esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
In this paper, a panel/viscous vortex particle hybrid method was developed and was coupled to the multibody dynamic code MBDyn to predict the blade unsteady aerodynamic loads. The lifting surfaces were modeled by vortex panels with constant dipole and source strength. The wake panels were shed from the trailing edge according to the Kutta conditon and were converted to viscous vortex particles as soon as they were convected downstream. The fast multipole method was utilized to accelerate the computation of the induced velocity and of its gradient. The proposed method was validated by comparison with the Caradonna-Tung rotor in hover and the AH-1G rotor in forward flight. Finally, the vortex particle code was coupled with MBDyn, and the HART II baseline model was simulated. The following conclusions can be drawn:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Collective** & **Longitudinal** & **Lateral** \\ \hline Experiment & 3.80\({}^{\circ}\) & \(-\)1.34\({}^{\circ}\) & 1.92\({}^{\circ}\) \\ Present & 3.925\({}^{\circ}\) & \(-\)1.116\({}^{\circ}\) & 1.347\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Trim parameters.  
Figure 17: HART II baseline case.  
When the vortex panel method was combined with the viscous vortex particle method, the predicted blade pressure, normal thrust, and wake geometry match well with experimental data and CFD results both in the hover and forward flight conditions.  
The classical particle-based fast multipole method was extended to a hybrid particle-panel computational domain by using a semi-analytical approach. The convergence of numerical tests show that, as the truncation order grows, a reasonable accuracy can be obtained; in addition, the numerical tests show that the presented algorithm has a computational complexity of \(O(N)\).  
When t vortex code was coupled with the computational structural dynamics code MBDyn, the results show that the VPM/MBDyn coupling approach can predict the unsteady airloads well and is capable of capturing the BVI phenomenon, a necessary requisite for a future, accurate evaluation of the rotor aeroacoustic response.  
Conceptualization, W.Z., M.M. and S.L.; funding acquisition, W.Z. and S.L.; methodology, W.Z. and M.M.; resources, W.Z.; software, W.Z. and M.M.; supervision, W.Z.; validation, W.Z.; writing--original draft, W.Z. and M.M.; writing--review and editing, W.Z. and M.M. and S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the China Scholarship Council (CSC, NO. 201806020058).  
Not applicable.  
The data can be obtained upon request from the corresponding author.  
The authors declare no conflicts of interest.
# Exploring the Role of Distraction in Weak Flow-Performance Link Based on VR Searching Tasks
## 7 Empirical Study 3: Effect of a Congruence Design Approach on the Weak Flow-Performance Link
### Results
#### 7.6.3 Effects on the Flow-Performance Link  
Correlation analysis and regression analysis were conducted under each of the two conditions. Results showed that the correlation between flow and performance was not significant in version 0, and flow did not significantly predict task performance (\(\beta\) = 0.215, \(p\) = 0.086). In comparison, data from version 2 showed that the correlation between flow and task performance was significant, and flow significantly predicted task performance (\(\beta\) = 0.255, \(p\) = 0.041). It could be seen that the correlation between flow and performance did increase to some extent after promoting congruence design.  
These results basically support H3 and demonstrate that distraction behavior can be transformed into task-relevant behavior by adding visual cues to provide attention guidance. When attention shifts from distractors to the task, the weak link will be alleviated.
## 3 Results and Discussion
### K-Nearest Neighbor  
Table 2 summarizes the results obtained with the pressure residuals of the water distribution network. The model's performance was assessed by the model's confusion matrix, accuracy report, receiver operating curve, precision, recall, and F1-score. The table shows that the K-NN classifier performed well with leakage localization and detection accuracy of 0.70%, recall = 0.70, F1-score = 0.70, and precision = 0.70%.  
The use of the K-NN classifier in leak localization across the two zones in order to provide the possible locations of leak events within the water distribution network can be identified in several possible locations, and the magnitudes of such leaks are 30, 35, 40, and 45 L/s for each node. Uncertainty conditions, such as noise and demand uncertainty, influenced the overall performance of the model; the uncertainty metrics were factored into the quality of data used in the training and testing of the model.
