esa-sceva/satcom-chunk-collection huggingface dataset:
# Three-Dimensional Human Pose Estimation with Spatial-Temporal Interaction Enhancement Transformer
## 2 Related Work  
### Three-Dimensional Human Pose Estimation  
There are two methods used for 3D human pose estimation, one of which is to predict 3D coordinates directly from the input image, known as the end-to-end method [24; 25; 26; 27]. Another is a 2D to 3D lifting technique, which utilizes an available 2D pose detector to generate 2D coordinates and then lifts them to the 3D pose through a lifting network [8; 9; 15; 28; 29]. However, the end-to-end approach takes RGB images as the input, which requires high computational consumption and is impractical in real-world applications [11]. In contrast, the inputs for lifting methods are 2D coordinates, which are simpler to process than RGB images. For example, Reference [29] suggested a residual network that is fully connected and lifts the keypoints of each frame from the 2D to the 3D space. Recent work [30] has shown that the lifting method can efficiently and accurately regress 3D poses utilizing detected 2D keypoints, thanks to the superior performance of 2D human pose estimation, and this method outperforms end-to-end approaches. As in [29; 30], our proposed model belongs to the category of 2D to 3D lifting.
## 4 Experimental Results and Analysis
### Experimental Results and Analysis of Pressure Performance
Figure 18 shows the wall pressure distribution curves of the optical cavity and test pieces 1#, 2#, and 3# in the butt joint experiment, where the first five measuring points were located on the optical cavity and the rest were located on the active supercharging supersonic diffuser. The results in the figure show that both diffuser 2# and diffuser 3# started smoothly, and the pressure of several measurement points in the frontmost optical cavity had a downward trend, whereas the pressure distribution in the optical cavity of experiment 1# was significantly high, which once again indicated that the airflow blockage phenomenon of experiment 1# was effectively addressed in experiments 2# and 3#. There were two pressure spikes in test piece 2# at measuring points 7# and 11#, which indicated that strong shock waves were generated there, whereas experiment piece 3# did not have apparent pressure spikes in these two places. This may be because the rear edge of the nozzle of the 3# test piece is slotted, which enhances the mixing effect of the platform and actives gases and reduces the Mach number of the gas flow; thus, the shock wave intensity is weak. In addition, although the initial pressure of the vacuum tank increases the pressure distribution along the diffuser, the pressure distribution in the optical cavity is not significantly affected. This also verifies that the active booster supersonic diffuser plays a role in resisting the high back pressure and preventing the influence of disturbance on the flow field in the optical cavity.
## 3 The Proposed Model
### Problem Formulation  
UNs and ANs are important for the NL problem in WSNs [24]. The energy consumption and cost of ANs are generally far greater than the typical SN. The localization technique is to optimize and find the location of the UN via ANs with known positions. The explanation of the NL problem is given in the following: (1) there are \(m\) anchors with known locations and \(n\) UNs with unknown positions; (2) consider that the ANs and UNs of the sensor network can be distributed on an \(sL\times L\) 2D plane and the nodes are uniformly and randomly distributed; (3) the measured distance error conforms to uniform distribution and the measured distance is \(d^{\prime}_{ij}\), as given in Equation (1):  
\[d^{\prime}_{ij}=d_{ij}\cdot(1+\tau\cdot\varepsilon), \tag{1}\]  
In Equation (1), \(d_{ij}\) shows the real distance from the \(i\)th to \(j\)th nodes, the distribution of \(\varepsilon\) is \(\sigma(0,1)\), and \(\tau\) refers to the error feature.  
Figure 1: The overall process of the CMLOA-NLA method.  
The classical DV-Hop method without range-based positioning comprises three stages: computing the UN coordinate location, computing the minimum number of hops (MNH) among anchors, and computing the evaluated distance from ANs to UNs.  
The basic steps of the DV-Hop method are discussed below.  
Step 1: the MNH can be computed by the flooding method, where the anchor broadcasts data to all the nodes of the WSN. Later the MNH between the UNs and ANs is computed, along with the MNH between the ANs themselves.  
