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Record W2891869238 · doi:10.1109/tap.2018.2869228

2-D Scanning Magnetoelectric Dipole Antenna Array Fed by RGW Butler Matrix

2018· article· en· W2891869238 on OpenAlexaff
Mohamed Mamdouh M. Ali, Abdel-Razik Sebak

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2018
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsBandwidth (computing)Dipole antennaRadiation patternOpticsDipoleBroadsidePhysicsComputer scienceAntenna (radio)Telecommunications

Abstract

fetched live from OpenAlex

In this paper, a 2-D scanning magnetoelectric (ME) dipole antenna array fed by printed ridge gap waveguide (PRGW) Butler matrix is proposed. The ME dipole antenna is designed to achieve a bandwidth wider than 20% at 30 GHz and stable gain of 6.5 ± 0.8 dB over the operating frequency bandwidth. A 4 × 4 planar PRGW Butler matrix is designed and constructed using a four PRGW hybrid couplers having a wide bandwidth performance. The overall performance of the Butler matrix exhibits about 5° phase error over the operating frequency bandwidth. The integration of ME dipole antennas with the designed Butler matrix results in four fixed beams, one in each quadrant at an elevation angle of 35° from the broadside to the array axis. The proposed passive beam switching network (BSN) has a wide bandwidth of 20% with radiation efficiency higher than 84% over the operating bandwidth. The proposed BSN shows a stable radiation pattern with a stable gain of 10.3 ± 0.2 dB, where the sidelobe level is less than -15 dB over the whole operating frequency band. The fabricated prototype of the proposed BSN is tested, where the measured and simulated results show an excellent agreement.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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

Citations93
Published2018
Admission routes1
Has abstractyes

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