esa-sceva/satcom-chunk-collection huggingface dataset:
# Hole Concealment Algorithm Using Camera Parameters in Stereo 360 Virtual Reality System
## 3 Algorithm Proposed to Conceal a Hole
### Camera Parameters of Neighboring Blocks  
In Figure 1, when an ERP image is made, the stitching algorithm is applied to the set of pictures that have been taken by a single camera (the left or right camera). As explained in Section 2, each picture in a set of pictures with the left or right views is warped and translated according to the intrinsic matrix K and extrinsic matrix R of each picture. The following equations are the intrinsic and extrinsic matrixes of picture \(I_{i}\).  
\[K(I_{i})=\left(\begin{array}{ccc}f_{x}(I_{i})&s(I_{i})&c_{x}(I_{i})\\ 0&f_{y}(I_{i})&c_{y}(I_{i})\\ 0&0&1\end{array}\right) \tag{1}\]  
Figure 2: Examples of left and right ERPs, where a hole is found in the left ERP: (**a**) \(B_{L}^{l}\), \(B_{L}^{c}\), \(B_{L}^{r}\) in the left ERP image; (**b**) \(B_{R}^{l}\), \(B_{R}^{c}\), \(B_{R}^{r}\) in the right ERP image.  
\[\text{R}(I_{i})=\left(\begin{array}{ccc}r_{11}(I_{i})&r_{12}(I_{i})&r_{13}(I_{i} )\\ r_{21}(I_{i})&r_{22}(I_{i})&r_{23}(I_{i})\\ r_{31}(I_{i})&r_{32}(I_{i})&r_{33}(I_{i})\end{array}\right) \tag{2}\]  
where elements \(f_{x}(I_{i})\) and \(f_{y}(I_{i})\) are the horizontal and vertical focal lengths of the camera used, respectively. \((c_{x}(I_{i}),\ c_{y}(I_{i}))\) is the location of the intersection between the Z axis of the world coordinate system and the plane of the image sensor. \(s\) is a skew coefficient. In (2), R is a rotation matrix used to represent the direction of camera lens when picture \(I_{i}\) is taken. \(K\) and R are derived from a homography matrix calculated by RANSAC. The parameters in the \(K\)'s and R's of all the pictures are optimized by the bundle adjustment based on the Levenberg-Marquardt algorithm [26].  
Figure 3: Process to detect the hole in an ERP image: (**a**) A stitched picture including a hole; (**b**) a black-and-white picture; (**c**) a binary image resulted from Step 2; (**d**) several contours of the candidate holes in the binary picture; (**e**) the map indicating the decided holes.  
As explained in Section 2, the blocks \(\{B_{R}^{c},\,B_{R}^{l},\,B_{R}^{r},\,B_{L}^{c},\,B_{L}^{l},\,B_{L}^{l}\}\) have their own \(K\)'s and R's, which can be represented by substituting \(\{B_{R}^{c},\,B_{R}^{l},\,B_{R}^{r},\,B_{L}^{c},\,B_{L}^{l},\,B_{L}^{r}\}\) into \(I_{i}\) in (1) and (2) as follows. As the equations have the same pattern, we represent those for \(B_{L}^{l}\) only.  
\[K(B_{L}^{l})=\left(\begin{array}{ccc}f_{x}(B_{L}^{l})&s(B_{L}^{l})&c_{x}(B_{ L}^{l})\\ 0&f_{y}(B_{L}^{l})&c_{y}(B_{L}^{l})\\ 0&0&1\end{array}\right) \tag{3}\]  
\[\text{R}(B_{L}^{l})=\left(\begin{array}{ccc}r_{11}(B_{L}^{l})&r_{12}(B_{L}^{ l})&r_{13}(B_{L}^{l})\\ r_{21}(B_{L}^{l})&r_{22}(B_{L}^{l})&r_{23}(B_{L}^{l})\\ r_{31}(B_{L}^{l})&r_{32}(B_{L}^{l})&r_{33}(B_{L}^{l})\end{array}\right) \tag{4}\]
# Graph-Level Label-Only Membership Inference Attack Against Graph Neural Networks
## 4 Graph-Level Label-Only Membership Inference Attack
### Threat Model
#### 4.1.2 Adversary's Knowledge and Capability  
**Label-Only Black-Box Setting.** It is assumed that the adversary cannot access the parameters or internal representations of the target model \(f_{t}\) and can only query the target model. The target model only provides prediction labels rather than prediction probability vectors. This represents a realistic scenario that is the most restrictive scenario, making it significantly more challenging for the adversary to attack.  
