esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence-Based Controller for DC-DC Flyback Converter
## 5 Results and Discussion
### Load Regulation  
Load regulation was performed by changing the value of the load resistance and checking the capability of the controller to maintain a constant output voltage. For the load resistance change of 10 \(\Omega\) to 14 \(\Omega\) and from 10 \(\Omega\) to 6 \(\Omega\), load current variations are shown in Figure 14. Reduced peak to peak voltage spike with reduced recovery time was observed at the time of load transience. The ANFIS controller offered excellent load regulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Unity Feedback** & **FLC** & **ANFIS** & **PID** \\ \hline Rise time (ms) & 1.0 & 0.8925 & 0.8827 & 0.98 \\ Settling time (ms) & NaN & 6.9 & 6.3 & 22.1 \\ Overshoot (\%) & 28.1803 & 11.0875 & 0.5603 & 19.5644 \\ Undershoot (\%) & 0.0022 & 0.0149 & 0.0205 & 0.3464 \\ Peak (V) & 30.7633 & 26.6610 & 24.1345 & 28.6955 \\ Peak time (ms) & 2.4 & 1.9 & 1.8 & 2.7 \\ Steady-state error (\%) & 7.54 & 1.83 & 1.04 & 1.79 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of response parameters of unity feedback, FLC, ANFIS, and PID.  
Figure 14: Output voltage and load current when the load resistance was (**a**) increased and (**b**) decreased.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PID Parameters** & **Values** \\ \hline Proportional gain \(K_{P}\) & 18.45 \\ Integral gain \(K_{I}\) & 99,999.88 \\ Derivative gain \(K_{D}\) & 0.00071 \\ \hline \hline \end{tabular}
\end{table}
Table 3: PID parameters.
## 2 Materials and Testing Program
### Microstructural Imaging  
The structure of stabilised soil was probed using a scanning electron microscope (SEM) model JEOL JSM-6490 LA housed at the microscopy facility of the University of Wollongong. A scanning electron microscope (SEM) capable of testing both in high and low vacuum settings was used in this study. This type of SEM can also be referred to as ESEM and can operate at low vacuum, which allows the testing of specimens in a moist condition, thus avoiding undesirable microstructural damage that can result from the drying process. The soil specimens were imaged at different magnifications to examine the microstructural effects of each stabiliser on various scales. Soil specimens were imaged before and after inundation. For each specimen, an Energy Dispersive X-ray Spectroscopy (EDS) test was also conducted. The EDS graphs show the distribution of different elements in the soil, which can indicate the presence and increase in the elements associated with the stabilisers (e.g., calcium, carbon, sulphur and heavy metals). Figure 4 shows the Energy Dispersive X-ray Spectroscopy (EDS) spectrum for the soil studied having no treatment.
## 4 Discussion
### Discussion of Major Quantitative Findings and Qualitative Feedback from the Users
#### 4.1.5 Satisfaction and Confirmation  
In terms of SAT and CON, although the quantitative results were not statistically significant and the hypotheses were not verified, an effect from different interactive interfaces on the specific measured item about the operating experience was found. The result of the Wilcoxon-signed rank test indicates that hand gesture recognition and joystick control influence the operating experience in confirmation, with a rank mean of Z = -2.869 and a level of significance of \(p<0.05\). Users have a higher confirmed operating experience when using hand gesture recognition (M = 5.49, SD = 1.523), as shown in Figure 8.  
Figure 8: Users’ subjective evaluation of the measured item of operating experience with respect to different interactive interfaces (i.e., hand gesture recognition/joystick control; \(\mathbf{{}^{**}}\)\(p<0.01\)).  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{3}{l}{**Qualitative question:**} \\
**Did the hand gesture recognition/joystick control make you joyful or was it attractive** \\ (e.g., fun and immersive)? \\ \hline \hline \multicolumn{3}{l}{Feedback for hand gesture recognition:} \\ - & Very cool. \\ - & Immersive. \\ - & Feels like real life. \\ - & Cool, no need to use any controllers. \\ - & It’s great. I was attracted by it. \\ - & Had a sense of real experience. \\ - & Very high-tech. \\ - & Unique experience. \\ - & It’s fun. \\ \multicolumn{3}{l}{Feedback for joystick control:} \\ - & Although it was good at controlling, I’m not impressed. \\ - & So fun! But not attractive to me. \\ - & Efficient and convenient operation. It was easy to use, but there was no feeling of freshness. \\ \hline \hline \end{tabular}
\end{table}
Table 9: Users’ Qualitative Feedback on PE for Hand Gesture Recognition and Joystick Control.  
