Integrated Satellite-Terrestrial Network Framework for Next Generation Smart Grid
Bibliographic record
Abstract
Satellite Internet as a representative of 6G offers a globally resilient service of communication, navigation, timing and remote sensing at multiple temporal and spatial scales. Facing the 6G era, the next generation smart grid (NGSG) will be constructed with the assistance of a unified space-ground network. This accounts for the unified network’s attributions of wide coverage, large base of sources and loads, and the complex environment. Hence, an integrated satellite-terrestrial network (ISTN) framework is proposed in this letter. It aims at empowering the NGSG under the massive renewable energy sources integration, which includes conditions of an intelligent urban grid, a reliable remote area grid, and a resilient grid to emergency. Heterogeneous data such as remote sensing and navigation signals are aggregated and leveraged in the proposed ISTN framework. Information entropy theory is used to coordinate the space- and ground-based networks elaborately, maximizing the utilization of network resources pointedly and efficiently. A cyber-physical platform is used based on an actual LEO satellite Internet test constellation to validate the proposed ISTN, which reveals the superiority and promising future.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".