Step 2: the mean distance per hop \(Hop_{i}\) of all the ANs and the evaluated distance \(d_{u,\,i}\) is measured by Equation (2):  
\[Hop_{i}=\frac{\Sigma_{i\
eq j}d_{i,j}}{\Sigma_{i\
eq j}h_{i,j}}=\frac{\Sigma_{ i\
eq j}\sqrt{\left(x_{i}-x_{j}\right)^{2}+\left(y_{i}-y_{j}\right)^{2}}}{ \Sigma_{i\
eq j}h_{i,j}} \tag{2}\]  
where \(\left(x_{i},y_{i}\right)\) shows the location coordinate of \(i\)th ANs, \(\left(x_{j},x_{j}\right)\) indicates the location coordinate of \(j\)th ANs, \(h_{i,j}\) shows the MNH from \(i\)th AN to \(j\)th nodes, and evaluated distance \(d_{u,\,i}\) from \(i\)th AN to \(u\) UN is expressed by Equation (3):  
\[d_{u,i}=Hop_{i}\times h_{u,i} \tag{3}\]  
where \(h_{u,\,i}\) signifies the MNH from UN \(u\) to \(i\)th AN.  
Step 3: the fitness function (FF) of the NL optimizer technique can be evaluated by Equation (4):  
\[f(x_{u})=\sum_{i=1}^{m}\sqrt{\left(x_{u}-x_{i}\right)^{2}+\left(y_{u}-y_{i} \right)^{2}}-d_{u,i} \tag{4}\]  
Here \(\left(x_{u},\,y_{u}\right)\) indicates the evaluated location coordinate of \(u\)th UNs, \(\left(x_{j},y_{j}\right)\) represents the location coordinate of \(i\)th ANs, \(d_{u,i}\) indicates the evaluated distance from \(u\)th UNs to \(i\)th Ans, and \(m\) indicates the number of ANs.
###### Abstract  
To address the time-variant reliability (TVR) issue during a structure's life cycle, we propose an innovative Kriging method named EDFK-FCTP. This active-learning TVR analysis is based on an error distance function (EDF) and the first crossing time point (FCTP). We employ Latinized partially stratified sampling (LPSS) to estimate the first four-order origin moments of the FCTP surrogate model. The convergence condition for establishing the optimal surrogate model for the FCTP is determined by the geometric mean of the probability density of training samples and the maximum error of the first four-order origin moments across two adjacent iterations. Utilizing the optimal surrogate model, the probability density function (PDF) of the FCTP is solved using kernel density estimation (KDE), thereby deriving the failure probability of the structure throughout its life cycle. Example analyses demonstrate that the calculation accuracy of our proposed method fulfills engineering requirements. Notably, it presents certain advantages over alternative methods, particularly in regions of low failure probability. For complex performance functions, our method offers significant computational efficiency benefits compared to other approaches.  
Keywords:time-variant reliability; whole life cycle; first crossing time point; Kriging surrogate model; kernel density estimation +
Footnote †: journal: Applied sciences
# Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting
## 6 Experiment
### Qualitative Comparisons  
Figure 4 presents sample results of the proposed OPV using our UAV datasets, which contain very small UAVs under various visual tracking environments (e.g., severe background clutter (UAV datasets 1, 2, 3, 4, and 5), large scale changes (UAV datasets 4 and 5), and moving cameras (UAV datasets 1 and 2)). As shown in Figure 4, the proposed OPV can accurately track small targets, even if the background regions contain many objects that appear similar to those of the targets.  
Figure 5 shows visual tracking results over time for several UAV datasets. Our method accurately detected and tracked UAVs, in which these objects could disappear and appear again due to UAVs' and camera motions. As shown in Figure 5(a),b, although UAVs change their sizes, our method robustly tracker the targets. As shown in Figure 5(c), although UAVs have very irregular motions, our method successfully tracker the targets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **SiamRPN++** & **DAT** & **SiamDW** & **ECO** & **OPV** \\ \hline AUC & 0.638 & 0.651 & 0.649 & 0.657 & **0.693** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative comparison using our unmanned aerial vehicle (UAV) datasets. The best results are indicated in bold type.  
