esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Causes of Excessive Noise and Vibrations of Live Steam Pipelines
## 3 Measurement of Vibrations of the Live Steam Pipelines
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{2}{c}{**Measurement Point**} & \multicolumn{2}{c}{**V\({}_{\text{RMS}}\)**} \\  & & **[mm/s]** & \\ \hline Level of 12 m & unit & A & B \\ \hline
1 & & 4.61 & 3.70 \\
2 & & 6.49 & 2.82 \\
3 & & 4.25 & 3.17 \\
4 & & 8.26 & 2.85 \\
5 & & 4.30 & - \\ \hline Level of 17 m & unit & A & \\ \hline
1 & & 8.18 & \\
2 & & 9.15 & \\
3 & & 3.91 & \\
4 & & 10.92 & \\
5 & & 7.76 & \\ \hline Level of 36 m & unit & A & \\ \hline
1 & & 4.60 & \\
2 & & 6.84 & \\
3 & & 3.99 & \\
4 & & 5.66 & \\
5 & & 5.65 & \\ \hline \hline \end{tabular}  
\(\blacktriangle\)—unit which generates high vibrations of the live steam pipeline; B—control unit, with low vibrations.  
\end{table} 
Table 1: The RMS values of vibration velocity in mm/s. Figure 7: Labeling of measurement points at the level of 17 m.  
similar power unit with identical parameters, except for the diameters of live steam pipeline, i.e., in power unit B, do not show this dominating frequency value, as illustrated in Figure 8b. The dominating vibration frequency in unit B is 7.5 Hz. Based on the obtained results of measurements of vibrations of the live steam pipeline taken by means of a laser vibrometer, the following were observed:  
* The highest recorded RMS vibration velocity (Table 1) in power unit A at the level of 17 m was V\({}_{\text{RMS,17,A}}\) = 10.92 mm/s;
* The lowest vibration level was recorded at the level of 36 m, where the RMS value of vibration was about 50% lower than the vibrations at the level of 17 m;
* The dominating vibration frequency recorded at all levels of the noise-generating power unit A was f\({}_{\text{A}}\) = 957 Hz, which is in close match to the structural modal modes identified in numerical simulations (see Section 6);
* In comparison, the results of measurements taken on power unit B, whose parameters are identical to those of the noise-generating unit A, show that the highest RMS value of vibration velocity (Table 1) was V\({}_{\text{RMS,12,B}}\) = 3.70 mm/s, with a vibration frequency of f\({}_{\text{B}}\) = 7.5 Hz;
* The mode shape of the pipeline in unit A has characteristics of a sinusoidal-wave shape transmitted along its axis. A mode shape of this type was not observed in unit B [8]  
Figure 8: Amplitude–frequency plot at the level of 12 m: (**a**) power unit A, (**b**) power unit B.
## 3 Results
### Evaluation Metrics
#### 3.1.2 Confusion Matrices  
One of the most common performance measurements of pattern classification is accuracy, which is defined as the portion of correct predictions to the total size of the dataset. The accuracy generally evaluates the overall classification results but cannot reflect which class the misclassification is from. Therefore, a bias towards the majority class, ignoring the minority class, could happen. The confusion matrix, which also takes the  
Figure 10: Movement indicator diagram: (**a**) no movement, so all black in red frame; (**b**) movement existing.  
particularities of the decisions into consideration, could be used to solve this issue. As two conditions, positive and negative, are determined, four possible outputs could be defined as true positive (TP), true negative (TN), false positive (FP), and false negative (FN). TP is the number of positive cases correctly predicted as positive. TN is the number of negative cases correctly predicted as negative. FP is the number of negative cases incorrectly predicted as positive, while FN is the number of positive cases that are incorrectly predicted as negative.  
