esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, we fabricated a type of Core-cladding Ge-Sb-Se glass fiber with a Ge\({}_{12.5}\)Sb\({}_{15}\)Se\({}_{72.5}\) core and Ge\({}_{15}\)Sb\({}_{10}\)Se\({}_{75}\) cladding and achieved a SC spectrum spanning from 2 \(\upmu\)m to 9 \(\upmu\)m by pumping the Ge-Sb-Se fiber with a core diameter of 11 \(\upmu\)m using a femtosecond laser pump at 3.8 \(\upmu\)m. We present the Raman response function of Ge-Sb-Se glasses calculated using a multiple vibrational mode model. Furthermore, we calculate the Raman gain spectrum using the Raman response function and compare it with the Raman gain spectrum obtained from experimentally measured Raman spectra.  
Figure 10: (**a**) Comparison of SC generated by simulation of Raman response function using the multiple–vibrational–mode model with experimental results. (**b**) Comparison of SC generated by simulation of Raman response function generated using the single Lorentzian model with experimental results. (**c**,**d**) represent the simulated SC spectral evolution of (**a**,**b**).  
This comparative analysis serves to verify the accuracy of this model. The simulation of SC generation is performed based on the Raman response functions obtained from both the multiple vibrational mode model and the single Lorentzian model, incorporating relevant parameters, such as dispersion and nonlinearity coefficients. A comparison between the simulated SC and the experimental SC reveals that the multiple vibrational mode model exhibits a superior fit with the experimental data. This finding underscores the influence of the Raman response function on the generation of SC and highlights the critical importance of an accurate Raman response function for precise simulation of SC generation. To the best of our knowledge, this study represents the first report on Ge-Sb-Se fiber SC generation utilizing the Raman response function obtained with a multiple vibrational mode model. Moreover, we assert that this model can be applied to other materials characterized by a multi-peaked Raman gain spectrum. By accurately capturing the material-specific characteristics, this model provides a foundation for more precise simulation and optimization of SC generation as well as Raman laser output.  
Conceptualization, Z.W.; Methodology, Z.W.; Software, X.P.; Formal analysis, Z.W.; Investigation, X.P.; Data curation, X.P.; Visualization, Y.D.; Project administration, Y.D. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Natural Science Foundation of Zhejiang Province (LY20F040003) and the Natural Science Foundation of Ningbo (202003N4158) and sponsored by the K. C. Wong Magna Fund of Ningbo University.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to further study.  
The authors declare no conflict of interest.
# Optimization of RIS-Assisted 6G NTN Architectures for High-Mobility UAV Communication Scenarios
## 4 Joint Optimization Framework for RIS Configuration and UAV Mobility  
Joint optimization of RIS phase shifts in high-mobility NTN scenarios presents several significant challenges. Firstly, the inherently non-convex and unit-modulus-constrained nature of the optimization problem (that is, each RIS element must maintain a phase shift within \([0,2\pi)\)) makes it computationally intensive and unsuitable for directly applying standard convex solvers. Secondly, in high-mobility environments such as UAV-assisted NTNs, CSI rapidly becomes outdated due to short coherence times and Doppler effects, thus demanding real-time or predictive optimization techniques. Furthermore, the dimensionality of the optimization problem scales linearly with the number of RIS elements, making it increasingly difficult to find optimal phase-shift configurations in large-scale RIS deployments. The coordination latency between the control node and RIS and the limited computational capacity of UAVs further constrain the feasibility of real-time reconfiguration. Furthermore, achieving joint optimization across multiple performance metrics, such as SINR, throughput, energy efficiency, and latency, often introduces conflicting trade-offs that complicate algorithm design. These challenges collectively require the development of lightweight, predictive, and adaptive algorithms capable of achieving near-optimal solutions in dynamic and resource-constrained NTN environments.  
