Exploitation Of Space Research Articles

A single-loop reliability-based design optimization using adaptive differential evolution

Reliability-based design optimization (RBDO) is an efficient tool for generating reliable and optimal solution under uncertainty of design variables. The major challenges in solving RBDO problems are generating optimal or near optimal reliable solution and higher computational cost. In this paper, a single-loop RBDO formulation is developed for addressing these challenges that uses shifting vector approach for achieving feasibility for violated constraints or performance functions. The formulation also incorporates target and trial vectors of differential evolution (DE) for guiding the algorithm. DE is also made adaptive by designing a heuristic parameter that controls two mutation operators for both exploration and exploitation of search space. The proposed RBDO method is tested on three mathematical and four engineering examples. The reliability of obtained solutions from RBDO methods are verified using Monte Carlo simulations with sample size of one million. The results of the proposed method are compared with various RBDO methods from the literature and a double-loop based DE method. It is found that the proposed method generates the best reliable solution for all examples.

Data clustering using leaders and followers optimization and differential evolution

Data clustering is an important research topic in data mining. Although cluster analysis based on optimization algorithms has attracted great attention, optimization-based techniques face difficulties due to the non-linear objective function and complicated search space. Leaders and followers optimization (LaF) introduced in the 2015 IEEE Congress on Evolutionary Computation, and differential evolution algorithm (DE) are two efficient evolutionary computation methods, and they own some special advantages. The key power of LaF is the exploration in multi-modal search spaces, but it has a poor performance in the exploitation. On the other hand, DE based on the DE/best/1 mutation operator significantly promotes the exploitation process. In this study, the strong properties of LaF and DE are combined to balance exploration and exploitation in the search space to discover the cluster centroids. Besides, the proposed clustering method, i.e., LaF-DE, does not need parameter settings, unlike the existing optimization-based partitional clustering methods. Hence, this study proposes a straightforward, parameter-free, and efficient novel hybrid algorithm for the optimization-based partitional data clustering problem. Many experiments on the functions from CEC2017 test suite show that LaF-DE has better optimization performance and higher stability than the state-of-the-art metaheuristic algorithms. LaF-DE has been compared with well-known clustering techniques on the UCI and Shape datasets. The experimental results and statistical tests indicate that LaF-DE outperforms the well-known partitional clustering methods on 8 of 12 datasets in terms of internal performance metrics. Besides, LaF-DE performs better than density peaks clustering on 9 of 12 datasets in terms of external performance metrics.

Deep Rock Mass Engineering: Excavation, Monitoring, and Control

With the continuing development of the global economy and society, the exploitation of underground space is undergoing an unprecedented prosperity period [...]

Switching of behavioral modes and their modulation by a geometrical cue in the ciliate Stentor coeruleus.

Protists ubiquitously live in nature and play key roles in the food web chain. Their habitats consist of various geometrical structures, such as porous media and rigid surfaces, affecting their motilities. A kind of protist, Stentor coeruleus, exhibits free swimming and adhering for feeding. Under environmental and culture conditions, these organisms are often found in sediments with complex geometries. The determination of anchoring location is essential for their lives. However, the factors that induce the behavioral transition from swimming to adhering are still unknown. In this study, we quantitatively characterized the behavioral transitions in S. coeruleus and observed the behavior in a chamber with dead ends made by a simple structure mimicking the environmental structures. As a result, the cell adheres and feeds in narrow spaces between the structure and the chamber wall. It may be reasonable for the organism to hide itself from predators and capture prey in these spaces. The behavioral strategy for the exploration and exploitation of spaces with a wide variety of geometries in their habitats is discussed.

