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Where Are We? Security, Misuse, and Manipulation of Location-based Systems
Full text linkLocation-based systems allow us to perform many tasks: we can check the weather wherever we are, navigate unfamiliar environments, and track our running workouts as well as valuables such as laptops or luggage. However, all of these examples require access to highly sensitive and private location data. Misuse of this data allows adversaries to identify individuals based on a few locations, track them digitally without consent, or manipulate location data to bypass location-based access policies, such as those implemented in cars or smart door locks.
In this thesis, we contribute to three emerging areas of location-based systems: (1) Security and misuse of Bluetooth offline-finding networks; (2) Security, reliability, and accuracy of localization with UWB; and (3) Secure location sharing via satellite. Our methodology is diverse. We reverse-engineer implementations to investigate their privacy and security protections, open up proprietary systems and provide open-source implementations. Furthermore, we develop tools to protect users from unwanted location tracking, research novel attack vectors and protections, and conduct user studies to understand the needs of victims of unwanted tracking.
To summarize our main research results: (1) We analyze Apple’s Find My network, uncover vulnerabilities that expose individuals’ location data, and demonstrate that the network can be misused for tracking and stalking. Based on our results, we open up the proprietary network and publish a framework that allows any programmable Bluetooth device to be integrated into the Find My network, making it locatable. To protect users from unwanted tracking attacks, we develop and publish the AirGuard app, which identifies malicious trackers following a person and alerts them. We conduct a survey with over 5,000 participants and find that 44% of stalking victims have suffered from unwanted location tracking. With the study results and AirGuard user data, we identify patterns and methods used by stalkers, and where trackers are hidden. (2) We measure the accuracy and reliability of Ultra-Wide Band (UWB) distance measurements in consumer smartphones and find that, while results are accurate in most cases, all devices measured a small number of large outliers. Furthermore, we analyze the security of UWB against distance manipulation attacks and develop a novel physical-layer attack that can reduce the measured distance by several meters. (3) Encryption and privacy protection mechanisms are often neglected in satellite communication. We evaluate the iPhone’s location-sharing via satellite feature and find robust security mechanisms with multi-layer encryption in place. Nevertheless, we identify bypasses around Apple’s restrictions and manage to use satellite services from restricted areas and send text messages encoded in location updates. We propose countermeasures for all vulnerabilities we find.
In addition to academic publications, our work has demonstrated real-world impact with the AirGuard apps for Android and iOS. They have been installed over 1.3million times in the last three and a half years. Several victims of unwanted tracking informed us that they had found trackers using our application, preventing further tracking. By pushing for better detection mechanisms, we influenced major companies: Apple and Google have proposed standardized tracking detection for all Bluetooth trackers
The physics of defect chemistry and the chemistry of defect physics
Full text linkDefect chemistry is the classical approach to evaluate point-defect concentrations in solids depending on the chemical activity of the (n−1) of n constituents by evaluating the mass action laws of a number of defect reactions conserving species, lattice sites, and charge. In an alternative approach, formation energies of individual defects can be calculated to determine the dependence on the Fermi level and on the chemical potentials of the reservoirs. This contribution provides the quantitative relationship between the two approaches, offering the opportunity to compare calculated defect formation energies with experimentally determined quantities. As an example, the application of the two approaches to the comparison of electronic and ionic compensation of doping and the influence of the band edge energies on it is given. This example demonstrates that the Gibbs energy of reduction and oxidation are essentially aligning the energy axis of ionic defects relative to that of electronic defects. In conjunction with the dependence of the valence band maximum and conduction band minimum energies on material composition, this offers the opportunity to tune the preference for electronic, ionic or mixed compensation of doping by two independent quantities
A Concept for Shared Control of Unmanned Aircraft Systems
Full text linkWith the rise of agile air mobility, combined with low operating costs and advanced autonomous technologies, unmanned aircraft systems (UAS) are increasingly being integrated into various industries, including transportation, agriculture, and media. Typically, UAS guidance is managed either by autonomous systems or pilots. Autonomous systems offer advantages like stability and quick responsiveness but may lack effective emergency handling. Conversely, pilots excel in environmental perception and adaptive learning but are susceptible to fatigue.
Inspired by this contradiction, the concept of shared control is introduced to harmonize these two control modes, leveraging their strengths while mitigating weaknesses. Following an extensive review of relevant literature, a rigorous definition of shared control for UAS operation is established, distinguishing it from assistant and filtering control methods.
Two real-world scenarios are selected to assess the feasibility of shared control. In the first scenario, collision avoidance, a comprehensive shared control system is developed. The pilot's control authority is dynamically allocated based on collision risk, seamlessly integrating commands from both the pilot and autonomous systems. Field experiments validate the system's effectiveness, while Monte Carlo simulations further demonstrate its ability to increase the probability of avoidance, revealing potential additional collision risks. Additionally, user experiments are conducted to evaluate user satisfaction with the system.
