Electronic parts in space inevitably subject to radiation effects leading to the degradation of electronic performance or even failure, so radiation performance of an electronic part must be assessed to ensure it work normally in space. At present, to assess the ion radiation effects on a semiconductor device is directly through irradiation tests. However, due to the scarcity of cyclotron resources, the test time is difficult to appoint and the cost is huge. Due to schedule and budget constraints, it is also impossible to conduct irradiation tests on all semiconductor devices in actual space missions. Therefore, assessment of the radiation effects on semiconductor devices through irradiation tests has caused difficulties. Radiation susceptibility of semiconductor device is determined by the design topology and fabrication technology, and the irradiation test data shows that similar semiconductor devices has similar radiation susceptibility, so a method to assess the radiation effects on semiconductor devices base on similarity theory is proposed at first time in this paper. This assessing method does not require irradiation testing and does not require separate sampling. It has the virtues of easy implementation, quick response and low cost, providing an efficient method of assessing radiation effects on semiconductor devices.
Earth Observation spacecraft play a pivotal role in various critical applications impacting life on Earth. Historically, these systems have adhered to conventional operational paradigms, namely the "mow-the-lawn" and "bent pipe" approaches. In these paradigms, operational schedules are formulated on the ground and subsequently uploaded to the spacecraft for execution. Execution involves either systematically acquiring vast amounts of data (mow-the-lawn) or targeting specific areas of interest as defined by end users or operators. We aim to depart from these traditional methodologies by integrating onboard Artificial Intelligence, real-time communication, and new observing strategies in one system called CogniSAT-6. These transformative innovations will amplify the amount, speed, and quality of the information yielded by such a system by up to an order of magnitude. Consequently, these advancements are poised to revolutionize conventional Earth Observation systems from static entities into dynamic, intelligent, and interconnected instruments for highly efficient information gathering. This paper provides an overview of the current state of the art in autonomous Earth Observation spacecraft and the application of onboard processing in Earth Observation spacecraft. An overview is given of the CogniSAT-6 mission, its concept of operations, system architecture, and data processing design. Since we believe that the technology presented here will have a significant impact on society, an ethical framework for such systems is presented. Finally, the benefits of the technology and implications for EO systems going forward are discussed.
The actual paper presents an in-depth study and experimental development of a class of rotorcraft, named as x-tilt, that features four tilting rotors. Initially, the equations of motion modeling the aerial robot are presented based on the Euler-Lagrange formulation. The model includes the aerodynamic effects induced by the rotorcraft's relative motion and propellers. For control purposes the aforementioned model is split into a nominal model and lumped disturbance terms, the latter encompassing endogenous and exogenous uncertainties. In this vein, the actual work propose a robust navigation strategy targeting a specific performance profile whose problem is formulated through the model predictive control (MPC) framework. To this end, two schemes are proposed, (i) an integral MPC and a (ii) MP sliding-mode Control (MPSMC). Both control schemes are linked to a extended-state Linear Kalman Filter (ES-LKF) that furnishes the states and lumped disturbance estimates. Moreover, a high-fidelity simulation is presented in detail to validate the effectiveness of the proposed controller within a realistic scenario. We finally present the experimental stage to validate the tilting-rotor configuration as well as the integral MPC.
The application of millimeter-Wave (mmWave) Radar sensors for people monitoring raised a lot of interest in the context of Active Assisted Living (AAL), especially since the processing of Radar signals can provide interesting information about the observed subjects. Correct recognition of the ongoing behavior, however, cannot disregard from detecting where the subject is positioned. Detection approaches, based on Constant False Alarm Rate (CFAR) algorithms, sometimes fail to correctly identify the presence of targets within the observed scenario, especially in complex environments such as indoors. This paper proposes the use of a mmWave Multiple Input Multiple Output (MIMO) Radar in combination with a You Only Look Once (YOLO) neural network-based algorithm for the detection of moving people in indoor environments by processing all the data cube information at the same time. Results are validated through experimental tests which involve subjects walking in linear or random mode, different Radar configurations, and different indoor environments. By exploiting at the same time information such as the angle, Doppler, and range distance of the target, the proposed approach proves to be very effective in the examined scenarios. Experimental results will be discussed in this work to demonstrate the effectiveness of the proposed method.
