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Record W2799433131 · doi:10.1109/lawp.2018.2830349

Formulation of Surface-Volume-Surface-EFIE for Solution of Three-Dimensional Scattering Problems on Composite Dielectric Objects

2018· article· en· W2799433131 on OpenAlexafffund
Zhuotong Chen, Reza Gholami, Jamiu Mojolagbe

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDielectricSurface (topology)ScatteringComposite numberMaterials scienceVolume (thermodynamics)Composite materialMathematical analysisMathematicsOpticsPhysicsGeometryOptoelectronics

Abstract

fetched live from OpenAlex

A novel formulation of the surface-volume-surface electric field integral equation (SVS-EFIE) is introduced for the solution of the scattering and radiation problems on the composite dielectric objects made of arbitrary number of piecewise homogeneous nonmagnetic dielectric regions. The regions composing the scatterer may or may not share common boundaries. Due to the fact that SVS-EFIE introduces the independent fictitious surface electric current densities on the boundary of each region, the proposed formulation allows for independent meshing of the scatterer regions according to its permittivity. As a result, the new SVS-EFIE formulation presents no complications in method-of-moments discretization at the material junctions. The proposed formulation exhibits the same CPU time and the memory complexities as traditional surface integral equation formulations when fields throughout the scatterer are to be computed. The new equation is validated through comparison of its solution against the Mie series and fields computed using the commercial software.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0030.001

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 head0.227
Teacher spread0.214 · 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
GenreMethods

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

Citations15
Published2018
Admission routes2
Has abstractyes

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Same venueIEEE Antennas and Wireless Propagation LettersSame topicElectromagnetic Scattering and AnalysisFrench-language works237,207