esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
By using fully Cartesian coordinates to represent a mechanism, the equations that define the reactions are less complex than those obtained with other methods, hence, the use of this kind of coordinates is suitable for complete balancing, minimization of reactions, and calculation of the ShF and ShM in mechanisms.  
The use of fully Cartesian coordinates to represent a four-bar linkage in conjunction with the Simplified Gradient Descent algorithm is a suitable methodology to optimize the balancing of mechanisms. It allows, when using three counterweights and giving more importance to static balancing, to reduce the ShF and ShM by 99.70% and 28.69%, respectively; and when importance is given to the dynamic balancing, to reduce the ShM and the ShF by 83.99% and 8.47%, respectively.  
The optimization algorithm was successfully applied to solve the problem. The use of linear combination of functions is a simple yet robust way to handle multi-objectives. The approximation of the derivatives based on Finite Difference allows guiding the algorithm and reducing human hand calculation mistakes.  
Comparison between Pareto fronts proves to be an adequate methodology for the sensitivity analysis of each counterweight. This method has proved that even when using only one counterweight, the ShF can be reduced by 78.74% when giving importance to the static balancing or the ShM can be reduced by 73.61% when giving importance to dynamic balancing.  
The box-plots of the partial derivatives with respect to each variable, histograms of volumes, and relations between area and thickens allow to analyze the proposed optimization limits in order to decide if they can be changed to obtain even better results.  
As future work, it is expected to use these algorithms and analysis to optimize more complex mechanisms in two and three dimensions.  
[PERSON] conceived and implemented the use of Fully Cartesian coordinates to define a mechanism. [PERSON] design the Optimization Algorithm. [PERSON]-[PERSON], [PERSON] designed the experimental methodology and analyze the results. [PERSON], [PERSON] validated the results. [PERSON]-[PERSON], [PERSON], [PERSON] wrote the paper. All authors reviewed the paper.  
This research received no external funding.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
ShF Shaking Force ShM Shaking Moment  
Figure 9: Pareto comparison when changing Counterweight-3 thickness upper limits.
## 2 Methods
### Feature Combination Evaluation

Table 12: ML metrics using combinations of ANC Test feature for Merged Dataset. Figure 5: Best accuracy obtained in Dataset 1—Coarse Tree (**a**) and Dataset 2—Quadratic SVM (**b**). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **Model** & **Accuracy [\%]** & **Precision [\%]** & **Recall [\%]** & **Specificity [\%]** & **F1 Score** & **AUC** \\ \hline A, DtS & Naive Bayes & 61.4 & 88.1 & 61.7 & 50.0 & 0.73 & 0.58 \\ N, DtS & Quadratic SVM & 67.1 & 83.3 & 73.7 & 68.9 & 0.78 & 0.69 \\ NC, DtS & Quadratic SVM & 63.4 & 77.4 & 64.4 & 51.3 & 0.70 & 0.62 \\ C, DtS & Gaussian SVM & 59.3 & 96.4 & 60.4 & 50.0 & 0.74 & 0.56 \\ A, N & Quadratic SVM & 70 & 82.1 & 69.7 & 65.9 & 0.75 & 0.66 \\ A, N, DtS & Quadratic SVM & 66.4 & 83.3 & 70.7 & 65.9 & 0.77 & 0.70 \\ A, NC & Cubic KNN & 62.9 & 73.8 & 67.4 & 54.2 & 0.70 & 0.61 \\ A, NC, DtS & Coarse Tree & 65 & 72.6 & 69.3 & 55.8 & 0.71 & 0.61 \\ A, N, NC & Quadratic SVM & 67.9 & 96.4 & 65.9 & 82.4 & 0.78 & 0.64 \\ A, N, NC, DtS & Quadratic SVM & 72.9 & 84.5 & 71.0 & 67.5 & 0.77 & 0.61 \\ A, N, NC, C, DtS & Quadratic SVM & 70.7 & 84.5 & 69.6 & 65.8 & 0.76 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: ML metrics using combinations ANC Test feature for Dataset 2.  
