esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
1. The macrolithotypes of coals in the Dacun block are dominated by semi-dull coals, followed by semi-bright coals. The organic macroals of coals are dominated by vitrinite, followed by inertinite, and exinite is generally rare. The coals are semi-anthracite to anthracite in coal rank.
2. CH\({}_{4}\) concentration accounts for 33.8-100% (avg. 85.21%) of the total gas component and, overall, takes on an increasing trend as the depth increases. The CH\({}_{4}\) weathering zone depth is 310 m. The coal seams, overall, have a high gas content, varying from 0.11 to 35.68 cm\({}^{3}\)/g (avg. 17.27 cm\({}^{3}\)/g). The gas content shows a \"increase\(\rightarrow\)decrease\" trend as the depth increases, and the critical depth is around 700 m.
3. The coals in the Dacun Block have low porosities (4.20-5.41%) and permeabilities (0.001 mD-2.85 mD). The porosity shows a positive correlation with the R\({}_{0,max}\). The permeabilities present a decreasing trend with the increase in depth. Moreover, a negative relationship exists between the coal permeability and minimum horizontal stress magnitude.
4. The coal reservoirs in the Dacun Block overall have a low abnormal pressure state. The fluctuation change relationship between the reservoir pressure coefficient and depth indicates that three sets of independent superposed gas-bearing systems possibly exist vertically in the Longtan Formation.
5. The coals in the Dacun Block have a strong adsorptivity; however, the gas saturation is low. Coals with high gas contents and reservoir pressures usually have high gas saturations. The CBM development in the Dacun Block needs a large depressurization of reservoir pressure due to the low ratio of critical desorption pressure to reservoir pressure.  
Figure 13: The relationship between the gas content and the critical desorption pressure (\(p_{cd}\)) of the coal reservoir in the Dacun Block.  
**Author Contributions:** Conceptualization, X.Z. and Y.W.; methodology, Z.Z. and X.Z.; software, Z.L. and X.L.; formal analysis, Z.L. and X.L.; data curation, X.Z. and Z.L.; writing--original draft preparation, X.Z. and Z.Z.; writing--review and editing, Z.Z. and Y.W.; supervision, Y.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the National Natural Science Foundation of China (No. 42130802).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 4 Experiments and Results
### Infrared Image Super-Resolution Experiment with Added White Gaussian Noise
In contrast, though the noise in the images reconstructed using the TGV-based method is reduced, some of the reconstructed images are significantly distorted compared with the original HR images. Furthermore, in the case of the TV4 reconstruction results, aside from image distortions, there are obvious \"spots\"; overall, the visual effect of reconstruction is poor. Finally, in terms of visual effects, the FTV4-based and proposed methods provide outstanding results; however, in terms of noise suppression, the results of the proposed method are superior. Figure 9: Super-resolution x2 results of the LR bird images with added white Gaussian noise (\(\sigma=20\)); (**a**–**d**,**i**–**l**) ground truth and the results of the Bicubic, MFT, TV, TGV, TV4, FTV4, and FTV4 Lp methods, respectively; (**e**–**h**,**m**–**p**) enlarged details from the rectangles in (**a**–**d**,**i**–**l**), respectively. Figure 10: Super-resolution x2 results of the LR passerby images with added white Gaussian noise (\(\sigma\) = 20); (**a–d,i–l**) ground truth and the results of the Bicubic, MFT, TV, TGV, TV4, FTV4 and FTV4 Lp methods, respectively; (**e–h,m–p**) enlarged details from the rectangles in (**a–d,i–l**), respectively.
## 2 Problem Definition
### Our View of the Problem  
In this paper, the task of joint relational triple extraction is divided into three subtasks:  
**Entity Extraction**. For the given sentence \(S\), this subtask identities all subjects and objects separately. The output of this subtask are the subjects set \(Y_{s}(S)=\{s_{i}\}\) and objects set \(Y_{o}(S)=\{o_{i}\}\).  
