Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonlinear control of librational motion of tethered satellites in elliptic orbits

A method to control the librational motion of a tethered satellite system in an elliptic orbit is presented. To simplify the analysis, gravity is treated as the only external force affecting the tethered satellite system, only in-plane motion is considered, and the flexibility and mass of tethers are neglected. The tethered satellite system treated in this paper consists of two subsatellites and a mother satellite, such as the space shuttle, connected together in series via massless tethers. This type of tethered satellite system has very important applications in Earth observation, space observation, communications, and satellite constellations. The librational motion of the tethered satellite system in an elliptic orbit is known to be chaotic and can be stabilized to undergo periodic motion by the delayed feedback control method. The periodic motion of a tethered satellite in a circular orbit is employed as the reference trajectory for tracking by the actual tethered satellite, which is in an elliptic orbit. The decoupling and model tracking control methods, based on differential geometric control theory are combined with the delayed feedback control method in a new approach to controlling the librational motion of the tethered satellite system in elliptic orbits. The results of numerical simulations show that the proposed control scheme has good performance in controlling the librational motion of a tethered satellite system in an elliptic orbit.
Sustainable Embedding of e-Learning: A New Model Based on TAM, EVT and QiU

This study attempts to understand the students' acceptance of e-learning in Higher Education (HE) institutions. To better understand the facets that influence the acceptance of the use of e-learning by students, the authors have focused on frameworks that have in common the analysis of Human-Computer Interaction (HCI) from the point of view of the user's perception. This paper provides a theoretical framework for sustainable e-learning applied in HE institutions and aims to build a conceptual model using TAM, EVT, and QiU, that would help better understand the diverse dimensions that could predict HE students' continuous use of e-Learning. The main contribution of this research is the identification of antecedents to the intention to use e-learning by integrating three well-known frameworks related to technology use/adoption, namely TAM, EVT, and QiU in reliance on the cognitive process - involving perception- rather than the properties of the software itself. Moreover, HCI, technology use, and e-learning literature helped to analyze the similarities as well as the significance of constructs, which the authors synthesized through an e-Learning Acceptance Model. The new model will also help potential future research.
Online Tuning of Pre-trained Deep Neural Network for Guidance Against Non-cooperative Pursuer

In the astronautics, it is widely acknowledged as a hard problem that using neural network to handle the intelligent observation and guidance when dealing with non-cooperative pursuing vehicles. Due to the difficulty of obtaining training data set from non-cooperative vehicles, the inconsistency between training data and utilization data, application of neural network controller is by far very limited. In order to tackle this problem, an online tuning scheme of neural network is proposed in this paper aiming to boost the observation and counter action abilities against non-cooperative pursuer. In the proposed methodology, the deep neural network controller is pre-trained through dataset from generative network before deployment, and fine-tuned online through real time observations data set. In this way, the calculation pressure is alleviated for onboard computer and the contribution of real time observation data set is utilized more correctly, thus guaranteeing the efficiency and effectiveness of online tuning. Simulation results suggested that, both in the observation ability and counteractive guidance to avoid the pursuer, the proposed method all trumped traditional networks without online tuning, which resulting a higher chance to survive the pursuit.
Cybersecurity Threats and Solutions of IoT Network Layer

Internet of Things (IoT) is a combination of technologies with many applications that attempt to enhance the quality of humans’ life. IoT appliances gather data without human direct interaction and analyse it to be used without complexity. This advantage leads the IoT to become the main key to operate the smart city applications. Most of the large cities are involving IoT to support smart city applications such as traffic control, weather monitoring, or water and electrical services management. However, some IoT appliances gather private and critical data, which turn them into a target for adversaries whom work on exposing this data either for personal gain or perform attacks on the system to damage the city’s infrastructure. There are several challenges in using IoT regarding data confidentiality, integrity, and authentication. Robust security solutions are essential requirements for IoT devices and their applications. Nevertheless, there are some IoT devices certified and secure, but they need to be updated once a breach has been detected or the version became outdated.Within this paper, we provide and discuss some of these challenges related to network layer of IoT architecture, how we can overcome them to ensure confidentiality, integrity, and authentication to prevent potential threats. Additionally, provide directions for future researches that aim to address security issues, and introduce new applications to achieve the goals. In addition, we proposed security frameworks to be standardized for IoT devices.
Application of Industrialised Building System in the Field of Construction toward Sustainability: Future Routes and a Theoretical Mapping

