esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Methods
#### 2.5.4 Geographically Weighted Regression  
Based on the previous period, the spatial and temporal differentiation of the groundwater depth was qualitatively analyzed using spatial autocorrelation analysis. In the process of the confluence of groundwater depth, the land cover type is an important influencing factor. Through geographically weighted regression, the response mechanism of recharge and convergence processes of different land types and groundwater depth was quantitatively analyzed. The specific remote sensing band index was selected to represent the land type that has a great influence on the groundwater depth and is represented by the MHRU. A geographically weighted regression model of the index corresponding to different land types and groundwater depth was constructed. Combined with spatial characteristics, quantitative analysis of the influence of surface characteristics of different soils on groundwater depth [65; 66; 67] was then carried out.  
Geographically weighted regression is based on the ordinary linear regression model, which embeds the spatial position of the data into the regression equation. The formula is as follows:  
\[y_{i}=\beta_{0}(u_{i},v_{i})+\sum_{k=1}^{P}\beta_{k}(u_{i},v_{i})x_{ik}+ \varepsilon_{i} \tag{6}\]  
where \((u_{i},v_{i})\) is the sampling point \(i\) coordinate, \(\beta_{k}(u_{i},v_{i})\) is the \(k\)th regression parameter at the sampling point \(i\), which is a function of the geographical position, and the weight function is obtained in the estimation process.  
The Gaussian kernel function to determine the bandwidth is as follows [68; 69; 70]:  
\[\omega_{ij}=exp\left(-\left(\frac{d_{ij}}{b}\right)^{2}\right) \tag{7}\]  
where \(b\) is a non-negative attenuation parameter describing the functional relationship between weight and distance, called bandwidth. The larger the bandwidth, the slower the weight is attenuated with distance, and the faster the weight is attenuated.
# Physical Layer Definition for a Long-Haul HF Antarctica to Spain Radio Link
## 4 Physical Layer Tests Analysis
### Modulation Tests
#### 4.4.1 Spread Spectrum Modulations
(**a**) Signaling dictionary structure; (**b**) Quasi-Quadriphase dictionary structure and IQ components.
## 6 Mechanical Properties
### Tensile Strength
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Materials** & **Condition** & **Yield Strength (mp)** &
\begin{tabular}{c} **Ultimate Tensile** \\ **Strength (mp)** \\ \end{tabular} & **Elongation (\%)** & **Reduction of Area (\%)** & **Ref.** \\ \hline IN625 & As-built & 743 & 103 & 31.4 & 48.6 & [59] \\ IN625 & LPBF-annealing & 386 & 910 & 54.4 & 56.6 & [59] \\ IN625 & As-built (multi-laser) & 585 & 864 & 58.6 & – & [61] \\ IN625 & + heat treatment & 507 & 827 & 69.3 & – & [61] \\ IN625 & As-built & 641.5 & 878.5 & 30 & – & [74] \\ MWCNT-IN625 & As-built & 788 & 998 & 19.1 & – & [74] \\ MWCNT-IN625 & LPBF + heat treatment & 585 & 1000 & 31.5 & – & [74] \\ IN625 & Wrought & 517 & 930 & 40 & – & [73] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mechanical properties of LPBF IN625 alloys from different literature. In summary, the typical textures that existed in LPBF IN625 alloy parts leads to the anisotropy of tensile strength and other mechanical properties. Heat treatments can significantly change the tensile strength and increase the ductility of LPBF IN625 alloy samples [59; 61; 74]. The significant strengthening effect of adding carbon nanotube into IN625 [74] indicates that adding second tiny strengthening phases could be a useful method to improve the mechanical properties of LPBF IN625. When using the highly efficient multi-laser LPBF to produce IN625 samples, the mechanical properties of the samples are still comparable with those produced by a conventional single laser method [61].
## 4 Experimental Results  
In this section, we verify the simulation result using two experiments: a lake trial and a sea trial. As in the simulation, each experiment compared the performance of the conventional CSS method and the proposed method.
