esa-sceva/satcom-chunk-collection huggingface dataset:
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 3 Energy-Efficient Coverage Control Using Memetic Algorithm  
### Problem Formulation  
Assume that there are \(G\) monitoring events in the underwater monitoring area \(A\), which can all be completely covered by the union of the sensing ranges of the \(N\) nodes. Then, the problem can be described as follows: after giving a set of nodes \(S=\left\{s_{1},s_{2},\cdots,s_{N}\right\}\) and an event set \(E=\left\{e_{1},e_{2},\cdots,e_{G}\right\}\) that can be covered by \(S\), find the smallest subset of sensors \(S^{\prime}\) so that \(E\) can be completely covered by \(S^{\prime}\). That is, if the perceived range of node \(s_{i}\) is expressed as \(r_{s}\), and the smallest subset of nodes found is \(S^{\prime}=\left\{s_{1},s_{2},\cdots,s_{m}\right\}\), \(1\leq m\leq N\), then \(S^{\prime}\) must satisfy \(A\subseteq r_{1}\bigcup r_{2}\bigcup\cdots\bigcup r_{m}\). This is a set-covering problem (SCP) of NP (Non-deterministic Polynomial) difficulty. In this study, SCP will be applied to node-optimization scheduling to solve the problems of energy efficiency and coverage, which can be described as follows:  
\[Minimize:\quad\sum_{i\in N}c_{i}\cdot q_{i} \tag{7}\]  
\[Subject\ to:\quad\sum_{i\in N}\gamma_{ji}\cdot q_{i}\geq 1\quad j\in G \tag{8}\]where \(c_{i}\ (i\in N)\) is the active cost of the \(i\)th node, and \(q_{i}\) is a key decision variable, which is the focus of this study (\(q_{i}=1\) indicates the node is active, and \(q_{i}=0\) means the node is asleep). The objective function in Equation (7) describes the number of least active nodes. We assume that the active costs of each node are the same. For example, \(c_{i}=1\). The constraint in Equation (8) indicates that each monitoring event \(e_{i}\) is covered by at least one node. When the network is in working condition, the optimization model described by Equations (7) and (8) is important, as it greatly improves the network energy efficiency and prolongs the lifetime of the network while guaranteeing coverage. The swarm intelligence optimization algorithm is a heuristic algorithm formed by simulating the laws of nature or biology [23; 24]. Similar to a genetic algorithm, particle swarm algorithm, memetic algorithm (MA), or fish school algorithm [25; 26; 27], it has the characteristics of self-learning, self-organization, and self-adaptation, and it provides new optimization methods for UASNs. Based on this, we developed a swarm intelligence optimization algorithm that simulates the evolutionary mechanism of organisms through iterative computations and explores the optimal solution in the solution space. To solve the SCP problem, we propose an effective scheduling algorithm of coverage control (ESACC) and apply it to the UASN. ESACC includes two optimization strategies: (1) node sleep scheduling based on a memetic algorithm, which can suppress redundant nodes in UASNs; and (2) a node wake-up scheme to solve energy-saving coverage optimization problems in each round. In UASNs, a round is a basic unit of time. The ESACC is described below.
## 5 Potential Challenges
### Challenges in Automatic Signal Detection  
In a NOMA-based system, the detection of multi-users and decoding of the signal at the receiving end is a crucial procedure. A trained neural network can replace the SIC receiver to substantially enhance the performance of the communication system. Differentiating between distinct modulation symbols needs the development of a DL model. The application of reinforcement learning at the receiving end, which should pose a correlation of the detection with the time-varying channel condition, may result in automatic user detection.
## 1 Introduction
This system also includes a user interface to alert workers in real-time when a defect is detected, thus enabling prompt corrective actions. This research presents a significant advancement in the field by automating the process of detecting missing welds, which traditionally relies on manual inspection. Our method aims to reduce rework, minimize hazards, and enhance product quality in the construction industry. The deployment of our framework is expected to yield high efficiency and accuracy in quality control processes for steel joist fabrication. Our work distinguishes itself by providing a holistic approach to welding quality control. Our framework seamlessly integrates hardware, software, and machine learning techniques for real-time defect detection in a factory setting. While recent advancements in welding quality control have shown promising results, there remain significant challenges and gaps in current methodologies. Our study aims to address these gaps by introducing an innovative approach. Furthermore, our framework is designed to achieve high accuracy and efficiency in weld defect detection, representing a potential advancement in automated quality control processes. Detailed comparisons and evaluations of our method in the context of existing research will be presented in the results and discussion sections. This paper outlines an innovative approach to enhancing welding quality control in steel joist prefabrication through the application of advanced computer vision and machine learning techniques. The ensuing sections will delve into our methodology and the specifics of our framework, detail its integration of hardware and software for real-time defect detection and discuss its practical effectiveness in an industrial setting.
