esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### The Multi-Level Location Models for Emergency Shelters
#### 3.1.3 Location Model of Central Emergency Shelters  
The location of a central emergency shelter should maximize the coverage of the facility points when the site is selected to meet the established travel distance criteria. Based on the principles of economics, the location of such central emergency shelters should be combined with the location of a fixed emergency shelter [11]. The maximum coverage model (MCM) is an important variant of the MPM, the basic assumptions are the same as those for the location of fixed emergency shelters. The specific mathematical models are expressed as follows.  
**Maximum coverage model (MCM)**  
\[MCM:\max z=\sum_{i\in D}a_{i}y_{i} \tag{8}\]  
\[s.\ t.\ \sum_{j\in V_{i}}y_{i}\leq x_{j},\forall i\in D \tag{9}\]  
\[D=\{D_{i}|i=1,2,\ldots,n\} \tag{10}\]  
\[V_{i}=\left\{i\middle|d_{ij}\leq r\right\} \tag{11}\]  
\[\sum_{j\in L}x_{j}=p(1\leq p\leq m) \tag{12}\]  
\[x_{j},y_{i}\in\{0,1\},\forall j\in L,\forall i\in D\ (j\leq i) \tag{13}\]  
\[L=\left\{L_{i}\middle|j=1,2,\ldots,m\right\} \tag{14}\]  
\[y_{i}=\left\{\begin{array}{c}1,if\ \textit{the demand point}\ D_{i}\ \textit{is covered by the facility point}\\ 0,\textit{otherwise}\end{array}\right. \tag{15}\]  
\[x_{j}=\left\{\begin{array}{c}1,if\ \textit{the facility point}\ L_{j}\ \textit{is identified}\ \textit{for emergency point}\\ 0,\textit{otherwise}\end{array}\right. \tag{16}\]where \(D\) is the set of demand points, \(n\) is the number of demand points, \(L\) is the set of candidate facility points, and \(m\) is the number of candidate facility points. \(V_{i}\) is the set of candidate service facilities for demand point \(i\). \(p\) is the number of service facilities planned to be established. \(r\) is the maximum service radius of the central emergency shelters, which is set to 8 km in this paper. \(a_{i}\) is the demand of demand points and, in this paper, we use the coverage population to represent it. \(d_{ij}\) is the parameter of the distance from point \(i\) to point \(j\).  
Equation (8) is to maximize the population of the covered demand points under limited resources. In this paper, the alternative facility points for the central emergency shelter are mainly based on the location results of the fixed emergency shelter. Equation (9) indicates that when demand point \(i\) is assigned to cover, one of the candidate facility points within its effective emergency distance must be selected as an emergency shelter. Equation (11) is the set of candidate service facilities for demand point \(i\), and the basic condition for its satisfaction is the parameter of the distance from point \(i\) to point \(j\) is less than the effective emergency distance. Equation (12) expresses that the number of designated facilities is equal to \(p\). Moreover, Equation (13) is the integral constraint condition of \(x_{j}\) and \(y_{i}\). Equations (10), (14)-(16) refer to the range and condition of \(x_{j}\) and \(y_{i}\).
## 3 Parameter Analysis and Optimization Design
### Parameter Analysis
#### 3.1.3 Impact of Lever's Bending Line Stiffness and Leverage Amplification Coefficient  
This section expounds the variation in \(f_{d}\) and \(f_{E}\) with the lever's bending line stiffness \(i_{le}\) as well as the lever's amplification coefficient \(\chi\). In Figure 9, \(i_{le1}\) refers to the bending line stiffness with the box section of 230 \(\times\) 140 \(\times\) 25 \(\times\) 25 (unit: mm). The impact of the stiffness and amplification coefficient is not the same. As is shown in Figure 9a, like \(i_{mc}\), \(f_{d}\) and \(f_{E}\) rapidly increase at first and then decrease slowly with the increase in \(i_{le}\). As is shown in Figure 9b, when \(k_{N}=0\), there exists an optimal leverage amplification coefficient \(\chi\) which enables \(f_{E}\) to reach the maximum. When \(k_{N}\
eq 0\), \(f_{d}\) and \(f_{E}\) increase with the increase in the lever's amplification coefficient \(\chi\). However, this does not mean that larger options can be selected without limitations. The larger \(\chi\) causes the lever and beam to bear a greater force, which requires a larger cross-section to maintain elasticity. SAP2000 (Version 23.1.0) is used for numerical simulation. The results indicate that the theoretical calculation results and numerical simulation results of \(f_{d}\) are very consistent under different \(\chi\) conditions.
