esa-sceva/satcom-chunk-collection huggingface dataset:
# State of the Art of Tactile Micro Coordinate Metrology
## 4 System Calibration and Performance Verification
### Probe Calibration
#### 4.1.2 Scanning  
The standard ISO 10360-4 [34] describes the scanning probing error \(T_{\mathrm{ij}}\) which is the range of radial distances obtained from four circular scans on a sphere: one full circle in an equatorial plane, a second full circle in a plane parallel to the first, a third half circle through the pole, and a fourth half circle in a plane perpendicular to the third at a distance of about 1/2 of the radius from the pole (Figure 5, left).  
This test was performed with the METAS probe on a 1 mm ruby sphere with a point density of 300 pts/mm [35]. The LS diameter fitted through the scan profiles resulted in 1.000806 mm, compared to the value of 1.000804 mm found by the independent probe sphere diameter determination as shown  
Figure 4: Determination of the single point probing error according to ISO 10360-5 on a 1 mm Si\({}_{3}\)N\({}_{4}\) sphere: (**left**) Arrangement of probing points on the reference sphere; (**right**) Deviation from average radius for four measurements with sphere rotated by 90\({}^{\circ}\).  
Figure 5: Determination of the single point probing error according to ISO 10360-4 on a 1 mm ruby sphere: (**left**) Arrangement of scanning profile planes A, B, C and D; (**right**) Measured profiles A to D resulting in a scanning probing error \(T_{\mathrm{ij}}\) = 87 nm.  
in Section 4.1.3. For \(T_{\rm jj}\) a value of 87 nm was obtained. The corresponding values for the four individual scans were 51 nm, 75 nm, 67 nm and 78 nm, respectively. It must be noted that these values comprise the form deviation of the reference sphere (53 nm), the uncompensated form deviation of the probing sphere and surface roughness contributions of both spheres, as no filtering was applied.
## 2 Problem Background
### Existing OppNet Routing Protocols for Sensed Data Collection
#### 2.2.3 Current State of OppNet Routing Protocols for Sensed Data Collection
While encounter-based routing protocols are more applicable for sensed data collection in terms of delivery guarantees, additional mechanisms are required to improve fairness under higher degrees of spatial locality without degrading throughput. Next, we briefly overview existing routing solutions for addressing the issue of unfairness in OppNets and also present their limitations for sensed data collection.
# Equivalent Analysis of Thermo-Dynamic Blow-Off Impulse under X-ray Irradiation
## 2 Finite Element Calculation Software
### Calculation Process  
The calculation process of electron beam energy deposition can be summarized as follows:  
In a unit mass thickness material \(S(E)\), the electron energy loss, mainly caused by electron collision ionization for lower-energy electrons, is as follows [28; 29]:  
\[S(E)=\frac{2\pi e^{4}N_{0}Z}{m_{0}c^{2}\beta^{2}A}\left\{\text{In}[\frac{m_{ 0}c^{2}\beta^{2}E}{2J^{2}(1-\beta^{2})}]-[2(1-\beta^{2})^{1/2}-1+\beta^{2}] \text{In}2+(1-\beta^{2})+\frac{1}{8}[1-(1-\beta^{2})^{1/2}]^{2}-\delta_{0}\right\} \tag{1}\]  
where \(N_{0}\) is the Avogadro constant, \(e\) is the electron charge, \(Z\) is the atomic number, \(A\) is the atomic weight, \(m_{0}c^{2}=0.511 MeV\) is the electron rest mass, \(\beta=\left\{1-[m_{0}c^{2}/(E+m_{0}c^{2})]^{2}\right\}\), \(E\) is the kinetic energy of electron, \(J(MeV)\) is the average ionization energy, \(\delta_{0}\) is the density effect correction factor and \(\delta_{0}\) is introduced to reduce energy loss by electron collisions through target polarization.  
