esa-sceva/satcom-chunk-collection huggingface dataset:
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 4 Personalized Privacy Protection
### Personalized Private Space Decomposition Framework Design  
#### 4.1.1 PSD  
When constructing a worker index in the database, we operate under the following assumptions: Firstly, the workers' residential places represent their current locations. Secondly, the platform possesses latitude and longitude coordinate data of workers. The third assumption is that the workers' residential places are distributed within a known area. In the simulation experiment, the residential location can serve as a substitute for real-time location data, facilitating comparisons with the spatial index privacy protection algorithm of other data structures. This section introduces the spatial index frame design for the spatial location of workers. The dynamic division of the spatial grid employs the dynamic spatial meshing strategy outlined in Section 3.1.  
It is worth noting that the data privacy budget of each grid is the same when the above algorithm dynamically divides the spatial grid, so it cannot meet personalized needs. This paper makes some improvements on this basis.
###### Abstract  
The exposure of humans to microplastics through food is a topic of great interest. Foods of marine origin, such as fish and salt, have been the most extensively studied in this regard. Conversely, foods considered less likely to be contaminated (such as dairy products) have been investigated to a lesser extent. This is the first study addressing the occurrence of microplastics in cheese. In this paper, we report the first analytical approach for cheese preparation, before a chemical analysis of microplastics in cheese was performed. Therefore, the most suitable digestion methods were investigated. Alkaline digestion (i.e., KOH 5 M, 50 \({}^{\circ}\)C, 48 h) achieved a digestion efficiency of \(97.5\pm 0.8\%\). To assess the feasibility of the preparative method proposed, a recovery rate of spiked polystyrene microbeads (-10 \(\upmu\)m) of \(98.5\pm 0.4\%\) was determined. Further, the effects of the digestion agent on the microbeads were also investigated. To confirm whether the preparative method allows for the confirmation of the plastic-nature of microparticles, a qualification of spiked microplastics (polystyrene, 150 \(\upmu\)m, and polyethylene terephthalate, 300 \(\upmu\)m) was performed using pyrolysis-gas chromatography-mass spectrometry.  
cheese; microplastics; sample preparation; preparative method; Py-GS-MS; human exposure +
Footnote †: journal: Applied Sciences  
Article  
First Approach for Defining an Analytical Protocol for the Determination of Microplastics in Cheese Using Pyrolysis-Gas Chromatography-Mass Spectrometry.
## 3 Results
### Observed Correlation of Rainfall, GNSS-IWV, and CAPE at the GNSS Station Locations
#### 3.1.5 Relation between Rainfall, CAPE, and GNSS-IWV Based on Quantile Regression  
As mentioned above, our regression analysis revealed an exponential relationship between rainfall and GNSS-IWV and a power-law relationship between rainfall and CAPE, which is supported by [4; 26]. We used a linear multivariable regression model (Equation (6)) to show the joint effect of both climatic variables on extreme rainfall at both station locations. Quantile regression is characterized as a frequently used statistical tool for analysing extreme rainfall events [51; 52; 53]. The quantile regression has the proficiency of being less sensitive to the presence of outliers and skewed distributions [51].  
Therefore, we analyse the joint effect of both climatic variables on extreme rainfall (0.9 quantile) as well as for the 0.75, 0.8, and 0.85 quantiles at both station locations using quantile regression [47; 54]. In order to exclude the relationship between the daily mean rainfall and both variables in the cold season, which is not an applicable relationship specially between rainfall and IWV [13], we rely on a quantile regression analysis for the austral summer season.  
Our results indicate the regression coefficient (\(\beta\)) close to the parcel theory (0.5), but generally lower values of (0.3 to 0.4) and the regression coefficient (\(\alpha\)) around (0.04 to 0.06) for extreme rainfall (0.9 quantile) at both stations (Figure 9). We observe a higher contribution of CAPE to rainfall for CATA station at the 0.9 quantile compared to lower quantiles and the lower contribution of GNSS-IWV to rainfall for CATA station at the 0.9 quantile compared to lower quantiles compared to lower quantiles. In contrast we indicate a lower contribution of CAPE to rainfall for TUCU station at the 0.9 quantile compared to lower quantiles and about the same contribution of GNSS-IWV to rainfall for TUCU station at the 0.9 quantile compared to lower quantiles.  
