esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability of a Compacted Sand Slope Model Subject to
## 3 Numerical Study
### Validation of Numerical Results by the Experimental Ones
#### 3.3.2 Case study No.2: \(\alpha=35^{\circ}\)  
From Figures 18-20, the comparison between models M6, M7 and M8 shows that the more the location of applied loading moves towards the crest slope, the more the failure load decreases.  
Figure 17: Evolution of the reaction force versus vertical displacement (M5).  
Figure 18: Evolution of the reaction force versus vertical displacement (M6).  
In turn, the HSM predictions of the failure load are in much better agreement with the experimental values recorded with test models M6 and M7. One can conclude that when the angle of compacted sand slope model increases from 30\({}^{\circ}\) to 35\({}^{\circ}\), the HSM predicts a realistic decrease in failure load compared to the prediction by Mohr-Coulomb model.  
From Figures 20-22, it is clear that the global experimental behavior fairly matches with predictions by the MC constitutive law both for the magnitudes of failure load and vertical displacement recorded under rigid plate RP3 at the crest side of the tested slope models M8, M9 and M10.  
Figure 19: Evolution of the reaction force versus vertical displacement (M7).  
Figure 20: Evolution of the reaction force versus vertical displacement (M8).  
Moreover, the HSM overestimates the recorded vertical displacement and underestimates the magnitude of failure load by 19%, 27% and 29% when the applied loading moves away from the slope side, respectively.  
As shown in Figure 23, the value of the vertical displacement under the rigid plate tends toward a maximum value of 16 mm, and the observed failure surface of this model (M9) fits with a log-spiral failure curve.  
Figure 21: Evolution of the reaction force versus vertical displacement (M9).  
Figure 22: Evolution of the reaction force versus vertical displacement (M10).  
Table 5 summarizes the relative differences between the experimental values of the failure load and its predictions by the Mohr-Coulomb and hardening soil models. Absolute values of these relative differences indicate that the MC model predicts the experimental values of the failure load much better (relative difference lower than 10%) for all models except model M6. For the latter, the HSM predicted the failure load much better, with a weak relative difference.
# An Image-Encryption Algorithm Based on Stage-Merging Bit Scrambling
## 3 Security Analysis
### Histogram Analysis  
An algorithm with an outstanding encryption effect is supposed to make the number of pixels corresponding to each pixel value after encryption relatively average, which can hide the statistical information of pixel values and improve security. Figure 9 shows the histogram comparison of [PERSON]'s plain image, scrambled intermediate cipher image, and cipher image. The pixel value distribution of the plain image is concentrated in a specific interval. After bit scrambling, the pixel value distribution becomes smooth and uniform, which hides the original information.
## 2 Preliminaries  
### Petri Nets (PNs)  
A Petri Net [52], also called a Place/Transition Net, is a four-tuple N = (\(P\), \(T\), \(F\), \(W\)). Both \(P\) and \(T\) are non-empty sets. Generally, \(P\) is a finite set of places, and \(T\) is a finite set of transitions, disjointed to each other. The set \(F\subseteq(P\times T)\cup(T\times P)\) is the arcs of this net, which is represented by one-way arrows from its nodes to denote a flow relation in a PN model. The mapping \(W:(T\times P)\cup(P\times T)\rightarrow\mathbb{N}\) assigns a weight to an arc: \(W(x,\ y)>0\) if \((x,\ y)\in F\), and \(W(x,\ y)=0\) otherwise; where \(x,\ y\in P\cup T\) and \(\mathbb{N}\) is the set of non-negative integers. We call \(x^{\bullet}=\{y\in P\cup T\ |(x,\ y)\in F\}\) the postset of \(x\), and \({}^{\bullet}x=\{y\in P\cup T\ |(y,\ x)\in F\}\) is called the preset of \(x\). A marking \(M\) is a multi-set of its places, which allocates tokens to each place of PNM. We denote \(M(p)\) as the number of tokens in place \(p\). The pair \((N,\ M_{0})\) is called a marked Petri net or a net system. A net is pure if \(\forall(x,\ y)\in(P\times T)\cup(T\times P),\ W(x,\ y)>0\), implying that \(W(y,\ x)=0\). The incidence matrix \([N]\) of the pure net \(N\) is a \(|P|\times|T|\) integer matrix with \([N](p,\ t)=W(t,\ p)-W(p,\ t)\).  
