esa-sceva/satcom-chunk-collection huggingface dataset:
# Calibration and Image Reconstruction in a Spot Scanning Detection System for Surface Defects
## 4 Experiment Results and Discussions
### Experiments of System Calibration  
The test sample is first scanned by the SS-SDES before adjustment. According to the setting values, the ideal system parameter vector is \(\psi_{0}=\left(0^{\circ},0^{\circ},135^{\circ},90^{\circ},0,0,20,8\pi,10^{6}\right)\). The corresponding mapping relationship \(\mathbf{g}_{\psi_{0}}\) is then established using the practical scanning trace model as described in Section 3.1. Figure 6a shows the original image reconstructed using \(\mathbf{g}_{\psi_{0}}\). As can be seen, apparent distortions are present in the image, but most defects remain independent and distinguishable, allowing the original image to be used for the extraction of feature sequence number sets.  
The extraction process of feature points is illustrated in Figure 6. Image processing techniques including Gauss smoothing, top hat transformation, threshold segmentation, and region selection are performed on the original image successively, to find out the regions of the specific scratches and dig pairs that are used for constraints establishment. The feature points are then extracted through skeletonization for scratches and centroid calculation for digs. Figure 6b shows some of the selected defect regions, and Figure 6c,d shows the extracted feature points of a dig and a scratch, respectively. In the calibration experiment, ten scratches and ten distance-marked dot pairs are selected. They are distributed in various parts of the image so that constraints are applied as much as possible to the entire image. With the feature points, the feature sequence number sets are obtained through the backtracking strategy, as described in Section 3.2, to subsequently construct the constraint function.  
Figure 6: Flow chart of the extraction of feature points and corresponding images. (**a**) Original image; (**b**) Selected defect regions; (**c**) Feature point of a dig; (**d**) Feature points of a scratch.  
Taking \(\psi_{0}\) as the initial guess, the Levenberg-Marquardt Algorithm is performed to obtain the calibrated system parameter vector \(\dot{\psi}\), completing the whole process of the proposed system calibration method. The nonlinear optimization takes about 46 s, and the weight factor \(\lambda=2\) is chosen through experimental comparison. Table 1 gives the system calibration results where the rotation velocity \(\omega\) is in the unit of revolutions per second (RPS). It can be seen that the calibrated values of most parameters deviate from the setting values slightly, except the azimuthal angle of rotation axis \(\varphi_{r}\) and the coordinate of the initial illuminated spot \((x_{i},y_{i})\). The former is due to the fact that the rotation axis may tilt towards any azimuth in space, and the latter is caused by the lack of registration during system assembly. What is more important, shown in Table 1, is that the value of the constraint function decreases significantly after calibration, indicating that the distortion of the image is well corrected.
## 1 Introduction
Limitations of the traveling wave and non-traveling wave (NTW) based determinations of fault location are identified. The rest of the paper is organized as follows: a brief explanation of HVdc grid protection is given in Section 2. Section 3 explains TW. Section 4 covers the application of TW for fault estimation in MT-HVdc links having high grounding resistance. Section 5 discusses FCL method for hybrid HVdc systems. Finding of fault location based on natural frequency of TW is provided in Section 6. Variation in voltage signals at pre and post-fault state is used for fault detection and explained in Section 7. Section 8 deals with FCL via symmetrical components of TW. Section 9 contains a morphological gradient based method. Shortcomings of TW based method is covered in Section 10. The dc fault location detection via non-traveling-based methods, i.e., mode extraction, rate of change of voltage and current, voltage level detection, under voltage and over current detection are discussed in Sections 11-15. Transient based fault estimation in MT-HVdc systems is covered in Section 16. Electromagnetic time reversal-based fault classification and location is discussed in Section 17. Short comings of non-traveling wave methods are described in Section 18. Sections 19 and 20 cover the comparison of TW and NTW methods and existing FCL techniques, respectively. Practical realization of TW based relays for FCL is added in Section 21. Section 22 elaborates on future possibilities for efficient FCL in MT-HVdc systems. Finally, conclusions are drawn in Section 23.
# Experimental Investigation into the Influence of Plasma Technology on Seed Surface Wettability
## 2 Materials and Methods  
### Materials and Preparing  
The seeds of spring wheat (_Tritcium aestivum_ L.), of the variety Tercia from the Selgen breeding company, were used as the representatives of cereals. The legume seeds were represented by winter pea (_Pisum sativum_ L.), of the variety Aviron, also from the Selgen breeding company (Sibrina, Czech Republic). The sunflower seeds (_Helianthus annuus_ L.), of the variety Bella, provided by Oseva Agro, Ltd. (Brno, Czech Republic), were selected as the representatives of oilseeds.  
