diff --git a/.aoi_cache/details.csv b/.aoi_cache/details.csv
new file mode 100644
index 0000000..8021818
--- /dev/null
+++ b/.aoi_cache/details.csv
@@ -0,0 +1,101 @@
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diff --git a/.aoi_cache/hit_age.csv b/.aoi_cache/hit_age.csv
new file mode 100644
index 0000000..8635292
--- /dev/null
+++ b/.aoi_cache/hit_age.csv
@@ -0,0 +1,101 @@
+obj_id,hit_rate,avg_age
+1,0.3273584905660377,0.26512968299711814
+2,0.5978987583572111,0.2476038338658147
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diff --git a/.aoi_cache/lambda_distribution.pdf b/.aoi_cache/lambda_distribution.pdf
new file mode 100644
index 0000000..708d39e
Binary files /dev/null and b/.aoi_cache/lambda_distribution.pdf differ
diff --git a/.aoi_cache/lambda_vs_access_count.pdf b/.aoi_cache/lambda_vs_access_count.pdf
new file mode 100644
index 0000000..9a38524
Binary files /dev/null and b/.aoi_cache/lambda_vs_access_count.pdf differ
diff --git a/.aoi_cache/objects_in_cache_over_time.pdf b/.aoi_cache/objects_in_cache_over_time.pdf
new file mode 100644
index 0000000..e018506
Binary files /dev/null and b/.aoi_cache/objects_in_cache_over_time.pdf differ
diff --git a/.aoi_cache/overall_hit_age.csv b/.aoi_cache/overall_hit_age.csv
new file mode 100644
index 0000000..31c7d3a
--- /dev/null
+++ b/.aoi_cache/overall_hit_age.csv
@@ -0,0 +1,9 @@
+,hit_rate,avg_age
+count,100.0,100.0
+mean,0.45981069707251754,0.24812847494166054
+std,0.1784163653354017,0.02303421599819579
+min,0.3020730503455084,0.19466666666666665
+25%,0.3337018368087684,0.2360698198198198
+50%,0.34650586181228565,0.24698476555859195
+75%,0.5146835706797419,0.25914839632437936
+max,0.9718145659031683,0.3275862068965517
diff --git a/aoi_cache_experiment_eval.ipynb b/aoi_cache_experiment_eval.ipynb
index 2892f02..f6f7a2f 100644
--- a/aoi_cache_experiment_eval.ipynb
+++ b/aoi_cache_experiment_eval.ipynb
@@ -65,17 +65,17 @@
      "data": {
       "text/plain": [
        "[{'ttl': '0.5s',\n",
-       "  'data': {'hit_rate': 0.458669533255314, 'avg_age': 0.2405818161600988}},\n",
+       "  'data': {'hit_rate': 0.4598106970725175, 'avg_age': 0.2481284749416605}},\n",
        " {'ttl': '1.0s',\n",
-       "  'data': {'hit_rate': 0.619673736493892, 'avg_age': 0.4976340127164911}},\n",
+       "  'data': {'hit_rate': 0.6106589394405821, 'avg_age': 0.5015452422120209}},\n",
        " {'ttl': '2.0s',\n",
-       "  'data': {'hit_rate': 0.769815289387402, 'avg_age': 1.0034930453709514}},\n",
+       "  'data': {'hit_rate': 0.7277601169941756, 'avg_age': 0.9972298475197796}},\n",
        " {'ttl': '3.0s',\n",
-       "  'data': {'hit_rate': 0.7906927824364712, 'avg_age': 1.479522176980214}},\n",
+       "  'data': {'hit_rate': 0.8159040915293513, 'avg_age': 1.4993537506269703}},\n",
        " {'ttl': '4.0s',\n",
-       "  'data': {'hit_rate': 0.8461611168860607, 'avg_age': 1.966244726179581}},\n",
+       "  'data': {'hit_rate': 0.8529286517016675, 'avg_age': 1.9959603082017117}},\n",
        " {'ttl': '5.0s',\n",
