esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Joint Resource Allocation and Trajectory Planning under Known Users' Location
### Alternative Algorithm for Solving P2  
Based on the Lagrangian duality method and SCA method, we propose a joint resource allocation and trajectory planning (JRATP) algorithm under known users' location in this subsection. The detailed JRATP algorithm is shown in Algorithm 1.  
```
Input: Initialize \(P_{o,n}^{1}[t]\), \(f_{n}^{1}[t]\), \(f_{u}^{1}[t]\), \(\textbf{q}_{u}^{1}[t]\) with feasible solution. Initialization: Set the radio environment parameters \(B\), \(\beta_{0}\), \(\sigma^{2}\), the operation parameters \(P_{u}\), \(\eta\), \(C_{tt}\), \(C_{u}\), \(\theta_{n}\), \(\theta_{u}\), and the tolerance error \(\varepsilon_{1}\), \(\varepsilon_{2}\) For each iteration \(t\)  Calculate \(P_{o,n}^{o\prime p,t}[t]\), \(f_{n}^{i,opt}[t]\) by Theorem 1 and calculate \(f_{u}^{i,opt}[t]\) according to (17);  Update \(\Delta\mu(i)\), and \(\mu(i+1)\) by subgradient formula; \(P_{o,n}^{i+1}[t]=P_{o,n}^{i,opt}[t]\), \(f_{n}^{i+1}[t]=f_{n}^{i,opt}[t]\), \(f_{u}^{i+1}[t]=f_{u}^{i,opt}[t]\), \(q_{u}^{j}[t]=q_{u}^{i}[t]\). For each iteration \(j\)  Using CVX to solve **P4.1** for given \(P_{o,n}^{opt,t}[t]\), \(f_{n}^{opt,t}[t]\), \(f_{u}^{opt,j}[t]\) and obtain \(\textbf{q}_{u}^{opt,t}[t]\); If \(\sum_{t=1}^{N}\parallel\textbf{q}_{u}^{j+1}[t]-\textbf{q}_{u}^{j}[t]\parallel \leq\varepsilon_{2}\), \(\textbf{q}_{u}^{i}[t]=\textbf{q}_{u}^{j}[t]\), break End if  update \(j=j+1\); End For  If \(||\sum_{t=1}^{T}(R_{c,n}^{i}[t]-R_{c,n}^{i-1}[t])+\sum_{t=1}^{T-1}(R_{o,n}^{i}[t ]-R_{o,n}^{i-1}[t])||\leq\varepsilon_{1}\), break End if  update \(i=i+1\); End For Output \(P_{o,n}^{opt}[t]\), \(f_{n}^{opt}[t]\), \(f_{u}^{opt}[t]\), \(\textbf{q}_{u}^{opt}[t]\).
```  
**Algorithm 1** Joint Resource Allocation and Trajectory Planning Algorithm under known users' location  
The complexity of Algorithm 1 comes from three aspects: (1) the computation of offloading power and CPU frequencies, (2) the computation of the dual variables, and (3) the application of CVX for computing UAV trajectory. Let \(L_{1}\) and \(L_{2}\) denote the number of iterations required for the outer loop and the inner loop of Algorithm 1. Let \(\phi\) denote the tolerance error for the subgradient method. Then, we can obtain the total complexity of Algorithm 1 as \(\mathcal{O}[L_{1}(2 NT+1/\phi^{2}+L_{2}T^{3})]\), where \(\mathcal{O}(\cdot)\) is the big-O notation.  
By solving each subproblem alternately, Algorithm 1 can guarantee convergence, while, due to the usage of SCA method and alternating optimization, the global optimum of problem **P2** cannot be strictly guaranteed.
# Research on Pure Hydrogen Production Using a Fuel-Processing System Combined with a PSA System
## 9 Calculation of the Reformer Yield  
The efficiency of the reformer-WGS system was analyzed using the Aspen Plus(r) simulation results. Figure 8 shows the calculated efficiency of the reformer-WGS system measured according to the reformer temperature change. The efficiency of the reformer system would be calculated as the ratio between the energy of produced hydrogen and the energy of supplied natural gas, which is shown in Equation (8) [16; 17; 42; 43].  
\[\eta_{1}=\frac{LHV_{H_{2}}\times n_{H_{2}}(produced)}{LHV_{CH_{4}}\times\left(n_ {CH_{4}}(burner)+n_{CH_{4}}(process)\right)} \tag{8}\]  
Figure 7: Fuel-processing system yield SC ratio.  
