@phdthesis{Zhelavskaya2020,
  author    = {Zhelavskaya, Irina},
  title     = {Modeling of the Plasmasphere Dynamics},
  doi       = {10.25932/publishup-48243},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-482433},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xlii, 256},
  year      = {2020},
  abstract  = {The plasmasphere is a dynamic region of cold, dense plasma surrounding the Earth. Its shape and size are highly susceptible to variations in solar and geomagnetic conditions. Having an accurate model of plasma density in the plasmasphere is important for GNSS navigation and for predicting hazardous effects of radiation in space on spacecraft. The distribution of cold plasma and its dynamic dependence on solar wind and geomagnetic conditions remain, however, poorly quantified. Existing empirical models of plasma density tend to be oversimplified as they are based on statistical averages over static parameters. Understanding the global dynamics of the plasmasphere using observations from space remains a challenge, as existing density measurements are sparse and limited to locations where satellites can provide in-situ observations. In this dissertation, we demonstrate how such sparse electron density measurements can be used to reconstruct the global electron density distribution in the plasmasphere and capture its dynamic dependence on solar wind and geomagnetic conditions. First, we develop an automated algorithm to determine the electron density from in-situ measurements of the electric field on the Van Allen Probes spacecraft. In particular, we design a neural network to infer the upper hybrid resonance frequency from the dynamic spectrograms obtained with the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrumentation suite, which is then used to calculate the electron number density. The developed Neural-network-based Upper hybrid Resonance Determination (NURD) algorithm is applied to more than four years of EMFISIS measurements to produce the publicly available electron density data set. We utilize the obtained electron density data set to develop a new global model of plasma density by employing a neural network-based modeling approach. In addition to the location, the model takes the time history of geomagnetic indices and location as inputs, and produces electron density in the equatorial plane as an output. It is extensively validated using in-situ density measurements from the Van Allen Probes mission, and also by comparing the predicted global evolution of the plasmasphere with the global IMAGE EUV images of He+ distribution. The model successfully reproduces erosion of the plasmasphere on the night side as well as plume formation and evolution, and agrees well with data. The performance of neural networks strongly depends on the availability of training data, which is limited during intervals of high geomagnetic activity. In order to provide reliable density predictions during such intervals, we can employ physics-based modeling. We develop a new approach for optimally combining the neural network- and physics-based models of the plasmasphere by means of data assimilation. The developed approach utilizes advantages of both neural network- and physics-based modeling and produces reliable global plasma density reconstructions for quiet, disturbed, and extreme geomagnetic conditions. Finally, we extend the developed machine learning-based tools and apply them to another important problem in the field of space weather, the prediction of the geomagnetic index Kp. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere, the radiation belts and the plasmasphere. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast and make short-term predictions of Kp, basing their inferences on the recent history of Kp and solar wind measurements at L1. We analyze how the performance of neural networks compares to other machine learning algorithms for nowcasting and forecasting Kp for up to 12 hours ahead. Additionally, we investigate several machine learning and information theory methods for selecting the optimal inputs to a predictive model of Kp. The developed tools for feature selection can also be applied to other problems in space physics in order to reduce the input dimensionality and identify the most important drivers. Research outlined in this dissertation clearly demonstrates that machine learning tools can be used to develop empirical models from sparse data and also can be used to understand the underlying physical processes. Combining machine learning, physics-based modeling and data assimilation allows us to develop novel methods benefiting from these different approaches.},
  language  = {en}
}
@article{NehkTiberiusKraus2021,
  author    = {Nehk, Nadia and Tiberius, Victor and Kraus, Sascha},
  title     = {Urban air mobility},
  series = {International journal of innovation and technology management},
  volume    = {18},
  journal   = {International journal of innovation and technology management},
  number    = {07},
  publisher = {World Scientific},
  address   = {Singapore},
  issn      = {0219-8770},
  doi       = {10.1142/S0219877021500334},
  pages     = {30},
  year      = {2021},
