esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 3 Methods
### Scalable Update Strategy
#### 3.2.1 Implicit Factorization  
For notational clarity, we reorder the rows and columns of our parameter matrices to separate active components from inactive ones. Specifically, we partition the matrices as follows:  
\[Z_{i}=\begin{pmatrix}Z_{iA}&Z_{iB}\end{pmatrix},\hat{Z}=\begin{pmatrix}Z_{A}&Z _{B}\end{pmatrix},\alpha=\begin{pmatrix}\alpha_{A}&\alpha_{B}\end{pmatrix},\beta=\begin{pmatrix}\beta_{A}&\beta_{B}\end{pmatrix},\] \[W=\begin{pmatrix}W_{A}&W_{B}\\ W_{C}&W_{D}\end{pmatrix},\Phi_{i}=\begin{pmatrix}\Phi_{iA}\\ \Phi_{iB},\end{pmatrix} \tag{30}\]  
Here, the subscript \(A\) denotes variables belonging to the active subspace (i.e., those corresponding to \(K^{(a)}\) basis functions), and \(B,C\), and \(D\) denote the inactive components.  
Notably, the cross terms \(W_{B}\) and \(W_{\text{C}}\) involve both active and inactive components; these are updated implicitly, as proven in the Appendix part.  
Following the strategy in [39], a basis function is deemed inactive if its precision exceeds a high threshold, i.e., \(\alpha_{j}>\epsilon^{-1}\) and \(\beta_{k}>\epsilon^{-1}\) as \(\epsilon\to 0\). In the limit, the inactive basis functions decouple from the active ones, leading to the following mean field factorization:  
\[\mathcal{Q}_{W} =\mathcal{Q}_{W_{A}}\mathcal{Q}_{W_{B}}\mathcal{Q}_{W_{\text{C}}} \mathcal{Q}_{W_{D}} \tag{31}\] \[\mathcal{Q}_{\bar{Z}} =\mathcal{Q}_{\bar{Z}_{A}}\mathcal{Q}_{\bar{Z}_{B}}\] (32) \[\mathcal{Q}_{Z_{i}} =\mathcal{Q}_{Z_{iA}}\mathcal{Q}_{Z_{iB}} \tag{33}\]  
The factorization of \(\alpha\) and \(\beta\) was already obtained in Equations (10) and (11). These factorizations allow us to decouple the update for the active subspace with the proof provided in the Appendix part.  
It implies that only updates for \(\mathcal{Q}_{Z_{iA}},\mathcal{Q}_{W_{A}},\mathcal{Q}_{W_{B}},\mathcal{Q}_{W_{C }},\mathcal{Q}_{\bar{Z}_{A}},\mathcal{Q}_{\bar{\beta}_{A}},\mathcal{Q}_{\sigma },\mathcal{Q}_{\eta}\) are required, as shown in Figure 2. This strategy reduces the computational complexity from \(\mathcal{O}(K^{6})\) to \(\mathcal{O}(K^{(a)})^{6}\). Moreover, we initialize \(W\) as an identity matrix and set the active \(\alpha_{A}\) to all ones and the inactive \(\alpha_{B}\) to infinite. In this way, we can initialize the remaining active dimensions \(K^{(a)}\), e.g., \(Z_{iA}\), using a modified, multi-instance version of a relevance vector machine [30], which performs fast analytical maximum-likelihood updates, as detailed in Appendix E in the Appendix part.
# Parameter Estimation of the Bishop Photovoltaic Model Using a Genetic Algorithm
## 3 Mathematical Formulation of the Optimization Problem
### Chu-Besley Genetic Algorithm Applied to the Problem of Parameter Estimation  
Equation (5) in Section 2 represents the problem addressed in this study and shows that iterative methods should be applied to find a solution. This study proposes two stages to solve said problem: (1) estimation of the SDM parameters and (2) estimation of Bishop parameters that can be used to represent the cell-level PV profiles in the first and second quadrants, where the GA is used to find the optimal parameter configuration. The following subsection describes the five fundamental steps of the GA.
