esa-sceva/satcom-chunk-collection huggingface dataset:
# Inverse Solution Error Analysis and Correction of Beam Steering System Based on Risley Prisms
## 4 Simulation and Experiment
### Discussion  
With the same value of system error, the larger the maximum deflection angle of the Risley prisms, the larger the forward and inverse solution error caused by the system error. The maximum deflection angle of the Risley prisms in this article is \(40^{\circ}\), but it is difficult for experimental setups to measure large angles with high resolution. We can only  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Pointing Error**} \\ \cline{2-3}  & **Maximum** & **RMS** \\ \hline
**Before correction** & 0.0997\({}^{\circ}\) & 0.0705\({}^{\circ}\) \\
**After correction** & 0.0059\({}^{\circ}\) & 0.0034\({}^{\circ}\) \\
**Descent rate** & 94.08\% & 95.18\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Experimental results of inverse solution error correction.  
Figure 13: Comparison of the inverse solution error before and after correction in the experiment. (a) Before correction. (b) After correction.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Deflection Angle**} & \multicolumn{2}{c}{**Pointing Error**} \\ \cline{2-3}  & & **Maximum** & **RMS** \\ \hline \multirow{3}{*}{\(\Delta\theta\)= \(4.86^{\circ}\)} & Before correction & 336.6017\({}^{\prime\prime}\) & 224.4371\({}^{\prime\prime}\) \\  & After correction & 20.6913\({}^{\prime\prime}\) & 11.0432\({}^{\prime\prime}\) \\  & Descent rate & 93.76\% & 95.08\% \\ \hline \multirow{3}{*}{\(\Delta\theta\)= \(3.24^{\circ}\)} & Before correction & 326.7776\({}^{\prime\prime}\) & 226.0153\({}^{\prime\prime}\) \\  & After correction & 25.4662\({}^{\prime\prime}\) & 5.7213\({}^{\prime\prime}\) \\  & Descent rate & 92.21\% & 97.47\% \\ \hline \multirow{3}{*}{\(\Delta\theta\)= \(1.62^{\circ}\)} & Before correction & 336.6017\({}^{\prime\prime}\) & 229.6042\({}^{\prime\prime}\) \\  & After correction & 18.9351\({}^{\prime\prime}\) & 9.7448\({}^{\prime\prime}\) \\ \cline{1-1}  & Descent rate & 94.37\% & 95.76\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Experimental results of forward solution error correction.  
experimentally verify with the existing experimental setup in a small angle; simulated pointing experiments are performed by numerical simulation to verify the algorithm at large angles. The inverse solution error correction accuracy of the experimental results is higher than the simulation results because the residuals after error correction are larger in the case of a large deflection angle.
## 4 Experiment and Results
### BIMCoder Validation
#### 4.2.2 Execution Accuracy on Fuzzy Queries  
Based on the results presented in Table 9 and Figure 5, when addressing fuzzy problems, the test results indicate that the ERNIE 3.0 model significantly outperforms Llama and SQLCoder. The challenges posed by colloquial and diverse query methods in the fuzzy dataset require strong semantic understanding, an area where ERNIE excels. The fusion model (BIMCoder) further enhances performance by leveraging ERNIE 3.0's understanding ability and SQLCoder's structural language generation capability. This integration leads to more stable and accurate results in exact matching and Execution Accuracy, surpassing even the ERNIE fine-tuned model.
## 4 Establishment of the PA-LM Model
### Process of PA Optimizing LM Model  
Firstly, the aforementioned eight hyperparameters of the LM model were selected as the optimized parameters of the PA, and the position values of all the particles were randomly initialized within the ranges of the eight hyperparameters. Then, the LM model was developed and then trained by the training data set. The detailed processes are shown below.  