A transition \(t\in T\) is enabled at marking \(M\) if \(\forall p\in{}^{\bullet}t,\ M(p)\geq W(p,\ t)\), which is denoted as \(M[t]\). Once an enabled transition \(t\) fires, it generates a new marking \(M^{\prime}\), denoted as \(M[t]M^{\prime}\), where \(M^{\prime}(p)=M(p)-W(p,\ t)+W(t,\ p)\). The set \(M[)\) represents all markings reachable from \(M\) by firing any possible sequence of transitions. The set \(M_{0}[)\) is the reachable markings of net \(N\) with initial marking \(M_{0}\), often denoted as \(R(N,\ M_{0})\). It can be graphically indicated by a reachability graph, which can, in turn, be denoted as \(G(N,\ M_{0})\). It is a directed graph in which each node represents a marking in \(R(N,\ M_{0})\), and arcs are labeled by the fired transitions.  
Let \((N,\ M_{0})\) be a net system with \(N=(P,\ T,\ F,\ W)\). A transition \(t\in T\) is live at \(M_{0}\) if \(\forall M\in R(N,\ M_{0}),\ \exists M^{\prime}\in R(N,\ M_{0}),\ M^{ \prime}[t]\). The pair \((N,\ M_{0})\) is live if \(\forall t\in T,\ t\) is live at \(M_{0}\). It is dead at \(M_{0}\) if \(\
exists\in T,\ M_{0}[t]\).  
A P-vector is a column vector \(I:P\rightarrow\mathbb{Z}\) indexed by \(P\). P-vector \(I\) is called a P-invariant (place invariant, PI for short) if \(I\
eq 0\) and \(I^{T}[N]=0^{T}\). P-invariant \(I\) is said to be a P-semiflow if \(I\geq 0\). Let \(I\) be a PI of \((N,\ M_{0})\) and \(M\) be a reachable marking from \(M_{0}\). Then, \(I^{T}M=I^{T}M_{0}\).
## 4 Conclusions  
Understanding the spatio-temporal variation of the WUE and its environmental control mechanism are of great significance to estimate the effect of the water cycle on the ecosystem carbon budget and to evaluate the spatio-temporal variation of the water circulation of ecosystems. This study represents the first attempt to assess the spatio-temporal variation of the GPP, ET, and WUE using remote sensing technology and to understand the interactions between carbon and water circulations in South Korea. We used the GPP with the MOD 17 GPP products and MOD 16 global ET products to derive regional scale map of the WUE which showed the spatial variability of the WUE in South Korea. Using MODIS products, we quantified the mean annual GPP as 21.91 and 25.91 gC\(\cdot\)m\({}^{-2}\cdot\)8-day\({}^{-1}\) and the mean annual ET as 13.85 and 15.84 g\(\cdot\)m\({}^{-2}\cdot\)8-day\({}^{-1}\) for the period of 2007 and 2008, respectively. The seasonal mean of the WUE was 2.86 and 2.92 g\(\cdot\)C\(\cdot\)g\(\cdot\)H\({}_{2}\)O\({}^{-1}\) in the dormant season and 1.81 and 1.88 g\(\cdot\)C\(\cdot\)g\(\cdot\)H\({}_{2}\)O\({}^{-1}\) in the growing seasons during 2007 and 2008. In case of spatial variation, it was identified that the WUE values near the coastline were higher than inland areas in dormant season because of overestimated GPP values near the coastline. The lower WUE was identified in the agricultural area such as cropland and rice paddy. The WUE was high in highly elevated areas as in case of the plateau which was located at a higher elevation resulted in lower ET and high WUE value. In case of temporal variation, Understanding the spatio-temporal pattern of WUE is the most significant to estimate the ecosystem carbon budget (photosynthesis of the vegetation) and to evaluate the spatio-temporal variation of water circulation. We investigated the interaction between the carbon and water circulation in terrestrial ecosystems and found that these ecological procedures were generated by the photosynthesis of the vegetation. The MODIS-based GPP, ET, and WUE map introduced in this study were appeared to be useful for understanding of the relationship between the water and carbon cycle with reasonable accuracy.
