esa-sceva/satcom-chunk-collection huggingface dataset:
# Personal Identification Using Embedded Raspberry Pi-Based Face Recognition Systems
## 3 Verification System
### Effectiveness of Recognition Depending on Facial Expressions
The system automatically concluded the image capture process upon reaching a total of one hundred photos. Users were required to complete the entire sequence of head movements and facial expression changes within this timeframe. All reference photos were taken with neutral facial expressions, similar to those used in ID or passport photos. For the test, the subjects attempted to replicate the same facial expression. Figure 11 shows samples of the example emotions and, therefore, the different facial representations. The test results are recorded in Table 6.  
The application's performance with varying facial expressions can be considered satisfactory. The system consistently produced correct results for common expressions such as smiling, sadness, or surprise. However, it encountered difficulties with more exaggerated expressions, such as the \"duck face\". For critical applications, such as access control systems for rooms containing sensitive data, it is essential to standardize the method of capturing test images by requiring a neutral facial expression. Standardizing image capture techniques helps minimize the risk of errors. Additionally, expanding the reference image database to include frontal face positions, side angles, and various basic expressions, such as smiling or sadness, would further enhance the system's reliability. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type of Facial** & **User No. 1** & **User No. 2** & **User No. 3** & **User No. 4** & **User No. 5** & **User No. 6** \\
**Expression** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** & **([PERSON])** \\ \hline smile & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\ sadness & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\ surprise & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\ duck face & [PERSON] & unknown & [PERSON] & [PERSON] & unknown & [PERSON] \\ clenched lips & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\ anger & [PERSON] & [PERSON] & [PERSON] & [PERSON] & unknown & [PERSON] \\ puffed cheeks & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] & [PERSON] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Face recognition results in varying facial expressions. Figure 13: Sample tested images with varying facial expressions: (**a**) User No. 1 ([PERSON]—sadness); (**b**) User No. 2 ([PERSON]—surprise); (**c**) User No. 3 ([PERSON]—surprise); (**d**) User No. 4 ([PERSON]—joy); (**e**) User No. 5 ([PERSON]—joy); (**f**) User No. 6 ([PERSON]–joy).
## 3 Results
### Regional Priority  
Figure 9 shows the boxplot and [PERSON]'s \(\delta\) results in RP category. According to the boxplot, the median increased from Certified through Silver and Gold to Platinum (\(2\), \(3\), \(3\), and \(4\) achieved points, respectively) with decreases in the range of the Certified and Silver projects through Gold to Platinum projects (\(1\)-\(4\), \(1\)-\(4\), \(2\)-\(4\), and \(3\)-\(4\) achieved points, respectively). According to [PERSON]'s \(\delta\), the RP category showed low achievement in Certified, Silver, and Gold projects and medium in Platinum projects. The RP category includes four possible bonus points, which can be obtained by performing regional-related credits in LT, SS, WE, EA, MR, and EQ categories [5]. However, according to the present results, RP was an unattractive category for Certified, Silver, and Gold projects and received little attention from Platinum projects.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Credit**} & \multirow{2}{*}{
