esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Impact of Multipath on Galileo System Measurements
## 4 Multipath Analysis
### Phase Multipath Analysis
The strong similarity in terms of the signal characteristics of both Galileo and GPS satellites as well as in terms of the frequency characteristics of the DD and CMC series confirms that the observed interference is caused by the impact of signal reflections (the multipath effect). The peak-to-peak amplitudes of specular multipath error for DD phase residuals are equal to 9.0 mm for E1 Galileo observations and 9.1 mm for L1 GPS (see Table 5). Again, the largest errors were observed for the entire frequency range characteristic of the multipath effect. For this range, however, the error for GPS observations is clearly smaller than that for Galileo observations (peak-to-peak amplitudes equal to 19.6 mm for Galileo and 13.6 mm for GPS). Figure 11: Double-difference phase residuals and scalograms for (**a**) Galileo E18 and (**b**) GPS G09 satellites. Figure 10: CMC observables and scalograms for (**a**) Galileo E18 and (**b**) GPS G09 satellites at the WUT2 site. Figure 12: CMC observable signal extractions using wavelets for (**a**) specular, (**b**) diffuse, and (**c**) specular and diffuse multipaths. Figure 13: DD phase residual signal extraction using wavelets for (**a**) specular, (**b**) diffuse, and (**c**) specular and diffuse multipaths.
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
## 3 The Novel Dihedral CR and SAR Data Set
### The Deviation Angles of the CR Array  
The misalignment of the CR's axis with the SAR incident angle affects the CR's scattering intensity. The accurate RCS reduction curve of the CR, based on the deviation angle, can be determined from the observed results under real SAR data. This information, combined with the required deformation monitoring accuracy, can be used to guide the design of the CR's size and inclination.  
The CRs on the Huangtupo slope are completely symmetrical under the ascending and descending SAR data. The two panels are also completely symmetrical. The RCS reduction by the deviation angle differences under different SAR tracks weakens the influence of system errors, such as the panel dielectric constant or sensor thermal noise, which can more accurately evaluate the RCS reduction caused by the change of incident angle. Additionally, it aids in assessing the quality of the CR product or the impact of the installation environment.  
One the Huangtupo slope, three types of dihedral CR with different horizontal tilt angles have been installed. The CRs with the square panels cannot tilt upwards, and their tilt angle is 0\({}^{\circ}\). The dihedral CRs with the semi-circular panels are not only horizontally positioned but also available in two inclined configurations with tilts of 10\({}^{\circ}\) and 15\({}^{\circ}\). Table 2 presents the deviation angles corresponding to the ascending and descending TSX data and overlapped Sentinel data for each type of CR. Each CR has two deviation angles in the TSX or Sentinel data.  
We define the deviation angle difference between the two deviation angles as follows:  
\[\Delta\beta=||\beta_{1}|-|\beta_{2}|| \tag{6}\]  
where \(\Delta\beta\) represents the deviation angle difference. The deviation angles for the CR under the ascending and descending TSX or overlap Sentinel data are denoted as \(\beta_{1}\) and \(\beta_{2}\).  
Figure 5: **(a,b)** Are the average intensity images of the Sentinel (Track 084) and TSX (ascending track) of the CR array on the Huangtupo slope; (**c,d**) are the enlarged intensity images of the CRs under the Sentinel and TSX data.  
From Table 2, it can be observed that the CRs with three different tilt angles exhibit six deviation angles under the ascending and descending TSX data or overlap Sentinel data. These deviation angles range from 1\({}^{\circ}\) to 16\({}^{\circ}\), resulting in six deviation angle differences, distributed between 3\({}^{\circ}\) and 12\({}^{\circ}\). In actual observations, the deviation angle is less than 20\({}^{\circ}\) because the SAR incident angles generally range from 25\({}^{\circ}\) to 45\({}^{\circ}\). The CR array in Huangtupo could provide comprehensive sampling for validating the RCS reduction with the misalignment.
## 5 Conclusions  
To reveal the characteristics of the evolution of the fatigue damage of the argillaceous shale found in the Fushun West Open-pit Mine, multi-stage cyclic loading tests were applied to the samples. The fatigue mechanical properties of the rocks were analyzed, and a cumulative fatigue damage model describing the evolution of the damage accumulation was proposed. The main conclusions are as follows.  
