esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mechanical Property Analysis  
Figure 10a shows the fatigue load stress-strain curves of the fractured granite after freeze-thaw cycles. The curves exhibit a \"sparse-dense-sparse\" pattern, and the cyclic load test can be divided into three stages. Stage 1: In the initial deformation stage (the first loading cycle), the axial stress increases suddenly, and the area of the hysteresis loop is larger and then gradually stabilizes. Stage 2: In the constant velocity deformation stage, the hysteresis loop area is small, and the overlapping area between successive hysteresis loops is large, indicating that the specimen is being slowly and irreversibly damaged. Stage 3: In the accelerated deformation stage, the area of the hysteresis loop gradually increases, the distance between hysteresis loops expands, and the overlapping area continuously decreases until the specimen becomes unstable and fails. This indicates that the irreversible damage of the specimen gradually expands, leading to instability and failure when the fatigue limit is reached.  
The damage caused by freeze-thaw cycles is reflected both in the reduction in peak strength and decreased fatigue resistance. After 0, 40, and 80 freeze-thaw cycles, the fatigue life of the specimen is 498, 21, and 5 cycles, respectively. The curve of fatigue life versus the number of freeze-thaw cycles is  
cycles follows an exponential decline. The fitting equation, as shown in Formula (1), has an \(R^{2}\) of 0.99, indicating a strong correlation between the test data and the fitting results.  
\[N_{t}=N_{0}+A\times\left[1-\exp\left(\frac{-\Gamma_{t}}{B}\right)\right] \tag{1}\]  
where \(N_{t}\) is the number of fatigue loads the rock can withstand, \(F_{t}\) is the number of freeze-thaw cycles the rock undergoes, \(N_{0}\) is the number of fatigue loads the rock can withstand without freeze-thaw cycles, and \(A\) and \(B\) are undetermined parameters.
# Deep Learning Applied to Scenario Classification for Lane-Keep-Assist Systems
## 3 Time Series Classification Using Convolutional Neural Networks (CNN)
### Depthwise Separable Convolutions  
Given an input tensor \(T_{i}\) with shape \(ch_{i}\times h_{i}\times w_{i}\), where \(ch_{i}\) is the input depth or number of channels, \(h_{i}\) and \(w_{i}\) are the height and width respectively, an ordinary convolution layer applies the kernel \(k\times k\) over all the channels of the input tensor simultaneously and the whole kernel is applied for \(ch_{o}\) times leading to the output tensor \(T_{o}\) with dimensions \(ch_{o}\times h_{o}\times w_{o}\). Thus, the dimension of the kernel \(K\) is defined as \(k\times k\times ch_{i}\times ch_{o}\), leading to a computational cost of \(h_{i}\cdot w_{i}\cdot ch_{i}\cdot ch_{o}\cdot k\cdot k\). Generally, the convolutional layer filters input features and computes new representations at the same time.  
On the contrary, the depthwise separable convolutions [29; 30] separate the feature filtering and feature computation into two parts. For the filtering part, a separate convolutional kernel \(K_{i}\) is applied for each of the input channels \(ch_{i}\). As all the channels have their individual kernels an additional layer is needed to combine these filtered features. Thus, the depthwise convolution is followed by a \(1\times 1\) convolution generating the new representation. The \(1\times 1\) layer is a linear combination of the filtered features generated in the depthwise convolution. Each kernel \(K_{i}\) has a dimension of \(k\times k\times 1\) and combined with the \(1\times 1\) convolution leads to a computational complexity of \(h_{i}\cdot w_{i}\cdot ch_{i}\cdot(ch_{o}+k\cdot k)\). The number of parameters compared to the ordinary convolutional layer is reduced by the factor:  
\[\frac{ch_{o}\cdot k\cdot k}{k\cdot k+ch_{o}} \tag{1}\]
# Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms
## 1 Introduction
Moreover, several algorithms have been introduced to fight against credit card fraud detection, but this challenge remains. Figure 1 depicts the credit card fraud transaction process. The following questions are addressed in this work: (1) Is a new incoming transaction consistent with normal customer behavior, i.e., does it correspond to that customer's regular spending patterns in terms of (a) frequency or the average number of transactions over a given time, (b) recency or the average time between the current and previous transaction, and (c) monetary value or the amount spent on that transaction?
