esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method
## 6 Experiments and Results
### Fault Feature Extraction Experiment
As shown in Figure 7a-c, choosing \"db10\" as the wavelet basis can clearly distinguish three types of faults and fault diameters. According to the above, \"db10\" is chosen as the wavelet basis and the WPT level is set to 4. Then, the energy entropy of the reconstructed signal of each node is calculated in the last level and forms the 16-dimensional feature vector for further fault diagnosis. It can be seen from this figure that the fault characteristics extracted from different fault types are also greatly different, which indicates that under different fault conditions, the energy concentration frequency band is different and different fault diameters also affect the energy entropy value of each frequency band. Therefore, the application of the WPEE in bearing fault feature extraction can reveal the energy distribution in various frequency bands in the form of entropy and effectively characterize the rolling bearing fault type. As shown in Figure 8, there is a large difference in the energy entropy between node 2, node 6, and node 14 under the different fault types. The wavelet packet energy entropy clustering diagram under different fault conditions for a load of 2 hp is shown in Figure 9. Figure 9 shows the energy entropy clustering of the second node, the sixth node, and the fourteenth node in the fourth level, in which \"BF1\", \"BF2\", \"BF3\", and \"BF4\", respectively, represent ball fault samples with fault diameters of 0.007, 0.014, 0.021, and 0.027 in and the inner race fault and outer race fault are in turn. From this figure, the features of different fault conditions show good clustering, but since only 3 of the 16 dimensions in the feature vector are taken as the display, there is some overlap between some fault conditions. Figure 8: The WPEE feature extraction results under different fault types of rolling bearing with a load of 2 hp.
# Analytical Solution for 2D Inter-Well Porous Flow in a Rectangular Reservoir
###### Abstract  
Inter-well fluid flows through porous media are commonly encountered in the production of groundwater, oil, and geothermal energy. In this paper, inter-well porous flow inside a rectangular reservoir is solved based on the complex variable function theory combined with the method of mirror images. In order to derive the solution analytically, the inter-well flow is modeled as a 2D flow in a homogenous and isotropic porous medium. The resulted exact analytical solution takes the form of an infinite series, but it can be truncated to give high accuracy approximation. In terms of nine cases of inter-well porous flow associated with enhanced geothermal systems, the applications of the obtained analytical solution are demonstrated, and the convergence properties of the truncated series are investigated. It is shown that the convergent rate of the truncated series increases with the symmetric level of well distribution inside the reservoir, and the adoption of Euler transform significantly accelerates the convergence of alternating series cases associated with asymmetric well distribution. In principle, the analytical solution proposed in this paper can be applied to other scientific and engineering fields, as long as the involved problem is governed by 2D Laplace equation in a rectangular domain and subject to similar source/sink and boundary conditions, i.e., isolated point sources/sinks and uniform Dirichlet or homogeneous Neumann boundary conditions.  
Keywords:analytical solution; inter-well porous flow; Laplace equation; method of mirror images; Euler transform
## 4 Results
### Spatially Varying Relationships between FVC and Driving Factors Explored by SGWR
#### 4.3.2 The Performance of SGWR Modelling in Comparison with GWR  
The adjusted R\({}^{2}\) of the GWR and SGWR models are visually displayed and highlight the spatial heterogeneity of the simulation effect. The modelling system performance can be demonstrated by the adjusted R\({}^{2}\) values, which usually describe the fitting degree of the regression model to observation data and remove disturbances from the explanatory variable on R\({}^{2}\). The adjusted R\({}^{2}\) of the SGWR model is the closest to 1; therefore, the SGWR model better explains regional vegetation coverage variations. The adjusted R\({}^{2}\) of the GWR model generally ranges from 0.4-0.8, whereas the average adjusted R\({}^{2}\) of the SGWR model is greater than 0.8 (Figure 7). The lower adjusted R\({}^{2}\) values in Heyuan and Yunfu indicate an insignificant association between vegetation coverage and model driving factors, whereas the relatively higher adjusted R\({}^{2}\) values in Guangzhou, Huizhou, and Dongguan indicate a stronger positive correlation. The high adjusted R\({}^{2}\) value of the SGWR verifies that the fitting accuracy is significantly better than that of the GWR model.  
By analysing the research times of the models, we found the highest adjusted R\({}^{2}\) in 2015. The SGWR improvement from 2000-2015 ranged from 0.21-0.45 and 0.26-0.67 in northern Guangdong and the Pearl River Delta, respectively, and more than 50% of counties had adjusted R\({}^{2}\) values higher than 0.8 in 2015 (35 more than in 2000).  
