esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 4 Theoretical Model and Validation
### Calculation Flow of Cuttings Bed Height  
The height of the cuttings bed is represented as \(H\), the height of the cuttings is denoted as c, and the annular cuttings bed height is determined by the following calculation: (1) if the sliding condition of the bottom single layer of cuttings is met, the height of the cuttings bed is designated as \(H=0\); if not, a cuttings bed will be established at the bottom of the annulus, and the height of the cuttings bed is designated as \(H=c\).  
(2) As cuttings are continuously generated and migrate, they eventually settle on the surface of the bottom cuttings bed. Assuming orderly arrangement, if the flow rate at the center of the upper cuttings meets the conditions for sliding, rolling, or the suspension of multi-layer cuttings, smooth transportation of the upper cuttings and an unchanged height of the cuttings bed can be achieved. If these conditions are not met, the cuttings will be deposited in the bottom cuttings bed, resulting in an increase in bed height by \(\Delta h=c\), and the height of the cuttings bed can be recorded as \(H=H+\Delta h\). (3) Repeat step (2) until the cuttings bed is satisfied. The cuttings starting condition and the height of the cuttings bed formed under the current flow condition are obtained (Figure 15). The calculation assumes that the cuttings are deposited in an orderly manner on the surface of the bed. The model also considers the cuttings' shape and size, as well as the flow dynamics within the annulus, to accurately predict the cuttings bed height under different operational conditions.  
Figure 15: Calculation flow of cuttings bed height.
## 2 Numerical Model
### Environmental Conditions  
Table 2 provides environmental conditions simulated in this study. Wave and current conditions are selected based on the real measurement data observed in southwest coast of South Korea for 2014-2016 as represented in Figure 2. Furthermore, fishing conditions for different wave heights are suggested in a guide [29]. According to the guide, fishing is prohibited when the wave height is higher than 5 m. From the guide and real measurement data, wave heights for analyses were determined. Current velocity at free surface is from 0.25 m/s to 1.5 m/s assuming that current velocity at bottom is 20% of current velocity at free surface. Wave and current directions are assumed to be perpendicular to the net entrance, i.e., collinear environments and net. Simulation time is 30 min for each case. Figure 3 provides an example case of generated time histories of sea surface from JONSWAP wave spectrum at significant wave height of 1.0 m, and the comparison of the corresponding input and regenerated wave spectra for verification.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & **Wave Condition** & & \\ \hline Significant wave & & 1.0 & 2.5 & 3.0 & 5.0 \\ height, \(H_{\text{s}}\) (m) & & 5.25 & 7.5 & 8.0 & 10.0 \\ Peak period, \(T_{p}\) (s) & & 1.0 & 1.0 & 3.3 \\ Enhancement & & & & \\ parameter, \(\gamma\) & & & & \\ \hline \multicolumn{5}{c}{**Current Condition**} \\ \hline Surface current & & & & \\ velocity (m/s) & & & 0.25, 0.50, 0.75, 1.00, 1.25, 1.50 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Environmental conditions for parametric study.  
Figure 2: Measured current velocity (**a**) significant wave height (**b**), and peak period (**c**) of southwest coast of South Korea in years of 2014–2016.
