esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Principles of Improved Harmonic Detection Methods
### Mallat Algorithm  
Wavelet transform can perform time-frequency analysis on signals because it has the ability of multi-resolution analysis [14; 15]. According to the idea of multi-frequency analysis, the wavelet transform expansion formula:  
\[\begin{split} f(t)&=\sum\limits_{k=-\infty}^{\infty}c _{j,k}\varphi(t-k)+\sum\limits_{k=-\infty}^{\infty}\sum\limits_{j=0}^{\infty}d _{j,k}\psi(2t-k)\\ c_{j,k}&=<s(n),\varphi_{j,k}(n)>,\ d_{j,k}=<s(n), \psi_{j,k}(n)>\end{split} \tag{1}\]  
where, \(\varphi(t)\) is a scale function, \(\psi(t)\) is a wavelet function, orthogonal to each other, and together constitute the basis function of the wavelet transform. \(c_{j,k}\) and \(d_{j,k}\) are the decomposed components, \(j\) is the scale factor, and \(k\) is the displacement factor.  
The Mallat algorithm is a fast wavelet transform algorithm based on analysis of resolution. It is also a mainstream method of using wavelet functions for decomposition and reconstruction. This method can be used to decompose and reconstruct time-varying parameters. The Mallat algorithm explains the physical meaning of scale function and wavelet function. During the decomposition process, the scale function is equivalent to a low pass filter (\(\overline{s}^{*}[n]\)), retaining the main information of the signal. The wavelet function is equivalent to a high pass filter (\(\overline{h}^{*}[n]\)), which retains the changes in the detail. In the reconstruction process, the low pass filter (\(g[n]\)) and the high pass filter (\(h[n]\)) are the reverse order of the decomposition process. The Mallat algorithm implements the decomposition and reconstruction of the three-layer discrete wavelet transform, as shown in Figure 1, where \(\overline{s}^{*}[n]\) and \(\overline{h}^{*}[n]\) are analysis filters, \(g[n]\) and \(h[n]\) are synthesis filters.
# Thermal Strain-Based Simplified Prediction of Thermal Deformation Caused by Flame Bending
## 2 Prediction of Thermal Deformation
### Determination of Virtual Temperature  
The SDB method uses the calculated thermal strain as a direct coefficient of thermal expansion for the shell element in the finite element method (FEM) and assigns a virtual temperature, which is calculated by the dimension of the heat affected zone as shown in Equation (18) as a boundary condition. Figure 6 explains the modeling concept of the virtual temperature. The virtual temperature is applied to predict the thermal deformation, which is determined by the shape of the HAZ. Therefore, the temperature distribution of the HAZ results in thermal deformation in the FEM analysis [8].  
\[T=\left\{\begin{array}{c}T_{top}=\frac{1}{b_{1}}\int_{d-\frac{h}{2}}^{-\frac {h}{2}}b_{Z}\times\left(1-\frac{4}{h}\times Z\right)dZ\\ \\ T_{btm}=\frac{1}{b_{2}}\int_{d-\frac{h}{2}}^{-\frac{h}{2}}b_{Z}\times\left(1+ \frac{4}{h}\times Z\right)dZ\end{array}\right. \tag{18}\]  
As shown in Figure 6, the size of shell elements is the same as the maximum breadth of the HAZ. If there is only one heating line, top and bottom temperature are input only at the  
Figure 5: Trapezoid shape heat affected zone (HAZ).  
Figure 6: Temperature boundary conditions imposed on the shell element.  
point along the heating line, and the other points must be zeros. However, as the number of heating lines increases and overlaps with each other, it is impossible to produce elements with uniform widths. For this reason, additional virtual temperatures are required for irregular grid mesh. Through the comparison of deformation by a single heating line, it is found that the deformation results are all the same if the temperature field is the same, even if the shell element size is not equal to the breadth of HAZ. The virtual temperature for any point (\(k\)) can be expressed as Equation (19) [8]. The temperature of all points is equivalent to 0, and the virtual temperature calculated in Equation (19) is input only to the nodes along the heating line. By using this method, the residual stress field can be shown clearly.  
\[T^{(k)}=\left\{\begin{array}{l}T^{(k)}_{top}=T_{top}\frac{b-a}{b}\\ \\ T^{(k)}_{btm}=T_{btm}\frac{b-a}{b}\end{array}\right. \tag{19}\]  
where \(b\) and \(a\) represent the breadth of the HAZ and the straight-line distance from the heating line as shown in Figure 7, respectively.