Figure 7: Quantile–Quantile plot on \(log_{e}\) scale of rainfall vs. \(log_{e}\) scale of CAPE essentially shows that most of the data are well behaved within the assumed relation for both stations (**A**) for TUCU and (**B**) for CATA. Below the 10 th percentile, rainfall and CAPE do not follow an identical distribution at both stations. Black dashed lines indicate the rainfall percentiles (the 10 th and 90 th percentiles).  
We then test the goodness of our joint model based on the goodness of fit criterion for a certain quantile [47]. Here, we select the 0.75, 0.8, 0.85, and 0.9 quantiles. Our results show a better fit at higher quantiles (extreme rainfall) for both CATA and TUCU stations (Figure 10).
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol
### Problematic Scenarios in DDC-MAC  
#### 4.4.1. Deafness and Hidden Terminal Problem  
Figure 6a illustrates a scenario of the hidden terminal problem due to the use of pure omni-directional antennas (i.e., \(s_{1}\), \(s_{2}\), \(r_{1}\), and \(r_{2}\)), and the deafness problem caused by using directional antennas (i.e., node \(s_{3}\), and \(r_{3}\)). Suppose that sender \(s_{2}\) intends to communicate with receiver \(r_{2}\); meanwhile, the channel of receiver \(r_{2}\) has been occupied due to overhearing the RTS packet from \(s_{1}\), so the RTS from \(s_{2}\) cannot be received by \(r_{2}\), hence the hidden terminal problem occurs. Additionally, the deafness problem occurs when sender \(s_{3}\) sends RTS through a pure directional antenna to the intended receiver \(r_{3}\), whose antenna beam is pointing in a different direction. As a result, node \(r_{3}\) is deaf to the RTS from sender \(s_{3}\).  
To address the two problems, in Scenario 1, the pairs \((s_{1},r_{1})\) and \((s_{2},r_{2})\) are equipped with adaptive antennas and communicate in DT-OR mode; thus, the two pairs can realize parallel transmission without interference. In Scenario 2, when node \(r_{2}\) overhears the RTS from \(s_{1}\), node \(s_{2}\) will switch to another channel according to ACBS mechanism and send the RTS again. In addition, the deafness problem can also be resolved naturally through the adaptive antenna of \(r_{3}\), which can receive the packet omni-directionally at all times. Then, node \(r_{3}\) points its beam in the direction of \(s_{3}\) shortly after receiving the RTS.
## 5 Application to the Satellite Module Layout Problem
### Results and Analysis
#### 5.2.3 Analysis of the Layout  
The convergence curves related to each of the containers and for the best optimal layout (among the 50 repetitions) are shown in Figure 12.  
Figure 11: Median and IQR of convergence curves of the upper level for 50 independent runs.  
Figure 12: Convergence curves for the four containers of the satellite module.  
Figure 12 shows that the convergence speed is higher for Surface 1 and Surface 4 than for Surface 2 and Surface 3. Indeed, as illustrated in Figures 13 and 14, the Surfaces 2 and 3 located near the geometrical center of the satellite module have the highest occupation rates due to the assignation of the components to the surface in order to minimize the terms of the inertia equations along the z axis. The higher the occupation rates, the more difficult it is to solve the constraints. Consequently, the number of iterations is increased and the convergence speed is slower. Moreover, for the same reasons, the first feasible layout is found in less iterations for the less occupied surfaces (1 and 2).  
Figure 13 shows the best obtained layout using the Oboe-CCEA algorithm [19] and the proposed algorithm. Figure 14 illustrates the best obtained layout using the Dynamic FS algorithm [19] and the proposed algorithm. In both figures, the functionally incompatible components are highlighted in orange and blue for the heat constraints and orange for the electromagnetic constraint. Moreover, the redder the components, the heaviest their corresponding masses.
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
## 4 Results
### Average Travel Time Results  
Figure 14 presents the average travel times across different cycle times (60 to 204 s) for various AV driving behaviors and penetration rates, along with human-driven vehicles. Travel times generally increase with longer cycle times for all scenarios.  
Cautious AVs consistently show the highest travel times across all cycle times and penetration rates, due to their conservative driving behavior, which underutilizes road capacity and leads to inefficiencies. Human-driven vehicles also exhibit high travel times but perform slightly better than cautious AVs, particularly at higher cycle times.  
