esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 CPS Resilience Quantitative Assessment Framework
### Physical Domain: System Performance Modeling
#### 3.2.2 CPS Resilience Mathematical Modeling  
In this section, a mathematical model is proposed to simulate the dynamic behavior of system performance under adverse events. CPSs are designed to fulfill one or more functional objectives. We postulate that for a given task or mission, there exists a function \(M(t)\) that represents the accomplishment of that mission, cumulative from the mission start time \(t_{0}\) up to the current time \(t\). The derivative of \(M(t)\) with respect to time, denoted as \(F(t)\), reflects the system's instantaneous performance level and thus serves as the basis for the performance curve. The relationship between \(M(t)\) and \(  
where \(A(t)\) represents the impact caused by adverse events and \(R(t)\) represents the impact on system recovery capability.  
Let \(Q(t)=A(t)+R(t)\); then the equation can be simplified as follows:  
\[\frac{dF}{dt}+Q(t)F(t)=+F_{N}R(t) \tag{3}\]  
This differential equation has a general solution as follows:  
\[F(t)=\exp\biggl{[}-\int_{0}^{t}Q(p)dp\biggr{]}\biggl{[}F(0)+F_{N}\int_{0}^{t}R (\tau)e^{\int_{0}^{\tau}Q(p)dp}d\tau\biggr{]} \tag{4}\]  
To better understand the model, we analyze three representative cases based on the system parameters \(A\) and \(R\).  
A. **Constant-coefficient model**  
Both \(A\) and \(R\) are assumed as time-invariant constants. This simplified assumption reflects a scenario in which the disturbance intensity and the system's recovery capability remain constant over time. Under this assumption, the solution is  
\[F(t)=\biggl{[}F(0)-\frac{F_{N}R}{Q}\biggr{]}e^{-Qt}+\frac{F_{N}R}{Q} \tag{5}\]  
where \(F(0)\) represents the initial condition of F.  
As \(t\rightarrow\infty\), the performance \(F(t)\) approaches a steady-state value:  
\[F_{\infty}=\lim_{t\rightarrow\infty}F(t)=F_{N}\frac{R}{A+R} \tag{6}\]  
Figure 4 illustrates the evolution of system performance over time under the constant-coefficient model. In general, the trajectory of the performance curve is determined by the relative strength of the adverse impact and recovery capacity, as well as the system's initial condition. When the system starts at nominal performance and the adverse effect dominates recovery, the performance exhibits exponential decay. Conversely, if the initial performance is low and the recovery effect outweighs the adverse impact, the system will gradually recover.  
This basic form captures the fundamental resilience dynamics where the system degrades at a steady rate and recovers proportionally to the performance gap. Although  
Figure 4: The curve variation of \(F(t))/F_{N}\) under different values of \(A\) and \(R\) when the initial condition \(F(t)=F_{N}\). As \(t\rightarrow\infty\), the value of \(F(t))/F_{N}\) will approach \(R/(A+R)\).  
simplistic, this model provides clear insights into baseline resilience behavior and serves as a reference point for analyzing more complex dynamics in later sections.
## 3 Results and Discussion
### The Effect of Filter Material Properties on Filtration Efficiency
#### 3.1.3 Comparison of the Filtration Efficiency of Filter Media with Different Surface Treatments  
The dipping process uses high-concentration emulsion to impregnate the needle felt as a whole, to achieve the effect of coating the surface of each fiber, which is equivalent to coating a layer of film on the surface of the fiber. The coating process is to use special equipment to coat the surface of the filter material, and then go through conventional processes such as the drying and heat setting. Both processes can improve the oxidation resistance, corrosion resistance, and temperature resistance of the filter material, and at the same time can improve the filtration accuracy of the filter material and prolong the service life of the filter material. After immersion treatment and coating treatment, the surface microstructure of the filter material was shown in Figure 4. It can be seen from Figure 3b that after the coating treatment, the surface of the filter material is coated with a film with different pore sizes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PPS (\%)** & **PI (\%)** & **PTFE (\%)** \\ \hline Before aging & 99.9790 & 99.9505 & 99.9824 \\ After aging & 99.9970 & 99.9921 & / \\ \hline \hline \end{tabular}
\end{table}
Table 3: Filtration efficiency of different material filtration materials.  
