esa-sceva/satcom-chunk-collection huggingface dataset:
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 2 Materials and Methods  
### Study Area  
China has a complex topography (Figure 1) and a wide variety of ecosystems [23], encompassing subtropical, tropical, temperate, boreal, and highland regions. Among these, forest and grassland ecosystems dominate in terms of extent. Forest ecosystems span across Northeast, North, South, Southwest, and Northwest China, covering approximately 22.96% of the country's total area. Meanwhile, grassland ecosystems thrive in the Qinghai-Tibet Plateau, Inner Mongolia, Xinjiang, and Northwest China, accounting for 41.94% of the nation's land area [24].  
In addition to forests and grasslands, China boasts a variety of other ecosystems, including wetlands, deserts, islands, and marine habitats. Its wetlands occupy a vast expanse of 535,300 square kilometers, ranking among the world's largest [25]. The country's deserts span an area of approximately 100,000 square kilometers and encompass notable regions such as the Taklamakan Desert, Kubuqi Desert, and Ordos Desert. Furthermore, China is home to numerous islands such as Taiwan, Hainan, Dongsha Islands, Spratly Islands, and Diaoyu Islands. Marine ecosystems envelop the East China Sea, South China Sea, and Yellow Sea, with the East China Sea representing the largest marine ecosystem in China.  
Figure 1: Topographical map of China and the distribution of the different provinces.  
From a socio-economic perspective, China stands as the world's most populous country [26]. It also maintains its position as the second-largest economy globally, with a total national GDP of RMB 134.9 trillion in 2022 and an economic growth rate of 5.2%. China possesses a substantial consumer market and export potential, projected to reach RMB 80 trillion and RMB 20 trillion, respectively, by 2030 [27].  
In summary, China harbors a vast range of ecosystems and presents significant socio-economic prospects. Moving forward, it is crucial for China to prioritize ecological and environmental preservation as a foundation for achieving sustainable economic growth.
# Conversation-Based School Building Inspection Support System
## 5 Discussions
### Potential Applications of the System  
Not only school buildings but also other public buildings located in areas with frequent earthquakes may require periodical inspections and seismic capability evaluations. Fire prevention and fire risk assessments are also critical for public buildings that gather massive numbers of people, such as gyms, libraries, and hospitals. Inspections targeting different safety aspects are required to grasp the situation of the buildings. However, the inspection of public buildings encounters similar difficulties to the school building inspection, including the restrictions of the paperwork process and the complexity of inspections. Thus, the introduced concept and the system framework in this study may be adapted to public buildings and serve as a feasible solution. The implemented system may notify the assessors to start inspections on specified dates or after particular incidents. By utilizing the feature for predefining checkpoints, the system may also support the assessors to complete inspections that target different topics such as seismic resistance, fire prevention, or other facilities.
# Status of the SXFEL Facility
## 1 Introduction  
Free-electron lasers (FELs) hold the ability to generate extremely high intensity, ultra-short, and coherent radiation pulses, which can open up new frontiers of ultra-fast and ultra-small sciences at the atomic scale. In the X-ray region, most of the existing FEL facilities, such as FLASH [1], LCLS [2], SACLA [3], PAL [4], SwissFEL [5], and European XFEL [6], are based on the self-amplified spontaneous emission (SAE) principle [7, 8]. While SASE FEL has the advantages of simple setup, technological maturity, and excellent transverse coherence, it typically has rather limited temporal coherence. In order to improve the temporal coherence of SASE, several seeding schemes, including external seeding [9, 10, 11, 12, 13, 14] or self-seeding [15, 16, 17, 18], have been developed in recent years. Among these schemes, high-gain harmonic generation (HGHG) [9] and echo-enabled harmonic generation (EEHG) [11, 12] have been proven as promising candidates for generating nearly Fourier-transform limited pulses with better stabilities of central wavelength and intensity [19, 20, 21, 22, 23]. To further extend the output wavelength of an external seeding FEL down to the X-ray regime, cascading stages of HGHG FEL with the fresh bunch technique [10] have been demonstrated both with SDUV-FEL [21] and FERMI FEL [24]. The cascaded HGHG at FERMI has already been applied for FEL user experiments and has prominent advantages in temporal coherence and controllable longitudinal phase.  
