfPt/U/mpyhz6/VDe/CFIoeFgX4zX/9WDt+eP5/dTh1m3exPTVy9g8It9MsS+1b4Xe44cYPzSGZwIP8OoLyYRcyuWV1u9DMCQF/vy6feLWfnreo6cP0Hv6e/wQsPW+DxRkaTkJK7cuGZ8PVurCX4VqvLGrJEAxMbfNGn/qNdwAnZsYP2ezQAMfrEPq3f8yLKta/jz7FF6ThvKvxu0pLxXOdyKl6THsy/x5v9GsydsP7sO72PUF5N5t8ubefY9Su5YUp5niVQcE4vy6SezeKH981SqVDHT9vXrfiA2Jpb/9OtrvOZSogRbt2wlNiaW2JhYArduo6pv1bwassgjZ+dSlNpvN6Bo8aIm15NvJxNz5gbO3i7YFP3nh/wSlUsScyYaAFsnO26cvM6ty3GkpqRyOTgce7di2DqmFcJuXojF3tWehmObUaRY1me03LoSx197LuLTpZrJP4jXj1yj9tsN8ajjZcZPLALr16/n6tWrjBgxghUrVpgUvu4WFBQEgL+/f4a2Jk2aALBnT9qS3+TkZEJCQnBxccHPzy9DfHof6fGXL1/m3Llz+Pj44O7unml8amoqwcHBAISGhnLr1i3q1auHnZ2dSaydnR3169fn5s2bxr3QgoODSU1NpXHjxhn6dnNzo2rVqpw9ezbD0k0Rczpx/DjJycnUqlXTeK12ndr8GXo4w4zNP0P/pHadWsZ/B6ysrKhVpxZ/HEr7/3ToH39Sp24dY7xXaS9Kl/bizz9Csba2Ys78z6jqW8Wkz5SUFOLj46lStQqz5szK8Hfn5s20pciXLobjV+Ofv7dWVlZU9nmK0D/uf+/wi5cA8KvxtLHdx+cprl2L5NKlv3jqqcpMmjoRKysrUlNT+e3X7Zw7d4669f7pT8Qcalashm2RIuw58s8s/l2HQ2hYtXaGgkPF0t7sPfq7ybU/zx6jcbW6f7c/yd5j/7RHXL/KtZgoY3u6IjZFmNhnJJNWzCEq9gb3alnbn2f8GvH+V1ON1xr51mFH6F7j78OvXebC1Us08q1DxdJPArD32EFje+jZo5Rx8+JJzydy/F2IWBoVx8Ri7A3ex8H9B+n/VuZTcg0GA19/sYRXe72Cg+M/PyQNf3cYl8Iv8UyTFjzTpAWxMTGM/WBMXg1b5JGzdbDFvbqH8feGVAMXfz2Ha1V3EmISKOpibxJvV7wod26kLXHxfrY8jl5O7Bn/G9sGbeTSjgvUGlQfK+u0BKxUTU+e/k9t7JxNfxi51/nNp3Gt6o5L+RIm1+uP8se1ipsZPqVYAmsz/npYnTt3JjAwkP79+2Nra5tpTFJSEuHh4bi6ulK8ePEM7d7e3gCcOXMGgEuXLpGYmIi3t3emTz3vjU/foL98+fKZ3r9cuXK5ik+Pe9D+RR6FyGuRlChRAlu7f/6eubm5kpCQQHR0tEnstWuRlPIoZXLN1c2Nq3/Pnoy8FolHJu1XIq5ib2+PfzN/k+LX8mUr8KnyFCVLlsS9lDv1G9Qztt25c4c136+lYaMGd93H9BCMKxER3LgRfd97u7m5Api8PyIibczRd+1LlpSYRP1aDXl70DDad3iBGjVrZPGtieROaVdPImOuk5T8z36SV6KvUayovcmeXgBXblyjrLvpw8dypcrgXtz1n3a3f9od7Ivh6lzC2J6ua/P2lHByYd4PSzMd0+hug1iyZTXh1y7fNU4P/oq6cs94InnCvTRXbqTtbVvWvbTJuADcXUzvLZbFkvI8S1QwP5XkOwkJCUz8cCJjxo3G3t4+05iQffu5euUqnTp3NLl+4cJFvEqX5vOvF7Pg8/kkJCQyY9qneTFskcfi5Jqj3LwQQ+WXqpCSmIK1rel/yq2LWJOanPa0PyEmgdSkFJ7uV5v6o5pQwseNw1/+btzMPyeS7yQTEfIX3nctp5SCyZKm29erV48SJUpkGxMdHY3BYMDFxSXT9vSCWfopkembc5srPv36vfFZjTs9Pv3EzQeNF3kUbt+5g52daQE6vYCVlGh6IMSd23ews713VqStcanwnTt3sM0wa9KWxKSMJ69+t3wlWzb9wjvvvpOhLTU1lXHvjyf+1i36vfEfANr96zm2bP6F7b/tIDk5mR/W/UDY4SPGQyuyu3eZsmWoUdOP6VM+ISY6hshrkSyctzDtM95z6MW3K79hyvTJbNq42bgnmYi5ONgXI+Gevw8Jf//9KWprukJg1W8/0uWZ53m+YStsrG3o1aYz9avUxO7vB0artv/ImB6DqOpdmaK2RZn5Ztoee3b3PFDq//yrfLHxO+4k3uFeFby8aVnLnznrTA+ocCiayTiTEihqa8eFq5cIOnKAzwZ+REnnEniWLMWHPdO2HrArkvnDLLEMlpTnWaKs19CI5KGF8xZR7elq+DdtkmXM1i1b8W/WBJcS//yQEhcXx4f//YjFXy2iRs20qfYfTfyQ//Tqx8Ahb1GqVKmsuhPJl06uOcqFwLP4vVEHp7LFsba1JinONLFPTU7Fxs4GgKPf/olHndKUblgWAL/Xa7NzdCDXDl3Bq36ZHN0z6vBVrO1scKuuv0+Sc61atcq2/e59wHIrOTkZIMuZZek/4CckJOQqPv2H5nuXed0v/lGNR+RRKGpXlMR7imDpxa57H1gWLWqXodCVmJiEfbG0OLuidiQlZtJ+Tz+rvlvNtMnTGTn6XZr4my4rTk5OZtyYD9ixfQeLvliAe6m0Jc3+zfwZMPBNRrz9LikpKdRvUI8XOjxvPAH2fveeNHUi7w4bSXP/Z3FydmLosCH8cSgURycnY7ytnS2+1XzxrebLtavX+O7b7+jVp+d9vkGRnLuTeIei9xSYi/793/r4hNsm1zfv/42Pvp3Fmg8WU8SmCL/+sYdvflmDi6MzAB8v/x8VS3sT9vk2kpKTWLRhOYdOhxEb/8+pyKVKuNHs6QYMnvPfTMfTqdm/OXQ6jKMXTt4zzoSM47Qtahxjz2lv8/0Hi4j8PpSYW7GM+WoqTarXM7m3SH5jkcWxe485fxBOd/0DJ/nHpo2biYqMolHdtOJY+g8Yv2zeSvCBtL1fdu/aw4BBphs9nj1zjtu3b1Olqo/xWlXfKqSmphJx+YqKY1KgHPvuMOHbz/P0f2rhWTdtKrt9CXtu/XXTJC4x9p+llrHno6nw78rGtiL2RXDwcOBOVHyO7xsZdo1SNTyNSzGl4Mpvpw8VLZr2lP3emR/p0n/AT9+v7EHj03+oTkzMOOslu/gH7f9+8Y6Ojpm251fK8yyLh2cpoqOjSU5ONh5SERkZhb29Pc7Fne+J9SAqMsrkWlRkJO7uafmWh4cHkZm0lyr1z559S7/6hpkzZjH83Xd4tecrJrFJSUm8N2I0QbuDmLdwDrVq1zJpf2PA6/T+Ty9u3ozDzc2Vke+8R5kyZXJ0b+8nvVm9dhVRUddxdnbi4oWLWFtbU7q0F+Hhlzh/7rzJQ9qKlSoYl2yKmMulyAjcXVyxsbYhJTVtFr9XSQ/i79wmOi4mQ/zkFXOYEbAIF0dnrkVHseq/Czh35SIA8Xdu023iWxR3cMaAgZvxcVxZfYhzEReN729brwVnIy5w+NyxTMfTrn4L1v29Cb/JOKMi8HI1/TnKy7UUl6+nLU0+/dc5ag9oS6kSbsTcukml0k+SkpLChauXcvfFSJ7Ib3leXrPI4li9evVyNVXPysqKI0eOPIIRyaP25ZLPjU/QAf438zMAhg1/G0hbehJ+MZza9yRJ6XtLnDl9Bt9qvgCcO3sOgLJP5GxWjEh+cPrHE4RvP4/fG7XxrPvP/7ddKpbk7KbTpCSmGGeLRZ+6TonKaXs+FC1hz63Lcbg/nbZnWWpSCrcjb1PsAU6XjDkbzZOttaRSHow5Zobdj7OzMzY2NsZljfdKX46YvlwyffliTuPvXTZ5r5iYmEzjs1oGmdt4Z2fnTNvzK+V5lqVK1SoUKVLk7w3tawPw+8Hfqf50NaytTZft+9Xw46svvsZgMGBlZYXBYODQwT94/c1+ANSo6cfvB3/nxZc7ABBxOYKIiCv4/b131w/rfmDmjFmMHP0ur/V6NcNYJoyfSPCeYOYvnmccS7qNGzbyZ+hh3hszEjc3V+7cuUPIvhAmTJ5w33unpqbyVv9BvPvecJ7yeQqAndt34VutKk5OTuzcsYtJH01i6/ZfjEXrI0eOUrGS/u0T8zp0Ooyk5GQa+dZhd1gIAE2frk/IiT8wGAwmsd2ffZGGVWvzzoIPuRYdhb2dPc/WbEKfT9KWIk97fSxh54/zzS/fA1DPpyYujs7sOXLA2EfDqrXZHbafrNT3qcmkFbMzXA8+epCmTzdg6ZYAAJ4oVZpypcoQfPQgVlZWbJryLSMWfmwsuj3fsBUHT/3JTc0ck3zMIvcce/PNN43/4JYoUYLSpUvn6OXlpdPS8qsyZcvg/aS38eXo6IijoyPeT6Ztjnzq5GmKFi1K2SfKmrzP08sT/6ZNmDD+Y46EHSHscBgTxn9Mu3+3xdVVG0JKwRB3+SZnN5yk/L8qU6KyKwkxd4yvkj5u2Je0J2zJH8T9dZOzG08RczaaMv5pf3fKNvPm7M8nuRZ6hVsRcRz59k+K2BfBvaZnju6dmpJKfEQcjmUK1g/nkrn8theFra0t5cqVIyoqilu3bmVov3DhAgCVK6fNnixbtiz29vbG6/eLr1Spksn1e128eDFP4wsK5XmWpVixYrR/8QUmfjSJw3+GsW3rr3zz9TJe+XtWV+S1SO7cSdurqE3b1ty8eZPpUz7h9KnTTJ/yCbdv3+a5ds8B0LV7F376YQP/t2YtJ46fYOyYcTzTohlPPFGWmOgYpkycRoeX2tPuX22JvBZpfKWkpBC0J5gf1v3AiPeG4+1dzth2/fp1AJ4s/yQBq75n6y+BnD93ntEj38fTy4umzfzve29ra2uK2dvz2czZnD93nm1bf2Xh/EX0659W1Gve/BmcnJz4+MOJnDt3np9/2siSL5fyxpuv5/UfhxRwtxPusPSXABa+PYV6PjV5sUlb3u3yJp+t/RIAz5KlsLdLK9CeCD/DgBde4+Wm/6Jy2QqsGDOXi9f+YmPIrwD8FRXB+J7vUM+nJnWe8uPb0bNZ8NMybtyMNt7v6fJVOHL+ZIZxADzp+QTFHZ0zbV/w4zf0bNWR/7Trjl8FX7557zN+2ruVcxEXMRgMxN+5zdTXx1C5bAVebNKW8T3fYfJ3c838bYm55bc8L69Z5Myxd955hwoVKjBmzBi8vb1ZsWIFNjY2j3tY8hhFRUXh7Oyc6V/EqZ9MYcb0mQwaMAQrrHi2VQtGjBz+GEYp8mhcO3QFQ6qBsxtOcnaDaQLTZvEL1BpUn7Clf7B34k6KeThS8616FHMrBkD55yqBAY6vDCMpLhGXSiWp805DbGxz9t/UpFtJGFIN2Dpog9XCID9Ot2/YsCHnzp0jKCiI1q1bm7Tt3r0bgPr16wNgbW1NvXr12LVrF8eOHaNq1arZxnt4eFChQgWOHTvG9evXMzx02b17N9bW1tStWxeA6tWr4+zszP79+0lKSjLZSywxMZF9+/bh6OhItWrVjPextrYmKCgow+eKiori2LFjVKxYETe3gnUirPI8y/PuqBFMmjCZ1/u8gZOzE28NHkDrNmn7BrZq3oYJkz7ixZc74OTkxJz5s5n40STWBPwfT/k8xdyFc3BwSPs3p2atmoz78L/Mn7OAmJgYGvs3ZvyEcQDs2RNEfHw8P6z7kR/W/Why/59/2cDWLVsB+PjDiXz84URjW5kypdm49WeqVa/G2A/e59PpM4mJjqFBowbMXTjbOLstu3sD/Hf8WCZ8+DHdO7+Cq5sro8eOolXrlgA4ODqw4PP5TJk4le6delDStSQjx7zLs62efUTfuBRmwxd+xIKhU/h1xmpibt1k/NJPWbtrIwARq3+nzyfvsHRLAAdP/slbs9/n0/7jcCteksDfd/P8f3sbZ5jNWf815b3KsXHyMlJTU1kWuIZRn082uZdnSXduxEVnOg7PkmkrcG5kspwz+OhB3vxsNBN6v4urcwm2HNjBGzNHGtsHfDaGxe9M4+D8jVyNjmTIvHGs273JHF+PPEL5Mc/LS1aGe+dvWpCZM2fy+eefM2LECF5/Pe+e3NxJyflePCKSkb1N2pK9wdtHPOaRiORvc5vn7cm7Idd2ma2v+qWamq0vgJYtW3Lp0iVCQkKMyxIB/vzzT7p06cJTTz3FihUrjEsQjx07Rvfu3XFxceGXX34xbm6/efNmhg4dSpMmTVi0aJHx+s6dO3njjTfw8/MjICDA2P9XX33FtGnTePnll5kyZYrxIc3q1asZN24c//rXv/jf//5njP/444/59ttvGTRoEEOHDjVenzVrFgsXLuT1119n5Mi7fsAYMIBff/2VyZMn06lTJyDtpL5Ro0bxww8/MH78eF55xXRfpoJCeZ5I/pSe51m1eeIxj0Qk/zP8Ep5n97LkPM8SWHRxLCUlhRdeeIHIyEgCAwNNkuFHSUmTyMNRcUzEPPK6OLb/2m6z9VWvlL/Z+oKsi2MA06ZN46uvvqJ06dK0a9eOuLg4fvrpJ5KTk1m0aBH+/qZjGTp0KJs3b6ZixYq0bNmSK1eusHHjRooVK8a3335rMqMsKSmJnj178vvvv+Pn50ejRo04e/YsW7dupXTp0qxcudJkuV9MTAxdu3bl3LlzNG7cGD8/P0JDQwkODsbX15dvv/3WZFP5ixcv0q1bN65fv06rVq2oUKECQUFBHD58mKZNm7Jw4cIsT7PM75TnieRPKo6JmE9eFscsOc+zBBZdHAMIDg5m/fr1dO7c2bhs4VFT0iTycFQcEzGPPC+ORe4xW1/13JvcP+gBZFccAwgICGDFihWcPn0aR0dH/Pz8GDx4MDVq1MgQm5yczJIlS/i///s/Ll68iIuLC/Xq1WPIkCHGfcDuFh8fz6JFi9iwYQMRERGUKlUKf39/hgwZgqdnxv37rl+/zty5cwkMDCQqKgovLy/atGnDgAEDjJvw3y08PJzZs2eza9cu4uLiKFu2LB06dKBv377GzcELKuV5IvmPimMi5pOnxTELzvMsgcUXxx4HJU0iD0fFMRHzUHFMxPyU54k8HBXHRMxHxTHLYZEb8ouIiEje00atIiIiIgWT8rzsqTgmIiIiAAX2aG4RERGRwk55XvasH/cAREREREREREREHhfNHBMRERFA0+1FRERECirledlTcUxEREQAJU0iIiIiBZWl5XkbN25kyZIlnDp1ChsbG2rXrs2gQYMyPWk8M5GRkSxcuJBff/2VK1euULRoUapVq0bv3r1p3br1A49HyypFRERERERERCRPLFiwgGHDhhEZGUnXrl1p06YNe/fupUePHuzcufO+77906RIvv/wyy5Ytw8PDg9dee43nnnuOsLAwBg0axNy5cx94TJo5JiIiIoA2ahUREREpqCwlzzt16hSzZ8/Gx8eHVatW4eDgAMBrr71Gjx49GDt2LFu2bMHe3j7LPqZMmcLVq1cZOnQogwYNMl4fOnQonTt3Zt68eTz33HP4+PjkeFyaOSYiIiJA2nR7c/0SEREREcthKXne0qVLSU1NZeDAgcbCGICvry+dO3fmypUrBAYGZvn++Ph4fv31V0qUKMGAAQNM2kqXLk2PHj1ITU1l27ZtDzQuFcdEREREREREROSRCwoKAsDf3z9DW5MmTQDYs2dPlu9PSUlh5MiRDB06FBsbmwztRYsWBeDWrVsPNC4tqxQRERHA8jZqFRERERHzsIQ8LykpifDwcFxdXSlevHiGdm9vbwDOnDmTZR/Ozs706dMn0zaDwcDmzZsBqFKlygONTcUxERERASxnLwoRERERMS9z5nmtWrXKtj2rZZHR0dEYDAZcXFwybU8vmN28eTNX41q2bBl//vknXl5eD3xipZZVioiIiIiIiIjII5WcnAyAra1tpu12dnYAJCQkPHDfa9euZcqUKdjY2DBt2rRsN/TPjGaOiYiICGAZ0+1FRERExPzMmedlt2F+dtL3A0tKSsq0PTExEcBko/6cWLx4MTNnzsTa2ppp06bRqFGjBx6bimMiIiICaFmliIiISEFlCXmes7MzNjY2WS6bjI2NBch0P7LMJCYmMm7cONatW4e9vT0zZ86875LPrKg4JiIiIiIiIiIij5StrS3lypXj/Pnz3Lp1C0dHR5P2CxcuAFC5cuX79hUTE8Nbb73FgQMHcHd3Z8GCBdSoUSPXY9OeYyIiIgKkTbc31y8RERERsRyWkuc1bNgQg8FAUFBQhrbdu3cDUL9+/Wz7iIuLo2/fvhw4cAAfHx8CAgIeqjAGKo6JiIjI3ywlaRIRERER87KUPK9Lly5YWVnx2WefmSyvPHbsGGvWrMnRSZPjx48nLCyMqlWrsnz5csqUKfNQYwItqxQRERERERERkTzg5+dH3759+eqrr2jfvj3t2rUjLi6On376ieTkZCZPnmw8tTI2NpalS5cCMGTIEADCwsL46aefAKhSpYqx/V41a9bkmWeeyfG4VBwTERERwDI2ahURERER87OkPG/UqFFUrFiRFStWsGLFChwdHWnQoAGDBw82WR4ZGxvL3LlzgX+KY9u3bze2r1+/Pst79OrV64GKY1YGg8HwoB+koLuTEv+4hyCSr9nbpB29O3j7iMc8EpH8bW7zT/P0fidiDputLx+Xp83Wl4g5Kc8TeTjpeZ5Vmyce80hE8j/DL+F5di/lednTnmMiIiIiIiIiIlJoaVmliIiIAGgjfREREZECSnle9lQcExEREcCy9qIQEREREfNRnpc9LasUEREREREREZFCSzPHRERE5G96oigiIiJSMCnPy46KYyIiIgJour2IiIhIQaU8L3taVikiIiIiIiIiIoWWZo6JiIgIoFOMRERERAoq5XnZU3FMREREACVNIiIiIgWV8rzsaVmliIiIiIiIiIgUWpo5JiIiIoA2ahUREREpqJTnZU/FMREREQE03V5ERESkoFKelz0tqxQRERERERERkUJLM8dEREQEsKwnisuWLWPixIlZti9fvpx69eoBkJCQwNKlS1m3bh2XLl3C2dmZFi1aMHToUDw8PDK8NzY2lsWLF7NlyxYiIiJwc3Ojbdu2DBo0CGdn5wzxV69eZe7cuezatYtr165RpkwZOnTowBtvvIGdnV2G+HPnzjFnzhxCQkKIiYnB29ubbt268corr2BtreeSIiIikvcsKc+zRCqOiYiICGBZe1EcOXIEgN69e2dasCpTpgwAycnJDB48mB07dlCnTh1atWrF6dOnCQgIYPv27QQEBODl5WV8X1xcHH369CEsLIxmzZrRtm1bQkND+frrr9m1axcrV67EycnJGH/lyhV69OjB5cuXee655yhXrhxBQUHMnj2bffv28cUXX2Bra2uMP3HiBK+99hq3b9/m+eefx9XVlW3btvHxxx8TGhrK9OnTH9VXJiIiIpIlS8rzLJGKYyIiImJxjh49StGiRRk1ahQ2NjZZxgUEBLBjxw46derE5MmTjddXr17NuHHjmDRpEnPmzDFeX7hwIWFhYQwZMoTBgwcbr8+aNYuFCxcyd+5cRo8ebbw+depULl26xLRp03jppZcASE1NZdSoUfzwww+sXLmSnj17GuPHjRtHbGws33zzDQ0aNABg2LBhvP7666xfv5527drRsmXLh/5+RERERMR8NLdfREREgLTp9ub69TASExM5deoUPj4+2RbGAJYsWYK1tTXDhw83ud61a1d8fHzYunUrV65cMfa7YsUKXFxc6N+/v0n8oEGDKFmyJN9//z2JiYkAREREsGnTJp566iljYQzA2tqa0aNHY2Njw/Lly43XDx48yKFDh3jmmWeMhTEAOzs7Ro0aBWASLyIiIpJXLCXPs1QqjomIiAiQNt3eXK+HcfLkSZKSkvD19c027vLly5w7dw4fHx/c3d0ztPv7+5OamkpwcDAAoaGh3Lp1i3r16mXYK8zOzo769etz8+ZNQkNDAQgODiY1NZXGjRtn6NvNzY2qVaty9uxZIiIiAAgKCgKgSZMmGeKrV69OiRIl2LdvHykpKTn4FkRERETMx1LyPEul4piIiIhYlPT9xqysrBg+fDjPPPMMNWrUoEOHDixfvpzU1FQAzp49C0D58uUz7adcuXIAnDlz5oHi0+Ny23+FChWyjE9MTCQ8PDzTdhERERF5PLTnmIiIiADmPcWoVatW2bYHBgZm2Xb06FEAVq1aRYMGDXjhhReIjIxk+/btTJgwgZCQEGbNmsWNGzcAcHFxybSf9Os3b94EMMaXKFEi2/jY2NiHir/feNLjRURERPJKQV0OaS4qjomIiMjfLCNpsrKyokyZMrz99tsme31FRkbSp08fNm7cSJMmTYxLI+9dIpku/XpCQgIASUlJACanS2YXn5ycbNb49Ovp8SIiIiJ5xzLyPEul4piIiIiYXXYzw+5n3LhxjBs3LsN1d3d3Ro8eTb9+/Vi7di29e/cGMG6gf6/06w4ODgDY29sD/xTJzBXv6OiYo/j06+nxIiIiImIZVBwTERERIH88T6xZsyYAFy5cyLBs8l4xMTEAFC9eHLj/ssbcxjs7O+cqXkRERCSv5Ic873FScUxEREQALOL0oaSkJI4ePUpCQgL169fP0B4fHw9A0aJFqVSpEpBWKMvMxYsXAahcuTJAnsWnX88s3tHRkdKlS2faLiIiIvKoWEKeZ8l0WqWIiIhYjKSkJLp3706vXr24fv16hvZ9+/YBUKtWLTw8PKhQoQLHjh3LNHb37t1YW1tTt25dAKpXr46zszP79+/PsPQxMTGRffv24ejoSLVq1QCoX78+1tbWBAUFZeg7KiqKY8eOUbFiRdzc3ABo2LAhAHv27MkQf/jwYaKjo6lTpw42NjYP8pWIiIiIyCOm4piIiIj8zcqMr9xxcHCgdevWpKamMnXqVFJTU41tFy5cYMaMGVhbW9OnTx8AunbtSnJyMtOnT8dgMBhjV69ezYkTJ2jbti0eHh5A2gb6L774IlFRUSxYsMDkvvPmzSM6OpoePXpQpEjaxHpPT0+aN2/O0aNHWbNmjTE2fWwpKSn07NnTeL1WrVr4+Piwbds2kwJZYmIi06dPBzCJFxEREck7jz/Ps2RWhrszSQHgTkr84x6CSL5mb5O2mfXg7SMe80hE8re5zT/N0/tduR1utr48iz2R6/dGRETwyiuvcOnSJapWrUrjxo2JjIwkMDCQ+Ph4xowZYyyOJSUl0bNnT37//Xf8/Pxo1KgRZ8+eZevWrZQuXZqVK1fi5eVl7DsmJoauXbty7tw5GjdujJ+fH6GhoQQHB+Pr68u3336Lk5OTMf7ixYt069aN69ev06pVKypUqEBQUBCHDx+madOmLFy40OR0ytDQUHr37k1SUhLt2rXD09OTbdu2cebMGTp27MiUKVNy/b2IeSjPE3k46XmeVZvc/3deRNIYfjFf7nU/lpLnWSoVxzKhpEnk4ag4JmIehbU4BhAdHc3ChQvZunUrERERODg4UKNGDfr160fjxo1NYuPj41m0aBEbNmwgIiKCUqVK4e/vz5AhQ/D09MzQ9/Xr15k7dy6BgYFERUXh5eVFmzZtGDBggHFT/buFh4cze/Zsdu3aRVxcHGXLlqVDhw707dvXeELl3Y4fP86cOXPYt28fiYmJeHt7061bN7p3764llRZAeZ7Iw1FxTMR8VByzHCqOZUJJk8jDUXFMxDzyvjh2yWx9eRYra7a+RMxJeZ7Iw1FxTMR88rY4pjwvOzqtUkRERACdYiQiIiJSUCnPy5425BcRERERERERkUJLxTERERERERERESm0tKxSREREALAqoEdzi4iIiBR2yvOyp5ljIiIiIiIiIiJSaGnmmIiIiAB6oigiIiJSUCnPy55mjomIiIiIiIiISKGl4piIiIiIiIiIiBRaWlYpIiIiAFhZabq9iIiISEGkPC97mjkmIiIiIiIiIiKFlopjIiIiIiIiIiJSaGlZpYiIiAA6xUhERESkoFKelz0Vx0RERORvSppERERECiblednRskoRERERERERESm0NHNMREREAD1PFBERESmolOdlT8UxERERAXTEt4iIiEhBpTwve1pWKSIiIiIiIiIihZZmjomIiMjf9ERRREREpGBSnpcdFcdEREQEUMokIiIiUlApz8uellWKiIiIiIiIiEihpZljIiIi8jc9UxQREREpmJTnZUfFMREREQF0ipGIiIhIQaU8L3taVikiIiIiIiIiIoWWimMiIiIiIiIiIlJoaVmliIiIAGClvShERERECiTlednTzDERERERERERESm0NHNMRERE/qYniiIiIiIFk/K87Kg4JiIiIoBSJhEREZGCSnle9rSsUkRERERERERECi3NHBMREREArKz0TFFERESkIFKelz0Vx0RERORvSppERERECiblednRskoRERERERERESm0NHNMREREAD1PFBERESmolOdlT8UxERER+ZvSJhEREZGCSXledrSsUkRERERERERECi3NHBMRERFApxiJiIiIFFTK87KnmWMiIiIiIiIiIlJoqTgmIiIiIiIiIiKFlpXBYDA87kGIiIjI43cnJd5sfdnbOJitLxERERF5OMrzsqfimIiIiIiIiIiIFFpaVikiIiIiIiIiIoWWimMiIiIiIiIiIlJoqTgmIiIiIiIiIiKFlopjIiIiIiIiIiJSaKk4JiIiIiIiIiIihZaKYyIiIiIiIiIiUmipOCYiIiIiIiIiIoWWimMiIiIiIiIiIlJoqTgmIiIiIiIiIiKFlopjIiIiIiIiIiJSaKk4JiIiIiIiIiIihZaKYyIiIiIiIiIiUmipOCYiIiIiIiIiIoWWimOSb2zcuJFu3bpRt25dGjRowJtvvkloaOjjHpZIvjZr1iyqVKlCbGzs4x6KiIgUYsrzRMxPeZ5Izqk4JvnCggULGDZsGJGRkXTt2pU2bdqwd+9eevTowc6dOx/38ETypXXr1rF48eLHPQwRESnklOeJmJ/yPJEHY2UwGAyPexAi2Tl16hTt27encuXKrFq1CgcHBwCOHj1Kjx49KF68OFu2bMHe3v4xj1Qkf0hOTmb27NksXryY9H8CQkJCKF68+GMemYiIFDbK80TMS3meSO5o5phYvKVLl5KamsrAgQONCROAr68vnTt35sqVKwQGBj7GEYrkH0FBQbRv355Fixbh5+dHyZIlH/eQRESkEFOeJ2I+yvNEck/FMbF4QUFBAPj7+2doa9KkCQB79uzJ0zGJ5Ffr16/n6tWrjBgxghUrVpj8ICIiIpLXlOeJmI/yPJHcK/K4ByCSnaSkJMLDw3F1dc10KrC3tzcAZ86cyeuhieRLnTt3ZvTo0ZQoUeJxD0VERAo55Xki5qU8TyT3VBwTixYdHY3BYMDFxSXT9vRE6ubNm3k5LJF8q169eo97CCIiIoDyPBFzU54nkntaVikWLTk5GQBbW9tM2+3s7ABISEjIszGJiIiIyMNTniciIpZCxTGxaEWLFgXSpt1nJjExEUDr6UVERETyGeV5IiJiKVQcE4vm7OyMjY1NltPpY2NjAXQ0sYiIiEg+ozxPREQshYpjYtFsbW0pV64cUVFR3Lp1K0P7hQsXAKhcuXJeD01EREREHoLyPBERsRQqjonFa9iwIQaDwXjU9912794NQP369fN6WCIiIiLykJTniYiIJVBxTCxely5dsLKy4rPPPjOZdn/s2DHWrFmDl5cXrVu3fowjFBEREZHcUJ4nIiKWoMjjHoDI/fj5+dG3b1+++uor2rdvT7t27YiLi+Onn34iOTmZyZMnG08zEhEREZH8Q3meiIhYAhXHJF8YNWoUFStWZMWKFaxYsQJHR0caNGjA4MGDqVGjxuMenoiIiIjkkvI8ERF53KwMBoPhcQ9CRERERERERETkcdCeYyIiIiIiIiIiUmipOCYiIiIiIiIiIoWWimMiIiIiIiIiIlJoqTgmIiIiIiIiIiKFlopjIiIiIiIiIiJSaKk4JiIiIiIiIiIihZaKYyIiIiIiIiIiUmipOCYiuZaamvq4hyAiIiIij4DyPBEpTFQcE8mhvXv3UqVKlUxfvr6+1K5dm7Zt2zJq1Cj+/PPPxz1c5s+fT5UqVRg9erTxWnh4OFWqVKFatWoP1ffFixcZMmQIv//++8MOM1sRERHG7zgn0v+M2rRp89D3Tr9vRETEQ/dlCfcRERGRrCnP+4fyPPNRnieSfxR53AMQyY/at29v8nuDwcCdO3c4duwY69at48cff2Ty5Mm89NJLj2eAj1jfvn25ePEivXv3ftxDERERETEr5XnK80Sk8FFxTCQXZsyYken11NRUZsyYwZdffsmHH37IM888g6urax6PLmuenp78/PPPWFlZPVQ/mmYvIiIiBZXyPOV5IlL4aFmliBlZW1vzzjvv4Orqyu3bt/n1118f95BM2NraUqlSJSpWrPi4hyIiIiKSryjPExEpuDRzTMTMbG1tKVu2LNevXycyMtJ4vUqVKri7u7NkyRLGjh3LkSNHKFGiBO+88w6dOnUCIDY2liVLlrBlyxYuXryIjY0NPj4+dOzYkU6dOmFjY5PhfqGhoXz++eccPHiQuLg4fH19efPNNzMdW3h4OK1atcLGxoYjR46YtCUmJvLdd9/x008/cfbsWaysrChXrhwdO3aka9eu2NnZ8X//93+MGTPG+J5XX30VgG+++YaGDRsar2/evJmVK1cSFhZGfHw8Xl5etGjRgv79++Ph4ZFhXLGxsXz55Zds2rSJy5cv4+7uTocOHcy6XOHs2bMsW7aM4OBgLl++TFJSEiVKlKB27dr06dOHunXrZvq+27dvM3PmTH788UciIyMpXbo0zz33HG+88QYuLi4Z4hMSEli+fLnxezQYDJQvX54XXniBnj17UrRoUbN9JhEREclbyvOU5ynPEymYVBwTMbPExETOnTsHQNmyZU3abt++Tb9+/bCxsaFFixYcPnwYPz8/AC5cuEDfvn0JDw/H3d2dhg0bkpqayv79+xk3bhxbt25l3rx52NraGvvbuHEjI0eOJCkpierVq1O3bl2OHTvGgAED8PHxyfGYb9y4Qb9+/QgLC8PR0ZF69ephZWVFSEgIH3/8Mb/99hsLFy7E29ub9u3bExgYSHx8PE2bNqVkyZK4u7sDaXtyjBkzhrVr12Jra8vTTz+Nh4cHhw8fZtmyZfz88898+eWX+Pr6Gu997do1evXqxZkzZ3Bzc6N58+ZER0ezaNEiAgMDc/vHYGL37t0MHDiQO3fuUKlSJfz9/blz5w5Hjhxhy5YtBAYGsmjRIpo1a5bhvYMHD+bMmTPUrVuXp59+mpCQED7//HM2b97Mt99+i6enpzH2+vXrvP7664SFhVG8eHFq166NnZ0dBw4c4JNPPmHTpk189dVXFC9e3CyfS0RERPKW8jzlecrzRAoog4jkSHBwsMHHx8fg4+OTZUxCQoJhzJgxBh8fH0ODBg0MN2/eNLalv/ell14y3L5922AwGAwpKSkGg8FgSE5ONrRv397g4+NjGD9+vLHdYDAYrl69aujUqZPBx8fH8MknnxivX7t2zVC3bl2Dj4+PYc2aNcbrKSkphk8++cR4v1GjRhnbLl68aPDx8TH4+vqajPudd94x+Pj4GF599VXD9evXTe7Rtm1bg4+Pj+Gbb74xXn/22WcNPj4+hpCQEJN+Pv/8c4OPj4+hXbt2hlOnTpmMac6cOQYfHx/Ds88+a7hz546xbdiwYQYfHx9D//79DfHx8cbrBw4cMNSpU+e+3/nd0v+MWrdubbyWnJxsaN68ucHHx8ewaNEik/jbt28bBg4caPDx8TG8/vrrJm3p961Vq5Zh3759xus3b9409OrVy+Dj42MYOHCgyXveeOMNg4+Pj2HQoEGG6Ohok/f079/f4OPjYxg2bFim97l8+XKOPqOIiIiYn/I85XkGg/I8kcJMe46J5MK7775r8hoxYgT9+vWjadOmrFmzBnt7e6ZPn46Tk1OG97766qvY29sDaXtXAGzbto3jx49TpUoVxo0bZ2wHKFWqFDNmzMDKyopvv/2WuLg4ANauXcvNmzdp27YtHTt2NMZbW1szYsQIk6d22bl27RobN27E1taWTz/9lJIlSxrb3N3dee+99yhfvvx9j6BOTk7m888/B2D69OlUqlTJZEyDBw+mbt26XLp0iR9//NHk3nZ2dkyZMoVixYoZ31OnTh2GDBmSo8+QnaioKBo1akTLli35z3/+Y9Jmb29P586dgbRjyzPTr18/6tevb/y9k5MTn3zyCba2tgQGBnLp0iUAjhw5wvbt2ylVqhTTpk0zmYrv5OTEtGnTcHJyYuPGjZw/f/6hP5eIiIg8GsrzMlKepzxPpKDTskqRXEj/Rz+djY0NDg4OPPHEE3To0IFXX32VChUqZPreatWqZbi2e/duABo3bpzpfhPly5enQoUKnDlzhkOHDtG0aVOCg4MBePbZZzPEW1lZ8dxzz3H06NH7fpa9e/eSmppKnTp1TKaOp2vZsiUtW7a8bz9HjhwhOjoaFxcX4xKCe7Vo0YIDBw4QFBRE586d2bt3LwaDgVq1amV62lO7du2YMmXKfe+dHQ8PD6ZOnZrh+vXr1zl58iQ7duwA0pZJZCaz/TA8PDyoWbMm+/fvJyQkhLJlyxr/DGvVqoWjo2OG96Tve7Fz506Cg4N58sknH+JTiYiIyKOiPC8j5XnK80QKOhXHRHLh+PHjuX5viRIlMlz766+/AFiyZAlLlizJ9v2XL18G4MqVKwB4eXllGleuXLkcjefq1asAlClTJkfxWUl/shYTE0OVKlWyjU3/vOmfoXTp0pnGeXl5YWtrS1JS0kONDSAkJIT/+7//48iRI1y4cIH4+HiAbI87t7W1zXJs6d9X+mdI/0y//PJLjj+/iIiIWB7leRkpz1OeJ1LQqTgmksfSp9jfLSUlBYDatWvzxBNPZPv+9CQpu3/sAYoUydlf7+Tk5BzF3U9qaiqQNkW/cePG2camb2Cb/hkMBkOWsUWKFHmopMlgMDBixAg2bNgAQIUKFWjRogUVK1akWrVq2NjYZHnqU5EiRTJ9wnv3mNM3zk3/M6xSpcp9N8nVEesiIiIFk/I85XnK80TyJxXHRCxA+jT3du3a0adPnxy/58SJE4SHh2fanv6k637Sj9zOaq+JpKQkVq1aRZkyZWjRokWmSV/6eADc3NyYMWNGju6dngCmP428V0xMDLdv385RX1nZsGEDGzZswNnZmYULF1KvXj2T9uxOSrp9+zaxsbGZnjqU/r2nP3FM//wNGjTgv//970ONWURERAoO5XnK80TE8mlDfhEL0LBhQwC2bt2aaXtMTAz//ve/eeWVVzhz5gwATZs2BWDTpk2Zvienx2PXrVsXKysrDh06xPXr1zO0HzhwgI8//piJEydmmTAB+Pn54eDgwMmTJ7PciHT8+PG89NJLLFu2DIBGjRpRpEgR/vjjD+Mygrtt27YtR58hOyEhIQA0adIkQ8IEGPeiSH8imlX73cLDwwkNDcXW1ta4iWv6n+Fvv/2W6VPapKQkunbtSrdu3YxjEhERkYJPeZ7yPBGxfCqOiViAf/3rX5QrV46QkBAmTZrEnTt3jG3x8fGMGjWK06dPExsba9wA9uWXX6ZUqVLs2rXLeHpQusWLF7Nv374c3btcuXK0adOGxMRERo4caTwlCdJOAJo0aRIA3bt3N15PP2UpNjbWeK1o0aL06tWL1NRUhg4dyunTp03us2bNGlavXs3Ro0epWbMmAK6urnTq1Ink5GSGDx9OdHS0Mf7YsWN88sknOfoM2UnfAPb3338nKirKeD01NZXly5ezevVqABISEjJ9/5QpUzh58qTx9zdu3GDEiBGkpKTQsWNH3N3dAahXrx516tTh4sWLjBw5kpiYGON7kpKS+Pjjj/njjz84f/48Tz/99EN/LhEREckflOcpzxMRy6dllSIWwM7Ojrlz5/L666/zzTff8NNPP1G9enVsbGz4/fffiYmJoVSpUsyZM8e4f4OLiwuffvopAwcOZMaMGaxdu5annnqKU6dOcerUKerWrcuBAwdydP+PPvqIc+fOsWvXLlq2bEm9evVISEjg999/59atWzRr1oy+ffsa4ytWrMjp06f54IMPWLduHX379qV27doMHjyYkydPEhgYyIsvvkj16tXx9PTk1KlTxiRq7Nix1KhRw9jXe++9x4kTJzh48CCtW7emYcOG3L59m3379uHr60tsbOxD7UXRtWtXVqxYwdWrV3nuueeoX78+VlZWHDlyhIiICCpXrsypU6eM90nfWwLSEkFPT09efvllGjRoQLFixdi7dy83b96kTp06jB492uRes2bNok+fPvz888/s3LmT6tWr4+joSGhoKNeuXcPBwYH58+ebHGUuIiIiBZvyPOV5ImL5NHNMxEJUrVqVH374gf79++Pm5kZISAj79+/Hy8uLAQMGsH79+gzHhjds2JDvv/+el19+mbi4OLZt24aNjQ1Tp07l1VdfzfG9XV1dWb16NcOHD6d06dLs3r2bkJAQnnzyScaNG8eiRYtMkolRo0bRuHFj4uLi2LlzJ8eOHQPSNi2dN28e06dPp27dupw9e5Zt27aRkJBA27Zt+e677+jVq5fJvZ2cnPjmm28YOXIkXl5e7Nq1ixMnTtClSxe++uqr+25Iez+lS5fm+++/56WXXsLFxYVdu3YREhKCh4cHo0ePZu3atfj4+JCcnMzOnTtN3mtjY8PSpUvp2rUrx48fZ8eOHXh4eDBixAiWLl2Kg4ODSbyXlxfff/89w4cPx9vbm9DQUIKCgihevDg9e/bkxx9/pE6dOg/1eURERCT/UZ6nPE9ELJuVIbvjQ0RERERERERERAowzRwTEREREREREZFCS8UxEREREREREREptFQcExERERERERGRQkvFMRERERERERERKbRUHBMRERERERERkUJLxTERERERERERESm0VBwTEREREREREZFCS8UxEREREREREREptFQcExERERERERGRQkvFMRERERERERERKbRUHBMRERERERERkUJLxTERERERERERESm0VBwTEREREREREZFC6/8BkrqH/ibvdfgAAAAASUVORK5CYII=",
