esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The fluid-structure interaction effect should not be disregarded when examining the vibration characteristics of hydraulic pipeline systems. The transfer matrix method (TMM) is an efficacious method for analyzing the vibration characteristics of hydraulic pipelines in the frequency domain, offering advantages such as simplicity and efficiency. However, the TMM suffers the problem of high frequency instability when dealing with long-span hydraulic pipelines, which restricts its practical application. Currently, several modified transfer matrix methods face challenges such as low computational efficiency and difficulties in handling complex boundaries. In response to these issues, this paper proposes a novel modified transfer matrix method known as the mixed variable transfer matrix method. This innovative method possesses clear physical significance and effectively prevents the transfer matrix from becoming singular without necessitating the subdivision of the pipeline length. Consequently, it addresses high-frequency instability while maintaining high computational efficiency. Moreover, this method is capable of addressing complex boundary problems by integrating boundary matrices, thereby demonstrating enhanced applicability compared to existing methods. The performance of the proposed method was validated through the utilization of classic Dubee pipeline impact test data, and the result shows maximum errors of 3.03% relative to the public data. Subsequently, an experiment was conducted on a section of hydraulic piping within a ship's steering system. A hydraulic fluid noise generator was established to induce fluid pulsation excitation to the pipeline, thereby simulating the actual boundary conditions encountered in a ship's hydraulic pipeline system so as to corroborate the efficacy of the proposed method in predicting the frequency domain vibration characteristics of a real hydraulic pipeline system. The experimental results indicate that the proposed method offers significant advantages in terms of high precision, efficiency, and stability, shows maximum errors of 4.35% relative to experimental data, and demonstrates promising prospects for engineering applications.  
hydraulic pipeline; fluid-structure interaction; frequency domain vibration characteristics; mixed variable transfer matrix method +
Footnote †: journal: Applied Sciences  
1
Footnote 1: email: {[[https://doi.org/10.3390/app142310847](https://doi.org/10.3390/app142310847)]([https://doi.org/10.3390/app142310847](https://doi.org/10.3390/app142310847))}
# A Physicist's View on Partial 3D Shape Matching
## 3 Implementation
### Computing the Wave Kernel Signatures  
The wave kernel signatures are computed from the eigenpairs of the Laplace-Beltrami (LB) operator for the triangular mesh on the surface of a shape. Many schemes have been proposed to approximate the LB operator on a discrete surface represented by a triangular mesh ([PERSON], 1965). We use the so-called cotangent scheme. In this scheme, we start with defining an operator LB on the mesh. This operator is given as a matrix \(L\) of size \(N\times N\), where \(N\) is the number of vertices in the mesh. \(L\) is set to \(M^{-1}W\), where M is a diagonal matrix whose element \(M(i,i)\) corresponds to the area associated with vertex \(i\) and \(W\) is the symmetric matrix of cotangent weights, as defined in ([PERSON], 1965), to account for possible boundaries within the mesh. The general eigenproblem \(W\phi=\lambda M\phi\) has real eigenvalues as solutions. We solve this problem by considering the standard eigenvalue problem \(Av=\lambda v\), where \(A=M^{-1/2}WM^{-1/2}\)([PERSON], 1965). The eigenvalues of the latter are the same as the eigenvalues of the generalized problem, and its eigenvectors \(v\) are associated with the eigenvectors \(\phi\) of the former according to \(\phi=M^{-1/2}v\).  
In all the numerical experiments presented in the next section, the parameters were fixed. We computed \(N=500\) eigenvalues of the LB operator and we evaluated at \(M=200\) values of \(\mathrm{e}\). We used \(e_{min}=\log(E_{1})+2\sigma\), where \(E_{1}\) is the smallest eigenvalue different from \(0\) and \(e_{max}=\log(E_{N})-2\sigma\), where \(E_{N}\) is the \(N\)-th eigenvalue. The increment \(\delta\) in energy was set to \(\frac{e_{max}-e_{min}}{M}\), and the variance \(\sigma\) (see Equation (5)) was set to \(7\delta\).  
To compare the WKS at the point \(x\) on a shape \(S_{1}\) and a point \(y\) on a shape \(S_{2}\), we follow the method proposed by [PERSON] et al. (2019) and define a distance using the \(L_{1}\) norm of the normalized signature difference:  
\[d_{WKS}(x,y)=\sum_{k=1}^{M}\left|\frac{WKS(x,e_{k})-WKS(y,e_{k})}{WKS(x,e_{k}) +WKS(y,e_{k})}\right|. \tag{29}\]  
Note that this implies that the same energy spectrum is used when computing the WKS for both shapes.
