esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of the Mycelium-Based Composites
The long-term water absorption, very often used for assessing the durability aspects of thermal insulating materials, seems to be the less sensitive parameter to distinguish the different MBCs because it varies roughly between 60%vol. for HERF-containing composites and 50% for other composites. Despite the significantly reduced (by more than 50%) apparent density of 100% HERF composite, i.e., increased overall porosity, compared to the MBC in our previous research [11], the long-term water absorption remains the same (around 60%vol). The water absorption of 100%SLDS composite is even reduced from ca.85%vol. to ca.50%vol. Still, the long-term water absorption can be assessed as quite high, which reveals that all MBCs have up to 60% open porosity, accessible for water. Long-term water absorption of conventional thermal insulating material is much lower--between 0.2%vol. for extruded polystyrene (XPS) to 2.8%vol. for EPS, after 2 months of immersion [56]. However, the water absorption of the bio-composites could be significantly modified, because it is not only due to the open (accessible to water) porosity but also to the water suction by the used substrate. The behavior of mycelium-based bio-composites during water immersion strongly depends on the hydrophobic properties of the fungal mycelium [26; 36] and the hydrophilicity of lignocellulosic fibers [57]. The hydrophobicity of basidiomycetes is mainly a result of the content of a low-weight protein, called hydrophobin. This protein was also reported to affect the process of adhesion of lignocellulosic particles [12; 26]. To limit the hydrophilicity of the lignocellulosic fibers and reduce the water absorbance of the mycelium HERF/SDLS-based bio-composites, further investigations are needed in order to obtain well-developed mycelium, fully enveloped substrate particles together with an intact outer mycelium layer that covers the entire bio-composite. Another option would be one that was reported to considerably decrease the water absorbance [55]. Unless modified on the surface or in volume (e.g., by reducing open porosity and water absorbance of mycelium and substrate), the MBCs would not be recommended for external thermal insulation because the high-water content would reduce the insulation properties, making the composites vulnerable to frost and to biological degradation. Further testing shall clarify the impact of water absorption on the bio-composite swelling and the mycelium changes. Thus, the present findings confirm that by optimizing the porous structure of MBCs, different physical and mechanical performances can be achieved according to the composites' application requirements.
###### Abstract
In particular, the suggested, and experimentally verified, successful VLC-VLS combination based on time multiplexing between the tasks of VLC and VLS allows to conclude that the combination of VLC and VLS is possible without any mutual interferences between VLC and VLS. That the general concept of sensing based on backscattered signals is of high interest also for technologies using other ranges of the electromagnetic spectrum, e.g., Wi-Fi, is shown in [18]. In that work, a gesture recognition system is presented, that, based on the channel state information of the Wi-Fi communication procedure, allows to determine the movement of a human hand. In the experimental setup of that study, the hand gesture was performed at a 50 cm to 60 cm distance to a sender-receiver setup, which had a distance of 1 m between the sender and receiver. In comparison to the results presented in that work, with our approach, we can handle also more complex tasks over a larger distance. Furthermore, we would like to argue that in a common setup, Wi-Fi sender and receiver components are not placed in such close vicinity to each other, whilst our setup follows the common placement of light sources, which is parallel to the floor with the light being emitted downward. In contrast to [18], we also verify the applicability of our solution approach in the presence of additional radiation of the same kind, in our case, ambient light. This article is divided into the following sections. In Section 2, Materials and Methods, we firstly describe the motivation of this work, the used materials and the experimental setup, which largely remained unmodified compared to [15]. In the subsections of Section 2, we subsequently describe in detail the implemented solution approaches for advancing and improving the system. In Section 3, the results of the experiments for movement direction determination as well as for the tasks of identification and sensing the speed are presented for the scenarios without and with additional ambient room lighting being present. In Section 4, we discuss the results and outline future research directions. Section 5 finally summarizes this manuscript.
