esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Parameter Selection and Data Reduction
### Data Augmentation
The mean absolute error(MAE), mean absolute percentage error(MAPE) and root mean square error(RMSE) of the generated data and the original data under different algorithms are shown in Tables 1 and 2. As can be seen from the tables, the generated samples can better improve each algorithm's accuracy. The reason is that the original data volume is relatively small and the \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**BP**} & \multicolumn{2}{c}{**SVR**} & \multicolumn{2}{c}{**ELM**} \\ \cline{2-7}  & **Original** & **Generated** & **Original** & **Generated** & **Original** & **Generated** \\ \hline MAE & 6.761 & 5.8759 & 8.0414 & 5.629 & 4.958 & 3.7806 \\ MAPE & 0.02219 & 0.0194 & 0.0266 & 0.0184 & 0.0163 & 0.0125 \\ RMSE & 8.7792 & 7.2729 & 10.25 & 6.9361 & 6.0941 & 5.0378 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of setting errors of grinding ore feeding. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{2}{c}{**BP**} & \multicolumn{2}{c}{**SVR**} & \multicolumn{2}{c}{**ELM**} \\ \cline{2-7}  & **Original** & **Generated** & **Original** & **Generated** & **Original** & **Generated** \\ \hline MAE & 0.5184 & 0.4183 & 0.5993 & 0.3826 & 0.4645 & 0.3108 \\ MAPE & 0.017 & 0.0137 & 0.0197 & 0.0124 & 0.01524 & 0.0103 \\ RMSE & 0.9114 & 0.5039 & 0.7644 & 0.5097 & 0.6263 & 0.5490 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of setting errors of water feeding. data interval is large, which makes it difficult to fit the data well. GAN can supplement the data to simulate some working conditions that are not in the original data but may occur, so that the database contains more working conditions. Although the generated data may not definitely have a positive effect on the optimization, the validity of the generated data can be proven after several generation experiments when all different classification methods show that the expanded data fit better than the original data.
# Study of the Algorithm for Wind Shear Detection with Lidar Based on Shear Intensity Factor
## 3 Test of the Improved Algorithm Based on Numerical Simulation
### Turbulent Wind Field  
Buildings, mountains, trees and other obstacles near the airport will hinder the flow of air and change the wind direction, causing an irregular turbulence of wind and affecting the takeoff and landing of aircraft. The turbulent wind field simulated in this paper is depicted in Figure 12, where the wind speed and direction change irregularly. The red straight line in Figure 12 is the slide path and the laser beam of the simulated aircraft in slide path scanning.  
The wind shear detection results under the turbulent wind field background are presented in Figure 13, where the black dotted line represents the headwind profile data in the slide path obtained by the simulated lidar. The results indicate that wind shear exists at multiple positions away from the aircraft landing point, but the change in the  
Figure 11: The results of wind shear detection under the low-level jet wind field background.  
Figure 12: The left figure is the simulated turbulent wind field in the 3-D coordinate system; the right figure is the profile view of the simulated turbulent wind field.  
wind speed is negligible. Simultaneously, both algorithms WSDSF and WSDHP accurately identify the maximum wind shear at 9000 m from the aircraft landing point. However, their calculated wind shear intensity factor values are small, 0.35 and 0.69, separately, and should be classified as moderate wind shear. In addition, the wind shear intensity calculated by algorithm WSDSF is slightly larger than that calculated by algorithm WSDHP. Concurrently, it should be observed that the wind shear detection results of the double ramp detection algorithm are more detailed and richer and find multiple locations with wind shear. Furthermore, the wind shear intensity value calculated from the double ramp detection algorithm is larger than that from algorithms WSDSF and WSDHP overall. As a result, the double ramp detection algorithm detects wind shear better when the wind shear intensity is low, with the maximum wind shear intensity calculated by algorithm WSDSF closer to the real result than the other two algorithms.
# Orbit Decomposition Method for Rotordynamic Coefficients Identification of Annular Seals
## 8 Conclusions  
In this article, the orbit decomposition method based on small perturbation and linear superposition theory is proposed for the rapid identification of frequency-dependent rotordynamic coefficients for annular seals. Through the analysis and comparisons with the transient CFD method, several conclusions are summarized as follows:  
1. Compared with the transient CFD method, the orbit decomposition method with steady state simulation can significantly reduce the computational time.
2. The orbit decomposition method and the transient CFD method have a similar accuracy in predicting the fluid-induced forces and rotordynamic coefficients.
3. The orbit decomposition method is valid when the whirl amplitude is less than 20% of the seal clearance. The accuracy of the orbit decomposition method decreases when the whirl amplitude is larger than 20% of the seal clearance.