The next step in electronic energy is decided by the law of energy logarithmic delay  
\[E_{n+1}=KE_{n} \tag{2}\]  
where \(K\) is 0.9576.  
The continuous slow-down used to approximately calculate the interval mass range of electron.  
\[\Delta S_{n+1}=\int_{E_{n+1}}^{E_{n}}\left|\frac{dE}{dS}\right|^{-1}\!dE \tag{3}\]  
The electronic coordinates of step \(n+1\) are as follows.  
\[X_{n+1}=X_{n}+\Delta S_{n+1}\frac{\cos\theta_{n+1}}{\rho(j)} \tag{4}\]  
where \(\rho(j)\) is the Lagrange interval density of \(j\). \(\theta_{n+1}\) is the positive angle between the electron motion direction and the \(X\)-axis of step \(n+1\).  
According to the Moliere Multiple Scattering Theory, the scattering angle is as follows [30],  
\[F(\theta)=\mathbf{f}^{(0)}(\theta)+\mathbf{f}^{(1)}(\theta)/B+\mathbf{f}^{(2)}( \theta)/B^{2},\omega=X_{c}B^{1/2}\theta \tag{5}\]  
where \(B\) and \(X_{c}\) are the parameters that depends on energy and path length, and it's directly related to energy. \(\theta\) is the discrete angle.  
The electronic polar angle is as follows.  
\[\theta_{n+1}=\cos^{-1}(\cos\theta_{n}\cos\omega+\sin\theta_{n}\sin\omega\cos\varphi) \tag{6}\]  
where \(\omega\) is the electronic azimuth, \(\varphi\) is the determined uniform sampling.  
If the deposition energy of each incident electron is \(\Delta E_{i}\) in the interval \(J\), the total deposition energy of each incident electron is as follows [8],  
\[E_{n}=\sum_{i=1}^{N}\Delta E_{i} \tag{7}\]  
The specific energy of unit energy flux in interval \(J\) is as follow.  
\[Q_{J}=\frac{E_{n}}{\rho_{0}\Delta xN} \tag{8}\]  
where \(\Delta x\) is the mesh step.  
According to the current and voltage waveform of experiment, the energy deposition of electron beam can be calculated, as shown as below:  
\[E_{R}(\mathrm{x},\mathrm{t})=U_{t}I_{t}Q_{J}\Delta T/S \tag{9}\]  
where \(U_{t}\) and \(I_{t}\) are the instantaneous voltage and current of REB diode, \(\Delta T\) is the time step, \(S\) is the spot area of electron beam on the target.
# Interference Alignment in Multi-Hop Cognitive Radio Networks under Interference Leakage
## 3 Outage Probability Analysis  
In this section, the exact outage probability expression is derived for two different cases: (i) the interference is perfectly aligned; (ii) the interference leakages occur due to imperfect IA. Outage probability can be defined as the outage probability of the overall system. In other words, the system is in outage if at least one of the hops is in outage. Mathematically, it can be expressed as:  
\[P_{\text{out}}=1-\prod_{i=1}^{K-1}(1-P_{\text{out}}^{(i)}), \tag{9}\]  
where \(P_{\text{out}}^{(i)}\) is the outage probability of the \(i\)th hop.
# IBP Based Caching Strategy in D2D
###### Abstract  
Device to Device (D2D) communication is a key technology in 5 th generation wireless systems to increase communication capacity and spectral efficiency. Applying caching into D2D communication networks, the device can retrieve content from other devices by establishing D2D communication links. In this way, the backhaul traffic can be significantly reduced. However, most of the existing caching schemes in D2D are proactive caching, which cannot satisfy the requirement of real-time updating. In this paper, we propose an Indian Buffet Process based D2D caching strategy (IBPSC). Firstly, we construct a geographical D2D communication network to provide high quality D2D communications according to physical closeness between devices. Then devices are divided into several social communities. Devices are ranked by their node importance to community in each community. The base station makes caching decisions for devices according to contrition degree. Experimental results show that IBPSC achieves best network performance.  