Several treatment variants were prepared for all crops. Each process variant for both discharges used was designed in the same way independently. Several seeds were placed on the adhesive tape inside the Petri dish to avoid seed turning or other movements during treatment. The Petri dish was placed on the holder under the plasma jet of the atmospheric plasma system. When low-pressure plasma discharge was used, the Petri dish was placed at the bottom of the vacuum chamber. Subsequently, the plasma treatment of prepared samples was performed under the preset process conditions.
## 3 Results and Analysis
### Analysis of the Steepest Ascent Method Results  
Based on the results of the two-level factorial test, the steepest ascent method was conducted to further determine the optimal range of the significant influencing factors. The non-significant influencing factors were held constant. Based on the defined ranges of the influencing factors, the initial levels for factors \(x_{2}\) and \(x_{3}\) were established at 0.3 \(\times\) 10\({}^{8}\) N\(\cdot\)m\({}^{-3}\)  
Figure 10: Pareto chart for a two-level factorial experiment. Note: \(x_{1}\) is the particle contact radius, \(x_{2}\) is the normal stiffness per unit area, \(x_{3}\) is the tangential stiffness per unit area, \(x_{4}\) is the critical normal stress, \(x_{5}\) is the critical tangential stress, and \(x_{6}\) is the bonding radius. The empty square columns in the figure represent six influencing factors.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Source** & **Effects** & **Degrees of Freedom** & **Mean Square** & **F** & **\(p\)** \\ \hline Model & 169,000 & 6 & 28,141.88 & 19.49 & \(<\)0.0001 ** \\ A-\(x_{1}\) & 539.24 & 1 & 539.24 & 0.373 & 0.5563 \\ B-\(x_{2}\) & 136,000 & 1 & 136,000 & 93.9 & \(<\)0.0001 ** \\ C-\(x_{3}\) & 19,185.07 & 1 & 19,185.07 & 13.29 & 0.0054 ** \\ D-\(x_{4}\) & 3754.61 & 1 & 3754.61 & 2.6 & 0.1413 \\ E-\(x_{5}\) & 9.14 & 1 & 9.14 & 0.006 & 0.9383 \\ F-\(x_{6}\) & 9003.42 & 1 & 9003.42 & 6.23 & 0.034 * \\ Residual & 12,996.85 & 9 & 1444.09 & & \\ \hline \end{tabular} Note. – * indicates extremely significant (\(p\) \(<\) 0.01), * indicates significant (0.01 \(\leq\)\(p\) \(<\) 0.05). \(x_{1}\) is the particle contact radius, \(x_{2}\) is the normal stiffness per unit area, \(x_{3}\) is the tangential stiffness per unit area, \(x_{4}\) is the critical normal stress, \(x_{5}\) is the critical tangential stress, and \(x_{6}\) is the bonding radius.  
\end{table}
Table 5: Significance analysis of the two-level factorial test results.  
with increments of 0.8 \(\times\) 10\({}^{8}\) N\(\cdot\)m\({}^{-3}\). For factor \(x_{6}\), the starting value was set to 1.3 mm, and the factor levels were determined using a gradient increment of 0.08 mm. The design and results of the steepest ascent method are presented in Table 6. As the levels of the significant influencing factors increased, the shear force also gradually increased. Since the simulated shear force in Group 2 (37 N) closely approximates the experimentally measured value, the factor settings from Groups 1 to 3 were selected for the subsequent Box-Behnken Design.
## 3 Results and Discussions
### Compressive Strength of Pervious Concrete  
The 28 d compressive strength of the pervious concrete and its relationships versus the effective porosity are shown in Figure 7. It is expected that the 28 d compressive strength of the pervious concrete decreased from 42 MPa to 11 MPa with the effective porosity increasing from 5.2% to 27.2%. It has been reported that the porosity has a decisive effect on the strength of concrete, at the same time, the pore structure (such as pore size, pore distribution, pore shape and orientation, etc.) also affects the compressive strength of the concrete [32; 33]. Work by [PERSON] [32] shows that there is a critical relationship between compressive strength and pore threshold radius of mortar. As threshold radius approaches gel pore size, compressive strength increases rapidly, indicating that pore size, rather than pore volume, is a limiting factor of compressive strength development. [PERSON]'s study [33] shows that  
Figure 7: Dry Density of the pervious concrete and its relationship versus the effective porosity and measured porosity.  