The Shanghai soft X-ray Free-Electron Laser Facility (SXFEL), as a phased project, is composed of the SXFEL test facility (SXFEL-TF), and the SXFEL user facility (SXFEL-UF). The main purpose of the SXFEL-TF is to promote FEL research in China, including exploring the possibility of the seeded X-ray FEL with two stages of cascaded HGHG-HGHG, or a new scheme based on an EEHG-HGHGcascade and performing research and development on X-ray FEL-related key technologies. After a series of discussions and comparisons organized by the Chinese Academy of Sciences, it was decided to establish this test facility in the campus of the Shanghai Synchrotron Radiation Facility (SSRF). The civil construction started at the end of 2014. The tunnel and technical buildings were ready in April 2016, and the installation was almost completed by the end of 2016. Currently, the test facility is under commissioning and is expected to be finished by the end of 2017. The upgrading of the test facility to the water window user facility, SXFEL-UF, has been undertaken by the collaboration between the Shanghai Institute of Applied Physics (SINAP) and Shanghai-Tech University. Shanghai-Tech University is in charge of developing science cases and experimental end-stations, and SINAP is responsible for the remaining parts of facility development, including upgrading the linac energy to 1.5 GeV, building a second undulator line, facility integration, and constructing the utility and SXFEL-UF buildings. The civil construction was started in November 2016, and the user facility is scheduled to be open to users in 2019.
## 3 Technical Challenges and Recent Efforts to Address Them
### Membrane Fouling
Such membranes were successfully fabricated by utilizing cellulose nanofiber composite [84], chitosan [85], and polycaprolactone [86]. Accordingly, in the future, membrane contactors with high performance and sufficient biodegradability should be targeted for the sustainable management of the overall system.
## 3 Results
### The Third Test: The [PERSON]'s San Francesco Bridge at Cosenza
In panel (a) we observe the movements of a point at a height of 80 m with respect to the instrument, as a function of the GPS time. The displacements due to the moving loads are easily recognizable by analyzing the 10 samples moving average, drawn in red: during the run of the first truck the point moves up to 15 mm towards the midspan. The point does not move during the stop and undergoes a further displacement during the run of the second truck. After a brief pause, the third movement begins, ending with the halt of the third truck. The total displacement is about 41 mm. It is worth remarking that the graph reflects the times of the events reported in Table 4.  
The comments made above can be repeated for the point at a height of 50 m, which movements are drawn in the panel (b). Displacements are reduced, as expected. As regards the point at 20 m height, the various phases are less evident. A final displacement of about 16 mm can be observed. Measurements were performed with a total station after positioning each of the three trucks. The station pointed targets placed on the pylon at various heights and its measurements were used for the official load test. The results fully agree with those obtained with our method. Also the inclination variations sampled by two sensors, positioned inside the pylon and connected wirelessly, are consistent with the splines extracted using the method described. Thanks to the acquisitions made by the GNSS receivers positioned on the trucks, the displacements of the points of the pylon can finally be correlated to the position of the mobile loads. Since the TLS position was obtained thanks to its GPS receiver, the coordinates of the each mobile vehicle, for each GPS time, can be converted into local coordinates in the TLS reference frame. The correlation is shown in Figure 22 in which the horizontal positions of a point of the upper part of pylon are represented as a function of GPS time. The point is 85 m above the TLS centre. The trend-line evidences runs and stops of the trucks. In the thumbnails, the GPS time and the instantaneous local coordinate x of the moving loads, represented by dots, are shown. The red, blue and green dots represent, respectively, the first, second and third trucks. Figure 20: Enlargements of the point clouds obtained at a height of 80 m above the TLS. Superimposed are the interpolating lines of the 2D point cloud provided by the TLS in line scanner mode. Figure 21: Horizontal displacements, as a function of the GPS time, for three points of the pylon during the test: (**a**) at 80 m above the TLS; (**b**) at 50 m above the TLS; (**c**) at 20 m above the TLS. Ordinates are the horizontal distances from the TLS.
