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Record W4405601145 · doi:10.1109/tmtt.2024.3512198

Design of a Helical-Shaped TM<sub>01</sub>–TE<sub>01</sub> Mode Converter Based on Eigenmode Expansion Method

2024· article· en· W4405601145 on OpenAlexaff
Sidi Liu, Hao Li, Meiling Ou, Jianing Zhao, Haiyang Wang, Yihong Zhou, Yiyun Wang, Jiaoyin Wang, Tingxu Chen, Mingyu Yang, Fadhel M. Ghannouchi, Biao Hu

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2024
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsEigenmode expansionNormal modeMode (computer interface)PhysicsMaterials scienceAcousticsComputer scienceVibration

Abstract

fetched live from OpenAlex

A novel helical-shaped circular TM01–TE01 mode converter working in the X-band is proposed in this article. By combining the polarization conversion of rays in free space and mode conversion in a waveguide, the mode conversion potential between TM$_{{0}m}$mode and TE$_{{0}{n}}$mode of helical corrugated waveguide (HCW) is fully explored. Through the eigenmode expansion method, the process of mode conversion in HCW can be clearly demonstrated, guiding the construction of the final helical-shaped TM01–TE01 mode converter. Simulation results reveal a peak conversion efficiency of 99.6% at 8.6 GHz and a bandwidth of 16.5% with over 95% conversion efficiency (center frequency 9 GHz). In addition, the mode converter has a power capacity of gigawatt level with a longitudinal length of only 3.81 wavelengths. To validate the structure, a prototype was fabricated and measured. The results of the experiment are in great agreement with the simulation, which verifies the effectiveness of this proposed mode converter.

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 communication0.0000.000
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 head0.015
GPT teacher head0.270
Teacher spread0.254 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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