Guest Editorial Signal Processing for Digital Twin in 6G Multi-Tier Computing Systems
Bibliographic record
Abstract
Digital twin (DT) has become a game-changing tech- nology in many smart applications, including smart cities, manufacturing, automotive, gaming, entertainment and climate resilience. DTs help push the boundaries of system reliability and are used to support a wide range of func- tions such as diagnostics and fault prediction. Keeping DT up-to-date requires communication means with low latency, high reliability, and high data security protection. The digital virtual twins of physical systems are then used to optimize performance of the system in real time, and one example for such systems is the sixth-generation (6G) wireless networks. There are many challenges in representing a physical system virtually, such as truereflectionof attributes,entanglementandcomposability. Entanglement refers to the truly complete exchange of information between physical objects and their logical twins, while composability deals with using the ex- isting twins of different entities to enable a complete twin- based service. A typical 6G service can be deployed using either a single or multiple twin objects. Multi-tier computing enables the distributed smart devices using the signal pro- cessing and wireless communication techniques to share their idle computing and storage resources, realising the efficient utilisation of multi-tier resources. The sharing of computing, communication and caching resources in multi-tier computing systems is maturing with the continuous development of signal processing and wireless communication technology to create an intelligent interconnected world for the metaverse.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.016 |
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".