esa-sceva/satcom-chunk-collection huggingface dataset:
# Facial Privacy Protection with Dynamic Multi-User Access Control for Online Photo Platforms
###### Abstract  
In the digital age, sharing moments through photos has become a daily habit. However, every face captured in these photos is vulnerable to unauthorized identification and potential misuse through AI-powered synthetic content generation. Previously, we introduced SnapSafe, a secure system for enabling selective image privacy focusing on facial regions for single-party scenarios. Recognizing that group photos with multiple subjects are a more common scenario, we extend SnapSafe to support multi-user facial privacy protection with dynamic access control designed for online photo platforms. Our approach introduces key splitting for access control, an owner-centric permission system for granting and revoking access to facial regions, and a request-based mechanism allowing subjects to initiate access permissions. These features ensure that facial regions remain protected while maintaining the visibility of non-facial content for general viewing. To ensure reproducibility and isolation, we implemented our solution using Docker containers. Our experimental assessment covered diverse scenarios, categorized as \"Single\", \"Small\", \"Medium\", and \"Large\", based on the number of faces in the photos. The results demonstrate the system's effectiveness across all test scenarios, consistently performing face encryption operations in under 350 ms and achieving average face decryption times below 286 ms across various group sizes. The key-splitting operations maintained a 100% success rate across all group configurations, while revocation operations were executed efficiently with server processing times remaining under 16 ms. These results validate the system's capability in managing facial privacy while maintaining practical usability in online photo sharing contexts.  
facial privacy protection; selective facial encryption; multi-user access control; deep-learning applications; online photo platform 2020
## 4 Results and Discussion
### Comparison of the First Roof Weighting Interval  
It can be seen from Figure 7 that the GA-BP model is used to train and predict the results of the first weighting interval with a smaller error than the BP model, and the GA-BP model has a larger and more stable determination coefficient during the 30 training sessions. It can be seen from Table 6 that the iteration times of the GA-BP model and BP model are 25 and 53 respectively. This indicates that the GA-BP model has a faster convergence rate. These results indicate that the optimized model can better predict the initial compressive interval.  
Figure 6: Comparison of the first roof weighting strength results. (**a**) The value of the first roof weighting strength predicted by the BP and GA-BP prediction models; (**b**) a comparison of the determination coefficients predicted by the BP and GA-BP models.
## 3 Results
### SOC Estimator of the Battery Pack  
In this section the results of the battery module discharge test and those of the Simulink simulation are provided.  
Figures 16 and 17 show the voltage and the current profiles of the battery module during the test, respectively.  
From Figure 16, it is evident that the voltage module, that is, half of the voltage of the overall battery pack, started from the voltage value of 29 V, that is, the open circuit voltage of the module, which suddenly decreased to 27.8 V when the lo  
Figure 16: The voltage profile of the battery module during the 2C-rate discharge test.  
Figure 17: The current profile of the battery module during the 2C-rate discharge test.  
battery module. The discharge test took about 40 min to be carried out; in the final part of the slope close to 2500 s, the \"knee\" of the curve is clearly visible and, from that point, the voltage starts to drastically decrease. Therefore, the performed discharging cycle can be considered to be complete; going beyond the \"knee\" point, the performance of the battery module could be irreparably compromised.  
Figure 17 shows that the current was held almost constant during the test, at a value close to 240 A. This current value implies that each battery cell supplied about 4 A, which corresponds to almost 2C-rate discharge. It is worth noting that for continuous-discharge usage this type of cell can supply a maximum of 2.75 A; it is then clear that the performed test is a stressful test for the battery module and the 2C-rate can be considered a high rate for a discharge cycle.  
The performed test was necessary to set some of the parameters of the implemented KiBaM to simulate the SOC profile of the battery pack during a collection scenario. The principal parameters correspond to the maximum deliverable capacity (C) and the initial capacity (C\({}_{i}\)).  
The SOC estimator was run for a collection scenario, and the simulation was ended when all of the operations included in Table 1 were carried out. Figure 18 shows the SOC trend of the battery pack during a collection scenario, which took about 5570 s to be performed.  
The SOC of the battery pack fell to a value of 0.33 after the collection scenario simulation was complete. This result means that the battery-pack SOC fell from 100% to 33% of its full charge during the considered collection scenario. The size of the battery pack, in terms of stored energy, is correctly sized for this type of application. In fact, to avoid overly stressing the battery pack, a margin higher than 30% of the SOC was guaranteed to complete the entire collection scenario. Preventing the DOD of the battery pack from reaching a large value ensures that its lifetime can be extended, with comparison to full DOD, as a function of the number of charge/discharge cycles performed.