Platooning AVs with 100% penetration demonstrate the best overall performance, achieving the lowest travel times. Their coordinated driving minimizes stop-and-go waves, resulting in highly efficient traffic flow. Aggressive AVs, while showing slightly higher travel times than platooning AVs, still perform well with lower travel times across all penetration rates. The differences between aggressive AVs and platooning AVs are slight, particularly at lower cycle times.  
Normal AVs exhibit moderate increases in travel times, offering a balanced performance between cautious and aggressive behaviors. In mixed AV environments, travel times are more stable, falling between the extremes of cautious and aggressive behaviors, providing smoother and more predictable traffic flow.  
Figure 14: Influence of AV behaviors on average travel time at various traffic signal cycle times. Source: processed by authors.
## 5 Results and Discussion
The amount of averaged MSE, minimum observed MSE, and Root Mean Squared Error (RMSE), and correct classification accuracy of MLP-NN-based classifier are sorted for test data in Table 8.  
Root mean squared error is known as the fit standard error; the lower value of RMSE indicated, the closer to the NN convergence value. It can be seen that the RMSE on testing samples are very small, which means that the NN classifies and estimate correctly. In addition, average classification accuracy on testing instances is obtained as 98.80%, indicating a reasonable classification. To show how close is actual output to the desired output, the comparative results of target and output of the back propagation (BP) neural network in all load conditions can be sorted as shown in Table 9. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Testing on CV Data**} \\ \hline
**Performance Measure** & **High Load** & **Medium Load** & **Low Load** & **No Load** \\ \hline cp.ErrorRate & 0.01667 & 0.01666 & 0 & 0.0333 \\ cp.CorrectRate & 0.98333 & 0.98333 & 1 & 0.9667 \\ mean\_ErrorRate & 0.01604 & 0.01626 & 0 & 0.0338 \\ mean\_CorrectRate & 0.9839 & 0.98373 & 1 & 0.9661 \\ Standard deviation & 0.00049 & 0.00050 & 0 & 0.0005 \\ No. of features & 5 & 9 & 5 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance of multi-layer perceptron neural network (MLP-NN)-based classifier on cross-validation data. Figure 12: Variation of average classification and MSE with a number of features as inputs in no Load. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{5}{c}{**Testing on Test Data**} \\ \hline
**Performance Measure** & **High Load** & **Medium Load** & **Low Load** & **No Load** \\ \hline Averaged MSE & 0.0055 & 0.003 & 0.0032 & 0.0046 \\ RMSE(Min. observed) & 0.0698 & 0.05 & 0.039 & 0.0654 \\ MSE(Min. observed) & 0.0048 & 0.0025 & 0.0015 & 0.0042 \\ Classification accuracy & 98.80\% & 98.80\% & 98.80\% & 98.80\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of MLP-NN-based classifier on test data. Figure 14: The Error between Real Output and Desired for Testing Data Set in 50% of Full Load. Figure 15: The Error between Real Output and Desired for Testing Data Set in 35% of Full Load.
# Innovative Data-Driven Machine Learning Approaches for Predicting Sandstone True Triaxial Strength
## 3 Strength Criteria
### HB Criterion  
The HB criterion, developed by [PERSON] et al. [7] through fitting true triaxial test data, is an empirical criterion widely used in the field of geotechnical engineering. Among various criteria in geotechnical engineering, the HB criterion stands out as one of the most extensively applied. Its specific form is as in Equation (21):  
\[\sigma_{1}=\sigma_{3}+\sigma_{c}\bigg{(}m\frac{\sigma_{3}}{\sigma_{c}}+1\bigg{)} ^{0.5} \tag{21}\]  
where \(m\) is a constant associated with the rock type. For intact sandstone, the range of \(m\) typically falls between 13 and 21 [37].
## 4 Test on Synthetic Data  
The first test can be made using synthetic data. These data are obtained from the family of probability density functions:  
\[f_{s}(x;r)=N_{(\sqrt{r},1)}(x), \tag{14}\]  
where \(N_{(\mu,\sigma^{2})}\) is normal distribution with mean \(\mu=r_{i}\) and variation \(\sigma^{2}=1\). The data are generated with 200 points in \(r_{i}=0.,0.25,...,50.\) and 300 random points for each \(r_{i}\). Those data are binned in order to obtain the matrix of probabilities (11) with size \(200\times 56\).  
