esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effects of Biophysical Properties on Estimating Phenology
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(T_{\text{air}}\)} & \multicolumn{2}{c}{PAR} & \multicolumn{2}{c}{LAI} & \multicolumn{2}{c}{Chl} \\ \cline{2-9}  & **S** & **A** & **S** & **A** & **S** & **A** & **S** & **A** \\ \hline GCC90 & 0.23 & 0.21 & 0.26 & 0.16 & 0.20 & 0.13 & 0.21 & 0.21 \\ NDVI\({}_{\text{Ground}}\) & 0.19 & 0.11 & 0.29 & 0.40 & 0.31 & 0.16 & 0.27 & 0.14 \\ SIF\({}_{\text{Ground}}\) & 0.33 & 0.26 & 0.21 & 0.10 & 0.06 & 0.16 & 0.09 & 0.27 \\ GPP & 0.37 & 0.30 & 0.20 & 0.07 & 0.04 & 0.20 & 0.10 & 0.34 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The Mean Absolution Error (MAE) of the surface air temperature (\(T_{\text{air}}\)), photosynthetically active radiation (PAR), leaf area index (LAI) and leaf chlorophyll content (Chl) versus the 90 th percentile of the green chromatic coordinates (GCC90), ground-based NDVI (NDVI\({}_{\text{Ground}}\)), ground-based SIF (SIF\({}_{\text{Ground}}\)) and tower-based GPP in both spring (S) and autumn (A). The phenological metrics derived from the tower-based GPP time series were used to define the spring and autumn. All time series in Table 3 are normalized and have a daily step. the main reason for the poor performance of reflectance-based data in estimating the GPP-based autumn metrics. \(\text{SIF}_{\text{Ground}}\) does not follow the trends of LAI and Chl in the fall as it does in the spring (Figure 3d). However, \(\text{SIF}_{\text{Ground}}\) still shows a strong similarity with GPP in the whole autumn season (Figure 3d, Table 3). Following the decline trend of GPP, SIF starts to decrease almost simultaneously even though both LAI and Chl still remain at a relatively high level (Figure 3c). It suggests that \(\text{SIF}_{\text{Ground}}\) tracks the GPP-based fall phenology through factors other than LAI and Chl (see below).
## 3 Case Studies
### Case Study 2: Emergency Scenario (Emergency Management)
#### 3.2.1 Case Study 2: Dataset Generation  
The dataset for this case study consists of one matrix (see Figure 9), which is used to train the Algorithm 2 (described below) and contains sensor data (10 different sensors per each sensing node of the WSN). This matrix simulates the 64 possible park conditions (i.e., the condition 0--no sensor  
Figure 8: Two examples of emergency plans in case of an environmental/structural alarm active at (a) node 3, and (b) nodes 1, 3, and 5.  
alarm--together with the 63 sensor alarm conditions defined above) and a number from 0 to 63 that represents all the possible park conditions. In more detail, the matrix was created using a MATLAB script that generates, for each sensor, a suitable value in the range of operations using a random function with a Gaussian distribution. Furthermore, some of the data is \"faulted\" to inject alarm conditions. The matrix contains 32,000 records of data (10 sensors for each of the six sensor nodes) and has been randomized and standardized during the preprocessing task. The last column of the matrix represents, for each record, the labeled alarm condition.  
Note that every row of the above matrix is a complete record of a single sensors' data acquisition and has a 1D grid-like structure (\(1\times 63\)). Importantly, the position of every column is fixed, i.e., containing a specific order of the sensor data (accelerometer, temperature, humidity, etc.). Hence, every row is a different set of sensors' data in a specific column position for a single acquisition. In this way, the NN is able to learn the relationship between data related to each alarm condition, using every single row as a full set of information.
