esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed RDH-ED Scheme Based on VTSIS  
The security environment in the cloud environment is variable and may be affected by factors, such as the secret security level, secret urgency, and adversary attack capability. Hence, it becomes imperative to tailor strategies in response to this dynamic security environment. The framework of the proposed scheme is illustrated in Figure 2, delineated into three primary stages: image encryption and data embedding, secret share updating, image reconstruction and secret extraction. Initially, the original carrier image is encrypted with VTSIS. At the same time, the secret is embedded into the polynomial coefficient redundant generated in the encryption process. After embedding, \(n\) shares generated by VTSIS are distributed to \(n\) different participants, with the threshold value \(T=k\). \(T\) can be adjusted according to the new security environment when the security environment is changed, and the values \(\{k,k+1,\ldots,h\}\) can be taken. The receiver can collect \(T\) shares randomly to extract the secret and recover the carrier image.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 5 Data-Driven Analysis
### Rainfall Impact on Signal Measurements  
Given the daily pattern observed in the signal measurements in Section 5.1.2, the average value is not an appropriate evaluation metric for investigating the relationship between rainfall and signal measurements. This is because any decrease in the average could result from rainfall, the daily pattern, or a combination of both. Variance remains a viable metric to explore, as rainfall intuitively induces greater diversities in signal fluctuations, expecting to vary signal variance. This section analyzes rainfall's influence on signal measurements through standard deviation (SD). Additionally, the effects of rainfall on signal measurements are examined through spectral time series analysis.
## 2 Materials and Methods
### Testing
#### 2.3.4 Electromagnetic Interference and Dielectric Constant Measurements  
EMI shielding efficiency of resulting polymer composites was tested in the frequency between 8.2 and 12.4 GHz using an Agilent Technology PNA series network analyzer (N5230C Agilent PNA-L, Agilent, Santa Clara, CA, USA) with a rectangular waveguide with cross-section of 23 \(\times\) 10 mm. An incident EM wave within the specific frequency range was applied and both the transmitted and reflected waves were collected. The instrumentation measures the magnitude and phase of the scattering parameters (S-parameters) of the sample upon the incident wave whose power is denoted as Pi. The power of the transmitted and reflected waves are denoted as Pt and Pr, respectively. The attenuation of incident EM waves is expressed in decibels (dB) and it can be expressed as given in Equation (1).  
\[\mathrm{EMI\;SE}\;=\;10\;\log\left(\frac{P_{t}}{P_{i}}\right) \tag{1}\]  
The reflected and transmitted waves through the shielding material can be mathematically represented by the complex S-parameters named S11, S22, S12 and S21 which are correlated to the reflectance and the transmittance, respectively, according to Equations (2) and (3):  
\[\mathrm{T}\;=\left(\frac{P_{t}}{P_{i}}\right)^{2}=\left|\mathrm{S12}\right|^{2 }=\left|\mathrm{S21}\right|^{2} \tag{2}\]  
\[\mathrm{R}\;=\left(\frac{P_{r}}{P_{i}}\right)^{2}=\;\left|\mathrm{S11}\right|^ {2}=\left|\mathrm{S22}\right|^{2} \tag{3}\]  
Figure 3: Schematic of the perpendicular and parallel section in a filament for transmission electron microscopy (TEM) observation.  
The absorbance (A), reflectivity (R), and transmittance (T) are correlated as in Equation (4):  
\[\text{A}\;+\;\text{R}\;+\;\text{T}\;=\;1 \tag{4}\]  
Considering the propagation of an electromagnetic wave through an electrically conductive specimen, the tested specimen can either absorb (SEA) or reflect (SER) the incident electromagnetic waves. This can be estimated according to Equations (5) and (6).  
\[\text{SER}\;=\;10\;\log\Bigl{[}1{-}(\text{S11})^{2}\Bigr{]} \tag{5}\]  
\[\text{SEA}\;=\;10\;\log\Biggl{\{}\Biggl{[}1{-}\frac{1{-}(\text{S11})^{2}-( \text{S21})^{2}}{1{-}(\text{S11})^{2}}\Biggr{]}\Biggr{\}} \tag{6}\]  
The overall EMI SE is thus given by both SER and SEA as follows.  
\[\text{EMI}\;\text{SE}\;=\;\text{SER}\;+\;\text{SEA} \tag{7}\]  
The dielectric constant and the loss factor of the samples were also determined using the Agilent 85071 software (Agilent, Santa Clara, USA). The software uses the Nicolson-Ross-Weir (NRW) algorithm and the S-parameters to calculate these values.  
