The interactions of the composite materials with the space radiation environment are a considerably complex process. These interactions can lead to the occurrence of collective and extensive effects on the composite materials. These effects have resulted in damage and degradation of the electronics and components of satellites and spacecraft, and anomalies in the operational parts of space devices and other aerospace systems. The prediction of the material performances in this complicated environment is essential and considered the fundamental item for the success of any space mission. The presented research work focuses on studying the impact of space radiation sources on the composite materials used for satellite and spacecraft systems. The effects of some of the irradiation sources on a variety of composite materials are discussed. The protection and mitigation techniques from radiation damages are addressed.

In the presented paper, a model describing the range equation, the energy received, the number of returned photons, and photoelectrons as well as the Time-Of-Flight, has been analytically studied. The data concerning the Satellite Laser Ranging (SLR) observed from the Changchun Observatory are considered to evaluate some of the parameters such as the energy received to ground telescope E_recv, the number of returned photons N_p and photoelectrons N_pe. The SLR-data are taking into our consideration with the summarised results for the observations taken from the first quarter of 2018. The model is applied on a variety of satellites with different heights and configurations. The results have shown and confirmed the dependency of these parameters on the properties of the laser and detector systems and the used setup.

One of the critical challenges for solar arrays operating in space is the occurrence of discharging and arcing on the array surfaces exposed to plasma environments. Arcs can lead to severe damage to cell interconnectors due to the generated high peak currents and therefore significantly impact the performance and reliability of spacecraft systems. Solar arrays with highly negative biases are particularly prone to frequent arcs, which can escalate into sustained arcs, causing harm to spacecraft and satellite components. The presented study aims to investigate discharging and arc spectra on solar cell surfaces through the analysis of arc images. Leveraging advanced Deep Learning (DL) methodologies, including Convolutional Neural Networks (CNN) and Transfer Learning, a robust predictive model has been developed to analyze arc behavior and identify defective cells based on image data. Furthermore, algorithms and image processing tools, such as Python and Maxim-DL, are employed to examine variations in intensities and spatial variation in the arced region. The findings highlight the prevalence of intensive arcs, particularly on mid-cells and interconnectors, and provide insights into the dynamics of sustained arc events. The image analysis can advance the understanding of arc evolution, enabling improved mitigation strategies for solar array systems in space applications.

The simulation of the Low Earth Orbit (LEO) plasma environment is essential for understanding the effects of space plasma interactions on space systems and subsystems. Spacecraft charging and arcs are among the most significant problems related to plasma impact. These can cause variations in the physical characteristics of materials and spacecraft systems. Ground-based experimental studies are proposed to investigate the effects of a simulated LEO plasma environment. The experiments have shown that plasma effect variations depend on the applied conditions and plasma properties. One of the key factors determining the estimated plasma properties is the source used to produce plasma. Various plasma sources have been developed to simulate the conditions and properties of LEO plasma. This study presents various sources applied to generate a simulated LEO plasma environment. Different techniques used for measuring plasma characteristics are presented in the paper. Examples of the operating conditions and plasma properties are also addressed.

Optical observation is one of the most common techniques used for characterizing the physical properties of unknown objects and debris in space. This research presents measurements and properties of the new object 96019 from ground-based optical methods. Optical observations of this small object were performed using a charge-coupled device (CCD) camera and the Santel-500 telescope at the Zvenigorod Observatory. The orbital elements and physical properties of this object, such as areato- mass ratio, have been determined. The results show that this small object has a low area-to-mass ratio, between 0.009 and 0.12 m2/kg. The light curve of object 96019 is given: Over the time intervals, variations in brightness are analyzed and the maximum brightness was found to be 12.4 magnitudes. The observational results show that, this object brightens by about three magnitudes over a time span of three minutes. Based on these observations, the characteristics and physical properties of this object are discussed.

The rapid growth of astronomical imaging data from next-generation surveys necessitates automated and scalable approaches to galaxy morphology classification that transcend the limitations of supervised methods requiring manual labels. We present an unsupervised multimodal deep learning framework that integrates ConvNeXt-derived visual embeddings with quantitative morphological parameters including concentration, asymmetry, smoothness, Gini, and M20 to uncover natural taxonomic structures within galaxy populations. Using a strictly purged and physically verified sample of 4950 galaxies from the Sloan Digital Sky Survey, we engineered a PyTorch-based Multimodal Autoencoder (MAE) to compress features into a dense 64-dimensional bottleneck, successfully resolving the inherent dimensionality imbalance. Clustering was executed exclusively within this robust latent space utilizing a probabilistic Gaussian Mixture Model (GMM). An explicit ablation study confirmed that this multimodal architecture optimizes structural cohesion compared to isolated modalities. Furthermore, we established the astrophysical integrity of the unsupervised clusters through a new proxy external validation against classical heuristic constraints, achieving a 52.7% baseline alignment. By utilizing GMM log-likelihoods, we isolated extreme physical anomalies (limiting the noise fraction to 2.0%), producing a physically coherent taxonomy that maps seamlessly to Early-Type, Late-Type, and Interacting systems. Each galaxy was processed in ~ 27.6 ms, demonstrating strong scalability for upcoming large-scale surveys such as LSST and Roman. This study establishes a foundation for unsupervised morphology analysis at survey scale, advancing our understanding of galaxy evolution through multimodal deep representation learning.