esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallel Reservoir Simulation with OpenACC and Domain Decomposition
## 3 Materials and Methods
### The GPU-Aided Domain Decomposition  
The aforementioned parallel method is based on physical domain decomposing. Thus, we introduce our scheme of domain decomposing as following.  
In this paper, domain decomposition method (DDM) is used as the method of parallel nonlinear equation Newton iteration which is frequently-used and effective in the serial software parallelization field. The basic point of DDM is that it divides the reservoir simulation domain into many small subdomains, each subdomain is assigned to a process to deal with. After calculation, processes communicate and update boundary data. Then we apply DDM on the GPU, and implement GPU-aided domain decomposition (GDDM). In Figure 3, GDDM divides reservoir domain \(\Omega\) into subdomain \(\Omega_{1}\), \(\Omega_{2}\), \(\Omega_{3}\), \(\Omega_{4}\), the same number CPU threads are launched, based on the GPU HypeQ feature, each CPU thread launches a group of GPU thread blocks to calculate the corresponding subdomain. The iteration repeats continuously until the global solution converges, i.e., \(||F(x)||\leq\tau_{t}||F(x_{0})||+\tau_{a}\), where \(F(x)\) is the linear model of \(F\) about \(x\), \(\tau_{t}\) is the relative error tolerance and \(\tau_{a}\) means the absolute error tolerance.  
Considering the influence of open boundary, we use the domain decomposition overlapping scheme, which uses the additive Schwarz method (ASM). In Figure 3, the simulation domain is divided into some subdomains which all contain a overlap part with other (for example \(\Gamma_{i}\) is original boundary of \(\Omega_{i}\), \(\Gamma_{ij}\) is boundary of \(\Omega_{i}\) in \(\Omega_{j}\)). The mathematical model \(\Omega\) is assumed as:  
\[\left\{\begin{array}{l}Lu=f,\ (\Omega)\\ u=g,\ (\partial\Omega)\end{array}\right. \tag{6}\]  
Differential Operator as,  
\[Lu=-\sum_{i,j=1}^{N}\frac{\partial}{\partial x_{j}}(A_{ij}\frac{\partial u}{ \partial x_{i}})+B(x)u \tag{7}\]  
where \(B\geq 0\), \(\Omega=\Omega_{1}\cup\Omega_{2}\cup\Omega_{3}\cup\Omega_{4}\), and \(j\
eq i\).  
ASM is described as follows:  
1. Choose the approximate solution \(u^{0}\in H_{g}^{1}(\Omega),n=0\),
2. Parallel compute boundary value of subdomain, \[\left\{\begin{array}{l}Lu_{i}^{n+1}=f,\ (\Omega)\\ u_{i}^{(n+1)}=u^{n},\ (\partial\Omega),i=1,..,n\end{array}\right.\] (8)
3. Extend \(u_{i}^{n+1}\) to \(Omega\)
4. If it does not meet the convergence conditions, make \(n:=n+1\), then go to step 2.  
Figure 3: GDDM method example.  
ASM is used as preprocessor to build a linear solver, and it is fully-parallel. Note that the number of overlaps is set as variable for considering its effect on iterative convergence.
