esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Habitat Suitability Models
#### 2.2.2 Environmental Database
To prevent the high correlation between environmental layers, we used Pearson's correlation analysis in R software [35] and excluded the environmental layers with correlation indices higher than 0.9 because high correlation between environmental layers may alter model predictions [36; 37]. The bathymry data was downloaded from the General Bathymetric Chart of the Oceans (GEBCO) on a resolution of 30 arc seconds and was re-sampled on the same spatial resolution as the rest of the variables. For the creation of the coastal type, a raster of Normalized Difference Vegetation Index (NDVI), downloaded from the Global Land Cover Facility (GLCF: [[http://www.landcover.org/](http://www.landcover.org/)]([http://www.landcover.org/](http://www.landcover.org/)); accessed on 13 September 2020), was implemented to differentiate between mangrove ecosystems and other types of non-submerged vegetation [38; 39]. The substratum type data were obtained by digitizing the sea sedimentology published by INAPESCA (1994) [40].
## 1 Introduction
In summary, the achieved process improvements of the static beam shaping methods do not allow high productivity while maintaining high quality for thick metal plates. A different attempt to solve the conflict between productivity and quality is beam oscillation as dynamic beam shaping method. That implies a superimposed modification of the laser beam movement regarding the feed direction. Thus, the beam oscillates inside the generated cut kerf and thereby shapes the interaction area into an arbitrary geometry [30; 31]. A main advantage of beam oscillation concept is the preserved intensity distribution of the fiber laser beam at simultaneously increased interaction area between laser beam and melt. Two of the most relevant processes using beam oscillation are electron [33; 34] and laser beam welding [35; 36; 37; 38]. In addition, first investigations on laser beam flame cutting of mild steel [39; 40; 41] and LBFC of stainless steel [2; 30; 31] have been performed with beam oscillation. All these investigations have demonstrated a high potential of process improvements regarding less process instabilities, faster processing speed, and advanced quality. The present study aims to improve the LBFC of mild and stainless steel using longitudinal, linear beam oscillation. By changing different process variables such as amplitude and frequency, the optimal cut edge quality at maximum reachable cutting speed is investigated. The acquired results using beam oscillation method are compared against static beam shaping for both treated steels. The achieved process improvements caused by beam oscillation result from a changed energy deposition. Detailed information provides the investigation of the heat-affected zone (HAZ) by means of metallographic cross-sectional analyses. Finally, a simplified model of the energy deposition provides a first attempt to explain the changed process mechanisms in LBFC using longitudinal, linear beam oscillation.
## 2 Materials and Methods
### Existing Test Data  
In 2009, quasistatic four-point bend testing of ash and maple dowels was completed at the FPL in Madison, WI [8]. These test data quantified how the MOE and MOR vary as a function of density for ash and maple. The density range tested for each wood species corresponded to the range of densities that are used to manufacture MLB-quality baseball bats. Figures 1 and 2 show MOE-density and MOR-density results, respectively, for the ash and maple dowels. These dowels had slope-of-grain (SoG) angles between \(\pm 3^{\circ}\), which is the current range of SoGs that are allowed for the manufacture of the baseball bats used by major-league players. A \(0^{\circ}\) SoG is when the direction of the wood grain is aligned with the axis of the bat. Figures 1 and 2 show how the MOE and MOR, respectively, of each of the wood species increases with increasing density. These test data reflect the wide scatter in material properties that are typical for wood. A linear regression on each set of data was completed. The resulting equations are:  
\[Maple\ MOE\ (Msi)=39.96\times\rho+1.279 \tag{1}\]  
\[Maple\ MOR\ (psi)=1002663\times\rho-2554 \tag{2}\]  
\[Ash\ MOE\ (Msi)=122.5\times\rho-0.875 \tag{3}\]  
\[Ash\ MOR\ (psi)=1383652\times\rho-10117 \tag{4}\]  
where \(\rho\) is the density in lb/in\({}^{3}\).  
Figure 1: Modulus of Elasticity (MOE) test results for ash and maple dowels with slope-of-grain angles spanning the range of \(\pm 3^{\circ}\).
