esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Analysis
### Stereo-Matching Experiments
Compared with the SURF and SIFT algorithms, the matching success rate with our proposed combination was slightly lower than that with the SIFT algorithm, but the real-time performance was much higher than with these two algorithms. Compared with the other five types of recombinant stereo-matching systems, our proposed combination is clearly in an advantageous position in terms of the number of detected feature points, matching success rate, and real-time performance.
# Feature-Preserved Point Cloud Simplification Based on Natural Quadric Shape Models
## 2 The Methodology
### Simplification Entropy of Key Points
#### 2.2.3 Curve-Keeping Simplification Entropy  
The curve intuitively has more influence on the geometric features. The curve value participates in the simplification through curve-keeping simplification entropy. In the local area, the three principal vectors, which can be represented by \(V_{0}\), \(V_{1}\), and \(V_{2}\), are found by PCA (principal component analysis). Corresponding to the three principal vectors, the eigenvalues are represented as \(\lambda_{0}\), \(\lambda_{1}\), and \(\lambda_{2}\), respectively.  
We create the covariance matrix \(C\) of point cloud \(R\):  
\[C= \frac{1}{k}\sum_{i=1}^{k}\left(p_{i}-\overline{p}\right)(p_{i}- \overline{p})^{T}. \tag{6}\]  
where \(k\) is the number of \(p_{i}\)'s neighbors; and \(\overline{p}\) is the centroid of \(p_{i}\).  
Using the singular value, matrix \(C\) can be decomposed as:  
\[C=\left[V_{0},V_{1},V_{2}\right]\left[\begin{array}{ccc}\lambda_{0}&&\\ &\lambda_{1}&\\ &&\lambda_{2}\end{array}\right]\left[\begin{array}{ccc}V_{0}\\ V_{1}\\ V_{2}\end{array}\right]. \tag{7}\]  
\(c_{curvature}\) is calculated by Equation (8):  
\[c_{curvature}=\left|\frac{\lambda_{0}}{\lambda_{0}+\lambda_{1}+\lambda_{2}}\right|. \tag{8}\]  
Figure 6: Clusters of boundary points: (**a**) the view of clusters; (**b**) the centers of clusters.  
The value of \(c_{curvature}\) reflects the curve of the surface. If the value of \(c_{curvature}\) is bigger, the surface is sharper. Taking the \"bunny\" dataset as an example, the local maximum and minimum values of \(c_{curvature}\) are extracted, as shown in Figure 7.  
From Figure 7, we know that the \(c_{curvature}\) value reflects the curve of the surface. Additionally, sharp surface features are important for the simplification. To protect the geometric features, most of the simplification algorithms consider that the sharper areas are important and should be protected to a large extent. However, in this paper, we consider the minimum of \(c_{curvature}\) to also be important for preserving smooth geometric features, or else a lot of holes will be produced in the reconstructed surface. Therefore, we set a threshold \(\theta\), which is the number of key points required for curve preservation.  
Figure 7 shows the feature points with different thresholds \(\theta\).  
Using the Kd-tree with threshold \(\sigma_{c}\), the neighbors of key points are created for curve surface features, which reflect the curve feature of the surface. When combined with the Gaussian kernel function, the curve-preserving simplification entropy can be defined as follows:  
\[\mathrm{H_{c}}=\log(\exp{(-\frac{(c_{curvature}-\mu_{c})^{2}}{2\sigma_{c}^{2}}) }^{-1}) \tag{9}\]  
where \(\sigma_{c}\) is the threshold to control the scale of the feature points' neighbors; and \(\mu_{c}\) is the average of \(c_{curvature}\).
