esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Reduced Element
### Main Ideas  
In this subsection, the main ideas for the reduced element are illustrated by a sample example from the practical computation. Without losing generality, the equation of motion with initial displacement \(u(0)=0\) is solved respectively by a quadratic and linear element on an initial step \([0,h]\) with the step-size (element length) \(h=3.5\). Related solutions are shown in Figure 2, in which \(u\) is the exact solution, \(u_{\text{L}}^{h}\) and \(u_{\text{Q}}^{h}\) denote the solutions of the conventional linear and quadratic elements respectively, and \(u_{\text{R}}^{h}\) is the solution of the reduced linear element. From Figure 2, the following observations can be made.  
Figure 1: Structure of the reduced linear element: linear element with quadratic nodal values.  
For the conventional linear element solution \(u_{\rm L}^{h}\), it is seen that the largest error occurs at the second end node. This is very unfavorable since, for an adaptive time-stepping approach, if the nodal errors happen to be exactly equal to the required tolerance, then there is no room for the errors to further accumulate in the subsequent solution steps, making the error control aborted. For the conventional quadratic element solution \(u_{\rm Q}^{h}\), although the overall errors have been tremendously reduced, the maximum error still occurs at second end node, and the similar failure mentioned above may be inescapable either. However, for the solution \(u_{\rm R}^{h}\) of the reduced linear element, which is simply a linear interpolation to the quadratic solution, it is seen that there are a couple of desirable properties and advantages:  
1. The reduced solution \(u_{\rm R}^{h}\) is not a standard linear element solution: its interior solution is \(O(h^{2})\) accuracy, but its nodal solution is inherited from \(u_{\rm Q}^{h}\) and attains \(O(h^{4})\) accuracy, which ideally makes the accuracy ratio exactly 2;
2. The maximum error occurs within the element, leaving the nodal errors virtually negligible, which provides sufficiently accurate initial values for the subsequent solution steps, and hence greatly slows down accumulation of nodal errors on a long time domain;
3. Since the full order solution \(u_{\rm Q}^{h}\) is much closer to the exact solution \(u\), i.e., one order higher than the reduced solution \(u_{\rm R}^{h}\), the full order solution \(u_{\rm Q}^{h}\) can serve as a convenient and qualified error estimator for a point wise error estimate over the reduced solution \(u_{\rm R}^{h}\).
# Navigation in GEO, HEO, and Lunar Trajectory Using Multi-GNSS Sidelobe Signals
This paper is structured as follows: Section 2 elaborated the observation simulation strategy, such as GNSS constellations and signals, GNSS transmitter radiation pattern and receiver antenna gain, as well as positioning strategy; results are carried out in Section 3, including \(C/N_{0}\), signal visibility, GDOP and navigation performance of GEO, HEO, and lunar trajectory; discussions are listed in Section 4; finally, the paper is concluded in Section 5.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 3 Applications of Artificial Intelligence in Viticulture
### Data-Driven Approaches for Optimizing Water and Nutrient Management  
Efficient water and nutrient management are crucial for optimizing grape yield and quality. AI-driven data analysis and precision agriculture techniques provide vineyard managers with the tools needed to manage these resources effectively.
## 3 Materials and Methods
### Probe
#### 3.1.1 Optical Parts  
Four light-emitting diodes (LEDs), also known as sources, were carefully chosen to interrogate specific information from the brain. The wavelengths used are 660 nm, 770 nm, 810 nm, and 880 nm. The 810 nm wavelength, also known as the isosbestic wavelength, has the unique characteristic of being absorbed to the same extent by both oxy- and deoxy-hemoglobin [18]. Therefore, it is an optimal wavelength to record an optical signal which is independent of blood oxygenation and could be correlated to intracranial volumetric changes.  
Wavelengths at 660 nm and 880 nm were also included in order to assess extracerebral oxygen saturation. The utilization of these wavelengths in pulse oximetry (SpO\({}_{2}\)) is widely reported [18]. Likewise, NIRS commercial devices generally use at least two infrared wavelengths (750-1000 nm) to calculate cerebral oxygenation parameters. These two wavelengths are usually selected on opposite sides with respect to the isosbestic point of the hemoglobin absorption spectra (810 nm). Accordingly, LEDs of 770 nm and 880 nm were included in the probe to implement spatially resolved spectroscopy (SRS) measurements.  
