esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Literature Review on DAS+PRS
### Feature Extraction  
Feature extraction approaches for DAS+PRS are divided into the three categories described in Section 3.2: time domain-based methods, frequency domain-based methods, and time-frequency domain-based methods. A summary of the systems that belong to each group is presented in Table 7. Next, we describe the main proposals in the literature.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reference** & **Feature Extraction Category** & **Feature Extraction Method** \\ \hline
[3] & & \\
[28] & & Signal Phase \\
[31] & Frequency domain &
\begin{tabular}{c} Tandem features \\ (MLP from Energy in Frequency Bands) + normalization \\ \end{tabular} \\ \hline
[17] & & STFFT \\ \hline
[18] & Time domain & LCR \\ \hline
[26] & Time-Frequency domain &
\begin{tabular}{c} DWT \\ (low + high free, decomposition) \\ \end{tabular} \\ \hline
[19] & Frequency domain &
\begin{tabular}{c} Singular Spectrum Analysis \\ \end{tabular} \\ \hline
[21] & Other &
\begin{tabular}{c} Morphology from \\ image processing \\ \end{tabular} \\ \hline
[46] & Frequency domain &
\begin{tabular}{c} Energy related from FFT \\ \end{tabular} \\ \hline
[27] & Time-Frequency domain &
\begin{tabular}{c} DWT \\ (low + high free, decomposition) \\ \end{tabular} \\ \hline
[23] & Frequency domain &
\begin{tabular}{c} Energy in Frequency Bands + normalization \\ \end{tabular} \\ \hline
[12] & & Raw signal \\
[13] & Time domain &
\begin{tabular}{c} Raw signal \\ \end{tabular} \\ \hline
[14] & & Normalized Raw Signal \\ \hline
[15] & & FFT + PSD \\ \hline
[22] & Frequency domain &
\begin{tabular}{c} Energy in Frequency Bands \\ \end{tabular} \\ \hline
[25] & & Energy in Frequency Bands + normalization \\ \hline
[45] & & Energy based from FFT + PCA \\ \hline
[20] & Time-Frequency domain &
\begin{tabular}{c} Energy in Frequency \\ Bands from DWT \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of the feature extraction (FE) approaches reported in the literature for DOFS+PRS. ’LCR’ stands for level crossing rate, ’MLP’ for multi-layer perceptron, ’STFFT’ for short time fast Fourier transform, ’PSD’ for power spectral density, ’PCA’ for principal component analysis, ’DWT’ for discrete wavelet transform, and ‘freq.’ for frequency.
###### Abstract  
Heavy metal contamination has long been a tough challenge. Recently, enzyme-induced carbonate precipitation (EICP) has been proposed to handle this problem. This paper aims to explore the efficacy, process, and mechanisms of EICP using crude sword bean urease extracts to remediate Zn\({}^{2+}\), Ni\({}^{2+}\), and Cr(VI) contamination. A series of liquid batch tests and geochemical simulations, as well as microscopic analyses, were conducted. The liquid batch test results show that Zn\({}^{2+}\), Ni\({}^{2+}\), and Cr(VI) can be effectively immobilized by the EICP method, and the highest immobilization percentage was observed for Zn\({}^{2+}\), reaching up to 99%. Ni\({}^{2+}\) and Cr(VI) were immobilized at 62.4% and 24.4%, respectively. Additionally, the immobilization percentage of heavy metals increased with the concentration of added Ca\({}^{2+}\). The simulation results and XRD results reveal that the organic molecules in crude sword bean urease can promote ZnCO\({}_{3}\), Zn(OH)\({}_{2}\), Zn\({}_{5}\)(CO\({}_{3}\))\({}_{2}\)(OH)\({}_{6}\), and NiCO\({}_{3}\) precipitation. The FTIR and SEM-EDS results provide evidence for heavy metal adsorption by the functional groups in crude urease and calcium carbonate. The liquid batch test results, as well as the simulation results and the microscopic analysis results, indicate that the mechanism of EICP in heavy metal remediation can be summarized as biomineralization to form heavy metal carbonate precipitates and metal hydroxide precipitates, adsorption by calcium carbonate, and adsorption or complexation or promoting nucleation by organic molecules.  
