esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Acceleration via LP Relaxation  
The computational intensity associated with UCP solutions has driven the development of sophisticated LP relaxation methodologies aimed at balancing solution quality and computational efficiency. These techniques have evolved beyond traditional approaches, incorporating innovative strategies to enhance both the quality of initial solutions and the efficiency of the optimization process.  
Contemporary research has demonstrated significant advancements in this domain. Recent studies [16] have introduced refined problem formulations combined with intelligent repair mechanisms for continuous relaxation variables, establishing a foundation for iterative solution enhancement. A particularly noteworthy approach [10] has emerged, focusing on strategic variable fixation through a heuristic analysis of commitment and generation variables, effectively reducing computational complexity while maintaining solution integrity. The field has further progressed with the introduction of advanced neighborhood search algorithms [17] that incorporate sophisticated relaxation-inducement techniques. Additionally, novel iterative methods [18] have been developed, combining localized branching strategies with targeted neighborhood exploration to optimize solution quality.  
Reference [19] solves multiple UCPs that share the same feasible region as the original problem but have slightly different objective functions. The method determines stable variables by averaging the LP relaxation solutions and enhances computational efficiency through an adjoint model without sacrificing optimality.
## 3 Performance Analysis  
### Ber  
In the MCIM-SR-DCSK-CC system, the overall BER is concluded by the BER of the polarity-modulated bits, the BER of the code index bits, and the BER of the code-modulated bits. Therefore, the BER expression of the proposed system can be obtained as  
\[P_{sys}=\frac{1}{m_{ci}+m_{cm}+1}P_{pm}+\frac{m_{ci}}{m_{ci}+m_{cm}+1}P_{ci}+ \frac{m_{cm}}{m_{ci}+m_{cm}+1}P_{cm} \tag{17}\]  
where \(P_{pm}\), \(P_{ci}\), and \(P_{cm}\) are defined as the BERs of the polarity-modulated bits, the code index bits, and the code-modulated bits, respectively. \(m_{ci}\) is the number of the code index bits. \(m_{cm}\) is the number of the code-modulated bits.
# Coastal Zone Classification Based on U-Net and Remote Sensing
## 2 Experimental Methods  
### Study Area and Experimental Environment  
#### 2.1.1 Study Area and Datasets  
The selected study area is Shuangyue Bay (\(114.553^{\circ}\)-\(115.420^{\circ}\) N, \(22.544^{\circ}\)-\(23.388^{\circ}\) E), a typical coastal zone in Guangdong Province, China. Located in Huidong County, Huizhou City, Eastern Guangdong, Shuangyue Bay is renowned for its distinctive dual crescent-shaped bays, creating a unique and picturesque coastal landscape. This characteristic makes it an ideal case for studying coastal geomorphology [63]. Like many coastal zones, Shuangyue Bay faces environmental pressures from human activities such as tourism, fishing, and urbanization, making it a representative area for studying the impact of human activities on coastal environments and developing management strategies [64]. The total length of the continental coastline in the study area is approximately 16 km, with a land area of about 55 km\({}^{2}\) (Figure 1). A scene of GF-2 high-resolution remotely sensed data, captured on 26 January 2017, was selected as the data source for this study ([[https://data.cresda.cn/#/2](https://data.cresda.cn/#/2) dMap]([https://data.cresda.cn/#/2](https://data.cresda.cn/#/2) dMap)).  
Figure 1: Study area. (**I**) Mixed forest and farm land areas. (**II**) Woodland-dominated areas. (**III**) Low-density artificial surface areas. (**IV**) High-density artificial surface areas. (**V**) Mixed land and water boundary zone.  
The GF-2 remote sensing satellite provides the highest resolution data available for civil land observation in China. It is equipped with four multispectral and one panchromatic image acquisition sensor. The multispectral image resolution is 4 m, covering three visible spectral bands (blue, green, and red) and one near-infrared band, with a 10-bit radiometric resolution. The wavelength ranges for the visible and near-infrared bands are 0.45-0.52 \(\upmu\)m, 0.52-0.59 \(\upmu\)m, 0.63-0.69 \(\upmu\)m, and 0.77-0.89 \(\upmu\)m, respectively. The panchromatic band has a resolution of 1 m and a wavelength range from 0.45 to 0.90 \(\upmu\)m [65; 66].
