esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Average Photosynthesis Rate at Plant Level
This indicates a strong adaptation of the plant's metabolism to cope with the limitation in light availability such as increasing the specific leaf area and reducing respiration. Excessive solar reduction caused by high shading rates can decrease the total and marketable yield of tomato grown in a greenhouse [35]. In contrast, light to moderate shading does not affect total and marketable yield [36; 37] and can even improve production under warm growing conditions and high solar radiation [35; 38]. [PERSON] et al. (2019) [39] found no differences in the total or cumulative marketable yield associated with the use of shading of 9.8% by photovoltaic panels. There are other crops that do not need so much sunlight for proper growth. Examples of this, and their interaction with semitransparent PV solar panels on the greenhouse roofs, have been described in [40]. In addition [41], in strawberry and raspberry cultivation, concluded that the parameters of fruit quality (sugars, anthocyanins, phenols, organic acids, etc.) are not affected by the shading of solar panels on the greenhouse roof. Moreover, tests with PV panels on the greenhouse roof (20%) in pepper cultivation have been described in [42]. These authors concluded that the quality of the pepper fruit is not affected.
## 4 Establishment of Contact Mechanics Model
### Solution of Porosity and Real Contact Area
#### 4.2.2 Contact Area Ratio  
(1) Influence of contact pressure on \(A_{\rm r}/A_{0}\)  
As shown in Figure 14, with the addition of contact pressure \(p\), \(A_{\rm r}/A_{0}\) increases, and the variation of \(A_{\rm r}/A_{0}\) is closer to linearity with smaller changing amplitude at smaller fractal dimension \(D\) and larger fractal roughness \(G\). With the increase of \(p\), the deformation of the asperity increases, resulting in the augment of the real contact area. Smaller \(D\) and larger \(G\) means the asperity has smaller curvature radius, so the value and increment amplitude of real contact area after compression is smaller, therefore, the value and variation range of \(A_{\rm r}/A_{0}\) is smaller.  
(2) Influence of topography parameters on \(A_{\rm r}/A_{0}\)  
As can be seen from Figure 15a1-a3, with the increase of \(D\), \(A_{\rm r}/A_{0}\) increases gradually, and the growth rate is bigger at smaller \(G\) and bigger \(p\). Seen in Figure 15b1-b3, as \(G\) becomes bigger, \(A_{\rm r}/A_{0}\) declines, and it declines more gently at smaller \(D\) and smaller \(p\). The influence of surface topography parameters on \(A_{\rm r}/A_{0}\) is mainly reflected by the curvature radius of surface asperity and the real contact area after deformation. From Figure 15 it is found that the influence of fractal dimension \(D\) on \(A_{\rm r}/A_{0}\) is greater than that of fractal roughness \(G\).  
Through the research, the contact condition and porosity of rough sealing surface can be better understood. In practice, by combining these findings and the specific working requirements (better lubrication or lower leakage), we can choose a rougher or smoother surface so as to determine the corresponding surface processing method. On the basis of the given surfaces, by measuring the relevant parameters and calculating according to the established model, the contact pressure under which the sealing surface has no percolation or has appropriate lubrication can be calculated.  
Figure 14: Change of \(A_{\rm r}/A_{0}\) with contact pressure \(p\).
## 4 Results
### Fracture Propagation Simulation During Refructuring
#### 4.5.1 Post-Refracturing Simulation  
The UFM, which calculates the stress field that causes fracture propagation using a coupled seepage-stress field model, is the basis for simulating re-fracture crack extension. The fracture extension criterion, which is determined iteratively for each node of the extension from fracture initiation and the interplay between hydraulic and natural fractures, makes it possible to simulate 3D fractures [45; 46; 47]. To mimic refracturing fracture  
Figure 14: Horizontal principal stress length distribution for different production times. (**a**) 2013. (**b**) 2017. (**c**) 2021. (**d**) 2024.  