4. The rotordynamic coefficients arising from the orbit decomposition method are not sensitive to the ellipticity.  
Finally, the accuracy of the present method is further demonstrated with the experimental data. The present method provides a novel approach for the fast and accurate identification of frequency-dependent rotordynamic coefficients for annular seals.  
The next research step is to use the proposed method to predict the rotordynamic coefficients of more annular seals, such as the hole-pattern seal, honeycomb seal, and pocket damper seal.  
Conceptualization, M.Z. and J.Y.; methodology, M.Z.; software, M.Z. and Q.G.; validation, M.Z., W.Z. and Q.G.; formal analysis, J.Y.; investigation, M.Z.; resources, J.Y.; data curation, M.Z.; writing--original draft preparation, M.Z.; writing--review and editing, J.Y.; visualization, M.Z.; supervision, J.Y.; project administration, J.Y.; funding acquisition, J.Y. and W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers 52075096 and 51875361.  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors would like to acknowledge the support of National Natural Science Foundation of China, grant numbers 52075096 and 51875361.  
The authors declare no conflict of interest.
# Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots
## 2 Target Multi-Robot System
The formation control algorithm proposed in this paper designs and suggests a swarm control algorithm that utilizes the information of both the leader, which is the (\(i-1\))-th robot, and the follower, which is the (\(i+1\))-th robot, for the \(i\)-th robot. In this paper, we make the following two assumptions: _Assumption 1:_ Communication between robots is minimal, and near real-time communication can be ensured. _Assumption 2:_ The positions of the robots are obtained to reduce signal noise and compensate for error accumulation. These two assumptions allow this paper to focus on proposing a coupled-error-based formation control algorithm. Although real-time communication is assumed in this paper, the realistic implementation of this assumption is feasible due to the existing technologies such as 5G communication, WIFI-6, mesh local networks, and low-altitude satellite communication, which enable communication with nearby robots. Furthermore, the assumption regarding position acquisition is also practically achievable, given that position information can be obtained with issues such as signal noise and error accumulation resolved through technologies like D-GPS (Differential Global Positioning System), sensor fusion, and SLAM (Simultaneous Localization And Mapping).
## 3 Automatic Language Identification Using Speech Rhythm Features
### Automatic Language Identification Using Rhythm Metrics  
The final step is to identify a language with the R-vector estimated from the given input speech. The general pattern classification techniques mentioned in Section 2 can be used for this step. However, the proposed R-vector-based LID approach has a different condition from the conventional approaches in terms of amount of features. The conventional approaches directly employing general acoustic features such as MFCCs accept features extracted from small-sized frame units, whereas the R-vector is estimated from an utterance or a sentence level. Hence, the amount of feature data in R-vector may be insufficient to train a complex model such as DNN. Instead, other methods using less training data, such as SVM and LDA, can be useful for R-vector-based LID. Among the two techniques, LDA  
Figure 3: A sample result of the consonantal and vocalic interval detection.  
Figure 2: The procedure for constructing two Gaussian Mixture Models (GMMs) (for consonants and vowels) for interval detection.  
requiring normally distributed data may lead to misclassification, as elements of R-vector are not correlated, thus making it very difficult to find an optimal linear decision boundary. For this reason, we employ the SVM technique for R-vector-based LID. In addition, we carefully propose another approach using a combination of R-vector and i-vector as LID features.
## 1 Introduction
Between 2011 and 2022, it was widely used. Disciplines include renewable energy, sustainable manufacturing, natural disasters, environmental pollution, landfill waste management, and many other issues that fall explicitly or implicitly under the theme of sustainable development [12]. The entropy method, rooted in information theory, is an unsupervised weight determination method that is widely used in performance evaluation [13]. [PERSON] et al. used entropy analysis and the TOPSIS method for employee performance evaluation, which improved the interpretability of the evaluation and helped to identify the strengths and weaknesses of each employee, thus furthering the development of the employees [14]. [PERSON] et al. used an evaluation model that combined entropy weight with improved D-S evidence theory to evaluate the performance of large infrastructure projects in terms of their operation and maintenance [15]. [PERSON] et al. applied the entropy method to the fuzzy comprehensive evaluation of the investment performance of electric power companies, which provided a theoretical basis for government performance evaluation [16]. The regression model based on machine learning algorithms has an absolute effectiveness frontier, surpassing traditional packet analysis methods in terms of analysis efficacy. [PERSON] et al. integrated the neural network model with the performance evaluation method to reduce the impact of statistical noise in the data and address the inherent issues with the data packets [17]. [PERSON] et al. improved the importance-performance analysis (IPA) algorithm by integrating the back-propagation neural network (BPNN) and the three-factor theory for effective performance analysis [18]. Therefore, this paper presents a BPNN-dominated K-means-random forest unsupervised factor importance analysis model for the performance analysis of university collaborative innovation centers. This study is aimed at the 76 collaborative innovation centers in Jiangsu Province, constructs evaluation indicators based on the performance statistics of the second phase, and assigns an importance index to each characteristic factor based on the model. Based on this importance index, this paper provides suggestions on how to promote the high-quality connotative development of the collaborative innovation center and further improve the construction efficiency, acting as a reference for the management and construction of the collaborative innovation center in the future.
