esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results
### Source Specification/Requirement Analysis
#### 4.1.2 Emission Rate Analysis--MEO  
Next, a MEO satellite pass was considered for the emission rate requirement analysis. For the purposes of our analysis, the O3b MEO orbit was simulated; therefore, the constellation was considered as flying across the equator. Additionally, the OGS location had to be specified for the calculation of the satellite's elevation angle over time. Therefore, the Helmos OGS (Latt: 37.98, Long: 22.20) location in Greece was selected for the simulation of the MEO satellite pass. It should be noted here that for OGSs with a higher latitude the maximum achievable elevation angle will be lower. For an MEO satellite and the Helmos OGS, the maximum achievable elevation angle was calculated to be just above 30 degrees. Figure 4 once again presents the end-to-end link loss alongside the projected normalized SKR over time. This analysis assumes a range of transmitted block sizes and incorporates tip-tilt correction under three distinct scenarios: (a) a scenario featuring a receiver aperture of 1 m and SNSPDs coupled with the telescope to the SMF, (b) a scenario with a 1.5 m receiver aperture also using SNSPDs and telescope-to-SMF coupling, and (c) a scenario involving a 2.3 m receiver aperture with free-space SPAD detection. That time, it was evident that, as shown in Figure 4, transmitted block sizes of \(N_{trans}>10^{12}\) bits were required to provide adequate post prosecting block sizes for all three scenarios. Given the above--as well as when considering that, then, the satellite was visible for a time window of more than 50 min--the required emission rate of the on-board source was calculated to be in the order of a few hundreds of MHz (ca. 300 MHz).  
Figure 3: End-to-end link loss over time, as well as the expected normalized SKR over time for different transmitted block sizes values, in telescope-to-SMF coupling, all while considering both (**a**) SNSPD detectors and (**b**) SPAD detectors.  
Considering the above, for the three distinct scenarios, the average SKRs of \(\sim\)30 bps, \(\sim\)75 bps, and \(\sim\)420 bps were, respectively, calculated. The best performance took place when seemingly very large telescope apertures (2.3 m) were employed and combined with a free-space SPAD detection that can bypass the limited telescope-to-SMF coupling efficiency tip-tilt correction provides. However, large apertures (>1.9 m) were found to be necessary for SPADs to support MEO-to-ground QKD, and this only worked when assuming free-space detection. Despite this, it is not clear whether SPAD detection is able to support repetition rates as high as 300 MHz due to the electronic limitations of the devices, such as having a high jitter. On the other hand, SNSPDs can provide low jitter, but they usually require fiber coupling. Nonetheless, MEO orbits can be feasible for QKD under certain assumptions, such as receiver aperture or OGS latitude, thus providing much longer time windows, yet limited SKRs.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
## 2 Related Background
### QR Code  
QR code is a two-dimensional array composed of light and dark modules. It has 40 versions corresponding to different sizes and payload. Version 1 is made up of \(21\times 21\) modules. The edge length of each subsequent version is four modules longer than that of the previous version. For each version, QR code has four error correction levels: L, M, Q and H. Error correction level L can correct up to approximately 7% codeword errors. Similarly, level M, Q, H can correct roughly up to 15%, 25%, and 30% of codeword errors, respectively.  
As shown in Figure 1, the structure of a typical QR code can be divided into two regions: function patterns and encoding region [23]. The function of finder pattern is to help the decoder to locate the region of a QR code image. The function of timing pattern can enable the symbol density and version to be determined and provide datum positions for determining module coordinates. Every data codeword is made up of eight modules. The same is true for every error correction codeword.  
In the _encoding_ stage, the input data (such as text or numbers) is encoded into a QR code image, which can be described by the following steps:  
1. Determine the type of data encoding according to the type of the input data.
2. Convert data characters into bit stream.
3. Convert the bit stream into codewords and assemble codewords into blocks for ECC encoding.
4. Error correction codeword encoding, which calculates error correction codewords for each block.
5. Place the codeword modules into the QR code matrix, along with the finder pattern, separator, timing pattern, and alignment pattern.
6. Masking: XOR (exclusive OR) the QR code matrix with a selected masking pattern to balance the B/W modules in encoding region.
7. Generate and place the format information and version information.  
Figure 1: Structure of a typical QR code (Version 7).  
In the _decoding_ stage, a captured color QR code image is processed and analyzed to extract the embedded data. This can be divided into the following steps:  
1. Convert color QR code image into a binary image. The brightness component of the color QR code image is extracted and compared to a global/local threshold to get binary image.
