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Record W2071256786 · doi:10.1504/ijspacese.2013.059267

Prospects of multiple global navigation satellite system tracking for formation flying in highly elliptical earth orbits

2013· article· en· W2071256786 on OpenAlexafffund
Erin Kahr

Bibliographic record

VenueInternational Journal of Space Science and Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGNSS applicationsGLONASSQuasi-Zenith Satellite SystemGlobal Positioning SystemConstellationVisibilityComputer scienceSatelliteGNSS augmentationRemote sensingSatellite navigationGalileo (satellite navigation)Orbit (dynamics)Low earth orbitSatellite systemAerospace engineeringReal-time computingGeodesyGeographyTelecommunicationsMeteorologyPhysicsEngineering

Abstract

fetched live from OpenAlex

Two formation flying missions are currently planned for highly elliptical orbit, NASA’s Magnetosphere Multi Scale Mission and ESA’s PROBA-3; however, neither of these missions will take advantage of the new positioning opportunities offered by multi-constellation GNSS and their modernised signal structures. This paper investigates the potential benefits through a detailed visibility simulation which includes GPS, Galileo, GLONASS, BeiDou, QZSS, WAAS, EGNOS, GAGAN, SDCM and MSAS. Results based on the PROBA-3 orbit demonstrate that the GNSS signals are marginally detectable by a standard GNSS receiver, and therefore the output of any visibility simulation is highly dependent on the input simulation parameters. Because small changes to the mission and receiver or unexpected GNSS signal levels can significantly impact the visibility, investing in weak tracking and multi-constellation GNSS is particularly advantageous to mitigate the impact of uncertainty in the HEO environment. Under the right conditions, regional systems are shown to be particularly advantageous.

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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.277

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 communication0.0000.001
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 head0.008
GPT teacher head0.220
Teacher spread0.212 · 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 teacher head, 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

Citations4
Published2013
Admission routes2
Has abstractyes

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