## 3 Edge Resources Classification  
Efficient optimization of the edge resources can enable real-time analytics and business intelligence, which can be used to gain a competitive advantage. One of the first steps in creating adaptive resource-management systems is determining the various application-specific performance characteristics and metrics of edge-based resources [33; 34]. It is challenging to gather different performance metrics in a timely and accurate manner due to the abundance of deployed hardware configurations with various architectures and versions [35]. Resource profiling is the first step to determine how resources are currently being used for the best management of the hardware, and it is necessary to identify efficient methods for controlling hardware and obtaining the best performance out of it. Hence, prior to resource profiling, a selection of the edge resources and their performance characteristics are identified, as depicted in Figure 1 and Table 1, containing the respective description and metrics.The psutil (process and system utilities) Python library [36] provides access to system and process information on any supported platform such as CPU, memory, disks, network, sensors, etc. The categorization is largely based on such utility functions that can monitor edge nodes to help create the resource profile, control access to system resources, and manage processes in progress. Figure 1: Edge resource metrics classification. In addition to the quantitative performance qualities indicated in Table 1, the following intangible elements can also improve edge performance. **Agility:** This involves preparing for unknown workloads and applications, reconfiguring component systems, and having edge management tools that adapt. It relates to the velocity component of big data and the capacity to process continuously flowing streams in a timely manner [37]. **Total Cost of Ownership:** Edge providers must weigh the costs and benefits of using industry-standard hardware, storage, support, and other services, as the redundant infrastructures required to support cloud computing have a significant impact on the TCO [38]. Detaching storage components from other parts of the system may help with scalability. **Serviceability:** Edge locations can be difficult to service due to distance, cost, and the volume of sites to service. The serviceability of a network is defined as its capacity to meet the needs of connected end devices (e.g., throughput, delay, and packet loss) [39]. In the 5G
## 5 Result and Discussion
### Compare with Existing Forecasting Schemes  
In order to compare our scheme with the existing schemes, we used the dataset R80 collected by [PERSON] et al. for testing. The R80 dataset contains 78 sequences with 29,243 ordered residues and 3566 disordered residues. Existing schemes include DISPRED2 [20], BVDEA [35], DisPSSMP [36], RONN [24], ISUnstruct [15], FoldIndex [14]. Table 8 shows the prediction results of each program.  
Considering the classification method used, we use GBDT-PE and LightGBM-PE as the abbreviations of our scheme. Among these solutions, the highest Sens, Spec, F1 and MCC are DISPRED2, RONN, LightGBM-PE, LightGBM-PE, respectively. Only our LightGBM-PE scheme and IsUnstruct scheme have MCC values exceeding 0.5. Similarly, the F1 values of LightGBM-PE, DisPSSMP and IsUnstruct all exceed 0.6. After a comprehensive comparison, the results of our scheme are roughly the same as those of BVDEA, DisPSSMP and IsUnstruct. DisPSSMP and BVDEA need to calculate the 188 and 120 features of each residue in the protein sequence, respectively, while our solution only needs to calculate 5 features, which has lower computational complexity and simpler decision curve calculation, so our solution is more robust than DisPSSMP and BVDEA, and requires fewer learning samples.
# Research on Fault Diagnosis Method for Rotating Machinery Based on Edge Computing
## 3 Theoretical Background and Edge Node Design
### Dempster-Shafer Evidence Theory  
Dempster-Shafer (DS) evidence theory (also known as the theory of belief functions) is a mathematical framework for reasoning under uncertainty that can be applied to feature-level fusion or decision-level fusion. It is based on Basic Probability Assignment (BPA) and calculates belief functions (Bel) and plausibility functions (PI) by combining information from different sources of evidence [28].  
Calculating the belief assignment for each feature: assume there are two features, A and B. Their belief assignments can be represented as two sets containing belief and plausibility values.  
Belief assignment of feature A:  
\[m_{\rm A}=\{(A,\rm Bel(A)),(A,\rm PI(A)),(\emptyset,1-Bel(A)-Pl(A))\} \tag{6}\]Belief assignment of feature B:  
\[\text{m}_{\text{B}}=\{(\text{B},\text{Bel}(\text{B})),(\text{B},\text{PI}(\text{ B})),(\emptyset,1-\text{Bel}(\text{B})-\text{PI}(\text{B}))\} \tag{7}\]  
where \(\text{Bel}(\text{A})\) and \(\text{PI}(\text{A})\) represent the belief and plausibility of feature A, respectively, while \(\text{Bel}(\text{B})\) and \(\text{PI}(\text{B})\) represent the belief and plausibility of feature B. \(\emptyset\) denotes the empty set, with its corresponding probability being the portion of belief assignment not allocated to A or B.  
Combining using the DS combination rule: the DS combination rule is used to combine two belief assignments, AAA and BBB, to obtain a fused belief assignment CCC. The calculation steps for the combination rule are as follows:  
Compatibility check: check whether \(\text{m}_{\text{A}}\) and \(\text{m}_{\text{B}}\) are compatible by examining if there is any conflict. A conflict is defined as the presence of at least one common element in both sets where the sum of their belief and plausibility values exceeds 0.  
