esa-sceva/satcom-chunk-collection huggingface dataset:
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
###### Abstract
Such an approach extracts the foreground image with only the moving object from the background through Gaussian mixture model (GMM) to generate a contour of a human body. This approach obviates the wearable sensor issues and does not need high-cost equipment. However, it is susceptible to environmental interference such as light, clothing, or overlapping portraits. There are also concerns about privacy with this approach. Another type of computer-vision-based approach for fall detection uses the human skeleton from Kinect and OpenPose or Deepcut to obtain joint points to identify and predict fall based on machine learning or deep learning [20; 21; 22]. Skeleton recognition is less likely to be disturbed by the environment than human contour recognition, with an improved privacy protection. Because training data are based on joint points and not on pictures, the training time is shorter. However, this approach suffers from poor feature quality and missing joint points, thus resulting in lower fall detection accuracy. This paper proposes a fall detection framework based on OpenPose's long short-term memory (LSTM) and gated recurrent unit (GRU) model. The proposed method reduces equipment costs, and does not require a user to wear sensors or the use of specific photography equipment. The framework has a short training time and protects user's privacy. The proposed framework adopts linear interpolation to compensate for the joint points loss and a relative position normalization method to improve the efficiency of the machine-learning algorithm and detection accuracy. The remainder of the paper is organized as follows: Section 2 introduces the proposed system architecture and methodology. Section 3 presents the evaluation of the proposed framework and experimental results. Section 4 provides a discussion on the interpolation approach for missing data recovery. Section 5 concludes the paper.
## 2 Problem Description and Methods
### Prediction and Optimization Methods
#### 2.2.3 Attention Mechanism  
The attention mechanism is inspired by the way humans selectively focus their attention on specific moments and regions amidst large amounts of information in order to emphasize key details while disregarding irrelevant information. LSTM units have limitations in retaining historical trajectory information, particularly when faced with long sequences. While the attention mechanism can effectively mitigate this issue by reducing the influence of useless information, thereby enabling better retention of all relevant trajectory data. Figure 6 provides a visual diagram illustrating the attentional mechanism. The Attention mechanism processes the data with Equations (18)-(20).  
\[e_{ij}=score\big{(}h_{i-1},s_{j}\big{)}, \tag{18}\]  
\[\alpha_{ij}=\frac{exp\big{(}e_{ij}\big{)}}{\sum_{k=1}^{n}exp\big{(}e_{ik}\big{)}}, \tag{19}\]  
\[c_{i}=\sum_{j=1}^{n}\alpha_{ij}h_{i}. \tag{20}\]  
where \(h_{i-1}\) represents the hidden state of the \(i\)-th LSTM unit prior to time \(j\), \(s_{j}\) represents the hidden state of the LSTM unit at the current time \(j\), \(e_{ij}\) represents the attention weight assigned to the \(i\)-th LSTM unit, \(\alpha_{ij}\) represents the normalized weight assigned to the \(i\)-th LSTM unit, \(c_{i}\) represents the final context vector. The \(score()\) in Equation (18) represents an attention function, such as tanh, Relu, or other functions. In this paper, we choose tanh instead of Relu as the attention function for the ship trajectory prediction problem  
Figure 5: Algorithm flowchart of WOA.  
because there are many negative values in the training data, the Relu function may cause many negative values to disappear during the process, and this may increase the error in the predicted results. The function tanh can be utilized to model complex nonlinear relationships, and its output range is constrained within \([-1,1]\). This constraint helps to mitigate the issues of gradient explosion and vanishing gradient.  
This paper uses the attention mechanism for the BILSTM layer, where the data features in the network are assigned different weights according to their importance, with an emphasis on critical information such as latitude and longitude to amplify its contribution to the prediction process.
