esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Input Parameters  
The input parameters database of the RESRAD-ONSITE code used in the present work for the Wolsong NPP was established by referring to overseas NPP decommissioning cases [3, 4], previous studies [17, 18, 19], and the site-specific variables of the Wolsong NPP site [10, 14, 20, 21, 22, 23, 24, 25, 26]. Consequently, based on the geological structure model of the Wolsong NPP site that was assumed above, the hydrological input parameters for each zone were taken from the NUREG/CR-6697 [17]. The area of the contaminated zone, which is a characteristic variable of the Wolsong NPP site, was applied based on a single unit of the Wolsong NPP, and the thickness of the contaminated zone was set to 0.15 m by referring to the case of the Rancho Seco NPP [3, 14]. Furthermore, with respect to the density of the saturated zone, the averaged data for quartz andesite, quartz diorite, and andesite, reported as the main bedrocks in the Wolsong Final Safety Analysis Report (FSAR), were employed. The hydraulic conductivity was input by referring to the data of a previous study on field permeability tests in the Wolsong NPP site [14, 20]. Moreover, for the water transport model, the non-dispersion model proposed by default in the RESRAD-ONSITE code for sites with a contaminated zone larger than 1000 m\({}^{2}\) was applied [10]. Furthermore, the input parameters related to diet in a contaminated site, assuming resident exposure scenario, were input by referring to the domestic annual consumption reports of South Korea [21, 22, 23, 24, 25]. In addition, regarding the precipitation and wind speed, the mean values of climate data measured in Gyeongju city from 2011 to 2020 were employed [26]. The detailed input parameters used in this study are listed in Tables 3 and A1 in Appendix A. Table A1 presents various input parameters used in the RESRAD-ONSITE code taken from various research works. The data obtained from NUREG/CR-7267 [19] were alternatively used in case of the absence of site-specific parameters available for the Wolsong NPP site.
## 4 Numerical Validation
### Example 3: A Nonlinear Absorber System  
The following equations of motion,  
\[\left[ \begin{array}{cc}m_{1}&0\\ 0&m_{2}\end{array} \right]\left\{ \begin{array}{c}\ddot{x}_{1}\\ \ddot{x}_{2}\end{array} \right\}+\left[ \begin{array}{cc}c_{1}+c_{2}&-c_{2}\\ -c_{2}&c_{2}\end{array} \right]\left\{ \begin{array}{c}\dot{x}_{1}\\ \dot{x}_{2}\end{array} \right\}+\left[ \begin{array}{cc}k_{1}+k_{3}&-k_{3}\\ -k_{3}&k_{3}\end{array} \right]\left\{ \begin{array}{c}x_{1}\\ x_{2}\end{array} \right\}+\left\{ \begin{array}{c}k_{2}x_{1}^{3}+k_{4}(x_{1}-x_{2 })^{3}\\ -k_{4}(x_{1}-x_{2})^{3}\end{array} \right\} \tag{43}\] \[=\left\{ \begin{array}{c}f_{0}\cos\omega_{f}t\\ 0\end{array} \right\},\]  
Figure 6: Frequency-amplitude response curves: (**a**) \(x_{1}\) vs \(\omega_{t}\)**(b**) \(x_{2}\) vs \(\omega_{t}\). These were computed from the numerical integration solutions of Equations (36) and (42). The parameter values used to obtain these plots were \(m_{1}=1\) kg, \(m_{2}=1.5\) kg, \(k_{1}=2\) N/m, \(k_{2}=3.5\) N/m, \(k_{3}=5\) N/m, \(c_{1}=0.066\) N-s/m, \(c_{2}=0.099\) N-s/m, \(\epsilon_{1}=1\) N /m\({}^{3}\), \(\epsilon_{2}=1\) N/m\({}^{3}\), \(\mathcal{Q}_{1}=0.2\) N. Here, the black, solid lines describe the numerical integration solutions of Equations (36), while the blue, dashed lines represent the numerical solutions obtained from the derived equivalent nonlinear equations of motion (42). Figure 7: Amplitude versus time response curves: (**a**) \(u_{1}\) vs \(t\), (**b**) \(u_{2}\) vs \(t\), (**c**) \(x_{1}\) vs \(t\), (**d**) \(x_{2}\) vs \(t\). Almost periodic solutions can be observed when the modal displacements are transformed back into the original coordinate systems \(x_{1}\) and \(x_{2}\) using the relationships (37). Here the solid line represents the numerical integration solution of Equation (36), while the dashed, colored line describes the approximate solution obtained from Equation (42). describe the dynamics of a nonlinear absorber attached to a single degree-of-freedom nonlinear oscillator [50]. Here, \(x_{1}\), \(m_{1}\), \(k_{1}\), \(k_{2}\) and \(c_{1}\) represent the displacement, mass, linear and nonlinear stiffnesses, and damping coefficient of the primary system, respectively, while \(x_{2}\), \(m_{2}\), \(k_{3}\), \(k_{4}\) and \(c_{2}\) are system parameters related to the nonlinear secondary absorber system, and \(f_{0}\) and \(\omega_{f}\) are the external excitation force and driving frequency, respectively.
