| 1. |
- Sorriso-Valvo, Luca, et al.
(författare)
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Turbulence-Driven Ion Beams in the Magnetospheric Kelvin-Helmholtz Instability
- 2019
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Ingår i: Physical Review Letters. - : AMER PHYSICAL SOC. - 0031-9007 .- 1079-7114. ; 122:3
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Tidskriftsartikel (refereegranskat)abstract
- The description of the local turbulent energy transfer and the high-resolution ion distributions measured by the Magnetospheric Multiscale mission together provide a formidable tool to explore the cross-scale connection between the fluid-scale energy cascade and plasma processes at subion scales. When the small-scale energy transfer is dominated by Alfvenic, correlated velocity, and magnetic field fluctuations, beams of accelerated particles are more likely observed. Here, for the first time, we report observations suggesting the nonlinear wave-particle interaction as one possible mechanism for the energy dissipation in space plasmas.
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| 2. |
- Abbas, Zainab
(författare)
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Scalable Streaming Graph and Time Series Analysis Using Partitioning and Machine Learning
- 2021
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Recent years have witnessed a massive increase in the amount of data generated by the Internet of Things (IoT) and social media. Processing huge amounts of this data poses non-trivial challenges in terms of the hardware and performance requirements of modern-day applications. The data we are dealing with today is of massive scale, high intensity and comes in various forms. MapReduce was a popular and clever choice of handling big data using a distributed programming model, which made the processing of huge volumes of data possible using clusters of commodity machines. However, MapReduce was not a good fit for performing complex tasks, such as graph processing, iterative programs and machine learning. Modern data processing frameworks, that are being popularly used to process complex data and perform complex analysis tasks, overcome the shortcomings of MapReduce. Some of these popular frameworks include Apache Spark for batch and stream processing, Apache Flink for stream processing and Tensor Flow for machine learning.In this thesis, we deal with complex analytics on data modeled as time series, graphs and streams. Time series are commonly used to represent temporal data generated by IoT sensors. Analysing and forecasting time series, i.e. extracting useful characteristics and statistics of data and predicting data, is useful for many fields that include, neuro-physiology, economics, environmental studies, transportation, etc. Another useful data representation we work with, are graphs. Graphs are complex data structures used to represent relational data in the form of vertices and edges. Graphs are present in various application domains, such as recommendation systems, road traffic analytics, web analysis, social media analysis. Due to the increasing size of graph data, a single machine is often not sufficient to process the complete graph. Therefore, the computation, as well as the data, must be distributed. Graph partitioning, the process of dividing graphs into subgraphs, is an essential step in distributed graph processing of large scale graphs because it enables parallel and distributed processing.The majority of data generated from IoT and social media originates as a continuous stream, such as series of events from a social media network, time series generated from sensors, financial transactions, etc. The stream processing paradigm refers to the processing of data streaming that is continuous and possibly unbounded. Combining both graphs and streams leads to an interesting and rather challenging domain of streaming graph analytics. Graph streams refer to data that is modelled as a stream of edges or vertices with adjacency lists representing relations between entities of continuously evolving data generated by a single or multiple data sources. Streaming graph analytics is an emerging research field with great potential due to its capabilities of processing large graph streams with limited amounts of memory and low latency. In this dissertation, we present graph partitioning techniques for scalable streaming graph and time series analysis. First, we present and evaluate the use of data partitioning to enable data parallelism in order to address the challenge of scale in large spatial time series forecasting. We propose a graph partitioning technique for large scale spatial time series forecasting of road traffic as a use-case. Our experimental results on traffic density prediction for real-world sensor dataset using Long Short-Term Memory Neural Networks show that the partitioning-based models take 12x lower training time when run in parallel compared to the unpartitioned model of the entire road infrastructure. Furthermore, the partitioning-based models have 2x lower prediction error (RMSE) compared to the entire road model. Second, we showcase the practical usefulness of streaming graph analytics for large spatial time series analysis with the real-world task of traffic jam detection and reduction. We propose to apply streaming graph analytics by performing useful analytics on traffic data stream at scale with high throughput and low latency. Third, we study, evaluate, and compare the existing state-of-the-art streaming graph partitioning algorithms. We propose a uniform analysis framework built using Apache Flink to evaluate and compare partitioning features and characteristics of streaming graph partitioning methods. Finally, we present GCNSplit, a novel ML-driven streaming graph partitioning solution, that uses a small and constant in-memory state (bounded state) to partition (possibly unbounded) graph streams. Our results demonstrate that \ours provides high-throughput partitioning and can leverage data parallelism to sustain input rates of 100K edges/s. GCNSplit exhibits a partitioning quality, in terms of graph cuts and load balance, that matches that of the state-of-the-art HDRF (High Degree Replicated First) algorithm while storing three orders of magnitude smaller partitioning state.