In this section, we present the proposed optimization framework for maximizing the performance of RIS-assisted UAVs in high-mobility scenarios. The framework focuses on optimizing the RIS phase shifts to enhance key performance metrics such as SINR, throughput, energy efficiency, and outage probability while minimizing latency. The framework consists of three main components:  
1. Real-time RIS phase-shift optimization.
2. Predictive coordination between UAVs and RIS.
3. Performance evaluation and trade-off analysis.
## 2 ML Methodologies
### Supervised Learning
#### 2.1.1 Classification
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \multicolumn{1}{c}{**Category**} & \multicolumn{1}{c}{**Algorithm**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Ref.**} \\ \hline \multirow{4}{*}{Hydrological modeling} & Logistic regression & Statistical modeling for hydrological data. & Predicting flood occurrence based on rainfall data. & [45] \\ \cline{2-5}  & Support vector machine & SVM-based modeling for hydrology. & Forecasting drought severity using climate data. & [46] \\ \cline{2-5}  & Naive Bayes classifier & Probabilistic classification for water quality. & Identifying waterborne contaminants in drinking water. & [47] \\ \hline \multirow{4}{*}{Water quality analysis} & Ridge classifier & Regularized classification for water quality. & Detecting sources of pollution in rivers and lakes. & [48] \\ \cline{2-5}  & Lasso classifier & Lasso-based classification for water resources. & Classifying land use for urban water management. & [49] \\ \cline{2-5}  & Elastic net classifier & Elastic net regularization for water data. & Monitoring and classifying water sources for quality. & [50] \\ \hline \multirow{4}{*}{Streamflow forecasting} & Least squares classifier & Linear regression for streamflow prediction. & Forecasting river discharge for flood risk assessment. & [51] \\ \cline{2-5}  & Stochastic gradient descent (SGD) classifier & Gradient-based modeling for streamflow. & Real-time streamflow forecasting for water resource planning. & [52] \\ \hline \multirow{2}{*}{Data-driven analysis} & Linear discriminant analysis (LDA) & Dimensionality reduction for water data. & Feature extraction for water quality classification. & [53] \\ \cline{2-5}  & & & & \\ \end{tabular}
\end{table} 
Table 1: _Cont._Table 3 provides an understandable overview of various nonlinear classification techniques. These methods are specifically designed for scenarios where the decision boundary separating different classes is not a linear function of the input features. In Table 4, we establish connections between different water management methods and their specific nonlinear classifications, along with practical applications and supporting references for verification. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Category** & **Algorithm** & **Description** & **Applications** & **Ref.** \\ \hline \multirow{6}{*}{Water resources classification} & Passive aggressive classifier & Online learning for water resource classification. & Land use classification for watershed management. & [54] \\ \cline{2-4}  & Quadratic discriminant analysis & Nonlinear classification for water data. & Ecosystem classification in aquatic environments. & [55] \\ \cline{2-4}  & Perceptron & Simple binary classification algorithm. & Initial water quality classification in field surveys. & [56] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._
## 5 Discussion and Future Work  
The algorithm in this paper is based on generative adversarial networks to achieve super-resolution reconstruction of low-quality images. Using deep learning, neural networks can learn a mapping between low-resolution and high-resolution images. Depthwise separable convolution combined with dense blocks is also introduced, and the DSC Dense Block suitable for super-resolution reconstruction was redesigned, greatly reducing the total number of network parameters and the complexity of the algorithm. A frequency energy similarity loss function is also proposed, based on the frequency domain, which better constrains the generator network, making the edges of the generated image  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \multicolumn{2}{c}{**PSNR/dB**} & \multicolumn{2}{c}{**SSIM**} \\ \hline
**Model** & **Set5** & **Set14** & **Set5** & **Set14** \\ \hline DSCSRGAN & **30.1438** & **27.1176** & **0.9224** & **0.8217** \\ WSRGAN & 28.2423 & 25.0850 & 0.8779 & 0.7396 \\ SRGAN & 28.7594 & 25.1159 & 0.9074 & 0.7766 \\ SRCNN & 26.8796 & 23.9469 & 0.7627 & 0.6571 \\ FSRCNN & 27.9020 & 24.6097 & 0.8057 & 0.6991 \\ ESPCNN & 28.1569 & 24.7584 & 0.8001 & 0.6962 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of the quantitative evaluation results of six methods with the Set5 and Set14 datasets.  
