esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spectrum Correction Method Based on Optimizing Turbulence Intensity
## 3 Results and Discussion
### Power Density Spectrum and Coherence Function Selection  
Kaimal spectrum is widely used to describe the spectral density during wind speed [21], and its simulation results are more conservative when applied to the fatigue load design of wind turbines. The function expression of Kaimal spectrum in IEC61400-1 4 th Edition [18] is as below, \(X_{k}=L_{k}/\overline{U}\) represents the ratio of the turbulence integral length to average wind speed (\(k=1,2,3\) represents the longitudinal, transverse and vertical component spectra, respectively):  
\[S(f)={\sigma_{k}}^{2}\frac{4X_{k}}{\left(1+6 fX_{k}\right)^{5/3}} \tag{18}\]  
IEC61400-1 uses an exponential form of coherence function, which is expressed as follows:  
\[Coh(r,f)=exp[-12((\frac{fr}{u})^{2}+\left(0.12\frac{r}{L_{k}})^{2})^{0.5}] \tag{19}\]  
In Equation (18), according to IEC61400-1, the parameters of the longitudinal component are calculated as follows:  
(1) Wind speed definition standard deviation \(\sigma_{1}\):  
\[\sigma_{1}=I_{ref}\big{(}0.75\overline{U}+b\big{)};\ b=5.6m/s \tag{20}\]  
where, \(I_{ref}=0.14\), determined by 15 MW wind turbine design grade Class IB; \(\overline{U}=10.59\ m/s\).  
(2) Integral scale parameter \(L_{1}\):  
\[L_{1}=8.1\Lambda_{1} \tag{21}\]  
The longitudinal integral scale parameters at the height of the hub \(Z\) are:  
\[\Lambda_{1}=\left\{\begin{array}{ll}0.7Z,(Z\leq 60\ m)\\ 42\ m,(Z\geq 60\ m)\end{array}\right. \tag{22}\]  
The horizontal and vertical components of turbulence spectrum parameters simulating a three-dimensional fluctuating wind speed are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Main Parameters** & \(\sigma_{k}\) & \(L_{k}\) \\ \hline the transverse (k = 2) & \(0.8\sigma_{1}\) & \(2.7\Lambda_{1}\) \\ the vertical (k = 3) & \(0.5\sigma_{1}\) & \(0.66\Lambda_{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Turbulence spectrum parameters of horizontal and vertical components.  
\begin{table}
\begin{tabular}{c c} \hline \hline capacity & 15 MW \\ wind turbine diameter \(D\) & 240 m \\ hub height \(Z\) & 150 m \\ the reference mean wind speed \(V_{ref}\) & 10.59 m/s \\ reference turbulence intensity \(I_{ref}\) & 0.14 \\ \hline \hline \end{tabular}
\end{table}
Table 2: 15 WM wind turbine parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **One-Dimensional** & **Two-Dimensional Space** \\ \hline number of simulated points \(m\) & 40 & 49 \\ simulated point spacing \(dr\) & \(6(k-j)\) & Calculate according to the \\ frequency sampling number \(N\) & \(2^{12}\) & \(2^{12}\) \\ analog time points \(M\) & \(2^{13}\) & \(2^{13}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Frequency domain simulation parameters.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### Rock-Fall Detection Process  
The rock-fall detection process occurs in three stages, filtering the image frame, background subtraction, and performing frame manipulation, as in Figure 6. Firstly, the blurring Gaussian filter was used to cancel out the noise from captured images. This noise was caused by swing weeds and tiny drops of rain. Secondly, the adaptive Gaussian mixture model was used to detect moving rocks in a sequence of a video frame that suffered from variations in the background illumination due to time of day and weather conditions.  
The model values a particular pixel as a mixture of Gaussians and performs two tasks: learning the background model and classifying pixels as background or foreground. Equation (4) represents the background model.  
\[\hat{P}\left(\overrightarrow{x}\left|\chi_{T}\right.,BG\right)=\sum_{m=1}^{M} \hat{\pi}_{m}\mathcal{N}\left(\overrightarrow{x};\overrightarrow{\mu}_{m}, \vartheta_{m}^{2}I\right) \tag{4}\]  
where \(\hat{P}\left(\overrightarrow{x}\left|\chi_{T}\right.,BG\right)\) is the background estimated, \(\overrightarrow{x}\) is value of a pixel at time t in grayscale color space, \(\chi_{T}\) is a training set, \(M\) is the number of the Gaussian components, \(\pi_{m}\) is a weight that describes how much of the data belong to the \(m\) component of the GMM, \(\overleftarrow{\mu}_{m}\) is estimates of the means, \(\vartheta_{m}^{2}\) is estimates of the variances, and \(I\) is the identity matrix.  
