esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 4 Hybrid-Precision Circuit Simulation Validation and Stress Analysis
### Development of the Hybrid-Precision Simulation Model for the Communication Module  
Since the analog components in the communication module are comparatively simple, they can be modeled using built-in SPICE device models provided by the Saber 2016 simulation software. Figure 10 shows the schematic diagram of the final simulation model for the minimum communication subcircuit. The module consists of 16 serial communication channels, with one serial communication channel presented as an example.  
Additionally, since the DS26C32 receives differential signals as input, the analog input voltage must be differenced before conversion to a digital signal. It cannot be directly simulated using a single-ended input port model. Therefore, in this article, Verilog-AMS is used to re-model the DS26C32, integrating differential signal processing and analog-to-digital conversion directly within the DS26C32 model.  
To validate the developed communication module circuit simulation model, a test circuit is designed where two communication modules communicate with each other. The schematic diagram of the test circuit is shown in Figure 11. During the simulation, command data are inputted into FPGA1, which forwards the command to FPGA2 while receiving telemetry data from FPGA2. FPGA2 processes the given telemetry data and transmits it back to FPGA1. Therefore, if the communication module functions correctly, the telemetry data returned by FPGA1 should contain the expected data.  
Figure 10: Schematic diagram of the minimum communication subcircuit.  
Figure 9: FPGA simulation test results (send/receive timing string).  
The band rate is set to 115,200, and the simulation test circuit performs five cycles of data transmission and reception (approximately 200 ms). The serial port module is used to monitor the telemetry data returned by FPGA1. The simulation results, as shown in Figure 12, indicate that FPGA1 successfully receives and returns the telemetry data. Since the expected data are correctly included in the response, it can be concluded that the communication module simulation circuit functions correctly.
###### Abstract  
In order to optimize the pressure relief gas extraction process for the 1504 working face in East 2 of Dalong Coal Mine based on its mining and gas conditions, a physical model of pressure relief gas extraction in the airspace using two preliminary extraction processes--a high-level oblique borehole and a directional long borehole--was established using COMSOL 6.2 software. The changes in the gas extraction effect of high-level oblique boreholes were analyzed through a simulation of the advancement of the working face, and the reasons for the low utilization rate of the high-level oblique boreholes were outlined. The effects of the horizontal distance of the directional long boreholes from the side of the air return lane, the borehole spacing, and the negative pressure of the boreholes on the gas extraction effect were analyzed, and the gas extraction process of the directional long boreholes was optimized and applied in the field. The results showed that the directional long borehole gas extraction process had a better extraction effect, a higher borehole utilization rate, and superior cost savings, and was thus was the preferred process. Additionally, the optimal parameters were a 30 m horizontal distance of the boreholes from the side of the air return lane, a 30 m spacing between the boreholes, and a 20 kPa negative extraction pressure.  
gas extraction; high-level oblique borehole; directional long boreholes; process optimization; upper corner gas control Article
## 2 Materials and Methods
### Modeling Plastic Behavior/Region During Indentation
#### 2.2.2 Finite Element Modeling  
The Berkovich-like shape indenter was applied in the finite element modeling using ABAQUS to investigate von Misses stress distribution. The indenter was assumed as a three-sided pyramid in shape with a sharp tip at the end. The size of the plate sample has been set large enough to simulate the realistic situation, and ignore the effect on the surrounding and bottom surfaces in the analysis. An example of the ABAQUS model with mesh is depicted in Figure 3. The indenter was modeled as a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\epsilon_{ind}\) & \multicolumn{3}{c}{\(\sigma_{df}\) (MPa)} \\ \cline{2-4}  & \(\alpha\)=2.6 & \(\alpha\)=2.5 & \(\alpha\)=2.4 \\ \hline