Explotar y gestionar el bosque entre la memoria y la práctica: La constitución de las dehesas forestales del monasterio de Cardeña, siglos X y XI

The aim of this article is to thoroughly analyse the practices of exploitation and management of the early medieval woods through the analysis of the constitution of the dehesas of the monastery of Cardeña, in Castile. In order to do so, two levels are identified which hardly ever are distinguishable: on the one hand, the monastic discourse about the possession projected over his wood properties and, on the other hand, the use and appropriation perceptible in these charters. This involves the comprehension of the exploitation of non-cultivated spaces according to an interaction with cultivated ones and the precision of different practices and logics of appropriation and management. The relationships between different users and with the monastery around woods over 10th and 11th centuries are analysed. This perspective could help us to reconsider the capacity of peasant groups to act over non-cultivated lands and to organize territory according to collective logics and negotiation with lords.

Intelligent short term traffic forecasting using deep learning models with Bayesian contextual hyperband tuning

AbstractAn intelligent transport system (ITS) is fully valuable only if it can dynamically and aptly integrate all the latest cutting‐edge technologies. An ITS focuses on providing services like promptly offering real‐time road traffic information to interested parties, finding ways to reduce the average waiting time and offer secure and reliable services for commuters using past statistics. Short‐term traffic prediction is one such area in which the research community has focused in the past decade. Existing models developed for prediction has scope to improve in terms of accuracy and training time. There is a necessity to develop a best‐performing model that is computationally affluent to train with the optimal hyperparameter configuration as input which leads to improved performance. This article proposes a model that captures the traffic flow trend present in the past data to predict the flow for a future time interval. This model is an amalgamation of seasonal global trend (SGT) model and long short‐term memory (LSTM) model with attention mechanism. A novel hyperparameter tuning algorithm is also proposed which is based on multi‐armed bandit strategy with context, incorporating the right trade‐off between exploitation and exploration of the hyperparameter space using successive halving. Experimental results conducted proves that our forecast model in combination with the proposed hyperparameter tuning algorithm outperforms the existing models like SGT, LSTM models in terms of accuracy and time.

Relationship between Topological Structure and Ecosystem Services of Forest Grass Ecospatial Network in China

Forest and grass ecological space is the key component of the ecosystem and plays a vital role in regulating the carbon, water, and energy cycle. The long-term exploitation of forest and grass ecological space and huge population pressure have gradually degraded the function of China’s ecosystem. Therefore, forest and grass ecological space plays an important role in maintaining the stability of the ecosystem. The relationship between forest and grass ecospatial network structure and ecosystem service has been the focus of research. In this study, the forest and grass ecospatial network is constructed based on the minimum cumulative resistance (MCR) model. Then, the topological indicators (degree, weight clustering coefficient, node weight, unit weight, weight distribution difference, betweenness, PageRank) of the forest and grass ecospatial network were calculated by combining the complex network theory to analyze the relationship between these topological indicators and the three ecosystems (water retention, soil conservation, carbon storage). Based on the ecological significance of topological indicators, we identified ecologically fragile areas and proposed areas and directions for optimizing the ecospatial structure. Results show that the spatial distribution of the three ecosystem services in the southeast region of China is higher than that in the northwest region of China and shows a gradual decrease from the east to the west. The degree, node weight, unit weight, PageRank, and betweenness were highly significant and positively correlated with the three ecosystem services, among which PageRank had the highest correlation with water retention (p < 0.01, R2 = 0.835). Based on the spatial distribution characteristics of the different topological indicators, the quantitative relationship between the structural characteristics of the forest and grass ecospatial network and ecosystem services is clarified, revealing the intrinsic connection between ecological processes and ecosystem services. Through rational optimization of the forest and grass ecospatial network, ecosystem services can be effectively improved and ecosystem stability can be enhanced.

