esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Contact Force Model of Polyhedral Elements for the Discrete Element Method
## 4 Comparison between DEM and Experimental Results
### Two Elements under Face-Face Contact Mode  
From Figure 14, it can be observed that the amplitude of the contact force peak obtained from simulation and experimental measurement is similar under different loading conditions, but the contact force-time curve obtained from simulation during the unloading stage is relatively smoother compared to the experimental measurement. This is because the specimen vibrates when it is subjected to an impact. Once the upper specimen separates from the lower block in the simulation, the contact force between the specimens becomes zero, whereas in the experiment, the contact force is not immediately reduced due to the sensitivity of the sensor. The relative error between the contact force peak obtained from the discrete element simulation and the experimental measurement under face-face contact is around 15%. The experimental results show that, after time t = 0.05 s, an overall waveform shift can be observed, and the waveform no longer stably oscillates around the zero value. The reason for this phenomenon is attributed to the instantaneous and high-energy input of the impact load. Under sufficiently high-energy conditions, it may lead to an unstable or uneven response in the thin-film pressure sensor. When the energy reaches a certain level, the instantaneous response can cause a waveform shift or drift after reaching the peak contact force. This situation does not affect the accurate capture of the peak contact force.  
Figure 13: Comparison of DEM and experimental contact force–time curves under edge-to-face contact mode.  
In the comparison of the simulation and experimental results of polyhedron unit contact forces under various contact modes mentioned above, we observed a small relative error in the maximum simulated contact force, and the simulated contact force exhibited a pattern similar to the experimental results. This suggests that a certain level of accuracy in simulation is achieved by the improved contact force model. To further substantiate the effectiveness of the improved contact force model, a series of packing experiments with cubic blocks were conducted in the subsequent section, and a comprehensive comparison was made between the experimental results and the simulations, both before and after the improvement.
## 5 Beyond Standard SDS
### Data-Driven SDS (DDSDS) for Hyperplane Estimation  
Data-driven SDS [18] retains all the core components and simplicity of standard SDS, as seen in the algorithm detailed in Section 2, with some crucial differences at each stage of the algorithm.
## 4 Experimental Results and Discussions
### TB Prediction Using Ensemble Model
Table 8 tabulates the comparison of the prediction performance of the proposed cascade algorithm and the existing studies. The comparison exhibits that the proposed algorithm provides preferable results for both the MC and SZ datasets. It provided significant improvement for the MC dataset. It achieved over 4% better accuracy than previous studies. To our knowledge, our cascade algorithm is the first to achieve over 90% accuracy for the MC dataset. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Network** & **Learning Rate (\(\alpha\))** & **Momentum (\(\gamma\))** &
