esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Calculation of Rock Discontinuity Parameters  
The PCA algorithm is employed for plane fitting on the extracted discontinuities to determine their normal vector \(\overrightarrow{u}\). The components \(u_{x}\), \(u_{y}\), and \(u_{z}\) of \(\overrightarrow{u}\) are then used in Equations (11) and (12) to calculate the dip direction (\(\alpha\)) and dip (\(\beta\)) of the discontinuity.  
\[\alpha=\left\{\begin{array}{ll}90^{\circ}-\tan^{-1}\frac{u_{y}}{u_{x}}&u_{x }>0\\ 270^{\circ}-\tan^{-1}\frac{u_{y}}{u_{x}}&u_{x}<0\end{array}\right. \tag{11}\]  
\[\beta=\cos^{-1}\left|\frac{u_{z}}{\sqrt{u_{x}^{2}+u_{y}^{2}+u_{z}^{2}}}\right| \tag{12}\]  
In total, 19 discontinuities previously studied by [PERSON] et al. [32] and [PERSON] et al. [18], as shown in Figure 8b-d, were selected for orientation calculation to validate the effectiveness of the proposed approach in this paper. Table 1 presents the dip direction and dip results of the 19 discontinuities extracted using the approach proposed in this study, along with the methods introduced by [PERSON] et al. [32] and [PERSON] et al. [18]. It is observed that 94.7% of the discontinuities exhibit orientation differences within 7\({}^{\circ}\), with an average difference remaining within 2\({}^{\circ}\). The maximum deviation in the dip direction appears on Discontinuity 12, which is attributed to surface undulations.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Discontinuity** & **[PERSON] et al.** & **[PERSON] et al. [18] (\({}^{\circ}\))** & **New** & **[PERSON] et al. [32] (\({}^{\circ}\))** & **[PERSON] et al. [18] (\({}^{\circ}\))** & **[PERSON] et al. [18] (\({}^{\circ}\))** \\
**ID** & [32] (\({}^{\circ}\)) & & **Method (\({}^{\circ}\))** & \(\Delta\)**1**********D**\(\mid\)** & \(\Delta\)**1******D**\(\mid\)** & \(\Delta\)**1****D**\(\mid\)** & \(\Delta\)**1****D**\(\mid\)** \\ \hline
11 & 246.24/39.02 & 244.62/38.38 & 246.25/39.03 & 0.01 & 0.01 & 1.63 & 0.65 \\
12 & 256.86/52.3 & 256.18/52.16 & 247.13/49.76 & 9.73 & 2.54 & 9.05 & 2.4 \\
13 & 70.26/35.8 & 251.04/36.17 & 250.23/35.79 & 0.03 & 0.01 & 0.81 & 0.38 \\
14 & 252.68/35.48 & 251.44/33.85 & 252.63/35.47 & 0.05 & 0.01 & 1.19 & 1.62 \\
15 & 249.74/35.91 & 250.82/36.83 & 249.33/35.55 & 0.41 & 0.36 & 1.49 & 1.28 \\
16 & 70.47/35.91 & 250.46/35.86 & 250.19/35.71 & 0.28 & 0.2 & 0.27 & 0.15 \\
17 & 255.12/32.82 & 253.19/33.46 & 254.90/30.87 & 0.22 & 1.95 & 1.71 & 2.59 \\
21 & 339.47/83.25 & 157.55/83.81 & 339.38/82.79 & 0.09 & 0.46 & 1.83 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of orientation results from different methods.  
Figure 9: Extraction results of individual discontinuity. One color per individual discontinuity.
## 2 Data  
The data used in this paper were sourced from the National Water and Climate Center (NRCS), which is affiliated with the U.S. Department of Agriculture. The agency has 226 automatic monitoring stations set up across the United States to monitor hourly changes in soil, atmosphere, precipitation, wind, and other elements in real time, and it has built the largest database of soil, water resource, and climate data in the United States. This study collected hourly meteorological data of 124 stations from 2010 to 2019, including 11 meteorological elements, namely, air temperature (TOBS), maximum air temperature (TMAX), minimum air temperature (TMIN), maximum wind speed (WSPDX), average wind speed (WSPDV), solar radiation (SRADV), dew point temperature (DPTP), air pressure (PVPV), relative humidity (RHUM), minimum humidity (RHUMN), and maximum humidity (RHUMX). According to the Koppen climate classification index, we divided the collected station data into six types of climate types, with each type represented by three letters. The main climate condition is represented by the first letter, which can be one of the following: (A) equatorial climate, (B) arid climate, (C) warm climate, (D) snow climate, or (E) polar climate. The second and third letters indicate precipitation and temperature conditions, respectively. For example, BWh indicates that the area is located in a dry and rainless desert climate. To obtain further information on the climate classification, please refer to [41]. Figure 1 illustrates the distribution of the 124 stations studied in the experiment.  
