esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Work
### Comparison to Our Previous Works  
In most of the above works, the authors only consider either encryption outsourcing or decryption outsourcing. The authors fail to achieve both encryption and decryption outsourcing simultaneously. Moreover, the authors only considered outsourcing and did not consider multiauthority and attribute revocations.  
In addition to the above existing works, we depicted the significant differences between this research work and [44]. In this version, the secret keys are produced dynamically by introducing the attribute-group keys and not only by attribute revocation. Hence, in [44], the secret keys are only produced with the help of attribute revocation and not by attribute-group keys. We provide the challenge game by the attacker and prove that the proposed scheme can tackle the attacker's challenge. Therefore, the work in [44] did not show any security challenge, which is the main limitation of the previous work.  
This work is an extension of our conference paper [45]. In [45], we only considered the security analysis of the proposed work. In this extended work, we extensively work on the computational analysis of our proposed work, which was the major deficiency of our previous work. In contrast, we simulate the results of the computing overhead of the key-generation algorithms, computing overhead of the encryption algorithm and computing overhead of the decryption algorithm. Moreover, in this work, we included the threat model that practically represents the implementation of this research work.  
The current version achieved all the processes of encryption/decryption, multiauthority attribute revocation and outsourcing computation based on ciphertext policies. On the other side, the previous work has a limited encryption and decryption process. In this regard, our simulation results in the previous version are limited to certain parameters, i.e., the time of decryption, encryption and key generation with the number of attributes. Keeping this view, we compared the results with up-to-date and more-relevant schemes, i.e., CP-ABE and MA-ABE, respectively. We extended our results not only to the time of decryption, encryption and key generation with the number of attributes but also to the time of decryption, encryption and key generation with the number of attributes per attribute authority.
## 1 Introduction
The equations used to estimate crop coefficient values based on vegetation indices, once calibrated and validated for a given location, can accurately estimate crop evapotranspiration [10; 21]. This study aims to assess the potential of multitemporal and multisensor EO data for crop parameter (\(K_{cb}\) values and the lengths of the crop's development stages) retrieval and crop type classification at high spatial (10 m) and temporal (five days) resolution with a focus on irrigated agriculture. For this purpose, EO data (Sentinel-1A + SPOT-5 Take-5) are evaluated for irrigation requirement estimation based on a soil water balance model (IrrigRotation) [22]. The main goals are to estimate crop parameters from NDVI and VV + VH backscattering time series and to calculate the crop irrigation requirements. On the other hand, the integration of Sentinel-1A VV + VH polarized data into the classification process is assessed and compared to the accuracies obtained only with spectral information from SPOT-5. This permits the determination of: (1) band combinations that provide better crop classification results; (2) the most critical dates for improved crop class discrimination; (3) and the number of observations required within a given growing season for a good classification accuracy.
## 4 Analysis
### Efficiency Analysis
#### 4.1.1 The Number of Detected Attacks  
According to the threat model in Section 2, the attack interval of the adversary has an exponential distribution \(t_{interval}\sim E(\lambda)\), where \(\lambda\) is the number of attacks in a time window \(T\). Denote the compromised probability of node \(i\) in a single attack as \(p_{i}\). Then, in a time window \(T\), the number of attacks against node \(i\) is \(\lambda p_{i}\). The average number of attestations on the node \(i\) is \(n_{v,i}=\dfrac{T}{T_{v}}\). Then, the probability of the attack perceived by one attestation is \(\frac{\lambda p_{i}\cdot t_{a}}{T}\), and the total number of detected attacks in the time window \(T\) is:  
\[n_{d,i}=\dfrac{\lambda p_{i}\cdot t_{a}}{T}\cdot n_{v,i}=\dfrac{\lambda p_{i} \cdot t_{a}}{T_{v}} \tag{31}\]  
In the original software-based attestation scheme, such as SWATT [11], the total number of detected attacks in the time window \(T\) is:  
\[n_{d,original}=\sum_{i=1}^{N}n_{d,i}=\sum_{i=1}^{N}\dfrac{\lambda\cdot t_{a}} {NT\beta}=\dfrac{\lambda\cdot t_{a}}{T\beta} \tag{32}\]  
In the LRMA, according to Definition 1, there is:  
\[n_{r}\cdot n_{d,i}=R_{i} \tag{33}\]  
where \(n_{r}\) is the number of periods to compute the risk value and \(R_{i}\) is the risk value of node \(i\).  
