esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of AEP Predictions for Commercial Wind Farms in Sweden
## 3 Methods
### Normalization to Wind Turbine Availability and Transformer Efficiency  
The P50 value corresponds to the availability that the owner can count on (i.e., the manufacturer guaranteed availability, which is typically 97% [15]). The time-based availability is the percentage of a given period that a wind turbine is available for operation, and for a given month, was calculated as follows:  
\[A_{t}=\frac{T_{m}-T_{d}}{T_{m}} \tag{1}\]  
where \(T_{m}\) is the total number of hours for the given month and \(T_{d}\) is the downtime in hours for the same month. The availability for the same month was used to calculate the electricity yield normalized for the typical industry standard of 97% availability (\(A_{std}\)):  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **No Data** & **With Data** & **Open Terrain** & **Forest Areas** & **Offshore** \\  & **Exclusion** & **Exclusion** & **Open Terrain** & **Forest Areas** & **Offshore** \\ \hline Wind turbines & 1162 & 880 & 309 & 493 & 77 \\ Total capacity (GW) & 2.68 & 1.98 & 0.67 & 1.12 & 0.19 \\ Observations (months) & 83,278 & 49,375 & 19,307 & 24,615 & 5416 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Vindstat data used for this study. In the columns to the right, the wind turbines after data exclusion are divided into three terrain types.  
Figure 1: Locations of wind turbines included in this study (green). Locations of index sites used for normal wind-year normalization (red); see Section 3.2.  
\[E_{n}=\frac{E\cdot A_{std}\cdot\eta_{t}}{A_{t}} \tag{2}\]  
where \(E\) is the electricity yield for the given month, and \(\eta_{t}\) is the transformer efficiency, which must be accounted for because the P50 value refers to the electricity measured at the grid connection point. The transformer efficiency is assumed to be comparable to the modern multi-megawatt wind turbines investigated in this study and is set to 99%, which is typical for Swedish AEP calculations.
## 3 Classification of Optical Waveguide Sensors
### Microring Structured Optical Waveguide Sensor
#### 3.1.2 Chalcogenide Microring Optical Waveguide Sensor
Chalcogenide glass has been identified as the material of choice for the design of highly specific and sensitive integrated sensors, because of their wide infrared transparent  
Figure 11: Horizontal slot waveguide runway resonator sensor top view. windows. [PERSON] et al. [43] proposed a compact microdisk resonator to achieve a new type of high-refractive-index contrast. The sensing material used in this study, chalcogenide glass, also has potential applications in biochemical sensing. The sensor has single-mode waveguide capability with core dimensions down to the submicron range. Through testing, the refractive index sensitivity was determined to be 182 nm/RIU. Based on the water absorption coefficient of \(\alpha=9.6\) cm\({}^{-1}\) at a wavelength of 1550 nm, the estimated absorption limiting factor \(Q\) was 19,000. [PERSON] et al. [44] proposed a nonlinear refractive index sensor based on a coupled waveguide MRR, which consists of a coupled circular resonator with radius \(R\). The sensitivity of the nonlinear microresonator refractive index sensor proposed in this study compared to the RI changes was two orders of magnitude higher than the theoretical sensitivity value of the linear microresonator RI sensor, and the \(LOD\) of chalcogenide glass reached \(10^{-12}\) RIU for Si.
# Microalgae as Potential Sources of Bioactive Compounds for Functional Foods and Pharmaceuticals
## 2 Materials and Methods
### Evaluation of Bioactivities  
#### 2.5.1 Antioxidant Activity  
DPPH assay was carried out according to the method described in [PERSON] et al. (2016) [31]. Briefly, extracts (22 \(\upmu\)L, at the concentrations of 0.1, 0.5 and 1 mg/mL) and DPPH solution (200 \(\upmu\)L, 120 Mm) were mixed in 96-well microplates and incubated for 30 min in the dark at RT. A microplate reader spectrophotometer was used to determine absorbance at 515 nm (BioTek 4 Synergy Multi-Detection, Agilent, Santa Clara, CA, USA). As a positive control, butylated hydroxytoluene (BHT, 0.1 mg/mL) was utilized. IC\({}_{50}\) was determined whenever the DPPH scavenging activity percentage (compared to a blank containing DMSO) was higher than 50% at a concentration of 1 mg/mL.  
