A Starting-Point-Congestion Method for Over-Loaded Radar Task Scheduling
Bibliographic record
Abstract
This paper proposes a novel scheduling method, the Starting-Point-Congestion (SPC) algorithm, designed to address the radar task scheduling problem in over-loaded scenarios. The SPC algorithm determines the mobility and urgency of tasks based on their priorities and crowdedness. All tasks are initially positioned at the start of the time window, awaiting reordering via the Random-Shifted-Start-Time (RSST) technique. The RSST process is constrained by the previously determined task mobility, meaning tasks with higher urgency (and thus lower mobility) have a greater likelihood of remaining within the time window and securing execution positions. Conversely, tasks with lower urgency have higher mobility, increasing the chance of them being moved out of the time window. Numerical simulations show that the schedule produced by the SPC algorithm is up to 13.1 times less costly than that generated by the Earliest-Start-Time (EST) algorithm, and up to 2.8 times better than using the RSST algorithm alone under certain loading conditions. The computational time of SPC is maintained below 8 milliseconds, thus it is considered both efficient and practical for real-time radar missions.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".