Figure 15: Distribution of deflection angles of principal stresses.  
extension, the refracturing construction parameters are entered based on the reconstructed seepage-stress field. A more complicated fracture behavior is produced because of communication between the large-scale volumetric fracturing and the spontaneous fracture, as seen by the simulation findings in Figure 16. Furthermore, as time passes, the stress field's direction changes, causing the in situ stress field to undergo complicated modifications that eventually result in a more complex fracture network morphology. The half-lengths of the fractures vary from 97 to 154 m. The following table provides a summary of the simulation findings seen in Figure 16.  
The degree of matching between the microseismic data and the refracturing fractures in Figure 17 confirms that the fabricated fractures of refracturing are primarily concentrated in the unreached area between the clusters of initial fracturing fractures, as shown in Figure 18. The fracture half-length is longer than that of the initial fracturing, which facilitates the refracturing fractures' passage through the unreached residual oil area between the wells. The volume of the modified reservoir increases by 125% because of recurrent fracturing, which also produces a complex fracture network. The direction of the fracture extension is slightly deflected, and the fracture continues to extend in the residual oil region.  
Figure 16: (a) Refracing fracture propagation simulation in well YP 3. (b) Superimposition of initial fracture propagation and refracturing fracture propagation graphs. The arrows points to the north direction.
# Path Algorithms for Contact Sequence Temporal Graphs +
## 2 Materials and Methods
### Algorithms for General TRG_Ours
#### 2.2.2 Foremost Paths Algorithm for TRG_Ours  
For our foremost paths algorithm, we modify Breadth First Search (BFS). As we only use \(V_{out}\) nodes, a node can be visited and its distance updated multiple times, but it can be expanded only once. By expanding a node, we mean putting it into the BFS queue and checking/updating its neighbors.  
Algorithm 2 is our foremost paths algorithm. The inputs are the same as for the shortest paths algorithm. The output is a vector of foremost path distances for each node in the original graph (\(foremost[]\)). We initialize the foremost distance to 0 for the source node and to infinity for the other nodes. Lines 4-26 implement the body of the BFS algorithm. Our main modification over the conventional BFS happens at lines 21-25 when we process the immediate chain neighbor of node \((u,t)\). For two reasons, we have separated the immediate chain neighbor \((u,\vec{t})\) from the other neighbors process in lines 9-20. First, in our implementation, we do not add the chain neighbors to the adjacency list of our nodes. Second, when we visit \((u,\vec{t})\), we do not need to update \(foremost[u]\) as it was already updated when we put \((u,t)\) in Q.  
The time complexity is \(O(n+e)\) where \(n\) and \(e\) are the number of nodes and edges in the transformed graph, respectively.  
```
1:functionFastestPaths(\(G\), \(s\), \(t_{start}\), \(t_{end}\)) \(\triangleright\)\(G=(V,E)\) is TRG_Ours graph and \(s\) is the source node
2:return Array \(foremost[]\) of foremost path durations
3: Initialize \(foremost[s]=0\), all other distances to infinity
4: Let Q be a queue
5: Initialize Q with nodes in \(V_{out}(s)\) with \(t_{start}\leq t\leq t_{end}\)
6:while\(Q\) is not empty do
7: Let \((u,t)\) be the head of Q
8: Remove \((u,t)\) from Q
9:for\((u^{\prime},t^{\prime})\in\) neighbors((u,t)) do
10: Let \(w_{link}\) be the weight of link between \((u,t)\) and \((u^{\prime},t^{\prime})\)
11: arrival_time \(\gets t+w_{link}\)
12:if arrival_time \(>t_{end}\)then
13: continue
14:endif
15:\(foremost[u^{\prime}]\leftarrow min(foremost[u^{\prime}]\), \(arrival\_time)\)
16:if\((u^{\prime},t^{\prime})\) is not visited and \(t^{\prime}\leq t_{end}\)then
17: Mark \((u^{\prime},t^{\prime})\) as visited
18: Add \((u^{\prime},t^{\prime})\) to \(Q\)
19:endif
20:endfor
21: Let \((u,\overline{t})\) be the next node in sorted order of \(V_{out}(u)\)\(\triangleright\) sorted in increasing order of time
22:if\((u,\overline{t})\) is not visited and \(\overline{t}\leq t_{end}\)then
23: Mark \((u,\overline{t})\) as visited
24: Add \((u,\overline{t})\) to Q
25:endif
26:endwhile
27:endfunction
```  
