esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Behavior Analysis and Stability Control of Tethered Satellite Formation Deployment
## 3 Dynamic Analysis of Simplified Models of Single-DOF and Two-DOFs
### Single-DOF (\(\theta\))  
Numerous studies show that tether in-plane angles are much larger than out-of-plane angles; when TSSs are operating in orbital planes, \(\phi=0\). In this case, Equations (15)-(17) can be rewritten as follows:  
\[\theta^{\prime\prime}=2(\theta^{\prime}+1)\frac{e\sin\
u}{\kappa}-\frac{3}{ \kappa}\sin\theta\cos\theta \tag{19}\]  
assuming that a spherical earth is considered. Orbital eccentricity, \(e\), is a small quantity; thus, the perturbation method was selected in order to calculate approximate analytical solutions for Equation (19), where \(e\) is regarded as a tiny perturbation that is substituted into Equation (19).  
\[\theta^{\prime\prime}-2(\theta^{\prime}+1)\frac{e\sin\
u}{1+e\cos\
u}+\frac{3 \sin 2\theta}{2(1+e\cos\
u)}=0 \tag{20}\]  
The power series form of the periodic solution can be written as follows.  
\[\theta_{p}(\
u,\ e)=e\cdot\theta_{1}(\
u)+e^{2}\cdot\theta_{2}(\
u)+e^{3}\cdot \theta_{3}(\
u)+e^{4}\cdot\theta_{4}(\
u)+e^{5}\cdot\theta_{5}(\
u) \tag{21}\]  
The linear ordinary differential equation is written as follows.  
\[\begin{split}\theta^{\prime\prime}{}_{1}+3\theta_{1}& =2\sin\
u\\ \theta^{\prime\prime}{}_{2}+3\theta_{2}&=2\theta^{ \prime}{}_{1}\sin\
u-\theta^{\prime\prime}{}_{1}\cos\
u,\ \theta^{\prime\prime}{}_{3}+3\theta_{3}&=2\theta^{\prime}{}_{2} \sin\
u-\theta^{\prime\prime}{}_{2}\cos\
u\\ \theta^{\prime\prime}{}_{4}+3\theta_{4}&=2\theta^{ \prime}{}_{3}\sin\
u-\theta^{\prime\prime}{}_{3}\cos\
u,\ \theta^{\prime\prime}{}_{5}+3\theta_{5}&=2\theta^{\prime}{}_{4} \sin\
u-\theta^{\prime\prime}{}_{4}\cos\
u\end{split} \tag{22}\]  
Equation (22) can be executed by the periodic initial condition, \(\theta_{i}(0,\ \theta_{i0})=\theta_{i}(2\pi,\ \theta_{i0})i=1,2,\ldots,5\), and the analytical solution of Equation (19) can be expressed as follows.  
\[\begin{split}\theta_{p}=e\sin\
u-\frac{3}{2}e^{2}\sin 2\
u+e^{3} \sin 3\
u-\frac{3}{26}e^{4}(5\sin 4\
u+13\sin 2\
u)\\ +\frac{3}{143}e^{5}(143\sin\
u+55\sin 3\
u-5\sin 5\
u)\end{split} \tag{23}\]  
Figure 2a shows the tether in-plane vibration angle versus the orbit's true anomaly expressed in radians with various \(e\). The system moves periodically and repeatedly with a period of \(2\pi\) in the direction of \(\theta\), and \(e=0.1,\ \theta_{\mathrm{max}}=5.93^{\circ}\) appears at \(1/4\) and \(3/4\) of the period, respectively. Figure 2b illustrates that \(\theta_{\mathrm{max}},\ \theta^{\prime}_{\mathrm{max}}\) increase as orbital eccentricity increases \(e\to 1\) (the elliptical orbit is flatter), which means that the system tends to move towards an unstable equilibrium state.
