esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Mask Detection Module
#### 3.3.2 Fine-Tuning with Transfer Learning  
DNN (Deep Neural Network) has gained significant image classification progress due to its better performance than other algorithms. Developing and training DNN from scratch is a lengthy and time-consuming task, because it needs huge computational power and resources. A transfer learning approach is introduced to reduce this complexity, which allows transferring the trained knowledge of the neural network in terms of parametric weights to the new model. We used five pre-trained models in our proposed framework, and the Inception V3 and VGG-16 achieved a higher accuracy than other models. The pre-trained models (VGG-16, Inception V3) can classify 1000 different classes, but we need three categories: FWPM, FWIPM, and FWOM instead of 1000. To do that, we replaced the last fully connected layer of the models with a new one with three output features instead of 1000. We accessed the VGG-16 and Inception V3 classifiers with the'model.classifier' method, a six-layer array. We also disabled the training for convolutional layers, as we only trained the fully connected layer. Figure 11 shows the fine-tuned model architecture.  
We also defined our loss function (cross-entropy) and the optimizer. The learning rate started at 0.001, and a StepLR object decreased by a factor of 0.1 every seven epochs. We iterated over all of the training batches for each epoch, computed the loss, and adjusted the network weights with loss.backward() and optimizer.step() methods. Subseequently, we evaluated the performance over the test dataset. At the end of each epoch, we showed the network progress (loss and accuracy). The accuracy told us how many predictions were correct.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Total Images** & **Training Images** & **Testing Images** \\ \hline Github (MAFA) [37] & 12,000 & 9600 & 2400 \\ Bing Dataset [40] & 4039 & 3232 & 807 \\ Github Dataset (Masked Face-Net) [36] & 67,562 & 54,049 & 13,512 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Datasets Description.  
Figure 10: Sample of dataset Images.
## 3 Experimental Tests and Results
### ZERO(tm) Machine Tests
#### 3.2.2 FEM-Based Validation of ZERO(tm) Machine Tests
The comparison of the measured data with the FEM simulation results, particularly at critical positions such as the maximum and intermediate Y-axis travel, shows a good agreement, confirming the reliability of the simulation in predicting machine behaviour under varying environmental conditions. Table 10 presents the numerical values of the established metrics for comparing the measured data with the values calculated by the FEM model for the two defined positions. At Position 1 (maximum Y travel), the FEM model effectively reproduced the significant thermal error changes induced by the temperature fluctuations, with both the mean absolute error (MAE) and root mean square error (RMSE) values indicating a satisfactory level of accuracy (13.1 \(\upmu\)m and 16.3 \(\upmu\)m, respectively). Similar correlations were observed at Position 2 (intermediate Y travel), with the MAE and RMSE values of 13.3 \(\upmu\)m and 17.3 \(\upmu\)m, respectively, further validating the robustness of the FEM model.
## 1 Introduction
In addition, a two-stage robust optimization was provided in [26] considering all possible component failure scenarios including both generator and transmission line contingencies. In the technical literature, the execution of N-k criterion usually costs more time and has therefore a negative impact on solutions efficiency. Moreover, it has not been fully studied. Consequently, the motivation of this paper is to represent the benefits of combining active set method with security constraints to improve the efficiency of N-1 criterion verification. This paper focuses on the optimal scheduling of active power in the power system with the integration of large-scale wind power generation. The reactive power generation, transmission, and consumption in the system are assumed to be maintained in normal operation. Therefore, the practical engineering DC power flow formulation and algorithm can be employed, resulting in a very efficient computational behavior. Meanwhile, the global optimality and convergence can be strictly guaranteed. In this paper, a robust co-optimization model is thus set up to find an optimal scheduled generation while guaranteeing the security of the power systems for any given wind power as well as any N-1 contingency of both transmission lines and generators. Compared with the prior art works, the major contributions of this paper are summarized as:  
1. The model proposed in this paper can generate a robust scheduling scheme immune to the largest fluctuation of the renewable energy output;
2. The zonal spinning reserves can enable the deliverability of spinning reserves, particularly when an outage of generators occurs;
3. The N-1 security criterion of unit and transmission lines is executed to guarantee the feasibility of the initial scheduling scheme. Moreover, the utilization of the active set method can improve the efficiency of solution. The rest of the paper is organized as follows: Section 2 presents the basic robust co-optimization model with energy and reserves to address the uncertainties from wind power generation. Section 3 employs the active set method to efficiently solve the co-optimization model in the consideration of N-1 contingencies for both generators and transmission lines. In Section 4, tests and comparisons under different scenarios are demonstrated. Finally, conclusions are drawn in Section 5.