###### Abstract  
Incorporating fibers into asphalt mixtures as additives and stabilizers can significantly enhance the performance of asphalt pavements. This study aimed to analyze the impact of using basalt and bamboo fibers as modifiers on the properties of asphalt mastics. The effects of different types of fibers on rutting resistance, fatigue resistance, elastic recovery, and low-temperature cracking performance were tested using frequency scanning, linear amplitude scanning (LAS), multiple stress creep and recovery (MSCR), elastic recovery, and bending beam rheometer (BBR) experiments. The study results suggest that adding fibers into asphalt mastics can effectively improve their stiffness, and the higher the fiber content, the better the stiffness enhancement. Moreover, the characteristic flow index of asphalt mastics grows gradually with the rise in temperature, indicating that these materials exhibit near-Newtonian fluid behavior at elevated temperatures. Furthermore, incorporating fibers significantly enhances the high-temperature rutting resistance of asphalt mastics. However, the addition of fibers did not demonstrate any appreciable benefits in terms of fatigue resistance. The elasticity of asphalt mastics cannot be significantly changed by fiber content without compromising their elastic recovery. Surprisingly, the study's findings showed that adding basalt fibers to asphalt mastics did not improve their resistance to low-temperature cracks. On the other hand, it was discovered that the ability of asphalt mastics to resist cracking at low temperatures could be made up for by the use of bamboo fibers as a modifier together with a raised temperature. Overall, it was discovered that bamboo fibers performed better than basalt fibers at improving the performance of modified asphalt mastics.  
asphalt mastic; basalt fiber; bamboo fiber; rutting; fatigue Article
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 3 Signal Processing
### Signal Model
Alternatively, the signal components with frequencies satisfying \(|f|\leq f_{s}\) (\(1-\alpha\)) can be extracted from each TV channel, i.e., only the flat spectrum can be used, to reduce the waveform influence of the used satellite digital TV signal (the amplitude-filtered spectrum), as indicated by Equation (9). However, this will also decrease the output SNR as the integration gain generated by the matched filtering is reduced. Therefore, in the concept-proof study stage, Equation (15) is used for the following process without considering the spectrum properties of the satellite digital TV signal.
## 5 Results And Discussion
### Vulcanization Characteristics  
By vulcanization, we mean a set of chemical reactions that convert NR into cross-linked elastomers. CMPTD, CMCS, COT samples and the reference formulation BC were vulcanized and the set of reactions rheometrically monitored. Studied parameters include \(t_{02}\) as scorch time, \(t_{90}\) as final vulcanization time, \(\Delta M\) as cross-link density, \(R_{E}\) as absorption of energy and \(\rho\) as material compactness, experimental results are collected in Table 3. As can see, all formulations exhibit \(t_{02}\) and \(t_{90}\) significantly lower than BC (especially the COT sample), indicating that leather waste performs the action of the accelerator. Collagen is a fibrillary protein, which also contains sulfurous amino acids such as cystine and methionine, therefore it is a source of additional sulfur. As soon as temperature exceeds 140 \({}^{\circ}\)C, sulfur released by amino acids is added to the vulcanization sulfur, hence the process takes place with higher sulfur content. Consistently, all formulations display a greater CRI than BC reference. From inspection of Table 3 one finds that \(\Delta M\), for CMTPD and CMCS compounds, is slightly lower than BC while for the COT formulation it turns out to be significantly lower than BC. The introduction of essential main chain modification reduces the resilience. Thus, data in Table 3 indicate that in all formulations the leather waste introduces modifications along the polymer chains, resulting in a  
Figure 1: Morphology of leather waste used to prepare rubber compounds. **(a)** post-tanning dyeing (PTD) non-shredded sample; **(b)** PTD shredded sample; **(c)** oil tanning (OT) sample.  
lower \(R_{E}\) value, compared to BC. CMPTD and CMCS compounds exhibit reactivity and mechanical properties similar to each other, but quite different from COT (see Table 3 and Figure 3). The different behavior is due to the specific tanning processes: fish oil for COT and chromium for PTD and CS waste. The two types of tanning are illustrated in Figure 2. In OT trimmings, a fraction of the unreacted tanning oil remains free, oil that can be solvent-extracted from the tanned leather (Figure 2). Although the action of tanning oil on collagen is not yet fully clarified, it is well-known that the oil reacts with some proteins to form aldehydes reacting with collagen [27]. During oxidation, the oil polymerizes to form a film adhering to the fibers increasing probability of creating cross-linked networks, as illustrated in Figure 2.
