esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Dynamic Load Identification of Beam Structures Using a Bayesian Method
## 2 The Bayesian Formulation of the Distributed Dynamic Load Identification
### Distributed Dynamic Load Identification Algorithm Based on the Bayesian Method  
The identification model of the distributed dynamic load is given by:  
\[\mathbf{y}=QA+\mathbf{y}_{noise} \tag{19}\]  
where \(\mathbf{y},Q,A\) represent the measurement response, dynamic calibration matrix, and the orthogonal polynomial coefficients, respectively. We assume the noise is the Gaussian noise and the prior distribution is the Markov random field, thus, the posterior probability density of the orthogonal polynomial coefficients is derived as Equation (20) based on the Bayesian inference:  
\[p(A\,|\mathbf{y})\propto(\sigma^{2})^{-\frac{\eta}{2}}\exp(-\frac{\|QA-\mathbf{y}\|_{2 }^{2}}{2\sigma^{2}})\lambda^{\frac{\eta}{2}}\exp(-\frac{1}{2}\lambda A^{ \mathsf{T}}\mathbf{W}A) \tag{20}\]  
To determine the parameters \(\lambda,\sigma^{2}\), the hierarchical Bayesian model is adopted via treating the parameters as random variables with its own prior distribution to obtain the prior probability distribution. The Gamma distribution is used as the prior distribution of \(\lambda\) and \(\tau=1/\sigma^{2}\), that is,  
\[p(\lambda)=\frac{\beta_{1}^{\alpha_{1}}}{\Gamma(\alpha_{1})}\lambda^{\alpha_{ 1}-1}\exp(-\beta_{1}\lambda) \tag{21}\]  
\[p(\tau)=\frac{\beta_{2}^{\alpha_{2}}}{\Gamma(\alpha_{2})}\tau^{\alpha_{2}-1} \exp(-\beta_{2}\tau) \tag{22}\]  
where \(\alpha_{1},\beta_{1},\alpha_{2},\beta_{2}\) denote the Gamma distribution parameters. Consequently, the hierarchical model for the joint posteriori probability density of the orthogonal polynomial coefficients \(A\) and the parameter \(\lambda,\tau\) is derived using:  
\[p(A,\lambda,\tau\,|\mathbf{y})\propto p(\mathbf{y}|A,\tau)p(A|\lambda\,)p(\tau)p( \lambda) \tag{23}\]  
where \(p(\mathbf{y}|A,\tau)\) is the likelihood function and \(p(A|\lambda\,)\) is the prior probability density of the orthogonal polynomial coefficients. Based on Equations (13) and (15), the expressions are defined as:  
\[p(\mathbf{y}|A,\tau)\propto\tau^{\frac{\eta}{2}}\exp(-\frac{\tau}{2}\|QA-\mathbf{y}\| _{2}^{2}) \tag{24}\]  
\[p(A|\lambda\,)\propto\lambda^{m/2}\exp(-\frac{1}{2}\lambda\|A\|_{2}^{2}) \tag{25}\]  
Combining Equations (21), (22), (24), and (25) with Equation (23), the posteriori probability density is rewritten as Equation (26). \[p(A,\lambda,\tau\,|\mathbf{y})\propto\tau^{\frac{\eta}{2}+\alpha_{2}-1}\lambda^{ \frac{\eta}{2}+\alpha_{1}-1}\exp(-\frac{\tau}{2}\|QA-\mathbf{y}\|_{2}^{2}-\frac{1} {2}\lambda\|A\|_{2}^{2}-\beta_{1}\lambda-\beta_{2}\tau) \tag{26}\]  
In this paper, the maximum a posteriori estimation given by Equation (27), as proposed in Equation (11), is performed to determine the values of the unknown variables. However, it should be noted that there are some limitations to solving various estimators of parameters through the posterior probability distribution with the common methods. Therefore, the maximum of the posterior density function can be converted to the minimizer of the functional, and then derived with the iterative methods.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 3 Objective Function Formulation
### Second Level  
The object function is shown in Equation (23). It is a mixed-integer linear programming (MILP) problem to search the optimal operating mode of the devices. The object is to minimize the operation cost of the MGs, which includes the cost of natural gas, cost of power interaction with the DN, cost of power interaction with the other MGs, and devices' maintenance cost in the MGs.  
