esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecasting Landslides via Three-Dimensional Discrete Element Modeling: Helong Landslide Case Study
## 4 Results and Discussions
### Post-Failure Analysis
The result showed that a lower friction coefficient led to a narrower deposit with a larger affected area and less depth. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Values from the** & **Values from the** \\  & **Shear Test** & **Landside Modeling** \\ \hline Number of particles & 3788 & 30872 \\ Radius (m) & 0.0025–0.01 & 0.5–2 \\ Particle density (kg/m\({}^{3}\)) & 2500 & 2500 \\ Friction between balls & 0.5 & 0.25 \\ Friction between balls and slip surface & 0.5 & 0.2–0.35 \\ \(E\) (MPa) & 20 & 20 \\ \(\
u\) & 0.4 & 0.4 \\ \(\overline{\sigma}_{c}\) (Pa) & \(2\times 10^{6}\) & \(2\times 10^{6}\) \\ \(\overline{\tau}_{c}\) (Pa) & \(1\times 10^{6}\) & \(1\times 10^{6}\) \\ \(\overline{k}_{n}\) (N/m\({}^{3}\)) & \(8\times 10^{10}\) & \(2\times 10^{8}\) \\ \(\overline{k}_{s}\) (N/m\({}^{3}\)) & \(4\times 10^{10}\) & \(1\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: DEM modeling numerical parameter values. Figure 10: Three-dimensional DEM. Understanding the dynamic landslide process is important. Here, we selected a friction coefficient of 0.25 as an example for analysis, and the result is shown in Figure 12. The whole potential landslide process lasted 25 s. To obtain detailed information about the run-out process, seven groups of balls were traced and their positions and velocities were recorded in every step (Figure 13a). The sliding mass experienced two stages: acceleration and deceleration (Figure 13b). The period of acceleration ranged from 0 to 15 s, whereas the deceleration occurred from 15 to 25 s. The maximum velocity of the sliding mass was 12 m/s, and the general velocity ranged from 4 to 10 m/s. The velocity of the upper mass was generally higher than the lower mass due to topographical factors. Combined with the swelling phenomenon at the foot of the slope, the potential landslide was a thrust type. Figure 11: Simulation results with friction coefficients of (**a**) 0.35, (**b**) 0.3, (**c**) 0.25, and (**d**) 0.2 to illustrate the final deposition areas.
# Real-Time Monitoring of Wind Turbine Bearing Using Simple Neural Network on Raspberry Pi
## 3 Experiment  
### Dataset  
There are many acceleration sensors on the generator bearing of a wind turbine. The data used in the experiment were the signal of one of the acceleration sensors. The data were collected from the turbine generator of a company. The vibration data were collected at 25,600 samples/second and a collection time of 3 s.  
The generator is a device that converts mechanical energy into electrical energy. It usually consists of a rotor and a stator, where the rotor is supported by bearings rotating on a shaft. The generator bearing is an important component that supports the rotor, and its proper functioning is critical to the performance and reliability of the generator. It is shown in Figure 4a.  
In order to monitor the health of generator bearings, acceleration sensors are installed on the bearings. An acceleration sensor is a device that measures the acceleration of an object. When a generator is in operation, the bearings are subjected to a variety of forces and vibrations that cause the bearings to change in acceleration. The acceleration sensor senses and measures these changes and converts them into a digital signal. It is shown in Figure 4b.  
Important information about generator bearing vibration can be obtained by analyzing the data collected by the acceleration sensor. Vibration data can include parameters such as the vibration amplitude, the frequency, and the time variation. These data are critical for monitoring the health of the bearing. Abnormal vibration data may indicate bearing failure or wear, such as loose bearings, overheating, or damage. By detecting these problems in time, the safety monitoring system can take appropriate repair and maintenance measures to prevent further damage or failure from occurring.  
Figure 3: (**a**) Acceleration sensor, (**b**) data logger, (**c**) Raspberry Pi board, and (**d**) performance evaluation.  
Therefore, acceleration sensor data acquisition is crucial for generator bearing monitoring and maintenance. It provides valuable information to better manage and optimize the generator system, ensuring its proper operation and extended service life.  
Two different states are Normal and Fault. Each state has 9600 segments in testing, 2400 segments in training, and each segment has 64 samples; details are shown in Table 1.