# Recent Progress on Graphene-Functionalized Metasurfaces for Tunable Phase and Polarization Control
## 3 Phase Modulation in Graphene Metasurfaces  
Metasurfaces have been applied with great success for complicated wavefront shaping because of their compactness and flexibility when compared to conventional bulky devices. Dynamic wavefront shaping is a highly-desirable feature towards practical applications including, but not limited to adaptive optics and satellite communication. Ideally, the phase shifters should cover the complete 2\(\pi\) phase range with high efficiency or even independent control of the amplitude and phase responses to implement arbitrary kinds of wavefront shaping. In reality, it is not easily achievable by simply tuning the optical properties of an atomic layer. The combination of graphene and a metasurface presents an effective solution to enhancing the interaction with increased phase shifting. The constitutive phase shifters in the graphene metasurface array are either graphene micro-/nano-structures with separate biasing or graphene-hybridized metallic/dielectric resonators with a single-location biasing. The former offers the maximum degree of freedom for dynamic wavefront shaping, and the latter is more practical in implementation.
## 2 The Synthetic Storm Technique Applied to Zenith Paths
Finally, because the relationship between \(A(t)\) and \(R(t)\) is monotonic, the on-site probability distribution \(P(R)\) is transformed directly into the \(P(A)\) of the zenith path through the variable transformation given by Equation (1). Figures 1-4 show examples of the \(P(A)\) obtained at several frequencies at the sites listed in Table 1. Because our analysis is oriented to find the outage probability when the radio-link power margin is not larger than \(A\) dB, \(P(A)\) is also the radio-link outage probability due to rain. Figure 1: Annual average probability \(P(A)\) (%) of exceeding the rain attenuation \(A\) (dB) indicated in abscissa, at the indicated sites: Spino d’Adda (SP, blue continuous line); Gera Lario (GL, black continuous line). line); Fucino (FC, cyan continuous line); Madrid (MD, green continuous line); Prague (PR, magenta continuous line); Tampa (TM, cyan dashed line); Norman (NR, magenta dashed line); White Sands (WS, green dashed line), at 16 GHz in zenith paths. When the power margin in the zenith radio-links of the GeoSurf satellite constellations is equal to \(A\) (dB), \(P(A)\) represents the outage probability. Figure 3: Annual average probability \(P(A)\) (%) of exceeding the rain attenuation \(A\) (dB) indicated in abscissa, at the indicated sites: Spino d’Adda (SP, blue continuous line); Gera Lario (GL, black continuous line); Fucino (FC, cyan continuous line); Madrid (MD, green continuous line); Prague (PR, magenta continuous line); Tampa (TM, cyan dashed line); Norman (NR, magenta dashed line); White Sands (WS, green dashed line), at 40 GHz in zenith paths. When the power margin in the zenith radio-links of the GeoSurf satellite constellations is equal to \(A\) (dB), \(P(A)\) represents the outage probability. Figure 2: Annual average probability \(P(A)\) (%) of exceeding the rain attenuation \(A\) (dB) indicated in abscissa, at the indicated sites: Spino d’Adda (SP, blue continuous line); Gera Lario (GL, black continuous line); Fucino (FC, cyan continuous line); Madrid (MD, green continuous line); Prague (PR, magenta continuous line); Tampa (TM, cyan dashed line); Norman (NR, magenta dashed line); White Sands (WS, green dashed line), at 30 GHz in zenith paths. When the power margin in the zenith radio-links of the GeoSurf satellite constellations is equal to \(A\) (dB), \(P(A)\) represents the outage probability. In the next section, we show how \(P(A)\) changes as the frequency increases from 16 to 100 GHz, for a fixed power margin \(A\), by defining a suitable outage probability factor.
