esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions and Conclusions  
### Error Analysis of Vertical Displacement  
The influence of the control parameters \(\varepsilon_{1}\) and \(h_{1}\) on the error of vertical displacement after \(n\)= 5 iterations with \(\alpha\)= 2.0 is then discussed.  
After five iterations, the improved homotopy analysis method is adopted to solve the Equations (3.21) and (3.22) to yield the solution to the original Equations (2.3b) and (2.4b) as below  
Figure 3: Horizontal displacement at free end.  
\[\begin{array}{l}V_{b}=0.33333\alpha+0.096738h_{1}\alpha^{3}+0.10197h_{1}^{2} \alpha^{3}\\ +0.052108h_{1}^{3}\alpha^{3}+0.011580h_{1}^{4}\alpha^{3}+0.028571\varepsilon_{1} h_{1}\alpha^{3}\\ +0.085714\varepsilon_{1}h_{1}^{2}\alpha^{3}+0.095238\varepsilon_{1}h_{1}^{3} \alpha^{3}+0.047619\varepsilon_{1}h_{1}^{4}\alpha^{3}\\ +9.5238\times 10^{-3}\varepsilon_{1}h_{1}^{5}\alpha^{3}-6.1695\times 10^{-3}h_{1} \alpha^{5}-3.3120\times 10^{-3}h_{1}^{2}\alpha^{5}\\ -8.2799\times 10^{-4}h_{1}^{3}\alpha^{5}-5.8320\times 10^{-3}\varepsilon_{1}h_{1} \alpha^{5}-8.1553\times 10^{-3}\varepsilon_{1}h_{1}^{2}\alpha^{5}\\ -4.5967\times 10^{-3}\varepsilon_{1}h_{1}^{3}\alpha^{5}-1.0215\times 10^{-3} \varepsilon_{1}h_{1}^{4}\alpha^{5}+2.5955\times 10^{-4}h_{1}\alpha^{7}\\ +7.4156\times 10^{-5}h_{1}^{2}\alpha^{7}+6.8257\times 10^{-4}\varepsilon_{1}h_{1} \alpha^{7}+4.3913\times 10^{-4}\varepsilon_{1}h_{1}^{2}\alpha^{7}\\ +1.0978\times 10^{-4}\varepsilon_{1}h_{1}^{3}\alpha^{7}-7.4415\times 10^{-6} \varepsilon_{1}h_{1}\alpha^{9}-4.2843\times 10^{-5}\varepsilon_{1}h_{1} \alpha^{9}\\ -1.2241\times 10^{-5}\varepsilon_{1}h_{1}^{2}\alpha^{9}+1.4103\times 10^{-6} \varepsilon_{1}h_{1}\alpha^{11}\end{array} \tag{5.1}\]  
When \(\alpha\)= 2.0, Equation (5.1) becomes  
\[\begin{array}{l}V_{b}=0.66667+0.60589h_{1}+0.71928h_{1}^{2}+0.39037h_{1}^{3} \\ +0.092637h_{1}^{4}+0.11027\varepsilon_{1}h_{1}+0.47469\varepsilon_{1}h_{1}^{2} \\ +0.62886\varepsilon_{1}h_{1}^{3}+0.34826\varepsilon_{1}h_{1}^{4}+0.076190 \varepsilon_{1}h_{1}^{5}\end{array} \tag{5.2}\]  
When \(\alpha\)= 2.0, the exact solution is \(V_{b}\)= 0.49346. When the iteration is five times, the relative error between the result by IHAM and the exact solution can be expressed as  
\[\Delta_{V}=\frac{\left|V_{b}^{[n=5]}-V_{b}\right|}{V_{b}}\times 100\% \tag{5.3}\]  
When \(h_{1}=-0.  
According to Equations (5.3) and (5.5), the relative error curve of control parameter \(\Delta_{V}-h_{1}\) is a quintic power function. When the relative error is less than \(\Delta_{V}\leq 1\%\), the convergence interval is \(R_{h_{1}}=[-1,-0.58]\), which is the effective region of \(h_{1}\).  
Figure 5 gives the relative errors curve \(\Delta_{V}-h_{1}\) of Equation (2.3b) when \(\alpha\!=\!2.0\), \(\varepsilon_{1}=24(\pi-2)/\pi^{3}\). From Figure 5, when \(h_{1}=-0.68\), the relative error is the minimum.