\[T\beta(R_{i}+\varphi)^{2}-T\beta N(R_{i}+\varphi)-n_{r}\cdot\lambda p_{i}t_ {a}(\bar{R}+\varphi)=0,\qquad i=1,2,\cdots,N \tag{34}\]Let \(F(p_{i})=R_{i}+\varphi\), and then, we have:  
\[F^{\prime}(p_{i})=\frac{Nn_{r}\mathcal{M}_{a}(\bar{R}+\varphi)}{(2R+\varphi)NT \upbeta-n_{r}\mathcal{M}_{a}p_{i}} \tag{35}\]  
\[F^{\prime\prime}(p_{i})=\frac{(T\upbeta NF^{\prime}-n_{r}\mathcal{M}_{a}) \cdot 2F^{\prime}}{(2R+\varphi)NT\upbeta-n_{r}\mathcal{M}_{a}p_{i}} \tag{36}\]  
As we can see, if we have \(n_{r}\leq\frac{(2R+\varphi)NT\upbeta}{\mathcal{M}_{a}p_{i}}\) and \(n_{r}\geq\frac{(2R+\varphi-N\bar{R}-N\varphi)NT\upbeta}{\mathcal{M}_{a}p_{i}}\), we can get \(F^{\prime}(p_{i})>0\) and \(F^{\prime\prime}(p_{i})>0\). Therefore, in the LRMA, the total number of detected attacks in the time window \(T\) is:  
\[n_{d,LRMA}=\sum_{i=1}^{N}\frac{R_{i}}{n_{r}}=\frac{1}{N}\sum_{i=1}^{N}F(p_{i}) \frac{N}{n_{r}}-\frac{N\varphi}{n_{r}}\geq F(\frac{1}{N}\sum_{i=1}^{N}p_{i}) \frac{N}{n_{r}}-\frac{N\varphi}{n_{r}}=n_{d,original} \tag{37}\]  
If and only if \(p_{1}=p_{2}=\cdots=p_{N}=\frac{1}{N}\), the equality holds.
## 3 Experiments
### X-ray Diffraction Experiment  
The X-ray diffraction (XRD) experiment on the thermal coatings of the aeroengine blade reflects the necessity of the upsampling algorithm. The experiment is carried out on the beamline BL14B1 at Shanghai Synchrotron. The 3D camera used is the Sizector 3D R600. Compared with the HD40, this camera has a larger standard field of view (XY) and z-axis measurement range and the ability to resist ambient light interference. However, the point cloud it generates is not dense enough, and experiments can only be performed at a scale of 1 mm (X-Y axis). We use the upsampling algorithm to upsample the collected point cloud and construct a 3D space motion coordinate system by generating the point cloud. The measurement accuracy at the X-Y axis can reach 0.5 mm.  
As mentioned in Seq.2 Calibration Experiment, the mapping relationship between the real sample and the point cloud is set up first. Then, we select five points in the original point cloud, and another four points from the upsampled point cloud for experiments. These four points are located between the five points of the original point cloud. The diffraction data are collected by the Rayonix MX225 CCD detector, and processed by the Fit2D software [18]. The location diagram of those diffraction points and the integration result of the XRD data are shown in Figure 8.  
Figure 6: Qualitative comparisons with PU-GAN. (**a–d**) are the ground truth and the upsampling point cloud of MPU, PU-GAN, GPU-GAN respectively. (**e–h**) are the point clouds at the outlet of the air-cooling channel enlarged corresponding to (**a–d**) point cloud respectively.  
Figure 7: Comparison of results after adding filters.  
The black points (A/C/E/G/I) are selected in the original point cloud, and the red points (B/D/F/H) are selected in the generated point cloud. The interval between them is 500 \(\upmu\)m. Figure 8b is the diagram showing locations of those points only on the x axis. By observing data curve E, F, and G in Figure 8c, the diffraction peak appears at ~31\({}^{\circ}\) (2\(\theta\)) and shows a trend of the diffraction signal weakening and then enhancing. This conclusion cannot be drawn without the information measured at the F point. The supplemented points from upsampling the data cloud can effectively complement the integrity of the original point cloud data and can obtain more continuous and complete experiment results.  
Further processing of the X-ray diffraction data can obtain more information on the thermal coatings of the blade, for example, the evolution of the stress on the coatings. The diffraction experiment here shows the effectivity of our upsampling method. The further data processing and results are not discussed here.
