Satellite networks face escalating cybersecurity threats from evolving attack vectors and systemic complexities. This paper proposes SatGuard, a novel framework integrating a three-dimensional penetration testing methodology and a nonlinear risk assessment mechanism tailored for satellite security. To address limitations of conventional tools in handling satellite-specific vulnerabilities, SatGuard employs large language models (LLMs) like GPT-4 and DeepSeek-R1. By leveraging their contextual reasoning and code-generation abilities, SatGuard enables semi-automated vulnerability analysis and exploitation. Validated in a simulated ground station environment, the framework achieved a 73.3% success rate (22/30 attempts) across critical ports, with an average of 5.5 human interactions per test. By bridging AI-driven automation with satellite-specific risk modeling, SatGuard advances cybersecurity for next-generation space infrastructure through scalable, ethically aligned solutions.
Bryan Better, Aboulghit El Malki Alaoui, Christine Espinosa
et al.
Lightweight aeronautical structures and power generation structures such as wind turbines are fitted with protected external layers designed and certified to withstand severe climatic events such as lightning strikes. During these events, high currents flow through the structural protection but are likely to induce effects deeper in the supporting composite material and could even reach or perforate pressurized tanks. In situ measurements are hard to achieve during current delivery due to the severe electromagnetic conditions, and the lightning strike phenomenon on these structures is not yet fully investigated. To gain a better understanding of the physics involved, similarities in direct damage between lightning-struck samples and those subjected to pulsed lasers and an electron gun are analyzed. These analyses show the inability of a pure mechanical contribution to fully reproduce the shape of the delamination distribution of lightning strikes. Conversely, the similarities in effect and damage with the thermomechanical contribution of electron beam deposition are highlighted, particularly the increase in core delamination due to the paint and the apparent similarities in delamination distribution.
A collectible rotor hybrid aircraft (CRHA) represents a novel type of vertical takeoff and landing (VTOL) unmanned aircraft configuration, combining the typical rotor and transmission systems of helicopters with the wing and propulsion systems of fixed-wing aircraft. Its weight estimation and parameter design during the conceptual design stage cannot directly use existing rotorcraft or fixed-wing methods. This paper presents a rapid key design parameter sizing and maximum takeoff weight (MTOW) estimation approach tailored to CRHA, explicitly scoped to the 5–8-metric-ton (t) MTOW class. Component weight models are first formulated as explicit functions of key design parameters—including rotor disk loading, power loading, and wing loading. Segment-specific fuel weight fractions for VTOL and transition flight are then updated from power calculations, yielding a complete mission fuel model for this weight class. A hybrid optimization framework that minimizes MTOW is constructed by treating the key design parameters as design variables and combining a genetic algorithm (GA) with sequential quadratic programming (SQP). The empty-weight model, fuel-weight model, and optimization framework are validated against compound-helicopter, tilt-rotor, and twin-turboprop benchmarks, and parameter sensitivities are evaluated locally and globally. Results show prediction errors of roughly 10% for empty weight, fuel weight, and MTOW. Sensitivity analysis indicates that at the baseline design point, wing loading exerts the greatest influence on MTOW, followed by power loading and disk loading.
The use of electrical motors and other remote systems are important tools in radiation environments. Certain harsh radiation environments, such as particle accelerators, require the use of remote systems during operation. Stepper motors are one motor, in particular, that have acquired interest in the nuclear field for use in these remote systems. The stepper motor's precision in rotation and holding is a highly desired quality. In this review, the stepper motor is examined for use in radiation environments. The different mechanical parts are introduced and examined from the broader literature of previous work. We attempt to explain the different radiation destructive mechanisms involved with each part of the stepper motor and how the mechanics are affected. Current and past research is explored, identifying either radiation hard alternatives or thresholds of the materials involved, and a general review of stepper motor radiation effects as available in literature.
