esa-sceva/satcom-chunk-collection huggingface dataset:
# Physicochemical Properties and In Vivo Hepatoprotective Effect of Polysaccharides from Grape Pomace
## 3 Results and Discussion
### Structural Characterization of GPPs  
#### 3.3.1 UV and FT-IR of GPPs  
GPPs with uniform composition were obtained by means of deproteinization, decolorization and ultrafiltration separation. The UV spectrum of GPPs is shown in Figure 2A. There are no special absorption peaks at 260 nm and 280 nm, which means that the nucleic acids or conjugated proteins were removed with GPP purification.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Source**} & **Sum of** & **Degree of** & **Mean** & **F-** & \multirow{2}{*}{_p-Value_} & \multirow{2}{*}{**Significance**} \\  & **Squares** & **Freedom** & **Square** & **Value** & & \\ \hline Model & 17.5 & 9 & 1.94 & 878.22 & \textless{}0.0001 & *** \\ A & 0.11 & 1 & 0.11 & 47.8 & 0.0002 & \\ B & 0.041 & 1 & 0.041 & 18.35 & 0.0036 & \\ C & 0.012 & 1 & 0.012 & 5.43 & 0.0526 & \\ AB & 2.025E-003 & 1 & 2.025E-003 & 0.91 & 0.3707 & \\ AC & 2.500E-005 & 1 & 2.500E-005 & 0.011 & 0.9183 & \\ BC & 0.012 & 1 & 0.012 & 5.47 & 0.0520 & \\ A\({}^{2}\) & 13.07 & 1 & 13.07 & 5905.47 & \textless{}0.0001 & *** \\ B\({}^{2}\) & 0.91 & 1 & 0.91 & 410.41 & \textless{}0.0001 & *** \\ C\({}^{2}\) & 2.12 & 1 & 2.12 & 957.52 & \textless{}0.0001 & *** \\ Residual & 0.015 & 7 & 2.561E-003 & & & \\ Lack of fit & 7.375E-003 & 3 & 3.483E-003 & 1.86 & 0.2766 & Not \\ Pure error & 8.120E-003 & 4 & 1.870E-003 & & & \\ Cor total & 17.51 & 16 & & & & \\ R-squared & 0.9991 & & & & & \\ Adj R-squared & 0.9980 & & & & & \\ CV\% & 0.70\% & & & & & \\ Pred & 0.9825 & & & & & \\ R-squared & & & & & & \\ Aded & 72.683 & & & & & \\ precision & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Analysis of variance (ANOVA) of the quadratic model and lack of fit.  
As shown in Figure 2B, the FT-IR spectrum of GPP at 4000-500 cm\({}^{-1}\) shows the characteristic peaks of polysaccharides. The strong absorption bands at 3390 cm\({}^{-1}\) and 2930 cm\({}^{-1}\) represent the stretching vibration of O-H and C-H in sugar molecules, respectively. The broad and intense band detected near 1610 cm\({}^{-1}\) is due to the stretching vibration of COO-, indicating the presence of uronic acid in the polysaccharide. The peak at 1380 cm\({}^{-1}\) is attributed to the C-H bending vibration, and absorption between 800 and 1200 cm\({}^{-1}\) is considered the fingerprint area that can reflect the vibration of the sugar ring. The two strong absorption bands observed at 1060 cm\({}^{-1}\) and 1100 cm\({}^{-1}\) are related to the C-O-C stretching of the glycosidic bond and confirmed that the main style of the monosaccharides in GPPs was the pyran type. In addition, absorption at 840 cm\({}^{-1}\) and 816 cm\({}^{-1}\) indicates the presence of \(\alpha\) Type A glycosidic bonds.
## 2 System Model
### Traffic Management and Data Analysis  
The data collected through the VANET are not only used for immediate safety measures but also contribute to long-term traffic management. The central base station collects traffic data from multiple clusters and applies the K-means algorithm to identify congestion patterns within different regions. By clustering vehicles based on their density and speed, the system can locate areas with high traffic density and predict potential congestion hotspots.
# Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs
## 3 Experiments and Results
### Performance Comparison  
First, we compared the accuracy of our proposed algorithm with seven other prediction algorithms. Then, a comparison experiment was conducted considering the difference in traffic flow characteristics between weekdays and weekends. After verifying the effectiveness of our proposed algorithm, ablation experiments were conducted in order to investigate whether the internal graph convolutional network or the external heterogeneous data fusion network in the algorithm played a more important role. Finally, the time consumption of our algorithm was compared with three other algorithms.
