Plasma Drift Velocity Observation Data and Simulation Results
Bibliographic record
Abstract
The repository contains MATLAB code and data associated with the scientific article. It includes: · Tools for visualizing observational ionosonde measurements and processing results obtained using the algorithm developed by the authors for calculating 3D ionospheric plasma drift velocities. · Tools for visualizing computer simulation data generated with algorithms for solving the inverse problem of plasma drift velocity determination, as described in the article. The MATLAB code reproduces the figures presented in the article. It was tested on Microsoft Windows 11 with MATLAB R2024b. · The file FIG_02.zip contains: A folder FIG_02/Fig/ Output images. A folder FIG_02/Code/ MATLAB code to calculate and plot the resolution of plasma drift velocity as a function of sounding duration 𝑇, for both vertical and horizontal components. To generate the output images, run the script Run_CalcPlot_VdRes_via_dFd.m. · The file FIG_03.zip contains: A folder FIG_03/Fig/ Output image. A folder FIG_03/Code/ MATLAB code to calculate and plot phase error as a function of signal-to-noise ratio (SNR). To generate the output image, run the script Run_CalcPlot_dPhas_via_SnR_analitic.m. · The file FIG_04.zip contains: A folder FIG_04/Fig/ Output image. A folder FIG_04/CodeData/ MATLAB code to load simulated data (stored in FIG_04/CodeData/DataMat/SimulationDataFIG04.mat), calculate and plot both phase error and angular displacement errors as functions of SNR across sounding frequencies from 2 MHz to 8 MHz. To generate the output image, run the script Run_CalcAndPlotDisplacement.m. · The file FIG_05.zip contains: A folder FIG_05/Fig/ Output images. A folder FIG_05/CodeData/ MATLAB code to load simulated data (stored in FIG_05/CodeData/DataMat/SimulationDataFIG05.mat), calculate and plot drift velocity estimation errors as a function of SNR for both horizontal and vertical components. To generate the output images, run the script Run_CalcAndPlot_VdErrorbar_via_FreqSNR.m. · The file FIG_06.zip contains: A folder FIG_06/Fig/ Output images. A folder FIG_06/CodeData/ MATLAB code to load simulated data (stored in FIG_06/CodeData/DataMat/SimulationDataFIG06.mat), calculate and plot the impact of zenith angle on drift velocity estimation errors for horizontal and vertical components. To generate the output images, run the script Run_CalcPlot_VdHZ_Errorbar_via_SNR_TETA0.m. · The file FIG_07.zip contains: A folder FIG_07/Fig/ Output images. A folder FIG_07/CodeData/ MATLAB code to load simulated data (stored in FIG_07/CodeData/DataMat/SimulationDataFIG07.mat), calculate and plot error asymmetries—defined as the ratios of underestimation to overestimation ranges—in determining horizontal and vertical drift velocity components as a function of SNR. To generate the output images, run the script Run_CalcPlotAssymetry_via_SNR_TETA0.m. · The file FIG_08.zip contains: A folder FIG_08/Fig/ Output images. The folder FIG_08/CodeData_Blissville/ contains MATLAB code to load ionosonde data (stored in FIG_08/CodeData_Blissville/DataMat/Bliss_20240126_0315.mat), collected at the Blissville site from January 27 to March 15, 2024. The code calculate and plot the drift data presented in the associated paper. To generate the output images, run the script PlotSynchroDuirnalVelocitiesBlissFig.m. The folder FIG_08/CodeData_MillstoneHill/ contains MATLAB code to load ionosonde data (stored in FIG_08/CodeData_MillstoneHill/DataMat/MH_20240126-0315.mat), collected at the Millstone Hill Haystack Observatory from January 27 to March 15, 2024. The code calculate and plot the drift data presented in the associated paper. To generate the output images, run the script 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".