Bibliographic record
Abstract
The launch of regional GEO multimedia satellites will initially support an Internet over satellite service in a star network configuration interconnecting users to an ISP. While a star network is ideally suited as an access technology for the bulk of Internet traffic including web browsing and email, the increasing need to support mesh networking for other applications is expected in the future. These applications include conferencing and business to business (B2B) eCommerce. For a GEO satellite in particular, this requires an on‐board processor to support single hop interconnectivity between small low cost terminals. This approach achieves the following advantages compared to a star configuration, which would require a double hop: (1) it minimises the latency by reducing the roundtrip delay in half; (2) it halves the capacity utilized; and (3) it provides signal regeneration, additional coding gain and switching. In general, some form of switching is required to interconnect beams in a multi‐beam system, typical of regional multimedia satellites which employ high gain spot beams to increase link margin and frequency reuse. In this paper the architecture and operation of the EMS SpaceMux TM on‐board processor is described which achieves these objectives for mesh networking. The first generation of SpaceMux TM supports the DVB‐RCS air interface and will operate as a demonstration of OBP mesh networking technology on the Telesat Anik‐F2 satellite due to be launched in the third quarter of 2002.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".