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Record W4406089989 · doi:10.1109/tap.2024.3523632

An Exact Synthesis Procedure for Minimum Quality Factor Spherical Wire Antennas

2025· article· en· W4406089989 on OpenAlexafffund
A. Murray, Ashwin K. Iyer

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2025
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Alberta
FundersMinistère de la Défense Nationale
KeywordsQuality (philosophy)Directional antennaComputer scienceAntenna (radio)PhysicsTelecommunications

Abstract

fetched live from OpenAlex

A design procedure for minimum- <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> spherical wire antennas of arbitrarily small electrical size is derived. Special cases of circularly polarized and self-tuned designs are investigated, where the latter is elliptically polarized and provides the lowest <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> for any configuration of radiated spherical waves in the electrically small regime. The designs are tested in simulation to find that the polarizations agree with the theoretical predictions. The basis for these theoretical predictions — spherical wave expansion — also provides <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> estimates which are attained in the self-tuned (lowest <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> electrically small spherical wire) case over a range of electrical sizes for a large number of wires. For a more practical number of wires, a special case is synthesized with electrical size ka = 0.25 to obtain a quality factor which is 1.03 times the lower bound on spherical wire antenna Q, the lowest <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Q</i> relative to this lower bound in the literature.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.941
Threshold uncertainty score0.843

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 studies0.0000.000
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 head0.266
Teacher spread0.247 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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