\begin{tabular}{c} **L2-Regularization’s** \\ **Decay Rate (\(\lambda\))** \\ \end{tabular} \\ \hline AlexNet & \(1.6\times 10^{-2}\) & \(9.0\times 10^{-1}\) & \(2.1\times 10^{-5}\) \\ GoogleNet & \(1.1\times 10^{-2}\) & \(8.4\times 10^{-1}\) & \(3.5\times 10^{-9}\) \\ InceptionV3 & \(1.0\times 10^{-2}\) & \(8.0\times 10^{-1}\) & \(1.5\times 10^{-10}\) \\ ResNet-50 & \(1.0\times 10^{-2}\) & \(9.0\times 10^{-1}\) & \(3.0\times 10^{-4}\) \\ ResNet-101 & \(1.3\times 10^{-2}\) & \(9.8\times 10^{-1}\) & \(1.5\times 10^{-6}\) \\ VGG-16 & \(1.0\times 10^{-2}\) & \(8.9\times 10^{-1}\) & \(1.0\times 10^{-4}\) \\ VGG-19 & \(1.0\times 10^{-2}\) & \(9.9\times 10^{-1}\) & \(2.5\times 10^{-4}\) \\ XceptionNet & \(1.0\times 10^{-2}\) & \(8.2\times 10^{-1}\) & \(3.6\times 10^{-10}\) \\ MobileNet & \(1.4\times 10^{-2}\) & \(9.1\times 10^{-1}\) & \(1.0\times 10^{-4}\) \\ SqueezeNet & \(2.1\times 10^{-1}\) & \(8.7\times 10^{-1}\) & \(9.0\times 10^{-6}\) \\ ShuffleNet & \(2.5\times 10^{-2}\) & \(9.1\times 10^{-1}\) & \(8.9\times 10^{-9}\) \\ DenseNet & \(1.0\times 10^{-2}\) & \(9.5\times 10^{-1}\) & \(1.0\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Optimal parameters for each DCNN. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{3}{c}{**MC Dataset**} & \multicolumn{3}{c}{**SZ Dataset**} \\ \cline{2-7}  & **F1** & **ACC** & **AUC** & **F1** & **ACC** & **AUC** \\ \hline AlexNet & 71.2 & 58.5 & 72.6 & 71.9 & 64.3 & 78.7 \\ GoogleNet & 84.2 & 85.4 & 95.2 & 82.6 & 83.9 & 93.3 \\ Inceptionv3 & 72.7 & 78.0 & 93.1 & 74.8 & 79.4 & 92.8 \\ ResNet-50 & 84.4 & 82.9 & 94.3 & 79.3 & 81.9 & 93.8 \\ ResNet-101 & 68.8 & 75.6 & 94.8 & 78.4 & 81.4 & 94.1 \\ VGG-16 & 68.1 & 63.4 & 79.5 & 65.2 & 68.8 & 76.6 \\ VGG-19 & 66.7 & 73.2 & 88.8 & 59.9 & 70.4 & 87.1 \\ XceptionNet & 78.9 & 80.5 & 92.9 & 75.6 & 79.9 & 94.5 \\ MobileNet & 90.5 & 90.2 & 96.4 & 92.9 & 93.0 & 98.0 \\ SqueezeNet & 87.0 & 85.4 & 93.6 & 76.9 & 80.4 & 93.0 \\ ShuffleNet & 68.8 & 75.6 & 95.0 & 85.7 & 87.4 & 96.9 \\ DenseNet & 89.5 & 90.2 & 97.6 & 94.2 & 94.5 & 99.6 \\ Ensemble & 92.7 & 92.7 & 99.0 & 95.3 & 95.5 & 99.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results from individual DCNNs and the ensemble of selected DCNNs (in %).
# Assisting Drivers at Stop Signs in a Connected Vehicle Environment
## 5 Performance Evaluation
### Performance Evaluation of the Proposed Protocol at X-Junction  
In this last set of experiments, we evaluate the performance of the proposed protocol at the most common road intersection (i.e., X-junction). Figure 7 graphically illustrates the performance of the proposed protocol in the X-junction road intersection. The braking distance of stopped vehicles is increased using this protocol, and thus, the safety conditions are enhanced. Figure 7c,d show that the stopping distance at low traffic density in both tested scenarios is highly increased compared to the high-traffic-density scenario for the same road intersection.  
The proposed protocol completely eliminated the accident rates in the X-junction as well. Figure 7e,f illustrate the performance of the proposed protocol in terms of average accident rates. In the scenario of assist-absent, the accident rates reached 6% and 8% in highly congested X-Junction road scenarios for the all-ways stop sign intersection and main road intersection scenarios, respectively. The proposed protocol (i.e., assist-used) successfully eliminated the accident rate in both scenarios.  
The throughput of the investigated road intersection is significantly increased, especially for highly congested scenarios. Figure 7g,h graphically represent the performance  
Figure 6: The performance evaluation of the driving assistance protocol at Y-junction.  
of the proposed protocol in terms of increasing the throughput of the investigated road intersection. We can also see from the figure that the throughput of the main road scenario is higher than the throughput of the all-ways scenario. This is due to the fact that vehicles traveling on the main road are not required to stop at the road intersection. However, on the all-ways stop sign intersections, all vehicles have to come to a full stop at the road intersection.
