esa-sceva/satcom-chunk-collection huggingface dataset:
# A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems
## 5 Experiment Results
### Experimental Configuration  
We evaluate our algorithms and competing algorithms on three benchmark problems as follows: (i) **Random Graphs**: we refer to the Erdos-Renyi model [42] to provide two random graphs, sparse random graphs (edge probability 0.1) and dense random graphs (edge probability 0.6); (ii) **Scale-free Networks**: we use the Barabasi-Albert (BA) [43] model to generate scale-free networks. Its parameters are \((m_{1}=15,m_{2}=7)\); and (iii) **Small-world Networks**: we use the Watts-Strogatz topology model [44] to generate small-world networks. The number of nearest nodes is 6 and the reconnect probability is 0.5.  
We set the number of agents from 50 to 100 for three benchmark problems, and the quantity interval was 10. Although PLSA and PGSA can use any form of utility functions (refer to Figure 1), we followed reference [36; 38] and used the utility function in the form of \(ax^{2}+bx+cxy+dy+ey^{2}+f\) to fairly present the comparison results, where \(a,b,c,d,e\), and \(f\) are random numbers in the range \([-5,5]\). In addition, we set the domains of all variables to \([-50,50]\) and the number of iterations to 500. For each experimental configuration, we independently ran each algorithm 30 times and took the average as the experimental result. The experiments were carried out on a computer equipped with Intel(R) Core(TM) i5-10500 CPU, 3.10 GHz processor, and 8 GB RAM.  
We considered three important parameters of PLSA, \(\lambda,T,andP_{m}\), and refer to reference [38] to set \(P_{m}=0.01\). For the parameters \((\lambda,T)\), we discretize \(\lambda\) into 0.01-0.9 and \(T\) into 50-100 to find a combination with the best solution quality on dense random graphs (\(|A|=100\)). Figure 3 shows the relative quality of solutions with different \(\lambda\) and \(T\), where we normalize the solution quality by _max-min normalization_ to present the difference. Specifically, the relative quality is \((|Q|-|Q_{max}|)/(|Q_{min}|-|Q_{max}|)\), where \(Q\) is the solution quality, \(Q_{max}\) and \(Q_{min}\) are the maximum and minimum solution quality, respectively. It is obvious from Figure 3 that the best combination \((\lambda,T)\) is \((0.9,100)\). In addition, we set \(K\) to 1000. The parameter configuration of the competing algorithms can refer to HCMS [36], PCD [35], C-CoCoA [37], and PCD-LD [38]. It is worth mentioning that we record the best solution found on the empirical evaluation for each algorithm.
## 5 Conclusions  
In this study, a relationship between crack aspect ratio and crack density was derived through combination of several constitutive equations, and subsequently verified by matching predicted and observed results from weathering experiments. The detailed conclusions that can be drawn from this study are summarized below.  
* Various parameters included in two constitutive equations were rearranged and simplified to define elastic wave velocity, with aspect ratio and crack density as the main variables to be solved.
* Ten rock columns were each subjected to three chemical and nine physical experimental weathering cycles. The elastic wave velocity was continually measured in each column, allowing predictions of their evolving aspect ratio and crack density.
* The reliability of the calculated aspect ratio and crack density data was verified by measuring the ratio of crack porosity to total porosity. These data allowed for the formulation of a relationship between aspect ratio and crack density that may be applied to a broad spectrum of samples.  
Funding: This research is funded by the Korea Agency for Infrastructure Technology Advancement (18 CTAP-CI45146-01).
