esa-sceva/satcom-chunk-collection huggingface dataset:
## A Survey of MAC Protocols for Cognitive Radio Body Area Networks  
**[PERSON] and [PERSON]***  
Department of Computer Engineering, Chosun University, 309 Pilmun-daero, Dong-gu,  
Gwangju 501-759, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-62-230-6032; Fax: +82-62-233-6896.  
Academic Editor: [PERSON]  
_Received: 13 February 2015 / Accepted: 15 April 2015 / Published: 20 April 2015_  
**Abstract:** The advancement in electronics, wireless communications and integrated circuits has enabled the development of small low-power sensors and actuators that can be placed on, in or around the human body. A wireless body area network (WBAN) can be effectively used to deliver the sensory data to a central server, where it can be monitored, stored and analyzed. For more than a decade, cognitive radio (CR) technology has been widely adopted in wireless networks, as it utilizes the available spectra of licensed, as well as unlicensed bands. A cognitive radio body area network (CRBAN) is a CR-enabled WBAN. Unlike other wireless networks, CRBANs have specific requirements, such as being able to automatically sense their environments and to utilize unused, licensed spectra without interfering with licensed users, but existing protocols cannot fulfill them. In particular, the medium access control (MAC) layer plays a key role in cognitive radio functions, such as channel sensing, resource allocation, spectrum mobility and spectrum sharing. To address various application-specific requirements in CRBANs, several MAC protocols have been proposed in the literature. In this paper, we survey MAC protocols for CRBANs. We then compare the different MAC protocols with one another and discuss challenging open issues in the relevant research.  
**Keywords:** wireless body area network; cognitive radio body area network; media access control; energy efficiency; network lifetime
## 3 Controller Design and Stability Analysis
### RBFNN-Based Observer  
In real-world outdoor applications, the bound for the uncertainty \(D_{\theta}\) in the roll channel is hard to estimate. Considering this, we chose an adaptive RBFNN observer to adapt the estimated the uncertainty value \(\hat{D}_{\theta}\) in the UAV system. The chosen RBFNN was a three-layer feed-forward neural network [34; 35].  
The vector in the input layer is \(Z=[e_{1},\dot{e}_{1}]^{\mathrm{T}}\). By using the weighted sum method, the output is derived as follows  
\[\begin{array}{c}\hat{D}_{\theta}=\sum_{j=1}^{N}W_{j}\phi_{j}(\mathbf{Z}),\quad j =1,2,\cdots,N\\ \phi_{j}(\mathbf{Z})=\exp(-\frac{\left\|\mathbf{Z}-M_{j}\right\|^{2}}{\sigma_{j}^{2}}) \end{array}, \tag{17}\]  
where \(W_{j}\) denotes the connective weight, \(N\) is the number of hidden nodes, \(M_{j}\) is the centre vector, and the positive scalar \(\sigma_{j}\) denotes the spread width. The receptive field function uses a Gaussian function in our designed neural network.
# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Supervised ML Solutions
#### 4.1.8 Time Series Analysis  
A time series is a compilation of temporal events, and time series datasets generally possess the following characteristics: high dimensionality, a substantial number of instances, and continuous updates [70]. Time series representation has three primary categories: model-based representation, non-data-adaptive representation, and data-adaptive representation. A primary objective of time series representation is to reduce dimensionality [71; 72]. In smart buildings, time series analysis is used to forecast elderly sleeping patterns. In [73], the authors proposed a wellness model utilizing seasonal autoregressive integrated moving average time series in conjunction with sleep activity scenarios. In the realm of data sensing in smart buildings, ref. [74] proposed a time series analytical approach to examine correlations among non-stationary time series.
