esa-sceva/satcom-chunk-collection huggingface dataset:
# Fourier Transform (FT) Analysis of the Stress as a Tool to Follow the Fatigue Behavior of Metals
## 1 Introduction
### Nonlinear Material Behavior during Fatigue  
When a sample is deformed in cyclic strain controlled tension/tension (T/T), the constant static deformation is larger than the strain amplitude (\(\varepsilon_{0}\)) of the dynamic deformation, to avoid compression at the strain minimum of each cycle. The ratio between the minimum (\(\varepsilon_{min}\)) and maximum (\(\varepsilon_{max}\)) deformation is described by the deformation ratio (\(R\)) and correlates with \(\varepsilon_{0}\) as [1]:  
\[R=\frac{\varepsilon_{min}}{\varepsilon_{max}}=\frac{\varepsilon_{max}-2 \varepsilon_{0}}{\varepsilon_{max}} \tag{2}\]  
The oscillatory time dependent elongational strain \(\varepsilon(t)\) can be described by a simplified complex notation as:  
\[\varepsilon^{*}(t)=\varepsilon_{s}+\varepsilon_{d}^{*}=\varepsilon_{s}+ \varepsilon_{0}\;e^{i\omega_{1}t} \tag{3}\]  
which can be decomposed into a static (\(\varepsilon_{s}\)) and a dynamic (\(\varepsilon_{d}\)*) part. In our previous works, a framework was established to quantify the mechanical nonlinear material behavior of polymers via Fourier transform of the stress response in strain controlled testing. The developed concepts are explained as follows. Under strain controlled sinusoidal testing, the stress response of polymers becomes nonlinear under sufficiently large amplitude oscillatory elongation (LAOE) [12]. Nonlinearity means in this context, that the stress response and the applied strain are not directly proportional to each other, so the linear material parameters such as storage (\(E^{\prime}\)) and loss (\(E^{\prime}\)) moduli loose their physical meaning as material parameters. Fourier transform is a very powerful tool to detect nonlinear contributions, as well as to analyze and quantify the resulting (nonlinear) stress waveform with very high sensitivity [12; 13]. This is done by analyzing the higher harmonics of the Fourier spectra in the frequency domain of the time dependent stress. For a sufficiently high number of data points in the time domain, the stress can be quantified in the frequency domain using a limited number of frequency at multiples of the fundamental frequency as \(I_{\left(\omega_{1}\right)}\), \(I_{\left(2\omega_{1}\right)}\), \(I_{\left(3\omega_{1}\right)}\), etc., while the remaining points of the spectra are considered as non-periodic and are assumed as noise as a first approximation [14; 15]. In this case, the harmonics analysis acts as a very efficient filter of the time data using \(\sigma(t)=\sigma(t+T)\) where \(T\) is the period time. For a periodic nonlinear asymmetric stress response, either caused by the loading conditions [16] or specimen anisotropy [17], the stress and the moduli depend on the deformation direction so \(\sigma^{*}(t)(\varepsilon+)-\sigma_{mean}\
eq-(\sigma^{*}(t)(\varepsilon-)- \sigma_{mean})\) or \(E^{\prime}(t)(\varepsilon+)\
eq E^{\prime}(t)(\varepsilon-)\) and \(E^{\prime\prime}(t)(\varepsilon+)\
eq E^{\prime\prime}(t)(\varepsilon-)\).
## 4 Cybersecurity and Its Threats
### Top Four Attacks
#### 4.1.3 Malware  
Malware is a general label for malicious software that spreads between computers and interferes with computer operations as described by [PERSON] [16]. Malicious software is designed to gain unauthorised access to cause damage. Malware can be deemed destructive, for example, deleting files or causing system crashes and stealing personal data. An example of malware includes viruses.  
Viruses can cause computer dysfunction but can also have more severe effects in terms of damaging or deleting hardware, software, or files. Viruses are self-replicating programs and can spread within computers. They require a host (such as a file, disk, or spreadsheet) in a computer to act as a carrier, but they cannot infect a computer without human action to run or open the infected file.  
Another example of malware, according to [PERSON] [16] is worms. Worms are also self-replicating programs, but they can spread autonomously, within and between computers, without requiring a host or any human action. The impact of Worms can therefore be more severe than Viruses, causing destruction across whole networks. Worms can also be used to drop Trojans onto the network system.  