## 3 Results and Discussion
### Characterization of PVA/BC/C-Ag Composite Membranes
#### 3.1.3 TG and DSC Analysis  
Figure 3 shows the thermogravimetric and differential thermal curves of a PVA/BC/C-Ag composite membrane. Figure 3a shows that the membrane's weight loss could be divided into three stages. The first stage occurred between 100 and 150 \({}^{\circ}\)C and was mainly caused by the evaporation of water from the material. The endothermic peak appeared, and the weight loss is approximately 5%. The second stage occurred between 200 and 300 \({}^{\circ}\)C; the weight loss was approximately 60%, mainly due to the decomposition of PVA/BC polymer side chains [24]. The DSC curve shown in Figure 3b shows that the PVA/BC/C-Ag composite membrane had endothermic peaks near 200 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, which may have been caused by the fusion of the BC crystal structure and the melt-phase transition of PVA.  
Figure 2: SEM images PVA (**a**), PVA/BC (**b**), PVA/C-Ag (**c**), C-Ag (**d**), and PVA/BC/C-Ag (**e**).  
The third stage occurs between 350 and 500 \({}^{\circ}\)C, mainly due to the fracture of C-C chains in the PVA/BC polymer membrane [25] and so-called carbonization; total weight loss was about 88%. The final weight remained stable, with the remaining components being mainly C-Ag and a small amount of charred PVA/BC ash. The thermogravimetric and differential thermal curves of the PVA/BC/C-Ag composite membrane indicated that the composite membrane will have excellent stability in actual water treatment where temperatures do not exceed 100 \({}^{\circ}\)C.
# A Masked-Pre-Training-Based Fast Deep Image Prior Denoising Model
## 4 Experimental Results
### Qualitative Comparison
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**PSNR (dB)**} & \multicolumn{4}{c}{**SSIM**} \\  & **Nam** & **PolyU** & **LDCT** & **Average** & **Nam** & **PolyU** & **LDCT** & **Average** \\ \hline BM3D & 41.19 & 38.65 & 31.73 & 37.19 & **0.9944** & **0.9880** & 0.9007 & 0.9610 \\ DnCNN & 36.90 & 35.15 & 31.32 & 34.46 & 0.9128 & 0.8817 & 0.7428 & 0.8458 \\ FFDNet & 41.88 & 38.47 & 31.64 & 37.33 & 0.9717 & 0.9557 & 0.7508 & 0.8927 \\ DRUNet & 43.08 & 39.50 & 31.52 & 38.03 & 0.9899 & 0.9744 & 0.7497 & 0.9047 \\ SwinIR & 42.94 & 39.37 & 31.50 & 37.94 & 0.9892 & 0.9732 & 0.7526 & 0.9050 \\ Restormer & 41.55 & 38.87 & 30.59 & 37.00 & 0.9900 & 0.9827 & 0.8859 & 0.9529 \\ MT & 35.38 & 35.34 & 31.15 & 33.96 & 0.9620 & 0.9544 & 0.7630 & 0.8931 \\ DIP & 40.41 & 37.90 & 31.42 & 36.58 & 0.9890 & 0.9831 & 0.8960 & 0.9560 \\ MPFDIP & **43.29** & **39.83** & **32.02** & **38.38** & 0.9942 & 0.9878 & **0.9098** & **0.9639** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Denoising effect across Nam, PolyU, and LDCT datasets. Figure 6: Comparison of denoising effects of various methods on Gaussian noise. (**a**) PSNR = 25.60 dB, DnCNN [14]; SSIM = 0.7714; (**b**) PSNR = 25.72 dB; SSIM = 0.7769, FFDNet [15]; (**c**) PSNR = 26.49 dB; SSIM = 0.8013, DRUNet [16]; (**d**) PSNR = 26.55 dB; SSIM = 0.8030, SwinIR [18]; (**e**) PSNR = 26.67 dB; SSIM = 0.8827, Restormer [19]; (**f**) PSNR = 19.17 dB; SSIM = 0.4487, MT [47]; (**g**) PSNR = 24.35 dB; SSIM = 0.8217, DIP [29]; (**h**) PSNR = 27.98 dB; SSIM = 0.9061, MPFDIP; (**i**) reference; (**j**) PSNR = 14.90 dB; SSIM = 0.3340, noisy.