## 2 Mathematical Model
### Optimization Scheme  
At present for challenging engineering problems, there are different numerical design techniques to achieve solutions. Since there are several optimization algorithms in the literature, it is very important to choose a suitable search algorithm for aerodynamic shape optimization. Here genetic algorithm (GA) is selected for the optimization process. To get an optimized result of both the airfoil NREL S-821 and RAE-2822, GA and flow solver XFOIL were linked with in-house MATLAB code to obtain the coefficients of an airfoil. These coefficients are generated with the help of design variables obtained from the CST method during the parametrization process. These design variables are used in the optimization process as the main task. As per the previous study, optimization problems are of two types, Constrained and unconstrained. These optimization problems based on the principle of natural selection are solved by a genetic algorithm. The process of GA drives biological evolution and is used to optimize the shape of the airfoil. The process of optimization for each scenario was performed with a default population size (Ps) of 50 and the maximum generation of 200. The conditions employed for aerodynamic flow parameters in the optimization process include Re, Ma, and AOA. The single-objective function for the optimization of an airfoil is expressed as:  
\[\text{Minimize}:f(x)=Cd \tag{12}\]  
The main goal is to attain the maximum value of aerodynamic performance by minimizing \(Cd\) and maximizing the L/D ratio. For aerodynamic shape optimization, GA is used in a large design space. The probabilities of operating crossover and mutation on each generation are 0.5 and 0.01, respectively. The optimized airfoil profile of NREL S-821 and RAE-2822 are shown in Figure 2.  
Figure 1: Airfoil generated by CST parameterization (**a**) S-821 and (**b**) RAE-2822.  
Figure 2: Optimized airfoil shape profile.
# Dark Type Dynamical Systems: The Integrability Algorithm and Applications +
## 2 Nonlinear Dynamical Systems and the Lax Type Representation  
### Generalized Eigenvalue Problem  
There is considered a uniform nonlinear dynamical system  
\[u_{t}=K[u] \tag{1}\]  
on a \(2\pi\)-periodic smooth functional manifold \(M\subset C^{\prime}(\mathbb{R}/\{2\pi\mathbb{Z}\};\mathbb{R}^{m})\), generated by a smooth vector field \(K:M\to T(M)\), where the nonlinear mapping \(K:\mathcal{J}^{\prime}()(\mathbb{R}/\{2\pi\mathbb{Z}\};\mathbb{R}^{m})\)\(\to\mathbb{R}^{m}\) for some finite \(r\in\mathbb{Z}_{+}\) is a Frechet differentiable nonlinear mapping. In addition, we take into consideration a vector fibre bundle \(E(M;\mathbb{C}(\mathbb{R}/\{2\pi\mathbb{Z}\};\mathbb{C}))\) over the manifold \(M\) with the space \(C(\mathbb{R}/\{2\pi\mathbb{Z}\};\mathbb{C})\) as its bundle jointly with a linear smooth differential operator mapping \(L:=L[u;\lambda]:L_{\infty}(\mathbb{R};\mathbb{C})\to L_{\infty}(\mathbb{R}; \mathbb{C})\) of a finite order \(k\in\mathbb{N}\) with domain \(\text{Dom}(L)=C^{k}(\mathbb{R};\mathbb{C})\) and meromorphically depending on a complex spectral parameter number \(\lambda\in\mathbb{C}\). For this linear differential operator expression  
\[L[u;\lambda]:=y^{(k)}+\sum_{s=\mathbb{I},k}l_{k}[u;\lambda]y^{(k-s)}, \tag{2}\]  
one can define a generalized eigenvalue problem as follows: a number \(\lambda\in\mathbb{C}\) belongs to the spectrum \(\sigma(L)\) of this operator, if the solution \(y\in C^{k}(\mathbb{R};\mathbb{C})\) of the equation  
\[L[u;\lambda]y(x;\lambda)=0 \tag{3}\]  
is bounded, that is  
\[||y||_{\infty}:=\sup_{x\in\mathbb{R}}\sum_{s=\overline{0},\overline{k}}|y^{(s )}(x;\lambda)|\ <\infty,\]  
where \(\|\cdot\|_{\infty}\) is the norm in the Banach space \(L_{\infty}(\mathbb{R};\mathbb{C})\).  