###### Abstract  
Potentially toxic elements (PTEs) have been found in high levels in rainwater, highlighting the importance of removing them when the water is intended for domestic use. In this work, white bean peel was evaluated as sorbent for the removal of a mixture of PTEs from rainwater, namely Zn(II), Cu(II) and Pb(II). A uniform experimental design was used to evaluate the sorption and to optimize the removal process by response surface methodology. The biosorbent reduced the PTEs concentration in the solution, and their removal increased with the increase of the initial concentration and with time. The removal of Cu(II) and Pb(II) was affected by the pH of the solution since, at pH 7.0 for Cu(II), and at pH 5.6 and 7.0 for Pb(II), a decrease occurred in the removal. The optimal conditions for removal, 6 h of contact time between the sorbent and the solution, were applied to rainwater samples spiked with the mixture of PTEs and resulted in removals of 30-90% for Zn(II), 11-78% for Cu(II), and 11-97% for Pb(II), generally lower than those expected by the models, 91% for Zn(II) and 52% for Cu(II), highlighting that the rainwater matrix interferes with the removal of PTEs by peel. However, the white bean peel may be an alternative as sorbent to reduce Zn(II), Cu(II), and Pb(II) concentrations in rainwater, since it is a natural and sustainable material.  
Article  
1  
Footnote 1: Department of Chemistry, University of Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro, Portugal; [EMAIL_ADDRESS]  
2 CICECO & Department of Chemistry, University of Aveiro, Campus Universitario de Santiago, 3810-193 Aveiro, Portugal; [EMAIL_ADDRESS]  
**Keywords: rainwater; potentially toxic elements; white bean peel; biosorption; uniform design; response surface methodology**
# A Novel Hybrid Harris Hawk-Arithmetic Optimization Algorithm for Industrial Wireless Mesh Networks
## 3 Problem Formulation
### Industrial Wireless Mesh Networks  
Proper planning is essential when implementing a wireless mesh network to ensure the optimal placement of mesh routers. Determining the ideal locations and the number of routers needed to achieve complete coverage and connectivity is crucial. Our approach assumes that mesh clients remain stationary and their locations are predetermined, as the placement of mesh routers in an industrial environment depends on the clients' locations. Despite this, finding the optimal placement of WMN routers in a timely and precise manner remains a computational challenge. To address this issue, we made certain assumptions about the placement of mesh routers in a wireless mesh network.  
* The devices connected to the mesh network stay in one place within a desired 2D region;
* Each router in the network has the same range for transmitting signals (Identical transmission);
* The routers are connected based on their transmission range to ensure connectivity.  
