esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this study, the effect of soil-structure interaction (SSI) on the dynamic behavior of low-frequency and high-frequency structures experiencing a high-frequency-dominant earthquake is analyzed. For this purpose, a fixed-end model without considering SSI and an embedded model with SSI were constructed, and the numerical analysis results of the two models were compared to quantitatively analyzing the effect of SSI on the major dynamic responses of the structure, such as natural frequency, peak response frequency, and maximum relative displacement. The conclusions from this study are as follows:  
Figure 11: FFT spectrum of acceleration responses at the top of the high-frequency structure for different soil types considering SSI effects: (**a**) WS-EW; (**b**) FI-NS.  
Figure 10: Maximum relative displacement of the high-frequency structure considering SSI effects: (**a**) EW direction; (**b**) NS direction.  
In the analysis results of the low-frequency structure, it was found that the peak acceleration amplitude decreases when SSI is considered for all four soil types considered. This is known to be due to the change in support conditions and the energy dissipation effect of the ground;
2. The maximum relative displacement of the low-frequency structure also decreased across all soil conditions when considering the SSI effect, with the most significant reduction observed on FI soil: 35.6% in the EW direction and 16.4% in the NS direction;
3. The maximum relative displacement of the short-period structure also decreased generally when the SSI was considered but increased by 22.8% and 55.1% for the EW direction of WS soil and NS direction of FI soil, respectively. From these results, it is observed that a larger response can be obtained when SSI is considered. The reason for this result is that the natural frequencies of the structure calculated considering the stiffness of the ground are in the frequency region where the amplitude of the free-field response is larger than otherwise;
4. For both low-frequency and high-frequency structures, considering the SSI results in additional damping as the energy dissipation of the ground is reflected in the analysis, which reduces the dynamic response. However, when SSI is considered, the support conditions of the structure change, resulting in the lower natural frequency of the structure and a change in the dynamic response. Therefore, depending on the overlap between the dominant frequency range of the input seismic wave and the natural frequency range of the structure, SSI consideration may be necessary for conservative design results.  
Conceptualization, W.C. and J.L.; methodology, J.L.; software, H.J. and J.Y.; supervision, W.C. and J.L.; writing--original draft, H.J.; writing--review and editing, J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF2018R1A6A1A0702581921).  
Not applicable.  
Data are contained within the article.  
The authors declare no conflicts of interest.
# Fine-Grained Action Recognition by Motion Saliency and Mid-Level Patches
## 4 Action Recognition with Graph Structure
### The Graph Structure  
In order to effectively capture the spatial context information during the action process, a graph structure is used to represent the relationship between mid-level patches [53; 54]. Firstly, all mid-level patches are constructed into an undirected action graph \(G=(V,E)\), where the node \(i\in V\) represents a mid-level patch, and the edge \((i,j)\in E\) represents the relationship between two patches. Initially, all nodes form a fully connected graph.  
In fact, not all the body parts have a cooperative relationship during the action process. In most cases, cooperation only involves parts of human body parts. Using a fully connected graph to represent the relationship between the patches will generate many redundant relationships and increase computation costs. Therefore, this paper adopts a graph segmentation algorithm to segment the action graph \(G\) and deletes unnecessary edges in which two endpoints have no cooperation relationship [55; 56]. For any two nodes \(i\) and \(j\), if two mid-level patches represented by the nodes move simultaneously for long enough, they are considered to have a motion cooperation relationship. Otherwise, there is no cooperation relationship between them.  
Suppose that \(n\) mid-level patches are captured in frame \(k\), and they are expressed as \(F_{k}=\left(f_{k}^{1},f_{k^{\prime}}^{2},\cdots,f_{k}^{n}\right)\). For each patch \(f_{k^{\prime}}^{i}\) we extract the DT feature. The motion trajectories of allfeature points in the patch \(f_{k}^{i}\) (absolute coordinate in the image) are expressed as \(tc_{i}=\left\{tc_{i}^{m}\right\}\). \(tc_{i}^{m}=\left\{tc_{i}^{m}\left[t\right]\right\}_{t=ts}^{td}\), where the vector \(tc_{i}^{m}\left[t\right]\) represents the coordinate vector \(\left(x,y\right)\) of feature point \(m\) at time \(t\), and \(\left[ts,td\right]\) is the time range of the trajectory of that point.  