## 3 Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline  & **0\%MVC** & **20\%MVC** & **40\%MVC** & **60\%MVC** & **80\%MVC** \\ \hline Young’s modulus (kPa): & & & & & \\ Male & \(35.86\pm 8.74\) & \(55.92\pm 14.73\) & \(70.00\pm 11.81\) & \(85.15\pm 18.95\) & \(103.79\pm 17.76\) \\ Female & \(27.47\pm 5.59\) & \(47.91\pm 16.76\) & \(63.16\pm 19.26\) & \(80.82\pm 19.67\) & \(102.39\pm 20.36\) \\ \(p\)-value & \(0.010\) * & \(0.168\) & \(0.247\) & \(0.769\) & \(0.728\) \\ Dynamic muscle stiffness (N/m): & & & & \\ Male & \(355.46\pm 26.07\) & \(442.96\pm 50.12\) & \(522.58\pm 53.04\) & \(556.63\pm 62.74\) & \(603.63\pm 70.70\) \\ Female & \(303.54\pm 45.66\) & \(371.12\pm 57.99\) & \(398.81\pm 88.15\) & \(464.69\pm 90.46\) & \(497.73\pm 98.25\) \\ \(p\)-value & \(0.005\) * & \(0.005\) * & \(<\)0.001 * & \(0.002\) * & \(0.007\) * \\ \hline \hline \end{tabular}
\end{table} 
Table 2: [PERSON]’s modulus and dynamic muscle stiffness of FDS in different intensity of muscle contraction. Figure 3: [PERSON]’s modulus of the flexor digitorum superficialis (FDS) muscle at different intensities of muscle contraction. In terms of the relationship between dynamic muscle stiffness given by MyotonPRO and [PERSON]'s modulus obtained from SWE, there was a moderate direct correlation between the values at all %MVC levels, as shown in Table 3. Statistical analysis indicated that there was a significant correlation between the muscle stiffness value at the 0%MVC and 40%MVC levels. However, a significant difference was not found at the remaining levels, that is, at 20%, 60% and 80%MVC.
## 3 Machine Learning Models
### Support Vector Machines (SVM)
#### 3.1.2 Healthcare Data  
Regarding the field of medical care, in the work of [PERSON] et al. [21], least-squares ARIMA and SVM models are applied to data on the daily confirmed cases of SARS-CoV-2 in the five countries with the highest number of cases during the time period of publication of this paper (Italy, Spain, France, United Kingdom, and the USA), with the aim of predicting the number of confirmed cases over a period of one month in these countries. The ARIMA parameters were optimized separately for each country. According to the conclusions of this paper, ARIMA and Least Squares SVM models have different handling approaches for data coming from different countries: the research results indicate a drop in errors (MAE, MSE, and RMSE) and an increase in prediction accuracy (coefficient determination) using the least squares SVM model, with a percentage difference of 80% in the prediction of the number of confirmed cases for Spain with respect to the ARIMA technique and a difference of 60% for the predictions concerning Italy and France.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Article** & **Algorithms** & **Metrics** & **Dataset** \\ \hline [PERSON] et al. [20] & ARIMA\((2,1,2)\times(2,1,2)_{12}\) & RMSE & gold price \\  & SVM & MAPE & \\ \hline [PERSON] et al. [21] & SVM & MAE & COVID-19 confirmed cases \\  & & MSE & \\  & & RMSE & \\ \hline [PERSON] et al. [22] & ARIMA\((0,1,2)\times(1,0,1)_{30}\) & MAPE & solar energy generation \\  & SVM & & \\  & ANN & & \\ \hline [PERSON] et al. [23] & ARIMA\((0,1,12)_{day}\) & MSE & ambient noise levels \\  & ARIMA\((0,1,10)_{night}\) & RMSE & \\  & SVM & MAPE & \\  & & \(R^{2}\) & \\ \hline  & ARIMA & \(R^{2}\) & drought forecasting \\  & WNN & MSE & \\  & SVM & NSE & \\  & & K-S distance & \\ \hline [PERSON] et al. [25] & ARIMA\((2,1,1)\) & MAPE & short time load forecasting \\  & SVM (for non-linear & MSE & \\  & load pattern) & & \\ \hline [PERSON] et al. [26] & ARIMA & various & urban water consumption \\  & BP & & \\  & SVM & & \\ \hline \end{tabular}
\end{table}
Table 2: Summary of ARIMA and SVM Comparison Studies in Time Series Forecasting. In the works of [PERSON] et al. and [PERSON] et al. the ARIMA parameters were optimized for each observation site.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 4 Discussion
