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Record W3121080819 · doi:10.1109/tmtt.2020.3048149

A Novel Design Technique for mm-Wave Mismatch Terminations

2021· article· en· W3121080819 on OpenAlexaff
Mahmoud Elsaadany, Mohamed Mamdouh M. Ali, Shoukry I. Shams, Tayeb A. Denidni, Ghyslain Gagnon

Bibliographic record

VenueIEEE Transactions on Microwave Theory and Techniques · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à MontréalConcordia UniversityÉcole de Technologie Supérieure
Fundersnot available
KeywordsBandwidth (computing)MicrowaveReflection (computer programming)Reflection coefficientElectronic engineeringFrequency bandAcousticsOpticsMaterials scienceComputer scienceEngineeringPhysicsTelecommunications

Abstract

fetched live from OpenAlex

The mismatch terminations are essential components in measurement setups of various active and passive microwave components. The main objective of such components is to provide a certain reflection level with a flat response over the operating bandwidth. The need for mm-wave mismatch terminations has increased due to the expansion of using mm-wave frequency ranges in future communication systems. The mm-wave bands are considered among the essential bands for the 5G communication system, which is expected to launch this year. In addition, the utilization of these bands is expected to increase in the 6G standard. In this article, we introduce a novel systematic procedure to design mismatch terminations. Moreover, two examples of mismatch terminations are designed with reflection levels of -9.5 and -12.7 dB, with the operating band of 32-38 GHz. For experimental validation, the proposed design is fabricated and measured, where a good agreement is achieved between the simulated and the measured response.

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)0.001
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: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.020
GPT teacher head0.235
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
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

Citations5
Published2021
Admission routes1
Has abstractyes

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