High-Altitude Pseudo-Satellite: Prototype Conceptualization and Development

HAPS (high-altitude pseudosatellites) are flight machines, airplane type, generally without pilot which fly in a definite zone at 18-22 km altitude, providing communication and surveillance services. These flight machines do not leave the atmosphere, and their purpose is to maintain a constant flight level for as long time as possible in the interest zone (e.g., five years) to fulfill their mission. HAPS energetic system proposed in this paper has to feed the electric propulsion system of HAPS (12.5 kW) and also to feed on-board equipment (navigation, data links, scientific equipment, etc.). On-board energy sources have to maintain HAPS in the interest zone for long periods. For this reason, it is used in the present solar power sources. A part of the generated energy is consumed on board; the rest is stored daytime and consumed nighttime. So, the system is provided with energy generation systems and also with storage and management systems. HAPS energetic system is a hybrid type, with two or more power sources. In this case, power sources are photovoltaic panels are used daytime and fuel cell are used nighttime, and also, a battery and/or a supercapacitor is used in transition periods from day to night and in peak load periods. In this paper, an electric power system used nighttime is designed and analysed. In this situation, the primary power source is the fuel cell and the secondary power sources are battery and/or supercapacitor. There are used numerical simulations models, developed in Matlab/Simulink, for all hybrid power source components: fuel cell stack, battery system, supercapacitors, conversion system, and fuzzy logic power management system. For a part of these components, there are used existing simulation models in Matlab/Simulink, adapted to these simulation requirements, and for others, there are designed and implemented simulation schemes according to these simulation requirements. An important component of the hybrid power source is the power conversion system which adapts the power sources parameters to consumer input requirements. A fuzzy logic power management system is designed.

The future European Air Traffic Management (ATM) System is currently being defined by the Single European Sky ATM Research (SESAR) programme. Iris is the European Space Agency programme to develop an Air-Ground communication system for the SESAR programme. Within the Iris Programme, ANTARES focuses on the development of a new satellite-based communication system based on the use of low-cost user terminals through the realization of a new satellite communication standard. The present paper describes the system design process defined and adopted in the ANTARES project to cope with the uncertainties of the user requirements The proposed process is based on the definition of System Architecture Options allowing to evaluate the impact of requirements variability on the satellite system design. Each System Architecture Option will implement a set of Design Options which are system or technology solutions adopted to design the system or its elements. An insight on the Design Option relevant to the airborne user terminal features is provided in the paper.KeywordsANTARESL-BandATMSESARSystem design options

In complex and heterogeneous landscapes, determining the spatial origin of measured fluxes is critical for interpreting eddy covariance (EC) data accurately. To address this, footprint models are used to simulate the transport of turbulence and quantify the contribution of different areas within the source region. These models rely on theoretical assumptions, such as homogeneous and stationary atmospheric conditions, which often deviate significantly from real-world conditions particularly in terrains with uneven topography or land cover. This discrepancy may lead to substantial uncertainties, as the models may fail to accurately represent the true flux contributions under these non-ideal conditions.To evaluate the reliability of the Flux Footprint Prediction (FFP) model (Kljun et al., 2015) and its performance under real-world conditions, we conducted three tracer release campaigns in the upwind region of a tall tower EC greenhouse gas observation system located at Hove (55.7169°N, 12.2375°E), a rural area west of Copenhagen, Denmark. The experiments utilized acetylene (C₂H₂) as the tracer gas, released at a controlled and precisely known emission rate.  The FFP model were assessed using data from different averaging intervals, enabling a detailed comparison of temporal resolutions and their impact on flux estimates.The observed fluxes were systematically compared with the model predictions, allowing us to identify discrepancies and provide critical insights into the strengths and limitations of the FFP model, particularly in rural and heterogeneous landscapes. Moreover, the analysis highlights the influence of averaging intervals on the agreement between measured and modelled fluxes. This work also provides a reference for applying tracer release experiments in heterogeneous terrain using the tall tower EC system, contributing to the understanding of experimental design and model validation in such environments.