      "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = pd.read_csv(\"../cnsm_final_datasets/cryptomining_evaluation.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>191</td>\n",
       "      <td>188</td>\n",
       "      <td>10765</td>\n",
       "      <td>50973</td>\n",
       "      <td>1.647758e+09</td>\n",
       "      <td>1.647758e+09</td>\n",
       "      <td>4444</td>\n",
       "      <td>52168</td>\n",
       "      <td>209</td>\n",
       "      <td>221.0</td>\n",
       "      <td>92.913939</td>\n",
       "      <td>8633</td>\n",
       "      <td>-0.384501</td>\n",
       "      <td>99.00</td>\n",
       "      <td>314.0</td>\n",
       "      <td>99</td>\n",
       "      <td>314</td>\n",
       "      <td>215</td>\n",
       "      <td>99</td>\n",
       "      <td>44.456430</td>\n",
       "      <td>81</td>\n",
       "      <td>38.755981</td>\n",
       "      <td>228.551015</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>1.183891</td>\n",
       "      <td>4.369635</td>\n",
       "      <td>-0.248564</td>\n",
       "      <td>6.233434e-07</td>\n",
       "      <td>6.566425e-07</td>\n",
       "      <td>-0.023256</td>\n",
       "      <td>1.278231</td>\n",
       "      <td>1.892272</td>\n",
       "      <td>0.385636</td>\n",
       "      <td>0.549860</td>\n",
       "      <td>False</td>\n",
       "      <td>0.668727</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666959</td>\n",
       "      <td>0.455309</td>\n",
       "      <td>0.451087</td>\n",
       "      <td>61.466667</td>\n",
       "      <td>60.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>4.655172</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.310345</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666959</td>\n",
       "      <td>3237</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-8.843324e-03</td>\n",
       "      <td>0.460736</td>\n",
       "      <td>0.999801</td>\n",
       "      <td>0.532736</td>\n",
       "      <td>0.052789</td>\n",
       "      <td>0.091441</td>\n",
       "      <td>0.00208</td>\n",
       "      <td>2.639437e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.280826e+04</td>\n",
       "      <td>14301.538431</td>\n",
       "      <td>0.199771</td>\n",
       "      <td>0.503294</td>\n",
       "      <td>0.230154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.727921</td>\n",
       "      <td>1.650374e+01</td>\n",
       "      <td>-0.467065</td>\n",
       "      <td>0.053616</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12</td>\n",
       "      <td>36</td>\n",
       "      <td>1104</td>\n",
       "      <td>39515</td>\n",
       "      <td>1.648240e+09</td>\n",
       "      <td>1.648240e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>50166</td>\n",
       "      <td>900</td>\n",
       "      <td>1103.5</td>\n",
       "      <td>595.227688</td>\n",
       "      <td>354297</td>\n",
       "      <td>-0.203830</td>\n",
       "      <td>333.00</td>\n",
       "      <td>1508.0</td>\n",
       "      <td>84</td>\n",
       "      <td>1508</td>\n",
       "      <td>1424</td>\n",
       "      <td>1508</td>\n",
       "      <td>66.136410</td>\n",
       "      <td>543</td>\n",
       "      <td>60.333333</td>\n",
       "      <td>1067.957597</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>-1.021458</td>\n",
       "      <td>2.682167</td>\n",
       "      <td>-0.512347</td>\n",
       "      <td>4.064469e-09</td>\n",
       "      <td>4.569391e-09</td>\n",
       "      <td>0.034894</td>\n",
       "      <td>1.127692</td>\n",
       "      <td>2.978095</td>\n",
       "      <td>0.782195</td>\n",
       "      <td>0.527571</td>\n",
       "      <td>False</td>\n",
       "      <td>0.844307</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.692308</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12612</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-3.455617e-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.540484</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>117</td>\n",
       "      <td>124</td>\n",
       "      <td>17036</td>\n",
       "      <td>28086</td>\n",
       "      <td>1.647402e+09</td>\n",
       "      <td>1.647402e+09</td>\n",
       "      <td>9033</td>\n",
       "      <td>37256</td>\n",
       "      <td>418</td>\n",
       "      <td>143.0</td>\n",
       "      <td>508.947934</td>\n",
       "      <td>259028</td>\n",
       "      <td>0.098115</td>\n",
       "      <td>143.00</td>\n",
       "      <td>463.0</td>\n",
       "      <td>114</td>\n",
       "      <td>1508</td>\n",
       "      <td>1394</td>\n",
       "      <td>143</td>\n",
       "      <td>121.757879</td>\n",
       "      <td>374</td>\n",
       "      <td>89.473684</td>\n",
       "      <td>652.523767</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.540330</td>\n",
       "      <td>2.182934</td>\n",
       "      <td>1.447668</td>\n",
       "      <td>6.068340e-09</td>\n",
       "      <td>6.392511e-09</td>\n",
       "      <td>-0.718750</td>\n",
       "      <td>3.567438</td>\n",
       "      <td>1.778511</td>\n",
       "      <td>0.362452</td>\n",
       "      <td>0.528996</td>\n",
       "      <td>False</td>\n",
       "      <td>0.722879</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.589412</td>\n",
       "      <td>0.683824</td>\n",
       "      <td>50.100000</td>\n",
       "      <td>69.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.931034</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.104680</td>\n",
       "      <td>False</td>\n",
       "      <td>1.172414</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7621</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.258144e+00</td>\n",
       "      <td>0.188585</td>\n",
       "      <td>0.996424</td>\n",
       "      <td>0.960068</td>\n",
       "      <td>0.036177</td>\n",
       "      <td>0.038381</td>\n",
       "      <td>0.00328</td>\n",
       "      <td>1.808859e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.309956e+06</td>\n",
       "      <td>-33132.694769</td>\n",
       "      <td>0.038255</td>\n",
       "      <td>0.501281</td>\n",
       "      <td>0.326268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.212822</td>\n",
       "      <td>1.160794e+01</td>\n",
       "      <td>-0.036356</td>\n",
       "      <td>0.584415</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>869</td>\n",
       "      <td>3899</td>\n",
       "      <td>1.646941e+09</td>\n",
       "      <td>1.646941e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>45339</td>\n",
       "      <td>848</td>\n",
       "      <td>577.0</td>\n",
       "      <td>547.001828</td>\n",
       "      <td>299211</td>\n",
       "      <td>-0.215769</td>\n",
       "      <td>570.00</td>\n",
       "      <td>1428.0</td>\n",
       "      <td>237</td>\n",
       "      <td>1428</td>\n",
       "      <td>1191</td>\n",
       "      <td>1428</td>\n",
       "      <td>64.504933</td>\n",
       "      <td>464</td>\n",
       "      <td>54.716981</td>\n",
       "      <td>979.016445</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>-1.060326</td>\n",
       "      <td>3.595966</td>\n",
       "      <td>-0.782617</td>\n",
       "      <td>8.682681e-23</td>\n",
       "      <td>1.294338e-22</td>\n",
       "      <td>0.351981</td>\n",
       "      <td>0.842347</td>\n",
       "      <td>1.921928</td>\n",
       "      <td>0.827729</td>\n",
       "      <td>0.486321</td>\n",
       "      <td>False</td>\n",
       "      <td>0.698970</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>True</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4240</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.496355e-17</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.753455</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.006667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>172</td>\n",
       "      <td>162</td>\n",
       "      <td>8944</td>\n",
       "      <td>45338</td>\n",
       "      <td>1.647734e+09</td>\n",
       "      <td>1.647734e+09</td>\n",
       "      <td>4444</td>\n",
       "      <td>43768</td>\n",
       "      <td>233</td>\n",
       "      <td>216.0</td>\n",
       "      <td>109.110036</td>\n",
       "      <td>11906</td>\n",
       "      <td>-0.362135</td>\n",
       "      <td>102.00</td>\n",
       "      <td>314.0</td>\n",
       "      <td>102</td>\n",
       "      <td>568</td>\n",
       "      <td>466</td>\n",
       "      <td>314</td>\n",
       "      <td>46.828342</td>\n",
       "      <td>87</td>\n",
       "      <td>37.339056</td>\n",
       "      <td>257.177500</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.466667</td>\n",
       "      <td>-0.742370</td>\n",
       "      <td>4.426724</td>\n",
       "      <td>0.479149</td>\n",
       "      <td>5.645564e-07</td>\n",
       "      <td>5.947150e-07</td>\n",
       "      <td>-0.235849</td>\n",
       "      <td>3.699268</td>\n",
       "      <td>1.965739</td>\n",
       "      <td>0.400608</td>\n",
       "      <td>0.491949</td>\n",
       "      <td>False</td>\n",
       "      <td>0.702060</td>\n",
       "      <td>0.774484</td>\n",
       "      <td>0.692117</td>\n",
       "      <td>0.498815</td>\n",
       "      <td>0.443182</td>\n",
       "      <td>112.233333</td>\n",
       "      <td>100.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>225.0</td>\n",
       "      <td>7.758621</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0.172414</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.692117</td>\n",
       "      <td>2210</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5.185349e-02</td>\n",
       "      <td>0.464925</td>\n",
       "      <td>0.998808</td>\n",
       "      <td>0.900468</td>\n",
       "      <td>0.066589</td>\n",
       "      <td>0.076886</td>\n",
       "      <td>0.03407</td>\n",
       "      <td>3.329463e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.058267e+04</td>\n",
       "      <td>12616.645142</td>\n",
       "      <td>0.098252</td>\n",
       "      <td>0.504591</td>\n",
       "      <td>0.285693</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.981977</td>\n",
       "      <td>2.625826e+01</td>\n",
       "      <td>-0.098340</td>\n",
       "      <td>0.075675</td>\n",
       "      <td>True</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075571</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>266</td>\n",
       "      <td>258</td>\n",
       "      <td>12623</td>\n",
       "      <td>73996</td>\n",
       "      <td>1.648406e+09</td>\n",
       "      <td>1.648406e+09</td>\n",
       "      <td>4444</td>\n",
       "      <td>42662</td>\n",
       "      <td>239</td>\n",
       "      <td>249.0</td>\n",
       "      <td>105.517771</td>\n",
       "      <td>11134</td>\n",
       "      <td>-0.387447</td>\n",
       "      <td>131.50</td>\n",
       "      <td>314.0</td>\n",
       "      <td>103</td>\n",
       "      <td>568</td>\n",
       "      <td>465</td>\n",
       "      <td>314</td>\n",
       "      <td>44.149695</td>\n",
       "      <td>82</td>\n",
       "      <td>34.309623</td>\n",
       "      <td>260.699252</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>-0.710781</td>\n",
       "      <td>4.435272</td>\n",
       "      <td>0.363573</td>\n",
       "      <td>1.418639e-07</td>\n",
       "      <td>1.494423e-07</td>\n",
       "      <td>-0.178082</td>\n",
       "      <td>3.975130</td>\n",
       "      <td>2.077865</td>\n",
       "      <td>0.423459</td>\n",
       "      <td>0.495116</td>\n",
       "      <td>False</td>\n",
       "      <td>0.732303</td>\n",
       "      <td>0.129320</td>\n",
       "      <td>0.452672</td>\n",
       "      <td>0.504930</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>143.400000</td>\n",
       "      <td>165.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>9.793103</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.165985</td>\n",
       "      <td>False</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.452672</td>\n",
       "      <td>2945</td>\n",
       "      <td>0.366667</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.184860e-04</td>\n",
       "      <td>0.556587</td>\n",
       "      <td>0.487241</td>\n",
       "      <td>0.666836</td>\n",
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       "      <td>0.179595</td>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>6.774217e+00</td>\n",
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       "      <td>0.578357</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1075573</th>\n",
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       "      <td>-0.547046</td>\n",
       "      <td>229.50</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075574</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2120</td>\n",
       "      <td>1.647941e+09</td>\n",
       "      <td>1.647941e+09</td>\n",
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       "      <td>0.199771</td>\n",
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       "      <td>5.758589e+06</td>\n",
       "      <td>0.133107</td>\n",
       "      <td>0.000002</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1075575</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>33</td>\n",
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       "      <td>24899</td>\n",
       "      <td>4683</td>\n",
       "      <td>1.646613e+09</td>\n",
       "      <td>1.646613e+09</td>\n",
       "      <td>443</td>\n",
       "      <td>41316</td>\n",
       "      <td>952</td>\n",
       "      <td>987.5</td>\n",
       "      <td>510.009804</td>\n",
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       "      <td>-0.302317</td>\n",
       "      <td>553.75</td>\n",
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       "      <td>True</td>\n",
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       "      <td>1.415267e+02</td>\n",
       "      <td>0.233831</td>\n",
       "      <td>0.142146</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1075576 rows × 83 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         DST_IP  SRC_IP  PACKETS  PACKETS_REV  BYTES  BYTES_REV    TIME_FIRST  \\\n",
       "0           NaN     NaN      191          188  10765      50973  1.647758e+09   \n",
       "1           NaN     NaN       12           36   1104      39515  1.648240e+09   \n",
       "2           NaN     NaN      117          124  17036      28086  1.647402e+09   \n",
       "3           NaN     NaN        7            9    869       3899  1.646941e+09   \n",
       "4           NaN     NaN      172          162   8944      45338  1.647734e+09   \n",
       "...         ...     ...      ...          ...    ...        ...           ...   \n",
       "1075571     NaN     NaN      266          258  12623      73996  1.648406e+09   \n",
       "1075572     NaN     NaN      224          212  10718      59974  1.646190e+09   \n",
       "1075573     NaN     NaN        6            4    463        465  1.648661e+09   \n",
       "1075574     NaN     NaN       20           40   1080       2120  1.647941e+09   \n",
       "1075575     NaN     NaN       33           20  24899       4683  1.646613e+09   \n",
       "\n",
       "            TIME_LAST  DST_PORT  SRC_PORT  MEAN  MEDIAN       STDEV     VAR  \\\n",
       "0        1.647758e+09      4444     52168   209   221.0   92.913939    8633   \n",
       "1        1.648240e+09       443     50166   900  1103.5  595.227688  354297   \n",
       "2        1.647402e+09      9033     37256   418   143.0  508.947934  259028   \n",
       "3        1.646941e+09       443     45339   848   577.0  547.001828  299211   \n",
       "4        1.647734e+09      4444     43768   233   216.0  109.110036   11906   \n",
       "...               ...       ...       ...   ...     ...         ...     ...   \n",
       "1075571  1.648406e+09      4444     42662   239   249.0  105.517771   11134   \n",
       "1075572  1.646190e+09      4444     48462   303   314.0   42.544095    1810   \n",
       "1075573  1.648661e+09        80     49122   256   256.0   74.953319    5618   \n",
       "1075574  1.647941e+09     46166      1883    62    62.0    0.000000       0   \n",
       "1075575  1.646613e+09       443     41316   952   987.5  510.009804  260111   \n",
       "\n",
       "         BURSTINESS      Q1      Q3  MIN   MAX  MIN_MINUS_MAX  MODE  \\\n",
       "0         -0.384501   99.00   314.0   99   314            215    99   \n",
       "1         -0.203830  333.00  1508.0   84  1508           1424  1508   \n",
       "2          0.098115  143.00   463.0  114  1508           1394   143   \n",
