esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Numerical Method and Its Validation
### Result Analysis  
Based on the responses of fixed-base structures and structures considering SSI, the \(e_{\text{bs}}\) and \(e_{\text{sd}}\) of RC-frames with different frequencies under seismic records with different spectral characteristics are shown in Figures 11 and 12, respectively. It can be seen from Figure 11 that in general, the absolute values of \(e_{\text{bs}}\) for most structures decrease with the increase in the soil shear wave velocity under three seismic records, and the absolute values of \(e_{\text{bs}}\) for most structures are more than 40% when the vs. is 150 m/s and reduced to below 20% when the vs. is 500 m/s. The above description indicates that in general, the effect of SSI on the maximum base shear of the structure decreases with a larger shear wave velocity. Moreover, the values of \(e_{\text{bs}}\) for most structures are negative, and the \(e_{\text{bs}}\) of the 3-story RC-frame is about \(-\)55% when the vs. is 150 m/s and the seismic record is RSN-587, indicating that considering SSI can reduce the maximum base shear of the structure in most cases. Conversely, Figure 11a shows that the values of \(e_{\text{bs}}\) in some cases are positive and the \(e_{\text{bs}}\) of the 3-story RC-frame is more than 30% when the vs. is 500 m/s. A similar phenomenon can also be seen in Figure 11c, indicating that considering SSI can also increase the maximum base shear of the structure in specific cases, especially for low-rise structures built on tough soil. The reason for the above phenomenon is that in certain cases, the resonance effect occurs when the dominant frequency of the ground motion recorded at the surface of tough soil approaches the frequency of the low-rise structure.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Frames**} & \multirow{2}{*}{**Story**} & \multicolumn{2}{c}{**Beam**} & \multicolumn{2}{c}{**Column**} & \multicolumn{2}{c}{**Pile Foundation**} \\ \cline{3-8}  & & **Section** & **Rebar (mm\({}^{2}\))** & **Section** & **Rebar (mm\({}^{2}\))** & **Diameter** & **Length** & **Total Rebar** \\  & & **(m)** & **(Top \(\times\) Bottom)** & **(m)** & **(Each Side)** & **(m)** & **(m)** & **(mm\({}^{2}\))** \\ \hline Frame-1 & 1 & 0.3 \(\times\) 0.6 & 1080 \(\times\) 940 & 0.5 \(\times\) 0.5 & 833.2 & 0.20  
Figure 12 shows that the curves of \(e_{sd}\) have a significant difference compared with the curves of \(e_{bs}\) shown in Figure 11 in some cases, indicating that the influence of SSI on the maximum base shear does not always have a positive correlation with that on the maximum story drift. The maximum value of \(e_{sd}\) of the 1-story RC-frame is up to 95%, as shown in Figure 12c, and a similar phenomenon can also be seen in Figure 12a,b, indicating that considering SSI can significantly increase the maximum story drift of low-rise structures built on tough soil. Figures 11 and 12 show that the influence of SSI on the structural maximum story drift and base shear is closely related to the structure frequency, spectral characteristic of the seismic record, and soil shear wave velocity.
# Multiple-Mode Orthogonal Time Frequency Space with Index Modulation
## 2 System Model
### The Transmitter
Then, at the transmitter, the data symbols are first converted to the TF domain through the ISFFT operation, which can be written in a matrix form as  
\[\mathbf{X}^{FT}=\mathbf{F}_{M}\mathbf{X}^{DD}\mathbf{F}_{N}^{H} \tag{3}\]  
where \(\mathbf{X}^{FT}\in\mathbb{C}^{M\times N}\), \(\mathbf{F}_{M}\in\mathbb{C}^{M\times M}\), and \(\mathbf{F}_{N}\in\mathbb{C}^{N\times N}\) are fast Fourier transform (FFT) matrices, and \((\cdot)^{H}\) denotes the conjugate transpose of a matrix. Then, the time domain signal \(\mathbf{S}\) is obtained by the \(M\)-point inverse fast Fourier transform (IFFT), which can be written as  
\[\mathbf{S}=\mathbf{F}_{M}^{H}\mathbf{X}^{FT} \tag{4}\]  
To avoid intersymbol interference (ISI) between OTFS blocks, a cyclic prefix (CP) is appended to the front of \(\mathbf{S}\) before transmission. The 1D transmit signal \(\mathbf{s}\) can be written as  
Figure 3: BPSK signal constellation of eight different modes. \[\mathbf{s}=vec\{\mathbf{A}_{CP}\mathbf{S}\} \tag{5}\]  
where \(\mathbf{A}_{CP}\in\mathbb{C}^{(M+N_{CP})\times M}\) is a CP addition matrix, \(N_{CP}\) is the length of CP, and the operation \(vec(\cdot)\) is a vectorization operator to a matrix.
# Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9 Cr-1 Mo Steel
## 4 Validation Testing  
The validation testing was carried out utilizing additional data acquired from the published literature [53; 54; 55] to further demonstrate the efficacy of the constructed GPR model with five screened important features for dataset out of model training and testing. A total of 21 creep rupture life data for ASME Grade 91 steel are included in the validation testing dataset. The five most important features screened in this study, namely the test stress, test temperature, tempering time, and the contents of S and Cr, were collected as input variables for the created creep rupture life prediction model. The prediction results using the validation testing dataset are displayed in Figure 9. The majority of the data points are close to the 45\({}^{\circ}\) line and are distributed inside the two-factor band lines, indicating adequate prediction accuracy. However, it is important to note that the RMSE of the validation dataset is estimated to be 0.279, which is much higher than that of the testing dataset (0.127). This is due to the fact that five data points fall outside the two-factor band lines, as shown in Figure 9b. The increased error in the validation dataset could be due to the following reasons. First, due to the variations in the test equipment, test procedure, data collection and post-processing methods, and specimen geometry between different data sources [56], the experimental creep data of 9 Cr-1 Mo steels that are currently accessible vary greatly from source to source. Second, different creep specimens made of the same material may have different initial microstructures and initial internal stress states, which result in variances in the overall creep responses [57]. As a result, different rupture life data may be obtained from the creep tests carried out by the same testing apparatus with materials supplied by the same manufacturer. For instance, under the same 700 \({}^{\circ}\)C/60 MPa testing conditions, the NIMS database [36] shows that the creep rupture life of modified 9 Cr-1 Mo steel varies from 364.6 to 447.5 h, exhibiting approximately 20% inaccuracy. These possible reasons may cause a higher error in machine learning-based creep life prediction using the validation testing dataset. However, given that the majority of data points fall inside the two-factor band lines, it can be stated that the GPR model with five crucial features that are screened is effective in accurately predicting the creep rupture life of modified 9 Cr-1 Mo steel.  
Figure 8: Prediction results of creep rupture life by SVR model: (**a**) Training and prediction results of creep life in log scale; (**b**) Predicted creep life vs. measured creep life.
## 5 Discussion  
According to the analysis and experiments above, it has been proven that the angular indexing error (also called the angular positioning error) of the mandrel of a GMM can be predicted if the errors of the mandrel are known. These geometric errors can be measured by proper techniques. The measured radial error motion of the lower center contains not only the radial error of the spindle but also the centering error of the lower center. In addition, the radial error motion of the spindle only considers its first harmonic. The residual errors of the mandrel's angular indexing errors, as shown in Figure 13, could be due to the remaining higher harmonics of the spindle's radial errors. It is noted that these residual errors still contain the original radial error of the spindle and the tilt induced radial error at the tip of the lower center.  
Although this paper presents the methodology of analyzing the angular indexing error of the GMM due to the coaxiality error of the mandrel, the radial error of the lower center tip, and the angular indexing error of the spindle, it can also apply to other machines with a similar mechanism, such as a gear grinding machine, cam grinding and measuring machines, or even a long lathe.
