esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Assembly Trials  
Mitigation of manufacturing errors can be achieved by reassembling a lens several times until a better lens is obtained. This method relies on rotating lens elements, interchanging lens elements and lens spacers, and on the statistical nature of the assembling process. Each time an objective lens is reassembled a Monte Carlo assembly trial is performed. Related to this methodology is the concept of selective assembly [21].  
As an example consider the telecentric objective lens [22] in Figure 5. This lens has a focal length of 15 mm, a field of view of \(\pm\)3.5 degrees, and an optical speed of F/1.5. Table 5 provides the sensitivities to aberrations for surface tilts of 0.1\({}^{\circ}\). As can be seen uniform coma dominates followed by linear astigmatism.  
The lens elements tolerances for fabrication were 3 Newton fringes for surface radius of curvature, \(\pm\)0.05 mm for central lens thickness, and \(\pm\)5 arc-minutes of lens wedge, except for the second and long lens that was specified with a maximum wedge of \(\pm\)2.5 arc-minutes. Thirty sets of the lens elements were manufactured.  
Based on the tolerances for wedge only, and performing a Monte Carlo analysis of 1000 trials, a histogram vs. wavefront error was produced as shown in Figure 5. The mean lens has a wavefront rms error of 0.08 waves, and the standard deviation is 0.037 waves rms. The yield curve also shown in Figure 5 indicates that 43.3% of the lenses would have a Strehl ratio of 0.08 or larger.  
The application of the objective lenses required them to be diffraction limited. The actual objective manufacture involved reassembling the lenses, adjusting lens spacers, rotating lenses, and interchanging lenses. Table 6 provides the performance in terms of Strehl ratio according to the number of objective lenses completed. It was possible to obtain out of the 30 sets, 21 with a Strehl ratio of 0.08 or higher; this is a yield of 70%. Thus assembly trials provide an improved manufacturing yield.  
Figure 5: Objective lens, yield curve, and histogram for 1000 Monte Carlo trials of rms wavefront error across the field of view.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Telecentric Objective Lens Surface Sensitivities in Waves**} \\
**Surface** & \(W_{UA}\) & \(W_{UC}\) & \(W_{LA}\) & \(W_{FT}\) \\ \hline STOP & 0.00 & 0.00 & 0.00 & 0.00 \\
2 & 0.00 & \(-0.11\) & 0.20 & 0.12 \\
3 & 0.00 & \(-0.65\) & 0.37 & 0.19 \\
4 & \(-0.01\) & \(1.18\) & \(-0.73\) & \(-0.40\) \\
5 & 0.00 & \(-0.28\) & 0.21 & 0.22 \\
6 & 0.00 & 0.54 & \(-0.33\) & \(-0.30\) \\
7 & 0.00 & \(-0.19\) & 0.23 & 0.21 \\
8 & 0.00 & 0.06 & 0.02 & \(-0.07\) \\ RSS & 0.01 & 1.50 & 0.96 & 0.64 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Telecentric objective lens. Surface sensitivities in waves.
# Boosting of Denoising Effect with Fusion Strategy
## 5 Discussion
Specifically, in digital medical treatment, the detailed features in images may be ignored by the NCSR algorithm, which is based on the non-local self-similarity of images; however, such features can be preserved by the external denoising method DnCNN. Thus, the proposed method can output better and more comprehensive images by combing the complementary information of the medical images denoised by the two methods, thereby providing more accurate data for clinical diagnosis and treatment. This will be crucial for feature extraction from images of lesions, three-dimensional reconstruction and multi-source medical image fusion, and other technologies that assist in diagnosis. Thus, the proposed method could be of immense value with regard to providing an alternative for boosting the denoising effect. The boosting algorithm developed in this study can be interpreted as an algorithm for the fusion of two initially denoised images. Thus, it is not limited to the noise models of algorithms such as AWGN, and can be adapted to other types of noise if it is allowed by the constituent denoising algorithms. In addition, a good discrimination between noise and image texture information can significantly improve the noise reduction effect, which is also the goal of many traditional denoising algorithms. Currently, researchers are continuing to improve the performance of the state-of-the-art denoising methods. In the future, we will determine complementary algorithms with better performances to deal with various denoising tasks by using our fusion strategy. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & _Cameraman_ & _[PERSON]_ & _House_ & _[PERSON]_ & _Monarch_ & _[PERSON]_ & **Average** \\ \hline \(\sigma=10\) & 0.253 & 0.614 & 0.255 & 0.540 & 0.270 & 0.257 & 0.365 \\ \(\sigma=30\) & 0.260 & 0.530 & 0.257 & 0.539 & 0.257 & 0.265 & 0.351 \\ \(\sigma=60\) & 0.260 & 0.531 & 0.259 & 0.531 & 0.289 & 0.274 & 0.357 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Fusion time (s) of the proposed method for processing the six selected images with sizes of \(256\times 256\) and \(512\times 512\) with noise level \(\sigma=10\), 30, and 60.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
