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Discrete gradient methods are a powerful tool for the time discretization of dynamical systems, since they are structure-preserving regardless of the form of the total energy. In this work, we discuss the application of discrete gradient methods to the system class of nonlinear port-Hamiltonian differential-algebraic equations - as they emerge from the port- and energy-based modeling of physical systems in various domains. We introduce a novel numerical scheme tailored for semi-explicit differential-algebraic equations and further address more general settings using the concepts of discrete gradient pairs and Dirac-dissipative structures. Additionally, the behavior under system transformations is investigated and we demonstrate that under suitable assumptions port-Hamiltonian differential-algebraic equations admit a representation which consists of a parametrized port-Hamiltonian semi-explicit system and an unstructured equation. Finally, we present the application to multibody system dynamics and discuss numerical results to demonstrate the capabilities of our approach.
Layout estimation and 3D object detection are two fundamental tasks in indoor scene understanding. When combined, they enable the creation of a compact yet semantically rich spatial representation of a scene. Existing approaches typically rely on point cloud input, which poses a major limitation since most consumer cameras lack depth sensors and visual-only data remains far more common. We address this issue with TUN3D, the first method that tackles joint layout estimation and 3D object detection in real scans, given multi-view images as input, and does not require ground-truth camera poses or depth supervision. Our approach builds on a lightweight sparse-convolutional backbone and employs two dedicated heads: one for 3D object detection and one for layout estimation, leveraging a novel and effective parametric wall representation. Extensive experiments show that TUN3D achieves state-of-the-art performance across three challenging scene understanding benchmarks: (i) using ground-truth point clouds, (ii) using posed images, and (iii) using unposed images. While performing on par with specialized 3D object detection methods, TUN3D significantly advances layout estimation, setting a new benchmark in holistic indoor scene understanding.
This paper presents a fault-tolerant attitude control scheme for satellites equipped with reaction wheels subjected to faults, formulated within a switched model predictive control (SMPC) framework. The proposed method partitions the satellite's maneuvering range into smaller regions and models the attitude dynamics using a switched system governed by a persistent dwell-time (PDT) switching signal. Within this framework, an optimal control problem is formulated to achieve both H2 performance (minimizing control cost) and L2-gain disturbance attenuation. This ensures the uniform ultimate boundedness (UUB) of the closed-loop system while accounting for the satellite's limited energy resources. A key contribution is the determination of the optimal number of sub-regions (i.e., subsystems) based on mission-specific requirements such as control accuracy, maneuver range, and convergence speed, thereby enabling accurate, wide-range maneuvers without unnecessary complexity. Additionally, a novel sliding mode fault estimator is developed to actively detect and estimate both loss-of-effectiveness and bias faults in the reaction wheels. The integration of this estimator into the control framework drives the UUB region towards zero, significantly reducing tracking error and enhancing overall system robustness. The effectiveness of the proposed scheme is validated through comprehensive simulations on a representative satellite model. (c) 2025 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
3D visual grounding (3DVG) aims to localize objects in a 3D scene based on natural language queries. In this work, we explore zero-shot 3DVG from multi-view images alone, without requiring any geometric supervision or object priors. We introduce Z3D, a universal grounding pipeline that flexibly operates on multi-view images while optionally incorporating camera poses and depth maps. We identify key bottlenecks in prior zero-shot methods causing significant performance degradation and address them with (i) a state-of-the-art zero-shot 3D instance segmentation method to generate high-quality 3D bounding box proposals and (ii) advanced reasoning via prompt-based segmentation, which utilizes full capabilities of modern VLMs. Extensive experiments on the ScanRefer and Nr3D benchmarks demonstrate that our approach achieves state-of-the-art performance among zero-shot methods.
Electrolyte protection against oxygen is a practical strategy to extend the service life of vanadium redox flow batteries, and this study compares two common approaches in a three-cell stack: protecting the electrolyte with a paraffin blanket versus removing oxygen via nitrogen injection. Nitrogen injection provides markedly better performance stability than paraffin shielding. This improvement is observed as 502 mAh more capacity and 28.1
