esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Prediction Method of the Ultra-Low-Cycle Fatigue Damage of Steel
## 4 Conclusions  
The finite element models of the base metal and weld specimens suitable for predicting ULCF were established on the basis of the calibrated parameters of CDM and CVGM. The predicted value of the damage under strain cyclic loading was compared with the test results to prove the effectiveness of the two prediction methods. The CDM model is better than CVGM in terms of predicting the damage of ULCF for steel and its joint. The specific research conclusions are as follows:  
1. Under stress triaxiality state, only the equivalent plastic strain cannot be used for fracture prediction. The crack initiation stage occupies most of the fracture life in the test.
2. The value toughness parameter \(\eta\) in CVGM for weld metal are the least one, and the dispersion coefficient of weld metal is the largest, thereby indicating that the weld metal can be easily damaged and has a large material uniformity.
3. The location of cracks initiation predicted by the CDM model for the base metal and weld specimens is slightly different from the test results, but the predicted post-fracture path and fracture surface are consistent with the test results. The CDM model can also predict the relationship between the crack initiation and fracture lives.
4. CVGM can predict the crack initiation and fracture lives well but cannot predict the relationship between the crack initiation and fracture lives and the post-fracture path. In addition, the location  
Figure 14: CDM model predicts the number of cycles to initial damage of base metal and weld specimens.  
of the cracks initiation predicted by CVGM for the base metal and weld specimens is different from the test results.
* Compared with the CVGM model, the CDM model can predict the fatigue life and the relationship between fatigue and fracture lives, the post-fracture path and the number of cycles to initial damage. In addition, the parameter calibration is simple, and the prediction error is small. The finite element size is not limited by the characteristic length, and the calculation efficiency is high. CDM can directly consider the effect of damage on the elastic modulus of materials and can be combined easily with the finite element software for easy engineering application.  
Conceptualization, Q.T.; methodology, Q.T. and X.X.; software, Q.T. and Y.L.; validation, Y.L. and H.Z.; formal analysis, Q.T. and Y.L.; resources, X.X.; data curation, Q.T. and Y.L.; writing--original draft preparation, Q.T., Y.L. and X.X.; writing--review and editing, Q.T.; project administration, Q.T.; funding acquisition, Q.T. and X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of China, grant number 51878606; National Science Foundation for Young Scientists of Jiang Xi, grant number 20161 BAB216113; Natural Science Foundation of Jiang Xi, grant number 20171 BAB206050; and China Postdoctoral Science Foundation, grant number 2017M611993.  
The authors declare no conflict of interest.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 3 Simulation Validation
### Eigen-Level Damage Detection Model  
The PNN classifier in the eigen-level fusion model was constructed as same as the above single PNN classifier model. To test the eigen-level data-fusion model, the three fusion feature groups involved were NC1 + NC2, NC2 + NC3 and NC1 + NC3. The IAs of training phase is 100% for all three classifiers. Table 3 shows the identification results of totally 1200 testing samples from the eigen-level model for noise levels \(\varepsilon\) = 1%, 2% and 3%, respectively. It can be seen that the identification accuracy all improved effectively with the different noise level. It is noted that the average IA is up to 73.8% when the noise level is 3%, which indicates that feature-level data fusion has improved the effectiveness of damage detection, and also has more robustness than the single PNN classifier, which uses only individual features.  
It can be found that, after the eigen-level data fusion processing, the average IAs improved remarkably at every noise levels; particularly for patterns 2 and 3, the IA was improved significantly at different noise levels. The average IA is up to 86.9% when the noise level is 2%, but the average IA was only 83.5% for single PNN classifier models, even when \(\varepsilon\) = 1%. These is further evidence that the eigen-level data fusion model, after data fusion processing, had a better damage detection capability. Because of the existence of redundant and complementary information in the eigen-level data fusion, the noise-tolerance and robustness of the eigen-level fusion model are consequently better than that of single PNN classifiers.
