This Special Issue contains selected papers from works presented at the 11th EASN (European Aeronautics Science Network) International Conference on Innovation in Aviation & Space, which was held from the 1st until the 3rd of September 2021. About 420 remote participants contributed to a high-level scientific gathering providing some of the latest research results on the topic, as well as some of the latest relevant technological advancements. Nineteen interesting papers, which cover a wide range of topics including characterization, analysis and design, as well as numerical simulation, are contained in this Special Issue.

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The application of advanced guidance and control to missiles, hypersonic vehicles, and unmanned aerial vehicles has long represented a research hotspot in academia, and the development of more advanced and intelligent guidance and control technology for flight vehicles has become a focus of research in recent years. In this reprint, studies on the mathematical theory and application of flight vehicle guidance and control are presented. This includes trajectory optimization, online planning, advanced intermediate guidance theoretical methods, terminal guidance theoretical methods, intercept guidance and pursuit and escape guidance, and other topics.

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Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective.

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Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective.

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Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective.

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Radar-related technology is mainly processed within the time and frequency domains but, at the same time, is a multi-dimensional integrated system including a spatial domain for transmitting and receiving electromagnetic waves. As a result of the enormous technological advancements of the pioneers actively discussed in this book, research and development in multi-dimensional undeveloped areas is expected to continue. This book contains state-of-the-art work that should guide your research.

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The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms.

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Environmental quality is crucial to our health, economy, lives, and for future generations. However, during the last few years, we have faced several serious challenges, which include, but are not limited to, climate change, unsustainable consumption and production, as well as various forms of pollution. These challenges are of global concerns for the sustainable development of human beings. This reprint has collected review and research papers aimed at a wide range of environmental topics: water and wastewater treatment and management, soil degradation and conservation, pollution control, the environmental impact of technologies, air quality and the indoor environment, and advanced environmental materials. Analytical methods developed for the determination of environmental pollutants were also welcomed. Papers dealing in the synthesis of adsorbents for the solid-phase extraction of environmental pollutants also fell within the scope of this Special Issue. Contributions describing novel and significant knowledge, scientific results, and advanced applications in the field of environmental sciences and technology were also welcomed. This reprint provides an integrated view of the trends in solving the problems associated with the achievement of sustainability in environmental technology.

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"Advances in UAV Detection, Classification and Tracking" is a comprehensive book that explores the latest techniques and advancements in unmanned aerial vehicle (UAV) detection, classification, and tracking. As UAV technology continues to evolve and become more accessible, there is a growing need for effective methods to detect, identify, and track these devices in various scenarios. This reprint provides a thorough overview of the state-of-the-art approaches for UAV detection, classification, and tracking, covering both theoretical and practical aspects.The reprint begins by introducing the basics of UAVs and their various applications, followed by a detailed overview of the challenges associated with UAV detection, classification, and tracking. The authors then present the latest techniques and algorithms used in the field, including machine-learning-based approaches, computer vision techniques, and sensor fusion techniques. The reprint also covers the challenges of real-world applications, such as dealing with occlusions, sensor noise, and environmental factors.With contributions from leading experts in the field, "Advances in UAV Detection, Classification and Tracking" is an essential resource for researchers, engineers, and practitioners working on UAV detection, classification, and tracking. It is also a valuable reference for graduate students and anyone interested in the latest advancements in this rapidly evolving field.

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The increasing demand for high data rate applications and the delivery of zero-latency multimedia content drives technological evolutions towards the design and implementation of next-generation broadband wireless networks. In this context, various novel technologies have been introduced, such as millimeter wave (mmWave) transmission, massive multiple input multiple output (MIMO) systems, and non-orthogonal multiple access (NOMA) schemes in order to support the vision of fifth generation (5G) wireless cellular networks. The introduction of these technologies, however, is inextricably connected with a holistic redesign of the current transceiver structures, as well as the network architecture reconfiguration. To this end, ultra-dense network deployment along with distributed massive MIMO technologies and intermediate relay nodes have been proposed, among others, in order to ensure an improved quality of services to all mobile users. In the same framework, the design and evaluation of novel antenna configurations able to support wideband applications is of utmost importance for 5G context support. Furthermore, in order to design reliable 5G systems, the channel characterization in these frequencies and in the complex propagation environments cannot be ignored because it plays a significant role. In this Special Issue, fourteen papers are published, covering various aspects of novel antenna designs for broadband applications, propagation models at mmWave bands, the deployment of NOMA techniques, radio network planning for 5G networks, and multi-beam antenna technologies for 5G wireless communications.

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This Special Issue introduces the latest developments in electromagnetic computing and artificial intelligence technology. Artificial intelligence technology can be applied to the modeling, analysis, and optimization design of microwave equipment, solving the routing problem of self-organizing networks in small unmanned aerial vehicle systems, calculating the radiation characteristics of antenna arrays on large electrical platforms, analyzing the impact of electromagnetic wave coupling on electronic devices, simulating the field distribution characteristics of electronic devices, and so on. With the help of artificial intelligence, designers can more conveniently, quickly, and accurately solve engineering problems.

