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Record W1490231314 · doi:10.1109/aero.2015.7119133

Design of a quasi optical transmission line for Cloud and precipitation radar system of ACE mission

2015· article· en· W1490231314 on OpenAlexaboutno aff
Vahraz Jamnejad, Ezra Long, Stephen L. Durden

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPrecipitation Measurement and Analysis
Canadian institutionsnot available
FundersRice University
KeywordsRadarRemote sensingCloud computingTransmitterSoftware deploymentComputer scienceAntenna (radio)Transmission (telecommunications)Transmission lineMan-portable radarAerospace engineeringSystems engineeringRadar engineering detailsElectronic engineeringEngineeringTelecommunicationsRadar imagingGeology

Abstract

fetched live from OpenAlex

We outline the design and test of a new quasi-optical transmission line (QOTL) for feeding the reflector system in an optional instrument for deployment on the Aerosol/Cloud/Ecosystems (ACE) mission. The QOTL used to connect the transmitter and receiver to the antenna is designed for operation at W-band (94 GHz) frequency. A test-bed has been developed for the various critical components of the radar system. The challenge in using quasi-optics for ACERAD is that it must accommodate dual-polarized operation. We have developed a way to accomplish this that extends the design implemented in CloudSat radar jointly developed by JPL/NASA, Canadian Space Agency and other agencies. The new design has been prototyped using various components on a laboratory optical bench. Critical components of the QOTL have been fabricated and tested. Some results are presented.

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: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.069
GPT teacher head0.262
Teacher spread0.193 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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