The location of mesh clients establishes the ideal positioning of mesh routers and it is represented by \(M=\{L(p_{1},q_{1}),L(p_{2},q_{2}),L(p_{3},q_{3}),\ldots,L(p_{n},q_{n})\}\). It is crucial to remember that a network \(\mathbb{N}\) may not be entirely linked, meaning it could consist of multiple distinct subgraphs. In order to improve the connectivity of the WMN, it is important to enlarge the largest subgraph in the network until it reaches its maximum capacity. A network is considered fully linked when all mesh routers are interconnected. The network coverage for the clients can be obtained using the following equation.  
\[\Omega(N)=\sum_{d=0}^{c}\Delta_{d} \tag{10}\]  
where, \(\Delta_{d}=\begin{cases}1\text{ if the router encloses the client d}\\ 0\text{ Otherwise.}\end{cases}\)Similarly, the network connectivity between the WMN routers will be measured using the expression below.  
\[\Phi(N)=\max_{d\in\{1,\ldots,h\}}|N_{d}|. \tag{11}\]  
The primary goal of the HHAOA is to enhance the efficiency of the WMN, aiming to maximize the network range and connection while minimizing the usage of mesh routers to alleviate network traffic. Moreover, when establishing the fitness function, the parameters \(\Phi\left(N\right)\), representing the level of connectivity among routers, and \(\Omega\left(N\right)\), stating the network coverage, were considered. The weighted sum technique is a straightforward approach for streamlining a multi-objective problem. By expertly combining each objective and assigning a user-determined weight, a single-objective problem is formed.  
\[F_{j}=\xi\cdot\left(1-\frac{\Phi(N)}{n}\right)+(1-\xi)\cdot\left(1-\frac{\Omega (N)}{m}\right); \tag{12}\]  
where, the m and n are the numbers of mesh network clients and routers, respectively. \(\xi\) is a weight-adjusting coefficient parameter that lies in the range of (0,1). Furthermore, the metrics for the statistical comparison will be carried out in terms of _mean_, _standard deviation_ (std.), and _best_ and _worst_.
## 3 Theoretical Formulations
### Equations of Motion  
The equations of motion of FG-GPL reinforced sandwich beams are derived from [PERSON]'s principle. Accordingly, we have  
\[\int_{t_{1}}^{t_{2}}(\delta U-\delta W-\delta K)dt=0, \tag{16}\]  
where \(U\) is the strain energy, \(W\) is the work done by external forces, and \(K\) is the kinetic energy. The virtual strain energy \(\delta U\) for the third-order shear deformable sandwich beams reads as follows  
\[ \begin{array}{rcl}\delta U&=&\int_{A}\int_{0}^{L}(\sigma_{xx}\delta\varepsilon _{xx}+\sigma_{xz}\delta\gamma_{xz})dxdA\\ &=&\int_{A}\int_{0}^{L}\Bigl{[}\sigma_{xx}\Bigl{(}\delta\varepsilon_{xx}^{(0) }+2\delta\varepsilon_{xx}^{(1)}+z^{3}\delta\varepsilon_{xx}^{(3)}\Bigr{)}+ \sigma_{xx}\Bigl{(}\delta\gamma_{xz}^{(0)}+z^{2}\delta\gamma_{xz}^{(2)}\Bigr{)} \Bigr{]}dxdA\\ &=&\int_{0}^{L}\Bigl{[}-\frac{\partial N_{xx}}{\partial x}\delta u-\frac{ \partial}{\partial x}\Bigl{(}N_{xx}\frac{\partial w}{\partial x}\Bigr{)} \delta w-\frac{\partial M_{xx}}{\partial x}\delta\phi+c_{1}\frac{\partial P_{ xx}}{\partial x}\delta\phi-c_{1}\frac{\partial^{2}P_{xx}}{\partial x^{2}}\delta w \\ &+&Q_{x}\delta\phi-\frac{\partial Q_{x}}{\partial x}\delta w-c_{2}R_{x}\delta \phi+c_{2}\frac{\partial R_{x}}{\partial x}\delta w\Bigr{]}dx\\ &+&\Bigl{[}N_{xx}\delta u+N_{xx}\frac{\partial w}{\partial x}\delta w+M_{xx} \delta\phi-c_{1}P_{xx}\delta\phi-c_{1}P_{xx}\frac{\partial}{\partial x}\delta w +c_{1}\frac{\partial P_{xx}}{\partial x}\delta w+Q_{x}\delta w-c_{2}R_{x} \delta w\Bigr{]}_{0}^{L},\end{array}  \tag{17}\]  