## 3 Manufacturing Techniques for Wearable Antennas
### Flexible Textile Wearable Antennas
#### 3.2.4 Inkjet and Screen Printed Antennas  
Inkjet and screen printing [50] can also be used to create conducting sections of wearable antennas. However, the substrates are generally paper or Kapton while textiles are not ideal printing substrates. As the name suggests screen printing requires a mask to be made for each design and therefore is less practical for different individual designs [51, 52]. RF transmission lines have been previously screen printed onto a cotton substrate [53].  
Figure 8: Simulated surface current of two different stitch direction patch antennas.  
Inkjet printing does not require a mask and designs can be created within minutes of receiving the computer file containing the geometry and hence enables great manufacturing flexibility. Typically, silver nanoparticles in solution are used to make thin conducting lines which are approximately 1 \(\upmu\)m thick. Therefore, printing on rough surfaces such as textiles is very challenging. In addition textiles are porous materials which make it more challenging to create continuous conducting lines [54]. Other challenges of inkjet printing on textiles include achieving a continuous conducting track with a high conductivity; robustness to stretching and inherent movement and resilience to high temperatures required to remove the non-conducting solvent from the ink.  
These challenges can be overcome by first screen printing an interface layer onto fabrics. This process is outlined in [55]. The inherent surface roughness of the cotton fabric is reduced by using screen printed interface layer and enables the printing of antennas with reasonable efficiencies with only one or two layers of ink. This process has been applied to dipoles [55] and patch antennas [56].  
Antenna performance can be improved by printing multiple layers but this increases material costs and manufacturing time and reduces the resolution of the lines. As the ink layer is thin, skin depth issues must be considered. This is likely to limit the applicability of inkjet printing on fabrics to higher frequencies.
# Fault Detection in Industrial Equipment through Analysis of Time Series Stationarity
## 4 Results  
### Variable Analysis  
Through individual analyses of the variables, an attempt was made to understand their behaviour over time. Figure 1 shows the plots of the three variables chosen for the year 2018. Regarding variable VCP 1, it is possible to observe a growing trend over time, but as shown in Figure 2, it also presents some discrepant values, which may happen for different reasons. As for variable VCP 2, it exhibits some irregularity and instability and shows seasonal behaviour due to the repetition of patterns throughout the time series, as revealed in Figure 3. Notably, there are values that are nearly zero and values associated with the mean value. Variable VCP 3, between February and May, demonstrates dynamic behaviour with some randomness in its values. From there, similar to the analysis conducted on the behaviour of variable VCP 2, some seasonal patterns start to emerge. The charts give an idea of the long-term behaviour of these variables, but it is clear that the signals are very noisy and it is difficult to identify clear patterns just from this visual information.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Var** & **VCP 1** & **VCP 2** & **VCP 3** \\ \hline AF & 76,608 & 76,608 & 76,608 \\ Mean & 0.87 & 2.05 & 1.87 \\ SD & 0.43 & 0.53 & 0.89 \\ Min & \(-\)0.05 & \(-\)0.04 & \(-\)0.01 \\
25\% & 0.71 & 1.88 & 1.42 \\
50\% & 0.83 & 2.12 & 1.81 \\
75\% & 0.97 & 2.39 & 2.01 \\ Max & 21.28 & 6.63 & 5.94 \\ \hline \end{tabular} AF—absolute frequency, SD—standard deviation, Min—minimum, Max—maximum.  
\end{table}
Table 6: Statistical data related to the second interval (R2), after linear interpolation.  
Figure 3: Seasonal decomposition of variable VCP 2.  
Figure 2: Seasonal decomposition of variable VCP 1.
