esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
## 6 Mechanical Model
### Fully Restrained Beam with Axial Force  
When a fully restrained beam is also subjected to axial force, the natural frequencies can be calculated according to [24; 25] as follows.  
\[f_{n,T\
eq 0}=f_{n,T=0}\Bigg{(}1+\gamma\frac{T}{P_{cr}}+\frac{1}{\frac{1}{ \alpha_{7}}\frac{P_{cr}}{T}+\frac{1}{B}}\Bigg{)}^{0.5} \tag{11}\]where \(\alpha,\beta,\ e,\ \gamma\) are parameters depending on boundary conditions and on the considered mode (see Table 1) and \(P_{cr}\) is the critical axial load, evaluated using the inertia moment, \(I\), of the cross section of the beam related to vertical bending:  
\[P_{\sigma}=\frac{4\pi^{2}EI}{L^{2}} \tag{12}\]  
The natural frequencies obtained under the different hypotheses described in the previous paragraphs are presented in Table 2. The outputs of each mode refer to similar modal shapes. The modal shapes of the first 5 vibration modes are shown in Figure 4 for simply supported beam and fully restrained beam conditions.  
If we compare the results of the tensed rope scheme with the simply supported scheme with axial force, it is possible to observe that for the low order modes (1 and 2) the rope-like behavior is common (\(11.1\cong 11.7\) and \(22.2\cong 26.4\)). On the contrary, when the modal order increases, the bending inertia starts playing a dominant role, increasing the natural frequencies (\(46.3>33.3,72.5>44.4\), etc.).  
From the comparison of column 3 with column 5, it can be seen that the effect of the full restraint at the ends is smaller for the low order modes (+26% for Mode 1) than for high order modes (+92% for Mode 5).  
The laboratory-tested rods should behave like tensed beams with an intermediate boundary condition between hinge and full restraint; therefore, the authors expected the rod specimens to have the first three natural frequencies within the limits listed below:  
* \(11.7\leq\ f_{1}\ \leq 14.7\) Hz (lab \(f_{1}\cong\ 14\) Hz)
* \(26.4\leq\ f_{2}\ \leq 40.7\) Hz (lab \(f_{2}\cong\ 31\) Hz)
* \(46.4\leq\ f_{3}\ \leq 82.4\) Hz (lab \(f_{3}\cong\ 53\) Hz)  
The values effectively measured in the lab confirmed these design hypotheses.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode** & \(\alpha\) & \(\beta\) & \(\gamma\) \\ \hline
**1** & 0.195 & 1.211 & 0.816 \\
**2** & 0.148 & 1.151 & 0.857 \\
**3** & 0.088 & 0.996 & 0.979 \\
**4** & 0.068 & 0.828 & 0.991 \\
**5** & 0.055 & 0.739 & 0.996 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary conditions parameters for fully restrained beam.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & & & **Frequency [Hz]** & & \\ \cline{2-5}
**Mode** & **1** & **2** & **3** & **4** & **5** \\  & **Tens. Rope** & **Simply Supp.** & **Simply Supp.** & **Fully Restrained** & **Fully Restrained** \\  & **T \(\
eq\) 0** & **T = 0** & **T \(\
eq\) 0** \\ \hline
**1** & 11.11 & 3.58 & 11.67 & 8.12 & 14.65 \\
**2** & 22.22 & 14.33 & 26.44 & 22.38 & 40.69 \\
**3** & 33.33 & 32.24 & 46.37
# Preparation of Magnetic Polymers for the Elimination of 3-Isobutyl-2-methoxypyrazine from Wine
## 2 Results and Discussion
### Characterisation of Polymers  
Figure 1 shows scanning electron microscopy (SEM) images of the various polymers produced by conventional and MW synthesis. The polymers were deemed to be micro- to meso-porous (<2 nm to 2-50 nm) and no backbone structural difference regarding compactness was found between the imprinted and non-imprinted polymers, nor with the MW synthesised polymers. In comparison to the regular polymers, images of the magnetic polymers implied the presence of metal (bright spheres, Figure 2) due to the incorporation of iron oxide nanoparticles (using commercial iron (II,III) oxide nanoparticles, which may or may not be purely magnetite in the products, so Fe\({}_{x}\)O\({}_{y}\) has been used). As with the non-magnetic polymers, the backbones of the MMIPs and MNIPs were similar, as were the microwave synthesised magnetic polymers.  
