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Record W3135560447 · doi:10.1109/tap.2021.3060079

Heuristic UTD Diffraction Coefficient for Three-Dimensional Dielectric Wedges

2021· article· en· W3135560447 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDiffractionRay tracing (physics)DielectricPolarization (electrochemistry)Mathematical analysisUniform theory of diffractionGeometrical opticsOpticsWedge (geometry)Reflection coefficientGeometryHeuristicPhysicsMathematicsMathematical optimization

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.234
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it