## 4 Discussion
For short observations, the entire observation interval is affected by multipath, and for multiple observations 3-4 h apart, the effect of multipath is reflected in increased variability in the results of a series of measurements. The mean value calculated from a series of multiply repeated measurements is closer to the conventional true value. In the project [20], a special Leica AT504 GG GNSS instrument designed primarily for stationary permanent stations, which is characterized by a specific construction of concentric shielding rings to reduce the influence of multipath, was used for GNSS measurements. It turned out that, in the operational conditions of the railway, the benefit of using this special instrument was not significant compared to the usual high-quality geodetic GNSS instruments. The integration of GNSS and IMU units allows for improved positioning accuracy in difficult observation conditions and is addressed in conjunction with moving objects (vehicles, unmanned vehicles, etc. ), e.g., in urban environments. For this purpose, a combined single-frequency multi-GNSS RTK/MEMS-IMU solution has been developed that, based on a Kalman filtering strategy, is well able to withstand remote measurements or low-quality observations [28]. In our case, the GNSS/IMU is not moving during the measurement and is horizontal. The difference between the accuracy of the spatial coordinate determination computed by the instrument and the accuracy computed from the variance of a series of repeated measurements was shown. The difference in accuracy with the IMU tilt compensation unit switched on and off was also shown, with a slight deterioration in accuracy when switched on. The results of GNSS tests with magnetometer sensors and a micro-electro-mechanical system (MEMS) and an inertial measurement unit (IMU) at vertical inclinations of 0 degrees, 15 degrees, 25 degrees, 35 degrees and 45 degrees are presented in [21]. The IMU-based tilt sensor gave more accurate results than the MEMS sensor. Using the IMU, it was possible to achieve an accuracy of better than 4 cm in the horizontal component at a tilt of 15 degrees from vertical. Vertical accuracy was less sensitive to tilt angles when using both sensor types. Multipath was found to be more pronounced at larger tilt angles, resulting in increased horizontal errors of up to decimeters. Comparing the methods used to determine the primary system of railway lines in Germany [14], China [15] and Poland [16], the construction method in the Czech Republic is mostly similar to that in Germany. In terms of accuracy, the density of GNSS points at approximately 1 km along the route followed by densification by terrestrial measurement methods seems to be optimal, where an accuracy of better than 10 mm can be achieved. The Chinese solution of a primary system of a pair of landmarks at about 4 km then leads to a slight deterioration in the accuracy of the secondary system.
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 1 Introduction
### Motivation  
Determining the required sample size for the identification of relevant effects is an essential component of the frequentist design of experiments (DOE) methodology framework [1]. Statistical power analyses are employed to determine the sample size in order to identify the technologically relevant effects with a defined probability by using the estimated variance of the data [2]. In general, the type II error is used alongside the type I error, and should be kept as small as possible. The type II error is the complement to the statistical power; therefore, it represents the risk of not detecting existing effects belowa certain threshold value while performing a test. For technical products, which have to endure a certain service life before they fail, such concepts lack implementation and application. From a life test perspective on such products, a type II error is made in a test if the test result does not demonstrate the required reliability when the product actually does meet the requirement in reality. By making use of the power, the planning of expenditures becomes possible; additionally, test configurations can be identified which cannot be realized from a practical point of view due to a too-high sample size requirement.
# Energy Saving Potential of PCMs in Buildings under Future Climate Conditions
## 4 Results
### Effect of PCM on Energy Consumption
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **PCM** & **Present (kWh)** & **2020 (kWh)** & **2050 (kWh)** & **2080 (kWh)** \\ \hline \multirow{3}{*}{Seoul, Korea} & Without PCM & 340,661 & 339,128 & 338,961 & 356,690 \\ \cline{2-6}  & BioPCM & 312,676 (8.21\%) & 311,373 (8.18\%) & 312,477 (7.81\%) & 332,044 (6.91\%) \\ \cline{2-6}  & DuPonEnergainPCM & 323,097 (5.16\%) & 320,756 (5.42\%) & 321,654 (5.11\%) & 340,715 (4.48\%) \\ \cline{2-6}  & RUBITHERMPCM & 318,423 (6.53\%) & 315,552 (6.95\%) & 316,537 (6.62\%) & 335,950 (5.82\%) \\ \hline \multirow{3}{*}{Tokyo, Japan} & Without PCM & 327,679 & 310,043 & 327,410 & 365,575 \\ \cline{2-6}  & BioPCM & 295,918 (9.69\%) & 282,812 (8.78\%) & 301401 (7.94\%) & 341,450 (6.60\%) \\ \cline{1-1}  & DuPonEnergainPCM & 306,854 (6.36\%) & 293,228 (5.42\%) & 312102 (4.68\%) & 351,656 (3.81\%) \\ \cline{1-1}  & RUBITHERMPCM & 301,838 (7.89\%) & 287,355 (7.32\%) & 306020 (6.53\%) & 345,558 (5.48\%) \\ \hline \multirow{3}{*}{Hong Kong, China} & Without PCM & 356,171 & 387,911 & 412889 & 452,682 \\ \cline{2-6}  & BioPCM & 337,832 (5.15\%) & 370,962 (4.37\%) & 397,553 (3.71\%) & 441,416 (2.49\%) \\ \cline{1-1}  & DuPonEnergainPCM & 346,177 (2.81\%) & 377,858 (2.59\%) & 403,101 (2.37\%) & 433,918 (1.94\%) \\ \cline{1-1}  & RUBITHERMPCM & 340,316 (4.45\%) & 371,729 (4.17\%) & 397,256 (3.79\%) & 438,635 (3.10\%) \\ \hline \hline \end{tabular} \({}^{1}\) Percent electricity saving with PCM.  
