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Novel Integral Equation Formulation for Scattering on Dielectric Objects Free of Low-Frequency and Oversampling Breakdowns

2022· article· en· W4297514138 on OpenAlexaff
Osman Goni, Vladimir Okhmatovski

Bibliographic record

Venue2022 International Conference on Electromagnetics in Advanced Applications (ICEAA) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOversamplingDielectricScatteringIntegral equationFrequency dependencePhysicsMathematical analysisComputer scienceMathematicsOpticsQuantum mechanicsNuclear magnetic resonanceTelecommunicationsBandwidth (computing)

Abstract

fetched live from OpenAlex

In our recent work [1] we have demonstrated analytic solution of a novel Surface-Volume-Surface Electric Field Integral Equation with magnetic current (SVS-EFIE-M formulation) for the problem of Hertzian dipole radiation in the vicinity of a homogeneous non-magnetic dielectric sphere. The SVS-EFIE-M stated with respect to magnetic current $\hat{n}\times I$ on object’s surface S is shown in (1), where $\overline{\overline{G}}_{m\in}$ is the magnetic field dyadic Green’s function of the homogeneous non-magnetic space with relative permittivity $\epsilon, \overline{\overline{G}}_{e0}$ is the electric field dyadic Green’s function of free-space, and $\hat{n}\times E^{inc}$ is the tangential component of the incident electric field on the surface S. It is obtained through single-source magnetic current based surface integral representation of the electric field inside the dielectric object (see first term in (1) representing $\hat{n}\times E, E$ being the total electric field) constrained by the classical Volume-EFIE (V-EFIE) enforced on the boundary of the object for the tangential component of the electric field.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0020.002
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 head0.020
GPT teacher head0.281
Teacher spread0.260 · 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 designTheoretical or conceptual
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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