esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
It is advisable to incorporate additional sensors to accurately measure water temperature, ensuring that the concurrent measurements of water level and inclination are not compromised by variations in temperature and salinity. Notably, it is feasible to measure either water level and temperature or inclination and temperature simultaneously, provided that salinity remains constant. The feasibility of concurrent measurement of water level, tilt, and temperature is contingent upon a significant temperature differential in the water utilized for the measurement applications, specifically exceeding 30 \({}^{\circ}\)C. It is important to note that the data corresponding to the shortest wavelength of subfigure a and the longest wavelength of subfigure d in Figures 3 and 4 do not overlap with other datasets. Thus, under these exceptional conditions, it is possible to accurately obtain the aforementioned measurements simultaneously. It is also worth mentioning that the wavelength of FBG3, utilized for calibration to adjust for wavelength measurement deviations induced by changes in air temperature, is consistently maintained at 1544 nm. Consequently, there is no requirement to compensate for wavelength changes due to fluctuations in air temperature in the context of this experiment. As previously discussed, the difference in buoyancy of the buoy at the same water level when tilted to the left or right results in a non-linear shift of the FBG wavelength. This phenomenon arises from the tilting of the FBG subsidence sensors; however, it is also primarily attributable to the geometric configuration of both the water container and the buoy utilized in the FBG subsidence sensor. Achieving a consistent FBG wavelength shift for identical tilt angle changes proves to be challenging and impractical, as it necessitates consideration of varying water levels and temperatures. Consequently, it is difficult to design a water container and buoy with an appropriate shape and mass that would ensure a uniform buoyancy change for every equal tilt angle variation, particularly when accounting for the diverse conditions presented by different water levels and temperatures. Consequently, a slight nonlinear wavelength drift is inevitable and may indirectly influence measurement accuracy. However, this challenge is not insurmountable. In comparison to a previous similar investigation [39], the sensitivity of the tilt measurement within this frame work still presents opportunities for optimization. These enhancements primarily hinge on the interplay between the design of the buoy and the configuration of the water container. For the existing scheme, if the size of the water vessel, the volume of the buoy, and the length of the TB are increased, the measurement sensitivity can be improved theoretically. Therefore, as measurement sensitivity improves, the resultant increase in the reflected FBG wavelength drift will diminish the likelihood of misinterpreting the measured tilt angle or water level, thereby fostering greater measurement accuracy.
# Monitoring Water Area Dynamics in Kashgar (2003-2023) Using Multi-Source Remote Sensing Data
## 3 Results and Analysis
### Spatial Patterns of Water Area Changes in the Kashgar Region
The permanent water bodies in the northwest, south, and northeast displayed decreasing trends, while the seasonal water bodies exhibited increasing trends. Overall, the total water body area in Kashgar exhibited an initial increase followed by a subsequent decrease between 2003 and 2023. This pattern was also reflected in the trends of the permanent water bodies, which initially increased and then decreased, while the seasonal water bodies showed an overall decreasing trend.
# Speed Optimization in DEVS-Based Simulations: A Memoization Approach
## 4 Analysis and Experiments on the DEVS Simulation Engine
### Coordinator in the DEVS Simulation Engine  
The DEVS-based simulation engine operates predictably and consistently when the life cycle (time advance) of each model is clearly defined and an accurate FSM (finite state machine) is defined. Performance of hierarchical simulations in the DEVS engine is controlled by a major unit called the coordinator, illustrated in Figure 1. The coordinator plays an important role in controlling the behavior and event processing priorities of the atomic model and the coupled model. The process of having the coordinator conduct the simulation can be divided into four steps.  
Figure 6 shows the phase in which the coordinator finds and selects the child model with the smallest time advance within the coupled model. In the case of an atomic model, the time for the model transition is reserved, so time advance can be found by approaching constant time.  
However, in the case of a coupled model, it is necessary to compare and reduce the time advance of the child node, so a graph search is performed to find and compare the atomic model with the least time advance. For models with equal time advance, the total order is determined through the SELECT function.  
After performing the internal transition of the DEVS model, which is displayed in green, the external transition occurs for internally coupled models, and then the time advance of the remaining models is advanced.  
Figure 6: Process of the coordinator conducting the simulation.  
A problem with the existing simulation engine occurred when selecting the target model for internal transition in Figure 6.  
To find the model with the least time advance, you need to know the total order for the entire DEVS model, which requires a recursive graph search, so the higher the complexity of the simulation, the higher the linear increase in the time spent on that search, as shown in Table 2. In the designed simulation environment, whenever 10,000 individual atomic models perform an internal/external transition, a time advance inquiry is requested of the connected atomic model for the entire coupled model.  