## 2 Geodynamic Framework
#### Seismicity of Croatia  
Regarding the geodynamic framework, the coastal part of Croatia is the most seismically active one. In historical records, the Dubrovnik area was the most seismically active area, with even 8 seismic events greater than IX MCS [53], out of which the strongest earthquake occurred in 1667, with intensity X MCS. Nevertheless, there have been other significant earthquakes along the Adriatic coast in the 15 th, 16 th, and 18 th centuries. During the last decades, also significant seismicity in the Central Adriatic has been noticed due to the increase in quality and number of regional seismological stations [53]. Considering the most populated area, namely the capital city Zagreb and its metropolitan area, large number of strong earthquakes occurred there in the 17 th century [53], of which the largest one had an intensity of M\({}_{w}\)6.3, occurring in 1880 in Zagreb.  
Considering the seismicity in Croatia from 1900, all significant earthquakes with M\({}_{w}\) > 5 (Biokovo earthquake in 1962 with M\({}_{w}\)6.1, Ston-Slano earthquake in 1996 with M\({}_{w}\)6.0, and the earthquake sequence on Jabuka island in south Adriatic in 2003 with M\({}_{w}\)5.5 [53]) occurred in the south Adriatic, except the Kupa Valley earthquake, occurred in Pannonian basin in 1909.  
Figure 2 displays the earthquakes that happened in the last 16 years, in the period between 2004-2021, over Croatian territory. The time span has been set to the last 16 years due to available seismic data distributed publicly by the European-Mediterranean Seismological Centre (EMSC) [58]. Left subfigure shows the earthquakes that happened in period between 2004 and December 2020, while the subfigure on the right side shows the earthquakes that happened in the period between 15 December 2020 and 1 February 2021. The main aftershocks are occurring along the NW-SE direction, following the Pannonian fault location (see Figure 2, subfigure on the right, orange dots).  
In the last 16 years until December 2020, more than a thousand seismic events happened on the Croatian territory, out of which about 25% were of magnitude \(M_{w}\)\(\geqq\) 2.5. Furthermore, in that period, there have been 19 (data by EMCS) occurrences of earthquakes \(M_{w}\)\(\geqq\) 4 and only one \(M_{w}\)\(>\) 5. This \(M_{w}\)5.4 earthquake was also a hazardous earthquake with the epicenter near the Croatian capital city, also dating from 2020 (March), which caused human loss and enormous material damage [59].
# Crop Growth Stage GPP-Driven Spectral Model for Evaluation of Cultivated Land Quality Using GA-BPNN
## 2 Materials and Methods
### Methods
#### 2.3.2 Selecting the Phases of GPP  
The important step in selecting the phases for CLQ evaluation was the determination of the relevant growth stage. In this study, Pearson product moment correlation that quantifies the linear relationship between two variables [35; 36] was applied to obtain the spectral variables with the highest coefficients at the significance level of 0.05. The correlation coefficient is calculated as:  
\[r_{i}=\frac{\sum_{n=1}^{N}\bigl{(}R_{ni}-\overline{R_{i}}\bigr{)}(y-\overline {y})}{\sqrt{\sum_{n=1}^{N}\bigl{(}R_{ni}-\overline{R_{i}}\bigr{)}^{2}\sum_{n= 1}^{N}(y-\overline{y})^{2}}}, \tag{4}\]  
where \(r_{i}\) is the correlation coefficient between growth stage and CLQ, \(N\) is the total number of CLQ samples. \(R_{ni}\) is the \(i\)th growth stage of the \(n\)th CLQ sample, \(\overline{R_{i}}\) is the average of the CLQ sample values in the \(i\)th growth stage, and \(y\) is the nth CLQ, \(\overline{y}\) is the average value of CLQ.  
Moreover, the Variance Inflation Factor (VIF) was applied to mitigate the collinearity among the GPP predictors, which is calculated as [37; 38]:  
\[\text{VIF}=\frac{1}{1-R_{i}^{2}} \tag{5}\]  
where, \(R_{i}^{2}\) is the determination coefficient between the \(i\)th predictor and the remaining independent variables. The larger the VIF, the greater the collinearity between the predictors. In general, the values from 0 to 10, 10 to 100, and equal to and greater than 100, respectively, imply no strong and serious collinearity.