(1) Initialize the particle parameters, including the number of particles \(n\), \(c_{1}\), \(c_{2}\), \(r\), and \(T_{\max}\) in the PA. (2) A particle \(X_{i}\),\(O\) (\(n_{1}\), \(n_{2}\), \(m\), \(L\), \(M\), \(P\), \(F\), and \(D\)) was randomly generated. The values of the particle \(X_{i}\),\(O\) were assigned to the hyperparameter of the LM model. (3) The initial data set was used to train and test the LM model and the corresponding COP values were calculated. The fitness value \(fit_{i}\) of the \(X_{i}\),\(O\) was calculated as follows:  
\[fit_{i}=0.5^{*}\sum_{j=1}^{J}\frac{\left|\hat{y}^{j}-y^{j}\right|}{y^{j}}*\frac {1}{J}+0.5^{*}\sum_{k=1}^{K}\frac{\left|\hat{y}^{k}-y^{k}\right|}{y^{k}}*\frac {1}{K} \tag{12}\]  
In Equation (12): \(y^{k}\) and \(y^{j}\) are the COP values in the testing and training sets, respectively, \(\hat{y}^{k}\) and \(\hat{y}^{j}\) are the predicted COP values by the PA-LM model in the testing and training sets, and \(\hat{J}\) and \(K\) are the size of the training and testing sets, respectively.  
(4) The \(pbest^{k}_{i,j}\), was initialized as the particle position in the initial state, and the \(gbest\)\({}^{k}_{i,j}\) was taken as the particle position with the lowest \(fit_{i}\). (5) During each iteration, the velocity and position values of the particles were updated based on the \(pbest^{k}_{i,j}\), and \(gbest\)\({}^{k}_{i,j}\) was calculated by Equations (9) and (10). Then, the new \(fit_{i}\) of the particles were calculated and the \(pbest^{k}_{i,j}\) and \(gbest\)\({}^{k}_{i,j}\) of the particles were updated. (6) After \(T_{\max}\) was reached, the \(gbest\)\({}^{k}_{i,j}\) (the corresponding hyperparameters) were the optimal values for the LM model.
# Reliability Analysis of CFRP-Packaged FBG Sensors Using FMEA and FTA Techniques
## 4 Reliability Analysis of CFRP-Packaged FBG Sensors Using the FTA Technique  
### A Brief Introduction of the FTA Technique  
Fault tree analysis (FTA) takes the unexpected fault event (top event) of the system as the target, finds out all the direct and potential factors leading to the occurrence of the top event step by step, and builds the logical relationship between the system and the basic events into an inverted tree graph through the corresponding logic gate symbols [15; 16; 17; 18]. The general FTA analysis process is shown in Figure 10.
## Channel Allocation in Wireless Networks with Directional Antennas  
**[PERSON] \({}^{1,\ast}\), [PERSON] \({}^{2}\) and [PERSON] \({}^{3}\)**  
\({}^{1}\) Faculty of Information Technology, Macau University of Science and Technology, Macau  
\({}^{2}\) Department of Computer Science and Engineering, The Chinese University of Hong Kong,  
Hong Kong; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200030, China; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +853-8897-2154; Fax:+853-2882-3280.  
_Received: 1 March 2013; in revised form: 29 March 2013 / Accepted: 2 April 2013 / Published: 16 April 2013_  
**Abstract:** In this paper, we study the channel allocation in multi-channel wireless _ad hoc_ networks with directional antennas. In particular, we investigate the problem: given a set of wireless nodes equipped with directional antennas, how many channels are needed to ensure collision-free communications? We derive the upper bounds on the number of channels, which heavily depend on the node density and the interference ratio (_i.e._, the ratio of the interference range to the transmission range). We construct several scenarios to examine the tightness of the derived bounds. We also take the side-lobes and back-lobes as well as the signal path loss into our analysis. Our results can be used to estimate the number of channels required for a practical wireless network (e.g., wireless sensor network) with directional antennas.  
**Keywords:** multiple channels; channel allocation; wireless _ad hoc_ networks; wireless sensor networks; directional antennas; graph theory
## 5 Train-to-Ground Communication Architectures with Leaky Waveguide  
The above simulations indicate that leaky waveguide could not only effectively suppress the high Doppler frequency shift, but also achieve an even field distribution inside the tube. However, the penetration loss caused by the heavy train body still exist. To establish wireless link between train user equipments and leaky waveguide, some solutions will be proposed in this section. These solutions can be classified into two categories, solutions based on Mobile Relays (MRs) and direct coverage solution.
# A Digital Denoising Method Based on Data Frequency Statistical Filtering
## 2 Image Denoising Method Based on Probability Statistics
### Noise Screening and Processing  
Through the design of the buffer data queue and statistical data queue, it can be seen that output M of the buffer data queue corresponds to the original value of the center point of the statistical data queue. The data transformation of the statistical data queue is equivalent to the statistical data window moving from left to right and from top to bottom for the original image, and the statistical data pool is equivalent to a neighborhood range of M pixel blocks. In addition, the size of the data pool can be adjusted by adjusting the size of L, and the capacity of the data pool can be changed. By calculating the data frequency of the M pixel block in the neighborhood range and comparing it with the preset frequency threshold, the noisy pixel blocks and effective pixel blocks can be separated. The noisy pixel blocks are processed separately to achieve the denoising function, as shown in Figure 7.
