diff --git a/analysis/PoR_test_analysis_with_multiple_storage_nodes.ipynb b/analysis/PoR_test_analysis_with_multiple_storage_nodes.ipynb
index 505a588..8b7608a 100644
--- a/analysis/PoR_test_analysis_with_multiple_storage_nodes.ipynb
+++ b/analysis/PoR_test_analysis_with_multiple_storage_nodes.ipynb
@@ -155,7 +155,9 @@
         "id": "tHlX0Ch8Dafz"
       },
       "source": [
-        "To verify the above functions we set up a small simulation, and evaluate the same probabilities empirically as well."
+        "To verify the above functions we set up a small simulation, and evaluate the same probabilities empirically as well.\n",
+        "In this case we also allow for an optional additional layer of erasure coding \"horizontally\" on the row, i.e. among chunks on the\n",
+        "same node."
       ]
     },
     {
@@ -171,16 +173,19 @@
         "  chunks = np.random.rand(N, B)\n",
         "  return chunks > p_chunk_error\n",
         "\n",
-        "def recoverable(chunks, e):\n",
-        "  bad = chunks.shape[0] - np.count_nonzero(chunks, axis=0) # bad chunks\n",
-        "  return (bad < e).all()\n",
+        "def recoverable(chunks, ecol, erow=0):\n",
+        "  # ecol: maximum number of recoverable errors in a column\n",
+        "  # erow: maximum number of recoverable errors in a row\n",
+        "  badcol = chunks.shape[0] - np.count_nonzero(chunks, axis=0) # bad chunks per column\n",
+        "  badrow = chunks.shape[1] - np.count_nonzero(chunks, axis=1)\n",
+        "  return not (chunks[badrow > erow][:, badcol > ecol]).any()\n",
         "\n",
-        "def Px_recoverable(p_chunk_error, n, k, tries=1000):\n",
+        "def Px_recoverable(p_chunk_error, n, k, mcol=0, tries=1000, random_chunks=random_chunks):\n",
         "  ## evaluate probability of dataset recoverable empirically\n",
-        "  e = n-k+1 # erased chunks meaning loss\n",
+        "  e = n-k # erased chunks meaning loss\n",
         "  count = 0 \n",
         "  for i in range(tries):\n",
-        "    count += recoverable(random_chunks(p_chunk_error), e)\n",
+        "    count += recoverable(random_chunks(p_chunk_error), e, mcol)\n",
         "  return count/tries\n",
         "\n",
         "def random_query(chunks, l):\n",
@@ -216,7 +221,7 @@
         "\n",
         "def Stats_random_query_on_chunks(p_chunk_error, n, k, l, tries = 1000):\n",
         "  ## TP: recoverable and test for rec. passed\n",
-        "  e = n-k+1 # erased chunks meaning loss\n",
+        "  e = n-k # maximum number of errors in a column\n",
         "  tp, fp, tn, fn = 0, 0, 0, 0 \n",
         "  for i in range(tries):\n",
         "    chunks = random_chunks(p_chunk_error)\n",
@@ -230,7 +235,7 @@
         "\n",
         "def Stats_random_multiquery_on_chunks(p_chunk_error, n, k, l, q=1, maxfail=0, tries = 1000):\n",
         "  ## TP: recoverable and test for rec. passed\n",
-        "  e = n-k+1 # erased chunks meaning loss\n",
+        "  e = n-k # maximum number of errors in a column\n",
         "  tp, fp, tn, fn = 0, 0, 0, 0 \n",
         "  for i in range(tries):\n",
         "    chunks = random_chunks(p_chunk_error)\n",