esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Model Selection
#### 3.4.3 Autoregressive Integrated Moving Average (ARIMA)  
ARIMA, a classic statistical model, uses patterns such as trends and seasonality to predict future scores in a series. It is a generalized version of the ARMA model specially designed to handle non-stationary time series. Unlike the ARMA model, which assumes stationarity of the analyzed time series, non-stationary time series must first undergo a transformation process to remove seasonality and trends through finite-point differentiation. A stationary time series is a combination of signal and noise. The ARIMA model separates the time signal from the noise and gives its forecast for a later time point. As indicated by the method's acronym, its structural components are the following [31; 32]:  
AR for autoregression: a regression model that uses the dependence relationship between an observation and several lagged observations (model parameter p).  
I for integration: calculating the differences between observations at different time points (model parameter d), aiming to make the time series stationary.  
MA for moving average: this approach considers the dependence that may exist between observations and the error terms created when a moving average model is used on observations that have a time lag (model parameter q) [33].  
One way to represent an AR model with order p, or AR (p), is through a linear process, as shown in Equation (8). The stationary variable is denoted by \(x\) and the constant by \(c\). The autocorrelation coefficients at lags 1, 2, \(\ldots\), p are represented by \(\mathcal{O}\), while the residuals are the Gaussian white noise series with a mean of zero and a variance of \(\sigma\)[34].  
\[x_{t}=c+\sum_{i=1}^{p}\varnothing_{i}x_{t-i}+\varepsilon_{t} \tag{8}\]  
Equation (9) represents an MA (q) order model, wherein the \(\theta\) terms denote the weights given to the current and previous values of a stochastic term in the time series. Here, \(\mu\) is the expectation of \(x\) and is generally assumed to be zero, while \(\theta\) equals one. We consider \(\varepsilon\) to be a Gaussian white noise series with a mean of zero and variance of \(\sigma\)[34].  
\[x_{t}=\mu+\sum_{i=o}^{q}\theta_{i}\varepsilon_{t-i} \tag{9}\]  
Equation (10) shows how these two models are combined to create an ARMA model of order (p, q), where \(\mathcal{O}\
eq 0\), \(\theta\
eq 0\), and \(\sigma>0\). The parameters represent the AR and MA orders p and q, respectively. ARIMA forecasting, also known as Box and Jenkins forecasting, can handle non-stationary time series data due to its \"integration\" step. This step involves differencing the time series, converting a non-stationary time series into a stationary one [34].  
\[x_{t}=c+\sum_{i=1}^{p}\varnothing_{i}x_{t-i}+\varepsilon_{t}+\sum_{i=o}^{q} \theta_{i}\varepsilon_{t-i} \tag{10}\]
## 4 Experiment
### Ablation Experiments  
In this subsection, we analyze the contributions of the two modules in the SSP-AA model and the task of subgraph-based prediction. We provide three different datasets for two sets of separate ablation experiments. Table 4 shows the impact on model performance when the skip knowledge module and the adaptive attention mechanism are successively stripped from the SSP-AA model. Table 5 displays the effect on model performance when the SSP-AA model is based on the entire graph structure and subgraphs. The results are the average AUC and relative error from five experiments.  
The results in Table 4 demonstrate that both modules enhance the model's performance to some extent on the Celegans and NS datasets, thereby confirming the effectiveness of each module. On the one hand, among the two modules, the adaptive attention mechanism has the most substantial impact, indicating that assigning different weights to neighbor nodes is necessary for the model's aggregation process. On the other hand, integrating hierarchical information through the skip knowledge module also assists in the aggregation process. Therefore, from the ablation study, it can be inferred that these two modules are crucial factors in improving model performance.  
Table 5 presents a performance comparison of the SSP-AA model for link prediction tasks based on subgraphs versus those based on the entire graph structure in two large datasets.  
