esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulations
### Discussion  
The simulation results demonstrate that the fault recovery performance of the TFR approach significantly reduced the clock drifting of the local clock in comparison to the standard PTP and the standard PTP under RSTP. This is due to the fact that the TFR approach detects the failure with a short duration, and it starts to recover it by using a handshake mechanism between the master and slave nodes. In contrast, the standard PTP does not include any fault recovery mechanism to identify the failure in the network, so it does not perform a recovery process to retrieve it.  
As a final point, the reason for the variation in the detection and recovery delay of the simulation results in comparison to the analytical results is due to the variation in the total network delay, specifically the queueing and transmission delays, and the latency of reestablishing the BMC and the synchronization hierarchy after the failure occurrence in the simulated network. More importantly, the small variation that has been stated in the results is owing to the fact that the sample failure scenario that has been used in the analysis and simulation validation includes the available links failure without isolating the GMC or synchronization reference from the network. Additionally, we did not consider a node failure scenario in the sample network, due to the fact that the node failure in the network leads to isolating the GMC and synchronization reference.
## 4 Security Notions
### Publicly Verifiable Property
#### 4.3.2 Temporal Verification  
The public verification method to the spatial aggregation is equally effective to the temporal verification. For any node, one of its pairwise nodes in the neighborhood gain its encrypted message formed as \(x^{i}_{(j,d)}+r^{i}_{(j,d)}\) in a billing period before computing its local temporal aggregation, and thus its total temporal aggregation is computed and verified by summing up local temporal aggregations from all its pairwise nodes.  
Thus, the billing user itself or any user node can verify the accuracy of the billing from the GW without revealing individual fine-grained data. Hence, they can detect if there is a malicious and fraudulent profile of the malicious GW and reports it to the CC in time.
## 5 Classification and Regression of the Runway Overrun Risk
### The Classification of Flights with Runway Overrun Risk  
#### 5.2.1 Classification Model and Evaluation Metrics  
For the classification task, we still use the nine features from Table 1. In this paper, all flights are sorted based on their risk values. The top 35% of flights with the highest risk values are selected as positive samples, while the remaining 65% of flights are considered negative samples. The threshold for distinguishing positive and negative samples is set at risk = 0.30. The following classification models are employed: LR, NNs, DTs, RFs, and SVM.  
The evaluation metrics include accuracy, precision, recall, and F1-score, with the following formulas:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{12}\]  
\[Precision=\frac{TP}{TP+FP} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Regression Model** & **MAE** & **RMSE** & **MAPE** \\ \hline
1 & LR & 0.0295 \(\pm\) 0.0025 & 0.0416 \(\pm\) 0.0036 & 0.1712 \(\pm\) 0.0242 \\
2 & NN & **0.0206 \(\pm\) 0.0024** & **0.0279 \(\pm\) 0.0039** & **0.1129 \(\pm\) 0.0217** \\
3 & DT & 0.0401 \(\pm\) 0.0053 & 0.0585 \(\pm\) 0.0081 & 0.1908 \(\pm\) 0.0356 \\
4 & RF & 0.0298 \(\pm\) 0.0036 & 0.0426 \(\pm\) 0.0065 & 0.155 \(\pm\) 0.0253 \\
5 & SVR & 0.0464 \(\pm\) 0.0063 & 0.0588 \(\pm\) 0.0097 & 0.2853 \(\pm\) 0.0509 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Regression results for runway overrun.  
Figure 10: Regression feature importance ranking.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Classification Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline
1 & LR & 0.8326 \(\pm\) 0.0376 & 0.7536 \(\pm\) 0.0824 & 0.7625 \(\pm\) 0.085 & 0.7522 \(\pm\) 0.0513 \\
2 & NN & **0.899 \(\pm\) 0.0331** & **0.8671 \(\pm\) 0.0637** & **0.8312 \(\pm\) 0.0971** & **0.8439 \(\pm\) 0.0552** \\
3 & DT & 0.8056 \(\pm\) 0.0444 & 0.7153 \(\pm\) 0.0836 & 0.7135 \(\pm\) 0.0802 & 0.71 \(\pm\) 0.0614 \\
4 & RF & 0.8372 \(\pm\) 0.0425 & 0.8103 \(\pm\) 0.0877 & 0.6792 \(\pm\) 0.0916 & 0.7341 \(\pm\) 0.07 \\
5 & SVR & 0.8576 \(\pm\) 0.0451 & 0.8018 \(\pm\) 0.0904 & 0.7833 \(\pm\) 0.0773 & 0.787 \(\pm\) 0.0562 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classification results for runway overrun.  
\[Recall=\frac{TP}{TP+FN} \tag{14}\]  
\[F1\text{-}Score=\frac{2 Precision\cdot Recall}{Precision\cdot Recall} \tag{15}\]  
where TP, TN, FP, and FN represent the numbers of true positive samples, true negative samples, false positive samples, and false negative samples, respectively.