rather than in Dataset 1, of 27%. As in the first evaluation, certain combinations of ANC features have a redundant role. In the context of wearable devices, it is desired to reduce the computation power, therefore, the best predictors of hypertensive would be A and N type. On the other hand, where computation power does not represent a constrain like a server computation, the selection with best performance would be implemented: A, N, NC, and DtS parameters. For the merged strategy evaluation, the overall accuracy scored between the performance of Dataset 1 and Dataset 2. The best accuracy was 63.2% similar to in the Dataset 2 case by A, N, NC, and DtS parameters with an F1 score of 0.60. In a nutshell, the prediction of detecting prehypertensive or hypertensive patients is around 70% even if the different datasets have been used with individual measurement protocols. The designed algorithm by sorting various signal morphologies in defined classes prove a good performance for BP prediction of Level 1. Therefore, the ANC Test(tm) represents a good starting point in this health metric evaluation.
# Parameter Observer Useable for the Condition Monitoring of a Capacitor
## 3 Results
### Experimental Results  
The experiments were performed for two cases in which the COUT capacitor in Figures 12 and 13 had the nominal values of 100 uF and 470 uF, respectively. The capacitors were of the type aluminium electrolytic capacitor Nichicon 100 uF/25 V and Panasonic polymer SMD electrolytic capacitor 470 uF/25 V, respectively. The discharges were performed each time in two stages through the resistors with the resistances \(R_{L}=29.89\ \Omega\) and \(R_{\rm a}=15.84\ \Omega\) while the setup environment temperature was constant 22 \({}^{\circ}\)C. The voltage across the output capacitor terminals was measured using a LeCroy WaveSurfer 104 MX digital oscilloscope (8-bit resolution) and saved in files for offline digital processing in the MATLAB-SIMULINK environment. The sampling frequency of the oscilloscope was several times higher than the corresponding frequency bandwidth of the capacitor discharging process according to the oversampling technique. Thus, we took measurements with a sampling frequency of 5 MHz. The results are shown in Figures 14-17 that refer to the use  
Figure 12: Diagram for determining the transient equivalent values of the COUT capacitor parameters.  
Figure 13: Simplified representation of the diagram in Figure 12.  
the processing of the measured voltage values filtered with the 51-point moving average filter (38), while Figure 15c,d corresponds to unfiltered data processing.  
\[y_{av}[n]=\frac{1}{51}\cdot\sum_{j=n-25}^{n+25}x[n-j]. \tag{37}\]  
Figure 15b,d shows, using the bisector method, the equivalent values \(\hat{T}_{el}=2.9066\cdot 10^{-3}\) s and \(\hat{T}_{el}=2.9071\cdot 10^{-3}\) s. The calculated values correspond to \(t_{1}=3.6087\cdot 10^{-3}\) s, and \(t_{1}=3.6137\cdot 10^{-3}\) s. Therefore, we consider that:  
\[\hat{T}_{el}=2.9\cdot 10^{-3}\text{ s}. \tag{38}\]  
Figure 16 shows the counterparts of Figure 15 for the second discharging stage. The equivalent values calculated for filtered (Figure 16a) and unfiltered (Figure 16c) records are \(\hat{T}_{el2}=1.1024\cdot 10^{-3}\) s, (Figure 16b) and \(\hat{T}_{el2}=1.1036\cdot 10^{-3}\) s (Figure 16d), respectively. They correspond to the moments \(t_{1}=3.6137\cdot 10^{-3}\) s and \(t_{1}=2.1036\cdot 10^{-3}\) s, respectively. Here, we will consider:  
\[\hat{T}_{el2}=1.1\cdot 10^{-3}\text{ s}. \tag{39}\]  
For the values in (38) and (39), the Formula (36) also leads to equivalent values  
\[\hat{C}_{e}=92.84\cdot 10^{-6}\text{ }F,\text{ \ \ }\hat{R}_{e}=1.5339\text{ }\Omega. \tag{40}\]  
In the case of the capacitor with a nominal value of 470 uF, the two discharging stages are shown in Figure 17a,b, respectively.  