# Energy Efficient Data Transmission for Sensors with Wireless Charging
## 3 Transmission in Limited \(K\) Periods
### Problem with \(C=2E\)
Therefore, even if the last period of energy is not completely used, the energy utilization is \(OPT-E>\frac{3}{4}(OPT-E)\)  
there are at most \(\lfloor\frac{K}{4}\rfloor E\) units of energy overflow. The energy utilization in the first \(4\lfloor\frac{K}{4}\rfloor\) periods is \(\frac{3}{4}\cdot 4\lfloor\frac{K}{4}\rfloor E\). Because \((K\mod 4)\leq 3\), we need to consider the following three cases. Case 1: \((K\mod 4)=1\), i.e., \(OPT=(4\lfloor\frac{K}{4}\rfloor+1)E\). Even if the whole last period of energy is unused, the energy utilization is \(\frac{3}{4}\cdot 4\lfloor\frac{K}{4}\rfloor E\geq\frac{3}{2}(OPT-E)\). Case 2: \((K\mod 4)=2\), i.e., \(OPT=(4\lfloor\frac{K}{4}\rfloor+2)E\). Assume \(x>E\) units of energy in the last two periods are unused. It means that all the remaining packets have size larger than \(x\). It contradicts the fact that the larger packet whose size is larger than \(x\) should be transmitted first. Therefore, in this case, at most \(E\) units of energy may be unused. This derives that the energy utilization is at least \(\frac{3}{4}(OPT-E)\). Case 3: \((K\mod 4)=3\), i.e., \(OPT=(4\lfloor\frac{K}{4}\rfloor+3)E\). Assume \(x>\frac{E}{2}+E\) units of energy in the last three periods are unused. It means that all the remaining packets have a size larger than \(x>\frac{3}{2}E\). It contradicts the fact that the larger packet whose size is larger than \(x\) should be transmitted first. Therefore, in this case, at most \(\frac{3}{2}E\) units of energy may be unused. This derives that the energy utilization is at least \(\frac{3}{4}(OPT-E)\). This completes the proof. ```
Input:\(S=\{s_{1},s_{2},\cdots,s_{n}\}\), \(C\), \(E\) and \(K\), assuming \(\forall s_{i}\leq C\) Output: Transmit a subset \(S^{\prime}\subset S\) of packets to maximize the total amount of data. 1: Sort \(S\) in non-increasing order, and let the ordered sequence be \(s_{1}\geq s_{2}\geq...\geq s_{n}\);
2:\(e=0\); \(k=0\)
3:for\(i=1\) to \(n\)do
4:while\(e<s_{i}\) and \(k<K\)do
5:\(e=e+E\); //Wireless charging
6:\(k=k+1\);
7:endwhile
8:if\(e>C\)then
9:\(e=C\); //Energy overflows
10:endif
11:if\(e<s_{i}\)then
12: exit;
13:endif
14: Transmit packet \(i\);
15:\(e=e-s_{i}\);
16:endfor
```  
**Algorithm 2**LargerFirst (\(S\))  
After transmitting a larger packet, the remaining energy can be dedicated to transmitting some shorter packets. Since the more energy remains in the battery, the more energy may overflow. Such an improvement is described in Algorithm 3. In many cases, Algorithm 3 can improve energy utilization. However, in the worst case, it has the same energy utilization as Algorithm 2.
# A Search Efficient Privacy-Preserving Location-Sharing Scheme in Mobile Online Social Networks
## 6 Discussion
### Security Analysis
As mentioned in the security model (i.e., Section 4.2), we suppose that the malicious users may be system insiders who can collude with \(SNS\). Thus, with the help of these insiders, \(SNS\) can obtain users' rough location range since base stations can locate the subscribed cell phones with an accuracy of 50 to 300 m. However, \(SNS\) can not get actual locations of users and can not always make sure of users' rough location range since the mapping relationship between grid cells' identifiers and their fake-IDs is different in each update cycle. Therefore, \(SNS\) can not get users' actual locations. Social Network Privacy. Since \(SNS\) manages users' social network graphs, we do not consider social network privacy on \(SNS\), and only need to analyze whether there is an issue of disclosing social network privacy on \(LS\). In the location query process, \(LS\) has nothing information about a user's query type (i.e., querying friends' locations or querying strangers' locations). Therefore, suppose that the number of pseudo-IDs sent from \(SNS\) is \(m\), the probability of inferring the user's social network privacy for \(LS\) can be computed as \(\frac{1}{m}\). In the location update process, all the users' pseudo-IDs are uploaded to \(LS\) and the index \(ind\), which is used to indicate the real pseudo-ID, is randomly changed. Therefore, the probability of inferring the user's social network privacy can be calculated as \(\frac{1}{2^{nk}}\), where \(n\) is the total number of users and \(k\) is the number of pseudo-IDs for each user. Since \(m\) and \(n\) are usually large numbers, it is impossible for \(LS\) to obtain the social network privacy. Therefore, users' social network privacy can be well protected. In addition, since \(SNS\) sends a subset of an issuing user's friends (i.e., part of the user's friends) to \(LS\) each time [11], our scheme can also prevent \(LS\) from inferring the complete social network relationship of the user.
