@book{PolyvyanyySmirnovWeske2008, author = {Polyvyanyy, Artem and Smirnov, Sergey and Weske, Mathias}, title = {Reducing the complexity of large EPCs}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-32959}, publisher = {Universit{\"a}t Potsdam}, year = {2008}, abstract = {Inhalt: 1 Introduction 2 Motivation and Goal 3 Fundamentals 4 Elementary Abstractions 5 Real World Example 6 Conclusions}, language = {en} } @book{PolyvyanyySmirnovWeske2008, author = {Polyvyanyy, Artem and Smirnov, Sergey and Weske, Mathias}, title = {The triconnected abstraction of process models}, publisher = {Universit{\"a}tsverlag Potsdam}, address = {Potsdam}, isbn = {978-3-940793-65-2}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-32847}, publisher = {Universit{\"a}t Potsdam}, pages = {17}, year = {2008}, abstract = {Contents: Artem Polyvanny, Sergey Smirnow, and Mathias Weske The Triconnected Abstraction of Process Models 1 Introduction 2 Business Process Model Abstraction 3 Preliminaries 4 Triconnected Decomposition 4.1 Basic Approach for Process Component Discovery 4.2 SPQR-Tree Decomposition 4.3 SPQR-Tree Fragments in the Context of Process Models 5 Triconnected Abstraction 5.1 Abstraction Rules 5.2 Abstraction Algorithm 6 Related Work and Conclusions}, language = {en} } @article{ProlSmirnovHoqueetal.2022, author = {Prol, Fabricio S. and Smirnov, Artem G. and Hoque, M. Mainul and Shprits, Yuri Y.}, title = {Combined model of topside ionosphere and plasmasphere derived from radio-occultation and Van Allen Probes data}, series = {Scientific reports}, volume = {12}, journal = {Scientific reports}, number = {1}, publisher = {Macmillan Publishers Limited, part of Springer Nature}, address = {London}, issn = {2045-2322}, doi = {10.1038/s41598-022-13302-1}, pages = {11}, year = {2022}, abstract = {In the last years, electron density profile functions characterized by a linear dependence on the scale height showed good results when approximating the topside ionosphere. The performance above 800 km, however, is not yet well investigated. This study investigates the capability of the semi-Epstein functions to represent electron density profiles from the peak height up to 20,000 km. Electron density observations recorded by the Van Allen Probes were used to resolve the scale height dependence in the plasmasphere. It was found that the linear dependence of the scale height in the topside ionosphere cannot be directly used to extrapolate profiles above 800 km. We find that the dependence of scale heights on altitude is quadratic in the plasmasphere. A statistical model of the scale heights is therefore proposed. After combining the topside ionosphere and plasmasphere by a unified model, we have obtained good estimations not only in the profile shapes, but also in the Total Electron Content magnitude and distributions when compared to actual measurements from 2013, 2014, 2016 and 2017. Our investigation shows that Van Allen Probes can be merged to radio-occultation data to properly represent the upper ionosphere and plasmasphere by means of a semi-Epstein function.}, language = {en} } @misc{SmirnovKronbergDalyetal.2020, author = {Smirnov, Artem G. and Kronberg, Elena A. and Daly, Patrick W. and Aseev, Nikita and Shprits, Yuri Y. and Kellerman, Adam C.}, title = {Adiabatic Invariants Calculations for Cluster Mission: A Long-Term Product for Radiation Belts Studies}, series = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, journal = {Postprints der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe}, number = {2}, issn = {1866-8372}, doi = {10.25932/publishup-52391}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-523915}, pages = {14}, year = {2020}, abstract = {The Cluster mission has produced a large data set of electron flux measurements in the Earth's magnetosphere since its launch in late 2000. Electron fluxes are measured using Research with Adaptive Particle Imaging Detector (RAPID)/Imaging Electron Spectrometer (IES) detector as a function of energy, pitch angle, spacecraft position, and time. However, no adiabatic invariants have been calculated for Cluster so far. In this paper we present a step-by-step guide to calculations of adiabatic invariants and conversion of the electron flux to phase space density (PSD) in these coordinates. The electron flux is measured in two RAPID/IES energy channels providing pitch angle distribution at energies 39.2-50.5 and 68.1-94.5 keV in nominal mode since 2004. A fitting method allows to expand the conversion of the differential fluxes to the range from 40 to 150 keV. Best data coverage for phase space density in adiabatic invariant coordinates can be obtained for values of second adiabatic invariant, K, similar to 10(2), and values of the first adiabatic invariant mu in the range approximate to 5-20 MeV/G. Furthermore, we describe the production of a new data product "LSTAR," equivalent to the third adiabatic invariant, available through the Cluster Science Archive for years 2001-2018 with 1-min resolution. The produced data set adds to the availability of observations in Earth's radiation belts region and can be used for long-term statistical purposes.