In addition, sensitivity, specificity, and balanced accuracy are usually used alongside the confusion matrix. Sensitivity is the ratio of TP over all positive cases, specificity is the ratio of TN over all negative cases, and the balanced accuracy is the mean value of sensitivity and specificity that eliminates the imbalance in the number of different classes of data.  
\[\text{sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{5}\]  
\[\text{specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}} \tag{6}\]  
\[\text{balanced accuracy}=\frac{\text{sensitivity}+\text{specificity}}{2} \tag{7}\]  
where precision measures the proportion of correctly predicted positive instances out of all instances predicted as positive.  
\[\text{precision}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{8}\]  
Recall, also known as sensitivity or true-positive rate, measures the proportion of predicted true positives out of all positives.  
\[\text{recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{9}\]  
F1 is commonly used for the evaluation of a model's accuracy and is particularly suitable for imbalanced datasets. It can be expressed as follows:  
\[\text{F1}=\frac{2\times\text{precision}\times\text{recall}}{\text{precision}+ \text{recall}} \tag{10}\]
# Dynamic Alternations in Mother-Infant Dyad's Gut Microbiota over the Period of Six Months
## 4 Results  
### Microbial Dynamics of Mother's and Infant's Faecal Microbiomes  
The dynamics of the faecal microbiome composition and diversity of the mother-infant dyad were tracked over a period of 212 days. Species-level taxonomic annotation was performed with MetaPhlAn3. In total, the faecal microbiota of mother and infant contained sequences representing 310 species that belonged to 10 phyla and 55 families. There were 18 major species in the complete dataset (average relative abundance > 1.0%; Figure 1A). Generally, the microbial complexity was rather low in the infant's faeces, having four major species, including _Bacteroides fragilis, Ruminococcus gnavas, Klebsiella pneumoniae,_ and _Klebsiella michiganensis_. Comparatively, the mother's faecal microbiota was relatively complex, comprising a number of major species, e.g., _Bacteroides vulgaris, Bacteroides steroris, Faecalibacterium prausnitzii, Bacteroides uniformis, Eubacterium rectale, Bifidobacterium pseudocatenulatum_, and _Alistipes putreditis_.  
Figure 1: \(Cont.\)  
The metagenomic data were subjected to PCoA (Bray-Curtis distance; Figure 1B). Symbols representing the mother and infant's microbiota showed distinct clustering patterns on the PCoA score plot, confirming great differences in the faecal microbiota between the two individuals and little variation among samples of the same person. The correlation between the gut microbiota of the mother and the infant was worth exploring.
## 4 Simulation Validation
### Localization and Imaging
(**a,c,e**): signals received from all sensing paths were taken. (**b,d,f**): signals received from all sensing paths but AC and BD were taken. appears on the actual location of the breathing crack at coordinates (118.75, 70), and the distance between the two horizontal lines within the crossover area accurately estimates the length of the breathing crack (about 3 mm) in the vertical direction. In addition, a probability-based diagnostic imaging (PDI) based on ToF for damage localization [27] was also analyzed and discussed. In this imaging method based on the elliptical loci, probability distribution value was assigned to the virtualized grid nodes in the structure, and the greater the probability value of this location, the more likely the damage location. Under the same situation as Figure 13a, the identification result using PDI is shown in Figure 13b. As can be seen from Figure 13a,b, both the crossover area determined by the proposed method and the highlighted damage region defined by the PDI with a threshold fall on the actual location of the breathing crack with a certain dimension. It was found that these two imaging methods based on nonlinear S0 modes can achieve localization and imaging of the breathing crack more precisely. Thus, the modified ellipse method using nonlinear S0 mode Lamb waves may be a suitable candidate for identifying and locating micro-damages.
