esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm
## 4 Dynamic Edge-Server Placement
### Dynamic Edge-Server Placement Algorithm  
This section describes the proposed dynamic and intelligent edge-server placement strategy. Initially, we place static edge servers in areas where the population is relatively fixed and dense. Dynamic edge servers are then deployed so that the average user latency is within a certain range. However, because of the variations in the user locations over time, the user delay also varies. When the average user delay exceeds a certain range, we take appropriate reduction measures. First, we try to reduce the average user latency by adjusting the location of the dynamic edge server. If this approach fails to meet our requirements, we increase the number of edge servers to ensure that the latency remains within an acceptable range. In contrast, when the average user latency is below a certain threshold, we force it within a certain range by reducing the number of dynamic edge servers. The reduced edge servers can be used for other purposes to compensate for the deployment cost. This flexibility enables dynamic deployment decisions based on real-time conditions.  
The solution steps are listed in Algorithm 2. With this dynamic and intelligent edge-server deployment strategy, the servers can better adapt to changes in crowd location, and thus, the quality of the user experience is optimized.  
```
0: Location of user distribution at different moments
0: Number and location of dynamic edge servers
1: Find the optimal location for static server placement using the ISO algorithm
2:forTime \(=1\) to \(t\)do
3: Optimize the dynamic-server placement using the ISO algorithm
4:if\(\min<\) delay \(<\) max then
5:do nothing
6:else
7:if delay \(>\) max then
8: Increase the number of dynamic servers
9: Compute the position and delay using the ISO algorithm
10:else
11: Reduce the number of dynamic servers
12: Calculate the position and delay using the ISO algorithm
13:endif
14:endif
15:endfor
```  
**Algorithm 2** Dynamic Edge-Server Placement Algorithm
# Explainable AI Frameworks: Navigating the Present Challenges and Unveiling Innovative Applications
## 4 Discussion
### XAI Frameworks
#### 4.1.26 DeepLIFT  
The Explainable AI (XAI) approach known as DeepLIFT (Deep Learning Important FeaTures) is used to assign the inputs of input characteristics to the results produced by a neural network model [67; 68]. By evaluating each neuron's stimulation within the network regarding a reference activation state usually selected as a baseline state like zero or the average activation it functions, DeepLIFT rates each feature according to its significance in terms of the model's predictions by calculating the differential in activations between the present state and the reference condition. By revealing which information properties are significant in shaping the model's judgments, these significance scores improve the ability to understand and comprehend neural network activity. DeepLIFT is very useful in fields like image analysis, financial forecasting, and healthcare diagnostics, where it is essential to understand feature contributions [53; 69].
# Improving 2-5 Qubit Quantum Phase Estimation Circuits Using Machine Learning
## 4 Discussion
#### Limitations and Drawbacks  
There are a few limitations of this method of enhancing QPE that are important to consider. First, this method requires the simulation of large datasets, which is computationally expensive for small qubit systems (like those considered here) and nearly impossible for many qubit systems since the computational difficulty is exponential. Since the accuracy of the traditional method increases exponentially with the number of qubits as well, a QPE system of several qubits free of noise would be better than the best performance of our method. Therefore, the useful application for this type of improvement to the standard QPE approach lies in circumstances where only a few qubits are available in the first register. This is, however, the circumstance in which the field of quantum computing finds itself now.  
More specific limitations in this experiment include the fact that only one type of error was examined in the simulated circuits. For a more complete picture, Pauli measurement, depolarizing, thermal relaxation, and reset errors (among many others) would need to be inserted into the simulations, though this would require more simulation for every type of noise that is modeled. Furthermore, only one phase was present in the distributions. For general applications, the state of the second register may not be an eigenstate of the unitary operator, which leads to multiple phases affecting the probability distribution from which we sample. Our models are not applicable to these scenarios. Simulations of more general situations with multiple types of error and multiple phases are underway in order to train more advanced machine learning models. Finally, this experiment only measured performance with absolute error in phase, not the periodic error for the phase, \(\widetilde{\theta}\), given by \(\min\left\{\left|\widetilde{\theta}-\theta\right|,\;1-\left|\widetilde{\theta }-\theta\right|\right\}\). It is possible to train models and perform analysis with this periodic error, and having both sets of models may be desired by some researchers. This is, again, an opportunity for further work.