From the data in Table 5, it can be intuitively seen that the performance of the model in the link prediction tasks based on subgraphs is higher than based on the entire graph structure. Given the two large datasets chosen, there will be a significant amount of noise present when the model carries out neighbor node aggregation operations in prediction tasks based on the entire graph structure, resulting in a decline in the final model performance. Furthermore, in the link prediction tasks based on subgraphs, due to the higher average degree of the two datasets, the connections between local nodes will be tighter. The attention mechanism can better aggregate similar neighbor nodes, hence the aggregation effect of the SSP-AA model in the link prediction tasks based on subgraphs will be better.
# An Overview of the Applications of Earth Observation Satellite Data: Impacts and Future Trends
## 3 Results
### Subject Analysis  
To help with understanding the subject areas to which research based on EO satellite data have been applied, we made an analysis of the important keywords in the articles that referenced the data [63]. This analysis was carried out using CiteSpace and the top 10 keywords (Table S5) were found to be remote sensing (5904), MODIS (5861), classification (4108), model (4079), vegetation (3502), algorithm (3116), Landsat (3020), validation (2408), area (2313), and satellite (2302). MODIS and Landsat have undeniably been the most important EO satellites of the past few decades, and the use of their data has helped to promote remote sensing applications. Image classification is the method that is most important to the processing of EO satellite data. EO satellite data are an essential input to the modeling of the Earth's fundamental systems and environment and to the validation of these models. Observation data and model simulations are necessary to our understanding of the Earth's systems, and EO satellite data are becoming more and more important in the modeling of the Earth's environment [79]. Validation is needed to confirm the accuracy of environmental products, and obtaining more accurate environmental information requires the continuous optimization of processing algorithms.
# Respiration Rate Extraction of Moving Subject Using Velocity Change in FMCW Radar
## 3 Results
### Respiratory Measurements of Subjects Moving Forward and Backward  
The respiration rate extraction accuracies of the conventional and proposed research methods are compared for ten subjects seated in a chair and moving forward and backward.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subject** & **Conventional** & **Proposed** \\ \hline Subject \#1 & 64.56\% & 92.97\% \\ Subject \#2 & 72.80\% & 81.62\% \\ Subject \#3 & 86.36\% & 97.08\% \\ Subject \#4 & 73.57\% & 90.81\% \\ Subject \#5 & 77.68\% & 89.14\% \\ Subject \#6 & 66.56\% & 75.05\% \\ Subject \#7 & 70.32\% & 80.92\% \\ Subject \#8 & 51.57\% & 87.26\% \\ Subject \#9 & 69.88\% & 73.97\% \\ Subject \#10 & 50.40\% & 90.38\% \\ \hline Average & 68.37\% & 85.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of respiratory rate accuracy for the case shown in Figure 5b.  
Figure 9: Respiration extraction results for the case shown in Figure 5b. (**a**) Subject \#1, (**b**) Subject \#2, (**c**) Subject \#3, (**d**) Subject \#4, (**e**) Subject \#5, (**f**) Subject \#6, (**g**) Subject \#7, (**h**) Subject \#8, (**i**) Subject \#9, and (**j**) Subject \#10.  
As shown in Figure 10, the conventional method alleviates the effect of chest movement displacement caused by respiration, owing to the change in distance from the radar. Nevertheless, the result is inaccurate because the signal beyond the set range-bin is measured. In contrast, the proposed method can extract the specific velocity change in the chest owing to respiration, regardless of the measurement range. In addition, because the velocity change frequency associated with movement other than chest movement exists individually, the respiration rate can be accurately determined. We experimented with the motion conditions shown in Figure 5c. Table 4 shows the respiratory accuracy calculation values for all subjects obtained through the experiment.  
Figure 10: Respiration extraction results for the case shown in Figure 5c. (**a**) Subject #1, (**b**) Subject #2, (**c**) Subject #3, (**d**) Subject #4, (**e**) Subject #5, (**f**) Subject #6, (**g**) Subject #7, (**h**) Subject #8, (**i**) Subject #9, and (**j**) Subject #10.
