TY - BOOK A1 - Polyvyanyy, Artem A1 - Smirnov, Sergey A1 - Weske, Mathias T1 - Reducing the complexity of large EPCs N2 - Inhalt: 1 Introduction 2 Motivation and Goal 3 Fundamentals 4 Elementary Abstractions 5 Real World Example 6 Conclusions T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 22 Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-32959 ER - TY - BOOK A1 - Polyvyanyy, Artem A1 - Smirnov, Sergey A1 - Weske, Mathias T1 - The triconnected abstraction of process models N2 - 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 T3 - Technische Berichte des Hasso-Plattner-Instituts für Digital Engineering an der Universität Potsdam - 26 KW - Hasso-Plattner-Institut KW - Hasso-Plattner-Institute Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus-32847 SN - 978-3-940793-65-2 PB - Universitätsverlag Potsdam CY - Potsdam ER - TY - JOUR A1 - Smirnov, Artem G. A1 - Kronberg, Elena A. A1 - Daly, Patrick W. A1 - Aseev, Nikita A1 - Shprits, Yuri A1 - Kellerman, Adam C. T1 - Adiabatic Invariants Calculations for Cluster Mission: A Long-Term Product for Radiation Belts Studies JF - Journal of Geophysical Research: Space Physics N2 - 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. KW - L-Asterisk KW - magnetosphere KW - electrons KW - model Y1 - 2019 VL - 125 IS - 2 PB - John Wiley & Sons, Inc. CY - New Jersey ER - TY - JOUR A1 - Smirnov, Artem A1 - Shprits, Yuri A1 - Allison, Hayley A1 - Aseev, Nikita A1 - Drozdov, Alexander A1 - Kollmann, Peter A1 - Wang, Dedong A1 - Saikin, Anthony T1 - Storm-Time evolution of the Equatorial Electron Pitch Angle Distributions in Earth's Outer Radiation Belt JF - Frontiers in astronomy and space sciences N2 - 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. KW - pitch angle KW - pitch angle distributions KW - electrons KW - radiation belts KW - magnetosphere KW - van alien probes Y1 - 2022 U6 - https://doi.org/10.3389/fspas.2022.836811 SN - 2296-987X VL - 9 PB - Frontiers Media CY - Lausanne ER - TY - THES A1 - Smirnov, Artem T1 - Understanding the dynamics of the near-earth space environment utilizing long-term satellite observations T1 - Verständnis der Dynamik der erdnahen Weltraumumgebung mit Hilfe von Langzeit-Satellitenbeobachtungen N2 - 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. N2 - Die erdnahe Weltraumumgebung ist ein hochkomplexes System, das aus mehreren Regionen und Partikelpopulationen besteht, die für den Satellitenbetrieb gefährlich sind. Die in den Strahlungsgürteln und dem Ringstrom gefangenen Teilchen können bei Weltraumwetterereignissen aufgrund der tiefen dielektrischen und oberflächlichen Aufladung erhebliche Schäden an Satelliten verursachen. Näher an der Erde liegt eine weitere wichtige Region, die Ionosphäre, die die Ausbreitung von Funksignalen verzögert und die Navigation und Positionsbestimmung beeinträchtigen kann. Als Reaktion auf Fluktuationen der solaren und geomagnetischen Aktivität können sowohl die Populationen der inneren Magnetosphäre als auch der Ionosphäre innerhalb von Minuten bis Stunden drastische und plötzliche Veränderungen erfahren, was eine Herausforderung für die Vorhersage ihres Verhaltens darstellt. Angesichts der zunehmenden Abhängigkeit unserer Gesellschaft von der Satellitentechnologie ist ein besseres Verständnis und eine bessere Modellierung dieser Populationen von größter Bedeutung. In den letzten Jahren wurden zahlreiche Raumsonden gestartet, um die Dynamik von Partikelpopulationen im erdnahen Weltraum zu untersuchen, was diesen in eine datenreiche Umgebung verwandelt hat. Um aus der Fülle der verfügbaren Beobachtungen wertvolle Erkenntnisse zu gewinnen, ist der Einsatz fortschrittlicher Modellierungstechniken unabdingbar, und Methoden des maschinellen Lernens gehören zu den leistungsfähigsten verfügbaren Ansätzen. Diese Dissertation nutzt langfristige Satellitenbeobachtungen, um die Prozesse zu analysieren, die die Teilchendynamik antreiben, und schafft interdisziplinäre Verbindungen zwischen Weltraumphysik und maschinellem Lernen, indem sie neue hochmoderne Modelle der innermagnetosphärischen und ionosphärischen Teilchendynamik entwickelt. Das