esa-sceva/satcom-chunk-collection huggingface dataset:
# Operational Flood Mapping Using Multi-Temporal Sentinel-1 SAR Images: A Case Study from Bangladesh
## 3 Results
For an accuracy assessment, the Sentinel-1 classification result for 29 August 2017, was evaluated with the waterbodies map derived from Landsat-8 (22 August 2017) using 4500 reference points collected from the Landsat-8 classification map. The overall accuracy of the 2017 August flood inundation map was found to be 96.44%, with a kappa value of 0.81, standard error kappa of 0.02, and a 95% confidence interval between 0.770 to 0.850 (Table 1). The evaluation of the flood map from SAR data compared to the optical image-based inundation map (Table S4) shows that for a particular cloud free window, the Landsat- (22 August 2017) based map produced an inundated area of 70% while the SAR-based map showed 59% of the areas inundated. Within the 70% Landsat-based inundated maps, a 53% inundated area was common, 17% of the area was an omission, and 6% of the area differed from the Sentinel-1-based study. A visual comparison of the Landsat-based (22 August 2017) and Sentinel-1-based (29 August 2017) flood extent is also shown in Figure 4.  
The accuracy of the 2017 Landsat-derived land cover map was found to be 87.51%, with a kappa value of 0.81, a standard error kappa of 0.02, a 95% confidence interval between 0.770 and 0.850, and a 0.906 maximum possible unweighted kappa, given the observed marginal frequencies (Table 2). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class Name** & **Flood** & **Other** & **Total** & **Accuracy** \\ \hline Flood & 1335 & 123 & 1458 & 91.56 \\ Other & 37 & 3005 & 3042 & 98.78 \\ Total & 1372 & 3128 & 4500 & n.a. \\ Producer’s accuracy (\%) & 97.30 & 96.07 & n.a. & n.a. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error matrix for the land cover map of 2017. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline Hill forest & 155 & 1 & & 1 & 8 & & 1 & 166 & 93.37 \\ Madhupur forest & & 2 & & 1 & & 1 & & 9 & 77.78 \\ Mangrove forest & & & 43 & 1 & & & & 44 & 97.73 \\ Rural settlement and & & 1 & 1 & 214 & & 60 & & 2 & 279 & 76.70 \\ homestead orchard & & & 1 & & 3 & & 1 & 1 & 6 & 50.00 \\ Cropland & & & & 59 & 1 & 722 & 3 & 12 & 797 & 90.59 \\ Barren area & & & & & 1 & 19 & 1 & 1 & 22 & 86.36 \\ Built-up area & & & & 3 & & 2 & 6 & 11 & 54.55 \\ Waterbodies & & & & & 18 & & 113 & 131 & 86.26 \\ Total & 156 & 9 & 45 & 279 & 4 & 812 & 23 & 7 & 130 & 1465 & n.a. \\ \hline Producer’s accuracy (\%) & 99.36 & 77.78 & 95.56 & 76.70 & 75.00 & 88.92 & 82.61 & 85.71 & 86.92 & n.a. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error matrix for the land cover map of 2017.
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 2 Basic Theory of Random Field
### Orthogonal Transform.  
As mentioned above, the random field is discretized into a set of correlated random variables based on the LAM (e.g., \([H]\) = \([H_{1}\), \(H_{2}\),..., \(H_{n}]\)T). However, these random variables cannot be directly used in the stochastic finite element analyses. The correlated random variables should be transformed into the independent random variables by means of orthogonal transform. The covariance values  
Figure 3: Discretization of a 2D random field.  