## 2 Materials and Methods
### Research Modeling and Hybrid Neural Network Development  
#### 2.6.1 ANN Architectural Design  
Typically, ANNs exist as organized layers that include interconnected input nodes, output nodes, and hidden layers (Figure 3). They are computing systems that were inspired by the biological/human neural network [44]. They are also a class of feed-forward models that accepts data into the dense inputs layer and outputs prediction results based on the number of neurons in the dense output layer. In the ANN model design, the input layer has 7 nodes representing the 7 (Year, Month, Day, Hour, \(\mathrm{T_{amb}}\), \(\mathrm{A_{S}}\), and \(\mathrm{W_{10}}\)) input data columns that are required to determine/predict solar radiation and 1 node on the output layer representing the target column (Gi). The network has 1 hidden layer with 2500 neurons which is followed by an activation function to add nonlinearity to the layer's computation. In this study, the rectified linear unit (ReLU) is applied as the nonlinear activation function for the ANN models. The model loss was calculated using the mean square error being a regression analysis, and the global minima are determined via the backpropagation gradient descent. Adam optimizer was implemented with a learning rate of 0.001 and a training batch size of 512. The model was carefully designed to optimally learn the features of the data without underfitting and overfitting which is observed in the evaluated metric score of the training data to the test. The model overfitting problem is avoided by increasing the number of epochs gradually while checking the model performance. In total, the ANN model was trained for the various number of epochs depending on input data. The number of epochs used in training each model is highlighted in Table 3.  
Figure 3: ANN model architecture.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### Key Concept and Applications
#### 4.5.2 Digital Twins in Plant Water  
[PERSON] [97] developed a ML-based DT framework to optimize agriculture biomass at large scales. It integrates real-world data from LiDAR and multispectral sensors with simulations to create digital replicas of agricultural systems. This DT optimizes key variables like plant density, water availability, light intensity, and soil quality, etc., to predict the biomass. A physics engine models plant growth, while Genetic Algorithms solve complex optimization problems. The framework is scalable, supporting small farms to large plantations.  
[PERSON] et al. [98] developed a DT model to simulate and optimize corn, wheat, and rapeseed yields using real-time data from IoT sensors, satellite imagery, and previous records. The model integrates key variables like soil moisture, rainfall, irrigation, temperature, nutrient levels, and climatic factors to adapt farming practices, including seeding and fertilization. It highlights the importance of water and nitrogen management in improving yields. It supports predictive analytics for resource optimization, offering a sustainable solution for precision agriculture.
# An Incentive Factor-Based Dynamic Comprehensive Evaluation on a High-Speed Railway Track
## 4 Case Study
### An Incentive Factor-Based DCE
The time factor-based DCE can clearly show the obvious risk. Moreover, it can also indicate the potential risk, as illustrated in Figures 4 and 5. Figure 3: Trend in the geometric parameters from K1023 to K1024 (unit: km): (**a**) Left surface irregularity, (**b**) right surface irregularity, (**c**) left track alignment irregularity, (**d**) right track alignment irregularity, (**e**) cross-level irregularity, (**f**) twist irregularity, (**g**) track gauge irregularity, and (**h**) the rate of track gauge irregularity. Figure 2: Comparison of the TQI and the incentive factor-based DCE. Figure 4: Trends in the geometric parameters from K1004 to K1005 (unit: km): (**a**) Left surface irregularity, (**b**) right surface irregularity, (**c**) left track alignment irregularity, (**d**) right track alignment irregularity, (**e**) cross-level irregularity, (**f**) twist irregularity, (**g**) track gauge irregularity, and (**h**) the rate of track gauge irregularity. Figure 5: Trend in the geometric parameters from K1067 to K1068 (unit: km): (**a**) Left surface irregularity, (**b**) right surface irregularity, (**c**) left track alignment irregularity, (**d**) right track alignment irregularity, (**e**) cross-level irregularity, (**f**) twist irregularity, (**g**) track gauge irregularity, and (**h**) the rate of track gauge irregularity. It is clear from Figures 4 and 5 that all the parameters did not exceed the management values. However, the twist and gage rate were very close to the upper and lower limits (see Table 14) on the segment from K1004.9 to K1005, as shown in Figure 4g,h, indicating potential risk. The fluctuations in P\({}_{prf,L}\) and P\({}_{prf,R}\) imply that the section was dangerous. Another potential risk can be identified from the trend lines of P\({}_{prf,L}\), P\({}_{prf,R}\), and P\({}_{gage}\), which are shown in Figure 5a,b,g. The minimums of P\({}_{prf,L}\) and P\({}_{prf,R}\) were close to the limit value on the section between 1067.3 and 1067.4, while the minimum of P\({}_{gage}\) was close to the limit value on the section between 1067 and 1067.2. The peaks and valleys of the curves in Figures 4 and 5 did not exceed the limit values, but significant fluctuations appeared. This indicates that the section may have been deteriorating, which is dangerous. All the trend lines in Figures 3-5, especially Figure 3, had an apparent peak and valley. Meanwhile, Figure 3 contains parameter values exceeding the limits, which indicate obvious risk. In Figures 4 and 5, some parameters had values very close to the limits, which suggest potential risks. These potential risks could not be identified in Figure 1, confirming that the incentive factor-based DCE method is much more accurate in identifying the risky sections than the TQI method. The results can be used by rail administrations for safety control and track maintenance, inspection, and rehabilitation.