\begin{tabular}{c} **Possible** \\ **Points** \\ \end{tabular} } & \multicolumn{4}{c}{**Achieved Points**} \\ \cline{3-6}  & & **Certified** & **Silver** & **Gold** & **Platinum** \\ \hline Enhanced indoor air & \multirow{2}{*}{2} & \(1.0\pm 2.0\) & \(1.5\pm 1.0\) & \(2.0\pm 1.8\) & \(2.0\pm 2.0\) \\ quality strategies & & 0.64 & 0.50 & _0.45_ & _0.40_ \\ \hline Low-emitting materials & \multirow{2}{*}{3} & \(0.0\pm 1.0\) & \(1.0\pm 2.0\) & \(1.0\pm 1.8\) & \(2.0\pm 1.3\) \\  & & 0.82 & 0.86 & 0.73 & 0.80 \\ \hline Construction indoor air & \multirow{2}{*}{1} & \(1.0\pm 1.0\) & \(1.0\pm 0.0\) & \(1.0\pm 0.0\) & \(1.0\pm 0.0\) \\ quality management plan & & **0.32** & **0.00** & **0.09** & **0.00** \\ \hline Indoor air quality assessment & \multirow{2}{*}{2} & \(0.0\pm 1.0\) & \(0.0\pm 2.0\) & \(1.0\pm 1.8\) & \(1.0\pm 1.3\) \\  & & 0.86 & 0.73 & 0.55 & 0.60 \\ \hline Thermal comfort & \multirow{2}{*}{1} & \(0.0\pm 1.0\) & \(0.0\pm 1.0\) & \(0.0\pm 1.0\) & \(1.0\pm 0.0\) \\  & & 0.59 & 0.59 & 0.55 & **0.00** \\ \hline Interior lighting & \multirow{2}{*}{2} & \(1.0\pm 1.0\) & \(1.0\pm 1.0\) & \(1.0\pm 0.8\) & \(2.0\pm 0.3\) \\  & & 0.82 & 0.64 & 0.73 & **0.20** \\ \hline Daylight & \multirow{2}{*}{3} & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) & \(0.0\pm 0.8\) & \(0.0\pm 1.3\) \\  & & 1.00 & 0.95 & 0.91 & 1.00 \\ \hline Quality views & \multirow{2}{*}{1} & \(0.0\pm 0.0\) & \(0.0\pm 1.0\) & \(1.0\pm 1.0\) & \(1.0\pm 0.3\) \\  & & 0.77 & 0.55 & _0.45_ & _0.20_ \\ \hline Acoustic performance & \multirow{2}{*}{1} & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) & \(0.0\pm 0.0\) \\  & & 0.95 & 1.00 & 0.91 & 1.00 \\ \hline \hline \end{tabular} Notes: Effect size is considered to be: very high if \(|\delta|<0.147\) (bold font), high if \(0.147<|\delta|<0.33\) (bold italic font), medium if \(0.33<|\delta|<0.474\) (talic font), and low if \(|\delta|\geq 0.474\) (normal font).  
\end{table}
Table 7: Credit points achieved for Indoor Environmental Quality (EQ) category of LEED-NCv4 projects: median \(\pm\) IQR (\(25\)th–\(75\)th) and [PERSON]’s \(\delta\) effect size between possible and achieved points in a credit.
## 4 Open Research Issues and Future Directions  
The use of DT and FL results in several open research issues, such as those relating to security, reliability, efficiency, privacy, and adaptability.  
Data security is very important in the present era, and IIoT, IoV, and IoD produce massive amounts of data. These data are vulnerable to cyber threats, and the security of these data is very important. FL helps address this issue, but there is still a need to develop more robust techniques to resist cyber threats.  
Similarly, the reliability of DT and FL is also an open research issue that needs to be addressed. DT and FL are considered more reliable; however, there is a need for schemes to address the reliability of DT and FL in the IIoT, IoV, and IoD.  
DT provides a virtual model of the networks, and the efficiency of the network can be analyzed using DT. However, the real-time deployment of the same DT model can face the problem of efficiency. This opens a new pathway for researchers to develop models that address the efficiency of DT-based networks and real-time networks. Moreover, the IoD also lacks the use of DT.  
Deployment of the DT network in the real world may face the problem of environmental adaptations. The same DT, which is more robust in the virtual world, may face a decline in performance when implemented in the real world.  
DT and FL can be used for the resource allocation in the reconfigurable intelligence surface (RIS) based wireless network. [PERSON] et al. demonstrated the resource allocation in RIS based wireless network [80]. However, they have not used DT and FL which can be  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Publication Year** & **Objective** & **FL** & **DT** & **Achievements** \\ \hline [76] & 2021 & Power control & Yes & No & Improved security, better QoS \\ [77] & 2021 & Roadside computing & Yes & No & Improved efficiency \\  & & Blockchain integrated & & & Higher security against cyber threats, \\  & & data accumulation & & & improved privacy \\  & & & & & Better image recognition, enhanced privacy \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of the use of FL in IoD.  
used to solve this problem more efficiently. Similarly, FL and DT can be used for the green communication. The techniques and challenges of the green communication are mentioned in [81].  