## 7 Results
### Channel-Specific Accuracy
For the aforementioned reasons, a reasonable approach for channel sensing could be to perform the sensing on Channel 20 or on the adjacent channels in order to maximize the classification accuracy. Figure 8: Location B, multiple IEEE 802.11 access points operating on Channels {1, 6, 7, 11}. (**a**) Channel-specific detection rate at Location B for a sampling window length of 150 ms.; (**b**) IEEE 802.11 power spectral density (PSD) as observed by the channel analyzer.
## 4 Experiments and Analysis
### Pruning Performance Comparison  
In order to further verify the efficiency of the FPRL method, the compression performance of the N2N [36] method is used for comparative analysis. The results are shown in Table 3.  
From Table 3, the proposed FPRL method achieves a much higher compression rate while reserving higher network accuracy after pruning than N2N on both datasets and all the networks. Taking the ResNet-18 network as an example, on the CIFAR100 dataset, N2N achieves 4.64\(\times\) parameter compression and the accuracy is reduced by 4.21%, while FPRL achieves 10.93\(\times\) parameter compression, and the network accuracy after pruning is only reduced by 2.04%. There are cases where the decrease in accuracy by the proposed FPRL method is greater than N2N. For example, on the CIFAR10 dataset, N2N achieves 11.10\(\times\) parameter compression for the ResNet-18 network and the accuracy is increased by 0.18%. This is due to the fact that the base networks by N2N are under-fitted, resulting in very low initial accuracy. The comparison results highly support that the proposed FPRL method can efficiently compress DCNNs with only a small decrease in network accuracy.
# Parameter Optimization for Low-Rank Matrix Recovery in Hyperspectral Imaging
## 3 Results
### Parameter Analysis for \(r\)  
The rank parameter \(r\) describes the upper bound for the rank of the low-rank matrix describing the noise-free signal of the data cube. The value should be as small as possible while not underestimating the noise intensity of the image data. Small values provide shorter computational times, while larger values provide  
In the following, we present the PSNR, the gradient of the PSNR denoted as \(\
abla\)PSNR, and computational times in seconds in MATLAB and Python, denoted as \(t_{Matlab}\) and \(t_{python}\), respectively, in Table 1. Lower rank values are better in terms of computational effort.  
The computational effort is significantly more efficient in MATLAB. The analysis for Indian Pines suggests choosing \(r=5\). This parameter choice provides a sufficient balance between PSNR and computational time.
