In low-Earth-orbit (LEO) satellite networks, the need for intelligent parameter-adjustment strategies has become increasingly critical due to the presence of highly dynamic channel conditions, limited spectrum resources, and complex interference environments. In this paper, a method for optimizing LEO satellite communication links based on deep reinforcement learning (DRL) is proposed. Through the optimization of the transmit power, the modulation and coding scheme (MCS), the beamforming parameters, and the retransmission mechanisms, adaptive link control is achieved in dynamic operational scenarios. A multidimensional state space is constructed, within which the channel state information, the interference environment, and the historical performance metrics are integrated. The spatio-temporal characteristics of the channel are extracted by means of a hybrid neural architecture that incorporates a convolutional neural network (CNN) and a long short-term memory (LSTM) network. To effectively accommodate both continuous and discrete action spaces, a hybrid DRL framework that combines proximal policy optimization (PPO) with a deep Q-network (DQN) is employed, thereby enabling cross-layer optimization of the physical-layer and link-layer parameters. The results demonstrate that substantial improvements in throughput, bit error rate (BER), and transmit-power efficiency are achieved under severely time-varying channel conditions, which provides a new idea for resource management and dynamic-environment adaptation in satellite communication systems.
Raimondas Pomarnacki, Domantas Bručas, Tomas Jačionis
et al.
In this article, the authors conducted electromagnetic radiation research on telecommunication base stations using Unmanned Aerial Vehicles (UAVs). Until now, UAVs have only been capable of performing visual inspections, without investigating electrical parameters. The authors suggest a method that involves using a spectrum analyzer and an automated flight with a predefined trajectory around the base stations to conduct electromagnetic radiation research, which is used by telecommunication regulatory organizations. Two different types of UAVs were used in this work: a drone and a fixed-wing aircraft, each with distinct characteristics. The authors successfully designed and tested both types of UAVs under real conditions and performed measurements. A specialized algorithm with software was developed for processing measurement results, which accurately presents the data in a graphical format. Experiments were performed, and the results, at distances of 200 m or further from the telecommunication base tower by changing the altitude by 5 m, were collected.
Space–air–ground integrated networks (SAGINs) have been considered to be the trend of future 6G network development. In the presence of hostile interferers/attackers especially under military application scenarios, the resilience of the SAGIN to various threats such as physical, electronic, and cyberattacks can be crucial to guarantee desirable networking performance. This motivates advanced enhancement and evaluation schemes for the resilience of SAGIN. In this paper, the SAGIN resilience enhancement process is divided into 4 resilience enhancement phases, namely, resistance, absorption, recovery, and reconfiguration. Then, different resilience enhancement methods are discussed and analyzed within each phase, respectively. Further, considering that SAGIN is coupled by several cross-domain subsystems, the indicator system for resilience capabilities evaluation and evaluation methods of SAGIN are given. Firstly, the resilience capability of each subsystem is evaluated based on 4 indicators, namely, resistance, absorption, recovery, and reconfiguration capability. Then, by combining the resilience capability of the space-based, air-based, and ground-based communication subnetworks, the overall resilience enhancement capability of SAGIN is evaluated.
Motor vehicles. Aeronautics. Astronautics, Astronomy
The complex layout of the airport surface, coupled with interrelated vehicle behaviors and densely mixed traffic flows, frequently leads to operational conflict risks. To address this issue, research was conducted on the recognition of characteristics and risk assessment for airport surface operations in mixed traffic flows. Firstly, a surface topological network model was established based on the analysis of the physical structure features of the airport surface. Based on the Monte Carlo simulation method, the simulation framework for airport surface traffic operations was proposed, enabling the simulation of mixed traffic flows involving aircraft and vehicles. Secondly, from various perspectives, including topological structural characteristics, network vulnerabilities, and traffic complexity, a comprehensive system for feature indices and their measurement methods was developed to identify risk hotspots in mixed traffic flows on the airport surface, which facilitated the extraction of comprehensive risk elements for any node’s operation. Finally, a weighting rule for risk hotspot feature indices based on the CRITIC–entropy method was designed, and a risk assessment method for surface operations based on TOPSIS–gray relational analysis was proposed. This method accurately measured risk indices for airport surface operations hotspots. Simulations conducted at Shenzhen Bao’an International Airport demonstrate that the proposed methods achieve high simulation accuracy. The identified surface risk hotspots closely matched actual conflict areas, resulting in a 20% improvement in the accuracy of direct risk hotspot identification compared to simulation experiments. Additionally, 10.9% of nodes in the airport surface network were identified as risk hotspots, including 3 nodes with potential conflicts between aircraft and ground vehicles and 21 nodes with potential conflicts between aircraft. The proposed methods can effectively provide guidance for identifying potential “aircraft–vehicle” conflicts in complex airport surface layouts and scientifically support informed decisions in airport surface operation safety management.