# Smart Nanocomposites for Nanosecond Signal Control: The Nano4 waves Approach
## 3 Results and Discussion
### Realization of Efficient Microwaves Absorbers
#### 3.3.3 Ink-Based Absorber  
The last kind of absorber presented in this paper has the most elaborate architecture, as shown in Figure 18. It combines a stack of insulating dielectric spacing layers with two patterns inkjet printed on thin polycarbonate films, each film being sandwiched between the insulating spacers. As shown in Figures 18b and 19a, the top pattern is a Jerusalem cross, while the bottom is a square ring enclosing a dipole cross. The ink preparation, inkjet procedure and absorber design method are fully described in [26]. Here, we compare the performances of absorbers realized with pristine MWCNTs-ink with that obtained with a Fe\({}_{3}\)O\({}_{4}\)@MWCNTs ink. As explained in [26; 27], the surface sheet resistance R\({}_{8}\) of the printed ink pattern is characterized as a function of the number of printed layers (Figure 19b), while Figure 19a shows the ink-printed patterns. The sheet resistance of the Fe\({}_{3}\)O\({}_{4}\)@MWCNTs ink is higher due to the insulating nature of magnetite, as explained before. The selection of proper values  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer** & **6-Layer Configuration from Air interface** & **Thickness \(d\) (mm)** & \(\varepsilon\) & \(\sigma\) \\ \hline
**1.** & GNPs (1 wt\%) & 0.8 & 3.97 & 0.0677 \\ \hline
**2.** & MWCNTs (0.5 wt\%) + GNPs (1 wt\%) & 0.4 & 6.949 & 2.068 \\ \hline
**3.** & MWCNTs (1 wt\%) + GNPs (1 wt\%) & 0.4 & 10.01 & 3.134 \\ \hline
**4.** & MWCNTs (1 wt\%) + GNPs (2.5 wt\%) & 0.8 & 14.15 & 6.59 \\ \hline
**5.** & MWCNTs (1 wt\%) + GNPs (5 wt\%) & 0.8 & 20.52 & 10.25 \\ \hline
**6.** & MWCNTs (1 wt\%) + GNPs (10 wt\%) & 0.8 & 40.82 & 13.83 \\ \hline \hline \end{tabular}
\end{table}
Table 4: EM (electromagnetic) properties of the constituent layers for 6-layer absorber.  
Figure 17: (**a**) Measured and computed (**a**) Reflection coefficient \((S_{11})\) (**b**) Absorption for 6-layer multilayer absorber.  
for (I) the numbers of layers, (II) the top and bottom patterns and (III) the adequate dimensions of each enables to achieve ultra-wideband high absorption and low reflection for MWCNTs only and for Fe\({}_{3}\)O\({}_{4}\)@MWCNTs based absorbers, as shown in Figure 20. The benefit of the presence of magnetite nanoparticles is clearly demonstrated in Figure 20, since the corresponding absorber has the highest absorption and lowest reflection over the wide 8 GHz bandwidth, and lowest thickness (3.5 mm for Fe\({}_{3}\)O\({}_{4}\)@MWCNTs device, versus 5 mm for MWCNTs-only device) ensuring optimal compactness.  
Figure 19: (a) ink-printed patterns (adapted from [26]: left Jerusalem cross, center square ring enclosing a dipole cross, right perspective view) (b) measured surface resistance for MWCNTs only-ink and for magnetite@MWCNTs-ink as function of number of prints.  
Figure 18: (a) cross-section (b) unit cell of ink-based absorber where a\({}_{x}\) = a\({}_{y}\) = 9 mm. Adapted from [27].
## 4 Mission Reliability Modeling, Allocation, and Analysis Method Design
### Task Reliability Modeling Design
#### 4.1.1 Structural Modeling  
The AADL model can describe the architecture of a system, the connections and information flow between components, as well as the properties of components, to support reliability analysis of the system. However, the AADL model does not require a description of the specific implementation within the components, as these details do not affect the reliability analysis of the system. The ship system of this study consists of three categories: detection and perception equipment, command and control system, and weapon module. The following will introduce them separately:  
(1) Detection and perception devices.  
Detection and perception devices can be regarded as device elements in AADL, containing an output port, output information, and fault conditions [48]. When constructing detection and perception devices, it is not necessary to describe the implementation details inside the components in detail.  
(2) Command and control system.  
