Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver
Bibliographic record
Abstract
Nowadays, with the incursion of low-cost GNSS receivers with modern characteristics, it is common to investigate and apply new methodologies and solutions with different receivers of this nature. Based on this fact, the performance of the solution obtained from the low-cost GNSS receiver is evaluated compared to a geodetic grade GNSS receiver at different sampling frequencies for the PPP-static and PPP-kinematic modes. For this, the original RINEX observation files were analyzed and decimated into different sampling rates as 0.1, 0.2, 1, 5, 15 and 30 s with TEQC software. All RINEX files were submitted to the Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) online service for processing with static and kinematic modes. The PPP-derived coordinates from the low-cost GNSS receiver were compared with the geodetic receiver to evaluate the obtained solution. The results reveal that the behavior of all studied sampling rates from the low-cost GNSS receiver are constant in achieved positioning. In addition, the achieved precision shows that it is recommendable to use a high sampling rate to obtain a cm level in PPP-static mode by using a low-cost GNSS receiver, this mode being the most accurate and potential alternative for structural health monitoring studies, mapping and positioning in urban areas.
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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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".