esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimization of DSPE Conditions
#### 3.2.4 Matrix Effects  
The matrix effect (ME) comes from the ionization process, where interferences in the matrix can inhibit or enhance the ionization efficiency of the analyte [41]. It can lead to errors in the quantitation of pesticides and should be considered when significant suppression or enhancement occurs [42]. Our study investigated the matrix effect of the target compounds in bottled water and honey products based on the ratio of the peak area between standards in the blank matrix solution and standards in the solvent. The blank matrix solution was obtained following the proposed DSPE scheme, where no studied pesticides were detected. For water samples, the matrix effects for all target compounds were between \(-2.7\%\) and \(+3.6\%\), which are ignorable for the accurate quantification by LC-MS/MS analysis based on the standards proposed by [PERSON] et al. [43]. However, in honey products, the matrix effects were \(+51.6\%\) and \(-29.9\%\) for imidacloprid and thiazolprid, respectively. Further rinsing steps were studied to remove the matrix effect of the DSPE materials after adsorption. Five solvents of 5 mL were examined by 1 min of vortex, and centrifugation at \(3200\times\)\(g\) for 2 min. As shown in Figure 4, an acceptable ME of all targets (within \(\pm 20\%\)) could be achieved by introducing a rinsing procedure with water containing \(10\%\) (\(v/v\)) methanol. Thus, it was selected as the washing solvent to decrease the matrix effect in honey.  
Figure 4: Matrix effects in honey products with different rinsing solvents ((**A**), petroleum ether; (**B**), hexane; (**C**), water; (**D**), 10% MeOH in water; (**E**), 30% MeOH in water). DSPE conditions: sample volume, 40 mL; the dosage of the sorbents, 50 mg; desorption solvent, 2 mL 0.5% (\(v/v\)) ammonium hydroxide in acetonitrile; desorption mode and time, 1 min of vortex, and 1 min of ultrasonication (The dashed box indicates that the matrix effect was within \(\pm 20\%\)).
# Performance Analysis and Testing of Spiral Quantitative Fertiliser Distributors in Orchards
## 3 Results and Analysis
### Regression Model Analysis  
To intuitively analyze the relationship between test indexes and factors, quadratic regression analysis and multiple regression fitting of test results were conducted using Design-Expert 13. Table 4 enlists the analysis of variance (ANOVA) results of the \(CV\) of uniformity. \(B^{2}\) is the primary influencing factor of the \(CV\) of uniformity, followed by \(A^{2}\), \(A\), \(B\), \(C\), \(BC\), \(AB\), \(C^{2}\) and \(AC\). Specifically, \(B^{2}\) is the quadratic term of the opening degree of the fertilizer outlet. \(A^{2}\) is the quadratic term for the spiral rotation speed of the conveyor. \(A\) is the spiral rotation speed of the conveyor. \(B\) is the opening degree of the fertilizer outlet. \(C\) is the advancing speed. \(BC\) is the intersection term for the opening degree of the fertilizer outlet and advancing speed. \(AB\) is the intersection term for the conveyor's spiral rotation speed and the fertilizer outlet's opening degree. \(C^{2}\) is the quadratic term for the advancing speed. \(AC\) is the intersection term for the spiral rotation speed of the conveyor and advancing speed. Among them, \(B^{2}\), \(A^{2}\), \(A\), \(B\), \(C\), \(BC\), \(AB\) and \(C^{2}\) significantly influenced  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{3}{c}{**Factor**} \\ \cline{2-4}  & **Spiral Rotation Speed A** & **Opening Degree of** & **Advancing Speed C** \\  & **(RPM)** & **Fertilizer Outlet B (\%)** & **(km/h)** \\ \hline \(-1\) & 30 & 30 & 1.5 \\ \(0\) & 55 & 40 & 2.25 \\ \(1\) & 80 & 50 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coding Table for Experimental Factor Level.  
the \(CV\) of uniformity (\(p<0.01\)). \(AC\) had insignificant effects (\(p<0.05\)). The regression model of the \(CV\) of uniformity can be expressed by Equation (12).  
\[\begin{split} CV=+120.99573-0.913972\times A-4.00637\times B+0.8450 00\times C\\ +0.004530-0.0052-0.190667\times BC+0.005975\times A^{2}\\ +0.049670\times B^{2}+1.25689\times C^{2}\end{split} \tag{12}\]  
The regression model for the coefficient of variation of homogeneity was highly significant. The lack of fit terms was not significant, indicating that the model had significant accuracy. There was no lack of fit. There were relatively few errors, and according to the fit statistic, \(CV=0.9905\) indicated that the model had a high degree of fit. The terms \(B^{2}\), \(A^{2}\), \(A\), \(B\), \(C\), \(BC\), \(AB\), and \(C^{2}\) had a highly significant effect on \(CV\), and the degree of effect of the different parameters did not show \(A>B>C\).
