@misc{Rostalski2025, type = {Master Thesis}, author = {Rostalski, Sarah-Maria}, title = {Radio Astronomy Essentials: Calibration Techniques with the 2.3m Radio Telescope at Rhine-Waal University}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:1383-opus4-21533}, school = {Hochschule Rhein-Waal}, pages = {88}, year = {2025}, abstract = {This thesis discusses calibration and, in this context, multiple ways of determining the system temperature of the 2.3-metre radio telescope at Rhine-Waal University of Applied Sciences in Kamp-Lintfort. Two methods for determining the system temperature are described here: the hot/cold-method and the derivation from the radiometer equation using measurement data from a source with a known brightness temperature, in this case, S7. Both methods deliver different results, depending on the measurements and assumptions on which they are based. The results show that the data basis and uncertainties should always be considered when analysing the observational data to be able to evaluate the results correctly. Overall, the telescope is quite suitable for educational observations, especially of the 21-cm line of neutral hydrogen. Furthermore, instructions have been created for the various tasks so that the results are reproducible and comprehensible. The entire process is made transparent, from planning the observations and setting up the telescope to measuring and analysing the data. In addition, basic concepts of radio astronomy are described in theory to provide valuable background knowledge. On this basis, the use of the telescope enables students and amateur astronomers to understand and apply the basic principles of radio astronomy and to plan and carry out their own observations.}, language = {en} } @masterthesis{Yankouski2025, type = {Bachelor Thesis}, author = {Yankouski, Matsvei}, title = {Simulation of Satellite Flares and their Impact on Ground-Based Observations}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:1383-opus4-22166}, school = {Hochschule Rhein-Waal}, pages = {62}, year = {2025}, abstract = {With a growing number of satellites on Low Earth Orbit (LEO) the problem of light pollution arises, which is caused by the light reflected from satellite modules and directed to the ground-based observatories. This phenomenon is called satellite "flares" or "glints". The goal of this work is to create a physical simulation model, which can recreate the conditions when the satellite flare is visible. The model is made in Python and uses a simplified geometry of reflection, orbit, solar panel parameters, and real-time TLE data. With the developed model it is possible to evaluate a variety of scenarios: single satellite, constellation and global maps of flare distribution with the given parameters. The simulation model allows for analysing the distribution of flares of time and space and checking the effectiveness of technical and legal norms. In the flow of the work numerical simulations are performed, heat maps are created, analysis and comparison with real data are made. The obtained results demonstrate the potential of this model as an instrument for planning and scheduling observations, developing less bright satellites and creating policy measures to decrease light pollution of the night sky. The results of this thesis are fundamental for expanding the model and integrating real data of all satellites.}, language = {en} } @masterthesis{Helou2025, type = {Bachelor Thesis}, author = {Helou, Ziad}, title = {Opportunities and challenges for the solar industry in Lebanon.}, school = {Hochschule Rhein-Waal}, year = {2025}, abstract = {Lebanon faces a significant energy crisis characterized by unreliable electricity supply, political instability, and outdated infrastructure. This thesis explores the potential of solar energy as a sustainable solution to address these challenges, focusing on economic, technical, and regulatory barriers while drawing comparative insights from Cyprus's renewable energy model. The research examines three primary questions regarding the economic and technical challenges hindering solar energy adoption in Lebanon, the lessons that can be learned from Cyprus to inform solutions, and the comprehensive strategies that can support the development of Lebanon's solar energy sector. The findings reveal that while Lebanon has experienced rapid growth in solar energy adoption, the sector is undermined by systemic issues such as low-quality imports, skill gaps, insufficient grid infrastructure, and the dominance of diesel generators. Comparative analysis with Cyprus underscores the importance of robust regulatory frameworks, financial accessibility, public-private partnerships, and accreditation systems for solar installers. Additionally, the analysis highlights the critical role of improving capacity factors in Lebanon, with findings showing that even a 1\% increase could supply power to over 16,000 homes, emphasizing the potential efficiency gains for Lebanon's solar energy sector. The thesis also evaluates Lebanon's progress toward its 2030 renewable energy targets, noting that while the country appears to have met these targets in percentage terms, the achievement is largely a result of reduced electricity generation rather than genuine renewable capacity growth. Recommendations include enforcing quality standards, establishing national accreditation systems, addressing corruption, and leveraging international funding for infrastructure modernization and workforce development. This research emphasizes that Lebanon's transition to renewable energy is not only a technical and economic endeavor but also a pathway to greater energy independence and environmental sustainability.}, language = {en} } @misc{Syed2025, type = {Master Thesis}, author = {Syed, Zaid Saleem}, title = {Development of an ML-based multivariate anomaly detection model for beehive monitoring using sensor and environmental data.