esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robotic Automatic Assembly System Based on Vision
## 3 Proposed Method
### Position Detection of Moving Workpiece With Incomplete Information  
In the process of moving the target object with the belt, due to the fast moving speed of the belt and the lag of shooting, the target object captured has repeated shooting and incomplete information in the camera field of view. The target recognition method based on the fitting of the smallest rectangular area of the assembled parts is studied, and the edge contour and feature matching are combined to realize the accurate detection of the moving target pose. Before performing contour extraction, image segmentation is performed, and foreground extraction is performed by binarization. The binarization method is as follows:  
\[det(x,y)=\left\{\begin{array}{l}\max\quad ifsrc(x,y)>threshold\\ \min\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad\quad otherwise \end{array}\right. \tag{10}\]  
Set the pixel on the image larger than the threshold to be maximal, and the other pixels to be minimal. The region where the pixel is maximal in the binarized image is the region of interest (ROI). In order to overcome the problem of some tiny black holes in the binarized image, a closed operation process of first dilating and then eroding is required. For the dilate operation:  
Figure 6: Hand eye calibration.  
\[dst\left(x,y\right)=dilate\left(src\left(x,y\right)\right)=\max_{\left(x^{\prime}, y^{\prime}\right)}src\left(x+x^{\prime},y+y^{\prime}\right) \tag{11}\]  
and for the erode operation:  
\[dst\left(x,y\right)=erde\left(src\left(x,y\right)\right)=\min_{\left(x^{\prime },\mathcal{A}\right)}src\left(x+x^{\prime},y+y^{\prime}\right) \tag{12}\]  
The target contour is extracted by Canny edge detection algorithm [36]. The performance of this algorithm depends largely on the Gaussian filter and the high and low threshold settings. The Gaussian filter is as follows:  
\[G\left(x,y\right)=\frac{1}{2\pi\sigma^{2}}e^{\frac{x^{2}+y^{2}}{2\sigma^{2}}} \tag{13}\]  
Aiming at the incomplete image problems, an algorithm of the minimum rectangle fitting of feature regions was proposed. Combined with the shape characteristics of the grabbing workpiece, the minimum circumscribed rectangle is used to fit the ROI of the workpiece appearing in the camera field of view, and the actual pose of the workpiece is solved by the eigenvalue of the rectangle.  
In the pixel coordinate system, the camera field size is X*Y, the actual length of the workpiece is H pixels, the workpiece center point is \(P_{r}\), and the position is \(\left(U,V\right)\). The length of the rectangular workpiece fitted in the field of view is \(h\), and the center point \(P_{f}\) coordinate is \(\left(u,v\right)\). The rotation angle of the rectangular area is \(\theta\). The specific mathematical schematic is shown in Figure 7.  
So the actual workpiece positional relationship is as follows:  
\[\left[\begin{array}{c}U\\ V\end{array}\right]=\pm\frac{1}{2}\left(H-h\right)\left[\begin{array}{c}\sin \theta\\ \cos\theta\end{array}\right]\pm\left[\begin{array}{c}u\\ v\end{array}\right] \tag{14}\]
# The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System
## 4 Conclusions  
### Discussion  
Since the recommendation system is essential in the business over the Internet, online marketing is providing a lot of benefits to the business owner for providing the movies to the consumer through the Internet or online entertainment. The number of movies has increased to become more overcrowded; finding a video that users are looking for through the existing technologies is very hard. For this reason, users need a system that can recommend their movie needs and the best technology about these; it is a recommendation system. However, the collaborative filtering method is a powerful method using in the recommendation system to predict user needs, as this method helps to satisfy their predictions. Inside the collaborative filtering mechanisms still have some issues like a cold-start problem, which is related to the new user login without having any prior Internet searching information or without their history issue before.  
