esa-sceva/satcom-chunk-collection huggingface dataset:
# Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts
## 5 System Development
### The System Implementation
In this package, the geographical coordinates of the points, which are  
Figure 11: (**a**) The user position display and (**b**) the tourist places display. \begin{table}
\begin{tabular}{c c c c c} \hline  & \(S_{i}\) & \(R_{i}\) & \(Q_{i}\) & \(\mathrm{\acute{Ranking}}\) \\ \hline L \(\mathrm{\acute{a}}\)miz & 0.3246 & 0.2136 & 0 & 1 \\ Menro & 0.35 & 0.267 & 0.143 & 2 \\ Kenzo & 0.505791 & 0.267 & 0.349 & 3 \\ Hatam & 0.703255 & 0.456 & 1 & 4 \\ \hline \end{tabular}
\end{table} 
Table 3: The results of the VIKOR method. the latitude and longitude, are converted to the navigation coordinates (x and y), and the navigation coordinates are then converted to the camera coordinates and displayed in points on the camera screen. In this study, the coordinates of each tourist place are entered into the AR class after the best items in the previous step are identified. In addition, after the relevant transformations are performed, the location is added to the environment as the AR. At first, by using the sensors inside the mobile device, the user's position is calculated as a latitude and longitude when the user enters the environment. Afterward, the system uses the user contexts in order to estimate the type of appropriate tourist place by using one of two methods, which includes the VIKOR method. After that, it converts the coordinates of the tourist place from the GPS coordinates, which are the latitude and longitude, by performing several conversions, which were previously described, to the phone screen coordinates. Among the items that are displayed to the user as the AR, the first item with the highest score is added to the environment, and the following items are added in order. The priority of each item is displayed to the user as a number, which is illustrated in Figure 12.  
In Figure 12, the places suggested to the user, which are restaurants and parks, are prioritized. The two restaurants, which include D.L.P. and Turin, are located closer to the user and are within the user's sight. Mehr restaurant and Gelayol Park are far from the user and are not in the user's sight. In this system, by using the information provided by the AR to the user, the user is allowed at any time to receive the location and the direction of the restaurant and park according to his or her current location. Moreover, the user's confusion regarding finding the exact location of the restaurant and the problems that a map can create for the user, which is due to the user centric in the AR, are prevented.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 4 Discussion
### The Correlation between Scallop Height and Surface Roughness  
The surface defects and scallop height of optical elements would cause the sagittal height of the optical surface to deviate from the theoretical shape of the optical design. These deviations are reflected in the different spatial frequencies in spatial distribution. Scholars divide the frequency band error into three parts: the Figure, Mid-Spatial Frequency (MSF), and High Spatial Frequency (HSF) [25; 35]. The scallop height is defined as the unremoved area between adjacent grinding wheel paths. Because the wheel path is periodic, the scallop height usually shows a periodic waviness error, which belongs to the category of Mid-Spatial Frequency errors. Surface roughness is the unevenness of small peaks and valleys in the microscopic area of the workpiece surface, which belongs to the category of High Spatial Frequency errors [36; 37]. Therefore, the scallop height and surface roughness belong to two different spatial frequency errors of the grinding surface of optical parts, and there is no direct relationship between them.
# Calibration of Magnetometers with GNSS Receivers and Magnetometer-Aided GNSS Ambiguity Fixing
## 2 Measurement Models  
In this section, we introduce a precise model for the magnetometer measurements.
# Techno-Economic Analysis of Flare Gas to Hydrogen: A Lean and Green Sustainability Approach
## 3 Results
### Levelized Hydrogen Cost (LCOH)  
According to [66], LCOH assesses the total costs associated with hydrogen production throughout its lifecycle, encompassing annual capital cost and operational expenses (OPEX), dividing it by annual hydrogen production. The LCOH was calculated using the formula of [20], and the annual discount rate was assumed to be 8%. The EU trading scheme carbon price is 73 EUR/tonnes, and UK ETS carbon sale is GBP 45/tonne  
\[\text{LCOH}\left(\frac{\text{USD}}{Kg\;H2}\right)=\frac{CAPEX\times CRF+OPEX+C_{carbon \;tax}-R_{carbon\;sale}}{P_{hydrogen}} \tag{8}\]  
where CRF is the capital recovery factor, calculated as follows:  
\[\text{CRF}=\frac{1}{A_{t,r}} \tag{9}\]  
\[\text{LCOH}\;=\;\text{USD}\;1.97/\text{Kg}\;\text{H}_{2}\]  
The LCOH value from SMR with CCS is lower than that reported by [65] and almost the same as that of [67]. This suggests that establishing an SMR plant in Nigeria is more cost-effective due to the country's abundant natural gas, which serves as a feedstock for hydrogen production. Therefore, the project presents a promising and economic opportunity for the government. Additionally, this hydrogen can be utilized and transported locally and internationally, as demand continues to rise, positioning Nigeria as a central hub for hydrogen production.
