esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Generation of In-Silico Data for Algorithm Validation
#### 2.2.3 Generation of Different Simulated Scenarios  
The parameters of the simulation algorithm were varied in order to test the ranking algorithm in different data scenarios. In total, 12 different simulation scenarios were considered, which are summarized in Table 2. The first 3 scenarios are characterized by different CV values, for which we tested 3 values: 10%, 30%, and 50%. Scenarios 4-6 are obtained by testing different values for N\({}_{\text{noise}}\), i.e., 3, 9, 12, which correspond to the values 11, 7, 5 for N\({}_{\text{pred}}\) and 6, 4, 3 for N\({}_{\text{corr}}\). In scenarios 7-9, we test different values of n\({}_{\text{train}}\), i.e., 250, 500, and 1500. Finally, the last 3 scenarios are characterized by different fractions of censored subjects, i.e., 0.1, 0.3 and 0.7. For each scenario, N = 50 training sets were simulated for a total of 600 simulated datasets.  
\begin{table}
\begin{tabular}{c c} \hline
**Variable** & **Model Coefficient** \\ \hline x\({}_{1}\) & 4.77 \\ x\({}_{2}\) & \(-\)4.69 \\ x\({}_{3}\) & \(-\)3.74 \\ x\({}_{4}\) & 2.93 \\ x\({}_{5}\) & \(-\)2.74 \\ x\({}_{6}\) & \(-\)2.29 \\ x\({}_{7}\) & \(-\)1.52 \\ x\({}_{8}\) & 1.02 \\ x\({}_{9}\) & \(-\)0.88 \\ x\({}_{10}\) & 0.70 \\ \hline \end{tabular}
\end{table}
Table 1: Coefficients of the true Cox model for the representative simulated dataset.  
Figure 1: Pearson correlation matrix of the representative simulated dataset. The variables x\({}_{1}\)–x\({}_{20}\) are the simulated candidate predictors, y represents the event times before applying the censoring.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Scenario** & **CV** & **N\({}_{\text{noise}}\)** & **n\({}_{\text{train}}\)** & **F\({}_{\text{c}}\)** & **N\({}_{\text{pred}}\)** & **N\({}_{\text{corr}}\)** & **N\({}_{\text{feat}}\)** \\ \hline
1 & 10\% & 5 & 1000 & 0.5 & 10 & 5 & 20 \\
2 & 30\% & 5 & 1000 & 0.5 & 10 & 5 & 20 \\
3 & 50\% & 5 & 1000 & 0.5 & 10 & 5 & 20 \\
4 & 30\% & 3 & 1000 & 0.5 & 11 & 6 & 20 \\
5 & 30\% & 9 & 1000 & 0.5 & 7 & 4 & 20 \\
6 & 30\% & 12 & 1000 & 0.5 & 5 & 3 & 20 \\
7 & 30\% & 5 & 250 & 0.5 & 10 & 5 & 20 \\
8 & 30\% & 5 & 500 & 0.5 & 10 & 5 & 20 \\
9 & 30\% & 5 & 1500 & 0.5 & 10 & 5 & 20 \\
10 & 30\% & 5 & 1000 & 0.1 & 10 & 5 & 20 \\
11 & 30\% & 5 & 1000 & 0.3 & 10 & 5 & 20 \\
12 & 30\% & 5 & 1000 & 0.7 & 10 & 5 & 20 \\ \hline \end{tabular}
\end{table}
Table 2: Summary of simulated scenarios.