Industrialized Building System (IBS) is a construction system in which components are factory-made in a measured atmosphere (on or off-site), placed, conveyed, and collected on site with minimal additional site activities, according to the Construction Industry Development Board Malaysia (CIDB). The application of IBS in construction projects could significantly improve site performance in terms of protection, quality, charge efficiency, production, sustainability and waste reduction. In the construction industry, sustainability has become increasingly essential. Meanwhile, due to the extremely difficult constraints that stakeholders encounter in adopting new technology and a scarcity of experienced labour, the degree of IBS implementation in Malaysia remains low. According to several studies, typical IBS is gradually being pushed to the background. However, no systematic study applying bibliometric examination of IBS studies to more investigate relevant study in this field was uncovered. This study offers an outline of the IBS current studies gathered from "Web of Science" database. To visualise the material included in the study scope, VOSviewer was combined with bibliometric analytic techniques. As a conclusion, six clusters were discovered that focused on these primary theme segments: IBS approach, prefabrication, construction, innovation, sustainability, and management. Based on the current gap, this paper makes recommendations for future IBS research. Assisting the IBS research community with research and development.
"Approximate Multipliers Based on Low-Power 4:2 Compressors for Error-Tolerant Applications"

Energy-efficient and high-performance general- purpose compute engines, as well as application specific integrated circuits, are highly demanded to facilitate the development of artificial intelligence and big data processing applications. However, with the end of Dennard’s scaling and Moore’s law it is becoming difficult to handle massive amounts of data and complex computations required in these applications. Approximate computing (AC) has emerged as an attractive paradigm in the digital design to address this unprecedented challenge. AC is driven by the observation that many state-of-the-art applications, such as classification, machine learning, data mining, robotics and communication, exhibit error-tolerant characteristics; therefore, a small amount of error (trades off the requirement of exact computation) can be introduced to achieve area, power, and speed benefits. AC techniques can be applied at both the software and hardware layers. At the hardware layer, arithmetic units (multipliers, adders, and dividers) are considered as hardware computational modules. Therefore, the approximation at hardware layer has been focused around the design of approximate arithmetic units. This paper presents approximate multipliers based on novel 4:2 compressors for error-tolerant applications. The proposed 4:2 compressors exhibit zero-mean error behavior while having a comparable hardware utilization with the existing state-of the-art designs. The hardware-efficient as well as the error-efficient designs of variable accuracy-power has been investigated to explore the maximum trade-off. All the designs are synthesized using Cadence Genus synthesis tool (TSMC 65 nm technology) and power is reported using Cadence Joules RTL power solution. A comprehensive error analysis is performed using well-known error metrics such as error distance (ED), mean average distance (MED), mean relative error distance (MRED) and normalized mean error distance (NMED). Moreover, all the designs are also compared with respect to power-delay product (PDP) and MRED to apprehend which designs are lying on the error-energy Pareto- optimal curve. A case study is also presented to demonstrate the applicability of the proposed designs in practical image processing application.
Trust Evaluation Model of Social Internet of Things Based on Multi-relationships

Trust plays a vital role in ensuring the security of the Social Internet of Things. Due to the increased mobility of intelligent devices, there are frequent interactions between machines and changes in social relationships. It leads to a possible data sparsity problem in the Social Internet of Things. Therefore, it is presented that the trust evaluation model is based on various social relationships. The model considers the impact of multiple dynamically changing social relationships between nodes on trust. The implicit social relationship is mined based on the stated social relationship and potential attributes connecting nodes. It addresses the scarcity issue and the slow start of the Social Internet of Things. Simulations on the Santander Smart Cities dataset demonstrate that the approach improves the accuracy and convergence of trust evaluation in both sparse and regular networks.
Factorized Sparse Approximate Inverse Preconditionings I. Theory

This paper considers construction and properties of factorized sparse approximate inverse preconditionings well suited for implementation on modern parallel computers. In the symmetric case such preconditionings have the form $A \to G_L AG_L^T $, where $G_L $ is a sparse approximation based on minimizing the Frobenius form $\| I - G_L L_A \|_F $ to the inverse of the lower triangular Cholesky factor $L_A $ of A, which is not assumed to be known explicitly. These preconditionings preserve symmetry and/or positive definiteness of the original matrix and, in the case of M-, H-, or block H-matrices, lead to convergent splittings.
Deep-Wrist: Reliable User Verification using Deep Representation of WristVein Patterns