## 3 Results and Discussion
### Validation of the Method  
The method of determining the FAs was validated using the 37-component FAME Mix standard. All FA components were identified by the retention times and chromatograms in the standard mix standards (Table 2, Figure 2).  
A quantitative analysis of the FAs was carried out and calibration curves were plotted between 20.2 ug/mL and 612 ug/mL. The calibration curves consisted of five concentrations. Table 2 showed the retention times, correlation coefficients, linear equations, ranges, limits of detection (LOD) and limits of quantification (LOQ) for each FA component. The correlation coefficients were more than 98%, confirming the detector's excellent response.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Type of Oil**} & \multicolumn{2}{c}{**Total Volume Produced in Kazakhstan, Thousand Tons**} \\ \cline{2-3}  & **2015** & **2020** \\ \hline Cottonseed & 11.6 & 10.6 \\ Rapeseed & 31.3 & 68.8 \\ Soybean & 12.3 & 23.2 \\ Sunflower & 217.3 & 320.1 \\ Safflower & 2.3 & 6.0 \\ Linseed & 6.2 & 29.1 \\ Maize & 0.04 & 0.3 \\ Total & 225.7 & 484.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The total volume of VOs produced in Kazakhstan, according to the Bureau of National Statistics of the Republic of Kazakhstan [14].  
The accuracy and precision of the method were determined by a replicate analysis of the FAME Mix standard. The repeatability for the standards, in terms of the calculated %RSD for the retention times, was not greater than 0.5%. When we calculated the peak areas that were not greater than 1.0% under the same conditions, the precision of the retention times was not greater than 0.3%. The LOD value was between 0.29 \(\upmu\)g/mL and 1.95 \(\upmu\)g/mL, and the LOQ values varied between 2.06 \(\upmu\)g/mL and 3.95 \(\upmu\)g/mL, which showed that the method is sensitive. According to the data determined above, this method could be used for the identification of FAs in VO products, conforming with the ICH guidelines [36].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\#} & **Fatty Acid** & \multirow{2}{*}{**RT (Mean)**} & \multirow{2}{*}{**R\({}^{2}\)**} & **Calibration Curve** & **Range** & **LOD** & **LOQ** \\  & & & & **Equation** & **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** & **(\(\upmu\)g/mL)** \\ \hline
1 & C4:0 & 7.086 & 0.9972 & y = 0.0039\(\times\)
## 3 Results  
It was noted that the sterilizing substance type and the presence of silver nanoparticles (AgNPs) in the media influenced disinfection efficiency (Table 1).  
Based on visual observations, it was noted that the highest number of explants without the symptoms of disinfection was obtained when they had been disinfected with both ethanol and sodium hypochlorite and placed in the media supplemented with silver nanoparticles (100%). Unfortunately, that mixture caused necrosis in all the explants. Comparing the number of contamination-free explants, it might have been observed that sodium hypochlorite allowed us to obtain more disinfected stem pieces than did ethanol (138 in comparison to 36). Ethanol was the least efficient in the disinfecting of explants, as only 12% were free of fungus or bacteria. The addition of AgNPs increased the number of disinfected explants up to 24%. Looking at the data obtained, it was observed that supplementation of the media with AgNPs allowed for the obtaining of a higher number of disinfected explants in all treatments, but, at the same time, it decreased the number of regenerating shoots.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Disinfecting** \\ **Substance** \\ \end{tabular} } & \multirow{2}{*}{**AgNPs**} & **No. of** & **No. of** & **No. of** \\  & & **Conlamination-Free** & **Explants (\%)** & **Explants (\%)** & **Regenerating** \\ \hline \multirow{2}{*}{NaOCI} & – & 61 & 36 & 25 \\  & + & 77 & 58 & 19 \\ \hline \multirow{2}{*}{ EtOH} & – & 12 & 0 & 12 \\  & + & 24 & 24 & 0 \\ \hline \multirow{2}{*}{ EtOH-NaOCl} & – & 62 & 50 & 12 \\  & + & 100 & 100 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The contamination rate and regeneration of _Aldrovanda vesiculosa_ in vitro, depending on the use of sodium hypochlorite (NaOCl) or ethanol (EtOH) for explants disinfection, and the addition of silver nanoparticles (AgNPs) to the 1/5 Murashige and Skoog (MS) culture media.