\[\text{min}C_{operation}=\sum_{i=1}^{N_{MG}}\left(C_{gas,i}+C_{grid,i}+C_{ex,i} +C_{mc,i}\right) \tag{23}\]  
where \(C_{operation}\) represents the total operation cost of MGs. \(C_{gas,i}\) represents the cost of natural gas of ith MG. \(C_{grid,i}\) is the power purchase cost of ith MG. \(C_{ex,i}\) represents the power interaction cost with other MGs of ith MG. \(C_{mc,i}\) represents the device maintenance cost of ith MG. The details of each cost are shown below:  
\[C_{gas,i}=\sum_{t=1}^{T}c_{gas,t}(F_{GT}(t)+F_{GB}(t))\Delta t \tag{24}\]  
\[C_{grid,i}=\sum_{t=1}^{T}c_{grid,t}P_{grid}(t)\Delta t \tag{25}\]  
\[C_{ex,i}=\sum_{t=1}^{T}c_{ex,t}P_{MG}(t)\Delta t \tag{26}\]  
\[C_{mc,i}=\sum_{t=1}^{T}\sum_{k=1}^{N_{M}}c_{k}(t)P_{k}(t)\Delta t \tag{27}\]  
where \(c_{gas,t}\) represents the gas price at time slot \(t\). \(c_{grid,t}\) is electricity price at time slot \(t\). \(c_{ex,t}\) is power interaction price between MGs at time slot \(t\). \(c_{k}\) and \(P_{k}(t)\) are unit operation and maintenance cost and power generated or consumed by GT, GB, WH, HX, AC, EC, photovoltaic array (PV), wind turbine (WT), ESS. \(N_{dv}\) is the number of devices in the MG.  
The multi-energy MGs contain a variety of constraints, including device output constraints and bus balance constraints. The device output constraints and power interaction constraints have been introduced in Section 2. As illustrated in Figure 2, the bus balance constraint in each MG can be written as:  
* Electric bus balance \[\begin{array}{l}P_{PV}(t)+P_{WT}(T)+P_{GT}(t)+P_{grid}(t)+\\ P_{dis}(t)+P_{MG}(t)=P_{Load}(t)+P_{ch}(t)+P_{EC}(t)\end{array}\] (28)
* Cooling load bus balance \[P_{EC}(t)COP_{EC}+H_{AC}=H_{cooling}(t)\] (29)
* Heating load bus balance \[H_{HX}(t)+H_{GB}(t)=H_{heating}(t)\] (30)
* Steam bus balance: \[H_{C}(t)+H_{h}(t)=H_{WH}(t)\] (31) where \(P_{Load}(t)\), \(H_{cooling}(t)\), \(H_{hating}(t)\) represent the electrical load, cooling load and heating load, respectively.
## 3 Simulation Results  
In this section, we compare the sparse channel estimation performance between the proposed algorithm and the convex regularized recursive maximum correntropy (CR-RMC) [20]. In addition, the numerical robustness of the proposed algorithm is compared with that of CR-RMC in the finite-precision environments.
## 4 Proposed Method
### Hardware Monitoring Module
#### 4.4.2 Data Integrity Check  
During program execution, when the HMM detects a new BB and starts to execute, the LHash calculation unit and DI check unit in the HMM will be activated. The instructions that are executed sequentially in the BB and the corresponding storage address are continuously inputted into the hardware-implemented LHash calculation unit until the instruction (branch or jump instruction) that marks the end of the BB is detected. At the same time, from the arrival of the first instruction of the BB, the starting address is sent to the Integrity Parameter Memory to find the pre-stored static integrity digest (LHashStatic). The LHash calculation unit maps the binary codes of all instructions of the currently executed BB into 96-bit binary numbers. Through the same selection method as when offline, the 16-bit dynamic data integrity digest (LHashDynamic) is obtained. Then, it will be compared with the LHashStatic. If there are differences, the DI has been destroyed by attackers.
