Data preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cdc1516e-90a4-4747-a7d6-18438bd210a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler, RobustScaler, LabelEncoder\n",
    "from sklearn.impute import KNNImputer\n",
    "from sklearn.decomposition import PCA, KernelPCA, TruncatedSVD\n",
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.pipeline import Pipeline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5b73f61c-2a87-4d43-8efe-25e0f47dfa56",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"PhiUSIIL_Phishing_URL_Dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "19db272c-89c4-40d1-8c08-8a0db104ccc6",
   "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>FILENAME</th>\n",
       "      <th>URL</th>\n",
       "      <th>URLLength</th>\n",
       "      <th>Domain</th>\n",
       "      <th>DomainLength</th>\n",
       "      <th>IsDomainIP</th>\n",
       "      <th>TLD</th>\n",
       "      <th>URLSimilarityIndex</th>\n",
       "      <th>CharContinuationRate</th>\n",
       "      <th>TLDLegitimateProb</th>\n",
       "      <th>...</th>\n",
       "      <th>Pay</th>\n",
       "      <th>Crypto</th>\n",
       "      <th>HasCopyrightInfo</th>\n",
       "      <th>NoOfImage</th>\n",
       "      <th>NoOfCSS</th>\n",
       "      <th>NoOfJS</th>\n",
       "      <th>NoOfSelfRef</th>\n",
       "      <th>NoOfEmptyRef</th>\n",
       "      <th>NoOfExternalRef</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>521848.txt</td>\n",
       "      <td>https://www.southbankmosaics.com</td>\n",
       "      <td>31</td>\n",
       "      <td>www.southbankmosaics.com</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>com</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.522907</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31372.txt</td>\n",
       "      <td>https://www.uni-mainz.de</td>\n",
       "      <td>23</td>\n",
       "      <td>www.uni-mainz.de</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>de</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.032650</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>597387.txt</td>\n",
       "      <td>https://www.voicefmradio.co.uk</td>\n",
       "      <td>29</td>\n",
       "      <td>www.voicefmradio.co.uk</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>uk</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.028555</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>554095.txt</td>\n",
       "      <td>https://www.sfnmjournal.com</td>\n",
       "      <td>26</td>\n",
       "      <td>www.sfnmjournal.com</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>com</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.522907</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>151578.txt</td>\n",
       "      <td>https://www.rewildingargentina.org</td>\n",
       "      <td>33</td>\n",
       "      <td>www.rewildingargentina.org</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>org</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.079963</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>244</td>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 56 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     FILENAME                                 URL  URLLength  \\\n",
       "0  521848.txt    https://www.southbankmosaics.com         31   \n",
       "1   31372.txt            https://www.uni-mainz.de         23   \n",
       "2  597387.txt      https://www.voicefmradio.co.uk         29   \n",
       "3  554095.txt         https://www.sfnmjournal.com         26   \n",
       "4  151578.txt  https://www.rewildingargentina.org         33   \n",
       "\n",
       "                       Domain  DomainLength  IsDomainIP  TLD  \\\n",
       "0    www.southbankmosaics.com            24           0  com   \n",
       "1            www.uni-mainz.de            16           0   de   \n",
       "2      www.voicefmradio.co.uk            22           0   uk   \n",
       "3         www.sfnmjournal.com            19           0  com   \n",
       "4  www.rewildingargentina.org            26           0  org   \n",
       "\n",
       "   URLSimilarityIndex  CharContinuationRate  TLDLegitimateProb  ...  Pay  \\\n",
       "0               100.0              1.000000           0.522907  ...    0   \n",
       "1               100.0              0.666667           0.032650  ...    0   \n",
       "2               100.0              0.866667           0.028555  ...    0   \n",
       "3               100.0              1.000000           0.522907  ...    1   \n",
       "4               100.0              1.000000           0.079963  ...    1   \n",
       "\n",
       "   Crypto  HasCopyrightInfo  NoOfImage  NoOfCSS  NoOfJS  NoOfSelfRef  \\\n",
       "0       0                 1         34       20      28          119   \n",
       "1       0                 1         50        9       8           39   \n",
       "2       0                 1         10        2       7           42   \n",
       "3       1                 1          3       27      15           22   \n",
       "4       0                 1        244       15      34           72   \n",
       "\n",
       "   NoOfEmptyRef  NoOfExternalRef  label  \n",
       "0             0              124      1  \n",
       "1             0              217      1  \n",
       "2             2                5      1  \n",
       "3             1               31      1  \n",
       "4             1               85      1  \n",
       "\n",
       "[5 rows x 56 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69bdd20b-aa6a-4df2-a6a1-649c7f87dcc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(235795, 56)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "edafcb89-d086-413c-a561-e69b2c8c2a40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 235795 entries, 0 to 235794\n",
      "Data columns (total 56 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   FILENAME                    235795 non-null  object \n",
      " 1   URL                         235795 non-null  object \n",
      " 2   URLLength                   235795 non-null  int64  \n",
      " 3   Domain                      235795 non-null  object \n",
      " 4   DomainLength                235795 non-null  int64  \n",
      " 5   IsDomainIP                  235795 non-null  int64  \n",
      " 6   TLD                         235795 non-null  object \n",
      " 7   URLSimilarityIndex          235795 non-null  float64\n",
      " 8   CharContinuationRate        235795 non-null  float64\n",
      " 9   TLDLegitimateProb           235795 non-null  float64\n",
      " 10  URLCharProb                 235795 non-null  float64\n",
      " 11  TLDLength                   235795 non-null  int64  \n",
      " 12  NoOfSubDomain               235795 non-null  int64  \n",
      " 13  HasObfuscation              235795 non-null  int64  \n",
      " 14  NoOfObfuscatedChar          235795 non-null  int64  \n",
      " 15  ObfuscationRatio            235795 non-null  float64\n",
      " 16  NoOfLettersInURL            235795 non-null  int64  \n",
      " 17  LetterRatioInURL            235795 non-null  float64\n",
      " 18  NoOfDegitsInURL             235795 non-null  int64  \n",
      " 19  DegitRatioInURL             235795 non-null  float64\n",
      " 20  NoOfEqualsInURL             235795 non-null  int64  \n",
      " 21  NoOfQMarkInURL              235795 non-null  int64  \n",
      " 22  NoOfAmpersandInURL          235795 non-null  int64  \n",
      " 23  NoOfOtherSpecialCharsInURL  235795 non-null  int64  \n",
      " 24  SpacialCharRatioInURL       235795 non-null  float64\n",
      " 25  IsHTTPS                     235795 non-null  int64  \n",
      " 26  LineOfCode                  235795 non-null  int64  \n",
      " 27  LargestLineLength           235795 non-null  int64  \n",
      " 28  HasTitle                    235795 non-null  int64  \n",
      " 29  Title                       235795 non-null  object \n",
      " 30  DomainTitleMatchScore       235795 non-null  float64\n",
      " 31  URLTitleMatchScore          235795 non-null  float64\n",
      " 32  HasFavicon                  235795 non-null  int64  \n",
      " 33  Robots                      235795 non-null  int64  \n",
      " 34  IsResponsive                235795 non-null  int64  \n",
      " 35  NoOfURLRedirect             235795 non-null  int64  \n",
      " 36  NoOfSelfRedirect            235795 non-null  int64  \n",
      " 37  HasDescription              235795 non-null  int64  \n",
      " 38  NoOfPopup                   235795 non-null  int64  \n",
      " 39  NoOfiFrame                  235795 non-null  int64  \n",
      " 40  HasExternalFormSubmit       235795 non-null  int64  \n",
      " 41  HasSocialNet                235795 non-null  int64  \n",
      " 42  HasSubmitButton             235795 non-null  int64  \n",
      " 43  HasHiddenFields             235795 non-null  int64  \n",
      " 44  HasPasswordField            235795 non-null  int64  \n",
      " 45  Bank                        235795 non-null  int64  \n",
      " 46  Pay                         235795 non-null  int64  \n",
      " 47  Crypto                      235795 non-null  int64  \n",
      " 48  HasCopyrightInfo            235795 non-null  int64  \n",
      " 49  NoOfImage                   235795 non-null  int64  \n",
      " 50  NoOfCSS                     235795 non-null  int64  \n",
      " 51  NoOfJS                      235795 non-null  int64  \n",
      " 52  NoOfSelfRef                 235795 non-null  int64  \n",
      " 53  NoOfEmptyRef                235795 non-null  int64  \n",
      " 54  NoOfExternalRef             235795 non-null  int64  \n",
      " 55  label                       235795 non-null  int64  \n",
      "dtypes: float64(10), int64(41), object(5)\n",
      "memory usage: 100.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39c75fdb-4759-4c9e-bd37-e3c9ca610934",
   "metadata": {},
   "source": [
    "## Checking for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4e31f3f-1603-424f-a289-4288f05f7b70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FILENAME                      0\n",
       "URL                           0\n",
       "URLLength                     0\n",
       "Domain                        0\n",
       "DomainLength                  0\n",
       "IsDomainIP                    0\n",
       "TLD                           0\n",
       "URLSimilarityIndex            0\n",
       "CharContinuationRate          0\n",
       "TLDLegitimateProb             0\n",
       "URLCharProb                   0\n",
       "TLDLength                     0\n",
       "NoOfSubDomain                 0\n",
       "HasObfuscation                0\n",
       "NoOfObfuscatedChar            0\n",
       "ObfuscationRatio              0\n",
       "NoOfLettersInURL              0\n",
       "LetterRatioInURL              0\n",
       "NoOfDegitsInURL               0\n",
       "DegitRatioInURL               0\n",
       "NoOfEqualsInURL               0\n",
       "NoOfQMarkInURL                0\n",
       "NoOfAmpersandInURL            0\n",
       "NoOfOtherSpecialCharsInURL    0\n",
       "SpacialCharRatioInURL         0\n",
       "IsHTTPS                       0\n",
       "LineOfCode                    0\n",
       "LargestLineLength             0\n",
       "HasTitle                      0\n",
       "Title                         0\n",
       "DomainTitleMatchScore         0\n",
       "URLTitleMatchScore            0\n",
       "HasFavicon                    0\n",
       "Robots                        0\n",
       "IsResponsive                  0\n",
       "NoOfURLRedirect               0\n",
       "NoOfSelfRedirect              0\n",
       "HasDescription                0\n",
       "NoOfPopup                     0\n",
       "NoOfiFrame                    0\n",
       "HasExternalFormSubmit         0\n",
       "HasSocialNet                  0\n",
       "HasSubmitButton               0\n",
       "HasHiddenFields               0\n",
       "HasPasswordField              0\n",
       "Bank                          0\n",
       "Pay                           0\n",
       "Crypto                        0\n",
       "HasCopyrightInfo              0\n",
       "NoOfImage                     0\n",
       "NoOfCSS                       0\n",
       "NoOfJS                        0\n",
       "NoOfSelfRef                   0\n",
       "NoOfEmptyRef                  0\n",
       "NoOfExternalRef               0\n",
       "label                         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e812b41-a777-434f-bf59-c4ee57bfa8d9",
   "metadata": {},
   "source": [
    "## Remove unnecessary columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "318db96a-29e8-40cc-81ea-14e03b7310f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['FILENAME', 'URL', 'Domain', 'Title'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a6f94dbe-c94b-4280-8de0-bac8a5b6d22c",
   "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>URLLength</th>\n",
       "      <th>DomainLength</th>\n",
       "      <th>IsDomainIP</th>\n",
       "      <th>TLD</th>\n",
       "      <th>URLSimilarityIndex</th>\n",
       "      <th>CharContinuationRate</th>\n",
       "      <th>TLDLegitimateProb</th>\n",
       "      <th>URLCharProb</th>\n",
       "      <th>TLDLength</th>\n",
       "      <th>NoOfSubDomain</th>\n",
       "      <th>...</th>\n",
       "      <th>Pay</th>\n",
       "      <th>Crypto</th>\n",
       "      <th>HasCopyrightInfo</th>\n",
       "      <th>NoOfImage</th>\n",
       "      <th>NoOfCSS</th>\n",
       "      <th>NoOfJS</th>\n",
       "      <th>NoOfSelfRef</th>\n",
       "      <th>NoOfEmptyRef</th>\n",
       "      <th>NoOfExternalRef</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31</td>\n",
       "      <td>24</td>\n",
       "      <td>0</td>\n",
       "      <td>com</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.522907</td>\n",
       "      <td>0.061933</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>28</td>\n",
       "      <td>119</td>\n",
       "      <td>0</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>de</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.032650</td>\n",
       "      <td>0.050207</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>39</td>\n",
       "      <td>0</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>29</td>\n",
       "      <td>22</td>\n",
       "      <td>0</td>\n",
       "      <td>uk</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.028555</td>\n",
       "      <td>0.064129</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>com</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.522907</td>\n",
       "      <td>0.057606</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>15</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "      <td>org</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.079963</td>\n",
       "      <td>0.059441</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>244</td>\n",
       "      <td>15</td>\n",
       "      <td>34</td>\n",
       "      <td>72</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   URLLength  DomainLength  IsDomainIP  TLD  URLSimilarityIndex  \\\n",
       "0         31            24           0  com               100.0   \n",
       "1         23            16           0   de               100.0   \n",
       "2         29            22           0   uk               100.0   \n",
       "3         26            19           0  com               100.0   \n",
       "4         33            26           0  org               100.0   \n",
       "\n",
       "   CharContinuationRate  TLDLegitimateProb  URLCharProb  TLDLength  \\\n",
       "0              1.000000           0.522907     0.061933          3   \n",
       "1              0.666667           0.032650     0.050207          2   \n",
       "2              0.866667           0.028555     0.064129          2   \n",
       "3              1.000000           0.522907     0.057606          3   \n",
       "4              1.000000           0.079963     0.059441          3   \n",
       "\n",
       "   NoOfSubDomain  ...  Pay  Crypto  HasCopyrightInfo  NoOfImage  NoOfCSS  \\\n",
       "0              1  ...    0       0                 1         34       20   \n",
       "1              1  ...    0       0                 1         50        9   \n",
       "2              2  ...    0       0                 1         10        2   \n",
       "3              1  ...    1       1                 1          3       27   \n",
       "4              1  ...    1       0                 1        244       15   \n",
       "\n",
       "   NoOfJS  NoOfSelfRef  NoOfEmptyRef  NoOfExternalRef  label  \n",
       "0      28          119             0              124      1  \n",
       "1       8           39             0              217      1  \n",
       "2       7           42             2                5      1  \n",
       "3      15           22             1               31      1  \n",
       "4      34           72             1               85      1  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "198c2991-d73c-463c-8af4-4c3b6fcb2349",
   "metadata": {},
   "source": [
    "## Label encoding on categorical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09ae28eb-07a2-41a4-89f6-3ef4f8074d74",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "794bb6cc-6e05-4e69-bc65-26522cb87ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['TLD'] = label_encoder.fit_transform(df['TLD'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "133b9643-25d3-4a55-a8e3-4e88e904c4b6",
   "metadata": {},
   "source": [
    "## Outlier detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6aa86393-68ab-464d-ba43-1361995af1ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Outlier detection and removal using Isolation Forest\n",
    "iso_forest = IsolationForest(contamination=0.05)  # Adjust contamination as needed\n",
    "df['outlier'] = iso_forest.fit_predict(df.select_dtypes(include=[np.number]))  # only numerical columns\n",
    "df = df[df['outlier'] == 1]  # keep inliers\n",
    "df.drop(columns=['outlier'], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c64948a4-134d-4681-bdf1-4b372f86d21d",
   "metadata": {},
   "source": [
    "## Standardisation of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b369ca90-35d4-4b0f-a75b-eb57820ac599",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1: Standardize the Data\n",
    "features = df.drop(columns=['label'])  # Assuming 'label' is the target variable\n",
    "scaler = StandardScaler()\n",
    "scaled_features = scaler.fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da5932a9-63aa-4c48-832f-0b1f5eb6f2a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-3.65559798e-02,  3.27756879e-01, -2.47379823e-02, ...,\n",
       "         5.14797924e-01, -1.35452825e-01,  8.45669812e-01],\n",
       "       [-5.32537825e-01, -6.26499577e-01, -2.47379823e-02, ...,\n",
       "        -1.64052318e-01, -1.35452825e-01,  1.82157310e+00],\n",
       "       [-1.60551441e-01,  8.91927648e-02, -2.47379823e-02, ...,\n",
       "        -1.38595434e-01,  6.08042327e-03, -4.03066653e-01],\n",
       "       ...,\n",
       "       [-9.85537105e-02,  2.08474822e-01, -2.47379823e-02, ...,\n",
       "        -2.82538589e-03,  6.08042327e-03,  2.47535539e-01],\n",
       "       [ 1.45138956e+00,  3.07124419e+00, -2.47379823e-02, ...,\n",
       "        -4.94991812e-01, -1.35452825e-01, -4.55534572e-01],\n",
       "       [ 8.74394815e-02,  5.66320993e-01, -2.47379823e-02, ...,\n",
       "         1.67732896e+00, -1.35452825e-01,  2.28329078e+00]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaled_features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f19c360-8fc5-426c-88e1-82aea728da71",
   "metadata": {},
   "source": [
    "## Applying PCA to find feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c518561e-1cf5-4de1-bbd0-71497c34c7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply PCA\n",
    "pca = PCA()  # Adjust the number of components as needed\n",
    "pca_features = pca.fit_transform(scaled_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e23df93c-332a-429e-862c-6c137a185bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame with the PCA features\n",
    "pca_df = pd.DataFrame(data=pca_features, columns=[f'PC{i+1}' for i in range(pca_features.shape[1])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5a9f7813-d825-4e9f-8358-82f86dd37de4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now that the PCA has been fitted, we can calculate the loadings\n",
    "# Get the loadings (weights) of the features\n",
