Andra-Ana-Maria GHEORGHIU, Ionut BUNESCU, Mihai-Vladut HOTHAZIE
et al.
This paper presents a detailed analysis of the Blade Element Theory (BET) as applied to the aerodynamic performance of propellers and rotor systems. BET, a low-fidelity but widely used method, models the rotor blade as a series of independent elements along its span, allowing local aerodynamic forces to be calculated based on flow conditions. The approach combines the Blade Element Theory with Momentum Theory, implemented in a MATLAB program for a forward flight helicopter, facilitating the computation of thrust, torque and power coefficients. To account for inflow distribution and induced velocity effects, actuator disk theory is applied at the rotor disk level, providing a consistent estimation of the induced velocity required for accurate flow velocity values. The rotor solidity is used to compare lifting rotor system to an ideal actuator disk. Results demonstrate that the coupled method of BET and Momentum Theory provides accurate and consistent aerodynamic predictions for use in rotor design, optimization and validation against experimental measurements or CFD simulations.
The transition toward Software-Defined Vehicles requires standardization and modularization of hardware decoupled from software, along with centralized electrical/electronic architectures. While electrified drive units, such as integrated in-wheel drives, are expected to realize the hardware standardization and unprecedented flexibility in vehicle design, their implementation remains constrained by complex signal wiring between the module and the vehicle body and by control units decentralized across them. This paper proposes a modular drive architecture that achieves complete hardware-software separation by leveraging the power packet dispatching system. We introduce a sensorless control method that estimates motor internal states, specifically winding current and rotor angle, solely from physical quantities measured on the vehicle side. This completely eliminates the need for physical sensors in the drive module, reducing it to a passive actuator governed by the vehicle-side power system via a standardized packet protocol. The proposed architecture significantly reduces wiring complexity and centralizes control logic, advancing fully standardized, plug-and-play platforms for next-generation electrified mobility.
The distant retrograde orbits(DROs) form a family of planar periodic orbits that are retrograde around the Moon in the Earth-Moon circular restricted three-body problem(CR3BP). Known for their "low-energy insertion, long-term stability, and global accessibility", DROs are regarded as ideal candidates for future cis-lunar exploration. Sun's gravity and solar radiation pressure(SRP), as primary perturbations in the Earth-Moon system, could significantly alter the orbit dynamics of spacecraft. Based on the above considerations, the effect of the Sun's gravity and SRP on the dynamics and geometry of Sun-resonant DROs within a quasi-bicircular problem(QBCP) is investigated. Each resonant DRO in the CR3BP bifurcates into at least two branches in the QBCP. Furthermore, the Sun's gravity can qualitatively alter the phase-space structure of most resonant DROs, transforming them from stable to unstable, which opens the possibility of low-energy transfer. Regarding SRP, it induces more complex changes in the phase-space structures and leads to more bifurcation types, such as tangent bifurcation, period-doubling bifurcation and second-class Hopf bifurcation, thus deriving richer families of orbits. It is worthily noted that SRP may help stabilize orbits, making it beneficial for station-keeping. Additionally, SRP can significantly alter the geometry of DROs. Especially when the spacecraft which has a relatively large lightness number, is pitched at certain angles, the planar DROs evolve into spacial orbits to make them more suitable for cis-lunar space missions.
Currently, there is an urgent need to create a high-quality automated risk assessment tool for the use of (unmanned aircraft) UAVs. There is no universal approach to risk management in unmanned civil aviation, and the risk assessment of the operator is largely individual. At the moment, no tool has been developed for plotting optimal routes for UAV flights in airspace, which would avoid piloting in areas with unacceptable risk. The article suggests the use of fully functional geographic information systems (GIS) to assess the risks of performing a flight mission. For a qualitative assessment of the risks of a particular flight assignment, it is proposed to take into account the situational component in the relevant segment of airspace and the ground (surface) situation. The article systematizes the main groups of factors that are important for assessing the risks of using BV. UAV flights are exposed to environmental factors, while posing a danger to surrounding objects. A formula for analyzing the spatial and temporal distribution of risk values in the airspace is derived. The minimum size of the simulation cell is proposed. A universal approach to assessing the risks of a UAV flight by various operators is substantiated, and a methodology for spatiotemporal analysis of the distribution of risk values based on the use of GIS is given. The results of the analysis of spatial and temporal information in the GIS environment make it possible to zone the airspace according to the degree of flight acceptability and build the optimal route outside areas with an increased risk of an aviation incident or accident. The developed spatio-temporal risk-oriented model can be used to support management decision-making in terms of building optimal routes for the movement of UVs.