  abstract  = {The growing global demand for efficient and sustainable urban mobility in metropolitan areas has created innovative approaches to new modes of transportation and vehicles. Using the Delphi method, this study explored the prospective development of urban air mobility (UAM), specifically the emergence of air taxis or vertical take-off and landing (VTOLs). The two-staged study examined 25 projections regarding technological and infrastructural aspects to propose a future scenario for UAM and air taxis for the next 5-10 years. The questioned experts confirmed most of the proposed statements from both areas but were undetermined regarding certain technological aspects. Considering the crucial impacts of regulation and certification as well as consumer perception and acceptance for UAM and air taxis, further research on these topics and their correlation is suggested.},
  language  = {en}
}
@article{KayemMeinelWolthusen2018,
  author    = {Kayem, Anne Voluntas dei Massah and Meinel, Christoph and Wolthusen, Stephen D.},
  title     = {A resilient smart micro-grid architecture for resource constrained environments},
  series = {Smart Micro-Grid Systems Security and Privacy},
  volume    = {71},
  journal   = {Smart Micro-Grid Systems Security and Privacy},
  publisher = {Springer},
  address   = {Dordrecht},
  isbn      = {978-3-319-91427-5},
  doi       = {10.1007/978-3-319-91427-5_5},
  pages     = {71 -- 101},
  year      = {2018},
  abstract  = {Resource constrained smart micro-grid architectures describe a class of smart micro-grid architectures that handle communications operations over a lossy network and depend on a distributed collection of power generation and storage units. Disadvantaged communities with no or intermittent access to national power networks can benefit from such a micro-grid model by using low cost communication devices to coordinate the power generation, consumption, and storage. Furthermore, this solution is both cost-effective and environmentally-friendly. One model for such micro-grids, is for users to agree to coordinate a power sharing scheme in which individual generator owners sell excess unused power to users wanting access to power. Since the micro-grid relies on distributed renewable energy generation sources which are variable and only partly predictable, coordinating micro-grid operations with distributed algorithms is necessity for grid stability. Grid stability is crucial in retaining user trust in the dependability of the micro-grid, and user participation in the power sharing scheme, because user withdrawals can cause the grid to breakdown which is undesirable. In this chapter, we present a distributed architecture for fair power distribution and billing on microgrids. The architecture is designed to operate efficiently over a lossy communication network, which is an advantage for disadvantaged communities. We build on the architecture to discuss grid coordination notably how tasks such as metering, power resource allocation, forecasting, and scheduling can be handled. All four tasks are managed by a feedback control loop that monitors the performance and behaviour of the micro-grid, and based on historical data makes decisions to ensure the smooth operation of the grid. Finally, since lossy networks are undependable, differentiating system failures from adversarial manipulations is an important consideration for grid stability. We therefore provide a characterisation of potential adversarial models and discuss possible mitigation measures.},
  language  = {en}
}
@article{JeltschBlaumBroseetal.2013,
  author    = {Jeltsch, Florian and Blaum, Niels and Brose, Ulrich and Chipperfield, Joseph D. and Clough, Yann and Farwig, Nina and Geissler, Katja and Graham, Catherine H. and Grimm, Volker and Hickler, Thomas and Huth, Andreas and May, Felix and Meyer, Katrin M. and Pagel, J{\"o}rn and Reineking, Bj{\"o}rn and Rillig, Matthias C. and Shea, Katriona and Schurr, Frank Martin and Schroeder, Boris and Tielb{\"o}rger, Katja and Weiss, Lina and Wiegand, Kerstin and Wiegand, Thorsten and Wirth, Christian and Zurell, Damaris},
  title     = {How can we bring together empiricists and modellers in functional biodiversity research?},
  series = {Basic and applied ecology : Journal of the Gesellschaft f{\"u}r {\"O}kologie},
  volume    = {14},
  journal   = {Basic and applied ecology : Journal of the Gesellschaft f{\"u}r {\"O}kologie},
  number    = {2},
  publisher = {Elsevier},
  address   = {Jena},
  issn      = {1439-1791},
  doi       = {10.1016/j.baae.2013.01.001},
  pages     = {93 -- 101},
  year      = {2013},
  abstract  = {Improving our understanding of biodiversity and ecosystem functioning and our capacity to inform ecosystem management requires an integrated framework for functional biodiversity research (FBR). However, adequate integration among empirical approaches (monitoring and experimental) and modelling has rarely been achieved in FBR. We offer an appraisal of the issues involved and chart a course towards enhanced integration. A major element of this path is the joint orientation towards the continuous refinement of a theoretical framework for FBR that links theory testing and generalization with applied research oriented towards the conservation of biodiversity and ecosystem functioning. We further emphasize existing decision-making frameworks as suitable instruments to practically merge these different aims of FBR and bring them into application. This integrated framework requires joint research planning, and should improve communication and stimulate collaboration between modellers and empiricists, thereby overcoming existing reservations and prejudices. The implementation of this integrative research agenda for FBR requires an adaptation in most national and international funding schemes in order to accommodate such joint teams and their more complex structures and data needs.},
  language  = {en}
}