# A Hybrid PAPR Reduction Method Based on SLM and Multi-Data Block PTS for FBMC/OQAM Systems
## 1 Introduction
However, the two hybrid PAPR reduction methods only combined the advantages of two conventional methods, but the overlapped nature of FBMC/OQAM symbols was ignored. [PERSON] et al. [27] proposed a hybrid PAPR reduction scheme using multi data PTS and TR-based methods. In [28], the authors proposed a hybrid scheme combined with improved bilayer PTS and clipping-filtering methods. Using these two proposed hybrid methods, the overlapped structure of FMBC/OQAM signals was explored, and PAPR reduction performances were obtained than those with conventional PAPR reduction methods. In this paper, a hybrid PAPR reduction scheme combined with SLM and multi data block PTS methods for FBMC/OQAM systems is investigated. This paper provides the following three main contributions:  
1. To the best of my knowledge, a hybrid scheme combined with SLM and PTS for FBMC/OQAM signals has not yet been studied in the literature. At first, a simple SLM-PTS method is presented. When the overlapped structure of FBMC/OQAM signal is considered, the adjacent multi data block overlapping effect on its PTS process is taken into account, and a more effective hybrid method is proposed based on SLM and multi data block PTS methods. 2. The superiority of the proposed method is that it combines the advantages of SLM and PTS methods, and the overlapped nature of FBMC/OQAM symbols is also considered. 3. The accuracy of the analytical results under different conditions is verified through numerical simulation. The different conditions include different numbers of phase sequences, different numbers of sub-blocks, and different numbers of data blocks. Simulation results demonstrate that the proposed approach has better PAPR reduction performance than traditional SLM, PTS and SLM-PTS schemes. The purpose of this paper is to propose an effective PAPR reduction method for FBMC/OQAM systems. I would like to convince readers with the potential of the proposed method as a high-performance PAPR reduction technology in FBMC/OQAM systems. The rest of the paper is organized as follows. The FBMC/OQAM signal and PAPR for FBMC/OQAM are presented in Section 2. Section 3 shows the traditional SLM and PTS schemes. In Section 4, a hybrid method based on SLM and multi data block PTS PAPR reduction scheme is proposed. Section 5 shows the PAPR reduction simulation performance of the proposed hybrid method associated with the SLM, PTS and simple SLM-PTS methods. Finally, the conclusions are given in Section 6.
# Effect of Slot Opening Width on the Air-Gap Magnetic Field of a Direct Drive Permanent Magnet Motor
## 5 Conclusions  
Herein, the effect of slot opening width on the air-gap magnetic field was investigated. In addition to the introduction of the exact analytical model, the analytical general solution of the air-gap magnetic field was obtained, thereby showing that the slot opening can affect the air-gap magnetic field distribution. Subsequently, the air-gap magnetic fields of DDPMM with closed and open slots were compared. Moreover, the finite element models of motors composed of unit motor were established to analyze the effect of slot opening width on the air-gap magnetic field based on different slot opening widths. The FEM results reveal that the air-gap magnetic field is affected by the slot opening width. Some characteristics are summarized as follows.  
1. The slot opening width affects the air-gap magnetic field distribution. The width and depth of the concaved magnetic field rise, and the harmonics of the air-gap flux density change with the increase in the slot opening width.
2. The effect of slot opening width on the air-gap magnetic field varies with different positions. As the slot opening position deviates from the center of the permanent magnet, the position of the concaved magnetic field shifts, and the main harmonic in the air-gap magnetic field gradually changes from the third harmonic to the second harmonic. Moreover, when the slot opening corresponds to the gap of two permanent magnets, the air-gap magnetic field distribution is less affected by the slot opening width.
3. The slot opening width has different effects on the air-gap magnetic field distribution in the motors composed by different numbers of unit motor. With the increase in the number of unit motor, the fundamental amplitude of the air-gap magnetic field in the motor increases, the total harmonic contents decrease, and the value of each harmonic also reduces.  
Therefore, different slot openings have certain effects on the air-gap magnetic field. This study provides a reference for optimizing the air-gap magnetic field distribution of a DDPMM.  
Figure 11: Air-gap magnetic field analysis of the DDPMM with 24 poles and 27 slots at different positions: (**a**) magnetic field distribution at position 1; (**b**) harmonics at position 1; (**c**) magnetic field distribution at position 2; (**d**) harmonics at position 2; (**e**) magnetic field distribution at position 3; (**f**) harmonics at position 3; (**g**) magnetic field distribution at position 4; (**h**) harmonics at position 4.  
**Funding:** This research is supported by the open fund of Henan Intelligent Power Transmission and Conversation Engineering Research Center, Henan University of Urban Construction (2019 SPBH08).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Materials and Methods
### Mutagenicity Assay: Ames Test  
The TA100 and TA98 strains of _Salmonella typhimurium_ were utilized for mutagenicity assay in absence and presence of metabolic activation, i.e., with and without S9 liver fraction. The tester strains used were selected because they are sensitive and detect a large proportion of known bacterial mutagens and are most commonly used routinely within the pharmaceutical industry [60]. The following specific positive controls were used, respectively, with and without S9 fraction: 2-nitrofluorene (2-NF) 2 \(\mu\)g/mL + 4-nitroquinoline N-oxide (4-NQO) 0.1 \(\mu\)g/mL, and 2-aminoanthracene (2-AA) 5 \(\mu\)g/mL. The final concentration of S9 in the culture was 4.5%.  