In Equation (8), \(n_{H_{2}(produced)}\) is the flow rate of produced hydrogen (mol/s) in the fuel-processing system, \(n_{CH_{4}(burner)}\) is the flow rate of the methane supplied to the burner, and \(n_{CH_{4}(process)}\) is the flow rate of methane supplied to the reformer as a raw material. LHV \(H_{2}\) and LHV \(CH_{4}\) are the lower heating value of hydrogen mass at the outlet and the lower heating value of \(CH_{4}\) at the inlet, respectively.  
The fuel-processing system yields at various temperature and pressure conditions for 10 kW H\({}_{2}\) production were calculated using Equation (8) and are summarized in Figure 8. As the conversion of CH\({}_{4}\) increases, less PNG is needed to make 10 kW H\({}_{2}\), and the lower the conversion of CH\({}_{4}\), the more PNG is needed. The amount of BNG supplied to the system according to the conversion of CH\({}_{4}\) is shown in Figure 5. As shown in Figure 8, fuel-processing system yield increases and stabilizes with increasing temperature at a fixed pressure. In addition, under the same temperature conditions, as the pressure increased, the CH\({}_{4}\) conversion rate decreased according to [PERSON]'s law, resulting in a decrease in yield. The maximum yield is 82.40% at a pressure of 1 atm and a temperature of 800 \({}^{\circ}\)C, which is estimated at the highest CH\({}_{4}\) conversion under this condition.
## 4 Results and Discussion
### Influence of Vertical Scale of Fluctuation (\(\delta_{v}\)) in the Random Field  
On account of the increasing of \(\delta_{v}\) from 2 to 8 m (e.g., case Ani-9, 10, 3, and 11 shown in Table 1), a mixed dataset consisting of the total samples in the four cases is generated to train a 2D-CNN model, which has the same architecture as shown in Figure 5, to predict the uniaxial bearing capacity factors of the skirted foundation considering the variation of \(\delta_{v}\) in the soil random field. A total of 1680 and 240 samples are selected for the training and validation datasets to accomplish the training process of the model, while the remaining 480 samples are used to estimate the prediction performance of the surrogate model.  
Figure 14 compares the uniaxial bearing capacity factors predicted by the 2D-CNN model and calculated by RFEM. It can be seen from Figure 14a,b that the CNN-predicted \(N_{CH}\) and \(N_{CM}\) are close to the calculated results, in which the R\({}^{2}\) value is more than 0.97 and the RMSE value is less than 0.11. According to the cumulative distribution of \(N_{CH}\) and \(N_{CM}\) shown in Figure 14c,d, it is evident that the proposed 2D-CNN model is feasible in analyzing the uniaxial bearing capacity factors of the skirted foundation in spatially variable soil with different \(\delta_{v}\), for the maximum relative error of the three evaluating indicators is 1.67%.  
Furthermore, the probability distribution of the relative errors between the predicted and calculated uniaxial bearing capacity factors is given in Figure 15. With the variation of \(\delta_{v}\) from 2 to 8 m, the proposed 2D-CNN model is capable of providing satisfactory prediction performance for the uniaxial bearing capacity factors by restricting the maximum relative error within 9%. More specifically, the Cis of the relative error located within 5% are 99.6% and 97.9% for the prediction of \(N_{CH}\) and \(N_{CM}\), respectively. The effect of \(\delta_{v}\) in the soil random field on the prediction performance of the uniaxial bearing capacity factors is similar to the influence of \(\delta_{h}\), as shown in Figure 13.  
Figure 14: Comparison of the predicted and actual uniaxial bearing capacity factors under mixed \(\delta_{v}\): (**a**) Scatter of \(N_{CH}\); (**b**) Scatter of \(N_{CM}\); (**c**) CDFs of \(N_{CH}\); (**d**) CDFs of \(N_{CM}\).  
The time required for 600 samples in the statistical analysis of tradition RFDM is about 327 min on a standard PC as described in Section 3, while the trained 2D-CNN model takes just 3 s, resulting a 6540-times improvement in calculation efficiency. Only the ultimate bearing capacity of the skirted foundation had been investigated in this study, while more than 600 simulations are essential for further reliability analysis, such as the failure mechanics and failure envelopes of foundation. At this point the trained 2D-CNN model is highly efficient to acquire numerous outputs in several seconds once the random filed data are generated, instead of time-consuming finite element calculations.