Today, many researchers are interested in developing various methods to improve accuracy, instead of collaborative filtering methodology. Therefore, to improve the accuracy in the system, we present the FCA network graph created from the personalized information of the user in the system to solve the issue of cold-start problems and offer the doParallel algorithm to solve the problem of execution consuming a long time to access to the vast dataset.  
In this manuscript, we proposed the efficiency of a doParallel algorithm and the FCA network graph applied to the recommendation system, which has shown the best accuracy of the movie  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Sequential** & **doParallel** \\ \hline Proposed method & 48 h & 3 h \\ \hline \hline \end{tabular}
\end{table}
Table 8: Speedup comparison.  
recommendation system. However, to evaluate the performance, collaborative filtering methods, the maximal clique methods, and the proposed methods are used to assess the movie lenses dataset.  
The experiment result of MAPE in Figure 8 shows the proposed method provides the result of MAPE with more accuracy than the existing method, the accuracy result from the proposed method is 89.73%, from the collaborative filtering method is 81.12% and from the maximal clique method is 72.45%. These results confirm that the proposed method is powerful and useful in the area of recommendation systems. The proposed method has shown that it can solve the cold-start problem and timely execution issue as well. The comparison shows that the appropriate performance of a collaborative filtering algorithm decreased from 24 h to 1 h, the maximal clique algorithm reduces from 29 h to 1 h and 20 min and the proposed method decreased from 48 h to 3 h.
# Modified Uncertainty Error Aware Estimation Model for Tracking the Path of Unmanned Aerial Vehicles
## 2 Tracking of UAVs Using a Kalman Filter Algorithm with Error and Uncertainty Sensitivity
### Uplation Phase  
The estimate of the update phase \(\hat{y}_{l+1|l+1}\) for the standard Kalman filter is described by the following equation.  
\[\min_{y}\Big{(}\|x-\hat{x}_{l+1|l}\|^{2}+\|z_{l+1}-I_{x}\|_{C^{-1}}^{2}\Big{)} \tag{26}\]  
where \(C=ww^{R}\succ 0\). To make Equation (26) meaningful in Equation (25), in this work \(G_{l}\succ 0\) is considered; thus, the predicted estimate is obtained from the following equation.  
\[\hat{x}_{l+1|l+1}=\hat{x}_{l+1|l}+w^{*} \tag{27}\]where \(w^{*}\) represents the argument of the improved estimation model described by the following equation.  
\[w^{*}=\underset{w}{\text{arg min}}\bigg{(}\left||w\,||_{D_{l+1|l+1}^{-1}}^{2}+ \left\|a_{l+1}-L_{w}\right\|_{C^{-1}}^{2}\right), \tag{28}\]  
where \(w\) describes the estimated variance calculated using the following equation.  
\[w=x-\hat{x}_{l+1|l} \tag{29}\]  
and \(e_{l+1}\) is an improved estimation model, determined by the following equation.  
\[e_{l+1}=x_{l+1}-L\hat{x}_{l+1|l} \tag{30}\]  
By modeling uncertainties and errors, we obtain a better estimator, as described below.  
\[w^{*}=\underset{w}{\text{arg min}}F\bigg{\{}\left||w\,||_{D_{l+1|l+1}^{-1}}^{2} +\left\|e_{l+1}-L_{w}+\big{(}\delta_{w}+\overline{\delta}_{w}V(|w|)\big{)} \right)\alpha_{l+1}^{w}||_{C^{-1}}^{2}\bigg{\}}, \tag{31}\]  
Here, \(\delta_{w}\overline{\delta}_{w}\) are new parameters added to the modified KF measurement model and \(\alpha_{l+1}^{w}\sim\mathcal{O}(0,I)\) defines the noise added to the model. Thus, the improved update model results from the following equation.  