# On Implementing a Two-Step Interior Point Method for Solving Linear Programs
## 2 Algorithm
### Relationship with Root Finding  
Define:  
\[\xi=\begin{bmatrix}x\\ y\\ s\end{bmatrix}\text{ and }F(\xi)=\begin{bmatrix}Ax-b\\ A^{T}y+s-c\\ \mu\mathbf{e}-XS\mathbf{e}\end{bmatrix},\]  
then a solution to the system (2) is \(\xi^{*}\) such that \(F(\xi^{*})=0\). The root \(\xi^{*}\) can be computed using [PERSON]'s method (in the neighbourhood of the root), which, given \(\xi\), finds \(\Delta\xi\) such that \(F(\xi+\Delta\xi)=0\)(20). Using [PERSON] series approximation (linear approximation) around \(\xi\), we have (20):  
\[F(\xi)+F^{\prime}(\xi)\Delta\xi\simeq 0\]  
where:  
\[\text{Jacobian }F^{\prime}=\begin{bmatrix}\frac{\partial F_{1}}{\partial\xi_{1} }&\frac{\partial F_{1}}{\partial\xi_{2}}&\frac{\partial F_{2}}{\partial\xi_ {3}}\\ \frac{\partial F_{2}}{\partial\xi_{1}}&\frac{\partial F_{2}}{\partial\xi_{2}} &\frac{\partial F_{2}}{\partial\xi_{3}}\\ \frac{\partial F_{3}}{\partial\xi_{1}}&\frac{\partial F_{3}}{\partial\xi_{2}} &\frac{\partial F_{3}}{\partial\xi_{3}}\end{bmatrix}=\begin{bmatrix}A&0&0\\ 0&A^{T}&I\\ S&0&X\end{bmatrix}\text{ and }\Delta\xi=\begin{bmatrix}\Delta x\\ \Delta y\\ \Delta s\end{bmatrix}.\]  
This is precisely the system that was derived earlier (4). This approximation yields an iterative algorithm, the Newton-Raphson method, for finding roots. Starting with an initial estimate \(\xi_{0}\), the current estimate \(\xi_{n}\) of the root is updated using the following rule for some appropriate step size \(\alpha\): \[\xi_{n+1}=\xi_{n}-\alpha(F^{\prime}(\xi_{n}))^{-1}F(\xi_{n})\] The proposed method generalizes the Newton-Raphson method as follows (assume any \(F\)). In the first step, an auxiliary point \(\xi_{n}\) is computed using a linear approximation to the function around the current estimate of the root \(\xi_{n}\). The average \(\xi_{n+1}\) of the auxiliary point and the current estimate is computed in the second step. In the third step, the current estimate is updated using the linear approximation to \(F\) around \(\xi_{n}\), but instead of using the Jacobian at point \(\xi_{n}\), the Jacobian is evaluated at the average \(\xi_{n+1}\). The update rules used in \(n^{th}\) iteration can be summarized as [20]: \[\xi_{n+1} = \xi_{n}-\alpha(F^{\prime}(\xi_{n}))^{-1}F(\xi_{n})\] \[\xi_{n+1} = \frac{1}{2}(\xi_{n+1}+\xi_{n})\] \[\xi_{n+1} = \xi_{n}-\alpha(F^{\prime}(\xi_{n+1}))^{-1}F(\xi_{n})\] This sequence of updates requires two evaluations of the Jacobian, once at point \(\xi_{n}\) and the second time at point \(\xi_{n+1}\). This requirement doubles the computation burden. In practice, we use information regarding \(F^{\prime}(\xi_{n+1})\) for the next iteration. Thus only a single evaluation of the Jacobian is needed in each iteration if we save the result from the previous iteration.