Figure 2: Reduction coefficient of variance vs. the correlation model.  
calculated by Equation (2) or Equation (4) for the random variables in the vector \([H]\) constitute the matrix \([C_{H}]\), as shown below:  
\[[C_{H}]=\left[\begin{array}{cccc}cov(H_{1},H_{1})&cov(H_{1},H_{2})&\cdots&cov(H _{1},H_{n})\\ cov(H_{2},H_{1})&cov(H_{2},H_{2})&\cdots&cov(H_{2},H_{n})\\ \vdots&\vdots&\ddots&\vdots\\ cov(H_{n},H_{1})&cov(H_{n},H_{2})&\cdots&cov(H_{n},H_{n})\end{array}\right]. \tag{5}\]  
From Equation (5), it can be seen that the \([C_{H}]\) is a real symmetric matrix. According to the theory of linear algebra, the matrix [A] can be expressed as:  
\[[A]^{T}[C_{H}][A]=[C_{\lambda}], \tag{6}\]  
\[[C_{\lambda}]=\left[\begin{array}{cccc}\lambda_{1}&&&0\\ &\lambda_{2}&&&\\ &&\ddots&\\ 0&&&\lambda_{n}\end{array}\right], \tag{7}\]  
where \([C_{\lambda}]\) denotes the \(n\) order diagonal matrix composed of the eigenvalues (\(\lambda_{i}\), \(i\)=1, 2,..., \(n\)) of the covariance matrix \([C_{H}]\), as shown in Equation (7); \([A]\) denotes the \(n\) order orthogonal matrix constructed by the eigenvectors of the matrix \([C_{H}]\); and \([Y]\) is a vector consisting of \(n\) random variables \(Y_{i}\) (\(i\)=1, 2,..., \(n\)), which obey a normal distribution:  
\[[Y]=[A]^{T}[H]. \tag{8}\]  
From Equation (8), the mean values of the random variables in \([Y]\) are described as Equation (9):  
\[\mu_{Y}=E[Y]=E[A^{T}H]=A^{T}E[H]. \tag{9}\]  
The matrix \([C_{Y}]\) indicates the autocorrelation among the random variables in [Y]:  
\[[C_{Y}]=E([Y][Y]^{T})=E([A^{T}H][A^{T}H]^{T})=A^{T}E([H][H]^{T})A=A^{T}C_{H}A=[ C_{\lambda}]. \tag{10}\]  
From Equation (10), it can be seen that the random variables in \([Y]\) are unrelated to each other, and the variance of random variable, \(Y_{i}\), corresponds to the main diagonal element in the matrix \([C_{\lambda}]\), namely, \(\sigma^{2}[Y_{i}]=\lambda_{i}\). At this point, the \([H]\) can be expressed by independent random variables in \([Y]\):  
\[[H]=[A][Y]. \tag{11}\]  
Based on the probability distribution of the random variable in \([Y]\), samples of random variables are generated. Then, they are substituted into Equation (11) to obtain the sets of samples of the discrete elements in the random field corresponding to random variables \([H]\), whose attributes are assigned to the relevant elements of the structural finite element (FE) model for reliability evaluation.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 3 JPS Path Optimization Based on Angle-Propagation Theta* Algorithm  
As shown in Figure 3, the JPS algorithm differs from the A* algorithm in the way it selects successor nodes, and the jump search method can avoid the calculation of a large number of redundant nodes in the A* algorithm. The JPS algorithm is undoubtedly an excellent path planning algorithm. However, because the search direction of the JPS algorithm is fixed in the pathfinding process, this will cause the JPS algorithm to miss some important path turning points in the search process, resulting in the JPS algorithm's result path and the shortest realistic path still exists. A common optimization method is the LOS optimization method. The LOS optimization method can only eliminate the redundant points on the path in some cases, while it cannot obtain the judgment due to the existence of obstacles between two nodes, as shown in Figure 4.  
Figure 3: JPS algorithm for natural neighbors and forced neighbors. (**a**) pruning rule of JPS algorithm, (**b**) pruning rule of JPS algorithm, (**c**) the way to determine the forced neighbor nodes, (**d**) the way to determine the forced neighbor nodes.  
Based on the disadvantages of the LOS optimization method, this paper proposes a vision triangle judgment rule and introduces the Angle-Propagation Theta* algorithm of the viewable angle for further optimization of the above problem.
