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Record W1600287067 · doi:10.23919/eumc.2011.6101963

Broadband substrate-integrated-waveguide six-port applied to the development of polarimetric imaging radiometer

2011· preprint· en· W1600287067 on OpenAlexaff
Ali Doghri, Tarek Djerafi, Anthony Ghiotto, Ke Wu

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicMicrowave Engineering and Waveguides
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsBroadbandRadiometerPolarimetryPort (circuit theory)WaveguideSubstrate (aquarium)Remote sensingMicrowave imagingOpticsMaterials scienceComputer scienceOptoelectronicsElectronic engineeringMicrowaveTelecommunicationsPhysicsGeologyEngineeringScattering

Abstract

fetched live from OpenAlex

In this paper, a Ka-band broadband substrate integrated waveguide (SIW) six-port technique proposed for polarimetric imaging radiometer is reported. Conventional polarimetric radiometer systems generally consist of a phase discriminator composed of couplers, crossovers and external loads. A six-port is proposed for the first time to replace this discriminator. This approach presents advantages such as lower cost and smaller size. Furthermore, the use of SIW technology allows integrability, compactness and low interference susceptibility. Simulation and measurement results show that the proposed six-port can operate over 20% of bandwidth centered at 34 GHz. To the knowledge of the authors, the achieved bandwidth is higher than other SIW six-ports reported in the literature. To validate the proposed application, the six-port is integrated in a front-end receiver. Measurements show achieved polarimetric channel symmetry of 1 dB and isolation of 29 dB which presents excellent performances. This six-port architecture presents advantages for implementation in passive millimeter-wave remote sensing systems.

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)0.000
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: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.020
GPT teacher head0.217
Teacher spread0.197 · 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

Citations15
Published2011
Admission routes1
Has abstractyes

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