## 5 Improved Tracking Differentiator (SYSTD)
### Parameter Tuning Method of SYSTD
It can be seen that increasing \(M_{1}\) can increase the cut-off frequency of the system. However, an excessively large \(M_{1}\) may lead to over-shoot in the high-frequency band of the system, causing survey errors for effective signals and weakening the filtering performance and stability of SYSTD. 3. Influence of parameter \(M_{2}\) on the frequency characteristics. First, fix \(M_{1}\) and \(R\), change \(M_{2}\), and then draw the Bode diagram of SYSTD as shown in Figure 8c. An increase in \(M_{2}\) is beneficial for improving the dynamic response of SYSTD. However, there may be a problem of over-shoot. Moreover, the impact of \(M_{2}\) on the dynamic performance of SYSTD is more obvious. Figure 8: Frequency characteristics of SYSTD under different parameters. (**a**) \(R\) changes independently. (**b**) \(M_{1}\) changes independently. (**c**) \(M_{2}\) changes independently. It can be seen from the above analysis that, in order to effectively extract the survey signal, it is necessary to first select an appropriate value of \(R\) so that the bandwidth of SYSTD can cover the spectrum of the survey signal; then, select appropriate \(M_{1}\) and \(M_{2}\) to satisfy Equation (48) and ensure the stability of the system; then, adjust \(M_{1}\) and \(M_{2}\) to further balance the contradiction between the filtering ability and the fast response of SYSTD; finally, combined with the basic constraint of Equation (40), select appropriate \(a_{41}\), \(a_{51}\), \(a_{42}\), and \(a_{52}\) to meet the requirements of \(M_{1}\) and \(M_{2}\). Generally, the parameters \(a_{51}\) and \(a_{52}\) are adjusted first to adjust the amplitude of SYSTD; then, the parameters \(a_{41}\) and \(a_{42}\) are adjusted to adjust the fast-tracking ability of SYSTD.
## 4 Results
### Performance Evaluation  
We used the five-fold stratified cross-validation (5-scv) procedure, due to the imbalance ratio of some datasets [49]. As a performance measure, we used the average classifier accuracy of the folds. The process was repeated 10 times.  
This algorithm was implemented and tested in a personal computer with the following specifications: Intel Core i7 970 3.20 Ghz, 24 Gb RAM memory, Windows 8.1 Pro 64 bits, Hard drive 1 TB.  
We tested the AISAC model in two scenarios: with respect to other immune-based learning classification systems (Section 4.4.1) and with respect to well-known learning classification systems (Section 4.4.2). In both scenarios, we used the Wilcoxon test [50] to establish the existence or lack thereof of significant differences in performance among the compared algorithms. We used a significance level of 0.1, for a 90% confidence interval. This particular statistical comparison was suggested in [51].
## 2 Results and Discussion
### Statistical Analysis
#### 2.3.1 Comparison of the Extraction Methods with the Solventless Method
For the SLE and MAE methods, at least a weak difference effect was observed between the SLE1-1 and MAE4 measurements compared to the SPME method (\(d>0.20\)). This may mean that unidentified compounds may show a lower intensity level with the third and fourth extraction methods.
# An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
## 3 System Model  
### Network Model  
In this paper, the network is composed of numerous sensors as well as a BS, as shown in Figure 1. Some physical information of the sensor field such as temperature and humidity are detected by sensors and they transmit their monitored data to its corresponding CHs. There are two types of roles for the sensors to play. Member nodes need to monitor the surroundings and send the monitored data to corresponding CHs. CHs not only need to detect the information of the environment, but also need to receive the data packages from their members and conduct data fusion. Finally, the fused data is uploaded to the BS by the CHs. The following assumption are made to conduct the simulation conveniently.  
1. All the sensors are deployed in a rectangle area by planes or other vehicles and they keep stationary after they are deployed.
2. Sensor nodes can be identified by their unique ID.
3. Each sensor owns the knowledge of its position by the equipment such as the Global Positioning System (GPS), and they can get the information of other nodes by information exchange.
4. All the sensors own the same initial energy and their batteries cannot be changed. Once they exhaust their energy, they will be useless.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Seismic Behavior of Triple Tunnel Complex in Soft Soil Subjected to Transverse Shaking
## 5 Parametric Study
### Effect of the Variation in Lining Thickness  
In order to study the effect of tunnel lining thickness, the tunnel lining thickness is kept variable from 0.4 m to 0.8 m, which constitutes 1.63% to 3.26% of the total tunnel width. All the other factors, i.e., inner structure's thickness, the amplitude of the IM and the C/H ratio are kept constant and the overall surface displacements are calculated. Apart from this, the normalized tunnel deformations and the bending moments are also computed. Normalized tunnel deformation is defined as the ratio of distortions between the top and bottom parts of the tunnel complex to the distortions at the same points in the free-field. This parameter is important in determining the flexibility of the tunnel lining. From the results shown in Figure 4b, it can be depicted that the normalized tunnel deformations are almost equal to 1 when the lining thickness is 1 m. It means that the lining thickness greater than 1 m would behave as rigid while a lesser thickness would act as flexible. The increased lining thickness causes a reduction in the flexibility of the tunnel and resistance to deformations increases. It also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**No.** & **Earthquake** & **Station** & **Year** & **Magnitude** & **Epicenter** & **Peak Ground** & **Peak Ground** \\
**Distance** & **Acceleration** & **PGA (g)** & **PGA (g)** & **PGA (m/s)** \\ \hline
1 & Coyote, USA &
\begin{tabular}{c} San Juan \\ Bautista \\ \end{tabular} & 1979 & 5.7 & 17.2 & 0.124 & 0.176 \\
2 & Kocaeli, Turkey & Arcelik & 1999 & 7.4 & 17 & 0.218 & 0.177 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input motion records.  
