esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Acquisition and Experiment
### Validation  
#### 4.3.1 Repeatability of the Detection Method  
Ten repeated measurements of the same track slab were carried out to verify the repeatability of the detection method in this paper, and all the supporting block planes were detected.
# Theoretical Study on a Cable-Bracing Inerter System for Seismic Mitigation
## 3 Parametric Study
### Multi-Objective \(H_{2}\) Norm Optimum Design
#### 3.3.1 Harmonic Excitation  
Using the performance-based optimization results in Table 1, the controlled and uncontrolled responses of the structure are compared under harmonic excitations. The natural frequency of the structure is 1 Hz and the frequency range of harmonic excitations is 0-3 Hz. Figure 9 shows displacement amplification factors of the uncontrolled and a CBIS-equipped SDOF structure. The peak value reduction effect is 71.75%. The response of a structure at the resonant frequency is significantly reduced by the CBIS.
# Mdpj _sciences_
## 4 Experiment Result and Analysis
### Prediction Performance  
As demonstrated by the experimental results presented in Table 3, the Mila model significantly outperforms comparative models in maintenance project prediction. Given that the average number of maintenance projects per instance is 5.21, this study defines multiple values of \(k\) = ([5,10,15,20,25,30,35]) to thoroughly assess the performance of \(R@k\). The results indicate that the Mila model surpasses all referenced baseline models across both shorter and longer prediction lists. Regarding the \(w\)-\(F_{1}\) metric, the Mila model registers a leading score of \(40.99\pm 0.21\%\), surpassing the highest-performing Chet model by 3.56%. Generally, a higher \(w\)-\(F_{1}\) score indicates a better balance between precision and recall achieved by the model. Consequently, the Mila model effectively predicts a higher number of accurate maintenance projects while minimizing incorrect predictions, which is critical in practical scenarios. Moreover, the Mila model demonstrates significant advantages across multiple \(R@k\) metrics. In summary, the Mila model not only demonstrates high accuracy and generalization capabilities in predicting maintenance projects but also reinforces its utility through consistent performance across various prediction list lengths, establishing a solid foundation for future research and practical applications.
# A Survey on Depth Ambiguity of 3D Human Pose Estimation
###### Abstract  
Depth ambiguity is one of the main challenges of three-dimensional (3D) human pose estimation (HPE). The recent strategies of disambiguating have brought significant progress and remarkable breakthroughs in the field of 3D human pose estimation (3D HPE). This survey extensively reviews the causes and solutions of the depth ambiguity. The solutions are systematically classified into four categories: camera parameter constraints, temporal consistency constraints, kinematic constraints, and image cues constraints. This paper summarizes the performance comparison, challenges, main frameworks, and evaluation metrics, and discusses some promising future research directions.  
human pose estimation; depth ambiguity; deep learning; human key points positioning; human object detection +
Footnote †: journal: Medical  
methods. This paper summarizes the performance comparison of various strategies based on the dataset Human3.6M.  
The main objective of this paper is to provide a summary of disambiguating methods that address the under-constrained nature of 3D HPE from single RGB inputs. We classify the mentioned methods based on the main constrain information into four categories. Then, we analyze the performances of the mentioned methods according to their categories and discuss the advantages and the limitations of the followed strategies. In the end, we discuss the existing problems of current research and evaluate future development trends.
## 3 Results
### Use Case of Assistance in Frequency Symbolic Method Usage Based on Anomaly Detection  
The idea, as shown in Figure 5, is to use a trained ML system as an \"Assistant\" for users of FSM implementation. For training, Supervised Machine Learning should be used. First, AI is trained by collecting a dataset of symbolic expression (obtained using FSM) features to corresponding inputs. Once trained, it could be used as an \"Assistant\" (supervisor) for the following FSM usage. For example, the ML system can detect anomalies and inform the user. An anomaly could be a human mistake, for example, when the conductivity matrix is not as usual or a parametric element is incorrectly connected.
