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Record W4308798753 · doi:10.1063/5.0127427

Design of a coaxial and compact TM01–TE01 mode converter based on helical corrugated waveguide for high-power microwave system

2022· article· en· W4308798753 on OpenAlexaff
Sidi Liu, Lihui Jiang, Hao Li, Jianing Zhao, Keqiang Wang, Haiyang Wang, Tianming Li, Yihong Zhou, Fadhel M. Ghannouchi, Biao Hu

Bibliographic record

VenueAIP Advances · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGyrotron and Vacuum Electronics Research
Canadian institutionsUniversity of Calgary
FundersSpecific Research Project of Guangxi for Research Bases and TalentsNational Natural Science Foundation of China
KeywordsMicrowaveCoaxialWaveguideTransverse modePhysicsMode (computer interface)Energy conversion efficiencyPower (physics)OpticsWavelengthTransverse planeOptoelectronicsMaterials scienceElectrical engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

In this paper, a novel X-band TM01–TE01 mode converter for high-power microwave (HPM) system is proposed. To realize the compact design, a helical corrugated waveguide with large deformation is employed to convert the TM01 mode into TE01 mode. By optimizing the structure parameters, the simulation results demonstrate that the maximum conversion efficiency of the proposed TM01–TE01 mode converter reaches 98.8% at 9.2 GHz. Moreover, this mode converter exhibits a conversion efficiency of over 95% in the operation band from 8.7 to 9.42 GHz, and the power capacity of the mode converter reaches GW-level in theory. More importantly, the overall longitudinal and transverse dimensions of the mode converter are less than three wavelengths. The results show the compact structure and high conversion efficiency of this mode converter, which has great potential application value in the HPM system.

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 score0.002

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
GPT teacher head0.291
Teacher spread0.274 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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