## 6 Application Cases
### EMU Bogie Wheel Pair System Fault Data  
A bogie, as the most important part of EMU, directly affects the security and stability of train operations. A bogie's O&M plan and repair method would certainly be associated with the repair degree. A total of 128 repair and maintenance records from a certain CRH series of EMU were collected, and the data of servicing work and routine security checks needed eliminating. Based on the Python platform, the Pandas function reads the fault repair data of the collected bogie key parts, removes the data set of the missing data, deletes the useless information of notice order number, train set number, fault feedback personnel, etc., and screens the fault information of 41 bogie key parts, as shown in Table 1.
# English Speech Emotion Classification Based on Multi-Objective Differential Evolution
## 4 Experimental Results and Analysis
### Experimental Analysis
#### 4.2.2 Simulation Results on the RF Classifier  
1. Hypervolume  
Table 6 shows the HV values of the multi-objective algorithms. The data obtained by the algorithms using eNTERFACE05, RAVDESS, SAVEE, and TESS were better than those obtained using the KNN classifier. MODE-NSF outperformed the other algorithms on four datasets, especially TESS, where it achieved a value of 0.8101, a result close to the theoretical optimal value. The Friedman test showed that their average ranks were 2.75, 3.75, 2.5, and 1. The Wilcoxon rank sum revealed that they performed well on 2, 1, 3, and 4 datasets. MODE-NSF and MOGA produced consistent statistical data using eNTERFACE05 and RAVDESS. Additionally, MODE-NSF and MODE exhibited a similar performance using RAVDESS, while MODE-NSF and NSGA-II achieved similar experimental results using eNTERFACE05, RAVDESS, and SAVEE.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Dataset** & **MOGA** & **MODE** & **NSGA-II** & **MODE-NSF** \\ \hline eNTERFACE05 & 2148.5126 & **187.7856** & 1860.2133 & 268.8662 \\ RAVDESS & 5858.3368 & 407.2977 & 2797.5543 & **295.3447** \\ SAVEE & 2226.02 & 180.2649 & 1802.0954 & **178.759** \\ TESS & 16,305.3481 & 954.1857 & 4681.1689 & **543.2745** \\ \hline \end{tabular}
\end{table}
Table 5: The average running time of the algorithms (second).  
2. Inverted generational distance  
Table 7 provides the IGD of the algorithms and their corresponding Wilcoxon rank-sum and Friedman test results. The IGD value of MODE-NSF was significantly smaller than that of the other algorithms, which means that the multi-objective solutions obtained by it were close to the Pareto front composed of all algorithms. The performance of MODE-NSF was better than the others. The Wilcoxon rank sum indicates that MOGA produced similar statistics to MODE-NSF using eNTERFACE05; in addition, MOGA, MODE, NSGA-II, and MODE-NSF performed well on 1, 0, 0, and 4 datasets. Their average ranks were 2.25, 3.5, 3.25, and 1, and the \(p\)-value was less than 0.05.  
Tables 6 and 7 confirm that the proposed MODE-NSF demonstrates exceptional distribution and convergence, and it is suitable for multi-objective feature selection.  
3. Pareto solutionsTable 8 presents the running time of the algorithms. The maximum time complexity of RF was larger than that of KNN, resulting in a longer run time for the algorithms compared to KNN. MODE-NSF had a superior operational efficiency. The algorithms ran longer for TESS than they did for eNTERFACE05 and SAVEE.
