esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction
## 2 Physics Engine Review
### Bullet  
Bullet is an open-source physics engine, commonly used for computer games, computer graphics, and robotics. The Bullet collision detection algorithm proceeds similarly to most physics engines that support primitive-shape and mesh objects. When calculating the contact force, Bullet uses polyhedral approximation, similar to ODE, and provides Dantzig, PGS, nonsmooth nonlinear conjugate gradient (NNCG [22]), and the sequential impulse (SI [23]) solver. The default setting in CoppeliaSim is SI. Similar to ODE, Bullet also reduces the contact points to reduce the computational burden when calculating the contact force; the difference is that the rules for this are known, which is that the contact point with the deepest penetration is always maintained, and up to 4 contact points are determined to maximize the area covered by the contact points. The integrator in Bullet is a semi-implicit Euler integrator. Additionally, unlike ODE, Bullet uses the generalized coordinate representation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Physics Engine** & **Object** & **Collision Detection** & **Contact Clustering** & **Contact Solver** & **Integration** & **Coordinate** \\ \hline ODE & Primitive and & & & Ignores some & LCP + & Semi-implicit & Maximal \\  & mesh & Built-in & contact points & Dantzig/PGS & Euler & \\ \hline Bullet & Primitive and & & & Maximizes & LCP + & & \\  & mesh & Built-in & contact area & Dantzig/PGS & Semi-implicit & \\  & & & & /NNCG/SI & Euler & \\ \hline Vortex & Primitive and & & & Details are not & & LCP + & \\  & mesh & & & disclosed & direct/iterative & \\  & & & & method & Euler & \\ \hline MuJoCo & Primitive and & & & Keeps only a & & Semi-implicit & \\  & convex mesh & & few contact & & & Euler/fourth- & \\  & & & points in front & optimization & order & \\  & & & & Runge-Kutta & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of physics engines.
## 4 Wireless Soil Moisture Sensing Applications
### Applications in Model Validation  
Soil moisture data from WSNs have been used extensively for the validation of distributed land surface models and rainfall runoff models. For instance, the soil moisture data provided by SoilNet was used to validate several distributed hydrological models, such as HydroGeoSphere [111; 112], MIKE-SHE [19; 20], and TerrSysMP [37; 113]. For example, [PERSON] et al. [19] used WSN soil moisture data to validate high-resolution long-term 3D simulations of a headwater catchment with the hydrological model ParFlow-CLM using empirical orthogonal function and wavelet coherence methods. Their results revealed that information on soil porosity heterogeneity effectively improves estimates of soil moisture patterns and that wet and dry seasons have a significant effect on temporal correlation between observed and simulated soil moisture. In another study, [PERSON] et al. [20] compared three distributed hydrological models for their ability to simulate soil moisture patterns at the catchment scale and were able to analyze the spatial error correlation between these models. More recently, long-term soil moisture data from several WSN in Germany have been used to validate an operational model-based drought monitor [114].
# PRC-Light YOLO: An Efficient Lightweight Model for Fabric Defect Detection
## 3 Fabric Defect Detection Based on PRC-Light YOLO
### Improved Feature Fusion Network
#### 3.2.2 CARAFE Upsampling Operator  
The upsampling method in the YOLOv7 feature fusion network is the nearest neighbor interpolation method [33]. The principle of this method is to identify the nearest neighbor pixel in the target image based on each pixel's position in the original image and subsequently replace the value of that pixel in the original image with the value of the nearest neighbor pixel. However, the image quality after scaling by this method is poor, and obvious jagged edges may appear. To address this issue, we introduce the CARAFE upsampling operator. This innovation significantly bolsters the neural network's semantic feature extraction capacity during the upsampling process within object detection tasks. Importantly, this improvement is made without significantly increasing the amount of computations and parameters. The CARAFE upsampling process is depicted in Figure 4.  
The CARAFE architecture is made of two primary modules: the upsampling kernel prediction module and feature reassembly module. These components work together to enable effective upsampling and feature reassembly within the network. In the upsampling kernel prediction module, assuming the upsampling ratio is \(\sigma\), the process begins with channel compression through a \(1\times 1\) convolution operation on the input feature map of size \(H\times W\times C\), resulting in a feature map of size \(H\times W\times C_{m}\). Subsequently, encoding the feature map using a \(k_{enc}\times k_{enc}\) convolutional kernel generates the recombined kernel \(k_{up}\times k_{up}\), obtaining a feature map with \(\sigma^{2}\times k_{up}^{2}\) channels. The channels are then spatially unfolded and subjected to softmax normalization, producing a feature map of size \(\sigma H\times\sigma W\times k_{up}^{2}\), where each feature value corresponds to an upsampling kernel. In the feature recombination module, through the mapping relationship, the region \(k_{up}\times k_{up}\)  
Figure 3: The structure of the RFB.  
centered in the input feature map is taken out, and then the dot product operation is performed with the upsampling kernel at the same position in \(\sigma H\times\sigma W\times K_{up}^{2}\) to obtain the output value.  
