esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Future Landslide Identification Using InSAR Techniques
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Landslide Inventory by Field Survey** & **Landslide Inventory by Field Survey and InSAR** \\ \hline Number of landslides & 93 & 129 \\ Altitude range & \textless{}3415 m & 25 potential landslides at high altitudes \textgreater{}3415 m \\ Slope distribution & Mainly concentrated in 20\({}^{\circ}\)–30\({}^{\circ}\) & Mainly concentrated in 20\({}^{\circ}\)–30\({}^{\circ}\) but 37.5\% and 70\% increases \\ Relief amplitude & Mainly concentrated in 45–58 m & in the ranges of 20\({}^{\circ}\)–30\({}^{\circ}\) and 30\({}^{\circ}\)–40\({}^{\circ}\) \\ Distance to rivers & Mainly distributed within 500 m & Dominant relief amplitude expands to 45–69 m \\ Distance to rivers & Mainly distributed within 500 m & Some landslides at a distance of greater than 1500 m from rivers are observed \\ Distance to road & Mainly distributed within 100 m & 25 landslide sites in an area located more than 250 m away from the roads \\ Slope curvature & Mainly located in [\(-\)1, 1] & Mainly located in [\(-\)1, 1] \\ Development pattern & Controlled by the strata and wedges & Not only controlled by the strata and wedges but also the combinations of strata bedding planes and wedges \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of findings in the current study. the landslide boundaries using different methods. In field investigation, the boundaries are usually determined by the presence of distinct cracks or prior experience and might not be precise when this evidence is not sufficient on a slope. However, the InSAR technique determines the boundaries by differentiating areas with ground deformation from areas without deformation with high accuracy [83]. Therefore, the InSAR results provide more precise landslide boundaries in the satellite observation geometry [84]. It also should be clarified that the actual landslide boundary may not be the same as that determined by InSAR but should at least cover the deformation area, considering the geometric effects of SAR images. In other words, the landslide boundary should be the union set of the field investigation and InSAR results. Thus, in order to improve the accuracy of landslide boundary determination, one approach is to combine the InSAR results with field investigation and another is to apply multi-sensor data with different observing directions. In summary, InSAR techniques can significantly improve the results of field investigation for landslide identification. In future efforts on studies of potential landslides, improved results can be obtained by using multi-sensor and long-wavelength SAR data and combining InSAR results and field investigation.
## 3 Results and Discussion
### Band Gap and Photocurrents  
Considering that the band structure plays a significant role in determining the photocatalytic activity of semiconducting materials, Figure 4a displays the UV-Vis absorption spectrum which shows a wide and strong absorption in the whole visible range of 400-700 nm by natural wolframite. Moreover, depending on the Kubelka-Munk formula, the optical absorption coefficient and the band gap energy (direct gap semiconductor) follow the Tauc plots as (\(\alpha\)h\(\
u\))\({}^{2}\) = A(h\(\
u\) - E\({}_{\text{g}}\)), where \(\alpha\), h, \(\
u\), and A are absorption coefficient, [PERSON]'s constant, light frequency, and a constant, respectively [34]. As shown in  
Figure 3: Powder XRD patterns (**a**) and Raman spectrum of natural wolframite (**b**).  
Figure 2: SEM image of natural wolframite (**a**) and energy dispersive X-ray spectrometry (EDX) mapping of different elements (**b–e**).  
Figure 4a (inserted picture), the estimated band gap (E\({}_{\text{g}}\)) was about 1.7 eV as calculated by the intercept of the tangent. Previous study indicated that a hydrothermal synthetic FeWO\({}_{4}\) photocatalyst had a band gap of 2.16 eV [31] and synthesized MnWO\({}_{4}\) had a band gap of 2.81 eV [29]. However, natural wolframite had a narrower band-gap and considerably better optical absorption in the visible region than MnWO\({}_{4}\) and FeWO\({}_{4}\), which should produce more photoelectron/hole pairs under visible-light illumination. Therefore, we concluded that natural wolframite was a great photoactive material and improved photocatalytic performance should be conferred by its considerably broadened visible light response.  