An increase in aircraft availability and readiness is one of the most desired characteristics of aircraft fleets. Unforeseen failures cause additional expenses and are particularly critical when thinking about combat jets and Unmanned Aerial Vehicles (UAVs). For instance, these systems are used under extreme conditions, and there can be situations where standard maintenance procedures are impractical or unfeasible. Thus, it is important to develop a Health and Usage Monitoring System (HUMS) that relies on diagnostic and prognostic algorithms to minimise maintenance downtime, improve safety and availability, and reduce maintenance costs. In particular, within the realm of aircraft structures, landing gear emerges as one of the most intricate systems, comprising several elements, such as actuators, shock absorbers, and structural components. Therefore, this work aims to develop a preliminary digital twin of a nose landing gear and implement diagnostic algorithms within the framework of the Health and Usage Monitoring System (HUMS). In this context, a digital twin can be used to build a database of signals acquired under healthy and faulty conditions on which damage detection algorithms can be implemented and tested. In particular, two algorithms have been implemented: the first is based on the Root-Mean-Square Error (RMSE), while the second relies on the Mahalanobis distance (MD). The algorithms were tested for three nose landing gear subsystems, namely, the steering system, the retraction/extraction system, and the oleo-pneumatic shock absorber. A comparison is made between the two algorithms using the ROC curve and accuracy, assuming equal weight for missed detections and false alarms. The algorithm that uses the Mahalanobis distance demonstrated superior performance, with a lower false alarm rate and higher accuracy compared to the other algorithm.
In the domain of Advanced Air Mobility (AAM), Simplified Vehicle Operations (SVOs) promise a reduction in handling complexity and training time for pilots. Designing a usable human–machine interface (HMI) for pilots of SVO-enabled aircraft requires a deep understanding of task and user requirements. This paper describes the results of two user research methods to gather these requirements. First, a traditional Helicopter Emergency Medical Service (HEMS) mission was examined using a Hierarchical Task Analysis (HTA). The findings were used to formulate a theoretical HTA for a single-piloted electric Vertical Take-Off and Landing (eVTOL) system in such a scenario. In the second step, qualitative interviews with seven subject matter experts (pilots and paramedic support) in HEMS operations produced vital user requirements for HMI development. Key findings emphasize the necessity of a simplified information presentation and collision avoidance support in the HMI.
Grigorios Kostopoulos, Konstantinos Stamoulis, Vaios Lappas
et al.
This study explores the shape-morphing behavior of 4D-printed structures made from Polylactic Acid (PLA), a prominent bio-sourced shape-memory polymer. Focusing on the response of these structures to thermal stimuli, this research investigates how various printing parameters influence their morphing capabilities. The experimental approach integrates design and slicing, printing using fused deposition modeling (FDM), and a post-printing activation phase in a controlled laboratory environment. This process aims to replicate the external stimuli that induce shape morphing, highlighting the dynamic potential of 4D printing. Utilizing Taguchi’s Design of Experiments (DoE), this study examines the effects of printing speed, layer height, layer width, nozzle temperature, bed temperature, and activation temperature on the morphing behavior. The analysis includes precise measurements of deformation parameters, providing a comprehensive understanding of the morphing process. Regression models demonstrate strong correlations with observed data, suggesting their effectiveness in predicting responses based on control parameters. Additionally, finite element analysis (FEA) modeling successfully predicts the performance of these structures, validating its application as a design tool in 4D printing. This research contributes to the understanding of 4D printing dynamics and offers insights for optimizing printing processes to harness the full potential of shape-morphing materials. It sets a foundation for future research, particularly in exploring the relationship between printing parameters and the functional capabilities of 4D-printed structures.
Willem van Lynden, Raoul Andriulli, Nabil Souhair
et al.
Ambipolar plasma thrusters are an appealing technology due to multiple system-related advantages, including propellant flexibility and the absence of electrodes or neutralizer. Understanding the plasma generation and acceleration mechanisms is key to improving the performance and capabilities of these thrusters. However, the source and plume regions inside are often simulated separately, and no self-consistent strategy exists which can couple these different simulations together. This paper introduces the MUlti-regime Plasma Equilibrium Transport Solver (MUPETS), a self-consistent coupled model integrating a fluid solver for the plasma dynamics in the source, which are collision-driven, with a kinetic Particle-In-Cell (PIC) code for the plasma dynamics in the magnetic nozzle, which involve expansion across a diverging magnetic field. The methodology begins by solving the plasma source with the classical Bohm condition at the thruster’s throat. The resulting plasma profiles (density, temperature, speed) are input into the PIC code for the magnetic nozzle. The PIC code calculates the plasma plume expansion and determines the electric field at the thruster’s throat. This electric field is then used as a boundary condition in the fluid code, where it replaces the Bohm assumption, and the fluid simulation is repeated. This iterative process continues until convergence. In comparing the MUPETS results with those for an experimental thruster, the plasma densities at the thruster’s throat differed by less than 2–5% between the fluid and PIC regions. The thrust predictions agreed with the experimental trend, and were kept well within the measurement’s uncertainty band. These results validate the effectiveness of the coupling strategy for enhancing plasma thruster simulation accuracy.