Si Pengju, Gao Zhifeng, Wang Huan, Gao Song, Zhu Chenqi, Zhang Dongkai, Sun Lifan, Ji Baofeng
To address the challenges of low resolution and sparse information in drone infrared images, this paper proposes a multi-domain divide-and-conquer drone infrared image super-resolution algorithm based on a state space model. The algorithm incorporates a state space model structure utilizing a selective scanning mechanism. By modeling long-range dependencies within features through the state space model, the approach effectively suppresses infrared image noise while enhancing the reconstruction capability for texture details and small target structures. During the feature mapping stage, a method based on Haar wavelet transform is designed to decouple high-frequency and low-frequency image features. Furthermore, spatial pyramid pooling and selective state space equations are employed to enhance local texture continuity and global semantic consistency. Finally, an adaptive multi-domain attention fusion method is introduced. This method aligns structural features across spatial and frequency domains using multi-scale convolution and incorporates an adaptive weighted cross-attention mechanism. It dynamically adjusts feature fusion weights via learnable parameters to improve model robustness. Extensive experimental results demonstrate that, compared to existing algorithms, the proposed method achieves superior reconstruction of infrared image details and textures, enhanced performance across multiple quantitative evaluation metrics.
To address issues such as insufficient tracking capacity and weak anti-jamming capability of defense systems caused by ballistic missile swarm attacks, poor dynamic adaptability and a lack of multi-band coordination of the traditional networked radar resource management methods, it is necessitated urgently making a breakthrough of the real-time resource optimization in high-density target environments. This paper proposes a joint task-resource optimization framework based on the posterior Cramer-Rao lower bound (PCRLB), constructs a lightweight PCRLB prediction model that reduces computational complexity through Monte Carlo approximation. It designs a two-stage decomposition algorithm to decouple the mixed-integer nonlinear programming problem into discrete radar-target assignment and continuous dwell time allocation phases, also develops a multi-band anti-jamming coordination mechanism to jointly optimize array parameters and frequency offsets. Simulations demonstrate that in a 75-target saturation attack scenario, the tracking position error is reduced by 42.3% compared with traditional methods, and the root mean square error (RMSE) is approaching to the PCRLB theoretical lower bound, and time consumption of the resource allocation algorithm is only 18.7 ms, which meeting millisecond-level real-time requirements, and SNR is increased by 15 dB for the multi-band coordination, while the false alarm rate is reduced by 60%. The proposed framework significantly enhances dense target tracking accuracy and resource utilization efficiency, providing both theoretical and technical support for ballistic missile defense systems with enhanced anti-saturation and anti-jamming capabilities.
Virtual reality (VR) is increasingly used to enhance the ecological validity of motor control and learning studies by providing immersive, interactive environments with precise motion tracking. However, designing realistic VR-based motor tasks remains complex, requiring advanced programming skills and limiting accessibility in research and clinical settings. MovementVR is an open-source platform designed to address these challenges by enabling the creation of customizable, naturalistic reaching tasks in VR without coding expertise. It integrates physics-based hand-object interactions, real-time hand tracking, and flexible experimental paradigms, including motor adaptation and reinforcement learning. The intuitive graphical user interface (GUI) allows researchers to customize task parameters and paradigm structure. Unlike existing platforms, MovementVR eliminates the need for scripting while supporting extensive customization and preserving ecological validity and realism. In addition to reducing technical barriers, MovementVR lowers financial constraints by being compatible with consumer-grade VR headsets. It is freely available with comprehensive documentation, facilitating broader adoption in movement research and rehabilitation.
The rapid introduction of automated driving functionalities in vehicles, has forced the necessity to systematically organize the methodologies for traffic safety assessment. Their safety impacts depend on many factors: human, vehicle, traffic, size of implementation. Modelling and simulations on the driver up to the societal level, play a vital role in assessment. This paper discusses the need, as well as an approach, to develop a multi-level safety assessment framework, facilitating the mitigation of risks. Via a research-driven format, this paper provides practitioners with a strategy to effectively perform safety assessment, offering practical, stepwise guidelines regarding the relevant models and tools.
Wazeer Zulfikar, Nishat Protyasha, Camila Canales
et al.
Adults who are minimally verbal with autism spectrum disorder (mvASD) have pronounced speech difficulties linked to impaired motor skills. Existing research and clinical assessments primarily use indirect methods such as standardized tests, video-based facial features, and handwriting tasks, which may not directly target speech-related motor skills. In this study, we measure activity from eight facial muscles associated with speech using surface electromyography (sEMG), during carefully designed tasks. The findings reveal a higher power in the sEMG signals and a significantly greater correlation between the sEMG channels in mvASD adults (N=12) compared to age and gender-matched neurotypical controls (N=14). This suggests stronger muscle activation and greater synchrony in the discharge patterns of motor units. Further, eigenvalues derived from correlation matrices indicate lower complexity in muscle coordination in mvASD, implying fewer degrees of freedom in motor control.
