esa-sceva/satcom-chunk-collection huggingface dataset:
# An Intelligent TCP Congestion Control Method Based on Deep Q Network
## 5 Results and Discussion  
### Experimental Environment  
A high-performance server is used to measure and compare the performance of TCP-DQN  
1. CPU: Intel (R) Xeon (R) silver 4110 CPU @ 2.10 GHz processor;
2. Memory: 32 g DDR4 memory;
3. GPU: NVIDIA Titan V;
4. Operating system: Red Hat 4.8.5-28.  
We implemented TCP-DQN method and tested its performance in virtual data space. The TCP-DQN is compared with the representative TCP congestion control algorithms such as cubic, NewReno, and HighSpeed. NewReno is a classic congestion control algorithm; HighSpeed is a congestion control algorithm designed for the high-speed network environment; cubic is the default TCP congestion control algorithm since Linux kernel version 2.6.19. According to our real test, the average network bandwidth of virtual data space is close to 67.8 Mbps, the delay is close to 56 ms, and the packet loss rate is close to 0.01%. The time step of TCP-DQN is set to 0.1 s. After training with 60,000 steps, the reward obtained has become stable, indicating that the algorithm has converged.  
As shown in Figure 3, we use a simple Muti-Layer Perception to build our DQN network architecture. There are two hidden layers and each layer has 64 nodes. The input layer has seven nodes, which are used to observe the current state. The output layer has 101 nodes, each represents the Q value of the corresponding action. The first node represents the action \(-50\), the second node represents the action \(-49\),..., and the last node represents the action \(50\). If exploration is not triggered in the current time-step, then the action with max Q value will be selected. For example, if the first output node has the max Q value, then the action \(-50\) is selected. According to Equation (5), the cwnd should be reduced by 50 segments.  
We set the core parameters of DQN as follows: gamma = 0.99, learning_rate = 0.0005, buffer_size = 50000, exploration_fraction = 0.1, exploration_final_eps = 0.02, exploration_in _ital_eps = 1.0. The exploration_fraction indicates the fraction of entire training period over which the exploration rate is annealed; exploration_final_eps indicates the final value of random action probability; exploration_initial_eps indicates the initial value of random action probability. At the first time-step, the agent will definitely explore the environment because the initial value of random action probability is set to 1. Then, the exploration rate will be annealed. When the exploration terminates (the exploration fraction reaches up to 10%), the exploration rate is annealed to 0.02.
# Heat Transfer Correlations for Star-Shaped Fins
## 1 Introduction
### Influence of Outside Tube Diameter
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Boundary Condition**} \\ \hline Air temperature at the inlet & \(T_{\text{in}}\) & K & 288 \\ Air velocity at the inlet & \(u_{\text{in}}\) & m/s & 1, 24 and 5 \\ Wall temperature of the internal tube & \(T_{\text{w}}\) & K & 353 \\ Gauge air pressure at the outlet of HE & \(p_{\text{out}}\) & Pa & 0 \\ Wall condition (ariside) & & & Hydraulically smooth wall \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Boundary conditions of numerical analysis. Figure 1: Tube with star-shaped fins. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Sizes of Fins and Tubes**} \\ \hline Material & \multicolumn{3}{c}{ stainless steel} \\ \hline \multirow{4}{*}{Tube data} & \(d_{0}\) & mm & 20 \\  & - & - & staggered \\  & \(\mathfrak{s}_{\text{t}}\) & mm & 50 \\  & \(\mathfrak{s}_{\text{l}}\) & mm & 40 \\  & \(N_{\text{l}}\) & - & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The tube and star-shaped fin data. The computational mesh was created in ANSYS Fluent meshing software by using a hybrid mesh where most of the volume was a structured mesh and the smaller part around the fins was an unstructured mesh. The fin and tube surface, on which convective heat transfer was performed, was covered with eight boundary layers. The thickness of the first layer was 0.025 mm to keep the dimensionless wall distance \(y^{+}<1\). The study of mesh independence was carried out for the basic variants 5, 6, and 7, focusing on \(Nu\) and \(Eu\), with mesh sizes of 5.8, 8.2, 10.8, and 15 million finite volumes. Results are presented in Table 6.  