\[\hat{x}_{l+1|l+1}=\hat{x}_{l+1}+Q_{l+1}\Big{(}z_{l+1}-L\hat{x}_{l+1|l}\Big{)}- M_{l+1}\gamma_{l+1} \tag{32}\]  
with  
\[M_{l+1}=\left[J-\omega+\omega N_{l=1}\right]^{-1}\omega, \tag{33}\]  
\[Q_{l+1}=Q_{l+1}L^{R}C^{-1}, \tag{34}\]  
\[N_{l=1}=D_{l+1|l}^{-1}+\wedge\Big{(}C^{-1}\Big{)}, \tag{35}\]  
\[\gamma_{l+1}=\frac{1}{2}\Delta\Big{(}C^{-1}\Big{)}S(w^{*}). \tag{36}\]
## 2 Three-Dimensional Numerical Simulation Analysis
### Three-Dimensional FDEM Numerical Modeling
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Unit** & **Rock–Body** & **Ore Body** & **Fault** & **Prefabricated Joints** \\ \hline Density & Kg/m\({}^{3}\) & 2765 & 2780 & 2770 & – \\ Modulus of deformation & GPa & 15.77 & 16.15 & 0.73 & – \\ Poisson’s ratio & – & 0.26 & 0.28 & 0.31 & – \\ Tensile strength & MPa & 0.26 & 0.34 & 0.04 & 0.1 \\ Cohesion & MPa & 5.07 & 5.22 & 0.28 & 0.75 \\ Angle of internal friction & \({}^{\circ}\) & 39.26 & 39.07 & 27.65 & 29.24 \\ Type I fracture energy & J/m\({}^{2}\) & 144 & 144 & 50 & 32 \\ Type II fracture energy & J/m\({}^{2}\) & 300 & 300 & 150 & 90 \\ Normal contact stiffness & GPa & 71.31 & 53.42 & 8.0 & – \\ Tangential contact stiffness & MN/m & 7130 & 5342 & 800 & – \\ Contact friction coefficient & – & 0.73 & 0.62 & 0.5 & – \\ Normal stiffness of nodal unit & GPa & 3600 & 3600 & 400 & 3600 \\ Tangential stiffness of nodal unit & GPa & 3600 & 3600 & 400 & 3600 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model input parameters. Figure 3: Overarching Model. (**a**) Geometric models. (**b**) Network models. horizontal principal stress direction in the mining area is oriented North-South (NS) and West-East (WE). Therefore, it is imperative to incorporate geostress boundary conditions into the numerical model by rotating the coordinates appropriately. The resulting geostress equilibrium, depicted in Figure 4, aligns with theoretical expectations, with the y-direction representing the vertical orientation. The simulation approach employs a fixed concentric square expansion rate for bottoming. This study details a simulation method that directly controls the removal of ore body units within the bottoming area within the program, as opposed to pre-cutting the bottoming area for each step in the model. Once stability is achieved after the previous step's bottoming, the program orchestrates the concentric expansion of the bottoming area in the previous step based on the square's concentric expansion. Bottoming progresses step by step within the defined range until the entire ore body has been bottomed. The principle guiding the bottoming process is centered on the shape's center falling within the bottoming range. If it lies outside this range, it is excluded from consideration. To optimize computation, a bottoming rate of 12 m per step is utilized, with the center of the square bottoming area serving as the origin. The ore body's length exceeds 500 m, while its width is under 150 m. Consequently, during simulation, bottoming is confined to the ore body's interior. If the bottoming area surpasses the ore body's width, bottoming proceeds only along the ore body's length. Each bottoming step includes two ore releases, with the process advancing to the next step once equilibrium is attained.
## 4 Simulation Verification
### Sudden Load Conditions  
In practical applications, sudden load changes can cause voltage fluctuations, which may affect the normal operation of the system. To assess the performance of the proposed control strategy in responding to sudden load changes, we will simulate the system response under varying load conditions. By comparing the performance of different control methods after the load change, we can further validate the adaptability and effectiveness of the proposed method in real-world load-variation scenarios. Figure 10 below shows the DC-side voltage waveform when the load changes suddenly to 50 \(\Omega\) and 300 mH at 0.25 s.  