Review of bio-inspired optimization applications in renewable-powered smart grids: Emerging population-based metaheuristics

The management of renewable-powered smart grids deals with nonlinear optimization problems featuring a variety of linear or nonlinear constraints, discrete or continuous optimization variables, involving high dimensionality of the solution space, and strict time requirements to identify the optimal or near-optimal solution. One promising approach for addressing such optimization problems is to apply bio-inspired population-based optimization algorithms, many such metaheuristics emerging lately. In this paper, we have identified the metaheuristics with the highest impact published recently and reviewed their applications in the management of renewable-powered smart energy grids using the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology and the Web of Science Core Collection as the reference database. Four main smart grid application domains we been analyzed: (i) energy prediction models’ optimization to reduce uncertainty (ii) energy resources coordination to handle the stochastic nature of renewables, (iii) demand response using controllable loads and flexibility while considering the consumers’ needs and constraints and (iv) optimization of grid energy efficiency and costs. The results showed the advantages of such metaheuristics for decentralized optimization problems with low computational time and resource overhead. At the same time, several issues need to be addressed to increase their adoption in the smart grid management scenarios: the lack of standard testing methodologies and benchmarks, efficient management of exploration and exploitation of the optimization search space, guidelines for metaheuristics application with clear links to the type of optimization problems, etc.

Spotted Hyena Optimization and Simulated Annealing-Based NLOS Nodes Localization Scheme for Improving Warning Message Dessimination in VANETs

An accurate localization technique is considered as the significant entity in vehicular ad hoc networks (VANETs) for facilitating emergency message data transmission in diversified critical safety applications. In VANETs, the system of global positioing is generally used for estimating the position of the vehicles in the network for attaining neighborhood awareness in the event of warning message dissemination. However, the existence of green foliages, buildings, indoor parking lots and urban streen canyons introduces NLOS situation that introduces unwanted errors that crumbles the degree of data dissemination in emergency situations. In this paper, spotted hyena and simulated annealing optimization algorithm (SHSAOA)-based positioning scheme was proposed for precise estimation of NLOS nodes. İt included the advantages of improved simulated annealing (SA) integrated into SHOA for establishing better balance between the process of exploitation and exploration in the search space. This positioning approach generated candidate solutions by deriving the merits of the trajectory-based charateristics of SA throughout the algorithmic development process in order to improve the local optimization process. This proposed SHSAOA utilized the distance infotmation that are associated with the vehicle trajectory, number of vehicles and error in distance information for assessing the precise location of the NLOS nodes in the network. The simulation results of the proposed SHSAOA scheme confirmed minimized localization error with maximized accuracy in transmission, warning message transmission rate, channel utilization degree and neighborhood awareness degree with different vehicular density and NLOS nodes.

Space Law and Arbitration. A Not-So-Outlandish Space Odyssey

The exploration and exploitation of space has attracted the attention of many States, as well as of private stakeholders. In the past couple of years, they have poured drastically increasing amounts of resources, financial and otherwise, into space-related activities. Despite the rapid economic and technological developments, however, the legal framework around space-related activities, including dispute settlement, has largely remained the same as the one first established in the 1960’s. The question thus arises whether such a framework can harbor the increasing demand of spacerelated disputes, and if not, what changes could be made, particularly in international arbitration, to address the specificities and complexities of this fast-growing field.

Enhanced Arithmetic Optimization Algorithm for Parameter Estimation of PID Controller

The Proportional-Integral-Derivative (PID) controller is a key component in most engineering applications. The main disadvantage of PID is the selection of the best values for its parameters using traditional methods that do not achieve the best response. In this work, the recently released empirical identification algorithm that is the Arithmetic Optimization Algorithm (AOA) was used to determine the best values of the PID parameters. AOA was selected due to its effective exploration ability. Unfortunately, AOA cannot achieve the best parameter values due to its poor exploitation of search space. Hence, the performance of the AOA exploit is improved by combining it with the Harris Hawk Optimization (HHO) algorithm which has an efficient exploit mechanism. In addition, avoidance of trapping in the local lower bounds of AOA–HHO is enhanced by the inclusion of perturbation and mutation factors. The proposed AOA–HHO algorithm is tested when choosing the best values for PID parameters to control two engineering applications namely DC motor regulation and three fluid level sequential tank systems. AOA–HHO has superiority over AOA and comparative algorithms.

