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Record W2414657622 · doi:10.1109/tap.2016.2576502

Full-Space Electronic Beam-Steering Transmitarray With Integrated Leaky-Wave Feed

2016· article· en· W2414657622 on OpenAlexafffund
Jeffrey Grant Nicholls, Sean V. Hum

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2016
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Ontario
KeywordsBeam steeringAperture (computer memory)OpticsBeamformingAzimuthPhased arrayPhased-array opticsBeam (structure)Antenna (radio)Computer scienceMaterials sciencePhysicsAcousticsTelecommunications

Abstract

fetched live from OpenAlex

A new low-profile, full-space, electronic beam-steering antenna architecture which combines the full-space beam-steering properties of reconfigurable transmitarrays with the low-profile feeding characteristics of leaky-wave antennas is proposed. The design uses an integrated leaky-wave feed to spatially distribute power across a reconfigurable transmitarray aperture in a low-profile manner while individual element phase control using varactor diodes enables full-space pencil-beam-steering. A $6\times 6$ element array was fabricated and experimentally verified, and full-space (both azimuth and elevation) beam-steering was demonstrated at angles up to 45° off broadside with a total efficiency for all scan angles on the order of 25%-35%. The design demonstrates a tenfold reduction in the overall thickness over the original transmitarray, improved aperture amplitude distribution control, as well as improved spillover control. The design also acts as a lower cost, scalable alternative to phased arrays as it does not require a complicated beamforming network and the feed and aperture are easily scaled.

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 score0.004

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.192
Teacher spread0.182 · 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

Citations88
Published2016
Admission routes2
Has abstractyes

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