Let us suppose that the defined above spectrum \(\sigma(L)\subset\mathbb{C}\) is under the isospectral deformation by virtue of the dynamical system (1), i.e.,  
\[d\sigma(L)/dt=0,\]  
for all \(t\in\mathbb{R}\). As, in general, the spectrum \(\sigma(L)\) is a smooth \(2\pi\)-periodic functional on \(M\), in the case under consideration, it will be invariant on \(M\). The sufficient condition for the considered above situation for the dynamical system (1) to occur consists in existence of depending on the admissible parameter \(\lambda\in\mathbb{C}\) another linear differential operator \(A:=A[u;\lambda]:L_{\infty}(\mathbb{R};\mathbb{C})\to L_{\infty}(\mathbb{R}; \mathbb{C})\), being a local functional on \(M\), such that for all \(u\in M\) and \(\lambda\in\mathbb{C}\) the following linear evolution equation  
\[\partial L/\partial t=[A,L] \tag{4}\]  
holds, where \([\cdot,\cdot]\) is the usual operator commutator. Then one easily ensues that the dynamical system (1) is the compatibility condition for the operator relationships (3) and (4) for all admissible \(\lambda\in\mathbb{C}\). The above representation of the dynamical system (1) in the form of (4) is called the Lax type representation.
## 2 Materials and Methods
### Structural Characterization  
X-ray diffraction (D8 Advance, Bruker, Germany) was used on the sintered and subsequently ground samples to determine the crystalline phases (CuK\(\alpha\) radiation, \(\lambda\) = 0.154 nm, 40 kV, and 100 mA). The spectra were obtained in an angular range of 20-70\({}^{\circ}\) (20) with a step size of 0.0334\({}^{\circ}\) every 100 s. Peak locations were fitted based on the form of a Lorentz peak. Also, the Inorganic Crystal Structure Database (ICSD) was used to determine the crystalline phases. Additionally, the relative volume fractions of the \(T\) (\(V_{T}\) ) and \(O\) (\(V_{O}\)) phases were determined as:  
\[V_{T}=\frac{\left[I_{\left(002\right)_{T}}+I_{\left(200\right)_{T}}\right]}{ \left[I_{\left(002\right)_{T}}+I_{\left(200\right)_{T}}+I_{\left(020\right)_{O }}+I_{\left(200\right)_{O}}\right]} \tag{1}\]  
\[V_{O}=\frac{\left[I_{\left(022\right)_{O}}+I_{\left(200\right)_{O}}\right]}{ \left[I_{\left(002\right)_{T}}+I_{\left(200\right)_{T}}+I_{\left(022\right)_{O }}+I_{\left(200\right)_{O}}\right]} \tag{2}\]  
where \(I_{\left(002\right)T},I_{\left(200\right)T},I_{\left(022\right)O}\), and \(I_{\left(200\right)O}\) are the integrated intensities of the tetragonal (002) and (200) peaks, and orthorhombic (022) and (200) peaks, respectively [9].
## 2 Materials and Methods
### Measurement Apparatus  
In rainfall events, the flow was measured, and at the same time, samples were collected for analytical tests. The measurement apparatus was installed in the separation chambers (monitoring point--Figure 1) of all treatment plants. The ISCO AV 2150 module flow meters (Teledyne ISCO, Lincoln, NE, USA) were employed to measure stormwater quantity. Stormwater samples were collected using ISCO 6712 portable samplers (Teledyne ISCO, Lincoln, NE, USA). Unstabilised samples were immediately transported to a chemical laboratory in order to determine the following quality indicators, i.e., HM: Ni, Cu, Cr, Zn, Pb, and Cd. The pH value was measured in accordance with the PN-EN ISO 10523:2012 method using SevenMulti(tm) meter (Metller Toledo, Columbus, OH, USA) [17]. Samples were digested in nitric acid using microwave oven (Multiwave 3000, [PERSON], Graz, Austria) and filtered with membrane filters (0.45 \(\upmu\)m). Concentrations of HM were determined by atomic emission spectrometry with inductively coupled plasma ICP Optima 8000 (Perkin Elmer, Waltham, MA, USA) with certified multi element standards (Perkin Elmer) [18]. The tipping bucket type rain gauge RG50 from SEBA Hydrometrie GmbH (Kaufbeuren, Germany) was used to measure rainfall depth. Measurement frequency ranged from 2 to 5 min, with a resolution of 0.1 mm.