enzyme-induced carbonate precipitation; crude sword bean urease; heavy metal remediation; calcium carbonate +
Footnote †: journal: Applied Sciences
## 4 Results and Analysis
### Evaluation and Validation
A ROC curve is a graph used to evaluate the performance of a classification model, with the horizontal coordinate being the false-positive rate and the vertical coordinate being the true-positive rate. The four solid lines of different colors in the curve reflect the model's ability to classify tunnel components in four service states. The closer the position of the curve to the upper left corner, the better the model's performance, i.e., its true classification rate is higher, and its false-positive rate is lower. The Area Under the Curve (AUC) can be used to quantitatively judge the accuracy of a model. In Figure 5a-c, it can be seen that the LGBM model developed in this study has high prediction accuracy  
Figure 4: A comparison of the evaluation metrics: (**a**) structural section; (**b**) civil section; and (**c**) non-structural section. for the three types of components. For structural components, its prediction ability for components with service status C (mediocre) is relatively poor, with an AUC of 0.86; for transportation and non-structural components, its prediction ability for components with service status B (good) is also relatively poor, with an AUC of 0.87.
# Two-Dimensional Histogram Shifting-Based Reversible Data Hiding for H.264/AVC Video
## 2 HS-based RDH Technique
### Proposed 2D HS-Based RDH Schemes
#### 3.2.3 Data Extraction and Video Recovery  
In the proposed scheme, the data extraction and video recovery can be completed by the inverse operation of embedding. From Figures 4 and 5, it can be observed that the in-degree of each point is one. Therefore, each coefficient pair in the marked video denoted by the point \((x^{\prime},y^{\prime})\) can be uniquely restored to the original coefficient pair in the cover video denoted by the point \((x,y)\) by following the opposite direction of the arrow ending at \((x^{\prime},y^{\prime})\), and at the same time, the embedded data can be obtained according to the rules of shifting \((x,y)\) to \((x^{\prime},y^{\prime})\).
## 3 Experiment
### Model Evaluation Indicators  
Given the distinct characteristics of IDC equipment data prediction and classification tasks, appropriate evaluation metrics are selected for regression and classification performance assessment. For regression tasks, which emphasize the accuracy of predicted values [18], we adopt the following widely used metrics: mean absolute error (MAE), mean squared error (MSE), and root mean squared error (RMSE). For classification tasks, which focus on discrimination correctness and robustness [19], we employ accuracy, precision, area under the curve (AUC), recall, and F1-score to provide a comprehensive evaluation.
# Investigation into the Flexural Toughness and Methods of Evaluating Ductile Concrete
###### Abstract  
The main purpose of this study was to investigate the flexural behavior of high-ductility fiber-reinforced concrete (HDC) and propose a suitable method for evaluating flexural toughness. The flexural strength, deformation, and toughness of HDC were investigated through four-point bending tests with specimens of \(40\times 40\times 160\) mm. The test parameters were fiber volume fractions (\(0\%\), \(1\%\), \(1.5\%\), and \(2\%\)), water-binder ratios (\(0.24\), \(0.26\), \(0.29\), and \(0.32\)), and ages (\(28\) d and \(56\) d). The experimental results showed that polyvinyl alcohol (PVA) fibers led to significant improvement in the flexural behavior of HDC due to its strain-hardening behavior and excellent crack dispersion capacity. The ultimate flexural strength of HDC with \(2\%\) PVA fibers of about \(15.32\) MPa showed an increase of up to \(221\%\). The deformation and flexural toughness ratios were \(23\) times and \(1.43\) times higher, respectively, than the specimens without fibers. A simple and practical method for evaluating the flexural toughness of HDC was proposed, which solved many problems with the existing methods. This method made full use of the peak load, which overcame the difficulty of identifying the initial crack information, solved the insufficient deflection limit, and provided a more comprehensive and accurate evaluation by selecting characteristic points evenly distributed throughout the loading process.  
