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Recently, energy efficiency (EE) has been pointed out as one of the key requirements within mobile networks. The development of intelligent algorithms providing Radio Access Networks (RAN) with EE features is possible when having access to the Key Performance Indicators (KPIs), and proper control actions, e.g., cell on/off switching, or Radio Frequency (RF) channel reconfiguration. These features together with a Machine Learning (ML) framework are available in the O-RAN architecture. This paper provides an overview of the EE framework according to the use cases specified by the O-RAN ALLIANCE. It is followed by the implementation of the Energy Saving rApp (ES-rApp). The rApp utilizes Deep Q-Learning (DQL) to increase EE through intelligent RF channel reconfiguration. Simulation results show up to a 24.8% EE gain over the static RF Channel Configuration (RCC).

In recent times, both geopolitical challenges and the need to counteract climate change have led to an increase in generated renewable energy as well as an increased demand for clean electrical energy. The resulting variability of electricity production and demand as well as an overall demand increase, put additional stress on the existing grid infrastructure. This leads to strongly increased maintenance demands for distribution system operators (DSOs). Today, condition monitoring is used to address these challenges. Researchers have already explored solutions for monitoring critical assets like switchgear and circuit breakers. However, with a shrinking knowledgeable technical workforce and increasing maintenance requirements, mere monitoring is insufficient. Already today, DSOs ask for actionable recommendations, optimization strategies, and prioritization methods to manage the growing task backlog effectively. In this paper we propose a vision of a grid-level cognitive assistance system that translates the outcome of diagnosis and prognosis systems into actionable work tasks for the grid operator. The solution is highly interdisciplinary and based on empirical studies of real-world requirements. We also describe the related work relevant to the multi-disciplinary aspects and summarize the research gaps that need to be closed over the next years.

Robotic and multi-sensor technologies are increasingly being adopted in a number of agricultural applications, including seeding, weeding, harvesting, fertilization, and crop monitoring and analysis. However, the lack of interoperability and the predominance of manufacturer-specific closed solutions demand a careful choice of devices, sensors and data processing platforms and hinder the flexible adaption of these systems to the individual farmer’s needs and knowledge exchange. The Horizon 2020 Agriculture Interoperability and Analysis System (ATLAS) project is aimed at overcoming these issues through an open, flexible and distributed interoperability network, which enables the seamless interconnection of sensor systems, machines, and data analysis tools. This paper presents the latest achievements in the context of the ATLAS project, concerning the development of robotic services for in-field crop monitoring and their integration into the ATLAS network.

The expansion of small satellite networks in earth's orbit has resulted in a plethora of earth optical imagery available to the civil, defense, and commercial sectors. Small satellites (less than 1000 kg in mass) and their constellations can be delivered rapidly and at low cost and are more difficult to target by adversaries—a key consideration in the defense industry. Yet, small satellite size constraints often result in reduced payload capacity, reduced power capacity, or loss of redundancy. Traditionally, the cost of an optical telescope on board a satellite scales at roughly the square of the aperture, meaning that it costs four times as much to double the resolution of the imaging hardware. However, deep learning has shown considerable success in the areas of super-resolution and enhancing the pixel resolution of optical imagery. These deep learning methods have the potential to provide optical resolution capabilities rivaling larger satellites and their telescopes, while maintaining the benefits of small satellites—smaller physical size (which lowers launch vehicle costs and provides a basis for large constellations), reduced manufacturing time, and lower manufacturing costs. By providing low-cost small satellite platforms with the same capabilities as larger satellites, the cost for high-resolution in-orbit optical imagery is reduced alongside time to orbit. In this work, we detail a deep-learning-based approach, which improves optical satellite imagery to five times the original pixel-based resolution without the need or expense of increasing the capabilities of the imager through larger telescope apertures. The approach—demonstrated on Terran Orbital's GEOStare SV2 mission imagery—is generally applicable to any optical satellite image and is agnostic to the mission, satellite manufacturer, optical payload specifications, or data source. This capability provides a basis for small satellite missions and constellations—and their optical payloads—to rival the native hardware-based resolutions available through larger satellites with wider telescope apertures at a significantly reduced cost.

