esa-sceva/satcom-chunk-collection huggingface dataset:
# A Unified Approach for the Control of Power Electronics Converters. Part II: Tracking
## 5 Tracking with Perturbations  
When the system is affected by external perturbations, the previous approach can be extended to reject them. Model (1) is modified accordingly in order to consider the effects of perturbations. It is expressed as  
\[\left\{\begin{array}{rcl}\frac{dx(t)}{dt}&=&(A_{0}+A_{u_{1}(t)u_{2}(t)\dots u _{m}(t)})x(t)+(B_{0}+B_{u_{1}(t)u_{2}(t)\dots u_{m}(t)})v_{in}(t)+(E_{0}+E_{u _{1}(t)u_{2}(t)\dots u_{m}(t)})w(t)\\ y(t)&=&(C_{0}+C_{u_{1}(t)u_{2}(t)\dots u_{m}(t)})x(t)+(F_{0}+F_{u_{1}(t)u_{2}(t )\dots u_{m}(t)})w(t)\end{array}\right. \tag{12}\]where \(w(t)\in\mathbb{R}^{r}\), \(w\in L_{2}\) is the perturbation. The matrices \(E_{0}\), \(E_{u_{1}(t)u_{2}(t)...u_{m}(t)}\), \(F_{0}\) and \(F_{u_{1}(t)u_{2}(t)...u_{m}(t)}\) are defined similarly as in Section 2. Following the developments of Section 2, we can obtain an augmented model written as  
\[\left\{\begin{array}{l}\dfrac{dx(t)}{dt}=A_{0}x(t)+B_{0}(x(t))\lambda(t)+E_{0 }(w(t))\lambda(t)\\ y(t)=C_{0}x(t)+D_{0}(x(t))\lambda(t)+F_{0}(w(t))\lambda(t)\end{array}\right. \tag{13}\]  
where, denoting \(M=2^{m}\),  
\[B_{0}(x)=\left[\begin{array}{cccc}A_{1}x+(B_{0}+B_{1})v_{in}&\ldots&A_{M}x+( B_{0}+B_{M})v_{in}\end{array}\right]\text{, }E_{0}(w)=\left[\begin{array}{cccc}E_{1}w&\ldots&E_{M}w\end{array}\right]\]  
\[D_{0}(x)=\left[\begin{array}{cccc}C_{1}x&\ldots&C_{M}x\end{array}\right]\text {, }F_{0}(w)=\left[\begin{array}{cccc}F_{1}w&\ldots&F_{M}w\end{array}\right]\]
# A Bearing Fault Diagnosis Method Based on Wavelet Denoising and Machine Learning
## 3 Results and Discussion
### Display of Various Index Results of Different Noise Reduction Methods
#### 3.2.2 Wavelet Packet Analysis Denoising Index Analysis  
The level of decomposition has a great influence on the denoising effect. If there are too many levels of decomposition, all the wavelet space coefficients of each level will be processed. It is easy to cause serious information signal loss, decrease in signal-to-noise ratio after denoising, and slow processing due to increased computation. If the level of decomposition is too small, the denoising effect is not ideal. For each signal, there is a level of decomposition that denoises best or is close to best. Therefore, when dealing with this problem, on the premise that the wavelet packet base and its vanishing moment are fixed, different levels of decomposition are selected, and the denoising effects are compared to find the most suitable level of decomposition for the denoising signal. Select the Symlets wavelet family, fix its vanishing moment to 6, change its level of decomposition, and perform four-level of decomposition and five-level of decomposition, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Wavelet** & **MSE** & **SSE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **SNR** \\ \hline db3 & \(1.20\times 10^{3}\) & \(5.04\times 10^{0}\) & \(3.50\times 10^{-2}\) & \(9.88\times 10^{-1}\) & \(4.46\times 10^{-6}\) & \(1.96\times 10^{1}\) \\ db6 & \(6.40\times 10^{-4}\) & \(2.61\times 10^{0}\) & \(2.52\times 10^{-2}\) & \(9.94\times 10^{-1}\) & \(2.59\times 10^{-6}\) & \(2.25\times 10^{1}\) \\ db9 & \(4.60\times 10^{-4}\) & \(1.89\times 10^{0}\) & \(2.15\times 10^{-2}\) & \(9.96\times 10^{-1}\) & \(2.30\times 10^{-5}\) & \(2.39\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analysis results of the first signal index of Type A fault.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Wavelet** & **MSE** & **SSE** & **RMSE** & **R\({}^{2}\)** & **MAE** & **SNR** \\ \hline db3 & \(3.87\times 10^{-3}\) & \(1.59\times 10^{1}\) & \(6.22\times 10^{-2}\) & \(8.64\times 10^{-1}\) & \(3.56\times 10^{-6}\) & \(8.69\times 10^{0}\) \\ db6 & \(1.91\times 10^{-3}\) & \(7.82\times 10^{0}\) & \(4.37\times 10^{-2}\) & \(9.33\times 10^{-1}\) & \(4.72\times 10^{-6}\) & \(1.18\times 10^{1}\) \\ db9 & \(1.33\times 10^{-3}\) & \(5.47\times 10^{0}\) & \(3.65\times 10^{-2}\) & \(9.53\times 10^{-1}\) & \(5.06\times 10^{-6}\) & \(1.33\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis results of the first signal index of Type B fault.  
