esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Optimization  
#### 4.2.1 Hidden Layer and Neuron  
Several optimizations are processed before evaluating the performance of the machine-learning algorithm. The optimized parameters are determined based on the accuracy and loss function defined in Section 3. Accuracy is a way to measure the performance of a classification model. The higher the accuracy, the better the algorithm. The accuracy of the perfect failure-detection algorithm is 1. On the other hand, the loss function considers the probability or uncertainty of the prediction depending on how different the prediction is from the actual value. The loss is the sum of the errors that occurred for each sample in the training. The perfect failure-detection algorithm has zero loss.  
The performance tests are firstly conducted with respect to the numbers of the hidden layers and neurons in each layer. Generally, ANN cannot analytically determine the best numbers of layers and neurons of each layer. In addition, using a single hidden layer makes it difficult to express the relationship between inputs and outputs since they are linearly linked. Therefore, the number of hidden layers and neurons should be optimized through the performance tests, as presented in Figure 8. In the optimization process, we utilized the signals from seven sensors (#2-8) that are equally spaced. The algorithm randomly selects 80% of the entire dataset for training from the first eight environmental conditions given in Table 3 and uses the remaining 20% of the dataset to check the accuracy and loss. While the epoch increases from 1 to 2000, accuracy and loss are evaluated. These procedures are also adopted in the next optimization process. As shown in Figure 8, the highest accuracy and lowest loss can be acquired with three hidden layers and 200 neurons, and these values are used in the ensuing study. In general, the higher the epoch, the higher the accuracy. Interestingly, increasing the hidden layers and neurons more than three and 200 does not provide higher accuracy.
## 3 System Model
### V2V Channel Model for Straight Road Scenario  
In the channel transmission model, considering the frequency non-selective fading channel, the complex channel fading envelope between the transmitting antenna and the receiving antenna can be expressed as [31]  
\[h_{TR}(t)=h_{TR}^{LOS}(t)+h_{TR}^{NLOS}(t). \tag{1}\]
# Symmetry Criterion and Far-Field Control of Photonic-Crystal Surface-Emitting Lasers
## 4 Conclusions  
In summary, we have investigated the far-field characteristics of PCSELs using 3D-FDTD simulation. We analyzed the evolution of FFPs from the perspective of group theory and demonstrated that the symmetry of FFPs is consistent with that of the in-plane PhC structure. The symmetry criterion to decrease the divergence angle is increasing the in-plane asymmetry of PhC. Especially, it will result in a circular single lobe when minimum symmetry operations are allowed. Further, we found that a reduction in the thickness of the PhC layer or the introduction of a more complex vertical epi-structure is bad for the formation of a single-lobed FFP because the modulation effect of the PhC on FFP is weakened in this case. However, increasing period number could counteract this negative effect. Moreover, the inverse relationship between the divergence angle and the size of the PhC cavity is provided analytically. When the size of PhC cavity reaches 1000 \(\upmu\)m, the beam divergence angle will be as low as 0.1\({}^{\circ}\). We believe that our work will contribute to revealing the mechanism of the influence of PhC geometry on far field and the physical origin of PCSELs' ultralow divergence angle.  
Conceptualization, Z.W. and C.T.; methodology, Z.W. and C.T.; software, Z.W., P.W. and H.L.; validation, P.W., B.M. and Y.W.; formal analysis, Z.W., B.M. and C.T.; investigation, Z.W. and H.L.; resources, L.W.; data curation, P.W.; writing--original draft preparation, Z.W.; writing--review and editing, B.M. and C.T.; visualization, Z.W.; supervision, L.W; project administration, C.T.; funding [PERSON], C.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key Research and Development Program of China (2018 YFB2201000), National Natural Science Foundation of China (61790584, 62025506).  
Not applicable.  
The authors declare no conflict of interest.
# A Reconfigurable Single-/Dual-Bandstop Filter with Controllable Equal-Ripple Performance
## 2 Design and Analysis of the Proposed Reconfigurable BSF
### State II: D-Bsf
#### 2.2.3 Design Examples of State II  
Figure 7 illustrates the altering curves of \(Z_{1}\), \(Z_{2}\), and \(Z_{4}\) versus \(Z_{3}\) with \(Z_{5}\) fixed as 3.0 \(\Omega\), 2.5 \(\Omega\), and 2.0 \(\Omega\), respectively, under the condition of \(RL_{D1}=10\) dB and \(RL_{D2}=15\) dB.  