## 6 Security Analysis and Discussion  
### Security and Robustness  
The security and robustness of authentication mechanisms are generally measured using two popular metrics: the false acceptance rate (FAR) and the false rejection rate (FRR). Generally, these two undesirable errors are defined considering the major PUF properties (intra-HD and inter-HD), and there is a tradeoff between them which can be controlled by a threshold for obtaining a suitable FAR and FRR [33]. The threshold is determined by effective parameters depending on the application. In this paper, FAR refers to the probability that a wrong response is verified as the true response from the target device and FRR is the probability of wrongly rejecting the target entity's response. Based on the proposed authentication mechanism, the classification of received response is a major stage in accepting or rejecting it. When the server sends a challenge to the target device, the probability that the corresponding response is rejected directly depends on the result of classification of the response, which is compared to the original class in the next stage. Thus, the accuracy of classification significantly affects the FRR, and the probability of misclassification determines the FRR. However, the FAR value is not directly influenced by the rate of classification error and depends on CNN features. Assuming that a wrong response from an invalid device is received, it can be classified in each class with the same probability due to the uniqueness of PUF responses generated by different DRAM blocks. Therefore, the FAR is about 1/N, where N indicates the number of classes of the trained CNN. In Figure 8, the values for the FAR and FRR for deep PUF are shown and compared to some other PUFs. The threshold used for controlling the tradeoff between FAR and FRR can be determined by the number of classes. With a smaller number of classes (N = 60), the FAR and FRR achieve an equal value (0.016), but as N increases, the FAR and the accuracy of classification decrease leading to a higher FRR. However, the dependence of accuracy on other features (e.g., number of samples in each class and CNN model) can address this issue and enable a desirable FRR. In such a scenario, it will be possible to minimize both the FAR and FRR considering the major features, such as increasing the accuracy and the number of CNN classes simultaneously.  
Figure 7: Probability of error in classification as a function of the number of measurements for the different number of classes (N = 20, 50 and 100).  
More generally, when a device is being authenticated, the probability of error during the response verification is influenced by both inter-device HD and inter-class HD. In Deep PUF, generating enough samples of each class using different ambient conditions, provides more involvement and minimizes the FRR. However, in other mechanisms, the response space is not large enough, and it is difficult to control both these errors.
# Toward Scalable Video Analytics Using Compressed-Domain Features at the Edge
## 2 Background
### Pixel-Domain-Based Moving Object Detection  
Due to more reliable features that can be extracted from the pixel data, the majority of moving object detection methods require full decoding of the video stream. In the pixel-domain, a video analytics server analyzes the red-green-blue image to determine the appearance of objects and spatial events. Ubiquitous real-time video analytics remains an open and exciting research problem, and it is fueled by the recent advantages of hardware and deep learning [18; 19; 20; 21; 22; 23]. There are two primary methods that are considered for moving object detection in the pixel-domain.  
2.2.1 Hybrid Model of Background Subtraction and Object Classification-Based Moving Object Detection  
The background subtraction method [24; 25] subtracts the current frame and background image to eliminate an image's background and then detects moving targets based on gray value differences. This method is the most commonly used object detection technique. There are a number of methods that have been used to build the background model, e.g., [26] proposed a novel real-time motion detection algorithm that integrates the temporal differencing method, double background filtering method, optical flow method, and morphological processing methods to obtain excellent performance. Moreover, the authors of [25] discussed modeling each pixel as a grouping of Gaussians using an on-line approximation to renew the model. The Gaussian distributions of the adaptive mixture model were then assessed to determine which model can be presumed to be obtained from a background process. This method's advantage is its simple implementation, low computational resource requirements, robustness in the presence of environmental noise, and dynamic background. However, it has limitations with shadows, background changes, as well as object localization and classification. Furthermore, object detection based on the background model cannot detect the individual objects in a group or a block object. In general, the aim of background subtraction is to separate foreground images from background ones in the form of blobs, followed by an object classification process. The detected blobs then help classify each blob into subclasses, as shown in Figure 4, which shows the complete process of this approach to perform smoke detection. Fortunately, deep learning, a relatively new technique in machine learning, enables very accurate classification of images using convolutional neural networks (CNNs) [27; 28; 29]. The capacity of CNNs can be controlled by varying their depth and breadth; moreover, they make strong and mostly correct assumptions about the nature of images (i.e., stationarity of statistics and locality of pixel dependencies). For example, by combining CNN and transfer learning, the authors of [30] achieved an average accuracy of 70.1% using the CIFAR-10 [31] dataset.  
Figure 3: Example of motion vectors’ extraction. (**a**) Test video sequence from our recorded video. (**b**) Test video sequence from VIRAT.
