esa-sceva/satcom-chunk-collection huggingface dataset:
# Sampling Rate Impact on Precise Point Positioning with a Low-Cost GNSS Receiver
## 2 Methodology
### Experimental Setup and GNSS Data Collection  
As was mentioned earlier, two observations were made at different dates, but it was considered that the same day and time should be used to maintain similar conditions. First, the campaign with the geodetic receiver (Figure 2a) was carried out on 7 February  
Figure 1: Flowchart for the methodology implemented for the present research.  
2020. Likewise, the observations with the low-cost GNSS receiver (Figure 2b) were carried out on 16 October 2020. In both campaigns, the location was on a stable forced-centering monument located on the roof of the Earth and Space Sciences faculty building, with a clean surrounding antenna environment and optimal weather conditions.  
The low-cost GNSS receiver ZED-F9P of the U-Blox series was used for generating the observations (Figure 3a). This low-cost GNSS receiver had 184 channels on the frequencies of GPS: L1C/A, L2C; GLO: L1 OF L2 OF; GAL: E1B/C, E5b; and BDS: B1I, B2I signals. A convergence time RTK < 10 s, cold start of 24 s and reacquisition time of 2 s were used, along with a navigation and tracking sensibility of 167 dBm, cold start of \(-\)148 dBm and hot start of \(-\)157 dBm. This was used with a SMA multiband antenna ANN-MB (Figure 3b).  
With the purpose of testing the impact of different sampling rates on PPP performance using a low-cost GNSS receiver, the sampling rates of each receiver were configured at the highest sampling rate that the equipment allows for. In this sense, the sampling rates were set up to 0.1 and 0.2 s for the low-cost GNSS and geodetic receivers, respectively, with a cut-off angle (elevation mask) of 15\({}^{\circ}\). The duration of the observation was setup for 2 h with the purpose of inspecting the data loss derived from the capability of the registering of the low-cost GNSS receiver. Once the observations were made, the original RINEX of both receivers were decimated with the TEQC software at 0.2, 1, 5, 15 and 30 s for the low-cost GNSS receiver, and likewise, 1, 5, 15 and 30 s for the geodetic receiver. In the same way, the multipath was calculated following the pseudorange multipath approach proposed by [35]. In addition, the signal-to-noise ratio was calculated with the TEQC  
Figure 3: Equipment used: (**a**) low-cost GNSS receiver; (**b**) U-Blox antenna.  
Figure 2: GNSS receivers used: (**a**) geodetic order receiver; (**b**) low-cost GNSS receiver.  
software for the original sampling rates (0.1 and 0.2 s), with the objective of finding some influence or degradation in the received signal. In addition, in order to analyze the obtained solution derived from the geodetic and low-cost GNSS receivers, the coordinates obtained by using both receivers in both methods were compared to the precise coordinates that were obtained by means of the Trimble Business Center [38] Software.
## 1 Introduction
### Machine-Learning Modeling
#### 1.2.2 Preprocessing Data  
After collecting and structuring the dataset, its variables need to be cleaned, processed, and filtered for the ML model. The processing stage includes several techniques, which can be human- or AI-designed, and they depend on the nature of the training data. For example, in natural language processing with text, preprocessing removes capitals [38]; in signal processing, wavelet transforms separate signals into their main components [39]; in image processing, convolution with the image filters extracts features [40]; in big data and data mining, dimensionality reduction is achieved [41].  
The preprocessing data stages include normalization based on algorithms, such as MIN-MAX normalization, decimal scaling, and Z-scores; filtering redundant and inconsistent data; transformations such as linear, quadratic, polynomial, and histogram transformations; removing noisy data with techniques such as ensemble filtering, cross-validated filtering, and interactive partitioning; feature selection with exhaustive, heuristic, filter, and wrapper methods; and discretization to change from analog systems to digital ones [42].  
Input features in ML modeling are representative when their information affects the output of the modeled system. Additionally, removing characteristics that are irrelevant or have low correlations from the results produces search spaces with lower complexity, boosting the capabilities of the training algorithm and improving the final model's efficiency [43; 44].  