The similarities between two p.d.f.'s can be calculated using distances which are defined in many ways. Here, we would use Hellinger distance and Kullback-Leibler divergence.  
For a specific \(r\), the Hellinger distance is defined as follows:  
\[H(r)^{2}=\frac{1}{2}\int\left(\sqrt{f_{s}(x;r)}-\sqrt{\hat{f}(x,r)}\right)^{2}dx \tag{15}\]  
Another integration along \(r\) is required in order to obtain distance for all \(r\)'s:  
\[\frac{1}{R_{2}-R_{1}}\int_{r_{1}}^{r_{2}}\int_{-\infty}^{\infty}\left(\sqrt{f _{s}(x;r)}-\sqrt{\hat{f}(x,r)}\right)^{2}dx\,dr. \tag{16}\]  
The Kullback-Leibler divergence for a specific \(r\) can be defined as follows:  
\[D_{KL}=\int f_{s}(x;r)(\log f_{s}(x;r)-\log\hat{f}(x,r))dx. \tag{17}\]Again, additional integration along \(r\) is required  
\[\frac{1}{R_{2}-R_{1}}\int_{r_{1}}^{r_{2}}f_{s}(x;r)(\log f_{s}(x;r)-\log\hat{f}(x,r))dx\,dr. \tag{18}\]  
Due to inherent randomness, the calculations were carried out 100 times. The results are presented in Table 1. Observe that Kullback-Leibler divergence is not symmetric. Its symmetrized version provides almost the same results as in Table 1.  
The reconstruction using the presented algorithm can be seen in Figure 1.
## 3 Methodology
### Cross-Layer Attention Upsampling (CLAU)
#### 3.3.3 Cross-Layer Attention Upsampling  
To further enhance the feature fusion capability of our lightweight network, we propose a learnable upsampling model called \"Cross-Layer Attention Upsampling (CLAU)\" (Figure 4). The inputs to the model consist of two tensors, \(\textbf{X}\in\mathbb{R}^{\mathbb{C}\times H\times W}\) and \(\textbf{Y}\in\mathbb{R}^{\mathbb{C}\times(S*H)\times(S*W)}\); **X** is a low-resolution deep feature map that contains rich semantic information while losing many details, while **Y** is a high-resolution shallow feature map that has detailed information and can help the network to detect small targets. We implement a nearest-neighbor upsamplingoperation on \(\mathbf{X}\) to achieve resolution alignment and obtain the tensor \(\mathbf{X}^{\prime}\). Here, \(C\) represents the number of channels, while \(S\) is the coefficient required for upsampling. Next, three standard \(1\times 1\) convolution operations are used to obtain the query, key, and value tensors:  
\[Q =Conv1d(Y,W^{Q}) \tag{7}\] \[K =Conv1d(X^{\prime},W^{K})\] (8) \[V =Conv1d(X^{\prime},W^{V}) \tag{9}\]  
where \(\mathbf{Q}\), \(\mathbf{K}\), \(\mathbf{V}\in\mathbb{R}^{C\times(SsH)\times(SsW)}\), and \(\mathbf{W}^{Q},\mathbf{W}^{K},\mathbf{W}^{V}\in\mathbb{R}^{C\times C\times 1 \times 1}\) are the respective weight tensors of the three \(1\times 1\) convolutions. Then, we reshape \(\mathbf{Q}\), \(\mathbf{K}\), \(\mathbf{V}\) and obtain the output \(\mathbf{Z}\in\mathbb{R}^{C\times(SsH)\times(SsW)}\) according to the Formula (6). We use the attention mechanism to help the deep network obtain the shallow feature information. The shallow feature map with small target details guides the up-sampling of the deep feature map, allowing the network to selectively adjust its attention and obtain the details of small targets.
## 2 Materials and Methods
### Data Processing and Statistical Analysis  
Ecosystem CO\({}_{2}\) fluxes were calculated for 30 min intervals using the Eddy Pro software (LI-COR Inc., USA). All necessary corrections and statistical tests were implemented under this processing. Footprint parameters were estimated using the model of [PERSON] et al. [54]. Quality flags were assigned to the calculated fluxes using the 0-2 flag policy according to [PERSON] and Foken [55]. All fluxes with flag 2 were excluded from the analysis following the recommendations from the data quality assessment [56]. Additionally, the 30 min flux values with flags 0 and 1 containing the spikes collected under rain and dew events and low turbulence were also filtered out. Storage terms calculated using the \"one-point\" approach following Greco and Baldocchi [57] were added to the flux values. The \"one-point\" approach allows one to calculate storage terms using CO\({}_{2}\) concentration measurements at the top of the tower when the profile measurements are not available. NEE data were u*-filtered, gap-filled, and partitioned into TER and GPP using the REddyProc package [58]. The u* thresholds varied between 0.29 and 0.52 m\(\cdot\)s\({}^{-1}\) in the period.  