After asking for qualitative feedback, we discovered that users' expectations were better confirmed while using hand gesture recognition, as shown in Table 10. This finding supports [PERSON] et al.'s [48] claim that consumers have expectations for gesture recognition. In contrast, the joystick controller shows a limitation for the virtual experience, which resulted in a lower confirmation. This also echoes our finding that the joystick controller's level of valence (pleasure) is lower than hand gesture recognition.
# A Comprehensive Safety Analysis Study for Concrete Core Dams
## 4 Stability Analysis  
In this program example, finite element analysis of slope stability is calculated by choosing upstream that designs the flood level which is 124.33 m and checks flood level which is 125.12 m. The bottom of the dam foundation and the left and right sides are fixed boundaries, and the dam surface is a free boundary. Calculation parameters are shown in Table 2. Calculation results are added to other conditions (seepage results of pore water pressure). Comparative results are shown in Table 3.  
Figure 13 and Table 4 show that: (1) for the calculation of \"design flood level\" and \"check flood level\" by Li Zheng software, the safety factor does not change. So the calculation of the Li Zheng software is not sensitive when the water level does not change significantly. However, FEM software analysis results are more reasonable; (2) considering the osmotic pressure, the calculation result of Li Zheng software is less than that of the FEM software, probably due to the fact that the Li Zheng calculation is based on the conventional limit equilibrium method, which only considers the structure gravity without considering the impact of the dam stress. The stress on the dam is mostly compressive stress, which has a positive effect on the stability of the slope. So the safety factor based on the finite element analysis of FEM software is larger; (3) through the same software, comparing considering seepage with not considering seepage, the osmotic pressure shows a greater impact in the calculation results in the calculation of sliding stable safety factor and the presence of osmotic pressure reduces safety factor; (4) by calculating the results calculated with the FEM and Li Zheng, it is concluded that the FEM is reliable while the Li Zheng, used widely in practical engineering, is considered to be the basic and original reference. (5) finite element analysis of slope stability does not need to make assumptions to part of the internal forces and the shape of slip surface, which makes the analysis research results of theoretical basis even tighter; and (6) the applications scheme of reinforcement is safe and reliable.  
Figure 12: Velocity vector diagram of the checking flood level by the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material** & \begin{tabular}{c} **Natural Unit Weight** \\ **(kN/m3)** \\ \end{tabular} & \begin{tabular}{c} **Saturated Unit Weigh** \\ **(kN/m3)** \\ \end{tabular} & \
\begin{tabular}{c} **Shear Strength** \\ **C (kPa)** \\ \end{tabular} &
\begin{tabular}{c} **\(\varphi\) (\({}^{\circ}\))** \\ \end{tabular} \\ \hline Sandy gravel I & 19.0 & 21.0 & 0 & 33 \\ mud gravel soil clay II\({}_{3}\) & 19.3 & 19.8 & 21.0 & 24.0 \\ Core wall soil II\({}_{2}\) & 19.6 & 19.85 & 21.0 & 24.0 \\ Mud sandy gravel soil of & 18.0 & 20.0 & 0 & 30\({}^{\circ}\) \\ dam foundation III & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material properties indicators of main dam.
## 4 Experimental Results
### Evaluation  
The performance evaluation of the proposed framework is shown based on the confusion matrix given in Figure 6. The horizontal axis displays the actual category index, while the vertical axis represents the predicted labels. The list of overall evaluation parameters has been displayed in Table 2. The \(T_{i,j}\) in the table denotes the distribution of actual \(i\)th values in the \(j\)th prediction class of the \(n\)th family.  
Figure 5: Variants of the Malware family in the database.  
Figure 6: Proposed model classification performance measurement through confusion matrix.  
The overall accuracy obtained with the expression is given in Equation (8), where the parameters \(T_{P}\), \(T_{N}\), \(F_{P}\), and \(F_{N}\) denote the true positive, true negative, false positive, and false negative respectively.  
\[Overall-Accuracy=\frac{T_{P}+T_{N}}{T_{P}+T_{N}+F_{P}+F_{N}} \tag{8}\]
## 2 Background Works
### Hash Function in Digital Fingerprinting  
Digital fingerprinting is a crucial technology related to the authentication of items. Therefore, we aimed to use this technology to verify the authenticity of craftworks and prevent duplication or forgery. Digital fingerprinting technology is primarily employed in content protection techniques to address issues of copyright protection and traitor tracing in P2P-based content distribution systems [7; 8; 9; 10; 11; 12; 13]. This technology is mainly applied to digital images, converting attributes such as color and shape into digital values. However, since craftworks are physical objects rather than digital images, there is a need for a method to represent these craftworks in digital data. Therefore, we sought to digitize the analog attributes of craftworks to apply digital fingerprinting technology effectively.  