where  
\[\left[ \begin{array}{c}N_{xx}\\ M_{xx}\\ P_{xx}\end{array} \right]=\int_{A}\sigma_{xx}\left[ \begin{array}{c}1\\ z\\ z^{3}\end{array} \right]dA,\ \ \left[ \begin{array}{c}Q_{x}\\ R_{x}\end{array} \right]=\int_{A}\sigma_{xz}\left[ \begin{array}{c}1\\ z^{2}\end{array} \right]dA. \tag{18}\]  
The virtual kinetic energy \(\delta K\) is defined as  
\[ \begin{array}{rcl}\delta K&=&\int_{A}\int_{0}^{L}\rho(z)\Bigl{(}\dot{u}_{1 }\dot{\delta u}_{1}+\dot{u}_{3}\dot{\delta u}_{3}\Bigr{)}dAdx\\ &=&\int_{0}^{L}\Bigl{[}\Bigl{(}m_{0}\ddot{u}+m_{1}\ddot{\phi}-c_{1}m_{3}\Bigl{(} \ddot{\phi}+\frac{\partial\ddot{w}}{\partial x}\Bigr{)}\Bigr{)}\delta u\\ &+&\Bigl{(}m_{1}\ddot{u}+m_{2}\ddot{\phi}-c_{1}m_{3}\ddot{u}-c_{1}m_{4}\Bigl{(} 2\ddot{\phi}+\frac{\partial\ddot{w}}{\partial x}\Bigr{)}+c_{1}^{2}m_{6}\Bigl{(} \ddot{\phi}+\frac{\partial\ddot{w}}{\partial x}\Bigr{)}\Bigr{)}\delta\phi\\ &+&\biggl{(}m_{0}\ddot{w}+c_{1}m_{3}\frac{\partial\ddot{u}}{\partial x}+c_{1} m_{4}\frac{\partial\ddot{\phi}}{\partial x}-c_{1}^{2}m_{6}\Bigl{(}\frac{\partial \ddot{\phi}}{\partial x}+\frac{\partial^{2}\ddot{w}}{\partial x^{2}}\Bigr{)} \Bigr{)}\delta w\Bigr{]}dx,\end{array}  \tag{19}\]  
with  
\[m_{i}=\int_{A}\rho(z)z^{i}dA=\sum_{k=1}^{2N+1}b\int_{z_{k}}^{z_{k+1}}\rho_{c}^{ (k)}z^{i}dz,\ \ \ i=0,1,2,3,4,6. \tag{20}\]  
In the total absence of external forces on the structure, it is \(\delta W=0\).
## 2 Material and Methods
### MODIS Land Products Subsets  
Collection 5 MODIS land product subsets for Fluxnet sites are readily available from the DAAC (Distributed Active Archive Center) at Oak Ridge National Laboratory [20]. These subsets represent MODIS product cutouts of 7 km by 7 km around the eddy flux towers, greatly simplifying data availability. We used both collection 5 MOD15A2 (FPAR canopy product [21]) used for computation of the collection 5.1 MOD17A2 (GPP) for the period ranging from 2001 to 2010. For Puechabon, we selected all pixels from the 7 km \(\times\) 7 km subset to represent the eddy tower footprint (see quality analysis and control for our site in [22] and [23]) with its mean and standard-deviation values because our site is representative of vegetation cover that extends largely across our study region [24] considering the surrounding area reduce errors with gridding artifacts and with spatial data with nested source of variations [25].  
\[GPP=FPAR\times PAR\times LUE_{\mathrm{max}}\times f(T_{\mathrm{min}})\times f (VPD) \tag{2}\]  
where \(LUE_{\mathrm{max}}\) is the biome-specific maximum light use efficiency (g\(\cdot\)C\(\cdot\)MJ\({}^{-1}\)), \(PAR\) is the photosynthetically active radiation (MJ), and the potential \(GPP\) is linearly scaled between 1 (no limitation) and 0 (whole limitation = no \(GPP\)) using biome-specific functions for minimum temperature, \(f\)(T\({}_{\mathrm{min}}\)) and vapor pressure deficit, \(f\)(\(VPD\)). The fraction of \(PAR\) absorbed by the vegetation cover \(FPAR\) is from MOD15 while meteorological inputs are from large spatial-scale meteorological data, obtained from NCEP-DOE Reanalysis II dataset (see supporting online material in [26]). Due to acknowledged effects of input climate data on MODIS17A2 GPP [21,27,28], we also tested for f(\(VPD\)) sensitivity to bias in climate data source (NCEP _vs._ tower climate).