# \(K\)-Means Cloning: Adaptive Spherical \(K\)-Means Clustering
## 2 K-Means Cluster Cloning
### Simulated Annealing Heuristic  
Simulated Annealing (SA) [68] is a generic probabilistic meta-heuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is a random-search technique and often used when the search space is discrete. For certain problems, simulated annealing may be more efficient than exhaustive enumeration provided that the goal is merely to find an acceptably good solution in a fixed amount of time, rather than the best possible solution.  
SA, as its name implies, exploits an analogy between the way in which a metal cools and freezes into a minimum energy crystalline structure, i.e., the annealing process. The SA algorithm successively generates a trial point in a neighborhood of the current solution and determines whether or not the current solution is replaced by this trial point based on a probability value. This value is related to the difference between their function values. Convergence to an optimal solution can theoretically be guaranteed only after an infinite number of iterations controlled by the procedure so-called cooling schedule. Algorithm 1 describes the main algorithmic steps of simulated annealing.  
For global search purposes, it does not only accept changes that decrease the objective function \(f\) (assuming a minimization problem), but also it makes some changes that increase it. The latter are accepted with a probability;  
\[\mathbf{P}=\exp\frac{-\Delta f}{T},\]where \(\Delta f\) is the increase in \(f\) and \(T\) is a control parameter, which by analogy with the original application is known as the system \"temperature\" irrespective of the objective function involved.  
```
1:Initialize \(x_{start}\), \(T_{\max}\), \(T_{\min}\) and \(L_{0}\).
2:Set \(p:=0\), \(x:=x_{start}\), and \(T:=T_{\max}\).
3:while\(T>T_{\min}\)do
4:for\(L:=1\) to \(L_{p}\)do
5: Generate \(y\).
6:if\(f(y)\leq f(x)\)then
7: then \(x:=y\).
8:else
9:if\(\exp((f(y)-f(x))/T_{p})>random[0,1]\)then
10: Set \(x:=y\).
11:endif
12:endif
13:endfor
14:endwhile
15:Set \(p:=p+1\).
16:Calculate epoch length \(L_{p}\).
17:Calculate control temperature \(T\).
```  
**Algorithm 1** Simulated Annealing  
SA has been used on a wide range of combinatorial optimization problems and achieved good results. In data mining, SA has been used for feature selection, classification, and clustering [69].
## 3 Case Study
### Energy-Dissipation Support Design  
It was essential, when designing the steel plate composite structure, to consider the characteristics of the project itself, and it was deemed necessary to employ a 3D laser scanner to assist with the design. This was used to monitor the holographic geometric parameters, which could, in turn, determine the deformation distribution values along the tunnel circumference, provide quantitative feedback for the reserved design to address the deformation, predict the deformation trend, and evaluate safety risks. Three-dimensional laser scanning has proved beneficial in improving construction quality, controlling construction costs, and ensuring on-site safety [27]. The scanned point cloud is shown in Figure 11 above. The most interesting section of data extracted from the 3D cloud map is shown in Figure 13. The left-side deformation is significantly higher than the right-side deformation, and there is a clear bias situation. As previously indicated, the original support design had failed to solve the problem of large-scale deformation, and once the overall structure had been damaged, the deformation magnitude increased sharply, presenting a risk of collapse of the tunnel, which severely augmented the site safety risks for personnel and machinery.  
Figure 12: Damage to the strengthening support implemented in the tunnel: (**a**) photographs of reinforcement support damage on site; (**b**) cloud image of 3D laser scanning.  
Figure 13: The deformation data of the original support.  
The specific scheme of the compression support design in this study was as follows: the supporting parameters of I-22b were maintained unchanged, two groups of steel plate combination structures were set at 25\({}^{\circ}\) around the vault, the left side was 37.8 cm high, the right side was 30.8 cm high, with a 40 cm reserve for further deformation. The thickness of the upper and lower steel plates was 1 cm, the height of the vertical plate was 28 cm, and its thickness was 8 mm. The composite structure of the steel plate and I-beam steel frame were connected by bolts, as shown in Figure 14.