-       "  'data': {'hit_rate': 0.8689161003980432, 'avg_age': 2.469801316710304}}]"
+       "  'data': {'hit_rate': 0.8725120313088975, 'avg_age': 2.498238888150122}}]"
       ]
      },
      "execution_count": 4,
@@ -102,7 +102,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 2 Axes>"
       ]
@@ -259,6 +259,24 @@
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>32</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>5</td>\n",
        "      <td>1</td>\n",
@@ -266,31 +284,13 @@
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>14</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
@@ -315,45 +315,45 @@
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "      <td>2</td>\n",
-       "      <td>45</td>\n",
        "      <td>1</td>\n",
-       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>97</th>\n",
        "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
-       "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>98</th>\n",
        "      <td>37</td>\n",
        "      <td>1</td>\n",
-       "      <td>52</td>\n",
+       "      <td>1</td>\n",
        "      <td>2</td>\n",
-       "      <td>13</td>\n",
+       "      <td>1</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>99</th>\n",
        "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>18</td>\n",
-       "      <td>3</td>\n",
-       "      <td>19</td>\n",
        "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>100</th>\n",
        "      <td>2</td>\n",
+       "      <td>4</td>\n",
        "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
+       "      <td>4</td>\n",
        "      <td>2</td>\n",
+       "      <td>1</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -363,17 +363,17 @@
       "text/plain": [
        "        0.5s  1.0s  2.0s  3.0s  4.0s  5.0s\n",
        "obj_id                                    \n",
-       "1          1     1     5     1     1     1\n",
-       "2          3     2     2     1     1     1\n",
-       "3          1     3     3     1     1     1\n",
-       "4          1     2     6     1     3     3\n",
+       "1          1     8    32     3     1     1\n",
+       "2          3     1     1     1     3    19\n",
+       "3          1     1     5     1     1     1\n",
+       "4          1     3    14     4     1     1\n",
        "5          2     1     1     1     2     1\n",
        "...      ...   ...   ...   ...   ...   ...\n",
-       "96         1     1     2    45     1     7\n",
-       "97         1     3     7     1     1     3\n",
-       "98        37     1    52     2    13     1\n",
-       "99         4     1    18     3    19     2\n",
-       "100        2     1     1     1     1     2\n",
+       "96         1     1     2     1     1     1\n",
+       "97         1     1     2     2     1     1\n",
+       "98        37     1     1     2     1     1\n",
+       "99         4     2     1     1     2     1\n",
+       "100        2     4     1     4     2     1\n",
        "\n",
        "[100 rows x 6 columns]"
       ]
@@ -396,7 +396,7 @@