## 3 Results and Discussion
### Intensification of Single-Phase Filtration  
In their work, ref. [42] distinguishes three forms of rock deterioration in the near wellbore zone: physical blocking of the pore space (damage to permeability), change in wettability, and increase in viscosity (formation of emulsion). Given that in our experiment there was no fluid replacement but single-phase filtration was performed at a constant temperature and relatively constant pressure, a change in the wettability of the sample during the experiment is unlikely. Due to the filtration of single-phase oil during the experiment, the occurrence of an emulsion in the sample itself is also unlikely. In this connection, the physical blocking of pore channels by particles of asphaltene and paraffin is considered the main form of permeability deterioration. Despite the fact that some papers indicate that the increase in permeability is associated with the development of internal cracks and deformations in the rock [16], it seems to us that such an effect on consolidated rock samples is unlikely. Given that the sample in the rig is in a compressed (by cuff) state, it is possible that new cracks will not be able to open. In this study, the nonlinear load is not characterized by high amplitudes; therefore, deformation of consolidated rock samples is hardly possible, and the main mechanism for restoring permeability is unblocking of the pore space.  
Figure 8: Displaced residual oil from the sample during water injection: (**a**) colored distilled water and oil droplets at the outlet of the sample; (**b**) a solvent (gasoline) before washing the rig parts; (**c**) a solvent after washing the rig parts.  
Figure 9: Graph of the water permeability of a rock sample increasing under nonlinear loading in the presence of residual oil inside the rock sample. The six stages of nonlinear load activation are highlighted in yellow. After activation stages, the permeability tends to decrease, and it stabilizes after the final stage of activation, corresponding to the removal of the maximum amount of trapped residual oil able to be moved by the effect of nonlinear loads.
# Estimation of Optimal Speed Limits for Urban Roads Using Traffic Information Big Data
## 3 Data Collection and Model Equation Development
### Research Methodology
#### 3.1.5 Model Test and Utilization  
When calculating the optimal speed limits for many accident points according to the completed model, if the difference between the actual 85 th percentile speed (V85) and the speed limit (RSL) is large in a section where many accidents occur, the speed limit can be considered unsafe.
# A Sustainable Composite Cementitious Material Manufactured by Phosphogypsum Waste
## 3 Results and Discussion  
### Reaction Degree of Phosphogypsum  
The reaction degree of phosphogypsum in composite cementitious material paste comprising 10% to 90% phosphogypsum is shown in Figure 1. As can be seen, a low reaction degree of phosphogypsum was demonstrated. Clearly, the reaction degree of phosphogypsum decreases dramatically as phosphogypsum incorporation is increased, which is equivalent to linear reduction. For example, the reaction degree of phosphogypsum at 3 days is 14.36%, 12.67%, 10.69%, 8.36% and 7.08% with 10%, 30%, 50%, 70% and 90% phosphogypsum, respectively. Based on this, it is not difficult to conclude that the reaction degree of phosphogypsum in composite cementitious material is greatly influenced by the content of phosphogypsum when the other parameters in the mixing proportion remained unchanged. The fundamental cause of this occurrence is that the inclusion of phosphogypsum reduces the concentration of cement, which reduces calcium hydroxide excitation, further reducing phosphogypsum activity. As a result, the lower the reaction degree, the higher the phosphogypsum incorporation. The curing time was extended from 3 to 28 days, and the phosphogypsum reaction degree gradually rose from 14.36% to 24.61% Although phosphogypsum incorporation rose from 10% to 90%, the change rule of the phosphogypsum reaction degree with increased curing time remained consistent. As a result, the phosphogypsum reaction degree in all combinations continued to rise with increased curing time. Even though the curing time is extended, the change rule remained: the higher the incorporation, the lower the activity. This phenomenon is developed around composite cementitious material as a result of a significant number of phosphogypsum calcium ions being dissolved, and a large amount of calcium hydroxide being produced, which increased the phosphogypsum reaction degree. In comparison with ground granulated blast-furnace slag, the reaction degree of phosphogypsum in all combinations is low. According to earlier research [3; 4] and experimental findings in this study, it was observed that the reaction degree of industrial waste (such as ground granulated blast-furnace slag, fly ash, and lithium slag) were connected to the mineral composition and cement reaction process.
## 3 Prototype and Design of the Device  
In this study, we used a self-designed and built proprietary device. It is a portable, independent structure that allows the identification of symbols of musical notation placed on the staff. It should also allow the user to generate a pdf or midi file of the recognized music notation. The prototype of the device is shown in Figure 1.  
