The Special Issue on Modeling and Data Analysis Methods for the SMILE mission

The Soft X-ray Imager (SXI) is a compact X-ray telescope with a wide $15.5^{\circ } \times 26.5^{\circ }$ 15.5 ∘ × 26.5 ∘ field-of-view (FOV) and is part of the scientific payload of the Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission. SMILE is a joint science mission between the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS) and is due for launch in 2026. SXI is capable of viewing large portions of Earth’s magnetosphere from the vantage point of the SMILE orbit. SXI is designed to detect the soft X-rays from the Solar Wind Charge eXchange (SWCX) process produced when heavy ions of solar wind origin interact with neutral particles in Earth’s exosphere. SWCX provides a mechanism for boundary detection within the magnetosphere, such as the position of Earth’s magnetopause, because the solar wind heavy ions essentially do not enter regions of closed magnetic field lines. The sensitivity of the SXI is such that it can potentially track movements of the magnetopause on timescales of a few minutes and the orbit of SMILE will enable such movements to be tracked for segments lasting many hours. The SXI has a large grasp (FOV times effective area) and a CCD-based detector system with an energy resolution comparable to contemporary X-ray astrophysics missions. SXI will, therefore, acquire high precision spectra capable of discriminating the composition of many of the solar wind heavy ions within the regions of interest. SXI is led by the University of Leicester (UL) in the United Kingdom (UK) with collaborating organisations on hardware, software and science support within the UK, Europe, China and the United States.

<p>The interaction between the solar wind and the Earth's magnetosphere, and the geospace dynamics that result, is one of the key questions in space plasma physics. In situ instruments on a fleet of solar wind and magnetospheric constellation missions now provide the most detailed observations of Sun-Earth connections over multiple scales, from the smallest of a few kilometres up to the largest of a few 10s of Earth radii. However, we are still unable to quantify the global effects of the drivers of such connections, including the conditions that prevail throughout geospace. This information is the key missing link for developing a complete understanding of how the Sun gives rise to and controls Earth's plasma environment and space weather. This is where SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) comes in.<br>SMILE is a novel self-standing mission dedicated to observing the solar wind - magnetosphere coupling via simultaneous in situ solar wind/magnetosheath plasma and magnetic field measurements, soft X-ray imaging of the magnetosheath, magnetopause and polar cusps, and UV imaging of the Northern hemisphere auroral oval. Remote sensing of the magnetosheath and cusps with soft X-ray imaging is made possible thanks to solar wind charge exchange (SWCX) X-ray emissions known to occur in the vicinity of the Earth's magnetosphere. SMILE is a joint mission between ESA and the Chinese Academy of Sciences (CAS) due for launch at the end of 2024. SMILE science objectives as well as the latest technical developments jointly undertaken by ESA and CAS and the international instrument teams will be presented.</p>

Imaging has been an important strategy for exploring space weather. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint Chinese Academy of Sciences (CAS) and European Space Agency (ESA) mission, aiming at studying the interaction between Earth’s magnetosphere and solar wind near the subsolar point via soft X-ray imaging. As the boundary of Earth’s magnetosphere, magnetopause is a significant detection target to mirror solar wind’s change for the SMILE mission. In preparation for inverting three-dimensional magnetopause, we proposed an OESA-UNet model to detect the magnetopause position. The model obtains magnetopause with a U-shaped structure, in an end-to-end manner. Inspired by attention mechanisms, these blocks are integrated into ours. OESA-UNet captures low and high-level feature maps by adjusting the receptive field for precise localization. Adaptively pre-processing the image provides a prior for the network. Availability metrics are designed to determine whether it can serve three-dimensional inversion. Lastly, we provided ablation and comparison experiments by qualitative and quantitative analysis. Our recall, precision, and f1 score are 93.8%, 92.1%, and 92.9%, respectively, with an average angle deviation of 0.005 under the availability metrics. Results indicate that OESA-UNet outperforms other methods. It can better serve the purpose of magnetopause tracing from an X-ray image.