Constrained robust Bayesian optimization of expensive noisy black‐box functions with guaranteed regret bounds

AbstractMany real‐world design problems involve optimization of expensive black‐box functions. Bayesian optimization (BO) is a promising approach for solving such challenging problems using probabilistic surrogate models to systematically tradeoff between exploitation and exploration of the design space. Although BO is often applied to unconstrained problems, it has recently been extended to the constrained setting. Current constrained BO methods, however, cannot identify solutions that are robust to unavoidable uncertainties. In this article, we propose a robust constrained BO method, constrained adversarially robust Bayesian optimization (CARBO), that addresses this challenge by jointly modeling the effect of the design variables and uncertainties on the unknown functions. Using exact penalty functions, we establish a bound on the number of CARBO iterations required to find a near‐global robust solution and provide a rigorous proof of convergence. The advantages of CARBO are demonstrated on two case studies including a non‐convex benchmark problem and a realistic bubble column reactor design problem.

Bayesian optimization with experimental failure for high-throughput materials growth

A crucial problem in achieving innovative high-throughput materials growth with machine learning, such as Bayesian optimization (BO), and automation techniques has been a lack of an appropriate way to handle missing data due to experimental failures. Here, we propose a BO algorithm that complements the missing data in optimizing materials growth parameters. The proposed method provides a flexible optimization algorithm that searches a wide multi-dimensional parameter space. We demonstrate the effectiveness of the method with simulated data as well as in its implementation for actual materials growth, namely machine-learning-assisted molecular beam epitaxy (ML-MBE) of SrRuO3, which is widely used as a metallic electrode in oxide electronics. Through the exploitation and exploration in a wide three-dimensional parameter space, while complementing the missing data, we attained tensile-strained SrRuO3 film with a high residual resistivity ratio of 80.1, the highest among tensile-strained SrRuO3 films ever reported, in only 35 MBE growth runs.

Markov-based genetic algorithm with [formula omitted]-greedy exploration for Indian classical music composition

Recent technological advancements have made it possible to automate some of the intricate activities involved in music production. In this paper, a heuristic method is proposed for automatic composition of Indian Classical Music (ICM). Composing ICM necessitates consideration of raga constraints which distinguishes it from other genres of music. The raga-based music cannot be generated through random exploration of search space. More specifically, certain disordering in note combinations can violate the raga constraints. To address the aforesaid issue, the paper proposes a novel method by combining genetic algorithm with Markov chain model attuned to ICM sequences. Moreover, the proposed method uses ϵ-greedy strategy to balance the exploration and exploitation of musical search space. In order to preserve the musical characteristics, domain-specific genetic operators (i.e., crossover and mutation) are defined. The evaluation function based on music theory is used to direct the search to favorable tracks. The Kullback Liebler (KL) divergence metric is used to analyze the probability distribution of output and sample data sequence. The results indicate that by regulating the exploration rate, the method is capable of generating melodic sequences while maintaining the desired musical note distribution. Moreover, the method converges faster than the conventional genetic algorithm.

Characterizing Production–Living–Ecological Space Evolution and Its Driving Factors: A Case Study of the Chaohu Lake Basin in China from 2000 to 2020