# Bi-Stability in Flexure Beams: Introducing a Setup for Experimental Characterization
## 2 Materials and Methods
### Bi-Stable Mechanism Design and Assembly  
The design of the bi-stable mechanism studied in this work is based on the conventional fixed-fixed beam topology introduced by [PERSON] et al. [22]. As illustrated in Figure 1b, the presented design consists of a slender beam divided in the center by a slider. Each side of the beam is called a flexure beam and is attached to the slider through a pivot joint. The other end of each flexure beam is fixed. A base made of acrylic has perforations to allocate all the components with cap bolts (Figure 2a). The input force is applied at the central element, causing the slider to move downward and generate a linear motion.  
The design of the components and the assembly strategy of the proposed bi-stable mechanism are inspired by the kit presented by [PERSON] et al. [31]. The proposed kit here consists of two elements: flexible slender beams and semi-rigid circular connectors with multiple slots. The elements can be press-fit by hand, allowing rapid assembly and testing of different compliant designs. From the original kit, we adapted the idea of having thin slots in the rigid components (sliders and connectors) to insert the individual flexure beams. The design of the slots locks the flexure beams in place. The fixed points at the ends of the flexure beam are referred to as anchors, and the component that receives the compression force to move along the slider is called the trigger. Figure 2b shows the components and layout. One end of the flexure beam is attached to the anchor, and the other is attached to the trigger. As shown in Figure 1b, the proposed design has two configurations. The length of the slider restrains the linear displacement, defining the form of the flexure beams in both initial and final configurations. To begin, the mechanism is set to the first configuration (first stable equilibrium position). When the force F is applied along the Y axis, the slider  
Figure 1: (**a**) Typical bi-stable force-displacement graph. (**b**) Stable and unstable equilibrium positions of the bi-stable mechanism used in this study.  
begins to move vertically until it overcomes the snap-through point and switches to the second configuration (second stable equilibrium position).
# A Set of Active Disturbance Rejection Controllers Based on Integrator Plus Dead-Time Models +
### Two Sets of Active Disturbance Rejection Controllers without and with Dead-Time Compensation  
Based on the main ADRC paradigms, in [20], the sequence of plant models (2) was used to design a set of controllers denoted as \(\text{ADRC}_{n},n=1,2,...\). The controllers have shown interesting properties and indicated a possible connection to solutions known as higher order ADRC. In this paper, we show how the mentioned set of controllers can be easily transformed into a new set of controllers denoted as \(\text{ADRC}_{n}(T_{d})\). These fully respect the dead time \(T_{d}\) of the IPDT model under consideration. The consequences of such a transformation are tested experimentally.  
Ideally, the model parameters \(T_{m}\) and \(K_{m}\) should be equal to the integral plant parameters \(T_{d}\) and \(K_{s}\). Since the dead time \(T_{d}\) considered in the controller tuning may differ from the dead time \(T_{m}\) obtained from the plant identification, we need to distinguish these two values. As presented by [PERSON] and [PERSON] [27], and discussed in numerous publications [36; 37], such plant approximations can be widely applied. Here, the approximations are based on drawing a tangent line through the inflection point of the plant step response (process reaction curve). Such approaches can widely be considered also in the ADRC domain (see e.g., [5]).
## 2 Key Factors Contributing to Spatial Resolution in PET Imaging
### Advanced Photon Depth-of-Interaction (DOI) Capable PET Detector Design  
Various detector designs have been proposed to achieve photon DOI measurement. One approach uses narrow scintillation crystal elements with a dual-end readout, where photodetectors are placed at both ends of the crystal to capture the DOI information [32; 33; 34]. Alternatively, light-sharing techniques have been developed, allowing DOI positioning to be determined by analyzing light distribution across photodetectors [35; 36; 31; 37]. This enables single-ended readout, where photodetectors are attached to only one end of the crystal element, a common setup in PET detector arrays employed in industry. Each method has its advantages and limitations. Dual-end readouts require twice as many photodetectors as single-ended designs, while single-ended readouts typically achieve DOI accuracy of around 2-3 mm.  
