PERMITTIVITY PROFILE ESTIMATION BASED ON NON-RADIATING EQUIVALENT SOURCE (2D CASE)
Bibliographic record
Abstract
Abstract—This paper presents a new approach to the electromagnetic inverse scattering formulation of the permittivity profile estimation. The proposed approach is particularly effective for the cases where unknown objects are made of a finite number of homogeneous regions. This approach prevents the need for the Born approximation initial guess and updating the internal total electric field iteratively. The solution to the inverse source problem and scattering problem is not unique. To address the non-uniqueness issue, we have defined the non-radiating objective functions. By minimizing this objective function and applying some constraints, we have been able to obtain a unique permittivity profile. The simulation results indicate that the low-contrast and high-contrast permittivity profiles are accurately estimated by the proposed method. The distinguishing feature of the proposed approach is that by including the non-radiating part of the equivalent source, the unknown permittivity profile becomes the solution to a minimization problem, which is much less computationally intensive as compared to existing methods using iterative field calculation over the entire domain, when applied to large (in terms of wavelength) objects. The high performance of the proposed method for noisy measured data has also been verified. 1.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".