Jeimmy Nataly Buitrago-Leiva, Ines Terraza-Palanca, Luis Contreras-Benito
et al.
<sup>3</sup>Cat-4 is the fourth member of the CubeSat series of UPC’s NanoSat Lab, and it was selected by the ESA Academy’s Fly Your Satellite! program in 2017. This mission aims at demonstrating the capabilities of nano-satellites, and in particular those based in the 1-Unit CubeSat standard, for challenging Earth Observation (EO) using Global Navigation Satellite System-Reflectometry (GNSS-R) and L-band microwave radiometry, as well as for Automatic Identification Systems (AIS). The following study presents the results of the thermal analysis carried out for this mission, evaluating different scenarios, including the most critical cases at both high and low temperatures. The results consider different albedos and orbital parameters in order to establish the optimal temperatures to achieve the best mission performance within the nominal temperatures, and in all operational modes of the satellite. Simulation results are included considering the thermal performance of other materials, such as Kapton, as well as the redesign of the optical properties of the satellite’s solar panels. The correlation with the thermal model and the TVAC test campaign was conducted at the ESA ESEC-GALAXIA facilities in Belgium.
Combat intent recognition refers to analyzing the enemy target's state information to interpret and judge the purpose of the enemy. With the increased knowledge of combat platforms, these time-series enemy state presents multi-dimensional and massive characteristics. Using neural networks to learn enemy state information has become a research trend in the face of such traits. To address these challenges, we propose a hierarchical aggregation model to recognize the intention of the target. The bottom layer of our model is based on convolutional neural network(CNN) to perceive behavior features, and the middle layer is based on Bi-LSTM(Bi-directional long short-term memory) to aggregate the long-time interdependence information between sub-intentions. The top layer focuses on higher-level features that contribute more to the recognition of intent through the attention mechanism and finally combines the global information to recognize the intention. Extensive experimental results show the superiority of our model in that the recognition accuracy achieves 88.83%, which can solve the problem of identifying air target intent on the modern battlefield.
In the recent years, visual navigation has been considered an effective mechanism for achieving an autonomous landing of Unmanned Aerial Vehicles (UAVs). Nevertheless, with the limitations of visual cameras, the effectiveness of visual algorithms is significantly limited by lighting conditions. Therefore, a novel vision-based autonomous landing navigation scheme is proposed for night-time autonomous landing of fixed-wing UAV. Firstly, due to the difficulty of detecting the runway caused by the low-light image, a strategy of visible and infrared image fusion is adopted. The objective functions of the fused and visible image, and the fused and infrared image, are established. Then, the fusion problem is transformed into the optimal situation of the objective function, and the optimal solution is realized by gradient descent schemes to obtain the fused image. Secondly, to improve the performance of detecting the runway from the enhanced image, a runway detection algorithm based on an improved Faster region-based convolutional neural network (Faster R-CNN) is proposed. The runway ground-truth box of the dataset is statistically analyzed, and the size and number of anchors in line with the runway detection background are redesigned based on the analysis results. Finally, a relative attitude and position estimation method for the UAV with respect to the landing runway is proposed. New coordinate reference systems are established, six landing parameters, such as three attitude and three positions, are further calculated by Orthogonal Iteration (OI). Simulation results reveal that the proposed algorithm can achieve 1.85% improvement of AP on runway detection, and the reprojection error of rotation and translation for pose estimation are 0.675∘ and 0.581%, respectively.
Shengchang Zhang, Chunhua Wang, Xiaoming Tan
et al.
The present study investigates the effects of upstream ramps on a backward-injection film cooling over a flat surface. Two ramp structures, referred to as a straight-wedge-shaped ramp (SWR) and sand-dune-shaped ramp (SDR), are considered under a series of blowing ratios ranging from M = 0.5 to M = 1.5. Regarding the backward injection, the key mechanism of upstream ramps on film cooling enhancement is suggested to be the enlargement of the horizontal scale of the separate wake vortices and the reduction of their normal dimension. When compared to the SDR, the SWR modifies the backward coolant injection well, such that a larger volume of coolant is suctioned and concentrated in the near-field region at the film-hole trailing edge. As a consequence, the SWR demonstrates a more pronounced enhancement in film cooling than the SDR in the backward-injection process, which is the opposite of the result for the forward-injection scheme. For the SWR, the backward injection provides a better film cooling effectiveness than the forward injection, regardless of blowing ratios. However, for the SDR, the backward injection could show a superior effect to the forward injection on film cooling enhancement, when the blowing ratio is beyond a critical blowing ratio. In the present SDR situation, the critical blowing ratio is identified to be M = 1.0.