# A Fast Ray-tracing Method for Locating Mining-induced Seismicity by Considering Underground Voids
## 3 Synthetic Tests
### Experiments  
#### 3.1.1 Case 1  
We first established a void model, as shown in Figure 7. This model has 180 triangular faces and 92 vertices (the first category of nodes). The vertices are represented by green circles in the figure. Then according to Equations (1)-(3), the number of nodes of the second category is calculated as 3304 (step size is 1). The red circle in the figure represents the second category of nodes. The total number of nodes is 3396.  
Figure 6: Schematic diagram of calculating the shortest path.  
Figure 7: A void model and nodes layout. The green circle and the red circle represent the first category of nodes (vertex) and the second category of nodes, respectively.  
The scope of the monitoring area is \(x\in[0,100],y\in[0,100],z\in[0,100]\), as shown in Figure 8. Eighteen receivers (green inverted triangle) and one source (red circle) are arranged in the monitoring area. The propagation velocity of the wave in the non-void is set to 5000 m/s. Then, we use the proposed method to calculate the shortest paths from the source to the receivers, as shown by the red line in Figure 8. We can see that the ray paths from the source to the receivers bypass the void and finds the shortest paths that propagate in the non-void along the surface of the void.
## 4 Electron Transfer-Based Assays
### Ferric-Reducing Antioxidant Power
#### 4.1.2 A Comparative Analysis of Methodological Approaches in Studies on Plant Materials
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tested Material** & **Standard** & **Substrates** & **Type and Time** & **Wavelength** & **Source** \\ \hline \multirow{4}{*}{Various kinds of fruits} & \multirow{4}{*}{FeSO\({}_{4}\)} & 1.8 mL FRAP reagent (2.5 mL of a & & \\  & & 10 mM TPTZ solution in 40 mM HCl, & & \\  & & 2.5 mL of 20 mM FeCl\({}_{3}\), 25 mL of & 10 min, 37 \({}^{\circ}\)C & 593 & [48] \\  & & 300 mM acetate buffer (pH = 3.6), & & \\  & & 40 \(\mu\)L of tested sample, 0.2 mL MQ & & \\ \hline \multirow{4}{*}{Apple} & \multirow{4}{*}{FeSO\({}_{4}\)} & 2 mL of FRAP reagent (10 mL of & & \\  & & 10 mM TPTZ in HCl, 10 mL of 20 mM & 10 min, room & \\  & & & FeCl\({}_{3}\), 100 mL of acetate buffer & temperature & 593 & [47] \\  & & & pH = 3.6), 50 \(\mu\)L of tested sample & & \\ \hline \multirow{4}{*}{Oat \(\beta\)-glucans} & \multirow{4}{*}{millimolar Fe\({}^{2+}\)} & 3 mL of FRAP reagent (1 mL of & & \\  & & 20 mM FeCl\({}_{3}\)\(\cdot\)6H\({}_{2}\)O, 1 mL of 10 mM & & \\  & & & TPTZ in 40 mM HCl, 10 mL of & & \\  & & & 300 mM acetate buffer, pH = 3.6), & & \\  & & & 0.1 mL of sample, 0.3 mL of MQ & & \\ \hline \multirow{4}{*}{Wine} & \multirow{4}{*}{FeSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O} & 1 mL of FRAP reagent (1 mL of & & \\  & & 20 mM FeCl\({}_{3}\)\(\cdot\)6H\({}_{2}\)O, 1 mL of 10 mM & & \\  & & & TPTZ in 40 mM HCl, 10 mL of & 300 mM acetate buffer, pH = 3.6), & \\  & & & 50 \(\mu\)L of tested sample, 0.45 mL of MQ & & \\ \hline \multirow{4}{*}{Radish} & \multirow{4}{*}{Ascorbic acid} & 0.19 mL of FRAP reagent (2.5 mL of & & \\  & & 20 mM FeCl\({}_{3}\)\(\cdot\)2.5 mL of 10 mM TPTZ, & & \\  & & 2.5 mL of 10 mM acetate buffer & & \\  & & & pH = 3.6), 10 \(\mu\)L of tested sample & & \\ \hline \multirow{4}{*}{Tomato juice and carrot juice} & \multirow{4}{*}{BHT} & 0.6 mL of FRAP reagent (50 \(\mu\)L of & & \\  & & 20 mM FeCl\({}_{3}\), 50 \(\mu\)L of 10 mM TPTZ, & & \\  & and carrot juice & & 0.5 mL of 300 mM acetate buffer & \\  & & & pH = 3.6), 100 \(\mu\)L of extract & & \\ \hline \multirow{4}{*}{Various meals in kindergarten} & \multirow{4}{*}{Ascorbic acid of FRAP reagent (20 mM FeCl\({}_{3}\), & & \\  & & 10 mM TPTZ in 40 mM HCl, 300 mM & 10 min, room & \\  & & acetate buffer pH = 3.6), 100 \(\mu\)L & temperature & 593 & [56] \\  & & & of extract & \\ \hline \multirow{4}{*}{Maple syrup} & \multirow{4}{*}{FeSO\({}_{4}\)} & 210 \(\mu\)L of FRAP reagent (20 mM & & \\  & & FeCl\({}_{3}\), 10 mM TPTZ in 40 mM HCl, & & \\  & & 300 mM acetate buffer pH = 3.6), & & \\  & & & 30 \(\mu\)L of extract & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of methodological approaches in the FRAP assay.
## 3 Results  
### CoP-Related Parameters  
Figure 2A,B shows the Unit Path and Area95 results, respectively. The two-way ANOVA analysis showed a significant main effect of the device (\(p<0.001\); \(\eta_{\text{p}}\)\({}^{2}=0.621\)) and body posture (\(p<0.001\); \(\eta_{\text{p}}\)\({}^{2}=0.816\)) for the Unit Path parameter.  
The Bonferroni post-hoc analysis revealed significantly lower (\(p<0.01\)) values for the Eggball (BS: 3.097 \(\pm\) 0.527 cm/s; US: 4.313 \(\pm\) 0.843 cm/s) with respect to Gym53 (BS: 3.678 \(\pm\) 0.457 cm/s; US: 5.247 \(\pm\) 0.732 cm/s) and Gym65 (BS: 3.122 \(\pm\) 0.418 cm/s; US: 5.340 \(\pm\) 0.896 cm/s). Furthermore, the two-way ANOVA analysis showed a significant main effect of the device (\(p<0.001\); \(\eta_{\text{p}}\)\({}^{2}=0.652\)) and body posture (\(p<0.001\); \(\eta_{\text{p}}\)\({}^{2}=0.887\)) for the Area95 parameter. The Bonferroni post-hoc analysis revealed significantly lower (\(p<0.001\)) values for the Eggball (BS: 0.473 \(\pm\) 0.189 cm\({}^{2}\); US: 3.049 \(\pm\) 1.329 cm\({}^{2}\)) with respect to Gym53 (BS: 0.674 \(\pm\) 0.180 cm\({}^{2}\); US: 4.857 \(\pm\) 1.938 cm\({}^{2}\)) and Gym65 (BS: 0.682 \(\pm\) 0.280 cm\({}^{2}\); US: 5.695 \(\pm\) 1.964 cm\({}^{2}\)). Table 1 shows the one-way ANOVA results for the Area95 (\(p<0.001\); \(\eta_{\text{p}}\)\({}^{2}=0.494\)) and Unit Path (\(p<0.001\); \(\eta_{\text{p}}\)\({}^{2}=0.516\)) in the EX condition. The post-hoc analysis showed a statistically significant difference (\(p<0.001\)) between Gym53 and Gym65 for the Area95. Moreover, significantly higher values for the Unit Path were detected in the Gym65 condition with respect to Eggball (\(p<0.01\)) and Gym53 (\(p<0.01\)).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Gym53** & **Gym65** & **Eggball** \\ \hline Area95 (cm\({}^{2}\)) & 31.86 \(\pm\) 13.24 & 45.86 \(\pm\) 15.47 \# & 37.68 \(\pm\) 17.43 \\ Unit Path (cm/s) & 10.44 \(\pm\) 1.58 \$ & 11.91 \(\pm\) 1.91 & 10.68 \(\pm\) 2.24 \$ \\ EMG (\(\mu\)V) & 61.77 \(\pm\) 45.41 & 60.76 \(\pm\) 48.41 & 54.11 \(\pm\) 31.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: One-way ANOVA results of the postural balance and EMG parameters for Gym53, Gym65, and Eggball during the EX condition. Data are presented as the mean \(\pm\) standard deviation. \(\#\) significantly different from Gym65 (\(p<0.001\)); \(\$\) significantly different from Gym65 (\(p<0.01\)).