There are several other challenges related to DT and FL, such as protocols and integration of DT and FL in networks. These issues provide future researchers with additional research directions.
# A Simplified Analysis Method for Seismic Response of
## 4 Example Verification
### Components Computational Efficiency Comparison  
It can be seen from the above results that the EBSM-RAM proposed in this paper can more accurately calculate the internal force of the pile foundation under the horizontal earthquake action. At the same time, the EBSM-RAM is more computationally efficient than the time history analysis. Taking the above example as an example, the number of finite element nodes in the time history analysis is about 300,000, the duration of the input seismic wave is 17 s, 15 cores (physical processor core of the central processing unit) are opened simultaneously on the server, and the calculation time of each example is about 24 h. When using the EBSM-RAM to calculate the same finite element model, the calculation time for each model is about 5 min. The scale of the finite element model will be larger than that of the above examples for different practical engineering problems. If the time history analysis method is used, the calculation time will be unacceptably large. The calculation time of the simplified method is much less affected by the size of the finite element model than that of the time history analysis method. The calculation results show that for multi-layer soil, the EBSM-RAM analysis method can reflect the position of shear stress mutation, especially for the foundation with apparent soft and hard layers. Since the EBSM-RAM is a static analysis, the calculation time is not affected by the duration of the seismic wave. Therefore, the simplified method has obvious advantages in expanding the analysis scale and improving the analysis efficiency.
## 4 Discussion
Figure 8: Illustration of all positions judgment from the sixteen participants with respect to the actual (reference) targets at 150 cm distance for (**a**) HMD and (**b**) SWD conditions. It shows that the overall position estimates in the SWD were better than the HMD position estimates. Overall, better exocentric distance judgment and lower horizontal position judgment in the frontal plane was obtained in the HMD. The position judgment error in the HMD was observed to pull every one of the targets towards either the right or to left (Figure 7a), which made the horizontal position accuracy below that of the SWDs. The systematic shift could be because of the possible effects of lens  
Figure 11: Average positions judgment with respect to actual (reference) target at a target distance of 150 cm from the participants for the exocentric distances (**a**) D5 and (**b**) D6 that shows a comparison between HMD and SWD. Figure 10: Average positions judgment with respect to actual (reference) target at a target distance of 100 cm from the participants for the exocentric distances (**a**) D3 and (**b**) D4 that shows a comparison between HMD and SWD. Figure 9: Average positions judgment with respect to reference target at a target distance of 65 cm from the participants for the exocentric distances (**a**) D1 and (**b**) D2 that compared HMD and SWD. position, which might be different from participant to participant and the way the participant's eye fit to the HMD lens, which can cause an offset to the center of the twelve targets while pointing. Moreover, as [PERSON], [PERSON] and [PERSON] [38] identified the significant effect of interpupillary distance (IPD) on size estimation, this might be one of the possible reasons for the observed results. Therefore, it is possible that the HMD could give a better precision in both the exocentric distance and the position estimates in the frontal plane, if the HMD had been impeccably mounted and flawlessly fit the participant's eyes. Furthermore, to use VR displays for industrial applications, it was recommended that we used different calibration methods [10]. For example, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [10] developed an objective stereo display calibration method and got a more precise perception. Furthermore, the results revealed that the intermediate target distance might be the fitting or ideal distance for the design and structure of 3D applications, as the highest performance in both distance judgment and position accuracy in the frontal plane (about 90.9% average accuracy) was obtained at a target distance of 100 cm from the participant. However, to our knowledge, no comparable investigations have been reported regarding the optimum distance for locating 3D application design; be that as it may, it would be premature to assume that this can be generalized to all VR conditions just yet. Generally, HMD can be used and recommended for tasks which need exocentric distance judgment accuracy, such as architecture [19], medical visualization [9], and geography, while SWD can be utilized for tasks which need location or position accuracy, such as military training.