Solar wind charge exchange produces emissions in the soft X-ray energy range which can enable the study of near-Earth space regions such as the magnetopause, the magnetosheath and the polar cusps by remote sensing techniques. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) and Lunar Environment heliospheric X-ray Imager (LEXI) missions aim to obtain soft X-ray images of near-Earth space thanks to their Soft X-ray Imager (SXI) instruments. While earlier modeling works have already simulated soft X-ray images as might be obtained by SMILE SXI during its mission, the numerical models used so far are all based on the magnetohydrodynamics description of the space plasma. To investigate the possible signatures of ion-kinetic-scale processes in soft X-ray images, we use for the first time a global hybrid-Vlasov simulation of the geospace from the Vlasiator model. The simulation is driven by fast and tenuous solar wind conditions and purely southward interplanetary magnetic field. We first produce global X-ray images of the dayside near-Earth space by placing a virtual imaging satellite at two different locations, providing meridional and equatorial views. We then analyze regional features present in the images and show that they correspond to signatures in soft X-ray emissions of mirror-mode wave structures in the magnetosheath and flux transfer events (FTEs) at the magnetopause. Our results suggest that, although the time scales associated with the motion of those transient phenomena will likely be significantly smaller than the integration time of the SMILE and LEXI imagers, mirror-mode structures and FTEs can cumulatively produce detectable signatures in the soft X-ray images. For instance, a local increase by 30% in the proton density at the dayside magnetopause resulting from the transit of multiple FTEs leads to a 12% enhancement in the line-of-sight- and time-integrated soft X-ray emissivity originating from this region. Likewise, a proton density increase by 14% in the magnetosheath associated with mirror-mode structures can result in an enhancement in the soft X-ray signal by 4%. These are likely conservative estimates, given that the solar wind conditions used in the Vlasiator run can be expected to generate weaker soft X-ray emissions than the more common denser solar wind. These results will contribute to the preparatory work for the SMILE and LEXI missions by providing the community with quantitative estimates of the effects of small-scale, transient phenomena occurring on the dayside.

The SMILE (Solar wind Magnetosphere Ionosphere Link Explorer) mission is a joint space science mission between the Chinese Academy of Sciences (CAS) and the European Space Agency (ESA), aiming to understand the interaction of the solar wind with the Earth’s magnetosphere in a global manner. As of May 2024, the SMILE mission is in phase-D with an expected launch date of September 2025. This report summarizes developments in the mission during the past two years.

The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) was proposed to the Chinese Academy of Science (CAS) and the European Space Agency (ESA) following a joint call for science missions issued in January 2015. SMILE was proposed by a team of European and Chinese scientists, led by two mission Co-PIs, one from China and one from Europe. SMILE was selected in June 2015, and its budget adopted by the Chinese Academy of Sciences in November 2016 and the ESA Science Programme Committee in March 2019, respectively. SMILE will investigate the connection between the Sun and the Earth using a new technique that will image the magnetopause and polar cusps: the key regions where the solar wind impinges on Earth’s magnetic field. Simultaneously, SMILE will image the auroras borealis in an ultraviolet waveband, providing long-duration continuous observations of the northern polar regions. In addition, the ion and magnetic field characteristics of the magnetospheric lobes, magnetosheath and solar wind will be measured by the in-situ instrument package. Here, we present the science goals, instruments and planned orbit. In addition the Working Groups that are supporting the preparation of the mission and the coordination with other magnetospheric missions are described.

Einstein Probe is a Chinese Academy of Science (CAS) mission due for launch at the end of 2022 with its primary goals to discover high-energy transients and monitor variable objects. The mission consists of two instruments, the Wide field X-ray Telescope (WXT), a lobster eye X-ray telescope consisting of twelve identical modules; and the Follow-up X-ray Telescope (FXT), which is a traditional Wolter X-ray telescope, which is jointly developed by CAS, the European Space Agency (ESA) and the Max Planck Institute for Extraterrestrial Physics (MPE). Each of the WXT modules comprise of 36 micro pore optics (MPOs), with a 375 mm focal length, a field of view of more than 3600 square degrees, a goal of 5 arcmin resolution and working in the energy range of 0.5-4 keV. As part of the collaboration between CAS and ESA, the University of Leicester (UoL) is acting as a testing facility and repeatability check for several elements of the WXT optic development. This includes calibration of individual MPOs, temperature measurements on a prototype module, calibration of a qualification module and full calibration of one of the flight module optics. The majority of these tests will be performed in the two X-ray facilities at the University of Leicester, the Tunnel Test Facility (TTF) and the Vertical Test Facility (VTF). Presented here are details of the tests carried out and the results obtained from the first batch of individual MPOs and the plan for the thermal tests of the prototype in the TTF.

