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Record W2918775705 · doi:10.1109/access.2019.2902596

High Gain Bow-Tie Slot Antenna Array Loaded With Grooves Based on Printed Ridge Gap Waveguide Technology

2019· article· en· W2918775705 on OpenAlexaff
Syed M. Sifat, Mohamed Mamdouh M. Ali, Shoukry I. Shams, Abdel-Razik Sebak

Bibliographic record

VenueIEEE Access · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsBow tieSlot antennaMaterials scienceAntenna efficiencyAntenna measurementAntenna gainWidebandRadiation patternOpticsAntenna (radio)Bandwidth (computing)Electrical engineeringAcousticsOptoelectronicsComputer sciencePhysicsEngineeringTelecommunications

Abstract

fetched live from OpenAlex

The development of wireless and satellite communication has led to a demand for high-performance microwaves and mm-wave components in terms of cost, losses, and fabrication complexity. Gap waveguide is one of the emerging technologies in 5G and mm-wave applications due to their low cost, low losses, and high power handling capability. In this paper, a groove-based wideband bow-tie slot antenna array is designed at 30 GHz based on printed ridge gap waveguide technology (PRGW). A two-section T-shaped ridge is designed to feed a bow tie slot placed on the upper ground of the PRGW. The gain of the proposed slot antenna is enhanced by using a horn-like groove. Then, the proposed high gain element is deployed to build up a 1 bow-tie slot antenna array loaded with three-layer groove antenna. The proposed antenna array is fabricated and measured, where the measured results show a -10-dB impedance bandwidth from 29.5 to 37 GHz (22%). The fabricated prototype achieves a high gain of 15.5 dBi and a radiation efficiency higher than 80% over the operating frequency bandwidth.

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 communication0.0000.000
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 head0.012
GPT teacher head0.225
Teacher spread0.213 · 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

Citations67
Published2019
Admission routes1
Has abstractyes

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