## 3 Results
### Accuracy Analyses of Experimental Results
#### 3.2.5 Effect of the Different Gender of Subjects on Accuracy  
Twenty-four healthy people, seven females, were involved in the study and completed all experimental processes. Next, we compared whether the different gender of subjects affected the accuracy, as shown in Table 12.  
After the independent samples t-test, there are no significant difference between the accuracies of different gender of subjects using components P300 and N2P3 to distinguish except for using component N200 (electrodes Fz, Cz, and Pz). This result implies that accuracy is not affected by the gender of users if the system uses component N2P3 to distinguish EEG output data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Subjects**} & \multicolumn{2}{c}{**N200**} & \multicolumn{2}{c}{**P300**} & \multicolumn{2}{c}{**N2P3**} \\  & **DG** & **SG** & **DG** & **SG** & **DG** & **SG** \\ \hline N09 & 62.00 & 46.00 & 64.00 & 54.00 & 72.00 & 62.00 \\ N10 & 56.00 & 60.00 & 66.00 & 54.00 & 86.00 & 70.00 \\ N11 & 66.00 & 30.00 & 56.00 & 72.00 & 64.00 & 58.00 \\ N12 & 34.00 & 64.00 & 60.00 & 60.00 & 52.00 & 80.00 \\ N13 & 66.00 & 66.00 & 62.00 & 42.00 & 64.00 & 54.00 \\ N14 & 52.00 & 68.00 & 84.00 & 50.00 & 78.00 & 78.00 \\ N15 & 60.00 & 58.00 & 78.00 & 70.00 & 78.00 & 62.00 \\ N16 & 44.00 & 52.00 & 64.00 & 66.00 & 60.00 & 74.00 \\ N17 & 62.00 & 72.00 & 68.00 & 52.00 & 72.00 & 74.00 \\ N18 & 58.00 & 72.00 & 66.00 & 58.00 & 70.00 & 78.00 \\ N19 & 70.00 & 64.00 & 62.00 & 66.00 & 80.00 & 74.00 \\ N20 & 48.00 & 64.00 & 82.00 & 72.00 & 78.00 & 76.00 \\ N21 & 58.00 & 64.00 & 54.00 & 66.00 & 64.00 & 74.00 \\ N22 & 48.00 & 68.00 & 66.00 & 52.00 & 62.00 & 78.00 \\ N23 & 66.00 & 48.00 & 68.00 & 56.00 & 76.00 & 60.00 \\ N24 & 62.00 & 62.00 & 60.00 & 44.00 & 56.00 & 42.00 \\ \hline Average & 56.83 & 60.17 & 63.75 & 57.92 & 69.50 & 68.75 \\ \hline \(t\)-test & \multicolumn{2}{c}{\(p\) = 0.2827} & \multicolumn{2}{c}{\(p\) = 0.0353 *} & \multicolumn{2}{c}{\(p\) = 0.7764} \\ \hline \hline \end{tabular}  
* \(p\) < 0.05.  