**Subject-Object Alignment**. For the given sentence \(S\) and the identified set of subjects \(Y_{s}(S)\) and set of objects \(Y_{o}(S)\), this subtask identifies all objects that correspond to the subjects set \(Y_{s}(S)\) and the subjects that correspond to the objects set \(Y_{o}(S)\). The output of this subtask are two entity pair sets \(Y_{s2p}(S,s_{i}|s_{i}\in Y_{s}(S))=\big{\{}(s_{i},o_{j})\big{\}}\) and \(Y_{o2p}(S,o_{i}|o_{i}\in Y_{o}(S))=\big{\{}(s_{j},o_{i})\big{\}}\). Then, the model can obtain the final identification of the entity pairs set \(Y_{(s,o)}(S)=\{(s,o)\}\) by aligning the two sets \(Y_{s2o}(S,s_{i}|s_{i}\in Y_{s}(S))\) and \(Y_{o2s}(S,o_{i}|o_{i}\in Y_{o}(S))\).  
**Relation Judgement**. For the given sentence \(S\) and identified set of entity pairs \(Y_{(s,o)}(S)\), this subtask forms all possible triples by identifying all possible relations for each entity pair \((s,o)\) in the set \(Y_{(s,o)}(S)=\{(s,o)\}\). The output of this task is \(T(S)=\big{\{}(s,r,o)|(s,o)\in Y_{(s,o)}(S),r\in R\big{\}}\).
# Real-Time Safety Decision-Making Method for Multirotor Flight Strategies Based on TOPSIS Model
## 3 Experimental Verification  
In this experiment, firstly, based on the Rflysim platform, the flight status of the multirotor was monitored in real time, and the information source of the condition assessment was established. Some of the collected flight data were used as training samples to train the failure level classification model of the multirotor based on the random forest algorithm. The remaining flight data were used as test samples to verify the performance of the model. The experimental results were compared with five other fault classification models. Finally,the optimal control strategy, namely, the real-time flight decision result, was obtained through the real-time attribute decision model of the multirotor based on TOPSIS.  
Real-Time Monitoring of Flight Status of Multirotor and Establishment of Status Evaluation Information Source  
Based on the Rflysim platform, using an existing dynamic model of multirotor aircraft, the fault injection module was constructed to simulate different types of fault injection and collect flight data under each fault condition. The fault types include abnormal motor speed fault, propeller tension coefficient fault, accelerometer scale factor and offset, gyro random walk noise variance, and barometer white noise variance. According to the simulation effect of the fault injection platform and the change in the flight data, the five fault types can be divided into three safety levels. They are disaster, danger, and slight. Among them, abnormal motor speed fault and propeller tension coefficient fault are disaster-level faults, accelerometer scale factor and deviation are danger-level faults, and gyroscope random walk noise variance and barometer white noise variance are slight-level faults. A time frame of 5 min was selected for the measured data, where the state of the multirotor in this segment of data exhibited different changes, such as violent shaking, slight shaking, acceleration, and hovering. A total of 900 groups of flight data of the multirotor with different fault levels were collected.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 2 Ionosphere Characteristics
### Technical Procedures and Metrics to Analyze the Ionospheric Variations  
The ionosphere is a dynamic medium in terms of propagation, especially if working with low-latitude or high-latitude. In this sense, in the procedure of design of a communications system to work exhaustively in a ionospheric environment, there are several preparatory processes to be conducted, and a set of metrics to analyze depending on the frequency of transmission, the latitude and the hour of the day or night.  
A sounding of the channel should be done, with the wider possible set of requirements to detect the best possible setup for the particular link. For this purpose, several metrics are defined to support the optimum setup depending on the sounding results.