}, language = {en} } @phdthesis{Smirnov2023, author = {Smirnov, Artem}, title = {Understanding the dynamics of the near-earth space environment utilizing long-term satellite observations}, doi = {10.25932/publishup-61371}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-613711}, school = {Universit{\"a}t Potsdam}, pages = {xxxvi, 286}, year = {2023}, abstract = {The near-Earth space environment is a highly complex system comprised of several regions and particle populations hazardous to satellite operations. The trapped particles in the radiation belts and ring current can cause significant damage to satellites during space weather events, due to deep dielectric and surface charging. Closer to Earth is another important region, the ionosphere, which delays the propagation of radio signals and can adversely affect navigation and positioning. In response to fluctuations in solar and geomagnetic activity, both the inner-magnetospheric and ionospheric populations can undergo drastic and sudden changes within minutes to hours, which creates a challenge for predicting their behavior. Given the increasing reliance of our society on satellite technology, improving our understanding and modeling of these populations is a matter of paramount importance. In recent years, numerous spacecraft have been launched to study the dynamics of particle populations in the near-Earth space, transforming it into a data-rich environment. To extract valuable insights from the abundance of available observations, it is crucial to employ advanced modeling techniques, and machine learning methods are among the most powerful approaches available. This dissertation employs long-term satellite observations to analyze the processes that drive particle dynamics, and builds interdisciplinary links between space physics and machine learning by developing new state-of-the-art models of the inner-magnetospheric and ionospheric particle dynamics. The first aim of this thesis is to investigate the behavior of electrons in Earth's radiation belts and ring current. Using ~18 years of electron flux observations from the Global Positioning System (GPS), we developed the first machine learning model of hundreds-of-keV electron flux at Medium Earth Orbit (MEO) that is driven solely by solar wind and geomagnetic indices and does not require auxiliary flux measurements as inputs. We then proceeded to analyze the directional distributions of electrons, and for the first time, used Fourier sine series to fit electron pitch angle distributions (PADs) in Earth's inner magnetosphere. We performed a superposed epoch analysis of 129 geomagnetic storms during the Van Allen Probes era and demonstrated that electron PADs have a strong energy-dependent response to geomagnetic activity. Additionally, we showed that the solar wind dynamic pressure could be used as a good predictor of the PAD dynamics. Using the observed dependencies, we created the first PAD model with a continuous dependence on L, magnetic local time (MLT) and activity, and developed two techniques to reconstruct near-equatorial electron flux observations from low-PA data using this model. The second objective of this thesis is to develop a novel model of the topside ionosphere. To achieve this goal, we collected observations from five of the most widely used ionospheric missions and intercalibrated these data sets. This allowed us to use these data jointly for model development, validation, and comparison with other existing empirical models. We demonstrated, for the first time, that ion density observations by Swarm Langmuir Probes exhibit overestimation (up to ~40-50\%) at low and mid-latitudes on the night side, and suggested that the influence of light ions could be a potential cause of this overestimation. To develop the topside model, we used 19 years of radio occultation (RO) electron density profiles, which were fitted with a Chapman function with a linear dependence of scale height on altitude. This approximation yields 4 parameters, namely the peak density and height of the F2-layer and the slope and intercept of the linear scale height trend, which were modeled using feedforward neural networks (NNs). The model was extensively validated against both RO and in-situ observations and was found to outperform the International Reference Ionosphere (IRI) model by up to an order of magnitude. Our analysis showed that the most substantial deviations of the IRI model from the data occur at altitudes of 100-200 km above the F2-layer peak. The developed NN-based ionospheric model reproduces the effects of various physical mechanisms observed in the topside ionosphere and provides highly accurate electron density predictions. This dissertation provides an extensive study of geospace dynamics, and the main results of this work contribute to the improvement of models of plasma populations in the near-Earth space environment.}, language = {en} } @article{LandisSaikinZhelavskayaetal.2022, author = {Landis, Daji August and Saikin, Anthony and Zhelavskaya, Irina and Drozdov, Alexander and Aseev, Nikita and Shprits, Yuri Y. and Pfitzer, Maximilian F. and Smirnov, Artem G.