\end{table}
Table 10: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**\(\alpha\) = 0.01, N = 480**} \\ \hline  & **Case** & \(T\)**-Value** & \(p\)**-Value** \\ \hline N200 & correct selected R vs. & 1.066 & 0.292 \\  & correct selected L & \multirow{2}{*}{\(-0.639\)} & \multirow{2}{*}{0.525} \\ P300 & correct selected R vs. & & \\  & correct selected L & & \\ N2P3 & correct selected R vs. & & \\  & correct selected L & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: Paired samples t-test results of average accuracies for every trial between correct selected R and correct selected L.
## 4 Parameters Analytical Calculation Model
### Compressor Total Adiabatic Efficiency Calculation Model  
Regarding the compression process, from the enthalpy-entropy diagram presented in Figure 5, the compressor total adiabatic efficiency is determined based on the entropy equality conditions.  
Related to the compressor instrumentation method presented above, and according to the established measurement parameters, the total adiabatic efficiency calculation model is defined by the following steps:  
\[p_{1.t}=p_{0.t}\cdot\sigma_{da.t} \tag{23}\]  
\[\pi_{C.t}=p_{2.t}/p_{1.t} \tag{24}\]  
\[s_{1.t}=s_{a}(T_{1.t})-R_{a}\cdot\ln(p_{1.t}/p_{0}) \tag{25}\]  
\[s_{1.t}=s_{2.t.id}=s_{a}(T_{2.t.id})-R_{a}\cdot\ln(p_{2.t.id}/p_{0}) \tag{26}\]  
\[s_{a}(T_{2.t.id})=s_{1.t}+R_{a}\cdot\ln(p_{2.t.id}/p_{0})\ \ \Rightarrow\ \ T_{2.t.id} \tag{27}\]  
\[l_{C.t.id}=h_{2.t.id}-h_{1.t}=i_{a}(T_{2.t.id})-i_{a}(T_{0}) \tag{28}\]  
\[l_{C.t}=h_{2.t}-h_{1.t}=i_{a}(T_{2.t})-i_{a}(T_{1.t}) \tag{29}\]  
\[\eta_{C.t}=\frac{l_{C.t.id}}{l_{C.t}}\ \ \Rightarrow\ \ \eta_{C.t} \tag{30}\]  
In the case of the second instrumentation method, the model calculation for the compressor total adiabatic efficiency and, implicitly, for the total overall pressure ratio is as follows.  
\[M_{2}=V_{2}/a_{2} \tag{31}\]  
\[a_{2}=\sqrt{k_{a}\cdot R_{a}\cdot T_{2.s}} \tag{32}\]  
Figure 5: Compressor enthalpy–entropy diagram.  
\[V_{2}=M_{a}/(\rho_{2.s}\cdot A_{2}) \tag{33}\]  
\[\rho_{2.s}=p_{2.s}/(R_{a}\cdot T_{2.s}) \tag{34}\]  
From Equations (33) and (34), and taking into account Equations (31) and (32) we obtain the following:  
\[V_{2}=(M_{a}\cdot R_{a}\cdot T_{2.s})/(p_{2.s}\cdot A_{2}) \tag{35}\]  
\[M_{2}=\frac{M_{a}\cdot R_{a}\cdot T_{2.s}}{p_{2.s}\cdot A_{2}}\cdot\frac{1}{ \sqrt{k_{a}\cdot R_{a}\cdot T_{2.s}}} \tag{36}\]  
\[M_{2}{}^{2}=\frac{M_{a}{}^{2}\cdot R_{a}{}^{2}\cdot T_{2.s}{}^{2}}{(p_{2.s} \cdot A_{2})^{2}\cdot k_{a}\cdot R_{a}\cdot T_{2.s}} \tag{37}\]  
\[M_{2}{}^{2}=\left(\frac{M_{a}}{p_{2.s}\cdot A_{2}}\right)^{2}\cdot\frac{R_{a }}{k_{a}}\cdot T_{2.s}=ct\cdot T_{2.s} \tag{38}\]  
\[T_{2.s}\cdot\left(1+\frac{k_{a}(T_{2.t})-1}{2}\cdot M_{2}{}^{2}\right)=T_{2.t} \tag{39}\]  
\[T_{2.s}\cdot\left(1+\frac{k_{a}(T_{2.t})-1}{2}\cdot ct\cdot T_{2.s}\right)=T_ {2.t} \tag{40}\]  
Therefore, in the end, it results in a second-degree equation depending on the static outlet temperature (\(T_{2.s}\)).  