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Aspects with Influence in PLC Systems Design
#### 5.2.1 Communications Architecture: MV _versus_ Lv  
The key point to develop the most appropriate architecture for the services to be delivered is to take a decision on which combination of PLC technologies are to be used in each segment of the grid, considering the constraints of the existing MV and LV grids topology. There is no single answer to this question, as the final configuration will be based on the bandwidth needed for the services to be provided, the existing electricity grid infrastructure, the existence of adequate technology and vendors, and the preferences of the utility.  
If we consider just the high bandwidth requirement, a BB PLC (also referred to as Broadband Power Line (BPL)) solution could be deployed in both MV and LV segments; if, on the contrary, only metering constraints are considered, just a fully NB PLC solution may be deployed in both segments. Combinations of both approaches can also be found, with BB solutions in the MV segment together with NB solutions for LV segment [88]; this mixed approach can also be applied in areas of the utility in combination with the other two approaches in other parts. As an extreme solution for very specific and particular grids UNB PLC remains as an alternative [33; 34; 35].
## 4 System Architecture  
As illustrated in Figure 16, the implemented NB-IoT system consists of two user equipment (UE1 and UE2) and the NB-IoT base station. The UE1 implements the NB-IoT transmitting chain to transmit its data to the base station, which in turn sends these data to the UE2. The UE2 implements the NB-IoT receiving block chain to receive the data from the implemented base station.  
Two field programmable gate arrays (FPGAs) and two NI universal software radio peripherals (USRPs) are used for both UEs. In addition, another NI USRP is deployed as the base station. For the software part, MATLAB is used to build the NB-IoT base station as well as the NB-IoT transmitting and receiving chains that are converted to RTL to implement them on the FPGAs. Additionally, GNU radio is used to interface with the USRPs, which act as the system's radio frequency (RF) front end.  
The connection between FPGA and the USRP in the UE1 can be illustrated as follows. The NB-IoT transmitting chain is implemented on the FPGA, which sends the generated orthogonal frequency-division multiplexing (OFDM) symbols to the USRP. Therefore, the USRP starts to transmit the symbols towards the base station.
## 6 Numerical Results
Specifically, according to the numerical results, the maximum and minimum values of the BER are decreased from 0.38 to 0.33 and \(8.1\times 10^{-9}\) to \(1.5\times 10^{-15}\), respectively. In addition, Figures 6 and 7 show that the effect of using the proposed filter increases the area with a lower BER. Therefore, it is clearly evidenced that the use of a VO\({}_{2}\) thin film as an optical filter in VLC links results in a significant enhancement of their performance. Even higher improvement could be achieved if the transmittance bandwidth of VO\({}_{2}\) thin films was narrowed by displacing the shorter cut-off wavelength to values closer to 600 nm in order to favorably exclude sunlight noise peaking at 500 nm. This might be accomplished with adequate doping, resulting in bandgap narrowing of VO\({}_{2}\) thin films. Apart from this potentiality, the decrease of the thickness of the film, as has been already mentioned, provides a straightforward possibility to boost the optical transmittance at 600-700 nm. Therefore, our present results and the mentioned optimization possibilities provide convincing evidence for the use of VO\({}_{2}\) thin films as a greatly efficient optical filter in VLC links.
## 4 Effect of Model Parameters on the Calculation Results
### Effect of Particle Densities on the Calculation Results  
In order to study the effect of different particle densities on the trajectories and collection coefficient calculation results, droplets and ice crystals were used as research objects, respectively. The motion characteristics of the droplets and ice crystals were calculated, and the effect of particle densities on the calculated results was compared. The calculation model was NACA0012, with a chord length of 1.0 m. The inflow airflow velocity was 120 m/s, the ambient pressure was 101,325 Pa, the angle of attack was 2\({}^{\circ}\), and the diameter was 20 \(\upmu\)m. The grid and flow field are shown in the Figure 7. The differences between the trajectories of ice crystals and droplets are shown in Figure 8. A comparison of the ice crystal and droplet collection coefficients for the same conditions is given in Figure 9.  
Figure 5: Droplet trajectories for the NACA23012 verification example.  
Figure 6: Comparison of collection coefficients for the NACA23012 verification example.  
