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.

The challenge of transforming partial point clouds into complete meshes still persists, with current methods facing issues like data accessibility constraint, shape preservation failure and poor robustness on real-scan data. Drawing inspiration from the structural information of objects to enhance the completion, we introduce an innovative weakly-supervised shape completion method leveraging structural decomposition without the necessity of SDFs during training. By representing objects as abstract structural frameworks and part details, our method initiates by forecasting the structure of the input partial point clouds, and individually restore each component through part decomposition completion and generation. Extracted part details are represented in images, which are porous and incomplete. Hence, we utilize a completion network to complete such details. For multiple results generation, a diffusion-based generation network is employed to generate a variety of details for the missing areas. The predicted structure and details are subsequently converted back into meshes, yielding the complete results. Since the details are depicted in images, our approach eliminates the need for SDFs during the training phase, achieving weakly-supervision. We conduct extensive comparisons on both artificial and real-scan datasets, demonstrating an average improvement of over 38.1% compared to the prior method, and achieving SOTA performance.

Six-dimensional movable antenna (6DMA) offers a potential solution to enhance wireless transmission performance by physically reconfiguring antenna positions and orientations. However, prevailing snapshot-based reactive methods are ill-suited for continuously tracking mobile user equipments (UEs) due to their neglect of antenna kinematic constraints and system latency. To address these limitations, in this paper, we propose a proactive approach by modeling UE tracking as a single, long-term 6DMA trajectory optimization problem to maximize sum spectral efficiency. Leveraging a channel knowledge map (CKM) for predictive data, our model holistically incorporates the system’s complex kinematics and physical constraints, including velocity limits and safety distances, to ensure a physically feasible trajectory. To solve this high-dimensional, non-convex problem, we develop a novel manifold optimization algorithm. This method maps the antenna’s rotational states onto the SO(3) Lie group and employs an adaptive penalty measure with tangent space backpropagation for an efficient solution. Simulation results demonstrate our approach significantly enhances sum spectral efficiency over benchmarks, while ensuring continuous and physically feasible antenna trajectories.

Structural representation is crucial for reconstructing and generating editable 3D shapes with part semantics. Recent 3D shape generation works employ complicated networks and structure definitions relying on hierarchical annotations and pay less attention to the details inside parts. In this paper, we propose the method that parameterizes the shared structure in the same category using a differentiable template and corresponding fixed-length parameters. Specific parameters are fed into the template to calculate cuboids that indicate a concrete shape. We utilize the boundaries of three-view renderings of each cuboid to further describe the inside details. Shapes are represented with the parameters and three-view details inside cuboids, from which the SDF can be calculated to recover the object. Benefiting from our fixed-length parameters and three-view details, our networks for reconstruction and generation are simple and effective to learn the latent space. Our method can reconstruct or generate diverse shapes with complicated details, and interpolate them smoothly. Extensive evaluations demonstrate the superiority of our method on reconstruction from point cloud, generation, and interpolation.

In this paper, we present LiDAR-Net, a new real-scanned indoor point cloud dataset, containing nearly 3.6 billion precisely point-level annotated points, covering an expansive area of 30,000m2. It encompasses three prevalent daily environments, including learning scenes, working scenes, and living scenes. LiDAR-Net is characterized by its non-uniform point distribution, e.g., scanning holes and scanning lines. Additionally, it meticulously records and an-notates scanning anomalies, including reflection noise and ghost. These anomalies stem from specular reflections on glass or metal, as well as distortions due to moving persons. LiDAR-Net's realistic representation of non-uniform distribution and anomalies significantly enhances the training of deep learning models, leading to improved generalization in practical applications. We thoroughly evaluate the performance of state-of-the-art algorithms on LiDAR-Net and provide a detailed analysis of the results. Crucially, our research identifies several fundamental challenges in understanding indoor point clouds, contributing essential insights to future explorations in this field. Our dataset can be found online: http://lidar-net.njumeta.com.