A low-budget OV7670 camera module was used as the scanner for image acquisition, and it was equipped with a CMOS sensor with a size of 0.3 Mpx and a resolution of 640 \(\times\) 480 pixels. To meet the functional requirements of the device, the Basy3 educational set by Digilent was used. The set includes analog and digital I/O ports, VGA (Video Graphics Array) output, USB port, built-in JTAG (Joint Test Action Group) programmer, buttons, switches, 16 luminescent diodes and fast memory. The key element of the set, responsible for communication between components and performing mathematical operations, is the programmable digital Artix-7 FPGA circuit [40].  
The image obtained from the camera in accordance with the RGB565 format was downloaded in two clock cycles to two variables with a 16-bit data type. In order to display it on the monitor via 12-bit VGA output, the image has been converted to RGB444 format.  
Black objects on a white background were to be identified; the color image was replaced with a grayscale image. The marking of objects appearing in the image was performed by thresholding the pixel values relative to the value of the previous image-reading iteration. The reading was to be interrupted when the entire object appeared in the lens. The identified image should have a resolution of 90 \(\times\) 240 pixels. In order to obtain the best quality samples, the acquired grayscale image was filtered and then binarized, thus obtaining a black and white image. The samples prepared in this way are identified by the neural network. The response of the network is the numerical equivalent of the recognized image on the basis of which the output image or sound is generated.  
The most common method of using a neural network on a field-programmable gate array system, recommended in the literature, was to load an array of weights or the entire learned neural network, rather than teaching it from the level of a digital system. The RBF neural network was used to recognize musical notation symbols. The preparation of learning sets, the learning process and tests were carried out in the Matlab environment  
Figure 1: The original device prototype [39].  
with the use of Netlab libraries. The code in the VHDL hardware description language of the neural network model was generated using the \"HDL Coder\" tool available in the Matlab environment. The methodology of using the developed hardware layer in the musical notation classification task can be presented in the flowchart depicted in Figure 2.  
Section 4 is intended to introduce the issues of reading notes as an image recognition task and describe possible solutions in this regard.
# Study on the Mechanism of the Variable-Speed Rotor Affecting Rotor Aerodynamic Performance
## 5 Conclusions  
The current work applied a dimensionless parameter analysis to analyze and reveal the mechanisms of a variable-speed rotor affecting the rotor aerodynamic performance under the conditions of various flight speeds and take-off weights. The analysis concluded the following:  
1. During hover and slow flight modes, the variable-speed rotor reduces the required power by reducing the blade dynamic pressure, whereas the non-uniform thrust distribution caused by a lower rotor speed suppresses improvements in aerodynamic performance.
2. During cruise and fast forward flight modes, the blade dynamic pressure and advancing blade tip compression loss reductions both improve the rotor aerodynamic performance.
3. During forward flight, the lower the rotor loading is, the greater the effect on reducing required power is. This effect is due to a reduction in the advancing blade tip compression loss in variable-speed rotors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Take-Off Weight** & **Advance Ratio** & **Blade Loading** & **Rotor Speed Ratio** & **Flight Speed** \\ \hline
1600 kg & 0.3 & 0.125 & 0.69 & 88 m/s \\
1900 kg & 0.3 & 0.125 & 0.75 & 96 m/s \\
2200 kg & 0.3 & 0.125 & 0.82 & 104 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 4: Variable-speed rotor high loading case.  
Figure 14: Drag coefficient, \(C_{D}\), with rotor speed for different rotor loadings.  
**Author Contributions:** [PERSON], [PERSON], [PERSON] and [PERSON] conceived the research and contributed to the literature search; [PERSON] conducted the simulation and analyzed the results; [PERSON] and [PERSON] wrote and revised the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Abbreviations**  
AoA: attack of angle  
CFD: computational fluid dynamics  
DPL: direct power loss  
HP: hub plane  
TPP: tip-path plane
# Electrospinning of Chitosan for Antibacterial Applications--Current Trends
## 5 Electrospinning of Chitosan
### Viscosity  
The solution viscosity of chitosan is the main element that hinders continuous fiber formation and is normally affected by chitosan's molecular weight, concentration, and solvent characteristics. Rigid D-glucosamine repeating units in chitosan are responsible for the formation of inter- or intramolecular hydrogen bonds and the increase in crystallinity. This leads to low solubility in pure water as well as in other organic solvents. A high concentration of CS leads to high viscosity, because due to repeating polar groups on the polymer, the increase in hydrogen bonding is affected. The protonation of primary amines in low pH using an acetic acid solution in water improves the water solubility of chitosan [92]. This is because in aqueous acidic conditions intrachain hydrogen bonding is prevented by the electrostatic repulsive interactions between positive ammonium groups, while the enhanced creation of interchain hydrogen bonds with water molecules improves the chitosan solubility. Solubilizing the polymer in 90% acetic acid is one way to electrospun pure chitosan at a 7 wt% concentration, as it reduces the viscosity by slowing down the coagulation rate [97]. If the viscosity is too high, the solution will become excessively viscous and fiber production will be hindered. In this case, the electrical voltage will not be sufficient to overcome the viscosity of the solution. If the viscosity is too low, the production of nanofibers will either not be possible, because polymer chains are not intertwined enough, or instead polymer beads will often be formed [20; 104].