The command and control system, as a software-hardware coupling system, includes basic support equipment such as computing service devices, network devices, display  
Figure 6: Reconfigurable ship electronic information system mission reliability design and analysis flowchart.  
devices, as well as software in the form of service components. Computing service devices can be regarded as System elements in AADL. Computing service devices are in the form of cabinets, the basic computing devices (Processor class), and basic storage devices (Memory class) are encapsulated into a module using the System class [49]. Network devices consist of switches and buses. Switch devices are modeled using the Device class in AADL, while buses are modeled using the Bus class [50]. The computing service devices are connected through network devices, and the software of the command and control system is modeled using the Process class of AADL.  
(3) Weapon module  
The weapon module includes short-range and long-range air defense equipment, electronic jamming devices, etc. Close-range and long-range air defense equipment have a range of 8 to 30 km and can carry out both close-range and long-range strikes. The model for striking weapon equipment is the Device element of AADL, and the output signal of the target control software is transmitted to the weapon module input [51].  
Model the detection and perception devices, command and control system, and weapon module separately, and connect them using port and access connections to obtain the structural diagram of the reconfigurable ship electronic information system. The correspondence between each module and AADL elements is shown in Table 5.
# Time Series Classification with Shapelet and Canonical Features
## 3 Definition and Background
### Definitions  
**Definition 1**.: _Time series. A time series \(T\) of length \(l\) is an ordered sequence of \(l\) observations of a variable, which can be expressed as \(T=\textless t_{1}\), \(t_{2}\),..., \(t_{l}\textgreater\), where \(t_{i}\in\mathbb{R}\)._  
Compared with the entire time series, here we pay more attention to the local fragments of the time series, which is a time series subsequence.  
**Definition 2**.: _Time series subsequence. A subsequence \(T_{i,m}=\textless t_{i},t_{i+1},\ldots,t_{i+m-1}\textgreater\) of time series \(T\) refers to a fragment consisting of consecutive \(m\) values from index \(i\) to index \(i+m-1\) in \(\mathbf{T}\)._  
In this article, any subsequence can be seen as a Shapelet. According to a Shapelet's discriminating ability, there will be expressions such as \"optimal Shapelet\" or \"optimal \(k\) Shapelets\".  
**Definition 3**.: _Distance metric. The distances between subsequences can be used to reflect their similarity. For two subsequences \(\mathbf{S}^{1}\) and \(\mathbf{S}^{2}\) with the same length \(m\), here we use the normalized Euclidean distance metric shown in Equation (1)._  
\[dist(\mathbf{S}^{1},\mathbf{S}^{2})=\sqrt{\frac{1}{m}{\sum_{i=1}^{m}{(S_{i}^ {1}-S_{i}^{2})}^{2}}} \tag{1}\]  
When calculating the distance between a subsequence \(\mathbf{S}\) with length \(m\) and a time series \(T\) with length \(l\) (\(l>m\)), the subsequence \(\mathbf{S}\) needs to slide over the time series \(T\) to find the best matching subsequence \(\mathbf{S}^{\prime}\), and then take the best matching distance (i.e., the distance between the subsequence \(S\) and \(S^{\prime}\)) as the distance between the subsequence \(S\) and the time series \(T\) (as shown in Equation (2)). Figure 2 describes the above process.  
\[subDist(S,T)=\min_{1\leq i\leq l-m+1}dist(S,T_{i,m}), \tag{2}\]  
It should be noted that most of classification methods based on Shapelet generally use \(Z\)-standard Euclidean disdance measurements in order to ensure that the amplitude offset of the subsequence is invariant--that is, \(Z\)-score is calculated according to Formula (3) before using Formula (1) to obtain the normalized European distance (\(\mu\) is the mean of subsequence \(S\) and \(\sigma\) is the standard deviation of \(S\)).  
\[S_{i}^{norm}=\frac{S_{i}-\mu}{\sigma},i=1,2,\cdots,m, \tag{3}\]  
For computational efficiency reasons, the RSFCF algorithm uses a more efficient normalized Euclidean distance metric when searching for the best match \(S^{\prime}\), and only uses the \(Z\) standardized Euclidean distance metric when calculating the best match distance.  