In the second scenario, aerial cinematography, a novel shared control method is developed based on optimal control methodologies. This approach incorporates a human input model and utilizes model predictive control techniques to build the shared control system. Through simulated flights, all intended functionalities are effectively realized. After completing the simulated aerial cinematography tasks, participants provide objective evaluations that align with anticipated satisfaction levels.
The next step involves exploring the applicability of shared control across various scenarios. Future shared control systems should also be optimized for seamless integration with autonomous systems that possess learning capabilities. Additionally, mitigating the potential risks associated with implementing shared control remains critical
A Holistic Framework for Developing Expert Systems to Improve Energy Efficiency in Manufacturing
Full text linkAmid growing environmental and societal concerns about energy use, companies face increasing pressure to adopt sustainable manufacturing practices. The European Union’s guiding principles, aimed in part at achieving climate neutrality and fostering green growth, underscore the need for systematic, data-driven approaches to energy efficiency. This involves the measurement, monitoring, and analysis of energy data. However, identifying efficiency potentials often relies on expert knowledge, which is becoming increasingly scarce due to skilled labor shortages. Expert systems offer a solution by consolidating and analyzing data to automatically identify energy-saving opportunities. These systems leverage stored expertise, applying it to measurement data to generate actionable insights, while their explicit knowledge representation and transparent reasoning facilitate knowledge transfer. Despite their potential, most expert systems are developed intuitively and tailored to specific applications, limiting their broader adoption. To address this, we propose a holistic framework for systematic expert system development, supported by defined personas and an expert system shell serving as a software template. The framework is demonstrated and evaluated through its application in a metalworking process chain
Additive Manufacturing of Copper — A Survey on Current Needs and Challenges
Full text linkAdditive manufacturing (AM) of copper is subject to dynamic development regarding available processes and the quality of produced parts. While challenging, AM processes for copper provide parts with a quality comparable to other metallic material groups like steels. The reasons for the lower prevalence of additive manufacturing of copper components in industrial applications are currently not sufficiently researched, especially in light of the significant progress made in the maturity of this technology. A survey is used to investigate the assessments of protagonists in the field of copper AM. The needs of current and potential users of copper AM are analyzed and outlined. This study reveals that the most relevant technical limitation for users is the reduced surface quality of parts, while overall processes need to become less costly and more reliable to find broader use. Answers given hint to a higher degree of automation, the possibility of multi-material processing, and the upscaling of machine and part sizes as relevant future trends in the copper AM sector
Probabilistic Circuits: Going Bayesian and Spectral with Densities and Time Series
Full text linkWhen using a machine learning model for a specific task, users typically want to understand the reliability of the output from the model. Therefore, estimating the uncertainty of the output is a crucial task. A key approach to capturing uncertainty is probabilistic modeling. Traditional probabilistic models have evolved significantly over time: with the current increase in the amount of data, the complexity of data types, and diverse inference demands, new probabilistic models are constantly being proposed to address more complex scenarios. However, despite these advances, probabilistic modeling still faces challenges when dealing with data types such as time series or mixed tabular data. They often struggle to efficiently encode time dependencies or fail to provide a unified view for discrete and continuous random variables. In addition, they do not naturally integrate with deep neural networks limiting their application to more challenging tasks.