Goal: Auto-injectors for self-administration of drugs are usually refrigerated. If not warmed up prior to the injection, ejection of the total drug volume is not guaranteed, as their spring and plunger mechanism cannot adjust for a change in viscosity of the drug. Here, we develop piezoelectric micro diaphragm pump that allows these modifications possible while investigating the effectiveness of this alternative dosing method. Methods: The dosing of highly viscous liquid of 25 mPa·s is made possible using application-specific micropump design. By comparing the analytical with experimental results, the practicality of the concept is verified. Results: Using a powerful piezoelectric stack actuator, the micropump achieves high fluid pressures of up to (368 ± 17) kPa. In order to assess the influence of viscosity, we characterize the fluidic performance of the designed micropump through 27G gauge needle for various water-glycerin mixtures. We find maximum flow rates of 2 mL/min for viscosities of up to 25 mPa·s. Conclusions: The developed micro diaphragm pump enables the development of smart auto-injectors with flow rate regulation to achieve drug delivery for high viscosity drugs through 27G needles.

In the context of the E-TEST Interreg project, Fraunhofer ILT develops high stability, narrow linewidth laser seed sources and amplifiers at a wavelength of approx. 2 μm for potential usage in a third-generation gravitational wave detector: the Einstein telescope. While for such an application, there are highest demands on laser parameters such as linewidth, spectral purity, and polarization, especially stability properties such as the relative intensity noise must be optimized. To achieve highest output power stabilities, we develop a multi-stage low-noise holmium-doped fiber amplifier at a wavelength of 2095 nm, which is core-pumped by low-noise thulium-doped fiber lasers at a wavelength of approx. 1950 nm. In this article, we present the concept of the full laser system, and the achieved results for the pre-amplifier section, which perfectly fulfills the E-TEST pre-amplifier requirements. Our pump source, a thulium-doped fiber amplifier, achieves approx. 2 W output power at a wavelength of 1950 nm with 5 MHz linewidth and a polarization extinction ratio of 28 dB. With our holmium-doped fiber amplifier, we demonstrate an output power of approx. 400 mW at a wavelength of approx. 2095 nm with a linewidth of 2 MHz and a polarization extinction ratio of 18 dB. For both currently free-running systems we analyze the relative intensity noise (RIN) and obtained for example RINs of approx. 10−6 Hz−0.5 at a frequency of 100 Hz, which shows the suitability of the concept to achieve highest stabilities. To evaluate our system, we perform a system performance analysis, where we present the influence of signal and pump wavelength, and signal and pump power on the achievable optical-optical efficiencies.

To guarantee sufficient surge current fault protection for power electronic converters, power semiconductors have to be tested under appropriate surge current conditions. Standard maximum surge current values include the permissible fault current amplitude, or the I2t-value; however, they might not be sufficient to describe a power semiconductor's performance under all potential fault conditions. Surge current sources based on passive components are state-of-the-art, but are limited to usually only one specific current waveform. This article describes the topology and the design of a new modular and highly dynamic surge current source for power semiconductor tests with adjustable current waveforms. The new modular converter concept is introduced, with two potential operation modes: High current mode (HCM) and dynamic current mode (DCM). The requirements for the surge current tester are defined, and the electrical and mechanical design are described, including the modulation scheme and control. Experimental investigations prove the function of the current source with peak currents up to 100 kA (HCM) and the realization of highly dynamic load current trajectories with peak currents up to 50 kA (DCM). The output current ripple is exceptionally small with a theoretical value of below 1%.