**Definition 4**.: _Time series classification. Given a training set \(\mathbf{D}=\{(T_{1},y_{1}),(T_{2},y_{2}),\cdots,(T_{n},y_{n})\}\) containing \(n\) instances, each instance consists of a time series and its corresponding class label. The time series classification task aims to learn a classification model by training set \(\mathbf{D}\) and uses the model to predict the categories of unlabeled time series._
## 3 Methodology
### Experimental Model  
A physical prototype of the proposed model, as shown in Figure 6a,b is developed in the laboratory environment. A 12 volt, 7 Ah lead-acid battery is installed with the solar module PV panel (V\({}_{\text{OC}}\) = 21.6 V, I\({}_{\text{SC}}\) = 3.40 A, P\({}_{\text{m}}\) = 50 W) along with a 5 W AC bulb (240 V, 50 Hz) as a load. In the working prototype, PV solar panel, battery, and loads are connected including fuse and circuit breaker as well as the MATLAB/SIMULINK 2019a software is interfaced with dSPACE 1104 controller on Windows 10 professional operating system in a personal computer (PC) acer with 4.00 GB RAM, Intel(R), Core(TM) i5-3230 CPU @ 2.60 GHz. The integration of MATLAB Tools for dSPACE Code Generation block diagram is shown in Figure 7. The voltage and current sensors measure the voltage and current from the corresponding elements and provides the signal to the designed personal computer (PC) model through dSPACE1104 electronic board. The SIMULINK model analyzes the data and evaluates the PV power, battery SOC, and load power for the BMS controller. It is worth mentioning that the aim of this paper focuses on the battery system management through the proposed BMS that controls the charging, discharging, and islanding modes of the battery.
# Observational Analysis of Variation Characteristics of GPS-Based TEC Fluctuation over China
## 3 Results and Discussion
### TEC Fluctuation Detection on 8 October 2003  
To better show the application of ROTI to detecting ionospheric TEC fluctuations based on both GPS observation data from the QION site in Hainan provided by Crustal Movement Observation Network of China (CMONOC) and the ionospheric scintillation data from the FUKE site in Hainan provided by the Space Environment Monitoring Network of the Chinese Academy of Sciences, the time period was selected as 8 October 2003, which was during an obvious occurrence of scintillation in Hainan over the lower latitudes of China. QION is the GNSS tracking site nearest to the FUKE monitoring site, at a distance of less than 100 km, so the difference in ionospheric irregular activities between these sites may be considered slight.  
Figures 2 and 3 (blue lines) show the time series of the ROT and ROTI derived from all of the observed GPS satellites at the QION site during the occurrence of ionospheric scintillation on 8 October 2003 (the results vary by universal time, UT). Figure 3 (red lines) shows the UT variation of the amplitude scintillation index \(S_{4}\) of all observed GPS satellites at the FUKE monitoring site over the same time period. Local time (LT) can be approximately calculated by adding 8 h to the UT (LT = UT + 8). As depicted in Figure 2, affected by ionospheric scintillation, there were obviously rapid and random fluctuations of ROT that mainly occurred at night. Figure 3 shows that most of the observed satellites, such as the pseudo random noise code (PRN) 4, 7, 8, 20, 27, and 28, experienced obvious TEC fluctuations during the time period between 7 p.m. LT (pre-midnight) and  
Figure 1: The locations of the 21 selected GNSS sites from CMONC.  
3 a.m. LT (post-midnight); there was an obvious loss of lock phenomena for satellites PRN 7 and 27. Significantly, very few TEC fluctuations were observed during any other time periods.
# Interval-Based LDA Algorithm for Electrocardiograms for Individual Verification
## 1 Introduction
More recently, Safie [44] introduced a new feature extraction method, known as Pulse Active Ratio (PAR), that is implemented on electrocardiogram signals for biometric authentication. The Pulse Active Ratio uses pulse width modulation (PWM) to generate new ECG feature vectors and thereby can adapt to changes in heart rate. However, such fiducial independent approaches require tremendous computational effort because the parameters should be generated and compared for every person listed in the database during the verification stage. Furthermore, the database is often updated with intra-subject variability, in which the computational cost increases exponentially as the size of the database increases. This computational cost problem can be mitigated significantly by combining the above-mentioned two approaches. In this paper, we propose an interval-based Linear Discriminant Analysis (LDA) algorithm, a type of hybrid algorithm that combines the complementary strengths of both fiducial dependent and fiducial independent features, and thereby is able to achieve higher accuracy of identification. The main characteristics of the proposed algorithm are as follows: (i) the use of an autocorrelation profile (ACP) of ECG signal; (ii) the use of short segments of the ACP to extract the interval-based feature vectors, which can be updated every 5 s, showing higher adaptability to intra-subject variation; (iii) the enrollment and verification of the individual's interval-based feature vectors; and (iv) the update of interval-based feature vectors when the user's identity claim is accepted. The remainder of the paper is organized as follows. The proposed LDA algorithm based on the short ACP segments to determine the feature vectors on an interval basis as enrollment process, the verification process, and the algorithm to update the feature vectors are described in Section 2. In Section 3, the experimental results are compared with the existing ECG identification algorithms based on the same public database. Finally, the conclusion is drawn in Section 4.