Figure 3: (**a–c**) Acceleration-Time history, the Fourier amplitude, and the spectral acceleration of (**a**) Coyote earthquake (1979), (**b**) Kocaeli earthquake (1999), and (**c**) comparison of the normalized spectral accelerations with the design spectrum of site class A (EC8).  
results in the reduced surface heave with increased resistance to seismic-induced bending moments, which can be seen in Figure 4a-d. The results show a similar trend obtained in the reference study [16] and by [PERSON] and [PERSON] [8]. From the figures, it is evident that the tunnel complex with thicker lining has greater resistance to the seismic-induced uplift, distortions, and bending moments. From the study, it was also noted that reduction in lining thickness from 0.4 m to 0.3 m made the tunnel complex very fragile and resulted in the collapse of the structure.  
Figure 4: \(Cont.\)
# Anti-Atopic Dermatitis Activity of _Cornus walteri_ and Identification of the Bioactive Compounds
## 2 Results and Discussion
### Isolation and Identification of the Compounds  
The bioactive MeOH extract of CWS was sequentially employed for preparative HPLC to obtain four fractions (F1-F4) according to the separation of the peaks. Chemical analysis of the F4 fraction was performed by repeated process of column chromatography and semi-preparative HPLC purification, which yielded six compounds (Figure 3). The structures of the isolated compounds (Figure 4) were determined to be lupeol (**1**) [28], benthic acid (**2**) [29], \(\alpha\)-stigmast-3,6-dione (**3**) [30], 3-\(O\)-acetylbetulin (**4**) [31], benthic acid methyl ester (**5**) [32] and norphytan (**6**) [33] by the interpretation of NMR spectroscopic data and comparing their NMR data (Figures S1-S8) with previously reported data and MS data obtained by LC/MS analysis.  
Figure 1: **Effect of CWS treatment on AD chemokines in human keratinocytes (HaCaT cells).** (**A**) Cell viability was measured after treatment of CWS (0, 2.5, 5, 10 and 20 \(\mathrm{\SIUnitSymbolMicro g}\)/mL) for 48 h. (**B**) HaCaT cells were treated with varying concentrations of CWS and induced with IL-4/IL-13 (50 \(\mathrm{\SIUnitSymbolMicro g}\)/mL each). The production ofotaxin-3 in culture supernatants was measured by ELISA (\(n=3\)). (**C**) HaCaT cells were treated with varying concentrations of CWS and induced with IFN-\(\gamma\) (100 \(\mathrm{\SIUnitSymbolMicro g}\)/mL). The production of TARC in culture supernatants was measured by ELISA (\(n=3\)). The data are expressed as the mean \(\pm\) standard deviation \(\#\#\)\(p<0.001\) indicate a significant difference between the induced group and untreated control; \(***\)\(p<0.001\) indicate a significant difference between the sample treatment group and induced group.  
Figure 3: Separation scheme of compounds **1**–**6.  
Figure 2: **Effects of CWS on mRNA expression of AD-related skin structural protein and chemokine in HaCaT cells.** Cells were treated with varying concentrations of CWS and induced with TNF-\(\alpha\) and IFN-\(\gamma\) (10 ng/mL each) or TNF-\(\alpha\) only (10 ng/mL). The mRNA expression levels of (**A**) FLG and (**B**) MDC were examined by RT-PCR. The data are expressed as the mean \(\pm\) standard deviation *** \(p<0.001\) indicates a significant difference between the induced group and untreated control; *** \(p<0.01\), *** \(p<0.001\) indicates a significant difference between the sample treatment group and induced group.