}, title = {NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux}, series = {Space Weather: the international journal of research and applications}, volume = {20}, journal = {Space Weather: the international journal of research and applications}, number = {5}, publisher = {American Geophysical Union}, address = {Washington}, issn = {1542-7390}, doi = {10.1029/2021SW002774}, pages = {18}, year = {2022}, abstract = {We present two new empirical models of radiation belt electron flux at geostationary orbit. GOES-15 measurements of 0.8 MeV electrons were used to train a Nonlinear Autoregressive with Exogenous input (NARX) neural network for both modeling GOES-15 flux values and an upper boundary condition scaling factor (BF). The GOES-15 flux model utilizes an input and feedback delay of 2 and 2 time steps (i.e., 5 min time steps) with the most efficient number of hidden layers set to 10. Magnetic local time, Dst, Kp, solar wind dynamic pressure, AE, and solar wind velocity were found to perform as predicative indicators of GOES-15 flux and therefore were used as the exogenous inputs. The NARX-derived upper boundary condition scaling factor was used in conjunction with the Versatile Electron Radiation Belt (VERB) code to produce reconstructions of the radiation belts during the period of July-November 1990, independent of in-situ observations. Here, Kp was chosen as the sole exogenous input to be more compatible with the VERB code. This Combined Release and Radiation Effects Satellite-era reconstruction showcases the potential to use these neural network-derived boundary conditions as a method of hindcasting the historical radiation belts. This study serves as a companion paper to another recently published study on reconstructing the radiation belts during Solar Cycles 17-24 (Saikin et al., 2021, ), for which the results featured in this paper were used.}, language = {en} } @article{SmirnovKronbergLatallerieetal.2019, author = {Smirnov, Artem G. and Kronberg, Elena A. and Latallerie, F. and Daly, Patrick W. and Aseev, Nikita and Shprits, Yuri Y. and Kellerman, Adam C. and Kasahara, Satoshi and Turner, Drew L. and Taylor, M. G. G. T.}, title = {Electron Intensity Measurements by the Cluster/RAPID/IES Instrument in Earth's Radiation Belts and Ring Current}, series = {Space Weather: The International Journal of Research and Applications}, volume = {17}, journal = {Space Weather: The International Journal of Research and Applications}, number = {4}, publisher = {American Geophysical Union}, address = {Washington}, issn = {1542-7390}, doi = {10.1029/2018SW001989}, pages = {553 -- 566}, year = {2019}, abstract = {Plain Language Summary Radiation belts of the Earth, which are the zones of charged energetic particles trapped by the geomagnetic field, comprise enormous and dynamic systems. While the inner radiation belt, composed mainly of high-energy protons, is relatively stable, the outer belt, filled with energetic electrons, is highly variable and depends substantially on solar activity. Hence, extended reliable observations and the improved models of the electron intensities in the outer belt depending on solar wind parameters are necessary for prediction of their dynamics. The Cluster mission has been measuring electron flux intensities in the radiation belts since its launch in 2000, thus providing a huge dataset that can be used for radiation belts analysis. Using 16 years of electron measurements by the Cluster mission corrected for background contamination, we derived a uniform linear-logarithmic dependence of electron fluxes in the outer belt on the solar wind dynamic pressure.}, language = {en} } @article{SmirnovBerrendorfShpritsetal.2020, author = {Smirnov, Artem and Berrendorf, Max and Shprits, Yuri Y. and Kronberg, Elena A. and Allison, Hayley J. and Aseev, Nikita and Zhelavskaya, Irina and Morley, Steven K. and Reeves, Geoffrey D. and Carver, Matthew R. and Effenberger, Frederic}, title = {Medium energy electron flux in earth's outer radiation belt (MERLIN)}, series = {Space weather : the international journal of research and applications}, volume = {18}, journal = {Space weather : the international journal of research and applications}, number = {11}, publisher = {American geophysical union, AGU}, address = {Washington}, issn = {1542-7390}, doi = {10.1029/2020SW002532}, pages = {20}, year = {2020}, abstract = {The radiation belts of the Earth, filled with energetic electrons, comprise complex and dynamic systems that pose a significant threat to satellite operation. While various models of electron flux both for low and relativistic energies have been developed, the behavior of medium energy (120-600 keV) electrons, especially in the MEO region, remains poorly quantified. At these energies, electrons are driven by both convective and diffusive transport, and their prediction usually requires sophisticated 4D modeling codes. In this paper, we present an alternative approach using the Light Gradient Boosting (LightGBM) machine learning algorithm. The Medium Energy electRon fLux In Earth's outer radiatioN belt (MERLIN) model takes as input the satellite