Trojans are a form of malware yet again, that appear to be legal programs but facilitate illegal access to a computer. They can perform functions, such as stealing data, without the user's knowledge and may trick users by undertaking a routine task while actually undertaking hidden, unauthorised actions.
## 2 Analytical Programming
### Contact-Point Response  
In practical applications, only the acceleration response from the accelerometer installed on the stationary vehicle is available, such as \(\hat{q}_{s}\). Although it has been proven that the vehicle response (\(\tilde{q}_{s}\)) contains the bridge frequency, it is clear that such a response also contains the vehicle's own frequency; however, the contact-point response (\(\tilde{u}_{c}\)) is free from the vehicle frequency, which could provide the bridge's true response at the location of the parked vehicle. This section elaborates on the process of how the contact-point response is calculated. The equation of motion for the stationary vehicle is expressed as follows:  
\[m_{s}\ddot{\tilde{q}}_{s}+k_{s}(q_{s}-u_{c})=0 \tag{9}\]  
where \(m_{s}\) and \(k_{s}\) are the mass and stiffness of the stationary vehicle, respectively. Deriving the equation of motion for the stationary vehicle twice with respect to time could yield the following:  
\[-k_{s}(\tilde{q}_{s}-\tilde{u}_{c})=m_{s}\frac{d^{2}\tilde{q}_{s}}{dt^{2}} \tag{10}\]  
The natural frequency of the stationary vehicle is known in practice and can be expressed as \(\omega_{s}=\sqrt{k_{s}/m_{s}}\). Consequently, the contact-point acceleration of the stationary vehicle can be computed as follows:  
\[\tilde{u}_{c}=\tilde{q}_{s}+\frac{d^{2}\tilde{q}_{s}}{\omega_{s}^{2}dt^{2}} \tag{11}\]  
The term \(\frac{d^{2}\tilde{q}_{s}}{dt^{2}}\) can be calculated by the central difference method for the discrete data of the recorded signal such that  
\[\frac{d^{2}\tilde{q}_{s}}{dt^{2}}=\frac{\left(\tilde{q}_{s}|_{i+1}-2\tilde{q} _{s}|_{i}+\tilde{q}_{s}|_{i-1}\right)}{\left(\Delta t\right)^{2}}, \tag{12}\]  
where \(\Delta t\) denotes the sampling interval and \(i\) is the _ith_ sampling point. In this study, the vertical acceleration of the stationary vehicle obtained from the FE model was used to  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Frequency (Hz)**} \\ \cline{2-3}  & **FE Model (Ls-Dyna)** & **Analytical (Equation (4))** \\ \hline
1 st & 2.082 & 2.084 \\ \hline
2 nd & 8.303 & 8.335 \\ \hline
3 rd & 18.589 & 18.755 \\ \hline
4 th & 32.817 & 33.342 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Bridge eigenvalues obtained from the FE model and analytical formulation.  
calculate the contact-point response. Afterwards, the contact-point response was compared to the true response of the bridge to check the efficiency of the proposed approach.
# A Rapid Method for the Determination of SBS Content Based on the Principle of Orthogonal Testing
## 2 Experimental
### Manova  
The results of the orthogonal test are usually analyzed in two ways: direct-viewing analysis and analysis of variance (ANOVA). The direct-viewing analysis method is also called the range analysis method, which determines the primary and secondary relationships among the influencing factors by calculating the range of data and comparing its size. However, when the test results corresponding to different levels of a certain factor are different, the direct-viewing analysis method cannot distinguish whether this difference is caused by different levels of the factor or experimental errors. ANOVA can make up for the limitations of direct-viewing analysis and provide reliable analysis results [23]. ANOVA is usually used in the significance test for differences between two or more sample data, which is also known as \(F\) test or analysis of variability [24]. Generally, due to the influence of various factors, the data obtained from the test will fluctuate. ANOVA can be used to evaluate the volatility of the test data and the influence of various factors on the test results to find certain regularity. ANOVA for orthogonal test can be carried out by SPSS (Statistical Program for Social Sciences) data analysis tool.  
