esa-sceva/satcom-chunk-collection huggingface dataset:
# New Framework for Human Activity Recognition for Wearable Gait Rehabilitation Systems
## 4 Proposed Approach for Lower Limb Activity Detection
### Preprocessing Phase
#### 4.1.2 Denoising  
The signal obtained during the detection and collecting phases might be influenced by a variety of circumstances. To improve the quality of the signal, the denoising phase is added to the preprocessing step before employing the decomposition technique. In general, a variety of noise types are generated, such as ambient noise from the human body and noise from the apparatus that records muscle signals. A high-pass filter can reduce both types of noise since they are generated by random variables. The irregular firing rate of the motor causes the signal to become unstable, which leads to the final component.  
The noise frequency components are approximately 0-19 Hz. Since combined noise and artifacts cannot be eliminated by filtering, the current method denoises the sensory signals using a discrete wavelet transform [41]. For instance, orthogonal Meyer wavelets and Daubechies wavelets are frequently used to lower noise in biological signals. The chosen wavelets usually have profiles that resemble the forms of action potentials from motor units. Based on wavelet analysis, wavelets break down signals into several time-scale components [42].  
From the literature, wavelets typically employed for denoising biological signals include the Daubechies (db2, db8, and db6) wavelets and orthogonal Meyer wavelet [43].  
Figure 10: (**a**) Example of filtered EMG signals representing fast walking pattern. (**b**) Example of filtered IMU signals representing ramp walking pattern.  
Typically, wavelets that resemble the MUAP in shape are selected. The resulting discrete wavelet coefficients are then thresholded using a universal threshold approach [44]. The original EMG signals and the denoised EMG signal of length 2000 samples are shown in Figure 11 using different thresholding approaches.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 3 Effect of Sub-Mirror Position Error on WFE
### Decenter in X and Y Direction  
The sub-mirrors at different positions were selected so that they were eccentrically shifted along the X and Y directions radially within the range of 2.5 \(\upmu\)m, and the variation of the systematic WFE RMS with respect to the amount of decenter was obtained as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Surface** & **Radius/mm** & **Thickness/mm** & **Conic** \\ \hline PM & \(-\)15,879.70 & \(-\)7169.00 & \(-\)0.99670 \\ SM & \(-\)1778.90 & 7965.30 & \(-\)1.65980 \\ TM & \(-\)3016.20 & \(-\)1844.10 & \(-\)0.65950 \\ FSM & \(+\infty\) & 3027.61 & 0 \\ Image & \(-\)3040.46 & - & - \\ \hline \end{tabular}
\end{table}
Table 1: Optical system parameter.  
Figure 3: Layout of segmented telescopes.  
For the sub-mirror X-direction decenter, three sub-mirrors with different X-positions, A1, B1, and C2, are selected respectively, and when the sub-mirror X-direction decenter error is 2.5 \(\upmu\)m, the RMS of the system's WFE is 0.0316\(\lambda\), 0.0815\(\lambda\), and 0.1564\(\lambda\), respectively. Among them, the X-coordinate value of C2 is the largest, and the theoretical and simulation results indicate that the sub-mirror X-direction decenter error has the largest effect on the system's WFE RMS of the system WFE RMS.  
For the sub-mirror Y-direction decenter, similar to the X-direction decenter, four sub-mirrors with different Y-positions, A2, B1, B2, and C1, are selected respectively, and when the sub-mirror Y-direction decenter error is 2.5 \(\upmu\)m, the RMS of the system's WFE is 0.0317\(\lambda\), 0.0585\(\lambda\), 0.1354\(\lambda\), 0.1766\(\lambda\), respectively, where the C1 has the largest influence on the RMS of the system's WFE.
## 1 Introduction
These findings confirm that the proposed decoupling approach effectively mitigates inter-axis coupling, reduces measurement errors, and improves both the accuracy and efficiency of 3D electric field measurement.
## 1 Introduction
Recent studies, such as on the multilayer CNN-GRUSKIP model proposed by [PERSON] et al. [24], which is based on transformers, and on the iTransformer model for the short-term multi-step prediction of highway traffic speeds [25], have provided new perspectives and methods in this field. These studies not only demonstrate the potential application of deep learning technology in traffic flow prediction, but also offer new approaches to enhance the accuracy and real-time capabilities of such predictions. In this paper, we present a new method, which aims to take advantage of land cover information to accurately extract moving vehicles, thereby increasing the accuracy of traffic flow parameter estimation. There are two important contributions made by this study. Firstly, although satellite video has been applied in traffic flow parameters at present, most research aims to explore its feasibility, and land cover information is rarely used. This point was studied in this study. Secondly, the detected moving vehicles were used to form trajectories, these trajectories became discontinuous when vehicles were obscured. In this paper, we propose to a method to repair the broken trajectories and then use them to recover the obscured vehicles.
