esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Relevance of Methods  
The overall performance of the proposed scheme is in accordance with the requirements of an operational agriculture monitoring system. Discrimination and characterization of the cultivated crop types, is of paramount importance in the development, conservation, and management of natural resources at both macro- and micro-scales [41]. The identification of crops, along with their distribution, management practices, and annual rotation schemes provide essential information for the implementation, control, and monitoring of agricultural policies and environmental measures imposed by the CAP [3].  
Open access to the unmatched features of the Sentinel mission, in temporal and spatial resolution, and their efficient exploitation, signal a new era in the field. A revisit time of five days ensures the proper construction of imagery time-series, able for the consistent and timely monitoring of the agricultural landscape. Additionally, the alternative Landsat data, featuring a three-fold reduction in temporal resolution, can offer inadequate number of quality images, particularly in heavily clouded regions.  
On the other hand, Sentinel-2's high resolution multispectral data enable the successful employment of a parcel-based approach and the generation of parcel-specific thematic information. Besides, alternative VHR imagery is unrealistically costly for operational, large-scale, and consistent monitoring. The proposed methods, which effectively take advantage of the Sentinel-2 attributes, could ably function as the backbone for the EO-assisted compliance validation of CAP requirements, especially to what concerns the enhancement of transparency and the simplification of subsidy administration. The scheme was designed to directly assist the CAP paying agencies in their compliance inspections, by utilizing the data used (LPIS and farmers' declarations) in their existing operations, to offer a monitoring alternative to their inefficient sample-based controls. The scheme is finally characterized by robustness, in the sense that it is largely independent of manual fine-tuning or case-specific optimizations and is thereby reproducible.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Pairs** & **n\({}_{\text{ab}}\)** & **n\({}_{\text{ba}}\)** & **\(\chi^{2}\)** & **_p-Value_** & **h** \\ \hline
1 & Sentinel (10 m) & Landsat (15 m) & 205 & 1073 & 589.53 & \textless{}0.0001 & True \\
2 & Sentinel (10 m) & Landsat (30 m) & 207 & 1204 & 704.47 & \textless{}0.0001 & True \\
3 & Landsat (15 m) & Landsat (30 m) & 371 & 500 & 19.11 & \textless{}0.0001 & True \\
4 & Sentinel (30 m) & Landsat (30 m) & 322 & 988 & 338.54 & \textless{}0.0001 & True \\
5 & Sentinel SVM & Sentinel RF & 114 & 577 & 310.23 & \textless{}0.0001 & True \\ \hline \hline \end{tabular}
\end{table}
Table 8: McNemar’s test results for all different classification pairs (spatial resolution is defined in parentheses).
# Safety Effects of Freeway Hard Shoulder Running
## 3 Results  
Estimates for each of three safety performance functions are given in Table 3. As expected, traffic volume, share of truck traffic, average speed, number of lanes, and length of road sections all have a significant effect on the total number of crashes and number of property damage only crashes. However, only truck traffic and section length have a significant effect on the number of serious crashes. On the other hand, lane width and shoulder width have been found to be insignificant.  
Table 4 shows the estimated long-term expected numbers of crashes for the 22 freeway sites with HSR versus the actual crash numbers in those sites. As expected, increases were found for all crash types in aggregate (the sum of all 22 sites). However, the increase in the number of serious crashes (those involving injuries and/or fatalities) was relatively small (\(\overline{x_{s}}=4.5-4.4\)) compared to property damage only (\(\overline{x_{pdo}}=17.9-15.5\)) and total crashes (\(\overline{x_{t}}=22.4-19.1\)). Thus, freeway motorists operating on HSR appeared to be involved in crashes more frequently, but most of the crashes were less severe. Part of the increase may result from drivers' risky behaviors within HSR areas. Some of the drivers may be unfamiliar with the changed freeway driving environment due to HSR and may engage in unexpected vehicle maneuvering.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & \multicolumn{4}{c}{**Actual Crashes**} & \multicolumn{4}{c}{**Expected Crashes**} \\  & \multicolumn{2}{c}{**(Crashes/3 years)**} & \multicolumn{4}{c}{**(Crashes/3 years)**} \\ \cline{2-7}  & **Total** & **Serious** & **PDO** & **Total** & **Serious** & **PDO** \\ \hline