For this study, a mesh of 15 million finite volumes was chosen to obtain the most accurate results. The turbulence model \(k\)-\(\omega\) SST was used, which has been described in detail in [31]. For other variants, the mesh quality elements were checked, as shown in Table 7. The numerical analysis was performed using the same criteria applied in [31, 32, 33]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Finite Volumes (millions)** & **5.8** & **8.2** & **10.8** & **15.0** \\ \hline \(Nu\) & 54.7 & 51.4 & 51.3 & 51.4 \\ \(Eu\) & 0.34 & 0.38 & 0.41 & 0.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The mesh independence test. Figure 3: The mesh detail. Figure 2: Geometric model of the heat exchanger.
## 5 Parametric Sensitivity Analysis
### Active Earth Pressure Distribution  
We assumed that the wall-soil \(\delta=\varphi_{0}/2\), and that the intermediate principal stress weight coefficient \(b=0\), 0.5, 1. According to the Specification of Seismic Design for Highway Engineering (JTG B02-2013) [39], the following parameter values were set: horizontal seismic coefficient \(k_{h}=0\), 0.2 (level VIII), 0.4 (level IX); vertical seismic coefficient \(k_{\varphi}=0,\ 0.5k_{h},\ k_{h}\). The horizontal seismic active earth pressure distributed along the height of the retaining structure is shown in Figure 6. Horizontal active earth pressure and depth are dimensionless.  
Figure 5(a) shows the horizontal active earth pressure distribution under different intermediate principal stress weight coefficients. Where \(k_{\varphi}=0.5k_{h}\), it can be seen that as the \(b\) values increased, the horizontal active earth pressure gradually decreased. It is worth noting that in the working condition without earthquake action (i.e., \(k_{h}=0,\ k_{\varphi}=0\)), the intermediate principal stress was not considered. The obvious difference indicates that the seismic action significantly influenced the earth pressure compared with other working conditions. In addition, it can be seen from Figure 5(b) that when \(b=0\), the horizontal active earth pressure gradually increased with the horizontal seismic coefficient, and the trend of non-linearity increased sharply. The main gaps were concentrated in the middle and lower parts of the retaining structure. On the contrary, as the vertical seismic coefficient increased, the horizontal active earth pressure demonstrated a clear and gradual decreasing trend. Under several working conditions, the distribution of active earth pressure presented the same trend, and the decreasing points were all near \(0.8H\).
## Appendix A  
For the model of a single-mode cylindrical fiber optic element described in Section 2.2.2, the monochromatic light flux from the source is incident on the cross section of the sample in the direction of its axis, taken as the coordinate axis OZ. Then, the difference in the path of the reflected and refracted rays, in the adopted geometric model \(\delta r=l_{2}\cos\Psi-l_{1}\), calculated from expressions \(z_{1}=\frac{\mathrm{d}}{2\mathrm{i}\Theta\Psi}\), \(z_{2}=\frac{\mathrm{d}}{2\mathrm{i}\Theta\Psi}\), taking into account the equalities \(z_{1}=l_{1}\cos\theta\), \(z_{2}=l_{2}\cos\gamma\) and, can be represented in the form \(\delta r=\frac{\mathrm{d}}{2}\left(\frac{1}{\sin\gamma}\cos\Psi-\frac{1}{ \sin\theta}\right)\). Further, an approximate expression for the phase difference of these rays \(\phi-\phi_{0}\approx\frac{\omega}{c}\times\delta r\), gives the expression \(\phi-\phi_{0}\approx\frac{\omega}{c}\times\frac{\mathrm{d}}{2\sin\theta}(n \cos\Psi-1)\), which is similar, both in the structure of the formula and in its content to the expression previously constructed, in Section 2.2.2, \(\phi-\phi_{0}=\frac{\omega}{c}(n\cos\Psi-1)\left(\overset{\rightarrow}{\tau} _{0}\overset{\rightarrow}{r}\right)\). Since vector \(\overset{\rightarrow}{r}\) oriented, as defined in Section 2.2.2, along the direction of the incident light ray (in the direction in which the unit vector is selected, \(\overset{\rightarrow}{\tau}_{0}\)), obviously characteristic of the model under study is the equality \(\frac{\mathrm{d}}{2\sin\theta}=r=l_{1}\). This result can be interpreted as another criterion for the reliability of the formula \(\phi-\phi_{0}=\frac{\omega}{c}(n\cos\Psi-1)\left(\overset{\rightarrow}{\tau} _{0}\overset{\rightarrow}{r}\right)\).  
