Stabilize weighted adaptive-process-noise ratios

[FlorianPfaff]

Summary

• validate per-source NIS aggregation weights as nonnegative finite scalars
• compute the weighted ratio with a scale-invariant online mean instead of sum(weight * ratio) / sum(weight)
• add regressions for maximum finite weights and invalid source-weight payloads

Bug

RollingNISProcessNoiseAdapter.ratio() directly accumulated weighted sums. Equal weights near np.finfo(float).max overflowed both numerator and denominator, producing NaN for a mathematically finite mean. NaN weights were also silently skipped because NaN > 0 is false.

Testing

• compiled the modified module and regression test with python -m py_compile
• executed an isolated regression that verifies maximum finite equal weights return 3.0 and infinite weights raise ValueError
• GitHub Actions should run the repository test matrix on this PR

[github-actions]

✅MegaLinter analysis: Success

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✅ REPOSITORY	gitleaks	yes		no	no	15.2s
✅ REPOSITORY	git_diff	yes		no	no	0.44s
✅ REPOSITORY	secretlint	yes		no	no	62.0s
✅ REPOSITORY	syft	yes		no	no	6.08s
✅ REPOSITORY	trivy-sbom	yes		no	no	8.22s
✅ REPOSITORY	trufflehog	yes		no	no	33.6s
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✅ YAML	v8r	11		0	0	11.97s
✅ YAML	yamllint	11		0	0	0.6s

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