On the use of combined surface integral equations for the analysis of high contrast penetrable objects
Bibliographic record
Abstract
Abstract Accurate solutions of electromagnetic scattering problems involving objects made of materials with large permittivity contrasts are considered. Problems are formulated with different commonly used combined surface integral equations (SIEs). All studied formulations are discretised through the method of moments with rooftop basis functions over flat quadrilaterals represented as bilinear surfaces, with razor‐blade functions being used for the testing procedure. The accuracy of the results is first investigated in detail for several frequencies and permittivity values using different numerical measures. It is shown that numerical instabilities may appear at frequencies corresponding to the physical resonances of the object, in particular in the near field and for large material parameter contrasts. The example of a dielectric resonator (DR) with cubic geometry is considered for the purpose of analysis, especially since to achieve smaller DR type antennas, it is necessary to use higher contrast materials. The accuracy of the combined surface integral equations to determine the natural resonant modes of the DR is investigated. It is found that the resonance modes can be accurately determined by exploring the radar cross section (RCS) of the DR in free space only if a proper combination of the electric and magnetic fields equations is applied.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".