Heuristic UTD Diffraction Coefficient for Three-Dimensional Dielectric Wedges
Bibliographic record
Abstract
This article presents a heuristic diffraction coefficient for 3-D dielectric wedges of arbitrary angles. First, the proposed diffraction coefficient is derived for 2-D wedges with soft and hard polarization, by enforcing continuity of the total field at every shadow boundary associated with the multiply reflected fields inside the wedge. The accuracy of the proposed heuristic 2-D solution is verified comparing with the UTD solution derived from Maliuzhinets' exact integration. Then, it is extended to 3-D wedges for arbitrary polarizations and incident/scattered directions by transforming ray-fixed to edge-fixed coordinate systems. The total field predicted using the proposed heuristic diffraction coefficient is continuous for arbitrary angle 3-D dielectric wedges, including interior wedges, and is close to the geometrical optics field far from the shadow boundaries. The proposed method is validated against a Method-of-Moment solution. Finally, we show the method can significantly improve the accuracy of ray-tracing modeling of wave propagation in arched tunnels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".