esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation Model of Tooth Surface Wear
### Calculation of Wear Depth  
Figure 7 illustrates the flow chart for calculating gear wear depth.  
Figure 7: Flow chart for calculation of gear wear depth.  
As shown in Figure 7, initially, when the tooth surface is unworn, the finite element is used to calculate the three parameters of wear coefficient \(K\), contact pressure \(P\), and sliding distance \(S\). Then, the Archard wear formula is used to calculate the wear depth at any point on the tooth surface \((\Delta h)_{n}\), and to determine whether the wear depth has reached the tooth surface reconstruction threshold. If it has not reached the threshold, the above wear calculation is repeated until the wear depth reaches the threshold. At this stage, the change in the tooth surface will have a greater impact on the contact pressure, which in turn will have a greater impact on the wear depth. Therefore, the worn finite element model should be used to determine the contact pressure and the corresponding wear depth. The above calculation steps are then repeated, and finally, the wear amounts at each stage of the tooth surface are accumulated. When the wear depth at any point on the tooth surface exceeds the maximum allowable wear \([h]\), it can be considered that the gear has reached its wear life, and the cycle ends.  
As shown in Figure 8, the tooth surface reconstruction in this paper was mainly achieved by superimposing the wear amount on the finite element mesh model to simulate the dynamic change process of tooth surface wear. By comparing the normal contact force under different wear amounts of the mesh model and combining it with data from wear-related literature, this paper uses 2.5 um as the threshold for tooth surface reconstruction.
# Integrated Thermal and Dynamic Analysis of Dry Automotive Clutch Linings
## 5 Combined Thermal-Dynamic Analysis
The difference in temperature between the new and worn friction linings (with respect to the rest of the clutch elements), can be attributed to the different thermal transport properties associated with the ageing of the worn lining. Figure 5: Predicted variations of angular displacement, angular velocity and angular acceleration for the new friction lining material during clutch engagement. The higher temperature for the new lining material is due to higher contact friction as well as increased take-up judder oscillations during clutch engagement. Stick-slip oscillations and the associated excess acceleration contribute to the quantity of generated contact heat. It is noteworthy that severe angular velocity variations occur around 0.5 s into the clutch engagement process induce a step-type rise in the temperature. However, as time elapses the slope of rise in angular velocity reduces and vibrations are attenuated. Therefore, the rise in temperatures slows down as shown in Figure 7a. This is in agreement with Figure 7b, where the temperature rise follows a smoother path. This behavior can be attributed to the reduced/lack of stick-slip oscillations in the case of worn lining material as can be seen in Figure 6. Another noteworthy observation is the dominant effect of COF in comparison with the thermal transport properties of the lining material, particularly its thermal conductivity. The new friction material has a thermal conductivity almost double that of the used friction material but reach higher operating contact temperatures. The effect of consecutive and prolonged clutch engagements is shown in Figure 8. Figure 8a,b present the temperature variation in the main clutch components with time for the new and used friction lining material respectively, for the first maneuver. Figure 8a shows that the bulk temperature of the friction lining material reaches a value just below 60 \({}^{\circ}\)C for a series of consecutive engagements at a constant realistic engagement time of 0.5 s. The pressure plate surface reaches a temperature of around 55 \({}^{\circ}\)C and the flywheel reaches a temperature of around 45 \({}^{\circ}\)C for the same conditions. The temperature variation between the pressure plate and the flywheel surfaces is because of the exposed free surface geometry of the flywheel facing the  
Figure 6: Predicted variations of displacement, angular velocity, and angular acceleration for the worn friction lining material during clutch engagement. Figure 7: Predicted clutch system temperature rises for a single engagement for (**a**) new and (**b**) worn friction linings. engine, leading to a greater convective heat transfer thus a lower temperature. The temperature of the used friction material after the same series of consecutive engagements reaches a temperature of approximately 50 \({}^{\circ}\)C, a difference of 10 \({}^{\circ}\)C compared to the new lining. The final temperature reached after the consecutive engagements can also be attributed to the different COF of the clutch materials.