existing linear functions relating the strength with pore size characteristics of cement-based material are inadequate in the context of concrete. Therefore, the compressive strength and effective porosity of concrete do not exhibit a simple linear function. The relationship between the compressive strength and effective porosity is fitted with different functions (see Figure 8). Among these, the correlation of the logarithmic function was best. Therefore, the logarithmic function, displayed as \(\mathrm{f_{c}}=72.9-18.4\times\mathrm{ln(P)}\), was selected to analyze and predict the compressive strength of pervious concrete under different effective porosities.
## 3 Mathematical Formulation
### Constraints
#### 3.2.2 The Second-Stage Constraints
\begin{array}{c}0\leq P_{ESS,2}^{disk,t}(\xi)=P_{ESS-grid,2}^{t}(\xi)+ P_{ESS-load,2}^{t}(\xi)\leq\left(1-u_{SS,2}^{t}(\xi)\right)P_{ESSmax}\\ P_{ESS,2}^{disk,t}(\xi)+P_{ESS-SR}^{t}\leq P_{ESSmax}\end{array} \right.\] (18) \[E_{ESSmin}\leq E_{ESS,2}^{t}(\xi)=E_{ESS,2}^{t-1}(\xi)+\eta P_{ESS,2}^{ch,t}( \xi)-P_{ESS,2}^{disk,t}(\xi)/\eta\leq E_{ESSmax}\] (19)
* Day-ahead market's constraints: \[0\leq P_{sell,2}^{t}(\xi)=P_{RES-grid,2}^{t}(\xi)+P_{ESS-grid,2}^{t}(\xi) \leq\left(1-u_{buy}^{t}\right)(P_{ESSmax}+P_{RESrated})\] (20) \[0\leq P_{buy,2}^{t}(\xi)=P_{grid-load,2}^{t}(\xi)+P_{grid-ESS,2}^{t}(\xi) \leq u_{buy}^{t}(P_{ESSmax}+Load_{max})\] (21)
* Active power balance constraint: \[\Pr\left(P_{RES,2}^{t}(\xi)+P_{ESS,2}^{disk,t}(\xi)+P_{buy,2}^{t}(\xi)=P_{ sell,2}^{t}(\xi)+P_{ESS,2}^{ch,t}(\xi)+P_{Df}^{t}+P_{D-error}^{t}P_{Df}^{t} \right)\geq 1-\varepsilon\] (22)Constraint shows the association between case 1 and case 2:  
During the reserve contract period, it is necessary to ensure that the VPP is always ready to provide reserve at any time. Therefore, even if the reserve is not called and generated, the VPP is not allowed to sell that part of the capacity to the energy market. Consequently, the VPP's buying/selling power in the energy market should be the same for cases 1 and 2. When there is no reserve contract, the VPP's buying/selling power is allowed to be adjusted to take full advantage of the power output of the RESs. In addition, since the ESS provides a part of reserve capacity, the energy in the ESS immediately before the contract period should be the same in both cases. The following constraints can secure these issues:  
\[\left\{ \begin{array}{l}-\left(1-u_{SR}^{t}\right)bigM\leq P_{sell,1}^{t}( \xi)-P_{sell,2}^{t}(\xi)\leq\left(1-u_{SR}^{t}\right)bigM\\ -\left(1-u_{SR}^{t}\right)bigM\leq P_{buy,1}^{t}(\xi)-P_{buy,2}^{t}(\xi)\leq \left(1-u_{SR}^{t}\right)bigM\end{array} \right. \tag{23}\]  
\[-\left(1-u_{SR}^{t}+u_{SR}^{t-1}\right)bigM\leq E_{ESS,1}^{t-1}(\xi)-E_{ESS,2}^ {t-1}(\xi)\leq\left(1-u_{SR}^{t}+u_{SR}^{t-1}\right)bigM \tag{24}\]
# An On-Orbit Task-Offloading Strategy Based on Satellite Edge Computing
## 3 Model Introduction and Problem Analysis
### Objective Function
The scheduling procedure should then satisfy  
\[T_{trans}^{i}=\frac{D_{i}}{B}hop_{i}, \tag{6}\]2  
Both task completion delay and system energy consumption are issues to consider during the satellite task scheduling process. The system cost obtained by weighting them together is defined as  
\[T_{trans}^{i}=\frac{D_{i}}{B}hop_{i}, \tag{6}\]3  
where \(\alpha\) and \(\beta\) are used as weights to indicate the importance given to latency and energy consumption, respectively. In summary, the optimization problem for dependent task scheduling based on \(SatTEG\) can be represented as follows:  
\[ \begin{split} min\ COST\\ s.t.&(2)(3)(4)(14)(17).\end{split}  \tag{19}\]