Vein-based biometrics are widely deployed in various security-based access control systems, considering their accuracy and usability. Further, vein biometrics are captured in a contactless fashion that can further enable the inflection free user verification that is highly suitable for real-life scenario. In this work, we present a novel framework based on the fusion of deep features with the pre-trained AlexNet as the backbone for reliable wristvein-based biometric verification. The proposed method is based on the AlexNet and performs the feature extraction from three different fully connected layers. Extracted deep features are further compared using the Linear Support Vector Machine (L-SVM) and combined to make the final verification decision. Extensive experiments are carried out on the dataset comprised of 100 unique wristvein patterns corresponding to 50 unique data subjects. Extensive experiments are performed with three different evaluation protocols, and the verification performance of the proposed method is benchmarked with five different deep features-based wristvein verification algorithms. The obtained results indicate the improved performance of the proposed method on all three evaluation protocols.
Deep Reinforcement Learning Based Anti-Jamming Using Clear Channel Assessment Information in a Cognitive Radio Environment

Jamming as a type of denial of service attack has proved to be destructive to communication systems. This paper investigates and implements an anti-jamming scheme in a dynamic jamming environment. In our study, we utilize the clear channel assessment (CCA) information available in the MAC layer of a standard IEEE wireless device. Consequently, we eliminate the need for additional equipment to obtain the raw spectrum information. This contrast existing works which need a priori knowledge of the jamming patterns or employ raw spectrum information. The CCA information of all available spectrum channels is utilized as input states to train a double deep q-network (DDQN) agent online to mitigate the effects of jamming. Numerical results show that the proposed anti-jamming approach is effective in different jamming scenarios.
A dynamical model for radiatively inefficient accretion flows with convection

We explore the time evolution of radiatively-inefficient accretion flows. Since these types of accretion flows are convectively unstable, we also study the effects of convection in the present model. The effects of convection are applied to equations describing angular momentum and energy. In analogy to the traditional α-prescription, we introduce the convection parameter αc to study the influences of convection on physical quantities. The model is studied in two cases: the transport of angular momentum due to convection inward and outward. We found the physical variables are sensitive to the parameter αc and are also dependent on the direction of angular momentum that is transported by convection. As for angular momentum transfer inward, the accretion flow can be convectively dominated and radial infall velocity becomes zero. Moreover, we found the radial dependence of the density and radial velocity takes an intermediate place between steady state radiatively-inefficient accretion flow and steady state advection-dominated accretion flow. This property is in accord with direct numerical simulation of radiatively-inefficient accretion flows.
Artificial neural network for bedload estimation in alluvial rivers

A talented soft computational technique is applied to predict bedload sediment discharge in rivers. The feedforward–backpropagated (Levenberg– Marquardt algorithm) Artificial Neural Network (ANN) architecture is employed without any restriction to an extensive database compiled from measurements in 16 different rivers. Following the assessment of several possible models, two dimensionless parameters were selected from an initial set of five for the prediction of dimensionless bedload discharge. The ANN method demonstrated an encouraging performance compared to other standard methods. The mean value and standard deviation of the bedload predictions of theANN model differ only slightly from the measured values. The coefficient of determination and the efficiency coefficient of the ANN method are higher than those of the traditional methods. The performance of the currently used ANN method demonstrates its predictive capability and the possibility of generalization of the modeling to nonlinear problems for river engineering applications.
CAE-UNet: An Effective Automatic Segmentation Model for CT Images of COVID-19

Since December 2019, COVID-19 has ravaged the world, severely affecting the quality of life and physical health of human society. Computed tomography (CT) imaging is an effective way to detect solid lung lesions as well as pulmonary ground-glass nodules and is an effective way to diagnose COVID-19. The automatic and accurate segmentation of COVID-19 lesion areas from CT images can determine the severity of the disease, which is essential for the diagnosis and treatment of COVID-19. A new model CAE-UNet(Combine-ASPP-ECA-UNet) is proposed in this paper for COVID-19 CT image segmentation based on UNet. The coding structure of UNet is replaced with the improved ResNet50 and incorporated with ECA attention module and atrous spatial pyramid pooling(ASPP). Fusing different sensory fields, global, local and spatial features to enhance the detail segmentation effect of the network. The experimental results on the CC-CCII show that the mIoU of the proposed CAE-UNet reaches 79.53%, which is better than some other mainstream methods. The proposed method achieves automatic and efficient segmentation of COVID-19 CT images.
A Review of Botryosphaeria Stem Blight Disease of Blueberry from the Perspective of Plant Breeding