## 3 Results and Discussion
### Biological Assays
The strong antioxidant activity of ciliroside, assessed using various colorimetric tests, including FRAP, DPPH*, ABTS*+, and Fe2+ chelating assays, has been previously reported in the literature [35; 36]. Moreover, [PERSON] et al. described its cytoprotective effect against the OH*-induced damage in mesenchymal stem cell and they found that the activity is linked with p-coumaroyl moiety in the molecular structure [36]. Also, ellagic acid belongs to a potent antioxidant agent. [PERSON] et al. reported its protective effects against oxidative stress induced by UV-B in skin cells, associated with the restoration of glutathione (GSH) and superoxide dismutase (SOD) levels which were reduced in fibroblasts subjected to UV-B irradiation [37]. Significant ROS scavenging  
Figure 5: Effect of extract in human skin fibroblast cells on (a) 2′,7′-dichlorohydrofluorescein fluorescence and (b) cell viability assessed using NR test. The cells were pretreated with different extract concentration and exposed on H2O2. The data are means (_n_ = 3) \(\pm\) SD. * Indicates statistically significant difference vs. H2O2-stimulated cells (_p_ < 0.05). activity was also reported for kaempfer 3-glucose [38; 39]. Furthermore, it has been evidenced that this flavonoid protect for cellular membrane against ROS [38]. The presence of these specific phenolics in the tested extract has also been beneficial, considering the other desirable properties of cosmetic formulation. In vivo studies have demonstrated that trilioside exhibits a protective effect against inflammation in human skin provoked by UV radiation [40; 41] and in a mouse ear inflammation model [35]. It also acts as a tyrosinase inhibitor, which may be useful in skin-whitening preparations [42]. Furthermore, [PERSON] et al. discovered that trilioside stimulates the production of ceramides, the components of the outermost layer of the epidermis, essential for maintaining skin hydration. Consequently, it supports moisture retention and improves skin barrier function [27]. In turn, ellagic acid has shown significant activity towards the inhibition of some metalloproteinases, including hyaluron, elastase, collagenase, being accountable for the breakdown of the extracellular matrix and loss of skin firmness [43] and exerts a photoprotective effect [43; 44].
# An Improved MOEA/D with Optimal DE Schemes for Many-Objective Optimization Problems
## 1 Introduction
In 2014, [PERSON] and [PERSON] [27] suggested a reference point-based NSGA-II for many-objective problems, similar to NSGA-II, called NSGA-III. NSGA-III still employed the Pareto non-dominated sorting method and added the decomposition concept to solve the many-objective optimization problems. In 2015, [PERSON] et al. [28] proposed a new algorithm that exploited the merits from both the dominance- and decomposition-based approaches to balance the convergence and diversity of the evolutionary process to solve the many-objective optimization very well. From these points of view, while solving this kind of many-objective optimization problem, the original MOEA/D will meet some limitations, such as the distributions of weighted vectors not being very uniform, and it just uses a single approach to decompose the MOPs. All of these limitations are encouraging researchers to study it. This provides the major impetus for us to study how to improve the performance of MOEA/D on solving the many-objective optimization problems. Therefore, an improved MOEA/D with optimal DE schemes (MOEA/D-oDE) is proposed in this paper Our purposes are to employ and use the advantages of different DE schemes. Firstly, MOEA/D-oDE uses the alterable decomposition approach. Besides, MOEA/D-oDE uses the optimal DE schemes. More precisely, the proposed algorithm MOEA/D-oDE combines the weighted sum approach and the Tchebycheff approach to produce a new scalarizing function to decompose the many-objective optimization problems. Additionally, we use the alterable DE's trial vector operators to improve the performance of MOEA/D in solving the many-objective optimization problems. The remaining part of this paper is arranged as follows. Section 2 presents the preliminaries of our study, including the basic idea of MOEA/D, the basic decomposition approaches and a simple introduction to the DE's trail vector design methods. Section 3 presents our newly-proposed algorithm, named MOEA/D-oDE, which attaches importance to how to decompose the many-objective optimization problems and how to make the results of the DE operators perform well. Section 4 presents the experimental studies on MOEA/D-oDE, comparing MOEA/D-oDE with MOEA/D and NSGA-II in solving the many-objective problems, and the experimental results demonstrate that MOEA/D-oDE outperforms or performs similarly to MOEA/D and NSGA-II. Finally, Section 5 concludes this paper concisely.