## 4 Simulations
### Hypothesis Testing
To assess the performance of the two models, we use the following three criteria: The average _Akaike information criterion_ (AIC), the average _Bayesian information criterion_ (BIC) and the average Pearson chi-squared statistic \(\chi^{2}_{n-q-1}\)[18]:  
\[\chi^{2}_{n-q-1}=\sum_{i=1}^{n}\frac{(x_{i}-\hat{\mu}_{i})^{2}}{\hat{\sigma}_{i }^{2}},\]  
where \(\hat{\mu}_{i}\) and \(\hat{\sigma}_{i}^{2}\) are the estimated mean and variance of \(X_{i}\) and \((n-q-1)=995\) is the degree of the Pearson chi-squared statistic because we use the MLEs \(\{\hat{\beta},\hat{\alpha}\}\) or \(\{\hat{\beta},\hat{\upsilon}\}\) to calculate \(\{\hat{\mu}_{i},\hat{\sigma}_{i}^{2}\}_{i=1}^{n}\). To show the differences in performance in the two models with the same data set, we first generate a data set from (A4) and estimate parameters with the GP\({}^{(0)}\) and CMP regression models 1000 times. Specifically, we can calculate the MLEs of parameters for the GP\({}^{(0)}\) regression model with the MM Algorithm (14) & (16). The MLEs of parameters of the CMP regression model can be calculated directly through the built-in 'glm.cmp' function in the COMPoissonReg R package. Next, we generate another data set from (B4) and estimate these parameters with the two models. By averaging the obtained results, the log-likelihood, AIC, BIC, \(\chi^{2}_{n-p}\) and the time cost of the system when the algorithm converged (denoted by Sys. Time) are reported in Table 9.  
According to Table 9, in Case (A4), we can see that the log-likelihood of the GP\({}^{(l)}\) regression model is larger than that of the CMP regression model, and the AIC and BIC of the GP\({}^{(l)}\) regression model are smaller than that of the CMP regression model. However, the values of the log-likelihood, AIC and BIC show an inverse numerical relationship between GP\({}^{(l)}\) and CMP regression models in Case (B4). So, the GP\({}^{(l)}\)/CMP regression model performs better log-likelihood, AIC, and BIC when the data is generated from GP\({}^{(l)}\)/CMP. For the Pearson chi-squared statistic, the GP\({}^{(l)}\) regression model outperforms the CMP regression model in Case (A4) because the value of GP\({}^{(l)}\) is closer to the degree of the Pearson chi-squared statistic, 995. In Case (B4), the \(\chi^{2}_{n-p-1}\) of the GP\({}^{(l)}\) regression model is greater than 995 by around 5, and the \(\chi^{2}_{n-p-1}\) of the CMP regression model is less than 995 by around 5, implying they have similar performances. For the cost of time, our proposed GP\({}^{(l)}\) regression model converges faster than the CMP model in simulation, in which the time cost of the GP\({}^{(l)}\) regression model is nearly half of the CMP regression model.
## 5 Case Study: Experimental Results
### Thematic and Temporal Analysis  
User-topic modeling can be used to analyze the behavioral characteristics of community users in terms of thematic and temporal factors. A temporal topic is one that users actively engage with over a certain time period. A temporal topic can be generated from a significant event, or it can develop naturally when some particular users are consistently active over a certain time period. An article with a thematic topic is consistently addressed over time, and involves users who are interested in this topic.
# Enhancing Precision in Magnetic Map Interpolation for Regions with Sparse Data
## 2 Improved Variogram of Kriging Algorithm for Sparse Data
### Derivation of Variogram Formula Based on Magnetic Dipole
Their relative positions are shown in Figure 2. The coordinate of point \(i\) is \(P^{i}=(x_{i},y_{i},z_{i})=(0,0,h)\), the coordinate of point \(j\) is \(P^{j}=(x_{j},y_{j},z_{j})\)\(=(0,-l,h)\), and the magnetic dipole coordinate is the origin and the magnetic moment is \((0,0,M)\); then, Equation (8) can be converted to the following:  
\[\gamma_{ij}=\tfrac{\mu_{0}^{2}M^{2}}{32\pi^{2}}E\left\{\frac{\left( 3\left(l^{4}\times l\right)^{2}+\left(3\left(h^{2}+l^{2}\right)^{5/2}-3l^{5}+ \left(h^{2}+l^{2}\right)\left(l^{3}-\left(h^{2}+l^{2}\right)^{3/2}\right) \right)^{2}\right}{h^{6}\left(h^{2}+l^{2}\right)^{5}}\right\} \tag{9}\] \[=\tfrac{\mu_{0}^{2}M^{2}}{32\pi^{2}}E\left\{\frac{\left(3\left(l/ \tan\varphi\right)^{4}l\right)^{2}+\left|3\left(\left(l/\tan\varphi\right)^{2} +l^{2}\right)^{5/2}-3\left(l/\tan\varphi\right)^{5}+\left(\left(l/\tan\varphi \right)^{2}+l^{2}\right)\left(\left(l/\tan\varphi\right)^{3}-\left(\left(l/\tan \varphi\right)^{2}+l^{2}\right)^{3/2}\right)\right|^{2}}{\left(l/\tan\varphi \right)^{6}\left(\left(l/\tan\varphi\right)^{2}+l^{2}\right)^{5}}\right\}\] \[=\tfrac{\mu_{0}^{2}M^{2}}{32\pi^{2}}E\left\{\frac{\left(9\left(l \tan\varphi\right)^{8}+\left|3\left(1-\cos\varphi\right)^{5}/\sin\varphi^{5}+ \left(\left(1/\tan\varphi\right)^{2}+1^{2}\right)\left(\left(1/\tan\varphi \right)^{3}-\left(\left(1/\tan\varphi\right)^{2}+1^{2}\right)^{3/2}\right) \right|^{2}}{l^{5}\left(\cos\varphi^{8}/\sin\varphi^{16}\right)}\right\}\] \[=\tfrac{\mu_{0}^{2}M^{2}}{32\pi^{2}}E\left\{\frac{T}{l^{3}}\right\}\]  