Massive Multiple-Input Multiple-Output (MMIMO) is one of the 5G key enablers. Though, most of the works consider MMIMO under assumptions of ideal hardware. It has been shown that Power Amplifiers (PAs) introduce nonlinear distortion while operating close to their saturation power. Moreover, these distortions are in some cases beamformed toward the user, preventing antenna array gain from solving this problem. One of the possible solutions is an adaptive adjustment of the PA operating point, measured by Input Back off (IBO), to find a balance between wanted signal power and nonlinear distortion power. This work proposes a Contextual Bandit-Based IBO Optimization (COBBIO) algorithm to find rate-maximizing IBO for a given user’s radio conditions using learning through interaction. The proposed solution is tested in a realistic analog beamforming MMIMO cell simulator with multiple functional blocks, e.g., precoder, user scheduler, and utilizing an accurate 3D Ray-Tracing radio channel model. COBBIO provides throughput gains both over fixed-IBO schemes and state-of-the-art analytical IBO adjustment algorithms. The highest gains were observed for the so-called cell-edge users, where up to 83% improvement over the state-of-the-art algorithm was observed for the proposed COBBIO algorithm.

The 6th generation of wireless mobile networks is emerging as a paradigm shifting successor to unifying the experience across the physical, digital, and human worlds, pushing boundaries on performance in capacity, throughput, latency, scalability, flexibility, and reliability, while prominently addressing new major factors, including sustainability, security and privacy, as well as digital inclusion. Many research institutions and initiatives worldwide have started investigations to make 6G a reality by approximately 2030. In Germany, federal funding from the German Ministry of Education and Research (BMBF) supports a large-scale 6G initiative, with its lighthouse project, called 6G-ANNA. The core aim of this project is to develop the key aspects of a holistic, sustainable, secure, and resilient 6G system design that will simplify and improve the interaction between humans, digital assets, and the physical environment. This paper shares the vision of the project’s main technical working areas and advances, spanning topics from radio access, integration of multiple networks, as well as automation and simplification in networking to new applications and testbed scenarios, including real-time digital twins and extended reality. The industrial impact and relevance of standardization makes 6G-ANNA uniquely positioned to lead and realize the vision of next-generation wireless mobile network technologies, systems, and applications.

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system which, in addition to affecting motor and cognitive functions, may also lead to specific changes in the speech of patients. Speech production, comprehension, repetition and naming tasks, as well as structural and content changes in narratives, might indicate a limitation of executive functions. In this study we present a speech-based machine learning technique to distinguish speakers with relapsing-remitting subtype MS and healthy controls (HC). We exploit the fact that MS might cause a motor speech disorder similar to dysarthria, which, with our hypothesis, might affect the phonetic posterior estimates supplied by a Deep Neural Network acoustic model. From our experimental results, the proposed posterior posteriorgram-based feature extraction approach is useful for detecting MS: depending on the actual speech task, we obtained Equal Error Rate values as low as 13.3%, and AUC scores up to 0.891, indicating a competitive and more consistent classification performance compared to both the x-vector and the openSMILE ‘ComParE functionals’ attributes. Besides this discrimination performance, the interpretable nature of the phonetic posterior features might also make our method suitable for automatic MS screening or monitoring the progression of the disease. Furthermore, by examining which specific phonetic groups are the most useful for this feature extraction process, the potential utility of the proposed phonetic features could also be utilized in the speech therapy of MS patients.

This paper introduces a method based on a deep neural network (DNN) that is perfectly capable of processing radar data from extremely thinned radar apertures. The proposed DNN processing can provide both aliasing-free radar imaging and super-resolution. The results are validated by measuring the detection performance on realistic simulation data and by evaluating the Point-Spread-function (PSF) and the target-separation performance on measured point-like targets. Also, a qualitative evaluation of a typical automotive scene is conducted. It is shown that this approach can outperform state-of-the-art subspace algorithms and also other existing machine learning solutions. The presented results suggest that machine learning approaches trained with sufficiently sophisticated virtual input data are a very promising alternative to compressed sensing and subspace approaches in radar signal processing. The key to this performance is that the DNN is trained using realistic simulation data that perfectly mimic a given sparse antenna radar array hardware as the input. As ground truth, ultra-high resolution data from an enhanced virtual radar are simulated. Contrary to other work, the DNN utilizes the complete radar cube and not only the antenna channel information at certain range-Doppler detections. After training, the proposed DNN is capable of sidelobe- and ambiguity-free imaging. It simultaneously delivers nearly the same resolution and image quality as would be achieved with a fully occupied array.