## 3 Cavitating Jet
### Periodical Shedding of Cavitation Cloud  
As mentioned above, the cavitation cloud sheds periodically [16; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27], and it forms the ring vortex cavitation; then, the ring vortex cavitation collapses, producing impacts. Thus, the periodical shedding of the cavitation cloud is an important phenomenon. Figure 11 reveals aspects of periodical shedding of the cavitation cloud of a cavitating jet [21]. In Figure 11, the jet flows from the left-hand side to the right-hand side. As shown in Figure 11a, the cavitation cloud breaks near the nozzle at 0.5, 0.9, 1.4 and 1.9 ms; thus, the shedding frequency \(f_{shadd}\) is about 2 kHz for \(p_{1}\) = 20 MPa and \(\sigma\) = 0.014. When \(p_{1}\) is increased to 30 MPa at a constant cavitation number, i.e., \(\sigma\) = 0.014, \(f_{shadd}\) is increased to 2.4 KHz, as shown in Figure 11b. When \(\sigma\) is increased from 0.014 to 0.02 at constant \(p_{1}\)= 20 MPa, \(f_{shadd}\) also increases from 2 to 3.2 KHz, as shown in Figure 11a,c. \(f_{shadd}\) is affected by not only \(p_{1}\) but also \(\sigma\). As is well known, the cavitating length \(L_{cav}\) and the width \(w_{cav}\) are affected by \(p_{1}\), \(\sigma\) and \(d\). In a previous report, \(f_{shadd}\) was measured experimentally, changing with \(p_{1}\), \(\sigma\) and \(d\), and the following relations were found [21].  
\[f_{shadd}\ \propto\ w^{-1} \tag{3}\]  
\[f_{shadd}\ \propto\ p_{1}{}^{0.45\pm 0.03} \tag{4}\]  
\[f_{shadd}\ \propto\ d^{-0.98\pm 0.14} \tag{5}\]  
\[f_{shadd}\ \propto\ o^{0.83\pm 0.10} \tag{6}\]  
When the Strouhal number, \(St\), is defined by \(f_{sadd}\), \(w\) and jet velocity at the nozzle exit, \(U\), which is calculated from \(p_{1}\), the following experimental equation is obtained [21].  
\[St=\ \frac{f_{shadd}w}{U}\ \approx 0.18\pm 0.02 \tag{7}\]  
This result suggests that the cavitation cloud shedding is a phenomenon governed by a constant Strouhal number. On the other hand, the nozzle outlet geometry affects the aggressive intensity of the cavitating jet [39; 74]. Details are described in Section 4.6. The \(f_{sadd}\) is also affected by the nozzle outlet geometry. For example, when the guide pipe with the cavitator was installed, the aggressive intensity of the jet was four times larger than that without the guide pipe and the cavitator [39], and \(St\) became nearly a quarter of that of the jet without the guide pipe and the cavitator [75], as \(f_{sadd}\) was decreased. Namely, the constant value of \(St\) should be unique to the nozzle outlet geometry. The investigation of \(St\) for various nozzles would be a future work, to enhance and/or control the aggressive intensity of the cavitating jet.  
Figure 10: Schematic diagram of local cavitation number [72].  
Figure 11: Aspects of periodical shedding of cloud cavitation: (**a**) \(p_{1}=20\) MPa, \(\sigma=0.014\); (**b**) \(p_{1}=30\) MPa, \(\sigma=0.014\); (**c**) \(p_{1}=20\) MPa, \(\sigma=0.02\)[21].
## 4 Results
### Extraction Methods  
DB-MS-PD were extracted by different methods (Figure 12). 7 studies (32%) assessed the potential of single features as biomarkers. Most studies (N = 14, 64%) combined multiple features and used ML models to provide a robust result. Logistic regression (N = 5, 33%) and random forest (3, 20%) was the most common between the ML models. Finally, only one study used raw data as input for a DL model (N = 1, 4%).
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 12 Applications of HCFs in Networks and Systems
### Stable Latency Applications
#### 12.2.2 Radio Networks  
A key example of an application sensitive to latency is fronthaul links to radio sites in a 5G cellular network. Phase synchronization of radio beams sent from multiple sites to a single user when multiple input multiple output (MIMO) technology is employed sets tight limits for differential latency in the links to all sites, as MIMO requires a matched phase for all beams aimed at the given location of the user. Without automatic monitoring and compensation of latency variations, the tolerable variation can be estimated as \(\pm\)1/36 th of the period of radio wave, corresponding to phase variations within \(\pm\)10\({}^{\circ}\). For 750 MHz, 3.6 GHz and 26 GHz frequency bands reserved for 5G networks in Poland, this corresponds to 11.11 ns, 2.31 ns and 0.32 ns, respectively.  