[PERSON] et al. and [PERSON] et al. introduced an innovative side-readout approach using an \(8\times 8\) LYSO (lutetium-yttrium-oxyorthosilicate) scintillator array with each array coupled to a position-sensitive avalanche photodiode (PSAPD). Each crystal element in the array was measured as \(0.91\times 0.91\times 1\) mm\({}^{3}\). Instead of the usual configuration, they arranged the array \"edge-on\" with respect to incoming photons. In addition to clearly resolving the minuscule crystals in a flood histogram, this side-coupled array allows for a depth-of-interaction (DOI) resolution of \(\sim\)1 mm (the dimension of each crystal element) and an energy resolution of about 10% FWHM owing to reduced light collection efficiency variations. The PSAPD's slim design also helps maximize the packing density of the crystal elements, making this novel configuration more efficient [38; 39].
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### Fuzzy Logic
#### 3.1.7 Drlc  
DFLC (Distributed Fuzzy Logic-Based Clustering) is a clustering algorithm based on the fuzzy logic which is executed on a distributed manner by nodes within the network [37]. DFLC considers the network as a tree in which a node can be sink (BS), root (CH), member (node within a cluster), parent or child node. A parent node is an intermediate node and receives data sent by a child node to the root. In order to efficiently select the CH, each node runs the fuzzy logic engine with five input parameters: the residual energy, the distance of a node to the other nodes in the tree (centrality), the distance to the BS, the number of hops, and the number of neighbor nodes of a node (node density). During the execution of the fuzzy logic engine only necessary nodes that have a higher probability of being selected as a new root node are considered. DFLC avoids the crash of the network when sensor nodes may not perform their responsibilities due to the energy depletion. When a node receives a Discovery message during the set-up phase, it stores the id of the sender node and denotes this node as a closest neighbor node to the sink node, thus if the network crashes, an alternative path can be used to adapt the system to failures without delays. DFLC is implemented in NS2 simulator to test and compare its performances in terms of the energy consumption, the number of nodes alive, the network lifetime, and the amount of received messages within five networks (100, 200, 300, 400, and 500 nodes). The proposed algorithm is compared to LEACH, ACAWT [38], FCH, and CHEF. The results of experimentations shown that DFLC outperforms the others algorithms in terms of all the experimental metrics.
# Superimposed Training Combined Approach for a Reduced Phase of Spectrum Sensing in Cognitive Radio
## 2 Superimposed Training System Model and Detection Problem
### Superimposed Training PU's Transmitter  
In practical wireless communications systems, training symbols known by the receiver are used to perform channel estimation and synchronization for reliable transmissions. Traditionally, separate time slots are used to transmit training symbols independently of the data symbols, which is known in the literature as the time domain multiplexed training (TDMT) scheme. This, certainly, requires the allocation of a fraction of the total bandwidth for the training sequence, as visualized in Figure 1 (top). As a result, the data rate of the PU to transmit signals in a specific frequency band is reduced. Alternatively to TDMT, the ST technique consists in adding a low-power periodic training sequence to the PU's data sequence before it is transmitted, as visualized in Figure 1 (bottom), increasing its data transmission bandwidth at the cost of requiring some additional processing [16]. In addition, the PU's receivers also achieve better synchronization and channel estimation by means of the ST sequence; as a consequence, its bit error rate is decreased.  