    "loadings = pca.components_.T * np.sqrt(pca.explained_variance_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "41544676-e9e9-4777-8c27-1f0229767b00",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate feature importance as the sum of absolute loadings for each feature\n",
    "importance = np.abs(loadings).sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "49c09f1b-3c95-496d-839b-e7b7d88a57aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a DataFrame for feature importance\n",
    "importance_df = pd.DataFrame({\n",
    "    'Feature': features.columns,\n",
    "    'Importance': importance\n",
    "}).sort_values(by='Importance', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0711a828-0201-47a0-9077-7cac0cb31a01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA/EAAAIjCAYAAABLQJsFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8g+/7EAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd1QVV9vw4d8BpDexoahgoYko1sSOisHeRezYS+y9S1HsDY0lUUEN9kLsjYhGVCwRYkHEQizBaFBQLIByvj/4mNcjRdA0n9zXWrMWZ2bvPXvmmOd977PLrVKr1WqEEEIIIYQQQgjxr6f1T3dACCGEEEIIIYQQeSNBvBBCCCGEEEII8ZmQIF4IIYQQQgghhPhMSBAvhBBCCCGEEEJ8JiSIF0IIIYQQQgghPhMSxAshhBBCCCGEEJ8JCeKFEEIIIYQQQojPhATxQgghhBBCCCHEZ0KCeCGEEEIIIYQQ4jMhQbwQQgghhPjLxcbG8tVXX2FmZoZKpSIkJOSf7pIQQnyWJIgXQggh/qWCgoJQqVTZHhMnTvxL7nn69Gm8vb1JTEz8S9r/FJnv48KFC/90Vz7aihUrCAoK+qe78adLTExEX18flUpFdHR0tmV69erF5cuXmTVrFhs3bqR69eps2rSJJUuW/K19dXV11fhvycLCgho1arBu3TrS09OzlA8LC6N9+/ZYWlqiq6tL0aJFadWqFbt27cq2/ejoaFQqFfr6+v/K/46EEJ8/nX+6A0IIIYTIna+vL2XKlNE4V7Fixb/kXqdPn8bHxwcvLy/Mzc3/knv8l61YsYLChQvj5eX1T3flT7V9+3ZUKhWWlpYEBwczc+ZMjeuvXr3izJkzTJkyhaFDhyrnN23axJUrVxg5cuTf2t+SJUsye/ZsAB4/fsyGDRvo27cvN27cYM6cOUq5GTNm4Ovri62tLQMHDsTa2pqEhAQOHDhAhw4dCA4OpmvXrhptf//991haWvL06VN27NhBv379/tZnE0L875MgXgghhPiXa9asGdWrV/+nu/FJXrx4gZGR0T/djX/My5cvMTQ0/Ke78Zf5/vvvad68OdbW1mzatClLEP/48WOAv+WHofT0dFJTU9HX18+xjJmZGd27d1c+Dxw4EHt7e5YvX46fnx8FChRgx44d+Pr60rFjRzZt2kSBAgWU8uPGjePw4cOkpaVptKtWq9m0aRNdu3blzp07BAcHSxAvhPjTyXR6IYQQ4jN38OBB6tWrh5GRESYmJrRo0YKrV69qlPnll1/w8vKibNmy6OvrY2lpSZ8+fUhISFDKeHt7M27cOADKlCmjTDeOi4sjLi4OlUqV7VRwlUqFt7e3RjsqlYpr167RtWtXChYsSN26dZXr33//PdWqVcPAwAALCws8PT25d+/eRz27l5cXxsbG3L17l5YtW2JsbIyVlRXffPMNAJcvX6ZRo0YYGRkpAea7Mqfonzx5koEDB1KoUCFMTU3p2bMnT58+zXK/FStW4OTkhJ6eHiVKlODrr7/OMmXa1dWVihUrcvHiRerXr4+hoSGTJ0/GxsaGq1evcuLECeXdurq6AvDkyRPGjh2Ls7MzxsbGmJqa0qxZM6KiojTaDgsLQ6VSsW3bNmbNmkXJkiXR19encePG3Lx5M0t/IyIiaN68OQULFsTIyIhKlSqxdOlSjTLXr1+nY8eOWFhYoK+vT/Xq1dmzZ0+ev4O7d+/y008/4enpiaenJ3fu3OH06dPKdW9vb6ytrYGM4FelUmFjY4Orqyv79+/n119/Vd6HjY2NUi8lJYUZM2ZQvnx59PT0KFWqFOPHjyclJUXj/iqViqFDhxIcHKx8N4cOHcpz/wEMDQ358ssvefHihfKDw7Rp07CwsGDdunUaAXwmd3d3WrZsqXEuPDycuLg45V2cPHmS+/fv56svQgjxITISL4QQQvzLJSUl8ccff2icK1y4MAAbN26kV69euLu7M3fuXF6+fMnKlSupW7culy5dUoKio0ePcvv2bXr37o2lpSVXr17l22+/5erVq5w9exaVSkX79u25ceMGmzdvZvHixco9ihQpogQ2+dGpUydsbW3x9/dHrVYDMGvWLKZNm4aHhwf9+vXj8ePHLFu2jPr163Pp0qWPGql9+/YtzZo1o379+sybN4/g4GCGDh2KkZERU6ZMoVu3brRv355Vq1bRs2dPatWqlWV5wtChQzE3N8fb25uYmBhWrlzJr7/+qgTNkBGM+vj44ObmxuDBg5Vy58+fJzw8XCPQS0hIoFmzZnh6etK9e3eKFSuGq6srw4YNw9jYmClTpgBQrFgxAG7fvk1ISAidOnWiTJky/P7776xevZoGDRpw7do1SpQoodHfOXPmoKWlxdixY0lKSmLevHl069aNiIgIpczRo0dp2bIlxYsXZ8SIEVhaWhIdHc2+ffsYMWIEAFevXqVOnTpYWVkxceJEjIyM2LZtG23btmXnzp20a9fug+9/8+bNGBkZ0bJlSwwMDChXrhzBwcHUrl0bgPbt22Nubs6oUaPo0qULzZs3x9jYGCMjI5KSkrh//z6LFy8GwNjYGMgYTW/dujWnTp1iwIABODo6cvnyZRYvXsyNGzeybIr3448/sm3bNoYOHUrhwoU1fgzIq9u3b6OtrY25uTmxsbFcv36dPn36YGJikuc2goODKVeuHDVq1KBixYoYGhqyefNm5ccxIYT4U6iFEEII8a8UGBioBrI91Gq1+vnz52pzc3N1//79Neo9fPhQbWZmpnH+5cuXWdrfvHmzGlCfPHlSOTd//nw1oL5z545G2Tt37qgBdWBgYJZ2APWMGTOUzzNmzFAD6i5dumiUi4uLU2tra6tnzZqlcf7y5ctqHR2dLOdzeh/nz59XzvXq1UsNqP39/ZVzT58+VRsYGKhVKpV6y5Ytyvnr169n6Wtmm9WqVVOnpqYq5+fNm6cG1D/88INarVarHz16pNbV1VV/9dVX6rdv3yrlli9frgbU69atU841aNBADahXrVqV5RmcnJzUDRo0yHL+9evXGu2q1RnvXE9PT+3r66ucO378uBpQOzo6qlNSUpTzS5cuVQPqy5cvq9VqtfrNmzfqMmXKqK2trdVPnz7VaDc9PV35u3HjxmpnZ2f169evNa7Xrl1bbWtrm6Wf2XF2dlZ369ZN+Tx58mR14cKF1WlpaRrPAqjnz5+vUbdFixZqa2vrLG1u3LhRraWlpf7pp580zq9atUoNqMPDw5VzgFpLS0t99erVPPW3QYMGagcHB/Xjx4/Vjx8/VkdHR6uHDx+uBtStWrVSq9Vq9Q8//KAG1IsXL85Tm2q1Wp2amqouVKiQesqUKcq5rl27qitXrpznNoQQIi9kOr0QQgjxL/fNN99w9OhRjQMyRloTExPp0qULf/zxh3Joa2vzxRdfcPz4caUNAwMD5e/Xr1/zxx9/8OWXXwLw888//yX9HjRokMbnXbt2kZ6ejoeHh0Z/LS0tsbW11ehvfr277tjc3Bx7e3uMjIzw8PBQztvb22Nubs7t27ez1B8wYIDGSPrgwYPR0dHhwIEDABw7dozU1FRGjhyJltb//b9P/fv3x9TUlP3792u0p6enR+/evfPcfz09PaXdt2/fkpCQgLGxMfb29tl+P71790ZXV1f5XK9ePQDl2S5dusSdO3cYOXJkltkNmTMLnjx5wo8//oiHhwfPnz9Xvo+EhATc3d2JjY3lwYMHufb7l19+4fLly3Tp0kU5l/nv8fDhw3l+/vdt374dR0dHHBwcNP6tNGrUCCDLv5UGDRpQoUKFPLd//fp1ihQpQpEiRXB0dGTZsmW0aNGCdevWAfDs2TOAfI3CHzx4kISEhCzvIioqKsvyFiGE+BQynV4IIYT4l6tZs2a2G9vFxsYCKIHN+0xNTZW/nzx5go+PD1u2bOHRo0ca5ZKSkv7E3v6f96esx8bGolarsbW1zbZ8duuO80JfX58iRYponDMzM6NkyZJKwPru+ezWur/fJ2NjY4oXL05cXBwAv/76K5DxQ8C7dHV1KVu2rHI9k5WVlUaQ/SHp6eksXbqUFStWcOfOHd6+fatcK1SoUJbypUuX1vhcsGBBAOXZbt26BeSexeDmzZuo1WqmTZvGtGnTsi3z6NEjrKyscmzj+++/x8jIiLJlyypr8vX19bGxsSE4OJgWLVrkWDc3sbGxREdHZ/le3+3Xu97/t/YhNjY2fPfdd0oqOFtbW4oWLapcz/xv5/nz53lu8/vvv6dMmTLo6ekp76JcuXIYGhoSHByMv79/vvoohBA5kSBeCCGE+Exl5rTeuHEjlpaWWa7r6Pzf/5n38PDg9OnTjBs3DhcXF4yNjUlPT6dp06bZ5sZ+3/vBcKZ3g833vTv6n9lflUrFwYMH0dbWzlI+cz10fmXXVm7n1f9/ff5f6f1n/xB/f3+mTZtGnz598PPzw8LCAi0tLUaOHJnt9/NnPFtmu2PHjsXd3T3bMuXLl8+xvlqtZvPmzbx48SLbUfBHjx6RnJz8Ud9reno6zs7OLFq0KNvrpUqV0vic3/dtZGSEm5tbjtcdHByAjI0R8+LZs2fs3buX169fZ/sj1aZNm5g1a1aO/x0JIUR+SBAvhBBCfKbKlSsHQNGiRXMNSJ4+fUpoaCg+Pj5Mnz5dOZ85kv+unIKMzJHe93dif38E+kP9VavVlClTBjs7uzzX+zvExsbSsGFD5XNycjLx8fE0b94cQNldPSYmhrJlyyrlUlNTuXPnTq7v/105vd8dO3bQsGFD1q5dq3E+MTFR2WAwPzL/bVy5ciXHvmU+R4ECBfLc/3edOHGC+/fv4+vri6Ojo8a1p0+fMmDAAEJCQjRSub0vp/dRrlw5oqKiaNy48T8S+NrZ2WFvb88PP/zA0qVLP/hDxK5du3j9+jUrV67M8n3FxMQwdepUwsPDNbI0CCHEx5I18UIIIcRnyt3dHVNTU/z9/bPkq4b/y82dOWr7/ijtkiVLstTJzOX+frBuampK4cKFOXnypMb5FStW5Lm/7du3R1tbGx8fnyx9UavVGunu/m7ffvutxjtcuXIlb968oVmzZgC4ubmhq6tLQECARt/Xrl1LUlJSnqeNGxkZZXm3kPEdvf9Otm/f/sE16TmpWrUqZcqUYcmSJVnul3mfokWL4urqyurVq4mPj8/SxocyEmROpR83bhwdO3bUOPr374+trS3BwcG5tpG5Q/37PDw8ePDgAd99912Wa69eveLFixe5tvtn8PHxISEhgX79+vHmzZss148cOcK+ffuAjHdRtmxZBg0alOVdjB07FmNj4w++CyGEyCsZiRdCCCE+U6ampqxcuZIePXpQtWpVPD09KVKkCHfv3mX//v3UqVOH5cuXY2pqqqRfS0tLw8rKiiNHjnDnzp0sbVarVg2AKVOm4OnpSYECBWjVqhVGRkb069ePOXPm0K9fP6pXr87Jkye5ceNGnvtbrlw5Zs6cyaRJk4iLi6Nt27aYmJhw584ddu/ezYABAxg7duyf9n7yIzU1lcaNG+Ph4UFMTAwrVqygbt26tG7dGshIszdp0iR8fHxo2rQprVu3VsrVqFEj19Hmd1WrVo2VK1cyc+ZMypcvT9GiRWnUqBEtW7bE19eX3r17U7t2bS5fvkxwcLDGqH9+aGlpsXLlSlq1aoWLiwu9e/emePHiXL9+natXryqbzn3zzTfUrVsXZ2dn+vfvT9myZfn99985c+YM9+/fz5KnPlNKSgo7d+6kSZMm6OvrZ1umdevWLF26NMv69fffx9atWxk9ejQ1atTA2NiYVq1a0aNHD7Zt28agQYM4fvw4derU4e3bt1y/fp1t27Zx+PDhbPeJ+DN17tyZy5cvM2vWLC5dukSXLl2wtrYmISGBQ4cOERoayqZNm/jtt984fvw4w4cPz7YdPT093N3d2b59OwEBAR+994MQQij+mU3xhRBCCPEh2aVUy87x48fV7u7uajMzM7W+vr66XLlyai8vL/WFCxeUMvfv31e3a9dObW5urjYzM1N36tRJ/dtvv2VJuaZWq9V+fn5qKysrtZaWlka6uZcvX6r79u2rNjMzU5uYmKg9PDzUjx49yjHF3OPHj7Pt786dO9V169ZVGxkZqY2MjNQODg7qr7/+Wh0TE5Pv99GrVy+1kZFRlrINGjRQOzk5ZTlvbW2tbtGiRZY2T5w4oR4wYIC6YMGCamNjY3W3bt3UCQkJWeovX75c7eDgoC5QoIC6WLFi6sGDB2dJ4ZbTvdXqjPR/LVq0UJuYmKgBJd3c69ev1WPGjFEXL15cbWBgoK5Tp476zJkz6gYNGmikpMtMMbd9+3aNdnNKAXjq1Cl1kyZN1CYmJmojIyN1pUqV1MuWLdMoc+vWLXXPnj3VlpaW6gIFCqitrKzULVu2VO/YsSPbZ1CrM75DQL127docy4SFhakB9dKlS3NMMZecnKzu2rWr2tzcXA1opJtLTU1Vz507V+3k5KTW09NTFyxYUF2tWjW1j4+POikpSSkHqL/++usc+/G+3L6f7ISGhqrbtGmjLlq0qFpHR0ddpEgRdatWrZT0gwsXLlQD6tDQ0BzbCAoK0khZKIQQn0KlVv8Nu7sIIYQQQvwLBQUF0bt3b86fP/+Xj+wKIYQQfwZZEy+EEEIIIYQQQnwmJIgXQgghhBBCCCE+ExLECyGEEEIIIYQQnwlZEy+EEEIIIYQQQnwmZCReCCGEEEIIIYT4TEgQL4QQQgghhBBCfCZ0/ukOCPFflZ6ezm+//YaJiQkqleqf7o4QQgghhBDiH6JWq3n+/DklSpRASyv3sXYJ4oX4h/z222+UKlXqn+6GEEIIIYQQ4l/i3r17lCxZMtcyEsQL8Q8xMTEBMv5DNTU1/Yd7I4QQQgghhPinPHv2jFKlSikxQm4kiBfiH5I5hd7U1FSCeCGEEEIIIUSeltnKxnZCCCGEEEIIIcRnQoJ4IYQQQgghhBDiMyFBvBBCCCGEEEII8ZmQIF4IIYQQQgghhPhMSBAvhBBCCCGEEEJ8JiSIF0IIIYQQQgghPhMSxAshhBBCCCGEEJ8JCeKFEEIIIYQQQojPhATxQgghhBBCCCHEZ0KCeCGEEEIIIYQQ4jMhQbwQQgghhBBCCPGZkCBeCCGEEEIIIYT4TEgQL4QQQgghhBBCfCYkiBdCCCGEEEIIIT4TEsQLIYQQQgghhBCfCQnihRBCCCGEEEKIz4QE8Z8RGxsblixZkmuZhw8f0qRJE4yMjDA3N/9b+vUxgoKC/tX9y46rqysjR478p7shhBBCCCGE+A+TIP5f4t69e/Tp04cSJUqgq6uLtbU1I0aMICEhIV/tLF68mPj4eCIjI7lx48Zf1Nv8ye7Hh86dO//p/YuLi0OlUimHhYUFDRo04KeffspXO2FhYahUKhITEzXO79q1Cz8/vz+xx0IIIYQQQgiRPzr/dAcE3L59m1q1amFnZ8fmzZspU6YMV69eZdy4cRw8eJCzZ89iYWGRp7Zu3bpFtWrVsLW1/Yt7/WkMDAwwMDD4S9o+duwYTk5O/PHHH8yaNYuWLVty48YNihUr9knt5vU7yK9FUQnoG6f+JW0LIcTfYWKVwv90F4QQQoj/DBmJ/xf4+uuv0dXV5ciRIzRo0IDSpUvTrFkzjh07xoMHD5gyZYpS9vnz53Tp0gUjIyOsrKz45ptvlGs2Njbs3LmTDRs2oFKp8PLyUkanIyMjlXKJiYmoVCrCwsIAePr0Kd26daNIkSIYGBhga2tLYGCgUv7+/ft06dIFCwsLjIyMqF69OhEREUDGjwZt2rShWLFiGBsbU6NGDY4dO6bUdXV15ddff2XUqFHKCDlkP51+5cqVlCtXDl1dXezt7dm4caPGdZVKxZo1a2jXrh2GhobY2tqyZ8+eLO+zUKFCWFpaUrFiRSZPnsyzZ8+U/gJs3LiR6tWrY2JigqWlJV27duXRo0dAxmh+w4YNAShYsKDyHjOf5d3p9E+fPqVnz54ULFgQQ0NDmjVrRmxsbLbfsRBCCCGEEEL8GSSI/4c9efKEw4cPM2TIkCwj05aWlnTr1o2tW7eiVqsBmD9/PpUrV+bSpUtMnDiRESNGcPToUQDOnz9P06ZN8fDwID4+nqVLl+apD9OmTePatWscPHiQ6OhoVq5cSeHCGaMqycnJNGjQgAcPHrBnzx6ioqIYP3486enpyvXmzZsTGhrKpUuXaNq0Ka1ateLu3btAxhT0kiVL4uvrS3x8PPHx8dn2Yffu3YwYMYIxY8Zw5coVBg4cSO/evTl+/LhGOR8fHzw8PPjll19o3rw53bp148mTJ9m2+erVKzZs2ACArq6ucj4tLQ0/Pz+ioqIICQkhLi5OCdRLlSrFzp07AYiJicn1PXp5eXHhwgX27NnDmTNnUKvVNG/enLS0tGzLp6Sk8OzZM41DCCGEEEIIIfJDptP/w2JjY1Gr1Tg6OmZ73dHRkadPn/L48WMA6tSpw8SJEwGws7MjPDycxYsX06RJE4oUKYKenh4GBgZYWloCGaPFH3L37l2qVKlC9erVgYwR/UybNm3i8ePHnD9/XplOXr58eeV65cqVqVy5svLZz8+P3bt3s2fPHoYOHYqFhQXa2trKqHdOFixYgJeXF0OGDAFg9OjRnD17lgULFigj45AROHfp0gUAf39/AgICOHfuHE2bNlXK1K5dGy0tLV6+fIlaraZatWo0btxYud6nTx/l77JlyxIQEECNGjVITk7G2NhYec6iRYvmuPlebGwse/bsITw8nNq1awMQHBxMqVKlCAkJoVOnTlnqzJ49Gx8fnxzfgRBCCCGEEEJ8iIzE/0tkjrR/SK1atbJ8jo6O/qR7Dx48mC1btuDi4sL48eM5ffq0ci0yMpIqVarkuB48OTmZsWPH4ujoiLm5OcbGxkRHRysj8XkVHR1NnTp1NM7VqVMny7NVqlRJ+dvIyAhTU1NlKnymrVu3cunSJXbu3En58uUJCgqiQIECyvWLFy/SqlUrSpcujYmJCQ0aNADIV5+jo6PR0dHhiy++UM4VKlQIe3v7HL+PSZMmkZSUpBz37t3L8/2EEEIIIYQQAiSI/8eVL18elUqVY+AXHR1NwYIFKVKkyEe1r6WV8RW/+yPB+9O9mzVrpqxb/+2332jcuDFjx44F+ODmc2PHjmX37t34+/vz008/ERkZibOzM6mpf81Gbe8G45CxTj5zan+mUqVKYWtrS7t27fD396ddu3akpKQA8OLFC9zd3TE1NSU4OJjz58+ze/dugL+sz5n09PQwNTXVOIQQQgghhBAiP2Q6/T+sUKFC1K9fH19fX2bOnImurq6S2uzhw4cEBwfTs2dPZUO4s2fPatQPDQ0lLi6OyMhIXFxcsrSfGfyHhobSs2dPrl+/To0aNbIt16tXL3r16kW9evUYN24cCxYsoFKlSqxZs4YnT55kOxofHh6Ol5cX7dq1AzJG5uPi4jTK6Orq8vbt21zfg6OjI+Hh4fTq1Uuj7QoVKuRaD+CPP/5ApVKxf//+LNc6duzI9OnTWbFiBaNGjeL69eskJCQwZ84c1q5dS0hIiPKDxbv9BXLts6OjI2/evCEiIkKZTp+QkEBMTEye+vyu0ZULSUAvhBBCCCGEyBMZiX+Pl5cXKpWKOXPmaJwPCQlRAum8unr1Kh4eHspadTs7O6ZPn87Lly81ytna2vLmzRucnJxYv3499+7d49ChQ8r08k2bNqGvr89vv/3G8ePHmTNnDjdu3OCbb77hwIEDufbBwMCAL7/8El9fX8qUKcOmTZuyjDhPnz6dH374gZs3b3L16lX27dunrNHv0qULlpaWtG3blvDwcG7fvs3OnTs5c+aM0vddu3YRGRlJVFQUXbt2zTIybmNjw8mTJ3nw4AF//PEHkBF4JyUlKbvmjxs3jqCgIFauXElsbCyLFi1i165dWQLs/Ni5cycNGzYkLi6O0aNHU7FiRbZt24auri7Lli3j6dOnJCUlZcn9bm1tjUqlYt++fTx+/Jjk5OQsbdva2tKmTRv69+/PqVOniIqKonv37lhZWdGmTZuP7rMQQgghhBBC5EZG4rOhr6/P3LlzGThwIAULFvyoNs6ePYubmxtubm7s37+fYsWKce7cOcaMGUNoaCjHjx9XRnyfPn1KmzZtMDExYeDAgTx58gQzMzMSEhLo3r07o0ePxtzcnOrVq/PixQu++eYb/Pz8MDU1ZcqUKfj6+ubal3Xr1lGpUiUOHz7MvXv3mDdvHl999ZVyXVdXl0mTJhEXF4eBgQH16tVjy5YtyrUjR44wZswYmjdvzps3b6hQoYKS2m7RokX06dOH2rVrU7hwYSZMmJBl13VfX18GDhxIuXLlSElJyXb9f9u2bVm6dCkLFixgxIgRlClThsDAQFxdXT/q/U+ZMoW5c+cyatQopk+fTvv27alVqxY3b96kc+fObN++nXv37qGnp8eWLVto3bq1UtfKygofHx8mTpxI79696dmzJ0FBQVnuERgYyIgRI2jZsiWpqanUr1//gz+qZEfyxAsh/hdIrnghhBDi7yEj8dlwc3PD0tKS2bNn51hm586dODk5oaenh42NDQsXLlSuqdVq+vbti6OjI7t27aJmzZpYW1vTqVMn9u7dy5kzZ1i8eDHwf7ndd+3axfr162natClPnz5FrVbTrl07NmzYgIuLCzY2Nvzxxx9s27aN+/fvs27dOuLj4+nduzcA169fp3bt2hw6dIgLFy5w4sQJICPveYUKFXjz5g2pqalERkby4MEDAgMDadu2LQBTp07l2rVrbNq0iSdPnhASEkKZMmWIioqiYcOGVKxYkSNHjlC+fHlOnDjB+fPnqVmzpjKV/uzZs+jp6eHo6EjXrl0JCwujadOm1K1bF3Nzc1q0aEGpUqW4evWqEsCPGzcOgCpVqqBSqXB1dWXw4MHcunWLFStWoKWlRf/+/XFwcGDFihXKe23bti3nzp2jSpUq6OvrU758ecqVKwdAiRIlUKvVpKam4u/vz8KFC5k/fz6NGjUiMTGR7777jp07dxIQEMCdO3eYOnUqtra2JCYmYm1tTYMGDfD09OT58+dMmzaN+Ph4Dhw4wM2bNzE3N+fy5cvcvHmTW7duARl55H19fUlKSiIwMJBXr17h7OxMcHDwn/VPUQghhBBCCCE0SBCfDW1tbfz9/Vm2bBn379/Pcv3ixYt4eHjg6enJ5cuX8fb2Ztq0acpobWRkJNeuXWP06NHKxnKZKleujJubG5s3bwayz+1+5MgREhISsp1K3qpVK+zs7JT6mcaNG8eYMWO4dOkStWrVolWrViQkJFCqVCni4+MxNTVlyZIlxMfH07lz5zy9h27dulGyZEnOnz/PxYsXmThxorKxXGRkJI0bN6ZChQqcOXOGU6dO0apVK2Ud+YsXLxg9ejQXLlwgNDQULS0t2rVrp0y1P3fuHADHjh0jPj6eXbt2ARlp2qZPn86sWbOIjo7G39+fadOmsX79eiBjzX3Lli2pUKECFy9exNvbO8t7Cg4OxtjYWElX975308bdunWLkJAQ9u3bx759+zhx4oTGUooPPUemiRMnMmLECKKjo3F3d8/2vpInXgghhBBCCPGpZDp9Dtq1a4eLiwszZsxg7dq1GtcWLVpE48aNmTZtGpCRr/3atWvMnz8fLy8vbty4AZBr7vdTp04BZJvb/UP1HRwclDKZhg4dSocOHQBYuXIlhw4dYu3atYwfPx5LS0tUKhVmZma55mp/3927dxk3bhwODg5AxjrwTPPmzaN69erKKDmAk5OT8ndmXzKtW7eOIkWKcO3aNSpWrKhsuFeoUCGNPs2YMYOFCxfSvn17AMqUKcO1a9dYvXo1vXr1YtOmTaSnp7N27Vr09fVxcnLi/v37DB48WGkjNjaWsmXLZtnJPjvp6ekEBQVhYmICQI8ePQgNDWXWrFl5eo5MI0eOVPqcE8kTL4QQQgghhPhUMhKfi7lz57J+/fos6d9yymkeGxursaN5XnO/5yQ/9d/NH6+jo0P16tU/OX/86NGj6devH25ubsyZM0eZRg7/NxKfk9jYWLp06ULZsmUxNTXFxsYGyD0X+4sXL7h16xZ9+/bF2NhYOWbOnKncOzo6mkqVKqGvr6/Ue/fZIX/vzcbGRgngAYoXL66Rdz6vz1G9evUP3kvyxAshhBBCCCE+lQTxuahfvz7u7u5MmjQpX/Xs7OwAcs39nlnmr6ifF1paWlmC3ffzx3t7e3P16lVatGjBjz/+SIUKFZSc6h/KH9+qVSuePHnCd999R0REBBEREUDuudgzd4H/7rvviIyMVI4rV65kSa2XGzs7O27fvp3lebLzobzzeX0OIyOjD95L8sQLIYQQQgghPpUE8R8wZ84cZTO6TJk5zd8VHh6OnZ0d2trauLi44ODgwOLFi7OsnY6KiuLYsWN06dIlx3t+9dVXWFhYaGyWl2nPnj3K6PC73g1y37x5w8WLF3Ocjg8Z0/ifP3/OixcvlHOZ6d7eZWdnx6hRozhy5Ajt27cnMDAQgEqVKhEaGppt25n50qdOnUrjxo1xdHTk6dOnGmWyy8VerFgxSpQowe3btylfvrzGUaZMGSDj3f/yyy+8fv0622cH6Nq1K8nJyRpT/d+VmJiYw1vJ/3MIIYQQQgghxN9J1sR/gLOzM926dSMgIEA5N2bMGGrUqIGfnx+dO3fmzJkzLF++XAkaVSoVa9eupUmTJnTo0IFJkyZhaWlJREQEY8aMoVatWowcOTLHexoZGbF69Wo8PT0ZMGAAQ4cOxdTUlNDQUMaNG0fHjh3x8PDQqPPNN99ga2uLo6Mjixcv5unTp/Tp0yfHe3zxxRcYGhoyefJkhg8fTkREhEYatVevXin3KlOmDPfv3+f8+fPKGvGrV6/y888/M2TIEAYNGoSuri7Hjx+nU6dOWFhYUKhQIVasWMHMmTP56aefePXqlcb9ixYtioGBAYcOHaJkyZLo6+tjZmaGj48Pw4cPx8zMjKZNm5KSksKFCxd4+vQpo0ePpmvXrkyZMoX+/fsrafEWLFiQ5dnGjx/PmDFjePDgAe3ataNEiRLcvHmTVatWUbduXUaMGKGUDwsLo2HDhlkC9IIFC1KoUCG+/fZbihcvzt27d5k4cWKO7/Rjja5cSEblhRBCCCGEEHkiQXwe+Pr6snXrVuVz1apV2bZtG9OnT8fPz4/ixYvj6+uLl5eXUqZ27dqcPXsWHx8fmjVrxvPnzyldujS9evVi0qRJ6Onp5XrPjh07cvz4cWbNmkW9evVITk7GyMiIGTNmMHLkSFQqFfB/o9DTpk1jzpw5REZGUr58efbs2UPhwjnn7LWwsOD7779n3LhxfPfddzRu3Bhvb28GDBjAyJEj2bVrF/fv32flypVAxsh9p06dlI3ZDA0Nadu2LVFRUdSsWRMDAwO++OILunTpgpaWFlu2bKFHjx48fPgQOzs7Zs+erbFJnI6ODgEBAfj6+jJ9+nTq1atHWFgY/fr1w9DQkPnz5zNu3DiMjIxwdnZWfvQwNjZm7969DBo0iCpVqlChQgXmzp2bZQO6uXPnUq1aNb755htWrVpFeno65cqV49mzZ1neS+3atYmPj8fMzEzjfOZzDB8+nIoVK2Jvb09AQMBH567PieSJF0KITyM56oUQQvyXqNSfuvua+Ft4eXmRmJhISEiIxvl3R5HfTZ32MZ48ecKXX36Jrq4u8+fPx8nJibi4OKZOnUpMTAxnzpyhbNmyALi6uuLi4sKSJUtybG/s2LGcP39eyVn/b5CXfv9dnj17hpmZGTNO3kbf2OTDFYQQQmRLgnghhBCfu8zYICkp6YOzdGVN/P+QhIQEunTpgpWVFYaGhjg7O2fJJ79jxw6cnZ0xMDCgUKFCuLm5Kevip0yZwm+//caxY8do1qwZpUuXpn79+hw+fJgCBQrw9ddfa7T15s0bhg4dipmZGYULF2batGnKZnmurq4sXLiQkydPolKplNFrlUqV5YcIc3NzZSp/amoqQ4cOpXjx4ujr62Ntbc3s2bOVsomJiQwcOJBixYqhr69PxYoV2bdvX56e38vLixMnTrB06VJUKhUqlYq4uDjCwsJQqVQaa+V37tyJk5MTenp62NjYZNmfwMbGBn9/f/r06YOJiQmlS5fm22+/zd8XJoQQQgghhBD5JEH8/5DXr19TrVo19u/fz5UrVxgwYAA9evTg3LlzAMTHx9OlSxf69OlDdHQ0YWFhtG/fHrVaTXp6Olu2bKFbt25ZcskbGBgwZMgQDh8+zJMnT5Tz69evR0dHh3PnzrF06VIWLVrEmjVrANi1axf9+/enVq1axMfHs2vXrjw9Q0BAAHv27GHbtm3ExMQQHByspHVLT0+nWbNmhIeH8/3333Pt2jXmzJmDtrZ2np5/6dKl1KpVi/79+xMfH098fDylSpXK0oeLFy/i4eGBp6cnly9fxtvbm2nTpmnsGQCwcOFCqlevzqVLlxgyZAiDBw8mJiYmx2dLSUnh2bNnGocQQgghhBBC5Iesif+M7Nu3D2NjY41z7+7ubmVlxdixY5XPw4YN4/Dhw2zbto2aNWsSHx/PmzdvaN++PdbW1kDGxn0Av//+O4mJiTnuaO/o6IharebmzZvUrFkTgFKlSrF48WJUKhX29vZcvnyZxYsX079/fywsLDA0NERXVzfLjwK5uXv3Lra2ttStWxeVSqX0E+DYsWOcO3dOI8Ve5vT+vDy/mZkZurq6GBoa5tqnRYsW0bhxY6ZNmwZk7NB/7do15s+fr7HvQfPmzRkyZAgAEyZMYPHixRw/fhx7e/ts2509e7ayp4AQQgghhBBCfAwZif+MNGzYUCN/emRkpDLyDRkBvZ+fH87OzlhYWGBsbMzhw4e5e/cuAJUrV6Zx48Y4OzvTqVMnvvvuuyw7sudni4Qvv/xS2WAPoFatWsTGxmr8sJBfXl5eREZGYm9vz/Dhwzly5IhyLTIykpIlSyoB/Ps+9Px5FR0dTZ06dTTO1alTJ8uzVapUSflbpVJhaWnJo0ePcmx30qRJJCUlKce9e/fy1S8hhBBCCCGEkCD+M2JkZJQlf7qVlZVyff78+SxdupQJEyZw/PhxIiMjcXd3JzU1Y+dzbW1tjh49ysGDB6lQoQLLli3D3t6eO3fuUKRIEczNzYmOjs723tHR0ahUKsqXL/9Jz6BSqbL8UJCWlqb8XbVqVe7cuYOfnx+vXr3Cw8ODjh07AhnT+nPzoef/sxUoUEDjs0qlIj09Pcfyenp6mJqaahxCCCGEEEIIkR8ynf5/SHh4OG3atKF79+5AxhryGzduUKFCBaWMSqWiTp061KlTh+nTp2Ntbc3u3bsZPXo0Hh4eBAcH4+vrqzHd/NWrV6xYsQJ3d3csLCyU8xERERr3P3v2LLa2tsoa9ewUKVKE+Ph45XNsbCwvX77UKGNqakrnzp1p0KABFy9eZOfOnZiamrJ3717u37/PjRs3sh2Nz8vz6+rqfnCmgKOjI+Hh4VnatrOzy/XZPpbkiRdCCCGEEELklYzE/w+xtbXl6NGjnD59mujoaAYOHMjvv/+uXI+IiMDf358LFy5w9+5ddu3axePHj5V18P7+/lhaWtKkSRMOHjzIvXv3OHnyJO7u7iQkJHDo0CHmzJmjtHf37l1at26NSqVi8+bNLFu2jBEjRuTax0aNGrFw4UKaNGmCubm5sn589+7dvHz5kkWLFrF582auX7/OtGnTlFkCN27coEGDBhQoUAB7e3tUKhUGBgaUK1eOKVOm5On5IWNX+YiICOLi4vjjjz+yHTkfM2YMoaGh+Pn5cePGDdavX8/y5cs11tsLIYQQQgghxD9BRuL/h0ydOpXbt2/j7u6OoaEhAwYMoG3btiQlJQEZI9wnT55kyZIlPHv2DGtraxYuXEizZs0AKFSoEGfPnsXX15eBAwfy8OFDLCwsaNasGUWLFmX//v3MnTuXgQMHAtCzZ09u3boFwNdff82IESMYMGBArn3s2rUr27dv59dff6VEiRIsWbKEr7/+mlOnTtGkSRO6d+/OvHnziI2NJSUlhcKFC3PgwAFlZkCRIkUoUqQIcXFxvHz5kqdPn+Lv70+LFi0++PyQkbu+V69eVKhQgVevXnHnzp0sfaxatSrbtm1j+vTp+Pn5Ubx4cXx9fTU2tfszLYpKQN/4r5nyL4QQ/0WSN14IIcT/MpU6PzuZif8sLy8vEhISuHnzJq1atWLevHkAhISE0K5dO2Wd+86dO5k+fTo3b96kePHiDBs2jDFjxgAZm+ZVrFgRQ0NDIiIi0NL6v4kgUVFRVKlShdmzZzNhwgRsbGz49ddfleu9evUiKCgIGxsbRo4cyciRI4GMXPVmZmYMHz6c2bNnc/nyZUaMGMGZM2cwNDSkQ4cOLFq0SNnV38vLi8TERKpUqcLy5ctJSUmha9euBAQEoKurC5DlHgAuLi60bdsWb29vIGNZwooVK9izZw9hYWEUL16cefPmKev38+LZs2eYmZkx4+Rt9I1N8veFCCGEyJEE8UIIIT43mbFBUlLSB5faynR6kWfa2tr4+/uzbNky7t+/n+X6h/KrR0ZGcu3aNUaPHq0RwEPGzvlubm5s3rwZgPPnz9O0aVM8PDyIj49n6dKl2fZJR0eHAgUKkJqayosXL3B3d6dgwYKcP3+e7du3c+zYMYYOHapRJzQ0lOjoaMLCwti8eTO7du36qNRv06ZNo0OHDkRFRdGtWzc8PT1z3BgQJE+8EEIIIYQQ4tNJEC/ypV27dri4uDBjxows197Nr25nZ4eXlxdDhw5l/vz5ANy4cQMg11z0mWWKFCmCnp4eBgYGWFpaYmZmlqV8amoqs2fPJikpiUaNGrFp0yZev37Nhg0bqFixIo0aNWL58uVs3LhRY228rq4u69atw8nJiRYtWuDr60tAQECuO8tnp1OnTvTr1w87Ozv8/PyoXr06y5Yty7H87NmzMTMzU45SpUrl635CCCGEEEIIIUG8yLe5c+eyfv36LKPOec2v/qkrOCZMmICxsTGGhobMnTuXOXPm0KJFC6Kjo6lcuTJGRkYa909PTycmJkY5V7lyZQwNDZXPtWrVIjk5Od9522vVqpXlc24j8ZInXgghhBBCCPGpJIgX+Va/fn3c3d2ZNGlSvuplpoXLLRd9dqnj3jdu3DgiIyO5f/8+T58+ZcKECfnqx4doaWnlmsv+Y0meeCGEEEIIIcSnkt3p/yYPHz6kR48enD59mgIFCpCYmPhPd+mTzJkzBxcXFyVFHHw4v7qLiwsODg4sXrwYT0/PLBvbHTt2jNmzZ2d7v6CgIEaOHIm5uTmFCxemfPnyWco4OjoSFBTEixcvlNH48PBwtLS0NPoZFRXFq1evMDAwADLy2xsbGyvT29/PZf/s2bNsd7E/e/YsPXv21PhcpUqVnF9aDiRPvBBCCCGEECKv/pMj8V5eXqhUKo2c55Cx07pKpcpXW1evXsXDw0NZw21nZ8f06dN5+fKlRrnFixcTHx9PZGSksu470+zZs9HW1lbWjn8OnJ2d6datGwEBAcq5D+VXV6lUrF27lmvXrtGhQwfOnTvH3bt32b59O61ataJWrVoaO8LnJigoCHNzc41z3bp1Q19fn169enHlyhWOHz/OsGHD6NGjB8WKFcPV1ZVz586RmppK3759uXbtGgcOHGD8+PGkpaUpPyoUKFCA+fPno1Kp0NLSolixYrx584ZXr15p3G/16tV06dKFGzduMGPGDM6dO5dlEz0hhBBCCCGE+DP9Z0fi9fX1lZznBQsW/Kg2zp49i5ubG25ubuzfv59ixYpx7tw5JZg9fvy4krbs1q1bVKtWDVtb2yztrFu3jvHjx7Nu3TrGjRv3Sc/1V3p/4zdfX1+2bt2qfM5LfvXatWtz9uxZfHx8aNasGc+fP6d06dL06tWLSZMmoaen99H9MzQ05PDhw4wYMYIaNWpopJh7V+PGjbG1taV+/fqkpKRQrVo1Ll26pFyvV68ely9fRq1WY2JiQrdu3ViyZAn79u1j7ty5Sjlzc3N+/vlnKlWqRPHixdm8eTMVKlTId78lT7wQQvy3SAo8IYQQn+I/ORIP4ObmhqWlZY7TtyEj57mTkxN6enrY2NiwcOFC5ZparaZv3744Ojqya9cuatasibW1NZ06dWLv3r2cOXOGxYsXAxl5x3fu3MmGDRtQqVQaQe2JEyd49eoVvr6+PHv2jNOnT2v0wdvbGxcXF9atW0fp0qUxNjZmyJAhvH37lnnz5mFpaUnRokWZNWuWRj2VSsXKlStp1qwZBgYGlC1blh07dmiUuXfvHh4eHpibm2NhYUGbNm2Ii4tTrnt5edG2bVtmzZrFkSNHuHz5MgArVqzA1tYWBwcHzM3N6dChg1LHyMiIggULYmhoSHJyMmFhYdy6dUu5HhcXR6VKlejatSuVKlVCR0cHIyMjmjdvrrHZXFBQED///DPbtm2jXbt2JCQkKPWzG63PfE+RkZHcvn0bXV1dmjRpwsKFC5Uc8e/y8fHhjz/+4Pnz58rMjEx6enqUKVOGZ8+e8eDBA+bNm8ewYcM0pthDRsq9wYMH8/r1a+7cuYOHh0eW+wghhBBCCCHEn+k/G8T/W3Ker127li5dulCgQAG6dOnC2rVrs/Tl1q1bHDx4kEOHDrF582bWrl1LixYtuH//PidOnGDu3LlMnTqViIgIjXq55TFPS0vD3d0dExMTfvrpJ8LDwzE2NqZp06akpv7fqHBoaCgxMTEcPXqUffv2ceHCBYYPH46vry8xMTEcOnSI+vXrK+VfvHjB6NGjuXDhAqGhoWhpadGuXbsso/hTpkxh7NixREZGYmdnR5cuXXjz5g0AERER9O3bl6FDhxIZGUnDhg2ZOXPmB7/TW7duERISwr59+9i3bx8nTpzIsmTiY8TFxXH48GFlVsXHkjzxQgghhBBCiE/1n51OD5o5z98Pnt/NeQ4ZO6tfu3aN+fPn4+Xllaec56dOnQKy5jzP9OzZM3bs2MGZM2cA6N69O/Xq1WPp0qUao8fp6emsW7cOExMTKlSoQMOGDYmJieHAgQPKpm1z587l+PHjfPHFF0q9zDzmAH5+fhw9epRly5axYsUKtm7dSnp6OmvWrFFGoQMDAzE3NycsLIyvvvoKyBhZX7NmjRLA7tq1CyMjI1q2bImJiQnW1tYam7m9OyoPGUsFihQpwrVr16hYsaJyfuzYsbRo0QLIGBV3cnLi5s2bODg4sHTpUpo2bcr48eOVd3/69GkOHTqU01epvKegoCBMTEwA6NGjB6GhoVlmKeTF5cuXMTY25u3bt7x+/Rogy7T8/Jo9ezY+Pj6f1IYQQgghhBDiv+0/OxKf6Z/Meb5582bKlStH5cqVAXBxccHa2lpjnTlkTMfPDEwBihUrRoUKFTRmABQrVoxHjx5p1Mstj3lUVBQ3b97ExMQEY2NjjI2NsbCw4PXr1xrT352dnTVGoJs0aYK1tTVly5alR48eBAcHa2ziFxsbS5cuXShbtiympqbY2NgAcPfuXY2+VKpUSfm7ePHiAEr/o6OjNX6MyO5ZsvP+eypevHiWd/LVV18REhLywbbs7e2JjIzk/PnzTJgwAXd3d4YNG6ZcV6vVGtP/80LyxAshhBBCCCE+1X8+iP8nc56vXbuWq1evoqOjoxzXrl1j3bp1GuUKFCig8VmlUmV77v0p67lJTk6mWrVqREZGahw3btyga9euSrnMVG2ZTExM+Pnnn9m8eTPFixdn+vTpVK5cWUmZ16pVK548ecJ3331HRESEMsX/3Sn67z9T5kyA/PQ/Ox96J6ampiQlJWWpl5iYiJmZmcY5XV1dypcvT8WKFZkzZw7a2tqfPIoueeKFEEIIIYQQn+o/PZ0+09+d8xwypmtfuHCBsLAwLCwslPNPnjzB1dWV69ev4+Dg8EnPlVse86pVq7J161aKFi2a72BSR0dH2ZV/xowZmJubU7BgQY4fP05MTAzfffcd9erVA1CWFOSHo6NjlvX9Z8+ezXc777O3t+fIkSNZzv/8888f/MFl6tSpNGrUiMGDB1OiRAlsbGz+tDXtkideCCGEEEIIkVcSxJNzzvMaNWrg5+dH586dOXPmDMuXL2fFihXA/+U8b9KkCR06dGDSpElYWloSERHBmDFjqFWrFleuXKFt27bZTt9eu3YtNWvW1NgULlONGjVYu3btJ+eN3759O9WrV6du3boEBwdz7tw5Ze1/t27dmD9/Pm3atMHX15eSJUvy66+/MmvWLCIiIrIEqK6urri4uODm5sbt27epX78+BQsW5MCBA6SnpxMWFkatWrUoVKgQ3377LcWLF+fu3btMnDgx3/0ePnw4derUoWDBggwZMoTixYt/cD18XgwePJjly