2. Use the binary image in last step to determine a sampling grid for modules and decode each module into bits: the dark and light modules are identified as \"1\" and \"0\", respectively.
3. Read the format and version information to determine the version of the symbol.
4. Eliminate mask by \"XOR\" operation.
5. Use error correction codeword decoder to obtain data codewords.
6. Decode the data codewords to obtain the embedded data (such as text or numbers).
## 3 Results
### Impact of Radio Conditions on DC  
Figure 7 presents box plots comparing the DC of various internet activities under 80 MHz bandwidth for good and poor radio conditions. Table 6 provides detailed statistical data, including the mean, median, P\({}_{95}\), max, and SD for both conditions. It is important to note that under worst radio conditions, only AP signals could be captured. Thus, the DC data for good radio conditions were calculated using the method described in Section 2.2.2, extracting AP-transmitted signals based on received signal amplitudes. The results indicate a significant influence of radio conditions on DC. As expected, for most network services, except FTP upload, the mean DC increases substantially under poor radio conditions: the mean DC of 4K video streaming increased from 3.1% to 19%, with a larger interquartile range (IQR) reflecting greater transmission instability; video calling: DC rose from 3.8% to 8.8%; the change in FTP downloads was from 43.1% to 88.6%; the largest relative change was observed in the case of Spotify audio streaming, with mean DC rising from 0.72% to 11.5%. These increases are attributed to degraded channel quality, which lowers the SNR. This leads to higher error rates and retransmission demands, while devices often reduce their MCS to maintain connection stability. These factors collectively worsen transmission efficiency, resulting in elevated DC. Conversely, the DC for FTP upload decreased under poor radio conditions. This can be explained by the reduced efficiency of continuous uplink data transmission from the UD to the AP, leading to a decline in AP-side DC under such conditions. Therefore, the counterintuitive conclusion is that the increased duration of active Wi-Fi signals may lead to higher exposure levels in the presence of \"weak signals\".  
Figure 6: One-second-averaged DCs as a function of Internet application per bandwidth: **(a)** 20 MHz, **(b)** 40 MHz, **(c)** 80 MHz, and **(d)** 160 MHz. The mean DC for each application over 6 min (marked with a star in the box plot) is labeled above each box.
## 2 Discussion on Water Pressure Reduction Coefficient of Sub-Sea Tunnel Lining  
### Numerical Analysis of External Water Pressure of Initial Support of Sub-Sea Tunnel  
The F5 structural fracture zone section of the sub-sea tunnel in the Daqing section of Qingdao Metro Line 8 is the engineering background, based on the numerical calculation model, and according to the stratum and support structure design of this section, the external water pressure of sub-sea tunnel lining is calculated. This section is located in the slightly weathered to moderately weathered tuff stratum with joint development and the permeability coefficient of surrounding rock k\({}_{r}\) = 0.6 m/d. The thickness of the grouting ring t\({}_{2}\) = 5 m and the permeability coefficient is considered as slightly weathered tuff, taking k\({}_{g}\) = 0.01 m/d. The initial support is 0.3 m thick C25 shotcrete, the impermeability grade is P6 [23], and its permeability coefficient k\({}_{i}\) = 1.0 \(\times\) 10\({}^{-8}\) m/d. The calculation results of external water pressure of model initial support are shown in Figure 1, and the water pressure at the marked position in the figure is shown in Table 1.  
Figure 1: The external water pressure of initial support of the sub-sea tunnel (unit: Pa).  
It can be seen from Figure 1 and Table 1 that under the condition of water blocking and drainage restriction, the external water pressure distribution of the initial support is uneven, and the maximum water pressure is 134 kPa. The total head at the vault of the tunnel is 52 m, and the water pressure at each part is less than 30% of the total head. It shows that the water pressure outside the tunnel lining is not hydrostatic pressure. The water pressure is different at different positions. The water pressures of monitoring points 1-4 are 11.53, 12.66, 18.74, and 31.54 kPa, respectively.
# Long-Term Forecasting of Electrical Loads in Kuwait Using Prophet and Holt-Winters Models
## 2 Methodologies
### Prophet Forecasting Method  
Prophet forecasting model incorporates data trends, seasonality, and holidays that allow the possibility of modeling complicated time-series features. Multiple seasonality can be fitted as daily, weekly, and yearly patterns. The mathematical representation of the decomposed time series model is hence described as:  
\[y(t)=g(t)+s(t)+h(t)+\varepsilon_{t} \tag{1}\]  
where g(t) represents the data trend function, s(t) represents the seasonality, and h(t) represents holidays effect that can be added within specific points of the data. The error term, \(\varepsilon_{t}\), represents any distinctive features of the data that are not fitted by the model.  
