In the maritime Internet of Things (MIoT), automatic identification system (AIS) devices serve as mobile sensing nodes at sea and rely on satellite-based reception and relaying for global ship situational awareness. However, their signals are susceptible to spoofing and deception, posing a potential threat to maritime security. Satellite constellation design enables the effective detection and localization of non-cooperative AIS signals by optimizing satellite orbits in targeted regions. Nevertheless, balancing positioning performance with deployment cost remains a central challenge in constellation design. This paper proposes an average positioning error (APE)-driven satellite constellation design scheme. First, a global AIS signal distribution model is constructed using real-world AIS data. The probabilities of different coverage multiplicities under dynamic network topologies are analyzed, and benchmark localization errors for various positioning regimes are established. Based on these insights, an analytical APE formula is derived to quantitatively evaluate the positioning performance of satellite constellations. Then, we model the constellation design as a multi-objective continuous optimization problem and propose a balanced adaptive constrained genetic algorithm (BACGA) to optimize the constellation configuration parameters. Simulation results show that under the minimum positioning error constraint, the proposed algorithm generates the optimal low-Earth orbit constellation configuration that meets accuracy requirements and achieves good coverage of key areas at low cost.

Industrial Internet of Things (IIoT) increasingly relies on Gigabit Ethernet (1000BASE-T) as the physical back-bone for interconnecting industrial devices, while the rapid growth of IIoT nodes has intensified concerns about physical-layer identity spoofing and unauthorized access. Recently, device fingerprinting has emerged as a promising approach to achieving secure authentication at the physical layer. However, existing 1000BASE-T fingerprint extraction methods rely on randomly scrambled signals, leading to degraded authentication reliability. In addition, the absence of radio-frequency (RF) frontend modules—commonly defined in wireless systems—within 1000BASE-T transmitters prevents the direct application of conventional hardware-imperfection models. To overcome these challenges, this paper first introduces test mode (TM) signals as highly consistent and controllable reference inputs, and on this basis, proposes an interpretable high-pass filter (HPF) fingerprint model. The model characterizes the high-pass response of the transmission link using a single-pole system, establishes a monotonic relationship between filter parameters and waveform morphology, and extracts stable fingerprint features accordingly. Furthermore, a closed-loop physical-layer authentication framework is developed, integrating signal acquisition, preprocessing, feature extraction, and device identification. Experimental results demonstrate that the proposed method achieves 100% identification accuracy under standard sampling conditions and preserves perfect recognition over the entire tested sampling-rate range. Moreover, the method exhibits substantially enhanced noise robustness compared with baseline methods, and retains 95.59% accuracy after a 30-day interval in temporal stability evaluations.

Bipartite graph representation learning has been successfully applied in downstream applications such as link prediction and recommendation tasks. However, the performance of existing bipartite graph representation learning models is extremely vulnerable to adversarial perturbations, resulting in incorrect predictions in downstream tasks. To address this issue, we propose Bipartite graph Black-box adversarial Attack based on Implicit relations (BBAI). First, we extract the explicit and implicit relations in the bipartite graph to generate the entity relationship matrix. Then, we apply the Feature Perturbation Theory to calculate the perturbation score of each candidate edge and flip the top few candidate edges with the highest scores. Finally, we train on the perturbed bipartite graph to generate bipartite graph node embeddings. We use the embeddings for downstream tasks to evaluate the quality of the embeddings. Experimental results show that BBAI can significantly disrupt the performance of bipartite graph embeddings by perturbing the structure of bipartite graphs. Supplemental materials including code and data are available at https://github.com/DengBW-1998/BBAI.

This paper addresses the challenge of model mismatch in traditional time-of-arrival (TOA)-based localization methods for near-field or far-field source. We propose a unified TOA-based localization model that operates effectively in both scenarios. To estimate source direction, we introduce an algebraic closed-form solution, the two-step weighted least squares based on the modified polar representation (TSWLSMPR) method, which mitigates the issues associated with model mismatch. Additionally, we derive the Cramér-Rao Lower Bound (CRLB) as a benchmark to assess the performance of the proposed method. Simulation results demonstrate that the TSWLS-MPR method achieves the CRLB under varying levels of measurement noise, source distance, and source direction, confirming its accuracy and efficiency.