Measurements were conducted at 23 \(\pm\) 1 \({}^{\circ}\)C, the reported spectra are representative of three replicated acquisitions for each specimen. The thickness of all the specimens was 2.0 mm.
# An ANN-Based Approach for Real-Time Scheduling in Cloud Manufacturing
## 4 Numerical Results  
### Experimental Environment Setting  
We conduct comparative experiments to verify the effectiveness of the proposed ANN-based approach for real-time scheduling, and the dataset is adapted from MPSPLib(www.mppsplib.com/download.php), which consists of multiple project instances defined in Table 2.  
According to the MT scale of projects in the aforementioned MPSPLib, the experimental dataset is grouped as follows:  
**Exp-30**: Each MP in this group contains 30 MTs;
**Exp-60**: Each MP in this group contains 60 MTs;
**Exp-90**: Each MP in this group contains 90 MTs;
**Exp-120**: Each MP in this group contains 120 MTs;
**Exp-mix**: MT scale of each MP in this group can be any value in \(\{30,60,90,120\}\).  
Each project in the MPSPLib is enabled with random features as Equations (33)-(37) to imitate the process of publishing MPs to the CMfg platform from the demanders and to enlarge the diversity of MS from the providers.  
\[L_{c,i}^{(k)} \sim\text{U}(\text{dist\_min},\text{dist\_max}) \tag{33}\] \[|\mathcal{I}_{k}| \sim\text{U}(\text{n\_min},\text{n\_max})\] (34) \[C_{i}^{(k)} \sim\text{U}(\text{c\_min},\text{c\_max})\] (35) \[\overline{V}_{i}^{(k)} \sim\text{U}(\text{v\_min},\text{v\_max})\] (36) \[p_{i}^{(k)} \sim\text{U}(\text{p\_min},\text{p\_max}) \tag{37}\]  
where \(\text{U}(a,b)\) represents a random distribution over the interval \([a,b]\), and the involved parameters are listed in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MT Label (\(j\))** & **Processing Time (\(p_{j}^{(c)}\))** & **MS Type (\(\kappa_{j}^{(c)}\))** & **MR Capacity (\(v_{j}^{(c)}\))** & **Successors (\(\mathcal{S}_{j}^{(c)}\))** \\ \hline
1 & 0 & n/a & 0 & 2, 3, 4 \\
2 & 8 & 0 & 4 & 6, 11, 15 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
31 & 2 & 2 & 2 & 32 \\
32 & 0 & n/a & 0 & n/a \\ \hline \hline \end{tabular} Processing Time (\(p_{j}^{(c)}\)) and MR Capacity (\(v_{j}^{(c)}\)) are dimensionless quantities.  
\end{table}
Table 2: Project Instance in MPSPLib (take file j301_1.sm as an example).
## 3 Results & Discussion
### MAE of Bioactive Compounds
#### 3.1.2 Effect of Solvent-to-Biomass Ratio  
The increase of solvent-to-biomass ratio, from 20 to 55 and eventually 90 \(\rm{mI_{solv}/g_{biom}}\), during MAE at 50 \({}^{\circ}\)C, 15 min and 550 watts led to extraction yield improvement (Figure 2a). However, chlorophylls (Figure 2c), carotenoids (Figure 2e), as well as the extract's antioxidant activity (Figure 2f) were not favored by the solvent-to-biomass ratio rise and therefore were decreased. The selected carotenoid content (Figure 2d) was negatively affected by the ratio increase, but lutein was slightly increased above 55 \(\rm{mI_{solv}/g_{biom}}\). Finally, total phenolic content (Figure 2b) initially improved from 20 to 55 \(\rm{mI_{solv}/g_{biom}}\), while further solvent-to-biomass increase led to reduced values.  