New aerodynamic aircraft concepts enable the storage of volumetric liquid hydrogen (LH2). Additionally, the low temperatures of LH2 allow technologies such as the superconductivity of electrical components. An increased power density of the onboard wiring harness and the electrical machine can be expected. Nevertheless, the power electronic drive inverter has to deliver high power and high switching frequencies ( $f_{\mathrm {PWM}}\text{s}$ ) under challenging conditions. Therefore, knowledge of the electric behaviour of different semiconductor materials under cryogenic temperatures is essential to answer the question: “Are modern power electronics a technology enabler or a system bottleneck?” This publication shows a comprehensive novelty study for cryogenic power electronics based on experimental-driven semiconductor investigations, mission profile-based considerations, requirement analyses of superconducting electrical machines, and studies of the cooling concepts. All aspects are discussed within one interdisciplinary publication. A cryogenic system cannot be considered without a feasible cooling concept. Different semiconductor structures based on various materials (silicon (Si), silicon carbide (SiC) and gallium nitride (GaN)) are evaluated for their suitability. The collected data and the literature review draw a technology feasibility studies supported by detailed cooling system analyses and superconducting electrical machine requirements. The power demand and high $f_{\mathrm {PWM}}$ lead to a SiC non-cryogenic inverter approach. Due to the detailed cooling system assessment, a loss reduction is achieved by optimising the junction temperature ( $T_{\mathrm {J}}$ ) under various load cases (LCs) out of the mission profile.

With the increase in potential uses of terahertz technology, the need for terahertz transceivers with application-oriented adaptive radiation patterns has emerged. Reconfigurable reflectarrays consisting of actuated sub-wavelength reflectors have been successfully used for terahertz beam steering and beamforming. They do not require a complex feeding network and associated electronics, enabling a compact and power-efficient terahertz beam steering system. However, the current reflectarray-based beam steering is accomplished by forming the reflectarray as a grating structure, which is accompanied by the problems such as grating lobes, limited steering range, and discrete steering angles. Here, we configure a MEMS-based reflectarray with the genetic algorithm to eliminate the grating lobes and open up the possibility of customizing its radiation pattern. We used single- and multi-objective optimization to find the optimal height profile of the reflectarray and verified the results by full-wave electromagnetic simulations. We measured the radiation patterns of four reflectarray phantoms, i.e. reflectarrays without the MEMS actuation systems. The measurement results agree well with the calculated ones, with the main beam deviating at most 2° from the target direction. Our work demonstrates how a genetic algorithm is used to shape a reconfigurable terahertz reflectarray to eliminate the grating lobes and tailor some specific featuress in its radiation pattern.

This paper introduces a novel deep learning based concept for image enhancement and distortion suppression in automotive radar signal processing. The deep neural network (DNN) is trained solely on virtual data that is generated by an automotive MIMO radar ray tracing simulator. The simulator mimics the raw data that would be provided by a specific automotive MIMO radar for which the signal processing is envisaged. This virtual radar sensor, which has the same properties as the real radar, creates the DNN training input data. The unique feature of our approach is that an advanced virtual radar sensor is used to create the target values for the DNN training. The advanced virtual sensor is a simulated radar that works on the same basic principle as the real radar and its resembling virtual sensor, but is significantly upgraded in terms of performance. In our experiments the advanced virtual sensor, e.g., has a notably larger array, i.e. a much better lateral resolution than the real radar. In addition, our advanced virtual sensor is not affected by multi-path effects, clutter, noise, beamforming sidelobes and other typical automotive radar distortions. The paper shows that a DNN trained on this advanced virtual sensor training data can deliver outstanding automotive radar signal processing results not only on simulated data but also on field data. The presented real-world automotive radar measurements show that multi-path, clutter and noise are efficiently suppressed by this DNN and that it has a remarkable ability to sharpen images and suppress sidelobes. The results presented in the paper suggest that the novel advanced DNN training concept based on virtual sensors offers opportunities that also go far beyond the radar signal processing capabilities illustrated. Hence, the concept put forward here is an attractive option for placing DNN-based radar signal processing on a novel and easy-to-implement foundation.