## 4 Performance Analysis of Islanding Detection Methods
### Response Time  
Due to the negative impact of islanding on the network components and utility personnel, the response time of the islanding detection method is very crucial and should be as small as possible. Most islanding detection methods have a response time ranging between half a second to two seconds, which is relatively high, especially under the  
Figure 4: Non-detected Zone.  
circumstance of uninterruptedly autonomous operation of an island. The response time of the passive method is more than the response time of the active method, while remote methods are faster compared to passive and active methods.
# Formation Control of Multiple Autonomous Mobile Robots Using Turkish Natural Language Processing
## 4 Materials and Methods
### Path Tracking  
The optimal path between the robot's origin and its destination point is found by the A\({}^{\star}\) algorithm. The algorithm calculates the path according to the size of the area used the distance to the target point, and node values are returned. These node coordinates are sent to the controller system as reference values, and the robot reaches the target. Figure 10 shows a block diagram of the process.  
As shown in Figure 10, the system first reads the coordinates of the next target node. The distance between the robot and the target is calculated according to Equation (27), and the angle is calculated according to Equation (28). If these calculated values are within the reference range, the robot stops and waits. If the error values are outside the reference range, the controller uses these values to determine the angular velocity and linear velocity of the robot. The robot is driven at these determined velocities. This process continues until the error values enter the reference range.  
\[d=\sqrt{\left(\begin{array}{c}x_{d}-x_{r}\end{array}\right)^{2}+\left(y_{d} -y_{r}\right)^{2}} \tag{27}\]  
\[\theta=\arctan\left(\frac{y_{d}-y_{r}}{x_{d}-x_{r}}\right) \tag{28}\]  
where \(x_{d},y_{d}\) are the coordinates of the node, \(x_{r},y_{r}\) are the coordinates of the robot, \(d\) is the distance between the robot and the node, and \(\theta\) is the angle between the robot and the node.
## 4 Time Integration Method  
In the previous section, the HHT time integration method used for reducing the computational time was mentioned and the detailed explanation can be as follows [29; 30]:  
\[Ma_{i+1}+(1+\alpha)Cv_{i+1}-\alpha Cv_{i}+(1+\alpha)r_{i+1}-\alpha r_{i}=(1+ \alpha)F_{i+1}-\alpha F_{i} \tag{1}\]  
\[d_{i+1}=\overline{d}_{i+1}+\Delta t^{2}\beta a_{i+1} \tag{2}\]  
\[v_{i+1}=\overline{v}_{i+1}+\Delta tya_{i+1} \tag{3}\]  
Figure 3: Numerical Algorithm used in FEAPH.  
Figure 2: Schematic Diagram and organization of Finite Element Analysis Program for Hybrid test (FEAPH) (**a**) Simplified RTHT system with FEAPH window; (**b**) Internal organization of FEAPH program.  
\[\widetilde{d}_{i+1}=d_{i}+\Delta tv_{i}+\Delta t^{2}\Big{(}\frac{1}{2}-\beta\Big{)} a_{i} \tag{4}\] \[\widetilde{v}_{i+1}=v_{i}+\Delta t(1-\gamma)a_{i} \tag{5}\]  
where the integral coefficient \(-\frac{1}{3}\leq\alpha\leq 0,\;\beta=\frac{(1-\alpha)^{2}}{4},\;\gamma=\frac{1}{2}-\alpha\).  