Therefore, this paper uses ANOVA to analyze the results of orthogonal tests. The orthogonal test is a multi-factor and single-index test. The influencing factors are SBS  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Level** & **B (SBS Content/\%)** & **C (Sulfur Content/\%)** & **D (Rubber Oil Content/\%)** \\ \hline I & 2\% & 0.0\% & 0\% \\ II & 4\% & 0.1\% & 2\% \\ III & 6\% & 0.2\% & 4\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Levels of different factors.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Parameters** & **SBS Modified Asphalt** & **Base Asphalt** \\ \hline Penetration(25 \({}^{\circ}\)C, 100 g, 5 s)/0.1 mm & 56 & 67.4 \\ Softening point/\({}^{\circ}\)C & 85.8 & 46.5 \\ Ductility(5 \({}^{\circ}\)C, 5 cm/min)/cm & 32 & – \\ Ductility(15 \({}^{\circ}\)C, 5 cm/min)/cm & & 115 \\ Mass change/\% & 0.11 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of Asphalt.  
content, sulfur content and rubber oil content. It is assumed that the effects of the three are independent of each other and there is no correlation. Thus, the multi-factor univariate ANOVA model with no interaction in SPSS is used to compare and analyze the test results of three factors and three levels. Multivariate analysis of variance (MANOVA) uses the \(F\) test, whose null hypothesis is H\({}_{0}\): the observed variable values at different levels of each factor have no significant difference. SPSS will automatically calculate the F value and provide the corresponding probability \(p\) value according to the \(F\) distribution table. It can be judged whether the different levels of each factor have a significant influence on the observed variables by comparing the \(p\) value with the magnitude of the significance level \(\alpha\).
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 3 Condition Monitoring  
As is described in [22], for the detection of gear cracks, the whole frequency spectrum should be monitored for changes, including increases and decreases in all amplitudes [22]. One possible algorithm is presented in [22] as the the Average-Log-Ratio (ALR) indicator, which indicates changes in the rotational harmonics. For more complex applications, not only should the harmonics of the rotational speed be monitored but also the areas in between, containing the rotational harmonics of the other components and sidebands.  
To monitor these frequencies, a family of CIs has been developed and presented for ultra-low-speed applications in [23]. To prove the assumption that these CIs can be used for higher speeds, they are applied amongst other indicators in the current study for the monitoring of hypoid gears. As described in [23], the CI called the relative spectral difference (\(RSD\)) is defined, whose calculation consists of a learning phase and an evaluation phase, described below.  
To determinate the RSD, the Fast-Fourier Transformation (FFT) \(S(f)\) with \(f\in\mathbb{R}\setminus\{0\}\) of an arbitrary vibration signal of interest \(s(t)\) has to be calculated, where \(S(f)\) is a vector with the FFT results for all frequencies \(f\).
## 3 Results and Discussion
### Validation of Model
Similarly, injected active power results at \(-0.40\%\) slip value seen in Figure 6b are 0.087 pu, 0.103 pu and 0.080 pu, respectively, for Measurement, the Developed Model and the SCP Model [12]. In the same figure, injected active power results at \(-1.20\%\) slip value are 0.364 pu, 0.347 pu and 0.320 pu, respectively, for Measurement, the Developed Model and the SCP Model [12]. Despite the change in rotor slip value, the injected active power and reactive power demand given in Figure 6a,b show that the Developed model and the SCP model give close results, and these results have the same trend as the measured ones. Actually, at BS EN 50160 [26], it is recommended that the value of the _VUF_ is less than 2% in the distribution systems. For this reason, in order to evaluate the Developed model better, the test results of the asynchronous generator working with the slip values between \(-0.4\%\) and \(-1.2\%\) under the unbalanced terminal voltage with _VUF_ = 2% are also given in Figure 7.  
The obtained and calculated reactive power demand results at \(-0.4\%\) slip value given in Figure 7a are 0.625 pu, 0.606 pu and 0.592 pu, respectively, for Measurement, the Developed Model and the SCP Model [12]. In the same figure, reactive power demand results at \(-1.20\%\) slip value are 0.684 pu, 0.632 pu and 0.630 pu, respectively, for Measurement, the Developed Model and the SCP Model [12]. Similarly, injected active power results at \(-0.4\%\) slip value seen in Figure 7b are 0.087 pu, 0.083 pu  
Figure 6: Variation of the reactive power demand (**a**), and injected active power (**b**) of the generator at the 5% _VUF_ value for different rotor slip values. and 0.073 pu, respectively, for Measurement, the Developed Model and the SCP Model [12]. In the same figure, injected active power results at \(-\)1.20% slip value are 0.390 pu, 0.359 pu and 0.346 pu respectively for Measurement, the Developed Model and the SCP Model [12]. It is seen in Figure 7 that the results obtained by the Developed model are in close agreement with the SCP model [12] and show the same trends and similar trends as the measured ones.