Previous evaluation of infrared light and cerebral tissue interaction at different source-detector (S-D) separations showed that higher S-D separation increases the penetration depth, but it also causes a decrease in the overall signal quality due to high absorption [21]. Accordingly, the probe design was subdivided into two individual sub-probes called proximal probe and distal probe (Figure 1). As this is a research probe, the proposed configuration will enable the flexibility to investigate the effect of S-D on optical signals on demand.  
The proximal sub-probe consists of the four LEDs (D1-D4), and one photodiode (Ph 0) placed 10 mm from the sources to detect the backscattered light from superficial tissues, which will allow arterial oxygen saturation (SpO2) measurements. The distal sub-probe includes two photodiodes (Ph 1 and Ph 2) to detect the backscattered light from cerebral tissues, which can be separated from the proximal probe between 25 and 50 mm. The sources and distal photodiodes will allow the evaluation of cerebral oxygenation parameters through SRS.  
Photoplethysmograms (PPG) from one of the distal photodiodes are acquired to obtain volumetric information from the cerebral tissue. However, due to the spatial sensitivity profile of a reflectance probe, it is likely that the signals are contaminated by extracerebral information. Therefore, extracerebral volumetric signals are also acquired by the proximal photodiode in order to subtract them from the contaminated brain signals. The latter should result in the acquisition of only cerebral pulsatile signals for the further estimation of ICP from changes in their morphological features.  
Table 1 describes the optical and electrical characteristics of the optical components selected to build the proposed multimodal probe.  
Figure 1: PCB design of the multimodal probe showing both proximal and distal sub-probes, with their respective optical components.
# Determination of Corrosion Rate in GAlvanized Pipes in Centralized Hot Water Supply Systems
## Materials and Methods  
To check the corrosion mechanism of galvanized steel pipes in the laboratory of the Moscow State University of Civil Engineering, studies were conducted in conditions as close as possible to operational ones. The experiments were carried out on tap water in Moscow. The water quality during the experiments was as follows:  
* pH = 7.9.
* The temperature of cold water (t\({}_{\text{cw}}\)) was 14-16 \({}^{\circ}\)C.
* The temperature of hot water (t\({}_{\text{hw}}\)) was 45-47 \({}^{\circ}\)C.
* Total alkalinity (\(A_{T}\)) was 2.5 meq/L.
* Total hardness (GH) was 2.7 meq/L.
* The content of calcium (Ca\({}^{2+}\)) was 1.84 meq/L.
* Total salinity was 210 mg/L.  
According to the Langelier and Risner indices (LSI and RSI, respectively), water can be classified as weakly corrosive and capable of the deposition of calcium carbonate.  
Pipe samples for analysis were made of steel pipe with a diameter of 15 mm and a wall thickness of 2.8 mm [2]. The zinc coating was applied by hot-dip galvanizing. The chemical composition of the sediment on the surface of the samples was studied using a scanning electron microscope (Quanta FEI 250, PHILIPS/FEI, Hillsborough, OR, USA) and energy dispersive spectroscopy (EDAX) at different values of accelerating voltage. The secondary electrons (SE) type of signal was used in the research.  
The corrosion rate was calculated based on the mass loss of the samples. Each pipe sample was weighed on a scale (Model AND GR-200, accuracy up to the fourth decimal place) before being inserted under the stream of hot water. After the exposure, the sample was dried at room temperature and reweighed. The difference between the two values of the sample mass (before and after the exposure) was the mass loss, which was related to the time of exposure to obtain values of corrosion rate.  
For a possible comparison of the results obtained by different authors, the corrosion rate values had the same dimension.
# Deep Learning-Based Acoustic Echo Cancellation for Surround Sound Systems
## 6 Conclusions  
This paper proposed a compressed complex spectrum mapping approach for surround AEC, and the method does not need to identify the acoustic echo paths explicitly, and thus does not suffer from the non-unique solution problem. The proposed method used the B-format signals instead of the far-end D-format loudspeaker signals as the reference signals of the AEC algorithm. Experimental studies showed that the model trained with the B-format signals was more robust than that trained with D-format signals against various loudspeaker layouts, including the standard and nonstandard 5.1 surround set-ups. The proposed algorithm outperformed the traditional PBFDLMS in both the single-talk and double-talk scenarios. Experimental results in real acoustic scenarios further confirmed the effectiveness of this method. In the near future, we will explore more effective network structures, such as Transformer-based networks [38], and compare them with this work.  