As shown in Figure 10, the improved control strategy achieves more stable control. Compared to traditional methods, this approach demonstrates superior performance in maintaining steady-state voltage and suppressing voltage fluctuations, effectively reducing voltage deviations and improving steady-state accuracy. Furthermore, the proposed method exhibits better convergence characteristics, enabling the system to stabilize more quickly in a shorter time, significantly enhancing the control response speed and precision. This ultimately improves the stability and reliability of the system under dynamic conditions.  
Taking phase A as an example, as shown in Figure 11a, the waveform of the load current under a sudden change at 0.25 s is displayed. Figure 11b shows the waveform of the compensating current injected by the device, while Figure 11c presents the compensated grid current waveform. Clearly, when the system encounters a sudden change in the nonlinear load, the proposed improved control method still maintains superior performance, significantly improving both the current waveform and the DC-side voltage waveform. Figure 12 show the THD of phase A current at different times after the load change.  
Figure 10: DC-side voltage waveform under sudden load conditions.
## 3 Experimental Testing of Coating by Oil Dispersion
### Conditions of Experimental Measurements  
Measurements have been made with precision, with the utmost effort to maintain identical conditions. The ambient temperature did not change, the pressure of the air in the pressure vessel was always the same before each measurement, and the time of duration of each period was kept as precisely as possible. Application tubes, which served as a substitute for steel pipes to be treated with anticorrosive protection, were yielding and did not retain their shape. This phenomenon was eliminated by their stiffening with solid steel tubes, as seen in Figure 15.  
One important detail was also the detail of the position of the nozzle when entering the test tube. The output nozzle position was very important, as seen in Figure 16B. It was necessary to keep its position in the pipe axis and also the application angle had to correspond to the axis of the pipe. Otherwise, if the nozzle or pipe nozzle is incorrect, it is likely that a larger amount of oil will settle in the initial part of the tube and consequently less oil will settle in the back as the oil mist will be less  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sector** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1. measurement & 56.47 & 5.54 & 3.88 & 2.61 & 2.10 & 3.38 \\
2. measurement & 38.83 & 9.17 & 2.10 & 2.61 & 2.36 & 1.85 \\
3. measurement & 54.69 & 17.00 & 11.59 & 4.97 & 3.57 & 4.71 \\ \hline Average value & 49.99 & 10.57 & 5.86 & 3.40 & 2.68 & 3.31 \\ \hline \end{tabular}
\end{table}
Table 2: Second set of measurements of oil film areal density (g/m\({}^{2}\))—a slim tube with the inner diameter of 25 mm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Sector** & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline
1. measurement & 42.84 & 3.00 & 2.55 & 1.91 & 1.89 & 0.05 \\
2. measurement & 34.65 & 3.00 & 1.59  
dense and will contain a weaker concentration of oil particles than it might have when the nozzle is correctly positioned.  
In view of the texture of the dispersion oil fraction itself, it was important to run the applicator for a few seconds without the application tube just before application for a few seconds at the beginning, until the applicator mixing valve filled with applied oil from the container, thereby displacing the air bubbles that were in the application set. These bubbles were formed by gravity in intervals between applications themselves. Another, no less important detail in the application of the dispersion oil fraction in the tube is its outlet part, i.e., the exhaust. It is important for the exhaust not to be covered; its free outlet should be unimpeded, as seen in Figure 16A. It is possible that further research will be able to propose a better aerodynamic element necessary to achieve the desired flow characteristic of the pipe outlet. From the point of view of measuring and observing identical conditions of measurement of tubes of different height clearance, it is appropriate to set identical conditions for the outlet of the pipe mouth.
