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Record W2761979321 · doi:10.1109/tmtt.2017.2757469

An Integrated Ka-Band Diplexer-Antenna Array Module Based on Gap Waveguide Technology With Simple Mechanical Assembly and No Electrical Contact Requirements

2017· article· en· W2761979321 on OpenAlexaff
Abbas Vosoogh, Milad Sharifi Sorkherizi, Ashraf Uz Zaman, Jian Yang, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
FundersH2020 European Research CouncilChalmers Tekniska HögskolaVINNOVA
KeywordsDiplexerSplitterAntenna (radio)PassbandBeam waveguide antennaElectronic engineeringDipole antennaRadiation patternAntenna feedElectrical engineeringEngineeringComputer scienceAntenna efficiencyOpticsBand-pass filterPhysics

Abstract

fetched live from OpenAlex

This paper presents the integration of a diplexer with a corporate feed network of a high gain slot array antenna at the Ka-band. A hybrid diplexer-splitter with a novel architecture is proposed to have compatibility for its direct integration with the feed network of the array antenna. A seventh-order hybrid diplexer-splitter is successfully integrated into a corporate feed network of a 16×16 slot array antenna. The proposed integrated diplexer-antenna module consists of three distinct metal layers without the need of electrical contacts between the different layers based on the recently introduced gap waveguide technology. The designed module has two channels of 650-MHz bandwidths each with center frequencies 28.21 and 29.21 GHz. The fabricated prototype provides good radiation and input impedance characteristics. The measured input reflection coefficients for both Tx/Rx ports are better than -13 dB with the measured antenna efficiency better than 60% in the designed passband, which includes the losses in the diplexer.

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 head0.012
GPT teacher head0.249
Teacher spread0.237 · 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

Citations89
Published2017
Admission routes1
Has abstractyes

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