## 3 Results and Discussion
### PCA Analysis  
Figure 2 shows the result of PCA analysis. First principal component explained 35.26% of the total variance, while the first two components explain over 56% of the variance. The first component was strongly positively related to WG and negatively related to DS, and the second component was strongly positively related to DP and negatively related to SP. The flours from grain from CONV have high positive values of the first and second principal components, so they had high values for features strongly positively correlated with those of principals. Generally, the flours from the grain of cultivars growing in CONV had highvalue of WG and GI, which indicates their very good baking value. For flours obtained from grain of cultivars growing in ORG and INT, we observed high values of DS and low values for traits such as WG, AC, FN, and WA. The PCA analysis confirms the correlations we observed in Figure 1; in PCA, we observe the same relationships between variables that were observed in the correlation analysis. Figure 2: PCA analysis for study traits. Abbreviations: AC—ash content; PC—protein content, WG—wet gluten; GI—gluten index, FN—falling number, WA—water absorption; DT—development time; SD—stability; DS—degree of softening; DN—quality number; TBL—total baking loss; DY—dough yield; BY—bread yield; BV—bread volume; DP—Dallman porosity; CD—crumb density; RS—resilience; SP—springiness; HD—hardness; ORG—organic; INT—integrated; CONV—conventional.
## Materials and Methods
### Datasets and Analysis
#### 2.2.3 Drought Indicator  
We used the self-calibrating Palmer Drought Severity Index (scPDSI; [53]) to assess the drought severity over the study region. The scPDSI indicates the variations in soil moisture, terrestrial water storage, and runoff [54; 55]. Because of the self-calibration procedure, the scPDSI improves the original PDSI by maintaining the consistent behavior of the index over diverse climatological regions, which makes spatial comparisons over wide regions more meaningful. Additionally, we used the climatic water deficit (Precipitation - Potential Evapotranspiration; P - PET) to account for the potential spatio-temporal mismatch of standardized scPDSI values and actual water shortage [56]. The PET and precipitation values, as well as the scPDSI dataset used in this study, were based on climatological data from the ERA-Interim reanalysis [57] and soil water holding capacity values from the digital world soils map of the Food and Agriculture Organization of the United Nations [58]. We selected the scPDSI dataset because it has already been successfully used to quantify drought conditions in the Amazon basin area [4].  
scPDSI drought severity is categorized at the pixel level depending on the index value. From one extreme to the other, the scPDSI follows a standard normal probability distribution function, where \(-4\) and \(+4\) values are extremely rare events [59]. Similar to previous studies employing the scPDSI [4; 53], we define two drought levels from scPDSI and focus on them. These levels are dry and normal, with five relevant scPDSI thresholds and drought classes (Table 2). This study defines drought affected sub-regions as areas with an scPDSI \(<-2\) and P - PET \(<0\) and non-drought as areas with an scPDSI between \(-1.0\) and \(1.0\) with P - PET \(\geq 0\).
# Responses of Seasonal Indicators to Extreme Droughs in Southwest China
## 1 Introduction
In addition, seasonal indicators are important, because any change of seasonality made by drought could have large impact on ecosystems [39]. Research on seasonal indicators and drought periodicity will provide theoretical basis for formulating drought mitigation plans and improving disaster reductionpractice, particularly with the exacerbating extreme weather in the coming decades [40; 41]. However, until now, the responses of seasonal indicators including vegetation phenology and seasonal variations of surface water to drought are still unknown. Vegetation phenology represents the key trait of an ecosystem, and its resistance to drought can estimate the stability of an ecosystem and reflect the feature of droughts [42]. Furthermore, droughts can influence the area and volume of surface water and the biological processes in aquatic ecosystems [9; 43]. Seasonal surface water plays an important part in drought process. Moreover, the surface water of SWC plays an important role in people's life for living and agriculture, etc. Thus, the changes of surface water could be strongly correlated to the drought. The new data of surface water area from long-time series of satellite images have been released due to the development of big data and cloud computing. The new data give us the new opportunity to fill the knowledge gap of seasonal indicators of droughts in SWC. This study made the effort to apply seasonal surface water and vegetation phenology as seasonal indicators to analyze the responses of vegetation and surface water to different droughts in SWC. Meteorological data, social statistical data and PDSI were also used to analyze the droughts in SWC from 2000 to 2017. The main aims of this study are to: (1) Analyze the responses of seasonal indicators to droughts in SWC; (2) Compare the difference of responses between seasonal indicators.
## 4 Experimental Validation  
The reliability of the damage indicator can be verified in two ways. Firstly, it is analyzed whether the texture characteristics characterized by the feature parameters are consistent with the development of the damage. Secondly, multiple sets of experiments were performed, and 30 images of continuous lesion areas were randomly acquired for data verification.
