@inproceedings{SildatkeKarwanniKraftetal.2020,
  author    = {Sildatke, Michael and Karwanni, Hendrik and Kraft, Bodo and Schmidts, Oliver and Z{\"u}ndorf, Albert},
  title     = {Automated Software Quality Monitoring in Research Collaboration Projects},
  series = {ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops},
  booktitle = {ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops},
  publisher = {IEEE},
  address   = {New York, NY},
  doi       = {10.1145/3387940.3391478},
  pages     = {603 -- 610},
  year      = {2020},
  abstract  = {In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem. Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ. Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible. In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production. Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.},
  language  = {en}
}
@inproceedings{DuranParedesMottaghyHerrmannetal.2020,
  author    = {Duran Paredes, Ludwin and Mottaghy, Darius and Herrmann, Ulf and Groß, Rolf Fritz},
  title     = {Online ground temperature and soil moisture monitoring of a shallow geothermal system with non-conventional components},
  series = {EGU General Assembly 2020},
  booktitle = {EGU General Assembly 2020},
  year      = {2020},
  abstract  = {We present first results from a newly developed monitoring station for a closed loop geothermal heat pump test installation at our campus, consisting of helix coils and plate heat exchangers, as well as an ice-store system. There are more than 40 temperature sensors and several soil moisture content sensors distributed around the system, allowing a detailed monitoring under different operating conditions.In the view of the modern development of renewable energies along with the newly concepts known as Internet of Things and Industry 4.0 (high-tech strategy from the German government), we created a user-friendly web application, which will connect the things (sensors) with the open network (www). Besides other advantages, this allows a continuous remote monitoring of the data from the numerous sensors at an arbitrary sampling rate.Based on the recorded data, we will also present first results from numerical simulations, taking into account all relevant heat transport processes.The aim is to improve the understanding of these processes and their influence on the thermal behavior of shallow geothermal systems in the unsaturated zone. This will in turn facilitate the prediction of the performance of these systems and therefore yield an improvement in their dimensioning when designing a specific shallow geothermal installation.},
  language  = {en}
}
@inproceedings{SattlerChicoCaminosUerlingsetal.2020,
  author    = {Sattler, Johannes Christoph and Chico Caminos, Ricardo Alexander and {\"U}rlings, Nicolas and Dutta, Siddharth and Ruiz, Victor and Kalogirou, Soteris and Ktistis, Panayiotis and Agathokleous, Rafaela and Jung, Christian and Alexopoulos, Spiros and Atti, Vikrama Naga Babu and Teixeira Boura, Cristiano Jos{\´e} and Herrmann, Ulf},
  title     = {Operational experience and behaviour of a parabolic trough collector system with concrete thermal energy storage for process steam generation in Cyprus},
  series = {AIP Conference Proceedings},
  booktitle = {AIP Conference Proceedings},
  number    = {2303},
  doi       = {10.1063/5.0029278},
  pages     = {140004-1 -- 140004-10},
  year      = {2020},
  abstract  = {As part of the transnational research project EDITOR, a parabolic trough collector system (PTC) with concrete thermal energy storage (C-TES) was installed and commissioned in Limassol, Cyprus. The system is located on the premises of the beverage manufacturer KEAN Soft Drinks Ltd. and its function is to supply process steam for the factory's pasteurisation process [1]. Depending on the factory's seasonally varying capacity for beverage production, the solar system delivers between 5 and 25 \% of the total steam demand. In combination with the C-TES, the solar plant can supply process steam on demand before sunrise or after sunset. Furthermore, the C-TES compensates the PTC during the day in fluctuating weather conditions. The parabolic trough collector as well as the control and oil handling unit is designed and manufactured by Protarget AG, Germany. The C-TES is designed and produced by CADE Soluciones de Ingenier{\´i}a, S.L., Spain. In the focus of this paper is the description of the operational experience with the PTC, C-TES and boiler during the commissioning and operation phase. Additionally, innovative optimisation measures are presented.},
  language  = {en}
}