diff --git a/perfanalysis/001_perf_analysis_vm_assess_w_and_wo_cache_01.png b/perfanalysis/001_perf_analysis_vm_assess_w_and_wo_cache_01.png
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_w_and_wo_cache_and_extended_01.png b/perfanalysis/001_perf_analysis_vm_assess_w_and_wo_cache_and_extended_01.png
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_eval_fix_01.png b/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_eval_fix_01.png
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_eval_fix_02.png b/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_eval_fix_02.png
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_eval_fix_03.png b/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_eval_fix_03.png
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_extended_oracle_cache_and_with_eval_fix_02 b/perfanalysis/001_perf_analysis_vm_assess_with_cache_and_extended_oracle_cache_and_with_eval_fix_02
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_without_cache_and_extended_oracle_cache_and_with_eval_fix_02 b/perfanalysis/001_perf_analysis_vm_assess_without_cache_and_extended_oracle_cache_and_with_eval_fix_02
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_without_cache_and_with_eval_fix_02 b/perfanalysis/001_perf_analysis_vm_assess_without_cache_and_with_eval_fix_02
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diff --git a/perfanalysis/001_perf_analysis_vm_assess_without_cache_and_with_eval_fix_02.png b/perfanalysis/001_perf_analysis_vm_assess_without_cache_and_with_eval_fix_02.png
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diff --git a/perfanalysis/perf_analysis_vm_assess.ipynb b/perfanalysis/perf_analysis_vm_assess.ipynb
index 95ca041..82c5f5a 100644
--- a/perfanalysis/perf_analysis_vm_assess.ipynb
+++ b/perfanalysis/perf_analysis_vm_assess.ipynb
@@ -15,7 +15,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "7f38bad5-adf7-4165-8f8f-5baf34014017",
    "metadata": {},
    "outputs": [],
@@ -76,38 +76,38 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 131,
    "id": "db1e25f0-0acf-4db5-8360-981ca4c3ee2b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.587383"
+       "581.622394"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "assess_vm(1).elapsed.total_seconds()"
+    "assess_vm(10).elapsed.total_seconds()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 66,
    "id": "6a3b0cf0-e9f9-4375-86cb-9fae50e5ecf2",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[1.01543, 0.955455]"
+       "[2.651261, 2.58381]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -118,24 +118,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 142,
    "id": "c30442bc-600f-4b35-bccf-1fdb6e58f536",
    "metadata": {},
    "outputs": [],
    "source": [
-    "vm_nbs = [1, 2, 5, 10, 20, 50, 100, 1000]\n",
+    "vm_nbs = [1, 2, 5, 10, 20, 50, 100, 1000, 2000, 3000]\n",
     "results  = [assess_vm_elapsed_time(x, 1) for x in vm_nbs]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 143,
    "id": "26f2cd85-311b-4419-b734-0b9a5aa9776d",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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p9Id26pxVdUaTL774IuvWpffXtRRqUmcyhc/ZO3I/N8z9556z1IH+ufu88sorWQhb0JWw00J5aZG8FIhSMKqyKEPRU+hM3c8UvGbfGi6Fr5rYeeeds8CaOstpKkNadOywww770f3SSYIU8tMldUTTwndpgcX5hfkkBdPU+U+X9HpJH5t2MkiLMs49quPnpNdU6vymx5k9MFetJr+or7kU4FMgTydA0smQ1M39qSH2s4f5tChjWpBtQRdVW9DXcBp9kS7peU91pvrSYodV4fnnnu/02k1TEqo62z+lJt+7NAokTc1Jl/S4KeCnURuzh/lFsSD11pTV64H6zjB7gHogreKcOlVpaO7cnal0O61IvrDSHtvJ3KtJV3WrF8c+21VduNm/tjQsuaYhdl7SUOYU1OdejX/2z5O61KmT/t///vdH90krrqe5xgtSc7r+U9u1/Zz0mCmgzL5dXVrp/OdW1a+ShpKneehpNfe0Y0Dqzq+77rrV70+PO/dw/dS5XXHFFX9yK765X18pgFc9bk228JvXay5t/ZZWjK+S1k9IOwGkznTVVIVF6eSntRJSVzx12tOIiarV/H8ucKafg7SyexopUxev4XRiYe6f4fXXX3+O57Imz3d67Y4ZMyY78TO3dBKuaj2KmjzW3PdJ34MU8hfmezs/Na13QaTva9XHA9RHOvMA9UAKGX379s22cUrhLu1VnjqPaQ/vFFbTll1pz/KFkRaJS9u7pW3r0h/FKUilLdZSxzl9nu222y7qWhoCXdU9TFtOpQ5eCtgpaKYu6cJI837TcOa0rVsaxpy2rUtD4lPnNA0fTl936lqn4JsWpksd1LS9Wwq8qUOcjqfgMb+pD2k4dfq+pOc9nRBI+3enzvqizOlNJ07SSZTUYU9d4tQ1T2sppGA1rznM85JGCfztb3/Lvp6qrciqpK2+0vzmtK1f+vpTaEtb5KXF3tIw7/lJ3dn03KU55Onj05z2FLxTCJ19NEdNpddr6gyn6Qhp//e0p336XqU55ylMp9f2okpD7W+++ebse5i63lXB7+fMb6u92noNp5+rNIUinWxIr5/0PUn3S6+fqhNrNXm+0+s7vZbTnu9V2zKmk09pxEh6LtPviTQnvyaPlRaUTFsFpsdIHfq0LV3V9oW1pab1Loj0GElaODGtk5BOqKTFQwHqjbyX0wegZlvT/dRWVVXuvvvuyq222irb4ild1lhjjWx7prfffnuOrenWWmuteX78vLamS2bMmFF5wQUXVHbo0KGycePGle3bt68866yzKqdNm/az24zNvjXdnXfeWaOvbfYtwao88MADleuuu25l06ZNK1ddddXKyy67rPKGG27I7pe2q/q5GtLXnS6zmzBhQuUJJ5yQbUu2xBJLVK600krZc/D111/PsSVa+lzpOUvb27Vp0ybbLi49H5MmTar8Kf/73/8qd9hhh2w7tbQF3DHHHFP5yiuvZDWnr/3nnveq52F2119/fWWnTp2yWtL3Nz3OvO4399Z0s0tfS9pyL21rOLsffvih8rTTTsu2ImvRokVWU7r+z3/+8ye3prvrrrsqd9xxx8rlllsuex5XXnnlymOPPbZy/PjxP/n8/NT364svvsi2fkvPW3rMddZZZ47nbPZtyq644orKBZW2Ymvbtm328YMGDZrnfeb1OpyXmm5NV5PX8Isvvpht2Ziew/Q9Ts/pbrvtVvn8888v8PM9efLk7Oc0bamX7peeyy222KLyyiuvrN4isiaP1bdv38pNN920snXr1tlWc+l1l7a7nH2byfm9huf3+2Ze3/ea1PtT3/O5t7tL3+MTTzyx8he/+EW2PZ4/e4H6pkH6J+8TCgDA4pNWZk8d1kceeSTvUgCAhWTOPACUkDREOq1ePvuifABA8dGZB4ASkFbuT/PP09z3r7/+Ots7Pq2MDwAUJ515ACgBaRGxtK3YjBkz4rbbbhPkAaDI6cwDAABAkdGZBwAAgCIjzAMAAECRaRQlZtasWfHZZ59FixYtokGDBnmXAwAAANXSTPjJkyfHiiuuGA0bzr//XnJhPgX59u3b510GAAAAzNfHH38cK6200nzfX3JhPnXkq56Yli1b5l0OAAAAVCsvL88a0FXZdX5KLsxXDa1PQV6YBwAAoBD93LRwC+ABAABAkRHmAQAAoMgI8wAAAFBkhHkAAAAoMsI8AAAAFBlhHgAAAIqMMA8AAABFRpgHAACAIiPMAwAAQJER5gEAAKDICPMAAABQZIR5AAAAKDLCPAAAABQZYR4AAACKjDAPAAAARUaYBwAAgCIjzAMAAECREeYBAACgyAjzAAAAUGSEeQAAACgywjwAAAAUGWEeAAAAiowwDwAAAEVGmAcAAIAiI8wDAABAkRHmAQAAoMgI8wAAAFBkhHkAAAAoMsI8AAAAFBlhHgAAAIqMMA8AAABFRpgHAACAIiPMAwAAQJER5gEAAKDINMq7AAAAAKgrFbMq49kPv4kvJ0+L5Vo0jU07LB1lDRtEsRPmAQAAqJeGvD4+LnjwfzF+0rTqY21bNY3zd+8SO6/dNoqZYfYAAADUyyB/3C0vzhHkk88nTcuOp/cXM2EeAACAeje0/oIH/xeV83hf1bH0/nS/YiXMAwAAUK88++E3P+rIzy5F+PT+dL9iJcwDAABQr3w5eVqt3q8QCfMAAADUK8u1aFqr9ytEwjwAAAD1yqYdls5Wrf//Z8jPKW1Ml96f7leshHkAAADqlbKGDWLfDpVRWRlRWTlrjvdV7TCftqcr5v3mhXkAAADqnVMO3D62KXs7mjesmOP4Cq2axr8O3bDo95lvlHcBAAAAUBsqKytj+vTp0aRJk2jQoEH0v/iUbPu5tGp9WuwuzZFPQ+uLuSNfRZgHAACgXgT5P/zhD/Hqq6/GQw89FEsttVR2PAX3zVdbJuobw+wBAAAoarNmzYrjjz8+/vrXv8aoUaPikUceifpOZx4AAICiDvLHHntsXHfdddnQ+vR29913j/pOmAcAAKAoVVRUxNFHHx033nhjNGzYMHt72GGHRSkQ5gEAACg6M2fOjN69e8ett94aZWVl0b9//zj44IOjVAjzAAAAFJ2xY8fGoEGDolGjRnHbbbfFfvvtF6VEmAcAAKDorL766jFs2LD47LPPYo899ohSI8wDAABQFH744Yd45513Yp111slub7zxxlGqbE0HAABAwZs2bVrsvffeseWWW8YzzzwTpS7XMP+vf/0r1l133WjZsmV22XzzzWPw4MHzvX9amTBtNTD7pWnTpou1ZgAAABav7777LhtKn/JiWvhuypQpUepyHWa/0korxaWXXhqdOnWKysrKuOmmm2LPPfeMl156KdZaa615fkwK/W+//Xb17RToAQAAqJ+mTp2a7Rs/atSoaNasWTz88MPRtWvXKHW5hvn0DZndRRddlHXrn3766fmG+RTeV1hhhcVUIQAAAHmZPHly7LrrrvH4449H8+bNs878VlttlXdZBaFg5sxXVFTEwIEDs7Muabj9/KThFKusskq0b98+6+K/8cYbP7tAQnl5+RwXAAAAClvKbjvvvHMW5NMI7eHDhwvyhRTmX3vttewMS5MmTeI3v/lN3HvvvdGlS5d53rdz585xww03xP333x+33HJLzJo1K7bYYov45JNP5vv4l1xySbRq1ar6kk4CAAAAUNhSRkwhvnXr1vHII4/EZpttlndJBaVBZZqsnqPp06fHuHHjYtKkSXHXXXfFddddF6NHj55voJ/djBkzYs0114yDDz44Lrzwwvl25tNl9rM7KdCnz5deGAAAABSm77//PsaOHZvlvlJRXl6eNaJ/LrPm3plfYoklYvXVV4+NNtoo66Kvt9568de//rVGH9u4cePYYIMN4r333vvZszmzXwAAACg8X3/9dfz5z3/OFkhPllxyyZIK8kWzAN68pKHzs3fSf26efRqmv8suu9R5XQAAANSdL774InbYYYd4/fXXs478Oeeck3dJBS3XMH/WWWdFjx49YuWVV85WKRwwYEA8+uijMXTo0Oz9hx9+eLRr1y7r2Cd9+vTJ5kmkTv7EiRPjiiuuiI8++iiOPvroPL8MAAAAFsH48eOjW7du8dZbb0Xbtm1j3333zbukgpdrmP/yyy+zwJ6+cWlOwLrrrpsF+e7du2fvT3PpGzb8/2cCfPvtt3HMMcfE559/Hm3atMmG5j/11FM1ml8PAABA4UkLmqcg/+6778ZKK60UI0eOjE6dOuVdVsHLfQG8Ql1MAAAAgLqVGrjbbbddfPDBB9mI7VGjRkXHjh2jlJUXywJ4AAAAlJ5p06ZlHfkU5Dt06BCPPfZYyQf5BSHMAwAAsNg1bdo0zj333OjcuXMW5FdZZZW8SyoqhtkDAACQm7SbWdpSnP/HMHsAAAAKyptvvpnNkU+LmlcR5BeOMA8AAECde+2116Jr167ZduQnnXRS3uUUPWEeAACAOvXyyy9nHfmvvvoqNthgg7jmmmvyLqnoCfMAAADUmRdeeCFbtX7ChAmxySabxCOPPBLLLLNM3mUVPWEeAACAOvHMM8/E9ttvH99++21svvnmMXz48GjTpk3eZdULwjwAAAC1Lm2c9tvf/jZblX2rrbaKoUOHZqu0UzuEeQAAAGpdgwYN4r777otevXrFkCFDokWLFnmXVK8I8wAAANSar7/+uvp6+/bt48Ybb4xmzZrlWlN9JMwDAABQK4YNGxYdOnSIO++8M+9S6j1hHgAAgEU2aNCg2H333WPKlCkxcODAbM48dUeYBwAAYJHcf//9sddee8X06dNj7733jttuuy2bM0/dEeYBAABYaHfffXfst99+MWPGjNh///3j9ttvjyWWWCLvsuo9YR4AAICFkoL7gQceGDNnzoyePXvGgAEDonHjxnmXVRKEeQAAABbKmDFjoqKiItt+7uabb45GjRrlXVLJ8EwDAACwUP7yl7/EJptsEgcffHA0bKhXvDh5tgEAAKixwYMHZwvdJWmRu0MOOUSQz4FnHAAAgBq55pprYpdddomDDjoomydPfoR5AAAAftbVV18dJ5xwQna9Y8eOUVZWlndJJU2YBwAA4CddccUV8Yc//CG7fuaZZ2a37SOfL2EeAACA+br44ovj9NNPz66fd9552W1BPn9WswcAAGCeLrnkkjjnnHOy63369Ilzzz0375L4P8I8AAAA87TlllvGUkstlYX4NLyewiHMAwAAME/bbLNNvPXWW9G+ffu8S2Eu5swDAACQqayszLrwr776avUxQb4wCfMAAADErFmz4sQTT4y+fftG9+7dY9KkSXmXxE8wzB4AAKDEpSB/3HHHxX/+859spfq08F2rVq3yLoufIMwDAACUsIqKijjmmGOiX79+0bBhw+zt4YcfnndZ/AxhHgAAoISD/BFHHBH9+/fPgnx627Nnz7zLogaEeQAAgBJ12WWXZQG+rKwsbrvttth///3zLokasgAeAABAiUoL3m277bZx5513CvJFRmceAACgxIbWp0580qJFixg5cmS26B3FRWceAACgREybNi323HPPuPjii6uPCfLFSZgHAAAoAd9//30W5B9++OFsL/mPPvoo75JYBIbZAwAA1HNTp06NPfbYIxtSv9RSS2WBfpVVVsm7LBaBMA8AAFCPTZ48OXbbbbd47LHHonnz5jFo0KDYeuut8y6LRSTMAwAA1FPl5eXRo0ePeOqpp6Jly5YxZMiQ2HzzzfMui1ogzAMAANRTgwcPzoJ869atY9iwYbHJJpvkXRK1RJgHAACopw488MCYMGFCbLbZZrHhhhvmXQ61SJgHAACoR77++uto2LBhLL300tnt3/72t3mXRB2wNR0AAEA98eWXX0a3bt1ixx13jIkTJ+ZdDnVImAcAAKgHxo8fH9tuu2289tpr8dlnn8VXX32Vd0nUIWEeAACgyH366adZkH/zzTdjpZVWitGjR0enTp3yLos6ZM48AABAERs3blw2tP7999+PlVdeOUaNGhUdO3bMuyzqmDAPAABQpMaOHRvbbbdd9rZDhw5ZkF9llVXyLovFwDB7AACAIjVjxoz44YcfYvXVV8+G1gvypUNnHgAAoEilefGpG9+iRYtYccUV8y6HxUhnHgAAoIikRe6GDh1afbtz586CfAkS5gEAAIrE66+/nq1av+eee8Zjjz2WdznkSJgHAAAoAi+//HIW5L/88svo0qVLrLXWWnmXRI6EeQAAgAL3wgsvZNvPTZgwITbZZJN45JFHYplllsm7LHIkzAMAABSwZ555Jrbffvv49ttvY/PNN4/hw4dHmzZt8i6LnFnNHgAAoEC99dZb0b1795g8eXJstdVWMWjQoGzlehDmAQAACnjrud122y3Gjx8fDz74YDRv3jzvkigQwjwAAECBKisri5tvvjlmzJgRSy65ZN7lUEDMmQcAACggw4YNi2OPPTYqKiqy240aNRLk+RGdeQAAgAKR5sTvs88+8cMPP8Q666wTJ5xwQt4lUaB05gEAAArAAw88EHvvvXcW5NPbX//613mXRAET5gEAAHJ2zz33xL777hvTp0+P/fffP26//fZYYokl8i6LAibMAwAA5CgF9wMOOCBmzpwZPXv2jAEDBkTjxo3zLosCJ8wDAADk5IsvvogjjjgiW+zu8MMPz1auTwvewc/xKgEAAMjJ8ssvHwMHDswWvrvmmmuyreigJhpUVlZWRgkpLy+PVq1axaRJk6Jly5Z5lwMAAJSgKVOmRPPmzfMugyLOrIbZAwAALEb//Oc/o0uXLvHBBx/kXQpFTJgHAABYTP7617/G8ccfHx9//HHcdttteZdDERPmAQAAFoMrr7wyTjrppOz6GWecEWeffXbeJVHEhHkAAIA6dskll8Rpp52WXf/jH/+Y3W7QoEHeZVHEhHkAAIA61KdPn+oufLp+4YUXCvIsMlvTAQAA1JHvv/8+7rnnnux66safeeaZeZdEPSHMAwAA1JEll1wyRowYEQ899FD07t0773KoRwyzBwAAqEWVlZXxxBNPVN9edtllBXlqnTAPAABQS2bNmhUnnnhibL311vHvf/8773KoxwyzBwAAqKUgf9xxx8V//vOfbIG7srKyvEuiHhPmAQAAFlFFRUUcc8wx0a9fvyzIp7e9evXKuyzqMWEeAABgEYP8EUccEf3794+GDRtmb3v27Jl3WdRzwjwAAMAiDK0/9NBDY+DAgdmw+ttuuy3233//vMuiBAjzAAAACyl14jt37hyNGzeO22+/Pfbee++8S6JENKhM+yaUkPLy8mjVqlVMmjQpWrZsmXc5AABAkUuR6u2334411lgj71IoocxqazoAAIAFMG3atDj33HNj6tSp2e204J0gz+JmmD0AAEANff/997HXXnvFsGHD4uWXX44HH3ww75IoUcI8AABADaRO/B577BEjR46MZs2axSmnnJJ3SZQwYR4AAOBnTJkyJXbdddd47LHHonnz5jF48ODYaqut8i6LEibMAwAA/MyCZLvssks8+eST2YJkQ4YMic033zzvsihxwjwAAMBP6NmzZxbkW7dunc2V32STTfIuCaxmDwAA8FP69u0bq6++ejzyyCOCPAVDZx4AAGAee8enLeeS9ddfP958881o1Eh8onDozAMAAMzmyy+/jC233DKeeuqp6mOCPIVGmAcAAPg/48ePj2233TbGjBkTRx55ZFRUVORdEsyT00sAAAAR8emnn0a3bt3inXfeiZVWWikefPDBKCsry7ssmCdhHgAAKHnjxo3Lgvz7778fK6+8cowaNSo6duyYd1kwX4bZAwAAJW3s2LHRtWvXLMh36NAhRo8eLchT8IR5AACgpF166aVZoE/bz6Ugv+qqq+ZdEvwsw+wBAICS9te//jWbG3/22WdHu3bt8i4HakSYBwAASs5nn30Wbdu2zfaSb9KkSVxzzTV5lwQLxDB7AACgpLz++uuxwQYbxMknnxyVlZV5lwMLRZgHAABKxiuvvJLtI//ll19m8+OnTp2ad0mwUIR5AACgJLz44ovZ9nMTJkyIjTfeOB555JFo3rx53mXBQhHmAQCAeu/ZZ5+N7bffPr755pvYbLPNYsSIEdGmTZu8y4KFJswDAAD12pgxY6J79+4xceLE2HLLLWPo0KHRqlWrvMuCRSLMAwAA9dpHH30UU6ZMia5du8aQIUOiZcuWeZcEi8zWdAAAQL120EEHRevWrWPrrbeOZs2a5V0O1AqdeQAAoN559NFHs73kq+y8886CPPWKMA8AANQrgwcPzsJ7Wrn+q6++yrscqBPCPAAAUG88+OCDsddee8UPP/wQa665poXuqLeEeQAAoF649957Y5999onp06fHfvvtF3fccUcsscQSeZcFdUKYBwAAil4K7vvvv3/MnDkzDj744LjtttuicePGeZcF9TPM/+tf/4p111032xoiXTbffPNsfstPufPOO2ONNdaIpk2bxjrrrBODBg1abPUCAACF57777ssCfEVFRRx22GHRv3//aNTIxl3Ub7mG+ZVWWikuvfTSeOGFF+L555/PFqjYc88944033pjn/Z966qnsh/Soo46Kl156KZsLky6vv/76Yq8dAAAoDJtsskl06NAhjjzyyOjXr1+UlZXlXRLUuQaVlZWVUUCWXnrpuOKKK7LAPrcDDzwwpk6dGg899FD1sc022yzWX3/9uPbaa2v0+OXl5dkiGJMmTcpGAwAAAMXv66+/zrJEw4ZmElPcappZC+aVnobEDBw4MAvrabj9vIwZMyZ22GGHOY7ttNNO2fH5SatYpidj9gsAAFDcUjNvwIAB1beXXXZZQZ6SkvtEktdeey0L79OmTYvmzZtnK1B26dJlnvf9/PPPY/nll5/jWLqdjs/PJZdcEhdccEGt1w0AAOTjb3/7W/z+97/PwnvKDmmkLpSa3E9dde7cOV5++eV45pln4rjjjotevXrF//73v1p7/LPOOisbnlB1+fjjj2vtsQEAgMXrqquuyoJ8ctppp8V6662Xd0lQmp35tO/j6quvnl3faKON4rnnnou//vWv8e9///tH911hhRXiiy++mONYup2Oz0+TJk2yCwAAUNzS4tmpWZf88Y9/jD59+kSDBg3yLgtKszM/t1mzZmXz3OclDcd/5JFH5jg2fPjw+c6xBwAA6ocU3KuCfJpGe+GFFwrylLRcO/Pph7FHjx6x8sorx+TJk7MFLB599NEYOnRo9v7DDz882rVrl817T9Jwmq5du2ZDa3bddddswby0pd1//vOfPL8MAACgDg0ePDjOP//87PrFF19cHeqhlOUa5r/88ssssI8fPz5ben/dddfNgnz37t2z948bN26OFSm32GKLLPCnITVnn312dOrUKe67775Ye+21c/wqAACAurTzzjvHCSecEKuuumqccsopeZcDBaHg9pmva/aZBwCAwpdiysyZM6Nx48bVtw2rpxSUF9s+8wAAAFXB/Xe/+13su+++MX369OyYIA8Ftpo9AADA7Ati//a3v812t0oBPq2pteOOO+ZdFhQcYR4AACgIFRUV8etf/zpuuOGGLMj369dPkIf5EOYBAICCCPJHHHFE9O/fP1sEO73t2bNn3mVBwRLmAQCAXKWF7g477LBs6+mysrJsB6sDDjgg77KgoAnzAABArt5555148MEHo1GjRnH77bfHPvvsk3dJUPCEeQAAIFddunSJQYMGZVtx7b777nmXA0VBmAcAABa7adOmxUcffRSdO3fObm+zzTZ5lwRFxT7zAADAYvX999/HXnvtFVtuuWW89tpreZcDRUlnHgAAWGy+++672GOPPeKRRx6JpZZaKiZMmJB3SVCUhHkAAGCxmDJlSuy2224xevToaN68eTZPfuutt867LChKwjwAAFDnJk+eHLvssks88cQT0aJFixgyZEhsscUWeZcFRUuYBwAA6lRapX7nnXeOp59+Olq1ahXDhg2LTTfdNO+yoKgJ8wAAQJ1K+8c3btw42rRpEyNGjIgNN9ww75Kg6AnzAABAnWrWrFk8/PDD8fHHH2d7ygOLztZ0AABArfvyyy/j2muvrb6d5skL8lB7dOYBAIBa9fnnn8f2228f//vf/+KHH36I3//+93mXBPWOMA8AANSazz77LLp16xZvv/12tGvXLnr06JF3SVAvGWYPAADUijQnvmvXrlmQX3nllbP95H/5y1/mXRbUSzrzAADAIhs7dmzWkf/www9j1VVXjVGjRmVvgbohzAMAAItk6tSpse2228ZHH30Uq622WowcOTLrzAN1xzB7AABgkbeeO/XUU6Nz587Z0HpBHupeg8rKysooIeXl5dGqVauYNGlStGzZMu9yAACg3vj+++9jySWXzLsMKInMqjMPAAAssDfeeCNbqf6bb76pPibIw+IjzAMAAAvk1VdfzebIDxkyJE455ZS8y4GSJMwDAAA19uKLL8Z2220XX3/9dWy00UZx1VVX5V0SlCRhHgAAqJHnnnsutt9++2xo/a9+9asYMWJELL300nmXBSVJmAcAAH7WmDFjYocddoiJEyfGFltsEcOGDYvWrVvnXRaULGEeAAD4SRUVFXH00Udnq2xvs802MXToUDtDQc6EeQAA4CeVlZXFfffdF4ccckgMGjQomjdvnndJUPLsMw8AAMxTGlJvKD0sXvaZBwAAFlradm6VVVaJwYMH510KMA/CPAAAMIeHHnoo9txzz6xDeNNNN+VdDjAPwjwAAFDt3nvvjX322SemT58e++67b/Tv3z/vkoB5EOYBAIDMnXfeGfvvv3/MmDEjDjrooBg4cGA0btw477KAeRDmAQCAGDBgQBbg0zZ0hx56aNaRb9SoUd5lAfMhzAMAADFixIiYNWtWHHHEEXHjjTcK8lDg/IQCAADx3//+N7beeuvo1atXNGyo5weFzk8pAACUqOHDh2fD6pOysrKsKy/IQ3HwkwoAACXo73//e+y4445ZgE/D64HiIswDAECJ+fOf/xy/+93vsutt27aNBg0a5F0SsICEeQAAKCGXXnppnHLKKdn1c845J7stzEPxEeYBAKBEXHjhhXHWWWdl1//0pz9ltwV5KE5WswcAgBKQgvt5552XXb/ooovi7LPPzrskYBHozAMAQAnYZJNNYokllojLL79ckId6QGceAABKwM477xxvvfVWdOjQIe9SgFqgMw8AAPVQZWVlnH/++fHuu+9WHxPkof4Q5gEAoJ5J+8b/9re/jT59+sT2228fU6dOzbskoJYZZg8AAPUsyP/617+O66+/Plup/oILLohmzZrlXRZQy4R5AACoJyoqKuLII4+Mm2++ORo2bBg33XRTHHrooXmXBdQBYR4AAOqBmTNnRq9evWLAgAFRVlYWt9xySxx00EF5lwXUEWEeAADqgTQ/PgX5Ro0axcCBA2PffffNuySgDlkADwAA6oGTTjopfvWrX8Vdd90lyEMJ0JkHAIAiXuwuzY1Pll566XjqqaeqbwP1m590AAAoQtOmTYvddtst/vGPf1QfE+ShdPhpBwCAIvPdd9/FHnvsEYMHD44zzzwzxo8fn3dJwGJmmD0AABSRKVOmxO677x6PPvpotn/8oEGDom3btnmXBSxmwjwAABSJyZMnxy677BJPPPFEtGjRIuvMb7nllnmXBeRAmAcAgCIwadKk6NGjR4wZMyZatWoVQ4cOzVavB0qTMA8AAEXg3nvvzYJ8mzZtYtiwYbHxxhvnXRKQI2EeAACKQO/eveOrr76K7t27x/rrr593OUDOhHkAAChQX3/9dTRp0iSbH5+cdtppeZcEFAhb0wEAQAH64osvYtttt41dd901pk6dmnc5QIER5gEAoMB89tlnWZB/44034v3338+CPcDshHkAACggn3zySRbk33rrrWjfvn2MHj06OnbsmHdZQIER5gEAoEB89NFH0bVr13j33Xdj1VVXzYL86quvnndZQAGyAB4AABSADz74ILp165YF+tSJHzVqVKy88sp5lwUUKJ15AAAoAN99911MmTIlfvnLX8Zjjz0myAM/SWceAAAKwNprrx0jR46MX/ziF9G2bdu8ywEKnDAPAAA5+d///hcTJkyIrbfeOru97rrr5l0SUCQMswcAgBy8+uqr2ar1PXr0iGeffTbvcoAiI8wDAMBi9uKLL8Z2220XX331VayxxhpWrAcWmDAPAACL0XPPPRfbb799fPPNN7HpppvGiBEjYumll867LKDICPMAALCYjBkzJnbYYYeYOHFibL755jFs2LBo3bp13mUBRUiYBwCAxeC1116LHXfcMcrLy7MF74YOHRqtWrXKuyygSFnNHgAAFoPOnTtn8+TTXvIPPvhgNGvWLO+SgCImzAMAwGKwxBJLxJ133hkVFRWx1FJL5V0OUOQMswcAgDoyZMiQOPnkk6OysjK73aRJE0EeqBU68wAAUAceeuih2HfffWP69Omx9tprx5FHHpl3SUA9ojMPAAC17L777ot99tknC/Lp7aGHHpp3SUA9I8wDAEAtuuuuu2L//fePGTNmxIEHHhgDBw7M5ssD1CZhHgAAasltt90WBx10UMycOTPrxt9yyy3RuHHjvMsC6iFhHgAAasHHH38cvXv3zlarT29vvPHGaNTIElVA3fDbBQAAakH79u2jX79+8fjjj8c111wTDRvqmwF1p0Fl1T4ZJaK8vDxatWoVkyZNipYtW+ZdDgAARe7777+PJZdcMu8ygBLLrE4XAgDAQkod+PXWWy8+/fTTvEsBSowwDwAAC+Evf/lLnHDCCfHuu+9mC98BLE7CPAAALKDLL788Tj755Oz6WWedFaecckreJQElRpgHAIAF0Ldv3zjjjDOy6+eff35cdNFF0aBBg7zLAkqM1ewBAKAG0rrRf/rTn6JPnz7Vof6cc87JuyygRAnzAABQA1OmTImBAwdWD7M/7bTT8i4JKGHCPAAA1ECLFi1i5MiRMXTo0DjyyCPzLgcocebMAwDATwytf/bZZ6tvt2vXTpAHCoIwDwAA8zBr1qw4/vjjY7PNNosBAwbkXQ7AHAyzBwCAeQT5Y489Nq677rpspfoffvgh75IA5iDMAwDAbCoqKuKoo46Km266KRo2bJi9PfTQQ/MuC2AOwjwAAPyfmTNnRq9evbJh9WVlZXHLLbfEQQcdlHdZAD8izAMAwP8F+UMOOSTuuOOOaNSoUbYN3b777pt3WQDzJMwDAEBE1olfccUVo3HjxnHnnXfGnnvumXdJAPPVoDLtt1FCysvLo1WrVjFp0qRo2bJl3uUAAFBA0p/Gr7/+eqyzzjp5lwKUqPIaZlZb0wEAULK+//776NOnT/Vq9WnlekEeKAaG2QMAUJK+++67bCj9iBEj4n//+182Rx6gWAjzAACUnClTpsTuu+8ejz76aDRr1iyOO+64vEsCWCDCPAAAJWXy5Mmxyy67xBNPPBEtWrSIwYMHx5Zbbpl3WQALRJgHAKBkpAWlevToEWPGjMkWmBoyZEhsttlmeZcFsMCEeQAASmal+v322y8L8m3atIlhw4bFxhtvnHdZAAvFavYAAJSEtFL9BRdcEKuuumqMHDlSkAeKWq5h/pJLLolNNtkkm6u03HLLxV577RVvv/32T37MjTfemP0inv3StGnTxVYzAADF15GvssUWW2R/b66//vq51gRQ1GF+9OjRcfzxx8fTTz8dw4cPjxkzZsSOO+4YU6dO/cmPa9myZYwfP7768tFHHy22mgEAKB5ffPFFdO3aNV566aXqY0sssUSuNQEU/Zz5tODI3F331KF/4YUXYptttpnvx6Vu/AorrLAYKgQAoFilpk+3bt3irbfeit69e2eBvmFDs0yB+qFhoa0umiy99NI/uy/oKqusEu3bt48999wz3njjjfne94cffojy8vI5LgAA1G+ffPJJ1pFPQT79zXj33XcL8kC9UjC/0WbNmhUnnXRStsfn2muvPd/7de7cOW644Ya4//7745Zbbsk+Ls19Sr+w5zcvP207UnVJv8wBAKi/0hTMFOTffffdbLG7NLVz9dVXz7ssgFrVoHL2FUFydNxxx8XgwYPjiSeeiJVWWqnGH5fm2a+55ppx8MEHx4UXXjjPzny6VEmd+RTo0yiANPceAID648MPP4ztttsuC/QdO3aMUaNGxcorr5x3WQA1ljJrakT/XGYtiH3mTzjhhHjooYfiscceW6AgnzRu3Dg22GCDeO+99+b5/iZNmmQXAADqv/POOy8L8p06dcq2n1vQvy0BikWuw+zToIAU5O+9997sl22HDh0W+DEqKiritddei7Zt29ZJjQAAFI9rr702jjzyyGxovSAP1Ge5dubTtnQDBgzI5r+nveY///zz7HgaUrDkkktm1w8//PBo165dNvc96dOnT2y22WbZvKeJEyfGFVdckZ19Pfroo/P8UgAAyHH7ubQjUtrxqFmzZnH99dfnXRJA/e7M/+tf/8rmAWy77bZZZ73qcvvtt1ffZ9y4cdm2IlW+/fbbOOaYY7J58rvssks2n+Cpp56KLl265PRVAACQl1dffTXWWWedrOEDUEoKZgG8QltMAACAwpb2je/evXtMmDAhNtxww2wh5arRnQD1PbMWzNZ0AABQU88//3x069YtC/KbbrppPPLII4I8UFKEeQAAisrTTz8d22+/fbZ+0uabbx7Dhg2L1q1b510WwGIlzAMAUDSefPLJ2HHHHbNhqFtvvXUMHTo0G44KUGqEeQAAisabb74ZkydPju222y4GDx6c7YgEUIpy3ZoOAAAWRNqOeNlll82680sttVTe5QDkRmceAICCNnr06Pj666+rb++1116CPFDyhHkAAArWww8/nHXh0xZ0acE7AP4fYR4AgIJ0//33x9577x3Tp0+Pjh076sYDzEaYBwCg4Nx1112x3377xYwZM+KAAw6IgQMHxhJLLJF3WQAFQ5gHAKCgpOB+0EEHxcyZM+OQQw6JW2+9NRo3bpx3WQAFRZgHAKCgOvIpwFdUVETv3r3jpptuikaNbMAEMDe/GQEAKBgbbrhhrLjiirHzzjvHv//972jYUO8JYF6EeQAACkZa6O65556L5ZZbTpAH+Al+QwIAkKt//vOf8eCDD1bfXmGFFQR5gJ+hMw8AQG7+8pe/xMknn5ytVP/yyy/HmmuumXdJAEXBKU8AAHJx+eWXZ0E+OeWUU2KNNdbIuySAoiHMAwCw2F100UVxxhlnZNfPP//87HaDBg3yLgugaBhmDwDAYlNZWRkXXHBBdkn69u0b55xzTt5lARQdYR4AgMXmvvvuqw7yl112WZx++ul5lwRQlIR5AAAWmz333DN69eoV6623XvzhD3/IuxyAoiXMAwBQ50PrKyoqolGjRtmWc/369TM/HmARWQAPAIA6M2vWrDj++OPj8MMPzwJ9IsgDLDphHgCAOgvyxx57bPzrX/+KgQMHxhNPPJF3SQD1hmH2AADUutSFP/roo+PGG2/Mhtant127ds27LIB6Q5gHAKBWzZw5M3r37h233nprlJWVRf/+/ePggw/OuyyAekWYBwCg1syYMSMOO+ywuP3227MF72677bbYb7/98i4LoN4R5gEAqDWvvfZatpd848aN44477oi99tor75IA6iVhHgCAWrPhhhvGvffem82Z32233fIuB6DeEuYBAFgk06ZNi/Hjx0eHDh2y2z169Mi7JIB6z9Z0AAAstO+++y722GOP2HLLLePdd9/NuxyAkiHMAwCwUKZOnZoNpR8+fHiUl5dn3XkAFg/D7AEAWGCTJ0+OXXfdNR5//PFo3rx5DB48OLbaaqu8ywIoGcI8AAALJHXh07z4p556Klq2bBlDhw6NzTbbLO+yAEqKMA8AQI1NnDgxdtppp3j22WejdevW2RD7jTfeOO+yAEqOMA8AwAJJ284tvfTSMWLEiNhggw3yLgegJAnzAADUWOrGDxs2LFvsbq211sq7HICSZTV7AAB+0pdffhk333xz9e3UlRfkAfKlMw8AwHylDvz2228fb775ZsyYMSOOOuqovEsCQJgHAGB+Pv300+jWrVu88847sdJKK0XXrl3zLgmA/2OYPQAAPzJu3LgsvKcgv8oqq8Rjjz0Wq6++et5lAfB/hHkAAOYwduzYLMi///770bFjxxg9enR06NAh77IAmI1h9gAAVJs0aVJss8028fHHH0enTp1i5MiR2RB7AAqLzjwAANVatWoVv/nNb2KNNdaIRx99VJAHKFANKisrK6OElJeXZ/9JpbPOLVu2zLscAICCNHXq1GjWrFneZQCUnPIaZladeQCAEvfaa6/F3nvvHZMnT64+JsgDFDZhHgCghL388sux3XbbxX333RdnnHFG3uUAUEPCPABAiXrhhReyfeQnTJgQm2yySVx00UV5lwRADQnzAAAl6Jlnnontt98+vv3229h8881j+PDh0aZNm7zLAqCGhHkAgBLz5JNPRvfu3bPFlbbaaqsYOnRottgSAMVDmAcAKCEzZsyIww8/PFvsbtttt40hQ4ZEixYt8i4LgAUkzAMAlJDGjRtni90deOCB8fDDD1u1HqBI2WceAKBE/gbytw9A4bPPPAAAmUGDBkWHDh3isccey7sUAGqJMA8AUI/df//9sddee8U333wT//3vf/MuB4BaIswDANRTd999d+y3337Zonf7779/3HDDDXmXBEAtEeYBAOqh22+/PVvkbubMmdGzZ88YMGBAtvgdAPWDMA8AUM/ccsstWYCvqKjItqG7+eabo1GjRnmXBUAtEuYBAOqRtFFRmic/a9asOOqoo6Jfv35RVlaWd1kA1DKnaAEA6pEGDRrErbfeGjvssEMcc8wx0bCh3g1AfeS3OwBAPTBq1KisG58sscQSceyxxwryAPWY3/AAAEXu6quvjm7dusWJJ56YDbMHoP4T5gEAitgVV1wRf/jDH7LrrVq1yrscABYTYR4AoEhdfPHFcfrpp2fXzzvvvLjooouyOfMA1H/CPABAkUlD6S+44II455xzstt9+vTJbgvyAKXDavYAAEXmT3/6Uxbgk0svvTTOOOOMvEsCYDET5gEAiszaa6+d7R1/+eWXx8knn5x3OQDkQJgHACgy+++/f6y//vrRqVOnvEsBICfmzAMAFLi0f3waVv/xxx9XHxPkAUqbMA8AUOBB/rjjjovzzz8/dthhh/jhhx/yLgmAAmCYPQBAgaqoqIhjjjkm+vXrFw0bNsxWr2/SpEneZQFQAIR5AIACNHPmzDjiiCPilltuyYJ8//79o2fPnnmXBUCBEOYBAAowyB922GExcODAbNX62267LVv0DgCqCPMAAAXm7LPPzoJ848aN4/bbb4+9994775IAKDAWwAMAKDBp7/h111037r77bkEegHnSmQcAKACVlZXRoEGD7PoKK6wQL774YjbEHgDmRWceACBn33//fey6665x0003VR8T5AH4KcI8AECOpk6dGrvttlsMHjw4TjzxxPj666/zLgmAImCYPQBATqZMmZJ15B977LFo3rx5DBo0KJZddtm8ywKgCAjzAAA5KC8vj1122SWefPLJaNmyZQwZMiQ233zzvMsCoEgI8wAAi9nEiRNj5513jmeeeSZat24dw4YNi0022STvsgAoIsI8AMBidsstt2RBfumll47hw4fHhhtumHdJABQZYR4AYDE7/vjjs4Xu0h7y6623Xt7lAFCEGlSmTU1LbH5aq1atYtKkSdn8NACAxeGrr77KFrlbcskl8y4FgHqQWW1NBwBQx8aPHx9du3aNffbZJ3744Ye8ywGgHjDMHgCgDn366afRrVu3eOedd2Ly5Mnx+eefxyqrrJJ3WQAUOZ15AIA6Mm7cuKwjn4L8yiuvHKNHjxbkAagVOvMAAHVg7Nixsd1222VvO3ToEKNGjRLkAag1OvMAALXs/fffj2222SYL8quvvrqOPAC1TpgHAKhl33zzTUycODE6d+6cBfn27dvnXRIA9Yxh9gAAtWyTTTaJESNGZPPkV1hhhbzLAaAeEuYBAGrB66+/HtOmTYuNN944u73pppvmXRIA9Zhh9gAAi+iVV16JbbfdNrp37x6vvvpq3uUAUAKEeQCARfDCCy9kq9ZPmDAhOnXqZH48AIuFMA8AsJCeffbZ2H777ePbb7+NzTbbLIYPHx5t2rTJuywASoAwDwCwEMaMGZMNq580aVJsueWWMXTo0GjVqlXeZQFQIoR5AIAF9NJLL8WOO+4Y5eXl0bVr1xgyZEi0bNky77IAKCFWswcAWEBp//i0/VzDhg3jgQceiKWWWirvkgAoMcI8AMACSuH9wQcfzML8kksumXc5AJQgw+wBAGpg8ODBcf7550dlZWV2u1mzZoI8ALnRmQcA+BlpKP3+++8f06dPjzXXXDMOOuigvEsCoMTpzAMA/IS777479t133yzI77ffftl1AMibMA8AMB+33357HHjggTFz5sw4+OCD47bbbovGjRvnXRYACPMAAPNyyy23RM+ePaOioiIOO+yw6N+/fzRqZIYiAIVBmAcAmMv7778fvXv3jlmzZsWRRx4Z/fr1i7KysrzLAoBqNTq9vM8++8SCuvbaa2O55ZZb4I8DAMjbaqutFv/85z/jpZdeimuuuSbbgg4ACkmDyqr9VX5C+g/sgAMOqPH2KwMGDIg333wzOnbsGIWmvLw8WrVqFZMmTYqWLVvmXQ4AUEB++OGHaNKkSd5lAFDCymuYWWs88etvf/tbjTvtd911V00fFgCgIKS/da677roYOXJkLLvssnmXAwA/qUZjxkaNGhVLL7101NTgwYOjXbt2Nb4/AECerrrqqvj9738fr732WjbCEADqRZjv2rXrAq3eutVWW9VoiNoll1wSm2yySbRo0SLr+u+1117x9ttv/+zH3XnnnbHGGmtE06ZNY5111olBgwbVuDYAgLn/Hjn11FOz63/84x/jxBNPzLskAPhZC7yay4svvpidta5y//33ZyH87LPPjunTpy/QY40ePTqOP/74ePrpp2P48OExY8aM2HHHHWPq1Knz/Zinnnoq2+f1qKOOyhalSZ87XV5//fUF/VIAgBLXp0+f7G+YqusXXnhhNGjQIO+yAKB2FsCbXeqkn3nmmbHvvvvGBx98EGuttVbsvffe8dxzz8Wuu+4aV199dSysr776KuvQp5C/zTbbzPM+Bx54YBb2H3rooepjm222Way//vrZCvrzWsgmXWZfTKB9+/YWwAOAEpb+/DnvvPOib9++1d359PcNABTLAngL3Jl/5513suBcNdw9he40t+zGG2+Mu+++e5GKTsUmPzU/f8yYMbHDDjvMcWynnXbKjs9L+s85PRFVlxTkAYDSNnHixLj55pur58sL8gAUm4YLcyZ71qxZ2fURI0bELrvskl1PIfnrr79e6ELSY5500kmx5ZZbxtprrz3f+33++eex/PLLz3Es3U7H5+Wss87KThJUXT7++OOFrhEAqB/atGmTLfD73//+N04++eS8ywGABVbzVe3+z8Ybb5wNSUvd8TQc/l//+ld2/MMPP/xRyF4Qae58mvf+xBNPRG1KC/HZLxYASA2Jl19+OTbYYIPsdseOHbMLAJREZz7NiU+L4J1wwglxzjnnxOqrr169t/wWW2yxUEWkx0pz4NMZ8pVWWukn77vCCivEF198McexdDsdBwCY3wjA4447LjbddNN44IEH8i4HABZ/Z37dddedYzX7KldccUWUlZUt8BnytP3LvffeG48++mh06NDhZz9m8803j0ceeSQbkl8lrYSfjgMAzK2ioiKOOeaY6NevX7ZS/bfffpt3SQCw+MP8/KQ93xdmaH1aPC9tb5f2mq+a954WqltyySWz64cffni0a9cuW8gu+f3vf5/te58Wq0mr5w8cODCef/75+M9//lNbXwoAUI+C/BFHHBH9+/ePhg0bZm979uyZd1kAsHiG2afV5RdkcbuVV145Pvroo5+9X5pvnxal23bbbaNt27bVl9tvv736PuPGjYvx48dX305D+dMJgBTe11tvvWx4/3333feTi+YBAKVn5syZceihh2YBPo0eTA0AQR6AkurMp+1bBg8enHXMa2LChAnZmfCfU5Mt7tPw+7ntv//+2QUAYF5mzJgRBx98cLZtbuPGjbNGwd577513WQCw+IfZ9+rVq/Y+KwBAHUqd+NatW8cSSyyRjeLbfffd8y4JAGpVg8qatMfrkfLy8myEQRre37Jly7zLAQDqcAX7V199NdZff/28SwGAWs+sC7w1HQBAIfr+++/jsssuy+bKJ2nBO0EegPqq1lazBwDIy3fffRd77LFHtn3tO++8E9dff33eJQFAnRLmAYCiNmXKlNhtt91i9OjR0bx58+jdu3feJQFAnRPmAYCinle4yy67xJNPPpnNK0y776RtbAGgvhPmAYCilLbO3XnnneOZZ57JVq4fOnRobLrppnmXBQCLxUItgPf+++/HH//4x2z/1i+//DI7ls6Ev/HGG7VdHwDAj6TNeNIc+RTkl1566WyuvCAPQClZ4DCf5qOts8462X+e99xzTzZPLXnllVfi/PPPr4saAQDm0KBBgzj33HOjffv2MXLkyNhwww3zLgkACjvMn3nmmdG3b98YPnx4LLHEEtXHu3XrFk8//XRt1wcAME/du3fPVq5fb7318i4FAAo/zL/22mux9957/+j4csstF19//XVt1QUAMIfx48dnzYO33nqr+ljTpk1zrQkAiibMpwVm0n+mc3vppZeiXbt2tVUXAEC1Tz/9NLbddtsYNWpUHH744dmceQAoZQsc5g866KA444wz4vPPP8/mq82aNSvbDubUU0/N/nMFAKhN48aNi65du2ZD6ldeeeW47bbbsr9BAKCULXCYv/jii2ONNdbIFpxJi9916dIlttlmm2xP17TCPQBAbRk7dmwW5NNOOh06dMgW4l1ttdXyLgsActegciHHqaWz5K+//noW6DfYYIPo1KlTFIPy8vJo1apVTJo0KVq2bJl3OQDAfKQAn+bIp785UoBPQ+xTMwEA6rOaZtZGC/sJ0jC3dAEAqAunn356FuQ7d+6c7SNvbR4AWIQwnxr5d911V3Z2/Msvv8zmzM8u7T0PALCorr/++lhyySXjyiuvjBVWWCHvcgCguMP8SSedFP/+979ju+22i+WXX94CNABArZkwYUIss8wy1Tvo3HLLLXmXBAD1I8z3798/677vsssudVMRAFCSXnnlldhhhx3i7LPPjj/84Q95lwMA9Ws1+zQRv2PHjnVTDQBQkl588cVssbuvv/46BgwYENOnT8+7JACoX2H+T3/6U1xwwQXx/fff101FAEBJefbZZ2P77bePb775Jn71q1/F8OHDY4kllsi7LACoX8PsDzjggLjttttiueWWi1VXXTUaN278ozPrAAA1MWbMmNh5552zbXi23HLLGDRokK1jAaAuwnyvXr3ihRdeiEMPPdQCeADAQnv88cezNXimTJkSXbt2jYceeiiaN2+ed1kAUD/D/MMPPxxDhw6Nrbbaqm4qAgBKwvPPP58F+TTE/oEHHoillloq75IAoP6G+fbt2xv+BgAssrRifdu2bWPPPffM9pMHAOpwAbyrrroqTj/99Bg7duyCfigAUOIee+yxmDRpUvXtgw46SJAHgMXRmU9z5b/77rtYbbXVsuFwcy+Al1aiBQCY24MPPhj77bdfbLTRRjFs2DDz4wFgcYb5q6++elE+HwBQgu6555448MADY+bMmdGuXbto0qRJ3iUBQOmtZg8AUFN33HFH9OzZMyoqKuLggw+Om2++ORo1WuA/QQCA2dTof9K092vVonfp+k+xOB4AUOXWW2+Nww8/PGbNmhWHHXZY9OvXL8rKyvIuCwCKXo3CfJs2bWL8+PGx3HLLRevWree5t3xlZWV2PJ11BwC47bbbsgCf/kY48sgj4z//+Y8gDwCLM8yPHDkyll566ez6qFGjautzAwD12HrrrRfLLrts7LPPPvHPf/4zGjZc4E10AIBFCfNdu3atvt6hQ4dsr/m5u/PprPvHH39ck4cDAEpAly5d4sUXX8wWvJvXqD4AYOEt8CnyFOa/+uqrHx1PW9Kl9wEApeuaa67JRvRVWWmllQR5AKgDC7yUbNXc+LlNmTIlmjZtWlt1AQBF5qqrropTTz01llpqqXjttdeiY8eOeZcEAPVWjcP8ySefnL1NQf7cc8/N/qOukha9e+aZZ2L99devmyoBgIJ2ySWXxNlnn139N4PRegBQIGH+pZdequ7Mp7PtSyyxRPX70vW0yE06Gw8AlJY+ffrE+eefn12/4IIL4rzzzsu7JACo92oc5qtWsT/iiCPir3/9q/3kAaDEpRP8Kbj37ds3u33xxRfHWWedlXdZAFASGlSm/4lLSHl5ebRq1SomTZrkhAQALOI+8j179syuX3HFFUboAcBizKwLvAAeAECy3377xV133RXbbLNN/P73v8+7HAAoKcI8AFBjaUBfujRs2DAaN26chXlbzwFAEewzDwCUplmzZsVxxx2XXdL1RJAHgHzozAMAPyttQ/vrX/86brjhhizApwVxN9tss7zLAoCSVaMw/8ADD9T4AffYY49FqQcAKMAgn8J7//79s+H16a0gDwBFEOb32muvOW6nM/KzL4I/+xC79B8+AFA/zJw5Mw4//PBs5fqysrIYMGBAHHDAAXmXBQAlr0Zz5tO8uKrLsGHDYv3114/BgwfHxIkTs8ugQYNiww03jCFDhtR9xQDAYjFjxow4+OCDsyDfqFGjuOOOOwR5ACjWOfMnnXRSXHvttbHVVltVH9tpp51iqaWWyubSvfnmm7VdIwCQg+eeey7uu+++6lXrTaUDgCIO8++//360bt36R8fTpvZjx46trboAgJxtscUW2bD6Zs2axS677JJ3OQDAbBpUzj75vQa22WabaNq0abb4zfLLL58d++KLL7L5dNOmTYvRo0dHISsvL89OPEyaNClatmyZdzkAUFC+//77mDBhQqy00kp5lwIAJam8hpl1gfeZT1vSjB8/PlZeeeVYffXVs0u6/umnn8b111+/qHUDADn57rvvYvfdd89O3H/88cd5lwMA1OYw+xTeX3311Rg+fHi89dZb2bE111wzdthhhzlWtQcAiseUKVNit912y0bYpWH1Kcy3b98+77IAgNoaZj+7NKy+SZMmRRXiDbMHgB//35jmxD/55JPRokWLbHeaNF8eAKhHw+zT9nQXXnhhtGvXLpo3bx4ffvhhdvzcc881zB4Aikz6QyHtSpOCfPrDYcSIEYI8ABSBBQ7zffv2jRtvvDEuv/zyWGKJJaqPr7322nHdddfVdn0AQB359ttvs2lyTz/9dLRp0yZGjhwZm266ad5lAQB1EeZvvvnm+M9//hOHHHJIlJWVVR9fb731qufQAwCFb8aMGdmid8suu2yMGjUqNtxww7xLAgDqagG8tGp9WgRvXsPv0x8FAEBxWG655eKRRx7JtqJba6218i4HAKjLznyXLl3i8ccf/9Hxu+66KzbYYIMFfTgAYDH6/PPP484776y+vcIKKwjyAFAKnfnzzjsvevXqlXXoUzf+nnvuibfffjsbfv/QQw/VTZUAwCL77LPPolu3bvHOO+9ERUVFHHTQQXmXBAAsrs78nnvuGQ8++GC22m3ahzaF+zfffDM71r1794WtAwCoQ2nf+K5du2Yn4NP+8ZtsskneJQEAee0zX4zsMw9AqRk7dmzWkU/bya666qrZYnfpLQBQQvvMpzP7n3zySfXtZ599Nk466aRshXsAoLB88MEHWUc+BfnVVlstHnvsMUEeAOqBBQ7zPXv2zM7oVy2ik/anTYH+nHPOiT59+tRFjQDAQvj666+zID9u3Lj45S9/GaNHj86G2AMAJRjmX3/99dh0002z63fccUess8468dRTT8Wtt94aN954Y13UCAAshGWWWSY7Cb/mmmtmQb5du3Z5lwQA5BXm017yTZo0ya6nRfD22GOP7Poaa6wR48ePr626AIBF1KBBg7j00kvj6aefzragAwBKOMynvWivvfbabK/54cOHx84771y93U3qAAAA+XnllVfi4IMPju+//7460FvwFQDqnwXeZ/6yyy6LvffeO6644opsv/n11lsvO/7AAw9UD78HABa/F198Mdsm9ptvvokVV1wxrrrqqrxLAgAKaWu6ioqKbLn8Nm3azLHtzVJLLRXLLbdcFDJb0wFQHz333HOx4447xsSJE+NXv/pVDBkyJFq3bp13WQBAHWXWBe7MJ2VlZdnc+TTUPuncubNtbgAgJ2PGjMmmvaX//LfYYosYPHiwE9YAUM8t8Jz5yZMnx2GHHZatiJu2u0mXdP3QQw/NzhwAAIvPE088kXXkU5DfZpttYujQoYI8AJSABQ7zRx99dDzzzDPx0EMPZUP50iVdf/755+PYY4+tmyoBgB/54Ycf4qCDDoopU6ZEt27dYtCgQdG8efO8ywIACnHOfLNmzbKz/ltttdUcx9OQ+zTEb+rUqVHIzJkHoD559tln48orr4ybbropllxyybzLAQAWU2Zd4M582n4uPfDc0rHZF8QDAOrG7CfO004yd9xxhyAPACVmgcP8H//4xzj55JPj888/rz6Wrp922mlx7rnn1nZ9AMBsHnzwwejYsWM2vQ0AKF0LPMx+gw02iPfeey+bp7fyyitnx8aNGxdNmjSJTp06/Wi/20JjmD0Axeree++NAw88MNtR5qijjorrrrsu75IAgGLZmm6vvfZa1NoAgAV05513Rs+ePWPmzJnZonfXXntt3iUBAMXUmS92OvMAFJvbbrst2xa2oqIi2wq2X79+0ajRAp+PBwBKeQE8AGDx6d+/fxbgU5Dv3bt33HjjjYI8ALDgw+zTHxN/+ctfspVz01z56dOnz/H+b775pjbrA4CSlQbPDRgwIGbNmhXHHHNMNrS+YUPn4QGAhejMX3DBBfHnP/85W4Antf3Tyvb77LNP9sfFn/70p7qpEgBKUIMGDeLuu++Ov//974I8ALBoc+ZXW221+Nvf/ha77rprtGjRIl5++eXqY08//XTWQShk5swDUOieeOKJ2HLLLbMwDwCUlvK6mjOf9pRfZ511suvNmzfPPkGy2267xcMPP7woNQNAyUuj37beeus455xzsmH2AAC1EuZXWmmlGD9+fHY9deSHDRuWXX/uueeyveYBgIVz2WWXxSmnnJJd15UHAGo1zO+9997xyCOPZNdPPPHEOPfcc6NTp05x+OGHx5FHHrmgDwcARMSFF14YZ555ZnY9rUHTt29fgR4AqLt95seMGZNdUqDffffdo9CZMw9AIUn/DZ9//vlZmE8uuuiiOPvss/MuCwAo8My6yBvVbr755tkFAFhwf/zjH+Piiy/Orl9++eVx2mmn5V0SAFAEahTmH3jggRo/4B577LEo9QBASUnrz6Th9Gnhu5NOOinvcgCA+jTMvqb72qY/RioqKqKQGWYPQKH53//+F126dMm7DACgvm1NN2vWrBpdCj3IA0De0v+XaV78l19+WX1MkAcA6nw1ewBg4YP8sccem82T33HHHWPGjBl5lwQA1Pcwv8suu2Rt/iqXXnppTJw4sfr2hAkTdBYAYD7S6LW0het1112XTV879dRTo3HjxnmXBQDU9zA/dOjQ+OGHH6pvp5V3v/nmm+rbM2fOjLfffrv2KwSAIpf+jzz88MPjpptuirKyshgwYEAceuiheZcFABSxGm9NN/c6eYu4PT0AlIQ0lD4F9zvuuCMaNWoUAwcOjH333TfvsgCAIrfI+8wDAPOXhtOnIJ+G1N95552x55575l0SAFBKw+zTtnPpMvcxAGD+Tj755OjcuXPcd999gjwAkM8w+969e0eTJk2y29OmTYvf/OY30axZs+z27PPpAaCUpf8zq054r7LKKvH6669nQ+wBAGpLjf+y6NWr1xy357VwT1rcBwBK2XfffRf77bdfHH300bHPPvtkxwR5AKC2NagssZXsysvLo1WrVtk2ey1btsy7HADqkSlTpsTuu+8ejz76aLRp0yY+/PDD7P8cAIDazqxaBQBQCyZPnhy77LJLPPHEE9GiRYt46KGHBHkAoM4I8wCwiNKZ8x49esSYMWOyAD906ND41a9+lXdZAEA9JswDwCL49ttvY6eddornnnsuG1o/fPjw2GijjfIuCwCo54R5AFgE//3vf7Mgv8wyy8SIESNi/fXXz7skAKAECPMAsAhOPfXU+Oqrr7IdXdZZZ528ywEASoTV7AFgAX399dfZ/yWNGzfOuxQAoEQza8PFWhUAFLnPPvsstt566+jZs2fMnDkz73IAgBJlmD0A1NAnn3wS3bp1i3fffTemTp0aX3zxRbRr1y7vsgCAEqQzDwA18NFHH0XXrl2zIL/qqqvG6NGjBXkAIDc68wDwMz744IOsI58CfceOHWPUqFGx8sor510WAFDCcu3MP/bYY7H77rvHiiuuGA0aNIj77rvvJ+//6KOPZveb+/L5558vtpoBKC3vvfde1pFPQb5Tp07Z/12CPABQ0mE+zTdcb7314pprrlmgj3v77bdj/Pjx1ZfllluuzmoEoLR9+umnMWHChFhjjTUMrQcACkauw+x79OiRXRZUCu+tW7euk5oAYHapKz9kyJDo3LlzLL/88nmXAwBQvAvgrb/++tG2bdvo3r17PPnkkz953x9++CHbp2/2CwD8lFdffTXeeOON6tvbbLONIA8AFJSiCvMpwF977bVx9913Z5f27dvHtttuGy+++OJ8P+aSSy6JVq1aVV/SxwDA/Lz00kux3XbbZQvevfPOO3mXAwAwTw0qKysrowCkhezuvffe2GuvvRZ4+GNaiKh///7z7cynS5XUmU+BftKkSdGyZctFrhuA+uP555/PRn1NnDgxNt100xg6dKhpXQDAYpUya2pE/1xmLfqt6dIfW0888cR839+kSZPsAgA/5emnn46ddtop+w90iy22iEGDBmX/kQIAFKKiGmY/Ly+//HI2/B4AFlZaf2XHHXfMgvzWW2+dLXgnyAMAhSzXzvyUKVOy/XurfPjhh1k4X3rppbOh82eddVa2JdDNN9+cvf/qq6+ODh06xFprrRXTpk2L6667LkaOHBnDhg3L8asAoJg999xzWUc+bZea5so/+OCD0axZs7zLAgAo3DCf5iamP5yqnHzyydnbXr16xY033pjtIT9u3Ljq90+fPj1OOeWULOAvtdRSse6668aIESPmeAwAWBBpy7m11147m5N23333Zf+/AAAUuoJZAK/QFhMAoHSk/xPS+ipNmzbNuxQAoMSV1zCzFv2ceQBYUA8//HBcddVV1bfTf5iCPABQTIp+NXsAWBD3339/7L///jFjxozo1KlT7LHHHnmXBACwwHTmASgZd911V+y3335ZkD/ggAOiR48eeZcEALBQhHkASsLAgQPjoIMOipkzZ8YhhxwSt956azRu3DjvsgAAFoowD0C9179//yzAV1RURO/eveOmm26KRo3MNAMAipcwD0C99uabb2Zbns6aNSuOPvrouP7666OsrCzvsgAAFom2BAD12pprrhmXX355fPjhh/H3v/89GjZ0HhsAKH7CPAD1UlrkrmpO/KmnnhqVlZXRoEGDvMsCAKgV2hMA1Dt/+ctfYquttopJkyZVHxPkAYD6RJgHoF5JQ+pPPvnkePbZZ7MV7AEA6iNhHoB6o2/fvnHGGWdk188///z49a9/nXdJAAB1wpx5AIpemg//pz/9Kfr06VMd6s8555y8ywIAqDPCPABFH+RTcL/kkkuy25dddlmcfvrpeZcFAFCnhHkAitpXX30V/fr1y67/+c9/jj/84Q95lwQAUOeEeQCK2nLLLRePPPJIPPnkk3HMMcfkXQ4AwGIhzANQdGbNmhVvvvlmrLXWWtntLl26ZBcAgFJhNXsAii7IH3vssbHxxhtnHXkAgFKkMw9A0aioqIijjjoqbrrppmjYsGGMHz8+75IAAHIhzANQFGbOnBm9evWKAQMGRFlZWdxyyy1x0EEH5V0WAEAuhHkACt6MGTPi0EMPjTvuuCMaNWoUAwcOjH333TfvsgAAciPMA1DQpk+fnnXg77333mjcuHHceeedseeee+ZdFgBAroR5AApamhufQnyTJk3innvuiV122SXvkgAAcifMA1DQ0rD6ND/+1VdfjY022ijvcgAACoKt6QAoON99911cffXV2TZ0SerMC/IAAP8/nXkACsrUqVNj9913j1GjRsXYsWOzUA8AwJyEeQAKxuTJk2PXXXeNxx9/PFq0aBH7779/3iUBABQkYR6AgjBp0qTo0aNHjBkzJlq1ahVDhgyJzTbbLO+yAAAKkjAPQO6+/fbb2HnnnePZZ5+NNm3axLBhw2LjjTfOuywAgIIlzAOQq7TIXdpuLgX5ZZZZJoYPHx4bbLBB3mUBABQ0q9kDkPs+8qeffnqsuOKK2aJ3gjwAwM9rUFlZWRklpLy8PJuLmeZmtmzZMu9yAJhtO7qllloq7zIAAIois+rMA7DYjR8/Pnbaaads67kqgjwAQM0J8wAsVp988kl07do1W+SuV69eUWIDxAAAaoUwD8Bi89FHH2VB/t13341VVlklbrzxxmjQoEHeZQEAFB1hHoDF4sMPP8yC/AcffBAdO3aM0aNHR4cOHfIuCwCgKAnzANS59957L7bZZpusM9+pU6csyKfOPAAAC0eYB6DOnXjiidlc+TXWWCMeffTRWGmllfIuCQCgqAnzANS5m266Kfbff/8syKf95AEAWDSNFvHjAWCeJk6cGK1bt86uL7fccnHHHXfkXRIAQL2hMw9ArXv55ZezufHXX3993qUAANRLwjwAter555+Pbt26xddffx3//e9/Y+bMmXmXBABQ7wjzANSap59+Orbffvv49ttvY/PNN4+hQ4dGo0ZmdAEA1DZhHoBa8eSTT8aOO+4Y5eXlsfXWW2dBvlWrVnmXBQBQLwnzACyytG/8TjvtFJMnT47tttsuBg8eHC1atMi7LACAekuYB6BWwvzUqVOje/fu8dBDD0WzZs3yLgkAoF4zkRGARXbuuefGyiuvHAcddFA0bdo073IAAOo9nXkAFsoTTzwR3333XXa9QYMG0bt3b0EeAGAxEeYBWGD3339/tv3cnnvuGdOmTcu7HACAkiPMA7BA7rrrrthvv/1ixowZsfTSS0dZWVneJQEAlBxhHoAaGzhwYDYvfubMmXHIIYfErbfeGo0bN867LACAkiPMA1Aj/fv3zwJ8RUVFNj/+pptuikaNrKMKAJAHYR6AGgX5Xr16xaxZs+Loo4+O66+/3vB6AIAcaakA8LO6dOkSLVu2jJ49e8Y//vGPaNjQuWAAgDwJ8wD8rI022iheeumlWHXVVbNt6AAAyJfWCgDzlDrwTz/9dPXtDh06CPIAAAVCZx6AH7n88svjjDPOyIbWv/HGG7HSSivlXRIAALPRmQdgDhdddFEW5JM//OEP0a5du7xLAgBgLjrzAGQqKyvjT3/6U/Tp0ye73bdv3zjnnHPyLgsAgHkQ5gHIgnwK7pdcckl2+7LLLovTTz8977IAAJgPYR6AuPHGG6uD/J///OdseD0AAIVLmAcgDj744Bg4cGDsvvvuccIJJ+RdDgAAP0OYByhRs2bNyraaS5emTZvG4MGDo2FD66ICABQDf7UBlGiQP/bYY7N58Wm+fCLIAwAUD515gBJTUVERRx99dDZPPgX4nj17xgYbbJB3WQAALABhHqCEzJw5M3r37h233nprlJWVRf/+/QV5AIAiJMwDlIgZM2bEYYcdFrfffns0atQoBgwYEPvvv3/eZQEAsBCEeYASMH369Gw4/d133x2NGzeOO+64I/baa6+8ywIAYCEJ8wAl4PHHH4977rknllhiiSzQ77bbbnmXBADAIhDmAUrA9ttvHzfccEOssMIKsfPOO+ddDgAAi0iYB6invv/++ygvL4/ll18+u50WvgMAoH6wqTBAPTR16tRsKP12220XX375Zd7lAABQy4R5gHpm8uTJ0aNHjxg5cmR8/PHH8eGHH+ZdEgAAtUyYB6hH0rD6NCc+LXjXsmXLGD58ePzqV7/KuywAAGqZOfMA9cTEiRNjp512imeffTZat26dBfmNN94477IAAKgDwjxAPfDNN99E9+7d48UXX4yll146RowYERtssEHeZQEAUEcMsweoB7777rusM/+LX/wiRo0aJcgDANRzOvMA9cBKK62ULXiXVrHv0qVL3uUAAFDHdOYBitT48ePjoYceqr69yiqrCPIAACVCmAcoQp988kl07do19tprr3jwwQfzLgcAgMVMmAcoMuPGjcuC/LvvvpsNr1977bXzLgkAgMVMmAcoIh9++GEW5D/44IPo2LFjjB49Ojp06JB3WQAALGbCPECReO+997IgP3bs2OjUqVMW5NM8eQAASo/V7AGKwOeff54F+c8++yzWWGONeOSRR2LFFVfMuywAAHKiMw9QBJZffvnYY489Yq211opHH31UkAcAKHE68wBFoEGDBnHNNddEeXl5tG7dOu9yAADImc48QIF6+eWX46ijjooZM2Zktxs2bCjIAwCQ0ZkHKEAvvPBCdO/ePb799ttsSP2FF16Yd0kAABQQnXmAAvPMM8/E9ttvnwX5zTffPE499dS8SwIAoMAI8wAF5Mknn8w68pMmTYqtttoqhg4dGq1atcq7LAAACowwD1AgHnvssdhpp51i8uTJse2228bgwYOjRYsWeZcFAEABEuYBCsDUqVNjv/32y97usMMO8fDDD0fz5s3zLgsAgAIlzAMUgGbNmsXtt98e++yzTzzwwAOx1FJL5V0SAAAFrEFlZWVllJC0R3Oaf5rmo7Zs2TLvcoAS9/3338eSSy6ZdxkAABRZZtWZB8jJ/fffH506dYo33ngj71IAACgywjxADu6+++5sjvynn34a11xzTd7lAABQZIR5gMUszY0/8MADY+bMmdGzZ8/429/+lndJAAAUGWEeYDG65ZZbsgBfUVERvXr1iptvvjkaNWqUd1kAABQZYR5gMbnxxhvj8MMPj1mzZsVRRx0VN9xwQ5SVleVdFgAARUiYB1gMUic+hfe0gchvfvOb+M9//hMNG/oVDADAwjG2E2AxSB34hx56KAv0v//976NBgwZ5lwQAQBHTFgKoQ88991z19bRP6EknnSTIAwCwyIR5gDpy5ZVXxqabbhqXX3553qUAAFDPCPMAdeDiiy+O0047Lbs+ZcqUvMsBAKCeEeYBalFa4O6CCy6Ic845J7vdp0+f7AIAALXJAngAtRjkzz333Ljooouy25dcckmceeaZeZcFAEA9JMwD1JKzzjorLrvssuz6VVddFSeffHLeJQEAUE8J8wC1pG3bttnbv/3tb3HiiSfmXQ4AAPVYrnPmH3vssdh9991jxRVXzLZquu+++372Yx599NHYcMMNo0mTJrH66qvHjTfeuFhqBfg5af/4l19+WZAHAKB+h/mpU6fGeuutF9dcc02N7v/hhx/GrrvuGtttt132B3Par/noo4+OoUOH1nmtAHObNWtWXHrppfHtt99WH0u/0wAAoK41qEwrNhWA1Jm/9957Y6+99prvfc4444x4+OGH4/XXX68+dtBBB8XEiRNjyJAhNfo85eXl0apVq5g0aVK0bNmyVmoHSk9FRUUcc8wx0a9fv9hss83iiSeeiLKysrzLAgCgyNU0sxbV1nRjxoyJHXbYYY5jO+20U3Z8fn744YfsyZj9ArAoZs6cGb17986CfMOGDbNh9YI8AACLU1GF+c8//zyWX375OY6l2ymgf//99/P8mLQ1VDqrUXVp3779YqoWqK9B/rDDDotbbrklC/ADBw6Mnj175l0WAAAlpqjC/MJuFZWGJ1RdPv7447xLAorUjBkzsqk9KcA3btw47rzzzth///3zLgsAgBJUVFvTrbDCCvHFF1/McSzdTvMIllxyyXl+TFr1Pl0AFlUaTn/33XfHEksskb3dbbfd8i4JAIASVVSd+c033zweeeSROY4NHz48Ow5Q1/7whz9Ehw4d4v777xfkAQAo3c78lClT4r333ptj67m05dzSSy8dK6+8cjZE/tNPP42bb745e/9vfvOb+Mc//hGnn356HHnkkTFy5Mi44447shXuAepC2vAj7baRdO7cOd56662sMw8AACXbmX/++edjgw02yC7JySefnF0/77zzstvjx4+PcePGVd8/dcRScE/d+LSX81VXXRXXXXddtqI9QG2bOnVq1oEfNmxY9TFBHgCAQlAw+8wvLvaZB2pi8uTJWZB/7LHHYrnllosPPvggmjVrlndZAADUc+U1zKxFtQAewOL6BdqjR4946qmnsl+g9913nyAPAEBBEeYBZjNx4sTYeeed45lnnonWrVtnQ+w32WSTvMsCAIA5CPMA/+ebb76JHXfcMV544YVsIc4RI0ZUr+kBAACFRJgH+D9//etfsyC/7LLLZttgrrvuunmXBAAA8yTMA/yfc889NyZMmBDHHXdcrLXWWnmXAwAA8yXMAyUthfc0N76srCwaNWoU//jHP/IuCQAACnufeYA8ffrpp7HFFlvE0UcfHbNmzcq7HAAAqDGdeaAkjRs3Lrp16xbvv/9+TJs2Lb788stYYYUV8i4LAABqRGceKDljx46Nrl27ZkG+Q4cO8dhjjwnyAAAUFWEeKCkpwKcgnwL96quvHqNHj45VVlkl77IAAGCBCPNAyXjnnXeyIJ+G2Hfu3DkL8u3bt8+7LAAAWGDCPFBSXfk0N75Lly7x6KOPxoorrph3SQAAsFAsgAeUjB49esRDDz0U66+/fiy33HJ5lwMAAAtNmAfqtVdeeSWaN28eq622WnZ7xx13zLskAABYZIbZA/XWCy+8ENttt122Bd1HH32UdzkAAFBrhHmgXnr22Wdj++23j2+//Tbatm0brVq1yrskAACoNcI8UO889dRTscMOO8SkSZNiyy23jGHDhkXr1q3zLgsAAGqNMA/UK48//njstNNOMXny5GwbuiFDhkTLli3zLgsAAGqVMA/Uq478zjvvHFOmTMmG2A8aNChb/A4AAOobq9kD9Ubnzp2zVevT/vH33ntvLLnkknmXBAAAdUKYB+qNZZZZJkaOHJl145s2bZp3OQAAUGcMsweK2oMPPhj//ve/q28vu+yygjwAAPWezjxQtO6555448MADY+bMmdGxY8fo3r173iUBAMBioTMPFKU77rgjDjjggCzIH3zwwbHddtvlXRIAACw2wjxQdG699dYswFdUVMRhhx0W/fv3j0aNDDQCAKB0CPNAUbnpppuyAD9r1qw48sgjo1+/flFWVpZ3WQAAsFgJ80DReOmll+KII46IysrKOPbYY+O///2vIA8AQEkyLhUoGhtssEGcc845MXHixPjb3/4WDRo0yLskAADIRYPK1OIqIeXl5dGqVauYNGlStGzZMu9ygBpIi9xVzYmv+pUlyAMAUMqZ1TB7oKBdddVV2ZZz3333XXWIF+QBACh1wjxQsC655JI49dRT49FHH822ogMAAP4fYR4oSH369Imzzz67+nrv3r3zLgkAAAqGBfCAgpLmxJ933nnRt2/f6u78mWeemXdZAABQUIR5oKCC/FlnnRWXXXZZdvvKK6+MU045Je+yAACg4AjzQMH47LPPsr3jk7/+9a/xu9/9Lu+SAACgIAnzQMFo165dDB8+PF544YU45phj8i4HAAAKljAP5GrWrFnx3nvvxS9/+cvs9oYbbphdAACA+bOaPZCbioqKrAO/0UYbxdNPP513OQAAUDSEeSC3IH/EEUfEDTfcEN99912MHTs275IAAKBoGGYPLHYzZ86Mww47LAYOHBhlZWUxYMCAOOCAA/IuCwAAioYwDyxWM2bMiIMPPjjuvvvuaNSoUdx+++2xzz775F0WAAAUFWEeWGx++OGHOPDAA+P++++Pxo0bx1133RV77LFH3mUBAEDREeaBxd6Zb9KkSdxzzz2xyy675F0OAAAUJWEeWGxSiE/D61955ZX41a9+lXc5AABQtKxmD9SptFL9v/71r6isrMxuN23aVJAHAIBFpDMP1JkpU6bEbrvtFqNHj45PP/00+vbtm3dJAABQLwjzQJ0oLy/P5sQ/+eST0bJlS/PjAQCgFgnzQK2bOHFi9OjRI55++ulo3bp1DB06NDbddNO8ywIAgHpDmAdq1TfffBM77bRTPP/887H00kvH8OHDY8MNN8y7LAAAqFeEeaDWzJw5szrIL7vssjFixIhYb7318i4LAADqHavZA7WmUaNG8bvf/S7atm0bo0aNEuQBAKCONKis2i+qhBblatWqVUyaNClblAuom1XsmzdvnncZAABQbzOrzjywSD777LPYY489Yvz48dXHBHkAAKhb5swDC+3jjz+Obt26xXvvvRfTp0+PIUOG5F0SAACUBJ15YKGMHTs2unbtmgX5VVddNa699tq8SwIAgJKhMw8ssA8++CC22267GDduXKy22mrZYnft27fPuywAACgZOvPAAnn33Xdjm222yYL8L3/5yxg9erQgDwAAi5kwDyyQY445Jj799NPo0qVLFuTbtWuXd0kAAFByhHlggdxyyy2x5557ZkPrV1hhhbzLAQCAkmTOPPCzJk+eHC1atMiur7TSSnHfffflXRIAAJQ0nXngJ7344ovZIne333573qUAAAD/R5gH5uu5556L7bffPr766qv4+9//HrNmzcq7JAAAQJgH5mfMmDGxww47xMSJE2PLLbeMQYMGRcOGfmUAAEAh8Jc58COPP/547LjjjlFeXp5tQzdkyJBo2bJl3mUBAAD/R5gH5pBWqd95551jypQp2RD71JFv3rx53mUBAACzEeaBOaTw/t1338VOO+0UDz74YDRr1izvkgAAgLnYmg6Yw+WXX56tXt+7d+9o2rRp3uUAAADzoDMPxFNPPRXTp0/Prjdo0CB+85vfCPIAAFDAhHkocffcc0907do1DjjggJgxY0be5QAAADUgzEMJu+OOO7IQP3PmzFhyySWzrjwAAFD4hHkoUbfeemscfPDBUVFREYcddlj0798/GjWyjAYAABQDYR5K0E033ZQF+FmzZsURRxwR/fr1E+QBAKCICPNQYlJwTwG+srIyfv3rX8d1110XZWVleZcFAAAsAK04KDGrr756Nj8+Bfq///3v5skDAEAREuahxGy99dbx0ksvRadOnQR5AAAoUobZQwn4xz/+Ea+88kr17V/+8peCPAAAFDFhHuq5Sy65JE488cTYYYcd4osvvsi7HAAAoBYI81CP9enTJ84+++zsegr0yy+/fN4lAQAAtcCceaiH0kr15513XvTt2ze7ffHFF8dZZ52Vd1kAAEAtEeahHgb5FNwvu+yy7PYVV1wRp556at5lAQAAtUiYh3rm3//+d3WQv/rqq+P3v/993iUBAAC1zJx5qGcOOeSQ2GqrreKaa64R5AEAoJ7SmYd6MrS+aqu5Fi1axKhRo6JRIz/eAABQX+nMQ5GrqKiIo48+unqxu0SQBwCA+s1f/FDkQf6II46I/v37R8OGDWOfffaJLl265F0WAABQx4R5KFIzZ86Mww47LAYOHBhlZWUxYMAAQR4AAEqEMA9FaMaMGdGzZ8+46667siH1t99+e9aVBwAASoMwD0Vm+vTpceCBB8Z9990XjRs3zgL9HnvskXdZAADAYiTMQ5EZNmxYFuSbNGkS99xzT+yyyy55lwQAACxmwjwUmd122y3+/ve/xy9/+cvYcccd8y4HAADIgTAPReC7776LadOmxdJLL53dPuGEE/IuCQAAyJF95qHATZkyJXbdddfo3r17TJw4Me9yAACAAiDMQwGbPHly9OjRIx599NF499134/3338+7JAAAoAAI81CgJk2alM2Jf+KJJ6JVq1YxYsSI2GijjfIuCwAAKADmzEMB+vbbb7Mg//zzz0ebNm2yIL/hhhvmXRYAAFAghHkoMBMmTIgddtghXn755Vh22WWzIL/eeuvlXRYAAFBAhHkowOH1X375ZSy33HLxyCOPxNprr513SQAAQIER5qHAdOzYMUaOHBmzZs2KNddcM+9yAACAAmQBPCgAn332WTacvkrnzp0FeQAAYL6EecjZxx9/HF27do3ddtstG1YPAADwc4R5yNHYsWOzIP/ee+9F27ZtY7XVVsu7JAAAoAiYMw85+eCDD2K77baLcePGZSF+1KhR0b59+7zLAgAAioDOPOTg3XffjW222SYL8r/85S9j9OjRgjwAAFBjOvOQ0xz58ePHZ4vcpZXrV1hhhbzLAgAAiojOPCxmK664YnTr1i3WWWedePTRRwV5AABggenMw2JWVlYWN954Y0yePDnatGmTdzkAAEAR0pmHxeDFF1+ME088MSoqKrLbjRo1EuQBAICFpjMPdey5556LHXfcMSZOnJgNsT/rrLPyLgkAAChyBdGZv+aaa2LVVVeNpk2bxq9+9at49tln53vfNDy5QYMGc1zSx0EhGjNmTOywww5ZkN9yyy3j+OOPz7skAACgHsg9zN9+++1x8sknx/nnn58NRV5vvfVip512ii+//HK+H9OyZctsJfCqy0cffbRYa4aaeOKJJ7KOfHl5ebYN3ZAhQ7LXLgAAQNGH+T//+c9xzDHHxBFHHBFdunSJa6+9NpZaaqm44YYb5vsxqRufVgCvuiy//PKLtWb4OWmV+p133jmmTJmSrVw/aNCgaN68ed5lAQAA9USuYX769OnxwgsvZMOQqwtq2DC7nYYnz08KSKusskq0b98+9txzz3jjjTfme98ffvgh64zOfoG6NGnSpNh7771j6tSpWWf+oYceimbNmuVdFgAAUI/kGua//vrrbHXvuTvr6fbnn38+z4/p3Llz1rW///7745ZbbolZs2bFFltsEZ988sk873/JJZdEq1atqi/pBADUpfQ669+/f+y1117Z63TJJZfMuyQAAKCeaVBZWVmZ1yf/7LPPol27dvHUU0/F5ptvXn389NNPj9GjR8czzzzzs48xY8aMWHPNNePggw+OCy+8cJ6d+XSpkjrzKdCn7qn5y9Sm9Dpr0qRJ9e30o5WmhAAAANRUyqypQfhzmTXXzvyyyy4bZWVl8cUXX8xxPN1Oc+FronHjxrHBBhvEe++9N8/3p3CVnoDZL1Db7r333uyk0vvvv199TJAHAADqSq5hfoklloiNNtooHnnkkepjadh8uj17p/6npGH6r732WrRt27YOK4X5u/POO2P//fePDz/8MP7+97/nXQ4AAFACGuVdQNqWrlevXrHxxhvHpptuGldffXW2cFha3T45/PDDs6H4ae570qdPn9hss81i9dVXz/buvuKKK7Kt6Y4++uicvxJK0W233RaHHXZYdlLp0EMPjSuvvDLvkgAAgBKQe5g/8MAD46uvvorzzjsvW/Ru/fXXz/bjrloUb9y4cdkK91W+/fbbbCu7dN82bdpknf005z5taweL080335yddEqjSXr37h3XXXddNm0EAACgXi+AV8iLCcBPSTsqpNEg6ccnnVy69tpr5zjpBAAAUG8XwINiNHPmzPjnP/+ZBfnf/va3gjwAAFB6w+yh2DRq1CiGDh0a/fr1i1NOOcWq9QAAwGKnnQg19PLLL1dfX2aZZeLUU08V5AEAgFwI81ADl112WWywwQbZkHoAAIC8CfPwM/r27Rtnnnlmdv2LL77IuxwAAABz5mF+0gJ3559/flx44YXZ7YsuuijOPvvsvMsCAAAQ5mF+QT4F90svvTS7ffnll8dpp52Wd1kAAAAZYR7mEeRTcL/qqquy23/5y1/ipJNOyrssAACAasI8zCWtUN+yZcvs+j/+8Y84/vjj8y4JAABgDg0qUxuyhJSXl0erVq1i0qRJ1YEN5uX555+PjTfeOO8yAACAElJew8xqNXuIiFmzZsWVV14ZU6ZMqT4myAMAAIVKmKfkVVRUxJFHHpnNk99rr72yOfMAAACFzJx5StrMmTOjV69eMWDAgCgrK4tjjjkmmzMPAABQyIR5StaMGTPikEMOiTvvvDMaNWoUAwcOjH333TfvsgAAAH6WME9Jmj59ehx00EFx7733RuPGjbNAv+eee+ZdFgAAQI0I85SkY489NgvyTZo0iXvuuSd22WWXvEsCAACoMQvgUZJOOumkaNeuXTzwwAOCPAAAUHR05ilJ6623Xrz33nvRtGnTvEsBAABYYDrzlIS0f/zuu+8eTzzxRPUxQR4AAChWOvPUe5MnT86G0qcg/9JLL+nIAwAARU+Yp16bNGlS7LzzzvH0009Hq1at4u677xbkAQCAoifMU299++23sdNOO8Vzzz0Xbdq0ieHDh8dGG22Ud1kAAACLTJinXpowYUJ07949G1a/zDLLxIgRI2L99dfPuywAAIBaIcxTL1166aVZkP/FL34RjzzySKyzzjp5lwQAAFBrhHnqpb59+8bXX38dp512WnTp0iXvcgAAAGqVME+9miPfunXraNCgQTRp0iT69es3x/srZlXGsx9+E19OnhbLtWgam3ZYOsoaNsitXgAAgIUlzFMvfPLJJ9GtW7fo0aNHXH311Vmgn92Q18fHBQ/+L8ZPmlZ9rG2rpnH+7l1i57Xb5lAxAADAwmu4CB8LBeGjjz6Krl27xrvvvhsPPPBAtvjd3EH+uFtenCPIJ59PmpYdT+8HAAAoJsI8Re2DDz7Ignx6u9pqq8Xo0aNj2WWXnWNoferIV87jY6uOpfen+wEAABQLYZ6i9d5772VBPnXmO3XqlAX5lVdeeY77pDnyc3fkZ5cifHp/uh8AAECxEOYpSm+//XYW5NNc+TXWWCML8u3atfvR/dJidzVR0/sBAAAUAmGeovT666/H559/HmuvvXY8+uij0bbtvBexS6vW10RN7wcAAFAIhHmK0r777hv33HNPjBw5MpZffvn53i9tP5dWrZ/fBnTpeHp/uh8AAECxEOYpGq+88kp8+umn1bf33HPP+MUvfvGTH5P2kU/bzyVzB/qq2+n99psHAACKiTBPUXj++edju+22yy7jxy/YVnJpH/l/HbphrNBqzqH06XY6bp95AACg2DTKuwD4OU8//XTstNNOUV5eni12t9RSSy3wY6TA3r3LCtmq9WmxuzRHPg2t15EHAACKkTBPQXvyySejR48eMXny5Nh6663j4YcfjhYtWizUY6Xgvvlqy9R6jQAAAIubYfYUrLTdXOrIpyCfhtcPHjx4oYM8AABAfSLMU5Aef/zxrCM/derU6N69ezz00EPRrFmzvMsCAAAoCIbZU5BWX331WGmllaJTp05x9913R9Om9oEHAACoIsxTkNq2bRuPPfZYtGnTJpo0aZJ3OQAAAAXFMHsKxv333x+33npr9e0VVlhBkAcAAJgHnXkKwl133RUHH3xwzJo1K1ZZZZXYaqut8i4JAACgYOnMk7uBAwfGQQcdFDNnzswC/WabbZZ3SQAAAAVNmCdX/fv3j0MOOSQqKiqid+/ecdNNN0WjRgaMAAAA/BRhntzccMMN0atXr2xo/dFHHx3XX399lJWV5V0WAABAwRPmycWYMWPiqKOOisrKyvjtb38b//73v6NhQy9HAACAmjCemVykefG/+93vokGDBvGXv/wlewsAAEDNCPMsVmlufBpKn8L71VdfnR0T5AEAABaMcc0sNpdffnnsueee8cMPP1SHeEEeAABgwQnzLBZ9+/aNM844Ix5++OG499578y4HAACgqAnz1Km0wN35558f5557bnWoT3vKAwAAsPDMmadOg/w555wTl1xySXb7sssui9NPPz3vsgAAAIqeME+dBfkU3K+88srsdlqx/qSTTsq7LAAAgHpBmKdOjB07Nq699trs+j/+8Y84/vjj8y4JAACg3hDmqRMdOnSIwYMHx1tvvRVHH3103uUAAADUKw0q03joElJeXh6tWrWKSZMmRcuWLfMup16ZNWtWfPTRR1mQBwAAoO4yq9XsC1TFrMoY8/6EuP/lT7O36XYhq6ioiKOOOio23njjeOWVV/IuBwAAoF4zzL4ADXl9fFzw4P9i/KRp1cfatmoa5+/eJXZeu20UmpkzZ0bv3r3j1ltvjbKysnjnnXdivfXWy7ssAACAektnvgCD/HH/X3t3Ah1Vefdx/J8QQgwkYTUhLLJICcgiCNggSguRsIiiQoGCpED1FVCpeiqba12QTTwIIlgLtoBBBFEicJpCwLJGdimLqAERiKgsCSAEyH3P//GdOTMhQHg78d478/2cM0zuvc9M7nCeM7m/+2xztvgFeZV78qzZr8ed5Pz589K/f38T5CMiIiQ9PV169epl92kBAAAAQFAjzDuIdqXXFvniOtR79ulxp3S5LygokD59+sj8+fOlbNmy8sEHH0jPnj3tPi0AAAAACHqEeQfJzjl2SYu8L43welzL2e3cuXOmBX7RokUSGRlpnu+55x67TwsAAAAAQgJj5h3kaP7ZgJYr7QnvdHbFqKgoWbx4saSmptp9SgAAAAAQMgjzDnJ9TFRAy5Wm6OhoycjIkM8//1ySk5PtPh0AAAAACCl0s3eQNnUrm1nrwy5zXPfrcS1nh9OnT8vs2bO92xUqVCDIAwAAAIANCPMOUiY8zCw/p4oGes+2Htdyv7T8/Hzp0qWLDBw4UMaNG+fdr5Pxrf/qR/lo2yHz7JTJ+QAAAAAgmNHN3mF0Hfnp/Vtess58go3rzOvYeA3y69evl9jYWGnfvr3Zr8vkFT3P6jaeJwAAAACEijDLskKqKTUvL0/i4uJMQNVg6lTawq2z1utkdzpGXrvW29Eif/z4cencubNkZ2dLxYoVJTMzU1q1amWCvK57X7TyeM5Qb0gQ6AEAAACgdDIrLfMOpcE9uX4VW8/hxx9/lE6dOsmWLVukcuXK8q9//UtatGhhbjRoi3xxd4Gs/wv0evzOxgm23IAAAAAAgGDHmHkUq6CgQFJSUkyQr1atmmRlZZkgr7THgG/X+uICvR7XcgAAAACAwCPMo1iRkZHy4IMPSkJCgqxatUqaNWt2zevcl7QcAAAAAODaEOZxWUOHDpU9e/ZI48Y/z7B/revcl7QcAAAAAODaEObh9e2330rPnj3NWHkPnXihKJ2MT2etv9xoeN2vx7UcAAAAACDwCPMwDhw4YJacW7hwoelefyU6qZ0uP6eKBnrPth5n8jsAAAAAKB2EeUhOTo4J8l9//bXUq1dPJk+efNXX6LJzuvxcQpx/V3rdZlk6AAAAAChdLE0X4r788kvp0KGDHDx4UBo0aCArV66UmjVrlui1Gth1+TmdtV4nu9Mx8tq1nhZ5AAAAAChdhPkQtnfvXhPkDx8+LElJSbJixQpJTEy8pvfQ4J5cv0qpnSMAAAAA4FJ0sw9RlmXJgAEDTJC/6aabzPJz1xrkAQAAAAD2IMyHqLCwMJk7d6507dpVsrKyJD4+3u5TAgAAAACUEN3sQ8yZM2ckOjra/HzjjTfKJ598YvcpAQAAAACuES3zIWTz5s1Sv359AjwAAAAAuBxhPkRs3LhROnbsKLm5uTJx4kQzZh4AAAAA4E6E+RCwdu1aufPOO+XkyZPSrl07+fjjj82YeQAAAACAOxHmg9zq1aslNTVV8vPz5Te/+Y0sX75cYmJi7D4tAAAAAMB/gTAfxFauXCldunSR06dPS0pKihkrX758ebtPCwAAAADwXyLMB7H3339ffvrpJ+ncubMsWbLEO4s9AAAAAMDdWJouiE2bNk0aNWokDz/8sJQrV87u0wEAAAAABAgt80E4a/2FCxfkYqEl2ftPSJ32PWXLt6fMNgAAAAAgONAyH0QWLlwoffr0kY5pT8qJeimSm3fOe6x6XJQ8172xdG5S3dZzBAAAAAD892iZDxLp6enSu3dvKVuvteyp0s4vyKvck2dlyJwtsnznEdvOEQAAAAAQGIT5IDBnzhzp16+f6Upf6+7HRYpZQ97Tyf6FJbvocg8AAAAALkeYd7lZs2bJgAEDpLCwUO77n6fkXJnLz1ivEf7IybOSnXPsFz1HAAAAAEBgEeZd7O2335ZBgwaJZVkyZMgQeeChYSV63dH8s6V+bgAAAACA0sMEeC6lXeXPVKgpsU07yF0db5cpE5+Vz/YfL9Frr4+JKvXzAwAAAACUHlrmXUgnsWs3bqVM3l4olbo+IWvLtZbbx2fJ8dPnzKz1l46Y/5nu1+Nt6lb+hc8YAAAAABBIhHmXGT5hljw8Z4sZ+150tvph87bK3c1/XnquaKD3bOvydGXCLxf3AQAAAABuQJh3kRdfelkW5oSZMfJFefZ8vP2ITPt9S0mI8+9Kr9vT+7dknXkAAAAACAKMmXcBDe9/+ctfZOw7CyXh92OvOlt9pfKRsmZEBzNrvU52p2PktWs9LfIAAAAAEBwI8y4I8qPHPC2T5yyR6IZtS/QaDfAa3JPrVyn18wMAAAAA/PII8w4P8v1GTJBPTzW4Yot8UcxWDwAAAADBjTDvYI+Me0fWhjWWMjElKx/2f2Pjma0eAAAAAIIbYd6hlu44LMtOVpewEg5zZ7Z6AAAAAAgdhHmHriM/dN7Wa3qNtshrkGe2egAAAAAIfoR5h7lYaMkLS3aVuPyA5BukS5PqzFYPAAAAACGEMO8wupycLi9XUhrkmbUeAAAAAEJLuN0ngEuXlSup6kx2BwAAAAAhiTDvMNeyrByT3QEAAABAaHJEmJ82bZrUqVNHoqKi5NZbb5Xs7Owrll+wYIEkJSWZ8k2bNpWlS5dKsNCWdm1xv1JE1/z+5u9bMtkdAAAAAIQo28P8/Pnz5YknnpDnnntOtmzZIs2bN5fU1FQ5evRoseXXrVsnffv2lcGDB8vWrVulR48e5rFz504JBtrSri3u6nKBfmrfFtK1GUEeAAAAAEJVmGVZlp0noC3xrVu3lqlTp5rtwsJCqVWrljz66KMycuTIS8r37t1bTp8+LRkZGd59v/71r+Xmm2+Wt95666q/Ly8vT+Li4uTkyZMSGxsrTl6eTme1950MT1vsWX4OAAAAAIJXSTOrrbPZFxQUyObNm2XUqFHefeHh4ZKSkiLr168v9jW6X1vyfWlL/uLFi4stf+7cOfPw/Y9xAw3sdzZOMLPb66R4Opae5ecAAAAAALaH+R9++EEuXrwo8fHxfvt1e8+ePcW+Jjc3t9jyur84Y8eOlRdeeEHcSIM7y84BAAAAABw3Zr60aau/dk/wPA4ePGj3KQEAAAAA4N6W+apVq0qZMmXku+++89uv2wkJCcW+RvdfS/ly5cqZBwAAAAAAwcLWlvnIyEi55ZZbZMWKFd59OgGebicnJxf7Gt3vW15lZmZetjwAAAAAAMHG1pZ5pZPZpaWlSatWraRNmzby+uuvm9nqBw4caI4PGDBAatSoYca+q+HDh0v79u1l0qRJ0q1bN0lPT5dNmzbJzJkzbf4kAAAAAACESJjXpea+//57efbZZ80kdrrE3PLly72T3H3zzTdmhnuPtm3byrx58+Tpp5+W0aNHS4MGDcxM9k2aNLHxUwAAAAAAEELrzP/S3LLOPAAAAAAg9OSVMLMG/Wz2AAAAAAAEG8I8AAAAAAAuQ5gHAAAAAMBlCPMAAAAAALgMYR4AAAAAAJchzAMAAAAA4DKEeQAAAAAAXIYwDwAAAACAyxDmAQAAAABwGcI8AAAAAAAuQ5gHAAAAAMBlCPMAAAAAALgMYR4AAAAAAJchzAMAAAAA4DKEeQAAAAAAXIYwDwAAAACAyxDmAQAAAABwGcI8AAAAAAAuQ5gHAAAAAMBlCPMAAAAAALgMYR4AAAAAAJchzAMAAAAA4DKEeQAAAAAAXIYwDwAAAACAyxDmAQAAAABwGcI8AAAAAAAuQ5gHAAAAAMBlCPMAAAAAALhMhIQYy7LMc15ent2nAgAAAACAH09W9WTXywm5MJ+fn2+ea9WqZfepAAAAAABw2ewaFxdX/EERCbOuFveDTGFhoRw+fFhiYmIkLCxMnHw3Rm84HDx4UGJjY+0+HeCyqKtwC+oq3IT6CregrsIt8lxUVzWia5BPTEyU8PDLj4wPuZZ5/c+oWbOmuIVWNKdXNkBRV+EW1FW4CfUVbkFdhVvEuqSuXqlF3oMJ8AAAAAAAcBnCPAAAAAAALkOYd6hy5crJc889Z54BJ6Ouwi2oq3AT6ivcgroKtwjGuhpyE+ABAAAAAOB2tMwDAAAAAOAyhHkAAAAAAFyGMA8AAAAAgMsQ5gEAAAAAcBnCvENNmzZN6tSpI1FRUXLrrbdKdna23aeEEDJ27Fhp3bq1xMTEyPXXXy89evSQvXv3+pU5e/asDBs2TKpUqSIVKlSQ+++/X7777ju/Mt98841069ZNoqOjzfv8+c9/lgsXLvzCnwah5NVXX5WwsDD505/+5N1HXYVTHDp0SPr372/q4nXXXSdNmzaVTZs2eY/rnMTPPvusVK9e3RxPSUmRffv2+b3HsWPHpF+/fhIbGysVK1aUwYMHy6lTp2z4NAhmFy9elGeeeUbq1q1r6mL9+vXlxRdfNHXUg/oKO3z66afSvXt3SUxMNH/vFy9e7Hc8UPVyx44dcvvtt5ssVqtWLRk/frw4EWHegebPny9PPPGEWTphy5Yt0rx5c0lNTZWjR4/afWoIEatXrzbhZ8OGDZKZmSnnz5+XTp06yenTp71lHn/8cVmyZIksWLDAlD98+LDcd999fhcCGo4KCgpk3bp18u6778rs2bPNFyxQGj777DOZMWOGNGvWzG8/dRVOcPz4cbntttukbNmysmzZMtm1a5dMmjRJKlWq5C2jF4tTpkyRt956SzZu3Cjly5c3f//1hpSHXoD+5z//Md/NGRkZ5sL2oYcesulTIViNGzdOpk+fLlOnTpXdu3ebba2fb7zxhrcM9RV20GtRzUba8FmcQNTLvLw8c917ww03yObNm2XChAny/PPPy8yZM8VxdGk6OEubNm2sYcOGebcvXrxoJSYmWmPHjrX1vBC6jh49qrfirdWrV5vtEydOWGXLlrUWLFjgLbN7925TZv369WZ76dKlVnh4uJWbm+stM336dCs2NtY6d+6cDZ8CwSw/P99q0KCBlZmZabVv394aPny42U9dhVOMGDHCateu3WWPFxYWWgkJCdaECRO8+7T+litXznrvvffM9q5du0zd/eyzz7xlli1bZoWFhVmHDh0q5U+AUNKtWzdr0KBBfvvuu+8+q1+/fuZn6iucQESsDz/80LsdqHr55ptvWpUqVfK7BtDv8IYNG1pOQ8u8w2jLkN4B0i4hHuHh4WZ7/fr1tp4bQtfJkyfNc+XKlc2z1lFtrfetp0lJSVK7dm1vPdVn7UIaHx/vLaN3RvVup94NBQJJe5Jo67pvnVTUVTjFxx9/LK1atZJevXqZoRwtWrSQt99+23s8JydHcnNz/epqXFycGWrnW1e1S6i+j4eW1+sEbYECAqVt27ayYsUK+eKLL8z29u3bZc2aNdKlSxezTX2FE+UEqF5qmTvuuEMiIyP9rgt0yKn2snKSCLtPAP5++OEH0+XT96JS6faePXtsOy+ErsLCQjP+WLuHNmnSxOzTL0r9gtMvw6L1VI95yhRXjz3HgEBJT083Q5K0m31R1FU4xddff226LeswutGjR5v6+thjj5n6mZaW5q1rxdVF37qqNwJ8RUREmBut1FUE0siRI80NTb35WaZMGXNt+vLLL5vuyYr6CifKDVC91GedL6Loe3iO+Q6PshthHsBVWzx37txp7sgDTnPw4EEZPny4Gfemk9QATr4xqi1Br7zyitnWlnn9btVxnRrmASd5//33Ze7cuTJv3jy56aabZNu2bebGvk46Rn0FnINu9g5TtWpVcwe06EzLup2QkGDbeSE0PfLII2ZikKysLKlZs6Z3v9ZFHRJy4sSJy9ZTfS6uHnuOAYGg3eh1ctCWLVuaO+v60EnudPIb/VnvpFNX4QQ6s3Ljxo399jVq1MispOBb167091+fi06Gq6su6MzM1FUEkq7ooa3zffr0McOQHnjgATOZqK52o6ivcKKEANVLN10XEOYdRrvb3XLLLWacku/dfN1OTk629dwQOnROEQ3yH374oaxcufKSrkZaR3VGZt96quOI9KLUU0/1+fPPP/f7wtTWU10GpOgFLfD/1bFjR1PPtNXI89DWT+0K6vmZugon0KFKRZf41PHIOluy0u9ZvUj0ravazVnHcPrWVb0xpTexPPQ7Wq8TdEwoEChnzpwxY4h9aWOT1jVFfYUT1Q1QvdQyOsO9zrnje13QsGFDR3WxN+yegQ+XSk9PN7Muzp4928y4+NBDD1kVK1b0m2kZKE1Dhgyx4uLirFWrVllHjhzxPs6cOeMt8/DDD1u1a9e2Vq5caW3atMlKTk42D48LFy5YTZo0sTp16mRt27bNWr58uVWtWjVr1KhRNn0qhArf2ewVdRVOkJ2dbUVERFgvv/yytW/fPmvu3LlWdHS0NWfOHG+ZV1991fy9/+ijj6wdO3ZY99xzj1W3bl3rp59+8pbp3Lmz1aJFC2vjxo3WmjVrzCoOffv2telTIVilpaVZNWrUsDIyMqycnBxr0aJFVtWqVa2nnnrKW4b6CrtWr9m6dat5iIj12muvmZ8PHDgQsHqpM+DHx8dbDzzwgLVz506TzfT7esaMGZbTEOYd6o033jAXn5GRkWapug0bNth9Sggh+uVY3GPWrFneMvqlOHToULN0h37B3XvvvSbw+9q/f7/VpUsX67rrrjMXAU8++aR1/vx5Gz4RQjnMU1fhFEuWLDE3jvSGfVJSkjVz5ky/47qs0jPPPGMuIrVMx44drb179/qV+fHHH81FZ4UKFczyiQMHDjQXt0Ag5eXlme9RvRaNioqy6tWrZ40ZM8ZvqS7qK+yQlZVV7DVqWlpaQOvl9u3bzXKi+h56Y0tvEjhRmP5jd+8AAAAAAABQcoyZBwAAAADAZQjzAAAAAAC4DGEeAAAAAACXIcwDAAAAAOAyhHkAAAAAAFyGMA8AAAAAgMsQ5gEAAAAAcBnCPAAAAAAALkOYBwAAjlCnTh15/fXX7T4NAABcgTAPAIDDde/eXTp37lzssX//+98SFhYmO3bskP3795ufy5QpI4cOHfIrd+TIEYmIiDDHtRwAAHA3wjwAAA43ePBgyczMlG+//faSY7NmzZJWrVpJs2bNvPtq1Kghf//73/3Kvfvuu2Z/qCkoKLD7FAAAKBWEeQAAHO6uu+6SatWqyezZs/32nzp1ShYsWGDCvq+0tDQT8n3ptu4vSVf3V155RQYNGiQxMTFSu3ZtmTlzpvf4qlWrTOv+iRMnvPu2bdvm1+Kv51mxYkXJyMiQhg0bSnR0tPTs2VPOnDljbiro76hUqZI89thjcvHiRb/fn5+fL3379pXy5cubmw/Tpk3zO66/949//KP5/4iNjZUOHTrI9u3bvceff/55ufnmm+Wvf/2r1K1bV6Kioq76mQEAcCPCPAAADqfd4wcMGGBCsmVZ3v0a5DUMa/j1dffdd8vx48dlzZo1ZlufdVu765fEpEmTTGv/1q1bZejQoTJkyBDZu3fvNZ2zBvcpU6ZIenq6LF++3NwEuPfee2Xp0qXm8Y9//ENmzJghH3zwgd/rJkyYIM2bNze/e+TIkTJ8+HDTK8GjV69ecvToUVm2bJls3rxZWrZsKR07dpRjx455y3z55ZeycOFCWbRokbnRAABAMCLMAwDgAtpS/tVXX8nq1av9Wtvvv/9+iYuL8ytbtmxZ6d+/v/ztb38z2/qs27q/JLp27WpC/I033igjRoyQqlWrSlZW1jWd7/nz52X69OnSokULueOOO0zLvN5UeOedd6Rx48amt8Fvf/vbS973tttuMyH+V7/6lTz66KPmdZMnTzbH9PXZ2dnmJobebGjQoIFMnDjR9ALwvSmgXet1mIH+bt/hBwAABBPCPAAALpCUlCRt27b1BnRtfdbJ74p2sfcN/xp6c3NzzbNul5RvANbu8wkJCaY1/Fpo1/r69et7t+Pj4033+goVKvjtK/q+ycnJl2zv3r3b/Kzd6XVoQZUqVcz7eB45OTnmRofHDTfcYLrhAwAQzCLsPgEAAFAyGty1tVrHkWurvIbl9u3bF1u2adOm5gaAdsFv1KiRNGnSpMRdzou24GugLywsND+Hh//cDuDb3V9b4UvyHld635LQIF+9enXTZb8obZ330PH2AAAEO1rmAQBwid/97ncmTM+bN890I9fWdg3El6PHNfheS6v81XhavHWpO49AjkvfsGHDJdt6M0Lp+HjtaaBzCOgQAN+HDgUAACCUEOYBAHAJ7VLeu3dvGTVqlAnTf/jDH65Y/sEHH5Tvv//ezP4eKBqca9WqZWaN37dvn3zyySdmwrxAWbt2rYwfP16++OIL0wNBhwjoJHgqJSXFdLvv0aOH/POf/zSz569bt07GjBkjmzZtCtg5AADgBoR5AABc1tVeZ6ZPTU2VxMTEK5bVFmxtsdbnQNGu8u+9957s2bPHjK0fN26cvPTSSwF7/yeffNIEc528Tt/3tddeM59VaS8EnQlfJ9QbOHCgmSSvT58+cuDAATP+HgCAUBJm+Q56AwAAAAAAjkfLPAAAAAAALkOYBwAAAADAZQjzAAAAAAC4DGEeAAAAAACXIcwDAAAAAOAyhHkAAAAAAFyGMA8AAAAAgMsQ5gEAAAAAcBnCPAAAAAAALkOYBwAAAADAZQjzAAAAAACIu/wvgWUq2H9Z0HoAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 1000x1000 with 1 Axes>"
       ]
@@ -158,7 +158,7 @@
     "\n",
     "# mean results with error bars\n",
     "ax.errorbar(vm_nbs, means, errors, fmt='o', linewidth=2, capsize=6)\n",
-    "ax.set_title('Performance analysis for VM assessment')\n",
+    "ax.set_title('Performance analysis for VM assessment with cache and evaluator fix')\n",
     "ax.set_ylabel('Elapsed time [s]')\n",
     "ax.set_xlabel('VM number')\n",
     "\n",
@@ -167,12 +167,112 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 144,
+   "id": "20caf548-ab01-424e-aabb-090cb14b5a3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filename = '001_perf_analysis_vm_assess_with_cache_and_eval_fix_04'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
    "id": "1917911c-2184-448f-92d5-283695a5c428",
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig.savefig('perf_analysis_vm_assess.png') "
+    "fig.savefig(filename+'.png') "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "22c2645b-faf7-4be5-8202-1a58a1bbe336",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# save results\n",
+    "import pickle\n",
+    "with open(filename, \"wb\") as fp:   #Pickling\n",
+    "    pickle.dump(results, fp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "id": "3b431c21-9c01-4c76-a7d7-31a174725301",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load all results\n",
+    "\n",
+    "with open(\"001_perf_analysis_vm_assess_without_cache_and_with_eval_fix_02\", \"rb\") as fp:   # Unpickling\n",
+    "    wo_cache = pickle.load(fp)\n",
+    "\n",
+    "with open(\"001_perf_analysis_vm_assess_without_cache_and_extended_oracle_cache_and_with_eval_fix_02\", \"rb\") as fp:   # Unpickling\n",
+    "    wo_cache_and_extended_oracle_cache = pickle.load(fp)\n",
+    "\n",
+    "with open(\"001_perf_analysis_vm_assess_with_cache_and_with_eval_fix_02\", \"rb\") as fp:   # Unpickling\n",
+    "    w_cache = pickle.load(fp)    \n",
+    "\n",
+    "with open(\"001_perf_analysis_vm_assess_with_cache_and_extended_oracle_cache_and_with_eval_fix_02\", \"rb\") as fp:   # Unpickling\n",
+    "    w_cache_and_extended_oracle_cache = pickle.load(fp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "id": "b84762e1-90c0-43f0-b149-a7ddd706e927",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def plot_results(results, ax, label):\n",
+    "    means = [np.mean(x) for x in results]\n",
+    "    errors = [np.std(x, ddof=1) / np.sqrt(len(x)) for x in results]\n",
+    "\n",
+    "    # linear regression\n",
+    "    coef = np.polyfit(vm_nbs,means,1)\n",
+    "    poly1d_fn = np.poly1d(coef) \n",
+    "    ax.plot(vm_nbs, poly1d_fn(vm_nbs), '--', label=label)\n",
+    "    ax.errorbar(vm_nbs, means, errors, fmt='o', linewidth=2, capsize=6)\n",
+    "    \n",
+    "# Plot the graph\n",
+    "fig, ax = plt.subplots(figsize=(10, 10), layout='constrained')\n",
+    "#plot_results(wo_cache, ax, 'without cache')\n",
+    "plot_results(w_cache, ax, 'with cache')\n",
+    "#plot_results(wo_cache_and_extended_oracle_cache, ax, 'without cache, with extended oracle cache')\n",
+    "plot_results(w_cache_and_extended_oracle_cache, ax, 'with cache, with extended oracle cache')\n",
+    "ax.legend()\n",
+    "\n",
+    "\n",
+    "# mean results with error bars\n",
+    "ax.set_title('Performance analysis for VM assessment')\n",
+    "ax.set_ylabel('Elapsed time [s]')\n",
+    "ax.set_xlabel('VM number')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "id": "d80677c5-61df-4438-b9d7-b9d420b2d640",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig.savefig('001_perf_analysis_vm_assess_w_cache_and_extended_01.png') "
    ]
   }
  ],