### The Influence of the Reference Channel SNR on the Performance of the Algorithms
\[\left|\eta_{3,9}[\lambda_{1},f_{d1}]\right|^{2}=\left\{\sum_{l=1}^{N-1}\sum_{k =1}^{N}\biggl{\{}z_{ref}[n]z_{ref}^{*}[n-l]FFT(z_{sur}^{*}[n-l]z_{sur}[n])e^{j2 \pi f_{d1}\frac{l}{l}}e^{-j2\pi\lambda_{1}k}\biggr{\}}\right\}^{2} \tag{54}\]  
\[\left|\eta_{2,3}[\lambda_{1},f_{d1}]\right|^{2}=\left\{\sum_{l=1}^{N-1}\sum_{k =1}^{N}\left\{z_{ref}[n]z_{ref}^{*}[n-l]|a_{1}|^{2}P_{l}(k)\right\}\right\}^{2} \tag{55}\]  
\[\left|\eta_{3,8}[\lambda_{1},f_{d1}]\right|^{2}=\left\{\sum_{l=1}^{N-1}\sum_{k =1}^{N}\biggl{\{}z_{ref}[n]z_{ref}^{*}[n-l]FFT(a_{1}z_{sur}^{*}[n-l]d[n-\lambda _{1}]e^{j2\pi f_{d1}\frac{l}{l}}e^{-j2\pi\lambda_{1}k}\biggr{\}}\biggr{\}}^{2} \tag{56}\]  
\[\left|\eta_{4,3}[\lambda_{1},f_{d1}]\right|^{2}=\left\{\sum_{l=1}^{N-1}\sum_{k =1}^{N}\biggl{\{}\left|A_{ref}\right|^{2}P_{l}^{*}(k)\cdot FFT(z_{sur}^{*}[n-l ]z_{sur}[n])e^{j2\pi f_{d1}\frac{l}{l}}e^{-j2\pi\lambda_{1}k}\biggr{\}}\right\}^ {2} \tag{57}\]  
Under different difference orders \(l\), the signals in Equation (54) are approximately uncorrelated. However, the signals in Equations (55)-(57) all contain completely uncorrelated items and incompletely correlated items, which mean that the correlation are relatively weak. Therefore, they are also considered to be approximately uncorrelated. Therefore, its accumulated gain can be approximately regarded as the incoherent accumulated gain of a signal with length \(\sum\limits_{l=1}^{N-1}\left(N-l\right)\).
## 4 Factors Affecting Pesticide Degradation  
### pH Value  
The effectiveness of chlorine dioxide in degrading pesticides is affected by the pH of the reaction system (Figure 3). According to studies, chlorine dioxide is more active under neutral and alkaline conditions and less active under acidic conditions [61]. The oxidation of chlorine becomes stronger with increasing pH. In addition, the pH value in the solution affects the degree of ionization of ionizable organic compounds [62]. For non-ionizable organic compounds, an increase in pH may affect the hydrogen bonding of organic compounds [35]. In the study of chlorine dioxide with the organophosphorus insecticides dimethoate and azamethiphos, complete degradation of azamethiphos was found at pH 9, and the degradation rate of dimethoate under pH 7 was 98.22% [36]. In the reaction system of organophosphorus acaricide phorate with chlorine dioxide, the phorate was completely degraded at pH 10.7 [33].  
The increase in pH value also has a significant impact on the ozone degradation system (Figure 4). The pH value can affect the solubility of ozone in water [63]. As the pH value increases, it promotes the decomposition of ozone to produce -OH, and some organic compounds react better with -OH, improving the degradation rate [64]. In the system where ozone reacted with five pesticides--alachlor, carbendazim, diuron, pyrimethanil, and tebuconazole--the degradation rate increased with increasing pH [65]. In the degradation of prometryne, a triazine herbicide commonly used in aquaculture, it was found that the best degradation of prometryne by ozone was achieved at pH 7, with a degradation rate of 77.63% [66].  
Figure 4: Mechanism of pH affecting ozone degradation of pesticides.  
Figure 3: Mechanism of pesticide degradation by pH affecting chlorine dioxide.  
In general, the pH value has a great influence on the degradation of pesticides by ozone and chlorine dioxide. In the reaction system of chlorine dioxide and ozone with pesticides, both showed that the degradation rate of pesticides was higher in neutral and alkaline conditions than in acidic conditions. However, the mechanism of the effect of pH on these two oxidants is different. For chlorine dioxide, the increase in pH increased the activity of chlorine dioxide. For ozone, the increase in pH promoted the decomposition of ozone, producing more -OH.