Control systems of modular multilevel converters can be partitioned into several levels. Such partitioning is becoming relevant in multi-vendor high-voltage direct-current systems where one party may deliver the converter hardware and associated control, whereas another party may deliver the system-related control. However, the exact control system partitioning has not been subject to research. This paper presents a method for partitioning control systems into two levels with minimum dependencies between the two levels. Furthermore, the impact of specific control designs on the partitioning and integration effort is discussed using an example. The presented method is useful for the design of multi-vendor control systems for modular multilevel converters whose partitioning otherwise would be based on engineering intuition.

Modern sensor networks—those used for autonomous driving, security systems, human motion tracking, or smart city/smart factory applications—are shifting to a more centralized data processing approach to enable efficient multimodal sensor fusion for optimal environment perception in complex dynamic situations. Among lidars and cameras, radars are typical for these applications, but they generate huge amounts of data, which cannot be transmitted or stored effectively in current setups. Consequently, manufacturers usually have to process the data “on sensor.” This results in transmitting only a few extracted features as point clouds or object lists to a central processing unit, which usually causes a significant loss of information. With this approach, advanced processing—such as enhancement of resolution by coherent combination of sensors or ghost target removal with advanced algorithms—is hardly possible. To overcome this, we suggest an alternative method by using signal-based compression with defined losses. The following topology will be proposed: the sensors encode raw data without prior radar-specific processing and after transmission, a central unit decodes and processes the radar data, thus benefiting from its more powerful heterogeneous processing system. We will analyze lossless compression algorithms with rate savings of about 30% to 65%, but the focus is on lossy compression algorithms that incorporate higher compression ratios by allowing negligible errors. It is shown that state-of-the-art multimedia compression algorithms can obtain rate savings of 99%, and radar specific algorithms can add a 50-fold gain on top, reaching 99.98%. To assess the distortions of compressed data, we then present different radar-specific evaluation metrics.

High voltage direct current (HVDC) grids are envisioned for large-scale grid integration of renewable energy sources. Upon realization, components from multiple vendors have to be coordinated and interoperability problems can occur. To address these problems, a multi-vendor HVDC system can benefit from a partially open control and protection system. Unwanted interactions can be investigated and solved more easily in partially open software compared to when applying black-boxed and vendor-specific software. Although a partially open approach offers these advantages, practical aspects, such as the implementation in a real station architecture, have to be addressed carefully. This paper covers this important topic, first by reviewing the required control and protection functions and second by discussing the choice for certain open and closed software parts, their implementation in physical units as well as the required communication and interfaces. The result from this discussion is a first proposal of a station architecture for a multi-vendor HVDC system using partially open control and protection. This architecture will be a helpful starting point to industry and academia working with research and harmonization on this topic as ad-hoc solutions in terms of practical aspects can be avoided.

ON-state conductance properties of vertical GaN n-channel trench MISFETs manufactured on different GaN substrates and having different gate trench orientations are studied up to 200 °C ambient temperature. The best performing devices, with a maximum output current above 4 kA/cm2 and an area specific ON-state resistance of 1.1 mΩ·cm2, are manufactured on ammonothermal GaN substrate with the gate channel parallel to the a-plane of the GaN crystal. The scalability of the devices up to 40 mm gate periphery is investigated and demonstrated. It is found that, in addition to oxide interface traps, the semiconductor border traps in the p-GaN layer limit the available mobile channel electrons and that the channel surface roughness scattering limits the channel mobility. Both strongly depend on the gate trench orientation and on the GaN substrate defect density.