To achieve this, hash functions were applied to digitally fingerprint the various characteristics of the product through the process of hash encryption, generating a unique identifier for each item. Hash functions began to be utilized when [PERSON] developed MD2 in 1989 and MD5 in 1992 [14]. A hash function is a function that converts an arbitrary bit string into a fixed-length bit string, and the result of the hash function is called a hash value. Regardless of the quantity and size of the input data, the hash value is expressed as a bit string of a fixed size determined by the hash function. It is also known as a one-way function because the original data cannot be retrieved from the converted hash value. Due to these characteristics, hash functions have been primarily used for detecting data forgery and alteration, quick data searches, communication security verification, and the safe storage of passwords. Recently, their scope of use has expanded to include electronic signatures, blocking illegal works, determining whether a website has been hacked, securing the reliability of cryptocurrency, and electronic voting.  
As such, hash functions in digital fingerprinting are used to uniquely identify data by converting varying amounts of data into a fixed-size hash value. This fingerprint can represent the original data in a concise and unique manner. Hash functions ensure data integrity; even a minor change in the data results in a different hash value when recalculated. This property is crucial in applications such as software distribution, file storage, and secure communication for verifying the reliability and integrity of data. In databases or large-scale systems, hash functions help uniquely identify and index data, which can be useful for efficiently deduplicating, searching, and managing large data sets. Overall, hash functions provide a method for creating digital fingerprints of data for various purposes where uniqueness, integrity, and security are needed. In our project, we aimed to create digital fingerprints for craftworks by digitizing their various attributes through hash encryption and generating a unique identifier for each product.
## 3 Boost Converter
### Determination of the Generator-Supplied Current  
In Figure 3, the mean current supplied by the generator within a given time period T (see Figure 2 and (11)) is:  
\[I_{g}=\frac{1}{T}\int\limits_{0}^{T_{ON}}i_{L}\ dt+\frac{1}{T}\int\limits_{T_{ON }}^{T_{X}}i_{L}\ dt \tag{12}\]  
Next we will analyze the behavior of the converter in the time intervals considered in (12). \(\mathbf{0}\leq\mathbf{t}\leq\mathbf{T_{ON}}\)  
In this time interval, current through the inductor grows, the diode is reverse biased and therefore its branch is disconnected. Figure 4 shows the equivalent circuit. From Figure 4 the following is deduced:  
\[V_{g}=(r+R_{S})i_{L}+L\frac{di_{L}}{dt}. \tag{13}\]  
Solving it for \(i_{L}\) and integrating the result into (12):  
\[\frac{1}{T}\int\limits_{0}^{T_{ON}}i_{L}\ dt=\frac{1}{T}\left(\int\limits_{0}^ {T_{ON}}\frac{V_{g}}{r+R_{S}}dt-\frac{L}{r+R_{S}}\int\limits_{I_{1}}^{I_{2}} di_{L}\right)=\frac{V_{g}}{r+R_{S}}\delta-\frac{f\cdot L}{r+R_{S}}\Delta I, \tag{14}\]  
where \(\delta\) and \(\Delta I\) are given by (3) and (6) respectively. On the other hand, from (13), (4) and Figure 2 it follows that:  
\[\int\limits_{I_{1}}^{I_{2}}\frac{di_{L}}{V_{g}-(r+R_{S})i_{L}}=\int\limits_{0 }^{\delta T}\frac{dt}{L}. \tag{15}\]  
By a simple variable change in the left-hand integral:  
\[\ln\biggl{(}\frac{V_{g}-(r+R_{S})I_{2}}{V_{g}-(r+R_{S})I_{1}}\biggr{)}=-\frac {r+R_{S}}{f\cdot L}\delta=-\gamma_{1}, \tag{16}\]  
where  
\[\gamma_{1}=\frac{r+R_{S}}{f\cdot L}\delta=\frac{\delta}{k_{1}}, \tag{17}\]  
and  
\[k_{1}=\frac{f\cdot L}{r+R_{S}}\cdot \tag{18}\]  
Figure 4: Equivalent boost converter circuit for \(0\leq t\leq T_{ON}\). Finally, by solving (16) for \(I_{1}\):  
\[I_{1}=\frac{V_{g}}{r+R_{S}}(1-e^{\gamma_{1}})+I_{2}\,e^{\gamma_{1}}. \tag{19}\]  
\(\mathbf{T_{ON}}\leq\mathbf{t}\leq\mathbf{T_{X}}\)  
In this time interval, the diode branch is connected to the generator. Figure 5 shows the equivalent circuit. Figure 2a shows how current through the inductor drops exponentially (up to \(t=T_{X}\)) in this time interval. At this point, if the current reaches 0 (DCM), it then keeps this value until new switch conduction (see Figure 2b). From Figure 5 the following is deduced:  
\[V_{g}-(V_{o}+V_{\gamma})=(r+R_{D})i_{L}+L\frac{di_{L}}{dt}. \tag{20}\]  
By solving it for \(i_{L}\) and integrating the result into (12):  
\[V_{g}=(r+R_{S})i_{L}+L\frac{di_{L}}{dt}. \tag{13}\]0  
where \(\delta_{f}\) is given by (5). On the other hand, from (20) and Figure 2 it follows that:  