Tables 3 and 4 show the index analysis results of the first fault signal of type A and type B after denoising by wavelet packet analysis.
# Multi-Grained Temporal Clip Transformer for Skeleton-Based Human Activity Recognition
## 2 Related Works
### Transformer for Skeleton-Based Activity Recognition  
Transformer [33] and its variants [18; 19; 34; 35; 36; 37; 38] have demonstrated strong effectiveness in the field of activity recognition. [PERSON] et al. [36] integrated self-attention and graph convolution to model spatial-temporal dependencies among joints. [PERSON] et al. [37] utilized attention blocks specifically designed to address the unique characteristics of skeleton data, enabling the modeling of spatial-temporal correlations among joints. [PERSON] et al. [38]  
Figure 1: The action of clapping can be regarded as consisting of three sub-actions, “Open hands”, “fold hands”, and “Put down hands”. The action of jumping can be regarded as consisting of two sub-actions, “Preparation Phase”, “Take off”, and “Landing”.  
explored the dependencies between joints and parts using cross self-attention mechanism within the intra-frame. [PERSON] et al. [35] applied self-attention to model multi-scale spatial-temporal features, and leveraged cross-attention to combine cross-features. However, these methods only focus on modeling dependencies between joints or body parts within a single frame, neglecting the correlations between joints in consecutive frames and failing to model the characteristics of sub-actions.
## 3 Results and Discussion
### Aggregates Characterization
#### 3.1.2 Resistance to Wear through the Micro-Deval Coefficient  
The micro-Deval test was performed with aggregates from the 10/14 fraction of SSA, resulting in a micro-Deval coefficient (MDE) of 9.9%. Previous studies [16; 28] also obtained SSA MDE values lower than 11%. The usual MDE values for granitic aggregates are slightly higher (around 15%), but do not exceed 20% [29], showing that SSA has high wear resistance. Thus, no limitations were identified for replacing natural aggregates with SSA in asphalt mixtures.
# Design Procedure of a Mono-Anchor Head for 2360 MPa High-Strength Steel Strand
## 5 Parametric Study
### Determination of Mono-Anchor Head Design Specifications  
The maximum stress obtained by parametric analysis refers to the stress at the top of the wedge hole. The concentration of external force was compared with the maximum stress of the 42 CrMo4 anchor head to be quantified based on Equation (6), calculated in Chapter 3. Concentration factor (k) is distributed according to the diameter and inclination angle as shown in Figure 20, and Equation (12) was derived so that it can be calculated by those parameters. Therefore, when the diameter and internal inclination angle are determined, the concentration factor can be calculated using Equation (15) and the maximum stress during elastic behavior can be calculated by applying the calculated concentration factor to Equation (6).  
\[k=284.8-20.58\theta-1.92D \tag{15}\]  
Figure 19: Plastic hoop strain on the top of the wedge hole.  
Figure 20: Stress concentration factor.  