position, a combination of geomagnetic indices and solar wind parameters including the time history of velocity, and does not use persistence. MERLIN is trained on >15 years of the GPS electron flux data and tested on more than 1.5 years of measurements. Tenfold cross validation yields that the model predicts the MEO radiation environment well, both in terms of dynamics and amplitudes o f flux. Evaluation on the test set shows high correlation between the predicted and observed electron flux (0.8) and low values of absolute error. The MERLIN model can have wide space weather applications, providing information for the scientific community in the form of radiation belts reconstructions, as well as industry for satellite mission design, nowcast of the MEO environment, and surface charging analysis.}, language = {en} } @article{SmirnovShpritsAllisonetal.2022, author = {Smirnov, Artem and Shprits, Yuri Y. and Allison, Hayley and Aseev, Nikita and Drozdov, Alexander and Kollmann, Peter and Wang, Dedong and Saikin, Anthony}, title = {Storm-Time evolution of the Equatorial Electron Pitch Angle Distributions in Earth's Outer Radiation Belt}, series = {Frontiers in astronomy and space sciences}, volume = {9}, journal = {Frontiers in astronomy and space sciences}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2296-987X}, doi = {10.3389/fspas.2022.836811}, pages = {15}, year = {2022}, abstract = {In this study we analyze the storm-time evolution of equatorial electron pitch angle distributions (PADs) in the outer radiation belt region using observations from the Magnetic Electron Ion Spectrometer (MagEIS) instrument aboard the Van Allen Probes in 2012-2019. The PADs are approximated using a sum of the first, third and fifth sine harmonics. Different combinations of the respective coefficients refer to the main PAD shapes within the outer radiation belt, namely the pancake, flat-top, butterfly and cap PADs. We conduct a superposed epoch analysis of 129 geomagnetic storms and analyze the PAD evolution for day and night MLT sectors. PAD shapes exhibit a strong energy-dependent response. At energies of tens of keV, the PADs exhibit little variation throughout geomagnetic storms. Cap PADs are mainly observed at energies < 300 keV, and their extent in L shrinks with increasing energy. The cap distributions transform into the pancake PADs around the main phase of the storm on the nightside, and then come back to their original shapes during the recovery phase. At higher energies on the dayside, the PADs are mainly pancake during pre-storm conditions and become more anisotropic during the main phase. The quiet-time butterfly PADs can be observed on the nightside at L> 5.6. During the main phase, butterfly PADs have stronger 90 degrees-minima and can be observed at lower L-shells (down to L = 5), then transitioning into flat-top PADs at L similar to 4.5 - 5 and pancake PADs at L < 4.5. The resulting PAD coefficients for different energies, locations and storm epochs can be used to test the wave models and physics-based radiation belt codes in terms of pitch angle distributions.}, language = {en} } @article{SmirnovKronbergDalyetal.2020, author = {Smirnov, Artem G. and Kronberg, Elena A. and Daly, Patrick W. and Aseev, Nikita and Shprits, Yuri Y. and Kellerman, Adam C.}, title = {Adiabatic Invariants Calculations for Cluster Mission: A Long-Term Product for Radiation Belts Studies}, series = {Journal of Geophysical Research: Space Physics}, volume = {125}, journal = {Journal of Geophysical Research: Space Physics}, number = {2}, publisher = {John Wiley \& Sons, Inc.}, address = {New Jersey}, pages = {12}, year = {2020}, abstract = {The Cluster mission has produced a large data set of electron flux measurements in the Earth's magnetosphere since its launch in late 2000. Electron fluxes are measured using Research with Adaptive Particle Imaging Detector (RAPID)/Imaging Electron Spectrometer (IES) detector as a function of energy, pitch angle, spacecraft position, and time. However, no adiabatic invariants have been calculated for Cluster so far. In this paper we present a step-by-step guide to calculations of adiabatic invariants and conversion of the electron flux to phase space density (PSD) in these coordinates. The electron flux is measured in two RAPID/IES energy channels providing pitch angle distribution at energies 39.2-50.5 and 68.1-94.5 keV in nominal mode since 2004. A fitting method allows to expand the conversion of the differential fluxes to the range from 40 to 150 keV. Best data coverage for phase space density in adiabatic invariant coordinates can be obtained for values of second adiabatic invariant, K, similar to 10(2), and values of the first adiabatic invariant mu in the range approximate to 5-20 MeV/G. Furthermore, we describe the production of a new data product "LSTAR," equivalent to the third adiabatic invariant, available through the Cluster Science Archive for years 2001-2018 with 1-min resolution. The produced data set adds to the availability of observations in Earth's radiation belts region and can be used for long-term statistical purposes.}, language = {en} }