https://fordatis.fraunhofer.de/handle/fordatis/126 2026-09-08T06:47:06Z https://fordatis.fraunhofer.de/handle/fordatis/508 Titel: Towards Dependable LLM-Based Systems in Manufacturing – Supplementary Material Datenautorinnen und Datenautoren: Van, Hoai My; Beck, René; Rocha, Melise; Weiss, Gereon Zusammenfassung: Analysis of dependability attributes for the operation phase of an exemplary LLM-based troubleshooting system consisting of various components (e.g. knowledge graph, retrieval pipeline). 2026-01-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/503 Titel: Towards Reliable LLM-based Agentic Applications in Industrial Systems - Supplementary Material Datenautorinnen und Datenautoren: Weiss, Gereon; Beck, René; Kreutz, Andreas; Otsuka, Satoshi Zusammenfassung: The document includes results of a System-Theoretic Process Analysis (STPA) of an industrial automation Troubleshooting Assistant which has been implemented as Multi Artificial Intelligence (AI) Agent. 2026-06-15T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/502 Titel: MODALSIM94: A Multimodal Physiological and Performance Dataset from Simulated Flight Workload Tasks Datenautorinnen und Datenautoren: Ramon Rodriguez, Marcel; Zafar, Shanza Ali; Zintl, Michael; Janetzko, Dominik; Holzapfel, Florian Zusammenfassung: MODALSIM94 is a multimodal dataset collected from 94 participants performing simulated flight workload tasks in a fixed-base research flight simulator. The dataset was designed to support research on cognitive workload assessment, pilot monitoring, adaptive automation, and human–machine interaction in aviation-relevant environments. Participants completed three five-minute flight scenarios corresponding to low, medium, and high workload conditions. Workload was manipulated through a secondary N-back working-memory task, with no N-back task in the low workload condition, a 1-back task in the medium workload condition, and a 2-back task in the high workload condition. The dataset includes synchronised physiological, behavioural, performance, subjective, and demographic data. Physiological measures comprise cardiac activity from an eSENSE Pulse sensor, respiration data from an eSENSE Respiratory sensor, and eye-tracking data from Dikablis 3 goggles. Flight and performance data include aircraft dynamics, control-related variables, trajectory information, tunnel-deviation metrics, and N-back task responses, accuracy, and reaction times. Subjective measures include NASA-TLX workload ratings, SART situation-awareness ratings, Karolinska Sleepiness Scale scores, Samn–Perelli fatigue ratings, and participant demographic information. All data are provided in open CSV/TXT formats and organised by modality. Each modality preserves its native temporal resolution, while participant identifiers, scenario labels, and elapsed-time references from scenario onset support temporal alignment across data streams. MODALSIM94 can be used for workload classification and regression, multimodal physiological modelling, flight-performance analysis, personalisation studies, and cross-domain research on cognitive-state monitoring. 2026-06-01T00:00:00Z https://fordatis.fraunhofer.de/handle/fordatis/370 Titel: Assured Resilience in Autonomous Systems – Supplementary Material Datenautorinnen und Datenautoren: Weiß, Gereon; Gansloser, Jens; Schwaiger, Adrian; Henne, Maximilian Zusammenfassung: Results for autonomous driving scenarios simulated with CARLA under different environment conditions. 2023-12-01T00:00:00Z