## 3 Results and Discussion
### Beam Failure Modes
Figure 19: Beam B-G3: (**a**) Concrete separation at the level of protective layer of concrete; (**b**) Cracks in concrete on the top side of the beam intersecting the groove with the epoxy infill. Figure 18: Beam B-G2: (**a**) Failure of the cross section above the support; (**b**) Failure of cross section in the span due to concrete separation. Failure of beams reinforced by the EB method with CFRP tapes (B-EC beams) occurs much earlier than in beams strengthened with CFRP bars by the NSM method (B-C beams). In general, the behavior of these beams is similar until the onset of tape separation in B-EC beams. Failure (fracture) occurs due to the sudden separation of the tape due to the loss of bond at the joint of concrete and epoxy adhesive (Figure 21a), after which the protective layer of concrete separates (Figure 21b). Failure due to the debonding of the reinforcement system is the most common failure of beams strengthened by the EB method, and it is also the most prominent deficiency of this method. Debonding failure prevention can be achieved by various anchoring systems such as: anchor spikes, U-Anchors, transverse wrapping, plate anchors, bolted angles and other miscellaneous systems [32]. The fracture load was 150 kN (at B-C beam 180 kN) where the deformability of this beam is very low with a very linear relationship between the load and the deflection of the beam. Figure 21: Beam B-EC: (**a**) Debonding at the joint of concrete and epoxy adhesive on the beam bottom side; (**b**) Concrete separation at the level of protective layer of concrete. Figure 20: Beam B-C: (**a**) Concrete separation at the level of protective layer of concrete (**b**) Arrangement of cracks.
# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 3 Types of Chiral Metasurfaces
### Deep-Learning-Enabled Design of Chiral Metasurface
DL-based design of an anisotropic metasurface with a full-phase attribute in the ultrawide band was also proposed in [81], and metasurface patterns were produced using GANs. The design procedure includes a forward deep neural network (DNN) predictor that is trained from the dataset created with different geometry data and the associated reflective spectra, and it predicts the phase responses and amplitude of a meta-atom structure with extremely high accuracy in seconds and a GAN trained on geometric data for an inverse design, as shown in Figure 7e. The dataset of geometric data and associated reflective spectra for the GAN-based inverse design was generated by assembling the binarized pattern images of the meta-atom and their respective reflective spectra produced by forward EM simulations. The dataset was used to train the forward DNN predictor to successfully forecast the phase and amplitude response of the meta-atom [81]. The GANs network, which underwent training using this geometric data, was used for the purpose of inverse design when the input reflective spectra were provided, generating candidate patterns that matched the target spectra through Euclidean distance calculations. However, generating the dataset was not easy due to the resource-intensive nature of traditional simulations and the need to successfully train GANs for accurate design generation while avoiding concerns such as insufficient gradients for the generator [81]. The inverse design outcomes of the patterns created in the intended phases are displayed in Figure 7f. The network received an anisotropic reflection of the phase data from the modified handwritten MNIST digital sample test (Figure 7f) and sector sample test (Figure 7g). The solid curve represents the anisotropic reflection phases of the sample test, while the circle represents the response of the created pattern. The comparison results indicate that the test samples and generated patterns agree very well. This result demonstrates that using a GAN to inverse-design anisotropic chiral metasurfaces with full-phase characteristics can be used for a range of potential applications, including metalenses, reflect arrays, and stealth surfaces [81]. DL-based design in metalenses enables precise control of light manipulation, leading to high-performance, compact imaging systems. Reflect arrays benefit from optimized designs for beam forming and steering designs, which improve communication and radar systems, while DL-based stealth surfaces enable enhanced radar-absorbing materials, which improve stealth technologies by decreasing radar signatures [78; 79; 80; 81].
## 4 Conclusions  
From the reported experimental work, modelling, and discussion, the following conclusions can be drawn:  
* The needle probe method provides a convenient and accurate approach to measure unsaturated soil thermal conductivity.
* At a certain water content, the thermal conductivity of sandy soil decreases with increasing porosity due the increase in the air content. At a certain porosity, the thermal conductivity of sandy soil increases with the water content due to the decrease in the air content. Capillary condensation plays an important role on the thermal conductivity at low water content and low porosity with small pore sizes because the condensed water will affect the ratio of the water volume to total pore surface area.
* There are two major types of empirical model proposed, which can be generalised as representing an exponential function and power function, respectively. The characterised exponential function has showed a good result for the thermal conductivity of soils of a certain porosity at varied water contents. However, the power function has showed a better performance for the conditions of both varied water contents and porosities.
* Intrinsically, the thermal conductivity at varied water content and porosity is related to the pore size distribution. Due to the intrinsic relation and the dependence on similar factors, both the thermal conductivity and water retention characteristics of unsaturated soils can be described using a unified model, which has demonstrated good accuracy.  
The experimental work was funded by the National Natural Science Foundation of China (NSFC). Project No. 41372247.  
[PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] did the modelling, analysis and discussion; [PERSON], [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.