## 4 Results and Discussion
### Model Validation and Experimental Results
#### 4.1.3 ANN-Fitting Model  
Based on deformation simulation results obtained from the FEM simulations under various pressure differentials, the ANN fitting function was established with the central deflection (unit: mm) as the independent variable and diaphragm swept volume (unit: mm\({}^{3}\)) as the dependent variable, as illustrated in Figure 12. The Pearson correlation coefficient (\(R\)) and mean squared error (_MSE_) serve as critical metrics for evaluating ANN fitting accuracy [29]. For the first-stage and second-stage diaphragms, the ANN fitting functions achieved the Pearson correlation coefficients of 0.9992 and 0.9995, respectively, with corresponding MSE values of 1.25 \(\times\) 10\({}^{7}\) and 4.17 \(\times\) 10\({}^{5}\). These results demonstrated excellent agreement between the ANN fitting and FEM simulation results.  
Figure 10: FEM results of the mesh independence analysis.
### Research Contribution and Paper Organization  
To explore this avenue and eliminate the need for predefined niching parameters to locate and maintain multiple optima, the present paper develops the following strategies:  
1. The first endows the particles with socio-emotional personalities. Based on an analogy pertaining to socio-emotional relations prevailing during the mammal reproduction period, the proposed approach introduces three particle types with specific personalities. Among these, some male particles are intrepid and explore the landscapes, while others are more prudent and preserve the found optima. The interactions between the socio-emotional particles give the swarm a natural ability to locate and maintain multiple optima and completely eliminate the need for predefined niching parameters.
2. The second strategy introduces a technique to help the particles visit secluded zones. This technique apportions the particles of the initial distribution to subdomains based on biased decisions. The biases reflect the subdomain's potential to contain optima. The procedure establishes this potential from a balanced combination of the jaggedness and the mean-average interval descriptors put forward in the study.
3. In addition, to control the number of particles required to populate the subdomains, the proposed investigation reduces the domain dimensionality based on a sensitivity index.
4. To boost the performance of the particles, the investigation also examines an economical strategy exploiting the information provided by contours formed by surrounding particles.  
After the literature survey presented in Section 2, the following sections detail the four strategies mentioned above (Sections 3-5). Section 3 begins with the pseudocode of the complete optimizer to show how combining these approaches leads to the proposed algorithm. This algorithm is designated as the Socio-Emotional Particle Swarm Optimizer or S-EPSO. Finally, Section 6 compares the performance of S-EPSO to those of two state-of-the-art algorithms over seventeen multimodal functions with multiple optima. Section 6 also demonstrates the S-EPSO's ability to deal with real-world constrained optimization problems.
## 2 Modeling and Analysis of Electric Actuator Control System
### Electric Actuator Modeling Considered Dead-Zone Non-Linearity  
Non-linearities such as dead-zone, backlash, time-delay, friction, and so on, which can be commonly found in electric actuators, are hard to model mathematically and have a great influence on the control performance of the system [23]. The basic parameters of the DC electric actuator in this paper are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Name** & **Value** \\ \hline \(i_{1}\) & Transmission ratio of gear 1 & 3 \\ \(i_{2}\) & Transmission ratio of gear 2 & 105 \\ \(P_{N}\) & Rated output of the actuator & 40 W \\ \(U_{N}\) & Rated voltage of the direct current (DC) motor & 28 V \\ \(L\) & The motor inductance & \(3.2\times 10^{-3}\) H \\ \(K_{E}\) & Back-emf coefficient of the motor & 0.0415 (rad/s) \\ \(K_{T}\) & Torque coefficient of the motor & 0.0416 N\(\cdot\)m/A \\ \(R_{a}\) & Resistance of the motor & 0.8 \(\Omega\) \\ \(J_{m}\) & Moment of inertia of the motor & \(3.6\times 10^{-6}\) Kg\(\cdot\)m\({}^{2}\) \\ \(T_{Lm}\) & Available output torque of the actuator & 30 N\(\cdot\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic parameters of the actuator.  
Figure 1: Pitch control system of the missile.  