Then, the relationship between the plastic hoop strain distributed in the C45 anchor head and the stress exceeding the yield strength was established. In Figure 21, the \(x\)-axis is the yield strength excess stress, and it is calculated as the difference between the maximum elastic stress for each configuration and the yield strength of the anchor head. The \(y\)-axis represents the plastic hoop strain occurring at the top of the wedge hole due to the stress exceeding the yield strength. The points shown in the graph were derived as a quadratic equation with 0 as the y-intercept as in Equation (16).  
\[\epsilon_{P.Hoop}=5.68\times 10^{-9}\big{(}\sigma_{concentration}-f_{y}\big{)}^{2 }+1.07\times 10^{-5}\big{(}\sigma_{concentration}-f_{y}\big{)} \tag{16}\]  
Taking into account the relationship between the yield strength and the plastic hoop strain, an additional FE analysis was performed for verifying Equation (16). The arbitrary designated yield strength values are 343 MPa and 800 MPa, and the calculations of the plastic hoop strain for each configuration based on Equation (16) and the FE analysis results were compared. The two verification results are described in Figure 22a,b, respectively. In the proposed equation, when the difference between the elastic maximum stress (\(\sigma_{concentration}\)) and determined yield strength (\(f_{y}\)) of the same configuration exceeds 300 MPa, the plastic hoop strain overestimates compared to FEM; when it is below 300 MPa or exceeds 700 MPa, the plastic hoop strain is underestimated compared to FEM. However, all cases here show similar results and it is judged that there will be no limit when evaluating whether the plastic hoop strain allowance is met. Based on the equations proposed in this paper, anchor head design parameters with safe performance can be determined. Figure 23 shows the minimum yield strength of steel that can be applied according to the shape of the anchor head.  
Figure 21: Plastic hoop strain for stress exceeding the yield strength.  
Figure 22: Comparison of the FEM and Equation (15) results at the specified yield strength.
## 4 Channel State Prediction via ML Algorithms
### Numerical Results  
In this section, we present model performance evaluation results from several experiments, in which we trained and tested the proposed DNN on the datasets \(\mathcal{D}_{X,\Delta\mathrm{s}}\) with \(X\in\{\mathrm{AoA},\mathrm{AoD}\}\) and \(\Delta\mathrm{s}=1,30,60,100\) cm.  
We start with Figure 8, presenting the achievable accuracy of predicting the coordinates of the elements in the frame via the considered metrics with respect to various values of the step size. As one may observe, the prediction probability of the AoD is better than the prediction probability of the AoA. The rationale is that, as we have seen in Section 3, the correlation between the consecutive frames is higher for the departing rays as compared to the incoming rays. Also, the proposed model can achieve approximately 97 % prediction accuracy for the step size of 1 cm, and the achieved prediction decreases to 75 % for the step size of 100 cm. This behavior is expected as the dependence between the consecutive frames decreases when the step size between two points on the trajectory increases.  
Further, in Figure 9, we present the observed MSE associated with predicting the path loss of the elements of the frame with respect to various values of the step size. The proposed model can achieve \(5\times 10^{-4}\) MSE for the step size of 1 cm. As one may observe, MSE remains almost intact up until the step size of 60 cm, and then it quickly increases to \(5\times 10^{-3}\) for the step size of 100 cm.  
Figure 8: Model accuracy as a function of the step size \(\Delta\mathrm{s}\).  
Figure 9: MSE as a function of the step size \(\Delta\mathrm{s}\).  
Figure 10 shows the model accuracy vs. the dataset size \(|\mathcal{D}|\) obtained via 5-fold cross-validation. We observe that the model accuracy does not improve substantially after \(|\mathcal{D}|=\) 20,000, which justifies our choice of the dataset size.  
Finally, in Figure 11, we present some frame predictions. Here, three data points corresponding to consecutive Rx trajectory steps were chosen randomly from validation subsets of datasets \(\mathcal{D}_{\text{AoA,1}}\), \(\mathcal{D}_{\text{AoA,60}}\), and \(\mathcal{D}_{\text{AoA,100}}\). Predicted frames are shown next to their ground-truth values, so as to illustrate the frame dynamics from step to step as well as the prediction efficiency. As one may observe, the difference between the true and predicted frames increases as the step size becomes larger. Specifically, for \(\Delta s=1\) cm, the proposed algorithms predict the actual values almost perfectly for all the frame numbers, while for \(\Delta s=100\) cm, there are occasional spikes in predicted data that become larger as the frame number increases.  
Figure 11: _Cont._  
Figure 10: Model accuracy as a function of the dataset size \(|\mathcal{D}|\) for step size \(\Delta s=30\) via 5-fold cross-validation.
