esa-sceva/satcom-chunk-collection huggingface dataset:
# Recognition of American Sign Language Gestures in a Virtual Reality Using Leap Motion
## 2 Methods
### Gesture Identification  
Leap Motion continuously displays user hand frames. The problem arises when we want to filter out the sequence of these shots when the gesture is started. If we send all the data to the gesture recognition system, the system will recognize these gestures poorly. This phenomenon is due to the fact that certain gestures may consist of several other types of gestures. This is often seen with a motion gesture. The motion gesture consists of a large number of frames, among which there are gestures without motion. To solve this problem, the states of the system are defined as follows:  
Figure 2: Outline of a system.  
* **Start.** The system is waiting for the user to start moving. If the hand started moving, the transition to the Waiting state will not start.
* **Waiting until the state changes.** If the system does not see the hand, the system returns to the start state. If the user does not move the hand, the system goes to the Stationary gesture lock state.
* **Stationary gesture lock state.** The user does not move his hand for two seconds and the gesture is fixed. Recorded hand model data is saved and converted to the gesture recognition state. If the user moves the hand in two seconds, the system's state changes to a Motion detection state.
* **Motion detection state.** If the device can not follow the user's hand, the recorded hand model data is saved and the system's state is changed to the Gesture recognition state.
* **Gesture recognition state.** Data captured in this state is sent to the gesture recognition subsystem. When the subsystem returns results, they are presented to the user and the system goes to the Data clearing state.
* **Data clearing state.** Clears unnecessary data and goes to the Start state.
## 3 Method
### Classification  
Accuracy (\(ACC\)), sensitivity (\(SEN\)), and specificity (\(SPE\)) are used as common evaluation indicators for classification [46] as follows:  
\[ACC=\frac{TP+TN}{TP+TN+FP+FN}\times 100\%, \tag{16}\]  
\[SEN=\frac{TP}{TP+FN}\times 100\%, \tag{17}\]  
\[SPE=\frac{TN}{TN+FP}\times 100\%, \tag{18}\]  
where true positive (\(TP\)), false negative (\(FN\)), true negative (\(TN\)), and false positive (\(FP\)) are the numbers of correctly classified falls, ADL classified falls, correctly classified ADLs, and fall classified ADLs, respectively. Accuracy, sensitivity, and specificity refer to the probabilities of accurately predicting all ADL and fall, fall of all falls, and ADL of all ADLs, respectively.
# Analysis and Design of a Transient-State Resonant Converter Used as a Frequency Multiplier
## 2 Resonant Converter Transient
### Optimization Constant \"\(g\)\" for Satisfying the Series Condition of the RLC Equivalent Circuit  
As shown in Figure 1, the transient analysis shown in [20] is applied to a series RLC circuit, as shown in Figure 4b, so the equivalent circuit of the resonant converter in Figure 4a must act as a series circuit. Figure 5a shows the equivalent circuit where the magnetizing inductance is being considered, which affects the transient behavior and underdamped response, since, depending on the value of the resistance \(\mathrm{R_{eq2}}\), the current can be diverted to the magnetizing inductance, affecting the desired operation of the circuit and no longer behave as a series circuit.  
Figure 4: Equivalent circuit reduction considering stray inductances, (**a**) Determination of equivalent components from reactance analysis between \(\mathrm{L_{ds}}\), \(\mathrm{L_{mp}}\), and \(\mathrm{R_{eq2}}\), (**b**) Equivalent circuit with equivalent passive components \(\mathrm{L_{eq1}}\), \(\mathrm{R_{s}}\).  
To solve the drawback presented by the magnetizing inductance \(\mathrm{L_{mp}}\) in the circuit, it is necessary to propose operating conditions for the current to flow only to the equivalent resistance \(\mathrm{R_{eq2}}\), as shown in Figure 5b.  
In the circuit of Figure 5a, if the inductive reactance of \(\mathrm{L_{mp}}\) is much larger than the equivalent resistance \(\mathrm{R_{eq2}}\), the current will flow only to \(\mathrm{R_{eq2}}\). Equation (7) introduces the optimization constant \"\(g\)\", which proposes the necessary operating condition for the current to flow only to \(\mathrm{R_{eq2}}\), indicating that the value of the magnetizing reactance \(\mathrm{X_{imp}}\) is at least 10 times greater than \(\mathrm{R_{eq2}}\).  
\[X_{Lmp}=\omega_{o}L_{mp}=gR_{eq2} \tag{7}\]  
where g is the optimization constant and its value is g \(\geq\) 10. Considering the constant \"\(g\)\" in the equivalent circuit of the resonant converter, it is necessary to determine the value of the load \(\mathrm{R_{L}}\) necessary to satisfy the operating condition proposed by (7). Considering (7) and solving for \(\mathrm{R_{L}}\):  
\[R_{L}=\frac{2\pi^{3}f_{o}L_{mp}}{8g\eta_{rec}}. \tag{8}\]  
Equation (8) defines the value required in the \(\mathrm{R_{L}}\) load at the output of the resonant converter for the equivalent circuit to behave as a series RLC circuit as shown in Figure 5b.
