esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Newly Proposed Evaluation Method and Analysis Procedure
### Typical Procedure of Newly Proposed CRITIC-GRA Evaluation Model
#### 2.1.3 CRITIC-GRA Multi-Factors Evaluation Model  
The CRITIC method is used to determine the weight values matrix \(W\) shown in Equation (14) for all preset schemes and considered indicators, which is then applied to the GRA method to determinate the importance rank of the selected or calculated ranging values of safety and economic indicators. The GRA method allows for the quantitative analysis of various influential factors. Combining the outcomes with these two methods provides a unique evaluation criterion for the slope dimension optimization considering the open stoping stability, mining capacity and costs in the same model, and the criterion is the final output value of grey correlation degree \(r_{i}\) shown in Equation (16).  
\[r_{i}=\sum_{j=1}^{n}w_{i}\zeta_{i}(j) \tag{16}\]  
where, \(r_{i}\) is the grey correlation degree of scheme \(i\), \(w_{i}\) is the weight value matrix of each factor shown in Equation (14), and \(\zeta_{i}\) is the grey correlation coefficient of scheme \(i\) shown in Equation (15).  
According to the magnitude of grey correlation degree \(r_{i}\), the preset schemes can be prioritized, and a higher grey correlation degree indicates a better suggested slope dimension scheme.  
The advantage of the proposed CRITIC-GRA method is that it completely eliminates the impact of subjective factors and finds the optimal scheme with the consideration of diverse factors. Moreover, the method imposes no constraints on the quantity or regularity of the considered indicators, which makes it possible to synthesize and analyze the open stoping stability index from the Mathews stability graph, the numerical simulated results and the economic indexes of mining capacity and costs.
## 2 Materials and Methods
### Study Area and Data Source
#### 2.1.2 Data Source  
The research data collected includes high-resolution satellite imagery, land use data, DEM (Digital Elevation Model) data, and auxiliary data such as administrative boundaries, water systems, and road networks. The high-resolution satellite imagery was obtained from the Land Observation Satellite Data Service Platform of the China Resource Satellite Application Center ([[https://data.cresda.cn/](https://data.cresda.cn/)]([https://data.cresda.cn/](https://data.cresda.cn/)), accessed on 11 January 2025), and the supplier of the high-resolution satellite imagery is the Guangdong Provincial Smart Farmland Protection Engineering Technology Research Center. Four cloud-free satellite images (with cloud coverage of less than 5%) were acquired, covering the study area. These images are classified as Level 1A and include both multispectral and panchromatic bands. The multispectral data of both GF-2 and GF-1 comprise four bands: red, green, blue, and near-infrared, with spatial resolutions of 4 m for GF-2 and 8 m for GF-1. Additionally, the panchromatic bands of GF-2 and GF-1 consist of a single band featuring spatial resolutions of 1 m for GF-2 and 2 m for GF-1, as detailed in Table 1. The image dates are 15 March 2021 and 6 December 2021. The land use data, sourced from the Hainan Provincial Academy of Agricultural Sciences, consists of vector data that reflect land use changes in 2021. The data layer includes three types of cultivated land: dryland, irrigated land, and paddy fields. These data serve as the baseline for cultivated land. The DEM elevation data, with a spatial resolution of 12.5 m, is from the Geospatial Data Cloud. Auxiliary data, including administrative boundaries, water systems, and roads, were obtained from the Hainan Provincial Academy of Agricultural Sciences. Information on the main crop types and their planting areas in the study region comes from the Hainan Provincial Statistical Yearbook.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset Name**} & \multirow{2}{*}{**Spectral Band**} & **Spectral Band** & **Temporal** & **Spatial** & \\  & & **Range/\(\mu\)m** & **Resolution/d** & **Resolution/m** & **Altitude/km** \\ \hline \multirow{4}{*}{GF-2} & B1-Blue & 0.45–0.52 & & 4 & \\  & B2-Green & 0.52–0.59 & & 4 & \\  & B3-Red & 0.63–0.69 & 69 & 4 & 631 \\  & B4-NIR & 0.77–0.89 & & 4 & \\  & PAN & 0.45–0.90 & & 1 & \\ \hline \multirow{4}{*}{GF-1} & B1-Blue & 0.45–0.52 & & 8 & \\  & B2-Green & 0.52–0.59 & & 8 & \\ \cline{1-1}  & B3-Red & 0.63–0.69 & 41 & 8 & 645 \\ \cline{1-1}  & B4-NIR & 0.77–0.89 & & 8 & \\ \cline{1-1}  & PAN & 0.45–0.90 & & 2 & \\ \hline DEM & & & 14 & 12.5 & 692 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of remote-sensing data [54].  
