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An IEEE 2030.5-Based Legacy Protocol Converter for Interoperable DER Integration
No full text214889214903Interoperability among diverse devices, from traditional substation control rooms to modern inverters managing components like Distributed Energy Resources (DERs), is a primary challenge in modern power systems. It is essential for streamlining decision-making and control processes through effective communication, ultimately enhancing energy management efficiency. This paper introduces the open-source Legacy Protocol Converter (LPC) grounded in the IEEE 2030.5 standard, which incorporates advanced features for improved adaptability. The LPC bridges legacy equipment using standard protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus with a light-weight asynchronous Neural Autonomic Transport System (NATS) communication system. In light of the limitations inherent in traditional synchronous RESTful systems - specifically those compliant with IEEE 2030.5 that are incapable of facilitating multiple endpoints - the adoption of asynchronous NATS is implemented. This approach can notably enhance communication flexibility and performance. The implementation is containerized for efficient service orchestration and supports the reusability of solutions. The LPC is engineered for seamless integration of DERs with Energy Management System (EMS), aggregation platforms, and Hardware-in-the-loop (HIL) testing environments. In this paper, the LPC has been tested and further developed in various use cases such as multi-physics optimization involving HIL and fast frequency services, e.g., virtual inertia and load shedding, each in a different architectural setup. The findings validate the applicability of LPC not only for devices within modern power systems, but also for heat pumps in the thermal energy sector, facilitating sector coupling. Moreover, the paper provides additional insights into LPC's functionality, reaffirming its efficacy as a scalable, robust, and user-friendly solution for bridging legacy systems through the enhanced IEEE 2030.5 standard designed for the monitoring and control of DERs.1
Weiterbildung zur Cybersicherheit
No full text737741Angesichts der Bedrohungslage und zunehmender Risiken kommt es nicht nur auf technische Schutzmaßnahmen an, sondern auch auf das Wissen und die erworbenen Fähigkeiten der handelnden Akteure in Bezug auf Cybersicherheit. Jedoch entwickelt sich Cybersicherheitswissen rasant weiter und kann auch schnell altern. Um Schritt zu halten und Anforderungen erfüllen zu können, ist kontinuierliche Weiterbildung notwendig. Nichtwissen und falsches Handeln können schwerwiegende Folgen haben. Doch unterschiedliche Aufgaben erfordern verschiedene Kompetenzen.491
Designing and Evaluating Malinowski's Lens: An AI-Native Educational Game for Ethnographic Learning
No full textThis study introduces 'Malinowski's Lens', the first AI-native educational game for anthropology that transforms Bronislaw Malinowski's 'Argonauts of the Western Pacific' (1922) into an interactive learning experience. The system combines Retrieval-Augmented Generation with DALL-E 3 text-to-image generation, creating consistent VGA-style visuals as players embody Malinowski during his Trobriand Islands fieldwork (1915-1918). To address ethical concerns, indigenous peoples appear as silhouettes while Malinowski is detailed, prompting reflection on anthropological representation. Two validation studies confirmed effectiveness: Study 1 with 10 non-specialists showed strong learning outcomes (average quiz score 7.5/10) and excellent usability (SUS: 83/100). Study 2 with 4 expert anthropologists confirmed pedagogical value, with one senior researcher discovering "new aspects" of Malinowski's work through gameplay. The findings demonstrate that AI-driven educational games can effectively convey complex anthropological concepts while sparking disciplinary curiosity. This study advances AI-native educational game design and provides a replicable model for transforming academic texts into engaging interactive experiences
Innovation boost for digitization in educational practice: Lockdown experiences of teachers and trainers Innovationsschub für die Digitalisierung in der Ausbildungspraxis: Lockdown-Erfahrungen von Lehrenden und Ausbildenden
No full text24124
Machine Learning-assisted GNSS Interference Monitoring through Crowdsourcing
No full text11191143The Global Navigation Satellite System (GNSS) community shows great interest in detecting and eliminating GNSS interference, i.e., jammers. State-of-the-art techniques employ either threshold-based mechanisms or supervised learning on raw data streams or features thereof. However, they require special expensive GNSS receiver hardware that needs to be placed in a fixed location. Instead, there is limited research on ubiquitous interference detection using mobile devices such as smartphones. But, advances in the smartphone ecosystem enable the support of GNSS measurements for real-time navigation and the Third-Generation Partnership Project (3GPP) enables the distribution of potential assistance information. However, there is limited research into crowdsourcing smartphone-based features to localize the source of any detected interference. Hence, we employ supervised learning to map effective GNSS features of Android-based smartphones to corresponding reference labels to reliably detect and classify sources of interference. From there, we localize the identified sources of interference. We use the 5G platform for decentralized synchronization and collection of features and corresponding reference positions (either an unbiased GNSS- or a 