## 3 Results  
The means and standard deviations for all variables are presented in Table 1 with a graphical presentation of percentage difference between experimental conditions and control in Figure 2. For non-normally distributed data (i.e., throws in set 1, throws in set 2, throws in set 3, total throws, and left-hand grip strength), data are presented as median and interquartile range. The mean and standard deviation of the non-normal data are as follows: throws in set 1 (control: 4.40 \(\pm\) 0.52; squat: 5.00 \(\pm\) 0.94; ballistic jumps: 4.90 \(\pm\) 0.57), throws in set 2 (control: 8.20 \(\pm\) 0.63; ballistic jumps: 9.30 \(\pm\) 0.82), throws in set 3 (control: 8.70 \(\pm\) 0.48; ballistic jumps: 9.00 \(\pm\) 0.82), total throws (ballistic jumps: 23.20 \(\pm\) 1.62), and handgrip strength of left hand (squat: 27.9 \(\pm\) 6.8). In the SJFT, the results revealed significant differences between the experimental conditions for throws in set 2 (F = 16.46; \(p\)! 0.001, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.65, large ES), total throws (F = 20.62; \(p\)! 0.001, \(\eta_{\mathrm{p}}\)\({}^{2}\)= 0.70, large ES), and the SJFT index (F = 16.34; \(p\)! 0.001, \(\eta_{\mathrm{p}}\)\({}^{2}\) = 0.65, large ES). Both the squat and ballistic jump exercises significantly improved performance compared to the control condition. For throws in set 2, significant improvements were observed following both the squat (\(p\) = 0.003, Hedge's g = 1.78, large ES) and ballistic jump exercises (\(p\) = 0.010, Hedge's g = 1.44, large ES). Similarly, total throws were significantly higher in the squat (\(p\) = 0.003, Hedge's g = 1.51, large ES) and ballistic jump (\(p\)! 0.001, Hedge's g = 1.37, large ES) conditions compared to the control. The SJFT index also showed improvements for both the squat (\(p\) = 0.010, Hedge's g = 0.80, moderate ES) and ballistic jump (\(p\)! 0.001, Hedge's g = 0.90, moderate ES) conditions. However, no significant differences were found between the squat and ballistic jump conditions for the throw in set 2, total throws, or the SJFT index. Additionally, there were no significant differences in throws in set 1, throws in set 3, or heart rate measurements (immediate and one-minute post-test) among the three experimental conditions. Regarding handgrip strength, a significant reduction in right-hand grip strength was observed following the squat condition compared to both the control (\(p\) = 0.008, Hedge's g = 0.19, trivial ES) and the ballistic jump condition (\(p\) = 0.014, Hedge's g = 0.23, small ES), while no differences were found between ballistic jumps and control for right-hand grip strength. There were no significant differences in left-hand grip strength or RPE across any of the conditions.
# Drag Reduction in Compressible Channel Turbulence with Periodic Interval Blowing and Suction
## 3 Results and Discussions
### Effect of Injected Mass Flow Rate and Kinetic Energy  
In order to further verify the dominant role of the injected mass flow rate in drag reduction, Table 3 gives a comparison of the supplementary cases C11 and C12 with C2 and C7 at \(Ma=0.8\) and \(Ma=1.5\), respectively. Global blowing/suction with a small \(I\) is implemented in C2 and C7, so they are chosen as the reference cases. Take C11 and C2 for example: the wall density \(\rho_{\text{w}}\) in both cases is 1.014. On the basis of Equation (16), the blowing velocity \(v_{\text{w}}\) in C11 is four times that in C2, but the blowing area in C11 is 1/8 of that in C2; hence, the injected kinetic energy in C11 is four times that in C2. As for the injected mass flow rate, owing to \(\dot{m}\propto\alpha\cdot I\), \(\dot{m}\) in C11 is half of that in C2. The settings of C11 guarantee that the wall mass flow rate of C2 is higher and the injection kinetic energy of C11 is greater. Apparently, the drag reduction rate \(DRR_{\text{B}}\) and the net energy-saving rate \(S\) in C2 are almost twice that in C11, indicating that the drag reduction is strongly related to the injected mass flow rate instead of the injected kinetic energy. The result of \(G\) reveals that the case with a greater mass flow rate has a higher control gain, but the average drag reduction rate shows little distinction. Table 2 illustrates that the wall mass flow rates in C2\(\sim\)C5 are the same, but the input power for C3, C4, and C5 is 4, 9, and 25 times more than that in C2, respectively. The drag reduction effect reveals that these four controlled cases are nearly identical, with a minimal difference between \(DRR_{\text{B}}\) and \(S\), suggesting that the injected kinetic energy variation in these cases is negligible relative to the drag reduction impact. This reiterates that the wall mass flow rate is the critical determinant for the micro-blowing/suction control effect. A similar conclusion is also found in C7 and C12 at \(Ma=1.5\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Case Number** & \(Ma\) & \(I\) & \(\alpha\) & \(\begin{array}{c}C_{\text{f,B}}\\ (\times 10^{-2})\end{array}\) & \(Re_{\tau,\text{B}}\) & \(\begin{array}{c}DRR_{\text{B}}\\ (\%)\end{array}\) & \(\begin{array}{c}DRR_{\text{S}}\\ (\%)\end{array}\) & \(\begin{array}{c}DRR_{\text{S}}\\ (\%)\end{array}\) & \(\begin{array}{c}S\\ (\%)\end{array
## 5 Experimental Results
### Time and Space Complexity Analysis of the AFF_CGE Model  
This paper conducts an in-depth analysis of the time and space complexity of models during the training and testing phases, aiming to evaluate the applicability of the AFF_CGE model in real-time detection scenarios. Specifically, we provide a comprehensive comparison of the proposed AFF_CGE method against a baseline model regarding their time and space complexities.  