1 & 26 & 8 & 18 & 21.4 & 6.9 & 14.9 \\
2 & 42 & 0 & 42 & 33.5 & 1.0 & 33.1 \\
3 & 68 & 0 & 68 & 55.9 & 1.7 & 55.2 \\
4 & 26 & 11 & 15 & 21.7 & 9.2 & 12.8 \\
5 & 11 & 4 & 7 & 9.4 & 3.6 & 6.1 \\
6 & 16 & 6 & 10 & 14.2 & 5.4 & 9.1 \\
7 & 12 & 1 & 11 & 11 & 1.1 & 10.1 \\
8 & 2 & 0 & 2 & 2.1 & 0.1 & 2.1 \\
9 & 11 & 4 & 7 & 10.3 & 3.8 & 6.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Estimated crash effects of HSR.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dependent Variables**} & \multicolumn{4}{c}{**Independent Variables**} \\ \cline{2-7}  & & **Ln(A)** & **Ln(T)** & **Ln(V)** & **N** & **L** \\ \hline \multirow{2}{*}{Total crashes} & coefficients & 0.938 & 0.430 & 2.567 & \(-\)0.454 & 0.123 \\  & \(p\)-value & 0.010 & 0.032 & 0.041 & 0.040 & \(<\)0.001 \\ \hline \multirow{2}{*}{Serious crashes} & coefficients & & 0.627 & & & 0.131 \\  & \(p\)-value & & \(<\)0.001 & & & \(<\)0.001 \\ \hline \multirow{2}{*}{PDO crashes} & coefficients & 1.188 & 0.462 & 3.013 & \(-\)0.654 & 0.113 \\  & \(p\)-value & 0.002 & 0.030 & 0.026 & 0.005 & \(<\)0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Safety performance function.
# An Orthogonal Wheel Odometer for Positioning in a Relative Coordinate System on a Floating Ground
## 3 Experiments
### The Experiment of Linear Motion
#### 3.2.4 The Experiment of Moving along a Square  
The mobile robot chassis moves in a square counterclockwise direction along the simulated floating platform as shown in Figure 23.  
Due to the size limitation of the floating platform, we choose the side length of the square to be 1.8 m. Figure 24 shows the movement track of the mobile chassis. The positioning effect of the positioning module is verified by the movement of the chassis along the square. The four points of the square are set to P\({}_{0}\), P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\). The actual motion scene is shown in Figure 25.  
Figure 23: Simulation floating experimental platform, size of 2.4 m \(\times\) 2.4 m.  
\begin{table}
\begin{tabular}{c c c} \hline
**The Theoretical Angle Value (*)** & **The Actual Angle Value (*)** & **Error (*)** \\ \hline
180.00 & 180.12 & 0.12 \\
360.00 & 360.42 & 0.42 \\
540.00 & 540.62 & 0.62 \\
720.00 & 721.15 & 1.15 \\ \hline \end{tabular}
\end{table}
Table 3: The actual movement value and error of the positioning system for each movement of 180\({}^{\circ}\) along the Z axis.  
Figure 24: The movement track of the mobile chassis. The length of the square side is 1.8 m. The four points of the square are set to P\({}_{0}\), P\({}_{1}\), P\({}_{2}\), and P\({}_{3}\). In the horizontal plane coordinate system XOY, the center of the chassis is center point O, OX direction is the initial motion direction of the mobile chassis, and OY direction is the vertical direction of moving robot. The initial position coordinates of the mobile robot (x, y) are the origin of the world coordinate system.  
The displacement transformation curve and angle change curve of the mobile robot chassis are shown in Figure 26. In the process of movement, the actual motion curve is basically consistent with the theoretical motion curve, with only a little fluctuation. The floating degree of the simulated platform can be reflected by the angle of chassis coiling around pitch and roll axis, as shown in the Figure 27.  
Figure 26: (**a**) Comparison diagram of actual motion curve and theoretical motion curve. The actual motion curve is basically consistent with the preset motion path. (**b**) The angle change curve of the mobile robot when it moves in a counterclockwise direction along a square.  
Figure 25: The actual motion scene. In the figure, 1, 2, 3 and 4 are the four stages of the movement process respectively.  
Table 4 records the actual movement value and error of the positioning system for each point along the square. It can be seen from Table 4 the positioning value after moving to each point along the square. In addition, after the positioning system returns to the origin after four points, the distance cumulative error is approximately 1.6 cm, and the angle cumulative error is approximately 1.42\({}^{\circ}\). The experiment shows that the positioning system can meet the requirements in the short distance.
