esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Case of the Southern District of Texas  
In the U.S. there are few hundred of the type of wind turbines we examine in this paper all installed in the years 2011-2012. Most of the times after 5 years, the owners of the turbines are finished with the OEM service contract and switch to self-operation and maintenance. The investigated parks are shown on Figure 2 in A and B sites.  
A financial advisor and asset management company, Lazard, creates a report every year about the history, current status, and the future trends of the energy price in the U.S. market. A decade ago, renewable energy prices, such as wind and solar energy, were higher than the conventional energy production methods. Solar and wind price dropped 86% and 67%, respectively. Since 2011, the onshore wind energy has the lowest LCOE [22]. The forecast shows further decrease for both solar and wind LCOE [23]. By 2020, the average cost of wind is projected to go below $40 and by 2050 below $30. These numbers are very important to be kept in mind when planning with long-term OPEX and potential life-time expansion of the wind turbines.  
The study was based on extensive data acquisition via Supervisory Control and Data Acquisition (SCADA). These types of systems have a network of electronic measuring devices, sensors, switches, etc. The setup of the systems allows the controlling processes to be executed both locally and remotely. Furthermore, its purpose is to support data analytics, generate alarms and transmit them to different  
Figure 2: Wind sites in the Southern District of Texas.  
specified locations. [24]. A system is normally a mix of a device which collects the data from the Programmable Logic Controller (PLC) of the Turbine's controlling unit, a central host unit which collects all the transmitted data and a number of monitoring and controlling software (can be standard or custom).
## 5 Discussion, Conclusions and Limitation and Industry Future
### Conclusions
On the other hand, the ability of the system to withstand endurance and reliability inevitably depends on the success of the system in communicating with the external environment; in other words, the stability of the system depends entirely on the ability of the system to adapt, change and evolve and respond to the environment, and since the environment, in turn, is constantly changing, this process of system adaptation must be a dynamic and sensitiveprocess. A stable system needs proper internal functioning and an environmentally friendly relationship, in other words, the stability inside and stability outside (in interaction with the environment) to act as a stable system as a whole. Sustainability in practice is a balance between environmental necessities and development needs and is achieved in two ways: by reducing pressures and increasing existing capacities. It is expected that the use of Industry 5.0 technologies will reduce transportation costs, make the national and global supply chain more productive, increase revenue levels and improve people's quality of life. This upgrade will not remain in a country, and we will see new markets and global economic growth due to reduced business costs; but economists believe that this huge change or leap will be the beginning of a human inequality in the manpower sector of companies and industries. This is because the workforce which previously relied on manual labor and experience will be replaced by new technologies and artificial intelligence. Simply put, perhaps the greatest opportunity of this period, which can itself be a threat, is the replacement of talent with manual labor. This factor causes society to be divided into two parts: talented and knowledgeable people with high income and people with skills, knowledge and low income. A social divide that may even lead to tensions in the community. Many economic analysts see this as justifying the concern of some workers and employees about their income and the dissatisfaction of the middle class with their job situation. However, [PERSON] et al. [64] argued that brand love is different from the concept of consumer satisfaction.
# A Comparative Study of Two Rule-Based Explanation Methods for Diabetic Retinopathy Risk Assessment
## 5 Experiments and Results
### Analysis of Computational Complexity  
Computational resources are crucial in any practical application. In this subsection, we analyse the time complexity of the system. The most costly parts of the system are the generation of neighbours, the construction of the decision tree and the construction of the rules in the DRSA method. So, we first present the theoretical analysis of the time complexity of each part, and then we show the experimental setup and the running time in seconds.  
We solve the generation of neighbours as a searching problem using the Uniform Cost Search algorithm. Hence, the total running time complexity of this part of the system is \(O(b^{1+\lfloor C^{*}/e\rfloor})\), where \(b\) is the branching factor and \(C^{*}\) is the cost of the optimal solution, assuming that every action costs at least \(e\)[34]. The time complexity for the decision tree algorithm is \(O(n)+O(m\cdot n\cdot log_{2}n)+O(n\cdot log_{2}n)\)[35]. The time complexity of the DRSA method is \(O(m^{2}\cdot n^{2})\). Here, \(m\) is the number of examples, and \(n\) is the number of attributes.  