## 3 Results
### The Effect of BMI  
BMI had different effects on smokers' and non-smokers' studied parameters. In the non-smoking group, BMI had an insignificant effect on all of the parameters studied (Table 5). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{5}{c}{**Age Group**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Smokers**} & \multicolumn{5}{c}{**Non-Smokers**} \\ \cline{2-7}  & **20–29** & **30–39** & **40–49** & **20–29** & **30–39** & **40–49** \\  & **(8)** & **(6)** & **(5)** & **(8)** & **(14)** & **(13)** \\ \hline TC mg/dL & 147.7 \(\pm\) 41.6 & 151.4 \(\pm\) 42.1 & 189.6 \(\pm\) 18.2 & 124.7 \(\pm\) 25.9 \({}^{\rm a}\) & 157.9 \(\pm\) 42.5 & 171.9 \(\pm\) 28.2 \({}^{\rm a}\) \\ \hline TG mg/dL & 197.5 \(\pm\) 134.2 & 192.0 \(\pm\) 89.8 & 378.9 \(\pm\) 259.5 & 126.7 \(\pm\) 106.4 & 276.3 \(\pm\) 279.9 & 251.5 \(\pm\) 123.7 \\ \hline HDL-C mg/dL & 37.0 \(\pm\) 8.7 & 41.2 \(\pm\) 9.5 & 48.2 \(\pm\) 13.0 & 47.24 \(\pm\) 17.3 & 46.4 \(\pm\) 17.2 & 39.9 \(\pm\) 13.8 \\ \hline LDL-C mg/dL & 56.8 \(\pm\) 11.05 & 41.0 \(\pm\) 8.48 & 73.5 \(\pm\) 32.05 & 52.25 \(\pm\) 15.59 \({}^{\rm b}\) & 70.82 \(\pm\) 27.85 & 88.9 \(\pm\) 26.77 \({}^{\rm b}\) \\ \hline TC/HDL ratio & 4.2 \(\pm\) 1.9 & 3.8 \(\pm\) 1.3 & 4.0 \(\pm\) 0.7 & 2.8 \(\pm\) 0.7 \({}^{\rm c}\) & 3.6 \(\pm\) 0.96 & 4.7 \(\pm\) 1.48 \({}^{\rm c}\) \\ \hline RCA (mm) & 0.7 \(\pm\) 0.15 & 0.71 \(\pm\) 0.11 & 0.78 \(\pm\) 0.16 & 0.51 \(\pm\) 0.06 \({}^{\rm d}\) & 0.64 \(\pm\) 0.17 & 0.76 \(\pm\) 0.19 \({}^{\rm d}\) \\ \hline LCA (mm) & 0.7 \(\pm\) 0.12 & 0.67 \(\pm\) 0.14 & 0.76 \(\pm\) 0.17 & 0.51 \(\pm\) 0.06 \({}^{\rm e}\) & 0.65 \(\pm\) 0.15 & 0.74 \(\pm\) 0.13 \({}^{\rm e}\) \\ \hline \hline \end{tabular}  
* The ages of the smokers had no significant effects on the studied parameters, which increased with the increase in the smokers’ ages, with the exception of the TC/HDL-C ratio. With regard to the non-smoking group, the total blood cholesterol, the LDL-C, the TC/HDL-C ratio and RCA’s and LCA’s IMTs increased significantly as the ages increased, whereas there was an insignificant increase in the TG and an insignificant decrease in the HDL-C:\({}^{\rm a}\) (\(p\)-value = 0.005), \({}^{\rm b}\) (\(p\)-value = 0.005), \({}^{\rm c}\) (\(p\)-value = 0.001), \({}^{\rm d}\) (\(p\)-value = 0.002) and \({}^{\rm e}\) (\(p\)-value \(<\) 0.001).  
\end{table} 
Table 4: Effect of age (years) on the calculated parameters’ mean and standard deviation for smokers and non-smokers.
# Decentralized and Secure Blockchain Solution for Tamper-Proof Logging Events
## 3 Materials and Methods
### Components of the System
#### 3.1.3 Message Broker  
The purpose of this component is to temporarily store event messages while the log module is handling them. The message broker was implemented in the DRACSC system using RabbitMQ, specifically version 3.10.5. It is based on AMQP, which brings the advantage of scalability and asynchrony [24]. Additionally, the software offers other benefits, such as decoupling with other system services or assuring that events will not be lost due to the public blockchain being highly congested at a specific moment or failures in the log module service that the broker feeds. Other factors influencing their choice include a lightweight design, which is crucial because of hardware resource limitations and ease of use for prototyping.