The assessment of redox metal (Fe and Cu) chelating activity of the extracts was conducted by spectrophotometric methods adapted to 96-well plates and expressed as chelation percentage, relative to DMSO as a blank. To prevent potential interferences related to the color of the extracts, absorbances were corrected by removing color control values (assay solvent with extract). Extracts with a percentage of metal chelating activity greater than 60%, were selected for IC\({}_{50}\) (mg/mL) determination.  
Copper chelating activity was determined according to the method described by [PERSON] et al. (2016) [32] with some modifications. Briefly, 30 \(\upmu\)L of extract (1 mg/mL) were mixed with 164 \(\upmu\)L of sodium acetate buffer (50 Mm, Ph 6), 6 \(\upmu\)L pyrocatechol violet (4 Mm) in the above buffer and 100 \(\upmu\)L of CuSO\({}_{4}\cdot\)5H\({}_{2}\)O (50 \(\upmu\)g/mL, _w/v_ in ultrapure water). The change in color was measured at 632 nm. As a positive control, the synthetic metal chelator Ethylenediaminetetraacetic acid (EDTA, 0.1 mg/mL) was used.  
Iron (II) binding ability was determined by measuring the formation of the Fe\({}^{2+}\)-ferrozine complex [32]. Briefly, 200 \(\upmu\)L of sodium acetate buffer (Ph 4.9), 30 \(\upmu\)L of extract (1 mg/mL) and 30 \(\upmu\)L of FeCl\({}_{2}\cdot\)4H\({}_{2}\)O (0.1 mg/mL in ultrapure water) were mixed in 96-well microplates and incubated for 20 min at RT. Then, 12.5 \(\upmu\)L ferrozine (40 Mm) was added to start the reaction. After 10 min at RT, the absorbance of the solution was measured at 562 nm. EDTA was used as positive control (0.1 mg/mL).
## 3 Methodology  
### RUSLE Equation  
Soil erosion is a complex phenomenon that can be studied through various models, including the widely used universal soil loss equation (USLE). The empirical nature of the revised universal soil loss equation (RUSLE) makes it a suitable approach for analyzing  
Figure 1: Location of the study area.  
soil erosion. Using this approach, cartographic maps can be created to understand soil erosion in specific regions, with several parameters directly or indirectly linked to soil erosion [52; 53]. Directly linked parameters such as slope length and steepness, rainfall intensity, and soil erodibility factor impact soil loss, while indirectly linked parameters such as crop management practices and soil conservation practices impact soil loss by influencing the direct parameters [54]. By predicting USLE parameters and creating cartographic maps, the model gains insights into erosion in vulnerable areas [55]. In this study, the RUSLE model was applied using a land cover map created from remotely sensed satellite data, soil types, and agricultural practices. One of the model's main characteristics is that it can be easily incorporated into GIS for improved analysis, making it a valuable option for this study. The methodology followed during this study is depicted in Figure 2, with the RUSLE equation (Equation (1)) developed by combining five input parameters that are interrelated and subject to variations in both space and time. The input data for each element depends on one another and can change over time and space, leading to the calculation of erosion at a pixel level.  
\[\text{A}=\text{R}\times\text{K}\times\text{LS}\times\text{C}\times\text{P} \tag{1}\]  
where A, average annual soil erosion (t-ha\({}^{-1}\)y\({}^{-1}\)); R, rainfall activity factor (MJ\(\cdot\)mm\(\cdot\)ha\({}^{-1}\).h\({}^{-1}\).y\({}^{-1}\)); K, erodibility of soil (t-ha\({}^{-1}\).h\(\cdot\)MJ\({}^{-1}\).ha\({}^{-1}\).mm\({}^{-1}\)); the rest of parameters are dimensionless as shown in Figure 2.  