The CARAFE achieves instance-specific content-aware processing by generating real-time adaptive convolution kernels and can aggregate contextual information over a larger receptive field. Therefore, we introduce the CARAFE upsampling operator to replace two upsampling operations in the Neck structure of the YOLOv7 model. According to the trade-off relationship between \(k_{enc}\) and \(k_{up}\), to maintain the performance and efficiency, \(k_{up}\) takes the value of 7 and \(k_{enc}\) takes the value of 5 in the experiments.
# Study on the Impact of Ventilation Methods on Droplet Nuclei Transmission in Subway Carriages
###### Abstract  
The environment inside subway carriages is relatively enclosed, putting passengers at risk of respiratory infections during viral pandemics such as COVID-19 and SARS. This paper uses the Euler-Lagrange method to simulate the distribution of droplet nuclei produced by coughing under six different operating conditions in a subway carriage. The study investigates the impact of different air supply characteristics and ventilation methods, including mixed ventilation (MV), floor-supply, and ceiling-return ventilation (SFRC), on the distribution of droplets. These results indicate that under MV mode, the dispersion range of droplets during a patient's cough is the largest, with an average droplet suspension rate (SR) of up to 77% at the initial moment. The SFRC system markedly diminishes droplet dispersion, decreasing the SR by 35%. Upon increasing the air supply velocity to 0.8 m/s, the SR diminishes to 6%, the probability of particles attaining a 2 m social distance (PRP) declines by roughly 70%, and the weighted average contamination range (CR) of coughing particles reaching a safe social distance reduces by 33.5%. These results may act as a guide for the subsequent design and optimization of airflow patterns in carriages to reduce the risk of cross-infection.  
Keywords:human coughing droplet nuclei; airborne spread; subway carriage; ventilation; infection risk
## 3 Results and Discussion
### Microstructural Analysis-Hardness
This observation is very important because it indicates a lack of potential brittle intermetallic phase formation. The presence of intermetallic phases at the interfacial area would cause deteriorating effects on the properties of final composite systems [52]. EDX analysis revealed no significant traces of oxide phases (especially associated with Al). This may be attributed to the use of elemental Al powder that was protected in the supply container and used at the very last moment of the overall process. This handling reduced the exposure time to the atmosphere that could result in potential oxygen absorption. Furthermore, graphite powder was employed as a lubricant during the mechanical processing stage. The presence of graphite creates a reduction atmosphere at the following stage of hot pressing, ensuring intensive oxygen removal from the powders. The hot-pressing stage was performed under vacuum reducing further the potential for oxygen contamination. Figure 4: Microstructure and elemental map of MoTaNbVW particulate in higher magnification (BE mode). Figure 5 presents the hardness measurements for the different systems produced in the present effort. The increase of the HEA particle content results in an increase of the overall composite hardness, indicating the reinforcing action of the hard and strong HEA phase. It should be noticed here that the selected HEA system possesses a hardness of 97 \(\pm\) 4 HRB (Rockwell B) [50]. Another interesting observation is the consistency of the values and the deviation of hardness measurements. This may be explained by the good microstructural homogeneity and the good particle distribution of the reinforcement in the matrix.
## 4 Conclusions  
The torrefaction of lignocellulosic biomass feedstocks prior to pelletizing reduces pelletizing efficiency, necessitating the addition of binders. However, the torrefaction of already-formed biopellets enhances pellet quality and obviates the need for binders during pelletizing. This research demonstrated that torrefaction significantly improves the fuel properties of biopellets made from various woody biomass feedstocks, including hardwood chip residue (HW), camellia oilseed cake (CO), and pruning remnants of the toothache tree (TA) and mulberry tree (MT). The effects of torrefaction varied depending on the type of woody biomass feedstock used. Specifically, torrefaction resulted in a substantial reduction in volatile matter content and an increase in heating value, enhancing the overall energy density of the biopellets. The mass yield of torrefied biopellets ranged between 60% and 70%, while the energy yield was between 80% and 95%. Additionally, torrefaction reduced the initial ignition time of the biopellets by 50-59% and prolonged their combustion duration by 15-24%, making them easier to ignite and burn more uniformly. Torrefied biopellets also exhibited a slower mass loss rate during thermal decomposition compared to non-torrefied biopellets, primarily due to the removal of moisture and volatile matter. This indicates enhanced stability and durability during combustion. Moreover, the use of unused forest biomass and wood chip residues from pulp mills for biopellet production not only provides a sustainable and efficient method for waste utilization but also contributes to environmental conservation by reducing reliance on fossil fuels.  
