Assessment of the performance of the TOPGNSS and ANN-MB antennas for ionospheric measurements using low-cost u-blox GNSS receivers
Bibliographic record
Abstract
Low-cost GNSS receivers have recently been gaining reliability as good candidates for ionospheric studies. In line with these gains are genuine concerns about improving the performance of these receivers. In this work, we present a comprehensive investigation of the performances of two antennas (the u-blox ANN-MB and the TOPGNSS TOP-106) used on a low-cost GNSS receiver known as the u-blox ZED-F9P. The two antennas were installed on two identical and co-located u-blox receivers. Data used from both receivers cover the period from January to June 2022. Results from the study indicate that the signal strengths are dominantly greater for the receiver with the TOPGNSS antenna than for the receiver with the ANN-MB antenna, implying that the TOPGNSS antenna is better than the ANN-MB antenna in terms of providing greater signal strengths. Summarily, the TOPGNSS antenna also performed better in minimizing the occurrence of cycle slips on phase TEC measurements. There are no conspicuous differences between the variances (computed as 5-min standard deviations) of phase TEC measurements for the two antennas, except for a period around May–June when the TOPGNSS gave a better performance in terms of minimizing the variances in phase TEC. Remarkably, the ANN-MB antenna gave a better performance than the TOPGNSS antenna in terms of minimizing the variances in pseudorange TEC for some satellite observations. For precise horizontal (North and East) positioning, the receiver with the TOPGNSS antenna gave better results, while the receiver with the ANN-MB antenna gave better vertical (Up) positioning. The errors for the receivers of both antennas are typically within about 5 m (the monthly mean was usually smaller than 1 m) in the horizontal direction and within about 10 m (the monthly mean was usually smaller than 4 m) in the vertical direction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".