1. As the loading frequency increased, the number of loading cycles and the fatigue life gradually increased. The morphology of the hysteresis curve changed with the loading time. In addition, the hysteresis curve changed from sparse to dense at each fatigue loading stage before failure, while the final failure stage was sparse.
2. For a given loading frequency, the axial, radial, and volumetric strains showed different trends. The cumulative strains in the argillaceous shale at the different loading frequencies were also different. Overall, the higher the frequency, the higher the cumulative strain during the final deformation. The damage process was divided into three stages based on the results of the deformation characteristics of the fatigue test.
3. The AE count and AE energy exhibited during the rupture of the rock were affected by the loading frequency, and the accumulated damage of the rock that occurred when the stress amplitude suddenly increased was greater than that at the fatigue loading stage. The entire process of the accumulative AE energy corresponded well to the accumulative AE count.
4. The rock exhibited two stages of damage evolution under incremental variable amplitude fatigue loads: (1) rapid damage accumulation at the beginning of the loading and (2) relatively smooth damage evolution thereafter. The proposed cumulative fatigue damage model can fit the damage accumulated throughout the rock fracturing process.  
These experiments were closer to the actual loading mode of the slope, which can provide basic data support for slope stability engineering. This study had certain limitations. Specifically, the experiments only considered the rock subjected to variable frequency loading and increased amplitudes. In future studies, the fatigue deterioration mechanisms of the rocks subjected to environments with complex stress disturbances, such as dry-wet cycles, freeze-thaw cycles, and temperature cycles, will be investigated.  
Conceptualization, S.G. and H.M.; methodology, X.W. and Y.W. ([PERSON]); investigation, S.G., P.W. and H.M.; resources, J.W., Y.W. ([PERSON]) and H.M.; writing--original draft preparation, S.G. 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 42130720) and the China Geological Survey (grant number DD20190318 and DD 20221816).  
Not applicable for studies not involving humans or animals.  
Not applicable.  
The study did not report any data.  
The authors declare no conflict of interest.  
Acoustic emission XRD X-ray diffraction SEM Scanning electron microscopy LVDT Linear variable differential transformer PAC Physical Acoustics Corporation
## 5 Conclusions and Discussion  
Considering that the outliers in the complex engineering environment have a serious effect on the location performance, the PCFWE method is proposed to improve the location accuracy in this paper. Firstly, the weight estimation is developed by transforming residuals to weights according to normal distribution. Updated weights are incorporated into the source location to provide a weighted least squares solution. Then, outliers are filtered by iterating between the weight estimation and source location. Linear equations, which are reconstructed with remaining measurements after filtering outliers, are ill-conditioned and need to be preconditioned to lower the condition number. Finally, the optimal source coordinates can be obtained by solving preconditioned linear equations. The proposed location method is verified by the experiment of pencil-lead breaks; the results show that the proposed PCFWE method achieves a stable and accurate location both with outliers and without outliers in measurements. However, how many outliers the proposed method can tolerate, and the sensitivity of the wave velocity, still need further investigation.
## 2 Theory of the Non-Detection Zone  
Based on previous research in low-frequency ultrasonic guided wave detection [9; 10], we use a square steel bar as an example and analyze the objective reasons for the appearance of the non-detection zone with a finite-length boundary. Firstly, this problem associated with the ultrasonic guided wave (UGW) becomes equivalent to the reflection of waves with different modes in a plate [11] and is the 2D equivalent of an ultrasonic guided wave simulation model. By taking full advantage of the characteristics of the low-frequency pulse wave [12], an analysis is performed on the changes in the pulse signal of the flaw identification and the creation of the non-detection zone in a conventional inspection.  
Prior to the analysis of the ultrasonic guided wave echo experiments and simulations, the following constraints are defined:  
* A single low-frequency incident pulse is used in the experiments and simulation and the waveforms of the other frequencies are not induced by the excited incident pulse at the corresponding frequencies.
* The mode of the incident pulse wave is a single \(S0\) mode and the frequency of the incident pulse is lower than the \(S1\) and \(A1\) cut-off frequencies so that the mode is simplified and easy to analyze [13].
* In a symmetrical plate-like structure, the reflected pulse is also a single mode \(S0\); in an asymmetrical plate-like structure (such as man-made flaws), the reflected pulses in the \(A0\) mode and \(S0\) mode are excited [14].