# A High Capacity Watermarking Technique for the Printed Document
## 3 Proposed Model
### Watermark Embedding Process  
Based on the previous introduction, the following is an example of embedding two bits of watermark information for each character. The watermark embedding process is as follows:  
1. The watermark information is obtained and arranged into a sequence \(w=\{w_{1},w_{2},\cdots,w_{L}\}\), \(w_{l}\in\{0,1\}\), where \(L\) is the watermark length.
2. Converting the text image into a binary image, denoising, removing characters whose pixels are less than a certain threshold \(T\), for example, punctuation, etc. This paper takes \(T\) greater than half of the average pixels of all characters.
3. The processed binary image is divided into character row units, and then the effective characters of each row are segmented to obtain a character set \(a=\left\{a_{ij}\right\}i=1,2,3,\cdots m;j=1,2,\cdots n\), where \(i\) is the row index and \(j\) is the column index. The corresponding black pixels set is \(b=\left\{b_{ij}\right\}i=1,2,3,\cdots m;j=1,2,\cdots n\).
4. Calculating the average value \(M\) of black pixels of all characters in the first row, then calculating the ratio set \(X\) of the remaining character black pixels to \(M\). And they are shown in Equations (9) and (10), respectively. \[M=\frac{1}{N}\sum b_{1j}\] (9) \[X=\left\{x_{1},x_{2},\cdots,x_{s}\right\}x_{s}=b_{s}/M\] (10)
5. Solving the quantization function Equation (6) according to the watermark information. If \(\{w_{2l-1},w_{2l}\}=2^{\prime}b00\), then solving \(f(\hat{x}_{l})=900\); else if \(\{w_{2l-1},w_{2l}\}=2^{\prime}b01\), then solving \(f(\hat{x}_{l})=400\); else if \(\{w_{2l-1},w_{2l}\}=2^{\prime}b10\), then solving \(f(\hat{x}_{l})=100\); else if \(\{w_{2l-1},w_{2l}\}=2^{\prime}b10\), then solving \(f(\hat{x}_{l})=0\); finding the appropriate solution \(\hat{x}_{l}\). Then we can get the print-scan invariant set of all characters after embedding the watermark information and the set is \(\hat{X}=\{\hat{x}_{1},\hat{x}_{2},\cdots,\hat{x}_{l}\}\).
6. Calculating the pixel flipping ratio set \(F=\hat{X}-X=\{f_{1},f_{2},\cdots,f_{s}\}\) for watermarked characters.
7. According to the pixel flipping ratio set \(F\), the pixel points of the corresponding characters are flipped with the pixel flipping strategy in Section 3.3.
## 5 Comprehensive Evaluation Example and Numerical Simulation
and A4 are set to 0.1, and the aggregation result is mapped from the 5-dimensional space to the 3-dimensional space. Figure 7 shows the eigenvalue's trend of A2 and A3's convex combination. The function value in this figure is \(f(x,y)=\max|eig(A_{2}^{x}\circ A_{3}^{y})|\); _eig_ represents a function to obtain the matrix eigenvalues. Obviously, there must be an optimal aggregation coefficient for the convex combination of multiple judgment matrices. The range of aggregation coefficients proposed in this paper: \(\{x,y|x\in[0.33,0.83],y\in[0.17,0.67]\}\subset[0,1]\times[0,1]\). From this, the aggregation coefficient according to the authority of the experts has a certain degree of uncertainty, which leads to a large fluctuation in the final consistency ratio(CR). Therefore, the aggregation coefficient with initial consistency factor proposed in this paper can decrease the floating of CR, thus increase the probability of obtaining a better solution. 6. Calculate the effectiveness improvement ratio = \((s_{2}\cdot w/s_{1}\cdot w)-1\) = 0.4779. After calculation, the comprehensive effectiveness of the second-generation TDRS system is ~48% higher than that of the first-generation system under given conditions. The effectiveness comparison between and iisystem is shown in Figure 8 and Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**Relative Score**} & \\ \cline{2-3}  & \(\mathbf{S_{1}}\)**:TDRSi** & \(\mathbf{S_{2}}\)**:TDRSii** & \\ \hline C1 & 2 & 3 & 0.1846 \\ C2 & 3 & 5 & 0.4041 \\ C3 & 5 & 8 & 0.6359 \\ C4 & 3 & 4 & 0.2166 \\ C5 & 4 & 5 & 0.5333 \\ C6 & 2 & 3 & 0.2260 \\ C7 & 3 & 4 & 0.1262 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The input parameter: score and weight. Figure 7: Eigenvalue trend of two matrices’s convex combination. Example Summary: This evaluation example is a typical multi-objective comprehensive evaluation process, which shows the complete application process of the group AHP consistency enhancement strategy proposed in this paper. Moreover, through numerical examples, it is proved that the strategy converges faster and has stronger consistency. At the same time, it also provides a model for comprehensive evaluation index system to evaluate the subsystem. In addition, the evaluation results also give the main improvement direction of TDRSS which is the backbone system of the space information network, and give an intuitive description of the performance gap between the two generation TDRSS.