According to the literature, an increase in the solvent-to-biomass ratio on MAE is considered contradictory, leading either to increased extraction yield [47; 48; 49] or enhanced yield, which afterward decreases [50; 51; 52; 53]. The solvent-to-biomass ratio increase offers a greater concentration gradient to the biomass-solvent system, contributes to the solvent's amount sufficiency and mixing adequacy and therefore offers faster and intensified diffusion phenomena [54; 55]. This could justify the yield's rise during the solvent-to-biomass ratio's increase from 20 to 90 \(\rm{mI_{solv}/g_{biom}}\), as well as the initial enhancement of total phenolic content from 20 to 55 \(\rm{mI_{solv}/g_{biom}}\). However, solvent increment during MAE demands  
Figure 5: The simultaneous effect of extraction temperature, duration, solvent-to-biomass ratio, and microwave power on MAE’s (**a**) yield, extract’s total (**b**) phenolic and (**c**) chlorophyll content, (**d**) selected and (**e**) total carotenoid content, and (**f**) antioxidant activity.  
higher microwave power and duration in order to reach the required temperature [15]. In addition, the solvent-to-biomass ratio's increase might lead to the dissolution and extraction of undesirable compounds and hence lower solvent selectivity toward components of interest [56]. The above reasons might be responsible for the decreasing values of the examined pigment concentration and antioxidant activity of the extracts during solvent-to-biomass ratio increase from 20 to 90 \(\mathrm{mL}_{\mathrm{solv}}/\mathrm{g}_{\mathrm{biom}}\), as well as the reduced phenolic content for ratio above 55 \(\mathrm{mL}_{\mathrm{solv}}/\mathrm{g}_{\mathrm{biom}}\).
## 4 Experimental Results
### Ablation Study
** Table 4 demonstrates a notable enhancement in the image quality of cover-stego and secret-reconstructed image pairs through the incorporation of the GAN. Specifically, in the forward hiding process, the PSNR value for the cover-stego image pair improved by 2.16 dB, and SSIM improved by 0.02. In the backward reconstruction process, the PSNR value for the secret-reconstructed secret image saw an improvement of 0.84 dB, with SSIM increasing by 0.02. The introduction of image-permutation adds complexity to the actual secret image, challenging the model in restoring it. Consequently, the image quality of the secret-reconstructed secret image pair saw a significant decrease. After training with the adversarial training network, the PSNR value and SSIM increased by 1.82 dB and 0.03, respectively. This indicates that GAN effectively promotes the convergence of the distribution of secret images and reconstructed secret images, enhancing image quality while maintaining high restoration accuracy. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Image-Premutation** & **GAN** & **Cover/Stego Pair** & **Secret/Reconstructed Pair** \\ \hline \(\times\) & \(\times\) & 33.82/0.92 & 35.18/0.93 \\ \(\times\) & \(\surd\) & 35.98/0.94 & 36.02/0.95 \\ \(\surd\) & \(\times\) & 33.61/0.92 & 30.41/0.90 \\ \(\surd\) & \(\surd\) & 33.09/0.91 & 32.23/0.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments on image-premutation and GAN. Figure 7: Illustrating a concealed visual comparison on images challenging for steganography. In the cover and secret images in the first column, the green box denotes areas cropped for image steganography. The first row of images on the right displays the stego images for each steganography method [22; 34]. The second row on the right showcases the reconstructed secret image, while the third row on the right depicts the error image corresponding to the cover image and the stego image (enhanced 20\(\times\)).
## 3 Applications of NMR in polysaccharide Research
### Environmental and Industrial Applications  
The environmental applications of NMR in polysaccharide research mainly focus on understanding microbial extracellular polysaccharides, soil stability, and biofilm dynamics. [PERSON] et al. explored O-polysaccharides from marine bacteria [12]. They found that these polysaccharides have the potential to develop biotechnological solutions for environmental challenges. For example, they can be used in bioremediation and biofilm management. [PERSON] et al. used advanced NMR technology to figure out the structure of bacterial lipopolysaccharidesaccharides. This helps us understand the composition of biofilms [69]. In the same way, [PERSON] et al. used ssNMR to study the molecular order of polysaccharides in biofilms. They obtained a deeper understanding of the structural role of these polysaccharides in microbial ecosystems [70].  