## 4 Case Studies of Real Wind Power Data
### Experimental Results
#### 4.2.5 Discussions  
Based on the experimental results, we observe that the NN-based models outperform the non-NN-based models. By breaking the training datasets into ramp and non-ramp training datasets for distinct classes of wind turbines, the performance of the NNs can be improved.  
Further, the proposed DSPOT-based ramp classifier can better split the ramp and non-ramp events using dynamic thresholds and therefore better capture the heterogeneous dynamics of wind farm generation. Moreover, the proposed DSN can automatically adapt to the changing dynamics of wind farm generation over time, and the model updating time for the DSN is low. Specifically, the number of ramp-up and ramp-down events in the training datasets is less than 4000, and updating the corresponding models takes only about 3-5 min using a machine with Dual-sockets Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20 GHz. The updating time is much less than the forecasting horizons, and therefore our model can work well in practice.  
As shown in the experiments, the performance improvement for the point forecast and the distributional forecast is smaller in the 10 min horizon while the improvement is higher in the 60 min horizon. This might be due to the fact that some baseline models (e.g., the AR, SNEAT, LSTM, and ANN) are not considering ramp events which leads to a deeper forecast degeneration with a longer prediction horizon. Although the SSEN in our previous work [38] considered the ramp events, it leverages a fixed threshold to distinguish the ramp events. Because our proposed framework adjusts the ramp thresholds dynamically, the accuracy results are superior compared to the existing benchmarks.
## 2 Data and Methods
### Modified TSM  
In order to account for the difference between NRCSs in upwind and downwind directions, the modified TSM was developed by introducing a correction term into the conventional TSM. The NRCS for sea surface can be written as:  
\[\sigma_{pp}^{0}\left(\theta^{\prime}\right)=\sigma_{pp}^{s}\left(\theta^{\prime }\right)+\sigma_{pp}^{c}\left(\theta^{\prime}\right) \tag{20}\]  
where the subscript \(pp\) indicates the VV or HH polarization state and \(\theta^{\prime}\) is the local incidence angle of each individual facet. \(\sigma^{s}\) is the standard term proportional to the wave spectral density at the Bragg wave number and can be calculated by the conventional TSM [30; 31]:  
\[\sigma_{pp}^{s}\left(\theta^{\prime}\right)=16\pi k_{i}^{4}\cos^{4}\theta^{ \prime}G_{pp}S\left(K_{Bx},K_{By}\right) \tag{21}\]  
where \(S\) is the wave spectrum of ocean surface, \(G_{pp}\) is the VV- or HH-polarized scattering coefficient, \(k_{i}\) is the radar wave number, \(K_{B}=2k_{i}\)sin\(\theta^{\prime}\) is the Bragg wave number, and \(K_{Bx}\) and \(K_{By}\) are the two components of Bragg resonance waves in range and azimuth directions. \(\sigma^{c}\) is the complementary term which is proportional to the sea surface bispectrum and can be given by [23]:  
\[\sigma_{pp}^{c}\left(\theta^{\prime}\right)=-{k_{i}}^{5}\cos^{3}\theta^{\prime }B_{a}\left(K_{B},\varphi\right)\left[4\left|f_{pp}\right|^{2}+1.5 Re\left[f_{ pp}\cdot F_{pp}\right]+0.125\left|F_{pp}\right|^{2}\right] \tag{22}\]  
where \(B_{a}\) is the imaginary part of sea surface bispectrum, and \(f_{pp}\) and \(F_{pp}\) are two polarization dependent coefficients. \(B_{a}\) can be written as [23]:  
\[B_{a}(K,\varphi)=-K_{0}^{6}\left(6-K^{2}s_{0}^{2}\cos^{2}\varphi\right)\cos \varphi\exp\left(-K^{2}s_{0}^{2}/4\right)/16 \tag{23}\]  
where \(s_{0}\) is the skewness parameter, which can be expressed as [23]:  
\[s_{0}=\zeta\xi\frac{\sigma_{R}}{\left(U_{12.5}-A/B\right)^{1/3}}U_{12.5}^{1/2} \tag{24}\]  
\[\zeta=\frac{\left(6/B\right)^{1/3}}{\sqrt{0.5C}} \tag{25}\]  
where \(A\) = 5.0 \(\times\) 10\({}^{-2}\), \(B\) = 42 \(\times\) 10\({}^{-3}\), and \(C\) = 5.1 \(\times\) 10\({}^{-3}\). \(A\) and \(B\) are the coefficients in the relation between skewness coefficient and wind speed, and \(C\) is the coefficient in the relation between total slope variance and wind speed, which are provided by [PERSON] and Munk in [42]. \(U_{12.5}\) denotes the wind speed at the height of 12.5 m over the ocean surface. \(\sigma_{R}^{2}\) is the small-scale vertical variance, and can be evaluated from:  
\[\delta=k_{i}\sigma_{R} \tag{26}\]  
\[\delta=0.205\log_{10}u_{*}-0.0125 \tag{27}\]  
where \(\zeta\) is an empirical coefficient to be determined. The value of \(\zeta\) depends on polarization, incidence angle, and wind speed. It can be obtained by fitting the predictions of modified TSM to the reference data. The mean NRCS at a certain incidence angle, wind speed, wind direction, and directional wave spectrum can be obtained by averaging the local NRCSs over surface samples.