where \(T=\frac{9\left(1/\tan\varphi\right)^{8}+\left|3\left(1-\cos\varphi\right)^{5} /\sin\varphi^{5}+\left(\left(1/\tan\varphi\right)^{2}+1\right)\left(\left(1/ \tan\varphi\right)^{3}-\left(\left(1/\tan\varphi\right)^{2}+1\right)^{3/2} \right)\right|^{2}}{\left(\cos\varphi^{8}/\sin\varphi^{16}\right)}\), and \(\varphi\) is the angle of the vector \(\overset{\rightarrow}{r}_{i}\) and \(\overset{\rightarrow}{r}_{j}\). As can be seen from the above equation, although the relative relationship between two points in the space of the magnetic map is relatively complex, its variogram is related to the third power of the relative position, that is, \(l^{3}\) of the above equation. The spherical variogram is related to the third power of the lag distance \(l\), and it can be seen from the constructed variogram graph that the spherical model is more in line with the trend of the variogram than other models. Therefore, the spherical variogram is chosen for research in this paper.
Sensors_**2009**, \(9\), 922-942; doi:10.3390/s90200922  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Modeling Gross Primary Production of Agro-Forestry Ecosystems by Assimilation of Satellite-Derived Information in a Process-Based Model**  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2,1, [PERSON] 1,**  
**[PERSON] 3,4, [PERSON] 4,5, [PERSON] 4 and [PERSON] 4**  
\({}^{1}\) Remote Sensing of Environmental Dynamics Lab., DISAT, University of Milano-Bicocca, Milano, Italy  
\({}^{2}\) Institute on Atmospheric Pollution (IIA), National Research Council (CNR), Roma, Italy  
\({}^{3}\) Department of Forest Science and Environment, University of Tuscia, 01100 Viterbo, Italy  
\({}^{4}\) European Commission, DG-JRC, Institute for Environment and Sustainability, Climate Change Unit, Ispra (VA), Italy  
\({}^{5}\) Institute for Mediterranean Agricultural and Forest Systems, National Research Council, Rende (CS), Italy  
E-Mails: [PERSON]] (M.M.); [EMAIL_ADDRESS] (L.B.); [EMAIL_ADDRESS] (R.C.); [PERSON](T.Z.); [EMAIL_ADDRESS] (G.M.); [EMAIL_ADDRESS] (G.S.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +39-02-6448-2848; Fax: +39-02-6448-2895  
_Received: 6 October 2008; in revised form: 10 February 2009 / Accepted: 12 February 2009/ Published: 13 February 2009_  
**Abstract:** In this paper we present results obtained in the framework of a regional-scale analysis of the carbon budget of poplar plantations in Northern Italy. We explored the ability of the process-based model BIOME-BGC to estimate the gross primary production (GPP) using an inverse modeling approach exploiting eddy covariance and satellite data. We firstly present a version of BIOME-BGC coupled with the radiative transfer models PROSPECT and SAILH (named PROSAILH-BGC) with the aims of i) improving the BIOME-BGC description of the radiative transfer regime within the canopy and ii) allowing the assimilation of remotely-sensed vegetation index time series, such as MODIS NDVI, into the model. Secondly, we present a two-step model inversion for optimization of model parameters. In the first step, some key econphysiological parameters were optimized against data collected by an eddy covariance flux tower. In the second step, important information about phenological dates and about standing biomass were optimized against MODIS NDVI. Results obtained showed that the PROSAILH-BGC allowed simulation of MODIS NDVI with good accuracy and that we described better the canopy radiation regime. The inverse modeling approach was demonstrated to be useful for the optimization of ecophysiological model parameters, phenological dates and parameters related to the standing biomass, allowing good accuracy of daily and annual GPP predictions. In summary, this study showed that assimilation of eddy covariance and remote sensing data in a process model may provide important information for modeling gross primary production at regional scale.  
