esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection of Induced GNSS Spoofing Using S-Curve-Bias
## 4 Experiments  
In this section, experiments are carried out to evaluate the performance of the proposed method. Based on the theoretical analysis of the previous section, the proposed induced spoofing detection method will be embedded into a conventional software GPS receiver which is developed in Civil Aviation University of China. The TEXBAT data sets [39] are used in all experiments.
# Delivering Extended Cellular Coverage and Capacity Using High-Altitude Platforms
## 4 HAP Beam Pointing for Extended Coverage  
This section provides the theoretical framework formulation and detailed discussion of our proposed enhanced and validated algorithm, which is an extension of our conference paper [9]. The algorithm, which aims to exploit the resulting geometry of a HAP beam footprint, gives the total number of cells \(N_{c}\) and a set of their boresight coordinates required to provide extended contiguous coverage and capacity.
# Feasibility Study and Prospects of Rock Fragmentation Using Ultrasonic Vibration Excitation
## 3 The Theoretical Feasibility of Ultrasonic Rock Breakage
### Mechanisms of Ultrasonic Action on Rock Mass  
Ultrasound is a sound wave with frequency higher than 20 kHz, and is in the form of mechanical vibration and energy transfer. When ultrasonic waves propagate in rock mass, physical and chemical changes of the rock occur due to the interaction of the ultrasonic waves with the two-phase or multi-phase media, resulting in a series of mechanical, thermal, electromagnetic, and chemical ultrasound effects. It is believed that the mechanisms of ultrasonic action include four important elements, being mechanical action, cavitation effect, thermal effect, and chemical action. This section will mainly focus on the mechanical and cavitation effects on the rock mass.  
(1) Mechanical effects  
During the process of ultrasonic wave propagation, the compression and expansion of the medium particles will lead to a change of pressure between the mediums. This pressure change will cause a mechanical effect between the mediums, which is mainly reflected by the change of the origin displacement, vibration velocity, acceleration, and sound pressure parameters [16]. Due to the short wavelength and the high frequency, the particle vibration caused by ultrasonic waves is minimal, but the mass acceleration, proportional to the square of the ultrasonic frequency, is significantly large--up to 3000 times that of the acceleration of gravity. Therefore, a small static stress can cause a huge impact load, and the effect stress will far exceed the strength of the rock, resulting in the formation of fine cracks at the rock surface, then followed by fracture propagation [17]. Based on this mechanism, ultrasound has been well used in the processing of stiff and brittle material.  
(2) Cavitation  
Cavitation is a unique form of ultrasonic action that can be categorised into steady-state cavitation and transient cavitation [18]. When the ultrasonics works on liquid, the resulting local high negative pressure can initiate a large number of voids or bubbles. As the energy of the ultrasonic wave increases, the liquid will undergo significant tensile stress. When the ultrasonic energy reaches a certain threshold, the bubbles will rapidly expand; then they suddenly break and cleave into more small bubbles and they generate a shock wave. The interaction between steady-state cavitation and transient cavitation contributes to the reactions including oscillation, growth, contraction, and other processes [19]. Currently, in the cutting and drilling using water jets, the introduction of ultrasonic cavitation can further improve work efficiency [20].