## 4 Result and Discussion
### Spatial and Temporal Characteristics of the Accuracy Statistics
Figure 9: Scatterplots for the observed vs. estimated daily ET\({}_{0}\) in the blind test of the ML model in the croplands of South Korea, according to year. A comparison with the operative MODIS PET product (MOD16A2 GF) was conducted for a more objective assessment of our ET\({}_{0}\) retrievals because the PET can be considered the same physical quantity as ET\({}_{0}\). Since MOD16A2 GF comprises 8-day composite data, we aggregated the daily ET\({}_{0}\) and in situ data into 8-day bins for these comparisons. Table 8 shows the accuracy statistics of the ML model and MODIS product against the in situ data collected during 2013-2019. Both 8-day composite datasets led to good model performance due to the effects of temporal smoothing. However, the results of the ML model were much better than those from the MODIS product for croplands in South Korea (Figure 11). The RMSE of our retrievals was 73% smaller than that of MODIS PET ((1.142\(-\)0.304)/1.142), and the ML model produced an almost perfect CC of 0.982. This difference in performance is because our ML model was locally optimized for South Korea, whereas the MODIS PET algorithm was not specifically designed for South Korea. This finding also indicates that the arrangement of the input features for the ML model was spatially and temporally appropriate. The 8-day LAI/FPAR and the daily NDVI created through time-series interpolation appeared suitable for the estimation of daily ET\({}_{0}\), as described previously [48; 49]. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multicolumn{3}{c}{**Observed ET\({}_{0}\)**} & \multicolumn{3}{c}{**Estimated ET\({}_{0}\)**} \\ \cline{2-7}  & **Min** & **Max** & **Mean** & **Min** & **Max** & **Mean** \\  & **(mm/day)** & **(mm/day)** & **(mm/day)** & **(mm/day)** & **(mm/day)** & **(mm/day)** \\ \hline March & 0.494 & 8.555 & 3.572 & 0.840 & 7.582 & 3.600 \\ April & 0.599 & 9.621 & 4.493 & 0.826 & 8.395 & 4.479 \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Retrieval** & **MBE (mm/day)** & **MAE (mm/day)** & **RMSE (mm/day)** & **CC 1** & **n** \\ \hline This study & \(-0.001\) & \(0.234\) & \(0.304\) & \(0.982\) & \(1883\) \\ MODIS & \(0.337\) & \(0.867\) & \(1.142\) & \(0.769\) & \(1883\) \\ \hline \hline \end{tabular} \({}^{1}\)\(p\)-value \(<0.001\).  
\end{table} 
Table 8: Comparison of the accuracy statistics of the ML model and Moderate Resolution Imaging and Spectroradiometer (MODIS) product using 8-day composite data for croplands in South Korea for the 2013–2019 period. Figure 10: Scatterplots for the observed vs. estimated daily ET\({}_{0}\) in the blind test of the ML model in the croplands of South Korea, according to month.
# A Review of In-Situ and Remote Sensing Technologies to Monitor Water and Sanitation Interventions
###### Abstract
This involves, in addition, increasing accountability of the operating environment for the providers, delegation, contracting, or licensing of operations, so that providers deliver services to the users. Remote sensing technologies may be of critical importance where limited information is available. For example, the spatial data behind remote sensing imagery can be used to feed a robust spatial econometric model to estimate the severity of infrastructure deprivation in the slums and shantytowns of a given city, where surveys typically are not conducted due to high data collection costs. In situ instruments vary from flowmeters and water quality sensors to motion detectors installed in latrines. These sensor technologies can be used both operationally or within a sampling frame. Collecting data via cellular phones, with online analytics and dissemination, is an increasingly common feature of water and sanitation programs.
