Passivity Verification of Delayed Rational Function Based Macromodels of Tabulated Networks Characterized by Scattering Parameters
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Bibliographic record
Abstract
In this paper, a generalized theory for passivity verification of delayed rational function (DRF) macromodels representing electrically long networks that are characterized by multiport tabulated scattering parameters is presented. In the proposed approach, passivity verification of DRF macromodels is formulated as a quasi-periodic frequency-dependent generalized eigenvalue problem, using which, the necessary search region for passivity violations is reduced to just a single period along the imaginary axis. Necessary theoretical foundations and the related proofs are developed. Further, a computationally more efficient method based on half-Hamiltonian size frequency-dependent generalized eigenvalue problem is developed. Numerical validations for both the full-size and half-size formulations are also presented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it