esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Although ginsenosides Rb1 and Rg3 have been identified as the significant ginsenosides found in red ginseng that confer anti-diabetic actions, it is unclear whether insulin-sensitizing effects are mediated by the individual compounds or by their combination. To determine the effect of ginsenosides Rb1 and Rg3 on adipocyte differentiation, 3T3-L1 preadipocytes were induced to differentiate the standard hormonal inducers in the absence or presence of ginsenosides Rb1 or Rg3. Additionally, we determined the effects of Rb1, Rg3, or their combination on the expression of genes related to adipocyte differentiation, adipogenic transcription factors, and the insulin signaling pathway in 3T3-L1 cells using semi-quantitative RT-PCR. Rb1 significantly increased the expression of CEBP\(\alpha\), PPAR\(\gamma\), and aP2 mRNAs. However, Rg3 exerted its maximal stimulatory effect on these genes at 1 \(\upmu\)M concentration, while a high concentration (50 \(\upmu\)M) showed inhibitory effects. Similarly, treatment with Rb1 and Rg3 (1 \(\upmu\)M) increased the expression of IRS-1, Akt, PI3K, GLUT4, and adiponectin. Importantly, co-treatment of Rb1 and Rg3 (9:1) induced the maximal expression levels of these mRNAs. Our data indicate that the anti-diabetic activity of red ginseng is, in part, mediated by synergistic actions of Rb1 and Rg3, further supporting the significance of minor Rg3.  
3T3-L1 adipocyte; differentiation; ginsenoside Rb1; ginsenoside Rg3; adipogenic transcription factor +
Footnote †: journal: Applied Sciences  
Article
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 5 Experimental Results and Discussions  
### Experimental Environment  
Before the experiment, to facilitate the display of the image, we encoded the original image into the gray scale of [0.255], and set the CFPN with standard deviation intensity in the [0.20] range. For scientific CIS, the general photo response nonuniformity (PRNU) is about 0.5%, and for consumer CMOS, it is about 2%. We set the noise intensity in the range of [0.20], so that the noise level of these two kinds of CIS can be completely expressed. To illustrate the effectiveness of the proposed algorithm (LRSUTV), we tested it from both the simulation and real data. In this paper, six scene-based FPN correction algorithms are selected for comparison experiments. They are wavelet [17; 39; 40], anisotropic total variation (UTV) [41; 42; 43], ASSTV [29], variational stationary noise remover (SNR), SILR [28], \(\ell_{0}\) sparse method (\(\ell_{0}\) sparse) [34], and the recommended method in this work.  
To comprehensively and objectively reflect the correction effect of FPN, we use several common quality evaluation methods to evaluate the noise removal effect of each algorithm, namely Mean Cross-track (\(x\)-axis stands for the column number of the image, and the \(y\)-axis represents the mean value of each column), PSNR and SSIM. The various Lagrange multipliers used in our algorithm are hard-tuned. In order to objectively compare the effects of the various methods, I have adjusted the parameters of all methods to the best of my ability.
# A Simple and Stable Atmospheric Pressure Electrodeless Water Vapor Microwave Plasma Torch
## 3 Results and Discussion
An XRD analysis of the ceramics can be seen in Figure 5a. The results show that the ceramics have a similar structure before and after discharge. To further explore the composition of the ceramic, we analyzed the ceramic material by energy dispersive spectroscopy (EDS) and found that the main elements are Si and O. Therefore, it can be found that the peak intensity of SiO\({}_{2}\) after discharge is significantly stronger than that before discharge, which indicates that the high temperature generated by discharge can promote the crystallinity of the crystal. Finally, we also carried out experiments with alumina ceramic tubes and obtained similar results, which shows that this device can also be used for the crystal growth of non-metallic materials. For different values of plasma input power, the emission spectrum of water vapor plasma of the device is obtained in the wavelength range of 250 to 1000 nm. The two-second integration time is applied, and the software PLASUA SpecLine (ver. 2.1, Plasus SpecLine, Kissing, Germany) is used to analyze and allocate the emission line. Shown in Figure 6 are the emission spectra of water vapor microwave plasma obtained by excitation at three different microwave powers of 900, 700, and 500 W, respectively. It illustrates that with the increase in power, the peak value of the spectrum is enhanced, and the emission line increases gradually. In this spectrum, the OH band is clearly identified with intense peaks in the range 302.1-308.9 nm [8]. H\(\alpha\) at 656.3 nm is also clearly identified [27]. In particular, the atomic oxygen line at 777 nm and 844 nm was remarkably enhanced. The strongest spectral line with a peak at 589.59 nm in the spectrum is most likely Na [28], which mainly comes from Na ions in water. In short, the emission spectrum confirms the dissociation of water molecules. Figure 4: The excitation process of water vapor microwave plasma torch. (**a**) The wick with flame discharges under the action of the microwave, causing evaporation and discharge of water at the top of the ceramic tube (multimedia view: Video S2, the upper part is the schematic diagram). (**b**) Process of water vapor plasma excited by microwave (multimedia view: Video S3, the upper part is the schematic diagram). (**c**) Temperature changes at the beginning of the excitation of water vapor microwave plasma. (**d**) Change of mass of ceramic tube after discharge with time. Figure 5: (**a**) Image comparison and XRD of the ceramic tube before and after discharge. (**b**) Analysis of the surface composition of ceramic materials. Figure 6: Spectral data of water vapor plasma with different power.
