The integration of artificial intelligence (AI) technologies across all segments of space systems, including the launch, space, ground, and user segments, holds immense potential to revolutionize space exploration, satellite operations, and communication networks. This paper presents a comprehensive overview of AI-powered systems in each segment, highlighting their key functionalities, benefits, and challenges. In the launch segment, AI algorithms can optimize launch vehicle trajectories, predict launch conditions, and facilitate the safety of space missions. Machine learning techniques can enable real-time decision-making and autonomous control during launch operations, improving launch success rates and reducing costs. Within space segment, AI-powered satellites can have enhanced capabilities in autonomous navigation, attitude control, and mission planning. These systems leverage AI algorithms to analyze sensor data, detect anomalies, and autonomously adapt to dynamic space environments, increasing mission resilience and flexibility. In the ground segment, AI-powered systems can facilitate satellite operations, data processing, and communication management. Intelligent ground stations utilize machine learning algorithms to optimize antenna pointing, schedule satellite contacts, and process large volumes of satellite data efficiently, enabling faster and more reliable communication services. Finally, in the user segment, AI technologies can enhance the user experience and enable innovative applications in space science, earth observation, and satellite-based services. AI-powered data analytics platforms can provide users with actionable insights from satellite imagery, sensor data, and telemetry, enabling informed decision-making and driving advancements in various domains. Despite the significant benefits offered by AI-powered systems across all segments, challenges such as data quality, algorithm robustness, and ethical considerations remain critical areas for further research and development. Addressing these challenges will be essential to fully harnessing the potential of AI in advancing space exploration, satellite operations, and space-based applications. Through research findings, and technological advancements, this paper aims to provide insights into the current state-of-the-art and future prospects of AI-powered systems in space, paving the way for continued innovation in space exploration.

Aminohydroxylation of alkene is an important method for synthesizing 1,2-amino alcohols, which are found in natural products, pharmaceutically active compounds, and several marketed drugs. Herein, we report the development of visible-light mediated copper-catalyzed aminohydroxylation and aminoalkoxylation of alkenes. In contrast to expensive iridium, ruthenium, or organic-dyes-based photocatalysts, this protocol takes advantage of economical copper phenanthroline complex as a photocatalyst for the generation of N-centred radical from N-amino pyridinium salt. Furthermore, this protocol features mild reaction conditions, a broad substrate scope, and regiospecific.

The collision avoidance constraints are prominent as non-convex, non-differentiable, and challenging when defined in optimization-based motion planning problems. To overcome these issues, this paper presents a novel non-conservative collision avoidance technique using the notion of convex optimization to establish the distance between robotic spacecraft and space structures for autonomous on-orbit assembly operations. The proposed technique defines each ellipsoidal- and polyhedral-shaped object as the union of convex compact sets, each represented non-conservatively by a real-valued convex function. Then, the functions are introduced as a set of constraints to a convex optimization problem to produce a new set of differentiable constraints resulting from the optimality conditions. These new constraints are later fed into an optimal control problem to enforce collision avoidance where the motion planning for the autonomous on-orbit assembly takes place. Numerical experiments for two assembly scenarios in tight environments are presented to demonstrate the capability and effectiveness of the proposed technique. The results show that this framework leads to optimal non-conservative trajectories for robotic spacecraft in tight environments. Although developed for autonomous on-orbit assembly, this technique could be used for any generic motion planning problem where collision avoidance is crucial.

The proposed work approaches machine learning based Lithium Iron-phosphate (LIP) battery state of charge (SoC) forecasting method for electric vehicles (EVs). The SoC must be accurately estimated to operate safely and steadily for an LIP battery. It is challenging for the user to determine the battery SoC during the charging segment because of the random nature of their charging process. One of the fundamental components of artificial intelligence, machine learning (ML) is rapidly transforming a wide range of fields with its capacity to learn from given data and resolve complex problems. A Grey Wolf optimization (GWO) algorithm is used to modify the most pertinent hyperparameters of the SVR model to enhance the accuracy of the predictions. The database used in the study was gathered from a laboratory setup and includes information on temperature, voltage, current, average voltage, and, average current. Initially, a preprocessing method for data normalization is utilized to improve the data quality and forecast accuracy. It is demonstrated that the accuracy of the suggested model for estimating the SoC of the LIP battery shows less than 3% MSE between the measured and expected states of charge.