     {
      "data": {
       "text/plain": [
-       "43624"
+       "372850"
       ]
      },
      "execution_count": 11,
diff --git a/experiments/No_Refresh_0_5s_ttl/details.csv b/experiments/No_Refresh_0_5s_ttl/details.csv
index 1d3dd2c..8021818 100644
--- a/experiments/No_Refresh_0_5s_ttl/details.csv
+++ b/experiments/No_Refresh_0_5s_ttl/details.csv
@@ -1,101 +1,101 @@
 obj_id,access_count,hits,misses,mu,lambda,hit_rate,avg_age
-1,122,41,81,0,1,33.61,0.02459016393442623
-2,382,235,147,0,3,61.52,0.15706806282722513
-3,127,38,89,0,1,29.92,0.10236220472440945
-4,113,33,80,0,1,29.2,0.08849557522123894
-5,244,121,123,0,2,49.59,0.11065573770491803
-6,116,40,76,0,1,34.48,0.09482758620689655
-7,656,474,182,0,5,72.26,0.16310975609756098
-8,128,39,89,0,1,30.47,0.046875
-9,114,37,77,0,1,32.46,0.08771929824561403
-10,115,33,82,0,1,28.7,0.06956521739130435
-11,246,129,117,0,2,52.44,0.09349593495934959
-12,132,50,82,0,1,37.88,0.08333333333333333
-13,139,50,89,0,1,35.97,0.08633093525179857
-14,120,35,85,0,1,29.17,0.058333333333333334
-15,261,134,127,0,2,51.34,0.13793103448275862
-16,225,109,116,0,2,48.44,0.10666666666666667
-17,120,39,81,0,1,32.5,0.06666666666666667
-18,117,39,78,0,1,33.33,0.07692307692307693
-19,360,213,147,0,3,59.17,0.10277777777777777
-20,117,40,77,0,1,34.19,0.09401709401709402
-21,134,48,86,0,1,35.82,0.07462686567164178
-22,147,55,92,0,1,37.41,0.12244897959183673
-23,142,50,92,0,1,35.21,0.07746478873239436
-24,264,140,124,0,2,53.03,0.10984848484848485
-25,123,42,81,0,1,34.15,0.11382113821138211
-26,141,50,91,0,1,35.46,0.0851063829787234
-27,128,45,83,0,1,35.16,0.0703125
-28,564,390,174,0,5,69.15,0.16666666666666666
-29,133,47,86,0,1,35.34,0.06015037593984962
-30,125,45,80,0,1,36.0,0.096
-31,115,31,84,0,1,26.96,0.06956521739130435
-32,468,308,160,0,4,65.81,0.17094017094017094
-33,116,37,79,0,1,31.9,0.034482758620689655
-34,498,335,163,0,4,67.27,0.18072289156626506
-35,132,39,93,0,1,29.55,0.09848484848484848
-36,100,29,71,0,1,29.0,0.07
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diff --git a/experiments/No_Refresh_0_5s_ttl/hit_age.csv b/experiments/No_Refresh_0_5s_ttl/hit_age.csv
index a07638a..8635292 100644
--- a/experiments/No_Refresh_0_5s_ttl/hit_age.csv
+++ b/experiments/No_Refresh_0_5s_ttl/hit_age.csv
@@ -1,101 +1,101 @@
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diff --git a/experiments/No_Refresh_0_5s_ttl/lambda_distribution.pdf b/experiments/No_Refresh_0_5s_ttl/lambda_distribution.pdf
index 1c8b8a1..4be49cb 100644
Binary files a/experiments/No_Refresh_0_5s_ttl/lambda_distribution.pdf and b/experiments/No_Refresh_0_5s_ttl/lambda_distribution.pdf differ
diff --git a/experiments/No_Refresh_0_5s_ttl/lambda_vs_access_count.pdf b/experiments/No_Refresh_0_5s_ttl/lambda_vs_access_count.pdf
index 55f1f0b..3e7133f 100644
Binary files a/experiments/No_Refresh_0_5s_ttl/lambda_vs_access_count.pdf and b/experiments/No_Refresh_0_5s_ttl/lambda_vs_access_count.pdf differ
diff --git a/experiments/No_Refresh_0_5s_ttl/objects_in_cache_over_time.pdf b/experiments/No_Refresh_0_5s_ttl/objects_in_cache_over_time.pdf