# Automated Tunnel Point Cloud Segmentation and Extraction Method
## 2 Materials and Methods
### Application of the GuSAC Algorithm
#### 2.1.1 Designed Tunnel Axis Point Cloud Extraction  
The designed tunnel axis, as the symmetrical centerline of the tunnel structure, provides a reference for precise tunnel positioning. It reflects the tunnel's alignment and orientation, serving as the foundation for establishing the coordinate system used in subsequent analyses. The GuSAC algorithm is built upon this alignment-representative designed tunnel axis, forming the operational plane for the tunnel.  
The extraction of the point cloud set representing the designed axis is typically obtained through coarse processing of vault point cloud data [9]. First, the initial coordinate system of the tunnel is adjusted. Then, sampling slices are taken at fixed intervals of M, with the sampling baseline determined by the tunnel's elevation values, and N slices are extracted. Finally, by calculating the centroid of the point cloud set within each slice, a smooth point cloud set of the designed axis is generated, laying a solid foundation for subsequent analyses.
## 2 Description of the Systems Under Investigation and Experimental Setups
### Random Vibration Testing
#### 4.2.3 Random Vibration Testing: Discussion on the Influence of the Central-Cut Width
\begin{table}
\begin{tabular}{c|c c c c c c c} \hline  & \multicolumn{2}{c}{**0.5\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{**0.25\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{**0.1\({}^{\prime\prime}\) Cut**} & \multicolumn{2}{c}{**No Cut**} \\  & **Random** & **FEA** & **Random** & **FEA** & **Random** & **FEA** & **Random** & **FEA** \\ \hline
**Mode 1** & 106.25 & 106.89 & 101.56 & 105.63 & 101.17 & 106.89 & 155.08 & 165.29 \\ \hline
**Mode 2** & 285.16 & 290.87 & 279.3 & 287.58 & 275.781 & 290.87 & - & 395.39 \\ \hline
**Mode 3** & 304.69 & 331.63 & 300.39 & 322.34 & 286.72 & 331.63 & 591.8 & 649.25 \\ \hline
**Mode 4** & 373.438 & 379.11 & 346.48 & 364.3 & 333.98 & 379.11 & 728.125 & 728.92 \\ \hline
**Mode 5** & 398.438 & 450.47 & 406.25 & 443.72 & 386.33 & 450.47 & 834.766 & 846.72 \\ \hline
**Mode 6** & 471.875 & 511.62 & 455.86 & 493.46 & 448.05 & 511.62 & - & 1073.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparative study between FEA and random vibration testing using excitation of \(1\times 10^{-7}\) V\({}^{2}\)/Hz. Figure 14: A 3-D comparison of all cut widths for the (**a**) side and (**b**) RC accelerometers using data from the \(1\times 10^{-4}\) V\({}^{2}\)/Hz excitation. not discussed. One important note to mention is the difference seen between the impact and random testing results. This difference concerns the dominant frequencies for the various central-cut configurations and the non-monotonic trend in the variation in the dominant frequency with respect to the central-cut width seen in the impact testing results. The other difference is the higher values of dominant frequencies that were obtained during random testing compared to impact testing. These differences may be due to the usage of a slip-table and stinger setup for random vibration testing, and hence, some additional stiffness may be added to the BARC systems, which may have a direct influence on the coupling between the removable component and the box assembly.
## 3 Results and Analysis
### Microstructures of the Welded Joints  
Figure 7 shows the microstructure of the fusion zone welded at 3, 4, and 5 Hz. It is observed that the grain size at 5 Hz is smaller, the grain size at 3 Hz is bigger, and the grain size at 4 Hz is in the middle. The reason for this phenomenon may be that the weld pool is periodically stirred by the thermal frequency, thereby the fluidity of the weld pool is improved by increasing the thermal frequency and the growth of grains is simultaneously inhibited (2018).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Thermal Frequency (Hz)** & **Weld Width (mm)** & **Weld Deep (mm)** & **Weld W/D Ratio** & **Porosity (N/mm)** \\ \hline
3 & 7.38 & 7.66 & 1.21 & 6 \\
4 & 7.66 & 7.64 & 1.29 & 3 \\
5 & 8.10 & 7.30 & 1.49 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Macrostructure details and porosity of weld beads.  