The potential of pulsed THz radiation for time-of-flight imaging applications is well recognized. However, advances in this field are currently severely limited by the low average power of ultrafast THz sources. Typically, this results in impractically long acquisition times and a loss in resolution and contrast. These limitations make imaging of the objects in real-life scenarios impossible. Here, conclusively, the potential of state-of-the-art high-average power THz time-domain spectrometer (TDS), driven by a 100-W class, one-box ultrafast oscillator for imaging applications is shown by demonstrating lensless THz imaging in reflection mode of a dielectric sample with low reflectivity. Images obtained with our home-built 20-mW average power THz-TDS system show a significant contrast enhancement compared to a state-ofthe-art commercial THz-TDS with less than 200 μW of average power. Our unique setup even allows us to obtain images of such an object with high-contrast hidden inside a medium-density fiberboard (MDF) box. This opens the door to THz time-of-flight imaging of concealed objects of unknown shape and orientation in various real-life scenarios which were so far impossible to realize.

A novel design and manufacturing pipeline for Additive Manufacturing is presented. The architecture of the pipeline is motivated by the observation that the conventional pipeline is unnecessarily complex. Most of the time, only a small set of programming steps suffices for 3D design and manufacture. In particular, the proposed method requires no complex hardware or software, and it generates the geometric data on the fly. This is demonstrated using a simplified evaluation of general volumetric sweeps. The method side-steps many of the problems of the conventional Additive Manufacturing pipeline. Several scripts are provided that illustrate the capabilities and the advantages of the proposed approach. These scripts are processed on a single-board computer and the parts are manufactured on a Fused Filament Fabrication type of 3D printer.

To provide users with high throughputs, the fifth generation (5G) and beyond networks are expected to utilize the Massive Multiple-Input Multiple-Output technology (MMIMO), i.e., large antenna arrays. However, additional antennas require the installation of dedicated hardware. As a result the power consumption of a 5G MMIMO network grows. This implies, e.g., higher operator costs. From this angle, the improvement of Energy Efficiency (EE) is identified as one of the key challenges for the 5G and beyond networks. EE can be improved through intelligent antenna switching, i.e., disabling some of the antennas installed at a 5G MMIMO Base Station (BS) when there are few User Equipments (UEs) within the cell area. To improve EE in this scenario we propose to utilize a sub-class of Machine Learning techniques named Reinforcement Learning (RL). Because 5G and beyond networks are expected to come with accurate UE localization, the proposed RL algorithm is based on UE location information stored in an intelligent database named a Radio Environment Map (REM). Two approaches are proposed: first EE is maximized independently for every set of UEs’ positions. After that the process of learning is accelerated by exploiting similarities between data in REM, i.e., REM-Empowered Action Selection Algorithm (REASA) is proposed. The proposed RL algorithms are evaluated with the use of a realistic simulator of the 5G MMIMO network utilizing an accurate 3D-Ray-Tracing radio channel model. The utilization of RL provides about 18.5% EE gains over algorithms based on standard optimization methods. Moreover, when REASA is used the process of learning can be accomplished approximately two times faster.