All these five contributing factors were incorporated individually in ArcGIS, and then the soil erosion was estimated empirically using the raster map function. The boundaries of the study area were set by using the spatial function of ArcGIS. The entire spatial and remotely sensed data were processed using ArcGIS Pro 2.8.0 software. The RUSLE parameters were calculated using satellite imagery, and digital elevation model data are shown in Table 1.  
Figure 2: The methodological framework of the current research study.
## 2 Material and Methods
### Time-Domain Methods for V-I Characteristics of EAF Arcs
#### 2.3.4 Nonlinear Resistance Model (Model 4)  
The V-I characteristic of the electric arc in EAFs can be represented using a nonlinear resistance model [42]. This method articulates the mathematical correlation between the current and the voltage of the electric arc as delineated in Equation (5):  
\[i=i_{0}\cdot\left(\frac{V}{V_{at}}\right)^{\alpha} \tag{5}\]  
In Equation (5), \(V\) and \(i\) represent the arc voltage and arc current, respectively, \(i_{0}\) represents the reference current utilized to establish the maximum arc voltage, and \(\alpha\) is a constant that characterizes the nonlinear behavior of the arc resistance. To obtain the required current level in Equation (5), this function must be utilized alongside the transfer function as \(\frac{1}{0.2e^{-\frac{V}{2}+2}}\) This transfer function has been established for a 60 MVA arc furnace based on experimental results [42]. This function should be empirically redefined for arc furnaces of varying dimensions.
## 2 ACS-SSI Algorithm and Its Verification
### Basic Principle of ACS-SSI  
Stochastic subspace identification (SSI) can achieve modal identification only with output signals relying on the system input data. The process can be summarized as follows. First, the control matrix and the observable matrix are obtained by the singular value decomposition of the covariance of the response data; then, the modal parameters of the state matrix can be derived [29]. The kinematic equations can be written as the discrete-state space stochastic equation  
\[\left\{\begin{array}{l}x(k+1)=\mathbf{A}x(k)+w(k)\\ y(k+1)=\mathbf{C}x(k)+v(k)\end{array}\right., \tag{1}\]  
where \(\mathbf{A}\) is the state space matrix, \(\mathbf{C}\) is the output space matrix, \(w(k)\) is the representative random input signal which includes the process noise, \(v(k)\) is the measurement of noise and \(k\) is the discrete time value. The state space matrix \(\mathbf{A}\) contains the modal information of the system.  
The state matrix \(\mathbf{A}\) is reconstructed by the stochastic subspace method and the modal parameters can be extracted from it. The matrix of \(l\)-channel measurement signals collected by \(l\) channels is \(y(t)\), in which the one with a better signal-to-noise ratio (SNR) is chosen and set as the \(k\)-th signal \(y_{k}(t)\). The correlation function of all the original signals for the \(k\)-th channel is:  
\[r_{k}(\tau)=\frac{1}{n}[y(t+\tau)\cdot y_{k}(t)], \tag{2}\]  
where \(\tau=1\), \(2\),..., \(N-n\) is the discrete time series of the correlation function, with \(N\) being the total length of the collected signal. The correlation function \(r_{k}(\tau)\in R^{ls\tau}\) is an \(l\)-way signal related to a set of \(\tau\) correlation functions. In order to improve the denoising effect, a multi-group experiment was carried out to obtain the \(n\)-group signal \(r_{k1}\), \(r_{k2}\),..., \(r_{kn}\); the \(n\)-group signal was averaged to obtain an average correlation function \(\overline{r_{k}(\tau)}\) used to replace the original \(y(t)\) as the input for modal identification.  
The subsequent steps are the same as the covariance-driven stochastic subspace method [26]: using the covariance of \(\overline{r_{k}(\tau)}\) to form the matrix \(T_{1/i}\), the singular value decomposition (SVD) is carried out to obtain the discrete state matrix \(\mathbf{A}\). After decomposing matrix \(\mathbf{A}\), the eigenvalue \(\lambda_{i}\) and eigenvector \(\Psi_{i}\) are obtained and then converted into natural frequency \(f_{ni}\), modal damping ratio \(\xi_{i}\), and modal vector \(v_{i}\).
