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Record W2606390924 · doi:10.1109/tmtt.2017.2690298

Design of 3-dB Hybrid Coupler Based on RGW Technology

2017· article· en· W2606390924 on OpenAlexaff
Shoukry I. Shams, Ahmed A. Kishk

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsConcordia University
Fundersnot available
KeywordsExtremely high frequencyMicrowaveRadarHybrid couplerElectronic engineeringPower dividers and directional couplersMicrowave transmissionFrequency bandElectronic circuitMillimeterComputer scienceEngineeringElectrical engineeringTelecommunicationsBandwidth (computing)OpticsPhysics

Abstract

fetched live from OpenAlex

Hybrid couplers are essential devices in various microwave circuits and systems, such as radar systems and beam forming networks. The development of this device is necessary along with the development of the new communication standards for the most modern guiding structures. One of the most recent and promising guiding technologies is the ridge gap waveguides, which is expected to play an essential role in the millimeter wave and submillimeter wave applications, not only the 5G communications but also other future communication. Therefore, standards are recommended to make use of the high-frequency guiding structures. In this paper, a design procedure for the hybrid couplers is presented. The frequency band of interest is centered at the 15 GHz, which can be deployed for both the 5G mobile communication and the airborne radar applications. The proposed design is fabricated and measured. The measured and simulated results are in excellent agreement.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.226
Teacher spread0.215 · 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

Citations87
Published2017
Admission routes1
Has abstractyes

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