## 3 Results
### New Shapes of Rehabilitation Devices
\begin{table}
\begin{tabular}{c c} \hline \hline
**Area of Application/Improvement** & **Detailed Description** \\ \hline Improved personalization & By analyzing data from medical scans and patient metrics, AI can design products tailored to specific physical conditions or limitations, improving recovery outcomes and more precisely adapting to the next steps in the therapeutic process, improving patient guidance during therapy, or changing solution parameters over time. \\ \hline Improved personalization & AI enables 3D-printed rehabilitation solutions, such as braces, prosthetics, and orthotics, to be precisely tailored to a person’s unique anatomy, providing greater comfort and effectiveness. \\ \hline Optimized design processes: & AI algorithms streamline design iterations, enabling the rapid prototyping and development of complex geometries that are lightweight yet durable, ideal for rehabilitation needs. \\ \hline Predictive adjustments & ML models can predict how a patient’s condition will evolve and adjust 3D-printed solutions accordingly, reducing the need for frequent replacements or modifications. \\ \hline Increased accessibility & Automation of the design and manufacturing process lowers costs, making personalized rehabilitation devices more affordable and accessible to a wider population. \\ \hline Improved performance tracking & AI-based 3D-printed devices can incorporate sensors to monitor patient progress, providing healthcare providers with real-time feedback for adaptive treatment plans. \\ \hline Faster time to market & AI accelerates the design and testing phases, enabling the faster delivery of personalized solutions to patients, which is crucial for timely rehabilitation interventions. \\ \hline Error reduction & Advanced AI-based simulations help identify and correct potential design flaws before production, ensuring higher device reliability and safety \\ \hline Scalable personalization & AI enables efficient mass personalization, allowing healthcare providers to meet the unique needs of multiple patients without significantly increasing manufacturing complexity. \\ \hline Sustainability & By optimizing material usage and reducing waste, AI-based 3D printing contributes to environmentally sustainable practices for creating personalized rehabilitation tools. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AI techniques and methods supporting the 3D printing of solutions for rehabilitation and physiotherapy (authors’ own elaboration).
## 3 Distributed Hybrid Formation Control
### Stability Analysis
\begin{array}{l}f(0)=1\\ \dot{f}(t)=-k_{\theta}te^{-\sqrt{k_{\theta}}t}\\ \forall t>0,0<f(t)<1\\ \lim\limits_{t\rightarrow+\infty}f(t)=0\end{array} \right. \tag{29}\]  
Substituting Equation (23) into Equation (28), we have  
\[\alpha_{i^{-}}^{*}\dot{\alpha}_{i}^{+}-\alpha_{i}^{*}\dot{\alpha}_{i}^{-}=( \alpha_{i^{-}}^{*}+\alpha_{i}^{*})\Delta\alpha_{i}^{0}f(t) \tag{30}\]Based on Equation (8), we obtain  
\[\left( \begin{array}{cccccc}\alpha_{n}^{*}&0&0&\cdots&0&-\alpha_{1}^{*}\\ -\alpha_{2}^{*}&\alpha_{1}^{*}&0&\cdots&0&0\\ 0&-\alpha_{3}^{*}&\alpha_{2}^{*}&\cdots&0&0\\ \vdots&\vdots&\vdots&\ddots&\vdots&\vdots\\ 0&0&0&\cdots&-\alpha_{n}^{*}&\alpha_{n-1}^{*}\end{array} \right)\check{\pi}= \left( \begin{array}{c}\alpha_{1}^{*}+\alpha_{n}^{*}\\ \alpha_{2}^{*}+\alpha_{1}^{*}\\ \alpha_{3}^{*}+\alpha_{2}^{*}\\ \vdots\\ \alpha_{n}^{*}+\alpha_{n-1}^{*}\end{array} \right) \tag{31}\]  
A general solution of (30) is expressed as  
\[\check{\alpha}(t)=x(t)\alpha^{*}+(\check{\alpha}(0)-\alpha^{*})f(t)\]  
Summing up each column vector of Equation (31), we have  
\[\sum\limits_{i=1}^{n}\alpha_{i}^{+}(t)=x(t)\sum\limits_{i=1}^{n}\alpha_{i}^{* }+(\sum\limits_{i=1}^{n}\alpha_{i}^{+}(0)-\sum\limits_{i=1}^{n}\alpha_{i}^{*} )f(t)\]  
According to \(\sum\limits_{i=1}^{n}\alpha_{i}^{+}(t)=2\pi\) and \(\sum\limits_{i=1}^{n}\alpha_{i}^{*}(t)=2\pi\), we have  
\[2\pi\text{=}2\pi x(t),x(t)=1.\]  
The solution of (30) is  
\[\check{\alpha}(t)=\alpha^{*}+(\check{\alpha}(0)-\alpha^{*})f(t)\]  
Written in a component form as  