index ff0321b..7144d73 100644
Binary files a/experiments/No_Refresh_0_5s_ttl/objects_in_cache_over_time.pdf and b/experiments/No_Refresh_0_5s_ttl/objects_in_cache_over_time.pdf differ
diff --git a/experiments/No_Refresh_0_5s_ttl/overall_hit_age.csv b/experiments/No_Refresh_0_5s_ttl/overall_hit_age.csv
index 516c754..31c7d3a 100644
--- a/experiments/No_Refresh_0_5s_ttl/overall_hit_age.csv
+++ b/experiments/No_Refresh_0_5s_ttl/overall_hit_age.csv
@@ -1,9 +1,9 @@
 ,hit_rate,avg_age
 count,100.0,100.0
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diff --git a/experiments/No_Refresh_1_0s_ttl/details.csv b/experiments/No_Refresh_1_0s_ttl/details.csv
index e46db5e..9139f6b 100644
--- a/experiments/No_Refresh_1_0s_ttl/details.csv
+++ b/experiments/No_Refresh_1_0s_ttl/details.csv
@@ -1,101 +1,101 @@
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diff --git a/experiments/No_Refresh_1_0s_ttl/hit_age.csv b/experiments/No_Refresh_1_0s_ttl/hit_age.csv
index 93f77f1..3e8cdc7 100644
--- a/experiments/No_Refresh_1_0s_ttl/hit_age.csv
+++ b/experiments/No_Refresh_1_0s_ttl/hit_age.csv
@@ -1,101 +1,101 @@
 obj_id,hit_rate,avg_age
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diff --git a/experiments/No_Refresh_1_0s_ttl/lambda_distribution.pdf b/experiments/No_Refresh_1_0s_ttl/lambda_distribution.pdf
index 99600af..3f8fc47 100644
Binary files a/experiments/No_Refresh_1_0s_ttl/lambda_distribution.pdf and b/experiments/No_Refresh_1_0s_ttl/lambda_distribution.pdf differ
diff --git a/experiments/No_Refresh_1_0s_ttl/lambda_vs_access_count.pdf b/experiments/No_Refresh_1_0s_ttl/lambda_vs_access_count.pdf
index 60a1598..cc0ec1f 100644
Binary files a/experiments/No_Refresh_1_0s_ttl/lambda_vs_access_count.pdf and b/experiments/No_Refresh_1_0s_ttl/lambda_vs_access_count.pdf differ
diff --git a/experiments/No_Refresh_1_0s_ttl/objects_in_cache_over_time.pdf b/experiments/No_Refresh_1_0s_ttl/objects_in_cache_over_time.pdf
index fa69997..9945c7c 100644
Binary files a/experiments/No_Refresh_1_0s_ttl/objects_in_cache_over_time.pdf and b/experiments/No_Refresh_1_0s_ttl/objects_in_cache_over_time.pdf differ
diff --git a/experiments/No_Refresh_1_0s_ttl/overall_hit_age.csv b/experiments/No_Refresh_1_0s_ttl/overall_hit_age.csv
index 6a19fba..a4713e0 100644
--- a/experiments/No_Refresh_1_0s_ttl/overall_hit_age.csv
+++ b/experiments/No_Refresh_1_0s_ttl/overall_hit_age.csv
@@ -1,9 +1,9 @@
 ,hit_rate,avg_age
 count,100.0,100.0
-mean,0.619673736493892,0.4976340127164911
-std,0.15169157416583476,0.07281557921922656
-min,0.41,0.2542372881355932
-25%,0.4959007993441279,0.46297849716446127
-50%,0.5411570894063774,0.48810190369540873
-75%,0.7357392018056838,0.5397796817625459
-max,0.9664941432852084,0.7083333333333334
+mean,0.6106589394405821,0.5015452422120209
+std,0.15132826274882488,0.019318631378919444
+min,0.483,0.46313799621928164
+25%,0.4998761674999377,0.48838831096845714
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+75%,0.7459501043980399,0.5153286948176583