The growing demand for sustainable and resource-efficient construction has driven the development of adaptive high-rise buildings, which employ active structural control to reduce material usage while maintaining high load-bearing performance. This paper presents a novel Model Predictive Control (MPC) strategy for active vibration damping in adaptive high-rise buildings that departs from conventional tuned mass dampers by employing a distributed actuator system directly integrated into the load-bearing structure. Unlike traditional passive systems, the proposed control strategy continuously modifies structural properties to enhance damping across multiple vibration modes. To ensure practical feasibility, the controller explicitly accounts for actuator force limitations. A comparative study examines different levels of abstraction in the MPC prediction model to balance computational efficiency and control performance. In particular, it evaluates the prediction accuracy and closed-loop system performance of high-order structural models, with and without nonlinear effects, against analytically derived and data-based reduced-order models. The proposed approach is experimentally validated on D1244, the world’s first adaptive high-rise building. Results demonstrate a 62% improvement in vibration damping over the passive case, proving its effectiveness under real-world conditions. These results highlight the significant potential of adaptive buildings to reduce resource consumption in civil engineering. • Adaptive structures enable resource-efficient construction through distributed actuation. • Active MPC strategy achieves 62% vibration damping in adaptive high-rise buildings. • Prediction model abstraction in the MPC balances real-time capability and performance. • Experimental validation confirms feasibility on world’s first adaptive high-rise.

Securing restricted zones such as airports, research facilities, and military bases requires robust and reliable access control mechanisms to prevent unauthorized entry and safeguard critical assets. Face recognition has emerged as a key biometric approach for this purpose; however, existing systems are often sensitive to variations in illumination, occlusion, and pose, which degrade their performance in real-world conditions. To address these challenges, this paper proposes a novel hybrid face recognition method that integrates complementary feature descriptors such as Fuzzy-Gabor 2D Fisher Linear Discriminant (FG-2DFLD), Generalized 2D Linear Discriminant Analysis (G2DLDA), and Modular-Local Binary Patterns (Modular-LBP) with Dempster–Shafer (DS) evidence theory for decision fusion. The proposed framework extracts global, structural, and local texture features, models them using Gaussian distributions to estimate belief factors, and fuses these belief factors through DS theory to explicitly handle uncertainty and conflict among descriptors. Experimental validation was performed on two widely used benchmark datasets, ORL and Cropped Yale B, achieving recognition rates exceeding 98%, which outperform traditional methods as well as recent deep learning-based approaches. Furthermore, the method demonstrated strong robustness under noisy conditions, maintaining accuracies above 96% with salt-and-pepper and Gaussian noise. These results highlight the effectiveness of the proposed integration strategy in enhancing accuracy, reliability, and resilience compared to single-descriptor and conventional fusion methods. Given its high performance and efficiency, the proposed method shows strong potential for deployment in real-world restricted-zone applications such as smart parking systems, secure facility access, and other high-security domains.

Car ownership in India is expected to skyrocket in the coming decades, strongly driven by rising incomes. This phenomenon provides unprecedented opportunities for automakers and equally unprecedented social and environmental challenges. Policymakers, urban planners and civil society see this car boom leading to an explosion in problems related to congestion, infrastructure, air pollution, safety, higher oil imports and climate change. For all these stakeholders to take effective action, good data on how people use their cars, their demand for mobility and their behavior in mobility is essential. Unfortunately, there is very little data on the Indian transport sector as a whole and virtually none on real-world vehicle performance and use. The rapid development of high quality mobile telecommunications infrastructure provides India with the opportunity to leapfrog the West in cheaply collecting vast amounts of useful data from transportation. In this paper, we describe a pilot project in which we use commercial smart phone apps to collect per second car driving data from the city of Pune, instantly upload it through 3G and prepare it for analysis using advanced noise filtering algorithms for less than $1 per day per car. We then use our data in an Autonomie simulation to show that India’s currently planned fuel economy test procedures will result in over-estimates of fuel economy of approximately 35% for a typical Indian car when it is operated in real world conditions. Supporting better driving cycle development is just one of many applications for smart phone derived data in Indian transportation.