The restoring force (\(r_{i+1}\)) as a known value can be calculated from the command displacement. At the next time step (\(i+1\)), Equation (1) can be rewritten as below.  
\[Ma_{i+1}^{(k)}+(1+\alpha)Cv_{i+1}^{(k)}-\alpha Cv_{i}+(1+\alpha)r_{i+1}^{(k)}- \alpha r_{i}=(1+\alpha)F_{i+1}-\alpha F_{i}-R_{i+1}^{(k)} \tag{6}\]  
Equation (6) can be expressed with equation (1) and \(R_{i+1}^{(k)}\) (residual force) can be as follows.  
\[R_{i+1}^{(k)}=M(a_{i+1}-a_{i+1}^{(k)})+(1+\alpha)C(v_{i+1}-v_{i+1}^{(k)})+(1+ \alpha)(r_{i+1}-r_{i+1}^{(k)}) \tag{7}\]  
Equation (7) can be rearranged for \(\Delta d_{i+1}^{(k)}\):  
\[R_{i+1}^{(k)}=M\frac{\Delta d_{i+1}^{(k)}}{\Delta t^{2}\beta}+(1+\alpha)C\frac {\gamma}{\Delta t\beta}+(1+\alpha)K_{ini,\;i+1}^{(k)}\;\Delta d_{i+1}^{(k)}=K^ {*}\;\Delta d_{i+1}^{(k)} \tag{8}\]  
where \(K_{ini,\;i+1}^{(k)}\;\Delta d_{i+1}^{(k)}=r_{i+1}-r_{i+1}^{(k)}\;,K
## 3 Results
### Functional Properties of FMLSP  
The scavenging of DPPH and ABTS radicals by FMLSP and VC initially increased and subsequently leveled off with increasing concentration, as shown in Figure 1C,D. Significant antioxidant activity was shown by FMLSP, according to the IC50 values derived from the curves. The IC50 values of VC (0.07 mg/mL and 0.14 mg/mL) were similar to the IC50 values of FMLSP for DPPH and ABTS radical scavenging, which were 0.36 mg/mL and 0.19 mg/mL, respectively.  
As can be seen from Figure 1E,F, the release of total flavonoids as well as total phenols from FMLSP during the simulated gastric digestion phase was not much different from that before digestion and decreased rapidly within 15 min from the beginning of the simulated gut digestion phase. This indicates that the total flavonoids and total phenols of FMLSP  
\begin{table}
\begin{tabular}{c c c c} \hline
**D10\%/\(\upmu\)m** & **D50\%/\(\upmu\)m** & **D90\%/\(\upmu\)m** & **D [4,3]/\(\upmu\)m** \\ \hline
4.21 \(\pm\) 0.16 & 8.09 \(\pm\) 0.20 & 18.52 \(\pm\) 0.57 & 8.52 \(\pm\) 0.33 \\ \hline \end{tabular}
\end{table}
Table 2: FMLSP particle size distribution.  
\begin{table}
\begin{tabular}{c c c} \hline
**Compound** & **Content (mg/g)** & **Compound** & **Content (mg** **GAE/g dw)** \\ \hline Ash & 82.28 \(\pm\) 0.04 & Polyphenol & 50.41 \(\pm\) 1.04 \\ Protein & 227.52 \(\pm\) 0.07 & Flavonoid & 91.30 \(\pm\) 2.92 \\ Fat & 31.93 \(\pm\) 0.08 & Polysaccharides & 58.41 \(\pm\) 1.39 \\ Dietary fiber & 305.18 \(\pm\) 5.28 & 1-DNJ & 0.89 \(\pm\) 0.008 \\ \hline \end{tabular}
\end{table}
Table 3: The chemical composition content of FMLSP.  
are stable in an acidic environment but easily oxidized under alkaline, light, and humid conditions. Therefore, the release was stable in the simulated gastric digestion stage, while it was partially oxidized in the simulated enteric digestion environment, leading to a decrease in the release.  
The inhibition rate of FMLSP on \(\alpha\)-amylase and \(\alpha\)-glucosidase activities showed a trend of increasing (Figure 1G and then leveling off in the experimental concentration range. The inhibition rate of FMLSP on \(\alpha\)-amylase peaked at 63.60% when the concentration hit 3 mg/mL. The inhibition rate of FMLSP on \(\alpha\)-glucosidase peaked at 86.15% when the concentration hit 4 mg/mL. The results indicated that FMLSP has the potential for hypoglycemia.