# Use of SMAP Soil Moisture and Fitting Methods in Improving GPM Estimation in Near Real Time
## 2 Study Area and Data Sets
### China Merged Perception Analysis (CMPA)  
In this study, the 0.1\({}^{\circ}\) gridded data of fusion hourly precipitation (V1.0) from CMPA ([PERSON] and [PERSON] [42]) is regarded as the ground truth. The dataset uses precipitation data from both ground and satellite sources. The ground observations come from more than 30,000 automatic stations (including national and regional automatic stations) in China. Real-time satellite retrieval CMORPH precipitation products [44] are selected for satellite inversion precipitation products, with an original spatial resolution of 8 km and temporal resolution of 30 min. CMPA first resamples CMORPH data to hourly, 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) satellite inversion precipitation products. Secondly, the system error of the CMORPH in China is reduced based on the hourly ground observations using the PDF matching method. Finally, the optimal interpolation (OI) method is used to combine these two data sources and generate the final fusion hourly precipitation data products. The overall error of this product is less than 10%, which is better than other products of the same type in China [45]. AMPA has been used in many hydrologic applications such as precipitation assessments, flood simulation, and uncertainty analysis [46; 47; 48; 49; 50; 51]. AMPA data can be download at National Meteorological Information Center ([[http://data.cma.cn/data/cdcdetail/dataCode/SEVP_CLI_CHN_MERGE_CMP_PRE_HOUR_GRID_0.10.html](http://data.cma.cn/data/cdcdetail/dataCode/SEVP_CLI_CHN_MERGE_CMP_PRE_HOUR_GRID_0.10.html)]([http://data.cma.cn/data/cdcdetail/dataCode/SEVP_CLI_CHN_MERGE_CMP_PRE_HOUR_GRID_0.10.html](http://data.cma.cn/data/cdcdetail/dataCode/SEVP_CLI_CHN_MERGE_CMP_PRE_HOUR_GRID_0.10.html))).
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 4 System Design
### Smart Contracts Design  
Smart contracts play a critical role in implementing the back-end business logic, such as user registration, transaction management, market rules, and payment settlement. To achieve better extensibility and make the system support different markets, we adopt a modular smart contract design. The core smart contracts and their interactions are shown in Figure 3.  
Figure 3: BPET smart contracts design.  
Different contracts interact with each other to exchange information through implementing interfaces. Registry provides all functions regarding user management, such as supplier/consumer registration, information updating, and deletion. PoolMarket defines the detailed market rules and records energy transactions. It determines the basic information required to submit an offer/bid and, more importantly, how the price is calculated according to associated market rules and auction models. This contract relies on the Registry smart contract to check user registration status. More market smart contracts (e.g., Bilateralmarket, BalanceMarket, and NewMarket) can be easily integrated into the system by properly implementing the interfaces, i.e., IMarket. This design limits the market type to order book markets, which necessitate registration and input of all demand and supply to calculate trading prices. Further modifications are required to support other market types, such as automated market makers and Oracle-based price mechanisms. EnergyToken provides a unified ERC20 [35] token as the payment method to exchange energy. We design the token as one type of stablecoin similar to the widely used Tether (USDT), USD Coin (USDC), and Binance USD (BUSD) in the current cryptocurrency market. Payment executes the payment process after each trading. It relies on the finalized pool price rate, the total dispatched energy quantity in the PoolMarket, and the payer's registration status in the Registry.
## 3 The Proposed Method
### The Locality Constraint for Commonality Representation  
Locality information of the data has played an important role in many real applications. By incorporating locality information for learning a dictionary, we can ensure that the samples with common features tend to have similar coding coefficients [7]. Further more, the dictionary atoms measure the similarity of the samples, which are more robust to the noise and outliers than the original samples. Hence, we use the atoms of the shared dictionary to capture the correlation among the common features and construct a locality constraint.  