## 2 Materials and Methods
### Ground-Measured Data  
#### 2.2.1 Solar Shortwave Radiation (SSR), Air Temperature (Tair), and GPP Data  
The data set of the RO1, NE1, NE2, and NE3 sites from Fluxnet 2015 provides hourly Tair, net ecosystem exchange (NEE), ecosystem respiration (Re), and SSR. The daily mean, minimum, and maximum Tair (i.e., Tmean, Tmin, and Tmax, respectively); NEE; and Re were computed based on hourly data. GPP was defined by NEE subtracting Re. The emergence and harvest days of three NE sites were obtained from the Carbon Sequestration Project (CSP) of the University of Nebraska ([[http://csp.unl.edu/public/](http://csp.unl.edu/public/)]([http://csp.unl.edu/public/](http://csp.unl.edu/public/)), accessed on 10 March 2019). The start of season (SOS) and end of season (EOS) of the RO1 site were defined by [PERSON] et al. (2019) with the reconstructed daily NDVI time series by [PERSON] et al. (2019). Consequently, the NE1, NE2, NE3, and RO1 sites have data availability for 1945, 1276, 987, and 214 days, respectively (Table 1). GPP ranges for four sites are presented in Figure 2.  
Figure 1: Locations of four flux sites (NE1, NE2, NE3, and RO1) and the MODIS pixel (red lines) with 250 m resolution corresponding to the sites.  
Figure 2: Time series of ground-measured daily maize GPP in NE1, NE2, NE3, and ROI sites.
# A Tool for Pre-Operational Daily Mapping of Floods and Permanent Water Using Sentinel-1 Data
## 1 Introduction
However, AUTOWADE works on the national scale and, for each S1 pass over Italy, it must process a number of image slices (Section 2.1) between 2 and 7, depending on the orbit. Hence, AUTOWADE does not use the approach proposed in [29] to determine the threshold, mainly because image tiling might be time-consuming. Through the BFE approach, AUTOWADE tackles the problem in a new way, taking advantage of the availability of a reference water mask to directly analyze portions of a SAR image that likely contain comparable amounts of water and non-water pixels. The open water mapping algorithm implemented in AUTOWADE has been quantitatively validated considering maps produced by the CEMS in the period 2019-2021 as a benchmark; for the method to detect flooded vegetation close to open water, a qualitative comparison with optical data and aerial photos has been carried out. Section 2 introduces the data used by AUTOWADE, as well as the data used to validate the AUTOWADE-derived product. Section 3 describes in detail the different modules that make up AUTOWADE, with particular emphasis on the open water mapping performed through the BFE approach. Section 4 presents the results that are discussed in Section 5, while Section 6 draws the main conclusions.
## 2 Research Methodology
### Robust Estimation Method  
When gross errors cannot be avoided, robust estimation methods can ensure more reliable results by fully utilizing valid information, excluding potentially harmful information and minimizing the impact of gross errors [24; 25].  
The IGG (Institute of Geodesy and Geophysics) III method is one of the more commonly used robust estimation methods in recent years [26]. In the traditional least-squares method, all the residuals (the difference between the observed value and the estimated value) are treated equally. The IGGIII method introduces a specific weight function, which appropriately adjusts the size of the residuals through a weight adjustment mechanism [27]; the specific weight function helps to weaken the adverse effect of gross errors on the estimation of unknowns to a certain extent and thus improves the stability of the estimation [28; 29]. The specific-weight IGGIII method is  
\[p_{i}=\left\{\begin{array}{cc}p_{i},&\left|\widetilde{v_{i}}\right|\leq k_{ 0}\\ p_{i}*\frac{k_{0}}{\left|\widetilde{v_{i}}\right|}\left(\frac{k_{1}-\left| \widetilde{v_{i}}\right|}{k_{1}-k_{0}}\right)^{2},&k_{0}<\left|\widetilde{v_ {i}}\right|\leq k_{1}\\ 0,&k_{1}\leq\left|\widetilde{v_{i}}\right|\end{array}\right. \tag{11}\]  
In the formula presented, \(k_{0}\) and \(k_{1}\) denote conciliation coefficients: \(k_{0}\) usually takes 1.0-1.5, and \(k_{1}\) usually takes 2.5-3.0 [30; 31]; \(\widetilde{v_{i}}\) denotes standardized residual; and the specific formula is shown below:  
\[\widetilde{v_{i}}=\frac{v_{i}}{\sigma_{0}} \tag{12}\]  
\(v_{i}\) denotes the residual of the ith observation, \(\sigma_{0}\) is the unit weight medium error, and the specific formula is shown below:  
\[\sigma_{0}=\sqrt{\frac{V^{T}V}{n-m}} \tag{13}\]  
In the formula presented, \(V\) denotes the residual vector, \(n\) denotes the number of observations, and \(m\) denotes the number of unknowns.
