esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Single Slot Analysis
#### Comparison of a Single Slot Jet and a Single Round Jet Configuration  
The flow physics of the jet impingement is strongly affected by the nozzle geometry. In this paragraph the numerical results obtained by a slot jet and by a round jet are compared. The same jet-wall distance, Reynolds number and outlet conditions have been considered in order to evaluate the performance of each case, as reported in Table 2.  
Figure 8: Turbulence kinetic energy contours of the single slot jet at H/B = 8, Re = 1.1 \(\times\) 10\({}^{4}\), with k-\(\epsilon\) model.  
Figure 7: Velocity contours of the single slot jet at H/B = 8, Re = 1.1 \(\times\) 10\({}^{4}\), with k-\(\epsilon\) model.  
The same settings were used both for the grid and for the numerical models. In the round jet configuration, a circular duct long enough to develop the flow was used to obtain the velocity and turbulence profiles. More details on the circular nozzle were reported in a previous paper [34], which also highlighted the best accuracy of the v2f model for those cases. In Figure 9 the Nusselt number distributions for the two configurations are compared for the jet-wall distances H/B = H/D = 2 (left) and 6 (right); in Table 3 the average Nusselt values and percentage differences are shown.  
For the configuration with low jet-wall distance (H/D = H/B = 2) the circular nozzle guarantees a higher average Nusselt number than the slot jet, due to the greater heat exchange near the jet axis (higher peak). In fact, there is a higher impact in the area near the jet, which has less chance of mixing with the surrounding stationary environment. The heat transfer distribution is not much lower than that of the slot jet, also at a certain distance from the axis. On the other hand, for a higher jet-wall distance (H/D = H/B = 6) the situation is considerably different. The round jet maintains a considerably higher Nusselt peak; in this case the outgoing flow has more space to mix with the stationary surrounding fluid (this advantage decreases moving away from the jet axis). For this configuration, the slot jet has higher performance and guarantees a uniform distribution with a higher average Nusselt number (16.5%). This better heat transfer uniformity leads to an effective advantage if the distance between the wall and the slot is not excessively low, otherwise the best impact in correspondence to the jet axis, guaranteed by the circular nozzle, also leads to a higher average Nusselt number.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Round Jet** & **Slot Jet** \\ \hline D or B [mm] & 18 & 9 \\ Re & 23,000 & 23,000 \\ T\({}_{\text{outlet}}\) [\({}^{\circ}\)C] & 30 & 30 \\ T\({}_{\text{wall}}\) [\({}^{\circ}\)C] & 50 & 50 \\ Velocity [m/s] & 19.64 & 39.27 \\ H/D or H/B & 2 and 6 & 2 and 6 \\ turbulent model & v2f & realizable k-\(\varepsilon\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summarizing of the main parameters for the comparison of a slot jet and a round jet.  
Figure 9: Nusselt trend comparison between slot and round jet, for H/B = H/D = 2 (left) and 6 (right).
# Rock Indentation Behavior: Effects of Penetration Rates and Indenter Types
## 4 Conclusions  
This study investigated rock indentation behaviors through tests on artificial rock, limestone, and granite specimens under varying penetration rates, with a particular focus on indentation stage classification and factors influencing rock fragmentation efficiency. The key findings are as follows:  
_Indentation Process:_ The penetration process with sharp indenters involves three stages--rock compression, crack propagation, and fragmentation--while blunt indenters lead directly from compression to fragmentation. During the compression and crack propagation stages, the rock undergoes multiple cycles of crushing and chipping, leading to shear failure of rock chips. Crack propagation is mainly influenced by tensile strength.  
Figure 7: Relationship between peaking force and fracture area during the test.  
In contrast, the rock penetration process with blunt indenters only experiences a single rock compression stage before transitioning to the fragmentation stage.  
_Effect of Indenter type:_ Rock fragmentation efficiency is higher with a spherical indenter than with a conical one under the same loading conditions. However, the maximum loading pressure is greater with the spherical indenter.  
_Impact of Penetration Rate:_ A positive linear relationship exists between the loading rate and peak force, fractured area, and indentation depth, indicating that higher loading rates improve fragmentation efficiency. Greater brittleness also enhances fragmentation.  
These findings contribute to improving models of rock failure and offer practical guidance for rock drilling and mining. For example, increasing the penetration rate of picks can effectively enhance rock fragmentation efficiency. However, continuous monitoring of tool wear progression is critically required, as this phenomenon necessitates elevated rock fracturing pressures to maintain equivalent productivity.  
