@article{LoechteRojasRuizGloesekoetter2021,
  author    = {L{\"o}chte, Andre and Rojas Ruiz, Ignacio and Gl{\"o}sek{\"o}tter, Peter},
  title     = {Battery State Estimation with ANN and SVR Evaluating Electrochemical Impedance Spectra Generalizing DC Currents},
  series = {Applied Sciences},
  volume    = {12},
  journal   = {Applied Sciences},
  number    = {1},
  isbn      = {978-84-1117-173-1},
  doi       = {10.3390/app12010274},
  pages     = {275},
  year      = {2021},
  abstract  = {The demand for energy storage is increasing massively due to the electrification of transport and the expansion of renewable energies. Current battery technologies cannot satisfy this growing demand as they are difficult to recycle, as the necessary raw materials are mined under precarious conditions, and as the energy density is insufficient. Metal-air batteries offer a high energy density as there is only one active mass inside the cell and the cathodic reaction uses the ambient air. Various metals can be used, but zinc is very promising due to its disposability and non-toxic behavior, and as operation as a secondary cell is possible. Typical characteristics of zinc-air batteries are flat charge and discharge curves. On the one hand, this is an advantage for the subsequent power electronics, which can be optimized for smaller and constant voltage ranges. On the other hand, the state determination of the system becomes more complex, as the voltage level is not sufficient to determine the state of the battery. In this context, electrochemical impedance spectroscopy is a promising candidate as the resulting impedance spectra depend on the state of charge, working point, state of aging, and temperature. Previous approaches require a fixed operating state of the cell while impedance measurements are being performed. In this publication, electrochemical impedance spectroscopy is therefore combined with various machine learning techniques to also determine successfully the state of charge during charging of the cell at non-fixed charging currents. Keywords: electrochemical impedance spectroscopy; artificial neural networks; support vector regression; zinc-air battery; state estimation; state of charge},
  language  = {en}
}
@article{WillingDresenGerlitzetal.2021,
  author    = {Willing, Markus and Dresen, Christian and Gerlitz, Eva and Haering, Maximilian and Smith, Matthew and Binnewies, Carmen and Guess, Tim and Haverkamp, Uwe and Schinzel, Sebastian},
  title     = {Behavioral responses to a cyber attack in a hospital environment},
  series = {Nature -- Scientific Reports},
  journal   = {Nature -- Scientific Reports},
  doi       = {10.1038/s41598-021-98576-7},
  year      = {2021},
  abstract  = {Technical and organizational steps are necessary to mitigate cyber threats and reduce risks. Human behavior is the last line of defense for many hospitals and is considered as equally important as technical security. Medical staff must be properly trained to perform such procedures. This paper presents the first qualitative, interdisciplinary research on how members of an intermediate care unit react to a cyberattack against their patient monitoring equipment. We conducted a simulation in a hospital training environment with 20 intensive care nurses. By the end of the experiment, 12 of the 20 participants realized the monitors' incorrect behavior. We present a qualitative behavior analysis of high performing participants (HPP) and low performing participants (LPP). The HPP showed fewer signs of stress, were easier on their colleagues, and used analog systems more often than the LPP. With 40\% of our participants not recognizing the attack, we see room for improvements through the use of proper tools and provision of adequate training to prepare staff for potential attacks in the future.},
  language  = {en}
}
@article{BrinkmannDresenMergetetal.2021,
  author    = {Brinkmann, Marcus and Dresen, Christian and Merget, Robert and Poddebniak, Damian and M{\"u}ller, Jens and Somorovsky, Juraj and Schwenk, J{\"o}rg and Schinzel, Sebastian},
  title     = {ALPACA: Application Layer Protocol Confusion - Analyzing and Mitigating Cracks in TLS Authentication},
  series = {30th USENIX Security Symposium},
  journal   = {30th USENIX Security Symposium},
  year      = {2021},
  language  = {en}
}