Approximately 10\({}^{7}\) bacteria were exposed to 6 concentrations of _R_ex, as well as to positive and negative controls, for 90 min in medium containing sufficient histidine to support approximately two cell divisions. After 90 min, the exposure cultures were diluted in pH indicator medium lacking histidine, and aliquoted into 48 wells of a 384-well plate. Within two days, cells which had undergone the reversion to His grew into colonies. Metabolism by the bacterial colonies reduced the pH of the medium, changing the color of that well. This color change can be detected visually. The number of wells containing revertant colonies were counted for each dose and compared to a zero-dose control. Each dose was tested in six replicates.  
The material was regarded mutagenic if the number of histidine revertant colonies was twice or more than the spontaneous revertant colonies.
## 4 Simulation Results
The proposed method, Ring Routing and line-based data dissemination, have stable performances in different number of sensor nodes; considering the performance of Railroad, when a node has sensor data, some information about these data would be sent to the closest node of the rail and a station would be created; naturally, by increasing the number of nodes, number of created stations and number of dissemination would be also increased; this results in considerable complications in performance of railroad for larger grids. Figure 14: Comparative performance: Average energy consumption for changing the number of sensor nodes. Figure 13: Comparative performance: Average reporting delay for variable speeds of a sink. Figure 15 indicates average reporting delay of protocols considering the change in the number of sensor nodes has patterns similar to average curves for energy consumption. Line-based data dissemination, Ring Routing and the proposed routing among a given number of nodes in the range have stable performance, while the Railroad performance worsens with increases in the number of nodes and the size of the grid. Routing for the proposed method has better reporting delay values than other algorithms. As seen in Table 2, the performances of four introduced protocols have been compared.
## 3 A Recommender of Tourist Activities Based on Clustering and Rule Mining
### Association Rule Mining
#### 3.4.2 ARM Metrics  
Several metrics can be used to evaluate the usefulness of mined rules. The three most popular ones are support, confidence and lift. They were used to select the best rules for the recommendation process, as will be described in the next section.  
* **Support**. It indicates how frequently an itemset occurs in a data set. It is the fraction of times an itemset appears among all the transactions being analysed. It can be denoted as the probability of occurrence of the itemset \(P(itemset)\). The support of a rule is the percentage of times that the antecedent and the consequent of the rule appear together.
* **Confidence**. It indicates how often a rule is found to be true. It is the proportion of times the consequent is found in the same transaction as the antecedent. It can be denoted as the conditional probability of the consequent appearing in the same transaction after the antecedent is found to be true \(P(antecedent|consequent)\).
* **Lift**. It was established to solve the problem on the confidence, being dependent only on the support of the antecedent. The order of the consequent and the antecedent in the rule does not matter, which makes the confidence metric a bit skewed because it considers the consequent to be dependent on the antecedent. The lift metric modifies this fact by considering the support of both the antecedent and the consequent. The mathematical formulation of these measures is the following: _For a rule: \(X\to Y\), where \(X\) is the antecedent and \(Y\) is the consequent_ Support: \[supp(X)=P(X);\;supp(Y)=P(Y);\;supp(X\to Y)=P(X\cup Y)\] (1) Confidence: \[conf(X\to Y)=P(X|Y)=\frac{supp(X\to Y)}{supp(X)}\] (2) Lift: \[lift(X\to Y)=\frac{supp(X\to Y)}{supp(X)*supp(Y)}\] (3)  
The three metrics were combined to rank the mined rules in the selection stage, as will be detailed shortly. In summary, the following ARM steps were executed for each individual cluster to mine useful rules:  
1. Create separate baskets with the POIs visited in the same day by each user.
2. Mine frequent itemsets of visited POIs with a maximum length of 3 and a minimum support of 0.001.
3. Build association rules from the itemsets uncovered in the previous step.
4. Compute the previous metrics for each rule.  
\begin{table}
\begin{tabular}{c c c c} \hline \multicolumn{2}{c}{**Apriori Params**} & \multicolumn{2}{c}{**Association Rule Params**} \\ \hline Min support & Max length & Metric & Minimum value \\ \hline
0.001 & 3 & Lift & 0 \\ \hline \end{tabular}
\end{table}
Table 4: ARM algorithm parameters.