## 7 Discussion
### Software Implementation  
The prototype was implemented using major AR frameworks provided by a smartphone OS, that is, Apple Inc.'s iOS. The primary advantage of utilizing these mature AR frameworks lies in their ease of development. Notably, they ensure cross-device consistency in image tracking and positioning despite variations in camera and sensor parameters. This approach does constrain developers' autonomy in certain respects. For instance, if a frame  
Figure 17: Examples of positioning error correction of exhibition objects via image-tracking using a smartphone’s camera. The blue dotted lines represent the user’s movement trajectory. The segments where the blue dotted lines are interrupted indicate areas where accumulated errors have been corrected. The corrected points are emphasized as red dots.  
work ceases to support a particular type of device following an update, the application will consequently lose compatibility with those devices.  
The software, based on the proposed framework, is considered scalable due to its infrastructure-free and standalone features. The calibration solution developed to resolve the accumulative error problem enables its application for long navigation routes in large indoor areas. Once developed, the software can be adapted to different facilities by replacing the data (e.g., floor plans, markers, targets, and geofences) and content (e.g., audio guides, descriptions, and photos).
## Results and Discussion
### Hemolysis and \(\beta\)-Glucuronidase Activity  
MKHJ 1-1, 1-2, 2-2, 13-2, 17-1, and 19-2 strains were found to exhibit \(\gamma\)-hemolysis (Table 2). Hemolytic properties can be divided into \(\alpha\)-hemolysis (which oxidizes hemoglobin to methemoglobin without destroying the erythrocyte membrane), \(\beta\)-hemolysis (which destroys the erythrocyte membrane, causing jaundice and anemia), and \(\gamma\)-hemolysis (without hemolysis). \(\alpha\)-Hemolysis causes the incomplete lysis of erythrocytes, resulting in a green-hued zone around the bacterial colonies, and \(\beta\)- hemolysis causes the complete lysis of erythrocytes, resulting in a clear zone around the bacterial colonies [29]. \(\beta\)-Hemolysis is considered harmful, whereas a-hemolysis and \(\gamma\)-hemolysis are considered safe [27].  
Probiotics should not produce harmful enzymes such as \(\beta\)-glucuronidase, which is the enzyme that causes colorectal cancer. \(\beta\)-glucuronidase catalyzes the hydrolysis the \(\rho\)-nitrophenyl-\(\beta\)-D-glucuron to the \(\beta\)-glucuron acid and nitrogenhead at introduces a yellow color [30]. In this study, \(\beta\)-glucuronidase enzyme activity was measured quantitatively, using \(\rho\)-nitrophenyl-\(\beta\)-D-glucuronide as a substrate, after by visually confirming color change. When observed visually, all the selected strains, except _E. coli_, used as a positive control, were colorless. The \(\beta\)-glucuronidase activity of the selected strains was significantly lower than that of _E. coli_ (0.81 \(\pm\) 0.16 U/mL). In the hemolysis and \(\beta\)-glucuronidase activity results, only 6 (MKHJ 1-1, 1-2, 2-2, 13-2, 17-1, and 19-2) among the 14 strains were verified as safe.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strains** & \begin{tabular}{c} **Kinds of** \\ **Fermented Food** \\ \end{tabular} & \
\begin{tabular}{c} **L-Asparaginase** \\ **Activity (U/mL)** \\ \end{tabular} &
\begin{tabular}{c} **L-Glutaminase** \\ **Activity (U/mL)** \\ \end{tabular} \\ \hline MKHJ 1-1 & Gochujang & 1.20 \(\pm\) 0.03 **\({}^{\ast}\)** & 0 \\
1-2 & Gochujang & 0.81 \(\pm\) 0.02 \({}^{\text{c}}\) & 0 \\
2-2 & Gochujang & 1.11 \(\pm\) 0.06 \({}^{\text{a}}\) & 0 \\
2-3 & Gochujang & 0.87 \(\pm\) 0.05 \({}^{\text{bc}}\) & 0 \\
4-2 & Soy sauce & 0.91 \(\pm\) 0.05 \({}^{\text{bc}}\) & 0 \\
4-3 & Soy sauce & 0.94 \(\pm\) 0.05 \({}^{\text{bc}}\) & 0 \\
8-1 & Doenjang & 0.96 \(\pm\) 0.02 \({}^{\text{b}}\) & 0 \\
8-4 & Doenjang & 0.91 \(\pm\) 0.00 \({}^{\text{bc}}\) & 0 \\
8-5 & Doenjang & 0.97 \(\pm\) 0.06 \({}^{\text{b}}\) & 0 \\
12-8 & Doenjang & 0.97 \(\pm\) 0.03 \({}^{\text{b}}\) & 0 \\
13-2 & Doenjang & 0.67 \(\pm\) 0.02 \({}^{\text{d}}\) & 0 \\
17-1 & Doenjang & 0.90 \(\pm\) 0.10 \({}^{\text{bc}}\) & 0 \\
19-2 & Doenjang & 0.89 \(\pm\) 0.03 \({}^{\text{bc}}\) & 0 \\
25-7 & Soy sauce & 0.96 \(\pm\) 0.04 \({}^{\text{b}}\) & 0 \\ \hline \hline \end{tabular} \({}^{\ast}\)Values with different letters indicate significant differences (_p_ < 0.05).  