Prophet trend function, \(g(t)\), can be represented by a piecewise linear growth model or a saturating growth model. Since the maximum load data do not exhibit a saturating growth, a piecewise linear growth model is utilized as:  
\[g(t)=\big{(}k+a(t)^{T}\delta\big{)}t+\big{(}m+a(t)^{T}\gamma\big{)} \tag{2}\]  
where \(k\) is the growth rate, \(\delta\) is adjustment rate, \(m\) is the offset parameter, and \(\gamma\) represents the trend changepoints, \(s_{j}\), and is set as \(-s_{j}\delta_{j}\), with \(a(t)\) defined as:  
\[a_{j}(t)=\left\{\begin{array}{ll}1&\quad if\ t\geq s_{j}\\ 0&\quad otherwise\end{array}\right. \tag{3}\]  
The change points allow the growth model to change the trend and can be utilized by the analyst-in-the-loop to fit the model better and produce reliable data forecasts. The Prophet model allows the user to specify the flexibility of the trend by the adjustment rate. It can also automatically detect the change points, or the data analyst can manually adjust them to describe trend-altering events in the time series.  
Figure 2: Modelling flowchart with analyst-in-the-loop.  
For the seasonality function, \(s(t)\), the time series' multiperiod seasonality can be modeled using the Fourier series to represent the daily, weekly, and yearly seasonality. The seasonality function is hence written as:  
\[s(t)=\sum_{n=1}^{N}\Bigl{(}a_{n}\cos\Bigl{(}\frac{2\pi nt}{P}\Bigr{)}+b_{n}\sin \Bigl{(}\frac{2\pi nt}{P}\Bigr{)}\Bigr{)} \tag{4}\]  
with P = 354.25 for yearly seasonality or P=7 for weekly seasonality. Lastly, to include holiday effects by the holiday function, \(h(t)\), the user can define a matrix list of holidays with dates to be incorporated into the time series as a matrix of regressors, \(Z(t)\), defined as:  
\[Z(t)=[1(t\,\epsilon\;\mathrm{D}_{1}),\ldots,\;1(t\,\epsilon\;\mathrm{D}_{L})] \tag{5}\]  
\[h(t)=Z(t)\kappa \tag{6}\]  
with \(D\) as the set of holiday dates, \(\kappa\;\sim\;Normal\Bigl{(}0,v^{2}\Bigr{)}\) with \(v\) as the holiday smoothing parameter.
# Coupled Thermomechanical Responses of Zirconium Alloy System Claddings under Neutron Irradiation
## 2 Thermomechanical Modelling Framework  
### Thermomechanical Model of UO\({}_{2}\) Fuel  
The thermal properties of UO\({}_{2}\) depend on the temperature, porosity, and burnup. In the present study, UO\({}_{2}\) fuel was the only considered heat source. The thermal conductivity of fuel UO\({}_{2}\) was calculated using the Fink-Lucuta model, which is valid from 298 to 3120 K [20; 21]:  
\[k_{\text{UO}_{2}}=k_{95}F_{D}F_{P}F_{M}F_{x}F_{R} \tag{1}\]  
where \(k_{95}\) is the thermal conductivity of unirradiated UO\({}_{2}\) at 95% theoretical density in W/(m\(\cdot\)K). In the present study, the temperature dependence of annealing on irradiation defects was considered in the thermal conductivity of UO\({}_{2}\).  
\[k_{95}=\frac{1}{0.041+2.165\times 10^{-4}T+\left(1+396e^{-6380/T}\right)^{-1}}+ \left[\frac{4.715\times 10^{9}}{T^{2}}\right]\exp\left(-\frac{16361}{T}\right) \tag{2}\]  
where \(T\) is temperature in \(K\), and \(F_{D}\) and \(F_{P}\) are the impact factors of dissolved and precipitated fission products on thermal conductivity, respectively.  
\[F_{D}=\left[\frac{1.09}{B^{2.265}}+\frac{0.0643}{B}\sqrt{T}\right]\times\arctan \left(\frac{1.09}{B^{2.265}}+\frac{0.0643}{B}\sqrt{T}\right)^{-1} \tag{3}\]  
\[F_{P}=1+\left(\frac{0.019B}{3-0.019B}\right)\left[1+\exp(\frac{1200-T}{100}) \right]^{-1} \tag{4}\]  
where \(F_{x}\) is the deviation from stoichiometry (1.0 for urania fuel), \(F_{R}\) and \(F_{M}\) are the impact factor of irradiated effect and the porosity, respectively, and \(B\) is fuel consumption in atom%, \(B\) = 50%.  
\[F_{R}=1-\frac{0.019B}{1+\exp(\frac{T-900}{80})} \tag{5}\]  
\[F_{M}=\frac{1-P}{1+(s-1)P} \tag{6}\]  
where \(P\) is the porosity and \(s\) is the shape factor that is spherical in this work, \(s\) = 0.5.  
