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Record W4226038434 · doi:10.1109/lawp.2022.3163603

Linear Antenna Array With Large Element Spacing for Wide-Angle Beam Scanning With Suppressed Grating Lobes

2022· article· en· W4226038434 on OpenAlexaff
Quanxin Ren, Bingyi Qian, Xiaoming Chen, Xiaoyu Huang, Qinlong Li, Jiaying Zhang, Ahmed A. Kishk

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2022
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
FundersNational Natural Science Foundation of China
KeywordsGratingAntenna (radio)PhysicsAzimuthRadiation patternOpticsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

An array with large element spacing is designed for wide scanning. The element is designed to suppress the grating lobe by combining two radiation modes. The two modes are the$\mathrm{T}{\mathrm{E}_{\boldsymbol{1}\delta 1}}$mode of a square dielectric resonator antenna (DRA) and the TM011mode of a monopole. The proper phase and magnitude combine the two modes to achieve the required radiation pattern. The element radiation pattern has an asymmetric radiation pattern with the main beam pointing to the anticipated scanning region and a low radiation level in the region of the expected grating lobe. A linear four-element array with an interelement spacing of 0.95λ0(λ0is the operation wavelength) is designed, fabricated, and measured for beam scanning in the E-plane to verify the design concept. The array's grating lobe is reduced by more than 11 dB within the scan angle of 15°–60° and bandwidth of 4.51–5.32 GHz. Good agreements between simulated and measured results are observed.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.004

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 head0.194
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 designSimulation or modeling
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

Citations30
Published2022
Admission routes1
Has abstractyes

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