Validation of a Transionospheric Propagation Scintillation Simulator for Strongly Scintillated GPS Signals Using Extensive High Latitude Data Sets
Bibliographic record
Abstract
The background ionosphere is a medium of partially ionized gases, developed due to interaction of different solar ionizing radiations with neutral gases present in Earth’s atmosphere. The ionization of the gas molecules results in the free electron density. This electron density depends on the neutral gas density and the solar flux and is also affected by recombination particularly after sunset. The ionization level varies with altitude, local time, season, location, geomagnetic and solar activity as well as solar cycle epoch. There is also a further stochastic electron density component consisting of time-varying electron density irregularities which themselves can be contained within meso-scale structures such as polar patches at high latitudes or plasma bubbles at low/equatorial latitudes. At high latitudes the occurrence of the irregularities increases with geomagnetic activity and will produce scintillation in both amplitude and phase on a transionospheric signal such as that from a GPS satellite received on the ground by a suitable receiver. The fluctuations in amplitude and phase of the signal termed amplitude and phase scintillation come about as a consequence of signal diffraction by the ionospheric irregularities resulting in self-interference of the carrier incident at the receiving antenna which can severely affect the GPS receiver performance. One method of investigating this physics-based scintillation is via construction of a transionospheric propagation simulator (e.g. the SPLN simulator) which can accurately and realistically model all the scintillation effects, even for strong scintillation conditions. This simulator can produce time series of amplitude and phase for different scintillation conditions which can, by being input to a software GPS receiver, test the robustness of different GPS receiver designs under a variety of scintillation conditions. Also, it is envisaged that, by comparison of its output for different scintillation conditions with a very extensive GPS data set received at high latitudes, it could be rigorously validated and utilised to form the basis of a scintillation forecast program for this region. The SPLN (St. Petersburg-Leeds-Newcastle) transionospheric simulator is capable of estimating the statistical characteristics of transionospheric radio signals in strong scintillation conditions and can simulate time series of the phase and log-amplitude of the signal, as well as its amplitude and phase spectra at a ground-based receiver. The technique used in this simulator is the hybrid method of complex phase and the random screen. The input parameters for this simulator are: electron density profile for background ionosphere obtained from the NeQuick model, the geomagnetic and solar activity indices, the spectral index, cross-field outer scale and aspect ratios of the irregularities and the carrier frequency of signal along with the elevation and azimuth angle of the visible satellite. The random realization of phase and log-amplitude on ground results from propagating downwards the complex amplitude of the field on a random screen situated below the ionosphere and is generated in terms of the propagation geometry and the anisotropic inverse spatial spectrum of the electron density irregularities in the ionosphere. The work focuses on the validation of the physics-based SPLN transionospheric propagation scintillation simulator with experimental scintillation measurements at different locations in the high latitude region. These include six GPS stations all equipped with GISTM based NovAtel GPS receivers capable of providing raw GPS observations and scintillation indices. Three GPS stations (Yellowknife [62.48o N, 114.48o W], Athabasca [54.72o N, 113.31o W], Calgary [51.08o N 114.13o W]) represent the mid-high latitude region in Northern Canada, while the other three stations (Bronoysund [65.6° N, 12.2° E], Bergen [60.0° N, 5.0° E],) are for the mid-high latitude region in Northern Europe. The experimental validation for different elevations and azimuths also aids the development of a mapping function for scintillation indices between vertical and oblique paths which is necessary for a planned scintillation forecasting program. Simulated time series of scintillated signals will be developed which are well suited for testing the robustness of GPS receivers for realistic scenarios including, in particular, strong scintillation conditions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".