## 2 Online Advertising Ecosystem--Key Components
Moreover, they enable advertisers to keep track of where their ads are served, and which ad spaces are most cost-effective. However, it should also be noted that many Ad exchanges struggle with the problem of low-quality inventory, as good quality ad spaces are often already purchased by big Ad networks. Examples of large Ad exchanges include Google DoubleClick, OpenX, AppNexus, SmartyAds, and AdECN. **Supply Side Platforms (SSPs)** serve publishers by managing and selling their inventories (i.e., available ad space) in multiple ad networks and exchanges [8]. Through strategy optimization, a publisher's SSP will aim to increase the publisher's profit by increasing the advertisers' demand for its ad inventory. Examples of real-world SSPs are Google's DoubleClick for Publishers (DFP), PubMatic, Rubicon Project, BrightRoll, and AppNexus Publisher Suite. **Demand Side Platforms (DSPs)** serve as advertisers' agents (i.e., representatives) in multiple ad networks and exchanges, and they are responsible for selecting the right audiences and the most appropriate media to represent the advertisers' ads. Examples of real-world DSPs are Google's DoubleClick Bid Manager (DBM), TubeMogul, Oath DSP, Amazon DSP (AAP), Sizmek, and TradeDesk. **Data Exchange or Data Management Platform (DMP)** is a third-party data aggregator that publishers and advertisers interact with to obtain additional user-related information, leading to better advertising. In general, DMPs collect users' data to profile them according to their interests. As such, DMPs can facilitate a publisher or an SSP to find the best match for their ad inventory (based on their audience's interests) among many different DSPs. To generate accurate user profiles, DMPs harvest vast amounts of data from the Internet and deploy advanced data mining tools. Lotame, Salesforce DMP, OnAudience.com, and Snowflake are examples of real-world DMPs.
## 1 Introduction
### Forward-Dynamics Simulation Models  
The development and application of forward-dynamics simulation models typically involve four stages: (1) model construction; (2) parameter determination; (3) model evaluation; (4) model application [8; 9; 10; 11; 12; 13]. The first three stages are often an iterative process until the model incorporates sufficient complexity to adequately represent the real physical system (i.e., each time model complexity is changed, the number of individual-specific parameter values to be determined may alter). Early models typically generated equations of motion from first principles using [PERSON]'s Second Law [14; 15], although software packages utilizing [PERSON]'s method [16] to model the dynamical system have become increasingly popular. To run the model, an iterative method (e.g., Euler method or Runge-Kutta) is used to advance the model dynamics over specified time intervals [17]. Model outputs comprise time histories of all calculated variables (e.g., whole-body orientation, linear and angular momentum, joint kinematics and/or kinetics, and possibly joint reaction forces). Once a model has been evaluated, typically by assessing how closely it can reproduce the recorded performances of an individual [9], it can be applied to investigate cause and effect relationships and optimal individual performance [12; 13; 18; 19; 20; 21]. Given the number of assumptions and constraints necessary to model the human body mathematically and computationally, these must be critically reviewed in relation to the true human dynamical system.