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 4 Experimental Verification
### Case 2:PUI Bearing Dataset
#### 4.2.3 Comparison with Other Networks  
Similarly, under the same experimental conditions, it is compared with the classical VSDP-TLVgg16, VSDP-TLEfficientNet, VSDP-TLesQueezeNet and VSDP-ResNet18 models. Four datasets (A, B, C and D) were introduced for diagnostic analysis, each containing a different number of training data samples. Among them, the combined number of training samples in datasets A, B, C and D is 240, 120, 60 and 36, respectively, and each dataset contains 480 test data samples. The number of training and testing samples is uniform across all fault categories in each dataset. As shown in Figure 20, the average accuracy of VSDP-TLSqueezeNet, VSDP-TLVgg16 and VSDP-TLEfficientNet is 94.32%, 97.24% and 97.18%. Although the three networks perform well on dataset A, their performance on dataset D decreases significantly, which may reflect the limitations of VSDP-TLSqueezeNet, VSDP-TLVgg16 and VSDP-TLEfficientNet in processing small-sample data. Compared with the re-trained VSDP-ResNet18, VSDP-TLResNet18 achieves an average accuracy of 99.11%. As the number of training samples decreases, the diagnostic accuracy of the four models also declines, but the diagnostic accuracy of VSDP-TLResNet18 on the four datasets is still relatively high, highlighting the effectiveness of the pre-trained and fine-tuned method in enhancing the model's generalization ability and adapting to various small-sample datasets.  
In order to better understand the generalization of VSDP-TLesNet18, Figure 21 shows the confusion matrix of five networks on dataset D. As can be seen from the figure, compared with VSDP-ResNet18, VSDP-TLesNet18 shows better performance in most categories, is superior to other networks and the number of misclassifications is reduced, especially in categories 1 and 3. This shows that the pre-trained and fine-tuned method is helpful for the model to better adapt and identify the characteristics of each category. VSDP-TLSqueezeNet has a lot of misclassifications in categories 0 and 4; a large proportion of samples in category 0 are misclassified to category 1, which indicates that VSDP-TLSqueezeNet is not enough to capture the characteristics of these fault categories on dataset D. Both VSDP-TLVgg16 and VSDP-TLEfficientNet incorrectly identify category 5, and VSDP-TLResNet18 can better identify category 5.  
Figure 20: Verification accuracy of different datasets under Case 2.
## 3 Results and Discussion
### Anttimicrobial Activity
#### 3.4.1 Grapefruit Essential Oil (GF-EO)  
Plant-derived EOs are one group of natural food preservatives. The effectiveness of EOs is associated with the presence of multiple volatile compounds with antimicrobial activity reported since ancient times. This results in the effectiveness and specificity of each EO against various microorganisms. The results of the antimicrobial activity measured by the disc diffusion method for the EO extracted from GF and miconazole (standard) are shown in Figure 4. The GF-EO showed a good antibacterial activity against Gm+ bacteria: _Bacillus cereus_, _Microococcus leuttus_, _Staphylococcus epidermidis_, and _Staphylococcus aureus_. The highest antibacterial activity was demonstrated by the EO against _S. aureus_ (15 mm), followed by the inhibition zone against _B. cereus_ (13 mm). Regarding _S. epidermidis_ and _M. leuttus_, the inhibition zone was 10 mm. _S. aureus_ and _S. epidermidis_ contain teichoic acid in the peptidoglycan layer and are inhibited by different citrus peel EOs and extracts [93]. In addition, the GF-EO also had a great inhibition spectrum toward the pathogenic yeast \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**DPPH**} & \multicolumn{2}{c}{**Nitric Oxide**} \\ \hline
**Concentration** & & & & \\
**(\(\mu\)g/mL)** & **GP-EO** & **Vit. C** & **GP-CE** & **GF-PE** & **Vit. C** \\ \hline
250 & 21.8 \(\pm\) 0.69 \({}^{\text{e}}\) & 23.73 \(\pm\) 0.53 \({}^{\text{e}}\) & 9.85 \(\pm\) 0.047 \({}^{\text{e}}\) & 43.32 \(\pm\) 0.32 \({}^{\text{e}}\) \\
500 & 35.7 \(\pm\) 0.37 \({}^{\text{d}}\) & 41.08 \(\pm\) 0.52 \({}^{\text{d}}\) & 14.63 \(\pm\) 0.18 \({}^{\text{d}}\) & 63.61\(\pm\) 0.42 \({}^{\text{d}}\) \\
1000 & 46.7 \(\pm\) 1.27 \({}^{\text{c}}\) & 64.57 \(\pm\) 0.59 \({}^{\text{c}}\) & 29.37 \(\pm\) 0.33 \({}^{\text{c}}\) & 77.83 \(\pm\) 0.51 \({}^{\text{c}}\) \\
1500 & 54.8 \(\pm\) 0.46 \({}^{\text{b}}\) & 81.85 \(\pm\) 0.46 \({}^{\text{b}}\) & 39.86 \(\pm\) 0.53 \({}^{\text{b}}\) & 85.25 \(\pm\) 0.32 \({}^{\text{b}}\) \\
2000 & 65.8 \(\pm\) 0.28 \({}^{\text{a}}\) & 96.09 \(\pm\) 0.14 \({}^{\text{a}}\) & 64.71 \(\pm\) 0.52 \({}^{\text{a}}\) & 92.94 \(\pm\) 0.28 \({}^{\text{a}}\) \\ \hline IC\({}_{50}\) & 1271.24 \(\pm\) 0.85 \({}^{\text{b}}\) & 734.42 \(\pm\) 0.43 \({}^{\text{c}}\) & 1656.19 \(\pm\) 0.71 \({}^{\text{a}}\) & 263.60 \(\pm\) 0.52 \({}^{\text{b}}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Percentage (%) inhibition of the GF-EO extracted from grapefruit peels in terms of DPPH and nitric oxide free radicals at different concentrations (\(\mu\)g/mL) and their IC\({}_{50}\) values compared to vitamin C (standard). Values represent the mean \(\pm\) standard deviation. Different letters mean significant differences between concentrations (\(p\) < 0.05).