Fluid Antenna (FA) systems hold significant potential for enhancing physical layer security (PLS) by dynamically adjusting the positions of transmit antennas to suppress information leakage to eavesdroppers. However, the joint optimization of secure beamforming and FA positions is very challenging and remains unsolved, given the mutually coupled, non-convex and NP-hard nature of the problem. In this paper, we investigate the FA-assisted multi-user multiple-input single-output (MU-MISO) system for maximizing the downlink secrecy rate. First of all, we propose an alternating optimization (AO) framework to decouple the problem. For efficient FA position optimization, we introduce a low-sampling successive selection and successive convex approximation (L3S-SCA) method, which first selects a proper port in discrete space and subsequently refines the FA positions via continuous optimization. For secure beamforming, we reformulate the problem as an unconstrained optimization on Riemannian manifold, eliminating the errors from relaxing per-antenna power constraints (PAPC). We design the necessary Riemannian tools and propose a Limited-memory Riemannian Broyden-Fletcher-Goldfarb-Shanno (LRBFGS) method with low computational complexity. Comprehensive convergence and complexity analyses are conducted, and simulation results demonstrate the advantages of FA-assisted secure beamforming, as well as the superiority of our proposed algorithms in terms of performance and complexity.

Existing graph adversarial defense algorithms cannot recover the implicit relationships of a bipartite graph that have been disrupted by adversarial attacks. To address this issue, this paper proposes Bipartite graph adversarial Defense algorithm based on graph Purification (BDP) to improve the robustness of bipartite graph embedding algorithms under adversarial attacks. Firstly, we obtain the poisoned graph disrupted by adversarial attack algorithms; secondly, we traverse all the edges in the poisoned graph, and delete the edges whose Jaccard similarity is less than the threshold; thirdly, we traverse the implicit edges in the graph, and recover the important edges deleted by the adversarial attack algorithms according to the implicit edge reconnection rule, so as to obtain the purified graph; Then, we input the purified graph into the training model to get the node embeddings. Finally, we input the node embeddings into the downstream task to detect their performance. The experimental results show that under the same adversarial attack settings, as the most advanced baselines, BDP can significantly improve the performance of bipartite graph embedding algorithms. At the same time, the experimental results also prove that using Jaccard similarity to measure node similarity can make BDP optimally perform. Supplemental materials including codes and datasets are available at https://github.com/DengBW-1998/BDP.

The current point cloud registration methods cannot effectively address low-overlap scenarios. Hence, we present a simple, flexible, and general framework titled GPR-Net for low-overlap point cloud registration. We use Geometric Dynamic Graph Convolutional Neural Network (GeoDGCNN) block to extract the reliable geometric features. Then the cross-attention based on the message passing formulation encodes the two pairs of point clouds for early information exchange, and thus can predict which points lie in the overlap region. Finally, a Random Sample Consensus(RANSAC) algorithm is used to estimate transformation between the source and the reference point cloud. The experimental results show that the registration recall of our method in the 3DLoMatch datasets reaches 67.9%.

This paper focuses on reducing the effect of systematic uncertainties on the near-field or far-field source direction-finding accuracy by introducing a calibration emitter, which suffers the same uncertainties as the actual source. We propose a modified polar representation (MPR)-based closed-form algebraic algorithm, i.e., the improved successive unconstrained minimization (SUM), to eliminate the common errors of time difference of arrival (TDOA) measurements, thereby refining the source direction-finding accuracy. The simulations show that the proposed algorithm can approach the Cramer–Rao Lower Bound´ (CRLB); and demonstrate the positive effect of the calibration emitter on the direction-finding performance under the condition of varying measurement error, source range, and sensor position error.

Due to the efficiency of exploring multiple views of the real-word data, Multi-View Clustering (MVC) has attracted extensive attention from the scholars and researches based on it have made significant progress. However, multi-view data with numerous complementary information is vulnerable to various factors (such as noise). So it is an important and challenging task to discover the intrinsic characteristics hidden deeply in the data. In this paper, we present a novel MVC algorithm based on deep matrix factorization, named Self-Weighted Multi-view Clustering with Deep Matrix Factorization (SMDMF). By performing the deep decomposition structure, SMDMF can eliminate interference and reveal semantic information of the multi-view data. To properly integrate the complementary information among views, it assigns an automatic weight for each view without introducing supernumerary parameters. We also analyze the convergence of the algorithm and discuss the hierarchical parameters. The experimental results on four datasets show our algorithm is superior to other comparisons in all aspects.