The rise in transmission line voltage has significantly raised the importance of insulator contamination studies to understand the physical principles governing this occurrence and to ascertain the flashover behaviour of contaminated high-voltage insulators. An essential factor influencing the performance and reliability of electrical transmission networks is the pollution of the insulators, which is considered as the most essential component of the flashover phenomena. The implications of the flashover phenomena on polymeric insulators performance used in transmission lines have been the subject of significant experimental research projects. The objective of this work is to address this research gap by creating a reliable, laboratory-based artificial pollution testing technique developed specifically for high-voltage polymeric insulators. The flashover phenomenon, the maximum degree of pollution that could be withstand, the effects of fan-shaped pollution, the influence of orientation angle, and the impact of flashover voltage on dry band position were all the subjects of investigation. The test findings showed that the flashover voltage varies with the position of the dry band, and the orientation angle has little effect on the insulator’s ability to flash over in dry circumstances. The insulators functioned better when tilted under damp conditions. An inverse relationship was found between the voltage at flashover and time, and an inverse relationship was found between the pollution severity and the time to flashover.

In this paper, actuator fault detection and reconstruction in consensus tracking of uncertain multi‐agent systems (MAS) is addressed. The communication is assumed to be connected undirected. An adaptive fault detection method is developed to detect actuator faults. A novel‐reinforced unscented Kalman filter (RUKF) is employed to reconstruct the faults by adjusting the noise covariance matrices of unscented Kalman filter (UKF) as well as to train neural network internal parameters by providing a set of previous measurements. A Chebyshev neural network (CNN) is incorporated to learn the uncertain plant. To prevent the neural network approximation errors a hyperbolic tangent function‐based robust control term is applied. The Lyapunov stability approach guarantees the stability of the proposed RUKF, which runs in conjunction with robust control method. Lastly, numerical simulations are presented to show the effectiveness of the proposed RUKF under actuator abrupt, intermittent, and transient fault conditions.

In the original version of the book and cover, the following belated correction has been incorporated: The volume editor name "Raghavendra Kumar Chaudhary" has been changed to "Raghvendra Kumar Chaudhary" throughout the book and cover. The book has been updated with the changes

A microgravity environment in space, the elasticity of the tether, and complex flexible appendages make the tethered-towing system a nonlinear and underactuated system, which is sensitive and difficult to stabilize. This letter develops a controller based on wave propagation for tethered towing of defunct satellites, and carries out a robustness analysis of the controller via numerical simulation.

In this paper, the problem of consensus tracking of uncertain multi-agent systems (MAS) with communication faults is addressed. The communication is assumed to be undirected. A reinforced unscented Kalman filter (RUKF) is employed to adapt the noise covariance matrices and to estimate the uncertain states of MAS as well as to train neural network internal parameters by providing a set of prior measurements. A Chebyshev neural network (CNN) is incorporated to learn the uncertain plant. To avert the neural network approximation errors a hyperbolic tangent function based robust control term is applied. The stability of the RUKF which is running simultaneously with the robust control term has been proven using Lyapunov stability approach. Numerical simulations are presented under different fault conditions to show the effectiveness of the proposed RUKF with 5% less computation power compared to adaptive unscented Kalman filter (AUKF).

Numerous electrode designs, such as a sphere, point, rod, and plane, are used to study the breakdown properties of gases where the field might be uniform or nonuniform. In this work, the electric field behaviour with the presence and absence of dielectric barrier Polyvinyl Chloride (PVC) was examined using the finite element method (FEM) to quantitatively assess how a dielectric obstruction affects the patterns of electric field and voltage distributions in electrode configurations that are oriented vertically. In this study, the spatial distribution of the electric field is examined over a range of barrier- and barrier-free electrode designs. The field analysis has been analysed using the COMSOL Multiphysics simulation tool, and experimental verification of each electrode design with and without a barrier was also conducted. Four distinct types of electrode configurations were selected. The field computation results show the manner in which the conditions of the environment, the dielectric barrier, and the types of electrodes affect the maximum electric field fluctuation. As a result, when the barriers were placed between the electrodes, the electric field intensity was at its peak.

In high-voltage equipment, unpressurized air is utilized generally as the principal insulating medium. Unfortunately, the tendency for the system to grow physically big is a flaw in the air-insulated design. However, using dielectric barriers might increase the breakdown voltage while also making the equipment smaller. Problems in high voltage techniques are mostly field oriented electrostatic problems. Many researchers have employed many numerical methods to solve the Laplace and Poisson equations for the fields among complicated electrode configurations. The breakdown properties of gases are widely studied using the needle-plane gap. The field pattern and distribution of voltage in a needle-plane gap with a vertical orientation were quantitatively analysed in this work using the finite element method (FEM). To determine the discharge phenomenon, the greatest field in the gap was tested for various with and without barriers concerning various locations between electrodes. Additionally, an approximate breakdown voltage simulation model is suggested and supported by the experimental findings.

