Rich and context-aware activity logs facilitate user behavior analysis and health monitoring, making them a key research focus in ubiquitous computing. The remarkable semantic understanding and generation capabilities of Large Language Models (LLMs) have recently created new opportunities for activity log generation. However, existing methods continue to exhibit notable limitations in terms of accuracy, efficiency, and semantic richness. To address these challenges, we propose DailyLLM. To the best of our knowledge, this is the first log generation and summarization system that comprehensively integrates contextual activity information across four dimensions: location, motion, environment, and physiology, using only sensors commonly available on smartphones and smartwatches. To achieve this, DailyLLM introduces a lightweight LLM-based framework that integrates structured prompting with efficient feature extraction to enable high-level activity understanding. Extensive experiments demonstrate that DailyLLM outperforms state-of-the-art (SOTA) log generation methods and can be efficiently deployed on personal computers and Raspberry Pi. Utilizing only a 1.5B-parameter LLM model, DailyLLM achieves a 17% improvement in log generation BERTScore precision compared to the 70B-parameter SOTA baseline, while delivering nearly 10× faster inference speed.

The Hongtu-1 (HT-1) synthetic aperture radar (SAR) system is the first spaceborne single-pass multibaseline (MB) interferometric SAR (InSAR) system based on four HT-1 SAR satellites flying in a cartwheel formation. This setup includes three secondary satellites that function as receive-only units to create a compact multistatic SAR system. The primary objective of the HT-1 mission is to generate a consistent global digital elevation model (DEM) at 1:50000 scale. In addition, the HT-1 mission will feature novel SAR imaging technology demonstrations. On March 30, 2023, four HT-1 satellites were successfully launched and started to provide spaceborne radar data services to users. This article provides a detailed description of the HT-1 MB InSAR system, including the SAR performance, the cartwheel formation designed for multistatic SAR data collection with desired baselines, and the uninterrupted synchronization link. The interferometric performance is thoroughly analyzed. With the recorded data, imaging and interferometric processing procedures are introduced, and the capabilities of single-pass MB InSAR DEM generation are demonstrated. Compared with those of ICESAT, the height errors are less than 2 m in flat terrain and less than 5 m in mountainous terrain. Moreover, the resolution and swath of multisatellite mosaic imaging are 3 m and 80 km, respectively. The repeat-pass differential InSAR measurement for surface deformation monitoring is also included.

The Hongtu-1 (HT-1) Synthetic Aperture Radar (SAR) constellation is the first in-orbit spaceborne single-pass multi-baseline interferometric SAR (InSAR) system. The system has the ability to conduct high-resolution earth observation and high-precision, high-efficiency terrain surveying. The highest resolution of the system is better than 0.5 m, and it has a 1:50000 scale global digital elevation model (DEM) and digital surface model (DSM) surveying capability. This paper provides a basic introduction to the HT-1 constellation, and demonstrates the advantages of single-pass multi-baseline InSAR results and its advantages over steep area.

Instantaneous attitude estimation for spacecraft through optical/ISAR fusion observation is a hot topic in space situational awareness. However, due to the imaging characteristics and domain gap between the two data types, it is difficult to autonomously estimate instantaneous attitude based on data fusion. In this paper, an automatic optical/ISAR multi-feature fusion instantaneous attitude estimation framework is proposed. We deploy semantic feature extraction networks to extract and associate two kinds of robust features in optical and ISAR images, and an attitude estimation algorithm considering cross-range scaling based on multiple features combination is proposed. Meanwhile, the inherent structure constraints of spacecraft are introduced. Simulation experiments demonstrate that the proposed framework achieves an average attitude estimation accuracy of 1.87°, while also exhibiting excellent robustness.

The microservice architecture has gained widespread adoption in smart manufacturing, enabling the collaborative completion of production tasks through the integration of multiple microservices. However, migrating microservices in dynamic environments poses challenges for maintaining production quality and service efficiency. First, there are complex dependencies between microservices, such as layered and chain structures, making microservice migration a difficult process. Second, large-scale production scenarios require rapid decision-making based on high-dimensional variables to adapt to the dynamic environment. Third, microservice migration can cause interruptions, so careful selection of microservices is crucial to minimize production stagnation during migration. To tackle these challenges, we develop an efficient and economical migration strategy ($E^{2}MS$). This approach considers the complex dependencies between microservices and optimizes the system cost by selecting appropriate microservices for migration. We formulate an integer non-convex quadratic programming problem and employ techniques such as variable reduction, penalty functions, and successive convex approximation (SCA) to solve it. The proposed strategy enables efficient decision-making for microservice migration in dynamic production environments and exhibits strong scalability. Our experimental results demonstrate the exceptional dynamic performance of the proposed method, significantly reducing the time required to obtain migration strategies and achieving a 90% reduction in microservice interruptions compared to other methods.