esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Sensitivity Analysis
#### 4.5.3 Effects of Information Absorption and Transformation Efficiency  
The evolution of strategies under different information absorption and transformation efficiencies of each agent is shown in Figure 11. The absorption and transformation efficiency reflects the ability to process information in implementing carbon abatement strategies, and the greater the ability, the more inclined to choose the \"abatement\" strategy. The Planning and Designing Party (X) is the most sensitive. When \(\gamma_{X}\lambda_{X}=0.02\), after 70 iterations for x, the stabilization fluctuation is at [0.20, 0.39]. When \(\gamma_{X}\lambda_{X}=0.08\), x converges to 1 after 262 iterations. When \(\gamma_{X}\lambda_{X}\in[0.4,\ 0.6]\), x converges to 1 at a rate of 0.005-0.009. When \(\gamma_{X}\lambda_{X}=1.8\), x converges rapidly to 1 at a rate of 0.031. For Y and Z, when the information processing capacity is too poor, Y and Z first converge towards 0 before shifting to converge towards 1 as the effect accumulates, with inflection points of (95, 0.16) and (137, 0.22), respectively. And u always converge to 1, with an evolutionary speed between 0.004 and 0.03. Increasing the information processing capacity does not affect U's strategy selection direction and only speeds up the evolution.  
Figure 10: Evolution pathways of the four parties under different abatement costs. (**a**) Evolution pathway for X; (**b**) Evolution pathway for Y; (**c**) Evolution pathway for Z; (**d**) Evolution pathway for U.
# Few-Shot Charge Prediction with Data Augmentation and Feature Augmentation
## 1 Introduction
We propose a text data augmentation strategy with Mixup, which enlarges the training samples by linear interpolation from word level and sentence level in the hidden layer space of text representation, so as to enrich the feature diversity and improve the training effect of the model on low-frequency charges. Then, from the perspective of feature augmentation: Firstly, the criminal law divides the charges into many categories, such as \"crimes against national security\" and \"crimes against public security\". Each category is subdivided into specific charges, and the specific charges have their own definitions. Therefore, the category charge definition can be formalized into a hierarchical structure, as shown in Figure 1. By exploring the similar relationship between different charges under the same category and the co-occurrence relationship between charge definition words, this paper constructs multiple groups of charge label heterogeneous graphs. Then, in order to capture the high-order features between heterogeneous graphs, a Dropedge graph convolutional network (DGCN) is designed to capture the distinguishable features between charges without over-smoothing and generate charge graph information. The confusing charges can be effectively distinguished by analyzing this information. At the same time, because each heterogeneous graph contains multiple charges, based on the information interaction between charges, knowledge can be transferred from high-frequency charges to low-frequency charges, so the prediction effect of low-frequency charges can be further improved. A feature fusion network is used to effectively integrate the charge graph information into the case to learn semantic-enhanced fact representation. Finally, this paper uses the semantic-enhanced fact representation to predict final charges. In addition, based on the prior distribution of different charges, this paper constructs a category prior loss function to pay more attention to low-frequency charges in the training process. To demonstrate the advantages of this paper's model, we conduct several experiments on real-work datasets. The experimental results show that the model in this paper outperforms other baseline models and achieves significant improvements for few-shot charges and confusing charges. The main contributions of this paper are as follows:  
1. We adopt the Mixup method to augment the training samples, which effectively compensates the problem of insufficient training samples for few-shot charges, and we design a category prior loss function to further focus on few-shot charges;
2. We creatively construct the charge label heterogeneous graph and a novel Dropedge graph convolutional network (DGCN) is designed to extract higher-order distinguishability features for effectively predicting few-shot and confusing charges;
3. Extensive experiments are conducted on real-world datasets, and our model can effectively predict few-shot and confusing charges and outperforms other models. The results show that our model outperforms all state-of-the-art methods.
## 5 Case Study  
To validate the robustness of the QOBO algorithm, it has been applied for addressing the studied problem of optimal configuration of the proposed multiple scenarios HRES, i.e., the PV/WT/diesel generator/BESS, PV/biomass, PV/diesel generator/BESS and WT/diesel generator/BESS. The proposed hybrid systems have been introduced in the isolated mode for satisfying the load requirements in the proposed site.  
The project is applied in Aswan, Egypt as shown in Figure 4. The annual load curve over a time interval of one hour is shown in Figure 5. Figures 6-9 present solar irradiation, temperature, wind speed and atmospheric pressure in the studied region. Four standalone scenarios of the hybrid system will be evaluated for covering the load demand in that site. These configurations are: (1) PV/WT/diesel/BESS, (2) PV/biomass, (3) PV/diesel/BESS and (4) WT/diesel/BESS. The proposed QOBO is validated on optimal sizing of these four hybrid systems and the optimization results are comprehensively compared with the corresponding ones obtained from BO, HHO, AEFA and IWO algorithms.  