Conceptualization, G.L. and C.Z.; methodology, G.L. and Y.K.; software and validation, G.L. and Y.K.; writing--original draft preparation, G.L.; writing--review and editing, Y.K. and C.Z.; supervision, X.L.; funding acquisition, Y.K. 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 under Grant 62101550.  
Not applicable.  
The authors declare no conflict of interest.
# Experimental Study of Accuracy of High-Rate GNSS in Context of Structural Health Monitoring
## 2 Methods and Materials
### Precise Point Positioning  
The PPP determines the position of a point with a stand-alone GNSS receiver. With the help of good quality satellite orbit and clock products, the PPP-derived position can reach a centimeter level. Dual-frequency ionospheric-free (IF) pseudorange and carrier phase observations are usually used to reduce the ionospheric effects. After the clock, orbit, and DCB (differential code bias) corrections, the linearized PPP IF observations can be expressed as:  
\[\left\{\begin{aligned} &\mathsf{P}_{IF}^{g}=\mathsf{\mu}_{r}^{g}x+c \cdot\overline{d}\mathsf{T}_{r}^{g}+T^{g}+\delta_{P}\\ &\Phi_{IF}^{g}=\mathsf{\mu}_{r}^{g}x+c\cdot\overline{d}\mathsf{ T}_{r}^{g}+\overline{N}_{IF}^{g}+T^{g}+\delta_{\Phi}\end{aligned}\right. \tag{3}\]  
with  
\[\left\{\begin{aligned} & c\cdot\overline{d}\mathsf{T}_{r}^{g}=c \cdot(d\mathsf{\mu}_{r}^{g}+\overline{\mathsf{P}}_{r,IF}^{g}/c)\\ &\overline{N}_{r,IF}^{g}=N_{r,IF}^{g}+\overline{B}_{r,IF}^{g}- \overline{B}_{IF}^{g}\end{aligned}\right. \tag{4}\]  
where \(\mathsf{P}_{IF}^{g}\) and \(\Phi_{IF}^{g}\) represent the corrected pseudorange and carrier phase observations: \(c\) is the speed of light in the vacuum; \(x\) is the 3D receiver coordinates; \(\overline{d}\overline{N}_{r}^{g}\) and \(\overline{N}^{g}\) denote, respectively, the reformed receiver clock error and phase ambiguity: the slant tropospheric delay is marked as \(T^{g}\), which contains the hydrostatic delay \(T_{d}^{g}\) and wet delay \(T_{w}^{g}\): \(\overline{P}_{r,IF}^{g}\) is the code hardware bias and grouped into the receiver clock offsets: \(\overline{B}_{r,IF}^{g}\) and \(\overline{B}_{IF}^{g}\) are the receiver and satellite phase hardware biases grouped into the ambiguity parameters, respectively. Based on the assumption that the multi-GNSS code observations share the same receiver clock, the code-specific inter-system bias (ISB) is considered for each system except for GPS.  
To obtain a PPP ambiguity resolution, three steps can be followed. First, the fractional cycle biases (FCBs) of the receiver are eliminated by the between-satellite single-difference (BSSD) algorithm [32], while the FCBs of a satellite can be corrected by the WL/NL FCB products or the observable-specific signal bias (OSB) products. The WL float phase ambiguity can be fixed by rounding. Finally, the least-squares ambiguity de-correlation adjustment (LAMBDA) method [33] is adopted to resolve the NL phase ambiguities. When WL and NL ambiguities are fixed, the IF-combined fixed-phase ambiguity can be derived by the following equation.  
\[\lambda_{IF}\widetilde{N}_{IF}^{g}=\frac{cf_{2}}{f_{1}^{2}-f_{2}^{2}} \widetilde{N}_{WL}^{g}+\frac{c}{f_{2}+f_{2}}\widetilde{N}_{NL}^{g} \tag{5}\]where \(\lambda_{IF}\) denotes the wavelength of the IF combined observations; \(f_{1}\) and \(f_{2}\) are the frequency of the two-carrier phase; and \(\bar{N}_{NL}^{g}\) and \(\bar{N}_{NL}^{g}\) represent the WL and NL fixed phase ambiguity in cycles, respectively.
# Fast Frequency Response of a Doubly-Fed Induction Generator for System Inertia Support
## 2 Frequency Control of Synchronous Generators  
To guarantee the stable operation of an electric power grid, the system frequency should remain nearly constant. If an imbalance between active power generation and consumption appears, defense plans of conventional synchronous plants against the frequency change are carried out to adjust the system frequency within an acceptable range in different continuation steps, which are roughly classified by inertia response, primary frequency control (governor response), and secondary frequency control [13]. The brief descriptions of inertia response and primary frequency control are provided in the following sections. Furthermore, the secondary frequency control is not included in this study because the purpose of it is to remove the system frequency error in a long timeframe.