Conceptualization, S.F.; methodology, S.F.; formal analysis, S.F. and Y.Z. ([PERSON]); resources, Y.Z. ([PERSON]); writing--original draft preparation, S.F. and Y.Z. ([PERSON]); writing--review and editing, S.F., Y.Z. ([PERSON]) and M.Y; funding acquisition, Y.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 51709150, Open Research Fund of Key Laboratory of Construction and Safety of Water Engineering of the Ministry of Water Resources, China Institute of Water Resources and Hydropower Research, grant number 202102, China Postdoctoral Science Foundation, grant number 2022 MD72382, and the APC was funded by Xi'an University of Science and Technology.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
## 5 Raspberry Pi-Based Implementation of the Suggested Algorithm
### Raspberry Pi Hardware Board  
Raspberry Pi is a nano-computer running on the Linux operating system that can be used to control electronic components for physical computing, exploring the Internet of Things (IoTs) applications, etc. Raspberry Pi is widely utilized in a wide range of application domains, including signal processing [48; 49; 50] and the IoTs [51; 52; 53]. Since Raspberry Pi is a Linux-based operating ecosystem, it offers the advantage of the Python programming language and its strong packages, especially the Message Passing Interface (MPI) [54] for parallel computing and QR code python module. Moreover, a recent version of the Raspberry Pi series, namely Raspberry Pi 4 model B [55], is equipped with a quad-core 64-bit CPU, which is very useful for running parallel algorithms. Raspberry Pi 4B has  
Figure 13: Proposed parallel encryption of the QR code using multi-core CPUs.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**QR Code Size**} & **Conventional** & **Parallel Encryption** & **ETIR (\%)** \\  & **Encryption in (s)** & **in (s)** & **ETIR (\%)** \\ \hline \(200\times 200\) & 0.9615 & 0.2767 & 71.2253 \\ \hline \(400\times 400\) & 1.8845 & 0.5276 & 72.0016 \\ \hline \(600\times 600\) & 2.8852 & 0.8248 & 71.4129 \\ \hline \end{tabular}
\end{table}
Table 3: Comparison in terms of execution time in seconds (s) between the conventional and parallel encryption of QR codes.  
three RAM options: 1 GB, 2 GB, and 4 GB. To connect the Raspberry Pi 4 model B, we can select the wireless connection mode via Gigabit Ethernet, Dual Band Wireless LAN port 2.4/5.0 GHz, or Bluetooth 5.0. Figure 14 shows the Raspberry Pi 4B hardware model with its various physical devices.  
In our work, we used Raspberry Pi 4 model B equipped with 2 GB of RAM and 1.5 GHz CPU for implementing our security scheme. The used programming language is Python, and the employed software packages are the Message Passing Interface (MPI) for Python [51; 54] and the QR code python module.
## 3 Results and Discussion
### Rotation Directions  
For a pair of closely spaced wind turbines, three different configurations are possible based on the rotation direction of each wind turbine. As shown by the black arrows in Figure 13a, the wind turbine is rotating in the opposite direction and upwards, indicating that the rotation direction of the blades in the gap between the two wind turbines is opposite to the wind direction; (b) shows that the wind turbine is rotating in the opposite direction and downwards; (c) shows that the wind turbine is rotating in the same direction. The maximum average power coefficient corresponding to different rotation directions is shown in Figure 12, with little difference between them. The highest efficiency corresponds to the opposite direction and downward rotation, which is 28.9%, and the gain factor _TPGF_ reaches 11.1%. The lowest efficiency corresponds to the opposite direction and upward rotation, with a 6% difference. All three rotation methods show higher efficiency than that of a standalone wind turbine. This indicates that regardless of the installation method, closely spaced dual turbine groups can improve the average conversion efficiency of the entire unit.  
Three different rotation modes' velocity wake fields are shown in Figure 13, demonstrating an asymmetry in the wake velocity, as indicated by the red arrows, which is a fundamental characteristic of the flow field. In particular, for (b) downward reverse rotation, the wake produces a convergence effect, resulting in a smaller wake velocity area where more wind energy can be absorbed by the turbine and converted into mechanical energy, leading to higher efficiency.
## 4 Experimental Results and Discussion
### K-Fold Cross Validation
#### 4.8.1 Comparative Analysis  
Based on the values presented in the k-fold cross-validation for loop and protruding fibers' detection, the following specific comments are listed:  
**(1)**: **mAP_0.5 and mAP_0.5:0.95**  
* The average values range from approximately 0.43 to 0.79, indicating the model's strong ability to accurately detect loop and protruding fibers in various scenarios.
