esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation on Eddy Current Sensor in Tension Measurement at a Resonant Frequency
## 4 Experimental Results and Discussion
### The Influence of Sleeve Structure on Sensor Performance
#### 4.3.2 Relative Sensitivity Coefficient  
The change in the impedance parameters of the sensor is proportional to the external tension and its normalized value can be calculated by:  
\[S=\frac{Z(T)-Z(T_{0})}{Z(T_{0})} \tag{10}\]  
where \(S\) is the relative sensitivity, \(Z(T_{0})\) and \(Z(T)\) are measured at 0 kN and at each 3 kN.  
Figure 13a,b shows the relative sensitivity change of the impedance magnitude and phase at the resonant frequency, respectively.  
Figure 13 illustrates that the sensor's relative sensitivity presents an increasing trend with the external tension changes from 0 kN to 15 kN with the loading step of 3 kN. Therefore, the data of impedance magnitude or impedance phase at the resonant frequency can be used to infer external tension. In addition, it is very clear that the presence of the sleeve structure improves the sensor's relative sensitivity. This improvement may be attributed to the fact that the sleeve structure allows more magnetic field lines to flow through the test specimen as a result of its higher permeability than the non-sleeve structure. This conclusion is supported by the finite element analysis in Section 3.  
Figure 13: Relative sensitivity change of impedance (**a**) magnitude and (**b**) phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Correlation Coefficient**} & \multicolumn{2}{c}{**Sleeve Structure**} & \multicolumn{2}{c}{**Non-Sleeve Structure**} \\ \cline{2-5}  & **Loading** & **Unloading** & **Loading** & **Unloading** \\ \hline Impedance magnitude & 0.991 & 0.986 & 0.981 & 0.982 \\ Impedance phase & 0.989 & 0.996 & 0.977 & 0.974 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison results of correlation coefficient.
# Computation of the Hausdorff Distance between Two Compact Convex Sets
## 3 Results  
We tested the Frank-Wolfe and projected gradient ascent algorithms on two pairs \((A,B)\) of compact convex sets: (1) where \(A\) is the box \([-\mathbf{1},\mathbf{1}]\) translated by \(\mathbf{1}\) and \(B\) is the intersection of the unit ball and the non-negative orthant; and (2) where \(A\) is the probability simplex and \(B\) is the \(\ell_{1}\) unit ball translated by \(\mathbf{1}\). These examples are representative, and for the second pair one can show that \(d_{H}(A,B)=\sqrt{p}\), where \(p\) is the dimension of the ambient space. See Proposition A2 of Appendix A. Table 1 presents our findings. The computation times are in seconds per trial across 100 random initializations and appear to scale affinely (a constant plus linear) in \(p\). The columns Maximum, Mean, and Std convey summary statistics of the converged values of the Hausdorff distance. The point-cloud method generated \(10^{4}\) random points to mimic each continuous set. Because the point-cloud method is non-iterative, a single run captured its performance. For the record, all computations were carried out on a MacBook Pro with a 2.3 GHz 8-core i9 chip and 16 GB of memory. Although the algorithms were embarrassingly parallel across trials, our Julia code is completely serial.  
The random point-cloud method was not remotely competitive with projected gradient ascent in either accuracy or speed on these sample problems. It did produce approximate distances that confirmed the best results of the iterative methods. As measured by the quality of its solution, projected gradient ascent also outperformed the Frank-Wolfe algorithm. The Frank-Wolfe method was probably too aggressive, perhaps because it moved directly to an extreme point of \(A\) in computing \(d(A,B)\). On the second problem, projected gradient ascent attained the global maximum across all trials. When the standard deviation of the converged values is positive, it follows that some trials were trapped by inferior local maxima. Both iterative algorithms benefit from the homotopy heuristic, which is fully deterministic. Accordingly, the standard deviations under homotopy equaled 0. Homotopy increased the computation times by less than an order of magnitude for 11 evenly spaced homotopy points. Finally, as \(p\) increased, both problems appeared easier to solve by the iterative methods. This behavior was particularly evident when \(p=1000\). In contrast, the random point-cloud solutions deteriorated as \(p\) increased.