\[\check{\alpha}_{i}(t)=\alpha_{i}^{*}+(\check{\alpha}_{i}(0)-\alpha_{i}^{*})f(t)\]  
which is rewritten as  
\[\alpha_{i}^{+}(t)=\alpha_{i}^{*}(1-f(t))+\check{\alpha}_{i}^{+}(0)f(t)\]  
According to Equation (29), we get  
\[\lim\limits_{t\rightarrow+\infty}\alpha_{i}^{+}(t)=\alpha_{i}^{*}\]  
satisfying the convergence condition, where \(\alpha_{i}^{*}>0\), \(\alpha_{i}^{+}(t)>0\). When \(t>0\), from Equation (29), we get \(f(t)>0\), \(1-f(t)>0\). Thus,  
\[\forall t>0,\alpha_{i}^{+}(t)>0\]  
satisfies the anti-collision condition. To sum up, we give the form of \(\omega_{i}^{*}(X_{i})\), \(\alpha_{i}^{*}(X_{i})\), \(\alpha_{\theta}^{*}(X_{i})\) as in Equations (16) and (24), and prove that the motions of multiple mobile robots satisfy the anti-collision (2) and the convergence condition (11).
## 5 Conclusions  
We compared seven LUE models (CASA, CFix, CFlux, EC-LUE, MODIS, VPM, and VPRM) for simulating terrestrial ecosystem GPP over the period 2000-2010, and our results revealed large discrepancies among models. Though calibrated by the observations from FLUXNET sites, LUE models estimated terrestrial GPP with large differences in the magnitude, spatial, and temporal variation. Differences in model formulations and model drivers can cause large discrepancies in the spatial and temporal changes of GPP estimates. In particular, water availability (indicated by ET, inverted VPD, and LSWI) and vegetation conditions (shown by NDVI, EVI, and FPAR) played more important roles than radiation and air temperature in regulating interannual variability of GPP across large vegetated areas, including the Amazon, South Africa, Australia, Southeast Asia, North America, and Western Europe. Although the LUE models have significant advantages, such as the simplicity of implementation and the ability to combine remotely sensed data, they are limited in considering the various impacts on plant photosynthesis. To improve our understanding of terrestrial GPP, model inter-comparison has been performed at the global scale and a comprehensive analysis revealing differences of global GPP estimates has been conducted. Our study suggested that LUE models need to examine the representation of vegetation indices and the nonlinear functional dependencies of GPP on environmental drivers, especially water availability.
## 1 Introduction
### Risk Assessment  
The International Maritime Organization (IMO) formulated a formal safety assessment (FSA) to prevent and reduce the occurrence of maritime accidents and to improve the safety of ships and maritime navigation [10; 11; 12]. The FSA includes hazard identification, risk assessment, risk control options, cost benefit assessment, and recommendations for decision making. In a risk assessment, the FSA uses mainly quantitative methods as risk assessment tools, such as using a Bayesian network to calculate the incidence rate, or a semi-empirical risk management analysis model [13], such as fault tree analysis (FTA) [14; 15; 16] and event tree analysis (ETA) [17; 18]. However, the aforementioned methods may have some pitfalls, such as ambiguous outcomes when two experts have totally opposite opinions on the same subject. Therefore, proposing a meaningful research approach to improve FSA is of paramount importance for the successful application of supportive maritime safety policy-making instruments [19].  
The occurrence of maritime accidents is normally caused by multiple factors and complicated processes rather than just one single factor [17; 20; 21]. The analytical aspects concerning preventative factors of ship collision accidents focus mainly on the risk factors of humans [11; 22; 23; 24; 25; 26; 27; 28], the ship or management [2; 28; 29], and the navigation and natural environments. A majority of the previous research explored the hazard assessments of post ship collision accidents; however, the simultaneous examination of the risk factors for ship collisions and the preventative and mitigation measures of a hazard assessment is limited. Therefore, a comprehensive evaluation of all three aforementioned aspects is needed.  