Device to Device; caching; Indian Buffet Process Article
# Evolution of the Complex Supply Chain Network Based on Deviation from the Power-Law Distribution
## 3 Evolution Model of a Complex SCN Based on the Deviation of the Power Law Distribution
### Model Condition Setting  
Assuming that the initial SCN contains m0 nodes and n0 edges, three conditional assumptions are set for the process of network evolution based on literature research and actual network analysis and are executed with a certain probability at each time step. The specific contents of the model assumption are as follows:  
* Add new nodes with probability \(p_{1}\). Each newly added node establishes an edge with another node through the preferred connection mode shown in Formula (2), and \(m\) new connected edges are generated.
* Add ct new continuous edges with probability \(p_{2}\), where parameter c is a constant that is much smaller than 1 and used to characterize different network features.
* \(n\) connected edges were removed and connected. Interruption and establishment of cooperation between enterprises in the network are common. To characterize this phenomenon, randomly select a node i and one of its connected edges \(l_{ij}\) with probability \(q\), remove this edge with probability \(q\prime\), and then use Formula (2) to select node \(j\prime\) to form a new connected edge \(l_{ij}\prime\). When the node degree value is larger, the value of \(q\prime\) is smaller, and the removal of edges between nodes follows the anti-optimal disconnection rule.  
In this model, all probability parameters \(p_{1}\), \(p_{2}\), \(q\) and \(q\prime\) are in the range of [0, 1], and when \(q=0\). \(q\prime=0\). It is assumed that the degree value ki is continuously changing, and the initial network structure will not have a great impact on the final evolution of the network. Therefore, the above three operations will have a greater impact on the SCN degree value \(k_{i}\).
## 5 Simulation Results
### Network Lifetime  
This section compares the network lifetimes of the PSO-LSA,EPOA-CHS, LEACH-IACA, and LEACH-ANT algorithms with the suggested ESO-GJO algorithm. The time span from the sensor network's inception to the death of the final node is referred to in this study as the network lifetime. Four different metrics, FND (First Node Death), HND (Half Nodes Death), MND (Most Nodes Death), and LND (Last Node Death), are analyzed for different protocols. Table 2 records the number of rounds in which each indicator occurs in different scenarios for ESO-GJO, PSO-LSA, EPOA-CHS, LEACH-IACA, and LEACH-ANT. Figure 4a-c shows more visually the results of ESO-GJO compared to other protocols for the four metrics in the section above for scenarios with 100 nodes, 200 nodes, and 300 nodes. Consequently, our suggested ESO-GJO can more effectively increase the network lifetime.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Value** \\ \hline Network Area & \(100\times 100\), \(200\times 200\), \(300\times 300\) (m\({}^{2}\)) \\ Number of nodes & \(N=100,200,300\) \\  & (50 m, 150 m) \\ Sink node location & (100 m, 250 m) \\  & (150 m, 350 m) \\ \(E_{0}\) & 0.5 J \\ Packet Size & 4000 Bits \\ \(E_{dec}\) & 50 nJ/bit \\ \(e_{fs}\) & 10 nJ/bit/m\({}^{2}\) \\ \(\epsilon_{mp}\) & 0.0013 pl/bit/m\({}^{4}\) \\ \(E_{DA}\) & 5 nJ/bit/signal \\ \(d_{0}\) & 70 m \\ \hline \end{tabular}
\end{table}
Table 1: Simulation-related parameter.  
Figure 3: Residual energy in each round. Scenarios with (**a**) 100 nodes; (**b**) 200 nodes; (c) 300 nodes.