\[V_{g}=(r+R_{S})i_{L}+L\frac{di_{L}}{dt}. \tag{13}\]1  
Now, by solving (22) as (15):  
\[V_{g}=(r+R_{S})i_{L}+L\frac{di_{L}}{dt}. \tag{13}\]2  
where  
\[V_{g}=(r+R_{S})i_{L}+L\frac{di_{L}}{dt}. \tag{13}\]3  
and  
\[V_{g}=(r+R_{S})i_{L}+L\frac{di_{L}}{dt}. \tag{13}\]4Finally, by solving (23) for \(I_{2}\):  
\[V_{g}=(r+R_{S})i_{L}+L\frac{di_{L}}{dt}. \tag{13}\]5  
The complete solution of (12) can be found by adding (14) and (21). However, both depend on \(\Delta I\), so this value must be calculated previously.
# A Survey of Adversarial Attacks: An Open Issue for Deep Learning Sentiment Analysis Models
## 2 Adversarial Examples and Deep Learning Models
### Taxonomy of Adversarial Attacks
#### 2.3.1 Model Access  
According to the attacker's knowledge about the model to be fooled (or victim model), three types of attacks can be carried out: _black box, white box_, and _grey box_. A victim model can suffer attacks under different levels of knowledge at the same time, or the attacks can be generated independently.  
* **Black box.** Black box attacks are applied when the architectures, parameters, activation, or loss functions are not accessible by the attacker. In this case, adversarial examples are generated by accessing the test dataset or querying the target model by making requests until a modification that allows for a change in the output is found.
* **White box.** Unlike black box attacks, white box attacks rely on knowledge of the complete details of the target model to be fooled.
* **Grey box.** Gray-box attacks occupy a middle ground between black box and white box attacks.
# A Deep-Learning-Based Bearing Fault Diagnosis Using Defect Signature Wavelet Image Visualization
## 1 Introduction
Subsequent to transforming the AE signals into a compact relevant 2-D representation, the images serve as input of a classifier to generate the decision making. Recently, machine learning-based methodologies for fault analysis have become prevalent and powerful algorithms in the field of bearing health monitoring since they have the capacity to gain valuable knowledge from the considerable amount of recorded data already extant. Among the various processes, K-nearest neighbor (KNN) [8], support vector machine (SVM) [9], and artificial neural networks [22] are popularly implemented for fault detection. Deep learning approaches have recently been considered a new branch of application for fault diagnosis. The deep learning algorithm comprises multiple stages of non-linear operation and shows an ability to automatically learn up to high-abstract features to more intelligently support decision-making. Deep learning algorithms such as the convolutional neural network (CNN) [23] and stacked auto-encoders [24] have been investigated in fault detection. Thus, our research also aims to design and employ a deep and capable CNN architecture to obtain high accuracy for bearing fault diagnosis. The specific contributions of this paper can be summarized as follows:  
1. To alleviate the limitations of previous methods used for transformation of1-D signals into 2-D images, a novel 2-D representation method is created by combining the envelope analysis and continuous wavelet transform (CWT) with filtering by the frequency range covering the bearing defect frequencies to generate the defect signature wavelet image (DSWI). The constructed DSWI is considered as the new signature, which solves the modulation problem, reduces the nonstationary effect in the signal, demonstrates the distinct patterns for the different types of faults in bearings, and closely relates to the defect frequencies in the envelope spectrum. 2. This study also introduces a specific architecture of the deep convolutional neural network (DCNN) for classifying multiple fault types that occur in bearings by learning the specific features from the DSWI representations. To estimate the performance of the proposed approach, it has been evaluated using the laboratory dataset collected from the bearing testbed. Finally, the results of the proposed method are compared with other methods presented in the literature. The remaining portions of this research are organized as follows: A description of the test rig, experiment setup, and data acquisition system is provided in Section 2. Section 3 describes the overall methodology of this study to construct the DSWI as the 2-D representation of the AE signal from the different types of bearing faults and the structure of the DCNN for classification. Section 4 discusses and explains the resultant performance of the proposed methodology using the different evaluations from the dataset, and Section 5 gives the conclusions of the paper.
# RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones
## 2 Materials and Methods
### RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm  
We walked through an urban canyon. RTK could not provide reliable and continuous positioning results in the urban canyon because of GNSS outage. The assistance of an IMU-based pedestrian navigation algorithm could be helpful. We tied the Xiaomi Mi 8 to our foot, and the Xiaomi Mi 8 collected the measurements of the accelerometer, the gyroscopes, and the magnetometers. By applying the Madgwick algorithm, we could estimate and record the Xiaomi Mi 8's attitude with these measurements. We counted the interval between two positioning results output by RTK. If the intervals between a positioning result and its last output and also its next output both exceeded the threshold, this positioning result would be ignored. The last valid positioning result before the GNSS outage was regarded as the initial position of the ZUPT aiding INS. We synchronized the outputs of RTK and the Madgwick algorithm so that we could provide the initial attitude, especially the yaw, for the ZUPT aiding INS. The outputs of the ZUPT aiding INS were regarded as positioning results during GNSS outage in the urban canyon. Once we passed through the city canyon, the ZUPT aiding INS stopped outputting positioning results, and RTK continued providing positioning results.
# Fourier Neural Operator Network for Fast Photoacoustic Wave Simulations
## 2 Materials and Methods
### Data Generation  
The MATLAB toolbox k-Wave was used for photoacoustic wave simulation and to generate data for training and testing the FNO network [16]. The simulation medium was defined as a 64 \(\times\) 64 computational grid, non-absorbing and homogenous with a speed of sound of 1480 m/s and density of 1000 kg/m\({}^{3}\). Forward simulations were performed with a time step of 20 ns for T = 151 steps. The initial photoacoustic pressure was initialized using anatomically realistic breast vasculature phantoms that were numerically generated [40]. The training dataset (\(n\) = 500) and testing dataset (\(n\) = 100) comprised images representing the initial photoacoustic pressure (the input to the FNO network) and the corresponding simulation of the photoacoustic wave propagation (output of the FNO network). Simulations for the Shepp-Logan, synthetic vasculature, tumor and Mason-M phantoms were also generated to evaluate the generalizability of the FNO network [16; 41].  
A second dataset was generated based on images from the Animals with Attributes 2 (AWA2) dataset originally developed for zero-shot image classification [42]. The AWA2 dataset has over 32,000 images categorized into 50 animal classes. This highly diverse dataset with many varied animals and backgrounds provides a more challenging task to train and evaluate the FNO. Forward simulations were performed with a similar medium as described previously, except with a 128 \(\times\) 128 computational grid for T = 302 steps. With the same medium, the adjoint simulations were also performed using a 128-sensor linear array at the top of the computational grid. Images from 10 animal classes were used to create a training dataset (\(n\) = 1500). Images from 5 different animal classes were used to create a testing dataset (\(n\) = 500). The forward and adjoint simulations had their own respective datasets.
# Experimental Investigation into the Thermal Performance of Personal Cooling Mechanisms
## 2 Materials and Methods
### Equipment
#### 2.2.2 Climatic Chamber  
During the tests, the thermal manikin was placed inside a WalkIn Model climatic chamber that measured 14 m in length, 3 m in width, and 5 m in height. This climatic chamber can be used at air temperatures ranging from \(-30.0\)\({}^{\circ}\)C to \(+60.0\)\({}^{\circ}\)C, with a relative humidity range of 30% to 90% and an air velocity of 12 m/s.