## 3 Materials and Methods
### Hyperparameter Optimization
Because testing all potential possibilities in a full factorial manner would be prohibitively costly, these hyperparameters are optimized using the Particle Swarm Optimization (PSO). PSO is a computer approach to solving problems that uses a population of possible solutions, referred to as particles, and moves them around in the search area using a simple mathematical formula based on their location and velocity. Each particle's movement is impacted by its local best-known location, but it is also steered toward the search space's best-known positions, which are updated when better places are discovered by other particles. The swarm is predicted to migrate to the best options because of this [54]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyperparameter** & **Description** & **Range** \\ \hline Activation Function & Neuron’s activation function & ReLU, SeLU, Sigmoid \\ \hline \multirow{2}{*}{Batch Size} & Group size of training data & \multirow{2}{*}{8, 16, 32} \\  & divisions & \\ \hline Epoch & Number of learning iterations & 20, 50, 100 \\ \hline \multirow{2}{*}{Kernel Count} & Kernel count of convolutional & \multirow{2}{*}{8, 16, 32} \\  & layer & \\ \hline \multirow{2}{*}{Kernel Size} & Kernel size of convolutional & \multirow{2}{*}{1, 2, 3} \\  & layer & \\ \hline \multirow{2}{*}{Layer Depth} & Number of layers constituting & \multirow{2}{*}{1, 2, 3} \\  & entire & \\ \multicolumn{2}{c}{network} & \\ \hline \multirow{2}{*}{Learning Rate} & Weight change updated & \multirow{2}{*}{0.01, 0.001, 0.0001} \\  & during learning & \\ \hline Loss Function & Function to calculate error & Binary crossentropy, L2 loss \\ \hline \multirow{2}{*}{Neuron Count} & Neuron count in the final & \multirow{2}{*}{8, 16, 32} \\  & fully-connected layer & \\ \hline \multirow{2}{*}{Stride} & Number of moving pixels of & \multirow{2}{*}{1, 2, 3} \\  & kernel during convolution & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Hyperparameters and their range for CNN models. The most significant parameters of a neural network are initial learning rate (\(\alpha\)0), learning rate decay factor (\(\phi\)), number of hidden neurons (h), and regularization strength (\(\lambda\)). Because all designs achieved comparable identification accuracy, hyper-parameters are frequently more essential than network architecture because a given architecture provides dramatically varying recognition rates with various hyper-parameter combinations. Finally, the best-performing model with the smallest loss value in the validation dataset is chosen. As the performance of the validation data set is factored into the model's hyper-parameter optimization, the final performance of the model is evaluated using a hold-out test set. This method yields an unbiased evaluation of performance. Performance metrics are generated independently for each objective to determine which targets are precisely forecasted. Then, the network's output is utilized to forecast breast cancer disease. The same performance measures as described above are used to assess these results.
## 3 Result and Discussion
### The Operation Characteristics of the PHP
#### 3.2.2 The Heat Transfer Characteristics of the PHP  
The PHP with the adiabatic section length of 180 mm was taken as an example to illustrate the heat transfer characteristics. The temperatures of the evaporation section and the condensation section are shown in Figure 9. It can be clearly seen that at the heat input of 20 W, the oscillating amplitude of the temperature was larger and the oscillating frequency of the temperature was lower compared with those at higher heat inputs. The main reason for this was that when the heat input was low, the flow velocity of the working fluids was small, and the movement of the liquid slugs was oscillatory in the PHP, as shown in Figures 6 and 7. Due to the oscillatory motion of the liquid slugs, the temperature oscillated with a high amplitude at the heat input of 20 W. When the heat input was higher, the oscillation frequency and the absolute velocity of the liquid slugs increased, and the liquid slugs in the PHP formed a more circulatory flow. Thus, the heat transfer process was more stable and the oscillation amplitude of the wall temperature was lower at higher heat input. In addition, Figure 9 shows that the time needed for the start-up of the PHP decreased as the heat input increased. The main reason for this was that new vapor plugs were easier to generate at higher heat input, and the driving force of the liquid slugs was larger. This is consistent with the previous experimental results [1; 21].  
