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Urban Air Mobility (UAM) is a new air transportation system for passengers and cargo in urban environments, enabled by new technologies and integrated into multimodal transportation systems. The vision of UAM comprises the mass use in urban and suburban environments, complementing existing transportation systems and contributing to the decarbonization of the transport sector. Initial attempts to create a market for urban air transportation in the last century failed due to lack of profitability and community acceptance. Technological advances in numerous fields over the past few decades have led to a renewed interest in urban air transportation. UAM is expected to benefit users and to also have a positive impact on the economy by creating new markets and employment opportunities for manufacturing and operation of UAM vehicles and the construction of related ground infrastructure. However, there are also concerns about noise, safety and security, privacy and environmental impacts. Therefore, the UAM system needs to be designed carefully to become safe, affordable, accessible, environmentally friendly, economically viable and thus sustainable. This paper provides an overview of selected key research topics related to UAM and how the German Aerospace Center (DLR) contributed to this research in the project "HorizonUAM - Urban Air Mobility Research at the German Aerospace Center (DLR)". Selected research results that support the realization of the UAM vision are briefly presented., Comment: 20 pages, 7 figures, project HorizonUAM

For the realization of the future urban air mobility, reliable information exchange based on robust and efficient communication between all airspace participants will be one of the key factors to ensure safe operations. Especially in dense urban scenarios, the direct and fast information exchange between drones based on Drone-to-Drone communications is a promising technology for enabling reliable collision avoidance systems. However, to mitigate collisions and to increase overall reliability, unmanned aircraft still lack a redundant, higher-level safety net to coordinate and monitor traffic, as is common in today's civil aviation. In addition, direct and fast information exchange based on ad hoc communication is needed to cope with the very short reaction times required to avoid collisions and to cope with the the high traffic densities. Therefore, we are developing a \ac{d2d} communication and surveillance system, called DroneCAST, which is specifically tailored to the requirements of a future urban airspace and will be part of a multi-link approach. In this work we discuss challenges and expected safety-critical applications that will have to rely on communications for \ac{uam} and present our communication concept and necessary steps towards DroneCAST. As a first step towards an implementation, we equipped two drones with hardware prototypes of the experimental communication system and performed several flights around the model city to evaluate the performance of the hardware and to demonstrate different applications that will rely on robust and efficient communications.

Increasing anthropomorphic robot behavioral design could affect trust and cooperation positively. However, studies have shown contradicting results and suggest a task-dependent relationship between robots that display emotions and trust. Therefore, this study analyzes the effect of robots that display human-like emotions on trust, cooperation, and participants' emotions. In the between-group study, participants play the coin entrustment game with an emotional and a non-emotional robot. The results show that the robot that displays emotions induces more anxiety than the neutral robot. Accordingly, the participants trust the emotional robot less and are less likely to cooperate. Furthermore, the perceived intelligence of a robot increases trust, while a desire to outcompete the robot can reduce trust and cooperation. Thus, the design of robots expressing emotions should be task dependent to avoid adverse effects that reduce trust and cooperation., Comment: Accepted at 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Supervised masking approaches in the time-frequency domain aim to employ deep neural networks to estimate a multiplicative mask to extract clean speech. This leads to a single estimate for each input without any guarantees or measures of reliability. In this paper, we study the benefits of modeling uncertainty in clean speech estimation. Prediction uncertainty is typically categorized into aleatoric uncertainty and epistemic uncertainty. The former refers to inherent randomness in data, while the latter describes uncertainty in the model parameters. In this work, we propose a framework to jointly model aleatoric and epistemic uncertainties in neural network-based speech enhancement. The proposed approach captures aleatoric uncertainty by estimating the statistical moments of the speech posterior distribution and explicitly incorporates the uncertainty estimate to further improve clean speech estimation. For epistemic uncertainty, we investigate two Bayesian deep learning approaches: Monte Carlo dropout and Deep ensembles to quantify the uncertainty of the neural network parameters. Our analyses show that the proposed framework promotes capturing practical and reliable uncertainty, while combining different sources of uncertainties yields more reliable predictive uncertainty estimates. Furthermore, we demonstrate the benefits of modeling uncertainty on speech enhancement performance by evaluating the framework on different datasets, exhibiting notable improvement over comparable models that fail to account for uncertainty., Comment: Accepted version

We report on the design and the construction of a sounding rocket payload capable of performing atom interferometry with Bose-Einstein condensates of $^{41}$K and $^{87}$Rb. The apparatus is designed to be launched in two consecutive missions with a VSB-30 sounding rocket and is qualified to withstand the expected vibrational loads of 1.8 g root-mean-square in a frequency range between 20 - 2000 Hz and the expected static loads during ascent and re-entry of 25 g. We present a modular design of the scientific payload comprising a physics package, a laser system, an electronics system and a battery module. A dedicated on-board software provides a largely automated process of predefined experiments. To operate the payload safely in laboratory and flight mode, a thermal control system and ground support equipment has been implemented and will be presented. The payload presented here represents a cornerstone for future applications of matter wave interferometry with ultracold atoms on satellites.

