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Design and Validation of Hybrid Couplers for Satellite Feed Networks in Ka and QV Bands

2025· article· en· W4415125827 on OpenAlexaff
Mahmoud Gadelrab, Ahmed Elsaadany, Shoukry I. Shams, Mahmoud Elsaadany, Ghyslain Gagnon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsApollo Microwaves (Canada)Concordia University
Fundersnot available
KeywordsCommunications satelliteReliability (semiconductor)SatelliteWidebandIntermodulationTransmission (telecommunications)SIGNAL (programming language)Power (physics)Coupling (piping)

Abstract

fetched live from OpenAlex

The performance of satellite communication systems depends on the efficiency and reliability of the feeding structure, which directly impacts link quality, data transmission rates, and signal reception sensitivity. This paper presents the design, optimization, and experimental validation of three hybrid couplers operating at Ka and QV bands, which are critical for modern low-Earth orbit (LEO) satellite applications. The proposed designs employ both E-plane and H-plane coupling techniques to achieve wideband operation, high isolation, and minimal insertion loss. The couplers are optimized for size, weight, and power (SWaP) considerations, ensuring suitability for space applications. A comprehensive study for thermal, power, and passive intermodulation analysis is performed to evaluate their performance under realistic operating conditions. Fabrication and experimental testing validate the proposed designs, showing an agreement between simulated and measured results.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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
Published2025
Admission routes1
Has abstractyes

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