Based on the shared dictionary \(D_{0}\in\mathbf{R}^{m\times k_{0}}\), we can construct a nearest neighbor graph \(M_{0}\in\mathbf{R}^{k_{0}\times k_{0}}\) as follows.  
\[M_{0}^{r,s}=\begin{cases}\exp(-\frac{\|d_{r}-d_{s}\|_{2}}{\delta}),&\text{if }d_{r}\in k \text{NN}(d_{s});\\ 0,&\text{else},\end{cases} \tag{5}\]  
where \(\delta\) is a parameter to control the exponential function, \(k\text{NN}(d_{s})\) denotes \(k\) nearest neighbors of atom \(d_{s}\), \(M_{0}^{r,s}\) indicates the similarity between the atoms \(d_{r}\) and \(d_{s}\). For convenience of calculation, we invariably set the parameters \(\delta=4\) and \(k=1\), as they have the stable values in the experiments.  
Once \(M_{0}\) is calculated, we construct a graph Laplacian matrix \(L_{0}\) as follows.  
\[L_{0}=G_{0}-M_{0},\ G_{0}=\text{diag}(g_{0}^{1},\ldots,g_{0}^{k_{0}}),\text{ and }g_{0}^{r}=\sum_{s=1}^{k_{0}}M_{0}^{r,s}. \tag{6}\]  
Since \(L_{0}\) is constructed based on the dictionary \(D_{0}\), it will be updated in coordination with \(D_{0}\) in the learning process.  
By now, we can obtain a locality constraint term for choosing graph Laplacian matrix \(L_{0}\) as follows.  
\[\frac{1}{2}\sum_{r=1}^{k_{0}}\sum_{s=1}^{k_{0}}(z^{r}-z^{s})^{2}M_{0}^{r,s}=Tr \big{(}Z_{0}^{\mathrm{T}}L_{0}Z_{0}\big{)}=\sum_{i=1}^{\mathrm{C}}Tr\big{(}Z_{0 }^{i\,\mathrm{T}}L_{0}Z_{0}^{i}\big{)}. \tag{7}\]  
Because the profile \(z^{r}/z^{s}\) and the atom \(d^{r}/d^{s}\) have a one-to-one correspondence, the above equation ensures that similar atoms encourage similar profiles [7]. Hence, the correlated information of the common features can be inherited by the coefficient matrix \(Z_{0}\) and the graph Laplacian matrix \(L_{0}\).
## 3 BDS Precise Orbit and Clock Products Generation
### Quality of the BDS WHU Orbits  
In order to externally evaluate BDS WHU orbits, two different methods presented in [PERSON] et al. [26] were employed: (1) comparison with the other ACs and (2) satellite laser ranging (SLR) validation.
# Link Quality Estimation from Burstiness Distribution Metric in Industrial Wireless Sensor Networks
## 2 System Model
### Link Burstiness Research Overview  
Physical property in wireless communications exhibits consecutive packet losses in a burst called a link burstiness. The authors in [24; 25] proposed mechanisms to solve the link burstiness problem. [PERSON] et al. [24] proposed a new metric, denoted as \(\beta\), showing that providing an inter-packet delay of 500 ms can avoid link burstiness. Even in the industrial fields, a 500 ms delay is a disadvantage for the network; one should not have to wait for an extended period of time to prevent packet failure when a link burst occurs. [PERSON] et al. [25] proposed a maximum burst length metric (denoted Bmax), estimated by long experiment data. The authors connected the scheduling algorithm and the routing algorithm called the least-burst-route to reduce the total number of burst lengths on the route. However, the algorithm to calculate Bmax was based on a long experiment data-trace before constructing the network. After processing the data-trace by computing the Bmax value, they applied it to the sensor network. They did not calculate the metric during network construction. That makes this algorithm hard to apply to an ad hoc network.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 6 Discussion and Open Research Challenges
To address the specific challenges derived from the utilization of nanosatellites for providing effective and cost-efficient IoT connectivity, we envision open research and implementation aspects  
Figure 14: A visual comparison of the reviewed medium access control (MAC) protocols in the context of nanosatellite IoT scenarios. from three perspectives: from the network protocols perspective, from the integration capabilities of CubeSats and from the evolution of the nanosatellites industry.