## 1 Introduction
[14] concluded that gLAI was preferable to total leaf area index, as nonphotosynthesizing portions of the canopy were not contributing to photosynthesis. Thus, in the LUEc, maximum light use efficiency (\(\varepsilon_{0}\)) is downregulated as environmental conditions change. Temperature, water stress, and phenology are used to estimate daily light use efficiency \(\varepsilon\) (e.g., [1,5,7,15]). However, to produce a more descriptive daily \(\varepsilon\), the original study [13] based the scalars on in situ mass and energy flux measurements, micrometeorological observations and gLAI measurements to regulate \(\varepsilon_{0}\). The main objective of this study was to evaluate the potential applicability of the LUEc-enhanced four-scalar light use efficiency approach [13] to provide regional GPP estimates. This was accomplished using input data derived from remotely sensed satellite data and gridded weather datasets to estimate daily GPP over the entire growing season in seven U.S. Corn Belt agricultural sites. Thus, this study also investigates the robustness of the scalar stressor parameterizations identified in the original study [13] for regions outside eastern Nebraska. LUEc performance was evaluated by comparing estimated and AmeriFlux measured GPP values and calculating root mean squared error (RMSE) and mean normalized bias (MNB).
## 4 Content Prediction of Free Calcium Oxide (f-CaO) in Cement Clinker Using Machine Learning
### Prediction Model Based on XGBoost  
The XGBoost algorithm is employed to predict the quality of cement clinker based on the free lime content. The XGBoost algorithm [18] uses gradient boosting trees as an ensemble learning method, iteratively training a series of decision tree models for prediction. In this approach, each decision tree model functions as a weak classifier, and even though their predictive ability might be relatively weak, the XGBoost algorithm combines them iteratively to obtain a strong classifier. This enables the modeling of complex nonlinear relationships and enhances prediction accuracy.  
Consider a training dataset \(D_{T}=\{\mathbf{x}_{i}=\langle x_{i,1},x_{i,2},\ldots,x_{i,j},\ldots,x_{i,N}\rangle,\,y_{i}\}\), \(i=1,2,\ldots,T\), a loss function \(l(y_{i},\hat{y}_{i})\), and a regularization term \(\Omega(f_{k})\), the overall objective function of XGBoost algorithm can be denoted as:  
\[\mathcal{L}(\phi)=\sum_{i}l(y_{i},\hat{y}_{i})+\sum_{k}\Omega(f_{k}),\]  
where \(\mathcal{L}(\phi)\) represents the objective function in the linear space, \(i\) stands for the \(i\)-th data sample, \(k\) denotes the \(k\)-th decision tree, \(\hat{y}_{i}\) signifies the predicted value corresponding to the \(i\)-th sample \(\mathbf{x}_{i}\), i.e., \(\hat{y}_{i}=\sum_{k}\hat{f}_{k}(\mathbf{x}_{i})\), and \(\sum_{k}\Omega(f_{k})\) represents the complexity of the \(k\)-th tree.  
Recall Figure 2 and Section 4.1, and note that since the predicted values under normal operating conditions are more valuable as references for guiding the control and optimization of the cement production, we utilize a ResNet50 deep learning model to classify and determine the operating conditions of the production line based on real-time thermal images, distinguishing between normal and abnormal operating conditions. Further, the DCS data records that are associated with the abnormal operating conditions are ruled out. In other words, the dataset used in the aforementioned model training in this section is the one that is frequently sampled from the production line data via the DCS system, with data records that reflect abnormal operating conditions eliminated, and is then preprocessed following the method in Section 4.4.
## 3 Study Area and Data Collocation  
The location of the study area is illustrated in Figure 3. It was the Trung Luong National Expressway section (with an overall length of nearly 140 km), a part of the Ho Chi Minh-Can Tho Expressway, Vietnam. The coordinates of the start and finish points of the proposed track are 10\({}^{\circ}\)25\({}^{\prime}\) north (N) and 106\({}^{\circ}\)18\({}^{\prime}\) east (E), and 10\({}^{\circ}\)41\({}^{\prime}\) N and 106\({}^{\circ}\)34\({}^{\prime}\) E, respectively. The project is anticipated to be completed at the end of 2019. With a designed speed of 120 km/h, the width of this road varies from 25.5 m (four-lane traffic in the first stage) to 33.0 m (six-lane traffic in the final stage) [34].  
Extensive geotechnical field testing was carried out to obtain the soil information by sampling and implementing soil tests including the standard penetration test [44], vane shear test [45], and cone penetration test [46]. The data were collocated from 73 boreholes where their depths varied from 12.5 to 70.5 m.  
