esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulations and Real Data Processing
### Evaluation of the Proposed CCAE Method  
In this subsection, simulations are performed to evaluate the proposed CCAE method on stretched signals. The CPU frequency of the computer is 3.30 GHz, and the memory size is 8 GB. The transmitted signal is an LFM waveform, and additive white Gaussian noise is added to the UR signal. Table 2 lists the detailed simulation parameters. The target is set as a multi-scatterer model containing 10 scatterers, and the length of the target is about 9 m. The velocity is 100 m/s, and the acceleration is set to zero.  
The energy concentration of the proposed CCAE method is investigated at a high SNR level, i.e., 30 dB. The spectrum of the cross-correlation result is shown in Figure 4, which shows that the energy of different scatterers is focused on one position. The estimated velocity of the proposed method is 99.9 m/s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Center Frequency (GHz)** & **Bandwidth (MHz)** & **Pulse Width (\%)** & **Sampling Frequency (MHz)** & **PRI (ms)** \\ \hline
9 & 200 & 100 & 10 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameters of the evaluation of the proposed CCAE method.  
Next, we compare the performances of the proposed CCAE method with different bandwidths and PRIs, under different SNR conditions. The SNR varies from \(-30\) dB to \(2\) dB. Under each SNR, \(1000\) Monte Carlo simulations have been carried out. Figure 5 shows the RMSE results. It is observed that when the bandwidth or the PRI increases, the RMSE decreases. To achieve the same RMSE, the test case with \(B=1\) GHz and \(T_{pr}=10\) ms requires the lowest SNR, and hence, it has the best performance. Under the same SNR, the RMSE performance of the case with \(B=1\) GHz, \(T_{pr}=10\) ms is about \(3\) dB lower than the case with \(B=0.5\) GHz, \(T_{pr}=10\) ms and the case with \(B=1\) GHz and \(T_{pr}=5\) ms. The performance difference is about \(6\) dB between the case with \(B=1\) GHz, \(T_{pr}=10\) ms and the case with \(B=0.5\) GHz, \(T_{pr}=5\) ms.
# Climate Warming Dominates Vegetation Productivity in the Hanjiang River Basin, China
## 2 Materials and Methods
### Data Sources
#### 2.2.2 Meteorological Data  
The meteorological data utilized in this study were obtained from the National Meteorological Science Data Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 3 December 2022). The dataset includes precipitation (Pre), temperature (Tem), sunshine hours (SSD), and wind speed (WS), spanning from 2000 to 2020. Monthly values of climate factors were obtained through data aggregation and calculation. Potential evapotranspiration (PET) was computed using the Penman-Monteith method [48; 49]. Furthermore, we used the Standardized Precipitation Evapotranspiration Index (SPEI), which characterizes drought at multiple scales, with SPEI1, SPEI3, and SPEI12 representing drought severity at monthly, seasonal, and annual scales in the study area, respectively. To ensure data consistency, the spatial resolution of each climate factor was interpolated to match the same spatial resolution as GPP using ANUSPLINE [50].
# Application of ChatGPT-Based Digital Human in Animation Creation
## 3 Development Environment Setup and Parameterization
### Unity Plug-in Drives Mouth and Real-Time Voice Synchronization  
The Unity plug-in utilized to drive the mouth and eye movements of the digital human is SALSA With RandomEyes Version 1.5.5 [29]. Upon importing the plugin, an analysis of its demo file was conducted. The analysis revealed that the script responsible for controlling the mouth drive is Salsa3D, which drives the Blendshape parameters of the digital human's mouth using sound data. On the other hand, the eye movement parameter is managed by the RandomEyes3D script, which randomizes blinking and eye turning movements by adjusting the Blendshape parameters of the digital human's eyes in a random time-based manner. Incorporating the data analysis from the demo, the digital human's parameters are configured accordingly. This integration ensures the digital human's mouth and eye movements are dynamically synchronized with sound and realistically mimic random eye rotations, creating a more-lifelike and -interactive experience for users.  
On the imported digital human, the Salsa3D component is added to the Inspector window to control the real-time synchronization of sound with the digital human's mouth movements. Additionally, the RandomEyes3D component is added to achieve random eye rotations.  
To set up the Salsa3D component, follow these steps:  
1. In the Skinned Mesh Renderer of the Salsa3D component, select the sub-level object of the digital human that contains the Blendshapes for the mouth. In the experiment, the Blendshapes for the digital human's mouth are located on the digital human's body.
2. The source of the Audio Source depends on the text-to-speech sound source. In the experiment, the AzureTTS sound source is selected.
3. Adjust the parameters of the digital human's mouth opening degree. The small-amplitude index of the sound corresponds to the small-amplitude mouth action in Blendshapes; the medium-amplitude index corresponds to the medium-amplitude Blendshape; the large-amplitude index corresponds to the high-amplitude Blendshape. This parameter adjustment ensures that the sound's amplitude controls the corresponding mouth movement size.  