Figure 3: Surface electron microscopy of (**a**) PPS, (**b**) PI, and (**c**) PTFE.  
Under standard test conditions, the PPS filter material (sample 1, sample 6) after immersion and the coating treatment was subjected to a comparative study of filtration efficiency. The experimental results were shown in Table 5. It can be seen from Table 5 that the filtration efficiency of the filter material before and after the aging of the two surface treatment processes was very high. However, compared to the coating treatment process, the filtration performance of the immersion treatment process was slightly superior. After the filter media was impregnated with PTFE, the PTFE emulsion entered the filter media and adhered to the surface of the fiber or filled the space between the fiber and the fiber, which inevitably reduced the internal pore size of the filter media and improved the filtration efficiency of the filter media for particulate matter. From the microscopic morphological analysis in Figure 4b, a microporous film was formed on the surface of the filter media after the coating treatment, which led to a reduction in porosity, thus affecting the filtration performance of the original filter media [33]. In addition, PTFE emulsion was not evenly distributed on the surface of the filter media, and the surface micropores were of different sizes, so the filtration accuracy of the coated filter media was greatly affected by the production process.
# Performance and Control Strategy of Real-Time Simulation of a Three-Phase Solid-State Transformer
## 4 Digital Real-Time Simulator
### Modulation Schemes  
Another challenge to simulate in real-time the SST is the control of the switching devices; for \(VSC_{i}\) and \(VSC_{o}\) the modulation strategy is carried out by a SPWM technique [23], in which three modulating sine-waves signals shifted \(\frac{2\pi}{3}\) rad among them are compared with a triangular carrier, the result per modulating signal controls a branch of the converter. However the generation of the structure by using Simulink blocks like triangular generators and comparators causes the comparison result in being linked to the sampling time \(T_{s}\); meaning that only when a \(T_{s}\) rising edge occurs the switching state will change, with the possible loss of commutation states as shown in Figure 5.  
One possible solution is the use of the RTE library, which allows the simulation algorithm to generate transition states between computation times \(T_{s}\). However the use of RTE-Comparator and RTE-Not blocks to generate the PWM signal only allows to create one transition event per time-step \(T_{s}\); to solve this the RTE library incorporates the RTE-SPWM block, whose operation principle is also shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of Inputs & 32 \\ Number of Outputs & 32 \\ Number of Switches & 64 \\ Maximum number of states & 150 \\ Calculation power & 25.6 GFLOPS \\ Solving time & \(\approx\)200 nS \\ \hline \end{tabular}
\end{table}
Table 2: eHS Parameters.  
However the RTE-SPWM block is optimized for DC-DC converters, and the simulation technique operates only positive voltages for the triangular wave. Figure 6 shows an arrangement of the RTE-SPWM block in order to handle the negative half-cycle of the carrier.  
For the DAB converter the modulation scheme is based on a conventional phase-shift, consisting of two 50% fixed duty cycle square-waves signals shifted \(\theta\) degrees among them; the proposed modulation scheme is shown in Figure 7. A block RTE-SPWM with a 50% ratio (\(D=0.5\)) is used to generate the control signals, and the \(\theta\) regulation is made through RTE-Delay buffers. However the use of these blocks only allows for positive values; for negative degree values for \(\theta\) (inverted power flow) the input is saturated to zero, to solve this for \(\theta\geq 0\) the switching signals are delayed on the secondary bridge and for \(\theta<0\) values the primary bridge control signals are lagged. The sign and conversion from degrees to time are solved with a gain \(K=\frac{1}{2\pi f_{sDAB}}\), and all the logic functions are made through RTE-Blocks.  
Figure 5: RTE-SPWM block and operation.  
Figure 6: RTE-SPWM block and operation.