## 3 Results and Discussion  
### Boundary Layer Profile  
#### 3.1.1 Reynolds Number Independence Study  
Dimensionless streamwise (\(U\)/\(U_{inlet}\)) and vertical (\(W\)/\(U_{inlet}\)) velocity profiles in the vertical direction (\(Z\)) at Line 1 in Figure 3 are compared in Figure 5 for different free stream wind speeds at the wind tunnel inlet (\(U_{inlet}\)), in order to identify the critical \(U_{inlet}\) above which the flow can be considered Reynolds number independent [41]. The results showed that under \(U_{inlet}\) of 4 m\(\cdot\)s\({}^{-1}\) and 6 m\(\cdot\)s\({}^{-1}\) conditions, the values of both \(U\)/\(U_{inlet}\) and \(W\)/\(U_{inlet}\) were different to the other cases. When \(U_{inlet}\) increased from 6 m\(\cdot\)s\({}^{-1}\) to 8 m\(\cdot\)s\({}^{-1}\), the average relative change of dimensionless air velocities was 0.391 and 0.006 for \(U\)/\(U_{inlet}\) and \(W\)/\(U_{inlet}\), respectively. In contrast, when \(U_{inlet}\) increased from 8 m\(\cdot\)s\({}^{-1}\) to 10 m\(\cdot\)s\({}^{-1}\), the average relative change was only 0.003 and 0.001 for \(U\)/\(U_{inlet}\) and \(W\)/\(U_{inlet}\), respectively. It indicated that when \(U_{inlet}\) exceeded 8 m\(\cdot\)s\({}^{-1}\), the dimensionless wind profile did not change considerably with further increase of the wind tunnel wind speed. Therefore, the Reynolds number independence was reached and a fully-developed turbulent flow was obtained at \(U_{inlet}\) of 8 m\(\cdot\)s\({}^{-1}\).
## 2 LiCSAR and LiCSBAS
### Visualization of the Results  
Interpretation of the derived time series is as important as the accurate derivation of the time series. However, the huge amount of associated data (e.g., \(\sim\)4 GB for 100 images of an entire frame) often makes smooth visualization and intuitive interpretation problematic. Selection of an appropriate reference area is also a critical step towards obtaining meaningful and accurate displacements for interpretation, as all measurements are relative rather than in an absolute reference frame [23]. However, it is not possible to interactively examine and find out the appropriate reference by trial and error in most software.  
LiCSBAS is equipped with an interactive time series viewer composed of two windows (graphical user interfaces). The first image window displays velocity, cumulative displacements, and noise indices (Figure 4a). When clicking on a pixel of interest, the associated time series with and without the spatiotemporal filter is promptly plotted in the second time series window (Figure 4b). Any network gaps are clearly displayed, which alerts the user to carefully interpret the time series connected by the temporal constraint (see Section 2.4.3). The reference area is shown by a black rectangle, and it can be interactively changed and examined in the image window.  
The viewer can smoothly display the results of the time series analysis, even with a huge file size, without consuming a large amount of computer memory, owing to the HDF5 file format, from which the requested part of a large dataset can be quickly read.
# In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks
## 5 Analysis
### Machine Learning Approaches
The performance of these trained models is then evaluated using the remaining 40 percent of the labeled dataset, referred to as the \"test dataset\". Various train-test splits, ranging from 50-50 to 90-10, were tested, and the 60-40 split was found to maximize accuracy while minimizing overfitting. Finally, the final trained model was applied to the unlabeled dataset to identify ties and ballasts. Initially, the measured distances from one of the LiDAR sensors (e.g., the left sensor) to tie or ballast surfaces were used as \"input\" to train the ML models using both Decision Tree and Logistic Regression algorithms. The difference between the left and right LiDAR sensor measurements was explored as an input for training the models. Finally, to improve the models further, the standard deviation of the difference between the left and right measurements was also evaluated to train the models. In summary, the measured distance from the LiDAR sensors to the tie or ballast surfaces was considered to be the \"input\" for training the ML model in the first place and then gradually upgraded to the difference between the left and right LiDAR sensors' measurements, and then the standard deviation of the difference between the left and right measurements was used to achieve higher accuracy and robustness. In Section 5.3, the logic behind these models will be described further, and the most efficient and robust model will be identified. Figure 11 illustrates how six different models were developed based on three different inputs (i.e., preprocessing methods) and two different algorithms.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 4 Conclusions  
We propose a novel federated learning method called FLGQM for defense against malicious clients. By designing a defense mechanism from geometric metrics, we can geometrically exclude malicious clients. By incorporating quality metrics in addition to geometric metrics, we can effectively detect and mitigate poisoning attacks that may go unnoticed when relying solely on geometric metrics. This comprehensive approach enhances the robustness of our defense mechanisms. Our approach combines both geometric and qualitative aspects, thus it can ensure the robustness of federation learning.  