Attenuated total reflectance Fourier transform infrared (ATR-FTIR) analysis was performed to further ensure the correct preparation of polymers. Figure 3 shows the FTIR spectra of Fe\({}_{x}\)O\({}_{y}\), Fe\({}_{x}\)O\({}_{y}\)@SiO\({}_{2}\), Fe\({}_{x}\)O\({}_{y}\)@SiO\({}_{2}\)-MPS, and MMIP. In line with the results of [PERSON] et al. [25], the adsorption peak at 579 cm\({}^{-1}\) found in each spectra was indicative that Fe\({}_{x}\)O\({}_{y}\) nanoparticles were present in these materials. The peaks around 1108 cm\({}^{-1}\) were attributed to Si-O-Si, revealing the formation of the silica shell. The strong peak at 1733 cm\({}^{-1}\) associated with the C=O functional group, and the lack of a peak at 1660 cm\({}^{-1}\) ordinarily attributable to C=C, indicated the successful formation of MMIP by polymerisation of magnetic nanoparticle-bound 3-(trimethoxysily)propyl methacrylate (MPS), ethylene glycol dimethacrylate (EGDMA), and methyl methacrylate (MMA). The peak at 2952 cm\({}^{-1}\), indicative of C-H stretches from methyl and methylene groups, confirmed MMIP coupling with MMA and EGDMA [36]. The FTIR spectra of MMIP, MW MMIP, and MW MNP coincided with MMIP, and MIP had quite similar FTIR spectra (not shown), except without an adsorption peak of Fe-O at 579 cm\({}^{-1}\). No variations were found between the different batches of polymers.  
Figure 1: Scanning electron microscopy (SEM) images of polymers prepared by conventional thermal synthesis for (**A**) molecularly imprinted polymer (MIP) and non-imprinted polymer (NIP) (**B**), and by microwave (MW) synthesis for (**C**) MW MIP and (**D**) MW NIP.
## 3 Data Analysis Approach
### Artificial Neural Network Model  
The artificial neural network (ANN) approach has the capability to learn from an experimental dataset to establish a high nonlinear correlation between surface roughness and input parameters [33]. Because a wide range of cutting parameters were adopted in machining tests, the response surface plot of the roughness against the cutting parameters are expected to show highly nonlinear characteristics. This study, therefore, employed an ANN technique to model and predict the roughness of the machined surface based on cutting parameters. A simple multilayered architecture of a typical ANN is shown in Figure 6.  
The network has three layers, namely, input, hidden, and output. The input and output layers are defined as nodes or neurons, and the hidden layer provides the relationship between the input and output layers using different numbers of neurons. The processing neurons in the hidden layer deliver the processed data from the neurons in the input layers to neurons in the output layer. For example, the input data to the neurons in the hidden layer can be characterized by the general form:  
\[x_{\text{hidden,j}}=\sum_{i=1}^{n}W_{ij}u_{i}+\theta_{j} \tag{5}\]  
where \(W_{ij}\) is the weight coefficient between the input neurons and hidden neurons, \(u_{i}\) is the value of the input, and \(\theta_{j}\) denotes the biases of the hidden neurons. The effectiveness of neural networks models is dependent on various characteristics such as network architecture, activation functions, and training algorithms. In this study, the MLP network was trained with the application of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, conjugate gradient, and the steepest descent training algorithm. To realize the best network, the activation functions--linear, exponential, hyperbolic, logistic, and sin--were changed in the hidden and output layers, wherein the hyperbolic function is given by  
\[f(x)=\frac{e^{2x}-1}{e^{2x}+1} \tag{6}\]  
In addition, the linear function was used as the output layer activation function. In ANN modeling analysis, the input neurons are the cutting depth, spindle speed, feed rate, and vibration level; the output is the surface roughness. A trial-and-error scheme has been used to determine the appropriate number of hidden neurons. Further, the error function was used to determine errors between actual values and calculated values during learning, testing, and validation, which is defined as  
Figure 6: Multilayered architecture of a typical artificial neural network.  
\[\text{Errors}=\sum_{i=1}^{N}(y_{i}-t_{i})^{2}, \tag{7}\]  
where \(N\) is the number of dataset in ANN modeling.