Figure 5: Perspective view of a mountain in image plane (**a**). Perspective corrected image in world plane (**b**).  
Figure 6: Rock-fall detection process.  
The moving rocks were considered as foreground clusters of pixels. A Bayesian decision was introduced to classify pixels as background (BG) or foreground (FG), which is comparing a background estimated value of a particular pixel with the threshold; if it is greater than the threshold, it is considered a background, otherwise, it is considered a foreground [49]. To track a continuous change in the image of the scene, the model updates the training data set at every reasonable period T and re-estimates the background model value. After the foreground pixel cluster is classified and segmented from a background, holes appear in the foreground due to imperfection introduced during segmentation. The dilation process was used to fill in those holes, and it is one of the morphological image processing tools which adds pixels to the boundaries of objects in an image.
## 1 Introduction  
Wire ropes are one of the most common structural members used for transmitting tensile forces. With high flexibility and considerable strength, wire ropes are in widespread use, especially in marine environments. Whether for handling cargo, mooring, or rigging, wire ropes can be found at shipyards, ports, ships, and offshore facilities. Considering the high loads they must maintain, and the harsh environmental and operational conditions coupled with pre-existing defects and/or imperfections in manufacturing that can serve as crack initiation points under fatigue loading conditions, it is easy to understand the need for the proper characterization of stress distribution and the remaining life of wire ropes. If not properly analyzed and designed, the collapse of wire ropes could have disastrous consequences [1; 2; 3].  
There has been a considerable amount of effort in describing the behavior of ropes and strands. Most analytical solutions generally exclude frictional and contact effects [4] and they are based on equilibrium equations related to the boundary conditions of actual problems. If trying to consider friction and contact, plus some other operational conditions, numerical techniques such as finite element (FE) analysis are usually employed.  
Some of the previous work on numerical simulations of ropes and strands include FE analysis of simple straight strands [5] or the development of a model for predicting the mechanical response of an independent wire rope core [6]. More recent research concentrates on the FE modeling of wire ropes (cables) with the inclusion of fatigue effects that take into account multiscale effects [7], the FE modeling of multi-strand wire ropes exposed to axial and torsion loads [8], or a combination of experimental analysis and finite element modeling to understand the behavior of a rope [9]. Furthermore, input parameters for numerical analysis can be introduced from experimental investigations of wire ropes, such as sea trials for ropes in marine applications [10].  
However, none of the reviewed references take into consideration the reduction of wire rope cross-section diameters caused by damage or the effect on the remaining service life. One of the advantages of wire ropes is the possibility of continued operation, even if some of the strands are broken, i.e., the rope diameter is reduced. This paper uses FE analysis to perform stress and fatigue analysis of different design types of wire ropes. The size of the wire rope cross section is varied in order to simulate progressive damage to wires, and the remaining service life is numerically predicted. The aim is to provide a better understanding of the mechanical behavior of the damaged wire ropes under various conditions and hence to be able to choose an appropriate wire rope design from engineering and economic points of view.