For a total of 315 soil specimens, the information of 10 key factors, namely, depth of sample (DOP), percentage of sand, percentage of silt, percentage of clay, percentage of moisture content (MC), wet density (WD), liquid limit (LL), plastic limit (PL), plastic index (PI), and liquidity index (LI), was used to construct the input dataset, and the shear strength was considered to be the dependent variable. Figure 4 depicts the shear strength versus the independent factors. Also, the descriptive statistics of the used dataset are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Features**} & \multicolumn{8}{c}{**Descriptive Index**} \\ \cline{2-9}  & **Mean** & **SE * & **Median** & **SD ** ** & **SV *** & **Skewness** & **Minimum** & **Maximum** \\ \hline Depth of sample (m) & 16.21 & 0.71 & 13.00 & 12.57 & 157.94 & 1.02 & 1.00 & 52.00 \\ Sand (\%) & 8.40 & 0.73 & 2.40 & 13.04 & 169.92 & 1.85 & 0.00 & 57.30 \\ Silt (\%) & 54.01 & 0.70 & 56.10 & 12.46 & 155.30 & \(-\)0.30 & 20.00 & 83.70 \\ Clay (\%) & 37.28 & 0.66 & 37.60 & 11.68 & 136.35 & 0.01 & 10.40 & 63.90 \\ Moisture content (\%) & 48.47 & 1.35 & 49.90 & 23.90 & 571.41 & 0.12 & 17.10 & 90.30 \\ Wet density (kg/m\({}^{3}\)) & 1767.68 & 12.09 & 1690.00 & 214.60 & 46,054.17 & 0.09 & 1470.00 & 2150.00 \\ Liquid limit (\%) & 54.47 & 0.84 & 54.90 & 14.89 & 221.65 & \(-\)0.23 & 21.40 & 79.90 \\ Plastic limit (\%) & 24.72 & 0.30 & 24.40 & 5.39 & 29.05 & 0.02 & 12.90 & 35.90 \\ Plastic Index (\%) & 29.75 & 0.56 & 30.40 & 10.00 & 99.92 & \(-\)0.27 & 6.60 & 49.70 \\ Liquidity index & 0.70 & 0.03 & 0.79 & 0.46 & 0.22 & 0.04 & 0.01 & 1.73 \\ Shear strength (kPa) & 42.12 & 1.76 & 20.81 & 31.31 & 980.23 & 0.55 & 8.27 & 130.00 \\ \hline \hline \end{tabular}  
* Standard error; ** standard deviation; *** sample variance.  
\end{table}
Table 1: Descriptive statistics of the used dataset.  
Figure 3: Location of the studied track.
## 6 Conclusions  
1. The restraint mechanism of isolation piles on the surface settlement trough induced by shield tunnelling is analyzed based on pile-soil-tunnel interaction mechanism. The friction between pile and surrounding soil is the principal reason of restraint effect. Based on the Melan solution and Loganathan formula, the analytical solution is derived. The analytical solution could reflect the restraint effect of isolation pile on the surface settlement trough from the internal mechanism;
2. Taking the Changsha Metro Line 5 from East Laodong Road Station to Huaya Station, where shield tunnelling passes through Huaya International Hotel at a close distance as an engineering background. The FDM-DEM coupling technique is introduced to establish the numerical model. The coupling numerical model could simultaneously satisfy the engineering analysis scale and reflect the friction characteristics between isolation pile and soil particles;
3. The FDM-DEM coupling model can simulate the impact of different ground loss rates on surface settlement trough by deleting and activating preset circle walls. The affected zone above the tunnel top has a wider range and a more uniform variation after shield tunnelling without isolation pile, while the range of affected zone decreases under the influence of isolation pile. The existence of isolation pile blocks the continuous development of soil displacement;
4. Comparing with field measured data, the applicability and reliability of the analytical solution and the FDM-DEM coupling model proposed in this study are verified. The ultimate surface settlement trough induced by shield tunnelling presents an asymmetric distribution due to the restraint effect of isolation pile on surrounding soil. The maximum surface settlement with isolation piles is less than that without isolation piles. Meanwhile, when isolation piles are pre-installed on one side of the tunnel, surface settlement tends to develop more on the side without isolation piles.  