## 1 Introduction
The proposed scheme realizes instant attribute revocation with the attribute-group key's help as well as ensures fine-grained access control. In this way, it also reduces the computational burden of the resource-constraint IoT devices. Hence, we propose 11 MA-CP-ABE for encryption and decryption, attribute revocation and outsourcing by reducing the computational burden and securing the system. 3. We provide a security analysis to validate the proposed CP-ABE approach. We also compare our results with [13; 14] in terms of the computational cost. Simulation findings show that our procedure outperforms the works in [13; 14]. The proposed method reduces the operation time for the encryption and decryption process. We outsource cryptography operations to the fog node, reducing the end user's computational cost. This paper is organized as follows: We illustrate the introduction, contributions and related works in Section 1. Section 2 illustrates the preliminaries of the work that include the system model and step-by-step process of the algorithm. The thread model is described in Section 3. Sections 4 and 5 describe the proposed scheme and performance analysis, respectively. Finally, the paper is concluded in Section 6.
## 3 Mathematical Modeling
### Equations of Motion
#### 3.1.1 Equilibria and Natural Orbital Dynamics for Symmetric Rotating NEOs  
The nature of equilibrium points in the vicinity of regularly rotating NEOs depends on the relative strength of the body's gravitational and centripetal forces and, as shown in [80], the amount of these equilibrium points in the gravity field of irregular bodies, comprising asteroids and comets, is not fixed, although always existing in at least one of them. Additionally, even if these equilibria exist, they may all be unstable [57; 66; 81]. Thus, even if relying on scarce pre-mission knowledge about the NEO's physical parameters, the stability analysis and the search for periodic orbits is a central issue for orbital dynamics in close-approach missions around NEOs.  
By defining the Effective Potential [75]  
\[V(\mathbf{r})=\ U(\mathbf{r})\ -\frac{1}{2}\|\boldsymbol{\omega}\times\mathbf{r} \|^{2}, \tag{3}\]  
and assuming the frequent case of a small body uniformly rotating around its maximum moment of inertia, Equation (1) transforms to  
\[\ddot{\mathbf{r}}+2\boldsymbol{\omega}\times\dot{\mathbf{r}}=-\
abla V( \mathbf{r})\,, \tag{4}\]  
which makes the Jacobian integral (mechanical energy) time-invariant, now named Jacobi constant \(J\) (Equation (5)),  
\[H(\mathbf{r},\dot{\mathbf{r}})=\frac{1}{2}\left[\,(\dot{\mathbf{r}}\cdot\dot{ \mathbf{r}})-\|\boldsymbol{\omega}\times\mathbf{r}\|^{2}\,\right]+U(\mathbf{ r})=\frac{1}{2}(\dot{\mathbf{r}}\cdot\dot{\mathbf{r}})+V(\mathbf{r})\ =J\,. \tag{5}\]  
The contour surfaces defined by the solutions of  
\[H(\mathbf{r},\boldsymbol{0})=V(\mathbf{r})=J\,, \tag{6}\]  
are the so-called Zero-Velocity Surfaces (ZVS), which delimit in space the allowed regions for the spacecraft motion [82]  
\[A=\{\,\mathbf{r}:\quad\dot{\mathbf{r}}\cdot\dot{\mathbf{r}}\geq 0\ \}\,, \tag{7}\]  
and thus provide a means for classification of the orbital motion into families of orbits that may be identified in correspondence to given values of the Jacobi constant, \(J\), in the time-invariant system defined by Equations (3)-(5).
# Experimental Study of Scour Hole Depth around Bridge Pile Using Efficient Cross-Section
## 4 Results and Discussion
### Scouring at Different Positions of Piles  
Figure 8 shows that the piles facing the water current first experienced maximum scouring compared to the preceding group of piles. It can be seen that the frontal area of upstream piles 1 and 5 experiences the maximum local scour of 10.7 mm and 10.65 mm. As regards the square piles, the same trend is observed in circular piles, with the scouring of 44.3 mm and 42.7 mm at the upstream piles 1 and 5. Notably, scouring at the downstream piles is less than at the upstream pile. The reason lies in the fact that as the water flows through different piles, the flow velocity decreases, thereby leading to a less intense horseshoe vortex. Moreover, the scouring material from the front scour holes is deposited in the rare scour holes, decreasing the scour depth at the rare piles. This phenomenon can be seen in Figure 6c, where the rear two downstream piles remain in their original position while the rest of the six piles displace. The main reasons are the excessive soil erosion in the upstream piles and the interaction of the scour holes.  