# A Reliable Scheme for Synchronizing Multimedia Data Streams under Multicasting Environment
## 3 Problem Statement
### Problem Scenarios  
In order to understand the problem clearly, let us consider a simple example. The scenario consists of one sender, two receivers, and the single data stream is being transmitted from a sender to receivers as shown in Figure 4.  
Receivers R1 and R2 received packet P1 from sender S1, calculate its playout time and put it in the jitter buffer. Jitter buffer holds the packet till its playout time has arrived. This wait is enforced to remove the jitter and provide the smooth playout as shown in Figure 5a-c.  
Figure 4: Topology with one sender and two receivers.  
Figure 3: Problem Statement use cases: (**a**) Use case 1; (**b**) use case 2.  
In Figure 5a both receivers have same base playout time (packet received time is same), but usually, this does not happen in real time applications because the propagation delay varies. To calculate the playout time, receiver adds local playout delay and jitter buffer delay to the base playout time.  
In Figure 5b both receivers have the same value for the delay at receiver (local playout delay + jitter buffer delay). However, this is not valid in the case of real-time applications because each receiver has its own processing speed. The local delay, in this case, varies due to the variation of network delays, jitter, and buffer delay among different receivers.  
In Figure 5c the actual playout time calculated by both receivers R1 and R2 is the same. This is very unlikely to happen as even if two packets travel through the same path in an IP network but their transmission delay will be different in most of the cases due to the general behavior of IP networks. If the destinations R1 and R2 are far apart, the probability of variations in delay increases. Other factors like clock skew and buffer delays at receivers make it difficult to achieve synchronized playout. Therefore, there is a clear need to design an algorithm to handle synchronized playout of multimedia data on all the participating receivers.  
In all three different cases the playout is smooth, i.e., receivers are synchronized with the source. This kind of synchronization is local synchronization. In this paper, we have developed a system that guaranteed the result similar to Figure 5a where both receivers are playing back at the same time.
# Minimum Barrier Distance-Based Object Descriptor for Visual Tracking
## 3 The Proposed Algorithm
### MBD-Based Patch Weighting
We summarize the MBD transform in Algorithm 1, and \(k\) is the number of iterations in the raster scan. As shown in Figure 3, one raster scan will only access the two neighbors of the current node since graph \(G\) is a map of the 4-neighbor. \(\pi_{x1}\) indicated by the green arrow represents a path from current node \(x\) to \(S\) and through its upper neighbor \(b_{1}\), and \(\pi_{x2}\) represents the path of the current node to \(S\) and through its left neighbor \(b_{2}\); if \(\beta_{G}(\pi_{x}\cup b_{2})<\beta_{G}(\pi_{x}\cup b_{1})\), then \(D(x)=\beta_{G}(\pi_{x}\cup b_{2})\). Finally, we can obtain the enhanced object descriptor by multiplying the feature descriptor with the corresponding patch weight as follows:  
\[\psi(\mathbf{z}_{t},\mathbf{r})=[D_{t}(1)\mathbf{f}_{1}^{T},...,D_{t}(64) \mathbf{f}_{64}^{T}]^{T}. \tag{6}\]  
Figure 3: Illustration of the raster scan pass and the inverse raster scan pass. The node x is the current visited patch and its masked neighbor area for 4-adjacency is denoted as b. ```
1:image \(G\), seed set \(S\), number of passes \(k\). 2:MBD map \(D\). 3:auxiliaries \(H\), \(L\). 4:Initialize: \(D(x)\gets 0\) when \(x\in E\), otherwise \(D(x)\leftarrow\infty\); set \(H\gets G\) and \(L\gets G\). 5:for\(i=1\):\(k\)do
6:if\(mod(i,2)=1\)then
7:\(ScanOrder\gets RasterScan\);
8:else
9:\(ScanOrder\gets InvRasterScan\);
10:endif
11:for each node \(x\) in \(ScanOrder\)do
12:for each neighbor node \(b\) of \(x\)do
13: compute \(\beta_{G}(\pi_{x}\cup b)\) according to Equation (4);
14:if\(\beta_{G}(\pi_{x}\cup b)<D(x)\)then
15: update \(D(x)\) according to Equation (5);
16:\(H(x)\leftarrow\max\{H(b),G(x)\}\);
17:\(L(x)\leftarrow\min\{L(b),G(x)\}\);