One of the most used commonly techniques for removing redundant and inconsistent data in the second stage is feature Selection (FS). FS also makes it possible to reduce size, increase the efficiency and accuracy of predictive learning, and reduce the complexity of the final model [42]. The different FS approaches reported in the literature are constituted theoretically and apply methods such as filtering, wrapping, and embedding through techniques involving search algorithms, statistical criteria, and information, distance, dependency, and consistency measures [42].
## 6 Positioning Algorithm Analysis
The real orbital parameters were \(a~{}=~{}20,000\), \(e~{}=~{}0.003\), \(i~{}=~{}40\), \(\Omega~{}=~{}40\), \(w~{}=~{}70\), \(M~{}=~{}0\). The initial orbital parameters estimated using one arc segment had high accuracy. Figure 15 indicates that the maximum error of orbital half-length diameter was 0.5 km; the maximum error of orbit eccentricity was 1.5 \(\times\) 10\({}^{-4}\), and the error of orbit inclination was below 6 \(\times\) 10\({}^{-60}\); the error of longitude of ascending node was also below 1 \(\times\) 10\({}^{-40}\). With the help of orbital parameter estimation, the initial orbital parameters can be used as feature vectors to accomplish target identification for different orbital arc segments. From Figure 16, it can be seen that the longitude of orbit parameters still maintained high calculation accuracy as the orbit height decreased, The real orbital parameters were \(a~{}=~{}7000\), \(e~{}=~{}0.002\), \(i~{}=~{}40\), \(\Omega~{}=~{}40\), \(w~{}=~{}70\), \(M~{}=~{}0\), the maximum error of orbital half-length diameter was below 0.1 km, the maximum error of orbit eccentricity was  
Figure 14: The positioning error of space debris varies with the height of the debris position at different observation distances when the star tracker’s own positioning error was 100 m. (**a**) The space debris observation distance of 50 km. (**b**) The space debris observation distance of 100 km. (**c**) The space debris observation distance of 200 km. \(6\times 10^{-4}\), and the error of orbit inclination and the error of longitude of the ascending node were both in the order of \(1\times 10^{-40}\). Figure 16: _Cont._  
Figure 15: The error curve of orbital parameters estimated by using different orbital arcs with the positioning error of 10 m high orbit \(a=20,000\), \(e=0.003\), \(i=40\), \(\Omega=40\), \(W=70\), \(M=0\). (**a**) Orbital half-length diameter estimation error. (**b**) Orbital eccentricity estimation error. (**c**) Orbital inclination estimation error. (**d**) Longitude of ascending node estimation error. (**e**) Argument of perigee estimation error. It can be seen from the positioning accuracy of space debris, the positioning accuracy of the star tracker itself largely determined the observation accuracy of the debris. Only by improving the positioning accuracy of the satellite itself, can the positioning accuracy of the observation target (that is, the space debris) be achieved, and finally the high-precision orbit determination of the target can be completed.
## 4 Discussion  
### The Impact of Policies and Land Use on NDVI  
Changes in policies and land use have a significant impact on vegetation. Between 2001 and 2023, NDVI values in China's climate zones showed notable changes, largely due to policy adjustments and land use transformations. The TMC climate zone, covering the eastern coastal regions, North China, and Northeast China, experienced a decline in NDVI values, likely due to rapid urbanization and industrialization, which increased environmental pressures. In contrast, the TCC climate zone, located in the northwestern inland areas such as Inner Mongolia and Xinjiang, saw degradation primarily driven by overgrazing and land reclamation [94]. This zone's vegetation is particularly sensitive to climate change due to the uneven distribution of precipitation. The SMC climate zone, which includes the middle and lower reaches of the Yangtze River and South China, also experienced a decline in NDVI values, mainly caused by urbanization reducing vegetation cover. The PMC climate zone, covering the Tibetan Plateau, faced a significant NDVI decline, likely due to habitat degradation from glacial retreat. Between 2004 and 2008, NDVI values in various climate zones generally showed a significant improvement trend. The increase in NDVI values in the TMC climate zone during this period was mainly due to the carbon sink effect brought about by the Grain-for-Green program. In the TCC climate zone, the growth in NDVI values was closely related to the implementation of grassland ecological compensation policies. The recovery of NDVI values in the SMC climate zone reflected the positive results of the collective forest rights system reform. In the PMC climate zone, the improvement in NDVI values demonstrated the important contribution of the Three Rivers Source ecological protection project. The increase in NDVI values in the TRMC climate zone aligned with the effective advancement of tropical rainforest conservation measures. However, in recent years, most climate zones have shown signs of vegetation stability and recovery, reflecting the continuous improvement and effective implementation of recent vegetation protection policies.  
