TLM-based modal-extraction approach for the investigation of discontinuities in the rectangular waveguide and the NRD
Bibliographic record
Abstract
This paper describes the development of a rigorous transmission-line matrix-based modal-extraction approach to analyze discontinuities in guided-wave structures in general, with particular attention to the nonradiative dielectric waveguide (NRD). The motivation for this paper arose from the need to ascertain the admittance of a slot in the ground plane of an NRD without relying on experimental data. These data enabled one to design an NRD-based slot array following the methodology of Malherbe (1984), Malherbe et al. (1984), and Ghosh et al.(1997). Previous work in this area relied on placing observation points sufficiently remote from the discontinuity in order to ensure the decay of scattered evanescent modes to appreciably low levels. The method discussed here obviates this requirement and allows the evaluation of generalized scattering-matrix coefficients arbitrarily close to the discontinuity, thus significantly reducing the computational overhead. Results pertaining to discontinuities in the NRD and the rectangular waveguide have been presented and shown to give good agreement with those in the literature and with measurements. The perfectly matched layer has been used as an absorbing boundary condition in our simulations. Finally, the results have been verified using the power-conservation and Poynting's theorems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".