An SMF cable has a temperature coefficient of latency of about 40 ps/km*K (Section 7.3). The difference between latencies in two aerial sections of cable attached to radio masts each 40 m long resulting from 20K difference of temperatures, e.g., when one site has sunny weather, and the other a cloudy one, is only 0.032 ns. The effect of a 10K temperature difference between two duct cables 10 km long, e.g., when one of them is laid along a pipeline, is much larger: 4 ns. The total differential latency between fronthaul links, 4.032 ns, is acceptable in a 5G network working in the 700 MHz band, but not in microwave ones.  
There is ongoing work on the monitoring and compensation of variable latencies in cable links for 5G Massive MIMO networks [129], but active equipment is expensive in comparison to a short length of cable. This leaves a niche for HCFs: short links (100s m max.), especially with exposed cables subjected to large variations in temperature. Besides 5G/6G radio sites, this includes phased-array radars and other multi-antenna systems.
# Multi-Sensor Fusion Positioning Method Based on Batch Inverse Covariance Intersection and IMM
## 5 Experiment
### Semi-Open Area Experiment  
We demonstrated the fusion positioning accuracy of the experiment under the semi-open area as in Figure 14, in front of the 4 th teaching building of Beijing University of Posts and Telecommunications. The positioning results of SICI(CV), SICI(CT), BICI(CV), BICI(CT) and BICI-IMM algorithms are shown in Figure 15. The positioning errors are statistically analyzed in Figure 16. The RMS positioning errors of SICI(CV), SICI(CT), BICI(CV), BICI(CT) and BICI-IMM fusion positioning algorithms are 1.838 m, 1.839 m, 1.564 m, 1.563 m and 1.44 m in Table 5. Compared with other algorithms, the accuracy of the BICI fusion positioning algorithm decreases the least, so BICI has better robustness and positioning accuracy. However, the performance of BICI-IMM is limited by the precision motion model, which is hard to obtain.  
Figure 16: The CDF of positioning error in the semi-open area.  
Figure 15: Comparison of positioning results in the semi-open area (local Cartesian coordinates coordinate system).
## 5 Experiments
### Collaborative Framework Performance Tests
#### 5.1.2 Regular Packet Communication Delay  
The most common packet transmitted by the collaborative framework is really small (only 37 bytes), is sent via UDP every 200 ms, and contains the 3D location and orientation of an element, as well as some metadata that indicate which object has moved and how. The update period is configurable on the framework. It is set to 200 ms as the default value, as it provides a good  
Figure 9: Operator consulting documentation (**left**) and two workers using the shared experience (**right**).  
compromise between real-time movement and non-excessive network load. Such a value allows for rendering around 12 frames between each update when running above 60 frames per second, which allows for a good movement smoothing on the client.  
With such packets, tests were performed for 10, 30, 40, 70, 90, and 110 concurrent users. The latency and the number of lost packets were tracked to determine how many users can be handled without harming the AR user experience. The tests were executed using two different wireless networks in order to evaluate how the wireless channel and protocol impacted the results: a 2.4 GHz network created with the Linksys router and a 5 GHz network operated by the TP-Link Archer router. The WiFi spectrum was checked before the tests to guarantee that the wireless routers operated in a WiFi channel with no overlapping networks.  
Using such networks, latency was measured from the time the packet was sent to the network from the source device and until such a packet was decoded and processed by the target device. If the message contained by the packed included the new coordinates of an object, the latency was measured until the new coordinates were assigned to the target object. It is important to note that the measured latency did not include virtual object rendering time, since it depends on the number of polygons of the 3D objects shown in an AR scene and therefore is not related to the performance of the proposed collaborative framework.  
In the previously described scenarios, the tests were performed several times and showed that the latency was very stable, requiring an average of 4.8 ms for the 2.4 GHz network and 3.3 ms when using the 5 GHz router. Such figures barely oscillate (only a fraction of millisecond) for the number of evaluated simultaneous users (between 10 and 110).  
According to the International Telecommunication Union (ITU) definition of the Tactile Internet, a seamless video experience requires latencies of up 10 ms, but if a human expects fast reaction times, a 1-ms latency would be needed [63]. The obtained results are somewhat in the middle of both ITU's assessments, but, as analyzed in the next subsection, they do not depend so much on the collaborative framework, but on the underlying wireless communications infrastructure.