Maintaining \(RL_{D1}\) = 10 dB, \(RL_{D2}\) = 15 dB, and \(RL_{S}\) = 25 dB, \(SR_{S}\) is selected as 16 dB, 18 dB, and 20 dB, respectively, and the corresponding variable parameters are ensured and summarized in Table 3. The circuit simulation results of corresponding \(S\)-parameters for the three examples (Example \(A_{1}\), Example \(A_{2}\), and Example \(A_{3}\)) are demonstrated in Figure 9.  
2 Example \(B\) With Controllable \(RL_{S}\)  
Maintaining \(RL_{D1}\) = 10 dB, \(RL_{D2}\) = 15 dB, and \(SR_{S}\) = 20 dB, \(RL_{S}\) is set as 25 dB, 27 dB, and 29 dB, respectively, and the corresponding variable parameters are ensured and summarized in Table 4. The circuit simulation results of corresponding \(S\)-parameters for the three examples (Example \(B_{1}\), Example \(B_{2}\), and Example \(B_{3}\)) are demonstrated in Figure 10.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \(RL_{S}\) (dB) & \(Z_{1}\) (\(\Omega\)) & \(Z_{2}\) (\(\Omega\)) & \(Z_{3}\) (\(\Omega\)) & \(Z_{4}\) (\(\Omega\)) & \(Z_{5}\) (\(\Omega\)) \\ \hline Example \(B_{1}\) & 25.0000 & 2.0879 & 0.7025 & 1.6618 & 2.4695 & 2.8860 \\ \hline Example \(B_{2}\) & 27.0000 & 2.1326 & 0.7253 & 1.6540 & 2.4317 & 2.7571 \\ \hline Example \(B_{3}\) & 29.0000 & 2.1710 & 0.7447 & 1.6494 & 2.4041 & 2.6586 \\ \hline \end{tabular}
\end{table}
Table 4: The design parameters for Example \(B_{1}\), Example \(B_{2}\), and Example \(B_{3}\). (\(RL_{D1}\) = 10 dB, \(RL_{D2}\) = 15 dB, and \(SR_{S}\) = 20 dB).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(SR_{S}\) (dB) & \(Z_{1}\) (\(\Omega\)) & \(Z_{2}\) (\(\Omega\)) & \(Z_{3}\) (\(\Omega\)) & \(Z_{4}\) (\(\Omega\)) & \(Z_{5}\) (\(\Omega\)) \\ \hline Example \(A_{1}\) & 16.0000 & 1.7119 & 0.9313 & 2.8948 & 2.6605 & 2.7201 \\ \hline Example \(A_{2}\) & 18.0000 & 1.8881 & 0.8050 & 2.1564 & 2.5290 & 2.7899 \\ \hline Example \(A_{3}\) & 20.0000 & 2.0878 & 0.7025 & 1.6618 & 2.4695 & 2.8860 \\ \hline \end{tabular}
\end{table}
Table 3: The design parameters for Example \(A_{1}\), Example \(A_{2}\), and Example \(A_{3}\). (\(RL_{D1}\) = 10 dB, \(RL_{D2}\) = 15 dB, and \(RL_{S}\) = 25 dB).  
Figure 9: Circuit simulation results of the \(S\)-parameters for Example \(A_{1}\), Example \(A_{2}\), and Example \(A_{3}\). (\(RL_{D1}\) = 10 dB, \(RL_{D2}\) = 15 dB, and \(RL_{S}\) = 25 dB).  
for the three examples (Example \(D_{1}\), Example \(D_{2}\), and Example \(D_{3}\)) are demonstrated in Figure 12.
## 5 The Application in Fatigue Detection
Here, we investigate the detection effects of various detection models under different fatigue intensities (the ratio of fatigue samples to normal samples). The values of 5%, 10%, 15% and 20% are used to simulate the samples under different fatigue intensities, and the detection model is trained with these four fatigue samples. The detection accuracy under different learning modes is shown in Figure 12.  
