Three-Dimensional Scattering From Uniaxial Objects With a Smooth Boundary Using a Multiple Infinitesimal Dipole Method
Bibliographic record
Abstract
The formulations for three-dimensional (3D) scattering from uniaxial objects with a smooth boundary using a multiple infinitesimal dipole method (MIDM) are introduced. The proposed technique uses two sets of infinitesimal dipole triplets (IDTs), including three co-located orthogonally polarized electric infinitesimal dipoles, distributed inside and outside of a scatterer to construct simulated fields. The dyadic Green’s functions of uniaxial materials are deployed in the MIDM so as to obtain the simulated fields. The singularity issues in using the uniaxial dyadic Green’s functions, which cannot be solved analytically so far for a general uniaxial medium, can be easily eliminated by using the proposed MIDM. In comparison to the traditional single-layered distribution scheme of IDTs, the proposed multiple-layered distribution scheme can handle the scattering from uniaxial objects accurately and efficiently. Several numerical examples are presented to study bistatic radar cross section (RCS) responses under different scenarios. Excellent agreement is achieved by comparing numerical results with those obtained from commercial software packages, while the simulation performance including CPU time and required memory is drastically improved by using the MIDM when computing a general uniaxial material or a relatively larger object. The proposed technique has its merits on simplicity, conciseness and fast computation in comparison to existing numerical methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".