The PU's transmitted sequence is given by [15]  
\[s[n]=t[n]+d[n], \tag{1}\]  
where \(t[n]\) denotes the ST sequence and \(d[n]\) denotes the data (or information) sequence. The ST sequence is a non-random periodic sequence with period \(P\) and average power given by \(\sigma_{t}^{2}=(1/P)\sum_{n=0}^{P-1}|t[n]|^{2}\). Note that \(N\) represents the sample index. Moreover, the data sequence is assumed to be zero mean with average power denoted by \(\sigma_{d}^{2}\). Generally, an ST system is characterized by the training-to-information ratio (TIR) value, which is defined as \(\alpha=\sigma_{t}^{2}/\sigma_{d}^{2}\). Small values of TIR are usually specified for the PUs since the ST sequence acts as input noise on the data sequence. Therefore, it is assumed that the PU sends the ST-based signal through a frequency band selected from those available for its data transmissions. In the SU network, the receivers must identify the idle bands, thus formulating the detection problem explained in the next section.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 2 Materials and Methods
### Unsupervised Classification Algorithm FBCCA
#### 2.1.2 CCA Analysis  
After obtaining \(n\times m\) subband filter matrix, we need to do the CCA analysis. CCA is a multivariate statistical method that maximizes the correlation between two sets of data, which is widely used in BCI frequency classification and detection based on SSVEP [20, 21]. The method is mainly used to calculate the correlation coefficient between the multi-channel EEG signal and the established reference sine and cosine signal to determine the maximum correlation of the signal. Figure 3 shows the process.  
Figure 2: Bode diagram of the filter bank.  
As shown in Figure 3, CCA can analyze multi-channel EEG signals simultaneously, providing more channel information and more successfully extracting features from SSVEP EEG data. When CCA is used to extract the SSVEP response frequency, two sets of multivariate variables are defined as \(X\) and \(Y\). Among them, \(X\) is the multi-channel EEG signal of SSVEP, and \(Y\) is the reference signal associated with the stimulation frequency \(f\).  
\[Y=\left(\begin{array}{c}\sin(2\pi ft)\\ \cos(2\pi ft)\\ \vdots\\ \sin(2\pi Nft)\\ \cos(2\pi Nft)\end{array}\right) \tag{3}\]  
As shown in (3), \(N\) denotes the number of harmonics in \(f\), and \(X\) denotes the number of channels. Assuming \(i\) stimulation frequencies exist, then \(Y_{i}\) is as follows:  
\[Y_{i}=\left(\begin{array}{c}\sin(2\pi f_{1}t)\cdots\sin(2\pi f_{i}t)\\ \cos(2\pi f_{1}t)\cdots\cos(2\pi f_{i}t)\\ \vdots\\ \sin(2\pi Nf_{1}t)\cdots\sin(2\pi Nf_{i}t)\\ \cos(2\pi Nf_{1}t)\cdots\cos(2\pi Nf_{i}t)\end{array}\right) \tag{4}\]  
CCA identifies a pair of vectors \(W_{X}\) and \(W_{Y}\) for each set of multidimensional variables \(X\) and \(Y\) and optimizes the correlation between related variables \(x=X^{T}W_{X}\) and \(y=Y^{T}W_{Y}\) through \(W_{X}\) and \(W_{Y}\), as shown in the following formula:  
\[\begin{array}{c}\max_{W_{X},W_{Y}}\rho=\frac{\varepsilon[x^{T}y]}{\sqrt{ \varepsilon[x^{T}x]\varepsilon[y^{T}y]}}=\\ \frac{\varepsilon[W_{X}^{T}XX^{T}W_{Y}]}{\sqrt{\varepsilon[W_{X}^{T}XX^{T}W_{ X}]\varepsilon[W_{Y}^{T}YY^{T}W_{Y}]}}\end{array} \tag{5}\]  
The maximum correlation coefficient \(rho\) between \(X\) and \(Y\) is obtained from (5). Different \(f\) are selected, and \(\rho\) is calculated. Then, the frequency corresponding to the maximum \(\rho\) is regarded as the SSVEP response frequency.
## 2 Theoretical Background
### PSO-GP Model Process  
Using the PSO to optimize the \(GP\) model includes the following 5 steps (see Figure 2):1. Randomly divide the database into training datasets and testing datasets with a ratio of 80% and 20% [43; 44; 45].
2. Scale the training datasets and testing datasets into the range of 0 to 1 for reducing the calculation difficulties.
3. Find the optimal hyper-parameter combination of the _GP_ model.
4. Develop the optimal training _GP_ model.
5. Check the predictive performance of the trained _GP_ model using testing datasets and verification metrics.