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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 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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.478
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.020
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 teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2022
Admission routes1
Has abstractyes

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