The division of the territorial space functional area is the primary method to study the rational exploitation and use of land space. The research on the Production–Living–Ecological Space (PLES) change and its motivating factors has major implications for managing and optimizing spatial planning and may open up a new research direction for inquiries into environmental change on a global scale. In this study, the transfer matrix and landscape pattern index methods were used to analyze the temporal changes as well as the evolution features of the landscape pattern of the PLES in the Chaohu Lake Basin from 2000 to 2020. Using principal component analysis and grey correlation analysis, the primary driving indicators of the spatial changes of the PLES in the Chaohu Lake Basin and the degree of the influence of various driving factors on various spatial types were determined. The study concluded with a few findings. First, from the standpoint of landscape structure, the Chaohu Lake Basin’s agricultural production space (APS) makes up more than 60% of the total area, and it and urban living space (ULS) are the two most visible spatial categories. Second, the pattern of the landscape demonstrates that the area used for agricultural production holds a significant advantage within the overall structure of the landscape. Although there is less connectedness between different landscape types, less landscape dominance, and more landscape fragmentation, the structure of different landscape types tends to be more varied. Third, the findings of the driving analysis demonstrate that the natural climate, population structure of agricultural development, and industrial structure of economic development are the three driving indicators of the change of the PLES. Finally, in order to promote the formation of a territorial space development pattern with intensive and efficient production space, appropriate living space, and beautiful ecological space, it is proposed to carry out land regulation according to natural factors, economic development, national policies, and other actual conditions.

From the history of space research in Ukraine. 1. The performance of space research as of 1991 year

The article displays the material of 1991 from the author’s archive, which is devoted to the problems of space research arrangement and the space industry authority in independent Ukraine. A brief description of the space research level in Ukraine at that time is given. The active participation of Ukraine in the activity of international organizations and the implementation of USSR space programs are highlighted. The significant achievements and acquirements of scientific and design teams in the space research branch are emphasized. Among them, there were the rocket design center DB “Pivdenne” (now — Yangel Yuzhnoye State Design Office), E. O. Paton Electric Welding Institute of NAS of Ukraine, and many other enterprises and scientific institutions in Ukraine. In new conditions, under the newly acquired independence, the formidable task of preserving the aerospace industry of Ukraine arose. Within this task, it was necessary to preserve scientific and developing potential and to provide perspectives for scientific and technical progress. All this was possible only within the framework of the concept that had to be developed and which would contain the main goals of the national politics of Ukraine as an independent state in the branch of exploration and exploitation of space. It was necessary to create the aerospace agency of Ukraine for the coordination of space programs and to develop a law to regulate its activities. One more task was to adopt the state program of space exploration by the Verkhovna Rada, the program, which had to provide for the progress of fundamental and applied space research.

A Novel Multiobjective Fireworks Algorithm and Its Applications to Imbalanced Distance Minimization Problems

Recently, multimodal multiobjective optimization problems (MMOPs) have received increasing attention. Their goal is to find a Pareto front and as many equivalent Pareto optimal solutions as possible. Although some evolutionary algorithms for them have been proposed, they mainly focus on the convergence rate in the decision space while ignoring solutions diversity. In this paper, we propose a new multiobjective fireworks algorithm for them, which is able to balance exploitation and exploration in the decision space. We first extend a latest single-objective fireworks algorithm to handle MMOPs. Then we make improvements by incorporating an adaptive strategy and special archive guidance into it, where special archives are established for each firework, and two strategies (i.e., explosion and random strategies) are adaptively selected to update the positions of sparks generated by fireworks with the guidance of special archives. Finally, we compare the proposed algorithm with eight state-of-the-art multimodal multiobjective algorithms on all 22 MMOPs from CEC2019 and several imbalanced distance minimization problems. Experimental results show that the proposed algorithm is superior to compared algorithms in solving them. Also, its runtime is less than its peers'.