# Beamforming Techniques for Resilient Navigation with Small Antenna Arrays +
## 4 Performance Analysis
### Suburban Scenario  
Suburban scenarios comprise those in the city outskirts and residential areas with small buildings, thus having a wider sky visibility than in urban environments. In this case, the LOSS and the replica are considered to impinge from 60\({}^{o}\) and 20\({}^{o}\) elevation, respectively. The azimuth angle is also kept here at 30\({}^{o}\) for both signals.  
Figures 3 and 4 depict the replica cancellation and MEE results, respectively, for the suburban scenario. The very same conclusions to those in the urban scenario can be extracted here. Conventional techniques such as the PHA and CAP are outperformed by the HYB and PBC. In terms of replica cancellation, whereas the former two provide practically the same performance as in the urban case, the latter two present a steeper behavior and do provide a gain of \(\sim\)5 dB along all replica delays. This means that a cancellation of 10 dB is now achieved in ULA2 with as little as 0.3 delay, whereas at this point the cancellation already exceeds 15 dB in URA4. In terms of MEE, conventional beamformers exceed values of 10 m while requiring signals to become separated enough to improve these results. In contrast, the HYB and PBC present MEE no larger than 5 m in ULA2 with only 0.3 replica delay, a point at which the MEE is already below 5 m in URA4. Therefore, the MEE performance in the urban case is even improved in the suburban case by an amount of 3-5 m. These results conclude on the feasibility to perform multipath and spoofing mitigation in ULA2 and URA4 arrays by implementing advanced beamforming techniques such as the HYB and PBC.  
Figure 2: MEE in an urban scenario with ULA2 (**top**) and URA4 (**bottom**) array distributions.
# Compressive Sensing-Based IoT Applications: A Review
## 5 Challenges and Research Trends
### Implementation on Multi-Core Platforms  
With the fast increase in the number of IoT devices, the big data transmitted to the computing platforms will put a lot of pressure on the real-time requirements and the processing will exceed the capabilities of single-core platforms. This scenario occurs in several IoT applications that require real-time video streaming. Subsequently, single-core platforms will soon fade and be replaced with multi-core platforms that have more relaxed computation and communication features [155]. Moreover, most IoT applications would utilize the end-user personal computer and mobile as the edge computing platform; thus, the implementation of reconstruction algorithms on multi-core platforms that are similar to the ones held by the end-user has been addressed in the literature for different applications. For instance, taking advantage of parallel computing, [PERSON] et al. presented in [106] the design and investigated the performance of BP algorithm on four different multi-core platforms, namely, Intel Core 2 Quad, Intel Core I7, PS3 cell and NVIDIA 8800 GTS. In addition, medical imaging applications have witnessed several implementation of CS reconstruction algorithms on multi-core platforms [156; 157; 158; 159]. More recently, [PERSON] et al. presented a hardware implementation of OMP on a heterogeneous mobile SoC based on the ARM BIG.LITTLE TM architecture for CS-based ECG monitoring [160]. The proposed architecture shows to be able to provide a real-time reconstruction for ECG signals which can fit well into IoT applications for long-term healthcare monitoring.
# Brake Light Detection Algorithm for Predictive Braking
## 5 Discussion
### Applicability in Industrial Applications  
The factory-installed camera setup of the vehicle could be used with proper optics. In this study, the camera and optics were installed parallel to the existing vehicle multi-purpose camera to ensure effortless access to a video feed of the road ahead. Most of the vehicle manufacturers use high-quality multipurpose cameras for road lane recognition and other fairly demanding partially, automated tasks, so the camera quality should be sufficient for this type of system as well. The computation power of the vehicle and factory installed control units could turn out to be a bottleneck for the algorithm task. In order for the algorithm to work with live video-feed from the camera, a substantial amount of computation power is required. The classification solution proposed here is lightweight but suffers from the object detection part, which should be replaced to reduce the computational power requirements while maintaining a good performance. Regarding the applicability of the developed algorithm in production grade systems, the algorithm has the benefit of not requiring an excessive amount of data for training. The colorspace threshold values are camera-dependent, meaning that the values must be adjusted when a different camera is used. Nevertheless, as shown in this paper, these threshold values and the random forest classifier training can be achieved with a few hundred images. Considering many other machine vision and machine learning applications, this amount of data is fairly modest for achieving a functional detection system.
## 3 Results and Discussion  
In this section, a quantitative evaluation of the system was performed following the performance metrics presented in Section 2.2.4. In addition, a qualitative evaluation was carried out, in which the output of the system was compared with the corresponding ground truth descriptions, and also verified by a lung specialist, in order to validate the results.
