esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Evolving Fuzzy Controller Composed of Univariate Fuzzy Control Rules
## 4 Results
### Results' Analysis
* Figure 4a-b shows that the proposed method has outperformed the SEDFLC evolving method [31], where both methods have used the same initial parameters, and that the SEDFLC method did not react as well as the self-evolving FLC to the new \(r(k)=0.065\) (\(1500\leq k<1800\)) region of operation, which is distant from the previous operating region. * It can be seen that the proposed self-evolving direct FLC controller design methodology successfully online designed the FLC controller, reaching a simple control structure, where each of the input variables (\(r(k)\) and \(y(k)\)) was described by six fuzzy control rules, whose membership functions are described in Figures 5c and 6c, and the final fuzzy control rules for \(x_{1}(k)=r(k)\) are described by: \[\text{Rule 1}: \text{IF }r(k)\text{ is }A_{1}^{1}\text{ THEN }q_{c}(k)\text{ is }90,\] \[\text{Rule 2}: \text{IF }r(k)\text{ is }A_{1}^{2}\text{ THEN }q_{c}(k)\text{ is }90,\] \[\text{Rule 3}: \text{IF }r(k)\text{ is }A_{1}^{3}\text{ THEN }q_{c}(k)\text{ is }101.1,\] \[\text{Rule 4}: \text{IF }r(k)\text{ is }A_{1}^{4}\text{ THEN }q_{c}(k)\text{ is }104.9,\] Figure 4:0 Figure 4:1 and the final fuzzy control rules for \(x_{2}(k)=C_{A}(k)\) are described by: Figure 4:2 Figure 4:3 Figure 4:4 Figure 4:5 (21) Figure 4:6 Figure 4:7  
Regarding the application of the proposed self-evolving method to other SISO processes, defined by (8), it is expected that the initialization and offline stage will be intuitively defined, similarly to the CSTR plant. The offline design is performed using only the range limits of the process's variables (input and output), which is mandatory information to control any process; and the thresholds used on the criteria to add or remove control rules are also intuitively defined, being given by a percentage of the variables range, while \(e\in[0,1]\) has also an intuitive meaning. The sliding window's size \(T_{M}\) and the consequent adaptation gain \(\gamma\) are parameters that must have some know-how about the process to be controlled; \(T_{M}\) must be defined in order to allocate relevant information to obtain the estimated control error \(e_{c}(k)\) and \(\gamma\) defined in order to have an acceptable performance in the beginning (until several control rules are created in order to cover several regions of operation). In terms of performance on other SISO processes, it is expected that the proposed method will have a better performance in industrial process with slow variations, which has happened in several industrial processes with, for example, sample times of \(0.5,1,2,\) and \(5\) s. In terms of the interpretation of the designed controller in other SISO processes, which is an important goal, due to the defined univariate control structure (2) and the defined criteria adding/removing control rules (mainly Criterion 2 that limits excessive partitioning of the input variables spaces), it is expected that the designed FLC will have a simple (interpretable) structure.
# Stability and Failure of the Edge-Closed Honeycomb Sandwich Panels with Face/Core Debonding
## 3 Test Results and Discussion
### Ultimate Failure Modes  
The failure mode of the specimen CD0-3 is shown in Figure 10. Due to the additional bending moment caused by the eccentric load, the structure of the CD0-3 bulged out along the inner sheet. The honeycomb core in the beveled area was crushed and sheared, which caused the sheets to lose support and form a large area of depression. Meanwhile, fiber crushing and delamination occurred in the reinforced area. Finally, the structure failed and could not continue to carry.  
The failure mode of the specimen CD40-3 is shown in Figure 11. The honeycomb core between debonding area and beveled area was crushed and sheared, so the inner and outer skins lost support and a large area bulged out. Meanwhile, fiber crushing and delamination occurred on the both sides of laminated area. As a result, the structure could not continue to carry.  
The failure mode of the CD80-3 specimen is shown in Figure 12. The honeycomb core in the defect area and the beveled area was crushed and sheared due to the local buckling of the specimens. The inner skin of the defect area was dented and the outer skin bulged. Damage continued to accumulate and the structure could not continue to carry. Unlike the failure modes of CD0 and CD40 specimens, no fiber breakage and delamination were found in the inner and outer skins on both sides of the lamination zone during the entire test.  