## 5 Hardware Design of the Proposed Algorithm
### Hardware Architecture of the proposed 3D Shape Reconstruction System
#### 5.3.1 Hardware Architecture of the Laser Spot Detection Algorithm  
The laser spots detection algorithm consists on highlighting the laser projection on the stereo RGB images in order to easy the extraction of the corresponding 2D coordinates. The first step is the binarisation of the RGB images based on pixel color information. White pixels will represent the laser projections and black ones the background. In Figure 9, we propose the hardware architecture of the segmentation step, which correspond in fact to the expression (Equation (2)) given in Section 4.2.  
The segmentation results demonstrate that the binarisation presents some outliers. These errors should be eliminated to avoid its effect on the final restitution of the karst 3D shape. Figure 10 illustrates the subsystems of the laser spots extraction module, which consists of respectively Cartesian2 Polar transformation, region-based process to eliminate outliers and the extraction of laser positions. The image center (\(x_{0},y_{0}\)) is estimated during the calibration process.  
In order to eliminate wrong laser projections, we use an approach based on region constraints. All pixels that are white ('bin' = '1'), that are inside the mirror projection {'\(r\)' < 'Rmiroir'}, and that the relation between \(x\) and \(y\) coordinates is close to one of the predefined set, or  
Figure 10: Hardware architecture of the region-based laser spot extraction module.  
Figure 9: Hardware architecture of the segmentation block.  
where the x coordinates is negative (\(x<0\)) are considered to form a cloud points. A selection is applied to choose which laser projection will be considered as an object and which will be merged with the image background. This is done by analyzing the values of the polar and Cartesian coordinates of each laser pixel. For a pixel belonging to a direction whose inclination angle is \"\(\theta\)\", its (\(x,y\)) coordinates are proportional to the \"extangent (\(\theta\))\" value. In our work, we use twelve laser pointers, which let us define twelve directions corresponding to \(\theta\in\left\{0,30,60,\ldots,330\right\}\) corresponding to the arctangent value \(\mathbb{C}\) {\(\pm 0.577\), \(\pm 1.732\)} and pixels belonging to the vertical axis {\(-3<y<3\)} and horizontal axis {\(-3<x<3\)}. Because of the symmetry of the arctangent function, two faced region is described by the same arctangent value. To differentiate between the two regions, we analyze the signs of the (\(x,y\)) coordinates (Figure 4b). Twelve selection blocks are designed to specify each region of the image (Figure 11). Each block contains the region characteristics defined to sort pixels which are identified as laser projection. For the gravity center calculation, we use a \"MCode\" blockset inserted to determine the median position value of all entering pixel values of a cloud points for each region.
## 3 Materials and Methods
### Monitoring Network and Equipment
Starting from June 2015, rainfall levels on the landslide were measured by a tilting bucket rain gauge installed at the landslide head (Figure 3). Nonetheless, some technical problems and malfunctions affected precipitation recording during the considered monitoring period which resulted in some data gaps (86% data coverage). Therefore, rainfall data collected from the Sara rain gauge (100% data coverage) were used. This station is located approximately 2.7 km south-east from the landslide under investigation.
# Enhancement of Sentinel-2A Images for Ship Detection via Real-ESRGAN Model
## 2 Data and Methodology
### Methodology
#### 2.2.1 Pre-Processing  
The pre-processing stage involves applying tiling, masking, and downsampling operations to the images. The tiling operation is performed on both datasets and is intended to train both the super-resolution and object detection models. Since the study area focuses on ports and sea surfaces, a masking operation is applied after tiling to remove other regions from the dataset. For training super-resolution models, HR-LR image pairs are required. In this context, the downsampling operation is applied to the HR images to generate HR-LR image pairs.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Port Name** & **Location** & **Area (ha)** & **Included** \\ \hline Amsterdam & The Netherlands & 9314.82 & Dataset 2 \\ Bremerhaven & Germany & 2840.25 & Dataset 2 \\ Busan & Republic of Korea & 4410.04 & Dataset 1 and Dataset 2 \\ Durrés & Albania & 518.59 & Dataset 1 and Dataset 2 \\ Hong Kong & China & 40,926.75 & Dataset 1 and Dataset 2 \\ Iskenderun & Türkiye & 843.31 & Dataset 1 and Dataset 2 \\ Izmir & Türkiye & 295.80 & Dataset 2 \\ Long Beach & USA & 7834.57 & Dataset 1 \\ Nantong & China & 69,377.14 & Dataset 2 \\ Ningbo-Zhoushan & China & 12,199.59 & Dataset 2 \\ Piraeus & Greece & 8385.77 & Dataset 1 and Dataset 2 \\ Qingdao & China & 4001.83 & Dataset 2 \\ Rizhao & China & 20,478.86 & Dataset 2 \\ Rotterdam & The Netherlands & 14,934.40 & Dataset 2 \\ Santos & Brazil & 736.26 & Dataset 1 and Dataset 2 \\ Taranto & Italy & 4363.43 & Dataset 1 and Dataset 2 \\ Tianjin & China & 44,598.57 & Dataset 2 \\ Zeebrugge & Belgium & 4429.12 & Dataset 2 \\ \hline \end{tabular}
\end{table}
Table 1: Detailed information about the ports.