Figure 8: Annual variation of wind speed over the year in Aswan.  
Figure 6: Solar irradiation over the studied region.  
Figure 7: Temperature variation in Aswan.
# Central Non-Linear Model-Based Predictive Vehicle Dynamics Control
## 2 Simulation Framework  
A simulation framework is used to implement the central predictive vehicle dynamics control and its validation. This framework is based on a co-simulation between IPG CarMaker and MATLAB & Simulink. Figure 1 illustrates the simulation framework. The multi-body simulation within the software IPG CarMaker is used for a realistic simulation of the vehicle. In addition to this realistic representation of the vehicle and the vehicle dynamics, IPG CarMaker also offers the possibility to edit and simulate the environment as well as driver models. In the context of the contribution, a vehicle of the sport utility vehicle class, a Lexus RX400h, is utilized. Due to the heightened center of gravity, this class of vehicle features higher tendencies towards movements in terms of rolling and pitching, which ultimately presents a more challenging task for the vehicle dynamics control. In order to accomplish the control and to achieve the control objectives, the vehicle is equipped with active stabilizers and semi-active dampers. The sensor equipment of the vehicle in IPG CarMaker is based on a minimalistic configuration. Available measured quantities are the longitudinal acceleration \(a_{x}\), the lateral acceleration \(a_{y}\), the yaw rate \(\dot{\psi}\), the steering wheel angle \(\delta_{\text{SW}}\), the velocity \(v\) and the wheel speeds \(n_{ij}\). Further fixed parameters of the vehicle are listed in Table 1.  
The implementation of all algorithms is done in MATLAB & Simulink. In addition to the central predictive control, this also includes the generation of reference trajectories representing the control targets, the simulation of the actuators with regard to a realistic mapping, as well as the implementation of state estimators, which estimate the states necessary for the control not determined by sensors. Examples for the implementation of these state estimators are presented in [19; 20; 21].  
In the following, the focus is on the central non-linear model-based predictive control algorithm.
## 2 Manoeuvre Prediction Model for Moving Targets at Sea  
### Basis for Model Selection  
At present, the prediction methods for moving target motion trajectory can be divided into data-driven and behaviour-driven moving target trajectory prediction methods [15]. The data-driven moving target trajectory prediction method is used to explore the hidden target behavioural characteristics behind the data using massive historical data and predicting the target's movement trend by fusing and matching with the current location data. There are four main categories of data-driven moving target trajectory prediction methods: probabilistic statistics, neural networks, deep learning, and hybrid models. The behaviour-driven moving target trajectory prediction method is used to predict the trajectory of a certain period of time in the future according to the relevant motion characteristics of the moving target. There are two main categories of behaviour-driven moving target trajectory prediction methods: kinetic modelling and intent recognition. Data-driven methods based on massive historical trajectory data have greater potential for application in long-timetrajectory dynamics prediction. The behaviour-driven approach is more explanatory and novel, but it requires too much information on the environment and target state and has the best results only in specific scenarios. Since the environmental complexity is large and the target state uncertainty is strong in the process of observing moving targets at sea; hence, this paper adopts a data-driven moving target trajectory prediction method based on data.  
The typical data-driven marine moving target motion prediction methods mainly include the dead estimation method, curve fitting method, and typical path method. The navigation position prediction method has higher requirements for the prior information of a manoeuvring target and is more accurate in judging the potential area of the target. Therefore, the navigation position prediction method is adopted in this section to establish a manoeuvring target prediction model. Next, model inference is performed in two dimensions, a two-dimensional planar coordinate system and a three-dimensional spatial coordinate system. Three-dimensional spatial model reasoning is more complicated, but the reasoning idea is basically the same as that in the two-dimensional coordinate system. The predictive model reasoning in the two-dimensional planar coordinate system is first extended to the three-dimensional spatial coordinate system after the reasoning is sufficient.