## 4 Results and Discussions
### Research Implication and Future Directions  
The major emphasis of this study is to identify the K-means hybrid developed for the purpose of automatic clustering. However, most of the reviewed articles concentrated efforts on finding solutions to the initial cluster centroid problems of the traditional K-means algorithm and the problem of local optimum convergence. In some other cases, the attention was not on improving the K-means clustering algorithm. Instead, the attention was on improving the performance of the corresponding MOA in handling the clustering problem. For the few that proposed improving the K-means clustering algorithm, their performances' limitations, such as increased \(d\) number of user-dependent variables and algorithm complexity, limit their performances. The same drawbacks also affect the hybridized algorithm extending the K-means algorithm for handling automatic clustering.  
Moreover, the number of research papers on the hybridization of K-means with MOA is relatively small compared with the number of existing MOAs and still smaller when the issue of automatic clustering is considered. There is a need for further research on finding new K-means hybridization that will enhance its performance in handling automatic clustering for big data clustering while maintaining its desirable quality of linear order complexity. In most hybridized algorithms, a higher execution time is required to obtain higher quality clustering results. Further, they are more computationally expensive due to the increase in the necessary iteration operation to achieve convergence. A computationally less expensive hybridized K-means algorithm that can handle automatic clustering will be highly desirable.
## 3 Methodology
### Normals and Curvature Estimation  
Surface normals and curvature are critical spatial geometric features for defect detection, effectively identifying defects large enough to cause significant changes in these attributes.  
As illustrated in Figure 9, defect regions typically exhibit significant deviations in normal vectors and pronounced curvature variations. Consequently, by defining appropriate thresholds for normal vector angular deviation or curvature variation rates, it is possible to preliminarily identify the approximate locations of defects. However, this approach may result in false positives or missed detections. In step **b** of the previous section, the three eigenvalues of the weighted covariance matrix were computed and arranged in descending order. Thus, the normal vector for each point can be derived by identifying the eigenvector \(\
u\) corresponding to the smallest eigenvalue according to \(\textbf{C}\
u=\lambda\
u\), as the eigenvalues of the weighted covariance matrix represent variance, with smaller variance indicating a higher concentration of data points. Similarly, the curvature \(\mathcal{C}\) of each point is also defined based on these eigenvalues [53], which can be expressed as follows:  
\[\mathcal{C}=\frac{\lambda_{2}}{\lambda_{0}+\lambda_{1}+\lambda_{2}} \tag{10}\]
# Behavioral and Performance Analysis of a Real-Time Case Study Event Log: A Process Mining Approach
## 3 Methodological Framework for Applying Process Mining in Practice
### Data Pre-Processing
#### 3.3.1 Log Preparation  
Each entry in an event log refers to a case and an activity and includes a time stamp showing when it occurred. Log preparation involves transforming data into a format used for process mining. This transformation includes a selection of sources, the identification of activities and events, the selection of the time period, and the conversion of data into a mineable format such as MXML or XES [2,38]. As mentioned before, the log data used in this research work fulfill the fundamental requirements of the log. The received log is in Excel format, and initially, it is converted into CSV format and XES format using the PrOM framework. Now, this converted log file can be used for the next phase.
# Optimal Design and Control of MMC STATCOM for Improving Power Quality Indicators
## 4 Modular Multilevel Converter-Based STATCOM (MMC STATCOM)
### DC-Link Capacitor  
In this section, the calculations are only provided on the upper leg, as there is no difference between the upper and lower legs [28]. For more simplification, the calculations are performed taking into account the following assumptions:  
