esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory of Cable Dynamics Analysis
### Description of the Single Cable Boundary Constraint  
Since the transverse and vertical directions of the cable are not coupled with each other, the influence of the vertical support constraint on the transverse vibration of the cable can be neglected when studying the transverse vibration of a single cable. In the transverse boundary conditions, it can be assumed that the transverse bearing displacement stiffness \(k_{A}{}^{a}\) and \(k_{B}{}^{a}\rightarrow\infty\) at both ends, and the corner stiffness \(k_{A}{}^{\theta}\) and \(k_{B}{}^{\theta}\) can be arbitrary values, that is, it is the composite boundary condition considering the actual situation, then Equation (14) can be transformed into the following equation:  
\[\frac{EI}{l^{3}}\left(\begin{array}{cc}k_{22}-\frac{l^{3}}{EI}k_{A}{}^{\theta}& k_{24}\\ k_{24}&k_{22}-\frac{l^{3}}{EI}k_{B}{}^{\theta}\end{array}\right)\left\{\begin{array} []{c}\theta_{a}l\\ \theta_{b}l\end{array}\right\}=\left\{\begin{array}{c}0\\ 0\end{array}\right\} \tag{17}\]  
where, the expressions \(k_{22}\), \(k_{24}\) and \(\Delta\) are shown in the Appendix A.  
Through the analysis of the calculation examples in the literature [13], it is reliable to propose that the tension-bending ratio \(\xi=l\sqrt{H/EI}\) is used to measure the influence of the boundary conditions on the dynamic characteristics of the cable. When the tension-bending ratio is \(\xi\geq 45\), the influence of the boundary conditions at both ends of the cable on the cable frequency can be neglected; however, when the tension-bending ratio is \(\xi\leq 45\), the influence of the actual boundary conditions must be considered for the calculation of the cable. Here, we only discuss and analyze the single cable boundary conditions for the tension-bend ratio of \(\xi\leq 45\).  
According to the relative values between the flexural stiffness of the cable and the boundary conditions, the dimensionless boundary condition coefficients \(v_{A}\) and \(v_{B}\) are introduced, where:  
\[\left\{\begin{array}{c}v_{A}=\frac{k_{A}{}^{\theta}}{k_{A}{}^{\theta}+k^{2} EI/l}\\ v_{B}=\frac{k_{B}{}^{\theta}}{k_{B}{}^{\theta}+k^{2}EI/l}\end{array}\right. \tag{18}\]  
In practice, the boundary condition of the cable is between simple and solid support, i.e., \(v_{A}\) and \(v_{B}\) can be any value in the interval [0, 1].  
Where, the corner stiffness can be expressed by the boundary condition coefficient from Equation (18), i.e.,  
\[\left\{\begin{array}{c}k_{A}{}^{\theta}=\frac{\zeta EIv_{A}}{l(1-v_{A})}\\ k_{B}{}^{\theta}=\frac{\zeta EIv_{B}}{l(1-v_{B})}\end{array}\right. \tag{19}\]  
Substituting Equation (19) into the frequency Equation (17) yields the cable frequency equation considering the boundary condition coefficients.  
\[\frac{EI}{l^{3}}\left(\begin{array}{cc}k_{22}-\frac{v_{A}}{1-v_{A}}\xi l^{2 }&k_{24}\\ k_{24}&k_{22}-\frac{v_{B}}{1-v_{B}}\xi l^{2}\end{array}\right)\left\{\begin{array} []{c}\theta_{a}l\\ \theta_{b}l\end{array}\right\}=\left\{\begin{array}{c}0\\ 0\end{array}\right\} \tag{20}\]
###### Abstract  
Terahertz (THz), as an advanced spectral technology, has unique absorption characteristics for most biological macromolecules. In this work, the theoretical fundamentals for the application of THz time-domain spectroscopy (THz-TDS) to molecular characterization and fingerprint peak detection of three plant growth regulators (PGRs), including 2,4-Dichlorophenoxyacetic acid (2,4-D), forchlorofenuron (CPPU) and indole-3-acetic acid (IAA) were researched. Meanwhile, the effects of eight types of window functions on THz spectra were studied when converting time-domain spectra into frequency-domain spectra by Fourier transform. Based on the optimal window function, the THz absorption coefficient and refractive index of PGRs in frequencies of 0.2-3 THz were extracted. The molecule structure and vibration mode of three PGR samples were simulated by using density functional theory (DFT). The results showed that the three PGRs had different fingerprint peaks. Characteristic absorption and anomalous dispersion of 2,4-D were found at 1.35, 1.57 and 2.67 THz, those of CPPU were found at 1.77 and 2.44 THz, and the absorption peak of IAA was located at 2.5 THz. The absorption peaks obtained from THz spectra were identified according to the theoretical calculation results of DFT. These fingerprint peaks in THz spectra were generated by the interior stretching vibration and external deformation vibration of molecular groups. The experimental results revealed the feasibility of identifying PGRs species and detecting residues using THz-TDS.  