# An Intelligent Cluster-Based Routing Scheme in 5G Flying Ad Hoc Networks
###### Abstract  
Flying ad hoc network (FANET) is an application of 5G access network, which consists of unmanned aerial vehicles or flying nodes with scarce resources and high mobility rates. This paper proposes a deep Q-network (DQN)-based vertical routing scheme to select routes with higher residual energy levels and lower mobility rates across network planes (i.e., macro-plane, pico-plane, and femto-plane), which has not been investigated in the literature. The main motivation behind this work is to address frequent link disconnections and network partitions in order to enhance network performance. The 5G access network has a central controller (CC) and distributed controllers (DCs) in different network planes. The proposed scheme is a hybrid approach that allows CC and DCs to exchange information among themselves, and handle global and local information, respectively. The proposed scheme is suitable for highly dynamic ad hoc FANETs, and it enables data communication between UAVs in various applications, such as monitoring and performing surveillance of borders, and targeted-based operations (e.g., object tracking). Vertical routing is performed over a clustered network, in which clusters are formed across different network planes to provide inter-plane and inter-cluster communications. This helps to offload data traffic across different network planes to enhance network lifetime. Compared to the traditional reinforcement learning approach, the proposed DQN-based vertical routing scheme has shown to increase network lifetime by up to 60%, reduce energy consumption by up to 20%, and reduce the rate of link breakages by up to 50%.  
flying ad hoc network; deep Q-network; reinforcement learning; 5G; QoS 2022202020209 February 20220209 February 20220209 February 202209 February 202209 February 202209 February 20209 February 20  
and underutilized spectrum. These features help to achieve improved spectral efficiency, coordinate different kinds of network cells (e.g., macrocells and small cells (SCs), including picocells and femtocells) for achieving reduced interference, and achieve network virtualization for sharing network-wide resources. These features cater for next-generation network scenarios characterized by ultra-densification, heterogeneous, and high variability, in order to achieve a better quality of service (QoS) of up to 10\(\times\) higher data rate, up to 1000\(\times\) lower delay, up to 99.999% higher reliability and availability, up to 100\(\times\) larger network coverage, and up to 10\(\times\) longer battery lifetime [2]. As an example of the new technologies, D2D enables neighboring nodes to perform direct communication among themselves without passing through a base station (BS), which can offload traffic from the BS to reduce network congestion while reducing delay and energy consumption.  
The rest of this section presents an overview of FANET, 5G, vertical clustering, as well as our contributions and the paper organization. Table 1 presents general notations, and Table 2 presents notations related to routing.
## 4 Case Study
### Impact of Multiple Slices on User Satisfaction
#### 4.5.2 Processing Capacity  
The insights derived from Figure 6 accentuate the positive impact of employing multiple slices on user satisfaction with the average processing capacity, especially in the presence of varying numbers of VUs. The figure manifests a clear trend in which the use of the slices contributes to a substantial improvement in the overall satisfaction of the VUs with respect to the processing capacity.  
Figure 5: User satisfaction with respect to mobility versus the number of slices for different number of VUs.  
Figure 6: User satisfaction with respect to average processing capability versus the number of slices for different number of VUs.  
As expected, the provision of more resources through the implementation of slices in each layer empowers the system to quickly meet the increasing demands of a growing VU population. This resilience is particularly noteworthy, as the figure demonstrates sustained improvements in processing capability satisfaction even with a higher influx of VUs. The results reiterate the superiority of the proposed DLaaS structure in efficiently catering to the computational needs of a dynamic and expanding user base.