# A Hybrid Grasshopper Optimization Algorithm Applied to the Open Vehicle Routing Problem
## 4 Computational Experiments
### Results with the Hierarchical Objective Function  
Table 2 presents the results obtained by the HGOALS for the benchmark problems using a hierarchical objective function. In this case, the first goal is to minimize the number of vehicles, and the second, the total traveling cost. Column \(n\) shows the size of the instance, \(Q\) shows the vehicles capacity, \(NV\) presents the number of vehicles and \(BKS\) the best-known solution value. Column \(Avg\) shows the average solution value after five runs, \(Best\) presents the value of the best solution value after five runs, \(\%gap\) shows the percentage \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instance** & \(n\) & \(Q\) & \(L\) \\ \hline C1–C5 & 50–200 & 140–200 & \(\infty\) \\ C6–C10 & 50–200 & 140–200 & 160–230 \\ C11–C12 & 100–120 & 200 & \(\infty\) \\ C13–C14 & 100–120 & 200 & 720–1040 \\ \hline O1–O8 & 200–440 & 550–1000 & \(\infty\) \\ \hline K01–K08 & 200–440 & 550–1000 & 650–1800 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test instances. difference between the best-known solution value and the best solution value obtained by the HGOALS, and \(CPU\) presents the average CPU time per run in seconds, after five runs. Row \(Avg\) presents the average for columns \(CPU\) and %\(gap\). Results in bold format show that the algorithm found the best know solution for the instance. Results in bold and underlined show the algorithm improved the best-known solution for the benchmark problem. The symbol ''\(\ast\)'' is used to indicate that the algorithm found solutions with a new minimum number of vehicles. When the number of vehicles from the best-known solution is different of the best solution obtained by the HGOALS, the number of vehicles used by the best-known solution is shown in column \(NV\). The results show that the algorithm was able to improve the best-known solutions for seven instances in group C, five on group K and one in group O. Therefore, the HGOALS is capable of improving the best-known solution for 15 of the 30 benchmark problems. Notice that 11 of the improved best-known solutions where from instances considering route length constraints. The algorithm also finds the best-known solutions for the other five instances. Other relevant results are the new best solutions for instances C6, C13, C14 and O4 that use fewer vehicles
# Capacity and Delay Analysis for Large Social-Aware Mobile Ad Hoc Wireless Networks
## 3 Preliminaries
### Social Model  
We adopt the model in Reference [26] mapping from the social connections to wireless network. For a given node \(i\), it select destination node \(j\) based on two aspects - social experiences and geographic location. The node is likely select the destination close to it.  
We define social relationship strength of node \(j\) with respect to node \(i\) as  
\[Rank_{i}(j)=|\{k\in N:\mathcal{L}(X_{k},X_{i})<\mathcal{L}(X_{j},X_{i})\}|, \tag{1}\]  
where \(N\) is the node set of networks, \(\mathcal{L}(X_{k},X_{i})\) is the distance between node \(k\) and node \(i\), \(|\cdot|\) represented the number of elements in the set.  
The probability that node \(i\) select node \(j\) as a destination is defined as  
\[P_{i\to j}=\frac{1}{F_{n}Rank_{i}^{\delta}(j)}, \tag{2}\]  
where \(F_{n}=\sum\limits_{i=1}^{n}\frac{1}{j^{2}}\) uses for normalization.  
\(F_{n}\) shows the social experiences which follows the power-law degree distribution and guarantees small-world properties. \(\delta\) is the parameter of social model. Notice that when \(\delta=0\) and \(F_{n}=0\) the network is pure mobile ad hoc network.