## 2 Experiment Setup
As shown in Table 2, four types of HNLFs, denoted by IDs A to D, are considered, where A represents dispersion-flattened HNLF (DF-HNLF), B is a standard HNLF, C is HNLF with stable phase-matching for improved nonlinear efficiency (HNLF-SPINE), and D is specified HNLF-SPINE with a lower value of fourth-order fiber dispersion coefficients. Note that the dispersion parameters are measured by an Agilent 86037C dispersion test set [22].
# Study on Elastoplastic Damage Coupling of Reservoir Mudstone Considering Permeability Change
## 5 Simulation Analysis of Seepage-Stress Coupling
### Numerical Simulation of Seepage-Stress Coupling  
The permeability evolution model is written as a USFLD subroutine of ABAQUS. Based on Soils (the fluid-solid coupling analysis step of ABAQUS), UMAT (the elastoplastic  
Figure 10: Simulation results of triaxial compression tests under different confining pressures. (**a**) Confining pressure = 10 MPa. (**b**) Confining pressure = 20 MPa. (**c**) Confining pressure = 30 MPa.  
damage coupling mechanical model subroutine) and USFLD (the permeability evolution model subroutine), the solution of seepage-stress coupling is realized [34].  
Taking the mudstone sample of the indoor triaxial permeability test as the research object, an axisymmetric finite element model was established as shown in Figure 11. The bottom and the left boundary are fixed in the y direction and the x direction, respectively. Firstly, 30 MPa confining pressure is applied. Then, 5 MPa pore pressure is applied at the bottom, and the water is drained through the top. Finally, the deviatoric stress is applied in stages using the top displacement control.  
The initial permeability coefficient of mudstone is 1 \(\times\) 10\({}^{-9}\) m/s. Based on the simulation results, the pore pressure evolution curve (Figure 12) and the permeability coefficient evolution curve (Figure 13) of the sample near the bottom were obtained. In Figure 12, the curve AB shows the stage of applying confining pressure, curve BC shows the pore pressure evolution stage after the initial pore pressure is applied, curves CD, EF and GJ show the stages with different axial compression, and curves DE and FG show the pore pressure evolution stages after stopping axial compression loading. Comparing the three pore pressure evolution stages, it can be seen that after applying axial pressure, the pore pressure dissipation rate significantly increases with the gradual increase in the permeability coefficient. As the axial pressure increases, the volume deformation of the rock sample changes from compression to expansion, the internal cracks rapidly develop, the pores rapidly penetrate, and the pore pressure in the curve GJ rapidly decreases. As shown in Figure 13, before the lowest point of the volumetric strain curve, that is, the compression-expansion critical point, the internal deformation of the rock sample is mainly compression, and the closure of the pores and fissures, plastic deformation and damage evolution simultaneously occur under compressive stress, so the permeability coefficient slightly increases with the axial compression loading. After the critical point, the plastic deformation and damage rapidly increase, and the pores and fissures inside the rock sample rapidly develop and connect, resulting in the sharp increase in the permeability coefficient and rapidly reaching the peak.  
Figure 11: Finite element analysis model in ABAQUS.
# Estimation and Healing of Coverage Hole in Hybrid Sensor Networks: A Simulation Approach
## 3 Preliminaries
### DT and Related Definitions  
DT is an important data structure in computational geometry ([PERSON] et al., 2017). The most significant property we use in this paper is that (1) maximum empty circle characteristic which means any four points do not construct a circle; (2) maximize the minimum angle which means the composition of triangle is reasonable and not too narrow. We can detect the coverage hole in the each delaunay triangle by discussing the \(R_{s}\) and the circumcenter \(C_{0}\) of triangle. If \(C_{0}\) is not covered by the sensor node of  
Figure 1: Communication and sensing models.  
triangle, there will exist coverage holes in the triangle. Given \(N\) static sensors \(S_{1}\), \(S_{2}\), \(S_{3}\)\(\cdots\), we can get the DT. For example, we choose 50 random nodes to generate DT by MATLAB as shown in Figure 2.