**(2)**: **Precision**  
* The average precision varies between approximately 0.69 and 0.74, showing the model makes a high number of correct detections. However, there is room for improvement, especially regarding false positives.
**(3)**: **Recall**  
* Average recall ranges from approximately 0.73 to 0.77, demonstrating the good recovery of loop and protruding fibers. This is consistent with the goal of minimizing false negatives.
**(4)**: **Accuracy**  
* The model shows consistent accuracy levels across the ten folds ranging from approximately 0.4800 to 0.5200, suggesting a stable performance across different data partitions. The average accuracy is about 0.5064, indicating that the model correctly classifies instances approximately 50.64% of the time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**K-Fold 10 Cross-Validation (YOLOv5s6-Hairiness)** & **mAP\_0.5** & **mAP\_0.5:0.95** & **Precision** & **Recall** & **Accuracy** \\ \hline
**kfold-10** & 0.7860 & 0.4593 & 0.7040 & 0.7663 & 0.5050 \\ \hline
**kfold-9** & 0.7794 & 0.4541 & 0.6981 & 0.7496 & 0.5067 \\ \hline
**kfold-8** & 0.7901 & 0.4589 & 0.7094 & 0.7655 & 0.5067 \\ \hline
**kfold-7** & 0.7909 & 0.4575 & 0.7327 & 0.7426 & 0.5200 \\ \hline
**kfold-6** & 0.7939 & 0.4568 & 0.7439 & 0.7264 & 0.5050 \\ \hline
**kfold-5** & 0.7881 & 0.4477 & 0.7099 & 0.7528 & 0.5100 \\ \hline
**kfold-4** & 0.7872 & 0.4599 & 0.7200 & 0.7503 & 0.5084 \\ \hline
**kfold-3** & 0.7507 & 0.4330 & 0.6954 & 0.7289 & 0.4800 \\ \hline
**kfold-2** & 0.7862 & 0.4581 & 0.7143 & 0.757 & 0.5084 \\ \hline
**kfold-1** & 0.7911 & 0.4568 & 0.7197 & 0.7362 & 0.5017 \\ \hline \hline \end{tabular}
\end{table}
Table 10: K-fold cross validation with k = 10 in optimized yolov5s6-yarn hairiness with augmentation.  
Variation in Results
* Metric values vary slightly between folds which is expected due to the nature of the method. This variation underscores the importance of k-fold cross-validation in assessing model robustness and generalization.
## 5 BER and Data Rate Calculation of the Proposed Network
### The Packet Interarrival Time Analysis  
[PERSON]'s law estimates the maximum capacity of the PU and the SU systems in bit/sec as  
\[R_{PU}=B\big{(}1+(1+K)SINR_{PU}\big{)}, \tag{25}\] \[R_{SU}=B\big{(}1+(1-\gamma)SINR_{SU}\big{)}. \tag{26}\]  
where \(B\) is the operating bandwidth, \(K\) is the SINR increase of the PU system, and \(\gamma\) is the power donation from the SU system to the PU system. Furthermore, the packet inter-arrival time analysis using the Poisson cumulative distribution function (CDF) describes the following:  
1. Maximum capacity of the PU system and the decrease in the data rate during the conversion of the user from an active case to an inactive case, i.e., out of service.
2. Minimum rate of the SU system and the increase in the SU data rate during the conversion of the PU from an active case to an inactive case. The Poisson distribution function is defined as \[P(\beta)=\frac{q^{\beta}}{\beta!}e^{-q},\] (27) where \(\beta=\ 0,2,3,\ \dots\) and \(q\) is the mean number of occurrences in the interval, i.e., the rate.
# Bearing Fault Diagnosis Method Based on Improved VMD and Parallel Hybrid Neural Network
## 3 Materials and Methods  
Combining a signal decomposition algorithm and neural network technology, a fault diagnosis method based on improved VMD, a parallel hybrid neural network is proposed. The method is mainly composed of fault signal processing and fault diagnosis models.