Keywords:Gross Primary Production; Phenology; BIOME-BGC; PROSPECT, SAILH; Poplar plantations. +
Footnote †: journal: Journal of Computational Physics
## 3 Results
### Comparison Experiments
#### 3.2.1 Scene I
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Networks**} & \multicolumn{4}{c}{**Water**} & \multicolumn{4}{c}{**Background**} \\ \cline{2-9}  & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** & **PA (\%)** & **IoU (\%)** & **Recall (\%)** & **F1-Score (\%)** \\ \hline U-Net & 67.25 & 65.46 & 96.10 & 79.13 & 99.29 & 91.53 & 92.13 & 95.58 \\ MF2 AM & 91.34 & 87.23 & 91.12 & 93.18 & 97.59 & 96.43 & 98.78 & 98.18 \\ Deeplabv3+ & 83.14 & 81.73 & 97.96 & 89.94 & 99.55 & 95.39 & 95.80 & 97.64 \\ Ours & 95.24 & 91.63 & 96.03 & 95.63 & 98.98 & 97.77 & 98.77 & 98.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of water detection results among various networks in Scene I.  
Figure 6: The detection results for water in the zoomed-in views of Scene I by various networks. (**a**) Independent testing SAR image. (**b**) Ground truth. (**c**) The extracted water result for U-Net; (**d**) for MF2 AM; (**e**) for DeepLabV3+; and (**f**) for SCAFF-Net. The blue color indicates correct detections of water, the green color highlights missed detections, the red color marks false alarms, and the dashed blue box indicates the highlighting of detailed differences.
## 4 Enhancing the Dynamic Multi-Hop Routing (EDMR) Protocol
### The Network Architecture Phase  
This phase is responsible for organizing all deployed sensor nodes into clusters. It includes three sub-phases: initialization, announcement, and table preparation. It is worth noting that the network architecture phase of the EDMR protocol builds upon the DMR phases previously described in Sections 3.2.1-3.2.3. For clarity, the flowchart shown in Figure 4 illustrates a summary of the initialization sub-phase. Figure 5 presents the announcement messages used by the Ns, CHs, and INs to create their RTs at the beginning of the round, while Figure 6 shows the updated messages used by them to update their RTs during the round. Finally, Algorithm 1 outlines the table preparation sub-phase. The table generated in this sub-phase is employed in the routing phase.  
Figure 3: The phases of the EDMR protocol.  
