@article{SattlerRoegerSchwarzboezletal.2020,
  author    = {Sattler, Johannes Christoph and R{\"o}ger, Marc and Schwarzb{\"o}zl, Peter and Buck, Reiner and Macke, Ansgar and Raeder, Christian and G{\"o}ttsche, Joachim},
  title     = {Review of heliostat calibration and tracking control methods},
  series = {Solar Energy},
  volume    = {207},
  journal   = {Solar Energy},
  publisher = {Elsevier},
  address   = {Amsterdam},
  doi       = {10.1016/j.solener.2020.06.030},
  pages     = {110 -- 132},
  year      = {2020},
  abstract  = {Large scale central receiver systems typically deploy between thousands to more than a hundred thousand heliostats. During solar operation, each heliostat is aligned individually in such a way that the overall surface normal bisects the angle between the sun's position and the aim point coordinate on the receiver. Due to various tracking error sources, achieving accurate alignment ≤1 mrad for all the heliostats with respect to the aim points on the receiver without a calibration system can be regarded as unrealistic. Therefore, a calibration system is necessary not only to improve the aiming accuracy for achieving desired flux distributions but also to reduce or eliminate spillage. An overview of current larger-scale central receiver systems (CRS), tracking error sources and the basic requirements of an ideal calibration system is presented. Leading up to the main topic, a description of general and specific terms on the topics heliostat calibration and tracking control clarifies the terminology used in this work. Various figures illustrate the signal flows along various typical components as well as the corresponding monitoring or measuring devices that indicate or measure along the signal (or effect) chain. The numerous calibration systems are described in detail and classified in groups. Two tables allow the juxtaposition of the calibration methods for a better comparison. In an assessment, the advantages and disadvantages of individual calibration methods are presented.},
  language  = {en}
}
@article{DachwaldWurm2011,
  author    = {Dachwald, Bernd and Wurm, Patrick},
  title     = {Mission analysis and performance comparison for an Advanced Solar Photon Thruster},
  series = {Advances in Space Research},
  volume    = {48},
  journal   = {Advances in Space Research},
  number    = {11},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0273-1177},
  pages     = {1858 -- 1868},
  year      = {2011},
  language  = {en}
}
@article{Staat1991,
  author    = {Staat, Manfred},
  title     = {Versagenswahrscheinlichkeit der Prim{\"a}rkreisdruckumschließung eines HTR-Moduls zur Prozeßw{\"a}rmegewinnung unter St{\"o}rfallbedingungen},
  series = {Jahrestagung Kerntechnik '91 / Kerntechnische Gesellschaft e.V. ; Deutsches Atomforum e.V},
  journal   = {Jahrestagung Kerntechnik '91 / Kerntechnische Gesellschaft e.V. ; Deutsches Atomforum e.V},
  publisher = {INFORUM},
  address   = {Bonn},
  pages     = {123 -- 126},
  year      = {1991},
  language  = {de}
}
@article{DuongNguyenStaat2015,
  author    = {Duong, Minh Tuan and Nguyen, Nhu Huynh and Staat, Manfred},
  title     = {Physical response of hyperelastic models for composite materials and soft tissues},
  series = {Asia pacific journal on computational engineering},
  volume    = {2},
  journal   = {Asia pacific journal on computational engineering},
  number    = {3 (December 2015)},
  issn      = {2196-1166},
  doi       = {10.1186/s40540-015-0015-x},
  pages     = {1 -- 18},
  year      = {2015},
  language  = {en}
}
@article{PancContiuBocanetetal.2019,
  author    = {Panc, Nicolae and Contiu, Glad and Bocanet, Vlad and Thurn, Laura and Sabau, Emilia},
  title     = {The influence of cutting technology on surface wear hardness},
  series = {Academic Journal of Manufacturing Engineering},
  volume    = {17},
  journal   = {Academic Journal of Manufacturing Engineering},
  number    = {3},
  issn      = {1583-7904},