This paper is a continuation on my revolutionary theory of solving the pointwise fluid flow approximation model for time-varying queues. Thus, the long-standing simulative approach has now been replaced by an exact solution by using a constant ratio 𝛽 (Ismail's ratio) , offering an exact analytical solution. The stability dynamics of the time-varying 𝑀/𝐸 𝑘 /1 queueing system are then examined numerically in relation to time, 𝛽, and the queueing parameters.
A time-varying current flows through an inductor. The time-varying magnetic field excites an induced electric field and an induced electromotive force is generated in the coil winding. The supply potential blocks the induced electromotive force from generating conduction current. The time-varying induced electric field excites a magnetic field, forming an electromagnetic wave. A current-carrying conductor and the inductor are fixed together to form a system. The conductor is subjected to an Ampere force in the magnetic field of the electromagnetic wave. Electromagnetic wave is independent of the inductor and independent of the system. The Ampere force is an external force. It can drive the system. Like rocket engines, the system enables propulsion in space. It has the advantage of requiring only electrical power, not propellant.
This paper describes the process followed to implement a system to characterize the complex permittivity of materials in the 10-330 GHz frequency band. Firstly, the method used and the system's calibration process are shown, consisting of a double calibration TRL (Thru-Reflect-Line) and GRL (Gated-Reflect-Line). Subsequently, a smoothing technique is used to improve the accuracy of the results. Finally, a test is performed on quartz and glass fiber samples, showing that the results are quite reliable over the entire measured bandwidth.
This work outlines the procedure to establish a system for assessing materials' complex permittivity and permeability within the 110-170 GHz frequency range. We present the employed methodology and the calibration procedure for the system, incorporating a dual approach using TRL (Thru-Reflect-Line) and GRL (Gated-Reflect-Line) methods. Following that, a smoothing technique is used to enhance the accuracy of the results. Tests were conducted on a HIPS sample to validate the system's performance, demonstrating the results' reliability across the entire measured bandwidth.
We address the crucial task of identifying changes in land cover using remotely sensed imagery. While most change detection methods focus on two images, we introduce an unsupervised approach that considers long image series (more than two), supporting a more nuanced differentiation between changed and unchanged areas. The proposed technique transforms input data to a new representation, capturing the target's spectral response changes over time. Areas with minimal response variation are identified as non-changing and distinguished from regions that have undergone modifications. The method further categorizes, utilizing statistical procedures, regions undergoing spatiotemporal modifications into seasonal or permanent changes. Experimental validation using simulated and real-world remote sensing image series demonstrates the effectiveness of the proposed approach.
The use of unmanned aerial vehicles (UAVs) for a variety of commercial, civilian, and defense applications has increased many folds in recent years. While UAVs are expected to transform future air operations, there are instances where they can be used for malicious purposes. In this context, the detection, classification, and tracking (DCT) of UAVs (DCT-U) for safety and surveillance of national air space is a challenging task when compared to DCT of manned aerial vehicles. In this survey, we discuss the threats and challenges from malicious UAVs and we subsequently study three radio frequency (RF)-based systems for DCT-U. These RF-based systems include radars, communication systems, and RF analyzers. Radar systems are further divided into conventional and modern radar systems, while communication systems can be used for joint communications and sensing (JC&S) in active mode and act as a source of illumination to passive radars for DCT-U. The limitations of the three RF-based systems are also provided. The survey briefly discusses non-RF systems for DCT-U and their limitations. Future directions based on the lessons learned are provided at the end of the survey.