## 3 Results
### Simulation Results
\begin{tabular}{c} **Case 5** \\ **Flow** \\ **Rate** \\ **Injection** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Case 6** \\ **Flow** \\ **Rate** \\ **Injection** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Case 7** \\ **Flow** \\ **Rate** \\ **Injection** \\ \end{tabular}  } & \multicolumn{2}{c}{\ \begin{tabular}{c} **Case 8** \\ **Flow** \\ **Rate** \\ **Injection** \\ \end{tabular}  } \\  & 25 & 300.78 & 25 & 285.38 & 25 & 250.43 & 25 & 220.23 & 25 & 186.08 & 16 & 174.75 & 16 & 118.39 & 14 & 83.43 \\  & 26 & 291.85 & 26 & 274.53 & 26 & 239.06 & 26 & 215.01 & 4 & 182.59 & 28 & 174.25 & 28 & 117.97 & 26 & 81.87 \\  & 10 & 277.25 & 10 & 261.24 & 14 & 224.62 & 14 & 202.12 & 26 & 181.91 & 3 & 170.38 & 3 & 116.51 & 80.35 \\  & 14 & 267.75 & 14 & 254.49 & 24 & 213.40 & 4 & 198.83 & 3 & 181.30 & 4 & 167.67 & 4 & 115.28 & 21 & 80.22 \\  & 13 & 256.22 & 21 & 245.76 & 13 & 211.85 & 16 & 195.30 & 16 & 178.90 & 6 & 163.00 & 6 & 114.94 & 3 & 79.82 \\  & 21 & 252.46 & 24 & 244.36 & 10 & 211.27 & 13 & 194.91 & 2 & 178.26 & 1 & 165.72 & 1 & 114.66 & 25 & 79.54 \\  & 24 & 250.37 & 13 & 243.13 & 16 & 202.94 & 3 & 194.69 & 1 & 177.89 & 17 & 165.44 & 17 & 114.12 & 10 & 79.25 \\  & 9 & 240.77 & 9 & 241.08 & 4 & 202.83 & 1 & 194.49 & 6 & 174.98 & 2 & 160.83 & 29 & 113.48 & 5 & 79.06 \\ \hline \multirow{2}{*}{ \begin{tabular}{c} 60 s \\ \end{tabular}  } & 15 & 240.74 & 15 & 230.18 & 15 & 202.76 & 2 & 194.16 & 19 & 170.37 & 29 & 159.82 & 2 & 113.06 & 27 & 78.11 \\  & 22 & 240.60 & 12 & 228.63 & 3 & 202.61 & 6 & 191.29 & 14 & 169.48 & 18 & 199.65 & 18 & 112.74 & 12
# DANet: A Semantic Segmentation Network for Remote Sensing of Roads Based on Dual-ASPP Structure
## 5 Conclusions  
This paper proposes the use of a dual-ASPP structure for encoding, which enhances the ability to extract roads in remote-sensing images. The dual-ASPP structure is designed  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Network Structure**} & \multicolumn{3}{c}{**Evaluation Indicators (\%)**} \\ \cline{2-4}  & _Re_ & _Road IoU_ & _F1-Score_ \\ \hline U-Net & 69.87 & 64.75 & 69.95 \\ DeepLab V3+ & 69.72 & 65.17 & 70.51 \\ D-LinkNet [34] & 70.18 & 64.68 & 70.55 \\ HRNet-V2 [35] & 69.93 & 64.22 & 70.85 \\ PSPNet & 69.78 & 64.52 & 70.33 \\ SegNet & 70.15 & 65.09 & 70.45 \\ SENet & 70.06 & 64.59 & 70.21 \\ DANet & 70.87 & 65.31 & 71.08 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation indexes with other network structure prediction results.  
to address the occlusion of objects such as clouds, trees, and buildings, as well as the varying width and length of roads. Expansive convolution is used for perceptual field expansion and multi-scale feature fusion, which effectively improves the performance of road extraction. The model is evaluated using the CHN-CUG Roads Dataset and compared with classical network structures in semantic segmentation. This study demonstrates that DANet can successfully extract road information even in the presence of shadow occlusion and can effectively extract information from complex, thin, and narrow roads. This study also proves the effectiveness of incorporating the ASPP structure twice in the encoding phase and using the DarkNet network structure for feature extraction, as shown through ablation tests. Using the CHN-CUG Roads Dataset, we have confirmed that the proposed network structure, \(Re\), has shown improvements of 1.26% in accuracy and 1.15% in _F1-score_ when compared to DeepLab V3+. Additionally, the _road IoU_ has shown a 1.27% improvement when compared to HRNet-V2.  
Conceptualization, Z.F. and G.L.; methodology, Z.F.; writing--original draft preparation, Z.F.; writing--review and editing, G.L.; funding acquisition, L.C. and S.Z. All authors have read and agreed to the published version of the manuscript.  
Jilin Provincial Department of Science and Technology: Jilin Scientific and Technological Development Program (No. 20210201130 GX).  
The data are not publicly available due to privacy.  
The authors declare no conflict of interest.