Figure 7: Microstructure photograph of fusion zone.  
[PERSON] et al. demonstrated that the arc length varied with thermal frequency [9]. The welding arc changing periodically, the welding arc makes the heat input and the arc force to vary in alternating high and low, which cause the oscillation of the weld pool. The oscillation of the molten pool is beneficial to the spillover of the bubbles in the molten pool and the refinement of grains, thus improving the quality of the welded joint [24; 25]. As soon as the varied weld pool size reaches a value where the intrinsic oscillation frequency of the weld pool is equal to the thermal frequency of DP-GMAW, the weld pool will be in resonance, with the pool oscillation amplitude reaching the maximum and the fusion zone obtaining the most refinement grain.  
When compared to 3 Hz and 4 Hz, the smaller gain size of fusion zone is obtained at 5 Hz. It is illustrated that the oscillation frequency of the molten pool at 5 Hz is closer to the intrinsic frequency of the molten pool. Smaller grain size may markedly lead to less porosity and crack sensitivity, which enhances the mechanical properties of the weld. Therefore, the thermal frequency of 5 Hz is more suitable for the welding of AA6061-T6 aluminum alloy.  
In the Al-Mg-Si alloy, the Mg\({}_{2}\)Si precipitate significantly influences the microhardness and the mechanical properties of welded joints, and the distribution of magnesium element greatly affects the formation of Mg\({}_{2}\)Si precipitate [26]. The EDS maps of Magnesium elemental in fusion zone is shown in Figure 8. It can be noticed that the distribution areas of magnesium at 3 Hz are sparser than that of 4 Hz and 5 Hz, and the content of magnesium at 3 Hz is higher than that of 4 Hz and 5 Hz. The results indicate the more Mg\({}_{2}\)Si precipitate are distributed in the matrix when the thermal frequency is higher, therefore, thermal frequency significantly affects the precipitation of Mg\({}_{2}\)Si.  
Figure 8: Energy dispersive spectrometer (EDS) maps of Magnesium elemental in fusion zone.
## 4 MIMO Performance Parameters
### Diversity Gain (DG)  
The benefits of variety are often attained when transmitters receive numerous broadcast stream versions through diverse channel paths. Therefore, finding a diversity gain value close to 10 dB indicates a strong diversity performance. Diversity gain may be calculated using Equation (3) from the envelope correlation coefficient. Figure 15 represents the \(DG\) of the presented design.  
\[DG=10\times\sqrt{1-\left(ECC\right)^{2}} \tag{3}\]  
Figure 14: The \(ECC\) response of the proposed two-port MIMO antenna structure.
# Magnetic Field-Based Vehicle Positioning System in Long Tunnel Environment
## 1 Introduction
To find a location using the magnetic field, make a magnetic fingerprint DB in advance, and in the location estimation phase, find a place with the most similar pattern between the current magnetic field data and the magnetic fingerprint DB. The attitude estimation and calibration of the smartphone must be resolved for position estimation with a magnetic sensor. Note that pedestrians perform various motions, which raises difficulties in estimating the attitude of the smartphones [21]. However, the problem of attitude estimation can be conveniently resolved for the vehicle navigation situation as the smartphone is fixed on the dashboard during driving. In addition, since the tunnel environment is expressed in 1-D, it is simpler to find the position of the vehicle in a tunnel than that of the pedestrian on the 2-D map. Furthermore, the magnetic sensor output in the smartphone is calibrated using the magnetic sensor DB value before entering the tunnel. As the current position can be estimated through GNSS, the bias is eliminated by comparing the magnetic DB value at the current position with the magnetic sensor output value. This study uses magnetic field sequence data to estimate vehicle positions inside a tunnel. Subsequence dynamic time warping (DTW) [22] is applied to compare the user buffer and magnetic DB. In the positioning phase, we generate the minor DB to reduce the computational complexity. Furthermore, this enhances the positioning accuracy. To demonstrate the performance of the proposed system, an experiment was conducted in the tunnel environment. The contributions of this study are as follows:  
* We propose the magnetic field based novel tunnel navigation system for drivers. The driver does not need any additional equipment apart from a smartphone, and there is no cost burden for infrastructure installation, as the magnetic field is utilized. The only requirements are the creation and management of the magnetic field DB. * We propose a convenient magnetic sensor calibration method for vehicle navigation, and obtained very accurate positioning results using magnetic field data in the tunnel environment. * In the positioning phase, the magnetic field sequence data are compared with the minor DB using subsequence DTW. By using the minor DB, the computational complexity is reduced and positioning accuracy is enhanced. The remainder of this article is organized as follows. The proposed system is introduced with a block diagram in Section 2. Subsequently, the concepts of the proposed system along with attitude estimation and calibration method are presented in Section 3. The experimental results are analyzed in Section 4, and finally, the conclusions are presented in Section 5.
