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Analysis of the radar channel and its variability in vital-sign sensing applications
No full textIn millimeter-wave (mmWave) radar-based vital-sign sensing, the radar sensor usually operates in the radiative near field of the target, which is the human body. Therefore, in contrast to far-field conditions, scattering cannot be quantified by a target-inherent radar cross section (RCS) as it strongly depends on the relative position and directivity of the radar antenna. To investigate the resulting consequences for the application, this article examines the radar channel transfer function, i.e., the ratio between the power waves feeding and being received by the antenna, with particular focus on its variability due to changes of the relative antenna position. The analysis is supported by a physical-optics-based simulation model tailored to practical circumstances. Measurements at 24 GHz and 61 GHz using antennas with different directivities and a human-like mannequin as a target confirm the simulative predictions. The radar channel suffers from significant variations, e.g., displacing the antenna by less than 1mm can cause a 30-dB receive power drop. A statistical analysis comprehensively evaluates the radar channel variability and clarifies the challenges for the application that stem from target near-field effects
Simulating Gadolinium-induced magnetic field variations for temperature sensing with magneto-mechanical resonators
No full textSmall-size magneto-mechanical resonators (MMR) represent an emerging class of passive, wireless sensors that combine a sensing functionality with a tracking option. The operation principle is based on a resonating rotor oscillation whose frequency is defined by the magnetic flux density of a stator magnet. One general sensing mechanism is the coupling of an external parameter to this resonator frequency. In this study, we investigate an approach for encoding a temperature information as a shift in the natural oscillation frequency utilizing the temperature-dependent magnetic properties of gadolinium (Gd). We perform an isolated simulation study on the temperature scaling of the magnetic field generation for stators coated with Gd of varying thickness. Our results show that the magnetic phase transition of Gd at its Curie temperature leads to a pronounced change in the magnetic permeability enabling a significant magnetic shielding behavior only for lower temperatures. In the transition regime, we find a peak sensitivity reaching 45.8 Hz/K exceeding existing values from the literature by up to a factor of 20. The findings of this work are an important step toward quantitative high-sensitivity temperature extraction with MMRs
Flexibilisierung von hafeninternen Containertransporten mit Zeitfensterbuchungen
No full textTruck appointment systems are a well-studied approach to reduce queues in front of container terminals and on their yard. Traditional systems are lacking in flexibility and tend to favour the terminal operators over other partners in the maritime supply chain. To enable a better fit of appointment systems to ports and to increase their flexibility, a systematic analysis of different parameters and strategies and their interdependencies is executed. The results show that longer time slots benefit terminal operators as well as trucking companies. Furthermore, the so-called flexibility options show some promise to increase efficiency with little extra effort. As there are many interdependencies, the parameters and strategies need to be chosen with care for every container terminal
Integrating optimal design of experiments and superstructure optimization under uncertainties for the design of membrane processes
No full textMembrane processes are attractive options for fluid separations as they enable highly selective separations under mild process conditions. The design and evaluation of membrane processes is however a complex task, due to the large variety of different separation principles and corresponding types of membrane processes, a variety of different membrane materials, and alternative process structures, as well as the limited availability of data and models that is especially a hurdle in the early-stage design of chemical processes. A model- and optimizationbased approach can help to design membrane separation processes systematically but requires the determination of a sufficiently accurate process model that usually requires time-consuming experimental work. In the early stage of process design, there are still many options regarding the process structure and operating parameters, and it is desirable to focus the experimental and modelling efforts only on promising process variants and operating conditions. For this purpose, the current work presents an integrated methodology that enables the application of optimization-based methods in early phase process design applied to a reaction-separation process. The application focusses on the selection and design of the membrane separation as an important aspect of the overall flowsheet. By performing a superstructure optimization in which uncertainties are explicitly considered, a set of promising flowsheet variants can be selected. Instead of a separated design of experiments for the model identification, an integral approach is pursued by selecting the most informative experiments on the basis of the impact of the model uncertainties on the design decisions, in order to quickly determine the most relevant parameters. In the specific case study, the methodology is used for the conceptual design of a membrane process where the membrane is used to separate a co-product from a complex reaction mixture while retaining the expensive homogeneous catalyst. Using the integrated approach only one set of experiments in addition to the initial screening experiments is necessary to identify the optimal flowsheet for the case study