This study presents a preliminary evaluation of candidate wick material and working fluid for a flat-loop heat pipe (F-LHP) designed to operate within the temperature range of 500–700 K. The selection process considered key thermal and physical parameters, including thermal conductivity, chemical compatibility between wick and fluid, capillary pressure generation, pressure drop across the wick structure, and structural integrity at elevated temperatures. A range of metallic and ceramic wick materials, along with suitable high-temperature working fluids, were reviewed and compared based on performance metrics and practical availability. Special attention was given to oxidation and corrosion resistance, capillary performance, and thermal stability under elevated-temperature conditions. Nine different porous wicks with distinct materials and microstructures—differing in pore size, porosity, and permeability—were analyzed in combination with seven different working fluids. The analysis focused on determining which combinations generated the highest capillary pressure and which exhibited the lowest flow resistance due to external flow, thereby enhancing the LHP’s performance. Based on these results, the study identifies the most effective wick–fluid pairings for F-LHP applications, offering an optimal balance of thermal performance and long-term reliability. These findings provide a foundation for further experimental validation and the development of prototypes.

This paper contributes to an ongoing development of a micro hydropower network system tailored for implementation along river networks in Malaysia. The concept involves deploying multiple micro hydropower generators to charge a battery positioned on the riverbank. This interconnected hydropower network work alongside similar network systems to either charge larger batteries or serve multiple communities, enhancing its scalability and utility. The primary aim of this study is to determine a better generator blade type, and explore the relationship between the arrangement of generator blades in the river and the resulting torque generated. An experiment is conducted to determine the superior turbine type. The simulation process integrates engineering simulation software (ANSYS) and 3D modeling software (CATIA). ANSYS is employed to simulate river conditions with specific flow rates, while CATIA facilitates the design of generator blades, subsequently imported into ANSYS for simulation. The objective of the simulation is to identify the blade arrangement yielding the highest maximum generated torque. Subsequently, an experiment involving 3D printing the selected turbine types and measuring their rotational speed under consistent water flow conditions using a digital tachometer is conducted. From the experiment, it is found that curved blade types outperform straight blade types, while from the ANSYS simulation it is found that a distance between generator blades ranging from 300mm to 400mm offers optimal performance. This comprehensive approach enables a thorough evaluation of turbine configurations and their real-world performance, advancing micro hydropower technology for sustainable energy generation along river networks.

Weight reduction is a critical factor in the design of spacecraft and rockets, driving the adoption of composite structures instead of traditional materials. Sandwich composite panels, commonly used in aerospace applications, offer high stiffness-to-weight ratios but are vulnerable to localized deformation under load. Conventional T and L joints, typically made of Aluminum, exacerbate this issue by causing localized stress concentrations, which can lead to permanent structural failure. Additionally, these joints require large supportive areas, increasing the overall weight of the satellite structure. To address these challenges, this study proposes the use of innovative edge inserts as a replacement for traditional joints. These edge inserts are embedded within sandwich panels, which consist of a honeycomb core sandwiched between two face sheets. A comprehensive sensitivity analysis was conducted to evaluate von Mises stress and elemental strain on the inserts under varying load conditions, identifying potential damage and failure modes. Experimental validation through pull-out tests further compared the theoretical analysis with real-world performance, highlighting the effectiveness of edge inserts in enhancing load transfer while minimizing weight. This study demonstrates the potential of edge inserts to significantly improve the performance and reliability of aerospace sandwich structures.

The growing use of electric vehicles (EVs) creates challenges in designing charging systems that are smart, dependable, and efficient, especially when environmental conditions change. This research proposes a fuzzy-logic-based PID control strategy integrated into a photovoltaic (PV) powered EV charging system to address uncertainties such as fluctuating solar irradiance, grid instability, and dynamic load demands. A MATLAB-R2023a/Simulink-R2023a model was developed to simulate the charging process using real-time adaptive control. The fuzzy logic controller (FLC) automatically updates the PID gains by evaluating the error and how quickly the error is changing. This adaptive approach enables efficient voltage regulation and improved system stability. Simulation results demonstrate that the proposed fuzzy–PID controller effectively maintains a steady charging voltage and minimizes power losses by modulating switching frequency. Additionally, the system shows resilience to rapid changes in irradiance and load, improving energy efficiency and extending battery life. This hybrid approach outperforms conventional PID and static control methods, offering enhanced adaptability for renewable-integrated EV infrastructure. The study contributes to sustainable mobility solutions by optimizing the interaction between solar energy and EV charging, paving the way for smarter, grid-friendly, and environmentally responsible charging networks. These findings support the potential for the real-world deployment of intelligent controllers in EV charging systems powered by renewable energy sources This study is purely simulation-based; experimental validation via hardware-in-the-loop (HIL) or prototype development is reserved for future work.