## 5 Results and Discussion  
### Equivalent Stress and Material Damage Corresponding to the Five States  
#### 5.1.1 Equivalent Stress Analysis  
To obtain the mechanical states corresponding to the five states, the simulation will be performed until the material of the flanging lip has incurred large damage and been squeezed out of the housing chamfer [20; 21; 22]. \"State 1\" is the state where the flanging lip just comes into contact with the roller, which is the simulation start time. Figure 6 shows the flanging lip deformation and equivalent stress of the outer ring in the latter four states.  
Figure 6a shows the equivalent stress of the outer ring in the limit state of \"State 2\", at this time, the flanging lip deformation has reached a certain degree, the maximum stress value is about 500 MPa. Meanwhile, there is no obvious effect on the inside of the outer ring. The staking quality in this state is mainly evaluated by the push-out load index. Figure 6b shows the equivalent stress of the outer ring in the limit state of \"State 3\", and the maximum stress value is about 580 MPa. At this time, the deformation quality is better, the fit is relatively stable, and the push-out load meets the requirements. If the stress is controlled below a small value, the bearing will not be \"locking\". This state is mainly controlled by the staking load, and thus the stress-strain distribution in the deformation area can be controlled. Figure 6c shows the equivalent stress of the outer ring in the limit  
Figure 6: Flanging lip deformation and equivalent stress of the outer ring in the latter four states: (**a**) Equivalent stress of “State 2”; (**b**) Equivalent stress of “State 3”; (**c**) Equivalent stress of “State 4”; (**d**) Equivalent stress of “State 5”.  
state of \"State 4\". At this time, the degree of fit between the flanging lip and the housing chamfer is improved, and the push-out load meets the requirements. However, the stress inside the outer ring is relatively large, the maximum stress value is about 610 MPa, and the limit state of undamaged material on the flanging lip is reached. In practice, in this state, the quality defects of the bearing \"locking\" often appear after roller staking. Figure 6d shows the equivalent stress of the outer ring at the limit state of \"State 5\". At this time, the structure of the flanging lip becomes thinner, the stress inside the outer ring is larger, and the maximum stress value is about 650 MPa. The material of the flanging lip is excessively squeezed or even squeezed out of the housing chamfer. Products in this state cannot meet practical application; therefore, feeding displacement must be controlled to avoid flanging lip deformation.
## 2 Biogenesis of Exosomes
Tetraspanins are proteins that play a key role in protein loading and exosome biogenesis. In the plasma membrane, tetraspanin-enriched microdomains (TEMs) are specialized platforms for protein signaling and receptor compartmentalization. Tetraspanin CD8+ and TEM together play a key role in sorting target receptors and intracellular components into the exosomes [25]. Bioactive molecules sorting into exosomes occurs either through ESCRT-dependent or ESCRT-independent mechanisms depending on the cell type origin. Plasma membrane-budded microvesicles (MVs) are a heterogeneous group of membrane vesicles that are produced with exosomes. They are generated by external budding of the plasma membrane and are of variable shapes, 100-1000 nm in size and are products of endothelial cells, red blood cells, and platelets. The small size and shape of exosomes make it easier for them to escape from the mononuclear phagocytic system cells, allowing them to stay longer in the general circulation [26].
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### NPSS Detection
#### 3.2.3 Scenario 3: Single-Satellite Visibility  
In the scenario shown in Figure 10, if no additional NPSSs are detected within 10 ms after the reception of the first NPSS, the terminal determines that it is in a single-satellite visibility scenario. In this case, the terminal records the arrival time of the NPSS as  
Figure 9: Two-satellite visibility scenario diagram.  
and the corresponding satellite ephemeris as \(S_{1}(x_{1},y_{1},z_{1})\), then enters PSM for a duration of \(T\) seconds. After exiting PSM, the terminal performs another NPSS detection. If three satellites are detected at that time, the terminal directly applies the method described in Scenario 1 for positioning. If only two satellites are detected, the terminal records the arrival times of the two NPSSs as \(L_{2},L_{3}\) and combines this information with the previously recorded arrival time \(L_{1}\) and satellite ephemeris \(S_{1}(x_{1},y_{1},z_{1})\) to perform the positioning calculation. In this case, the observation value a is given by:  
\[\begin{split}\Delta t_{1}&=L_{2}-L_{1}-T\\ \Delta t_{2}&=L_{3}-L_{1}-T\end{split} \tag{27}\]  
If the terminal still detects only a single satellite at this time, it records the arrival time of the NPSS as \(L_{2}\) and the corresponding satellite ephemeris as \(S_{2}(x_{2},y_{2},z_{2})\), then re-enters PSM for another duration of \(T\) seconds. Upon waking, the terminal repeats the above procedure. If the single-satellite visibility condition persists, it records the NPSS arrival time as \(L_{3}\) and the satellite ephemeris as \(S_{3}(x_{3},y_{3},z_{3})\). The observation value at this point is given by:  
\[\begin{split}\Delta t_{1}&=L_{2}-L_{1}-T\\ \Delta t_{2}&=L_{3}-L_{1}-2T\end{split} \tag{28}\]  
Substituting into Equation (23), the terminal coordinates can be obtained. The entire TDOA positioning process is presented in Algorithm 2.