This paper performed outlier detection on the experimental data and deleted the data items that contained outliers to avoid the influence of outliers on some machine-learning methods that rely on certain assumptions and calculations, such as linear regression, logistic regression, support vector machine, etc.  
When imputation is applied directly to the original data, the extent of discrepancy between the imputation result and the true value cannot be assessed due to the unavailability of the true value for the missing data. This paper selected the non-missing records of all stations, randomly deleted some units according to the proportion, recorded the deleted part as the true value, and then imputed data according to the proposed framework, so that the difference between the imputed value and the true value could be evaluated. The missing value rate in this study was set at 10%, 20%, 40%, 60%, and 80%.
# Integrated Hydrological Analysis of Little Akaki Watershed Using SWAT-MODFLOW, Ethiopia
## 2 Materials and Methods
### Method of Recharge Estimation  
SWAT model, a physically-based basin-scale spatially distributed continuous daily time step and computationally efficient hydrological model [15], is used for recharge estimation. It was developed to simulate the impact of land use (management practices) on quantity, as well as the quality of water, sediment, and agricultural chemical yields, in big complex watersheds [16]. ArcSWAT, the interface of the GIS divides the watershed into hydrological response units (HRUs) that contain similar characteristics of land use,  
Figure 1: Little Akaki Watershed.  
Figure 2: Average rainfall and temperature measured at observatory.  
soil, slope, and management. HRU is used to describe the spatial heterogeneity within a watershed [16; 17]. The inputs data required for the GIS interface of ArcSWAT 2012 to simulate the stream flow includes Digital Elevation Model (DEM), soil and land use spatial data reclassified as per the SWAT format, and daily weather data from the representative stations.  
A 30 m by 30 m resolution ASTER global DEM (ASTER GDEM) version 2.0 [17] was used. For the Land Use and Land Cover requirement, the open access global land-cover map datasets ([[http://www.globallandcover.com/GLC30](http://www.globallandcover.com/GLC30) Download/download.aspx]([http://www.globallandcover.com/GLC30](http://www.globallandcover.com/GLC30) Download/download.aspx), accessed 15 January 2020), high-resolution (30 m \(\times\) 30 m), released by the government of China [18] was used.  
The soil data from the Harmonized World Soil Database (HWSD) v 1.2 [19] was extracted and used for this study. The spatial resolution of the data is about 1 km (30 arc seconds \(\times\) 30 arc seconds).  
The input meteorological data used to run the SWAT model were the conventional weather data from 1988 to 2004 from five stations in and around the Little Akaki sub-basin (Addis Ababa Bole, Addis Ababa Observatory, Holota, Ginchi, and Teiji) obtained from the National Meteorological Agency of Ethiopia. The hydrological flow data, daily streamflow data for the Little Akaki gauging station was obtained from Ethiopia Ministry of Water, Irrigation, and Electricity department of Hydrology.  
The observed daily streamflow data from 1992-2003 was used for the model calibration and validation. The calibration and validation analysis, the sensitivity, and uncertainty were used in the SUFI-2 algorithm in the SWAT-CUP [20]. The SWAT model simulation output was imported to SWAT-CUP for analysis. The SUFI-2 algorithm computes the 95% prediction uncertainty (95 PPU) range through an iterative procedure and attempts to capture the observed streamflow data within the band.
# Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms
## 2 Materials and Methods
### Deep Convolutional Models Learning Strategy and Configuration  
All deep convolutional models considered in this work (Section 3) have been written in Python using the open-source library _Keras_. They are trained adopting an early stopping rule to avoid overfitting and minimize the generalization error. In practice, the original training dataset is randomly split into a proper training set (85% of data) and a validation set (15%) (not to be confused with the fully independent test dataset described above, which is never seen during the training) based on which both training (hindcast) and validation losses are updated during the network optimization. The validation loss, in particular, is used as an estimate of the generalization error, and early stopping consists in terminating the iterative learning as soon as its values start to increase. As the estimations can be rather noisy, early stopping admits a \"patience\" parameter, which defines the number of epochs to be completed before the loss function minimum can be considered such. Here we have set the patience equal to 20 for the SRCNN model (whose computations are very fast but require many more epochs to converge) and reduced it to five for all deeper models. The adaptive moment estimator, Adam, is applied for the stochastic optimization of models' parameters [51], with the learning rates (l\({}_{\rm r}\)) and numerical stability constants (\(\varepsilon\)) kept as in the original implementations of the baseline networks (i.e., l\({}_{\rm r}\) = 3 \(\times\) 10\({}^{-4}\) and \(\varepsilon\) = 10\({}^{-7}\) for SRCNN; \(\rm{I_{r}}=10^{-4}\) and \(\varepsilon=10^{-8}\) for the other networks). The latter values are also adopted for dADR-SR. Within the dADR-SR model, we have also tested the implementation of a DropBlock strategy [52] to improve the network regularization with minimal performance differences.
