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.

data/sec_brute.csv

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