The influence of in situ pitch-angle cosine coverage on the derivation of solar energetic particle injection and interplanetary transport conditions

Modelization of solar energetic particle (SEP) events aims at revealing the general scenario of SEP injection and interplanetary propagation and relies on in situ measurements of SEP distributions. In this paper, we study to what extent the LEFS60 and LEMS30 electron telescopes of the Electron Proton Alpha Monitor (EPAM) on board the Advanced Composition Explorer are able to scan pitch-angle distributions during near-relativistic electron events. We estimate the percentage of the pitch-angle cosine range scanned by both telescopes for a given magnetic field configuration. We obtain that the pitch-angle coverage is always higher for LEFS60 than for LEMS30. Therefore, LEFS60 provides more information of the directional distribution of the observed particles. The aim of the paper is to study the relevance of the coverage when fitting LEFS60 particle measurements in order to infer the solar injection and the interplanetary transport conditions. By studying synthetic electron events, we obtain that at least 70% of the pitch-angle cosine range needs to be scanned by the telescope. Otherwise, multiple scenarios can explain the data.     

Thermoviscoelastic Analysis of Stress in Composite Structures- Micro-to-Structural Approach

Manufacturing of composite materials is usually accompanied with residual stresses. These stresses should be evaluated and assessed. To this end, a micromechanical model for periodic material whose temperature dependent constituents behave as thermorheologically complex materials （TCM） has been developed. This model, referred as the high fidelity generalized method of cells （HFGMC）, takes into account the detailed interaction be- tween the fiber and resin, their volume ratios, the fibers distribution and their waviness. This model is linked, in conjunction with a special UMAT subroutine, to the ABAQUS finite element code for prediction of the response of thermoviscoelastic composite structures during cool down process. The present investigation shows the effect of the cool down rate on the residual stress developed in the composite cylindrical structures.     

The automated prediction of solar flares from SDO images using deep learning

In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).     

New method of enhancement using wavelet transforms applied to SODISM telescope

PICARD is a space-based observatory hosting the Solar Diameter Imager and Surface Mapper (SODISM) telescope, which has continuously observed the Sun from July 2010 and up to March 2014. In order to study the fine structure of the solar surface, it is helpful to apply techniques that enhance the images so as to improve the visibility of solar features such as sunspots or faculae. The objective of this work is to develop an innovative technique to enhance the quality of the SODISM images in the five wavelengths monitored by the telescope at 215.0?nm, 393.37?nm, 535.7?nm, 607.1?nm and 782.2?nm. An enhancement technique using interpolation of the high-frequency sub-bands obtained by Discrete Wavelet Transforms (DWT) and the input image is applied to the SODISM images. The input images are decomposed by the DWT as well as Stationary Wavelet Transform (SWT) into four separate sub-bands in horizontal and vertical directions namely, low-low (LL), low–high (LH), high-low (HL) and high–high (HH) frequencies. The DWT high frequency sub-bands are interpolated by a factor 2. The estimated high frequency sub-bands (edges) are enhanced by introducing an intermediate stage using a Stationary Wavelet Transform (SWT), and then all these sub-bands and input image are combined and interpolated with half of the interpolation factor α/2, used to interpolate the high-frequency sub-bands, in order to reach the required size for IDWT processing. Quantitative and visual results show the superiority of the proposed technique over a bicubic image resolution enhancement technique. In addition, filling factors for sunspots are calculated from SODISM images and results are presented in this work.