For the parameter settings of the RandomEyes3D component, follow these steps:  
1. In the Skinned Mesh Renderer of the RandomEyes3D component, find the sub-level object of the digital human that contains the Blendshapes for the eyes.
2. The Eye Shape Indexes in the RandomEyes3D component correspond to the Blendshapes for Eye Up, Down, Left, Right, and Blink. Place the corresponding Blendshapes in the appropriate positions to achieve the desired eye movements, including up, down, left, right, and blinking.  
By configuring the Salsa3D and RandomEyes3D components with the appropriate Blendshapes and parameters, the digital human can realistically synchronize its mouth movements with the sound and exhibit random eye rotations, providing a more-engaging and -lifelike animation experience. The parameter settings are displayed in Figure 4.
## 1 Introduction
[29] used an unsupervised clustering based on the multiple eigenspaces algorithm to carry out topological mapping hierarchically using omnidirectional images. More recently, [PERSON] et al. [30] proposed the use of a differential clustering method to improve the compression of telemetry data. We propose a method to build hierarchical maps through a combination of clustering methods and global appearance descriptors. We compare the performance of spectral and self-organizing maps' clustering. In addition, an exhaustive experimental evaluation is carried out to assess the performance of the method in mapping and localization tasks, and we evaluate the influence of the most relevant parameters in the results. This is an interesting problem in the field of mobile robotics because, as pointed out before, global appearance descriptors are a straightforward way of describing visual information, but they contain no metric information, comparing to local-features' descriptors. Additionally, no deep study to assess the performance of global-appearance descriptors in hierarchical mapping can be found in the literature. The experiments show that the proposal that we present is a feasible alternative to build robust compact maps, despite the phenomenon of visual aliasing, which is present in the sets of images that we have used in the experiments. The present paper continues and extends the study presented in [31], which is a comparative evaluation in which the performance of some descriptors was assessed to create compact models and estimate the position of the robot. The contributions of the present paper are the following: (a) a new method to compact the visual model is proposed; (b) the trade-off compactness-accuracy-computational cost is addressed, and the performance of the compact models is compared to raw models (with no compaction); (c) a comparison between compression through direct methods and compression through clustering methods to solve the localization task is evaluated; and (d) new indoor environments with different topologies are included in the experimental section. The remainder of the paper is structured as follows: Section 2 outlines the global appearance descriptors that will be tested throughout the paper. After that, Section 3 shows the clustering approaches used to compress the models. Next, Section 4 presents the method to obtain the localization within the compact models. Section 5 presents the experimental results of clustering and localization and also the discussions about the results. Finally, Section 6 outlines the conclusions and future research lines.
# A Review of Adsorbents for Heavy Metal Decontamination: Growing Approach to Wastewater Treatment
## 5 Adsorption Isotherm
### Freundlich Isotherm  
The Freundlich equation is used to present sorption value [91]. The Freundlich isotherm explains the physical sorption of the solute particle from metal solution to the adsorbent. The Freundlich adsorption isotherm is explained as below:  
\[\mathrm{q_{e}=K_{f}\,C_{e}}^{1/n} \tag{5}\]  
The term \(\mathrm{q_{e}}\) is the quantity of solute in milligrams sorbed by one gram of sorbent at equilibrium (mg/g), \(\mathrm{C_{e}}\) is the equilibrium amount of adsorbate in solution after sorption (mg/L) and \(\mathrm{K_{f}}\) is the Freundlich constant (L/mg): n is the heterogeneity factor. Adsorption data well fit to Freundlich isotherm indicates that solute sorbed on the exterior of the sorbent is forming many layers. The suitability of the Freundlich equation is examined by \(\mathrm{logq_{e}}\) against \(\mathrm{logC_{e}}\) by the use of the logarithmic form of Equation (5).  
\[\mathrm{log\,q_{e}=log\,K_{f}+1/n\,log\,C_{e}} \tag{6}\]  
The above equation is a straight-line having intercept \(\mathrm{log\,K_{f}}\) and slope equal to \(1/n\).