## 2 Materials and Methods  
### Culinary Frying Oils and Fats  
Sunflower, corn, canola, extra-virgin olive and coconut oils were all purchased from local UK retail stores, as was soybean oil and pork lard for the LF benchtop NMR investigations (the sunflower, corn, canola and coconut oils were all refined products). Each oil was then de-identified in the laboratory via its transference to coded but unlabelled storage containers. The specified SFA, MUFA and PUFA contents of these oils were 11.0%, 28.0% and 61.0% (\(w\)/\(w\)) for sunflower oil; 14.0%, 23.4% and 62.6% (\(w\)/\(w\)) for corn oil; 7.0%, 64.4% and 28.5% for canola oil; 13.0%, 77.4% and 9.4% for extra-virgin olive oil; and 90.7%, 7.5% and 1.8% (\(w\)/\(w\)), respectively, for coconut oil. The percentage of omega-3 FAs in these samples was estimated by our own previously published \({}^{1}\)H NMR method [52], and these contents, predominantly that of linolenic acid, were found to be 0.29%, 8.35%, 0.73% and 0.00% (\(w\)/\(w\)) for the sunflower, canola, extra-virgin olive and coconut oil products tested, respectively. Moreover, the smoke-points of these oils were 228 \({}^{\circ}\)C for sunflower oil, 234 \({}^{\circ}\)C for corn oil, 226 \({}^{\circ}\)C for canola oil, 195-205 \({}^{\circ}\)C for extra-virgin olive oil, and 203 \({}^{\circ}\)C for coconut oil. Further experiments involved a commercially available sample of refined soybean oil (smoke-point 233 \({}^{\circ}\)C); its acylglycerol FA composition was 15.3, 25.3 and 59.4 molar % for SFAs, MUFAs and PUFAs, respectively, the latter including 9.3 molar % linolenic acid. A locally purchased pork lard (tallow) sample was found to contain 46.6, 47.5 and 5.9 molar % SFAs, MUFAs and PUFAs, respectively. The molar % contents of total UFAs, PUFAs, MUFAs and SFAs of these oils were estimated by modifications of previously reported \({}^{1}\)HNMR methods that are outlined in Ref. [22]. This involved a consideration of the intensities of integrated intelligently selected \({}^{1}\)H NMR buckets containing their acylglycerol _bis_-allylic-CH=CH-CH\({}_{2}\)-CH=CH-, -CH\({}_{2}\)-CH=CH-, \(\alpha\)-CH\({}_{2}\)-CO\({}_{2}\)- and glyceryl backbone-CH\({}_{2}\)COOR function resonances (\(\delta\) = 2.73-2.88, 1.96-2.13, 2.26-2.37 and 4.10-4.15/4.28-4.33 ppm, respectively), and the use of equations specified for the estimation of these contents. \({}^{1}\)H NMR-determined molar % FA contents obtained were in very good agreement with those specified by the manufacturers (i.e., within \(\pm\)0.25 molar % of the values provided above).
# Detecting Grounding Grid Orientation: Transient Electromagnetic Approach
## 5 Conclusions and Future Work  
Grounding grid drawings are often lost and mishandled, altering the status of the grid from known to unknown. In this paper, a new method to measure the orientation of the grounding grid was presented. The method was not only independent of the current injection that brought the disturbing inhomogeneity of the surface magnetic flux density, but also did not enhance the effect of the surrounding EME. The transmitter-receiver pair of the TEM system was moved along a circle above the surface such that the vertical conductor acted as the pole of the circle. According to the mesh spacing of the grounding grid between 3 m to 7 m, the radius of the circle was constrained between 0 m to 3 m. Once the equivalent resistivity \(\rho_{r}\) was determined from the secondary magnetic field \(H_{z}\), high \(\rho_{r}\) and low \(\overline{H_{z}}\) at a point on the circle laid the basis for orientation detection of the grounding grid. Moreover, the proposed method was also investigated for complex mesh configurations including the presence of a diagonal branch and an unequally spaced mesh configuration. As an application, the paper used TEM to measure the orientation of the grounding grid. Simulation results showed that the diagnosis was feasible.  
There is a great need for further research to detect grounding grid orientation. This includes a grounding grid with an unequal mesh spacing and a diagonal branch. Furthermore, the depth of the grounding grid must also be considered in future research because it is critical for fault diagnosis.  
A.Q. proposed the idea and drafted the manuscript. I.U.H. performed the simulations and composed the figures. M.A. and N.N.Q. arranged the funding. This research received no external funding. This research is funded by the Deanship of Scientific Research at the University of Ha'il. The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Average Equivalent Resistivity (\(\Omega\)m)**} \\ \cline{2-4}
**Measuring** & **Equal** & **Equal** & **Unequal** \\
**Point** & **Mesh** & **Spacing and** & **Mesh** \\  & **spacing** & **Diagonal Branch** & **Spacing** \\ \hline \(P_{1}\) & 0.2889 & 0.2976 & 0.2916 \\ \(P_{2}\) & 0.2752 & 0.2736 & 0.2758 \\ \(P_{3}\) & 0.2889 & 0.2911 & 0.2889 \\ \(P_{4}\) & 0.2752 & 0.2752 & 0.2933 \\ \(P_{5}\) & 0.2944 & 0.2752 & 0.2900 \\ \(P_{6}\) & 0.2752 & 0.2982 & 0.2944 \\ \(P_{7}\) & 0.2889 & 0.2752 & 0.2889 \\ \(P_{8}\) & 0.2752 & 0.2752 & 0.2758 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Equivalent resistivity calculated based on TEM.