From calculations similar to those of the 100 uF capacitor, we obtained \(\hat{T}_{el}=13.5\cdot 10^{-3}\) s and \(\hat{T}_{el2}=4.9\cdot 10^{-3}\) s, respectively:  
\[\hat{C}_{e}=443.6\cdot 10^{-6}\text{ }F,\text{ \ \ }\hat{R}_{e}=0.7324\text{ }\Omega. \tag{41}\]
## 5 Finite Element Model Updating
### Analysis of the Influence of Global Elasticity Modulus' Reduction--Model 5  
In this section, an analysis of the reduction in the global modulus of elasticity of the walls was carried out, since the value obtained by experimental testing using the flat jack test is much higher than the expected value for this type of structure obtained by conducting experimental tests on similar types of structures which were from 1200 to 2000 MPa. Model 4 was taken as the initial model in this analysis, while the change of the global modulus of elasticity of the walls \((E_{GL,red})\) was carried out with a step of 500 MPa (Figure 14). The obtained results of the change in the value of the natural frequency depending on the change in the global modulus of elasticity \((E_{GL,red})\) for the first mode shape are shown in Figure 14. In this model, activation of 90% of the mass was achieved in the first 35 modal forms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mode, \(i\)** & \(\mathbf{f_{i}^{exp}}\) & \(\mathbf{f_{i}^{num}}\) & \(\mathbf{\Delta f_{i}^{exp,num}}\) \\  & **[Hz]** & **[Hz]** & **[\%]** \\ \hline
1 & 2.98 & 3.78 & \(-26.8\) \\
2 & 3.96 & 5.29 & \(-33.6\) \\
3 & 4.45 & 6.29 & \(-41.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the experimentally \(\left(f_{i}^{exp}\right)\) and numerically \(\left(f_{i}^{num}\right)\) obtained natural frequency values in form of deviation \(\left(\Delta f_{i}^{exp,num}\right)\) for the numerical Model 4.  
Figure 13: The finite elements of the model with a reduced modulus of elasticity at the (**a**) ground and (**b**) first floor.  
The changes in the natural frequency values obtained by numerical modal analysis with respect to the experimental values are visible. The modulus of elasticity around the 2000 MPa gives a very small difference compared to the experimentally obtained results. The next table (Table 8) shows a comparison of the experimentally determined values of the natural frequencies with the frequencies obtained by the numerical modal analysis of Model 5, to which the global modulus of elasticity in the amount of 2000 MPa is assigned.  
From the results of the comparison (Table 8), a small difference in natural frequency can be observed, the value of which is acceptable for further analysis. In addition, the value of the global modulus of elasticity of the walls in the amount of 2000 MPa represents the expected value for this type of structure.
## 3 Method
Both AOMs were set up in double pass configuration such that, the first order diffracted beam is retroreflected back towards the AOM, whereas the zero order beam is blocked, as seen in Figure 2. At the exit of the 1 st-AOM, the laser beam has a frequency of \(\simeq\)\(\omega_{0}+400\) MHz, and it is then directed onto the 2 nd-AOM. With a correct voltage applied to the 2 nd-AOM driver, \(\omega_{\pm}\) beam is directed towards the interaction cell with a frequency that closely matches that of \(\omega_{0}\) (i.e., \(\omega_{\pm}\simeq\omega_{0}\)). At this stage, two mutually coherent and independent beams are created, where \(\omega_{0}\) depends solely on the properties of the ECDL, and \(\omega_{\pm}\) depends on the properties of both the ECDL and the individual AOMs. The two beams \(\omega_{0}\) and \(\omega_{\pm}\) are recombined, so that the two beams co-propagate through the vapor cell. To create a dark state that is sensitive to a magnetic field, the two beams are put through a quarter wave plate (\(\lambda/4\)), which creates a right and left circular polarization \(\sigma_{+}\) and \(\sigma_{-}\), respectively, before going through the interaction cell. Upon the application of a magnetic field collinear to the direction of the beams, \(\omega_{0}\) and \(\omega_{\pm}\) excite different Zeeman transitions. Finally, when a triangular waveform is applied to the 2 nd-AOM driver at low frequency and amplitude, this will produce a frequency modulation for \(\omega_{\pm}\), which is prerequisite to generate a CPT signal.
## 1 Introduction  
Cross-correlation is an established technique to calculate the time-of-flight of ultrasonic signals with applications ranging from medicine to industrial quality control [1, 2, 3]. The cross-correlation function, CCF, takes two ultrasonic echoes as inputs and returns a function that mathematically quantifies the similarity of two signals in the time-regime [4]. The maximum of the CCF marks the best match and provides the possibility to determine a relative shift between the signals.  