## 4. Clamping Resonators
### Finite-Element Simulations  
Frequency domain finite-element analysis [17] was performed in order to provide confidence in the experimental results presented in Figures 2 and 4. This simulation assumes a plane wave solution and uses the frequency domain by assuming \(e^{i\omega t}\) dependence, hence for a forward traveling wave the wavenumber \(k_{x}\) is negative-valued. Perfect matching layers are used on the ends of the model to prevent \"end-wall\" reflections. The remaining parts of the domain are divided into buffer, excitation, incident/transmit, and \"affected\" regions. The purpose of this model is to evaluate the transmission and reflection qualities of reflectors defined in the \"affected\" region. A similar model was used in Hsu [18], but we apply a body-load excitation instead of line excitation to excite a single mode. The displacement component \(u_{x}\) is plotted along the x-axis in Figure 7. The \(y\)-direction width of the model was chosen to correspond with the spacing of the resonators, which equates to a periodically repeating array of resonators in the \(\pm y\)-directions since periodic boundary conditions are applied to these edges. The clamping resonators are effective at prohibiting the propagation of much of the S0 Lamb wave, while the frequency-matched resonators are completely ineffective. The wavenumber spectrum was calculated using the complex-displacements measured from the transmitted wavefield and is shown in Figure 8. At the wavenumber of the S0 mode we see that the frequency-matched resonators only reduce the spectrum by approximately 7%, while the clamping resonators reduce it by 90%.  
Figure 6: (**a**) Schematic of transducer positioning with respect to the resonator array for measuring reflected waves; (**b**) The 2 DFFT of the incident/reflection measurements. The diagonal blue lines are the dispersion curves for the forward and backward propagating S0 Lamb wave. The vertical black line is where the wavenumber-spectrum slice data at a frequency of 53.9 kHz are obtained for (**c**) the wavenumber spectrum.
# A Direct Search Algorithm for Global Optimization
## 4 Experimental Study
### Performance Evaluation  
The evaluation of the GDS algorithm has been accomplished by solving the 24 functions in Table 1 using the fixed set of parameters values explained in the previous subsection. The performance of GDS is compared to DIRECT [51]. DIRECT is a deterministic global optimization algorithm based on the domain partition strategy. Each iteration includes two phases. The first phase identifies hyper-rectangles that potentially contain a global optima. In the second phase, the hyper-rectangles identified in the first phase are partitioned into smaller hyper-rectangles, and the cost function is evaluated at the centers of the hyper-rectangles. The convergence properties guarantee that if no termination criteria are included, DIRECT will exhaustively sample the domain. The implementation of DIRECT in MATLAB by [PERSON] [64] has been used in this study. The experiments have been performed in a laptop PC equipped with a dual core processor Intel (R) Core (TM) i5-3317U CPU @1.70 GHz running MATLAB R2014a under the Windows operating system.  
The comparative study has been performed as follows: For both algorithms, GDS and DIRECT, a fixed number of function evaluations is used as a stopping condition, and the error in computing the global minimum value is obtained. Each problem has been solved 15 times, and the median of the error is tabulated for each function. The study has been accomplished for the 24 functions of Table 1 and for different dimensions of the function domain and different numbers of function evaluations. The default parameters of DIRECT [64] have been used, but using the number of function evaluations as stopping conditions.  
In Table 2, the results for functions of dimensions five and 10 and a number of function evaluations equal to \(10^{3}\) and \(10^{4}\) are shown, while in Table 3, the same results are shown for dimensions 10 and 20 and \(10^{4}\) and \(10^{5}\) function evaluations. GDS and DIRECT obtained different results for different functions. In general, for a small number of function evaluations, DIRECT gets a smaller median of the error. However, as the number of function evaluations increases, the improvement in approaching the minimum value is higher in GDS. One of the outstanding features of GDS is its simplicity. This can be checked by comparing the CPU time with DIRECT. The CPU time for all of the problems, dimensions  
Since GDS is simple and computationally efficient, it can be successfully applied to problems of high dimensions where the number of function evaluations to obtain a good result is large for any direct search algorithm.
# Chinese Event Detection without Triggers Based on Dual Attention
## 4 Experiment
### Experiment Results Analysis
#### 4.2.1 Performance Comparison Analysis
It is possible to improve the model effect by adding pre-training models such as BERT, which also proves the power of pre-training models in NLP. By fusing character-level and lexical-level information, NPN obtains a hybrid representation that captures internal character composition and accurately classifies event information. NPN attempts to deal with the word-trigger mismatch, but with limited improvement in precision and recall. TLNN focuses on solving the trigger-word mismatch problem by creating a path to link the cell states of all words between the start and end positions of the word. Compared with other baselines, it significantly improves the model's recall. However, TLNN ignores the significance of document context, resulting in limited progress in precision and an overall F1-score. JMCEE focuses on solving the problem of role overlap and uses shared feature representations of pre-trained language models to jointly predict event trigger words and event arguments. It has achieved breakthroughs in accuracy rate, recall rate, and F1-score, but it does not solve the problem of trigger-word mismatch. This results in a dramatic performance degradation in the process from trigger identification to classification. TBNNAM reduces the expensive manual labeling cost and explores event detection without triggers for the first time, which is close to the best baseline model in terms of event detection accuracy. However, skip-gram embedding coding does not consider context, and the MSE loss function vanishes as the bias derivative output probability value approaches 0 or 1. Limited by these two problems, the recall of TBNNAM is low, leading to the model's poor results on the F1-score. ALBERT reduces the number of parameters in the BERT pre-training model and extracts text features from large-scale unlabeled text data to construct word embedding vectors in context. In addition, ALBERT combines BiLSTM to capture important context and improve the model's predictive power. Consequently, EDWTDA has the best overall experimental results, with significant improvements in precision and recall. To avoid word-trigger mismatch and improve the model's recall, EDWTDA detects events without trigger words, uses a local attention mechanism to simulate hidden event trigger words, and applies a global attention mechanism to capture essential documents. To improve the model's generalization ability, EDWTDA converts the event detection task into a binary classification task and makes up for the imbalance between positive and negative samples by means of the focal loss function.