## 3 GNC System with LOS-Based Model
### Coverage Path for the Path-Following Algorithm  
The coverage path for the path-following algorithm in this study was obtained using a simple back-and-forth pattern in a predefined rectangular coverage area. The coverage area included no decomposition, while the path was parallel to the major axis, sweeping parallel to the North axis or parallel to the East axis to avoid unnecessary turns of the AUV. This approach fits in a search and rescue mission scenario, where the search area can be extensive without knowing the target's position. The zigzag motion generation or seed spreader motions are thoroughly documented [24; 39; 40].  
Figure 7 shows the implementation of the coverage path algorithm. The predefined rectangle coverage area is defined by the first two corners, origin (\(N_{1}\), \(E_{1}\)) and second corner to be inspected (\(N_{2}\), \(E_{2}\)), as well as the width \(W_{\text{area}}\) of the coverage area. Additionally, the coverage radius \(R_{\text{AUV}}\) of the AUV is predefined according to the perception sensor used in the vehicle. Based on the corners position and width, the algorithm calculates the length \(L_{\text{area}}\) and rotation angle \(\alpha_{\text{area}}\) of the coverage area.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller** & \(K_{\mathbf{P}}\) & \(K_{\mathbf{I}}\) & \(K_{\mathbf{D}}\) \\ \hline North & 151.366 & 0.0106 & 1081.310 \\ East & 151.366 & 0.0106 & 1081.310 \\ Down & 151.366 & 0.0106 & 1081.310 \\ Yaw & 63.425 & 0.0037 & 271.85 \\ LOS & 0.0333 & 0.0 & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PID controller parameters for the AUV.  
Figure 7: Coverage path definition based on Algorithm 1: Coverage path (dotted red line) is generated based on the dimensions \(l_{\text{area}}\), \(w_{\text{area}}\), and first two corners of the coverage area (\(N_{i}\), \(E_{i}\)), along with the coverage AUV radius \(r_{\text{AUV}}\).  
Figure 8 illustrates the AUV trajectory in the harbor of Sant Feliu de Guixols, Girona, Spain, with the definition of each waypoint based on the coverage path algorithm. The coverage radius is limited to \(R_{\text{AUV}}=5\) m, allowing more waypoints for the coverage path in the small field testing area. Meanwhile, the coverage area width is \(W_{\text{area}}=-20\) m, where the negative sign indicates that the AUV covers the left side of the vehicle.
# Spatio-Temporal Variations of Ecosystem Water Use Efficiency and Its Drivers in Southwest China
## 4 Discussion
### Potential Inaccuracies and Limitations  
Uncertainty in GPP and ET data is the main source of ambiguity in WUE. Since both GPP and ET are calculated with relatively complex computational models and various input data, potential uncertainties can exist in space and time and ultimately affect the accuracy of WUE. The PML-V2 product used in this study has been validated for GPP and ET in some studies and has concluded that it has similar or higher accuracy than other products [18; 60; 61]. For example, this product was validated using 95 flux sites worldwide, ET and GPP had high correlation with flux stations, with correlation coefficients of 0.83 and 0.85, respectively [18]. [PERSON] et al. used 26 flux stations in China to be verified GPP and ET, and their correlation coefficients were both 0.87 [62]. In addition, the PML-V2 product also has a spatial resolution of 500 m, and the higher spatial resolution is more suitable for conducting research in areas with high heterogeneity. For this reason, this study used the highly accurate PML-V2 product to largely reduce the errors caused by WUE. In addition, the temperature and precipitation data were generated by interpolation of thin plate splines applicable to mountainous areas [63], which reduced the uncertainty of our results to some extent.  
