Surface Susceptibility Bianisotropic Matrix Model for Periodic Metasurfaces of Uniaxially Mono-Anisotropic Scatterers Under Oblique TE-Wave Incidence
Bibliographic record
Abstract
A bianisotropic matrix technique is presented for the development of a homogenized surface susceptibility model of metasurfaces with arbitrary uniaxially mono-anisotropic scatterers, illuminated by obliquely incident TE waves. Based on the sole assumption that the scatterers can be described by point-dipoles, the proposed formulation establishes a simple relation between the homogenized metasurface susceptibility matrix and the scatterer polarizability matrix. For this purpose, the exact interaction coefficients, associating the metasurface local field vectors with the dipole moment vectors, are extracted in terms of rapidly convergent series. The resulting analytical expressions for the interaction coefficients are applicable to both near- and far-field problems. Moreover, the derived formula for the surface susceptibility matrix reveals the existence of off-diagonal terms, corresponding to a magnetoelectric coupling effect at the lattice level. The accuracy of the method is verified via comparisons with full-wave-simulation results for several metasurfaces of planar resonators and magnetodielectric spheres. It is observed that the efficiency of the model is contingent upon the electrical size of the scatterers rather than the lattice periodicity, since the former determines the validity of the point-dipole approximation, which is the only assumption throughout the analysis.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".