* When the frequency-thickness product (\(f\cdot d\)) is maintained, the group velocity of the \(S0\) and \(A0\) modes remains unchanged under ideal conditions [15].
## 2 Materials and Methods
### Characterization of AuNPs  
The formation and stability of the synthesized AuNPs were recorded at 300-700 nm by using a UV-visible (UV-vis) spectrophotometer (Thermo Scientific Evolution 201, USA). The morphology of AuNPs was characterized through transmission electron microscopy (Philips Tecnai F20, Netherlands). The particle size and particle-size distribution of AuNPs were obtained using a dynamic light scattering spectrometer (Malvern Nano ZS, UK). The stability of AuNPs was evaluated by storing AuNPs in deionized water, 5% bovine serum albumin, 5% NaCl or phosphate buffered saline (PBS) (pH 7.4) at 26 \({}^{\circ}\)C for 3 months. The solutions were monitored using a UV-vis spectrophotometer during the storage period.
## 6 Dynamic Responses of Floating OWTs
### Mit/Nrel Tlp  
The given wave condition adopted for dynamic response of MIT/NREL TLP is JONSWAP spectrum (significant wave height: \(H_{s}\) = 5 m, peak period: \(T_{p}\) = 8.7 s, and peak-enhancement factor: \(\gamma\) = 2.4). As illustrated in Figure 19a,b, The RAO analysis of surge and heave with the mooring load conducted by the hydrodynamic simulator will be in comparison with the results from NREL design code, Fatigue, Aerodynamics, Structures, and Turbulence (FAST), executed by [PERSON] and [PERSON] [35]. FAST is NREL's primary CAE tool, enabling coupled nonlinear aero-hydro-servo-elastic simulation of wind turbines in the time domain. It is clearly shown that the surge RAO is considerably larger than the heave RAO because the mooring system is much stiff in the vertical plane. Although second-order mean drift forces are considered to simulate slowly varying horizontal-plane drift motions through [PERSON]'s approximation method in their study, our results are generally in good agreement with FAST's results except for a slight shift of the peak in the surge RAO. Furthermore, [PERSON] [36] indicated  
Figure 18: Time histories of wave-load components at (a) the surge natural frequency (\(f_{p}\) = 0.014 Hz), (b) the heave natural frequency (\(f_{p}\) = 0.052 Hz), and (c) the pitch natural frequency (\(f_{p}\) = 0.054 Hz) for the UMaine-Hywind spar.  
that the TLP represents the most attractive option as a result of its low Root Mean Square (RMS) accelerations and negligible heave and pitch motions.  
The time histories in Figure 20a,b show that the surge oscillation excites individual wave-load components by couplings with the taut-leg mooring system to oscillations at the surge and heave natural frequencies, respectively. It is obvious that the mooring load has a greater contribution to motion restriction of the MIT/NREL TLP at the heave natural frequency than that at the surge natural frequency. For taut lines of a TLP with no part of the cables lying on the seabed, the linearity assumption of the stiffness matrix is a relatively good approximation to vertical forces.  
Figure 19: The performance of (**a**) surge and (**b**) heave RAOs as a function of wave frequency for the MIT/NREL TLP by comparing hydrodynamic simulator with FAST.