## 3 Analysis and Comparison of Machine Learning-Based Anomaly Detection
### Machine Learning Models
#### 3.1.7 k-Nearest Neighbors (KNN) Classifier  
The k-nearest neighbors (KNN) classifier is a non-parametric and instance-based machine learning technique used for classification and regression. It is based on the idea of similarities between features. In KNN, each data point is assigned a category based on the classification of its \(k\)-nearest neighbors, where the user selects \(k\) as a hyper-parameter. The predicted class for a new data point can be determined by the majority class among the \(k\)-nearest neighbors, as expressed in Equation (7):  
\[\hat{y}=\operatorname*{argmax}_{c}\sum_{i=1}^{k}I(y_{i}=c) \tag{7}\]  
where \(c\) shows each class label and the predicted class is represented by \(\hat{y}\). Therefore, \(y_{i}\) denotes the class label of the \(i\)-th nearest neighbor. In this formula, \(I(\cdot)\) is the indicator function, which returns 1 if the condition inside is true and 0 otherwise.  
In the study, we conducted a comparative analysis and determined that using five neighbors (the optimal value of \(k\)) is appropriate. The algorithm determines the distance of a new data point from the rest of the dataset, usually using the Euclidean distance metric, to decide its classification. It picks the most frequent label among the \(k\)-nearest data points for classification tasks, and for regression tasks, it takes the mean of the selected points. The algorithm's performance is highly dependent on the value of \(k\); smaller values make it vulnerable to local noise, while larger values can lead to over-smoothing of the decision border [50]. KNN is computationally efficient during training, but its prediction method can be time-consuming, particularly when working with large datasets, due to the need to calculate distances to all data points.
## 4 Results
### Analysis of the Forested Areas' Exchange Status
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Land Use Types** & **Transfer in (Gain)** & **Transfer out (Loss)** & **Net Transition** \\ \hline \multirow{6}{*}{2005–2010} & Agricultural land & 940.99 & 1214.2 & \(-\)273.29 \\ \cline{2-5}  & Artificial surfaces & 75.69 & 61.42 & 14.26 \\ \cline{2-5}  & Forested areas & 403.00 & 668.72 & \(-\)265.72 \\ \cline{2-5}  & Grassland & 1214.01 & 460.07 & 753.94 \\ \cline{2-5}  & Bare land & 521.30 & 628.68 & \(-\)107.38 \\ \cline{2-5}  & Wetland & 13.36 & 135.17 & \(-\)121.81 \\ \hline \multirow{6}{*}{2010–2015} & Agricultural land & 1017.63 & 1127.68 & \(-\)110.05 \\ \cline{2-5}  & Artificial surfaces & 93.81 & 42.56 & 51.24 \\ \cline{2-5}  & Forested areas & 854.16 & 389.85 & 464.30 \\ \cline{2-5}  & Grassland & 355.37 & 1263.04 & \(-\)907.68 \\ \cline{2-5}  & Bare land & 897.68 & 432.03 & 465.65 \\ \cline{2-5}  & Wetland & 53.93 & 17.37 & 36.55 \\ \hline \multirow{6}{*}{2015–2020} & Agricultural land & 1269.56 & 1149.99 & 119.56 \\ \cline{2-5}  & Artificial surfaces & 112.14 & 68.92 & 43.22 \\ \cline{2-5}  & Forested areas & 293.99 & 702.12 & \(-\)408.13 \\ \cline{2-5}  & Grassland & 845.14 & 442.01 & 403.12 \\ \cline{2-5}  & Bare land & 677.50 & 792.98 & \(-\)115.49 \\ \cline{2-5}  & Wetland & 25.24 & 67.53 & \(-\)42.29 \\ \hline \end{tabular}
\end{table} 
Table 3: Quantitative analysis of land type shifts at the interval level (km\({}^{2}\)). In summary, agricultural land, bare land, and artificial surfaces had the most significant impact on forested areas. However, the complex and fluctuating relationship between forests and other land use types underscores the uncertainty surrounding the impact of land use changes on ecosystem services, particularly in forested areas. Accordingly, enforcing land use regulations that promote sustainable forest resource use and protect agricultural land from transition is fundamental in the Thies region.