## 2 Experimental
### Characterization Techniques for Composite Beads and Instruments  
#### 2.3.1 Characterization of Composite Beads  
To evaluate the properties of composite beads vs. their control, samples from each series of beads were previously dried by lyophilization as follows: beads freshly taken from the liquid medium were placed in Falcon tubes, kept in the refrigerator (at \(-20\)\({}^{\circ}\)C) for 24 h, and then lyophilized (at \(-55\)\({}^{\circ}\)C) for 48 h.  
_Structure and composition:_ Fourier-Transformed Infrared (FTIR) spectra were recorded in Attenuated Total Reflectance (ATR) mode on a Thermo Fisher Summit Proequipment (Madison, WI, USA) running 16 scans at 4 cm\({}^{-1}\) resolution in the 4000-400 cm\({}^{-1}\) range. The lyophilized beads were ground in a ball mill before analysis.  
_Thermal stability:_ Thermogravimetric analyses (TGA) coupled with differential thermal analysis (DTA) were performed using a Mettler Toledo TGA/SDTA851e thermogravimeter (New Castle, DE, USA) at a heating rate of 10 \({}^{\circ}\)C/min, under 80 mL/min synthetic air flow in the 25-1000 \({}^{\circ}\)C temperature range.  
_Morphology:_ The morphology of beads was investigated using Scanning Electron Microscopy (SEM). SEM images for the reference and hybrid samples were recorded on a Hitachi TM4000 plus II benchtop (Tokyo, Japan) at an accelerating voltage of 15 kV with a cooling stage. Before SEM analysis, samples were uniformly coated with gold by cold sputtering using a Sputter Coater Q150R ES Plus (Quorum; Cambridge, UK) to create an electrically conductive thin film (\(\sim\)5 nm film thickness), thus inhibiting \"charge-up\", reducing thermal damage and increasing the emission of secondary electrons. The lyophilized beads were cut in two for analyzing the inner morphology.  
_Porosity and specific surface:_ Using nitrogen porosimetry, the specific surface area of beads, the pore surface area, pore size, and volume were evaluated using a NOVA 2200 e (Quantachrome Instruments, Boynton Beach, FL, USA) under a nitrogen atmosphere at \(-196\)\({}^{\circ}\)C (liquid nitrogen temperature). The lyophilized beads were ground in a ball mill before analysis and degassed at 40 \({}^{\circ}\)C for 4 h under vacuum.
## 4 Discussion
### Comparison between Model-Agnostic Metrics  
As shown in Figure 7, MMD and FID were able to distinguish the similarity between the real and generated sets \(\rho(\Phi(\mathbb{P}_{r\_test})\), \(\Phi(\mathbb{P}_{g}))\) at the middle axial level and the both end levels. In contrast, the 1-NN LOO accuracy exhibited a smaller variance in the \(\rho(\Phi(\mathbb{P}_{r\_test})\), \(\Phi(\mathbb{P}_{g}))\) for each axial level than the MMD and FID shown, and even the variance of \(\rho(\Phi(\mathbb{P}_{r\_test})\), \(\Phi(\mathbb{P}_{g}))\) for A\(\beta\)-negative images is greater than \(\rho(\Phi(\mathbb{P}_{r\_train})\), \(\Phi(\mathbb{P}_{r\_test}))\) (Table 2). The 1-NN LOO accuracy demonstrates that the variance that occurs in the similarity estimation is larger than that of MMD and FID in identical datasets owing to the nature of the estimation of classification performance, which is sensitive to the number of data [69].  