## 2 Dynamic Modeling
### Restoring Force and Restoring Moment  
In the inertial coordinate system, the direction of the HAUV subjected to gravity in the water is opposite to the positive direction of the axis, but the direction of buoyancy is the same as the positive direction of the axis. Buoyancy is obtained by using Archimedes' principle, where gravity and buoyancy are, respectively, as follows:  
\[\begin{array}{ll}f_{g}&=-mgI_{1}^{T}\left[\begin{array}{ccc}0&0&1\end{array} \right]^{T}\\ &=-mg\left[\begin{array}{ccc}-\sin\theta&\sin\phi\cos\theta&\cos\phi\cos \theta\end{array}\right]^{T}\\ \tau_{g}&=\left[\begin{array}{ccc}0&0&0\end{array}\right]^{T}\\ \\ f_{b}&=k_{s}\rho_{w}Vgf_{1}^{T}\left[\begin{array}{ccc}0&0&1\end{array} \right]^{T}\\ &=k_{s}\rho_{w}Vg\left[\begin{array}{ccc}-\sin\theta&\sin\phi\cos\theta&\cos \phi\cos\theta\end{array}\right]^{T}\\ \tau_{b}&=r_{b}\times f_{b}\end{array} \tag{9}\]  
Here, \(\rho_{w}\), \(V\), and \(g\) are defined as the density of water, the volume of the HAUV watertight chamber, and the gravitational acceleration, respectively. Since the origin of the coordinate system is chosen to be located at the center of gravity of the HAUV, no torque is generated by gravity. The center of buoyancy is \(r_{b}=\left[\begin{array}{ccc}x_{b}&y_{b}&z_{b}\end{array}\right]^{T}\). Based on the above analysis, the restoring force is expressed as a vector:  
\[g_{v}=-\left[\begin{array}{c}f_{g}+f_{b}\\ \tau_{g}+\tau_{b}\end{array}\right] \tag{10}\]  
**Remark 1**: _It is worth noting that, according to the basics of ship principles, the stability of a floating body is closely related to the relative position of the center of buoyancy to the center of gravity, which changes dynamically. The special elongated structure of the coaxial HAUV leads to a minimal change in the horizontal position of the center of buoyancy during its trans-domain motion. The simplified rule of change in the vertical position of the center of action of the wind force and the center of buoyancy is given here:\[z_{b} =\begin{cases}\begin{array}{c}0,z>h/2\\ k_{s}z_{b0}-(h/2+z)/2,-h/2\leq z\leq h/2\\ z_{b0},z<-h/2\\ 0,z>h/2\\ h/2-(h/2+z)/2,-h/2\leq z\leq h/2\\ 0,z<-h/2\end{array}\end{cases} \tag{11}\]  
where \(z_{b0}\) is the vertical coordinate value of the center of buoyancy in the body-fixed coordinate system under fully submerged HAUV conditions.  
The position of the center of action of the wind force and the center of buoyancy in the body-fixed coordinate system can be approximated by using mathematical calculations.  
\[r_{b} =\begin{bmatrix}z_{b}\sin(\theta)&-z_{b}\sin(\phi)&z_{b}\end{bmatrix}^{ \mathsf{T}} \tag{12}\] \[r_{w} =\begin{bmatrix}-z_{w}\sin(\theta)&z_{w}\sin(\phi)&z_{w}\end{bmatrix}^{ \mathsf{T}}\]
# Nonlinear Dynamics of a Resonant-Impact Dielectric Elastomer Actuator
###### Abstract  
In recent years, with the rapid development of soft robots, dielectric elastomer actuators (DEAs) as a novel type of soft actuators have been widely studied. However, DEAs often suffer from low instantaneous output force/power, especially in high payload damping conditions, which limits their applications in certain scenarios. Inspired by the vibro-impact mechanisms found in many engineering systems (e.g., pile driving and percussive drilling), a resonant-impact DEA system was proposed in the authors' previous work to potentially address this limitation. However, due to the complex nonlinearities and unique electromechanically coupled forcing mechanism of DEAs, no nonlinear dynamic model was developed to perform systematic investigations and optimization. In this paper, a nonlinear dynamic model of the resonant-impact DEA system is developed by considering multiple nonlinearities, viscoelasticity, and electromechanical coupling. Using both a numerical model and extensive experiments, the nonlinear dynamics of the resonant-impact DEA system are studied in depth. The effects of several key parameters, including excitation voltage amplitude, constraint gap, constraint stiffness, and number of DEA layers, on the dynamic response of the system are characterized. The findings reported in this paper can provide guidance for the performance optimization of resonance-impact DEA systems and their applications.  
