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    On-Chip Bandpass Filter for Superconducting Devices
    (2026) Winkel, Job
    On-chip lumped element bandpass filters offer a pathway to tightly integrate noise suppression directly at the chip level in superconducting quantum devices. Despite the widespread use of filters in cryogenic qubit setups, co-fabricated lumped element bandpass filters remain relatively unexplored. This work evaluates their feasibility, design constraints, and performance when embedded directly on a superconducting qubit chip, paving the way for scalable quantum architectures. The filter design follows a standard radio frequency (RF) synthesis approach, adapted to cryogenic operation, co-fabrication constraints, limited footprint, and superconducting drive requirements. A scalable design flow is developed to implement arbitrary-order bandpass filters using lumped inductors and capacitors. Electromagnetic (EM) simulations are employed to extract effective component parameters and refine circuit models beyond ideal lumped element approximations. Simulations show that on-chip lumped element bandpass filters can achieve welldefined passband characteristics and support higher-order architectures. However, ideal and extended lumped element models alone are insufficient to predict device response accurately. Direct optimization with computationally intensive EM simulations were therefore necessary to achieve reliable filter performance. The filter response also directly influences the thermal noise spectrum experienced by the qubit. Modeling indicates that appropriately designed bandpass filters can reduce unwanted thermal occupation, providing a tool for engineering and investigating the qubit’s EM environment. A prototype device was fabricated and characterized at cryogenic temperatures. The measured response did not exhibit the intended passband, with analysis pointing to fabrication issues, particularly unreliable capacitor connections, rather than limitations of the filter concept or design methodology. Overall, this work establishes a simulation-driven platform for co-fabricated lumped element bandpass filters in superconducting quantum circuits. The results demonstrate their feasibility, scalability, and potential for controlled thermal noise engineering in cryogenic quantum hardware.
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    Retrieval-Augmented Generation for Sustainable Material Data Handling in Automotive Value Chain
    (2026) Tran, John; Larsson, Daniel
    Applying large language models (LLMs) to industrial material data workflows has the potential to improve efficiency. However, conventional LLMs are limited by hallucinations, depend on proprietary training data, and are costly to update. This thesis explores Retrieval-Augmented Generation (RAG) as an alternative approach, in which an LLM generates responses grounded in a restricted, domain-specific corpus of documents and databases and provides source citations for them. The study is carried out in an industrial setting with two main data domains: an internal SQL materials database with tabular material properties, and a corpus of unstructured textual documents, including supplier documents, corporate standards, and environmental product declarations. A RAG system is developed that (1) indexes both textual and tabular data, (2) retrieves relevant chunks via dense vector search, and (3) generates source-grounded responses. The work investigates whether a RAG model that explicitly integrates both domains can outperform a baseline tuned for unstructured text and explores which tabular serialization format yields the most semantically informative embeddings for pretrained embedding models. To achieve this, we first constructed LLM-based pipelines to generate documentand table-based test sets with ground-truth chunk annotations, and implemented a modular RAG pipeline with separate indices for textual and tabular data. Then, we experimented with multiple retrieval strategies, ranging from concatenating the retrieval results to using cross-encoders to weigh them. In addition, several fusion strategies were tested to evaluate whether they could improve retrieval accuracy when operating across different domains. Experiments are conducted comparing nine tabular serialization strategies, studying performance as a function of index size, chunk size, and top-k, and evaluating different fusion modes and embedding models. The evaluation metrics used are Hit Rate, Recall, Precision, F1-score, and Mean Reciprocal Rank. Results show that enriched serialization, which converts tabular rows into natural-language statements, yields stronger tabular retrieval performance than a standard key-value-based format, without degrading performance on document retrieval. Larger chunk sizes and higher top-k values systematically improve retrieval metrics, highlighting both the difficulty of relying solely on similarity search and the benefits of cross-encoder reranking on larger candidate sets. A domain-aware weighted fusion retriever further improves overall retrieval performance over the optimized baseline with only moderate computational overhead. These findings demonstrate that semantically rich tabular representations and domain-aware fusion can enhance RAG performance on heterogeneous industrial material data.
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    Drive Cycle Analysis for the Electric Powertrain in Terminal Tractors: A Data-Driven Approach to Performance Evaluation
    (2026) Magnusson, Fabian; Marjanovic, Edwin
    The transition toward electrified powertrains in industrial vehicles places increased demands on understanding real world operational behavior. Terminal tractors operate under highly variable and transient conditions, making traditional standardized drive cycles insufficient for accurate performance evaluation and optimization. This thesis presents a data driven framework for analyzing, categorizing, and simulating operational drive cycles of electric terminal tractors based on real world field data. Multivariate time series data collected from electric terminal tractors were preprocessed and segmented into individual drive cycles using application specific operational signals. A set of interpretable features capturing both steady state and dynamic behavior was extracted for each cycle. Dimensionality reduction and feature selection were performed using Principal Component Analysis and Principal Feature Analysis to retain the most informative characteristics while maintaining interpretability. The results were that only 17 principal components out of the original 24 were needed to describe 95% of the explained variance. Multiple clustering techniques, including Hierarchical Agglomerative Clustering, K-Means, K-Medoids, and a convolutional autoencoder based clustering, were applied and evaluated using internal validation metrics. The resulting clusters revealed two distinct operational regimes, representative usage patterns, and outlying behaviors across the fleet. These two operational regimes were defined as low load and high load, where the low load cluster is defined by its lower variance and more stable values, while the high load cluster is defined by higher variance and a broader range of values in torque for example. Representative and atypical drive cycles from each cluster were subsequently integrated into a simulation model of the electric power train to evaluate component behavior, energy usage, and engine efficiency operation under different load configurations. The results demonstrate that data driven cycle analysis can effectively characterize real world usage patterns and provide valuable insights for how powertrain components, such as the engine and battery, are affected during operation. The proposed methodology offers a scalable framework for leveraging operational data to identify dominant operating regimes and guide structured evaluation of the electrical powertrain in terminal tractors.
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