1. There is a small ripple in the DC capacitor voltage.
2. Only the fundamental component of the switching function is considered.  
In the steady-state condition, the upper leg switching function is defined as:  
\[s_{1}(t)=M\sin(\omega t) \tag{16}\]  
where M denotes the modulation index. From Equations (9), (12) and (16), the following formula is produced:  
\[C\frac{dV_{c}}{dt}=\frac{1}{4}MI\cos\varphi_{i}+\frac{K_{I}}{2}MI\sin(\omega t )-\frac{1}{4}MI\cos(2\omega t+\varphi_{i}) \tag{17}\]  
After integration, the following equation exists:  
\[V_{c}=V_{DC}-\frac{MIK_{I}\cos(\omega t)}{2\omega C}-\frac{MI\sin(2\omega t+ \varphi_{i})}{8\omega C} \tag{18}\]where \(V_{DC}\) denotes the average DC voltage. The ripple in the current of the upper leg is described by the following formula:  
\[\Delta V_{c}=-\frac{MIK_{I}\cos(\omega t)}{2\omega C}-\frac{MI\sin(2\omega t+ \varphi_{i})}{8\omega C} \tag{19}\]  
By similarity, the ripple in the current of the lower leg is calculated as follows:  
\[\Delta V_{c}=-\frac{MIK_{I}\cos(\omega t)}{2\omega C}-\frac{MI\sin(2\omega t+ \varphi_{i})}{8\omega C} \tag{20}\]  
When the phase angle between the voltage and current \(\varphi_{i}\) equals \(\pm 90^{\circ}\) during the static VAr generating condition and by ignoring the resistance of the inductor, Equation (19) will be converted to:  
\[\Delta V_{c}=-\frac{MIK_{I}\cos(\omega t)}{2\omega C}+\frac{MI\sin(2\omega t)} {8\omega C} \tag{21}\]  
To obtain the maximum value of the peak-to-peak ripple voltage of the DC capacitor, Equation (21) is differentiated, which results in:  
\[\Delta V_{cpp}=\frac{MI(K_{I}+1)^{2}}{4\omega C} \tag{22}\]  
Accordingly, the capacitance of the DC capacitor can be defined as:  
\[C\geq\frac{MI(K_{I}+1)^{2}}{4\omega\Delta V_{cpp}} \tag{23}\]  
From Equations (14) and (20) it is clearly shown that the voltage ripple contains the fundamental and the second harmonic, which dramatically increases the rating of the capacitor, resulting in an increase in the total cost of the system. An accurate determination of the capacitance value in each sub-model must be taken into account, as a higher value of the capacitance leads to an increase in the total cost of the MMC converter, and on the other hand, a lower value increases the ripple level in the output voltage.
## 1 Introduction
First, we simulated GPP using the GLOPEM-CEVSA model, the same meteorological datasets, and various FPAR products from GIMMS NDVI3g, GIMMS NDVI1g, and the Moderate Resolution Imaging Spectroradiometer (MODIS) FPAR product (MOD15A2). Second, we validated the derived GPP estimates using the eddy covariance derived GPP data and conducted a comparison among the three GPP results as well as the MODIS primary production products (MOD17A2). With this study, we aim to set the stage for the first continuous 30-year records of GPP for Southeast Asia, which we hope to use to improve our understanding of the climatic and anthropogenic responses of ecosystems that have occurred in the region over the previous three decades.
# Fast Equipartition of Complex 2D Shapes with Minimal Boundaries +
## 6 Experimental Evaluation
### Applications of the Proposed Methods  
The proposed methods can be applied to segmentation tasks, such as the tree detection problem [6], where the objective is to identify trees in aerial images. In cases of dense forests and low-quality imagery, unsupervised and deep learning methods often struggle to achieve accurate segmentation. Figure 19 shows a promising result of the proposed methods _SEP-PSO FBC_ and _SEP-FBC_ on the tree detection problem under a low quality dense forest image. In this example, even a human expert is almost impossible to detect the trees. _SEP-FBC_ has been applied on the largest region of the bitmap image derived by RGBVI index as used in [6]. The number of trees was determined by dividing the area of the largest region by the typical size of a tree. The tree boundaries are colored blue. Under the assumption that the trees are equal sized, 188 trees were detected by the proposed methods. As expected, the \(NL\) of _SEP-PSO FBC_ (2.713) is lower than that of _SEP-FBC_ (2.806), indicating that the segmentation produced by _SEP-PSO FBC_ is probably more preferable.  
