MétaCan
Menu
Back to cohort
Record W4405721100 · doi:10.3390/s24248183

High-Performance CP Magneto-Electric Dipole Antenna Fed by Printed Ridge Gap Waveguide at Millimeter-Wave

2024· article· en· W4405721100 on OpenAlexafffund
Zahra Mousavirazi, Mohamed Mamdouh M. Ali, Peyman PourMohammadi, Peng Fei, Tayeb A. Denidni

Bibliographic record

VenueSensors · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsInstitut National de la Recherche Scientifique
FundersFonds de recherche du Québec – Nature et technologies
KeywordsExtremely high frequencyWidebandOptoelectronicsBandwidth (computing)Dipole antennaOpticsMaterials scienceAxial ratioAntenna (radio)PhysicsElectrical engineeringCircular polarizationEngineeringTelecommunicationsMicrostrip

Abstract

fetched live from OpenAlex

This paper presents a high-performance circularly polarized (CP) magneto-electric (ME) dipole antenna optimized for wideband millimeter-wave (mm-wave) frequencies, specifically targeting advancements in 5G and 6G technologies. The CP antenna is excited through a transverse slot in a printed ridge gap waveguide (PRGW), which operates in a quasi-transverse electromagnetic (Q-TEM) mode. Fabricated on Rogers RT 3003 substrate, selected for its low-loss and cost-effective properties at high frequencies, the design significantly enhances both impedance and axial ratio (AR) bandwidths. The antenna achieves an impressive impedance bandwidth of 31% (25.24-34.50 GHz) and an AR bandwidth of 24.9% (26.40-33.91 GHz), with a peak gain of up to 8.4 dBic, demonstrating a high cross-polarization level. The experimental results validate the high-performance characteristics of the antenna, making it a robust candidate for next-generation wireless communication systems requiring CP capabilities.

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 score0.002

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 communication0.0000.000
Open science0.0000.000
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 head0.010
GPT teacher head0.194
Teacher spread0.183 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueSensorsSame topicMicrowave Engineering and WaveguidesFrench-language works237,207