## 3 System Function Realization
### Monocular Camera Rough Distance Measurement  
According to the principle of camera imaging, the size of the object in the camera image is proportional to its actual size and related to the focal length. Thus, a similar triangle method can be used for the monocular camera coarse distance measurement. \(W\) is the actual width of the object, \(W^{\prime}\) is the pixel width of the object on the imaging plane, \(d\) is the distance between the object and the camera lens, and f is the focal length of the camera. The proportional relationship between the object width and distance is derived from the similarity of triangles. Using this relationship, the distance between the object and the camera lens can be calculated as shown in Equation (5).  
\[d=\frac{W\times f}{W^{\prime}} \tag{5}\]  
Firstly, the camera is calibrated to determine its pixel focal length. Subsequently, this focal length and the true width of the vehicle (\(W\)) are input into the system. The image is then imported, and the system is provided with the target detection box width (\(W^{\prime}\)) calculated by YOLOv8, as well as the pixel coordinates of the target. Using these values, the target's real-world distance is calculated using Equation (5). The final ranging result is then output. The schematic diagram and flow chart of the monocular ranging algorithm are shown in Figure 8.  
Figure 8: The schematic diagram and flow chart of the monocular ranging algorithm. (**a**) Algorithm schematic diagram; (**b**) algorithm flow chart.  
Figure 7: The performance of the trained model. (**a**) P-R curve; (**b**) detection performance.
## 3 Results and Discussion
Si toxicity is linked to erosive gastritis promotion, which can lead to internal bleeding and ulcers [84,85]. The RDD is 19 mg/kg for women and 40 mg/kg for men [86]. Geophagic presented mean values of 456,435 (sand) and 318,063 (silt) mg/kg. No physiological function in the human body is attributed to Al, which is considered non-essential [87]. Samples revealed a higher concentration high than RDD for adults of 1 mg/kg [75], with a mean of 77,615 mg/kg on the total fraction (sand) and 164,576 mg/kg in silt fraction. In general, Al is removed by the kidneys; however, prolonged consumption can lead to toxicity [88]. Bone and neurological disorders and learning and memory disturbances associated with Al were also reported [88]. Barium is a non-essential element and is potentially toxic [89], with RDD of 0.02 mg/kg [90]. Toxic effects include breathing difficulties; increased blood pressure; changes in heart rate; stomach irritation; muscle weakness; changes in nerve reflexes; swelling of the brain; and damage to the liver, kidneys, and heart [91]. Geophagic samples' mean concentration was 66.5 and 491 mg/kg in sand and silt fractions, respectively. WHO established an RDD of 1 mg/kg for Br, with concentrations in this study ranging from 0.5 to 21.3 mg/kg in sand fraction and 0.4 to 35.5 mg/kg in silt fraction, with some samples posing a potential toxicity risk for consumers [82]. Cr toxicity in humans has been linked to damaged blood cells, livers, nervous systems, and kidneys. In this study, the mean concentration was 110 and 230 mg/kg in sand and silt fractions, respectively, posing a potential risk. Nickel is necessary for the absorption of Fe by the human body, with toxicity related to miscarriages and foot deformities, with RDD = 1.1 mg/kg [92]. The silt fraction revealed a Ni mean of 69.1 mg/kg and a sand fraction of 31.2 mg/kg, representing a potential risk. Vanadium can cause distress, with toxicity directly proportional to the increase in valances states, instigating depressed growth, diarrhea, and depressed food intake [93]. The average concentration in the sand fraction was 37.2 mg/kg and in the silt fraction 195 mg/kg, higher than an RDD of 0.002 mg/kg [94]. [PERSON]'s correlation of sand and silt fraction is presented in Table 4. In sand fraction, pairs Na/Ca, S/Cl, Fe/Mn, K/Mn, and Zn/S, and silt fraction Ca/Cu, Cl/Na, Fe/S, and Mg/S showed a significant positive correlation, suggesting a common source, possibly of geogenic nature. Pairs P/Fe, K/P, and P/Mn, in sand fraction and K/Zn in silt fraction presented a significant negative correlation, which may indicate different sources. Previous studies in dry environments with relatively high temperatures reported geophagic materials enriched in Pb, Mn, and Cu and established a link with anthropogenic sources,such as industry, traffic, and asphalt emissions resuspension, associated with higher PTEs adsorption and deposition in environments with temperatures \(>\) 20 \({}^{\circ}\)C [23; 94; 95]. Sources applicable to the climatic season and markets studied. Silt fraction correlation analysis, associated pairs, with significant positive correlation.