Effect of soil Young’s modulus on Sub-rectangular tunnels behavior under quasi-static loadings

Tunnels are an important component of the transportation and utility system of cities. They are being constructed at an increasing rate to facilitate the need for space expansion in densely populated urban areas and mega-cities. The circular and rectangular tunnels cannot completely meet the requirements of underground space exploitation regarding the cross-section. Sub-rectangular tunnels are recently used to overcome some drawbacks of circular and rectangular tunnels in terms of low utilization space ratio and stress concentration, respectively. However, the behavior of the sub-rectangular tunnels under seismic loading is still limited. This need to be regarded and improved. This paper focuses on conducting a numerical analysis to study the behavior of the sub-rectangular tunnels under seismic loadings. Here seismic loadings in this study are represented by quasi-static loadings. Based on the numerical model of the circular tunnel that was validated by comparison with analytical solutions, the numerical model of the sub-rectangular tunnel is created. This paper is devoted to highlight the differences between the behavior of the sub-rectangular tunnels compared with the circular ones subjected to quasi-static loadings. The soil-lining interaction, i.e., full slip and no-slip conditions are particularly considered. The influence of soil’s Young’s modulus on the sub-rectangular tunnel behavior under quasi-static loading is also investigated. The results indicated that soil’s Young’s modulus significantly affects static, incremental, and total internal forces in the tunnel lining under quasi-static loadings. Special attention is a significant difference in total internal forces in the sub-rectangular tunnel lining in comparison with the circular tunnel ones and the stability of the lining tunnel for both the full slip and no-slip conditions when subjected to quasi-static loadings.

Analytical model for collision probability assessments with large satellite constellations

At a time when space debris are already a growing issue in the space sector, the deployment of large constellations, made of hundreds to thousands of satellites, is of concern from an environmental point of view. In the next decade, the space sector will undergo a considerable change as the population of active satellites is about to quintuple. This scenario will pose new challenges regarding space traffic management, generating the demand for more powerful and efficient tools.In this study, an analytical model for collision probability assessments between de-orbiting or injecting space objects and satellite constellations is presented. Considering the first to be subjected to a continuous tangential acceleration, its spiraling motion would result in a series of close approaches in the proximity of a constellation. The mathematical description of the crossing dynamics relies on the assumptions of circular orbits and independent collision probabilities, but does not require to propagate the satellites’ orbit. The statistical model presented in the current work constitutes an efficient tool for the evaluation of the mean collision probability related to this type of events. A comparison with a propagation method has been performed for validation purposes, showing that the model is capable of delivering accurate results, also in case of elliptical crossing orbits.The statistical model has been used to assess the risk connected to constellation’s satellites replacement, once they have reached their programmed End-of-Life. The environmental impact of the full replacement of 12 approved constellations is analysed by means of average collision probability. In particular, it is shown that the key features for space exploitation sustainability are the maximum thruster acceleration, the size of the satellites and the true anomaly phases between constellations’ and crossing satellites. The consequences of an in-orbit collision are also investigated by assessing the collision risk generated by the formation of a debris cloud.The results corroborate the need for international standards for space traffic management as an exponentially increasing satellite population could trigger a chain reaction of collisions, making Low Earth Orbit inaccessible for decades.

A two-layer mono-objective algorithm based on guided optimization to reduce the computational cost in virtual screening

Virtual screening methods focus on searching molecules with similar properties to a given compound. Molecule databases are made up of large numbers of compounds and are constantly increasing. Therefore, fast and efficient methodologies and tools have to be designed to explore them quickly. In this context, ligand-based virtual screening methods are a well-known and helpful tool. These methods focus on searching for the most similar molecules in a database to a reference one. In this work, we propose a new tool called 2L-GO-Pharm, which requires less computational effort than OptiPharm, an efficient and robust piece of software recently proposed in the literature. The new-implemented tool maintains or improves the quality of the solutions found by OptiPharm, and achieves it by considerably reducing the number of evaluations needed. Some of the strengths that help 2L-GO-Pharm enhance searchability are the reduction of the search space dimension and the introduction of some circular limits for the angular variables. Furthermore, to ensure a trade-off between exploration and exploitation of the search space, it implements a two-layer strategy and a guided search procedure combined with a convergence test on the rotation axis. The performance of 2L-GO-Pharm has been tested by considering two different descriptors, i.e. shape similarity and electrostatic potential. The results show that it saves up to 87.5 million evaluations per query molecule.