For elucidating the photocatalytic activity of natural wolframite, electrochemical measurements corresponding to different applied potentials were performed by LSV dark/light curves under N\({}_{2}\) and O\({}_{2}\) conditions. Figure 4b compares the dark/light LSV curve for a wolframite-coated graphite electrode at a potential of \(-\)0.65-0.0 V. With N\({}_{2}\), the current densities were similar to each other, owing to the absence of an electron acceptor. However, when O\({}_{2}\) appears, higher current density was observed. Notably, a measurable photocurrent was observed. The average photocurrent density was \(-\)319 mA\(\cdot\)m\({}^{-2}\) at \(-\)0.6 V versus SCE, which was 175% greater than the dark current density (\(-\)182 mA\(\cdot\)m\({}^{-2}\)) at the same voltage. The enhanced cathodic photocurrents indicated that the wolframite had p-type semiconducting nature. In addition, the value of the photocurrent was influenced by the potential change and the increment of current density was more significant with higher cathodic potentials. As a semiconducting mineral with a suitable band gap of 1.7 eV, wolframite absorbed visible light energy and generated photoexcited electrons/holes, suggesting the wolframite has great potential to be used as a cathode photocatalyst in MFCs.
## 1 Introduction
The previous studies have made significant theoretical and numerical advancements in understanding bedforms and their effects. However, there remains a lack of the quantitative analysis of bedforms and the thorough examinations of empirical data. Moreover, the existing empirical formulas require enhancements in terms of applicability and accuracy, especially under complex bed conditions such as dune bedforms. Prior research has not adequately distinguished between the features of 2D and 3D dune bedforms, overlooking their differences in development and sediment transport capabilities, which results in the misinterpretation of dune characteristics. The traditional formulas for sediment transport rates have been designed for uniform sediment and are less effective when applied to dune bedforms, leading to inaccuracies in predicting the sediment transport processes. Furthermore, the resistance characteristics of dune bedforms and the impact of bedload transport have not been comprehensively explored, resulting in an incomplete understanding of the development of dune bedforms and sediment transport mechanisms. To address these limitations, this study established a flow-sediment movement and 3D bedform measurement system within a large-scale flume and conducted a series of movable-bed dune experiments. The objectives of this study were to (1) quantitatively analyze the features of 2D and 3D dune bedforms, (2) investigate the impact of dune bedforms on the bedload transport rate, and (3) examine the relationships between bed resistance, dune morphology, and sediment transport. The goal was to elucidate the processes by which dune bedforms influence sediment transport and bed resistance through experimental observations and theoretical analysis, thereby enhancing the accuracy of sediment transport predictions and providing scientific support for river management and engineering practices.
## 2 Study Area and Materials  
### Study Area  
The Heihe River Basin (HRB) (37.65\({}^{\circ}\)-42.71\({}^{\circ}\) N, 97.05\({}^{\circ}\)-102.12\({}^{\circ}\) E), with an area of approximately 143,300 square kilometers, is the second-largest endorheic basin in Northwest China. The HRB includes upstream, midstream, and downstream areas. Multi-element, multi-scale, and distributed and incorporated satellite-airborne-ground-based observation networks have been established in the region, including the Watershed Allied Telemetry Experimental Research (WATER) experiment (2007-2011) [51] and the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment (2012-2015) [52; 53; 54; 55]. The average altitude of the Zhangye oasis, which belongs to the typical temperate continental arid climate, is about 1770 m. The average annual temperature is 7.3 \({}^{\circ}\)C, the average annual relative humidity is 52%, the main wind direction is northwesterly, the average annual precipitation is 130.4 mm, and the average annual evaporation is 2003 mm (small evaporation pan observation value).  
Figure 1 shows the area of interest, which is located in the midstream oasis area of the HRB. Oasis cultivation is located along the Heihe River and surrounded by the Gobi Desert. This agricultural area covers 18 irrigation districts and more than 4000 pumping wells. The study area contains 12 of these irrigation districts (Daman, Shangsan, Ganjun, Yingke, Xigan, Wujiang, Liyuan River, Shahe, Liaoquan, Yanuan, Banqiao, and Pingchuan; Figure 1). In 2012, summer maize was mainly planted in the oasis area. The soil texture of the oasis is mainly pink loam soil. Irrigation of the farmland is mainly channel irrigation. The irrigation water is from the nearby Heihe River using traditional flood irrigation during growth seasons.