The experiments were carried out on a 64- bit computer, with AMD Ryzen 7 3700U (4 Cores, 2.3 GHz) and 16 GB RAM, running Windows 11 operating system. The number of examples in the test set is 1111. For each example, we generate 800 neighbours and use them to derive the explanation. Table 3 shows the elapsed time of generating the neighbours, building the decision trees and constructing the rules in seconds. As expected, the most expensive part of the system is the generation of the neighbours. The decision tree is slightly faster than the DRSA, which is expected as the DRSA complexity is quadratic, whereas the decision tree complexity is logarithmic. The estimated time to obtain an explanation is 9.051 s using the C-LORE-F method and 9.268 s using the DRSA method.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Min** & **Max** & **Average** \\ \hline Neighbours Generation & 5.453 & 19.252 & 8.882 \\ C-LORE-F & 0.093 & 1.149 & 0.169 \\ DRSA & 0.125 & 2.556 & 0.386 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Running time comparisons.  
Figure 7: Proximity of counterfactual examples, in Categorical and Continuous attributes. The numbers in the \(y\)-axis represent the metrics values (the higher values, the better performance), while the \(x\)-axis represents the number of generated counterfactual instances.
# Probabilistic Shaping for Finite Blocklengths: Distribution Matching and Sphere Shaping
## 3 Signaling Schemes  
### Uniform Signaling  
In uniform signaling, a \(k\)-bit uniform sequence \(u^{k}=(u_{1},u_{2},\ldots,u_{k})\) is encoded by a rate \(R_{\rm c}=k/n_{\rm c}\) FEC code, as shown in Figure 3 (top). Afterwards, the coded sequence \(c^{n_{\rm c}}\) is divided into \(m\)-bit vectors \((c_{1},c_{2},\ldots,c_{m})\), each of which is mapped to a channel input symbol \(x\in\mathcal{X}\) via the symbol mapper. Finally, assuming that \(n_{\rm c}/m=n\), the sequence \(x^{n}\in\mathcal{X}^{n}\) is transmitted over the channel. The transmission rate of this construction is \(R=k/n\) bit/1-D. We will compare the uniform and shaped signaling techniques at the same transmission rate \(R\), as it is obviously the only fair comparison as recently discussed in (Section IV-A in [61]) and [83].
# Morphological Analysis of a Collapsing Cavitation Bubble near a Solid Wall with Complex Geometry
## 4 Morphological Analysis Method Exerted on the Cavitation Bubble Collapse Model
### Evolution of Bubble Profiles
#### 4.2.2 Geometric Features of Solid Walls  
As shown in Figure 12, three solid walls with the same fractal dimension are selected as rigid boundaries to further analyze the effect of the surface geometry of the solid wall on the collapsing bubble. From Figure 12, it can be found that the actual distance between the bubble and the solid wall's surface has a great difference in these three solid walls. The difference between the solid walls a and b is up to 25 lu. Figure 13 depicts the morphological metric curves of the Minkowski functionals for \(\lambda=1.5\) and \(D=1.03274\).  
The three Minkowski measures for the collapsing bubble near a solid wall with a complex geometry no longer show similar trends, as shown in Figure 13. In the initial stage of bubble collapse, the actual distance from the bubble to solid wall's surface plays a major role in the morphological evolution of the collapsing bubble. As a result, the curves of \(F(t)\) and \(U(t)\) have a certain difference before \(t=300\) ts. Except for the actual distance between the bubble and the solid wall's surface, the variations in the surface geometry of the solid wall also influence the dynamic process of the collapsing bubble. For instance, the first collapse near the solid wall b occurs before that near the solid wall c, but the corresponding second collapse occurs after that near the solid wall c. This is because the surface geometry of the solid wall influences its retarding effect on bubble collapse and leads to differences in the size of the collapsing bubble at the first collapse and the length of the collapse time. Therefore, the geometric feature of the solid wall plays a significant part in the evolution of the bubble profiles, thereby affecting the bubble collapse process.  