\end{table} 
Table 9: Peak annual electricity demand by the HVAC system in each region. to 26 \({}^{\circ}\)C, which all three PCMs did by solidifying, and that resulted in consumption reductions for electricity and gas during the cooling season. Similar cases were observed in Tokyo and Hong Kong. The higher temperature fluctuation values of the PCM 25 and PCM 29 meant that, in the thermostat range (20-26 \({}^{\circ}\)C), they offered the highest consumption reductions in electricity and gas at the present, as well as in 2020, 2050, and 2080. Therefore, it is important to select a PCM melting point temperature range suited to both the local climate and local thermostat set points. A higher melting point does not always mean higher energy savings. Figure 17 shows that DuPonEnergainPCM (21.6 \({}^{\circ}\)C), followed by RUBITHERMPCM (25 \({}^{\circ}\)C), better reduced gas consumption in Tokyo, Seoul, and Hong Kong than BioPCM (29 \({}^{\circ}\)C), and Figure 16 shows that, in 2050 and 2080, Hong Kong will likely depend on electrical energy instead of gas for cooling and heating (Table 10): Figure 17 shows gas savings in Hong Kong of 83.03%, 91.47%, and 92.60%, which is unreasonable when the highest electricity savings in Hong Kong is only 5%. As shown in the simulation results for Hong Kong, by
## 3 Analysis and Results
### Methods of Using Results  
The road segment division method, based on spatial partitioning of individual vehicle speed data, inherently produces rectangular areas due to the Geohash approach. As a result, these divisions do not align precisely with the linear contours of the roads, leading to segments that vary in interval lengths and widths, even within the same partition level. This variability creates segment sizes that differ based on spatial characteristics rather than consistent road-based intervals. However, adjacent segments with similar characteristics can be merged to highlight sections where traffic patterns are uniform. These consistent characteristics represent areas where individual vehicle speeds are relatively similar.  
In contrast, variations in lane-specific speeds may arise within the same segment, particularly at accident sites or road diverging/merging points, leading to heterogeneous traffic patterns even within a single segment. Such instances of dissimilar individual vehicle speeds reflect the complexity of traffic conditions in these areas. Figure 8 presents examples of heterogeneous speeds, derived from actual individual vehicle data, that illustrate the presence of unstable traffic flow.  
Table 2 presents data for the upward direction of Expressway 50, detailing the number of spatial divisions, the number of segments, the average number of data points per segment, and the average of representative values. Figure 9 illustrates the speed-homogeneous space-partitioning function derived from the comprehensive dataset analysis. Based on this function, segments where the space mean speed is comparatively low are identified as having uneven speed distributions within the segments, a condition defined as unstable flow.  
This refined analysis of traffic flow, particularly the differentiation of speed by lane in response to accidents and merging/diverging zones, provides essential situational awareness for autonomous vehicles. Such information is especially critical in scenarios where sensor performance may be compromised by adverse weather or other environmental factors, offering crucial data on traffic conditions ahead. These data not only support autonomous and semi-autonomous vehicle systems but also enhance safety for conventional vehicles by notifying drivers of real-time speed changes in their immediate path.  
By providing detailed insights into speed variations within traffic flows, this segmentation approach can play a transformative role in traffic management, allowing for more  
Figure 8: The relationship between speed-homogeneous spaces and unstable traffic flow.  
precise analysis and improved response strategies across various traffic situations. Ultimately, this granularity in traffic information supports better decision making for all vehicle types, contributing to increased safety, smoother traffic flow, and heightened efficiency on the roadways.