## 3 Sidebands and Their Correlation Analysis  
In the experiment, reconstruction of the generated APTs was accomplished using the technique of correlation-based reconstruction of attosecond pulses (CoBRA) inspired by the RABBITT scheme. For the implementation, neon was used as the target gas for the two-color photoionization scheme. A significant difference between the RABBITT and CoBRA schemes is the number of sidebands between consecutive harmonics. In our scheme, due to the frequency relation between the seed laser and the NIR laser, between each pair of consecutive harmonics, two sidebands were found, whose intensities are given by:  
\[S_{q,q+1}^{(\pm)}=a_{q,q+1}^{(\pm)}\pm b_{q,q+1}^{(\pm)}\cos(3\omega_{\text{ NIR}}\tau+\varphi_{q+1}-\varphi_{q}), \tag{1}\]  
where \(\varphi_{q}\) is the phase of harmonic H\({}_{q}\), \(\tau\) is the delay between the APT and the NIR fields, \(a_{q,q+1}^{(\pm)}\), \(b_{q,q+1}^{(\pm)}\) are coefficients depending on the photoelectron energy and the intensity of the two harmonics and of the NIR field, while \(+(-)\) corresponds to the sideband with higher (lower) energy. Each sideband (\(S_{q,q+1}^{(\pm)}\)) is a result of the interference of two pathways, each resulting from the ionization by the harmonic H\({}_{q}\) (H\({}_{q+1}\)) and absorption (emission) of a different number of NIR photons. As a result of the different number of exchanged photons reaching a particular sideband (\(S_{q,q+1}^{(\pm)}\)), the final states reached via the two pathways present the same final energy, but a different parity. The interference between the two pathways, therefore, does not lead to a variation of the total intensity of the sidebands when the relative phase between the two paths is changed (as in the RABBITT case), but to an asymmetric emission of the sideband photoelectrons. The strong-field approximation (SFA) [27] on which Equation (1) is derived, confirms a complete cancellation of the oscillations of the sideband intensities if the signals are integrated over the total solid angle of \(4\pi\,\mathrm{sr}\). In the experiment, the photoelectron spectra were acquired by a magnetic bottle electron spectrometer (MBES) configured to collect the photoelectrons in \(2\pi\,\mathrm{sr}\). This detection configuration allows one to observe oscillations of the sideband intensities upon changing the relative phase of the two pathways (i.e., upon changing the relative delay between the XUV and NIR fields). From the definition of the intensity of the sidebands, an oscillating component of the sidebands (\(P_{q,q+1}\)) is constructed, using the definition:  
\[P_{q,q+1}=\frac{S_{q,q+1}^{(+)}-S_{q,q+1}^{(-)}}{S_{q,q+1}^{(+)}+S_{q,q+1}^{(- )}}=\alpha_{q,q+1}\cos(3\omega_{\mathrm{NIR}}\tau+\varphi_{q+1}-\varphi_{q}). \tag{2}\]  
where \(\alpha_{q,q+1}\) is a coefficient obtained with the approximations \(a_{q,q+1}^{(+)}\,(b_{q,q+1}^{(+)})\approx a_{q,q+1}^{(-)}\,(b_{q,q+1}^{(-)})\). In the correlation plots, we use the oscillating component of the sidebands to visually represent the different degree of correlation between the sidebands.
# An Approach for Selecting the Most Explanatory Features for Facial Expression Recognition
## 3 Methodology
### Feature Extraction Methods
#### 3.1.3 Pre-Trained Deep Models  
Several deep learning algorithms have been proposed and applied to FER [29; 44; 45], particularly from an explainable artificial intelligence (XAI) point of view [46; 47; 48]. Our interest was in the combination of different representation spaces. Thus, we selected pre-trained models to obtain facial expression representations. The training of deep learning architectures for FER poses a problem when using data sets such as CK+, JAFFE, and BU-3 DFE, due to the small number of elements in these sets. Models trained with such data sets are prone to overfitting, which can hinder objective analysis when determining the contribution of the characteristics of the model to the system. Therefore, we selected general classification models (i.e., object classification and facial classification models) that were pre-trained on extensive databases, including the VGG-face [35] and VGG [49] models. In the case of the VGG-face model, as a feature, we selected the output of the Rectifier Linear Unit (ReLU) layer 33. For the VGG model, we selected the output of the ReLU layer 34. The size of both vectors was 4096.