Heart disease has been the leading cause of mortality globally. The necessity for quick access to trustworthy, dependable, and practical processes for early diagnosis and disease management pertains to numerous risk factors for heart disease. In the current global environment, detecting heart disease through early-onset manifestations is challenging. This has the potential to be fatal if not stopped in time. In isolated, semiurban, or rural locations without access to heart specialists, accurate risk prediction and analysis may be essential for the early detection of cardiac issues. Artificial Intelligence (AI) and robotics are currently used in medical research. This addresses the urgent need for better ways to find, diagnose, and treat heart disease. To close the gap between theory and reality, we offer a dataset on cardiovascular disease that has been carefully put together. The variables in the dataset are age, gender, subtypes, symptoms, risk factors, and result variables that can be either 1 or 0. The
Environmental movements of the late 20th century resulted in sweeping legislation and regulatory actions to reduce the prevalence of diverse pollutants. Although the consequences of noise pollution to public health, the environment, and the economy have been recognized over the same time period, noise has received far less policy attention. Correspondingly, even while recent decades have seen robust advancements in assessing the impacts of noise pollution, solutions and actual reductions in environmental noise have remained seemingly out of reach. To address this shortcoming, we developed a prospectus for environmental noise reduction through technology-forcing policies. Technology-forcing describes intent to encourage technological solutions for pollution control through policy and regulations, and has been a critical component of national and global progress in reducing environmental pollutants. We take advantage of the unique policy history for noise in the United States - which initially enacted, but then abandoned federal noise regulation. We compare this history against outcomes from contemporaneous environmental legislation for air, water, and occupational pollution control, to demonstrate the potential for technology-forcing to reduce noise pollution. Our review then identifies promising solutions, in the form of existing technologies suitable for innovation and diffusion through technology-forcing regulations and incentives. Based on this review, we outline a program for noise policy development that is intended to support efforts to reduce environmental noise pollution worldwide. The proposed program consists of three steps, 2 which are to (i) identify dominant sources of noise pollution, (ii) combine legislative or regulatory provisions with suitable systems of enforcement and incentives, and (iii) anticipate and prepare for stages of technological change. This work is intended to support and advocate for noise policies designed around technology-forcing, to advance technologies that not only improve public health and sustainable development, but ensure that these benefits are distributed equitably.
The reachable and feasible sets of spacecraft are important tools for many areas of astrodynamics such as mission design, space situational awareness, and assessment of potential threats. Whilst these may be obtained by solving many optimal control problems in the indirect formulation, this process is slow and can suffer from convergence issues with a lack of a good initial guess. This work presents an analytical method of rapidly estimating the reachable and feasible sets utilizing the D matrix, an error state transition matrix under linearity assumptions for Keplerian motion which employs time as a state variable and true anomaly as the reference variable. Analytical expressions for the control laws are derived to produce the maximum/minimum change in orbital radius, time-offlight, and out-of-plane height. Moreover, an analytical expression for the control efficiency is derived to allow thrust activation only when it is efficient to do so, allowing a target fuel consumption to be attained. By mixing the contributions of these controls, the complete reachable or feasible sets can then be rapidly swept over with minimal effort, which is demonstrated in a series of numerical simulations. The results show that the optimal control problem solution presents only minor improvements, highlighting the capacity of the D matrix to provide marginally sub-optimal results within a fraction of the computational time.
Unmanned Aerial Vehicles play a crucial role in various operations especially where human life must be protected. This work presents an adaptable intelligent system suitable to enhance the efficiency and effectiveness of drone swarm operations in a 3D dynamic environment. The system incorporates several modules, including an Ant Colony Optimization (ACO)-based path planning algorithm, collision avoidance mechanism, messaging system, and a hybrid navigation approach, which evaluates the application requirements to decide to prioritize the desired formation of the swarm or the path length and flight time. The proposed system is adaptable and can optimize to several optimization parameters, including solution quality, time consumption, mission completeness, and average divergence. The experiments show that the system consistently provides high-quality paths, achieving around 97% path quality in most cases, and never declines below 90%, even in challenging scenarios. The collision avoidance module ensures 100% mission completeness successfully navigating drones around obstacles and maintaining an optimal path. Moreover, the hybrid navigation approach demonstrates the ability to maintain desired formations while dynamically adapting to obstacles. The systemâ\euro™s performance shows its potential for real-world applications, ensuring efficient and autonomous operations in different missions.