# An IoT System for Remote Monitoring of Patients at Home
## 1 Introduction
Second, an authentication scheme called the Buddy-ACK (Acknowledgment) authorization scheme is proposed in this study. In the Buddy-ACK authorization scheme, a specific piece of biomedical data can be accessed only after both a patient and the related medical staff are authorized. We call the patient and the related medical staff \"buddies\". A piece of biomedical data can be accessed only after the acknowledgement (or consent) of the buddy is obtained. The remainder of this paper is organized as follows. Section 2 describes some related studies, and Section 3 explains the structure of the monitoring IoT system for patients at home proposed in this paper. Section 4 discusses the multiclass Q-learning scheduling algorithm proposed in this study, while Section 5 discusses communication protocol conversion mechanisms between oneM2M and ISO/IEEE 11073. Section 6 identifies some security issues and describes the Buddy-ACK authentication scheme proposed in this study. Section 7 shows the results of some experiments using the system constructed in this study, and engages in a discussion based on the results. Finally, Section 8 draws some conclusions and discusses some possible directions for future research.
## 1 Introduction
### Directly Related Works  
In the context of the channel assignment problem, heuristics are of significant importance for two key reasons. Firstly, heuristics enable the rapid identification of feasible solutions, making them well-suited for real-time applications with satisfactory outcomes. Secondly, and equally vital, they facilitate the generation of a well-distributed initial population (i.e., the first generation) for all Bio-inspired Evolutionary Algorithms.  
A popular greedy algorithm for solving the MS-FAP is the heuristic algorithm based on the frequency allocation strategy with node degree reordering (F/DR). This algorithm was originally proposed in [41; 42] and further refined in [43]. It incorporates a cell prioritization strategy and an exhaustive frequency allocation algorithm for channel assignment [43]. This strategy is highly valuable in CAP because it enables the finding of solutions that, in many cases, nearly approximate the global optimum while requiring little computing complexity, execution time, and implementation cost [44].  
For these reasons, the F/DR algorithm has been modified and applied in several problems, including Max-FAP with non-uniform cells, channel assignment incorporating adjacent channel information, and power allocation problems [45; 46; 47]. It has also been integrated as part of other meta-heuristics, as a local optimization algorithm to tune each generation [48; 49; 50]. Despite the algorithm's simplicity, low computational cost and effectiveness, we identified limited literature focused on enhancing this basic heuristic for MS-FAP [43; 51; 52; 53]. This could possibly be attributed to its integration with other meta-heuristics, where the algorithm assumes a secondary role. The F/DR algorithm can be improved because its cell prioritization strategy employs an ordering that favors cells with a higher level of difficulty related to the criteria used to create the sorted list. For instance, employing this heuristic may result in poor channel allocation in scenarios with high-demand cells that do not electromagnetically interfere with their neighboring cells.
# A Novel Cloud Approach for Connected Vehicles
## 2 Related Works
### Vehicular Cloud Computing
In [15], the authors present a framework in order to easily access ITS components as cloud nodes. It is composed of three layers. The end user refers to either a user or a server/data center. A node submits requests and receives responses through the communication layer. The communication layer aims to select the relevant link to be used between two actors depending on the component features. Additionally, it provides access to the next layer, the cloud. The cloud layer is either a classic data center or a dynamic cloud composed of ITS actors. Even if this method seems to be easy to implement, it will induce network overhead since it generates a high amount of data [27]. In [28], the authors propose to combine distant cloud and vehicular cloud into their architecture. In this architecture, the nature of the cloud type (static, dynamic, or infrastructure) has no impact on the solution. Indeed, it is based on the virtualization of the components in order to handle heterogeneous resources. The paradigms of context and ecosystem for the vehicles are presented in [29]. The context is a logical vision of one or several physical entities (e.g., a mall with its parking). The ecosystem which is defined as a set of contexts bound by a pattern (e.g., mobility pattern, habit pattern). The ecosystem aims to bring data closer to the user of another context. It also allows the slicing of geographic areas in hierarchical ecosystems (e.g., country and its regions, region and its cities). Geographical relevance is an important criterion for vehicles. Additionally, this hierarchical vision is aligned with the naming method adopted for information-centric networking.