## 1 Introduction
Although the previous studies have obtained promising results, there is still a need to develop and apply more efficient methods in terms of accuracy and computational burden, mainly considering that detecting faulty transformers using vibration analysis is not accessible due to various factors such as the burden of high-level noise, nonstationary properties of the vibration, and the current signals of the transformer. Recently, the use of quaternions has been presented as an effective tool for fault diagnosis, as shown by their growing implementation in various studies. For instance, [PERSON] et al. [13] utilized current and vibration signals to detect faults in electric motors, while [PERSON] et al. [14] introduced a novel signal decomposition technique based on quaternions used to identify gear faults [PERSON] et al. [15] proposed the quaternion block sparse representation for signal recovery and classification which is applied in color face recognition. They also proposed the quaternion block sparse representation for signal recovery and classification applied in color face recognition. Moreover, quaternions have proven effective in spectral analysis for diagnosing bearings and level gear faults in [16; 17] respectively. These notable applications highlight the versatility and potential of quaternions in fault detection across the different components of electrical machines. This study introduces quaternion signal analysis QSA to diagnose SCTs in electrical transformers. The method involves the creation of a model that captures the transformer's concurrent behavior for four physical variables in the time domain. This method considers each sample as a point, forming shape trajectories over time and providing high levels of accuracy from a small number of samples. The obtained QSA-based model and its statistical features, i.e., variance and standard deviation, are used to classify various severities of SCTs (i.e., 0, 5, 10, 15, 20, 25, and 30 SCTs) in a real electrical transformer using as physical variables the current signal and the vibration signals acquired from tri-axial accelerometer. As a classifier, the k-Nearest-Neighbors (KNN) method is used. As a final observation, this study contributes by proposing a new methodology based on the QSA technique that allows fast processing using a small number of samples while achieving good accuracy results above 97.5% during classification. This paper is organized into six sections. In Section 1, an introduction and the related works concerning SCT damage diagnosis are presented. Section 2 describes the background of QSA, statistical features, and the KNN method. In Section 3, the experimental setup is presented. The results are shown in Section 4, and discussions and the future works of the proposed methodology are presented in Section 5. Finally, conclusions are drawn in Section 6.
## 5 Results and Discussions
### Numerical Stability Analysis of AB Cross-Section Under Permafrost Thawing  
Under a permafrost condition, the water present in the pores or voids is frozen, which helps to keep the materials intact and improves stability as seepage is not allowed through the body of the natural dam. Under permafrost thawing, the water present in the voids under a frozen condition is melted, which loosens the material and allows the water to keep through the body. This future condition is simulated in the numerical modelling and analysis to assess the stability of the natural dam and the potential danger of a glacial lake outburst flood.  
Considering the same scenario, in time to come, the water present in the voids of the embankments and permafrost surrounding Gurudongmar Lake will melt due to a rise in temperature under changing climate conditions. Moreover, the scenario will be aggravated if there is a sudden release of water from the feeder lake due to any mass movement. The melting of permafrost and an increase in the depth of the active layer would contribute to  
Figure 10: Elevation and slope of the Gurudongmar Lake area (ALOS-PALSAR DEM). Two possible scenarios: Scenario 1 of a rock and ice avalanche into the feeder lake (A and B) causing overtopping, affecting the stability of the supraglacial debris dam and frontal moraine dam (C and D), and Scenario 2 of a shallow debris slope failure into the feeder lake (A) leading to overtopping or failure of the supraglacial dam and moraine dam (C and D) (modified after [PERSON] et al., 2023 [56]).  
the build-up of pore pressure inside the dam. Under these conditions, the stability of the AB section existing on the downstream side of the lake will be more critical. The numerical analysis of the stability of the AB dam cross-section is performed considering the scenario where there is a 10 m rise in the water table going up to the crest of the dam and the pore pressure builds up due to permafrost melting. Figure 11 shows the steps involved and the output obtained from the numerical analysis.  
From the results of the numerical analysis, it is anticipated that the stability of the downstream of the AB natural dam section will be compromised if there is a sudden rise in the water level in the lake or due to permafrost melting under steady seepage conditions.