# Optimized Detection Algorithm for Vertical Irregularities in Vertical Curve Segments
## 4 Optimization of Detection Algorithms for Vertical Curve Segments
### Reference Cancellation Method
Formulate the linear equation and solve for estimated line parameters. Given the line equation \(y=ax+b\), calculate the estimated line equation \(y=a_{0}x+b_{0}\) based on segmentation points \(m_{1}(x_{1},y_{1})\) and \(m_{2}(x_{2},y_{2})\). \[\left\{ \begin{array}{l}a=a_{0}+\xi_{a}\\ b=b_{0}+\xi_{b}\end{array} \right.\] (10) where \(a\) and \(b\) are the parameters to be determined; \(a_{0}\) and \(b_{0}\) are the approximate parameter values; \(\xi_{a}\) and \(\xi_{b}\) represent the correction values for the parameters. 2. Derivation of Single-Point Linear Fitting Residual. Calculate the distance \(s_{i}\) from point \(m_{i}(x_{i},y_{i})\) to the line \(y=a_{0}x+b_{0}\), and perform a second-order Taylor expansion on \(s_{i}\) to obtain the single-point fitting residual. \[s_{i}=\frac{a_{0}x_{i}-y_{i}+b_{0}+\xi_{b}}{\sqrt{a_{0}^{2}+1}}+\frac{x_{i}+a_ {0}y_{i}-a_{0}b_{0}}{\left(a_{0}^{2}+1\right)^{3/2}}\xi_{a}\] (11)
3. Substitute multiple points into Equation (11) to obtain the linear fitting residual equation and solve for the unknown parameters. \[S=A\xi-B\] (12) \[\xi=\left(A^{T}A\right)^{-1}(A^{T}B)\] (13) where \(S=\left( \begin{array}{c}s_{1}\\ \cdots\\ s_{n}\end{array} \right)\), \(\xi=\left( \begin{array}{c}\xi_{a}\\ \xi_{b}\end{array} \right)\), \(A=\left( \begin{array}{c}\frac{x_{1}+a_{0}y_{1}-a_{0}b_{0}}{\left(a_{0}^{2}+1 \right)^{3/2}},\frac{1}{\sqrt{a_{0}^{2}+1}}\\ \cdots\\ \frac{x_{n}+a_{0}y_{n}-a_{0}b_{0}}{\left(a_{0}^{2}+1\right)^{3/2}},\frac{1}{ \sqrt{a_{0}^{2}+1}}\end{array} \right)\), \(S=\left( \begin{array}{c}\frac{y_{1}-a_{0}x_{1}-b_{0}}{\sqrt{a_{0}^{2}+1}}\\ \cdots\\ \frac{y_{n}-a_{0}x_{n}-b_{0}}{\sqrt{a_{0}^{2}+1}}\end{array} \right)\). Figure 10: Comparison of detection beam pitch angle over mileage. The algorithm flow is shown in Figure 11.  
A segment of simulation data from a specific condition in Section 3 was selected for acceleration integration and pitch angle integration, with the selected pitch angle data undergoing mean smoothing filtering and orthogonal least-squares fitting. The calculation results are shown in Figure 12. As observed in Figure 12a, both the acceleration integration and pitch angle integration results capture the height variation in the track profile in the vertical alignment space. The acceleration integration result additionally includes the vertical oscillations induced by track irregularities. Figure 12b presents the difference between the two results; a minor trend component remains due to deviations between the identified and actual track segmentation points. Nevertheless, the overall track profile shape has been largely removed, and applying a high-pass filter at this stage would significantly improve detection accuracy.
## 2 Construction Method of EMR-SF along the Road Collection Data
### Construction of the Grid Points in the LRBF
#### 2.2.1 Maximum Influence Distance of the Sample Points  
The value of the Gaussian basis function \(\varphi(\|\mathrm{x}-\mathrm{x}_{i}\|)\) in Equation (1) will affect the result of \(F(x)\), which can be expressed as:  
\[\varphi(\|\mathrm{x}-\mathrm{x}_{i}\|)=\left\{\begin{array}{c}e^{-x^{2}\| \mathrm{x}-\mathrm{x}_{i}\|^{2}}\;0  
where x represents the grid points, and xi is the sample points. \(\|\mathrm{x}-\mathrm{x}_{i}\|\) is the distance from the grid points to the sample points. \(L\) represents the maximum influence distance of the sample points; that is, when the distance from the grid points to the sample points is greater than \(L\), the value \(\varphi(\|\mathrm{x}-\mathrm{x}_{i}\|)\) tends to zero, and the grid points are almost unaffected by the sample points.  
As the distance from the grid point to the sample point increases, the value of the Gaussian function decreases (Figure 3). When the distance increases to a certain value, the value of the Gaussian basis function is almost unchanged, and this value is used as the maximum influence distance \(L\). The maximum influence distance \(L\) can be obtained by integrating the Gaussian basis function. The integral formula of the Gaussian basis function is:  
\[F(d)=\int_{0}^{d}e^{-e^{2}r^{2}}dr \tag{3}\]  
where \(F(d)\) is the integral value of the Gaussian basis function from 0 to \(d\), and \(d\) is the upper bound of the integral. \(\varepsilon\) is the shape parameter of the Gaussian basis function.
