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Compression Mount Connector-to-SICL Transition Optimization at Millimeter-Wave Frequencies

2022· article· en· W4321844099 on OpenAlexaff
Aditya Singh, Carlos E. Saavedra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Antenna and Metasurface Technologies
Canadian institutionsQueen's University
Fundersnot available
KeywordsCable glandCoaxialPrinted circuit boardMaterials scienceCompression (physics)BendingSubstrate (aquarium)Insertion lossFabricationMillimetre waveOptoelectronicsElectrical engineeringAcousticsEngineeringComposite materialPhysicsGeology

Abstract

fetched live from OpenAlex

A transition optimization for vertical launch solder-less compression mount connector to substrate integrated coaxial lines (SICL) transition is presented up to K and Ka bands. The design for the coaxial-to-SICL transition is delineated while emphasizing on the key design parameters considering fabrication feasibility using printed circuit board (PCB) technology. Through full-wave simulations, an improvement of 1.07 dB in the insertion loss exhibited by the coaxial-to-SICL transition is demonstrated by using an array of plated through hole Vias in the vicinity of the transition. Furthermore, potential causes of performance degradations due to imperfect ground connections and SICL bending due to compression are considered. It is shown that SICL bending minimally impacts the performance, however, inadvertent effects such as an air gap may prominently impact the connector 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

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 science0.0000.000
Research integrity0.0000.000
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 head0.206
Teacher spread0.185 · 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
Published2022
Admission routes1
Has abstractyes

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