If two patches are both in motion for at least \(\delta\) frames, it is considered that there exists a motion cooperation relationship between them. We set \(T_{i}=\underset{\forall m\in f_{k}^{i}}{\bigcup}\left[ts_{m},td_{m}\right]\), and the adjacency matrix \(M_{k}\) of graph \(G\) in frame \(k\) is defined as Equation (5). \(T_{i}\) is the time interval in which patch \(i\) is in motion.  
\[M_{k}(i,j)=\left\{\begin{array}{l}1,if|T_{i}\cap T_{j}|>\delta\\ 0,\qquad otherwise\end{array}\right.. \tag{5}\]
# Data Interpretation Technology of GPR Survey Based on Variational Mode Decomposition
## 6 Conclusions  
Based on the VMD and the Hilbert transform theory, the VMD technique was introduced into the GPR data interpretation process, and the instantaneous frequency analysis method of the GPR signal was also established. The analysis results showed that the proposed method has better outcomes than the traditional methods. Finally, the method was applied to a real engineering exploration. The conclusions are as follows:  
1. The VMD method is better at overcoming the influences of mode aliasing and endpoint effects as compared to the traditional methods EMD and EEMD.
2. The time-frequency spectrum of the GPR signal can be obtained using VMD. The slice section of the 3D time-frequency data body that was constructed from the GPR scan line data displays the information of the detection target from multiple angles.
3. VMD separates the IMF of the GPR signal and has obvious advantages when compared to the traditional method EMD and EEMD.
4. According to the time-frequency spectrum of the GPR signal based on VMD, the processing method can highlight the abnormal areas and it shows good applicability in the discovery of the cavern anomalies.  
Conceptualization, J.X.; Methodology, J.X.; Software, J.X.; Formal Analysis, B.L.; Writing--Original Draft Preparation, J.X.; Writing--Review & Editing, J.X. and B.L.  
This research was funded by the Open Research Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering (2017 SDSJ05), the project of the Hubei Foundation for Innovative Research Groups (2015 CFA025) and the Open Research Fund of Key Laboratory of Hydraulic and Waterway Engineering of the Ministry of Education (Chongqing Jiaotong University, Grant No. SLK2017A02).  
The authors declare no conflict of interest.
# IeALL: Dam Deformation Prediction Model Based on Combination Model Method
## 6 Experiments
### Analysis of Results
#### 6.3.2 Analysis of IEALL
It can be concluded through the above verification experiments that the IGWO algorithm can effectively optimize the parameter of the EALL, thereby improving the predictive accuracy  
Figure 9: Variation curve of training times and RMSE value of prediction results: (**a**) variation curve of _epoch_ and RMSE; (**b**) variation curve of _batch_size_ and RMSE; (**c**) variation curve of \(num\) and RMSE; (**d**) variation curve of \(\eta\) and RMSE. of the model. Each reaches the optimum when \(epoch=153\), \(batch\_size=36\), \(num=13\), \(\eta=0.0077\), which are also set as the optimal parameters obtained after IGWO optimization. Experiment 3: IEALL prediction results and comparison. The optimal parameters obtained after IGWO optimization are brought into the EALL to obtain the prediction results of the IEALL model. The results are compared to the model with the EALL and shown in Figure 10.  
As can be seen from the figure, the predicted values of the IEALL model are closer to the actual values and the predicted effect is better. The results of the evaluation of the prediction effects of the two models are shown in Table 6.  
From Table 6, it can be seen that the RMSE, MAPE, and MAE values of the IEALL model are closer to 0 than EALL, which can be judged that the IEALL model is more effective in dam deformation series prediction compared to EALL.
# Noise Cancellation Method for Mud Pulse Telemetry Based on Discrete Fourier Transform
## 4 Discussions  
To evaluate the proposed method quantitatively, the noise cancellation output is further processed for demodulation. The traditional pump noise cancellation method mentioned in Section 2.2 is not able to deal with the situation of two pumps operating at  
Figure 14: Frequency spectrum contrast. (Blue: the input signal; Red: the output signal.)  
Figure 12: Pressure signal input.  
Figure 13: Noise cancellation output.  
Figure 15: Pure pump noise block contrast. (Red: the input signal; Blue: the output signal.)different frequencies. To make a comparison with the traditional method, the working frequency of these two pumps while drilling was located around 1 Hz. Table 2 illustrates the bit error rate (BER) of the demodulation results with these two de-noising methods at different depths and the corresponding SIRs. The DFT-based noise cancellation method demonstrates its effectiveness under different depths and different data rates. Furthermore, a lower BER and a higher SIR can be obtained with the DFT-based noise cancellation method. To summarize, the proposed method shows better performance and applicability in field experiments as well.  