Abstract In this paper, we propose a spectral vanishing viscosity method for the triangular spectral element computation of high Reynolds number incompressible flows. This can be regarded as an extension of a similar stabilization technique for the standard spectral element method. The difficulty of this extension lies in the fact that a suitable definition of spectral vanishing viscosity operator in non-structured elements does not exist, and it is not clear that if a suitably defined spectral vanishing viscosity provides desirable dissipation for the artificially accumulated energy. The main contribution of the paper includes: 1) a well-defined spectral vanishing viscosity operator is proposed for non-standard spectral element methods for the Navier-Stokes equations based on triangular or tetrahedron partitions; 2) an evaluation technique is introduced to efficiently implement the stabilization term without extra computational cost; 3) the accuracy and efficiency of the proposed method is carefully examined through several numerical examples. Our numerical results show that the proposed method not only preserves the exponential convergence, but also produces improved accuracy when applied to the unsteady Navier-Stokes equations having smooth solutions. Especially, the stabilized triangular spectral element method efficiently stabilizes the simulation of high Reynolds incompressible flows.
Engineering (General). Civil engineering (General), Motor vehicles. Aeronautics. Astronautics
due to the variability and uncertainty of some process parameters under investigation and limited uncertainties and confusions, the controller design faces problems. the controller is performed locally using the information of neighboring agents and the corresponding graph has a spanning tree. fuzzy systems are used as a general approximator and the parameters of the fuzzy system are adjusted in such a way that the tracking error of each agents and the stability of the uniform ultimately bounded of the closed loop system are guaranteed. 1- considering of the nonlinear non-affine of multi-agent system, 2- The unknown dynamics of the agents, 3- The convergence of the tracking error and the formation error to zero, 4- The use of fuzzy systems as a general estimator, are the main advantages of the presented method. Finally, in the simulations performed on the quadrotor, the leader-follower formation for the desired mission is realized and according to the set criteria, the proposed methodology is satisfactory.
Mechanical engineering and machinery, Motor vehicles. Aeronautics. Astronautics
The goal of the work is to research the case of isotopy of spacecraft compartments in the problem of on-board equipment location. A definition of this engineering topological property is given in the article. The most frequently used spacecraft compartment hull shapes and various compositions of their casings are considered. Also, general strategies of taking decisions that determine the choice of modeling technologies and performance factors are discussed. A method of comparing various layouts on the strength of the object location performance factors was developed. The problem of on-board equipment accommodation is solved. Generalized mathematical models of performance factors are presented. Design models and diagrams are constructed. The results obtained make it possible to upgrade algorithms of accommodation.
This manuscript investigates the use of a reinforcement learning method for the guidance of launch vehicles and a computational guidance algorithm based on a deep neural network (DNN). Computational guidance algorithms can deal with emergencies during flight and improve the success rate of missions, and most of the current computational guidance algorithms are based on optimal control, whose calculation efficiency cannot be guaranteed. However, guidance-based DNN has high computational efficiency. A reward function that satisfies the flight process and terminal constraints is designed, then the mapping from state to control is trained by the state-of-the-art proximal policy optimization algorithm. The results of the proposed algorithm are compared with results obtained by the guidance-based optimal control, showing the effectiveness of the proposed algorithm. In addition, an engine failure numerical experiment is designed in this manuscript, demonstrating that the proposed algorithm can guide the launch vehicle to a feasible rescue orbit.
Designing blades for efficient energy transfer by turning the flow and angular momentum change is both an art and iterative multidisciplinary engineering process. A robust parametric design tool with few inputs to create 3D blades for turbomachinery and rotating or non-rotating energy converters is described in this paper. The parameters include axial–radial coordinates of the leading/trailing edges, construction lines (streamlines), metal angles, thickness-to-chord ratio, standard, and user-defined airfoil type among others. Using these, 2D airfoils are created, conformally mapped to 3D stream surfaces, stacked radially with multiple options, and they are transformed to a 3D Cartesian coordinate system. Smooth changes in blade curvature are essential to ensure a smooth pressure distribution and attached flow. B-splines are used to control meanline curvature, thickness, leading edge shape, sweep-lean, and other parameters chordwise and spanwise, making the design iteration quick and easy. C2 curve continuity is achieved through parametric segments of cubic and quartic B-splines and is better than G2. New geometries using an efficient parametric scheme and minimal CAD interaction create watertight solid bodies and optional fluid domains. Several examples of ducted axial and radial turbomachinery with special airfoil shapes or otherwise, unducted rotors including propellers and wind and hydrokinetic turbines are presented to demonstrate versatility and robustness of the tool and can be easily tied to any automation chain and optimizer.