## 3 Multiple Access Interference Characterization and Capture Diversity
### Capture Diversity
Instead, capture diversity is given by the combination of spatial macro-diversity diversity and the dynamic of the medium access protocol. Specifically, the randomness induced by the channel access negotiation scheme is such that simultaneously transmitting nodes are typically placed at different distances from the receivers, thus contributing to the near-far phenomenon, which encourage capture effect. Therefore, capture diversity can be interpreted as a form of network-level diversity.
## 2 An Isolated, High-Power, Photovoltaic Pumping System (Monitored System)  
The current work proposes a non-intrusive data logging and storage system for monitoring the performance of an isolated HPPVPS (there is no grid hybridization), which is in the Aragon region of Spain and is used for direct irrigation of tree fruit orchards (Figure 1) [12].  
As shown in the layout of Figure 2[13], the high-power photovoltaic pumping system consists of the following main components: PVG, submersible pump, induction motor, hydraulic (irrigation) network, variable-frequency drive and system controller. The PVG powers the three-phase induction motor of a centrifugal pump through a variable frequency drive (controller includes a DC-to-AC power converter). The variable-frequency drive of such a system can include a pressure controller, volume flow controller or MPPT (maximum power point tracker) [14], depending on the reference parameter of the controller (pressure, volume flow or PV generated power) [13].
## 5 Evaluations
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Time Lags**} & \multicolumn{2}{c}{**Evaluation Metrics**} & \multirow{2}{*}{**S50**} & \multirow{2}{*}{**S60**} & \multirow{2}{*}{**S70**} & \multirow{2}{*}{**T50**} & \multirow{2}{*}{**T60**} & \multirow{2}{*}{**T70**} \\ \cline{3-3} \cline{5-8}  & \multicolumn{1}{c}{**Metrics**} & & & & & & \\ \hline \multirow{3}{*}{1} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.800 & 0.793 & 0.805 & 0.813 & 0.820 & 0.813 \\  & RMSE (kWh) & 23.60 & 23.92 & 23.30 & 22.82 & 22.39 & 22.85 \\  & CVRMSE (\%) & 0.260 & 0.263 & 0.256 & 0.251 & 0.246 & 0.251 \\ \hline \multirow{3}{*}{2} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.736 & 0.807 & 0.792 & 0.804 & 0.806 & 0.806 \\  & RMSE (kWh) & 26.84 & 23.14 & 24.03 & 23.39 & 23.25 & 23.22 \\  & CVRMSE (\%) & 0.295 & 0.255 & 0.264 & 0.257 & 0.256 & 0.255 \\ \hline \multirow{3}{*}{3} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.817 & 0.817 & 0.832 & 0.826 & 0.840 & 0.823 \\  & RMSE (kWh) & 22.60 & 22.56 & 21.65 & 22.06 & 21.15 & 22.23 \\  & CVRMSE (\%) & 0.249 & 0.248 & 0.238 & 0.243 & 0.233 & 0.245 \\ \hline \multirow{3}{*}{4} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.829 & 0.835 & 0.844 & 0.858 & 0.855 & 0.849 \\  & RMSE (kWh) & 21.72 & 21.44 & 20.84 & 19.88 & 20.12 & 20.55 \\ \cline{1-1}  & CVRMSE (\%) & 0.239 & 0.236 & 0.229 & 0.219 & 0.221 & 0.226 \\ \hline \multirow{3}{*}{5} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.857 & 0.850 & 0.840 & 0.866 & 0.863 & 0.862 \\ \cline{1-1}  & RMSE (kWh) & 19.97 & 20.49 & 21.07 & 19.37 & 19.54 & 19.65 \\ \cline{1-1}  & CVRMSE (\%) & 0.220 & 0.225 & 0.232 & 0.213 & 0.215 & 0.216 \\ \hline \multirow{3}{*}{6} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.850 & 0.856 & 0.857 & 0.873 & 0.874 & 0.875 \\ \cline{1-1}  & RMSE (kWh) & 20.37 & 20.01 & 20.01 & 18.82 & 18.76 & 18.71 \\ \cline{1-1}  & CVRMSE (\%) & 0.224 & 0.220 & 0.220 & 0.207 & 0.206 & 0.206 \\ \hline \multirow{3}{*}{7} & \multicolumn{2}{c}{R\({}^{2}\) (\%)} & 0.862 & 0.785 & 0.847 & 0.866 & **0.877** & 0.872 \\ \cline{1-1}  & RMSE (kWh) & 19.61 & 23.82