       "3         -0.215769  570.00  1428.0  237  1428           1191  1428   \n",
       "4         -0.362135  102.00   314.0  102   568            466   314   \n",
       "...             ...     ...     ...  ...   ...            ...   ...   \n",
       "1075571   -0.387447  131.50   314.0  103   568            465   314   \n",
       "1075572   -0.753756  314.00   314.0  100   314            214   314   \n",
       "1075573   -0.547046  229.50   282.5  203   309            106   203   \n",
       "1075574   -1.000000   62.00    62.0   62    62              0    62   \n",
       "1075575   -0.302317  553.75  1428.0  106  1428           1322  1428   \n",
       "\n",
       "         COEFFICIENT_OF_VARIATION  AVERAGE_DISPERSION  PERCENT_DEVIATION  \\\n",
       "0                       44.456430                  81          38.755981   \n",
       "1                       66.136410                 543          60.333333   \n",
       "2                      121.757879                 374          89.473684   \n",
       "3                       64.504933                 464          54.716981   \n",
       "4                       46.828342                  87          37.339056   \n",
       "...                           ...                 ...                ...   \n",
       "1075571                 44.149695                  82          34.309623   \n",
       "1075572                 14.040955                  20           6.600660   \n",
       "1075573                 29.278640                  53          20.703125   \n",
       "1075574                  0.000000                   0           0.000000   \n",
       "1075575                 53.572458                 458          48.109244   \n",
       "\n",
       "         ROOT_MEAN_SQUARE  PERCENT_BELOW_MEAN  PERCENT_ABOVE_MEAN  \\\n",
       "0              228.551015            0.366667            0.633333   \n",
       "1             1067.957597            0.428571            0.571429   \n",
       "2              652.523767            0.700000            0.300000   \n",
       "3              979.016445            0.600000            0.400000   \n",
       "4              257.177500            0.533333            0.466667   \n",
       "...                   ...                 ...                 ...   \n",
       "1075571        260.699252            0.500000            0.500000   \n",
       "1075572        306.403003            0.066667            0.933333   \n",
       "1075573        261.428767            0.500000            0.500000   \n",
       "1075574         62.000000            0.000000            0.000000   \n",
       "1075575       1076.811064            0.500000            0.500000   \n",
       "\n",
       "         PEARSON_SK1_SKEWNESS  PEARSON_SK2_SKEWNESS  FISHER_MI_3_SKEWNESS  \\\n",
       "0                    1.183891              4.369635             -0.248564   \n",
       "1                   -1.021458              2.682167             -0.512347   \n",
       "2                    0.540330              2.182934              1.447668   \n",
       "3                   -1.060326              3.595966             -0.782617   \n",
       "4                   -0.742370              4.426724              0.479149   \n",
       "...                       ...                   ...                   ...   \n",
       "1075571             -0.710781              4.435272              0.363573   \n",
       "1075572             -0.258555             13.985490             -2.703785   \n",
       "1075573              0.707107              6.830918             -5.123189   \n",
       "1075574              0.000000              0.000000              0.000000   \n",
       "1075575             -0.933315              3.663655             -0.567110   \n",
       "\n",
       "         FISHER_PEARSON_g1_SKEWNESS  FISHER_PEARSON_G1_SKEWNESS  \\\n",
       "0                      6.233434e-07                6.566425e-07   \n",
       "1                      4.064469e-09                4.569391e-09   \n",
       "2                      6.068340e-09                6.392511e-09   \n",
       "3                      8.682681e-23                1.294338e-22   \n",
       "4                      5.645564e-07                5.947150e-07   \n",
       "...                             ...                         ...   \n",
       "1075571                1.418639e-07                1.494423e-07   \n",
       "1075572                6.925974e-06                7.295960e-06   \n",
       "1075573                0.000000e+00                0.000000e+00   \n",
       "1075574                0.000000e+00                0.000000e+00   \n",
       "1075575                7.035601e-09                7.411443e-09   \n",
       "\n",
       "         GALTON_SKEWNESS   KURTOSIS   ENTROPY  SCALED_ENTROPY  HURST_EXPONENT  \\\n",
       "0              -0.023256   1.278231  1.892272        0.385636        0.549860   \n",
       "1               0.034894   1.127692  2.978095        0.782195        0.527571   \n",
       "2              -0.718750   3.567438  1.778511        0.362452        0.528996   \n",
       "3               0.351981   0.842347  1.921928        0.827729        0.486321   \n",
       "4              -0.235849   3.699268  1.965739        0.400608        0.491949   \n",
       "...                  ...        ...       ...             ...             ...   \n",
       "1075571        -0.178082   3.975130  2.077865        0.423459        0.495116   \n",
       "1075572         0.000000  17.921043  0.420026        0.085599        0.542103   \n",
       "1075573         0.000000   0.250000  1.000000        1.000000        0.563329   \n",
       "1075574         0.000000   0.000000  0.000000        0.000000        1.000000   \n",
       "1075575         0.088933   1.520808  2.571629        0.524085        0.633057   \n",
       "\n",
       "         BENFORD_LAW_PRESENTED  P_BENFORD  NORMAL_DISTRIBUTION  \\\n",
       "0                        False   0.668727             0.000000   \n",
       "1                        False   0.844307             0.000000   \n",
       "2                        False   0.722879             0.000000   \n",
       "3                        False   0.698970             0.000000   \n",
       "4                        False   0.702060             0.774484   \n",
       "...                        ...        ...                  ...   \n",
       "1075571                  False   0.732303             0.129320   \n",
       "1075572                  False   0.367697             0.000000   \n",
       "1075573                  False   0.477121             0.000000   \n",
       "1075574                  False   0.301030             0.001000   \n",
       "1075575                  False   0.817697             0.483657   \n",
       "\n",
       "         CNT_DISTRIBUTION  TIME_DISTRIBUTION  AREA_VALUES_DISTRIBUTION  \\\n",
       "0                0.666959           0.455309                  0.451087   \n",
       "1                0.000000           0.000000                  0.888889   \n",
       "2                1.000000           0.589412                  0.683824   \n",
       "3                0.000000           0.000000                  0.500000   \n",
       "4                0.692117           0.498815                  0.443182   \n",
       "...                   ...                ...                       ...   \n",
       "1075571          0.452672           0.504930                  0.375000   \n",
       "1075572          1.000000           0.467647                  0.857143   \n",
       "1075573          0.000000           0.000000                  0.660714   \n",
       "1075574          0.555556           0.500000                  0.875000   \n",
       "1075575          0.678395           0.557500                  0.769231   \n",
       "\n",
       "         MEAN_SCALED_TIME  MEDIAN_SCALED_TIME  Q1_SCALED_TIME  Q3_SCALED_TIME  \\\n",
       "0               61.466667                60.0            30.0           105.0   \n",
       "1                0.000000                 0.0             0.0             0.0   \n",
       "2               50.100000                69.0             7.0            78.0   \n",
       "3                0.000000                 0.0             0.0             0.0   \n",
       "4              112.233333               100.0            68.0           187.0   \n",
       "...                   ...                 ...             ...             ...   \n",
       "1075571        143.400000               165.0            75.0           195.0   \n",
       "1075572         15.900000                16.0             9.0            25.0   \n",
       "1075573          0.000000                 0.0             0.0             0.0   \n",
       "1075574        150.000000               150.0            75.0           225.0   \n",
       "1075575        133.800000               181.0            58.0           181.0   \n",
       "\n",
       "         DURATION  MEAN_DIFFTIMES  MEDIAN_DIFFTIMES  MIN_DIFFTIMES  \\\n",
       "0           135.0        4.655172               1.0            0.0   \n",
       "1             0.0        0.000000               0.0            0.0   \n",
       "2            85.0        2.931034               1.0            0.0   \n",
       "3             0.0        0.000000               0.0            0.0   \n",
       "4           225.0        7.758621               2.0            0.0   \n",
       "...           ...             ...               ...            ...   \n",
       "1075571     284.0        9.793103               1.0            0.0   \n",
       "1075572      34.0        1.172414               1.0            0.0   \n",
       "1075573       0.0        0.000000               0.0            0.0   \n",
       "1075574     300.0       15.000000              15.0           15.0   \n",
       "1075575     240.0        8.275862               0.0            0.0   \n",
       "\n",
       "         MAX_DIFFTIMES  MEAN_SCALED_DIFFTIMES  SIG_SPACES  SWITCHING_METRIC  \\\n",
       "0                 15.0               0.310345       False          2.000000   \n",
       "1                  0.0                    NaN       False          1.692308   \n",
       "2                 28.0               0.104680       False          1.172414   \n",
       "3                  0.0                    NaN       False          1.500000   \n",
       "4                 45.0               0.172414       False          2.000000   \n",
       "...                ...                    ...         ...               ...   \n",
       "1075571           59.0               0.165985       False          2.000000   \n",
       "1075572            2.0               0.586207       False          0.206897   \n",
       "1075573            0.0                    NaN       False          2.000000   \n",
       "1075574           15.0               1.000000       False          0.000000   \n",
       "1075575           62.0               0.133482       False          1.172414   \n",
       "\n",
       "         TRANSIENTS  CNT_ZEROS  CNT_NZ_DISTRIBUTION  BIGGEST_CNT_1_SEC  \\\n",
       "0             False        0.0             0.666959               3237   \n",
       "1              True        0.0             0.000000              12612   \n",
       "2              True        0.0             1.000000               7621   \n",
       "3              True        0.0             0.000000               4240   \n",
       "4             False        0.0             0.692117               2210   \n",
       "...             ...        ...                  ...                ...   \n",
       "1075571       False        0.0             0.452672               2945   \n",
       "1075572       False        0.0             1.000000               8478   \n",
       "1075573       False        0.0             0.000000                512   \n",
       "1075574       False        0.0             0.555556                248   \n",
       "1075575        True        0.2             0.532519              13235   \n",
       "\n",
       "         DIRECTIONS  PERIODICITY  VAL  TIME     MIN_POWER     MAX_POWER  \\\n",
       "0          0.300000        False    0   0.0 -8.843324e-03      0.460736   \n",
       "1          0.214286        False    0   0.0 -3.455617e-17           NaN   \n",
       "2          0.366667        False    0   0.0 -1.258144e+00      0.188585   \n",
       "3          0.200000        False    0   0.0 -1.496355e-17           NaN   \n",
       "4          0.300000        False    0   0.0 -5.185349e-02      0.464925   \n",
       "...             ...          ...  ...   ...           ...           ...   \n",
       "1075571    0.366667        False    0   0.0  4.184860e-04      0.556587   \n",
       "1075572    0.033333        False    0   0.0 -7.364835e-01      0.395958   \n",
       "1075573    0.500000        False    0   0.0 -0.000000e+00           NaN   \n",
       "1075574    0.047619         True   62  15.0 -2.371520e+06  71761.889150   \n",
       "1075575    0.800000        False    0   0.0 -3.307937e+00      4.103020   \n",
       "\n",
       "         MIN_POWER_FREQ  MAX_POWER_FREQ  POWER_MEAN     POWER_STD  POWER_MODE  \\\n",
       "0              0.999801        0.532736    0.052789      0.091441     0.00208   \n",
       "1              0.540484             NaN         NaN           NaN     0.00000   \n",
       "2              0.996424        0.960068    0.036177      0.038381     0.00328   \n",
       "3              0.753455             NaN         NaN           NaN     0.00000   \n",
       "4              0.998808        0.900468    0.066589      0.076886     0.03407   \n",
       "...                 ...             ...         ...           ...         ...   \n",
       "1075571        0.487241        0.666836    0.079205      0.088774     0.00461   \n",
       "1075572        0.998808        0.499559    0.119351      0.031113     0.12707   \n",
       "1075573        0.006865             NaN         NaN           NaN     0.00000   \n",
       "1075574        0.066664        0.199771 -541.868615  33766.633513     0.00000   \n",
       "1075575        0.758024        0.991855    0.175090      0.165833     0.03034   \n",
       "\n",
       "         SPECTRAL_ENERGY  SPECTRAL_ENTROPY  SPECTRAL_KURTOSIS  \\\n",
       "0           2.639437e+02               NaN       5.280826e+04   \n",
       "1                    NaN               NaN                NaN   \n",
       "2           1.808859e+02               NaN       1.309956e+06   \n",
       "3                    NaN               NaN                NaN   \n",
       "4           3.329463e+02               NaN       5.058267e+04   \n",
       "...                  ...               ...                ...   \n",
       "1075571     3.960268e+02       1187.836327       4.759685e+04   \n",
       "1075572     5.967566e+02               NaN       6.179043e+05   \n",
       "1075573              NaN               NaN                NaN   \n",
       "1075574    -2.709343e+06               NaN       2.431071e+07   \n",
       "1075575     8.754488e+02               NaN       5.195789e+05   \n",
       "\n",
       "         SPECTRAL_SKEWNESS  SPECTRAL_ROLLOFF  SPECTRAL_CENTROID  \\\n",
       "0             14301.538431          0.199771           0.503294   \n",
       "1                      NaN          0.006667                NaN   \n",
       "2            -33132.694769          0.038255           0.501281   \n",
       "3                      NaN          0.006667                NaN   \n",
       "4             12616.645142          0.098252           0.504591   \n",
       "...                    ...               ...                ...   \n",
       "1075571       12230.932591          0.332480           0.504016   \n",
       "1075572      -20277.831294          0.499559           0.502997   \n",
       "1075573                NaN          0.006667                NaN   \n",
       "1075574     -346532.690866          0.199771           0.083215   \n",
       "1075575        6296.161282          0.991855           0.509584   \n",
       "\n",
       "         SPECTRAL_SPREAD  SPECTRAL_SLOPE  SPECTRAL_CREST  SPECTRAL_FLUX  \\\n",
       "0               0.230154             NaN        8.727921   1.650374e+01   \n",
       "1                    NaN             NaN             NaN            NaN   \n",
       "2               0.326268             NaN        5.212822   1.160794e+01   \n",
       "3                    NaN             NaN             NaN            NaN   \n",
       "4               0.285693             NaN        6.981977   2.625826e+01   \n",
       "...                  ...             ...             ...            ...   \n",
       "1075571         0.252373        0.083842        7.027141   3.308845e+01   \n",
       "1075572         0.281717             NaN        3.317582   6.774217e+00   \n",
       "1075573              NaN             NaN             NaN            NaN   \n",
       "1075574         0.058398             NaN     -132.434112   5.758589e+06   \n",
       "1075575         0.313779             NaN       23.433810   1.415267e+02   \n",
       "\n",
       "         SPECTRAL_BANDWIDTH  PERIODICITY_SCDF  LABEL  \n",
       "0                 -0.467065          0.053616   True  \n",
       "1                  0.000000               NaN  False  \n",
       "2                 -0.036356          0.584415  False  \n",
       "3                  0.000000               NaN  False  \n",
       "4                 -0.098340          0.075675   True  \n",
       "...                     ...               ...    ...  \n",
       "1075571            0.179595          0.068190   True  \n",
       "1075572           -0.499249          0.578357   True  \n",