# Deep Learning Methods to Analyze the Forces and Torques in Joints Motion
## 2 Data and Modeling Details
### Solving Kinetic Quantities with Critical Points  
As demonstrated in Figure 2a, the MASK-RCNN algorithm identifies the positions of joints in real-time and the positions must be converted to kinetic quantities including velocity, acceleration, angular velocity and angular acceleration. Assume that the positions of points A and B at time \(t\) are \(\mathbf{p}_{A}(t)\) and \(\mathbf{p}_{B}(t)\). The velocities and accelerations at time \(t\) are first- and second-order time derivations of positions, which can be calculated numerically by adopting the five-point central finite difference method (FDM) as  
\[\mathbf{v}_{A} = \frac{1}{\delta t}\bigg{(}\frac{1}{12}\mathbf{p}_{A}(t-2)-\frac{ 2}{3}\mathbf{p}_{A}(t-1)+\frac{2}{3}\mathbf{p}_{A}(t+1)-\frac{1}{12}\mathbf{p} _{A}(t+2)\bigg{)}\, \tag{14}\] \[\mathbf{a}_{A} = \frac{1}{\delta t^{2}}\bigg{(}-\frac{1}{12}\mathbf{p}_{A}(t-2)+ \frac{4}{3}\mathbf{p}_{A}(t-1)-\frac{5}{2}\mathbf{p}_{A}(t)+\frac{4}{3} \mathbf{p}_{A}(t+1)-\frac{1}{12}\mathbf{p}_{A}(t+2)\bigg{)}. \tag{15}\]\(\delta t\) is the inverse of the FPS, which tells the time elapse of two consecutive images. \(\mathbf{p}(t)\) is the position vector at time \(t\). With the five-point central FDM approach, the error can be controlled at the level of \(\delta t^{4}\).  
The rotation-related quantities can be evaluated similarly. Assume that there is a rod \(\mathbf{AB}\) represented by the coordinates of its both ends, the vector \(\mathbf{AB}(t)=\mathbf{p}_{B}(t)-\mathbf{p}_{A}(t)\) has its angle evaluated as  
\[\theta(t)=arctan\bigg{(}\frac{\mathbf{p}_{By}(t)-\mathbf{p}_{Ay}(t)}{\mathbf{ p}_{Bx}(t)-\mathbf{p}_{Ax}(t)}\bigg{)}\;. \tag{16}\]  
With the FDM, the angular velocity and angular acceleration can be expressed as  
\[\omega_{A,AB} = \frac{1}{\delta t}\bigg{(}\frac{1}{12}\theta_{A,AB}(t-2)-\frac{2 }{3}\theta_{A,AB}(t-1)+\frac{2}{3}\theta_{A,AB}(t+1)-\frac{1}{12}\theta_{A,AB} (t+2)\bigg{)}\;, \tag{17}\] \[\alpha_{A,AB} = \frac{1}{\delta t^{2}}\bigg{(}-\frac{1}{12}\theta_{A,AB}(t-2)+ \frac{4}{3}\theta_{A,AB}(t-1)-\frac{5}{2}\theta_{A,AB}(t)+\frac{4}{3}\theta_{ A,AB}(t+1)-\frac{1}{12}\theta_{A,AB}(t+2)\bigg{)}\;. \tag{18}\]
###### Abstract  
Wireless sensor networks (WSNs) play a huge part in arising innovations like smart applications, the Internet of Things, and numerous self-designed, independent applications. Energy exhaustion and efficient energy consumption are principal issues in wireless sensor networks. Energy is a significant and valuable asset of sensor nodes; early energy depletion ultimately leads to a shorter network lifetime and the replacement of sensor nodes. This research proposes a novel Power-Efficient Cluster-based Routing (PECR) algorithm. It takes in clustering using K-Means, the arrangement of Cluster Heads (CHs) and a Main Cluster Head (MCH), the optimal route choice, communication in light of the energy utilization model, cluster heads, and main cluster head alternation based on residual energy and relative location. PECR decreases traffic overburden, restricts energy usage, and at last, expands the network lifetime. Sensor nodes sense the information and transmit traffic to a Base Station (BS) through a legitimate channel. The results confirm it decreases the traffic overhead and effectively utilizes the energy assets. The simulation results show that PECR's performance is 44% more improved than LEACH, EC, EECRP, and EECA algorithms. It is suitable for networks that require a stretched life.  