erste Ziel dieser Arbeit ist es, das Verhalten von Elektronen im Strahlungsgürtel und Ringstrom der Erde zu untersuchen. Unter Verwendung von ~18 Jahren Elektronenflussbeobachtungen des Global Positioning System (GPS) haben wir das erste maschinelle Lernmodell des Elektronenflusses im mittleren Erdorbit (MEO) entwickelt, das ausschließlich durch Sonnenwind und geomagnetische Indizes gesteuert wird und keine zusätzlichen Flussmessungen als Eingaben benötigt. Anschließend analysierten wir die Richtungsverteilungen der Elektronen und verwendeten zum ersten Mal Fourier-Sinus-Reihen, um die Elektronen-Stellwinkelverteilungen (PADs) in der inneren Magnetosphäre der Erde zu bestimmen. Wir führten eine epochenübergreifende Analyse von 129 geomagnetischen Stürmen während der Van-Allen-Sonden-Ära durch und zeigten, dass die Elektronen-PADs eine starke energieabhängige Reaktion auf die geomagnetische Aktivität haben. Außerdem konnten wir zeigen, dass der dynamische Druck des Sonnenwindes als guter Prädiktor für die PAD-Dynamik verwendet werden kann. Anhand der beobachteten Abhängigkeiten haben wir das erste PAD-Modell mit einer kontinuierlichen Abhängigkeit von L, der magnetischen Ortszeit (MLT) und der Aktivität erstellt und zwei Techniken entwickelt, um die Beobachtungen des äquatornahen Elektronenflusses aus Daten mit niedrigem Luftdruck mit Hilfe dieses Modells zu rekonstruieren. Das zweite Ziel dieser Arbeit ist die Entwicklung eines neuen Modells der Topside-Ionosphäre. Um dieses Ziel zu erreichen, haben wir Beobachtungen von fünf der meistgenutzten Ionosphärenmissionen gesammelt und diese Datensätze interkalibriert. So konnten wir diese Daten gemeinsam für die Modellentwicklung, die Validierung und den Vergleich mit anderen bestehenden empirischen Modellen nutzen. Wir haben zum ersten Mal gezeigt, dass die Ionendichtebeobachtungen von Swarm-Langmuir-Sonden in niedrigen und mittleren Breiten auf der Nachtseite eine Überschätzung (bis zu ~40-50%) aufweisen, und haben vorgeschlagen, dass der Einfluss leichter Ionen eine mögliche Ursache für diese Überschätzung sein könnte. Zur Entwicklung des Oberseitenmodells wurden 19 Jahre lang Elektronendichteprofile aus der Radio-Okkultation (RO) verwendet, die mit einer Chapman-Funktion mit einer linearen Abhängigkeit der Skalenhöhe von der Höhe angepasst wurden. Aus dieser Näherung ergeben sich 4 Parameter, nämlich die Spitzendichte und die Höhe der F2-Schicht sowie die Steigung und der Achsenabschnitt des linearen Trends der Skalenhöhe, die mit Hilfe von neuronalen Feedforward-Netzwerken (NN) modelliert wurden. Das Modell wurde sowohl anhand von RO- als auch von In-situ-Beobachtungen umfassend validiert und übertrifft das Modell der Internationalen Referenz-Ionosphäre (IRI). Unsere Analyse zeigte, dass die größten Abweichungen des IRI-Modells von den Daten in Höhen von 100-200 km über der F2-Schichtspitze auftreten. Das entwickelte NN-basierte Ionosphärenmodell reproduziert die Auswirkungen verschiedener physikalischer Mechanismen, die in der Topside-Ionosphäre beobachtet werden, und liefert sehr genaue Vorhersagen der Elektronendichte. Diese Dissertation bietet eine umfassende Untersuchung der Dynamik in der Geosphäre, und die wichtigsten Ergebnisse dieser Arbeit tragen zur Verbesserung der Modelle von Plasmapopulationen in der erdnahen Weltraumumgebung bei. KW - Ionosphere KW - radiation belts KW - ring current KW - space physics KW - empirical modeling KW - machine learning KW - gradient boosting KW - neural networks KW - Ionosphäre KW - empirische Modellierung KW - Gradient Boosting KW - maschinelles Lernen KW - neuronale Netze KW - Strahlungsgürtel KW - Ringstrom KW - Weltraumphysik Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-613711 ER - TY - JOUR A1 - Smirnov, Artem G. A1 - Kronberg, Elena A. A1 - Latallerie, F. A1 - Daly, Patrick W. A1 - Aseev, Nikita A1 - Shprits, Yuri A1 - Kellerman, Adam C. A1 - Kasahara, Satoshi A1 - Turner, Drew L. A1 - Taylor, M. G. G. T. T1 - Electron Intensity Measurements by the Cluster/RAPID/IES Instrument in Earth's Radiation Belts and Ring Current JF - Space Weather: The International Journal of Research and Applications N2 - 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. Y1 - 2019 U6 - https://doi.org/10.1029/2018SW001989 SN - 1542-7390 VL - 17 IS - 4 SP - 553 EP - 566 PB - American Geophysical Union CY - Washington ER - TY - GEN A1 - Smirnov, Artem G. A1 - Kronberg, Elena A. A1 - Daly, Patrick W. A1 - Aseev, Nikita A1 - Shprits, Yuri A1 - Kellerman, Adam C. T1 - Adiabatic Invariants Calculations for Cluster Mission: A Long-Term Product for Radiation Belts Studies T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - 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. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1192 KW - L-Asterisk KW - magnetosphere KW - electrons KW - model Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-523915 SN - 1866-8372 IS - 2 ER - TY - JOUR A1 - Smirnov, Artem A1 - Berrendorf, Max A1 - Shprits, Yuri A1 - Kronberg, Elena A. A1 - Allison, Hayley J. A1 - Aseev, Nikita A1 - Zhelavskaya, Irina A1 - Morley, Steven K. A1 - Reeves, Geoffrey D. A1 - Carver, Matthew R. A1 - Effenberger, Frederic T1 - Medium energy electron flux in earth's outer radiation belt (MERLIN) BT - a Machine learning model JF - Space weather : the international journal of research and applications N2 - 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. KW - machine learning KW - radiation belts KW - electron flux KW - empirical modeling KW - magnetosphere KW - electrons Y1 - 2020 U6 - https://doi.org/10.1029/2020SW002532 SN - 1542-7390 VL - 18 IS - 11 PB - American geophysical union, AGU CY - Washington ER - TY - JOUR A1 - Smirnov, Artem A1 - Shprits, Yuri A1 - Allison, Hayley A1 - Aseev, Nikita A1 - Drozdov, Alexander A1 - Kollmann, Peter A1 - Wang, Dedong A1 - Saikin, Anthony T1 - An empirical model of the equatorial electron pitch angle distributions in earth's outer radiation belt JF - Space Weather: the International Journal of Research and Applications N2 - In this study, we present an empirical model of the equatorial electron pitch angle distributions (PADs) in the outer radiation belt based on the full data set collected by the Magnetic Electron Ion Spectrometer (MagEIS) instrument onboard the Van Allen Probes in 2012-2019. The PADs are fitted with a combination of the first, third and fifth sine harmonics. The resulting equation resolves all PAD types found in the outer radiation belt (pancake, flat-top, butterfly and cap PADs) and can be analytically integrated to derive omnidirectional flux. We introduce a two-step modeling procedure that for the first time ensures a continuous dependence on L, magnetic local time and activity, parametrized by the solar wind dynamic pressure. We propose two methods to reconstruct equatorial electron flux using the model. The first approach requires two uni-directional flux observations and is applicable to low-PA data. The second method can be used to reconstruct the full equatorial PADs from a single uni- or omnidirectional measurement at off-equatorial latitudes. The model can be used for converting the long-term data sets of electron fluxes to phase space density in terms of adiabatic invariants, for physics-based modeling in the form of boundary conditions, and for data assimilation purposes. KW - pitch angle KW - radiation belt KW - model KW - magnetosphere KW - van allen probes; KW - electrons Y1 - 2022 U6 - https://doi.org/10.1029/2022SW003053 SN - 1542-7390 VL - 20 IS - 9 PB - American Geophysical Union CY - Washington, DC ER - TY - JOUR A1 - Landis, Daji August A1 - Saikin, Anthony A1 - Zhelavskaya, Irina A1 - Drozdov, Alexander A1 - Aseev, Nikita A1 - Shprits, Yuri A1 - Pfitzer, Maximilian F. A1 - Smirnov, Artem G. T1 - NARX Neural Network Derivations of the Outer Boundary Radiation Belt Electron Flux JF - Space Weather: the international journal of research and applications N2 - 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. KW - radiation belts KW - forecasting (1922, 4315, 7924, 7964) KW - machine learning (0555) Y1 - 2022 U6 - https://doi.org/10.1029/2021SW002774 SN - 1542-7390 VL - 20 IS - 5 PB - American Geophysical Union CY - Washington ER -