While advanced interference elimination methods, such as time-frequency analysis approaches and adaptive filtering, offer superior interference suppression efficacy and higher adaptability, they come with increased computational complexity and implementation challenges. Specifically, machine learning methods achieve the highest SIR improvement and THD reduction but require substantial computational resources and extensive training data, making them less feasible for real-time and resource-constrained applications.  
On the other hand, the DFT-based method, though exhibiting lower adaptability and slightly inferior interference suppression compared to the most advanced methods, provides a balanced trade-off with its high computational efficiency, ease of implementation, and adequate performance for harmonic interference elimination. This makes the DFT-based approach particularly suitable for applications where real-time processing and resource constraints are critical considerations.  
Our experimental results demonstrate that DFT-based interference elimination effectively achieves the desired level of harmonic suppression, maintaining the integrity of the fundamental signal while attenuating harmonics efficiently. In the context of our specific application, the performance gains from more complex time-frequency or adaptive algorithms did not justify the additional computational overhead as the DFT method sufficiently meets performance objectives. The DFT, especially when implemented using the fast Fourier transform (FFT) algorithm, exhibits low computational complexity. This efficient computational complexity makes DFT highly suitable for real-time applications, enabling the rapid transformation of time domain signals into the frequency domain.
## 2 Methodology
### Watermark Encryption  
To prevent the watermark information from being easily identified and attacked, the initial watermark image W needs encryption. The Arnold algorithm Arnold (1964) is a classic scrambling algorithm that encrypts the watermark image, which transforms the coordinates (x,y) of each vertex on a N \(\times\) N watermark image into (x',y') as follows:  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\end{array}\right]=\left[\begin{array}{cc}1&b\\ a&ab+1\end{array}\right]\left[\begin{array}{c}x\\ y\end{array}\right]mod(N) \tag{1}\]  
where \(a\) and \(b\) are positive integers, mod is the remainder function, \(N\) is the width of the matrix. These parameters determine the scrambling cycles.  
This study chooses a binary image with copyright information as the initial watermark image \(W\) and scrambles it to obtain an encrypted watermark image \(W^{\prime}\) based on the Arnold algorithm. The encrypted watermark is transformed into an ordered sequence that consists of only 0 and 1 and is further divided into r groups with 8-bit digits in each group. The 8-bit digits in each group are transformed into an 8-bit unsigned integer I (ranging from 0 to 255). The 8-bit unsigned integers of the \(r\) groups together form a watermark sequence \(W_{n}\), which stores the encrypted watermark information and is used to generate zero-watermarks in subsequent operations.
# Multi-User AF Relay Networks with Power Allocation and Transfer: A Joint Approach
## 3 Proposed Power Allocation and Transfer Algorithm
### Master Problem
Next, suppose \(\sum_{i=1}^{N_{T}}e^{\tilde{\mathbf{W}}_{r}^{\star},\tilde{\mathbf{W}}_{r}^{ \star}}=\sum_{i=1}^{N_{T}}\left[E_{r,i}+\sum_{k=1}^{N}\beta_{k}\tilde{\delta}_ {k,i}^{\star}\right]\) is fulfilled with strict inequality, for \(\big{(}\tilde{\mathbf{P}}_{s}^{\star},\tilde{\mathbf{W}}_{r}^{\star}, \boldsymbol{\delta}^{\star},\boldsymbol{\Omega}_{s}^{\star},\boldsymbol{\Omega }_{r}^{\star}\big{)}\). By reducing \(\tilde{\delta}_{k,T}^{\star},\forall k\) and increasing \(e^{\tilde{\mathbf{P}}_{k,T}^{\star}}\) and \(\boldsymbol{\Omega}_{k,T-1}^{\star}\), we can get an ameliorated sum rate, while fulfilling other conditions in \((\mathbf{P4})\). Thus, (T.2) directly contradicts with the optimality of \(\big{(}\tilde{\mathbf{P}}_{s}^{\star},\tilde{\mathbf{W}}_{r}^{\star}, \boldsymbol{\delta}^{\star},\boldsymbol{\Omega}_{s}^{\star},\boldsymbol{\Omega }_{r}^{\star}\big{)}\). Hence, it is proved. Now, we update the values of \(\mathbf{\Omega}_{s}\) and \(\mathbf{\Omega}_{r}\) with the help of the following lemma. Lemma 1: _To obtain the maximum sum rate, the auxiliary variables \(\mathbf{\Omega}_{s}\) and \(\mathbf{\Omega}_{r}\) should be updated as \(\Omega_{k,m}^{(1)}=\sum_{i=1}^{m}\left(e^{\tilde{\mathbf{P}}_{k,i}^{(0)}}+P_{k, i}^{c}\right)\) and \(\Omega_{r,m}^{(1)}=\sum_{i=1}^{m}\left(e^{\tilde{\mathbf{W}}_{r,i}^{(0)}}+P_{r, i}^{c}\right)\), for \(m=1,\ldots,N_{T}-1,\ \forall k\)._  