# Biochar-Improved Growth and Physiology of _Eltretia asperula_ under Water-Deficit Condition
## 3 Results
### Effect of Biochar on Growth Characteristics
#### 3.1.2 Effect of Biochar on Plant Height and Number of Leaves  
Water-stress conditions significantly reduced the plant height and number of leaves of _Eltretia asperula_ plants in all without WB and WB treatments. However, WB treatments showed a significant increase in the plant height and number of leaves of _Eltretia asperula_ plants in both non-WSC and WSC. The lowest values of the plant height and number of leaves of _Eltretia asperula_ plants were recorded with the without-WB treatment under both non-WSC and WSC. The highest values of plant height of _Eltretia asperula_ plants were recorded with the 15 tons ha\({}^{-1}\) WB treatment but were not statistically different with plants in the 20 tons ha\({}^{-1}\) WB treatment under both non-WSC and WSC. The highest values of the number of leaves of _Eltretia asperula_ plants were recorded with the 15 tons ha\({}^{-1}\) WB treatment but not statistically different with plants in the 20 tons ha\({}^{-1}\) WB treatment but not statistically different with plants in the 20 tons ha\({}^{-1}\) WB treatment under WSC (Table 3).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Biochar Rates** & **Treating** & **Plant Height** & **Leaf Number** \\
**(tons ha\({}^{-1}\))** & **Conditions** & **(cm)** & **(Leaves)** \\ \hline \multirow{2}{*}{0} & Non-WSC & 36.10 bc & 24.20 c \\  & WSC & 27.26 e & 19.40 d \\  & Non-WSC & 39.08 b & 26.40 c \\
5 & WSC & 31.46 d & 24.00 c \\  & Non-WSC & 45.36 a & 32.40 b \\
10 & WSC & 32.68 cd & 24.60 c \\  & Non-WSC & 47.50 a & 41.20 a \\  & WSC & 38.96 b & 26.80 c \\  & Non-WSC & 44.90 a & 35.80 b \\
20 & WSC & 37.45 b & 25.00 c \\ \hline \end{tabular}
\end{table}
Table 3: Effect of biochar on plant height and number of leaves of _Eltretia asperula_ under non-WSC and WSC.  
Figure 1: Growth rate of the plant height of _Eltretia asperula_ in response to biochar treatments in concentrations of 0 ton ha\({}^{-1}\) (**A**), 5 tons ha\({}^{-1}\) (**B**), 10 tons ha\({}^{-1}\) (**C**), 15 tons ha\({}^{-1}\) (**D**), and 20 tons ha\({}^{-1}\) (**E**) under the non-WSC and WSC. Vertical bars represent mean \(\pm\) SD, \(n=15\).
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 2 Hardware Architecture
### Packaging and Connection  
To emphasize its ability, a satellite-based wireless sensor was assembled in a waterproof package with all components integrated. The fully assembled wireless node pictured in Figure 1b was connected using a series of wires carrying power and signals. A 200 mm (length) \(\times\) 150 mm (width) \(\times\) 100 mm (height) single enclosure with a clear door was employed as the enclosure. The power bank was fastened to a plastic board of the enclosure to directly power the Raspberry Pi computer. The accelerometer sensor was directly connected the I\({}^{2}\)C ports from the GPIO pins of the Raspberry Pi 3 Model B+, and the satellite device was connected to the computer via a USB port using the UART communication interface. Both were two-way communications to ensure the implementation of sending measurements and receiving commands. A highly sensitive accelerometer was mounted on the floor of the enclosure to measure the ground motion. By using an IP66 level plastic outdoor enclosure and six 5 gram silica gel desiccant packages, the whole packaging was waterproofed to ensure the functionality of electric components under severe environments. The entire packaged wireless sensing node is demonstrated in Figure 1c.
## 3 Results and Discussion
### Influence on VOOs Quality and Composition  
#### 3.2.1 Quality Parameters  
According to oil basic quality parameters (FFA, PV, K\({}_{232}\), K\({}_{270}\), \(\Delta\)K), all investigated samples were in the range established for the EVOO quality category (Table 4).  
In respect of FFA, a quality parameter related to hydrolytic deterioration of oil, only a slight decrease was noted in the T\({}_{3}\) sample from 2019. Irrigation had a positive effect on PV value, a parameter related to the primary oxidation of oil, and a slight decrease of PV was detected in both years in all irrigated treatments. However, this observation was especially marked in the T\({}_{2}\) and T\({}_{3}\) treatments. For the parameters connected to the secondary oxidation of oil, irrigation had no effect on K\({}_{232}\), while a converse effect on K\({}_{270}\) values was determined in 2019 and 2020.  
