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Characterization of Equatorial Low Latitude Ionospheric Scintillation of GPS Signals: An E-POP Experiment

2020· article· en· W3043212147 on OpenAlexaff
Ali Mohandesi, D. J. Knudsen, S. Skone

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsGNSS applicationsIonosphereScintillationGPS signalsInterplanetary scintillationGlobal Positioning SystemPrecise Point PositioningGeodesySunsetRemote sensingLatitudeGeologyEnvironmental sciencePhysicsAssisted GPSComputer scienceTelecommunicationsGeophysicsDetectorOpticsPlasma

Abstract

fetched live from OpenAlex

<p>Ionospheric irregularities are a major error source in GNSS positioning and navigation as they affect trans-ionospheric signal propagation. They cause random, rapid fluctuations in the intensity and phase of the received signal, referred to as ionospheric scintillations. From a global point of view, GNSS signal scintillations are more severe and frequent in the equatorial region and during post-sunset hours. Characterizing irregularities that interfere most with navigation signals requires high-temporal resolution of measurements. In this work we utilize high-rate upward-looking measurements accomplished by the GAP RO receiver on CASSIOPE (Swarm Echo) satellite to study GPS signal scintillations and irregularities associated with them. This was done by reorienting CASSIOPE by approximately 90 degrees for short periods during November and December, 2019 while it passed through low-latitude region during post-sunset hours local time. High-rate GAP RO measurements provide a unique opportunity to investigate small-scale irregularities that are responsible for signal scintillations.</p>

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.230
Threshold uncertainty score0.269

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 head0.019
GPT teacher head0.226
Teacher spread0.207 · 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 designBench or experimental
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

Citations0
Published2020
Admission routes1
Has abstractyes

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