Our evaluation on two datasets shows that FLGQM can achieve robustness against most malicious clients. Moreover, even in scenarios involving a substantial number of malicious clients, FLGQM can successfully train a global model that performs comparably to the global model learned by FedAvg in the absence of attacks.  
There are several potential avenues for improvement in future work. One possibility is to explore the development of an optimal method, potentially utilizing other geometric statistics, to enhance the accuracy of geometric metrics. We will also explore the design of a local parameter poisoning attack against FLGQM and subsequently optimize FLGQM to enhance its resilience against such attacks. In addition, we consider the introduction of a reputation mechanism to enable a better approach to union and to coalition client selection.  
Conceptualization, S.L.; methodology, S.L. and X.X.; validation, F.W.; formal analysis, S.L. and X.X.; investigation, S.L.; data curation, X.X. and Q.Z.; writing--original draft preparation, S.L. and X.X.; writing--review and editing, S.L. and C.Z.; supervision, Y.J.; funding acquisition, M.W. 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 (No. 62076139), Jiangsu Key Development Planning Project (BE2023004-2), Natural Science Foundation of Jiangsu Province (Higher Education Institutions) (20 KJA520001), Jiangsu Hongxin Information Technology Co., Ltd Project (JSGS2301022 EGN00), Future Network Scientific Research Fund Project (No. FNSRFP-2021-YB-15).  
Not applicable.  
Figure 4: Accuracy of compared federated learning algorithms on MNIST with different attacks.
# Evaluating the Impact of Drone Signaling in Crosswalk Scenario
## 3 Materials and Methods
### Algorithm
#### 3.4.2 Phase 2: Progressive Speed Adjustment  
During this phase, the drone focuses on the car progression up to the braking distance. The idea is to compare speed and acceleration profiles with reference profiles \(v_{c,ref}\) and \(a_{c,ref}\), and this operation is repeated at the expiration of each perception and reaction distance \(d_{c,p-r}\) up to the braking distance.  
The reference profiles are designed for energy minimization purposes and are similar to those presented in Reference [43] (road speed sectioning). The reference speed varies linearly with respect to the distance (constant speed grade \(\delta v\), see Equation (14)), and the acceleration is then a negative constant (it is a deceleration before the driver starts to brake; see Equation (15)).  
\[v_{c,ref2}(d_{c})=\frac{\delta v}{d_{c,t}-d_{b,ref}}\cdot d_{c}+(v_{0}+\delta v -\frac{d_{c,t}\cdot\delta v}{d_{c,t}-d_{b,ref}}), \tag{14}\]  
\[a_{c,ref2}(d_{c})=\frac{-\delta v}{d_{c,t}-d_{b,ref}}, \tag{15}\]  
\[d_{b,ref}=d_{stop}+\frac{v_{0}^{2}}{2\cdot f_{1}\cdot g}. \tag{16}\]  
If \(v_{c}(d_{c,t}-k\cdot d_{c,p-r})\geq v_{c,ref2}(d_{c,t}-k\cdot d_{c,p-r})\) or \(a_{c}(d_{c,t}-k\cdot d_{c,p-r})\geq a_{c,ref2}(d_{c,t}-k\cdot d_{c,p-r})\) (considering the \(k^{th}\) iteration), this means that either the vehicle is still traveling at too high speed or that it is not decelerating enough. In this case, the drone sends an alert to the driver to reduce the speed, unless the drone considers that the trip is safe and thus it remains silent. The drone forecasts and anticipates the braking distance. However, the most important part of the process holds in the last meters before the crosswalk. If the vehicle has very well predicted its speed within the allotted time, phase 3 does not take place if the pedestrian had time to leave the vehicle driver's visual line of sight.  
Figure 4: Proposed algorithm consisting in three phases: phase 1 (determining the driver behavior), phase 2 (progressively adjusting the vehicle speed), and phase 3 (braking).