## 2 Background
### Satellite-to-Ground Optical Communication  
To enable satellite-to-ground optical communication, low earth orbit satellites are being equipped with laser transmitters. Line of sight is required for transmission between the telescope and a satellite and is only maintained for a few minutes during the satellite pass, after which the telescope must reposition to track another satellite.  
The atmosphere has a characteristic turbulence timescale on the order of \(\sim\)1 ms, which varies significantly with the satellite's elevation angle and turbulence conditions. This value can be as short as 0.2 ms and as high as 10 ms. Turbulence is generally the strongest at the lowest elevations due to the effective thickness of the atmosphere. However, the ground layer has a longer coherence time when compared to the high-altitude jet stream where the timescales are much shorter. The optical path from the satellite to the telescope is also changing due to the continuously changing satellite elevation angle. If an approximate solution can be found within a few milliseconds, the atmosphere can be considered to be in a quasi-static state and a static turbulence profile for the purposes of training can be considered valid. This paper is focused on optical ground stations near sea-level and therefore we adopt 1 ms as the target coherence timescale, neglecting the rapidly changing high altitude optical path column and high wind speeds.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 3 Atomic Clocks and Time Scale Metrics
### Performance Metrics for Time Scale  
Evaluation metrics for atomic clocks include accuracy and stability.
# A Semi-Analytical Approach for the Linearized Vibration of
## 3 Linearized Vibration of Beams with the Effect of Static and Thermal Load
### Solution Procedures
#### 3.2.3 Linearized Random Vibration Analysis  
Following the linearized modal analysis, the mode-superposition technique is employed to determine the random response analysis of beams with the effect of both static and thermal loads. The acoustic pressure load is assumed to be a harmonic excitation  
\[P_{1}(t)=e^{i\omega_{p}t} \tag{27}\]  
in which \(\omega_{p}\) is the frequency of the acoustic pressure load. The analysis in this study omits the effects of large deformations caused by thermal stress and static forces on structural damping. And the fraction of critical damping is adopted in to evaluate the structural response under noise loading instead damping coefficient. The vibration equation with critical damping is converted into modal form as follows:  
\[\ddot{q}_{j}(t)+2\zeta_{j}\omega_{j}\dot{q}_{j}(t)+\omega_{j}^{2}q_{j}(t)=F_{ j}\omega_{j}^{2}e^{i\omega_{p}t}\ \ (j=1,2,\cdots) \tag{28}\]where \(q_{j}\) is the modal coordinate. \(\omega_{j}\) and \(\zeta_{j}\) are the natural circular frequency and the fraction of critical damping of the \(j\)th mode. \(F_{j}\) is the pressure amplitude in modal coordinates and has the form  
\[F_{j}=\frac{1}{\omega_{j}^{2}}\int_{0}^{L}\widetilde{\phi}_{j}(x)\mathrm{d}x \tag{29}\]  
where \(\widetilde{\phi}_{j}\) is the \(j\)th normal mode in modal coordinates. The complex frequency response function \(H_{j}(\omega_{p})\) of the structure under unit excitation is obtained  
\[H_{j}(\omega_{p})=F_{j}\frac{1-s^{2}-2\mathrm{i}\zeta s}{\left(1-s^{2}\right) ^{2}+\left(2\zeta s\right)^{2}} \tag{30}\]  
in which \(s_{j}=\omega_{p}/\omega_{j}\).  
The displacement complex frequency response function \(H(x,\,\omega_{p})\) is solved by the mode-superposition method using the first \(n\) modes.  
\[H(x,\omega)=\sum_{j=1}^{n}\widetilde{\phi}_{j}(x)H_{j}(\omega) \tag{31}\]  
The acceleration complex frequency response function \(H_{acc}(x,\,\omega_{p})\) can be obtained by  
\[H_{acc}(x,\omega_{p})=\omega^{2}H(x,\omega_{p}) \tag{32}\]  
Considering that the acoustic pressure is a stationary random process and the dynamic acceleration response under random excitations can be calculated by  
\[S_{acc}(x,\omega_{p})=H_{acc}(x,\omega_{p})S_{F}(\omega_{p})H_{acc}^{\mathrm{ T}}(x,\omega_{p}) \tag{33}\]  
where the \(S_{F}\) and \(S_{acc}\) are the power spectral density (PSD) functions of dynamic excitation and acceleration response, respectively.