Experimental Setup and Method Overview:  
Baseline (BS-F + XGBoost): The base feature set (BS-F) is directly input into the XGBoost classifier for training and testing.  
AFF_CGE + XGBoost: This method comprises two phases. In the first phase, the base feature set (BS-F) is processed through the integrated AFF_CGE model to generate new feature representations of encrypted traffic. In the second phase, the learned feature representations are fed into the XGBoost classifier for training and testing.  
Metrics for Complexity Analysis: To thoroughly assess the time and space complexities of the models, we employ the following key metrics:  
* Time: The duration required for the model to complete a full training cycle or predict all test samples, measured in milliseconds (ms).
* Processing Speed: The number of samples the model can process per second during training or testing, measured in samples per second.
* Memory: The peak memory usage of the model during training or testing, measured in megabytes (MB).  
Experimental Environment and Data: The experiments were conducted on a CentOS Linux release 7.9 system equipped with 64 GB of RAM and an Intel(R) Core(TM) i9-12900K processor (Intel, Santa Clara, CA, USA). The training phase utilized traffic data from 5438 IPs, while the testing phase employed traffic data from 3628 IPs.  
The experimental results, as shown in Table 7, indicate that during the training phase, although the AFF_CGE method exhibits increased time and space complexity compared to the baseline model, its resource demands remain within a manageable range, easily handled by a standard PC (with a single training cycle requiring only 291 ms and a peak memory usage of 578.54 MB). In the testing phase, the AFF_CGE model also demonstrates exceptional real-time detection capabilities, with a maximum memory usage of only 578.54 MB and a total processing time of just 53.6 ms for all test samples. Notably, the feature representation extraction phase can process 154,540 samples per second, while the detection phase reaches up to 1,047,637 samples per second, clearly demonstrating its efficiency and usability in large-scale traffic analysis tasks.
# Adaptive Speech Separation Based on Beamforming and Frequency Domain-Independent Component Analysis
## 6 Conclusions  
We propose an adaptive and fast speech separation based on ICA and beamforming. The maximum number of mixed speech components per frequency bin is determined by using a positioning algorithm to prevent false sound sources. Then, the separation matrix of ICA is initialized according to sound source localization to notably reduce the iteration time and avoid ambiguity of permutation. PCA dimension reduction allows weighing the beamforming and ICA application for speech separation in real environments. Simulation and experimental results of multiple tests verify the high performance of the proposed algorithm.  
The beamforming algorithm in the proposed method only uses the simple DSB-based approach when ICA is not necessary. In future work, we will explore methods to improve the algorithm performance when sound sources are located as similar angles, which is a common situation in real environments.  
[PERSON] and [PERSON]. ([PERSON]) conceived and designed the study. [PERSON] and [PERSON] performed the experiments. [PERSON] and [PERSON]. ([PERSON]) wrote the paper. [PERSON], [PERSON] ([PERSON]), [PERSON] and [PERSON]. ([PERSON]) reviewed and edited the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China, grant number2018 YFB1307500, the Natural Science Foundation of China under Grant, grant number 61973059 and 61532007.  
The authors declare no conflict of interest.
## 2 RPC Model and Bundle Adjustment  
As a projection model commonly used in commercial satellite imagery, the RPC model is very important for DSM generation from satellite images. The proposed DSM generation method refines the parameters of the RPC model based on an improved bundle adjustment optimization algorithm, so this section will briefly introduce the principles of the RPC model and the bundle adjustment optimization algorithm.