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Record W2957492955 · doi:10.1109/tap.2019.2927628

Active Circular Retro-Directive Array

2019· article· en· W2957492955 on OpenAlexfundno aff
Anil Chepala, Vincent Fusco, Neil Buchanan

Bibliographic record

VenueIEEE Transactions on Antennas and Propagation · 2019
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilQueen's UniversityQueen's University Belfast
KeywordsDirectiveRetransmissionComputer scienceCircular bufferOpticsPhysicsAcousticsTelecommunications

Abstract

fetched live from OpenAlex

Retro-directive arrays provide retransmission of signals back toward their source direction without prior knowledge of the source location. In this paper, we explore the use of an active circular retro-directive array for up to 360° retro-directive monostatic coverage. We also show by selectively choosing which bank of array elements are to be conjugated how we can control sectoral retro-directive coverage. The simulated and experimental results are provided for a 16-dipole element active circular retro-directive array at 2.4 GHz.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.197
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 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

Citations8
Published2019
Admission routes1
Has abstractyes

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