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| 3. |
- Alberti, Tommaso, et al.
(författare)
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Multifractal and Chaotic Properties of Solar Wind at MHD and Kinetic Domains : An Empirical Mode Decomposition Approach
- 2019
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Ingår i: Entropy. - : MDPI AG. - 1099-4300. ; 21:3
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Tidskriftsartikel (refereegranskat)abstract
- Turbulence, intermittency, and self-organized structures in space plasmas can be investigated by using a multifractal formalism mostly based on the canonical structure function analysis with fixed constraints about stationarity, linearity, and scales. Here, the Empirical Mode Decomposition (EMD) method is firstly used to investigate timescale fluctuations of the solar wind magnetic field components; then, by exploiting the local properties of fluctuations, the structure function analysis is used to gain insights into the scaling properties of both inertial and kinetic/dissipative ranges. Results show that while the inertial range dynamics can be described in a multifractal framework, characterizing an unstable fixed point of the system, the kinetic/dissipative range dynamics is well described by using a monofractal approach, because it is a stable fixed point of the system, unless it has a higher degree of complexity and chaos.
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| 4. |
- Chiappetta, Federica, et al.
(författare)
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Energy Conversion through a Fluctuation-Dissipation Relation at Kinetic Scales in the Earth's Magnetosheath
- 2023
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Ingår i: Astrophysical Journal. - : Institute of Physics Publishing (IOPP). - 0004-637X .- 1538-4357. ; 957:2
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Tidskriftsartikel (refereegranskat)abstract
- Low-frequency fluctuations in the interplanetary medium represent a turbulent environment where universal scaling behavior, generated by an energy cascade, has been investigated. On the contrary, in some regions, for example, the magnetosheath, universality of statistics of fluctuations is lost. However, at kinetic scales where energy must be dissipated, the energy conversion seems to be realized through a mechanism similar to the free solar wind. Here we propose a Langevin model for magnetic fluctuations at kinetic scales, showing that the resulting fluctuation-dissipation relation is capable of describing the gross features of the spectral observations at kinetic scales in the magnetosheath. The fluctuation-dissipation relation regulates the energy conversion by imposing a relationship between fluctuations and dissipation, which at high frequencies are active at the same time in the same range of scales and represent two ingredients of the same physical process.
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| 5. |
- Servidio, Sergio, et al.
(författare)
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Compressible turbulence in Hall Magnetohydrodynamics
- 2007
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Ingår i: Planetary and Space Science. - : Elsevier BV. - 0032-0633 .- 1873-5088. ; 55:15, s. 2239-2243
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Tidskriftsartikel (refereegranskat)abstract
- By direct numerical simulations we investigate the nonlinear dynamics of a compressible Hall Magnetohydrodynamic (MHD) plasma. At small scales, where the Hall effect dominates, we found an increase of the compressibility of the system and the breakdown of the strong link between velocity and magnetic fields, typical of usual MHD. Moreover, we find that small-scale fluctuations are characterized by an anti-correlation between density and magnetic field intensity. These features characterize the excitation. of a quasi-perpendicular magnetosonic turbulence that can be interpreted as the small-scale signature of the break-down of the MHD nonlinear energy cascade due to Hall effect. Fluctuations with the same properties, based on measurements by Cluster spacecraft in space plasma turbulence during different magnetopause crossings, have been recently observed.