## 2 Physics-Driven Models of Sound Transmission Loss
### Methodology of the STL Analyses
#### 2.1.2 Correction Factor  
Based in the Rayleigh-integral method, [PERSON] et al. [27] propose to apply a correction factor to the acoustic transparency of the infinite plate \(\tau_{\infty}\) to account for effects of the plate size, such that the acoustic transparency of a finite plate could be approximated by:  
\[\tau_{fin}=(\sigma_{R}\cos\theta)\tau_{\infty}, \tag{19}\]  
where the radiation efficiency \(\sigma_{R}\) is given by  
\[\sigma_{R}(\theta,\phi,a,b)=\frac{\Re(Z_{fin}(\theta,\phi,a,b))}{\rho_{0}c_{0}}. \tag{20}\]  
The structural impedance \(Z_{fin}\) is evaluated as:  
\[Z_{fin}=\frac{j\rho_{0}\omega}{S}\int_{S}\int_{S}e^{-i(k_{x}x+k_{y}y)}G(x,y;x ^{\prime},y^{\prime})\times\ e^{-i(k_{x}x^{\prime}+k_{y}y^{\prime})}dxdydx^{ \prime}dy^{\prime}, \tag{21}\]  
where \(S\) designates the surface of the panel, and \(G\) is the half-space Green's function. [PERSON] et al. [28] proposes analytical simplifications, so that \(\sigma_{R}\) simplifies into:  
\[\sigma_{R}(\theta,\phi,a,b)=\frac{\omega b}{16c_{0}}\int_{-1}^{1}\frac{a}{b}F (\theta,\mu(\phi),a,b)d\mu, \tag{22}\]  
where \(\mu=(4\phi/\pi-1)\). This equation can be evaluated using Gaussian numerical integration. The function \(F(\theta,\mu(\phi),a,b)\) and the details of its derivation are found in [28]. Although the correction factor approach is a simplified approach, the resultant modified STL curve is coherent with experimental curves with band average [23].
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
###### Abstract  
The performance of classic radar geometry based on the line-of-sight (LOS) signal transmitted from radar to the target in the free space is affected by multipath echoes in urban areas, where non-line-of-sight (NLOS) signals reflected by obstacles are received by the radar. Based on prior information of the urban situation, this article proposes a novel two-stage localization algorithm with multipath exploitation in a dense clutter environment. In the offline stage, multipath propagation parameters of uniformly distributed samples in the radar field of view are predicted by the ray-tracing technique. In the online stage, a rough location of the target is estimated by the maximum similarity between measurements and the predicted parameters of reference samples at different locations. The similarity is described by the likelihood between measurements and the predicted multipath parameters with respect to all possible associated hypotheses. A gating threshold is derived to exclude less likely hypotheses and reduce the computational burden. The accurate target location is acquired by a non-linear least squares (NLS) optimization of the associated multipath components. Simulation results in various noise conditions show that the proposed method provides robust and accurate target localization results under dense clutter conditions, and the offline pre-calculation of ray-tracing ensures the real-time performance of the proposed localization algorithm. The root mean square error (RMSE) of simulation results shows the advantage of the proposed method over the existing method. The presented results suggest that the proposed method can be applied to NLOS target localization applications in complex environments.  
m +
Footnote †: journal: Applied sciences  
ultipath exploitation; non-line-of-sight (NLOS); target localization; data association; non-linear least squares (NLS) localization; dense clutter
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 4 Experiment and Results
### Ablation Experiments on LEVIR-CD
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Parameters** & **PR (\%)** & **RC (\%)** & **OA (\%)** & **Kappa (\%)** & **IoU (\%)** & **F1 (\%)** \\ \hline \(a=0.6,b=0.3\) & 92.20 & 90.78 & 99.34 & 91.15 & 84.33 & 91.49 \\ \(a=0.6,b=0.5\) & 92.45 & 91.02 & 99.36 & 91.23 & 84.47 & 91.73 \\ \(a=0.5,b=0.4\) & 92.92 & 90.89 & 99.38 & 91.46 & 84.77 &
## 1 Introduction
To eliminate these drawbacks, alternative media with the reduced or zero fluoride content for the production of silicon are being investigated. In particular, this concerns the KCl-KI-KF-K\({}_{2}\)SiF\({}_{6}\)[32; 33] and CaCl\({}_{2}\)-CaO-SiO\({}_{2}\) melts [34; 35; 36; 37], although their use also implies the absence of moisture in the reactor and increased requirements for the preparation of molten electrolytes and instrumentation. In previous works, we have shown the fundamental possibility of obtaining nanosized silicon fibers by electrolytic refining of silicon in a low-fluoride KCl-K\({}_{2}\)SiF\({}_{6}\) system [38; 39]. The advantage of this system is that its anionic composition virtually does not change. That provides stabilization of the