esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Analysis Based on Fracture Mechanism
### Corrosion Expansion Model  
Corrosion of the reinforcement in concrete causes the corrosion product to expand. The pressure resulting from expansion acts on the surrounding concrete and may cause damage if the generated tensile and shear stresses in concrete exceed its strength. In the present study, expansion due to the reinforcing bar corrosion was modelled by applying radial displacement to the region around the reinforcing bar. In actual structures, corrosion is more progressive in those areas that face the reinforcement side than in those that face the concrete interior, and the corrosion expansion behaves slightly differently from the corrosion expansion model used in the present study. The experimental results used to assess the validity of the present numerical results were obtained by the electrolytic corrosion of RC. In general, sufficient reinforcement corrosion occurs for such electrolytic corrosion, in which the cover thickness and tightness of the concrete are controlled so that its cover surface is filled adequately with salt water. Therefore, unlike the situation in actual structures, where the chloride ions and water content are locally different, the reinforcing bar corrosion model was considered to be appropriate within the scope of the present study and was used on that basis.  
In actual structures, there is a need to model the locality of corrosion, but this is a subject for future analysis. Furthermore, the present research neglected the nonlinear effect and compressibility of the corrosion products, which we considered to be beyond the scope of this study because our aim was to simulate the generation of cracks in concrete due to corrosion expansion using the expanded damage model. Simulating the actual phenomena would require modelling (i) the Young's modulus of the corrosion products, (ii) the spatial distribution of corrosion formation, and (iii) the nonlinear effect and compressibility of the corrosion products.
## 3 Pre-Processing of Multi-Sensor Data from the Unmanned Ship
### Network Positioning of Zigbee System  
In order to meet the multi-node effective communication of the unmanned-ship Zigbee positioning system, \(n\) reference nodes were set in the positioning network coordinate system in this paper, so that the blind node on the unmanned ship is in a network composed of \(n\) reference nodes with a known signal strength and position coordinates [18]. The specific steps are as follows:  
Step 1 Define the \(RSSI\) theoretical value of the signal strength indication of the received power, and calculate the distance between the blind node and the reference node.  
\[RSSI=-(10\vartheta\cdot\text{lg}d+A) \tag{11}\]  
From Equation (11), we can obtain  
\[d=10^{-\frac{A+RSSI}{10\vartheta}} \tag{12}\]  
where \(\vartheta\) is the path loss coefficient, \(d\) is the distance between the blind node and the reference node, \(RSSI\) is the signal strength value, and \(A\) is the strength of the initial signal at the transmitter.  
Step 2 Establish the following distance equations among the blind node and the reference nodes in the plane:  
\[\left[\begin{array}{c}\left(x_{1}^{R}-x^{2}\right)^{2}+\left(y_{1}^{R}-y^{ 2}\right)^{2}\\ \left(x_{2}^{R}-x^{2}\right)^{2}+\left(y_{2}^{R}-y^{2}\right)^{2}\\ \vdots\\ \left(x_{n}^{R}-x^{2}\right)^{2}+\left(y_{n}^{R}-y^{2}\right)^{2}\end{array} \right]=\left[\begin{array}{c}d_{1}^{2}\\ d_{2}^{2}\\ \vdots\\ d_{n}^{2}\end{array}\right] \tag{13}\]where \(\forall\ i\in[1,\ n]\), \(d_{i}\) is the distance between the blind node and the \(i\)th reference node.  
