User Requirement Analysis of Resilient PNT System
Bibliographic record
Abstract
Firstly, this paper provided a brief introduction to the concepts of Positioning, Navigation and Timing (PNT) and Resilient, and proposed that the fundamental premise for building a resilient PNT system is to identify the diverse needs of users in terms of accuracy, availability, continuity, and other indicators across different typical scenarios. Subsequently, it analyzed user requirements for various scenarios, including aviation routes, maritime navigation, agricultural surveying, train control, vehicle navigation, and emergency response, in different environments such as near-earth, urban, jungle, indoor, and underwater environments. The analysis took into account factors such as the accuracy, availability, continuity, integrity, terminal cost, and form of PNT. Based on this analysis, this paper summarized the user requirements for resilient PNT systems in different scenarios, presenting a comprehensive table of typical user requirements. Furthermore, it suggested that resilient PNT terminals should be cost-effective, compact, low-power, and highly compatible. Finally, the diverse user requirements were summarized and analyzed, providing a research foundation for developing resilient PNT solutions for different typical scenarios, such as near-earth, urban, jungle, indoor, and underwater environments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".