## 2 Materials and Methods
### Optimal Triangulation Network Design  
Accurate estimation of crustal strain rates from GNSS velocity fields critically depends on the spatial configuration of observation stations. A widely adopted method involves dividing the study region into non-overlapping triangular elements, within which strain tensors are calculated. However, the geometric quality of these triangles--particularly internal angles and aspect ratios--directly affects the numerical stability and spatial reliability of the strain estimates. Elongated or narrow triangles with acute angles are prone to instability and error amplification.  
To mitigate these limitations, this study employs Delaunay triangulation, which maximizes the minimum internal angle of each triangle and avoids poorly shaped elements, thereby improving geometric regularity. This method enhances the robustness and accuracy of strain field computations, particularly in areas with uneven GNSS station distribution. In contrast to interpolation techniques such as kriging, inverse distance weighting (IDW), or spline functions, which often overlook triangulation geometry or introduce smoothing effects, the Delaunay approach preserves the spatial configuration of the observation network and retains local deformation signals.  
Delaunay triangulation is also compatible with probabilistic and Bayesian modeling frameworks increasingly used in modern geodetic analyses to quantify uncertainty in strain fields. Its ability to adaptively preserve resolution in dense areas while minimizing interpolation artifacts in sparse regions makes it particularly effective for tectonic applications [54; 56; 57; 58].  
In this study, a total of 49 non-overlapping triangles were constructed using 33 GNSS stations--23 from the national Continuously Operating Reference Station (CORS) network and 10 additional campaign-mode sites. This configuration ensures dense spatial coverage across the southeastern Korean Peninsula, particularly near major fault systems.  
Figure 2 shows the constructed triangulation network. Blue squares denote permanent CORSs, while black circles represent temporary campaign stations deployed to improve spatial resolution. Red lines mark the Yangsan (west) and Ulsan (east) Faults--both seismically active and structurally significant. Triangles are indexed (1-49) to enablespatially resolved analysis. Notably, attention was given to the fault-intersection zone, where crustal strain is expected to localize due to fault interaction dynamics.  
This triangulation forms the geometric foundation for two-dimensional strain tensor computation and provides a high-resolution basis for analyzing crustal deformation and assessing seismic hazard in southeastern Korea.
## 1 Introduction
Empirical Mode Decomposition (EMD) can decompose the signal to be processed into a series of Intrinsic Mode Functions (IMFs), which possess properties such as orthogonality, completeness, and adaptability, allowing for descriptions of the signal at different scales. However, the EMD algorithm suffers from boundary effects and modal aliasing, where one or more modes often contain signals of different scales or similar signals during the decomposition [10]. Based on the EMD algorithm, several improved algorithms have been proposed, such as Intrinsic Time Decomposition (ITD) and Variational Mode Decomposition (VMD). However, the ITD algorithm lacks the ability to precisely identify local extrema. To address the shortcomings of recursive modal decomposition methods (such as EMD and ITD) in processing MT signals, particularly their limitations in handling nonlinear and nonstationary characteristics, this paper introduces the VMD algorithm to investigate the signal-to-noise separation problem under strong cultural noise interference. This method transforms the modal estimation into a variational problem and continuously updates the center frequencies of each mode in the frequency domain, effectively overcoming issues such as modal aliasing and boundary effects faced by EMD and ITD. The VMD algorithm has been widely applied in the fields of geophysics, including microseismic data denoising [11] and signal-to-noise identification [12], the interpretation of GPR survey data [13], transient electromagnetic data denoising [14], magnetotelluric impedance estimation [15], magnetotelluric data denoising [16], and audio magnetotelluric data denoising [17]. However, previous research on denoising MT data still lacks precise studies on selecting the appropriate number of modes and parameters. To address this, this paper employs the PSO-optimized VMD method, leveraging the global search capabilities of Particle Swarm Optimization (PSO) to suppress noise in MT data containing strong cultural noise, with the aim of improving the quality of field data and enhancing the accuracy and reliability of interpretations of subsurface electrical structure information.