The study focuses on the analysis and optimization of energy supply systems for unmanned aerial vehicles (UAVs), specifically FPV drones equipped with 10-inch propellers, to increase flight duration and operational efficiency under various civilian application scenarios. With the rapid expansion of drone usage in fields such as agriculture, environmental monitoring, logistics, and technical inspection, the issue of selecting the most efficient power source has become particularly relevant. The research aims to determine the optimal type of battery in terms of weight, energy density, endurance, and cost-effectiveness, ensuring reliable operation and sufficient energy reserve for different mission profiles. The article presents a comparative analysis between a lithium-polymer (LiPo) battery pack (6S, 10Ah) and three lithium-ion (Li-Ion) battery assemblies based on Molicel P42A cells with different configurations: 6S2P, 6S3P, and 6S4P. The analysis includes both theoretical calculations and practical considerations derived from known empirical data. Each configuration was evaluated based on three main criteria – flight time, maximum range, and overall economic viability. Additionally, the influence of payload mass (0–3 kg) on flight duration and efficiency was modeled for six key mission scenarios: observation and video surveillance, light and medium cargo delivery, heavy cargo delivery, one-way missions, and high-speed flight operations. Methodologically, the work takes into account motor efficiency (0.8–0.85), aerodynamic drag, and the voltage behavior of both LiPo and Li-Ion systems under load. The results demonstrate that Li-Ion batteries offer a higher specific energy density (up to 180 Wh/kg) and lower cost per watt-hour compared to LiPo alternatives, though their discharge characteristics limit maximum current output. Among the tested configurations, the Li-Ion 6S3P assembly showed the most balanced performance, providing a flight time of up to 45 minutes without payload and maintaining stable voltage throughout the discharge cycle. The LiPo 6S 10Ah pack remains advantageous for high-current, short-duration missions such as racing or rapid-response operations due to its superior instantaneous power output. The study concludes that no single battery configuration is universally optimal for all use cases. However, the Li-Ion 6S3P pack can be considered the most versatile and practical solution for multi-purpose civilian FPV platforms, combining reasonable cost, low weight, and extended endurance. The article also provides recommendations for selecting power systems depending on the mission profile and discusses the practical aspects of battery assembly, including the use of bi-metal copper and nickel-plated steel strips (0.15×10 mm) to ensure low resistance and reliable current transfer during high-load operation. The findings highlight the need for further experimental validation under real-world flight conditions, as well as future research focused on optimizing propeller geometry, assessing temperature impacts, and integrating hybrid power systems for small UAVs. The proposed analytical framework and results can serve as a methodological foundation for engineers, drone developers, and energy system designers seeking to enhance the performance and energy efficiency of modern FPV drones

