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Record W4408428429 · doi:10.5194/egusphere-egu25-14321

Investigating the Role of Geomagnetic Activity in Loss of Navigational Capability in the Swarm Satellite Mission

2025· preprint· en· W4408428429 on OpenAlexaff
D. J. Knudsen, Hossein Ghadjari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicInertial Sensor and Navigation
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsEarth's magnetic fieldSwarm behaviourSatelliteComputer scienceRemote sensingGeodesyGeographyAerospace engineeringArtificial intelligenceEngineeringPhysicsMagnetic field

Abstract

fetched live from OpenAlex

The Swarm satellite mission, launched by the European Space Agency in 2013, investigates Earth's ionosphere using advanced onboard instruments, including GPS receivers capable of tracking signals from up to eight satellites simultaneously. Loss of navigational capability, defined as periods when fewer than four GPS satellites are tracked, poses significant challenges for precise positioning and mission operations. Before 2020, the frequency of these events was relatively low, with fewer than 200 occurrences for Swarm A and C, and fewer than 100 for Swarm B. After 2020, the number of events increased dramatically, exceeding 1,400 for Swarm A and C, and 400 for Swarm B. While geomagnetic activity directly affects high-latitude regions, less than 10% of these events occur in the high-latitude ionosphere, suggesting indirect influences of magnetic storms on other latitudes through associated phenomena. This study investigates the correlation between geomagnetic activity and loss of navigational capability, exploring whether geomagnetic indices and magnetic storms act as precursors or are unrelated to these events. The findings will provide insights into the interplay between space weather and satellite-based navigation, contributing to improved operational resilience in future satellite missions.

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)0.001
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 score0.509
Threshold uncertainty score0.523

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.011
GPT teacher head0.247
Teacher spread0.235 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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