The coefficient of thermal expansion was calculated using Equation (7):  
\[\frac{\Delta L}{L}=K_{1}T-K_{2}+K_{3}\exp(\frac{E_{D}}{K_{T}}) \tag{7}\]  
where \(E_{D}\) and \(K_{T}\) are the thermodynamic parameters and Boltzmann constant, \(E_{D}\) = 6.9 \(\times\) 10 \(-\) 20 J and \(K_{T}\) = 1.38 \(\times\) 10\({}^{-23}\) J/K respectively. The values of \(K_{i}\) are \(K_{1}\) = 1.0 \(\times\) 10\({}^{-5}\) K\({}^{-1}\), \(K_{2}\) = 3.0 \(\times\) 10\({}^{-3}\) K\({}^{-1}\) and \(K_{3}\) = 4.0 \(\times\) 10\({}^{-2}\) K\({}^{-1}\) respectively. The density and Poisson's ratio are 10.96 g/cm\({}^{3}\) and 0.316, respectively.
## 2 Results and Discussion
### Phenylethylanoids and Benzenoids  
The characteristic rose-like odor (very lasting, though mild and warm rose and rose-honey note) of Rosaceae plants is due to the phenylethanol 2-phenylethyll alcohol. However, being highly soluble in water, the 2-phenylethyll alcohol is usually lost in the distillation process (or collected in the rose water), and its concentration in the rose oil, derived by the traditional hydro distillation methods, is low. Our study reveals that 2-phenylethyll alcohol is the most abundant single component in SPME _R. alba_ aroma profile, with a maximal concentration of 33.93% in the mixed petals sample, extracted at room temperature. The same sample shows maximal concentration for benzyl alcohol and benzaldehyde, 13.78 and 4.04%, respectively. The higher extraction temperature decreases their amounts, reaching the minimum value of 8.41% for 2-phenylethyll alcohol and 2.62% for benzyl alcohol at 60 \({}^{\circ}\)C. In addition, _phenyl acetaldehyde_ was found in a maximal concentration of 1.69% in the mixed petals sample.  
Figure 4: Effect of the extraction temperature (30–60 \({}^{\circ}\)C) on the VOCs of _R. alba_ mixed whole blossom sample. Legend: Wmix—mixed whole blossom; the numbers 30, 45, 60 indicate SPME performed at 30, 45, 60 \({}^{\circ}\)C, respectively.  
It is worth mentioning that methyl eugenol, a phenyl propanoid, which is naturally occurring in _R. damascena_ as a minor component and its content is regulated by the ISO 9842:2024 [35] due to its allergenic and potentially cancerogenic properties, is absent in _R. alba_ fresh blossom VOCs. This fact is in agreement with the studies about the chemical composition of the _R. alba_ essential oil [1], making white oil-bearing rose a prospective alternative to Damask rose for perfumery, cosmetics, and medical applications, with a very low toxic potential [2; 4; 5].
## 3 Results and Discussion
### Influence of Free Surfaces on the Performance of Cutting Head
#### 3.4.2 Free Surface at the Rear Side of Rock Mass  
When the boundary condition of rock mass is like that shown in Figure 3c, and the rear side of the rock mass is the free surface, deformations of rock mass in the \(y\)- and \(z\)-directions are shown in Figure 18. For the deformation of rock mass shown in Figure 18, it could be found that the rock mass near the end plate has undergone an obvious deformation in the \(z\)-_direction_. The main reasons for this obvious deformation are the following:  
1. There is no constraint applied on the rear side of the rock mass, and the rock mass is deformed along the \(z\)-direction due to the compression of the cutting forces of conical picks on the end plate of the cutting head during the cutting process;  
Figure 16: Deformation of rock mass if the top side of rock mass is a free surface. (**a**) boundary condition of rock mass; (**b**) deformation in \(y\)-direction; (**c**) deformation in \(z\)-direction.  
2. The top and bottom sides of the rock mass are fully constrained, which means that even if some cutting forces from conical pick are applied on those regions, no displacement occurs. Therefore, the bulging deformation of the rock mass at the rear side of the rock mass would appear.  