###### Abstract  
We systematically investigated the anchorage performance of self-drilling anchor bolts in strongly weathered dolomite through integrated field pull-out tests and FLAC3D numerical modeling. The study incorporates symmetry principles in both experimental design and numerical simulations to ensure balanced force distribution and model simplification. Experimental data collected from a slope reinforcement project demonstrated that grouting parameters of 0.8 MPa pressure and 0.8 water-cement ratio achieved an interfacial bond strength of 0.147 MPa, surpassing the recommended value by 22.5%. A modified FLAC3D pile element, calibrated against RS6-01 anchor bolt test data, exhibited improved alignment with load-displacement curves, converging to 272 kN ultimate capacity at 26.1 mm displacement. Symmetrical anchor configurations in the numerical model reduced computational complexity while maintaining accuracy in stress distribution analysis. Through orthogonal experimental design, symmetry-driven parameter optimization identified a 7 m bolt length, 30\({}^{\circ}\) installation angle, and 2 m spacing as the most effective configuration. This solution increased the slope safety factor by 19.98% while reducing displacements by 46-62%. The symmetry in anchor spacing and angular alignment contributed to uniform stress redistribution, enhancing slope stability. The findings highlight the synergy between symmetry principles and geotechnical reinforcement strategies.  
self-drilling anchor bolt; steep loose slope; slope stability +
Footnote †: journal: Applied Sciences
# Pressure Study on Pipe Transportation Associated with Cement Coal Gangue Fly-Ash Backfill Slurry
[PERSON] and [PERSON], LO [18] compared the CFD modeling technique and the discrete element method (DEM) for solid distributions in horizontal pipelines. The CFD is universally accepted by investigators as solving complex flow problems. As one type of novel backfilling material, currently, there is not much published research papers regarding the CFD simulation on the CGFB pipe transportation. [PERSON] [19] uses experimental methods to research the pressure drop of CGFB in the pipe loop. [PERSON] et.al [20] researches similar materials to the CGFB in a horizontal circular pipe in the lab. Based on the experimental study,Wu [19] reported a qualitative conclusion but without much quantitative data, which is not enough to conclude a function or equation to predict or guide the CGFB slurry pipe transportation further. Thus, employing the CFD simulation method to study CGFB slurry pipe transportation in detail is essential. In the present work, an attempt has been made to reveal the flow movement features in the CGFB slurry pipeline using the commercial CDF code FLUENT in the aspects of pressure drop and velocity distribution. A simulation study was used to optimize the hydraulic conveying velocity and determine the CGFB slurry transportation's crucial parameters in a straight and bending pipe.
## 2 Problem  
Based on Reference [10], we introduce the problem and necessary definitions for the robots, occupancy grid, and constraint construction shortly: Here, we utilise a distributed system of \(P\in\mathbb{N}\) mobile robots. The robots follow a time discrete model stated as a control-affine system:  
\[z_{p}^{+}=f(z_{p},u_{p}):=f_{0}\left(z_{p}\right)+\sum_{i=1}^{m}f_{i}\left(z_{p} \right)u_{p,i},\qquad p\in[1:P] \tag{1}\]  
with smooth vector fields \(f_{0}\) and \(f_{i}\colon\mathbb{R}^{d}\to\mathbb{R}^{d}\), \(d\geq 2\), \(i\in\{1,\ldots,m\}\), \(d,m\in\mathbb{N}\) where, for each vector field \(f_{i}\), \(i\in\{1,\ldots,m\}\), one control value \(u_{p,i}\) is imposed. A successive state of a robot is denoted by \(z_{p}^{+}\) and determined by \(f_{i}\) based on its current state \(z_{p}\) and imposed control \(u_{p}\). The state is represented here without loss of generality by the planar coordinates \(\left(x_{p},y_{p}\right)^{\top}=z_{p}\in\mathbb{R}^{d}\). Constraints for state and control are given by  
\[z_{p} \in Z:=[-\tilde{x},\tilde{x}]\times[-\tilde{y},\tilde{y}]\times \widetilde{Z}\qquad\text{with }\widetilde{Z}\subseteq\mathbb{R}^{d-2}\text{ and }\tilde{x},\tilde{y}>0, \tag{2}\] \[u_{p} \in U\subset\mathbb{R}^{m}, \tag{3}\]  
respectively, with \(U\) containing the origin.  