\end{table}
Table 1: L-asparaginase and L-glutaminase activity of selected strains.
## 2 Methods
### Proposed Method
#### 2.2.2 Detail Feature Extractor  
The DC block has mainly consisted of depthwise convolution (DWC) of the kernel size \(3\times 3\) and Point-wise Convolution (PWC) of the kernel size \(1\times 1\), to reduce the computation cost and the number of parameters compared to the traditional convolution operation. Rather than the traditional convolution operation, dilated convolution (DC) has been employed to acquire a greater receptive field. The DC block can be expressed as follows:  
\[X_{D}=X+PWC(GELU(PWC(BatchNorm(DWC(X))))) \tag{1}\]  
where \(X\) is the input of the HSIs. \(DWC_{r}(\cdot)\) is the depthwise dilated convolution with the dilation rate \(r\). \(BatchNorm\) is a standard batch normalization layer. \(GELU\) is an activation operation. \(PWC(\cdot)\) denotes a PWC operation with kernel size \(1\times 1\), which is equivalent to a linear layer.  
Three DC blocks with a similar structure have been introduced consecutively, with the only adjustment of the dilation rate in the DWC stage. In the first block, the dilation rate \(r=1\) was utilized, corresponding to a standard non-dilated convolution. We continuously increased the value of \(r\) until it reached its upper limit value, which is constrained by the patch size. Figure 2 illustrates this stage.
# Research on Soil Pesticide Residue Detection Using an Electronic Nose Based on Hybrid Models
## 4 Discussion  
This paper proposed a hybrid model based on an electronic nose system to prevent the electronic nose system from misjudging samples that have not participated in training. The experimental results showed that the combination of the electronic nose system and the model could classify healthy soil with an accuracy of 99.3%, and the pesticide type recognition accuracy was 99.27%. This result indicated that the proposed model combined with the electronic nose system has significant application value in the field of soil pesticide residue detection. In Table 5, we summarized the papers on the application of an electronic nose in pesticide detection. As can be seen from the table, the previous research had shown that electronic nose systems could be used to detect pesticide residues, and the results of this study supported such conclusions. Unlike the previous works, this paper considered the problem that untrained samples might lead to the misjudgment of the electronic nose  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature Extraction Methods** & **FT** & **MAX** & **Mean** & **WT** \\ \hline Classifier & & & KNN & \\ Recognition rate(\%) & 98.98 & 96.98 & & 97.92 & 98.23 \\ Classifier & & & RF & \\ Recognition rate(\%) & 95.63 & 98.13 & & 97.92 & 98.23 \\ Classifier & & & SVM & \\ Recognition rate(\%) & 95.42 & 99.27 & & 99.27 & 99.17 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identification results of the pesticide residue contamination type detection module using KNN, RF, and an SVM.  
system and solved this problem using the proposed hybrid model, which greatly enhanced the potential of the electronic nose system in the field of pesticide residue detection.  
Although the hybrid model combined with an electric nose proposed in this study can accurately predict pesticide residues in soil, its study was conducted in a laboratory with fewer interference factors. In the future, it can be applied to agricultural detection to verify the reliability of this method.
