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Record W2005799076 · doi:10.1109/tmtt.2010.2086373

A Planar Reconfigurable Aperture With Lens and Reflectarray Modes of Operation

2010· article· en· W2005799076 on OpenAlexaff
Jonathan Y. Lau, Sean V. Hum

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2010
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsVaricapDirectivityBeamformingPlanarBroadsideAperture (computer memory)OpticsPlanar arrayFabricationMaterials scienceOptoelectronicsEngineeringElectronic engineeringComputer scienceElectrical engineeringPhysicsAcousticsCapacitanceAntenna (radio)

Abstract

fetched live from OpenAlex

This paper presents the design and experimental characterization of a planar 6 × 6 fully reconfigurable array operating at 5.7 GHz, capable of functioning both as a reconfigurable array lens and a reconfigurable reflectarray. First, the design of the array element, which consists of two varactor diode-loaded patches coupled by a varactor diode-loaded slot, is reviewed. Next, the design and fabrication of a planar array is described, and the varactor biasing scheme is discussed in detail. Experimental results demonstrating 2-D beamforming are presented, where a broadside directivity of 20.8 dBi and a beam-scanning range of 50° by 50° is achieved for the lens mode. The ability of the array to also function as a reflectarray is also demonstrated, achieving a directivity of 19.4 dBi at broadside and a beam-scanning range of 60° by 30°. Not only is this array able to demonstrate full 2-D beamforming, it is also low-cost and easy to fabricate, making it very attractive for applications where high-gain beam-scanning is needed.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.215
Teacher spread0.207 · 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

Citations80
Published2010
Admission routes1
Has abstractyes

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