The mechanical transmission part of the actuator consists of two gear reduction units. Ignoring the elastic deformation of the structure, other non-linear factors, and taking the change of the load as the disturbance to the actuator system, and only considering the voltage dead-zone of the motor.  
The actuator model can be mathematically described as:  
Voltage balance equation:  
\[U_{a}(t)=R_{a}I_{a}(t)+L\frac{dI_{a}(t)}{dt}+E_{a}(t) \tag{1}\]  
where \(U_{a}(t)\) is the armature input voltage, \(I_{a}(t)\) is the armature current, and \(E_{a}(t)\) is the back electromotive force of the actuator DC motor.  
Electromotive force equation:  
\[E_{a}(t)=K_{E}\omega(t)=K_{E}\frac{d\theta_{m}(t)}{dt} \tag{2}\]  
where \(\omega(t)\) is the angle rate of the motor shaft, \(\theta_{m}(t)\) is the motor angle position.  
Electromagnetic torque equation:  
\[T_{m}(t)=K_{T}I_{a}(t) \tag{3}\]  
where \(T_{m}(t)\) is the magnetic torque of the motor.  
Torque balance equation:  
\[T_{m}(t)=J_{m}\frac{d\omega(t)}{dt}+T_{L}(t) \tag{4}\]  
where \(T_{L}(t)\) is the load torque of the motor shaft.  
Angular output relation equation:  
\[\delta(t)=\frac{\theta_{m}(t)}{i_{1}i_{2}} \tag{5}\]  
where \(\delta(t)\) is output deflection angle of the actuator.  
The input/output relationship of the dead-zone model [24] is:  
\[v_{o}=f(v_{i})=\left\{\begin{array}{ll}v_{i}-d_{-},&v_{i}<d_{-}\\ 0,&d_{-}\leq v_{i}<d_{+}\\ v_{i}-d_{+},&v_{i}\geq\ d_{-}\end{array}\right. \tag{6}\]  
where \(v_{i}\), \(v_{o}\), are the input and output signal to the dead-zone non-linear link, respectively. \([d_{-}d_{+}]\) characterizes the width of the dead-zone. When the voltage dead-zone is considered in Equations (1)-(5), the modified block diagram of the non-linear electric actuator is shown in Figure 2.
# Interval Uncertainty Optimization Method for Electromagnetic Orbital Launcher
## 4 Process of Interval Uncertainty Optimization Method  
### Interval Uncertainty Optimization System  
A two-layer optimization structure system is employed to address the transformed problem, with the outer layer focusing on the optimization of the design variables and the inner layer focusing on the optimization of the uncertainty variables. The flowchart of this two-layer optimization system is depicted in Figure 4. This paper specifically applies the two-layer optimization approach to the structural optimization of modern electrical equipment.  
In the outer layer optimization, multiple design vector individuals are generated using an optimization algorithm. Each individual then undergoes the inner layer optimization process. The inner layer utilizes an optimization algorithm and a high-precision proxy model to maximize and minimize the responses within the uncertainty domain. This process determines the midpoint and radius values of the objective function and subsequently calculates the evaluation function value for the objective.  
Finally, the algorithm evaluates the stopping criterion. If the criterion is satisfied, the optimization process concludes, and the values of the design variables corresponding to this point represent the optimized results. If the criterion is not satisfied, the optimization search continues.
## 3 Theoretical Formulation of Turbulent Gas Flow Modeling
### Turbulence Models
#### 3.2.2 \(k\)-\(\omega\) Turbulence Model  
This turbulence model introduces a specific dissipation rate of kinetic energy (\(\omega\)) instead of a dissipation rate of kinetic energy (\(\epsilon\)). The realizable and RNG \(k\)-\(\epsilon\) turbulence models perform fairly well in a variety of nozzle flow problems; however, they require empirical damping functions in the viscous sub-layer which are not accurate in the presence of an adverse pressure gradient [25]. In such complex flows where the separation and reattachment of flows occur, the \(k\)-\(\omega\) model provides better results. Therefore, in this study, the \(k\)-\(\omega\) turbulence model along with the above discussed three \(k\)-\(\epsilon\) turbulence model was tested and compared.  
