Optical satellite networks, supported by optical inter-satellite links (OISLs), provide reliable and low-latency optical connectivity. However, periodic and predictable sun outage events significantly compromise OISL availability, leading to frequent OISL interruptions and reduced network reliability. Existing routing algorithms often overlook the regularity of sun outage-induced interrupts and their differentiated impacts on services, resulting in degraded service performance. To address this challenge, this paper proposes a sun outage-enhanced time discretization OISL model and introduces a sun outage link-aware routing (SOLR) algorithm. By incorporating joint awareness of sun outage patterns and service requirements, SOLR employs an adaptive optimization mechanism to dynamically adjust routing decisions within temporal windows. Experimental results demonstrate that SOLR extends stable path durations by 39.9%, reduces interruption rates by 28.5%, and decreases blocking rates by 36.4%, significantly outperforming link-state-based routing algorithms. By effectively mitigating the impact of sun outages, SOLR ensures continuous optical service connections. This interruption-tolerant framework bridges network modeling and service provisioning, offering a robust solution for mission-critical service in optical satellite networks.

In recent years, research on satellite networks has gained significant attention, with their capability for seamless global coverage and meeting real-time communication demands serving as a key solution to address deficiencies in ground communication network coverage and to improve the real-time transmission of services. Traditional satellite networks, originally employed for singular purposes such as data relay, are gradually transitioning to satellite internet to support various Internet-based services. The integration of satellites with ground networks, known as the Satellite-Terrestrial Integrated Network (STIN), has become an inevitable trend, making the deployment of ground stations (GSs) a critical issue in the STIN construction. Traditional GS deployment strategies are insufficient to meet the real-time demands of emerging services. In this context, a GS deployment strategy based on the data center-user gravity model (GSD-DG) is proposed, where the influence of all data center factors on GS deployment is considered. This approach takes into account constraints such as satellite connectivity, user traffic, and data center gravity. The integration of GS with data centers plays a pivotal role in enhancing the internet service latency performance. Simulation results indicate that the proposed strategy significantly reduces service latency by 24.5% compared to the benchmark, providing a more effective GS deployment solution to further optimize the STIN service latency performance.

Static code analysis tools (SATs) are widely used to detect potential defects in software projects. However, the usability of SATs is seriously hindered by a large number of unactionable warnings. Currently, many warning prioritization approaches are proposed to improve the usability of SATs. These approaches mainly extract different warning features to capture the statistical or historical information of warnings, thereby ranking actionable warnings in front of unactionable warnings. Such features are extracted by extremely relying on domain knowledge. However, the precise domain knowledge is difficult to be acquired. Also, the domain knowledge obtained in a project cannot be directly applied to other projects due to different application scenarios among different projects. To address the above problem, we propose a pattern mining-based warning prioritization approach based on the warning-related Abstract Syntax Tree (AST). To automatically mine actionable warning patterns, our approach leverages an advanced technique to collect actionable warnings, designs an algorithm to extract the warning-related AST, and mines patterns from ASTs of all actionable warnings. To prioritize the newly reported warnings, our approach combines exact and fuzzing matching techniques to calculate the similarity score between patterns of the newly reported warnings and the mined actionable warning patterns. We compare our approach with four typical baselines on five open-source and large-scale Java projects. The results show that our approach outperforms four baselines and achieves the maximum MAP (0.76) and MRR (2.19). Besides, a case study on Defect4J dataset demonstrates that our approach can discover 83% of true defects in the top 10 warnings.

Huibin Zhang, Zhengkun Zhang, Yao Zhang, Jun Wang, Yufan Li, Ning Jiang, Xin Wei, Zhenglu Yang. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

The location technologies, such as GPS and telegraphy, are producing more and more data of moving objects. Spatio-temporal database is needed to manage these data, so as to solve the problems in spatio-temporal applications. However, there are few prototypes of spatio-temporal database systems yet. This is because attention is always paid on some specified aspects not the whole system. In this paper, we present the design and implementation of a spatio-temporal database prototype for managing moving objects. A Trajectory Model is proposed in this paper, where the moving objects' movements in 2D plane over time are treated as trajectories in the 3D spatio-temporal space. Referring to R tree and the trajectory oriented indexing method TB tree, a new indexing method: S-TB tree is proposed in the prototype. Distance based queries and trajectory topology based queries are all supported by the prototype. The distance based queries includes point queries, range queries. This paper also presents different strategies to process these queries. Finally, two application cases are presented: traffic control and historical queries of the storms on Earth.

Spatial sampling is a significant part of query processing in spatial database. In this paper, an adaptive data-driven sampling for selectivity estimation is proposed in spatial database. This technique presents an efficient sampling for spatial data, especially for two-dimensional line and polygon data, and it make the sample size fit the limit of time and memory, or a user-defined parameter. The data-driven sampling technique is compared with various techniques on different type of datasets in our experimental study, and it out outperforms the other techniques over a broad range of query workloads and datasets.