Earth’s magnetopause is a thin boundary separating the shocked solar wind plasma from the magnetospheric plasmas, and it is also the boundary of the solar wind energy transport to the magnetosphere. Soft X-ray imaging allows investigation of the large-scale magnetopause by providing a two-dimensional (2-D) global view from a satellite. However, it is challenging to derive information about the three-dimensional (3-D) magnetopause from a 2-D X-ray image. By performing 3-D global hybrid simulations, we obtain the soft X-ray imaging of Earth’s magnetopause under different solar wind conditions. The soft X-ray images observed by a hypothetical satellite are shown, and the location of the magnetopause, the cusps, and the magnetosheath are all identified in the X-ray images. Although there is a large amplitude fluctuation of the X-ray emissivity in the magnetosheath, the maximum X-ray intensity matches the tangent directions of the magnetopause well, which indicates that the magnetopause location can be identified from the 2-D X-ray images. Moreover, the magnetopause location can be identified with different positions of the satellite. We also find that solar wind conditions have little effect on the magnetopause identification. The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) mission will provide the X-ray images of the magnetopause for the first time, and our global hybrid simulation results can help better understand the 2-D X-ray images of the magnetopause from a 3-D perspective, with particle kinetic effects considered. 

Electromagnetic fields and currents connect various regions of the earth's near space environment extending upto the magnetopause. Realization of this fact has lead to the concept of Global Electric Circuit (GEC) to describe the electromagnetic environment of the earth's atmosphere. Solar wind - magnetosphere - ionosphere coupling forms a vital component of GEC. Magnetospheric substorms represent a global interaction between the solar wind, the magetosphere, and the ionosphere. This article gives an overview of the solar wind - magnetosphere- ionosphere coupling processes with emphasis on the nonlinear particle dynamics in the magnetotail. Those aspects of the substorm processes which involve the chaotic dynamics are highlighted. Various methods based on nonlinear particle dynamics, linear prediction filtering techniques, phase space reconstruction techniques, and dynamical anologue models of geomagnetic activity are reviewed. It is shown that the solar wind- magnetosphere - ionosphere system behaves as a strongly coupled nonlinear dynamical system which could be driven from regular to chaotic behavior with low dimensionality when the solar wind forcing is strong enough.

SUMMARYThis paper considers novel Bayesian non-parametric methods for stochastic epidemic models. Many standard modeling and data analysis methods use underlying assumptions (e.g. concerning the rate at which new cases of disease will occur) which are rarely challenged or tested in practice. To relax these assumptions, we develop a Bayesian non-parametric approach using Gaussian Processes, specifically to estimate the infection process. The methods are illustrated with both simulated and real data sets, the former illustrating that the methods can recover the true infection process quite well in practice, and the latter illustrating that the methods can be successfully applied in different settings.

This paper discusses educational tools, resources and experience in teaching physics-of-failure (PoF) modeling and data analysis methods used in accelerated life testing (ALT) and accelerated degradation testing (ADT). The concepts introduced in this paper include several probabilistic and highly technical approaches such as the PoF and mechanistic approaches to reliability prediction and assessments as well as advanced Bayesian analysis methods used in ALT and ADT to analyze life data obtained through various methods of constant and variable stress testing efforts. Other resources include codes and algorithms from the readily available computational tools R, MATLAB, and WinBUGS for solving advanced model and life estimation methods related to PoF, ALT and ADT analyses. This paper provides an overview of these resources and several example problems.