Step 3 Based on the reference node's distance equation set established by Equation (13), the linear transformation equation \(2A\left[\begin{array}{c}x^{z}\\ y^{z}\end{array}\right]=B\) of the blind node coordinates of the unmanned ship was constructed, where \(A\) and \(B\) are expressed as follows:  
\[A=\left[\begin{array}{ccc}x_{n}^{R}-x_{n}^{R}&y_{1}^{R}-y_{n}^{R}\\ x_{2}^{R}-x_{n}^{R}&\vdots&y_{2}^{R}-y_{n}^{R}\\ x_{n-1}^{R}-x_{n}^{R}&y_{n-1}^{R}-y_{n}^{R}\end{array}\right]B=\left[\begin{array} []{ccc}d_{n}^{2}-d_{1}^{2}-(x_{n}^{R})^{2}+(x_{1}^{R})^{2}-(y_{n}^{R})^{2}+(y_ {1}^{R})^{2}\\ d_{n}^{2}-d_{2}^{2}-(x_{n}^{R})^{2}+(x_{2}^{R})^{2}-(y_{n}^{R})^{2}+(y_{2}^{R} )^{2}\\ \vdots\\ d_{n}^{2}-d_{n-1}^{2}-(x_{n}^{R})^{2}+(x_{n-1}^{R})^{2}-(y_{n}^{R})^{2}+(y_{n- 1}^{R})^{2}\end{array}\right] \tag{14}\]  
Step 4 The coordinates (\(x^{z}\), \(y^{z}\)) of the blind node in the ZigBee system network are obtained by the least-squares method.  
\[\left[\begin{array}{c}x^{z}\\ y^{z}\end{array}\right]=\frac{1}{2}{(A^{T}A)}^{-1}A^{T}B \tag{15}\]
# RIS-Assisted High-Speed Communications with Time-Varying Distance-Dependent Rician Channels
## 5 Numerical Evaluations and Discussion
### Deployment Analysis  
Figure 9 illustrates ergodic SE against different positions of the BS, i.e., \(z_{\text{BS}}=30\) m, \(50\) m, and \(70\) m. It can be seen that \(z_{\text{BS}}=30\) m can achieve the highest SE, as expected. This is because the path losses between the MR and the BS and between the RIS and the BS all decrease. Similarly, as Figure 10 shows, the outage probabilities of different \(z_{\text{BS}}\)s have the same pattern as Figure 9, due to the reduction of \(d_{0}(t)\) and \(d_{\text{BS}}^{\text{min}}\). However, Figure 11 reveals that decreasing \(z_{\text{BS}}\) would increase the optimized DS. Therefore, we should make a tradeoff between ergodic SE and DS, when deploying the BS. Specifically, deploying the RIS near the BS can enhance the SE and reduce the outage probability, at the expense of increasing the DS. Moreover, it should be emphasized that the negative sign of the DS in Figures 7 and 11 only denotes the level of the deviation, rather than representing that there exists a negative frequency in reality.  
Figure 10: Outage probabilities for different positions of the BS.  
Figure 9: Ergodic SEs for different positions of the BS.
# Using Machine Learning to Predict Indoor Acoustic Indicators of Multi-Functional Activity Centers
## 4 Results and Discussions
### Construction of the ML Model  
#### 4.2.1 Data Observation  
The correlation matrix between the numerical target value and the feature is shown in Figure 17, which was used to observe the degree of correlation between the variables. Its value was between \(-1\) and \(1\), and this graph was used to find the variables that had a greater impact on the target parameters.  
Among acoustic indicators, the geometric information of space had a great influence, but the correlation between the height of the stage and the acoustic indicators was relatively low. In addition, the equivalent sound absorption area had a low correlation with the acoustic indicators. However, the correlation matrix has limitations. The correlation coefficient only considers the linear relationship between the two variables, and a strong correlation does not necessarily indicate a causal relationship. Furthermore, variables other than these two that may affect the correlation cannot be presented.
# A Survey on Satellite Communication System Security
## 5 Defense in Satellite Communication Systems  
In this section, we explore countermeasures for the attacks discussed earlier, examining the methods for each attack through a classification criterion. The overall classification is depicted in Figure 11.
# A Reinforcement Learning-Based Congestion Control Approach for V2V Communication in VANET
## 4 Evaluation  
In this section, we evaluate the performance of the QBACC approach using a framework of Vehicles in Network Simulation (Veins) [38], which contains a basic implementation of the IEEE 802.11p and IEEE 1609 protocols in order to facilitate the testing of V2V networks. Veins connect a widely used network simulation tool, an objective modular network testbed in C++ (OMNeT++) [39], and the traffic mobility simulator simulation of urban mobility (SUMO) [40]. To evaluate the Q-learning performance, we simulated a 20 km highway with four lanes (two in each direction). We only considered data from a 4 km stretch in the middle of the highway to eliminate inaccuracies caused by vehicles entering or exiting the simulation. The simulation involved either 300 or 500 vehicles, with random velocities ranging from 80 to 130 km/h, to produce a dynamic traffic flow with varying vehicle densities. The two vehicle numbers represent low- and high-density traffic scenarios. The simulation time was 1000 s, but we only analyzed data from 350 to 750 s when vehicles were present within the 4 km stretch.  