Figure 1: Location and Digital Elevation Model (DEM) of the study area.
# Integrative Factors of E-Health Laboratory Adoption: A Case of Indonesia
## 5 Results and Data Analysis
### Analysis of the Measurement Model
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Construct** & **Items** & \
\begin{tabular}{c} **Factor** \\ **Loading** \\ \end{tabular} & **AVE** & **CR** &
\begin{tabular}{c} **Cronbach’s** \\ **Alpha** \\ \end{tabular} \\ \hline Technology Characteristics (TCC) & TCC1 & 0.84 & & & & \\ Technology Characteristics & TCC2 & 0.858 & & & & \\ (TCC) & TCC3 & 0.843 & 0.711 & 0.945 & 0.932 \\  & TCC4 & 0.882 & & & & \\  & TC1 & 0.927 & & & & \\ Task Characteristics (TC) & TC2 & 0.953 & 0.887 & 0.959 & 0.936 \\  & TC3 & 0.945 & & & & \\  & TTF1 & 0.866 & & & & \\ Task Technology Fit (TTF) & TTF2 & 0.894 & & 0.734 & 0.943 & 0.926 \\  & TTF3 & 0.886 & & & & \\  & TTF4 & 0.906 & & & & \\  & IQ1 & 0.819 & & & & \\  & IQ2 & 0.887 & & & & \\ Information Quality (IQ) & IQ3 & 0.877 & 0.764 & 0.942 & 0.922 \\  & IQ4 & 0.886 & & & & \\  & IQ5 & 0.898 & & & & \\ Accessibility (AC) & AC1 & 0.844 & & & & \\  & AC2 & 0.856 & 0.723 & 0.839 & 0.617 \\  & DE1 & 0.887 & & & & \\ Design (DE) & DE2 & 0.912 & 0.801 & 0.923 & 0.876 \\  & DE3 & 0.886 & & & & \\  & PI1 & 0.884 & & & & \\ Personal Innovativeness (PI) & PI2 & 0.886 & & & & \\  & PI3 & 0.847 & 0.718 & 0.91 & 0.868 \\  & PI4 & 0.768 & & & & \\  & PU1 & 0.768 & & & & \\ Perceived Usefulness (PU) & PU2 & 0.85 & 0.678 & 0.894 & 0.842 \\  & PU3 & 0.803 & & & & \\  & PE1 & 0.691 & & & & \\ Perceived Ease of Use (PE) & PE2 & 0.924 & 0.722 & 0.885 & 0.801 \\  & PE3 & 0.914 & & & & \\  & AI1 & 0.897 & & & & \\ Adoption Intention (AI) & AI2 & 0.911 & 0.811 & 0.928 & 0.884 \\  & AI3 & 0.894 & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Convergent validity. In this study, we also calculated the Variance Inflation Factor (VIF). These values obtained ranged from 1.248 to 4.712 and were still smaller than 10.0 [115; 116]. There was no VIF value above 10.0, so it could be stated significantly that there was no multicollinearity problem in the dataset of the e-health laboratory user questionnaire in this study.
