An Innovative Infrastructure Based on Shape-Adaptive RIS for Smart Industrial IoTs
Bibliographic record
Abstract
Industrial Internet of Things (IoTs) applications will have higher demands in terms of system capacity and wireless transmission performance in the future. Reconfigurable intelligent surfaces (RIS) for the wireless transmission environment reconstruction is currently a research hotspot in the field of wireless communication. In this paper, RIS is applied to industrial IoTs scenarios, and an innovative infrastructure based on shape-adaptive RIS suitable for smart industrial IoTs is proposed. Based on the many advantages of RIS, the proposed infrastructure can meet the significant demands of industrial IoTs in terms of network coverage, system capacity, transmission rate, and communication security. Further, in order to achieve flexible RIS phase adjustment in wider ranges, in this paper, a shape-adaptive RIS mechanism is proposed to regulate RIS-reflected waves in the shape domain, which can be achieved by scalable RIS unit sizes and macroscopically changeable physical shapes. This mechanism can reduce the requirements for RIS-controller and artificial-unit design and expand the spatial distribution of reflected beams, whereas this is difficult to achieve by conventional regulations in the spatial, time, and frequency domains. The simulation results show that, in terms of system-coverage probability, the system performance of RIS is better than that of general communication systems, and the performance of the deformable RIS-based system is the best. In terms of channel gain, the performance of the deformable RIS is related to its surface size. Moreover, the path loss caused by the long-distance transmission of wireless signals can be compensated for by increasing the RIS surface area.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".