Figure 3: Quantitative comparison on deep learning-based methods using the OTB dataset. We compared the proposed method (OPV) with SIAMese network Deeper and Wider (SiamDW), SIAMese Region Proposal Network (SiamRPN++), Siamese INstance search Tracker (SINT), Deep Attentive Tracking (DAT), Target-Aware Deep Tracking (TADT), Efficient Convolution Operators (ECO), and Continuous Convolution Operator Tracker (C-COT).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ECI** & **C-COT** & **SINT** & **OPV** \\ \hline FPS & 7 & 1 & 5 & 87 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computational costs of OPV in terms of frames per seconds (FPS).  
Figure 4: Qualitative results 1 using our UAV datasets. Red boxes denote visual tracking results of the proposed method.  
Figure 5: Qualitative results 2 using our UAV datasets. Red boxes denote visual tracking results of the proposed method.  
Figure 7 demonstrates that the proposed method can successfully track UAVs with medium or large size, in which large objects are relatively insensitive to background clutters and object proposal methods can accurately estimate object candidate regions.
## 2 HSI: Basic Concepts
### HSI: Device Components
#### 2.1.2 Wavelength Dispersion Systems  
Wavelength dispersion systems split the incident broadband light or the reflected radiation from the sample into different wavelengths before reaching the detector. Depending on the application, device cost, and sensitivity, different systems like filter wheels, electronically tunable filters (ETFs), and imaging spectrographs (i.e., diffraction grating) can be used.  
A filter wheel is a set of discrete bandpass filters mounted on a wheel generally used in multispectral imaging systems (usually able to detect up to 10 wavelengths). They are sensitive to vibration, slow wavelength switching, unchangeable spectral resolution, and image unmatching owing to filter movement. However, they are cost-effective and simple to implement [4].  
Concerning ETFs, they disperse the wavelengths one at time (like a bandpass filter) with a moderate spectral resolution (5-20 nm) and broad wavelength range (400-2500 nm). Differently from filter wheels, such filters can be easily customized in an array, reducing mechanical vibration issues [4; 10].  
Instead, diffraction gratings split instantaneously the broadband light into different wavelengths without moving parts. They are an optical surface composed of a series of closely packed grooves that have been engraved or etched into the grating's surface. Diffraction gratings can be either transmissive or reflective. As light transmits through or reflects off a grating, the grooves cause the light to diffract, dispersing the light into its component wavelengths [4; 11].
# Experimental Study on Torsional Shear Testing of
## 2 The Torsional Shear Testing Device and the Specim Size
### The Option of Specim Size  
As the size of the specimen depends on the size of the aggregate particle, and the end effects of the specimen are considered to be of such importance, the specimen's geometry is related to the measurement of the deformation and stress of the material.  
The majority wall thickness criterion was established in an earlier experiment in soil materials research, where the magnitude of the wall thickness was related to the size of the aggregate particle. [PERSON] [13] suggested that the minimum specimen cross section was 10 to 25 times the average sized aggregate. According to Japanese standards for laboratory shear tests using hollow cylinders, the minimum wall thickness is 5 times the maximum-sized aggregate when well-graded samples are used [14]. However, the soil specimen achieves this wall thickness criterion simply, as the asphalt mixture differs from the soil when the sized aggregate is excessively large. The height of the specimen must be large enough to minimize the end effects. [PERSON] [15] demonstrated that the influence of the specimen height in a torsional shear test is negligible when the height is 4 times the average of the radius. [PERSON] [13] revealed the height was negligible when it was is 1.5 or more times the average of the radius, since the increases in the precision of the gauge stress was not obvious.  
Figure 1: The torsional shear testing device.  