# Deep-Sea Underwater Cooperative Operation of
## 2 Related Studies on Cooperative Operations  
### Manned and Unmanned Submersibles  
In mother-child cooperative operations, the manned submersible acts as the mother and transports the unmanned cable-controlled submersible into complex operating environments. Furthermore, this operation provides opportunities for exploiting the unique advantages associated with each of the manned and unmanned submersibles, such as operating cooperatively under the leadership of the manned submersible, emphasizing the complementarity of manned and unmanned submersibles, and implementing coordinated actions by sharing significant amounts of information, task integration, resource optimization, and jointly completing the tasks [22].  
In 1986, the Woods Hole Oceanographic Institution (WHOI) used the Alvin HOV to remotely control the small ROV called \"Jason Junior\" and approached the wreck of the Titanic (3800 m) for the first time in history. They captured detailed photographs and videos, and investigated the exterior and interior of the wreck. Jason Junior was equipped with optical detection equipment, such as cameras, which were mounted in the sampling basket of the small ROV. The small ROV was remotely controlled by the Alvin using a 60 m fiber optic cable to reach the dangerous or complex areas that the Alvin could not reach.  
Based on a single HOV/single ROV mother-child cooperation, Russia improved the scale and quantity of mother-child cooperation and expanded the cooperative operational mode of double HOV/double ROV mother-child cooperation. In 2005, the Russian engineers of the Mir HOV and American experts jointly developed related deep-sea live broadcast technology and used two Mir HOVs to broadcast a live video of the wreck of the Titanic, thereby, realizing the first deep-sea live broadcast. The Mir HOV deployed the Jake and Elwood, which are small ROVs for a close-up live video broadcast of the exterior and interior of the Titanic wreck [23].  
In 2014, the Jiaolong HOV of China was equipped with the Longzhu ARV to conduct deep-sea test applications in the Caiwei Sea mountain (2424 m) in the northwestern Pacific Ocean. According to the planned cooperative operational process, they completed the interactive shooting between each other (as shown in Figure 1), and the underwater operational image of the Jiaolong HOV was obtained for the first time. This resulted in the development of a new mother-child cooperative operating model wherein different types of submersibles in China complemented each other's advantages and cooperated with each other [24].
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## 4 Conclusions  
In this paper, we studied the degradation modeling and RUL prediction of cutting tools based on a cutting precision criterion. The cutting tool suffers continued wear in usage, which decreases the cutting precision of the machining process, i.e., the roughness of the surface in this study. Although cutting precision is of more practical value, it is indirectly measurable and its degradation pattern is more complex. On the other hand, the wear state of a tool is a more directly measurable characteristic in practice, and its degradation pattern is more traceable. Therefore, we proposed to model the degradation process of tool wear as a proxy for the degradation of cutting precision, and linked the  
Figure 12: MAE between the estimated RUL and the true RUL.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Prediction Distribution Falls within 1 \(\pm\) 0.1 of True RUL** \\ \hline IG process model & 72\% \\ Particle filtering model & 60\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Distributions of predicted RUL based on deviation accuracy.  
roughness of the surface requirement to a random threshold for the wearing of a tool. A degradation model based on the IG process was proposed for the tool wearing process, and the RUL prediction method was also studied. The following conclusive remarks were reached in this study:  
1. An IG process model with a variable drift coefficient was used to characterize the degradation of the tool wearing process subjected to individual heterogeneity in dynamic working environments;
2. The surface roughness requirement was linked to a random threshold for the wearing of the cutting tool, and the RUL prediction method was developed based on the proposed degradation model with a random failure threshold.
3. Finally, the applicability and effectiveness of the proposed method was validated using the wearing data of cutting tools in a milling experiment; the MAE was 4.33.  