The configuration parameters for the evaluation are summarized in Table 3.  
The performance of our proposed QBACC congestion control approach is evaluated by comparing it with other techniques using the dynamic traffic model described above. For all approaches, a fixed transmission power of 20 mW is utilized.  
1. 10 Hz: BSMs are transmitted at a constant rate of 10 BSM/s.
2. 5 Hz: BSMs are transmitted at a constant rate of 5 BSM/s.
3. MDPRP: An RL-based congestion control algorithm proposed in [28].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Vehicle** \\ **Density** \\ \end{tabular} & \
\begin{tabular}{c} **Estimated** \\ **BSM/s** \\ \end{tabular} &
\begin{tabular}{c} **Estimated** \\ **CBR** \\ \end{tabular} & \multicolumn{5}{c}{**Transmission Rate (BSM/s)**} \\ \cline{1-7}  & 1 & 0.0402 & 66 & 71 & 75 & 77 & 82 \\ \hline
1 & 10 & 0.3532 & 68 & 77 & 84 & 89 & 100 \\ \hline
5 & 1 & 0.0806 & 67 & 72 & 77 & 83 & 54 \\ \hline
5 & 10 & 0.7252 & 63 & 60 & 56 & 54 & 13 \\ \hline
15 & 1 & 0.1816 & 71 & 86 &
## 7 Implementation and Evaluation
### Service Selection  
Given the workflow, the first step in creating the service composition is the selection of suitable services from the service registry. As stated before, we need OWL-S in addition to WSDL for describing our services. Our service registry is realized using the open-source RDF store Sesame [41], configured to use a MySQL database to store the RDF graph.  
For each of the roles in the workflow, a set of candidate services need to be discovered and selected from the set of available services. This filtering is based on the type (e.g., transcoding) and the available operation of each service.  
If not reported otherwise, all measurements are averages over a hundred independent runs and were performed an IBM T43 with 1.5 GB of memory and a 2.0 GHz Intel PentiumM processor.  
Figure 13: Selecting service candidates from the repository.  
Figure 13 shows the overall time necessary for accessing suitable services inside the service repository. This time is composed of locating the service in the database and retrieving the respective service description. The overall times are reasonably small for repositories containing up to 5000 services, and selecting up to 5 candidates for each role in the workflow. This leads to an offset delay of around 3 s which even in video on demand frameworks is considered practical. A more detailed analysis has shown that locating the service candidates is actually very fast, even for large databases (average \(<\)200 ms). However, time for retrieving the service description from the sesame database considerably increases with the number of services. Therefore, selecting new services on-the-fly for replacing failing services is not an option in the current implementation, and has to rely on selecting several services at the initial selection step.
## 2 Methodology
### Multipath Transmission Algorithm  
This paper proposes a framework for multipath transmission algorithms based on hierarchical culling. The core operation involves performing three different types of simplification on the network topology graph. After the NSGA-II-based multi-constraint optimal path algorithm determines the first path that satisfies the multi-constraint requirements, the framework performs different simplification operations based on varying situations to obtain the second path.