This causes a very serious cost problem in the RTL-DEVS simulation model environment, where it is necessary to process a short time or zero-time advance for processing electrical signals.  
To improve this problem, there is a need to improve the time spent on graph exploration. Techniques such as priority queueing and memoization can be introduced. We want to propose a solution to the problem from the perspective of dynamic programming by utilizing the memoization technique to reduce the time spent on the graph exploration of the coupled model.
## 3 Improved Artificial Potential Field Method Based on Joint Space  
### Traditional Artificial Potential Field Algorithm  
In APF, the robotic arm is subjected to two forces when moving to the target point. One force is the attractive force that pulls the robot towards the target position, while the other force is the repulsive force exerted by obstacles on the robot. Under the combined force of these two forces, the robotic arm can quickly reach the target position without colliding with obstacles in this process. The force analysis of the robotic arm in the potential field is shown in Figure 1. The force on each joint and link of the robotic arm is shown in Figure 2.  
\(F_{rep}\) represents the repulsive force; \(F_{att}\) represents the attractive force; and \(F_{n}\) is the combined force of the two forces, which makes the manipulator reach the target position.  
The attraction field produced by \(F_{att}\) can be obtained by Equation (4):  
\[U_{att}(\theta_{1},\cdots,\theta_{n})=\frac{1}{2}\mu d^{2}\Big{(}q,q_{goal} \Big{)} \tag{4}\]  
Figure 1: The force of the robotic arm in the APF.  
Figure 2: Force on each joint and link of the robotic arm.  
where \(U_{att}(\theta_{1},\cdots,\theta_{n})\) is the attraction field, \(\mu\) is the correction coefficient of the attractive potential field, \(q\) represents the current position configuration of the robot, \(q_{goal}\) represents the position configuration of the robot when reaching the target position, and \(d\Big{(}q,q_{goal}\Big{)}\) is the distance between the robot and the target position.  
The repulsive potential field produced by \(F_{rep}\) can be obtained by Equation (5):  
\[U_{rep}(\theta_{1},\cdots,\theta_{n})=\begin{cases}\frac{1}{2}k\Big{(}\frac{1 }{d(q,q_{obs})}-\frac{1}{d_{0}}\Big{)}^{2},d(q,q_{obs})\leq d_{0}\\ 0,d(q,q_{obs})>d_{0}\end{cases} \tag{5}\]  
where \(U_{rep}(\theta_{1},\cdots,\theta_{n})\) is the repulsive potential field, \(k\) is the correction coefficient of the repulsion potential field, \(q_{obs}\) represents the position configuration of the robot when colliding with an obstacle, \(d(q,q_{obs})\) is the real-time distance between the robotic arm and the obstacle, and \(d_{0}\) represents the influence radius of the obstacle. When \(d(q,q_{obs})>d_{0}\), the repulsion field will not affect the movement of the robot.  
The total potential energy function can be expressed as Equation (6):  
\[U(\theta_{1},\cdots,\theta_{n})=U_{att}(\theta_{1},\cdots,\theta_{n})+U_{rep} (\theta_{1},\cdots,\theta_{n}) \tag{6}\]
###### Abstract  
Ensuring the best possible stability of UTC(k) (local time scale) and its compliance with the UTC scale (Universal Coordinated Time) forces predicting the [UTC-UTC(k)] deviations, the article presents the results of work on two methods of constructing time series (TS) for a neural network (NN), increasing the accuracy of UTC(k) prediction. In the first method, two prepared TSs are based on the deviations determined according to the UTC scale with a 5-day interval. In order to improve the accuracy of predicting the deviations, the PCHIP interpolating function is used in subsequent TSs, obtaining TS elements with a 1-day interval. A limitation in the improvement of prediction accuracy for these TS has been a too large prediction horizon. The introduction in 2012 of the additional UTC Rapid scale by BIPM makes it possible to shorten the prediction horizon, and the building of two TSs has been proposed according to the second method. Each of them consists of two subsets. The first subset is based on deviations determined according to the UTC scale, the second on the UTC Rapid scale. The research of the proposed TS in the field of predicting deviations for the Polish Timescale by means of GMDH-type NN shows that the best accuracy of predicting the deviations has been achieved for TS built according to the second method.  