This paper presents a novel Perceptual Motor Learning (PML) framework integrated with Active Inference (AIF) to enhance lateral control in Highly Automated Vehicles (HAVs). PML, inspired by human motor learning, emphasizes the seamless integration of perception and action, enabling efficient decision-making in dynamic environments. Traditional autonomous driving approaches--including modular pipelines, imitation learning, and reinforcement learning--struggle with adaptability, generalization, and computational efficiency. In contrast, PML with AIF leverages a generative model to minimize prediction error ("surprise") and actively shape vehicle control based on learned perceptual-motor representations. Our approach unifies deep learning with active inference principles, allowing HAVs to perform lane-keeping maneuvers with minimal data and without extensive retraining across different environments. Extensive experiments in the CARLA simulator demonstrate that PML with AIF enhances adaptability without increasing computational overhead while achieving performance comparable to conventional methods. These findings highlight the potential of PML-driven active inference as a robust alternative for real-world autonomous driving applications.
Large-eddy and direct numerical simulations generate vast data sets that are challenging to interpret, even for simple geometries at low Reynolds numbers. This has increased the importance of automatic methods for extracting significant features to understand physical phenomena. Traditional techniques like the proper orthogonal decomposition (POD) have been widely used for this purpose. However, recent advancements in computational power have allowed for the development of data-driven modal reduction approaches. This paper discusses four applications of deep neural networks for aerodynamic applications, including a convolutional neural network autoencoder, to analyze unsteady flow fields around a circular cylinder at Re = 100 and a supersonic boundary layer with Tollmien–Schlichting waves. The autoencoder results are comparable to those obtained with POD and spectral POD. Additionally, it is demonstrated that the autoencoder can compress steady hypersonic boundary-layer profiles into a low-dimensional vector space that is spanned by the pressure gradient and wall-temperature ratio. This paper also proposes a convolutional neural network model to estimate velocity and temperature profiles across different hypersonic flow conditions.
Radiation energy increases with temperature according to an approximately T4 law. As voltage increases, the energy radiation decreases and deviates slightly from this law, revealing tunable emissivity by adjusting the electric field.
As 3D acquisition equipment picks up steam, point cloud registration has been applied in ever-increasing fields. This paper provides an exhaustive survey of the field of point cloud registration for laser scanners and examines its application in large-scale aircraft measurement. We first researched the existing representative point cloud registration algorithms, such as hierarchical optimization, stochastic and probability distribution, and feature-based methods, for analysis. These methods encompass as many point cloud registration algorithms as possible; typical algorithms of each method are suggested respectively, and their strengths and weaknesses are compared. Lastly, the application of point cloud registration algorithms in large-scale aircraft measurement is introduced. We discovered that despite the significant progress of point cloud registration combining deep learning and traditional methods, it is still difficult to meet realistic needs, and the main challenges are in the direction of robustness and generalization. Furthermore, it is impossible to extract accurate and comparable features for alignment from large-scale aircraft surfaces due to their relative smoothness, lack of obvious features, and abundance of point clouds. It is necessary to develop lightweight and effective dedicated algorithms for particular application scenarios. As a result, with the development of point cloud registration technology and the deepening into the aerospace field, the particularity of the aircraft shape and structure poses higher challenges to point cloud registration technology.
Collision probability is employed for evaluating whether there will be a dangerous encounter between 2 space objects. The fidelity of the collision probability mainly depends on the accuracies of orbit prediction and covariance prediction for the space objects. In this paper, the collision probability between the Tsinghua Gravitation and Atmosphere Science Satellite, Q-Sat, and the space debris with a North American Aerospace Defense Command ID of 49863 on 2022 January 18 was calculated. The 2 objects approached each other dangerously close and the event was reported. First, the atmospheric density model is modified by a dynamic approach-based inversion to improve the accuracy of orbit prediction for the Q-Sat. Next, predictions of position error covariance are carried out. Orbits of the next 24 hours are predicted, and the predicted orbits are compared with the actual orbits of the Q-Sat. Backpropagation neural network was trained for predicting the position error covariance. For the space debris, the 2-line element data are employed. Orbit predictions for the space debris are also conducted and compared with the actual orbit. Another backpropagation neural network for predicting the position error covariance for the space debris is trained. Using the covariances from the backpropagation neural network, the error ellipsoids of the 2 objects are established. The error ellipsoids are later projected to the encounter plane to calculate the collision probability. Different from the reports from other institutes, the closest distance between the Q-Sat and the space debris calculated by the current method was 2.71 km. The collision probability was 1.16 × 10−11. It was not a dangerous encounter event. The onboard precise orbit determination device enabled improved orbit determination precision and orbit prediction accuracy, which is important for space safety management.
