The extensive coverage of satellite constellations has rendered the satellite–terrestrial integrated network (STIN) a pivotal solution for communication and computation services in internet of vehicles (IoVs) rescuing in remote or disaster areas with limited terrestrial networks. To optimise network resource utilisation and service quality, the integration of the service function chain (SFC) into STIN‐enabled IoV rescuing systems has become essential. However, traditional SFC‐based STIN systems encounter challenges in flow scheduling flexibility, stemming from the sequential execution of subtasks on satellites equipped with virtual network functions (VNFs). This leads to a trade‐off between data volume reduction and the additional communication and computation energy costs incurred in the orbit. To address this issue, this paper introduces a space ground collaborative SFC (SGC‐SFC) flow scheduling strategy. This strategy enables the execution of subtasks on either VNF‐equipped satellites or the ground vehicle formation, contingent on network conditions. Firstly, we carry out a computation–space–time graph (CSTG) model specifically for the STIN‐enabled IoV rescuing system with SFC. This model integrates the computational layer into the space–time graph (STG), accurately capturing the data volume reduction characteristics and sequential execution constraints of SFC in the STIN‐enabled IoV rescuing system. Secondly, a SGC‐SFC flow scheduling algorithm is designed to identify a set of feasible paths with minimal energy cost and maximum processable data volume. Simulation results validate the effectiveness and robustness of our proposed SGC‐SFC under diverse conditions.

With advancements of in‐vehicle computing and Multi‐access Edge Computing (MEC), the Internet of Vehicles (IoV) is increasingly capable of supporting Vehicle‐oriented Edge Intelligence (VEI) applications, such as autonomous driving and Intelligent Transportation Systems (ITSs). However, IoV systems that rely solely on vehicular sensors often encounter limitations in forecasting events beyond current roadways, which are critical for regional transportation management. Moreover, the inherent temporal dependency in VEI application data poses risks of interruptions, impeding the seamless tracking of incremental information. To address these challenges, this paper introduces a joint task offloading and resource allocation strategy within an MEC environment that collaboratively integrates vehicles and Roadside Infrastructure Sensors (RISs). The strategy carefully considers the Doppler shift from vehicle mobility and the Tolerance for Interruptions of Incremental Information (T3I) in VEI applications. We establish a decision‐making framework that actively balances delay, energy consumption, and the T3I metric by formulating the task offloading as a stochastic network optimization problem. Utilizing Lyapunov optimization, we dissect this complex problem into three targeted subproblems that include optimizing local computational capacity, MEC computational capacity and comprehensive offloading decisions. To tackle the efficient offloading, we develop algorithms that separately optimize offloading scheduling, channel allocation and transmission power control. Notably, we incorporate a Potential Minimum Point (PMP) algorithm to boost parallel processing and simplify computational scale through matrix decomposition. Evaluations of our algorithm show that it excels in both complexity and accuracy, with accuracy improvements ranging from 74.3% to 114.0% in asymmetric resource environments. Simulation and experimental studies on offloading performance validate the effectiveness of our framework, which significantly balances network performance, reduces latency, and improves system stability.

The authors propose to use formatting templates and implicit formatting semantics information for automatic metadata identification and segmentation. The pure texts and their corresponding formatting information including line height, font type, and font size, are recognized in parallel to guide metadata identification. The authors use implicit formatting semantics, such as the change of formatting, formatting templates and implications, explicit formatting layouts, as well as predefined frequently occurred keywords database to increase the extraction accuracy. Unlike other OCR-based approaches, the authors use open source PDFBox package as the basic preprocessing tool to get pure texts and formatting values of the document contents. On top of PDFBox they built their own pipeline program, namely, PAXAT, to implement their approaches for metadata extraction. 10177 papers from arXiv, ACM, ACL and other publicly accessed and institution-subscribed sources are tested. The overall extraction accuracy of title, authors, affiliations, author-affiliation matching are 0.9798, 0.9425, 0.9298, and 0.9109, respectively.