esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Texture Extraction and \(V^{25}_{cmax,0}\) Models
#### 2.4.4 Convolutional Neural Network  
In this study, we adopted two typically convolutional neural network architectures, including AlexNet [38] and ResNet [54], to extract spatial information from UAV images. ResNet is more complex and has a deeper structure than AlexNet (Figure S4). It is important to note that the input images should maintain the same size when fed into the CNN. Since images with a resolution of 6.5 cm/pixel are too large for the CNN to handle efficiently, UAV images were resampled to a 1m resolution and clipped by the external rectangle (see Section 2.1.2) to maintain the same size. Pixels outside the flux footprint climatology were set as 0. The five-band multispectral images were concatenated into an input tensor (430 \(\times\) 420 \(\times\) 5). The input images were preprocessed using the standardization and then fed into the CNN to estimate field-scale \(V_{cmax,0}^{25}\).  
The different combinations of texture features for \(V_{cmax,0}^{25}\) were evaluated (Table 3). For the mean and standard deviation of the 5-band reflectance, GLCM-based texture features, and LBPH-driven models, the deep neural network (DNN) was employed to estimate field-scale \(V_{cmax,0}^{25}\).  
\[\begin{array}{l}\mathbf{h}_{1}=f_{0}(\mathbf{w}_{0}\mathbf{x}+\mathbf{b}_{0})\\ \mathbf{h}_{l}=f_{l}(\mathbf{w}_{l-1}\mathbf{h}_{l-1}+\mathbf{b}_{l-1})\\ \mathbf{y}=\mathbf{w}_{l}h_{l}+\mathbf{b}_{L}\end{array} \tag{22}\]  
where \(x\) is the input layer, namely the combination of the 5-band reflectance and corresponding texture features; \(h_{l}\) is the \(l\)-th hidden layer; \(w_{l}\) and \(b_{l}\) are the weights and bias of the \(l\)-th layer; \(f_{l}\) is the activation function of the \(l\)-th layer. The performance of the deep neural network highly relies on hyperparameters, including the number of hidden layers, the number of neurons, the selection of activation function, and the dropout value. We used the genetic programming algorithm to optimize the hyperparameters. The search space was set as follows: (1) the maximum number of hidden layer was set as 4; (2) the number of neurons for each hidden layer was selected from the set [16,32,64,128,256,512,1024]; (3) the activation function for each hidden layer was selected as either ReLU or sigmoid; (4) the dropout rate for each hidden layer was selected from an interval of 0 to 0.5. The daily \(V_{cmax,0}^{25}\) and corresponding multispectral surface reflectance for each land cover type were randomly apportioned into training and validation sets, with an 80-20 split. The former was used to train the weights and biases, and the latter was employed to evaluate the generalization error.
## 5 Model Application
### Sensitivity Analysis of Different Influencing Factors  
We constructed different ANN models to understand factors influencing link travel time, such as weather, and temperature. As mentioned in Section 3, the input features of neural network model includes day of the week (\(wd_{i}\)), which 30 min of the day (\(wh_{i}\)), expected speed (\(E_{i}(v)\)), standard deviation of speed (\(D_{i}(v)\)), degree ratio between target link and adjacent link (\(\Delta R_{i}(degr)\)), length ratio between target link and adjacent link (\(\Delta R_{i}(leng)\)), temperature (\(temp\)) and rainfall (\(rain\)). The output of ANN models was the travel time ratio between target link and adjacent link (\(\Delta R_{i}(t)\)). The role each feature plays in predicting travel time in the neural network model needed to be verified. Therefore, different models were constructed with different factors and the sensitivity of factors on travel time prediction was analyzed. We constructed different neural network models by combining input features and used the average absolute percentage error (MAPE) to measure the performance of these ANN models. To construct models conveniently, we use simple variable  
The ANN model without rain was trained as Model H. The input feature of Model H includes \(F_{1}\), \(F_{2}\), \(F_{3}\), \(F_{4}\), \(F_{5}\), \(F_{6}\) and \(F_{7}\).  