18:endif
19:endfor
20:endfor
21:endfor
```  
**Algorithm 1** Minimum Barrier Distance transform.
## 3 System Description
### Methodology
#### 3.1.2 Conventional Placement  
Being represented by Case VII in Figure 3, it has been used as the traditional placement approach (CLQ) throughout this study to establish a comparison with the proposed technique. Additionally, this is the part where the illuminance and communication parameters of CLQ were calculated. It was run in parallel to the iterative analysis, so, altogether, there were twelve cases under consideration (see: Figure 3).
# Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
## 2 Related Work
### Autoencoder Algorithm  
Autoencoder is a neural network whose training goal is to minimize the reconstruction error between the output and input [21]. It is frequently utilized in feature extraction [22], image classification [23], and noise reduction [24]. Autoencoder is also commonly used in data compression. It is made up of two components: encoder network and decoder network. An autoencoder can encode data \(X\) into a low-dimensional hidden vector \(Z\), and a decoder can reconstruct the hidden vector into data \(X^{\prime}\). The following is its mathematical formula.  
\[Z=\sigma_{e}(WX+b) \tag{3}\]  
\[X^{\prime}=\sigma_{d}\big{(}W^{\prime}Z+b^{\prime}\big{)} \tag{4}\]  
\(\sigma_{e}\) and \(\sigma_{d}\) are the activation functions for the encoder and decoder. \(W\) and \(b\) are respectively the weight and bias.  
Since the autoencoder is trained on several normal data, its aim is to minimize the reconstruction error [25], so the trained autoencoder has a good ability to reconstruct normal data, but insufficient ability for anomalous data because it has not been trained. Considering that the reconstruction error of normal data will be smaller while those of anomaly data will be relatively larger [26], the reconstruction error can be used to distinguish whether it is an abnormality. The calculation formula of the reconstruction error is shown in Formula 5 below.  
\[e=X-X^{\prime} \tag{5}\]
# Well-Distributed Feature Extraction for Image Registration Using Histogram Matching
## 3 Method
### Feature Matching, Transformation, and Re-Sampling  
After extracting features with good distribution, the next steps in image registration are finding correspondence between features from the reference and the sensed images, transforming the model and re-sampling. In our method, we use Euclidian distance to match the features. To find the transformation model, the B-Spline based method proposed by [PERSON] et al. [23] is used. B-splines are locally controlled, less dependent on image texture and more robust to noise. Finally, the transformed image is re-sampled. The size of neighborhood and the choice of interpolation method may affect the resultant image. Among the various methods, we apply bi-cubic interpolation [24] by using \(4\times 4\) neighborhood pixels. The bi-cubic interpolation is computationally more expensive; however, it provides better results than nearest neighbor and bi-linear based interpolation methods.
# Methodology for Improving Scanning Performance Loading an Array Element with a 3D All-Metal WAIM
## 2 Proposed Unit-Cell and Equivalent Circuit  
### Description of the Structure  
The basic unit-cell considered in this manuscript is based on the structure proposed by some of the authors in [14], consisting of an innovative periodic cell that supports TEM modes when illuminated by an impinging wave under normal incidence. It is formed by four metallic corners, as it is sketched in Figure 1a. Such a structure was employed in [12] to design a polarizing screen. For the purpose of designing a WAIM, the unit-cell can be loaded by a shunt impedance placed at the loading plane (as it was also performed in [14] in order to synthesize the polarization conversion and to provide physical support to the four metallic corners, because they are floating).  
Figure 1: TEM unit-cell: (**a**) general structure, (**b**) incidence planes considering \(Y\) polarization.  
The interest in using this kind of 3D unit-cell is to benefit from its multiple degrees of freedom, broad operation bandwidth and avoidance of dielectric materials. The possibility to interact independently with the fields impinging in the two main scanning planes (\(H\)- and \(E\)-_planes_, taking as reference the \(Y\) polarization like in Figure 1b) is particularly appealing for the WAIM application.