# An Overview of GIS-Based Assessment and Mapping of Mining-Induced Subsidence
## Mine subsidence Risk Assessment and Mapping
### General Procedure  
As mentioned in Section 2, subsidence risk can be quantified by multiplying the subsidence hazard index, exposure intensity index, and vulnerability index. Consequently, in the process of creating the subsidence risk map, in addition to the subsidence hazard mapping procedure (Figure 2), generations of an exposure map and a vulnerability map as well as multiplication of the three maps is added, as shown in Figure 6. In this section, an exposure map shows an element at risk caused by mining subsidence, and a vulnerability map shows the element at risk. Strictly, the resulting subsidence risk map contains the expected annual cost or loss from an absolute point of view [(20)]. This is ascribed to the subsidence hazard already containing the concept of the probability of occurrence. Because subsidence risk mapping is a complex and difficult task, most of these studies lack a verification of the risk result.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Hazard Factor** & **Exposure Factors 1** & **Vulnerability Factors** & **Risk Item** & **Spatial Resolution** & **Country** \\ \hline [PERSON] and & Terrain deformation & & & & & \\ [PERSON] [46] & (category) & Building & Building resistance (strength) & Building damage & 100 m x 100 m & Poland \\ \hline [PERSON] et al. [47] & Terrain deformation & & & & \\ (sinking rate) & & & & & \\  & & & & & \\  & & & & & \\  & & & & & \\ \hline [PERSON] and & & & & & \\ [PERSON] [48] & Susceptibility & Land and Road network & Rating each class for land and road network by experts opinion & Relative risk map (very low to very high) & 10 m x 10 m & Macedonia, Greece \\ \hline [PERSON] et al. [35] & Susceptibility & Building and Infrastructure & Distributions and depth of mine drift & Relative risk map (very low to very high) & 5 m x 5 m & Republic of Korea \\ \hline [PERSON] [49] & Hazard & Agricultural (crop) area & Soil subsidence sensitivity & Com yield loss & 10 m x 10 m & United States of America \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of three elements considered in assessing and mapping mine subsidence risk within a GIS environment.
## 2 Materials and Methods
### Image Acquisition  
Image data were acquired using an X61 Thinkpad laptop and an acquisition program written in Visual Basic version 6 (Microsoft, Seattle, WA, USA). The camera was connected to the laptop using the Ethernet port and the filter controller was connected using a USB port. The native resolution of the camera, 1360 H \(\times\) 1024 V pixels, was binned by three to yield a final resolution of 453 \(\times\) 341 pixels. Binning was used to reduce noise, and to increase light sensitivity and dynamic range of acquisitions. The acquisition software facilitated the automated capture of a sequence of images from 460 to 720 nm at 5 nm increments. Using a frame rate of 5 fps, a complete hyperspectral data set can be acquired in less than 45 s. The frame rate determines the maximum potential exposure time for individual images. The 12-bit resolution of images was preserved by storing images using the 16-bit TIF greyscale format.  
The camera supports gain settings from 0 to 32, and the acquisition software allows individual gains to be set for each acquisition wavelength. For this study, gain routines were modified so that the gain across acquisition wavelengths was identical for a single hyperspectral data set; four data sets for each test circumstance were acquired with gains incremented for individual data sets from 20 to 32 in steps of 4. For more details concerning the acquisition program, please refer to a previous publication [12].