Conceptualization, Y.S.; methodology, Y.S.; validation, K.H., Y.S. and X.K.; investigation, Y.S. and X.H.; resources, Y.S.; data curation, Y.S. and R.L.; writing--original draft preparation, Y.S.; writing--review and editing, K.H.; visualization, Y.S. and Q.W.; supervision, K.H.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52078060, the National Science Foundation of Hunan Province, grant number 2020 JJ4606, the International Cooperation and Development Project of Double-First-Class Scientific Research in Changsha University of Science & Technology, grant number 2018 IC19 and the Innovative Program of Key Disciplines with Advantages and Characteristics of Civil Engineering of Changsha University of Science & Technology, grant number 18 ZDXK05.  
Not applicable.  
**Data Availability Statement**: Some or all data and models from the study are available from the corresponding author by request.  
The authors declare no conflict of interest.
# Modeling and Performance Analysis of Satellite Network Moving Target Defense System with Petri Nets
## 3 Related Work
The authors concluded from the theoretical analysis that the satellite network under dual-users makes the average delay greater due to the presence of resource competition. Ref. [39] modeled each operating phase of the microsatellite system separately based on Time Petri Nets (TPN). This research work was very interesting, but unfortunately the results were not very exploitable. To facilitate a quick overview of these research works, Table 1 summarizes the literature presented in this section. In summary, researchers have performed a wide range of applications in protocol correctness verification, availability analysis, fault detection, and performance evaluation of satellite networks based on Petri Net. However, in terms of performance evaluation, the aforementioned works are almost focused on the satellite network itself, application protocols or its networking mode. As far as we know, there is no research on performance evaluation of satellite networks based on active defense, and there is a lack of theoretical guidance for the deployment of proactive security mechanisms. Consequently, in this paper, we propose a performance evaluation scheme of satellite network moving target defense system based on SPN.
## 4 Measurement Quality Analysis  
When a signal is received and processed by the receiver, code-phase and carrier-phase measurements are generated. For precise positioning applications, algorithms such as the real-time kinematics (RTK) algorithm use carrier-phase measurements to calculate positioning results [25; 28]. If the baseline length between two receivers is less than 10 km and the receivers are located at similar altitudes, the double difference algorithm can remove most of error in the measurements except receiver noise and multi-path error [29]. For the RTK algorithm, the stability of the measurement is most important. As a result, the only thing that needs to be analyzed is whether the stabilities of different GNSS measurements are on the same level. In order to achieve centimeter-level positioning, resolving integer ambiguity is the most important issue. A stable integer ambiguity solution requires a stable carrier measurement [30]. Therefore, analyzing the stability of the measurement is very important for precise positioning applications. For the general user, the code-phase measurement is accurate enough. However, the error sources in the ranging measurement, including satellite clock and orbit errors, ionospheric error, tropospheric error, multipath error, receiver clock bias, and receiver thermal noise, influence positioning performance [17]. By using the correction model, a GPS and BDS user can acquire more accurate positioning results. Unfortunately, the general models cannot remove all the errors in the ranging measurement [31]. Because the location of the reference station used in  
Figure 12: BDS cycle slip with respect to C/N\({}_{0}\) and satellite elevation angle.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **GPS** & \multicolumn{3}{c}{**BDS**} \\ \hline
**Orbit** & MEO & GEO & IGSO & MEO \\ \hline
**Mean C/N\({}_{0}\)** & 34.1 & 36.0 & 30.7 & 43.3 \\
**Mean Ele. C** & 15.4 & 41.7 & 9.4 & 27.7 \\ \hline \hline \end{tabular}
\end{table}
Table 8: SQA3 statistical results.  
this research is known, an accurate range between the satellite and the receiver can be calculated. This research shows the ranging residual difference for GPS and BDS after using all models to remove the error sources. MQA is thus divided into four parts:  
1. MQA1: Measurement stability analysis
2. MQA2: Double difference zero-baseline analysis
3. MQA3: Measurement error source analysis
4. MQA4: Measurement pseudorange residual analysis  
The data used in this section are the same data used in Section 2, which were recorded on the 22 nd of July 2014 for 24 h. The raw measurements are separated into code-phase pseudorange and carrier-phase observables. These two measurements were directly output by the receiver without any smoothing filter or data transformation. The data were recorded at a rate of 1 Hz. In this section, the statistical results were estimated using all the data. The total numbers of data points for GPS and BDS were 870,874 and 899,137, respectively.