esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Method Validation
#### 3.5.2 Method Precision and Accuracy of Two Methods  
The precision of the detection methods described in this study was deduced by calculating the relative standard deviations (RSDs) of the obtained signal intensities of the selected analytes at two fortification concentrations (0.5 and 5 \(\mu\)g/L). The intra-day precision was calculated on the basis of five replicate detection (\(n=5\)) results of each concentration in GC-QqQ-MS/MS detection system within one day. On the other hand, the inter-day precision was calculated on the basis of six days' detection (\(n=6\)) results [34]. As shown in Tables S2 and S3, the intra-day (\(n=5\)) and inter-day (\(n=6\)) precision in this study for these selected oxygenate compounds were found in the range of 1.99-8.87% and 1.18-12.7% with the P&T-GC-QqQ-MS/MS detection system, and 1.19-9.65% and 1.09-9.78% with the HS-SPME-GC-QqQ-MS/MS detection system, respectively.  
The accuracy of detection method was assessed based on the recovery of these selected oxygenate compounds in two kinds of real water samples without any fuel oxygenates. These two water samples were spiked with the organics to two individual concentrations (0.5 and 5 \(\mu\)g/L) [36]. The accuracy  
Figure 6: Effect of addition of NaCl on signal intensities of the twelve analytes (20 \(\mu\)g/L). The peak area at no salt added is used as a reference (100%). (**a**) the peak area ratio of TBA, MTBE, DIPE, ETBE, TBF, and TAME; (**b**) the peak area ratio of benzene, toluene, ethylbenzene, m+p–xylene, and o–xylene.  
of P&T-GC-QqQ-MS/MS detection method and HS-SPME-GC-QqQ-MS/MS detection method exhibited excellent recoveries. The accuracy of P&T-GC-QqQ-MS/MS was ranging from 80.2-114.2% for surface water and 80.5-120.1% for groundwater. The accuracy of HS-SPME-GC-QqQ-MS/MS was ranging from 73.9-123.1% for surface water and 73.7-117.4% for groundwater as shown in Tables S2 and S3.  
Chromatograms of the extracted quantitative identification ions obtained in P&T-GC-QqQ-MS/MS system and HS-SPME-GC-QqQ-MS/MS system are shown in Figure S2.
###### Abstract  
Tunneling, especially in urban areas, affects many structures on the ground, which directly influences the usability and stability of the structures. The settlement of and axial stress on the pile foundation are important factors that determine the behavioral characteristics of the pile foundation. Therefore, this study uses numerical analysis and machine learning to derive a prediction model of pile axial stress due to tunnel excavation adjacent to the piled raft. Numerical analysis data were utilized for machine learning purposes, and the effects of the input data on the prediction model were scrutinized. The numerical analysis revealed that the change in the pile axial stress resulting from tunnel excavation differed depending on the pile's location, with the greatest axial stress reduction occurring in the center of the piled raft. Furthermore, the rate of reduction was higher in soils with lower relative densities. Several algorithms were employed to derive the prediction model, with tree-based algorithms displaying notable performance in predicting pile axial stress. Additionally, preprocessing the data with appropriate feature engineering techniques exhibited superior predictive power, and incorporating settlement data aided in enhancing the prediction model's performance.  
pile-tunnel interaction; piled raft; numerical analysis; axial stress; machine learning Article  
Prediction and Analysis of Axial Stress of Piles for Piled Raft Due to Adjacent Tunneling Using Explainable AI. _Applied_  
1  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON] Prediction and Analysis of Axial Stress of Piles for Piled Raft Due to Adjacent Tunneling Using Explainable AI. _Appl. Sci._**2023**, _13_, 6074. [[https://doi.org/10.3390/app13106074](https://doi.org/10.3390/app13106074)]([https://doi.org/10.3390/app13106074](https://doi.org/10.3390/app13106074))
## 4 Experiments and Results
### Corrosion Level Classification Network Experiment and Analysis
Images with corrosion level B have a 0.08 probability of being incorrectly predicted as level A and a 0.02 probability of being incorrectly predicted as level C or D. Images with corrosion level C have a 0.04 probability of being incorrectly predicted as level B and a 0.1 probability of being incorrectly predicted as level D. Images with corrosion level D have a 0.04 probability of being incorrectly predicted as level C.  