Combination formula: if \(\text{m}_{\text{A}}\) and \(\text{m}_{\text{B}}\) are compatible, then calculate the combined belief \(\text{Bel}(\text{AB})\) and plausibility \(\text{PI}(\text{AB})\):  
\[\text{Bel}(\text{AB})=\frac{\text{Bel}(\text{A})\cdot\text{Bel}(\text{B})}{1- \text{K}} \tag{8}\]  
\[\text{PI}(\text{AB})=\frac{\text{PI}(\text{A})\cdot\text{PI}(\text{B})}{1- \text{K}} \tag{9}\]  
where \(\text{K}=\sum_{\text{X}\in\{\text{A},\text{B}\}}\text{Bel}(\text{X})\cdot \text{PI}(\emptyset)\) is the degree of conflict. If \(\text{m}_{\text{A}}\) and \(\text{m}_{\text{B}}\) are not compatible, combination is not possible and it is necessary to handle the conflict or adopt alternative strategies.
# A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management
## 3 The Continuous Monitoring and Reallocation (CoMoRe) Method
### Description of the Proposed Method Application  
Initially, when the change begins, the first allocation will apply Equation (5) and achieve maximum performance by allocating each employee to a specific task. After this point, RtC will emerge and begin to affect the performance of the workers. The innovative idea of the proposed method is that it considers changes in performance at regular intervals and optimally reassigns human resources. Thus, the RtC of each employee is considered during the change, and the overall performance is improved with this reallocation. Performance is considered time-dependent and can be described by Equation (9) [51]:  
\[P_{ij}(t+1)=P_{ij}(t)-R_{ij}(t) \tag{9}\]  
where \(R_{ij}(t)\) is RtC at time \(t\), \(P_{ij}(t)\) is performance at time \(t\), and \(P_{ij}(t+1)\) is performance at time \(t+1\).  
In order to apply the proposed method, the following steps should be followed:  
* The initial values of performance (\(P_{ij}(t)\), \(t=0\)) achieved by each worker and for each task are defined in a matrix of size \(n\times n\).
* The allocation of workers to tasks is optimized through the implementation of the Hungarian algorithm. This approach results in the maximum total performance according to Equation (5).
* The total performance is calculated as the sum of performance values for optimal pairs \(i,j\), as determined in step 2.
* The values of resistance to change (\(R_{ij}(t)\)) that are presented by each worker for each task are calculated and stored in a matrix of size \(n\times n\).
* The new values of performance (\(P_{ij}(t)\), \(t=t+1\)) are recalculated using Equation (9).
* If the change period (\(T\)) is not complete, steps 2-6 are repeated with the new matrix values.
* When the change period (\(T\)) is complete, the allocation of workers to tasks is optimized through a final implementation of the Hungarian algorithm. This maximizes total performance according to Equation (5).
* Finally, based on the optimal allocation (determined in step 7), the total performance is calculated as the sum of performance values for optimal pairs \(i,j\).  
The flowchart for this algorithm is shown in Figure 2, and the pseudocode follows as Algorithm 1.  
```
1:Initialize \(P_{ij}(t=0)\)
2:repeat
3:OptimalAllocation = HungarianAlgorithm(\(P_{ij}(t)\))
4:TotalPerformance = 0
5:for all\((i,j)\) in OptimalAllocation do
6: TotalPerformance += \(P_{ij}(t)*x_{ij}\)
7:endfor
8:\(R_{ij}(t)\) = CalculateResistance()
9:for\(i=1,\ldots,n\)do
10:for\(j=1,\ldots,n\)do
11:\(P_{ij}(t+1)=P_{ij}(t)-R_{ij}(t)\)
12:endfor
13:endfor
14:\(t=t+1\)
15:until\(t=T\)
16:OptimalAllocation = HungarianAlgorithm(\(P_{ij}(t=T)\))
17:TotalPerformance = 0
18:for all\((i,j)\) in OptimalAllocation do
19: TotalPerformance += \(P_{ij}(t=T)*x_{ij}\)
20:endfor
```  
**Algorithm 1** CoMoRe.  
Figure 2: The procedure and flowchart of the CoMoRe algorithm.
