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Record W3084849267 · doi:10.1109/access.2020.3022965

A Frequency-Scanned Slow-Wave Waveguide Antenna at Millimeter-Wave Frequencies

2020· article· en· W3084849267 on OpenAlexaff
Amirmasoud Ohadi, George V. Eleftheriades

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBeamwidthRadiation patternOpticsAntenna measurementAntenna factorBeam waveguide antennaAntenna gainAntenna (radio)Coaxial antennaAntenna efficiencyPhysicsAntenna apertureDipole antennaAcousticsMaterials scienceTelecommunicationsComputer science

Abstract

fetched live from OpenAlex

In this paper we describe the design, theory, implementation, and measurement results of a novel frequency-scanned slow-wave slotted waveguide antenna. The slow-wave antenna is fabricated by periodically loading a standard WR22 waveguide using cylindrical posts. The antenna has a backward radiation and can steer the beam from -72° to -12° by changing the frequency from 32.5 GHz to 37.4 GHz. The gain of the antenna remains within a 3 dB range from the maximum gain of 17 dBi throughout the steering range. The antenna is 29λ in length and 0.69λ in width. The antenna radiation efficiency is between 74% and 92% throughout its frequency range which allows for further extension of its length in order to achieve a higher gain and a smaller beamwidth. Moreover, the small lateral width of the antenna allows placing several of them side-by-side to either narrow or facilitate scanning the beam in the transverse plane. The antenna has a fast and constant scanning rate of 4.3° over a 1% bandwidth and a return loss of less than -10 dB throughout its operating frequency range.

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: Methods · 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 communication0.0000.001
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 head0.048
GPT teacher head0.235
Teacher spread0.187 · 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
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

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