In an ultrasonic system, the shape of the signals and, therefore, the shape of the CCF is influenced by many factors in practice. Major parameters include defects in the material, positioning of the sensor, coupling conditions, damping and reflections [5, 6, 7]. For regular excitation patterns, such as a sine wave or an impulse stimulation, the CCF exhibits many local maxima with similar correlation factors as the global maximum because of the resonant eigenfrequencies of the transducer, causing ambiguity [8, 9, 10].  
There are different strategies to address the problem of ambiguous CCF results. A recent study used a purely algorithmic approach, based on machine learning, to identify phase shifts in cross-correlated signals [11]. However, this requires prior training of the algorithms and the generation of large amounts of labeled training data. Anotherexperimental approach, the so-called coded excitation, modulates the excitation signal to reduce the self-similarity of echoes [12, 13]. This principle was originally developed for applications in radar technology like UWB short-range radar sensing or FMCW [14, 15]. It was adapted to ultrasonic methods but is so far mainly used in medical applications, such as commercial scanners or other medical imaging applications [16, 17, 18]. When using coded excitation signals, excessive natural frequency behavior [19, 20] and nonlinear piezoelectric behavior [21, 22, 23] are usually seen as inconveniences and the experimental limits are set in a way that they do not influence the signals.  
The approach presented in this paper modulates the ultrasound signal based on coded excitation both in the linear and nonlinear regime of the piezoelectric sensor to generate cross-correlation results with optimized unambiguity and, thus, achieves a higher robustness against disturbance. To explore a wide range of coded excitation signals, a simulation model was first developed and experimentally validated. Based on the resulting optimal excitation signals, further empirical optimization of the signal-to-noise ratio leveraging the nonlinear piezo regime yields improvement of robustness for CCF-based time-of-flight measurements.
## 4 Experimental Results and Analysis
### Datasets  
Experimental analysis is performed for the selected algorithms by using four classical real network datasets. The selected datasets are of different sizes and come from different network fields as much as possible, including virus networks, gene networks, and protein networks. The weight and direction of each network linking edge are ignored in this experiment.  
(1) The international aviation network where the SARS virus spread [44] comprises 224 nodes and 2247 edges. The nodes represent the countries where flights arrived while the edges represent the routes between two countries. The important node set of the network is defined as the countries where the SARS virus spread at the early stage.  
(2) The Genepath human gene signaling network [45] comprises 6306 nodes and 57,340 edges. Nodes represent genes while edges represent the relationship between nodes. The important node set of the network is defined as the Alzheimer's disease gene.  
(3) The mouse protein interaction network [46] comprises 1187 nodes and 1557 edges. Nodes represent mouse proteins while edges represent the interaction between proteins. The important node set of the network is defined as mouse protein kinase.  
(4) The yeast protein network [47] comprises 5093 nodes and 24,743 edges. The nodes represent proteins while edges represent the relationship between proteins. The important node set of the network is defined as the important protein of the yeast network.  
The basic topology characteristics of the four real networks used in this work are shown in Table 1.  
Here, \(n\) refers to the number of nodes in the network, \(m\) refers to the number of edges in the network, \(n_{1}\) refers to the number of important nodes in the network, \(\overline{k}\) refers to the average degree of the network, and \(C\) refers to the average clustering coefficient of the network.
# The Application of Deep Learning and Image Processing Technology in Laser Positioning
## 3 Convolutional Neural Network  
The major difference between the Convolutional Neural Network (CNN) and the traditional multilayer perception network is the convolutional and pooling layers. The two layers enable the CNN to recognize image (or voice) details, whereas the other neural networks only extract data for computation. The three layers of CNN are briefly described below.
## 4 Implementation Details
### Evaluation Metrics  
Comparing images to assess the accuracy of segmentation is critical for evaluating progress in this field of research. To evaluate segmentation performance, we use the following four metrics: mean intersection over union (_mIoU_), Dice similarity coefficient (DSC), precision (\(P\)), and recall (\(R\)). These metrics are mathematically defined with the following equations:  
\[P=\frac{TP}{TP+FP} \tag{8}\]  
\[R=\frac{TP}{TP+FN} \tag{9}\]  
\[mIoU=\frac{TP}{TP+FP+FN} \tag{10}\]  
where \(FP\) refers to the number of false positives, \(TP\) refers to the number of true positives, \(TN\) refers to the number of true negatives, and \(FN\) refers to the number of false negatives.  