In the area of renewable energy, [PERSON] et al. used ssNMR to study how termites enzymatically break down wood polysaccharides. This research provided important insights into the lignocellulosic degradation pathways that are involved in biofuel production [10]. The study shows that NMR can connect the structure of polysaccharides with enzyme function, driving progress in sustainable energy technologies. The industrial use of NMR has also been expanded to include the study of hydrogels and composites. These findings are very important for improving hydrogels in drug delivery, agriculture, and tissue engineering.  
In agricultural science, [PERSON] and [PERSON] determined the structure of _Clostridium perfingens_ capsule polysaccharides. They elucidated the role of NMR in studies of bacterial polysaccharides [71]. [PERSON] et al. used pulsed field gradient NMR to study covalent polysaccharide gel. They pointed out its potential in structural engineering [72]. [PERSON] et al. used solid-state NMR to investigate how saliva proteins and polysaccharides interact in calcified structures. This has helped us learn more about dental materials [73]. These examples illustrate that NMR can be used in many ways to solve difficult problems in the environment and industry. When we combine understanding at the molecular level with practical applications, NMR has become an essential tool to promote sustainable technologies and industrial innovation. These examples show that NMR is playing an increasingly important role in dealing with the complex challenges related to the use of industrial polysaccharides.
# C-SAR/02 Satellite Polarimetric Calibration and Validation Based on Active Radar Calibrators
###### Abstract  
Quad-polarization synthetic aperture radar (SAR) satellites are important detection tools in Earth observation and remote sensing; in particular, they are of great significance for accurately interpreting radar data and inverting geophysical parameters. Polarimetric calibration is particularly critical to eliminate the effects of distortion in polarized SAR data. The C-SAR/02 satellite launched by China is an important part of the C-band synthetic aperture radar (SAR) constellation, and the quad-polarization strip I (QPSI) is an important imaging mode for its sea-land observation. The relevant research on its polarimetric calibration is still lacking. This study's polarimetric calibration of C-SAR/02 was performed based on the active radar calibrator (ARC) method using four independently developed L/S/C multi-band ARCs and several trihedral corner reflectors (CRs). The polarimetric calibration distortion matrix varies along the range direction; the polarimetric calibration distortion matrix and polarimetric calibration accuracy along the range direction were analyzed, incorporating the devices in different range directions to calculate the distortion matrix. This approach improved the accuracy of the polarimetric calibration results and the effect of the quantization application of the C-SAR satellites. Moreover, our experimental results indicate that the method presented herein is suitable for the C-SAR/02 satellite and may also be more universally applicable to C-SAR-series satellites.  
polarimetric calibration; active radar calibrator; SAR 2024  
1
# Development and Operation of Track Condition Monitoring System Using In-Service Train
###### Abstract
The system is currently installed in two regional railway lines in Japan and continuously monitors track conditions [20; 21]. More recently, a track condition monitoring system using a smartphone has been proposed as an alternative to dedicated vehicle vibration measurement equipment [22; 23; 24; 25]. If a mathematical model is available that can represent the vehicle's motion, the model-based estimation method can be applied. In this case, it is necessary to solve the inverse problem of estimating the track geometry from the vibration of the vehicle. Kalman filter-based methods were proposed to estimate the track geometry from car-body motions [26; 27; 28; 29; 30]. As large amounts of measurement data become available on a daily basis, it becomes necessary to process these data automatically to diagnose track conditions. The root mean square (RMS) of car-body acceleration is highly correlated to the general health of tracks [31]. A machine learning technique for identifying track degradation based on the measured RMS values of a car-body acceleration has been developed [20]. [PERSON] et al. proposed a new method for automatically classifying the type and degradation level of track faults using a convolutional neural network (CNN). In this study, the car-body acceleration was imaged using the continuous wavelet transform (CWT) [32]. [PERSON] et al. analysed possibilities for train-type identification in railway switches and crossings based on accelerometer data by using neural networks [33]. Various methods of monitoring track conditions have been proposed. However, few papers refer to long-term changes in the condition of the track and future predictions. In this study, we conduct long-term measurements using the track condition monitoring system [20; 21] and evaluate changes in the track conditions over time using car-body vibration data. The map-matching method causes an error of several tens of metres, depending on the GNSS reception conditions. Thus, to improve the position identification accuracy, we use the point at which changes are observed in the curvature estimated from the yaw angular velocity (yaw rate) of the vehicle to correct the position information obtained via the map-matching method; subsequently, we evaluate the effectiveness of the method and diagnose the track condition.