# A Survey on Fault Diagnosis of Rolling Bearings
## 4 Rolling Bearing Fault Type Recognition
### Deep Learning Based Fault Type Recognition Methods
The results demonstrated that the proposed method could extract features adaptively from bearing vibration signals regardless of the speed or load changes. [PERSON] et al. [152] used a denoising AE to extract features from the bearing original vibration signals and inputted the extracted features to the BP network classifier. DBN is formed by stacking multiple restricted Boltzmann machines (RBMs), where the output layer of the former RBM is used as the input layer of the latter RBM. These RBMs are trained in a greedy hierarchical manner and can gradually learn expressive features from the data. [PERSON] et al. [153] used the directional gradient histogram of the vibration signals as input features to the DBN model for bearing fault recognition. In [154], the time-domain and frequency-domain features extracted from the different sensor signals were fused as the machine health indicators through a multiple two-layer sparsity AE and used to train a DBN for further classification. [PERSON] et al. [155] developed a novel rolling bearing fault recognition method called continuous DBN with locally linear embedding, which computes a new comprehensive feature index based on locally linear embedding to quantify rolling bearing performance degradation and uses a GA to optimize the DBN parameters for adapting to the signal characteristics. Considering that the rolling bearing vibration signal is essentially a time series, RNNs with time memory functions have gradually attracted attention. RNNs can effectively analyze and process the time information of the data by establishing the connection between multiple cycle units and mapping the whole history of the input data to the target vector. To address the long-term dependency, improved methods of RNNs were developed, such as long-short-term memory (LSTM) and gated recurrent units (GRUs), which are more effective for bearing fault recognition [156; 157; 158]. [PERSON] et al. [156] investigated the performance of RNN, LSTM, and GRU in fault diagnosis, finding that LSTM performed the best and the ensemble of RNN, LSTM, and GRU could not enhance its performance. [PERSON] et al. [157] developed convolutional bi-directional LSTM combining CNN and LSTM, where CNN extracted the robust local features from original signals and LSTM encoded temporal information on the outputs of CNN. [PERSON] et al. [158] constructed a deep GRU for effectively learning features of bearing vibration signals and applied the artificial fish swarm algorithm to obtain the optimal parameters of the GRUs.
# UAV IoT Framework Views and Challenges: Towards Protecting Drones as \"Things\"
## 2 Overview on UAVs as Members of IoT  
### UAVs for Wireless Networks  
#### 2.1.1 Use Cases for Wireless Networking with UAVs  
The use of UAVs as key entities of next-generation wireless networks constitutes one of the most promising applications of the corresponding technologies. A number of promising use cases are thoroughly detailed in Reference [10] and presented below.  
* UAV-carried flying base stations that complete heterogeneous 5G systems to enhance the coverage and capacity of existing wireless access technologies.
* UAV-based aerial networks that allow reliable, flexible, and fast wireless connections in public-safety scenarios.
* UAVs that support terrestrial networks for disseminating information and enhancing connectivity.
* UAVs as flying antennas that can be deployed on demand to enable mmWave communications, massive MIMO, and 3D network MIMO.
* UAVs that are used to provide energy-efficient and reliable IoT uplink connections.
* UAVs that form the backhaul of terrestrial networks to allow agile, reliable, cost-effective, and high-speed connectivity.
* UAVs able to cache popular content and efficiently serve mobile users by following their mobility patterns.
* UAVs that act as users of the wireless infrastructure for surveillance, remote-sensing, and virtual-reality cases, and package-delivery applications.
* UAVs that collect vast amounts of city data and/or enhance cellular network coverage in a smart-city scenario.
## 3 Discussion
### Comparison of the Scaling Effect between Closures  
The aim of this study was to determine if closure permeability, such as the type of closure, was impacting VSC flavour scalping in model and Shiraz wines.  
In both matrices, no effect of closure permeability was observed at any time of analysis, except in the Shiraz wine for the sum of VSCs at 6 h of stirring, where C1 had a higher concentration scalped compared to C4. This could mean that the adsorption kinetics could be faster with the tightest closure. Yet, after 3 and 7 days, there were no significant differences between closures, meaning that the permeability of micro-agglomerated cork closures could not influence the scalping phenomenon of VSCs during wine storage.  
Other works suggested that flavour scalping could be the cause of a difference in DMS concentration of several Shiraz wines bottled with closures with a high OTR difference [20]. This experiment presented here showed that this hypothesis could probably not explain the differences of DMS levels in these wines.  
Surface treatment on closures was also checked for flavour scalping. Indeed, micro-agglomerated closures usually receive a surface treatment to allow the material to adhere to the glass surface. As different kinds of treatments were used on the closures investigated above, the analysis of the same closure processed with different surface treatments (T1, T2, T3, and T4) was performed to check their influence on flavour scalping. In this experiment, four different surface treatments commonly used in the industry were studied, as well as closures with no surface treatments. Closures C1 and C2 were studied in a model wine (Figure S2), and closure C1 was also monitored in a Shiraz wine (Figure S3), after 3 and 7 days of stirring to reach the equilibrium. For the total amount of VSCs, as well as for each of them, surface treatment was not a significant factor for the scalping phenomena, despite the conditions considered.
