esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Internal Crack Characteristics
#### 3.3.2 Internal Fissure Distribution Characteristics  
The RA-AF value in the sound-emission signal qualitatively characterizes the distribution of the different types of cracks propagating during the rock damage process [31]. The values were then used to analyze the main pattern when the test sample failed. RA = rise time/amplitude, AF = Ring counts/duration time.The cutting cracks corresponded to a small RA and a large AF for the sound-emission signal, and cracks transecting the cutting cracks led to a large RA value and a small AF value [32; 33; 34]. The corresponding boundary values currently do not have a uniform criterion and are usually determined based on the load time and the ratio of the AF and RA maximum values. Based on this description, we selected \(\varphi_{0}\) = AF/RA = 60 as the criterion for determining the stretch and cutting cracks. Next, we calculated the RA-AF values for the sample under different early damages and then constructed the RA-AF diagram, as shown in Figure 9. The split ratios of the different phases of each sample during the destruction process are listed in Table 3. In the compression and elastic phases during sample destruction, stretch cracks dominate, and in the nearest stage of instability, the percentage of cuts increases significantly.  
As shown in Figure 9, when the initial disturbance was 0%, the main failure mode of the samples was tensile failure, with tensile cracks accounting for 60.71%. With an increase in early damage, the proportion of shear cracks increased significantly; the proportion of tensile cracks recorded for 20% and 40% early damage decreased to 47.38% and 39.35%, respectively. When the test sample was characterized mainly by tensile cracks, the strength of the samples decreased. In contrast, the strength increased when shear cracks dominated, which was consistent with the change in the uniaxial compressive strength of the coal-cement grout. When the early damage increased to 60%, the sample was dominated by tensile failure; the proportion of tensile events increased significantly, whereas the proportion of shear events decreased. The strength of the samples reduced under these damage conditions. This occurred because new cracks developed during the compaction stage for the samples with 60% early damage, and the new cracks gradually expanded and extended after being subjected to uniaxial compression. This process results in tensile stress that affects the interior of the rock body and generates more tensile cracks.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Initial Damage** & **Compression** & **Elastic Phase** & **Failure Phase** & **Post-Peak Phase** \\  & **Phase (\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
0\% & 73.37 & 61.28 & 52.94 & 22.73 \\
20\% & 53.09 & 63.64 & 51.67 & 43.82 \\
40\% & 80.15 & 65.89 & 36.27 & 24.53 \\
60\% & 73.68 & 84.00 & 47.04 & 39.03 \\ \hline \end{tabular}
\end{table}
Table 3: Percentage of tensile cracks in samples at four stages under different early damages.
## 2 Materials and Methods
### Methods  
The data-processing flow is shown in Figure 2. Following the figure from top to bottom, first the GLASS data were preprocessed to extract ET, GPP, and LAI data. The WUE was then calculated from the GPP and ET data and the climate factors such as temperature, precipitation, wind speed, relative humidity, sunshine duration, and water vapor pressure were interpolated from the original climate data at the meteorology stations. Next, the spatial distribution and variation trend of the WUE were calculated using the linear-regression method. After that, grey relational analysis method was performed to analyze the importance of all influencing factors to WUE and the important factors were chosen. Finally, ridge regression was carried out using the WUE and the selected factors. The contributions of important influencing factors to the WUE trend were quantified. Based on this, the dominant influencing factors of the WUE variation was determined.  
Figure 2: Flowchart of the study. LAI: leaf-area index; PRE: precipitation; TEM: temperature; WS: wind speed; RH: relative humidity; SD: sunshine duration; EA: water vapor pressure.
## 2 A New Class of Linear Multistep Formulae
### Stability of the \(L_{2}\)Mf  
For the stability analysis of the new linear multistep method, the following standard test equations can be considered:  
\[\left\{\begin{array}{l}\frac{\mathrm{d}x}{\mathrm{d}t}=\lambda x\\ x(0)=1\end{array}\right. \tag{10}\]  
where \(\mathrm{Re}(\lambda)<0\).  