       "1075573            0.000000               NaN  False  \n",
       "1075574            0.133107          0.000002  False  \n",
       "1075575            0.233831          0.142146  False  \n",
       "\n",
       "[1075576 rows x 83 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sklearn\n",
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "df_validation['LABEL'] = df_validation.LABEL.astype('category')\n",
    "df_validation[\"LABEL_code\"] = df_validation[\"LABEL\"].cat.codes\n",
    "\n",
    "bool_features = [\"TRANSIENTS\", \"BENFORD_LAW_PRESENTED\", \"SIG_SPACES\", \"PERIODICITY\",\"PERIODICITY_SCDF\"]\n",
    "\n",
    "\n",
    "for F in list(df_validation.keys()):\n",
    "    if F in bool_features or F == \"LABEL\":\n",
    "        continue\n",
    "    if F in distribution_features:\n",
    "        df_validation.replace([np.inf], 0.5, inplace=True)\n",
    "        df_validation.replace([-np.inf], 0.5, inplace=True)\n",
    "        df_validation.loc[df_validation[F].isnull(), F] = 0.5\n",
    "    elif F in [\"DIRECTIONS\",\"NORMAL_DISTRIBUTION\"]: \n",
    "        df_validation.replace([np.inf], 0.5, inplace=True)\n",
    "        df_validation.replace([-np.inf], 0.5, inplace=True)\n",
    "        df_validation.loc[df_validation[F].isnull(), F] = 0.5\n",
    "    elif F in frequency_features: \n",
    "        df_validation.replace([np.inf], -1, inplace=True)\n",
    "        df_validation.replace([-np.inf], -1, inplace=True)\n",
    "        df_validation.loc[df_validation[F].isnull(), F] = -1\n",
    "    else:\n",
    "        df_validation.replace([np.inf], 0, inplace=True)\n",
    "        df_validation.replace([-np.inf], 0, inplace=True)\n",
    "        df_validation.loc[df_validation[F].isnull(), F] = 0\n",
    "\n",
    "\n",
    "for F in bool_features:\n",
    "    df_validation.loc[df_validation[F].isnull(), F] = False\n",
    "    df_validation[F] = df_validation[F].astype('bool')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botnet: 392577\tclear: 682999\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df_validation[df_validation['LABEL_code'] == 1])}\\tclear: {len(df_validation[df_validation['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_validation = clean_dataset(df_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "botnet: 392577\tclear: 682999\n"
     ]
    }
   ],
   "source": [
    "print(f\"botnet: {len(df_validation[df_validation['LABEL_code'] == 1])}\\tclear: {len(df_validation[df_validation['LABEL_code'] == 0])}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}99.20 & 99.58 & 98.21 & 98.89 \\\\\n",
      "\\hline\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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//fYbTZs25dSpUxbtN23aRKNGjVi9ejVJkyalevXqGAwGDh06RO/evfn222/jfehnihQpqF+/vnn4bYECBahfv75FUObp6YmbmxsbN24kffr0VK9enSxZsrB582bc3d3NibWXefDggTkR1KBBg5e2r1ixIqtXr2bo0KEWc95je578/Pxo0qQJ69evJ2/evFSoUIGAgACWLVtGy5YtefLkCRAZHNWvX59MmTIBUKxYMerXr0/58uVf6XVGZ+HChXz99dccPHiQ3LlzU716dbJmzcrOnTv58ssvWb58+Uv38fjxY9q1a8eIESM4c+YMrq6uVKtWjadPn7Jo0SIaNGjA8ePHo9122rRp9O3bl0ePHlGxYkVy5MjB33//Te/evc3Ti19Hrly5KFasGH5+fhw+fDjK+pCQELZs2WIONKPj5+dH48aNmTJlCrdv36Z48eJUrVqVtGnT4uPjw8CBA5k8ebK5fc2aNc0J1UyZMkX7OzElcVOnTk3FihWxtbXlk08+iXJsW1tbJk6ciKOjI8eOHWPx4sXmdQ8fPjRfQ71797aaxDX9/ZjaZMiQgfr161O/fn3zl4PYXq+v+lqiU6BAAYYMGQJEFs3duXMnw4cPByLv7ufLly/KNjdv3uTo0aMAuLu7v9JxKlasyJgxY16aSIvOyZMnAShRokSUdQsXLqRFixZs2rSJDBkyUKNGDXLlysXOnTvp0KGDxXUR222OHTuGu7s7GzZsIGXKlFSrVo2CBQty4sQJvvvuO7799ltz21e59kTk/bVjxw4g8sbci++PU6ZMoUOHDvz111/kyJGD6tWrkypVKn7//XcaNWr0yjMOhg4dSs+ePdmzZw8ff/wxNWrUwNnZmatXrzJ//nxatmzJ06dPgf/FISa1atWy+Fx5kekG74YNG6K9YbV161aePn1K+fLlzV9cg4OD6dKlC8OGDePs2bMULFiQypUrExgYyKxZs2jatCm3bt16pde2YcMGwsLCyJMnzyvViR05ciS//PILn376qXlZWFgYvXr1om/fvhw+fBgnJyeqV69OkiRJ+O2332jUqFGMI81Wr15Np06duH79OqVLlyZ//vycPn2a4cOHWyQMa9asaZHcMn1ex7Z0R0hICO3bt2fatGn4+/tTokQJypcvz+PHj1myZAlNmjR5peL2hw8fpl69eixZsoTg4GCqV69OkSJFOHXqFB4eHnz55ZfRFlcPCgqiZcuWLFmyhGzZslGlShVCQkJYs2YNbm5uMSYOrDHF5xs2bIh2/Y4dO3j06JHVG59//PEH7u7u/P7776RKlYrKlStTsmRJHj16xLp163Bzc+PcuXNAZPmQ+vXrkyFDBuB/MfeLv5NJkyaxevVqSpcujZOTE3ny5In22E5OTnzzzTcA/PDDDxbnf/Pmzfz222/mm4gvzlx5cT/WvgvE5Xp91dcSnV69elGiRAmCgoIYOHAg8+fPZ/fu3eTIkYNJkyZFO3th48aNGI1GChcu/MoPx+rfvz99+/Yla9asr9w3k5jiu9h8j4nNNmPGjGHw4MH4+vpiMBioXr06qVOnZuPGjbRs2ZJdu3YBr37tyXvCKLF27Ngxo8FgMFatWtUYHh5uXt6vXz+jwWAwjhkzJso21apVMxoMBmOBAgWMGzduNC8PDg42fvPNN0aDwWBs1KiRefnRo0eNBoPBWLt2bWNgYKB5eXh4uHH48OFGg8Fg7Natm3n5pUuXjIUKFTIaDAbjkiVLjBEREeZ1Z8+eNVasWNFoMBiMs2bNMi8/ePCg0WAwGGvWrGnRV4PBYDQYDMZ///03yuuIaZuBAwcaDQaDccaMGVHOlbOzs7Fo0aLGv/76y2Ldrl27jC4uLsZChQoZz549G+VYLzKdk4IFCxpDQ0Nf2j46cTlPBoPB2Lp1a+N///1nXnf9+nVj6dKljQaDwbhq1SqLY7Vv395oMBiMq1evtlge07kyMV0rhw8fNhqNkdeIq6ur0dnZ2Xju3DmLtuvWrTMaDAZj6dKljWFhYebl0f0OBwwYYDQYDMYGDRoYb9++bV4eEhJiHDNmjNFgMBjLlStnfPjwoXldmzZtjAaDwejs7Gxcs2aNxbHHjh1rNBgMxooVK0b7OqLzfL+WLl1qNBgMRg8Pjyjttm7dajQYDMZ58+YZb9y4Ye7D80x/b1999ZUxJCTEvDwiIsI4Z84co8FgMBYrVszivKxevdpoMBiM7du3t9jX87/j5/tj+vuOaTvT+S9SpIjx6tWrRqPRaOzZs6fRYDAYO3XqZHF9WbNmzRqjwWAwtmnTxmJ5XK/X6F7L6+jTp495XwaDwTh+/PgY25rORYUKFV77OC8yXXdTp061WB4aGmq8e/euce3atcZSpUoZXV1do/xN7N+/32gwGIyFChUybtmyJcq6YsWKGQ0Gg3HdunVx2qZTp05Gg8FgXLZsmUX7ixcvGkuUKGE0GAzGM2fOmJfHdA2JSMJk+uwxGAzGGzduRFn/7Nkz4/Xr140LFiwwurq6Gg0Gg3HSpEkWbTZt2mQ0GAzG8uXLG48fP26xztPT0+jk5GQsWbKkRVwRXYzg7e1tNBgMxpIlSxovXrxosZ/n33Oejy+NxpjjuRfjjIiICGONGjWMBoPB6OPjE+W1mt6Tn9//d999ZzQYDMYWLVoY/fz8zMuDg4ONQ4cONRoMBqO7u3uUfUXHw8PDaDAYjIMGDXql9tGZMmWKOTa/cOGCeXlERIRx3rx5RoPBYCxatKj5s9po/N+5NhgMxrlz51p8Ti5atMhoMBiMhQsXNj569Mi8/N9//zVv86LXjZ/Xr19vNBgMxpYtWxqDg4PNy4ODg41dunQxGgwG4+jRo83Lo/scefDggbFUqVJGg8Fg/OGHHyzi4xs3bhjr1atnNBgMxmHDhlksN/X1s88+M167ds287t69e8batWsbDQaD8aeffor+ZL/gxX41bdrUaDAYjEePHo3S9quvvjI6Ozsb79y5Y5w6darRYDAYhwwZYl4fEBBg/nvasGGDxbYPHz40urm5GQ0Gg3HEiBEW60zX6Iuxqul37OTkZPz777/Ny02/6+i2i4iIMH/Gd+zY0Wg0Go23b982n+f169e/0nkxGmP+LhCX6zWm1/Kq/v33X/N3F9M17uvrG2P7jh07Rvv+9rqsvacGBwcbL1++bH5fadeunUX8bjTG7nvM627j5+dndHJyMpYuXTrK3/Hs2bPN+3peTNeevF80ciwOTKPGmjZtapFhb9GiBRA5FPPx48fRbtuoUSM+++wz87/t7e0ZPXo06dOn58yZM+Yh7qYh2RkzZrSYB29ra0uPHj0YNmyYxZMxTcVVGzVqRJs2bSzmgRcoUICxY8cCkdMS46v45auYM2cO4eHh9OzZ02IoOkDVqlXp1KkToaGhzJ8//6X7Mp2TdOnSxbqWVlzP0/fff2++CwSRI6Bq164NRA7FfhOCgoJ48uQJDg4OUepM1K9fHw8PD7777juLaVwv8vf3Z+3atdjZ2TF16lSL/SRNmpTBgwdTrFgx7t27Z76+n1e7du0od/k6deoERBYjjm4ayct8+umnJE2alK1bt0aZGmHqq2l4fnQyZsxIpUqV+Pbbb0maNKl5uY2NDa1btwYi7xi9br2Dzp07m//7ZfW/THeNnz17hoeHB15eXmzdupVMmTLxww8/xLkoc1yv19d5LdEZOXKkefpkxowZzXdTo2Oa/hFTUdvHjx/Tv3//GH+iM336dItHfRcqVIjy5cvz7bffEhISwrx586KMLPz1118B6NKlC3Xq1LFYV65cOfMU1lmzZsVpG9P70ccff2zRPl++fIwdO5YJEyZYPMxARN5fNWrUsHgvcnJyokiRItSsWZNx48ZhNBrp2bNnlOLTpvcMDw8PihYtarHOzc2N+vXrExgY+NLR30+ePKFOnTp07949ysi0fPnymUejPD8d/3XY2NjQpEkTIOpTom/fvs3hw4dJmzYtNWvWBCKnQXp5eWFvb8/PP/9s8b5vb2/PiBEjyJkzJ8ePH7c6ld3k+Zg3NkJCQli4cCEA48aNs5iuZ2NjQ6dOnahXrx5Pnz41lwp4XpEiRejcubPF52SrVq1IliwZISEh5gL88c30urNnz24xCsne3p6BAwcyYsQIi+8M0Vm1ahUPHz6kdOnS9O/f3yI+zpkzJ1OmTMHW1pbVq1dHOxJswIABFiNd0qdPT+PGjYHYx7WmUYsvjh67f/8+3t7eVKhQIcbf9X///UeNGjVo0qQJ9erVs1iXOnVq88i0GzduvFafSpQoQfHixc3/thYT2djYMH78eNKlS8e+fftYvXo1AwYM4OHDhzRr1sxqbPoq4nq9vs5riU7WrFnNcQ1A69atcXFxibG9afRcTKPAdu/eHWNsF913Coj6nuri4kLdunVZvnw5xYsXZ9asWRazfmLzPSa22xiNRhwdHaPEcB06dGDw4MHv7MEK8mYpORZLjx49YvPmzdjZ2ZmnVJqUKlWKvHnz8ujRoyj1jkwaNWoUZVmyZMmoVKkSAAcPHgSgePHi2Nvbc+TIEdzd3Zk3bx7nzp3DaDSSKVMm2rZtazEtx7Td88Pon1epUiUyZMjAo0ePXrvOV2xFRESY+1WhQoVo25iKce7fv/+l+zMlQKKrMfGq4nKe0qRJE+10ssyZMwOYp1XGt/Tp02MwGHjy5AkNGzZk8uTJHDp0yJwMadOmDZ9++ikODg4x7uPQoUMYjUaKFCnCRx99FGW9jY2NOeAwnaPnRTc1MFOmTOZkjWkqx+u+rooVKxIQEGBRNy0gIIDdu3dTrlw587mNzuDBg/n1118tfiePHz/m5MmTrFmzxty310kGOzo6RnlCz8t899135MiRg0OHDvHdd99ha2vLpEmT4iUxEpfrNTav5UWenp7mRP/du3dZsmRJjG2N/z8VxxhDDbng4GDWr18f4090DAaDOQFZv359Pv/8c6pVq0auXLl4+vQpX3zxhUXgFR4ebq79ENM5q1+/Pra2tly6dIn//vsvVttAZNIMIh+RPnz4cLZt20ZgYCAQmUxu1KjRG3v6lYi8XTVr1jS/D9WuXZs0adIAkV/Wx44dy759++jRo4fFl9S7d++akwsvi4FelkCqVasWU6dOtag1FR4ezvXr19myZYs5URCXm59NmjTB1taWrVu3Wuxn7dq1GI1GPv/8c3MC59ChQ4SGhvLJJ59E+z6XNGlS83T5V0mOmRI6sY3vTp48yePHj8mUKRNly5aNto3pBl90/YkuxkmSJIl5ulRsYpxXUaZMGWxsbFi/fj0dO3Zk2bJl5lpTefPmpVWrVlZLM8D/4oSYEjb58uWjcOHChIeHR1vG4vkki0lc49p69ephZ2fH5s2bLeqdbdq0yXzDLyb58+dn0qRJjBs3zmK5v78/e/fuNQ8ieN1r3dnZ+bXaZ8qUidGjRwMwfPhwfHx8yJ8/Px4eHq+1n+jE9Xp93dfyopCQEJYtW2b+92+//WY12WiK62IqzXPx4sUYY7uY6u4+/55av3596tatS5kyZXB0dOTo0aO4ublZJKVj8z0mNtsYDAYyZMjAjRs3aNy4MTNnzuTEiROEh4fj4OBAhw4dzDcJJHFJmI+wew+YCvEnT56cQYMGRVlv+iBZtmyZefTK82L6spo9e3YA8xevLFmy8PPPPzN06FB8fX3x9fUFImsSVapUiUaNGpm/nAHmu0HW5jnnypWLe/fuxaqGQGwEBASYz4e12gIAd+7cITQ01GIE0ItMH9YPHz58aduYxOU8pU6dOtr2pjsbMSUF4sOUKVPo3bs3Fy5cYPbs2cyePZvkyZNTpkwZ6taty+eff271fJhei7VkiWlddNeH6YvA82xtbbG1tSU8PDzWtezq16/Prl272LBhA9WrVwciazqEhoa+9JoBuHbtGitXruTYsWNcvXrVPILNxsbmpcma6ET3Ol8mVapUjBkzhg4dOhAWFkaLFi1iDHZeV1yu19i8lucdPnyYn376CVtbW5o1a4anpyc//vgjxYsXjzICAjCPqDS9h70offr0Ue5C+/n5RRlR+rzatWvHeIdux44d9OnTh+HDh5M1a1aqVKlCQECAOWCO6VpPmTIlGTJk4M6dO/j7+2NnZ/fa22TOnJl+/frh5+fHtm3bWLVqFatWrcLW1pYiRYpQo0YN3NzcSJcuXYyvTUTeH4MHD7Z4MuHTp08ZMmQImzZtYvLkyRQpUiRKcfHnaxWVLFnS6v5fpa7U06dPWbt2LTt37uTSpUv4+fmZR4ybbgbFJQ7JmjUrFSpUYO/evezatcs8itY0ksxUl+z5/p45cybGuqAvtrXGFN/du3cvVn1/1c9KiP4zKqbPS1N8F9/1ek1cXFwYOXIk48aNY//+/eYbxab6X02bNn1pDbZXfe2+vr5R4gRbW9toi6vHNa7NmDEj5cqVw9vbm/3795sHAKxbt45UqVJRo0YNq9tHRESwe/du1q9fz/nz57l586a5ZlpsR+SnTZv2tbepWbMmDRs2NP8NjBkzJl6e8BrX6zU2r+V5o0aN4vTp0xQoUIBkyZJx/Phx+vTpw4oVK6Kto5Y5c2auXr3KnTt3ot2fqfj+82bOnMmUKVNi7MOL76kmT58+ZfTo0fz222906tSJzZs34+DgEKvvMbHZJlmyZMyYMYNvvvmGixcvMmXKFKZMmYKjoyMVKlSgXr161KpVK84zQyThUXIslkyjFJ4+fWr1btilS5fYt29flLuFMb2pmj6Ank9w1KhRgwoVKrBnzx727t2Lj48PV69eZc2aNaxZswZ3d3dGjRplsb01pg93ayOMXnUfr+L5u0Uxjch4XlhYmNUET/78+UmePDlPnz7l+PHjlCpV6qV9/fHHHylUqBAVK1YkderUcTpPb+uN8MWnCkHkHcR169Zx5MgR/vrrL3x8fDhz5gx//fUXf/31F0uWLGHx4sUxPor6dV53dB+Mb+q116hRg5QpU7Jz506ePHlCihQpWLt2LSlSpIjyRKcXLVu2jNGjRxMREUGmTJlwdXUlT548FChQgDJlylCtWrXXDmhjM/UQ4K+//jL/944dO+jdu3e8jByLy/Ua29cCkcFY3759CQsLo0uXLnzzzTf4+/uze/du+vbtyx9//BHly4QpYXbnzh2uXLnyWgViY8OUgFq2bBlLly6lSpUqrxzIP3/OYrMNRD5MYfr06Vy6dIkdO3Zw8OBBjh07xvHjxzl+/Djz5s1j8eLFL/3iKCLvn+TJkzNx4kT8/Pw4evQonTp14o8//rCYKmZ6z0iRIsVLkwHWns4IcPXqVdq3b4+fnx8ODg4UKlSIChUqkC9fPooVK8aKFSvi5QlpTZs2Ze/evaxdu5Y6depw4sQJrly5grOzs0UhbtNry5UrF66urlb3Wbhw4Zcet0iRIqxYscLq052f5+vri7e3N6VKlXppLGhieq9/mzHO82KKR9zd3fn000/ZtWsX3t7eHD58mNu3b7Ny5UpWrlxJnz596NatW4z7TahxbYMGDfD29mbDhg1UqlSJq1evcuLECZo2bWo1wRQcHMyXX36Jj48PNjY2GAwGateuTd68eSlcuDC3b982PyDodcQmJgoKCrJ4cvyqVauivTn4Jli7XuMS3/3+++94enri4ODAhAkTSJ48OY0bN+bUqVNMnDiRYcOGRdmmaNGi+Pj4mEfZv0nJkydn+PDhbN++nVu3brFjxw4+++yzWH2Pie13n2LFirF161YOHDjAnj178PHx4cKFC2zZsoUtW7ZQqVIl5syZYzHtU95/So7FwtGjR7lw4QKOjo7s27cvxiTTN998w4YNG1iyZEmU5Ji/v3+0X5pNdSJerCmVLFkyateuba5r5e/vz+rVq5k2bRqenp60aNGCQoUKkSVLFq5fv87169djvBNheoqmaZh4TEyjbqKrYfXw4UOr2z4vXbp02NvbExISgoeHR5xHsiRPnpxKlSrx559/snHjxpcGRAcOHDDXEtq4cSOpU6eO1/MUW6ZgJLokGGCemhXdds8HgkFBQezatYvRo0dz+vRpvLy86NixY7TbmqY9WBs2bXrdsa35ERum6/uPP/5gx44duLq6cuzYMRo3bkzy5Mlj3O727duMHTuWiIgIPDw8aN26tUWQFxQU9Mbu9L5o//79LFq0iFSpUlGqVCl27drF0KFDLepTxda7uF7DwsLo27cvd+7coUSJEvTu3RuA8ePH06BBA27dusXgwYOZOXOmxXb58uWjUKFCnD59mlWrVkU7sja+mUZqmN4/06ZNi4ODA8HBwdy4cSPaBN2jR4/MdegyZsxI6tSpX3ub5+XLl498+fLRpUsXwsLCOHz4MBMnTuTMmTNMnjyZ2bNnx+trFpGEIWnSpEyaNIn69evz33//MXDgQObNm2deb/rctbOzi3MNylGjRuHn50e5cuWYMmVKlHhq7ty5sd7382rUqEHatGnZs2eP+cmAYDlqDP430ss0BS6uTE/pu3HjBr6+vi8dLbVo0SI2bNiAi4sLv/32m7k/1p4Wf+3aNeDNxjixjZ8dHR1p0KCBeYrXtWvXWLJkCUuWLGH69Om4u7vH+BmfJUsWLl++zPXr1y1mlDzvTce10alVqxYpUqRg27ZtjBw50nwtvWxWwMKFC/Hx8SFbtmz88ssvUUZkPv908Dft+++/59atW1SsWJHTp0+zevVqqlatav5OFlvv6no9e/Ys33//PQBDhgyhQIECAOYnbC9ZsoQyZcpEuTldv359fvnlF44dO8a5c+fM270pDg4OfPTRRwQEBJjju9h8j4nLd5+kSZNSuXJlKleuDMCDBw/YtGkTEyZMYO/evezYsSPO14EkLKo5Fguenp4AL63vZKpFtnv37ih/kHv27InSPigoyLzcVEds0aJFVK9ePcoXqyxZsvD1119jMBiA/w1XN03jiunRyXv27CEgIIC0adO+dK666Q5mdMNnjx49Gu020QV9SZIkMT+GN6bHEa9Zs4ZPP/2UIUOGWO2TialgqqenJydOnIix3ZMnT8wBW8WKFfnkk0+A+D1PsWUqcB7d+T137lyUGg+mR3R36dLFYnmqVKmoX7++OZiy9sj00qVLY2tri6+vb7QfxkajkY0bNwLEGFy9KaZRhRs3bjT/Xl4WPB0/fpywsDDSpUsXpVA9WP6dPX/nKL7vkj548ICBAwdiNBoZNGgQEyZMIFOmTOzcuZMVK1bEef/v4nr94YcfOHLkCOnSpWPy5MnmWjDp06c3P+Z7x44d5mKyzxs0aBB2dnYsWrQo2ve6F5ke1x1bptospmnpSZIkMU9fiumcbdiwAaPRiMFgIH369LHa5tGjRzRr1oyKFSsSHBxsbpckSRLKlStH9+7dAcvpRBqCL5L45MiRw3wjwNvb22L0VrZs2cidOzePHj3i0KFD0W4/a9Ys6tevz+TJk60ex1QrqkOHDlESY48fP+bvv/8G4j79z97engYNGhAaGsr27dvZvHkz9vb2UUb/m2IKHx+fGG/ode3alaZNm7J58+aXHjdt2rTmh1qNHDnS4n31RYcOHWLr1q0A5huCLi4upEyZkjt37sR4rk21Ld9kjPO68fMPP/xA5cqVzYkjk48++ohhw4bh6OhIWFiY1XIoL4sTLl26xJkzZ0iSJAmlS5d+5dcSVylSpKB69eo8fvyY3bt3s2HDBnLkyPHSPpiu9U8//TRKYgz+F9+96RugGzduZN26daRNm5YJEyaYa415eHjEuTzNu7heAwMD6dmzJ8+ePePzzz83/71B5Cg/0wM5hgwZEuW7q5OTE25ubgD069ePR48eWT1WaGgo586di3Vfw8LCzN9VcuTIAcTue0xsttm0aRN16tSJMjoxXbp0tG7d2lxL8VWmi8v7Rcmx1xQYGGj+gLdWSBIiP6hy585NREQES5cutVg3a9Ysi4KYT58+ZcCAAQQGBlKxYkXzsPW8efNy69YtFi5cyD///GOxjxMnTnD58mWSJElCoUKFAGjfvj1JkyZlzZo1UZ56dP78efObeps2bV5aq8vUhwULFlh8+Ozdu5eVK1dGu40pWfhikGR6Wt748eMtpp4BnDp1ivHjx3P58mXy5s1rtU8mRYoUoVOnToSHh9OhQwe8vLyiFOW8efMmX331FWfOnCF16tSMGDHCvC4+z1NsmZIYW7dutfgAunfvXrRDxQsUKMCNGzfYs2dPtE/+MQUK1qY2ZMuWjXr16hEeHk6fPn0sPtjDwsKYOHEix44dI3369OZk29tSrlw5MmXKhLe3N3/88QfZsmWjTJkyVrcx1XJ68OCBxZB3AB8fH/N0Y8AiyI7pOo0tDw8P/vvvPypXroybmxtp0qRh5MiRAEyYMIHLly/Haf9v+3rdsmULCxcuxMbGhh9++CFKoeWyZcvy1VdfATBp0iRzLUST0qVLM2DAACIiIujatSuTJk2Ktl7GpUuXGDRoEL169QJidzf70KFDFk8ONjHVvZg7d26UpPyhQ4f48ccfASxGWb7uNo6OjtjZ2XHnzh1++OEHi1ECoaGh5qD2+b/J+L72RCRhcHd3N39mTZgwwaJulumm1uDBg6NMGdyzZw+zZ8/mwoULLx2JYZpxsGPHDosbPvfv36dPnz7mka0vJpVM09deZ9S/6f106tSp3Lt3zzya7HnZs2enXr16BAUF0aNHD/NTFyEyaTFjxgx27drF+fPnX1pQ3qRfv37kypWLkydP0qpVK06dOhWlzfbt2+nZsyehoaFUr17d/CRHBwcH2rZtC0Se60uXLpm3MRqNLFq0iE2bNpEsWTJatmz5yufidb1u/JwrVy78/f2ZPn16lITL9u3befToEalTp7ZapsDd3Z3UqVPj4+PD5MmTLWYl3L59mz59+mA0Gqlfv/5bHTkGmOPJKVOmcO3aNerXr//SG0Wma33fvn0WD0IIDQ1l8uTJ5gc4xXStx8dn7O3bt/nuu+8AGDFiBBkzZqRevXrUrl2bgIAABg0aFKf6fm/7ejUajQwYMMA8Ot4Upz7Pw8ODvHnzEhgYSN++faN8t/Lw8KBIkSJcunSJ+vXrs2XLlihtTEn1pk2bmhPYr3vNhYWFMX78eB48eED69OmpWrUqELvvMbHZxsnJyVzC6MXE5bVr18xJ7udHt8bntSfvjqZVvqY1a9bw7NkzcufObR4NFRMbGxuaNm3K5MmT+f33383TkiAyMdKuXTuKFy9OhgwZOHLkCPfu3SNv3rwWT2apVKkSTZs2ZfXq1TRs2BBXV1cyZszInTt3OH78OBEREXzzzTfmEROffPIJY8eOZciQIXz//fcsWrSIAgUKcP/+ff7++2/Cw8P5/PPP+frrr1/6Wjt37szff//N1q1bqV27NgULFuTmzZucPn2aZs2a8dtvv0XZxpTcWrFiBTdu3KBKlSq4u7tToUIF+vfvz48//shXX31F/vz5yZMnj/l1GI1GPv300xinA0bn22+/JVmyZEyfPp1hw4YxadIknJ2dSZs2Lbdv3+bUqVOEh4eTI0cOZsyYYTEdLT7PU2x99tlnzJkzxxwolC1b1vzEvMyZM1O6dGmLef2Ojo6MHDmSgQMH8s033zBnzhzy5MnD48ePOXr0KE+ePKFKlSp8+umnVo/73XffcePGDY4fP07t2rUpWbIkKVOm5MSJE/j5+ZEuXTqmTp361oMnW1tb6tWrx8KFC7l27RpdunR5aT2F0qVLU7RoUU6cOEG7du0oWbIkadOm5cqVK1y4cIG0adOSKVMm7ty5w71798wjB03X6enTp+nQoQMGg+GVRy2+yMvLi23btpE6dWrzE40gcnpIo0aNWLNmjfkx1rFNXL3N6/Xy5cvmc/HVV1+ZC+i+qGfPnvj4+PD333/Tp08f1qxZY/Gwig4dOpA7d26GDx/OL7/8wrx58yhQoADZs2cnPDycq1evmkd8OTg40KxZM/r06RPtsf7880/z1AKTsLAwrl27xpkzZ4DIJ6w9/6j7599zunfvjrOzMx9//DH//vsvx48fByKTYaa7pLHdZuTIkbRq1YolS5awbds2c9L79OnT/Pfff+TMmZMePXqY28fntSciCcuoUaNo0KABAQEBjBo1ip9//hkANzc3zpw5w/Lly2nZsiUFCxYkZ86c5pgKIpPuL/v87ty5MyNHjsTT05PDhw9jMBh4+PAhR48eJSQkhE8++YSLFy+ak2QmefPm5cyZM3Tv3p2CBQvSv39/q0XAIfKGnGmKPESdUmliiikOHTpE3bp1KVSoEOnSpePMmTPcunWLJEmS8NNPP5E1a9ZXOYWkTJmSlStX8vXXX5trU+XNm5c8efJgY2PD2bNnzSPk69Wrx9ixYy0SLT169OCff/5hx44dNGjQgOLFi5M+fXrOnTvH1atXSZEiBePGjYv2qePx5XXj52bNmrFt2za8vb2pWbMmxYsXJ02aNOZtbG1t8fDwsFpmIn369EydOpXu3bsze/Zs1q5dS5EiRXj8+DGHDx8mODiYsmXLxqpOV1xVqFCBDBkycPHiReDlswIA2rVrx+bNmzl//jw1atTA1dWV8PBwTpw4wYMHD6xe63v27GHq1Kn4+PjQsGHDWD1VMCIiwjxooW7duhbxxXfffcfhw4fZv38/CxYsoFOnTq+9f5O3eb3Onj2bXbt2kSxZMqZMmWKewfK8FClSMHnyZNzc3Dh58iQ//PADQ4cONa93cHBg6dKl/PTTTyxevJjevXvj6OiIs7Mz6dOn5+HDh5w5c8aciM+RIwe9e/eO8Xc+btw4i+vaaDTy5MkTfH19uXv3Lg4ODkycONGir7H5HvO62+TLl4+ePXsybdo02rVrh4uLC9mzZycgIICjR48SGhpK8+bNLZ7yGl/XnrxbSo69JtOUyld5Ywdo3LgxU6dOJTAwkDVr1piX//zzz6xYsYK1a9dy4sQJsmbNSrNmzejSpUuUYuqjRo2icOHCrFu3jgsXLnDixAnSpk1LtWrVaNOmjXkKpkmDBg1wcnJi/vz5HDx4kB07dpAmTRoqVqyIu7v7K/+hVqlShUWLFjFnzhxOnDjB7t27yZcvH+PGjaNevXrRJseaN2/OuXPn2L59O3v27CFlypS4u7sDkcFCiRIlWLRoEX///Te7du0ibdq0lC5dmubNm1O3bt3XLmrYs2dPatSowapVqzh69CgnTpwgODgYR0dHSpQoQe3atXFzc4u26Gd8nafYSp48OStWrGDmzJns3LkTb29vMmbMiJubGz169DDXA3heo0aNyJAhA0uXLuXkyZNcunSJlClTUrBgQRo0aECzZs1eeg5TpUrFkiVL8PT0ZP369Rw9epSIiAiyZ89Oly5daNeunfmJg29bgwYNzNP0XuVvzM7Ojvnz5zNv3jy2bt3KiRMnsLGxIXv27HTs2JFOnToxZ84cli5dyq5du8x39QsUKMCgQYNYtGgRR44c4ebNmwwePPi1+3v16lXGjh0LwNChQ6OMsBo6dCj79+/n9OnT/Pzzz3z77bevfQyTt3G9PnnyhJ49e/L48WNKly5tHtEVHTs7O3788UcaNWpkrj82Y8YMizbVq1enbNmy7Ny5k61bt3Lx4kUOHDhAWFgY6dOnp1q1apQtW5YGDRpYfXDBhQsXuHDhgsWyZMmSkTFjRurWrUvDhg3NTzl9XufOnSlevLj5PefixYukS5eOOnXq0KZNm2indbzuNk5OTnh6evLLL7/g4+ODt7c3SZIkIVeuXDRr1oyOHTtaJA1juvY03VLk/ffRRx/Ro0cPJk2axObNm2nQoIH5vWnEiBFUqVKFlStXcuLECS5cuEDGjBmpUqUKrVu3tvrEXpPWrVuTJUsWFixYwKVLl9i5cyfp0qWjcuXKtGnTBkdHR5o2bYq3tzchISHm4tJjxoxhxIgRnDt3joCAAC5fvvzS5BhEJm1Onz5NtmzZotTONTHFFF5eXqxfv55z584RGhpKtmzZaNKkCR07djSXAHlVGTNmZPny5WzcuJEtW7Zw5swZ8+j4zJkz8/nnn9OsWbNop5olTZqUGTNmsHbtWlavXs25c+d49uwZWbNmpU2bNrRr146PPvrotfrzul43fk6SJAkzZsxg8eLFbNmyBV9fX0JDQ0mfPj2ff/45HTp0wMXF5aXHLVeuHOvWrWPevHns3buXnTt3kjJlSooWLUrjxo1p1KhRnIq4x1aSJEn47LPPWLJkCS4uLq80S6Rw4cJ4eXkxY8YMfH192b17N6lSpSJ//vw0bNiQBg0aUKlSJW7dusX58+fND73p2rUrt2/fZt++fezZs4e8efPGKj765ZdfOHz4MBkyZLCYeQKRo6CGDx9O3759+emnnyhfvnys62+9rev1wIEDTJ06FYBhw4ZZfUiQKU4ZOXIkixcvpkyZMhbn0MHBgcGDB9OmTRs2bdrE7t27uXnzJsePH8fBwYEsWbJQu3ZtqlatSvXq1a1ecy+O0re1tSVlypTkypWL+vXr07p16yhPmYzN95jYbNOjRw8+/vhjvLy8OHfuHGfOnMHR0ZEyZcrQrFmzKDcz4uvak3fLxhiX8aAiIiIiIiIiIiLvMdUcExERERERERGRD5aSYyIiIiIiIiIi8sFSckxERERERERERD5YSo6JiIiIiIiIiMgHS8kxERERERERERH5YCk5JiIiIiIiIiIiHywlx0RERERERERE5IOV5F13QERERBIGm1o5421fxm03421fIiIiIhI3ivOsU3IsGvF50Yh8iExvls/Cn7zjnoi835LZpXjXXRBJdBTnicSN4jyR+KNYL+FQckxEREQi2di86x6IiIiIyJugOM8qJcdEREQkkiqRioiIiCROivOs0ukREREREREREZEPlkaOiYiISCQNtxcRERFJnBTnWaXkmIiIiERSzCQiIiKSOCnOs0rTKkVERERERERE5IOlkWMiIiISScPtRURERBInxXlWKTkmIiIikTSeXERERCRxUpxnlU6PiIiIiIiIiIh8sDRyTERERCJpuL2IiIhI4qQ4zyolx0RERCSSYiYRERGRxElxnlWaVikiIiIiIiIiIh8sjRwTERGRSLa6pSgiIiKSKCnOs0rJMREREYmkmElEREQkcVKcZ5WmVYqIiIiIiIiIyAdLI8dEREQkkp5iJCIiIpI4Kc6zSskxERERiaSYSURERCRxUpxnlaZVioiIiIiIiIjIB0sjx0RERCSSnmIkIiIikjgpzrNKyTERERGJpJhJREREJHFSnGeVplWKiIiIiIiIiMgHSyPHREREJJKeYiQiIiKSOCnOs0rJMREREYmkWhQiIiIiiZPiPKs0rVJERERERERERD5YGjkmIiIikXRDUURERCRxUpxnlZJjIiIiEkm1KEREREQSJ8V5VmlapYiIiIiIiIiIfLA0ckxEREQi6YaiiIiISOKkOM8qJcdEREQkkp5iJCIiIpI4Kc6zStMqRURERERERETkg6WRYyIiIhJJNxRFREREEifFeVYpOSYiIiKR9BQjERERkcRJcZ5VmlYpIiIiIiIiIiIfLI0cExERkUi6ZSYiIiKSOCnOs0rJMREREYmk4fYiIiIiiZPiPKuUOxQRERERERERkQ+WRo6JiIhIJN1QFBEREUmcFOdZpeSYiIiIREpgw+0PHz7ML7/8wvHjxwkNDSVXrlw0adKEVq1aYW9vb24XERGBp6cnK1eu5Nq1azg4OFC2bFl69+5Nnjx5ouw3ODiYRYsWsWbNGm7duoWjoyNVq1alV69eZM6cOUr7wMBA5s6dy59//omfnx8ZMmSgTp06dO/eHUdHxyjt//vvP6ZPn463tzd37twhe/bsNGjQgM6dO1v02+Tq1atMmzaNw4cP8/DhQ3Lnzk3z5s1p1aoVtrYa5C8iIiLxIIHFeQmNjdFoNL7rTiQ0NrVyvusuiLzXjNtuAvAs/Mk77onI+y2ZXYq3ejybLgXjbV/GuWfitL2npyfDhw8nVapU1K1bl+TJk/PXX39x/fp1Pv30UyZPnozN/wd5w4YNw8vLC4PBQOXKlfHz82PLli04ODiwfPlyChQoYN5vWFgY3bp1Y8+ePRQvXpySJUty6dIlduzYQebMmfHy8iJr1qzm9kFBQbRr147Tp09TqVIlnJ2d8fX15eDBg+TPn5+VK1eSKlUqc3t/f39atmzJv//+S+3atcmVKxcHDhzg1KlTlC1bll9//ZWkSZOa21+4cIE2bdrw9OlT6tWrR/r06dm5cydXrlyhYcOGTJw4MU7nUaJSnCcSN4rzROLP24z1ElKclxBp5JiIiIhESiCDlC5fvsyoUaPIli0bS5YsIWfOyGRG//79adeuHZs3b8bd3Z3y5cuzZ88evLy8qFixInPmzCFJksjQplGjRnTu3JkhQ4bw+++/m/ft5eXFnj17aNq0KWPHjjUv9/T0xMPDgzFjxjBt2jTz8tmzZ3P69Gl69uxJjx49zMsnT57M7NmzmT59OoMGDTIvHz9+PLdu3WLChAk0atQIiBzZNnDgQNatW8fKlStp27atub2HhweBgYEsXryY0qVLA9CnTx++/PJL1q5dS926dalevXo8nl0RERH5ICWQOC+h0ukRERGRSDY28fcTB4sXLyYkJIShQ4eaE2MADg4O9O3bl6ZNmxIWFgbAwoULAejdu7c5MQZQqVIlqlatyunTpzl+/Lh5+cKFC7G1taVfv34Wx3R3d8dgMLB9+3b8/f0BCAkJYfny5aRJk4YuXbpYtO/evTvp0qXjt99+IyQkBMA8Yi1//vzmxBiAra0tgwYNws7OjmXLlpmXHz16lOPHj1O5cmVzYgzA3t6egQMHAli0FxEREYm1BBLnJVRKjomIiEiCsnPnTnMdsBeVLVuWsWPHUrlyZcLCwjh8+DBp0qTBxcUlStsKFSoAsH//fgD+/fdfrl69isFgIGPGjNG2j4iI4ODBgwD4+vry+PFjSpYsGaVWmL29PaVKleLRo0f4+voCcPDgQSIiIihXrlyUfWfIkIECBQpw5coV/Pz8ADhw4AAA5cuXj9K+UKFCpE2bFh8fH8LDw2M8VyIiIiISd0qOiYiISCSbePyJpQcPHuDv70++fPkIDAxk1KhRVKlSBRcXFz777DMWLlxIREQEALdu3SIkJITcuXOb6489L3fu3EDkNE2AK1euAPDxxx9He+xcuXLFqr2pXWz3H91DA0ztQ0JCuHnzZrTrRURERF5ZAojzEjLVHBMREZFItvEX7dSoUcPq+h07dkS7/Pkpjc2aNSM0NJRq1aphNBrZuXMn48aN4+TJk/z44488ePAAgDRp0kS7r9SpUwPw6NEjgJe2Ny1/sX3atGmttg8MDIxT+5f1x9ReREREJNbiMc5LjJQcExERkQTj8ePHAJw5c4bChQszf/58c5KoT58+tG7dmg0bNlCjRg0yZ84MYPH0x+eZpkIGBwcDEBoaarH8Vdu/6v5NddDiq71puam9iIiIiLwZSo6JiIhIpHgssBrTyLCXsbOzM//3kCFDLEZVZciQgT59+tC7d2/WrVtH9+7dgf8lsV5kKpSfIkXkY9KTJUtmsfxV27/u/l/WPmXKlK/U3rTc1F5EREQk1hJpIf34ouSYiIiIREoAMZOjoyMANjY20RbZL1y4MADXrl0zT180TYN8kWk6oml65YvTJl/08OHDaNvHNK0xtu1Nr/F124uIiIjEWgKI8xIyFeQXERGRBCNXrlwkTZoUo9EY7Ygq01TE5MmTkyNHDpIlS8b169ej3Zdp+SeffAJAvnz5LJa/6MaNG++kvWl5dO1TpkxJtmzZol0vIiIiIvFDyTEREREBIkdrxddPbNnb21OsWDEAvL29o6w/ceIEAM7Oztja2lKyZEkePHjAuXPnorTdt28fAKVKlQIgc+bM5MmTh3PnznH//v1o29va2lKiRAkAChUqhKOjI0eOHImSqAsJCcHHx4eUKVNSsGBB83FsbW05cOBAlH3fu3ePc+fOkTdvXjJkyABAmTJlANi/f3+U9qdOnSIgIIDixYtbTDUVERERiY2EEOclZEqOiYiICJBwgqZ27doB8OOPP3L37l3z8jt37jB9+nRsbGxwd3cHMP//hAkTLGqJ7d27l7/++osiRYpQtGhR83J3d3fCwsKYOHEiRqPRvNzT05MLFy5Qp04dc6F/e3t7GjZsyL1795g1a5ZFH2fMmEFAQAAtW7YkSZLIKhVZsmShSpUqnD17ltWrV5vbRkREMH78eMLDw2nbtq15uaurKwaDgZ07d1okyEJCQpg4cSKARXsRERGR2EoocV5CZWN8PjIUAGxq5XzXXRB5rxm33QTgWfiTd9wTkfdbMrsUb/V4dn2KvrzRKwr/+UScth89ejRLliwhbdq01K1bF4Dt27dz9+5dvv76a3r37m1u26tXL7Zu3UrevHmpXr06/v7+bN68meTJk7N06VIKFChgbhsaGkrbtm05duwYLi4ulC1blitXrrB9+3ayZcvGypUryZo1q7n9w4cPcXd35+rVq5QrVw4XFxd8fX05ePAgzs7OLF26lFSpUpnb37hxg+bNm3P//n1q1KhBnjx5OHDgAKdOnaJixYrMnj3b4umUvr6+tG/fntDQUOrWrUuWLFnYuXMnly9fpkmTJowbNy5O51GiUpwnEjeK80Tiz9uM9RJSnJcQKTkWDQVNInGjoEkkfrzt5FiSvvEXNIVNjnvQtGnTJpYtW8aZM2ewsbHBycmJ9u3bm5Nl5mOFhbFw4UJ+//13bty4QZo0aShZsiQ9e/Y01/V63pMnT5gzZw4bN27Ez8+PTJkyUaFCBXr27EmWLFmitL9//z7Tp09nx44d3Lt3j6xZs1KrVi26du1q8TRNk5s3bzJ16lS8vb0JCgoiR44cNGjQgI4dO5qfUPm88+fPM23aNHx8fAgJCSF37tw0b96cFi1aaErlG6A4TyRuFOeJxJ+3GesltDgvoVFyLBoKmkTiRkGTSPx428kx+36u8bavkJ+Ox9u+ROKT4jyRuFGcJxJ/3maspzjPOtUcExERERERERGRD1aSd90BERERSRgSa4FVERERkQ+d4jzrlBwTERERQEGTiIiISGKlOM86TasUEREREREREZEPlkaOiYiICKA7iiIiIiKJleI865QcExEREQAUM4mIiIgkTorzrNO0ShERERERERER+WBp5JiIiIgAGm4vIiIiklgpzrNOyTEREREBFDSJiIiIJFaK86zTtEoREREREREREflgaeSYiIiIAGCD7iiKiIiIJEaK86xTckxEREQADbcXERERSawU51mnaZUiIiIiIiIiIvLB0sgxERERAUA3FEVEREQSJ8V51ik5JiIiIgDYKmoSERERSZQSWpy3efNmFi5cyMWLF7Gzs6NYsWJ0796dIkWKvNL2z549Y86cOWzatIlbt26RPHlyXF1d6dq1KyVKlHjt/mhapYiIiIiIiIiIvBWzZs2iT58+3L17F3d3d2rVqsWhQ4do2bIle/fufen2YWFhdOrUiZkzZ2Jra0vr1q2pUqUKBw4coG3btmzbtu21+6SRYyIiIgKoUKuIiIhIYpVQ4ryLFy8ydepUDAYDq1atIkWKFAC0adOGli1bMnToUP7880+SJUsW4z7WrVvH33//TenSpZk/fz5JkyYFwM3NjQ4dOvD9999TvXp17OzsXrlfGjkmIiIiQGTQFF8/IiIiIpJwJJQ4b9GiRURERPD111+bE2MAzs7ONGvWDH9/f3bs2GF1HydOnACgadOm5sQYQJkyZcifPz937tzh+vXrr9UvJcdEREREREREROSNO3DgAAAVKlSIsq58+fIA7N+/3+o+0qZNC8DNmzctloeGhnL//n1sbW3NbV6VkmMiIiICRD7FKL5+RERERCThSAhxXmhoKDdv3iR9+vSkTp06yvrcuXMDcPnyZav7adq0KSlTpmTevHls3LiRoKAgbt26xcCBA7lz5w7u7u6kS5futfqmmmMiIiICJJxaFCIiIiISvxJCnBcQEIDRaCRNmjTRrjclzB49emR1P7lz52blypUMGjSIfv36Wazr2bMnX3/99Wv3TckxERERERERERF5JTVq1LC6PqaaYWFhYQAWdcKeZ29vD0BwcLDV/QcGBjJ58mTOnDlD0aJFKVasGPfv32fHjh3MmzePjBkz0qJFi5e9DAtKjomIiAiQMO4oioiIiEj8SwhxnoODAxA5vTI6ISEhABaF+qMzYMAAdu3aRY8ePejZs6d5+e3bt2ndujUjRozg448/pmzZsq/cNyXHREREBEgYQZOIiIiIxL/4jPNe9jTJmDg6OmJnZxfjtMnAwECAaOuRmfj7+7Nr1y6yZ89O9+7dLdZlz56dfv360b9/f1atWvVayTEV5BcRERERERERkTcqadKk5MqVi3v37vH48eMo669fvw7AJ598EuM+/v33XwDy5s2LrW3UlJaTkxMQOYrsdSg5JiIiIkDkHcX4+hERERGRhCOhxHllypTBaDRy4MCBKOv27dsHQKlSpWLcPlOmTEDkEy2NRmOU9VeuXAEgc+bMr9UvJcdEREQESBiP+BYRERGR+JdQ4jw3NzdsbGyYMmWKxfTKc+fOsXr1arJmzUrNmjVj3D5HjhwUK1aM27dvM3v2bIt19+/fZ/LkyQA0bNjwtfqlmmMiIiIiIiIiIvLGubi40LFjR+bPn0/9+vWpW7cuQUFBbNiwgbCwMMaOHWt+amVgYCCLFi0CsCi8P378eNq0acPPP//MX3/9RcmSJbl//z47d+4kICCA1q1bW02wRUfJMREREQFUkF9EREQksUpIcd7AgQPJmzcvy5cvZ/ny5aRMmZLSpUvTo0cPihQpYm4XGBjI9OnTAcvk2Mcff8yaNWuYM2cOu3btYtGiRdjb21OwYEFatWrFZ5999tp9sjFGN0nzA2dTK+e77oLIe8247SYAz8KfvOOeiLzfktlZf4x1fMsxpmq87evW0L/ibV8i8UlxnkjcKM4TiT9vM9ZTnGedao6JiIiIiIiIiMgHS9MqRUREBADbBDTcXkRERETij+I865QcExEREUBPmRQRERFJrBTnWadplSIiIiIiIiIi8sHSyDEREREBEtZTjEREREQk/ijOs04jx+SNsk9qz/Seo7n/+yn8PI8xptNA87pGFepyZt4uHq07z97Jv1Psk8LR7mNu34mMaNvPYlm+7B+zZdxSHq07z7Vlh+jv1tVifcXCpTkyYxNB6y5wbPZWahSraF5n3HYz2p+2NZvG4ysXeTdCQkJo0qAZh32OmJf9e/tfun/VgzLFy/F5nQZs3fxntNv+MvtXPIYMt1gWGPiI7zy+p1qlGlStUA2PIcMJDHz0Rl+DvDs28fg/EUm8HJI68Gu/STz44zS3V/5Nv2ZdYmxbq0Rljs/+k0frzrNtwgoMOfNarP+m2VdcXryfB3+cZn7/H0mZLPLJbVWKlIsxZsuVKTsAuTJlZ+OYxTxe/w//LPTGrfLn0fahWeV65icsirxpwcHBjBj2HRXLVKJG5VosWrA4xrZnz5yjdfO2lClejlburTlz+ozF+s0bN1OvTn3KFC9Hn579ePDggXndy2K006dO065Ve8oUL0eDzxqxfu16i33/c+Ef2rfpSOliZWna0A2fQ4ct1i9dvIyaVWtTrmQFRgz7jqdPn0bpf3RxJ8DRI0dp0awVZUqUw71xcw7uP/jyEydvnOI865QckzdqytffU6t4ZeoMbkOrcT3o/GkrutRrTcGPDCwfPJ1xK6ZT9KvaHL90ho2jF5HcIZnF9t+6d6PzZ60sltnY2LBx9CLuBNynWLe6dJ0ymGGte9GyWiMAMqXNwPpRC1j51zpcutTEc/d61n4/nxwZswGQ1b2Yxc+EVTO56neDtfujTxiIvC+Cg4MZ2H8wly5eMi8LCwujR7deJEmShFWrV9C+UzuGDBzKP/9ctNh288bNzJoxO8o+R38/mgvnLzBj9jRm/TKTy5euMHL4yDf+WkREJOH6ocswShqKUP3b5nw9bSgj2vSlaaV6UdoV/MjAxtGLWHvgT0p8/SlH/znFzh88zQmwLvVa8127fgxZMIEKfRqTI2NWlg+ZDsD+M0eixGx7fA/yh/cWbty5jZ2tHRvHLCY0LJRi3erwg9cslg6aSqGPnSz6kCZlaqZ+rc8teXt++mEyZ06f4ZcFcxkyfDBzZs5l29ZtUdo9efKUHl17UrxEMVZ4LaOoa1F6dO3FkyeRSaiTvqf4zmMkXb/uwpIVi3gUGIjHkBHm7a3FaI8ePaL7Vz1wLebK6rVefNWtC995jOTY0ePm9V990Y18+fLy2xpPatSsTr9e/bh37z4A2//czuwZs/H4bhi/LJiL74mTTP5xikX/o4s7Ae7du0+v7r2p+2kdVq/xonbd2vTu2Rd/P/94O8cib4KSY/LGpHNMyxd1W9B58gAOnz/OzmP7+PG3uZQpUIzaJSpz+tp5lmxfzeV/rzF43jiyZchCwY8MADimSIWXxxwGNe/O9f9uWew3S7pMHL90mm5TB3Px1hU2++xkx7F9VCxcCoAKhUoRFh7OJK/ZXPG7zrgV03kWGkxZ5+IA+D+4Y/5J7pCMXo068eVP3xL4RKNh5P116eIl2rZox80bNyyWe+/xxt/PjzETRvNxno9xa96MSpUrcuLYCSAyeTb6+zGMGPY9OXPltNj2yZOnbP9zB4OGDqJgoYI4F3RmwOD+7Nyxi+Dg4Lf22uTtsbGxibcfEUmcUiRLzpeftqT3zBEcu3iKNfu2MNFzFj0adojStlv9duw/8zcjFk3iws3LDPx1DA8fB9K6RmMAejbsyI+/zWXlrrWcuXaB9hP78nmZmhhy5iU0LNQiZqvmWh6XPAXoPPlbAD4rU51cmbLRdkJvLty8zNyNy9jks5PyBUta9OGHLsO49O+1N35eRCAydvpj9RoGDB6Ac0FnatSsTocv2rNy+aoobbdu3opDMgf6fduXvPnyMmDwt6RMmcKcSFu5fCW169aifsP6GJwMjBk/Gu893ty8eeulMZqfnz8VKlWgb/8+5MyVk3r1P+OT/J9w/NhxANatWU+KFCkYOnwIuT/Kzdc9u5H7o9ycOXUagGVLltO6bWuqVK1MYZdCeHw3jLW/rzWPHosp7gQ4fuw4dnZJ6PBFe3LmysmXX32Bg70Dvid839BZl1elOM+6BFlzLCQkhO3bt3PgwAEuX75MYGAgISEhpEiRAkdHR/Lnz0/JkiWpVasWSZIkyJcgQMXCpXj4+BF7fP83jHbCqhkAtK3ZlEIfOVG+UEkOnPmbjnWa8/BxIJduRwYvebLmIpm9A8W/rsvCbydb7Nfv/n+0GPO1+d/lC5WksksZvp46BIB7gQ/ImCY9jSt+yh/em2lYvg6OyVNy8srZKH0c2b4/O455s+OYd7y/fpG36e8jf1OqTCl69O5O2RLlzcsPHz5C6bKlSZUqlXnZz9P/9zf15MlT/rnwD0tXLmbJoqUW+7S1tWHazCkUcLa8Cx8eHs6TJ09wcHB4Q69G3pXEGuxIwqNY7/1VNG9BkiZJwv4z/5tG5X3qMENb9cLGxgaj0Whenjdbbg6dPWax/ckr5yhXsARzNy4jb7aPOHTuf+v97v/HnYf3KFewBBduXjYvT2KXhNEdvmXM8mncC4ycVla1SDl2HNvHoydB5naNv/vS4liVi5SlatFy9JoxnM1jl8TPCRCx4sL584SFheHqWtS8rFjxYvw6Zx4RERHY2v5vbMpJ35MUK+5q/uy1sbHBtbgrJ4770rBxA3xPnKTTlx3N7bNmy0q2bFk5ecKXajWqWY3R8uf/hDHjRwMQERHBnt17uXr1KiVKRg4WOHL4b6pWr4KdnZ152+Wey8z7OH3qDF27/69sTZGiLoSGhnLh/AWKuhaNMe4ESJs2DQEBAWzftoMaNauza8dfPH78mPyG/HE6txJ3ivOsS3DRxr59+xg6dCj+/v4WH67P8/HxYfny5WTLlo0xY8ZQrly5t9xLeRV5s+bmqv8N2tZsypCWPbFPmpQFWz0Zs3wqq3avp0G52uz7eQ1h4WFERERQb1h7AoIeAuB7+Sz1PTq89BhXlx7koyw5WX9gG6u9NwGw9+Qhpq9dyG8ec4gwRpDELgkdfuhrEWRBZJ2KVtUaUb5Po/h+6SJvnXsL92iX37pxi+w5svPzT1PYsG4j6dKlpVv3blSvWQ2A1KkdWbRsYbTbJkuWjAqVKlgsW7ZkOQan/KRLly5e+y8iHw7Feu+3bOmzcPfhfULDQs3L/AMiR+NnSJ2Ouw/v/2/5gzvkyJjVYvtcmbJz/1HA/9Zn+N/6FMmSk94xLRlTp7fYxr1KfdKmSsOMdYvMy/Jmy81V/5uM+2IwbWs25e7D+4xY/CNr928FIuvezu0zge7ThhISGorI23D3zl3Spk1LUvuk5mUZMqQnODiYgIAA0qf/37V9585d8n1iWYMvfYYMXPr/0hd379wlc+ZMUdb7+/33yjFaaEgoZUuWJywsDLfmzShStAgAt27cpLBLIUaOGMVfO3eTPUd2vhnQj2LFXXn06BHBwcEWx06SJAlp0qbB3+8/IOa4E6B4ieI0b9Wc/n2+xdbWlvDwcEaO+Z6P83z8KqdQ5J1JUNMqfX19+eqrr3j48CGtW7dm1qxZbNy4kb1793Lo0CH27t3Lxo0bmTVrFq1ateLBgwd89dVXnDp16l13XaKRKnlK8ufIw1eft6HjpH70nzuaXo060bdpZzKkTkfW9JnoPm0oZXrWZ/H21Szo/xOZ0mZ4rWM0HdmFz4e1xzVfISZ3/c583LzZcvPdkp8o3eNzRi+bwtSvR+KUK5/Ftl982pIjF3zxOXcsmj2LJA5Pnjxh3Zp1BD58xLSZU/i8wef07/stp/9/2PzrWLFsJX9u2Ubf/n3fQE8lIbCxib8fkego1nv/pUiWnODQEItlwSGR/3ZIajmieNVf63GrXI96ZWpgZ2tHu1rNKOVUFPukkYmDVbvXM7hldwrk/gSHpA789FVkPSXTepMu9Vrz6+YVPAt5Zl6WKnlKOtR2I12qNNT36MDi7b/x2/A5lDBEfvn3aN2boxdPsu3vPfF7AkSsePrsGfb2ltevvb09EJmoet6zp8+wT2r/QtukhPz/39OzZ89Iah/N+hf+/sB6jLZ05WLGTRzLls1bWbwwcgTlkydPWfDrAjJmzMiMOdMpUaoEXTt3w+9fP549jfw7i3LspPbmvlnz5MkTbt24SdfuX7Fs1RI6f/UlE8ZO5MrlKy/dVt4sxXnWJaiRYzNnziRp0qSsWLGCAgUKRNsmU6ZM5MuXj2rVquHm5kbLli2ZPn06s2dHLSQt71ZYeDhpUqam1dge5rphuTNl5+sGkcmsk1fOMfP/7wB2mTyAs/P+omOd5kxcNfOVj/H3hci5631nf8+yQVPpP3cUA9y7YYMNo5b+DMCxi6coU6AYvRt/YZ56CdCs0mfM3rA0ut2KJBp2SZKQJm1aho0Ygq2tLc4FnTn29zFWe/1OocKFXnk/q1Z4MmHsRL4d1J/yFTSCQ968JUuWMHr06BjXL1u2jJIlI2sLBQcHs2jRItasWcOtW7dwdHSkatWq9OrVi8yZM0fZNjAwkLlz5/Lnn3/i5+dHhgwZqFOnDt27d8fR0TFK+//++4/p06fj7e3NnTt3yJ49Ow0aNKBz587mLz3Pu3r1KtOmTePw4cM8fPiQ3Llz07x5c1q1amUxpcbk1KlTTJ8+nZMnT0ZOPcmfn3bt2lG/fv3XOWXvBcV6779nIc9weOELvcP//x08CbZ8mt3WI3/x/dLJrB4+lyR2Sdh1Yj+Lt60mTcrIv7NRy34mb7bcnP5lJ6FhoczZuIzjl04T+NxUyUxpM1CpcGl6TBtmse+w8HDuBT6g29TBGI1Gjl08RaXCpenyWWumhgTT5bPWuHSp+SZOgUiMHOwdCHkhCWZKKCVLZvngMQcH+yiJrpCQUJIlj2xn72BPaEg061/Yj7UYLal9UpwLOuNc0Jk7/91hxdIVtOvQFrskdjg5F+Drnt0AcC5YgAP7DrBh3UaauEXWBIxy7NAQc9+sWTBvIUajka5ff/X/+3bmpO9Jli1ZzrARQ1+6vbw5mlZpXYJKjh07dozPP/88xmDpRQUKFODzzz9n586db7hnEhv/3vfnafAzi4L6529eJlem7ISFhzF1zXzzcqPRyInLZ/goc46X7jdz2oyUK1jCPGwe4My1CzjYO5A6RSpKGIpw4rLlY5CPXTxN4Tz/m5OfM1M2Cn3sZLEPkcQoY8aM2IDFF/KP8nzMP+cvvPI+Fs1fzE+TJtOvf19at2318g3kvZWQgqYzZyLfx9u3bx9twip79uzA/z+RtUcP9uzZQ/HixalRowaXLl3Cy8uL3bt34+XlRdas/5u2FRQURIcOHTh9+jSVKlWiTp06+Pr6smDBAry9vVm5cqVFjT5/f39atmzJv//+S+3atcmVKxcHDhxg6tSp+Pj48Ouvv5L0uVEuFy5coE2bNjx9+pR69eqRPn16du7cyahRo/D19WXixIkWr+PAgQN06dIFe3t76tWrR/LkydmyZQv9+/fn0qVL9OnTJz5P6zunWO/9d+uuHxnTpMfO1o7wiHAAsqbLzJNnT83lMZ43dvk0JnnNIU1KR+4E3GPVsFlc9Y8s4v3k2VOaj+5G6hSOGDHy6EkQ/p7Huer3vyLfdUpW5YrfdU5dPWex33/vR07LfX5q7vmblymSx5mmFT8jfeq0XFq8DwA728i6So/WneernwexfOcf8XtSRP5f5iyZCAgIICwszFwv8e7deyRLlgzH1I4vtM3Mvbv3LJbdu3uXjBkjpzNmzpyZu9Gsz5Qpo/nfMcVoN2/e4trVa1So+L96YHnz5eHBgwAAMmbKSJ4Xpjl+9PFH+Pn5kTZtWhwcHLh79x558uYBIj9rHwY8tDh2TM6ePouhgMFiWQHnAlx84UnpIglNgkqOhYeHRxsAW5MqVSoeP378hnokcXHw7FGSOyQjf448/HMrchitc+5PuOp/g9v3/CmY2/JN0ylnPg6fP/HS/ebJlpvfR/xCrlaluX3PD4AShiL89+Au9wIfRO77I8uCjwVy5+OK33Xzv8sUKM71/25x487tuL5MkQStSFEXfpnzK+Hh4eaiq1cuXyZ7juyvtP26Nev4adJkvh3UnzbtWr/JrkoCkJCSY2fPnsXBwYGBAwdaFAx+kZeXF3v27KFp06aMHTvWvNzT0xMPDw/GjBnDtGnTzMtnz57N6dOn6dmzJz169DAvnzx5MrNnz2b69OkMGjTIvHz8+PHcunWLCRMm0KhRIyCyuPHAgQNZt24dK1eupG3btub2Hh4eBAYGsnjxYkqXLg1Anz59+PLLL1m7di1169alevXqQORogiFDhmBnZ4enpyf58kVO/+/evTstW7Zkzpw51K1b95UTSe8DxXrvv+OXThMaFkZZ5+LsO30YiHwI0+ELJ6LUkGtRrSFlChSj76zvuBNwj2T2yahWtDwdfoic+jXhy6Gcvnaexdt+A6CkoShpUjqy/8zf5n2UKVCMfaeP8KKDZ48yrFVvbG1tiYiIAP4XZ05bu4BlzyXAyhQoxrLB03DtWgf/B3fi94SIPMepgBNJkiTB98RJipcoBsCxo8coVLhglJHDLkVcmP/rAoxGo/lhFsePnuDLr74AImO4Y0eP0bBxAwD8/vXDz88fl/+vG2YtRjvpe5Ix349h++5t5pFmZ86cJW++yGRXkSIu/H3kb4ttrl65wqf1PsXW1pZChQty7OgxSpWOHKHte9yXJEmSYHCy/P4WnUyZM3H5kmWt5ytXrpAj58sHQciblZDivIQoQdUcy5cvH9u2bSM4OPiV2gcFBbFlyxby5s378sby1l24eZkNB7ez8NvJFMnrTO2SVRjUvDuz1i/hl03L6fxZK9rUbEq+7B8z7ovBfJQlB4u2eb10v4fPH+fvf3yZ338Szrnz82np6vzQeShjVkR++fl18wo+K12dPk2+JE/W3PRu/AV1S1Zl5rrF5n0U/tiJM9f+eWOvXSSh+LReXSIiIhgzchzXr11n1QpP9u3dT1O3Ji/d9mHAQ8aNnkCDRvWp+2kd7t65a/4JDw9/C72XD1VISAgXL17EYDBYTYwBLFy4EFtbW/r162ex3N3dHYPBwPbt2/H39zfvd/ny5aRJk4YuXbpYtO/evTvp0qXjt99+M0+B8fPzY8uWLeTPn9+cGIPIkZiDBg3Czs6OZcuWmZcfPXqU48ePU7lyZXNiDCLrzQwcOBDAov3WrVu5ffs2jRs3NifGAFKnTk3v3r2JiIhg+fLlr3LK3huK9d5/T4OfsWibF7N7j6OkoSgNy9ehv9tXTPljHgBZ0mUimX3kl/ELNy/T9fM2NK74KZ/kyMPywdO5cec2mw/vAuD2PT9GtO1LSUNRiud3YemgqczasIQH/1+wH2KO2VbsWoutrS0ze44lX/aP6Va/HZ+WqsYvm5bz4FEAl25fNf/cuht5M/XS7asEPVWiVd6c5MmTU7/h54z+fgynTp5m5/ZdLF6whFb/P6rr7p27PHsWWdOrVp2aPHr0iInjfuDSxUtMHPcDT58+pXbd2gC4t3Bjw7qN/L76Dy6cv8DQwR5UrlqJnDlzvDRGq1KlMqlSpWLUd6O5evUamzZsZuG8RXT+KvKJrm7Nm3Hh/D/Mmj6b69euM2PaTG7euEW9+vUij93SnUXzF7Nz+y5OnTzN6JFjadKsMcmTJ3/pOWjSrDHee/axZNFSbt64ydLFy9i3d7/VIv4iCUGCSo516NCBGzdu0Lx5c/7880+CgoKibff06VN27txJ69at8fPzo3VrjWZIqFqP68nF21fxnvwHiwf8zPR1C5m2Zj6eu9fTY/owhrTswbFZW6hQqCTVv23OnYB7L91nREQEDUd8weNnTzkwdS2/9pvI1DXzmfr/Qdmhs0dp8n1n2td2w3fuNtrWbMpnQ9tx5tr/ppFlSZeRB0EBb+pliyQYqVKlYs6vs7h65QpNG7qxbMlyJvw4HueCzi/ddv/+A/9f0H89NarUsvjx8/N/C72Xt83GxibefuLin3/+ITQ0FGdn69fpv//+y9WrVzEYDGTMGHWqR4UKFYiIiODgwYNAZDH4x48fU7JkySi1wuzt7SlVqhSPHj3C1zeynuXBgweJiIiI9kmJGTJkoECBAly5cgU/v8gv3gcOHACgfPnyUdoXKlSItGnT4uPjY04um9pXqFAhSnvTPkxtEgvFeolDv9nf8/c/J9k1yZMZPccwYtGP/OG9GQA/z2M0rxpZL+/oPyfpNnUIP3bx4O8ZkU8VrzesvXmE2bS1C1h3YBubxy5h85glbDi0nf5zRlkcK6aY7dGTIGoNbEmB3J9w6pft9G78Bc3HfM2xi3p4g7xb/Qd+Q8FCznzZoTNjR4+jW4+u1KxVA4AaVWqxdfOfQGSMNm3mVI7+fYyWbq3xPXGS6bOnkSJFZAKqqGtRPL4bxpwZc2nXqgOpU6dm5JjvgZfHaClSpmDWLzO5898dWjRtybQp0/l2cH+q1Yh8Wnn2HNmZ9ctMdv+1h6YN3di9aw/TZ08lS5bIOp2fflaXTp07Mur70XT9sisuRQrTt3+fV3r9RYoW4acpk1i/Zj3NGrmzYd1GZsyexif58718Y3mjEkqcl1DZGGN6hvY7MmvWLKZNm2b+0EyfPj1p0qQhadKkhIaGEhgYyP37983rO3bsyIABA+K1Dza1csbr/kQ+NMZtNwF4Fv7kHfdE5P2WzC7FWz2e0+S68bav8323xHpbLy8vhg0bRvPmzQkKCuLIkSMEBATw8ccf07x5c1q2bImtrS379++nY8eO1K1blylTpkTZz7Jlyxg5ciRdu3alb9++5v1+8cUX0cYOEydOZN68eYwePRo3NzfzVMvhw4dHm5zp3bs3W7ZsYcGCBZQvX57+/fuzfv165s6dS5UqVaK0b9asGSdPnuTPP//ko48+omXLlhw9epSNGzfyySefRGlfrlw5AgICOHHiRLSF/99X7zrWU5wnEjeK80Tiz9uM9RJKnJdQJaiaYwDdunWjdu3aLFy4kIMHD3Lz5k3u3fvfaCI7Ozty585NmTJlcHNzo3Dhwu+wtyIiIhKdGjVqWF2/Y8eOGNedPXsWgFWrVlG6dGk+//xz7t69y+7duxk5ciSHDx9m8uTJPHjwAIA0adJEux/T8kePHgGY26dNm9Zq+8DAwDi1f1l/XrV96tSpuX//PkFBQaRPnz7aNu8jxXoiIiKS0CS45BhE1qMYNSpySHVYWJj5iR8ODg44Ojqan/whIiIi8SehDJO3sbEhe/bs9O7d26LW1927d+nQoQObN2+mfPny5tFUMY2qMi031bcKDQ0FsHi6pLX2YWFh8dretPx192+qgZaYKNYTERF5uxJKnJdQJfjII0mSJNHWEREREZH4FZ9Bk7WRYS/j4eGBh4dHlOUZM2Zk0KBBfPHFF/zxxx+0b98eiDl5ZFqeIkXklAXTE7tMSbL4ap8yZcpXam9a/qrtX+xPYqVYT0RE5M1Tcsy6BFWQX0RERMSaokWLAnD9+vUo0yZf9PDhQyByeiJEndYYX+0dHR3fSPvAwEBsbGxIlSpVtOtFREREJH4oOSYiIiJAwniKUWhoKL6+vhw+fDja9U+eRBaAdnBwIF++yCdfXb9+Pdq2N27cADAXu39b7U3Lo2ufMmVKsmXL9tL2QUFB3L9/n3z58mFrq3BNRERE4iYhxHkJmaItERERAcDGJv5+Yis0NJQWLVrQrl077t+/H2W9j48PAK6urmTOnJk8efJw7ty5aNvu27cPW1tbSpQoAUChQoVwdHTkyJEjUaYyhoSE4OPjQ8qUKSlYsCAApUqVwtbWlgMHDkTZ97179zh37hx58+YlQ4YMAJQpUwaA/fv3R2l/6tQpAgICKF68OHZ2di9tv2/fPgBKliwZ3WkSEREReS0JIc5LyJQcExERkQQjRYoU1KxZk4iICMaPH09ERIR53fXr15k0aRK2trZ06NABAHd3d8LCwpg4cSJGo9Hc1tPTkwsXLlCnTh0yZ84MRBa4b9iwIffu3WPWrFkWx50xYwYBAQG0bNnSXAw+S5YsVKlShbNnz7J69WpzW1PfwsPDadu2rXm5q6srBoOBnTt3WiS8QkJCmDhxIoBF++rVq5MxY0a8vLw4f/68eXlgYCBTp07FxsaGNm3axPpcioiIiMirsTE+H0kKADa1cr7rLoi814zbbgLwLPzJO+6JyPstmd3bLcReePrn8bavUz02xHpbPz8/WrVqxa1btyhQoADlypXj7t277NixgydPnjB48GBzciw0NJS2bdty7NgxXFxcKFu2LFeuXGH79u1ky5aNlStXkjVrVvO+Hz58iLu7O1evXqVcuXK4uLjg6+vLwYMHcXZ2ZunSpRY1vm7cuEHz5s25f/8+NWrUIE+ePBw4cIBTp05RsWJFZs+ebfG0SV9fX9q3b09oaCh169YlS5Ys7Ny5k8uXL9OkSRPGjRtn8Vq3b99Or169cHBwoF69eqRMmZItW7bg5+dHz5496dGjR6zPo0RPcZ5I3CjOE4k/bzPWSyhxXkKl5Fg0FDSJxI2CJpH48baTYy4z6sfbvk52Xx+n7QMCApg9ezbbt2/Hz8+PFClSUKRIEb744gvKlStn0fbJkyfMmTOHjRs34ufnR6ZMmahQoQI9e/YkS5YsUfZ9//59pk+fzo4dO7h37x5Zs2alVq1adO3a1Vwk/3k3b95k6tSpeHt7ExQURI4cOWjQoAEdO3Y0P3HyeefPn2fatGn4+PgQEhJC7ty5ad68OS1atDBPqXzekSNHmDlzJidOnMBoNJIvXz7at2/P55/HXxAr/6M4TyRuFOeJxJ+3GeslpDgvIVJyLBoKmkTiRkGTSPz4kJNjIm+K4jyRuFGcJxJ/lBxLOJK86w6IiIhIwpBYnz4kIiIi8qFTnGedkmMiIiICJN6nD4mIiIh86BTnWaenVYqIiIiIiIiIyAdLI8dEREQE0HB7ERERkcRKcZ51So6JiIhIJAVNIiIiIomT4jyrNK1SREREREREREQ+WBo5JiIiIoCG24uIiIgkVorzrFNyTERERACNthcRERFJrBTnWadplSIiIiIiIiIi8sHSyDEREREBNNxeREREJLFSnGedkmMiIiICKGgSERERSawU51mnaZUiIiIiIiIiIvLB0sgxERERAXRHUURERCSxUpxnnZJjIiIiAugpRiIiIiKJleI86zStUkREREREREREPlgaOSYiIiKAhtuLiIiIJFaK86xTckxEREQABU0iIiIiiZXiPOs0rVJERERERERERD5YGjkmIiIigO4oioiIiCRWivOsU3JMREREAAVNIiIiIomV4jzrNK1SREREREREREQ+WBo5JiIiIgDohqKIiIhI4qQ4zzolx0RERATQcHsRERGRxEpxnnWaVikiIiIiIiIiIh8sjRwTERERQHcURURERBIrxXnWKTkmIiIigIImERERkcRKcZ51mlYpIiIiIiIiIiIfLI0cExEREUBPMRIRERFJrBTnWafkmIiIiAAabi8iIiKSWCnOs07TKkVERERERERE5IOlkWMiIiISSXcURURERBInxXlWvVJyzMPDI9YHsLGxYeTIkbHeXkRERN4ODbf/MCnOExERSfwU51n3SskxLy8vbGxsMBqNr30ABU0iIiIiCZfiPBEREfnQvVJyrEePHm+6HyIiIvKO2eqG4gdJcZ6IiEjipzjPOiXHREREBNBw+w+V4jwREZHET3GedfFWkD88PJywsDAcHBzia5ciIiIikgAozhMREZH4tHnzZhYuXMjFixexs7OjWLFidO/enSJFirzyPrZv386iRYs4c+YMAB9//DFt2rShYcOG2NravlZ/Xq/1Cy5cuMCgQYOoXLkyhQsXZvjw4QAMGzaMBQsWxKp2hYiIiLwbtjY28fYj7z/FeSIiIolHQorzZs2aRZ8+fbh79y7u7u7UqlWLQ4cO0bJlS/bu3ftK+5g8eTLdu3fnypUrNGzYkAYNGuDv78+gQYP44YcfXrtPsR45tmbNGjw8PAgNDTUvMwVJR48eZfXq1Zw6dYpJkyZp+J6IiMh7QJ/XYqI4T0REJHFJKJ/XFy9eZOrUqRgMBlatWkWKFCkAaNOmDS1btmTo0KH8+eefJEuWLMZ9HDx4kNmzZ1OwYEEWLFhA2rRpAejTpw9NmjRh/vz5tGjRgo8++uiV+xWrkWNnzpxh2LBh2Nra0q1bN1atWmWxvkuXLqRNm5ZNmzaxZcuW2BxCRERERN4BxXkiIiLypixatIiIiAi+/vprc2