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 5 Performance Analysis  
To verify the performance of the algorithm, we consider a scenario with one MS and four SSs serving a time-sensitive task. The distance between each SS and the MS is generated randomly. The AWGN power is set to \(\sigma^{2}=1\times 10^{-8}\) W. We assume that all satellites have the same computing capability. The default values of typical parameters are listed in Table 2 and the distribution map of satellites in the scenario is shown in Figure 3.  
Figure 3: The distribution map of satellites in the scenario.  
Figure 2: The flowchart of the DB-IPDD algorithm.
# Error Probability of a Coherent \(M\)-ary PSK FSO System Influenced by Phase Noise
###### Abstract  
In this paper, we aim to develop an analytical framework for design and analysis of new generation mobile networks fronthaul/backhaul links based on the application of free-space optical (FSO) technology. Taking the receiver hardware imperfections into account, we present an efficient analytical approach in analyzing average symbol error probability (SEP) of the coherent FSO system employing \(M\)-ary phase-shift keying (PSK). Optical signal transmission is influenced by pointing errors and atmospheric turbulence. The signal intensity fluctuations caused by atmospheric turbulence are modeled by general Malaga (\(\mathcal{M}\)) distribution, which takes into account the effect of multiple scattered components. We estimate the range of the signal-to-noise ratio at which the SEP floor appears, as well as the value of this non-removable error floor. The results illustrate that the effect of imperfect phase error compensation on the SEP is more critical under weaker turbulence conditions and for higher order modulation formats. Based on the analytical tools presented here, it is possible to estimate tolerable value of standard deviation of phase noise for the given value of SEP. This value of standard deviation is an important parameter in designing the phase-locked loop filter in the receiver.  
refe-space optics; phase-shift keying; error probability; phase noise; atmospheric turbulence  
**MSC**: 33C20; 65B10; 42A20; 60E05
# Fuzzy Relationship between Kansei Images: A Grey Decision-Making Method for Product Form
## 1 Introduction
### Contributions  
To address the problems of existing design decision-making methods ignoring the fuzzy relationship between Kansei images and that constant weights result in a decrease in the accuracy of cognitive evaluation results, this paper combines the variable weights theory with CGTDM for multiple Kansei images, and constructs a grey decision-making model for product form driven by fuzzy relationship between Kansei images. The main contributions of this paper are as follows:1. Variable weight theory and the grey target decision-making model are widely used in the field of engineering technology, but they are less often used to study the fuzzy relationships between product forms and Kansei images. Here, we introduce them to the field of design. Through an innovative combination, the paper proposes a grey decision-making model for product form driven by a fuzzy relationship between Kansei images, which is more in line with the actual decision-making process.
2. While solving the problem of the low accuracy of cognitive evaluation results caused by constant weights, this study overcomes the one-sidedness of ignoring the fuzzy relationship between Kansei images in the CGTDM for multiple Kansei images. It provides a more reliable method for multiple Kansei images decision-making of products.
3. Compared to other decision-making models, the grey decision-making model constructed in this study can improve the discrimination among decision-making results and make it easier for designers to select design reference samples.