## 2 Data
### Sentinel-1 A/B  
We used 73 Copernicus Sentinel-1 A/B dual-polarization C-band (5.405 GHz \(\cong\) 5.6 cm) SAR images taken between February 2016 and March 2018. Table 1 summarizes the temporal distribution of the data where a sequence denotes up to three sequential images connected to the same near-coincidental altimeter track. All images are extra wide swath ground range detected with medium resolution, i.e., 40 m \(\times\) 40 m pixel size and range and azimuth resolution of 93 m and 87 m, respectively. The polarization is horizontal transmit and receive (co-polarization, HH) and horizontal transmit and vertical receive (cross-polarization, HV). The data is preprocessed using the Sentinel Application Toolbox (SNAP) applying the following steps: precise orbit \(\rightarrow\) noise removal \(\rightarrow\) (optional) slice assembly \(\rightarrow\) calibration to \(\sigma^{0}\)\(\rightarrow\) terrain correction to UTM with ASTER 1 sec GDEM.
# A Secure and Efficient Authentication Scheme for Fog-Based Vehicular Ad Hoc Networks
## 3 Preliminaries
### System Model  
The system model includes the cloud server (\(CS\)), fog node (\(FN_{j}\)), and vehicle user (\(V_{i}\)).
* Cloud server (\(CS\)): The \(CS\) is a fully trusted entity that initializes the system setup and provides registration services for \(V_{i}\) and \(FN_{j}\) and stores the verification values derived from their identities for authentication.
* Fog Node (\(FN_{j}\)): \(FN_{j}\) is a semi-trusted entity in the protocol that has its own computing capabilities and storage capacity. The fog node mediates the authentication messages transmitted between \(CS\) and \(V_{i}\). Once the authentication phase is complete, \(FN_{j}\) establishes a shared session key with \(CS\) and \(V_{i}\). \(FN_{j}\) has data storage servers and is a wireless communication facility in VANET environments.
* Vehicles (\(V_{i}\)): Each \(V_{i}\) employs its on-board unit to communicate with other vehicles or infrastructure and collect real-time traffic information. In addition, \(V_{i}\) is considered untrustworthy in fog-based VANETs, so the adversary can perform attacks after registering as a legitimate user.
## 4 Discussion
Consequently, the correction accuracy achieved by the SATP system demonstrates high precision and remains well within the clinically acceptable range, even for mild discrepancies. These results support the feasibility of integrating SATP into clinical practice as a preventive and compensatory solution for LLD. Furthermore, the system has the potential to reduce the frequency of visits to the prostheticist, as minor adjustments can be made automatically, thereby eliminating the need for frequent professional intervention. Despite the system's notable advantages, it is imperative to acknowledge potential limitations that must be taken into account. The precision of LLD evaluation is contingent upon the quality of the captured image, which is susceptible to external factors such as lighting conditions or the resolution of the smartphone camera. A further limitation is that the current solution is optimized for users with transtibial amputations and would require adaptations for other types of prostheses. In future research, we aim to address the impact of lighting conditions on the algorithm and extend the correction method to other amputated anatomical segments. Future research will address the impact of lighting conditions on the algorithm and extend the correction method to other amputated anatomical segments. The goal is to simplify the system by reducing the number of markers and minimize environmental errorsto reduce error accumulation over time. Additionally, we will evaluate the adjustment frequency during clinical trials in collaboration with medical specialists. At this experimental stage, validation was carried out on a mannequin in a static and controlled environment. The aim was to demonstrate the technical feasibility of the proposed method for correcting a simulated LLD. While this approach does not fully replicate the biomechanical complexity of a real human user, it enabled an initial evaluation of the system's accuracy and the operating principle of the SATP. However, this approach falls short in fully capturing the intricate biomechanical nuances inherent in real-world applications. The limitations of static testing will be addressed in subsequent research phases, which will include clinical studies and dynamic testing on human users. The proposed method in this study offers a novel approach to the assessment of and compensation for LLD in transtibial amputee patients by leveraging accessible technologies. Notwithstanding the challenges associated with optimization for enhanced accuracy, the proposed solution offers considerable potential for clinical implementation, with the potential to reduce the necessity for manual adjustments and contribute to an enhancement in the quality of life of patients.