According to Equation (3), the corresponding characteristic equation was obtained:  
\[\rho(z)-q\sigma(z)=0,q=\lambda h \tag{11}\]  
where the stability domains of different \(s\) values in Equation (3), supposing, were shown in Figures 1 and 2. In Figures 1 and 2, the outside regions of boundary curves were the stability domain of the new LMF. Obviously, the numerical stability regions of the new linear multistep methods contained the entire left-half complex plane, so the new LMF were A-stable.  
According to the Dahlquist barriers theorem [19], new two-step LMF were second-order methods, and other LMF were first-order methods. The main coefficient of the local truncation error for the three-step LMF and the four-step LMFs are \(-0.6683\) and \(-0.3009\), respectively.  
In addition, Equation (9) was substituted into Equation (3); hence the following equation was obtained:  
\[\frac{x_{n+s}}{x_{n+s-1}}=\frac{-\widetilde{\alpha}_{s-1}}{1-\beta_{s}\lambda h }+\frac{-\sum\limits_{i=0}^{s-2}\widetilde{\alpha}_{i}\frac{x_{n+i}}{x_{n+s-1 }}}{1-\beta_{s}\lambda h} \tag{12}\]  
where \(|x_{n+s}/x_{n+s-1}|\to 0\) if \(\Re(\lambda h)\rightarrow-\infty\). Therefore, the new linear multistep formulae were L-stable [19].  
In summary, the new linear multistep methods are low-order methods. So it is named as low-order LMF (L\({}_{2}\)MF) method. In numerical stability, the L\({}_{2}\)MF method was both A-stable and L-stable. Therefore, compared with the A-stable second-order implicit trapezoidal integration method, the L\({}_{2}\)MF method has strong damping characteristics, which can effectively suppress the numerical oscillations, and hence is suitable for electromagnetic transient simulations in power systems.  
Figure 1: Stability region of new linear multistep formulae (LMF) for even numbers of \(s\).  
Figure 2: Stability region of new LMF for odd numbers of \(s\).
## 2 Preliminaries and Problem Formulation
### Problem Formulation  
Consider the heterogeneous multi-agent system consisting of \(m\) first-order agents and \(N-m(N>m)\) second-order agents. The system is modeled as  
\[\left\{\begin{aligned} \dot{x}_{i}(t)&=f_{i}(x_{i}(t),t)+u _{i}(t),&\quad i\in I_{m}\\ \left\{\begin{aligned} \dot{x}_{i}(t)& =v_{i}(t),&\quad i\in I_{N-m}\\ \dot{v}_{i}(t)&=g_{i}(x_{i}(t),v_{i}(t),t)+u_{i}(t), \end{aligned}\right.&\quad i\in I_{N-m}\end{aligned}\right. \tag{1}\]  
where \(I_{m}=\{1,2,\ldots,m\}\) denotes the set of first-order agents, \(I_{N-m}=\{m+1,m+2,\ldots,N\}\) denotes the set of second-order agents, \(x_{i}(t)\in R^{n}\), \(v_{i}(t)\in R^{n}\), \(u_{i}(t)\in R^{n}\) are the position state, velocity state, and control input of the \(i\)th agent, respectively. \(f_{i}(\cdot)\) and \(g_{i}(\cdot)\) are continuous nonlinear functions.  