\[\frac{k_{a}(T_{2.t})-1}{2}\cdot ct\cdot T_{2.s}{}^{2}+T_{2.s}-T_{2.t}=0 \tag{41}\]  
\[a\cdot T_{2.s}{}^{2}+b\cdot T_{2.s}+c=0\ \ \Rightarrow\ \ T_{2.s1,2}=\left(-b\pm\sqrt{\Delta}\right)/2\cdot a \tag{42}\]  
\[M_{2}=\sqrt{ct\cdot T_{2.s}}\ \ \Rightarrow\ \ M_{2} \tag{43}\]  
\[p_{2.t}=\left(1+\frac{k_{a}-1}{2}\cdot M_{2}{}^{2}\right)^{(k_{a}/(k_{a}-1))} \cdot p_{2.s} \tag{44}\]  
\[\pi_{C.t}=p_{2.t}/p_{1.t}\ \ \Rightarrow\ \ \pi_{C.t} \tag{45}\]  
Otherwise, in the case of the second method, after determining the outlet total pressure (\(p_{2.t}\)), the model calculation for the total adiabatic efficiency is the same as the model presented for the first method.
## 3 Basic Principles of the Proposed Method
### Kernel Spectral Regression  
Kernel spectral regression (KSR) is a dimensionality reduction method based on manifold learning and subspace [18]. The KSR assumes that the original data is embedded in the low-dimensional manifold of the high-dimensional observation space, and each sample is kept adjacent to it by the manifold learning algorithm, so as to mine the low-dimensional manifold structure contained in the high-dimensional data. KSR only needs to solve a set of regularized least squares problems, which results in huge savings of both time and memory. KSR can make efficient use of label and local  
Figure 2: (**a**) The tiling of the ideal frequency plane by a cone adapted shearlet system. (**b**) The frequency tiles of a discrete nonseparable shearlet transform (DNST) filter corresponding to the first scale. **(c)** The frequency tiles of a DNST filter corresponding to the second scale.  
neighborhood information to discover the intrinsic discriminant structure in the data. The algorithmic procedure is stated below.  
1. Constructing the adjacency graph. Let G denote a graph with \(m\) nodes. The \(i\)-\(th\) node corresponds to the sample \(x_{i}\). If \(x_{i}\) shares the same label with \(x_{j}\), put an edge between nodes \(i\) and \(j\).
2. Choosing the weights: \[w_{ij}=\begin{cases}\frac{1}{\ell_{i}},&\text{if $x_{i}$ and $x_{j}$ both belong to the $k-th$ class}\\ 0&\text{otherwise}\end{cases}\] (5) where \(\text{W}_{ij}\) is the weight of the edge joining vertices \(i\) and \(j\).
3. Responses generation. Find \(y_{0},y_{1},\cdots,y_{c-1}\), the largest \(c\) generalized eigenvectors of eigenproblem. \[\text{W}\mathbf{y}=\lambda\text{D}\mathbf{y}\] (6) where \(D\) is a diagonal matrix whose (\(i\), \(i\))-\(th\) element equals to the sum of the \(i\)-\(th\) column of W, \(c\) is the number of classes.