Keywords:terahertz time-domain spectroscopy; window function; plant growth regulators; absorption coefficient; refractive index; density functional theory +
Footnote †: journal: Applied Physics Letters
## 6 Conclusions and Perspectives  
This study proposed a methodology for the probabilistic characterization of the input parameters of a simple chlorination model including an acceleration factor for the diffusion coefficient of chloride in concrete. On the basis of the results obtained, the following conclusions are drawn:  
1. The chloride content at different depths increases when the beams are loaded and for larger loading intensity.
2. The methodology, based on the Bayesian network approach, allows integrating data from experimental trials to determine the parameters of a model. It was also useful to separate the cracking effects from the diffusion of chloride ions mechanism through an acceleration factor.
3. The characterized means of the parameters \(\mathrm{C_{s}}\) and \(\mathrm{D}\), close to the experimental values, show the usefulness of the Bayesian approach for this type of study.
4. The acceleration factor increases with the intensity of the load and is higher for the cyclic load, which resulted in larger width cracks on the beams.
5. Static and cyclic loads reduced the corrosion initiation time by 1.1 and 1.31 years, respectively, compared to the unloaded case.  
In addition, one limitation of this study is the use of a simple chlorination model (Collepardi model) that does not take into account several parameters such as concrete aging and environmental conditions. Further work should consider chlorination models more representative of the chloride diffusion process. Another aspect to improve the methodology is to consider mechanics-based cracking models and to combine it with chlorination models. With these improvements, the acceleration factor could take into account crack characteristics (width, length, density, etc.), crack initiation and propagation mechanisms, and loading in a comprehensive way.  
Conceptualization, H.M.I, E.B.-A. and R.M.P; Data curation, X.-H.W; Funding acquisition, E.B.-A.; Methodology, H.M.I., and E.B.-A.; Software, H.M.I.; Supervision, E.B.-A., R.M.P. and S.E.A.; Validation, E.B.-A., R.M.P., S.E.A. and X.-H.W.; Writing--original draft, H.M.I.; Writing--review and editing, H.M.I, E.B.-A., R.M.P., S.E.A. and X.-H.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Regional Council of 'Pays de la Loire' within the framework of the BUENO 2018-2021 research program (Durable Concrete for Offshore Wind Turbines).  
The authors declare no conflict of interest.  
Figure 14: Probability of initiation corrosion.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 4 Discussion  
### Advantages of the Micro-Capsule Network Model  
Addressing the challenges of small sample sizes, limited individual sample sizes, complex neural network architectures, and subtle epileptic features, this study proposes a micro-capsule network model for epilepsy recognition tasks. Compared with existing capsule network models, the proposed approach demonstrates superior performance for small-sample epilepsy classification, particularly on datasets with limited sample sizes.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Category** & **Acc** & **Spec** & **Sen** \\ \hline AB vs. E & 99.94 \(\pm\) 0.07\% & 99.86 \(\pm\) 0.14\% & 99.98 \(\pm\) 0.04\% \\ C vs. E & 99.49 \(\pm\) 0.41\% & 99.62 \(\pm\) 0.36\% & 99.67 \(\pm\) 0.22\% \\ D vs. E & 99.62 \(\pm\) 0.02\% & 99.53 \(\pm\) 0.16\% & 99.71 \(\pm\) 0.02\% \\ A vs. C vs. E & 99.06 \(\pm\) 0.22\% & 98.59 \(\pm\) 0.003\% & 98.59 \(\pm\) 0.003\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: The performance of the micro-capsule network model through paired EEG dataset cases for epileptic detection.  