  pages     = {205 -- 210},
  year      = {2019},
  language  = {en}
}
@article{Laack2005,
  author    = {Laack, Walter van},
  title     = {Ohne Geist l{\"a}uft wenig! Teil 2: Zur Unfreiheit verdammt? Eine etwas andere Sicht der Libet-Experimente},
  series = {Die Drei : Zeitschrift f{\"u}r Anthroposophie in Wissenschaft, Kunst und sozialem Leben. 75 (2005), H. 3},
  journal   = {Die Drei : Zeitschrift f{\"u}r Anthroposophie in Wissenschaft, Kunst und sozialem Leben. 75 (2005), H. 3},
  isbn      = {0012-6063},
  pages     = {25 -- 35},
  year      = {2005},
  language  = {de}
}
@article{Laack2005,
  author    = {Laack, Walter van},
  title     = {Ohne Geist l{\"a}uft wenig! Teil 1: Kann aus Neuronen Bewusstsein entstehen?},
  series = {Die Drei : Zeitschrift f{\"u}r Anthroposophie in Wissenschaft, Kunst und sozialem Leben. 75 (2005), H. 2},
  journal   = {Die Drei : Zeitschrift f{\"u}r Anthroposophie in Wissenschaft, Kunst und sozialem Leben. 75 (2005), H. 2},
  isbn      = {0012-6063},
  pages     = {31 -- 38},
  year      = {2005},
  language  = {de}
}
@article{Laack2005,
  author    = {Laack, Walter van},
  title     = {Elektro- und Ultraschalltherapie},
  series = {Station{\"a}re Naturheilkunde : Handbuch f{\"u}r Klinik und Rehabilitation / Andr{\´e}-Michael Beer. [Autoren: Peter Altmeyer ...]},
  journal   = {Station{\"a}re Naturheilkunde : Handbuch f{\"u}r Klinik und Rehabilitation / Andr{\´e}-Michael Beer. [Autoren: Peter Altmeyer ...]},
  publisher = {Elsevier, Urban und Fischer},
  address   = {M{\"u}nchen},
  isbn      = {3-437-56890-6},
  pages     = {162 -- 171},
  year      = {2005},
  language  = {de}
}
@article{SchifferFerrein2018,
  author    = {Schiffer, Stefan and Ferrein, Alexander},
  title     = {ERIKA—Early Robotics Introduction at Kindergarten Age},
  series = {Multimodal Technologies Interact},
  volume    = {2},
  journal   = {Multimodal Technologies Interact},
  number    = {4},
  publisher = {MDPI},
  address   = {Basel},
  issn      = {2414-4088},
  doi       = {10.3390/mti2040064},
  pages     = {15},
  year      = {2018},
  abstract  = {In this work, we report on our attempt to design and implement an early introduction to basic robotics principles for children at kindergarten age. One of the main challenges of this effort is to explain complex robotics contents in a way that pre-school children could follow the basic principles and ideas using examples from their world of experience. What sets apart our effort from other work is that part of the lecturing is actually done by a robot itself and that a quiz at the end of the lesson is done using robots as well. The humanoid robot Pepper from Softbank, which is a great platform for human-robot interaction experiments, was used to present a lecture on robotics by reading out the contents to the children making use of its speech synthesis capability. A quiz in a Runaround-game-show style after the lecture activated the children to recap the contents they acquired about how mobile robots work in principle. In this quiz, two LEGO Mindstorm EV3 robots were used to implement a strongly interactive scenario. Besides the thrill of being exposed to a mobile robot that would also react to the children, they were very excited and at the same time very concentrated. We got very positive feedback from the children as well as from their educators. To the best of our knowledge, this is one of only few attempts to use a robot like Pepper not as a tele-teaching tool, but as the teacher itself in order to engage pre-school children with complex robotics contents.},
  language  = {en}
}
@article{SchwabedalSippelBrandtetal.2018,
  author    = {Schwabedal, Justus T. C. and Sippel, Daniel and Brandt, Moritz D. and Bialonski, Stephan},
  title     = {Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning},
  doi       = {10.48550/arXiv.1809.08443},
  year      = {2018},
  abstract  = {Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.},
  language  = {en}
}