In summary, this research highlights the advantages of torrefaction as a promising technology for producing high-quality, energy-dense solid biofuel from a variety of woody biomass feedstocks. The enhanced fuel properties, improved combustion efficiency, and sustainable utilization of waste biomass underscore the potential of torrefied biopellets as a viable alternative to traditional fossil fuels.  
Research and investigation, [PERSON]-H.K., [PERSON]-G.L., [PERSON]-H.P., [PERSON]-H.P., [PERSON]-S.P. and [PERSON]-[PERSON]; tables and figures preparation, T.-G.L. and [PERSON]-H.P.; data curation, J.-H.P., [PERSON]-S.P. and [PERSON]-S.L.; writing--review and editing, [PERSON]-G.L. and C.-H.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Basic Science Research Program via the National Research Foundation of Korea (NRF), funded by the Ministry of Education (Grant No. 2022R1I1A3053045). It was also supported by the Korea Basic Science Institute (National Research Facilities and Equipment Center) grant funded by the Ministry of Education (Grant No. 2022R1A6C101B724).  
Not applicable.  
Data Availability Statement: The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author. The authors declare no conflicts of interest.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 2 Methodology
### Contour Mission Flight Planning  
This study proposes a method to keep the shooting distance consistent based on the contour principle and DEM information to maintain the image quality as much as possible.  
The method proposed in this study to generate contour-type aerial routes is shown in the flowchart in Figure 3, steps S1-S7. First, in S1, the user provides information about the aerial photo area, camera, and mapping requirements. In S2, the system extracts the terrain information from the DEM and calculates the flying height and shooting baseline. In S3, the location of the first flight line is calculated based on the relationship between  
Figure 1: The route planning and actual image location site of single grid-type. The Rn is the nth route, and Dn is the shooting distance of the nth route.  
Figure 2: The schematic scene of the common problem for UAV capture photos in the rugged slope area. The transverse flight Rn for the slope is similar Rn in Figure 1. (**a**) The figure shows the change in shooting distance due to the difference in ground height when using same altitude photography in the slope area. (**b**) The image size and overlap differ due to different shooting distances. Where a, b are the different shooting distances, Pn is the size of the nth image, and c, d, e are the various overlaps.  
the lowest and highest elevations and the boundary line. In S4, the system will consider the distance between the routes and the topographic changes corresponding to the routes, and use either S5 or S6 to generate the routes, depending on the situation, until the highest point elevation is calculated and the routes for the whole area are planned. The entire process uses the principle of contour to maintain the consistency of the shooting distance, which can improve the image quality and coverage, thus improving the effect of aerial photography by UAV.  
The following are specific descriptions of the various steps in the process.
## 3 Materials and Methods
### Collection and Preprocessing of EEG Data  
The EEG signals in this study were collected from 28 electrodes embedded in an elastic cap arranged according to the 10/20 system, as illustrated in Figure 2. Throughout the process of collecting, the impedance of each electrode was maintained below 10 k\(\Omega\), and the sampling rate for the EEG signals was set at 250 Hz. Preprocessing was conducted using Python's SciPy. During the preprocessing phase, the low-pass filter of EEG data had a cutoff frequency set at 30 Hz, while the high-pass filter's cutoff was set at 0.1 Hz. Filtering was executed independently on each channel. Subsequently, a reference transformation was applied to the EEG data to eliminate common noise and background signals across channels. This step involved calculating the average value across all channels at each time point and subtracting this average from each channel's signal. Finally, baseline correction was executed by determining the mean value for every channel over the entire recording duration and subtracting this mean from each respective time point within that channel.
## 5 Numerical Examples
### Mesh Refinement Study
Analyzing the results for all three meshes showed that the homogenized (volume-averaged) stress-strain curves were nearly identical, indicating that even the coarsest mesh is sufficiently refined to accurately capture the macroscale response. However, local quantities are expected to converge more slowly, which was indeed the observation made in this study. For example, Figure 24 shows the effect of mesh refinement levels on the axial stress in the subset of Slice 1. Figure 23: CPFEM simulation results in Slice 1 at strain level 0.02: (**a**) axial stress \(\sigma_{xx}\); (**b**) equivalent plastic strain \(\epsilon_{\text{eq}}^{\text{pl}}\); (**c**) hardening \(\hat{\tau}\). Figure 22: CPFEM simulation results in Slice 1 at strain level 0.0026: (**a**) axial stress \(\sigma_{xx}\); (**b**) equivalent plastic strain \(\epsilon_{\text{eq}}^{\text{pl}}\); (**c**) hardening \(\hat{\tau}\).