## 2 Preliminaries
### A \((k,n)\)-XVC Scheme  
It is proven that a \((k,n)\)-XVC scheme has higher contrast and lower pixel expansion than the \((k,n)\)-OVC scheme [25; 26]. It simply requires that the decryption of the secret from the shadows are assisted by the small, cheap, and lightweight computational devices, which can be flexibly connected through the internet network. When such mobile devices are available, a \((k,n)\)-XVC schemes can be widely adopted to substitute a \((k,n)\)-OVC scheme. Many schemes focused on a \((k,n)\)-XVC scheme to decrease the construction complexity [21; 23; 24; 25; 26]. Let \"\(\otimes\)\" denote the Boolean \"XOR\" operation and \(\otimes(B_{i}|U)\) denote the Boolean XOR-ing of the row \(j\), \(j\in U\) of \(B_{i}\), \(i=0,1\). The formal definition of a \((k,n)\)-XVC scheme is presented as follows [26].  
**Definition 2** ([26]).: _With a set of two \(n\times m\) Boolean basis matrices, \(B_{0}\) and \(B_{1}\), the result of a column permutation of \(B_{i}\) defines the color of the \(m\) subpixels in each one of the \(n\) shadows when encoding a pixel \(i\in\{0,1\}\). Such basis matrices constitute a \((k,n)\)-XVC scheme if the following conditions are met._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Pixel** & **Shadow 1** & **Shadow 2** & **XOR** \\ \hline \hline \end{tabular}
\end{table}
Table 1: The encoding and decoding process of the shadows in a \((2,2)\)-OR-based \((k,n)\)-VC (OVC) scheme.  
**(1)**: _Contrast Condition:_ \(H(\otimes(B_{1}|U))\geq h^{\prime}\) _and_ \(H(\otimes(B_{0}|U))\leq l^{\prime}\) _for_ \(|U|=k\)_, where_ \(0\leq l^{\prime}<h^{\prime}\leq m\)_._
**(2)**: _Security Condition:_ \(H(\otimes(B_{1}|U))=H(\otimes(B_{0}|U))\) _for_ \(|U|\leq k-1\)_._  
It is analogous to Section 2.1. The following is a more intuitive description of a \((k,n)\)-XVC scheme through a simple example of a (2, 2)-XVC scheme.  
**Example 2**.: _The form of \(B_{0}\) and \(B_{1}\) is as follows when \(n=2\) and \(k=2\)._  
\[B_{0}=\left[\begin{array}{cc}1&0\\ 1&0\end{array}\right],\ \text{ and }\ B_{1}=\left[\begin{array}{cc}1&0\\ 0&1\end{array}\right].\]  
_The encoding and decoding process of the shadows is shown below in Table 2._  
Similar to the analysis in Section 2.1, the (2, 2)-XVC scheme has perfect security. When stacking one shadow, all black and white pixels are 1B1W. We have 2B0W and 0B2W for black and white secret pixels when stacking two shadows. The contrast is \(\alpha=1/2\), which is \(2^{k-1}\) times of the contrast in Section 2.1.
# An Experimental Investigation of Passive Jet Control Method on Bridge Tower Wake
## 1 Introduction
### Passive Jet Rings  
The present study adopts four kinds of passive jet rings to take control of the tower wake flow, which refers to the design in [PERSON] et al. (2013). The ring is an annular flow channel with  
Figure 1: Schematic diagram of test layout.  
slits made by a 1 mm thick plastic plate, as Figure 2 shows. The top view of the ring is a rounded rectangular box, of which the outer size is 323 mm \(\times\) 257 mm, with a 43 mm radius rounded corner in each corner, and the inner size is 283 mm \(\times\) 217 mm; thus, it can closely fit the tower model. The intermediate part of 18 mm thickness, considering the thickness of the plate, constitutes the hollow flow channel. The height of the passive jet ring is 100 mm. Several rectangular slits are distributed around the side walls of the ring. The major differences between these four categories of jet ring are the size and the distribution of these slits. The size of slits in the first and third categories is 80 mm \(\times\) 30 mm, and that in the second and fourth categories is 60 mm \(\times\) 30 mm. For the first category, there are nine and seven slits on each long and short side, respectively, and for the second category, there are seven and five slits. For the third and fourth categories, the slits of the first and second categories on the long side are sealed, seven and five slits are distributed on each short side, respectively, and no slits are arranged on the long side. The clear spacing between two slits on each side is 30 mm. All the slits adopt axisymmetric distribution on both two principal axes.  