cluster-based; internet of things (IoT); routing protocols; wireless sensor network (WSN) Article
# Generative Adversarial Networks for Data Augmentation in Structural Adhesive Inspection
## 2 Materials and Methods
### Dataset Characterization  
The dataset considered in this study consists of images collected from a structural adhesive application cell located at Introsys S.A. facilities in Castelo Branco, Portugal, a company specializing in industrial automation (particularly in the automotive sector) that operates in the international market since 2004. It encompasses two stations, one for the adhesive application carried out by an ABB IRB 2400 robotic arm, and the other for the visual quality inspection with two Teledine cameras, as depicted in Figure 1. Additional descriptions of the cell are available in [19; 20]. For this study two types of defects are considered based on the requirements of the use case at hand, namely discontinuities and blobs (excess of material). Still, the approach can easily accommodate additional defect types, so long as they are identified during the labeling process. To generate the original dataset, the controller was manually reconfigured by an operator for each product to produce different defects of these types at varying positions during the adhesive application.  
For the training set of 143 real images used in the GAN model, the original images were cropped to a resolution of 1024 \(\times\) 1024 to match the input format of StyleGAN2-ADA, namely square-shaped resolution and the same power-of-two dimensions, and centered on the adhesive bead. The resulting model was then used to generate 536 synthetic images of defective beads to augment the original dataset.  
Regarding the object detection model to automate quality inspection, 116 real images from the original dataset were selected (removing those that had no defects of the contemplated types), with this set being split 50/50 into training and validation sets. The initial training set of 58 images was then augmented with the synthetic set, resulting in a training set of 594 1024 \(\times\) 1024 images, which were manually annotated using LabelImg ([[https://github.com/tzutalin/labelImg](https://github.com/tzutalin/labelImg)]([https://github.com/tzutalin/labelImg](https://github.com/tzutalin/labelImg)), accessed on 9 March 2021). The real training set is imbalanced, consisting of 75 instances of discontinuity defects and 34 of excess defects, since the former occur more frequently in the present use case. The augmentation addresses the class imbalance, resulting in 582 instances of discontinuities and 580 of excess defects.  
Figure 1: (**left**) The structural adhesive application station showing a discontinuity defect. (**right**) The quality inspection station downstream.
# Joint Optimization of Service Migration and Resource Allocation in Mobile Edge-Cloud Computing
## 4 Proposed A2C-Based Algorithm
### Dynamic Migration and Resource Allocation Algorithm Based on A2c
The agent then interacts with the environment: it inputs the observed state into the actor network, which returns the corresponding action distribution (line 9). The agent samples an action from this distribution (line 10), executes it, and receives the resultant reward and the next state (lines 11-12). This process repeats until the predefined update frequency is met. When it is time to update the networks, the observed states at time step \(t+1\) are fed into the critic network to obtain the average reward for that state (line 16). We then backtrack to time step \(t-t_{g}+1\), compute the advantage function using (27), and update the parameters of the actor and critic networks according to (29) and (31) (lines 18-21). The interaction with the environment continues until the termination of the algorithm. Ultimately, the agent consistently produces actions that yield favorable rewards, demonstrating that the A2C-based approach has effectively optimized migration and resource allocation strategies in the MECC system. This system is now capable of executing pre-trained policies for efficient management. ```
Input : The number of ESs \(m\), the number of users \(n\) Output : Trained policy with parameter \(\theta_{a}\)
1 Randomly set the initial weights for both the Actor and Critic networks;
2 Initialize the MECC system;
3 Set global counter \(i\) and step counter \(t=1\);
4 Set \(t_{g}\), \(\gamma\), \(EP\_num\), \(T\), learning rate \(l_{a}\), \(l_{c}\);
5 forepisode \(i\gets 1\)to EP_num do
6 Reset the system state ;
7 for\(t\gets 1\)to T do
8\(s_{t}\leftarrow\) Observed system state;
9 The Actor network takes \(s_{t}\) as input and outputs the policy distribution of action \(\pi(a_{t}|s_{t})\);
10 Agent samples the action \(a_{t}\) according to \(\pi(a_{t}|s_{t})\);
11 Execute the action \(a_{t}\);
12 Obtain the reward \(r_{t}\), get new state \(s_{t+1}\);
13 if\(t^{\gamma}\!a_{t}g==0\)then
14 // \(t_{g}\) is the frequency of update neuronal network \(R\gets V_{\theta_{c}}(s_{t+1})\);
15 for\(q\gets t\)to\(t-t_{g}+1\)do
16\(R\gets r_{q}+\gamma R\);
17\(A(s_{q},a_{q})\gets R-V_{\theta_{c}}(s_{q})\);