Recent developments in explainable artificial intelligence promise the potential to transform human-robot interaction: Explanations of robot decisions could affect user perceptions, justify their reliability, and increase trust. However, the effects on human perceptions of robots that explain their decisions have not been studied thoroughly. To analyze the effect of explainable robots, we conduct a study in which two simulated robots play a competitive board game. While one robot explains its moves, the other robot only announces them. Providing explanations for its actions was not sufficient to change the perceived competence, intelligence, likeability or safety ratings of the robot. However, the results show that the robot that explains its moves is perceived as more lively and human-like. This study demonstrates the need for and potential of explainable human-robot interaction and the wider assessment of its effects as a novel research direction., Comment: Accepted at 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)

Bose-Einstein condensates (BECs) in free fall constitute a promising source for space-borne matter-wave interferometry. Indeed, BECs enjoy a slowly expanding wave function, display a large spatial coherence and can be engineered and probed by optical techniques. On a sounding rocket, we explore matter-wave fringes of multiple spinor components of a BEC released in free fall employing light-pulses to drive Bragg processes and induce phase imprinting. The prevailing microgravity played a crucial role in the observation of these interferences which not only reveal the spatial coherence of the condensates but also allow us to measure differential forces. Our work establishes matter-wave interferometry in space with future applications in fundamental physics, navigation and Earth observation., Comment: 7 pages, 3 figures

Microgravity eases several constraints limiting experiments with ultracold and condensed atoms on ground. It enables extended times of flight without suspension and eliminates the gravitational sag for trapped atoms. These advantages motivated numerous initiatives to adapt and operate experimental setups on microgravity platforms. We describe the design of the payload, motivations for design choices, and capabilities of the Bose-Einstein Condensate and Cold Atom Laboratory (BECCAL), a NASA-DLR collaboration. BECCAL builds on the heritage of previous devices operated in microgravity, features rubidium and potassium, multiple options for magnetic and optical trapping, different methods for coherent manipulation, and will offer new perspectives for experiments on quantum optics, atom optics, and atom interferometry in the unique microgravity environment on board the International Space Station.

Collected data, which is used for analysis or prediction tasks, often have a hierarchical structure, for example, data from various people performing the same task. Modeling the data's structure can improve the reliability of the derived results and prediction performance of newly unobserved data. Bayesian modeling provides a tool-kit for designing hierarchical models. However, Markov Chain Monte Carlo methods which are commonly used for parameter estimation are computationally expensive. This often renders its use for many applications not applicable. However, variational Bayesian methods allow to derive an approximation with much less computational effort. This document describes the derivation of a variational approximation for a hierarchical linear Bayesian regression and demonstrates its application to data analysis.

Online mental health treatment has the premise to meet the increasing demand for mental health treatment at a lower cost than traditional treatment. However, online treatment suffers from high drop-out rates, which might negate their cost effectiveness. Predictive models might aid in early identification of deviating clients which allows to target them directly to prevent drop-out and improve treatment outcomes. We propose a two-staged multi-objective optimization process to automatically infer model structures based on ecological momentary assessment for prediction of future symptom development. The proposed multi-objective optimization approach results in a temporal-causal network model with the best prediction performance for each concept. This allows for a selection of a disorder-specific model structure based on the envisioned field of application.

Space offers virtually unlimited free-fall in gravity. Bose-Einstein condensation (BEC) enables ineffable low kinetic energies corresponding to pico- or even femtokelvins. The combination of both features makes atom interferometers with unprecedented sensitivity for inertial forces possible and opens a new era for quantum gas experiments. On January 23, 2017, we created Bose-Einstein condensates in space on the sounding rocket mission MAIUS-1 and conducted 110 experiments central to matter-wave interferometry. In particular, we have explored laser cooling and trapping in the presence of large accelerations as experienced during launch, and have studied the evolution, manipulation and interferometry employing Bragg scattering of BECs during the six-minute space flight. In this letter, we focus on the phase transition and the collective dynamics of BECs, whose impact is magnified by the extended free-fall time. Our experiments demonstrate a high reproducibility of the manipulation of BECs on the atom chip reflecting the exquisite control features and the robustness of our experiment. These properties are crucial to novel protocols for creating quantum matter with designed collective excitations at the lowest kinetic energy scales close to femtokelvins., Comment: 6 pages, 4 figures

Quantum sensors based on coherent matter-waves are precise measurement devices whose ultimate accuracy is achieved with Bose-Einstein condensates (BEC) in extended free fall. This is ideally realized in microgravity environments such as drop towers, ballistic rockets and space platforms. However, the transition from lab-based BEC machines to robust and mobile sources with comparable performance is a challenging endeavor. Here we report on the realization of a miniaturized setup, generating a flux of $4 \times 10^5$ quantum degenerate $^{87}$Rb atoms every 1.6$\,$s. Ensembles of $1 \times 10^5$ atoms can be produced at a 1$\,$Hz rate. This is achieved by loading a cold atomic beam directly into a multi-layer atom chip that is designed for efficient transfer from laser-cooled to magnetically trapped clouds. The attained flux of degenerate atoms is on par with current lab-based BEC experiments while offering significantly higher repetition rates. Additionally, the flux is approaching those of current interferometers employing Raman-type velocity selection of laser-cooled atoms. The compact and robust design allows for mobile operation in a variety of demanding environments and paves the way for transportable high-precision quantum sensors., Comment: 22 pages, 6 figures