# A Real-Time Hybrid Approach to Combat In-Browser Cryptojacking Malware
## 2 Literature Review
### Hybrid Approaches
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref. No** & **Approach** & **Dataset** & **Features** & **Prevention** & **Result** \\ \hline
[15] Static & Alexa: 33k & JS API consumption of resources & TPR: 95.95\% \\ \hline
[16] & Alexa 1 Million & WASM signatures & N/A \\ \hline
[19] & PublicWWW & Images frames of cryptojacking & Accuracy: 98.97 \% \\ \hline
[20] & Alexa 1 Million & Web socket training & N/A \\ \hline
[21] & Network Traffic (Stratum) & Network’s metadata & Recall: 91 \% \\ \hline
[22] & Alexa 1 Million & CPU usage & N/A \\ \hline
[23] & Memory of Browser (1160 snapshots) & Stack features, heap snapshots & Precision: 95\% \\ \hline
[24] & N/A & Network features & TPR: 92\% \\ \hline
[18] Dynamic & Alexa: 100k & CPU memory & N/A & Accuracy: 97\% \\ \hline
[25] & N/A & Hardware cache events & Precision: 97.9\% \\ \hline
[26] & Manually created dataset (420 instances) & HPC values & Precision: 100\% \\ \hline
[27] & Alexa: 1 M & CPU and WASM based & N/A \\ \hline
[28] & Alexa: 600,000 & JavaScript execution/ compilation & TPR: 97.9\% \\ \hline
[29] & Alexa: 100,000 & Hashes based & TPR: 100\% \\ \hline
[30] & Alexa: 500 & WASM & F1 score: 98\% \\ \hline
[33] & Alexa 8000, 8156 samples from Coinhive, etc. & Network traffic, CPU speed, subprocesses & F1 score: 99.25\% \\ \hline
[34] & \multirow{2}{*}{Hybrid} & 1200 samples & CPU features and HPC & Precision: 96\% \\ \cline{3-5}
[10] & PublicWWW & CPU usage, code analysis & Notification & N/A \\ \hline
[11] & In-browser cryptojacking samples & Blacklist behavior & Kills the process & N/A \\ \cline{3-5}
[12] & Alexa 1 M & Code analysis + CPU usage & Suspension of process & FNR 1.83\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the state of the art on in-browser cryptojacking malware. that uses alternative programming languages, such as JavaScript obfuscation techniques, can easily bypass their detection system. For example, Coinhive [36], a mining-service provider frequently used for cryptojacking attacks, can not be detected by MineThrottle.
## 3 The MPC Controller Design for the Controllable Pitch Propeller
### Simulation  
Based on the above theoretical derivation, a comparative simulation of the MPC controller and PID controller is explored, as shown in Figure 7. The error between the input signal and the output is defined at the moment \(k\) as \(\Delta y_{mpc/PID}=y-y_{mpc/PID}\). \(y\) indicates the input signal, and \(y_{mpc/PID}\) represents the output of the MPC or PID controller. The absolute value of the total errors between the output value and the input signal is determined from the initial moment to the \(k\) as \(E_{mpc/PID}\), i.e., \(E_{mpc/PID}=\sum\limits_{i=0}^{k}\big{|}y-y_{mpc/PID}\big{|}\).  
The tracking signal results are shown in Figure 8. It can be seen that the PID controller tracks the reference signal in a closer distance over time. In contrast, the output signal of the MPC controller maintains a certain error with the tracking signal. The relative error curves of the two controllers are presented in Figure 9. In the 12-14 s and 18-21 s intervals, the relative error of the PID controller is smaller than that of the MPC controller and varies greatly for the remaining time. The absolute error curves of the two controllers are plotted in Figure 10. Specifically, the PID controller has an increasingly larger absolute error than the MPC controller over time. This suggests that the MPC controller outperforms the PID controller in terms of accuracy and stability.  
The simulation results with a step signal incorporated into the system are shown in Figure 11. Two error bands of 5% and 2% were set during the simulation. The regulation time of the MPC was 6.716 s and 6.896 s, and no overshoot was detected. The regulation time of the PID reached 6.705 s and 6.885 s, and the presence of a 1% overshoot was visible. These results indicate that the MPC had good accuracy and response speed under the step signal.  
Figure 7: Connection to the model predictive control (MPC) controller.  
Figure 8: Tracking signal results of the two controllers.  
Figure 10: Absolute errors of both controllers.  
Figure 9: Relative errors of the two controllers.