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 2 Coupled CFD-DEM Mathematical Equations
### Contact Model Equations  
In this paper, the Hertz-Mindlin nonlinear contact model combined with the linear adhesion model are used to describe the forces between particles with other particles or between particles with the solar panel surface, assuming that the contact between two spheres with radii of \(R_{1}\) and \(R_{2}\) is elastic (if the second object is the solar panel surface, \(R_{2}\) tends to be infinite), and the normal overlap is \(\alpha\), \(\alpha=R_{1}+R_{2}-D_{12}\), where \(D_{12}\) is the distance between the centers of two spheres. The contact model is shown in Figure 1. The normal force between particles is \(\widetilde{F}_{n}\), and its magnitude, \(F_{n}\), can be expressed as the following [27],  
\[F_{n}=\frac{4}{3}E^{*}(R^{*})^{1/2}\alpha^{3/2} \tag{7}\]  
where \(E^{*}\) and \(R^{*}\) are the equivalent modulus of elasticity and the equivalent radius, respectively, which are determined by the following formulas: \(1/E^{*}=\left(1-v_{1}^{2}\right)/E_{1}+\left(1-v_{2}^{2}\right)/E_{2}\) and \(1/R^{*}=1/R_{1}+1/R_{2}\). Here, \(E_{1},\
u_{1},E_{2},\
u_{2}\) are the elastic modulus and Poisson's ratio of two spheres. The magnitude of the normal damping force, \(\widetilde{F}_{nd}\), can be expressed by:  
\[F_{nd}=-2\sqrt{\frac{5}{6}}\beta\sqrt{S_{n}m^{*}}v_{n}^{ref} \tag{8}\]  
where \(\beta=\ln e/\sqrt{\ln^{2}e+\pi^{2}}\), \(e\) is the recovery factor, \(S_{n}=2E^{*}\sqrt{R^{*}\alpha}\), \(m^{*}=m_{1}m_{2}/(m_{1}+m_{2})\) is the equivalent mass, and \(v_{n}^{ref}\) is the normal component of relative velocity. In addition, the magnitude of the tangential damping force between particles, \(\widetilde{F}_{t}\), can be expressed as:  
\[F_{t}=-2\sqrt{\frac{5}{6}}\beta\sqrt{S_{t}m^{*}}v_{t}^{rel} \tag{9}\]  
where \(S_{t}\) is the tangential stiffness, \(S_{t}=8G^{*}\sqrt{R^{*}\alpha}\), and \(G^{*}\) is the equivalent shear modulus. Assuming \(G_{1},G_{2}\) are the shear modulus of two spheres, then \(1/G^{*}=\left(2-v_{1}^{2}\right)/G_{1}+\left(2-v_{2}^{2}\right)/G_{2}\).  
In order to describe the cohesion force between the particles and the solar panel, the linear cohesion model was used, which can modify the default Hertz-Mindlin contact model for particle-particle and particle-geometry interactions by adding a normal cohesion force, \(F_{nc}\), which is expressed as:  
\[F_{nc}=KA \tag{10}\]  
where \(K\) is the adhesion energy density, \(A\) is the contact area and \(F_{nc}\) represents the adhesion between the particles and the solar panel caused by van der Waals forces and electrostatic forces [28], which are difficult to give an accurate calculation method for.  
In this study, the effect of the particles volume was neglected because in general dusty weather the mass density of sand/dust particles is relatively small and collisions between particles are rare.  
Figure 1: The contact model of dust and solar panel.
## 4 Case Study  
### Case Setting  
The example in this paper is based on the interconnection network of 6-node heat network, 20-node gas network of Belgium and IEEE 39-bus power network. In this paper, the unit wind abandonment \(C_{cut}\) is set to 0.09 $/(kW\(\cdot\)h) in [PERSON] et al. [39]. In the ladder-type carbon trading mechanism, the system's carbon trading benchmark price \(C_{tax}\) is 30 $/t, the growth rate of carbon tax \(\gamma_{tax}\) is 0.3 and the interval length of total carbon emissions \(l\) is 4 \(\times\) 10\({}^{7}\) t in [PERSON] et al. [40]. The specific network structure diagram is shown in Figure 4. The 39-bus 10-unit power system has two wind power clusters, one CHP unit, two gas turbines and five coal-fired units. The CHP unit located at bus 30 of the power grid is connected to node three of the gas network and node one of the heat network, and the electric boiler located at bus 32 of the power grid is connected to node one of the heat network. P2G located at bus 31 of the power grid is connected to node seven of the gas network and node one of the heat network, gas turbines located at bus 33 and bus 37 of the power grid are connected to node six and node nineteen of the gas network, respectively, carbon capture devices are installed at bus 39 of the power grid. The CHP unit boundary vertex parameters are shown in Table 1. Electricity, heat, gas load and forecast output parameters of wind power are shown in Figures 5 and 6. In this paper, CPLEX solver was used to solve the problem, the time step was 1 h. The dynamic optimization results of one day and 24 h were analyzed.  
Figure 4: Electric–gas–heat integrated energy system structure diagram.  
Figure 5: Output power forecasting of wind power and load.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Vertex Number** & **Electric Power/MW** & **Thermal Power/MW** \\ \hline
1 & 81 & 104.8 \\
2 & 215.0 & 180.0 \\
3 & 247.0 & 0 \\
4 & 98.8 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary vertex parameter of CHP.  