This paper presents a novel, power- and hardware-efficient, multiuser, multibeam RIS (Reflective Intelligent Surface) architecture for multiuser MIMO, especially suited to operate in very high frequency bands (e.g., high mmWave and sub-THz), where channels are typically sparse in the beamspace and line-of-sight (LOS) is the dominant component. The key module is formed by an active multiantenna feeder (AMAF) with a small number of active antennas, placed in the near field of a RIS with a much larger number of passive controllable reflecting elements. We propose a pragmatic approach to obtainÂ a steerable beam with high gain and very low sidelobes. Then K independently controlled beams can be achieved by closely stacking K such AMAF-RIS modules. Our analysis includes the mutual interference between the modules and the fact that, due to the delay difference of propagation through the AMAF-RIS structure, the resulting channel matrix is frequency selective even in the presence of pure LOS propagation.Â We consider a 3D geometry and show that “beam focusing” is in fact possible (and much more effective in terms of coverage)Â also in the far-field, by creating spotbeams with limited footprint both in angle and in range.Â Our results show that: 1) Â simple RF beamforming without computationally expensive baseband digital multiuser precoding is sufficient to practically eliminate multiuser interference when the users are chosen with sufficient angular/range separation, thanks to the extremely low sidelobes of the proposed module; 2) the impact of beam pointing errors with standard deviation as large as 2.5 degÂ and RIS quantized phase-shifters with quantization bits > 2 is essentially negligible; 3) The proposed architecture is more power efficient and much simpler from a hardware implementation viewpoint than standard RF beamforming active arrays with the same beamforming performance. As a side result, we show also that the array gain of the proposed AMAF-RIS structure grows linearly with the RIS aperture, in line with classical results for standard reflector antennas.
Developing aerial robots that can both safely navigate and execute assigned mission without any human intervention â\euro“ i.e., fully autonomous aerial mobility of passengers and goods â\euro“ is the larger vision that guides the research, design, and development efforts in the aerial autonomy space. However, it is highly challenging to concurrently operationalize all types of aerial vehicles that are operating fully autonomously sharing the airspace. Full autonomy of the aerial transportation sector includes several aspects, such as design of the technology that powers the vehicles, operations of multi-agent fleets, and process of certification that meets stringent safety requirements of aviation sector. Thereby, Autonomous Advanced Aerial Mobility is still a vague term and its consequences for researchers and professionals are ambiguous. To address this gap, we present a comprehensive perspective on the emerging field of autonomous advanced aerial mobility, which involves the use of unmanned aerial vehicles (UAVs) and electric vertical takeoff and landing (eVTOL) aircraft for various applications, such as urban air mobility, package delivery, and surveillance. The article proposes a scalable and extensible autonomy framework consisting of four main blocks: sensing, perception, planning, and controls. Furthermore, the article discusses the challenges and opportunities in multi-agent fleet operations and management, as well as the testing, validation, and certification aspects of autonomous aerial systems. Finally, the article explores the potential of monolithic models for aerial autonomy and analyzes their advantages and limitations. The perspective aims to provide a holistic picture of the autonomous advanced aerial mobility field and its future directions.
Standard Direction of Arrival (DOA) estimation methods are typically derived based on the Gaussian noise assumption, making them highly sensitive to outliers. Therefore, in the presence of impulsive noise, the performance of these methods may significantly deteriorate. In this paper, we model impulsive noise as Gaussian noise mixed with sparse outliers. By exploiting their statistical differences, we propose a novel DOA estimation method based on sparse signal recovery (SSR). Furthermore, to address the issue of grid mismatch, we utilize an alternating optimization approach that relies on the estimated outlier matrix and the on-grid DOA estimates to obtain the off-grid DOA estimates. Simulation results demonstrate that the proposed method exhibits robustness against large outliers.