###### Abstract  
A two-year study (June 2022-May 2024) on the reliability and maintainability of technological equipment at the Patars basalt quarry identified critical wear issues in metal components impacting operational continuity. The analysis focused on identifying causes of operational interruptions and evaluating solutions to improve equipment performance. Results showed that speed and load significantly impact wear rate and material selection significantly influences abrasion resistance. Laboratory tribological tests provided valuable data on the influence of basalt properties on wear, complementing field data. The study highlighted the low reliability of critical components, such as the sorting station trough, front loader bucket knife, and excavator bucket tooth, necessitating frequent replacements. For example, the trough has only a 40% probability of operating without defects after 182 days, with average roughness reaching 1.2 \(\upmu\)m and wear profile height up to 22.5 \(\upmu\)m. Similarly, the bucket knife and tooth require replacement at significantly reduced intervals compared to their nominal operating life to achieve 80% reliability. To address these findings, the study proposes two solutions: (1) manufacturing experimental prototypes with alternative materials for the trough to improve wear resistance and reliability; and (2) on-site welding reconditioning of metal components to reduce costs and downtime.  
abrasive wear; reliability; maintainability; mining equipment; basalt quarry; tribological tests; wear-resistant materials Article
# Multi-Population Parallel Wolf Pack Algorithm for Task Assignment of UAV Swarm
## 3 The Proposed Multi-Population Parallel Wolf Pack Algorithm (MPPWPA)
### Approximate Average Division Method  
As is known, the optimization performance of heuristic algorithms is closely related to population size. Within a certain range, the more individuals the population includes, the better the optimization performance will be [45]. In essence, multi-population optimization reduces the population size of an optimization unit and makes up for the performance loss by absorbing high-quality individuals from other optimization units. The contribution of a sub-population to global optimization depends on the quality of its best individuals, because high-quality individuals can affect the exploration of global optimal solutions. In MPPWPA, all sub-populations are optimized by the WPA, so the size of each sub-population is the same. In order to equalize the quality of all sub-populations, an approximate average division method is proposed. The number of sub-populations is set as \(Num\). All individuals are sorted in ascending order and every \(Num\) individuals constitute a segment, then the individuals in each segment are randomly assigned to mass sub-populations. In this way, the population can be divided almost uniformly.  
Detailed steps are shown in Figure 11.  
Step 1: The population \(Pop\) is sorted in ascending order;  
Step 2: The first \(\frac{N}{Num}\) individuals are labeled as members of the elite sub-population;  
Step 3: \(Pop\) is divided into multiple segments with the same length \(Num\);  
Step 4: \(Num\) individuals in each segment are randomly assigned to \(Num\) mass sub-populations.  
The proposed pretreatment and approximate average division method maximize the competitiveness of each sub-population, and thus, they can be used in population migration. Mass sub-populations to are coalesced to \(Pop\), and then pretreated and re-divided. The problems of migration rate and individual selection in population migration are also avoided successfully.  
Figure 10: Optimization process of elite-mass population distribution.  
It should be noted that the migration probability (\(P_{m}\)) is used to determine whether to carry out population migration after each iteration. If \(rand()<P_{m}\), all mass sub-populations fuse and the population will be re-divided; otherwise, all sub-populations continue to be optimized. The flow of the MPPWPA is shown in Figure 12.
