The lower of two coinciding curves is the wide one, the random-guess
reference is dotted everywhere and never borrows a scheme colour, the
three collection SNRs in the known-plaintext figure differ by face and
width rather than by dash alone, and the legend floor now prints at six
points with an assertion that fails if any figure drags it lower. Both
table generators list the proposal first.
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.
exp_learned.py completes the learned side of the result stages, so every
figure can carry both realizations of keyed masking. real() holds the
structured artifacts aside and restores them, since exp_real_sec writes
fixed file names.
replot_security.py ranks legend handles from one declared order at all
three ax.legend call sites, so entries no longer follow plot-call order
and drift between figures.
exp_learned.py mirrors every structured result stage for the learned key
family at the same SNRs, frame counts and seeds, so Figs. 2, 3, 4 and 7
and Tables IV and VI can carry both realizations of keyed masking.
replot_security.py gains a style registry: colour identifies the scheme
and line style the role, so a curve learned in one figure reads the same
in the next. Previously OMA was grey in two figures and teal in a third,
and blue meant the eavesdropper in one figure and the permutation key in
another.
Every OMA reference takes the L/16 combining gain so the comparison
stays resource matched, four hardcoded copies of the configuration are
replaced by MAIN_D or the main curve, and stage_J's K-by-L Gaussian
draw becomes its exact scalar Beta equivalent.
Give the orthogonal-access jammer its own Rayleigh channel in
oma_ser_jammed, matching the convention every simulated scheme already
used. Without it the closed-form curve faced a jammer at full power in
every frame while the Monte Carlo curves faced a fading one, which
inverted the ordering of the comparison.
Measure the outsider error rate for the fixed-key and naive-refresh
cases as well, and emit the two refresh tables from make_tables.py, so
no cell of the paper is hand-typed.
KiHoLee