timescale predicting: time series analysis; GMDH type neural network; data preparation 1
###### Abstract  
Intelligent reflecting surface (IRS) is a promising technique which aims to shift the paradigm of uncontrollable wireless environment to a controllable one by adding the function of reconfigurability using multiple passive reflecting elements. In this work, optimal beamforming design for maximising achievable rate with respect to variable location of the IRS is considered. In particular, a single-cell wireless system that employs an IRS to aid communication between the user and an access point (AP) equipped with multiple antennas is adopted. An optimisation problem is formulated which aims to maximise the achievable rate, subject to signal-to-interference-plus-noise ratio (SINR) constraint of each individual user as well as the total transmit power constraint at the AP. The problem is solved by jointly optimising the transmit beamforming using active aerial array at the AP and the reflection coefficients using passive phase shifting at the IRS. Since the original optimisation problem is strictly non-convex, the problem is solved optimally by solving a corresponding power minimisation problem. Rigorous simulations have been carried out and the results demonstrate that the IRS-enabled system outperforms benchmark systems and employs significantly fewer RF power amplifiers.  
intelligent reflecting surface; location optimisation; 6G; wireless Article
## 5 ST-CXI System and Example Observations
Table 3 shows the main specifications of the latest ST-CXI system. The X-ray energies mainly used are 17.8 keV and 35 keV, and the observation fields of view (interference pattern sizes) at each energy are 50 \(\times\) 35 and 24 \(\times\) 35 mm\({}^{2}\), respectively, as shown in Figure 5. A fiber-coupled X-ray imager (Andor Zyla 5.5 HF), which converts the incident X-rays into visible light by using a phosphor (CsI) and transfers it to the sCMOS visible light camera through an optical fiber, is mainly used. The pixel size is 6.5 \(\upmu\)m, and the number of pixels is 2560 \(\times\) 2160. The field of view is 16 \(\times\) 13 mm, so the X-ray imager currently limits the available field of view. The maximum frame rate is 50 fps, and the typical exposure time for obtaining an X-ray interference pattern is about 4 s. The fringe scanning method with the three steps described above is used for quantitative detection of the phase shift. A wedge-shaped acrylic plate is placed in the reference beam path and moved up and down to shift the phase. Movement of the phase plate, rotation of the sample, and acquisition of the interference images are controlled by a control program (CTRL7) compatible with the Saga light source data KArte System (SAKAS) charting system, which is under development at SAGA Light Source and published on GitHub ([[https://github.com/SAGALS-IMG/](https://github.com/SAGALS-IMG/)]([https://github.com/SAGALS-IMG/](https://github.com/SAGALS-IMG/)), accessed on 1 April 2023). Figure 6 shows example images from a three-dimensional observation of a formalin-fixed mouse brain [32]. The X-ray energy was set to 17.8 keV, and the exposure time for each interference pattern was 4 s. Phase maps for each projection angle were obtained using the three-step fringe scanning method. The number of CT projections was 500 for a 360\({}^{\circ}\) rotation, and the total measurement time was about 3 h. Since the dynamic range of CXI is as narrow as \(\pm\)0.1 g/cm\({}^{3}\), the sample was placed and rotated in a 4%-formalin solution to keep the density difference between the sample and the surrounding area within the dynamic range. The sectional (a,b) and three-dimensional volume rendering (c) images clearly show the cerebral cortex, hippocampus, and striatum and were obtained without the use of a supplemental agent. They also clearly show that the cerebral cortex and hippocampus density differ. Since the phase shift is approximately proportional to the mass density, the density resolution was estimated to be about 0.3 mg/cm\({}^{3}\) from the standard deviation of the phase fluctuations (\(\Delta\)\(p\)-4 \(\times\) 10\({}^{-4}\) rad) in the outer area of the sample. The spatial resolution was estimated to be 20-30 \(\upmu\)m from the line profile of the boundary between the blood vessels and soft tissue.
# An Effective Water Body Extraction Method with New Water Index for Sentinel-2 Imagery
## 6 Conclusions  
An effective water body extraction method for Sentinel-2 contributes to large-scale water body mapping to understand surface water dynamic changes. This paper fully considers the unique band characteristics and analyzes the spectral response mechanism of Sentinel-2A data. A new water index, namely, the SWI, is proposed. The enhancement and extraction performances of various water body types from a typical study area are compared and the extraction accuracy is verified. The conclusions can be summarized as follows:  
1. Compared with NDWI, the SWI calculated using vegetation-sensitive red-edge and SWIR bands can achieve better enhancement performance for various water body types, including purer water, turbid water, salt water, and floating ice. The contrast values between water bodies and nonwater bodies of the SWI are larger than those of  
Figure 8: Performance comparison of water extraction in urban areas. (**(a)** Suzhou, (**b**) Jiaxing, (**c**) Wuxi, (**d**) Huzhou, (**a1**) building shadows, (**b1**) small water body, (**c1**) narrow river channel).  