## 4 Results and Discussion
### Discussion  
The time-series feature representation based on contrastive learning was used to improve parcel-based crop mapping. First, classification performance in two study areas was compared and analyzed. Then, based on the parcel-based crop maps resulting from the XGB-FR classification, accuracy evaluations and comparisons were conducted to discuss the number of training samples, the benefit of type-wise contrastive learning, the sensitivity of dimensions in feature representation, and assistance from multitemporal composition and vegetation indices.
## 6 Conclusions  
This paper proposes a single-stage entity-relation joint extraction model based on the HT-BES multi-dimensional labeling strategy MD-SERel that addresses the challenges presented by pesticide registration texts, including the high information density, complex logical structures, large spans between entities, and heterogeneous entity lengths. Additionally, the proposed model overcomes issues such as overlapping triplets, redundant computation, and exposure bias that exist in traditional joint extraction methods. The model enhances the representation capability of the complex corpora by integrating semantic and syntactic features. It also introduces the HT-BES multi-dimensional labeling strategy, which, through parallel scoring functions and fine-grained classification components, transforms the extraction task into a multi-label labeling task, enabling single-stage entity-relation joint extraction. This approach improves the accuracy of overlapping triplet extraction, reduces redundant computation, and avoids exposure bias. Experimental results demonstrate that the MD-SERel model performs well on both the pesticide dataset and general datasets, maintaining a stable extraction performance when handling complex logical structures and multiple triplets. It achieves fast inference speeds and high F1 scores. In the future, the scoring function could be further optimized by introducing an attention mechanism tofully capture the correlations between head and tail entities and their relationships, thereby improving the model's extraction effectiveness.  
**Author Contributions:** C.D.: methodology, formal analysis, investigation, writing--original draft, and data curation. S.X. ([PERSON]): formal analysis, methodology, and writing--review and editing. Y.C.: conceptualization and writing--review and editing. S.X. ([PERSON]): writing--review and editing. X.M.: supervision and funding acquisition. L.X.: methodology, conceptualization, supervision, and funding acquisition. S.X. ([PERSON]): supervision and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported by the Key R&D Project of Henan Province (241111111500).  
**Data Availability Statement:** No data from this study are available due to privacy or ethical restrictions.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### LC-MS Method Development
#### 3.1.1 Sample Preparation  
Arachidonic acid is a polyunsaturated fatty acid, a component of the cell membrane phospholipids, especially phosphatidylcholine, phosphatidylchinoline, and phosphatidylinositol [26,27]. In the bloodstream, it is usually incorporated in plasma lipoproteins. Human plasma contains about 1% free AA [28].  
Previous studies [29] have indicated that there is no considerable difference in AA concentrations between serum and plasma. However, plasma can form microfibrinogen clots during freezing and long-term storage, and therefore we preferred blood serum as a biological matrix for AA analysis [30]. With the aim of reducing the risk of gel-separator or anticoagulant interferences, we decided to collect the blood sample and separate the serum in tubes with a clot activator.  
Most of the published methods for LC analysis of AA in blood serum include derivatization of the target analyte [31,32] or solid-phase extraction [33,34], both time-consuming procedures. We used a liquid-liquid extraction (LLE) procedure, which, in addition to the purification and isolation of the target analyte, also contributes to the precipitation of serum proteins and the release of AA into the organic layer. Considering that AA is a long-chain lipophilic fatty acid, several organic solvents in different combinations and ratios were tested as extractants: ethanol, methanol, 2-propanol, chloroform, and methanol [35,36,37,38]. An extractant composed of dichloromethane and methanol in a ratio 2/1 was the most effective, both in terms of extracting efficiency of the target analyte from the biological matrix with the least interfering substances and as a protein precipitant. To improve the extraction efficiency of AA, a second LLE step was performed using pure dichloromethane. The optimized LLE procedure for AA was simple and suitable for routine work.