Analytical Framework for End-to-End Delay Based on Unidirectional Highway Scenario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In a sparse vehicular ad hoc network, a vehicle normally employs a carry and forward approach, where it holds the message it wants to transmit until the vehicle meets other vehicles or roadside units. A number of analyses in the literature have been done to investigate the time delay when packets are being carried by vehicles on both unidirectional and bidirectional highways. However, these analyses are focusing on the delay between either two disconnected vehicles or two disconnected vehicle clusters. Furthermore, majority of the analyses only concentrate on the expected value of the end-to-end delay when the carry and forward approach is used. Using regression analysis, we establish the distribution model for the time delay between two disconnected vehicle clusters as an exponential distribution. Consequently, a distribution is newly derived to represent the number of clusters on a highway using a vehicular traffic model. From there, we are able to formulate end-to-end delay model which extends the time delay model for two disconnected vehicle clusters to multiple disconnected clusters on a unidirectional highway. The analytical results obtained from the analytical model are then validated through simulation results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 it