As a special case, consider the model of a single-mode cylindrical fiber optic element, the refractive index of which at wavelengths in the visible optical range satisfies the condition \(n\to 1\). In this case, according to the geometric model described in Section 2.2.2, \(\delta r\approx\mathrm{b}\mathrm{t}\mathrm{g}\gamma\).  
According to the geometric model we accept, we determine the difference in the path of the refracted and incident rays \(\delta r\approx\frac{\mathrm{b}\sin\theta}{\sqrt{n^{2}-\sin^{2}\theta}}\), where b--parameter added to (Section 2.2.2). Then, the phase difference between the incident and refracted wave takes the following form:  
\[\phi-\phi_{0}=\frac{\omega}{c}\frac{\mathrm{b}\sin\theta}{\sqrt{n^{2}-\sin^{2} \theta}} \tag{1}\]  
based exp. (1) and exp. \(\phi-\phi_{0}=\frac{\omega}{c}(n\cos\Psi-1)r\), and introducing the notation \(u=\sqrt{n^{2}-\sin^{2}\theta}\), we construct the characteristic equation \(u^{2}-u\cos\theta-\frac{\mathrm{b}\mathrm{t}\mathrm{g}\Theta}{r}=0\), which allows us to calculate the refractive index of the optical fiber  
\[u=\frac{\cos\theta+\sqrt{\cos^{2}\theta+\frac{\mathrm{b}\mathrm{t}\mathrm{g} \Theta}{r}}}{2}. \tag{2}\]
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Link Faults  
This section describes the performance of all the algorithms in equal fault case scenarios, considering different fault types (pole to pole and pole to ground), varying the fault location along the link and the fault resistance. All the faults occur at t = 6.5 s. The faults are located at the beginning, midpoint and end of links L24 and L34, which are 12 and 50 km long, respectively. For link L24, the measurements are taken at relays R24 and R42, and for link L34, at relays R34 and R43. The predefined threshold values are equal for the bus and link faults, except for the LIVRD algorithm.
## 5 Wind Fatigue Tests
### Comparison between Numerical Analysis and Experimental Results  
The corresponding numerical analysis is carried out for the two working conditions in Table 4 and compared with the test results, as shown in Figure 14. The trend of the numerical analysis results is consistent with the test results. The value is slightly smaller than the test results because the initial defects in the production and installation of the roof panels were not considered in the establishment of the numerical model.  
Figure 14: Comparison between numerical analysis and test results of roof damage: (**a**) influence of wind pressure; (**b**) influence of purlin distance.  
The following formula is used to measure the differences between the two methods:  
\[\varepsilon=1-\frac{\textit{Simulated damage value}}{\textit{Test damage value}}. \tag{7}\]  
When the design wind pressures were 3.0, 3.6, and 4.5 kPa in the group I working conditions, the corresponding fatigue damage obtained from the numerical analysis were 0.54, 0.81, and 1.31, as shown in Figure 14a. According to Table 4, the ranges of \(\varepsilon\) values under these three loads were 19-46%, 19-38%, and \(-\)31-34.5%, respectively. The last group of numbers are the positive and negative deviations, which indicate the effectiveness of the numerical simulation method by comparing the simulated values and the range of the test values. Most deviation values are positive, and the maximum value is 46%. This notion means that up to 46% of damage could be underestimated by the numerical analysis. According to Table 3, the damage value of NELCS loaded in this study is much more conservative than that directly loaded by the actual wind pressure-time history. This study considers that this 46% deviation is acceptable.  
The influence of purlin distance on roof damage value was investigated when the design value of wind pressure was 4.5 kPa. When the roof purlin distance was shortened from 1000 mm to 800 and 600 mm, the fatigue damage could be reduced from 1.31 to 0.61 and 0.15 by numerical simulation analysis, respectively, as shown in Figure 14b. The damage of the roof panels can be effectively reduced, and the wind resistance of the whole roof system can be improved by shortening the purlin distance.