MMD and 1-NN LOO accuracy showed clear differences in \(\rho(\Phi(\mathbb{P}_{r\_test})\), \(\Phi(\mathbb{P}_{g}))\), whereas FID had some axial level at which there was no difference between \(\rho(\Phi(\mathbb{P}_{r\_train})\), \(\Phi(\mathbb{P}_{r\_test}))\) and \(\rho(\Phi(\mathbb{P}_{r\_test})\), \(\Phi(\mathbb{P}_{g}))\) (Figure 7). This suggests that the distribution of medical image samples for which the FID measures the similarity is not suitable for measuring with the FID using the Gaussian kernel, which also suggests that proper care should be taken when measuring the similarity for medical image synthesis.
# A Study on Reactive Ultra-Fine Fly Ash and Sulfoaluminate Cement in Self-Leveling Mortar
###### Abstract
[PERSON] [21] found that as the fly ash content in blended cement paste increased, the paste containing finer fly ash released more heat of hydration than that with coarser fly ash. XRD analysis revealed a denser microstructure in the paste with finer fly ash. The fluidity of the freshly mixed cement paste slightly improved with the increase in fly ash fineness. Additionally, the hardened blended cement paste containing 20% fly ash exhibited compressive strength comparable to that of the control paste at both 7 and 28 days, primarily due to the higher reactivity of the finer fly ash compared with the coarser fraction. [PERSON] [22] studied the effects of fly ash with different particle sizes on the performance of clay-cement mortar used in flood embankment construction. Ultra-fine, fine, and medium fractions of fly ash were obtained by using dry air separation and sieving. The results indicated that the rheological properties of the fresh mortar were significantly influenced by these components. Finer fly ash showed higher reactivity and a rapid increase in viscosity, with its spherical shape reducing internal friction during initial technical operations. As the particle size decreased, the compressive strength of the hardened mortar increased, forming a dense and uniform microstructure, offering advantages in economic feasibility, engineering performance, and ecological sustainability. [PERSON] [23] demonstrated that blending optimal amounts of PUFA and silica fume produced a dense microstructure. XRD peaks confirmed the presence of additional hydration peaks in the mixed samples. The mixture with 10% PUFA and 15% silica fume exhibited high strength, lower permeability, and enhanced particle packing, making it suitable for water-retaining structures. Although the previous developments in self-leveling mortar mentioned in many studies are fruitful, to the best of the authors' knowledge, research on self-leveling mortar containing PUFA has not been conducted. The goal of this study is to find appropriate mix proportions for self-leveling mortar by using type I Portland cement, SAC, and PUFA. The use of SAC should shorten the setting time, increase early-age strength, and reduce shrinkage. However, as SAC is used with type I Portland cement, the interference between two cement types might result in a reduction in compressive strength. Meanwhile, the addition of SAC may result in segregation, since the specific weight of SAC is lower than that of Portland cement. In order to resolve the problems linked to the addition of SAC, PUFA is used because it has strong pozzolanic activity, and can prevent segregation, similar to conventional fly ash. The performance of self-leveling mortar is also investigated. In the following sections, the materials used and the experiments conducted are described in Section 2, the results and some discussions on the observed facts are presented in Section 3, and the conclusions based on the experimental results are drawn and stated in Section 4.
