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Record W4313203610 · doi:10.1109/tap.2022.3231285

A Partially Reflecting Surface Antenna With a Non-Resonant Cavity and a Phase-Correcting Surface for Gain Enhancement

2022· article· en· W4313203610 on OpenAlexaff
Xiaolei Ren, Yuehe Ge, Zhizhang Chen, Hai Zhang

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsDalhousie University
FundersNatural Science Foundation of Fujian ProvinceNational Natural Science Foundation of China
KeywordsAntenna (radio)Surface (topology)LambdaPhase (matter)OpticsPhysicsMathematicsMathematical analysisTopology (electrical circuits)Computer scienceGeometryTelecommunicationsCombinatoricsQuantum mechanics

Abstract

fetched live from OpenAlex

Partially reflecting surface (PRS) resonant antennas have attracted significant attention for the past two decades. However, how to improve their peak gain is still a challenge. This article proposes a nonresonant PRS antenna that does not require the conventional resonant condition that limits design flexibility and peak gain. It consists of a PRS, a ground, a small feed, and a transparent phase-correcting surface (PCS) placed above the PRS for phase compensation. The ray-tracing method is used to analyze the proposed nonresonant PRS antenna, and numerical simulations are conducted to verify its effectiveness. Our theoretical and numerical results show that the gain of a PRS antenna with a nonresonant cavity reaches its peak when the cavity height is between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.6\lambda $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$0.9\lambda $ </tex-math></inline-formula> . Three prototypes are fabricated and tested, and the measured results show that peak gains of over 25 dBi and 3 dB gain bandwidths of more than 10% can be achieved at the same time.

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: none
Teacher disagreement score0.637
Threshold uncertainty score0.840

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 studies0.0010.000
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 head0.027
GPT teacher head0.281
Teacher spread0.254 · 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

Citations15
Published2022
Admission routes1
Has abstractyes

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