## 4 Results
### Case Study of PLS-MARS Method
#### 4.2.3 Comparison of the PLS-MARS Model with Typical Prediction Models
Although the PLS and SVM models had relatively higher simulation and prediction accuracies, their model accuracies were lower than the PLS-GWR and PLS-MARS models that considered the local difference. The Moran's I indexes in Table 1 indicate that both the PLS-MARS model and PLS-GWR model could maintain the spatial heterogeneity distribution characteristics of data. In addition, random spatial residuals without autocorrelation could further verify the effectiveness of the PLS-GWR model and the PLS-MARS model. Compared with the PLS-GWR model, the PLS-MARS model, which considered the nonlinear relationship between SOM content and VNIR spectra, had the highest simulation and prediction accuracies among the prediction models. Since PLS-GWR and PLS-MARS were constructed based on the same PCs, their AICc values were comparable. Nevertheless, the AICc values indicated that the proposed PLS-MARS model had higher model quality than the PLS-GWR model. In summary, the PLS-MARS model calibrated by the samples obtained utilizing the MVARC-R-KS method indicated immense potential for building a prediction model of SOM content based on the VNIR spectra in the riverside region.
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 1 Introduction
### Satellite-Based Monitoring with GRACE  
The Gravity Recovery and Climate Experiment (GRACE), launched in 2002, and its successor, GRACE Follow-On (GRACE-FO), developed by NASA and the German Aerospace Center (DLR), provide a powerful tool for monitoring GWS changes [2,3]. These satellites measure global gravitational field variations, enabling accurate estimates of water storage at regional and national scales. Over the past two decades, GRACE data have been used to assess groundwater fluctuations in regions such as the United States [4], Northern India [5], East Africa [6], and the Indus Basin in Pakistan [7], demonstrating the method's reliability and scientific value [8, 9, 10, 11]. This approach offers a scalable solution for studying groundwater in complex hydrogeological settings.
## 2 Results and Discussion
### The Effect of Solvent in an Asymmetric DA Reaction Catalyzed by ICCC 6
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Entry**} & \multirow{2}{*}{**Solvent**} & \multirow{2}{*}{**Time (h)**} & \multicolumn{5}{c}{**9**} \\ \cline{3-6}  & & & **Yield (\%)**b & _Exo/Endo_b & _exo (Exo) (\%)_c & _ee (Endo) (\%)_c \\ \hline
1 & THF & 60 & 18 & 51/49 & 57 & 67 \\
2 & CH2 Cl2 & 60 & 25 & 50/50 & 61 & 68 \\
3 & CH3 CN & 60 & 29 & 52/48 & 66 & 74 \\
4 & MeOH & 38 & 91 & 56/44 & 88 & \textgreater{}99 \\
5 & H2O & 60 & 97 & 52/48 & 82 & \textgreater{}99 \\
6 & Dry MeOH & 38 & 92 & 44/56 & 85 & 94 \\
7 & 95/5 THF/H2O & 60 & 87 & 52/48 & 85 & 97 \\
8 & 95/5 CHCN/H2O & 60 & 96 & 53/47 & 81 & 92 \\
9 & 95/5 MeOH/H2O & 38 & 99 & 54/46 & 92 & \textgreater{}99 \\
10 & 85/15 MeOH/H2O & 50 & 99 & 56/44 & 91 & 99 \\
11 & 75/25 MeOH/H2O & 62 & 99 & 50/50 & 89 & 98 \\ \hline \hline \end{tabular}  
* Reactions were conducted with **7** and **8** (3 equiv.) at 25 \({}^{\circ}\)C using **6d10B** (10 mol %). b By 1H NMR spectroscopy.
* By GC analysis (CHIRALDEX \(\beta\)-PH).  