Figure 6: Local estimates for Population factor of Fractional Vegetation Cover (FVC) calculated by the SGWR model.  
The SGWR fixes some parameters across space and allows others to change spatially to simultaneously represent stationary and non-stationary spatial relationships and processes between FVC and the driving factors. Another advantage of the SGWR model is its ability to explore the spatial relationships between the two regimes.  
Figure 7: Local adjusted R\({}^{2}\) for fractional vegetation cover calculated by the GWR and SGWR models.  
heterogeneity and homogeneity of the relationship between fractional vegetation cover and explanatory variables with the geographic variability test. Nonetheless, the SGWR model should be used with caution. Global or local variables in the geographic variability test affect the final analysis results and should be carefully selected. Additionally, the spatial relationship in the model is established by the spatial weight matrix, and the spatial weight matrix bandwidth setting is uncertain.
# Design of Indoor Visible Light Communication PAM4 System
## 5 Results and Analysis
### System Experiment  
The BER is one of the most important parameters in the communication system, which reflects the performance of the communication system. The communication rate is set to 10.0 MB bit/s, and the communication distance is 0.5 m, 1 m, 1.5 m, 2 m, and 2.5 m, respectively. The performance of the BER of the indoor visible light communication system based on PAM4 is tested.  
The experimental system diagram is shown in Figure 20. The experimental results are shown in Figure 21. During the experiment, 221,720 symbols were sent. As can be  
Figure 19: The PAM4 decoding simulation result.  
Figure 17: The PAM4 coding simulation result.  
Figure 18: The PAM4 waveform display result.  
seen from Figure 21, the BER is very large when the communication distance is very close. This is because the light intensity is too large, and the photodetector is saturated, resulting in the photodetector being unable to receive the signal. As the distance slowly increases, this situation will disappear; the BER can basically be maintained at 10\({}^{-6}\) within the communication distance of 1.5 m, but as the distance increases, the BER becomes larger and larger, and even after the distance exceeds 1.5 ms, the BER increases sharply, mainly because the intensity of the optical signal received by the photodetector decreases with the increase in distance. The optical signal will attenuate and diverge through the channel. A condenser lens can be added in front of the photodetector, or the luminous power of the LED can be increased. The BER obtained in this way will decrease under the same experimental conditions. This can be seen from Figure 21. After the distance exceeds 1.5 m, the BER obtained by the condenser lens is significantly reduced by combining hardware pre-equalization technology in the LED driver circuit to expand the modulation bandwidth of the LED, using pulse shaping to enhance the decoding performance of the receiver, optimizing the performance of the photovoltaic receiving circuit, adding a blue light filter in front of the photodetector to filter out the yellow light element with slower response, and reducing the response time of the photodetector. The BER will be further reduced by improving and optimizing the above aspects.
## 3 Modeling of the System
### Equipment Cost Accountings  
The equipment cost is used to compare the economics of the proposed three system configurations, so we make the following assumptions:  
1. The cost of the core equipment is considered the total cost of the system. In actuality, the cost of the core equipment consists of only part of the total cost, with pipes, construction fee, and engineering design as additions. Nevertheless, in this design process, we assume these costs are proportional to the total amount. Hence, we can obtain sufficient analysis to conduct the comparison of the proposed three system configurations, considering thermodynamics and thermoeconomic performance.
2. Characteristic parameters are used to calculate the cost of different core equipment. However, geometric design parameters and flow forms of turbomachinery will affect the purchasing price of the device, which will undoubtedly lead to some deviations compared to actual cost of the S-CO\({}_{2}\) system. However, this deviation can be acceptable for these three kinds of systems because we can consider it as consistent.  
Figure 5: The nodalization of the heat exchanger.  
The common cost calculation of the S-CO\({}_{2}\) cycle is mostly high temperature, while the temperature involved in this paper is relatively low. Hence, we use the trans-critical CO\({}_{2}\) cycle as the cost reference, which corresponds to more similar temperature levels. The functions built to calculate the equipment costs are presented in Table 2. They are obtained from [PERSON] et al [38] and [PERSON] et al [10].  