Figure 11: Compressive failure mode of CD40-3. (**a**) Failure mode of inner skin; (**b**) Failure mode of outer skin.  
Figure 10: Compressive failure mode of CD0-3. (**a**) Failure mode of inner skin; (**b**) Failure mode of outer skin.
# Numerical Voids Detection in Bonded Metal/Composite Assemblies Using Acousto-Ultrasonic Method
## 3 Model Description
### Simulation of the Receiver Sensor  
Several receiver sensor distances from the emitting sensor were investigated, from 28 mm to 230 mm (Figure 2b). The sensors were assumed to be sensitive only to the out-of-wave plane velocity or displacement. Two kinds of sensors were simulated: a perfect point sensor denoted PS and an R15 sensor. The perfect sensor is a virtual sensor that acquires signals at a single measurement point (a node) without averaging effects and is sensitive to all frequencies. A real sensor has a contact surface with the sample that must be considered. The R15 sensor consists of a piezoelectric ceramic with a 7.25 mm radius. Moreover, the sensor has an aperture effect, which results in a non-uniform response over its entire active surface (Figure 3a). It has its maximum sensitivity at its center. Sensitivity is described by the parameter \(k\), which represents the sensitivity of a point at a distance r from the center of the sensor surface. It is given by Equation (2) [42]:  
\[k(r)=1-\frac{1}{1+exp^{(-2r+12)}} \tag{2}\]  
The influence of the physical sensor is considered by calculating an average out-of-plane wave velocity over the sensor surface (\(S\)), accounting for its sensitivity:  
\[V_{s}=\frac{1}{\int_{S}kdS}\int_{S}vkdS \tag{3}\]  
In practice the average wave velocity is approximated by a discrete sum over the velocities \(v\) calculated at the mesh nodes. The frequency sensitivity of the sensor must also be considered. The frequency sensitivity function of the R15 sensor as a receiver is given in Figure 3b. The calibration curve is obtained by the reciprocity method [44]. Its bandwidth  
\begin{table}
\begin{tabular}{c c c} \hline  & \(\mathbf{\kappa}\) (\(\mathbf{s^{-1}}\)) & \(\mathbf{\beta}\) (\(\mathbf{s}\)) \\ \hline Steel & 500 & 4\(\cdot 10^{-10}\) \\ \hline Composite [43] & 10,000 & 6\(\cdot 10^{-10}\) \\ \hline Epoxy [43] & 50,000 & \(10^{-8}\) \\ \hline \end{tabular}
\end{table}
Table 2: Rayleigh parameters.  
Figure 3: (\(\mathbf{a}\)) Aperture function for the R15 sensors: evolution of the \(k\) factor versus the distance from the center of the sensor surface \(r\). (\(\mathbf{b}\)) Reception response of the R15 sensor obtained with the reciprocity method on a steel block.  
is centered on 50-400 kHz. This transfer function is added in the post-processing phase in the Fourier domain, following Equation (4):  
\[V_{s}(\omega)\times H_{R15}(\omega)=V_{R15}(\omega) \tag{4}\]  
where \(V_{R15}\) is the velocity calculated considering the sensor, \(V_{s}\) (\(\omega\)) represents the weighted mean out-of-plane velocity on the surface, \(H_{R15}\) (\(\omega\)) is the sensitivity function and \(\omega\) is the pulsation. In this study, the coupling medium was not considered. All numerical velocity signals were filtered with a 20 kHz to 1200 kHz Butterworth band-pass filter. Several sensor locations between 28 mm and 230 mm from the transmitter were analyzed (Figure 2b).
## 3 Neural Networks Hardware Accelerators  
The applying of neural network models in real systems leads to the need to use specialized computing devices. The huge number of simple operations inherent to NN models is a challenge, even for a state-of-the-art CPU. At the same time, the dataflow in NN allows high parallelization, so computing systems that support simultaneous execution of large amounts of calculations are worth using.  
A simple and powerful solution to the problem is the use of GPUs [4; 5]. Due to their multicore architecture, GPUs can significantly reduce time costs of training and using DNNs in many applications. In addition, the simplicity of the development on the GPU  
Figure 1: Artificial neuron with three inputs.  