# Long Short-Term Memory Neural Network Applied to Train Dynamic Model and Speed Prediction
## 3 Methodology
### LSTM Network Structure
Through multiple iterations, ideally, the parameters will gradually converge to the global optimum while the training process is finished. However, due to the substantial number of hyperparameters and the deep network structure, the gradient descent algorithm may be trapped to the local optimum, resulting in the degradation of output accuracy and the prolongation of training time. To cope with this problem, in this paper, the Adaptive Moment estimation (Adam) optimization algorithm [50] is employed to avoid low efficiency and local optimum. Note that, even though only one hidden layer exits in the network, the LSTM network is considered as a deep learning algorithm due to the recurrent nature. Similar to convolutional neural networks, more hidden layers can be stacked to seize high-dimensional characteristics. However, previous studies show that adding hidden layers may not significantly improve the algorithm performance over a single layer [51; 52]. Thus, in this paper, the aforementioned standard LSTM network structure is adopted where one hidden layer is deployed to save storage and computing resource. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Name** & **Explanation** & **Type** \\ \hline \(\mathbf{i}_{t}\) & output of input gate at \(t\)th time step & vector \\ \(\mathbf{W}_{i}\) & weight matrix of input gate & matrix \\ \(\mathbf{h}_{t}\) & output of the recurrent hidden layer at \(t\)th time step & vector \\ \(\mathbf{b}_{i}\) & bias vector of the input gate & vector \\ \(\sigma(\cdot)_{i}\) & activation function of input gate & function \\ \(d_{x}\) & dimension of the recurrent hidden layer input & integer \\ \(d_{h}\) & dimension of the recurrent hidden layer output & integer \\ \(d_{c}\) & dimension of the state vector & integer \\ \(\mathbf{f}_{t}\) & output of forget gate at \(t\)th time step & vector \\ \(\mathbf{W}_{f}\) & weight matrix of forget gate & matrix \\ \(\mathbf{b}_{f}\) & bias vector of forget gate & vector \\ \(\sigma(\cdot)_{f}\) & activation function of forget gate & function \\ \(\mathbf{o}_{t}\) & output of output gate at \(t\)th time step & vector \\ \(\mathbf{W}_{o}\) & weight matrix of output gate & matrix \\ \(\mathbf{b}_{o}\) & bias vector of output gate & vector \\ \(\sigma(\cdot)_{o}\) & activation function of output gate & function \\ \(\mathbf{\xi}_{t}\) & state update vector at \(t\)th time step & vector \\ \(\mathbf{W}_{c}\) & weight matrix of state update vector & matrix \\ \(\mathbf{b}_{c}\) & bias vector of state update vector & vector \\ \(\mathbf{c}_{t}\) & current cell state vector at \(t\)th time step & vector \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Illustration of parameters in the LSTM network structure.
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
## 1 Introduction
To the best of our knowledge, they are first proposed for the air traffic field. The second task is to comprehensively evaluate the reliability of the air traffic network based on the reliability indexes. Considering that human thinking is characterized by fuzziness, air traffic managers are primarily concerned about whether reliability is high. When the network reliability is evaluated comprehensively, it is necessary to convert quantitative reliability into qualitative reliability. Thus, the reliability evaluation of the air traffic network is fuzzy. Because of the complexity and uncertainty of air traffic operation, the reliability evaluation is also stochastic. There are many comprehensive evaluation methods, such as neural networks and fuzzy comprehensive methods. Although these measure fuzziness, they do not take randomness into account, and the determination of index weights is subjective. The reliability of the air traffic network is a combination of fuzziness and randomness. The uncertainty transformation model was presented by Prof [PERSON] based on traditional fuzzy set theory and probability statistics [27]. The fuzzy set and uncertainty transformation model have been widely used in data mining and evaluation of the effectiveness of complicated systems [28; 29; 30; 31; 32; 33; 34]. We introduce the uncertainty transformation model into the reliability evaluation method to achieve a balance between quality and quantity. The remainder of this paper is organized as follows: Section 2 presents the definition of the reliability of the air traffic network. In Section 3, the reliability measurement of the air traffic network is presented, including the modeling of the air traffic network, the measurement of connection reliability, travel-time reliability, and capacity reliability, and the comprehensive evaluation of the reliability of the air traffic network based on the uncertainty transformation model. The empirical analysis of the reliability of the US air traffic network is given in Section 4. Finally, conclusions are presented in Section 5.