## 3 Results and Discussion
### DPPH Radical Scavenging Activity
#### 3.3.1 No Treatment BCSO and TQ Probe  
The unheated BCSO was shown an IC50 value equal to 4.13 \(\pm\) 0.36 mg/mL (Table 2), which was very close to those reported in the literature and simultaneously relatively lower compared to other oils [18]. The low IC50 level indicates strong antioxidant activity; therefore, the obtained IC50 value confirmed the high radical scavenging ability of BCSO. We obtained for TQ IC50 a value equal to 72.31 \(\pm\) 0.26 mg/mL, which indicated its lower radical-scavenging capacity than control samples, Trolox, or \(\alpha\)-tocopherol (Table 2). Other results indicated that TQ also possessed a significantly lower antioxidant potential compared to gallic acid, ascorbic acid, or some others volatile components of BSCO [35; 42; 43; 44]. Moreover, we can find that the antiradical properties of antioxidants could depend on the solvent used during the experiment [45]. Therefore, the DPPH test was also made for the methanolic extract of BCSO, because methanol is reported to be the highest extraction solvent of TQ and has the highest free radical scavenging activity compared to other extraction solvents [35]. However, in our case, the higher IC50 value found for the BCSO methanolic extract reflected its weaker antioxidant activity against DPPH radicals. This difference can be explained by the presence in the ethyl acetate oil solution of additional lipophilic components exhibiting antioxidant properties, which the methanolic extract did not contain. The antioxidant properties of the lipophilic part of the oils were investigated in [46].
## 4 Conclusions  
The increased implementation of DSM applications to control EV charging and ES systems will significantly change the load characteristics of substation power transformers when combined with the effects of PV systems. The load profile will be flatter and possibly possess higher harmonic distortion levels than the historical load profile. The new characteristics will cause excessive heating within the unit, which results in increased aging and risk of permanent damage. If capacity limits were developed utilizing historical load profiles with cyclic characteristics, then the units will experience higher than anticipated temperatures for loads at or above the nameplate ratings.  
Harmonic distortion may also contribute to excessive heating, which can result in excessive aging and/or damage. Even at low penetration levels of PV, EV charging, and ES applications, current harmonic distortion can exceed 10%.  
To establish new capacity limits that reflect a flatter load profile with a higher harmonic content, the modeling methods described in IEEE Std. C57.91-2011 and Schijndel, Wouters, and Wetzer can be combined to identify the new peak demand limits [2; 10]. Options for reducing the effects caused by excessive internal heating include derating existing capacity limits for units still in service and possibly augmenting the design of new units by reducing their losses and increasing their solid dielectric characteristics.  
[PERSON] led the research effort, coordinated the field testing with Xeel Energy, and developed testing parameters. [PERSON] and [PERSON] developed the thermal model of the 50 MVA SPX unit utilizing Matlab. [PERSON] also performed the analysis regarding the impact of design variations. [PERSON] was responsible for quality control of the project and verifying data results. All authors contributed to the development of the manuscript.  
The research was sponsored by California State University, Fresno.  
The authors declare no conflicts of interest.
# Synthetical Modal Parameters Identification Method of Damped Oscillation Signals in Power System
## 3 Validation of SMPI with Simulated Signals
### Validation of Identified Results  
Substituting the matching results in Table 4 into the classic decaying oscillation signal mathematical equation [30; 31], the governing equation of three components in DLFO is  
\[X_{i}=A_{i}e^{(-2\pi f_{i}\xi_{i}\Delta t)}\text{cos}(\sqrt{1-\xi_{i}^{2}}\cdot 2 \pi f_{i}\Delta t+\theta_{i})i=1,2,3 \tag{26}\]  
In which the subscript \(i\) indicates the \(i\)-th dominant mode and \(X_{i}\) is the \(i\)-th dominant component.  
In light of Equation (26), the three components of DLFO signal A are reconstructed as Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**True Modes** & **Frequency (Hz)** & **Amplitude (\(\upmu\)m)** & **Damping Ratio** & **Phase Angle (Rad)** \\ \hline
1 & 0.3318 & 2.0785 & 0.0589 & 1.5942 \\
2 & 0.7807 & 3.9976 & 0.0258 & 1.6011 \\
3 & 1.0072 & 5.7695 & 0.1119 & \(-\)1.5118 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Modal parameters matching results of signal A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Nodes** & **Frequency (Hz)** & **Amplitude (\(\upmu\)m)** & **Phase Angle (Rad)** \\ \hline
1 & 0.3305 & 2.0785 & 1.5942 \\
2 & 0.7808 & 3.9976 & 1.6011 \\
3 & 1.0069 & 5.7695 & \(-\)1.5118 \\
4 & 1.9077 & 0.0179 & \(-\)0.1935 \\
5 & 2.4386 & 0.0354 & \(-\)0.6599 \\ � & � & � & � \\
99 & 49.4514 & 0.0014 & \(-\)2.7861 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Modal parameters of signal A identified with Prony approach.  
