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Record W4315783791 · doi:10.1109/lawp.2023.3236614

Half-Annular Leaky-Wave Antenna With Suppressed Open Stopband: Design and Experimental Testing

2023· article· en· W4315783791 on OpenAlexaff
Maksim Kuznetcov, Davide Comite, Symon K. Podilchak, Paolo Burghignoli, Alessandro Galli, A.P. Freundorfer, Yahia M. M. Antar, Paolo Baccarelli

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsRoyal Military College of CanadaQueen's University
Fundersnot available
KeywordsStopbandPlanarLeaky wave antennaAntenna (radio)MathematicsOpticsMicrostrip antennaMathematical analysisTopology (electrical circuits)PhysicsCombinatoricsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

A new half-annular bull-eye leaky-wave antenna (LWA) design with suppressed open stopband (OSB) and integrated feeding is presented. The 2-D aperture offers a pencil beam with scanning through broadside and with stable gain values as a function of frequency. To achieve directional radiation, a TM$_{0}$surface wave supported by a grounded dielectric slab is properly perturbed by means of a suitable planar-periodic double-strip aperture. In addition, the proposed structure offers improved performance and reduced design complexity with respect to previous LWAs having a nonsuppressed OSB. Dispersion analyses based on a well-established method-of-moments approach are also performed to characterize the fast spatial harmonic supported by the structure and to determine the size, position, and periodicity of the strips needed for optimal OSB suppression. Simulations and experiments have been carried out to validate the antenna performance against the optimized unit cell. The fabricated planar-periodic LWA also provides continuous beam scanning from$-35^{\circ }$to$40^{\circ }$, with a peak realized gain of 21 dBi.

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)0.001
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: Not applicable · 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
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.031
GPT teacher head0.226
Teacher spread0.195 · 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 designNot applicable
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

Citations10
Published2023
Admission routes1
Has abstractyes

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