18 Calculate the Loss of Actor by (28);
19 Update \(\theta_{a}\) of the Actor network according to (29);
20 Calculate the Loss of Critic by (30);
21 Update \(\theta_{c}\) of the Critic network according to (31);
22
23 end for
24
25 end for
26 Change environment's state by \(s_{t+1}\);
27
28 end for
```  
**Algorithm 1**Training of A2C-based dynamic migration and resource allocation algorithm.
## Appendix B Leak Characteristics  
When the gas is leaking from a pressurized container into atmospheric air, the Reynolds number (Re) for the leakage flow will be much higher than for the atmospheric air leaking into an evacuated vessel and will reach into the turbulent region. For the turbulent flow through a small hole, the flow characteristic must be averaged over a period of time. The Reynolds number for the small hole model can be described as follows:  
\[Re=\frac{\rho UD}{\mu},\] (A21)  
where \(U\) is the mean leakage velocity, \(D\) is the diameter of a hole and \(\mu\) is the dynamic viscosity of the fluid.  
The mass flow rate Q of the gas at the leak hole is divided into sonic flow and subsonic flow on the basis of the critical pressure ratio (CPR):  
\[\text{CPR}=\frac{P_{a}}{P_{cr}}=\left(\frac{2}{\gamma+1}\right)^{\frac{\gamma }{\gamma-1}},\] (A22)  
\[Q=C_{D}A_{lh}P_{0}\sqrt{\frac{2\gamma}{\gamma-1}\frac{M}{RT_{1}}\left[\left( \frac{P_{a}}{P_{0}}\right)^{\frac{2}{\gamma}}-\left(\frac{P_{a}}{P_{0}}\right) ^{\frac{2+1}{\gamma}}\right]\left(\frac{2}{\gamma+1}\right)^{\frac{2+1}{ \gamma-1}}P_{0}<P_{cr},\] (A23)  
\[Q=C_{D}A_{lh}P_{0}\sqrt{\frac{\gamma M}{RT_{1}}\left(\frac{2}{\gamma+1} \right)^{\frac{2+1}{\gamma-1}}P_{0}\geq P_{cr},\] (A24)  
where \(P_{a}\) is the ambient atmospheric pressure, \(P_{cr}\) is the critical pressure, \(P_{0}\) is the charged gas pressure, \(A_{lh}\) is the leakage hole area, \(C_{D}\) is the flow correction factor of the leakage hole (0.6\(\sim\)1.0), \(\gamma\) is the isentropic index and \(M\) is the molar mass of the gas. For nitrogen gas, CPR = 0.528. When \(p_{a}=1.01\) bar, \(p_{cr}=1.91\) bar.  
Since the mass flow rate of the gas at the leakage hole is given as \(Q=\rho A_{ll}U\), the mean flow velocity can be given by the following:  
\[U=\frac{Q}{\rho A_{ll}}=\frac{4Q}{\rho\pi D^{2}}. \tag{100}\]
# Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality
###### Abstract  
Quality monitoring is important for farmland protection. Here, high-resolution remote sensing data obtained by unmanned aerial vehicles (UAVs) and long-term ground sensing data, obtained by wireless sensor networks (WSNs), are uniquely suited for assessing spatial and temporal changes in farmland quality. However, existing UAV-WSN systems are unable to fully integrate the data obtained from these two monitoring systems. This work addresses this problem by designing an improved UAV-WSN monitoring system that can collect both high-resolution UAV images and long-term WSN data during a single-flight mission. This is facilitated by a newly proposed data transmission optimization routing protocol (DTORP) that selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted, additionally combining individual scheduling algorithms and routing algorithms appropriate for three different distance scales to reduce the energy consumption incurred during data transmission between the nodes in a cluster. The performance of the proposed system is evaluated based on Monte Carlo simulations by comparisons with that obtained by a conventional system using the low-energy adaptive clustering hierarchy (LEACH) protocol. The results demonstrate that the proposed system provides a greater total volume of transmitted data, greater energy utilization efficiency, and a larger maximum revisit period than the conventional system. This implies that the proposed UAV-WSN monitoring system offers better overall performance and enhanced potential for conducting long-term farmland quality data collection over large areas in comparison to existing systems.  