Assuming that our systems in Equation (1) are driftless (\(f_{0}\equiv 0\)), describing the kinematic model of a mobile robot, we assume that robots may immediately stop by the following assumption:  
**Assumption 1** (Immediate Hold).: _Each robot can come to an immediate hold:_  
\[\forall\,z_{p}\in Z\quad\exists\,\overline{u}_{p}\in U\quad:\quad z_{p}=f(z_{p},\overline{u}_{p}). \tag{4}\]  
Note that delays of actuator dynamics, which might invalidate this assumption are not taken into account. For a finite horizon \(N\) and individual initial positions \(z_{p}(0)\), for each robot \(p\), a finite control sequence is defined by  
\[\mathbf{u}_{p}=(u_{p}(0),u_{p}(1),\ldots,u_{p}(N-1)),\]  
which is utilised to formulate the predicted state trajectory of robot \(p\) as  
\[\left(z_{p}^{u}\left(0;z_{p}^{0}\right),z_{p}^{u}\left(1;z_{p}^{0}\right), \ldots,z_{p}^{u}\left(N;z_{p}^{0}\right)\right).\]
## 1 Introduction
Sun tracking was achieved by rotation in a similar way to standard blinds, plus minor movement to control the distance between the cylinders, which allowed lighting control. In order to assess the performance of the developed solar system ([PERSON] et al., 2019) in the case of facade-integrated applications, dynamic energy performance simulations were conducted ([PERSON] et al., 2019). The aim was to determine the feasibility of the proposed system in terms of covering the energy demands of a two-storey house in three different locations: Lisbon (Portugal), Barcelona (Spain) and Genoa (Italy). The solar fractions achieved showed a satisfactory performance, proving the relevance of the proposed system in relation to the goals pursued ([PERSON] et al., 2019). This research is a big step forward because it is quite difficult to have as a starting point a small-scale laboratory system, laboratory-scale fabrication, modelling and simulations, with the aim of developing a prototype of realistic dimensions. Testing a system under real conditions gives rise to challenges, and, in addition to this, drawbacks are identified and the feasibility of the proposed system is determined. To sum up, the literature on CPVT shows that there are numerical and laboratory-scale studies on these technologies, but there are very few experimental studies on novel CPVT systems with direct immersion. In order to fill this scientific gap and considering the importance of PV technologies for high ([PERSON] et al., 2018) and low ([PERSON] et al., 2018; [PERSON] et al., 2020) concentration applications, the present study aims to perform the following:  
* Experimentally evaluate the performance of a facade-integrated CPVT system under real operating conditions (the experimental set-up was built according to the required dimensions: intermediate-size system). * Evaluate a PV-cell cooling technique that has not been extensively studied. * Provide valuable information about CPVT modules, prototypes of realistic dimensions and innovative building-integrated solar concentrators, contributing to the enrichment of the literature on CPVT technologies. * Examine the performance of a system and its assembly that have been designed considering an appropriate control strategy for tracking solar altitude. * Monitor the performance of the proposed system in terms of electrical and thermal energy production. * Assessment of the qualitative performance of the facade-integrated system in terms of illumination and thermal envelope performances. The article is structured as follows: Section 2 covers system design; Section 3 is about construction and assembly process; in Section 4, the experimental results are provided; and in Section 5, the main results are explained.
## 3 Results
### Energy and Power Requirements
The dotted green line in the figure indicates the power requirements exceeding the maximum output of a single S333.3 unit, highlighting that only ten minutes of operation in non-consecutive intervals would necessitate a second coupled unit. Figure 15: Demand requirements and maximum potential of regenerative braking for a 1300 t freight train: (**a**) From Cartagena to Murcia; (**b**) From Murcia to Cartagena. Figure 16b illustrates the throttle and dynamic braking notch positions. The data reveal that the use of a double unit is only required for short, specific periods and that the N8 notch (maximum output power) is not the most frequently used. This insight provides a foundation for defining a blueprint for the hauling power requirements. Based on this analysis, it can be hypothesized that a second locomotive unit with lower traction power (in the range of 1100-1400 kW) could adequately fulfill operational requirements. While this configuration might slightly increase trip duration, it could significantly reduce energy demands, thereby improving overall efficiency. It is worth noting again that S269 locomotives, although providing lower tractive effort than diesel units at low speeds (<30 km/h), exhibit superior tractive effort at medium speeds (30-50 km/h). Additionally, 269 locomotives weigh 32 tons less than the S333.3 series, which could further contribute to operational efficiency (as illustrated in Figure 14a).