In the \(k\)-\(\epsilon\) turbulence model, the turbulent viscosity is calculated using Equation (7). The model coefficients \(C_{\mu}\), \(C_{1\epsilon}\), and \(C_{2\epsilon}\) are considered to be constant and are determined empirically, whereas the \(k\)-\(\omega\) turbulence model uses the empirical coefficients different  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cases** & **Case 1** & **Case 2** & **Case 3** & **Case 4** \\ \hline Turbulence models & Realizable \(k\)-\(\epsilon\) & Standard \(k\)-\(\epsilon\) & RNG \(k\)-\(\epsilon\) & Standard \(k\)-\(\omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation cases with different turbulent models.  
from those of the \(k\)-\(\epsilon\) turbulent model to calculate turbulent viscosity. The turbulent kinetic energy for this model is the same as shown in Equation (5), whereas the specific dissipation rate of kinetic energy (\(\omega\)) is given by Equation (8). The turbulent viscosity equation for the \(k\)-\(\omega\) turbulence model is given by Equation (9). Detailed information about the \(k\)-\(\omega\) turbulent model can be found in the research work of [PERSON] [24; 30]. The coefficients and their values used in the four turbulence models are given in Table 2.  
The turbulent kinetic energy dissipation rate equation:  
\[\
abla\bullet(\rho\textbf{u}\omega)=\
abla\bullet\left(\left(\mu+\frac{\mu_{t }}{\sigma_{\omega}}\right)\
abla\omega\right)+\alpha\frac{\omega}{k}G_{k}-\rho \beta f_{\beta}\omega^{2} \tag{8}\]  
where,  
\[\mu_{t}=\alpha^{*}\frac{\rho k}{\omega} \tag{9}\]
## 3 Results
### Lucas 2018 Validation
Figure 6: Results of SVM classification for 2023: unclassified areas are shown in white (code 0); overestimated areas are marked as code 1; underestimated areas are marked as code 3. LUCAS 2018 data points are represented as black dots. Figure 7: Results of CART Classification for 2023: Unclassified areas are shown in white (code 0); code 8 indicates improper classification of water; LUCAS 2018 data points are represented as black dots. Interestingly, the average accuracy (ACC) metric, at 0.9324, appeared inflated compared to OA. This discrepancy emphasizes the need to carefully interpret accuracy metrics in the context of classification performance. From the results summarized in Tables 12-16, the following insights can be drawn:  
* The average accuracy (ACC) is significantly higher than the Overall Accuracy (OA) (0.93 vs. 0.83, respectively). * For each class, ML accuracy metrics (ACC, NPV, TNR) are consistently high, averaging around 0.90 or above. * ML error metrics are low, typically below 0.10, and much smaller than remote sensing metrics such as OME and CME. * The corresponding metrics from remote sensing and ML approaches are closely aligned. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **1** & **3** & **5** & **6** & **8** \\ \hline
**TP** & 73 & 26 & 150 & 34 & 2 \\
**TN** & 248 & 305 & 159 & 265 & 337 \\
**FP** & 11 & 7 & 22 & 16 & 2 \\
**FN** & 11 & 5 & 12 & 28 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Binary confusion matrix RF 2023 on LUCAS 2018 validated data (notice TN). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **ACC** & **PA** & **UA** & **OME** & **CME** \\ \hline
**1** & **0.9359** & 0.8690 & 0.869 & 0.131 & 0.131 \\
**3** & **0.9650** & 0.8387 & 0.7879 & 0.1613 & 0.2121 \\
**4** & **0.9009** & 0.9259 & 0.8721 & 0.0741 & 0.1279 \\
**6** & **0.8717** & 0.5484 & 0.6800 & 0.4516 & 0.3200 \\
**8** & **0.9883** & 0.5000 & 0.5000 & 0.5000 & 0.5000 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: RF 2023 accuracy metrics: OA= 0.8309, ACC average = 0.9324 (compare PA and UA with tpr and ppv in Table 16; OME and CME with FNR and FDR in Table 15). Figure 9: Zoomed-in section of Figure 6: SVM classification—center of Krakow City; overestimation is class code 8; incorrect Water classification, see Vistula River. The analysis demonstrates that the RF algorithm performs the best in classifying the Krakow ROI, with the highest overall accuracy achieved using a validated LUCAS database. The findings underscore the importance of database quality, as inaccuracies in the original LUCAS dataset led to lower classification performance. Despite high average accuracy metrics, the observed discrepancies with OA highlight the need for a nuanced evaluation of classification methods, particularly when assessing specific classes like Water. These results contribute to refining classification methodologies and improving land cover mapping reliability.