Moreover, in the calculation process of the cooler, the constant heat transfer coefficient between water and supercritical CO\({}_{2}\) is set to 1600 W-m\({}^{-2.\circ}\)C\({}^{-1}\). This value is obtained by averaging the heat exchange process between water and S-CO\({}_{2}\) in a larger pressure range [39].
# Role of Restored Underwater Images in Underwater Imaging Applications
## 2 Proposed Restoration Method
### Transmission Map Estimation
#### 2.2.2 Transmission Map of Backscattered Signal  
Comprehensive studies have been conducted on the estimation of spectral attenuation backscattering coefficients, with Mie theory used to predict spectral behavior. [PERSON] et al. [24] use Slow Descent Rate Optical Profiler (Slow DROP), to experimentally calculate backscattering coefficients of particulate matters in five research cruises at five different wavelengths covering the visible spectrum, over a period of three years. The values selected based on the wavelengths of interest are shown in Table 1.  
Total backscattering coefficient \(\beta_{B}^{c}\) is a summation of pure water backscattering coefficient \(\beta_{BW}^{c}\) and particulate matter backscattering coefficient \(\beta_{BP}^{c}\),  
\[\beta_{B}^{c}(\lambda)=\beta_{BW}^{c}(\lambda)+\beta_{BP}^{c}(\lambda) \tag{18}\]  
Transmission map due to backscattered signal may be derived from Equations (3) and (18) as follow,  
\[t_{B}^{c}(x)=e^{-\beta_{B}^{c}\widetilde{d}(x)}\;\;\;\;\;c\in\{r,g,b\} \tag{19}\]  
Spectral attenuation coefficients of the direct \(\beta_{D}^{c}\) and backscattered \(\beta_{B}^{c}\) signals may be used to estimate the raw transmission map, using Equation (14). The estimated transmission is then further refined by using a guided filter [26], instead of soft matting [11], because of its better refinement properties.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Wavelength** & \(\beta_{BW}^{c}\)**(\(\lambda\)) [25]** & \(\beta_{BP}^{c}\)**(\(\lambda\)) [24]** & \(\beta_{B}^{c}\)**(\(\lambda\))** \\ \hline
450 nm & \(2.2\times 10^{-3}\) & \(1.75\times 10^{-2}\) & \(1.97\times 10^{-2}\) \\
540 nm & \(1.0\times 10^{-3}\) & \(1.26\times 10^{-2}\) & \(1.36\times 10^{-2}\) \\
620 nm & \(0.6\times 10^{-3}\) & \(1.33\times 10^{-2}\) & \(1.39\times 10^{-2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Determining the total backscattering attenuation coefficient \(\beta_{B}^{c}(\lambda)\).  
Scene radiance recovery involves the use of the estimated background light and transmission map to form the final scene radiance. The refined transmission map is used in Equation (2) to acquire the final restored image.
## 3 Results and Discussion
### The Protein Fraction of MI Silages  
The dynamics of the protein fractions of ML silages are shown in Table 2. Proteolysis is a common phenomenon in silage and is the main factor affecting the protein quality of silage [7]. In the first stage, under the action of plant proteolytic enzymes, most of the proteins are degraded into non-protein nitrogen, such as free amino acids, oligopeptides and dipeptides, then these hydrolysis products are further converted to ammonia-N by microorganism activities [7]. When the ensiling time was prolonged, the content of non-protein nitrogen increased (\(p<0.01\)) from 122 g/kg TN to 380 g/kg TN, which indicated extensive proteolysis in the ML silage. The increase in non-protein nitrogen indicated a decline in the nutritional value of silage proteins, considering that the utilization efficiency of non-protein nitrogen in ruminants is lower than that of true protein [30]. Thus, inhibitors, which could inhibit protease and microbial activities, were necessary. After 30 days of ensiling, the highest non-protein nitrogen content was observed in the CK, whereas 2% CA decreased (\(p<0.05\)) non-protein nitrogen content. Similarly, the addition of CA in alfalfa silages showed a quite different non-protein nitrogen content which decreased with the addition of 0.5% CA after 60 days of ensiling [31]. Moreover, compared with the CK, the non-protein nitrogen content varied less and had a lower content (310 vs. 380 g/kg DM) in the silages treated with 2% CA, implying that the proteins could be preserved better and more stably in silages treated with 2% CA. Ammonia-N, reflecting the degree of deamination of amino acids and peptide, is