## 3 Performance Analysis
### Expectation of SINR  
The expectation of SINR can be used to design a QoS-based beamforming scheme [30]. Using the PDF of SINR in (19), the expectation of SINR for Bob can be derived as  
\[\begin{split}\mathbb{E}[\mathrm{SINR}_{b}]&=\int_{ 0}^{+\infty}\gamma p_{\mathrm{SINR}_{b}}(\gamma)d\gamma\\ &=\frac{1}{\Gamma(\alpha_{b})}\beta_{b}^{\alpha_{b}}\beta_{b, \mathrm{AN}}^{\alpha_{b}\mathrm{AN}}\int_{0}^{+\infty}\gamma^{\alpha_{b}}e^{- \beta_{b}\gamma}U(\alpha_{b,\mathrm{AN}};\alpha_{b}+\alpha_{b,\mathrm{AN}}+1; \beta_{b}\gamma+\beta_{b,\mathrm{AN}})d\gamma\end{split} \tag{27}\]  
According to [40], we have the equation  
\[e^{-x}U(a;b;x)=G_{1,2}^{2,0}\bigg{(}x\left|\begin{array}{c}a-b+1,\\ 0,1-b\end{array}\right.\bigg{)} \tag{28}\]  
where \(G_{p,q}^{m,n}\left(x\left|\begin{array}{c}a_{1},\cdots,a_{p}\\ b_{1},\cdots,b_{q}\end{array}\right.\right)\) is the [PERSON]'s \(G\)-function ([41], 9.301). Then, by performing a change of variable \(t=\beta_{b}\gamma+\beta_{b,\mathrm{AN}}\), a closed-form expression for expectation of SINR for Bob can be derived using [42]  
\[\begin{split}\mathbb{E}[\mathrm{SINR}_{b}]&=\frac{1}{\Gamma( \alpha_{b})}\beta_{b}^{\alpha_{b}}\beta_{b,\mathrm{AN}}^{\alpha_{b,\mathrm{AN }}}\beta_{b,\mathrm{AN}}\int_{0}^{+\infty}\gamma^{\alpha_{b}}G_{1,2}^{2,0} \bigg{(}\beta_{b}\gamma+\beta_{b,\mathrm{AN}}\left|\begin{array}{c}-\alpha _{b},\\ 0,-\alpha_{b}-\alpha_{b,\mathrm{AN}}\end{array}\right.\bigg{)}d\gamma\\ &=\frac{\beta_{b,\mathrm{AN}}^{\alpha_{b,\mathrm{AN}}}}{\beta_{b} \Gamma(\alpha_{b})}e^{\beta_{b,\mathrm{AN}}}\int_{\beta_{b,\mathrm{AN}}}^{+ \infty}(t-\beta_{b,\mathrm{AN}})^{\alpha_{b}}G_{1,2}^{2,0}\bigg{(}t\left| \begin{array}{c}-\alpha_{b},\\ 0,-\alpha_{b}-\alpha_{b,\mathrm{AN}}\end{array}\right.\bigg{)}dt\\ &=\frac{\alpha_{b}}{\beta_{b}}\beta_{b,\mathrm{AN}}^{\alpha_{b}+ \alpha_{b,\mathrm{AN}}+1}e^{\beta_{b,\mathrm{AN}}}G_{2,3}^{3,0}\bigg{(}\beta_{ b,\mathrm{AN}}\left|\begin{array}{c}-\alpha_{b},\\ -\alpha_{b}-1,0,-\alpha_{b}-\alpha_{b,\mathrm{AN}}\end{array}\right.\bigg{)} \end{split} \tag{29}\]  
For the case \(g_{\mathrm{AN}}=0\), the expectation of SINR for Bob can be derived using ([41], 3.326), such that  
\[\begin{split}\mathbb{E}[\mathrm{SNR}_{b}]&=\int_{0}^{+\infty }\gamma p_{g_{\mathrm{S}}|\mathbf{H}_{b}\mathbf{w}_{b}|^{2}}(\gamma)d\gamma \\ &=\frac{\beta_{b}^{\alpha_{b}}}{\Gamma(\alpha_{b})}\int_{0}^{+ \infty}\gamma^{\alpha_{b}}e^{-\beta_{b}\gamma}d\gamma\\ &=\frac{\alpha_{b}}{\beta_{b}}.\end{split} \tag{30}\]  
Moreover, the expectation of SINR for [PERSON] can be obtained by replacing \(\alpha_{b}\), \(\beta_{b}\), \(\alpha_{b,\mathrm{AN}}\), and \(\beta_{b,\mathrm{AN}}\) with \(\alpha_{e}\), \(\beta_{e}\), \(\alpha_{e,\mathrm{AN}}\), and \(\beta_{e,\mathrm{AN}}\) in (29) and (30).