Proof. : To maximize the sum rate, the auxiliary variables \(\mathbf{\Omega}_{s}\) and \(\mathbf{\Omega}_{r}\) in the outer loop can be illustrated as  
\[[\mathbf{\Omega}_{s}^{\star},\mathbf{\Omega}_{r}^{\star}]=\arg\max_{\mathbf{ \Omega}_{s}\mathbf{\Omega}_{r}}\tilde{f}_{LB}^{\star}(\mathbf{\Omega}_{s,} \mathbf{\Omega}_{r})\,, \tag{38}\]where \(\tilde{f}_{LB}^{\star}(\mathbf{\Omega}_{s},\mathbf{\Omega}_{r})\) denotes the sum rate achieved by the Algorithm 1. It is very hard to exhaustively search over all values of \(\mathbf{\Omega}_{s}\) and \(\mathbf{\Omega}_{r}\) due to the very high computational complexity. Therefore, we propose a two-step technique to find the values of \(\mathbf{\Omega}_{s}\) and \(\mathbf{\Omega}_{r}\). As the first step, relax the battery storage constraints in (\(\mathbf{P}\mathbf{4}\)) by assuming \(\Omega_{k,m}^{(0)}=\infty\) and \(\Omega_{r,m}^{(0)}=\infty\), for \(m=1,\ldots,N_{T}-1\), and \(k=1,\ldots,N\), and find the optimal \(\left(\tilde{\mathbf{\mathrm{P}}}_{s}^{\star(0)},\tilde{\mathbf{\mathrm{W}}}_{r}^{ \star(0)},\delta^{\star(0)}\right)\) using the Algorithm 1. In the next step, we recompute the optimal solution by updating \(\Omega_{k,m}^{(1)}=\sum_{i=1}^{m}\left(e^{\mathbf{\mathrm{P}}_{k,i}^{\star(0)}}+P_ {k,i}^{\mathrm{c}}\right)\) and \(\Omega_{r,m}^{(1)}=\sum_{i=1}^{m}\left(e^{\tilde{\mathbf{\mathrm{W}}}_{r,i}^{\star (0)}}+P_{r,i}^{\mathrm{c}}\right)\), for \(m=1,\ldots,N_{T}-1\), \(\forall k\). This enables us to rectify the solutions by following the track of \(\Omega_{k,m}^{(1)}\) and \(\Omega_{r,m}^{(1)}\).
## 1 Introduction  
Food spoilage during storage is a global problem as 25% of the world's gross primary agricultural and fishery products are wasted through chemical deterioration and microbial spoilage every year [1]. Enzymatic autolysis, oxidation, and microbial growth are the three main mechanisms leading to the spoilage of fish. To extend the shelf life of fish, different types of preservation methods, such as drying, smoking, priming, fermentation, canning, freezing, and chilling, are used. Of these methods, low-temperature storage and application of chemicals for controlling water activity, enzymatic, oxidative, and microbial spoilage remain the most common preservation techniques in the industry [2, 3]. However, increase in environmental awareness has increased the demand for natural and non-toxic biological preservatives for usage in food processing, storage, and transportation. Tea polyphenols extracted from green tea are biological preservatives that contain polyphenolic compounds, such as catechins, flavanols, and phenolic acids with antibacterial and antioxidative activities [4, 5]. Tea polyphenols have previously been used to maintain the quality and extend the shelf life of fish, such as silver carp [6, 7], crucial carp [8], large yellow croaker [9], and weever [10]. To expand the range of their applications, it is necessary to explore the use of tea polyphenols on additional fish species.  
Crisp grass carp (_Clenopharyngodon idella_ C. e t V.), a widespread freshwater fish in China, has firm muscles and higher concentrations of crude proteins, fats, and amino acids, than the common grass carp (_Clenopharyngodon idella_) [11]. In our previous study, _Aeromonas_ and _Pseudomonas_ were found to be the major microbial organisms in the air- and vacuum-packed crisp grass carp fillets responsible for spoilage during chilled storage [12]. There have been no studies on the use of tea polyphenols to extend the shelf life of crisp grass carp during storage at 4 \({}^{\circ}\)C. This study aimed to investigate the effects of a tea polyphenol dip treatment on the shelf life of crisp grass carp during cold storage at 4 \({}^{\circ}\)C.