## 4 Discussion
### Limitations  
Although the proposed solution seems promising, further research is needed to address the identified limitations. The requirements and gathered feedback are considered independent of the sample. However, it should be mentioned that one limitation is the sample selection, which is considered a convenience sample and is not representative in terms of gender or age. Also, the quantity of the sample size itself (specifically for UES) is a limitation, which should be addressed in future work by including more participants in playtesting sessions. A major advantage of including therapists in the requirement analysis is their increased knowledge about the problem space and their expertise in finding solutions for incorporating exercises into a serious game. However, the therapists were not included in the usability evaluation, which can also be seen as a limitation. The usability evaluation could also benefit from a more diverse sample of participants and an extensive evaluation period to gain more insights into the VR game and its usage. In addition, the demographics and the recruitment of only healthy participants are also limitations in terms of the presented results. This currently restricts the ability to answer to what extent the rehabilitation of wrist injuries is supported through the usage of this VR serious game. To answer this, a clinical trial or intervention study is necessary. A subsequent study should also include therapists to improve the overall quality and results. In conclusion, the results seem promising and show great potential in using a serious game with the MetaQuest sensor, but further research is necessary to address the mentioned limitations. Overall, this publication should be seen as a good starting point for further advancements and additional research in VR utilizing \"MetaQuest\" to support the wrist and hand.
###### Abstract  
Functional connectivity derived from functional magnetic resonance imaging (fMRI) is used as an effective way to assess brain architecture. There has been a growing interest in its application to the study of intrinsic connectivity networks (ICNs) during different brain development stages. fMRI data are of high dimension but small sample size, and it is crucial to perform dimension reduction before pattern analysis of ICNs. Feature selection is thus used to reduce redundancy, lower the complexity of learning, and enhance the interpretability. To study the varying patterns of ICNs in different brain development stages, we propose a two-step feature selection method. First, an improved support vector machine based recursive feature elimination method is utilized to study the differences of connectivity during development. To further reduce the highly correlated features, a combination of F-score and correlation score is applied. This method was then applied to analysis of the Philadelphia Neurodevelopmental Cohort (PNC) data. The two-step feature selection was randomly performed 20 times, and those features that showed up consistently in the experiments were chosen as the essential ICN differences between different brain ages. Our results indicate that ICN differences exist in brain development, and they are related to task control, cognition, information processing, attention, and other brain functions. In particular, compared with children, young adults exhibit increasing functional connectivity in the sensory/somatomotor network, cingulo-opercular task control network, visual network, and some other subnetworks. In addition, the connectivity in young adults decreases between the default mode network and other subnetworks such as the fronto-parietal task control network. The results are coincident with the fact that the connectivity within the brain alters from segregation to integration as an individual grows.  
Keywords:functional connectivity; intrinsic connectivity networks; brain developmental differences; feature selection +
Footnote †: journal: Applied Sciences
## 2 Results and Discussion
### Polyphenolic Compounds Analysis by HPLC Supported by FTIR and Confocal Microscopy
#### 2.1.6 Anti-Inflammatory Properties  
The analysis of VOCs in the examined wine samples indicated the presence of various compounds, known for interesting biological effects, including anti-inflammatory properties, such as fatty acids and their esters; terpen, such as alpha-pinene and limonene; alcohols including 1-butanol, hexanal, 1-hexanol, octanoic acid, ethyl ester, and methyl-laurate. Thus, as the ultimate step of this study, the anti-inflammatory potential of pomegranate, kiwi, and persimmon wines was determined, using an LPS-stimulated RAW 264.7 macrophage model. Three different parameters were evaluated to assess the significance of anti-inflammatory activity, namely nitric oxide, IL-6, and TNF-\(\alpha\) release (Figure 5). All wine samples demonstrated notable anti-inflammatory effects in vitro, with pomegranate, kiwi, and persimmon wines showing decreasing efficacy in that order. Notably, dose-dependent differences were mostly indiscernible for persimmon wines. These findings correlate well with the composition of active polyphenic compounds within these wines.  