Nilabha Das, Laxman Rao S. Paragond, Balkrushna H. Waghmare
This work aims to develop an integrated control strategy for Brushless Direct Current Motors for a wide range of applications in robotics systems. The controller is suited for both high torque - low speed and high-speed control of the motors. Hardware validation is done by developing a custom BLDC drive system, and the circuit elements are optimised for power efficiency.
Cellular migration is crucial for biological processes including embryonic development, immune response, and wound healing. The myosin-clutch model is a framework that describes how cells control migration through the interactions between myosin, the clutch mechanism, and the substrate. This model is related to how cells regulate adhesion, generate traction forces, and move on compliant substrates. In this study, we present a five-dimensional nonlinear autonomous system to investigate the influences of myosin, clutches, substrate, and external load on the system's stability. Moreover, we analyze the effects of various parameters on fixed points and explore the frequency and amplitude of the limit cycle associated with oscillations. We discovered that the system demonstrates oscillatory behavior when the velocity of the myosin motor is relatively low, or when the ratio of the motor attachment rate to motor detachment rate is relatively high. The external load shares a fraction of the force exerted by myosin motors, thereby diminishing the force endured by the clutches. Within a specific range, an increase in external load not only diminishes and eventually eliminates the region lacking fixed points but also decelerates clutch detachment, enhancing clutch protein adherence.
Recently, the research on drone-view object detection (DOD) has predominantly centered on efficiently identifying objects through cropping high-resolution images. However, it has overlooked the distinctive challenges posed by scale imbalance and a higher prevalence of small objects in drone images. In this paper, to address the challenges associated with the detection of drones (DODs), we introduce a specialized detector called DroneNet. Firstly, we propose a feature information enhancement module (FIEM) that effectively preserves object information and can be seamlessly integrated as a plug-and-play module into the backbone network. Then, we propose a split-concat feature pyramid network (SCFPN) that not only fuses feature information from different scales but also enables more comprehensive exploration of feature layers with many small objects. Finally, we develop a coarse to refine label assign (CRLA) strategy for small objects, which assigns labels from coarse- to fine-grained levels and ensures adequate training of small objects during the training process. In addition, to further promote the development of DOD, we introduce a new dataset named OUC-UAV-DET. Extensive experiments on VisDrone2021, UAVDT, and OUC-UAV-DET demonstrate that our proposed detector, DroneNet, exhibits significant improvements in handling challenging targets, outperforming state-of-the-art detectors.
Accurately predicting proton flux in the space radiation environment is crucial for satellite in-orbit management and space science research. This paper proposes a proton flux prediction method based on a hybrid neural network. This method is a predictive approach for measuring proton flux profiles via a satellite during its operation, including crossings through the SAA region. In the data preprocessing stage, a moving average wavelet transform was employed to retain the trend information of the original data and perform noise reduction. For the model design, the TPA-LSTM model was introduced, which combines the Temporal Pattern Attention mechanism with a Long Short-Term Memory network (LSTM). The model was trained and validated using 4,174,202 proton flux data points over a span of 12 months. The experimental results indicate that the prediction accuracy of the TPA-LSTM model is higher than that of the AP-8 model, with a logarithmic root mean square error (logRMSE) of 3.71 between predicted and actual values. In particular, an improved accuracy was observed when predicting values within the South Atlantic Anomaly (SAA) region, with a logRMSE of 3.09.
New technologies are increasingly being implemented in people’s daily lives and with the growth of smart devices around the globe, the users’ needs and demands have changed in favor of more technological cities, cars, houses, and airplanes. Therefore, it is important to define the stakeholder’s needs and requirements to understand which technologies, smart or not, can be implemented on the cabin to support or even fulfill stakeholder needs. Consequently, those technologies enhance airplane operation and increase product competitiveness for airlines. This paper is the first of a two-part series where design thinking tools are applied to establish high-level requirements based on the concept of a “Smart Cabin” for regional airplanes from 60 to 120 seats. To achieve this goal, a series of methods such as stakeholders’ studies, personas creation and user journey methods are used. The Smart Cabin concept aims to enhance the passenger experience by granting a new level of cabin comfort, customization and connectivity that allows the reduction of airplane time on ground because of the real-time monitoring of airplane cabin components that enables the prediction of maintenance procedures, creates new profits and revenues opportunities for services, provides a more sustainable airplane operation and derived services, and creates new business opportunities for all companies that integrate regional aviation ecosystem.