\end{table} 
Table 3: Asymmetric DA reaction catalyzed by ICCC 6 a.  
higher reactivity and enantioselectivity than **12d20C** (entry 6 versus entry 5). The enantioselectivity was comparable to the corresponding model and linear-type polymeric catalysts. The improvement of the yield was observed when low-crosslinked ICCC **6d10B** was used due to the increase of the accessibility of substrates to the catalytic site (entry 7 versus entry 6) (Figures S19 and S20). The effect of corona length on the catalytic performance was also examined. Both the yield and enantiomeric excess were decreased when **6d10B-200** was used (entry 8). This result indicated that the increase of steric hindrance near the catalytic site significantly affected the catalytic performance. We also surveyed the effect of the immobilization method. The covalently core-corona polymer nanosphere-immobilized MacMillan catalyst **13** developed by Z. Fu group afforded _endo_-enriched **9** in 95/5 CH\({}_{3}\)CN/H\({}_{2}\)O, and the catalytic activity was lower than that of **6d10B** (entry 9) [38]. These results clearly indicated that the combination of the core-corona structure and ionic immobilization was critical to increasing the catalytic activity. The asymmetric reaction between (_E_)-2-methoxycinnamaldehyde and **8** afforded the Diels-Alder adducts **14** (Figures S13-S15) with 99% yields with 91% ee (_exo_) and 88% ee (_endo_) (Figure 4, Figures S21 and S22). The stability of ionic bonding between phenylsulfonate and chiral imidazolidinonium was also checked. We carefully checked the reaction mixture after the reaction, and no leaching of [PERSON] catalyst was observed by TLC and elemental analysis. Finally, the core-corona catalyst used in entry 7 was easily and quantitatively recovered by centrifugation and could be reused twice with maintaining the catalytic performance (entry 10 and 11).
## 3 Results
### Work Stability and Movement Pattern  
The analysis of work stability and movement patterns was conducted using data collected from IMU sensors positioned at the participants' waist and ankle locations. These sensors captured triaxial acceleration and angular velocity at a 100 Hz sampling rate, providing detailed insights into body movements and postural dynamics during task execution. This section presents the findings related to motion stability and movement characteristics across different task types and intensity levels.
## 2 Models and Data
### Radiative Transfer Model: AccuRT  
AccuRT is an accurate, efficient, and easy-to-use radiative transfer simulation tool that can be used to generate radiance and irradiance data at user-defined vertical locations in a coupled atmosphere-water system [27]. Note here that \"water\" refers to both the liquid and solid phase (snow and ice). The physical properties of each of the two slabs that constitute the coupled system, the radiative energy input at the top of the upper slab (TOA), and the boundary conditions at the bottom of the lower slab (water bottom) must be specified. To facilitate these specifications, AccuRT uses a set of radiatively significant constituents called \"materials\" that account for the wavelength dependence of inherent optical properties (IOPs). For our simulated dataset, we include the _earth_atmospheric_gases_, _aerosols_, and _snow_ materials in our main configuration file. The wavelength range was specified to be 380-2500 nm with 10 nm spacing to meet the needs of NASA's Ames GlobalHyperspectral Synthetic Data [28]. Once the input parameters have been specified, the AccuRT code will solve the radiative transfer equation and provide irradiances at desired vertical locations as well as radiances at desired vertical locations and in desired directions in the coupled atmosphere-snow system. In our case, the desired location for irradiances is the top of the snow layer, where the ratio of the upward and downward spectral irradiance is used to compute the spectral snow albedo. The input solar spectrum at the top-of-the-atmosphere (TOA) is ATLAS-3 (shifted to air wavelengths) for 200 nm to 407 nm, ATLAS-2 for 407.8 nm to 419.9 nm, and MODTRAN (v3.5) for 419.9 nm to 800 nm [29; 30]. Between 200 and 800 nm, the solar irradiance has a spectral resolution of 0.05 nm. For wavelengths of 800 nm, the ASTM G173-03 [31] spectrum is adopted with a spectral resolution of 1 nm between 800 nm and 1700 nm and 5 nm between 1700 nm and 4000 nm. For radiances, we want the upward radiance at the TOA in specified directions that would correspond to a desired sun-satellite geometry (solar zenith angle, observation polar angle, and relative azimuth angle) and snow grain size and impurity concentration. A pixel in a satellite image obtained over a snow surface is defined by these five parameters.