As asphalt mixtures are different than soils and granular materials, initial experimental work to apply torsional loads selected cylinder specimens which were glued into end platens by using a binder such as epoxy resin. The results revealed that the mordanting area was dependent on the bonding strength and increased as the binder increased. Most test specimens subjected to axial symmetry loads were damaged at the end platen and this did not accurately trace the theoretical failure position. The specimens were under an additional confined boundary deformation since the influence of the bonding force could not be ignored.  
According to the American standards of bituminous mixtures for repeated torsional shear tests using a dynamic shear rheometer (ASTM D7552) [9] on prismatic specimens with a geometry of 50 mm in height and a sectional size of 10 mm \(\times\) 12 mm, and according tot the Chinese standard test methods of bituminous mixtures [16] for uniaxial compression tests (T 0714) using a prismatic specimen with a geometry of 80 mm in height, with both side lengths of 40 mm, this paper used prismatic test specimens with a particular jig and fixture on the loading platens which clamped the specimen ends. The prismatic test specimens of AC13 used were 40 mm for the side length for a maximum nominal aggregate size ratio of 3, and used a height of 120 mm, which was 3 times the side length.
###### Abstract  
Physical activity (PA) is influenced by the built environment. However, little is known about the types of built environment where adults spend their time, and at what levels of PA they engage in those environments. Understanding the effect of the built environment on PA requires insight into PA behavior at different types of locations (e.g., home, work, shopping centers, and sports facilities). Therefore, this study describes where adults aged 45-65 years were active with moderate-to-vigorous intensity (MVPA), and examines associations of socio-demographic factors and neighborhood with MVPA at these locations. Participants' (\(N=308\)) PA was measured for seven days using accelerometers and GPS-devices. Adults spent most minutes of MVPA at home and work. Highest MVPA-ratios of total time spent at a location were achieved in sports facilities and during transport. Neighborhood characteristics and socio-demographic factors such as work status, health status and household structure, had significant effects on MVPA at various locations and on total MVPA. Understanding PA behavior at various locations may provide insights that allow professionals in different domains (e.g., health, landscaping, urban planning) to develop strategies to stimulate PA.  
physical activity; global positioning system (GPS); accelerometer; activity locations +
Footnote †: journal: Journal of Environmental Research and Public Health
# A Data Processing Framework for Polar Performance Diagrams
## 2 Problem Formulations and Theoretical Backgrounds
### From Real World Data to Polar Diagrams  
As mentioned before, one way to obtain polar diagrams which reflect the real-world conditions of a given sailing vessel is to make extensive measurements and use techniques from data science to construct a polar diagram from them. When measuring real-world sailing data, there are effects from factors that have been assumed to be idealised (such as currents and waves) and measurement errors. Furthermore, the polar diagram should only contain information of the maximum boat speeds, where in reality there are phases of acceleration and the top speed is only obtained after a certain amount of time. Thus, the data obtained from real-world measurements inevitably contains errors and inaccuracies and needs to be handled carefully.  
Furthermore, different types of sailing vessels produce different kinds of polar diagrams. Therefore, a relevant problem is to find a flexible, customisable data-processing model, specialised for the creation of polar diagrams. To the best of our knowledge, such a data-processing model has not yet been discussed in the literature.
## 2 Computational Setup
### Characterization of a Flow Control Device  
Flow control devices were applied to the numerical simulation using four continuous slots located at the back surface of the model (Figure 7). The width of these slots is b = 4 mm, and the angle between the control jet and the rear surface of the vehicle is defined as the jet angle \(\beta\) (Figure 7). An input velocity boundary condition was imposed on the slots for modeling the flow control devices.  