**Shadow Dataset and Shadow Model.** It is assumed that the adversary owns a shadow dataset from the same domain as the target model's training dataset, with no overlap between the shadow and target datasets. Additionally, the adversary knows the architecture and training algorithm of the target model, as well as the membership and non-membership status of the samples in the shadow dataset used for the shadow model. The adversary can use the shadow dataset to train a shadow model with the same architecture as the target model to mimic its behavior.
## 3 Results  
ST showed the deepest mean penetration at 3- and 6-mm levels (3 mm: 348.4 \(\pm\) 120.1 \(\upmu\)m; 6 mm: 640.2 \(\pm\) 163.43 \(\upmu\)m) in comparison with that achieved using System B (3 mm: 214.1 \(\pm\) 152.4 \(\upmu\)m, \(p<0.05\); 6 mm: 263.6 \(\pm\) 175.4 \(\upmu\)m; \(p<0.01\)), while compared with T, ST reported the statistically significant deepest mean penetration at 6 mm (470.3 \(\pm\) 291.5 \(\upmu\)m; \(p<0.05\)) (Figure 2).  
Figure 1: (a) Ultrasonic tip designed for the study. (b) Handpiece SONIC flex quick 2008/L Air Scaler (KaVo Dental Ltd., Corinium Industrial Estate, Amersham Bucks, UK).  
ST registered the highest values of infiltration at both levels (3 mm: 773.2 \(\pm\) 292.0 \(\upmu\)m; 6 mm: 1156.8 \(\pm\) 117.9 \(\upmu\)m), reporting statistically significant differences at 6 mm in comparisons with T (3 mm: 594.2 \(\pm\) 188.5; 6 mm: 708.2 \(\pm\) 420.3, \(p\) = 0.015) and SB (3 mm: 545.91 \(\pm\) 242.58, 6 mm: 564.5 \(\pm\) 388.4, \(p\) < 0.001) respectively.  
Although the percentages of seal penetration were higher for T at 3 mm (0.6 \(\pm\) 0.1) and ST at 6 mm (0.7 \(\pm\) 0.1), no statistically significant differences emerged.  
The ST area of penetration at 6 mm (15,997.1 \(\pm\) 8566.5 \(\upmu\)m\({}^{2}\)) resulted in significantly higher values than those observed with T (8092.9 \(\pm\) 6115.0, \(p\) = 0.0282) and SB (7696.0 \(\pm\) 7585.4, \(p\) = 0.0282). At 3 mm, no statistically significant differences emerged. All results are reported in Table 1.
## 3 Results and Discussions
### DCA Correction
The error in antenna electronic miss-pointing \(f_{em}\) arises from the radar antenna's directional deviation and orbital information discrepancies, leading to a deviation between the actual and theoretical beam-pointing angles. Typically, land is employed as reference data for correcting the DCA of 0. This correction is achieved utilizing the pixel points on land as shown in Figure 6a. The averaged DCA along the range direction is illustrated in Figure 4b for each sub-swath. Upon subtracting this component, what persists is the geophysical Doppler shift. After eliminating the non-geophysical factors, the remaining (geophysical) Doppler shift is directly linked to the ocean surface velocity. This velocity is primarily influenced by the orbital velocities of sea surface waves, establishing the connection between the Doppler shift and surface wind [13], as well as surface currents [15]. Consequently, it becomes essential to eliminate this sea-state influence \(f_{w}\) to extract the current-related velocity. Figure 4: Map of (**a**) the predicted (geometric) Doppler centroid and (**b**) the obtained Doppler centroid anomaly after subtracting the predicted Doppler centroid and the suppression of outliers. Figure 5: (**a**) Demonstration of de-scalloping of DCA. The solid curve in blue denotes the averaged DCA along the azimuth direction, and the dashed line in blue is the third-order polynomial fit. The black solid curve is the scalloping signal to be removed for each sub-swath. (**b**) The map of DCA after scalloping corrections.