# Abrasion-Resistant and Temperature Control of
## 2 Anti-Scouring and Abrasion-Resistant Performance Test on Concrete
### Test Results and Discussions
#### 2.3.3 PVA Fiber Effect
In addition, as shown in Figure 7c, the anti-scouring and abrasion-resistant strength of concrete also slightly increased with increasing mixture of the PVA fiber. At the test age of 90 day, when the mixtures of PVA fiber were 0.8%, 0.9% and 1.0%, the anti-scouring and abrasion-resistant strengths of the concrete specimen were 11.09 h(g/cm\({}^{2}\))\({}^{-1}\), 11.48 h(g/cm\({}^{2}\))\({}^{-1}\) and 11.62 h(g/cm\({}^{2}\))\({}^{-1}\), respectively. Compared to the dosage of 0.8%, the anti-scouring and abrasion-resistant strengths of the concrete specimen at the dosages of 0.9% and 1.0% increased by 3.52% and 4.45%, respectively. This was because after doping a suitable amount of PVA fiber in concrete, when cement solidified, PVA fiber would present a 3D irregular distribution in cement paste, form a multi-directional supporting system, effectively improve the anti-scouring and abrasion-resistant performance of the concrete member and reduce the micro-cracks induced by the shrinkage of cement matrix. Under water flow scouring and abrasion by other forces, the concrete component would always fail for a crack, and the larger the crack, the easier the failure. In this case, doping PVA fiber could effectively reduce the formation of micro-cracks and other weak links in the concrete member and thus improve the anti-scouring and abrasion-resistant performance of concrete.
## 5 Challenges and Future Research Trends
### Attention Mechanism in Noise Processing  
In prior research, noise processing in channel estimation often relies on image super-resolution algorithms or denoising neural networks. However, these approaches may destroy the actual channel information [99]. The attention mechanism [100] is a significant deep learning technique different from traditional neural networks, which treat all input data equally important. It assigns varying weights to different parts of the data, focusing more precisely on the most critical information. Therefore, adding the attention mechanism to channel estimation could be a valuable direction for future research. By doing so, the deep learning model would focus more on the effective information in the received signal and reduce noise impact by adjusting feature weights, thereby achieving more accurate channel estimation.
## 3 Materials and Methods  
This study presents the design of a global error model for the autonomous global positioning method. The main procedure of the model construction process involves the computation of partial derivatives of the system of equations composed of Equations (1) and (2). This system of equations represents an implicit function system that includes two independent variables: the solar azimuth angle (\(A_{\mathrm{s}}\)) and the altitude angle (\(l_{\mathrm{s}}\)). The two dependent variables are the longitude (\(\eta\)) and the latitude (\(\psi\)). This research aims to analyze the variations in the dependent variables caused by \(\Delta A_{\mathrm{s}}\) and \(\Delta l_{\mathrm{s}}\) at different times and geographical locations. In addition, this study aims to evaluate actual geographical deviation (\(\Delta L\)) associated with the changes in the two dependent variables. Consequently, the designed  
Figure 5: The global distribution of the number of solutions to the system of equations: (**a**) the solar declination is zero; (**b**) the solar declination is \(5^{\circ}\); (**c**) the solar declination is \(10^{\circ}\); (**d**) the solar declination is \(23^{\circ}27^{\prime}\).  
global error model enables a rapid estimation of the \(\Delta L\) value based on the changes in the independent variables in practical applications.  
First, the expressions for the four partial derivatives of the implicit function equation system are derived, and then the expression for the global positioning distance error (\(\Delta L\)) is obtained.  
Next, the conditions under which the error model is valid are studied, assuming that the implicit function equation system is differentiable during the derivation process of the positioning error expression. When this system is differentiable, the total differentials denote the best linear approximation of the total increments for small changes in \(A_{\text{s}}\) and \(h_{\text{s}}\), that is, \(\Delta\eta\approx\text{d}\eta\) and \(\Delta\psi\approx\text{d}\psi\). The findings of this study indicate that the error model is valid for specific geographical locations. However, for some geographical locations, the proposed model is demonstrated to be invalid.