## 4 Experiments and Results
### Experimental Procedure  
The experiment was divided into two parts, as shown in Figure 12: the introduction and usage of HybridFilm in the first part, and the introduction and usage of the PyMOL Scenes functionality in the second part. To mitigate potential learning effects, the order of the experimental tasks was randomly assigned to participants through draw lots method. The participants in the experiment were randomly assigned to two groups, with 8 individuals in each group. A 5 min break was provided between the two parts.  
The first part involved the introduction and usage of HybridFilm. The introduction lasted approximately 5 min, during which participants were briefed on the research objectives and significance, followed by a demonstration of the system's various features. Participants were then instructed to wear the head-mounted display and engage with the system, including tasks such as submitting and switching scenes, for about 10 min. Upon completing this portion, participants were asked to remove the HMD and fill out a questionnaire assessing the system's usability and usefulness.  
The second part focused on the introduction and usage of the PyMOL Scenes functionality. Similar to the first part, the introduction lasted around 5 min, followed by a demonstration of the PyMOL Scenes features. Participants then used these features for approximately 10 min. Afterward, participants completed the user experience questionnaire, a widely used tool in user experience research, which assessed their subjective experience with the system and the PyMOL Scenes functionality.  
The user experience questionnaire benchmark, completed by participants, is a questionnaire that evaluates various dimensions of subjective experience using a 7-point Likert scale [50]. Finally, participants were interviewed in a semi-structured format to discuss the benefits and challenges of using the system. The interview lasted approximately 5 min. All survey data were subsequently compiled and analyzed.
# A Review on Nanocomposite Materials for
## 4 Cathode
### Olivine, LiMPO\({}_{4}\) (M = Fe, Co, Mn and Their Mixtures)
Utilizing graphene in LiFePO\({}_{4}\) composites resulted in achieving near theoretical specific capacity (i.e., 170 mAh/g), exceptional rate capability and improved the cycle life due to intimate contact between non-aqueous electrolyte and the LiFePO\({}_{4}\) active materials as well as enhanced the charge and mass transfer [110; 111]. The schematic of the graphene wrapped LiFePO\({}_{4}\) particles are shown in Figure 11[111]. [PERSON] et al. reported that electrochemically exfoliated graphene wrapped commercial LiFePO\({}_{4}\) particles can deliver 208 mAh/g capacity which is more than the theoretical capacity of LiFePO\({}_{4}\). This extra capacity is attributed to the reversible redox reaction between Li ion in the electrolyte and the electrochemically exfoliated graphene sheets [112]. Further improvement in the performance of LiFePO\({}_{4}\) was obtained by encapsulating one-dimensional LiFePO\({}_{4}\) nanowires (-20-30 nm) into CNTs (i.e., LiFePO\({}_{4}\)@CNT). These core-shell structures were synthesized by a sol-gel route followed by the post-heating treatment [113]. The dispersion of the CNT and its intimate contact with the LiFePO\({}_{4}\) active material are critical for the performance enhancement [114]. By utilizing nitrogen doped CNT (i.e., N-CNT), instead of pristine  
Figure 11: Schematic diagram of the graphene nanosheet/LiFePO\({}_{4}\) structure. Adapted with permission from [111], © Royal Society of Chemistry, 2012. Figure 10: Schematic illustrations of the porous LiFePO\({}_{4}\)–CNT composite: (**a**) showing the corresponding electron transport and ion diffusion mechanisms; (**b**) is an enlarged zone from (**a**). Adapted with permission from [109], © Royal Society of Chemistry, 2010. CNT, it is possible to facilitate the uniform dispersion of CNT in the LiFePO\({}_{4}\) composite due to higher defect density and hydrophilic properties of N-CNTs. Additionally, nitrogen groups enhance the electronic conductivity of N-CNT as a result of increased electron density [114]. One of the limitations of LiFePO\({}_{4}\) is its low operating voltage (i.e., 3.42 V), which limits its energy density. It is possible to push the operating voltage to higher voltages (i.e., 4-4.2 V) using vanadium-based cathodes such as LiVPO\({}_{4}\)F and LiVOPO\({}_{4}\)[115; 116; 117; 118; 119; 120; 121]. Novel porous materials such as metal-organic frameworks (MOF) were also studied as electrode materials for Li-ion batteries [118; 119; 122; 123; 124]. Metal organic phosphate open frameworks (MOPOF) and their reduced graphene oxide nanocomposite derivatives showed promising Li storage, but further work is required to improve their rate capability and cycle life [118; 119].
