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Low-Profile Electronic Beam-Scanning Leaky-Wave Antenna Composed of Longitudinal Cells

2021· article· en· W3195726472 on OpenAlexaff
Nima Javanbakht, Rony E. Amaya, B. Syrett, J. Shaker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsCommunications Research Centre CanadaCarleton University
FundersAgence Nationale de la Recherche
KeywordsVaricapLeaky wave antennaCapacitanceMaterials scienceBeam (structure)Antenna (radio)OpticsOptoelectronicsDiodeBeam steeringSubstrate (aquarium)Microstrip antennaElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

A novel electronic beam-scanning leaky-wave antenna (LWA) is presented in this paper. The proposed LWA was realized on a low-profile substrate integrated waveguide (SIW). Integrating varactor diodes into the ground plane and sweeping their capacitances leads to variations in the propagation constant and main-beam pointing angle. The center frequency was chosen as 27.8 GHz to accommodate 5G mm-wave applications. The length, width, and thickness of the LWA are 148 mm, 24 mm, and 0.127 mm, respectively. Sweeping the varactor diodes’ capacitance leads to a 16° beam-scanning range with a peak realized gain of 8.2 ± 0.2 dBi at 27.8 GHz. The low-profile, small gain variation, electronic beam-scanning, and medium gain are among the features of the proposed LWA that make it a suitable candidate for 5G beam-scanning applications.

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 score0.001
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 communication0.0000.001
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 head0.011
GPT teacher head0.199
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

Citations1
Published2021
Admission routes1
Has abstractyes

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