# Laser R-Test for Angular Positioning Calibration and Compensation of the Five-Axis Machine Tools
## 5 Experiment and Results
### Angular Positioning Verification without Milling Workpiece  
For the system verification, we compare the measured results of the Laser R-test with commercial instrument XR-20 (Renishaw, UK) after compensations, and the measurement is executed five times for each instrument. The measured results shown in Figures 22 and 23 are as follows: for XR-20, the accuracy is 4.7 arcsec and the repeatability is 5.5 arcsec; for the  
Figure 21: The A-axis after compensation.  
Figure 20: The A-axis before compensation.  
Laser R-test, the accuracy is 5.37 arcsec and the repeatability is 4.32 arcsec. The maximum residual error between the Laser R-test and XR-20 is 4.4 arcsec as shown in Figure 24. The accuracy of XR-20 seems to be better for this case, but the repeatability of the Laser R-test is better. The Laser R-test had a better repeatability of 1.2 arcsec and worse accuracy of 0.67 arcsec compared to XR-20. In the measurement of the multi-body system, such as the five-axis machine tool, many components influence the accuracy of the measured results. The accuracy of measurements depends on measuring conditions. In this case, better repeatability describes the higher reliability of measurements.  
The measured results show that this system shall be applied to the compensation of the rotary axis positioning. The error curve has an apparent convergence, so the system's principles and methods are feasible in this experiment. However, these are just measurements. In order to demonstrate the feasibility of this research, in the next section, we apply the compensation values achieved from commercial instrument XR20-W and our Laser R-test on two actual workpieces.  
Figure 23: The angular positioning measurement (Laser R-test).  
Figure 22: The angular positioning measurement (Renishaw XR20).
## 1 Introduction
In the article presented in [19] the multi-objective function is designed based on Taguchi experimentation, where the objective functions are cutting quality, production speed, and energy consumption reduction. In the article presented in [20], a series of advanced multi-objective algorithms for the optimization of turning parameters such as cutting speed, feed, and depth of cut are tested by experimental tests of cutting forces and temperatures, determining material removal rates and surface roughness, and applying four optimization algorithms; values were obtained for the defined functions. This article will be used later to make the comparison with the method of interest. Table 1 shows the results obtained by various tests carried out and taken into consideration by the author. In the article shown in [21] a multi-objective optimization study of the turning process is carried out based on the Taguchi experiment and by modifying the values of five parameters: tool tip radius, tool holder length, spindle speed, feed rate, and cutting depth, from which the objective functions were constructed: surface roughness, roundness deviation, and material removal capacity. The article defined in [22] uses neural network-based prediction models to minimize the effect of vibration on the milling process. This article was of interest because the topic it addresses is complementary to that developed in the present article. The article presented in [23] proposed a multi-objective optimization model for the parameters involved in CNC turning, considering energy consumption in transient and steady states, as well as tool life. After reviewing the bibliography described in [14; 15; 16; 17; 18; 19; 20; 21; 22; 23], it is concluded that the authors do not present a combination of parameters that involve the machine power, surface \begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Material** & **Algorithm** & \(\mathbf{V_{c}}\) [**m/min**] & \(\mathbf{F_{v}}\) [**mm/rev**] & \(\mathbf{a_{c}}\) [**mm**] \\ \hline
1 & AISI 5140 & ANOVA & 190 & 0.06 & — \\
2 & SKD61 & Taguchi & 60 & 0.05 & 0.1 \\
3 & AISI 1050 & Taguchi and RSM & 200 & 0.07 & 1.2 \\
4 & EN10503 & Taguchi and VIKOR & 78.62 & 0.08 & 0.5 \\
5 & AISI 1050 & ANOVA & 135 & 0.214 & — \\
6 & AISI 1040 & Taguchi and ANOVA & 300 & 0.3 & 1.0 \\
7 & AISI 1040 & RSM, H-ABC, and Taguchi & 280 & 0.18 & 1.0 \\ \hline \end{tabular}
\end{table} 
Table 1: Results of the machining process given by the literature [20]. roughness, and structural integrity as restrictions to building a mathematical optimization model that involves minimizing the machining cycle time. Based on the argument previously stated, it is concluded that the topic that the authors develop in this paper is innovative, since it will contribute to the optimization and systematization of the machining process, because it is about presenting an integrative method that takes into account not only the requirements for machining the piece but also restrictions of the machine tool and the work material in order to guarantee the structural integrity and reliability of the machining.