The three signal components are superimposed to obtain a synthesized signal after SMPI as Equation (27).  
\[\mathbf{Y}=\sum\mathbf{X}_{\mathbf{mi}} \tag{27}\]  
where \(Y\) is the synthesized signal with SMPI.  
Subsequently, the comparison between \(Y\) and \(c_{EMD}(t)\) is shown in Figure 8. And the comparison of estimated modal parameters of SMPI with true modal parameters is described in Table 5.  
In light of Table 5, the value of estimated modal parameters is very close to that of true modal parameters, and the relative errors between them are comparatively small. The frequencies calculated by SMPI are all within 1% relative error. And the relative error of damping ratios and phase angles are less than 3% and 4%, respectively. As for the relative error of amplitude, the smallest is less than 0.1%, and the biggest one is not more than 4%.  
As Figure 8, the curve of signal \(Y\) synthesized with the identified results of SMPI is found in good coincidence with that of \(c_{EMD}(t)\), whose dominant modal parameters have been extracted successfully.  
Figure 8: Comparison of the signal \(Y\) and \(c_{EMD}(t)\).  
Figure 7: Dominant components of signal A identified by SMPI.
# Sep-RefineNet: A Deinterleaving Method for Radar Signals Based on Semantic Segmentation
## 5 Conclusions  
Aiming at the problems of poor anti-noise ability and over-reliance on manual parameters of traditional methods of radar signals deinterleaving, this paper proposes a method of combining encoding FCM and semantic segmentation network named Sep-RefineNet. In this paper, the FCM is used to extract the PRI features in the pulse stream, and then, the semantic segmentation method is used to identify the texture, shape, and structural features of the FCM. Finally, the deinterleaving task is completed intelligently by position decoding. Experiments show the effectiveness and anti-noise ability of the proposed method, which can accurately complete the deinterleaving, avoiding the traditional process of threshold judgment and pulse sequence search. When the aliasing pulse rate and missing pulse rate are 20%, the F1-Score of our method can still reach 0.9. The future work will consider how to increase the attention to the diagonal elements of FCM for better completion of deinterleaving. In addition, in the case of few-shot, it is possible to optimize the model and improve the practicability.  
Conceptualization, W.R., Z.Y., W.C., X.L. and Y.M.; investigation and analysis, X.L., W.R. and Z.Y.; software, Y.M.; validation, Y.M. and W.R.; writing--original draft preparation, Y.M. and W.R.; writing--review and editing, Y.M. All authors have read and agreed to the published version of the manuscript.  
The present is funded by the National Natural Science Foundation of China (no. 62131019).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
# Ozone Activation on TiO\({}_{2}\) Studied by IR Spectroscopy and Quantum Chemistry
## 3 Results
### Results of Calculations
As to the geometry of ozone adsorbed on Ti\({}_{20}\)O\({}_{40}\) cluster, the valence angle \(\alpha\) decreased slightly as compared with free ozone molecules, like in the case of TiO\({}_{2}\) cluster, and the distance between the O\({}^{1}\) and O\({}^{2}\) atoms also increased, while that between the O\({}^{2}\) and O\({}^{3}\) atoms became shorter (the numbering of O atoms always starts from that bound to Ti). The geometry of ozone molecules in monodentate complexes with four- and five-coordinated sites of Ti\({}_{20}\)O\({}_{40}\) cluster was almost the same in bond lengths and valence angles, but as expected, the distance from the TiO\({}_{2}\) surface to the nearest O\({}^{1}\) ozone atom was shorter in the case of the stronger four-coordinated Ti\({}^{4+}\)center. Similar trends were observed for bidentate complexes; however, despite the shorter distance from the TiO\({}_{2}\) surface, the bidentates lost to the monodentates in terms of adsorption energy, and the bidentate complex with the five-coordinated titanium atom was more strongly bound (by about 2 kJ/mol) than that with the four-coordinated one. We calculated the frequencies of all the isotopomers of mono- and bidentate complexes of ozone on four- and five-coordinated Ti atoms; the results are presented in Table 3. These data corrected by the corresponding factors were used for a comparison of the observed and calculated splitting (Figure 1). A mutual arrangement of band positions of \(\
u_{1}\) and \(\
u_{3}\) modes is shown in Figure 7. One can see that for all four considered structures of the Ti\({}_{20}\)O\({}_{40}\) cluster the \(\
u_{1}\) band of isotopically mixed ozone was split into three groups of lines,  
Figure 6: The optimized geometry of the monodentate (**I**) and bidentate (**II**) ozone complexes on the 4-coordinated Ti\({}_{4+}\) ions, and of the monodentate (**III**) and bidentate (**IV**) complexes with 5-coordinated Ti\({}_{4+}\) ions of the anatase cluster Ti\({}_{20}\)O\({}_{40}\). as in the case of the complex with the molecular TiO\({}_{2}\) cluster. Similarly, the band of v\({}_{3}\) vibration was split into four pairs of close lines 0.2-2.5 cm\({}^{-1}\) apart.