## 4 Performance Evaluation Results and Discussion
### Experiments and Results
#### 4.2.5 Result of Experiment 5
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Shallow Domain Knowledge**} & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Precision**} & \multirow{2}{*}{**Sensitivity**} & \multirow{2}{*}{**Specificity**} & \multirow{2}{*}{**F1-Score**} \\ \cline{2-2} \cline{5-8}  & **Start** & & & & & & & \\
**Index** & **Index** & & & & & & \\ \hline \multirow{8}{*}{TwoLead} & 0 & 0 & 56.19 & 0.72 & 0.56 & 0.56 & 0.47 \\  & 0 & 10 & 73.22 & 0.79 & 0.74 & 0.74 & 0.73 \\  & 0 & 20 & 69.62 & 0.81 & 0.70 & 0.70 & 0.67 \\  & 0 & 30 & 77.35 & 0.78 & 0.78 & 0.78 & 0.78 \\ \cline{1-1}  & 0 & 40 & 64.71 & 0.66 & 0.65 & 0.65 & 0.65 \\ \cline{1-1}  & 0 & 50 & 78.75 & 0.80 & 0.78 & 0.78 & 0.78 \\ \cline{1-1}  & 0 & 60 & 72.52 & 0.73 & 0.73 & 0.73 & 0.73 \\ \cline{1-1}  & 0 & 70 & 85.25 & 0.86 & 0.86 & 0.86 & 0.86 \\ \cline{1-1}  & 0 & 82 & 85.25 & 0.86 & 0.85 & 0.85 & 0.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Performance evaluation of SDK-Injection among various length of shallow domain knowledge on the non-disease ECG. highest accuracy of 85.25% was obtained when the shallow domain knowledge range was set to the index 70, where the difference in pattern values started to accumulate the most, and 82, which was the total length of the pattern. The precision, sensitivity, specificity, and f1-score, shown in Table 9, also showed similar patterns to the change in accuracy in Figure 13. In addition, with index 70, where the difference in values between patterns started to accumulate the most, was set as the shallow domain knowledge range, each showed the highest value. The accuracy and precision had the same values for the index 70 and the total length of the pattern, which was 82, but the sensitivity, specificity, and f1-score were slightly higher at index 70 than at index 82. In other words, considering the sensitivity, specificity, and f1-score, it is most appropriate to set the shallow domain knowledge with index 70, where the difference in pattern values is accumulated the most, rather than the entire length of the pattern, which is 82.
## 3 Results and Discussion
### MMRTG Emissivity Analysis  
The MMRTG is estimated to emit \(\sim\) 2 kW of thermal power ([PERSON] et al., 2008 [19]), which will be transferred through both radiative and convective processes. A straightforward method to model this heat transfer is to parameterize the MMRTG as a cylinder, as suggested by [PERSON] et al. [3]. In our study, the MMRTG was modeled as a cylinder with a diameter of 0.66 m and a length of 1 m. According to [PERSON] et al. (1997) [20], it is important to highlight that there are models for studying heat emissivity that take cooling systems into account. These models are based on the use of the Nusselt number ([PERSON] et al. [21]).
## 4 Comparison with Other Research Studies  
As anticipated in the introduction, different experiments have been conducted on modular acoustic barriers, carried out on scaled or real-size prototypes. Authors have applied [PERSON]'s law to different forms of metamaterial acoustic barriers; however, the adopted methodologies are varied and comprise preliminary studies conducted with numerical models or acoustic measurements applied to real models (scaled of real size), or both. On this basis, the authors of this paper decided to undertake acoustic measurements of scaled models only.  