###### Abstract  
Space-based wide-field surveillance systems are of great significance in maintaining the security of space resources by avoiding collisions between space targets. However, their performance is hindered by stray light phenomena. The nonuniform background noise caused by stray light significantly hampers subsequent target detection, leading to a high frequency of false alarms. To solve this problem, we propose a robust and accurate nonuniform background elimination method based on image block self-adaptive gray-scale morphology (IBSGM). First, we define two kinds of structural operators with different sizes and domains, which make full use of the difference between the target pixels and surrounding background pixels. Then, we block the original surveillance image and find the size of the largest target in each block by the minimum bounding rectangle method to determine the optimal size of the structural operator suitable for each block. Finally, we perform morphological processing using the defined structural operators to eliminate nonuniform backgrounds from images. Experimental results on simulated and real image datasets demonstrate that the proposed IBSGM method has higher precision in eliminating the nonuniform background when compared to other methods.  
stray light; wide-field surveillance; minimum bounding rectangle; gray-scale morphology; background elimination +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON].  
Stray Light Nonuniform Background Elimination Method Based on Image Block Self-Adaptive Gray-Scale Morphology for Wide-Field Surveillance. _Appl. Sci._**2022**, 12, 7299. [[https://doi.org/10.3390/app12147299](https://doi.org/10.3390/app12147299)]([https://doi.org/10.3390/app12147299](https://doi.org/10.3390/app12147299))  
Academic Editor: [PERSON]
# Design and Implementation of C-Band Large-Power Planar Butler Matrix in SRS
## 2 Principle and Design Method  
### Principle  
A Butler matrix is a beamforming network [19]. Figure 1 shows the schematic of a 4 \(\times\) 4 Butler matrix designed in this article. The input signal at input ports P1 to P4 is redistributed in the Butler matrix through four 3 dB hybrid couplers, two crossovers, two 45\({}^{\circ}\) phase shifters, and two 0\({}^{\circ}\) phase shifters. The Butler matrix delivers signals to its output ports with equal magnitudes and a phase increment between them of 45\({}^{\circ}\), \(-\)135\({}^{\circ}\), 135\({}^{\circ}\), and \(-\)45\({}^{\circ}\) depending on which input port is excited. The phase difference between output ports is fixed when any input port is fed. The wideband performance of a Butler matrix is mainly due to the wideband design of its components, e.g., couplers, crossovers, and phase shifters.
## 4 Comparison between Two Models
### Acceleration Responses of the Main Structures
#### 4.3.3 Arias Intensity  
In order to further analyze the dynamic response of the lining, the Arias intensity (\(I_{A}\)) was introduced, which can quantify the magnitude of the energy of earthquakes and characterize the situation of potential structural hazards induced by earthquakes [14]. The simplified expression is as follows:  
\[I_{A}=\frac{\pi}{2g}\int_{0}^{T_{d}}a^{2}(t)dt \tag{6}\]  
where \(a(t)\) is the earthquake acceleration time history, \(g\) is the acceleration of gravity, and \(T_{d}\) is the total duration of the earthquake acceleration record.  
The lining measurement points were compared with the input seismic waves of the shaking table to obtain the \(I_{A}\) and the energy amplification factor (EAF) (EAF = \(I_{A}\) of the acceleration measurement points/\(I_{A}\) of the shaking table). Energy response-frequency curves of the main structures are shown in Figure 13.  
It can be observed that the variation rule of the EAF of the lining was very similar to that of the AAE, and the EAF reached the peak value when \(\beta\) was close to 1. Compared with the AAF, the EAF of the hall vault (A1 and A3) in the JRM model was more significant near the fundamental frequency of the rocks, and the maximum values of the EAF under the transverse and the longitudinal excitation were 7.1 and 9.1, respectively. This indicates that the force of the JRM site produced a greater energy response to the station structure, and the station structure was more prone to damage.  
In addition, the energy response intensity under transverse excitation was greater than that under longitudinal excitation in the HRM model, while the energy response intensity  
Figure 12: Acceleration response–frequency curves of the main structures: (**a**) transverse excitation; (**b**) longitudinal excitation.  
under longitudinal excitation was greater than that under transverse excitation in the JRM model, which is consistent with the previous analysis.