From the calculation results, it is clear that the ice crystals trajectories were different from the droplet trajectories. Ice crystals have a more obvious tendency to deviate from their original trajectories under the action of airflow in the process of movement. This is because, when the diameters are the same, droplets and ice crystals are subject to the same air drag. The density of ice crystals is lower than that of droplets, and the acceleration  
Figure 8: Comparison of the trajectories of ice crystals and droplets.  
Figure 7: Flow field calculation results: (**a**) grid of airfoil; (**b**) flow field.  
Figure 9: Calculation results of ice crystal and droplet collection coefficients.  
due to air drag is greater on ice crystals. Therefore, droplets are more susceptible to the action of air flow and deviate from their original trend of movement. It can be seen that the collection coefficient of the ice crystals at the same location was smaller than that of the droplets. This was also due to the fact that ice crystals are more susceptible to the effect of the airflow field, so the proportion of ice crystals collected was lower. In this paper, it was considered that the ice is entirely formed of ice crystals, and the density of ice is not very different from water, so the difference between the two is small. However, t actual ice crystals may contain air, resulting in a lower density of the ice crystals compared to pure ice. In this case, the ice crystal motion trajectories would be further offset from the original trend, and the collection coefficient would be smaller.
# Using Noise Level to Detect Frame Repetition Forgery in Video Frame Rate Up-Conversion
## 2 Background  
FR directly inserts the new frames by duplicating the adjacent frames, and it can increase the frame rate of a video, i.e., supposing the \(t\)-th frame is the to-be-inserted frame, which is given by,  
\[f[t]\ =f[t+1] \tag{1}\]  
where \(f[t]\) is the to-be-inserted frame, \(f[t+1]\) is the adjacent original frame. As shown in Figure 1, the original frame rate can be multiplied by periodically inserting interpolated frames, which means that there always exist original frames which are consistent with interpolated frames in content. Therefore, FR forgery in video will be detected only if some repeated frames are detected in a certain time domain. Existing works use two types of detection methods, i.e., residual detection and similarity detection, which are briefly introduced as follows.
## 1 Introduction  
The net volume of municipal solid waste (MSW) in China is 215 million tons per year with an annual growth rate of 3-5%, and the number of MSW workers is over 4 million [1]. MSW contains larger amounts of heavy metals, seriously endangering human health [2; 3; 4; 5]. In many cities of China, including Guangzhou, Beijing, Shanghai, Dalian, Chengdu and Nanchang, the mean As, Cd, Hg, Cr, and Pb concentrations in MSW were found to exceed their corresponding threshold values of the national standard [6; 7; 8]. MSW workers are the main force of waste collection, sorting, transportation, and disposal of waste. This leads to serious considerations regarding potential health risk of MSW workers due to long working time and high frequency exposed to MSW heavy metals. It is of great significance to discuss the possible sources of heavy metals in MSW and quantitatively evaluate their effects on the health of MSW workers.  
The source apportionment of MSW heavy metals and its health risk assessment have become a hot topic of academic research around the world [4; 9; 10; 11; 12; 13; 14; 15]. Previous studies found that components of metal, metal-coated materials, food waste, soil, plastic, and paper were the main heavy metal sources of MSW [9]. Many health risk studies mainly focused on the impact of toxic heavy metals in the emissions from MSW incinerators on nearby citizens [3; 4; 13]. However, relatively little research has been done on the health risks of MSW workers who are directly exposed to MSW heavy metals.  
MSW workers are a particular population that has made great contributions to urban living environment. However, due to lack of self-protection awareness due to high illiteracy, many of them suffered from different types of occupational disease like headaches, nausea, allergies and cancers [11; 15; 16]. The occupational risks mainly derived from different exposure pathways for a variety of toxic pollutants, such as toxic heavy metals through hand-mouth ingestion, dermal absorption, and air inhalation [17]. The main purpose of this study were: (1) to determine the sources of the heavy metals in the Eastern Guangdong MSW using the Pearson correlation and principal component analysis (PCA) methods; (2) to assess health risks caused by heavy metals posed to MSW workers using a Monte Carlo simulation combined with the US Environmental Protection Agency (US EPA) health risk assessment model. The research results could provide a scientific basis for MSW management and MSW workers' health protection in the Eastern Guangdong.