# Pyrophyllite: An Economic Mineral for Different Industrial Applications
## 4 Enrichment of Low-Grade Pyrophyllite Ore
### Leaching  
In some cases, chemical treatment is used when physical treatment does not remove iron contaminants effectively from low-grade pyrophyllite ore. Leaching is the most important chemical treatment process for clay minerals and can use either organic acids (e.g., citric acid and oxalic acid) or inorganic acids (e.g., hydrochloric acid, sulfuric acid, and sodium hypochlorite). However, there are limitations when using inorganic acids with clay minerals due to environmental pollution and the contamination of products with Cl and SO\({}_{4}\)\({}^{-2}\). Therefore, organic acids are more widely used. Oxalic acid is preferred for dissolving iron contaminants in clay minerals because of its high leachability under different conditions [105; 106]. The effectiveness of oxalic acid in dissolving Fe from low-grade pyrophyllite ore to improve the quality of pyrophyllite was investigated in previous studies [8; 107; 108]. The critical parameters affecting the dissolution of Fe from pyrophyllite ore are the particle size, acid concentration, solid/liquid ratio, leaching time, temperature, and stirring speed. A study was also conducted to remove iron from pyrophyllite using oxalic acid leaching. The main gangue minerals were quartz, muscovite, and orthoclase (Table 6). Fe, mostly goethite, was present as inclusions or intergranular spaces within the silicates. When oxalic acid was used to dissolve iron using a concentration of 0.3 M, a temperature of 90 \({}^{\circ}\)C, a pulp density of 5%, a particle size below 100 microns, and a leaching time of 60 min, it was able to remove up to 99.3% of the iron in the pyrophyllite [8]. Oxalic acid is utilized with other chemical processes such as calcination, filtering, and drying to improve the quality of pyrophyllite by increasing its whiteness and brightness. These processes were applied to pyrophyllite micro powder with a particle size of 325-1200 meshes, and whiteness of greater than 87% was obtained [107; 108].  
There were previous attempts to use ammonia as a solvent to improve the quality of low-grade pyrophyllite ore via the dissolution of iron because ammonia is characterized by a low rinsing cost, low toxicity, and high efficiency in separating iron components [35]. In a previous study, the effectiveness of using an ammonia solution to remove iron from pyrophyllite was further investigated. The associated gangue minerals were quartz and dickite, and euhedral cubic pyrites were also observed (Table 6). The effect of variables such as the ammonium sulfate amount, particle size, addition of hydrogen peroxide, and sulfuric acid concentration were investigated. The study concluded that iron removal using an ammonia leaching solution could be effective [35].