It can be seen from Figure 10 that under different fatigue intensities, the training detection model can detect fatigue events better. The detection accuracy of SVM is 92.5% when there are fewer fatigue samples (fatigue intensity is 5%) and the miss detection rate is relatively high. This is mainly due to the imbalance of samples, which makes the classification plane of SVM shift. The imbalance of samples affects the effect of machine learning. Compared with other algorithms, the detection accuracy of this method can be maintained at a relatively high level (the lowest is 98.6%), which shows that the proposed algorithm is stable and can also obtain better detection results in the case of unbalanced samples. Figure 11: Box diagram of detection accuracy. Figure 12: Detection accuracy under different fatigue intensities.
## 4 A Case Study  
### Site Overview  
Working face 205 is located in the 2# panel of a 1000-m-deep coal mine in the Binchang mining area, Shaanxi Province, China. It is the fifth longwall face, with working faces 201-204 having already been mined. The widths of working face 205 in inclination and strike directions are 200 m and 1450 m, respectively. The layout of working face 205 is shown in Figure 3a. The average thickness and dip angle of the 4# main mineable coal seam are 9.7 m and 0\({}^{\circ}\)-8\({}^{\circ}\), respectively. The 4# coal seam has strong burst propensity [6]. In this working face, fully mechanized top-coal curing mining is adopted, and the coal mining height and caving height are 3.5 m and 5.5 m, respectively. In situ stress tests indicate that the three-dimensional stresses are 41.8 MPa, 22.5 MPa, and 17.5 MPa, and the 4# coal seam is severely stressed.  
An \"SOS\" micro-seismic monitoring system with 23 geophones was installed in the coal mine to monitor the mining-induced seismicity during coal mining. The placement of the geophones was regularly optimized as the working face advanced to improve the locating accuracy of mining-induced seismicity [20]. A 940-m-deep borehole ZY-1 was constructed in working face 205 and a 930-m-long distributed optical fiber was installedin the borehole to detect the overburden strata movement during coal mining, as shown in Figure 3b. The distributed optical fiber can capture the deformation of the strata under mining activity and is now widely promoted in coal mines [14]. When the deep borehole was put into operation on 6 December 2020, it was 292 m away from working face 205.
# Ramjet Nozzle Analysis for Transport Aircraft Configuration for Sustained Hypersonic Flight
## 3 Conceptual Design of a Ramjet Nozzle
### Flow through a Convergent-Divergent (C-D) Nozzle  
For C-D nozzles, as the ambient pressure is gradually reduced compared to a reference reservoir pressure. Meanwhile, the flow in the convergent section increases until sonic velocity, which is achieved at the throat (minimum area section). Further reduction in the ambient pressure does not result in an increase in flow velocity. As the flow proceeds past the throat into the divergent section, however, it accelerates further to supersonic speed as depicted in Figure 4a [6], with nozzle nomenclature provided in Figure 4b [7]. The pressure differential between the exit pressure (\(\text{P}_{\text{e}}\)), referred to as back pressure, and the reservoir pressure leads to the flow achieving supersonic speeds through the nozzle [8].  
Most of the gross nozzle trust, produced from jet momentum with additional thrust from pressure, will cause an imbalance between the throat static pressure and ambient pressure. In contrast to convergent nozzles, at high Nozzle Pressure Ratios (\(NPR=pt/p0\)), the C-D nozzle allows expansion to take place inside the nozzle to maximise the thrust being produced. The area ratio between the throat (\(A^{*}\)) and the exit area (\(A_{e}\)) impacts the flow significantly. Downstream of the throat, the nozzle gradually increases in the cross-sectional area, creating the characteristic parabolic bell profile, which allows the exhausted gases to expand, creating the required thrust. They can be characterised by the expansion area ratio (\(\varepsilon\)) where:  
\[\varepsilon=\frac{A_{e}}{A^{*}} \tag{1}\]
## 4 Verification on the Mesoscale Equivalent Model
### Model Verification with Penetration Tests
#### 4.2.1. Numerical Model  
Since no obvious yaw phenomenon is observed in the penetration process, quarter finite element models based on the equivalence method are established, as shown in Figure 14. The keyword \"CONSTRAINED_GLOBAL is used to simulate the symmetric boundary condition by defining two global boundary constraint planes. As for the outer surfaces of targets, the fixed boundary condition is imposed according to the experimental setup. Matrix elements (SOLID164) within five times the projectile diameter are refined, and the minimum matrix element size is 0.8 cm. There are 352,512 steel fiber elements (BEAM161) uniformly distributed in the target with 90 cm thickness and 587,571 steel  
\begin{table}
\begin{tabular}{c c c c c} \hline \(n\) & **1** & **3** & **4** & **5** \\ \hline computation time & 1400 h 16 min & 51 h 54 min & 23 h 18 min & 11 h 35 min \\ \hline \end{tabular}
\end{table}
Table 4: The estimated computation time of penetration simulations on target U-2 under different amplification factors.  