4. Regularized kernel least squares. Find \(c-1\) vectors \(\alpha_{1,\cdots},\alpha_{c-1}\in R^{m}\). \(\alpha_{k}(k=1,\cdots c-1)\) is the solution the linear equations system. \[(\text{K}+\alpha\text{I})\alpha_{k}=\mathbf{y}_{k}\] (7) where \(K_{ij}=K\big{(}x_{i},x_{j}\big{)}\). It can be easily verified that function \(f(x)=\sum\limits_{i=1}^{m}\alpha_{i}^{k}K(x,x_{i})\) is the solution of the following regularized kernel least square problem: \[\min_{f\in H_{k}}\sum\limits_{i=1}^{m}\big{(}f(x_{i})-y_{i}^{k}\big{)}^{2}+ \alpha\|\ f\ \|_{k}^{2}\] (8)
5. KSR Embedding: Let \(\Theta=[\alpha_{1},\alpha_{2},\cdots,\alpha_{c-1}]\), \(\Theta\) is a \(m\times(c-1)\) transformation matrix. The samples can be embedded into \(c-1\) dimensional subspace by \[x\to z=\Theta^{\text{ T}}K(;x)\] (9) where \(K(;x)=[K(x_{1},x),K(x_{2},x),\cdots,K(x_{m},x)]^{\text{ T}}\).
###### Abstract  
Moving differential and dynamic window moving averaging are simple and well-known signal processing algorithms. However, the most common methods of obtaining sufficient signal-to-noise ratios in distributed acoustic sensing use expensive and precise equipment such as laser sources, photoreceivers, etc., and neural network postprocessing, which results in an unacceptable price of an acoustic monitoring system for potential customers. This paper presents the distributed fiber-optic acoustic sensors data processing and noise suppression techniques applied both to raw data (spatial and temporal amplitude distributions) and to spectra obtained after the Fourier transform. The performance of algorithms' individual parts in processing distributed acoustic sensor's data obtained in laboratory conditions for an optical fiber subjected to various dynamic impact events is studied. A comparative analysis of these parts' efficiency was carried out, and for each type of impact event, the most beneficial combinations were identified. The feasibility of existing noise reduction techniques performance improvement is proposed and tested. Presented algorithms are undemanding for computation resources and provide the signal-to-noise ratio enhancement of up to 13.1 dB. Thus, they can be useful in areas requiring the distributed acoustic monitoring systems' cost reduction as maintaining acceptable performance while allowing the use of cheaper hardware.  
distributed acoustic sensing (DAS); denoising; noise reduction; optical fiber sensors; signal-to-noise ratio (SNR); data processing
## 1 Introduction
However, computer vision techniques, especially deep-learning-based object detection, require a large amount of proper task-oriented high-quality data for training and tuning to achieve the desired performance [24]. The changing lighting conditions and jerking of the camera on mobile robots can also affect the performance of deep-learning-based object detection models [25], which makes it difficult to implement solely computer-vision-based techniques in real-world manufacturing applications. Considering the aforementioned challenges, this paper has the following aims:  
* This paper aims to develop a vision-Lidar data fusion method for mobile robots to achieve accurate autonomous docking and recharging in a manufacturing environment. * This paper contributes to the transition of state-of-the-art real-time object detection methods from general public datasets to real-world manufacturing tasks by combining deep-learning-based techniques to identify charging stations in a complex manufacturing environment; we then use a Lidar-based approach to localize the detected wireless charger and dock the mobile robot to it for recharging. * An indoor manufacturing environment with an enclosed space where a wireless charging station is situated is considered for the implementation of the docking procedure. The proposed method is analyzed and discussed based on the autonomous docking and recharging of a Husky robot made by Clearpath Robotics. * A YOLOv7-based method is used to detect the charging station for the robot to navigate to the desired location. The process of planning a path to the charger can be achieved with waypoints using the SLAM method, which is not discussed in this paper. Afterward, the Lidar sensor is used, along with the detected results from the camera,to determine the distance from the charger and side wall to achieve an accurate pose estimation and then successfully dock the robot to the charging station. The proposed method can be easily adapted to different types and numbers of wireless chargers in a manufacturing environment. The distance data between the Lidar and the camera can be calibrated to achieve accurate alignment and pose estimation. This paper is structured as follows: The related work is presented in Section 2; Section 3 explains the proposed method in detail; Section 4 contains the results; and Section 5 comprises the discussion and conclusions of this paper.