## 3 Result and Discussion
### Photocatalytic Results
As shown in Figure 9A, the degree of mineralization decreased slightly by about 10% from the first to the second run and remained constant in the three subsequent runs, indicating that the material has good photocatalytic stability for the degradation and mineralization of DCF under the applied conditions. Figure 8: Effect of calcination temperature on TOC removal for TiO2 synthesized at (**A**) 40 \({}^{\circ}\)C, (**B**) 70 \({}^{\circ}\)C, and (**C**) 100 \({}^{\circ}\)C (‘-’ = without calcination, TOC values were determined after irradiation of 120 min). Figure 7: (**A**) Plot of relative DCF concentration versus reaction time for TiO2 samples synthesized at different temperatures and calcined at 500 \({}^{\circ}\)C and (**B**) plot for the estimation of the apparent pseudo-first-order rate constant (for B: \(c_{0}\) = DCF concentration when turning on the light). The proposed mechanism for DCF degradation across TiO\({}_{2}\) upon irradiation with simulated sunlight is shown in Figure 9B. By irradiating with light with an energy equal to or greater than the band gap of TiO\({}_{2}\), electrons are transferred from the valence band (VB) of TiO\({}_{2}\) to its conduction band (CB), creating positively charged holes in its valence band. The reaction of these electrons and holes with dissolved oxygen or water molecules forms reactive oxygen-containing species (superoxide radicals (-O\({}_{2}^{-}\)) and hydroxyl radicals (-OH)), which degrade DCF and its formed intermediates into CO\({}_{2}\) and water.
# Privacy-Preserving Outsourced Artificial Neural Network Training for Secure Image Classification
## 4 The Framework and Construction for Privacy-Preserving Image Classifier
### System Model  
_IPPNN_ has four types of entities: a group of users, a neural network training server (\(\mathcal{S}_{\mathcal{NN}}\)), an auxiliary server (\(\mathcal{S}_{\mathcal{UI}}\)) which does not collude with \(\mathcal{S}_{\mathcal{NN}}\), and a trusted third-party authority (\(\mathcal{TPA}\)) to enable functional encryption.  
* Users: Each user owns a dataset which contains multiple training samples and is willing to contribute his samples in a privacy-preserving way. In our system, the responsibility of a user is only to provide his data in well-designed encrypted version to the servers \(\mathcal{S}_{\mathcal{UI}}\) and \(\mathcal{S}_{\mathcal{NN}}\).
* \(\mathcal{S}_{\mathcal{NN}}\): A neural network training server to collaboratively build a global neural network model for image classification without model parameters information leakage. The server \(\mathcal{S}_{\mathcal{NN}}\) orchestrates the private training process with the help of the server \(\mathcal{S}_{\mathcal{UI}}\). Finally, \(\mathcal{S}_{\mathcal{NN}}\) is the owner of the image classifier.  
* \(\mathcal{S}_{\mathcal{A}\mathcal{U}}\): An auxiliary server to help the trainer server \(\mathcal{S}_{\mathcal{NN}}\) to complete a privacy-preserving neural network training. It does not possess any model updates parameters or optimized model weights.
* \(\mathcal{TPA}\): A trusted third-party authority \(\mathcal{TPA}\) is responsible for building the underlying cryptosystem, delivering the public key to each user and providing private key service to the server \(\mathcal{S}_{\mathcal{A}\mathcal{U}}\).
## 2 Theoretical Derivations
### Calculation of Prestress Loss, \(\sigma_{st,l}\)
#### 2.1.7 Prestress Loss Due to Long-Term Relaxation of the Strand, \(\sigma_{l6}\)  
The deformation of the strand will change with time, and the stress will decrease accordingly when the strand is subjected to a constant external force, which is the prestress loss due to the long-term relaxation of the strand. The greater the tensile force of the steel strand, the more obvious the stress relaxation effect is. The relaxation generally occurred earlier in the process, without considering the quality of the strands. This effect can be basically completed after one year, and then gradually calmed. The relaxation loss, \(\sigma_{l6}\), is related to the relaxation coefficient, \(k\), and can calculated according to the following formula:  
\[\sigma_{l6}=k\sigma_{st} \tag{21}\]  
where \(k\) is the relaxation coefficient and is related to the quality of the steel. For the cold-drawn thick steel bar, \(k\) is taken as 0.05 for one-time tensioning and 0.035 for ultra-tensioning. As for the steel wires and steel strands, \(k\) is considered to be 0.07 for one-time tensioning and 0.045 for ultra-tensioning [17]. For low-relaxation steel wires, the value of \(k\) can be taken to be 0.002 when no data are available, which we have learned from our experience.  