The main purpose of the research was to analyze the distribution of Arsenic (As), Cadmium (Cd), Copper (Cu), Iron (Fe), Nickel (Ni), Cobalt (Co), Mangan (Mn), and Zinc (Zn) in soft tissues, shells, and associated surface sediments of Cerithidea obtusa (C. obtusa) mangrove snails collected from Sungai Besar Sepang. The concentration of iron (Fe) was found to be the highest in relation to other toxic elements in sediments, soft tissues, and shells of C. obtusa. The concentrations of Cu and Zn in soft tissues of C. obtusa were found to exceed the concentrations in sediments, indicating bioaccumulation of these metals. Metal pollution was assessed with the Enrichment Factor (EF), Geoaccumulation Index (Igeo), and Pollution Factor (CF). EF, Igeo, and CF were 0.34 to 22.41, -3.37 to 2.65, and 0.14 to 9.42, respectively. The results indicate that sediments in Sungai Besar Sepang are contaminated with As and Zn. According to the bivalve bioaccumulation results, the soft tissues of C. obtusa act as a macro-concentrator for Cu and Zn. As a result, it is suggested that ongoing monitoring of releases of heavy metals from anthropogenic sources and stricter environmental protection measures should be implemented.

The spiders were collected using in situ counts, net sweeping, pitfall traps, litter sampling and bark trap collection methods. A total of 79 spider species with 56 genera under 17 families were noted. The spider family Salticidae was found to be with high relative abundance during kharif 2019 (21.98%) and rabi 2019–20 (24.09%) than any other spider families. The biodiversity indices were worked out, such as Shannon-Weiner Index (3.31- kharif and 3.65- rabi), Simpson Index (0.94- kharif and 0.96- rabi), Margalef Index (9.61- kharif and 11.4- rabi) and Pielou's Index (0.054- kharif and 0.049- rabi).

In areas with high levels of pollution and vandalism, one remedy suggested is to place polymeric insulators on high-voltage transmission networks.. They are widely used although having some advantages compared to their light weight and ease of handling, which is an appealing aspect, they also come with a wide range of financial and operational issues. The electric field distribution simulation results for medium voltage composite insulators that have been constructed ideally are presented in this work in both polluted and unpolluted environments. The Comsol Multiphysics tool conducted simulations for both standard and optimized insulators. This paper's major goal was to examine the impact of electric field distributions in relation to pollution circumstances. The influence of the pollution layer's conductivity and thickness was examined together with the electric field's dispersion. According to the modelling findings, significantly contaminated ecosystems had higher maximal electrical field stresses than clean or slightly polluted ones did. The outcome of this investigation will further our understanding of how polymeric insulators behave in diverse contaminated settings.

There is a method used for growing plants. The method for growing plants is called hydroponics. In this method the crops are grown on water, rich in essential nutrients. This method provides a solution to the problem in watering the plants. Plants found in houses, workplaces and other public places will have persons to water the plants manually. An automation system with moisture sensors provided near the plant pot is used to detect whether the soil of the plant pot is wet or dry. The LCD notification board displays soil moisture level on the screen. The Pump motor is used to pump water to the plant-based on the display in LCD screen. Therefore, this proposed design will automatically monitor and control plants’ watering.

Oil palm (Elaeis guineensis) is one of the most nutrient-intensive plantations in Malaysia. It requires many nutrients to reach its full potential and production stage. The main issue is that nutritional stress is a crop health issue caused by a lack of nutrient components to sustain crop growth requirements. We aimed to review the nutrient deficiency of oil palm based on an observation and literature study. The leaflet of the oil palm tree was analyzed for this study. The result showed that nutrient deficiency in oil palm is a common issue. Since oil palm is a heavy feeder plant that requires a well-balanced supply of nutrients, the continuous use of sustainable (eco-friendly) biofertilizers at a cost-effectively optimal level is necessary to achieve the maximum yield of oil palm. Though there are some inevitable causes of nutrient deficiencies, good management program should be carried out to lessen this problem and improve oil palm health and production.

The abundance and diversity of ant fauna was studied from June 2019 to February 2020 using 3 different collection methods (pitfall trap, in situ counts and net sweeping method) and 3251 individuals under 6 subfamilies of Formicidae were recorded. The most abundant subfamily was Myrmicinae with 46.90% species followed by Formicinae (37.22%). The biodiversity analysis with Shannon-Wiener index, Simpson's index and Equitability index revealed that the diversity was more for Myrmicinae (1.5750, 4.6831 and 0.7574) and Margalef richness index maximum with Ponerinae (0.9320). Among the 38 ants observed, the population of Pheidole spathifera (Forel) (Myrmicinae) was the maximum during August, 2019 and the least during June, 2019.