@@ -192,7 +292,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.0"
+   "version": "3.13.3"
   }
  },
  "nbformat": 4,
diff --git a/perfanalysis/perf_analysis_vm_assess_with_cach_on_server_phases.png b/perfanalysis/perf_analysis_vm_assess_with_cach_on_server_phases.png
new file mode 100644
index 0000000..e6ff737
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diff --git a/perfanalysis/perf_analysis_vm_assess_with_cached_for_each_phases.png b/perfanalysis/perf_analysis_vm_assess_with_cached_for_each_phases.png
new file mode 100644
index 0000000..d1f68b7
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diff --git a/perfanalysis/perf_analysis_vm_assess_with_pools_cache.png b/perfanalysis/perf_analysis_vm_assess_with_pools_cache.png
new file mode 100644
index 0000000..952c6b3
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diff --git a/perfanalysis/perf_analysis_vm_assess_with_server_phases_with_direct_impacts.png b/perfanalysis/perf_analysis_vm_assess_with_server_phases_with_direct_impacts.png
new file mode 100644
index 0000000..fa0977b
Binary files /dev/null and b/perfanalysis/perf_analysis_vm_assess_with_server_phases_with_direct_impacts.png differ
diff --git a/perfanalysis/perf_analysis_vm_assess_without_cache.png b/perfanalysis/perf_analysis_vm_assess_without_cache.png
new file mode 100644
index 0000000..5b14161
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diff --git a/perfanalysis/server_data_creator.ipynb b/perfanalysis/server_data_creator.ipynb
new file mode 100644
index 0000000..08769a8
--- /dev/null
+++ b/perfanalysis/server_data_creator.ipynb
@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c35fd21b-7cc2-408f-84fc-f7f99de9c7d2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CSV file 'workers.csv' created with 2000 workers.\n",
+      "CSV file 'workers_impacts.csv' created with 2000 hardware entries.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import csv\n",
+    "import random\n",
+    "\n",
+    "def generate_workers_csv(n, filename=\"workers.csv\"):\n",
+    "    header = [\n",
+    "        \"id\",\"pool_id\",\"dc_id\",\"power\",\"lifespan\",\"hdd_quantity\",\"hdd_unit_size\",\n",
+    "        \"ssd_quantity\",\"ssd_unit_size\",\"ram_quantity\",\"ram_unit_size\",\"gpu_quantity\",\n",
+    "        \"gpu_model_name\",\"fan_quantity\",\"psu_total_weight\",\"load_rate\",\"comment\",\"quantity\"\n",
+    "    ]\n",
+    "    \n",
+    "    with open(filename, mode=\"w\", newline=\"\") as file:\n",
+    "        writer = csv.writer(file)\n",
+    "        writer.writerow(header)\n",
+    "        \n",
+    "        for i in range(n):\n",
+    "            row = [\n",
+    "                f\"worker-{i}\",         # id\n",
+    "                \"worker\",              # pool_id\n",
+    "                \"fr01\",                # dc_id\n",
+    "                random.uniform(50, 70), # power (random float)\n",
+    "                1,                      # lifespan\n",
+    "                \"\", \"\",                  # hdd_quantity, hdd_unit_size\n",
+    "                \"\", \"\",                  # ssd_quantity, ssd_unit_size\n",
+    "                random.uniform(30, 64), # ram_quantity (GB)\n",
+    "                \"GB\",                   # ram_unit_size\n",
+    "                \"\", \"\",                  # gpu_quantity, gpu_model_name\n",
+    "                \"\", \"\",                  # fan_quantity, psu_total_weight\n",
+    "                \"\", \"\",                  # load_rate, comment\n",
+    "                1                        # quantity\n",
+    "            ]\n",
+    "            writer.writerow(row)\n",
+    "    \n",
+    "    print(f\"CSV file '{filename}' created with {n} workers.\")\n",
+    "\n",
+    "def generate_workers_impact_csv(n, filename=\"workers_impacts.csv\"):\n",
+    "    header = [\n",
+    "        \"hw_id\",\"lc_step\",\"ADPe\",\"ADPf\",\"AP\",\"CTUe\",\"CTUh_c\",\"CTUh_nc\",\n",
+    "        \"Epf\",\"Epm\",\"Ept\",\"GWP\",\"GWPf\",\"GWPlu\",\"IR\",\"LU\",\"ODP\",\"PM\",\n",
+    "        \"POCP\",\"WU\",\"TPE\",\"GWPb\"\n",
+    "    ]\n",
+    "    \n",
+    "    with open(filename, mode=\"w\", newline=\"\") as file:\n",
+    "        writer = csv.writer(file)\n",
+    "        writer.writerow(header)\n",
+    "        \n",
+    "        for i in range(n):\n",
+    "            row = [\n",
+    "                f\"worker-{i}\",              # hw_id\n",
+    "                \"manufacturing\",        # lc_step\n",
+    "                *[random.uniform(0, 100) for _ in range(len(header) - 2)]\n",
+    "            ]\n",
+    "            writer.writerow(row)\n",
+    "    \n",
+    "    print(f\"CSV file '{filename}' created with {n} hardware entries.\")\n",
+    "\n",
+    "# Example usage\n",
+    "generate_workers_csv(2000)\n",
+    "generate_workers_impact_csv(2000)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
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diff --git a/perfanalysis/workers.csv b/perfanalysis/workers.csv
new file mode 100644
index 0000000..04bd6a1
--- /dev/null
+++ b/perfanalysis/workers.csv
@@ -0,0 +1,2001 @@
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diff --git a/perfanalysis/workers_impacts.csv b/perfanalysis/workers_impacts.csv
new file mode 100644
index 0000000..92c1772
--- /dev/null
+++ b/perfanalysis/workers_impacts.csv
@@ -0,0 +1,2001 @@
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+worker-1998,manufacturing,30.603870757286124,72.75065101621449,18.057639573588112,99.29215308666562,83.87235276084567,1.228616032373575,23.514445227474223,21.25144137895678,94.34320984478988,3.7435345939233367,46.258511665733415,29.737365856328346,85.5822886758599,41.915462366926825,66.08687294295173,46.37825280693535,58.07764345524916,7.491749163829386,12.762289053621533,80.22836213973471
+worker-1999,manufacturing,43.59614947901667,30.318525099350758,17.215964037221386,82.73921723779426,15.684977561324143,52.50911242597335,98.50757993111013,9.691999096283045,63.14327526479616,16.11060142332954,24.439371444190826,79.00678087958929,19.06614222850974,40.90735745009969,27.336482828150643,96.13450634013387,8.036956472831402,32.46943807124979,36.408411754496214,95.0942202677256
diff --git a/src/main/kotlin/org/cloud_assess/service/VirtualMachineService.kt b/src/main/kotlin/org/cloud_assess/service/VirtualMachineService.kt
index 7d90289..15c5584 100644
--- a/src/main/kotlin/org/cloud_assess/service/VirtualMachineService.kt
+++ b/src/main/kotlin/org/cloud_assess/service/VirtualMachineService.kt
@@ -7,8 +7,10 @@ import ch.kleis.lcaac.core.datasource.in_memory.InMemoryConnectorKeys
 import ch.kleis.lcaac.core.datasource.in_memory.InMemoryDatasource
 import ch.kleis.lcaac.core.lang.SymbolTable
 import ch.kleis.lcaac.core.lang.evaluator.Evaluator
+import ch.kleis.lcaac.core.lang.expression.EProcessTemplate
 import ch.kleis.lcaac.core.lang.expression.EProcessTemplateApplication
 import ch.kleis.lcaac.core.lang.register.DataKey
+import ch.kleis.lcaac.core.lang.register.ProcessKey
 import ch.kleis.lcaac.core.lang.value.RecordValue
 import ch.kleis.lcaac.core.math.basic.BasicNumber
 import ch.kleis.lcaac.core.math.basic.BasicOperations
@@ -16,6 +18,7 @@ import org.cloud_assess.dto.VirtualMachineListDto
 import org.cloud_assess.model.ProductMatcher
 import org.cloud_assess.model.ResourceAnalysis
 import org.springframework.stereotype.Service
+import java.lang.IllegalStateException
 
 @Service
 class VirtualMachineService(
@@ -38,16 +41,17 @@ class VirtualMachineService(
         val cases = cases(vms)
         val vmsConnector = inMemoryConnector(vms)
         val sourceOps = defaultDataSourceOperations.overrideWith(vmsConnector)
-        val evaluator = Evaluator(
-            symbolTable.copy(
-                data = symbolTable.data.override(
-                    DataKey(overrideTimeWindowParam),
-                    period,
-                )
-            ),
-            BasicOperations,
-            sourceOps,
+
+        val vmTemplates = cases.entries.map { it.value.template }
+        val st = symbolTableWith(vmTemplates, symbolTable.copy(
+            data = symbolTable.data.override(
+                DataKey(overrideTimeWindowParam),
+                period,
+            ))
         )
+
+        val evaluator = Evaluator(st, BasicOperations, sourceOps)
+
         val productMatcher: (String) -> ProductMatcher = { id ->
             ProductMatcher(
                 name = "vm",
@@ -58,8 +62,7 @@ class VirtualMachineService(
         val analysis = cases.entries
             .map {
                 val arguments = it.value.arguments // TODO: override total_vcpu/ram/storage
-                val trace = evaluator.with(it.value.template)
-                    .trace(it.value.template, arguments)
+                val trace = evaluator.trace(it.value.template, arguments)
                 val systemValue = trace.getSystemValue()
                 val entryPoint = trace.getEntryPoint()
                 val program = ContributionAnalysisProgram(systemValue, entryPoint)
@@ -83,9 +86,9 @@ class VirtualMachineService(
         val totalStorage = with(helper) { vms.totalStorage.toDataExpression() }
         val cases = vms.virtualMachines.associate {