## 3 Results
The relationships between VIs and LAI were most notably affected at MTA \(>60^{\circ}\); at a lower MTA, the effect of leaf angle was less evident (Figure 7), especially for BNDVI, GARI, GNDVI, NDVI, and MSR\({}_{705}\) at LAI \(>3\) (Figure 7a,f,g). The effect of MTA on the VI-LAI relationship increased as a function of decreasing LAI for BNDVI, GNDVI, MSR\({}_{705}\), NDVI, and SAVI; for the remaining indices, the trend was unclear. Across the whole studied LAI variation range, the VI-LAI relationships for MSAVI and SR were most strongly affected by MTA, as the point clouds corresponding to the distinct MTA levels are clearly separable in Figure 7d,h. On the other hand, SR was the least saturating VI with LAI, and the relationships were nearly linear for the whole LAI range at MTA 15-50\({}^{\circ}\) (Figure 7h). The leaf chlorophyll content only weakly affected the relationship between BNDVI, MSAVI, NDVI, SAVI, and LAI (Figure 8a,d,f,g), as the point clouds corresponding to the different Cab values overlap in the figure. For the other indices (GARI, GNDVI, MSR\({}_{705}\), and, to a smaller extent, SR; Figure 8b,c,e,h), relationships with LAI were clearly affected by Cab, with the influence of Cab generally increasing as a function of LAI. Figure 7: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline
**Vegetation Index** & **MTA = 15\({}^{\circ}\)** & **MTA = 30\({}^{\circ}\)** & **MTA = 50\({}^{\circ}\)** & **MTA = 70\({}^{\circ}\)** \\ \hline BNDVI & 0.98 & 0.99 & 0.99 & 0.95 \\ GARI & 0.72 & 0.80 & 0.88 & 0.93 \\ GNDVI & 0.72 & 0.80 & 0.88 & 0.93 \\ MSAVI & 0.98 & 0.98 & 0.98 & 0.94 \\ MSR\({}_{705}\) & 0.73 & 0.83 & 0.91 & 0.94 \\ NDVI & 0.93 & 0.97 & 0.98 & 0.95 \\ SAVI & 0.93 & 0.97 & 0.98 & 0.95 \\ SR & 0.95 & 0.98 & 0.99 & 0.95 \\ \hline \end{tabular}
\end{table} 
Table 5: Kendall’s rank correlation coefficient (\(\tau_{k}\)) between vegetation indices and LAI in PROSAIL-simulated data for different MTA values at a fixed Cab (45–50 \(\upmu\)g cm\({}^{-2}\)). All correlations were statistically significant (\(p<0.01\)). Figure 7: Correlation between vegetation indices and the leaf area index (LAI) for a fixed Cab (45–50 \(\upmu\)g cm\({}^{-2}\)) and different leaf mean tilt angles (MTA = 15, 30, 50, 70\({}^{\circ}\)): (**a**) BNDVI, (**b**) GARI, (**c**) GNDVI, (**d**) MSAVI, (**e**) MSR\({}_{705}\), (**f**) NDVI, (**g**) SAVI, (**h**) SR. Canopy reflectance simulated with PROSAIL. Figure 8: _Cont._
###### Abstract  
Although thermosetting polyphenylene oxide- (PPO) based composites with excellent dielectric properties have been widely accepted as superior resin matrices of high-performance copper clad laminate (CCL) for 5G network devices, there has been limited information regarding the composition-process-structure-property relationships of the systems. In this work, the effects of peroxide initiator concentration on the structure and dielectric properties of a free radical cured ultralow loss PPO/Triallyl isocyanate (TAIC) composite system were studied. As expected, the glass transition temperature (T\({}_{\text{g}}\)) and storage modulus increased with the advancing of crosslinking, whereas the dielectric loss showed an \"abnormal\" rise with the increase in crosslink density. Extensive studies were carried out by varying the initiator contents and characterizing the structure with spectroscopy, thermal analysis, and positron annihilation lifetime spectrum (PALS) techniques. The results show that the competition of polarity, crosslink density, free volume, and free TAIC are the key factors determining the dielectric properties of the composites.  
copper clad; thermosetting PPO; dielectric properties; peroxide initiator Article
## 3 Results and Discussion
### Physicochemical Characterization of SHGCs
#### 3.1.6 Crude Protein Content  
Crude protein content ranged from 34.8% to 36.9%, with significant differences observed between SHGC-C and SHGC-U, as well as between SHGC-PP and SHGC-U (\(p\) = 0.0180). Notably, supplementation with _Ulva_ sp. increased crude protein content by nearly 2% compared to the other samples. This aligns with the higher crude protein content of _Ulva_ sp. (15.6%) relative to _P. palmata_ (14.4%), which may account for the observed differences.  
The crude protein content in the control sample (34.8%) exceeded values reported for Italian goat cheeses at the end of ripening (2-4 months), which range from 23.0% to27.5% WW [66], as well as for Greek \"Xinotypi\" goat cheese after 22 days of ripening (29.77 \(\pm\) 0.52%) [57]. However, the determined values are comparable to those reported for Spanish semi-hard goat cheese (\"Majorero\", Canary Islands) after 30 days of ripening (36.3%) [74].