In summary, the corrosion level determination model YOLOv10-vit-cls performs the best in the corrosion level classification task, outperforming the baseline YOLO networks. It can also leverage the backbone of the corrosion detection network as pre-trained weights. During training, it converges the fastest, achieving an accuracy of 0.917, which meets the requirements for corrosion level classification.
## 3 Methodology
### Multi-Angle VSR
#### 3.1.1 View Classification  
In the view classification part, we at first estimate at which angle face images were recorded among the following five candidates in this work: \(0^{\circ},30^{\circ},45^{\circ},60^{\circ}\) and \(90^{\circ}\). The estimation was carried out for each lip image in one sequence, using the 2D CNN model illustrated in Figure 2. The 2D CNN model employs a simple and common architecture; convolutional and pooling layers are repeatedly applied followed by FC layers, to obtain a classification result. After processing the above step for all the input images, we determine the angle which is the most often chosen.  
Figure 1: An architecture of our proposed multi-angle AVSR method.  
Figure 2: A 2D CNN model for view classification. Numbers in parentheses mean data shapes. For example, (64,64,1) indicates 64 \(\times\) 64 (image size) \(\times\) 1 (channel).
# GF-1 Satellite Imagery Data Service and Application Based on Open Data Cube
## 2 Methods
### Data Preprocessing Based on the FLASH and RPC Methods
#### 2.1.2 Geometric Normalization Based on the HighImgCorrect Method  
The HighImgCorrect method is a geometric correction program developed by our team for a variety of Chinese satellite imagery data (such as GF-1, GF-2, GF-6, and HJ). Compared with IDL and other conventional tools, the method used in this paper has the advantages of one-stop data operation, high efficiency, and quickness. Due to differences in imaging method, satellite platform, and imaging parameter precision, multisource remote sensing images are not fully consistent geog spatially. Therefore, geometric normalization is required to achieve geospatial consistency of multisource remote sensing images. To improve the processing efficiency of GF-1 satellite imagery data, we adopted the HighImgCorrect geometric normalization method. First, a pyramid tile structure was constructed for the baseline images, and the baseline tile data were published. Then, the level and name of the tiles to be downloaded were calculated according to the resolution and geographic coordinates of the images to be geometrically normalized, and the corresponding data were downloaded from the baseline tile data to form the standard tileset. Next, the images to be geometrically normalized were geometrically corrected with the tiles in the standard tileset. After that, the generated images of the previous step were matched with the tiles in the standard tileset to obtain multiple control points, and the calculation results of these control points were used to evaluate accuracy. Lastly, the geometric correction was terminated immediately if the accuracy met the requirements. Otherwise, a new round was conducted until the accuracy requirements were met.
# Optimizing HAWK Signature Scheme Performance on ARMv8
## 3 HAWK Optimization Through Profiling
### Profiling of the HAWK Signature Scheme
#### 3.1.1 Process of Key Generation  
The results of analyzing the operations used in the key-generation process are shown in Table 4. The calculation processes that generate the greatest overhead during the key-generation process are the NTRUSolve calculation process and the Rebuild_CRT calculation process. NTRUSolve has a 66.9% weight in the key-generation process, and the Rebuild_CRT process has a 27.9% weight. These two processes account for 95% of the key-generation process. Therefore, if it can be analyze the operations that are frequently used inside the two operation processes and optimize the operations, it can be improve the performance of the key-generation process of the HAWK Signature.  