# Experimental and Numerical Analysis of an Innovative Combined String-Cable Bridge
## 3 Test Results and Analysis
### Experimental Results
#### 3.1.2. Asymmetrical Loading
When analyzing the behavior of the asymmetrically loaded bridge model, it is necessary to note that the displacements of the part of the model not loaded with live load (nodes 3R, 5R, 7R) are directed upwards. The higher the values of the load ratio \(\gamma\), the larger these displacements are in absolute terms (see Tables 7-9). However, in their absolute size, these displacements are significantly smaller than the vertical displacements of the loaded part (on average, about 50% when \(\gamma=1\)). The axial forces of the bridge model elements in the string and cable under asymmetrical loading are presented in Table 10.  
From the data in Table 10, the tensile forces in the string under asymmetric loading are higher than in the case of symmetrical loading (see Table 6). Additionally, the cable forces increase with increasing pre-tension values (0.472 kN, 1.084 kN, 1.506 kN). In this case, the axial forces of the cable change little and are smaller in absolute magnitude (at any value of \(\gamma\)) than the string axial forces.
# Shock Wave Formation and Cloaking in Hyperelastic Rods
## 3 Secular Equations
### FE Formulation  
The governing equations are solved by the 1D FE method for spatial discretization and the FD method in the time-domain coupled with an explicit Lax-Wendroff energy conservation numerical scheme. To achieve a conditionally stable numerical algorithm, the Courant-Friedrichs-Lewi condition was imposed on the time increment \(\Delta t\):  
\[\Delta t<\frac{\Delta x}{\max c(\varepsilon)} \tag{3j}\]  
where \(\Delta x\) is the spatial increment (mesh size); and \(c(\varepsilon)\) is the rod velocity, defined by Equation (2o). For example, considering a particular case of Yoeh potential that relates to a Neo-Hookean material at \(n=1\) in Equation (2o), and a fairly large strain interval \(-0.5<\varepsilon<0.5\), gives:  
\[\max c(\varepsilon)\approx 2.958\sqrt{\rho^{-1}A_{1}} \tag{3k}\]  
At \(n>1\) expression for \(\max c(\varepsilon)\) will depend upon ratios \(A_{k}/A_{1}\), \(k=1,\dots,n\), provided \(A_{1}\
eq 0\).  
For spatial discretization, 2-node linear truss elements with no flexural or torsional stiffness were used. The total number \(N\) of elements varied in the range \(8K\leq N\leq 24.8K\). The mesh convergence analysis revealed almost identical displacement fields for the mesh with \(N\geq 10.2K\). Thus, \(N=10.2K\) was adopted for performing computations in the next section. Another remark regards the use of a median filter to reduce non-physical oscillations:  
\[f*(t_{i})=\frac{1}{2m+1}\sum_{k=1}^{m}f(t_{i\pm k}) \tag{3l}\]  
Herein, \(2m+1\) is the filter order; \(f\) is the unfiltered function; \(f*\) the filtered function. In the next section, median filters of orders \(7\div 15\) are used.  
Finally, the adopted physical nonlinearity prevents the use of a non-reflecting boundary condition at the right end of a rod of finite length; Figure 1. Thus, to eliminate waves reflected from the right end and arriving to a point of observation located at a distance \(l\) from the left end, the rod length \(L\) should satisfy the following condition:  
\[\frac{l}{\min c(\varepsilon)}<\frac{2L-l}{\max c(\varepsilon)} \tag{3m}\]  
ensuring that the slowest pulse reaches the point of observation without interfering with the fastest pulse reflected from the right end.