## 4 Discussion
### Results of the Discharge Method
Objectively, the permeability should not change too much for different samples of the same group; however, for high-pressure subsections (IV and V), it exhibits significant alterations. That is probably because the collected data do not truly reflect the flow rate due to the rapid change of pressure in the chamber. For the steady-state case, the measured permeability ranges from 3.55 to 3.73. A gray belt that indicates the varying range is plotted in Figure 11 for an easy comparison. Clearly, the permeability determined using a medium-pressure dataset (II and III) accords with that of the steady-state results. In theory, the permeability should be determined in small flow regions. However, when the pressure in the chamber changes too slowly, it is probable that the measurement noise might be magnified and the low signal-to-noise ratio would result in significant uncertainties. Considering these, the datasets with the pressure ranging from 250 kPa to 350 kPa are extracted and used to determine the permeability \(K\) and inertial coefficient \(\beta\), which are listed in Table 4. Actually, the dataset with the pressure ranging from 250 kPa to 350 kPa is only a small subset of the whole range. Taking the 11 mm sample (group 2) as an example, 250 kPa-350 kPa corresponds to the Reynolds number of 1.13-1.87. It is thus concluded that the data in the form of f versus Re taken with the discharge method cannot completely reflect the characteristics compared to the steady-state method. Figure 10: Pressure in the chamber versus mass flow rate obtained by the discharge method for the five porous media of group 2. Figure 9: Pressure in the chamber versus time during discharge process for the five porous media of group 2.
## 2 Materials and Methods
### High-Performance Liquid Chromatography (HPLC) Test for Reference Analysis  
The anthocyanins content in black rice was measured using high-performance liquid chromatography (HPLC) by injected 20 \(\upmu\)L sample extract into the system. The extraction process was carried out by dissolving the powder of black rice (1 g) into 30 mL of 1% HCL/40% CH\({}_{3}\)OH at 4 \({}^{\circ}\)C for 24 h. Before analysis, all of the samples were filtered through a 0.45 \(\upmu\)m membrane filter. The HPLC system for quantification of anthocyanins was operated using an Agilent 1200 series HPLC (Boehlingen, Germany) system comprising of a quaternary pump, an Agilent 1200 series diode-array detector (DAD) operating at 530 nm, ChemStation software, a 1200-well-plate autosampler, and a Tosoh ODS-120T column (250 mm \(\times\) 4.6 mm i.d., Tokyo, Japan) protected with a Nova-Pak C\({}_{18}\) guard insert column (Water, Milford, MA, USA). During the measurement, the column temperature was maintained at 30 \({}^{\circ}\)C.  
The mobile phase included solvent A (5% formic acid/H\({}_{2}\)O _v/v_) and solvent B (5% formic acid/acetonitrile _v/v_), entering the system at a flow rate of 0.7 mL/min. The applied gradient program was: 0-20 min, 10-30% B; 20-25 min, 30-60% B; 25-26 min, 60-10% B; and 26-35 min, 10% B. The anthocyanins concentration was obtained by comparing the HPLC peak area to external standard calibration curves. The standard calibration curve (\(R^{2}\geq\) 0.999) was provided by injecting 0.5-1 ug of anthocyanins into 20 \(\upmu\)L of 1% HCl/40% CH\({}_{3}\)OH. The total anthocyanins content and individual type of anthocyanins were analyzed, and the result was presented in mg/g.
# Parameter Optimization Based BPNN of Atmosphere Continuous-Variable Quantum Key Distribution
## 2 Transmittance and Security Analysis  
In this section, the main purpose is to analyze the security of the atmospheric channel. Due to the fluctuating of the atmospheric channel, we first analyze the transmittance of the channel according to different turbulence intensity. In addition, we then apply the transmittance analyzed above to the calculation of the secret key rate, in order to obtain the CVQKD security analysis under the atmospheric channel.  
The Rytov variance is used for depicting the turbulence intensity, and its expression is [40; 41]  
\[\sigma_{R}^{2}=1.23C_{n}^{2}k^{7/6}L^{11/9}, \tag{1}\]  
where \(C_{n}^{2}\) is the refraction index structure parameter, \(k\) denotes the optical wave number, and \(L\) represents the horizontal propagation distance. In the case of horizontal propagation, \(C_{n}^{2}\) can be seen as a constant. As shown in Table 1, the value of \(C_{n}^{2}\) we used is on the basis of long-term radiosonde measurements in Hefei, Anhui, China [42].