Figure 11: Jet velocity and boundary length vs. time under the different fractal dimensions: (**a**) jet velocity and (**b**) boundary length.  
Figure 12: Different solid walls with the same fractal dimension: (**a**) solid wall a, (**b**) solid wall b, and (**c**) solid wall c.  
Figure 14 depicts the evolution of the boundary length and jet velocity of the collapsing bubble near the three different solid walls from \(t=550\) ts to the first collapse. The trend of the jet velocity shows clear differences before the first collapse due to the differences in the surface geometry of the solid wall. For instance, the evolution of the jet velocity of the collapsing bubble near the solid wall a is similar to that in Figure 11. However, the jet velocities near solid walls b and c increase almost throughout the process. Moreover, the evolution of the corresponding boundary length of the collapsing bubble appears to have no apparent connection. Therefore, the surface geometry of the solid boundary affects the bubble collapse process, and there are no evident trends in the jet velocity evolution.
# Enhancing Design for Additive Manufacturing Workflow: Optimization, Design and Simulation Tools
## 4 Optimization Strategy
### Gradient-Based TO  
Available commercial software as Altair Hyperworks (Altair Engineering, Troy, MI, USA) [52], SolidWorks (Dassault Systemes, Velizy-Villacoublay, France), MSC (MSC Software Corporation, Newport Beach, CA, USA) [53], Comsol Multiphysics (Comsol Inc., Stockholm, Sweden) [54], Ansys (Ansys Inc., Canonsburg, PA, USA) and Abaqus (Abaqus Inc., Velizy-Villacoublay, France) solves the TO problem by using the gradient-based density approach, associated to the density interpolation scheme method SIMP or power-law approach. The density approach is characterized by a design variable assigned to discretized elements representing an artificial density of material [55]. This variable ranges from 0 to 1 in the case of void elements or solid material, respectively. In general, platforms provide as objective function minimizing compliance to find the maximum stiffness layout material distribution, aiming to reduce the computing complexity of gradient-based algorithms [56]. Alternatively, other optimization algorithms are available through MATLAB or Python codes and integrated into FEM commercial software [57].  
The solution of gradient-based TO strategy is found through an iterative process called nested-formulation [58]. The iterative loop consists of five main steps [59]: FEM analysis, sensitivity analysis, filtering techniques, optimality algorithm updating design variables and post-processing.  
In general, the stiffness matrix structure and the displacement field are found via FEM. Then, the density design variables are assigned into the design domain as constants for each finite element. By using a sensitivity analysis, the process calculates the partial derivatives of the objective function concerning the value of the design variable, at each element. For the particular case of compliance objective function, the derivative is always negative [60], thus indicating that increasing element-density yields a decrease of the overall compliance, and a stiffer structure. Aiming at reducing the numerical instabilities, the next step is applying density or sensitivity filtering techniques to impose a length-scale restriction on the design domain, thus limiting the spectrum of possible feasible solutions. The following phase uses an optimality algorithm method like optimality criteria (OC) [61] or the method of moving asymptotes (MMA) [62] that approximate the value of the objective function by using the sensitivity results. In this manner, the optimization task is divided into sub-problems so that solving is more efficient. The last step consists of updating the design variable values. The cycle is repeated until numerical convergence. Additional post-processing via projection filters might be required to improve contour boundaries.