## 3 Results and Discussion
efficiency and realized peak gain at 2.45 GHz were 86.3% (62.6%) and 4.3 dBi (3.3 dBi), respectively. The sufficient antenna efficiency indicates that the power collected from the transceivers can be radiated to free space. Since these performance indices also jointly evaluated the reflection coefficients, a shift of resonant frequencies was responsible for the slight discrepancies between the simulations and the measurements for the two arrays. Figure 10 demonstrates the simulated and measured radiation patterns at 28.0 GHz, 38.0 GHz, and 2.4 GHz. The main beams of the two capacitive coupled patches were directed broadside. For the antenna array operated at 28.0 GHz, the half-power beam widths (HPBWs) were about 90\({}^{\circ}\). In addition, the 2.45 GHz antenna emitted conventional loop-wise radiation patterns, and thus, it is suitable for WLAN applications. The maximum  
Figure 9: (**a**) Antenna efficiency and (**b**) realized peak gain for the 28 GHz unit element, 38 GHz unit element, and 2.45 GHz loop antenna. difference between the simulated and measured main beams was less than 3 dB for each of the patterns. Considering the errors related to fabrication, measurement, and calculation, this discrepancy is not significant. To demonstrate the capability of 5G beamforming, Figure 11 presents the simulated realized peak gains and the scanning ranges for the 28 GHz and 38 GHz arrays. The maximum gains for the proposed arrays were 16.7 dBi and 16.4 dBi, respectively. In terms of a 3 dB scan loss, the scanning ranges at 28 GHz and 38 GHz were about \(\pm\)45\({}^{\circ}\). Figure 10: Radiation patterns for (**a**) the 28 GHz unit element, (**b**) 38 GHz unit element, and (**c**) 2.45 GHz loop antenna.
## 3 Results
### Spatiotemporal Variation Characteristics of Drought in Xinjiang
#### 3.1.3 Trend Analysis of Drought in Different Ecological Regions of Xinjiang  
At the 3-month SPEI timescale, the MK test results across the ecological zones of Xinjiang were generally consistent, with all zones showing a slight increase in drought severity, except for R3, which remained relatively stable. The trends for SPEI-6 and SPEI-12 were similar, although R1 and R2 shifted from a slight to a significant increase in drought severity compared with SPEI-3, indicating a transition from short- to long-term drought in parts of northern Xinjiang. Conversely, the 12- and 24-month SPEI timescales revealed more pronounced differences in the drought trends. At these longer timescales,  
Figure 4: Spatial distribution of multi-year average SPEI in Xinjiang from 2000 to 2020 across different timescales. (**a**) Spatial distribution of SPEI at a 3-month timescale (SPEI-03). (**b**) Spatial distribution of SPEI at a 6-month timescale (SPEI-06). (**c**) Spatial distribution of SPEI at a 12-month timescale (SPEI-12). (**d**) Spatial distribution of SPEI at a 24-month timescale (SPEI-24). Red areas indicate drier conditions, whereas blue areas represent wetter conditions.  
the drought severity in the northern R1 and R2 zones further intensified, with R2 reaching a Z-value of \(-5.51\) at the 24-month scale, indicating an extremely significant long-term drought trend in this area. Simultaneously, the R3 zone shifted from stability to a slight increase in drought severity, suggesting that this zone was primarily affected by long-term climatic drought, whereas the R5 zone transitioned from a slight increase to relative stability, showing signs of drought alleviation (Table 3). Overall, the SPEI analysis across different timescales revealed the spatiotemporal characteristics of drought in the ecological zones of Xinjiang. Particularly, Northern Xinjiang exhibited significant mid- to long-term drought intensification, whereas the southern ecological zones generally showed a slight increase in drought severity. Notably, the R5 zone showed indications of drought relief at the SPEI-24 timescale.
# A Level-Set-Based Density Method for Buckling Optimization of Structure with Curved Grid Stiffeners
## 5 Numerical Examples
### Example 3  
The design domain of this case study is a rectangular shape with dimensions of \(10\,\mathrm{mm}\times 20\,\mathrm{mm}\). The bottom edge is completely fixed, and a vertically downward distributed load is applied on top with a length of \(2\,\mathrm{mm}\) and a magnitude of \(F=1\,\mathrm{N/mm}\). Boundary conditions are depicted in Figure 16. The wavelength parameters in Equations (5) and (6) are set to \(\mu_{1}=\mu_{2}=2.1\); \(200\times 400\) finite elements is used; and \(20\times 40\) CS-RBF knots are uniformly distributed. The gradient constraint parameter \(\xi\) in Equations (19) and (20) is configured as 0.3. The maximum allowable volume \(\overline{V}\) is set to be \(V^{0}\).  