## 1 Introduction
They mention that this method is reliable for groundwater quality assessment and serves as a useful tool for decision-makers for efficient groundwater quality monitoring and management mainly in agricultural areas which have a great influence on groundwater recharge and quality. In recent years, population growth and the increase of agricultural areas and indus-trial activities have intensified water demand, threatening the sustainable use of ground-water in the Cuernavaca aquifer. Despite this situation has been locally evidenced, no formal studies have been carried out to demonstrate the effect of these activities on groundwater resources. The novelty of this study lies in describing the hydrological and hydrochemical conditions of this aquifer to identify its vulnerability. The geohydrorological features of the Cuernavaca aquifer are described using Gibb, Piper, and Schoeller diagrams. Groundwater quality evaluation is carried out based on time series analysis, water quality distribution maps, and water quality indices. This study performs a multivariate and correlation analysis to identify possible pollution sources and proposes better water management strategies for this aquifer.
## 1 Introduction
Providing URC for services with different latency requirements is considered in [11]. However, no distinction is made in URC literature between communication under normal power grid operation conditions and under blackout conditions. The contribution of this paper is to show how LTE/5G PLMN communication can ensure reliable communications for selected MTC devices during emergency conditions, without giving these devices additional priority over other network users during normal conditions or adversely affecting the service to prioritised users during emergencies. This makes LTE/5G PLMN usable for critical applications where reliable communications are needed. The paper demonstrates this by firstly developing the communication requirements to enable an islanded microgrid to recover from blackout (Section 2). Then we examine how LTE/5G PLMN communication can ensure reliable communications for selected MTC devices during emergency conditions, without giving these devices additional priority over other network users during normal conditions or adversely affecting the service to prioritised users during emergencies (Section 3). Then the use of one such LTE/5G communication feature, the PLMN-based Access Barring method, to achieve reliable MTC communication during blackout is demonstrated in a lab environment (Section 4). Finally, we quantify the reduction in recovery time, using Continuous Time Markov Chain (CTMC) modelling of the recovery of the power and communications systems from blackout by conventional means compared to the autarkic microgrid-based approach (Section 5).
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 4 Effect of Metal-Based NPs on Medicinal and Aromatic Plants
### Silver-Based NPs
#### 4.1.2 Elicitor Effect
Cell suspension cultures (CSC) of _Momordia charantia_ elicited with AgNPs showed pronouncedly higher levels of Ag, malondialdehydedehyde (MDA), and H\({}_{2}\)O\({}_{2}\) compared to non-elicited ones, and in their extracts, considerably enhanced total phenolic content (TPC) and total flavonoid content (TFC) compared to the control were observed. In CSC elicited by 5 mg/L AgNPs. the amounts of flavonoids, hydroxybenzoic, and hydroxycinnamic acids greatly exceeded the amounts observed in the control [121].
## 2 Materials and Methods
### Methods
#### 2.2.2 ESR Measurements  
ESR measurements were made using an X-band EMX-10 spectrometer from Bruker (Bruker Co., Billerica, MA, USA). The magnetic field modulation frequency was 100 kHz. The samples were measured in Pasteur pipettes. The measurements were conducted at 260 K to assess whether nanoparticles can enter cells at specific nanoparticle/cell ratios (magnetic sweep width of 13 mT, modulation amplitude of 0.2 mT). The temperature during the measurements was controlled using a Bruker temperature controller unit (ER 4131 VT). Then, the samples were measured at 295 K (magnetic sweep width of 8 mT, modulation amplitude of 0.01 mT). The spectrometer was operating with the following settings: microwave power of 7.97 mW, sweep time of 41.9 s, conversion time of 40.96 ms, and the time constant was 40.96 ms. The concentration of free radicals in the studied samples was calculated from the integrated intensity of ESR signals from the attached TEMPOL spin label with an accuracy of 5%. All ESR experiments were repeated three times.
