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Record W4390871535 · doi:10.1109/lgrs.2024.3354293

Galileo High Accuracy Service in Real-Time PNT, Geoscience and Monitoring Applications

2024· article· en· W4390871535 on OpenAlexfundno aff
Tomasz Hadaś, Kamil Kaźmierski, Iwona Kudłacik, Grzegorz Marut, Szymon Madraszek

Bibliographic record

VenueIEEE Geoscience and Remote Sensing Letters · 2024
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsnot available
FundersU.S. Naval ObservatoryNatural Resources CanadaNarodowe Centrum Badań i RozwojuCentre National d’Etudes Spatiales
KeywordsGNSS applicationsGalileo (satellite navigation)Global Positioning SystemComputer scienceSatelliteReal-time computingService (business)Satellite systemRemote sensingTelecommunicationsGeologyAerospace engineeringEngineering

Abstract

fetched live from OpenAlex

Satellite transmission of orbit and clock corrections is critical for real-time positioning, navigation and timing (PNT), geoscience applications, safety and liability critical services based on Global Navigation Satellite Systems (GNSS) precise positioning. In response to such a demand, Galileo has established a High Accuracy Service (HAS) of almost global coverage for GPS and Galileo. We validate the quality of HAS corrections and investigate service performance in a variety of applications. Decimeter-level accuracy of HAS corrections leads to static and kinematic positioning with precision of a few centimeters and sub-decimeter, respectively, and timing precision of a single nanosecond. Other GNSS-derived products meet the requirements of real-time GNSS meteorology and allow for monitoring coseismic vibrations. Although other Internet correction streams offer superior results, HAS provides better performance than nominal and nearly global coverage.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.007

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations21
Published2024
Admission routes1
Has abstractyes

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Same venueIEEE Geoscience and Remote Sensing LettersSame topicGNSS positioning and interferenceFrench-language works237,207