## 4 Phantom Measurements
### Results
The first column contains D690 TOF and non-TOF images at 48 updates, the second SIGNA TOF and non-TOF images at 48 updates, the third Signa TOF and non-TOF images at update numbers given by the rule to match the D690 48 update contrast (32 for TOF, 96 for non-TOF in Single configuration, 128 for non-TOF in Double configuration). We can visually appreciate how, if in the Single configuration hot rods are visible both with TOF and without it, in the Double configuration hot rods visibility strongly depend on their position within the FOV. With TOF, the rule maintains hot rod visibility while reducing noise; in non-TOF it helps recovering hot rods, even if it at increased noise levels. The results obtained on experimental phantoms are in good agreement with the theoretical predictions. There are only two apparent discrepancies between the theory and experimental data, which can, however, be explained. First, in the non-TOF D690 data at 48 updates, the smallest sphere CR in the Double configuration is comparable instead to lower than the CR in the Single one. This may be due to the sphere position at the border of the phantom and not in the center. The rule derivation shows indeed that the dependence on the background diameter decreases the closer a pixel is to the hot area border. Secondly, with TOF, on both scanners, CR in the Single configuration is slightly higher than in the Double one. Equation (14) was derived assuming no scatter. In Section 2.2 we found that scatter slows convergence, in a way that it is proportional to its fraction over the total true counts amount. It can be therefore expected that, when increasing object size, the scatter fraction slightly increases, explaining this effect. This was not seen in simulations, where the scatter fraction is held constant. Figure 5: Non-TOF Reconstructions of the phantoms in different conditions. **Upper:** Single acquisitions. Matched non-TOF reconstructions for D690 not shown for clarity **Lower:** Double acquisition, HCR only for SIGNA shown for clarity. **Left (a,d)**: D690 acquisitions, 48 updates. **Center (b,e)**: SIGNA acquisitions, 48 updates. **Right (c,f)**: SIGNA acquisitions, 32 updates according to the proposed rule.
## 3 Results and Discussion
### Greenness, Blueness, and Whiteness Evaluation
#### 3.1.2 WAC Assessment
The main advantages of these methods are their low costs and speed of implementation (B1 and B2). The MEPS (manually) and USAEME methods also have high scores, of over 90 points. However, the remaining methods (SBSE, SPME, SPE), due to the costs of purchasing accessories, automatic attachments (e.g., thermal desorption for SBSE), and derivatization reagents, received a low rating for category B1. Additionally, the total rating of these methods was lowered by the B3 category, which is influenced by the requirements for \"advanced instruments and greater operator skills and experience\". Whiteness  
Whiteness is a summary assessment of the three components (red, green, and blue) and shows the overall usefulness of the procedure. This assessment tool enables the analyst to select a procedure that will meet the given expectations. The assessment results, together with knowledge of the matrix composition, amounts, properties, and expected concentrations of analytes, laboratory equipment, economic opportunities, and analyst skills will help to select the most appropriate procedure from among the highest-rated procedures. The highest whiteness rating (Table 2, Figure 2) of 100% was awarded to SDME, with only the red principle rated at less than 100 points. Assuming that the authors of this paper consider a result of more than 90% to indicate the whiteness of the method, six out of nine microextraction methods achieved satisfactory results. However, the lowest whiteness ratings were received by SBSE (72.3%) and SPE (77.5%), for which the blue principles had the greatest impact on the assessment.
## 4 Case Study and Results Analysis  
### Data Selection and Pretreatment  
For the sake of verify the forecasting accuracy of the method, this paper selects the load data and meteorological data of the distributed energy system in China from 0:00 on 18 June 2018 to 24:00 on 18 June 2019. The charge data from 0:00 on 14 June 2018 to 24:00 on 14 June 2019 are selected as training samples to establish univariate time series. The charge data from 0:00 on 18 June 2019 to 24:00 on 18 June 2019 are utilized as testing sample, with the information collection frequency being 15 min. Simultaneously, the maximum temperature, average temperature, minimum temperature, season type, month, precipitation regime, day type, wind speed, humidity, and the load value at the same time in the previous five days are considered as the candidate set for feature selection, totaling 30 candidate features, as shown in Table 1.  