[PERSON] et al. (2016) found that quality parameters of olive oil did not allow identifying a clear pattern of variation in respect of soil water availability. FFA was not affected by irrigation of Frantoio cv. trees, while PV and K\({}_{232}\) values decreased with increase of water deficit but only in one year of the three year investigation ([PERSON] et al., 2019). Conversely, [PERSON] et al. (2019) reported the higher value of FFA in irrigated Chemlali oil samples compared to rainfed  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**2019**} & \multicolumn{6}{c}{**2020**} \\ \cline{2-10}  & **C** & **T\({}_{1}\)** & **T\({}_{2}\)** & **T\({}_{3}\)** & **C** & **T\({}_{1}\)** & **T\({}_{2}\)** & **T\({}_{3}\)** & **EVOO \({}^{*}\)** \\ \hline FFA \% & \multirow{2}{*}{\(0.20\pm 0.00\,^{*}\)} & \multirow{2}{*}{\(0.19\pm 0.01\,^{\text{a}}\)} & \multirow{2}{*}{\(0.18\pm 0.01\,^{\text{a}}\)} & \multirow{2}{*}{\(0.17\pm 0.01\,^{\text{b}}\)} & \multirow{2}{*}{\(0.19\pm 0.00\,^{*}\)} & \multirow{2}{*}{\(0.19\pm 0.01\,^{\text{a}}\)} & \multirow{2}{*}{\(0.18\pm 0.01\,^{\text{a}}\)} & \multirow{2}{*}{\(0.18\pm 0.01\,^{\text{a}}\)} & \multirow{2}{*}{\(0.18\pm 0.01\,^{\text{a}}\)} & \multirow{2}{*}{\(0.18\pm 0.01\,^{\text{a}}\)} & \multirow{2}{*}{\(\leq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\)\(\geq\geq\)\(\geq\geq\)\(\geq\geq\)\(\geq\geq\)\(\geq\geq\)\(\geq\geq\)\(\geq\geq\)\(\geq\geq\geq\)\(\geq\geq\geq\)\(\geq\geq\geq\)\(\geq\geq\geq\geq\geq\)\(\geq\samples. [PERSON] et al. [46] did not find any effect of irrigation on FFA or spectrophotometric indices of Arbequina olive oils during a three year study period. Several studies also reported negligible or no effect on oil quality parameters (FFA, PV, K\({}_{232}\), K\({}_{270}\)) with respect to irrigation [15; 47; 48; 49].
## 2 Retrofitting Method Using Viscous Dampers
### Design Method
#### 2.2.1 Step1: Initial Placements of Dampers to Satisfy Code-Specific Requirements  
Code-specific requirements under rare earthquakes are used in the initial design of dampers. The inter-story drift ratio of story \(I\) (\(\delta_{i}\)) is used as the requirement, by considering that \(\delta_{i}\) should be less than 1/50 for RC frame under rare earthquakes [3]. In Region 8, design intensities are PGA = 0.07 g, 0.2 g, and 0.4 g for frequent, occasional and rare earthquakes, respectively. Note that Step 1 can be implemented with any procedure provided that the design requirements are met. The following procedure is suggested:  
1. For each GM, scale PGA to 0.4 g. Select a layout pattern of the damper placement; e.g., the pattern shown in Figure 3.
2. Use a uniform placement from a small damping coefficient, e.g., \(C_{i}\) = 20 kN\(\cdot\)(s/m), as shown in Section 2.1 or from any other value based on the designer's experience.
3. Calculate \(\delta_{i}\) for each story. If \(\delta_{i}\leq\) 1/50 for any story, the initial placement of dampers satisfies code-specific requirements; stop the procedure, otherwise, increase the damping coefficients \(C_{i}\) until the requirement \(\delta_{i}\leq\) 1/50 is met for story \(i\). This procedure is performed until \(\delta_{i}\leq\) 1/50 for any story. In such a way, \(C_{i}\) of each story is determined.  
Figure 3: Placement layout of viscous dampers in the structure (planar frame for illustration).  
Figure 2: Selected GMs compatible with the design spectrum.  
The use of the above procedure results in 7 schemes for initial placement patterns of dampers corresponding to the 7 GMs listed in Table 3. Table 4 provides the initial placements of dampers to satisfy code-specific requirements. Large discrepancies are observed in both the distribution pattern of damping coefficients among stories and the total damping coefficient (sum of damping coefficients of all stories). This observation reveals that although the GMs are compatible with the design spectrum, different demands may arise from the uncertainties of GMs. For example, to satisfy code-specific requirements, a total damping coefficient of 4,170,000 N-(s/m) is needed for GM 3 while only 440,000 N-(s/m) is needed for GM 2.  