energy characteristics of silicon-containing electroactive ions and possibility of controlling the morphology of silicon deposits. Preliminary electrolysis tests [38; 39] show that it is difficult to obtain nanosized silicon deposits from molten salts. In particular, this is due to the varied concentration of silicon-containing ions in the melt, the semiconducting nature of silicon, and increased sensitivity of the deposit morphology to oxygen impurities. Despite the active interest in Si-based anodes for LIBs [3; 4; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 40], the works devoted to the utilization of electrolytic silicon deposits as the LIB anode material are limited [41; 42; 43; 44; 45; 46]. The highest characteristics for silicon obtained by electrodeposition are on carbon cloth. The capacity was more than 1100 mAh\(\cdot\)g\({}^{-1}\) by the 200 th cycle at a discharge current of 500 mA\(\cdot\)g\({}^{-1}\), while the silicon nanopowder itself, without the use of carbon fabric, demonstrates very low capacity--less than 500 mAh\(\cdot\)g\({}^{-1}\) by the 5 th cycle and less than 200 mAh\(\cdot\)g\({}^{-1}\) by 200 th cycle [40]. In recent years, most researchers are focused on silicon electrodeposition from CaCl\({}_{2}\)-based melts. In work [42] carried out by researchers from the USA and China, electrodeposited nanofibers in LIB demonstrate a capacity of 714 mAh\(\cdot\)g\({}^{-1}\) after 500 cycles at C/2 current. According to [45], China launched a large-scale test production of electrodeposited silicon with capacity more than 2000 mAh\(\cdot\)g\({}^{-1}\) after 70 cycles at a current of 0.1 C; capacity at a current of 5 C is about 1600 mAh\(\cdot\)g\({}^{-1}\). It is noted that for practical use of the produced silicon they made Si/graphite mixture with a total capacity of 650 mAh\(\cdot\)g\({}^{-1}\)[47]. In this regard, in this work, we studied the effect of SiO\({}_{2}\) on the morphology of nanosized silicon deposits during the electrolysis of KCl-K\({}_{2}\)SiF\({}_{6}\) melts. For this, silicon deposits were obtained from the KCl-K\({}_{2}\)SiF\({}_{6}\) and KCl-K\({}_{2}\)SiF\({}_{6}\)-SiO\({}_{2}\) melts at different cathode overvoltage. Obtained deposits were analyzed and tested on their electrochemical characteristics as a part of an Si-based anode of a LIB.
## 1 Introduction
Thus, a direct demand model based on the GWR model is used to model the factors affecting transit ridership during different periods (evening rush hours, nonrush hours, average weekday ridership, average weekend ridership, and average daily ridership per week). Feature selection is conducted via the backward stepwise method before the GWR process. The applicability and effectiveness of the framework are demonstrated using the Shenzhen Metro network as a case study. Ridership data for 118 Shenzhen Metro stations were collected using the Automated Fare Collection (AFC) system in 2013. Four types of potential factors are considered: land use, socio-economics, the accessibility of intermodal transport, and network structure information. This study makes both methodological and empirical contributions, as follows. Methodologically, by quantifying the network structure factors using measurements in the domain of a complex network, comprehensive information and significant regression performance are obtained. To the extent of our knowledge, little research has been done in this way before. Empirically, different GWR models are implemented for different time periods, which can both model the local relationships between the metro station ridership and its potential influencing factors and interpret the temporal variation in the influencing factors of ridership. Therefore, the study contributes to metro planning and periphery development from both spatial and temporal perspectives. The findings also have several practical implications for urban and transport policymakers, thus bridging the gap between theory and practice. The study also advances the literature of transit ridership modeling from a local perspective. In addition, its framework can be extended to other areas, such as analyzing the influencing factors of public health conditions (virus and disease spreading) and customer volume at different locations. The remaining parts of the paper are organized as follows. A comprehensive review of the research on modeling and analyzing transit ridership and its influencing factors is provided in Section 2. The data collected for the study are described in Section 3. Section 4 introduces the methods of estimating transit station ridership and identifying the key influential factors. Section 5 details and analyzes the results of the model implementation. Section 6 concludes and provides practical recommendations.