## 5 Key Technologies Supporting Deliberate Attack Defense for IECPS
### Optimized Scheduling of IECPS
#### 5.3.1 Stochastic Optimization Scheduling  
Stochastic optimization is a method that uses probability theory to handle the impacts of uncertainty. The stochastic optimization for deliberate attack defense generally includes the following key steps: firstly, defining the quantitative indicators and constraints for the defense against deliberate attacks, with the objective function usually being the expected value or chance constraint. Secondly, using probability distributions or historical data to generate possible attack scenarios. Lastly, solving the stochastic optimization model using methods such as two-stage programming or dynamic programming, resulting in defense strategies that consider uncertainty. Among these methods, two-stage programming is the most commonly used method, which is divided into pre-decision and post-adjustment, corresponding to the decision-making process before and after uncertainty occurs. However, the assumed probability distributions may contain inaccuracies in characterizing the uncertainty. Furthermore, since stochastic programming converts uncertainty models into deterministic models by generating numerous scenarios, and considering the complex operational state of IECPS, the model's solution scale can become very large, making it difficult to solve. Therefore, it is necessary to reasonably apply methods such as scenario reduction techniques and extreme scenarios to optimize the selection of deliberate attack scenarios [203]. Additionally, one can consider retaining key uncertainty factors while neglecting less significant ones, thereby reducing the number of random variables in the stochastic optimization problem. This approach diminishes the difficulty of solving and enhances solution speed and efficiency.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**System**} & \multicolumn{2}{c}{**Methods**} & \multirow{2}{*}{**Multi-Stage Model**} & \multirow{2}{*}{**Data Driven**} & \multirow{2}{*}{**Ref.**} \\ \cline{2-2} \cline{5-6}  & **Stochastic Optimization** & & & & [201] \\ \hline Electric-Heat-Gas & \(\surd\) & & \(\surd\) & & [202] \\ Integrated System & & \(\surd\) & \(\surd\) & & [203; 204] \\ \hline Integrated power and gas system & \(\surd\) & & \(\surd\) & & [205] \\  & & \(\surd\) & & & [206] \\ \hline Integrated heat and electric system & \(\surd\) & & \(\surd\) & \(\surd\) & [207] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of Optimal Scheduling Studies Considering Uncertainty.
## 3 Results and Discussion
### Relative Gene Expression  
Apart from determination of the ATDC5 cells viability, also the relative expression of the selected genes, which are crucial for the process of chondrogenesis, was under scrutiny (Figure 9). The long expansion time and multiple passaging often lead to \"dedifferentiation\" of chondrocytes, which is defined as the gradual loss of differentiation molecular markers. Chondrocytes cultivated in vitro on the plastic surfaces dedifferentiate particularly quickly to less specific cells with enhanced ability to proliferate [60]. As dedifferentiation progresses, the expression of genes encoding the extracellular matrix components, such as collagen type II (_Col2a1_) or SRY (sex determining region Y)-box 9 (_Sox9_), transcription factor is changed, whilst at the same time the cells acquire an increased fibrobl phenotype, characterized by the expression of the genes encoding collagen type I (Col1a) or runt related transcription factor 2 (_Runx2_) [61]. Specifically, _Sox9_ transcription factor enhances expression of _Col2a1_ while overexpression of _Runx2_ is connected with the appearance of hypertrophic chondrocytes and leads to the appearance of osteoblasts followed by cell mineralization [61; 62].  
It was shown that some biophysical stimuli, like three-dimensional space for growth, are sufficient for cells to differentiate to chondrocytes [63]. The surfaces of BNC scaffolds were uneven with a porous structure, which allowed the cells to penetrate into the biopolymer structure. Therefore, pure BNC scaffolds slightly enhanced the differentiation process [14; 58]. What is more, some findings proved that the negatively charged surfaces of scaffolds could promote cell dedifferentiation [64].  
The _Col1a1_ expression was found to be nearly the same in case of all BNC/\(\kappa\)-Car composites as compared to the ATDC5 cells growing on either plastic surface (data not shown) or pure BNC. In case of the majority of BNC/\(\kappa\)-Car composites the expression level of _Col2a1_ was slightly higher than in the control sample. The only exception, for which the level of expressed _Col2a1_ was statistically increased was the BNC/\(\kappa\)-Car\({}_{0.8}\) composite. _Sox9_ expression was significantly enhanced in case of all the composites with higher \(\kappa\)-Car concentration, namely BNC/\(\kappa\)-Car\({}_{0.5}\), BNC/\(\kappa\)-Car\({}_{0.8}\), and BNC/\(\kappa\)-Car\({}_{2.0}\). Finally, low expression level of _Runx2_ shows that none of the composites tested induced the chondrocyte hypertrophy. Thus, the results of this study showed that BNC/\(\kappa\)-Car composites supported the dedifferentiation of ATDC5 cells to more chondrogenic phenotype, as demonstrated by the increase in the expression of chondrogenic marker genes _Col2a1_, _Sox9_ and the decrease of _Col1a1_ and _Runx2_ expression.