+max,0.9901008644183672,0.5511669658886894
diff --git a/experiments/No_Refresh_2_0s_ttl/details.csv b/experiments/No_Refresh_2_0s_ttl/details.csv
index b1771ff..153ca8e 100644
--- a/experiments/No_Refresh_2_0s_ttl/details.csv
+++ b/experiments/No_Refresh_2_0s_ttl/details.csv
@@ -1,101 +1,101 @@
 obj_id,access_count,hits,misses,mu,lambda,hit_rate,avg_age
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diff --git a/experiments/No_Refresh_2_0s_ttl/hit_age.csv b/experiments/No_Refresh_2_0s_ttl/hit_age.csv
index 180c846..044712e 100644
--- a/experiments/No_Refresh_2_0s_ttl/hit_age.csv
+++ b/experiments/No_Refresh_2_0s_ttl/hit_age.csv
@@ -1,101 +1,101 @@
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diff --git a/experiments/No_Refresh_2_0s_ttl/lambda_distribution.pdf b/experiments/No_Refresh_2_0s_ttl/lambda_distribution.pdf
index a6cfa75..837eb62 100644
Binary files a/experiments/No_Refresh_2_0s_ttl/lambda_distribution.pdf and b/experiments/No_Refresh_2_0s_ttl/lambda_distribution.pdf differ
diff --git a/experiments/No_Refresh_2_0s_ttl/lambda_vs_access_count.pdf b/experiments/No_Refresh_2_0s_ttl/lambda_vs_access_count.pdf
index 895a611..bfecb3a 100644
Binary files a/experiments/No_Refresh_2_0s_ttl/lambda_vs_access_count.pdf and b/experiments/No_Refresh_2_0s_ttl/lambda_vs_access_count.pdf differ
diff --git a/experiments/No_Refresh_2_0s_ttl/objects_in_cache_over_time.pdf b/experiments/No_Refresh_2_0s_ttl/objects_in_cache_over_time.pdf
index fff705a..e036323 100644
Binary files a/experiments/No_Refresh_2_0s_ttl/objects_in_cache_over_time.pdf and b/experiments/No_Refresh_2_0s_ttl/objects_in_cache_over_time.pdf differ
diff --git a/experiments/No_Refresh_2_0s_ttl/overall_hit_age.csv b/experiments/No_Refresh_2_0s_ttl/overall_hit_age.csv
index 91bb2cf..5952dac 100644
--- a/experiments/No_Refresh_2_0s_ttl/overall_hit_age.csv
+++ b/experiments/No_Refresh_2_0s_ttl/overall_hit_age.csv
@@ -1,9 +1,9 @@
 ,hit_rate,avg_age
 count,100.0,100.0
-mean,0.769815289387402,1.0034930453709514
-std,0.11953590678844736,0.07668973688754598
-min,0.6071428571428571,0.8424242424242424
-25%,0.66377403112603,0.953261395578711
-50%,0.743622126799697,1.0
-75%,0.8858906487772468,1.0517550319096711
-max,0.9904940003116721,1.2195121951219512
+mean,0.7277601169941756,0.9972298475197796
+std,0.09830526281613299,0.027196423303013563
+min,0.6460784313725491,0.9346879535558781
+25%,0.6626105290467492,0.9791110398620806
+50%,0.6745982073285375,0.999388379204893
+75%,0.7991587383707855,1.0166569842562063
+max,0.9974911652507664,1.0602240896358543
diff --git a/experiments/No_Refresh_3_0s_ttl/details.csv b/experiments/No_Refresh_3_0s_ttl/details.csv
index e4ff8e6..5a20551 100644
--- a/experiments/No_Refresh_3_0s_ttl/details.csv
+++ b/experiments/No_Refresh_3_0s_ttl/details.csv
@@ -1,101 +1,101 @@
 obj_id,access_count,hits,misses,mu,lambda,hit_rate,avg_age
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diff --git a/experiments/No_Refresh_3_0s_ttl/hit_age.csv b/experiments/No_Refresh_3_0s_ttl/hit_age.csv
index 3bec2d8..60d0502 100644