# Separating Built-Up Areas from Bare Land in Mediterranean Cities Using Sentinel-2A Imagery
## 5 Results and Discussion
### Validation on Independent Test Regions

Table 6: Classification accuracies of the different approaches for the Ankara region. Figure 12: (**a**) RGB image of Konya from Sentinel-2A, (**b**) the classified Sentinel-2A, (**c**) the classified multi-index (NDBI, NDVIre, and MNDWI), and (**d**) the classified multi-index (NDTI, NDVIre, and MNDWI) images. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Type** & **Overall Accuracy** & **Kappa** \\ \hline Sentinel-2A image (10 Bands) & 0.51 & 0.42 \\ Multi-index (NDBI+NDVIre+MNDWI) & 0.58 & 0.48 \\ Multi-index (NDTI+NDVIre+MNDWI) & 0.92 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the overall accuracy and Kappa statistics for the Ankara region. The analysis of the two independent test regions showed similar characteristics and supported the effectiveness of the NDTI-based multi-index set in different land and seasonal conditions. Notably, the roofs of buildings in industrial areas were made of aluminium-based materials and concrete, whereas the roofs of the other buildings in the built-up areas were made of tiles in the main and in the test regions. The difference in the roof material resulted in different spectral responses and enabled separation in the original spectral bands and the multi-index data for these classes. Thus, the mixing problem was mainly between bare land and the built-up areas and between bare land and industry. Further analysis was required to evaluate the performance of the proposed approach in regions where the industrial and built-up areas were composed of similar roof materials. The results from the study and the test regions demonstrated that although built-up indices have been shown to highlight built-up areas, their performance was limited in heterogeneous landscapes, where urban areas and bare lands were mixed. A shortwave infrared-based index improved the separation of urban areas and bare lands, as shown in this research work. In addition, the newly added red-edge spectral bands of Sentinel-2A enhanced the vegetation cover detection and mapping.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 4 Model Training and Evaluation
### Data Pre-Processing  
The RSSI dataset consisted of 5191 data and was filtered using the moving average filters. The moving average (MA) filter is the most common and widely used filter in digital signal processing because it is the most accessible digital filter in this kind of application. The row RSSI data in both the time and frequency domains are shown in Figure 3. Where we have only plotted the RSSI data received from three access points to understand the fluctuation and nature of the signals. The RSSI values received from access points one, two and three are shown in black, orange and blue color in the Figure 3.  
After the filtering, four columns were added to the dataset, as shown in Table 2, where zones A, B, C, and D were shown as per Figure 2. This modification was performed as we needed to train our ML models as a classification task. Table 2 shows the original dataset, where the location column denotes the location ID allocated for each location, and OO2, PO1, PO2 and PO3 are the RSSI levels received by the anchor nodes. Table 3 shows the dataset modified for classification purposes, where four new columns are added.  
Figure 3: RSSI data with frequency and time domain.
## 2 Materials and Methods
### Post-Processing of Change Parcels
#### 2.4.2 Post-Processing  
The post-processing of the bare land change parcels mainly includes edge simplification, parcel aggregation, hole-filling, and area/confidence filtering, and the methods and processing guidelines are as follows:  
(1) Edge simplification: The bare land change parcels automatically extracted by the deep CNN have irregular shapes and jagged edges. To reduce the redundant nodes, the Douglas-Peucker algorithm is used for the edge simplification of the parcels, which simplifies the data by identifying and removing smaller nodes in the polygon that affect the overall shape of the line elements. The tolerance parameter is used to determine the degree of simplification of the edges, which is the maximum allowable vertical distance between each fold and the newly created line. The greater the tolerance, the greater the degree of simplification. This method uses a tolerance of a 1-2-pixel width for the simplification of the bare land change parcels.  
(2) Parcel aggregation: Using the simplified parcels, the \(P_{com}\) value is calculated based on the proximity evaluation model proposed in this study. The threshold \(P_{thresh}\) is set to aggregate any two parcels larger than it. For instance, M, N, and s\({}_{0}\) in Figure 5 are aggregated into one parcel to form a closed continuous region. The confidence score \(p_{F}\) of the new aggregated parcel F is obtained from the confidence of the original parcels \(p_{M},p_{N}\), calculated based on area weighting.  
(3) Filtering and filling: According to the requirements for practical applications of change detection, smaller change parcels are removed based on a certain area threshold, \(A_{thresh}\). Another area threshold for holes in parcels, \(H_{thresh}\) is set, with holes smaller than the threshold being filled. Finally, a confidence range \(p\) can be specified based on business needs to output different parcels with different confidence levels.