Cutoff Wavenumber Analyses of Metallic Waveguides Filled With Homogeneous Anisotropic Materials Using the MFCM
Bibliographic record
Abstract
We present a multifilament current method (MFCM) to accurately and efficiently determine the cutoff wavenumbers (cutoff frequencies or eigenfrequencies) of homogeneous anisotropic material-filled metallic waveguides with arbitrary shapes. A set of$z$-directed filamentary line sources placed outside of a waveguide are responsible for generating the simulated fields within the anisotropic material-filled waveguide in light of the derived 2-D anisotropic dyadic Green’s functions. Instead of solving an excitation-free eigenvalue problem, a substantial line source presented as an excitation is incorporated into the MFCM to formulate an eigenmode analyses technique without spurious eigenmodes. The internal field intensity response versus free space wavenumber are subsequently simulated, and a high magnitude response can be observed when an eigenmode is excited. In this case, the cutoff wavenumbers of physical eigenmodes are revealed by maximum locations of simulated field intensity response. Several numerical examples with different configurations on a waveguide are investigated. The simulated results are compared with those obtained from commercial software packages and an excellent agreement is achieved. In addition, the study of the oscillating phenomenon on magnitudes of filamentary sources and the ill-conditioning issue of constructed impedance matrix with respect to the placement of filamentary sources are also discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".