# Image Mosaicing Applied on UAVs Survey
## 3 Stitching Methods
### Feature-Based: Global Single Transformation
#### 3.1.6 BRISK  
The Binary Robust Invariant Scalable Keypoints (BRISK) algorithm [66] is a feature point detection and description algorithm with scale invariance and rotation invariance. It constructs the feature descriptor of the local image through the grayscale relationship of random point pairs in the neighborhood of the local image and obtains the binary feature descriptor. The key concept of the BRISK descriptor makes use of a pattern used for sampling the neighborhood of the key point. Two subsets of distance pairings are defined: one each for the short-distance and long-distance pairings, \(S\) and \(E\), respectively. BRISK loses information about the image colors, which can provide more key points for matching points. Owing to this reason, a CBRISK algorithm is proposed to maintain the information of the RGB color channels [72]. To decrease computation time, the SBRISK development shifts the binary vector rather than rotating the image pattern or constellation, as many other descriptors do [73].
## 2 Results and Discussion
### Optimization of the Extraction Procedure
#### 2.1.1 Screening Design
In this type of fiber, the extraction mechanism is based on adsorption into pores, which is a stronger and more efficient mechanism than absorption, making this fiber more suitable for trace analysis. Analyzing the screening design results, the larger the sample volume, the greater the instrumental response. Therefore, the maximum sample volume tested, 10 mL, was chosen (half of the vial capacity). These results are consistent with two other studies found in the literature that use HS-SPME for the analysis of VMSs in water, although GC-MS was used in both [12; 13].
## 4 Experimental Environments and Findings  
This section considerably evaluates our suggested ConvResBiGRU-SE model via experimental validation on two public HAR datasets: UCI-HAR and WISDM. Initially, we outline the experimental setup, detailing the hardware configuration, software environment, and implementation specifics. Subsequently, we provide a comprehensive performance analysis, contrasting our model with various baseline methods utilizing standard assessment indicators, including accuracy, precision, recall, and F1-score. The experimental findings demonstrate the efficacy of our suggested approach in identifying human actions from wearable sensor data.
## 3 Material and Methods
### Machine Learning Model Descriptions
#### 3.3.2 Decision Tree and Random Forest  
Decision trees are a modeling technique used in classification and regression problems and are widely preferred in the fields of data mining and machine learning [33]. A decision tree is a hierarchical structure that represents a dataset and consists of a set of decision rules. The purpose of this structure is to separate the samples in the dataset into classes using simple decision structures. While creating the decision tree, the feature that best divides the dataset is selected at each node. Starting from the root of the tree, each branch classifies the data or makes regression predictions until the last leaf nodes. Advantages of decision trees include the ease of understanding of the model, the low need for data preprocessing, and the ability to work with both numerical and categorical data. However, decision trees can be prone to overfitting; this is when the model fits the training data very well but performs poorly on new, unseen data [25].  
Random forest (RF) is a powerful machine learning algorithm based on combining predictions produced by multiple decision trees, used for classification and regression problems [33]. By combining the predictions made by decision trees, RF minimizes the overfitting problem that a single decision tree may encounter and generally produces more stable results. This method, developed by [PERSON] in 2001, is based on the principle of training each decision tree on randomly selected subsets and thus increases the diversity and robustness of the model. Random forests are preferred in many application areas, thanks to their capacity to determine the importance of variables, especially in high-dimensional datasets. The success of this method can be further increased by the appropriate selection of the number of variables and number of trees depending on the size and complexity of the dataset.