## 6 Case Study and Discussion
### Simulation Results
#### 6.3.2 Case 2  
In Case 2, the numbers of EVBCDIs in three microgrids are optimized, while installation capacities of RESs are kept the same as Case 1. The optimal numbers of EVCDIs for RMG1, RMG2, and OBMG are 14, 15, and 29. WTs installation capacities for RMG1, RMG2, and OBMG are 240 kW, 160 kW, and 320 kW. PV installation capacities for RMG1, RMG2, and OBMG are 180 kW, 120 kW, and 240 kW. Since the two-day operation profiles of RMG1 are identical to those in Case 1, the operation profiles of RMG2 are shown here. The two-day operation profiles of RMG2 and OBMG in Case 2 are shown in Figure 7.  
Tendency of EV charging and discharging is similar with Case 1. However, the numbers of EVCDIs in both RMGs and OBMG are optimized. Comparing Figure 7 with Figure 6, it is obvious that the total charging and discharging power of EVs increased. The total energy transmitted between RMGs and OBMG increased, and more energy with lower prices are transported to the places and times with higher prices. Therefore, the operation cost of the system decreased, and the total cost also decreased.  
In Case 2, total cost of one year is $193,867.50. The costs for RMG1, RMG2, and OBMG are $67,491.10, $37,374.50, and $89,001.90, respectively.
## 4 Experiments
### Performance Evaluation  
We compared our detection performance across our selected baselines to evaluate the efficiency of the proposed method. Then, we analyzed the impact of the change in substitution rate and the number of votes on the detection performance and compared it against the RS&V method. We evaluated the performance of adversarial attack detection using accuracy and F1 score. Accuracy in Equation (1) represents the ratio of correctly classified samples to the total number of samples. The F1 score in Equation (4), calculated as the harmonic mean of Precision and Recall, provides a balanced measure that considers the baseline. Table 3 presents model accuracy and F1 score against adversarial attacks, including PWWS, TextFooler, BAE, and DeepWordBug. It compares performance without any defense and demonstrates improvements when defenses are introduced, including baseline methods RSV and FGWS, alongside our proposed method. The Accuracy column in Table 3 shows the classifier accuracy on the test dataset. The rows with N/A values in the Method column of Table 3 indicate the accuracy of a model under a specified adversarial attack. This accuracy is measured when we generate the 1000 adversarial examples by giving random inputs from a dataset. Interestingly, models trained on the AG News dataset show high robustness against BAE adversarial attacks.  
\[\text{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
where  
* \(TP=\text{True Positives}\)
* \(TN=\text{True Negatives}\)
* \(FP=\text{False Positives}\)
* \(FN=\text{False Negatives}\)  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]  
\[F1=2\cdot\frac{\text{Precision}\cdot\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{4}\]
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 2 Materials and Methods
### Experimentation
#### 2.2.2 NASA-TLX Scale  
NASA-Task Load Index (TLX) was developed by [PERSON] [22], which includes six dimensions: mental demand, physical demand, temporal demand, performance, effort, and frustration [23]. NASA-TLX consists of two stages: one is the scoring stage, which aims to separately evaluate the six subscales, and the other is the weighting stage, where the weight of each factor is determined by comparing the importance of six factors in pairs. The weight of each factor is multiplied by the scale score of each factor, and finally added up to calculate the overall index of overall mental workload (see Equation (2)).  
\[\textit{Overall Mental Workload Score}=\sum_{i=1}^{6}Scale_{i}\times\textit{Weight}_{i} \tag{2}\]  
where _Overall Mental Workload Score_ is the index of NASA-TLX; \(Scale_{i}\) represents the scale scores of factor \(i\); \(Weight_{i}\) are the weight scores of factor \(i\); and the corresponding factors include mental demand, physical demand, temporal demand, performance, effort,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Subtask** & **Area** & **Instruction** \\ \hline \multirow{3}{*}{SYS} & \multirow{3}{*}{1} & Monitor the scales of F1–F4 and click the corresponding scale with the mouse when the dynamic targets touch the upper or lower three bars of any scale. \\  & & \\  & & \\ TRA & 2 & Keep the target at the grid center by joystick in MANUAL \\  & & & mode and no action is required in AUTO mode. \\ RES & 3 & Monitor the status of pumps numbered 1–8 and click the corresponding pump with the mouse when a failure occurs. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the MATB subtasks.  
Figure 1: User interface of the MATB (**1**: SYS, **2**: TRA, **3**: RES).  
and frustration level with subscript \(i\) as 1-6, respectively. Higher scores mean a higher mental workload.