# An Optimal Authentication Scheme through Dual Signature for the Internet of Medical Things
## 1 Introduction
ECC, on the other hand, only needs 160 bits, which needs lower computational and transmission costs than RSA while still being able to offer the same level of security. Despite having better performance than RSA, ECC still has significant overhead costs associated with calculation and transmission. Since IoT devices are low-power and unable to do extensive processing, it is required somewhat to reduce the cost of computation and communication overhead. This feature is provided  
Figure 1: Internet of Medical Things (IoMT) [9]. by HEC, which utilizes only 80 bits of store space, reducing the amount of computing work and the cost of transmission while delivering an equivalent level of security [13].
# Anchor-Based Method for Inter-Domain Mobility Management in Software-Defined Networking
## 5 Performance Evaluation
### Simulation Setup  
For our experiments, we used a machine running a Linux operating system (Ubuntu 20.04 LTS 64-bit) with an Intel(r) Core(tm) i7-6600U CPU operating at a frequency of 2.60 GHz to 2.81 GHz and 20 GB of RAM. On the host system, we used Mininet-WiFi, which is an open source software, as the network emulator [21] to create a WiFi environment where we had multiple domains managed by an SDN controller, along with a mobile node and a corresponding node located in different domains. This emulator is ideal for our case because during emulation, mobile nodes have the ability to move while running standard Linux network software or Internet services. Mininet-WiFi is an extension of the Mininet software-defined network emulator [21]. The Mininet-WiFi developer did not modify any existing Mininet functionality, but added new functionality. We consider a fully connected network with more than three nodes. Cases where the nodes are less than or equal to three are not covered in this article. Indeed, with a number of nodes less than or equal to three, the options for determining the mobility anchor are extremely limited, if not non-existent. The nodes represent the domains of our network.  
We prepared four grid network architectures of different sizes: \(2\times 3\), \(3\times 3\), \(3\times 4\), and \(3\times 5\) and the architecture according to [19], as indicated, respectively, in Figure 7, Figure 8, Figure 9, Figure 10, and Figure 4. The context of our study takes place after a reconnection of the mobile node in the new domain, thus we have the known position of the corresponding node identified as the source node, and the various destination positions depending on the size of the grid to evaluate the position of the mobile node. To demonstrate the effectiveness of our method, we study two cases. The first one to determine the route taken and the second one to calculate the data transfer delay.  
In Mininet-WiFi, we modified the example provided by the simulator for mobility by adding additional domains based on the architecture, and we established links between these domains to represent our architectures. The simulation tests were conducted with variable bandwidths and delays on each link. We repeated the experiment 1000 times for each architecture
# Characterization of the 2014 Indus River Flood Using Hydraulic Simulations and Satellite Images
## 2 Materials and Methods
### Data and Model  
#### 2.2.1 Remote Sensing Data for Land-Use/Land-Cover Type Classification  
Images from the operational land imager (OLI) and thermal infrared sensor (TIRS) onboard the Landsat-8 satellite were obtained from the United States Geological Survey (USGS: [[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)) (accessed on 6 March 2021)) to identify the land-use/land-cover (LULC) types of the river basin. The study area is covered by Path 151 of the Landsat-8 satellite, providing images at a temporal resolution of approximately 16 days.  
Three satellite images were obtained over the study area: before the flood (July 22), during the flood (August 7), and after it (September 24). Google Earth images were also obtained for comparison with the LULC types derived from the Landsat images.