Figure 14: Variation of different gradients during optimization processes.  
In this example, the initial layout of the stiffeners is oriented in two directions, at 45\({}^{\circ}\) and \(-\)45\({}^{\circ}\), as illustrated in Figure 17a. Figure 17b exhibits the final outcome, where the stiffeners distribution at the bottom, near the center region, resembles that of Example 2, optimized in the direction of the load path, thereby enhancing the load-bearing capacity. The stiffeners in the upper region follow an arch-shaped path, similarly aimed at improving the load-bearing capacity. Figure 18a-c represents the first-, second-, and third-order buckling modes corresponding to the optimization results, respectively, demonstrating the absence of duplicate first-order BLFs.  
The plots depicting the evolution of the critical BLF and the volume of stiffeners are illustrated in Figure 19. The optimization procedure converged eventually, with the critical BLF \(\lambda_{1}\) increasing by 29.8%.  
Figure 16: The design domain and boundary conditions for Example 3.  
Figure 19: Convergence history of critical BLF and stiffeners volume.  
Figure 17: Initial design and optimized result.  
Figure 18: Optimization of the first three buckling modes for the grid stiffener structure. (**a**) First buckling mode, (**b**) second buckling mode, (**c**) third buckling mode.
## 2 Characteristics of Time Delay Complex Chen System  
Consider the following time delay complex Chen system:  
\[\left\{\begin{array}{rcl}\dot{y}_{1}&=&a_{1}(y_{2}-y_{1}),\\ \dot{y}_{2}&=&(a_{2}-a_{1})y_{1}+a_{2}y_{2}-y_{1}y_{3}(t-\tau),\\ \dot{y}_{3}&=&-a_{3}y_{3}(t-\tau)+(1/2)(\dot{y}_{1}y_{2}+y_{1}y_{2}),\end{array}\right. \tag{1}\]  
where \(0\leq\tau\leq\tau_{m}\) is the time delay factor, \(y_{1}=u_{1}+ju_{2}\) and \(y_{2}=u_{3}+ju_{4}\) are the complex state variables, and \(y_{3}=u_{5}\) is the real state variable. Overbar \(\bar{y}_{1}(\bar{y}_{2})\) stands for the complex conjugate of \(y_{1}(y_{2})\), and \((a_{1},\,a_{2},\,a_{3})^{\mathrm{T}}\) is the real parameter vector.  
Separating the real and imaginary parts of each variable in System (1), we obtain  
\[\left\{\begin{array}{rcl}\dot{u}_{1}&=&a_{1}(u_{3}-u_{1}),\\ \dot{u}_{2}&=&a_{1}(u_{4}-u_{2}),\\ \dot{u}_{3}&=&(a_{2}-a_{1})u_{1}-u_{1}u_{5}(t-\tau)+a_{2}u_{3},\\ \dot{u}_{4}&=&(a_{2}-a_{1})u_{2}-u_{2}u_{5}(t-\tau)+a_{2}u_{4},\\ \dot{u}_{5}&=&-a_{3}u_{5}(t-\tau)+(u_{1}u_{3}+u_{2}u_{4}).\end{array}\right. \tag{2}\]
## 2 RISs and Their Integration with NTNs
### RISs and Their Advantage Against Dynamic Switching  
RIS refers to a flat surface comprising several metamaterial reflection components with adjustable phase transitions, enabling manipulation of incoming EM waves' phase shifts. This cutting-edge technology allows for the dynamic control of the wireless environment and identification of reflected EM wave behaviors [20]. Controlling transmitted signals via a reflection array was first introduced in 1997, creating reflection displays using metamaterial elements to manage millimeter wave (mmWave) reflecting surfaces effectively [21; 22]. Their work suggests improvements in production, component design, and input layout while demonstrating the beamforming capabilities of reflecting panels in the millimeter wave (mmWave) range. Utilizing RISs can improve communication effectiveness significantly.  