## 1 Introduction
The research involves an individual approach to assessing spoken word recordings with a neural network for emotions to be classified into three categories: positive, neutral, and negative. Unlike conventional speech emotion recognition (SER) algorithms used in human-computer interaction systems, which typically handle a wider, more detailed set of emotions (such as happiness, disgust, and fear), this study opts for the three broader categories, which are more suitable for mental health assessment. To ensure the model's robustness and generalizability, a diverse and comprehensive dataset was created using speech samples from four publicly available datasets focused on voice-based emotion detection. This comprehensive dataset supports model training and testing, minimizing the risk of overfitting and enhancing the algorithm's performance on new, previously unseen data collected under different recording conditions. This way, the algorithm's adaptability and flexibility are sustained. To further boost the model's adaptability to various audio environments and settings, the research employs a range of data augmentation techniques, including noise addition, time stretching, and pitch shifting. The research addresses privacy concerns regarding voice recordings by not storing the raw voice recordings. Instead, the NeuroPredict platform only stores the final classification decision. The integration of this algorithm within the NeuroPredict platform improves the health monitoring process. The platform encompasses data from IoMT devices with self-reported information to provide regular and thorough emotional state monitoring for patients. This integration enables preventative measures and prompt updates to treatment procedures, ultimately improving patient care. The structure of this paper is as follows: Section 2 presents a literature review in the field of targeted neurodiseases, predictive modeling, and voice analysis for emotion detection. Section 3 presents the NeuroPredict platform, with the IoMT and deep learning algorithms based on data fusion from multiple modalities and the proposed emotion detection algorithm based on voice features. Section 4, Results, presents the performance of the mental state detection algorithm based on voice features and the integration of the algorithm into the platform. The final section discusses the results and future work.
## 4 Applications of WSN in Urban Areas
### Healthcare Applications
* Decentralized network topologies, such as star-connected configurations, offer flexibility and targeted monitoring, enhancing diagnostic accuracy and healthcare outcomes. * The methodologies emphasize the importance of appropriate network topologies and architectures. Centralized and star-connected topologies are reliable and efficient, while decentralized topologies offer insights into complex healthcare system challenges.
# Special Issue \"Novel Approaches and Applications in Ergonomic Design\"
## 12 A Linkage Representation of the Human Hand Skeletal System Using CT Hand Scan Images  
Although a linkage representation of the hand skeletal system is widely used in hand models by defining the segments and link lengths of the hand, an inappropriate linkage representation is commonly constructed based on bone length data or bony landmark locations. [PERSON] et al. [11] proposed a novel method to estimate finger joint centers of rotation (CoRs) using 3D hand motions reconstructed from computed tomography (CT) scans and constructing participant-specific linkages from the derived hand joint CoRs. The study constructed regression equations to predict the locations of CoRs for the thumb CMC joint and the MCP joints of the other four fingers using the data of hand length and hand breadth. This method was also used to predict internal hand link lengths using hand length data for a linkage representation of the hand. Future research is needed to examine the applicability of the proposed CoR estimation method using various hand postures.
## 1 Introduction
### Literature Review
The study presented in [23] focuses on minimizing the operating costs of a 30-node MG. To this effect, several scenarios are analyzed that involve different combinations of PVs and WTs while accounting for the inherent uncertainty of these renewable technologies. A genetic algorithm (GA) is employed to manage the active power injected by renewable generators. While this approach delivers a satisfactory performance, it could be enhanced by incorporating the PGA variation of this methodology. Additionally, the framework could be expanded by introducing objective functions aimed at improving the system's technical conditions. In [24], a GNDO algorithm is implemented to improve the economic performance of a power system that includes thermal units, PVs, and/or WTs within the framework of four case studies. The proposed strategy outperforms other benchmark techniques presented in the paper, but it does not consider technical constraints such as line current limits or nodal voltage variations. To reduce power losses in a 28-node test system, the work by [25] employs the JAYA algorithm, which achieves this goal through the strategic placement and intelligent management of PVs and WTs. Although the approach exhibits improvements in terms of technical performance compared with other benchmark techniques and the base case, it does not account for line current constraints or reactive power injection from the WTs. In [26], the authors aim to minimize the operating costs and energy losses of a 30-node power network. To this effect, an MVO for managing its PVs and WTs is applied. The proposed strategy outperforms all other benchmark methods under study. However, this paper does not specify the technical constraints considered in the analysis. To provide a comprehensive and critical overview of prior research focused on improving MG performance through intelligent wind generation management, Table 1 compiles some key studies, outlining the methodologies adopted, the test systems utilized, the indicators considered for optimization, and the type of power managed--be it active, reactive, or a combination of both.
## 3 Experimental Verification on the Inherent Characteristics of the Pipeline
### Modal Analysis on the Pipeline Filled with Fluid  
Modal analysis on the pipeline filled with fluid is carried out, and the experiment schematic is shown in Figure 6. The hydraulic hoses are connected with fittings on both sides of the \(x\)-axis as shown in Figure 3, the other directional branch pipe is plugged with plugs.  