In the Section 4, our results are compared with previous related studies, and most of them are consistent. Thus, making our results of the present study highly reliable and transferable. However, as those limitations mentioned earlier, there is much room for improvement in future research. For study areas with complex karst landforms with high spatial heterogeneity in the subsurface, as in this study, perhaps the use of higher resolution data would improve the reliability of the study results of the region. Temperature and precipitation are the most commonly used, easily accessible, and representative meteorological variables. Vegetation EVI, as a biophysical parameter of the leaf canopy, is an important indicator of the effectiveness of ecological projects. Thus, we adopted it as the driving factor of WUE change in this paper. However, CO\({}_{2}\) concentration [4], vapor pressure deficit [64], leaf area index, soil water, and other meteorological variables (e.g., relative humidity, sunshine hours) [65] all play an important role in the influence of WUE, besides temperature, precipitation, and vegetation EVI. Hence, future studies employing multifactorial analysis of the effects of ecosystem WUE are warranted to ensure that these conclusions are comprehensive, specific, and highly reliable.  
Figure 7: Relationship between cumulative afforestation areas and EVI in the study area from 2003 to 2017.
## 4 The IDCP Algorithm
### The Algorithm
#### 4.2.2 Node Assignment  
As one can see from the definitions of EST and LST, the start time of each node can slide between its EST and LST. Hence, the EST and LST of each node reflect its mobility, which is defined as:  
\[Mobility(n_{i})=LST(n_{i})-EST(n_{i}) \tag{21}\]  
The value of \(Mobility(n_{i})\) implies the flexibility of scheduling on node \(n_{i}\).  
Constrained by the dependency of tasks, \(n_{i}\) shall not be scheduled before its parents or after its children. A position that satisfies this constraint is called a candidate position. Let the set of candidate positions of node \(n_{i}\) be \(CP(n_{i})\). Let \(EST(n_{i},m)\) and \(LST(n_{i},m)\) denote the EST and LST of node \(n_{i}\) if it is scheduled onto the position \(m\) among all of its candidate positions respectively, which are given as  
\[EST(n_{i},m)=\max(EST^{G}(n_{i}),EST(n_{m-1})+T_{ex}(n_{m-1})) \tag{22}\]  
\[LST(n_{i},m)=\min(LST^{G}(n_{i}),LST(n_{m+1})-T_{ex}(n_{m})) \tag{23}\]  
where \(n_{m-1}\) and \(n_{m+1}\) denotes the node on the last position and next position of \(m\).  
Likewise, let \(Mobility(n_{i},m)\) represent the slide range of node \(n_{i}\) if it is scheduled onto position \(m\) among the candidate positions:  
\[Mobility(n_{i},m)=LST(n_{i},m)-EST(n_{i},m) \tag{24}\]  
Among all the candidate positions in \(CP(n_{i})\), \(n_{i}\) is scheduled onto the position with the greatest \(Mobility(n_{i},m)\). In this way, the high flexibility of the node is preserved, offering more room for future optimization. After assigning node \(n_{i}\) to the proper candidate position, \(n_{i}\) is removed from \(S_{n}\).  
Theorem 3.1: _If the current schedule is feasible, the node-assignment operation maintains the feasibility, provided that:_  
\[Mobility(n_{i},m)\geq 0 \tag{25}\]  
After the node \(n_{i}\) is scheduled onto a new position \(m\), the EST value of each node can be retained recursively from the entry node since the dependency of tasks has been considered in the node-assignment operation. Similar to EST, the LST value can also be obtained recursively. Accordingly, \(n_{i}\) can be scheduled onto its new position \(m\), with its starting time sliding between the range \([EST(n_{i},m),LST(n_{i},m)]\), which is valid if \(Mobility(n_{i},m)\geq 0\). Theorem 3.1 ensures that the feasibility of the solution is not violated after node-assignment operation is executed.
## 2 Point Cloud Data Registration Method  
### Methodology  
The goal of point cloud coarse registration is to reduce the rotation and translation dislocation between the reference point cloud and point cloud to be registered, and provide a good initial value for fine registration [17; 31]. The goal of fine registration is to obtain the optimal registration parameters and minimize the errors [38]. In this paper, the corresponding point pairs were obtained by extracting the SIFT features, the error point pairs were eliminated based on the random sample consensus (RANSAC) algorithm, the initial matrix was determined by utilizing the singular value decomposition (SVD) algorithm to complete the coarse registration. In order to improve the overall point cloud data registration speed, K-D tree was used to search for the nearest point in the ICP algorithm to complete the fine registration, as shown in Figure 1.  
Figure 1: Point cloud data registration method.