## 3 Application
### CFD Simulation  
Figure 5 shows the computational domain for the BWB aircraft simulations. The flow is simulated at the freestream velocity \(V_{\infty}\) = 27.78 m/s, which is the usual flight condition of this type of unmanned blended wing body aircraft [47]. The velocity inlet boundary and pressure outlet boundary conditions are used at the inlet and outlet of the computational domain. The no-slip boundary condition is applied on the surface of the aircraft. The symmetry boundary condition is applied at the top, bottom, front, and back sides of the computational domain. The steady Reynold-averaged Navier-Stokes (RANS) equations are solved using the Spalart-Allmaras turbulence model to compute the flow. The distribution of the grid around the aircraft is presented in Figure 6. To efficiently generate mesh elements for the simulation, the grid is refined as it approaches the aircraft. To properly capture the effect of the wall, the 20 structure inflation layers are generated near the surface of the aircraft with a 1:1 growth ratio. To solve the momentum equations, the SIMPLE algorithm is used with second-order spatial discretization schemes. When the residuals of the conservative variables are less than \(10^{-5}\), the simulations are treated as converged. To simulate the flow around BWB aircraft, the open-source CFD software OpenFOAM v7 (London, United Kingdom) is used.  
To analyze the reliability of the simulation, the grid sensitivity test is carried out, as presented in Table 1. The CFD simulations are conducted when the angle of attack is zero with the three types of grids (entitled to coarse, intermediate, and fine meshes). The total number of elements for the coarse-to-fine meshes is approximately 5.51, 8.75, and 13.08 million, respectively. The simulation results among these three meshes are compared  
Figure 5: Computational domain for the BWB aircraft CFD simulation with boundary conditions.  
Figure 6: The computational mesh around the BWB aircraft. The red boxed area in (**a**) is magnified in (**b**).  
with respect to the drag coefficient. The value provided in the last column of the table is the percentage of the drag coefficient difference that the corresponding mesh has compared with the fine mesh. For the coarse mesh, the relative error is computed as 2.4%. This error is reduced by up to approximately 1% at the intermediate mesh, showing the robustness of the current study's CFD simulation. Based on the results of the grid sensitivity test, the intermediate mesh is selected for the remaining BWB's CFD simulations.
## 4 Results
### Data Comparison  
Height displacement from RGB+D and the control data were calculated between four epochs for BEAM 1. and five epochs for BEAM 2.  
Matching points from each epoch were filtered manually and only the ones that were located on the top of the beam were used. Epochs were named based on the image file name to avoid confusion data in processing. The difference of the height displacements from the points and the model (residuals) as a reference were calculated. Statistical parameters for residuals for BEAM 1 are shown in Table 5 and for BEAM 2 in Table 6.  
Standard deviation of \(N\) scalar residuals \(R\) was defined as:  
\[St.dev_{R}=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\lvert R_{i}-\mu\rvert^{2}}, \tag{8}\]  
where \(\mu\) is the mean of \(R\):  
\[\mu=\frac{1}{N}\sum_{i=1}^{N}R_{i}. \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Epochs** & **216–218** & **218–219** & **219–220** & **220–221** \\ \hline Number of points & 33 & 57 & 65 & 65 \\ Standard deviation [mm] & 0.6 & 0.8 & 0.9 & 0.7 \\ Standard deviation error [mm] & 0.1 & 0.1 & 0.1 & 0.1 \\ Average [mm] & 0.2 & \(-\)0.5 & 0.3 & 0.2 \\ Minimum [mm] & 0.0 & 0.0 & 0.0 & 0.0 \\ Maximum [mm] & 1.8 & 2.4 & 2.2 & 1.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical parameters for residuals for BEAM 2.
Standard deviation error is defined as:  
\[St.dev.error_{R}=\frac{St.dev_{R}}{N}. \tag{10}\]  
Standard deviation error is used for quantifying the uncertainty around an estimate of the mean.  
Distributions were fitted to residuals using the maximum likelihood method, with a significance level of 95%. The residuals were distributed normally with some outlier elements, as shown in Figures 15 and 16.  
Based on residual data, it was calculated that the precision of the image matching and fusion part of the RGB+D is \(\pm 1\) mm, for this test case, with a GSD of 0.3 mm. Achievable precision of the RGB+D method was then calculated as a square root of the sum of  
Figure 16: Normalized histograms and distribution of residuals for epochs for BEAM 2. (Source: Own compilation).  
Figure 15: Normalized histograms and distribution of residuals for epochs for BEAM 1. (Source: Own compilation).  
squares of the standard deviation of 3D point measurement of Trimble SX10 [18] and determined RGB+D. The calculated achievable precision for determining height displacement is \(\pm\)2.7 mm.