Figure 5: The impact of different fruits wines on the release of IL-6, TNF-\(\alpha\), and NO in LPS-stimulated RAW 264.7 macrophages. RAW cells were pre-treated with wines at different concentrations (the numbers  
in parentheses indicate concentrations in \(\upmu\)g/mL) for 1 h, followed by the addition of 10 ng/mL of LPS to induce inflammation. Values are presented as the mean \(\pm\) SD of three experiments. The results are set together with untreated RAW cells (UNTR) and cells treated with LPS and dexamethasone as the reference drug (LPS + DEX). A statistical analysis was performed using one-way ANOVA with * \(p<0.05\), against the LPS-stimulated cells. The black line indicates significant differences between the used doses. Abbreviations are as follows: POME--pomegranate wine; KIWI--kiwifruit wine. PERS--persimmon wine.  
Notably, this is the first study presenting the anti-inflammatory potential of pomergnate, kiwi, and persimmon wines. Previously, only [PERSON] et al. [30] confirmed the anti-inflammatory properties of fermented pomergnate waste extract, rich in ellagic acid, demonstrating its safety across a wide concentration range of 5-100 \(\upmu\)g/mL--comparable results were replicated in our study. RAW 264.7 cells were pre-incubated with this pomergnate extract at a concentration of 50 \(\upmu\)g/mL before LPS treatment. Results showed that pre-treatment with the extracts significantly reduced the LPS-induced expression of TNF-\(\upalpha\), IL-1\(\upbeta\), and iNOS. Furthermore, the anti-inflammatory activities of kiwi fruits and their by-products are linked to their polyphenolic compounds, in addition to other antioxidants present in the fruit of _Actinidia arguta_[31]. For persimmon vinegar, [PERSON] et al. [32] have also confirmed its anti-inflammatory properties.
# A Maturity Model Proposal for Industrial Maintenance and Its Application to the Railway Sector
## 6 Conclusions  
The presented maturity model shows the potential of allowing a gradual adoption of more advanced maintenance strategies, starting from preventive strategies and ending with prescriptive strategies. The main conclusions of this work are the following:The 18 domains extracted using grounded theory methodologies are present in each of the maintenance areas (asset, status and maintenance) and in the four levels of maintenance maturity analyzed (preventive, condition-based, predictive and prescriptive).
2. The criticality of the asset and the failure modes need to be analyzed first. The more mature strategies (predictive and prescriptive) are mainly suitable for critical assets.
3. Another key point to consider is to address the problem holistically. Once the maturity level is identified and the levels are targeted, M3 provides the actions to take. Each of these should not be approached as a standalone problem. The overall objective is to maintain the possibility of integration and interoperability of the different solutions that are already implemented.
4. M3 is a maturity model which addresses all maintenance strategies, not just a specific one, as most of the proposals in the literature do. It also provides a tool for the assessment of the maturity level, rather than providing concrete solutions for a specific strategy. Finally, it provides a roadmap for implementation.
5. The discussion carried out using the example of railway infrastructure maintenance demonstrates the applicability of the model presented in this paper. The criticality of the asset selected (track geometry) has a direct impact on safety, and acquiring the prescriptive maintenance level is the final objective. The different initiatives to improve and acquire higher levels of maintenance maturity have also been analyzed, although these techniques are not yet extensively used by IMs.
6. Considering that the model presents methods used in several sectors, M3 presents the possibility of being applied to another sector where critical assets are involved.  
Conceptualization, I.E., S.B. and S.A.; Formal analysis, I.E., U.A. and S.A.; Investigation, I.E., U.A. and S.A.; Methodology, I.E., S.B. and S.A.; Resources, I.E., U.A. and S.A.; Supervision, S.A.; Writing--original draft, I.E. and S.A.; Writing--review & editing, I.E., U.A., S.B. and S.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.