# Sensing and Device Neighborhood-Based Slot Assignment Approach for the Internet of Things
## 4 Proposed Neighborhood-Enabled TDMA Approach
### TDMA-Enabled Communication Strategy
These devices may also receive such messages from other server modules \(S_{j}\) but are eager to join this particular server \(S_{j}\), as it has a minimum possible distance and a maximum value of RSSI. Thus, these devices send a response message (preferably an updated one) to the respective server module \(S_{j}\), wherein the join request value is set to one or true, i.e., \"\(Join-Request\;:\;1\)\". It is to be noted that the response message is sent as soon as the back-off timer is expired. In situations when numerous devices \(C_{i}\) are anxious to submit their packets simultaneously, random waiting time is provided to reduce the likelihood of packet collision. Server-1 \(S_{j}\) splits the sliding window into equal slots according to the number of asking devices, in this case, 6 (six), when it gets these signals and once its back-off timer has elapsed, as illustrated in Figure 1.
# A Linear Strong Constraint Joint Solution Method Based on Angle Information Enhancement
## 2 5G Reference Signals
### The SRS  
In 5G uplink positioning, the Sounding Reference Signal (SRS) is used as the positioning signal. The SRS is not a reference signal designed specifically for positioning, but is mainly used to estimate the frequency information of the uplink channel, perform frequency selective scheduling, estimate the uplink channel, etc. At the same time, it can also achieve beamforming scheduling of the base station through the detection of the uplink channel. Due to its autocorrelation and cross-correlation with the positioning reference signal, it is designated as the reference signal for uplink positioning in the 5G Release 16 standard.  
The SRS has introduced antenna rotation function in 5G mobile phones, and there are different broadcasting methods on different networks. In non-standalone networks (NSA), 1T1R (1-Transmitter-1-Round) and 1T4R rotation methods are used, while in standalone networks (SA), the 4T4R rotation method is used. The specific rotation method details are as follows:  
1T1R, only fixed on one antenna to provide SRS information feedback to the base station, does not support SRS rotation.  
1T4R, the terminal alternately transmits SRS signals on four antennas, selecting one antenna to transmit at a time; NSA terminals often use this mode.  
2T4R, the terminal alternately transmits SRS signals on four antennas, selecting two antennas to transmit at a time; SA terminals often adopt this mode.  
In 5G, the SRS can be configured with [1,2,4] OFDM symbols, which can be consecutive [1,2,4] OFDM symbols located in the last six symbols of a slot. In addition to the periodic SRS and the Aperiodic SRS defined in the LTE SRS, the 5G SRS has added the semi persistent SRS, which has flexibility between periodic and non-periodic SRS.
# Design of a Compact Ultra-Wideband Microstrip Bandpass Filter
###### Abstract  
A compact ultra-wideband bandpass filter based on a multilayer printed circuit board (MPCB) structure is proposed in this paper. RO4450F prepre is used to bond three RO4350B dielectric substrates with different thicknesses in the MPCB structure. The upper surfaces of the three dielectric substrates are respectively provided with copper-coated structures with different patterns. The blind holes and the defected ground structure (DGS) are added to the MPCB of an ultra-wideband bandpass filter. Two groups of loaded quarter-wavelength terminal-open stubs introduce two transmission zeros, which improves the roll-off rates and stopband characteristics, while simple DGS composed of rectangular slots introduces two resonance points in the passband to improve the return loss. Simulation and measurement are consistent. The insertion loss at the center frequency of 12.795 GHz is 0.58 dB and the fractional bandwidth of 3 dB is 40.33% from 10.215 GHz to 15.375 GHz. This bandpass filter can be widely used in wireless and satellite communication.  
defected ground structure; insertion loss; return loss; transmission zero; ultra-wideband bandpass filter +
Footnote †: journal: Electronics Electronics
## 4 Discussion  
### Potential Mechanisms of FDS Dominance of LST  
Our findings suggest that FDS dynamics strongly influenced LST over the European continent, contributing to a significant warming trend observed from 1986 to 2016. This trend (Figure 7) is attributable to biogeophysical feedback mechanisms; sustained increases in forest LAI led to reduced surface albedo, consequently amplifying surface warming via the enhanced absorption of shortwave radiation. This interpretation aligns with multi-scale satellite observations documenting a widespread global vegetation greening trend in recent decades [51; 52; 53]. Although the drivers of global greening are regionally heterogeneous [54], involving factors such as CO\({}_{2}\) fertilization [51; 53; 54], nitrogen deposition [53], climate change [52; 53; 54; 55; 56], and land cover changes [53; 57], our attribution analysis reveals a distinct characteristic for Europe. The RF models demonstrate that forest expansion in this region during 1986-2016 was primarily driven by FDS rather than the conventionally recognized climatic factors. This finding contrasts sharply with [PERSON] et al. [53]'s conclusion that high-latitude greening is mainly climate-driven, suggesting potential unique regulatory mechanisms in European forest dynamics. From a biophysical perspective, forest dynamics significantly modulate the surface energy balance by altering key parameters like surface albedo and evapotranspiration [1; 6; 16; 17], inducing net warming or cooling effects [58]. This study quantifies these competing processes, showing that the positive radiative forcing from albedo reduction intensifies with latitude, while negative forcing from increased ET diminishes poleward. This spatial heterogeneity results in the warming effect dominated by albedo changes prevailing over the cooling effect from ET across the European continent. These findings corroborate those of [PERSON] et al. [15] and underscore the crucial role of FDS-related forest dynamics in modulating European continental climate change.