5czfPhw+vXrh56eHvv372fz5s3s3bs32zpBQUGMHDmSxMREKlWqhL+/P8uXL1euP3jwgMjISI061tbWH522UAghhBBCCCFyI0H8//dX5DwfMmRItvdKTU3l+++/Z8KECdle79ChAwsXLsTf31859/btW1QqVZad8HPj4+PDli1blED43TzmhoaGnDx5kgkTJtC+fXueP3+OlZUVpUqV0ki39j5zc3N27dqFt7c3r1+/xtbWls2bN9OgQQMAtmzZwvDhw6lYsSL29vYEBATg6uqa5z4DfPnll3z33XcMGDCA+fPn07RpU6ZOnYqfn1++2nlf2bJlOXnyJFOmTMHNzY3U1FQcHBzYvn07TZs2/WD9UaNG4eXlpfG9LViwgAULFmiU27hxI927d89zvyRPvBBCiH87yW0vhBD/Hir1p+zMJnLl5eVFYmJitiPxixYtIjAwkNu3b2NhYUGrVq2YN2+esit95gjwhg0bmDhxIjdu3ODmzZvo6enRr18/fvzxRywtLZk1axaTJ09m5MiRSv70xMREChYsqGzyVr16dRYvXqxsoBcVFcXIkSO5cOECKpUKW1tbVq9eTXJyMg0bNtTo54wZM/D29lZG4pcsWZLlWeLi4ihTpgyXLl3CxcWFsLAwGjZsyLFjx5gwYQLXrl3DxcWFwMBAjSULP/zwAz4+Ply7do0SJUrQq1cvpkyZgo5Oxm9LNjY2Gs/1vg/VV6lUfPfdd+zfv5/Dhw9jZWXFwoULad26tdLGnj17GDNmDPfu3aNWrVp4eXnh5eXF06dPlfR22b0PGxsbBgwYwM2bN9m+fTsFCxZk6tSpDBgwIId/DVk9e/YMMzMzZpy8jb6xyYcrCCGEEP8QCeKFEOKvlRkbJCUlfXCp7X9+Y7t/ipaWFgEBAVy9epX169fz448/KinVMr18+ZK5c+eyZs0arl69StGiRenZsye//fYbYWFh7Ny5k2+//TbLDuydOnUCYPr06Vy8eJGqVavSuHFjnjx5AmRMpS9ZsiTnz5/n4sWLTJw4kQIFClC7dm2WLFmCqakp8fHxxMfHM3bs2I9+xilTprBw4UIuXLiAjo4Offr0Ua799NNP9OzZkxEjRnDt2jVWr15NUFBQntPB5bW+j48PHh4e/PLLLzRv3pxu3bop7+HOnTt07NiRtm3bEhUVxcCBA5kyZYpS90PvY+HChVSvXp1Lly4xZMgQBg8eTExMTI59ljzxQgghhBBCiE8lQfw/ZOTIkTRs2BAbGxsaNWrEzJkz2bZtm0aZtLQ0VqxYQe3atbG3t+fu3bscO3aM7777ji+++IKqVauyZs0aXr16pdQ5deoU586dA6B8+fLY2tqyYMECzM3N2bFjB5CR7s3NzQ0HBwdsbW3p1KkTlStXRldXFzMzM1QqFZaWllhaWmrkq8+vWbNm0aBBAypUqMDEiRM5ffq0knPdx8eHiRMn0qtXL8qWLUuTJk3w8/Nj9erVeWo7r/W9vLzo0qUL5cuXx9/fn+TkZOX9rF69Gnt7e+bPn4+9vT2enp4ayyU+9D6aN2/OkCFDKF++PBMmTKBw4cIcP348xz7Pnj0bMzMz5ShVqlReX6UQQgghhBBCALIm/h+TuXv99evXefbsGW/evOH169e8fPlSyT+uq6urkU89JiYGHR0dqlatqpwrX768xiZqUVFRJCcnY2RkpLEu+9WrV0r+99GjR9OvXz82btyIm5sbnTp1oly5cn/6M+aUC7506dJERUURHh6uMXL+9u3bLO8gJ3mt/24fjIyMMDU1VWYuxMTEUKNGDY12a9as+VHPlxnovz8r4l2TJk1i9OjRyudnz55JIC+EEEIIIYTIFwni/wFxcXG0bNmSwYMHM2vWLCwsLDh16hR9+/YlNTVVCUANDAxy3WQuO8nJyRQvXpywsLAs18zNzQHw9vama9eu7N+/n4MHDzJjxgy2bNlCu3btPvXRNOSWCz45ORkfHx/at2+fpZ6+vv4H285r/Q/ljv8U+W1bT08PPT29P+XeQgghhBBCiP8mCeL/ARcvXiQ9PZ2FCxcqu82/P5U+O/b29rx584ZLly5RrVo1AG7evMnTp0+VMlWrVuXhw4fo6OhgY2OTY1t2dnbY2dkxatQounTpQmBgIO3atUNXV5e3b99+2gPmQdWqVYmJiaF8+fL/SH3IeJ8HDhzQOHf+/HmNz3/X+xBCCCGEEEKIvJAg/i+WlJSUJY944cKFSUtLY9myZbRq1Yrw8HBWrVr1wbYcHBxwc3NjwIABrFy5kgIFCjBmzBiNEXs3Nzdq1apF27ZtmTdvHnZ2dvz222/s37+fdu3a4eTkxLhx4+jYsSNlypTh/v37nD9/ng4dOgAZO8InJycTGhpK5cqVMTQ0VGYGPH78WONZ/vjjD3x9fbMEvnkxffp0WrZsSenSpenYsSNaWlpERUVx5coVZs6cqZTLKQ97XuvnZuDAgSxatIgJEybQt29fIiMjCQoKAv5v5kBu7+N9Dx8+ZP78+fj4+LB7927atm2bp36MrlzogztQCiGEEEIIIQRIEP+XCwsLo0qVKhrn+vbty6JFi5g7dy6TJk2ifv36zJ49m549e36wvQ0bNtC3b1/q16+PpaUls2fP5urVq2zZsoWRI0cye/ZsDhw4wJQpU+jduzePHj3izZs3dOvWjWLFiqGtrU1CQgI9e/bk999/p3DhwrRv3x4fHx8gY0d2Dw8PmjdvTmpqKlpaWpQrV47U1FROnDjBpk2bNPpTtGhRDhw4QKNGjYCMoPfXX38F0FirP2zYMI167u7u7Nu3D19fX+bOnUuBAgVwcHCgX79+GuVyy8P+fv3Xr1/nOXAGKFOmDDt27KBNmzbMmzcPQJnufuTIETp16kTt2rUZNGgQnTt3JiEhQUkx977o6Gh+//13OnfuzJIlSzSe/UMkT7wQQoj/NZKSTggh/jqSJ/4zd//+fUqVKsVXX33FyZMn0dfX5/bt20oQGRISQrt27cjr13z27Fnc3Nxwc3Nj8uTJFCtWjHPnzjFmzBhKlSrF8ePH0dXVBaBjx44YGRmxfv16pb6NjQ19+/alf//+Gu2amJhgZGT0Jz119j6UVz4nKpWKwMBAmjZtqqT0e/36NT///DPOzs55amPfvn20atWK9PT0PO9jIHnihRBC/K+SIF4IIfJH8sT/D/vxxx/Zs2cPd+7c4fTp03h6emJjY0OxYsVwc3NTRudzsnPnTpycnNDT08PGxoaFCxcq19RqNX379sXR0ZFdu3ZRs2ZNrK2t6dSpE3v37uXMmTMsXrwYyAiYd+7cyYYNG1CpVBqp2UxMTJSUbJlHZgAfFhaGSqXi8OHDVKlSBQMDAxo1asSjR484ePAgjo6OmJqa0rVrV16+fKm06erqytChQxk6dChmZmYULlyYadOmKT9OuLq68uuvvzJq1ChUKhUqlYoXL15gamqqpNbLFBISgpGREc+fP2fFihUAvH79mqNHjxIYGMigQYN48+aNRrq4e/fu4eHhgbm5ORYWFrRp04a4uDggY6PAVq1aAaClpZXvzQiFEEIIIYQQIq8kiP/MpKWlMXnyZJycnGjXrh1FihQhLCwMLS0ttLW18ff3Z9myZdy/fz9L3YsXL+Lh4YGnpyeXL1/G29ubadOmKevAIyMjuXbtGqNHj1Y23MtUuXJl3Nzc2Lx5M5CxAVzTpk3x8PAgPj6epUuX5us5vL29Wb58OadPn1YC5CVLlrBp0yb279/PkSNHWLZsmUad9evXo6Ojw7lz51i6dCmLFi1izZo1AOzatYuSJUvi6+tLfHw88fHxGBkZ4enpSWBgoEY7gYGBdOzYERMTE2JjYwEYPnw4fn5+jBo1CktLSwBlxkFaWhru7u6YmJjw008/ER4ejrGxMU2bNiU1NZWxY8cq98i8d3ZSUlJ49uyZxiGEEEIIIYQQ+SFr4j8z7u7uuLu753i9Xbt2uLi4MGPGDNauXatxbdGiRTRu3Jhp06YBGTvUX7t2jfnz5+Pl5cWNGzcAcHR0zLZtR0dHTp06BUCRIkXQ09PDwMBACXozTZgwgalTp2qcO3jwIPXq1VM+z5w5kzp16gAZewRMmjSJW7duUbZsWSBjqv7x48eZMGGCUqdUqVIsXrwYlUqFvb09ly9fZvHixfTv3x8LCwu0tbWVWQCZ+vXrR+3atYmPj6d48eI8evSIAwcOcOzYMQAWL17MkiVL0NbW5rfffsPX15f09HRsbGzw8PAAYOvWraSnp7NmzRpllD0wMBBzc3PCwsL46quvlPR977+Ld82ePVvZe0AIIYQQQgghPoaMxP8Pmjt3LuvXryc6OlrjfHR0tBI4Z6pTpw6xsbEaadQ+dZuEcePGERkZqXFUr15do0ylSpWUv4sVK4ahoaESwGeee/TokUadL7/8UmOqeq1atbL0/X01a9bEyclJWbf//fffY21tTf369TXKLV68mMjISA4ePEiFChVYs2YNFhYWAERFRXHz5k1MTEwwNjbG2NgYCwsLXr9+za1bt/L8XiZNmkRSUpJy3Lt3L891hRBCCCGEEAJkJP5/Uv369XF3d2fSpEkaa9U/xM7ODsgI9t/fUT/zfGaZ3BQuXPiD+dsLFCig/K1SqTQ+Z55LT0/PS7c/qF+/fnzzzTdMnDiRwMBAevfunWXduqWlJeXLl6d8+fIEBgbSvHlzrl27RtGiRUlOTqZatWoEBwdnabtIkSJ57oeenp6y+70QQgghhBBCfAwJ4v9HzZkzh8qVK3P69GnlnKOjI+Hh4RrlwsPDsbOzQ1tbGxcXFxwcHFi8eDGenp5oaWkRFxdHmTJl2LJlC8eOHaN///6oVCqePn36yX1UqVTs3r07z+UjIiI0Pp89exZbW1u0tbWBjDXs2Y3Kd+/enfHjxxMQEMC1a9fo1atXljIBAQEEBQUREhJCzZo1qVatGrNmzWLp0qVUrVqVrVu3UrRo0b8kn7vkiRdCCCGEEELklUyn/5O5urpmm+IsKChIWTft7e2t7KCura1NqVKlGDBgAE+ePNGoY2Njw5IlS3K81+7du/nyyy8xMzMjODiYH3/8Ubm3s7Mznp6ePH/+XCk/ZswYQkND8fPz48aNG6xfv57ly5czduxYICOoXrt2LdeuXaNDhw6cO3cOtVrNt99+y7hx46hVqxYdOnT44Dt4/vw5Dx8+1Diy28QtPj6eZs2aASip2SIjI3Ns9+7du4wePZqYmBg2b97MsmXLaNq0KSqVisTERGxsbDh58iQPHjzgjz/+UOoVLFiQ9u3bM27cOL766itKliz5wWcYOXIkq1ev5sGDB3Tr1o3ChQvTpk0bfvrpJ+7cuUNYWBjDhw/PdgNBIYQQQgghhPiryEj8P8TJyYljx47x9u1boqOj6dOnD0lJSWzdujVP9UNDQ+ncuTOzZs2idevWTJo0iQcPHpCWlqaU8ff3Z+fOncrnqlWrsm3bNqZPn46fnx/FixfH19dXY8p97dq1OXv2LD4+PjRr1oznz59TunRpevXqxaRJkzh37twH+zZ9+nSmT5+ucW7gwIGsWrVK41xum8Blp2fPnrx69YqaNWuira3NiBEjcHNzU9Le+fr6MnDgQMqVK0dKSorG2v6+ffuyadMm+vTpk6d7NW3alDJlyjBr1ixWrFjByZMnmTBhAu3bt+f58+dYWVnRuHHjP2UEfVFUAvrGqZ/cjhBCCPFfIrnohRD/VTIS/w/R0dHB0tISKysr3Nzc6NSpE0ePHs1z/b1791KnTh3GjRuHvb09u3btIiIigm+++UYpExQUhKOjoxLMenl5sXHjRrp160bBggVJSkri5cuXvHnzhnHjxmFhYUHJkiW5cOECO3bsICEhgRs3bnDr1i06dOiAoaGhRh9CQkJYuHAhXbp0wcrKCkNDQ0xMTNi0aRNqtVo5GjRogI6ODiNHjqRjx464urpibm6OSqUiJCQELy8vZcZAlSpVUKlUhIWFERAQQIECBXj48CGQsY5+5cqV9O7dGycnJ2bNmqWxtv3LL79k1KhR6Ovrc+jQIRwdHZVUcFevXqVQoUK0adOGt2/fMnr0aMzNzSlUqBDjxo2jVKlSGs+mVqvp2bMnBw8exMDAAHd3d1q1asXjx4959eoVZcqU4ddff8XExATI2IfAysoqy48XQgghhBBCCPFnkiD+XyAuLo7Dhw8recnzwtLSkqtXr3LlypV83evHH3/kt99+4+TJkyxatIgZM2bQsmVLChYsSEREBIMGDWLgwIF5nib++vVrqlWrxv79+7ly5QoDBgygR48eWUbs169fj66uLuHh4VlG5AGl/LFjx4iPj2fXrl3Ur1+fsmXLsnHjRqVcWloawcHBuY6ov3z5kgULFrBx40YOHz7MzZs3mTZtGgMHDkRXV5eFCxcSFBTEunXrOHXqFE+ePMmyNn/27Nls2LCBVatWcfXqVUaNGkX37t05ceIEKpWK9evXc/78eQICAgAYNGjQB4N4yRMvhBBCCCGE+FQynf4fcvnyZYyNjXn79i2vX78GMvK459WwYcP46aefcHZ2xtrami+//JKvvvqKbt265boDuoWFBQEBAWhpaWFvb8+8efN4+fIlkydPBjLSoM2ZM4dTp07h6en5wX5YWVkpa+oz+3X48GG2bdtGzZo1lfO2trbMmzcvx3Yyd3kvVKiQxjT7vn37EhgYSNGiRYGMGQivX79WcrhnJy0tjVWrVlGuXDm8vb25c+cOOjo6TJo0CYAlS5YwadIk2rdvD8CqVas4fPiwUj8lJQV/f3+OHTtGrVq1AChbtiynTp1i9erVNGjQACsrK1avXk3Pnj15+PAhBw4c4NKlS+jo5PyflOSJF0IIIYQQQnwqCeL/Ifb29uzZs4fXr1/z/fffExkZybBhw/Jc38jIiP3793Pr1i2OHz/O2bNnGTNmDEuXLuXMmTNZpr5ncnJyQkvr/yZgFCtWjIoVKyqftbW1KVSoUJYc7Tl5+/Yt/v7+bNu2jQcPHpCamkpKSkqW+1erVi3Pz/YuLy8vpk6dyrp16/jyyy9p3bo1Hh4eGBkZ5VjH0NCQcuXKARmbCFauXJkOHTpgbGxMUlIS8fHxfPHFF0p5HR0dqlevriw7uHnzJi9fvqRJkyYa7aampmqk3uvUqRO7d+9mzpw5rFy5Eltb21yfZdKkSYwePVr5/OzZsyzT+IUQQgghhBAiNxLE/8lMTU1JSkrKcj4xMREzMzPls66urpJLfc6cObRo0QIfHx/8/Pzydb9y5cpRrlw5+vXrx5QpU7Czs2Pr1q307t072/LZ5WP/lBzt8+fPZ+nSpSxZsgRnZ2eMjIwYOXIkqamaG7XlFnTnpmjRorRq1YrAwEDKlCnDwYMHCQsLy7VOds/z7iZ3H5KcnAzA/v37sbKy0rj27iyHly9fcvHiRbS1tYmNjf1gu5InXgghhBBCCPGpJIj/k9nb23PkyJEs53/++Wfs7OxyrDd16lQaNWrE4MGDKVGixEfd28bGBkNDQ168ePFR9T9GeHg4bdq0oXv37kBGqrgbN25QoUKFfLWTuR9Adnne+/XrR5cuXShZsiTlypWjTp06H91fMzMzChUqRIMGDXj69Cnm5ua8efOGixcvUrVqVQAqVKiAnp4ed+/epUGDBjm2NWbMGLS0tDh48CDNmzenRYsWNGrUKN99kjzxQgghhBBCiLySje3+ZIMHD+bGjRsMHz6cX375hZiYGBYtWsTmzZsZM2ZMjvVq1apFpUqV8Pf31zj/4MEDIiMjNY6nT5/i7e3N+PHjCQsL486dO1y6dIk+ffqQlpaWZRr4n2H69Om0bds2y3lbW1uOHj3K6dOniY6Opnr16sTGxvLDDz+go6ND4cKFiYyM5NKlS6SkpOTYftGiRTEwMODQoUP8/vvvGrMZ3N3dMTU1ZebMmTnOMMiP0aNHU7BgQY4fP87169cZMmQIiYmJynUTExPGjh3LqFGjWL9+Pbdu3WLgwIFYWVmxfv16ALp27cqqVau4fv06zZo1w9DQkDZt2nDv3r1P7p8QQgghhBBC5ERG4v9kZcuW5eTJk0yZMgU3NzdSU1NxcHBg+/btNG3aNNe6o0aNwsvLiwkTJihrpRcsWMCCBQs0ym3cuJEGDRrwzTff0LNnT37//XcKFixIlSpVOHLkCPb29n/Z871v6tSp3L59G3d3dwwNDbG3t8fMzIwvv/ySdevWkZCQQPv27Tl//jy1a9cmLCxMScv2Lh0dHQICAvD19WX69OnUq1dPmTavpaWFl5cX/v7+9OzZ85P7PH78eB49eoSXlxdaWlr06dOHdu3aafxw4OfnR5EiRZg9eza3b9+mQIECaGtrU6ZMGR4/fsyePXsoWrQoUVFRvHnzhhMnTtCzZ09cXV25detWvvojeeKFEEKITyM544UQ/yUqdX4WC4v/LC8vLxITEwkJCWHHjh34+Phw8+ZNDA0NqVKlCj/88ANGRkZ4e3sTEhJCZGSkRv3r169TuXJlxo0bx8yZMwF4+vQpI0aMYO/evaSkpNCgQQMCAgKUDeKCgoIYOXIk33//Pd26dSM5OZl27dqxYcMGtm/fzowZM0hKSqJHjx4sXrwYbW1tIONHjqVLlxITE4ORkRGNGjViyZIlyg73YWFhNGzYUJlOn3mfrVu3MnLkSO7du0fdunUJDAykePHiAFmeK7vnHDBgAHv37iU+Pj5P7/TZs2eYmZkx4+Rt9I2z/rAhhBBCiLyRIF4I8bnLjA2SkpI+uNRWptOLfImPj6dLly706dOH6OhowsLCaN++/Qc3jnNwcKBZs2bs2rVLOefl5cWFCxfYs2cPZ86cQa1W07x5c9LS0pQyL168wMfHh5SUFObNm0dYWBjt2rXjwIEDHDhwgI0bN7J69Wp27Nih1ElLS8PPz4+oqChCQkKIi4vDy8sr1/69m1v+5MmT3L17VyN1Xl4YGBhk2dDvXZInXgghhBBCCPGpZDq9yJf4+HjevHlD+/btsba2BsDZ2TlPdR0cHJRN/2JjY9mzZw/h4eHUrl0bgODgYEqVKkVISAidOnUC4M2bN1y5coXBgwczZswYYmNj2bhxI7///jvGxsZUqFCBhg0bcvz4cTp37gxAnz59lHuWLVuWgIAAatSoQXJyMsbGxtn27d3c8gBDhw7F19c3z+/l4sWLbNq0KdeN7SRPvBBCCCGEEOJTyUi8yJfKlSvTuHFjnJ2d6dSpE9999x1Pnz7NU121Wo1KpQIgOjoaHR0djXzthQoVwt7enujoaOWcoaEhr169YvHixUBGXnsbGxuNYLxYsWIaee0vXrxIq1atKF26NCYmJsoO83fv3s2xb+/mlgcoXry4RpvZuXz5MsbGxhgYGFCzZk1q1arF8uXLcyw/adIkkpKSlEM2wRNCCCGEEELklwTxIl+0tbU5evQoBw8epEKFCixbtgx7e3vu3LnzwbrR0dGUKVMmX/fLb177Fy9eKLvZBwcHc/78eXbv3g2Q61T3j8ktb29vT2RkJNHR0bx69Yo9e/ZQrFixHMvr6elhamqqcQghhBBCCCFEfsh0epFvKpWKOnXqUKdOHaZPn461tTW7d+9m9OjROda5fv06hw4dYtKkSQA4Ojry5s0bIiIilOn0CQkJxMTE5DvH/Pv3SUhIYM6cOcoO/xcuXPjo9nKjq6tL+fLlP7kdyRMvhBBCCCGEyCsJ4kW+REREEBoayldffUXRokWJiIjg8ePHODo6KmXevHnDw4cPSU9PJyEhgbCwMGbOnImLiwvjxo0DMvLLt2nThv79+7N69WpMTEyYOHEiVlZWtGnT5qP7V7p0aXR1dVm2bBmDBg3iypUr+Pn5ffJzCyGEEEIIIcS/gQTxIl9MTU05efIkS5Ys4dmzZ1hbW7Nw4UKaNWumlLl69SrFixdHW1sbMzMzKlSowKRJkxg8eDB6enpKucDAQEaMGEHLli1JTU2lfv36HDhwIMvU9kwPHz7kwIEDREdHo6enR9GiRXFxcSE1NRUDAwMAihQpQlBQEJMnTyYgIICqVauyYMECWrdu/dHPHBcXh4+PD3Z2dh/dRm4kT7wQQgjx7yIp64QQ/2aSJ158FuLi4qhTpw7m5ub4+vri7OxMWloahw8f5ttvv+X69etZ6qSlpeX4g0B+712mTBkuXbqEi4vLJ7eXSfLECyGEEP9OEsQLIf5ukide/M8ZMmQIKpWKc+fO0aFDB+zs7HBycmL06NGcPXsWyFirv3LlSlq3bo2RkREzZ86kfPnyLFiwQKOtyMhIVCoVN2/e1KjXrFkzDAwMKFu2rEbe+czN+KpUqYJKpcLV1RWA9PR0fH19KVmyJHp6eri4uHDo0KEcn0HyxAshhBBCCCE+lQTx4l/vyZMnHDp0iK+//hojI6Ms183NzZW/vb29adeuHZcvX6Zv37706dOHwMBAjfKBgYHUr19fY1O6adOm0aFDB6KioujWrRuenp5Kqrtz584BcOzYMeLj49m1axcAS5cuZeHChSxYsIBffvkFd3d3WrduTWxsbLbPMXv2bMzMzJQjc+M9IYQQQgghhMgrCeLFv97NmzdRq9U4ODh8sGzXrl3p3bs3ZcuWpXTp0nh5eRETE6ME4mlpaWzatIk+ffpo1OvUqRP9+vXDzs4OPz8/qlevzrJly4CMdfaQkcfe0tISCwsLABYsWMCECRPw9PTE3t6euXPn4uLiwpIlS7Ltm+SJF0IIIYQQQnwqCeLFv15+tm2oXr26xucSJUrQokUL1q1bB8DevXtJSUmhU6dOGuVq1aqV5XPmSHx2nj17xm+//UadOnU0ztepUyfHepInXgghhBBCCPGpJIgX/3q2traoVKpsN697X3bT7fv168eWLVt49eoVgYGBdO7cGUNDw7+iq0IIIYQQQgjxl5IUc+Jfz8LCAnd3d7755huGDx+OkZERrq6uytT1xMREjXXx72vevDkFChTA0NAQHR0dTp48maXM2bNn6dmzp8bnKlWqAKCrqwvA27dvleumpqaUKFGC8PBwGjRooJwPDw+nZs2a+Xq+0ZULyai8EEIIIYQQIk8kiP+P8fLyIjExkZCQEI3zYWFhNGzYkKdPn+YaEOfFy5cv8fPzY9u2bTx48AATExMqVKjA6NGjadOmzUe1+c0331CnTh1q1qyJr68v8+fPR6VSERAQwMqVK3Od+q6trY27uzvBwcGUK1eOWrVqKc9boUIFALZv30716tWpW7culSpVIjU1lbVr1wJQtGhRDAwMOHToECVLlkRfXx8zMzPGjRvHjBkz+Pnnn7ly5Qpt27YlMjKS4ODgfD2b5IkXQgghxKeQlHhC/LfIdHrxpxs0aBC7du1i2bJlXL9+nUOHDtGxY0cSEhI+us2yZcvy888/07BhQ8aMGUPdunVp27YtoaGhrFy58oP1mzdvDmRsfPeu27dvA+Dj48OWLVuUAH7QoEFKgK+jo0NAQACrV6+mRIkSyg8Rw4cPZ/To0Rw5coQbN25w6NAh9uzZg62t7Uc/pxBCCCGEEELkRoJ4kUVCQgJdunTBysoKQ0NDnJ2d2bx5s0aZHTt24OzsjIGBAYUKFcLNzY0XL14AsGfPHiZPnkzz5s2xsbGhWrVqDBs2TGNH+KdPn9KzZ08KFiyIoaEhzZo1y5KaLTw8HFdXVwwNDSlYsCBeXl74+fkRFxdHrVq16NixIz/88AOurq5s3LiRatWq0aNHDywtLenatSuPHj1S2vrjjz8A8PT01LjHsGHDgIwd6I8cOcLr168xNTXVmBKfmJjI2bNnefXqFUZGRmhpaREVFYWWlhbW1tY8f/4ctVpNVFQUzZo1Iygo6NO/BCGEEEIIIYTIhgTxIovXr19TrVo19u/fz5UrVxgwYAA9evRQ0rTFx8fTpUsX+vTpQ3R0NGFhYbRv317ZRd7S0pIDBw7w/PnzHO/h5eXFhQsX2LNnD2fOnEGtVtO8eXPS0tIAiIyMpHHjxlSoUIEzZ85w6tQpWrVqpbEu/V1paWn4+fkRFRVFSEgIcXFxeHl5kZKSwv3795XAumjRohr1Ro4cCcD+/ftz7GunTp149OgRBw8e5OLFi1StWpXGjRvz5MkTOnfuzJgxY3ByciI+Pp74+Hg6d+6cbTspKSk8e/ZM4xBCCCGEEEKI/JA18f9B+/btw9jYWOPcu8GxlZUVY8eOVT4PGzaMw4cPs23bNmrWrEl8fDxv3ryhffv2WFtbA+Ds7KyU//bbb+nWrRuFChWicuXK1K1bl44dOyrp2GJjY9mzZw/h4eHUrl0bgODgYEqVKkVISAidOnVi3rx5VK9enRUrVijtOjk55fhM747yly1bloCAAGrUqEFgYCBff/015cqVy7Ze5i71O3fuZOnSpZiZmWlcP3XqFOfOnePRo0fo6ekBGfnhQ0JC2LFjBwMGDMDY2BgdHR0sLS1z7B/A7Nmz8fHxybWMEEIIIYQQQuRGRuL/gxo2bEhkZKTGsWbNGuX627dv8fPzw9nZGQsLC4yNjTl8+DB3794FoHLlyjRu3BhnZ2c6derEd999x9OnT5X69evX5/bt24SGhtKxY0euXr1KvXr18PPzAyA6OhodHR2++OILpU6hQoWwt7dXNqjLHInPq4sXL9KqVStKly6NiYmJsmN8/fr1efv2Ld9++22OddPS0ihevDhz587Nci0qKork5GQKFSqEsbGxcty5c4dbt27luX8AkyZNIikpSTnu3buXr/pCCCGEEEIIISPx/0FGRkaUL19e49z9+/eVv+fPn8/SpUtZsmQJzs7OGBkZMXLkSFJTM3ZQ19bW5ujRo5w+fZojR46wbNkypkyZQkREBGXKlAGgQIEC1KtXj3r16jFhwgRmzpyJr68vEyZMyFMfDQwM8vw8L168wN3dXdmBvkiRIty9exd3d3elz7nR0dFh1qxZeHl5MXToUI1rycnJFC9enLCwsCz18ruLv56enjKaL4QQQgghhBAfQ4L4/7B3c62/Kzw8nDZt2tC9e3cA0tPTuXHjhrJbO4BKpaJOnTrUqVOH6dOnY21tze7duxk9enS296pQoQJv3rzh9evXODo68ubNGyIiIpTp9AkJCcTExCj3qFSpEqGhoXmafn79+nUSEhKYM2cOpUqVAuDChQsA1KtXL9e1+Zk6derE/Pnzs9yvatWqPHz4EB0dHWxsbDSef/fu3bRt2xZdXd0c1+rnheSJF0IIIYQQQuTVZz+d3svLi7Zt22Y5HxYWhkqlIjEx8ZPv4e3tjUqlynI4ODh8cj//bQoWLMi+fftYt26d8pz9+/fn999/V8pERETg7+/PhQsXuHv3Lrt27eLx48c4OjoCGT8OrF69mosXLxIXF8eBAweYPHkyDRs2xNTUFFtbW9q0aUP//v05deoUUVFRdO/eHSsrKyV9W/fu3Tlz5gxGRkbo6upSrFgxKlWqxJkzZ7L0uXTp0ujq6rJs2TJu377Nnj17lKn7+TFnzhzWrVun7LIP4ObmRq1atWjbti1HjhwhLi6O06dPM3z4cGWTPCMjI65cucLWrVv5448/SElJyfe9hRBCCCGEECIvZCQ+j5ycnDh27JjGOR2dv//1paamoqur+5e137NnTyZOnMioUaM4deoUBgYGlChRgrZt25KUlASAqakpJ0+eZMmSJTx79gxra2sWLlxIs2bNAHB3d2f9+vVMnjyZly9fUqJECVq2bMn06dOV+wQGBjJixAhatmxJamoq9evX58CBAxQoUIC0tDQGDhxIjRo1ePnyJTExMbx8+ZIXL16QnJycpc9FihQhKCiIyZMnExAQQNWqVVmwYAGtW7fO17M3atSIRo0aceTIEeWcSqXiwIEDTJkyhd69e/P48WMsLS2pX7++MurftGlTAPr378/z588JDAzEy8srz/ddFJWAvvGHp/0LIYQQQnzIxCqF/+kuCCH+Yp/9SHxefGrec0DZffzdo3DhjP+RvH79OoaGhmzatEkpv23bNgwMDLh27Rre3t6sX7+eH374QRndzlxjfe/ePTw8PDA3N8fCwoI2bdoQFxentJM5gj9r1ixKlCiBvb09cXFxqFQqdu3aRcOGDTE0NKRy5coao9Q5PXNQUBAhISFZ3pGrqysNGjSgYMGCODo6cujQIZKTk3n8+DF+fn6sX7+eHj164OTkhIuLC9evX2fChAm8fv2amJgYhg4dio2NDX5+fkRHR3PlyhVatWrFypUrSUhIoEmTJtSuXRtDQ0M6duyInp4ejRs3xtzcHD09Pezs7ChbtiwAV69e5datW2zdupVffvmFlJQUnj9/zq1bt2jSpAmQMTti6dKlykyLLl26sHv3blJSUti0aROtWrUiMDAQbW1tQkJC6N+/P3p6enTu3Jl79+7h6uqKWq1myZIluLi4sG7dOkqXLk14eDiDBw+mR48ezJs3D0tLS8qVK0exYsV48OABqamp3L17l+DgYC5evAigzMjInLYveeKFEEIIIYQQf5X/RBD/qXnPP8TBwYEFCxYwZMgQ7t69y/379xk0aBBz586lQoUKjB07Fg8PD5o2barkEq9duzZpaWm4u7tjYmLCTz/9RHh4OMbGxjRt2lRjQ7bQ0FBiYmI4evQo+/btU85PmTKFsWPHEhkZiZ2dHV26dOHNmzd5eub8unjxIh4eHnh6enL58mW8vb2ZNm1aloB1wYIFVK5cmUuXLjFt2jQAXr58SUBAAFu2bOHQoUOEhYXRrl07Dhw4wIEDB9i4cSOrV69mx44dQMbIupaWFjt27PikteaZ9541axYbNmwgPDycxMREPD09NcrcunWLgwcPcujQITZv3szatWtp0aIF9+/f58SJE8ydO5epU6cSERGR7T0y3+mxY8eIj49n165d2ZaTPPFCCCGEEEKIT/U/MZ3+r857DnD58uUs9+jevTurVq0CYMiQIRw4cIDu3bujq6tLjRo1GDZsGADGxsYYGBiQkpKikUv8+++/Jz09nTVr1qBSqYCMaebm5uaEhYXx1VdfARlrrtesWaNMo88cqR87diwtWrQAwMfHBycnJ27evImDg8MHnzknK1as0Eg3N3DgQBYuXMiiRYto3LixEpjb2dlx7do15s+frzF1vFGjRowZM0b5/NNPP5GWlsbKlSuVXO0dO3Zk48aN/P777xgbG1OhQgUaNmzI8ePH6dy5M1ZWVgQEBDB+/Hh8fHyoXr06DRs2pFu3bspofV6lpaWxfPlyJZ3d+vXrcXR05Ny5c8p7SE9PZ926dZiYmCh9iYmJ4cCBA2hpaWFvb8/cuXM5fvy4Rlq8TEWKFAEy0uTllite8sQLIYQQQgghPtX/RBDfsGFDVq5cqXEuIiJC2V397du3+Pv7s23bNmVKdEpKCoaGhoBm3nN3d3e++uorOnbsSMGCBZX27O3t2bNnj8Y93t9RfN26ddjZ2aGlpcXVq1eVwDwnUVFR3Lx5ExMTE43zr1+/1shB7uzsnO06+EqVKil/Fy9eHIBHjx7h4ODwwWfOSbdu3ZgyZYryOTONWnR0tLLpXKY6deqwZMkS3r59i7a2NgDVq1fP0qahoaESwAMUK1YMGxsbjR9FihUrxqNHj5TPX3/9NT179iQsLIyzZ8+yfft2/P392bNnjzKlPi90dHSoUaOG8tnBwQFzc3Oio6OVIN7GxkbjOyhWrBja2tpoaWlpnHu3fx9j0qRJGrv3P3v2TFlXL4QQQgghhBB58T8RxP8dec91dXWz3ON9UVFRvHjxAi0tLeLj45XAOifJyclUq1aN4ODgLNcyR3czny87BQoUUP7O/MEgPT09T8+cEzMzsw8+Z26y6+u7/czsa3bnMvueycTEhFatWtGqVStmzpyJu7s7M2fOpEmTJkqA/e6Sh7S0tI/q88f2L78kT7wQQgghhBDiU/1PBPEf8lfkPX/fkydP8PLyYsqUKcTHx9OtWzd+/vlnDAwMALLNJV61alW2bt1K0aJF//Q84Xl55vxwdHQkPDw8yz3s7OyUUfi/UmZKv9OnTwP/9yNHfHy8MmMiMjIyS703b95w4cIFZdQ9JiaGxMREJR3ex8hcznDnzh0AZZbEx67flzzxQgghhBBCiLz6T2xsZ2trq4y0R0dHM3DgwHzlPYeMYPDhw4cax7ttDBo0iFKlSjF16lQWLVrE27dvNdak29jY8MsvvxATE8Mff/xBWloa3bp1o3DhwrRp04affvqJO3fuEBYWxvDhwzVmEvwVz5xfY8aMITQ0lDJlytCrVy/Wr1/P8uXLlWcMCgri7t27QMbO8Zm78Pfp04dnz54xYMAAnjx5otGmjY0NS5YsyXKvyMhImjRpgkqlYufOndy8eZO1a9eybt06ZUp/+fLlKVWqFN7e3sTGxrJ//34WLlyYpa0CBQowbNgwIiIiuHjxIl5eXnz55Ze57gvwrszsALkpWrQoBgYGHDp0iN9//11JxSeEEEIIIYQQf7b/xEj81KlTuX37Nu7u7hgaGjJgwIB85T2HjLRn70+P19PT4/Xr12zYsIEDBw5w6dIldHR00NHR4fvvv6du3bq0bNmSZs2a0b9/f8LCwqhevTrJyckcP34cV1dXTp48yYQJE2jfvj3Pnz/HysqKxo0bf/LI7IeeOb+qVq3Ktm3b6NmzJ99//z1hYWH4+vrmmA/dycmJY8eOsXnzZqZNm8ahQ4dISkpi69atH7xXyZIlKVmyJJCRt15bWxsbGxt8fHwYNWoUkBGcb968mcGDB1OpUiVq1KjBzJkz6dSpk0ZbhoaGTJgwga5du/LgwQPq1avH2rVrP+od5ERHR4eAgAB8fX2ZPn069erVU1II5oXkiRdCCCHEn0lyxQvxv02lzmseNSHIyCfv4uKSZQQ9KCiIkSNHkpiYiLe3NyEhIRrT28eMGUNgYKDGaLyNjQ0jR45k5MiRWe4TFxdHmTJluHTpEi4uLlmup6enM3fuXL799lsePnyInZ0d06ZNo2PHjgCEhYXRsGFDjh07xoQJE7h27RouLi4EBgZib2+vtDNz5kwCAgJ49eoVnTt3pnDhwhw6dIjIyEi8vb2z7CZ//PhxbGxsKFOmDDt37mTZsmVERERga2vLqlWrqFWrVp7f5bNnzzAzM2PGydvoG5t8uIIQQgghRB5IEC/E5yczNkhKSvrggO5/Yjq9+GfFxcVx+PDhbHfY/1izZ89mw4YNrFq1iqtXrzJq1Ci6d+/OiRMnNMpNmTKFhQsXcuHCBXR0dOjTp49yLTg4mFmzZjF37lwuXrxI6dKlNbIcjB07Fg8PD5o2bUp8fDzx8fHUrl1bo+2xY8cSGRmJnZ0dXbp04c2bNzn2WfLECyGEEEIIIT7Vf2I6vfj7Xb58GWNjY96+fcvr168BWLRo0Z/SdkpKCv7+/hw7dkwZ+S5btiynTp1i9erVNGjQQCk7a9Ys5fPEiRNp0aIFr1+/Rl9fn2XLltG3b1969+4NwPTp0zly5AjJyckAGBsbY2BgQEpKSrb538eOHUuLFi0A8PHxwcnJiZs3b+Lg4JBtvyVPvBBCCCGEEOJTyUi8+EvY29sTGRnJ+fPnmTBhAu7u7gwbNuxPafvmzZu8fPmSJk2aYGxsrBwbNmzg1q1bGmUrVaqk/J25p0FmvveYmJgsG9zldcO7D7WdnUmTJpGUlKQc9+7dy/O9hBBCCCGEEAJkJF7kk6mpabab4yUmJmJmZqZ81tXVVfLNz5kzhxYtWuDj44Ofn98n9yFzpHz//v1YWVlpXHs/D/u7+d5VKhXAJ+d7/9i2JU+8EEIIIYQQ4lPJSLzIF3t7e37++ecs53/++Wfs7OxyrDd16lQWLFjAb7/99sl9qFChAnp6ety9e5fy5ctrHKVKlcpzO/b29pw/f17j3PufdXV1Pzr/uxBCCCGEEEL82f71I/EPHz6kR48enD59mgIFCpCYmPjJbXp5eZGYmEhISMgnt/VvldvO79l5d3f53AwePJjly5czfPhw+vXrR79+/dDX1+fMmTPs3bs3x3q1atWiUqVK+Pv7s3z5cuX8gwcPNHaxB7C2tlb+jomJydKWk5MTY8eOZdSoUaSnp1O3bl2SkpIIDw/H1NSUXr165emZhw0bRv/+/alevTq1a9dm69at/PLLL5QtW1YpY2Njw+HDh4mJiaFQoUIasw3+LKMrF/rklIJCCCGEEEKI/4Z8BfFeXl6sX7+e2bNnM3HiROV8SEgI7dq1Iz/Z6q5evYqPjw/Hjx9XcrN7enoyceJEDA0NlXKLFy8mPj6eyMhIjQDq9OnTzJw5kzNnzvDq1StsbW3p3bs3I0aMQFtbG/hwmrK/wsuXL/Hz82Pbtm08ePAAExMTKlSowOjRo2nTps3f0gfIGFE2MjL6pDZSU1NZsmQJwcHBxMbGYmhoiL29PePGjePMmTO4ubnx5MkTihQpwvbt22natGmu7Y0aNQovLy8mTJigjJgvWLCABQsWaJTbuHEjdevWBcDT0zNLO/fu3aNcuXK8ePGC2bNnc/v2bczNzalatSqTJ0/O8f79+vXT+NytWzdu377N0KFDSU5Opk+fPnh5ebFr1y5lerxKpUJXVxdnZ2fS0tKUFHMAzZs3Z/z48Xn+oSQnkideCCGEEJ8DSV0nxL9Dvkfi9fX1mTt3LgMHDqRgwYIfddOzZ8/i5uaGm5sb+/fvp1ixYpw7d44xY8YQGhrK8ePHlXRkt27dolq1atja2ir1d+/ejYeHB7179+b48eOYm5tz7Ngxxo8fz5kzZ9i2bZsShP1d3r59i0qlYtCgQURERLBs2TIqVKhAQkICp0+fJiEh4W/tT5EiRT6pfmpqKu7u7kRFReHn50edOnUwNTXl7NmzLFiwgHXr1uHi4qLkjW/btq1S19vbG29v7yxtenp6agTlcXFx2d47813m9qOQSqVCT0+P69evZ3vd1dU1S31jY2NGjBihBOEA06ZNo1SpUowcOZK1a9fSpEkTLCwsMDMz49ixY7x9+5bo6Gj69OlDrVq1cHV1BUCtVmu0Y25unq8fsYQQQgghhBDiY+R7TbybmxuWlpbMnj07xzI7d+7EyckJPT09bGxsWLhwoXJNrVbTt29fHB0d2bVrFzVr1sTa2ppOnTqxd+9ezpw5w+LFi4GMqcw7d+5kw4YNqFQqvLy8ePHiBf3796d169Z8++23uLi4YGNjQ79+/Vi/fj07duxg27ZtAJQpUwaAKlWqoFKplAAs04IFCyhevDiFChXi66+/Ji0tTbmWkpLC2LFjsbKywsjIiC+++IKwsDDlelBQEObm5uzZs0djjfaePXuYPHkyzZs3x8bGhmrVqjFs2DCN/OQ2Njb4+fnRpUsXjIyMsLKy4ptvvtHoW2JiIv369aNIkSKYmprSqFEjoqKiNMrs3buXGjVqoK+vT+HChWnXrp3GPZYsWaJ8XrRoEc7OzhgZGVGqVCmGDBmibBCXnSVLlnDy5ElCQ0P5+uuvcXFxoWzZsnTt2pWIiAiNH1XS09MZP348FhYWWFpaZgngP3TvnN5lWFgYNWvWxMjICHNzc+rUqcOvv/6abX+9vb1xcXFh48aN2NjYYGZmhqenJ8+fP8/xGV++fMmiRYt48OABb9++ZcaMGRw7dozKlSujo6ODpaUlVlZWuLm50alTJ44ePZpjW0IIIYQQQgjxd8h3EK+trY2/vz/Lli3j/v37Wa5fvHgRDw8PPD09uXz5Mt7e3kybNo2goCAAIiMjuXbtGqNHj0ZLS/P2lStXxs3Njc2bNwMZU8KbNm2Kh4cH8fHxLF26lCNHjpCQkMDYsWOz3LtVq1bY2dkp9c+dOwfAsWPHiI+PZ9euXUrZ48ePc+vWLY4fP8769esJCgpS+ggwdOhQzpw5w5YtW/jll1/o1KkTTZs2JTY2Vinz8uVL5s6dy5o1a7h69SpFixbF0tKSAwcO5Bo8AsyfP5/KlStz6dIlJk6cyIgRIzSCxE6dOvHo0SMOHjzIxYsXqVq1Ko0bN+bJkydAxs7s7dq1o3nz5ly6dInQ0NBc06NpaWkREBDA1atXWb9+PT/++CPjx4/PsXxwcDBubm5UqVIly7UCBQpoTNVfv349RkZGREREMG/ePHx9fTWeJS/3fv9dWlhY0LZtWxo0aMAvv/zCmTNnGDBgQK4zLG7dukVISAj79u1j3759nDhxgjlz5uRYXqVSceDAAWbPnk1ycjJ79+5l586dGmviIWPGwOHDh5XZIR8rJSWFZ8+eaRxCCCGEEEIIkR8ftbFdu3btcHFxYcaMGaxdu1bj2qJFi2jcuDHTpk0DwM7OjmvXrjF//ny8vLy4ceMGAI6Ojtm27ejoyKlTp4CMKeF6enoYGBhgaWkJ8MH6Dg4OSpnMKeWFChVS6mcqWLAgy5cvR1tbGwcHB1q0aEFoaCj9+/fn7t27BAYGcvfuXUqUKAHA2LFjOXToEIGBgfj7+wOQlpbGihUrqFy5stLut99+S7du3ShUqBCVK1embt26dOzYkTp16mjcv06dOsq+AnZ2doSHh7N48WKaNGnCqVOnOHfuHI8ePVJSki1YsICQkBB27NjBgAEDmDVrFp6envj4+ChtvtuP9727btvGxoaZM2cyaNAgVqxYkW352NjYLDMXclKpUiVmzJgBgK2tLcuXLyc0NJQmTZrk+d7vv8snT56QlJREy5YtKVeuHJDzd54pPT2doKAgTExMAOjRowehoaHMmjUr2/IGBgYcO3ZM2dQvc9f9X375hcuXL2NsbMzbt295/fo1kPFv+1PMnj1b4/sSQgghhBBCiPz66BRzc+fOZf369URHR2ucj46OzjZgjY2N1UjV9anrhz+1vpOTk7IBHkDx4sV59OgRAJcvX+bt27fY2dlhbGysHCdOnODWrVtKHV1dXSpVqqTRbv369bl9+zahoaF07NiRq1evUq9evSz50WvVqpXlc+a7jIqKIjk5mUKFCmnc/86dO8r9IyMjady4cZ6f99ixYzRu3BgrKytMTEzo0aMHCQkJvHz5Mtvy+Xm/77+Dd99lXu/9/ru0sLDAy8sLd3d3WrVqxdKlS4mPj8+1HzY2NkoAn10/8sPe3p7IyEjOnz/PhAkTcHd3Z9iwYR/VVqZJkyaRlJSkHPfu3fuk9oQQQgghhBD/PR8dxNevXx93d3cmTZqUr3qZucTfD/4zRUdH55pv/FPrZypQoIDGZ5VKRXp6OgDJycloa2tz8eJFIiMjlSM6OpqlS5cqdQwMDLKd3l2gQAHq1avHhAkTOHLkCL6+vvj5+ZGamrcdyJOTkylevLjGvSMjI4mJiWHcuHHKvfMqLi6Oli1bUqlSJXbu3MnFixeVNfg59cnOzi7HTePel9u7zOu9s3uXgYGBnDlzRkn/Zmdnx9mzZz+qHwCmpqYkJSVlqZeYmJgldZyuri7ly5enYsWKzJkzB21t7U8eRdfT08PU1FTjEEIIIYQQQoj8+KQ88XPmzMHFxQV7e3vlnKOjI+Hh4RrlwsPDsbOzQ1tbGxcXFxwcHFi8eDGenp4a6+KjoqI4duxYrpvmffXVV1hYWLBw4UJq166tcW3Pnj3ExsYqo96Za5jfnQGQF1WqVOHt27c8evSIevXq5atudipUqMCbN294/fq10qf3g9GzZ88q08WrVq3Kw4cP0dHR0dgB/V2VKlUiNDSU3r17f/D+Fy9eJD09nYULFyrvO3PzP8hIHfjLL79o1OnatSuTJ0/m0qVLWdbFp6WlkZqamqcUdh+6d6bk5GRGjhypsRkfZHwXVapUYdKkSdSqVYtNmzbx5ZdffvC+2bG3t+fIkSNZzv/8888f/OFn6tSpNGrUiMGDBytLLP4skideCCGEEEIIkVefFMQ7OzvTrVs3AgIClHNjxoyhRo0a+Pn50blzZ86cOcPy5cuV9c8qlUpJ5dWhQwcmTZqEpaUlERERjBkzhlq1auWad9vIyIjVq1fj6enJgAEDGDp0KKampoSGhjJu3Dg6duyIh4cHAEWLFsXAwIBDhw5RsmRJ9PX1s4y4ZsfOzo5u3brRs2dPFi5cSJUqVXj8+DGhoaFUqlSJFi1a5FjX1dWVLl26UL16dQoVKsS1a9eYPHkyDRs21AjUwsPDmTdvHm3btuXo0aNs376d/fv3AxkZAAoVKqTsrq+trY2pqSlmZmb06NEDb29vZsyYQePGjSlXrhyenp68efOGAwcOMGHChCx9Kl++PGlpaSxbtoxWrVoRHh7OqlWrlOtLly4lODhYybHu6upKxYoVqVOnDo0bN8bPz48yZcpoPLeJiQlVqlT54OZs7997zZo12f5IY2RkpLHk4M6dO3z77be0bt2aEiVKEBMTQ2xsLD179gTg+vXrJCUlkZiYiLm5ea59yHT79m2uXLnC8OHD6devH3p6euzfv59Nmzbx9u1bpa3IyEiioqJQqVSoVCqKFStG/fr1cXBwwN/fn+XLlwPw8OFDNm7cmGXvAGtr63ylX5Q88UIIIYT4XyR55YX4a3z0dPpMvr6+GlOWq1atyrZt29iyZQsVK1Zk+vTp+Pr64uXlpZSpXbs2Z8+eRVtbm2bNmlG+fHkmTZpEr169OHr0qLKZW046duzI8ePHuXv3LvXq1cPe3p7FixczZcoUtmzZokzL1tHRISAggNWrV1OiRAnatGmT5+cKDAykZ8+ejBkzBnt7e9q2bcv58+cpXbp0rvXc3d1Zv349X331FY6OjgwbNgx3d/cso89jxozhwoULVKlShZkzZ7Jo0SLc3d2BjB863NzcKF26NJaWlmhpaaGrq4uZmRkLFy6kZcuW1K1bl+3bt7Nnzx5cXFxo1KiRshv/+ypXrsyiRYuYO3cuFStWJDg4WCOQNjMzw9DQUKOOjo4OR48eZfz48axevZr27dsDGXsJLF26lJMnT1KiRAkuX76c47r67O597NixbMupVCqN9eyGhoZcv36dDh06YGdnx4ABA/j6668ZOHBgLm8/dyYmJtStW5fr16/j5ubGF198wbZt27LNaa+lpUV8fDwPHjxg586dxMTE8Pz5c9asWaOxlv3nn39WZgtkHpk/xgghhBBCCCHEn02l/tQd4kS+2djYMHLkyFxnHHh5eZGYmEhISIjG+R9//JHGjRvz3Xff0a9fPxITExk7diw//PADKSkpVK9encWLF2vsVD9z5kwCAgJ49eoVnTt3pnDhwhw6dIjIyMgs9/Ly8mL9+vUa97xz5w4AZcqU4dKlS7i4uAAZGwBWqlSJH374gdatWwOwceNGli5dSkxMDEZGRjRq1IglS5ZQtGhR4uLilNkFmXr16kVQUBCurq64uLgo0+mfPn3KiBEj2Lt3LykpKTRo0ICAgAAlP31YWBgNGzbk6dOnmJubKzvMb926lZEjR3Lv3j3q1q1LYGAgxYsXz/Wd5tRWYmKiUmbZsmUMHz6cpKQkZUbF+33Or2fPnmFmZsaMk7fRNzb5cAUhhBBCiM+IjMQLkXeZscG78UZOPnkkXvy9GjVqROXKlZWc9x/KJx8cHMysWbOYO3cuFy9epHTp0qxcuTLH9pcuXUqtWrXo378/8fHxxMfHU6pUqSzlXr16xYYNGwA08qenpaXh5+dHVFQUISEhxMXFKbMwSpUqxc6dOwGIiYkhPj5eY6PAd3l5eXHhwgX27NnDmTNnUKvVNG/enLS0tBz7/vLlSxYsWMDGjRs5efIkd+/eZezYsbm8zbx59OgRu3fvRltbWyOjQX5JnnghhBBCCCHEp/qkNfHin+Hg4MAvv/ySp3zyy5Yto2/fvsoGeNOnT+fIkSMkJydn27aZmRm6uroYGhpiaWmZ5Xrt2rXR0tLi5cuXqNVqqlWrppHqrk+fPsrfZcuWJSAggBo1apCcnIyxsTEWFhZAxn4FOa1lj42NZc+ePYSHhyubFwYHB1OqVClCQkLo1KlTtvXS0tJYtWrV/2PvzuN6yv4Hjr8+RUm7tRBFO5GsyS4TM3ZjbShjn7Fr7EvLIGMtDTNjK752YQyGoZElJPGx1YRoGJNdJUbafn/06P58FBXznfGdeT8fj/t49Ln33HPOvcXjcT7nnPdbySs/atQo/P393/QqXys1NRUDAwNyc3OV7QJjxowpVjC/15E88UIIIYQQQoh3JTPxf4OkpKQ3LqUvSm5uLiqVqlj55BMSEmjcuLHG/a9+LoktW7Zw7tw5wsPDsba2JjQ0VCO1W2xsLJ07d6Z69eoYGhrSqlUrAG7evFnsNuLj4ylVqhRNmjRRzpUvXx47O7vXphaEvH30+QN4eLc88YaGhqjVas6cOcOiRYtwcXFhzpw5b1VXPskTL4QQQgghhHhXMhP/Pyg+Ph4rKysln3xkZGSBMsWN2F5SFhYW2NjYYGNjQ1ZWFt27d+fSpUvo6ury9OlTPDw88PDwYMOGDVSsWJGbN2/i4eHx2nz0f6bC8sS/HPLByMiIX3/9tcB9KSkpaGtra8yya2lpYW1tDeSlTUxMTGTkyJGsX7/+rfunq6tbZNBGIYQQQgghhHgTGcT/j/n555+5ePEi48ePp1q1akXmk7ezs+PIkSOsX7+eEydOULp0aZycnN7Yho6ODtnZ2UX25eOPP2bWrFksX76c8ePH88svv/Dw4UMCAwOVffRnzpwpUDfwxvodHBzIysoiOjpaWU7/8OFDEhIScHR0LLJfr2NnZ8fmzZvJyMjQGEyfPXsWKyurAl8CvGzKlCnUqlWL8ePH4+Li8tZ9KIzkiRdCCCGEEEIUlyyn/4t5e3ujUqkIDAzUOL9r1y4lNV6+jIwM7ty5w+3btzl79ixz586la9eudOrUiYEDB2Jubo6pqSnW1taULl0aKysrBg0axKRJk5TB8+jRo1m3bh3x8fHs2rWLESNGcOHCBeLi4l4bVd3S0pLo6GiSkpJ48OCBRgrBfCqViu+//54xY8YQGBjIs2fPlPR+y5Yt4/r16+zevVsj9zvk5VBXqVTs2bOH+/fvF7o338bGhq5duzJ06FCOHz/O+fPn+eSTT6hataqSJjA0NLS4r1wxf/58/vjjDwYOHEhsbCzXrl1jzZo1yvl8q1atIjU1FZVKpbzXZcuW0aVLF2bNmqWUO3LkCOfOnUOtVmscd+/eLXHfhBBCCCGEEKI4ZCb+b1CmTBnmz5/P8OHDMTU1fW25/fv3Y25uTqlSpTA1NaVevXoEBwfj5eXF6dOncXd3p1WrVhgYGHDkyBFu3brF+vXrMTU1VfKpe3p6MnfuXBITE+nRowe9e/fG29ubb7755rXt+vj44OXlhaOjI3/88YeSYq4wXl5eTJ8+nZCQEPT19bGwsGDbtm0EBwfj4uLCwoULlfRzAFWrVsXPz48pU6YwaNAgBg4cWGBA/uLFC9auXcvYsWPp1KkTL168oGXLluzbt++Ns+VF0dLSYvTo0fzyyy906dKF1NRUrK2t8fDwKLDMvlSpUty6dYvMzExiY2Px8vKiS5cuhIeHc/r0aSWuwNGjR6lfv77GvQEBAcyYMaPY/Vp8/iFlDP772w2EEEIIIf5qkmZOiD+fzMT/Ddzd3TEzM2PevHmvLdO5c2ccHR3R0dGhatWqTJ48mYMHDzJo0CBUKhWDBw/GwcGBH374gS1btnDnzh2ysrKIjY3l4cOHbN++HcibVY+LiyMjI4P09HSys7OJi4vTGAyHhoZq5E63tbVlypQp2Nvbo6urS9u2bQkLCyMzMxNnZ2dl6X737t3R19fHyMiISpUq4efnx82bN0lKSiIjI4Nhw4bRuXNnHj9+TEhICBUrVsTIyIjDhw+zf/9+cnJyCA0NxdfXl5SUFOrUqYOVlRVlypTB1NSULl26YGFhQW5uLjExMYwcOZKnT58qz1WvXj1l739kZCStW7dm4cKFmJubU758eQ4ePFhgL36lSpXYsWMHt2/fJj09HbVajYuLi8YqCGtraz766CPMzMywsLCgW7duuLu7c/nyZXJzczUCA+7cuZPc3FyNoyQDeCGEEEIIIYQoCRnE/w20tbWZO3cuy5Yt47fffitwPTY2lt69e9O3b18uXryIr68vM2fOVGas1Wo1cXFxTJgwAS0tzV9hvXr1cHd3Z9OmTUDekm87Ozs8PDw4evQolStX5tChQ29MlXbs2DEGDhzI2LFjiYuL49tvvyU0NFSJzh4TEwPA2rVrSU5OJiYmhj59+jBx4kRq166t5Jfv06cPUHQue4Br164RHh7Ojh07UKvVJCcn069fPz799FPi4+OJjIykR48eGoHqXnX48GESExM5fPgwYWFhhIaGvtWy+1ddunSJEydOKPv535bkiRdCCCGEEEK8K1lO/zfp3r07zs7OzJ49m9WrV2tcW7x4Me3atWPmzJlA3sx4XFwcCxYswNvbmytXrgB5AeAK4+DgwPHjx4G8meeHDx+SlJTEsWPHsLOzIzw8nAkTJry2b/nL3b28vIC8fO8BAQFMmjSJ2bNnU7FiRSAvAv7LueQNDAwoVaqUxrni5LKHvCX069atU+o+e/YsWVlZ9OjRgxo1agAUGZDP1NSUkJAQtLW1sbe356OPPiIiIoKhQ4e+8b7C7NmzBwMDA7KyssjIyEBLS4uQkJAS1/MyyRMvhBBCCCGEeFcyE/83mj9/PmFhYQVyn8fHx+Pm5qZxzs3NjatXr2pEdX/TrHQ+PT093Nzc6Nu3L0+fPuXs2bP06NHjjfecP38ef39/jdzzQ4cOJTk5mWfPnpXgCSlWLnvIC3iXP4CHvBUF7dq1w8nJiV69erFy5UoeP378xrZq166Ntra28vld8sS3adMGtVpNdHQ0Xl5eDBo0iJ49e75VXfkkT7wQQgghhBDiXclM/N+oZcuWeHh4MHXqVLy9vYt9n62tLZA32H81qFr++fwybyM9PR0/P79CB/tlypQpcV3FyWX/6vJ+bW1tDh48yIkTJ/jpp59YtmwZ06dPJzo6Gisrq0LbKixP/MuR9Y2MjEhNTS1wX0pKCsbGxhrn9PX1lTzxa9asoV69eqxevZrBgwe/8XnfRPLECyGEEEIIId6VDOL/ZoGBgTg7O2NnZ6ecc3BwICoqSqNcVFQUtra2aGtr4+zsjL29PUuWLKFv374a++LPnz/PoUOH3hg0ryguLi4kJCQog9jClC5dukCu98Lyy7u4uBSZyz4yMpKEhIQC51UqFW5ubri5uTFr1ixq1KjBzp07mTBhAqGhoUqQu+Kys7MjNja2wPmzZ89qvP/C+lGxYkWGDh3KkCFDOHfuHM7OziVq+00kT7wQQgghhBCiuGQQ/zfy9vYmJSUFT09PgoODlfOtWrVi+PDhTJ8+HS8vL06ePElISAjLly8H8gaVq1evpn379vTs2ZOpU6diZmZGdHQ0EydOxNXVlXHjxin1ZWVlcfbsWezt7UlKSsLExITU1FQOHz5Mq1atNCKz16hRg1mzZtGpUyeqV6/Oxx9/jJaWFufPn+fSpUt8+eWXQF50+IiICNzc3NDV1cXU1BRLS0tu3LiBWq2mWrVqGBoa4u7ujqurK926deOrr77C1taW33//nb1799K9e3caNmxIs2bNePjwoca7iY6OJiIigg8++IBKlSoRHR3N/fv3XxsHoDBBQUHUrl1b+Tx+/HiaN29OmTJlOHfuHNnZ2WzatImTJ08q77Yw+/fv59ixY1SoUIGRI0dSp04d5Vr+877MxsbmjYEDhRBCCCGEEOJtySD+PeDv78+WLVuUz/lL4Xfs2MGCBQswNzfH399fY8l9s2bNOHXqFH5+fnTs2JEnT55QvXp1vLy8mDp1qrJsOyUlhaNHj5Kdnc3y5ctp1KgRpUqVolmzZuzevZvdu3dr9GX9+vV88skn7NmzB39/f+bPn0/p0qWxt7dnyJAhSrlFixYxYcIEVq5cSdWqVUlKSqJnz57s2LGDNm3akJKSwtq1a/H29mbfvn1Mnz6dQYMGcf/+fczMzGjZsiWVKlUiKysLHR0djb3skLf0/ejRoyxdupS0tDRq1KjBokWL6Nix41u/52bNmjFhwgSWLl2Km5sbWlpaODk5ERERoTEwf1ViYiJVqlRh5MiRLF68mEmTJlGqVN4/ncICBB47dozmzZsXu1+SJ14IIYQQomQk/7z4N5PAdn+xV3OyQ96sdkZGRoFAdSdPniQ5OZlmzZqxZMkSypYti5OTk5I+zsnJie3bt/Ptt99iZ2fH7du3Wb58OV26dFGWmk+bNo2cnByuXr2Kl5cXjo6O2Nra8uDBA548eUJmZia5ubk8evSIAQMGMHr0aMqWLcvSpUsJDQ3l2bNnpKamMnLkSL744gt27dqFjY0NvXr1ombNmly/fp2kpCSSkpLQ09NjypQpPH78mNzcXLy9vVm6dCl16tRh6dKlbNiwgczMTL799lvi4+OxtrZWoui/LCsrixUrVnDq1Cmys7MZO3YsTZs25dChQxrvrF+/fkyaNIly5cqxf/9+jSXu+Uv3L1++jEqlUj7XqVMHAwMDHj16xIMHD2jVqhVjxoxh/fr1WFpaYmxszPPnz1m/fj2Qt1pi9OjR3Lx5k6lTp1K2bFn09fXJyMhg9OjRVKxYEV1dXdzc3Dh9+jS5ubklGsALIYQQQgghREnIIP499/z5cxo0aMDevXu5dOkSw4YNY8CAAZw+fRrgjfnUc3Jy2Lx5M56enlSpUqVA3fkp4SBvsHrmzBl2797NyZMnyc3N5cMPPyQzM1Mp/+zZM+bMmcO6deuIiooiJSWFvn37AnmDZnd3d9auXavRRv5s/Mv79qdMmUJgYCDx8fHUrVu3QL/mz5/Phg0bWLt2LVFRUaSlpRX44gMgLCwMfX19oqOj+eqrr/D39+fgwYNA4bnsXycxMZFdu3axZ88e9uzZw5EjRwgMDATyluT7+/tTrVo1jXomTZpEeHg4YWFhnD17Fmtrazw8PHj06NFr25E88UIIIYQQQoh3Jcvp/2b5+chf9nJwuKpVq+Lj46N8Hj16NAcOHGDr1q00btyY5OTk1+ZTv3fvHo8fP8be3v6Nfbh69Sq7d+8mKiqKZs2aAbBhwwYsLCzYtWsXvXr1AiAzM5OQkBCaNGkC5A2iHRwcOH36NI0bN2bIkCGMGDGCxYsXo6ury9mzZ7l48SLff/+9Rnv+/v60b9/+tf1ZtmwZU6dOpXv37gCEhISwb9++AuXq1q3L7Nmzgbx96CEhIURERNC+ffvX5rIvTE5ODqGhoRgaGgIwYMAAIiIimDNnDsbGxhgaGqKtra3U8/TpU1asWEFoaKiyvH/lypUcPHiQ1atX88UXXxTajuSJF0IIIYQQQrwrmYn/m+XnI3/5WLVqlXI9OzubgIAAnJycKFeuHAYGBhw4cICbN28Cb86nXpw88pCXkq5UqVLK4BygfPny2NnZaeSwL1WqFI0aNVI+29vbY2JiopTp1q0b2tra7Ny5E8jbOtCmTZsCUekbNmz42r6kpqZy9+5dGjdurJzT1tamQYMGBcq+Oov/tnnhLS0tlQF8cepJTEwkMzMTNzc35Vzp0qVp3Lixxvt6leSJF0IIIYQQQrwrGcT/zfLzkb98VK1aVbm+YMECgoKCmDx5MocPH0atVuPh4cGLF3mB0PLzqf/44484OjqybNky7OzsuHHjBhUrVsTExIRffvnlL3kWHR0dBg4cyNq1a3nx4gUbN27k008/LfSZ/wxF5YX/q+spiq6uLkZGRhqHEEIIIYQQQpSEDOLfc1FRUXTt2pVPPvmEevXqUbNmTa5cuaJRJj+fup+fH+fOnUNHR4edO3eipaVF37592bBhA7///nuButPT08nKysLBwYGsrCyio6OVaw8fPiQhIQFHR0flXFZWFmfOnFE+JyQkkJKSopH2bciQIRw6dIjly5cry/xLwtjYmMqVK2vsYc/Ozubs2bMlqgcKz2X/Z6hVqxY6OjpERUUp5zIzM4mJidF4X0IIIYQQQgjxZ5M98e85Gxsbtm/fzokTJzA1NWXx4sXcvXtXGSwWlU99zpw5REZG0qRJE+bMmUPDhg0pXbo0x44dY968ecTExGBjY0PXrl0ZOnQo3377LYaGhkyZMoWqVavStWtXpS+lS5dm9OjRBAcHU6pUKUaNGkXTpk01lr47ODjQtGlTJk+ezKeffoqenl6Jn3n06NHMmzcPa2tr7O3tWbZsGY8fP9bIZ18cheWyL460tDTu3r2Lvr4+pUuXxtfXV+O6vr6+Eq2/XLlyVK9ena+++opnz54xePDgEvURYEK98jIrL4QQQgghhCgWGcS/52bMmMH169fx8PCgbNmyDBs2jG7dupGamgoUnU+9XLlynDp1isDAQL788kt+/fVXTE1NcXJyYsGCBRgbGwN5UdzHjh1Lp06dePHiBS1btmTfvn0aS83Lli3L5MmT6d+/P7dv36ZFixasXr26QJ8HDx7MiRMnCl1KXxyTJ0/mzp07DBw4kOfPn/PixQucnJwoU6aMUubBgwcEBQWxdOnS19bzai77vXv3snz5ctLS0tDV1aVGjRqYm5sXWDp/5MgRsrOzuXDhAsbGxmzcuJHffvuNpUuXMm7cOAD69+/P7t27+fDDD8nNzUVXV5cWLVpoRPMvLskTL4QQQghRcpIrXvxbqXKLG/1M/KuFhoYybtw4UlJSiiwbEBDAtm3buHDhwju36+3tzebNm8nMzGT8+PEsXLgQgF27dtG9e/diB+87deoU7u7uuLu7M23aNCpXrszp06eZOHEiFhYWHD58GB0dHQA+/vhj9PX1CQsLU+63tLRk3LhxjBs3Tlnp0KlTJ8aOHYuJiQlJSUns3r2bMWPGYGVlVaw+paWlYWxszOyj1yljYFj0DUIIIYQQQiGDePFPkj82SE1NLXKVruyJF3+a9PR0Ll26REhICKNHj37ren799VdWrlzJlStXePz4MZUrVyYnJ0eJul+Y8PBwateuja6uLpaWlixatEi5lpuby+DBg3FwcGDHjh00btyYGjVq0KtXL3744QdOnjzJkiVLgLzBenh4OOvWrUOlUuHt7V2graioKFJTU1m1ahX169fHysqKNm3asGTJkmIP4IUQQgghhBDibcggXvxpRo0aRYMGDWjduvVbL6UH0NLSIjQ0lEaNGvHjjz/y5MkTAgIClGXtr4qNjaV379707duXixcv4uvry8yZMwkNDQVArVYTFxfHhAkT0NLS/JOvV68e7u7ubNq0CYCYmBg6dOhA7969SU5OJigoqEB7ZmZmZGVlsXPnzmKvBADIyMggLS1N4xBCCCGEEEKIkpBBvCgWb2/vIpfSh4aGkpGRwZYtW9DW1n7rtiwsLJTZ7v79+9OyZUtmzJiBs7Mzs2fPLlB+8eLFtGvXjpkzZ2Jra4u3tzejRo1iwYIFAEo0/5ej6L/MwcFBKVOxYkV0dXXR09PDzMxMiRnwsqZNmzJt2jT69+9PhQoV6NixIwsWLODu3btvfK558+ZhbGysHBYWFiV6L0IIIYQQQgghg3jxP2P+/PmEhYURHx+vcT4+Ph43NzeNc25ubly9elUjxdyfGf5hzpw53Llzh2+++YbatWvzzTffYG9vz8WLF197z9SpU0lNTVWOW7du/Wn9EUIIIYQQQvw7yCBe/M9o2bIlHh4eTJ06tUT32draAhQY/OeLj49XypRE+fLl6dWrFwsXLiQ+Pp4qVaoogfcKo6uri5GRkcYhhBBCCCGEECUhKebEa70ckf11fH192bVrF2q1+i/pU2BgIM7OztjZ2SnnHBwciIqK0igXFRWFra0t2traODs7Y29vz5IlS+jbt6/Gvvjz589z6NAh5s2bV6J+vBqtX0dHh1q1avH06dMSP5PkiRdCCCGEEEIUl8zE/0lUKtUbD19fX5KSklCpVK8d8IaGhirltbW1MTU1pUmTJvj7+yt54fN5e3vTrVu3/+ozxcTEMGzYMOWzSqVi165dGmV8fHyIiIj4r/YD8r4scHZ2xsnJCU9PT4KDg5VrEydOJCIigoCAAK5cuUJYWBghISH4+PgQGhqKlpYWv/zyC2fOnEFfX59OnTpx9uxZtm3bRufOnXF1dX3jFxWv2rNnD99++y2ZmZlcuXKFhIQEFi5cyL59++jatet/4emFEEIIIYQQIo/MxP9JkpOTlZ+3bNnCrFmzSEhIUM4ZGBjw4MGDIusxMjIiISGB3NxcUlJSOHHiBPPmzWPt2rVERUVRpUqV/0r/C1OxYsUiyxgYGGBgYPAX9Ob/+fv7s2XLFuWzi4sLW7duZdasWQQEBGBubo6/vz/e3t6EhoYq7/Ty5cvMnj2bffv2sW/fPmrWrImXlxdTp05FV1cXgOzs7EL3zufk5FCqVN4/F0dHR3R1dXn+/DnOzs7o6upiY2PDqlWrGDBgQImfZ/H5h5QxePGWb0MIIYQQ4t9LcsWLfyOZif+TmJmZKYexsTEqlUrjXHEHuvn3mZub4+DgwODBgzlx4gTp6elMmjSp2P25dOkSHTt2xMDAgMqVKzNgwACNLxGePHmCp6cn+vr6mJubs2TJElq3bq0xI21pacnSpUuVnwG6d++OSqVSPufPkOfLXyEwd+5cKleujImJCf7+/mRlZfHFF19Qrlw5qlWrxtq1azX6O3nyZGxtbSlbtiw1a9Zk5syZZGZmEhoaSrdu3fDz8+P8+fOoVCqsrKz49ttvlS86hgwZwogRI7h16xbNmzdn9+7d+Pj4FHin7dq14/jx43z55ZeoVCouXrxIrVq1qFKlCrt371YG50FBQSxZsoSBAwdiamqKnp4ev//+uzK4r1mzJt7e3hgaGrJx40YqVKjAhQsX2LRpkwSrE0IIIYQQQvxXySD+f0ClSpXw9PRk9+7dGtHWXyclJYW2bdtSv359zpw5w/79+7l79y69e/dWykyYMIGoqCh2797NwYMHOXbsGGfPnn1tnTExMQCsXbuW5ORk5XNhfv75Z37//XeOHj3K4sWLmT17Np06dcLU1JTo6GhGjBjB8OHDNXK+GxoaEhoaSlxcHEFBQaxcuZIlS5YA0KdPHyZOnEjt2rVJTk4mOTmZPn36ANCrVy/u3bvHjz/+SGxsLC4uLrRr145Hjx69tn96enrk5OSQlZUFwLNnz5g/fz6rVq3i8uXLVKpUCW9vb86cOcOqVasYMmQIubm5LF68mMzMTKWeZ8+eMWfOHNatW0dUVBQpKSn07dv3te1KnnghhBBCCCHEu5JB/P8Ie3t7njx5wsOHD4ssGxISQv369Zk7dy729vbUr1+fNWvWcPjwYa5cucKTJ08ICwtj4cKFtGvXjjp16rB27do3fkGQv7TexMQEMzOzNy61L1euHMHBwdjZ2fHpp59iZ2fHs2fPmDZtGjY2NkydOhUdHR2OHz+u3DNjxgyaNWuGpaUlnTt3xsfHh61btwJ5g24DAwNKlSqlrGzQ09Pj+PHjnD59mm3bttGwYUNsbGxYuHAhJiYmbN++vdC+Xb16lW+++YaGDRtiaGgIQGZmJsuXL6dZs2bY2dlx+/Ztdu/ezapVqxg+fDg//fQTK1eu5O7duxoxATIzMwkJCcHV1ZUGDRoQFhbGiRMnOH36dKFtS554IYQQQgghxLuSPfH/I/KXcqtUqiLLnj9/nsOHDxe6hD8xMZE//viDzMxMGjdurJw3NjbWiPj+LmrXrq0RAb5y5crUqVNH+aytrU358uW5d++ecm7Lli0EBweTmJhIeno6WVlZRUZsP3/+POnp6ZQvX17j/B9//EFiYqLyOTU1FQMDA3Jycnj+/DnNmzdn1apVynUdHR3q1q2rfI6Pj6dUqVI0adJEYwvCsmXLNNLUlSpVikaNGimf7e3tMTExIT4+XuPd5ps6dSoTJkxQPqelpclAXgghhBBCCFEiMoj/HxEfH4+RkVGBAWth0tPT6dy5M/Pnzy9wzdzcnGvXrv03uqgoXbq0xmeVSlXouZycHABOnjyJp6cnfn5+eHh4YGxszObNm1m0aNEb20lPT8fc3JzIyMgC10xMTJSfDQ0NOXv2LFpaWpibm6Onp6dRVk9Pr1hfjrwrXV1dJYCeEEIIIYQQQrwNGcT/D7h37x4bN26kW7duGjPcr+Pi4kJ4eDiWlpZKRPWX6evro1KpsLGxQU9Pj5SUFFJTU7ly5QotW7Z8bb2lS5cu1p784vjtt984cuQI48aN48SJE9SoUYPp06cr13/99VeN8jo6OgXadnFx4c6dO5QqVUoJtFcYLS0trK2ti903BwcHsrKyiI6OplmzZgA8fPiQhIQEHB0dlXJZWVmcOXNGmXVPSEggJSUFBweHYrcFkideCCGEEEIIUXyyJ/5vkJCQgFqtRq1W06VLF1QqFXPmzAHyls3fuXOHNWvWoFKpWLNmDc2aNcPY2JjAwECNelJTU5V61Go14eHhdOrUiRUrVpCQkICpqSlDhgzh0qVLHDhwgEGDBpGdnc3KlSsxNjamUqVKrFmzhsuXL1OlShWePn1KUFAQenp6WFpacv/+fa5evaq0Z2lpSUREBHfu3OHx48fv9A6qVKmCq6srADY2Nly/fp2JEyeSmJhIcHAwO3fu1ChvaWnJjRs3UKvVPHjwgJ9++okPPviARo0a0a1bN3766SeSkpI4ceIE06dP58yZMwXazI+c/6qsrCxUKhUpKSkAREVFAeDm5oZKpaJChQrUqVOHypUra+SBV6lUdOnShejoaGJjY/H29qZp06aFLqUXQgghhBBCiD+DzMT/DQqLYP7VV18xd+5c0tLSMDc3V5Z3f/vtt3h5eTF27NgCs7WRkZHUr19f41z16tXZv38/z549Y/LkyaxZs4a1a9diY2NDx44d0dLSIjExkY4dO6JSqRgwYABGRkbo6upiampK9+7d+eKLL0hKSuKjjz5i+fLlVKlShenTp7No0SImTJjAypUrqVq1KklJSW/9DrS0tNDR0QGgS5cuAKxcuZLvvvuOjz76iJkzZ+Lr66uU79mzJzt27KBNmzakpKQwefJkALZu3cqCBQsYNGgQ9+/fx8zMjJYtW1K5cuW37hvkLcH38PDgp59+Ij09HR0dHSpVqqSxLUBLS4uGDRvSv39/bt++TYsWLVi9enWJ25I88UIIIYQQfw7JGy/+DWQm/r/A29tbmdV9maWlJbm5uRqHl5cXnTp1okqVKvz666/K+R07dgAQHR3NzJkzOXjwILVr10ZXVxdLS0ucnJyUsjk5OTg6OtKwYUNu3LhB48aNad26NdHR0Zw7d47c3FwGDRrEkiVLsLKyIjw8nE2bNrFx40Z69epFcnIyBgYGPHz4EGdnZ6pXr07Lli158uQJs2bNYtasWSQkJNC5c2euXr3KuXPncHBwwMDAgBUrVuDk5KQEgAsNDWX9+vUaOei7du2KWq3WyEH/6nuBvNz16enpnDp1inHjxnHkyBHatGmDoaEhFStW5MaNGxw8eJDc3Fw6dOgA5A22g4ODmTNnDmXLlmXlypXExsbi4OBAhw4d8PDwKPR38fLvas+ePQXOa2lpsW3bNlJTU3n+/Dlz5szhwoULSlo4b29vmjdvjrW1NYmJiTx//pyDBw9SvXr1Yv6VCCGEEEIIIUTJySD+PaCtrc3cuXNZtmyZRu70fLGxsfTu3Zu+ffty8eJFfH19mTlzJqGhoQCo1Wri4uKYMGFCgT3z9erVw93dnU2bNgF5+d47dOhA+/btWb58OePGjePs2bPKIPzl5eIAY8eOJTc3l++//x74a3PQe3p6Uq1aNWJiYoiNjWXKlCkFAuS97NmzZyxcuJD169dz9OhRbt68iY+Pz2vLF9e9e/fYuXMn2traaGtrv3U9kideCCGEEEII8a5kOf17onv37jg7OzN79uwCS7IXL15Mu3btmDlzJgC2trbExcWxYMECvL29uXLlCsBrA6o5ODgoOdkrVqyIrq4u2dnZrFq1ii+++AIdHR1yc3MZPXo0FSpoLkEqV64clSpVUpbOv5yDPt+aNWuwsLDgypUrmJubExYWxsaNG2nXrh2QNzivUqXKa5/91Rz0+W7evMkXX3yBvb09kLd3/k0yMzP55ptvqFWrFgCjRo3C39//jfe8Tn5autzcXJ49ewbAmDFj0NfXf6v6IC9PvJ+f31vfL4QQQgghhBAyE/8emT9/PmFhYRq5yCEvvZybm5vGOTc3N65evaoRsT0/l3xxlC9fntjYWNLT03n06BGVK1d+7UA7NzdX2aP/cg76/CN/kJ2YmMj169f/tBz0EyZMYMiQIbi7uxMYGKiR+70wZcuWVQbwkJdO7+Vc9CVhaGiIWq3mzJkzLFq0CBcXFyX44NuaOnUqqampynHr1q13qk8IIYQQQgjx7yOD+PdIy5Yt8fDwYOrUqSW6z9bWFqDA4D9ffHy8UqakHj58yP3797GysgL+Pwf9y1Hx1Wo1V69efWN6urfh6+vL5cuX+eijj/j5559xdHQsELX+ZYXlon/5iw0jIyNSU1ML3JeSkoK2trbGLHt+WjoHBwcmTJhA06ZNGTly5Ds9j66uLkZGRhqHEEIIIYQQQpSELKd/zwQGBuLs7Kwxc+3g4KCkPcsXFRWFra0t2traODs7Y29vz5IlS+jbt6/Gvvjz589z6NAh5s2bV6CtO3fuMGDAAE6cOMEff/xRaH+CgoLQ0tJSUrMVlYO+Zs2aqFQqRo4cyd69ewFITk7mwoULXLp0iaCgoELT070uB72trS22traMHz+efv36sXbtWrp3715oXyEvsN64ceM0gtk5OzujVquxs7Nj8+bNZGRkoKurq1w/e/YsVlZWb9xvP2XKFGrVqsX48eNxcXF5bbm3IXnihRBCCCGEEMUlM/HvCW9vb1QqFXv37sXT05Pg4GAAdu3axcaNG4mIiCAgIIArV64QFhZGSEiIErRNpVKxevVq4uLi6NmzJ5s3b6ZTp04YGRnh7OyMrq4uqampyt7ufEuWLCE5ORm1Wk21atV48uQJERERtG/fHhMTE7S1tQkICMDR0VEZZH7++ec8evSIfv36ERMTQ2JiokYOekNDQypXrszx48c5fPiwMpOek5NDnz59SE5OxtjYmDt37jB+/HhUKhVlypQBYMGCBSQnJ/P48WP++OMPRo0aRWRkJL/++itRUVHExMS8dt//m0RERAB5gfJUKhUDBw4kNjaWa9eusWbNGpYuXcrEiRPfWIeFhQXdu3dn1qxZGufv37+PWq1GpVKxZMkS1Go1d+/eLXEfhRBCCCGEEKI4ZCb+PVKmTBnmz59PZGQkW7Zs0bi2detWZs2aRUBAAObm5vj7++Pt7a1cb9asGadOnWLs2LH069cPlUpF1apV8fb2pnHjxkybNo0jR45w+PBh5Z7ExEQaNGiAjY0NWlpaygBVS0uLSpUq0bx5c9q2bcutW7d4+vQpAFWqVCEqKorJkyfzwQcfkJGRQY0aNejQoYOyAqBWrVo8ePBA+SLB3t4eAwMDqlWrphG4rmnTpuzcuZOMjAwWL15McHAw1apVU4LkPXz4kIEDB3L37l0qVKhAjx493iowXPny5YG8wHnHjh1jypQpdOnShdTUVGrVqsXixYsZPHhwkfWMHz8eV1dXTp8+rez537hxIxs3bgTy9vADBAQEMGPGjGL3T/LECyGEEEL8uSRfvPgnk5n4v1loaCi7du0CwN3dHTMzMzZs2EBGRobGfu6ePXty+fJlNm3ahIGBAdOnT8fS0pJFixYpZerUqcPdu3dp2LAhWVlZ3Lp1i+DgYD755BN++OEHTp48yZIlS9i1axeRkZGEh4ezbt06VCoVrVu3ZufOnZQqVYqMjAySk5PZs2cPEyZMUPLL58vIyOCPP/4gMzMTQ0NDGjZsyPTp05Xgd6VKlaJDhw48ffoUOzs7IiMjefLkCYGBgbRu3RrIG8A3adIEMzMzatSoQVBQEC4uLnTt2pWkpCR0dHQIDQ2ld+/eVKhQgZSUFE6fPs2pU6cAaN26Nbm5uezatYvq1avz2Wef0aZNGx4+fKj0s1u3bsyePRtnZ2fl3Ny5c8nJyeGzzz7DyMiItLQ0hgwZwm+//Ubv3r0xMTFhwoQJtGrVSonIny8uLg5HR0datGiBubk5derUITc3lxo1amiUW7Vq1Vv/PQghhBBCCCHEm8gg/j3yd+SL7927N8nJyQQFBWFmZkZWVhY7d+58baT74uSJf/LkCQkJCSQmJjJ79mxq1KiBtrY2ly5dYseOHQXqzM3N5dixY/zyyy/o6Ogo50eNGsXJkyfZvHkzFy5coFevXnTo0IGrV68CEB0dzeDBgxk1ahRqtZo2bdrw5ZdfFvmeIyIiSEhI4ODBg+zZs4fMzEw8PDwwNDTk2LFjREVFYWBgQIcOHXjxIm+GfMWKFXz++ecMGzaMixcvsnv3bqytrZV3CQXz3L9K8sQLIYQQQggh3pUsp3/P/NX54vX09JQl7k2bNmXatGn079+fESNG0LhxY9q2bcvAgQOpXLkyUHSe+Pwo+GfPnqVevXro6OhgYGCAs7MztWvX1ujP8uXLWbVqFS9evCAzM5MyZcowZswYIC9H/Nq1a7l586aS+s7Hx4f9+/ezdu1a5s6dS1BQEB06dGDSpEnK+zhx4gT79+9/4zvW19dn1apVyhcG//nPf8jJyWHVqlXKaoK1a9diYmJCZGQkH3zwAV9++SUTJ05k7NixSj2NGjVS3iUUzHP/KskTL4QQQgghhHhXMhP/Hvor88W/as6cOdy5c4dvvvmG2rVr880332Bvb8/FixeBovPEQ16O9X79+ik56Hv06IGBgUGBtjw9PVGr1URFRdGxY0emT59Os2bNALh48SLZ2dnY2tpqtHXkyBGlnfj4eJo0aaJRp6ura5HP6OTkpDHjf/78ea5du4ahoaHSTrly5Xj+/DmJiYncu3eP33//nXbt2r3FG/1/kideCCGEEEII8a5kJv499HK++JeD1xXl5Xzx9evXL3C9uPniy5cvT69evejVqxdz586lfv36LFy4kLCwMCVP/Pz58wvcZ25uXuy+AhgbGytL0rdu3Yq1tTVNmzbF3d2d9PR0tLW1iY2NRVtbW+O+wr4QKImX88EDpKen06BBAzZs2FCgbMWKFQtsTXhburq6GqnthBBCCCGEEKKkZBD/nvor88W/iY6ODrVq1VKi0xeVJ/5tGRgYMHbsWHx8fDh37hz169cnOzube/fu0aJFi0LvcXBwIDo6WuNcfuC7knBxcWHLli1UqlTptfnaLS0tiYiIoE2bNoVef12eeyGEEEIIIYT4M8kg/j3l5OSkkS8eYOLEiTRq1IiAgAD69OnDyZMnCQkJYfny5cD/54tv3749PXv2ZOrUqZiZmREdHc3EiRNxdXVl3Lhxr21zz549bN68mb59+2Jra0tubi4//PAD+/btY+3atUBenviVK1fSr18/Jk2aRLly5bh27RqbN29m1apVBWbNi0ulUrFu3TquXLlCeHg4H3/8MZ6engwcOJBFixZRv3597t+/T0REBHXr1uWjjz5izJgxuLm5sXDhQrp27cqBAweK3A9fGE9PTxYsWEDXrl3x9/enWrVq/Prrr+zYsYNJkyZRrVo1fH19GTFiBJUqVaJjx448efKEqKgoRo8eDfz/IN/NzQ1dXV1MTU2L3f6EeuVf++WBEEIIIYQQQrxMBvHviePHjyuz3fn8/f018sW7uLgUmi8+NDQUtVrN0qVLlXzxo0aNokmTJpQuXZrq1avj5eXF1KlT37ic29HRkbJlyzJx4kRu3bqFrq4uNjY2rFq1iuzsbCXoG8APP/zA999/j7a2NpaWlhp54t/E19eXXbt2YWJionE+OTkZU1NToqKi8PX1pUePHqxdu1YJKHf79m0qVKhA06ZN6dSpk1LPypUrmT17NrNmzcLd3Z0ZM2YQEBBQaNuWlpZUqlRJCZSX76uvvlLeU48ePXj06BE5OTlAXvA9CwsLevXqxYIFCwgJCcHHx4cKFSqQnJyMhYUF3bp1Y9GiRUyYMIGVK1dStWrVAunp3kTyxAshhBBC/PdJ7njxT6HKfZcoaOJP4+3tTUpKipIzviRat26Ns7MzS5cuVc5FRkbSpk0bHj9+XGDAXBK5ublkZ2fzn//8h7Fjx5KQkEBubi43btzgs88+Q1dXt8CS9jfJH3yr1eq37tPb1mNpacm4ceMKrEZ4tS5vb2/u3r3L2rVryczMJDY2Fi8vL0aMGKERC0ClUrFz5066dev2Vs+QlpaGsbExs49ep4yB4VvVIYQQQgghikcG8eJ9lj82SE1NLXKVrkSn/x9w6dIlOnbsiIGBAZUrV2bAgAE8ePAAyBtwHjlyhKCgIFQqFSqViqSkJGXvtqmpKSqVSgmQl5OTw7x587CyskJPT4969eqxfft2pa3IyEhUKhU//vgjDRo0QFdXV0lLp1KpMDMzw9zcnGbNmjF48GBOnz6tke988uTJ2NraUrZsWWrWrMnMmTPJzMwEIDQ0FD8/P86fP6/0NT/HvUql0vgC4+LFi7Rt2xY9PT3Kly/PsGHDSE9Pf+078vb2plu3bixcuBBzc3PKly/P559/rrRdUrq6upiZmSkz7e7u7hw8ePCt6hJCCCGEEEKIP4sM4t9zKSkptG3blvr163PmzBn279/P3bt36d27NwBBQUG4uroydOhQkpOTlSXe4eHhACQkJJCcnExQUBCQl6t83bp1fPPNN1y+fJnx48fzySefcOTIEY12p0yZQmBgIPHx8dStW7dAv+7du8fOnTvR1tbW2AdvaGhIaGgocXFxBAUFsXLlSpYsWQJAnz59mDhxIrVr11b62qdPnwJ1P336FA8PD0xNTYmJiWHbtm0cOnSIUaNGvfFdHT58mMTERA4fPkxYWBihoaHKlwTv4tKlS5w4cUIjLd3byMjIIC0tTeMQQgghhBBCiJKQPfHvuZCQEOrXr8/cuXOVc2vWrMHCwoIrV65ga2uLjo4OZcuWxczMTClTrlw5ACpVqqQsp8/IyGDu3LkcOnRIyades2ZNjh8/zrfffkurVq2U+/39/Wnfvr1GX1JTUzEwMCA3N5dnz54BMGbMGI2UbTNmzFB+trS0xMfHh82bNzNp0iT09PQwMDCgVKlSGn191caNG3n+/Dnr1q1T6g4JCVFS21WuXLnQ+0xNTQkJCUFbWxt7e3s++ugjIiIiGDp06Otf8Gvs2bMHAwMDsrKyyMjIQEtLi5CQkBLX87J58+bh5+f3TnUIIYQQQggh/t1kEP+eO3/+PIcPHy40N3piYmKx8r7nu3btGs+ePSswOH/x4kWBvPINGzYscL+hoSFnz54lMzOTH3/8kQ0bNjBnzhyNMlu2bCE4OJjExETS09PJysoqceT1+Ph46tWrp/HlgJubGzk5OSQkJLx2EF+7dm2NVQHm5uZcvHixRG3na9OmDStWrODp06csWbKEUqVK0bNnz7eqK9/UqVOZMGGC8jktLQ0LC4t3qlMIIYQQQgjx7yKD+Pdcenq6MgP9KnNz8xLXBbB3716qVq2qce3VqPUvD6DzaWlpYW1tDeTlaE9MTGTkyJGsX78egJMnT+Lp6Ymfnx8eHh4YGxuzefNmFi1aVKJ+vq3SpUtrfFapVEqUeQAjIyNSU1ML3JeSkoKxsbHGOX19feVZ16xZQ7169Vi9ejWDBw9+6/7p6uq+MTuAEEIIIYQQQhRFBvHvORcXF8LDw7G0tKRUqcJ/XTo6OmRnZxc4B2icd3R0RFdXl5s3b2osnX9bU6ZMoVatWowfPx4XFxdOnDhBjRo1mD59ulLm119/LbKvr3JwcCA0NJSnT58qXyZERUWhpaWFnZ3dW/fXzs6O2NjYAufPnj37xnq1tLSYNm0aEyZMoH///ujp6SnX5s2b99bR6fNJnnghhBBCCCFEcckg/j2SmppaIGXasGHDWLlyJf369WPSpEmUK1eOa9eusXnzZlatWqXkaY+OjiYpKQkDAwPKlStHjRo1UKlU7Nmzhw8//BA9PT0MDQ3x8fFh/Pjx5OTk0Lx5c1JTU4mKisLIyAgvL68i+/hyrvh8DRo0UH7W0tJi2bJljBkzhi+++IKdO3dqlLW0tOTq1atKPSqVSpkF37JlC23atMHT05PZs2fj5eVFZmYmDx484M6dOwwYMOC1S+mLY/z48bRo0YI5c+bQo0cPsrOz2bRpEydPnmT58uWvvS8pKYn+/ftTqVIlvv76a3x8fJRrz549K/A7s7GxKXQlgxBCCCGEEEK8KxnEv0ciIyML7E0fPHgwUVFRTJ48mQ8++ICMjAxq1KhBhw4d0NLKSy7g4+ODl5cXjo6O/PHHH9y4cQNLS0v8/PyYMmUKgwYNYuDAgYSGhhIQEEDFihWZN28e169fx8TEBBcXF6ZNm1asPiYnJys/b9myhWnTpvHs2TP27dtH/fr1CQwMZPbs2UDefv6ZM2fi6+ur3NOzZ0+WLFnCmTNnAFi8eDEeHh44OjoSGRmJs7MzUVFRHDhwgLFjx3L06FG0tLTw9vZm8eLF7/J6adasGT/++CP+/v4sWrQILS0tnJyciIiIoE6dOkXe36dPH7766itGjhypDNIvXbpU4Hd27NgxmjdvXux+LT7/kDIGL0r2MEIIIYQQosQkV7z4J1Dl5ubm/t2dEP+bQkNDGTduHCkpKRrnk5KSsLKy4ty5czg7Oxf7vnv37lG7dm08PDz4z3/+A+Tlf09JSdHIIf+yS5cu8cUXX3Ds2DH09fX54IMPWLJkCRUq5P0H3bp1a+rWrUuZMmVYtWoVOjo6jBgxQuOLhV9++YUhQ4Zw5swZatasSXBwMO3bt2fnzp1069atwOqDVq1aERkZqfStefPmLFq0iBcvXtC3b1+WLl1aYH9+YdLS0jA2Nmb20euUMTAssrwQQgghhHg3MogX76v8sUFqamqRW20lT7x4b1SqVAlPT092795d5L55yAtI17ZtW+rXr8+ZM2fYv38/d+/epXfv3hrlwsLC0NfXJzo6mq+++gp/f38OHjwI5MUM6NatG2XLliU6OprvvvtOY08/wOnTpwE4dOgQycnJ7NixQ7lWktz0kideCCGEEEII8a5kOb14r9jb2/PkyRMePnxIpUqV3lg2JCSE+vXrM3fuXOXcmjVrsLCw4MqVK0r6vbp16ypL/G1sbAgJCSEiIoL27dtz8OBBEhMTiYyMVHLXz5kzRyMNX8WKFQEoX758gfz2JclNL3nihRBCCCGEEO9KZuLFeyV/d0dhAfRedf78eQ4fPoyBgYFy2NvbA5CYmKiUq1u3rsZ95ubm3Lt3D4CEhAQsLCw0BueNGzcudn8Ly02fX/erpk6dSmpqqnLcunWr2O0IIYQQQgghBMhMvHjPxMfHY2RkRPny5Yssm56eTufOnZk/f36Ba+bm5srPReWPfxclqVvyxAshhBBCCCHelQzi/4Xy07WdOHGC0qVLFwgw93e5d+8eGzdupFu3bkrk/TdxcXEhPDwcS0tLSpXK+1NWqVTs3LkTa2trkpKSOHLkCFWrVn1tHXZ2dty6dYu7d+8q6etiYmI0yujo6AAUa5/+25A88UIIIYQQQojikuX0fzNvb29UKhWBgYEa53ft2lWsJeUvu3z5Mr1796ZixYro6upia2vLrFmzePbsmUa5JUuWkJycjFqt5sqVK0De4LewCPDe3t5069ZN+dy6dWtUKhUqlYqhQ4fy5MkT5s2bx8tJDn777TcADhw4gFqt1jgyMzM5fvw4qampSj0VK1akUaNGNG7cGGNj4wLvIjU1tUA9t27d4vPPP+fRo0f069ePmJgYZQn9smXLyM7OxsLCAldX1zfO6rdv355atWrh5eXFhQsXiIqKYsaMGco7gbyAe3p6ekrgvNTU1De+IyGEEEIIIYT4b5GZ+PdAmTJlmD9/PsOHD8fU1PSt6jh16hTu7u64u7uzd+9eKleuzOnTp5k4cSIREREcPnxYmVFOTEykQYMG2NjYvFVbQ4cOxd/fn//85z/MmjWLWbNmYWJiwsiRIzXKTZkyhSlTpmice3UfuEql4vnz58TFxWFqasqFCxcKzEpHRkYWyMU+ePBgVq1aRVRUFJMnT+aDDz4gIyMDAH19fbS0tFCpVOjo6Lx2Vj83N5fc3Fx27drFkCFDaNSoETVr1mTBggV07tyZMmXKAFCqVCmCg4Px9/dn1qxZtGjRgsjIyBK/t9eRPPFCCCGEEH8tSTUn/pfJTPx7wN3dHTMzM+bNm/faMuHh4dSuXRtdXV0sLS1ZtGiRci03N5fBgwfj4ODAjh07aNy4MTVq1KBXr1788MMPnDx5kiVLlgBgaWlJeHg469atQ6VS4e3tXeL+li1bFjMzM3x8fHj27Bl169ZVUrYBVKtWDYBz584pA+X8o1q1ajRv3hxjY2Nyc3PJycnhyZMnBAYGcvv2bY12vv/+ey5cuICuri5WVlb4+vqSmZlJbm4uq1at4urVqwwePJh9+/Zhbm7O999/D8Cnn36KSqVSltPnP2NkZCQqlYrhw4dz8eJFdHV1OX78OLa2tnz00UdUqVKFpKQkxo0bB4C1tbXSF1dXV+rWrYu+vj6xsbG0aNGCmTNn4uzsTFhYGN9//z0qlYqgoCCNHPRCCCGEEEII8WeSmfj3gLa2NnPnzqV///6MGTNGGQTni42NpXfv3vj6+tKnTx9OnDjBZ599Rvny5fH29katVhMXF8fGjRsLzDrXq1cPd3d3Nm3axOTJk4mJiWHgwIEYGRkRFBSEnp7eW/c7NzeX48eP88svv7z1rD7k7YXfuXMn2traSqT3Y8eOMXDgQIKDg2nRogWJiYkMGzYMgNmzZ5OTk0OPHj2oXLky0dHRpKamKoPvokyZMoWFCxdSs2ZNTE1NGTBgAMeOHSMgIAAtLS2mTJmCSqXit99+o1atWty+fZuWLVvSunVrfv75Z4yMjIiKiiIrKwsfHx/i4+NJS0tj7dq1AJQrV67QdjMyMpTVAoDkiRdCCCGEEEKUmAzi3xPdu3fH2dmZ2bNns3r1ao1rixcvpl27dsycORMAW1tb4uLiWLBgAd7e3sq+dgcHh0LrdnBw4Pjx4wDKfnk9Pb0COc+La/ny5axatYoXL16QmZlJmTJlGDNmTInqSE1NxcDAgNzcXGXP/pgxY9DX1wfAz8+PKVOm4OXlBUDNmjUJCAhg0qRJzJ49m0OHDvHLL79w4MABqlSpAsDcuXPp2LFjkW37+/sreeAzMjLYvn07lStXZvjw4VSoUIH27duTlZXFt99+S6tWrfj6668xNjZm8+bNSjT6/Bz0AHp6emRkZBT5PiVPvBBCCCGEEOJdyXL698j8+fMJCwsjPj5e43x8fDxubm4a59zc3Lh69apGxPSXg8v9N3l6eqJWq4mKiqJjx45Mnz6dZs2alagOQ0ND1Go1Z86cYdGiRbi4uDBnzhzl+vnz5/H399fIAT906FCSk5N59uwZ8fHxWFhYKAN4yFvyXhwNGzZUfr527RovXrzg0aNHlCpVipSUFLZv387WrVuVQHlqtZoWLVoUSCdXUpInXgghhBBCCPGuZCb+PdKyZUs8PDyYOnVqifaq588Kx8fHFwgAl3/+5ZnjwhgaGhaIug6QkpKCsbGxxjljY2Nlv/jWrVuxtramadOmuLu7F7vPWlpaSh0ODg4kJiYycuRI1q9fD+TlgPfz86NHjx4F7s0POPe28mf789sB2Lt3b4FUdPk53d9ly8Gr9UmeeCGEEEIIIcS7kEH8eyYwMBBnZ2fs7OyUcw4ODkRFRWmUi4qKwtbWFm1tbZydnbG3t2fJkiX07dtXY1/8+fPnOXTo0BuD5kFevvTY2Fhl+Trk5UU/f/48Q4YMee19BgYGjB07Fh8fH86dO1fitHj5pkyZQq1atRg/fjwuLi64uLiQkJCgEVzuZQ4ODty6dYvk5GTMzc2BvAj9JeXo6Iiuri43b96kVatWhZapW7cuYWFhZGZmFjobr6Oj80455CVPvBBCCCGEEKK4ZDn9X8TS0pKlS5cWWc7JyQlPT08lmjygpIkLCAjgypUrhIWFERISgo+PD5CXpm316tXExcVRqVIl+vbty82bN9m2bRudO3fG1dW1yKBvEyZMYNWqVSxfvpyrV6+iVqsZNmwYjx8/fuMgHmD48OFcuXKF8PBwjfMJCQmF5okvTHZ2NpmZmUo/Z82axbp16/Dz8+Py5cvEx8ezefNmJYe7u7s7tra2eHl5cf78eY4dO8b06dPf2M/CGBoa4uPjw/jx4wkLCyMxMZGzZ8+ybNkywsLCABg1ahRpaWn07duXM2fOcPXqVdavX09CQgKQ97u9cOECCQkJPHjw4LXPKIQQQgghhBDvSpX7V22k/hvdv3+fWbNmsXfvXu7evYupqSn16tVj1qxZBfaa/zf7oK+vT9myZTXOe3t7k5KSwq5du5RzSUlJ2NjYkJWVpexzDw8PZ+bMmVy5cgUtLS1yc3MxNjbGzs6OIUOG8Mknn/DLL7/Qtm1b0tPTyc7Opnr16vTr14+pU6dqtNutWzdMTEwIDQ3V6MvGjRtZvHgxV65coWzZsjRo0IB58+ZRt25dAHx9fZXAbFpaWlSpUoWOHTsSGBjItGnTOH78OBcuXODmzZtYWVkV+h5u3bpF3759iY6O1hjsZmdns3//fjp16kR0dDSNGzfmwIED+Pv7c+7cOUqXLo29vT1Dhgxh6NChQF4wwKNHj5Keno6lpSXBwcF06NCBnTt30q1bN5KSkrCysuLcuXM4OzsTGRlJmzZt+M9//oOnp6fSdm5uLk2aNCE+Pp6MjAxMTEzIysri8ePHQN4y+MqVK6Orq8vt27eV1Q9ffvklrVq1IiIigsDAQE6ePEl6ejqHDx+mdevWRf5NpKWlYWxszOyj1yljYFhkeSGEEEII8deSfPLir5I/NkhNTS1yle6/Yjl9z549efHiBWFhYdSsWZO7d+8SERHBw4cP/7I+VKxYsdDzrw6kIW9md+XKlRqz5507dyYkJIQ7d+4QEBCAm5sbRkZGnDp1ioULF1K/fn2cnZ2pXbs2zs7Ob5z1f/kLg5f179+f/v37FzifnZ2tLJOvXbs2hw4dIjs7m/j4eD799FNSU1PZsmWLRv/f9N2QtbU1FSpo/oeora3NRx99pHGfh4cHHh4er63H2NiYFi1aaDzPy/e/2o/8gfXLe+IhbyWDo6MjVapUUepq3bo1tra2+Pv7k5GRwc8//8ywYcMIDg5m5MiRQN6XLZCXUu6nn356bT+FEEIIIYQQ4s/yj19On5KSwrFjx5g/fz5t2rShRo0aNG7cmKlTp9KlSxcgbxC3YsUKOnbsiJ6eHjVr1mT79u0a9UyePBlbW1vKli1LzZo1mTlzZoFl0z/88AONGjWiTJkyVKhQge7duyvXXl1Ov3jxYpycnNDX18fCwoLPPvtMCbJWmKVLl3L06FEiIiL4/PPPcXZ2pmbNmvTv35/o6GiNPO05OTlMmjSJcuXKYWZmhq+vr0ZdRbUdGhqKiYkJu3fv1tgzDlCqVCnMzMyoWrUq7u7u9OrVi4MHDyr3ZmdnM3jwYKysrNDT08POzo6goCDluq+vL2FhYXz//feoVCpUKhWRkZEkJSWhUqlQq9VK2SNHjtC4cWN0dXUxNzdnypQpZGVlvfYdtW7dmjFjxrzx2UuibNmymJmZUaNGDQYNGkTdunU1nrWkMjIySEtL0ziEEEIIIYQQoiT+8YP4/PRku3btIiMj47XlZs6cSc+ePTl//jyenp707dtXI9WboaEhoaGhxMXFERQUxMqVKzX2re/du5fu3bvz4Ycfcu7cOSIiImjcuPFr29PS0iI4OJjLly8TFhbGzz//zKRJk15bfsOGDbi7uxcafb506dIas8thYWHo6+sTHR3NV199hb+/v8bgszhtP3v2jPnz57Nq1SouX75MpUqVCrSblJTEgQMH0NHRUc7l5ORQrVo1tm3bRlxcHLNmzWLatGls3boVAB8fH3r37k2HDh1ITk4mOTm50PR0t2/f5sMPP6RRo0acP3+eFStWsHr1ar788svXvqPiPPvbyM3N5dixY/zyyy8az1pS8+bNw9jYWDksLCzeqV9CCCGEEEKIf59//HL6UqVKERoaytChQ/nmm29wcXGhVatW9O3bV9nrDdCrVy8lgFtAQAAHDx5k2bJlLF++HEAJqAZ5s+o+Pj5s3rxZGfzOmTOHvn37KnvGAerVq/fafr28VN7S0pIvv/ySESNGKO296urVq8XaZw150dRnz54NgI2NDSEhIURERNC+fftit52Zmcny5csLPMPFixcxMDAgOzub58+fA3kz+/lKly6t8Q6srKw4efIkW7dupXfv3hgYGKCnp0dGRgZmZmavfYbly5djYWFBSEgIKpUKe3t7fv/9dyZPnsysWbM0IvCX5NlLYvny5axatYoXL16QmZlJmTJlGDNmTInryTd16lQmTJigfE5LS5OBvBBCCCGEEKJE/vEz8ZC3J/73339n9+7ddOjQgcjISFxcXDT2o7u6umrc4+rqqjETv2XLFtzc3DAzM8PAwIAZM2YoS8wB1Go17dq1K3afDh06RLt27ahatSqGhoYMGDCAhw8f8uzZs0LLlyT+4MtfTgCYm5tz7969ErWto6NToB7IS0WnVquJiYlh8uTJeHh4MHr0aI0yX3/9NQ0aNKBixYoYGBjw3Xffabyr4oiPj8fV1VUjZZ2bmxvp6en89ttvb/3sJeHp6YlarSYqKoqOHTsyffr0QlcNFJeuri5GRkYahxBCCCGEEEKUxL9iEA9QpkwZ2rdvz8yZMzlx4gTe3t7KjG1RTp48iaenJx9++CF79uzh3LlzTJ8+nRcvXihl9PT0it2XpKQkOnXqRN26dQkPDyc2Npavv/4aQKPOl9na2vLLL78Uq/5Xc5mrVCpycnJK1Laenl6hOd91dHSwtramTp06BAYGoq2trTHzvnnzZnx8fBg8eDA//fQTarWaQYMGvfa5/mxvenbI2xaRmppa4L6UlBSMjY01zhkbG2NtbU2jRo3YunUrISEhHDp06L/TcSGEEEIIIYQohn/8cvrXcXR01IhqfurUKQYOHKjxOX//+YkTJ6hRo4ZGHvJff/1Vo766desSERHBoEGDimw7NjaWnJwcFi1apCwLz98z/jr9+/dn2rRpnDt3rsC++MzMTF68eFEg6nphDh06xIsXL/j2229Zu3YtKSkpRbb9JjNmzKBt27aMHDmSKlWqEBUVRbNmzfjss8+UMomJiRr36OjokJ2dXWh99evXZ+fOnTg4OBAeHk5ubq7yZUJUVBSGhoZUq1btrftrZ2dHbGwsXl5eyrns7GzOnz+vbKcojIGBAWPHjsXHx4dz584V+gXH25pQr7zMygshhBBCCCGK5R8/iH/48CG9evXi008/pW7duhgaGnLmzBm++uorunbtqpTbtm0bDRs2pHnz5mzYsIHTp0+zevVqIG9v9c2bN9m8eTONGjVi79697Ny5U6Od2bNn065dO2rVqkXfvn3Jyspi3759TJ48uUCfrK2tyczMpGnTpsTExNCrVy9OnDihXN+1axeDBg3SmBkeN24ce/fupV27dgQEBNC8eXPlWebPn8+0adPYsmULUVFRHDt2jH379tG3b1+mTJmi0XZUVBS5ublMmDCBLl26sH79eiWCu6mpqUbZwMDAAve/ytXVlbp16zJ37lxCQkKwsbFh3bp1HDhwACsrK9avX09MTIxG3nhLS0sOHDhAQkIC5cuXLzADDvDZZ5+xdOlSRo8ezahRo0hISGD27NlMmDBB+eJj+/btr81HX5jIyEjOnDnD5cuXsbe3p3379jx9+pRly5bx66+/aszYHzlyhCNHjhAUFIShoSF2dnaMHTuWK1euEB4ezscff6xkMEhISCjQVu3atQusCnidxecfUsbgr1mpIIQQQgghSk7yxYv3yT9+Ob2BgQFNmjRhyZIltGzZkjp16jBz5kyGDh1KSEiIUs7Pz4/NmzdTt25d1q1bx6ZNm3B0dASgS5cujB8/nlGjRuHs7MyJEyeYOXOmRjutW7dm27Zt7N69G2dnZ9q2bcvp06cL7VO9evVYvHgxly5dAmDnzp0agfMKo6ury8GDB5k0aRLffvstTZs2pVGjRgQHB/Phhx8ycOBAXrx4Qd26dfHy8mLOnDmEhobSvn17jcHpkydPaNiwIWvWrKF169Zs2LABExMTAH755ReSk5NZunQpRkZGBfa6v8748eNZtWoVt27dYvjw4fTo0YM+ffrQpEkTHj58qDErDzB06FDs7Oxo2LAhFStWJCoqqkCdVatWZd++fZw+fZp69eoxYsQIBg8eXOR7Ko6goCDWrFlDgwYN6NChA3fu3MHMzAxDQ0ONcu3btyc5OZkzZ87g5ubGoEGD6NSpE76+vhrvtG/fvtSvX1/juHv37jv3UwghhBBCCCFepcotScS0fyiVSsXOnTvp1q3bX9qut7c3Dx8+5Nq1a3Tu3JmvvvoKyJuJ7969uxLMLjw8nFmzZnHt2jXMzc0ZPXo0EydOBPIC3tWpU4eyZcsSHR2tEbX9/Pnz1K9fn3nz5jF58mQsLS01tgF4eXkRGhqKpaUl48aN04ha/6p9+/Yxbtw4bt26RdOmTfHy8mLQoEE8fvwYExMTfH192bVrl0ae96VLl7J06VKSkpIAiImJUbYEZGZm4uzszJIlS3BxcVHuefl38eLFCyZMmEB4eDiPHz+mcuXKjBgxgqlTpwIU6LdKpWLlypXs3buXAwcOULVqVRYtWkSXLl2AvJn4Nm3aKH1+WWF1vfw38eTJE4yMjAgKClIi1IeGhjJu3DhSUlLe/It+jbS0NIyNjZl99DplDAyLvkEIIYQQQvwtZCZe/Lfljw1SU1OL3Gr7j5+Jf99pa2szd+5cli1bVmjU9djYWHr37k3fvn25ePEivr6+zJw5U4msr1ariYuL01hmnq9evXq4u7uzadMmIG8Q3aFDB3r37k1ycjJBQUHF6uOtW7fo0aMHnTt3Rq1WM2TIkCKX2RfmyZMneHl5cfz4cU6dOoWNjQ0ffvghT548KbR8cHAwu3fvZuvWrSQkJLBhwwYsLS3f2Iafnx+9e/fmwoULfPjhh3h6evLo0aMS9/VlWVlZytaKd8kTn5GRQVpamsYhhBBCCCGEECXxj98T/7+ge/fuODs7M3v2bGWwmG/x4sW0a9dOWb5va2tLXFwcCxYswNvbmytXrgDg4OBQaN0ODg4cP34cgIoVK6Krq4uenl6BHO2TJ08usFT9xx9/pEWLFqxYsYJatWqxaNEiIC843MWLF5k/f36JnrNt27Yan7/77jtMTEw4cuQInTp1KlD+5s2b2NjY0Lx5c1QqFTVq1CiyDW9vb/r16wfA3LlzCQ4O5vTp03To0KFEfQXo168f2tra/PHHH+Tk5GBpaUnv3r1LXE++efPmaUTyF0IIIYQQQoiSkpl48pak/9VL6V81f/58wsLCNHLTQ16+dDc3N41zbm5uXL16VSPC+7vuivjiiy9Qq9UaR8OGDZU+NGnSRKO8q6tridu4e/cuQ4cOxcbGBmNjY4yMjEhPT39tDnlvb2/UajV2dnaMGTOGn376qcg2Xs4Tr6+vj5GR0VvniV+yZAlqtZoff/wRR0dHVq1aRbly5d6qLoCpU6eSmpqqHLdu3XrruoQQQgghhBD/TjKIf0+0bNkSDw8PZb93cdna2gIUGPzni4+PV8q8SYUKFbC2ttY49PT0it0PLS2tAl8kZGZmanz28vJCrVYTFBTEiRMnUKvVlC9f/rU55F1cXLhx4wYBAQH88ccf9O7dm48//viN/XhTnvj8vSXFzRNvZmaGtbU1H3zwAWvXrqVPnz5v/YUA5AUnNDIy0jiEEEIIIYQQoiRkOf17JDAwEGdnZ+zs7JRzDg4OBaK3R0VFYWtri7a2Ns7Oztjb27NkyRL69u1bILDdoUOHmDdvXpFtp6Wl0b59e06cOEHp0qU1grU5ODiwe/dujfKnTp3S+FyxYkXu3Lmjkdf95SB3+f1evnw5H374IZC31/7Bgwdv7JeRkRF9+vShT58+fPzxx3To0IFHjx691Yy4jY0NWlpaWFpacu7cOZydnQG4fv06qampb/yyo3HjxjRo0IA5c+YUGksgNzeX4cOHs337dh4/fqxRf1EkT7wQQgghhBCiuGQQ/x5xcnLC09NT2XseGBjIxIkTadSoEQEBAZQrV45Ro0ahp6fH8uXLgbyZ5tWrV9O+fXt69uzJ1KlTMTMzIzo6mokTJ1KvXj1Onz5NxYoVSUtLo3Tp0tSqVYtnz55RtmxZpe2DBw9y7949Dh48iJGREXfu3KFx48YaS77zB+d9+vThyJEjGn1v3bo19+/f56uvvuLjjz9m//79/Pjjjzx+/Jhdu3bRrVs3bGxsWL9+PQ0bNiQtLY0vvvjijbP9ixcvxtzcnPr166OlpcW2bdswMzMrEFm+uAwNDenTpw+bNm0iMjISY2NjTp8+Td++fQFo3rw5pqamODk5FXr/uHHj6N69O5MmTaJq1aoAZGdno1arOX78OGvXrmXlypXUrFmTOnXqvFUfhRBCCCGEEOJNZBD/nvH392fDhg1A3j754cOHs3XrVmbNmqUEsfP398fb21u5p1mzZpw6dQo/Pz86duzIkydPqF69Ou3atWPr1q3UqFGDvXv3UrlyZT7++GPi4uJo3749hw8fVqKt5we/e3n/vYGBAf7+/gwdOpSDBw8ye/Zsfv/9d27dusXcuXP59NNPlbIODg4sX76cuXPnEhAQQM+ePfHx8WH69OlKmdWrVzNs2DBcXFywsLBg7ty5+Pj4vPZdGBoa8tVXX3H16lW0tbVp1KgR+/btKxCFvyRmz57Npk2bCA4OZtq0aVSokJcuJDw8nGbNmvHgwQPmzJkDUCB1XIcOHbCysmLOnDnKlyjp6enUr19fKTNo0CBq1arFtWvXit2nxecfUsag8C0FQgghhBDif4OkoRN/FdkT/zcKDQ1l165dGucsLS0ZMGAAnTp1wszMjHnz5tGzZ08uX77Mtm3bAJSBb3h4OLVr10ZXV5fOnTvj6urKw4cPefHiBVevXuX06dM4OjqyY8cOGjduTI0aNYiJieHEiROcPHmSJUuWFNovLy8vcnNzKV++PIaGhpiZmTFgwACuX7/O8+fPiYqKwsrKCgB7e3sePnwIwIgRI3BycqJRo0asXbuW7777DsiLvq9SqejevTsxMTH88ccfLFiwgLlz53Lnzh2Cg4Px8/MjKytLCTKoUqnIysrCwsKC3Nxcxo8fT/PmzRk0aBDr16/H0tKSx48fc+rUKSVFXW5uLmXKlKF58+aYmJhQvnx5mjdvTosWLZRn09XVBWDHjh08e/aMo0ePAlCzZk3MzMyoU6cO06ZNA9BYsn/p0iU+/PBDbt26RXh4uPI7ys3NxcvLS+P9ZWVllfAvQQghhBBCCCGKRwbx76n/hfzxANWrV2fIkCEAfP3115w4cYKwsDC0tLSIiYkBYO3atSQnJyufjx07xsCBAxk7dixxcXF8++23hIaGKjPg+Xx9fenevTsXL15UZv0TExPZtWsXe/bsYc+ePRw5coTAwEDlnqdPnzJhwgTOnDlDREQEWlpadO/eXQluV5Q//viDdevWAf+fEz4lJYW2bdtSv359zpw5w/79+7l7966Sbi4oKAh/f3+qVaum8ZyvkjzxQgghhBBCiHcly+nfY+9z/vh83333HS1btmTKlCkEBwezatUqqlevrtQLYGJiolGvn58fU6ZMUWawa9asSUBAAJMmTWL27NlKuf79+zNo0CCNtnNycggNDcXQ0BCAAQMGEBERoXwB0LNnT43ya9asoWLFisTFxb1xn3qzZs3Q0tLi2bNn5Obm0qBBA9q1awdASEgI9evXZ+7cuRr1WlhYcOXKFWxtbTE0NERbW7vA+3uZ5IkXQgghhBBCvCsZxL/n5s+fT9u2bQvsHY+Pj6dr164a59zc3Fi6dOmfnj/+5f33AFWrVkVPT0+pe+HChQwfPpw+ffrQv3//Ius8f/48UVFRGjPv2dnZPH/+XCPgXn6e+pdZWloqA3gAc3NzjbRvV69eZdasWURHR/PgwQNlBv7mzZtvHMRv2bIFe3t7Ll26xKRJkwgNDVXS1Z0/f57Dhw9jYGBQ4L7ExMRipfCDvDzxEyZMUD6npaVhYWFRrHuFEEIIIYQQAmQQ/957OX/8q4PpN3k5f/zLgdfylTR//JscPXoUbW1tkpKSyMrKolSpN/9Zpaen4+fnR48ePQpcK1OmjPKzvr5+getvygMP0LlzZ2rUqMHKlSupUqUKOTk51KlT57W56PNZWFhgY2ODjY0NWVlZdO/enUuXLqGrq0t6ejqdO3dm/vz5Be4zNzd/Y70v09XVVfbkCyGEEEIIIcTbkEH8/4C/M3/8o0ePUKlUr817vmXLFnbs2EFkZCS9e/cmICBAY8l46dKlNVYGALi4uJCQkMCQIUNwdnZm6dKlxXwTb/bw4UMSEhJYuXKlEswuf8tASXz88cfMmjWL5cuXM378eFxcXAgPD8fS0rLILyjehuSJF0IIIYQQQhSXBLb7m3h7e9OtW7cC5yMjIwkLCyMzM1M5l58/Pjg4WDk3ceJEIiIiCAgI4MqVK4SFhRESEqIsu1epVHz99decP38eQ0NDdHR0KFeuHI6Ojri7u+Pq6sq4ceOK7Gd6ejoADx484M6dO9y5c0cJyPbbb78xcuRI5s+fT/PmzVm7di1z587l1KlTyv2WlpZERERw584dHj9+DMCsWbNYt24dTZs2pX///sTHx7N582aMjY0LDOgjIyNRqVSoVCr8/Py4cOECtWvXViLfv8zU1JTy5cvz3Xffce3aNX7++WeN5esqlYqffvpJ457CvkBQqVSMGTOGwMBAnj17xueff86jR4/o168fMTExJCYmcuDAAQYNGlTgCwohhBBCCCGE+G+Smfj/Ef7+/mzZskX57OLiouSPDwgIwNzcvED++NDQUKpUqUK1atW4fPkyT548QVtbm6ZNm7Jly5ZiLe3OT0PXvn175dzw4cNZsWIF3t7eNG7cmFGjRgHg4eHByJEj+eSTT1Cr1RgYGLBo0SImTJjAypUrqVq1KklJSXh4eLBnzx78/f0JDg6mdOnS2Nvbv3GWOyEhge+++459+/YxfPhwRo4cSa1atTTKaGlpsXnzZsaMGUOdOnWws7MjODiY1q1bF+cVa/Dy8mL69OmEhIQwadIkoqKimDx5Mh988AEZGRnUqFGDDh06vFPO+nySJ14IIYQQ4n+f5IkXfxWZiX9PbdiwgYcPH9KvXz+qVq2Ko6Mjtra2bNy4USnTs2dP/Pz8sLOz4969e8ybNw93d3eePn0KwO7du/Hz8+P48eM8fvyYzMxM7t+/zw8//KAEj1OpVOzatYtdu3Yp6elMTEwIDQ0lKSmJGzduALBp0yZcXV3R1dXl+PHjHD16lEOHDrF//36OHDmCSqXiwIEDHDt2jNu3b9OlSxfu3btHqVKlKFWqFHp6ejRr1oxnz54BeQP+0qVLM2zYMFJTU9HT0+PRo0eMHz8elUoFoLFSoVKlSixcuJC4uDjGjBmDlZUVZ8+eZdy4cSQlJWFpacnSpUtxd3cnLi6O58+fo1KpOHz4MLm5ucqqg+HDhyt1h4aGKun06tevj0qlUt7BgwcPaNGiBf7+/hgZGTF9+nRWrFjB48ePefbsGX369OHw4cP85z//wdLSktmzZ9O0aVMlZ70QQgghhBBC/DfIIP499vz5cxo0aMDevXu5dOkSw4YNY8CAAZw+fRqA5ORk+vXrx6effkp8fDyRkZH06NFDiRpvZmbGvn37/pSB5RdffMHEiRM5d+4crq6udO7cmYcPH2qU8fX1JSQkhBMnTnDr1i169+7N0qVL2bhxI3v37uWnn35i2bJlhda/Y8cOqlWrhr+/P8nJySQnJxdaLjc3l/3793Pz5k2aNGlS7P4XlrO+T58+TJw4kdq1aytt9unTh5ycHLp27cqjR484cuQIBw8e5Pr16/Tp00ejzqJy1r9K8sQLIYQQQggh3pUsp/8b7dmzp0Daspf3WFetWlUjtdzo0aM5cOAAW7dupXHjxiQnJ5OVlUWPHj2oUaMGkLd/Pt93332Hp6cn5cuXp169ejRv3pyPP/4YNze3Evd11KhRSg72FStWsH//flavXs2kSZOUMl9++aVS9+DBg5k6dSqJiYnUrFkTyAsYd/jwYSZPnlyg/nLlyqGtrY2hoWGhudarVasG5A2Ec3Jy8Pf3p2XLlsXu/+ty1hsYGFCqVCmNcwcPHuTixYvcuHFDSQG3bt06ateuTUxMDI0aNQKKzln/KskTL4QQQgghhHhXMhP/N2rTpg1qtVrjWLVqlXI9OzubgIAAnJycKFeuHAYGBhw4cICbN28CUK9ePdq1a4eTkxO9evVi5cqVSvA4yEtPd/36dSIiIvj444+5fPkyLVq0ICAgoMR9dXV1VX4uVaoUDRs2JD4+XqNM3bp1lZ8rV65M2bJllQF8/rmXc7qXxLFjxzTe0dy5c1mxYsVb1VWU+Ph4LCwsNHK4Ozo6YmJiovHMReWsf9XUqVNJTU1Vjlu3bv1X+i+EEEIIIYT455JB/N9IX18fa2trjaNq1arK9QULFhAUFMTkyZM5fPgwarUaDw8PJee5trY2Bw8e5Mcff8TR0ZFly5ZhZ2en7GOHvBRvLVq0YPLkyfz000/4+/sTEBCg1KFSqZTl9/lejoxfEi/ncFepVEXmdC8JKysrrK2tqV27NoMGDWLAgAEaM95aWlp/2nMUV0mfT1dXFyMjI41DCCGEEEIIIUpCBvHvsaioKLp27conn3xCvXr1qFmzJleuXNEoo1KpcHNzw8/Pj3PnzqGjo8POnTtfW6ejoyNZWVk8f/4cyFtm/vL+86tXryrB5172ctq4rKwsYmNjcXBweNdH1KCjo1PslG3a2tr88ccfyudXnyMtLU3jywwoPGd9YW06ODhw69YtjZnyuLg4UlJScHR0LPbzCCGEEEIIIcSfTfbEv4FKpWLnzp2F5nP/K9jY2LB9+3ZOnDiBqakpixcv5u7du8pAMjo6moiICD744AMqVapEdHQ09+/fVwbXrVu3pl+/fjRs2JDy5csTFxfHtGnTaNOmDTt27GDcuHF07NiRkJAQXF1dyc7OZvLkyQVmmAG+/vprbGxscHBwYMmSJTx+/JhPP/30T3tWS0tLdHV1OXr0KH379kVXV5cKFf4/Tce9e/d4/vw5GRkZnD59mvXr1/Pxxx8r19u2bUtoaCidO3fGxMSEWbNmoa2tXaCNiIgI3Nzc0NXVxdTUFEtLS27cuIFaraZatWoYGhri7u6Ok5MTnp6eLF26lKysLD777DNatWpFw4YN/7RnzjehXnmZlRdCCCGEEEIUy796EH/nzh3mzJnD3r17uX37NpUqVcLZ2Zlx48bRrl27/3r7T58+ZdCgQRw8eJD79+9TpUoVrKyslOszZszg+vXreHh4ULZsWYYNG0a3bt1ITU0FwMjIiKNHj7J06VLS0tKoUaMGixYtomPHjkBeGrewsDA+++wztLW1sbCwoFOnTsyaNYuyZcvy4YcfkpWVxaBBg2jRogVVqlQhKCiI2NjYAn0NDAwkMDAQtVqNtbU1u3fv1hhkF9ejR48wMTEhJSVF43xMTAyXL19m7Nix1KpVi4yMDI3l8XZ2dkDefnwLCwuGDx+Or6+vcn3q1KncuHGDTp06YWxsTEBAAEeOHMHPzw8/Pz9KlSpFuXLl2LRpk0bO+p49e7Jjxw7atGlDSkoKa9euxdvbm++//57Ro0fTsmVLnj9/Ts2aNdm7d2+Jn7c4JE+8EEIIIcQ/h+SLF/9tqtxXNxL/SyQlJeHm5oaJiQn+/v44OTmRmZnJgQMH+O677/jll1/+KzPxL168QEdHhzNnztCuXTvq1KnDtGnTsLe358mTJ3z//ff8/PPPHDly5E9r09LSknHjxim50v9OoaGhjBs3rsAg/r/B29ubu3fvsnbtWjIzM4mNjcXLy4sRI0Ywf/78YtfTunVrnJ2dWbp06Z/av7S0NIyNjZl99DplDAyLvkEIIYQQQrz3ZBAv3kb+2CA1NbXIVbr/2j3xn332GSqVitOnT9OzZ09sbW2pXbs2EyZM0Nj//eDBA7p3707ZsmWxsbFh9+7dyrXs7GwGDx6MlZUVenp62NnZERQUpNGOt7c33bp1Y86cOVSpUgU7Oztyc3Px9vbGxsaGY8eO8dFHH1GrVi2cnZ2ZPXs233//vXL/xYsXadu2LXp6epQvX55hw4aRnp5eoP6FCxdibm5O+fLl+fzzz5Wgbq1bt+bXX39l/PjxqFQqVCoVkDeYNjExUerx9fXF2dmZ9evXY2lpibGxMX379tXIMW9paVlgIOvs7KwxI7548WKcnJzQ19fHwsKCzz77TOlvZGQkgwYNIjU1VelL/r2v1n3z5k26du2KgYEBRkZG9O7dm7t375aov5AXTM7MzAwLCwu6deuGu7s7Bw8eVK4/fPiQfv36UbVqVcqWLYuTkxObNm3SeL9HjhwhKChI6XNSUhIAly5domPHjhgYGFC5cmUGDBjAgwcPEEIIIYQQQoj/ln/lIP7Ro0fs37+fzz//HH19/QLXXx7c+vn50bt3by5cuMCHH36Ip6cnjx49AvLyhFerVo1t27YRFxfHrFmzmDZtGlu3btWoLyIigoSEBA4ePMiePXtQq9VcvnyZiRMnoqVV8FeQ3/7Tp0/x8PDA1NSUmJgYtm3bxqFDhxg1apRG+cOHD5OYmMjhw4cJCwsjNDSU0NBQAHbs2EG1atXw9/cnOTlZI/jbqxITE9m1axd79uxhz549HDlyhMDAwOK8UoWWlhbBwcFcvnyZsLAwfv75ZyWXfLNmzVi6dClGRkZKX3x8fArUkZOTQ9euXXn06BFHjhzh4MGDXL9+nT59+rxTfy9dusSJEyfQ0dFRzj1//pwGDRqwd+9eLl26xLBhwxgwYACnT58GICgoCFdXV4YOHar02cLCgpSUFNq2bUv9+vU5c+YM+/fv5+7du/Tu3fu17WdkZJCWlqZxCCGEEEIIIURJ/Cv3xF+7do3c3Fzs7e2LLOvt7U2/fv0AmDt3LsHBwZw+fZoOHTpQunRp/Pz8lLJWVlacPHmSrVu3agzm9PX1WbVqlTJ4zB/kF9X+xo0bef78OevWrVO+bAgJCaFz587Mnz+fypUrA2BqakpISAja2trY29vz0UcfERERwdChQylXrhza2toYGhpiZmb2xvZycnIIDQ1Vcp8PGDCAiIgIjVRuRXl5yb6lpSVffvklI0aMYPny5ejo6GBsbIxKpXpjXyIiIrh48SI3btxQcrWvW7eO2rVrExMTQ6NGjYrd3z179mBgYEBWVhYZGRloaWkREhKiXK9atarGFwmjR4/mwIEDbN26lcaNG2NsbIyOjg5ly5bV6HNISAj169dn7ty5yrk1a9ZgYWHBlStXsLW1LfBc8+bN0/h7EUIIIYQQQoiS+lfOxJckDEDdunWVn/X19TEyMuLevXvKua+//poGDRpQsWJFDAwM+O6777h586ZGHU5OThqzv8VtPz4+nnr16mmsFnBzcyMnJ4eEhATlXO3atTUisZubm2v0sbgsLS2VAfHb1nPo0CHatWtH1apVMTQ0ZMCAATx8+LDQtHWvEx8fj4WFhTKAh7zUeCYmJsTHx5eov23atEGtVhMdHY2XlxeDBg2iZ8+eyvXs7GwCAgJwcnKiXLlyGBgYcODAgQK/w1edP3+ew4cPY2BgoBz5X8okJiYWes/UqVNJTU1VjpdT2AkhhBBCCCFEcfwrB/E2NjaoVCp++eWXIsu+mm5NpVKRk5MDwObNm/Hx8WHw4MH89NNPqNVqBg0axIsXmpHGX12ynz9LW5z2i+NNffwz69HS0irwBUT+3nvICxbYqVMn6tatS3h4OLGxsXz99dcABd7Jn6E4z62vr4+1tTX16tVjzZo1REdHs3r1auX6ggULCAoKYvLkyRw+fBi1Wo2Hh0eR/U1PT6dz586o1WqN4+rVq7Rs2bLQe3R1dTEyMtI4hBBCCCGEEKIk/pXL6cuVK4eHhwdff/01Y8aMKTDITklJ0dgX/zpRUVE0a9aMzz77TDn3ulnYlzk7O+Po6MiiRYvo06dPgX3x+e07ODgQGhrK06dPlT5GRUWhpaWlpFwrDh0dHbKzs4td/nUqVqyosac+LS2NGzduKJ9jY2PJyclh0aJFyjO9Gh/g1b74+vqya9cujTIODg7cunWLW7duKbPxcXFxpKSk4Ojo+Nb919LSYtq0aUyYMIH+/fujp6dHVFQUXbt25ZNPPgHyluhfuXJFo51X+xwZGcn+/fuxsbHB0tKSUqXe7Z+R5IkXQgghhBBCFNe/chAPecvg3dzcaNy4Mf7+/tStW5esrCwOHjzIihUrNJZtv46NjQ3r1q3jwIEDWFlZsX79emJiYjRyvRdGpVKxdu1a3N3dadGiBdOnT8fe3p709HR++OEHfvrpJ44cOYKnpyezZ8/Gy8sLX19f7t+/z+jRoxkwYICyH744LC0tOXr0KH379qV37940atQIZ2dnjTJqtZqLFy8CeQPrl/duW1hY0LFjR1xdXVm/fj2dO3fGxMSEJk2aaMx8W1tbk5mZybJly+jcuTNRUVEsWbIEgOrVq5OTk0PlypVJT09n586dtGjRQmMmP5+7uztOTk54enqydOlSsrKy+Oyzz2jVqhUNGzYs9nPfuXOHAwcOKBH5K1WqRLNmzYC837+Pjw82NjZs376dEydOYGpqyuLFi7l7967GIN7S0pLo6GiSkpIwMDBQnvnx48f069ePSZMmUa5cOa5du8bmzZtZtWqVxvaGokieeCGEEEKIfx5JNSf+W/6Vy+kBatasydmzZ2nTpg0TJ06kTp06tG/fnoiICFasWFGsOoYPH06PHj3o06cPTZo04eHDhxqz8m/SuHFjzpw5g7W1NUOHDsXBwYEuXbpw+fJlJdVa2bJlOXDgAI8ePaJRo0Z8/PHHtGvXTiMwW3H4+/uTlJRErVq1OHHiRLHuqV27Nn5+flSrVo21a9eyf/9+bt68SatWrejUqRMfffQRZcuWpXz58so99erVY/HixcyfP586derg6+vL48ePAdi2bRuXLl3im2++wcrKigEDBlCxYkWioqIKtK1Sqfj+++8xNTWlZcuWuLu7U7NmTbZs2VLsZ375y4GEhAR+//13tm3bRnx8PDk5OXz11Vc8ffqUGTNm4OLigoeHB61bt6ZSpUp069ZNoy4fHx+0tbVxdHSkYsWKyr77/fv3k52dzQcffICTkxPjxo3DxMSk0IwDQgghhBBCCPFnUOWWJMqb+J/XunVrnJ2dC+R7Dw0NZdy4caSkpChL3NVqtXJ94sSJrF27VkmvB3kz1OPGjdOISJ/v9OnTNGnShKVLlzJ27NgC1/O3DOS3NXHiRGbOnMnjx4/p2LEjK1euVILW7d+/ny+//JJLly6hra2Nq6srQUFB1KpVC8jbi29lZcXmzZtZvnw50dHRfPPNN1haWtKmTRseP36sbI/YuHEjnp6e/PLLLyQnJ9OmTRv27dvHjBkzuHjxIj/99BOurq588cUXbN68mbS0NBo2bMiSJUuUqPiRkZG0adOGPXv2MHXqVK5cuYKzszOrVq2iTp06xf5dpKWlYWxszOyj1yljYFj0DUIIIYQQ4n+GzMSLksgfG6Smpha51VamDEWRkpKSOHDggEaE/aJs2LABAwOD165MeDnmQFH53p8+fcqECRM4c+YMERERaGlp0b179wJB7KZMmcLYsWOJj4/Hw8Oj0Hb19PQAzUB7U6ZMITAwkPj4eOrWrcukSZMIDw8nLCyMs2fPYm1tjYeHh8YXGABffPEFixYtIiYmhooVK9K5c+dCtwfkkzzxQgghhBBCiHclg3hRqIsXL2JgYICenh5WVlZcvnyZyZMnF/v+q1evUrNmzQIR5AuTn++9Tp06tGjRQsn3nq9nz5706NEDa2trnJ2dWbNmDRcvXiQuLk6jnnHjxtGjRw+srKwwNzcv0E5ycjILFy6katWqGoEB/f39ad++PbVq1UJXV5cVK1awYMECOnbsiKOjIytXrkRPT08jqj3A7Nmzad++PU5OToSFhXH37l127tz52uecN28exsbGyvFyCj0hhBBCCCGEKA4ZxItC2dnZoVariYmJYfLkyXh4eDB69Ohi31+SXRpF5Xu/evUq/fr1o2bNmhgZGWFpaQlQIJf764LeVatWDX19fapUqcLTp08JDw/XWFXw8n2JiYlkZmbi5uamnCtdujSNGzcuEOzQ1dVV+blcuXLY2dm9MSCi5IkXQgghhBBCvKt/bXT6fysjIyNSU1MLnE9JScHY2Fj5rKOjg7W1NQCBgYF89NFH+Pn5ERAQUKx2bG1tOX78OJmZmUXOxheV771z587UqFGDlStXUqVKFXJycqhTp06BXO6vpgrMd+zYMYyMjKhUqZLGlwVF3fdn09XVRVdX9y9pSwghhBBCCPHPJIP4v9DLweP+LN7e3qSkpBTItf6y/EBsjx8/xs7Ojp9++qlAmbNnz2Jra/vaOmbMmEHbtm0ZOXIkVapUKbJf/fv3Jzg4mOXLl78xsN2b5AesA1i5ciUtWrQA4Pjx40W2/zIrK6si28pXq1YtdHR0iIqKokaNGkBepPuYmJgCAfxOnTpF9erVgbx0c1euXMHBwaFEfQPJEy+EEEIIIYQovn/Ucnpvb29UKhUqlYrSpUtTuXJl2rdvz5o1awoEQfs79OnThytXrhS7fP6zvO7w9fUlKCiI0NBQ5Z7WrVsXGi0+38iRI7ly5QpjxozhwoULJCQksHjxYjZt2sTEiROVcteuXUOlUikB5lxdXalbty5z587lo48+QqVSkZKSwu3bt1Gr1RrH48ePadKkCZMmTWLixIlMmjSJr7/+Wkkd16tXL8LCwor9HkxMTPjuu++4du0aP//8MxMmTNC4vmnTJqWPJiYm1K9fn3nz5hW7/pfp6+szcuRIvvjiC/bv309cXBxDhw7l2bNnDB48WKOsv78/ERERXLp0CW9vbypUqFAgPZ0QQgghhBBC/Jn+cTPxHTp0YO3atWRnZ3P37l3279/P2LFj2b59O7t376ZUqb/vkfX09JTo6MWRnJys/LxlyxZmzZpFQkKCcs7AwAADA4MS9aFmzZocPXqU6dOn4+7uzosXL7C3t2fbtm106NBBo6yFhQWhoaFMmTIFgPHjx+Pt7U1ubi7m5uY8e/aMhQsXsnDhQo371q9fzyeffML8+fNp0KABX3/9NV9//TUA06dPp0+fPnh5eRW7z/PmzSM4OJg6depgZ2dHcHAwrVu3BmDNmjX4+/sr76hWrVpcuHCBS5culei9vCwwMJCcnBwGDBjAkydPaNiwIQcOHMDU1BT4/xz0gYGBjB07lqtXr+Ls7MwPP/xQogj++Raff0gZgxdFFxRCCCGEEP9Iko5OlMQ/aiYe8vYdm5mZUbVqVVxcXJg2bRrff/89P/74ozJjffPmTbp27YqBgQFGRkb07t2bu3fvKnX4+voqUdCrV6+upErLzs7mq6++wszMjEqVKjFnzhyNthcvXoyTkxP6+vpYWFjw2WefkZ6erlwPDQ3VWNad38769euxtLTE2NiYvn378uTJEwDMzMyUw9jYGJVKpXHOwMAAb29vZfbX29ubI0eOEBQUpMzWJyUlFXhHjRo1YtasWdjZ2ZGRkcHt27f5+eefefr0qdKvhg0b0qlTJx48eEBUVBQAffv2ZdasWXh4eFCpUiXGjRtHbm4uubm5rFu3jgYNGmBgYICPjw/9+/fn3r179O7dm7CwMJ49ewbA5cuXmTVrlrJaYNasWfTv3x9ra2t0dXWpXr06T58+1eh3pUqVqFy5MlpaeX+uOjo65Obm0q1bN3bv3k3fvn3Jzc2lS5cu1K5dm379+jFnzhxat25Nbm4uJiYmrFmzhtq1a6Orq4u5uTmjRo1SrqelpWn8PQwcOJDp06dz//59nj9/jru7O0OHDmXVqlVYWVnh4eFBbm4uzZs3p2nTphgZGREfH8+4ceM4f/58yf5ghRBCCCGEEKIE/nGD+MK0bduWevXqsWPHDnJycujatSuPHj3iyJEjHDx4kOvXr9OnTx+NexITE/nxxx/Zv38/mzZtYvXq1Xz00Uf89ttvHDlyhPnz5zNjxgyio6OVe7S0tAgODuby5cuEhYXx888/M2nSpDf2ragc6SURFBSEq6srQ4cOJTk5meTk5ELTmCUmJtKhQwd69uzJhQsX2LJlC8ePH2fUqFEa5XR0dPD09GTt2rXKudDQUD799NMCdWZmZhIQEMD58+fZtWsXSUlJeHt7A3kz+uHh4QAkJCSQnJxMUFAQkBexPTAwkJkzZxIXF8fGjRupXLmyRt3Tp0/Hx8cHtVqNra0t/fr1IysrC8j7ouPUqVP8+uuvr30vK1as4PPPP2fYsGFcvHiR3bt3K0H7ivv3cO3aNcLDw9mxYwdqtRqAXr16ce/ePX788UdiY2NxcXGhXbt2BfLJ55M88UIIIYQQQoh39Y9bTv869vb2XLhwgYiICC5evMiNGzeUAe66deuoXbs2MTExNGrUCMgb3K1ZswZDQ0McHR1p06YNCQkJ7Nu3Dy0tLezs7Jg/fz6HDx+mSZMmABp70S0tLfnyyy8ZMWIEy5cvf22/8nOk50dNz8+R/uosf3EYGxujo6ND2bJlMTMze225efPm4enpqfTXxsaG4OBgWrVqxYoVKyhTpoxS9tNPP6VFixYEBQURGxtLamoqnTp1wtfXV6POlwf2NWvWJDg4mEaNGpGeno6BgQHlypUD8mbV81cjPHnyhKCgIEJCQpTl9bVq1aJ58+Yadfv4+PDRRx8B4OfnR+3atbl27Rr29vbMnj2bHj16YGlpia2tLa6urnz44Yd8/PHHysz9l19+ycSJEzUC7OX/nov79/DixQvWrVtHxYoVgbzgeqdPn+bevXtKxPmFCxeya9cutm/fzrBhwwp9735+fq/9vQghhBBCCCFEUf4VM/GQl7dcpVIRHx+PhYWFxgy1o6MjJiYmGjm+X81dXrlyZRwdHZWBYf65l/OZHzp0iHbt2lG1alUMDQ0ZMGAADx8+VJaSF6aoHOn/DefPnyc0NFTZU29gYICHhwc5OTncuHFDo2y9evWwsbFh+/btrFmzhgEDBhQaVyA2NpbOnTtTvXp1DA0NadWqFVAwl/vL4uPjycjIoF27dm/sb926dZWfzc3NAZR3ZG5uzsmTJ7l48SJjx44lKysLLy8vOnToQE5ODvfu3eP3339/bRvF/XuoUaOGMoCHvHeYnp5O+fLlNd7jjRs3SExMLLQtyRMvhBBCCCGEeFf/mpn4+Ph4JV1ZcRSWu/xN+cyTkpLo1KkTI0eOZM6cOZQrV47jx48zePBgXrx4QdmyZYvdzn87kn56ejrDhw9nzJgxBa7lp0x72aeffsrXX39NXFwcp0+fLnD96dOneHh44OHhwYYNG6hYsSI3b97Ew8OjQC73lxU3yN/L70ilUgEUeEd16tShTp06fPbZZ4wYMYIWLVpw5MgRGjZsWKw2ivJqLvn09HTMzc2JjIwsUPZ16ewkT7wQQgghhBDiXf0rBvE///wzFy9eZPz48VSrVo1bt25x69YtZfY1Li6OlJQUHB0d37qN2NhYcnJyWLRokTJbv3Xr1j+l/yWho6NDdnb2G8u4uLgQFxen7AsvSv/+/fHx8aFevXqFvqNffvmFhw8fEhgYqLzTM2fOFOgXoNE3Gxsb9PT0iIiIYMiQIcXqS3Hk9/Hp06cYGhpiaWlJ27Zt2blzZ4EUcA4ODm/19+Di4sKdO3coVaoUlpaW79RfyRMvhBBCCCGEKK5/3HL6jIwM7ty5w+3btzl79ixz586la9eudOrUiYEDB+Lu7o6TkxOenp6cPXuW06dPM3DgQFq1avVOs7bW1tZkZmaybNkyrl+/zvr16/nmm2/+xCcrHktLS6Kjo0lKSuLBgweFzupPnjyZEydOMGrUKNRqNVevXuX7778vENgu3/jx42nbti0REREa55OSklCpVMpe/Pxn3717NwEBARpla9SogUqlYs+ePdy/f5/09HTKlCnD5MmTmTRpEt27d1ci6ucfxVk5MXLkSAICAoiKiuLXX3/l1KlTDBw4kIoVK+Lq6grkRdsvU6YM169f5+rVq5w9e5Zly5YBvPXfg7u7O66urnTr1o2ffvqJpKQkTpw4wfTp0wt8gSGEEEIIIYQQf5Z/3Ez8/v37MTc3p1SpUpiamlKvXj2Cg4Px8vJSZsi///57Ro8eTcuWLdHS0qJDhw7KoO5t1atXj8WLFzN//nymTp1Ky5YtmTdvHgMHDvwzHqvYfHx88PLywtHRkT/++KPAHnfI22N+5MgRpk+fTosWLcjNzaVWrVoFIrK/rHTp0gWWlOerUKECoaGhTJs2jeDgYFxcXFi4cCFdunRRylStWhU/Pz+mTJnCoEGDGDhwIKGhocycOZNSpUqxYMECVCoVVapUYeDAgYwZM4Zbt27RuHHjNz6vu7s7a9asYcWKFTx8+JAKFSrg6upKREQE5cuXB8DLy4vnz5+zZMkSpkyZQoUKFfj444+BvOX5b/P3oFKp2LdvH9OnT2fQoEHcv38fMzMzWrZsWSC6flEkT7wQQgghhMgnOeNFUVS5ubm5f3cnxPvN29ublJQUdu3apXE+MjKSNm3a8PjxY7Kzsxk1ahRHjx7l8ePH1KpVi2nTptGvXz+l/Pbt2/Hz8+PatWuULVuW+vXr8/3336Ovr4+vry+7du1S0re9bP369QQFBZGQkIC+vj5t27Zl6dKlVKpUiZycHKpXr8706dMZOXKkcs+5c+do0KABN27cUFYBvLyc/rfffuOLL77gwIEDZGRk4ODgwNdff61kGlixYgULFy7k1q1bWFlZMWPGDAYMGKDUr1KpWLlyJXv37uXAgQNUrVqVRYsWaXxxUZS0tDSMjY2ZffQ6ZQwMi75BCCGEEEL848kg/t8pf2yQmppa5Fbbf9xyevH3eP78OQ0aNGDv3r1cunSJYcOGMWDAACUQXnJyMv369ePTTz8lPj6eyMhIevToQXG+Q3pTDnotLS369evHxo0bNe7ZsGEDbm5u1KhRo0B96enptGrVitu3b7N7927Onz/PpEmTlK0HO3fuZOzYsUycOJFLly4xfPhwBg0axOHDhzXq8fPzo3fv3ly4cIEPP/wQT0/P1+aIB8kTL4QQQgghhHh3MhMviuTt7c1//vMfjfzxkBek7vnz5zx+/LjQiOydOnXC3t6ehQsXcvbsWRo0aEBSUlKhA2tfX18CAgI0ItY7OjoWGg3/zJkzNGrUiCdPnmBgYIBarcbFxYWkpCSqV6+uzM7PmDGDESNGAGjMxH/33Xf4+PiQlJSk5K9/mZubG7Vr1+a7775TzvXu3ZunT5+yd+9epb4ZM2Yoe/+fPn2KgYEBP/74Ix06dCj0Pfr6+haaJ15m4oUQQgghRD6Zif93kpl48adr06YNarVa41i1apVyPTs7m4CAAJycnChXrhwGBgYcOHBAyRNfr1492rVrh5OTE7169WLlypU8fvxYr7XawAAAfTRJREFUow07OzuN+sPDw4Gic9A7Ozvj4OCgzMYfOXKEe/fu0atXr0KfRa1WU79+/UIH8JCXjtDNzU3jnJubm0beeNDMX6+vr4+RkZGSv74wkideCCGEEEII8a5kEC+KRV9fH2tra42jatWqyvUFCxYQFBTE5MmTOXz4MGq1WiNPvLa2NgcPHuTHH3/E0dGRZcuWYWdnpxF4T0dHR6N+CwsLJQe9kZERGzZsICYmhp07dwJo5KD39PRUBvEbN26kQ4cOSmC7VxU3P31RXs5fD3mz84VlA8inq6uLkZGRxiGEEEIIIYQQJSGDePGniIqKomvXrnzyySfUq1ePmjVrcuXKFY0yKpUKNzc3/Pz8OHfuHDo6OsqA/HVezkHfokUL7O3tC53t7t+/P5cuXSI2Npbt27fj6en52jrr1q2LWq1+7f51BwcHoqKiCjzfm/LGCyGEEEIIIcRf4R+XYk78uSIjIwkLC+PD/2vvvqOquLqHj38vqHSwoWADlaoixRYldhQwGlEUJQYl1hh7D1GwYMCGBUviExWIsTdiLNgiGlGxYiUW1KAGxQqCERR4/+Blft4ACmpiyf6sNWtxZ86c2TPgSvY9Z85u167QNitXrmTnzp1UrFiRgwcPUqZMGWbPns3t27eVxLdv375s3bqVX375hQoVKhAbG8udO3ewtbV94fWrVaum1KD/8ssvOXv2bL4a9ADm5uY0adKEPn36kJWVRUpKCqVLl+bhw4f52np7exMUFISHhwfBwcGYmppy8uRJunTpwqZNmxgzZgxeXl44Ojri4uLCL7/8wsaNG/npp59QqVScPHmyeA/xJUbal5NReSGEEEIIIUSRSBL/DipKSbeCFpIrjsJKul27do3q1atz8uRJHBwcaNKkCV5eXmRkZLywPy0tLZycnHB1dUVXV5f+/fvj4eFBSkqKcjw9PZ127dqRmpqKmZkZISEhuLu7v7BfY2PjQmvQHzt2DEdHx3zn2NnZ4ePjo5ST+7tSpUqxc+dORo0aRbt27Xj27JnaKLuHhwfz5s1j1qxZDBs2jOrVqxMWFsZHH330wlhfldSJF0IIIYQQz5PF7cSLSBIvXqhUqVKsWbOmwGMtWrQgJyeH8PBwNDQ08n3p8DxjY2Nq1KhRYB14yP1SYdKkSQUe8/b2Vqs3D5CTk0N0dDQAFy5cUBvJ1tfXR0dHR+3d978XYTAzM2P9+vVq+1QqlfLzwIED1erOQ+4XHIX1BxQ46i+EEEIIIYQQb5K8E/+eunfvHt7e3lSuXBldXV3s7OxYtWqVWpv169djZ2eHjo4O5cqVw8XFhfT09GJdJzo6GpVKpZaghoeHU61aNXR1denUqRP37t3Ld960adOoWLEiBgYG9OnThydPnuRrs2TJEmxtbdHW1sbGxoZFixYpx65du4ZKpWLjxo20bNkSXV1d7O3tOXToUL5+KlSogImJibLp6+sTHh6eb7bCzz//jJOTE9ra2tSoUYPJkyfz7NmzQu/9yJEjODo6oq2tTf369fNNo3/w4AE9evTA2NgYHR0dLC0tCQsLK7Q/IYQQQgghhHhdMhL/nnry5An16tVj3LhxGBoasnXrVnx8fKhZsyYNGzYkKSkJb29vZsyYQadOnXj06BG//fZbgSPIxREbG0ufPn0IDg7Gw8ODqKgoJk6cqNZm7dq1TJo0iYULF/Lxxx+zfPlyQkNDqVGjhtJmxYoVBAQEsGDBAhwdHTl58iT9+vVDT0+PXr16Ke3Gjx/PrFmzsLS0ZPz48Xh7e3P58mVKlCjen+5vv/1Gz549CQ0NpWnTpiQkJNC/f3+AfPEDpKWl0b59e9q0acNPP/3E1atXGTZsmFobf39/zp8/z/bt2ylfvjyXL1/mr7/+KjSGjIwMtdcSUlNTi3UPQgghhBBCCCFJ/Dtqy5Yt6Ovrq+3LyspSfq5cuTKjR49WPg8ZMoQdO3awdu1aJYl/9uwZnTt3xszMDMh9V/x5Z86cyXeNlyX58+bNw83NjbFjxwJgZWXFwYMHiYqKUtrMnTuXPn360KdPHwCmTp3K7t271UbjJ06cSEhICJ07dwagevXqnD9/nsWLF6sl8aNHj+aTTz4BYPLkydSuXZvLly9jY2OjtKlSpYpajH/88Ue+uCdPnszXX3+t9F2jRg0CAwMZO3ZsgUn8ypUryc7OZunSpWhra1O7dm1u3LihNsU+MTERR0dH6tevD+QurvciwcHBTJ48+YVthBBCCCGEEOJFJIl/R7Vs2ZLvvvtObV9sbCyff/45kJvQBwUFsXbtWm7evElmZiYZGRno6uoCYG9vT+vWrbGzs8PV1ZW2bdvSpUsXypQpo/RnbW3N5s2b1a5x8+ZNWrRoUWhc8fHxdOrUSW1f48aN1ZL4+Ph4vvzyy3xt9u7dC0B6ejoJCQn06dOHfv36KW2ePXuGkZGR2nl169ZVfjY1NQUgOTlZLYn/7bffMDAwUD4/f495Tp06RUxMDN9++62yLysriydPnvD48WPluT1/D3Xr1kVbW1vtHp43cOBAPD09OXHiBG3btsXDw4MmTZrku3YePz8/Ro4cqXxOTU2latWqhbYXQgghhBBCiL+TJP4dpaenh4WFhdq+GzduKD/PnDmTefPmMXfuXOzs7NDT02P48OFkZuaucq6pqcmuXbs4ePAgO3fuZP78+YwfP57Y2FiqV68O5C5a9/drFHea+qtIS0sD4IcffqBRo0ZqxzQ1NdU+lyxZUvk5b+G57OxstTbVq1d/6Wr9aWlpTJ48WRn5f97ziXpxuLu788cff7Bt2zZ27dpF69atGTRoELNmzSqwvZaWFlpaWq90LSGEEEIIIYQASeLfWzExMXTs2FEZmc/OzubixYtqpdJUKhXOzs44OzsTEBCAmZkZmzZt4rPPPuPHH3/k2rVrhdZSL4ytrS2xsbFq+w4fPlxgm549exbYpmLFilSqVIkrV67Qo0ePF15v5syZrFixosjxFcbJyYkLFy7k+9KiMLa2tixfvpwnT54oSf7f7xNyV93v1asXvXr1omnTpowZM6bQJL4wUideCCGEEEIIUVSSxL9lvr6+REREEBwczNdff63sT0pKQqVSFfqOuqWlJevXr+fgwYOUKVOG2bNnk5SURHp6OsbGxjx8+BAjIyM8PDwYM2YMp0+f5s6dO9ja2jJnzhzS0tKwtrZm3759Sp8HDx7km2++AaBhw4ZYW1vTtGlTtesOHTqUxo0bo1KpWLt2Lbdv31am0mdkZFCpUiXu37/P8ePHqV+/Ps7OzqxYsYJz586pLWw3efJkhg4dipGREW5ubmRkZHDs2DEePHigNuX8RcLDw4vUDiAgIID27duzYcMGBgwYgK+vL6dOneLs2bNMnTo1X3m7ffv2ce/ePXR0dNDU1KRcuXLKLIfn+wwMDGTRokU0a9aMLVu2YGtrW+SYhBBCCCGEEKK4JIl/B2hrazN9+nQGDBhQ4PvcBZkwYQJXrlzB1dUVXV1d2rVrx5MnT9DX12fr1q08evSIUaNGERYWxrJly7CwsCAkJAR3d3eWLl2KqakpKpWKChUqALBp0ya8vLzo0qULAJGRkfz555+MGDEC+L8F7z766CMgd9q7t7c37dq1Y8KECQQGBrJp0yb09fW5f/8+Pj4+jB07lidPnuDp6cnAgQPZsWOHEn/fvn3R1dVl5syZjBkzBj09Pezs7NReCXiTXF1d2bJlCx07dmTOnDl8//332NjY0Ldv3wLblyxZko8++ohHjx5x6dIl9PT0ePTokVqbUqVKATB8+HD09fVp2rQpq1evLnZss0/dQ1v/zd+zEEIIIYR4P33tWP5thyDeYVIn/h3g4uKCiYkJwcHBQO4Is5+fn1qbDRs2MGjQIEqVKoWDgwNhYWFERkby6NEjbt26xZEjR3BwcODmzZs0bNiQ1q1bExcXx4kTJwDo06cPgwcPxtzcnA0bNnD69GlOnTqFr68v6enp9OvXj08//ZRVq1aRk5NDu3bt6Nu3LytXrgRg586davH4+fmhp6fHmjVrGDVqFA8fPmTZsmXK6u+ff/45d+7c4dGjR1SsWJFNmzZx8eJFatSogb+/P0+fPuWzzz7j5MmT+Pn5Ua1aNXx8fBg5ciTa2tqYm5vTvHlzjI2NlWvGxMRgaGjIzZs3gdzV4O3t7ZX34X19ffHw8GDWrFn4+fmhqanJoEGDePr0KZCbyJuYmDBjxgxSUlKIjY1VW1jP3t4eDw8P5XPFihU5e/YsGRkZXLlyBXd3dxwdHXFwcAByv0gBWLNmDffu3SMyMlJZb0AIIYQQQggh/gmSxL8DNDU1CQoKYv78+WqL1+U5fvw4Xl5edO/enTNnzjBp0iT8/f2V6eRxcXGcP3+ekSNHoqGh/iu1t7fHxcWFVatWAXD06FHc3Nzw8vIiKSmJefPmsXPnTu7du6dWsi5Phw4dsLKyUs7PU69ePeULAcgtt7Z//358fHzy9WFgYEB4eDjnz59n3rx5/PDDD8yZM0etzeXLl9mwYQMbN24kLi4uXx8rV67E29ubFStWvPA9+r1795KQkMDevXuJiIggPDy8WNPuC3P27FkOHjyojL6/ioyMDFJTU9U2IYQQQgghhCgOSeLfEZ06dcLBwaHAmuWzZ8+mdevW+Pv7Y2Vlha+vL4MHD2bmzJkAXLx4EaDQ97FtbW2VNsbGxmhpaaGjo4OJiQlGRkYvPd/GxkZp87zevXuzbNkyIHf2QLt27dRGzvNMmDCBJk2aYG5uTocOHRg9ejRr165Va5OZmcmPP/6Io6OjWlk5gIULF/LVV1/xyy+/0L59+wJjzFOmTBkWLFiAjY0N7du355NPPmHPnj0vPKcwW7ZsQV9fH21tbezs7EhOTmbMmDGv1Bfk1ok3MjJSNikvJ4QQQgghhCguSeLfIdOnTyciIoL4+Hi1/fHx8Tg7O6vtc3Z25tKlS2RlZSn7ClsEr6hedH5BI9Cff/45hw4d4sqVK4SHh9O7d+8Cz12zZg3Ozs6YmJigr6/PhAkTSExMVGtjZmZW4BcA69evZ8SIEezatYvmzZu/9B5q166tVqbO1NSU5OTkl55XkJYtWxIXF0dsbCy9evXiiy++wNPT85X6gtxXEFJSUpTt+vXrr9yXEEIIIYQQ4r9Jkvh3SLNmzXB1dc33PvzLWFlZAeRL/vPEx8crbQpiaWn5SueXK1eO9u3b06dPH548eYK7u3u+NocOHaJHjx60a9eOLVu2cPLkScaPH59v8To9Pb0Cr+3o6IixsTHLli0r0pcUz9eVh9wye8/XlTc0NCQlJSXfeXmr+f89JgsLC+zt7Vm2bBmxsbEsXbr0pTEURktLC0NDQ7VNCCGEEEIIIYpDVqd/x0ybNg0HBwesra2Vfba2tsTExKi1i4mJwcrKihIlSrBx40ZsbGyYM2cO3bt3V3sv/tSpU+zatUutfvzfubq6UrZsWUJCQmjSpInasc2bN3Pp0iXmzp1b4Lm9e/emXbt2jBs3Tm0EPM/BgwcxMzNj/Pjxyr4//vjjhc8gz7Vr10hPTycmJoYWLVqgqanJggULinRuYaytrTl+/Hi+/SdOnFB75n+noaHBN998w8iRI/nss8/Q0dF5rTieJ3XihRBCCCGEEEUlI/GvoUWLFgwfPjzf/vDwcGXF9EmTJqFSqVCpVGhqalK1alX69+/P/fv31c4xNzdn7ty52NnZ0aNHD2XhN5VKxcqVK9m1a5fSj0qlYubMmfj6+pKUlES7du1YunQp586dQ1NTk59++onExETWrVtHhw4dMDY2VqvR/nd6enosXryYn3/+mf79+7N9+3ZUKhUaGhr07NmTfv360a5dOyC3fj3kvsN/7do13NzcuHPnDlOmTCmwb0tLSxITE2nevDlt2rQhNDSUTZs2Fes5W1lZsXfvXjZs2JDveT979oyBAwdSrVo1li9fTlRUFK6urvm+9MgzYsQItm7dyrfffkt8fDxnz55l/PjxHDp0iGHDhr0wjq5du6KpqcnChQvV9l+9epW4uDi1LT09vVj3KIQQQgghhBBFISPx/4LatWuze/dusrKyiI+Pp3fv3qSkpLBmzZoC20+ZMkU5lpc0N2/enOTkZNLT06lQoQJ9+vRhxIgRyuh3kyZN2LhxI+7u7gwePJjHjx9TrVo1evXqxdWrV0lLS3thjF26dGHv3r18++23dOvWDch9R75x48b873//U9pFRESonadSqShfvvA6lp9++ikjRowgNDSUZ8+eUa5cOfz9/Zk0adKLH9rfWFtb8+uvvyoj8iEhIUDuaL2+vj4REREsXLiQO3fu0KJFC+7du1dgP02aNGH79u1MmTKFkJAQNDQ0sLOzY8+ePdSpU+eFMZQoUYLBgwczY8YMBg4cqLwCMHLkyHxtf/vtNz7++OMi3ZvUiRdCCCGEEM+TOvHiRWQk/l9QokQJTExMqFy5Mi4uLnTt2pVdu3YBuaP2kZGRau3Nzc3JyMggJycHExMTTExMMDU1pVevXmRmZnLjxg0mT56MpqYmKpVKOT/vnfSUlBSePn1KlSpVCAwMpEQJ9e9qNm7ciLW1NdWrV0dHRwd7e3vWr19P06ZNiYqK4vTp0wDUqFGDX3/9lTt37ijnhoWF4e/vr9ZfVlYWffr0oXr16piammJlZcWpU6eU47q6umRkZJCVlcWaNWsYMWKEEvONGze4cOECiYmJ6OnpUb9+fWJjY4Hcuu+VK1dm+fLlmJub89FHH9GyZUvlC4Dhw4eTnp7O9OnTadmyJevXr2ffvn34+fnx6aefAjB37lwiIyMZMGAAFStWRFtbm5EjR/L1119z//597t69y+DBgxk4cCBaWlqYm5sTEhKi9nsxNzcnMDCQnj17EhQURLt27dDT0+PAgQN8/PHHaGtrU6VKFYYMGUJaWho5OTlFTuCFEEIIIYQQojgkif+XXbt2jR07drxWvfHCHDlyBIDdu3eTlJTExo0bC2wXHBzMjz/+yPfff8+5c+cYMWIEn3/+Ofv27VNrFxERQYkSJVi8eDEABw4c4MGDB3To0EGtXXZ2NlWqVGHdunWcP3+egIAAvvnmG6WM3OjRo/Hy8sLNzY2kpCSSkpJo0qQJaWlpNG/enJs3b7J582ZOnTrF2LFj1RaiS0hIIDIyki1btrBlyxb27dvHtGnTANDX10dfX5/IyEgyMjIKvNfs7Gzc3d2JiYnhp59+4vz580ybNk2ZwXD8+HG8vLzo3r07Z86cYdKkSfj7++erLT9r1izs7e05efIk/v7+JCQk4ObmhqenJ6dPn2bNmjUcOHCAwYMHF/r7kTrxQgghhBBCiNcl0+n/BWfOnEFfX5+srCyePHkC5NZ+f9PySrSVK1cOExOTAttkZGQQFBTE7t27ady4MZA74n7gwAEWL16sVsZNX1+f/v37c+XKFQCWLVvG559/nm8F+JIlSzJ58mTlc/Xq1Tl06BBr167Fy8sLfX19dHR0yMjIUIsrPDycO3fucPToUcqWLQuAhYWFWt/Z2dmEh4djYGAAgI+PD3v27OHbb7+lRIkShIeH069fP77//nucnJxo3rw53bt3V2rN7969myNHjqitsP/8+gCzZ8+mdevWyuwCKysrzp8/r6w5kKdVq1aMGjVK+dy3b1969OihvKNvaWlJaGgozZs357vvvkNbWzvfsw8ODlZ7TkIIIYQQQghRXDIS/y+wtrYmLi6Oo0ePMm7cOFxdXRkyZMhbieXy5cs8fvyYNm3aKCPZ+vr6/PjjjyQkJORr37t3b9atW8etW7dYt25dobXgFy5cSL169TA2NkZfX5///e9/+WrB/11cXByOjo5KAl8Qc3NzJYGH/HXfPT09+fPPP9m8eTNubm5ER0fj5OSkjKTHxcVRpUqVQkvsxcfH4+zsrLbP2dmZS5cukZWVpeyrX7++WptTp04RHh6u9gxdXV3Jzs7m6tWrBV5L6sQLIYQQQgghXpeMxL+GotYcL1WqlDLCPG3aND755BMmT55MYGDgvxZrnrwF7rZu3UrlypXVjmlpaeVrb2dnh42NDd7e3tja2lKnTh3i4uLU2qxevZrRo0cTEhJC48aNMTAwYObMmcq77YUpSpm2l9V9B9DW1qZNmza0adMGf39/+vbty8SJE/H19X1jpeD+Xsc+LS2NAQMGMHTo0Hxtq1WrVmAfWlpaBT5jIYQQQgghhCgqSeJfg7W1NTt37sy3/8SJE4WO/AJMmDCBVq1aMXDgQCpVqvTG4sl7z/75EeS/q1WrFlpaWkrZt6Lo3bs3X331Fd99912Bx2NiYmjSpAlfffWVsu/vo/qlSpXKF1fdunVZsmQJ9+/ff+FofHHVqlVLWZSubt263Lhxg4sXLxb4O7G1tc1Xji4mJgYrK6sC697ncXJy4vz58/mm/78KqRMvhBBCCCGEKCqZTv8aBg4cyMWLFxk6dCinT5/mwoULzJ49m1WrVqm9P/13jRs3pm7dugQFBantv3nzZr564w8ePChyPBUqVEBHR4eoqChu375d4CwBAwMDRo8ezYgRI4iIiCAhIYETJ04wf/78fOXj8vTr1487d+7Qt2/fAo9bWlpy7NgxduzYwcWLF/H39+fo0aNqbczNzZVndPfuXZ4+fYq3tzcmJiZ4eHgQExPDlStX2LBhA4cOHSrS/d67d49WrVrx008/cfr0aa5evcq6deuYMWMGHTt2BHJL8zVr1gxPT0927drF1atX2b59O1FRUQCMGjWKPXv2EBgYyMWLF4mIiGDBggWMHj36hdceN24cBw8eZPDgwcTFxXHp0iV+/vnnFy5sJ4QQQgghhBCvS0biX0ONGjXYv38/48ePx8XFhczMTGxsbFi3bh1ubm4vPHfEiBH4+voybtw4qlatCuSugD5r1iy1dsuXL+fzzz8vUjwlSpQgNDSUKVOmEBAQQNOmTYmOjs7XLjAwEGNjY4KDg7ly5QqlS5fGycmJb775ptB+X1QLfsCAAZw8eZJu3bqhUqnw9vbmq6++Yvv27Uqbfv36ER0dTf369UlLS2Pv3r20aNGCnTt3MmrUKNq1a8ezZ8+oVasWCxcuLNL96uvr06hRI+bMmUNCQgJPnz6latWq9OvXT+1eNmzYwOjRo/H29i60fnxAQAABAQEYGRkxfPhwvvjiCxwcHHBwcMjXNjw8nC+++ALIXQtg4cKFaGhoYGxsTP/+/YsU+/OkTrwQQgghhCiI1IsXBVHl5OTkvO0ghPi33Lp1S/l5zZo1BAQEcOHCBWWfvr4+d+/epXr16pw8ebLQJH7YsGFcuHCBnJwcHj58yMGDBwkODiYrK4uYmJgivSaRmpqKkZERE/dfQVvf4KXthRBCCCHEf4sk8f8deblBSkrKS1+1lZF48Z/yfIk7IyMjVCpVvnJ8d+/efWk/z59namqKra0tHTp0oHbt2owdO5affvop3zkZGRlq9eylTrwQQgghhBCiuOSdeCHekAoVKtCjRw82b95c4OKCwcHBGBkZKVveaxRCCCGEEEIIUVSSxAvxBtnY2PDo0aMC372XOvFCCCGEEEKI1yXT6YV4g/KWmFCpVPmOSZ14IYQQQgghxOuSkXgh3qD4+HgMDQ0pV67c2w5FCCGEEEII8QGSkXgh3pDk5GRWrlyJh4cHGhpF/35spH25l65AKYQQQgghhBAgSbz4D1uyZAkpKSmoVCpKlixJtWrV6NmzJ5999hmAWum5PLVr1wZyp83funVLKTF36NAhgoKCMDIyYtq0acWKQ+rECyGEEEKI4pDSc/9tksSL/7QSJUpw/fp1MjIy2LZtG4MGDSItLQ2A7t2752uftxhdamoqpqamqFQqDA0Nsba2plevXgwbNkxG1YUQQgghhBD/GHknXvxnWVhY8Mknn2BiYoKZmRkDBw7ExcWF3377jZCQEOrUqYOuri5VqlRh4MCBPHr0iCpVqtC1a1cMDAxYt24d2dnZPHz4kNjYWOzs7DA1NeXRo0dv+9aEEEIIIYQQHyhJ4oV4jo6ODpmZmWhoaBAaGsq5c+eIiIjg119/ZezYsQDo6enRvXt3wsLC1M4NCwujS5cuGBgYFNh3RkYGqampapsQQgghhBBCFIck8UKQ+4777t272bFjB61atWL48OG0bNkSc3NzWrVqxdSpU1m7dq3Svm/fvuzYsYOkpCQgd1G7bdu20bt370KvERwcjJGRkbJVrVr1H78vIYQQQgghxIdFknjxn7Zlyxb09fXR1tbG3d2dbt26MWnSJHbv3k3r1q2pXLkyBgYG+Pj4cO/ePR4/fgxAw4YNqV27NhEREQD89NNPmJmZ0axZs0Kv5efnR0pKirLlvV8vhBBCCCGEEEUlSbz4T2vZsiVxcXFcunSJv/76i4iICO7cuUP79u2pW7cuGzZs4Pjx4yxcuBCAzMz/W0W+b9++hIeHA7lT6b/44gtUKlWh19LS0sLQ0FBtE0IIIYQQQojikNXpxQfr1q1b+Pj4cPDgQUqWLMnDhw/ztdHT08PCwkJt3/Hjx8nOziYkJESp9/78VPo8n3/+OWPHjiU0NJTz58/Tq1evV4pT6sQLIYQQQgghikpG4sW/wtfXF5VKla+GemRk5AtHrwty7tw5vLy8MDY2RktLCysrKwICApSp7nnmzJlDUlIScXFxXLx4Udl/8uRJunbtypo1a/jll1+wtLSkX79+ShsLCwuePn1KjRo1MDAwQFtbm8mTJ6v1nZWVxeLFiylZsiTDhg1DQ0MDT09PlixZUqx7EUIIIYQQQojikJF48a/R1tZm+vTpDBgwgDJlyrxSH4cPH8bFxQUXFxe2bt1KxYoVOXLkCKNGjWLPnj3s3buXUqVKAZCQkEC9evWwtLRUzt+yZQuenp64urrStGlTAAIDA1m3bh3+/v6sWbOGu3fvoqGhwf3793n27BkNGjSgXr16zJs3T+ln8uTJLF68mBEjRjBlyhSCgoIoW7YsDx48KPY9zT51D239zJc3FEIIIYQQ4v/72rH82w5BvCWqnJycnLcdhPjw+fr6cu/ePS5fvkyHDh2YMWMGkDsS36lTJ/L+DDds2EBAQACXL1/G1NSUIUOGMGrUKCB3Bfm82u2xsbHKVHeAU6dO4ejoSHBwMOPGjcPc3Jw//vhDOd6rVy8WLVqEmZkZH3/8MZs2bcoX48OHDyldujTDhw/n1KlT7N27t9D7cXBwoFOnTtSoUYMRI0bw559/Kl8eFFVqaipGRkZM3H8Fbf2Cy9IJIYQQQghREEniPyx5uUFKSspLX7WV6fTiX6OpqUlQUBDz58/nxo0b+Y4fP34cLy8vunfvzpkzZ5g0aRL+/v7K4nFxcXGcP3+ekSNHqiXwAPb29ri4uLBq1SoAjh49ipubG15eXiQlJTFv3jx27NjB3bt3lXrvf1e6dGkATExMOHfuHGfPni30XoyNjdm6dSvffvstAwYMKFICL3XihRBCCCGEEK9Lknjxr+rUqRMODg5MnDgx37HZs2fTunVr/P39sbKywtfXl8GDBzNz5kwA5Z11W1vbAvu2tbVV2uS9L6+jo4OJiQlGRkZcunQJABsbmxfGOGTIEBo0aICdnR3m5uZ0796dZcuWkZGRobSxsrLi6NGjXLhwgcjISL788ku2b9/+wn6lTrwQQgghhBDidUkSL/5106dPJyIigvj4eLX98fHxODs7q+1zdnbm0qVLZGVlKfte9Q2Qop6np6fH1q1buXz5MhMmTEBfX59Ro0bRsGFDZfG8hQsXkpWVxZEjR+jXrx/Jycl06NCBvn37Ftqv1IkXQgghhBBCvC5J4sW/rlmzZri6uuLn51es86ysrADyJf954uPjlTYvOv/3338v0vVq1qxJ3759WbJkCSdOnOD8+fOsWbNGOa6hoUGDBg0YPnw4GzduJDw8nKVLl3L16tUC+5M68UIIIYQQQojXJavTi7di2rRpODg4YG1trewzNzcnNDSUGTNmKHXdPTw8qFq1Kpqamjg4OGBjY8OcOXPo3r17voXtdu/eTXBwcKHXbNu2LeXLl2fGjBkvXNiuIObm5ujq6pKenl5o/7Vq1QJ4YZuCSJ14IYQQQgghRFFJEi8K5OvrS0REBMHBwXz99dfK/r+vJl8U586dIzo6mqSkJLS0tDAzM6N79+5069aN0NBQpZ2BgQH3799n2LBhdOvWjYiICLS1tfH39wdApVIpo+iamprKedbW1jx+/JjGjRszfPjwQuPQ09NjyZIldO3alU8//ZShQ4diYWHB3bt3Wbt2LYmJiaxevZpJkybx+PFj2rVrh5mZGQ8fPiQ0NJSnT5/Spk0bALp06YKzszNNmjTBxMSEq1ev4ufnh5WV1UvfuRdCCCGEEEKIVyVJvCjUm6zrXrZsWT766CN+/PFHpa67sbEx2dnZStv09HSaN2/Orl27WLRoEaampgQGBtKvXz+1PqdMmUJsbCwHDx4kLS2NzMxMevXqhZ+fH1paWkq75/vO07FjRw4ePEhwcDCfffYZqampVK1alVatWjF16lQAmjdvzsKFC+nZsye3b9+mTJkyODo6snPnTmXmgKurK6tWrSI4OJiUlBRMTExo1aoVkyZNokSJ4v2zkjrxQgghhBDiVUiZuf8meSdeFMrFxQUTE5MXTlHfsGEDtWvXRktLC3Nzc0JCQpRjOTk59OnTB1tbW65du8a+ffswMzOja9eu/PLLL5w8eZIpU6aQk5ODubk5GzZsYN++fZw/f57PPvuMP/74gzFjxhAZGal2TTs7O7Zs2cL9+/fJzMzkypUr9OnTBz09PdasWUPz5s3R1tamc+fOhISE4O3tTeXKldHV1cXOzo5Lly6xYcMGkpOTefLkCZUrV6ZUqVIsWLCAMmXK0L17d1xdXYmPj+ezzz4jPT2dS5cu8ejRIyWGfv36ERoaSr169ShZsiRPnjzh2bNn6OnpvfHfgxBCCCGEEELkkSReFOpt13V/FV9//TXDhg0jPj4eV1dXnjx5Qr169di6dStnz56lf//++Pj4cOTIEbXzIiIiKF++PEeOHGHIkCEMHDiQrl270qRJE06cOEHbtm3x8fFRVqd/+PAhrVq1wtHRkWPHjhEVFcXt27fx8vIqNDapEy+EEEIIIYR4XZLEixd6m3XdC+Pt7Y2+vr6yPT9SP3z4cDp37kz16tUxNTWlcuXKjB49GgcHB2rUqMGQIUNwc3Nj7dq1an3a29szYcIELC0t8fPzQ1tbm/Lly9OvXz8sLS0JCAjg3r17nD59GoAFCxbg6OhIUFAQNjY2ODo6smzZMvbu3avc099JnXghhBBCCCHE65IkXrzU26rrXpg5c+YQFxenbHmLzQHUr19frW1WVhaBgYHY2dlRtmxZ9PX12bFjB4mJiWrt6tatq/ysqalJuXLlsLOzU/ZVrFgRgOTkZCB3Nfy9e/eqfZmQt6BdQkJCgXFLnXghhBBCCCHE65KF7cRLPV/X3dfXt8jnPV/X3dHRMd/xl9V1L4yJiQkWFhZq++7cuQOQ7530mTNnMm/ePObOnYudnR16enoMHz6czEz1heRKliyp9lmlUqntU6lUwP8tlpeWlkaHDh2YPn16vvhMTU0LjFtLS0tt4T0hhBBCCCGEKC5J4kWRFFTX3dbWlpiYGLV2MTExWFlZvZG67m9CTEwMHTt25PPPPwdyk/CLFy8qNd1flZOTExs2bMDc3LzYq9ELIYQQQgghxKuS7EMUiZ2dHT169FCr6z5q1CgaNGhAYGAg3bp149ChQyxYsIBFixYBuaPXS5cupU2bNnh6euLn54eJiQmxsbGMGjXqpXXd3wRtbW2WLVuGl5cX1apVY/bs2dy+ffu1k/hBgwbxww8/4O3tzdixYylbtiyXL19m9erVLFmyRK2O/cuMtC+HoaHha8UjhBBCCCGE+G+QJF4U2ZQpU1izZo3y2cnJibVr1xIQEEBgYCCmpqZMmTJFbcp9kyZNOHz4MJMnT8bd3Z1Hjx5RrVq1Auu6F6ZTp04Ayoh3REQEbm5uaGtrv/RcHx8f1q9fT5cuXdDV1aV///54eHiQkpJSrHs3NzdX+1ypUiViYmIYN24cbdu2JSMjAzMzM9zc3PKtxP8yUideCCGEEEK8KqkV/9+jynnTq44J8Qb5+vpy+/ZtwsLCePr0KcePH6dXr158+eWXBb6P/nfR0dG0bNmSBw8eULp06VeOw9zcnOHDh7/RmQOpqakYGRkxcf8VtPUN3li/QgghhBDiv0OS+A9DXm6QkpLy0lm6sjq9eOdpaWlhYmJC1apV8fDwwMXFhV27dgG5tdeHDh1KhQoV0NbW5uOPP+bo0aP5+oiJiaFu3bpoa2vz0UcfcfbsWbXjGzZsoHbt2mhpaWFubk5ISIhyrEWLFvzxxx+MGDEClUqlLHL3xx9/0KFDB8qUKYOenh61a9dm27Zthd6H1IkXQgghhBBCvC5J4sV75ezZsxw8eJBSpUoBMHbsWDZs2EBERAQnTpzAwsICV1dX7t+/r3bemDFjCAkJ4ejRoxgbG9OhQweePn0KwPHjx/Hy8qJ79+6cOXOGSZMm4e/vT3h4OAAbN26kSpUqTJkyhaSkJJKSkoDc9+IzMjLYv38/Z86cYfr06ejr6xcau9SJF0IIIYQQQrwueSdevPO2bNmCvr4+z549IyMjAw0NDRYsWEB6ejrfffcd4eHhuLu7A/DDDz+wa9culi5dypgxY5Q+Jk6cqNSTj4iIoEqVKmzatAkvLy9mz55N69at8ff3B3JL450/f56ZM2fi6+tL2bJl0dTUxMDAABMTE6XPxMREPD09lXryNWrUeOF9+Pn5MXLkSOVzamqqJPJCCCGEEEKIYpGRePHOa9myJXFxccTGxtKrVy+++OILPD09SUhI4OnTpzg7OyttS5YsScOGDYmPj1fro3HjxsrPZcuWxdraWmkTHx+v1geAs7Mzly5dIisrq9C4hg4dytSpU3F2dmbixImcPn36hfehpaWFoaGh2iaEEEIIIYQQxSFJvHjn6enpYWFhgb29PcuWLSM2NpalS5e+7bDo27cvV65cwcfHhzNnzlC/fn3mz5//tsMSQgghhBBCfMBkOr14r2hoaPDNN98wcuRILl++DMCECRNYvnw5AE+fPuXo0aP5VpE/fPgw1apVA+DBgwdcvHgRW1tbAGxtbVm8eDEnT54kMjISyF0Iz8rKSqn3XqpUqQJH5atWrcqXX37Jl19+iZ+fHz/88ANDhgwp1j1JnXghhBBCCCFEUUkS/x7z9fXl4cOHSuKZ502VVQOYNGkSkydPBkBTU5PSpUtTq1YtOnfuzMCBA4tU5/1N69q1K2PGjOG7776jf//+/PLLL0RFRVGtWjVmzJjB48eP6dOnj9o5U6ZMoVy5cjx79gw3NzdMTEzw8PAAYNSoUaxevZratWtz8eJFDh06xIIFC1i0aJFyvrm5Ofv376d79+5oaWlRvnx5hg8fjru7O1ZWVjx48IC9e/cqXwwUh9SJF0IIIYQQr0pKzP33SBIvXqp27drs3r2b7Oxs7t27R3R0NFOnTmX58uVER0djYPDv1jgvUaIEAwcOZMaMGVy9ehUtLS18fHx49OgR9evXZ8eOHZQpU0btnGnTpjFs2DAuXboEwLx585QV7p2cnFi7di0BAQHMnDkTU1NTpkyZgq+vr3L+lClTGDBgADVr1iQjI4OcnByysrIYNGgQN27cwNDQEDc3N+bMmfOvPQchhBBCCCHEf4+8E/+Bu3fvHt7e3lSuXBldXV3s7OxYtWqVWpv169djZ2eHjo4O5cqVw8XFhfT0dOV4iRIlMDExoVKlStjZ2TFkyBD27dvH2bNnmT59utIuIyOD0aNHU7lyZfT09GjUqBHR0dHK8ZfVVT937hzt27fH0NAQAwMDmjZtir+/P5GRkfj6+uLh4cG3335LpUqVWLJkCcnJydSuXZsaNWpw584dnjx5QkxMDMeOHcPd3R0dHR169+7NunXraN++PWfPniUzM3fEu1u3bqhUKlq0aAHAL7/8gqWlJZmZmfzxxx8MGTJErf786NGjWbJkCU+ePCEnJ4fo6GgWLFjA4sWLqVOnDmlpaVy+fJm7d+/+A79FIYQQQgghhMglSfwH7smTJ9SrV4+tW7dy9uxZ+vfvj4+PD0eOHAEgKSkJb29vevfuTXx8PNHR0XTu3JmcnJwX9mtjY4O7uzsbN25U9g0ePJhDhw6xevVqTp8+TdeuXXFzc1NGv19UV/3mzZs0a9YMLS0tfv31V44fP07v3r159uyZ0v+ePXu4cOECu3btYsuWLYXG5u/vj6enJ6dOnaJHjx50795dWYk+7753795NUlKSWvzPK2r9+fHjxxMSEsKxY8coUaIEvXv3LjSujIwMUlNT1TYhhBBCCCGEKA6ZTv+ey6uh/rznF2CrXLkyo0ePVj4PGTKEHTt2sHbtWho2bEhSUhLPnj2jc+fOmJmZASh1z1/GxsaGnTt3Ark108PCwkhMTKRSpUoAjB49mqioKMLCwggKCnphXfWFCxdiZGTE6tWrKVmyJJBbr/15enp6LFmyRJkGX5iuXbvSt29fAAIDA9m1axfz589n0aJFGBsbA1CuXDm1mu/PK079+W+//ZbmzZsD8PXXX/PJJ5/w5MkTtLW18/UbHBysrC8ghBBCCCGEEK9Ckvj3XMuWLfnuu+/U9sXGxvL5558DuQl9UFAQa9eu5ebNm2RmZpKRkYGuri4A9vb2tG7dGjs7O1xdXWnbti1dunTJ9055QXJyclCpVACcOXOGrKysfIl3RkYG5cqVA3Lrqg8cOJCdO3fi4uKCp6cndevWBSAuLo6mTZsqCXxB7OzsXprAg3pN+LzPcXFxLz0vT3Hqz+fFD2BqagpAcnKyshL+8/z8/Bg5cqTyOTU1lapVqxY5LiGEEEIIIYSQJP49l1dD/Xk3btxQfp45cybz5s1j7ty52NnZoaenx/Dhw5V3wzU1Ndm1axcHDx5k586dzJ8/n/HjxxMbG0v16tVfeO34+HilTVpaGpqamhw/flwpy5Ynb6ZA3759cXV1ZevWrezcuZPg4GBCQkIYMmQIOjo6RbrXd83zXzrkfaGRnZ1dYFstLa23spq/EEIIIYQQ4sMhSfwHLiYmho4dOyoj89nZ2Vy8eJFatWopbVQqFc7Ozjg7OxMQEICZmRmbNm1SGzX+u99//52oqCj8/PwAcHR0JCsri+TkZJo2bVroeQXVVe/atStxcXFcvXqVyMhIUlJSXuueDx8+TM+ePWnRogUODg7Exsbi6OjI48eP+eqrr5R4CyvBV7NmTUqVKkVMTIzyikFh9efzREZGKvXhp0yZwrJly4ocr9SJF0IIIYQQQhSVLGz3gbO0tFRG2uPj4xkwYAC3b99WjsfGxhIUFMSxY8dITEykdevW3Lx5kzNnzihtnj17RlhYGCqVijNnzjB//nyaN2+Og4OD8n64lZUVPXr0oGfPnmzcuJGrV6+yevVq7OzsMDIyQktLizJlytCjRw/Onz/PiRMnlLrqc+bMoWTJkhgZGdG0aVOOHTvG5s2bcXR0pGzZsmhra7N+/XqOHj1KcnLyS+953bp1LFu2jMePH3Po0CGOHDnC4MGDiYiI4OjRo2hrazNu3DiePHnCxx9/jEqlIiIigl9++QUrKytCQ0P58ssvGTNmDFFRUZw/f55+/foVWH8+z4ABA3BxcQF44ZcfQgghhBBCCPE6ZCT+AzdhwgSuXLmCq6srurq69O/fHw8PD2W029DQkP379zN37lxSU1PR0tKiRIkSREZGMnv2bCC39FvequstWrSgVq1a+Pn5MXDgQLXp4WFhYUydOpVRo0Zx48YNsrKyMDEx4bvvvsPZ2Zkvv/yStWvXsmrVKsqXL6/UVR8wYACNGjVi1KhRjBkzhubNm/PXX39hbGxMREQEderUYeDAgVy6dEmt9F1hJk+ezOrVqzl27BgGBgasWrWKWrVqsWzZMmrVqoWPjw9Tpkxh5syZGBgY0K9fP1JSUnjw4AHe3t7079+fkJAQPD09X1p/HnJfJUhOTqZJkyaEh4fnW2jwZWafuoe2fmaxzhFCCCGEEOJVfe1Y/m2HIF6DKudltcTEf4qvry/37t3j8uXLdOjQgRkzZgC508U7deqklJ7bsGEDAQEBXL58GVNTU4YMGcKoUaOA3AXv6tSpg66uLrGxsWho/N+Ej1OnTuHo6EhwcDDjxo3D3NycP/74Qzneq1cvPDw86Nq1K3/99RclShT+PdPZs2cZM2YMv/32G3p6erRt25affvqJTZs24eHhoUynnzt3Li1atGDfvn3Kuc2bNyc6OlqtTZ569ephZmamlJ/LyMhg/PjxrFq1iocPH1KnTh2mT59OixYtiI6OpmXLlmpx7d27V6k//yKpqakYGRkxcf8VtPUNXtpeCCGEEEKIN0GS+HdPXm6QkpLy0ldtZTq9yEdTU5OgoCDmz5+vtkhenuPHj+Pl5UX37t05c+YMkyZNwt/fn/DwcCB3pfnz588zcuRItQQeclfDd3FxYdWqVQAcPXoUNzc3vLy8SEpKYt68eZiYmPDs2TM2bdpUaL36hw8f0qpVKxwdHTl27BhRUVFqrwn83caNG+nXrx+NGzcutD58Tk4Ov/32G7///rvaKviDBw/m0KFDrF69mtOnT9O1a1fc3Ny4dOkSTZo04cKFC0DuFxtJSUk0adKkwBikTrwQQgghhBDidUkSLwrUqVMnHBwcmDhxYr5js2fPpnXr1vj7+2NlZYWvry+DBw9m5syZAFy8eBEAW1vbAvu2tbVV2hgbG6OlpYWOjg4mJiYYGRnx0Ucf8c033/DZZ59Rvnx53N3dmTlzplqSvmDBAhwdHQkKCsLGxgZHR0dlMbk///wz3zXLli2Lrq4upUqVwsTEhLJlyyrHFi1ahL6+PlpaWjRr1ozs7GyGDh0KQGJiImFhYaxbt46mTZtSs2ZNRo8ezccff0xYWBilSpWiQoUKyjVMTEwKLYMXHByMkZGRskl5OSGEEEIIIURxSRIvCjV9+nQiIiLy1UaPj49Xq6EO4OzszKVLl8jKylL2vc6bGt9++y23bt3i+++/p3bt2nz//ffY2NgoC+6dOnWKvXv3oq+vr2w2NjYALy2N93c9evQgLi6OmJgY3N3dGT9+vDKafubMGbKysrCyslK71r59+0hISCjWdfz8/EhJSVG269evF+t8IYQQQgghhJCF7UShmjVrhqurK35+fvj6+hb5PCsrKyA32Xd0dMx3PD4+XmnzIuXKlaNr16507dqVoKAgHB0dmTVrFhEREaSlpdGhQwemT5+e7zxTU9MixwpgZGSEhYUFAGvXrsXCwoKPPvoIFxcX0tLS0NTU5Pjx42hqaqqdV9wF7KROvBBCCCGEEOJ1SRIvALh16xY+Pj7s3bsXlUql7J82bRoODg5YW1sr+2xtbYmJiVE7PyYmBisrKzQ1NXFwcMDGxoY5c+Zw4cIFfv75Z+Li4oDcEfTdu3cTHBxcrPhKlSpFzZo1ldXpnZyc2LBhA+bm5i9c/K649PX1GTZsGKNHj+bkyZM4OjqSlZXFjh07GDZsWKG15V+H1IkXQgghhBBCFJVMp38H+fr6olKpmDZtmtr+yMhItQS7KM6dO4eXl5fy7rmVlRUBAQE8fvxYrd2cOXNISkri008/Veqdnzp1ivHjx1OyZElllfpu3brxxRdfsGfPHgIDA7l48SIREREsWLCA0aNHA6BSqVi6dCnnz59n7dq1PH78mMTERNatW0eHDh1o3Lgxw4cPV679+++/ExERgUqlokSJEhgaGlKhQgX69evH2bNnuXDhArNmzWLbtm107NgRgEGDBnH//n28vb05evQoCQkJ7Nixgy+++EJtSv+rGDBgABcvXmTDhg1YWVnRo0cPZs+ezZIlS7h//z5HjhwhODiYrVu3vtZ1hBBCCCGEEKK4ZCT+HaWtrc306dMZMGBAgbXJi+Lw4cO4uLjg4uLC1q1bqVixIkeOHGHUqFHs2bOHvXv3KouwJSQkUK9ePVQqFdnZ2dy5c4fWrVvTvn17NmzYQOfOncnMzKRSpUrUrFmTtWvXEhAQQGBgIKampkyZMkVtyn2TJk04fPgw3bp148qVK1hYWFCtWjV69eqFn59fvmnlpUuXJj4+nuzsbE6dOsXUqVMJCwtj6dKlGBkZYWlpyZIlS/Dx8QGgUqVKxMTEMG7cONq2bUtGRgZmZma4ubnlWxG/uMqWLUvPnj2ZNGkSnTt3JiwsjKlTpzJ16lQGDhxI+fLl+eijj2jfvv1rXSeP1IkXQgghhBBvg5Saez/JSPw7ysXFBRMTkxdOO9+wYQO1a9dGS0sLc3NzQkJClGM5OTn06dMHW1tbNm7cSMOGDTEzM6Nr16788ssvHDp0iDlz5gBgbm7Ohg0b+PHHH4mIiKB06dLExMSQkpLCkiVL+OSTT8jIyCAnJ4c5c+ZQvXp1PD09GTNmDLq6uvzxxx/KKPzzswXs7Ozw8vKiTp06zJ8/n4yMDEJCQvD19SUlJUWJtXv37piZmWFiYkKlSpVwd3cnJiaGs2fPUrJkSQYNGsSRI0fw9fXlwYMH9OzZkzJlymBvb89ff/3FkSNHePz4MfHx8djb21OmTBm2bNlCUlIS//vf/+jSpQuPHz/G0dGRa9euUaZMGYYOHUpWVhbR0dHMnTuX5cuXU79+fQwMDDAxMSE1NZVff/0VDQ0NSpYsScuWLbl27RrJycn8+eeffPrppzRt2pQdO3bQuHFj9PT0mDZtGklJSf/En4MQQgghhBBCAJLEv7Peh1rtRXX58mXWrl3LL7/8QlRUFCdPnuSrr7566Xk2Nja4u7ur1XT39fXl2LFjbN68mUOHDpGTk0O7du14+vSp0ubx48eEhoayevVqoqKiiI6OplOnTmzbto1t27axfPlyFi9ezPr165Vznj59SmBgIKdOnSIyMpJr1669dDG/x48fM2vWLJYvX87+/ftJTExUvswoiNSJF0IIIYQQQrwuSeLfYe96rfaievLkCT/++CMODg40a9aM+fPns3r1am7duvXSc21sbLh27RoAly5dYvPmzSxZsoSmTZtib2/PihUruHnzJpGRkco5T58+5bvvvsPR0ZFmzZrRpUsXDhw4wNKlS6lVqxbt27enZcuW7N27Vzmnd+/euLu7U6NGDT766CNCQ0PZvn07aWlphcb29OlTvv/+e+rXr4+TkxODBw9mz549hbaXOvFCCCGEEEKI1yVJ/DvuXa7VXlTVqlWjcuXKyufGjRuTnZ3NhQsXXnpuTk6OMj0/Pj6eEiVK0KhRI+V4uXLlsLa2Vns+urq61KxZU/lcsWJFzM3N1UrCVaxYkeTkZOXz8ePH6dChA9WqVcPAwIDmzZsDkJiYWGhsf7+OqampWp9/J3XihRBCCCGEEK9Lkvh33PO12ovj+VrtBSlurfZZs2YRHx9PpUqVmDVrFgAaGhr5viR4flr7mxAfH0/16tWLdU7JkiXVPqtUqgL3ZWdnA5Ceno6rqyuGhoasWLGCo0ePsmnTJgAyMwtfcK6gPl/0pYmWlhaGhoZqmxBCCCGEEEIUhyTx74Fp06Ypi9HlKU6t9rxkNU9erXZvb+9ixfH3Wu3GxsY8evRI+Qwo9eCfl5iYyJ9//ql8Pnz4MBoaGmq15wvy+++/ExUVhaenp3LPz549IzY2Vmlz7949Lly4QK1atYp1L3+/zr1795g2bRpNmzbFxsbmhSPqQgghhBBCCPG2SIm594CdnR09evQgNDRU2Tdq1CgaNGhAYGAg3bp149ChQyxYsIBFixYB/1ervU2bNnh6euLn54eJiQmxsbGMGjUqX632v9uyZQurV6+me/fuWFlZkZOTwy+//MK2bdsICwsDoFGjRujq6vLNN98wdOhQYmNjlYX1nqetrU2vXr2YNWsWqampDB06FC8vL0xMTJQ2z54949atW2RnZ3Pv3j2io6OZOnUqDg4OjBkzBgBLS0s6duxIv379WLx4MQYGBnz99ddUrlxZqR//KqpVq0apUqWYP38+X375JWfPniUwMPCl56WlpTF8+HDmzp37ytcGGGlfTkblhRBCCCGEEEUiSfx7YsqUKaxZs0b57OTkVORa7ZMnT8bd3Z1Hjx69sFb782rVqoWuri6jRo3i+vXraGlp5avVXrZsWX766SfGjBnDDz/8QOvWrZk0aRL9+/dX68vCwoLOnTvTrl077t+/T/v27ZUvG/KcO3cOU1NTNDU1MTIyolatWvj5+TFw4EC1OMPCwhg2bBjt27cnMzOTZs2asW3bNrWp7Y8fP1beo4fcLxFKlizJ6dOnqVu3br57NTY2Jjw8nG+++YbQ0FCcnJyYNWsWn3766Ut+K2+G1IkXQgghhBBvg9SJfz+pcl63fpgQ7xhfX19u376tzBi4desWEyZM4PTp0y9cqK64WrRogYODwyuPxKempmJkZMTE/VfQ1jd4Y3EJIYQQQghRFJLEvzvycoOUlJSXztKVd+LFB0lLSwsTExNMTExwcHDg66+/5vr169y5cweAcePGYWVlha6uLjVq1MDf319tUb5Jkybh4ODA8uXLMTc3x8jIiO7du/Po0aNCr7l161aMjIxYsWLFP35/QgghhBBCiP8mmU4vPnhpaWn89NNPWFhYUK5cOQAMDAwIDw+nUqVKnDlzhn79+mFgYMDYsWOV8xISEoiMjGTLli08ePAALy8vpk2bxrfffpvvGitXruTLL79k5cqVtG/fvsA4MjIyyMjIUD6npqa+4TsVQgghhBBCfOgkiRcfpC1btih14dPT0zE1NWXLli1oaOROPpkwYYLS1tzcnNGjR7N69Wq1JD47O5vw8HAMDHKnuvv4+LBnz558SfzChQsZP348v/zyi1JfviDBwcFMnjz5jd2jEEIIIYQQ4r9HknjxQWrZsiXfffcdAA8ePGDRokW4u7tz5MgRzMzMWLNmDaGhoSQkJJCWlsazZ8/yvXtibm6uJPAApqam+UrPrV+/nuTkZGJiYmjQoMELY/Lz82PkyJHK59TUVKpWrfq6tyqEEEIIIYT4D5F34sUHSU9PDwsLCywsLGjQoAFLliwhPT2dH374gUOHDtGjRw/atWvHli1bOHnyJOPHjyczU32F+OdXvIfcsn3Z2dlq+xwdHTE2NmbZsmW8bI1ILS0tDA0N1TYhhBBCCCGEKA4ZiRfvrFu3buHj48PBgwcpWbIkDx8+fOW+VCoVGhoa/PXXXxw8eBAzMzPGjx+vHP/jjz9eqd+aNWsSEhJCixYt0NTUZMGCBcXuQ+rECyGEEEIIIYpKRuJFkfj6+qJSqZg2bZra/sjISLWa7EVx7tw5vLy8MDY2RktLCysrKwICAnj8+LFauzlz5pCUlERcXBwXL14Ecqe4F6WkW0ZGBrdu3eLWrVvEx8czZMgQ0tLS6NChA5aWliQmJrJ69WoSEhIIDQ1l06ZNxbqH51lZWbF37142bNjA8OHDX7kfIYQQQgghhHgZGYkXRaatrc306dMZMGAAZcqUeaU+Dh8+jIuLCy4uLmzdupWKFSty5MgRRo0axZ49e9i7dy+lSpUCcleHr1evHpaWlsW+TlRUFKampkDuSvQ2NjasW7eOFi1aADBixAgGDx5MRkYGn3zyCf7+/kyaNOmV7gnA2tqaX3/9VRmRDwkJKfK5s0/dQ1s/8+UNhRBCCCGE+AdIvfj3i4zEiyJzcXHBxMSE4ODgQtts2LCB2rVro6Wlhbm5uVoym5OTQ58+fbC1tWXjxo00bNgQMzMzunbtyi+//MKhQ4eYM2cOkDvivmHDBn788UdUKhW+vr4FXk+lUrF48WLat2+Prq4utra2DBgwgEuXLtG8eXN0dXWpU6cOq1atwtPTE8j9cuDChQtoamqSk5NDQkICderUUZuuP2DAACpXroyOjg7Vq1dn5cqVzJ07V22kPTIykrS0NIyNjTE0NGTQoEHs3LmzWAm8EEIIIYQQQhSHJPGiyDQ1NQkKCmL+/PncuHEj3/Hjx4/j5eVF9+7dOXPmDJMmTcLf35/w8HAA4uLiOH/+PCNHjlRKveWxt7fHxcWFVatWAXD06FHc3Nzw8vIiKSmJefPmFRpXYGAgPXv2JC4uDhsbGz777DMGDBiAn58fx44dIycnh8GDByvt09LSaNeuHXv27OHkyZO4ubnRoUMHEhMTlTY9e/bkzz//JDo6mg0bNvC///0v38r0Xbt2JTk5me3bt3P8+HGcnJxo3bo19+/fLzDOjIwMUlNT1TYhhBBCCCGEKA5J4kWxdOrUCQcHByZOnJjv2OzZs2ndujX+/v5YWVnh6+vL4MGDmTlzJoDyXrutrW2Bfdva2ipt8t6X19HRwcTEBCMjo0Jj+uKLL/Dy8sLKyopx48Zx7do1evTogaurK7a2tgwbNozo6Gilvb29PQMGDKBOnTpYWloSGBhIzZo12bx5MwC///47u3fv5ocffqBRo0Y4OTmxZMkS/vrrL6WPAwcOcOTIEdatW0f9+vWxtLRk1qxZlC5dmvXr1xcYZ3BwMEZGRsom5eWEEEIIIYQQxSVJvCi26dOnExERQXx8vNr++Ph4nJ2d1fY5Oztz6dIlsrKylH0vK8VWXHXr1lV+rlixIgB2dnZq+548eaKMfKelpTF69GhsbW0pXbo0+vr6xMfHKyPxFy5coESJEjg5OSl9WFhYqK0DcOrUKdLS0ihXrhz6+vrKdvXqVRISEgqM08/Pj5SUFGW7fv36m3sIQgghhBBCiP8EWdhOFFuzZs1wdXXFz8+v0HfVC2JlZQXkJvuOjo75jsfHxyttiuP5eu55K+UXtC+vxvvo0aPZtWsXs2bNwsLCAh0dHbp06ZKvTvyLpKWlYWpqqjbCn6d06dIFnqOlpYWWllaRryGEEEIIIYQQfydJvHgl06ZNw8HBAWtra2Wfra0tMTExau1iYmKwsrJCU1MTBwcHbGxsmDNnDt27d1d7L/7UqVPs3r37hYvmvSkxMTH4+vrSqVMnIDchP3v2rJLsW1tb8+zZM06ePEm9evUAuHz5Mg8ePFD6cHJy4tatW5QoUQJzc/PXikfqxAshhBBCCCGKSqbT/0f5+vri4eHx0naTJk1CpVIRERHBzz//TNWqVenfvz+VK1emR48ehIaGKm3zysQFBgZy8eJFIiIiWLBgAaNHjwZyR8SXLl3K+fPn8fT05MiRIyQmJrJu3To6dOhA48aN/5U665aWlmzcuJG4uDhOnTrFZ599ho6ODo0bNwbAxsYGFxcX+vfvz5EjRzh58iT9+/dHR0dHSfRdXFxo3LgxHh4e7Ny5k2vXrnHw4EHGjx/PsWPH/vF7EEIIIYQQQvw3yUi8eKnatWtTu3ZtUlJSGD16NL179yYlJYXp06ezZs0apZ2TkxNr164lICCAwMBATE1NmTJlitqU+yZNmnD48GEmT56Mu7s7jx49olq1avTq1Qs/P79/Zbr57Nmz6d27N02aNKF8+fKMGzeO1NRUpT49wI8//kifPn1o1qyZUlbv3LlzaGtrA7lfSGzbto3x48fzxRdfcOfOHUxMTGjWrJnyXn6R45E68UIIIYQQ4h0nteTfHTISL1i/fj12dnbo6OhQrlw5XFxcSE9PV46XKFGCNWvWEBUVhYuLC127dmXXrl2Ym5uTkZFBTk4OS5YswdbWlh49epCVlcXcuXP5448/GD16NJmZmQwePBhTU1O0tbVp37499erV4969e2RmZpKQkEClSpXw9PRER0eHGjVqsH79eiIjI5XydGfOnKFVq1bcvn2bwMBA+vfvT1paGjk5OXh4eCgzC9avX4+JiQmtW7dm0KBBPH36lBYtWpCTk8PKlSuxtLTExsaGc+fO0a5dOxITExk0aJDa8/jmm2/w8PBg27ZtPHnyhGvXrtG0aVOSk5M5dOiQ0m7NmjXs2rWLe/fuUaNGDb7++mt++uknWXVeCCGEEEII8Y+Rkfj/uKSkJLy9vZkxYwadOnXi0aNH/Pbbb4WuIH/t2jV27NihNmq9YsUKAgICWLBgAY6Ojpw8eZJ+/fqhp6dHr169CA0NZfPmzaxdu5Zq1apx/fr1fCuz+/v7M23aNObNm8fy5cuVWvO2trakp6fj6upK48aNOXr0KMnJyfTt25fBgwcrST7A3r17MTU1Ze/evVy+fJlu3brh4OBAv379OHbsGEOHDmX58uU0adKE+/fv89tvvxV4jz169CA4OJjFixfTtm1bkpKSlDrz48aNK9I9FyQjI4OMjAzls9SJF0IIIYQQQhSXJPH/cUlJSTx79ozOnTtjZmYGqJdng9xRcH19fbKysnjy5AmQOyU9z8SJEwkJCaFz584AVK9enfPnz7N48WJ69epFYmIilpaWfPzxx6hUKuU6z+vatSt9+/YFIDAwkF27djF//nwWLVrEypUrefLkCT/++CN6enoALFiwgA4dOjB9+nRl+nqZMmVYsGABmpqa2NjY8Mknn7Bnzx769etHYmIienp6tG/fHgMDA8zMzApcIR9yXx+oXr06AQEBjBgxAgMDA0qXLo2Dg4NS4/5l91yQ4OBgJk+eXITfihBCCCGEEEIUTKbT/8fZ29vTunVr7Ozs6Nq1Kz/88IPaKuyQu1p7XFwcR48eZdy4cbi6ujJkyBAA0tPTSUhIoE+fPmr10qdOnarUS/f19SUuLg5ra2uGDh3Kzp0788WRt6jc85/z6tDHx8djb2+vJPCQW38+OzubCxcuKPtq166Npqam8tnU1JTk5GQA2rRpg5mZGTVq1MDHx4cVK1bw+PHjQp/LwIEDKVu2LI8fP+bWrVtkZmbSu3fvIt9zQaROvBBCCCGEEOJ1SRL/H6epqcmuXbvYvn07tWrVYv78+VhbW3P16lWlTalSpbCwsKBOnTpMmzYNTU1NZUQ5LS0NgB9++IG4uDhlO3v2LIcPHwZyF7y7evUqgYGB/PXXX3h5edGlS5c3fi/P14aH3MXn8mrDGxgYcOLECVatWoWpqSkBAQHY29vz8OHDAvvy9vbmwoULnDhxgoMHD3L9+nW6detW5HsuiJaWFoaGhmqbEEIIIYQQQhSHTKcXqFQqnJ2dcXZ2JiAgADMzMzZt2sTIkSMLbD9hwgRatWrF1KlT2bRpE5UqVeLKlSv06NGj0GsYGhrSrVs3unXrRpcuXXBzc+P+/fuULVsWgMOHD9OzZ0+l/eHDh5Xp7ra2toSHh5Oenq6MxsfExKChocGECRM4fvw4T58+pV27di+8zxIlSuDi4oKLiwsTJ06kdOnS/Prrr8qU+OdVqVKF5s2bs2LFCv766y/atGmDl5cXDg4OzJ07t0j3XFRSJ14IIYQQQghRVJLE/8fFxsayZ88e2rZtS4UKFYiNjeXOnTvKu9+RkZFqo/KQO9W9bt261K5dG3d3dyZPnszQoUMxMjLCzc2NjIwMjh07xoMHDxg5ciSzZ8/G1NQUR0dHNDQ0WLduHSYmJpQuXVrpc926ddSvX5+PP/6YOXPmcPjwYX7//XeWLl1K1apVefr0KZ9//jmBgYHcuXOHIUOGUKtWLe7fv09cXBz+/v7K+/onT54kKCiIrVu38uTJEywtLalRowYNGjSgS5culClThm3btpGdnY21tXWhz6ZHjx5MnDiRzMxM5syZw5IlS5RjL7tnIYQQQgghhPgnSBL/H2doaMj+/fuZO3cuqampmJmZERISgru7+wvPGzFiBL6+vkyePJm+ffuiq6vLzJkzGTNmDHp6etjZ2TF8+HAgdyr7jBkzuHTpEpqamjRo0IBt27ahofF/b3NMnjyZ1atXM3DgQJ4+fUr9+vVZuHAhFStW5MiRIwwZMoRff/2VqKgodHV18fT0JDk5GSMjIywtLdHW1ubJkyds2bIFT09PXF1dcXNz488//2TevHnMmTOH//3vfyxatEhJ7FetWkXt2rULvccuXbowePBgNDU18fDwUEviX3bPxSF14oUQQgghxLtO6sS/OySJ/496vjRbVFRUoe0cHBwwNzfPt7979+54e3tz/PhxqlatSpMmTYiLi2PDhg3Mnz+f2NhYJk2ahImJCf369aNfv34AHDhwAD8/P5o0aUL58uXp1KkTAJUqVWLHjh3UqVMHXV1dYmNjlSTfzMwMKysrHB0dCQ4OZty4cZibm/PHH38A8OOPP9KrVy9WrlyJmZkZ7dq1Y9OmTWrxrl69mocPHyqj//v27WPMmDH4+PhQtmxZevXqxdSpU5X26enpDB06lBIlSmBgYMDixYvzPQNPT09OnDhBcnIyDx8+5MmTJ5QpU+blD18IIYQQQgghXpEsbCfeqPHjxzN69Gji4uKwsrLC29ubZ8+eAZCQkICbmxuenp6cPn2aNWvWcODAAeXcuLg4zp8/z8iRI9VG6SF3FX0XFxdWrVoFwNGjR3Fzc8PLy4ukpCTmzZvHjh07uHv3LmPHji0wtrwE/ubNm7Rr144GDRpw6tQpvvvuO5YuXaqWxI8ZM4Z9+/bx888/s3PnTqKjozlx4oRaf4MHD+bQoUOsXr2a06dP07VrV9zc3Lh06VKB18/IyCA1NVVtE0IIIYQQQojikJF48UaNHj2aTz75BMidIl+7dm0uX76MjY0NwcHB9OjRQ5lybmlpSWhoKE2bNiUzM5OLFy8CKO/j/52tra2S9BsbG6OlpYWOjg4mJiYASvJsY2PzwhgXLVpE1apVWbBgASqVChsbG/7880/GjRtHQEAAjx8/ZunSpfz000+0bt0agIiICKpUqaL0kZiYSFhYGImJiVSqVEm596ioKMLCwggKCsp3XakTL4QQQgghhHhdksSLN6pu3brKz6ampgAkJydjY2PDqVOnOH36NCtWrFDa5OTkAGBnZ8fp06fV9hVXUc+Lj4+ncePGqFQqZZ+zszNpaWncuHGDBw8ekJmZSaNGjZTjZcuWVVsE78yZM2RlZWFlZaXWd0ZGBuXKlSvwun5+fmqL3qWmplK1atUixSyEEEIIIYQQIEm8eMOer9WelyTn1WpPS0tjwIABDB06NN951apVU1aXj4+PV8rLPS8+Pj5f0vy8vGO///47jRs3fvWbKIK0tDQ0NTU5fvw4mpqaasf09fULPEdLSwstLa1/NC4hhBBCCCHEh03eiRf/GicnJ86fP4+FhUW+rVSpUjg4OGBjY8OcOXOUxD/PqVOn2L17N97e3oX237ZtW8qXL8+MGTMKPP7w4UMgd1r+oUOH1EbuY2JiMDAwoEqVKtSsWZOSJUsSGxurHH/w4IEy3R/A0dGRrKwskpOT891L3vR+IYQQQgghhHjTZCRevFRKSgpxcXFq+wqbMv4i48aN46OPPmLw4MH07dsXPT09zp8/z65du5T305cuXUqbNm3w9PTEz88PExMTYmNjGTVqFI0bN35hCbdBgwZRo0YNtm7dyqeffsrQoUOxsLDg7t27rF27lsTERFavXs1XX33FtGnTcHBwYM2aNVy4cIGJEycqC+rp6+vTp08fxowZQ7ly5ahQoQI+Pj48fvxYuZaVlRU9evSgZ8+ehISE4OjoSFBQEFu2bGHJkiXKugBFMdK+HIaGhsV+nkIIIYQQQoj/Hkni3wJfX18ePnxIZGSk2v7o6GhatmzJgwcPlJXUX9WkSZOURdQ0NTWpUqUKnTp1IjAwsNDp3oWJjo7ON729T58+xY6pbt267Nu3j/Hjx9O0aVNycnKoWbMm3bp1U9o0adKEw4cPM3nyZNzd3Xn06BHVqlWjV69e+Pn5vXQ6uqmpKQsXLiQ4OJgOHTooU/TzrFmzhkuXLhEZGcmkSZOwt7enbNmy9OnThwkTJijtZs6cSVpaGh06dMDAwIDmzZtz5swZtb7CwsKYOnUqo0aN4ubNm2hpaaGpqUm1atWK9VykTrwQQgghhPjQSF35f44q51VXEROv7N9K4tevX8/u3bt59uwZMTEx9O7dm88//7zAmufvk6dPn6q9e5/n78/V19eX27dvExYWptbO2Ng433vsLxMeHs7w4cOVKfkFmTRpEpGRkflmLRQmNTUVIyMjJu6/gra+QbHiEUIIIYQQ4l0mSXzx5OUGKSkpL52lK+/Ev6Pu3buHt7c3lStXRldXFzs7O6VGep7169djZ2eHjo4O5cqVw8XFhfT0dOV4iRIlMDExoUqVKnTr1o0ePXqwefNmAJYvX079+vUxMDDAxMSEzz77jOTkZOXcBw8e0KNHD4yNjdHR0cHS0lJJhjMzMxk8eDCmpqZoa2tjZmZGcHAwkFtmrX379ko/c+fORaVSERUVpeyzsLBgyZIlQO6id1OmTKFKlSpoaWnh4OCg1vbatWuoVCrWrFlD8+bN0dbWZsWKFWRlZTFy5EhKly5NuXLlGDt2bIGr02tpaWFiYqK2aWpq0qJFC7Wp+RkZGYwePZrKlSujp6dHo0aNiI6OfuHvaNq0aVSsWBEDAwP69OmTb9RfCCGEEEIIId40SeLfUU+ePKFevXps3bqVs2fP0r9/f3x8fDhy5AgASUlJeHt707t3b+Lj44mOjqZz584vLLOmo6NDZmbutO2nT58SGBjIqVOniIyM5Nq1a/j6+ipt/f39OX/+PNu3byc+Pp7vvvuO8uVzv00LDQ1l8+bNrF27lgsXLrBixQrMzc0BaN68OQcOHCArKwuAffv2Ub58eSUhvnnzJgkJCbRo0QKAefPmERISwqxZszh9+jSurq58+umnSs33PF9//TXDhg0jPj4eV1dXQkJCCA8PZ9myZRw4cID79++zadOmV37egwcP5tChQ6xevZrTp0/TtWtX3Nzc8sWRZ+3atUyaNImgoCCOHTuGqakpixYteuE1MjIySE1NVduEEEIIIYQQojjknfi3ZMuWLfneTc9LfAEqV67M6NGjlc9Dhgxhx44drF27loYNG5KUlMSzZ8/o3LkzZmZmQG6t9cIcP36clStX0qpVKwB69+6tHKtRowahoaE0aNCAtLQ09PX1SUxMxNHRkfr16wMoSTpAYmIilpaWfPzxx6hUKuX6AE2bNuXRo0ecPHmSevXqsX//fsaMGaNMcY+OjqZy5cpYWFgAMGvWLMaNG0f37t0BmD59Onv37mXu3LksXLhQ6Xf48OF07txZ+Tx37lz8/PyUfd9//z07dux46XN2d3dn3bp1am0SExMJCwsjMTGRSpUqAbkzCqKioggLCyMoKChfv3PnzqVPnz7K2gBTp05l9+7dLxyNDw4OVtYpEEIIIYQQQohXISPxb0nLli2Ji4tT2/KmmENuQh8YGIidnR1ly5ZFX1+fHTt2kJiYCIC9vT2tW7fGzs6Orl278sMPP/DgwQO1a5w5cwZ9fX10dHRo2LAhjRs3ZsGCBUBuUt+hQweqVaumLNwGKP0PHDiQ1atX4+DgwNixYzl48KDSr6+vL3FxcVhbWzN06FB27typHCtdujT29vZER0dz5swZSpUqRf/+/Tl58iRpaWns27dPuVZqaip//vknzs7OanE7OzsTHx+vti/vywTIXS0/KSmJRo0aKftKlCih1qaw5xwaGpqvzZkzZ8jKysLKygp9fX1l27dvHwkJCfnaQ27N+uevD7y0Nr2fnx8pKSnKdv369Re2F0IIIYQQQoi/k5H4t0RPT08Zjc5z48YN5eeZM2cyb9485s6di52dHXp6egwfPlyZDq+pqcmuXbs4ePAgO3fuZP78+YwfP57Y2FiqV68OgLW1NZs3b6ZEiRJUqlSJUqVKAZCeno6rqyuurq6sWLECY2NjEhMTcXV1Vfp3d3fnjz/+YNu2bezatYvWrVszaNAgZs2ahZOTE1evXmX79u3s3r0bLy8vXFxcWL9+PQAtWrQgOjoaLS0tmjdvTtmyZbG1teXAgQPs27ePUaNGvdLzehUFPee/S0tLQ1NTk+PHj+db8K64K/m/iJaW1ktX1xdCCCGEEEKIF5Ek/h0VExNDx44d+fzzz4HcBeAuXrxIrVq1lDYqlQpnZ2ecnZ0JCAjAzMyMTZs2MXLkSABKlSpVYAL7+++/c+/ePaZNm0bVqlUBOHbsWL52xsbG9OrVi169etG0aVPGjBnDrFmzADA0NKRbt25069aNLl264Obmxv379ylbtizNmzdn2bJllChRAjc3NyA3sV+1ahUXL15U3oc3NDSkUqVKxMTEKKPzeffesGHDQp+NkZERpqamxMbG0qxZMwCePXvG8ePHcXJyKvIzzuPo6EhWVhbJyck0bdq0SOfY2toSGxtLz549lX2HDx8u9rVB6sQLIYQQQgghik6S+HeUpaUl69ev5+DBg5QpU4bZs2dz+/ZtJYmPjY1lz549tG3blgoVKhAbG8udO3ewtbV9ad/VqlWjVKlSzJ8/ny+//JKzZ88SGBio1iYgIIB69epRu3ZtMjIy2LJli9L37NmzMTU1xdHREQ0NDdatW4eJiYlSFq9Zs2Y8evSILVu2MG3aNCA3ie/SpQumpqZYWVkp16lcuTITJ06kZs2aODg4EBYWRlxcHCtWrFCL5/vvv+fw4cNK+bZhw4Yxbdo0jIyMGDBgAJ07d+b27dv8/PPPqFQqtXP//jnPvn37mDdvnvK5WbNmTJ48GR0dHcaOHasWo7m5udqigW3atGHq1KmEh4eTlZWFrq4uaWlpRXr+QgghhBBCCPGqJIl/R02YMIErV67g6uqKrq4u/fv3x8PDg5SUFCB3FHv//v3MnTuX1NRUzMzMCAkJwd3d/aV9GxsbEx4ezjfffENoaChOTk7MmjWLTz/9VGlTqlQp/Pz8uHbtGjo6OjRt2pTVq1cDYGBgwIwZM7h06RKampo0aNCAbdu2oaGRu8RCmTJlsLOz4/bt29jY2AC5CXJ2drbaiDvkjminpqYyatQokpOTqVWrFps3b8bS0vKF9zBq1CiSkpKUxf90dXXx9PTk/v37hIeHA1CzZk2qVavG3r17gdzSeHmvFHTu3Jm7d+9Ss2ZNwsLCePr0KXPnzmXZsmXcuHEDlUqFm5sbo0aNIjMzk+7du/P48WMAzp8/z4wZM3B2dubs2bNkZmZSr149kpOTX1gdoDCzT91DWz+z2OcJIYQQQgjxvpI68q9OlfMqWYcQb4ivry8PHz4kMjKS9evXM3nyZC5fvoyuri6Ojo78/PPP6OnpMWnSJCIjI5WR+DzXrl2jevXqnDx5EgcHB7Vj5ubmDB8+XK0efGHXfl54eDjDhw/n4cOHyr6goCD8/f1JS0tj8eLFzJs3j6tXr77WvaempmJkZMTE/VfQ1jd4rb6EEEIIIYR4n0gSry4vN0hJSXnpq7ayOr14J7xK3ft/k46ODtnZ2Tx79gwTExOSkpLYv39/sfqQOvFCCCGEEEKI1yXT6cU7oSh17/NK5j3v30jyL126xPfff0/9+vUxMDCga9eu7Nixg+bNm2NiYsJHH31E69at6dmz5wu/NZM68UIIIYQQQojXJSPx4p1QlLr31tbWajXf4+Li2LZt2z8ST0pKCvr6+ujq6mJtbU3FihWVxfY0NTUJCwvjxo0bzJgxg8qVKxMUFETt2rVJSkoqtE+pEy+EEEIIIYR4XZLEi3dCXt377du3U6tWLebPn4+1tbXae+d5JfOe3/JG7d80AwMD4uLiOHv2LOnp6ezfv19tVX3IXbXex8eHBQsWcO7cOZ48ecL3339faJ9aWloYGhqqbUIIIYQQQghRHDKdXvwrClos7u9eVvf+36ShoYGFhUWhx/++KF6ZMmUwNTUlPT292NeSOvFCCCGEEEKIopKR+A+Er68vKpUKlUpFyZIlqVixIm3atGHZsmVkZ2e/7fDo1q0bFy9eLPR4bGwsQUFBHDt2jMTERDZu3EhSUhLr1q37F6PM79q1a6hUKrVV8RcvXsyhQ4dITk4mISGBc+fOMW7cOM6dO0eHDh3eXrBCCCGEEEKID56MxH9A3NzcCAsLIysri9u3bxMVFcWwYcNYv349mzdvpkSJt/fr1tHRQUdHp9DjBdW9r1+/PhUrVvwXoyyahg0b8uzZM06dOkXt2rXR19endu3aREZG0rx582L3J3XihRBCCCGEeLM+5BJ2MhL/AdHS0sLExITKlSvj5OTEN998w88//8z27dsJDw8HIDExkY4dO6Kvr4+hoSFeXl7cvn1b6WPSpEk4ODiwbNkyqlWrhr6+Pl999RVZWVnMmDEDExMTKlSowLfffqt27dmzZ2NnZ4eenh5Vq1blq6++Ii0tTTkeHh5O6dKl812ndevWxMXF8dFHH1G6dGkSEhJ48uQJFy5cwNbWVq3932vEnz17loEDB6Knp4erqys+Pj7cvXtXOW5ubs6VK1cYO3YsZcuWxcTEhEmTJqnFNG3aND7++GO0tbWpVasWu3fv5osvvlCeV/Xq1QFwdHREpVLRokULHB0dadq0KW3atGHq1KmULFmSs2fPEhUVxdOnT1/lVyeEEEIIIYQQRSJJ/AeuVatW2Nvbs3HjRrKzs+nYsSP3799n37597Nq1iytXrtCtWze1cxISEti+fTtRUVGsWrWKpUuX8sknn3Djxg327dvH9OnTmTBhArGxsco5GhoahIaGcu7cOSIiIvj1118ZO3bsC2NLSEggMjKSLVu2sGXLFvbt28e0adOKdF8PHz6kVatWODo6cuzYMaKiorh9+zZeXl5q7SIiItDT0yM2NpYZM2YwZcoUdu3aBUBWVhYeHh7o6uoSGxvL//73P8aPH692/pEjRwDYvXs3SUlJbNy4UTm2d+9eEhIS2Lt3LxEREYSHhyvJf0GkTrwQQgghhBDidcl0+v8AGxsbTp8+zZ49ezhz5gxXr16latWqAPz444/Url2bo0eP0qBBAwCys7NZtmwZBgYG1KpVi5YtW3LhwgW2bduGhoYG1tbWTJ8+nb1799KoUSMAhg8frlzP3NycqVOn8uWXX7Jo0aJC48rOziY8PBwDAwMAfHx82LNnT75R/oIsWLAAR0dHgoKClH3Lli2jatWqXLx4UVlJvm7dukycOBEAS0tLFixYwJ49e2jTpg27du0iISGB6OhoTExMAPj2229p06aN0qexsTEA5cqVU9rkKVOmDAsWLEBTUxMbGxs++eQT9uzZQ79+/QqMWerECyGEEEIIIV6XjMT/B+Tk5KBSqYiPj6dq1apKAg9Qq1YtSpcuTXx8vLLP3NxcSawBKlasSK1atdDQ0FDbl5ycrHzevXs3rVu3pnLlyhgYGODj48O9e/d4/PhxoXH9/TqmpqZqfb7IqVOn2Lt3L/r6+spmY2MD5I7w56lbt67aec9f48KFC1StWlUtOW/YsGGRrg9Qu3ZtNDU1ixy/1IkXQgghhBBCvC4Zif8PiI+PV97tLoqSJUuqfc5b8f7v+/JWvb927Rrt27dn4MCBfPvtt5QtW5YDBw7Qp08fMjMz0dXVLfJ1irqSflpaGh06dGD69On5jpmamr6Ra7xMcfvW0tJCS0vrjVxbCCGEEEII8d8kSfwH7tdff+XMmTOMGDGCKlWqcP36da5fv66Mxp8/f56HDx9Sq1atV77G8ePHyc7OJiQkRBmtX7t27RuJvzBOTk5s2LABc3PzV15139ramuvXr3P79m1lFfyjR4+qtSlVqhSQ+/78P0XqxAshhBBCCCGKSqbTf0AyMjK4desWN2/e5MSJEwQFBdGxY0fat29Pz549cXFxwc7Ojh49enDixAmOHDlCz549ad68OfXr13/l61pYWPD06VPmz5/PlStXWL58Od9///0buaeUlBTi4uLUtuvXrzNo0CDu37+Pt7c3R48eJSEhgR07dvDFF18UOeFu06YNNWvWxNnZGWtra2JiYpgwYQKQO6oOUKFCBXR0dFi1ahUqlYrffvvtjdyXEEIIIYQQQrwKGYn/gERFRWFqakqJEiUoU6YM9vb2hIaG0qtXL2WE/Oeff2bIkCE0a9YMDQ0N3NzcmD9//mtd197entmzZzN9+nT8/Pxo1qwZwcHB9OzZ86Xn+vr68vDhQyIjI9X2R0dHExERAeSWd3tenz59WLJkCTExMYwbN462bduSkZGBmZkZbm5uau/uL1u2jHnz5uW7bl7fHh4e3Lp1ixMnTtC3b19mzpypTNPv2LEjJUqUIDQ0VFkcb8SIERw7dqxYz+dlpE68EEIIIYQQ/673uY68KicnJ+dtByH+u16UxLds2ZIHDx6o1Zcvrjt37igj8wcPHsTT05MLFy4o09d1dHQwMjJS2sfExPDxxx/j4uKilKKD3Pf+q1evzsmTJ3FwcHjleJ6XmpqKkZERE/dfQVvf4OUnCCGEEEIIId6Idy2Jz8sNUlJSXvqqrUynF++8e/fu4e3tTeXKldHV1cXOzo5Vq1aptVm/fj12dnbo6OhQrlw5XFxcSE9Px9jYGBMTE0xMTChbtiyQO0U+b9+vv/6Kj48PtWrVYvfu3XTq1AnIXW1fpVKhUqmIjo4uMK6zZ8/i7u6Ovr4+FStWxMfHh7t37xZ6H1InXgghhBBCCPG6JIkX77wnT55Qr149tm7dytmzZ+nfvz8+Pj4cOXIEgKSkJLy9venduzfx8fFER0fTuXNnijLJ5NGjR2zbto3ff/8dX19f2rRpQ8eOHXFzcyMpKYmkpCSaNGmS77yHDx/SqlUrHB0dOXbsGFFRUdy+fRsvL69CrxUcHIyRkZGyPV/qTwghhBBCCCGKQt6JF2/dli1b0NfXV9v3/OJ0lStXZvTo0crnIUOGsGPHDtauXUvDhg1JSkri2bNndO7cGTMzMwDs7OyKdO2ePXty5coVIiMjiYuLA/5viv/z9eP/bsGCBTg6OhIUFKTsW7ZsGVWrVuXixYtYWVnlO8fPz4+RI0cqn1NTUyWRF0IIIYQQQhSLJPHirWvZsiXfffed2r7Y2Fg+//xzIDehDwoKYu3atdy8eZPMzEwyMjKU+vP29va0bt0aOzs7XF1dadu2LV26dKFMmTL/WMynTp1i7969+b58AEhISCgwiZc68UIIIYQQQojXJUm8eOv09PSwsLBQ23fjxg3l55kzZzJv3jzmzp2LnZ0denp6DB8+nMzM3BXdNTU12bVrFwcPHmTnzp3Mnz+f8ePHExsbS/Xq1f+RmNPS0pRV7P/O1NT0H7mmEEIIIYQQQkgSL955MTExdOzYURmZz87O5uLFi9SqVUtpo1KpcHZ2xtnZmYCAAMzMzNi0aZPa9PWiKlWq1EtrzTs5ObFhwwbMzc0pUeL1/hmNtC/30hUohRBCCCGEEAJkYTvxHrC0tFRG2uPj4xkwYAC3b99WjsfGxhIUFMSxY8dITExk48aN3LlzB1tb21e6nrm5OadPn+bChQvcvXuXp0+f5mszaNAg7t+/j7e3N0ePHiUhIYEdO3bwxRdfvPQLACGEEEIIIYR4VZLEi3fehAkTcHJywtXVlRYtWmBiYoKHh4dy3NDQkP3799OuXTusrKyYMGECISEhuLu7v9L1+vXrh7W1NfXr18fY2JiYmJh8bSpVqkRMTAxZWVm0bdsWOzs7hg8fTunSpdHQkH9WQgghhBBCiH+GKqcodbiEEG9camoqRkZGpKSkyHR6IYQQQggh/sOKkxvIkKEQQgghhBBCCPGekCReCCGEEEIIIYR4T0gSL4QQQgghhBBCvCckiRdCCCGEEEIIId4TksQLIYQQQgghhBDvCUnihRBCCCGEEEKI94Qk8UIIIYQQQgghxHtCknghhBBCCCGEEOI9IUm8EEIIIYQQQgjxnpAkXgghhBBCCCGEeE9IEi+EEEIIIYQQQrwnJIkXQgghhBBCCCHeE5LECyGEEEIIIYQQ7wlJ4oUQQgghhBBCiPeEJPFCCCGEEEIIIcR7QpJ4IYQQQgghhBDiPVHibQcgxH9VTk4OAKmpqW85EiGEEEIIIcTblJcT5OUILyJJvBBvyb179wCoWrXqW45ECCGEEEII8S549OgRRkZGL2wjSbwQb0nZsmUBSExMfOk/VCGKIzU1lapVq3L9+nUMDQ3fdjjiAyJ/W+KfIn9b4p8if1vin/Km/7ZycnJ49OgRlSpVemlbSeKFeEs0NHKXpDAyMpL/qIh/hKGhofxtiX+E/G2Jf4r8bYl/ivxtiX/Km/zbKurAnixsJ4QQQgghhBBCvCckiRdCCCGEEEIIId4TksQL8ZZoaWkxceJEtLS03nYo4gMjf1vinyJ/W+KfIn9b4p8if1vin/I2/7ZUOUVZw14IIYQQQgghhBBvnYzECyGEEEIIIYQQ7wlJ4oUQQgghhBBCiPeEJPFCCCGEEEIIIcR7QpJ4IYQQQgghhBDiPSFJvBBvycKFCzE3N0dbW5tGjRpx5MiRtx2SeM/t37+fDh06UKlSJVQqFZGRkW87JPGBCA4OpkGDBhgYGFChQgU8PDy4cOHC2w5LfAC+++476tati6GhIYaGhjRu3Jjt27e/7bDEB2batGmoVCqGDx/+tkMR77lJkyahUqnUNhsbm389DknihXgL1qxZw8iRI5k4cSInTpzA3t4eV1dXkpOT33Zo4j2Wnp6Ovb09CxcufNuhiA/Mvn37GDRoEIcPH2bXrl08ffqUtm3bkp6e/rZDE++5KlWqMG3aNI4fP86xY8do1aoVHTt25Ny5c287NPGBOHr0KIsXL6Zu3bpvOxTxgahduzZJSUnKduDAgX89BikxJ8Rb0KhRIxo0aMCCBQsAyM7OpmrVqgwZMoSvv/76LUcnPgQqlYpNmzbh4eHxtkMRH6A7d+5QoUIF9u3bR7Nmzd52OOIDU7ZsWWbOnEmfPn3edijiPZeWloaTkxOLFi1i6tSpODg4MHfu3LcdlniPTZo0icjISOLi4t5qHDISL8S/LDMzk+PHj+Pi4qLs09DQwMXFhUOHDr3FyIQQomhSUlKA3GRLiDclKyuL1atXk56eTuPGjd92OOIDMGjQID755BO1/+cS4nVdunSJSpUqUaNGDXr06EFiYuK/HkOJf/2KQvzH3b17l6ysLCpWrKi2v2LFivz+++9vKSohhCia7Oxshg8fjrOzM3Xq1Hnb4YgPwJkzZ2jcuDFPnjxBX1+fTZs2UatWrbcdlnjPrV69mhMnTnD06NG3HYr4gDRq1Ijw8HCsra1JSkpi8uTJNG3alLNnz2JgYPCvxSFJvBBCCCGKbNCgQZw9e/atvAMoPkzW1tbExcWRkpLC+vXr6dWrF/v27ZNEXryy69evM2zYMHbt2oW2tvbbDkd8QNzd3ZWf69atS6NGjTAzM2Pt2rX/6itAksQL8S8rX748mpqa3L59W23/7du3MTExeUtRCSHEyw0ePJgtW7awf/9+qlSp8rbDER+IUqVKYWFhAUC9evU4evQo8+bNY/HixW85MvG+On78OMnJyTg5OSn7srKy2L9/PwsWLCAjIwNNTc23GKH4UJQuXRorKysuX778r15X3okX4l9WqlQp6tWrx549e5R92dnZ7NmzR94BFEK8k3Jychg8eDCbNm3i119/pXr16m87JPEBy87OJiMj422HId5jrVu35syZM8TFxSlb/fr16dGjB3FxcZLAizcmLS2NhIQETE1N/9Xryki8EG/ByJEj6dWrF/Xr16dhw4bMnTuX9PR0vvjii7cdmniPpaWlqX0TfPXqVeLi4ihbtizVqlV7i5GJ992gQYNYuXIlP//8MwYGBty6dQsAIyMjdHR03nJ04n3m5+eHu7s71apV49GjR6xcuZLo6Gh27NjxtkMT7zEDA4N8a3bo6elRrlw5WctDvJbRo0fToUMHzMzM+PPPP5k4cSKampp4e3v/q3FIEi/EW9CtWzfu3LlDQEAAt27dwsHBgaioqHyL3QlRHMeOHaNly5bK55EjRwLQq1cvwsPD31JU4kPw3XffAdCiRQu1/WFhYfj6+v77AYkPRnJyMj179iQpKQkjIyPq1q3Ljh07aNOmzdsOTQgh8rlx4wbe3t7cu3cPY2NjPv74Yw4fPoyxsfG/GofUiRdCCCGEEEIIId4T8k68EEIIIYQQQgjxnpAkXgghhBBCCCGEeE9IEi+EEEIIIYQQQrwnJIkXQgghhBBCCCHeE5LECyGEEEIIIYQQ7wlJ4oUQQgghhBBCiPeEJPFCCCGEEEIIIcR7QpJ4IYQQQgghhBDiPSFJvBBCCCGEEEII8Z6QJF4IIYQQ7xRfX188PDzedhiFunbtGiqViri4uLcdSpHcuXOHgQMHUq1aNbS0tDAxMcHV1ZWYmJi3HZoQQohXUOJtByCEEEII8b7IzMx82yEUm6enJ5mZmURERFCjRg1u377Nnj17uHfv3j92zczMTEqVKvWP9S+EEP9lMhIvhBBCiHdaixYtGDJkCMOHD6dMmTJUrFiRH374gfT0dL744gsMDAywsLBg+/btyjnR0dGoVCq2bt1K3bp10dbW5qOPPuLs2bNqfW/YsIHatWujpaWFubk5ISEhasfNzc0JDAykZ8+eGBoa0r9/f6pXrw6Ao6MjKpWKFi1aAHD06FHatGlD+fLlMTIyonnz5pw4cUKtP5VKxZIlS+jUqRO6urpYWlqyefNmtTbnzp2jffv2GBoaYmBgQNOmTUlISFCOL1myBFtbW7S1tbGxsWHRokWFPruHDx/y22+/MX36dFq2bImZmRkNGzbEz8+PTz/9VK3dgAEDqFixItra2tSpU4ctW7a81nMCOHDgAE2bNkVHR4eqVasydOhQ0tPTC41XCCHEy0kSL4QQQoh3XkREBOXLl+fIkSMMGTKEgQMH0rVrV5o0acKJEydo27YtPj4+PH78WO28MWPGEBISwtGjRzE2NqZDhw48ffoUgOPHj+Pl5UX37t05c+YMkyZNwt/fn/DwcLU+Zs2ahb29PSdPnsTf358jR44AsHv3bpKSkti4cSMAjx49olevXhw4cIDDhw9jaWlJu3btePTokVp/kydPxsvLi9OnT9OuXTt69OjB/fv3Abh58ybNmjVDS0uLX3/9lePHj9O7d2+ePXsGwIoVKwgICODbb78lPj6eoKAg/P39iYiIKPC56evro6+vT2RkJBkZGQW2yc7Oxt3dnZiYGH766SfOnz/PtGnT0NTUfK3nlJCQgJubG56enpw+fZo1a9Zw4MABBg8e/KJftRBCiJfJEUIIIYR4h/Tq1SunY8eOyufmzZvnfPzxx8rnZ8+e5ejp6eX4+Pgo+5KSknKAnEOHDuXk5OTk7N27NwfIWb16tdLm3r17OTo6Ojlr1qzJycnJyfnss89y2rRpo3btMWPG5NSqVUv5bGZmluPh4aHW5urVqzlAzsmTJ194H1lZWTkGBgY5v/zyi7IPyJkwYYLyOS0tLQfI2b59e05OTk6On59fTvXq1XMyMzML7LNmzZo5K1euVNsXGBiY07hx40LjWL9+fU6ZMmVytLW1c5o0aZLj5+eXc+rUKeX4jh07cjQ0NHIuXLhQ4Pmv+pz69OmT079/f7V9v/32W46GhkbOX3/9VWi8QgghXkxG4oUQQgjxzqtbt67ys6amJuXKlcPOzk7ZV7FiRQCSk5PVzmvcuLHyc9myZbG2tiY+Ph6A+Ph4nJ2d1do7Oztz6dIlsrKylH3169cvUoy3b9+mX79+WFpaYmRkhKGhIWlpaSQmJhZ6L3p6ehgaGipxx8XF0bRpU0qWLJmv//T0dBISEujTp48ywq6vr8/UqVPVptv/naenJ3/++SebN2/Gzc2N6OhonJyclJH0uLg4qlSpgpWVVYHnv+pzOnXqFOHh4Wqxurq6kp2dzdWrVwuNVwghxIvJwnZCCCGEeOf9PalVqVRq+1QqFZA7NfxN09PTK1K7Xr16ce/ePebNm4eZmRlaWlo0btw432J4Bd1LXtw6OjqF9p+WlgbADz/8QKNGjdSO5U19L4y2tjZt2rShTZs2+Pv707dvXyZOnIivr+8Lr1kcf39OaWlpDBgwgKFDh+ZrW61atTdyTSGE+C+SJF4IIYQQH6zDhw8rCeODBw+4ePEitra2ANja2uYrsxYTE4OVldULk+K8VdefH4XOO3fRokW0a9cOgOvXr3P37t1ixVu3bl0iIiJ4+vRpvmS/YsWKVKpUiStXrtCjR49i9ft3tWrVIjIyUrnmjRs3uHjxYoGj8a/6nJycnDh//jwWFhavFasQQgh1Mp1eCCGEEB+sKVOmsGfPHs6ePYuvry/ly5dXatCPGjWKPXv2EBgYyMWLF4mIiGDBggWMHj36hX1WqFABHR0doqKiuH37NikpKQBYWlqyfPly4uPjiY2NpUePHsUe5R48eDCpqal0796dY8eOcenSJZYvX86FCxeA3EXxgoODCQ0N5eLFi5w5c4awsDBmz55dYH/37t2jVatW/PTTT5w+fZqrV6+ybt06ZsyYQceOHQFo3rw5zZo1w9PTk127dnH16lW2b99OVFTUaz2ncePGcfDgQQYPHkxcXByXLl3i559/loXthBDiNUkSL4QQQogP1rRp0xg2bBj16tXj1q1b/PLLL8pIupOTE2vXrmX16tXUqVOHgIAApkyZgq+v7wv7LFGiBKGhoSxevJhKlSopyfDSpUt58OABTk5O+Pj4MHToUCpUqFCseMuVK8evv/5KWloazZs3p169evzwww/KqHzfvn1ZsmQJYWFh2NnZ0bx5c8LDw5Wyd3+nr69Po0aNmDNnDs2aNaNOnTr4+/vTr18/FixYoLTbsGEDDRo0wNvbm1q1ajF27FhlpsGrPqe6deuyb98+Ll68SNOmTXF0dCQgIIBKlSoV65kIIYRQp8rJycl520EIIYQQQrxJ0dHRtGzZkgcPHlC6dOm3HY4QQgjxxshIvBBCCCGEEEII8Z6QJF4IIYQQQgghhHhPyHR6IYQQQgghhBDiPSEj8UIIIYQQQgghxHtCknghhBBCCCGEEOI9IUm8EEIIIYQQQgjxnpAkXgghhBBCCCGEeE9IEi+EEEIIIYQQQrwnJIkXQgghhBBCCCHeE5LECyGEEEIIIYQQ7wlJ4oUQQgghhBBCiPfE/wOlcuyNmSOlHwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot feature importance\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(importance_df['Feature'], importance_df['Importance'], color='skyblue')\n",
    "plt.xlabel('Importance Score')\n",
    "plt.title('Feature Importance After PCA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0aab6230-c1c4-4ea2-86c8-77fa004b3ccc",
   "metadata": {},
   "source": [
    "## Dropping the columns below threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0fd92fe2-c792-44be-b8c6-6babfb76b6a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "threshold = 4  # Adjust threshold as needed\n",
    "features_to_drop = importance_df[importance_df['Importance'] < threshold]['Feature']\n",
    "df_reduced = df.drop(columns=features_to_drop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "416206f5-bf20-4b1c-bf23-88ed6ea9237f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine PCA features with the relevant label column\n",
    "result_df = pd.concat([df_reduced['label'], pca_df], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c8b54d70-eec8-4b1f-b011-7c25d949d9e7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Columns dropped: ['NoOfSelfRedirect', 'NoOfDegitsInURL', 'TLDLegitimateProb', 'NoOfJS', 'HasCopyrightInfo', 'NoOfLettersInURL', 'DegitRatioInURL', 'NoOfOtherSpecialCharsInURL', 'URLLength', 'HasExternalFormSubmit', 'HasSocialNet', 'Crypto', 'IsDomainIP', 'URLSimilarityIndex', 'NoOfEmptyRef', 'LargestLineLength', 'NoOfAmpersandInURL', 'NoOfPopup', 'HasObfuscation', 'NoOfObfuscatedChar', 'ObfuscationRatio']\n",
      "Reduced DataFrame shape: (224005, 31)\n"
     ]
    }
   ],
   "source": [
    "# Display the result\n",
    "result_df.head()\n",
    "print(f\"Columns dropped: {features_to_drop.tolist()}\")\n",
    "print(\"Reduced DataFrame shape:\", df_reduced.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ef20980a-6fe7-443f-a43b-c2c43906a1ba",
   "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>DomainLength</th>\n",
       "      <th>TLD</th>\n",
       "      <th>CharContinuationRate</th>\n",
       "      <th>URLCharProb</th>\n",
       "      <th>TLDLength</th>\n",
       "      <th>NoOfSubDomain</th>\n",
       "      <th>LetterRatioInURL</th>\n",
       "      <th>NoOfEqualsInURL</th>\n",
       "      <th>NoOfQMarkInURL</th>\n",
       "      <th>SpacialCharRatioInURL</th>\n",
       "      <th>...</th>\n",
       "      <th>HasSubmitButton</th>\n",
       "      <th>HasHiddenFields</th>\n",
       "      <th>HasPasswordField</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Pay</th>\n",
       "      <th>NoOfImage</th>\n",
       "      <th>NoOfCSS</th>\n",
       "      <th>NoOfSelfRef</th>\n",
       "      <th>NoOfExternalRef</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.061933</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.581</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.032</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>34</td>\n",
       "      <td>20</td>\n",
       "      <td>119</td>\n",
       "      <td>124</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>254</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.050207</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.391</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.087</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "      <td>217</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>647</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.064129</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.517</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.069</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19</td>\n",
       "      <td>231</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.057606</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.038</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26</td>\n",
       "      <td>503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059441</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0.606</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.030</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>244</td>\n",
       "      <td>15</td>\n",
       "      <td>72</td>\n",
       "      <td>85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DomainLength  TLD  CharContinuationRate  URLCharProb  TLDLength  \\\n",
       "0            24  231              1.000000     0.061933          3   \n",
       "1            16  254              0.666667     0.050207          2   \n",
       "2            22  647              0.866667     0.064129          2   \n",
       "3            19  231              1.000000     0.057606          3   \n",
       "4            26  503              1.000000     0.059441          3   \n",
       "\n",
       "   NoOfSubDomain  LetterRatioInURL  NoOfEqualsInURL  NoOfQMarkInURL  \\\n",
       "0              1             0.581                0               0   \n",
       "1              1             0.391                0               0   \n",
       "2              2             0.517                0               0   \n",
       "3              1             0.500                0               0   \n",
       "4              1             0.606                0               0   \n",
       "\n",
       "   SpacialCharRatioInURL  ...  HasSubmitButton  HasHiddenFields  \\\n",
       "0                  0.032  ...                1                1   \n",
       "1                  0.087  ...                1                0   \n",
       "2                  0.069  ...                1                1   \n",
       "3                  0.038  ...                1                1   \n",
       "4                  0.030  ...                1                1   \n",
       "\n",
       "   HasPasswordField  Bank  Pay  NoOfImage  NoOfCSS  NoOfSelfRef  \\\n",
       "0                 0     1    0         34       20          119   \n",
       "1                 0     0    0         50        9           39   \n",
       "2                 0     0    0         10        2           42   \n",
       "3                 0     0    1          3       27           22   \n",
       "4                 0     1    1        244       15           72   \n",
       "\n",
       "   NoOfExternalRef  label  \n",
       "0              124      1  \n",
       "1              217      1  \n",
       "2                5      1  \n",
       "3               31      1  \n",
       "4               85      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b2cef13d-7b44-4a7c-b2d9-df6106d3db97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224005, 31)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reduced.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a256e348-338d-427e-9365-8c96152486df",
   "metadata": {},
   "source": [
    "## Applying Z-score normalisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d2f29a03-4fc2-49f8-a6b1-4bde2833bf55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate the features and the target label\n",
    "features = df_reduced.drop(columns=['label'])  # Exclude the target label if it's in the data\n",
    "target = df_reduced['label']  # Assuming 'label' is your target column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a96640bb-3ec5-4eb3-8f7c-7eee5c7db3cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the StandardScaler (which performs Z-score normalization)\n",
    "scaler = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "97806b00-4a22-43fc-9b80-8bd6c8effa86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fit the scaler to the features and transform them\n",
    "scaled_features = scaler.fit_transform(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9683cd60-c6d1-4f5d-bdc0-87cadb8f3543",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the scaled features back into a DataFrame\n",
    "scaled_df = pd.DataFrame(scaled_features, columns=features.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5b0bda23-0e35-4a9b-94d5-fc245aff64be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the scaled features with the target label\n",
    "final_df = pd.concat([scaled_df, target.reset_index(drop=True)], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d4692859-0bee-4360-962c-a57ffd4a387c",
   "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>DomainLength</th>\n",
       "      <th>TLD</th>\n",
       "      <th>CharContinuationRate</th>\n",
       "      <th>URLCharProb</th>\n",
       "      <th>TLDLength</th>\n",
       "      <th>NoOfSubDomain</th>\n",
       "      <th>LetterRatioInURL</th>\n",
       "      <th>NoOfEqualsInURL</th>\n",
       "      <th>NoOfQMarkInURL</th>\n",
       "      <th>SpacialCharRatioInURL</th>\n",
       "      <th>...</th>\n",
       "      <th>HasSubmitButton</th>\n",
       "      <th>HasHiddenFields</th>\n",
       "      <th>HasPasswordField</th>\n",
       "      <th>Bank</th>\n",
       "      <th>Pay</th>\n",
       "      <th>NoOfImage</th>\n",
       "      <th>NoOfCSS</th>\n",
       "      <th>NoOfSelfRef</th>\n",
       "      <th>NoOfExternalRef</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.327757</td>\n",
       "      <td>-0.612241</td>\n",
       "      <td>0.702262</td>\n",
       "      <td>0.564266</td>\n",
       "      <td>0.395062</td>\n",
       "      <td>-0.270236</td>\n",
       "      <td>0.555651</td>\n",
       "      <td>-0.089864</td>\n",
       "      <td>-0.117188</td>\n",
       "      <td>-0.960848</td>\n",
       "      <td>...</td>\n",
       "      <td>1.229496</td>\n",
       "      <td>1.32186</td>\n",
       "      <td>-0.303655</td>\n",
       "      <td>2.747573</td>\n",
       "      <td>-0.537215</td>\n",
       "      <td>0.217040</td>\n",
       "      <td>1.209409</td>\n",
       "      <td>0.514798</td>\n",
       "      <td>0.845670</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.626500</td>\n",
       "      <td>-0.452245</td>\n",
       "      <td>-0.870528</td>\n",
       "      <td>-0.568653</td>\n",
       "      <td>-1.298737</td>\n",
       "      <td>-0.270236</td>\n",
       "      <td>-1.011426</td>\n",
       "      <td>-0.089864</td>\n",
       "      <td>-0.117188</td>\n",
       "      <td>0.775975</td>\n",
       "      <td>...</td>\n",
       "      <td>1.229496</td>\n",
       "      <td>-0.75651</td>\n",
       "      <td>-0.303655</td>\n",
       "      <td>-0.363958</td>\n",
       "      <td>-0.537215</td>\n",
       "      <td>0.526076</td>\n",
       "      <td>0.265536</td>\n",
       "      <td>-0.164052</td>\n",
       "      <td>1.821573</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.089193</td>\n",
       "      <td>2.281599</td>\n",
       "      <td>0.073146</td>\n",
       "      <td>0.776391</td>\n",
       "      <td>-1.298737</td>\n",
       "      <td>1.463224</td>\n",
       "      <td>0.027793</td>\n",
       "      <td>-0.089864</td>\n",
       "      <td>-0.117188</td>\n",
       "      <td>0.207560</td>\n",
       "      <td>...</td>\n",
       "      <td>1.229496</td>\n",
       "      <td>1.32186</td>\n",
       "      <td>-0.303655</td>\n",
       "      <td>-0.363958</td>\n",
       "      <td>-0.537215</td>\n",
       "      <td>-0.246515</td>\n",
       "      <td>-0.335110</td>\n",
       "      <td>-0.138595</td>\n",
       "      <td>-0.403067</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.268653</td>\n",
       "      <td>-0.612241</td>\n",
       "      <td>0.702262</td>\n",
       "      <td>0.146167</td>\n",
       "      <td>0.395062</td>\n",
       "      <td>-0.270236</td>\n",
       "      <td>-0.112419</td>\n",
       "      <td>-0.089864</td>\n",
       "      <td>-0.117188</td>\n",
       "      <td>-0.771377</td>\n",
       "      <td>...</td>\n",
       "      <td>1.229496</td>\n",
       "      <td>1.32186</td>\n",
       "      <td>-0.303655</td>\n",
       "      <td>-0.363958</td>\n",
       "      <td>1.861453</td>\n",
       "      <td>-0.381718</td>\n",
       "      <td>1.810056</td>\n",
       "      <td>-0.308308</td>\n",
       "      <td>-0.130233</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.566321</td>\n",
       "      <td>1.279885</td>\n",
       "      <td>0.702262</td>\n",
       "      <td>0.323519</td>\n",
       "      <td>0.395062</td>\n",
       "      <td>-0.270236</td>\n",
       "      <td>0.761845</td>\n",
       "      <td>-0.089864</td>\n",
       "      <td>-0.117188</td>\n",
       "      <td>-1.024006</td>\n",
       "      <td>...</td>\n",
       "      <td>1.229496</td>\n",
       "      <td>1.32186</td>\n",
       "      <td>-0.303655</td>\n",
       "      <td>2.747573</td>\n",
       "      <td>1.861453</td>\n",
       "      <td>4.273142</td>\n",
       "      <td>0.780376</td>\n",
       "      <td>0.115973</td>\n",
       "      <td>0.436420</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   DomainLength       TLD  CharContinuationRate  URLCharProb  TLDLength  \\\n",
       "0      0.327757 -0.612241              0.702262     0.564266   0.395062   \n",
       "1     -0.626500 -0.452245             -0.870528    -0.568653  -1.298737   \n",
       "2      0.089193  2.281599              0.073146     0.776391  -1.298737   \n",
       "3     -0.268653 -0.612241              0.702262     0.146167   0.395062   \n",
       "4      0.566321  1.279885              0.702262     0.323519   0.395062   \n",
       "\n",
       "   NoOfSubDomain  LetterRatioInURL  NoOfEqualsInURL  NoOfQMarkInURL  \\\n",
       "0      -0.270236          0.555651        -0.089864       -0.117188   \n",
       "1      -0.270236         -1.011426        -0.089864       -0.117188   \n",
       "2       1.463224          0.027793        -0.089864       -0.117188   \n",
       "3      -0.270236         -0.112419        -0.089864       -0.117188   \n",
       "4      -0.270236          0.761845        -0.089864       -0.117188   \n",
       "\n",
       "   SpacialCharRatioInURL  ...  HasSubmitButton  HasHiddenFields  \\\n",
       "0              -0.960848  ...         1.229496          1.32186   \n",
       "1               0.775975  ...         1.229496         -0.75651   \n",
       "2               0.207560  ...         1.229496          1.32186   \n",
       "3              -0.771377  ...         1.229496          1.32186   \n",
       "4              -1.024006  ...         1.229496          1.32186   \n",
       "\n",
       "   HasPasswordField      Bank       Pay  NoOfImage   NoOfCSS  NoOfSelfRef  \\\n",
       "0         -0.303655  2.747573 -0.537215   0.217040  1.209409     0.514798   \n",
       "1         -0.303655 -0.363958 -0.537215   0.526076  0.265536    -0.164052   \n",
       "2         -0.303655 -0.363958 -0.537215  -0.246515 -0.335110    -0.138595   \n",
       "3         -0.303655 -0.363958  1.861453  -0.381718  1.810056    -0.308308   \n",
       "4         -0.303655  2.747573  1.861453   4.273142  0.780376     0.115973   \n",
       "\n",
       "   NoOfExternalRef  label  \n",
       "0         0.845670      1  \n",
       "1         1.821573      1  \n",
       "2        -0.403067      1  \n",
       "3        -0.130233      1  \n",
       "4         0.436420      1  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f36f2e38-e828-4599-9637-d61cbdc98964",
   "metadata": {},
   "source": [
    "## Converting into CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fff7408a-be24-43e7-b103-8b63204f3b53",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_df.to_csv(\"Preprocessed data.csv\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "gpu",
   "language": "python",
   "name": "gpu"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}