# New Gap-Filling Strategies for Long-Period Flux Data Gaps Using a Data-Driven Approach
## 2 Materials and Methods
### Site and Data Description
Except for 2007 and 2014, we filled the flux data gaps using the marginal distribution sampling method. For nighttime CO\({}_{2}\) flux correction and partitioning NEE into GPP and RE, we applied the friction velocity (\(u^{*}\)) correction method with the modified moving point test method for determining \(u^{*}\) threshold [19], and extrapolated nighttime values of RE into the daytime values using the RE equation with a short-term (air) temperature sensitivity of RE from the nighttime data [1]. GPP was calculated by subtracting NEE from RE.
# Decision Framework for Predictive Maintenance Method Selection
## 3 Design of the Framework Elements
### Element 2A: Definition of the Ambiton Level  
The second identified problem was the commonly observed mismatch between the ambition level of the company and the initially available data and knowledge. Therefore, both a definition of the ambition levels (this subsection) and the data types (as defined as element 2B in the next subsection) are required. The ambition level is defined as the level of detail that is required in the maintenance decision making process. Therefore, it is important to consider the four aspects set out below:  
1. Is it required to predict the lifetime of individual assets? - If not, only a generic prediction can be made for a fleet or group of assets; - If individual predictions are required, also monitoring of individual assets is required.
2. Should variations in the usage of the assets be included? - If not included, maintenance will be based on calendar time; - Typical variations to be included are operating hours, driven kilometres, start/stops; - If variations are to be included, they must be monitored or registered during operation.
3. Should variations in environmental conditions be included? - If not included, maintenance will be based on average conditions; - Operation at other conditions may yield accelerated degradation; - If variations are to be included, they must be monitored or registered during operation.
4. Do future conditions differ from current or historical conditions? - If there is no difference, an extrapolation of a general trend can be made; - Otherwise, these variabilities have to be included in the prediction using a model.  
Based on these four aspects, five different ambition levels (AL) can be defined, as shown in Table 1. The decision scheme in Figure 3 then provides a guideline for selecting the appropriate ambition level.  
Figure 3: Guideline for the selection of the ambition level.
# FSI Application in the Parametric Analysis of Blade-Disk Connection of Turbojet Engine Compressor
## 4 Results
### Coefficient of Friction  
In the analysis of the classic structure, where the rotor blade and the load-bearing element are connected at the root, friction occurring on the surfaces of contacting and cooperating elements plays an important role. In the adopted methodology, friction was considered by using the calculations for the appropriate type of contact, i.e., friction, which in ANSYS software is defined as Frictional [25]. The value of the friction coefficient defined in the program depends on the type of material from which the cooperating components are made. Usually, this value is determined experimentally, but a numerical solution can also be sought. To check how much influence the adopted value of the friction coefficient has on the maximum stresses in the compressor stage, several numerical simulations have been performed. The load was assumed to be a centrifugal force set by the appropriate rotational speed of the rotor. The results obtained are summarized in Figure 12.  
Figure 11: Effect of P2 change on stresses.  
The verification was carried out in the friction coefficient range of 0-0.5. The results are presented for four selected rotational speeds, the values of which correspond to the actual operating ranges of the turbine engine compressor rotor. In this case, the speed of 15,183 rpm corresponded to 100% N2, i.e., the maximum rotational speed of the high-pressure compressor. Trend lines had a similar course for rotational speeds less than the maximum rotor speed. With the increase in the coefficient of friction, the stress value decreased. It increased slightly after exceeding -0.35. It is also seen that the local minimum changed with increasing speed. An increase in the coefficient of friction from 0.1 to 0.3 caused a decrease in maximum stress values by about 5%, depending on the value of rotation speed. It can also be seen that the increase in rotational speeds caused the optimal point of the curve to shift towards higher coefficients of friction. Therefore, taking the friction coefficient values in the range of 0.1-0.3 for the calculation did not critically affect the maximum values of stresses in the compressor rotor stage.  