# Insights into Segmentation Methods Applied to Remote Sensing SAR Images for Wet Snow Detection
## 2 Data and Method
### BERA Bulletins
#### 2.4.2 Scores
The Hamming distance is calculated as follows:  
\[h=\frac{b+c}{n} \tag{19}\]  
b- False Alarm Rate (FAR): This score measures the proportion of false snow detection cases with respect to the number of snow pixels detected. It is defined by:  
\[FAR=\frac{b}{a+b} \tag{20}\]  
c- True Detection Rate (Hit Rate): This score measures the proportion of snow detection cases that are true with respect to the number of snow pixels observed. It is defined by:  
\[HR=\frac{a}{a+c} \tag{21}\]  
d- Heidke Skill Score (HSS): This score measures the benefit of SAR image segmentation compared to a random snow pixel distribution. It varies between \(-\infty\) and 1, with a negative value corresponding to a degraded detection compared to a random distribution, a null value and Sentinel-2 images at observation dates close to each other, the PSSP score provides a comprehensive view of the snowpack's essential features at the mountain massif scale. RMS errors or correlations are computed to measure the distance between the obtained probability snow product curves. In addition to the scores already presented, it was necessary to measure the execution time of each segmentation algorithm to thoroughly compare their performances. A method that provides excellent results but takes too long to run may not be practical for processing large amounts of data. The runtime is dependent on the configuration of the machine used. Here, we provide some details for comparison purposes: cpu 20 cores of 2.30 GHz, 96 GB RAM memory. With the selected scores, we aimed to distinguish between segmentation methods using correlation values, Hamming distance, HR rates, FAR rates, HSS values, and probability curves. The ideal approach should have high correlation values, low Hamming distance, high HR rates, low FAR rates, high HSS values, and the best-fitting probability curves with minimal runtime. Figure 10 shows a flowchart of all data processing. As illustrated in this figure, the reference image was 25 August 2017. This article does not address the impact of reference image selection on wet snow detection. The study in [42] showed that examining the cross-correlations between images calculated over an entire time series allows for a better selection of reference images (with less similarity to the winter period). This approach was tested in [38] to better detect avalanche debris. A further study, regarding reference image impact on wet snow, will be the subject of dedicated research.
## 3 Results
### RQ5: What Research Designs Are Used in the Studies?  
According to the data extraction results (see Table 4), a quarter (25%) of the published articles are no-experimental design, which is characterized by no random assignment  
Figure 4: Publications per year and type.  
design, no control group and no multiple measures. Two-thirds of the research was conducted with a single-case experimental design, and 8.3% (\(n=3\)) reported a quasi-experimental design with a control group. [PERSON] et al. [68] conducted a pre-posttest control group design, which grouped 11 individuals (10 male and 1 female) into control (\(n=5\)) and experimental group (\(n=6\)). It is not a random assignment experiment, for they used a non-probabilistic intentional sampling method. [PERSON] et al. [74] and [PERSON] et al. [53] presented a posttest control group design, which compared the evaluation of different groups of participants at the end of the evaluation.  
In addition to the simplest pretest-posttest single-case experimental design, there also includes a multiple baseline single-case design that increases the internal validity by testing multiple baselines, alternating treatment single-subject design and withdrawing single-case designs.
## 2 Materials and Methods  
1-propyl-1-methylpiperidinium bis(trifluoromethylsulfonyl)imide [1P1 Mpip][NTf2](purity \(\geq\)99%), 1-propyl-3-methylpyrididinium bis(trifluoromethylsulfonyl)imide [1P3 Mpy][NTf2] (purity \(\geq\)99%), and 1-propyl-1-methylpyrrolidinium bis(trifluoromethylsulfonyl)imide [1P1 Myr][NTf2] (purity \(\geq\)99%) were supplied by loitec (Germany), and 1-propyl-1-methylpyrrolidinium bis(fluorosulfonyl)imide [1P1 Myr][NTf2] (purity \(\geq\)99%) [1P1 Myr][Nf2] (purity \(\geq\)99%) were purchased from Solvionic (France). Ionic liquids were dried in vacuum conditions (p = 0.2 Pa) at moderate temperature, and then they were stored under argon gas.  