Figure 7: Trends in NDVI changes and the contributions of meteorological factors in China from 2023 to 2100: panels (**a–d**) illustrate the temporal evolution of NDVI trends and the relative influence of each meteorological factor under the SSP126, SSP245, SSP370, and SSP585 scenarios, respectively.
## 2 Methodology  
### Fundamentals of GBR  
GBR determines displacements by measuring phase differences in the backscattered signal. Frequency modulation of the signal enables the distinction of multiple scattering points by their range to the GBR.Commercially available GBRs, such as IBIS-FS (IDS, Pisa, Italy), typically have a modulation bandwidth of 200 MHz at a centre frequency of 17.2 GHz, resulting in a range resolution of 0.75 m. IBIS-FS achieves a sampling rate of 200 Hz. In default operation, the GBR faces the bridge underside at an angle, as shown in Figure 2. This setup can lead to significant differences in measurement uncertainty for the bridge points. The uncertainty mainly depends on two factors. First, the scattering properties of the points determine the noise content in the backscattered signal. Lower noise content is achieved with larger reflective features, while higher noise content usually results from cluttering objects. These objects are, for example, attachments to the bridge's underside, such as railings or pipes, which cannot be distinguished from the reflective features if they have the same range to the GBR. In general, a higher signal-to-noise ratio (SNR) directly results in higher measurement precision. The SNR results from the amplitude variation  
\[\text{SNR}=\frac{m_{a}^{2}}{\sigma_{a}^{2}} \tag{1}\]  
where \(m_{a}\) is the mean and \(\sigma_{a}\) is the standard deviation of the amplitude [42]. An estimate of the displacement precision results from [41]  
\[\sigma_{d}=\frac{\lambda}{4\pi}\cdot\frac{1}{\sqrt{2\,\text{SNR}}} \tag{2}\]  
Typically, a displacement precision of 0.02 mm can be achieved with an \(\text{SNR}\geq 35\) dB. A second uncertainty factor is a necessary projection from the line of sight measurements to the vertical axis. The projection can lead to a significant scaling error if the actual displacement vector has more than one component [38]. However, the second factor has less relevance for determining natural frequencies. Besides reducing the measurement uncertainty through improved setups or additional processing steps, it is also important to reliably estimate this uncertainty [2]. Consideration of the uncertainty can provide valuable information in further analysis of the processed GBR measurements, for example, as input to the test for goodness of fit described in Section 2.2.3.  
Figure 1: Overview of the frequency estimation approach for damage detection with GBR displacements.
# Unified Friction Formulation from Laminar to Fully Rough Turbulent Flow
## 3 Switching Functions, Friction Factors, New Formulation, and Comparative Analysis
### Switching Functions  
The unified formula valid for all hydraulic cases depends directly on the switching functions \(y_{1}\), \(y_{2}\), and \(y_{3}\). Switching functions provide for a smooth transition between different hydraulic regimes. A connection of curves created by separate formulas for laminar flow \((a)\), smooth turbulent flow \((c_{1})\), and fully developed rough turbulent flow \((c_{3})\), is not smooth without help from the carefully selected switching functions \(y_{1},y_{2}\), and \(y_{3}\). These three functions are rational and generated in HeuristicLab, a software environment for heuristic and evolutionary algorithms [27]. The proposed switching functions have a simple form and therefore can be incorporated easily into computer codes without any significant burden on the Central Processing Unit [28]. The proposed switching functions \(y_{1}\), \(y_{2}\), and \(y_{3}\) are respectively represented by Equations (7)-(9) and Figures 5-7:  
\[y_{1}=1-\frac{1048}{\frac{4.489}{10^{30}}\cdot Re^{6.}\left(0.148\cdot Re- \frac{2.306\cdot Re}{0.00313\cdot Re\cdot e+9.646}\right)+1050} \tag{7}\]  
\[y_{2}=1.012-\frac{1}{0.02521\cdot Re\cdot e+2.202} \tag{8}\]  
\[y_{3}=1-\frac{1}{0.000389\cdot Re^{2}\cdot e^{2}+0.0000239\cdot Re+1.61} \tag{9}\]  
In Equations (7)-(9), symbols \(y_{1}\), \(y_{2}\), and \(y_{3}\) denote switching functions.  