IMwNnZmWbNmuHv78+OHTus7mPevHkAjBs3zpwYA0iTJg3ffPMNbm5uPHz48LX6Favk2C+//EJ4eDhTp06ld+/eFC1a1GJ9o0aN+OWXXwDw9PSMzSFERETkLbONxx95fynOExERSXwSSpx34MABACpUqBBlXfny5QHYv39/jNsHBwezf/9+PvnkEwoUKBBl/Weffcbo0aNfq3YZxHJa5eHDhylUqBBVqlSJsU3hwoVxdXXl0qVLsTmEiIiIvGUJuVbY5cuXadKkCR999BFr1661WBcREYGnpycrV67k2rVrODg4ULZsWXr37k2ePHmi7Cs4OJhFixaxZs0abt26haOjI1WrVqVXr15kzpw5SvvAwEDmzp3Ln3/+iZ+fHxkyZKBOnTp0794dR0fHKO3/++8/pk+fjre3N3fu3CF79uw0aNCAzp07Y29vH6X91atXmTZtGocPH+bhw4fkzp2b5s2b06pVq9cuJhsfFOeJiIgkPgkhzgsNDeXmzZukT5+e1KlTR1mfO3duIDLui8k///xDWFgYTk5O3Lp1i2nTprF3714ePXpEvnz56NChAw0bNnztvsUqORYQEECJEiVe2i5TpkycOnUqNocQERERASAsLIxvv/2Wp0+fRrt++PDheHl5YTAYaNWqFX5+fmzZsoU9e/awfPlyi7uKYWFh9OjRgz179lC8eHFq1KjBpUuX8PLyYvfu3Xh5eZE1a1Zz+6CgIDp06MDp06epVKkSderUwdfXlwULFuDt7c3KlStJlSqVub2/vz8tW7bk33//pXbt2uTKlYsDBw4wdepUfHx8+PXXX0maNKm5/YULF2jTpg1Pnz6lXr16pE+fnp07dzJq1Ch8fX2ZOHHiGzij1inOExEREWtq1KhhdX1M0yIDAgIwGo2kSZMm2vWmhNmjR49i3Le/vz8Ad+7coXHjxqRNm5a6desSFBTE9u3bGTBgAJcvX6Zv376v8lLMYpUcS5cuHdeuXXtpuytXrpAuXbrYHEJERETesoRSqPVF06dPjzEJs2fPHry8vKhYsSJz5swhSZLI0KZRo0Z07tyZIUOG8Pvvv5vbe3l5sWfPHpo2bcrYsWPNyz09PfHw8GDMmDFMmzbNvHz27NmcPn2anj170qNHD/PyyZMnM3v2bKZPn86gQYPMy8ePH8+tW7eYMGECjRo1AiJHtg0cOJB169axcuVK2rZta27v4eFBYGAgixcvpnTp0kBkMdkvv/yStWvXUrduXapXrx6Hs/f6FOeJiIgkPgkhzgsLCwOwuFH4PNMI++Dg4Bj38fjxYwB8fHyoWrUqU6dOxcHBAYAbN27g5ubG7NmzqV69epTSENbEaqx+qVKlOH/+PN7e3jG22b17N//8888r3XkUERGRdy8hPeLb5NixY8ydO5eaNWtGu37hwoUA9O7d25wYA6hUqRJVq1bl9OnTHD9+3KK9ra0t/fr1s9iPu7s7BoOB7du3m+9IhoSEsHz5ctKkSUOXLl0s2nfv3p106dLx22+/ERISAmAesZY/f35zYgzA1taWQYMGYWdnx7Jly8zLjx49yvHjx6lcubI5MQaRgeHAgQMBLNq/LYrzREREEp/4jPN27Nhh9ScmpiTW80/Dfp4ppnq+UP+L7OzszP/9/fffm/cJkCtXLjp37gzAunXrXv3kEMvk2JdffomtrS09e/Zk4cKFXLhwwbzu0aNH/PHHHwwYMABbW1vat28fm0OIiIjIB+7x48cMGDCAjz76KEoyCyLvPh4+fJg0adLg4uISZb2p0KupqOu///7L1atXMRgMZMyYMdr2ERERHDx4EABfX18eP35MyZIlo9QKs7e3p1SpUjx69AhfX18g8rHiERERlCtXLsq+M2TIQIECBbhy5Qp+fn7A/wrSmorPPq9QoUKkTZsWHx8fwsPDYz5Jb4DiPBEREXkTHB0dsbOzi3HaZGBgIEC09chMTOUsMmfObFEKw6Rw4cIArzQK/nmxSo4VLFiQESNGEBISwoQJE2jYsCE2NjasX7+e0qVLM2TIEB4+fMg333yDq6trbA4hIiIib5mNjU28/cSHcePGcfv2bSZOnGhxV9Dk1q1bhISEkDt37miP+WJR1ytXrgDw8ccfR3u8XLlyxaq9qV1s9x/dQwNM7UNCQrh582a0698UxXkiIiKJT0KI85ImTUquXLm4d++eeXrk865fvw7AJ598EuM+8ubNC/xvlNmLTFM3kydP/lp9i/UjkNzd3Vm2bBlVq1YlefLkGI1GjEYjSZMmpWzZssyfP58vvvgitrsXERGRtyyhPOIbIgu5enl50bVr12hHhQE8ePAA4JWLur6svWn5i+3Tpk1rtb3pLmds27+sP6b2b5PiPBERkcQlocR5ZcqUwWg0mkfQP2/fvn1AZImHmOTKlYucOXMSEBAQbU3aEydOAODs7Pxa/YpVQX4TV1dXZs2ahdFo5MGDB0RERJAuXTqLOaAiIiLy4YntU4wA7t69y7BhwyhcuDDdunWLsd3rFnU11bd4cYrky9q/6v5ftz8va29abq0o7ZukOE9ERETim5ubG56enkyZMoUyZcrg6OgIwLlz51i9ejVZs2aNsdasSdu2bRk3bhyjR49m3rx5pEyZEogclb9gwQKSJUtmUf/1VcQpOWZiY2ND+vTp42NXIiIi8o7EZyH9uBg2bBiPHz9m4sSJFkX2X/S6RV2TJUtmsfxV27/u/l/W3hTAvay9abmp/buiOE9EROT9l1DiPBcXFzp27Mj8+fOpX78+devWJSgoiA0bNhAWFsbYsWPNNxQDAwNZtGgRAD179jTvo127dhw+fJjt27dTr149atWqRVBQEH/++SdPnjxh5MiRZM+e/bX6Fafk2NmzZ1m2bBmHDh3C39+fpEmTkj17dsqXL0+bNm3MtTVEREQk4YvPR3xbGxlmzcqVK9m1axeDBw8mX758Vtuapi++alHXF6dNvujhw4fRto9pWmNs25vukL5u+7dNcZ6IiEjiEZ9xXlwNHDiQvHnzsnz5cpYvX07KlCkpXbo0PXr0oEiRIuZ2gYGBTJ8+HbBMjtna2jJ16lS8vLzMP0mTJqVo0aJ07tw52ocjvUysk2O//vorkydPJiIiAqPRCETeEf3nn3/4559/8PT0ZPz48dSpUye2hxAREZEPzMaNG4HIYvzjxo2Lsv7cuXM4OTmRI0cOtm/fTrJkyczFW1/0YlFXU7ItpvY3btx4J+1Ny6NrnzJlSrJlyxbt+jdJcZ6IiIi8SW5ubri5uVltkzNnTs6fPx/tOjs7O1q0aEGLFi3ipT+xSo55e3szadIkkiRJQuvWralZsyY5cuTAaDRy8+ZNNmzYwB9//EH//v3JnTv3axdCExERkbcvIQy3b9y4MaVLl46yPDAwkMWLF5MxY0ZatGiBo6Mjtra2lCxZEm9vb86dO0eBAgUstnmxqGvmzJnJkycP586d4/79+1GmCu7btw9bW1tKlCgBQKFChXB0dOTIkSOEhoZa1AYLCQnBx8eHlClTUrBgQfNxbG1toy0we+/ePc6dO0fevHnJkCEDEFmQFmD//v20bt3aov2pU6cICAigUqVKb73Gl+I8ERGRxCchxHkJWaySYwsWLMDGxoapU6dSvXp1i3W5c+emfPnylC5dmkGDBjFr1iymTp0aL50VERGRNychhExNmjSJdvnNmzfNybHnh9W7u7vj7e3NhAkTmDNnjrlGxd69e/nrr78oUqQIRYsWtWg/YcIEJk6cyLhx48xTDDw9Pblw4QKffvopmTNnBiIL6Dds2JClS5cya9YsevXqZd7PjBkzCAgI4MsvvzTXRcuSJQtVqlRh165drF69mqZNmwIQERHB+PHjCQ8Pp23btuZ9uLq6YjAY2LlzJ/v376d8+fJAZOJt4sSJABbt3xbFeSIiIolPQojzErJYJcd8fX1xdXWNEjA9r1GjRixZsoRDhw7FunMiIiIi1tSpU4c6deqwdetWGjZsSPXq1fH392fz5s2kSpWKUaNGWbRv27Ytf/75J3/88QcXL16kbNmyXLlyhe3bt5MtWzYGDRpk0b5Xr154e3szY8YMjh49iouLC76+vhw8eBBnZ+coT9McOnQovr6+DB06lJ07d5InTx4OHDjAqVOnqFixYpTpA2PGjKF9+/Z06dKFunXrkiVLFnbu3Mnly5dp0qQJVapUeTMnzgrFeSIiIvKhsY3NRmFhYea7qtbkzJnznT1+XERERF6PrY1NvP28TT/99BPffvstNjY2LF68mIMHD1KrVi1WrVoVZapl0qRJmT9/Pl27diUgIICFCxdy5swZ3NzcWLVqFVmzZrVonyZNGlasWEHr1q3Njwe/desWnTp1YtGiRaRKlcqifa5cufD09KRBgwYcO3aMxYsX8+TJE/r06cOMGTMspmYCFClShJUrV1K1alX27NnDsmXLSJo0KcOHD2f06NFv5oS9hOI8ERGRxOd9jfPeFhujqcrqa2jXrh1Xr15l+/bt5ukLLzIajdSuXZsMGTKwcuXKOHf0bbKplfNdd0HkvWbcdhOAZ+FP3nFPRN5vyexSvNXjtd7ydbzta1ndmfG2L3m7FOeJiDWK80Tiz9uM9RTnWRerkWO9e/fm/v37DBo0KMbHoY8bN45bt27x9dfx9wsQERERkTdLcZ6IiIh8aF6p5piHh0eUZTlz5mTz5s3s27eP6tWrkytXLpIlS4a/vz/e3t5cvnyZYsWKcfnyZSpXrhzvHRcREZH4ZZNIh8mLdYrzREREEj/Feda90rTKAgUKYGNjw8uaRtfGxsaGs2fPxq2Xb5mG24vEjYbbi8SPtz2tst2fPeJtX4trT4+3fcmbpThPRF6H4jyR+PM2Yz3Feda90sixHj3i7ySKiIiISMKhOE9EREQ+dEqOiYiICAAabP9hUpwnIiKS+CnOs+6VkmMiIiKS+CXWR3OLiIiIfOgU51kXp+TYyZMnuXHjBiEhIRbLIyIiCA4O5t69e+zevRsvL684dVJERERE3i7FeSIiIvKhiFVy7OnTp3Tp0oUjR45YbWc0GvVEBBERkfeE7igKKM4TERFJjBTnWWcbm43mz5/P4cOHsbOzo2DBgmTPnh0bGxvKlCmDs7MzdnZ2GI1G8uTJw5QpU+K7zyIiIvIG2NjYxNuPvL8U54mIiCQ+ivOsi1VybPv27djY2LBo0SJWr15Nr169MBqNDBkyhN9//50dO3ZQuHBhbty4Qc6cely2iIiIyPtCcZ6IiIh8aGKVHLt+/TpFihShRIkSABQuXBij0cixY8cAyJIlCz///DNGo5GFCxfGW2dFRETkzbG1sYm3H3l/Kc4TERFJfBTnWRermmPBwcFky5bN/O+PPvoIW1tb/vnnH/OynDlz4urq+tJ6FSIiIpIwJM5QR16X4jwREZHER3GedbEaOZYmTRoePXpk/neSJEnIkiULFy9etGiXMWNG7t69G7ceioiIiMhbozhPREREPjSxSo4VKFCAo0ePcv/+ffOyPHnycOrUKYvHfV+/fp3kyZPHvZciIiLyxmm4vYDiPBERkcRIcZ51sUqOffbZZzx9+pTmzZuzadMmACpUqEBQUBAeHh5cunSJ2bNnc/bsWT766KN47bCIiIi8GQqaBBTniYiIJEaK86yLVXKsUaNGVKhQgRs3brB582YA3NzccHR0ZN26dXz++edMmTIFGxsbWrZsGa8dFhEREZE3R3GeiIiIfGhiVZDfzs6OX375hdWrV5uXpU6dml9++YVBgwZx9epV7O3tadOmDY0bN463zoqIiMibY5NI7wTK61GcJyIikvgozrMuVskxAFtbW9zc3CyWubq6smXLFu7du0eqVKlwcHCIcwdFRETk7YjVcHJJlBTniYiIJC6K86yLdXLMmgwZMryJ3YqIiIjIO6Y4T0RERBKbV0qOrV+/Pk4HqV+/fpy2FxERkTdPw+0/TIrzREREEj/Feda9UnLs22+/jdOJVNAkIiKS8CXWpw+JdYrzREREEj/Feda9UnKsVKlSb7ofIiIiIvIOKM4TERGRD90rJceWLFnypvshIiIi75juKH6YFOeJiIgkforzrHsjBflFRETk/aNaFCIiIiKJk+I865Qci4Zx28133QWRRCGZXYp33QURERELivNE4ofiPBFJTJQcExEREQBs0R1FERERkcRIcZ51So5F42n443fdBZH3WnK7lAC03vL1O+6JyPttWd2Zb/V4Gm4vH4InYY/edRdE3mspkjgCYPNZ7nfcE5H3n3HT9bd2LMV51tm+6w6IiIiIiIiIiIi8Kxo5JiIiIoCeYiQiIiKSWCnOs07JMREREQHARrUoRERERBIlxXnWxUty7Nq1a9y+fZs0adJQsGBBAgMDSZ06dXzsWkRERETeIcV5IiIiktjFqebYqlWrqF69OnXr1qVTp04sWrQIgJ49e9K1a1fu378fL50UERGRN8/GxibefuT9pzhPREQk8VCcZ12sR44NHz4cLy8vjEYjyZMn5+nTpxiNRgBu377NzZs3ad++PStXriRlypTx1mERERF5M1SLQkwU54mIiCQuivOsi9XIsa1bt+Lp6UmuXLlYuHAhR44csVg/d+5cChUqxMWLF1m1alW8dFRERERE3jzFeSIiIvKhiVVybMWKFdjb2/Prr79StmxZ7OzsLNbnyZOHuXPnkixZMjZt2hQvHRUREZE3ywbbePuR95fiPBERkcRHcZ51sZpWeebMGUqUKEHu3LljbJM+fXpKlCjByZMnY905EREReXs03F5AcZ6IiEhipDjPulil/J49e0aKFCle2i5JkiQ8e/YsNocQERERkXdAcZ6IiIh8aGI1cixbtmycPXsWo9EY45MKIiIiOHfuHFmzZo1TB0VEROTtSKxPH5LXozhPREQk8VGcZ12sRo5VqVKFf//9l7lz58bYZt68efj5+VGpUqVYd05ERETeHpt4/J+8vxTniYiIJD6K86yL1cixzp07s27dOn7++WeOHz9OhQoVALh37x7bt29nx44drF27llSpUtGpU6d47bCIiIiIvDmK80RERORDE6vkWKZMmZg7dy5ff/01u3bt4q+//sLGxob9+/ezf/9+jEYjqVOnZsqUKWTPnj2++ywiIiJvgAq1CijOExERSYwU51kXq+QYQJEiRdiyZQu//fYb+/fv5/bt24SHh5MlSxbKlClD8+bNSZ8+fXz2VURERN6ghFSL4tmzZyxevJj169dz48YNUqRIQenSpenatSsFChSwaBsREYGnpycrV67k2rVrODg4ULZsWXr37k2ePHmi7Ds4OJhFixaxZs0abt26haOjI1WrVqVXr15kzpw5SvvAwEDmzp3Ln3/+iZ+fHxkyZKBOnTp0794dR0fHKO3/++8/pk+fjre3N3fu3CF79uw0aNCAzp07Y29vH6X91atXmTZtGocPH+bhw4fkzp2b5s2b06pVK2xt383j0hXniYiIJC4JKc5LiGyMRqPxXXcioXka/vhdd0HkvZbcLiUArbd8/Y57IvJ+W1Z35ls93pi/R8fbvoaWGBbrbUNCQujYsSNHjhyhUKFClClThvv377N582bCw8OZPn061apVM7cfNmwYXl5eGAwGKleujJ+fH1u2bMHBwYHly5dbJNPCwsLo1q0be/bsoXjx4pQsWZJLly6xY8cOMmfOjJeXl0WR+aCgINq1a8fp06epVKkSzs7O+Pr6cvDgQfLnz8/KlStJlSqVuf3/tXffcVWW/x/HXwdkCCJOBE1UVHCEe2u5R0OzVIzc38rMXWZqZmZ+c+W3frlHQzPN3JbmSKxcuDUUxT3AxAEqoAgcOL8/iJMnEBERjvB++uAPr/tzX/d1bvPhp8+5xpUrV/D39+fy5cu0adOG0qVLExgYyNGjR2nQoAFfffUVdnZ25viTJ0/SvXt3YmNjeeGFFyhSpAhbt27l3LlzvPTSS0yZMiXT71HSdscYndNDEHmiOeVL/lLA8LxnDo9E5Mln+uVitj3LWvI8a5XpmWMiIiKSu9hk7pyeLLdo0SL2799Phw4dmDJlivmbzu7du+Pv78/YsWN55plnyJcvH9u2bWP58uU0adKEuXPnki9fcmrTsWNH3nzzTT744ANWrVpl7nv58uVs27aNTp06MWHCBHP7smXLGDNmDJ9++inTp083t8+ZM4fg4GAGDRrEwIEDze1ffPEFc+bMYcaMGYwcOdLcPmnSJC5dusTkyZPp2LEjkDyzbcSIEfz0008sXbqUHj16mOPHjBlDVFQU3333HfXq1QNg6NChvPHGG6xdu5Z27drRokWLLHy7IiIikhdZS55nrTJVHBszZkyGYw0GA5988klmHiMiIiLZyFqm258/f55ChQoxaNAgizH5+vpSoUIFjh8/zqVLlyhTpgwLFiwAYMiQIebCGMAzzzxDs2bN+O233zh8+DA1atQAYMGCBdjY2PDuu+9aPNPPz49FixaxZcsWrly5QokSJYiPj2fJkiW4urrSt29fi/gBAwbw448/smLFCt59913s7e3NM9YqVqxoLowB2NjYMHLkSNavX8/ixYvNxbGDBw9y+PBhmjZtai6MAdjb2zNixAheeeUVFi9enO3FMeV5IiIiuY+15HnWKlPFseXLl2MwGLjfisyUl24ymZQ0iYiIyEMZP34848ePT9UeGxvLpUuXyJcvH4ULF8ZoNLJv3z5cXV3x9fVNFd+4cWN+++03du3aRY0aNbh8+TLnz5+nUqVKFCtWLM34kydPsnv3bl566SWCgoK4ffs2LVu2TLVXmL29PXXr1mXz5s0EBQVRp04ddu/eTVJSEg0bNkzVd9GiRalUqRLBwcGEh4fj7u5OYGAgAI0aNUoVX7VqVQoVKsTevXtJTEzE1tY2w+/vUSnPExERkbwmU8Wxe5cV3CsxMZGoqCgOHjzI8ePH6dSpE+3atXukAYqIiEj2sNZvFO/cucPRo0f54osviIqK4o033qBgwYJcuHCB+Ph4fHx80hy7p2fyfjhnz54F4Ny5cwCULVs2zeeULl06U/Hnzp2jTp06GYoPDg7m7NmzuLu7m+PTOjQgJf7IkSOEhYVRpkyZNGMeB+V5IiIiuY+15nnWIkuLY/eaOXMms2bNokuXLpl5hIiIiGQzG7IuaWrZsmW61wMCAjLUz/79++nWrZv59/7+/rz33nsA3LhxAwBXV9c07y1YsCAA0dHRGYpPaf93fKFChdKNj4qKeqT4B40nJT67KM8TERHJfbIyz8uNHtuObAMGDMDd3Z1Zs7L3pC0RERHJPWxtbenRowddu3alRIkS/PDDDwwZMoT4+HiMRiOAxemP90pZChkXFwdAQkKCRXtG4zPa/8OO50HxKe0p8dZEeZ6IiIjkJo/1tMqqVauye/fux/kIERERySJZOd0+ozPDHqRmzZrUrFkTgJiYGF5//XU2bdpEjRo1qFu3LvBPEevf4uPjAXBycgLA0dHRoj2j8Q/b/4PinZ2dMxSf0p4Sb22U54mIiDw5tKwyfY/1LM/w8PD7JnwiIiJiXWwMhiz7eRwKFChgXlK5ZcsW8/LFlGWQ/5ayHDFleeW/l03+261bt9KMv9+yxszGu7i4ZCre2ijPExEReXJYe56X0x5LcSwpKYlFixYRFBREhQoVHscjREREJBdKTEwkMDCQzZs3p3k9ZRP8yMhISpUqhaOjIxcvXkwzNqU9JRcpX768Rfu/hYaG5kh8Snta8c7Oznh4eKR5PacozxMREZHcJlPLKtu2bXvfa0ajkRs3bhAbG4vBYMDPzy/TgxMREZHsY7CCjVptbGwYNGgQMTExbNu2DTc3N4vrR48eBZJPhLSxsaFOnTrs2LGDkJAQKlWqZBG7c+dOAPPySzc3N8qVK0dISAiRkZEUKVIkVbyNjQ21a9cGkpcNuri4sH//fhISEiz2BouPj2fv3r04OztTpUoV83NsbGwIDAxM9bkiIiIICQnBy8uLokWLAlC/fn0Adu3aZXHoQMrnvHnzJs888wy2trYP8QYfnfI8ERGR3Mca8jxrlqmZYxcuXLjvz6VLl7hz5w729vb07dtXpxiJiIg8IWwMNln2k1kGg4EOHTpgMpmYNGkSSUlJ5mtXrlxh8uTJQPKplYC5ODN58mSLvcS2b9/O77//TrVq1ahevbq53c/PD6PRyJQpUzCZTOb2ZcuWcfLkSdq2bWsuyNnb2/PSSy8RERHB7NmzLcY5c+ZMbt68ib+/P/nyJX/XWKJECZo2bcrx48dZuXKlOTYpKYlJkyaRmJhIjx49zO01atTA29ubrVu3smvXLnN7fHw8U6ZMAbCIzy7K80RERHIfa8jzrJnBdG9mmEF79+697zUbGxucnJzw8vIybzT7pIlNvJ3TQxB5ouW3Td48utvG/jk8EpEn2+J22XsS4JdBn2dZX0OqvZvpe6OioujWrRsnT57Ex8eHRo0acfPmTbZs2UJ0dDT9+vXjnXfeMccPHjyYTZs24eXlRYsWLbhy5QobNmwgf/78fP/99xYzyhISEujRoweHDh3C19eXBg0acO7cObZs2YKHhwdLly7F3d3dHH/r1i38/Pw4f/48DRs2xNfXl6CgIHbv3k3lypX5/vvvKVCggDk+NDSUrl27EhkZScuWLSlXrhyBgYEcPXqUJk2aMGfOHIsZaEFBQfTq1YuEhATatWtHiRIl2Lp1K2fPnuWVV15h4sSJmX6PmZXb87w7xrT3nBORjHHKl7wPouF5zxweiciTz/RL2lsxPA7WkudZq0wVx3bs2MHTTz9t3gg3t1FxTOTRqDgmkjWyuzg27cgXWdbXYN93HhyUjtu3bzNv3jw2btzIpUuXcHR0pFq1avTq1YumTZtaxBqNRhYsWMCqVasIDQ3F1dWVOnXqMGjQIPO+Xve6c+cOc+fOZf369YSHh1O8eHEaN27MoEGDKFGiRKr4yMhIZsyYQUBAABEREbi7u9O6dWv69etn3lT/XmFhYUybNo0dO3YQExNDqVKl6NChA3369EmzoHTixAmmT5/O3r17iY+Px9PTk65du/Lqq69m+5JKyP15nopjIo9GxTGRrJOdxTFryvOsUaaKY82bN8dgMLB169bHMaYcp+KYyKNRcUwka2R3cWz6kf/Lsr4G+Q7Nsr4ke+X2PE/FMZFHo+KYSNbJzuKYteV5GzZsYMGCBZw+fRpbW1tq1qzJgAEDqFatWqb627t3L7169aJ58+bMmvXwOXSmFotev36dqlWrZuZWEREREbFiyvNERETkcZo9ezZDhw7l+vXr+Pn50bp1a/bs2YO/vz/bt29/6P6io6MZMWKExV61DytTp1WWKVOGs2fPZvqhIiIiYn1sDDrFSJTniYiI5EbWkuedPn2aadOm4e3tzY8//oiTkxMA3bt3x9/fn9GjR7N58+aH2tv0k08+4a+//nqkcWVq5tjHH39MeHg4/fr1Y8+ePURHa3q6iIjIk86Qhb/kyaU8T0REJPexljxv4cKFJCUl0b9/f3NhDKBy5cp07tyZK1euEBAQkOH+fvnlF3766SdatWr1SOPK1MyxqVOn4uLiwh9//MEff/wBJJ9eZLhPJfLo0aOZH6GIiIiIZBvleSIiIvK4BAYGAtC4ceNU1xo1asSiRYvYtWsXL7zwwgP7unLlCuPGjaN+/fr06NGDLVu2ZHpcmSqOHT58OFVbYmJipgchIiIiOc9apttLzlKeJyIikvtYQ56XkJBAWFgYRYoUoWDBgqmue3omH/SRke0dTCYTo0aNwmg0MmnSJEJDQx9pbJkqjj3MFDcRERF5MhgMmdptQXIZ5XkiIiK5T1bmeS1btkz3+v1yiZs3b2IymXB1dU3zekrBLCNbOixatIidO3cyceJESpYsmT3FsZ49e9K4cWPeeustAEqVKvVIDxURERER66A8T0RERLKD0WgEwM7OLs3r9vb2AMTFxaXbz+nTp5k6dSqtWrXilVdeyZKxZag4tnfvXtzd3bPkgSIiImKdtJF+3qQ8T0REJPfLyjwvs7PMHRwcgOTllWmJj48HsNio/98SEhIYPnw4zs7OfPLJJ5kaR1oytaxSREREch9r2ItCRERERLKeNeR5Li4u2Nra3nfZZFRUFECa+5GlmDZtGseOHWPmzJkULVo0y8am4piIiIiIiIiIiDxWdnZ2lC5dmgsXLnD79m2cnZ0trl+8eBGAChUq3LeP9evXAzBgwIA0rwcEBODj40O9evVYtGhRhsem4piIiIgAYLCCbxRFREREJOtZS55Xv359zp8/T2BgIK1atbK4tnPnTgDq1q173/t79uyZ5syzS5cusXr1asqVK8cLL7zw0HuoZrg4FhERwb59+x6q8xTpfTARERGxDjbacyzPUp4nIiKSu1lLntelSxeWLVvGl19+Sf369XFxcQEgJCSElStX4u7unqpodq/evXun2b5nzx5Wr16Nl5cXgwYNeuhxZbg4tmvXLnbt2vXQDzAYDBw7duyh7xMRERGR7KE8T0RERLKDr68vffr04ZtvvqF9+/a0a9eOmJgY1q1bh9FoZMKECeZTK6Oioli4cCFApgpeDyPDxTGTyZSpB2T2PhEREcle1jLdXrKf8jwREZHczZryvBEjRuDl5cWSJUtYsmQJzs7O1KtXj4EDB1KtWjVzXFRUFDNmzACsqDjWoUMHpkyZ8jjHIiIiIjnIYLDJ6SFIDlGeJyIikrtZW57XpUsXunTpkm7MU089xYkTJzLUX/369TMcmxbrejsiIiIiIiIiIiLZSKdVioiICGA9G7WKiIiISNZSnpc+FcdEREQEsK69KEREREQk6yjPS5+WVYqIiIiIiIiISJ6VoZljAwcOxMfH53GPRURERHKQQdPt8yTleSIiIrmf8rz0Zbg4JiIiIrmbptvnTcrzREREcj/leenTskoREREREREREcmztCG/iIiIADrFSERERCS3Up6XPhXHREREBACDQRPKRURERHIj5Xnp09sREREREREREZE8SzPHREREBNApRiIiIiK5lfK89Kk4JiIiIoBOMRIRERHJrZTnpU/LKkVEREREREREJM/SzDEREREBNN1eREREJLdSnpc+FcdEREQE0HR7ERERkdxKeV76tKxSRERERERERETyLM0cExEREQBsNN1eREREJFdSnpc+FcdEREQE0HR7ERERkdxKeV76tKxSRERERERERETyLM0cExEREQAM+s5MREREJFdSnpc+FcdEREQE0HR7ERERkdxKeV76VDoUEREREREREZE8SzPHREREBACDTjESERERyZWU56VPxTEREREBwEbT7UVERERyJeV56dOyShERERERERERybM0c0xEREQA65puHxMTw/z589m8eTNhYWHky5ePihUr0qVLF7p06WIRGxcXx8KFC1mzZg2XLl3CxcWFZs2aMXjwYNzc3FL1HRUVxbx589i8eTPh4eEULVqUtm3bMmDAAFxcXFLFX716lRkzZrBjxw6uXbtGyZIl6dChA2+++Sb29vap4s+fP8/06dPZt28ft27dwtPTk65du/Laa69hY5P6e8mjR48yY8YMjhw5wu3bt6lYsSI9e/akffv2j/AGRURERP5hTXmeNVJxTERERADrOcUoKiqK1157jVOnTlGpUiVeffVV7t69S0BAAB9++CEHDx5k4sSJABiNRgYOHMi2bduoVasWLVu25MyZMyxfvpw//viD5cuX4+7ubu47JiaG3r17ExwczDPPPEPbtm0JCgri22+/ZceOHSxdupQCBQqY469cuYK/vz+XL1+mTZs2lC5dmsDAQKZNm8bevXv56quvsLOzM8efPHmS7t27ExsbywsvvECRIkXYunUr48ePJygoiClTplh81sDAQPr27Yu9vT0vvPAC+fPnZ+PGjbz33nucOXOGoUOHPt6XLSIiInmCteR51krFMREREbEqM2fO5NSpU/j5+TFu3DjzbKvhw4fj7+/PqlWraNeuHU2bNmX58uVs27aNTp06MWHCBHMfy5YtY8yYMXz66adMnz7d3D5nzhyCg4MZNGgQAwcONLd/8cUXzJkzhxkzZjBy5Ehz+6RJk7h06RKTJ0+mY8eOACQlJTFixAh++uknli5dSo8ePczxY8aMISoqiu+++4569eoBMHToUN544w3Wrl1Lu3btaNGiBQDx8fF88MEH2NrasmzZMsqXLw/AgAED8Pf3Z+7cubRr145KlSpl8RsWERERkXtpzzEREREBwIBNlv08ivXr12MwGBg+fLjFMsSCBQvy5ptvArBlyxYAFixYgI2NDe+++65FH35+fnh7e7NlyxauXLkCJBejlixZgqurK3379rWIHzBgAIULF2bFihXEx8cDEB4ezsaNG6lYsaK5MAZgY2PDyJEjsbW1ZfHixeb2gwcPcvjwYZ599llzYQzA3t6eESNGAFjEb9q0ib/++ouXX37ZXBhL+ZxDhgwhKSmJJUuWPPwLFBEREfkXa8nzrFXu/FQiIiLy0AwGQ5b9ZFZiYiJ9+/ZlyJAhFCxYMNX1lD2+bt++zeXLlzl//jze3t4UK1YsVWzjxo1JSkpi9+7dAAQFBXH79m3q1KmTaq8we3t76tatS3R0NEFBQQDs3r2bpKQkGjZsmKrvokWLUqlSJc6dO0d4eDiQvEQSoFGjRqniq1atSqFChdi7dy+JiYkW8Y0bN04Vn9JHSoyIiIjIo7CGPM+aqTgmIiIiVsPW1paePXvy9ttvp3l948aNAPj4+HDu3DkAypYtm2Zs6dKlATh79ixAhuNT4jLbf7ly5e4bHx8fT1hY2AP7L1CgAEWKFCEsLMw8k01EREREHg8Vx0RERAQgCyfbP55vFLds2cKmTZtwcnLi5Zdf5saNGwC4urqmGZ/SHh0dDWCOL1SoULrxUVFRjxT/oPFkNL5gwYIkJSURExOT5nURERGRjLL2PC+naUN+ERERAbL2FKOWLVumez0gIOCh+tu5cyfDhg0DYOzYsbi5uZGQkACQaolkipT2uLg4AHP8vadLphdvNBqzND6l/WH718wxEREReVS5dTlkVtHMMREREbFqa9eu5a233uLu3bu899575s3xHR0dgfsXj1LanZycLOJTimRZFe/s7Jyh+JT2jMb/ezwiIiIi8nho5piIiIgAYMjCafIPOzMsLSaTic8//5x58+Zha2vLuHHjePXVV83X/71s8t9u3boFYN7Y/9/LGrMq3sXF5ZHjixcvnio+KioKg8FAgQIF0uxPREREJKOyMs/LjTRzTERERADrOsUoPj6ewYMHM2/ePJycnJg9e7ZFYQygfPnyAFy8eDHNPkJDQwGoUKFCtsantKcV7+zsjIeHxwPjY2JiiIyMpHz58tjYKF0TERGRR2NNeZ41UrYlIiIiVsVoNDJgwAA2b96Mu7s7P/zwA02bNk0V5+bmRrly5QgJCSEyMjLV9Z07d2JjY0Pt2rUBqFq1Ki4uLuzfvz/VUsb4+Hj27t2Ls7MzVapUAaBu3brY2NgQGBiYqu+IiAhCQkLw8vKiaNGiANSvXx+AXbt2pYo/evQoN2/epFatWtja2j4wfufOnQDUqVPnPm9JRERERLKKimMiIiICkIVnGD1aejF9+nS2bduGu7s7S5cupVKlSveN9fPzw2g0MmXKFEwmk7l92bJlnDx5krZt2+Lm5gYkb3D/0ksvERERwezZsy36mTlzJjdv3sTf3598+ZJ3nShRogRNmzbl+PHjrFy50hyblJTEpEmTSExMpEePHub2GjVq4O3tzdatWy0KXvHx8UyZMgXAIr5FixYUK1aM5cuXc+LECXN7VFQU06ZNw2Aw0L1794d6dyIiIiJpsZY8z1oZTPdmkgJAbOLtnB6CyBMtv23yZtPdNvbP4ZGIPNkWt5uVrc/74/LmLOurqUebTN139epVWrZsSXx8PM2bN6dq1appxnl5efHCCy+QkJBAjx49OHToEL6+vjRo0IBz586xZcsWPDw8WLp0Ke7u7ub7bt26hZ+fH+fPn6dhw4b4+voSFBTE7t27qVy5Mt9//73FHl+hoaF07dqVyMhIWrZsSbly5QgMDOTo0aM0adKEOXPmWJw2GRQURK9evUhISKBdu3aUKFGCrVu3cvbsWV555RUmTpxo8Tm2bNnC4MGDcXBw4IUXXsDZ2ZmNGzcSHh7OoEGDGDhwYKbeo9zfHWPae9SJSMY45UveN9HwvGcOj0TkyWf6Je2tGx4Ha8jzrJmKY2lQcUzk0ag4JpI18mJxbO3atbz//vsPjGvZsiWzZiW/nzt37jB37lzWr19PeHg4xYsXp3HjxgwaNIgSJUqkujcyMpIZM2YQEBBAREQE7u7utG7dmn79+pk3yb9XWFgY06ZNY8eOHcTExFCqVCk6dOhAnz59zCdO3uvEiRNMnz6dvXv3Eh8fj6enJ127duXVV181L6m81/79+5k1axZ//vknJpOJ8uXL06tXL1588cWMvDJ5SCqOiTwaFcdEso6KY9ZDxbE0qDgm8mhUHBPJGtldHNt2+dcs6+tZj9ZZ1pdIVlJxTOTRqDgmknWyszimPC99+XJ6ACIiImIdcuvpQyIiIiJ5nfK89Kk4Jjlm7eqfGDv641TtBoOBQ8EHCDkWwn/HTeD0qdN4VfDiw7EfUKVqlVTxmzf+yvvvjuDwsYPmtri4OCaOn8SWXwNwdHCkZ58e9OzTI9W9Ik+KEk7F6V2lK96FvIhJuMPmC7+z/vwWAHwKl6dHpS54OJfgyp2rLD6xiuCIfzb3ntdyKs52Thb9/efXd4hLjKOOW3XeqfWWxbW94Qf58vBXADTyqMsrFZ6nqGNhzkeFsShkOWdvXQDuP6tpdtBCdvy1J8s+u4iIZK+4uDgm/ncyAb9uxdHBgR59etCzd9qHQ4QcD+HTcROT87Xy5Rk9dhRVqlZOFffV3K+5eCGUTyZ8DMD+vft5s0+/NPv85dd1eJR051jwcSZ9OoXTJ09ToWJ53hs5jGrVfc1xixZ8z5JFP3Dz5k1q1qrJiA/fp0yZ5NlM8fHxzJw2m42/bCI2NpY6dWsz4oPhlHBPXmodfjmcCeMncXD/IQq6FqRbD3+69XztUV6bSIY42Dkws/94OjV+jtj4OKaunMvnq+enGdu65jN89vpoynuUYXfIIQbM+pCTl86ar4/t9g5vtH0VZwcnNh/axsBZY7gelXx6c8miJfjyrXG0qN6I2Li7/Lj9Zz5YMIW4hDgAypYozfzBk2lYuRYXrl5i6NyP+fXQdnPfzz5dny/f+hjvUl4EnT/OW9NHEXTuOE19G/D75GVpjtezVwNCr/2VVa9KJFupOCY5pu1zbWjcpJH590ajkTf7vMWzzZ4h9k4sA/sN5vkXn+OTCR+z/MeVDOo3hHWbfiK/U37zPVFR0UyZ8Fmqvr/47P84FnyM+d/O5fJflxkzaiweJT1o3bZVtnw2kaxkwMB7tftz9tYFPtg1EXcnNwZW/w834m5yNCKEYbXeZu2Zjey9coiGHnV4t2Y/hm8fR2TcTQo7uOJs58TQP8YQn5hg7jMuMTkxKlXAgwNXg/j66BLztYSk5DifwuV58+nufHV0MSdvnqG1Z1Perz2AIX+MIS4xjv5bR1qM87myLWjgUZsDV//Mhrcij4MBfaMoIvDF1C85dvQ4876Zw+W/LvPRBx/j4eGeKo+KvRPLoH5DeO7F5xj36cesWLaSwW8P5eeNayzytQ3rNzJn5jyef/E5c1v1GtX59feNFv29P2wUhQq54lHSnciISN56/W3atG3NuP+OZef2nbz9xgBWrF2GR0l3flm3gXlzvmLClP/iWcaTuTPnMXTAO6z6eQUGg4HZM+by25bf+HTyeAoXLsyXn09j2JDhLFq6EIPBwPvDRuFR0p3Fyxdx9sxZPnj/QzxKetCiVfPH+3Ilz/vs9Q+oU7EaLUb5U8atFAuHfc6Fq5dYufMXi7gqnt6sH7eAictmsvi3Nbze9lW2TlyKT99m3L57h77PdeP1Nl3pNmUwEdE3mD1gAl8NmULH8W8AsOKDOdyIucUzwztTxKUQ3wz9jMTERN7/ZgIAa8bM58j5EOoMeZGODduyesx8Kr/VgtBrf1G2RGk2fPIdk1fMZsnvaxjeqR9rP/oK7zebsev4Ady71bYY67JRs4iIuqHCmJVTnpe+3HkGpzwRHB0dKVa8mPln/c+/gMnEkHcHs2nDJhwcHXhn+FC8ynvx/qj3cHJ2YvMmy3XSX0z9P57yfMqiLfZOLKtXrmH4qOFUrlKZFq1a0Pv1Xixd8mN2fjyRLOPq4MLFqDC+Df6BK3eu8ef1YIIjTuBduALehcqTZEpi/fktXIuN4Kezm0hISqBCoXIAlCzgzo27N7kWG8Gt+CjzT4qSBdwJi/7L4todY2zyc+0LsubMBnZe3su12AhWn/4FF/sClCqQfPLfvffY29rRtkwz5h/9nljj3ex/SZIlDAZDlv2IyJMp9k4sa1au5f1Rw6hcpRItWjWn13968OMPqWeKbNq4GQdHR955bwhe5csxfOQwnJyd+HVT8sxmo9HIp59MZNyY8TxVupTFvXb2dhZ54L69+zl96jRjxn0IwLqf1lOokCsffDSScl5l6d6rGzVq1WD5jysAiImOYei7g3nm2SaUKeNJ79d7cf7cBW5E3gDg5zXrGDCkP3Xq1qZ8BS/GjPuQ4KPHuHgxlKhbURz58whvvvU6Zcp40rxFMxo1bsje3Xsf56sVwckhP2+09WfI3I85dOYoawI3MWXFHAa275Uq9u0XurPr+AHGfv85Jy+dZcQ3E7h1J5puzV8G4Pk6zflx289sO7qH4AsnmbJiDi1rNAbA56nyNKxcmz5fvMexiyfZEbyXj77/H6816whA8+qNKO9RhremjyIk9DSTls0k8PhB/tOmKwCDOvRmz4lDfLLk/zj913mGzvuYxKQkKpeuQIIxgSs3rpl/mldriG+ZSrw5bUT2vETJNOV56VNxTKzCrZu3+PbrBQx+dzD29vYEBR2hZq0a5r94BoOBGrVqEHQ4yHzP/n0H2L93P2/0fd2irxMnTmI0GqlRo7q5rUatGhwNOkpSUlL2fCCRLHQzLorpf37N3b9ne3kX8qJSkQocjzxJTMJtXOwLUKdEDQBqu1Unfz5HQmMuAfBUAQ8u37l6375LObsTfp/re68cYu3Z5G/17WzseK5sC27FRXEpJjxVbOcKLxIcccJiOaeIiDx5UvKo6vfkUTVr1eBoUHCqPOrIn0epUau6Zb5WszpBfybna7F3Yjl14hTf/bCAatWr3feZCQlGZk2fzRt9/0PhwoUACAu9ROUqlS1OePX2rmDu28+/C538XgEgOjqGZT8so3wFLwoXKUxSUhL/nfwJDRrWT/WsmOgYHBwdcMzvyNrVP5OQYOT8ufP8eSgIn8o+mXhjIhlX3asKdvnysev4AXPbjuB91Pepmarg4OXuyZ4Thy3ajpwPoWGlWgBERN/ghbotKFm0BI72Dvg368ChM8EAhN+4RtsPu3P15nWL+12dkw9TaOBTk4NnjnInLvafcRzbZ+67mW8DVu36Z2ZnbNxdKrz+DEHnjlv0l882H//tOZxPf5xORNSNzLwSEauhZZViFZYtXY5b8eLm6frXr12nfIXyFjFFixbh9KkzQPI+EuPH/pdRY0ZiZ2dnEXf92nUKFSqEnb2dxb1xcXHcvHmLIkUKP+ZPI/L4fNl0PMXyF+Xg1SPsDT+ECRObL/zOkBpvYDKZsLWxZe6R77h8O7ngVdLZHQcbe0bXG4qHUwkuRIey6PgKc0HMw7kEvsUq08GrLTYGG/aEH2TFqXUkmhLNz6xaxIeRdQcBMCtogXlJZoqijoVpVLIuH++emk1vQR4XG31nJpLnpZVHFSlaNM086vq163hV8LK4v2jRIpw+nZyvuRR0YcHibx74zF83/Up0VAx+/l3+6adYEU6eOGkRFx5+hZs3blq0rVm1lnFjxmNvb8/MedPNsxr+XRhbsugHChUuhLd3Rezs7Rg1egSTPp3CD98vJTExkQ4d2/Nyp44PHKvIo/Ao4sb1W5EkGP/Z6uLKzevkd3CkqEth835hKe2lipawuL90MQ8iY24B8MmSL/n542+4tGgfxkQjlyOv0vDdjgDcuh3F5oPbzPcZDAYGtu9NwOGd5nH8FXHFou8rN67xVDEPILkwdyculmWjZvPs0/UIvniSgbM+4njoKYt7/J55kULOBZm5buEjvhnJDsrz0qe3IznOZDKxeuUaXu3+qrntbuzdVEUve3t7EuLjAZg3ez6Vq1SiUeOGqfq7ezcWe/vU9wLm+0WeVP93aD5TD8yijMtT9KjcGUdbB9ycirHq9Ho+2j2FNWc20LNy8ub8kFwcc7ZzYs2ZDXx+aA7xiQl8UHcIjrYOFHMsgmM+B4xJRqYf/polIato7FGP13xesXhmaMxffLhrEitPreMt3x5UcC1rcb3ZU404e+sCZ26dz6a3II+LptuLyN27dy0KY4A5r/p3HnX37l1zjpXCzt6e+PgEHsbK5at5udNLODo6mttatm7B0SPBrFq+GqPRyK4dgfz+2x8kJBgt7q3foD4/rPielzt35J1Bw7gUdilV/79t/Z1FC75n0NCB5s927uw5nm32DAuXfMu4/45ly+YAflm34aHGLfKwnBzyE2e0/HsUl5D8ewc7y79LP277mS5NXuCFei2xtbGlZ8vO1PWujn2+5P+Gy5Z4ijtxsbz4cR+ajvAj7Pplvnkn7S8qp/znA2qVf5rR3035ZxwJqceRMoYC+Z2Z3GcU247u4bmPehF67TJbJizB2dHygKe+z73GV5uWcjfe8otTsU7K89KnmWOS44KPHuPqlau0e66tuc3ewYGEBMvEKj4+Hsf8jpw+dZqVy1exYk3ap6TY2zukSsri/07m7k26RJ5E56IuAmAXsoL+1XsTlxiPAQOrzyQn9OejQinvWpZ2ZZrz7bGlTN4/A1sbW/Nsr1lB3zKt2afUcvNl1+X99A14j9sJdwC4EB2GwWCgf7XefB+yAhMmAKLio4mKj+ZCdBgVCpWjpecznD5y3jymeiVqEhC6IxvfgoiIPC4ODg4kpMqjkn//7zzK3sHenGOlSIiPx9HRIcPPi4yI5NCBQ4wc/b5Fe4WKFRgzbjRTJkzl008m4lPJG79Xu7Bv736LOI+S7niUdGfEBz4c2HeAn9euo9+Af05h/i3gd0YMG8Wrr/nxSueOAOzZvZfVK9eycet6HB0dqfp0Fa5evcpXc7+2ODRAJKvdjY/DIZ9lESylIHXvEkeATQf+YNyS/2Pl6Dnks8nHb0GBfBewElfnggB8N+wLhn8zgfV7AwDwm9ifCwsCqedTg733LMec1GcUQzu+TtdJAwi+kDwb825CHEX/VehysLM3j8GYaOTnvVuY8fMCAN6cNoLQhbvp0KA1P/y+FoDirkV5pmo9Bs4ekwVvRiTnWWVxLCYmJtP3FihQIAtHItlh145d1Kpdk4KuBc1tbiWKc/16hEXc9esRFCtWjC2bA4i6FcWLbTsAmPe/aFi7MR9+PJpST5Xi5s2bGI1G8uVL/k884noEjo6OuBR0yaZPJZJ1Ctq7ULGQl8UpkJdiLmNnY4enSykuRIdZxF+ICuMpl+Rp8UaTEWPiP9+yJyQZuXYngsIOhQDMhbEUf8WEY29rTwE7Z4rnL0oSSZyPCr3nueHmDfkBijgW5imXkjqhMpfQKUaSHZTnWbfibsXvk0c5pMqj3NzciEgjXytevFiGn7drZyClnipJRe8Kqa699HIHXuzwApGRNyhevBj/N/VLSpZM/vdt3579FHcrRtlyZYHkGRHlvMpZLLvc+Msmxoz6iE5+nXhv5DBz+/Hg43iWKW1R7POp7MPX8x68BFTkUVyKCKeYaxFsbWxJTErewsK9cHHu3I3l5u2oVPETfpzB1JXzcHV24dqtCH4cNYvzV0Ip7loUT7dS/Hn2mDk27PplrkdFUsbtKXNxbFq/cbz9Qg+6fzaEVTv/mRl56Xo4VT29LZ7lXtiNy5HJ225cjrxKSOgZ87UEYwLnr4ZRulhJc1vb2k05dyWUo+e13+yTQnle+qyyOFanTp1MTdUzGAwcO3bswYFiVY4EHaFGrRoWbdWq+fLNVwswmUwYDAZMJhN/HjzM62+9TvMWzSy+1TsSdJTRIz7kx1U/ULRYUWxsbMiXLx9H/jxCzdo1ATh08DBVn66CjY1WEsuTxy1/UYbWfJPBv4/mRlzyPhPlXD25FRfNjbu3KFXAwyLeo0AJrt1J/p+Vz58dx5ozG9h2aTcADrb2uDu78dftK/gWq8yAan0Y/Pto4pOSZwWUKfgU0fExRCfE0KVie4o7FWXy/hnmvsu6lrYollVwLcv12Egi7moTVhHJGOV51s2nks/fedRRatauASTnUVWerpoqj/Kt/jTffrXQMl879Cevv/WfDD/vaNBRqtesnqp93579rFi+kslTJ1K8eDFMJhM7d+yis18nABZ8vRCPku58+PFoABITEzkRcpLX/t6mY8/uvYwZ9RFd/f0sCmOQXAAMvRhKQnyCeZnl+bPnKVnK8kRNkax2+GwwCUYjDSrVYuexfQA0qVqXfaf+xGQyWcS+2rQD9X1q8s68cVy7FYGjvQPNqzWk9+fDiIy+yd34u1TxrMiJsOQiVtGChSnqUphz4cmrDD56bSj9nu/Oq5MGsnLnLxZ97z5xiJF+/XG0dzAviWxStS47gpPHtDvkENW9Kpvj7fLZ4eXuyfmr/3whW9+nBjuPWc7kFHmSWWWl4K233jL/A1uoUCE8PDwy9OPu7v7gzsXqnD51Bq/ylpu5tmrbiujoaKZMnMqZ02eZMnEqsbF3aduuDa6FXPEs42n+cSvhBoBnGU+cnZ3Jnz8/7V96kf+Om8DRI8Fs3fIb3327iNd6+OfExxN5ZGduXeBcVCh9fXtQytmd6sWq4u/zMmvPbuT3sJ3UKFaVdmVaUDx/UdqVaU71YlX4NTR5E9bD147SqcILVC5SkVIFPHjbtzeRd29w+NpRTt04S0JSAm8+3R0PZzeqF6uCv88rrDv3KwBbw3ZQpYgPbcs0p4RTcTpVeIHyrmXZeH6reWxPuZTkUszlHHkvkvW0F4VkB+V51i1/fkdefOkFPv1kAsFHgvkt4HcWLVhkLjpdv3adu3fvAtCqTUuio6P5bNL/OHP6LJ9N+h+xsbG0ads6w887ffoMXl5eqdrLlPVk2+/bWbZ0BWGhYUwcP5moqGjav/QiAF1e7cxPa9axYd1Gzp87z4RPJhEXF0f7l17EaDTy8YefUKtObXq/3ovr166bfxLiE3i22bPky5ePcWPHc+H8Bf74bRvfzP8W/+5ds+ANitxfbNxdFgasYM7ACdSpWI2XGrbhvVf68uXa5FmLJQoXx9E+eVnyyUvn6Pd8N15u1I4KJcuy5P3phF77iw37fyMxKZFvf13O1Nc/5Jmn61G1jDffv/clu08cYv+pICqVrsAY/8FMWj6LHcf2UaJwcfMPwB9HdhN67TLfvvM/qnh6M6JLf+p5V+frzUsB+L+1X9Op8XP0e747FUqWZWb//3I3Po51e7aYP8vTZXw4dvEU8uRQnpc+g+nfJWorsWbNGkaNGkW1atVYsmSJxTHOj1ts4u1se5ZA/ZoN+WL6/2jUpJFF+5Ggo3w6bgLnzp6jondFPhz7AZWqVEp1/769+3mzd18OHztobouNjeXTTyYSsDmAAi4F6PWfnnTv2e2xfxZJlt/WGYBuG/vn8Ehyj0IOrvSu3JWqRX2IS4xj88U/+OnsJgBqFfelc8UXKeFUnMu3r/LDydUERyRPcbezyYdfxQ409KhD/nz5ORZ5km+PLSXy75lepQp40KNSZyoUKsdd4122hu5g1Zl/vl2sWfxp/Lxfwt2pOKExl1l0fDmnbp41X+9T5VWc7PIz889vs/Ft5B2L283K1uftu5Z1e8fVLd4ky/qS3Ccn87w7xuhse9aTKjb2LhM+mUjAr1uT86g+PejW8zUAalatw7j/jqXDy+2B5Jlfn34ykXNnz1PRuwKjx46iUuXU+dpHH3wMwCcTPrZof6V9Z17r4W+eEXav7X/s4Iup/8fly+FUq+bLyA9HUM6rrPn6mlVr+farhVwJv0K16snXvcqXI+jPI/R6rU+an23+t3OoU6/O38W8qQQfCaZw4cJ0fc2P13r459r/6ctKTvmSl9canvfM4ZE8mfI7ODJ7wAQ6NX6OW7ej+WzlXL5c+zUApl8u0vvzd1m4ZQUAvVt34SP/IRQtWJiAwzvpP/NDwm8kL310sHPgvz3f49WmHchv78ivh7YzaPZHXI+KZESX/kzqMzLN56f8uZX3KMPXQz+jvk8NTv91gaHzxhFw+J88oEOD1kzuM4qyJZ5i/6kjvDV9FMcu/nOC7LE5Afzf2m+Yt2HxY3lPeYXpl4vZ9izleemz2uIYwOeff878+fMZNmwYb7zxRrY9V8UxkUej4phI1lBxTHKznMrzVBwTeTQqjolkHRXHrIdVLqtMMWTIEMqWLcvcuXOJikq9QaGIiIhkHUMW/hJ5EOV5IiIi2Ud5Xvqsujhma2vL2LFjadWqFadOaT2ziIjIY2UwZN2PyAMozxMREclGyvPSZZWnVd6rQYMGNGjQIKeHISIiIiJZTHmeiIiIWAOrL46JiIhI9sit0+RFRERE8jrleelTcUxEREQAdEqbiIiISC6lPC99Vr3nmIiIiIiIiIiIyOOkmWMiIiICaLq9iIiISG6lPC99Ko6JiIgIoKRJREREJLeytjxvw4YNLFiwgNOnT2Nra0vNmjUZMGAA1apVy9D9169fZ86cOfz2229cuXIFBwcHqlSpQq9evWjVqtVDj0fLKkVEREREREREJFvMnj2boUOHcv36dfz8/GjdujV79uzB39+f7du3P/D+S5cu8fLLL7No0SLc3Nzo3r07bdq0ITg4mAEDBjBjxoyHHpNmjomIiAigjVpFREREcitryfNOnz7NtGnT8Pb25scff8TJyQmA7t274+/vz+jRo9m8eTOOjo737WPixIlcvXqVwYMHM2DAAHP74MGD6dy5MzNnzqRNmzZ4e3tneFyaOSYiIiJA8nT7rPolIiIiItbDWvK8hQsXkpSURP/+/c2FMYDKlSvTuXNnrly5QkBAwH3vv3PnDr/99huFChWiX79+Ftc8PDzw9/cnKSmJrVu3PtS4VBwTEREREREREZHHLjAwEIDGjRunutaoUSMAdu3add/7ExMTGT58OIMHD8bW1jbVdQcHBwBu3779UOPSskoREREBrG+jVhERERHJGtaQ5yUkJBAWFkaRIkUoWLBgquuenp4AnD179r59uLi40Lt37zSvmUwmNm3aBICPj89DjU3FMREREQGsZy8KEREREcla1pDn3bx5E5PJhKura5rXUwpm0dHRmep/0aJFHDlyBHd394c+sVLFMRERERERERERyZCWLVume/1+e4YZjUYA7Ozs0rxub28PQFxc3EOPafXq1UycOBFbW1smT56c7ob+aVFxTERERADrmG4vIiIiIlnPGvK8lP3AEhIS0rweHx8PYLFRf0bMmzePzz//HBsbGyZPnkyDBg0eemwqjomIiAhgHdPtRURERCTrZWWel95pkulxcXHB1tb2vssmo6KiANLcjywt8fHxjBkzhjVr1uDo6Mjnn3/+wFlt96PimIiIiIiIiIiIPFZ2dnaULl2aCxcucPv2bZydnS2uX7x4EYAKFSo8sK9bt27x9ttvc+DAAYoVK8bs2bOpVq1apsdmk+k7RUREJFcxZOEvEREREbEe1pLn1a9fH5PJRGBgYKprO3fuBKBu3brp9hETE0OfPn04cOAA3t7eLF++/JEKY6DimIiIiPzNWpImEREREcla1pLndenSBYPBwJdffmmxvDIkJISVK1dm6KTJsWPHEhwcTKVKlVi8eDElS5Z8pDGBllWKiIiIiIiIiEg28PX1pU+fPnzzzTe0b9+edu3aERMTw7p16zAajUyYMMF8amVUVBQLFy4EYNCgQQAEBwezbt06AHx8fMzX/6169eo8++yzGR6XimMiIiICaEN+ERERkdzKmvK8ESNG4OXlxZIlS1iyZAnOzs7Uq1ePgQMHWiyPjIqKYsaMGcA/xbE//vjDfH3t2rX3fUbPnj0fqjhmMJlMpof9ILldbOLtnB6CyBMtv23yxordNvbP4ZGIPNkWt5uVrc87eetolvXl7fp0lvUlkpXuGNM+IUtEMsYpnwsAhuc9c3gkIk8+0y8Xs+1ZyvPSpz3HREREREREREQkz9KyShEREQHQRvoiIiIiuZTyvPSpOCYiIiKAde1FISIiIiJZR3le+rSsUkRERERERERE8izNHBMREZG/6RtFERERkdxJeV56NHNMREREgOTp9ln1k9W++OILfHx8iIqKSvP6hg0b6Nq1K7Vr16ZevXq89dZbBAUFpRmblJTE0qVL6dixIzVr1qRBgwYMHTqUc+fOpRkfFxfHvHnzeP7556levTpNmjThww8/5OrVq2nGR0VFMXXqVNq0aUO1atVo3rw5kyZNIjo67VMSr169ykcffUSLFi3w9fWlbdu2zJw5k/j4+Ay8GREREZEHs+Y8zxqoOCYiIiJWbc2aNcybN+++12fPns3QoUO5fv06fn5+tG7dmj179uDv78/27dtTxX/00UeMHTuWxMREXnvtNRo3bsyvv/5Kp06dCAkJsYg1Go0MHDiQ//3vf7i6utKzZ0+qVavG8uXL6dSpE+Hh4RbxMTEx9O7dm/nz5+Pp6UmvXr3w9PTk22+/xd/fn5iYGIv4K1eu8Oqrr7J8+XJ8fX3p1asXBQoUYNq0abz55pskJCQ8wpsTERERkYzQskoREREBrO8UI6PRyLRp05g3bx4mkynNmNOnTzNt2jS8vb358ccfcXJyAqB79+74+/szevRoNm/ejKOjIwDbtm1j+fLlNGnShLlz55IvX3Iq1LFjR958800++OADVq1aZe5/+fLlbNu2jU6dOjFhwgRz+7JlyxgzZgyffvop06dPN7fPmTOH4OBgBg0axMCBA83tX3zxBXPmzGHGjBmMHDnS3D5p0iQuXbrE5MmT6dixI5A8s23EiBH89NNPLF26lB49ejzimxQREZG8ztryPGujmWMiIiICJCdNWfXrUQUGBtK+fXvmzp2Lr68vhQsXTjNu4cKFJCUl0b9/f3NhDKBy5cp07tyZK1euEBAQYG5fsGABAEOGDDEXxgCeeeYZmjVrRnBwMIcPH7aIt7Gx4d1337V4rp+fH97e3mzZsoUrV64AEB8fz5IlS3B1daVv374W8QMGDKBw4cKsWLHCvFwyPDycjRs3UrFiRXNhDMDGxoaRI0dia2vL4sWLM/7SRERERO7DmvI8a6TimIiIiFidtWvXcvXqVYYNG8aSJUssCl/3CgwMBKBx48aprjVq1AiAXbt2Ackz0fbt24erqyu+vr6p4lP6SIm/fPky58+fx9vbm2LFiqUZn5SUxO7duwEICgri9u3b1KlTB3t7e4tYe3t76tatS3R0tHkvtN27d5OUlETDhg1T9V20aFEqVarEuXPnUi3dFBEREZGspeKYiIiIANa1UWvnzp0JCAigb9++2NnZpRmTkJBAWFgYRYoUoWDBgqmue3p6AnD27FkALl26RHx8PJ6enmmO8d/xKRv0ly1bNs3nly5dOlPxKXEP27+IiIhIZllTnmeNtOeYiIiIANa1F0WdOnUeGHPz5k1MJhOurq5pXk8pmKWcEnnjxg2ALItPaf93fKFChdKNTzlx82HjRURERDLLmvI8a6TimIiIiGS5li1bpnv93n3AMstoNALcd2ZZytLGuLi4TMWnnBT57yWSD4p/XOMRERERkcdDxTEREREBnrxvFB0cHIB/ilL/lrLxfcp+ZQ8bn3LCZUp7RuMftv8HxTs7O6d5XURERCSjnrQ8L7upOCYiIiIAWbqHRFbMDHsQFxcXbG1tzcsa/y1lOWLKcsmU5YsZjf/3ssl/u3XrVprx91sGmdl4FxeXNK+LiIiIZFRu3Sssq2hDfhEREXki2dnZUbp0aSIiIrh9+3aq6xcvXgSgQoUKAJQqVQpHR0dz+4Piy5cvb9H+b6GhodkaLyIiIiKPh4pjIiIiAiRPt8+qX9mlfv36mEwmAgMDU13buXMnAHXr1gXAxsaGOnXqcOPGDUJCQh4Y7+bmRrly5QgJCSEyMjLNeBsbG2rXrg1A1apVcXFxYf/+/amWSsbHx7N3716cnZ2pUqWK+Tk2NjZpjj0iIoKQkBC8vLwoWrRoht+HiIiISFqexDwvO6k4JiIiIsCTecR3ly5dMBgMfPnllxbLH0NCQli5ciXu7u60atXK3O7n5wfA5MmTLfYS2759O7///jvVqlWjevXqFvFGo5EpU6ZgMpnM7cuWLePkyZO0bdsWNzc3IHkD/ZdeeomIiAhmz55tMc6ZM2dy8+ZN/P39yZcveVeLEiVK0LRpU44fP87KlSvNsUlJSUyaNInExER69OiRFa9JRERE8rgnMc/LTtpzTERERJ5Yvr6+9OnTh2+++Yb27dvTrl07YmJiWLduHUajkQkTJlicNtm2bVvatm3Lpk2beOmll2jRogVXrlxhw4YNFChQgPHjx1v036NHDzZv3szq1as5ffo0DRo04Ny5c2zZsgUPDw9GjhxpET948GB27NjBzJkzOXjwIL6+vgQFBbF7924qV67M22+/bRE/evRogoKCGD16NFu3bqVcuXIEBgZy9OhRmjRpQpcuXR7fyxMRERERAAyme78GFQBiE1PvWyIiGZffNvlktW4b++fwSESebIvbzcrW5/1150KW9VXSqUyW9QXQokULLl26xL59+8wb2t9r+fLlLFmyhDNnzuDs7Iyvry8DBw6kWrVqqWKNRiMLFixg1apVhIaG4urqSp06dRg0aJB5H7B73blzh7lz57J+/XrCw8MpXrw4jRs3ZtCgQZQoUSJVfGRkJDNmzCAgIICIiAjc3d1p3bo1/fr1M2/Cf6+wsDCmTZvGjh07iImJoVSpUnTo0IE+ffqYT7SUrHPHmPYBCyKSMU75kg8JMTzvmcMjEXnymX5Je9/Rx8Ga8zxroOJYGlQcE3k0Ko6JZI3sL45lXYJW0kn/0yTWScUxkUej4phI1sne4pjyvPRozzEREREREREREcmztOeYiIiIAOTSs4dERERERHle+lQcExEREYBce/qQiIiISF6nPC99WlYpIiIiIiIiIiJ5lmaOiYiIyN/0jaKIiIhI7qQ8Lz0qjomIiAiglElEREQkt1Kelz4tqxQRERERERERkTxLM8dERETkb/pOUURERCR3Up6XHhXHREREBNApRiIiIiK5lfK89GlZpYiIiIiIiIiI5FkqjomIiIiIiIiISJ6lZZUiIiICgEF7UYiIiIjkSsrz0qeZYyIiIiIiIiIikmdp5piIiIgA+kZRREREJLdSnpc+zRwTEREREREREZE8S8UxERERERERERHJs7SsUkRERAAwGDTdXkRERCQ3Up6XPs0cExERERERERGRPEvFMRERERERERERybO0rFJEREQAnWIkIiIiklspz0ufimMiIiLyNyVNIiIiIrmT8rz0aFmliIiIiIiIiIjkWZo5JiIiIoC+TxQRERHJrZTnpU/FMREREQF0xLeIiIhIbqU8L31aVikiIiIiIiIiInmWZo6JiIjI3/SNooiIiEjupDwvPSqOiYiICKCUSURERCS3Up6XPi2rFBERERERERGRPEszx0RERORv+k5RREREJHdSnpceFcdEREQE0ClGIiIiIrmV8rz0aVmliIiIiIiIiIjkWSqOiYiIiIiIiIhInqVllSIiIgKAQXtRiIiIiORKyvPSp5ljIiIiIiIiIiKSZ2nmmIiIiPxN3yiKiIiI5E7K89Kj4piIiIgASplEREREcivleenTskoREREREREREcmzNHNMREREADAY9J2iiIiISG6kPC99Ko6JiIjI35Q0iYiIiOROyvPSo2WVIiIiIiIiIiKSZ2nmmIiIiAD6PlFEREQkt1Kelz4Vx0RERORvSptEREREcifleenRskoREREREREREcmzNHNMREREAJ1iJCIiIpJbKc9Ln2aOiYiIiIiIiIhInqXimIiIiIiIiIiI5FkGk8lkyulBiIiISM67m3gny/pytHXKsr5ERERE5NEoz0ufimMiIiIiIiIiIpJnaVmliIiIiIiIiIjkWSqOiYiIiIiIiIhInqXimIiIiIiIiIiI5FkqjomIiIiIiIiISJ6l4piIiIiIiIiIiORZKo6JiIiIiIiIiEiepeKYiIiIiIiIiIjkWSqOiYiIiIiIiIhInqXimIiIiIiIiIiI5FkqjomIiIiIiIiISJ6l4piIiIiIiIiIiORZKo6JiIiIiIiIiEiepeKYiIiIiIiIiIjkWSqOyRNjw4YNdO3aldq1a1OvXj3eeustgoKCcnpYIk+0L774Ah8fH6KionJ6KCIikocpzxPJesrzRDJOxTF5IsyePZuhQ4dy/fp1/Pz8aN26NXv27MHf35/t27fn9PBEnkhr1qxh3rx5OT0MERHJ45TniWQ95XkiD8dgMplMOT0IkfScPn2a9u3bU6FCBX788UecnJwAOH78OP7+/hQsWJDNmzfj6OiYwyMVeTIYjUamTZvGvHnzSPknYN++fRQsWDCHRyYiInmN8jyRrKU8TyRzNHNMrN7ChQtJSkqif//+5oQJoHLlynTu3JkrV64QEBCQgyMUeXIEBgbSvn175s6di6+vL4ULF87pIYmISB6mPE8k6yjPE8k8FcfE6gUGBgLQuHHjVNcaNWoEwK5du7J1TCJPqrVr13L16lWGDRvGkiVLLP5HREREJLspzxPJOsrzRDIvX04PQCQ9CQkJhIWFUaRIkTSnAnt6egJw9uzZ7B6ayBOpc+fOjBw5kkKFCuX0UEREJI9TnieStZTniWSeimNi1W7evInJZMLV1TXN6ymJVHR0dHYOS+SJVadOnZwegoiICKA8TySrKc8TyTwtqxSrZjQaAbCzs0vzur29PQBxcXHZNiYREREReXTK80RExFqoOCZWzcHBAUiedp+W+Ph4AK2nFxEREXnCKM8TERFroeKYWDUXFxdsbW3vO50+KioKQEcTi4iIiDxhlOeJiIi1UHFMrJqdnR2lS5cmIiKC27dvp7p+8eJFACpUqJDdQxMRERGRR6A8T0RErIWKY2L16tevj8lkMh/1fa+dO3cCULdu3eweloiIiIg8IuV5IiJiDVQcE6vXpUsXDAYDX375pcW0+5CQEFauXIm7uzutWrXKwRGKiIiISGYozxMREWuQL6cHIPIgvr6+9OnTh2+++Yb27dvTrl07YmJiWLduHUajkQkTJphPMxIRERGRJ4fyPBERsQYqjskTYcSIEXh5ebFkyRKWLFmCs7Mz9erVY+DAgVSrVi2nhyciIiIimaQ8T0REcprBZDKZcnoQIiIiIiIiIiIiOUF7jomIiIiIiIiISJ6l4piIiIiIiIiIiORZKo6JiIiIiIiIiEiepeKYiIiIiIiIiIjkWSqOiYiIiIiIiIhInqXimIiIiIiIiIiI5FkqjomIiIiIiIiISJ6l4piIZFpSUlJOD0FEREREHgPleSKSl6g4JpJBe/bswcfHJ82fypUrU7NmTdq2bcuIESM4cuRITg+XWbNm4ePjw8iRI81tYWFh+Pj4UKVKlUfqOzQ0lEGDBnHo0KFHHWa6wsPDze84I1L+jFq3bv3Iz055bnh4+CP3ZQ3PERERkftTnvcP5XlZR3meyJMjX04PQORJ1L59e4vfm0wm7t69S0hICGvWrOHnn39mwoQJdOzYMWcG+Jj16dOH0NBQevXqldNDEREREclSyvOU54lI3qPimEgmTJ06Nc32pKQkpk6dytdff83HH3/Ms88+S5EiRbJ5dPdXokQJfvnlFwwGwyP1o2n2IiIiklspz1OeJyJ5j5ZVimQhGxsb3nnnHYoUKUJsbCy//fZbTg/Jgp2dHeXLl8fLyyunhyIiIiLyRFGeJyKSe2nmmEgWs7Ozo1SpUkRGRnL9+nVzu4+PD8WKFWPBggWMHj2aY8eOUahQId555x06deoEQFRUFAsWLGDz5s2EhoZia2uLt7c3r7zyCp06dcLW1jbV84KCgpg/fz4HDx4kJiaGypUr89Zbb6U5trCwMFq2bImtrS3Hjh2zuBYfH88PP/zAunXrOHfuHAaDgdKlS/PKK6/g5+eHvb09q1atYtSoUeZ7unXrBsB3331H/fr1ze2bNm1i6dKlBAcHc+fOHdzd3WnWrBl9+/bFzc0t1biioqL4+uuv2bhxI5cvX6ZYsWJ06NAhS5crnDt3jkWLFrF7924uX75MQkIChQoVombNmvTu3ZvatWuneV9sbCyff/45P//8M9evX8fDw4M2bdrw5ptv4urqmio+Li6OxYsXm9+jyWSibNmyvPjii/To0QMHB4cs+0wiIiKSvZTnKc9TnieSO6k4JpLF4uPjOX/+PAClSpWyuBYbG8vrr7+Ora0tzZo14+jRo/j6+gJw8eJF+vTpQ1hYGMWKFaN+/fokJSWxf/9+xowZw5YtW5g5cyZ2dnbm/jZs2MDw4cNJSEigatWq1K5dm5CQEPr164e3t3eGx3zjxg1ef/11goODcXZ2pk6dOhgMBvbt28f48eP5/fffmTNnDp6enrRv356AgADu3LlDkyZNKFy4MMWKFQOS9+QYNWoUq1evxs7Ojqeffho3NzeOHj3KokWL+OWXX/j666+pXLmy+dnXrl2jZ8+enD17lqJFi9K0aVNu3rzJ3LlzCQgIyOwfg4WdO3fSv39/7t69S/ny5WncuDF3797l2LFjbN68mYCAAObOncszzzyT6t6BAwdy9uxZateuzdNPP82+ffuYP38+mzZt4vvvv6dEiRLm2MjISN544w2Cg4MpWLAgNWvWxN7engMHDvDZZ5+xceNGvvnmGwoWLJgln0tERESyl/I85XnK80RyKZOIZMju3btN3t7eJm9v7/vGxMXFmUaNGmXy9vY21atXzxQdHW2+lnJvx44dTbGxsSaTyWRKTEw0mUwmk9FoNLVv397k7e1tGjt2rPm6yWQyXb161dSpUyeTt7e36bPPPjO3X7t2zVS7dm2Tt7e3aeXKleb2xMRE02effWZ+3ogRI8zXQkNDTd7e3qbKlStbjPudd94xeXt7m7p162aKjIy0eEbbtm1N3t7epu+++87c3rx5c5O3t7dp3759Fv3Mnz/f5O3tbWrXrp3p9OnTFmOaPn26ydvb29S8eXPT3bt3zdeGDh1q8vb2NvXt29d0584dc/uBAwdMtWrVeuA7v1fKn1GrVq3MbUaj0dS0aVOTt7e3ae7cuRbxsbGxpv79+5u8vb1Nb7zxhsW1lOfWqFHDtHfvXnN7dHS0qWfPniZvb29T//79Le558803Td7e3qYBAwaYbt68aXFP3759Td7e3qahQ4em+ZzLly9n6DOKiIhI1lOepzzPZFKeJ5KXac8xkUx47733LH6GDRvG66+/TpMmTVi5ciWOjo5MmTKFAgUKpLq3W7duODo6Asl7VwBs3bqVEydO4OPjw5gxY8zXAYoXL87UqVMxGAx8//33xMTEALB69Wqio6Np27Ytr7zyijnexsaGYcOGWXxrl55r166xYcMG7Ozs+N///kfhwoXN14oVK8b7779P2bJlH3gEtdFoZP78+QBMmTKF8uXLW4xp4MCB1K5dm0uXLvHzzz9bPNve3p6JEyeSP39+8z21atVi0KBBGfoM6YmIiKBBgwa0aNGC//znPxbXHB0d6dy5M5B8bHlaXn/9derWrWv+fYECBfjss8+ws7MjICCAS5cuAXDs2DH++OMPihcvzuTJky2m4hcoUIDJkydToEABNmzYwIULFx75c4mIiMjjoTwvNeV5yvNEcjstqxTJhJR/9FPY2tri5OTEU089RYcOHejWrRvlypVL894qVaqkatu5cycADRs2THO/ibJly1KuXDnOnj3L4cOHadKkCbt37wagefPmqeINBgNt2rTh+PHjD/wse/bsISkpiVq1allMHU/RokULWrRo8cB+jh07xs2bN3F1dTUvIfi3Zs2aceDAAQIDA+ncuTN79uzBZDJRo0aNNE97ateuHRMnTnzgs9Pj5ubGpEmTUrVHRkZy6tQptm3bBiQvk0hLWvthuLm5Ub16dfbv38++ffsoVaqU+c+wRo0aODs7p7onZd+L7du3s3v3bsqUKfMIn0pEREQeF+V5qSnPU54nktupOCaSCSdOnMj0vYUKFUrV9tdffwGwYMECFixYkO79ly9fBuDKlSsAuLu7pxlXunTpDI3n6tWrAJQsWTJD8feT8s3arVu38PHxSTc25fOmfAYPD48049zd3bGzsyMhIeGRxgawb98+Vq1axbFjx7h48SJ37twBSPe4czs7u/uOLeV9pXyGlM/066+/Zvjzi4iIiPVRnpea8jzleSK5nYpjItksZYr9vRITEwGoWbMmTz31VLr3pyRJ6f1jD5AvX8b+ehuNxgzFPUhSUhKQPEW/YcOG6cambGCb8hlMJtN9Y/Ply/dISZPJZGLYsGGsX78egHLlytGsWTO8vLyoUqUKtra29z31KV++fGl+w3vvmFM2zk35M/Tx8XngJrk6Yl1ERCR3Up6nPE95nsiTScUxESuQMs29Xbt29O7dO8P3nDx5krCwsDSvp3zT9SApR27fb6+JhIQEfvzxR0qWLEmzZs3STPpSxgNQtGhRpk6dmqFnpySAKd9G/tutW7eIjY3NUF/3s379etavX4+Liwtz5syhTp06FtfTOykpNjaWqKioNE8dSnnvKd84pnz+evXq8eGHHz7SmEVERCT3UJ6nPE9ErJ825BexAvXr1wdgy5YtaV6/desWzz//PK+99hpnz54FoEmTJgBs3LgxzXsyejx27dq1MRgMHD58mMjIyFTXDxw4wPjx4/nvf/9734QJwNfXFycnJ06dOnXfjUjHjh1Lx44dWbRoEQANGjQgX758/Pnnn+ZlBPfaunVrhj5Devbt2wdAo0aNUiVMgHkvipRvRO93/V5hYWEEBQVhZ2dn3sQ15c/w999/T/Nb2oSEBPz8/Ojatat5TCIiIpL7Kc9Tnici1k/FMREr8Nxzz1G6dGn27dvHp59+yt27d83X7ty5w4gRIzhz5gxRUVHmDWBffvllihcvzo4dO8ynB6WYN28ee/fuzdCzS5cuTevWrYmPj2f48OHmU5Ig+QSgTz/9FIBXX33V3J5yylJUVJS5zcHBgZ49e5KUlMTgwYM5c+aMxXNWrlzJsmXLOH78ONWrVwegSJEidOrUCaPRyLvvvsvNmzfN8SEhIXz22WcZ+gzpSdkA9tChQ0RERJjbk5KSWLx4McuWLQMgLi4uzfsnTpzIqVOnzL+/ceMGw4YNIzExkVdeeYVixYoBUKdOHWrVqkVoaCjDhw/n1q1b5nsSEhIYP348f/75JxcuXODpp59+5M8lIiIiTwblecrzRMT6aVmliBWwt7dnxowZvPHGG3z33XesW7eOqlWrYmtry6FDh7h16xbFixdn+vTp5v0bXF1d+d///kf//v2ZOnUqq1evpmLFipw+fZrTp09Tu3ZtDhw4kKHnjxs3jvPnz7Njxw5atGhBnTp1iIuL49ChQ9y+fZtnnnmGPn36mOO9vLw4c+YMH330EWvWrKFPnz7UrFmTgQMHcurUKQICAnjppZeoWrUqJUqU4PTp0+YkavTo0VSrVs3c1/vvv8/Jkyc5ePAgrVq1on79+sTGxrJ3714qV65MVFTUI+1F4efnx5IlS7h69Spt2rShbt26GAwGjh07Rnh4OBUqVOD06dPm56TsLQHJiWCJEiV4+eWXqVevHvnz52fPnj1ER0dTq1YtRo4cafGsL774gt69e/PLL7+wfft2qlatirOzM0FBQVy7dg0nJydmzZplcZS5iIiI5G7K85TniYj108wxEStRqVIlfvrpJ/r27UvRokXZt28f+/fvx93dnX79+rF27dpUx4bXr1+fFStW8PLLLxMTE8PWrVuxtbVl0qRJdOvWLcPPLlKkCMuWLePdd9/Fw8ODnTt3sm/fPsqUKcOYMWOYO3euRTIxYsQIGjZsSExMDNu3byckJARI3rR05syZTJkyhdq1a3Pu3Dm2bt1KXFwcbdu25YcffqBnz54Wzy5QoADfffcdw4cPx93dnR07dnDy5Em6dOnCN99888ANaR/Ew8ODFStW0LFjR1xdXdmxYwf79u3Dzc2NkSNHsnr1ary9vTEajWzfvt3iXltbWxYuXIifnx8nTpxg27ZtuLm5MWzYMBYuXIiTk5NFvLu7OytWrODdd9/F09OToKAgAgMDKViwID169ODnn3+mVq1aj/R5RERE5MmjPE95nohYN4MpveNDREREREREREREcjHNHBMRERERERERkTxLxTEREREREREREcmzVBwTEREREREREZE8S8UxERERERERERHJs1QcExERERERERGRPEvFMRERERERERERybNUHBMRERERERERkTxLxTEREREREREREcmzVBwTEREREREREZE8S8UxERERERERERHJs1QcExERERERERGRPEvFMRERERERERERybNUHBMRERERERERkTzr/wEKkKegWr1m5gAAAABJRU5ErkJggg==",
