Supercapacitors are widely used in China due to their high energy storage efficiency, long cycle life, high power density and low maintenance cost. This review compares the differences of different types of supercapacitors and the developing trend of electrochemical hybrid energy storage technology. It gives an overview of the application status of supercapacitors in China’s smart grid and Energy Internet in detail. Some strategies and constructive suggestions are put forward to solve the existing problems. With the improvement of the grid-connected capacity of new energy power generation during the 14th Five-year Period of China, the supercapacitor market in China will usher in a good development opportunity. The role of the supercapacitor in achieving carbon peak carbon neutralization is prospected.

Spacecrafts are critical systems that have to survive space environment effects. Due to its complexity, these types of systems are designed in a way to mitigate errors and maneuver the critical situations. Spacecraft delivers to the ground operator an abundance data related to system status telemetry; the telemetry parameters are monitored to indicate spacecraft performance. Recently, researchers proposed using Machine Learning (ML) / Telemetry Mining (TM) techniques for telemetry parameters forecasting. Telemetry processing facilitates the data visualization to enable operators understanding the behavior of the satellite in order to reduce failure risks. In this paper, we introduce a comparison between the different machine learning techniques that can be applied for low earth orbit satellite telemetry mining. The techniques are evaluated on the bases of calculating the prediction accuracy using mean error and correlation estimation. We used telemetry data received from Egyptsat-1 satellite including parameters such as battery temperature, power bus voltage and load current. The research summarizes the performance of processing telemetry data using autoregressive integrated moving average (ARIMA), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory Recurrent Neural Network (LSTM RNN), Deep Long Short-Term Memory Recurrent Neural Networks (DLSTM RNNs), Gated Recurrent Unit Recurrent Neural Network (GRU RNN), and Deep Gated Recurrent Unit Recurrent Neural Networks (DGRU RNNs).

Due to the space environment's hazards and challenges, aerospace systems are continuously exposed to many failures, such as the degradation of the subsystem performance, sensor faults, connection loss, or equipment damage. Therefore, the effective fault diagnosis for detecting and identifying any failures or unusual behaviors can be recognized as the fundamental and critical role of aerospace systems' predictive health management process. This paper proposes a novel fault diagnosis approach using Deep Learning (DL) technique. DL has recently become a popular approach in artificial intelligence (AI) due to its supremacy in accuracy and fast inference with the huge amount of data and highest order network structures. The proposed approach consists of two main phases; the feature selection phase by Binary Grasshopper Optimization Algorithm (BGOA), and the learning and prediction phase by Artificial Neural Networks (ANNs) with voting ensemble method. The proposed approach named BGOA-EANNs has been validated and evaluated its efficacy by comparing two existing diagnosis techniques using two types of aerospace health diagnosis datasets; satellite power system and aircraft engines. The experimental results demonstrated the effectiveness and superiority of the proposed approach.

The study of climate change has become an important topic because of its negative impact on human life. The North-East African part lacks the studies for climate change detection, despite it being one of the most affected parts worldwide. The relationship between the emission of greenhouse gases (GHGs) and climate change is an important factor to understand. To investigate this linkage, we used machine-learning (ML) models based on essential climate variables (ECVs) to investigate the relationship between the GHGs and the rhythm of climate variable change. The article investigates how ML techniques can be applied to climatic data to build an ML model that is able to predict the state of climate variables for the short and long term. By selecting a candidate model, it will help in climate adaptation and mitigation, also determine at what level GHGs should be kept and their corresponding concentrations in order to avoid climate events and crises. The used models are long short-term memory, autoencoders, and convolutional neural network (CNN). Alternatively, the dataset has been selected from U.K. National Centre for Earth Observation and Copernicus Climate Change Services. We compared the performance of these techniques and the best candidate was the Head—CNN; based on performance metrics such as root-mean-squared-error: 5.378, 2.395, and 15.923, mean-absolute-error: 4.157, 1.928, and11.672, Pearson: 0.368, 0.649, and 0.291, and R ² coefficient: 0.607, 0.806, and 0.539 for the ECVs temperature, CO ₂ , and CH ₄ , respectively. We were able to link the GHG emission to ECVs with high accuracy based on the reading of this geographic area.

In Earth Observation (EO) applications, the new trend moves toward using small satellites architectures like constellations and swarms. These architectures are low-cost, simple, and have relatively short development time. The Blockchain proved high efficiency in terrestrial systems due to its reliability, security, and latency reduction. In mobile satellite communication system, Blockchain represents a revolution in how to validate data integrity and create secure prioritized data exchange. In this paper, we present a survey about the adoption of Blockchain in space industry and its applications. This study presents challenges, suggested solutions and open research points in building satellite systems based on blockchain.