# Independent System Calibration of Sentinel-1B
## 4 Geometric Calibration
### The Pixel Localization Accuracy in Range  
The range delay offset is estimated by comparing the predicted signal round trip time between the SAR antenna and the deployed target (in Zero-Doppler plane) derived from geometry and the corresponding range delay measured by the radar derived from the actual range pixel position of the reference target in the SAR image based upon estimating its Impulse Response Function (IRF) in the sub-pixel domain.  
For this, two effects have to be taken into account: the internal SAR instrument delay and a signal delay along the propagation path. The latter is mainly induced by the tropospheric refraction along the hydrostatic path and corrected as described in [3].  
Figure 14: Azimuth offset for all acquired overpasses derived from corner reflectors for SM (blue), IW (red) and EW mode (green).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mode**} & \multicolumn{2}{c}{**Azimuth Offset**} & \multicolumn{2}{c}{**Range Offset**} \\  & \(\mu[\mathbf{m}]\) & \(\sigma[\mathbf{m}]\) & \(\mu[\mathbf{m}]\) & \(\sigma[\mathbf{m}]\) \\ \hline SM & \(-2.2\) & \(0.2\) & \(-0.5\) & \(0.5\) \\ IW & \(-3.0\) & \(1.2\) & \(-0.7\) & \(0.2\) \\ EM & \(-2.8\) & \(0.8\) & \(-0.7\) & \(0.3\) \\ \hline all & \(-The remaining range offset is depicted in Figure 15 as a function of look angle; the different modes are identified by color (SM: blue, IW: red, EW: green). No mode-dependent trend is found as indicated in Table 3; the overall range offset is in sub-pixel domain with a bias of \(-0.6\) m. The remaining standard deviation of the range offset is a measure for the pixel localization in range. The 0.4 m also confirms a high geometric accuracy in range.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 4 System Model
### Base Station's Power Model  
As seen in Figure 11, the resources utilized or the configuration (in terms of the bandwidth and antenna configuration) in the base station has an impact on its instantaneous power consumption, the user performance, and its activity factor [21]. A base station's power model quantifies the impact of resources used on its power consumption. An accurate power model helps us to analyze the impact of various components on the overall power consumption. Furthermore, a model depends on the base station type, and as a result, it is necessary to ensure that the implemented model accurately models the base station under consideration. A base station's total power consumption \(P_{total}\) can be divided into the load-dependent and load-independent parts as expressed in (13). The load-dependent parts consist of the baseband and power amplifier power consumption as given in Equations (11) and (12).  
\[P_{baseband}=f\left(M_{ij},B_{ij}\right) \tag{11}\]  
\[P_{PA}(p)=M_{ij}\Big{[}\frac{1}{(1+\epsilon)\eta}(p+\epsilon P_{max,PA})\Big{]} \tag{12}\]  
\[P_{total}=\underbrace{P_{baseband}+P_{PA}}_{P_{total-dependent}}+ \underbrace{P_{syn}+M_{ij}P_{\text{bs}}+P_{fixed}}_{P_{total-independent}} \tag{13}\]  
Here, \(p\) is the load experienced by the power amplifier (PA), \(\eta\) is the efficiency of the PA, \(\epsilon\) is a PA-dependent constant, and \(P_{syn}\) is the local oscillator power consumption. More detailed equations showing the impact of \(M_{ij}\) and \(B_{ij}\) on the baseband's \(P_{baseband}\) and the power amplifier's \(P_{PA}\) power consumption can be found in [21]. It is important to note that in a TowerCo scenario, the power amplifiers could experience different loads and consequently have different power consumption values.  
Figure 11: Activity factor of a base station.
# Seeking Optimal GNSS Radio Occultation Constellations Using Evolutionary Algorithms
## 3 Results
### Performance Evaluation of the Optimal Constellations  
As mentioned in Section 2.5, a uniform point-to-point distribution should be characterized by a low COV value. Hence, after the optimal constellations are obtained based on the evolutionary algorithms, their performances for GNSS RO observation over the globe are further evaluated using the COV. For each optimal constellation, the COV value corresponding to the ROE distribution within each 3 h is calculated. Therefore, there are 8 COV values for one day. The mean and standard deviation of the 8 COV values are also reported in Table 6. For a certain number of satellites, the optimal configurations for the two LEO constellation patterns are compared according to the means and standard deviations listed in this table. The smaller the mean and standard deviation, the better the performance of the LEO constellation.  
Figure 5: The distribution of ROEs observed within each three hours of one day by the final optimal LEO constellations with 2D-LFC pattern composed of 6 (**a**) and 12 satellites (**b**).  