**Remark 1**.: _The heterogeneous multi-agent system in this paper consists of first-order multi-agent systems and second-order multi-agent systems, where the first-order multi-agent systems only have position dynamics' equations, while the second-order multi-agent systems have separate dynamics' equations for both position and velocity._  
**Definition 2**.: _The heterogeneous multi-agent system (1) can achieve finite-time consensus when and only when given an arbitrary initial state of each agent, there exists a constant \(T>0\) such that_  
\[\left\{\begin{aligned} &\lim_{t\to T}\lVert x_{i}(t)-w_{i}x_{\delta} \rVert=0,\qquad i\in I_{N},\\ &\lim_{t\to T}\lVert v_{i}(t)\rVert=0,\qquad\qquad i\in I_{N-m}. \end{aligned}\right. \tag{2}\]  
_and \(x_{i}(t)=w_{i}x_{\delta}\) and \(v_{i}(t)=0\) for \(t>T\), where \(T\) is a function about the initial state vector value, \(x_{\delta}\) is the position state that the group of agent \(i\) wants to reach, and \(w_{i}(i=1,2,\ldots,N)\) is defined in Lemma 1._  
**Assumption 1**.: _The signed graph \(\mathcal{G}\) associated with MASs (1) is structurally balanced. The topology of the communication network composed by (1) contains a directed spanning tree._  
**Assumption 2**.: _The nonlinear functions \(f_{i}(x_{i}(t),t)(i=1,2,\ldots,m)\) and \(g_{i}(x_{i}(t),v_{i}(t),t)\)\((i=m+1,m+2,\ldots,N)\) satisfy_  
\[\begin{aligned} &\lVert w_{i}f_{i}(x_{i}(t),t)\rVert\leq \sigma_{1i}\lVert w_{i}x_{i}(t)-x_{\delta}\rVert,\qquad i\in I_{m},\\ &\lVert w_{i}g_{i}(x_{i}(t),v_{i}(t),t)\rVert\leq\sigma_{2i} \lVert w_{i}v_{i}(t)\rVert,\quad i\in I_{N-m}.\end{aligned} \tag{3}\]  
_where \(\sigma_{1i}\geq 0\)\((i=1,2,\ldots,m)\) and \(\sigma_{2i}\geq 0\)\((i=m+1,m+2,\ldots,N)\) are the constants._
## 3 Materials and Methods
### Forecasting Models
#### 3.2.3 Long Short-Term Memory  
LSTM is an advanced architecture of RNN. LSTM has a long-range dependency that makes it more accurate than conventional RNNs. The basic structure of LSTM is considered to be a memory cell for remembering and explicitly propagating unit outputs in different time steps. The memory cell of LSTM uses cell states to remember the information of temporal contexts. It also has a forget gate, an input gate, and an output gate to control information flow between different time steps. The mathematical challenge associated with learning the long-term dependencies in the structure of recurrent neuralnetworks is called the vanishing gradient problem. As the length of the input sequence increases, it becomes harder to capture the influence of the earliest stages, and the gradients to the first few input points vanish and become equal to zero. The LSTM model, which consists of a forget gate, an input gate, and an output gate, compensates for the disadvantages of RNN. The structure of the LSTM network is shown in Figure 6.  
Activation functions that are commonly used in the LSTM network are sigmoid and hyperbolic tangent [12; 24]. These activation functions of LSTM are calculated for forget gate \(f_{t}\), input gate \(i_{t}\), output gate \(o_{t}\), candidate vector \(c^{\prime}{}_{t}\), cell state \(c_{t}\), and hidden state \(h_{t}\) using the following formulae, respectively:  
\[\begin{array}{c}f_{t}=\ \sigma\Big{(}w_{xf}x_{t}+\ w_{hf}h_{t-1}+\ b_{f} \Big{)}\\ i_{t}=\ \sigma(w_{xi}+\ w_{hi}h_{t-1}+\ b_{i})\\ c^{\prime}{}_{t}=\ tanh(w_{xc^{\prime}}x_{t}+\ w_{hc^{\prime}}h_{t-1}+\ b_{c^{ \prime}})\\ o_{t}=\ \sigma(w_{x0}x_{t}+\ w_{ho}h_{t-1}+\ b_{o})\\ c_{t}=\ f_{t}*c_{t-1}+\ i_{t}*c^{\prime}{}_{t}\\ h_{t}=\ o_{t}*tanh(c_{t})\end{array} \tag{3}\]  
In the above equations, \(w_{xf}\), \(w_{xi}\), \(w_{xc^{\prime}}\), and \(w_{x0}\), used with \(x_{t}\), are, and \(w_{hf}\), \(w_{hi}\), \(w_{hc^{\prime}}\), and \(w_{ho}\), used with \(h_{t-1}\), are the weight of each gate. The operator * means the element-wise multiplication. In addition, \(b_{f}\), \(b_{i}\), \(b_{c^{\prime}}\), and \(b_{o}\) are the biases used at each gate. The prediction problem using LSTM creates input data in a pattern of a constant length, as shown in Figure 7. The input of the LSTM model is controlled by the window size (\(w\)) and the horizon factor (\(h\)). The window size indicates the number of consecutive time-steps that are used in feature vector X, while the horizon factor represents how far ahead predictions are being made. In Figure 7, \(w=3\) and \(h=2\).  