# Energy Efficient Routing Protocol for an IoT-Based WSN System to Detect Forest Fires
###### Abstract  
Around a third of the world's surface area is covered with forests. This is about 4-5 billion hectares of land. We are losing an additional 3 million hectares of forest cover each year than we did in 2001. Hence, researchers worldwide have been working on the identification and prevention of forest fires more aggressively. The wireless sensor network technology for forest fire detection was established as a result of the shortcomings of standard forest fire detection techniques in real time. This paper proposes an energy-aware Internet of Things (IoT) based Wireless Sensor Network (WSN) model with an efficient clustering and routing technique for forest fire detection. The proposed model is named Energy Efficient Routing Protocol (EERP). The model decreases the energy utilization in sensor nodes by minimizing idle listening in cluster heads. EERP also minimizes the transmission of redundant data by allowing only the sensor nodes close to an event to report the event. Furthermore, the model ensures that sensor nodes with low energy levels do not become cluster heads. EERP uses multi-hop routes to send data from source nodes to the Base Station. The performance of EERP is compared with that of some existing Medium Access Control (MAC) protocols in various scenarios. The simulation results prove that the proposed model reduces energy consumption in sensor nodes significantly.  
wireless sensor network; time division multiple access; forest fire detection; energy conservation; clustering; Internet of Things +
Footnote †: journal: Applied Sciences
## 2 Modeling Methodology for Blade Profile with SV  
### Geometry Parameters and Aerodynamic Conditions  
A representative blade section of the axial flow compressor cascades with SVs used in the study is shown in Figure 3. Both the trailing edge axial position and the blade outlet metal angle of the SVs were set exactly the same as those of PBs. Both the SVs and PBs were generated using circular-arc camber lines with the thickness distribution of the NACA65 profile and the same blade maximum thickness-chord ratio.  
Figure 3: Geometry definitions of splittered compressor cascades.  
In order to model the flow in blades with SVs in the present study, the basic correlations referred from some classical models were established at first. The basic correlations were derived based on a reference blade family. The reference blade family was characterized by an identical blade inlet metal angle of 63\({}^{\circ}\) and maximum thickness-chord ratio of 6.0%. Blade inlet metal angle, instead of stagger angle or inflow angle used as blade orientation by [PERSON] (1977), was treated as the orientation parameter in the study. On the other hand, the reference aerodynamics conditions were maintained with an inflow Mach number of 0.1, an axial velocity density ratio (AVDR) of 1.0, and a Reynolds number of 1.0 \(\times\) 10\({}^{6}\). All off-reference conditions have been corrected under the same Reynolds number so as to extend the application of the model.  
In the study, two new geometry parameters are defined for splittered cascades. The geometry definitions of the two parameters are shown in Figure 3. One is the effective passage inlet (EPI), which is a line from the leading edge of PBs perpendicular to the suction surface of adjacent PBs. The other one is critical length \(L_{\text{c}}\), which is a line parallel to the chordwise direction of PBs from the trailing edge of SVs to the EPI. The critical length ratio (CLR) is the ratio of the critical length \(L_{\text{c}}\) to the chord of PBs. According to the research by [PERSON] et al. (2016), the effects of a SV on upstream flow field become more significant when the leading edge of a SV is upstream of the EPI, or when the chord length of a SV is longer than \(L_{\text{c}}\). Therefore, the two geometry parameters are expected to act as important factors of the model.
# Simplified Polydispersion Analysis of Small-Angle Scattering Data
## 2 SAS Theoretical Background
### Polydisperse Inhomogeneities  
When the inhomogeneities still have the same shape, but their dimensions are spread over an SDF \(N(R)\), Equation (3) takes the following form:  
\[\frac{d\Sigma}{d\Omega}(Q)=(\Delta\rho)^{2}\int N(R)V(R)^{2}|F(Q,R)|^{2}dR \tag{9}\]  
where \(R\) is the linear dimension describing the inhomogeneities, such as, for example, the radius of a sphere, \(N(R)dR\) is the number density of inhomogeneities with dimension between \(R\) and \(R+dR\), \(V\) is the volume, and \(|F(Q,R)|^{2}\) is the form factor of the inhomogeneities of dimension \(R\). It is important to underline that the SCS \(\frac{d\Sigma}{d\Omega}(Q)\) shall be measured in absolute units, i.e., in cm\({}^{-1}\), by proper calibration of the experimental data, in order to obtain important physical quantities of the inhomogeneities, such as the total number per unit volume \(N\) and the volume fraction \(c_{v}\).  