# Effects of Random Values for Particle Swarm Optimization Algorithm
## 4 Experiments and Analysis
### Application and Analysis
#### 4.3.2 Analysis  
According to the experimental results and comparisons, it can be concluded that the performances of standard PSO and LDIW-PSO algorithms are all highly improved by expanding the range of random values. This is because that the large-scale random values are helpful in increasing the velocity of particles, and then the particles avoid falling into the local optima. As shown in Figure 7, in the local optima areas (A or C), if the velocity cannot be increased or its direction cannot be changed, the particle will gradually fall into the local optima and cannot jump out. However, if the velocity of particle can be increased or changed according to a certain probability, the global optima will be obtained more easily and quickly.  
For the standard PSO and LDIW-PSO algorithms, if the random values are set as 0.5 or generated by \(U[0,1]\), the diversity of particle is decreased, and the variation range of particle velocity is limited in a narrow band. Therefore, the probability of escaping local optima is very small. This is because the particle velocity gradually tends to 0 when the particle falls into local optima. In addition, the random value is a positive/negative number, which may lead to the monotonous variation of particle velocity. This also decreases the possibility of escaping local optima. However, the PSO algorithms with large-scale random values (distributed in \(U[-1,1]\) or \(G(0,1)\)) can overcome these problems to some extent. Furthermore, for a low dimensional practical optimization problem, the random values generated by \(U[-1,1]\) or \(G(0,1)\) can improve the diversity of particles, but the local searching ability may be decreased due to the finite particles. However, keeping the balance between local search and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & \(\mathbf{r_{1},r_{2}}\) & \(\mathbf{U[0,1]}\) & **0.5** & \(\mathbf{U[-1,1]}\) & \(\mathbf{G(0,1)}\) \\ \hline \multirow{4}{*}{SPSO} & Average solution & 5975.93 & 5975.93 & 5975.93 & 5975.94 \\  & Standard deviation & 0.00 & 0.00 & 0.01 & 0.01 \\  & The worst solution & 5975.93 & 5975.93 & 5975.96 & 5975.99 \\  & The best solution & 5975.93 & 5975.93 & 5975.93 & 5975.93 \\ \hline \multirow{4}{*}{LDIW-PSO} & Average solution & 5975.93 & 5975.93 & 6001.34 & 6026.76 \\  & Standard deviation & 0.00 & 0.00 & 139.18 & 193.41 \\ \cline{1-1}  & The worst solution & 5975.93 & 5975.93 & 6738.24 & 6738.26 \\ \cline{1-1}  & The best solution & 5975.93 & 5975.93 & 5975.93 & 5975.93 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparisons of standard PSO and LDIW-PSO algorithms with different types of random values for pressure vessel design.  
Figure 7: Schematic diagram of particles’ velocity.  
global search is very important for the performances of these PSO algorithms. So, the PSO algorithm with random values distributed in \(U[0,1]\) and deterministic PSO algorithm (\(r=0.5\)) have better local searching ability for some low dimensional optimization problems.
## 2 Beidou-Binocular Cooperative Navigation Model
### Binocular Vision Recognition and Perception Module
#### 2.2.2 Main Functional Modules of Binocular Vision
The core components of the DeepLab algorithm are as follows:  
* Dilated Convolution: By inserting holes in the convolution kernel, dilated convolution expands the receptive field while maintaining computational efficiency, capturing multi-scale contextual information. The output of dilated convolution can be expressed as follows: \[y[i]=\sum_{k=1}^{K}x[i+r\cdot k]\cdot w[k]\] (9) where \(r\) is the dilation rate, \(K\) is the kernel size, \(x\) is the output feature, and \(w\) is the convolution kernel weights. * Atrous Spatial Pyramid Pooling (ASPP): ASPP extracts multi-scale features by using convolution kernels with different dilation rates in parallel, combining local information with global context. The output of ASPP is the fused result of multi-scale features. * Conditional Random Field (CRF) Post-processing: CRF post-processing is used to introduce contextual information into the prediction results and optimize the segmentation boundaries through a fully connected CRF, refining the results. DeepLab uses the cross-entropy loss function to optimize the classification of each pixel:  
\[\mathcal{L}_{D}=-\frac{1}{N}\sum_{i=1}^{N}\sum_{c=1}^{C}y_{i}^{c}\log{\hat{y} _{i}^{c}} \tag{10}\]  
where \(N\) is the total number of pixels in the image, \(C\) is the number of classes, and \(y_{i}^{c}\) and \(\hat{y}_{i}^{c}\) are the true class and predicted probability of pixel \(i\), respectively. In the BBCN model, DeepLab is used to perform pixel-level semantic segmentation on the bounding box regions output by the object detection module (YOLO). For example, after YOLO detects a transformer, DeepLab further segments its pixel region, generating precise shape and boundary information for the obstacle. The segmentation results, combined with the depth map, are then input into the local path planning module for dynamic obstacle avoidance. Figure 4 summarizes the collaboration between YOLO and DeepLab. Through the collaboration of YOLO and DeepLab, the BBCN model can precisely perceive the environment and provide support for dynamic obstacle avoidance and path planning. 3. Dynamic Object Detection and Tracking: The binocular vision system can also use optical flow methods or tracking algorithms to detect and track dynamic objects in real time. Supposing the pixel coordinates of the target point at time \(t\) and \(t+1\) are \(\mathbf{p}_{t}\) and \(\mathbf{p}_{t+1}\), respectively, the motion trajectory can be estimated using the optical flow method: \[ \begin{cases}\mathbf{p}_{t+1}=\mathbf{p}_{t}+\Delta\mathbf{v}\\ \Delta\mathbf{v}=\left[\frac{\partial{t}}{\partial{x^{\prime}}},\frac{\partial {t}}{\partial{y}}\right]\cdot\Delta t\end{cases} \] (11) where \(\Delta\mathbf{v}\) is the velocity change, and \(\frac{\partial{t}}{\partial{x}}\) and \(\frac{\partial{t}}{\partial{y}}\) are the image intensity gradients. Through dynamic object tracking, the system can promptly perceive environmental changes and update the path.