dielectric elastomer actuators; resonant-impact systems; soft actuators; nonlinear dynamics Article
# Predicting Soccer Player Salaries with Both Traditional and Automated Machine Learning Approaches
## 3 Overview of Data and Preprocessing
### Data Preprocessing
#### 3.2.1 Feature Encoding  
To transform categorical variables into numerical formats suitable for machine learning, we use Label Encoding [34] for low-cardinality features and Hashing Encoding [35] for high-cardinality features. Label Encoding assigns a unique integer to each category:  
\[LE(c_{i})=i\quad\forall\;c_{i}\in C \tag{1}\]  
where \(LE(c_{i})\) is the encoded value of category \(c_{i}\) and \(C\) is the set of unique categories. While efficient, this may introduce ordinal relationships not present in the data. Hashing Encoding applies a hash function to map categories to a fixed number of buckets, mitigating issues of order and high dimensionality:  
\[HE(c_{i})=\text{hash}(c_{i})\mod k\quad\forall\;c_{i}\in C \tag{2}\]  
To select an encoder, we use the following rule based on cardinality:  
\[E(F)=\begin{cases}HE(F)&\text{if }|C(F)|>10\\ LE(F)&\text{if }|C(F)|\leq 10\end{cases} \tag{3}\]  
This strategy balances computational efficiency and model performance by adapting to the nature of the categorical features.
## 6 Model Building and Analysis
### Regression Analysis  
In this step, the Best Subset Regression function was used where Rsq increases with the number of variables added to the equation; however, Rsq(adj) varies as a peak, and it increases only if the added variable contributes to a better fit of the equation. The best subset reported is the highest Rsq and Rsqadj value. Additionally, Mallow's coefficient Cp should be equal or close to equal to the number of variables, as shown in Table 4 [45].  
For this data, the best subset identified included 20 factors (qualitative and quantitative). After multiple iterations for various subsets shortlisted from AHP, the best subset analysis was carried out. The highest Rsq value achieved was 88.92%, Rsqadj, at 78.97%. Figure 9 shows the seven shortlisted qualitative variables for modeling purposes. There are two main reasons for this. Firstly, the model deals with personal opinions, which are highly qualitative and difficult to model. Secondly, the majority of participants who responded in the survey fall between rank 1 to 200. Therefore, this model can best predict the ranking for organizations that fall between this range. This was the main reason for not utilizing the regression prediction model to assess organizational performance assessment.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source** & **DF** & **Adj SS** & **Adj MS** & **F-Value** & \(p\)**-Value** \\ \hline Regression & 35 & 6.566 & 0.187 & 8.94 & 0.000 \\ Growth Rate 1 year & 1 & 0.0073 & 0.0073 & 0.35 & 0.557 \\ CAGR 3 years & 1 & 0.0000 & 0.0000 & 0.00 & 0.994 \\ General Building & 1 & 0.0175 & 0.0175 & 0.83 & 0.367 \\ Manufacturing & 1 & 0.3113 & 0.3113 & 14.84 & 0.000 \\ Power & 1 & 0.1274 & 0.1274 & 6.07 & 0.018 \\ WWS & 1 & 0.4173 & 0.4173 & 19.89 & 0.000 \\ Industrial & 1 & 0.1337 & 0.1337 & 6.37 & 0.016 \\ Transportation & 1 & 0.0140 & 0.0140 & 0.67 & 0.419 \\ Hazardous Waste & 1 & 0.4151 & 0.4151 & 19.78 & 0.000 \\ Telecom & 1 & 0.2815 & 0.2815 & 13.42 & 0.001 \\ \% CM at risk & 1 & 0.0373 & 0.0373 & 1.78 & 0.190 \\ Revenue per employee (\$MIL) & 1 & 0.2340 & 0.2340 & 11.16 & 0.002 \\ No. of employees & 1 & 0.0137 & 0.0137 & 0.66 & 0.423 \\ Total number of years in business & 1 & 0.0453 & 0.0453 & 2.16 & 0.150 \\ Market diversification entropy & 1 & 0.0850 & 1.0850 & 51.71 & 0.000 \\ Clear vision mission and goals & 4 & 0.4851 & 0.1212 & 5.78 & 0.001 \\ Competitive strategy & 2 & 0.6449 & 0.3224 & 15.37 & 0.000 \\ Availability of knowledge & 3 & 0.0324 & 0.0108 & 0.52 & 0.674 \\ Business experience & 3 & 0.2885 & 0.0962 & 4.58 & 0.008 \\ Employee cultural environment & 3 & 0.3015 & 0.1005 & 4.79 & 0.006 \\ Training for employee & 3 & 0.0575 & 0.0191 & 0.91 & 0.443 \\ Market condition/customer engagement & 2 & 0.1103 & 0.0552 & 2.63 & 0.085 \\ Error & 39 & 0.8183 & 0.0209 & & \\ Total & 74 & 7.3846 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Regression analysis best subset.