--- a/experiments/No_Refresh_3_0s_ttl/hit_age.csv
+++ b/experiments/No_Refresh_3_0s_ttl/hit_age.csv
@@ -1,101 +1,101 @@
 obj_id,hit_rate,avg_age
-1,0.7322834645669292,1.4516129032258065
-2,0.7837837837837838,1.5775862068965518
-3,0.7264957264957265,1.5058823529411764
-4,0.728,1.4175824175824177
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-8,0.7155963302752294,1.5
-9,0.9047619047619048,1.4764542936288088
-10,0.7322834645669292,1.5698924731182795
-11,0.7295081967213115,1.4943820224719102
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-13,0.7154471544715447,1.375
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diff --git a/experiments/No_Refresh_3_0s_ttl/lambda_distribution.pdf b/experiments/No_Refresh_3_0s_ttl/lambda_distribution.pdf
index 6ac69b0..52b9f00 100644
Binary files a/experiments/No_Refresh_3_0s_ttl/lambda_distribution.pdf and b/experiments/No_Refresh_3_0s_ttl/lambda_distribution.pdf differ
diff --git a/experiments/No_Refresh_3_0s_ttl/lambda_vs_access_count.pdf b/experiments/No_Refresh_3_0s_ttl/lambda_vs_access_count.pdf
index 3581c0e..c2e35ea 100644
Binary files a/experiments/No_Refresh_3_0s_ttl/lambda_vs_access_count.pdf and b/experiments/No_Refresh_3_0s_ttl/lambda_vs_access_count.pdf differ
diff --git a/experiments/No_Refresh_3_0s_ttl/objects_in_cache_over_time.pdf b/experiments/No_Refresh_3_0s_ttl/objects_in_cache_over_time.pdf
index 92e08a3..183faf9 100644
Binary files a/experiments/No_Refresh_3_0s_ttl/objects_in_cache_over_time.pdf and b/experiments/No_Refresh_3_0s_ttl/objects_in_cache_over_time.pdf differ
diff --git a/experiments/No_Refresh_3_0s_ttl/overall_hit_age.csv b/experiments/No_Refresh_3_0s_ttl/overall_hit_age.csv
index db2cc05..c88cae8 100644
--- a/experiments/No_Refresh_3_0s_ttl/overall_hit_age.csv
+++ b/experiments/No_Refresh_3_0s_ttl/overall_hit_age.csv
@@ -1,9 +1,9 @@
 ,hit_rate,avg_age
 count,100.0,100.0
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diff --git a/experiments/No_Refresh_4_0s_ttl/details.csv b/experiments/No_Refresh_4_0s_ttl/details.csv
index 3a19846..4a28b06 100644
--- a/experiments/No_Refresh_4_0s_ttl/details.csv
+++ b/experiments/No_Refresh_4_0s_ttl/details.csv
@@ -1,101 +1,101 @@
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diff --git a/experiments/No_Refresh_4_0s_ttl/hit_age.csv b/experiments/No_Refresh_4_0s_ttl/hit_age.csv
index acccfe9..e066d99 100644
--- a/experiments/No_Refresh_4_0s_ttl/hit_age.csv
+++ b/experiments/No_Refresh_4_0s_ttl/hit_age.csv
@@ -1,101 +1,101 @@
 obj_id,hit_rate,avg_age
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diff --git a/experiments/No_Refresh_4_0s_ttl/lambda_distribution.pdf b/experiments/No_Refresh_4_0s_ttl/lambda_distribution.pdf
index fa61beb..22974ce 100644
Binary files a/experiments/No_Refresh_4_0s_ttl/lambda_distribution.pdf and b/experiments/No_Refresh_4_0s_ttl/lambda_distribution.pdf differ
diff --git a/experiments/No_Refresh_4_0s_ttl/lambda_vs_access_count.pdf b/experiments/No_Refresh_4_0s_ttl/lambda_vs_access_count.pdf
index 0e8bf1e..d0e8f67 100644