0 & 1 & 1 & 1 \\
0.004 & 15 & 16 & 17 \\
0.011 & 43 & 44 & 46 \\
0.017 & 66 & 69 & 72 \\
0.023 & 92 & 95 & 99 \\
0.028 & 109 & 114 & 118 \\
0.034 & 134 & 139 & 145 \\
0.040 & 157 & 163 & 170 \\
0.046 & 181 & 188 & 196 \\
0.051 & 201 & 209 & 217 \\
0.057 & 224 & 233 & 243 \\
0.060 & 236 & 246 & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Effective indentation stress and indentation strain with different indentation stress constrain factor, \(\alpha\), at \(h=5\) μm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{Depth}\), \(h\) & \(\mathbf{Hardness}\), \(\mathbf{H}\), \(\mathbf{[GPa]}\) & \(\mathbf{Reduced}\) \(\mathbf{Modulus}\), \(\mathbf{Young}\)’s \(\mathbf{Modulus}\), \(\mathbf{Constraint}\) \(\mathbf{Factor}\) \(\mathbf{[GPa]}\) & \(\mathbf{C_{ps}}\) \(\mathbf{[GPa]}\) & \(\mathbf{\alpha}\) \\ \hline
1 & 0.255 \(\pm\) 0.043 & 4.924 \(\pm\) 0.045 & 4.354 & 1.87 \\
5 & 0.260 \(\pm\) 0  
discrete rigid body using C3D10 quadratic elements with a reference point to control the indentation process through defined displacement. The plate was meshed using C3D8R linear elements with the bottom surface fixed in all directions. The surface-to-surface contact between the rigid indenter and the plate sample was set with a friction coefficient of 0.3. The model followed von Mises yield criteria with isotropic hardening and rate-independent. The constitutive model was assumed to be elastic-plastic with linear elastic behavior up to the yield stress and plastic strain determined through the process described in Section 2.2.1.  
Considering the accuracy of the result and computational resources, mesh size, one-fifth of the depth, (e.g., mesh size = 0.2 \(\upmu\)m for depth = 1 \(\upmu\)m) was applied to numerical analysis. More detailed information about mesh size selection can be found in Supporting Information S2.
# Hybrid Approach for Indoor Localization Using Received Signal Strength of Dual-Band Wi-Fi
## 5 Proposed Ranging Method
```
\(<\)Offline stage\(>\) Input  Training set \(\mathbf{R}^{\prime}\): The set of dual-band RSS vector, \(\mathbf{r}_{i}=[r_{i}^{2,4},r_{i}^{5,2}]\), and ground truth distance \(d_{i}\) for each sample  Validation set \(\mathbf{V}^{\prime}\): The set of validation data, which are same kind of data as, but not included in the training set  Initialize a neural network described in Figure 4 with random weights, biases, and normalization parameters. for all training data grouped by mini-batch size of \(K_{B}\), do  Forward calculation for the NN  The output \(y_{i}\) is a result of the last layer of the NN  Calculate the average MSE between \(y_{i}\) and \(d_{i}\) in the mini-batch  Update parameters of the NN, by using back-propagation endfor  Find best-fit parameters of the NN that minimize MSE for the validation set Output Trained NN as a deep regression model \(<\)Online stage\(>\) Input  Test data location of \(N\) APs and \(N\) RSS vector  Set a confidence threshold \(Th_{c}\) for each \(i\)-th AP do  Predict \(\widehat{d}_{i}\) using the trained NN endfor  Find target position \(\mathbf{\hat{x}}\), using Equations (5)-(7) Output : Position estimate of target device \(\mathbf{\hat{x}}=[\hat{x},\hat{y}]^{\mathsf{T}}\)
```  
**Algorithm 1** Proposed localization procedure. The number of hidden blocks and the number of hidden nodes constituting the proposed model are user-defined hyperparameters. The deeper the neural network is, the more complex the nonlinear function will be. However, there is a performance limit when the structure is excessively complex compared to the input data. In the next section, an analysis of the performance depending on the hyperparameters is presented.
## 3 Results
### Cell Viability and Proliferation  
The transfection of the miR-320a mimic caused a significant reduction in cell viability in MG-63 cells (\(p\) = 0.046) (Figure 2A). A similar trend was observed in U2 OS viability after mimic transfection, while an increase of viability (\(p\) = 0.045) was observed upon the inhibition of miR-320a. In contrast, hOBs viability was not affected by miR-320a transfection.  
The overexpression of miR-320a significantly decreased MG-63 and U2 OS proliferation (\(p\) = 0.028 and 0.04, respectively), which correlated with the viability results (Figure 2B). In contrast, the  
Figure 1: miR-320a expression levels assessed by qPCR in human osteoblast (hOB), U2 OS, and MG-63 cells. Data represent the mean \(\pm\) SD (\(n\) = 2). \({}^{*}\)\(p\)\(<\) 0.05 for hOB cells.  
