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Record W2153789080 · doi:10.1109/appeec.2009.4918329

Electromagnetic Fields of Energized Conductors in Multilayer Medium with Recursive Methodology

2009· article· en· W2153789080 on OpenAlexaff
Simon Fortin, Yixin Yang, Jinxi Ma, F. Dawalibi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsSafe Engineering Services & Technologies (Canada)
Fundersnot available
KeywordsElectrical conductorConductorSuperposition principleComputationDipolePermittivityElectromagnetic fieldElectric fieldPerfect conductorNumerical analysisPhysicsMaterials scienceComputational physicsMathematical analysisOpticsComputer scienceMathematicsDielectricOptoelectronicsGeometryAlgorithmScattering

Abstract

fetched live from OpenAlex

This paper discusses the computation of electromagnetic fields due to energized thin-wire conductors in a horizontal multilayer medium. The computation uses a full wave solution for the Hertz vector potential caused by an electric dipole located in the medium. The dipole coefficients are obtained analytically using a recursive algorithm. All the layers can have arbitrary resistivity, permeability, permittivity, and thickness. The field generated by a linear conductor is obtained by integrating the contribution of dipoles along the conductor. The field of an arbitrary conductor network is obtained by superposition of the contribution of all the network conductors. The energized conductors and the calculation points can be in any layer of the medium. Numerical results obtained with this method are presented and compared to those obtained with a completely numerical method: it is shown that the recursive method yields identical results to the numerical method, and that it is much faster.

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)0.000
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 score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.261
Teacher spread0.244 · 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

Citations9
Published2009
Admission routes1
Has abstractyes

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