###### Abstract
(1) We integrate a safety filter based on ECBF into the predictive control system to constrain the movement of the end-effector, which guarantees human safety during collaboration. Additionally, a human motion prediction module is adopted to reduce the idle time for agents in HRC compared to the turn-taking collaboration. (2) Since safe HRC requires an accurate estimate of the minimum distance between agents to ensure safe distance, a point-cloud-based perception system is proposed using a skeleton-aware mesh recovery system to capture human boundary information. (3) We develop several real-life human-robot collaboration manufacturing scenarios to test the safety and efficiency of the proposed human motion prediction enhanced HRC control methodology. This paper is organized as follows: Section 2 describes the overall HRC system. Related work is introduced in Section 3. The formulations and derivations of the target problem are presented in Section 4. Section 5 discusses the methodology for solving the problem, consisting of formulations for the system and obstacle detection, and introduces the NMPC algorithm and the solver used in the optimization problem. The controller design is introduced in Section 6. Then, Section 7 describes the experimental setup and presents the results of the simulations conducted to evaluate the performance of the proposed NMPC-ECBF-based control solution for human-robot collaboration. Finally, Section 8 provides the conclusions. **Notation:** The following notation is used throughout the paper. Let \(\mathbb{R}\), \(\mathbb{R}_{+}\), \(\mathbb{R}^{n}\), \(\mathbb{R}^{m\times n}\) and \(\mathbb{N}\) denote the sets of real numbers, the sets of positive real numbers, the set of real vectors of length \(n\), the set of \(m\) by \(n\) real matrices, and non-negative integers, respectively. Let \(x\), \(\mathbf{x}\), and \(\mathbf{X}\) denote scalar, vector and matrix quantities, respectively, with \(\mathcal{X}\) a set, and \(X\) a constant. For any non-negative integers \(k_{1}<k_{2}\), the finite set \(\{k_{1},\ldots,k_{2}\}\) is denoted by \(\mathbb{N}_{[k_{1},k_{2}]}\). For \(\mathbf{x}\in\mathbb{R}^{n}\), \([\mathbf{x}]_{+}=\max\{0,\mathbf{x}\}\) is defined element wise. Given a set \(C\), \(InfC\) gives the maximum lower bound of the set \(C\), while \(supC\) gives the least upper bound of the set \(C\).
# Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
## 3 Swin Transformer-Based Sub-Pixel Displacement Measurement Method
### The Process of the ST-SDM  
As shown in Figure 3, the processing of the ST-SDM can be divided into seven steps as follows:  
Step 1: The target image, Tar, is rotated clockwise by four angles (0\({}^{\circ}\), 180\({}^{\circ}\), 90\({}^{\circ}\), and 270\({}^{\circ}\)) to obtain four new target images, which are overlapped with the reference image, Ref, to obtain four 3-channel images, Img1(1), Img2(1), Img3(1), and Img4(1), respectively.  
Step 2: Taking images Img1(1), Img2(1), Img3(1), and Img4(1) as input images, calculate the sub-pixel displacement values at the \"0.1 pixels\" level using the first level classifier, F-ST, of the ST-SDM, and obtain the sub-pixel displacement values \((u_{1},v_{1})\).  
Step 3: The new target image, Tar2, is obtained after applying translation displacement \((-u_{1},-v_{1})\) to the target image Tar, and the reference image, Ref, and the four images obtained after rotating Tar2 by four clockwise rotations (0\({}^{\circ}\), 180\({}^{\circ}\), 90\({}^{\circ}\), and 270\({}^{\circ}\)  
Figure 4: Rotation-relative labeled value method.  
are overlapped, respectively, to obtain four 3-channel images, Img1(2), Img2(2), Img3(2), and Img4(2).  
Step 4: Taking images Img1(2), Img2(2), Img3(2), and Img4(2) as input images, calculate the sub-pixel displacement values at the \"0.01 pixels\" level using the second level classifier, S-ST, of the ST-SDM, and obtain the sub-pixel displacement values \((u_{2},v_{2})\).  
Step 5: The new target image, Tar3, is obtained after applying translation displacement \((-u_{2},-v_{2})\) to the target image Tar2, and the reference image, Ref, and the four images obtained after rotating Tar3 by four clockwise rotations (0\({}^{\circ}\), 180\({}^{\circ}\), 90\({}^{\circ}\), and 270\({}^{\circ}\)) are overlapped, respectively, to obtain four 3-channel images, Img1(3), Img2(3), Img3(3), and Img4(3).  
Step 6: Taking images Img1(3), Img2(3), Img3(3), and Img4(3) as input images, calculate the sub-pixel displacement values at the \"0.001 pixels\" level using the third level classifier, T-ST, of the ST-SDM, and obtain the sub-pixel displacement values \((u_{3},v_{3})\).  
Step 7: Obtain the final sub-pixel displacement values \((u,v)=(u_{1}+u_{2}+u_{3},v_{1}+v_{2}+v_{3})\).