###### Abstract  
Due to the limitations of seismic exploration instruments and the impact of the high frequencies absorption by the earth layers during subsurface propagation of seismic waves, recorded seismic data usually lack high and low frequency information that is needed to accurately image geological structures. Traditional methods face challenges such as limitations of model assumptions and poor adaptability to complex geological conditions. Therefore, this paper proposes a deep learning method that introduces the attention mechanism and Bi-directional gated recurrent unit (BiGRU) into the Transformer neural network. This approach can simultaneously capture both global and local characteristics of time series data, establish mappings between different frequency bands, and achieve information compensation and frequency extension. The results show that the BiGRU-Extended Transformer network is capable of compensating and extending the synthetic seismic data sets with the limited frequency band. It has certain generalization capabilities and stability and can effectively handle various problems in the data reconstruction process, which is better than traditional methods.  
attention mechanism; signal reconstruction; frequency extension; BiGRU-extended transformer +
Footnote †: journal: Applied sciences
## 5 Experiments and Discussion
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & & **RMSE** & **MAE** & **Average** & **Time** \\ \hline \multirow{2}{*}{LSTM} & Training & \(2.4444\) & \(2.5225\) & \(2.78\times 10^{0}\) & 5.2 \\  & Testing & \(1.5544\) & \(1.51228\) & \(5.99\times 10^{0}\) & 5.5 \\ \hline \multirow{2}{*}{RNN} & Training & \(3.1776\) & \(4.0786\) & \(4.81\times 10^{0}\) & 5.5 \\  & Testing & \(3.1786\) & \(4.0795\) & \(4.01\times 10^{0}\) & 3.7 \\ \hline \multirow{2}{*}{CNN} & Training & \(2.1611\) & \(2.0650\) & \(3.78\times 10^{0}\) & 1.4 \\  & Testing & \(2.1711\) & \(2.0660\) & \(2.88\times 10^{0}\) & 1.5 \\ \hline \multirow{2}{*}{CONV-LSTM} & Training & \(2.0800\) & \(2.0478\) & \(2.841\times 10^{0}\) & 1.12 \\  & Testing & \(2.0900\) & \(2.0488\) & \(1.8812\times 10^{0}\) & 1.2 \\ \hline \multirow{2}{*}{RMSDC} & Training & \(1.2534\) & \(2.0459\) & \(2.26\times 10^{0}\) & 0.65 \\  & Testing & \(2.0635\) & \(1.0559\) & \(2.17\times 10^{0}\) & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical error values of the proposed RMSDC technique for road examination based on the climate change in training and testing datasets at (Tx = 5, Ty = 2).
###### Abstract  
Bearings are the elements that allow the rotatory movement in induction motors, and the fault occurrence in these elements is due to excessive working conditions. In induction motors, electrical erosion remains the most common phenomenon that damages bearings, leading to incipient faults that gradually increase to irreparable damages. Thus, condition monitoring strategies capable of assessing bearing fault severities are mandatory to overcome this critical issue. The contribution of this work lies in the proposal of a condition monitoring strategy that is focused on the analysis and identification of different fault severities of the outer race bearing fault in an induction motor. The proposed approach is supported by fusion information of different physical magnitudes and the use of Machine Learning and Artificial Intelligence. An important aspect of this proposal is the calculation of a hybrid-set of statistical features that are obtained to characterize vibration and stator current signals by its processing through domain analysis, i.e., time-domain and frequency-domain; also, the fusion of information of both signals by means of the Linear Discriminant Analysis is important due to the most discriminative and meaningful information is retained resulting in a high-performance condition characterization. Besides, a Neural Network-based classifier allows validating the effectiveness of fusion information from different physical magnitudes to face the diagnosis of multiple fault severities that appear in the bearing outer race. The method is validated under an experimental data set that includes information related to a healthy condition and five different severities that appear in the outer race of bearings.  