Figure 8: The mean velocity (absolute value) of the liquid slug PHP with the adiabatic section of 180 mm at a steady operation state.  
Figure 9: _Cont._To analyze the heat transfer performance of the PHP, the thermal resistance (\(R\)) was calculated as follows:  
\[R=\left(\overline{T}_{\mathrm{e}}-\overline{T}_{\mathrm{c}}\right)/Q \tag{46}\]  
where \(\overline{T}_{e}\) and \(\overline{T}_{\mathrm{c}}\) are the mean temperature of the evaporation section and the condensation section, respectively, and \(Q\) is the heat input. The thermal resistance of the PHP represents the heat transfer performance of the PHP. Figure 10 shows the thermal resistance and flow velocity in the PHP with different lengths of the adiabatic section length. Because the flow of liquid slugs became more intense at higher heat input, the thermal resistances of the PHPs reduced as the heat input increased. For example, when the heat input was 20 W, the thermal resistance of the PHP with 180 mm adiabatic length was 0.78 \({}^{\circ}\)C/W. The thermal resistance of the PHP reduced to 0.42 \({}^{\circ}\)C/W when the heat input increased to 80 W.  
Figure 9: The wall temperature of the PHP with the adiabatic section of 180 mm at the heat input of (**a**) 20 W, (**b**) 25 W, (**c**) 30 W, (**d**) 40 W, (**e**) 60 W, and (**f**) 80 W.
## 1 Introduction
The performance of these LFM detection methods drastically declines in this condition. As discussed above, channel multi-path negatively affects the performance of traditional detector. Time-reversal (TR) provides an opportunity to utilize multi-path. TR can make the extended signal focusing and lower the effects of multi-path. In ultrasound and acoustic domain, [PERSON] et al. have demonstrated that TR has the ability of super-resolution focusing. In their work [18; 19], they showed that the resolution is only limited by the correlation of channel passed by transmitted signal, while it is no longer dependent on the sensor aperture size. They also validated this theory by acoustic and seismic imaging experiments. Furthermore, they proposed TR cavity [20], iterative TR [21], TR operator decomposition (DORT) [22] and other methods [23; 24] for target detection. [PERSON] et al. verified the focusing ability of TR in ocean experiments [25; 26; 27]. It is worth noting that TR focusing ability is based on the reciprocity of the channel, while long time delays will produce large mismatches that lead to TR focusing degradation[28]. In the electromagnetic field, TR focusing has been confirmed in [29]. [PERSON] and [PERSON] derived TR detectors, analyzed the performance and verified their theory by real electromagnetic data [30; 31]. In addition, TR focusing has also acquired considerable attention and been applied in other fields, such as communication [32; 33], positioning [34; 35] and imaging [36]. In this paper, we propose a TR-FrFT-based method for LFM signal detection in the underwater multi-path environment. In our work, TR can be regarded as a pre-filter to mitigate the effect of multi-path, and then the signal is transformed into the optimal order fractional Fourier domain to be detected. The main contribution of this paper is summarized as:  
(1) We propose a novel method for LFM signal detection in underwater multi-path environment. The new method can achieve energy focusing on the optimal order fractional Fourier domain and realize low signal to noise ratio detection. (2) Simulations and lake experiments were conducted and verified the effectiveness of the proposed method for LFM signal detection. (3) Compared with the FrFT and matched filter, the proposed method has superior detection performance in the underwater multi-path environment. The rest of this paper is organized as follows. In Section 2, after providing preliminaries of FrFT and the related detection method, problem statement is introduced. In Section 3, firstly, the theory about time-reversal is briefly presented. Secondly, we devise the proposed detection method. Simulations and lake experiments compared with other methods are provided in Section 4. Section 5 concludes the work.