Figure 4: Super-resolution image reconstruction effect comparison schematic diagram. From left to right: DSCSRGAN, SRGAN, WSRGAN, SRCNN, FSRCNN, ESPCNN. Corresponding PSNR, SSIM, and MSE are shown below the figure. (4\(\times\) upscaling) (Best viewed in color).  
sharper, richer in detail, and more natural. The reconstruction of some degraded images with missing details, insufficient illumination, and noise pollution still needs to be improved. By further improving the network structure and optimizing the loss function, the algorithms can be used in the fields of infrared image restoration and medical image reconstruction. We will explore intrinsic parallelism of convolutional neural network (CNN) [21] and consider for future studies the use of another framework in SISR. This single image super-resolution reconstruction algorithm is an improvement over previous work, and still has the potential for further refinement.  
Conceptualization, Z.J., Y.H., and L.H.; Data curation, Z.J. and Y.H.; Formal Analysis, Y.H.; Funding Acquisition, Z.J. and L.H.; Software, Y.H.; Writing--Review and Editing, Z.J., Y.H., and L.H. All authors have read and agreed to the published version of the manuscript.  
Sponsored by Nature Science Foundation of China (61876049, 61762066, 61572147) and project of Science and Technology plan in GuangXi (AC16380108) and Guangxi Key Laboratory of Image and Graphic Intelligent Processing (GIIP201701, GIIP201801, GIIP201802, GIIP201803) and project of postgraduate innovate plan in GuangXi (2019 YCXS043).  
The authors declare no conflict of interest.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 5 Performance Evaluation
### Comparation of Different Algorithms  
We compared our presented schema with CCMAR and ECDRA. In order to equally evaluate the performance of different algorithms, we adopted the same network model to execute the algorithms. As is commonly discussed in similar work, the network lifetime is commonly defined as the time when the first dead node appears in the network. We compared the network lifetime of different algorithms and the simulation result is shown as Figure 7. From Figure 7, we can clearly see that the schema we present had a better performance in the aspect of network lifetime compared to CCMAR and ECDRA. CCMAR adopts chain structure for intracluster transmission, which causes the heavy burden of forwarding. Additionally, intercluster communication in CCMAR adopts direct communication between CHs and the mobile sink, which greatly increases the energy consumption of CHs. ECDRA also adopts a mobile sink to rotate in the sensor field; however, it only considered the mobile sink moving in the outer side of the sensor field. Therefore, sensors in the boundary dissipate more energy and that hastens their demise. Our proposed schema performed best because it calculated the energy for different routing paths and chooses the optimal one.  
Then, we compared the energy consumption between different algorithms and the result is illustrated as Figure 8. We can clearly see from Figure 8 that our presented schema still had the best performance. Too much data forwarding resulted in the unnecessary energy dissipation in CCMAR and that led to a higher energy consumption compared to the other two algorithms. ECDRA consumed more energy than our presented algorithm because the average communication distance in ECDRA was much longer than that in our presented schema.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter Name** & **Value** \\ \hline Network radius (R) & [100, 200, 300, 400] m \\ Mobile sink radius (r) & [0, 0.25R, 0.5R, 0.75R, R] \\ Mobile sink number (MN) & [1, 2, 3, 4, 5] \\ Mobile sink speed (w) & [\(\pi\)/20, \(\pi\)/10, \(\pi\)/5] \\ Cluster number (CN) & [3, 4, 5, 6, 7] \\ Number of nodes (N) & 100 \\ Packet length (l) & 500 bits \\ Initial energy (\(E_{0}\)) & 0.5 J \\ Energy consumption on circuit (\(E_{elec}\)) & 50 nJ/bit \\ Free-space model parameter (\(\varepsilon_{fs}\)) & 10 pJ/bit/m\({}^{2}\) \\ Multi-path model parameter (\(\varepsilon_{mp}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ Distance threshold (\(d_{o}\)) & \(\sqrt{\frac{\varepsilon_{fs}}{\varepsilon_{mp}}}\)m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.