Pulse experiments such as the Galvanostatic Intermittent Titration Technique (GITT) are commonly used to parameterize lithium-ion batteries (LIBs) in physics-based models but have recently been used to parametrize equivalent circuit models (ECMs). ECM parameter data can be created with GITT with just a single experiment, providing both state of charge (SOC) open circuit voltage (OCV) relationships, and voltage dynamics over a current pulse that can be used to fit electrical circuit components. A high-resolution parameter set can be obtained by titrating over small SOC intervals and can more accurately capture battery behavior. Additionally, using a GITT with longer relaxation time will provide more accurate OCV readings, however these conditions result in longer experiment times that can tax laboratory resources. This study investigates the impact of GITT relaxation time on the accuracy of high-resolution, GITT-obtained ECM parameters for LIBs in marine duty cycles, using lithium-tungsten oxide (LWO) coated nickel-rich NMC 811 cathode materials. GITT was performed with varying relaxation times (5 h, 2 h, 1 h, 30 min, and 15 min) to collect SOC-OCV curves as well as electric circuit parameters. The results show that longer relaxation times improve the accuracy of SOC-OCV curves and ECM parameters, but shorter relaxation times may better replicate real-world battery conditions for marine cycles. Validation experiments using constant current discharge and ship cycle profiles demonstrated that ECMs built with longer relaxation times have lower average errors. However, 1-hour relaxation time experiments provide a balanced approach, with an average error difference of 7.54mV and 80% less experiment time versus 5-hour relaxation time experiments for cells cycled within 90% to 10% SOC. This study suggests potential for optimizing GITT relaxation times based on specific application needs and highlights the importance of supplementing continuous efforts to improve NMC 811 cycling stability in high voltage windows with parallel modeling studies.

Background/Objectives: This paper presents the various fields surveillance has become important to ensure security and safety, research into motion detection and other sensory information has been conducted in order to equip a mobile robot with surveillance capability. Methods/Statistical Analysis: Apart from that this study proposed to learn the continuous surveillance it is also sensitive to Metal, Bomb, Fire and Electromagnetic field. It has the ability to carry loads up to 50 kgs. Findings: The Robot has a Wireless Camera which constantly monitors the location and transfers the live audio/video to the remote control center. In control section, a RF transmitter and receiver module operating at 433.92 MHz (Zigbee band) to send and receive data. Applications/Improvements: This paper was designed for special applications like military and industrial purpose with special attention to surveillance report.

The combination of service virtualization and edge computing allows for low latency services, while keeping data storage and processing local. However, given the limited resources available at the edge, a conflict in resource usage arises when both virtualized user applications and network functions need to be supported. Further, the concurrent resource request by user applications and network functions is often entangled, since the data generated by the former has to be transferred by the latter, and vice versa. In this paper, we first show through experimental tests the correlation between a video-based application and a vRAN. Then, owing to the complex involved dynamics, we develop a scalable reinforcement learning framework for resource orchestration at the edge, which leverages a Pareto analysis for provable fair and efficient decisions. We validate our framework, named VERA, through a real-time proof-of-concept implementation, which we also use to obtain datasets reporting real-world operational conditions and performance. Using such experimental datasets, we demonstrate that VERA meets the KPI targets for over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$96\%$</tex-math></inline-formula> of the observation period and performs similarly when executed in our real-time implementation, with KPI differences below 12.4%. Further, its scaling cost is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$54\%$</tex-math></inline-formula> lower than a centralized framework based on deep-Q networks.

In modern days, increasing weapon-related threats in public places have created an immediate need for intelligent surveillance systems to detect crime in real-time. Traditional surveillance systems have struggles with recognizing small objects, occlusion, and the time it takes to respond, which makes them ineffective in crowded and fast-changing situations. To overcome these challenges, the suggested system combines closed-circuit television (CCTV) surveillance cameras with advanced deep learning methods, image processing, and computer vision techniques for real-time crime prediction and prevention. This study proposes a hybrid deep learning framework that merges a Faster region convolutional neural network and Mask Region Convolutional Neural Network, named FMR-CNN. The novel approach FMR-CNN represents a significant advancement towards improving object recognition and segmentation of images and videos. It has been combined with YOLOv8 to increase the real-time detection speed and localization accuracy significantly. Such a combination enables the concurrent utilization of high-resolution spatial context information and rapid frame-wise predictions, thus making it well-suited for continuous video surveillance tasks. The model was trained and tested on a five labeled class annotated dataset, where MobileNetV3 features are extracted to simulate real-world surveillance conditions. Experimental results show the hybrid model attains detection accuracy of 98.7%, average precision (AP) of 90.1, and speed of 9.2 frames per second (FPS), and generalizes to varied lighting, occlusion, object scales, and reduced computational complexity, making it highly effective for crime prevention. Using these models benefits police departments and law enforcement agencies, as it allows them to detect criminal offenses earlier and avoid untoward situations.