## 3 Experimental and Numerical Simulation Setup
### Experimental Setups
#### 3.1.2 Wind Speed above the Bridge Deck  
In addition to the study of the flow pattern above the bridge deck near the pylon zone, the wind speed profile is also measured in the wind tunnel experiment. The results are used to analyze the influence of the pylon on the wind speed distribution above the bridge deck. Dantec's hot-wire anemometers (DENTEC Dynamics company, model number of 55P11, Denmark) are applied to measure the wind speed. According to the Cartesian coordinate system defined in Figure 3, the detailed positions of the measuring points in test model are given in the following.  
1. [label=0]
2. Upstream main lane (Y = -0.184 m): x = 0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.8, 1.0, 1.2, 2.0 m;
3. Downstream main lane (Y = +0.184 m): x = 0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.6, 0.8, 1.0, 1.2, 2.0 m;
4. For each longitudinal measuring position, the height z = 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.04, 0.05, 0.06, 0.08, 0.1, 0.12 m.  
There are 336 measuring points in total. These include 14 measuring positions in the upstream main lane and also the relative 14 measuring positions in the downstream main lane, and each position has 12 measuring points along the height. The upstream lanes represent vehicle lanes that  
Figure 3: The positions of measuring points. **(a)** Longitudinal measuring point arrangement. **(b)**Vertical measuring point arrangement (A-A Section).  
Figure 2: Segmental model.  
are close to the inlet, and the downstream lanes represent the lanes that are far from the inlet flow. Furthermore, the central railing is the separation of upstream and downstream lanes.
# PSO-SVM Based Performance-Driving Scheduling Method for Semiconductor Manufacturing Systems
## 4 Empirical Study
### Experimental Results
#### 4.3.2 The Proposed Scheduling Method  
Simulation tests and comparisons with CDR serve to validate the proposed PSO-SVM-based scheduling solution. To avoid contingency, we conducted tens of experiments with various dispatching parameters. Different dispatching parameters could lead to different performances. Table 7 displays the simulation outcomes. The proposed scheduling method refers to the scheduling methodology that includes CDR, performance driving, and feedback mechanism. We perform normalization processing on the simulation results to examine the simulation findings. The results of normalization are displayed in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**NO.**} & \multicolumn{2}{c}{**Fab-Wide**} & \multicolumn{2}{c}{**Equipment-Related**} & \multicolumn{2}{c}{**Wafer-Related**} \\ \cline{2-7}  & **MOV** & **PR** & **EU\_P** & **EU\_O** & **PCSR** & **ODR** \\  & **(Steps)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline
1 & 96,682 & 39.61 & 77.11 & 73.28 & 96.04 & 89.71 \\
2  
From Table 7 and Figure 8, we can draw the following conclusions:  
1. Compared to CDR, the proposed scheduling method improved MOV, PR, EU_P, EU_O, PCSR, and ODR by 7.85%, 5.11%, 8.76%, 8.15%, 6.60%, and 7.33%, respectively. This illustrates the usefulness and effectiveness of the \"performance-driving\" and \"feedback mechanism\";
2. The proposed scheduling method can satisfactorily improve performance and respond to the changing manufacturing environment;
3. Different dispatching parameters could result in different performances. Therefore, we could improve performances by changing dispatching parameters appropriately. However, the production status is ever-changing, so we cannot confirm dispatching parameters that are suitable for all production status. We can make the scheduling system capable of adaptively adjusting dispatching parameters.
## 2 Mathematical Model
### Governing Equation and Solving Conditions  
Based on the model's symmetry, the right half of the model is used as the focus of the study. The governing equation for the drawdown within the confined aquifer is formulated based on Darcy's law and mass conservation as well as the aforementioned assumptions.  
\[k_{\text{h}}\frac{\partial^{2}s}{\partial x^{2}}+k_{\text{v}}\frac{\partial^{ 2}s}{\partial z^{2}}=\mu_{\text{e}}\frac{\partial s}{\partial t},\ (0\leq x\leq R,\ 0\leq z\leq M) \tag{1}\]  
where \(s(x,z,t)\) [L] is the drawdown of groundwater; \(k_{\text{h}}\) [LT\({}^{-1}\)] and \(k_{\text{v}}\) [LT\({}^{-1}\)] are the hydraulic conductivity in the \(x\) and \(z\) direction; and \(\mu_{\text{e}}\) [L\({}^{-1}\)] is the specific storage.  