## 3 Code Roots Recognition and Generator Polynomial Reconstruction
### Discussion of the Element \(\alpha^{2^{\pi}-1}\)
According to the basic character of the BCH codes, a generator polynomial has \(2t\) roots with consecutive degrees (the \(2t\) roots do not form all the code roots, but include all the distinct roots), where \(t\) is the correction capability of the codes. In other words, if \(\alpha\) is a primitive element in \({\rm GF}(2^{m})\), the generator polynomial \(g(x)\) of a BCH code for correcting \(t\) errors has \(\alpha\),\(\alpha^{2}\),\(\alpha^{3}\),..., \(\alpha^{2t}\) as its roots [3]:  
\[g(\alpha^{i})=0,\;\;i=1,2,...,2t \tag{34}\]  
After the estimation of code roots based on a randomly chosen primitive polynomial \(p(x)\) and the estimated degree \(m\), we can calculate the number of roots and the correction capability \(t\). Then, we can traverse all the primitive polynomials over \({\rm GF}(2^{m})\) and get the one that makes the code roots be in accordant with the character of BCH codes as the primitive polynomial of the being recognized code. As an example, we still consider the BCH(63, 51) code referred in Section 3. The codewords are encoded based on the primitive polynomial \(p_{\rm i}(x)=x^{\rm\,\,\,\,}+x+1\) and the code roots are \(\alpha\), \(\alpha^{2}\), \(\alpha^{4}\), \(\alpha^{8}\), \(\alpha^{16}\), \(\alpha^{32}\), \(\alpha^{3}\), \(\alpha^{6}\), \(\alpha^{12}\), \(\alpha^{24}\), \(\alpha^{48}\), and \(\alpha^{33}\). The first six ones and the last six ones are two groups of conjugate roots. Here \(\alpha\) is a primitive root in \(\mathrm{GF}(2^{6})\) based on the primitive polynomial \(p_{1}(x)\), _i.e.,_\(p_{1}(\alpha)=0\). But in the recognition procedure, we do the calculations over \(\mathrm{GF}(2^{6})\) with another primitive polynomial \(p_{2}(x)=x^{6}+x^{5}+1\) and let the symbol \(\beta\) be a root of \(p_{2}(x)\). As a result, we can get the code roots \(\beta^{15}\), \(\beta^{30}\), \(\beta^{60}\), \(\beta^{57}\), \(\beta^{51}\), \(\beta^{39}\), \(\beta^{31}\), \(\beta^{62}\), \(\beta^{61}\), \(\beta^{69}\), \(\beta^{55}\), and \(\beta^{47}\)after the recognition as shown in Figure 3. It is easy to verify that the estimated code roots also form two groups of conjugate roots, thus the error-correction capability \(t\) equals two. Now we traverse all the other primitive polynomials and find the one under which the code roots are in accordant with the characters of BCH codes.
# Analysis of Wind-Induced Vibration Response of Transmission Wire Under Crosswind in Canyon Terrain
## 7 Wind Vibration Characteristics of Wire Under Pulse Wind Speed Input  
### Wind Vibration Mechanism of Wire Under Pulse Wind Speed Input  
Figure 15 shows the time-history curves of the inlet wind speed, the pre-line wind speed, the lateral force on the wire, the displacement of the wire in the z-direction, the lift force on the wire, and the displacement of the wire in the y-direction under step condition 5. Figure 16 shows the pressure cloud image around the wire in the central section at different times under condition 5. We selected the time of each phase of the change in the pulse wind equally, as seen in Figures 15 and 16.  
Figure 14: Change curve and trend line.  