## 1 Introduction
Additionally, it is expected that not every data augmentation method will be applicable to all potentials (P300, ERD/ERS, SSVEP). In recent years, deep-learning techniques have been employed for data augmentation, with autoencoders (AE) and generative adversarial networks (GAN) being two common strategies. The impact of noise addition on time series is discussed in [34], where it was concluded that although noise can disrupt the amplitude and phase information, it does not change the spectral feature distribution. In [35], a data augmentation method based on graph empirical mode decomposition was introduced to generate EEG data, merging the benefits of the multiplex network model and the graph version of classical empirical mode decomposition. In [36], the authors explored the constraints of DA for EEG in emotion recognition. Direct geometric transforms and noise addition can impair the time domain features, potentially resulting in a negative DA impact. The issue of limited training data and a proposed solution are discussed in [37], where the authors employed the LST algorithm to transform SSVEP data across different users and devices to compile a larger dataset. In [38], a novel DNN model named FB-EEGNet for SSVEP target detection is introduced. This model integrates features from multiple neural networks to leverage information from various sub-bands and non-target stimulus data. Furthermore, it uses multiple labels for each sample and optimizes the parameters of FB-EEGNet across different stimuli to encompass information from non-target stimuli.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **CG before** & **CG after** & **WBVEG before** & **WBVEG after** & \\  & **Mean (SE)** & **Mean (SE)** & **Mean (SE)** & **Mean (SE)** & **\(p\)**-Value** & **\(p\)**-Value** \#** \\ \hline VAS (0–10) & 4.11 (0.56) & 3.94 (0.57) & 0.564 & 4.58 (0.62) & 3.23 (0.61) & 0.001 * & 0.041 * \\ TUG-test (s) & 14.87 (0.88) & 13.84 (0.78) & 0.002 * & 19.42 (1.96) & 16.44 (1.50) & \(<\)0.001 * & 0.045 * \\ BORG SCALE (score) & 1.66 (0.56) & 1.58 (0.51) & \(>\)9.999 & 2.15 (0.54) & 2.15 (0.54) & 0.627 & 0.452 \\ ATF (cm) & 17.53 (2.54) & 16.31 (2.53) & 0.103 & 18.81 (2.08) & 14.64 (2.05) & \(<\)0.001 * & 0.043 * \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Pain level, functionality (TUG test and anterior trunk flexion (ATF)), and subjectively perceived exertion rating (BORG Scale CR-10) assessments before and after the intervention and between groups. The data on the rating of perceived exertion (BORG scale) did not present significative differences in any comparison (Table 2).
## 3 Methods
### Supervised Learning Based Methods
#### 3.1.2 Extreme Gradient Boosting (XGBoost)  
XGBoost is an advanced implementation of the gradient boosting framework, designed to enhance model performance and computational efficiency. XGBoost constructs an ensemble of weak learners, typically decision trees, in a sequential manner, where each new tree is trained to correct the errors made by the ensemble of previous trees. The model iteratively improves the predictive accuracy by minimizing a defined objective function, which consists of two main components: a loss function that measures the discrepancy between the actual and predicted values and a regularization term that controls the model complexity [19]. The objective function is minimized as follows:  
\[L(\theta)=\sum_{i=1}^{N}l(y_{i},\widehat{y}_{i})+\sum_{k=1}^{K}\Omega(f_{k}) \tag{3}\]  
where  
* \(l(y_{i},\widehat{y}_{i})\) is the loss function that quantifies the difference between the true value \(y_{i}\) and the predicted value \(\widehat{y}_{i}\). Common loss functions used include the Mean Squared Error (MSE) for regression tasks and log-loss for classification tasks;
* \(\Omega(f_{k})\) is the regularization term that penalizes the complexity of each individual tree \(f_{k}\) in the model, helping to prevent overfitting. This term is defined as \[\Omega(f_{k})=\gamma T+\frac{1}{2}\lambda\sum_{j=1}^{T}w_{j}^{2}\] (4) where \(T\) is the number of leaf nodes in the tree, \(w_{j}\) represents the weight of leaf \(j\), \(\gamma\) controls the complexity of the model by imposing a penalty for adding more leaves, and \(\lambda\) is a regularization parameter that penalizes large weights.  
XGBoost optimizes the objective function using second-order gradient descent, which takes into account both the first-order (gradient) and second-order (Hessian) information, making it highly effective at capturing complex patterns in the data. Furthermore, XGBoost incorporates additional enhancements, such as shrinkage (learning rate), column subsampling, and handling missing values, which contribute to its superior predictive performance and versatility across various machine learning tasks. These features make XGBoost a powerful tool for tackling both regression and classification problems in a wide range of applications [20].
