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Record W2972432391

Cavity-excited Ferroelectric Lens Antenna for Low-Sidelobe Beam Steering

2019· article· en· W2972432391 on OpenAlexaff
Huan Li, Mohamad Mantash, Zhenjiang Zhao, Tayeb A. Denidni

Bibliographic record

VenueEuropean Conference on Antennas and Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBeam steeringOpticsBeam waveguide antennaAntenna (radio)Lens (geology)Phased arrayWaveguideRadiation patternPeriscope antennaBeam (structure)Materials sciencePhysicsEngineeringElectrical engineering
DOInot available

Abstract

fetched live from OpenAlex

Utilizing the technique of cavity excitation, this paper proposes a novel beam steering antenna with low sidelobe level. The whole beam steering antenna consists of two parts, i.e., a parallel plate waveguide based ferroelectric lens and a cavity-type feeding source. By independently tuning the DC bias voltage of each column of the ferroelectric lens, the beam of feeding source can be effectively collimated and steered in the far-field. By eliminating the spillover radiation at lens edge, low-sidelobe beam steering can be implemented. Full-wave simulated results show that the proposed antenna can achieve sidelobe levels of -34 dB at 0° steering angle and -25 dB at 30° steering angle. Without involving the complex feeding networks, the proposed lens antenna provides a promising alternative of complicated phased array antennas.

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 score0.003

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.213
Teacher spread0.191 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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