# A Novel FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages
###### Abstract  
In the field of navigation and positioning, the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system is known for providing stable and high-precision navigation services for vehicles. However, in extreme scenarios where GNSS navigation data are completely interrupted, the positioning accuracy of these integrated systems declines sharply. While there has been considerable research into using neural networks to replace the GNSS signal output during such interruptions, these approaches often lack targeted modeling of sensor information, resulting in poor navigation stability. In this study, we propose an integrated navigation system assisted by a novel neural network: an inverted-Transformer (iTransformer) and the application of a frequency-enhanced channel attention mechanism (FECAM) to enhance its performance, called an INS/FECAM-iTransformer integrated navigation system. The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. In the experimental part, a public dataset and a private dataset are used for testing. The best experimental results show that compared to a pure INS inertial navigation system, the position error of the INS/FECAM-iTransformer integrated navigation system reduces by up to 99.9%. Compared to the INS/LSTM (long short-term memory) and INS/GRU (gated recurrent unit) integrated navigation systems, the position error of the proposed method decreases by up to 82.4% and 78.2%, respectively. The proposed approach offers significantly higher navigation accuracy and stability.  
FECAM-iTransformer; GNSS outage; frequency domains; INS/GNSS integrated navigation systems +
Footnote †: journal: Applied  
Academic Editor [PERSON]  
FECAM-iTransformer Algorithm for Assisting INS/GNSS Navigation System during GNSS Outages. _Appl. Sci._**2024**, _14_, _8753_. [[https://doi.org/10.3390/app14198753](https://doi.org/10.3390/app14198753)]([https://doi.org/10.3390/app14198753](https://doi.org/10.3390/app14198753))
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Quantization-Based Real-Time Super Resolution  
We are currently observing a rapid expansion and pervasive implementation of deep neural networks across a multitude of disciplines. However, these DNN models, particularly CNNs, typically comprise an enormous number of parameters and demand high computational resources, tethering them closely to high-end hardware. This poses a significant hindrance to their broader applications, such as deployment on mobile devices.
## 3 Results
### Simulation
#### 3.1.2 Bidirectional Transmission  
In order to explore the effect of transmission distance on the laser detection capability, a complex underwater simulation environment was constructed. Relevant study has shown that the minimum particle size in the surface of the South China Sea is about 3 \(\upmu\)m ([PERSON] et al.) [41], so we chose calcium carbonate powder with a relative refractive index of 1.59 and the size of 3 \(\upmu\)m as scattering particles. We controlled the detection distance to be 1 m in our simulation and changed the attenuation coefficient of the medium by adjusting the quantity of the added particles, that is, adjusting the attenuation lengths (AL). In order to describe the corresponding attenuation coefficient and scattering angle distribution during transmission more realistically, the properties of the calcium carbonate powder and the attenuation efficiency factor \(Q_{e}\) and scattering efficiency factor \(Q_{s}\) represented by Equation (4) were combined to obtain the corresponding attenuation coefficient through the calculation of Equation (7).  
In the underwater scattering medium set as above, the IMC method was executed to simulate the underwater detection of artificial targets: \"Diver\" and \"Double Slit\". They were binary images with two regions, the image part (A) and the background part (B). The normal of the microfacet follows a Gaussian distribution and can be obtained by the Marsaglia-Bray algorithm, where the expectation is 0, and the standard deviation is related to the surface roughness. The approximation of the silver plated part A is the specular reflection \(f_{s}\) with roughness of 0.001 and part B is based on the diffuse reflection \(f_{d}\)[36]. Since we adopted the coaxial structure of transceiver, and the coordinate transformation of photons in the scattering process was always kept in the global coordinate system, the image coordinate system and pixel coordinate system of the detector were also established under it, so the imaging results of the ideal optical system in the object space could be directly described by the position of the photon. Targets with side length of 5 cm \(\times\) 5 cm were placed in the water tank 1 m away from the incident window, and the image resolution was 200 \(\times\) 200. To ensure they could be illuminated, the laser properties were confirmed by forward transmission simulation of IMC, where the divergence angle was 200 mrad and the initial spot diameter was 1 mm.  
Figure 2: The spatiotemporal broadening of laser beams transmitted in Jerlov II type water.