Figure 2: Effects of miR-320a on cell viability (**A**) and proliferation (**B**) in primary hOBs (\(n\) = 3), MG-63 (\(n\) = 3), and U2 OS cells (\(n\) = 3) at 48 h post-transfection. Data represent the mean \(\pm\) SD. \({}^{*}\)\(p\)\(<\) 0.05.  
ovexpression of miR-320a (\(p\) = 0.045) significantly increased hOB proliferation. In this case, the inhibitor transfection did not affect the proliferation in any cell type.
## 3 Mathematical Model
### Model Validation  
Model verification is an important procedure for ensuring the accuracy and reliability of numerical simulation results. To increase the reliability of the suction efficiency of road cleaners, road cleaners, two experimental validations were performed. The application of the pickup head geometric model was first confirmed by the factors affecting the efficiency of particle suction. The second verification was to verify the accuracy of the simulations of the absorption efficiency of particles by a pickup head.  
In the validation, the data used in the present study to evaluate the CFD model were obtained from [PERSON] et al. [17]. Based on the efficiency of the sand particles used in the pickup head suction port for use in the experiment, and in the numerical simulation verification, we were able to determine the effect of the observed difference in the sweeper traveling speed on the suction efficiency. The sweeping field experiments were carried out at a sweeper traveling speed of 6-14 km/h, pressure drop of 2000 Pa, sand particle sizes from 45 to 160 \(\upmu\)m, density of 2500 kg/m\({}^{3}\), and the direction of movement of the sweeper was Y = 900 mm in the plane Z = 20 mm. The size of the experimental road surface at 2.0 \(\times\) 1.8 mm. A road sweeper pickup head was used as the road cleaning device.  
In the second validation, the concentration obtained by the DPM was compared with those in the experiments conducted by [PERSON] et al. [19]. The overall absorption efficiency of the particles was assessed by conducting sweeper traveling speeds of 5-14 km/h, pressure drops of 1400-2900 Pa, particle sizes of 45-152 \(\upmu\)m, and reverse-blowing flow rates of 1227-3120 m\({}^{3}\)/h.  
Based on the two experiments, a geometric model and consistent boundary conditions were established. The simulation data agreed well with the experimental data. When the traveling speed was 6-16 km/h, the average deviation between the simulation and experimental results was less than 10%. However, when the sweeper speed was 12-16 km/h, the simulated values were slightly higher than the experimental ones. There may be a reason for the sufficient accuracy of the DPM model in simulating the sweeper pickup head particle suction efficiency. As for the concentration, the numerical simulation results are normalized, and the average concentration at speeds of 6 to 10 km/h is very consistent with the experimental data. However, there is a small deviation from the normalized average concentration at a sweeper speed of 6-10 km/h, which may be due to the difference in the airflow. Despite some slight differences, the realizable k-\(\epsilon\) turbulence model is feasible for solving fluid flow and solid particles.
## 7 Conclusions and Future Works  
In the context of vehicular networks, the process of verifying data in NDN can lead to significant delays due to their highly dynamic nature. In this paper, we introduced NDN-BDA, a decentralized data authentication mechanism based on blockchain technology, which is both efficient and straightforward. Our proposal was thoroughly evaluated for efficiency against various attacks, and its functionality was validated using the widely used AVISPA tool.  
As future work, we aim to enhance our solution by including a certificate revocation process and expanding it to a distributed mechanism that utilizes multiple Management Servers maintaining a global blockchain.  
**Author Contributions:** The authors equally contributed to this work in all aspects with more implementation-related efforts from A.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was derived from the R&D project PID2021-122580 NB-I00, funded by MCIN/AEI/10.13039/501100011033 and \"ERDF A way of making Europe\".  
**Data Availability Statement:** All implementation details, sources, and data will be delivered upon request to the corresponding author [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 9: HLPSL specification code used to define the simulation environment and communication sessions.
