A Hybrid BEM/WTM/DFT Technique for Analysis of the EM Scattering From Open-Ended Circular Cavities With Cylindrically Periodic Terminations
Bibliographic record
Abstract
The cavity problem, formulated using a magnetic field integral equation (MFIE), can be solved by the iterative physical optics (IPO) method, the progressive physical optics (PPO) method or the hybrid boundary element method/wavelet transform method (BEM/WTM). In this paper, we focus on open-ended circular cavities with cylindrically periodic terminations. The cavity problem is formulated using an electric field integral equation (EFIE), based on the dyadic Green function technique, instead of an MFIE. The discrete Fourier transform (DFT) technique is applied by exploiting the periodicity of the termination to reduce the original computational size by a factor of N/sub s/ with corresponding computational savings of a factor of N/sub s//sup 2/, where N/sub s/ denotes the number of blades in the cylindrically periodic termination. Two kinds of dyadic Green functions are derived to reduce the integral range in the EFIE, thus accelerating the scattering solutions for different cavity applications. The BEM/WTM method is combined with the DFT to obtain sparse impedance matrices that can be efficiently solved using sparse solvers. The proposed hybrid BEM/WTM/DFT technique provides an alternative means of effectively analyzing the electromagnetic (EM) scattering from large-size circular cavities with cylindrically periodic terminations. Numerical results are presented to demonstrate the merits of the method.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 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".