In the comparison experiment, we used the same dataset to train different ANN models and the same dataset was used to test those trained models using MAPE. Consequently, we conducted experiments using the same training dataset and four groups of testing dataset for each trained model to test the trained model. It can reflect the influence of model constituted by different factors on link travel time prediction. As shown in Table 11, it quantifies the influence of different factors. In general, model M has smaller MAPE than other models. Figure 11 illustrates the influence of different models on the performance of ANNs under the condition of different testing dataset. As shown in Figure 11, different factors influence the prediction of link travel time. Model M had greater prediction accuracy overall, as the MAPE value was lower. Models with the three factors day of the week, 30 min period of the day, and the expected speed of adjacent link influenced link travel time prediction had a higher value of MAPE than those models without them. The expected speed of an adjacent link had the greatest effect on link travel time prediction among those three factors; the biggest MAPE value appeared in the model excluding expected speed in link travel time prediction. The degree ratio and temperature slightly influence link travel time prediction. Rainfall affects link travel time prediction but is not as important as time of day as expressed by 30 min interval, expected speed of adjacent links, or day of the week. The MAPE value was smaller when rainfall was excluded from the model, as shown in the sensitivity analysis seen in Figure 11.
# Towards Federated Learning with Byzantine-Robust Client Weighting
## 3 Experimental Evaluation
### Experimental Setup
#### 3.1.3 The Clients and Attackers  
We examine a _model negation attack_[5]. In this attack, each attacker \"pushes\" the model towards zero by always returning a negation of the server's model. When the data distribution is balanced, this attack is easily neutralized since Byzantine clients typically send easily  
Figure 2: Histogram of the sample partitions of the MNIST (**left**) and Shakespeare (**right**) datasets.  
detectable extreme values. However, in our unbalanced case, we demonstrate that without our preprocessing step, this attack cannot be mitigated even by robust aggregation methods.  
In order to provide comparability, we additionally follow the experiment shown by [PERSON] et al. [12], in which 10% of the clients use a _label shifting attack_ on the MNIST task. In this attack, Byzantine clients train normally except for the fact that they replace every training label \(y\) with \(9-y\). The values sent by these clients are then incorrect but are relatively moderate in value, making their attack somewhat harder to detect.  
We first execute our experiment without any attacks for every server aggregation and preprocessing combination. Then, for each attack type, we repeat the process two additional times: (1) with a single attacker that declares 10 million samples, and (2) with 10% attackers that declare 1 million samples each.
## 3 Results
### Field Tests and Applications under Real Conditions

Table 6: Surface variation and omnivariance values of the point cloud of the rests of the column, fourth case study. Figure 12: Sections of the point clouds of the laboratory room; scalar field refers to the planarity value: 1 cm resolution scan (**a**), 1.5 cm resolution scan (**b**), 2 cm resolution scan (**c**). Figure 13: Sections of the scan closure zones: section at the closure of the scan of the lateral surface (**a**), section at the vertical closure of the scan on the third column drum (**b**). Observing the sections, it can be seen that the scan with a resolution of 1.5 cm (Figure 14b,e) was most affected by surface splitting related to drift problems. Surface splitting occurred in the horizontal direction when the operator scanned the side surface of the object and in the vertical direction when the operator scanned the column drum at the top of the column to close the scan. The 1 cm resolution scan (Figure 14a,d) showed minor surface splitting on the third column drum compared to the 1.5 cm resolution scan. The 2 cm resolution scan appears to be the best of the three; the scan only presented small problems at the junctions between one column drum and another. In addition, in order to assess the accuracy and precision of the point clouds obtained in the third and fourth case studies, the cloud-to-cloud (C2C) distance was calculated with respect to a point cloud surveyed with the TLS and photogrammetric method. In the third case study, the 3D point cloud was obtained using a HDS3000 Terrestrial Laser Scanner, which has a position accuracy of 6 mm@50m. In the fourth case study, a photogrammetric survey using digital single lens reflex camera and a structure from motion-multi-view stereo (SfM-MVS) approach was performed [23]. In particular, a Nikon D3300 with a Nikkor 20 mm f/2.8D fixed focal lens was used for the 3D survey [24]. The point cloud was built in an Agisoft Metashape environment; the root mean square equivalent (RMSE), evaluated on six ground control points (GCPs), was 0.001 m.  