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| 6. |
- Telloni, Daniele, et al.
(författare)
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Does Turbulence along the Coronal Current Sheet Drive Ion Cyclotron Waves?
- 2023
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Ingår i: Astrophysical Journal. - : American Astronomical Society. - 0004-637X .- 1538-4357. ; 944:2
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Tidskriftsartikel (refereegranskat)abstract
- Evidence for the presence of ion cyclotron waves (ICWs), driven by turbulence, at the boundaries of the current sheet is reported in this paper. By exploiting the full potential of the joint observations performed by Parker Solar Probe and the Metis coronagraph on board Solar Orbiter, local measurements of the solar wind can be linked with the large-scale structures of the solar corona. The results suggest that the dynamics of the current sheet layers generates turbulence, which in turn creates a sufficiently strong temperature anisotropy to make the solar-wind plasma unstable to anisotropy-driven instabilities such as the Alfven ion cyclotron, mirror-mode, and firehose instabilities. The study of the polarization state of high-frequency magnetic fluctuations reveals that ICWs are indeed present along the current sheet, thus linking the magnetic topology of the remotely imaged coronal source regions with the wave bursts observed in situ. The present results may allow improvement of state-of-the-art models based on the ion cyclotron mechanism, providing new insights into the processes involved in coronal heating.
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| 7. |
- Telloni, Daniele, et al.
(författare)
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Linking Small-scale Solar Wind Properties with Large-scale Coronal Source Regions through Joint Parker Solar Probe-Metis/Solar Orbiter Observations
- 2022
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Ingår i: Astrophysical Journal. - : IOP Publishing Ltd. - 0004-637X .- 1538-4357. ; 935:2
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Tidskriftsartikel (refereegranskat)abstract
- The solar wind measured in situ by Parker Solar Probe in the very inner heliosphere is studied in combination with the remote-sensing observation of the coronal source region provided by the METIS coronagraph aboard Solar Orbiter. The coronal outflows observed near the ecliptic by Metis on 2021 January 17 at 16:30 UT, between 3.5 and 6.3 R (circle dot) above the eastern solar limb, can be associated with the streams sampled by PSP at 0.11 and 0.26 au from the Sun, in two time intervals almost 5 days apart. The two plasma flows come from two distinct source regions, characterized by different magnetic field polarity and intensity at the coronal base. It follows that both the global and local properties of the two streams are different. Specifically, the solar wind emanating from the stronger magnetic field region has a lower bulk flux density, as expected, and is in a state of well-developed Alfvenic turbulence, with low intermittency. This is interpreted in terms of slab turbulence in the context of nearly incompressible magnetohydrodynamics. Conversely, the highly intermittent and poorly developed turbulent behavior of the solar wind from the weaker magnetic field region is presumably due to large magnetic deflections most likely attributed to the presence of switchbacks of interchange reconnection origin.
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| 8. |
- Telloni, Daniele, et al.
(författare)
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Observation of a Magnetic Switchback in the Solar Corona
- 2022
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Ingår i: Astrophysical Journal Letters. - : Institute of Physics (IOP). - 2041-8205 .- 2041-8213. ; 936:2
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Tidskriftsartikel (refereegranskat)abstract
- Switchbacks are sudden, large radial deflections of the solar wind magnetic field, widely revealed in interplanetary space by the Parker Solar Probe. The switchbacks' formation mechanism and sources are still unresolved, although candidate mechanisms include Alfvenic turbulence, shear-driven Kelvin-Helmholtz instabilities, interchange reconnection, and geometrical effects related to the Parker spiral. This Letter presents observations from the Metis coronagraph on board a Solar Orbiter of a single large propagating S-shaped vortex, interpreted as the first evidence of a switchback in the solar corona. It originated above an active region with the related loop system bounded by open-field regions to the east and west. Observations, modeling, and theory provide strong arguments in favor of the interchange reconnection origin of switchbacks. Metis measurements suggest that the initiation of the switchback may also be an indicator of the origin of slow solar wind.
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