## 5 Detection Methods: Modulation Spectroscopy for Wideband Absorption
### Optimizing the Modulation Index  
Since the recorded harmonic signals used in WMS are heavily dependent on the spectral line profile and modulation index adopted in the WMS system, the situation for sensing a larger molecular gas with a broadband spectrum is quite different. Moreover, the similarity of signal waveform between a broadband spectrum and the intrinsic optical fringes interference will seriously complicate the signal processing [30] and deteriorate the sensing sensitivity and precision.  
The modulation index always plays a pivotal role in WMS-based measurement. A modulation index of 2.2 is recognized as the optimum to achieve the maximum SNR with isolated Gaussian or Lorentzian line profile. For broadband spectrum, however, the reordered harmonic signal would broaden to overlap with the adjacent spectrum, interference, and optical fringes with the so-called modulation index optimum. The overlapping may deteriorate and even disable the WMS measurement. So the modulation index determination should balance the spectra discrimination and the SNR in WMS with broadband spectrum.  
We investigated the determination of modulation index for the broadband absorption, taking the carbon disulfide (CS\({}_{2}\)) spectrum around 2177.6 cm\({}^{-1}\) as an example. To evaluate the spectra discrimination of the WMS-2f signal, we particularly defined a parameter, SD [27]:  
\[SD=\frac{h_{CD}}{0.5\times(h_{A}+h_{B})}, \tag{5}\]  
where \(A\) and \(B\) are the adjacent valleys of neighboring WMS-2f signals, \(C\) is the middle point of the line connecting \(A\) and \(B\), point \(D\) at the signal curve is vertical to point \(C\), \(h_{A}\) and \(h_{B}\) are the amplitude of absorption valley \(A\) and \(B\), respectively. \(h_{CD}\) is the height difference between point \(C\) and \(D\). The parameter \(SD\) is a constant from 0 to 1, which represents the WMS-2f completely overlapping and parting, respectively, as shown in the insert panel of Figure 3b.  
The parameter \(SD\) and normalized amplitude of WMS-2f signals under different modulation index was plotted in Figure 3c, which reverse with the modulation index. To balance the \(SD\) and normalized amplitude of WMS-2f signals, the modulation index around 1.0 should be a good compromise for optimizing WMS with the band spectrum of CS\({}_{2}\) detection.
# YOLO-RRL: A Lightweight Algorithm for PCB Surface Defect Detection
###### Abstract
To address the challenge of accurately identifying printed circuit board (PCB) defects, a lightweight model called LW-YOLO is proposed, which employs a bidirectional feature pyramid network, a partial convolutional module, and a minimum point distance intersection-and-parallel ratio loss function. [PERSON] et al. [16] enhance the YOLOv4 model by using a modified backbone network and activation function and propose YOLOv4-MN3, which reduces the parameter space and the multiply-and-accumulate operation to make it more efficient. [PERSON] et al. [17] proposed a YOLO-HMC network to enhance defect detection using HorNet, MCBAM, and CARAFE, which solves the challenge of accurately identifying small defects due to the compact layout of printed circuit boards (PCBs). Nevertheless, the existing methodologies continue to encounter certain limitations in the detection of surface defects on printed circuit boards. First, the datasets used to train deep learning models for PCB defect detection are often scarce and uneven, which makes it challenging to develop effective models [18]. Second, the images of printed circuit boards (PCBs) are frequently characterized by complex backgrounds and multiple types of defects, increasing the detection difficulty. Furthermore, the deep learning model has a high demand for computational resources, which represents a limitation for real-time inspection applications in real production. In the context of industrial applications of PCB defect detection models, there is a clear requirement for model standards that combine speed and accuracy and are easy to deploy. In light of the aforementioned issues, an enhanced model, designated YOLO-RRL, has been developed to address the pivotal challenges in PCB surface defect detection. The research presented here focuses on the following aspects:  
* The lightweight model design seeks to enhance the original redundant structures and modules within the network, thereby increasing their efficiency in resource-constrained environments. This is achieved while maintaining or even improving the performance of the model. * A multi-scale feature extraction mechanism has been introduced to enhance the model's capability to deal with complex backgrounds and multiple defect types. * Data enhancement is a process that extends the defective sample dataset to improve the performance of the model in the context of data scarcity and imbalance.