Directed acyclic graph (DAG) blockchain is a new paradigm of blockchain that solves the poor scalability and low throughput caused by a single-chain structure for IoT applications in the blockchain. However, for DAG-structured blockchains, it is difficult to determine the order of blocks. Additionally, there is also lack of secure and efficient ways to generate blocks in parallel. To address these problems, we propose an efficient DAG blockchain architecture. First, a novel heaviest chain rule based on the block weight is proposed to guide the selection of the parent and uncle blocks for a new block, which realize that appending is ordering. Second, a tree-based gossip protocol (TBGP) is proposed to improve consensus efficiency by reducing message redundancy. Furthermore, federated learning is used to select nodes constructing the tree-based gossip network (TBGN). Finally, compared to the random gossip protocol (RGP), the simulation results indicate TBGP can effectively reduce communication redundancy and improve consensus efficiency.

A novel giant magnetoimpedance (GMI) noncontact current sensor with spiral structure double-probe is designed. Differing from the formerly reported sensors, the probes of this sensor consist of annealed commercial amorphous ribbons which are curled to spiral tubes. Two couples of permanent magnets are applied to provide bias magnetic field. The distance between the probes and the permanent magnets is fixed at 2.4 cm. The sensor shows sensitivity of 1 V/A in the current range of plusmn1.5 A, measurement precision of less than 0.16% at room temperature, and good thermal stability in the temperature range between -20 and 30degC.

Blockchain technology is a competitive solution to address the prevailing data security concerns in unmanned aerial vehicle (UAV) networks. However, wireless communication links between UAV nodes are vulnerable to electromagnetic interference and competition due to limited spectrum resources. Consequently, a comprehensive analysis of blockchain performance within UAV network environments is imperative for the effective deployment and optimization of blockchain technology. This paper presents two theoretical models. The first model assesses the impact of the Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) channel access protocol on the latency and throughput of the chained HotStuff consensus algorithm. The second model considers the movement characteristics of UAV nodes in three-dimensional space and the complexity of the communication environment. The aim of the second model is to calculate the consensus failure probability of UAV networks under electromagnetic interference. The results demonstrate that the theoretical values closely match the actual simulation outcomes.

Low Earth Orbit (LEO) satellite equipped with image inference capabilities (LEO-IISat) offer significant potential for Earth Observation (EO) missions. However, the dual challenges of limited computational capacity and unbalanced energy supply present significant obstacles. This paper introduces the Accuracy-Energy Efficiency (AEE) index to quantify inference accuracy unit of energy consumption and evaluate the inference performance of LEO-IISat. It also proposes a lightweight and adaptive image inference strategy utilizing the Markov Decision Process (MDP) and Deep Q Network (DQN), which dynamically optimizes model selection to balance accuracy and energy efficiency under varying conditions. Simulations demonstrate a 31.3% improvement in inference performance compared to a fixed model strategy at the same energy consumption, achieving a maximum inference accuracy of 91.8% and an average inference accuracy of 89.1%. Compared to MDP-Policy Gradient and MDP-Q Learning strategies, the proposed strategy improves the AEE by 12.2% and 6.09%, respectively.