Figure 5: The training iteration times and accuracy of the micro-capsule networks for different dataset cases of EEG epileptic detection.  
Results indicate that the model significantly improves performance when applied to small-sample epileptic data. Traditional neural networks for epileptic detection employ deeper architectures to achieve higher recognition rates, which increases computational complexity. In contrast, the novel micro-capsule-based method offers distinct advantages:  
The proposed method eliminates the need to reconstruct epileptic EEG signals in the capsule network layer. Additionally, its fully connected structure minimizes network connections, reducing both layer count and architectural complexity.  
To enhance feature extraction, the method leverages dimensionality enhancement techniques for EEG signal preprocessing. Combined with convolutional neural network layers, it preserves spatial relationships in subtle epileptic EEG signals, improving feature quality.  
By representing EEG features as local signal vectors and employing dynamic routing with fewer training parameters, the model reduces overfitting and accelerates neural network convergence.
## 5 Case Study
Of the two NN-based estimation methods, the proposed DR approach provides better optimal solutions when compared with that of the NN-based estimation method that uses the conventional log-sigmoid transfer function because the variance obtained from the proposed DR approach is almost zero in this particular example. Therefore, in terms of EQL, the best optimal solutions can be obtained using the NN-based DR estimation method. The criterion spaces (squared bias vs. variance) of the three estimated functions by using the conventional LSM approach based on RSM, the N-based estimation method using the log-sigmoid transfer function, and the proposed DR approach are illustrated in Figure 7. A green cross marks the optimal settings in each figure. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Estimation** \\ **Model** \\ \end{tabular} } & \multicolumn{2}{c}{**Optimal Factor Settings**} & \multicolumn{2}{c}{**Process**} & \multicolumn{2}{c}{**Process**} & \multicolumn{2}{c}{**Process**} & \multirow{2}{*}{**EQL**} \\ \cline{2-2} \cline{5-8}  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & & & & \\ \hline LSM & 1.000 & 0.072 & \(-\)0.250 & 494.672 & 5.328 & 1977.533 & 2005.917 \\ Feed-forward NN & \(-\)0.067 & 0.170 & 1.000 & 499.321 & 0.679 & 397.507 & 397.967 \\ Proposed method & 0.133 & \(-\)0.014 & 0.096 & 499.885 & 0.115 & 103.900 & 103.913 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative results of various methods. Figure 6: Estimated process mean and standard deviation response functions from the feed-forward NN approach with the proposed transfer functions: (**a**) mean (\(R^{2}=67.17\%\)); (**b**) standard deviation (\(R^{2}=53.49\%\)). Figure 7: Criterion spaces of the three estimated functions: (**a**) LSM based on RSM; (**b**) log-sigmoid transfer function-based NN; (**c**) proposed NN-based DR method.
###### Abstract  
This study aims to develop a modularized simulation system to estimate dynamic responses of floating Offshore Wind Turbines (OWTs) based on the concepts of spar buoy and Tension Leg Platform (TLP) corresponding with two typical mooring lines. The modular system consists of the hydrodynamic simulator based the Cummins time domain equation, the Boundary Element Method (BEM) solver based on the 3D source distribution method, and an open-source visualization software ParaView to analyze the interaction between floating OWTs and waves. In order to realize the effects of mooring loads on the floating OWTs, the stiffness and damping matrices are applied to the quasi-static mooring system. The Response Amplitude Operators (RAOs) are compared between our predicted results and other published data to verify the modularized simulation system and understand the influence of mooring load on the motion responses in regular or irregular waves. It is also demonstrated that the quasi-static mooring system is applicable to different types of mooring lines as well as determining real-time motion responses. Eventually, wave load components at the resonance frequencies of different motion modes for selected floating OWTs would be present in the time domain.  