condition monitoring; fault severity; bearings; feature calculation; feature extraction; information fusion; neural network; Linear Discriminant Analysis +
Footnote †: journal: Applied Sciences
# Error Model of a Measurement Chain Containing the Discrete Wavelet Transform Algorithm
## 4 Application of the Proposed Analysis Method  
To verify the presented thesis, a prototype of a measuring circuit processing a time-varying voltage signal \(s(t)\) from the range \(\hat{s}(t)\in[0;1]\) V was built. The first part of the measurement chain is an amplifier which adjusts the parameters of the processed signal \(s(t)\) to the operating range of the ADC. This amplifier has a transmittance of \(G_{y}(j\omega)\), which is responsible for introducing dynamic errors into the processed signal which can be described in a deterministic way. The output signal \(y(t)\) of the amplifier is processed by the ADC, whose successive output samples \(x(i)\) are processed by the discrete wavelet transform algorithm. A diagram of the measurement circuit is shown in Figure 3. The target gain of the analog part is \(\hat{K}_{y}(\omega)=3.29\) V/V, and the phase shift \(\phi_{y}(\omega)\) should be zero. The processed signal is sampled with a constant frequency of \(f_{s}=48\) kHz, and, on the basis of \(N=128\) samples of the input value, \(M=128\) samples of the output values of the measurement chain are determined. The additional D/D conversion element converts the original output signal \(c(i)\) of the ADC into output samples \(x(i)\) where \(\hat{x}(i)\in[0;1]\) V, which in effect eliminates the static gain of the measuring amplifier. According to presented assumptions the \(x(i)\) quantity, which is a data source for the DWT algorithm, can be described as:  
\[\dot{x}(i)=s_{s,x}\dot{s}\left(iT_{p}\right)=\dot{s}\left(iT_{p} \right), \tag{53}\] \[\dot{x}(i)=\dot{s}\left(iT_{p}\right)+e_{x,\Sigma}(i), \tag{54}\]  
where static gain \(s_{s,x}=1\) V/V and \(e_{x,\Sigma}(i)\) is described in next part of paper.  
The exemplary measurement circuit was built on the basis of the STM32F411 microcontroller and the 12 bit built-in ADC [26]. The DWT algorithm was implemented using the DSP instruction and the \"CMSIS DSP\" library [27]. The amplifier was based on the MCP 6002 operational amplifier [28], operating in a non-inverting configuration with a gain of 3.29 V/V. The reference voltage source for the ADC, and, at the same time, the power source of the operational amplifier, was the LD33 CV integrated circuit [29]. The reference voltage was additionally filtered to reduce noise.
# A New Concept of Reconfigurable Antenna Structure Based on an Array of RF-MEMS Switches
RF-MEMS show very good performance in switching speed (reduced up to a few \(\upmu\)S), scattering parameters and power consumption, and they demonstrated to work properly up to 100 GHz. This work presents a new kind of reconfigurable antenna based on MEMS switches. The proposed structure acts similarly to a field-programmable gate array (FPGA), a configurable integrated circuit. It consists of square gold blocks that can be connected thanks to a set of electronic MEMS switches. By properly activating the MEMS switches, the gold blocks can be configured and interconnected together to obtain different geometrical structures able to implement frequency reconfigurable antennas, impedance transformers and other basic RF components. As a proof of concept, an experimental prototype consisting of \(7\times 7\) gold patches interconnected by means of a set of RF-MEMS switches is designed, fabricated, numerically, and experimentally assessed. The proposed solution is monolithic since all the structures are made in a single technology and a single chip, like MEMS switches. This is the main key of force, which makes the proposed prototype compact, simple, and with very low power consumption. The paper is structured as follows: Section 2 details the reconfigurable matrix structure based on RF-MEMS. A detailed description of the development and fabrication of a prototype is reported in Section 3. Section 4 is devoted to the numerical and experimental assessment of a prototype. Finally, Section 5 reports the conclusions and ideas for further work.