In this study, the flow control methods are steady blowing, synthetic and unsteady jets. A uniform velocity input boundary condition Uj = U0 is applied to the continuous slots to define a steady blowing jet. Similarly, a sinusoidal velocity profile Uj = U0 sin(2\(\pi\)f\({}_{\mathrm{c}}\)t) was used with the synthetic jet, thereby performing the cyclic blowing and suction. In order to produce a jet that solely blows or sucks in a control cycle for an unsteady jet technique, a semi-sinusoid profile Uj = \(\pm\) i \(\mathrm{U}_{0}\)sin(2\(\pi\)f\({}_{\mathrm{c}}\)t) was specified on slot exit, where + stands for the blowing jet and - represents the suction jet. Numerical results from the work of [PERSON] et al. [24] using an unsteady jet to the edges at the back wall of the 25\({}^{\circ}\) [PERSON] model showed that, in most cases, the use of a blowing jet is more effective in reducing aerodynamic drag. Therefore, only a blowing jet was used in this study. The control frequency fc is identical to the one in the numerical study of [PERSON] et al. [24], in which they range from [34; 68; 272] Hz. Figure 8 illustrates the output signals from a synthetic and an unsteady jet device with the control frequency of 34 Hz.  
Figure 6: CFL distribution on the symmetry plane (z = 0).  
Figure 7: Schematic of a control device setup.
## 5 Modeling Results
### Model Evaluation  
To evaluate the model performance, the difference between the predicted and true values of every incident was calculated. The prediction values for AL were obtained from the individual models using the test data set (sample of 130 subjects). The random forest model yielded the closest values to the observed values among the three models, while the ANN model tended to overestimate AL compared with the observed values. Previous research has also indicated this overestimation issue in ANN prediction models [59]. The results are presented in Table 2.  
In the examples in Figure 7, the blue lines show the predicted outputs of the AL values obtained from the three models on Seoul roads. Overall, the outputs of each model do not differ significantly, although the AL values correspond to different levels. In particular, the ANN has more blue lines on the roads, while the random forest model tends to have the fewest blue lines on the roads, as shown in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Observed Values**} & \multicolumn{4}{c}{**Predicted Values**} \\ \cline{3-5}  & & **Random Forest** & **ANN** & **Decision Tree** \\ \hline
1 & 0.08 & 0.19 & 0.47 & 0.49 \\
2 & 0.28 & 0.16 & 0.43 & 0.49 \\  & : & : & : & : \\
50 & 0.2 & 0.20 & 0.47 & 0.47 \\
51 & 0.4 & 0.23 & 0.47 & 0.47 \\
52 & 0.08 & 0.28 & 0.80 & 0.55 \\
53 & 0.2 & 0.21 & 0.43 & 0.47 \\
54 & 0.16 & 0.22 & 0.43 & 0.24 \\
55 & 0.40 & 0.25 & 0.80 & 0.38 \\  & : & : & : & : \\
129 & 0.2 & 0.14 & 0.20 & 0.55 \\
130 & 0.36 & 0.19 & 0.47 & 0.43 \\ Mean & 0.21 & 0.22 & 0.46 & 0.37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Predicted accident level (AL) values obtained from three models.  
Figure 6: Mean out-of-bag estimate (OOB) over 10.  
The performance of the models was evaluated in detail using the MSE and RMSE, which were calculated for the test data. Low values of MSE and RMSE indicate that a model can be considered superior. These quantities are defined as follows:  
\[\text{MSE}=\frac{1}{N}\sum\
olimits_{i=1}^{n}{{{{\left({{t_{mi}}-{t_{gi}}} \right)}^{2}}}},\ \ \text{RMSE}=\sqrt{\frac{1}{N}\sum\
olimits_{i=1}^{n}{{{{\left({{t_{mi}}-{t_{gi}}} \right)}^{2}}}}}. \tag{7}\]  
For model evaluation, \(t_{mi}\) is the \(i\)th observation value, \(t_{gi}\) is the \(i\)th model value, \(N\) is the number of trained data, and \(m\) is the number of parameters in the model (the total number of weights and invariables in the net structure). The MSE and RMSE values for the models are shown in Table 3. The lower MSE and RMSE of the random forest model indicate its superior performance among the three models. Meanwhile, the ANN model exhibits higher values of MSE and RMSE, which is assumed to be because the ANN model overpredicted the AL.