The incremental dynamic analysis (IDA) [42] is performed to check the collapse-resistant capacity of the frame after the initial placements of dampers. The structural collapse state is determined according to FEMA (Federal Emergency Management Agency) 350 [43], i.e., the 20% tangent slope method, which defines the point on the IDA curve with a tangent slope that reduces to 20% of the initial elastic slope as the collapse capacity point, or according to the inter-story drift ratio (IDR) is larger than 10%. The collapse-resistant capacities are \(S_{\text{a}}(T_{1})\) = 0.16 g, 0.14 g, 0.22 g, 0.28 g, 0.19 g, 0.19 g, and 0.22 g after retrofitting by the 7 schemes, with increases of only 0.01 g, 0.0 g, 0.08 g, 0.03 g, 0.07 g, 0.05 g, and 0.0 g with respect to the original frame.
## 3 Red Shift of HHG in Resonant Systems
### Four-Step Model of MHOHG in Resonant Systems
This four-step model is different from the four-step mechanism proposed by [PERSON] [28]. In the latter model, the resonant state is an autoionizing state embedded in the continuum. The sequence of the above two models is also different. In the latter, the electron is ionized from the ground state first, then oscillation in the laser field, transition the resonant state, and recombination to the ground state finally. Resonant MHOHG described by the above four-step model have novel features [20, 21], which do not occur in the three-step model of atomic HHG. For example, resonance enhanced harmonic generation, multiple frequency harmonic series, double plateau, and a larger cutoff energy. Another important feature presented in the inset of Figure 2 is the redshift of MHOHG above the ionization threshold. No such shift appears in the lower-order MHOHG in the perturbative regime with photon energy less than the ionization potential \(I_{p}\) since it is of pure molecular bound state character [29]. It clearly shows that the red shift is a nonadiabatic effect. To understand the red shift in detail, we explore the role of EE and enhanced ionization (EI) [30, 31] in MHOHG. The latter is a nonlinear characteristic effect of molecules in intense laser fields. We present the population of the resonant \(2\,p\sigma\) state and the total ionization rate \(\Gamma(t)\) as a function of time in Figure 4, from which one concludes that EE and EI are not synchronous. For laser intensity below the saturation threshold, the excitation to the localized resonant state \(2\,p\sigma\) on the RLE will strongly enhance the ionization rate of the system on the FLE. In this way, the red shift generated on the FLE will be larger than the blue shift on the RLE. An overall noticeable red shift can be observed in the MHOHG process. The time delay between EE and subsequent EI plays an important role in producing a noticeable \"redshift\" in MHOHG for very short pulses. Figure 2: **(a)** Molecular high-order harmonic generation (MHOHG) spectra of HeH\({}^{2+}\) in linearly polarized laser fields; **(b)** wavelet analysis of the harmonic generation. The insets in **(a)** are the enlarged areas of the MHOHG spectrum. The left inset shows the harmonics in the turtabative regime without shift. The right inset presents the harmonics near the cutoff with noticable red shift. The internuclear distance R = 4 a.u. is fixed. The initial state is the ground \(1s\sigma\) state. The laser polarization is along the \(z\) axis, _i.e._, the orientation angle \(\chi=0^{\circ}\). The peak laser intensity is \(I=3.5\times 10^{15}\) W/cm\({}^{2}\). The wavelength is 400 nm. The pulse has a \(\cos^{2}(\pi t/\tau)\) shape \(f(t)\) with a duration of 15 optical cycles as illustrated in **(b)**.
## 4 Discussion
### Effect of Boron Substitution on Density  
Table 1 presents density measurements for the glass series, highlighting the impact of boron substitution on glass density. The observed decrease in density with increasing boron concentration can be attributed to several factors: atomic radii, bonding characteristics, atomic mass. Regarding the atomic radii, boron (ionic radius: 0.23 A, covalent radius: 0.82 A) has smaller covalent and similar ionic radii compared to silicon (ionic radius: 0.26 A, covalent radius: 1.11 A). This difference allows boron to achieve a higher packing factor ratio, contributing to lower density with more boron substitution. Regarding bonding characteristics, we previously saw that boron forms three- and fourfold coordinate bond complexes, with four-coordinate bonds creating tetrahedral structures similar to silicon. This bonding flexibility affects the structural density of the glass. Lastly, boron's lower atomic mass compared to silicon further reduces the overall density of the glass as boron concentration increases, despite similar structural volumes.  
These factors collectively explain the trend of decreasing density with increased boron substitution in the glass series.