## 4 Results and Discussion
### Comparison of Landing Stability Optimization Methods  
Landing stability optimization methods for different aircraft configurations, including traditional tube-and-wing aircraft, aeroengine-powered BWB aircraft, and electric BWB aircraft with novel thrust control configurations, are compared in Table 1. Traditional tube-and-wing aircraft utilize trailing-edge flaps to increase lift and deploy spoilers and engine thrust reversers to decelerate. Aeroengine-powered BWB aircraft employ belly flaps and spoilers to achieve similar effects, but in electric BWB aircraft, where engine thrust reversers cannot be used, a novel thrust control configuration is adopted to reduce speed and maintain lift without decreasing the fan rotation speed. The advantages and disadvantages of each design reveal key differences in their structural complexity, operational ease, stability, and safety. Traditional tube-and-wing aircraft are easier to operate and maintain, while BWB aircraft offer better lift and stability control, but face challenges with flow separation and aerodynamic coupling. Electric BWB aircraft enhance landing safety and controllability with their novel thrust control, but require more complex designs and precise control to avoid instability.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**To Maintain Lift** & **Traditional Tube-and-Wing Aircraft** & **Aeroengine-powered BWB Aircraft** & **Electric BWB Aircraft** \\ \hline \multirow{4}{*}{Methods} & 1. Trailing-edge flaps used to increase lift. & 1. Belly flaps used to increase lift. & 1. Novel thrust control configuration behind \\  & 2. Spoilers and engine thrust reversers used to slow down. & 2. Spoilers and engine thrust reversers used to slow down. & BLI fans used to reduce speed and maintain lift without decreasing fan rotation speed. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of landing stability optimization methods for different aircraft configurations.  
Figure 24: Variation in \(C_{L}\) and \(T\) with deflection angles under constant-pitch-moment conditions.
## 4 Experimental Evaluation
### Application to Testbed
It should be noted that both the no-pushing approach and the motion planning pushing approach are fundamentally constrained to a maximum success rate of 33% due to the lack of grasp position information. In contrast, the proposed controlled pushing approach significantly improves the success rate by addressing practical issues, such as maintaining the fingertip condition to prevent changes in frictional force. Additionally, as represented in Figure 13, the use of a simple two-finger gripper introduces inevitable changes in the gripping angle after pushing manipulation, which are accommodated for by adapting the scenario to ensure successful slot insertion. To summarize, in situations where the accuracy of the initial grasp position cannot be guaranteed, or real-time visual feedback of the grasp state is unavailable, the proposed control-based pushing primitive proves significantly more effective than the motion planning approach. It is noted that the robot's operating efficiency is noticeably slower than that of humans. Despite this, the validity of the proposed bushing machining process lies in its achievement of automation of the target industrial process by consistently supplying the buffer without depletion.
## 2 Conditional Random Fields  
In the CRF model, the conditional probability distribution is factored according to a graph consisting of a set of nodes, \(Y\), and a set of edges, \(E\). The nodes represent random variables, \(y_{i}\), that take values in a finite set of classification labels. Inter-node edges encode dependency relations among the random variables. The corresponding observation variable, \(x_{i}\), could be, for example, average intensity or color vector (red, green, blue) of the pixel patch at the position \(i\), and the variable \(y_{i}=\{0,1\}\) could represent whether the pixel patch contains man-made structures. It should be noted that the CRF-based approach relaxes the assumption of conditional independence of the observed data; thus, the observation variables \(x_{i}\) and \(x_{j}\) can include components, which are dependent. For example, the observation variable \(x_{i}\) could include average intensities and/or intensity gradients over the pixel patch, \(i\), and over neighboring pixel patches. In contrast to the MRF model, the CRF model relaxes the restrictive assumption of conditional independence of the observations in MRFs. This allows a convenient factorization that makes efficient modeling possible. By the Hammersley and Clifford theorem [PERSON] and [PERSON] (1954), under the mild assumption that \(P(Y|X;\theta)>0\) for all possible \(Y\) values, in a CRF, the conditional probability distribution can be given in a factored form as:  
\[P\left(y\mid x\right)=\frac{1}{\text{z(x)}}\prod_{c\in C}\psi_{c}\left(y_{c} \cdot x_{c},\theta\right) \tag{1}\]  
where \(z(x)=\sum_{y\in Y}\prod_{c\in C}\psi_{c}\left(y_{c}\cdot x_{c},\theta_{c}\right)\)\(x_{c}\) is a feature vector, \(\psi_{c}\) are factors that are specified for the application (see below), and \(y_{c}\) are the corresponding labels. In Equation (1), the factors \(\psi_{c}\) correspond to maximal cliques in the graph and is usually a member of the exponential family. The term \(C\) is the set of all maximal cliques in the graph, where a maximal clique of a graph is a fully connected subset of nodes that cannot be further extended. The function \(z(x)\) is the partition function required for \(P(y|x)\) to sum to one over all possible values of \(y\). We use lower case letters to depict realized values of the corresponding random variables (which are upper case). We give particular examples of Equation (1) in Equations (2) and (3) below. The probability \(P(Y|X;\theta)\) must sum to 1 over all possible values of the \(Y\) vector The partition function, \(Z\), is difficult to calculate because enforcing the normalization \(\sum_{y}P(Y\mid X;\theta)=1\) requires a summation over \(2^{x^{2}}\) pixels in the case of an _n_-by-_n_ square image for a binary-valued class label \(Y\), which is computationally nearly impossible for modest values of \(n\) such as \(n\) = 100. The vector \(Y=\{y_{1},y_{2},...,y_{x^{n}}\}\) is the vector of class labels (one label for each pixel). In applications, e.g., computer vision, a pairwise CRF model is often used. This model takes into consideration only unary and pairwise cliques.
