DTChecker: A Real-Time Signal Monitoring and Property Specification Tool for Digital Twins
Bibliographic record
Abstract
Specifying and monitoring temporal requirements in Digital Twin (DT) systems is challenging, as writing formal specifications in temporal logic is often complex and inaccessible to domain experts. This typically necessitates close collaboration with software engineers, introducing communication overhead and slowing development. We present DTChecker, a reusable selfcontained monitoring tool for DT systems built on RabbitMQbased architectures. The tool enables domain experts to write temporal specifications in a browser-based editor with language server support. These specifications are automatically translated into Signal Temporal Logic (STL) formulas and evaluated in real-time on data streams from sensors or services. Robustness scores are streamed to a front-end dashboard to visualize how well the system satisfies the specified requirements over time. This enables domain experts to write and verify temporal properties easily, thereby improving the real-time monitoring of the DT. We demonstrate the tool through integration with an open-source incubator DT case study. Video demonstration: https://youtu.be/elyhSOiGuc4?si=V2TTsXgRcYaAU2X
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 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 | 0.000 |
| 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 | 0.001 | 0.002 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".