## 2 Results and Discussion
### Impact of Cluster-Zone Leaf Removal on Grape Phenolic Profiles  
#### 2.2.1 Anthocyanins  
Five monomeric anthocyanins and their acetylated and coumarylated forms were identified (Table 2). The total concentration of anthocyanins in Marselan grapes was approximately 30% higher than in Cabernet Sauvignon grapes, and Malvidin-type anthocyanins were the most abundant in both cultivars. Additionally, Marselan grapes exhibited higher proportions of methylated and coumarylated anthocyanins and a lower proportion of acetylated forms. In 2021, all three LR treatments increased the concentration of malvidin-3-\(O\)-glucose and its acylated and coumarylated forms. However, no statistically significant differences were observed in the total anthocyanin concentration between the control and the three LR groups. In the Cabernet Sauvignon grapes of 2022 vintage, both LR1 and LR2 significantly increased the total anthocyanin concentration compared to LR3 and the control group. LR2 exhibited the most pronounced effect, with significantly higher concentrations of all anthocyanins compared to the control group. A comparable pattern was observed in the Marselan grapes of 2022 vintage, with all three LR treatments resulting in an approximate 10% increase in the total anthocyanin concentration. Among the treatments, LR2 resulted in the highest concentration of malvidin-type anthocyanins, while LR1 resulted in the highest concentration of other types of anthocyanins. A number of studies have indicated that leaf removal promotes anthocyanin accumulation by enhancing light exposure and temperature [5; 11; 12], and our investigation also verifies these conclusions. Furthermore, it has been proposed that elevated temperatures could enhance the acylation and methylation rates of anthocyanins [17]. However, no such phenomenon was observed between the two years, nor were any consistent patterns identified among the different leaf-removal treatments.
## 4 Security Analysis of the Intercept-and-Resend Attack
Therefore, any noise induced by rotational angles \(\frac{\pi}{4}<\theta<\frac{5\pi}{4}\) results in a decrease in the QBER in \(Q_{1}\) due to the periodic nature of the function. The slow increase in the QBER in \(Q_{1}\) compared to \(Q_{1}\) can be attributed to the fact that in case \(Q_{2}\), the quantum states prepared by Eve do not experience any noise in the channel before reaching Bob, as illustrated by Figure 1b. However, when \(\theta=\frac{\pi}{4}\), the case that results in phase flip errors, the QBER is at 50%, i.e., half of the sifted key bits will be erroneous. Furthermore, from Figure 2, we observe that a noise induced by the rotation of the prepared quantum state by an angle of 0.35 radians will cause a QBER of 11%, and this corresponds to the lower bound on the tolerable error rate for the BB84 protocol against general attacks. Furthermore, a rotational angle of 0.53 radians will cause a 25% QBER, a value corresponding to the bound on the error rate brought about by the intercept-resend attack. In Figure 3, we investigate the amount of information [PERSON] can gain from her intercept-resend strategy. Our results show that the maximum information that [PERSON] can acquire is 0.5 bits, which occurs at a noise level of 0.5 and in a noise-free environment (i.e., at \(\varepsilon=0\)). Furthermore, we observe that [PERSON] randomly sends \(H(A)=2\) bits of information; this implies [PERSON] gains utmost 25% of the information [PERSON] sends to [PERSON]. Thus, this confirms that [PERSON] can obtain a minimum of 0.4 bits of information at a 12% noise level. Figure 2: Comparison of the variation of the quantum bit error rate \(Q\) with the rotation angle \(\theta\) for the case where there is no eavesdropping (\(Q_{0}\)), the case where the eavesdropper performs the attack outlined in the strategy shown in Figure 1a (\(Q_{1}\)), and the case where the eavesdropper performs the attack outlined in the strategy shown in Figure 1b (\(Q_{2}\)).
## 4 Results  
In this section, we present several results including simulated and experimental data to compare the difference between the proposed method with DS, SA, and GSC in terms of lateral resolution, contrast, and sidelobe level. The simulated data is obtained by Field II [32; 33]. It is a simulation tool widely used in the medical ultrasonic imaging field to verify the performance of beamforming algorithms. In the present study, we simulate a 3.33 MHz, 64-element linear array with a 0.24 mm inter-element spacing along with 71.4 MHz sampling frequency and 1500 m/s sound velocity. Scattering points and a circular cyst are introduced as detection targets in the simulation experiment. Moreover, the experimental data is collected from a medical ultrasound imaging system with the same parameters as the simulated one.