The above analysis also considered the case without friction, i.e., one in which the coefficient of friction was equal to zero (in ANSYS, the contact conditions adopted in this way are equivalent to another available type of contact, the so-called Frictionless [25]). However, it should be remembered that this is only a theoretical case. It was included to check the effects of applying such a condition in the analysis of the blade-disk connection. In an existing construction, in the blade-disk connection between adjacent surfaces, there is always at least minimal friction.
## 3 Results and Discussion
### Segmentation Performances
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Helminth Ova Species** & \multicolumn{2}{c}{**Technique**} & \multicolumn{2}{c}{**Accuracy (\%)**} & **IoU (\%)** \\ \hline \multirow{4}{*}{ALO} & Machine learning segmentation & \multirow{2}{*}{FCM} & 98.54 & 59.82 \\  & Deep learning segmentation & & VGG-16 & 99.28 & 75.69 \\  & Deep learning segmentation & ResNet-18 & 99.22 & 69.82 \\  & & ResNet-34 & 99.30 & 75.80 \\ \hline \multirow{4}{*}{EVO} & Machine learning segmentation & \multirow{2}{*}{FCM} & 97.94 & 40.81 \\  & Deep learning segmentation & & VGG-16 & 98.89 & 49.29 \\  & & ResNet-18 & 98.80 & 54.36 \\  & & ResNet-34 & 98.86 & 55.48 \\ \hline \multirow{4}{*}{HWO} & Machine learning segmentation & \multirow{2}{*}{FCM} & 99.48 & 73.25 \\  & Deep learning segmentation & & VGG-16 & 99.16 & 62.68 \\  & & ResNet-18 & 99.20 & 66.32 \\  & & ResNet-34 & 99.11 & 61.87 \\ \hline \multirow{4}{*}{TTO} & Machine learning segmentation & \multirow{2}{*}{FCM} & 98.75 & 51.58 \\  & Deep learning segmentation & & VGG-16 & 99.72 & 75.09 \\ \cline{1-1}  & Deep learning segmentation & & ResNet-18 & 99.69 & 77.06 \\ \cline{1-1}  & & ResNet-34 & 99.68 & 74.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative measure for the segmentation performance of helminth ova images. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Helminth Ova Species** & **Accuracy (\%)** & **IoU (\%)** \\ \hline Ground Truth & \multicolumn{2}{c}{Segmentation Image} & \\ \hline  & & 99.30 & 88.54 \\ \hline ALO & \multicolumn{2}{c}{ALO} & \\ \hline  & & 93.35 & 70.37 \\ \hline EVO & \multicolumn{2}{c}{EVO} & \\ \hline  & & 98.28 & 83.41 \\ \hline HWO & \multicolumn{2}{c}{HWO} & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Segmentation performance of helminth ova images for ResNet-34.
# Additive Manufacturing for Sensors: Piezoresistive Strain Gauge with Temperature Compensation
## 2 Materials and Methods
### Data Analysis
#### 2.4.2 Repeatability  
Sensor intrinsic repeatability between consecutive cycles was calculated by considering resistance measurements \(\overline{R}_{ij}\), indexed by test cycle \(j=1,\ \ldots,\ n\) (where \(n\) is the number of cycles), and step \(i=1,\ldots,n_{T}\) (where \(n_{T}\) is the number of defined steps). Each \(\overline{R}_{ij}\) has been calculated as the mean of measurements of the labeled signal portion corresponding to that cycle and step (i.e., when force and resistance are stable) to reduce noise. Therefore, repeatability has been calculated as follows:  
\[r=\underset{i}{\text{mean}}\left(\underset{j}{\text{std}}\ \overline{R}_{ij} \right)i=1,\ldots,n_{T}\ \ j=1,\ldots,\ n\]  
where standard deviations are evaluated in sets corresponding to the same step and different cycles.