@inproceedings{GerstnerHackenberg, author = {Gerstner, Mathias and Hackenberg, Rudolf}, title = {Context-aware forecasting of mobile network quality for autonomous vehicle connectivity}, series = {Vehicular analytics 2025 : the second conference on vehicular systems}, booktitle = {Vehicular analytics 2025 : the second conference on vehicular systems}, publisher = {IARIA}, isbn = {978-1-68558-320-0}, doi = {10.35096/othr/pub-8616}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-86166}, pages = {7}, abstract = {As autonomous driving becomes increasingly feasible, the German government has introduced a legal framework to enable the operation with Level 4 automated driving functionality. A key requirement is the maintenance of a continuous connection between such vehicles and a remote technical supervisor. If this link is lost, the vehicle must transition into a safe state by bringing itself to a controlled stop. To mitigate the risk of connection loss, accurate forecasting of mobile network availability along routes is essential. This paper presents an Exploratory Data Analysis (EDA) based on 38 measurement runs collected over ten months along a rural 64 km route in Germany. The dataset includes passive mobile network signal quality parameters, Global Navigation Satellite System (GNSS) position and precision data, as well as contextual features, such as speed, driving direction, day of the week, weather, and distance to the connected base station. Although mean values capture overall tendencies for areas with consistently good or poor coverage, they fail to capture the variability necessary for reliable prediction on a per-trip basis. Notably, some route segments show high variance in signal quality across different measurement runs. This variability is assumed to result from changing environmental influences, such as weather or traffic conditions at different times. Our analysis reveals weak but statistically relevant correlations between several contextual features (e.g., temperature ≈ -0.2) and network quality indicators. The inclusion of weather parameters or the day of the week has been shown to lower the Mean Absolute Error (MAE) compared to a prediction based only on measurements from the past. These findings underscore the importance of contextual information and localized modeling to predict network availability for safety-critical systems, such as autonomous vehicles.}, language = {en} } @unpublished{LaubmannReschke, author = {Laubmann, Julia and Reschke, Johannes}, title = {Tackling fake images in cybersecurity - interpretation of a StyleGAN and lifting its black-box}, doi = {10.48550/arXiv.2507.13722}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-83930}, pages = {11}, abstract = {In today's digital age, concerns about the dangers of AI-generated images are increasingly common. One powerful tool in this domain is StyleGAN (style-based generative adversarial networks), a generative adversarial network capable of producing highly realistic synthetic faces. To gain a deeper understanding of how such a model operates, this work focuses on analyzing the inner workings of StyleGAN's generator component. Key architectural elements and techniques, such as the Equalized Learning Rate, are explored in detail to shed light on the model's behavior. A StyleGAN model is trained using the PyTorch framework, enabling direct inspection of its learned weights. Through pruning, it is revealed that a significant number of these weights can be removed without drastically affecting the output, leading to reduced computational requirements. Moreover, the role of the latent vector -- which heavily influences the appearance of the generated faces -- is closely examined. Global alterations to this vector primarily affect aspects like color tones, while targeted changes to individual dimensions allow for precise manipulation of specific facial features. This ability to finetune visual traits is not only of academic interest but also highlights a serious ethical concern: the potential misuse of such technology. Malicious actors could exploit this capability to fabricate convincing fake identities, posing significant risks in the context of digital deception and cybercrime.}, language = {en} } @masterthesis{Bachl, type = {Bachelor Thesis}, author = {Bachl, Hannes}, title = {Initial 5G synchronisation using a time-domain correlator}, address = {Regensburg}, doi = {10.35096/othr/pub-8904}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-89048}, school = {Ostbayerische Technische Hochschule Regensburg}, pages = {x, 63, XXI}, abstract = {The following Bachelor's thesis explores the implementation of a fre- quency correlator in the time-domain as required for the initial synchro- nisation in 5G mobile networks. Exact time domain synchronisation is required for modern mobile networks to allow reconstruction of the em- ployed physical modulations schemes without producing excessive amounts of interference. This is the first step in the discovery process of mobile radio cells. Initial the theoretical foundations of time synchronisation mechanisms as used in 5G networks are explored, including the physical modulation schemes employed. The different implementations of the synchronisa- tion algorithms are considered and a single algorithm is chosen for im- plementation. The chosen algorithm is then implemented and the implementation and findings are discussed. The hardware basis for the implementation is a Xilinx RFSoC, a high speed FPGA with associated analog-to-digital con- verts designed for RF signal processing. Finally there will be a discussion of possible optimisations and problems encountered during implementation.}, language = {en} }