## 3 Hanger-Specific Testing and Analysis
### Hanger-Specific Test Content/Method  
The detection methodology for hanger illness relied upon a visual examination approach, supplemented by the use of measurement instruments, marking tools, and camera techniques. A watercraft specifically designed for positioning was used to obtain the precise geographical coordinates of the lower anchor head. To ensure optimal lighting conditions, it is recommended to proceed with the following steps: Firstly, open the cover plate of the anchor box. Subsequently, inspect the lower anchor head for any water accumulation. Additionally, assess the aging condition of the grease within the anchor box. Furthermore, examine the corrosion status of both the inner and outer wire fasteners of the anchor head. Moreover, evaluate the deformation and corrosion conditions of the anchor pad plate.  
Then, the corrosion condition of the pier head steel wire and the pier head nut can be assessed. Lastly, it is advised to document these observations through photography and written records. Upon reaching the upper anchor head position above the arch rib  
Figure 5: Tied-arch bridge in this study: (**a**) structure of the hanger, (**b**) elevation of the bridge (unit: cm), (**c**) disease of the lower anchor head: corrosion of lower anchor head components, and (**d**) disease of the lower anchor head: water seepage in the lower anchor head.  
using a boarding truck, it is necessary to conduct an assessment for potential issues such as water seepage, white precipitation, breakage, cracks, and other ailments in the sealing anchor concrete. Additionally, measurements of crack length and width, as well as the area affected by the disease, should be taken. It is also important to document these findings through photography and written records.
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 3 Reviewing Positioning Systems and Their Capabilities  
When reviewing positioning systems for Unmanned Underwater Vehicles (UUVs), it is essential to consider the diverse technologies that enable precise navigation and mission execution in challenging underwater environments. These systems are designed to overcome the unique limitations of underwater operations, such as signal attenuation [95], limited visibility [93], and environmental interference [96]. Positioning technologies vary in terms of their mechanisms, ranging from acoustic [96], optical [60,79], and electromagnetic methods [74] to advanced systems like Synthetic Aperture Sonar (SAS) [66] and Simultaneous Localization and Mapping (SLAM) [71]. Each of these methods offers specific advantages depending on the mission requirements, water depth, environmental conditions, and level of precision needed. Whether through direct data gathering via sonar or laser scanning or more complex techniques like gravity gradiometry [91] and photogrammetry [70], UUVs rely on a combination of systems to ensure accurate positioning. Understanding these technologies, along with their strengths, limitations, and applications, is crucial for advancing underwater exploration, resource management, and scientific research.
## 4 Results and Discussion  
The theoretical method needs to test in the real world to be more reliable for engineers. For this purpose, in this study, five bridges in Semnan province in Iran have been selected and implemented the proposed method for them. These bridges as following:  
* Bridge No. 1: The bridge of Shahmirzad road intersection,
* Bridge No. 2: The bridge of Sari road intersection,
* Bridge No. 3: The bridge on 73 rd km of Semnan-Damghan road,
* Bridge No. 4: The bridge on 6 th km of Semnan-Jandaq road,
* Bridge No. 5: The bridge on 12 th km of Semnan-Jandaq road.  
First, all bridges are inspected by the authors. After that, the BCI of all bridges was determined based on the proposed method in this study. Finally, the prioritization of all bridges was conducted. The results of inspection and rating of bridges are given in the following subsections.
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## Materials and Methods
### Ionospheric Correction Models
#### NTCM  
The NTCM is a user-friendly TEC model that covers all levels and a global scale of solar activity [18]. This empirical approach describes the TEC/ionospheric delay dependencies on the geographic location, local time, and solar irradiance and activity. Considering the irradiation angle of the sun, the TEC variation with the local time is divided into diurnal, semi-diurnal and ter-diurnal harmonic components in the NTCM. The solar activity level is provided by the solar radio flux index F10.7. The 12 model coefficients, together with the covariance matrix in NTCM are fixed and determined by LS adjustment with observation data sets. The standard deviations for the coefficients are utilized to compute 95% confidence intervals. The input parameters of the NTCM are the F10.7 index, day of year, local time in hours, and the geographic and geomagnetic latitude in degrees [5].