$k$-Connectivity Analysis of One-Dimensional Linear VANETs
Bibliographic record
Abstract
In a 1-D linear vehicular ad hoc network (1-DL-VANET), some vehicles may leave the network (e.g., at highway exits), which may make the 1-DL-VANET disconnected. Thus, it is important to analyze the connectivity of the 1-DL-VANET. When removal of any (k- 1) arbitrary nodes from a network does not disconnect the network, the network is said to bek-connected. In this paper, we investigate thek-connectivity of the 1-DL-VANET. Sufficient and necessary conditions are derived for the 1-DL-VANET to bek-connected, and based on this, a method is provided, with the help of matrix decomposition, to obtain expression of the probability of the 1-DL-VANET beingk-connected. The expectation of the maximum number of tolerable vehicle departures is also derived. Simulation results confirm the accuracy of our analysis and indicate that the expectation of the maximum number of tolerable vehicle departures almost linearly increases with the total number of vehicles.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".