esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methodology
### Synchronization of Transmission Conveyor and Robot Arm  
In order to accomplish the automation production line, we used a Syntec robot and a microcontroller-controlled conveyor to transfer the workpiece to the stamping die. Furthermore, for the dynamic transmission workpiece to take place successfully, a formula needs to be proposed to synchronize the movement of the robot and the conveyor.  
First of all, the gripping position, \(P_{gripping}\), needs to be recognized. To achieve the gripping pose, the point cloud of the object in the reference position has to be considered. The robot arm will be set to this position and its coordinates will be saved to the database for calculating the later position change of the object. After the pose estimation system figures out the relative position differences between the target point cloud and the reference point cloud, the gripping position of the target object, \(P_{gripping}\), will be calculated as the sum of the reference position stored in the database, \(P_{database}\), and the relative position, \(P_{estimate}\). Equation (24) and Figure 6 describe the grab position of the target object.  
\[P_{gripping}=P_{database}+P_{estimate} \tag{24}\]  
Figure 7 depicts the home position of the robot, \(P_{robot}\), and the scanning position of the conveyor, \(P_{scan}\), to the gripping position, \(P_{gripping}\). These positions, \(P_{robot}\) and \(P_{scan}\), need to be known in advance and pre-set in the database to synchronize the operation of the robot and the conveyor.  
The travel time for the robot to move from the home position to the gripping position of the object, \(t_{R\to G}\), is calculated as the distance between the robot's initial position, \(P_{robot}\), and the gripping position, \(P_{gripping}\), divided by the feed rate of the robot in linear movement, \(FL\). The following will demonstrate how to find \(t_{R\to G}\):  
\[t_{R\to G}=\frac{\left|P_{robot}-P_{gripping}\right|}{FL} \tag{25}\]  
The time, \(t_{S\to G}\), for the conveyor to move from the scan position to the gripping position is calculated by the distance of the scan position of the object, \(P_{scan}\), to the gripping position, \(P_{gripping}\), divided by the speed of the conveyor, \(v_{converor}\). \(t_{S\to G}\) will be expressed by:  
Figure 6: Construction of the target object gripping pose.  
Travel times \(t_{R\to G}\) and \(t_{S\to G}\) have to be synchronized for the robot to successfully pick the workpiece while the conveyor is moving; therefore, \(t_{R\to G}=t_{S\to G}\). Substitute into Equations (25) and (26), obtaining:  
\[v_{conver}=\frac{FL\times\left|P_{scan}-P_{gripping}\right|}{\left|P_{robot}-P_{ gripping}\right|} \tag{27}\]  
Equation (27) indicates that the \(v_{conver}\) will be the linear function of the feed rate of the robot in linear movement, \(FL\).
# ESCAPE: Evacuation Strategy through Clustering and Autonomous Operation in Public Safety Systems
## 6 Numerical Results
### Comparative Evaluation
#### 6.3.3 Third Scenario: Benefits of Learning  
Finally, in order to demonstrate and quantify the benefits of the introduced reinforcement-learning component, we compared our proposed approach with two other approaches that do not implement any learning process; instead, evacuees choose the evacuation route that they want to join right away, following specific metrics each time. In particular, the alternative approaches considered in this comparison are described as follows:  
1. The first alternative approach is that evacuee \(m\) chooses evacuation route \(e\) with lowest respective distance \(d_{m,e}\).
2. The second approach is that evacuee \(m\) chooses evacuation route \(e\) with greatest evacuation route \(\lambda_{e}\).  
Figure 7 presents the average reward function of all three approaches (including the proposed ESCAPE framework), and it is clearly observed that our approach significantly outperformed the two alternatives. This observation was expected as, through the reinforcement-learning component, evacuees acting as stochastic learning automata are able to thoroughly examine the disaster area in terms of how congested evacuation routes are. As a result, evacuees are distributed in the evacuation routes much more efficiently than in the other two approaches, where evacuees make their choice right away based on simple metrics. As far as the two alternative methods are concerned, the one where evacuee \(m\) chooses evacuation route \(e\) that has greatest evacuation rate \(\lambda_{e}\) had worse performance than the approach where the evacuee chooses the route with the lowest distance \(d_{m,e}\).  