With the rise of high resolution multiple input multiple output (MIMO) systems, radar became an important sensor in the development of Advanced Driver Assistance Systems (ADAS) and autonomous driving applications. Autonomous driving will rely strongly on artificial intelligence. Since most modern classification algorithms are based on neural networks, they require huge amounts of data to perform well, especially in unexpected traffic situations. Radar sensor simulation can potentially produce a great variety of training data for machine learning algorithms, which makes it an important cornerstone in the development of ADAS. Furthermore, with radar simulators, different antenna configurations and various edge cases can be simulated. In this work, a versatile ray tracing toolchain based on the shoot and bouncing rays (SBR) approach is presented. The program is able to simulate complex urban environments including realistic clutter, by utilizing simplistic reflection models. The program does not only produce realistic radar images, but also generates camera-like images using the same materials. Furthermore, this work deals with the adaption of the SBR method to radar sensors with an arbitrary number of transmit (TX)- and receive (RX) antennas, which enables the simulation of large MIMO arrays. A novel performance optimization approach is proposed for large numbers of TX antennas, which reduces the runtime dramatically. The quality of the simulation is verified by measuring a complex and realistic scenario with a high resolution automotive MIMO radar. Also, a study of the effect on quality and runtime is being investigated for various optimization approaches, including the proposed method.

Imaging radar is a key perception technology for automotive and industrial applications. A lot of progress has been made with high channel count systems, deploying, for example, 12 transmit and 16 receive channels with cascaded monolithic microwave integrated circuit solutions. Nevertheless, fully automated driving requires even higher angular resolution for drive-under/drive-over decisions and exact predictions of object trajectories in dense urban driving scenarios. Both problems can be solved by increasing the antenna size and building larger radars. However, there is a physical limit to what can be placed on the front of a car, and manufacturing very large arrays is quite difficult. Thus, coherent automotive radar networks are a way to achieve high spatial resolution and obtain the complete velocity vector of an object from a single measurement. This solution is commercially attractive, as the sensor can remain relatively small, and complexity can be moved from the physical hardware to algorithms and processing. Two different test setups, each comprised of two multiple-input multiple-output radar units in the 76-77 GHz band, are presented in this article. To obtain azimuth and additional elevation information, the setups use a 1D and a 2D antenna array, respectively. Processing-based coherent evaluation is employed to create an additional radar image with doubled azimuth resolution and improved signal-to-noise ratio and to enable the estimation of vectorial target velocities. These benefits are presented and compared with optical reference images in traffic scenarios.

While 5G is being deployed and the economy and society begin to reap the associated benefits, the research and development community starts to focus on the next, 6th Generation (6G) of wireless communications. Although there are papers available in the literature on visions, requirements and technical enablers for 6G from various academic perspectives, there is a lack of joint industry and academic work towards 6G. In this paper a consolidated view on vision, values, use cases and key enabling technologies from leading industry stakeholders and academia is presented. The authors represent the mobile communications ecosystem with competences spanning hardware, link layer and networking aspects, as well as standardization and regulation. The second contribution of the paper is revisiting and analyzing the key concurrent initiatives on 6G. A third contribution of the paper is the identification and justification of six key 6G research challenges: (i) “connecting”, in the sense of empowering, exploiting and governing, intelligence; (ii) realizing a network of networks, i.e., leveraging on existing networks and investments, while reinventing roles and protocols where needed; (iii) delivering extreme experiences, when/where needed; (iv) (environmental, economic, social) sustainability to address the major challenges of current societies; (v) trustworthiness as an ingrained fundamental design principle; (vi) supporting cost-effective global service coverage. A fourth contribution is a comprehensive specification of a concrete first-set of industry and academia jointly defined use cases for 6G, e.g., massive twinning, cooperative robots, immersive telepresence, and others. Finally, the anticipated evolutions in the radio, network and management/orchestration domains are discussed.

Quantum technology has made tremendous strides over the past two decades with remarkable advances in materials engineering, circuit design, and dynamic operation. In particular, the integration of different quantum modules has benefited from hybrid quantum systems, which provide an important pathway for harnessing different natural advantages of complementary quantum systems and for engineering new functionalities. This review article focuses on the current frontiers with respect to utilizing magnons for novel quantum functionalities. Magnons are the fundamental excitations of magnetically ordered solid-state materials and provide great tunability and flexibility for interacting with various quantum modules for integration in diverse quantum systems. The concomitant-rich variety of physics and material selection enable exploration of novel quantum phenomena in materials science and engineering. In addition, the ease of generating strong coupling with other excitations makes hybrid magnonics a unique platform for quantum engineering. We start our discussion with circuit-based hybrid magnonic systems, which are coupled with microwave photons and acoustic phonons. Subsequently, we focus on the recent progress of magnon–magnon coupling within confined magnetic systems. Next, we highlight new opportunities for understanding the interactions between magnons and nitrogen-vacancy centers for quantum sensing and implementing quantum interconnects. Lastly, we focus on the spin excitations and magnon spectra of novel quantum materials investigated with advanced optical characterization.