## 4 Results and Discussion
### Variation of \(L\)ulc and NPP
#### 4.1.2 Total NPP Variations against LULC  
As the above stated, LULC change can directly alter the ecological system's NPP. Investigation into total NPP variations against LULC changes is beneficial to identify the impacts on NPP quantity and structure. The statistic mean NPP values of different land covers in Guangzhou are listed in Table 2 during the two periods. Among them, the mean values of forests were higher than the average, while that of grassland were the lowest. Evergreen needleleaf forests had the lowest NPP among the three forests classes, probably due to large areas of sapling forest with lower productivity [59, 60, 61]. Introducing broadleaf to needleleaf forests to accelerate the conversion to mixed forests was an effective way to maintain a high level of regional forest NPP [59, 62]. In the initial period, needleleaf forests converted into mixed forests and broadleaf forests are one case from low-NPP covers to high NPP-covers, as illustrated in Table 3, suggesting result of rising total NPP of mixed and broadleaf forests in the first period. In addition, the NPP of croplands had the largest increment of \(48.4\times 10^{6}\) gC due to elevated areas, for example in the surrounding region of some districts in the 2003 classification mapping (Figure 2b). Decline in NPP mainly occurred in the destroyed needleleaf and shrublands.  
Figure 3: Conversion areas among land use/land covers during (**a**) 2001–2003, (**b**) 2003–2006.  
Figure 2: Land cover/land use (LCLU) classification mapping (**a**) 2000, (**b**) 2003, (**c**) 2006.  
On the contrary from 2003 to 2006, many forests covers were changed into shrulands and cropland/vegetation mosaics, while the NPP did not have an obvious increase. The obvious decrease in total NPP of grasslands, needleleaf and broadleaf forests reached 79.5 \(\times\) 10\({}^{6}\) g\(\cdot\)C, 26.7 \(\times\) 10\({}^{6}\) g\(\cdot\)C and 22.1 \(\times\) 10\({}^{6}\) g\(\cdot\)C, respectively.  
In general, the reduction of total NPP was mainly from the contribution of needleleaf and broadleaf forests, shrulands and grasslands, which had most conversion areas to croplands. With urban expansion and rapid economic development, as depicted in Figure 2c, LULC changes mainly displayed from high cove forests to croplands in suburb and exurb areas such as Luogang, conghua and Zengcheng districts. This process led to an increase in land fragmentation and a decrease in NPP until it reached present state. It is reported that the urban expansion mechanism will inevitably lead to reduction of the surrounding land covers, such as the common croplands, grasslands, shrulands, _etc._ Then, the further land reclamation in exurb would be used to balance the structure of croplands [63,64]. These variations should be visible in the following spatial-temporal dynamics under the LULC changes.
###### Abstract  
_Purpose_: Patient satisfaction can identify specific areas of improvement in community pharmacy services. Currently in Pakistan, no evidence exists in this regard. This study was conducted to determine the needs of patients and the current standards of pharmacies. _Methods_: A cross-sectional study was conducted between October 2016 and June 2017. A pilot tested questionnaire was used to collected the data from 1088 patients of 544 community pharmacies. Likert scale and one way ANOVA was used to analyze the data. _Results_: The response rate of community pharmacies was 80% and that of purchasers was 68.1%. The mean age of participants was 35.2 years. The mean overall satisfaction score of participants was 2.78/5.00. Many patients were dissatisfied (1.65/5.00) with parking facilities provided by pharmacies. Pharmacy service time fulfilled the requirements of most patients (4.16/5.00). The counseling person's good attitude (3.99/5.00) was credited by purchasers. Level of patient satisfaction with the availability of medicines (3.19/5.00), safe storage of medicines in pharmacy stores (3.66/5.00), and quality of medicines (3.41/5.00) were almost moderate. Many patients were very satisfied (4.35/5.00) with readable instructions for their medications. Approximately half of the patients were dissatisfied with the waiting time. Many patients were also dissatisfied (2.28/5.00) with the knowledge of the counseling person. Patients perceived that staff interest in patient recovery (2.24/5.00) was low. No significant difference in level of satisfaction with regard to participant's characteristics was found. _Conclusions_: The current study demonstrated a low level of patient satisfaction with regard to community pharmacy services in Pakistan. These services need improvement.  
patient satisfaction; counseling; community pharmacy; dispensing; medicine; services; Pakistan 2018 15 2914 10.3390/jemph15122914 10.