The initial conditions are, respectively, described as  
\[s\big{|}_{t=0}=0\ \ \ \ (0\leq x\leq R,\ 0\leq z\leq M) \tag{2}\]  
The lateral boundary conditions are expressed as  
\[\frac{\partial s}{\partial x}\bigg{|}_{x=a}=\left\{\begin{array}{ll}0&0<z<d \\ -\frac{Q}{2k_{\text{h}}(M-d)}&d\leq z<M\end{array}\right. \tag{3}\]  
Figure 1: Schematic diagram of strip excavation pit with cut-off wall in confined aquifer with finite lateral dimensions.  
\[s|_{x=R}=0\ \ \ \ \ (t>0,0\leq z\leq M) \tag{4}\]  
Without considering the recharge of the adjacent aquifers, the vertical boundary conditions can be written as  
\[\left.\frac{\partial s}{\partial z}\right|_{z=0}=\left.\frac{\partial s}{ \partial z}\right|_{z=M}=0\ \ \ \ \ (t>0,0\leq x\leq R) \tag{5}\]  
where \(Q\) [L2T-1] is the discharge per unit width; \(a\) [L] is half the width of the excavation pit; \(M\) [L] is the thickness of the confined aquifer; \(R\) [L] is position of the constant-head boundary; and \(z\) [L] stands for the depth below the excavation pit.
## 4 Discussion  
### Comparison of TROPOSIF and eSIF Downscaling Data  
The main advantage of eSIF over other reconstructed SIFs lies in its clearer and more straightforward relationship with the original SIF, making it more suitable for studying the  
Figure 10: The variations in the linear relationships among eSIF, TROPOSIF, VIs, and GPP as the buffer zone diameter of flux sites changed: (**a**,**c**) are the SHBI site and (**b**,**d**) are the SHB2 site.  
\"true\" connection between SIF and GPP [27]. Using the GOSIF product as a representative of reconstructed SIF, the comparison of eSIF and reconstructed SIF in tracking the seasonal variations in GPP is shown in Figure 14. GOSIF exhibits smoother seasonal variation without corresponding changes with the decline in GPP, while eSIF demonstrates a more consistent variation pattern. This indicates that eSIF has a significant advantage in tracking seasonal changes in GPP. TROPOSIF is the true SIF directly retrieved from satellites. It is converted from instantaneous SIF on clear-sky days to daily-scale SIF by multiplying with a day-length scaling factor; hence, the daily-scale SIF can only represent SIF on clear-sky days [27; 29]. In this study, the linear relationship between the downscaled TROPOSIF and the SCOPE-simulated SIF was slightly better than that with the eSIF (Figure 5), while the linear relationships of the eSIF and TROPOSIF with the GPP were comparable (Figure 6). The primary advantage of SIF lies in its ability to accurately reflect the vegetation's photosynthetic capacity [2; 50], making the development of SIF products capable of accurately tracking seasonal variations in GPP essential. However, compared to the eSIF, the downscaled TROPOSIF performed poorly in terms of consistency with seasonal variations in GPP. This is because the original TROPOSIF only represents SIF on clear days, thus affecting the downscaled results by not accounting for all weather conditions. As a result, the downscaled TROPOSIF failed to consider all weather conditions. The downscaled TROPOSIF values for the growing season of 2021 in the study area were significantly higher than those of 2020 (Figure 15), while the eSIF values remained almost equal between the two years (Figure 4), which could result in considerable errors in interannual variation. During the summer of 2021, the PAR data were notably lower compared to 2020 (Figure 15), suggesting the likelihood of more overcast days in 2021. Consequently, this could result in a considerable increase in the TROPOSIF value for 2021 compared to the SIF value across all weather conditions. Additionally, the downscaled model shows that the importance of PAR as an independent variable is low for TROPOSIF (only 3.7%), while it is 10.9% for eSIF (Figure 15), indicating a weaker relationship between TROPOSIF and PAR.