## 2 Gimbal System
### Stabilization and Aiming  
The camera fitted on the innermost frame is inertially stabilized and controlled by the gimbal system. Furthermore, the system is required to align its optical axis in elevation and azimuth with a LOS joining the camera and target. Figure 6 describes the angular geometry of how the gimbaled camera aims at the target, where \(\theta\) is the pitch angle of the UAV body, \(\delta\) is the boresight angle, \(\lambda\) is the LOS angle, \(\theta_{P}\) is the pitch angle of the gimbal frame, and \(\varepsilon\) is the boresight error angle.  
There are four operation modes, namely, the preset angle mode, search mode, stabilize mode, and tracking mode as shown in Figure 7. When the gimbal system is powered on and initialized, its direction is kept at \(\delta=0\) in inertial space. In preset angle mode, the optical axis of the camera will be set to a given angle and the control system will maintain the desired direction despite disturbances. Then, the system may switch to search mode, in which the gimbal will rotate circularly between its minimum and maximum angle to search a larger range of area.  
When a target is confirmed, the control system will switch to the tracking mode and keep the target in the center of the camera view. In addition, if the target is lost and cannot be recaptured in a few seconds, the system will return to the search mode and try to find it again. Figure 8 shows how the control system works which contains two loops: tracking loop and stabilizing loop.  
Figure 6: Angular geometry of the gimbal system.  
Based on the image information and measurement data received from angular sensors, the tracking loop generates a rate command to direct the boresight towards the target LOS so that the pointing error can be kept near zero. On the other hand, the stabilizing loop isolates the camera from UAV motion and external disturbances, which would perturb the aim-point. The control loops in roll, elevation, and azimuth channels are related by the cross coupling unit based on the gimbal system dynamics, which may be defined as the impact on one axis with the rotation of another [56].
## 3 Results
### Feedback Appropriateness and Effectiveness  
The main effect of frustration on the participant's rating of feedback appropriateness was not significant, \(F\)(1,39) = 1.33, \(p\) = 0.26. The main effect of etiquette strategies on the participant's rating of feedback appropriateness was significant, \(F\)(4,156) = 12.31, \(p\) < 0.001. The interaction was not significant, \(F\)(4,156) = 1.26, \(p\) = 0.29. Figure 7a indicates significant (\(p\) < 0.05) pairwise differences between groups when they do not share a letter, based on [PERSON]'s HSD. In the low frustration condition, bald was significantly different from positive-politicness; no-feedback was significantly different from all four etiquette strategies. In the high frustration condition, positive-politicness was significantly different from bald, negative-politicness, off-record, and no-feedback; negative-politicness was significantly different from no-feedback. Across frustration conditions, high/bald was significantly different from low/positive-politicness and low/negative-politicness; high/positive-politicness was significantly different from low/politicness, low/negative-politicness, and low/negative-politicness; high/positive-politicness was significantly different from low/positive-politicness and low/no-feedback; high/off-record was significantly different from low/positive-politicness and low/negative-politicness; high/no-feedback was significantly different from all four etiquette strategies in low frustration.  
Figure 6: Mean and standard error of satisfaction with (**a**) feedback and (**b**) performance (\(n\) = 40).  
Feedback was marginally significantly more effective in low frustration than high frustration, \(F\)(1,39) = 3.06, \(p\) = 0.088, d = 0.14. The main effect of etiquette strategies participant's rating of feedback effectiveness was significant (\(F\)(4,156) = 10.31, \(p\)! 0.001. The interaction was not significant, \(F\)(4,156) = 1.07, \(p\) = 0.37. Figure 7b indicates significant (\(p\)! 0.05) pairwise differences between groups when they do not share a letter, based on [PERSON]'s HSD. In the low frustration condition, no-feedback was significantly different from all four etiquette strategies. In the high frustration condition, positive-politicness was significantly different from bald, negative-politicness, off-record, and no-feedback; negative-politicness was significantly different from no-feedback. Across frustration conditions, both high/bald and high/negative-politicness were significantly different from low/positive-politicness and low/no-feedback; high/positive-politicness was significantly different from low/no-feedback; high/off-record was significantly different from low/positive-politicness and low/negative-politicness; high/no-feedback was significantly different from all four etiquette strategies in low frustration.