## 6 Discussion and Conclusions
### Conclusions  
Through a comprehensive uniaxial compression test, this study meticulously analyzes the characteristics of electromagnetic radiation (EMR) and micro-seismic (ME) signals generated during the compression failure of sandstone under static loads, as well as the intricate coupling processes between them. The following conclusions have been drawn.  
Upon the application of pressure, as it propagates to the free surface of the rock sample, fractures emerge, gradually expanding into larger cracks. Notably, during the stage of fracture expansion, abnormal EMR signals manifest, confirming the production of EMR signals during rock breakage. It is observed that these EMR signals generally exhibit low amplitudes. As the impact stress diminishes, the EMR signals recede, affirming that low-frequency EMR results from charge movements on the surface of fractured rock fragments [36]. The time synchronization between the EMR signal and the ME signal, coupled with their high similarity in the frequency domain, underscores the homology between EMR generated during rock failure and ME. In conditions marked by small ME and subsequent small ME ruptures, an early warning precursor signal emerges before the main rupture signal. In the context of coal mining, where major disasters like water, fire, gas, dust, and roof instability are encountered, the link between water, gas, and roof disasters with the fracture and slip instability of rock strata is crucial during the mining process. Proposing the main fracture precursor signal of EMR for coal and rock mass as an early warning signal holds significant engineering relevance for the monitoring and early warning of dynamic disasters in coal and rock masses [37].  
Author Contributions: Conceptualization, Q.L.; methodology, B.A.; investigation, L.H.; writing--original draft preparation, L.H.; writing--review and editing, Q.L.; supervision, B.A.; validation, Q.L.; formal analysis, L.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant No. 52274117).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors wish to acknowledge the support they received. The reviewers are gratefully acknowledged for their valuable comments on the manuscript.  
The authors declare no conflict of interest.
# Predicting Shot Accuracy in Badminton Using Quiet Eye Metrics and Neural Networks
## 5 Results
### Feature Importance Results and Analysis  
Building on the SHAP analysis mentioned in Section 4.6.3., we further examined the relative importance of different features in predicting shot accuracy. SHAP values are calculated by comparing the model's prediction with and without each feature, averaged over all possible feature combinations.  
* QE duration (SHAP value: 0.385).
* Racket speed at impact (SHAP value: 0.312).
* QE onset (SHAP value: 0.287).
* Wrist angle at impact (SHAP value: 0.245).
* Shuttlecock trajectory (initial angle) (SHAP value: 0.198).
* Body posture (trunk rotation) (SHAP value: 0.173).  
Table 9 presents these SHAP values, illustrating their relative importance in predicting shot accuracy.  
Figure 5: Learning curves.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feature** & **SHAP Value** \\ \hline QE Duration & 0.385 \\ Racket Speed at Impact & 0.312 \\ QE Onset & 0.287 \\ Wrist Angle at Impact & 0.245 \\ Shuttlecock Trajectory Angle & 0.198 \\ Body Posture (Trunk Rotation) & 0.173 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Feature importance (SHAP values).  
To further visualize the impact of these features on model predictions, we plotted a SHAP dependency graph for the top two features in Figure 6.  
This plot demonstrates how QE duration and racket speed interact to influence the model's predictions, with longer QE durations and higher racket speeds generally associated with an increased likelihood of successful shots.
## 5 Simulation Results
### Basic Performance  
The performance of all methods is examined for the topology shown in Figure 11. In order to investigate the basic performance, the intermittent interval is set to a constant value which is the same for all nodes. Although shorter intermittent intervals are important for improving the performance in IRDT, extremely short intervals cause frequent transmission of IDs, which appears to interfere with other communication. Therefore, we examine the basic performance in the case where the intermittent interval is set to 0.1 and 1.0 s. We clarify the performance characteristics of IRDT by comparing them with those of RI-MAC and X-MAC. In IRDT, each node transmits an ID packet and waits for an SREQ packet. The time for ID transmission is 1.92 ms, \(T_{ws}\) is set to 2 ms, and in X-MAC each node periodically waits for 4 ms for a short preamble. In addition, X-MAC and RI-MAC use minimum hop routing, where sender nodes select one receiver node out of the neighboring nodes with minimum hop count from the sink node.