Bibliographic record
Abstract
Control loops have the schematic form while (active) {wait for trigger; do something to a device;} and they are the building blocks of most realtime programs. Control loops in RTLinux can be just a few lines of code or can involve the synchronized performance of thousands of loops. People have used RTLinux for some of the most demanding real-time applications as well as for quick experiments. Whether you are developing a 100-microsecond duty cycle magnetic bearing controller [3], a jet engine control and hardware-in-loop simulation (as did Pratt & Whitney), or a simple robotic controller using a sound card as an improvised analog-to-digital (A/D) device (several Japanese universities), you need the same ingredients, the same principles, and a good understanding of the device or plant you are controlling.This chapter covers basic control loop design and emphasizes issues of moving data and control information between the real-time loop and the outside world. We provide abbreviated treatment of scheduling and synchroniza- tion. Our experience is that pure priority scheduling1 satisfies 90% of all requirements and slot schedulers2 satisfy most of the rest. Both are built into RTLinux. Synchronization is not hard if a few basic rules are followed [8] and proper attention is paid to making the design robust. The RTLinux programming model is most effective when engineers can properly modularize their programs and reuse the powerful software found in Linux or BSD.
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.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.076 | 0.030 |
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".