floating offshore wind turbine; mooring system; RAO; panel method; modular system; offshore wind +
Footnote †: journal: Applied Sciences
## 3 Test Results and Analysis
### Pile Top Load-Settlement Relationship
As shown in Figure 12a, in the context of pile tip grouting, the value of pile lateral friction resistance increases as the distance to the pile tip decreases, reaching a range of 13.5 kPa to 42.8 kPa within a depth of 1.50 m to 1.75 m. According to the reinforcement effect of cement grout on pile skin friction, the diffusion range of cement grout is independent of grouting volume and ranges from a depth of 1.25 m to 1.75 m. However, the lateral friction resistance of the upper part of each pile with tip grouting is similar to that of N00 and has little effect on the bearing capacity. As shown in Figure 12b, the grout diffusion range of the pile side grouting group ranges from 0.50 m to 1.75 m in depth, which is more comprehensive than the coverage of pile tip grouting. The reinforcement range of grout on the pile side below the outlet is obviously larger than that above the outlet (the location of the pile side outlet is 0.875 m deep), which indicates that the downward  
Figure 11: Ultimate bearing capacity and pile top settlement with different grouting pressures. diffusion range of grout is greater than that of the upward diffusion. The reason for this phenomenon is that the setting of pile-soil unloading makes the grout preferentially split downward under the action of gravity. With the increase in grouting volume from 1 L to 3 L, the maximum value of pile shaft resistance reaches 32.6-54.2 kPa, all of which are located at the pile side grouting outlet. The pile lateral friction resistance decreases with the increase in the distance from the outlet. As depicted in Figure 12c, the pile lateral friction resistance distribution of the combined grouting group exhibits both of the aforementioned characteristics, with the larger value of pile lateral friction resistance located near the pile side grouting outlet and pile tip. Figure 13 shows the ultimate pile lateral friction distribution of the tip grouting pile and side grouting pile with different grouting pressures. There is no great change in the pile side lateral reinforcement enhancement region when the pressure changes from 0.5 MPa to 0.7 MPa. The influence of different grouting pressures on the lateral resistance is not obvious. Figure 12: Distribution of ultimate pile lateral friction resistance with different grouting volumes. (**a**) Pile tip grouting group; (**b**) pile side grouting group; (**c**) combined grouting group.
# Line Matching Based on Viewpoint-Invariance for Stereo Wide-Baseline Aerial Images
## 6 Conclusions  
This paper proposes an improved method for the line matching of wide-baseline images based on viewpoint-invariance. Perspective transformation matrices are established using the exterior orientation elements provided by POS and the original images are rectified into conformal images. By processing images whose distortion effects of large viewing angles have been eliminated, the number and accuracy of the matched line segments can be improved with no loss in time consumption meanwhile. With the better line matching outcomes, more quantitative and more effective 3D structural line information of buildings can be acquainted in subsequent 3D city reconstruction, compared with current state-of-art methods which perform line matching directly on original images. This method can be extended to other close-range photogrammetry fields, as well as the matching of other geometric features, such as points, circles, curves, etc.  
This method can be applied to normal UAV aerial photogrammetry. When the angle-measuring accuracy of POS is higher than \(5^{\circ}\), this method is recommended in order to achieve better matching results. In some special circumstances, such as low-cost IMU configuration, sharply shacked UAV platform, or sharp turning on MAV (Manned Aerial Vehicle) platform, however, the rotation angles for cameras will deviate significantly. It can be used in several matched point or line features from images to perform the initial orientation of the camera [27; 28] or subsequent aerial triangulation [29], which can also obtain the pose information of the camera accurately, to some extent.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON], [PERSON] and [PERSON] analyzed the data and compiled the discussion; [PERSON] wrote the paper and reviewed by [PERSON] All authors reviewed and approved the final manuscript.  
This research was partially supported by funding from the National Natural Science Foundation of China, No. 51474217, No. 41701533 and Key Research and Development Program of Guangxi, No. AA17204086. Authors would like to thank China TOPRS Technology Co., Ltd. and [PERSON] for providing the aerial images.  