## 3 Results and Discussion
The absence of growth inhibition zones in the case of the alginate-graphene composite may be due to the presence of polymer. The polymer chains surrounding the graphene sheets round off their sharp edges. The mechanism of graphene's bactericidal action is directly related to the sharp edges of the sheets, which cause the mechanical destruction of cell membranes. Thus, smoothing the edges leads to the deactivation of the antimicrobial action [58]. The zone of growth inhibition for _Escherichia coli_ when exposed to nanogold may also result from the impairment of proteins, lipids and nucleic acids present in bacterial cell membranes [59,60]. The absence of inhibition zones for microbial growth after 48 h of incubation in the case of nanogold is not a result that excludes its antimicrobial activity. The antimicrobial properties of nanometals are influenced by their shape, size or surface-to-volume ratio. It also depends on the diffusion mechanisms in the growth media. One of the described ways of the influence of nanometals on microorganisms is the ion release--the lower activity of nanogold can be caused by the slower ion release compared to nanosilver [61,62, 63]. It seems reasonable to continue microbiological studies over longer incubation periods or increased nanogold concentrations [60].
###### Abstract  
Multibeam echosounder (MBES) backscatter data are influenced by underwater sound absorption, which is dependent on environmental parameters such as temperature, salinity, and depth. This study leverages CTD datasets from the Korea Oceanographic Data Center (KODC) to analyze and visualize the spatiotemporal variations in absorption parameters in the East Sea of Korea, which are subject to pronounced variability over time and space. The legacy MBES backscatter data, originally processed using generalized absorption parameters that neglected spatiotemporal variations, were compared with the calibrated data. The calibration process included inverse calculation of water temperature with depth-specific average salinity values from the nearest KODC stations. This calibration revealed discrepancies of up to 2.1 dB in backscatter intensity across survey lines, highlighting the potential misrepresentation of legacy MBES backscatter data due to site-specific absorption variability having been overlooked. By addressing these discrepancies, this study underscores the importance of incorporating spatiotemporal absorption variability into MBES calibration workflows. This integrated approach not only enhances the reliability of legacy MBES data but also provides valuable insights for marine resource management, seafloor mapping, and environmental monitoring in highly dynamic marine environments such as the East Sea of Korea.  
underwater acoustics; sound absorption coefficient; conductivity-temperature-depth; multibeam echosounder
# User-Information-Aware D2D Multicast File Distribution Mechanism
## System Model  
### Problem Description  
In the cell, the members of the D2D multicast cluster are composed of the geographically adjacent users that request the same video from the base station, and the cluster head is selected from the members of the cluster. The base station firstly sends the requested video to the cluster head through the cellular downlink, and then the cluster head distributes the video to other members via D2D multicast communication. In this way, the redundant transmission can be reduced for the same video, improving the utilization of the spectrum and offloading the traffic of the base station.  
Assuming that the D2D link reuses the uplink channel resource of a cellular user in the same cell, let \(P_{h}\) denotes the D2D transmit power of cluster head. As mentioned in the above, the performance of D2D multicast communication is limited to the worst SINR of receivers. \(SINR^{\min}\) is the worst SINR of receivers, \(u_{\rm m}\) denotes the receiver with the worst SINR, and then \(SINR^{\min}\) can be denoted as:  
\[SINR^{\min}=\frac{p_{h}g_{hm}}{n_{0}+l_{cm}} \tag{1}\]where \(n_{0}\) is the noise power of the AWGN model, \(L_{cm}\) is the interference signal power of user \(u_{m}\) received from the cellular user, and \(g_{hm}\) is the channel gain between the cluster head user and user \(u_{\text{m}}\). Therefore, according to Shannon Theorem, the channel capacity of the D2D multicast communication can be expressed as:  
\[C_{total}=NB\log_{2}(1+SINR^{\text{min}}) \tag{2}\]  
where \(N\) is the number of receivers in the D2D multicast cluster, \(B\) is the bandwidth. Observing (1) and (2), we know the throughput can be greatly improved by increasing the number of members in the D2D multicast cluster in certain conditions, dramatically promoting the energy and spectrum efficiency.