## 2 Experimental Investigation of Column
### Axial Compression Test Results and Discussions
#### 2.4.5 Load-Transverse Strain Curves  
The axial compression load versus transverse strain (\(P\)-\(\varepsilon_{\rm T}\)) curves of the C159\(\times\)6-R26.5 sample is shown in Figure 11. Comparing the curves of Figure 10, it can be generally found that the transverse strain (\(\varepsilon_{\rm T}\)) is smaller than the longitudinal strain. It can be found from Figure 11 that at the initial loading, the transverse strain increases slowly, due to the fact that the constraint of outer steel tube to core concrete is a relatively weak. With the increasing \(P\), the constraint effect of steel tube on core concrete is strengthened. After entering the elastic-plastic stage, the transverse deformation of concrete is greater than that of the steel tube, resulting in the extrusion of outer steel tube by concrete and the rapid increase of \(\varepsilon_{\rm T}\).  
It also can be found from Figure 11 that the \(\varepsilon_{\rm T}\) of undamaged samples (i.e., C159\(\times\)6-R26.5-H0) is less than that of damaged samples. In addition, for undamaged samples before entering into plastic stage, the \(\varepsilon_{\rm T}\) values of all points are basically the same, which also reflects the fact that the sample is vertically and uniformly compressed. For damaged samples, i.e., C159\(\times\)6-R26.5-H2 to H4, except \(\varepsilon_{\rm T}\) at the position where the strain gauge 2 is pasted from the tension strain to compression strain, the \(\varepsilon_{\rm T}\) at the other position are tension strains during the whole deformation process; this may be due to the different deformation rates of steel tube and core concrete. At the concave side in the middle of the column (i.e., the position where the strain gauge 6 is attached), the \(\varepsilon_{\rm T}\) is almost the biggest, which is consistent with the position where the longitudinal strain gauge 5 is attached. With the increase of \(H\), most of \(\varepsilon_{\rm T}\) is larger, and the deformation rate of sample is higher.  
Figure 11: _Cont._  
Figure 10: Typical axial force versus longitudinal strain (\(P\)-\(\varepsilon_{\rm L}\)) curves of C159\(\times\)6-R26.5-H0 to H4. (a) \(H=0\) m; (b) \(H=2\) m; (c) \(H=3\) m; (d) \(H=4\) m.
## 5 Novel Combined Extraction Techniques
### Ultrasound and Supercritical Fluid Assisted Extraction  
Supercritical fluid extraction (SFE) of natural products has drawn particular interest in recent years, due to its low critical constant, non-toxic and non-flammable properties. For SFE, carbon dioxide (CO\({}_{2}\)) is the most commonly employed solvent for the extraction of bioactive and antioxidant compounds from vegetal materials, following its moderate operating temperature, low-cost, non-flammable and environmentally friendly nature. Combination of US with CO\({}_{2}\) resulted into an increase in the extraction yield, which could be associated with enhanced mass transfer rate. Better stability of CO\({}_{2}\), due to its inert nature and process operation at low temperature leads to minimal damage to vital components of the extract. Bioactive compounds were obtained with high purity from red pepper (_Capsicum bacatum L. var. pendulum_) via CO\({}_{2}\) based supercritical fluid extraction assisted by ultrasound (SFE-US) [89]. [PERSON] et al. [90] obtained 9-38% and 10-86% enhanced yield of phenolics, as well as antioxidants respectively from dry oregano via introducing a multiple circular sonotrode type ultrasound reactors, which were operating along with CO\({}_{2}\). The process demonstrated the importance of reactor configuration for obtaining better extraction yield. For the extraction of antioxidant compounds from blackberry bagasse, [PERSON] et al. [91] adopted SFE-US method, which indicated the effect of ultrasound on SFE for enhancing the global yield and the quality of extract. SFE-US extraction has also been used for the extraction of capsaicinoids from malagueta pepper (_Capsicum frutscens L._) [92] and oil from almonds [93]. The yield of SFE was enhanced up to 77% following the introduction of ultrasound during capsaicinoids extraction while considering same parameters for all type of extractions and four major capsaicinoids (capsaicin, dihydrocapsaicin, nordihydrocapsaicin, and homodihydrocapsaicin) were quantified successfully. During the extraction of oils from almond, the yield also enhanced up to 20% than treatments without ultrasound. The introduction of ultrasound reduces pressure, temperature, the flow rate of CO\({}_{2}\) and the overall extraction time as well. An enhancement in yield can be attributed to both the mechanical and thermal effects of ultrasound.