# A Deadbeat Current and Flux Vector Control for IPMSM Drive with High Dynamic Performance
## 4 Test Bench Configuration  
A standard test bench system for the IPMSM drive to verify the effectiveness of the proposed DB-CFVC scheme is shown in Figure 5a. The specifications of the target IPMSM are given in Table 1. The core controller is a digital signal processor (DSP) of Texas Instrument, TMS320F28379D, with a sampling and PWM switching frequency of 10 kHz. An IGBT VSI is used to feed the IPMSM with current limited at 10 A and DC bus voltage supplied at 100 V. The rotor speed of the PMSM is calculated by taking the derivative of rotor position (i.e., \(\theta_{\text{m}}\)), in which \(\theta_{\text{m}}\) is measured by an encoder with 5000 pulse/rev. The phase current and output torque are also measured independently by current and torque sensors. For data collection, the estimated values are exported via DAC ports of the DSP; then, the data are measured and analyzed by a Teledyne Leroy MDA803A oscilloscope. Furthermore, a real-time hardware-in-the-loop (HIL) MR series II toolkit, as shown in Figure 5b, is used to evaluate tests as motor parameters change instead of the real-test platform [3] (since the HIL allows conveniently changing the motor parameters).  
The PI gains of the speed loop are chosen as the method reported in [31], i.e., \(K_{Pu}=0.1256\) and \(K_{I\omega}=0.3933\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Specification** & **Value** & **Unit (SI)** \\ \hline DC voltage, \(V_{\text{dc}}\) & 100 & V\({}_{\text{DC}}\) \\ Rated current, \(i_{\text{rated}}\) & 10 & A\({}_{\text{pk}}\) \\ Maximum current, \(i_{\text{max}}\) & 20 & A\({}_{\text{pk}}\) \\ Base speed, \(N_{\text{base}}\) & 2300 & rpm \\ Rated torque, \(T_{\text{e,rated}}\) & 2.9 & N\({}_{\text{m}}\) \\ Pole-pairs, \(p\) & 4 & - \\ Stator resistance, \(R_{\text{s}}^{0}\) & 315 & m\(\Omega\) \\ Permanent magnet flux linkage, \(\mathbf{\
abla}_{\text{m}}\) & 48.2 & mWb \\ Torque constant, \(K_{\text{T}}\) & 0.2892 & N\({}_{\text{m}}\)/A \\ \(d\)-axis inductance, \(L_{\text{q}}^{0}\) & 2.03 & mH \\ \(q\)-axis inductance, \(L_{\text{q}}^{0}\) & 2.84 & mH \\ \hline \end{tabular}
\end{table}
Table 1: Target IPMSM specifications.
# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
## 3 Case Study
### Relationship Between Fuel Consumption and IRI  
The unloaded and loaded status of the truck is also an important factor affecting fuel consumption, so the study investigated the relationship between fuel consumption and IRI under unloaded and loaded separately. Restricted by the mine's prohibition of truck modification, the fuel consumption data obtained is the daily fuel consumption data of each truck in the dispatch system. However, as the Haerwusu open pit coal mine is a near-horizontal deposit, the main transport section in the cycle operation of trucks transporting rocks for loading, haulage, and dumping is the non-working face road, while the ramp accounts for a smaller proportion of the total transport.  
