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Record W6912139526 · doi:10.5281/zenodo.16848449

Plasma Drift Velocity Observation Data and Simulation Results

2025· dataset· en· W6912139526 on OpenAlexaff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2025
Typedataset
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsMATLABDrift velocityAngular velocityIonosondeCode (set theory)Phase velocityVisualizationPlasmaFunction (biology)

Abstract

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The repository contains MATLAB code, data and some additional documentations associated with the article: “Plasma Drift Velocity Estimation Technique for Ionosondes with Flexible Antenna Geometry” by O. Koloskov, A. Kashcheyev, P. T. Jayachandran. It includes: · Numerical simulation results characterizing plasma drift velocity reconstruction errors as functions of the prescribed drift velocity magnitude, drift velocity azimuth, and angular extent of the reflecting ionospheric region (SimulationAdditionalFigures.pdf). · MATLAB tools for visualization and post-processing of simulation data generated using the forward and inverse modeling algorithms described in the article. · A description of experimental testing of the plasma drift velocity estimation algorithm using data from a prototype modular ionosonde (Observations.pdf). · MATLAB tools for visualization and analysis of experimental ionosonde measurements and derived plasma drift velocity parameters. The provided MATLAB code reproduces all figures presented in the article and its supplemental materials. The code was tested under Microsoft Windows 11 using MATLAB R2024b. Simulation and figure datasets SimulationAdditionalFigures.zipContains the folder SimulationAdditionalFigures, which includes: o SimulationAdditionalFigures.pdf with additional simulation results illustrating drift velocity reconstruction errors as functions of initial drift velocity, drift velocity azimuth, and angular dimensions of the reflecting region. o SimulationAdditionalFigures/Fig/ — image files corresponding to these simulation results. FIG_02.zipContains: FIG_02/Fig/ — output figures. FIG_02/Code/ — MATLAB code for calculating and plotting plasma drift velocity resolution as a function of sounding duration T for both vertical and horizontal components.To generate the figures, run Run_CalcPlot_VdRes_via_dFd.m. FIG_03.zipContains: FIG_03/Fig/ — output figure. FIG_03/Code/ — MATLAB code for calculating and plotting phase error as a function of signal-to-noise ratio (SNR).To generate the figure, run Calc_dPhas_via_SnR_Estimation.m. FIG_04.zipContains: FIG_04/Fig/ — output figure. FIG_04/CodeData/ — MATLAB code and data (SimulationDataFIG04.mat) for calculating and plotting phase error and angular displacement errors as functions of SNR over sounding frequencies from 2 MHz to 8 MHz.To generate the figure, run Run_CalcAndPlotDisplacement.m. FIG_05.zipContains: FIG_05/Fig/ — output figures. FIG_05/CodeData/ — MATLAB code and data (SimulationDataFIG05.mat) for calculating and plotting drift velocity estimation errors as functions of SNR for both horizontal and vertical components.To generate the figures, run Run_CalcAndPlot_VdErrorbar_via_FreqSNR.m. FIG_06.zipContains: FIG_06/Fig/ — output figures. FIG_06/CodeData/ — MATLAB code and data (SimulationDataFIG06.mat) for analyzing the influence of zenith angle on drift velocity estimation errors for horizontal and vertical components.To generate the figures, run Run_CalcPlot_VdHZ_Errorbar_via_SNR_TETA0.m. FIG_07.zipContains: FIG_07/Fig/ — output figures. FIG_07/CodeData/ — MATLAB code and data (SimulationDataFIG07.mat) for calculating and plotting error asymmetry (ratio of underestimation to overestimation) in drift velocity estimation as a function of SNR.To generate the figures, run Run_CalcPlotAssymetry_via_SNR_TETA0.m. Experimental data Observations.pdfProvides a detailed description and analysis of experimental testing of the plasma drift velocity estimation algorithm. The testing was performed using a prototype modular ionosonde developed at the Radio and Space Physics Laboratory, the University of New Brunswick.Plasma drift velocities derived from this prototype were compared with standard drift velocity estimates obtained from a simultaneously operating Digisonde DPS-4D located at the Millstone Hill, Haystack Observatory, USA. FIG_S1.zipContains: FIG_S1/Fig/ — output figures. FIG_S1/CodeData_Blissville/ — MATLAB code and data (Bliss_20240126_0315.mat) for loading, processing, and plotting drift velocity data collected at the Blissville site from 27 January to 15 March 2024.To generate the figures, run PlotSynchroDiurnalVelocitiesBlissFig.m. FIG_S1/CodeData_MillstoneHill/ — MATLAB code and data (MH_20240126-0315.mat) for processing and plotting synchronized drift velocity data from the Millstone Hill Digisonde over the same period.To generate the figures, run PlotSynchroDiurnalVelocitiesMHfig.m.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.003

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 head0.033
GPT teacher head0.264
Teacher spread0.231 · 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 designSimulation or modeling
Domainnot available
GenreDataset

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
Published2025
Admission routes1
Has abstractyes

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