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Record W3178354638 · doi:10.52912/jsta.2021.1.1.33

Development and Field Test of the NEXTSat-2 Synthetic Aperture Radar (SAR) Antenna Onboard Vehicle

2021· article· en· W3178354638 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Space Technology and Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Analysis
Canadian institutionsKootenay Association for Science & Technology
FundersMinistry of Science and ICT, South Korea
KeywordsAntenna (radio)Synthetic aperture radarAntenna apertureComputer scienceTransceiverRemote sensingRadiation patternElectrical engineeringEngineeringTelecommunicationsGeologyWireless

Abstract

fetched live from OpenAlex

Based on the requirements of a total weight of 42 kg or less, the NEXTSat-2 SAR (synthetic aperture radar) system was developed. As the NEXTSat-2 is a small-sized satellite, the SAR system was designed to account for about 40% of the dry mass of the payload relative to the total mass. Among the major components of the SAR system - which are an antenna, an RF transceiver, a baseband signal processor, and a power unit - a part with a particularly large dry mass is the antenna, the core of the SAR system. Whereas various selections are possible in consideration of gain and efficiency when designing the antenna, the micro-strip patch array antenna was adopted by reflecting the dry mass, power, and resolution required by the NEXTSat-2 project. In order to meet the mission requirement of the NEXTSat-2, the antenna was developed with a frequency of 9.65 GHz, a gain of 42.7 dBi, and a return loss of –15 dB. The performance of the antenna was verified by conducting a field test onboard the vehicle.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.268
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

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

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 head0.006
GPT teacher head0.197
Teacher spread0.191 · 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