high-ductility fiber-reinforced concrete; flexural behavior; toughness; evaluation method
###### Abstract  
Core switching on different links in optical networks enables network operators to allocate network resources more flexibly, so as to reduce the network request blocking ratio under limited resources. Facing a differentiated network environment and diversified user demands, network operators need to optimize multiple objectives that are independent and diversionary of each other, and to provide multiple resource allocation schemes whose objective values do not dominate each other. For the static routing, spectrum, and core assignment (RSCA) problem in elastic optical networks with multi-core fiber (MCF-EONs), there is no literature that simultaneously considers core switching and multiobjective optimization algorithms. This paper improves the existing models and algorithms to adapt to the RSCA problem. In this paper, the RSCA problem is formulated as an integer linear programming model to minimize both network request blocking and crosstalk ratios simultaneously by considering core switching and inter-core crosstalk. To solve the model efficiently, we, therefore, design a joint routing and core coding scheme supporting core switching and propose a multiobjective evolutionary algorithm based on decomposition with adaptation and multi-strategy fusion (MOEA/D-AMSF), which integrates the new mechanisms of hybrid initial population generation, adaptive crossover, and double-layer and multi-point mutation in different iteration stages. These new mechanisms accelerate algorithm convergence and enhance solution diversity. Simulation results show that the proposed algorithm can obtain more dominated and diverse solutions compared with the existing multiobjective algorithm without considering core switching.  
routing, spectrum, and core assignment (RSCA); hybrid evolutionary algorithm (HEA); joint routing and core coding; core switching; elastic optical networks with multi-core fiber (MCF-EONs) +
Footnote †: journal: Applied sciences
# Stability Analysis of the Huasushu Slope Under the Coupling of Reservoir Level Decline and Rainfall
## 1 Introduction
[21] analyzed monitoring data for the displacement field of Baiijaobao slope over the last 10 years, revealing the deformation evolution mechanism of this slope under three scenarios: rainfall, reservoir water level decline, and coupling of the two. [PERSON] et al. [22; 23; 24] studied the Baihetan reservoir area and explored the combined effects of water level fluctuation and rainfall, deepening our understanding of the landslide mechanism. Other scholars have further analyzed landslide causes through numerical simulations [3; 25; 26; 27; 28; 29; 30]. Although many studies have confirmed the impact of reservoir water level fluctuations on slope stability, these studies have focused on specific engineering cases and lacked a general analysis of slope behavior under different geological conditions and climatic influences. In addition, most of these studies have relied on two-dimensional or simplified models that only partially consider the three-dimensional complexity of slopes and the joint effects of multiple factors, such as water level fluctuations and rainfall. These simplifications limited the accuracy of predicting slope behavior in actual environments. To overcome these deficiencies, this study considers the Huasushu slope in Fengjie County; uses numerical analysis methods; establishes a model for the joint effect of reservoir water level fluctuations and rainfall; constructs two-dimensional and three-dimensional numerical models; conducts a detailed analysis of slope deformations under different working conditions; and proposes a landslide prediction model. Our results not only provide a theoretical basis for analyses of the Huasushu slope, but may also serve as a reference for slope stability control projects in similar geological conditions.