The LSTM model in this section and the SLSTM model in the following section were trained using a Python package called Keras on top of the Tensorflow backend. For a fair comparison, the same hyperparameters were used for LSTM and SLSTM in the experiment. The detailed specification of the hyperparameter setting is given in Section 4.  
Figure 6: Schematic representation of long short-term memory (LSTM) model.
## 2 PAH Sample Pretreatment in Food and Quantitative Analysis
Other approaches are being steadily explored for improved clean-up efficiency, such as alumina, Florisil\({}^{\circledR}\), chitosan, diatomaceous earth, zirconia-based sorbents (Z-Sep and Z-Sep\({}^{+}\)), Enhanced Matrix Removal-Lipid (EMR-Lipid), LipiFiltr\({}^{\circledR}\), ChloroFiltr\({}^{\circledR}\), CarbonX, and Cleanert\({}^{\circledR}\) NANO [26; 34; 38]. Common analytical approaches for the identification and quantification of PAHs in a wide range of food matrices make use of high-performance liquid chromatography (HPLC) with an ultraviolet (UV) or photo-diode array (PDA) detector and gas chromatography (GC) coupled with a flame ionization detector (FID) [2; 15; 28]. However, these techniques are exceedingly costly, time-consuming, and labor-intensive and no longer match today's needs regarding selectivity and sensitivity requirements [15; 28]. In contrast, LC and GC techniques coupled with mass spectrometry (MS) surpass the performance of DAD and FLD methods, being widely used in PAH determination in food matrices [2; 13; 39]. Official methods employing LC determination include ISO 15302:2007 for benzo[a]pyrene (BaP) (withdrawn), revised in 2017 [40], in crude or refined edible oils and fats using reverse-phase HPLC-FLD; ISO 15753:2006 (withdrawn), revised in 2016 [41], for 15 PAHs in animal and vegetable fats and oils, involving a clean-up on C18 and Florisil cartridges followed by HPLC-FLD; ISO 22959:2009 [42] for 17 PAHs in animal and vegetable fats and oils using LC-LC coupling and on-line donor-acceptor complex chromatography (DACC) and FLD; and CEN/TS 16621:2014 [43] for BaP, benzo(a)anthracene (BaA), chrysene (Chr), and benzo(b)fluoranthene (BBF) in foodstuffs using HPLC-FLD, based on SEC clean-up. Methods for PAH determination employing GC techniques include CSN EN 16619:2015 [44] for BaP, BaA, Chr, and BBF in foodstuffs (extruded wheat flour, smoked fish, dry infant formula, sausage meat, freeze-dried mussels, edible oils, wheat flour) using pressurized liquid extraction (PIE); size exclusion chromatography (SEC) and SPE clean-up, followed by GC-MS; and PD ISO/TR 24054:2019 [45] for 27 PAHs (including 16 EPA) in animal and vegetable fats and oils using LL extraction and silica gel column clean-up, followed by GC-MS. In order to address the increasing need for more precise PAH confirmation, modern LC-MS/MS and GC-MS/MS techniques are being developed in addition to conventional analytical methods [2; 26; 28; 46].