Stem blight of blueberry caused by fungal pathogens in the family Botryosphaeriaceae presents a major challenge to global blueberry production. Since its first documented outbreak in North Carolina, USA in the 1950s, Botryosphaeria stem blight has been reported in the blueberry production regions of more than nine countries across five continents. The lack of effective management strategies or resistant cultivars makes disease control especially challenging. With the goal of illuminating directions for future Botryosphaeria stem blight management, especially through resistant-cultivar development, this review summarizes the latest information on the distribution and causal pathogens of this disease, the pathogenicity of fungal species, disease resistance of blueberry cultivars, and currently recommended management practices. DNA sequencing techniques have revealed multiple fungal species that are associated with this disease. However, a lack of reliable methods to screen cultivars for stem blight resistance remains a major bottleneck for the development of resistant cultivars. Future studies should focus on at least four key areas: (1) the development and adoption of uniform and reliable screening protocols; (2) utilization of diverse and well-characterized Botryosphaeriaceae isolates for germplasm screens; (3) field evaluations of cultivar resistance and management practices; and (4) exploration of new tools for disease management and prevention.
Dimension Reduction Techniques for Image Classification

The world is surrounded by a huge amount of data increasing day by day. The increase of data leads to the presence of high-dimensional data causing a challenge in data mining as it becomes costly in computation time and memory space. Moreover, high-dimensional data can affect the classification accuracy of machine learning algorithms because of the existence of redundant and irrelevant features, and that can cause the Curse of Dimensionality problem. Therefore, Dimensionality Reduction has been introduced to solve the Curse of Dimensionality problem by feature extraction techniques. Since there is a lack in the performance of some machine learning models because of high-dimensional data, this paper introduces two common dimensionality reduction methods, and conducts an empirical comparison between , Principal Component Analysis (PCA) and Auto-Encoders (AE) to study their effect in improving the performance of classification in high-dimensional data. The study uses three classification models, K-Nearest Neighbour(KNN),Support Vector Machine (SVM), and Random Forest (RF) to perform the classification in the MNIST, and Fashion-MNIST datasets. The results have been compared , analyzed, and show that AE has a better effect on improving performance of KNN, and SVM classifiers on MNIST dataset, as the SVM accuracy is improved from 94% to 97% when the AE used to reduce dimension with a percentage of 95%,90%, and 50%. Moreover, KNN accuracy has been improved by AE from 91% to 95% , when dimension is reduced with a percentage of 95%,90%, and 50%. On the other hand, the accuracy of the same classifier has been improved in Fashion-MNIST dataset from 81% to 83%, when dimension is reduced by AE with a percentage of 90% and 50%.
Shear-Flexural Buckling of Cantilever Columns under Uniformly Distributed Load

Approximate buckling formulas for shear–flexural buckling of cantilever columns subjected to a uniformly distributed load are derived, based on Timoshenko’s energy method. In this method the deflection curve at buckling is approximated by a trial function. Instead of trying to describe all possible buckling modes with one trial function, two trial functions are used: one to describe shear dominated localized buckling, another to describe bending dominated global buckling. It is investigated whether the bending dominated global buckling modes can best be described using polynomial functions, trigonometric functions, or a function defined by the lateral (flexural and shear) deflection of the cantilever column under uniformly distributed lateral load. The results of the derived formulas are compared to the exact solution and other approximate buckling formulas found in the literature. Attention is drawn to the fact that the shear–flexural buckling load cannot exceed the shear buckling load.
Measurements of the microstructural, microchemical and transition temperature gradients of A15 layers in a high-performance Nb3Sn powder-in-tube superconducting strand

In the past 5 years there has been a remarkable increase in layer critical current density, Jc, for Nb3Sn. At ∼5000 A mm−2 (12 T, 4.2 K), the average layer critical current density is now double that of the best ITER central solenoid model coil strand. The improvements in critical current density are a result of increased Sn content in the superconducting A15 phase and better compositional homogeneity, and perhaps other reasons not yet understood. The first indication that such large increases in critical current density of the Nb3Sn layer were possible was in the powder-in-tube strands produced by Shape Metal Innovation. The design of these strands allows us to accurately measure the composition gradient as well as the gradient in critical temperature across the A15 layer, and we report these measurements here for a wide range of heat treatments. Inductive Tc measurements were used to measure the radial gradient of Tc, while specific heat measurements revealed the position-insensitive inhomogeneity of the A15 layer. Composition gradients were measured by energy dispersive x-ray spectroscopy in a field emission scanning electron microscope. The three characterizations show that the composition and Tc gradient is quite shallow near the central Sn source and only becomes steep adjacent to the Nb sheath of each filament, a result that is beneficial to maximizing the fraction of the layer with high Tc. This strong nonlinearity of the Tc and composition gradient means that excellent properties are obtained, in spite of the built in composition gradients inevitable in any filamentary design of composite. The gradients are much smaller than reported for bronze-route conductors but higher than the overall layer gradient found in the latest generation of high critical current density internal Sn design strands. Coupled to recent modelling simulations, our data show the great value of high-Sn designs in developing high layer Jc values in Nb3Sn conductors.