Small unoccupied aerial systems (sUAS) are increasingly used for field data collection and remote sensing purposes. Their ease of use, ability to carry sensors, low cost, precise manoeuvrability, and navigation makes them versatile tools. The goal of this study is to investigate if sUAS multispectral imagery can be utilized to measure turbidity and total suspended solids (TSS) of small streams. sUAS multispectral imagery and water samples at varying depths were collected before and after rain events on three sampling dates in 2019 from Moores Creek in Lanett, Alabama (AL), United States of America (USA), which was restored in 2017. The water samples were processed for TSS and turbidity and related to pixel values from the multispectral imagery. Linear regression was used to develop models for TSS and turbidity. The models were then tested on Moores Mill Creek in Chewacla State Park, AL, USA. For Lanett, TSS and turbidity regression models for low flows had coefficients of determination (R 2) values of 0.77 and 0.78, respectively. During high flows, different single bands and band ratios were required for comparable R 2 values, suggesting separate models may be needed for high and low flow events. When the Lanett models were applied to Chewacla State Park, predicted TSS and turbidity were not comparable to measured values indicating that location-specific models may be required. Future research should incorporate depth as a variable since streambed visibility likely impacts results, along with other modelling and data analysis methods, such as machine learning.

Recently, there has been an explosive growth in the development of the concept of Industry. One of the most important elements of this concept is the application of methods of mathematical modeling and intelligent data analysis to create models of production processes and final products based on the processing of signals coming from intelligent sensors. A set of such models, which represent a virtual representation of industrial processes, systems and equipment, is called twin. Digital twins use data from sensors installed on production lines or on the basis of final products to predict equipment failures, optimize product quality and reduce the environmental impact of production processes. The model sets, underlying digital twins, can be described as workflows consisting of a set of computational services, each of which is a model of a process step. To provide flexible support for cloud computing to perform digital twins, we offer the concept of Micro-Workflows, which combines the power of the Scientific Workflows concept, the flexibility of container technology and the sustainability of the Stream Processing approach in distributed computing systems.

In recent years, text mining has become a branch of Natural Language Processing. This chapter focuses on the analysis of textual data, illustrated by examples treated with the software R, which will be used to manipulate texts and strings, to transform them by word embedding methods and to analyse them by supervised and unsupervised statistical and machine learning methods. It discusses methods of topic modeling and sentiment analysis. There are two types of methods in textual data analysis: descriptive methods and predictive methods. The chapter describes the various lexical analyses that are often performed to categorize documents or to detect the topics contained in a set of documents. The analysis of textual data is most often done on vector representations of the data, which are stored in matrices containing the documents in rows and the terms in columns. A recent application of natural language processing is sentiment analysis.

Introduction. The development and enhancement of the management efficiency of the forestry sector in the Macroregion of the Northwest and the Arctic zone of the Russian Federation are strategically significant for the country's development, oriented towards global competitiveness. The aim of this study is to analyze the prospects for personnel training within the system of secondary vocational education for the forestry sector in the Northwest and Arctic zones of the Russian Federation. Materials and Methods. The materials for the study were statistical data from two forms of state federal reporting for 2022: No. 1-T (prof) "Information on the number and demand for workers in organizations by professional groups" and form No. SPO-1 "Information about the educational organization that carries out educational activities in educational programs of secondary vocational education." The study employed methods of descriptive statistics, economic-mathematical modeling, and comparative data analysis. Results of the Study. An extended list of 23 directions and specialties of secondary vocational education organizations training qualified personnel for the forestry complex in the Arctic Macroregion and Northwest Russia has been identified. An assessment of the provision of the economy of enterprises and organizations in the forestry sector with qualified personnel has been presented. The analysis revealed 12 entities in the macroregion whose personnel needs for mid-level specialists can be met by graduates from their own region, as well as 13 entities in the macroregion that need to train their personnel beyond the macroregion in other regions of Russia to meet the demand for qualified workers and employees. A mapping tool has been developed, comprising 85 subjects of the Russian Federation, divided into three groups based on the total admission to specialties and directions related to the forestry sector. Discussion and Conclusion. The mapping tool obtained demonstrates that the regions with the highest preparation of forestry specialists are located far from the Arctic zone and Northwest Russia. Meanwhile, the main forest resources of the macroregion's entities are unevenly distributed across their territories, concentrated mainly in the central and southern parts. The proposed mapping tool contributes to addressing the issues of personnel training for the forestry sector through the involvement of all interested actors: applicants, employers, secondary vocational education organizations, and regional executive authorities.