The integrity of electronic health record (EHR) is susceptible to compromise by hardware failures, software errors, or human errors. To date, numerous data integrity verification schemes have been proposed, but most face challenges related to third-party auditing and communication overhead. To address this, a novel EHR integrity verification scheme based on a multi-branch authentication tree is presented in this paper. By integrating an edge-based batch processing mechanism with data identity labeling technology, a low-overhead data verification framework is constructed, effectively reducing communication load. A minimal multi-branch tree structure is innovatively designed to enable parallel authentication and batch signing of data blocks. Concurrently, a random security code generation algorithm is introduced to ensure data security. Experimental and analytical results demonstrate that the proposed scheme maintains correctness, efficiency, and security, consistently achieving 100 % precision in detecting corrupted EHR data replicas. This scheme provides an efficient and reliable data integrity guarantee mechanism for EHR within edge computing environments and contributes significantly to building a trustworthy medical service system.

Wearable device-based Human Activity Recognition (HAR) has attracted considerable interest with the rapid development of the Internet of things (IoT), and Federated Learning (FL) has been widely adopted in this domain for its ability to collaboratively train models across decentralized devices while preserving privacy. However, its performance is hindered by data heterogeneity arising from variations in the placement of the wearable devices, user behaviors, and physiological characteristics. In this work, we present LDA-FedHAR, a federated HAR framework designed for wearable devices by capturing more common knowledge from aligned client HAR data. It performs Local HAR Data Alignment (LDA) on each client, which is an entirely on-device alignment method that operates independently on local HAR data. By computing the transformation matrix solely from local HAR data and applying it to the data itself, LDA projects heterogeneous client data into a unified space, thereby reducing inter-client discrepancies at the source. To further enhance efficiency and robustness, we propose two IMU-specific variants, LDA(S-IMU) and LDA(C-IMU), which explore intra-and inter-IMU correlations based on practical placements of wearable devices. Experiments are conducted on 4 public HAR datasets: HHAR, Shoaib2014, OPPORTUNITY++, and PAMAP2. The results show that LDA effectively reduces inter-client discrepancies, and LDA-FedHAR along with its variants consistently outperforms state-of-the-art FL methods. Moreover, the improvements achieved by integrating LDA into other FL methods highlight its applicability.

As the space environment becomes more complex, efficient detection and tracking of resident space objects (RSOs) in Earth's orbit is increasingly important. However, current single-satellite detection systems are limited by observation range and computational power, making real-time RSO awareness difficult. To address this challenge, we propose a collaborative detection model with reconfigurable satellites that leverages a federated learning framework to enable situational awareness and object detection across multiple nodes. Specifically, we designed a Modular Self-Reconfigurable Satellite consisting of four modules, with each module equipped with two cameras. By stitching together their fields of view, the constellation offers comprehensive situational awareness of the surrounding 2π annular space. The approach overcomes the limitation of single-point computing power by aggregating and updating network parameters among reconfigurable satellites, effectively addressing the risks associated with multi-modules data transmission. We conduct extensive experiments to validate the effectiveness of our proposed reconfigurable satellite in detecting RSOs. Experimental results show that the optical system constructed in this paper can effectively perceive 6th magnitude stars with an exposure time of 1 second, and the proposed detection model exhibits a maximum increase in accuracy of 5.2% compared with the single satellite model.

Mirror scanning image mode (MSIM) effectively expands the observation range while maintaining the resolution and has become a prominent research focus in the field of Earth observation; however, it faces the inherent limitation of discontinuous imaging, which poses a major obstacle to achieving seamless multistrip coverage. Additionally, while MSIM expands the imaging range, it also introduces complex time-varying relative motion between the image and focal plane, further increasing the difficulty of image motion compensation. Effectively addressing the limitations of discontinuous imaging and image motion has, therefore, become a critical challenge in the advancement of MSIM. In order to address these difficulties, we introduce a novel image mode, namely deceleration-based MSIM (DMSIM). This mode integrates the satellite’s pitch maneuver with MSIM, effectively expanding the overlap and aiding in image motion compensation. Specifically, DMSIM first constructs a set of parameter constraint equations to define the reasonable range for satellite and payload motion speeds, thereby ensuring seamless coverage of the target area. Subsequently, within the defined speed range, image motion compensation is achieved by designing the satellite’s pitch attitude maneuver; moreover, scaled-down tests and digital simulations are designed to validate our mode. Experiment results indicate that this mode achieves a swath width of$600\times 600$km while the ground sample distance is at the meter level. Meanwhile, the average modulation transfer function (MTF) is 0.088, highlighting its great potential for broad coverage and sharp imaging.

Establishing an effective situation prediction model for cyber security can know the active situation of future network malicious events in advance, which plays a vital role in cyber security protection. However, traditional models cannot achieve sufficient prediction accuracy when predicting cyber situations. To solve this problem, the initial location information of the sparrow population is optimized, and a sparrow search algorithm based on the Tent map is proposed. Then, the BP neural network is optimized using the improved sparrow search algorithm. Finally, a situation prediction model based on the sparrow search algorithm and BP neural network is proposed, namely T-SSA-BPNN. The simulation results show that the convergence speed and global search ability of the prediction model are improved. It can effectively predict the network security situation with high accuracy.