**Algorithm 2** Foremost paths algorithm for TRG_Ours
# VSFCM: A Novel Viewpoint-Driven Subspace Fuzzy C-Means Algorithm
## 1 Introduction
Moreover, the separation term is added between the clusters to minimize the compactness of the subspace clusters and maximize the projection subspace of each cluster. On the basis of the CDCI and the viewpoint, combined with the above fuzzy feature weight processing mode, the viewpoint-driven subspace fuzzy c-means algorithm VSFCM is established. The innovations of this study are reflected in the following aspects. First of all, a new cut-off distance is proposed, improving the cluster center initialization effect and also serving as a new viewpoint selection method, which can better adapt to the clustering data structure and speed up the clustering process. Secondly, the fuzzy weight is introduced, that is, a weight that is added to each cluster and dimension to reflect the contribution degree of each feature to the clustering. Among them, smaller weights are assigned to features with a larger proportion of noise point values to reduce their participation in clustering and indirectly weaken their impact on clustering results. Finally, a new method of separation between clusters is given. The average value of the initialized cluster centers is used as the reference point for separation between clusters. Additionally, combined with the optimized weight allocation, the distance between clusters can be effectively increased. The paper is organized as follows. Section 2 reviews existing related algorithms. Section 3 describes the proposed clustering initialization method CDCI and the viewpoint-driven subspace fuzzy c-means algorithm VSFCM in detail. Section 4 presents the experimental results of the VSFCM algorithm and several other relevant algorithms on artificial data sets and some data sets of machine learning. Section 5 gives a summary and outlook for further research.
## 4 Methodology and Data
### Calculation of Total Factor Productivity for Yangtze River Inland Ports
The SEC value indicates whether changes in the port's scale have contributed to productivity growth, with a value greater than 1 signifying an improvement in scale efficiency and a value less than 1 indicating a reduction in scale efficiency. In conducting this analysis, we utilized the data envelopment analysis (DEA) method to calculate output distance functions. DEA is a non-parametric technique that evaluates the performance of a set of homogeneous decision-making units (DMUs) with multiple inputs and outputs by creating efficiency frontiers based on a piecewise linear surface [43; 47]. In studies related to port TFP, the use of DEA to calculate distance functions and thereby construct the Malmquist TFP index has been widely applied [6; 30; 40; 42; 48; 49; 50]. For more technical details on the global Malmquist productivity index based on DEA, please refer to the relevant literature by [PERSON] and [PERSON] [33]. In our study of the TFP of Yangtze River inland ports, we selected specific input and output variables based on the analysis of the early literature and the availability of data. [PERSON] and [PERSON] [3] argued that using cargo throughput and container throughput as output indicators can accurately reflect the production objectives of Yangtze River inland ports. Following this logic, we also adopted these output indicators. At the same time, considering that the number and length of berths are direct determinants of quay transfer operation efficiency and are also key indicators of port production and service capacity [51], we decided to use the number and length of productive berths at Yangtze River inland ports as the main input indicators. The descriptive statistics of these indicators are presented in Table 1.  
The port sample includes 16 major inland ports in the Yangtze River Basin from 2009 to 2019. Introduced in order from upstream to downstream, the specific list of these 16 ports is as follows: Chongqing, Yichang, Wuhan, Huangshi, Jiujiang, Anqing, Chizhou, Tongling, Wuhu, Ma'anshan, Nanjing, Zhenjiang, Yangzhou, Taizhou, Changzhou, and Nantong. The primary considerations for selecting these specific ports were the availability of data and the representativeness of these ports. This sample scope is similar to the research by [PERSON] and [PERSON] [3], which explored the impact of internet development levels on the TFP of Yangtze River ports. This further confirms the representativeness and applicability of our chosen port sample. The port data used in this paper are all sourced from the China Statistical Yearbook, China Port Statistics Yearbook, and the Shanghai International Shipping Institute.