}, school = {Hochschule Rhein-Waal}, year = {2025}, abstract = {Anomaly detection is an important aspect of beehive monitoring, enabling beekeepers to take timely action and preventing economic losses caused by the decline of the bee population. Most existing beehive monitoring systems lack machine learning (ML) capabilities for anomaly detection and rely on threshold-based methods or expensive, complex designs. This thesis utilized the DigiBee prototype, a cost- effective monitoring system, to develop an ML-based anomaly detection model using Isolation Forest. Data on temperature, humidity, sound, vibration, and weight from four active beehives was collected between May 1, 2024, and July 25, 2024, alongside environmental data—temperature, relative humidity, and precipitation—from nearby meteorological stations. Exploratory data analysis revealed several limitations: uniform outputs from vibration sensors, calibration errors in weight sensors, and multiple gaps during the data collection period. Correlation analysis indicated a limited influence of external weather variables on internal beehive conditions, while internal sensor parameters displayed stronger correlations with each other. Five models were developed, using either beehive data alone or a combination of beehive and weather data. Results showed that beehive-specific models achieved higher accuracy in detecting localized anomalies, whereas models combining data from multiple beehives generalized better but underrepresented hive-specific issues. Models incorporating weather parameters, especially precipitation, introduced noise and unnecessary dimensions, which, when excluded, improved the model's prediction by focusing on hive-specific patterns and ensuring anomaly detection remained contextually relevant.}, language = {en} } @masterthesis{Boulmane2025, type = {Bachelor Thesis}, author = {Boulmane, Aya}, title = {Development of a Web-Based Tool for Estimating Lifecycle CO₂ Emissions of Electric vs. Diesel Vehicles in Germany}, school = {Hochschule Rhein-Waal}, year = {2025}, abstract = {Due to rapid technological development, the transport sector has emerged as one of the most difficult areas to reduce decarbonization, balancing its high global greenhouse gas emissions. In Germany, the shift from internal combustion engine vehicles (ICEVs) to electric vehicles (EVs) has become a key focus of national and EU climate policies. Nevertheless, the transition to EVs is associated with a multitude of factors that need to be considered, including emissions from manufacturing, the energy used during operations, and post-operational treatment. This thesis focuses on the design, development, and refinement of a web-based CO₂ Lifecycle Emission Calculation tool that compares cradle-to-grave emissions of EVs and diesel ICEVs based on different driving scenarios in Germany. It is based on Life Cycle Assessment (LCA) Methodology as indicated in ISO 14040/14044. With the tool, the manufacturing, operational, and end-of-life emissions are estimated based on validated datasets from peer-reviewed literature and authoritative bodies (IPCC, IEA, UBA). The tool's modular structure, which was developed in Python with the Streamlit framework, also allows for other languages to be added, selection of the region's grid mix, scenario comparisons, and results visualization in multi-level dashboards. Findings of the literature review show that the tool is accurate for both types of vehicles as the known lifecycle emissions ranges for both types are captured. Carbon payback periods for EVs ranged from less than three years to over eight years depending on the grid context, from renewable-rich to coal-intensive. Sensitivity analysis demonstrates that both grid CO₂ intensity and annual mileage hold the greatest influence over lifecycle results. This research contributes to sustainability as it fills the void that exists between academic LCA models and consumer-facing applications with a transparent, reproducible, and user-configurable decision-support tool. Improvements can be made by incorporating real-time grid emission data, expanding the vehicle datasets, and including additional CO₂ environmental impact categories.}, language = {en} } @misc{Afzal2025, type = {Master Thesis}, author = {Afzal, Umair}, title = {AI-Driven Comic Generation: Exploring the Creative Potential of Generative AI in Digital Storytelling}, url = {http://nbn-resolving.de/urn:nbn:de:hbz:1383-opus4-22581}, school = {Hochschule Rhein-Waal}, pages = {111}, year = {2025}, abstract = {Creative sectors have accepted AI into their industry for establishing modern methods of generating digital content. This study introduces a novel AI-based framework based on AI technology, using GANs along with Stable Diffusion models to automate comic development. This study looks at juxtaposing narrative outlining through text with automated visual generation toward an integrated system that produces adaptable comic panels with appropriate visual structure. The research methodology that the project has followed can be built on three cornerstones: advanced GAN schemes for text generation and pre-processing, followed by image synthesis through Stable Diffusion. A specially developed algorithm for speech bubbles determined the optimal placement of that text, so it would function well and maintain a semblance of beauty. By iteratively refining and tuning the model, this system was evaluated. Initial observations regarding visual coherence and narrative alignment were hopeful, but further tests using quantitative metrics-for instance, FID for images and BLEU for text, as well as broader user feedback-would be needed to validate the efficacy of the model entirely. The impediments were, however, GAN mode collapse, irregular speech-bubble layout, and inconsistent artistic styles. Further research would uncover the potential role of AI systems in easing the comic generation process for creators, educators, and designers of digital content to enhance accessibility and efficiency. This method shows promising applicability in various domains like automated narratives, customizable comics, and educational material. Further along the way, the scientists plan to improve panel storytelling, create an intuitive interface, and expand the dataset to include more artistic styles. Such enhancements could maximize the gain from automated comic conception.}, language = {en} }