In this study, we calculate the dimensionless coefficient of suction ([PERSON] et al., 2015):  
\[C_{\text{suc}}=\frac{S_{\text{suc}}}{Hl}=\frac{hd(n_{\text{a}}\sin\alpha+n_{ \text{b}}\cos\alpha)}{H(a\sin\alpha+b\cos\alpha)}\]  
where \(S_{\text{suc}}\) is the summary area of the suction slits on one passive jet ring projection to the plane perpendicular to the direction of flow; \(l=a\text{sin}\ \alpha+b\text{cos}\ \alpha\) is the total projection length of the ring perpendicular to the incoming flow; \(H\) is the height of the passive jet ring; \(h\) is the length of the suction slit; \(d\) is the width of the suction slit; \(a\) and \(b\) are the length of the long and short side of the ring, respectively; \(n_{\text{a}}\) and \(n_{\text{b}}\) are the number of the slits distributed on each long and short side, respectively, and \(\alpha\) is the yaw angle of the ring.
## 3 Results
### Functional Suitability
#### 3.1.1 Functional Completeness  
Functional completeness analyses the main functions of software tools in terms of the tasks and objectives of climate adaptability. The experiment with different software tools revealed a range of climate adaptation parameters in different software tools. Table 2 gives an overview of functional completeness and defines the measures of different software tools based on several categories, including green and blue measures, climate analysis and economic impact. Software tools tested in the objective experiments revealed a different focus on climate adaptation. For instance, ENVI-met and Ladybug can simulate a wide range of climate aspects including sun, temperature, radiation, wind and thermal comfort, whereas GreenScenario and AST evaluate green and blue measures as well as economic impact. CitySim covers the fewest climate adaptability aspects for outdoor spaces, producing simulations of shaded areas, surface temperature and radiation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **ENVI-Met** & **Ladybug** & **CitySim** & **GreenScenario** & **AST** \\ \hline \hline
**Green MEASUUSES** & & & & & \\ \hline
**Green factor** & & & & & \\ \hline
**Biodiversity** & & & & & + \\ \hline
**Shaded area** & + & + & + & & \\ \hline
**BLUE MEASUUSES** & & & & & \\ \hline
**Rainwater management** & & & & + & + \\ \hline
**Evapotranspiration** & + & & & + & + \\ \hline
**CLIMATE ANALYSIS** & & & & & \\ \hline
**Solar analysis** & + & + & & & \\ \hline
**Surface temperature** & + & & + & & \\ \hline
**Radiation** & + & + & + & & \\ \hline
**Air pollution** & & & & + & \\ \hline
**Wind analysis** & + & + & & & \\ \hline
**Thermal comfort** & + & + & & & \\ \hline
**Thermal load** & + & & & & \\ \hline
**Thermal storage** & + & & & & \\ \hline
**Heat island reduction** & + & + & & + \\ \hline
**CO2 reduction** & + & & & + & \\ \hline
**ECONOMIC IMPACT** & & & & \\ \hline
**Maintenance costs** & & & + & + \\ \hline
**Construction costs** & & & + & + \\ \hline  & 11p. & 6p. & 3p. & 8p. & 5p. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of functional completeness of different software tools (source: authors).
# Synthetic Face Discrimination via Learned Image Compression
## 5 Discussion  
In this research, we performed thorough tests to assess the proposed method for the discrimination of synthetic face images. We used a deep learning-based image compression technique to detect synthetic face images. Additionally, we utilized the discrete wavelet transform to improve the detection process. This was established by noting that the diagonal details of a real and a synthetic face image display significant differences. On the other hand, the approximation details of real and synthetic faces are fairly similar, while the horizontal and vertical details have some differences, but not enough to be considered noteworthy. Instead of using image-specific features for this process, we compressed face images and measured the quality of their reconstruction, thus revealing their real or synthetic origin.  
These experiments showed some interesting results. We proved that not only is it possible to use compression for deepfake detection, but the accuracy of such an attempt is comparable to that of GAN-based methods on GAN-generated faces. The robustness of the proposed technique was also tested against several image processing operations, proving beneficial against certain types of attacks, e.g., Gaussian noise. An important benefit of this approach is the reduced size of the neural networks needed for its implementation. While ResNet50--to which we compare our technique--is very efficient, it actually uses 49 layers.  
Figure 11: Accuracy of the proposed technique and ResNet50 when JPEG compression with various quality factors is applied to the image.  
Figure 12: Accuracy of the proposed method and ResNet50 when Gaussian noise of different variance values is applied to the image.  
On the other hand, the proposed technique requires only 30 layers, 24 of which are used for the compression of the images and the other 6 for the classification. Consequently, there is lower computational complexity, which is highly beneficial. Finally, our method can successfully identify artificial images created using stable diffusion, in contrast to ResNet50, which seems to do well only on GAN-generated images. This means that the proposed method is more universal in regard to the images it can correctly classify.