# A New Monocular Pose Estimation Method for the Coplanar P4P Problem
## 2 Methodology
### To Calculate the Parallelogram's Pose Parameters Relative to the Camera  
As shown in Figure 3, the coordinates of the parallelogram's four vertices in the camera system \(S\)-\(X_{S}Y_{S}Z_{S}\) have been obtained. Then, their coordinates in the parallelogram's object system \(B\)-\(X_{B}Y_{B}Z_{B}\) can be obtained based on the definition of the object system. The object's pose parameters relative to the camera, the rotation matrix \(\mathbf{R}_{B}\) and the translation vector \(\mathbf{T}_{B}\), that  
\[\begin{bmatrix}X_{Si}\\ Y_{Si}\\ Z_{Si}\end{bmatrix}=\mathbf{R}_{B}\begin{bmatrix}X_{Pi}\\ Y_{Pi}\\ Z_{Pi}\end{bmatrix}+\mathbf{T}_{B}\quad i=1,2,3,4 \tag{8}\]  
can be calculated by coordinate transformation from the four vertices' coordinates in the two systems, respectively.  
Here, if no scale information is known, an unknown scale factor will exist between the estimation results of the object's position and the real values. While the estimation results of the object's attitude angles are independent of any scale information.  
Additionally, the parallelogram object's shape parameters can also be obtained after all the vertices' positions are obtained.
# ForecastNet Wind Power Prediction Based on Spatio-Temporal Distribution
## 2 Materials and Methods
### Wind Power Prediction Model
#### 2.2.5 Evaluation Metrics  
Common error evaluation metrics such as root mean squared error (RMSE), mean absolute error (MAE), etc., are only applicable to deterministic prediction results and cannot be applied to probabilistic prediction results. In interval probabilistic prediction, the prediction interval coverage is a major metric for evaluating uncertain predictions. The formula is shown in Equation (11):  
\[R_{cover}=\frac{c}{v}\times 100\%, \tag{11}\]  
where \(R_{cover}\) is the interval coverage, \(c\) is the number of true values in the test set that fall within the prediction interval, and \(v\) is the total number of true values in the test set.  
Figure 8: Hidden layer structure of ADFN.  
interval. A higher coverage indicates a more accurate prediction. Under a given confidence level, the coverage of the prediction interval cannot be lower than the requirement of the confidence level; otherwise, the prediction result is unreliable. In wind power prediction, interval coverage is one of the important metrics used to assess the prediction accuracy. However, relying solely on the interval coverage is insufficient to evaluate quality of the interval predictions; the interval width metric is also essential. The interval average width metric describes the average width of the prediction interval, which is usually used to assess the accuracy and effectiveness of interval prediction. When the interval coverage is higher, the average width of the interval should be smaller, indicating that the model can provide a more accurate range of intervals in the prediction process. Relatively speaking, the model prediction results of the interval coverage are high-quality, but the average width of the interval is too large may have certain defects. The calculation of the average width of the interval metric is shown in Equation (12):  
\[\delta_{mean}=\frac{1}{v_{t}}\sum_{t=1}^{v}\frac{(U_{t}-L_{t})}{(Y_{\text{max }}-Y_{\text{min}})}, \tag{12}\]  
Figure 10: A wind power prediction framework based on spatio-temporally distributed ForecastNet.  
Figure 9: Schematic of multi-step prediction.  
where \(Y_{\max}\) is the maximum value of wind power in the test set at the prediction time \(t\), \(Y_{\min}\) is the minimum value of wind power in the test set at the prediction time \(t\), \(U_{t}\) is the upper boundary of the prediction interval, and \(L_{t}\) is the lower boundary of the prediction interval.  
The larger the average interval width indicator, the larger the power interval obtained from the prediction, and the more valid the information it can provide. Combining the two metrics, interval average width and interval coverage, to jointly judge the uncertain prediction results is of practical significance.