Directional discontinuities (DDs) are defined as abrupt changes of the magnetic field orientation. We use observations from ESA’s Cluster mission to compile a database of events: 4216 events are identified in January-April 2007, and 5194 in January-April 2008. Localized time-scale images depicting angular changes are created for each event, and a preliminary classification algorithm is designed to distinguish between: simple - isolated events, and complex - multiple overlapping events. In 2007, 1806 events are pre-classified as simple, and 2410 as complex; in 2008, 1997 events are simple, and 3197 are complex. A supervised machine learning approach is used to recognize and predict these events. Two models are trained: one for 2007, which is used to predict the results in 2008, and vice-versa for 2008. To validate our results, we investigate the discontinuity occurrence rate as a function of spacecraft location. When the spacecraft is in the solar wind, we find an occurrence rate of ~2 DDs per hour and a 50/50 % ratio of simple/complex events. When the spacecraft is in the Earth’s magnetosheath, we find that the total occurrence rate remains around 2 DDs/h, but the ratio of simple/complex events changes to ~25/75 %. This implies that about half of the simple events observed in the solar wind are classified as complex when observed in the magnetosheath. This demonstrates that our classification scheme can provide meaningful insights, and thus be relevant for future studies on interplanetary discontinuities.

This paper describes a catalogue of simultaneous observations of the Earth’s magnetosheath by ESA’s Cluster and NASA’s MMS missions. The catalogue is built from a visual inspection of summary plots provided by the two missions complemented by an analysis of high-resolution magnetic field data. The catalogue includes 117 events when Cluster 4 and MMS 4 crossed simultaneously the magnetosheath between January-April, 2017-2021. We also determine the bow shock geometry for each event based on two different approaches: a) a minimum variance analysis of in-situ magnetic field measurements, and b) a geometrical approach which considers a bow shock model parameterized by OMNI data. A description of spacecraft trajectory during each event is also provided. Additional data describe the relative distances between Cluster 4 and MMS 4, a classification of each event as either quasi-parallel or quasi-perpendicular, and the distribution of events per magnetospheric flank. The time intervals for the Cluster - MMS conjunctions included in the catalogue, as well as all associated figures and tables discussed in this paper are made available through an independent online data repository, and can be freely downloaded and used by any interested researcher.

We built an integrated nonlinear analysis software -INA- designed to study space plasma turbulence and intermittency. The MATLAB programming environment was used for the algorithmic development and implementation of methods for spectral analysis, multiscale fluctuations and multifractal analysis. The performance of INA is demonstrated using magnetic field measurements from the Cluster 3 spacecraft during an inbound pass through the Earth’s magnetosheath region. We show how specific features of the power spectral density (PSD) can be mapped to localised time-frequency regions in the spectrogram representation, and identify multiple intermittent events using the wavelet-based local intermittency measure (LIM). Multiscale probability density functions (PDFs) showed clear departures from Gaussianity, signifying the presence of intermittency. Structure functions (SFs) and rank-ordered multifractal analysis (ROMA) revealed the multifractal nature of the analysed signal. INA is freely distributed as a standalone executable file to any interested user, and provides an integrated, interactive, and user-friendly environment in which one can import a dataset, customize key analysis parameters, apply multiple methods on the same signal and then export high-quality, publication-ready figures. These are only a few of the many distinguishing features of INA.

The analysis in real time of space data variability is essential for scientists and space mission controllers. Automated tools designed to extract key descriptors of variability are needed and solutions to adapt such algorithms for on-board computers are rare. This paper describes the design of an automated system for detecting directional discontinuities of a physical quantity and its implementation in Field-Programmable Gate Array (FPGA). The system is currently adapted for solar wind or terrestrial magnetosheath magnetic field directional discontinuities, i.e. sharp changes of the magnetic field directionality. Our detection algorithm uses analysis windows of adjustable width and averaging procedures in order to reduce the effects of random fluctuations. A sliding-window approach is designed for continuous monitoring and detection of magnetic directional discontinuities. A software implementation of the algorithm was tested using in-situ magnetic field measurements, and emphasised improvements of performance when using analysis windows of adjustable width. The FPGA implementation of the detection algorithm is built on DILIGENT Nexys 4 DDR featuring a comercial Xilinx Artix-7 device and is designed to be ported to space qualified infrastructure. The FPGA system was tested with synthetic and laboratory signals, and provides results in very good agreement with the software implementation. The FPGA system provides an efficient real-time monitoring solution using minimal computational and energy resources, and reducing the main on-board computer utilization.

Intermittency is a fundamental property of space plasma dynamics, characterizing turbulent dynamical variables as well as passive scalars. Its qualitative and quantitative description from in-situ data requires an accurate estimation of the probability density functions (PDFs) of fluctuations and their moments, particularly the flatness, a normalized fourth order moment of the PDF. Such a statistical description needs a sufficiently large number of samples to be meaningful. Due to inherent technological limitations (e.g. limited telemetry bandwidth) not all samples collected on-board the spacecraft can be sent to the ground for further analysis. Therefore, a technology designed to process on-board the data and to compute the flatness is useful to fully exploit the capabilities of scientific instruments installed on robotic platforms, including nanosatellites. We designed, built and tested in laboratory such a technology based on Field Programable Gate Arrays (FPGA) . The solution uses the FloPoCo framework with customized arithmetic operators; the computation block is a pipelined architecture which computes a new value of the flatness in each clock cycle. The design and implementation achieves optimization directives of the FPGA resources relevant for operation in space, like area, energy efficiency, and precision. The technology was tested in laboratory using Xilinx SRL16 or SRLC32 macros and provides correct results validated with test time series provided by magnetic field data collected in the solar wind by ACE spacecraft. The characteristics and performance of the laboratory prototype pave the way for a space qualified version.