## 2 Performance Assessment
### Signal-to-Noise Ratio Simulations
#### 2.2.2 Signal-to-Noise Ratio Equation  
A study is undertaken in the simulations of how the SNR varies with the system constant \(K_{s}\) [W\(\cdot\)m\({}^{3}\)]. The aim is to find the values of the system constant, \(K_{s}\), which allow SNR values higher than 5 to be reached. This threshold is considered sufficient for the application of most common automatic detection algorithms [21]. Since the lidar system should measure at high temporal resolution, the required SNR values must be computed for single pulse emission. The SNR expression for single pulse emission is given by [14]:  
\[SNR(R)=\frac{\xi_{0}K_{s}U(R)}{\left[\frac{2 qF}{R_{in}}\big{(}K_{s}U(R)+K_{s} L_{b}\big{)}\xi_{0}+NEP_{n}^{2}\right]^{1/2}B_{N}^{1/2}} \tag{6}\]  
\(K_{s}\) [W\(\cdot\)m\({}^{-3}\)] is the system constant given by:  
\[K_{s}=\frac{E_{s}A,c}{2} \tag{7}\]  
where \(E_{0}\) [J] is the energy emitted per laser pulse, \(A_{r}\) [m\({}^{2}\)] is the effective receiver area and \(c\) [ms\({}^{-1}\)] the speed of light.  
\(K_{b}\) [m\({}^{2}\cdot\)nm\(\cdot\)sr] is the background-radiance system constant, given by:  
\[K_{b}=A,\Omega,\Delta\lambda \tag{8}\]  
where \(\Omega_{r}\) [sr] is the receiver-system acceptance solid angle and \(\Delta\lambda\) [nm] is the interference filter bandwidth.  
\(U(R)\) [m\({}^{-3}\)] is the lidar backscattered signal from range \(R\) [m], given by:  
\[U(R)=\frac{\beta_{tot}(R)}{R^{2}}\exp\left[-2\int\limits_{0}^{R}\alpha_{tot}( r)dr\right] \tag{9}\]  
where \(\beta_{tot}(R)\) [m\({}^{-1}\cdot\)sr\({}^{-1}\)] is the total atmospheric volume backscattering coefficient, \(R\) [m] is the range and \(\alpha_{tot}(r)\) [m\({}^{-1}\)] is the total atmospheric volume extinction coefficient.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\lambda\) & **905 nm** & **1064 nm** & **1.5 μm** & **Spray Drift Cloud** \\ \hline \(\alpha_{tot}\) [**km\({}^{-1}\)]** & 0.138 & 0.111 & 0.068 & 10 \\ \(\beta_{tot}\) [**km\({}^{-1}\cdot\)sr\({}^{-1}\)]** & \(5.813\times 10^{-3}\) & \(4.883\times 10^{-3}\) & \(3.306\times 10^{-3}\) & 0.5 \\ \(\text{L}_{b}\) [**W\(\cdot\)cm\({}^{-2}\cdot\)nm\({}^{-1}\cdot\)sr\({}^{-1}\)]** & \(10^{-6}\) & \(4\times 10^{-7}\) & \(4\times 10^{-8}\) & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Opto-atmospheric parameters and solar background radiance for the studied wavelengths.  
\(\xi_{0}\) is the optics transmission factor, \(R_{io}\) [A/W] is the photodetector intrinsic current responsivity, \(q\) [C] is the electron charge, \(F\) is the excess noise factor, \(NEP_{m}\) [ W/\(\sqrt{\mathrm{Hz}}\) ] is the noise equivalent power of the photoreceiver module and \(B_{N}\) [Hz] is the equivalent noise bandwidth at reception.