# A Framework for Symmetric-Quality S3D Video Streaming Services
## 1 Introduction
One of the core technologies of the hybrid 3 DTV system is Scalable High Efficiency Video Coding (SHVC) [8]. SHVC is an encoding technology that supports video services at various resolutions, enabling the simultaneous encoding of high-resolution video for fixed receivers (TVs) and low-resolution video for mobile receivers (e.g., smartphones). This allows for the optimization of bandwidth usage while meeting diverse resolution requirements. SHVC takes advantage of the strong correlation between the left and right images by dividing them into a base layer (BL) and an enhancement layer (EL). This method effectively generates the base layer for the right-view video and the enhancement layer for the left-view video simultaneously, making it highly efficient in the hybrid 3 DTV system [9]. This approach is referred to as Cross-View SHVC. However, hybrid 3 DTV systems also have potential drawbacks. When the resolution difference is within a certain allowable range, a binocular suppression effect (BSE) can occur, where a lower-resolution or distorted image is suppressed in one eye [10]. As a result,viewers perceive the higher-resolution image more dominantly, making the overall image quality degradation less noticeable. However, if the resolution difference is excessively large or the distortion and quality degradation are severe, the quality of experience (QoE) of the synthesized 3D video may deteriorate. To address these issues, various methods for ensuring symmetric image quality have been proposed, such as improvements to the conventional Cross-View SHVC structure and adjustments of factors like camera distance, field of view (FOV), and screen distance to reduce geometric distortions [11, 12, 13, 14]. Additionally, a conditional replenishment algorithm that leverages the high correlation between binocular videos has also been developed [15]. This algorithm enhances the quality of the right image by selecting the better blocks when comparing the upscaled right image and the high-resolution left image with the original, thus mitigating the asymmetry between the left and right images. Furthermore, it is easy to implement and has fast processing speeds, as it loads images in block units according to the mode without additional computation in the decoder. In this paper, we propose a new structure that combines the conditional replenishment algorithm with the SHVC codec to overcome existing limitations and significantly enhance the quality of the right-view video to the level of the left-view video. In Section 2, we explain SHVC, Cross-View SHVC for symmetrical quality, and the conditional replenishment algorithm, which will be compared with the proposed method. Section 3 describes the proposed method, while Section 4 discusses the characteristics and performance of the sequences used in the experiments. Finally, Section 5 presents the main conclusions.
## 3 Results and Discussion
### PSD Results
#### 3.1.3 Graphite PSD  
Graphite is a resilient, dual composition, and carbon-based material. It has a unique characteristic that imparts resiliency and the ability to recover its initial size and volume up to 120% [22]. During the dry particle size distribution experiment on graphite, the analysis showed that the particle size ranged from 1190 to 74 \(\upmu\)m. The overall performance of graphite with the different carrier fluid systems showed that the material performed with a very minimal size reduction with all carrier fluid systems and that the carrier fluid types did not affect the graphite size degradation performance, as shown in Figure 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Drilling Fluid Systems** & **Average Size Degradation (\%)** \\ \hline Clay base & 0\% \\ \hline Polymer base & 1\% \\ \hline Sodium Chloride (NaCl) systems & 3\% \\ \hline Calcium Chloride (CaCl\({}_{2}\)) systems & 0\% \\ \hline Average & 1\% \\ \hline \end{tabular}
\end{table}
Table 6: Graphite size degradation (30 min shearing only).  
\begin{table}
\begin{tabular}{c c} \hline
**Drilling Fluid Systems** & **Average Size Degradation (\%)** \\ \hline Clay base & \(-\)44\% \\ \hline Polymer base & \(-\)32\% \\ \hline Sodium Chloride (NaCl) systems & \(-\)29\% \\ \hline Calcium Chloride (CaCl\({}_{2}\)) Systems & \(-\)26\% \\ \hline Average & \(-\)33\% \\ \hline \end{tabular}
\end{table}
Table 7: Marble size degradation (30 min shearing only).  
\begin{table}
\begin{tabular}{c c} \hline
**Drilling Fluid Systems** & **Average Size Degradation (\%)** \\ \hline Clay base & \(-\)4\% \\ Polymer base & \(-\)3\% \\ Sodium Chloride (NaCl) systems & \(-\)5\% \\ Calcium Chloride (CaCl\({}_{2}\)) systems & \(-\)4\% \\ Average & \(-\)4\% \\ \hline \end{tabular}
\end{table}
Table 5: Walnut size degradation (30 min shearing only).  
The size degradation analysis of graphite confirmed that the material did not experience any size reduction but showed a slight size expansion. The detailed size degradation results are shown in Table 6. The overall percentage size degradation of graphite in all fluids was reported at an average of 1% size increase. This increase in size resulted from the high resiliency of graphite, which confers the ability to recover more than its original size.