a more precise index compared with non-protein nitrogen. Consistent with the non-protein nitrogen content, the content of ammonia-N increased (\(p<0.01\)) when the ensiling time was prolonged, which was related to the deamination of amino acids and peptides caused by microbial activities [8]. During ensiling, the content of ammonia-N decreased (\(p<0.01\)) in the silages treated with CA, indicating that CA inhibited the deamination of microorganisms. This inhibition could suggest that the amount of free amino acids, oligopeptides and dipeptides in non-protein nitrogen increased with the addition of CA, thereby improving the nutritive value of non-protein nitrogen. Similarly, [PERSON] et al. [31] observed that ammonia-N decreased from 132 g/kg TN to 31.7 g/kg TN with the addition of 0.5% CA. Compared with the CK, the relatively low content of ammonia-N in the silages treated with CA might be due to direct acidification and the antimicrobial properties of CA inhibiting the growth of undesirable microorganisms, such as _Clostridium_ and _Enterobacter_, which is in accordance with the decrease in coliform bacteria counts [13]. Moreover, the
## 2 Materials and Methods
### Data Analysis
#### 2.6.2 Structural Alteration Index Across Forest Classes  
Based on field data, we estimated the Structural Alteration Index (\(SAI\)) for each site (see [65; 78] for details), a well-established proxy for ecosystem degradation previously tested in other forests in Argentina (e.g., [29; 58]). The \(SAI\) is a multivariate index that summarizes the structural and compositional deviation of the sampled forest relative to a non-disturbed \"reference\" site, previously defined for the study area [65; 78].  
To maintain a balanced ratio between variables and the number of plots, we selected variables for \(SAI\) calculation in each environment (foothill or plain) using a multivariate approach. Specifically, we conducted a Principal Components Analysis (PCA) on site-by-structure-composition variable matrices, retaining those variables that contributed the most to data variability (e.g., explaining PCA1 and PCA2) to calculate \(SAI\) (see S2, Table S2.1; Figures S2.1 and S2.2).  
The variables retained for \(SAI\) calculation included litter dry weight (ton-ha\({}^{-1}\)), bare soil (%), Vertical Heterogeneity Index (VHI) [58], Horizontal Heterogeneity Index (HHI) [58; 65], woody species seedling density (seedlings-ha\({}^{-1}\)), tree density (trees-ha\({}^{-1}\)), basal area (m\({}^{2}\).ha\({}^{-1}\)), and cover percentages of key species: _G. decoricians_, _C. etherebignana_, _P. praccox_, _N. flexuosa_, and _A. quebracho-blanco_ in the low (0-2 m) and medium (2-8 m) vegetation layers, as well as _L. divariata_ cover in both layers (see S2, Table S2.1; Figures S2.2 and S2.4). These variables describe the most important ecological characteristics of Arid Chaco Forest ecosystems, including vegetation physiogonomy, resilience, soil composition, and key woody species [20; 49].  
Based on multivariate matrices (sites \(\times\) selected structure and composition variables), \(SAI\) values for each site were computed using the Mahalanobis Distance (MD) [79] from the reference forest community (e.g., well-preserved forest). Sample sizes were \(n=30\) for plains and n = 19 for foothills. The Mahalanobis Distance accounts for variable correlation and weights each variable by its variance, making it applicable across different scales and independent of measurement units [79]. The Structural Alteration Index (\(SAI\)) was calculated as follows:  
\[SAI=\left[(MD_{i}\times 100)\times\left(MD_{max}\right)^{-1}\right]SAI= \left[(MD_{i}\times 100)\times\left(MD_{max}\right)^{-1}\right] \tag{1}\]  
where \(MD_{i}\) is the Mahalanobis Distance between the \(i\)-th site and the reference site, and \(MD_{max}\) corresponds to the maximum registered \(MD\) value. Based on \(MD\), \(SAI\) integrates structural and compositional variables, accounting for variable correlation and scaling differences. \(SAI\) captures both the deviation of degraded sites from reference conditions and the multidimensional nature of ecosystem alteration. The inclusion of physiognomic and species-specific variables indicative of degradation strengthens the ecological relevance of \(SAI\) in the context of the Arid Chaco Forest [78].