In this article, we put forward the substantial challenges in cyber resilience in the domain of autonomous systems and outline foundational solutions to address these challenges. These solutions fall into two broad themes: resilience-by-design and resilience-by-reaction. We use several application drivers from autonomous systems to motivate the challenges in cyber resilience and to demonstrate the benefit of the solutions. We focus on some autonomous systems in the near horizon (autonomous ground and aerial vehicles) and also a little more distant (autonomous rescue and relief). For resilience-by-design, we focus on design methods in software that are needed for our cyber systems to be resilient. In contrast, for resilience-by-reaction, we discuss how to make systems resilient by responding, reconfiguring, or recovering at runtime when failures happen. We also discuss the notion of adaptive execution to improve resilience, execution transparently and adaptively among available execution platforms (mobile/embedded, edge, and cloud). For each of the two themes, we survey the current state, and the desired state and ways to get there. We conclude the paper by looking at the research challenges we will have to solve in the short and the mid-term to make the vision of resilient autonomous systems a reality. This article came out of discussions that started at the NSF-sponsored Grand Challenges in Resilience Workshop held at Purdue in 2019 with the co-authors contributing to going into the depth of the issues and then this article.

The post-pandemic future will offer tremendous opportunity and challenge from transformation of the human experience linking physical, digital and biological worlds: 6G should be based on a new architecture to fully realize the vision to connect the worlds. We explore several novel architecture concepts for the 6G era driven by a decomposition of the architecture into platform, functions, orchestration and specialization aspects. With 6G, we associate an open, scalable, elastic, and platform agnostic het-cloud, with converged applications and services decomposed into micro-services and serverless functions, specialized architecture for extreme attributes, as well as open service orchestration architecture. Key attributes and characteristics of the associated architectural scenarios are described. At the air-interface level, 6G is expected to encompass use of sub-Terahertz spectrum and new spectrum sharing technologies, air-interface design optimized by AI/ML techniques, integration of radio sensing with communication, and meeting extreme requirements on latency, reliability and synchronization. Fully realizing the benefits of these advances in radio technology will also call for innovations in 6G network architecture as described.

Brazil, with more than 8 million km2, presents six different biomes, ranging from natural grasslands (Pampa biome) to tropical rainfall forests (Amazônia biome), with different land-use types (mostly pasturelands and croplands) and pressures (mainly in the Cerrado biome). The objective of this article is to present a new method to discriminate the most representative land use and land cover (LULC) classes of Brazil, based on the PROBA-V images. The images were converted into vegetation, soil, and shade fraction images by applying the linear spectral mixing model. Then, the pixel-based, highest proportion, annual mosaics of the fraction images, and their corresponding standard deviation images were derived and classified using the random forest algorithm. The following LULC classes were considered: forestlands, shrublands, grasslands, croplands, pasturelands, water bodies, and others. An agreement analysis was conducted with two available LULC maps derived from the Landsat satellite, the MapBiomas, and the Finer Resolution Observation and Monitoring-Global Land Cover (FROM-GLC) projects. Forestlands (412 million ha) and pasturelands (242 million ha) were the two most representative LULC classes; and croplands accounted for 30 million ha. The results presented an overall agreement of 69% and 58% with the MapBiomas and FROM-GLC projects, respectively. The proposed method is a good alternative to support operational projects of LULC map production that are important for planning biodiversity conservation or environmentally sustainable land occupation.