## 3 Biomechanics of Articular Cartilage
### Mechanical Properties of Articular Cartilage
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline
**Measurement Method** & **Measured Parameters** & **Test Characteristics** & **Advantages** & **Limitations** & **References** \\ \hline Confined compression test & Aggregate modulus, Hydraulic permeability & Cartilage compressed in a cylindrical chamber, without lateral expansion & Measurement of liquid-solid properties (biphasic model) & No mapping of natural anatomical conditions & Not & \\ \hline Unconfined compression test & Compressive Young’s modulus, Hydraulic permeability, Poisson’s ratio & Cartilage compressed freely vertically, with possible outflow of fluid and lateral expansion & Simple to carry out, well modelled & Strong dependence of the results on the boundary conditions and sample geometry & Strong & \\ \hline Indentation test & Aggregate modulus, Hydraulic permeability, Poisson’s ratio & Local indentation pressure on the cartilage surface & Possibility of in situ and in vivo testing & High sensitivity to tissue heterogeneity and alignment & [PERSON] et al. [184] \\ \hline Tensile constant strain rate & Tensile Young’s strength & Cartilage stretching at a constant speed & Mapping of collagen fibre behaviour & Difficulties with sample preparation, risk of damage & [PERSON] et al. [177] \\ \hline Tensile stress relaxation & Tensile equilibrium modulus & Maintaining constant strain and observing stress reduction & Analysis of viscoelastic properties & Long measuring time & [PERSON] et al. [177] \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of methods for measuring the mechanical properties of AC.
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 2 Methodology  
### Wind Farm Simulation Tool  
For simulations, a wind farm simulation tool, which was constructed in the previous study, was modified to have communications with the wind farm controller. The wind farm controller has a control algorithm based on a simplified farm model embedded in the controller and performs a model predictive control. The simulation tool is originally based on SimWindFarm [24] but modified to have [PERSON]'s eddy viscosity wake model and new wind turbine and farm controllers [20; 25]. It consists of individual wind turbine models, wake model, wind propagation model, and wind generator, as shown in Figure 1.  
The simulation tool was coded with C language to achieve short simulation time. It can simulate the load variation of a wind turbine for different wind turbine states subjected to different winds, but still the wind propagation is dependent on the [PERSON]'s frozen turbulence assumption. The individual wake fields generated by wind turbines are combined to be applied to downstream wind turbines with the \"Sum of Squares of Velocity Deficit\" equation. The equation is used for a well-known commercial wind farm design tool [26] and is shown in Equation (1).  
\[\text{Deficit}_{n}=\sqrt{\sum_{k=1}^{n-1}(\text{Deficit}_{kn})^{2}} \tag{1}\]  
The \"Sum of Squares of Velocity Deficit\" equation shown in Equation (1) is also known as the quadratic sum model, where \(k\) represents the total number of turbines constituting the wind farm and \(n\) represents the number of upstream wind turbines that make a wake effect on them [27]. The wind speed deficit in Equation (1) is defined as  
\[\text{Deficit}=\frac{\Delta\text{U}}{\text{U}_{\infty}} \tag{2}\]  
where \(\Delta\text{U}\) means the difference between the freestream wind speed and the wind speed in the wake region, and \(\text{U}_{\infty}\) means the freestream wind speed.  