As explained in Algorithm 1, NTRUSolve is a problem of finding a polynomial that satisfies \(fG\times Gf=1\) condition using sampled f and g to generate F and G. Considering the conditions, polynomial multiplication, addition and modular operations can be expected to be commonly used operations in NTRUSolve. CRT is the Chinese remainder theorem. Rebuild_CRT is a method to divide a large number into operations of a smaller number using a series of remainders for coprime modular values, and then restore it back to a large number. For more detailed analysis, the operations used internally in NTRUSolve and Rebuild_CRT were additionally analyzed.  
As can be seen in Table 5, the operations mainly used in the two processes are polynomial multiplication and addition, and modular operations. In the HAWK algorithm implementation, the two operations are implemented as \(add\_mul\_small()\) and \(mod\_small\_unsigned()\) functions. \(add\_mul\_small()\) performs the operation of multiplying a polynomial by an integer and adding it with another polynomial, and the \(mod\_small\_unsigned()\) function performs a modular operation of dividing the polynomial by small prime numbers and finding the remainder. The two operations(\(add\_mul\_small(),mod\_small\_unsigned()\)), including the NTRUSolve and Rebuild_CRT processes, account for 11% and 42% of the total key-generation process, respectively, and together they account for 53%. Therefore, we analyzed and optimized the implementation code with the goal of optimizing the two operations.
# Aging Impairs Unimanual and Bimanual Hand-Grip Force Control Capabilities
## 3 Results
### ROC Curve Findings  
The ROC curve analysis showed that the rRMSE significantly differentiated aging at 10% MVC (Table 2): (a) unimanual dominant hand at 10% MVC (AUC = 0.813), (b) unimanual non-dominant hand at 10% MVC (AUC = 0.785), and (c) bimanual hands at 10% MVC (AUC = 0.808). The analysis on %CV at 10% MVC significantly distinguished between younger and older adults: (a) unimanual dominant hand at 10% MVC (AUC = 0.789), (b) unimanual non-dominant hand at 10% MVC (AUC = 0.756), and (c) bimanual hands at 10% MVC (AUC = 0.713). Given that an AUC above 0.7 was considered indicative of a fair level of diagnostic quality [40,41], the AUC of rRMSE in unimanual dominant and bimanual hand conditions at a 10% MVC level showed good quality. The specific details of the ROC curve analysis results are shown in Figure 4 and Supplementary Table S1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Task** & **Variable** & **AUC** & **SE** & \(p\)**-Value** & **Cut Point** & **Sensitivity** & **Specificity** & **Accuracy** \\ \hline Unimanual & rRMSE & 0.813 & 0.054 & \(p\) \(<\) 0.001 * & 2.274 & 0.938 & 0.656 & 0.797 \\ \cline{2-9} MVC 10\% & \%CV & 0.789 & 0.056 & \(p\) \(<\) 0.001 * & 2.017 & 0.875 & 0.656 & 0.766 \\ \hline Unimanual & rRMSE & 0.785 & 0.059 & \(p\) \(<\) 0.001 * & 3.116 & 0.688 & 0.875 & 0.781 \\ \cline{2-9} MVC 10\% & \%CV & 0.756 & 0.060 & \(p\) \(<\) 0.001 * & 2.425 & 0.688 & 0.750 & 0.719 \\ \hline Bimanual hands & rRMSE & 0.808 & 0.055 & \(p\) \(<\) 0.001 * & 2.248 & 0.625 & 0.875 & 0.750 \\ \cline{2-9} MVC 10\% & \%CV & 0.713 & 0.066 & \(p\) \(=\) 0.003 * & 1.779 & 0.531 & 0.875 & 0.703 \\ \hline \hline \end{tabular}
\end{table}
Table 2: ROC curve analysis on variables that had a significant group effect in ANOVA.  
Figure 3: Force variability variables for the younger and older groups. (**a**) SD showing a significant Group \(\times\) Hand Condition \(\times\) Force Level interaction. (**b**) %CV showing a significant Group \(\times\) Force Level interaction. Asterisk (*) indicates a significant difference between groups. Ampersand sign (**&**) means a significant difference between bimanual and unimanual (both dominant and non-dominant) hands in the younger group. Section sign (**§**) indicates a significant difference between bimanual and unimanual (both dominant and non-dominant) hands in the older group. Number sign (**#**) denotes a significant difference between Force Levels in the younger group. Dollar sign (**§**) means a significant difference between Force Levels in the older group (\(p\) \(<\) 0.05).