Figure 5: Switching function \(y_{1}\) as a function of the Reynolds number, with the main purpose of providing a transition between laminar flow \((a)\) and smooth turbulent flow \((c_{1})\).  
Figure 6: Switching functions \(y_{2}\) and \(y_{3}\), with the main purpose of providing a transition between smooth turbulent flow \((c_{1})\) and fully developed rough turbulent flow \((c_{3})\).
## 3 Results
### Interfragmentary Movement Analysis (IFM)
#### 3.1.2 Shear Strain  
The distribution of shear strain among the various fractures shows that none of the configurations report an optimal behavior in blocking sliding motions in the fracture plane (0% shear strain). Comparing the models SQBP C1 and SPP C2, when the elementary transverse fracture is considered the former has lower shear strain values and almost 82% of the values are within the acceptable range (0-15)%, while the latter is characterized by a greater variability of shear strain in a range between 4% and 65% and with an acceptability percentage of 18% (Figure 8c). As far as the T-shaped fracture is concerned, in its transverse component a similar behavior between the two models is visible: for the SQBP C1 the shear strain values range between 5% and 77%, whilst for the SPP C2 range between 10% and 35%. Respectively, the percentage of couples of points within the acceptable range is 9% and 14%. For the vertical component of the T shaped fracture, the SQBP C1 values range between 18% and 27%, meaning that no points satisfy the acceptable range; in SPP C2 configuration, on the other hand, the shear strain is wider, between 10% and 50%, but with 20% of the points within the acceptable range.  
If the C2 screws configurations models are compared, it can be noted that for the T-shaped fracture the differences are minimal: in the transverse fracture the range of shear strain distribution (10%, 36%) is almost identical (Figure 8d), and it is feasible to assess the same value of median equal to 26% with the same percentage of values belonging to the acceptable range (16.64%). In the vertical component of the T shaped fracture, a range of distribution equal to (10%, 50%) is reached, with median equal to 22%, for the SPP plate. The SQBP plate reaches a very similar range of values (9%, 47%) with median 21%. For both plates, the same percentage of 20% of the points within the acceptable range was computed. The substantial difference between the two plate is in the elementary transverse fracture: SPP plate has an acceptable percentage of 18% while the SQBP plate the percentage increases to 90%; in this latter, indeed, the range of shear strain is confined between 5 % and 16% with a median of 9%.
# Wideband Channel Characterization for 6G Networks in Industrial Environments
## 2 Overview of Radio Wave Propagation for 6G Systems
### Propagation Mechanism  
The wireless communication systems at a frequency band above 100 GHz are expected to be strongly influenced by the variations of the reflective angle and the scattering of different stratified materials over the frequency and incident angle, as they will affect both LoS and NLoS propagation [20]. Hence, the impacts of reflections and scattering from different materials in radio propagation above 100 GHz have gained attention from different research groups in the literature [20; 21; 22; 23; 24]. In [20], the reflection terahertz time domain spectroscopy measurements and matching transfer matrix simulations of the frequency dependent reflection coefficient of multilayer building materials were presented for a set of angles in the 100-500 GHz frequency band, where a plaster sample, covered with white paint, and a double pane glass window were studied as examples for two commonly encountered multilayer structures in indoor scenarios. Based on the simulations performed over frequencies from 1 GHz to 1 THz in [24] using three different materials with incident angles of 10\({}^{\circ}\)-90\({}^{\circ}\), it was shown that the received scattered power increases as the frequency increases, and the scattered power falls off sharply when the incident wave becomes grazing and most of the incident power is reflected. In [21], the THz links' performance was studied in an indoor environment with different surface reflections (wall, ceiling, and furniture) based on narrowband measurements at different frequencies in the range of 0.1-1 THz (100, 200, 300, 400, and 625 GHz). The reflection and scattering from different materials, glass, plastic, hardwood, concrete, and aluminum, were also investigated in typical indoor office environments [22] based on time domain spectroscopy measurements in the 0.3-3 THz band and ray-tracing simulation. Considering the reflections from the walls, floor, and ceiling and randomly picked reflection points, simple deterministic propagation models were derived in [23] for LoS and NLoS indoor environments. In [25], the effective attenuation was measured at 100 GHz for typical building materials such as wood, tiles, concrete bricks, and a gypsum plate, and it was observed that the effective attenuation of most of the building materials is polarization sensitive. [PERSON] et al. [26] studied the diffraction from objects, such as wedges, edges, and cylinders, including the human body and different materials, such as metal and the wood knife-edge model, and reported that the best agreements were found between the uniform geometrical theory of diffraction and the measurement results at 300 GHz in indoor environments. However, it was also concluded that the diffraction from edges or wedges can be neglected almost everywhere in a room scenario using ray-tracing tools [26].