# Analysis of the Dynamic Behavior of Multi-Layered Soil Grounds
## 3 Results and Discussion
### Peak Ground Acceleration  
Peak Ground Acceleration (PGA) can serve as an indicator of the maximum ground-shaking experienced during an earthquake. Analyzing the PGA in multi-layered soil profiles involves considering the response of each soil layer to seismic waves and their interaction with adjacent layers. Layer thickness influences the amplification or attenuation of ground motion. Figure 12 shows the Peak Ground Acceleration (PGA) graphs derived from the acceleration-time history graphs.  
Figure 12: PGA profiles for the experiment and numerical analysis: (**a**) Case 1; (**b**) Case 2; (**c**) Case 3; (**d**) Case 4.  
In Figure 12a, the PGA of the DEEPSOIL results is generally lower than that of the experimental and ABAQUS results. However, in Figure 12b-d, the PGA of the DEEPSOIL results tends to be higher compared to those of the experimental and ABAQUS results. This is attributed to the effective amplification of DEEPSOIL on weathered soil. Additionally, the densely bound particles in weathered soil contribute to a higher velocity of acoustic wave propagation, facilitating rapid acceleration amplification. The PGA of the experimental and ABAQUS results shows closer agreement.  
Different soil layers exhibit distinct amplification characteristics. Areas with softer or thicker soil layers may experience higher PGA due to greater amplification of the seismic waves. Overall, analyzing PGA in multi-layered soil profiles requires consideration of various soil properties and wave propagation mechanisms.
## 3 Feature Extraction of Acoustic Signal  
### Double-Domain Feature Extraction Method  
The original acoustic signal has many characteristics. Dual-domain feature extraction is used to select suitable features for research and classification. First, the energy ratio of the acoustic signals is judged based on the time domain characteristics. Then, the sixth-order linear prediction coefficient (LPC) of the acoustic signal is obtained based on the frequency domain characteristics.
## 3 Results  
### The Biodegradation of UDMH by Activated Sludge  
Figure 1 shows the change of UDMH concentration per day before and after treatment, and the change in the daily degradation rate. In Figure 1, the degrading percentage of UDMH (24 h) increased gradually. At the beginning of the seven days, the degrading percentage was only just over 30%, which suggests the activated sludge did not form the dominant bacteria consortium to biodegrade UDMH. As the acclimation continued, the bacteria that had the capacity of biodegradation UDMH formed the domain bacterial consortium, and the degrading percentage rose to 70% after 15 days. On the 28 th day, the degrading percentage steadily increased to about 80% and the acclimation could be accomplished. At first, the addition of UDMH prevented the growth of bacteria and made a delay in the start of the log growth phase [32]. As soon as the bacteria adapted to UDMH, they smoothly entered the log growth phase in the course of acclimation. The concentration of chemical oxygen demand (COD) was also detected with the potassium dichromate (K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\)) method [33]. The final degrading percentage of COD (24 h) was 84.5%, which proved that the treatment by activated sludge system was effective.