Motor vehicles. Aeronautics. Astronautics, Astronomy
Silvia Romero-Azpitarte, Alba Guerra, Mercedes Alonso
et al.
The CISRU project has focused on the development of a software suite for planetary (and terrestrial) robotics, fully abstracted from the robotic platform and enabling interaction between rovers and astronauts in complex tasks and non-structured scenarios. To achieve this, a high level of autonomy is required, powered by AI and multi-agent autonomous planning systems inherited from ERGO/ADE and the PERASPERA program. This communication presents the system developed in CISRU, focusing on the modules of AI-based perception and the interaction between astronauts and robots.
Abstract Electronic band structure and optical conductivity of single-layer graphene could be altered by applied uniaxial strain. Valley and space inversion symmetries are broken. Dirac cones are deformed. We investigate the effect of uniaxial strain on the radiative properties of graphene from the perspective of direction and modulus. Optical conductivity exhibits wealthy phenomenon due to the degeneracy of the energy band broken by strain. The total energy radiation exhibits a novel behavior of periodicity in θ , in accordance with the symmetry of the hexagonal honeycomb lattice.
Alexandru SOLOMON, Valentin Claudiu OLTEI, Alina BOGOI
In this paper we address the subject of mathematical modelling, more precisely the optimization of algorithms for numerically solving partial differential equations. The problem proposed to be tackled in this paper is the implementation of an algorithm for solving partial differential equations in a significantly faster way than that obtained through applying finite difference schemes. The proper orthogonal decomposition (POD) method is a modern and efficient method of reducing the number of variables that occur as a result of applying centred difference schemes to partial differential equations, thus reducing the running time of the algorithm and the accumulation of truncation errors. Therefore, the POD method has been implemented to obtain a reduced order scheme applied to different partial differential equations, with some practical applications and comparisons with the analytical solutions.
With the development of high-altitude and long-endurance unmanned aerial vehicles (UAVs), optimization of the coordinated energy dispatch of UAVs’ energy management systems has become a key target in the research of electric UAVs. Several different energy management strategies are proposed herein for improving the overall efficiency and fuel economy of fuel cell/battery hybrid electric power systems (HEPS) of UAVs. A rule-based (RB) energy management strategy is designed as a baseline for comparison with other strategies. An energy management strategy (EMS) based on fuzzy logic (FL) for HEPS is presented. Compared with classical rule-based strategies, the fuzzy logic control has better robustness to power fluctuations in the UAV. However, the proposed FL strategy has an inherent defect: the optimization performances will be determined by the heuristic method and the past experiences of designers to a great extent rather than a specific cost function of the algorithm itself. Thus, the paper puts forward an improved fuzzy logic-based strategy that uses particle swarm optimization (PSO) to track the optimal thresholds of membership functions, and the equivalent hydrogen consumption minimization is considered as the objective function. Using a typical 30 min UAV mission profile, all the proposed EMS were verified by simulations and rapid controller prototype (RCP) experiments. Comprehensive comparisons and analysis are presented by evaluating hydrogen consumption, system efficiency and voltage bus stability. The results show that the PSO-FL algorithm can further improve fuel economy and achieve superior overall dynamic performance when controlling a UAV’s fuel-cell powertrain.
Although partially autonomous driving (AD) systems are already available in production vehicles, drivers are still required to maintain a sufficient level of situational awareness (SA) during driving. Previous studies have shown that providing information about the AD's capability using user interfaces can improve the driver's SA. However, displaying too much information increases the driver's workload and can distract or overwhelm the driver. Therefore, to design an efficient user interface (UI), it is necessary to understand its effect under different circumstances. In this paper, we focus on a UI based on augmented reality (AR), which can highlight potential hazards on the road. To understand the effect of highlighting on drivers' SA for objects with different types and locations under various traffic densities, we conducted an in-person experiment with 20 participants on a driving simulator. Our study results show that the effects of highlighting on drivers' SA varied by traffic densities, object locations and object types. We believe our study can provide guidance in selecting which object to highlight for the AR-based driver-assistance interface to optimize SA for drivers driving and monitoring partially autonomous vehicles.