## 6 Conclusions  
In this investigation, CGS specimens were designed using a negatively exponential continuous gradation equation, and a permeability coefficient database was constructed by integrating CFD-DEM simulated seepage tests. The predictive performance of three machine learning models--Back-Propagation Neural Network (BPNN), Gaussian Process Regression (GPR), and Random Forest (RF)--was systematically compared with that of traditional empirical models. Grey relational analysis (GRA) was employed to quantify the contribution of input parameters, while particle swarm optimization (PSO) combined with four-fold cross-validation enhanced the models' generalization capability, thereby demonstrating the advantages of data-driven methods in evaluating the permeability of coarse-grained soils. The main conclusions are summarized as follows:  
1. Grey relational analysis revealed that the gradation parameters \(\alpha\), \(\beta\), maximum particle size (\(d_{\text{max}}\)), and porosity (\(n\)) significantly influence the permeability coefficient (grey relational coefficient \(R>0.6\)). Consequently, these four parameters should be jointly employed as input variables, obviating the need for dimensionality reduction.
2. Among the models evaluated, BPNN achieved superior accuracy (\(R^{2}=0.99\), MAE \(=1.5\), RMSE \(=2.9\), U\({}_{95}=0.4\)), demonstrating its robust nonlinear mapping capability to accurately capture higher-order interactions affecting the permeability coefficient. In contrast, GPR exhibited the lowest accuracy (\(R^{2}=0.92\)) due to limited kernel adaptability and noise sensitivity, while RF (\(R^{2}=0.97\)) encountered challenges in modeling the smooth variations of continuous variables.
3. Compared to the traditional empirical model (\(R^{2}=0.9031\)), the BPNN's predictive accuracy improved by 10.13%, with high consistency observed between training and validation results. These findings confirm that data-driven methods effectively overcome the limitations imposed by theoretical assumptions and enhance the reliability of permeability assessments for coarse-grained soils, offering an efficient solution for the intelligent prediction of geotechnical parameters.  
The innovation of this study lies in the integration of CFD-DEM simulated datasets based on negatively exponential continuous gradation to capture realistic seepage behavior of coarse-grained soils, combined with the use of grey relational analysis to ensure the physical interpretability and relevance of input variables. Furthermore, a particle swarm optimization (PSO) strategy coupled with k-fold cross-validation was implemented to enhance the generalization capability of machine learning models. The systematic comparison of BPNN, RF, and GPR further reveals the superior performance of BPNN in modeling complex nonlinear relationships, offering a more accurate and data-efficient approach for permeability prediction than existing empirical or machine learning methods.
## 5 Conclusions  
This paper presents a signal processing method for weak signal detection. By combining sparse representation with singular value decomposition, Gaussian white noise is removed by sparse representation for primary filtering, and useful information in the signal is extracted by weighted singular value decomposition for secondary filtering to achieve the purpose of denoising. This multi-layer filtering method is mainly taken into account that sparse representation is sensitive to Gaussian noise, while the improved SVD method can select useful components in signals by using the appropriate indicator. After two layers of filtering, the useful components in the signal can be extracted. Two experiments verify the effectiveness of the proposed method and the experimental results show that SRSVD  
Figure 11: Denoising the 100 th minute signals envelope spectra of 4 different methods. (**a**) L1-kurtosis, (**b**) RSVD, (**c**) VMD, and (**d**) SRSVD.  
Figure 12: Denoising the 50 th minute signals envelope spectra of 4 different methods. (**a**) L1-kurtosis, (**b**) RSVD, (**c**) VMD, and (**d**) SRSVD.  
has advantages in fault signal extraction compared with the L1-kurtosis method, RSVD, and VMD. Future research can focus on useful information indexes to extract weak signals more effectively.  
Conceptualization, H.M.; methodology, H.M.; software, H.M.; validation, H.M.; formal analysis, H.M. and S.L.; investigation, H.M.; resources, S.L.; data curation, X.L.; writing--original draft preparation, H.M.; writing--review and editing, S.L.; visualization, H.M.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (No. 2018 YFB2003300), National Science and Technology Major Project, China (No. 2017-IV-0008-0045), and the National Natural Science Foundation of China (No. 51975276).  
Not applicable.  
The datasets in Section 4.2 can be found on this website: [[https://drive.google.com/open?id=1_ycmC46](https://drive.google.com/open?id=1_ycmC46) PARiytk82 ShnFfYQsaXv3_VK]([https://drive.google.com/open?id=1_ycmC46](https://drive.google.com/open?id=1_ycmC46) PARiytk82 ShnFfYQsaXv3_VK) (accessed on 18 April 2022).  
The authors declare no conflict of interest.