# An Efficient 5G Data Plan Approach Based on Partially Distributed Mobility Architecture
## 5 Numerical and Simulation Results  
### Numerical Results  
Based on the mathematical expressions given in Section 4, we compare the performance of the 4G network and 5G network architecture by varying the parameters expressed in Table 1.  
Figure 10 shows the effect of queuing delay (Q) on cumulative transmission delay at each node in the network. This figure compares three approaches: 4G network, fully distributed, and proposed approach (partially distributed method). The delay increased linearly for all networks. The results show that the proposed method achieves competitive performance. This result is because the data packets in the 5G network are sent through an optimal path--however, the data route in the 4G is delivered by P-GW and S-GW.  
Figure 11 demonstrates the impact of hop count between the eNB and C.E. on the transmission delay by varying \(\delta\). The proposed architecture shows superior performance compared with the 4G network due to the optimized route of data packets between the M-GW and eNB instead of a centralized anchor between the S-GW and P-GW in 4G.  
Figure 12 compares intra-gateway handover in both networks. The result shows the impact of hop count between eNB and C.E (\(\delta\)). Varying \(\delta\) significantly impacted the handover delay for the 4G network. However, the proposed network demonstrates significant enhancement because eNB performs the routing with C.E directly. By contrast, in the 4G network, handover is implemented between the P-GW and MME.  
Figure 11: The impact of varying \(\delta\) on the total transmission delay.  
Figure 10: The impact of the queuing delay (Q) at each node.  
Figure 13 illustrates the inter-gateway handover delay. The result compares the handover delay between the eNBs in the same network (\(\epsilon\)). The impact of varying \(\epsilon\) shows a notable effect on the 4G network delay because the MME changes the bearer via S-GW and P-GW as a centralized anchor. Nevertheless, The 5G architecture is insensitively affected by varying \(\epsilon\) because the C.E performs path routing with the M-GW.
## 2 Materials and Methods
### Experimental Setup  
#### 2.2.1 Model Selection  
To comprehensively evaluate the predictive performance of different machine learning algorithms for binary classification tasks, seven models were selected: Logistic Regression, Random Forest, Gradient Boosting (specifically XGBoost, LightGBM, and CatBoost), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs) using a multi-layer perceptron classifier architecture. This collection of models spans both classic (e.g., Logistic Regression, Random Forest, SVM) and modern ensemble or deep learning approaches (e.g., XGBoost, LightGBM, CatBoost, ANN), thus offering a diverse testbed for performance comparisons across various complexities of function approximation. By including regularized linear models, tree-based ensembles, kernel methods, and neural networks, the experimental design captures a broad spectrum of hypothesis spaces, ensuring robust insights into which methods excel under differing data conditions.  
A key motivation for selecting these models lies in their complementary strengths: for instance, Logistic Regression often provides interpretable coefficients, Random Forest and Gradient Boosting methods can capture complex interactions through ensembles, SVMs are effective in high-dimensional spaces with appropriate kernel design, and ANNs can  
Figure 1: Comparative visualization of the dataset before and after pre-processing, including weight-based resampling, standardization, and Principal component analysis.  
approximate non-linear relationships given sufficient training data. Moreover, these seven models are commonly used as baselines or benchmarks in predictive analytics, making them well-suited for a comparative study aimed at identifying the most effective modeling strategies for innovation outcome prediction.
# A Highly Integrated Millimeter-Wave Circularly Polarized Wide-Angle Scanning Antenna Unit
## 2 Design of a Circularly Polarized Wide-Angle Scanning Antenna Unit
### Antenna Unit Structure Design
(**b**) The field distribution of the SIW filter structure. Figure 2: The structure of the radiating square waveguide incorporating the circular polarizer. from the top waveguide port, the matching load initially converts the waveguide mode into the coplanar waveguide (CPW) mode. Subsequently, it absorbs the electromagnetic waves using resistive films positioned on both sides of the CPW. In addition, the matching resistors are made using the PCB buried resistor process, which etches away the metal on the surface of the PCB to reveal the resistors that have been buried therein. Combining all antenna components forms the complete antenna unit model. Figure 5 displays the structural model of the antenna unit from various viewpoints.