# Research on Image Mosaic Method Based on Fracture Edge Contour of Bone Tag
## 2 Basic Method
### Feature Descriptors  
In this paper, the ORB algorithm is used to extract feature descriptors. The ORB algorithm [20] is improved on the basis of the FAST algorithm [21]. The advantage lies in the efficient completion of feature extraction and feature description. Rotation invariance. The algorithm adopts binary description, which reduces storage space, improves the speed of feature descriptor generation, and reduces the time required for feature matching. The feature key points are in the prominent area of the image, and the ORB algorithm finds a special area in the image and calculates the corresponding feature vector for each key point, which can identify specific objects in the image.  
ORB determines the feature vector by the moment method, takes the feature point as the center of the circle, sets the feature point area with radius r, the moment method findsthe centroid of all feature points in the area, and connects the feature point and the centroid to form a vector, this vector is used as the feature direction vector. The method of moments is defined as follows:  
\[m_{pq}=\sum x^{q}y^{p}I(x,y)\ \ \ \ p,q\in\{0,1\} \tag{7}\]  
On an edge-detected patch, \(I(x,y)\) is the gray value at \((x,y)\) and \(m_{pq}\) is the moment of the image. The centroid \(C\) of the image block is calculated by the method of moments, and the coordinates are  
\[\begin{cases}C_{x}=\frac{m_{10}}{m_{00}}\\ C_{y}=\frac{m_{10}}{m_{00}}\end{cases} \tag{8}\]  
Connect the geometric center O and the centroid \(C\) of the image block to obtain a direction vector, take the feature point as the original coordinate, and obtain a direction angle, which is defined as  
\[\alpha=\arctan(m_{01}/m_{10}) \tag{9}\]  
The orientation angle of the feature points of each ORB image patch can be represented by \(\alpha\). When ORB calculates the feature descriptor, it is expressed in binary and is defined as  
\[\tau=(p;x,y)=\begin{cases}1&p(x)\leq p(y)\\ 0&p(x)>p(y)\end{cases} \tag{10}\]  
In which, \(p(x)\) and \(p(y)\) are the grayscale values of a pair of points at \((x,y)\) respectively.  
While ORB extracts feature descriptors, it has the characteristics of rotation invariance and scale invariance. Scale invariance is to perform Gaussian blur transformation of different scales on the image, down-sampling, and performing FAST feature point detection for each layer of pyramids. Obtain the key points of images of different scales, and finally extract the sum of the feature points from n images of different scales as the image feature descriptor. ORB assigns orientations to key points, creating the same vector regardless of the orientation of the key points, making it rotationally invariant.
# Simulation of Wave Propagation Using Finite Differences in Oil Exploration
## 3 Results
### Investigation of the Stability of the Generated Results  
The researchers observed that the developed pseudocode analyzed and evaluated different oilfield geological samples within the selected domain. The programming simulator proposed in this study was adapted for all the proposed scenarios, producing a satisfactory response that adjusted to the geological behavior in the presence of hydrocarbons in the reservoirs. Notably, they evaluated the stability of the code in a realistic situation using ultrasound equipment for reservoir detection. The simulation software responded consistently to the wave propagation analysis on the oil surface, producing satisfactory results.  
To test the stability of the solutions, the researchers subjected the pressure to prolonged durations, considering its time-dependent nature. As the pressure progressed over several units of computational time, its graph exhibited a smooth and consistent pattern throughout this evolution. The absence of unexpected peaks in the pressure graph during the progression was observed and ensured. For maintaining solution stability, calculating the interdependent mesh sizes (dx, dy, dz, and dt) required meticulous consideration to select the appropriate number of points for each mesh. A comprehensive examination of the meshes and spacings in the \(x\), \(y\), \(z\), and \(t\) directions was conducted, ensuring their mutual dependence and adherence to the limits imposed by the CFL condition. The paper specified stability conditioned by the CFL condition (refer to Equation (15)), implying a specific relationship that must be fulfilled. Figure 1 illustrates the smooth evolution up to 150 s, with no occurrence of peaks.