To compare the point clouds, it was necessary to subsample in order to obtain a statistically fair comparison. Table 7 shows the C2C values of mean and standard deviation of C2C distance for the third and fourth case studies between smartphone point clouds and reference point clouds; in addition, in Appendix B, we reported the histograms (Figure A6). Figure 14: Sections of the point cloud in the closure zones: vertical section (A-A) of the point cloud with resolution 1 cm (**a**), vertical section (A-A) of the point cloud with resolution 1.5 cm (**b**), vertical section (A-A) of the point cloud with resolution 2 cm (**c**), horizontal section (B-B) of the point cloud with resolution 1 cm (**d**), horizontal section (B-B) of the point cloud with resolution 1.5 cm (**e**), horizontal section (B-B) of the point cloud with resolution 2 cm (**f**).
## 5 Conclusions  
The dispersion and multimodal characteristics of Lamb waves have demonstrated the difficulty of signal separation for each component modes. The blind signal separation method has also been used in the field of ultrasonic guided waves. However, the separation method used in the past can only deal with the signals separated under certain conditions, and the experimental conditions are harsh. For the conventional one-shot and one-receiving Lamb wave signals, one location point can only measure a set of signals, and it is impossible to directly use the blind separation method for signal separation. On the basis of summarizing the advantages and disadvantages of previous blind separation methods, combining with the dispersion compensation method, the signal dispersion is simplified and then combined with the blind separation method. The multimodal Lamb wave signal could be separated.  
Aiming at the separation, we have presented a mode separation method for multimodal ultrasonic Lamb waves using dispersion compensation and fourth-order cumulant ICA in this paper. The four-order cumulant ICA algorithm is used to separate each mode signal from the overlapping Lamb wave signals after the dispersion compensation. Experimental results confirm the effectiveness of the proposed method. It is also noticed that there are some errors for the separated signals in the experiments. The introduction of particle swarm optimization or their optimization methods [24; 25; 26] to the present method might reduce the errors further. Regardless, the present method is helpful for the signal processing of multimodal Lamb wave dispersion.  
[PERSON] and [PERSON] designed, accomplished, and evaluated this study.  
This research was funded by project grants from the 333 high level personnel training project Jiangsu Province of China, the Natural Science Foundation of Jiangsu Province of China (BK20161536), and the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
The authors declare no conflict of interest.
# Simulation Model and Method for Active Torsional Vibration Control of an HEV
## 2 Materials and Methods
### Structure and Working Principles of a Series-Parallel Hybrid System
#### 2.1.3 Torsional Vibration Active Control System  
In this paper, the torsional vibration active control module is added to the original vehicle control system. The hierarchical position in the simulation platform is shown in Figure 3, and the specific control strategy logic is shown in Figure 4.  
Figure 3: Simulation platform architecture.  
Figure 2: Logic threshold energy management strategy.  
When the vehicle is running, the driver operates the accelerator pedal or the brake pedal according to road conditions and the state of the vehicle. Using this input and the information measured by the vehicle control system, an instruction set is sent to each component actuator, and the corresponding actions are performed.  
The specific logic of the active torsional vibration control strategy is: Through controller and driver behavior, a real-time working mode can be obtained, and future mode switching can be predicted thereby predicting the system's excitation signal or future large disturbance signals. With the real-time status information of the vehicle collected by the sensors, the state observer or estimator can be designed to estimate the key position torque fluctuation, angular acceleration, and speed signals that cannot be obtained by the sensors. Based on this real-time information, an active control strategy controls the torsional vibration using the engine, motors, and clutch. In this paper, we select the existing speed sensors, for example, based on the Hall sensor to measure the engine crankshaft front/flywheel and the wheel, based on the resolver to measure the angle and speed of the two motors.  