# Cell/Module Integration Technology with Wire-Embedded EVA Sheet
## 4 Conclusions  
The module technology proposed in this paper is to manufacture a wire-embedded EVA sheet module with a cell/module integrated process. The wire-embedded EVA sheet module was fabricated by varying the number of wires to 6, 12, 18, and 24 with wire diameters of 0.26, 0.32, and 0.36 mm. The module with a wire diameter of 0.36 mm and 12 wires showed the highest efficiency, at 20.28%. The series resistance was significantly reduced, however, and the short-circuit current loss did not significantly decrease, despite the increased optical loss as the wire diameter increased. In addition, when the number of wires was increased, the resistance of the finger electrode decreased because the length of the finger electrode between the wire and the wire decreased; however, the optical loss increased because of the shading area. The cell/module characteristics of the tabbing module and the wire-embedded EVA sheet module were compared. The ratio of the difference between the calculated values and the measurement value for power of the five-busbar tabbing module and wire-embedded EVA sheet module were approximately 3.245% and 2.619%, respectively. The power of the wire-embedded EVA sheet module was confirmed to be better than that of five-busbar tabbing module. It was possible to manufacture a high-power module that sufficiently compensated for the disadvantages of the tabbing module. In addition, the wire-embedded EVA sheet module can be applied to thin wafers because it can remove heat and mechanical stress generated by the solar cell owing to the high-temperature tabbing process.  
[PERSON] was the main author and carried out the experiments and analyzed the data. [PERSON] reviewed and analyzed the data and formatting. [PERSON] conceived the ideas of the analyzed results. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Korea Electric Power Corporation (Grant number: R17 XA05-1), the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20193020010650).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Experimental Results and Analysis  
### DQN Experimental Results and Analysis  
#### 4.1.1 Experimental Results and Analysis of Determining the Number of DQN Neural Networks  
The training results are shown in Figure 12 and Table 4, and the results of 100-140 experiments are taken as the experimental scores of different neurons in the final stage. Figure 13 is the boxplot of the results of 100-140 experiments in each group. Group3 had the highest average score and the lowest standard deviation. This proved that Group3 scored significantly higher than the other groups in the final stage.  
Figure 11: The complex underground scenario for training the LHD machine.  
Figure 12: Comparison of the training results of different neurons numbers in the hidden layers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Group** & **Mean** & **Standard Deviation** & **Minimum** & **Median** & **Maximum** \\ \hline
1 & \(-1.58001\) & 2.07777 & \(-5.11647\) & \(-1.54344\) & 1.82062 \\
2 & \(-14.39721\) & 1.09886 & \(-15.60397\) & \(-14.70304\) & \(-11.90487\) \\
3 & 3.08222 & 0.45751 & 2.67798 & 2.86441 & 4.21315 \\
4 & \(-7.8594\) & 1.7001 & \(-11.80333\) & \(-7.15154\) & \(-6.29695\) \\
5 & 0.39877 & 2.04473 & \(-1.75212\) & \(-0.21574\) & 4.82521 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Descriptive statistics of 100–140 training scores.  
Figure 13: Comparison of 100–140 training scores.  
To determine whether the autonomous driving action strategy of the LHD machine conformed to the expected trajectory, a further analysis of the actions taken by the LHD machine during the Group3 training was performed, and the actions taken by the LHD machine in each event in the last stage of the training were recorded. As shown in Table 5, the LHD machine initially carried out a straight operation. When it runs to approximately step 5, the LHD machine carried out calibration and was far away from the right tunnel. When it was roughly running in the center of the tunnel, it ran to the right again to ensure that the direction of the LHD machine was consistent with the center of the tunnel, which was roughly consistent with the expected walking path of the LHD machine; and the score of each event also reflected a good overall operation. Therefore, the experiment showed that when the number of neurons was set to 15, the experiment could achieve good results.
## 3 Typical Space-Based Situational Awareness Facilities

Table 1: Key performance parameters of space situational awareness satellite payloads. diversity of space targets, there is a growing trend towards multi-spectral joint detection, such as combining visible light with infrared detection or utilizing multi-band infrared detection. Regarding the selection of monitoring constellations and the number of satellites, with the advancement of small satellite technology and space satellite formation techniques, space situational awareness payloads are increasingly integrated with small or multiple satellites. Additionally, a single monitoring satellite may carry various sensors to cover a broader monitoring range and multiple spectral bands. In the choice of space camera, off-axis three-mirror optical systems and even more complex optical systems are becoming more widely used to address the growing demand for wide-field space-based surveillance.