Various strategies have been employed in recent years to achieve metamaterial tunability, including embedding switching apparatus externally and integrating positive intrinsic-negative (PIN) diodes with metasurface components [23]. Nowadays, reconfiguration capabilities on metasurfaces have become achievable, and several research teams have developed prototypes. Studies have explored software-controlled passive patch antennas for static beam development, showcasing advancements in beamforming capabilities and  
Figure 2: Taxonomy of RIS-assisted NTN-based 6G communications along with blocks illustrating the interconnection of research themes, forming a cohesive framework to accomplish RIS-assisted NTN-based 6G communications.  
reconfiguration in wireless systems [24; 25]. Several advancements in RIS technology have been achieved recently, showcasing diverse capabilities and advantages:  
1. A PIN-based 256-unit RIS with excellent energy density and cost-effectiveness has been developed [25; 26; 27].
2. Researchers have successfully regulated orientation and transmission reflection using a variable anisotropic computerized sequence alignment metasurface [28]. It was shown that doubling the number of antenna elements from 400 to 800 increased the achievable rate to 2 bps/Hz.
3. NTT Docomo from Japan has created a highly transparent two-dimensional metamaterial, allowing for covert usage in exhibitions and advertisements [29].  
These advancements highlight the superiority of RIS technology over dynamic switching methods, as RISs offer several advantages, such as low power consumption and simplified interference management. This capability positions RISs as critical enablers for intelligent wireless environments, particularly for 6G [30; 31].
## 2 Materials and Methods
### Testing Protocol  
Participants were asked not to perform any resistance exercises for 24 h prior to the start of the experimental session. Additionally, they were advised to maintain their regular dietary habits and refrain from the consumption of any supplements or stimulants, except for habitual supplementation such as creatine in the week preceding the experiment. At the beginning of the experimental session, body composition was assessed using a multi-channel bioelectrical impedance analysis in a laboratory setting using the InBody 720 device (InBody, Tokyo, Japan). The first resting measurement of the muscle viscoelastic properties of the Achilles tendon, biceps femoris muscle, semitendinosus muscle, rectus femoris muscle, and sternocleidomastoid muscle, were directly taken before the commencement of VO2 max measurement. All measurements of the viscoelastic properties of the muscles were performed in the same room as the VO2 max measurement. All tests were conducted by the same person trained to operate the Myoton device.  
The second sample was taken immediately after the completion of the VO2 max measurement. The reliability between the trials (within the session) of a selected muscle group (2 trials per participant, N = 21, total measurements = 42) was tested using the intraclass correlation coefficient (ICC) model. [PERSON]'s classification scales (1993) were  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristic** & **M \(\pm\) SD** \\ \hline Age [years] & 24 \(\pm\) 5 \\ Body mass [kg] & 75 \(\pm\) 7 \\ Body fat [\%] & 12.1 \(\pm\) 2.7 \\ Body height [cm] & 180 \(\pm\) 6 \\ Resistance training experience [years] & 7.9 \(\pm\) 2.2 \\ VO2 max [mL\(\cdot\)kg\({}^{-1}\cdot\)min\({}^{-1}\)] & 54.9 \(\pm\) 3.7 \\ \hline \hline \end{tabular}  
* –Mean, SD—Standard deviation.  
\end{table}
Table 1: Descriptive characteristics of the study participants.  
applied to interpret the ICC: very high = 1.00-0.90; high = 0.89-0.70; moderate = 0.69-0.50; low = 0.49-0.26. This indicates that the ICC value across the time points was in a range of 0.71-0.94, with the exception of BF (0.56). A high reliability coefficient indicates that the applied tests represent a consistent measurement of data regarding the viscoelastic properties of muscles among soccer players.