Scheduling for multifunction radar via two-slope benefit functions
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
The scheduling of tracking and surveillance looks for multifunction radar is considered. A technique called the sequential scheduler is proposed, whereby tracking looks and high-priority surveillance looks are scheduled first, and lower-priority surveillance looks are then scheduled to occupy gaps in the radar time line. A method called the two-slope benefit function (TSBF) sub-scheduler is used and requires that each tracking look and high-priority surveillance look has a benefit function, which specifies benefit as a function of start time. This method accounts for both look priority and target dynamics in formulating a look schedule. If the radar is overloaded with tracking look requests, the TSBF sub-scheduler down-selects a set of looks that can be scheduled, using a method that favours higher priority looks. Looks are scheduled to maximise the total benefit, and it is shown that the resulting maximisation is equivalent to a linear program which can be solved efficiently using the simplex method. A technique called the gap-filling sub-scheduler is used to schedule lower-priority surveillance looks. An example is presented which illustrates the properties of the sequential scheduler.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it