## 3 Results and Discussion
### Imaging in Bright Daylight  
Next, we performed a series of outdoor test in bright daylight. The measurements were made at stand-off distances of 150 m (microwave radar antenna), 1900 m (building baluster), 2500 m (house), and 4000 m (tower crane)--these distances correspond to accessible locations with unobstructed views from our roof laboratory facility. These distances were measured by our LiDAR system based on the time of flight (TOF) function. Given that strong solar background illumination is the primary source of noise, effective denoising capabilities are crucial. Current LiDAR system employs a combination of temporal, spatial, spectral, and polarization filtering techniques to address this issue. We conducted scans at varying distances of a microwave radar antenna, building baluster, residential structure, and tower crane. The resulting imaging outcomes are presented in Figure 4 with a reconstructed image pixel resolution of 128 \(\times\) 128 and per-pixel acquisition time of 0.1 ms. The profiles and details can be clearly observed. The steel structure size of the tower crane, measuring approximately 5 cm in width, showcases a remarkable spatial resolution of the LiDAR system surpassing 5 cm at 4000 m, corresponding to an angle resolution of 0.01 mrad. The detection range is limited by sunlight interference during daytime testing, and at greater distances, the SNR decreases significantly, making image reconstruction challenging. In addition to increasing the laser power, enhancing the per-pixel acquisition time can further enhance the range. However, since the coexistence between signal and noise impedes significant enhancement of the SNR, the degree of increased distance is unsatisfactory.
# Recent Developments in Materials Science for the Conservation and Restoration of Historic Artifacts
## 6 Smart Materials--A Potential Breakthrough in Heritage Science
### Smart Materials--Potential Applications in Conservation Science  
Smart materials have a wide range of potential applications in the field of cultural heritage conservation, including both direct (e.g., as adaptive supports and barriers, materials for different restoration technologies, and antimicrobial treatments) and indirect use (such as antimicrobial for environmental monitoring and protection).  
Smart materials can be used to reinforce or create adaptive supports that respond to changes in load or environmental conditions. For example, shape-memory alloys can be applied for retrofitting cultural heritage buildings, especially those found in seismic areas, in order to provide optimal support for such delicate artifacts [115]. The development of smart coatings can protect cultural heritage artifacts by serving as protective barriers against environmental pollutants, while their further functionalization can provide additional characteristics, such as thermal and mechanical resistance, or even UV protection [116; 117].  
Biocompatible smart adhesives can be used for repairing artifacts while allowing for easy removal if future restoration work is needed [118]. These adhesives may respond to specific triggers such as heat or solvent exposure. Certain smart polymers that exhibit either intrinsic antimicrobial properties or act as carriers for potent antimicrobial agents help prevent biological deterioration (e.g., biofilm formation) in different support materials [119; 120].  
As indirect applications, smart materials integrated with sensor technologies can monitor environmental conditions such as temperature, humidity, and light exposure in real time [102]. These data allow conservators to take proactive measures to protect artifacts from potential damage [121]. Finally, smart materials designed with self-regulating capabilities that adjust their properties based on environmental stimuli can be used to develop energy-efficient windows that can change opacity based on sunlight intensity, helping regulate indoor climate conditions [122].
## 1 Introduction
Because of the KTP's immunity to chemical reactions in some chemical solutions, it can provide possible application in harsh environments. In this study, simulations and experiments have been evaluated to indicate the advantages of the KTP-based birefringence sensor for fluidic liquid concentration measurements. In particular, the varied concentrations of hydrochloric acid (HCL) have been successfully measured with a very low concentration level of 0.0088 wt. %.