wireless sensor network; unmanned aerial vehicles; scheduling algorithms; integrated design +
Footnote †: journal: Sensors  
_Article_  
Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality
## 3 Laser Point Cloud Registration
### Fine Registration  
Building upon the coarse registration results, the trimmed iterative closest-point (TrICP) algorithm is employed for fine registration. TrICP is an optimized version of the classic ICP algorithm, which mitigates the impact of outliers on registration accuracy by trimming point pairs with excessive distance residuals [31]. The specific computational steps are as follows:  
Step 1: Adopt the Delaunay triangular mesh method [32] to pair nearby points between the bore profile laser point clouds \(p_{\rm t}\) and the standard bore profile point cloud \(p_{\rm s}\), and calculate the distance residuals \(E(n)\) for different point pairs [33]:  
\[E(n)=\left\|p_{\rm t,n}-p_{\rm s,n}\right\|_{2} \tag{22}\]  
Step 2: Extract the point pairs (\(p_{\rm t,tr,n}\), \(p_{\rm s,tr,n}\)) with \(E(n)\) less than the trimming threshold \(E\)th, and calculate the centroid of the point clouds based on these pairs:  
\[p_{\rm t,o}=\frac{1}{N_{\rm tr}}\sum_{n=1}^{N_{\rm tr}}p_{\rm t,Tr,n},\quad p_{ \rm s,o}=\frac{1}{N_{\rm tr}}\sum_{n=1}^{N_{\rm tr}}p_{\rm s,Tr,n} \tag{23}\]  
Step 3: Obtain the rotation matrix \(R_{\rm r}\) and translation vector \(T_{\rm r}\) through singular value decomposition [34]:  
\[U\Sigma V^{\rm T}=\sum_{n=1}^{N}\left(p_{\rm s,tr,n}-p_{\rm s,o}\right)\left( p_{\rm t,tr,n}-p_{\rm t,o}\right)^{\rm T} \tag{24}\]\[\left\{\begin{array}{l}R_{\rm r}=U\Bigg{[}\begin{array}{cc}1&0\\ 0&\det(UV^{\rm T})\end{array}\Bigg{]}V^{\rm T}\\ T_{\rm r}=p_{\rm s,o}-R_{\rm r}p_{\rm r,o}\end{array}\right. \tag{25}\]  
Step 4: Update the coordinates of the laser point cloud \(p_{\rm r}\) and calculate the average residual \(J\) as follows:  
\[p_{\rm r}=R_{\rm r}p_{\rm r}+T_{\rm r} \tag{26}\]  
\[J=\sqrt{\frac{1}{N}\sum_{n=1}^{N}\|R_{\rm r}p_{\rm r,n}+T_{\rm r}-p_{\rm s,n}\| _{2}^{2}} \tag{27}\]  
Step 5: Repeat these steps until the residual reduction between two consecutive iterations is less than the preset threshold \(\Delta J_{\rm set}\):  
\[J_{i-1}-J_{i}<\Delta J_{\rm set} \tag{28}\]
# Node-Based Optimization of GNSS Tomography with a Minimum Bounding Box Algorithm
## 3 New Node-Based Tomographic Approach  
The present research presents an innovative tomographic modeling approach that takes advantage of the greater flexibility of the node-based GNSS tomographic approach. It also adapts the tomographic region and the position of nodes to make sure that the adjusted region fits the distribution of the GNSS signals at the specified epoch. This new approach also adjusts the nodes' distribution as evenly distributed as a classical method. Further, the new tomographic model also significantly reduces the parameters to be solved by only solving nodes with nearby GNSS signals. This approach also increases not only the computing efficiency but also the accuracy of the tomographic results.  