## 2 Materials and Methods
### Applied Models
The number of neighbours (k) was tuned through cross-validation, and SMOTE was applied during training to improve minority class representation. Ensemble-based algorithm:  
RF: The randomForest and caret packages [48; 50] were utilised to implement RF classifiers, adjusting the number of variables randomly sampled at each split (mtry) and number of trees (ntree). SMOTE was used during training to correct class imbalance and improve generalisation. RF provides robustness to overfitting and improves generalisation by aggregating multiple trees. Deep learning-based algorithm:  
DL: We utilised the h2o library [51] to develop a DL model with multiple hidden layers (128-64-32-16 neurons) and Rectified Linear Unit (ReLU) activations. The model training incorporated stratified 5-fold cross-validation, early stopping based on log-loss minimisation, and adaptive learning rates. Unlike the classical models where SMOTE was used, class imbalance was addressed using balance_classes = TRUE along with customised \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **R Libraries** & **Method** & \begin{tabular}{c} **Tuned** \\ **Parameters/Settings** \\ \end{tabular} & \begin{tabular}{c} **Cross-** \\ **Validation** \\ \end{tabular} \\ \hline DT & caret, rpart & rpart & \begin{tabular}{c} cp = \{0.001, 0.011, 0.021, \\ 0.031, 0.041, 0.051], \\ SMOTE \\ \end{tabular} & 5-fold \\ \hline NB & caret, klaR & nb & Default settings, SMOTE & 5-fold \\ \hline SVM & caret, kernlab & svmRadial & \begin{tabular}{c} C = \{0.25, 0.5, 1\}, sigma = \{0.01, 0.05, 0.1\}, SMOTE \\ \end{tabular} & 5-fold \\ \hline k-NN & caret & knn & k = \{3, 5, 7, 9\}, SMOTE & 5-fold \\ \hline RF & \
\begin{tabular}{c} caret, \\ randomForest \\ \end{tabular} & rf & mtry = \{2, 4, 6\}, ntree = 100, SMOTE & 5-fold \\ \hline DL & h2o, caret & h2o.deeplearning &
\begin{tabular}{c} Hidden layers: \{128, 64, 32, \\ 16\}, epochs = 200, early stopping (log-loss), \\ balanced classes \\ \end{tabular} & 5-fold \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Machine learning models, R libraries, and parameters used. class_sampling_factors, which assigned higher weights to minority classes during training. This approach ensured better representation of rare classes without synthetic oversampling.
# A Shearlets-Based Method for Rain Removal from Single Images
## 5 Numerical Experiments
### Comparison Tests of the Synthetic Data
The quantitative results of four different methods in terms of RelErr, PSNR, and SSIM value are shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Rainy Type**} & \multicolumn{8}{c}{**Heavy**} & \multicolumn{8}{c}{**Light**} \\ \hline  & \multicolumn{4}{c}{**Background**} & \multicolumn{4}{c}{**Rainy Streaks**} & \multicolumn{2}{c}{**Times(s)**} & \multicolumn{2}{c}{**Background**} & \multicolumn{4}{c}{**Rainy Streaks**} & \multicolumn{2}{c}{**Times(s)**} \\ \hline
**Image** & **Methods** & **PSNR** & **SSIM** & **ReErr** & **PSNR** & **SSIM** & **ReErr** & **PSNR** & **SSIM** & **ReErr** & **PSNR** & **SSIM** & **ReErr** & \\ \hline Pic1 & 15 ICCV & 28.79 & 0.8673 & 11.832 & 27.51 & 0.4831 & 15.642 & 71.54 & 29.84 & 0.9020 & 10.273 & 27.16 & 0.495 & 11.416 & 78.32 \\  & 16 CVPR & 30.49 & 0.8892 & 7.682 & 30.67 & 0.6417 & 7.394 & 948.27 & 30.76 & 0.9123 & 10.791 & 30.52 & 0.533 & 10.376 & 941.96 \\  & 18 UTV & 30.83 & 0.9027 & 7.591 & **30.91** & 0.6793 & 7.427 & **0.87** & 32.87 & 0.9345 & 5.964 & 32.79 & 0.6497 & 5.873 & **0.68** \\  & **Ours** & **31.62** & **0.9115** & **8.244** & 30.76 & **0.6954** & **7.082** & 2.39 & **33.28** & **0.9452** & **5.417** & **33.19** & **0.6719** & **5.314** & 2.26 \\ Pic2 & 15 ICCV & 27.12 & 0.8117 & 12.027 & 27.01 & 419.13 & 21.343 & 7.934 & 28.96 & 0.9396 & 10.127 & 28.874 & 0.4019 & 10.127 & 82.37 \\  & 16 CVPR & 28.54 & 0.6629 & 9.264 & 28.31 & 0.5763 & 10.028 & 926.485 & 29.94 & 0.9187 & 9.0 Figure 7: Relative error (RelErr), peak signal to noise ratio (PSNR), structural similarity index (SSIM) performances on 20 images.
# Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression
## 3 Materials and Methods
### Labeled Dataset Building  
Data used in this article are taken from [21]. The data table columns represents respectively the run number, conductivity, pH of the solution, the chromium concentration, the chromium removal rate, and experimentation efficiency class. This class is obtained by labeling (0) each elimination rate lower than 80% and (1) for each one with an efficiency of higher than 80%. Table 1 represents our learning data set:  
\begin{table}
\begin{tabular}{l l l l l l} \hline
**Run** & **CD (mA/cm\({}^{2}\))** & **pH** & **Concentration (ppm)** & **Removal (\%)** & **Class** \\ \hline
1 & 20.0 & 4.00 & 1000.00 & 71.40 & 0 \\
2 & 20.0 & 9.0 & 1000.0 & 99.9 & 1 \\
3 & 13.0 & 6.5 & 750.0 & 98.5 & 1 \\
4 & 13.0 & 6.5 & 117
# Geometric Nonlinear Analysis of the Catenary Cable Element Based on UPFs of ANSYS
###### Abstract
[PERSON] proposed an exact expression for the equivalent stiffness of the cable element based on the principle of virtual work, which can be used as an approximate expression for the calculation of geometric nonlinearity [44]; [PERSON] and [PERSON] developed a three-dimensional catenary cable element based on the Absolute Nodal Coordinates Formula (ANCF) and verified the accuracy of the element by comparing the static and dynamic calculation results [45]; [PERSON] et al., analyzed the computational efficiency and degree of convergence of nine different dynamic relaxation methods in the geometric nonlinearity of the cable element [46]; [PERSON] et al., investigated the thermoelastic response of the catenary cable element by the finite difference approach [47; 48]. In summary, the advanced nonlinear theory of catenary cable element has been quite abundant, but most researchers still use the method of preparing finite element program to apply it to engineering examples, and the preparation of the program will lead to a series of shortcomings such as unfriendly pre- and post-processing interface, unable to store a large amount of computational data, low computational efficiency and not having a visualization function. ANSYS, an extensive general purpose finite element program, provides users with a secondary element development platform-UPFs, which facilitates researchers to develop units by combining proposed more advanced finite element algorithms with ANSYS secondary development tools. In order to improve the accuracy and efficiency of ANSYS to calculate the nonlinearity of complex structures, [PERSON] et al., then developed planar beam elements using the more advanced CR algorithm [49], and the authors also developed planar truss elements by using the variational approach [50]. When the pre-stress of the cable structure is high and the network topology is involved, the accurate initialization solution is essential for the convergence of the nonlinear structure. For this reason, in this paper, the initial configuration of the catenary cable element is first derived by dichotomous iteration, and then the algorithm of the element is derived based on the explicit expression of the tangent stiffness matrix of the two-node catenary cable element in the UL column and combined with the secondary development tools (UPFs) in the ANSYS platform. The two-node three-dimensional elastic catenary cable element (user101) is developed. The element only needs to obtain its single stiffness matrix, which is automatically formed in the ANSYS program for the total stiffness and operates the same way as the other elements. The method makes use of the powerful pre- and post-processing and visualization capabilities of the ANSYS program to combine a more accurate algorithm with a commercial program and has been validated by comparison with examples and natural bridges to show that the method improves the efficiency and accuracy of the calculation and has solid practical value.