# Development of a New Rubber Buckling-Restrained Brace System for Structures
## 9 Results and Performance Evaluation
### Inter-Story Drift Response  
The drift ratio between the stories is calculated as the proportion of the inter-story displacement at a certain point in the study frame to the respective story height. Tables 15 and 16 represent the variation and comparison of story drift responses of the study frames in bare, BRB, and RBRB models through DBE and MCE TH records in the X and Y-directions. The maximum story drift is found in the intermediate fifth story for braced frames, which is lower than the permissible drift limit of 2% as given in ASCE/SEI 7-10 (2010). It is also observed that the bare frame has almost 70.18% and 85.31% more drift compared to BRB and RBRB in the abovementioned story (in Darfield TH record), as shown in Figure 53, respectively, which is unacceptable based on ASCE code. Hence, all BRBFs and RBRBFs exhibited acceptable overall performance under DBE and MCE-level ground motions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & **Ridge** & **Roof** & **6 th** & **5 th** & **4 th** & **3 rd** & **2 nd** & **1 st** & **Base** \\ \hline \multirow{3}{*}{Darfield} & **Bare** & 0.047367 & **0.046096** & 0.042465 & 0.035022 & 0.026367 & 0.019739 & 0.013738 & 0.005203 & 0 \\ \cline{2-10}  & BRB & 0.010043 & 0.008904 & 0.009016 & **0.008005** & 0.008181 & 0.007301 & 0.00585 & 0.002634 & 0 \\ \cline{2-10}  & RBRB & 0.00449 & 0.003121 & 0.004609 & **0.006048** & 0.006328 & 0.005374 & 0.004148 & 0.002075 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Maximum inter-story drift ratio based on DBE in X-direction.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & **Story** & **Ridge** & **Roof** & **6 th** & **5 th** & **4 th** & **3 rd** & **2 nd** & **1 st** & **Base** \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Cape \\ Mendocino \\ \cline{2-10}  & BRB & mm & 167.19 & **139.482** & 116.636 & 92.514 & 71.845 & 48.735 & 26.732 & 8.487 & 0 \\ \cline{2-10}  & RBRB & mm & 104.105 & **90.383** & 80.339 & 70.404 & 57.109 & 39.615 & 22.601 & 7.933 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Maximum story displacement based on MCE in X-direction.  
Figure 52: Maximum story displacement. (**A**) DBE TH Cape Mendocino in X-direction. (**B**) MCE TH Cape Mendocino in Y-direction.  
It has to be mentioned that implementing RBRBs causes the structure performance to have 67.9% less drift compared to BRB due to the aforementioned added resistance of high-performance rubber.
## 4 Discussion
### Feature Selection  
Given the large number of extracted features (177 in total), an objective and standardized feature selection approach was necessary prior to applying machine learning models. To this end, statistical analyses--[PERSON]'s test and the Mann-Whitney U test--were employed to assess feature effectiveness in differentiating the two groups (DD and control), resulting in a reduction from 177 to 40 features. A comparable statistical feature selection methodology has been used in studies analyzing handwriting patterns in [PERSON]'s disease patients [55].  
To further reduce dimensionality, we implemented recursive feature elimination (RFE), which has demonstrated efficacy in prior research [50] as a feature selection method. Combining statistical selection with RFE enables the removal of features based on simple group differences, while RFE further enhances refinement by iteratively eliminating less informative features according to model performance. This hybrid statistical/RFE approach balances computational efficiency with model-driven feature relevance [56].  
Firstly, the datasets were split by LOOCV, and an optimal feature subset for a given set length was found. We used logistic regression as an RFE evaluator, accompanied by L1 and L2 penalty terms. L1--Lasso regularization is used to prevent overfitting and enables a more generalizable model. L2--Ridge regularization handles multicollinearity effectively and enables a more stable and robust model, especially when dealing with many correlated features.
# Impact Analysis and Optimization of Key Material Parameters of Embedded Water-Stop in Tunnels
## 3 Optimization of the Bonding between the Water-Stop and Concrete
### Experimental Scheme  
In this experiment, quartz sand with particle size ranges of 16-26 mesh, 26-40 mesh, 40-80 mesh, and 80-120 mesh, commonly available epoxy resin, and natural rubber sheet materials were selected. Due to the small size of the specimens in this experiment, and the fact that the adhesion comes mainly from the adhesion of cement paste and rubber, the ordinary Portland cement blocks were used in this experiment, and the compressive strength was 42.5 MPa. After forming a coating of quartz sand and epoxy resin on the rubber sheet material, a mold was used to cast the cement onto the rubber specimens with the coating, forming 12 groups of specimens, as shown in Table 3. The strengths of the shear bond and the tensile bond are taken as evaluation indicators in this experiment.