# Scheduling the Exchange of Context Information for Time-Triggered Adaptive Systems
## 4 Scheduling for Adaptive Time-Triggered Systems
### Establishment of Multi-Schedule Graph
#### 4.5.3 Schedule Reconstruction  
Algorithm 3 shows the implementation of the schedule reconstruction algorithm used in the genetic algorithm. The outcome of the genetic algorithm processes the relative execution order of each task, including task allocation, message path index, and message priority ordering. The process is initiated by duplicating the message list to prevent alterations to the original data. It then identifies processors affected by the current scheduling context and updates task allocations and node lists accordingly. The algorithm emphasizes the importance of precedence, constructing a partial schedule based on prior data and dynamically processing tasks based on predecessor completion. Key to its functionality is the adjustment of message sizes according to path costs and the prioritization of messagesfor each receiver. The final phase involves merging the newly computed schedule with unchanged segments of the preceding schedule.
```
1:Input:\(A_{o}[t]\), \(R_{o}[t]\), \(T_{o}[m]\), \(P_{o}[m]\), process_times
2:Output:schedule
3:Determine affected processors
4:Identify tasks scheduled in the current context
5:Update node list and task allocation based on scheduled tasks
6:Construct a partial schedule from previous data
7:Initialize processor list and task completion times
8:for all message \(m\) in message list do
9:\(p\leftarrow\texttt{message\_path\_index}[m.id]\)
10:\(d\leftarrow\texttt{path\_costs}[p]\)\(\triangleright\) hop-based delay from k-means-selected path
11:\(m.\texttt{size}\gets m.\texttt{size}+d\)\(\triangleright\) inject \(d_{x_{i}}\) into message
12:Update message dictionary with \((\texttt{sender},\texttt{size},\texttt{priority})\)
13:endfor
14:Sort messages by priority for each receiver
15:Set current time per processor and initialize completed tasks
16:while there are ready tasks do
17:Process each task considering predecessors and processor times
18:Update schedule and task completion times
19:endwhile
20:Merge the new schedule with the unchanged part of the previous schedule
21:return merged schedule, affected processors
```  
**Algorithm 3** Schedule Reconstruction
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
###### Abstract  
Accurate crowd flow prediction is essential for traffic guidance and traffic control. However, the high nonlinearity, temporal complexity, and spatial complexity that crowd flow data have makes this problem challenging. This research proposes a dynamic graph convolutional network model (Res-DGCN) based on the residual network structure for crowd inflow and outflow prediction in urban areas. Firstly, as the attention layer, the spatio-temporal attention module (SA) is employed for capturing the spatial relationship between the target node and the multi-order adjacent nodes by processing the features of the human flow data. Secondly, a conditional convolution module (SCondConv) is used to enhance the model's capacity for learning about the shifting characteristics of crowd flow to obtain spatial dependence. Finally, we train the model with the Huber loss function to lower the model's sensitivity to outliers and achieve optimal convergence. In two public datasets, the mean absolute error (MAE) of the proposed model is improved by 5.2% and 9.4%, respectively, compared to the baseline models, and the root mean square error (RMSE) is improved by 4.8% and 8.8%, confirming the model's usefulness for crowd flow prediction tasks.  
graph neural network; intelligent transportation system; traffic flow prediction; attention mechanism; urban people flow prediction +
Footnote †: journal: Applied sciences
## 5 Results and Discussion
### Sentiment Classification
#### 5.1.3 Results Based on Yelp 2014 Dataset  
Table 3 represents a comparative analysis of the Sentiment Classification model for several performance metrics through altering training data. The analysis of TD-Spider TaylorChoA-GRU for the precision, recall, and F-measure is listed below. In 90% of training data, the precision of the developed TD-Spider TaylorChoA-GRU is 0.952. The recall of TD-Spider TaylorChoA-GRU is 0.965 for 90% of training data and, considering 90% of training data, the F-measure of the developed model is 0.959.  
Figure 6 represents the accuracy and loss for the Sentiment Classification model. When the iteration is 0, the accuracy is 0.611. Similarly, when the iteration is 0, the loss is 0.397. Hence, the proposed method provides a better performance.  