Figure 6: Comparative analysis—Scenario 2: average reward function of all alternative strategies.  
Figure 7: Comparative analysis—Scenario 3: average reward function of all alternative approaches.
# A Study on the Mechanism of the Precipitation-Induced Slope Instability of Colluvium
## 5 Numerical Simulation Analysis Based on GeoStudio
### Simulation Results and Analysis  
As can be seen from Figure 16, with an increase in slope angle, the time required for pore water pressure to reach the maximum value is shorter. With rainfall infiltration, the pore water pressure inside the slope gradually increases from the surface to the slope, and the range of saturated soil at the foot of the slope gradually expands to its middle and rear. A large amount of rainwater was collected in a short time on the 40\({}^{\circ}\) slope, and the pore water pressure rose rapidly. The toe of the 30\({}^{\circ}\) slope is in a state of high pore water pressure for 60-90 min, which greatly accelerates the failure process of the toe of the slope, but the pore water pressure on the upper part of the slope has not reached the peak. The pore water pressure of the 20\({}^{\circ}\) slope changes slowly throughout the whole rainfall process, which is beneficial to the stability of the slope.  
In Figure 17, it can be seen that the saturation of the 30\({}^{\circ}\) slope rises faster at the beginning of the rainfall, which is because the slope angle of the 30\({}^{\circ}\) slope is slightly larger and the infiltration surface is larger, and the slope of 40\({}^{\circ}\) is steeper and a large amount of rainwater flows into the foot of the slope, resulting in the two slopes reaching saturation almost simultaneously. Combining this with the results in Figure 16, it can be seen that the foot of the 30\({}^{\circ}\) slope is saturated at 75 min, while the top of the slope has not reached the peak of pore water pressure at 90 min, which may also be the cause of the traction landslide. In the period of 60-90 min, the 30\({}^{\circ}\) slope collects more rainwater due to the large infiltration surface, but it is not completely saturated, while the 40\({}^{\circ}\) slope reaches the saturated state with a small infiltration surface, indicating that the drainage of the 40\({}^{\circ}\) slope is affected, and a large amount of rainwater exists in the slope, which provides favorable conditions for overall sliding.  
Although the numerical calculation model cannot achieve the rapid release process of pore water pressure and saturation due to the sliding of the sliding mass, it is still of great significance to understand the stress state adjustment process before slope instability.  
Figure 15: Numerical calculation model.  
Figure 16: Variation process of pore water pressure.  
Figure 17: Variation diagram of slope saturation with time.
# Influence of Waste Filler on the Mechanical Properties and Microstructure of Epoxy Mortar
###### Abstract  
This paper presents experimental investigations on epoxy mortar produced using industrial wastes. In some recent studies, coal bottom ash and polyethylene terephthalate (PET) waste have been chosen as a filler to replace sand, and fly ash and silica fume have been chosen as micro fillers for epoxy mortar production; enhanced results in terms of compressive and tensile strengths and durability have been achieved. However, these approaches failed to boost the strength and durability compared to the epoxy steel slag, epoxy sand, epoxy marble dust, and epoxy polyvinyl chloride (PVC) waste. This present research work has investigated the influence of waste filler on the mechanical properties and microstructure of epoxy mortar, produced by using sand and industrial wastes, i.e., steel slag, marble dust, and polyvinyl chloride waste. Based on the composition ratio, the prepared samples of epoxy resin mortar containing 25% epoxy binder (epoxy resin plus epoxy hardener) and 75% filler (1:3) were compared to the cement mortar. However, each specimen of epoxy resin mortar was prepared by mixing with different fillers. The properties such as compressive strength, tensile strength, and microstructural changes were measured using different characterization techniques including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared radiation spectroscopy (FTIR), and scanning electron microscopy and energy dispersive X-ray spectroscopy (SEM-EDX). From the obtained results, it was found that the strength of the specimens increases when blended with steel slag and marble dust, which is attributed to their peak densities and enhanced particle interactions. The XRD, SEM, FTIR, and SEM-EDX analyses showed the formation of calcium, magnesium, and other phases in the microstructure of epoxy resin-based mortars. This resulted in lower water absorption and porosity, as well as improvements in both compressive and tensile strengths. This research can help in understanding the important role of different industrial wastes as feasible fillers in epoxy resin-based composites.  