## 3 Methodology Description
### Experimental Approach
#### 3.1.2 Sloshing Force, \(F_{slosh}\)  
The vertical sloshing force acting on the SDOF system is experimentally calculated following the procedure shown in [8] for a similar setup. Denoting the load-cell measurement as \(T_{LC}\), the vertical sloshing force is:  
\[F_{slosh}=2T_{LC}+m_{s}a, \tag{4}\]  
where the solid moving mass \(m_{s}\) accounts for the masses of the C-shaped wooden structure, tank, mechanical bearing system and attachments. An in-depth explanation of the derivation of Equation (4) can be found in [8]. Using the liquid mass \(m_{l}\) and its initial acceleration due to the tank movement \(\omega_{0}^{2}y_{0}\) as characteristic values, the vertical sloshing forces presented in this paper are non-dimensionalized with \(F_{0}=m_{l}\omega_{0}^{2}y_{0}\).
## 3 Experiment and Results
### Building Extraction Results and Analysis
#### 3.3.3 RADARSAT-2  
The buildings in this data image mainly include factory buildings, residential buildings, and tall buildings. Non-buildings include forests, vegetation, roads, and railways. Among them, roads and railways have many similarities to buildings in terms of the scattering mechanism and spatial structure, which have a significant influence on the extraction of buildings. The building extraction results under different methods are shown in Figure 6.  
The three methods using CNN have resulted in a significant improvement in the overall extraction of the building, and the accuracy of the outlines and targets of the building has also improved. In the method of using convolutional neural networks, the feature sets, and methods we use can make the railways and highways better differentiated from buildings than the other two methods. There is still the phenomenon that some railways are mistakenly divided into buildings in the experiment. In detail, our approach can more accurately describe the boundary of the building. The building extraction accuracy is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & **Method** & **AR (\%)** & **FAR (\%)** & **F1-Score (\%)** \\ \hline Eigenvalue & Threshold & 64.11 & 25.13 & 69.07 \\ PauliRGB + G\({}^{0}\) & SVM & 84.03 & 45.06 & 66.44 \\
6D-Vector [38] & CNN & 93.62 & 29.99 & 80.11 \\ Polarimetric Features [39] & CNN & 94.29 & 30.82 & 79.80 \\ PauliRGB + G\({}^{0}\) & CNN & **94.37** & 21.76 & 85.55 \\ PauliRGB + G\({}^{0}\) & CNN + Superpixel & 93.64 & **17.89** & **87.49** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different methods of building extraction effects under RADARSAT-2 data.  
Figure 6: RADARSAT-2 image building extraction results. (**a**) [PERSON]’s threshold extraction method; (**b**) PauliRGB and G\({}^{0}\) statistical texture parameters using SVM results; (**c**) RVCNN; (**d**) PFDCNN; (**e**) PauliRGB and G\({}^{0}\) Statistical texture parameters using CNN; (**f**) as the result of introducing superpixel constraints in (**e**).  
From the experimental results of three different types of data, the proposed method can improve the extraction effect of buildings compared to other methods. The main improvement is the suppression of false alarm rates in building extraction. This method can distinguish buildings from similar buildings. In the case of the building extraction accuracy rate, the false alarm rate has been reduced by at least 12%, and F1 has increased by more than 7%.
# Graphene Passively Q-Switched Nd:YAG Laser by 885 nm Laser Diode Resonant Pumping
## 4 Conclusions  
Taking advantage of the pump of an 885 nm LD and the use of graphene as an SA, a passively Q-switched Nd:YAG laser was demonstrated in this paper. At the absorbed pump power of 11.7 W, the maximum average output power was 1.8 W with a slope efficiency of 51.2% when the SA was absent, and the average output power was decreased to 639 mW with a slope efficiency of 25.3% when the SA was inserted. The pulse width decreased  
Figure 5: A pulse profile of the threshold pump power. (insert) A pulse sequence of the Q-switched laser.  
Figure 6: Beam quality of the Q-switched Nd:YAG laser.  
from 4.06 us to 2.06 us, and the PRF increased from 24.7 kHz to 102.7 kHz, corresponding to the absorbed power from 9.21 W to 11.7 W. At the maximum average output power of 639 mW, the \(M^{2}\) factor was measured to be 1.25 using the knife-edge method. This research demonstrated that the graphene passively Q-switched Nd:YAG laser being pumped by the 885 nm LD is an efficient way to generate radiation with several-microsecond-level pulse width and a hundred-kilohertz-level PRF.  
Conceptualization, L.X. and Y.L.; methodology, L.X.; validation, L.X., Y.L. and Y.D.; investigation, J.C., W.Z. and T.D.; resources, T.L. and Y.J.; data curation, L.X.; writing-- original draft preparation, L.X.; writing--review and editing, L.X. and Y.L.; supervision, T.L. and T.D.; project administration, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.