Binary files a/experiments/No_Refresh_4_0s_ttl/lambda_vs_access_count.pdf and b/experiments/No_Refresh_4_0s_ttl/lambda_vs_access_count.pdf differ
diff --git a/experiments/No_Refresh_4_0s_ttl/objects_in_cache_over_time.pdf b/experiments/No_Refresh_4_0s_ttl/objects_in_cache_over_time.pdf
index c6684fe..676c410 100644
Binary files a/experiments/No_Refresh_4_0s_ttl/objects_in_cache_over_time.pdf and b/experiments/No_Refresh_4_0s_ttl/objects_in_cache_over_time.pdf differ
diff --git a/experiments/No_Refresh_4_0s_ttl/overall_hit_age.csv b/experiments/No_Refresh_4_0s_ttl/overall_hit_age.csv
index fada11a..dafb901 100644
--- a/experiments/No_Refresh_4_0s_ttl/overall_hit_age.csv
+++ b/experiments/No_Refresh_4_0s_ttl/overall_hit_age.csv
@@ -1,9 +1,9 @@
 ,hit_rate,avg_age
 count,100.0,100.0
-mean,0.8461611168860607,1.966244726179581
-std,0.06904890299740231,0.12918574131498722
-min,0.75,1.588648184618492
-25%,0.7946428571428571,1.8798211706342576
-50%,0.8120158760642724,1.9792294446835987
-75%,0.8918173495078966,2.054958791208791
-max,0.9984873695356224,2.2560975609756095
+mean,0.8529286517016675,1.9959603082017114
+std,0.06775773364762111,0.0367534968685268
+min,0.783,1.8934131736526947
+25%,0.7991498632946001,1.9758327369329602
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+75%,0.891341196533501,2.0179247095256945
+max,0.9979367689491483,2.1116446578631454
diff --git a/experiments/No_Refresh_5_0s_ttl/details.csv b/experiments/No_Refresh_5_0s_ttl/details.csv
index 50ab84b..556b6f4 100644
--- a/experiments/No_Refresh_5_0s_ttl/details.csv
+++ b/experiments/No_Refresh_5_0s_ttl/details.csv
@@ -1,101 +1,101 @@
 obj_id,access_count,hits,misses,mu,lambda,hit_rate,avg_age
-1,136,113,23,0,1,83.09,2.0808823529411766
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diff --git a/experiments/No_Refresh_5_0s_ttl/hit_age.csv b/experiments/No_Refresh_5_0s_ttl/hit_age.csv
index 5903e50..297e993 100644
--- a/experiments/No_Refresh_5_0s_ttl/hit_age.csv
+++ b/experiments/No_Refresh_5_0s_ttl/hit_age.csv
@@ -1,101 +1,101 @@
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diff --git a/experiments/No_Refresh_5_0s_ttl/lambda_distribution.pdf b/experiments/No_Refresh_5_0s_ttl/lambda_distribution.pdf
index 13b919e..ac290f2 100644
Binary files a/experiments/No_Refresh_5_0s_ttl/lambda_distribution.pdf and b/experiments/No_Refresh_5_0s_ttl/lambda_distribution.pdf differ
diff --git a/experiments/No_Refresh_5_0s_ttl/lambda_vs_access_count.pdf b/experiments/No_Refresh_5_0s_ttl/lambda_vs_access_count.pdf
index fd026a9..f8e194c 100644
Binary files a/experiments/No_Refresh_5_0s_ttl/lambda_vs_access_count.pdf and b/experiments/No_Refresh_5_0s_ttl/lambda_vs_access_count.pdf differ
diff --git a/experiments/No_Refresh_5_0s_ttl/objects_in_cache_over_time.pdf b/experiments/No_Refresh_5_0s_ttl/objects_in_cache_over_time.pdf
index 41ad058..d4a5f11 100644
Binary files a/experiments/No_Refresh_5_0s_ttl/objects_in_cache_over_time.pdf and b/experiments/No_Refresh_5_0s_ttl/objects_in_cache_over_time.pdf differ