## 3 End-to-End SNR Statistics  
The variable-gain relaying and fixed-gain relaying are considered in our work. From (6), the equivalent SNR at D can be obtained as:  
\[\gamma_{eq}=\frac{E_{s}G^{2}h_{RF}^{2}(\eta_{e}I)^{t}}{G^{2}(\eta_{e}I)^{t}N_ {01}+N_{02}}=\frac{\frac{E_{s}h_{RF}^{2}}{N_{01}}\frac{(\eta_{e}I)^{t}}{N_{02 }}}{\frac{(\eta_{e}I)^{t}}{N_{02}}+\frac{1}{G^{2}N_{01}}} \tag{18}\]  
For the variable-gain relaying scheme, amplifying gain \(G^{2}=\frac{1}{E_{s}h_{RF}^{2}+N_{01}}\)[16]. Substituting it into (18), the equivalent instantaneous SNR can be derived as:  
\[\gamma_{eq}^{V}=\frac{\gamma_{RF}\gamma_{FSO}}{\gamma_{RF}+\gamma_{FSO}+1} \tag{19}\]  
The fixed-gain relaying is semi-blind relaying, in which the channel state information (CSI) of the RF link is not required at the relay (i.e., the relay R has a fixed gain \(G\)). It is assumed that \(C=1/\big{(}G^{2}N_{01}\big{)}\). Thus, the equivalent instantaneous SNR can be written as [32]:  
\[\gamma_{eq}^{F}=\frac{\gamma_{RF}\gamma_{FSO}}{\gamma_{FSO}+C} \tag{20}\]
## Nomenclature  
### Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} RFR & Random Forest Regressor \\ D2D & Device-to-Device \\ DTN & Delay-Tolerant Network \\ Opp-Nets & Opportunistic Network \\ RFEPM & Random Forest Energy Prediction Model \\ GM & Gaussian Model \\ MANET & Mobile Ad Hoc Network \\ IT & Identity Table \\ HT & History Table \\ CC & Current Context \\ OMNs & Opportunistic Mobile Networks \\ MSE & Mean Square Error \\ GCN & Graph Convolution Network \\
**Variables** \\ Variables with descriptions used in this paper: \\ \(F_{n}\) & Input feature & \(r_{j}\) & Encounter events [0, 1] \\ \(u_{n}\) & User variable & \(C_{n}\) & Cell \\ \(E_{j}\) & Random variable & \(W\) & Weight \\ \(R_{i}\) & Random tree index & \(y_{i}\) & Output variable \\ \(x_{i}\) & Input feature instance & \(G\) & Aggregate function \\ \(N\) & Sample size & \(L\) & Leaf node \\ \(B_{i}\) & Bootstrap sample index & \(E_{T_{x}}\) & Expected encounter value \\ \(D\) & Data (search/data space) & \(D_{t}\) & Training dataset \\ \(D_{t}e\) & Test dataset & \(Dsn\) & Selected sub-space of training data \\ \(n_{s}n\) & No. of samples in selected sub-space of data & \(O_{j}\) & Output of tree \\ \(U_{j}\) & Co-ordinate of each node & \(C_{n}(U,E)\) & Cuts of new tree \\ \(U,E\) & Co-ordinates of \(r_{j}\) & \(r_{j}\) & Set of random classification output \\ \(\mu_{n}(U,E)\) & Time of each co-ordinate & \(J\) & Varies \(1\leq j\leq 3\) \\ \(E_{f}\) & Estimation function & \(M_{i}\) & \(\mu_{i}\) belongs to set of \(\mu\) (learning set) \\ \(R_{i}\) & Rule for predicting value of \(W\) \\ \end{tabular}
# Intelligent Identification of Trend Components in Singular Spectrum Analysis
## 3 Separability Improvement
### Methods of Separability Improvement
Also, consider the variant with elementary grouping with \(k=t\) and \(I_{j}=\{j\}\). Since the algorithm is iterative, it has a stopping criterion that includes the maximum number of iterations \(n_{iter}\), and tolerance; the latter will be taken by default to be \(10^{-3}\) and we will not specify it in the parameters. There is also a parameter \(\kappa\), which is responsible for improving the contribution separability; it will be taken equal to 2 by default. So, the reference to the algorithm is IOSSA(\(t\), \(n_{iter}\), \(I\)) in the case of non-elementary grouping and IOSSA(\(t\), \(n_{iter}\)) when grouping is elementary. Figure 2: Methods of separability improvement. Note that since \(\text{IOSSA}(t,n_{iter},I)\) requires an initial group of components \(I\), it matters which decomposition of the matrix \(\mathbf{Y}\) is input (the other methods do not care). Therefore, before applying IOSSA, it makes sense to apply some other method, such as FOSSA, to improve the initial decomposition and make it easier to build the initial grouping \(I\). The third method we will consider is the ESPRIT-motivated OSSA (**EOSSA**) algorithm. This method is new, so we will provide the full algorithm and its justification in Section 5. The method can improve strong separability. Unlike IOSSA, it is not iterative; instead, it relies on an explicit parametric form of the time series components. However, when the signal is infinite-rank time series or if the rank is overestimated, this approach may result in reduced stability. The reference to the algorithm is EOSSA(\(t\)).