# Simplified Integrity Checking for an Expressive Class of Denial Constraints
## 4 A Simplification Procedure for Obtaining a Pre-Test  
We report here the main steps of the simplification procedure described in [15], whose operators will be used as building blocks in the next section to handle an expressive class of integrity constraints. The procedure consists of two main steps, After and Optimize, which, together, are the main components of the simplification procedure Simp, as sketchily described in Figure 1. The former receives a schema \(S\) and an update \(U\), and builds a schema \(S^{\prime}\) that, while applying to the current state, represents the given integrity constraints in the state after the update \(U\). The latter eliminates redundancies from After's output \(S^{\prime}\) and exploits the consistency assumption, i.e., that the integrity constraints in \(S\) hold in the state before update \(U\), so as to emit a simplified schema \(S^{*}\). We observe that these operators work at the program level instead of the data level, so they will target program specifications (in datalog) rather than datasets. In the experiments and examples discussed here and in Section 5, we shall indeed tests our operators against specifications of integrity constraints (most of which are taken from the cited literature). For easier reference, Table 1 summarizes the main operators and symbols defined in this section, along with a synthetic explanation of how they work. A mere condition for checking integrity without actually executing the update is given by the notion of weakest precondition [6; 84; 83]. **Definition 3** (Weakest precondition). : _Let \(S=\langle IDB,\Gamma\rangle\) be a schema and \(U\) an update. A schema \(S^{\prime}=\langle IDB^{\prime},\Gamma^{\prime}\rangle\) is a weakest precondition (WP) of \(S\) with respect to \(U\) whenever \(D\models\Gamma^{\prime}\Leftrightarrow D^{U}\models\Gamma\) for any database \(D\) based on \(IDB\cup IDB^{\prime}\)._  
To employ the consistency assumption, we extend the class of checks of interest as follows. **Definition 4** (Conditional weakest precondition). : _Let \(S=\langle IDB,\Gamma\rangle\) be a schema and \(U\) an update. Schema \(S^{\prime}=\langle IDB^{\prime},\Gamma^{\prime}\rangle\) is a conditional weakest precondition (CWP) of \(S\) wrt \(U\) whenever \(D\models\Gamma^{\prime}\Leftrightarrow D^{U}\models\Gamma\) for any database \(D\) such that \(D\models\Gamma\)._  
A WP is also a CWP but the reverse does not necessarily hold.
## 3 Simulation Results and Analysis
### Simulation and Analysis of Short-Term Collision Probability
#### 3.1.2 Results and Analysis  
In accordance with the filtered data, a large portion of the space targets were close to each other for a long time. For this part of the target data, the orbital period of the space target was used to calculate the closest distance of the trajectory point in each period, and the closest distance to the trajectory point and its vicinity were considered. This trajectory point was the most likely to collide, and other trajectory points would not collide, thereby eliminating other trajectory points in each orbital cycle of the target. The approaching situation of the space target within 7 days was obtained after eliminating the long-term approaching target data. Figure 12 shows the statistics for different types of proximity situations.  
As shown in Table 2, many close points will be missed when the ellipsoid error is small due to the small relative screening distance. With the gradual increase in error, the relative screening distance and the number of targets that meet the proximity conditions increased. The time of the closest track point retained was filtered in accordance with the  
Figure 11: Distribution map of the number of space targets remaining in the apogee–perigee screening along the semi-major axis of the orbit.  
relative distance, and two points were selected before and after the track point. Lagrangian interpolation was used to calculate the closest moment and the motion state of the two targets at that moment and bring them into the Laplacian-based method. The collision probability calculation method of the Si transform was used to obtain the collision risk situation of the space target. Figure 13 shows the collision probability of space rendezvous targets greater than \(10^{-5}\) times.  
As shown in Table 3, the number of collisions between constellation satellites and space debris greater than \(10^{-5}\) reached seven times within the 7-day simulation period. The collision risk increased by about 30% to 40% after the constellation was deployed  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Close Target Type** & **Day One** & **Day Two** & **Day Three** & **Day Four** & **Day Five** & **Day Six** & **Day Seven** \\ \hline Number of times close to all targets & 7 & 50 & 101 & 238 & 620 & 894 & 1371 \\ Number of times close to constellation satellites & 3 & 11 & 20 & 46 & 126 & 191 & 283 \\ Number of close encounters between space debris objects & 4 & 39 & 81 & 192 & 494 & 703 & 1088 \\ \hline \end{tabular}
\end{table}
Table 2: Close proximity of space targets in the shell.  
Figure 12: The statistics for different types of proximity situations.  
Figure 13: Collision frequency of space rendezvous targets greater than \(10^{-5}\) times.  
compared with before the deployment of the constellation by comparing the number of constellation satellites with a collision probability greater than \(10^{-5}\) and the number of collisions between space debris with a probability greater than \(10^{-5}\).