Reliability Analysis of TSN Networks Under SEU Induced Soft Error Using Model Checking
Bibliographic record
Abstract
The fault-tolerance under Single Event Upset (SEU) is a crucial requirement for space and avionics applications due to the harsh radiation environment. The communication network is one of the components that can be affected by SEUs. Therefore, we are interested in the transmission reliability of Time-Sensitive Networking (TSN) under SEUs. TSN network allows deterministic Time-Triggered (TT) communication by introducing a new traffic shaper, namely, Time Aware Shaper (TAS). The TAS is configured to control the traffic flow based on the time and traffic class. Specifically, we inject the SEUs in the TAS. These configurations are stored in a list of entries, namely, Gate Control List (GCL). In this paper, we introduce a new framework to investigate the vulnerability of the TSN network to SEUs. This analysis allows the identification of SEU-induced communication failures in critical time-triggered traffic. The proposed framework introduces a Priced Timed Automata (PTA) model for TSN networks based on the network topology and traffic. The proposed model allows the injection of SEU propagation in Time Aware Shaper (TAS) configuration list. In particular, we investigate the impact of SEUs on the flow of TT traffic using model checking. The proposed analysis framework is carried out on two synthetic test cases in addition to a realistic case study from the space area based on the switched network in Orion Crew Exploration Vehicle (CEV). The main finding of this study is revealing a corner case in which multiple path redundancy fails to resile an SEU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".