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Record W2588172454 · doi:10.1109/temc.2017.2655497

Mixed-Potential Integral Equation for Full-Wave Modeling of Grounding Systems Buried in a Lossy Multilayer Stratified Ground

2017· article· en· W2588172454 on OpenAlexaff
Hamidreza Karami, Keyhan Sheshyekani, Farhad Rachidi

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsGroundEarthing systemLossy compressionIntegral equationFunction (biology)Ground planeOblique caseMathematical analysisGeometryGeologyMathematicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

The paper presents a comprehensive approach for full-wave modeling of grounding systems buried in a multilayer stratified ground. The dyadic spectral domain Green's function associated with the multilayer stratified ground is first derived. This Green's function is then evaluated by numerical solution of Sommerfeld's integrals along the deformed path in the complex plane. The proposed method is general as it can deal with arbitrarily shaped grounding electrodes buried in soils with any number of layers. The case of horizontal, vertical, and oblique electrodes buried in soils with different layers is analyzed. The accuracy of the proposed approach is first validated by comparing the results with those obtained by NEC-4 for a homogeneous soil, and with those obtained by finite difference time domain method for a multilayer soil.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.058
GPT teacher head0.273
Teacher spread0.215 · 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

Citations39
Published2017
Admission routes1
Has abstractyes

Explore more

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