## 2 Basic Geological Conditions of the Study Area  
The study area locates in Yarlung Tsangpo River basin, and contains the range from Nang County to Jiacha County (Figure 1b) in Linzhi City, Tibet autonomous region. It is 56 km long in the east-west direction and about 7 km wide on each sides of the Yarlung Tsangpo River, with an area about 540 km\({}^{2}\). According to the \"Plan for the prevention and control of geological disasters in Linzhi City, Tibet\" [23], Linzhi City was divided into nine key geological hazard prevention areas combined with the risk of geological hazards, population density and the scale of major human engineering construction activities. The study area is located in the third key prevention area of Linzhi city, where landslides and rockfalls are the major geological hazards. There are more than 20 habitations including towns, villages and counties in study area, such as Nang County, Zhongda Town, and Nang Town. There are also many major projects, such as provincial highway 306 and Central Tibet Grid Interconnection Project distributed along Yarlung Tsangpo River. These urban settlements, highways, power lines, and other major projects are the main victims of geological disasters along the Yarlung Tsangpo River.  
The altitude of the study area is between 3000 m to 5200 m (as shown in Figure 2), and the maximum relief is up to 2200 m, which is the alpine topography in plateau areas. This area crosses the Yarlung Tsangpo Structure Zone (YTSZ) and Gangdise-Nianqintanggula tectonic plate in a north-south direction, located at the north margin of the eastern section of the YTSZ, and adjoins with the southern edge of the Gangdise magmatic arc. Since the Miocene, the Indian plate has been subducting northward, and the Qinghai-Tibet plateau has been uplifted continuously, which lead to extremely strong tectonic activities in the study area. Many researchers have investigated the influences of tectonic activity on geohazards through geomorphological analysis, geophysical surveys, and finite-difference analysis etc., and proved that tectonic activity was one of the main factors impacting the slope stability [24; 25; 26]. The faults in the area are mainly distributed along the east-west direction, and the rest are distributed along the north-south direction. The famous Great Count Thrust (GCT) is distributed in parallel with the YTSZ from east to west [27]. Along the YTSZ, earthquakes over magnitude MS7.0 have occurred for many times, such as the MS7.7 earthquake in 1947 in the southeast of Nang County [28], and the MS7.0 earthquake in 1947 in Longzi County [29].  
Lithologies in the study area are mainly dioritic rocks, granitic rocks, conglomerate, Lang County ophiolite melange rocks, phyllite rocks from north to south. The metamorphism grade of rock mass in the study area is gradually deepened from west to east, and the deformation is also gradually strengthened.  
Figure 1: Location and regional geological map of study area (**a**). Location of study area (**b**). Regional geological map of study area (modified from [21; 22]).
## 2 System Overview  
### The Proposed System  
Figure 1 shows the proposed power and data parallel transmission circuit, where \(L_{\text{T}}\), \(C_{\text{T}}\), \(C_{\text{P}}\), and \(C_{\text{S}}\) constitute the LCC-S power compensation network, while \(C_{\text{DP}}\) and \(C_{\text{DS}}\) constitute the S-S data compensation network. \(Q_{1}\)-\(Q_{4}\) constitute a full bridge inverter and \(D_{1}\)-\(D_{4}\) constitute a rectifier. The output voltage control is employed by using backward data transmission (i.e., from the secondary side to the primary side). It should be noted that in the rotary steering system, forward data transmission (i.e., from the primary side to the secondary side) is also needed to issue control instructions. The data-processing circuits for backward and forward data are symmetrical. To simplify the analysis, only the backward data transmission circuit is shown in Figure 1, and the parasitic resistances of the coils are ignored. The power resonant circuit, composed of \(L_{\text{PP}}\), \(C_{\text{P1}}\), and \(L_{\text{SS}}\), \(C_{\text{S1}}\), is added to the power transfer channel to suppress the influence of power transfer on data transfer. The data resonant circuit composed of \(L_{\text{PP}}\), \(C_{\text{P2}}\), and \(L_{\text{SS}}\), \(C_{\text{S2}}\) is added to ensure that the data are loaded onto \(L_{\text{S}}\). Here, \(L_{\text{PP}}\) and \(L_{\text{ss}}\) are not magnetically coupled, and they are additional reactive elements incorporated into the primary- and secondary-site circuits. \(M\) is the mutual inductance between \(L_{\text{P}}\) and \(L_{\text{S}}\). To control the output voltage, the secondary controller uses the A/D module to sample the output voltage after the voltage divider, and  
then transmits the information to the primary side through the data transmission channel. After receiving the output voltage data, the primary-side controller controls the inverter through the proportional integral (PI) controller.