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Record W2514442695 · doi:10.1109/antem.2016.7550152

The impedance boundary condition implementation for the 3D random auxiliary sources method

2016· article· en· W2514442695 on OpenAlexaff
M. A. Moharram, Ahmed A. Kishk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicElectromagnetic Scattering and Analysis
Canadian institutionsConcordia University
Fundersnot available
KeywordsHFSSBoundary (topology)InfinitesimalElectrical impedanceBoundary value problemComputer scienceFlexibility (engineering)Function (biology)Impedance parametersMathematical analysisMathematical optimizationMathematicsAntenna (radio)Microstrip antennaEngineeringTelecommunicationsElectrical engineering

Abstract

fetched live from OpenAlex

The Random Auxiliary Sources (RAS) method is used to solve the electromagnetic scattering problem from structures with impedance boundary conditions. Basically, the RAS method exploits a uniformly distributed random infinitesimal electric/magnetic dipoles as the expansion function, where their unknown amplitudes are used to directly satisfy the boundary conditions. Typically, the flexibility of the formulation and implementation of the developed RAS method enables an easy adaptation for arbitrary boundary conditions. The results of the proposed implementation are compared to analytical expressions for canonical geometries as well as the full-wave analysis of HFSS of several cases.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score0.647

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.314
Teacher spread0.307 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2016
Admission routes1
Has abstractyes

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