###### Abstract  
In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system's safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation.  
trajectory replanning; UAVs; motion prediction; gradient-based optimization +
Footnote †: journal:  
0000-0001-999-4000-0001-9999-4000-0001-9999-40001-9999-40001-9999-40001-9999-40001-9999-40001-9999-40001-9999-40001-99999-40001-9999-40001-99999-40001-9  
Drawing inspiration from CHOMP [14; 15], gradient-based optimization has become a widely used approach for replanning. In [16], a local replanning system was constructed by optimizing polynomial trajectories. Similarly, ref. [17] integrated a sampling-based topological path searching approach with path-guided B-splines trajectory optimization for real-time UAV replanning. Optimization-based trajectory replanning has achieved significant success, leading to numerous subsequent methods built upon this foundation (e.g., [18; 19; 20]). However, these approaches typically involve many parameters (e.g., control points for B-splines and polynomial coefficients) that require careful tuning. More importantly, these methods do not anticipate the motion of surrounding moving obstacles, which is a critical prerequisite for safe and efficient motion planning [21].  
In this work, we present an online trajectory replanning approach specifically designed for fixed-wing UAVs, which incorporates predictions for moving obstacles into a gradient-based optimization framework. Our method represents the trajectory using uniformly discretized waypoints, serving as the basis for defining the optimization cost function. This function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic constraints, similarity to the reference trajectory, and trajectory smoothness. Additionally, moving obstacles are predicted by combining physics-based methods with pattern-based methods, allowing for the predicted obstacle movements to be integrated into the local trajectory replanning framework. This integration enhances the safety of the system.
# Collaborative Filtering Model of Graph Neural Network Based on Random Walk
## 5 Discussion
### Parameter Discussion
#### 5.1.1 Optimal Number of Convolution Layers  
In this study, the effect of the \(K\) values of the convolutional layers on the recommended performance is first discussed. Figure 3 shows the recall@20 and precision@20 of the proposed method at different \(K\) values. The results in Figure 3 show that the performance of the algorithm is relatively low when the number of convolutional layers is small. As the number of convolutional layers increases, the performance of the proposed algorithm gradually improves but reaches a peak and then starts to decrease. It indicates that as the number of convolutional layers increases, the perceptual field of the spectral convolutional network becomes gradually larger, but a continuous increase will risk gradient disappear  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Metrics** \\ **Entity** \\ \end{tabular} } & \multicolumn{4}{c}{**Recall**} & \multicolumn{4}{c}{**Precision**} \\ \cline{2-10}  & **recall@20** & **recall@40** & **recall@60** & **recall@80** & **recall@100** & **p@20** & **p@40** & **p@60** & **p@80** & **p@100** \\ \hline NGCF & 0.1284 & 0.1874 & 0.2276 & 0.2601 & 0.2901 & 0.0401 & _0.0277_ & _0.0216_ & _0.0199_ & _0.0167_ \\ \hline Light-GCN & 0.1279 & 0.1916 & 0.2199 & 0.2577 & 0.2703 & 0.0427 & _0.0301_ & _0.0264_ & _0.0207_ & _0.0183_ \\ \hline DGCF & 0.1402 & 0.2037 & 0.2264 & 0.2685 & 0.3049 & 0.0432 & _0.0328_ & _0.0256_ & _0.0232_ & _0.0177_ \\ \hline GCMC & 0.1459 & 0.1722 & 0.2347 & 0.2451 & 0.2602 & 0.0488 & _0.0367_ & _0.0309_ & _0.0254_ & _0.0221_ \\ \hline NCF & 0.1599 & 0.1837 & 0.2301 & 0.2413 & 0.2717 & 0.0491 & _0.0382_ & _0.0327_ & _0.0301_ & _0.0255_ \\ \hline SpectralCF & 0.1977 & 0.2216 & 0.2614 & 0.2761 & 0.3299 & 0.0683 & _0.0508_ & _0.0491_ & _0.0467_ & _0.0423_ \\ \hline RW-GCF & 0.2021 & 0.2342 & 0.2787 & 0.2901 & 0.3402 & 0.0801 & _0.0611_ & _0.0593_ & _0.0508_ & _0.0466_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model Comparison: HetRec.  
ance and gradient explosion, and it is experimentally verified that the evaluation index will reach a peak when the value of \(K\) is 3, which is the best result, as shown in Figure 3 below.