In order to better compare the impact of each module on the low carbon and economy of the system, six scenarios are set up, as shown in Table 2.
# Monitoring and Analysis of Slope Geological Hazards Based on UAV Images
## 2 Study Area  
The study area is situated in Shandong Province, China, and consists of the remaining slope of an abandoned quarry (Figure 1), covering an area of approximately 42,000 m\({}^{2}\). The region is characterized by low hills composed of sedimentary rocks, with altitudes primarily ranging from 300 m to 400 m.  
Since the 1960s, rapid urban development and the expansion of built-up areas have led to excessive logging and quarrying, causing significant damage to the region's mountains. Large areas of exposed rock have resulted in weathering and fragmentation, and human engineering activities have triggered rock collapses, landslides, and other geological hazards. The region features complex and steep terrain with significant undulations, compounded by previous quarrying methods, including step blasting. As a result, the mountain slopes are characterized by stepped cliffs. The area has undergone man-made repairs, but the northwest and northeast slopes remain as stepped exposed cliffs, while the south side still features exposed, broken slopes. Several roads pass through the region, with a production road on the eastern side directly adjacent to the slope. The road slopes on both sides exceed  
Figure 1: The map of the study area.  
80\({}^{\circ}\), making the area highly susceptible to slope geological hazards, posing a significant threat to public safety, engineering projects, and the urban ecological landscape.
## 1 Introduction
The approximately half-yearly periodic terms in the frequency drift rate are discussed, and some possible reasons are given. The performance of BDS-3 satellite atomic clocks with gradual improvement of frequency stability during the test period was analyzed; the results show that the daily frequency stability of BDS-3 satellite atomic clocks has reached a level superior to 8 \(\times\)\(10^{-15}\) after 2021. The rest of this study is arranged as follows. Section 2 introduces the basic information of the BDS satellite and the clock offset data used in this study; the effectiveness of the data preprocessing method is shown. Section 3 introduces the working principle and model of the satellite atomic clock, and then the calculation method of the main indicators is described; thus, the long-term characteristic analysis process of the satellite atomic clock is given. In Section 4, the performance indicators of the BDS on-orbit satellite atomic clock are calculated, and the long-term variation characteristics from July 2020 are analyzed. Thereafter, in Section 5, the necessary overall analysis is given and some long-term characterization features are revealed, including the slowly changing periodic term of the fitting residual and frequency drift rate, the changing trend of the frequency stability, and so on. Eventually, Section 6 gives the conclusion of this study.
## 4 Case Studies
### Non-Rotatable Objects
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Optimization Strategy**} & \multirow{2}{*}{
\begin{tabular}{c} **Objective** \\ **Function** \\ \end{tabular} } & \multicolumn{6}{c}{**Number of Objects**} \\ \cline{3-8}  & & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \multirow{3}{*}{Concurrent} & \(f_{T1}\) & 100\% & 84\% & 51\% & 42\% & 23\% & 5\% \\  & \(f_{A1}\) & 100\% & 78\% & 42\% & 46\% & 27\% & 9\% \\ \hline \multirow{3}{*}{Ordered} & Diagonal length-wise ordering & \(f_{DC}\) & 100\% & 100\% & 41\% & 49\% & 45\% & 20\% \\  & \(f_{DBL}\) & 100\% & 100\% & 99\% & 99\% & 100\% & 94\% \\  & \(f_{A1}\) & 100\% & 100\% & 81\% & 81\% & 73\% & 75\% \\ \cline{2-8}  & Area-wise ordering & \(f_{DC}\) & 100\% & 100\% & 100\% & 51\% & 6\% & 7\% \\ \hline \multirow{3}{*}{Ordered} & \(f_{DBL}\) & 100\% & 100\% & 100\% & 100\% & 100\% & 13\% & 24\% \\  & \(f_{A1}\) & 100\% & 100\% & 98\% & 100\% & 9\% & 6\% \\ \hline \end{tabular}
\end{table} 
Table 2: Success percentages in packing all objects of each set given in Table 1 for different optimization strategies and objective functions. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Quantity** & **Height** & **Width** \\ \hline
2 & 20 & 80 \\
2 & 20 & 40 \\
1 & 20 & 20 \\ \hline \end{tabular}
\end{table} 
Table 3: Quantities and dimensions of the rectangles used in the second study that involves no rotations.