Figure 13: Uniaxial compressive curves of specimens.  
fiber elements in the target with 150 cm thickness. The outer shell and inner filling of the projectile are modeled by the solid element SOLID164 and are connected by joint nodes. The eroding surface-to-surface contact algorithm is used to simulate the contact behavior between the projectile and matrix.  
In the process of penetrating, the outer shell of the projectile withstands high temperatures, which will soften the material. Thus, the Johnson-Cook model [30; 31] (\"MAT_015) applied for metals subjected to large strains, high strain rates, and high temperatures, coupled with *EOS_GRUNESEN, is adopted to build the outer shell. Material model *MAT_003 is used to build the inner filling of the projectile. The material models and parameters for the projectile are listed in Table 5.
# UAV Formation Trajectory Planning Algorithms: A Review
## 4 Intelligent Algorithm
### Heuristic Algorithm
#### 4.1.7 Artificial Bee Colony  
The Artificial Bee Colony algorithm (ABC) is an optimization method to imitate the intelligent foraging behavior of bees. The process of the algorithm follows: First, assign a hired bee to the initial honey source and search according to certain rules to generate a new honey source. Then, use the greedy selection method to retain the honey source with high fitness and calculate the probability that the honey source found by the hired bee will be followed. Last, follow the peak using the same method as the hired bee. If the nectar source satisfies the condition of being abandoned, the corresponding hired bee becomes a scout bee and randomly searches in the search space to generate a new nectar source, obtaining the global optimal trajectory through the local optimization behavior of each individual artificial bee. Figure 17 is a trajectory diagram of the Artificial Bee Colony algorithm (ABC).  
[PERSON] et al. [94] proposed an improved Artificial Bee Colony (IABC) algorithm, which optimizes the trajectory points only according to the cost value of the trajectory and solves the problem of long convergence time of the traditional Artificial Bee Colony algorithm.  
[PERSON] et al. [95] proposed a hybrid algorithm based on Artificial Bee Colony algorithm (ABC) and A*. The algorithm uses the ABC algorithm to complete the preliminary planning, then uses the A* algorithm to plan the specific trajectory points, and finally combines the adaptive time coordination method to obtain the optimal trajectory, which solves the problem of three-dimensional multi-UAV trajectory planning.  
Figure 17: A kind of ABC trajectory diagram (Black areas: obstacles; blue line: optimal trajectory).  
[PERSON] et al. [96] proposed a multi-UAV task assignment and trajectory planning method for disaster medical rescue. The algorithm uses the fitness function considering the current number of iterations and the maximum number of iterations and an Adaptive Genetic Algorithm (AGA) for task allocation; then, a balanced search strategy is added to improve the Artificial Bee Colony algorithm (IABC), and trajectory planning solves the problem of poor convergence efficiency and calculation effect of the traditional Artificial Bee Colony algorithm.  
Table 5 summarizes the content of the Pigeon-Inspired Optimization algorithm, Fruit Fly Optimization algorithm and Artificial Bee Colony algorithm in our review.
## 3 Results
### Color Fidelity Test  
The second objective trial aims to determine the fidelity of the perceived color to the real one, conducted using the Natural Color System test. This test uses a collection of color samples with varying parameters of chromaticity and brightness, identified in polar coordinates. As described in the methodology, respondents must identify the position of the color samples in the polar coordinates. Accordingly, the chromaticity deviation--colloquially identified as saturation--and the hue divergence can be quantified as separate parameters. The color fidelity is therefore the result of applying color deviation over a value of 100%; hence, a chroma fidelity score of 85% implies a deviation of 15%. Figure 12 represents the chromaticity and hue fidelities according to four color ranges (yellow to red, red to blue, blue to green, and green to yellow), defining the average score for each light source, as well as the color deviation for both parameters.  