diff --git a/experiments/No_Refresh_5_0s_ttl/overall_hit_age.csv b/experiments/No_Refresh_5_0s_ttl/overall_hit_age.csv
index 9a6e03d..b56c3d5 100644
--- a/experiments/No_Refresh_5_0s_ttl/overall_hit_age.csv
+++ b/experiments/No_Refresh_5_0s_ttl/overall_hit_age.csv
@@ -1,9 +1,9 @@
 ,hit_rate,avg_age
 count,100.0,100.0
-mean,0.8689161003980432,2.469801316710304
-std,0.05362014513420393,0.13549611807744597
-min,0.78,2.0483199198321485
-25%,0.8270676691729323,2.3846153846153846
-50%,0.841726618705036,2.4849237260629664
-75%,0.9100820467221729,2.5632199275958536
-max,0.9991551939924906,2.7454545454545456
+mean,0.8725120313088975,2.498238888150122
+std,0.054253138996698945,0.05047201928154133
+min,0.825,2.34533183352081
+25%,0.8319067910758455,2.4694216233979627
+50%,0.8375154569422085,2.494494857834241
+75%,0.9093495418588469,2.5267119984567903
+max,0.9978256165047042,2.6239907727797003
diff --git a/experiments/avg_ages.md b/experiments/avg_ages.md
index ba3a913..f7d5a3c 100644
--- a/experiments/avg_ages.md
+++ b/experiments/avg_ages.md
@@ -1,8 +1,8 @@
 |      |   avg_ages |
 |:-----|-----------:|
-| 0.5s |   0.240582 |
-| 1.0s |   0.497634 |
-| 2.0s |   1.00349  |
-| 3.0s |   1.47952  |
-| 4.0s |   1.96624  |
-| 5.0s |   2.4698   |
\ No newline at end of file
+| 0.5s |   0.248128 |
+| 1.0s |   0.501545 |
+| 2.0s |   0.99723  |
+| 3.0s |   1.49935  |
+| 4.0s |   1.99596  |
+| 5.0s |   2.49824  |
\ No newline at end of file
diff --git a/experiments/hit_rates.md b/experiments/hit_rates.md
index 7db1536..07ecab2 100644
--- a/experiments/hit_rates.md
+++ b/experiments/hit_rates.md
@@ -1,8 +1,8 @@
 |      |   hit_rates |
 |:-----|------------:|
-| 0.5s |    0.45867  |
-| 1.0s |    0.619674 |
-| 2.0s |    0.769815 |
-| 3.0s |    0.790693 |
-| 4.0s |    0.846161 |
-| 5.0s |    0.868916 |
\ No newline at end of file
+| 0.5s |    0.459811 |
+| 1.0s |    0.610659 |
+| 2.0s |    0.72776  |
+| 3.0s |    0.815904 |
+| 4.0s |    0.852929 |
+| 5.0s |    0.872512 |
\ No newline at end of file
diff --git a/experiments/hr_and_age_vs_ttl.png b/experiments/hr_and_age_vs_ttl.png
index 2f48f09..b32fe8d 100644
Binary files a/experiments/hr_and_age_vs_ttl.png and b/experiments/hr_and_age_vs_ttl.png differ
diff --git a/multi_aoi_cache_simulation.ipynb b/multi_aoi_cache_simulation.ipynb
index 8b05ac0..1578e78 100644
--- a/multi_aoi_cache_simulation.ipynb
+++ b/multi_aoi_cache_simulation.ipynb
@@ -25,7 +25,7 @@
     "# Constants\n",
     "SEED = 42\n",
     "DATABASE_OBJECTS = 100 # Number of objects in the database\n",
-    "ACCESS_COUNT_LIMIT = 100   # Total time to run the simulation\n",
+    "ACCESS_COUNT_LIMIT = 1000   # Total time to run the simulation\n",
     "EXPERIMENT_BASE_DIR = \"./experiments/\"\n",
     "TEMP_BASE_DIR = \"./.aoi_cache/\"\n",
     "\n",
@@ -331,7 +331,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Progress: 100%|████████████████████████████████████████████████████▉| 999/1000 [00:07<00:00, 131.29it/s]"
+      "Progress: 100%|████████████████████████████████████████████████████▉| 999/1000 [00:06<00:00, 162.60it/s]"
      ]
     },
     {