# TAEO-A Thermal Aware & Energy Optimized Routing Protocol for Wireless Body Area Networks
## 3 System Model
### Thermal Model  
The Specific Absorption Rate (SAR) is an effective tool to identify the rate at which the heat is absorbed by tissue per unit of its mass. It estimates the heat that is absorbed due to RF waves exposure for a specified duration and for a specific device. Using SAR, limits can be identified for a sensor node to curtail the dissipation of heat accordingly. SAR depicts the probable biological concern on tissues due to RF communication. For mobile phones, the Federal Communication Commission (FCC) has provided a safe SAR range for adherence [22; 23]. These are highly beneficial in designing wireless communication systems. The surpassing of SAR limits leads to damage to the tissues [24]. SAR can be evaluated through the Equation (4):  
\[SAR=\left(\frac{\sigma E^{2}}{\rho}\right) \tag{4}\]  
where, value gives conductivity of the tissue is represented by \(\sigma\), \(\rho\) is the density, and E is the induced electromagnetic field indicating the strength.
## 3 HTP Tooth Profile Design Using IH Tooth Profile and Cycloidal Curve
### Analysis of the HTP Tooth Profile and Conventional Tooth Profiles Through Schematic Comparison  
#### 3.4.1 Comparison Among Cycloidal, Harmonic, and Exact Curves  
Figures 3 and 4 compare the trajectories of the cycloidal curve (Cycloid), the exact curve (Exact), and the harmonic curve (Harmonic). In Figure 3, the trajectories are shown without scaling the x-axis displacement, clearly revealing differences between the curves. The harmonic curve, designed with actual gear meshing in mind, exhibits noticeable deviations from the exact curve.  
In contrast, Figure 4 applies a 1/2 scale adjustment to the \(x\)-axis displacement to enable a more direct comparison. After this scale correction, the trajectory of the cycloidal curve closely matches that of the exact curve. This suggests that the cycloidal curve can serve as an appropriate analytical model for exact meshing conditions. The harmonic curve also exhibits a similar trajectory shape, though slight differences remain.  
As shown in the figures, a consistent deviation is observed between the harmonic (IH) curve and the exact curve. The harmonic curve also differs from the cycloidal curve. These discrepancies reflect the characteristics of the harmonic tooth profile under actual meshing conditions. More precise comparisons may be achieved through additional scale correction.
# The MADAG Strategy for Fault Location Techniques
## 2 Preliminary
### Fault Localization Technique  
Spectrum-based fault localization (also known as coverage-based fault localization) is a family of approaches to identify the faults in a program. Well-known similarity coefficients of the SBFL technique include Jaccard [17], Tarantula [18], and Ochiai [9]. These approaches collect the spectrum information by executing a subject program with its test suite. The program spectrum often includes information about whether a program element (e.g., a branch, a function, or a statement) is hit in an execution. A test case corresponds to a testing result (passed or failed). All the spectrum of the test cases forms a matrix mentioned in Figure 1. On the basis of the matrix, the above-mentioned approaches compute the suspiciousness score for each element and rank them according to their suspiciousness for the programmer to inspect. [PERSON] et al. [26] indicated that strategy and time cost of test case prioritization are factors to affect the effectiveness of SBFL techniques, not coverage granularity. So, we consider program elements to be statements in this study. Program elements could just as easily have been switched to other elements such as functions, branches, or predicates.  
The key idea is for SBFL techniques to create a ranking of the most probable faulty elements such that these elements can then be inspected by programmers in the order of their suspiciousness until a fault is found. Table 1 lists the formulas of three similarity coefficients of the SBFL technique: Jaccard, Tarantula, and Ochiai. \(N_{\mathit{ef}}\) refers to the number of failed test cases that execute the statement. \(N_{\mathit{uf}}\) refers to the number of failed test cases that do not execute the statement. \(N_{\mathit{es}}\) refers to the number of successful test cases that do not execute the statement. \(N_{\mathit{es}}\) refers to the total number of successful test cases. \(N_{\mathit{f}}\) refers to the total number of failed test cases.  