As deduced from Figure 12, the color fidelity provided by the selected luminaires is suitable for most color ranges, with some nuances, as in the case of the color discrimination test. A general overview shows that daylight sources--CIE D65 and CIE D50--provide better color fidelity in most cases, except for the red to blue range, as seen in Figure 12B, where neutral and cool LED lamps--LED 5500 K (LED55) and Multichannel LED (MC LED)--offer better results. These electric light sources allow suitable fidelity in most color ranges, with a chromaticity deviation  
Figure 12: Quantification of the Natural Color System trial, showing average fidelities for chromaticity (saturation) and hue, as well as the color deviation for both parameters. From left to right and top to bottom. (**A**) Upper left: Yellow to red hues. (**B**) Upper right: Red to blue hues. (**C**) Lower left: Blue to green hues. (**D**) Lower right: Green to yellow hues.  
lower than 10%, except in the yellow to red hues, as observed in Figure 12A. Moreover, warm LED lamps--incandescent lamp (INC27) and LED 2700 K (LED27)--give poorer chromaticity and hue fidelity than the other luminaires, mainly in blue to yellow hues, as deduced from Figure 12B,D. This is because, as explained in the previous trial, the spectral distribution of the luminaires emitting warm-colored light is unbalanced, providing more energy in the long wavelength range and less in the short wavelength range, as seen in Figure 3.
# Efficient Machine Learning-Based Prediction of Solar Irradiance Using Multi-Site Data
## 1 Introduction
### Problem Formulation  
Solar irradiance, defined as the measure of light energy output from the sun received at the Earth's surface, serves as the foundation for harnessing solar energy [4]. This energy can be captured primarily through two methods: photovoltaic (PV) cells and concentrated solar power (CSP) plants.  
* Photovoltaic cells: 1. Operation: PV cells, commonly arranged into solar panels, directly convert sunlight into electricity. 2. Usage: This method is widely used in residential, commercial, and utility-scale solar energy systems.
* Concentrated solar power (CSP) plants: 1. Operation: CSP plants use mirrors or lenses to concentrate sunlight onto a small area, generating heat. This heat is then used to produce electricity.  
Scale: Typically, large-scale operations requiring substantial infrastructure and investment.
* 3. Advantage: CSP can provide a consistent and reliable renewable energy source.  
To effectively quantify and forecast PV power production, global horizontal irradiance (GHI) is a critical meteorological parameter. GHI measures the total irradiance received on a horizontal surface and is influenced by factors such as location, time, weather, and season. It comprises three main components:  
1. Direct normal irradiance (DNI): Solar radiation received directly from the sun, unaffected by the atmosphere.
2. Diffuse radiation (DI): Indirect radiation scattered by atmospheric particles, clouds, or other meteorological elements.
3. Albedo: The ratio of the reflected to incident radiation flux.  
Understanding, predicting, and measuring these components is essential for designing efficient solar irradiance-based systems. A graphical representation of these entities is provided in Figure 1, adapted from [16].  
A key strength of this work lies in the adaptability of the proposed solution across six distinct solar forecasting sites. This versatility is attributed to the extensive preprocessing and analysis steps, which leverage multiple time-shifted versions of GHI values along with their relevant linked features. The application of autocorrelation and partial autocorrelation analyses facilitates the identification of optimal time shifts, ensuring the best possible performance from the regressors. This was validated through an in-depth analysis of metric variations across different shift configurations. Furthermore, the study highlights the varying responses of different ensemble compositions across sites, with models consistently achieving high \(R^{2}\) values. The integration of explainable AI (XAI) techniques further enhances interpretability, allowing for a comprehensive investigation and validation of results by identifying the most critical features. In terms of generalization, the proposed approach outperforms those in the existing literature by effectively addressing previously overlooked challenges, thereby advancing the state of the art in solar forecasting.  
Figure 1: GHI components and solar PV interception.