# 3-D SRIM Simulation of Focused Ion Beam
## 5 Conclusions  
In this paper, an application-oriented incident model was realized by solving the Newton-Lorentz ion motion equations with aberrations and Coulomb repulsion forces being considered. The generated application-oriented ion beam current density distribution was used to simulate ion-solid interaction  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Point-Like Incident** & **Gaussian Distribution Beam** & **Application-Oriented Model** \\ \hline Longitudinal Range & 280 Å & 280 Å & 280 Å \\ Longitudinal Straggle & 103 Å & 104 Å & 104 Å \\ Lateral Proj. Range & 65 Å & 106 Å & 188 Å \\ Lateral Proj. Straggle & 83 Å & 133 Å & 503 Å \\ Radial Range & 102 Å & 166 Å & 296 Å \\ Radial Straggle & 59 Å & 88 Å & 657 Å \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation results of three incident models.  
Figure 6: RR/RS varies with FWHM value while the beam spot (20–80% rise distances) is 14 nm and the beam spot (12–88% rise distances) is 21 nm.  
Figure 7: Milling experiment of 30 keV FIB on the silicon substrate.  
by modifying the SRIM script file. Results show that the penetration depth of 30 keV Ga\({}^{+}\) in silicon is 28 nm and the radial range is 29.6 nm under 50 pA with this new model. Compared with the point-like incident beam and the Gaussian distribution beam, the application-oriented model has a better agreement with the experimental result. Also, ion-solid interaction under different beam energy and beam currents can be realized with this new model.  
When high energy ion bombards samples, it will generate lots of heat and make organic samples deformed. The transformation of the kinetic energy to the thermal energy needs to be studied for organic samples. With 3-D ion beam current density distribution, the application-oriented model will be a potential thermal source in simulating the process of FIB bombarding organic samples in the future.  
conceptualization, W.L.; methodology, L.Z. and W.L.; formal analysis, W.L., L.Z. and W.A.K.; writing-original draft preparation, L.Z.; writing-review and editing, W.L., Y.C. and Y.M.  
This research was supported by the National Natural Science Foundation of China (Grant No. 11205012) and scientific research and instrument development project of Chinese Academy of Sciences (Grant No. YZ201410).  
The authors declare no conflict of interest.
# Numerical Investigation of Erosion Wear in the Hydraulic Amplifier of the Deflector Jet Servo Valve
## 3 Materials and Methods
### Erosion Rate Model  
According to [9], the erosion rate can be obtained by  
\[E_{v}=f(\alpha)E_{90} \tag{11}\]  
\[E_{90}=K(H_{vw})^{k_{1}}(\frac{u_{P}}{u^{*}})^{k_{2}}(\frac{d_{P}}{d^{*}})^{k _{3}} \tag{12}\]  
\[f(\alpha)=\big{[}sin(\alpha)\big{]}^{n_{1}}\Big{\{}1+H_{vw}\big{[}1-sin(\alpha )\big{]}\Big{\}}^{n_{2}} \tag{13}\]  
in which \(\alpha\) is the impact angle; \(E_{v}\) represents the volumetric erosion rate at the impact angle \(\alpha\); \(E_{90}\) is the erosion rate at a normal impact angle; \(u^{*}\) and \(d^{*}\) are the reference impact velocity and the reference diameter of the particle, respectively, with the values 104 m/s and 0.326 um; \(u_{P}\) and \(d_{P}\) are the impact velocity and the particle diameter, respectively; \(H_{vw}\) is the Vickers hardness of the target material; \(f(\alpha)\) is the correlation between the erosion rate and the impact angle, with dimensionless units. And according to [22], \(K=50\), \(k_{1}=-0.12\), \(k_{2}=2.3(H_{vp})^{0.038}\) and \(k_{3}=0.19\), where \(H_{vp}\) is the Vikers hardness of particles; \(n_{1}\) and \(n_{2}\) depend on the particle type and the target hardness, determined as follows [8].  
\[n_{1}=s_{1}(H_{vw})^{q_{1}} \tag{14}\]  
\[n_{2}=s_{2}(H_{vw})^{q_{2}} \tag{15}\]  
where \(s_{1}=0.71\), \(q_{1}=0.14\), \(s_{2}=2.4\) and \(q_{2}=-0.94\).  
After impact the particle bounces off the wall, which can be estimated or represented using the momentum-based restitution coefficients, with the following assumption [25].  
\[\left\{\begin{array}{l}e_{n}=1.0-0.4159\alpha+0.4994\alpha^{2}-0.292\alpha^{ 3},\\ e_{t}=1.0-2.12\alpha+3.0775\alpha^{2}-1.1\alpha^{3},\end{array}\right. \tag{16}\]  
where \(e_{n}\) and \(e_{t}\) are the restitution coefficients in normal and tangential directions, respectively, which are essential for the particle trajectory calculation.  