## 4 Numerical Validation
### Numerical Validation of Two-Layer SVD  
The rub-impact fault can cause frequency modulation and amplitude modulation in its vibration signal. Herein, a signal with frequency modulation and amplitude modulation is considered to demonstrate two-layer SVD and HT.  
\[\begin{array}{l}s(t)=(1+0.2\cos(59\pi t)+0.2\cos(67\pi t))\times\\ \cos(103\pi t+0.2\sin(101\pi t)+0.2\sin(89\pi t)+0.2\sin(79\pi t))\end{array} \tag{26}\]in which the fundamental frequency is 51.5 Hz; modulation frequencies of IF are 50.5 Hz, 44.5 Hz, 39.5 Hz; oscillating amplitudes are 10.1, 8.9, 7.9. Gaussian white noise with a signal-to-noise ratio (SNR) of 3 dB is added into \(s(t)\). The instantaneous amplitude (IA) is  
\[1+0.2\cos(59\,\pi\,\mathrm{t})+0.2\cos(67\,\pi\,\mathrm{t}) \tag{27}\]  
instantaneous frequency (IF) is  
\[51.5+10.1\cos(101\,\pi\,\mathrm{t})+8.9\cos(89\,\pi\,\mathrm{t})+7.9\cos(79\, \pi\,\mathrm{t}) \tag{28}\]  
The signal with noise and its spectrum is illustrated in Figure 1. The sampling frequency is 5120 Hz, the duration of the signal is 2 s, so the frequency resolution of Figure 1f is 0.5 Hz.  
Filter signal using lowpass filter, the cut-off frequency of which is 131 Hz. Then, two-layer singular value decomposition is applied to the signal. The signal is reshaped into a Hankel matrix, which has 5121 rows and 5120 columns. After the first layer, singular value decomposition is applied to the signal; the singular values are illustrated in Figure 2a. The IF is reshaped into a Hankel matrix, which has 5120 rows and 5119 columns. After the second layer, singular value decomposition is applied to IF; the singular values are illustrated in Figure 2b.  
Figure 1: Simulated signal. **(a)** The waveform of simulated signal between 0 and 0.4 s; **(b)** The waveform of simulated signal between 0.4 and 0.8 s; **(c)** The waveform of simulated signal between 0.8 and 1.2 s; **(d)** The waveform of simulated signal between 1.2 and 1.6 s; **(e)** The waveform of simulated signal between 1.6 and 2 s; **(f)** Fourier spectrum of simulated signal.  
The purified IF and ideal IF are revealed in Figure 3 and Fourier spectrum of purified IF and ideal IF are revealed in Figure 4, where the red line is the ideal IF and the blue line is the purified IF. It is illustrated that the extracted IF agrees well with the ideal IF. The absolute error of oscillating amplitude of purified IF is shown in Table 1; the maximum absolute error is 0.41 Hz.  
Figure 3: The waveform of the IF for simulated signal (red: ideal; blue: purified). (**a**) The waveform of the IF between 0 and 0.4 s; (**b**) The waveform of the IF between 0.4 and 0.8 s; (**c**) The waveform of the IF between 0.8 and 1.2 s; (**d**) The waveform of the IF between 1.2 and 1.6 s; (**e**) The waveform of the IF between 1.6 and 2 s.  
Figure 2: The singular values. (**a**) Singular values of signal; (**b**) Singular values of IF.
# A Study on Crowd Evacuation Model Considering Squeezing Equilibrium in Crowded Areas
## 1 Introduction
[39] proposed an extended floor field cellular automaton (CA) model that was formulated by leveraging the leader-follower behavior rule that was evident in pedestrian group behaviors to capture and evaluate the influence of group behaviors on crowd evacuation. [PERSON] et al. [40] found that the most important result was that the density can be precisely controlled by xw, d and the gap. However, the traditional social force model was prone to stagnation in the simulation process of pedestrian evacuation, especially when a lot of pedestrians passed through the multi-exit complex environment with U-shaped obstacles. To this end, the acting forces between pedestrians and between pedestrians and obstacles in the traditional social force model are improved to find out the relationship in the two cases which are within the influence range and are not intersected, and which are intersected and not greater than the maximum degree of squeezing, and to solve it for parameter optimization. For the effective evacuation of the crowd in a complex and dense scene, the above methods are employed to deeply analyze the relationship between the evacuation time and the degree of squeezing.