5G-position). We evaluate both state-of-the-art and our methods on data from our large-scale real-world measurement campaign. Our dataset covers realistic effects such as multipath, motion dynamics, and variations in distance and power between jammer and sensors. We show that our selected features are optimal for detection, classification, and localization and are robust against multipath and dynamic environments. Our experiments also show that our novel deep learning pipeline (based on U-Net) outperforms state-of-the-art techniques, reliably detects (F2 > 93%) and classifies (F2 > 90.22%) six different interference classes (with 33 subclasses), and predicts uncertainty (< 16%). For localization, our supervised coarse-grained region classification and fine-grained position regression (MAE < 2.5 m) enable applications such as lane-detection when we combine the information from four smartphones over the 5G platform
Big Data Analytics in Laboratory Medicine: A Path towards Predictive Healthcare
No full text3542Predictive healthcare is now possible because of the fusion of big data analytics and laboratory medicine, which has ushered in a new era of revolutionary healthcare procedures. This integration makes use of data science to analyze large and varied datasets, including genetic data, laboratory test results, and electronic health records. Predictive models arise in this paradigm, providing previously unheard-of chances for improved diagnoses, personalized therapy, and more effective healthcare procedures. The many applications of big data analytics in laboratory medicine are examined in this study. Personalized medicine customizes therapies based on unique patient features, while predictive diagnostics allow for earlier and more accurate disease detection. The capacity to manage population health proactively and the acceleration of medication research and development both support an all-encompassing and focused approach to healthcare. Reduced turnaround times, resource allocation, and optimized laboratory operations contribute to increased operational efficiency. For measuring, the research study used smart PLS software and generated informative results, including descriptive statistics, correlation coefficient and algorithm model between them. Big data analytics-enabled real-time monitoring creates early warning systems for possible health problems, allowing for prompt actions. Moreover, cost optimisation techniques surface, guaranteeing that healthcare services stay efficient while avoiding excessive financial strain. Anyhow these encouraging developments, there are still issues to be resolved, including data protection issues, ethical issues, and the requirement for standardised procedures. The overall research found a direct path towards predictive healthcare. The broad adoption of big data analytics in laboratory medicine will depend on how well these difficulties are addressed as the field develops, securing its position as a keystone in the quest for predictive healthcare and better patient outcomes.59
Storing sensor concepts with in-memory computing, completely without auxiliary electrical energy Speichernde Sensorkonzepte mit in-memory-Computing, ganz ohne elektrische Hilfsenergie
No full text106109Sensors for condition monitoring have so far been implemented by digital microprocessor systems. Sensor signals are digitized, stored and processed. The extracted information is stored electronically. The concepts presented in this contribution use the non-electrical sensor signal itself as energy supply to directly extract and store the information in a nonelectronical way. They do not require any auxiliary electrical energy for data acquisition, evaluation and storage of the information
Magnetic Cleanliness Verification of Miniature Satellites for High Precision Pointing
No full textFraunhofer EMI developed the ERNST (Experimental Spacecraft based on Nanosatellite Technology) mission which is a mid-wavelength infrared imaging satellite. The 12U nanosatellite is based on commercial off-the-shelf CubeSat components where appropriate parts were available. In particular, it is carrying a capable commercial infrared telescope for Earth observation. Like for other nanosatellite missions, a magnetic cleanliness verification was performed on the relevant components of the satellite to assure proper pointing performance. These magnetic cleanliness requirements tend to be proportional to the satellites' moment of inertia which scales approximately with the fifth power of the satellites cubic side length. On the other hand, the measured magnetic field strength drops with the third power of the distance to the magnetic dipole source. Thus, the signal to noise ratio of magnetic field measurements in the satellites far field decreases significantly with the satellites size. As a consequence, the robust identification of a multi dipole model of the magnetization for a miniaturized satellite becomes unfeasible in standard laboratory environments. To qualify the ERNST nanosatellite, Fraunhofer EMI and IPM developed a test setup for precise characterization of small residual dipole moments
Optimal Design of Building Energy Supply—A Case Study
No full text281287In this paper, we optimize the design of a new office building’s energy supply with respect to costs and carbon emissions for one example year. The aim is to gain an overview over the impact of the supply design decision. For that, we combine known concepts to one decision support workflow. We simulate the expected heating loads of the building with a thermal network and use them to set up a mixed-integer linear program for the problem, which we can solve with one or both objectives.Part F378