## 1 Introduction
The study found that despite similar e-government initiatives, different legal frameworks and managerial tools led to different outcomes between the administrations in the two countries examined. Similarly, we also adopted the institutional case study approach to examine how different legal frameworks have led to different outcomes in the long-term. Specifically, we examined how digital government strategies are differently pursued between different administrations and countries, using the cases of Korea and the United States. We examined and compared how administrations in Korea and the United States have promoted their digital government policies since the establishment of the e-government acts in these countries. We also compared the legislation process and contents of the E-Government Act in both countries, and have argued that the difference in the e-government act has indeed differently affected how administrations in Korea and the United States have promoted their digital government policies. While the Korean E-Government Act was mainly motivated by a need to reduce documents and has led to the absence of a strong dedicated organization with budget authority for digital government implementation, the E-Government Act in the United States is comprehensive and has provided legal basis for a strong dedicated organization under the OMB that has resulted in consistent digital government promotion strategies across administrations. This research contributes to the literature by examining the long-term influences of the legal framework on government policies in the context of e-government and digital government. Despite using a case study approach based on various sources of information (e.g., examination of legal documents, engagement in the legislative drafting process, interviews with stakeholders), this study was not without its limitations; similar future studies that examine and compare text documents should employ content analysis or comparative document analysis methods to allow statistical analyses and the use of validation techniques. The next section examines the changes in policies for digital government implementation in Korea and the United States over the past two decades. After describing the legislation process and content of the e-government acts in Korea and the United States in the third section, we compared the e-government acts in Korea and the United States and identified three important features of the e-government acts that have influenced the implementation of digital government in Korea and the United States. We conclude with a discussion on the lessons learned and the policy implications for implementing a strong governance structure.
# Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data
MOD17 daily GPP and annual NPP products are derived from the LUE model. FPAR is related to GPP assimilation, and LAI is related to respiration when estimating NPP. However, daily MODIS Net Photosynthesis (PSNnet) does not include the calculation of growth respiration and maintenance respiration associated with living wood when compared with NPP. In this case, developing a more reliable algorithm to estimate global daily GPP and NPP by integrating high quality GLASS data and eco-physiological processes is of great significance. The aims of this paper are (i) to generate more accurate and continuous global GPP and NPP products with GLASS data; (ii) to validate the GPP and NPP estimates, and to compare the results with products; and (iii) to analyze the temporal and spatial variation of global GPP and NPP from 2004 to 2012.
# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 3 Unified Soil Classification System (USCS)
### Reporting  
Once the soil group symbol is assigned, the final classification, along with the relevant data, should be documented and reported for engineering and geotechnical applications. The USCS provides a systematic and standardized approach for soil classification, which is essential for geotechnical engineering, construction, and other applications involving soil properties and behavior.  
Section 3 thoroughly outlines the USCS procedure, detailing the steps involved in the USCS process. This includes key phases such as sample collection and preparation, grain size distribution analysis, Atterberg limit determination, classification based on grain size, use of the plasticity chart, assignment of group symbols and names, and validation and documentation. This study further uses the random forest method from machine learning to develop an automated soil classification model for the USCS procedure. Detailed explanations of each step are provided in the following section.