the NDWI, which indicates that the SWI can effectively separate different types of water bodies and nonwater bodies;
2. The integration of the SWI and Otsu algorithm is an effective method for accurately extracting various water body types. Visual evaluation suggests that the extraction accuracy of the SWI is better than that of the NDWI for Chaka Salt Lake and Chain Lake. Quantitative assessment shows that the overall accuracy of water body extraction using SWI for Taihu Lake and the Yangtze River (92.75% and 93%, respectively) is higher than that using the NDWI;
3. Although the water body extraction spatial resolution of the SWI is 20 m, the extraction performance of the SWI is better than that of the NDWI with a resolution of 10 m. Moreover, the new method can effectively extract large water bodies and wide river channels by eliminating shadow noise in urban areas.  
Conceptualization and methodology, W.J. and Y.N.; data collection, H.J. and X.Q., data collection, W.J. and X.L.; data analysis, W.J. and Y.N.; writing--original draft preparation, Z.P.; review and editing, G.H., J.L., K.Y., and J.F. 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 (51779269), the National Key Research and Development Program of China (2017 YFB0504105 and 2017 YFA0605003), and the Innovation Talent Project of Research Center of Flood and Drought Disaster Reduction of the Ministry of Water Resources.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Experiments
### Glyph Restoration Effectiveness Analysis  
Figure 9 presents the visualization results of glyph restoration by the proposed method on a historical document image and a stone inscription image. For the historical document example, despite the presence of paper aging, stains, and complex background textures, the model effectively preserves the stroke details of characters while suppressing irrelevant background noise. The restored output closely aligns with the ground-truth label in terms of character layout, spacing, and overall typesetting, demonstrating strong structural consistency.  
For the stone inscription example, the model is able to extract clear character contours and reconstruct structurally complete text regions, even under challenging conditions such as intricate carved textures, low foreground-background contrast, and partial surface erosion. This performance benefits from the integration of a multi-scale feature fusion  
Figure 8: Sample results of character-level detection and recognition by the proposed model. (**a,b**) Examples from the test set. (**c**) Real-world image. The Chinese characters are the primary visual content used to evaluate model performance and are not intended for linguistic interpretation. Blue frames indicate the detected bounding boxes for individual Chinese characters.  
strategy, which enables the model to capture both fine-grained stroke details and global character structures, thereby improving restoration quality in degraded scenarios.  
It is worth noting that the glyph restoration task is not performed as a separate image preprocessing step, but is integrated into an end-to-end multi-task framework jointly with text detection and recognition. This design is expected to support mutual enhancement among the three tasks. Glyph restoration has the potential to improve the structural modeling of characters for detection and recognition, while detection and recognition are able to provide more precise spatial guidance and semantic constraints for restoration.  
While the restoration results are generally satisfactory, some limitations remain. In regions with severe erosion or extremely low signal-to-noise ratios, particularly evident in the stone inscription example, residual background noise or incomplete stroke recovery may still occur. Future work may explore transformer-based global attention to further enhance restoration quality under extreme deterioration conditions.
# New Optimal Design of Multimode Shunt-Damping Circuits for Enhanced Vibration Control
## 2 Methodology
### Finite-Element Model of the Laminated Composite Beam  
Figure 1 shows the system under study which consists of a cantilever host beam made of an elastic material with two piezoelectric patches bonded on its top and bottom surfaces at distance \(x_{p}\) from the fix end. It is assumed that the layers are bounded perfectly, and the bond between them is negligible. The host beam has length \(L\), thickness \(h\) and width \(b\). The \(xy\)-plane is the beam's midplane, and the longitudinal and thickness axes are located in the \(x\)- and \(z\)-direction, respectively. The poling direction of the piezoelectric patches is assumed to be along the \(z\)-axis. Additionally, it is assumed that they are covered by fully conductive electrodes of negligible thickness. The electrodes of the top patch are connected to a passive shunt circuit. The bottom piezoelectric patch can be used as actuator. All the layers, piezoelectric and elastic, are considered to be thin so that the plane stress state can be applied.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 4 The Design Procedure of Circuit Component Parameters  
For meeting the performance requirements of the developed converter, some design prerequisites are listed as follows:  
1. It must have a wide voltage range.
2. There must be soft switching of power switches.
3. Cost and size should be reduced under high frequency operation.  
A good circuit design of resonant circuits is founded on selecting appropriate resonance parameters. Figure 9 illustrates a complete design flowchart of the bidirectional symmetric resonant converter with the step-by-step design procedures being described in the following.