## 3 Results and Analysis
### Recording by Digital Holography  
Figure 4 presents profiles of elastic waves in the polystyrene bar within the areas of 50-110 mm and 100-160 mm, obtained by digital holography. As can be seen from Figure 4 while travelling along the waveguide, the wave pattern evolves, the wave amplitude decreases and its width increases. Besides this, short disturbances, clearly seen on the main long wave in Figure 4a, almost disappear in Figure 4b, which can be explained by the relatively fast damping of linear, high-frequency components with the attenuation coefficient \(\alpha_{lin}=0.25\,\mathrm{cm}^{-1}\)[47]. The two plots demonstrate the gradual formation of the nonlinear solitary wave in the waveguide. The attenuation coefficient for the solitary wave observed in these areas is an order of magnitude smaller than that for linear disturbances and is about \(\alpha_{nonlin}=0.02\,\mathrm{cm}^{-1}\)[47]. The parameters of the forming and formed soliton determined from the graphs in Figure 4 are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Holographic Interferometry** & \multicolumn{2}{c}{**Digital Holography**} \\ \hline
**Waveguide Area (mm)** & **40–70** & **100–150** & **50–110** & **100–160** \\ \hline amplitude (\(10^{-4}\)) & 2.4 \(\pm\) 0.2 & 2.1 \(\pm\) 0.3 & 2.9 \(\pm\) 0.05 & 2.6 \(\pm\) 0.05 \\ width (mm) & 45 \(\pm\) 3 & 59 \(\pm\) 7 & 46 \(\pm\) 2 & 55 \(\pm\) 2 \\ FWHM (mm) & 24 \(\pm\) 2 & 33 \(\pm\) 5 & 20 \(\pm\) 2 & 27 \(\pm\) 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of nonlinear elastic strain waves in different areas of the polystyrene waveguide, determined from graphs in Figures 3 and 4. The error bars were obtained from the analysis of several recorded profiles of strain waves and statistical analysis of these data.  
Figure 3: (**a**) Stitching of four consecutive holographic interferograms demonstrating formation of strain soliton from the shock wave in the polystyrene rod at the distance of 40–70 mm from the rod input. The frame size is 30 mm, the size of the field of view obtained by stitching is 70 mm. (**b**) Stitching of three consecutive holographic interferograms showing soliton with oscillating tail in the polystyrene bar at the distance of 100–150 mm from the bar input. The frame size is 50 mm, the size of the field of view obtained by stitching is 100 mm. Stitching lines are indicated on the interferograms with white (**a**) and black (**b**) arrows. Fast-decaying short-wavelength oscillations in the graph (**a**) are indicated by yellow arrows.
## 3 Methodology
### Application Type for Supervised Learning  
In this work, the BRFFNN and LSGB were used to treat bandwidth estimation as a supervised regression task during simulation, and one-hot encoding was manually used to deal with the categorical Application_Type feature. To make sure the models could learn  
Figure 1: General flow of the methodology.  
unique traffic behavior, this procedure involved developing binary indicator variables for every application (such as the video, voice, and data categories). By allowing the models to identify application-specific bandwidth requirements, this encoding enhanced prediction performance, particularly for delay-sensitive services like VoIP and video streaming.
## 4 Results and Discussion  
### Experiment Data  
There werearound three kinds of scrap samples, totally about 4.5 kg scrap was tested at about 1.5 kg per material. However, not all acquired AE signals were available for further processing and analysis. Several abnormal impacts generated irregular AE signals, which did not transfer enough energy for vibration but only for acceleration of rebound velocity. Big sizes of scrap pieces led occasionally to multi-impacts. Small mass scrap pieces could probably generate only weak signals which were covered in the background noise. Examples of weak signals and irregular signals are shown in Figure 5.  
Figure 4: Feature extraction from PSD spectrum.  
The AE signals from abnormal impacts and the signals of extreme low intensity were excluded. During the experiments, all of the scraps with diameters less than 8 mm resulted in inadequate intensity of the AE signals. Also, due to the crushing process, there were still several pieces of scrap with diameter larger than 23 mm remaining. The scraps with equivalent diameters of less than 8 mm and larger than 23 mm of PP, ABS, and SMA were 12.2%, 14.6%, and 13.8% in mass, respectively. Considering that 2% of PP, 5% of ABS, and 4% of SMA scraps in the mass were not flake structure, there were still 85.8% of PP scraps, 80.4% of ABS scraps, and 82.2% of SMA scraps remaining.  