Phenol (purity \(\geq\)99.5%) and o-cresol (purity \(\geq\)99%) were supplied by Sigma-Aldrich (Germany), and 2-chlorophenol (purity \(\geq\)99%) was purchased from Merck Scuclart (Germany). 4-amineantipyrine method reagents: Potassium hexacyanoferrrate (III) (purity \(\geq\)99%), potassium sodium L(+)tartrate tetrahydrate (purity \(\geq\)99%), ammonium chloride (purity \(\geq\)99%), and 4-amineantipyrine (purity \(\geq\)97%) were purchased from VWR Prolabo Chemicals (Belgium), and ammonium hydroxide solution (25% wt NH\({}_{3}\)) was supplied by Sigma-Aldrich (Germany). All the aqueous solutions were prepared using Milli-Q water.  
In order to evaluate the extraction efficiency of these four solvents, the ternary systems {water + phenolic compound + IL} were studied. The samples were prepared by weighing, using a Mettler Toledo Excellent plus XP205 analytical balance (USA) with an uncertainty of \(\pm\) 3\(\cdot\)10\({}^{-4}\) g. The used ratio of IL: aqueous phase selected for all these experiments was 1:3 (_v/v_). The mixtures were introduced in flasks and sealed with silicone rubber covers and stirred for 2 hours at 298.15 K in a Polyscience thermostatic bath (USA) (\(\pm\)0.1 K), and then temperature controlled with a digital thermometer ASL model F200 with an uncertainty of \(\pm\)0.01 K, coupled to a Phoenix Instrument RSM-03-10 K magnetic stirrer (Germany). Then, they were left to settle down for 2 hours to ensure the correct separation of both phases. Finally, a sample of aqueous phase was collected, and the phenol concentration was determined with a Jasco V-630 UV-Vis spectrophotometer (Japan), with an uncertainty of \(\pm\)0.002 Abs (from 0 to 0.5 Abs) and \(\pm\)0.003 (from 0.5 to 1 Abs), respectively. The method used to quantify the phenolic concentration is the 4-amineantipyrine method [16], which is based on the formation of a colored dye between a phenolic compound and 4-amineantipyrine, in the presence of potassium ferricyanide at basic pH. The absorbance of the formed dye was measured at 510 nm by UV-Vis spectrophotometry, and the extraction efficiency was calculated following the equation:  
\[E\%=((C_{i}-C_{f})/C_{i})\times 100 \tag{1}\]  
where \(C_{i}\) and \(C_{f}\) denote the initial and the final concentration in mg\(\cdot\)L\({}^{-1}\) of phenols in the aqueous phase, respectively.
# LEO-Augmented GNSS Based on Communication Navigation Integrated Signal
###### Abstract  
Low Earth Orbit (LEO) is of great benefit for the positioning performance of Global Navigation Satellite System (GNSS). To realize the system of LEO-augmented GNSS, three methods to integrate communication and navigation signal for LEO communication system with the least influence on the communication performance are analyzed. The analysis adopts the parameters of IRIDIUM signal as restrictions. This paper gives quantitative comparison of these methods considering CN0(carrier noise power spectral density rate) margin, pseudorange accuracy, Doppler accuracy, and communication loss. For method 1, a low-power navigation signal is added to the communication signal. For method 2, the navigation signal is launched in one or more frames. For method 3, the navigation signal is launched in the frequency band separated to the communication signal. The result shows that the pseudorange accuracy of method 2 is far below method 1 and method 3. However, the difference of Doppler accuracy among the three methods can be emitted. Detailed analysis shows that method 1 is practicable when the communication and navigation signal power rate is 15 dB. It achieves the balance of pseudorange accuracy and bit error rate (BER) performance under this condition. Comprehensive comparison of these methods is given in the last. The result shows that the CN0 margin of the navigation signal for method 3 can be 13.04 dB higher than method 1, based on the accuracy threshold considered in this paper. Methods 1 and 3 have the advantage of high accuracy and high CN0 margin respectively. However, method 3 causes high communication capacity loss. Considering that the main disadvantage of GNSS signals is low CN0, method 3 is a good choice for the LEO-augmented GNSS system. Methods 1 and 3 can be combined to realize both high accuracy and high CN0 margin if possible.  
communication and navigation integration; LEO; GNSS; burst signal; IRIDIUM +
Footnote †: journal: Sensors