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Loop Proximity Effects in Three-Loop Antenna Systems

2021· article· en· W3207111398 on OpenAlexafffund
Christopher G. Hynes, Rodney G. Vaughan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectromagnetic Compatibility and Measurements
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLoop antennaLoop (graph theory)DipolePhysicsAntenna (radio)Dipole antennaMagnetic dipoleMoment (physics)RADIUSElectric fieldCurrent loopPlane waveElectromagnetic fieldAcousticsVoltageElectrical engineeringOpticsEngineeringComputer scienceMathematicsClassical mechanicsAntenna factor

Abstract

fetched live from OpenAlex

Three-loop antenna systems (TLASs) are one of the few sensors with the ability to simultaneously detect all of the components of the electromagnetic field. The theory of TLASs ignores the presence of the other orthogonal loops and assumes that the response is that of a dual-loaded loop in isolation. This paper investigates the loop proximity effects for the cases of plane wave and centrally located dipole moment sources, as a function loop wire radius. It is shown through simulations that the responses of the TLAS sensor are unaffected by the presence of the other loops when considering just the magnetic field component of the plane wave and the magnetic dipole source. However, the TLAS reponses to the electric field component of the plane wave and the electric dipole moment can be significantly affected.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.209
Teacher spread0.193 · 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 designBench or experimental
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

Citations1
Published2021
Admission routes2
Has abstractyes

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