According to [PERSON] [17], the conical picks on the end plate of the shearer drum suffer severe wear during the cutting process. When there is a free surface close to the end plate of the shearer drum as shown in Figure 18, larger deformation on rock mass close to the end plate of the shearer occurs, and the interval between rock mass and the end plate of the shearer drum increases, which would be helpful to reduce the cutting force on shearer drum.  
When there is a free surface on the rear side of the rock mass, the cutting force on the shearer drum during the clockwise and counterclockwise rotation is shown in Figure 19. According to the figure, the cutting forces were similar under the different directions of rotating cutting. From the perspective of mean cutting forces, it was approximately 1870 kN in the clockwise cutting condition while in the counterclockwise cutting condition, the mean cutting force is approximately 1822 kN. Compared to the mean cutting force under the anti-clockwise cutting condition, the mean cutting force under clockwise cutting conditions increased by about 48 kN, an increase of about 2.63%. Therefore, the mean cutting force under clockwise cutting conditions is much larger than that under counterclockwise cutting conditions.  
Figure 17: Rock cutting force in the \(y\)-direction if the top side of the rock mass is a free surface.  
Figure 18: Deformation of rock mass if the rear side of rock mass is a free surface. (**a**) boundary condition of rock mass; (**b**) deformation in \(y\)-direction; (**c**) deformation in \(z\)-direction.
# GNSS-Based Vertically Monitoring of Super-Tall Buildings
## 5 Verticality Monitoring Result and Analysis of the W Tower
### Calculating Deflection of the Vertical Components for the Monitoring Point  
Now, let us calculate the deflection of the vertical components using the Earth gravity field model and the GZGEOID model [32]. EGM2008 (to degree 2160) and EIGEN-6C4 (to degree 2190) ([[http://icgem.gfz-potsdam.de/](http://icgem.gfz-potsdam.de/)]([http://icgem.gfz-potsdam.de/](http://icgem.gfz-potsdam.de/))) were used to calculate deflections of the vertical by Equations (8) and (9). [PERSON] et al. have estimated the accuracy of the EGM2008 model with 466 first-order astrogeodetic points in the east (\(>\)102\({}^{\circ}\) E) of China. The estimated accuracy of deflection of the vertical components from the EGM2008 model are \(\pm\)1.734\({}^{\prime\prime}\) and \(\pm\)1.649\({}^{\prime\prime}\) in the north-south and east-west components, respectively [33], which is main reason for selecting the EGM2008 and EIGEN-6C4 (including GOCE (Gravity field and steady-state Ocean Circulation Explorer) data) models.  
Considering \(\sigma_{\Delta h}=\pm\)8 mm and \(\sigma_{\Delta H}=\pm\)1.2 mm [32], and assuming that orthometric height differences and ellipsoidal height differences are not correlated, we obtain \(\sigma_{\Delta N}\approx\pm\)8 mm. If the accuracy of the deflection of the vertical components is expected to be about 1 arc second, the \(\Delta s\) is about 1.6 km according to Equation (11). Finally, eight points along the north-south (A2, A1, C1, and C2) and east-west (B2, B1, D1, and D2) directions are chosen to calculate the deflection of the vertical components (see Figure 9), The distances from A1, B1, C1, and D1 to XT are about 30 arc seconds, and the distances from A2, B2, C2, and D2 to XT are about 60 arc seconds. The geoid height difference \(\Delta N\) can be calculated from the GZGEOID model and the EGM2008 and EIGEN-6C4 gravity field models, respectively, and we can obtain the deflection of the vertical components according to Equation (10). The results are shown in Table 7. The Table 7 shows that the results from the GZGEOID, EGM2008, and EIGEN-6C4 models have good consistency. The average deflection of the vertical components from the GZGEOID, EGEM2008, and EIGEN-6C4 models are shown in Table 8, respectively. For instance, the deflection of the vertical component \(\xi\) from GZGEOID is the average of 3.80\({}^{\prime\prime}\), 4.25\({}^{\prime\prime}\), 3.91\({}^{\prime\prime}\), and 4.36\({}^{\prime\prime}\), and the deflection of the vertical component \(\eta\) from GZGEOID is the average of \(-\)6.04\({}^{\prime\prime}\), \(-\)5.80\({}^{\prime\prime}\), \(-\)6.04\({}^{\prime\prime}\), and \(-\)5.80\({}^{\prime\prime}\), and so on.  
Figure 8: Differences in position in relation to the coordinate time series of the monitoring point: (**a**) \(x\) direction; (**b**) \(y\) direction; (**c**) \(H\) direction.  
Figure 9: Distribution of Points for calculating the deflections of the vertical components.