# The Performance and Fouling Control of Submerged Hollow Fiber (HF) Systems: A Review
## 5 Parameters Affecting the Performance of a Submerged Hollow Fiber System
### Membrane Properties and Module Configurations
#### 5.1.2 Fiber/Module Arrangement  
Experimental efforts have consistently established that a lower HF packing density, either by having fewer fibers [108], widening the HF module [187; 188], or by varying the module configuration [97; 189], is linked to a lower fouling tendency. Other than the HF packing density, modules that are designed to enhance either lateral flow [190] or lateral movement of the fibers [188] are known to further improve the performance of submerged HF systems. In a carefully controlled arrangement with nine fibers in a matrix, a detailed analysis has shown that the performance of individual fibers varied with their position in the module, such that the fibers at the edge performed best, while those in the center surrounded by other fibers performed the worst [108]. The underlying reason for this was tied to the lower cross-flow velocity at the center of the module and the 'flux competition' for the surrounded fiber. Interestingly, whereas [PERSON] and [PERSON] [108] found that a single fiber outperformed a multi-fiber HF module in a single-phase (no bubbling) system due to module blocking, [PERSON] and [PERSON] [191] found that a multi-fiber module performed better than a single fiber in the presence of bubbles (i.e., two-phase flow) due to inter-fiber interactions leading to mechanical erosion of the foulant layer.  
Simulation results by [PERSON] et al. [192] revealed that MBRs fitted with hollow fibers in a vertical orientation experienced 25% more membrane surface shear in the filtration zone than horizontally oriented fibers at the same aeration intensity. They also found that the addition of baffles in the membrane modules is a feasible way to promote turbulence and shear in the upper section of the membrane module.
## 2 Methodology
### Combined DBN and Particle Filter-Based Approaches for RUL Prediction
\begin{tabular}{l l} Step 1 & Let the last 100 rows be equal to the testing set, and set the rest of the data to the training set. \\  & Approximate the state transition model. \\  & & Reconstruct the time series of the signal features into a matrix with an \\  & & a & embedding dimension of \(d\). This will serve as the input, and the output \\  & & & will be the mapped state \(x_{t}\), where \(t=d-1,\ldots,N_{training}-L-1\). \\ Step 2 & & b & Create \(B\) bootstrapped data sets using the data created in Step 1. \\  & & & Initialize the weights and biases of the FNN by training the DBN on the \\  & & c & input data with all the data except for the last 100 rows. The rest of the \\  & & & data is the testing set. \\  & & & Train the FNN. Fine tune the weights in a supervised fashion by \\  & & d & minimizing the loss function on the training set and by using the \\  & & & back-propagation algorithm. \\  & & e & Predict the \(x_{t+L}\). This is accomplished by subtracting the network's \\  & & & output \(x_{t}\) by \(L\), i.e., \(x_{t+L}=x_{t}-L\). \\  & & f & Let \(t=t+1\), and update the training vector with input features \\  & & & \((z_{t},z_{t-1},\ldots,z_{t-d+1})\). \\  & & g & Repeat Steps 2e-2f, until all 100 points have been predicted. \\  & & Estimate the measurement distribution. Create a new dataset of the form \((x_{t},z_{t})_{t=1}^{N_{training}}\) \\ Step 3 & & Generate \(B\) bootstraps from this training dataset and apply Equations (22)-(28) to obtain the \\  & & measurement distribution. The DBN can be used for estimates in Equations (22) and (27). \\ Step 4 & & Estimate the state. The predicted state \(\hat{x}_{t+L}\) can be obtained by sampling \\  & & \(\{x_{t+L}^{1},x_{t+L}^{2},\ldots\ x_{t+L}^{B}\}\) with probability \(w_{t}^{i}\) [from Equation (15)]. \\ \end{tabular}   
This method allows a complete data-driven approach to be developed in conjunction with a particle filter for RUL estimation. Equation (10), the proposal distribution is built up using training tuples of the form \(([z_{t},z_{t-1},\ldots,z_{t-d+1}],x_{t})\). The bootstrapping approach is then used to collect the statistics for the mean and variance. Equation (11), the measurement model is then modeled by Equations (22)-(28). Here the DBN can serve two purposes. It can be used to reduce the dimensionality of the measurement \(z_{t}\) and it can also to be used in order to initialize the weights of the neural network.