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Record W3000377253 · doi:10.1109/tap.2020.2964940

A Dual-Polarized Magnetoelectric Dipole Array Based on Printed Ridge Gap Waveguide With Dual-Polarized Split-Ring Resonator Lens

2020· article· en· W3000377253 on OpenAlexaff
Abdolmehdi Dadgarpour, Nima Bayat-Makou, Marco A. Antoniades, Ahmed A. Kishk, Abdel-Razik Sebak

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsToronto Metropolitan UniversityUniversity of TorontoConcordia University
Fundersnot available
KeywordsOpticsPhysicsDipole antennaReflection coefficientAntenna (radio)Radiation patternAntenna gainSplit-ring resonatorAntenna efficiencyResonatorMaterials scienceTelecommunications

Abstract

fetched live from OpenAlex

In this article, a 1x4 dual-polarized magnetoelectric (ME) dipole antenna array is proposed. The antenna is excited by a dual-polarized fork-shaped microstrip line based on printed ridge gap waveguide technology. To achieve additional gain, a dual-polarized split-ring resonator (SRR) is designed, which has a mu-near zero (MNZ) feature. This scatterer works as a meta-lens to convert the radiated spherical wave to a plane wave for both polarizations. Integrating four layers of the proposed inclusions on top of the ME dipole antenna results in an increase in the antenna gain by a maximum of 3 dB. The proposed 1 x 4 antenna array was fabricated and tested, and the measured reflection coefficient at Port 1 is less than -10 dB over the frequency band of 28-35 GHz, while the measured realized gain varies from 14.3 to 15.4 dBi, and the measured radiation efficiency varies from 78% to 88%. The measured reflection coefficient at Port 2 for the orthogonal polarization is less than -8 dB over the frequency band of 28-35 GHz, with a measured gain of 14.8-16 dBi, and the measured radiation efficiency varies from 81% to 90%.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.203
Teacher spread0.187 · 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

Citations52
Published2020
Admission routes1
Has abstractyes

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