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Record W2549004003 · doi:10.1002/navi.144

An Analysis of SBAS Signal Reception in Space

2016· article· en· W2549004003 on OpenAlexafffund
Erin Kahr, Oliver Montenbruck, Kyle O’Keefe

Bibliographic record

VenueNAVIGATION Journal of the Institute of Navigation · 2016
Typearticle
Languageen
FieldEngineering
TopicSatellite Communication Systems
Canadian institutionsUniversity of Calgary
FundersUniversity of TorontoUniversity of Calgary
KeywordsCubeSatGeostationary orbitGNSS augmentationGeodesyGlobal Positioning SystemEphemerisRangingRemote sensingComputer scienceTelecommunicationsGNSS applicationsGeographySatelliteEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

WAAS, EGNOS, GAGAN, and MSAS data have been collected from the CanX-2 CubeSat. The suitability of SBAS for navigation in geostationary orbits (GEO), where SBAS satellites may be permanently in view, was assessed. The analysis revealed that all tracked SBAS transmit enough power to be tracked over Earth's limb. GAGAN has the narrowest gain pattern, WAAS has a similar pattern to EGNOS but a 2–4 dB higher transmit power, and MSAS has the lowest signal power but more even global coverage, with stronger power transmitted towards the edge of the Earth. SBAS ranging typically agrees with GPS single-point positions to within +/−10 m for WAAS and +/−20 m for MSAS and GAGAN. It was determined that the SBAS ranging capability is useable in GEO and other high orbits, provided that fast correction data are applied to the broadcast ephemeris, and the lower accuracies are accounted for. Copyright © 2016 Institute of Navigation

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.273
Teacher spread0.253 · 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 designSimulation or modeling
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

Citations2
Published2016
Admission routes2
Has abstractyes

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Same venueNAVIGATION Journal of the Institute of NavigationSame topicSatellite Communication SystemsFrench-language works237,207