## 2 Materials and Methods
### Dimension Reduction with DCT  
Discrete cosine transform (DCT) is a spatial to frequency domain transformation method, which represents a sequence of data in the form of a sum of cosine functions that oscillate at different frequencies. The DCT is an orthonormal transform in which \(y=Cx\) and \(x=C^{-1}y\) are defined in [31] as follows:  
\[\begin{array}{c}y(k)=\sqrt{\frac{2}{N}}\alpha(k)\sum\limits_{n=0}^{N-1}x(n) \cos\frac{(2n+1)k\pi}{2N};\\ k=0,\,1,\ldots N-1\end{array} \tag{2}\]  
\[\begin{array}{c}x(n)=\sqrt{\frac{2}{N}}\sum\limits_{k=0}^{N-1}\alpha(k)y(k) \cos\frac{(2n+1)k\pi}{2N};\\ n=0,\,1,\ldots N-1\end{array} \tag{3}\]  
\[\begin{array}{c}\alpha(0)=\frac{1}{\sqrt{2}};\,\alpha(k)=1;\,k\
eq 0.\end{array} \tag{4}\]  
Similar to Haar wavelet transform (HWT) and discrete Fourier transform (DFT), DCT concentrates the signal energy on a small number of low-frequency DCT coefficients. In the frequency domain,  
Figure 5: Limb vector and its 3D angles with \(X\), \(Y\), and \(Z\) axes in egocentric coordinate system.  
compression (dimension reduction) is achieved by keeping the low-frequencycomponents (the most useful information) and discarding the high-frequency components that normally represent noise. Therefore, performing DCT can reduce the data size and noise level.  
Table 2 demonstrates an example for the construction of DCT coefficients in frequency domain and indicates the lossy inverse transform as follows.  
In the above table, an input vector composed of 10 number of angle values ranging from 0 to \(\pi\) are passed through DCT (discrete cosine transform) and resulting DCT coefficients in frequency domain are illustrated. Additionally, in order to show how the inverse DCT is a lossy method, the resulting values after inverse DCT are also illustrated in the table.  
According to the table, values after inverse DCT operation are not exactly same with the input values, which indicates that DCT is a lossy compression method. On the other hand, frequency levels are sorted in ascending order from low frequency to high frequency in the frequency domain. In this regard, dimension reduction is achieved by keeping the low-frequency information and discarding the high-frequency information in the frequency domain. In other words, instead of employing the input vector in spatial domain, low-frequency DCT coefficients in frequency domain are used as the feature vector. By this method, the most useful information is kept and unnecessary information is discarded, which reduces not only the feature dimension but also the noise level at the same time.
# High-Speed Welding of Stainless Steel with Additional Compensatory Gas Jet Blow Molten Pool
## 1 Introduction
[25] analyzed the effects of various flux-cored arc welding (FCAW) parameters on weld width and tensile properties of weld metal extracted from butt joints. [PERSON] et al. [26] used a parametric model and the elements birth and death in single-pass butt welded joints to simulate the weld filler variation with time. [PERSON] et al. [27] studied the effect of area-filling paths on residual stresses developed during twin-wire arc weld-deposition. [PERSON] et al. [28] investigated a three-component shielding gas mixture used in tandem with narrow gap pulsed gas metal arc welding (GMAW), and the effect of its composition on arc behaviors and weld formation. [PERSON] et al. [29; 30] studied the pulsed metal inert gas (MIG) high-speed welding of stainless steel and proposed a new method using a compensatory gas jet impinging on the molten pool. They showed that this method could significantly inhibit the formation of humping bead and undercut defects during high-speed welding and increase the welding speed of single wires by nearly three times under equivalent heat input conditions. Moreover, the appearance of the weld was significantly improved, and the microstructure and mechanical properties of the weld were obviously better than those obtained by conventional welding. This paper studies the formation mechanism of hump weld bead and undercut, and puts forward \"welding pool compensation gas jet method\" based on the idea of human intervention in the movement of welding pool, and uses this new method to study the effects of gas jet on controlling the formation of welding pool, stirring the pool, isolating air protection, and accelerating cooling.