## 5 Experiments
### Performance of DNNs Under Different Multipath Fading Conditions  
To explore the adaptability of the CDRNN model in diverse multipath fading scenarios, we manipulate the number of multipaths and the corresponding path gain, multipath delay, and Rician K-factor of the fading model corresponding to dataset 2 in Section 3.2, thereby constructing two additional distinct fading models. Among them, the channel model corresponding to dataset 1, which is more desirable, exhibits a smaller path gain and simpler multipath delay compared to the parameters in Section 3.2. We set its Rician K-factor to 8. On the other hand, the channel model corresponding to dataset 3, which is more adverse, has a relatively large path gain and more complex multipath delay. We assign the Rician K-factor to 2. The simulation results are presented in Figure 11. In the third scenario, when SNR is greater than 0 dB, the average recognition rates of the CDRNN, VViT, and AWN models stand at 75.88%, 68.95% and 67.06%, respectively, with negligible difference in performance among the three models. When SNR is less than or equal to 0 dB, their recognition rates are 29.41%, 27.09% and 24.64%, respectively. The CDRNN model continues to possess the highest accuracy across the three fading scenarios compared to the other two models. Even in the third unfavorable channel environment, its recognition accuracy undergoes relatively less reduction and its performance remains more stable. However, the performance of the AWN is highly unstable and poorly adapted to  
Figure 10: **(a-h)** Accuracy of specific modulation types over various DNNs. Under the channel model in Section 3.2, simulation experiments were carried out to make a comparison of the recognition accuracy of different modulation types.  
the channel environment. When the channel environment is more favorable, the overall disparity in recognition accuracy between dataset 1 and dataset 2 is not significant, but the recognition rate in the third harsh environment is worse. Particularly when SNR is less than 12 dB, its performance declines significantly. The performance of the MobileNet model lies between that of the AWN and CDRNN model.
# Phytoplankton as CO\({}_{2}\) Sinks: Redirecting the Carbon Cycle
## 1 Introduction  
We are undoubtedly witnessing the beginning of a change in our planet's climate [1]. Since large-scale climate changes have occurred periodically on the Earth, it remains unclear to what extent this fluctuation is triggered by human activities developed in the last 300 years or if it is a process that would have occurred anyway. What seems evident is that human activity, primarily the exponential burning of fossil fuels, is greatly accelerating the process [2]. Since there are no precedents for a similar event, our civilization is not yet aware of the many direct effects that this acceleration of global warming may have on our daily lives. Although the scientific community is constantly recording increasingly representative figures about the significant relationship between the increase in CO\({}_{2}\) in the atmosphere and the increasingly frequent climate anomalies, the measures being implemented at the international level are lax and insufficient and involve a sudden change in our energy system that no country is willing to assume responsibility.  
Since human activities have imbalanced the carbon cycle toward its gaseous form, it seems logical that humans take the necessary measures to counteract this imbalance by boosting the natural inverse mechanisms. Photosynthesis is the planet's natural toolto decarbonise the atmosphere in the short term. Thus, it is logical to think that controlling and stimulating this process could be the quickest way to curb the accumulation of this greenhouse gas. However, without human intervention, this mechanism cannot counter this explosive release of carbon. Phytoplankton cultivation technology emerges as one of the most mature, efficient, and versatile technologies for achieving this goal, compared to other CO\({}_{2}\) capture methods such as chemical and physical processes or land reforestation with higher plants [3]. This work provides an overview of the numbers in stimulating photosynthesis on a global scale through the extensive and controlled culture of phytoplankton, as well as a documented discussion of the significant technological and geopolitical challenges governments would face to implement this biological capture of CO\({}_{2}\) worldwide.
# Combining Dilution of Precision and Kalman Filtering for UWB Positioning in a Narrow Space
## 3 Experiments and Results
### Experimental Protocol
#### 3.1.2 Experimental Hardware  
The hardware used in this experiment is shown in Figure 4. The bottom plate adopts an STM32F103 series single-chip microcomputer as the main control chip. Peripheral circuits include an LED display, charging interface, etc. The module is configured with DecaWave's DWM1000. The hardware can be configured as a base station or a tag via USB instructions.
## 3 Method  
The proposed MI detection and localization stages are shown in a flow-chart form in Figure 1a,b, respectively. The MI detection stage comprises the filtering of VCG signal, segmentation of VCG recordings into beats, decomposition of VCG beat into multi-scale VCG tensors using MVMD, and deep CNN to detect MI pathology. Similarly, the localization stage consists of the classification of AMI, IMI, ALMI, ASMI, ILMI, and IPLMI beats usingMI detected multi scale VCG tensor data. The following section briefly discuss each part of the flow-chart, as shown in Figure 1.