# VANET Secure Reputation Evaluation & Management Model Based on Double Layer Blockchain
## 4 Design of the DBREMM
### Consensus and Up-Chain  
Whether it is a block of either the event chain or reputation chain, the secure consensus protocol needs to be made before the chain is up-chained to ensure the security and reliability of the block content. At each preset fixed time slot, or after enough lists are generated, The RSU broadcasts the consensus request to vehicles under its coverage. Vehicles with idle computing power that reply on time will become the candidate miners. Following a certain predetermined secure consensus, the miners are selected securely. Then those miners up-chain the event status list \(C_{event}\) and the reputation update list \(C_{reputation}\) to the event chain and reputation chain separately. Finally, both newest chains are broadcast to every node in the network, and the nodes then update their own ledger.  
In our model, we use the MPoR algorithm designed in our previous work to complete the consensus. The time overhead and the reliability and anti-attack capability of MPoR were proved. For the specific consensus process, please refer to the author's previous article [47], as the information will not be repeated here.
## 5 Experiments and Results
### Drone Path Planning for PFBs Inspection
#### 5.1.1 Object Detection via Haar-Cascade Object Detection
Once annotations, training, and stage additions were completed on this revised model, it was tested in real-time and deemed an improvement from the last. This model performance is further illustrated in Figure 11(b). Verifying model performance for distance calculations, testing was conducted with the newly fitted model and various known PFB distances. The drone then hovered at the defined traversal altitude to avoid collision with the fans (\(\approx\)140 cm), where a fan would be set at 200 cm, 300 cm, and 400 cm away from the drone. The bounding box widths were saved into a designated CSV file for 48 frames, allowing averaging in real-time detection data. In the implemented experiments, the fan was placed at exact distances from the drone camera. However, the _Tello EDUI_ drones have a moment of instability after takeoff, causing the drone to drift away from the takeoff location. The discrepancy between the calculated and actual distance in the experiments is noteworthy, as the distance may vary from pre-takeoff to post-takeoff. To mitigate this issue, a helipad was strategically positioned to align with the specified distances from the fan. After takeoff, The auxiliary camera was employed to center the drone precisely over the helipad. Following this alignment step, distance calculations commenced with the drone hovering at the specified distance from the fan. This meticulous and thorough approach ensures a consistent and accurate starting point for distance calculation testing. Using the measured \(w_{r}\) value of 39 cm, the graphical representation of Figure 13 compares the calculated and actual distance values. Notably, the comparison plot reveals a linear relationship, with the average computed distances closely aligning with the actual distances and an average error of less than 6% for all three scenarios. This observation suggests adequate real-time accuracy and consistency in the calculated distance measurements relative to the actual distances. Accurate estimations are vital to determine how much the  
Figure 12: Improved Haar feature-based cascade classifier where the false detection of the QR codes is removed and the bounding boxes of the further fan are better fitted and centered. Model predictions are shown in red. (**a**) Inadequate Haar object-detection model; (**b**) Improved Haar object-detection model. drone needs to move forward and how much to shift the drone laterally to position the fan to the center of the drone's camera frame for PFB inspection.
# Fault Diagnosis of Wind Turbine Planetary Gear Based on a Digital Twin
## 1 Introduction
Compared with existing research, the main contributions of this paper are summarized as follows: (1) A digital twin virtual model based on the collected parameters is built, scripts are written to complete the accurate behavior mapping between the virtual model and the physical entity, and the interaction between virtual and real data is realized through data acquisition, transformation, transmission, and other methods to complete the real-time visual status monitoring of the wind turbine; (2) A data-driven fault diagnosis method based on empirical mode decomposition (EMD) and atom search optimization-support vector machine (ASO-SVM) is proposed to diagnose and predict the operating state of planetary gear by identifying strain signals of outer gear rings of the wind turbine planetary gearbox, which effectively improves the accuracy of fault diagnosis and does not require a large amount of data for a calculation that requires a long time; and (3) The combination of the fault diagnosis method and digital twin technology can realize the online fault diagnosis and location, realize the high efficiency and intelligence of fault diagnosis, and solve the problem of the wind turbine planetary gear fault diagnosis being untimely and difficult to maintain. Finally, the system's feasibility is verified on the laboratory's wind turbine fault diagnosis test bed. The rest of this article is organized as follows. Section 2 introduces the construction method and architecture of the digital twin system of the wind turbine transmission system, including the construction of a twin model, the composition of twin data, acquisition, and data transmission, and analysis of virtual-real interaction. Section 3 introduces the fault diagnosis method of EMD-ASO-SVM based on a digital twin. Section 4 is an experimental example of the wind turbine's digital twin planetary gear fault diagnosis system. It verifies the feasibility of the planetary gear fault diagnosis system based on a digital twin. Section 5 discusses the contributions of this paper and future research directions. Section 6 provides the conclusions of the report.