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 4 Embedded Programming
### System-Level Integration of Dynamic Memory Management
#### 4.2.1 Linked List Structure Memory Model  
This design employs a linked-list data structure to manage sampled geospatial points. A Node structure in Table 3 stores latitude/longitude coordinates and a pointer to the subsequent node, forming a singly linked list. Upon acquiring new coordinates, memory is dynamically allocated to instantiate this structure.  
The structure forms a one-way linked list. When the new coordinate data is collected, the corresponding memory space is dynamically allocated and the data is stored in the form of the structure.
# Sound Field Modeling Method and Key Imaging Technology of an Ultrasonic Phased Array: A Review
## 2 Modelling Method
### Finite Element Method  
FEM was proposed in the 1950s. It is an effective approximate calculation method and is widely used in the field of engineering calculation. FEM analysis divides the object into a finite number of cells. The cells are regarded as under a formation of rigid bodies connected through a finite number of nodes. The force between the elements is transferred through the nodes, and then each unit matrix is established based on the energy principle. After inputting boundary conditions such as loads and constraints, the computer is used to calculate and analyze the object characteristics [24]. At present, the FEM analysis software is ANSYS software [25; 26]. The finite element analysis of ultrasonic arrays sound field using ANSYS software is generally divided into the following four steps, as shown in Figure 4.  
Figure 4: The file chart of ANSYS finite element sound field simulation.  
Figure 3: Pathfinding mode. (**a**) Basic mode; (**b**) P2P mode; (**c**) P2M mode.  
In 1994, [PERSON] et al. [27] used FEM to carry out an accurate computer simulation of complex ultrasonic transducers, but how to generate planar ultrasonic wave sources with arbitrary incident angles on a given defect is a difficult problem for FEM. [PERSON] et al. [28] and [PERSON] et al. [29] used semi-analytical FEM to solve the problem of ultrasonic beam focusing when an object with a complex geometric shape propagates. In 2015, [PERSON] et al. [30] used FEM combined with the directivity of metal materials to analyze the focused sound field distribution of an OPFC ultrasonic phased array transducer and found that OPFC material had a broad prospect for the development of ultrasonic array transducers. In 2019, [PERSON] et al. [31] used the commercial finite element software PZ Flex to conduct FEM research in three dimensions and developed two kinds of high-frequency ultrasonic array transducers for specific applications. In the 2020s, [PERSON] et al. [32] studied the ultrasonic time of TOFD ultrasonic array technology FEM used for 2-D geometric material defect detection, established a finite element model through the finite element software ANSYS, compared the error rate between simulation and experiment, and found the feasibility of finite element detection. Because this method needs a large amount of meshing, it requires the high performance of a computer. Currently, FEM is mostly used to simulate 2D thin workpiece detection.
# Efficient Wireless Monitoring and Control of a Grid-Connected Photovoltaic System
## 4 Communication Module  
A WSN consists of nodes, which are sensor devices, and a coordinator or host, which is the controller of the network. The nodes send their data wirelessly to the coordinator. The choice of the technology to be used depends on the requirements of the target application, with considerations such as latency, security requirements, or battery life. Amongst the various WSN standards, such as the well-known IEEE 802.11 (Wi-Fi) and IEEE 802.15, the IEEE 802.15.4 standard shows high flexibility and is specifically designed for low power, low data rates, and low-cost sensor communication. For these networks, Wi-Fi is inappropriate because, even though it provides high data rates and superior range, its power consumption is an important disadvantage. Bluetooth low energy (BLE) is an interesting option for WSNs, but it has a short range. The IEEE 802.15.4 defined only the lowest two layers of the protocol stack, the physical layer (PHY) and the medium access control layer (MAC). The upper layers of the protocol stack are defined separately by other architectures, such as 6 LoWPAN, ZigBee, ISA100.11a, and WirelessHART.