Figure 5: Performance analysis based on (**a**) accuracy and (**b**) loss for Sentiment Classification using Yelp 2013 dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Data (\%)** & **HSACN** & **MCNN** & **DCBVN** & [3] & [34] & [17] & **Proposed** \\ \hline \multirow{4}{*}{Precision} & 60\% & 0.775 & 0.799 & 0.833 & 0.858 & 0.875 & 0.878 & 0.887 \\  & 70\% & 0.785 & 0.804 & 0.848 & 0.865 & 0.895 & 0.899 & 0.905 \\  & 80\% & 0.801 & 0.824 & 0.865 & 0.884 & 0.914 & 0.918 & 0.925 \\  & 90\% & 0.833 & 0.854 & 0.895 & 0.917 & 0.947 & 0.950 & 0.952 \\ \hline \multirow{4}{*}{Recall} & 60\% & 0.785 & 0.801 & 0.854 & 0.865 & 0.885 & 0.890 & 0.899 \\  & 70\% & 0.795 & 0.824 & 0.875 & 0.887 & 0.905 & 0.910 & 0.914 \\  & 80\% & 0.814 & 0.835 & 0.885 & 0.898 & 0.925 & 0.929 & 0.937 \\  & 90\% & 0.848 & 0.865 & 0.903 & 0.917 & 0.955 & 0.960 & 0.965 \\ \hline \multirow{4}{*}{F-measure} & 60\% & 0.780 & 0.800 & 0.843 & 0.862 & 0.880 & 0.884 & 0.893 \\  & 70\% & 0.790 & 0.814 & 0.861 & 0.876 & 0.900 & 0.904 & 0.910 \\ \cline{1-1}  & 80\% & 0.808 & 0.830 & 0.875 & 0.891 & 0.920 & 0.923 & 0.931 \\ \cline{1-1}  & 90\% & 0.840 & 0.860 & 0.899 & 0.917 & 0.951 & 0.955 & 0.959 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparative analysis of Sentiment Classification for Yelp 2014 dataset in terms of precision, recall and F-measure.
# ILRS Reference Point Determination Using Close Range Photogrammetry
## 3 Satellite Observing System Wettzell
### Analysis and Results
#### 3.2.4 Impact of the Stochastic Model
The intracorrelations of the horizontal coordinate components decrease below the 0.3-level. The average values excluding the first 11 coefficients are \(\bar{p}_{xx}=0.15\) and \(\bar{p}_{yy}=0.1\) for the \(x\)- and \(y\)-components, respectively. The correlations of the \(z\)-components remain higher and stagnate at about \(\bar{p}_{zz}=0.4\). The reason is the selected datum of the frame. Whereas the horizontal components of the mounted markers at the telescope are surrounded by the DN points, the vertical components of these points are located above the defined datum. Thus, the higher correlations of the vertical components indicate the dependencies between the chosen datum and the object points, and characterize the network extrapolation. These invaluable properties get neglected, if a simplified stochastic model is applied. To derive reliable and traceable results, it is highly recommended to introduce the fully populated dispersion to the analysis procedure. Figure 8: Comparison of the resulting standard deviations of the coordinate components \(X\), \(Y\), \(Z\) of the reference point and the axis offset \(e_{\text{AO}}\) derived by three different measurement configurations, denoted by i, ii, and iii, as well as five different stochastic models. The stochastic model defined by the identity matrix, the diagonal matrix, and the block-diagonal matrix is denoted by \(\boldsymbol{\Sigma_{\text{I}}}\) (dark grey), \(\boldsymbol{\Sigma_{\text{D}}}\) (light grey), \(\boldsymbol{\Sigma_{\text{P}}}\) (dark red), and \(\boldsymbol{\Sigma_{\text{M}}}\) (red), respectively. \(\boldsymbol{\Sigma}\) (light red) indicates the stochastic model introducing the fully populated dispersion.
## 4 Case Study  
### Measurement and Compensation of Spatial Symmetry Error  
To improve machining efficiency in the detection and compensation process, actually, the quantity of detected points can be reduced to two points for each tooth surface. Because the gear shaping cutter has high accuracy and the processed helix angle is constant, the three-dimensional spatial symmetry error can be reduced to a one-dimensional displacement deviation. Meanwhile, in order to compare and validate the detected data, it is necessary to detect two points for each tooth surface. Hence, in the actual machining process, the spatial symmetry error amount is calculated based on the data for a total of eight detected points on four tooth surfaces.  