The wind turbine model is a combination of first or second order dynamic sub-models [28] and simulates wind turbine power output and tower loads dynamically for turbulent winds. The model  
Figure 1: Schematic of wind farm model used for simulation including wind turbine and farm control.  
receives wind speed and power command from a wind farm controller and calculates electrical power, blade pitch angle, generator speed, and the tower load. The simplified order sub models include drive-train model, generator model, pitch actuator model, tower model, and controller model. The aerodynamic conversion is calculated in an aerodynamic model which is a three-dimensional look-up table with a function of blade pitch angle and tip speed ratio obtained from a commercial wind turbine dynamic simulation tool. The wind turbine controller includes a basic torque and collective pitch control with a power demand tracking to achieve power output commanded from a wind farm controller. The validation of the dynamic wind turbine model was made in the previous study [20].
# Low-Power Wireless Data Transfer System for Stimulation in an Intracritical Visual Prosthesis
## 6 Discussion  
### Scaling  
A scaled-down carrier frequency of 1 MHz and data rate of about 125 kbps are used for easy implementation on circuit board for proof of principle. The data rate is sufficient for testing since the 125 kbps data rate is close to the lower bound of the requirements of the data rate for the final system. The system will be easily scaled up to operate at 12 MHz and at a higher data rate up to 4 MHz, as our IC simulation on CMOS 0.18 \(\upmu\)m at 1.8 V has shown [17]. It is important to stress that the power consumption of the receiver scales linearly with frequency due to its predominantly digital circuits. Consuming below 1 mW at 12 MHz on IC simulation clearly indicates that tuning to a 6.7 or 13.56 MHz ISM band is easily possible although low-power communication via a 5-10 mm magnetically coupled (inductive) link is quite different from electromagnetic RF communication using antennas.
## 3 Results and Discussion
### Delta \(r_{eb}\)  
For each of the 15 combinations of energy levels and interval times (15 boxes) in the dataset of 90 samples, a corresponding range of \(r_{eb}\) values (\(\Delta r_{eb}\)) was calculated and is depicted in Figure 6a. The outlier at the leftmost point (an upright triangle) for the energy level 0.258 mJ was excluded in the regression line calculation. Notably, we observe a change in slope (d\(\Delta r_{eb}\)/dt) from positive to negative in the regression lines.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Energy (m)** & **(ti)c (s)** \\ \hline
0.288 & 63.9 \\ \hline
0.318 & 70.6 \\ \hline
0.348 & 77.4 \\ \hline
0.378 & 84.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: (ti)c of each energy level.  
Between laser energies of 0.32 mJ and 0.36 mJ there is an invariant point of energy, characterized by a constant \(\Delta r_{eb}\) across all pulse intervals, forming a horizontal line. Under such energy operation, the variation (\(\Delta r_{eb}\)) of \(r_{eb}\) is independent of the interval time. Higher energy levels with longer pulse intervals lead to higher \(r_{eb}\) values and reduced variability, thereby maintaining high-quality laser ablation.  
Conversely, the findings depicted in Figure 5(b), which are based on the 48-sample dataset, diverge from those in Figure 5(a). Notably, the regression lines for both energy levels, 0.258 mJ and 0.318 mJ, exhibit an identical downward trend in \(\Delta r_{eb}\) as the pulse interval increases.  
Considering the variation in \(\Delta r_{eb}\), to maintain \(\Delta r_{eb}\) below 0.15, green laser ablation of Si\({}_{3}\)N\({}_{4}\) at operating energies of 0.258-0.378 mJ can adopt a baseline interval time of the initial baseline multiplied by 1/\(\sqrt{2}\), as shown in Figure 5(c). Additionally, for the operating energies of 0.288-0.378 mJ during Si\({}_{3}\)N\({}_{4}\) laser ablation, \(\Delta r_{eb}\) can be kept below 0.1.  
The slope values of the regression lines from Figure 5(a),b are plotted with energy to generate Figure 6(a),b. In Figure 6(a), the plot of d\(\Delta r_{eb}\)/dt versus energy intersects the x-axis at (0.350 mJ, 0), indicating an energy level at which \(r_{eb}\) is independent of interval time.  
In Figure 6(b), however, when plotting the two negative slope values and extending the regression line to intersect the x-axis, it yields a negative energy level of \(-0.197\) mJ. This value is inapplicable within the scope of this study.