Further work is required to extend the proposed model's generalizability for handling the multiple cutting conditions observed in real cutting processes, such as turning, planing, and grinding. In addition, the distribution of the failure variable threshold is subject to confirmation by experimental and statistical analyses.  
Y.H.: Conceptualization, methodology, software, and writing--original draft. Z.L.: data curation, validation, and investigation. W.D. and W.Z.: supervision and writing--review and editing. B.W.: writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Fundamental Research Foundation of China (No. JSZL2019601A003 and YWF-21-BJ-J-727) and Technical foundation program of the Ministry of Industry and Information Technology of China (No. JSZL2019601C006).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 The Proposed Clustering-Based Bubble Meshing Method
### Procedures of the Proposed Bubble Method  
#### 3.2.1 Procedure 1: Inputted Geological Model  
Conventional input information includes the geometric boundary of the given solution area, which can be described by the planar straight line graph. And the parametric surface will be meshed by mapping method. However, geological structures and conditions are generally quite complex so that it cannot be well represented by parametric surfaces but can be well represented of triangulated surfaces. And the mesh generation of shared faces between adjacent bodies must be consistent for the  
Figure 4: The workflow of the proposed bubble method.  
Figure 5: Procedure of the clustering-based bubble method for a three-dimensional model.  
aim of topological consistency. Thus, it needs to input object file format to control the surface mesh and promise the topologically consistent in this method, see Figure 5a.  
For the generation of the high-quality mesh according to the surface mesh, we implement the clustering algorithm to divide surface points. Cluster analysis [25; 26] is a multivariate statistical analysis method. The basic principle is: investigate m data points in m-dimensional space, define the distance between points and points of a certain character, set m data points to form n types (\(n\leq m\)), and then combine the two types with the smallest distance into one Class, and recalculate the distance between classes.  
This paper uses the K-means clustering method [27] for the classification of the surface vertices. First, each surface vertice become a bubble with a radius, which is the half of average of the distances of all points incident to this point, see Figure 5b. Then according to the geometric distance distribution of spatial relationships, surface points are divided into several groups, see Figure 5c. The most optimal number of groups in surface points we can obtain based on the Sum of the Squared Errors index (SSE), Calinski-Harabaz index (CH) [28] and Silhouette index [29]. The larger CH value and the Silhouette value, the smaller the SSE value, then select the corresponding number of clusters as the optimal number.
## 4 Results and Discussion for Geiger Domes
### Unstable Regions  
The periodic force applied to one upper node in the vertical direction (see Figure 7) was taken into account. A constant part of the load \(P=5\) kN was assumed. The impact of the initial prestress \(S\) on the limits of the main unstable regions in the plane of parameters \(v\) (pulsatility index) and \(\eta\) (resonance frequency) (14) was investigated. The pulsatility index \(v\) in the range of 0 to 0.75 was considered, and the impact of the initial prestress \(S\) on the resonance frequencies \(\eta\) was investigated. In order to compare the behaviors of all domes, the same maximum prestress level \(S_{\rm max}=50\) kN was adopted (the maximum cable effort was \(W_{\rm max}=0.95\) for domes with a closed upper section (domes type A) and \(W_{\rm max}=0.93\) for domes with an open upper section (domes type B). In turn, the minimum prestress level \(S_{min}\) is an individual characteristic of every dome. Type B domes have a lower value \(S_{\rm min}=2\) kN, whereas type A domes have a much higher value. \(S_{\rm min}=24\) kN for a regular dome, and \(S_{\rm min}=35\) kN for a modified dome.  
The main unstable regions corresponding to various frequencies depending on the initial prestress were determined. The four main selected unstable regions are presented in this paper. The influence of three levels of initial prestress \(S\) on the shape and range of these regions was determined. At this point, it should be noted that in the case of type B domes, the number of frequencies, depending on the initial prestress level \(S\), corresponded to infinitesimal mechanisms. In turn, in case of type A domes, there were three additional frequencies that depended on the prestress [18].