## 4 Results
### Dataset Quantity  
A first impression about the quality of the OSM dataset can be gathered by investigating the quantity of the collected information in the designated areas. Figure 5 shows the total number of Nodes, Ways and Relations collected in each urban area per km\({}^{2}\). The results show that Paris has the highest contributed object density, which is partially based on a large data import of cadastral building information and not necessarily only based on active contributions. However, for Osaka different data imports were applied to the OSM dataset as well but did not show the same results as in Paris when considering the object density. Especially the concentration of Relation information, which is added via a more complex process and is usually conducted by experienced contributors, separates the more advanced cities such as Berlin, London, Moscow and Paris from less complete cities such as Cairo, Istanbul and Seoul.  
Figure 4: **(a)** Number of active contributors; and **(b)** Percentage of mapper group contributions per urban area (Aug.–Oct. 2012).  
Figure 5 also supports prior findings about the strong concentration of OSM data in European urban areas in comparison to other continents, in which Istanbul is the only exception for a European urban area with lower data density and Moscow an exception for a non-European urban area with strong data collection efforts. Overall the number of collected OSM objects in the tested areas correlates with the number of contributors per area. For Nodes and Ways the results showed a R\({}_{\rm S}\) value of 0.6783 and for Relations one of 0.720.
## 4 Results and Discussion
### Numerical Simulation for the Cold Room Temperature Distribution Profile  
Numerical analysis was carried out to understand the cooling effect inside the cold room and, hence, obtain the temperature distribution profile. Figure 8a demonstrates the results of the simulation, which indicates dense lines at low temperature in the region in front of the evaporator (position A) till the opposite wall is reached, following which, the temperature rise is observed that continues as the number of passes increases, and, finally, after absorbing the heat in the room, the highest temperature is observed at the corner of the cold room below the evaporator (position B). This confirms further the placement of PCM trays on the walls, opposite and adjacent to it, for further numerical analysis of PCM heat exchanger material. The temperature in the cold room varied in the range from 26.8 to 7.8 \({}^{\circ}\)C, which mainly depends on the heat ingress with maximum heat transfer observed in the upper region of the cold room, specifically the region in front of the evaporator.  
Figure 8: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline
**Properties** & **Value** \\ \hline Melting temperature & \(-2\) \({}^{\circ}\)C \\ Freezing temperature & \(-3\) \({}^{\circ}\)C \\ Latent heat & 329 kJ/kg \\ Solid density & 1060 kg/m\({}^{3}\) \\ Liquid density & 985 kg/m\({}^{3}\) \\ Solid thermal conductivity & 2.2 W/mK \\ Liquid thermal conductivity & 0.35 W/mK \\ Thermal stability & \(\sim\)1000 cycles \\ \hline \end{tabular}
\end{table}
Table 5: Properties of phase change material.
# Agriculture 5.0 in Colombia: Opportunities Through the Emerging 6G Network
## 4 Results and Discussion
### Future Prospects by Agricultural Domain  
The table below (Table 10) outlines projected developments in key agricultural domains based on the anticipated capabilities of 6G networks. These projections illustrate how Colombian agriculture could evolve through the integration of ultra-low latency communications, edge AI, and massive IoT connectivity.  
These future projections emphasize the potential of 6G to act not merely as a communication upgrade but as a structural enabler of intelligent and inclusive agricultural transformation in Colombia. By supporting decentralized decision-making, real-time responsiveness, and end-to-end data integration, 6G could bridge technological gaps that currently limit the scalability of smart farming initiatives. However, realizing these benefits will require coordinated efforts in infrastructure deployment, regulatory adaptation, and digital capacity-building to ensure that rural producers--particularly those in remote or underserved areas--can equitably access and benefit from these advanced technologies.  