## 3 The Proposed Method
### Multi-Attribute NMS
If the surrounding occluded pedestrians of a pedestrian have been retained, the suppression factors \(x\) and \(y\) are assigned the respective constants \(C_{2}\) and \(C_{3}\) for further suppression. The experiments in the subsequent section show that values of 3 and 4 for \(C_{2}\) and \(C_{3}\) obtain the optimal performance. ```
0:\(B=b_{1},\ldots,b_{n},S=s_{1},\ldots,s_{n}\), \(D=d_{1},\ldots,d_{n},C=c_{1},\ldots,c_{n},N_{t}\)
0:\(B\) is the list of initial bounding boxes; \(S\) is the list of corresponding confidence scores; \(D\) is the list of corresponding density attributes; \(C\) is the list of corresponding count attributes; \(N_{t}\) is the rigid NMS threshold. Output:\(F\)
1:begin:
2:\(F\leftarrow\varnothing\)
3:While\(B\
eq\varnothing\)do
4:\(m=argmaxS\)
5:\(M=b_{m}\)
6:\(N_{m}=max(N_{t},d_{m})\)
7:\(F\gets F\cup(s_{m},M);S\gets S-s_{m};B\gets B-M\)
8:for\(b_{i}\) in \(B\)do
9:if\(IoI(M,b_{i})\geq N_{m}\) then
10: if\(c_{i}\geq 0\) then
11:\(s_{i}=s_{i}\times f_{s}(M,b_{i},d_{M})\)
12: else
13:\(s_{i}=s_{i}\times(f_{s}(M,b_{i},d_{M}))^{C_{3}}\)
14:\(c_{i}=c_{i}-1\)
15: else if\(IoI(M,b_{i})\geq N_{t}\) then
16: if\(c_{i}\geq 0\) then
17:\(s_{i}=s_{i}\times f_{w}(M,b_{i},d_{M})\)
18: else
19:\(s_{i}=s_{i}\times(f_{w}(M,b_{i},d_{M}))^{C_{2}}\)
20:\(c_{i}=c_{i}-1\)
21:endfor
22:endwhile
23:return\(F\)
24:end
```  
**Algorithm 1**The procedure of Multi-Attribute NMS.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 1 Introduction
Hence, services are transferred from the cloud servers to the edge-assisted IoT devices, providing AI and data storage closer to end-users [6]. At the same time, the IoT-based healthcare system consists of various devices with different requirements. These IoT devices pose several challenges, like higher battery lifetime requirements, interference from other devices, environmental signal attenuation, poor reliability due to more delay, etc. In general, accurate diagnosis, prompt treatment, and excellent patient care define high-quality healthcare services. Technically, the smooth operation of medical monitors and equipment assures the patient that his or her information will smoothly and promptly reach the doctor's computer. This will guarantee that the patient receives appropriate medical attention on time. This enhances the quality of healthcare service by improving response time and decreasing waiting between patients and doctors in state-of-the-art medical facilities as well as remotely. The quality of service parameters in the wireless link are of utmost priority, which in turn provides improved signal receptions, lowers packet loss ratios, and minimizes power drain. In addition to this, distributed AI services and IoT data present several QoS challenges, like battery lifetime, delay jitter, etc.
## 2 Materials and Methods
### Selection of the Machine Learning Model and Libraries Necessary to Implement the Learning Process  
For the project, the YOLOv8 model was selected to evaluate the powder distribution process during 3D printing performed using the laser powder bed fusion (LPBF) method. This model, developed by Ultralytics, leverages deep learning algorithms for object detection and segmentation. YOLO (You Only Look Once) is an algorithm that processes entire images in a single pass, rather than dividing them into smaller segments. The architecture of the YOLOv8 model comprises multiple neural networks, each designed to perform specific tasks, subsequently passing the results to downstream algorithms.  
The YOLOv8 model employs a convolutional neural network (CNN) structured into two primary components:  
**Backbone**: This component is responsible for extracting features from images and reducing their resolution to facilitate further processing using downstream algorithms. The model employs a pyramid network to detect objects of various sizes and scales within the image. This task is executed through convolutional networks that filter data segments and enhance relevant features. These networks comprise several cooperating layers that collectively process spatial data.  