epoxy resin; filler; marble dust; microstructure; polyvinyl chloride
## 4 Experimental Implementation and Performance Evaluation
### Performance Analysis with CCR set at 8 Hz
Without loss of generality, our approach can be extended to more general topologies. As anticipated above, considering a network with a generic topology, the distance between BR and LEN is \(\Delta\)-dependent. Figure 8: Time required to complete the DAO-EBR phase with CCR set to 8 Hz (metric T-DAO-EBR). Figure 7: Comparison among times required by the different phases of the discovery procedure, with CCR set to 8 Hz. Figure 9: Time required to complete the SCR-RES phase with CCR set to 8 Hz (metric T-SCR-RES). with a general topology, every path between the BR and the LEN can be interpreted as a subnetwork with linear topology with additional neighboring interfering devices (especially because of multiple access interference) that do not belong to this path. As intuitively expected, these devices will likely cause collisions, thus introducing additional delays and increasing energy consumption in the network. Therefore, our experimental results (in terms of energy consumption and delay) can be interpreted as lower bounds for the performance of networks with general topologies, provided that \(N\) is interpreted as the number of hops of the path between a node of interest (for instance, the LEN) and the BR, i.e., as the node's depth.
# Optimization of Excavator Bucket Structure by a Coupled Simulation Method
## 2 Adams-EDEM Coupled Simulation Method
### Establishment of Adams-EDEM Coupled Simulation Model
#### 2.2.3 Construction of Different Ore Particle Models  
In order to simulate the actual excavation process, the particle size distribution and mass proportion of Mengdong mudstone, Shanxi gritstone, Shanxi coal rock and Wuhai mudstone were determined through a geological survey and the particle models of typical ores were obtained using a 3D scanner.  
Mengdong mudstone is a gray-black thick block or layered silty mudstone that is partially mixed with light gray lenticular argillaceous fine-coarse sandstone. The sandstone is loose, the silt content in the mudstone is low, and the fine sandstone is the main component. The geological conditions of Mengdong mudstone is shown in Figure 6.  
Shanxi gritstone is originally grayish-white, but after weathering, it is earthly yellow. It is a medium-thick to super-thick layered gritstone mixed with dark gray thin layer silty mudstone, which reflects the characteristics of braided river sedimentation. The geological conditions of Shanxi gritstone is shown in Figure 7.  
Shanxi coal, produced horizontally, is black giant thick stratified bright coal with a primary banded structure, as shown in Figure 8.  
Figure 8: Geological conditions of Shanxi coal.  
Figure 6: Geological conditions of Mengdong mudstone.  
Figure 7: Geological conditions of Shanxi gritstone.  
Wuhai mudstone is a dark gray medium-thick to thick layered silty mudstone, rich in plant leaf and stem fossils, and the individual fossils are relatively large and the structure is relatively complete. The geological conditions of Wuhai mudstone is shown in Figure 9.  
The 3D models of different ore particles were obtained using a 3D scanner. Finally, the models were imported into the discrete element software EDEM.  
The particle size distribution and particle models of typical ores are shown in Table 4. The material properties of the bucket and ores are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ore Type** & **Ore Particle Size (mm)** & **Mass Proportion (\%)** & **Particle Model** & **Ore Type** & **Ore Particle Size (mm)** & **Mass Proportion (\%)** & **Particle Model** \\ \hline \hline \multirow{4}{*}{\
\begin{tabular}{c} Mengdong mudstone \\ \end{tabular} } & 0–50 & 40 & & 0-100 & 26 \\  & 50–100 & 40 & & Shanxi & 100–300 & 50 \\  & 50–100 & 40 & & gritstone & & \\  & 100–300 & 15 & & 300–500 & 12 \\  & \(>\)300 & 5 & & \(>\)500 & 12 \\ \hline \hline \multirow{4}{*}{
\begin{tabular}{c} Shanxi coal \\ \end{tabular} } & 0–100 & 70 & & 0–50 & 40 \\  & 100–300 & 10 & & 50–100 & 15 \\  & 300–500 & 10 & & 100–300 & 12 \\  & \(>\)500 & 10 & & \(>\)300 & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Particle size distribution and particle models of typical ores.  