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The book presents a collection of papers focused on recent progress in key areas of photogrammetry for environmental research. Applications oriented to the understanding of natural phenomena and quantitative processes using dataset from photogrammetry (from satellite to unmanned aerial vehicle images) and terrestrial laser scanning, also by a diachronic approach, are reported. The book covers topics of interest of many disciplines from geography, geomorphology, engineering geology, geotechnology, including landscape description and coastal studies. Mains issues faced by the book are related to applications on coastal monitoring, using multitemporal aerial images, and investigations on geomorphological hazard by the joint use of proximal photogrammetry, terrestrial and aerial laser scanning aimed to the reconstruction of detailed surface topography and successive 2D/3D numerical simulations for rock slope stability analyses. Results reported in the book bring into evidence the fundamental role of multitemporal surveys and reliable reconstruction of morphologies from photogrammetry and laser scanning as support to environmental researches.

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Unmanned aerial vehicles (UAVs) are being increasingly used in different applications in both military and civilian domains. These applications include surveillance, reconnaissance, remote sensing, target acquisition, border patrol, infrastructure monitoring, aerial imaging, industrial inspection, and emergency medical aid. Vehicles that can be considered autonomous must be able to make decisions and react to events without direct intervention by humans. Although some UAVs are able to perform increasingly complex autonomous manoeuvres, most UAVs are not fully autonomous; instead, they are mostly operated remotely by humans. To make UAVs fully autonomous, many technological and algorithmic developments are still required. For instance, UAVs will need to improve their sensing of obstacles and subsequent avoidance. This becomes particularly important as autonomous UAVs start to operate in civilian airspaces that are occupied by other aircraft. The aim of this volume is to bring together the work of leading researchers and practitioners in the field of unmanned aerial vehicles with a common interest in their autonomy. The contributions that are part of this volume present key challenges associated with the autonomous control of unmanned aerial vehicles, and propose solution methodologies to address such challenges, analyse the proposed methodologies, and evaluate their performance.

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Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops.

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This book collects 14 articles from the Special Issue entitled “Deep Learning Applications with Practical Measured Results in Electronics Industries” of Electronics. Topics covered in this Issue include four main parts: (1) environmental information analyses and predictions, (2) unmanned aerial vehicle (UAV) and object tracking applications, (3) measurement and denoising techniques, and (4) recommendation systems and education systems. These authors used and improved deep learning techniques (e.g., ResNet (deep residual network), Faster-RCNN (faster regions with convolutional neural network), LSTM (long short term memory), ConvLSTM (convolutional LSTM), GAN (generative adversarial network), etc.) to analyze and denoise measured data in a variety of applications and services (e.g., wind speed prediction, air quality prediction, underground mine applications, neural audio caption, etc.). Several practical experiments were conducted, and the results indicate that the performance of the presented deep learning methods is improved compared with the performance of conventional machine learning methods.

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The characteristics and abilities of autonomous drones pose major chal­lenges for liability law. Nowadays only personal injury and damage to property on the ground are covered by strict liability (Art. 64 para. 1 Swiss Aviation Act). Injured parties are in danger of being left without legal pro­tection in the event of mid-air collisions, as claims for damages cannot be asserted on the basis of erroneous decisions by an algorithm, either through liability for wilful or negligent wrongdoing (Art. 41 para. 1 Swiss Code of Obligations) or product liability. The same applies to purely pecu­niary loss. The question of liability for wilful or negligent wrongdoing arises only if duties of care were violated when using autonomous drones. Such duties of care may ensue from permit and operating regulations. Currently autonomous flights without the possibility of direct control and beyond a pilot’s field of vision are allowed only with special permits. As international efforts show, such barriers will come down in future. Appropriate licensing and operating regulations as described in this dissertation will therefore be required. At the same time it will be necessary to extend strict liability under aviation law for unmanned aircraft that are not steered by a pilot to damage in the event of mid-air collisions and to purely pecuniary damage. The specific legal formulations and their legislative implementation are proposed and discussed here for this purpose.

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This Special Issue on the “Dynamic Behavior of Materials” collected recent research findings on the dynamic behavior of various materials. A collection of fifteen peer-reviewed research articles were included in this Special Issue. The main topics covered include processing technology, state-of-the-art characterization, testing, theoretic modeling, and simulation. We hope that this Special Issue will be interesting for the academic community, and also hope that it will shed light on future research activities in this field.

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This reprint compiles the articles, communications, and review articles from top researchers describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles. This reprint encompasses the key topics related to drone design, communications as well as autonomous Flight and Navigation. Special attention is paid to the drones’ applications to environmental and earth sciences, including in agriculture, forestry, water, and marine environments; other innovative applications are also explored including in relation to the field of application, such as the inclusion of new deep learning techniques.

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