# Adaptive DCS-SOMP for Localization Parameter Estimation in 5G Networks
## 4 Proposed Method
### Sensing Matrix Construction  
According to [14], the azimuth angle belongs to the interval \([0,2\pi]\) and the elevation angle belongs to the interval \([0,\pi]\). Therefore, we have \(q_{az}\) candidates for azimuth angles uniformly spaced within the interval \([0,2\pi]\), and \(q_{el}\) candidates for elevation angles uniformly spaced within the interval \([0,\pi]\). We define \(\mathbf{\tilde{\phi}}^{(0)}=[\tilde{\phi}^{(0)}_{1},\dots,\tilde{\phi}^{(0)}_{q_ {az}}]\), \(\mathbf{\dot{\phi}}^{(0)}=[\hat{\phi}^{(0)}_{1},\dots,\hat{\phi}^{(0)}_{q_{el}}]\) as candidates for AoD azimuth and elevation, respectively, and \(\mathbf{\tilde{\theta}}^{(0)}=[\tilde{\theta}^{(0)}_{1},\dots,\tilde{\theta}^{(0 )}_{q_{el}}]\), \(\mathbf{\dot{\theta}}^{(0)}=[\hat{\theta}^{(0)}_{1},\dots,\hat{\theta}^{(0)}_{q_{el }}]\) as candidates for AoA azimuth and elevation, respectively. We introduce the matrix \(\mathbf{U}^{(0)}_{t}\) of dimensions \(N_{t}\times q_{az}q_{el}\) containing the response vectors for each possible combination of azimuth and elevation for AoD:  
\[\mathbf{U}^{(0)}_{t}=[\mathbf{a}_{t}(\mathbf{\tilde{\phi}}^{(0)},\mathbf{\dot{\phi}}^{ 0})]. \tag{5}\]  
Similarly, we create the matrix \(\mathbf{U}^{(0)}_{r}\) of dimensions \(N_{r}\times q_{az}q_{el}\) containing the response vectors for each possible combination of azimuth and elevation for AoA:  
\[\mathbf{U}^{(0)}_{r}=[\mathbf{a}_{t}(\mathbf{\tilde{\theta}}^{(0)},\mathbf{\dot{\theta} }^{(0)})]. \tag{6}\]  
Finally, we obtain the sensing matrix \(\mathbf{\omega}^{(0)}\) as follows:  
\[\mathbf{\omega}^{(0)}[n]=(\mathbf{U}^{(0)}_{t}\mathbf{x}^{(g)}[n])^{T}\otimes\mathbf{ U}^{(0)}_{r}, \tag{7}\]  
where \(\mathbf{x}^{(g)}\) represents the transmitted data and \(\otimes\) denotes the Kronecker product. The sensing matrix \(\mathbf{\omega}\) has dimensions of \((N_{t}M_{t})\times(q_{az}q_{el})^{2}\). As a result, the number of elements in \(\mathbf{\omega}\) is directly related and highly sensitive to the values of \(q_{az}\) and \(q_{el}\).  
In Section 5, we have demonstrated that increasing the number of candidates (\(q_{az}\), \(q_{el}\)) for DCS-SOMP is crucial to achieving accurate estimations of 2D-AoD and 2D-AoA. However, an increased number of candidates also leads to increased memory usage, as shown in Figure 2. Our proposed modification to DCS-SOMP introduces an adaptive search approach, dynamically adjusting the sensing matrix during each iteration, as detailed in Section 4.3, to address this issue.
## 1 Introduction
Decision tree methods also have been commonly proposed to reduce training data because the training time is proportional to \(O\left(np^{2}\right)\) where \(p\) represents discrete input variables [23] so is faster than traditional SVMs. The decision tree method recursively decomposes the input data set into binary subsets through independent variables when the splitting condition is met. In supervised learning, decision trees, bringing forth random forests, are one of the most popular models because they are easy to interpret and computationally inexpensive. Indeed, taking advantage of decision trees, several researches combining SVMs with decision trees have been proposed for large-size SVM problems. [PERSON] et al. [24] presented a method that uses a binary tree to decompose an input data space into several regions and trains an SVM classifier on each of the decomposed regions. Another method using decision trees and [PERSON]'s linear discriminant was also proposed for large-size SVM problems in which they applied [PERSON]'s linear discriminant to detect 'good' data samples near the support vectors [25]. [PERSON] et al. [26] also utilized a decision tree to select candidate support vectors using the support vectors annotated by SVM trained by a small portion of training data. Their approaches, however, are limited in that it cannot properly handle the regions that have nonlinear relationships. The ultimate aim in dealing with large-scale SVM problems is to reduce the training time and memory consumption of SVMs without compromising the performance. For this goal, it would be worth finding good support vector candidates as a data-reduction method. Thus, in this paper we present a method that finds support vector candidates based on decision trees that works better than previous methods. We determine the decision hyperplane using support vector candidates chosen among the training dataset. In this proposed approach, we introduce a new concept, relative support distance, to effectively find candidates using decision trees in consideration of nonlinear relationships between local observations and labels. Decision tree learning decomposes the input space and helps find subspaces of the data where the majority class labels are opposite to each other. Relative support distance measures a degree that an observation is likely to be a support vector, using a virtual hyperplane that bisects the two centroids of two classes and the nonlinear relationship between the hyperplane and each of the two centroids. This paper is organized as follows. Section 2 provides the overview of SVMs and decision trees that are exploited in our algorithm. In Section 3, we introduce the proposed method of selecting support vector candidates using relative support distance measures. Then, in Section 4, we provide the results of experiments to compare the performance of the proposed method with that of some existing methods. Lastly, in Section 5, we conclude this paper with future research directions.