Figure 2: Feedforward neural network with two hidden layers.  
makes it easy to implement and maintain solutions. Today, GPU-based accelerators are widely used in desktop, server, and cloud systems, as well as in some large mobile systems, such as self-driving cars. The main disadvantage of these accelerators is their high power consumption, which limits their use in many autonomous and mobile systems.  
Further research to improve the characteristics of accelerators has led to the development of highly specialized devices based on FPGA and ASIC. Due to their flexible architecture, low-level implementation, and fine-tuning optimization, these solutions outperform GPU-based counterparts while consuming less power [7; 8; 9; 10; 11]. The disadvantages of such solutions are the complexity and cost of developing, embedding, and maintaining. In addition, the accelerators often support only a certain model or architecture of NN, which limits their reuse for other tasks.  
In recent years, hybrid computing systems have become popular. These systems include a CPU and an auxiliary neuroprocessor simultaneously. The CPU performs common tasks and delegates machine learning tasks to the neuroprocessor [22]. Because of the division of responsibilities, hybrid systems effectively solve a wide class of applied tasks. But the neuroprocessors are designed to fit prevalent needs, and the problem of the narrow focus of accelerators has not been completely eliminated yet.  
One possible solution to this problem is applying dynamically reconfigurable hardware accelerators.
# Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review
## 4 Volumetrics
### Algorithmic Approaches
#### 4.5.12 Ternary Search Tree-Based Differential Evolution (TSTDE) Algorithm  
The TSTDE is introduced as a novel approach to solving the 3D-BPP. The core innovation lies in the incorporation of a ternary search tree model for generating the initial population in the differential evolution process. Unlike conventional practices, TSTDE avoids the use of upper and lower bounds for trial individuals. Instead, the ternary tree model is leveraged to assess the feasibility of generated solutions, contributing to a unique and adaptive initialisation process. Similar to the termination criterion in HBTS, where it entails surpassing a predetermined computational limit, the developers of TSTDE recognise the difficulty in establishing practical termination conditions and similarly choose a termination approach based on a maximum number of iterations. Key parameters include the maximum number of iterations, crossover probability, scaling factor, and population size [8].  
Experimental results, conducted using Bischoff and Ratcliff instances with varying cargo types, demonstrate the effectiveness of TSTDE. Comparative analyses against established optimisation algorithms, such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), Simulated Annealing (SA), and Ant Colony Optimisation (ACO), reveal the superiority of TSTDE in handling the 3D-BPP [8].
# Assimilation of Dynamic Combined Finite Discrete Element Methods Using the Ensemble Kalman Filter
## Appendix A Brief FDEM Overview and HOSS Model Parameters  
The combined finite-discrete element method was conceived to represent the transition from continuum to discontinuum when a material breaks or fractures. The method formulation is based on the assumption that a material is composed of a group of deformable discrete entities, called discrete elements, that interact with each other via cohesive and contact forces. The governing equation that FDEM resolves, in its more general form, is given by  
\[\mathbf{M}\frac{\partial^{2}\mathbf{x}}{\partial t^{2}}+\mathbf{C}\frac{ \partial\mathbf{x}}{\partial t}=\mathbf{f}, \tag{1}\]  
where \(\mathbf{M}\) and \(\mathbf{C}\) are the lumped mass and the damping matrices, respectively, while \(\mathbf{x}\) is the vector of displacements and \(\mathbf{f}\) is the vector of forces acting on the nodes of the finite element mesh. Vector \(\mathbf{f}\) groups the forces due to material deformability, contact interaction, and cohesion with other elements, boundary conditions, and so forth. FDEM implementations use explicit time integration schemes [28; 51] to resolve Equation (1). Therefore, there is no need to assemble global matrices for the whole system. In turn, Equation (1) is resolved on an element-by-element basis.  
