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Record W2902157966 · doi:10.1109/lawp.2018.2872937

Wideband Low Axial Ratio and High-Gain Sequentially Rotated Antenna Array

2018· article· en· W2902157966 on OpenAlexaff
Wei Hu, Daniele Inserra, Guangjun Wen, Zhizhang Chen

Bibliographic record

VenueIEEE Antennas and Wireless Propagation Letters · 2018
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsDalhousie University
FundersFundamental Research Funds for the Central UniversitiesNational Natural Science Foundation of China
KeywordsWidebandBandwidth (computing)Antenna gainAntenna arrayAntenna noise temperaturePhysicsDipole antennaMicrostrip antennaAntenna measurementAntenna factorAntenna (radio)Electrical engineeringOpticsComputer scienceEngineeringTelecommunications

Abstract

fetched live from OpenAlex

A 2 × 2 sequentially rotated antenna array with high gain and wide axial ratio (AR) bandwidth is designed. The antenna element consists of a microstrip patch fed with a series power divider through four L-shaped probes. Low AR ≤ 1 dB is guaranteed by reiterating the employment of the series power divider at the array level, and optimizing the antenna element impedance matching bandwidth performance. A prototype of the antenna array is manufactured for the ultrahigh frequency radio frequency identification bandwidth, with overall size 1.32 × 1.32 × 0.065 λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> . Measurement shows a S11 ≤ -10 dB bandwidth of 36.5% (0.76-1.1 GHz), an AR ≤ 3 dB bandwidth of 28.8% (0.823-1.1 GHz) with AR ≤ 1 dB within 0.846-1.015 GHz (18.2%). Moreover, a peak gain of 13.2 dBic, and a 3 dB gain bandwidth of 26.1% are observed. The presented antenna exhibits outstanding AR performance and high gain despite its low design and implementation complexity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.197
Teacher spread0.189 · 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 teacher head, 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

Citations22
Published2018
Admission routes1
Has abstractyes

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