Truck Appoinment Systeme bei hafeninternen Containertransporten: Gestaltungsmöglichkeiten und Annpassungstrategien
No full textControlling organic semiconductor self-assembly through cylindrical nanoconfinement
No full textControlling the self-assembly of organic semiconductors at the nanoscale is critical for advancing high-performance electronic and photonic devices, yet remains challenging due to their intrinsic anisotropic crystallization and sensitivity to processing conditions. Here, we demonstrate that cylindrical nanoconfinement within anodic aluminum oxide membranes provides a versatile platform to precisely tune the molecular orientation and phase behavior of the prototypical organic semiconductor 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene (C8-BTBT-C8). Combining temperature-dependent high-resolution synchrotron X-ray scattering with optical birefringence measurements, we uncover that confinement geometries (pore diameters 25–180 nm) and surface chemistry govern the emergence of distinct smectic A textures, featuring molecular layers either parallel or perpendicular to the pore axis. The competition between axial and radial smectic layering is modulated by pore size, surface hydrophilicity, and thermal history, enabling reversible control over domain orientations and transitions between liquid crystalline and crystalline states. Notably, nanoconfinement stabilizes the smectic phase over an expanded temperature range compared to bulk, while inducing complex multi-domain configurations owing to geometric constraints and anchoring conditions. Our results elucidate fundamental mechanisms by which anisotropic nanoscale confinement directs the self-organization of highly conjugated organic molecules, with implications for optimizing directional charge transport and anisotropic optical responses in organic–inorganic hybrid nanoarchitectures. This study establishes nanoconfinement as a powerful strategy to engineer morphology and functional properties in organic semiconducting materials with nanoscale precision
Energiesystem Deutschland
No full textNachdem zuvor ausgewählte Rahmenbedingungen für die Nutzung erneuerbarer Energien in Deutschland betrachtet wurden, widmet sich das folgende Kapitel der Darstellung des derzeitigen Energiesystems des Landes und dessen jüngster Entwicklung. Hierbei wird zunächst der Primärenergieverbrauch analysiert, welcher die gesamte Energieversorgung der deutschen Volkswirtschaft einschließlich sämtlicher Verbrauchssektoren umfasst. Anschließend wird die Stromerzeugung näher betrachtet, da sie eine zentrale Rolle bei der Transformation des Energiesystems spielt und zunehmend durch erneuerbare Energien geprägt wird
Enhancing privacy through unlinkable data sharing with User-in-the-Loop access control
No full textIn our information-driven society, the volume of data generated by individuals has grown significantly. Protecting the privacy of individuals is becoming more challenging, as this data can reveal detailed insights into personal preferences and behavior. To address this challenge, we introduce a user-centric, privacy-preserving data-sharing solution that leverages a central data storage service, hereafter referred to as the data intermediary. By integrating local differential privacy with user-in-the-Ioop access control, our system enables data providers to securely and unlinkably store their data at the intermediary. Data consumers can localize and request data via the intermediary. The data providers are included in access decisions without disclosing their identity nor by enabling the linking of their data. We evaluated our approach using theoretical analysis and simulations. Our findings indicate that our system achieves c-privacy and safeguards data providers against external and internal attackers, malicious data consumers, and an honest-but-curious intermediary. Moreover, our method reduces the message overhead for data discovery in our system by more than half compared to existing approaches
Identification of hybrid systems with dynamics-based modeling through symbolic regression
No full textHybrid systems combine both continuous and discrete behavior. These systems serve as models in many fields, including control systems, robotics, and industrial processes. However, due to their complexity, finding an accurate model is a challenge. This paper presents a holistic approach to learning models of hybrid systems using symbolic regression. Our method leverages symbolic regression to automatically discover accurate and interpretable mathematical models in the form of hybrid systems from observed data. An advantage of our algorithm is that it detects transitions between different behavioral modes of a system based on the inherent dynamics. From learned expressions for the dynamical behavior of a system, we form a hybrid system by combining the learned expressions with a decision tree determining the current behavioral mode from data. This hybrid decision tree serves regression, prediction, and further related tasks. Our results demonstrate that symbolic regression can effectively identify the underlying dynamics of a real hybrid system and predict output signals on new input data with high accuracy