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Modelling of sub-ionospheric VLF signal propagation during a solar flare: an autonomous approach

2024· article· en· W4405676248 on OpenAlexfundno aff
Sayak Chakraborty, Tamal Basak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBluetooth and Wireless Communication Technologies
Canadian institutionsnot available
FundersNational Centers for Environmental InformationNational Oceanic and Atmospheric AdministrationDepartment of Science and Technology, Ministry of Science and Technology, IndiaCanadian Patient Safety Institute
KeywordsPhase noiseNoise (video)Computer scienceElectronic engineeringAcousticsOscillator phase noisePhase (matter)PhysicsElectrical engineeringTelecommunicationsEngineeringNoise figureArtificial intelligenceBandwidth (computing)

Abstract

fetched live from OpenAlex

The sub-ionospheric Very Low Frequency (VLF) signal propagation effects is a well established tool to investigate D-region ionosphere, especially during solar flare effects.We institute an integrated multistage model to simulate VLF signal during solar flares.Firstly, we autonomously solve 'D-region electron continuity equation (DECE)' and obtain electron density profile (N e ).Secondly, we extract Wait's parameters from N e using numerical fitting techniques.Thirdly, we simulate VLF signal amplitude profile (A sim ) using Long Wave Propagation Capability (LWPC) framework with the help of the Wait's parameters.Lastly, we compare A sim with its observational counterpart (A obs ).For performing the entire analysis, we choose a C3.2-class solar flare whose VLF signal response was recorded at the receiving station of Indian Centre for Space Physics (ICSP) for two transmitter signals, namely, VTX/18.2kHz and NWC/19.8kHz.We report an agreement between A sim and A obs .We discuss the justified limitations of the model and conclude.

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.660
Threshold uncertainty score0.380

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.219
Teacher spread0.191 · 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
GenreMethods

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
Published2024
Admission routes1
Has abstractyes

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