In order to accurately simulate fracture processes, HOSS uses a number of parameterizations in its hybrid finite-discrete element scheme. The parameterizations include a softening curve to simulate bond breakage (i.e., fracture) behavior between finite elements' interfaces in tangential and normal directions. This is achieved via a combined single and smeared crack model [52] implemented at the finite element's interfaces. There are a number of key model parameters that play a significant role in this crack model. The parameters we focus on in this work include tensile and shear strength (\(\sigma_{n}^{max}\) and \(\sigma_{t}^{max}\)), maximum relative displacements in the normal and tangential direction (\(\delta_{n}^{max}\) and \(\delta_{t}^{max}\)), and the parameters that determine the shape of the softening curve (\(a,b,c\)). The specific fracture energy dissipated at the interface during fracturing is calculated as  
\[G=\int_{0}^{1}z(x)\sigma^{max}g^{max}dx, \tag{2}\]  
where  
\[z(x)=\left[1-\frac{a+b-1}{a+b}\exp\left(x\bigg{(}\frac{a+cb}{(a+b)(1-a-b)} \bigg{)}\right)\right]\left[a(1-x)+b(1-x)^{c}\right] \tag{3}\]  
is the functional form for the displacement-softening evolution [52], and\[x=\begin{cases}0&\text{if }\delta\leq\delta^{e}\\ 1&\text{if }\delta>(\delta^{e}+\delta^{max})\\ \frac{\delta-\delta^{e}}{\delta^{max}}&\text{if }\delta^{e}<\delta\leq(\delta^{e}+ \delta^{max}).\end{cases} \tag{10}\]  
The displacement-softening curve is defined by three parameters (\(a\), \(b\), and \(c\)), see Equation (11). In the current work, the displacement-softening curve is utilized to describe both Mode I and Mode II fracture behavior. A description of the variables listed in Equations (10)-(10) can be found in [18].
## 3 Research Method
### Data Analysis  
Relationships raised in the research model were assessed by the selected data, for which the chosen method of analysis was the modeling of structural equations through PLS. According to the characteristics of the model (predictive) and the sample (less than 250 observations), the minimum sample size depended on the number of relationships between variables in the research model [79; 80]. In this case, at least 70 observations were suggested for 5 relationships. This sample complied with this condition, having a sample size of 92, which was suggested by G-Power software for a medium effect size (f2 = 0.15), at a power of 0.8 for an alpha level of 0.05.  
This study used SmartPLS 3 software [81]. The estimation of the model was completed in two steps [82]. In the first step, the measurement model was analyzed, where the link between indicators and the variable they represent was verified. In the second step, the structural model was analyzed to examine how valid the relationships between the proposed variables in the model were. To test the measurement model in the first step, the nature of the direction of causality between the indicator and the construct should be analyzed. In our model, all variables had a formative specification, since the indicators directly helped create the variable. In other words, the direction of causality went from the indicators to the variable being measured or from the indicators to the construct. A variable with formative indicators implied that the indicators did not need to be highly correlated with each other, because each indicator collected different aspects of the variable which can occur independently [83].  
For the structural model, relationships between the variables proposed in the research model were validated through the path coefficient (\(\beta\)) and their level of statistical significance with a Bootstrap test using 5000 subsamples and also illustrated the variance R\({}^{2}\) of the variables explained. In order to contrast the hypotheses of mediation, the first thing to be assessed were the direct effects caused to the model without introducing any mediating variables. After that, the mediating variables were introduced in order to assess the indirect effects the mediation relationships had on the mode to then calculate the VAF value.
# Glucose Data Classification for Diabetic Patient Monitoring
## 6 Classification Data in Healthcare
### Random Forest  
Random Forest is a machine-learning algorithm; it is a flexible and easy-to-use algorithm that produces, even without hyper-parameter settings, an optimal result most of the time. It is also one of the most used algorithms because of its simplicity and the fact that it can be used for both classification and regression tasks, because with random forest, one can also handle task regression using the random forest regressor. Random forest has almost the same parameters as a decision tree. In the field of health care, it is used to identify the correct combination of components in medicine and to analyze a patient's medical history to identify diseases. Finally, in e-commerce, random forest is used to determine whether a customer will really appreciate a product or not.  
The data measured by the glucose sensor are processed by the Arduino card and sent directly to the patient's smartphone via Wi-Fi or 4G, which in turn sends these data daily to the cloud and to the smartphone of the doctor. The monitoring center has the right to access data from the cloud via local connection. Table 1 shows the value of glucose levels measured with the glucose sensor and processed by the Arduino Card into 15 days for one diabetic patient with an average of two times per days.