# Adaptive Point Learning with Uncertainty Quantification to Generate Margin Lines on Prepared Teeth
## 3 Methods
### Post-Processing
#### 3.6.3 Removing Outliers  
Before the prediction of spline parameters, it was necessary to remove outliers from the predicted point cloud to obtain smooth and precise splines. Our novel post-processing was based on local density and first component of principal component analysis (PCA). To evaluate if a point is an outlier [57], first we calculate the average distance \(d_{i}\) for each point \(p_{i}\) based on its Euclidian distances with its k nearest neighbors \(q_{j}(j=1\ldots,k)\) such as:  
\[\bar{d}_{i}=\frac{1}{k}\sum_{j=1}^{k}\text{dist}(p_{i},q_{j}) \tag{8}\]  
Then we calculate the local density LD of \(p_{i}\) defined as:  
\[LD(p_{i})=\frac{1}{k}\sum_{q_{j}\in KNN(p_{i})}\exp\left(-\frac{\text{dist}(p _{i},q_{j})}{\bar{d}_{i}}\right) \tag{9}\]  
where KNN is the k nearest neighbors of \(p_{i}\). Then, from the observation of LD for each case, we define a threshold \(\epsilon=0.4\) to flag every point \(p_{i}\) with LD inferior to \(\epsilon\) as an outlier. We decided to take k = 50 in our analysis. The parameters for \(\epsilon\) and k were chosen by qualitative observation. For example, we observed that when the LD value for a point was below 0.4, this point would be isolated.  
Using the local density calculations alone, another problem could arise when the predicted point cloud has some gaps. Some points will be identified as outliers because they do not have enough neighbors while they are not outliers in reality (Figure 5). To overcome this issue, we proposed to check whether a point \(p_{i}\) identified as an outlier follows the tendency of its neighborhood (k = 50). We used PCA first component to determine tendency (Figure 5), and made a projection \(p_{i}^{\prime}\) of \(p_{i}\) to the tendency ray. If the distance dist(\(p_{i}\), \(p_{i}^{\prime}\)) is greater than a relative threshold of 0.1, then \(p_{i}\) is considered an outlier. The post-processing process is illustrated in Figure 6.  
Figure 5: Identifying outliers with (**a**) local density only; (**b**) with local density and PCA; (**c**) first component of PCA. Purple represents outliers in (**a,b**). With both local density and PCA, less outliers are observed.
## 1 Introduction
Both the O\({}_{2}\)-A and O\({}_{2}\)-B bands have potential for tower-based SIF retrieval, but in this study, we focused on the atmospheric correction at the O\({}_{2}\)-A band because, (1) the absorption effect at the O\({}_{2}\)-B band is much weaker than that at the O\({}_{2}\)-A band, so the atmospheric correction for tower-based SIF observation at the O\({}_{2}\)-B band is not so important as at the O2-A band; (2) in practice, the SIF retrieved at the O\({}_{2}\)-A band is more frequently used than that at the O\({}_{2}\)-B band due to the robustness of retrieval. Different from [PERSON] et al. [30] which mainly focused on theoretical analysis, this study aims to simplify the problem and to provide an operational solution for the atmospheric correction of tower-based observations of both the downwelling irradiance and upwelling radiance to support long-term, continuous spectral measurements. First, the radiative transfer process for tower-based SIF observation is analyzed theoretically; then, the atmospheric radiative transfer parameters are simulated using the MODerate resolution atmospheric TRANmission 5 (MODTRAN 5) model [31] for different conditions; and, finally, look-up tables (LUTs) for fast estimation of the upward and downward atmosphere transmittances between the tower-based sensor and top of canopy (TOC) are constructed. The influences of the temperature and pressure on the atmospheric transmittance are also compensated for using a corrector factor of RTPL. The performance of the method is then evaluated using field measurements.