## 3 PSSS Assessment Using Developed Efficient Probabilistic RES Model
### Stability Index Calculation  
Let \(\lambda_{o}=\alpha_{o}+j\omega_{o}\) denotes the \(o^{th}\) eigenvalue of the power system with real part \(\alpha_{o}\) and imaginary part \(\omega_{o}\). Damping factor \(\xi_{o}\) can be calculated as [32]:  
\[\xi_{o}=\frac{-\alpha_{o}}{\sqrt{\alpha_{o}^{2}+\omega_{o}^{2}}} \tag{5}\]  
Damping constant and damping factor are taken as output random variables in this paper, and their PDFs are calculated using the combined method of cumulant and Gram-Charlier expansion technique [2; 26]. The following stability indices are then defined, which provides information about the SSS margin [22]:  
\[P(\alpha_{o}<\alpha_{c})=\int_{-\infty}^{\alpha_{c}}f_{\alpha_{o}}(x)dx \tag{6}\]  
\[P(\alpha_{o}>\alpha_{c})=1-P(\alpha_{o}<\alpha_{c}) \tag{7}\]  
\[P(\xi_{o}<\xi_{c})=\int_{-\infty}^{\xi_{c}}f_{\xi_{o}}(x)dx \tag{8}\]  
where \(f_{\alpha_{o}}\) is the PDF of the damping constant for \(o^{th}\) eigenvalue, \(\alpha_{c}\) is the critical margin for the damping constant, \(\xi_{c}\) is the critical margin for the damping factor, and \(f_{\xi_{o}}\) is the damping-factor PDF for the \(o^{th}\) eigenvalue. Equation (7) provides probabilistic statistical information about SSS. A high value of this index indicates a low likelihood of a system being small-signal stable and vice versa. Similarly, Equation (8) provides the damping-factor probability of the \(o^{th}\) eigenvalue being less than the critical damping factor, and a lower value is desirable for this index. Both \(f_{\alpha_{o}}\) and \(f_{\xi_{o}}\) can be constructed with the aid of the Gram-Charlier expansion [2; 26]. More rigorous details on cumulant and PSSS theory can be found in References [2; 4; 8; 9; 10; 26; 29]  
Figure 3: Graphical illustration of PDF computation of PVG.
## 2 Materials and Methods
### Insecticidal Activity and Phytotoxicity Assessment of EF Fumigation in a Middle Size Vinyl House  
Insecticidal activity on SPW adults and phytotoxicity to yellow melon plants were evaluated in a 350 m\({}^{3}\) vinyl house (23 m \(\times\) 3.8 m \(\times\) 4.0 m) at Gyeongsang National University, Jinju, South Korea. Ninety-six yellow melon plants were transplanted 2 weeks after seeding into the vinyl house. Eight weeks after transplanting, the yellow melon plants were fumigated with EF 2.0 and 1.5 g m\({}^{-3}\) using portable vaporizers for 2 and 4 h under 29 \(\pm\) 1 \({}^{\circ}\)C, 90 \(\pm\) 10% RH. During this fumigation, we used four vaporizers because each vaporizer can cover only 100 m\({}^{3}\). Moreover, to measure the EF concentration, sample lines for EF gas sampling were placed onto 6 spots (3.0, 1.5, and 0.8 m above soil surface at two midpoints) in the vinyl house. After vaporization, the concentration was determined at 0.5, 1.0, 2.0, and 4.0 h by collecting the EF gas using an air pump (DOA-P704-AC, Gast Manufacturing, Inc., Benton Harbor, MI, USA) from each sampling spot into a Tedlar(r) bag. Next, the amount of EF gas was then measured using the same gas chromatography mentioned above. At the end of fumigation, the vinyl house was opened and then aerated for 1 h naturally.  
The effect of EF fumigation on SPW was evaluated. Twenty SPW adults were inoculated on a tobacco leaf disk in a breeding dish (4.5 cm diameter). In addition, six breeding dishes containing the whitefly adults were placed on the leaves of yellow melon at the six spots in the vinyl house before EF vaporization. The mortality of SPW adults was determined over 24 h after fumigation. It was considered dead if SPW adults did not move their appendages after touching a fine brush.  
Assessments of phytotoxic damage to the three different growth stages of yellow melon plants were done for up to 1 week after vaporization under 27 \(\pm\) 1 \({}^{\circ}\)C, 70 \(\pm\) 10% RH. The overall visible damage index, chlorophyll contents, and colors of leaves were measured using the same methods mentioned above. After the 2 and 4 h EF fumigation, the gates of the vinyl house were opened and ventilated. To check safety for workers (fumigators), the EF concentrations in the air of the entrance and exit gates of the vinyl house were monitored at 5, 10, 20, 30, and 60 min after ventilation. All measurements were repeated three times.