The active control method employed in this paper is: During the start and stop mode of the APU, PID calculation is performed on the engine-ISG speed difference, and the ISG motor output torque is actively controlled to reduce the resonance degree. When switching from pure electric drive mode to hybrid drive mode, the angular acceleration fluctuation of the TM is monitored in real time, and the reverse torque of the TM is output to reduce the switching impact. The specific method is shown in Figure 5:  
Figure 4: Torsional vibration active control strategy diagram.  
Figure 5: Active torsional vibration control method.  
The PID active control method and control parameter selection in the start-stop process are mainly based on the literature [11]. When the mode is switched, this paper uses the amplitude of the angular acceleration of the TM and multiplies it by its own moment of inertia as the compensation torque.
### Wavelet Transformation
#### 1.1.2 Continuous Wavelet Transform (CWT)  
Continuous wavelet transform is the convolution of the signal x(t) with the scaled and translated version of the mother wavelet function \(\Psi\)(t). CWT is given by the following equation:  
\[X(a,b,t)=\int_{-\infty}^{+\infty}x(t)\cdot\frac{1}{\sqrt{|a|}}\Psi\left(\frac{ t-b}{a}\right)dt \tag{2}\]  
Figure 1: db4 and sym4 wavelets. The sym4 wavelet is from the Symlet family, and db4 is from the Daubechies family. These wavelets show similarities with the ECG signal waveform and are used in analyzing the ECG signal waveforms.  
where a is the scaling parameter and b is the translational parameter.  
CWT is used in time-frequency analysis to process spectrograms and filtering purposes of time-localized frequency components.
###### Abstract  
Measurements taken on a historical dike in the Netherlands over one year showed that interaction with the atmosphere led to oscillation of the piezometric surface of about 0.7 m. The observation raised concerns about the long-term performance of similar dikes and promoted a deeper investigation of the response of the cover layer to increasing climatic stresses. An experimental and numerical study was undertaken, which included an investigation in the laboratory of the unsaturated behavior of a scaled replica of the field cover. A sample extracted from the top clayey layer in the dike was subjected to eight drying and wetting cycles in a HYPROPIM device. Data recorded during the test provide an indication of the delayed response with depth during evaporation and infiltration. The measurements taken during this continuous dynamic process were simulated by means of a finite element discretization of the time-dependent coupled thermohydraulic response. The results of the numerical simulations are affected by the way in which the environmental loads are translated into numerical boundary conditions. Here, it was chosen to model drying considering only the transport of water vapor after equilibrium with the room atmosphere, while water in the liquid phase was added upon wetting. The simulation was able to reproduce the water mass balance exchange observed during four complete drying-wetting cycles, although the simulated drying rate was faster than the observed one. The numerical curves describing suction, the amount of vapor and temperature are identical, confirming that vapor generation and its equilibrium is control the hydraulic response of the material. Vapor generation and diffusion depend on temperature; therefore, correct characterization of the thermal properties of the soil is of paramount importance when dealing with evaporation and related non-steady equilibrium states.  
HYPROP; TH coupled analysis; suction; evaporation; non-steady equilibrium Article
## 4 Experiment Setup and Results
### Results  
In our experiments, we observed a decrease in energy consumption of the chosen movements in the interval from 10% to almost 40%, as can be seen in Table 2. The optimization used a PSO swarm size  
Figure 8: End positions of the optimized trajectories.  
of 30 particles and required up to two hundred iterations (eras) for this result. We compare measured electric current in individual joints during movej movement and optimized movement in the Figure 9. The physical reason for the energy reduction lies, at least partly, in finding a path during which the center of mass of the robotic arm is on average closer to the vertical axis passing through the robot base.  