The total prestress loss of the prestressed wires can calculate by Equation (22):  
\[\sigma_{st,l}=\sum_{1}^{6}\sigma_{i} \tag{22}\]
# Development of a Combined Orchard Harvesting Robot Navigation System
## 1 Introduction
### Objective of the Study  
The objective of this research was to develop a master-slave combined harvesting robot navigation system that operates in the same row, while considering fruit tree height and branch growth. In this navigation system, the combined harvesting robot can perform within-row cooperative harvesting and cross-row continuous turning. In-row cooperative harvesting means that the combined harvesting robots can repeatedly stop in a fixed formation within a fruit tree row, while the picking robots stop and carry out the picking operation according to the fruit tree planting array, put the picked fruits into the fruit baskets of the transport robots, and then move on to the next tree when the harvesting is finished. Continuous turns across rows require the combined robot to continuously track the turn path to reduce the drive time when not operating. For this purpose, a combined harvesting robot navigation system was developed as a navigation method, with communication technology, navigation technology, and control technology, and the effectiveness of the navigation system was verified in orchard experiments [12].
## 3 Results and Discussion
### The Phase Formation Mechanism and Mechanical Properties
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Replacing** \\ **Atom** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Atomic** \\ **Fraction** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **B** \\ **G** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **G** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **E** \\ **F** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **v** \\ **G/B** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **G/B** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **C12-C44** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **H\({}_{V}\)** \\ \end{tabular} } \\ \hline \multirow{4}{*}{Al} & 0.25 & 265.66 & 179.77 & 440.05 & 0.22 & 0.68 & \(-\)33.95 & 23.4 \\  & 0.5 & 253.87 & 180.32 & 437.41 & 0.21 & 0.71 & \(-\)103.76 & 24.99 \\  & 0.75 & 248.28 & 189.51 & 453.22 & 0.2 & 0.76 & \(-\)77.16 & 28.35 \\  & 1.0 & 246.66 & 211.87 & 494.13 & 0.17 & 0.86 & \(-\)141.87 & 35.42 \\ \hline \multirow{2}{*}{Fe} & 0.25 & 255.7 & 169.55 & 416.58 & 0.23 & 0.66 & \(-\)29.87 & 21.91 \\  & 0.5 & 242.8 & 143.8 & 360.28 & 0.25 & 0.59 & 32.91 & 16.82 \\  & 0.75 & 213.99 & 115.99 & 294.73 & 0.27 & 0.54 & 17.84 & 12.76 \\ \hline \multirow{2}{*}{Cu} & 0.25 & 264.16 & 152.89 & 384.48 & 0.26 & 0.58 & 8.77 & 17 \\  & 0.5 & 244.78 & 120.28 & 310.05 & 0.29 & 0.49 & 41.94 & 11.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: The calculated bulk modulus \(B\) (GPa), shear modulus \(G\) (GPa), [PERSON]’s modulus \(E\) (GPa), [PERSON]’s ratio \(v\), [PERSON]’s ratio \(G/B\), Cauchy pressure \(C_{12}\)–\(C_{44}\), and Vickers hardness \(H_{V}\) for Ti\({}_{\{1-x\}}\)M\({}_{x}\)N intermetallics. As a whole, the random distribution of the Ti-N compounds and the Al-Cu-Ti intermetallics contributed to great enhancements in wear resistance. During the thermal diffusion process, the inter-diffusion of the Ti atoms and Cu atoms promotes the formation of hard intermetallic grains, where some loose structure and pores might be generated. In addition, the grain growth leads to a rough surface, which results in a quick displacement at the initial stage. Especially for the F3 sample, the deficient Ti atom made it quite difficult to form enough Cu-Ti intermetallic, not to mention the Ti-N compounds, and the inter-diffusion between the Cu and Ti atoms led to surface grain coarsening.
# Integrating the IoT and Blockchain Technology for the Next Generation of Mining Inspection Systems
## 5 Evaluation  
To effectively evaluate our case study, we implemented it as a fully-working prototype. The cloud inspection platform was implemented using Docker (version 20.10.7-0 ubuntu5 20.04.2) and Docker Compose (version 1.29.2) as containerization engine and multi-container manager, respectively. The platform was deployed on a cloud virtual machine (Digital Ocean General Purpose droplet) based on Ubuntu 20.04.3 LTS with 8 GB of RAM, 160 GB of SSD, and 4 vCPU.  
Then, the blockchain module runs on nodes using the official Geth client (version 1.10.1-stable) on independent virtual machines with 4 GB of RAM, 20 GB of SSD, and 4 vCPU on an OpenStack server using a clean Linux Ubuntu installation (version 18.04).  
For the IoT platform, and based on the results and evaluation presented in our previous works [26; 36], we opted for an ESP32 microcontroller. In particular, we selected the WROver-E 32-Bit ESP32 as the most suitable microcontroller in the 10 USD cost range. The board has an 80 MHz clock chipset with 1024 KB of program space and 320 KB of memory. It also includes Serial, USB, Wifi, and BLE interfaces for communication.  
