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Record W2109715976 · doi:10.1109/aps.2012.6349033

A waveguide-microstrip structure for millimeter-wave spatial power combining

2012· article· en· W2109715976 on OpenAlexaff
Dachuan Sun, Zhizhang Chen, Yiqiang Yu, Bo Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsDalhousie University
Fundersnot available
KeywordsReturn lossMicrostripExtremely high frequencyKa bandPower (physics)Insertion lossWaveguideMaterials sciencePower dividers and directional couplersMillimeterComputer scienceElectronic engineeringOptoelectronicsElectrical engineeringOpticsTelecommunicationsPhysicsEngineeringAntenna (radio)

Abstract

fetched live from OpenAlex

In this paper, a novel structure is proposed for power dividing and combining at millimeter-wave bands. It presents lower loss compared to circuit level power combining since its power-combining occurs in air space; in comparison with the traditional spatial power combining, it uses metal enclosures to prevent possible radiation loss. Simulations were performed on the proposed structure and a prototype was developed and tested. It was found that efficiencies of power combining in the Ka band and E band are 93.3% and 71.6%, while the bandwidths of return loss less than -20dB are more than 3GHz in the Ka band and 6GHz in the E band, respectively. The advantages of it include: low loss since power combining occurs in free space; simple structure and easy fabrication due to the use of conventional waveguide and microstrip structures; easy integration with MMICs because of the use of microstrip lines; good efficiency performance.

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: 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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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
Published2012
Admission routes1
Has abstractyes

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