## 4 Conclusions  
This study determined the effect of drying air temperature (T), ultrasonic power (P) and intermittency ratio of ultrasound generator (A) on moisture effective diffusion coefficient (Deff), saponin content (Sp) and color change (dE) during the ultrasound-assisted heat pump drying of Bo Chinh ginseng. The increase in drying air temperature and ultrasonic power shortened the drying time and increased the moisture effective diffusion coefficient significantly, while the color change increased. In particular, the drying time was reduced from 12.5 h at 35 \({}^{\circ}\)C to 8.9 h at 40 \({}^{\circ}\)C and to 5.8 h at 55 \({}^{\circ}\)C, and at ultrasonic power of 40 W, 80 W and 160 W, the drying time was 8.8 h, 7.8 h and 7 h. The moisture diffusion coefficient increased in the range of 3.81 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s to 5.92 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s when increasing the temperature and ultrasound power in the range as mentioned. The saponin content after drying tends to increase gradually with the drying temperature from 35 \({}^{\circ}\)C to 45 \({}^{\circ}\)C and ultrasonic power from 40 to 120 W, but decreases with the drying temperature from 45 \({}^{\circ}\)C to 55 \({}^{\circ}\)C and ultrasonic power from 120 to 160 W. The optimization results determined the optimal drying conditions as drying air temperature of 45.2 \({}^{\circ}\)C, ultrasonic power of 127.7 W and intermittency ratio of ultrasound generator of 0.18 and the output yield as moisture diffusion coefficient of 5.23 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s, saponin content of 96.4% and color change index of 5.66.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{3}{c}{**Drying Regime at Optimum Parameter T = 45.2 \({}^{\circ}\)C; P = 127.7 W; A = 0.18**} \\ \hline
**Optimum Criteria** & **Theory** & **Experiment** & **Error (\%)** \\ \hline Deff (\(\times\)10\({}^{-10}\) m\({}^{2}\)/s) & 5.23 & 5.05 & 3.44 \\ Sp (\%) & 96.4 & 94.1 & 2.39 \\ dE & 5.66 & 5.40 & 4.59 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of experimental and theoretical results at optimal drying mode.  
**Author Contributions:** Conceptualization, H.N. and Q.-H.L.; methodology, H.N. and Q.-H.L.; software, T.-D.L. and V.-K.P.; validation, Q.-H.L. and V.-K.P.; formal analysis, Q.-H.L.; investigation, V.-K.P.; resources, H.N. and V.-K.P.; data curation, Q.-H.L.; writing--original draft preparation, Q.-H.L.; writing--review and editing, H.N.; visualization, T.-D.L.; supervision, H.N. and Q.-H.L.; project administration, H.N.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The authors thank the financial support from the Ministry of Education and Training, Vietnam, under grant number B 2021_NLS_01.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 4 Protocol Analysis and Covert Channel Design
### Analysis of Covert Storage Patterns
#### 4.2.1 Size Modulation (PS1)  
Pattern  
The hidden information is encoded by choosing different PDU sizes. As specified in TS 38.323 [10], the maximum PDCP PDU size is 9000 bytes, without further requirements or limitations. However, the packet sizes of different layers are highly dependent on the radio resource allocation. Therefore, it is theoretically possible to use the PDU size of the PDCP for a covert channel, but, from a practical point of view, manipulation of the packet size is only possible if the entire 5G radio stack is considered. Therefore, the modulation of PDU size is less suitable for hiding information in PDCP.