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 2 Proposed Method
### Parallel Acceleration of Computational Layers in CNN
#### 2.1.3 Data Remapping for the Convolutional Layer Computation Kernel
The data within the layer is divided according to the pixel location of the data, and each part of the data is divided by a multiple of the total length and width to reduce the number of cycles. Parallel acceleration of computational layers in CNN decomposes the computational process of CNN models in three dimensions: input channel, output channel, and pixel. Parallel acceleration of these dimensions significantly improves the efficiency of the CNN algorithm. Traditional methods focus on the computational level of convolutional operations and do not improve enough on the structural level of the algorithm.
## 2 Materials and Methods
### Vibration Transfer Path Analysis (OTPA) Method
#### 2.4.2 Improved OTPA Method  
In practical applications, however, the input matrix \(\mathbf{X}(\omega)\) is often ill-conditioned (i.e., its condition number is excessively large) due to potential correlations among the multi-source excitation signals or the presence of noise interference. Directly inverting this matrix can make the results highly sensitive to minor errors, thereby compromising the accuracy and stability of the transfer function estimation. To address the challenges of modeling and analyzing such complex MIMO systems, various engineering fields have explored advanced solutions. For instance, in the field of power electronics control, researchers have adopted data-driven methods like machine learning to replace traditional physical models, aiming to cope with system nonlinearities and uncertainties [54]. To overcome the challenges encountered in this study, an improved OTPA method based on Singular Value Decomposition (SVD) was employed. SVD is a mathematical tool that decomposes an arbitrary matrix into the product of three matrices:  
\[\mathbf{X}=\mathbf{U}\mathbf{\Sigma}\mathbf{V}^{\mathbf{T}} \tag{7}\]  
In this equation, \(\mathbf{U}\) and \(\mathbf{V}\) are unitary matrices, and \(\mathbf{\Sigma}\) is a diagonal matrix whose diagonal elements are the singular values \(\sigma_{i}\), arranged in descending order. SVD can reveal the intrinsic structure of a matrix and can be used for data dimensionality reduction and noise filtering.  
To further enhance the stability and accuracy of the OTPA calculation, this study introduces a proportional truncation technique for singular values. The core idea is to set to zero any singular values in the matrix \(\Sigma\) that are smaller than a certain percentage of the  
Figure 6: Schematic diagram of the measurement point distribution on the chassis frame( The numbered points correspond to the physical locations as follows: 1—Left Front, 2—Left Middle, 3—Left Rear, 4—Right Rear, 5—Right Middle, and 6—Right Front).  
largest singular value (a threshold of 0.4% was used in this study). This truncation operation effectively filters out noise and insignificant components from the data while preserving the most dominant energy information in the signal. Consequently, it significantly reduces the condition number of the matrix, thereby improving the stability of the solution.  
The specific process of this improved method is illustrated in Figure 7. By setting to zero the singular values that fall below a predefined threshold, we can adaptively select the optimal number of singular values for truncation. This approach avoids the potential information loss associated with fixed truncation, thereby ensuring the effectiveness of the OTPA model's optimization. Based on this, the contribution of each excitation source to the target point can be calculated. For a specific transfer path \(\mathbf{j}\), its contribution can be expressed as:  
\[\mathbf{Y}_{ij}=\mathbf{X}_{i}\mathbf{H}_{j} \tag{8}\]