      "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_code']  # 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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"Cryptomining\"\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])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_proba = best_model.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None 0.5 99.89365747500703 99.926551421206 99.76371801446449 99.8450683282249\n",
      "None 0.51 99.89398670883054 99.92990777591612 99.76132164544282 99.84554354760567\n",
      "None 0.52 99.89266977353651 99.93230234445143 99.7550910859865 99.8436180827385\n",
      "None 0.53 99.89135283824248 99.93517687110761 99.74838125272586 99.8416916921719\n",
      "None 0.54 99.89102360441898 99.93805373478355 99.74454706229122 99.84120663765849\n",
      "None 0.55 99.88871896765443 99.94188699181134 99.73400303859592 99.83783680049129\n",
      "None 0.56 99.88723741544864 99.9452428527513 99.72633465772661 99.83566875619242\n",
      "None 0.57 99.88723741544864 99.94860167741068 99.7229797410963 99.8356632367865\n",
      "None 0.58 99.88575586324285 99.95243883105216 99.71483208642266 99.83349408112247\n",
      "None 0.59 99.88460354486057 99.9548372218165 99.70908080077068 99.83180776659428\n",
      "None 0.6 99.88361584339005 99.95723620987891 99.70380878892303 99.83036166317552\n",
      "None 0.61 99.87966503750796 99.95963207519968 99.68990984859741 99.82458876744214\n",
      "None 0.62 99.87719578383165 99.96058961776735 99.68176219392376 99.82098119582642\n",
      "None 0.63 99.8748911470671 99.96202781102348 99.67361453925014 99.81761284005606\n",
      "None 0.64 99.87110495809677 99.9644254713625 99.66019487272885 99.81207834628944\n",
      "None 0.65 99.86945878897923 99.96634550664436 99.6534850394682 99.80967010284536\n",
      "None 0.66 99.86682491839116 99.96874549213828 99.64342028957724 99.80581778555468\n",
      "None 0.67 99.86501413236188 99.9697049842514 99.63718973012092 99.80317039682768\n",
      "None 0.68 99.86221564486206 99.9706637683111 99.62808352783861 99.79907965519477\n",
      "None 0.69 99.85941715736224 99.97162274605243 99.61897732555632 99.79498850111148\n",
      "None 0.7 99.8571125205977 99.97354382825615 99.61035039707835 99.79161665146205\n",
      "None 0.71 99.85283248089209 99.97594476919006 99.59549290914407 99.78535620306167\n",
      "None 0.72 99.84805859045123 99.97786524747616 99.57967687360112 99.7783737967234\n",
      "None 0.73 99.84246161545161 99.98074812775542 99.56050592142786 99.77018450078165\n",
      "None 0.74 99.83867542648126 99.98267072941788 99.5475655287109 99.76464372343237\n",
      "None 0.75 99.83307845148164 99.98314917792061 99.53079094555929 99.75645724551704\n",
      "None 0.76 99.82879841177603 99.98459116014118 99.51689200523367 99.75019335994736\n",
      "None 0.77 99.82023833236484 99.98555031620725 99.49101121979976 99.73766774130003\n",
      "None 0.78 99.81546444192398 99.98651148450747 99.47615373186548 99.73067969142284\n",
      "None 0.79 99.80904438236557 99.98795407128299 99.45602423208355 99.72127980624143\n",
      "None 0.8 99.80196585516016 99.99036079542716 99.43301908947562 99.71091112259242\n",
      "0.8 0.81 99.7968627308958 99.99084129899303 99.41768232773701 99.7034380993122\n",
      "0.8 0.82 99.78583339780829 99.99180256724306 99.38461243523813 99.68728291882097\n",
      "0.8 0.83 99.77249942795623 99.99179936420953 99.34579125708726 99.66774852746724\n",
      "0.8 0.84 99.76147009486873 99.9932441561951 99.31224209078404 99.65157967379777\n",
      "0.8 0.85 99.75291001545753 99.9937250813326 99.28684057915447 99.63902910613409\n",
      "0.8 0.86 99.73792987648793 99.99468801761647 99.24226811535162 99.61705730670054\n",
      "0.8 0.87 99.72113895148904 99.99468539982703 99.19338218730978 99.5924220332703\n",
      "0.8 0.88 99.70780498163698 99.99468331915551 99.15456100915893 99.57285011250286\n",
      "0.8 0.89 99.6938125441379 99.99516462049523 99.1133434619864 99.55230131565645\n",
      "0.8 0.9 99.67126002722763 99.99564523152851 99.04720367698863 99.51916478658573\n",
      "0.8 0.91 99.64113513237669 99.99564137209606 98.95949657079593 99.47487088568566\n",
      "0.8 0.92 99.61166870517276 99.99660694896852 98.8727480122119 99.43150188093591\n",
      "0.8 0.93 99.57495913385166 99.99708849518873 98.7653906800416 99.37742327501398\n",
      "0.8 0.94 99.53001871694286 99.99757050397466 98.63406965765472 99.31114022579159\n",
      "0.8 0.95 99.46911045959396 99.99756612829425 98.45673835005199 99.22117063487272\n",
      "0.8 0.96 99.35091551695471 99.99804605449553 98.11214048473754 99.0461168512905\n",
      "0.8 0.97 99.21741120152238 99.99901912221246 97.72249088181587 98.84764924323957\n",
      "0.8 0.98 99.02151707653533 99.99901336398483 97.15215505466118 98.55502992527192\n",
      "0.8 0.99 98.53128791333249 99.99949932158074 95.7243983915571 97.81525939203385\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(99.71091112259242, 0.8)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = None\n",
    "best = 0\n",
    "for _t in range(50, 100, 1):\n",
    "    _t = _t / 100\n",
    "\n",
    "    y_pred = []\n",
    "    for p_0, p_1 in y_pred_proba:\n",
    "        if p_1 >= _t:\n",
    "            y_pred.append(1)\n",
    "        else:\n",
    "            y_pred.append(0)   \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(t, _t, accuracy, precision, recall, F1)\n",
    "    if precision >= 99.99 and F1 > best:\n",
    "        best = F1\n",
    "        t = _t\n",
    "best, t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_validation=df_validation[features]  # Features\n",
    "y_validation=df_validation['LABEL_code']  # Labels\n",
    "    \n",
    "y_pred_proba = best_model.predict_proba(X_validation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.4154654e-03, 9.9658453e-01],\n",
       "       [9.9869031e-01, 1.3097017e-03],\n",
       "       [9.9960905e-01, 3.9092908e-04],\n",
       "       ...,\n",
       "       [9.6181458e-01, 3.8185429e-02],\n",
       "       [9.9967128e-01, 3.2872244e-04],\n",
       "       [9.9996674e-01, 3.3265675e-05]], dtype=float32)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = []\n",
    "for p_0, p_1 in y_pred_proba:\n",
    "    if p_1 >= 0.96:\n",
    "        y_pred.append(1)\n",
    "    else:\n",
    "        y_pred.append(0)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\multirow{-6}{*}{\\rotatebox{90}{\\centering \\textbf{Our approach}}} & \\cellcolor{Gray} \\textbf{All} & \\rowcolor{LigthGray}95.29 & 99.99 & 87.12 & 93.11 \\\\\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": [
    "\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\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.1862874, 0.35720602, 0.014357813, 0.07739055, 0.19080752, 0.014357813]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PERIODICITY_SCDF      0.000000\n",
       "SPECTRAL_SKEWNESS     0.000000\n",
       "TIME                  0.000000\n",
       "PERIODICITY           0.000000\n",
       "SPECTRAL_BANDWIDTH    0.000000\n",
       "                        ...   \n",
       "SWITCHING_METRIC      0.045270\n",
       "MEAN_DIFFTIMES        0.063053\n",
       "TRANSIENTS            0.112688\n",
       "CNT_DISTRIBUTION      0.160066\n",
       "MEDIAN_SCALED_TIME    0.241753\n",
       "Length: 76, dtype: float32"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "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": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['PERIODICITY_SCDF', 'SPECTRAL_SKEWNESS', 'TIME', 'PERIODICITY',\n",
       "       'SPECTRAL_BANDWIDTH', 'BENFORD_LAW_PRESENTED', 'MAX_POWER_FREQ', 'MEAN',\n",
       "       'PEARSON_SK2_SKEWNESS', 'PEARSON_SK1_SKEWNESS', 'Q3_SCALED_TIME',\n",
       "       'COEFFICIENT_OF_VARIATION', 'GALTON_SKEWNESS', 'SPECTRAL_CREST',\n",
       "       'CNT_NZ_DISTRIBUTION', 'FISHER_PEARSON_g1_SKEWNESS', 'POWER_MEAN',\n",
       "       'PERCENT_BELOW_MEAN', 'SPECTRAL_FLUX', 'PACKETS', 'P_BENFORD',\n",
       "       'BURSTINESS', 'MAX_DIFFTIMES', 'AREA_VALUES_DISTRIBUTION',\n",
       "       'SPECTRAL_CENTROID', 'VAL', 'Q1_SCALED_TIME', 'SPECTRAL_ENERGY',\n",
       "       'BIGGEST_CNT_1_SEC', 'SPECTRAL_KURTOSIS', 'STDEV',\n",
       "       'FISHER_PEARSON_G1_SKEWNESS', 'SPECTRAL_SLOPE', 'MAX_POWER',\n",
       "       'ROOT_MEAN_SQUARE', 'BYTES_REV', 'NORMAL_DISTRIBUTION',\n",
       "       'TIME_DISTRIBUTION', 'MEAN_SCALED_TIME', 'KURTOSIS', 'Q1', 'MODE',\n",
       "       'SCALED_ENTROPY', 'PERCENT_ABOVE_MEAN', 'CNT_ZEROS', 'MIN_POWER_FREQ',\n",
       "       'MEAN_SCALED_DIFFTIMES', 'BYTES', 'DURATION', 'SPECTRAL_ENTROPY', 'MIN',\n",
       "       'PERCENT_DEVIATION', 'SPECTRAL_SPREAD', 'MIN_DIFFTIMES',\n",
       "       'SPECTRAL_ROLLOFF', 'POWER_STD', 'MAX', 'FISHER_MI_3_SKEWNESS',\n",
       "       'HURST_EXPONENT', 'PACKETS_REV', 'MIN_MINUS_MAX', 'MIN_POWER',\n",
       "       'SIG_SPACES', 'AVERAGE_DISPERSION', 'DIRECTIONS', 'Q3', 'ENTROPY',\n",
       "       'MEDIAN_DIFFTIMES', 'POWER_MODE', 'MEDIAN', 'VAR', 'SWITCHING_METRIC',\n",
       "       'MEAN_DIFFTIMES', 'TRANSIENTS', 'CNT_DISTRIBUTION',\n",
       "       'MEDIAN_SCALED_TIME'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'PERIODICITY_SCDF': 0.0,\n",
       " 'SPECTRAL_SKEWNESS': 0.0,\n",
       " 'TIME': 0.0,\n",
       " 'PERIODICITY': 0.0,\n",
       " 'SPECTRAL_BANDWIDTH': 0.0,\n",
       " 'BENFORD_LAW_PRESENTED': 0.0,\n",
       " 'MAX_POWER_FREQ': 0.0002947673783637583,\n",
       " 'MEAN': 0.0004647105233743787,\n",
       " 'PEARSON_SK2_SKEWNESS': 0.0005062959971837699,\n",
       " 'PEARSON_SK1_SKEWNESS': 0.0005169132491573691,\n",
       " 'Q3_SCALED_TIME': 0.0005253424751572311,\n",
       " 'COEFFICIENT_OF_VARIATION': 0.0005803302046842873,\n",
       " 'GALTON_SKEWNESS': 0.000585466914344579,\n",
       " 'SPECTRAL_CREST': 0.0006020539440214634,\n",
       " 'CNT_NZ_DISTRIBUTION': 0.0006750334869138896,\n",
       " 'FISHER_PEARSON_g1_SKEWNESS': 0.0006854453240521252,\n",
       " 'POWER_MEAN': 0.0008199396543204784,\n",
       " 'PERCENT_BELOW_MEAN': 0.0008237945148721337,\n",
       " 'SPECTRAL_FLUX': 0.0009062871686182916,\n",
       " 'PACKETS': 0.0009786856826394796,\n",
       " 'P_BENFORD': 0.0011158973211422563,\n",
       " 'BURSTINESS': 0.0011614245595410466,\n",
       " 'MAX_DIFFTIMES': 0.00116637849714607,\n",
       " 'AREA_VALUES_DISTRIBUTION': 0.001207812805660069,\n",
       " 'SPECTRAL_CENTROID': 0.0012547281803563237,\n",
       " 'VAL': 0.0012571880361065269,\n",
       " 'Q1_SCALED_TIME': 0.0012942214962095022,\n",
       " 'SPECTRAL_ENERGY': 0.0013177626533433795,\n",
       " 'BIGGEST_CNT_1_SEC': 0.0013672152999788523,\n",
       " 'SPECTRAL_KURTOSIS': 0.0014866336714476347,\n",
       " 'STDEV': 0.001523209735751152,\n",
       " 'FISHER_PEARSON_G1_SKEWNESS': 0.001574940513819456,\n",
       " 'SPECTRAL_SLOPE': 0.001600647228769958,\n",
       " 'MAX_POWER': 0.0016370357479900122,\n",
       " 'ROOT_MEAN_SQUARE': 0.0016397213330492377,\n",
       " 'BYTES_REV': 0.0019850735552608967,\n",
       " 'NORMAL_DISTRIBUTION': 0.0020241132006049156,\n",
       " 'TIME_DISTRIBUTION': 0.0020839832723140717,\n",
       " 'MEAN_SCALED_TIME': 0.002227989723905921,\n",
       " 'KURTOSIS': 0.002508345525711775,\n",
       " 'Q1': 0.0025507735554128885,\n",
       " 'MODE': 0.002599729225039482,\n",
       " 'SCALED_ENTROPY': 0.0026795801240950823,\n",
       " 'PERCENT_ABOVE_MEAN': 0.0026807875838130713,\n",
       " 'CNT_ZEROS': 0.0026976021472364664,\n",
       " 'MIN_POWER_FREQ': 0.0031393233221024275,\n",
       " 'MEAN_SCALED_DIFFTIMES': 0.0033017718233168125,\n",
       " 'BYTES': 0.003360348055139184,\n",
       " 'DURATION': 0.00353861041367054,\n",
       " 'SPECTRAL_ENTROPY': 0.0038782008923590183,\n",
       " 'MIN': 0.0038897525519132614,\n",
       " 'PERCENT_DEVIATION': 0.003949787467718124,\n",
       " 'SPECTRAL_SPREAD': 0.004466350190341473,\n",
       " 'MIN_DIFFTIMES': 0.004534496460109949,\n",
       " 'SPECTRAL_ROLLOFF': 0.004823715891689062,\n",
       " 'POWER_STD': 0.004938540980219841,\n",
       " 'MAX': 0.006886354647576809,\n",
       " 'FISHER_MI_3_SKEWNESS': 0.006966636516153812,\n",
       " 'HURST_EXPONENT': 0.007452708203345537,\n",
       " 'PACKETS_REV': 0.00803370587527752,\n",
       " 'MIN_MINUS_MAX': 0.008057840168476105,\n",
       " 'MIN_POWER': 0.009990801103413105,\n",
       " 'SIG_SPACES': 0.01036944892257452,\n",
       " 'AVERAGE_DISPERSION': 0.01213803794234991,\n",
       " 'DIRECTIONS': 0.016483087092638016,\n",
       " 'Q3': 0.022037358954548836,\n",
       " 'ENTROPY': 0.024236168712377548,\n",
       " 'MEDIAN_DIFFTIMES': 0.035811617970466614,\n",
       " 'POWER_MODE': 0.03623376414179802,\n",
       " 'MEDIAN': 0.036816325038671494,\n",
       " 'VAR': 0.03822765499353409,\n",
       " 'SWITCHING_METRIC': 0.04526951164007187,\n",
       " 'MEAN_DIFFTIMES': 0.06305255740880966,\n",
       " 'TRANSIENTS': 0.11268845200538635,\n",
       " 'CNT_DISTRIBUTION': 0.160066157579422,\n",
       " 'MEDIAN_SCALED_TIME': 0.24175302684307098}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_importances.to_dict()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def perform_val(selected_features, best_hyperparams=None, cycles=25):\n",
    "    if best_hyperparams is None:\n",
    "        df_to_tunne = df_features.sample(100000)\n",
    "        best_hyperparams = tunne_together_XGBoost_classification(df_to_tunne, selected_features, verbose=False)\n",
    "    \n",
    "    X=df_features[selected_features]  # Features\n",
    "    y=df_features['LABEL_code']  # 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_code']  # 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": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [05:04<00:00,  3.05s/trial, best loss: -0.9885280964417655]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.31289703495559656, 'gamma': 6.639927648641983, 'max_depth': 35.0, 'min_child_weight': 0.0, 'n_estimators': 200.0, 'reg_alpha': 0.0, 'reg_lambda': 0.8419839067837609}\n",
      "100%|██████████| 100/100 [03:28<00:00,  2.09s/trial, best loss: -0.9954950543531486]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.8319299637713123, 'gamma': 2.1291997335989556, 'max_depth': 40.0, 'min_child_weight': 2.0, 'n_estimators': 350.0, 'reg_alpha': 2.0, 'reg_lambda': 0.483128165540427}\n",
      "100%|██████████| 100/100 [04:06<00:00,  2.46s/trial, best loss: -0.9935064935064936]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.06421099862939211, 'gamma': 8.119302676935412, 'max_depth': 50.0, 'min_child_weight': 9.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6715195474721241}\n",
      "100%|██████████| 100/100 [04:19<00:00,  2.60s/trial, best loss: -0.9923759891912758]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.4253423837549576, 'gamma': 1.4296395348574436, 'max_depth': 20.0, 'min_child_weight': 17.0, 'n_estimators': 300.0, 'reg_alpha': 2.0, 'reg_lambda': 0.45994344835221335}\n",
      "100%|██████████| 100/100 [04:42<00:00,  2.82s/trial, best loss: -0.9884560593407673]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.21070151346011123, 'gamma': 2.378930963311418, 'max_depth': 50.0, 'min_child_weight': 9.0, 'n_estimators': 400.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6678467740743735}\n",
      "100%|██████████| 100/100 [04:07<00:00,  2.47s/trial, best loss: -0.9925318494655148]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.34152143114553035, 'gamma': 5.672744198661694, 'max_depth': 40.0, 'min_child_weight': 12.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.6525645842995369}\n",
      "100%|██████████| 100/100 [00:37<00:00,  2.65trial/s, best loss: -0.9549137545359645]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.2590780926886683, 'gamma': 5.986865209445059, 'max_depth': 45.0, 'min_child_weight': 20.0, 'n_estimators': 300.0, 'reg_alpha': 0.0, 'reg_lambda': 0.48184977332160406}\n",
      "100%|██████████| 100/100 [03:54<00:00,  2.34s/trial, best loss: -0.9929395724789405]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.25606028348120374, 'gamma': 4.426154201413016, 'max_depth': 35.0, 'min_child_weight': 4.0, 'n_estimators': 150.0, 'reg_alpha': 1.0, 'reg_lambda': 0.014606065737318763}\n",
      "100%|██████████| 100/100 [03:42<00:00,  2.23s/trial, best loss: -0.9912207562901565]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.0936177201648048, 'gamma': 5.439635789930925, 'max_depth': 20.0, 'min_child_weight': 4.0, 'n_estimators': 150.0, 'reg_alpha': 0.0, 'reg_lambda': 0.3721054959829425}\n",
      "100%|██████████| 100/100 [04:57<00:00,  2.98s/trial, best loss: -0.9930780773512755]\n",
      "The best hyperparameters are :  \n",
      "\n",
      "{'colsample_bytree': 0.6294300921727014, 'gamma': 4.19124962464093, 'max_depth': 30.0, 'min_child_weight': 5.0, 'n_estimators': 250.0, 'reg_alpha': 0.0, 'reg_lambda': 0.11906410016665095}\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": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "\n",
    "problem = \"Cryptomining\"\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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