Moreover, the truck dispatch system of the Haerwusu open-pit mine adopts a fixed allocation method, and most trucks have relatively fixed daily transport routes. Therefore,  
Figure 11: IRI statistical results.  
fuel is mainly consumed in the non-working face road. For this study, the horizontal part of the non-working face road is selected at a sampling interval of 150 m. And the average IRI of roads at 150 m sampling intervals was used to analyze the linear relationship with daily fuel consumption.  
Based on the transport records in the truck dispatch system of the Haerwusu open pit coal mine, the transport routes of the trucks were obtained, and the specific trajectories of the trucks were determined in the DEM based on the positions recorded by GPS in order to calculate the IRI. The total number of data points is 188. The fitted relationship between the fuel consumption of the loaded trucks and the IRI is shown in Figure 12.  
As this subsection focuses on relatively smooth roads, the magnitude and distribution of IRI values are relatively small. Overall, a more obvious linear relationship and clustering phenomenon is shown.  
With Q representing fuel consumption, the fitting equation is as follows:  
\[Q=14.24 RI+1698.56 \tag{3}\]  
The results of the regression model show [PERSON]'s coefficient of 0.973, which indicates that the regression model has good fitting ability and can better reflect the relationship between the smoothness index of the truck trajectory line and the fuel consumption of loaded trucks. For unloaded trucks, as shown in Figure 13, the total number of data points is 227, and the IRI value data are distributed between 12 and 28, and the regression results are shown in Equation (4), with a [PERSON]'s coefficient of 0.945, indicating a good fit.  
\[Q=9.791 RI+1274.36 \tag{4}\]  
Figure 12: Relationship between IRI and fuel consumption for loaded trucks.  
Figure 13: Relationship between IRI and fuel consumption for unloaded trucks.
# A Novel Hybrid Model for the Prediction and Classification of Rolling Bearing Condition
## 4 Experimental Analysis
### Experimental Results and Analysis
#### 4.2.3 Feature Extraction  
Next, the features were extracted from the sensitive IMFs. The power spectral density (PSD) distribution of the original signal are shown in Figure 11, which demonstrates that the sensitive modes are the 2 nd IMF to the 6 th IMF. However, the 1 st is obviously redundant for the downstream feature extraction and classification processing. Then, we extracted the T-F features from the 2 nd IMF to the 6 th IMF to form a new feature set and also normalized the set.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Condition** & **Series** & **RMSE** & **MAPE** & **MAE** \\ \hline \multirow{2}{*}{IF} & Original & \(5.2744\times 10^{-3}\) & \(3.0023\times 10^{-1}\) & \(4.1815\times 10^{-3}\) \\  & Subseries & \(3.6904\times 10^{-4}\) & \(2.2367\times 10^{-2}\) & \(2.6025\times 10^{-4}\) \\ \hline \multirow{2}{*}{NS} & Original & \(5.3683\times 10^{-3}\) & \(3.1319\times 10^{-1}\) & \(4.2680\times 10^{-3}\) \\  & Subseries & \(4.6082\times 10^{-4}\) & \(3.0578\times 10^{-2}\) & \(2.9941\times 10^{-4}\) \\ \hline \multirow{2}{*}{BF} & Original & \(5.2354\times 10^{-3}\) & \(3.0846\times 10^{-1}\) & \(4.1308\times 10^{-3}\) \\  & Subseries & \(8.1888\times 10^{-4}\) & \(4.3402\times 10^{-2}\) & \(2.6100\times 10^{-4}\) \\ \hline \multirow{2}{*}{OF} & Original & \(5.1080\times 10^{-3}\) & \(3.0283\times 10^{-1}\) & \(4.0719\times 10^{-3}\) \\  & Subseries & \(2.4100\times 10^{-3}\) & \(2.9969\times 10^{-2}\) & \(1.5100\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of performance criteria of ARMA-ANN model for the original series and subseries.  
Figure 11: The energy ratio distribution of subseries for IF data by VMD decomposition.  
Figure 10: Prediction result of ARMA-ANN model for IF original time series.
# UAV Application for Typhoon Damage Assessment in Construction Sites
## 2 Construction Sites and Natural Disasters
In order to address the current shortcoming, this study carried out the safety inspection of construction sites before the natural disaster, combining the advantages of UAV safety inspection and the characteristics of disaster factors, building vulnerability, and exposure in risk management, in addition, to improving the ability of safety inspection and providing support for the reduction in accidents related to natural disasters on construction sites through the establishment of UAV visual operation platform and post-image technology.