The authors declare no conflict of interest.
###### Abstract  
This paper makes use of sloshing reduced-order models to investigate the effects of sloshing dynamics on aeroelastic stability and response of flying wing structure. More specifically, a linear frequency-domain operator derived by an equivalent mechanical model is used to model lateral (linear) sloshing dynamics whereas data-driven neural-networks are used to model the vertical (nonlinear) sloshing dynamics. These models are integrated into a formulation that accounts for both the rigid and flexible behavior of aircraft. A time domain representation of the unsteady aerodynamics is achieved by rational function approximation of the fully unsteady aerodynamics obtained via the doublet lattice method. The case study consists of the so called Body Freedom Flutter research model in two different configurations with one or two tanks partially filled with liquid with a mass comprising 25% of the aircraft structure. The results show that linear sloshing dynamics are able to change the stability margin of the aircraft in addition to having non-negligible effects on rigid body dynamics. On the other hand, vertical sloshing acts as a nonlinear damper and eventually provides limit cycle oscillations after flutter onset.  
flexible aircraft; aeroelasticity; linear and nonlinear sloshing; equivalent mechanical models; neural networks +
Footnote †: journal: Applied  
00184 Rome, Italy  
* Correspondence: [EMAIL_ADDRESS]
# Focus on Disaster Risk Reduction by ResNet-CDMV Model After Natural Disasters
## 6 Conclusions  
To investigate the types of secondary disasters that can be caused by common everyday objects after natural disasters, this study constructed image datasets of six secondary disaster factors--fire, inflammable objects, explosive objects, toxic substances, trapped persons, and dangerous buildings--extracted visual features of secondary disaster factors through the ResNet-CDMV algorithm, and correlated them with corresponding secondary disaster events. In terms of improving the early warning accuracy of secondary disaster events, this study makes two contributions:  
* In addition to the secondary disaster factors, the dataset includes common non-secondary disaster factor items in everyday life that are similar to the visual characteristics of the secondary disaster factors. Their existence can effectively improve the identification accuracy of the secondary disaster factors.
* The CDMV kernel comprises a combination of multiple dominant models, and this overcomes the cognitive bias of a single CNN model for different types. This method can effectively improve the identification accuracy of secondary disaster factors in the'softmax' layer.  
The ResNet-CDMV method obtained in this study has an mAP value of 87% for the identification of secondary disaster factors. For this method, Faster-RCNN, SSD, CornerNet, CenterNet, and YOLOv7 object detection algorithm increased by 9.33%, 11.83%, 13%, 11%, and 8.17%, respectively. The high-precision early warning platform of secondary disaster events based on the ResNet-CDMV method has important practical significance for the prevention and management of secondary disasters after the occurrence of natural disasters.  
**Author Contributions:** [PERSON] conceived the main idea of the train and control methods, set up the simulation, and trained networks. [PERSON] performed experiments together and helped set up hardware for the experiments. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by National Key R&D Program of China (2023 YFC3011100).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data needed to evaluate the conclusions in the paper are present in the paper.  
**Acknowledgments:** We thank the participant who participated in this study for their time and effort.  
**Conflicts of Interest:** The authors declare that they have no competing interests.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 6 Limitations  
Despite the effectiveness of our approach, there are several limitations that could impact performance. First, we rely on a hard alignment between text and audio, which may introduce errors when transcription and audio segmentation are not perfectly synchronized. This can lead to misaligned speaker change points, affecting downstream processing. Second, the pre-trained Bert model used in our approach does not inherently contain speaker information. As a result, the textual representation lacks explicit speaker cues, which could otherwise improve the detection of speaker transitions.  
A more effective approach would involve a joint model that simultaneously performs automatic speech recognition and speaker change detection, reducing alignment errors. Alternatively, a stacked model architecture, where ASR is followed by SCD in a sequential manner, could improve robustness by leveraging intermediate speech representations. These models can be integrated with pre-trained audio models to further improve performance. Future research should investigate joint modeling of automatic speech recognition and speaker change detection to improve alignments between text and audio, or use hidden embeddings from a pre-trained ASR model to improve SCD performance.