## 4 Discussion and Comparison with Experiments  
The Equations obtained above (2,4,6,7,12-15,17,19,21,22,53-57,66-68,73-81,86-93) completely solve the problem of choosing the irradiation parameters for creating filaments by single pulses with taking into account the numerous factors listed above for obtaining LFC with a better smoothness. The key parameters are the energy of a single laser pulse in the material \(E_{1}\), the pulse duration \(\tau\), the step of filaments following in the material \(s_{1}\) and the PRR \(f\). Besides, based on these parameters, the LFC throughput \(\Omega\), the relative velocity of the beam and material \(u\) and the average power of the pulse-periodic beam \(P\) are also calculated. The dependences of these parameters on the filament VED \(w\), as well as the radius \(r_{f}\) and the length of the filament \(H\) are found. The allowed ranges of \(w\) variation are given by limitations (12), which eliminate significant losses of the filament energy to thermal radiation. The bounds \(w_{1}\) and \(w_{2}\) of these ranges of \(w\), as well as the VED threshold \(w_{mod}\) for creating the critical for separation modification zone in the material around the filament, depend on the optical, thermophysical and strength properties of the material.  
A schematic of the algorithm for a sequential calculation of the parameters \(E_{1}\), \(\tau\), \(s_{1}\) and \(f\) in the model is shown in Figure 7 when the energy transfer mechanism by heat conduction dominates in the formation of the impact zone around the filament. For an alternative shock-wave modification mechanism, smooth separation according to the model is not achieved, but this scheme, if we put in it \(\eta\) = 0 and \(f_{1}\) = \(f_{2}\), can be used to calculate the parameters \(E_{1}\), \(\tau\), \(s_{1}\) and \(f\), which in this case provide for a separation with more straight and more uniform in diameter filaments. The algorithm scheme shown contains 45 parameters in total, their physical meaning is explained in the model above. This number includes the input parameters: of radiation, of material and size of filaments, as well as the actual unknown parameters to be found--these are the irradiation parameters \(E_{1}\), \(\tau\), \(s_{1}\) and \(f\) (shown in Figure 7 in yellow), which determine the smooth separation. The search for the unknown \(E_{1}\), \(\tau\), \(s_{1}\) and \(f\) is performed in five consecutive stages. To illustrate the use of this algorithm, we calculate in the sections below these parameters and compare them with the experiments.
# Nanotechnology, Nanomedicine, and the Kidney
## 4 Nanotechnologies for Renal Imaging
### Multimodal Imaging  
At the end of the last century, multimodal imaging has been developed to overcome the weaknesses of the present imaging system. For example, it is possible to overcome MRI's low sensitivity by combining it with PET imaging, and at the same time, overcoming PET's poor spatial resolution. Recently several multimodal imaging approaches have been developed to achieve high-resolution spatial images in real-time, such as Positron emission tomography-magnetic resonance imaging (PET/MRI), ultrasound/MRI, and computed tomography (CT)/MRI. Special NPs have been developed for these technologies, adding another level of improvement either by improving the image quality or providing a potential treatment to restore kidney functions. The \({}^{64}\)Cu-radiolabeled Fe\({}_{3}\)O\({}_{4}\)-levodopa (LDOPA) NPs were designed as PET/MRI imaging probes [51]. For kidney imaging by SPECT, gamma counting, and MRI, 111 In-gamma radiolabeled SPIONs were generated [52]. Polyethylene glycol incorporated-Mn\({}^{2+}\)-chelated melanin NPs radiolabeled with \({}^{89}\)Zr (\({}^{89}\)Zr-MMPP) showed the ability to scavenge reactive oxygen species (ROS) and reduced oxidative stress [53]. When applied into an acute kidney injury mice model, PET/MRI bimodal imaged kidney uptake and further showed that MMPP NPs could efficiently restore renal function in the AKI mice model via antioxidative protection [53].
# A Python Framework for Crop Yield Estimation Using Sentinel-2 Satellite Data
## 2 Materials and Methods
### Methods
#### 2.5.2 Satellite Data Processing  
All vegetation indices (NDVI, GNDVI, EVI, LAI, RE-PAP) used in this study were calculated in the ESA SNAP software using the 'Band Maths' tool. Following the computation and extraction of the new data via the 'Bands Extractor' processor, the results were imported into QGIS. In QGIS, the mean index values for each date were extracted at the field level for each field using the Zonal Statistics tool.