## 3 Electric Field Detection Method of Degraded Insulators Based on Sensitive Insulators
### Detection and Test Platform of Degraded Insulators
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Zero-Value Position**} & \multicolumn{6}{c}{**Space Electric Field Strength (kV/m)**} \\ \cline{2-10}  & **No Degradation** & **\#1** & **\#3** & **\#5** & **\#7** & **\#9** & **\#11** & **\#13** & **\#15** \\ \hline
1 & 88.7 & 83.5 & 88.5 & 88.0 & 89.5 & 89.7 & 89.9 & 90.8 & 88.6 \\
2 & 76.6 & 82.2 & 70.6 & 75.0 & 77.8 & 78.2 & 78.8 & 78.6 & 76.0 \\
3 & 66.5 & 73.2 & 61.6 & 66.4 & 67.0 & 68.1 & 68.8 & 68.2 & 66.5 \\
4 & 58.3 & 65.2 & 58.8 & 60.1 & 59.1 & 59.9 & 60.7 & 60.9 & 58.3 \\
5 & 50.5 & 54.9 & 50.8 & 45.3 & 51.9 & 52.0 & 52.5 & 52.1 & 51.5 \\
6 & 42.3 & 46.9 & 43.5 & 45. According to Table 4, no matter where (any position of #1-#15 insulator strings) the zero-value insulator is set, there shall be at least two \(\sigma_{ij}\) with more than 5.0% in the last three insulators at the low-voltage end. Based on the criterion for the sensitive insulator method in Section 2.1, three insulators of the insulator string close to the cross arm side can be judged as sensitive insulators. Therefore, under a voltage level of 220 kV, the sensitive insulator of the insulator string is generally at the low-voltage end, and during detection, the space electric field strength of the sensitive insulator set can be collected first to judge whether this insulator string is degraded.
# Urban Quarry Ground Vibration Forecasting: A Matrix Factorization Approach
## 7 Discussion
### Normalization  
Without normalization, the PPV prediction accuracy declined compared to using ANN for normalization. A possible reason is that the value ranges differed significantly among parameters, making it challenging to lead to an appropriate solution. In WNMF, the difference between the original matrix and the decomposed matrix is defined as the loss function, and updates are repeated to minimize it. If the value range in the original matrix varies greatly due to parameters, the computational cost increases, and there might be cases where it does not converge to the optimal solution. The data used in this study varied greatly in value range depending on the parameters, which could have led to a decline in prediction accuracy. Moreover, uniformly performing normalization resulted in a lower PPV prediction accuracy compared to using ANN for normalization. This could be attributed to setting the same normalization value range for all parameters. Despite each parameter possibly having different impacts on the PPV, the same normalization range was set for all parameters. It can be presumed that focusing on learning from parameters that have a significant impact on PPV would improve prediction accuracy. In contrast, with ANN-based normalization, the weights calculated by ANN were quantified as the impact on PPV, and the normalization value range was varied according to those weights. As a result, it's conceivable that normalization using ANN resulted in higher prediction accuracy than uniform normalization.
# Predicting and Modeling Wildfire Propagation Areas with BAT and Maximum-State PageRank
## 1 Introduction
The Binary-Addition-Tree (BAT) proposed by [PERSON] [46] is similar to the depth-first-search (DFS) [47; 22; 48], breadth-first-search (BFS) [49; 50; 51], universal generating function methodology (UGFM) [43; 44; 45; 46; 47; 48; 49; 50; 51], etc. However, BAT is easier to understand and convenient to code. The most important advantage of BAT is its flexible unique search procedure, and its valuable and easy implementation [46]. Hence, it has been adapted to solve the proposed wildfire propagation. This study aims to calculate the probability of the wildfire propagation areas and quickly determine the areas that require immediate protection. Using the proposed novel state concepts and traditional PageRank, whose details are provided in Section 2.3, this study proposes an innovative methodology based on BAT [46] and PageRank [54; 55] to predict the probable areas affected by the wildfire propagation that is modeled using the scale-free network generated from the Barabasi-Albert model [45] and assess the risk of wildfire spread. Furthermore, the proposed maximum-state PageRank is implemented to quickly identify the node that requires immediate protection. The rest of this study is organized as follows. An overview of the wildfire propagation models, scale-free networks [42; 43; 44; 45], Barabasi-Albert model [45], and BAT [46] are presented in Section 2. The pseudo-code, details of the BAT in solving the given problem, and discussion regarding the novelty of the proposed BAT, innovative states, state PageRank, state probability, and maximum-state PageRank are presented in Section 3. An experiment is presented in Section 4 to demonstrate the applicability of the proposed BAT. Furthermore, the analytical results are reported in this section. Section 5 concludes and summarizes the discussion.