The simulation analysis of the liquid-filled pipeline is carried out. As the flow rate of the pump is 1.5 L/min, the flow rate and inlet pressure in the pipeline are calculated to be 0.3 m/s and 2 MPa, respectively. After the simulation, the results are transmitted to the mechanical module as pressure loads for the pre-stress modal analysis and frequency response analysis. As the coupling effect of the liquid and pipeline should be considered, hydraulic oil is equivalent to incompressible metal material with the same density. The bulk modulus is 160 MPa, and Poisson's ratio is zero. The hydraulic oil is simulated with solid units. The contact between the pipe and the support is bonded, and the constraint between the hydraulic oil and the inner wall of the pipe shows no separation, respectively. The liquid material parameters are shown in Table 3.  
Figure 8: Comparison of the mode shapes of the liquid-filled pipeline.  
Figure 7: Comparison of FRF between the experiment and simulation.  
The comparison of the dynamic characteristics of the experiment and simulation is shown in Table 4 and Figure 7. The first three orders of natural frequency of the piping system from the experiment and simulation are similar, and the maximum error is under 6%. This is verified by comparing the mode shapes of the liquid-filled pipeline as shown in Figure 8. The reasons for that discrepancy include the accuracy of the instrument, the processing error of the pipeline, and the simplification of the four-way joint by the finite element model. Moreover, the acceleration sensor could add a small quantity of mass, and the additional mass of the longer part of the pipeline is not considered. Generally speaking, the experimental and the numerical results are in good agreement and, therefore, the accuracy of the proposed methodology was verified.
## 3 Results
### Particulate Organic Carbon and Chlorophyll-a Concentration Modelling Optimization
Here we consider the implication of a natural boundary condition for this relationship: when \(n_{r}=1\) then \(C_{i}=0\). Including this constraint results in a slightly modified form of relationship  
\[C_{i}=a_{C}(n_{r}(660\,\mathrm{nm})-1) \tag{16}\]  
We note that this is equivalent to forcing the [PERSON] [19] regression (Figure 2 therein) through \((n_{r},\,C_{i})=(1,0)\). Given the experimental uncertainties noted in [PERSON] [19] and the similarity of the \(a\) and \(b\) components of the original regressions, we believe that the form proposed by Equation (16) is broadly comparable but better reflects the physical relationship under investigation. Similarly, in the case of \(Chl_{i}\) and \(n_{i}\) [PERSON] [19] provides a clear physical basis for a simple linear relationship based on earlier work by [PERSON] and [PERSON] [34]. A small offset is found in the resulting best-fit regressions, which is either small and positive (see Equation (5) here, [PERSON] [19]), or small and negative (see Equation (7) here, [PERSON] et al. [20]). The initial model suggests that \(n_{i}(675\,\mathrm{nm})\) ought to be a simple linear function of \(Chl_{i}\), but in practice the small offset could represent residual absorption by pigments other than ChlA ([PERSON] [19]), or it could be a statistical artefact associated with limitations in data quality (the discrepancy in sign between the two aforementioned studies is possibly significant). In either case, a slightly modified version of the \(n_{i}\) and \(Chl_{i}\) relationship can be given as  
\[Chl_{i}=a_{chl}n_{i}(675\,\mathrm{nm})+const. \tag{17}\]  
However, it must be noted that the information contained in our data does not provide an indication of the appropriate value for the constant offset in Equation (17): indeed, the absence of a well-determined pair of best parameters when optimization is executed on both a and b terms of the linear model shows that minimal error for the dataset may be reached for any value of the offset. Therefore, the only likely way forward is to use the single well-defined scenario available i.e., the assumption of negligible residual absorption by pigments other than \(Chl_{i}\). Under this assumption, the constant offset equals zero. Once applied to Equations (16) and (17), the optimization procedure thus defined identified minimal RMSE values for relationships  
\[C_{i}=6880(n_{r}(660\,\mathrm{nm})-1) \tag{18}\]  
Figure 9:0  
Comparisons of the optimized model results with the measured POC and ChlA values of the UKCW dataset are shown in Figure 9:1 RMS%E values for the comparisons were found to be 50.2% and 45.2% for POC and ChlA respectively. Figure 9:2 Comparison of (**a**) POC and (**b**) ChlA values as determined by the optimized models of Equations (17) and (18) vs. their respective measured values. The dashed grey lines indicate the 1:1 relationship.