## 2 Data Transfer in Underwater Channels  
### Transmission Loss  
Acoustic waves emitted from a transducer undergo an attenuation in signal strength as they propagate. According to [2], if spherical spreading is assumed, the transmission loss (\(TL\)) of acoustic waves becomes:  
\[TL=20\log d+\ \alpha\cdot d \tag{1}\]  
where \(d\) represents the distance between wave source and destination and \(\alpha\) means the absorption coefficient depending mainly on frequency. Also, if cylindrical spreading is assumed, \(TL\) becomes:  
\[TL=10\log d+\ \alpha\cdot d \tag{2}\]  
where transmitted acoustic waves are trapped between two parallel and perfectly reflecting surfaces.  
Figure 1: Transmission loss with respect to communication distance and frequency.  
Figure 1 shows \(TL\) with respect to \(d\) and frequency in spherical spreading and sea water with the assumptions that the temperature is 10 degrees Celsius, salinity is 35 per mil, and pH is 7.  
From the results, it is observed that the transmission loss on a dB scale is nearly proportional to the logarithm of distance up to 100 m. In other words, the second term of the right side in Equation (1) is negligible due to the relatively small value of the absorption coefficient. Meanwhile, when the distance exceeds around 100 m, the transmission loss increases exponentially due to the second term. Acoustic waves having high frequency are especially susceptible to absorption with the increase of distance and thus they experience large transmission loss gradually.
## 3 Maritime Traffic Generator
```
1 procedure:PSEUDOCODE FOR VDG_1 Get_COLREG2 PSN()
2 Input COLREG_case, Range
3\(a_{crit}\)13= overtaking tolerance (default 45\({}^{\circ}\))
4\(a_{crit}\)14= head-on tolerance (default 5\({}^{\circ}\))
5\(a_{crit}\)15= crossing aspect limit tolerance (default 5\({}^{\circ}\))
6 select case Determining the Appropriate Rule set()
7 case (Vessel is overtaken(stand-on)): start_Bearing = 112.5, end_Bearing = 247.5
8 case (Vessel is overtaking(give-away)): start_Bearing = \(-a_{crit}\)13, end_Bearing = +\(a_{crit}\)13
9 case (Vessel is head-on)(): start_Bearing = \(-a_{crit}\)14, end_Bearing = +\(a_{crit}\)14
10 case (Vessel is crossing(give-away))(0): start_Bearing = \(-a_{crit}\)14, end_Bearing = 247.5
11 case (Vessel is crossing(stand-on)): start_Bearing = \(a_{crit}\)14, end_Bearing = 112.5
12 Bearing \(\leftarrow\)get_Guassian(start_Bearing, end_Bearing)
13 Position \(\leftarrow\)get_PSN(Range, Bearing)
14 end procedure
```  
**Algorithm 1** COLREG-based position judgment algorithm  
```
1 procedure:PSEUDOCODE FOR VDG_2 Get_CPA2 SOG_COG()
2 Input DCPA, TCPA, own_Position, own_SOG, own_COG, other_Position
3 SOG_itr = step for SOG iteration (default 0.1 knots)
4 COG_itr = step for COG iteration (default 0.1\({}^{\circ}\))
5 forSOG_itr, COG_itr do
6 Get_DCPA(own_Position, own_SOG, own_COG, other_Position, SOG_itr, COG_itr)
7 if (SOG_itr < SOC_small && COG_itr < COG_small) then
8 break
9 endif
10 endfor
11 SOG \(\leftarrow\)SOG_itr
12 COG \(\leftarrow\)COG_itr
13 end procedure
```  
**Algorithm 2** Ship vector judgment algorithm