KiHoLee
138897aa8d
The package was missing every script behind the KM (lrn.) curves and both learned table rows: exp_learned's driver, the merge that folds the learned rows into sec_compare.csv and refresh_summary.csv, and the report that reads the learned numbers back. It was also missing sec_keylen_perm.csv, so Fig. 3 could not be regenerated at all, and the two diagnostics that answer why a fixed key beats a learned one here and where a learned mask would win instead. The learned artifacts themselves are regenerated. They were trained on the cross-entropy alone, which drifts to disjoint sparse supports: 99 percent of each key's energy on about six of the 64 entries, so a digit is decided over a sixth of its period and the key set is a choice of support rather than a dense direction in R^L. They are now the regularized keys of Section V-C, and check_consistency asserts which of the two families the figures draw. Verified from inside this repository: replot_security.py rebuilds all seven result figures, make_tables.py reproduces both result tables, and check_consistency.py passes every check that does not need the manuscript. The README now lists what ships. Its run list, layout and figure map had none of the learned pipeline, named two tables the manuscript renders as prose, and gave Fig. 3 no data file for its permutation curve.
1.3 KiB
1.3 KiB
| 1 | check | claim | empirical | abs_err | tol | verdict |
|---|---|---|---|---|---|---|
| 2 | V1 legit self-alignment | 1.0 | 0.9994724071424732 | 0.000527592857526793 | 0.02 | PASS |
| 3 | V2a eve mean advantage | 0.0 | 0.0006469982936097643 | 0.0006469982936097643 | 0.003 | PASS |
| 4 | V2b eve SER @ 0dB | 0.99609375 | 0.995 | 0.0010937500000000044 | 0.015 | PASS |
| 5 | V2b eve SER @ 10dB | 0.99609375 | 0.9888333333333333 | 0.007260416666666658 | 0.015 | PASS |
| 6 | V2b eve SER @ 20dB | 0.99609375 | 0.9881666666666666 | 0.007927083333333362 | 0.015 | PASS |
| 7 | V2b eve SER @ 80dB | 0.99609375 | 0.9896666666666667 | 0.0064270833333333055 | 0.015 | PASS |
| 8 | V3a bias slope in kappa | 1.0 | 0.9932 | 0.0068 | 0.03 | PASS |
| 9 | V3b random mask E|corr| | 0.09973557010035818 | 0.10187042771408686 | 0.002134857613728683 | 0.002992067103010745 | PASS |
| 10 | V4a blind jammer projection mean | 0.0 | 0.00026396107284673695 | 0.00026396107284673695 | 0.003 | PASS |
| 11 | V4b blind jammer projection variance | 0.01558576233568703 | 0.015476787953278994 | 0.00010897438240803532 | 0.0001558576233568703 | PASS |
| 12 | V5 matched bias over blind RMS | 8.0 | 8.0 | 0.04 | 1.0 | PASS |
| 13 | V6 coded-OMA outage @ 10 dB | 0.0406 | 0.040575 | 0 | 0 | REFERENCE |
| 14 | V7 symbolic identities | exact | exact | 0 | 0 | PASS |
| 15 | V8 cross-period remainder | 0.0 | 0.000337 | 0.000337 | 0.0005 | PASS |
| 16 | V9 score-variance ratio | 2.8 | 2.8252 | 0.0252 | 0.05 | PASS |
| 17 | V10 format-matched OMA at 10 dB | 0.055 | 0.05520 | 0.00020 | 0.001 | PASS |
| 18 | V11 OMA closed form vs Monte Carlo | 0.081245 | 0.080925 | 0.0039 | 0.01 | PASS |