## 3 Results  
### Evaluation of Sentinel-1A Soil Moisture  
#### 3.1.1 Microwave Soil Moisture vs. In Situ Measurements  
The temporal evolutions of the different soil moisture products and the daily accumulated precipitation at the test site are shown in Figure 3. The wet and dry season in our study site can be clearly identified by the precipitation and the in situ soil moisture: high values occur from July to November and low values occur from December to June. In general, the microwave soil moisture, including S1A, shows similar trends to the in situ measurements. SMAP and SMOS are the most similar among all of the soil moisture products, and they also have a better agreement with the in situ soil moisture over the whole period. ASCAT, AMSR2, and S1A_0.25 match the in situ measurements well in the wet season, but they overestimate the soil moisture by about 0.03 m\({}^{3}\)/m\({}^{3}\) in the dry season. The finer-resolution soil moisture, i.e., S1A_1K, shows a good agreement most of the time, but shows some overestimations during the dry season. The correlation and error of the microwave soil moisture products are shown in Table 2. High agreements with the in situ measurements can be seen for SMAP, SMOS, and S1A_0.25, with \(R\) values of greater than 0.7 and a ubRMSE of less than 0.04 m\({}^{3}\)/m\({}^{3}\), among which S1A_0.25 has the lowest ubRMSE of 0.018 m\({}^{3}\)/m\({}^{3}\). S1A_1K has a ubRMSE of 0.053 m\({}^{3}\)/m\({}^{3}\), which is comparable with AMSR2 and ASCAT.
## 3 Proposed Method
### Data Transmission  
This block focuses on the efficient transmission of data between the different components of the system. It facilitates the flow of information from the trace monitoring component to the real-time processing component and then to the result representation component. This block ensures that the data is communicated effectively and securely throughout the system. This block was developed using Kafka [9].
# A Carbon Benefits-Based Signal Control Method in a Connected Environment
## 3 Problem Statements  
The studied scenario is an isolated signalized intersection, as depicted in Figure 1. It highlights the application scope of the proposed method. The scenario is a typical four-leg, three-lane (in each direction) signalized intersection. These three lanes have different uses:a left-turn lane, a straight-through lane, and a right-turn lane. The signal plan has a fixed number of phases and execution order. Each phase is divided into an effective green interval and a clearance time (including yellow and all-red times). The clearance time is pre-designed and fixed within the system. The proposed method optimizes the start and end times of each phase during the optimization process. The maximum and minimum green times are in line with standard signal design practices.  
In this study, the scenario operates within a connected traffic environment. In this context, all vehicles can communicate in real time with roadside units through V2V and V2I technologies. The roadside control center can obtain real-time vehicle status information (position, speed). The control area extends from the upstream section to the location before the stop line, which is selected as 300 m in this study. Within the control area, the lane-changing behavior of vehicles is not considered. The control center provides the required speed commands to CBCVs at an update frequency of \(N_{sg}\Delta t\).
## 4 Channel Allocation Based on Multi-Agent Deep Reinforcement Learning  
In this section, we first treat the channel allocation problem as an independent optimization problem and propose the use of multi-agent deep reinforcement learning for the channel allocation problem. We then introduce the network model of the multi-agent deep reinforcement learning method and provide the corresponding training algorithm.
## 3 Results and Discussion
### Effects of Using Free Access Topographic and Landuse Data
Of course, this result needs to be confirmed by applications of this modeling approach to other urban catchments with different shapes and slopes. It is important to notice that the resolution of road topographic data may not be a key factor for urban 2D/1D modeling (excepted urban flood modeling).
# High-Fidelity Drone Simulation with Depth Camera Noise and Improved Air Drag Force Models
## 4 Conclusions and Discussion  
### Conclusions  
In this work, we present a realistic drone simulation that incorporates accurate models for the depth sensor and air drag force. In our work, the depth sensor model incorporates the noise behavior of the depth camera as a Gaussian process and factors in the impact of various materials at different distances. Additionally, the enhanced rotor drag force model incorporates thrust values along with the drone's velocity and rotation to enhance drag force accuracy. These models were identified, validated through experiments, and subsequently implemented in Ubuntu with ROS and PX4 programs. They were then visualized using Unreal Engine 4, creating a valuable simulation tool for testing and evaluating autonomous drone systems.