# Machine Learning-Powered Rainfall-Based Landslide Predictions in Hong Kong--An Exploratory Study
## 3 The Pilot Study--ML-Powered Natural Terrain Landslide Susceptibility Analysis
### Background
 } \\  & \multicolumn{2}{c}{\ \begin{tabular}{c} By predicted \\ probability of \\ landslide \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} By predicted \\ probability of \\ landslide \\ \end{tabular}  } \\  & \multicolumn{2}{c}  
As the conventional statistical approach requires the grouping of data and predefining correlations manually for curve-fitting, the resolution of the analysis is limited and the undertaking of a non-linear multivariate analysis is difficult. Hence, ML techniques become a popular means to model complex landslide problems and a promising predictive performance has been yielded [14]. In light of this, the GEO has recently commenced a pilot study to explore the potential improvements that can be brought to the natural terrain landslide susceptibility model with ML. This study is different from the aforementioned studies in that:  
1. Data sampling commonly used in previous studies to deal with an imbalanced dataset is not adopted. This helps improve the resolution of the landslide susceptibility maps (see more details in Section 3.3);
2. A new feature selection framework has been proposed in this study for identifying features with physical significance to landslide occurrences in the ML model development (see more details in Section 3.5) to ensure domain knowledge on landslide characteristics, which is often lacking in previous studies, is duly considered for the development of physically meaningful ML models. This pilot study is carried out based on the previous work by [PERSON] & [PERSON] (2016) [2]. Year-based analyses with landslide and conditioning factors and data covering the same period from year 1985 to 2008 were considered. The overall workflow of this study is illustrated in Figure 3. Each step will be discussed in sequence in the following.
## 3 Results and Discussion
### The Potential of Strain F19 for Enhancing Nitrogen Removal from Low-C/N Domestic Sewage  
To verify the application of strain F19 for domestic wastewater treatment (Figure 6A), a representative low-C/N domestic wastewater with 100.00-120.00 mg/L COD, 45.00-55.00 mg/L TN, 40.00-50.00 mg/L NH\({}_{4}\)\({}^{+}\)-N, 3.00-5.00 mg/L NO\({}_{3}\)\({}^{-}\)-N, and an untraceable amount of NO\({}_{2}\)\({}^{-}\)-N was used for nitrogen removal assessment. After 8 days of continuous influent, the concentrations of COD, TN, NH\({}_{4}\)\({}^{+}\)-N, and NO\({}_{3}\)\({}^{-}\)-N were significantly decreased to 32.61, 3.74, 1.05, and 2.68 mg/L, respectively, but there were undetectable amounts of NO\({}_{2}\)\({}^{-}\)-N. After 20 days, the concentrations of TN and NH\({}_{4}\)\({}^{+}\)-N in the culture system were stable (Figure 6B). However, nitrogen parameters increased rapidly due to long-term low-carbon resources, resulting in a dramatic decline in removal efficiency. When an appropriate carbon source (C/N = 2:1) was supplemented into the culture system after 20 days, concentrations of TN and NH\({}_{4}\)\({}^{+}\)-N were further reduced to less than 4.80 and 1.20 mg/L, respectively (Figure 6B), and these levels were maintained throughout the rest of the experiments. Furthermore, there were no changes in nitrate or nitrite concentrations in the culture system. These results showed that strain F19 could efficiently remove nitrogen released from low-C/N domestic sewage without the addition of an external carbon source. These excellent performance attributes, coupled with the convenience of a single reaction chamber, make strain F19 a promising candidate for nitrogen removal from domestic wastewater.  
Figure 5: (**A**) Nitrogen removal rates and (**B**) accumulation of NO\({}_{3}\)\({}^{-}\)-N in cultures containing different concentrations of ammonia.  
Figure 6: Characterization of strain F19 in a culturing and purification platform with low-C/N domestic wastewater. (**A**) Nitrogen removal purification tank. (**B**) Profile of nitrogen concentration during domestic sewage treatment with strain F19.