## 2 Literature Review
### Advanced Machine Learning Techniques for VPN Classification
Graph neural networks (GNNs) can be classified as a class of deep learning models in order to handle graph-structured data; various domains, including molecular biology, social network analysis, and computer vision, have applied them. In order to represent the network traffic as a graph, the authors used GNN to stimulate the relationship between various network flows as shown in a study by [PERSON], [PERSON], and [PERSON] [32] where edges represented the relationships between them while nodes corresponded to the flows. The classification of flows as VPN or non-VPN traffic was ensured through training the GNN model thereby outperforming conventional machine learning methods and achieving high accuracy. Likewise, a GNN-based approach was proposed for encrypted traffic classification including VPNs [32] in another study conducted by [PERSON], [PERSON], and [PERSON] (2021). Through extracting features from payload content and packet headers, they created a graph representation of network traffic. After training the GNN model, it classified the traffic, excelling the machine learning techniques in terms of performance.
## 2 Materials and Methods
### pNDE Imaging System
The pNDE damage detection area was 15 mm \(\times\) 15 mm, and the recorded data included \(500\times 500\) positions with a step length of 30 \(\upmu\)m per step. Accurate 3D imaging of the measured microstructures and potential damage in composites is critical in order to demonstrate the developed system for NDE applications. In our study, all the collected pNDE data was studied in the ultrasonic A-scan, B-scan, and C-scan presentation modes. A schematic of the three ultrasonic presentation modes is shown in Figure 1c. Each presentation mode provided a different way to evaluate the inspected region. A-scan displayed the ultrasonic signal energy as a function of time in the ultrasonic propagation direction. B-scan provided the display of ultrasonic signal energy regarding the linear position of the transducer, resulting in the plot to show the transverse cross-section of the detected composite plate. C-scan allowed a plan-type view of the location and size of damage in the detected sample. The combination of the three ultrasonic presentation modes allowed a comprehensive demonstration of the detected multi-scale damage in the composite structures.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Bayesian Inference Methods  
Bayesian inference methods are rarely used in real-time SR tasks due to their computational complexity. Gaussian processes (GPs) are used to represent the data frame space at HR and give a probabilistic foundation for SR. GPs are non-parametric models that are flexible enough to represent the uncertainty in the SR problem. GPs may be used to estimate the HR digital frame and offer uncertainty estimates associated with the reconstructed image by considering the SR process as a Bayesian inference issue.  
[PERSON] et al. [70] presented a low-dimensional sparse Bayesian learning with a Doppler compensation (LDSBL-DC) approach for improving azimuth resolution in aerial forward-looking imaging while reducing computing complexity. The solution addresses the issue of Doppler centroid variation induced by pitching angle changes through generating a Doppler compensation matrix that removes this spatial variance. This permits the Doppler convolution matrix to be created only once. In addition, they present a low-dimensional projection model that use singular-value decomposition to compress high-dimensional echo data into a lower-dimensional form. By merging Doppler correction with this projection model, a novel imaging technique is developed, with imaging parameters estimated via sparse Bayesian learning (SBL). Matrix transformations are used to further minimize computing complexity when estimating target scattering coefficients. The method's low computing complexity, obtained by Doppler correction and low-dimensional projection, allows for rapid processing of forward-looking imaging data. This makes them easier to integrate into real-time systems, where fast and precise image processing is critical. In addition, ref. [71] introduced a Bayesian deep learning technique for assessing and comprehending uncertainty in SR-guided wave array imaging. It uses a Monte Carlo (MC) dropout approach within multi-scale deep learning models for approximate Bayesian inference, which helps to quantify uncertainties in imaging subwavelength defects. This technique distinguishes between aleatoric uncertainty, which stems from the data, and epistemic uncertainty, which is linked with the model itself.
