A multi-technique approach for characterizing the SVN49 signal anomaly, part 2: chip shape analysis
Bibliographic record
Abstract
Due to a satellite internal reflection at the L5 test payload, the SVN49 (PRN1) GPS satellite exhibits a static multipath on the L1 and L2 signals, which results in elevation-dependent tracking errors for terrestrial receivers. Using a 30-m high-gain antenna, code and carrier phase measurements as well as raw in-phase and quadrature radio frequency samples have been collected during a series of zenith passes in mid-April 2010 to characterize the SVN49 multipath and its impact on common users. Following an analysis of the receiver tracking data and the IQ constellation provided in Part 1 of this study, the present Part 2 provides an in-depth investigation into chip shapes for the L1 and L2 signals. A single reflection model is found to be compatible with the observed chip shape distortions and key parameters for an elevation dependent multipath model are derived. A good agreement is found between multipath parameters derived independently from raw IQ-samples and measurements of a so-called Vision Correlator. The chip shapes and their observed variation with elevation can be used to predict the multipath response of different correlator types within a tracking receiver. The multipath model itself is suitable for implementation in a signal simulator and thus enables laboratory testing of actual receiver hardware.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.000 |
| 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".