## 3 Results and Discussion
As explained above, within the existing methodology of advanced exergy analysis, avoidable exogenous and avoidable endogenous exergy destruction were calculated with Equations (5) and (6). It could be concluded that these parts of exergy destruction cannot be obtained without estimating the endogenous \(\dot{E}^{EN}_{D,CM}\) and unavoidable endogenous \(\dot{E}^{UN,EN}_{D,CM}\) exergy destruction first. However, within the existing approach, it was impossible to estimate the \"pure\" endogenous parts of exergy destruction within the components due to incomplete thermodynamic \"idealization\" within the evaporator and the condenser. Only the pinch point temperature differences equal to zero could be accepted. For such conditions, exergy destruction rates within these heat exchangers were not equal to zero--irreversibilities were not completely removed [2; 14]. As a result, the endogenous and unavoidable endogenous exergy destructions for the compressor, which were equal to 4.850 kW and 1.031 kW, respectively, were not \"pure\" (i.e., they included some exergy destruction within the evaporator and the condenser). This drawback was afterwards implicitly included in the values of avoidable endogenous and exogenous parts' exergy destruction (\(\dot{E}^{AV,EN}_{D,CM}\) and \(\dot{E}^{AV,EX}_{D,CM}\)). This could explain the estimation of such a large difference (70%) between appropriate values of the avoidable parts of exergy destruction for the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Component**} & **Author’s Data** & **[PERSON] and [PERSON] [2]** & & **Author’s Data** & **[PERSON] and [PERSON] [2]** & \\ \cline{2-6}  & \(\dot{E}^{AV,INT}_{D,k}\)**, kW** & \(\dot{E}^{AV,EN}_{D,k}\)**, kW** & \(\dot{E}^{AV,EN}_{D,k}\)**, kW** & \(\dot{E}^{AV,EN}_{D,k}\)**, kW** & \(\dot{E}^{AV  
compressor obtained on the basis of the existing methodology of advanced exergy analysis and within the proposed approach. When estimating \(\dot{E}^{EN}_{D,CD}\) and \(\dot{E}^{UN,EN}_{D,CD}\), some amount of irreversibility within the evaporator was included. As a result, this inclusion affected the values of avoidable endogenous and exogenous parts' exergy destruction (\(\dot{E}^{AV,EN}_{D,CD}\) and \(\dot{E}^{AV,EX}_{D,CD}\)) for the condenser. A similar explanation could be applied for the evaporator.
# Revising the Observation Satellite Scheduling Problem Based on Deep Reinforcement Learning
## 5 Conclusions  
Observation satellite task scheduling policy plays a crucial role in providing high-quality space-based information services. In previous studies, many algorithms based on traditional optimization methods such as GA and SA have been successfully applied in the EOSSP. However, these methods depend upon a mathematical model, and with the increase in the task scale, they may fall into local optimum. In this paper, the EOSSP is considered as a time-continuous model with multiple constraints and, inspired by the progress of DRL and its model-free characteristics, a DRL-based algorithm is proposed to approach the EOSSP. In addition, to decrease the complexity of the solution, an improved graph-based minimum clique partition algorithm is proposed for the task clustering preprocess; this is a relatively new attempt in handling EOSSP optimization. The simulation results show that the DDPG algorithm combined with the task clustering process is practicable and achieves the expected performance. In addition, this solution has a higher optimization performance compared with traditional metaheuristic algorithms (GA, SA and GA-SA hybrid algorithm). In terms of scheduling profits, the experimental results indicate that the DDPG is feasible and efficient for the EOSSP even in a relatively large-scale situation.  
Note that, in the present work, satellite constellation task scheduling problems were not addressed. In a future study, we will attempt to adopt multi-agent DRL methods to study the multiple satellite EOSSP.  
Conceptualization, Y.H. and Z.M.; methodology, Z.M.; software, Y.H.; validation, S.W. and Z.M.; formal analysis, B.C.; investigation, S.W.; resources, S.W.; data curation, Y.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the China State Key Laboratory of Robotics (Grant No: 19Z1240010018), the Office of the Military and Civilian Integration Development Committee of Shanghai (Grant No: 2020-jmrh1-kj25), and National Natural Science Foundation of China (Grant No: U20B2054, U20B2056).  
Not applicable.  
The authors are grateful for the support and help of lab classmates.  
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.