Effect of additional distance measurements on satellite positioning
Bibliographic record
Abstract
A prototype of pseudlite (PL), a ground based emitter of GPS signals has been developed at the University of Warmia and Mazury and tested from the hardware point of view. It has also been adapted to work with the Javad Alpha GNSS receiver and with an IFEN SX-NSR software receiver. This paper shows results of studies regarding effects of additional pseudolite-like distances on the accuracy of kinematic satellite positioning. At this stage of the research, only simulated observations with various accuracie shave been used in the analyses.These simulated observations are in the form of distances between PL transmitters and two GNSS receivers forming a baseline.These distances are expressed in cycles and treated as phase measurements. They are double differenced with the reference satellite phase measurements and used along with real observations in a uniform functional model to determine the baseline. The pseudolites are going to be used in engineering geodetic applications such as deformation monitoring, where often independent positions between the main observational epochs are required.Thus the developed software works in the kinematic mode. The studies show that the additional observations may help to provide high accuracy of determined positions, but any inaccuracies of these observations affect the results more than similar errors of satellite observations. ARTICLE INFO
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".