###### Abstract  
A significant amount of the literature is focused on converters that supply the required voltage with low input-current ripple to electronic devices. A hybrid converter that combines boost and Cuk converters was developed recently. This hybrid converter achieved a relatively low input-current ripple based on earlier strategies. This paper proposes a new model that simulates a hybrid power converter system using a unified evolutionary algorithm (EA). As part of this paper, we present an improved framework for a hybrid power converter. Moreover, a unified EA is developed to incorporate differential evolution (DE) and genetic algorithm (GA) properties in order to analyze the proposed modified hybrid power converter. This research describes a modified hybrid power converter that optimizes the zero-ripple duty cycle (\(D_{Z}\)) through the proposed algorithm for minimizing the input-current ripple. Based on our simulation results, comparing the proposed method with the baseline algorithm reveals that the proposed approach is significantly more efficient than the baseline algorithm and achieves the minimum input-current ripple in different gain values. In addition, we observe that the proposed algorithm performs better than the DE and GA algorithms in terms of obtaining low input-current ripple results. Therefore, the proposed hybrid algorithm is becoming more efficient with hybridization.  
power converter; differential evolution; genetic algorithm; unified framework
# Impulsive Multiple-Bipartite Consensus Control for Networked Second-Order Multi-Agent Systems
## 1 Introduction
The above discussion naturally prompts us to consider a problem, that is, how to combine the impulse control algorithm and the second-order dynamics characteristics to give a reasonable and feasible distributed impulsive multiple-bipartite control protocol by using only the neighbor information of agents, which forms the main motivation of this article. In this paper, a distributed impulsive multiple-bipartite consensus protocol is designed for second-order MASs. By adopting an improved Laplacian matrix associated with the network topology, the sufficient conditions under which all second-order MASs reach multiple-bipartite consensus are presented. Compared with the relevant existing results in the literature, the innovation of this paper lies in the three following aspects. Firstly, compared with the consensus continuous protocols studied in [24; 25; 26; 27; 31], a new designed impulsive multiple-bipartite consensus control protocol corresponding to the instantaneous network connectivity only relies on the single signum information that instantaneously exchanges with agents' neighbors at discrete moments. Secondly, compared with the traditional Laplacian matrix, an improved Laplacian matrix associated with directed graph topology with acyclic partition and a structurally balanced structure is introduced to solve the stability analysis of controlled systems. Thirdly, an explicit expression of multiple-bipartite consensus states can be obtained using the proposed Laplacian matrix, which can be used to develop a unified approach yielding the desired multiple-bipartite consensus. The rest of this paper is designed as follows. Section 2 presents preliminaries and problem formulation. Section 3 proposes the impulsive multiple-bipartite consensus control schemes for second-order MASs and carries out the stability analysis. The simulations and conclusions are presented in Section 4 and Section 5, respectively.
# Effective Seed Scheduling for Directed Fuzzing with Function Call Sequence Complexity Estimation
## 4 Implementation
### Instrumentation  
Regarding selective instrumentation, we keep the collection of functions identified in SelectFuzz and selectively instrument them to gather data on the code and function coverage. Functions not included in this collection remain intact but are excluded from the code coverage measurement process. Consequently, they remain executable. The process of instrumentation is facilitated by making adjustments to AFL's LLVM-based compiler.  
To obtain the function sequence and coverage information, we need to obtain the execution path exercised by the test cases. We record that the execution path consists of basic blocks in a new shared memory called path_bits. Specifically, regarding the performance overhead, large memory allocations bring a significant time overhead to the memset function, which is executed frequently. Therefore, the size of the path_bits cannot be too large. Considering that the length of the path to be recorded cannot be too small and the size of the basic block id is 2 bytes, we set the length to 217 (131,072).  
We modify the way to generate the random number of the basic block during the instrumentation. We design the data stored in the path_bits. We allocate a memory region of 8 bytes for each basic block, where the upper 4 bytes are utilized to store the id of the current function, while the lower 4 bytes are employed to store the id of the current basic block. We modify the afl-fuzz.c, afl-llvm-pass.so.cc, and afl-llvm-rt.o.c to implement the recording logic. Two new global pointers pointing to func_bits and a counter of recording the current position in func_bits are set respectively in afl-llvm-pass.so.cc.