SBFL is a statistical fault localization approach as compared to a probabilistic one with ultra-low computational complexity. Therefore, we attempt to use SBFL techniques after the test case prioritization (or test case reduction) techniques have selected the test cases.
## 4 Employability Scenario: Assessing the Significance of the Enhanced Visibility Estimation Process
### Evaluating the Proposed Visibility Calculation Method  
With the dust concentrations (\(M\)) obtained from Equation (19), visibilities (\(V\)) can be calculated using any combination of visibility equations adhering to our findings. Having the lowest average RMSE, the averaged sum of E3 and E5 is taken as an example for evaluation, and the results are presented in Table 6 in addition to the calculated dust concentrations and wind speeds with corresponding inaccuracies.  
In addition to scattered evidence [32], the visibilities in Table 6 and the environmental conditions in [26] can be further supported by considering a meteorological standard that was set for reporting purposes. In 1979, the China Meteorological Administration (CMA) [33; 34; 35] established a standard  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Wind Speed, m/s** & **Calculated Dust Concentration, \(\mathrm{\mu g/m^{3}}\)** & **Calculated Visibility, km** \\ \hline
0.6 (\(\pm\)3\%) & 45.14 (\(\pm\)0.57) & 30.51 (-0.24, +0.25) \\
3.6 (\(\pm\)3\%) & 316.2 (-26.84, +28.95) & 9.9 (-0.53, +0.55) \\
3.8 (\(\pm\)3\%) & 424.03 (-32.52, +35.22) & 8.16 (-0.45, +0.46) \\
7.3 (\(\pm\)3\%) & 4913.78 (-698.38, +814.09) & 0.43 (-0.046, +0.087) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Corresponding dust concentrations and visibilities for the reported wind speeds in [26].  
for dividing SDS outbreak sequences into four levels. Each level is a description of a meteorological process associated with a projected range of horizontal visibility, and they are as follows: (1) floating dust (\(V<10\) km); (2) blowing dust (1 km \(\leq V\leq 10\) km); (3) sand/dust storm (0.5 km \(\leq V\leq 1\) km); and (4) severe sand/dust storm (\(V<0.5\) km).  
In comparison, of the four environment conditions defined by [26], three conditions, along with the calculated visibilities, despite the associated errors, exhibit a high degree of range-compliance in accord with the CMA's levels (Table 7). It is worth noting that the clear sky condition was not included since it corresponds to unobstructed horizontal visibility that extends beyond 10 km.  
Furthermore, it has been statistically inferred that for a span of two decades, the lowest storm-erupting wind speed was 5 m/s [36], which validates the 4 th tabulated environment condition since the reported wind velocity was 7.3 m/s. Besides, with visibility of 0.56 km, it shows exceptional conformity to the CMA's specified visibility projections (0 \(\leq\) visibility \(\leq\) 1 km) for SDS events.  
As for the remaining conditions with wind velocities below 5 m/s, it is still considered within an acceptable CMA classification range given the overlapping projections of horizontal visibility ranges, which are generally below 10 km.
# Predictors of Engagement in Virtual Reality Storytelling Environments about Migration
## 3 Hypotheses Development  
### Immersion  
Immersion has been defined in the literature from two perspectives: as an objective feature of a technical system or as a psychological state created by the perception of presence and interaction [47]. Immersion can be defined as \"a phenomenon experienced by an individual when they are in a state of deep mental involvement in which their cognitive processes (with or without sensory stimulation) cause a shift in their attentional state such that one may experience disassociation from the awareness of the physical world\" ([36], p. 6). Immersion occurs when the user focuses on what is being told, so good and well-told stories foster better immersion scenarios [48]. Digital storytelling (such as scenarios with VR and 360\({}^{\circ}\) storytelling) tries to maximize different elements of a storytelling process, including immersion [25]. In a study about the exposure to different levels of immersion to analyze motivation, engagement, performance, and spatial reasoning, it was found that moderate or higher levels of immersion might create a sense of novelty and, therefore, increase the level of engagement [49].  
Research on immersion in VR scenarios has shown that a higher level of immersion can enhance engagement when compared with other types of media. Moreover, sensors and controls are elements that make users feel as if they were really part of the virtual world. Thus, VR scenarios can consider using virtual versions of the hand controllers inside to enhance the immersion [25].  
**Hypothesis (H1).**_The sense of immersion in the virtual reality storytelling environment has a positive and significant effect on engagement._