The \(DSC\) metric measures the degree of overlap between the ground truth and predicted segmentation and can be written as follows:  
\[DSC(G,P)=\frac{2|G\cup P|}{|G|+|P|} \tag{11}\]  
where \(P\) is the predicted mask and \(G\) is the ground truth.
## 5 Conclusions  
In this paper, based on the VGG16 network model, the S.E. attention-mechanism module was added to improve the model's ability to extract the image features in the corrosion-salience region and suppress the information related to useless features. At the same time, the loss function was improved by using the fusion of central loss function and cross-entropy loss function, which can increase the inter-class distance between different pipe-corrosion patterns and reduce the intra-class distance within the same corrosion pattern. The accuracy of pipe-corrosion classification was further improved. The final experimental results show that the proposed model achieves an accuracy of 95.266% on the pipe corrosion dataset, which is 5.444%, 3.077%, 2.248%, 0.592%, 1.894%, and 2.840% higher than the traditional AlexNet, traditional VGG, Lian-VGG, the SE-VGG models, the ResNet50 model, and the DenseNet121 models, respectively. The experimental results met the standard requirements, providing a new approach to detecting damage to the inner walls of water-supply pipes.  
Conceptualization, Q.Z. and L.L.; methodology, L.L.; software, L.Z.; validation, Q.Z., L.L. and L.Z.; investigation, L.Z.; writing--original draft preparation, L.L.; writing--review and editing, Q.Z.; visualization, L.L. and L.Z.; supervision, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Grant No. 51804248), the Shaanxi Provincial Science and Technology Department Industrial Research Project (Grant No. 2022 GY-115), the Beilin District Applied Technology R&D Project (Grant No. GX2114), and the Shaanxi Provincial Education Department Service to Local Enterprises (No. 22 JC050).  
Not applicable.  
Data availability Statement: Data is available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Results
### The TARA Showcasing
#### 4.1.1 High-Risk Scenarios  
The scenarios obtaining the highest scores concern attacks on the means of communication as well as attacks involving physical access to the on-board computer. The former remains relatively simple to deal with as mitigation methods, such as data encryption, can be enough and are likely to be implemented by the Original Equipment Manufacturer (OEM). Consequently, it is impossible to determine whether data in transit are authenticated and whether integrity checks are carried out. However, as the encryption, authentication, and integrity checks are software-based without requiring any hardware substitution, such a setup can be implemented promptly by a team dedicated to system hardening. On the contrary, attacks involving physical access to the on-board computer require different mitigation strategies that require further hardware changes.  
As many of the current CAVs are prototypes, physical security for access to the digital systems is not a high priority at the moment. This can be attributed to the experimental nature and rapid development requirements of the vehicle, which include relatively easy access to the on-board computer. However, we have to mark this as a major security risk, and it may remain a high risk if no proper anti-tempering solutions are employed. The L4V is supplied with a keyboard that can allow the user to escape the OEM's program and access the host operating system. On the same note, several active USB ports are present on the machine attracting malicious intentions to plug a rogue device into the vehicle to damage the system or steal information. Theoretically, access to such ports allows the total destruction of the on-board computer via a \"USB Killer\", which is able to physically destroy a computer by several 240 V discharges sent into the USB port. Nowadays, such attacks can even be performed remotely and without the computer being turned on, thanks to the USB Killer V.4 [56].
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
## 2 Corrosion Fatigue Lifespan Model Based on EIFS Theory
### EIFS Theory
Small cracks with the same stress intensity factor range propagate at a rate several times faster than what is predicted by the data for long crack propagation. However, the mechanism of small crack growth is not yet fully understood, and the propagation of small cracks largely depends on the material's microstructural features, including grain size, grain orientation, and initial defect shape. These structural factors exhibit significant uncertainty due to their stochastic nature. This introduces additional challenges for probabilistic life prediction. To overcome these difficulties, the EIFS concept avoids the analysis of small crack growth and instead utilizes an equivalent initial crack size that matches the fatigue life of the specimen during the analysis of long crack growth. The detailed calculation process will be discussed in Section 2.2.