## 5 Experimental Verification and Discussion
### Filter Measurement Data  
The probe moved at a non-uniform speed on the circumference of the connecting rod journal, so the sampling data were arranged at non-equal intervals. According to Equation (1), the true sampling angle can be obtained. The circular profile of the connecting rod journal was obtained by the distribution of the sampling data corresponding to the  
Figure 6: Experimental prototype of measuring crankshaft.  
Figure 7: Measuring structure and probe arrangement of connecting rod journal.  
real sampling angle, which is shown in Figure 8. It can be found that with the rotation of the crankshaft, the sampling points on the connecting rod journal are first sparse and then dense, and the sampling interval is first long and then short. The corrected contour data were consistent with the real sampling angle distribution.  
Based on the true distribution of the sampling angle and the data of the circumference of connecting rod journal measured by the sensor, the connecting rod journal P1, P2, P3 and P4 were filtered, respectively, by the method mentioned in this paper. The cut-off wave number was 50 UPR, and the filtered contours of the four connecting rod journals can be obtained as shown in Figure 9a-d.  
Figure 8: Radial sampling data of the connecting rod journal.  
Figure 9: Filter profile of non-equal for all connecting rod journals: **(a)** The filtered contour of journal P1; **(b)** the filtered contour of journal P2; **(c)** the filtered contour of P3; **(d)** the filtered contour of P4.
# Research on the Influence of Anxiety Psychology on Unsafe Behavior Among Construction Workers
## 4 Model Validation and Result Analysis
### Regression Analysis
#### 4.4.2 Mediation Effect Testing
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Dependent Variable**} & \multicolumn{2}{c}{**Organizational Climate**} & \multicolumn{4}{c}{**Unsafe Behaviors**} \\  & \multicolumn{2}{c}{**Model 6**} & \multicolumn{2}{c}{**Model 7**} & \multicolumn{2}{c}{**Model 8**} \\  & \(\beta\) & **t** & \(\beta\) & **t** & \(\beta\) & **t** \\ \hline Control variable & & & & & \\ Gender & 0.033 & 1.035 & 0.055 & 1.039 & 0.044 & 0.044 \\ Age & 0.073 ** & 0.631 & 0.061 ** & 0.522 & 0.073 ** & 0.072 ** \\ Educational background & \(-0.133\) * & \(-1.229\) & \(-0.135\) * & \(-1.445\) & \(-0.136\) * & \(-0.134\) * \\ Experience & \(-0.069\) & \(-1.517\) & \(-0.019\) & \(-1.644\) & \(-0.021\) & \(-0.021\) \\ Level of anxiety & \(-0.359\)** & \(-1.626\) & \(-0.316\)** & \(-1.735\) & \(-0.310\)** & \(-1.465\) \\ \hline Independent variable & & & & & \\ Anxiety psychology & \(-0.383\)** & \(-1.595\) & 0.071 ** & 1.629 & & \\ \hline Mediating Variable & & & & & & \\ Organizational climate & & & & & & \\ \hline R\({}^{2}\) & 0.459 & & 0.645 & & 0.625 \\ R\({}^{2}\) (Adjusted) & & 0.446 & & 0.639 & & 0.618 \\ F & & 3.655 *** & & 4.357 *** & & 4.263 *** \\ \hline \end{tabular}
\end{table} 
Table 9: Regression analysis of the mediating effect of organizational climate. Comparing Model 2 and Model 10, the regression coefficient of anxiety psychology on unsafe behaviors decreased markedly from \(\beta\) = 0.679 (\(p\) < 0.001) to \(\beta\) = 0.105 (\(p\) < 0.05), meeting the criteria for full mediation. This confirms that safety competency fully mediates the anxiety psychology-unsafe behavior relationship, supporting Hypothesis H7. Model 11 demonstrated that safety competency alone significantly predicts unsafe behaviors (\(\beta\) = \(-\)0.003, \(p\) < 0.01) with robust model fit (R\({}^{2}\) = 0.664, adjusted R\({}^{2}\) = 0.616, F = 4.194, \(p\) < 0.001), thereby validating Hypothesis H5 (\"Safety competency is negatively correlated with unsafe behaviors\").