There were still quite a few weak signals found in the diameter range of 8-13 mm, which took more than 30% of all acquired signals. Considering this was a feasibility study, these weak signals were also excluded from further characterization. Although these signals were more intense than the signals with scrap diameters less than 8 mm, they were still not fully reliable because of the limitation of electronic devices, in which much useful signal information was hidden by noise.  
The weak impact AE signals of scraps were clear enough for further characterization using more sensible electronic devices, such as AE pickup and sound acquisition hardware with higher S/N ratio and sensitivity. The proportions of PP, ABS, and SMA scraps with diameters from 8-13 mm were 16.8%, 16.6%, and 15.6% in mass respectively. With increasing scrap diameter, the intensity of the AE signals obviously also increased.  
The scraps with abnormal impact of PP, ABS, and SMA were 4.4%, 2.1% and 2.7% in mass, respectively. Therefore the materials available for feature extraction remained 64.6% of PP, 61.7% of ABS, and 63.9% of SMA in mass, respectively. Overall there were about 1200 scrap impacts of each kind of material successful in feature extraction.  
Figure 5: A weak signal and an irregular signal and their power spectrum, (**a**) low mass impact with only noise features; (**b**) low mass with double impact of one particle.
## 3 Results and Discussion
### Temperature Stability
#### 3.1.4 The Temperature Stability of the Wall after One Coat of Paint  
Figure 8a illustrates the temperature measurements behind the painted block wall over three days, with room temperatures of 20.4 \({}^{\circ}\)C, 20.6 \({}^{\circ}\)C, and 20.4 \({}^{\circ}\)C and corresponding humidity levels of 56%, 49%, and 51%. The temperature behind the wall remained consistently about 3.0\({}^{\circ}\)C lower than the room temperature. In Figure 8b, for the coated brick wall, with room temperatures over three days of 20.4 \({}^{\circ}\)C, 20.6 \({}^{\circ}\)C, and 20.4 \({}^{\circ}\)C and humidity levels of 56%, 49%, and 51%, the temperature behind the wall stabilized at roughly 2.4 \({}^{\circ}\)C below the room temperature after painting. Figure 8c shows the temperature measurements behind the coated earth wall, with room temperatures of 20.4 \({}^{\circ}\)C, 21.3 \({}^{\circ}\)C, and 20.7 \({}^{\circ}\)C and humidity levels of 84%, 78%, and 78%, respectively. Here, the temperature behind the wall stabilized at about 0.9 \({}^{\circ}\)C below the room temperature after the paint application. The preliminary conclusions suggest that the insulation effect of the coating on the block wall is superior to that on the brick wall, and the insulation effects across all three wall types appear to be relatively stable over time.
## 4 A Case Study: DQN-Based Intelligent Thermal Management System
### Thermal Management Process Using a DQN-Based Intelligent Strategy  
Figure 13(a) shows a thermal management process conducted under the DQN decision. Figure 13(b) shows the reduction of monitoring temperature in an IDC prototype, indicating the optimal cooling policy successfully conducted by DQN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Training 16,000 Episodes** & **Deploying** \\ \hline Time & 35.59 h (128,112.47 s) & 1.46 \(\pm\) 0.1 s \\ Devices & NVIDIA GeForce RTX 4070 SUPER (28 GB) with Intel Core i7-9700 CPU, \\  & & \multicolumn{1}{c}{Beihang University, Beijing, China.} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Time consumption of DQN in training process and deploying process.  
Figure 13: Optimal actions of DQN and corresponding IT power consumption at different training steps.  
In the running process, the DQN model can save the sensor's log and use it as memory to enhance the training. Yet, because of the speed of the temperature transition in the real model DC, this function of DQN cannot be effectively evaluated and presented. Derived from the concept of post-training in DQN, this should enable the DQN decision-making agent to fit into the real DC quickly and to have an adaptive ability.  
In the test of the DQN decision-making agent on the real-scaled DC model and Digital Twins, it is possible to automatically control the cooling system's set point using the light information collected from the real DC. The temperature results demonstrated that the DQN can perform a function to lower the PUE and prevent overheating. The DQN decision will change with the temperature and IT consumption information that was collected from a real DC model. It can adjust the decision-biased direction by changing the reward ratio in the DQN training, and it can also apply a post-training method to a real DC. We can also store the running data in the database; DQN will draw memory from the database and improve its accuracy towards a particular DC.