Technology, Motor vehicles. Aeronautics. Astronautics
Dillon O’Reilly, Georg Herdrich, Felix Schäfer
et al.
Pulsed plasma thrusters (PPT) have demonstrated enormous potential since the 1960s. One major shortcoming is their low thrust efficiency, typically <30%. Most of these losses are due to joule heating, while some can be attributed to poor efficiency of the power processing units (PPUs). We model PPTs to improve their efficiency, by exploring the use of power electronic topologies to enhance the power conversion efficiency from the DC source to the thruster head. Different control approaches are considered, starting off with the basic approach of a fixed frequency flyback converter. Then, the more advanced critical conduction mode (CrCM) flyback, as well as other optimized solutions using commercial off-the-shelf (COTS) components, are presented. Variations of these flyback converters are studied under different control regimes, such as zero voltage switching (ZVS), valley voltage switching (VVS), and hard switched, to enhance the performance and efficiency of the PPU. We compare the max voltage, charge time, and the overall power conversion efficiency for different operating regimes. Our analytical results show that a more dynamic control regime can result in fewer losses and enhanced performance, offering an improved power conversion efficiency for PPUs used with PPTs. An efficiency of 86% was achieved using the variable frequency approach. This work has narrowed the possible PPU options through analytical analysis and has therefore identified a strategic approach for future investigations. In addition, a new low-power coaxial micro-thruster model using equivalent circuit model elements is developed.This is referred to as the Carlow–Stuttgart model and has been validated against experimental data from vacuum chamber tests in Stuttgart’s Pulsed Plasma Laboratory. This work serves as a valuable precursor towards the implementation of highly optimized PPU designs for efficient PPT thrusters for the next PETRUS (pulsed electrothermal thruster for the University of Stuttgart) missions.
The nonequilibrium response and fluctuations of Markovian systems, both near and far from equilibrium, are best understood by varying their parameters along dynamical equivalence classes. In this note, I illustrate this approach for an analytically solvable molecular motor toy model.
Raman spectroscopy has been efficiently used to recognize breast cancer tissue by detecting the characteristic changes in tissue composition in cancerization. In addition to chemical composition, the change in bio-structure may be easily obtained via polarized micro-Raman spectroscopy, aiding in identifying the cancerization process and diagnosis. In this study, a polarized Raman spectral technique is employed to obtain rich structural features and, combined with deep learning technology, to achieve discrimination of breast cancer tissue. The results reconfirm that the orientation of collagen fibers changes from parallel to vertical during breast cancerization, and there are significant structural differences between cancerous and normal tissues, which is consistent with previous reports. Optical anisotropy of collagen fibers weakens in cancer tissue, which is closely related with the tumor’s progression. To distinguish breast cancer tissue, a discrimination model is established based on a two-dimensional convolutional neural network (2D-CNN), where the input is a matrix containing the Raman spectra acquired at a set of linear polarization angles varying from 0° to 360°. As a result, an average discrimination accuracy of 96.01% for test samples is achieved, better than that of the KNN classifier and 1D-CNN that are based on non-polarized Raman spectra. This study implies that polarized Raman spectroscopy combined with 2D-CNN can effectively detect changes in the structure and components of tissues, innovatively improving the identification and automatic diagnosis of breast cancer with label-free probing and analysis.
Karine Klippel, Elisa Valentim Goulart, Gilberto Fisch
et al.
The Alcantara Launch Center (ALC) is the main Brazilian access to space. It is positioned over a complex terrain, and it has some important buildings for assembling, integration and launching activities, such as the Mobile Integration Tower. Being in a region of prevalent trade winds, the flow interaction between the complex terrain and the buildings can affect the safety of operations on the platform, and the dispersion of toxic gases emitted during the launching. The main objective of this work was to study the influence of topography and buildings on the atmospheric flow of ALC using computational fluid dynamics (CFD) techniques. Three geometries were considered: simplified terrain (case 1), smooth complex terrain (case 2), and roughness complex terrain (case 3). The flow conditions over ALC were simulated using the ANSYS Fluent 19.0 CFD commercial code. The numerical simulations used a realizable κ-ε to model turbulence effects and the results
presented a good agreement with the in-situ field measurements for the most complex geometry (case 3). The topography clearly influences the flow pattern at ALC, with the cliff influence over the wind being the major cause for establishing the flow patterns.
Technology, Motor vehicles. Aeronautics. Astronautics