Figure 9: Geological conditions of Wuhai mudstone.
## 3 Analysis
### Kolmogorov-Smirnov Test  
To statistically test the results, a Kolmogorov-Smirnov test was used. This test is a nonparametric goodness-of-fit test used to test the hypothesis that two populations have the same distribution. Let \(x_{1}\),..., \(x_{m}\) be the observations from the simulation of the first group with cumulative distribution function (CDF) \(F_{1}\), and let \(y_{1}\),..., \(y_{n}\) be the observations from the simulation of the second group with CDF \(F_{2}\). The null hypothesis is presented in Equation (2).  
\[H_{o}:F_{1}(x)=F_{2}(x)\text{ for all }x \tag{2}\]  
The Kolmogorov-Smirnov test statistic is defined in Equation (3).  
\[D=\max\bigl{|}F_{1}(x)-F_{2}(x)\bigr{|} \tag{3}\]  
The hypothesis is rejected if the test statistic, \(D\), is greater than the Kolmogorov-Smirnov critical value calculated using the functions presented in Equations (4)-(6) [33; 34].  
\[S(D)=Q_{KS}\biggl{[}\biggl{(}\sqrt{N_{e}}+0.12+\frac{0.11}{\sqrt{N_{e}}} \biggr{)}\times D\biggr{]} \tag{4}\]  
\[N_{e}=\frac{N_{1}N_{2}}{N_{1}+N_{2}} \tag{5}\]  
\[Q_{KS}(x)=2\sum_{i=1}^{\infty}(-1)^{i-1}e^{-2i^{2}x^{2}} \tag{6}\]  
where  
\(S(D)=\) level of significance,  
\(N_{e}=\) effective number of data points,  
\(N_{1}\), \(N_{2}=\) number of data points in the two distributions, and  
\(Q_{KS}=\) monotonic function for computing significance level.  
The results are presented in Table 3. As the computed \(p\)-value is greater than the significance level \(p\)-value of 0.05, the null hypothesis H\({}_{0}\) cannot be rejected in the case of age, type of clothing, carrying bags, and using mobile phones at the 95% confidence level.
## 3 Pose Estimation for Multi-Marker Systems
### Outlier Detection  
The temporal stability analysis algorithm monitored the consistency of measurements over time to identify sudden anomalies. For each _i-th_ marker, a moving average of rotation vectors was calculated:  
\[MA_{i}(t)=\frac{1}{k}\sum_{j=0}^{k-1}r_{cm,i}(t-j) \tag{9}\]  
In Equation (9), \(MA_{i}(t)\) is the moving average at time t. \(k\) and \(r_{cm,i}(t)\) denote the rotation vector of the _i-th_ marker at time \(t\). The temporal stability was determined by calculating the difference between the current measurement and the moving average:  
\[diff_{i}=\left|r_{cm,i}(t)-MA_{i}(t)\right| \tag{10}\]  
Figure 2: Spatial relationship between coordinate systems of camera, _i-th_ marker, and representative marker.  
A measurement was classified as an outlier if \(diff_{i}\) exceeded a predetermined threshold, indicating an abrupt change in the measured orientation that was likely due to environmental factors or measurement errors. Measurements designated as outliers were omitted from subsequent pose estimation calculations at time \(t\).
## 4 Experiments
### Setup
#### 4.1.2 Metrics  
We evaluated the sequential recommendation performance using five metrics, namely the AUC (area under the curve of ROC), accuracy, precision, recall and the F-Measure. These metrics were calculated through the confusion matrix. The confusion matrix is shown in Table 2.  
Accuracy refers to the ratio of correct prediction results to total samples:  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+FN+TN} \tag{18}\]  
Precision refers to the proportion of samples that are truly positive among all samples that are predicted to be positive:  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{19}\]  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Datasets** & **Users** & **Items** & **Records** \\ \hline MOOCS & 9986 & 1029 & 21,507 \\ Book-Crossing & 105,283 & 271,379 & 278,860 \\ Movielens-1m & 610 & 9742 & 108,375 \\ Steam & 12,393 & 5155 & 200,000 \\ Amazon Beauty & 1,210,271 & 1,249,274 & 2,023,070 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The statistics of the five datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**True** & **Predictions** \\ \hline - & Positive & Negative \\ Positive & True positive (TP) & False negative (FN) \\ Negative & False positive (FP) & True negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The confusion matrix.  