# Interplanetary Magnetic Field Bx Effect on Field-Aligned Currents in Different Local Times
## 1 Introduction
(2014) [17] noticed some significant imbalances in the responses of aurora intensity to IMF Bx in both hemispheres. Specifically, auroral brightness is higher in the 15-19 MLT sector when IMF Bx is negative in the NH, whereas in the SH, auroral brightness is stronger in the 16-20 MLT sector when IMF Bx is positive. These findings suggest that IMF Bx may similarly influence FACs. However, the effects of IMF Bx on FACs across different local times and hemispheres remain unclear, warranting further detailed investigation. This study provides a statistical analysis of the influence of IMF Bx on the modulation of FACs using six years of Swarm satellite observations. We systematically investigate how the polarity of IMF Bx governs the peak current densities and magnetic latitude distributions of FACs. Our analysis specifically explores the local time dependence, seasonal variability, and interhemispheric asymmetries in FAC responses to IMF Bx forcing. Section 2 shows the data processing methods employed in this study, and Section 3 outlines the statistical results. Section 4 discusses these results, and Section 5 concludes with a summary.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Heart Rate Extraction
#### 3.1.2 Near-Infrared Spectroscopy  
Near-infrared spectroscopy (NIR) is a widespread non-invasive technology to evaluate muscle oxidative metabolism including peripheral and cardiorespiratory measurement. It is an optical device that transmits optical signals in a tissue and receives the reflection of light [12]. The reflection of light intensity might be affected by the amount of hemoglobin in the blood. [PERSON] [13] employed a near-infrared spectroscopy (NIRS) muscle oxygen monitor  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Parameters** & **Technology Used** & **Articles** \\ \hline \multirow{3}{*}{Blood pressure} & Photoplethysmography (PAT) & [51; 52] \\ \cline{2-3}  & Pulse Transmit time (PPT) & [53] \\ \hline \multirow{4}{*}{Energy expenditure} & Thermal Imaging & [63; 64; 65; 66; 67; 68] \\ \cline{2-3}  & RGB-Depth & [67; 69] \\ \cline{2-3}  & Near-infrared spectroscopy & [70; 71] \\ \hline \multirow{4}{*}{Respiratory rate} & Near-Infrared spectroscopy & [13; 14; 15] \\ \cline{2-3}  & Video Based & [72; 73; 74; 75; 76; 77] \\ \cline{2-3}  & Doppler-based system & [11] \\ \cline{2-3}  & Infrared Camera & [78] \\ \cline{2-3}  & RGB-Depth & [46; 72] \\ \hline \multirow{4}{*}{Muscle fatigue} & Video Based (RGB) & [79; 80; 81; 82; 83; 84; 85] \\ \cline{2-3}  & Infrared Camera & [86] \\ \cline{2-3}  & Thermal Camera & [86] \\ \hline Oxygen Uptake (VO2) & Doppler-based system & [13; 35] \\ \hline Others (muscle oxygenation) & Near-Infrared spectroscopy & [70] \\ \hline Kee Load estimation & Camera Based & [87] \\ \hline Delayed Onset Muscle Soreness (DOMS) & Infrared Technology & [35] \\ \hline Exercise intensity analysis (Facial expression) & Camera Based & [44; 85; 88; 89] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._system to measure the relative change in muscle oxygenation, with the heart rate, oxygen uptake, production of carbon dioxide (VCO2), and respiratory exchange ratio. The results showed a high correlation between relative change in oxy-hemoglobin concentration and heart rate. The experiments were performed on a treadmill with an incremental protocol. [PERSON] [14] proposed an image sensing device using NIR, which acquires ECG signals and after a series of signal processing, the signal can be classified into four classes. Resting, walking, running, and lying were the four classes that can classify according to the ECG signal. They extracted HR, breathing rate, etc.