# BDS-3 Triple-Frequency Timing Group Delay/Differential Code Bias and Its Effect on Positioning
## 2 Data and Methods
### Triple-Frequency Satellite TGD/DCB Correction Model for BDS-3
#### 2.3.1 TGD/DCB Correction Model with Broadcast Satellite Clock
\begin{array}{l}\overline{b}_{IF_{1123}B_{2231}}^{s,brdc}=e_{i}\cdot DCB_{ C2I-C6I}+e_{j}\cdot(DCB_{C1X-C6I}-DCB_{C1X-C5X})\\ \overline{b}_{IF_{1123}B_{2231}}^{s,brdc}=e_{i}\cdot DCB_{C1X-C6I}+e_{j}\cdot( DCB_{C1X-C6I}-DCB_{C1X-C5X})\\ \overline{b}_{IF_{1123}B_{231}}^{s,brdc}=e_{i}\cdot DCB_{C2I-C6I}+e_{j}\cdot( DCB_{C1X-C6I}-DCB_{C1X-C7Z})\\ \overline{b}_{IF_{1123}}^{s,brdc}=e_{i}\cdot DCB_{C1X-C6I}+e_{j}\cdot(DCB_{C1X- C6I}-DCB_{C1X-C7Z})\\ \overline{b}_{IF_{1123}}^{s,brdc}=0\end{array} \right. \tag{21}\]  
For UC123, the correction formula is the same as that for single frequency. \[\left\{ \begin{array}{l}\overline{b}_{UC_{B1I}}^{s,brdc}=DCB_{C2I-C6I}\\ \overline{b}_{UC_{B1C}}^{s,brdc}=DCB_{C1X-C6I}\\ \overline{b}_{UC_{B2n}}^{s,brdc}=DCB_{C1X-C6I}-DCB_{C1X-C5X}\\ \overline{b}_{UC_{B2n}}^{s,brdc}=DCB_{C1X-C6I}-DCB_{C1X-C7X}\\ \overline{b}_{UC_{B3I}}^{s,brdc}=0\end{array} \right. \tag{22}\]Furthermore, according to Equation (18), it is possible to transform Equations (19), (21) and (22) into the TGD correction.
# Simulation Model for Correction and Modeling of Probe Head Errors in Five-Axis Coordinate Systems
###### Abstract  
Simulative methods are nowadays frequently used in metrology for the simulation of measurement uncertainty and the prediction of errors that may occur during measurements. In coordinate metrology, such methods are primarily used with the typical three-axis Coordinate Measuring Machines (CMMs), and lately, also with mobile measuring systems. However, no similar simulative models have been developed for five-axis systems in spite of their growing popularity in recent years. This paper presents the numerical model of probe head errors for probe heads that are used in five-axis coordinate systems. The model is based on measurements of material standards (standard ring) and the use of the Monte Carlo method combined with select interpolation methods. The developed model may be used in conjunction with one of the known models of CMM kinematic errors to form a virtual model of a five-axis coordinate system. In addition, the developed methodology allows for the correction of identified probe head errors, thus improving measurement accuracy. Subsequent verification tests prove the correct functioning of the presented model.  
Coordinate Measuring Machines (CMM); five-axis system; accuracy modeling; Monte Carlo method; probe head; virtual model +
Footnote †: journal: Applied  
Article
# UAV-BS Site Planning Based on Circular Coverage Strategy
## 4 Methodology  
This study focuses on UAVCN communication energy consumption and the full coverage of users in the target area. The circle coverage strategy is used for modeling, and the proportional relationship between the small circle radius and the number of small circle radii is fitted by a power function under the given large circle radius. The goal is to minimize power loss under the condition of ensuring full coverage. Newton iteration is used to give the number of UAV-BS deployed to meet the target and the optimal coverage radius to determine the optimal UAV-BS site planning strategy.