1. Both the lateral force and the z-displacement increased with the rise in the pulse wind and reached the maximum value when the pulse wind started to fall;
2. After the pulse wind disturbance, the lateral force quickly returned to the pre-disturbance value because the pressure on both sides of the wire was basically equal;
3. After the pulse wind disturbance, the z-displacement presented periodic fluctuation, and the amplitude gradually decreased;
4. The lift force increased under the pulse disturbance. However, the vertical direction (y)-displacement continued to fall during this period, which was caused by the combined force of gravity, wire tension, lift, and lateral forces still having a vertical downward component;  
Figure 16: Pressure around the wire.  
Figure 15: Time-history curves of wind speed, stress, and wire displacement.  
* A further analysis showed that after the pulse wind disturbance, the y-displacement oscillations underwent a decay process with a group of three wave peaks (the green dashed line in the figure) as the period. The oscillation period of three wave peaks was formed in the y-direction mainly because the positive and negative displacements in the y-direction were not synchronized with those in the z-direction. The positive and negative displacements in the y-direction were opposite to those in the z-direction during the first two wave peaks, and the positive and negative displacements in the third wave peak were the same as those in the z-direction.  
Under the pulse wind speed, the midpoint of the wire with the maximum displacement was selected for monitoring. Figure 17 shows the displacement trajectory of the midpoint of the wire under the wind speed of condition 5. The x-direction was perpendicular to the plane of the coordinate axis, and the point corresponding to 0 on the axis in the z-direction was the starting point of the displacement of the midpoint of the wire. The figure shows that the wire swung in the shape of the number \"8\". The swing trajectory gradually moved closer to the middle as the swing process progressed, and the stroke trajectory of each cycle gradually decreased. It was predicted that the wire would eventually reach a stable equilibrium state.
# Optimization Strategy for Electric Vehicle Routing under Traffic Impedance Guidance
## 3 Electric Vehicle Characteristics Model
### Electric Vehicle Energy Parameter Characteristics  
According to the classification and statistical results of EVs, the battery capacity (C\({}_{P}\)) follows a gamma distribution, which can be expressed by the following Equation (12):  
\[f(C_{p};\mu,\sigma)=\frac{1}{\beta^{\alpha}\Gamma(\alpha)}\cdot C_{p}^{-1} \cdot e^{-\frac{C_{p}}{\beta}} \tag{12}\]  
Here, \(\alpha\) and \(\beta\) are the parameters of the gamma distribution [26]. The upper limit of the battery capacity is set to 72, and the lower limit is set to 10.  
When EVs arrive for travel, the battery may not be in a fully charged state. In this article, the initial battery capacity of the EV is denoted as C\({}_{0}\), and the initial state of charge (SoC0) follows a normal distribution N(0.8, 0.1) [27], as shown below:  
\[\left\{\begin{array}{l}C_{0}=SoC0\cdot C_{p}\\ f(SoC0;\mu,\sigma)=\frac{1}{\sqrt{2\pi\cdot\sigma}}\cdot e^{-\frac{(SoC_{0}-p) ^{2}}{2\sigma^{2}}}\end{array}\right. \tag{13}\]  
Figure 4: Schematic diagram of road network area division.  
According to the actual driving characteristics of EVs, their power consumption is closely related to the real-time traffic speed \(\rm{v_{ij}}\) of each road segment. Many studies assume a constant driving speed, so the exact calculation of energy consumption for an EV trip is not possible. In this article, the following model is used to calculate the energy consumption per unit distance (\(\Delta\rm{E}\)) on each road segment [28], as shown in Equation (14):  
\[\Delta E=0.21-0.001v_{ij}+\frac{1.513}{v_{ij}} \tag{14}\]  
Based on the initial state of charge and the energy consumption per unit distance, the state of charge (SoC1) during the driving process of the EVs can be obtained as shown in Equation (15):  
\[SoC1=SoC0-\frac{\Delta E}{0.9C_{p}} \tag{15}\]