Then the Finite Volume commercial code ANSYS/FLUENT is employed to solve the continuity, momentum, turbulence and discrete phase equations. The simulation is based on the hypotheses of steady-state and incompressible flow, and the second order upwind spatial discretization scheme is employed.
# Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
## 4 TPEG Framework
### Low Cost Monitoring  
For an event-monitoring and detection application, each node senses the data according to the monitoring command when it moves within the area of RSU. Each node works in LCS mode and  
Figure 2: Phases of TPEG.  
generates data. The weight of data \(d\) on event \(e\) is \(w(d,e)\), and \(d\) is reported to the RSU if its weight is larger than a predefined threshold:  
\[w(d,e)\geq\tau_{0} \tag{4}\]  
As mentioned in Section 3, we assume the weight of data to be mainly decided by their corresponding cost, where data with lower cost have smaller weight on detecting an event. Example 2 shows low cost monitoring of the pavement surface deformation example:  
**Example 2**: _By default, vehicles takes photos of the road at 300\(\times\)300 px every 30 s and make a prediction and determine the confidence about the event of pavement surface deformation at different positions. Lower resolution photos incur less sensing and transmission cost, yet are less effective for the event detection compared with those of higher resolution. These low level photos might trigger the gathering of detailed data, i.e., 1080 \(\times\)1080 px per 5 s, for further analysis by the image-understanding systems [35]._
## 3 Results
### Simulation Environment  
Table 2 shows the simulation environment. In the simulation environment, the minimum unit of the key space is an hour, and the length of the key space is 16 bits (2\({}^{16}\) = 65,536 h). The number of physical nodes is from 250 to 1000, and all the physical nodes run the same number of virtual nodes from 2\({}^{1}\) to 2\({}^{4}\). Each virtual node is assigned to the specific key at random and constructs shortcut links to other virtual nodes based on the method described in [21]. In this paper, the author compares the proposed method to the case of no virtual nodes (or each physical node runs only one virtual node) because the proposed scheme aims to highly distribute the loads of physical nodes by node virtualization.  
Figure 5: An example of virtual node placement by physical nodes.  
First, this simulation measures the fairness index (_Fl_) of the number of the assigned data among physical nodes. The fairness index is calculated by the following:  
\[Fl=\frac{\left(\sum_{i=0}^{n}x_{i}\right)^{2}}{n\sum_{i=0}^{n}x_{i}^{2}} \tag{1}\]  
where \(0\leq Fl\leq 1\), and it indicates that the numbers of the assigned data to each physical node, \(x_{1},\ldots,x_{n}\), are more similar if it is closer to 1. When \(Fl=1\), \(x_{1}=\ldots=\)\(x_{n}\). The number of the assigned data are 10,000, and those assigned keys are determined based on three patterns, random, Gaussian, and Zipf's. The first pattern is determined at random within all of the key space. The Gaussian pattern is determined based on Gaussian distribution whose mean is the center of the key space (32,768) and the standard deviation is 65,536/10= 6553.6. The Zipf's pattern is determined based on Zipf's law (Zipf distribution) [23; 24]. Figures 6-8 show examples of each data key distribution, respectively. In addition, the simulation measures the number of messages among virtual nodes as communication loads to process interval queries. The sent query is only one, but it contains 52 keys from the random point and forwarded them to those assigned nodes while those intervals are 168 h (the total length is 8736 h = 52 weeks). The author executes the simulation 20 times for each environment and calculates the average of the measured values.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Value** \\ \hline The min. unit of the key space & 1 h \\ The length of the key space & 16 bits (216 = 65,536 h) \\ The number of nodes & 250, 500, 750, 1000 \\ The number of virtual nodes per physical node & 2, 4, 8, 16 \\ Node’s key & Assigned at random \\ Comparison method & No virtual nodes \\ The number of data & 10,000 \\ Data pattern & Uniform, normal distribution, Zipf distribution \\ Query pattern & 12 keys with a 720 h interval \\ Simulation count & 20 \\ Evaluation items & Fairness index, Avg. number of messages \\ \hline \end{tabular}
\end{table}
Table 2: Simulation environment.  
Figure 6: The number of messages among virtual nodes for one query.