             val content = """
-                process __main__ {
+                process __${it.id}__ {
                     products {
-                        1 u __main__
+                        1 u __${it.id}__
                     }
                     inputs {
                         $period vm from vm_fn(
@@ -128,4 +131,16 @@ class VirtualMachineService(
             content = content,
         )
     }
+
+    private fun symbolTableWith(templates: List<EProcessTemplate<BasicNumber>>, st: SymbolTable<BasicNumber>): SymbolTable<BasicNumber> {
+        val processTemplates = templates.map {
+            val processKey = ProcessKey(it.body.name)
+            if (st.processTemplates[processKey] != null) throw IllegalStateException("Process ${it.body.name} already exists")
+            processKey to it
+        }
+
+        return st.copy(
+            processTemplates = this.symbolTable.processTemplates.plus(processTemplates)
+        )
+    }
 }
diff --git a/trusted_library/02-pooling/pool.lca b/trusted_library/02-pooling/pool.lca
index 5e2a1cc..8dbbbec 100644
--- a/trusted_library/02-pooling/pool.lca
+++ b/trusted_library/02-pooling/pool.lca
@@ -9,33 +9,11 @@ process pool_server_fn {
     products {
         1 hour pool_server
     }
-    //    inputs {
-    //        1 hour pool_server from pool_server_fn( id = id, lc_step = "manufacturing" ) match ( phase = "embodied" )
-    //        1 hour pool_server from pool_server_fn( id = id, lc_step = "transport" ) match ( phase = "embodied" )
-    //        1 hour pool_server from pool_server_fn ( id = id ) match ( phase = "use" )
-    //        1 hour pool_server from pool_server_fn( id = id, lc_step = "end-of-life" ) match ( phase = "embodied" )
-    //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
+    inputs {
+        1 hour pool_server from pool_server_fn( id = id, lc_step = "manufacturing" ) match ( phase = "embodied" )
+        1 hour pool_server from pool_server_fn( id = id, lc_step = "transport" ) match ( phase = "embodied" )
+        1 hour pool_server from pool_server_fn ( id = id ) match ( phase = "use" )
+        1 hour pool_server from pool_server_fn( id = id, lc_step = "end-of-life" ) match ( phase = "embodied" )
     }
 }
 
@@ -50,33 +28,33 @@ process pool_server_fn {
     products {
         1 hour pool_server
     }
-//    inputs {
-//        for_each server from server_inventory match ( pool_id = id ) {
-//            server.quantity * 1 hour server from server_fn( id = server.id, lc_step = lc_step ) match ( phase = phase )
-//        }
+    inputs {
+        for_each server from server_inventory match ( pool_id = id ) {
+            server.quantity * 1 hour server from server_fn( id = server.id, lc_step = lc_step ) match ( phase = phase )
+        }
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }
 
 process pool_server_fn {
@@ -89,39 +67,40 @@ process pool_server_fn {
     products {
         1 hour pool_server
     }
-//    inputs {
-//        for_each server from server_inventory match ( pool_id = id ) {
-//            server.quantity * 1 hour server from server_fn( id = server.id ) match ( phase = phase )
-//        }
+    inputs {
+        for_each server from server_inventory match ( pool_id = id ) {
+            server.quantity * 1 hour server from server_fn( id = server.id ) match ( phase = phase )
+        }
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }
 
 /*
 Network
 */
 
+
 process pool_network_fn {
     params {
         id = "client_vm"
@@ -129,34 +108,34 @@ process pool_network_fn {
     products {
         1 hour pool_network
     }
-//    inputs {
-//        1 hour pool_network from pool_network_fn( id = id, lc_step = "manufacturing" ) match ( phase = "embodied" )
-//        1 hour pool_network from pool_network_fn( id = id, lc_step = "transport" ) match ( phase = "embodied" )
-//        1 hour pool_network from pool_network_fn ( id = id ) match ( phase = "use" )
-//        1 hour pool_network from pool_network_fn( id = id, lc_step = "end-of-life" ) match ( phase = "embodied" )
+    inputs {
+        1 hour pool_network from pool_network_fn( id = id, lc_step = "manufacturing" ) match ( phase = "embodied" )
+        1 hour pool_network from pool_network_fn( id = id, lc_step = "transport" ) match ( phase = "embodied" )
+        1 hour pool_network from pool_network_fn ( id = id ) match ( phase = "use" )
+        1 hour pool_network from pool_network_fn( id = id, lc_step = "end-of-life" ) match ( phase = "embodied" )
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }
 
 process pool_network_fn {
@@ -170,33 +149,33 @@ process pool_network_fn {
     products {
         1 hour pool_network
     }
-//    inputs {
-//        for_each network from network_inventory match ( pool_id = id ) {
-//            network.quantity * 1 hour network from network_fn( id = network.id, lc_step = lc_step ) match ( phase = phase )
-//        }
+    inputs {
+        for_each network from network_inventory match ( pool_id = id ) {
+            network.quantity * 1 hour network from network_fn( id = network.id, lc_step = lc_step ) match ( phase = phase )
+        }
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }
 
 process pool_network_fn {
@@ -209,33 +188,33 @@ process pool_network_fn {
     products {
         1 hour pool_network
     }
-//    inputs {
-//        for_each network from network_inventory match ( pool_id = id ) {
-//            network.quantity * 1 hour network from network_fn( id = network.id ) match ( phase = phase )
-//        }
+    inputs {
+        for_each network from network_inventory match ( pool_id = id ) {
+            network.quantity * 1 hour network from network_fn( id = network.id ) match ( phase = phase )
+        }
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }
 
 /*
@@ -249,34 +228,34 @@ process pool_storage_fn {
     products {
         1 hour pool_storage
     }
-//    inputs {
-//        1 hour pool_storage from pool_storage_fn( id = id, lc_step = "manufacturing" ) match ( phase = "embodied" )
-//        1 hour pool_storage from pool_storage_fn( id = id, lc_step = "transport" ) match ( phase = "embodied" )
-//        1 hour pool_storage from pool_storage_fn ( id = id ) match ( phase = "use" )
-//        1 hour pool_storage from pool_storage_fn( id = id, lc_step = "end-of-life" ) match ( phase = "embodied" )
+    inputs {
+        1 hour pool_storage from pool_storage_fn( id = id, lc_step = "manufacturing" ) match ( phase = "embodied" )
+        1 hour pool_storage from pool_storage_fn( id = id, lc_step = "transport" ) match ( phase = "embodied" )
+        1 hour pool_storage from pool_storage_fn ( id = id ) match ( phase = "use" )
+        1 hour pool_storage from pool_storage_fn( id = id, lc_step = "end-of-life" ) match ( phase = "embodied" )
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }
 
 process pool_storage_fn {
@@ -290,33 +269,33 @@ process pool_storage_fn {
     products {
         1 hour pool_storage
     }
-//    inputs {
-//        for_each storage from storage_inventory match ( pool_id = id ) {
-//            storage.quantity * 1 hour storage from storage_fn( id = storage.id, lc_step = lc_step ) match ( phase = phase )
-//        }
+    inputs {
+        for_each storage from storage_inventory match ( pool_id = id ) {
+            storage.quantity * 1 hour storage from storage_fn( id = storage.id, lc_step = lc_step ) match ( phase = phase )
+        }
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }
 
 process pool_storage_fn {
@@ -329,32 +308,32 @@ process pool_storage_fn {
     products {
         1 hour pool_storage
     }
-//    inputs {
-//        for_each storage from storage_inventory match ( pool_id = id ) {
-//            storage.quantity * 1 hour storage from storage_fn( id = storage.id ) match ( phase = phase )
-//        }
+    inputs {
+        for_each storage from storage_inventory match ( pool_id = id ) {
+            storage.quantity * 1 hour storage from storage_fn( id = storage.id ) match ( phase = phase )
+        }
+    }
+//    impacts {
+//        1 kg_Sb_Eq ADPe
+//        1 MJ_net_calorific_value ADPf
+//        1 mol_H_p_Eq AP
+//        1 CTUe CTUe
+//        1 CTUh CTUh_c
+//        1 CTUh CTUh_nc
+//        1 kg_P_Eq Epf
+//        1 kg_N_Eq Epm
+//        1 mol_N_Eq Ept
+//        1 kg_CO2_Eq GWP
+//        1 kg_CO2_Eq GWPb
+//        1 kg_CO2_Eq GWPf
+//        1 kg_CO2_Eq GWPlu
+//        1 kBq_U235_Eq IR
+//        1 u LU
+//        1 kg_CFC_11_Eq ODP
+//        1 disease_incidence PM
+//        1 kg_NMVOC_Eq POCP
+//        1 m3_world_eq_deprived WU
+//        1 MJ_net_calorific_value TPE
 //    }
-    impacts {
-        1 kg_Sb_Eq ADPe
-        1 MJ_net_calorific_value ADPf
-        1 mol_H_p_Eq AP
-        1 CTUe CTUe
-        1 CTUh CTUh_c
-        1 CTUh CTUh_nc
-        1 kg_P_Eq Epf
-        1 kg_N_Eq Epm
-        1 mol_N_Eq Ept
-        1 kg_CO2_Eq GWP
-        1 kg_CO2_Eq GWPb
-        1 kg_CO2_Eq GWPf
-        1 kg_CO2_Eq GWPlu
-        1 kBq_U235_Eq IR
-        1 u LU
-        1 kg_CFC_11_Eq ODP
-        1 disease_incidence PM
-        1 kg_NMVOC_Eq POCP
-        1 m3_world_eq_deprived WU
-        1 MJ_net_calorific_value TPE
-    }
 }