Although each research study offers an original approach, a comparison can be made with other investigations despite differing methodologies, applications, materials, and analysis. On this basis, some analogies can be found with respect to the studies conducted on the \"sonic crystal assisted barrier\", as defined by [PERSON] and [PERSON] [17], composed of  
Figure 9: 3D view of the spatial distribution related to configuration 6 at 5 kHz (**a**) and 6.3 kHz (**b**). Distance between source and edge of barrier (_Xs_) is equal to 800 mm. Distance between receiver and edge of barrier (_Xr_) is variable and corresponds to 25 positions of a 200 \(\times\) 200 mm grid.  
multiple-row scatterers of two different diameters coupled with a conventional opaque barrier and tested with plane-circular or concave rigid scatterers, or even concave scatterers with absorption in the cavity. By considering only the results obtained for the plane-circular scatterers, major sound attenuation was detected around at 1 kHz against the peaks found between 5 kHz and 6300 kHz related to the scaled 1:10 models of this research study, which shall be equivalent to 500 Hz and 630 Hz for real-size dimensions.  
Despite the similarities in terms of the band gap, the configuration of the scatterers is different between the reference and the current studies: in the reference research, the barrier is composed of a regular grid of three rows of 0.05 m diameter scatterers, three rows of 0.13 m diameter scatterers, and a plane opaque barrier of 0.2 m thickness. The novelty of this paper introduced by the authors consists of displaying the centers of the circular scatterers to be staggered instead of being installed on a regular grid. The purpose of this methodology is intended to increase the amplitude of the band gap rather than to avoid the installation of any conventional opaque barrier, which instead is intended to be completely replaced.
# Pose Tracking and Object Reconstruction Based on Occlusion Relationships in Complex Environments
## 3 Experiments
### Results and Analysis
#### 3.4.2 Self-Made Indoor Interaction Dataset  
To further validate the generalization of our method to various objects and its ability to track object poses during interaction actions in indoor scenes from both first-person and third-person perspectives, experiments on object tracking were conducted on a self-made indoor interaction dataset. Partial results are shown in Figure 5. Overall, the sizes of the bounding boxes match the true scales of the target objects, and the bounding boxes fit closely with the target objects, accurately reflecting their orientations. When horizontally observing the tracking results of each object over time, the motion of each object includes but is not limited to translation, rotation, occlusion by hands or other objects, and so on. The bounding boxes predicted by our method accurately reflect the poses of the target objects regardless of how they move or rotate. Vertically, various objects are distinguished by their sizes, textures, surface materials, etc., and our method successfully adapts to different objects, demonstrating its excellent generalization performance once again.  
We employ the open-source software Visual SfM 0.5.26 for pre-reconstruction of the tracked objects from multiple viewpoints. The correctly reconstructed results served as the ground truth for this experiment, used for calculating the Chamfer distance. A comparison between the reconstruction results of our method and BundleSDF is shown in Table 3, following the same order as Figure 5. From the data in the table, it can be observed that for the reconstruction results of all objects, our method outperforms the comparative method BundleSDF, with an average reconstruction losses than half of BundleSDF. Particularly, regarding the reconstruction loss for the switch, our method achieved a loss of only 0.21, significantly better than BundleSDF's 1.58. However, it is worth mentioning that both methods did not achieve satisfactory reconstructions for _airpods_. Objectively, this is due to the small size of the _airpods_, further reducing the visible area when handheld, and their smooth surface lacking any usable texture. Nevertheless, even in such circumstances, our method maintained tracking throughout the continuous 475-frame video data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ADD (\%) \(\uparrow\)** & **ADD-S (\%) \(\uparrow\)** & **CD (cm) \(\downarrow\)** \\ \hline BundleSDF [12] & 92.27 & 96.43 & 0.51 \\ NICE-SLAM [31] & 10.42 & 25.68 & 54.91 \\ SDF-2-SDF [32] & 15.19 & 34.88 & 9.65 \\ Ours & **92.56** & **96.51** & **0.50** \\ \hline \hline \end{tabular} The symbol \(\uparrow\) indicates that higher values represent reconstruction results that are closer to the ground truth, while the symbol \(\downarrow\) indicates that lower values represent reconstruction results that are closer to the ground truth. **Bold** values represent the best performance.  
\end{table}
Table 2: The comparative results of different methods on the HO-3D dataset.
# An Efficient Single-Anchor Localization Method Using Ultra-Wide Bandwidth Systems
## 4 Localization Algorithm  
In this section, we present a peak detection algorithm for the anchor to effectively determine the Resp message. Then we introduce the phase of arrival estimation method to obtain phase observations, and design an ML-based localization algorithm to achieve high-accuracy localization.
## 3 Results and Discussion
### Characterization of the Synthesized CNCs  
Figure 6 (secondary electron image) displays a scanning electron micrograph (SEM) of the CNCs grown on the stainless steel disk. The pitch and diameter of the CNCs are both approximately 0.1-0.2 \(\upmu\)m.  