## 2 Materials and Methods  
### Study Site and Experimental Factors  
Broccoli plants cv. Cezar were grown in an experiment conducted in 2021-2022 on a horticultural farm (52\({}^{\circ}\)37\({}^{\prime}\)17.6\({}^{\prime\prime}\) N, 18\({}^{\circ}\)30\({}^{\prime}\)07.9\({}^{\prime\prime}\) E and 53\({}^{\circ}\)12\({}^{\prime}\)61.1\({}^{\prime\prime}\) N, 19\({}^{\circ}\)01\({}^{\prime}\)91.7\({}^{\prime\prime}\) E) in the Kuyavian-Pomeranian Voivodeship (mid-northern Poland). Due to minor differences in the analyzed parameters, the results were expressed as the mean values from both cultivation dates. The experiment consisted of two stages. The biometric parameters of broccoli plants were determined in the first stage. The chemical composition of the edible parts of broccoli was determined in the second stage, which involved two experimental factors. The first factor was the edible parts of broccoli: florets, leaves, and stalks. The second factor was the effect of soil type on the growth and yield of broccoli plants, and the chemical composition of the edible parts of broccoli. The following soil types were analyzed [19]:  
1. [label=.]
2. Eutric Cambisol, a subtype of Cambisols (quality class IVa and good rye complex according to the Polish soil classification system), developed from boulder clay, humus horizon--30 cm;
3. Albic Luvisol (Arenic), a subtype of Luvisols, deeply flattened, developed from clay (quality class IIIa according to the Polish soil classification system);
4. Degraded chernozem, developed from light loam with low sand content, underlain by medium-heavy loam, humus content--1.9 (quality class IVb according to the Polish soil classification system).  
The chemical composition of the analyzed soil types is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Soil** & **pH-H\({}_{2}\)O** & **N-NO\({}_{3}\)** & **P** & **K** & **Mg** & **N-NH\({}_{4}\)** \\  & & & & **(mg dm\({}^{-3}\))** & & \\ \hline I & 7.1 & 18 & 175 & 152 & 84 & 29 \\ II & 8.3 & 48 & 219 & 364 & 132 & 30 \\ III & 8.3 & 29 & 230 & 306 & 142 & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of experimental soils.
# Study and Investigation on 5G Technology: A Systematic Review
## 4 5G Technologies
### Machine Learning Techniques for 5G
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Author**} & \multirow{2}{*}{**Key Contribution**} & \multirow{2}{*}{**ML Applied**} & \multicolumn{3}{c}{**Network Participants**} & \multicolumn{3}{c}{**5G Network Application**} & \multicolumn{1}{c}{**Parameter**} \\  & & & \multicolumn{3}{c}{**Component**} & & \multicolumn{3}{c}{**5G Network Application**} & \multicolumn{1}{c}{**Parameter**} \\ \hline  & & & **RAN** & **Core** & **LB** & **SDN** & **RAN** & **RA** & **SEC** \\ \hline [PERSON] et al. [68] & Network traffic prediction & LSTM and DNN & ✓ & ✓ & * & ✓ & ✓ & ✓ & X \\ \hline [PERSON] et al. [15] & Network slice admission control algorithm & Machine Learning and Deep Learing & ✓ & X & ✓ & ✓ & ✓ & ✗ \\ \hline [PERSON] et al. [69] & 5G Security & Machine Learning & X & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. [70] & Resource Allocation & Machine Learning & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ & ✗ \\ \hline [PERSON] et al. [71] & Low Latency communication & Unsupervised clustering & X & ✓ & X & ✓ & ✓ & ✓ & ✗ \\ \hline [PERSON] et al. [72] & Resource Management & LSTM, CNN, and DNN & ✓ & ✓ & ✗ & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. [73] & 5G mmWave Vehicular communication & FML (Fast machine Learning) & X & ✓ & * & ✓ & ✓ & ✓ & ✗ \\ \hline [PERSON] et al. [74] & Intrusion Detection System & Machine Learning & X & ✓ & X & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. [75] & 5G Network Slicing & Machine Learning & X & ✓ & X & ✓ & ✓ & ✓ & ✓ \\ \hline [PERSON] et al. [76] & Physical-Layer Channel Authentication & Machine Learning & X & ✓ & X & X & X & ✓ \\ \hline [PERSON] et al. [77] & Intelligent Network Data Analytics Function in 5G & Machine Learning & ✓ & X & ✓ & X & ✗ & * & * \\ \hline [PERSON] et al. [78] & Optimal 5G network slicing & Machine Learning and Deep Learing & X & ✓ & X & ✓ & ✓ & ✓ & * \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The state-of-the-art ML-based solution for 5G network. This section includes various works done on 5G ML by different authors. Table 10 shows the state-of-the-art work on the improvement of various parameters such as energy efficiency, Quality of Services (QoS), and latency with 5G ML.