Since the collected data is not public, the main statistical indicators are listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \(F_{1},\dots,F_{5}\) & \(LT_{-i},i=1,2,3,4,5\) represents the loads at the same moment in the \(t-i\)th day \\ \(F_{6},\dots,F_{11}\) & \(MAX_{-i},i=0,1,2,3,4,5\) represents the \(t-i\)th day’s maximum temperature \\ \(F_{12},\dots,F_{17}\) & \(AVCT_{-i},i=0,1,2,3,4,5\) represents the \(t-i\)th day’s average temperature \\ \(F_{18},\dots,F_{23}\) & \(MIN_{-i},i=0,1,2,3,4,5\) represents the \(t-i\)th day’s minimum temperature \\ \(F_{24}\) & \(S_{att}\) represents the season in which day \(t\) is located, 1 is spring, 2 is summer, 3 is autumn, 4 is winter. \\ \(F_{25}\) & \(M_{t}\) represents the month in which day \(t\) is located \\ \(F_{26}\) & \(P_{t}\) represents the \(t\)th day’s precipitation \\ \(F_{27}\) & \(Hol_{t}\) represent whether day \(t\) is holiday, 0 is holiday, 1 is not holiday. \\ \(F_{28}\) & \(Wk_{t}\) represent whether day \(t\) is weekend, 0 is weekend, 1 is not weekend. \\ \(F_{29}\) & \(W_{t}\) represents the wind speed on day \(t\) \\ \(F_{30}\) & \(H_{t}\) represents the humidity on day \(t\) \\ \hline \end{tabular}
\end{table}
Table 1: The whole candidate features.  
For the better training and learning of the proposed model, all input data should be normalized:  
\[Y=\{y_{i}\}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}}\quad i=1,2,3,\ldots,n \tag{23}\]  
In the formula, \(x_{i}\) represents the actual value, \(x_{min}\) denotes the minimum value of the sample, \(x_{max}\) denotes the maximum value, and \(y_{i}\) denotes the standardized load.  
Furthermore, KPCA is adopted to make principal component analysis of 30 reference vectors and extract 4 sets of vectors as the input vector of the proposed model.
## 4 Numerical Results
### Results of Parametric Analysis  
The influence of the \(D/t\) ratio, internal pressure, and material strain-hardening (represented by the YPL for YPT pipes, and PLUS ratio for RHT pipes) on the CLS of X80 pipes is outlined herein for the three different loading conditions considered. The PLUS ratios, defined as the ratios of respective values of the proportionality limit stress (as indicated by the stress-strain curve labels in Figure 1) to the specified value of the ultimate proof stress (703 MPa), are obtained as: PL486 = 0.691, PL446 = 0.634, PL406 = 0.577, PL366 = 0.520. The ratio of applied internal pressure to the circumferential yield pressure is herein referred to as the \"pressure factor\", \(f_{p}\), and various levels of internal pressurization are assigned numeric designations (0.0, 0.2, 0.4, 0.6, and 0.8) according to respective percentages (0%, 20%, 40%, 60%, and 80%) of the yield pressure. For combined axial compression + bending, the ratio of the applied stress to the limit stress of the pipe (obtained under uniform axial compression conditions) is herein referred to as the \"compression factor\", \(f_{c}\), and various levels of axial compression are assigned numeric designations (0.2, 0.4, 0.6, and 0.8) according to respective percentages (20%, 40%, 60%, and 80%) of the limit stress.
## 5 Experiments and Results
### Qualitative Analysis
#### 5.2.1 Heatmap Regression and Bounding Box Predictions
The attention layers give better filter responses, focusing more on the object of interest. Figure 21: Bounding box predictions of real images and video frames. (**a–d**) Real images; (**e–h**) Video frames; (**i–l**) Hard samples.