According to Table 6, as the number of satellites varies between 6 and 12, all the optimized 2D-LFCs and 3D-LFCs provide mean COV values for 3 h-distributions of ROEs lower than 0.6 and most of the standard deviations of the COV values are lower than 0.02. This indicates that both the optimal 2D-LFC and 3D-LFC configurations are of proper point-to-point ROE distributions in each 3 h, and the temporal variation of the observation uniformity is generally small.  
Table 6 also shows that, among the seven choices for the number of LEO satellites \((n=6,7,8,\ldots\ldots 12)\), the COV mean values of the optimal 2D-LFCs are lower than those of the optimal 3D-LFCs with \((n=6,7,8,10,12)\). For the other two constellations, the COV mean values provided by 3D-LFCs are slightly higher. This indicates that the optimized 2D-LFCs generally result in slightly more uniform point-to-point distributions of 3 h-ROEs compared to the optimized 3D-LFCs. As the number of LEO satellites varies between 6 and 12, the standard deviations of the COV values for the optimal 2D-LFCs are mostly lower than the corresponding optimal 3D-LFCs, which shows that the temporal variations of the ROEs distribution from 2D-LFCs are mostly smaller than those from the optimized 3D-LFCs.
## 3 Results and Discussion
### The Environmental Impact Analysis of Building Materials  
Tables 6 and 7 summarise the environmental impact assessment results from each building material for the three different slab systems. In this study, the major building materials were considered to assess the environmental impact during the construction of slab systems. The major building materials were ready-mixed concrete, rebars, forms for the OSS and the FPS. In the case of the VDS,  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Environmental Impact** & **FPS–OSS** & \begin{tabular}{c} **Reduction** \\ **Ratio** \\ \end{tabular} & **VDS–OSS** & \
\begin{tabular}{c} **Reduction** \\ **Ratio** \\ \end{tabular} & **VDS–FPS** &
\begin{tabular}{c} **Reduction** \\ **Ratio** \\ \end{tabular} \\ \hline GWP (kg-CO\({}_{2}\)-eq/m\({}^{2}\)) & \(\begin{array}{c}6.22\times\\ 10^{0}\end{array}\) & \(\begin{array}{c}5.59\times\\ 10^{1}\end{array}\) & \(\begin{array}{c}25.57\\ 10^{1}\end{array}\) & \(\begin{array}{c}4.47\times\\ 10^{1}\end{array}\) & \(\begin{array}{c}22.45\\ 8.93\times\\ 10^{-2}\end{array}\) & \(\begin{array}{c}16.56\\ 1.56\end{array}\) \\ AP (kg-PO\({}_{4}\)\({}^{3-}\) eq/m\({}^{2}\)) & \(\begin{array}{c}3.30\times\\ 10^{-3}\end{array}\) & \(\begin{array}{c}0.67\\ 10^{-1}\end{array}\) & \(\begin{array}{c}1.21\times\\ 32.42\end{array}\) & \(\begin{array}{c}1.18\times\\ 10^{-1}\end{array}\) & \(\begin{array}{c}31.53\\ 1.51\times\\ 10^{-4}\end{array}\) & \(\begin{array}{c}2.13\times\\ 10^{-4}\end{array}\) & \(\begin{array}{c}2.55\\ 10^{-4}\end{array}\) \\ ODP (kg-CFC11 eq/m\({}^{2}\)) & \(\begin{array}{c}9.40\times\\ 10^{-2}\end{array}\) & \(\begin{array}{c}24.7\\ 10^{-1}\end{array}\) & \(\begin{array}{c}1.03\times\\ 10^{-3}\end{array}\) & \(\begin{array}{c}27.32\end{array}\) & \(\begin{array}{c}8.58\times\\ 10^{-3}\end{array}\) & \(\begin{array}{c}2.28\) \\ 2.89\times\\ 33.34\end{array}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Reduction in environmental impact indicators.  
three additional building materials, namely, anchoring steel materials, steel decking, and void formers, were applied to construct the VDS, compared to the OSS and the FPS.  
The environmental impact of ready-mixed concrete is the highest factor for all of the environmental impact indicators in the three slab systems. While rebars were the lowest environmental impact element in the case of the OSS and the FPS, void formers were the smallest impact factor of all environmental indicators in the VDS. In this study, the VDS indicates less environmental impact from building materials, even though it would require additional building materials for construction. Differences in environmental impacts between the OSS and the FPS were relatively small, since a significant amount of building materials would be required for drop panels, which prevent punching shear in the FPS. In addition, the environmental impacts from the building materials for the VDS were considerably reduced, because the self-weight of the slab was lowered, and the number of beams and girders decreased.