## 2 Materials and Methods
### Air-Gap Membrane Distillation (AGMD)  
Membranes efficiency was evaluated in desalination process using air-gap membrane distillation (AGMD) technique and experimental rig presented in Figure 2. The AGMD experimental setup and the detailed experimental protocol are described elsewhere [PERSON] _et al._ (2011); [PERSON] _et al._ (2011). AGMD process was realized at temperate of feed equal to T\({}_{\mathrm{f}}\) = 90 \({}^{\circ}\)C and permeate equal to T\({}_{\mathrm{p}}\) = 5 \({}^{\circ}\)C. The air gap width was equal to 5 mm (Figure 2). Measurements were realized for pure water and for the following feed concentrations of NaCl aqueous solutions: 0.25, 0.5, 0.75, and 1.0 M. Rejection coefficient of sodium chloride (Equation (2)) was controlled by ion chromatography (Dionex DX-100 Ion Chromatograph).  
\[\mathrm{R_{NaCl}}=\left(1-\frac{C_{p}}{C_{f}}\right)\times 100\ (\%) \tag{2}\]  
where C\({}_{p}\) and C\({}_{f}\) stand for the NaCl concentration in permeate and feed solution, respectively.  
Figure 2: Scheme of a setup used in AGMD experiments (1—thermostated feed tank; 2 and 6—pump; 3—AGMD thermostated membrane module with air gap = 5 mm; 4—measuring cylinder; 5—balance; 7—cooling system).
## 4 Experimental Research  
### Model Design  
In order to validate the proposed adjustment method of pre-stress deviation, a 5-m diameter cable-bar tensile structure model was designed and constructed, as shown in Figure 3. It was made up of 12 pieces of symmetrical cable-bar units, which contained cables and bars. The cables were categorized into three groups: hoop cables, ridge cables and diagonal cables. Hoop cables consisted of hoop cable 1 (denoted as HC1) and hoop cable 2 (denoted as HC2); ridge cables consisted of ridge cable 1 (denoted as RC1) and ridge cable 2 (denoted as RC2); and diagonal cables consisted of diagonal cable 1 (denoted as DC1) and diagonal cable 2 (denoted as DC2). Meanwhile, the compression bars consisted of Bar 1 and Bar 2. In this study, the 12 DC1 were selected as the active members, while the remaining were the passive members.  
During the experiment, all the 12 DC1 were also used to lay out the measuring points. The resistance foil strain gauge, BX120-5 AA, was applied to monitor the force of the member, and a static data logger was utilized to obtain the strain. Prior to the experiment,  
Figure 3: A cable-bar tensile structure model. (**a**) The whole model; (**b**) A symmetrical cable-bar unit.  
the relation between the force and the strain of the 12 DC1 must be defined, and the least square method was applied as follows:  
\[N=k\varepsilon \tag{16}\]  
where \(N\) and \(\varepsilon\) represented the force and the strain, and \(k\) was the coefficient to be determined by Equation (17) and shown in Table 1, where the number n in the n-DC1 represented the unit number. Table 1 indicated that the relationship between force and strain of each monitoring point was nearly linear.  
\[k=\frac{\sum_{i}N_{i}\varepsilon_{i}}{\sum_{i}\varepsilon_{i}^{2}} \tag{17}\]
## 3 Results and Discussion
### W-TCP Coatings on MgO-MgSZ Rods
As there is not enough time for pores to escape when the laser scanning speed is relatively high and the layer thick enough, those pores aggregate to form small cavities onto the bioactive layer. Eutectic substrate/bioactive coating interface (**c**). Figure 12: FESEM transversal cross-section image showing the microstructure of a directionally solidified off—eutectic ceramic in the MgO–ZrO\({}_{2}\) system with excess of ZrO\({}_{2}\) (**a**). W–TCP coated rod (**b**). Close-up view of the coating-off eutectic interface (**c**). The dendritic structures seen at the interface and coating were CaMgSZ. The in vitro behavior of the W-TCP bioeutectic ceramics and glasses has been studied by [PERSON] et al. [35]. Both materials have the ability of forming a HAp layer in SBF. It is known that bioactive glasses bond with bone more rapidly than its ceramic counterparts and their dissolution products stimulate beneficial response from the body [36]. However, the biodegradable nature of glass can cause the bioactive coating to dissolve over time. Therefore, new compositions should be explored to optimize dissolution and bone integration of the coatings.