## 1 Introduction  
Tennis is an intermittent sport that involves short-intense and medium-low-intensity long-term physiological capacities, as well as sudden biomechanical activities like acceleration, deceleration, change of direction (COD), and striking [1]. A player runs a total of \(-8\)-\(12\) m per point [2]. In a competitive match, players perform more than 1.000 CODs [3]. Players perform \(\sim\)4 CODs per rally, but in a long rally, more than 15 CODs are applied [4]. Most of the time, lateral direction changes are more common than forward movements in ralles [5]. Additionally, although data show that COD movements in tennis are within \(\sim\)4 m [4], 80% of all tennis shots are played at a distance of less than 2.5 m [6]. Therefore, COD abilities should be monitored to improve players' performance [7].  
In tennis, since many physical components like speed, agility, COD, and jumping that are necessary for success are power-based [8; 9; 10], upper and lower body strength and power are important performance determinants [11]. Additionally, due to the importance of CODs in many sports, conditioners and coaches aim to use the most appropriate tests to accurately measure the physical quality of CODs [12; 13]. When the literature is examined, many agility tests have been developed and used to assess COD ability [14; 15; 16]. The most common of these tests are the pro-agility shuttle [17; 18; 19; 20; 21], the three-cone drill or L-run test [17; 19; 22], the T-test agility [23; 24; 25], and the Illinois agility test [21; 25; 26; 27]. Although most COD tests are largely correlated with athletes' ability to accelerate and decelerate [28],several studies have shown a weak and inconsistent relationship between CODs and straight-line sprinting [16; 29; 30; 31; 32].  
Previous studies have reported a significant relationship between COD and jump tests [21; 33; 34; 35; 36; 37; 38]. These results suggest that athletes' jump performances could be one of the factors affecting athletic skills like COD. However, the relationship between vertical jumps and COD in tennis players remains uncertain.  
Considering the specific demands of the sport is essential when selecting the most appropriate COD test to accurately represent an athlete's strengths and weaknesses. The quest for the optimal COD test in tennis players to determine performance most accurately has long been investigated and remains an intriguing topic for sports scientists. Since leg neuromuscular qualities are important determinants of COD performance [39], there is a need for a reliable tennis-specific test to assess COD ability associated with vertical jump tests. Furthermore, we could not find a tennis-specific reliable COD test correlating with vertical jump tests for adolescent tennis players.  
Based on this information, the objectives of this study were as follows: (1) to examine the reliability of the 77/COD test in adolescent tennis players; (2) to determine the relationship between vertical jump (Squat Jump-Countermovement Jump) height and the 77/COD test.
# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 2 STJL Model Applied to Online Multi-Object Tracking
### Tracking State Prediction  
The Kalman filter predicts a target's position in the current frame based on the dynamics of its previous state. In multi-object tracking with uncalibrated cameras and no motion data, the target's state vector \(X_{kc}\) is defined as \(X_{kc}=\left[u,v,\gamma,hi,\dot{x},\dot{y},\dot{\gamma},\dot{h}\right]^{\text {T}}\), where \((u,v)\) are the pixel coordinates of the detection box center, \(\gamma\) is the aspect ratio, \(hi\) is the height, and \((\dot{x},\dot{y})\) and \((\dot{\gamma},\dot{h})\) represent the velocities and rates of change for aspect ratio and height. The prediction step computes the state \(X_{kc}^{\prime}=F\cdot X_{kc-1}\) using the state transition matrix \(F\). The predicted error covariance matrix is calculated as \(P_{kc}^{\prime}=F\cdot P_{kc-1}F^{T}+Q\), where \(P_{kc}^{\prime}\) represents the predicted error covariance and \(Q\) is the process noise covariance.  
In the update phase, the Kalman filter refines its prediction using current observations \(Z_{kc}=H\cdot X_{kc}\), where \(H\) is the observation model. The Kalman gain \(K_{kc}=P_{kc}^{\prime}\cdot H^{\text{T}}\). \((H\cdot P_{kc}^{\prime}\cdot H^{\text{T}}+R)^{-1}\) optimizes the update by minimizing the error. The updated state is \(X_{kc}=X_{kc}^{\prime}+K_{kc}\cdot(Z_{kc}-H\cdot X_{kc}^{\prime})\), and the updated error covariance is \(P_{kc}=P_{kc}^{\prime}-K_{kc}\cdot H\cdot P_{kc}^{\prime}\), where \(X_{kc}\) is the object's state, \(X_{kc}^{\prime}\) is the predicted state, and \(P_{kc}\) is the updated error covariance.