## 4 Experiments and Result
### System Experiment Environment  
Table 1 shows the experimental environment. The experiment was conducted in several buildings on the campus of Changwon National University using different networks. All experiments used two different PCs, one laptop, and Oculus Rift S and HTC Vive Pro Eye as wired or wireless VR devices. In addition, to mitigate the possibility that the experiment may not proceed normally due to extraneous conditions, such as VR motion sickness in the participants, researchers were placed at each experimental site.  
### System Experiment Environment  
The experiment was designed to compare the proposed system with traditional video education to confirm the educational effect and immersion experience provided by our system. Both training methods covered the same aircraft maintenance scenarios. In addition, the same educator conducted the course for 18 min. The training materials and content were reviewed by an in-service aircraft engineer with 30 years of experience--who has also advised the educator.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **PC 1** & **Notebook** & **PC 2** \\ \hline \multirow{2}{*}{CPU} & AMD Ryzen 7 1700X Eight-Core & Intel® CoreTM 91-0980 HK CPU @ & AMD Ryzen 7 1700X Eight-Core \\  & Processor 3.40 GHz & 2.40 GHz 3.10 GHz & Processor 3.40 GHz \\ \multirow{2}{*}{RAM} & 16.0 GB & 32.0 GB & 16.0 GB \\ \cline{1-1}  & NVDIA GeForce GTX 1060 6 GB & NVDIA GeForce RTX 2080 Super & NVDIA GeForce GTX 1060 6 GB \\ \cline{1-1}  & HTC Vive pro Eye & Oculus Rift S & HTC Vive pro Eye (Wireless) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experiment environment.  
Figure 6: Aircraft maintenance simulation.
# An Algorithm of Acoustic Emission Location for Complex Composite Structure
## 2 Algorithms
### Correction of Travel Time Error for Straight Path Inside a Block  
The discretization of the continuum will inevitably cause errors although it brings convenience to describe complex structures. The error caused by spatial discretization is related to grid density. Specifically, the calculation accuracy as well as the calculation amount increase with increasing grid density. In addition, the shortest path algorithm also brings errors. For a CCS, AE rays are straight inside a block and deflected only at the block boundaries. However, the ray path inside the block is often not a straight line, but a polyline according to the above method due to the limitation of adjacency radius and the  
Figure 1: Schematic diagram showing node adjacency relationship. Pink and blue areas denote two different blocks; circles indicate grid nodes; black solid and open circles are the adjacent and non-adjacent nodes of the red nodes, respectively. \(R_{a}\) is the adjacency radius (\(R_{a}\) is equal to 4 cells here). (**a**) shows the node adjacency relationship inside a block; (**b**) shows the node adjacency relationship at block boundary.  
node discretization. Moreover, the length of each segment of the polyline is less than or equal to the adjacency radius. As a result, the travel time is calculated along the polyline path with error. This error will increase with the decrease in adjacency radius. Table 1 lists the maximum relative error of straight path travel time for various adjacency radii.  
Since the rays should be straight lines inside a block, we can correct the path and travel time of the rays according to the straight-line path. In our algorithm, it is easy to determine whether the ray reaches the block boundary since block boundaries are all located at nodes. Thus, the ray path and travel time in a block can be corrected when a ray reaches block boundary but does not enter another block. Namely, a path segment should be corrected as a straight line if the nodes, except the starting and ending nodes, of the path segment are intra-block nodes (Figure 2). Accordingly, the corresponding travel time is the straight-line distance between the starting and ending nodes of the path segment divided by the wave velocity of the block. By doing this, the ray path obtained by our algorithm will be straight inside a block and only deflected at the interface between blocks. As a result, the accuracy of the travel time calculation is improved.
## 3 The Proposed Model  
In this study, an effective OMLIDS-PBIoT technique was developed with the utilization of the BC and ML models for accomplishing security in the smart city environment. The presented OMLIDS-PBIoT technique employs different stages of subprocesses, namely pre-processing, GEO-FS, RVFL-based classification, and HBO-based parameter optimization. Moreover, BC technology is exploited for secure data transmission in the IoT enabled smart city environment.