Development of Service Guide for Mobile Broadcast Considering Interoperability
Bibliographic record
Abstract
최근 모바일 방송 표준 중 전송계층과 서비스계층 사이에 IP계층이 존재하는 DVB-IPDC, OMA-BCAST와 같은 표준이 상용화되었다. 특히 OMA-BCAST는 IP 추상계층을 통해 여러 전송계층과 인터페이스가 가능하다. 이런 모바일 방송 표준에서 사용자 인터페이스와 관련된 중요한 부분인 서비스가이드는 표준별로 서로 다른 스키마를 가지며 복잡한 구조를 가진다. 스키마가 호환되지 않아 한 종류 이상의 서비스계층을 지원하고자 하는 단말에서는 표준별로 별도로 서비스계층을 구현해야 한다. 본 논문에서는 기존 모바일 방송 표준인 DVB-IPDC와 OMA-BCAST와의 호환성을 고려하면서도 기본적인 사용자 시나리오를 만족할 수 있는 간결한 서비스가이드 스키마를 개발하였다. 개발된 스키마는 독립된 서비스가이드 스키마로 사용될 수 있고, 기존 모바일 방송 클라이언트 통합 환경에도 응용될 수 있다. Recently mobile broadcast standards that have IP layer between transport layer and service layer such as DVB-IPDC, OMA-BCAST have been commercialized. Specially, IP abstract layer of OMA-BCAST makes it possible to interface diverse transport layers. Service guide, that is important for user interface, of these mobile broadcast standards has complex structure and heterogeneous schema. ESG schema is not interoperable, therefore terminals should have various implementations of service layer to support more than one service layer at a time. In this paper, not only for enabling interoperability of DVB-IPDC and OMA-BCAST but for supporting basic scenarios of mobile broadcast, simple ESG schema was developed. This new schema can be used as an independent ESG schema, especially for the integrated mobile broadcast middleware environment.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.017 |
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".