Figure 5: Calculated electric field strength on the surface of CNC FE disk (**a**) without and (**b**) with focusing cup design.  
Figure 6: SEM micrographs of carbon-nanocoils at (**a**) low magnification and (**b**) high magnification.  
A TEM image of the CNCs synthesized at 600 \({}^{\circ}\)C is shown in the inset of Figure 7. The CNCs synthesized at this temperature had a diameter of around 100 nm and an amorphous structure. A Pd catalyst particle was observed in the micrograph (the red circle). Also shown in Figure 7 is the energy dispersive X-ray (EDX) spectrum that was obtained within the red circle to reveal the chemical composition of the catalyst particle (The Cu signal came from the Cu grid supporting the CNC samples for TEM analysis).  
Figure 8 shows the Raman spectrum of the synthesized CNCs. The D-band at around 1350 cm\({}^{-1}\) and the G-band at 1580 cm\({}^{-1}\) are clearly seen; they are related to the structural defects and the graphite lattice, respectively, in carbonaceous materials. The intensity ratio \(I_{\textrm{D}}\)/\(I_{\textrm{G}}\), which is an indicator of the crystallinity of carbon materials, is 0.78. This number is similar to those obtained for the CNCs in previous studies [39, 42].  
Figure 8: Raman spectrum of as-grown CNC film.  
Figure 7: TEM image of CNCs (inset) and EDX spectrum taken within the red circle, which contained one Pd catalyst particle.
## 2 Materials and Methods
### In Vivo Measurements  
Preliminary in vivo experiments were performed in order to test the performance of the system in a real case. Different finger-tapping experiments were performed to check system sensitivity in functional studies of the brain. Two healthy volunteers were recruited from the lab and informed consent was obtained (Subject one: male, 30 years old. Subject two: male, 43 years old). The competent ethics committee authorized the study.  
The optical probe was placed over the left motor area and it was centered (according to the international 10-20 system for the Electro Encephalography -EEG- (electrode placement) at the C3 point. The measurement protocol was composed of 20 s of baseline, 20 s of motor task (finger opposition at a frequency rate as high as possible) and 20 s of recovery. This sequence was repeated five times with the right hand (controlateral hand) and five times with the left hand (ipsilateral hand). The entire protocol was repeated four times for each subject--using a short source-detector separation (\(\rho\) = 6 mm for both subjects) with and without the gating approach and using a long source-detector separation (\(\rho\) = 20 mm for subject 1 and \(\rho\) = 30 mm for subject 2) with and without the gating approach. The short source-detector separation distance was limited to 6 mm because of the size of the fiber terminator at the probe side, while the long source-detector separation was chosen in order to guarantee a photon count as closer as possible to 1 Mcps. The same criterion was followed for the choice of the applied gating. Measurements were performed in a dimmed room to decrease the amount of background light.  
In all the measurements, reemitted photons at both wavelengths were collected together with an integration time of 1 s. The photon time of flight distributions were divided into 25 time windows of 500 ps each. For each time window, we estimated the value of the quantity \(\Delta\)u\({}_{\text{a}}\)vt at both the wavelengths [24], where \(\Delta\)u\({}_{\text{a}}\) is the variation of the absorption coefficient in the activated brain cortex, while v is the speed of light in the medium and t, the time spent by photons in the activated brain cortex. From this quantity at both wavelengths, we derived the changes in the amount of oxygenated hemoglobin (\(\Delta\)O\({}_{2}\)Hb) and deoxygenated hemoglobin (\(\Delta\)HHb), expressed in an arbitrary unit in the activated brain cortex. As mentioned in the phantom measurements section, we used the same time window for both gated and non-gated experiments in order to consider photons investigating the same depth. From the \(\Delta\)O\({}_{2}\)Hb and \(\Delta\)HHb curves, we can estimate contrast and CNR of the changes in the hemoglobin species with respect to the baseline.
## 3 Results and Discussion
### Assessment of Building Base Shear and Displacement (Drift)
#### 3.1.3 Macquarie Island--High-Risk Seismic Area  
Macquarie Island, characterized by a high seismic risk with a hazard factor of 0.6 in this study, presents significant challenges for structural resilience. Figure 21 depicts the variation of shear force (Figure 21a) and building drift (Figure 21b) for the Macquarie Island region. The base shear force for traditional masonry in this region ranged from approximately 430 kN to 1225 kN, signifying a considerable magnitude. The adoption of SIM panels, both Type 1 with a gap and Type 2 without a gap, demonstrated a substantial reduction in base shear in the range of 41% to 59% and 69% to 87%, respectively, when compared with traditional masonry. However, it was observed that all SIM panels in Level 1 for this location reached their maximum frictional capacity.  