Recall refers to the proportion of samples that are actually positive that were predicted to be positive:  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{20}\]  
The \(F_{1}\)-score is the harmonic mean of the precision and recall. Normally, precision and recall are contradictory to each other. Thus, in order to evaluate the quality of the model, \(F_{1}\) is used as a comprehensive measure of precision and recall:  
\[F_{1}=\frac{2 PR}{P+R} \tag{21}\]  
where \(P\) denotes precision and \(R\) denotes recall.  
AUC is defined as the area under the ROC curve (receiver operating characteristic curve). The abscissa of the ROC curve is the false positive rate and the ordinate is the true positive rate. AUC is used as a metric to visually compare which model has better generalization performance, and this metric will not be affected by the imbalance of the data category of the categorized data. The larger the value is, the better the generalization effect of the model is. The AUC calculation method is as follows:  
\[AUC=\frac{\sum_{\text{ins}_{i}\in\text{positive }\text{rank}_{\text{ins}_{i}}-\frac{M \times(M+1)}{2}}}{M\times N} \tag{22}\]  
where \(M\) represents the number of positive samples, \(N\) represents the number of negative samples and \(rank_{\text{ins}_{i}}\) is the ranking value of the predicted probability of the positive sample with the index of \(\text{ins}_{i}\) in the predicted probability of all samples.
## 2 Development of Finite Element Model of Axially Loaded RC Columns for Impact Load  
Two experimentally tested reinforced concrete (RC) columns were selected for the development of the finite element (FE) model for vehicle impact. One column is a standard RC column (Imp3), while the other column (ImpA2) has been retrofitted with two layers of aramid fiber-reinforced polymer (AFRP) sheets [15]. The cross-section and length of the columns were 300 \(\times\) 300 mm\({}^{2}\) and 3200 mm, respectively. The span of the columns for the impact test was 3000 mm. Four longitudinal reinforcements of 18 mm diameter were used. The clear concrete cover on all sides was 25 mm. Stirrups of 8 mm diameter with 150 mm spacing were provided along the length of the columns. The yield strengths of longitudinal bars and stirrups were 465.7 and 512.3 MPa, respectively. The ultimate strengths of the bars were 583.7 and 613.0 MPa, respectively. The compressive strength and split tensile strength of the concrete was 30.7 and 2.8 MPa, respectively. The maximum aggregate size was 12 mm, and the density of the concrete was 2380 kg/m\({}^{3}\). The thickness of each layer AFRP sheet was 0.2 mm. Detailed properties of the concrete and AFRP can be obtained from [15]. In the experiment, the axially loaded RC columns were impacted laterally by a falling weight [15]. Figure 2 shows the schematic diagram of the test setup. The applied axial load was 375 kN, which is approximately 15% of the axial capacity of the column. The axial load was applied at the left end of the column while the other end was restrained for axial movement. Elastomeric bearing pads were used at both ends. The mass of the falling weight was 585 kg, and the drop heights were 3 and 4.5 m to generate the targeted impact velocities for the standard RC column (Imp3) and the FRP-retrofitted column (ImpA2), respectively. The dimension of the impact was 400 \(\times\) 400 \(\times\) 700 mm\({}^{3}\).  
Figure 2: Schematic diagram of test setup for the drop weight impact on the RC column [15].  
In this study, the non-linear finite element program LS-DYNA was used to model the axially loaded RC column for impact load. Using the available information from the experiment, a finite model was developed, as shown in Figure 3. The center of the impact is 1000 mm away from the right support of the assembly, which simulates the car impact point on the column. The concrete, projectile, roller supports, end plates, and elastomeric pad were modeled as 8-node solid elements. From the mesh sensitivity analysis, it was found that the displacement-time history converged at an element size of 20 and less. Therefore, the element of 15 mm in size was used for concrete in the FE model. The reinforcements were modeled as beam elements, following previous studies [26; 32]. For the modeling of the AFRP-retrofitted column, the AFRP sheet was modeled using 4-node thin shell elements with bending and membrane capabilities. The maximum size of the shell element was 15 mm.