As shown in Figure 10, the positioning end plane is defined as the reference plane, and a total of four points whose heights are, respectively, \(h_{1},\ H-h_{1},\ h_{2}\) and \(H-h_{2}\) are detected by the on-line measuring device. As shown in Figure 11, corresponding to the heights of \(h_{1}\), \(-h_{1}\), \(h_{2}\) and \(H-h_{2}\), points \(A_{1}(X_{A1},\ Y_{A1},\ Z_{A1})\) and \(B_{1}(X_{B1},\ Y_{B1},\ Z_{B1})\), \(C_{1}(X_{C1},\ Y_{C1},\ Z_{C1})\) and \(D_{1}(X_{D1},\ Y_{D1},\ Z_{D1})\), \(A_{2}(X_{A2},\ Y_{A2},\ Z_{A2})\) and \(B_{2}(X_{B2},\ Y_{B2},\ Z_{B2})\), \(C_{2}(X_{C2},\ Y_{C2},\ Z_{C2})\) and \(D_{2}(X_{D2},\ Y_{D2},\ Z_{D2})\) are respectively detected. Based on the calculation and compensation methods in the actual machining process, the X-axis coordinates are chosen to calculate the spatial symmetry error. Therefore, the displacement error \(e_{1}\) and \(e_{2}\) can be written as follows:  
\[e_{1}=\frac{X_{B1}-X_{A1}}{2}-\frac{X_{D1}-X_{C1}}{2} \tag{9}\]  
\[e_{2}=\frac{X_{B2}-X_{A2}}{2}-\frac{X_{D2}-X_{C2}}{2} \tag{10}\]  
Then, through comparing the calculated error, the spatial symmetry error \(e\) in the actual machining process can be written as follows:  
\[e=\frac{e_{1}+e_{2}}{2} \tag{11}\]  
The actual detection and compensation process is shown in Figure 12. The diagram of the NC shaping machine drive system is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c} \hline Module-\(m\) (mm) & 1.65 \\ \hline Tooth-\(z\) & 28 \\ \hline Pressure angle-\(\alpha\) (\({}^{\circ}\)) & 20 \\ \hline Helix angle-\(\beta\) (\({}^{\circ}\)) & 15 \\ \hline Pitch diameter-\(d\) (mm) & 56.172 \\ \hline \end{tabular}
\end{table}
Table 1: Basic parameters of herringbone gear.  
Figure 11: Schematic diagram of actual detection process based on SSEDC.  
Figure 10: Schematic diagram of requirement for corresponding detected points.  
Figure 12: Flow chart of the actual detection and compensation process.  
Figure 13: Diagram of the shaping machine drive system and on-line symmetry error detection device.
# Super-Frequency Sampling for Thermal Transient Analysis
## 2 Super-Frequency Sampling
### Period Estimation  
The aforementioned experimental procedure comprises two independent periods: \(\Delta t_{\rm s}\) and \(\tau_{\rm set}\). Depending on the experimental setup, this poses additional challenges. If both periods are not linked to a common fundamental clock, the actual values might differ from the expected ones. Although this difference may be small in absolute terms, it results in a significant deviation for longer time scales.  
One approach is to consider the period of the signal, \(\tau_{\rm set}\), as not exact and to construct it from the data themselves. The algorithm described in the following text is based on the work by [PERSON] [15]. When using a non-exact value for \(\tau_{\rm set}\), the collapsing procedure (1) results in an increasing misalignment of the thermal transients, which appears as high-frequency noise in the reconstructed signal. Assuming that the correct signal period, \(\tau_{\rm true}\)  
Figure 1: Example of a thermal transient measured with super frequency sampling: signal as-measured (**left**) and reconstructed after collapsing the time series (**right**).  
results in the curve with the lowest noise, the uncertainty in the signal can be quantified by the cost function:  
\[V(\tau)=\sum_{i}\left|T(t_{i}^{\prime})-T(t_{i+1}^{\prime})\right|. \tag{3}\]  
Here, all temperature differences between two neighboring data points are accumulated. When two thermal transients have a sufficiently large relative offset in time, the relative ordering of the data points changes in the collapsed time series. Consequently, the nearest neighbor changes and with it \(V(\tau)\). A simplified Rader algorithm is applied; a flow chart is shown in Figure 2. The search for the true value of the sampling period, \(\tau_{\text{true}}\), begins in the interval \(\tau_{\text{set}}\pm 0.005\cdot\tau_{\text{set}}\). A total of 1001 evenly distributed trials for the sampling period, \(\tau_{\text{set}}\), within this range are selected. For these periods, the cost function (3) is calculated. Next, the trial period with the minimum cost function is set as the next best guess for the true value, \(\tau_{\text{true}}\). If there are multiple trial periods with the same minimum error, a median of these periods is used as the new \(\tau_{\text{true}}\). Subsequently, 1001 trial periods in the range \(\tau_{\text{true}}\pm 0.0625\cdot\Delta\tau\) are chosen and the previous steps are repeated. \(\Delta\tau\) is the difference between the largest trial period and the smallest trial period, \(\tau\), of the previous iteration. The iteration is stopped if more than 50 trial periods have the same cost value, or if a set number of iterations has been reached. Depending on the application, the termination parameters and interval sizes can be adjusted.  
Figure 2: Flow chart to determine the true period, \(\tau_{\text{true}}\), that minimizes the cost function \(V(\tau)\).