# Efficient Zero-Sequence Impedance Measurement in Autotransformers Using Low-Voltage Excitation
## 4 Simulation Verification  
### Double-Winding Autotransformer One-Phase and Two-Phase Disconnection Test  
The method in this paper can be applied to the measurement of the zero-sequence impedance of different autotransformers. Low-voltage field tests can be carried out using common commercial UPS power supplies with a rated voltage of 380 V and a power size of 500 kVA. To verify the practicality of the proposed method, a simulation model of a 31.5 MVA 220/66 kV three-phase, three-column YNa-type autotransformer was constructed in MATLAB/Simulink 2018b. The parameters of the testing transformer are shown in Table 2. Simulation verification of one-phase and two-phase disconnection tests was carried out, and the results are illustrated in Tables 3 and 4.  
Figure 18: The measurement process of the zero-sequence impedance.  
In accordance with the pertinent national standards for oil-immersed transformers, the positive-sequence short circuit impedance \(Z_{\text{HL-1}}\) in Equations (7), (9) and (11) can be assumed as 11%. Additionally, the values of \(V\) and \(I\) used in calculating the zero-sequence impedance can be taken as the averages of \(V_{ab}\) and \(V_{ca}\), and \(I_{ab}\) and \(I_{ca}\), respectively. Comparing the calculated results with the standard values, the error for the one-phase disconnection test on the high-voltage side is 1.88%; for the one-phase disconnection test on the low-voltage side, it is \(-\)2.01%. In the case of the two-phase disconnection test, the errors are 0.73% and 1.15% on the high- and low-voltage sides, respectively.  
As evident from the provided table, the proposed disconnection test method demonstrates a higher accuracy in calculating the zero-sequence impedance of a double-winding autotransformer. In comparison to the standard measurement method, the error in calculating the zero-sequence impedance of the transformer using the low-voltage disconnection test is less than 2%.
## 3 Results
### Current and Signal Modulation  
Experimental results have demonstrated that the electronic board is capable of modulating current injection in the tested configuration, thereby affecting the EL signal itself. Figure 4 shows three oscilloscope measurements monitoring the current in the tested PV module using the electronic board for self-powered modulation. Some noise is observed, particularly in the half- and full-sine modulations. This noise could be reduced by using a higher PWM frequency, employing a low-pass filter with a lower cutoff frequency, or increasing the execution speed of the code, which allows changing the duty cycle of the MOSFETs a higher number of times per second. However, the noise will be partially or entirely filtered by the camera, as it integrates the signal during the exposure time of each capture.  
Once the image sequence has been acquired, the evolution of the average pixel intensity throughout the sequence can be analyzed. Figure 5 illustrates this evolution during the measurement process with different modulation types. In all cases, it can be observed that the EL signal to be extracted corresponds to an average value of around 20 digital counts, compared to the background signal generated by the sun, which ranges from 10,000 to 12,000 digital counts. This means that the EL signal accounts for only 0.15-0.2% of the total signal, which may complicate its extraction, as even minor noise can significantly affect results. It is important to note that a higher ratio of background to EL signal may be observed when testing other modules, as monocrystalline solar cells, particularly those with PERC and TOPCon technologies, exhibit higher luminescence intensities.  
Moreover, Figure 5 shows how the background signal also varies during the evolution of the measurement, which can be explained by minor changes in irradiance. These changes may affect the quality of the measurement, as rapid fluctuations in irradiance would lead to larger changes in pixel intensity than those generated by the signal modulation, making sunlight background extraction impossible.  
The FFT allows transforming the signal evolution from the time domain to the frequency domain. Therefore, a signal peak corresponding to the modulation frequency (4 Hz) is expected to be found. By performing the FFT on the average pixel intensity of all the images in the sequence and plotting the FFT amplitude (see Figure 6), it is possible to detect this signal peak as well as signal transmission at different wavelengths, which correspond to noise.  
Figure 3: Processing approach flow diagram.  
Figure 4: Module current for square, full sine and half sine wave modulation (Measured with oscilloscope RS PRO Siglent SHS820X).  
Figure 5: Signal evolution in image sequences for square, full sine and half sine modulation.