# Development of a Two-Dimensional Hybrid Sediment-Transport Model
## 5 Conclusions  
In this study, a 2D single-phase nonequilibrium sediment-transport model has been developed for nonuniform, noncohesive sediments in unsteady turbulent flows. The model was discretized using FVM on a hybrid unstructured-mesh system with high adaptivity for geometrically-complex domains. The conventional suspended-load transport equation and the bed-load transport equation were solved separately in the model. For the suspended-load gradient, the velocity gradient, and the pressure gradient at the edges, a second-order accurate method was used to take account of the mesh irregularities and avoid interpolations for the vertex values. For the possible nonphysical oscillations induced in the wetting-and-drying process, a multipoint momentum interpolation correction method was integrated to maintain both numerical stability and accuracy.  
The sediment transport predictions were validated by a benchmark degradation case for the erosion process with armoring effects, a benchmark aggradation case for the deposition process, and a naturally meandering river was used for the validation of long-term unsteady sediment-transport processes. In general, the simulation results agreed well with the measured data, which indicates success in the development of this model. Finally, the model was successfully applied to simulate sediment transport in the Jiji Reservoir, which was significantly affected by typhoon events. In the future, this sediment-transport model will be improved and expanded in: (1) more applications for soil erosion and gully erosion in agricultural lands and (2) a cohesive sediment-transport process.  
Conceptualization, Y.Z.; methodology, Y.Z.; validation, Y.Z.; writing--original draft preparation, Y.Z.; writing--review and editing, D.W. and M.A.-H.; project administration, M.A.-H. All authors have read and agreed to the published version of the manuscript. This work is supported by the U.S. Department of Agriculture, Agricultural Research Service, through the Cooperative Agreement No. 6060-13000-030-00D between the USDA-ARS National Sedimentation Laboratory (USDA-ARS-NSL) and the University of Mississippi/National Center for Computational Hydroscience and Engineering (NCCHE).  
Not Applicable.  
Data Availability Statement: All data in examples and application are available upon request.  
The authors would like to thank [PERSON] from National Sedimentation Laboratory (NSL), USDA Agricultural Research Service (ARS) for his suggestions and help in this study. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
# Inversion of Rayleigh Wave Dispersion Curve Extracting from Ambient Noise Based on DNN Architecture
## 4 Conclusions and Discussions
### Discussions  
DNN provides a promising alternative method for inversion. It performs inversion by learning and extracting features from a large training dataset, allowing for fast mapping of dispersion data to shear wave models with good training. However, improving the accuracy and generalization ability of DNN inversion requires more complex and suitable neural network models, as well as more realistic and comprehensive sample datasets.  
In this study, we investigated three parameters that significantly impact the convergence of the training process: the number of training samples, learning rate, and number of hidden layers. However, it should be noted that the complexity of the neural network model may lead to differences in parameter settings for different models. The optimal parameters obtained in this study are not fixed rules. In many cases, for other specific data sequences, it is necessary to determine these optimal parameters through repeated testing.  
Furthermore, the randomness in the selection of velocity models for DNN training datasets can affect the learning process. In our research, we only use synthetic data to validate the network performance. However, the adequacy and diversity of the samples are still worth considering, which is a topic we must explore in the future.  
Conceptualization and methodology, Q.M. and Y.C.; formal analysis, Q.M. and Y.C.; data curation, Q.M. and Y.C.; writing--original draft preparation, Q.M. and Y.C.; writing--review and editing, F.S.; visualization, Y.C.; project administration, Q.M.; funding acquisition, Q.M. and T.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Depth Range (m)** & \multicolumn{2}{c}{**DL Inversion (m/s)**} & \multirow{2}{*}{**Liu 2015 (m/s) [37]**} \\  & **S1** & & **S2** \\ \hline
0–10 & 80–200 & 110–200 & 120–170 \\
11–20 & 150–250 & 200–250 & 150–230 \\
21-30 & 260–340 & 250–320 & 200–300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of \(V_{S}\) between general statistics and DL inversion (Table 1).  
Funding: This research was funded by the National Natural Science Foundation of China (Nos. 42272327) and the Social and Livelihood Project of Shandong Province (2021, 202131001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Acknowledgments:** The authors would like to thank [PERSON] and [PERSON], who participated in recording the ambient noise in the field. We also appreciate two anonymous reviewers for their valuable comments to improve the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.