Two potential approaches, including rough set theory (RST) and bow-tie analysis (BTA), can be integrated to form a comprehensive risk assessment framework. RST is a decision analysis tool aimed at extracting information and relevance from imprecise, incomplete, and uncertain data or human thoughts. This method is commonly used in medicine [30; 31], transportation [32], finance [33], risk management [34], and decision analysis [35]. BTA is a qualitative analysis technique that integrates the FTA and the ETA [36] and focuses on the relationship between risk factors and incidents. This analysis conducts a comprehensive risk assessment and helps junior management or executive managers to further achieve the highest standards of safety. Recently, BTA has been widely used in risk assessment analysis in various fields, such as medical safety [37; 38], petrochemical engineering safety [39; 40], marine risk management [41], traffic safety [42], and aviation technical safety [43].
# Digital Twins: Enabling Interoperability in Smart Manufacturing Networks
## 3 Digital Twins
### Barriers to Digital Twin Integration in the Smart Manufacturing Sector  
It can be said that no industry stands to benefit more from IoT advancements when compared to the manufacturing sector. Using edge devices, integrating data storage and edge computing within industrial appliances, manufacturing data can be collected allowing for improvement in predictive maintenance and energy efficiency [21]. This will reduce overall costs and maintain the preferred reliability and production time. Some of the main advantages to adopting a digitalising strategy and deploying digital twins in manufacturing are listed below:  
1. Preventive maintenance: IoT capabilities will improve operational intelligence, which is essential to smart manufacturing. This sector will gain from the availability of numerous sensors able to provide real-time information regarding equipment performance. The data can aid in predicting and preventing equipment malfunction when integrated with machine learning (ML) and artificial intelligence (AI).
2. Enhanced process efficiencies and troubleshooting: Interoperability and digital transformation work together to improve manufacturing process efficiencies. For example, using deep learning neural networks and advanced visual recognition, robotic systems can accurately and quickly scan connected objects for quality control in real time. Specialised equipment can be fixed remotely by specialists using augmented reality (AR), made possible by 5G networks' high bandwidth and low latency support.
3. Increased security with built-in security features: Interoperability and digital transformation will provide increased security with built-in security features, integrating security into the core network architecture and allaying manufacturers' security fears about adopting IoT [22].
4. Sandbox scenario testing: The charm of digital twinning is that it enables businesses to construct specialised, virtual replicas of their web infrastructure and to do predictive research tailored to their requirements. Employing digital twins to assess the viability of system applications and data architecture can help businesses intending to implement new systems or migrate to the cloud.
5. System Scalability: There is little doubt that there are huge opportunities for large-scale digital twins, which can assist manufacturing sites greatly by enhancing asset maintenance, increasing company transparency, and improving and deepening decision-making.
###### Abstract  
One of the prerequisites for the safe exploitation of surface mines is the stability of the working and final slopes of the mine. In order to ensure this, it is necessary to carry out detailed field and laboratory geomechanical tests of the soil and, based on the obtained results, make calculations related to stability analyses. The results obtained in this way are used for dimensioning the slope of exploitation slopes (excavation). Landslides occur when the ultimate shear strength is reached, and therefore, the adequate definition of shear strength parameters is one of the essential prerequisites for successfully solving the stability problem. Unlike earlier tests in Serbia, when the residual shear strength parameters were determined based on the usual conventional methods (direct shear apparatus, triaxial apparatus), this time, in addition to the direct shear apparatus, a ring shear apparatus was also chosen for testing. The paper shows the method of determining the residual shear strength parameters of high plasticity gray clays and siltstones of roof sediments from open pit mine Drmno, using direct and ring shear apparatus. The results show that the residual angle of internal friction for gray clays obtained with the ring shear apparatus is 9.9-10.8\({}^{\circ}\), and for the siltstone, it is 11.8-12.9\({}^{\circ}\), both of which are lower than the values obtained with the direct shear apparatus. In addition, correlations between the residual parameters of soil shear resistance and some physical indicators (plasticity index, clay content) are provided, showing high correlation coefficients. The proposed correlations should be used only when time and financial constraints prevent the execution of actual tests to determine residual shear strength, as concrete experimental procedures provide a much more reliable assessment of the residual strength properties of the soil.  
residual strength of soil; ring shear apparatus; direct shear apparatus; slope stability; open pit mines 2024 14 10325 10.3390/app142210325