## 5 Experiments and Analysis
### Evaluation Metrics
#### 5.3.4 Transfer Learning Validation Experiment  
To verify the influence of the training method of transfer learning on the classification effect, the deformable residual network designed in Section 4.2 was used to train and classify the model on SAS images directly, which is denoted as ResNet_No, for comparison with the method trained using transfer learning in this study. The classification accuracy obtained by the two methods is shown in Table 5, and it can be seen that training the model through transfer learning under the condition of small samples was beneficial for improving the classification accuracy.
## 2 Theory and Formulation  
### Material Properties  
For a ceramic-metal FGM beam (see Figure 1) having a length \(L\), width \(b\), and thickness \(h\), the geometry of the beam, its material properties, and volume fraction are expressed in Equations (1)-(3). Considering the rule of mixture, the effective material properties \(P\) can be written in terms of volume fraction, as shown below:  
\[P(z)=P_{m}V_{m}+P_{c}V_{c} \tag{1}\]  
where \(P_{m}\), \(P_{c}\), \(V_{m}\), and \(V_{c}\) denote the material properties of the metal and ceramic associated with the given subindices, respectively.  
\[V_{m}+V_{c}=1 \tag{2}\]  
Note that the distribution of the power law for the volume fraction of ceramic material can be expressed as follows:  
\[V_{c}=\left(\frac{z}{h}+\frac{1}{2}\right)^{p} \tag{3}\]  
where \(p\), which is a number greater than zero, takes a value in the range \(0\leq p\leq\infty\) and is called the power law index. In the case of \(p=0\), the material becomes fully ceramic.  
Figure 1: FGM beam.  
Considering the above relationship, the material properties in terms of Young's modulus and mass density, as shown below:  
\[\begin{split} E(z)&=E_{m}+(E_{c}-E_{m})V_{c}\\ \rho(z)&=\rho_{m}+(\rho_{c}-\rho_{m})V_{c}\end{split} \tag{4}\]  
[PERSON]'s ratio is assumed to be constant in our study.
## 3 Results and Discussions
### Influence of External Wind
#### 3.2.2 N Wind  
When the external wind direction was N, all entrances and exits were on the leeward side. Figure 10 displays the characteristics of the smoke concentration distribution over time in the station hall under Case 7. Figure 10a illustrates how the smoke gradually diffused symmetrically to both sides. Figure 10b demonstrates that when smoke first emerged at monitoring points C\({}_{\text{A10}}\) and C\({}_{\text{B10}}\) at about the same time, it did so at monitoring points C\({}_{\text{A20}}\) and C\({}_{\text{B20}}\) just 22 s later. According to the findings, smoke was largely dispersed towards the fire source center and spread constantly to both ends due to the combined effects of external wind and the TSE system.  
As all three entrances were all on the leeward side, the impact of the external wind on the station hall was not immediately apparent. The station hall exhibited a state of negative pressure due to the suction of the TSE system, as seen in Figure 10c. Outdoor air entered the station hall through the three leeward entrances as a result of the difference in pressure between the inside and outside, and the corresponding wind speeds were 0.2 m/s, 0.19 m/s, and 0.29 m/s. Because the smoke grew over time and steadily got worse in the station hall, the TSE system was unable to quickly discharge it.  
Figure 11 displays the influence of the N wind speed change from 1 to 5 m/s. The smoke diffused symmetrically to both sides, with the fire source as the center, as shown in Figure 11a. According to Figure 11b, the location of the CO\({}_{2}\) concentration center and the smoke concentration at A-end and B-end were similar to those in Case 0, indicating that the N wind direction had little impact on the spread of smoke. As seen in Figure 11c, the greatest smoke diffusion distance was 38.3 m, which represented an increase of just 4.76% over Case 0. Therefore, neither the direction nor the speed of the N wind had a major impact on the capability of the TSE system to control smoke.  
Figure 9: Smoke concentration distribution characteristics in the station hall under different speeds of SSE wind. (**a**) Distribution contours, (**b**) longitudinal distribution, (**c**) diffusion distance.  