In the new approach, GNSS signal paths are assumed to be straight lines for simplification. A temporal resolution of 20-min GNSS observations is selected for the tomographic sampling epoch. Different vertical intervals between two consecutive tomographic planes at different altitudes are adopted due to the nonuniformness of water vapor distribution in the vertical dimension. The new node-based tomography modeling process is achieved through the following steps: (1) determine the tomographic region dimension at each epoch by the minimum bounding box algorithm; (2) determine the nodes position and density at each of the different tomographic planes; (3) construct and solve the tomographic equations. Each of the above steps is elaborated below.
## The Impact of Different Aerosol Models
### Components of XCO\({}_{2}\) Retrieval Errors
The forward model parameter error always far exceeds the corresponding interference error, except in some cases when six or seven aerosol parameters are retrieved. Therefore, the total retrieval error of XCO\({}_{2}\) induced by aerosols depends primarily on the forward model parameter error. Although more cases show larger interference errors when retrieving more aerosol parameters, there remain more aerosol parameter combination groups with small forward model parameter errors. Thus, the retrieval of seven aerosol parameters is better for reducing the total XCO\({}_{2}\) retrieval error associated with aerosols (five for MA). Additionally, the forward model error exhibits a larger range for DD and UI than other aerosol types, indicating a greater impact on CO\({}_{2}\) retrieval. In conclusion, the effect of DD on CO\({}_{2}\) retrieval exceeds that of other aerosol types. The stronger scattering of DD results in larger interference for CO\({}_{2}\) retrieval error. To reduce the aerosol effect on CO\({}_{2}\) retrieval, we sought to identify an aerosol model that would permit a large amount of CO\({}_{2}\) information while simultaneously minimizing the total XCO\({}_{2}\) errors generated from the aerosol parameters, which include both the forward model error and interference error. Considering that fewer aerosol-induced retrieval errors of XCO\({}_{2}\) occur in cases with more retrieved aerosol parameters, the retrieval of seven aerosol parameters is recommended for UI, BB and DD, but only five parameters are recommended for MA due to the lower amounts of aerosol information. When the number of aerosol parameters to be retrieved has been determined, the selection of different aerosol parameter combination groups depends on the comparison of the sum of the forward model parameter errors and the interference errors.
###### Abstract
Therefore, by conducting a comprehensive analysis of prior academic work, as shown in Table 1, it is evident that the implementation of the EWF model has not been frequently utilized in resolving the issue of water-drop impact on wing surfaces with consideration of the far-field effect. Simultaneously, there is little research being conducted about the liquid film on the surface of airfoils presently. Hence, the primary aim of this work is to integrate the Euler-Euler approach and the EWF model, thereby simulating the water-drop impact on the airfoil surface and its liquid film flow. Ultimately, in comparison to previous research endeavors, the novelty of the numerical simulation presented in this paper can be attributed to the following key point: a three-dimensional model of the NACA653-218 airfoil blade, possessing low resistance and utilized in the transport aircraft C919, was constructed. Through simulation, the flow characteristics of liquid film during smooth flight were analyzed. \begin{table}
\begin{tabular}{c c c} \hline
**Reference** & **Research Object** & **Research Approach** \\ \hline
[17] & cell culture & \\ \hline
[19] & Gas-liquid Cylindrical Cyclones & \\ \hline
[20] & Containment cooling system & EWF model \\ \hline
[21] & Containment condenser & \\ \hline
[22] & Atmospheric pressure tower & \\ \hline \end{tabular}
\end{table} 
Table 1: Academic research on EWF models in recent years.