## 5 Potential Use Cases and Applications  
Enterprises of different types, sizes, and domains have increasingly needed to deliver QoE, high bandwidth and low latency including data backup, disaster recovery, email use, office virtualization environments, software development and testing, big data analytics, and web applications used by customers. For instance, the healthcare sector is using cloud-based services to develop better suited patients' personal needs treatments. Financial sector providers use cloud-based services to operate necessary capabilities that detect and prevent fraudulent operations in real time. Video game makers based on IEC and 5G technologies provide online games service to their customers around the globe. Here are some open cases and applications using IEC and 5G services [66; 67; 68].
## 3 Results and Discussion
### Thermal Analysis Using Differential Scanning Calorimetry (DSC)  
The DSC data of the samples are shown in Table 3 and illustrated in Figure 8, where 8A corresponds to the fruit bagasse fiber, 8B the fruit bagasse fiber after organosolv treatment, 8C the leaf bagasse fiber and 8D the leaf bagasse fiber after organosolv treatment. In all kinetics, the first event attributed to moisture loss from the samples was between 50 and 60 \({}^{\circ}\)C, and the second event occurred between 130 and 150 \({}^{\circ}\)C. This behavior was  
Figure 7: X-ray diffractograms of (**A**) fruit bagasse fiber before organosolv, (**B**) fruit bagasse fiber after organosolv, (**C**) leaf bagasse fiber before organosolv and (**D**) leaf bagasse fiber after organosolv. Ic = Indice de refracción. Values with different letters indicate significant differences (\(p<0.05\)) using a Tukey test; average \(\pm\) SD, \(n=3\).  
possibly associated with the fusion of the degraded sugars that remained in the solution after hydrolysis. Finally, the third event was between 210 and 230 \({}^{\circ}\)C and was related to cellulose degradation [5].  
Making a comparison between the fiber of the bagasse of the fruit before and after the organosolv treatment (Figure 8A,B, respectively), an increase in the To, Tp and \(\Delta\)H after organosolv treatment was observed because when losing the amorphous material composed of hemicellulose and lignin, a higher temperature is required to begin to degrade the crystalline material and to finish disorganizing the fiber (Tp). The \(\Delta\)H value also increased, because more energy per unit mass is required to disorganize the crystalline material.  
Finally, the Tc was reduced because the fiber no longer had an amorphous material protection or coating. In the case of the leaf fiber before and after organosolv treatment (Figure 8C,D, respectively), its thermal properties were affected, and an increase in To and Tp was observed while the values of \(\Delta\)H and Tc were reduced.
## 4 Description of the Developed Method
### Evaluation of the Estimation and Restart Condition of the Calculation  
For evaluating the estimation performed with the LKF and determining the need to reevaluate the number of variables used in the LKF, two values are employed.  
The maximum allowable error (MAE) represents the maximum value that can occur in the LKF estimation and is 10% of the value of the main spectral component obtained in the DFT.  
\[\text{MAE}=0.1*C_{11} \tag{25}\]  
where \(C_{11}\) is the value of the 50 Hz spectral component obtained in the DFT.  
The root mean square error (RMSE) represents the average value of the error committed by the estimation during the previous 10 ms. It is calculated using the equation:  
\[\text{RMSE}=\sqrt{\frac{\sum\limits_{t=1}^{T}{{{({{\hat{y}}_{t}}-{{y}_{t}})}^{ 2}}}}{T}} \tag{26}\]  
where,  
\(T\) = number of samples used in the calculation.  
\(\hat{y}_{t}\) is the value of the estimation at time t made with the LKF.  
\(y_{t}\) is the value of the input signal at time \(t\).  
The proposed method compares the output of the LKF with the maximum allowable error. If the result is greater, the RMSE is calculated, and the maximum allowable error is compared again. If the result is negative, the method determines that there has been a disturbance and the system has returned to its steady state, so it outputs the values of the estimations made by the LKF. If the result is positive, it determines that there has been a sufficiently high change in the input values to justify performing a new DFT and running the entire algorithm again to implement the LKF with a new number of variables. Figure 8 depicts the stages and phases of the method. The equations related to every process have been included in its corresponding block.