## 2 Materials and Methods
### RL Agents in Portfolio Optimization  
In general, RL agents can be broadly categorized into value-based, policy-based, and actor-critic methods. Value-based methods, such as DQNs [24] and Double DQNs [38], estimate the value of each possible action in a given state and select the action with the highest value. Policy-based methods, like PPO [26], directly optimize the policy that maps states to actions, enabling them to handle both discrete and continuous action spaces effectively. Actor-critic methods, such as DDPG [25] and SAC, combine both approaches: the actor determines the actions, while the critic evaluates the value of those actions. These distinctions allow RL agents to address varying portfolio optimization challenges, including action space granularity and decision stability.  
Appendix A provides a comprehensive overview of these algorithms, including their architectures, advantages, and limitations.  
A DQN is effective in discrete action spaces but is limited by overestimation bias, particularly in volatile financial environments. This bias can lead to suboptimal portfolio decisions, as the algorithm tends to overestimate the value of certain actions in noisy or stochastic market conditions. In the context of portfolio optimization, where asset prices are subject to fluctuations, such overestimation can compromise the robustness of allocation strategies [24; 38].
## 3 Test Results and Discussion  
Table 3 summarizes the experimental results, including failure load, maximum displacement, developed maximum tensile strain on the GFRP bars, as well as the developed maximum compressive strain on the concrete barrier wall for all specimens.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Criteria**} & \multicolumn{6}{c}{**Specimen**} \\ \cline{2-7}  & **B-1** & **B-2** & **B-3** & **B-4** & **B-5** & **B-6** \\ \hline Experimental failure & & & & & & \\ load (kN) & 169 & 183 & 130 & 163 & 160 & 187 \\ \hline Experimental failure & & & & & & \\ load per meter (kN/m) & 187 & 203 & 144 & 182 & 178 & 208 \\ \hline Experimental resisting & & & & & & \\ moment per meter & 186 & 201 & 143 & 180 & 176 & 205 \\ (kN.m/m) & & & & & & \\ \hline Developed design & & & & & & \\ moment per meter & 123 * & 144 * & 103 ** & 126 ** & 123 * & 144 * \\ (kN.m/m) & & & & & & \\ \hline Capacity/demand ratio & & & & & & \\ in design (experimental & & & & & \\ failure moment/ & 1.51 & 1.40 & 1.38 & 1.43 & 1.43 & 1.42 \\ developed design & & & & & & \\ moment) & & & & & & \\ \hline Capacity/demand ratio & & & & & & \\ using 0.85 & & & & & & \\ environmental & & & & & & \\ reduction factor & & & & & & \\ \hline Top front displacement & & & & & & \\ (mm) & & & & & & \\ \hline Bottom back & & & & & & \\ displacement (mm) & 3.81 & 4.27 & 8.43 & 13.42 & 0.73 & 3.46 \\ \hline Overhang deflection & & & & & & \\ (mm) & & & & & & \\ \hline Front uplift (mm) & 0.39 & 1.62 & 3.66 & 5.30 & 0.97 & 0.96 \\ \hline GFRP micro-strain & 6504 & 4765 & 5802 & 5495 & 15,848 & 7226 \\ \hline Concrete micro-strain & \(-818\) & \(-868\) & \(-688\) & \(-1045\) & \(-893\) & \(-701\) \\ \hline Observed failure & & & & & & \\ mechanism & & & & & \\ \hline \multicolumn{7}{c}{} \\ \end{tabular}
\end{table}
Table 3: Experimental results.