Large electromagnetic scattering computation using iterative progressive numerical method
Bibliographic record
Abstract
The progressive numerical method (PNM) is an effective way of dealing with electromagnetic scattering by electrically large objects. The PNM is based on the moment methods (MM). It is known that the solutions of the electric or magnetic field integral equations, using the MM, can be reduced to a matrix equation. The process of the PNM is started by selecting a small region at the centre of the illuminated side of the scatterer to reduce the interactions from the remaining sections of the object. However it is usually difficult to do so for asymmetrical scatterers. It is also noted that the accuracy of the solutions for the TE case is poorer than that of the TM case. This is due to the fact that for the TE case the induced currents are circumferential. An iterative step is incorporated with the PNM for better accuracy. To examine the behavior of the solution using iterative PNM, a perfect conducting infinite rectangular cylinder (TM case) is assumed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".