Uncertainty in the NEE, TER, and GPP associated with the random error in the flux measurements fluxes and data post-processing were calculated using the REddyProc package [58] as the standard deviation (SD) of the fluxes. The annual and seasonal random uncertainty aggregation for CO\({}_{2}\) fluxes was calculated considering the autocorrelation between the residuals using the empirical autocorrelation function [59].  
Since the daily TER, GPP, and NEE values did not show a normal distribution ([PERSON]'s test, \(p<0.05\)), the statistical significance of the interannual differences in CO\({}_{2}\) fluxes was estimated using the Mann-Whitney U-test (M-W U-test) and Kruskal-Wallis ANOVA (K-W test) with [PERSON]'s post hoc test. The differences were considered to be statistically significant under the \(p\)-value <0.05.
## 1 Introduction
In these studies, they investigated alveolar preservation using bovine cortical lamina and fibrin glue. The etiology of third molar inclusion is still under discussion; however, craniofacial development is certainly an important aspect of third molar inclusion [21], as inclusion is often associated with poor growth in the length of the mandible [4; 5; 6; 7]; therefore, it is associated with a type II skeletal class [22]. Consequently, it is also important to consider the decrease in the space between the lower second molar and the mandibular ramus. These factors occur due to a change in craniofacial growth during the developmental process, as a result of a decrease in the masticatory activity of the maxillary bones, which thus causes a decrease in their development. Another demographic factor that has been implicated in the etiology of impacted wisdom teeth is age. A systematic review and meta-analysis by [PERSON], [PERSON], et al. (2017) demonstrated that older age was a significant risk factor for impacted wisdom teeth, with the highest prevalence of impaction occurring in patients over the age of 25. The authors proposed that older age may result in increased bone density and decreased jawbone size, contributing to the development of impaction [23]. In addition to morphological and demographic factors, a significant genetic component has also been identified in the etiology of impacted wisdom teeth. In a twin study, [PERSON] et al. (2018) reported heritability estimates ranging from 40 to 60% for the development of impacted wisdom teeth. The authors suggested that the genetic contribution may be due to the influence of genetic factors on tooth size and jaw size [24]. Lastly, poor oral hygiene can also increase the risk of impaction. [PERSON], [PERSON] et al. (2021) found that patients with impacted wisdom teeth were more likely to have periodontitis and gingivitis compared to patients without impacted wisdom teeth. The authors proposed that poor oral hygiene may lead to an increase in the risk of impaction by creating an environment that is conducive to bacterial growth [25]. OPTs are extensively utilized in the field of dentistry due to their ability to offer a swift, cost-effective, and low radiation dosage option, while also providing a bilateral perspective of the mandible, unlike more complex diagnostic instruments). Furthermore, it is regarded as a suitable radiographic approach for evaluating the space occupied by the lower third molar and the linear and angular dimensions of the mandible [2; 26]. This retrospective radiograph study aimed to evaluate a possible correlation between the characteristics of the mandibular ramus and lower third molar inclusion by comparing a group of subjects with lower third molar inclusion to a second group with normal eruption to establish an early means of predicting this pathology. The comparison was made using linear and angular measurements taken on digital panoramic radiographs [2].
## 4 Experiments and Results
### Error Discussion
#### 4.5.1 Misdetection Error  
A misdetection case of a Flower-type cloud (Image 156f6 aa) is shown in Figure 11. As shown in Figure 11, MaskRCNN generates a partially correct prediction mask, but  
Figure 10: Two satellite images and the segmentation results of three models.  
CloudRCNN (auxiliary) considers that there is no flower-like cloud in the image. The misdetection may be due to the class imbalance between the background and foreground. Our model performs additional supervision on the foreground and background. With more pixels in the background than in the foreground in the dataset, the model will be more inclined to predict pixels as background classes.