## 3 Modeling and Analysis of Bare Pipe
### Calculation Result Verification  
Since \(St\) has no definite value in the critical Reynolds number interval [18], according to the empirical formula for predicting \(St\) through Re in the literature [19], when \(Re=1.5\times 10^{5}\sim 3.4\times 10^{5}\), \(St\) satisfies the following formula:  
\[St=0.1848+8.6\times 10^{-4}\times\left(Re/\left(1.5\times 10^{5}\right)\right)^{4.6} \tag{14}\]  
when the wind speed is \(6\) m/s, \(R_{e}=3.126\times 10^{5}\), which conforms to the interval required by the above formula. The theoretical value of \(St\) calculated by Equation (14) under this condition should be 0.21. Comparing the \(St\) calculated by the numerical model with the theoretical value can verify the validity of the model. According to the work of [PERSON] et al., a non-dimensional time-step \(\Delta tU_{\infty}/D\) of 0.01 was used and the simulation was run for 300 time units [20]. The purpose of controlling the total number of grids is achieved by adjusting the number of circumferential grids of the pipe, thereby verifying the grid independence. The calculation results of different grid numbers are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Boundary Conditions** \\ \hline Left & Velocity inlet, the velocity magnitude is set to wind speed \\ Right & Pressure outlet, the gauge pressure is set to 0 \\ Up, down, front, and back & Symmetry \\ Circular cylinder & No slip wall \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary condition settings.  
Figure 2: Near-wall grid of pipe.  
In Table 2, \(f_{s}\) is the vortex shedding frequency, \(\overline{C_{D}}\) is the mean value of the drag coefficient, and \(C_{Lrms}\) is the root mean square value of the lift coefficient. By comparison, we found that for the cases B and C, the maximum error was only 2%, and the error between the \(St\) of case B and the theoretical value was 3.8%; this justified the choice of model, grid, and time step. After considering calculation time and accuracy, 5.1 million grids (case B) was finally selected for subsequent analysis.  
The parameters of all the cases involved in the paper are shown in Table 3.  
The vortex shedding frequency, Strouhal number, lift coefficient, and drag coefficient of the pipe under different wind speed conditions were calculated, and the results are shown in Table 4.  
As seen in Table 4, as the wind speed increased, \(St\) also increased, while the drag and lift coefficient decreased accordingly. When the wind speed was 8 m/s, \(f_{s}\) was close to the first-order natural frequency \(f_{n}\) of the pipe, and the structure was prone to wind vortex-induced resonance. Therefore, the wind speed of 8 m/s was selected for subsequent calculation and analysis.
# Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics
## 3 Summary of ROS-Based RL Applications
### Notable Works Using Off-Policy
#### 3.2.6 Robot Control II  
In [74], the authors developed a technique called \"_Slictl!_\" that enables robots to learn \"_food-slicing tasks_\". The framework follows the \"_real2 sim2 real_\" [74] approach, combining \"_a high-fidelity cutting simulator and a robotics simulator_\" to train a robot before deploying it in the real world scenario. This approach addresses the challenges of training robots for tasks involving tools and delicate objects that can be inefficient, unsafe, and lead to damage or waste.  
The authors in [74] trained the robot to execute cutting motions by using the SAC RL algorithm. The authors used the dual simulation environment, which integrates two simulators: \"_DiSECT_\" to simulate the cutting process and Gazebo to simulate the robot's movements and interactions with the environment. \"_DiSECT_\" [74] focuses on simulating the intricate physics of the cutting process, including modeling knife interactions with food items, crack formation, and the forces involved. Gazebo, on the other hand, simulates the robot's kinematics and dynamics, focusing on the broader environmental interaction. An ROS played a key role in integrating and facilitating communication among the system's components, enabling data exchange between the simulators, the real robot, the RL control  
Figure 11: Structure of indoor robotics, including five main parts: “_world, robot, ROS components, monitor, and learning_”. For more information, please refer to Table 4.  
policy, and the low-level compliance controller. For more information on the hardware and software used, please see Table 4.  
The above-mentioned works have made a significant contribution by developing a practical and effective method to train robots in the complex task of slicing food. Their dual simulation approach makes it easier for the policies, strategies, and behaviors learned during training to be applied in real-world scenarios. This method has broader implications for the development of robots capable of handling complex manipulation tasks across diverse domains, such as food processing industries, and potentially in the field of surgical robotics.