```
Definitions \(\mathrm{N[i]}\)\(\in\) the set of \(\mathrm{Ns}\) located at \(\mathrm{distance}\geq\mathrm{d_{o}}/2\) from the \(\mathrm{CH}\) \(\mathrm{N[j]}\)\(\in\) the set of \(\mathrm{Ns}\) located in the lower levels \(\mathrm{CH[m]}/\mathrm{IN[m]}\)\(\in\) the set of \(\mathrm{CH}\)s/\(\mathrm{IN}\)s located at \(\mathrm{distance}\geq\mathrm{d_{o}}/2\) from the \(\mathrm{BS}\) \(\mathrm{CH[n]}/\mathrm{IN[n]}\)\(\in\) the set of \(\mathrm{CH}\)s/\(\mathrm{IN}\)s located at \(\mathrm{distance}\geq\mathrm{d_{o}}/2\) from the \(\mathrm{BS}\) \(\mathrm{RT}\): Routing Table Ann_Mess: Announcement message Join_Req: Join Request message Acc_Mess: Acceptance message
1: begin
2: for each (\(\mathrm{N[i]}\))
3: if (received Ann_Mess from \(\mathrm{N[j]}\)) then
4: send Join_Req to \(\mathrm{N[j]}\);
5: end if
6: for each (\(\mathrm{N[j]}\)) received Join_Req
7: if (has available in its TDMA table) then
8: assign a slot to \(\mathrm{N[i]}\);
9: send _Acc_Mess_ to \(\mathrm{N[i]}\);
10: end if
11: end for
12: for each (Acc_Mess received from \(\mathrm{N[j]}\))
13: compute cost (\(\mathrm{N[i]}\), \(\mathrm{N[j]}\));
14: add (\(\mathrm{N[j]}\), cost (\(\mathrm{N[i]}\), \(\mathrm{N[j]}\)));
15: sort \(\mathrm{RT[i]}\) in ascending order;
16: end for
17: end for
18: for each (\(\mathrm{CH[m]}/\mathrm{IN[m]}\))
19: if (received Ann_Mess from \(\mathrm{CH[n]}/\mathrm{IN[n]}\)) then
20: send Join_Req to \(\mathrm{CH[n]}/\mathrm{IN[n]}\);
21: end If
22: for each (\(\mathrm{CH[n]}/\mathrm{IN[n]}\))
23: if (has available in its TDMA table)
24: assign a slot to \(\mathrm{CH[m]}/\mathrm{IN[m]}\);
25: send Acc_Mess to \(\mathrm{CH[m]}/\mathrm{IN[m]}\);
26: end if
27: end for
28: for each (Acc_Mess from \(\mathrm{CH[n]}/\mathrm{IN[n]}\))
29: compute cost (\(\mathrm{CH[m]}/\mathrm{IN[m]}\), \(\mathrm{CH[n]}/\mathrm{IN[n]}\));
30: add (\(\mathrm{CH[n]}/\mathrm{IN[n]}\), cost (\(\mathrm{CH[m]}/\mathrm{IN[m]}\), \(\mathrm{CH[n]}/\mathrm{IN[n]}\)));
31: sort \(\mathrm{RT[m]}\) in ascending order;
32: end for
33:end for
```  
**Algorithm 1**Table Preparation Sub-Phase  
Figure 4: The initialization sub-phase of the EDMR protocol.  
Figure 5: Components of the announcement message for: (**a**) Ns; and (**b**) CHs/INs.
# A Process-Oriented Method for Tracking Rainstorms with a Time-Series of Raster Datasets
## 3 PoAIR Algorithm
### Matching and Tracking Successive Rainstorm Objects in a Rainstorm Sequence
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Name** & **Semantics** \\ \hline \multirow{5}{*}{Spatial features} & Centroid (\(C_{\mathrm{x}}\),\(C_{\mathrm{y}}\)) & The position of a rainstorm, including X and Y coordinates \\ \cline{2-3}  & Area (\(A\)) & The spatial coverage of a rainstorm object \\ \cline{2-3}  & Perimeter (\(P\)) & The perimeter of a rainstorm object \\ \cline{2-3}  & Length of Major Axis (\(L\)\(M\)) & The length of the minimum bounding rectangle of a rainstorm object \\ \cline{2-3}  & Width of Major Axis (\(W\)\(M\)) & The width of the minimum bounding rectangle of a rainstorm object \\ \cline{2-3}  & Eccentricity (\(E\)) & The ratio between the \(W\)\(M\) and the \(L\)\(M\) \\ \cline{2-3}  & Rectangularity (\(R\)) & The ratio between \(A\) and the area of the minimum bounding rectangle of a rainstorm object \\ \cline{2-3}  & Roundness (\(Sd\)) & The ratio between the radius of the internally and externally tangent circles of a rainstorm object \\ \cline{2-3}  & Shape Index (\(SI\)) & The ratio between the \(A\) and the \(P\) of a rainstorm object \\ \hline Temporal feature & Occurrence Time & The starting time of a rainstorm object \\ \cline{2-3}  & Total precipitation & The total rainfall (in mm) of a rainstorm object in a snapshot \\ \cline{2-3}  & Mean intensity & The mean rainfall intensity (in mm/h) of a rainstorm object \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features selected to track rainstorm objects.