Figure 17: A sensitivity analysis of the \(NL\) metric for the parameter \(\lambda\) and different values of \(N\) under the _SEP-FBC_ method and the MPEG7 dataset.  
Figure 18: The percentage of shapes where the sequence \(L(R(S,N))\) is not monotonically increasing for (**a**) LEMS and (**b**) MPEG-7 datasets, with \(N\leq 9\).
## 3 Results
### Between-Session Reliability in Isometric Belt Squat Test
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **CV\% (95\%CD)** & **ICC (95\%CI)** & **SEM (95\%CI)** & **SWC 0.2 (95\%CI)** & **\(p\) value** & **ES (g) (95\%CI)** \\ \hline Peak force (N) & 6.8 & 0.94 & 351.3 & 265.4 & 0.932 & \(-\)0.01 \\  & (5.2–9.8) & (0.85–0.97) & (268.7–507.2) & (203.0–383.2) & 0.932 & (\(-\)0.63 to 0.62) \\ \hline Relative peak force (N/kg) & 6.9 & 0.97 & 4.6 & 47 & 0.627 & \(-\)0.03 \\  & (5.3–10.0) & (0.92–0.99) & (3.5–6.6) & (3.6–6.7) & (\(-\)0.65 to 0.59) \\ \hline Force at 50 ms (N) & 14.9 & 0.75 & 238.1 & 91.6 & 0.111 & \(-\)0.26 \\  & (11.4–21.5) & (0.48–0.89) & (182.2–343.9) & (70.1–132.3) & & (\(-\)0.89 to 0.36) \\ \hline RFD 0–50 ms (N-s) & 48.6 & 0.74 & 3243.4 & 1234.5 & & 0.01 \\  & (37.1–70.1) & (0.47–0.89) & (2481.4–4683.7) & (944.4–1782.7) & 0.972 & (\(-\)0.62 to 0.63) \\ \hline Impulse 0–50 ms (N-s) & 13.2 & 0.67 & 9212.4 & 3127.3 & & \(-\)0.05 \\  & (10.1–19.1) & (0.35–0.85) & (7048.0–13,303.3) & (2392.5–4516.0) & & (\(-\)1.08 to 0.18) \\ \hline Force at 100 ms (N) & 21.4 & 0.65 & 424.3 & 138.8 & & 0.06 \\  & (16.4–30.9) & (0.31–0.84) & (324.6–612.7) & (106.2–200.5) & & (\(-\)0.56 to 0.68) \\ \hline RFD 0–100 ms (N-s) & 27.1 & 0.89 & 2117.6 & 1222.9 & & 0.04 \\  & (20.7–39.1) & (0.75–0.95) & (1620.1–3058.0) & (935.6–1765.9) & 0.682 & (\(-\)0.58 to 0.67) \\ \hline Impulse 0–100 ms (N-s) & 15.2 & 0.72 & 24,267.6 & 8909.6 & 0.397 & \(-\)0.14 \\  & (11.6–22.0) & (0.43–0.88) & (18,566.2–35(044.1)) & (6816.4–1286.1) & & (\(-\)0.77 to 0.48) \\ \hline Force at 150 ms (N) & 17.8 & 0.78 & 419.4 & 172.8 & & \(-\)0.03 \\  & (13.6–25.6) & (0.54–0.91) & (320.9–605.6) & (132.2–249.5) & 0.842 & (\(-\)0.03 to 0.59) \\ \hline RFD 0–150 ms (N-s) & 34.2 & 0.72 & 2637.3 & 964.5 & & 0.09 \\  & (26.2–49.4) & (0.43–0.88) & (2017.7–3808.4) & (737.9–1392.7) & 0.609 & (\(-\)0.54 to 0.71) \\ \hline Impulse 0–150 ms (N-s) & 17.7 & 0.68 & 47,313.1 & 16,140.0 & & \(-\)0.21 \\  & (13.6–25.6) & (0.36–0.86) & (36,197.3–68,323.4) & (12,348.0–23,307.3) & 0.241 & (\(-\)0.84 to 0.41) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Between-session reliability of isometric belt squat test.