## 3 System Modeling
### Evaluation Metrics
#### 3.3.2 Average Residual Requirements  
\(ARR\) is used to measure the residual quantity of unresponded access demands in the system. It reflects the system's real-time processing capability and load management status, serving as a key metric for evaluating task backlog, system load, and response timeliness.  
The number of unresponded demands \(|Que_{i}|\) in each queue is counted for the current round, and the residual demand \(ARR_{i}\) is calculated independently for each queue. The system's average residual requirements \(ARR\) is defined as the average of all \(ARR_{i}\) values:  
\[ARR_{i}=|Que_{i}|,\quad ARR=\frac{1}{3}\sum|Que_{i}|,\quad i\in\{low,medium, high\} \tag{11}\]\(ARR\) quantifies the system load and task backlog by counting unresponded demands across all queues, aiding in the evaluation of scheduling strategies under high-concurrency scenarios. Variations in \(ARR_{i}\) among different queues can also provide optimization insights for in situ data management strategies. A smaller \(ARR\) indicates that the system effectively accounts for demand accumulation across queues, ensuring timely responses to most demands and enhancing overall system performance and reliability.
# A Truly Second-Order and Unconditionally Stable Thermal Lattice Boltzmann Method
## 5 Conclusions  
In this paper, we present an unconditionally stable thermal lattice Boltzmann method (USTLBM) for numerical simulation of 2D/3D incompressible thermal flows. This method is developed from the recently proposed simplified thermal lattice Boltzmann method (STLBM) by truly ensuring the second-order of accuracy in time. Specifically, USTLBM is derived by reconstructing solutions to the macroscopic governing equations recovered from lattice Boltzmann equation and resolved in a predictor-corrector scheme. Final formulations of USTLBM only involve the equilibrium and the non-equilibrium distribution functions. In practical computation, the equilibrium distribution function is evaluated by the macroscopic variables, while the non-equilibrium term is approximated by the difference between two equilibrium distribution functions at different locations and time levels. Such treatment means that USTLBM can directly track the evolution of macroscopic variables rather than the distribution functions, which lowers the cost in virtual memories and facilitates implementation of physical boundary conditions.  
Von Neumann stability analysis can be performed to prove that USTLBM is an unconditionally stable scheme. Numerical experiments are carried out on cases with coarse meshes and in high Rayleigh numbers to validate this conclusion. Accuracy test of USLTBM reveals that it ensures the second-order of accuracy in space while being able to achieve more accurate results than STLBM at the same mesh size.  
Four numerical examples, including 2D natural convection in a square cavity, mixed convection in a lid-driven cavity, natural convection in a concentric annulus and 3D natural convection in a cubic cavity, are presented to validate the robustness and the flexibility of USTLBM. It turns out that the results obtained by USTLBM agree well with reference data in the literature; and the method can be flexibly coupled with Lagrangian interpolation algorithm to handle problems on non-uniform or body-fitted meshes and with curved boundaries.  
[PERSON] offered the idea of present work. [PERSON] performed the programming, the validation, as well as the analysis for numerical stability of the method. [PERSON] joined in the discussions and offered valuable suggestions. All three authors are involved in the editing and revising process of the present paper.  
The authors declare no conflict of interest.