In Figure 10, we can see that it would be difficult to program such a movement by hand. Joint 1 remains at the beginning of the movement in the initial position, while joint 2, which is mainly responsible for the reduction of torque due to gravity, goes closer to the vertical axis that intersects the robot base. After this decrease of momentum in inertia, lower energy consuming rotational acceleration in joint 1 is performed. Similarly, joint 4 in the optimized case goes to the final position faster than in the reference case, because it leads to a faster route for more momentum and energy-efficient position.  
Interestingly, the energy optimization with proper total energy consumption computation, i.e., either taken from a reasonable theoretical model or real measurements (our case), leads to a \(\dot{\theta}_{i}(t)\sim 0\) on the trajectory end. Accordingly, this is a natural result of energy optimization, unlike many cases of energy optimization methods, where we first mathematically enforce zero joint velocities at  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Movement Type**} & \multicolumn{2}{c}{**Energy Consumed (Mean)**} & \multirow{2}{*}{**Savings**} \\ \cline{2-3}  & **Movej [J]** & & \\ \hline Movement A & 495 & 377 & 23\% \\ \hline Movement B & 634 & 400 & 37\% \\ \hline Movement C & 340 & 305 & 10\% \\ \hline Movement D & 400 & 335 & 16\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of energy consumption for given movements.  
Figure 9: Joint currents for movement A : the optimized trajectory (including control points of Bezier curves) and trajectory obtained by execution of default movej command on UR3.  
the end of the trajectory by hand, and we then optimize the trajectory. It is a natural result of the energy optimization process that there is a soft start and a slowdown at the end of the trajectory. For other cases, movements B and C, we have observed similar tendencies to undergo such energetically reasonable behavior during the optimization process.  
During the optimization process, one can see that the robot arm movements converge to movements that resemble more and more natural human-like motions, which we assume to be energetically optimized.
## 3 Results
### Normal Distribution and Variance Homogeneity  
Only six variables (age: \(p=0.002\); bicipital skinfold: \(p=0.020\); suprailiac: \(p=0.025\); Yo-Yo IRI: \(p=0.010\); mathematics: \(p=0.003\); science: \(p=0.028\)) were not normally distributed. Four parameters (mass: \(p=0.036\); %BF: \(p=0.033\); mathematics: \(p=0.026\), SJ: \(p<0.001\)) were heterogenous in variance. Otherwise, all \(p\)-values were \(>0.053\) (BMI), indicating variance of other variables was heterogenous.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Test** & **Session One Mean \(\pm\) SD** & **Session Two Mean \(\pm\) SD** & **ICC (95\% CI)** & **CV (\%) (95\% CI)** \\ \hline
15 m sprint (s) & 2.75 \(\pm\) 0.29 & 2.81 \(\pm\) 0.29 & **0.98** (0.82–0.99) & **1.9** (1.4–2.8) \\
30 m sprint (s) & 5.07 \(\pm\) 0.75 & 5.03 \(\pm\) 0.77 & **0.94** (0.88–0.97) & **6.9** (5.4–10.7) \\ Agility T-half test (s) & 7.10 \(\pm\) 0.58 & 7.13 \(\pm\) 0.58 & **1.00** (0.99–1.00) & **0.4** (0.3–0.5) \\ CMJ (cm) & 34.8 \(\pm\) 5.32 & 33.6 \(\pm\) 5.33 & **0.99** (0.31–1.00) & **1.4** (1.1–2.1) \\ SJ (cm) & 30.8 \(\pm\) 7.43 & 30.1 \(\pm\) 7.31 & **0.98** (0.96–0.99) & **1.1** (0.8–1.6) \\ Medicine ball throw (m) & 7.92 \(\pm\) 0.90 & 7.76 \(\pm\) 0.93 & **0.98** (0.89–1.00) & **1.2** (0.9–1.8) \\ \hline \end{tabular}
\end{table}
Table 1: Physical fitness tests obtained from two sessions two weeks apart (\(n=31\)). Descriptive statistics (mean \(\pm\) standard deviation (SD)) and intrarater reliability are presented for each test. Intraclass correlation coefficient (ICC) \(\geq\) 0.75 and coefficient of variation (CV) \(\leq\) 10% are highlighted in bold.