Re-run the key-length attack sweep and emphasize the main-configuration row

stage_F was missed in the structured-family regeneration, so sec_brute.csv
and sec_sens.csv still carried learned-family results. Re-running it moves
the L=16 brute-force point from 0.7495 to 0.5916, which now agrees with
sec_brute_cmp.csv rather than contradicting it.

make_tables.py bolds the Walsh-Hadamard row the way it already bolds the
proposed and invariant rows, since that family is the main configuration.

code/make_tables.py

@@ -67,9 +67,15 @@ def compare_table():
 def maskfam_table():
     print("% Table: key families (from sec_maskfam.csv)")
     for r in csv.DictReader(open(DATA / "sec_maskfam.csv")):
-        cells = [cell(r[k], False) for k in
+        # the structured family is the main configuration, so its row is
+        # emphasized the same way the proposed row is in the comparison
+        b = r["family"] == "hadamard"
+        cells = [cell(r[k], b) for k in
                  ("legit_ser", "eve_ser", "eve_ones_ser", "mask_xcorr")]
-        print(f"{NAME[r['family']]} & " + " & ".join(cells) + r" \\")
+        name = NAME[r["family"]]
+        if b:
+            name = r"\textbf{" + name + "}"
+        print(f"{name} & " + " & ".join(cells) + r" \\")
 
 
 def real_table():

data/sec_brute.csv

@@ -1,29 +1,29 @@
 L,K,best_rho,eve_ser
-8,1,0.2918601623,0.9961626487
+8,1,0.2918601623,0.9945244994
-8,10,0.6307837307,0.9703819549
+8,10,0.6307837307,0.9550597921
-8,100,0.8190062809,0.8850038404
+8,100,0.8190062809,0.8147158732
-8,1000,0.9077793813,0.7200369393
+8,1000,0.9077793813,0.5723297059
-8,10000,0.9535904264,0.5318095675
+8,10000,0.9535904264,0.3866372342
-8,100000,0.9757162716,0.4169265965
+8,100000,0.9757162716,0.3157766639
-8,1000000,0.9872786315,0.339091505
+8,1000000,0.9872786315,0.2845244539
-16,1,0.2228791779,0.9991641146
+16,1,0.2228791779,0.9990085712
-16,10,0.4570522499,0.9957471368
+16,10,0.4570522499,0.9943536935
-16,100,0.6321070191,0.9833336645
+16,100,0.6321070191,0.9769163907
-16,1000,0.7417848118,0.9546712761
+16,1000,0.7417848118,0.9311708657
-16,10000,0.8164950053,0.9000406219
+16,10000,0.8164950053,0.8399011082
-16,100000,0.8673534005,0.8378011935
+16,100000,0.8673534005,0.7274791752
-16,1000000,0.9048131336,0.7494679145
+16,1000000,0.9048131336,0.5915534824
-32,1,0.1399813941,0.9997996279
+32,1,0.1399813941,0.999821061
-32,10,0.3205750013,0.9991500426
+32,10,0.3205750013,0.9991354579
-32,100,0.4660256564,0.9971309056
+32,100,0.4660256564,0.996404209
-32,1000,0.5620279439,0.9934165308
+32,1000,0.5620279439,0.991343747
-32,10000,0.6393936736,0.9852959659
+32,10000,0.6393936736,0.9815239297
-32,100000,0.6993164916,0.9746142481
+32,100000,0.6993164916,0.9634132739
-32,1000000,0.7479539255,0.9565757219
+32,1000000,0.7479539255,0.9349306487
-64,1,0.0987436915,0.9998965347
+64,1,0.0987436915,0.9999096477
-64,10,0.2263401688,0.9996584104
+64,10,0.2263401688,0.9997303409
-64,100,0.3389944824,0.9991447783
+64,100,0.3389944824,0.9991750164
-64,1000,0.4155608514,0.9984268536
+64,1000,0.4155608514,0.9981802181
-64,10000,0.4757575011,0.9974241507
+64,10000,0.4757575011,0.9967597446
-64,100000,0.5299797378,0.9957096073
+64,100000,0.5299797378,0.994275692
-64,1000000,0.5741312045,0.9933056882
+64,1000000,0.5741312045,0.9910114047

data/sec_sens.csv

@@ -1,23 +1,23 @@
 rho,eve_ser
-0,0.99997875
+0,0.999985
-0.1,0.999955
+0.1,0.99994625
-0.2,0.99976375
+0.2,0.9998125
-0.3,0.99947625
+0.3,0.99964375
-0.4,0.99873
+0.4,0.99860875
-0.5,0.99717375
+0.5,0.99641875
-0.6,0.992395
+0.6,0.989405
-0.65,0.983195
+0.65,0.9817575
-0.7,0.9776825
+0.7,0.96633
-0.75,0.9594925
+0.75,0.9402875
-0.8,0.91810625
+0.8,0.87396
-0.84,0.88158125
+0.84,0.80818
-0.88,0.8247575
+0.88,0.7000025
-0.9,0.792455
+0.9,0.62012
-0.92,0.67340125
+0.92,0.51693625
-0.94,0.602535
+0.94,0.4271675
-0.96,0.50181875
+0.96,0.3636825
-0.97,0.47287375
+0.97,0.33119875
-0.98,0.37558375
+0.98,0.30263125
-0.99,0.32493625
+0.99,0.2772575
-0.995,0.30185625
+0.995,0.26772625
-1,0.27494375
+1,0.25672125