## 1 Introduction
However, in practice, damaged and defective data is relatively rare and costly. There are also many challenges in the engineering application of this technology, including the lack of training data, the imbalance between damaged and undamaged data, and the impact on the construction and subsequent processing of the overall dataset. Therefore, in this study, we hope to find a method to expand the acquired damaged signal samples without affecting accuracy, and improve this situation through data augmentation, combined with innovative classifiers  
Figure 2: Schematic diagram of ultrasonic signal excitation propagation and reception. to optimize signal classification. In the signal dimension transformation method, commonly used methods have many limitations and complex parameters, and the effect of feature extraction for Lamb waves is generally not ideal. Therefore, this study attempts a new signal-to-image conversion method, which can extract Lamb wave features more specifically, simplify the processing process, and improve the effect of signal dimension transformation. In addition, this study uses attention mechanisms to capture the time and feature dependencies of time series, improving the quality of data generation and subsequent signal classification. This overall framework for deep learning and signal processing is the focus of this study  
The rest of this article is organized as follows. Section 2 is about the design of the experimental scheme and the introduction of relevant experimental equipment and materials. Section 3 includes the selection and design of data enhancement algorithm and signal classification algorithm. Section 4 presents the experimental test results of the proposed method and the comparison of common methods. Finally, Section 5 concludes this article.
# Generative Artificial Intelligence-Enabled Facility Layout Design Paradigm
## 3 Reference Architecture of Facility Layout Design 4.0
### Overview of the Reference Architecture  
As shown in Figure 2, the proposed FLD 4.0 architecture consists of four key layers: the supporting database and tools, core engine, main functions, and applications. This architecture emphasizes semantic interoperability, utilizing the AAS as a robust semantic framework for asset management. The AAS provides standardized descriptions of equipment and processes, enabling seamless integration across various systems and platforms in an Industry 4.0 context [73]. The structured ontology knowledge map based on AAS offers rich insights into asset relationships and operational processes. Additionally, the domain knowledge base, grounded in a large semantic model and advanced knowledge engineering practices, ensures contextual layout relevance and adaptability [74; 75]. The core engines comprise advanced components such as 3D visualization, artificial intelligence data processing, and big data analytics. These elements work in tandem to facilitate real-time monitoring and decision making based on current operational conditions. The main functions encompass user-centric features that enhance interactivity, including layout generation, virtual simulation, and digital pairing. The user interface incorporates feedback mechanisms to continuously refine the output based on user input, ensuring that the design meets practical needs. The application layer focuses on comprehensive solutions for facility layout design, production simulation, digital delivery, and the creation of dynamic digital twins. These digital twins not only mirror physical assets but also integrate real-time data inputs to predict performance and guide operational optimization effectively.  
This architecture no longer divides the facility layout design process into top-down sub-processes such as demand analysis, conceptual design, detailed design, layout optimization, and design delivery [76]. It achieves end-to-end layout design by automating the workflow from user requirements to final layout generation, minimizing the need for manual inference and intermediate steps. Furthermore, the architecture outlines functions such as repository management through the AAS, knowledge modeling specific to layout design using advanced natural language processing (NLP) techniques, 3D visualization and simulation, data collection capabilities from the IoT ecosystem, and layout prediction through embedded knowledge mapping. These functions operate as independent services, interacting via standardized interfaces to achieve modularity and loose coupling, which enhance system flexibility and scalability.  
Figure 2: Reference architecture of facility layout design 4.0.