Bibliographic record
Abstract
High-powered direct broadcast satellites can be used to transmit large volumes of data directly to extensive users or user groups. This technology is based on a technique of carrying IP packets over an DVB/MPEG-2 (digital video broadcasting) stream. Transmission of IP packets over an DVB/MPEG-2 stream is not new technology; however, providing a guaranteed QoS (end-to-end) in such networks is a novel approach, which has not been widely researched. There exists a need to provide a multi-tier QoS architecture for such networks. For example, a satellite data broadcasting company has a large satellite earth station capable of communicating data over satellite up to the full transponder rate, typically greater than 100 Mbps. A teleport such as this prefers to sell portions of its bandwidth to other data broadcasting companies, such as government data hubs, banking institutions and Internet service providers (ISPs). These middle companies in turn sell bandwidth to end users. The problem is the teleport needs to offer different levels of QoS to the middle companies, who in turn need to provide different levels of QoS to end users. This problem is compounded by the need to sell differentiated services to competing companies. Furthermore, end users typically would pay for constant, variable, or unspecified bit rate services. In this paper we present a design of a multi-tier QoS architecture to suit the needs of providing multi-tier levels of differentiated services. We analyze this design using a queuing model which shows promising results
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".