Figure 10: _Cont._  
Figure 11: _Cont._  
Figure 10: Smoke concentration distribution characteristics with time and pressure distribution contours in the station hall under Case 7. **(a)** Distribution contours, (**b**) variation at the monitoring points, (**c**) pressure distribution contour at 360 s.
## 4 Experiments
### The Training Process  
We trained the TSE-UNet model for a total of 60 epochs, and the curves illustrating the variations in loss and PSNR throughout the entire training procedure are shown in Figure 7. From the figure, it is evident that the convergence process of our method was stable, reaching convergence around approximately 45 epochs.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of** & **PSNR** & **SSIM Loss** & **Latency** & **Frequency** \\ Frames & & & \\
8 & 27.19 & \(9.49\times 10^{-5}\) & 14.44 ms & 69.26 Hz \\
16 & 27.52 & \(8.38\times 10^{-5}\) & 21.22 ms & 47.12 Hz \\
32 & 27.31 & \(9.52\times 10^{-5}\) & 33.64 ms & 29.72 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results on the number of frames. The used dataset is CARLA32-128.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Datasets** & CARLA32-128 & Ruby32-128 \\ \hline
**Latency** & 21.22 ms & 23.85 ms \\ \hline
**Frequency** & 47.12 Hz & 41.92 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average computational latency and frequency of TSE-UNet model on point cloud super-resolution for different datasets.
## 6 Result Discussion
### Result Accomplished
#### 6.3.4 Analysis of Response Time  
Response time is a critical parameter to consider, especially for security systems; higher response time is detrimental for any form of the network where a malicious node will have an opportunity to reframe its attack strategy further. The computation of response time is programmatically carried out as the time between the start point of the first request of the source node to propagate data (to the neighbouring node, or CIN or IoT access point) until the data are successfully forwarded to the destination node. Figure 9 highlights that the proposed system offers a superior performance of highly reduced response time compared to the existing scheme.  
The outcome exhibited in Figure 9 shows that TRM's contribution was more about incorporating the distributed scheme in order to maintain sensitive transactional information in distributed cloud storage in encoded form. However, a deeper insight into TRM shows that it offers faster availability of its service to store the sensitive payoff information, or to generate the information based on the request of RIN or CIN. It eventually means that the confirmation of security facts for any target node during data transmission is almost  
Figure 8: Comparative analysis of cost.  
Figure 9: Comparative analysis of response time.  
instantaneous because of TRM. Apart from this, both modules of GTC and CSC exchange information upon any event, which causes speedy delivery of services related to \(GT\), \(QM\), roles, the path of propagation, and optimally selected CIN. This operation to distribute service availability is quite challenging for any existing game model resulting in increased response time.
## 1 Introduction  
At the recent International Telecommunication Union Radiocommunication (ITU-R) Working Party (WP) 5D meeting, a new recommendation was approved which highlightssix usage scenarios of IMT-2030 [1]. As a typical usage case of the hyper-reliable and low-latency communication scenario, the industrial scenario has received widespread attention again. It extends the Ultra-Reliable and Low-Latency Communication (URLLC) of IMT-2020 and covers specialized use cases that are expected to have more stringent requirements for enhanced reliability, low latency, and precise positioning [2].  
To satisfy the high performance metrics of IMT-2030 for the industrial scenario, communication technology continues to develop from sub-6 GHz toward high-frequency bands [3]. Firstly, sub-6 GHz bands have low propagation loss and a strong coverage capability which can provide extensive coverage for the industrial scenario, which has a larger physical size than an ordinary indoor scenario [4]. Secondly, to meet the typical business requirements of an ultra-wide bandwidth and ultra-low-latency transmission in the industrial scenario, high-frequency communication technologies such as millimeter wave (mmWave) and terahertz (THz) bands are applied [5]. Thirdly, excessive electromagnetic interference will be generated due to the operation of the abundant equipment in the industrial scenario, and the visible light band is regarded as a solution to interference problems due to the significant frequency isolation between the light and radio waves. Furthermore, an abundance of metallic scatterers in the industrial scenario produces specular reflection which results in an increasing intensity of reflection and dense multipath components (MPCs) [6]. Due to the different capabilities for the reflection, diffraction, and scattering of signals in different frequency bands, the impact of the frequency is more pronounced than in traditional indoor hotspot scenarios. Hence, it is imperative to explore the propagation characteristics for all frequency bands within an industrial scenario and elucidate the distinct channel properties across various frequency bands.