Figure 8: Scour depths contours for pile group: (**a**) circular; (**b**) square; (**c**) lenticular.
## 5 Conclusions  
We investigated the seasonal variations in the ratio of the GPP to the far-red SIF across various PFTs using both satellite and tower-based data. The GPP/SIF ratio for forest types was generally higher than that of grass and crop types. There were also noticeable differences in the seasonal pattern of the GPP/SIF ratio between the selected samples experiencing low-temperature stress (below 10 \({}^{\circ}\)C) and those growing in relatively warm conditions (above 10 \({}^{\circ}\)C throughout the year). For the data with a wide temperature range, including low-temperature conditions, we observed a positive relationship between the GPP/SIF ratio and temperature; however, the relationship did not consistently respond to temperature when considering data for temperatures above 10 \({}^{\circ}\)C throughout the year. Overall, the results demonstrate that the relationship between the GPP/SIF ratio and temperature was not constant among PFTs, as the GPP/SIF ratio decreased under low-temperature conditions for some PFTs experiencing low-temperature stress. Therefore, our findings highlight the importance of incorporating the temperature into SIF-based GPP estimation.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs14153716/s1](https://www.mdpi.com/article/10.3390/rs14153716/s1)]([https://www.mdpi.com/article/10.3390/rs14153716/s1](https://www.mdpi.com/article/10.3390/rs14153716/s1)).  
Conceptualization, X.L.; Formal analysis, L.L.; Methodology, J.C.; Writing--original draft, J.C.; Writing--review and editing, X.L., Y.M. and L.L. 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 41825002, 42071310.  
The ERA5-Land reanalysis dataset is available at [[https://cds.climate.copernicus.eu/.FLUXCOM](https://cds.climate.copernicus.eu/.FLUXCOM)]([https://cds.climate.copernicus.eu/.FLUXCOM](https://cds.climate.copernicus.eu/.FLUXCOM)) GPP products with customized ensemble estimates are available on request to [PERSON] ([EMAIL_ADDRESS]). The XTS site data are available from the authors upon request. Niwot Ridge site data are available at a data repository ([[https://data.caltech.edu/records/1231](https://data.caltech.edu/records/1231)]([https://data.caltech.edu/records/1231](https://data.caltech.edu/records/1231))) hosted at the California Institute of Technology.  
This work was supported by the National Natural Science Foundation of China (grant 41825002, 42071310). We also thank the California Institute of Technology for providing the data for the Niwot Ridge site. We are grateful to [PERSON]'s team for supplying the experiment equipment and flux data for the XTS site. [PERSON], [PERSON] and [PERSON] contributed to the experiment design and research method. [PERSON] and [PERSON] performed analysis and writing of the manuscript.  
The authors declare no conflict of interest.
## 5 Conclusions  
This study proposed a novel index, namely, the RISI, for extracting impervious surface distribution from Landsat 8 imagery and validated it using three images covering urban areas of China in winter and summer. The results of the extraction were compared with those obtained using the NDBI, IBI, NDISI, NDII, and CBI. The main conclusions are as follows:  
(1) The RISI effectively distinguishes impervious surfaces from other ground objects, especially bare soil, and gives higher recalls, precisions, and F1 scores, of which the average values for the three case images were 95%, 91%, and 93%, respectively. The validation confirmed the robustness of the RISI.  
(2) The use of the coastal band after a 0-1 transformation can further increase the difference between impervious surfaces and bare soil.  
(3) The workflow has been shown to have good applicability in the extraction of impervious surface distribution from Landsat imagery.  
It is still necessary to perform validation using more images with complex compositions and distributions of ground objects to refine the workflow so that it suits more situations.  
[PERSON] performed the experiments, analyzed the data, and wrote the paper; [PERSON] revised and commented on the manuscript; [PERSON] performed the data processing and examination of the experiments.  
This study is supported by the National Natural Science Foundation of China (No. 41471283).  
The authors would like to thank the reviewers and the editor for precise and excellent comments that greatly improved this paper.  
The authors declare no conflict of interest.