# Privacy-Aware Access Protocols for MEC Applications in 5G +
###### Abstract  
Multi-access edge computing (MEC) is one of the emerging key technologies in fifth generation (5G) mobile networks, providing reduced end-to-end latency for applications and reduced load in the transport network. This paper proposes mechanisms to enhance user privacy in MEC within 5G. We consider a basic MEC usage scenario, where the user accesses an application hosted in the MEC platform via the radio access network of the mobile network operator (MNO). First, we create a system model based on this scenario. Second, we define the adversary model and give the list of privacy requirements for this system model. We also analyze the impact on user privacy when some of the parties in our model share information that is not strictly needed for providing the service. Third, we introduce a privacy-aware access protocol for the system model and analyze this protocol against the privacy requirements.  
MEC; 5G; IoT; identity privacy; data unlinkability; data confidentiality 2022
## 4 Simulation Validation
### Signal Processing  
The Lamb wave signals were received from all sensing paths in the intact and the current states, separately, using the excitation-acquisition procedure as described in Section 3.1. The group velocity was determined first by the process depicted in Section 3.3. The group velocities at the fundamental and double frequency were 5348.4 and 5319.1 m/s, respectively. The arrival time was collected sequentially, and the process of extracting linear and nonlinear features was essentially the same with that in Section 3.4.
###### Abstract  
Obtaining mathematical models of nonlinear cyber-physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed.  
pneumatic artificial muscle; nonlinear systems; artificial neural network; edge artificial intelligence; programmable logic controller
## 3 Cooperative Communication Algorithms
### End-to-End Reliability Model
In order to achieve this goal, users in the game will cooperate with each other to achieve a \"win-win\" scenario. In this case, the product of collective income function is maximized. According to the above model, joint optimization is used to model for the providers and users of the network service. The purpose of optimization is to calculate a traffic configuration \(\alpha_{p}^{s,d}\) to make the network link load relatively balanced. At the same time, users can experience shorter delay [15; 16]. It can be described with the following optimization model:  
\[\max_{|\alpha_{p}^{s,d}|}\left(\Pi\Big{(}1-\theta_{p}\Big{)}+\underset{p}{ \Pi}\frac{1}{t(s)}\right) \tag{12}\]  
\[\sum_{(s,d)}\sum_{p}\underset{derive}{\sum}\underset{p}{\sum}\underset{derive}{ \sum}\underset{p}{\sum}\underset{i,j}{\sum}\underset{(p)}{\sum}\underset{i,j} {\sum}\quad\forall(i,j)\in E \tag{13}\]  
\[\sum_{p}\alpha_{p}^{s,d}=1\quad\forall s,d\in V,s\
eq d \tag{14}\]  
\[\alpha_{p}^{s,d}\geq 0\quad\forall s,d\in V,s\
eq d,\forall p\in P^{s,d} \tag{15}\]  
Formula (14) ensures that the traffic on the link is not larger than the link capacity to avoid congestion. Formula (15) ensures that all requirements are transmitted over the network. Formula (14) has \(M\) constraints, Formula (15) has \(N^{2}-N\) constraints and Formula (16) has \(\Big{(}N^{2}-N\Big{)}\Big{|}P_{avg}\Big{|}\) constraints. Among them, \(\Big{|}P_{avg}\Big{|}\) is the average number of paths between nodes and \(\mathcal{O}\Big{(}N^{2}\Big{|}P_{avg}\Big{|}\Big{)}\) is the number of variables.