Figure 20: Variation of shear force and building drift for medium risk seismic region: (**a**) Base shear; (**b**) Building drift.  
The implementation of Type 1 SIM panels with a gap, designed to resist the substantial horizontal force, resulted in a maximum drift of approximately 100 mm, raising concerns about potential structural damage. Consequently, the feasibility of using Type 1 SIM panels in high-seismic areas like Macquarie Island is questionable. On the other hand, Type 2 SIM panels without a gap exhibited lower drift and reduced the base shear by up to 87%, making them a potentially effective solution for this region.
# A Boundary Distance-Based Symbolic Aggregate Approximation Method for Time Series Data
## 2 Related Work
### The Distance Calculation by SAX  
For example, a sequence data of length \(n\) is converted into w symbols. The specific steps are as follows:  
Divides time series data into w segments of the same size according to the Piecewise Aggregate Approximation (PAA) algorithm. The average value of each time segment for example \(\overline{C}=\overline{C_{1}},\overline{C_{2}},\ldots,\overline{C_{w}}\) the \(i_{th}\) element of \(\overline{\overline{C}}\) is the average of the \(i_{th}\) segment and is calculated by the following equation:  
\[\overline{C_{i}}=\frac{w}{n}\sum_{j=(n/w)(i+1)+1}^{(n/w)i}C_{j} \tag{1}\]  
where \(C_{j}\) is one time point of time series C, using breakpoints to divide space into \(\alpha\) equiprobable regions are determined  
These breakpoints are arranged in list order as \(B=\beta_{1},\beta_{2},\ldots,\beta_{\alpha-1}\), They satisfy Gaussian distribution, and the spacing between \(\beta_{i}\) and \(\beta_{i+1}\) is 1/\(\alpha\).  
Finally, the divided s time series segments are represented by these breakpoints. The SAX algorithm can map segments' average values to alphabetic symbols. The mapping rule of SAX is as follows, if it is smaller than the lower limit of the minimum breakpoints, it is mapped to 'a', and then greater than a bit smaller than the second breakpoints lower limit is mapped to 'b'. The symbols after these mappings can roughly indicate a time series.  
Given two time series Q and C, the two time series are of the same length \(n\), which is divided into w time segments. \(\hat{Q}\) and \(\hat{C}\) are the symbol strings after they are transformed into SAX algorithm representation, then the SAX distance between Q and C can be expressed as follows:  
\[\text{MINDIST}\big{(}\hat{Q},\hat{C}\big{)}=\sqrt{\frac{n}{w}}\sqrt{\sum_{i=1 }^{w}\left(dist(\hat{q},\hat{c})\right)^{2}} \tag{2}\]  
Among them, the \(\text{dist}(\hat{q},\hat{c})\) can be obtained according to Table 1, the query method can be expressed as the following equation:  
\[\text{dist}(\hat{q},\hat{c})=\left\{\begin{array}{cl}0&\text{if }\big{|}\hat{q}-\hat{c} \big{|}\leq 1\\ \beta_{max(\hat{q},\hat{c})-1}-\beta_{min(\hat{q},\hat{c})}&\text{otherwise} \end{array}\right. \tag{3}\]
###### Abstract  
Determining the advanced mining influence range of an underground working face is crucial for preventing dynamic disasters, such as coal bursts and gas outbursts. In this study, the occurrence of advanced seismicity before the working face as well as its correlation with the acoustic emission (AE) activity of coal and rocks under axial loading was analyzed. Based on the results, a novel statistical method to determine the advanced mining influence range based on advanced seismicity data was proposed and then validated with a case study. The results show that advanced seismicity is caused by the combined effects of static and dynamic stresses at the working face. This seismicity can be used to assess the mining influence degree of the working face on the advanced coal and rock mass, and determine the advanced mining influence range. Using the novel statistical method, the normalized curves for the total number and total energy of the advanced mining-induced seismicity can be plotted. Then, the advanced mining influence range can be determined using thresholds. The thresholds can be established based on the AE activities observed in coal and rock samples under axial static loading. In the case study in this research, the thresholds for the total seismic number and total seismic energy are 0.076 and 0.052, respectively. The corresponding advanced mining influence ranges are 275 m and 245 m, respectively. Field monitoring confirms an advanced mining influence range of 255 m, which validates the results obtained using the novel statistical method.  
underground coal mining; dynamic disasters; advanced mining influence range; statistical analysis; mining-induced seismicity