## 4 Discussion
### Error Source of the Spatiotemporal Change Detection Method  
The main source of commission is the specific land (or surface) cover change with the manifestation of a reduction in greenness at a small spatial scale, which closely resembles the discoloration process caused by PWD. For example, if there is a small area of logging coincided with the tree wilting in the forest, the temporal behaviors and spatial patterns of these two events are very similar. In this situation, the proposed method may not be able to determine to which kind of cover change it belongs. A conceivable solution is to track subtle temporal behaviors using dense image time series instead of the bi-temporal analysis. It should also be admitted that the spatiotemporal change detection method used in this paper may not effectively detect large continuous wilted pine trees because they are probably filtered out in the area-screening step. However, this situation is very rare except in the case in which the infected pine forest has not been treated for many years. Although the spatiotemporal change detection method in this study showed better accuracies, it is worth noting that the result is only for the newly added wilted trees between the two times. The time interval between the two image should not be too long to avoid the excessive land (or surface) cover change confused with tree wilting. In addition, the requirements for coregistration and relative radiation normalization of the spatiotemporal change detection method are relatively high.
# Demodulation of EM Telemetry Data Using Fuzzy Wavelet Neural Network with Logistic Response
## 3 Methodology
### Defining the Input
This reduces the training time and increases accuracy with noisy data. Finally, the \(S_{att}(t)\) and \(S_{dat}(t)\) data are reduced by selecting the signal at the phase-characterizing section within a time series data, that is, less than a quarter of a wavelength  
Figure 4: Description of EM MWD response post-processing results in preparation for fuzzy wavelet neural network demodulation with a logistic response. into and out of each signal (Figure 4) and used as the input for the neural network with reduced network size and an increase in accuracy even with noisy data. Demodulation of a BPSK signal can be considered a two-stage process: translation back to baseband, with the recovery of the bandlimited message waveform and the regeneration from the bandlimited waveform back to the binary message bitstream. Therefore, the neural network system will produce an output for each set of inputs presented to it. The choice of input size, as well as the number of connections, determines the balance between the spatial and temporal information that the neural network is attempting to learn. In this study, a completely spatial neural network will have the same number of inputs as samples per signal type and will only output one binary figure per input during training and simulation. Instead of the network, to output the reconstructed noiseless combined phase change system, logical output with binary values in the form of [1000000], [01000000], or [00000001] to represent either of the eight characterized combined phase shift signals is produced. This is rearranged in a logical response of ones and zeros: [1000000] = 0, [0100000] = 1, [00100000] = 0, [00010000] = 1, [00001000] = 0, [00000100] = 1, [00000001] = 0, [00000001] = 1, accordingly, to reproduce the coded transmitted signal. This type of detection is similar to number detection using multiple frames of low-resolution data [29], with the final output being the detected number.
## 3 Results and Discussion
### Effect of Reuse of Pretreatment Solution on Soluble Protein Removal in Corncob  
#### 3.1.1 Effect of pH Change of the Pretreatment Solution on Soluble Protein Removal in Corncob  
The concentration of the soluble protein and the increment of soluble protein in solution after pretreatment of corncobs are presented in Table 1. The corncob was primarily reacted with the deionized water at 100 \({}^{\circ}\)C for 0.5 h in order to better remove the soluble proteins from the corncob. However, after adjusting the mass fraction of sulfuric acid solution to 0.1% and continuing the reaction for 1 h, it can be seen from Table 1 that the obtained pretreatment liquid had a pH value of 3.1. After the first reuse round of the pretreatment liquid, the increment of soluble proteins in the pretreatment liquid was 67.2 mg, which exhibits a very small difference as compared to the increment of soluble proteins in the solution after pretreatment of the corncob with the new added pretreatment liquid (68.4 mg). With the pretreatment liquid being reused continuously, the resulting solutions have pH values in the range of 3.1-3.2. No significant differences were found for the increment of soluble proteins in different reuse rounds (from the first to the third reuse round) of the pretreatment liquid comparing to the increment of the soluble proteins in the solution after corncob pretreatment with the new added liquid, indicating that the pretreatment liquid with a pH value of 3.1-3.2 exhibits nearly equivalent effect on removing the soluble proteins from corncob as compared to the newly added liquid.