Finally, the mobile app was installed and tested on mobile devices running the Android OS version 9.
# Modeling the Dynamic Behavior of a Pilot-Operated Solenoid Valve for an Ultra-High Pressure Vessel
## 2 Modeling the Solenoid Motion
### Modeling the Motion Equation by Phase  
The solenoid valve can be described as an equation of motion with one degree of freedom because the plunger moves only in the vertical direction. As explained in Section 2.1, the solenoid movement is divided into two Phases. In Phase 1 where only the pilot plunger moves, free body diagram (FBD) is depicted as Figure 3a, while FBD in the second phase as Figure 3b. Where all parameters are: attraction force (F\({}_{\text{a}}\)), preload force of spring preload (F\({}_{\text{P}}\)), mass of the pilot plunger (m\({}_{\text{p}}\)), mass of the main plunger (m\({}_{\text{m}}\)), mass of the spring seat (m\({}_{\text{s}}\)), mass of the spring guide (m\({}_{\text{g}}\)), spring coefficient (k), and damping coefficients (c\({}_{\text{1}}\) and c\({}_{\text{2}}\)).  
Figure 2: Principle of the pilot-operated solenoid valve: (**a**) initial state; (**b**) application of an electric voltage; (**c**) pressure equilibrium state; (**d**) opening of the flow passage.  
In Phase 1, the pilot plunger moves under the influence of the attraction force, and the equation of motion can be written as Equation (1) with the initial conditions Equation (2).  
\[\mathrm{m}_{\mathrm{p}}\ddot{\mathrm{x}}\ +\ c_{1}\dot{\mathrm{x}}\ =\ \mathrm{f}_{a},\ \mathrm{for}\ 0\leq\mathrm{t}<\mathrm{t}_{1} \tag{1}\]  
\[\mathrm{x}(0)=0,\dot{\mathrm{x}}(0)=0. \tag{2}\]  
In Phase 2, all the drive unit of the solenoid move together under the influence of the attraction force and the spring preload. The equation of motion can be written as Equation (3) with the initial of Equation (4).  
\[\big{(}\mathrm{m}_{\mathrm{p}}+\mathrm{m}_{\mathrm{m}}+\mathrm{m}_{\mathrm{s} }+\ \mathrm{m}_{\mathrm{g}}\big{)}\ddot{\mathrm{x}}+\mathrm{c}_{2}\dot{ \mathrm{x}}+\ \mathrm{kx}=\mathrm{f}_{a}+\mathrm{f}_{p},\ \mathrm{for}\ \mathrm{t}_{1}<\mathrm{t}\leq \mathrm{t}_{2} \tag{3}\]  
\[\mathrm{x}(\mathrm{t}_{1})=\mathrm{p}_{1},\ \mathrm{x}(\mathrm{t}_{1})= \mathrm{v}_{1}. \tag{4}\]
## 4 Cable-Breaking Simulation
### Analysis Condition Selection  
Because the stress levels of different cables of a cable-stayed bridge are different, and they are in the natural environment for a long time, the erosion and damage of cables are also uncertain, and thus, the possible breaking positions are random. Therefore, when the cables in different positions are broken, the influence on the structural stress and deformation is also different. Thus, the dead load deformation of the structure after the cables were broken at different positions was calculated, and the maximum deflections of the main girder and the displacement of the tower top were extracted, as shown in Figure 6.  
Figure 5: Solution diagram of the cable-breaking interval time.  
It can be seen from Figure 6a that the maximum deflection of the main girder first decreased and then increased with the breaking of cables at different positions from #M29-#M1. After the #S1-#S29 cables were broken, the maximum deflection of the main girder shows a nonlinear decreasing trend. The breakage of the #M24 cable in the main span had the greatest influence on the deflection of the main girder, and the maximum deflection of the main girder was \(-0.160\) m, with an increase of 0.073 m. The breakage of the #S29 stay cable on the side span also had a great effect on the deflection of the main girder, and the maximum deflection of the main girder was \(-0.141\) m, with an increase of 0.054 m. As shown in Figure 6b, with the breakage of the #M29-#S29 cables, the displacement at the top of the cable tower was less nonlinear. The breakage of the #S29 cable had the greatest influence on the cable tower alignment, and the displacement increase was 0.051 m. As far as the structural deformation of the cable-stayed bridge is concerned, the breakage of the #M24 cable in the main span and the #S29 cable in the side span had the greatest influence on the structural deformation; therefore, these two types of cables were selected for the subsequent dynamic analysis of successive breakage.