* Convolutional layers: These layers apply sets of filters (kernels) to process regions of the input data, detecting relevant features and patterns.
* Activation layers: These layers apply activation functions to introduce non-linearity, enabling the model to learn more complex relationships in the data.
* Pooling layers: These layers reduce the dimensionality of the image data, which improves learning efficiency and simplifies the model.
* Dropout layers: These layers mitigate overfitting by randomly resetting neuron values during training, effectively discarding random units.
* Flattening layers: These layers transform the feature maps generated by previous layers into single vectors, which are then passed to the fully connected layers.  
**Head**: This component is responsible for object detection, processing the information provided by the preceding convolutional layers to determine the locations and assign appropriate classes to the detected objects. The algorithm outputs information in the form of location loss (bounding box loss) and classification loss (classification loss). Location loss quantifies the error between the predicted coordinates of the bounding box around the object and the actual coordinates provided in the training data. Classification loss measures the error between the predicted and actual classes of the detected objects.  
The Google Colaboratory platform was selected for implementing the machine learning process. This platform leverages cloud computing resources to facilitate work with the  
Figure 3: Augmentation process: original sample (**a**) and sample after augmentation (**b**) (rotation and contrast changed).  
Python programming language. It is based on the Jupyter Notebook environment, enabling code execution in discrete steps, which simplifies fault diagnosis.
## 3 Materials and Methods
### Model Formulation and Analysis
#### 3.3.2 Model Formulation  
The EBL location optimization model can provide the optimal scheme to determine the distribution of an EBL on the road network. Dynamic demand distribution is considered. For transit passengers, an EBL can significantly improve time reliability. However, for private vehicle passengers, after the implementation of an EBL, the road capacity will decrease and the traffic will be more congested. The EBL location optimization model is expressed as follows:  
\[min\ f_{1}=\sum_{s}u^{\prime}l_{s}\varphi_{s}+\sum_{s}u(t_{s}^{in-vehicle}(1- \varphi_{s})+t_{s}^{EBL}\varphi_{s})y_{s}+\sum_{s}ut_{s}^{car}y_{s}^{car} \tag{19}\]  
\[min\ f_{2}=\sum_{s}\int_{0}^{\gamma_{s}}c_{s}(\xi)\mathrm{d}\xi, \tag{20}\]  
s.t.  
\[\sum_{s}l_{s}\varphi_{s}\leq L_{max}\ \ \forall s\in S \tag{21}\]  
\[\varphi_{s}\in\{0,1\}\ \ \forall s\in S \tag{22}\]  
\[y_{s}=\sum_{o}\sum_{d}\sum_{p}y_{od}^{p}\delta_{od}^{ps} \tag{23}\]  
\[\sum_{p\in P}y_{od}^{p}=q_{od}\ \forall o\in V,\forall d\in V \tag{24}\]  
\[y_{od}^{p}\geq 0\ \forall o\in V,\forall d\in V,\forall p\in P \tag{25}\]  
\(f_{1}\) considers the tradeoff among the transit passenger's travel time cost, the private car passenger's travel time cost, and the construction cost of an EBL. In \(f_{2}\), dynamic transit demand is assigned. The objective function of \(f_{2}\) can reflect the sum of the results of integrating the general cost. As Equation (14) shows, extra waiting delay is determined by the real-time volume of waiting passengers The Beckman model is used, as \(f_{2}\) shows, which can demonstrate that the optimal solution is equivalent with user equilibrium [41].  
Equation (20) can be expressed as follows:  
\[\int_{0}^{y_{s}}c_{s}(\xi)d\xi=ut_{s}^{in-vehicle}+u(t_{s}^{uating}+d_{s})+u^{ \prime}l_{s} \tag{26}\]  
Equation (21) reflects the constraint of the maximum length of alternative EBL schemes. Equation (24) reflects that the sum of passengers on all paths should equal the demand of each OD pair. Equation (25) means that the decision variable is non-negative.