# InoT Sensor Network for Wild-Animal Detection near Roads
## 3 Design of DSN Hardware
### Energy Consumption and Harvesting of the DSM Node
Since manufacturers rarely provide the parameters and characteristics of batteries when they work at low temperatures, several lithium-ion (Panasonic (Osaka, Japan) and  
Figure 6: Charging current schedule from 22 March–13 April 2021. The average current generated during the entire period was \(I_{0}\) = 16.8 mA. Figure 7: Charge currents generated by solar panels on 25 March 2021 (cloudy, rain, blue graph) and 26 March 2021 (sunny, red graph). Samsung (Suwon, Republic of Korea) 18650 type) and nickel-metal hydrate batteries (Panasonic Eneloop pro) were tested. For Panasonic Eneloop batteries, their capacity practically did not change at temperatures of 20 \({}^{\circ}\)C and 0 \({}^{\circ}\)C. Figure 8 shows the test results of these batteries (connected two in a series) with a current of 400 mA at different temperatures. In the DSN prototypes, it was decided to use three AA size 2450 mAh Panasonic Eneloop pro batteries due to their safety (compared to Li-ion batteries) and capacity stability over a wide range of temperatures. The discharge curve is given in Figure 8.  
The block diagram in Figure 2 shows the components of the DSN. The energy costs of the DSN node will be the sum of the energy costs of the individual elements, which can be expressed by the formula:  
\[I_{SUM}=3\times I_{IPR}+I_{RF}+I_{MCLU}+2\times I_{LED} \tag{3}\]  
The DSN node will spend most of its time in sleep mode when its energy consumption will be the lowest. According to the data presented in the manufacturer's documentation [42; 43; 44; 50], the energy consumption of the DSN node will be:  
\[I_{SUM\_sleep}=3\times 20\ \mu\text{A}+1\ \mu\text{A}+10\ \mu\text{A}+2\times 0\ \mu\text{A}=71\ \mu\text{A} \tag{4}\]  
In active mode, the DSN node will consume:  
\[I_{SUM\_active}=3\times 20\ \mu\text{A}+20\ \mu\text{A}+2\ \mu\text{A}+2\times 50 \ \mu\text{A}=122.6\ \mu\text{A} \tag{5}\]  
It is likely that the appearance of wild animals on the road section during the day will not be very frequent, but false alarms due to the movement of other warm objects are also possible. DSN is the expected periodic wake-up of the processor and short-term activation of the receiving part of the radio communication module (to detect the activation of other DSNs). This will also slightly increase the standby current. In any case, in the absence of many sensor activations during the day, it is very likely that the average current consumed by the DSN electronics will be less than 1 mA. Even assuming that the average DSN current consumption will be 1 mA, the module should last over three months from a fully charged battery (2450 mAh).
## 5 Conclusions  
A novel data analysis based on the scenario of separating landslide source and runout areas with the topographic attributes was presented in this paper to improve landslide detections. To explore the feasibility of the proposed approach, four hierarchical experiments were designed. First, elevation was a critical variable according to the factor analysis. Second, the candidate models were constructed using the RF and cost-sensitive analysis and the limited training samples extracted from the top five largest areas of the inventory polygons. The polygon-based cross-validation was also used for the verification. Third, the candidate models verified using different polygon-based samples indicated that the No. 1 model using the RF method with a cost of 5000 and the No. 3 model based on the RF were the most favorable for predicting the larger polygon (area \(\geq\) 100,000 m\({}^{2}\)) samples. For the smaller polygon (area of 50,000-100,000 m\({}^{2}\)) samples, the No. 3 model provided acceptable results. The accuracies of developed models were above 0.8 in most cases, outperforming the performances of the SVM and LR. Finally, mapping the predicted distributions revealed that the detection of landslide sources provided accurate results with lower missing rates. The extraction for the runout areas revealed excellent user's and producer's accuracies.  
The gap in recognizing landslides has existed in terms of remote sensing and geotechnical engineering. More precisely, the landslide detections extracted from remotely sensed images without additional interpretations to eliminate the runout areas should be termed the landslide affected area and should not be directly treated as a landslide inventory or database for further analyses. This study provided a systematic procedure following a supervised data mining-based approach to separate landslide source and runout signatures. The tradeoff between the size areas of the training data and the predicted range of the developed models was emphasized in the results. More research is warranted regarding the effect of the elevation-based threshold and the binary classification based on the detections of landslide sources only to produce fully automatic landslide inventory productions.  
This study was supported, in part, by the Ministry of Science and Technology of Taiwan under project No. 109-2121-M-035-001 and 108-2119-M-035-003.  
The author would like to thank [PERSON] in Center for Space and Remote Sensing Research, National Central University, Taiwan, for providing the landslide inventory and the digital elevation model. The author also thanks [PERSON] in Department of Civil Engineering, Feng Chia University, Taiwan, for refining the figures in the article.  
The author declares no conflicts of interest.