In this thesis, we investigate modeling challenging data types, including time series and mixed tabular data with probabilistic circuits, which allow for efficient and flexible probabilistic inference and can also be vectorized to work jointly with deep neural networks. First, we model the time series into the leaf nodes of a probabilistic circuit by utilizing Gaussian processes, and use product nodes to sequentially encode both the output dimensions and the covariate space, resulting in multi-output mixture of Gaussian processes (MOMoGPs). This results in the Bayesian case, enabling efficient computation for multi-input, multi-output regression tasks, and we then show its application in a real-world energy production use case. Secondly, to model the joint distribution of the entire time series, we leverage the Whittle assumption and model the time series in the spectral domain with its Fourier coefficients, resulting in Whittle sum-product networks (WSPNs), one of our spectral cases. This method not only preserves the time series dependencies but also enables efficient and flexible inference for, e.g., anomaly detection via density estimation and forecasting via conditional sampling. It is further extended to work jointly with other deep neural networks to provide useful uncertainty estimates in autoencoding and time series prediction. Lastly, we go one step further in the spectral domain by leveraging the structure of probabilistic circuits to model the characteristic function of probability distributions, resulting in characteristic circuits (CCs). By modeling densities in the spectral domain, characteristic circuits provide a unified view for discrete and continuous random variables, and can represent distributions that do not have closed-form probability density functions. We also show that characteristic circuits can be easily adapted and extended for causal inference in hybrid domains. We validate the proposed MOMoGPs, WSPNs, and CCs with both synthetic and real-world data sets. At the end of the thesis, we highlight interesting directions for future research on probabilistic models for challenging data types
Multiple Chaperone DnaK–FliC Flagellin Interactions are Required for Pseudomonas aeruginosa Flagellum Assembly and Indicate a New Function for DnaK
Full text linkThe DnaK (Hsp70) protein is an essential ATP‐dependent chaperone foldase and holdase found in most organisms. In this study, combining multiple experimental approaches we determined FliC as major interaction partner of DnaK in the opportunistic bacterial pathogen Pseudomonas aeruginosa. Implementing immunofluorescence microscopy and electron microscopy techniques DnaK was found extracellularly associated to the assembled filament in a regular pattern. dnaK repression led to intracellular FliC accumulation and motility impairment, highlighting DnaK essentiality for FliC export and flagellum assembly. SPOT–membrane peptide arrays coupled with artificial intelligence analyses suggested a highly dynamic DnaK–FliC interaction landscape involving multiple domains and transient complexes formation. Remarkably, in vitro fast relaxation imaging (FReI) experiments mimicking ATP‐deprived extracellular environment conditions exhibited DnaK ATP‐independent holdase activity, regardless of its co‐chaperone DnaJ and its nucleotide exchange factor GrpE. We present a model for the DnaK‐FliC interactions involving dynamic states throughout the flagellum assembly stages. These results expand the classical view of DnaK chaperone functioning and introduce a new participant in the Pseudomonas flagellar system, an important trait for bacterial colonisation and virulence
Two Optimization Approaches for a Small‐Scale Power‐to‐Ammonia Cycle
Full text linkAmmonia is a promising carbon‐free energy vector. Small‐scale renewable power‐to‐ammonia (P2A) is particularly suited for isolated agricultural areas where ammonia can be used as fuel and fertilizer. This work compares two approaches to simulate and optimize the steady‐state behavior of a novel small‐scale P2A process: Aspen Plus® and MOSAIC®. Aspen Plus® is a commercial flow sheeting software whereas MOSAIC® is a freeware where equations and thermochemical properties need to be specified by hand. It can be shown that the results of MOSAIC® and Aspen Plus® are qualitatively comparable, but not identical. This suggests that the model in MOSAIC® can be improved further, starting with the implementation of a more accurate numeric reactor kinetics and equation of state
Alumina Supported Iron Catalysts for Selective Acetylene Hydrogenation Under Industrial Front‐End Conditions
Full text linkThe removal of acetylene traces from ethylene streams coming from the steamcracker is carried out in the industry on an annual scale of several million tons using Pd‐Ag/Al₂O₃ catalysts. The substitution of palladium containing catalysts with more abundant, cheap, and nontoxic materials is a first crucial step toward a more sustainable chemical industry. As iron is one of the most abundant metals and can be mined in almost all regions worldwide, it is an ideal catalyst material. In this work, we present the development of α‐alumina supported iron catalysts with 1, 5, and 10 wt% iron loading and their application in the selective acetylene hydrogenation under industrially applied front‐end conditions. The catalysts were prepared via simple incipient wetness‐impregnation and were analyzed via XRD, XRF, TPR, TEM, and N₂‐physisorption. The catalysts were subsequently calcined, reduced, and tested in the selective acetylene hydrogenation. After an activation phase, the catalysts show excellent activity and selectivity in the acetylene hydrogenation at 90 °C without significant ethylene hydrogenation. The excellent catalytic activity underline the great potential of iron based catalysts as an alternative to conventional Pd‐containing materials
Parameterising Local Reactive Power Control Characteristics of Distributed Energy Resources Through Time Series Based Optimal Power Flow Calculations
Full text linkThis paper presents a method for determining local reactive power control characteristics for distributed energy resources. Based on historical time series, an optimal reactive power dispatch is calculated. The optimal reactive power dispatch minimises a multi‐criteria objective function that allows the reduction of grid losses while complying with a desired vertical reactive power exchange with the overlaid grid and with operational constraints. From the resulting optimal operating points, individual reactive power control characteristics can be determined by piecewise linear regression and then be clustered to reduce the complexity of the grid planning process. The method thus allows the integration of the information contained in the historical time series on the volatile power flow behaviour and offers the possibility of systematic parameter determination. In contrast, conventional grid planning uses empirical values or conservative estimates. In contrast to offline or online optimisations during operation; however, no real‐time capable information and communication infrastructure is required. To validate the performance of the method, operation with the cluster control characteristics is compared with offline optimisation and simple parameterisation approaches for local reactive power control characteristics