Despite the fact that 6G technology is still in its infancy on a global scale, Colombia is beginning to witness the emergence of pilot projects and sector-specific use cases that demonstrate its transformative potential. Key sectors such as agriculture, healthcare, energy and tourism are beginning to adopt emerging technologies, including artificial intelligence and the Internet of Things (IoT). The Colombian agricultural sector, in particular, is well placed for this transition, as evidenced by initiatives promoted by MinTIC. However, these endeavors remain fragmented and lack a comprehensive state policy that strategically articulates the evolution towards a rural digital ecosystem based on 6G technology. In order to avoid the perpetuation of historical disparities with this new generation of networks, it is essential that a robust public agenda is established. It is imperative that such an agenda promotes infrastructure investment, encourages applied research and fosters public-private and community partnerships to develop comprehensive solutions. It is only through this approach that 6G technology can fulfill its potential as an effective tool for territorial transformation, sustainable productivity and digital inclusion across various sectors.
# Droplets Transmission Mechanism in a Commercial Wide-Body Aircraft Cabin
## 3 Results and Discussion
### Droplets Infection Risk Assessment
#### 3.3.5 Performance Comparison of Different Ventilation Systems  
Figure 10 shows the overall cabin infection risk under different ventilation systems based on the average infection risk of 45 passengers from row two to row six.  
As shown in Figure 10, displacement ventilation has the lowest infection risk among the four ventilation systems when the passengers B, D and E are the sources of infection. The infection risk of sidewall mixing ventilation was the highest when the passengers A, B, C and D are the infectious sources. Overall, the infection risk of mixing ventilation is greater than that of displacement ventilation, which is related to air distribution. The annular flow of mixing ventilation leads to a larger dispersion range of droplets so that more passengers are affected, while the direct flow of displacement ventilation can quickly take away the droplets and inhibit their dispersion. Personalized ventilation system makes use of the advantages of displacement ventilation to reduce the infection risk of passengers under mixing ventilation. Compared with sidewall mixing ventilation system, the infection risk of passengers is reduced by up to 27%. In addition, under the same ventilation system, when passenger C is the infectious source, the cabin environment is at the highest risk.
###### Abstract  
Integrated photonics is a cutting-edge field that merges optics and electronics on a single microchip, revolutionizing how we manipulate and transmit light. Imagine traditional bulky optical systems condensed onto a chip smaller than a fingermail, enabling faster communication, more efficient sensors, and advanced computing. At its core, integrated photonics relies on guiding light through waveguides etched onto semiconductor substrates, analogous to how wires conduct electricity in traditional electric circuits. These waveguides can route, modulate, and detect light signals with unprecedented precision and speed. This technology holds immense promise across various domains. Despite its immense potential, integrated photonics faces challenges, including manufacturing complexities and integration with existing electronic systems. However, ongoing research and advancements continue to push the boundaries, promising a future where light-based technologies seamlessly integrate into our everyday lives, powering a new era of innovation and connectivity.  
integrated photonics; material platforms; switches; modulators; waveguide lasers +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Density Functional Theory (DFT) Calculations
Major vibrational bands that were observed in the (Bar-CLA) complex's FTIR spectrum were found to resemble simulated IR spectrum patterns. The slight difference between theoretical and experimental frequencies is due to the theoretical values being acquired in the gas phase whereas experimental values were obtained in the solid phase. Therefore, to reduce these differences, the scaling factor 0.8924 was used. For the (Bar-CLA) complex, the simulated frequency for v(N-H) appears at 3277 & 3372 cm\({}^{-1}\). The band appearing at 1514 cm\({}^{-1}\) represents v(C=O). The bands that appeared at 1460 cm\({}^{-1}\) is due to v(C=C), 1425 cm\({}^{-1}\) is for \(\delta\)(N-H), and 1375 is for \(\delta\)(C-H). These calculated vibrational frequencies vary to a small extent from experimental results due to neglecting the incompleteness and anharmonicity of the basis set [59].