## 3 Materials and Methods
### Datasets  
The study's dataset, which included recordings, diagnostic data, and demographic information, is collected from several open-source databases freely available to download(Table 1). To generate this large dataset, five different sources were used. All the datasets contain 12-lead ECG recordings where the sample frequencies ranges from 257 Hz to 1 KHz. The datasets also include the demographic information such as age, sex, and types of diagnosis. There are 27 categories of ECG classes (diagnosis) which are presented along with SNOMED CT codes (Systematized Nomenclature of Medicine Clinical Terms). The following subsections detail the specific sources comprising the dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Database** & **Total Recordings** & **Recordings in Test Set** & \begin{tabular}{c} **Recordings in** \\ **Validation Set** \\ \end{tabular} & \
\begin{tabular}{c} **Recordings in** \\ **Training Set** \\ \end{tabular} &
\begin{tabular}{c} **Total Patients** \\ \end{tabular} \\ \hline CPSC & 13,256 & 1463 & 1463 & 10,330 & 9458 \\ INCART & 74 & 0 & 0 & 74 & 32 \\ PTB & 22,353 & 0 & 0 & 22,353 & 19,175 \\ G12 EC & 20,678 & 5167 & 5167 & 10,344 & 15,742 \\ Undisclosed & 10,000 & 10,000 & 0 & 0 & Unknown \\ Total & 66,361 & 16,630 & 6630 & 43,101 & Unknown \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dataset description.  
Figure 1 shows the distribution of ECG signal lengths in the dataset. The figure shows that 95% of the ECG signals in the dataset have a length of 5000 samples. The remaining 5% of the ECG signals have lengths that range from 5500 to 115,200 samples.  
Figure 2 visualizes the distribution of cardiac abnormalities in the utilized datasets. The vertical axis represents the number of abnormalities, and the horizontal axis lists the names of the 27 classes in abbreviated form. The abbreviations used, corresponding to the Diagnosis and SNOMED CT codes, are provided in Appendix A, Table 1. It is worth noting that \"Sinus Rhythm (SNR)\" appears as the most frequent abnormality in the graph.
# A Survey of Advanced Border Gateway Protocol Attack Detection Techniques
## 2 Inter-Domain Routing and BGP  
As the default inter-domain routing protocol for the Internet, the BGP has been described as a path-vector and distance-vector variant protocol [18; 19]. Although inter-networked routing domains (ASs) communicate using a shared protocol (BGP), they are autonomous entities administered by a single authority [20; 21]. ASs are often large, complex groups of networks. Internetworked ASs are also not simply physically or geographically bound but rather formed by corporate, organizational, and political factors; topological-centric inferences about Internet operations can be flawed, with potentially important information on Internet interactions and dynamics lost in abstraction [3; 4].  
Every BGP message is structured with a consistent header that includes a marker, length, and type fields, totaling 19 octets in size. The marker field, which is 16 octets long and set entirely to 1, signals the beginning of a message. The length field, taking up 2 octets, specifies the overall message size, header included. The type field identifies the message's category, which can be one of five types: OPEN, UPDATE, NOTIFICATION, KEEPALIVE, and ROUTE REFRESH, as specified in various RFCs (e.g., 1771, 4271, 2918, and RFC 7313 [22; 23; 24; 25]). The initiation of a TCP session triggers the sending of an OPEN message, marking the start of the BGP message exchange necessary to reach the ESTABLISHED state. The termination and maintenance of sessions are communicated through NOTIFICATION and KEEPALIVE messages, respectively.  
The storage of routes within a BGP peer is managed across three key databases, collectively referred to as the Routing Information Base (RIB): Adj-RIB-In, Adj-RIB-Out, and Loc-RIB (Figure 1). Adj-RIB-In is responsible for holding routes acquired from UPDATE messages from other peers, essentially reflecting the routes learned from neighboring peers that contribute to the path selection process. Conversely, Adj-RIB-Out archives routes that are disseminated to other peers, and Loc-RIB maintains the peer's currently selected best routes, as determined by the Adj-RIB-In data and the peer's internal path selection criteria. Modifications to routing information, such as announcements, withdrawals, or updates to existing attributes, are communicated following the RIB data exchange.