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Formulation of Surface-Volume-Surface-EFIE for Solution of 2D Scattering Problems on Composite Dielectric Objects under TM Polarization

2018· article· en· W2909550285 on OpenAlexaff
Zhuotong Chen, Reza Gholami, Vladimir Okhmatovski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectric-field integral equationIntegral equationDielectricMathematical analysisScatteringPolarization (electrochemistry)Boundary value problemCurrent densitySurface (topology)MathematicsPhysicsOpticsGeometryChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

This work generalizes the Surface-Volume-Surface Electric Field Integral Equation (SVS- EFIE) for the solution of the 2D scattering problems on the composite penetrable non-magnetic objects under TM polarization. The SVS-EFIE formulation for homogeneous scatterers features a single unknown surface current density on the boundary. In order to generalize SVS- EFIE to the case of composite scatterers, two independent electric surface current densities must be introduced on the joint boundaries between different dielectric materials. On each of the remaining boundaries between the scatterer and the background medium only a single unknown surface current density is introduced similarly with the original SVS- EFIE. To validate the new formulation its solution is compared against solution of the traditional Volume-EFIE (V-EFIE).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.013
GPT teacher head0.241
Teacher spread0.229 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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