MétaCan
Menu
Back to cohort
Record W4410915108 · doi:10.1051/swsc/2025026

Intermittency in the integrated power of ionospheric density fluctuations

2025· article· en· W4410915108 on OpenAlexafffund
Hossein Ghadjari, D. J. Knudsen, Georgios Balasis, S. Skone

Bibliographic record

VenueJournal of Space Weather and Space Climate · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of Calgary
FundersCanadian Space Agency
KeywordsIntermittencyIonospherePhysicsGeophysicsMeteorology

Abstract

fetched live from OpenAlex

The occurrence of ionospheric irregularities poses a significant challenge to Global Navigation Satellite Systems (GNSS) by disrupting signal propagation and causing loss of lock (LOL) events. This study investigates the integrated power spectral density (PSD) of electron density fluctuations at various scales using nearly ten years of Swarm satellite data. The analysis focuses on the post-sunset equatorial ionosphere, a region prone to ionospheric irregularities. GPS receivers on Swarm A and C, orbiting at altitudes between 430 and 460 km, suffer more frequent loss of navigational capability (LNC) than Swarm B (at 530 km). The observed power-law distribution of integrated power at small spatial scales (<30 km) points to scale-free behavior, which is a hallmark of complex systems and may be associated with self-organized criticality (SOC). The study also employs multifractal detrended fluctuation analysis (MFDFA) to demonstrate the multifractal and intermittent nature of the fluctuations. The findings highlight the importance of intermittency and strong bursts in understanding the LNC and LOL events in the ionosphere. This research contributes to a deeper understanding of the dynamics of ionospheric irregularities and offers potential for improved forecasting and mitigation of their impact on GNSS.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.197
Threshold uncertainty score0.337

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 head0.004
GPT teacher head0.231
Teacher spread0.227 · 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 designObservational
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
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Space Weather and Space ClimateSame topicIonosphere and magnetosphere dynamicsFrench-language works237,207