# Frequency Selective Surfaces: A Review
## 4 Types of FSSs
### FSS Based on Array Elements  
In this review article, we cover three types of FSSs based on array elements, including basic element type FSSs, convoluted/meandered FSSs, and fractal FSSs.
## 3 PS-DHI Applications
### Biological
A light linearly polarized in the same direction of the azimuth of the compensating quarter-wave plate QWP\({}_{1}\) and obtained by the input polarizer P\({}_{1}\), enters in a Mirau microscope objective (MMO) by the beam splitter cube BS\({}_{2}\). Therefore, the object (reflected from the sample) and reference (reflected from the reference mirror M) beams are orthogonally linear polarized after passing twice through QWP\({}_{1}\) and QWP\({}_{2}\) (see Figure 9b). Another quarter-wave plate (QWP\({}_{3}\)) transforms the orthogonal linear polarizations into L-state and R-sate circular polarizations. The light collimated by the MMO is focused by the tube lens TL, whose back focal plane corresponds to the input plane of the add-on 4G optics module. Here the L-state and R-sate circular polarizations images created in the sample and reference path overlap, and a polarization directed geometric-phase grating (GPG), with a spatial period \(\Lambda=9\)\(\upmu\)m (corresponding to \(2\uppi\) rotation of the anisotropy axis) is positioned. The polarization state of the object and reference beams is changed from L-state to R-state circular polarization and vice versa by passing through the GPG, whereas the geometric phase changes as \(\pm 2\upvarphi\), where \(\varphi\) is the periodic spatial change of the angular orientation of the anisotropy axis (see Figure 9c). This geometric-phase modulation leads to a tilt of the object and reference waves with the orthogonal circular polarizations in directions of \(+1\)st and \(-1\)st diffraction order with the mutual angle of \(8^{\circ}\) for the central wavelength. The polarizer P\({}_{2}\) and the lens L\({}_{2}\) give the polarization projection and the Fourier transform, respectively; then, the off-axis hologram is recorded on the CCD. By using an optical path difference compensator to the back focal plane of the lens L\({}_{1}\), the length of the object and reference beams optical paths can be aligned, allowing the successfully use of the MMO in biological experiments using broadband light. The Q4 GOM has been tested for quantitative imaging of diverse cells classes: human cheek cells, blood smear and spontaneously transformed rat embryonic fibroblast cells. As example, the images obtained for human cheek cells are reported in Figure 9d. Results are very impressive, since they demonstrated an accuracy well below 5 nm, opening new research directions in the quantitative retardance imaging of anisotropic biological samples [76].
## 5 Conclusions  
In this paper, a comprehensive power-sharing strategy was proposed to properly coordinate the energy from the load power demand to the PEMFC, BAT, and SC. The proposed methodology was constructed based on hierarchical techniques with high- and low-level control systems. In the high-level control, the FLRs were applied to determine the reference PEMFC power based on the status of the BAT SOC and the required power of load under different operating scenarios. To regulate the energy sources to match these references, low-level control with DC/DC converters dynamics and regulators were systematically analyzed based on each device's characteristics as the necessary criterion. To guarantee the stability of the DC bus voltage, and thus for the whole system qualification, the adaptive PID controller was employed to maintain the bus voltage around the desired value regardless of the load change. Simulation results displayed that the proposed method could match the load requirements, keep the stability of the DC bus with a smaller voltage ripple, and achieve higher working performance for the hybrid tramway system rather than the other approaches. Moreover, the achievements in this study not only guarantee the power supply efficiency but also reduce hydrogen consumption and prolong the lifespan of energy sources. However, the challenges of optimal fuel economy and improving the PEMFC efficiency were not comprehensively addressed in this study, which should be explored in depth. Furthermore, the advanced configuration with a bidirectional converter should be installed to control the power flow of SC, which can enhance the system performance, rapidly compensate for the high peak power, and prolong the lifetime of energy devices. Consequently, this study serves as a premise to develop advanced EMSs for hybrid PEMFC applications in the future. **Author Contributions:** [PERSON] was the supervisor providing funding and administrating the project, and he reviewed and edited the manuscript. [PERSON] carried out the investigation, methodology, analysis, validation, made the MATLAB simulation, and wrote the original manuscript. [PERSON] supported the model and methodology in MATLAB simulations, and checked the manuscript. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was financially supported by the \"Hydrogen Electric Tram Demonstration Project\" through the Ministry of Trade, Industry & Energy (MOTIE) and Korea Institute for Advancement of Technology (KIAT) and this work was supported by \"Regional Innovation Strategy (RIS)\" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021 RIS-003). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The load profile data presented in this study are available in [19; 20; 33; 45]. Conflicts of Interest: The authors declare no conflict of interest.