The number density of the inhomogeneities \(N\), i.e., the total number of inhomogeneities per unit volume, is then given by  
\[N=\int N(R)dR \tag{10}\]  
and the \(n\)-th momentum of the size distribution function \(\langle R^{n}\rangle\) is defined by  
\[\langle R^{n}\rangle=\frac{1}{N}\int N(R)R^{n}dR \tag{11}\]For polydisperse inhomogeneities, the [PERSON] approximation described above (Equation (5)) is still valid; however, the effective gyration radius \(R_{g*}\) takes the following form [1]:  
\[R_{g*}^{2}=\frac{\langle V^{2}R_{g}^{2}\rangle}{\langle V^{2}\rangle} \tag{12}\]  
where \(\langle V^{2}R_{g}^{2}\rangle\) and \(\langle V^{2}\rangle\) are the average values of \(V^{2}R_{g}^{2}\) and of \(V^{2}\) of the inhomogeneities, respectively.  
The SCS at \(Q=0\) is defined as  
\[\frac{d\Sigma}{d\Omega}(0)=(\Delta\rho)^{2}N\langle V^{2}\rangle \tag{13}\]  
In the case of a sharp interface between the inhomogeneities and the matrix, or solvent, thanks to the properties of \(|F(Q,R)|^{2}\), the Porod approximation yields  
\[\frac{d\Sigma}{d\Omega}(Q)\cong\frac{2\pi(\Delta\rho)^{2}N\langle S\rangle}{Q ^{4}}=\frac{A_{p}}{Q^{4}} \tag{14}\]  
where \(\langle S\rangle\) is the average surface of the inhomogeneities, and for simplicity, we define the constant of proportionality of the asymptotic behaviour \(A_{p}\), as the Porod constant.
## 7 Comparison of DYMO-Auction with the FL-MTSP  
Overview of the FL-MTSP: The FL-MTSP approach is a centralized solution designed to solve the multiple-depot multiple traveling salesman problem. The solution uses the fuzzy logic algebra to combine two objectives: the total traveled distance by all the robots and the maximum traveled distance by any robot. The solution is composed of two main phases: the assignment phase and the tour construction phase. In the first phase, the inputs of the fuzzy logic system were computed and combined into a single fuzzy metric, reducing the problem to a single objective optimization problem. Then, based on the fuzzy logic system output, each target will be assigned to the robot with the minimum output value. After allocating all targets, an improvement process starts trying to balance the workload among the robots. If the number of targets won by a robot is larger than the ratio of the number of targets to the number of robots, the farthest target is selected and added to the nearest robot. For the tour construction phase, a TSP solver (i.e., TSP_LKH solver [29]) was used. A detailed description of the FL-MTSP is provided in [16].  
Results: As shown in Figure 13, the deviation of the load balancing objective for the FL-MTSP is up to 35% (case of 36 tasks). Even though the FL-MTSP is an off-line approach (i.e., the tasks are known from the beginning), this deviation is reasonable as the main objective of the FL-MTSP is to provide a trade-off between the total traveled distance and the maximum traveled distance by any robot. However, for the DYMO-Auction, load balancing is one of the optimized objectives.  
We also compare the DYMO-Auction with the FL-MTSP in terms of the total traveled distance. This objective increases by 45% using DYMO-Auction, as shown in Figure 14. For FL-MTSP, the TTD has a significant impact on the assignment as the goal of this approach is to provide a trade-off solution where only two objectives (i.e., the total distance and the maximum distance) compete. However, for DYMO-Auction, three objectives compete to generate a solution. It is also worth noting that FL-MTSP requires global knowledge about the tasks from the beginning.  
Figure 15 shows the average task quality per robot. It is clearly shown that the task quality deviates in a negative direction, and in a positive direction. For each robot, the deviation becomes higher with the increase in the number of tasks because there will be more tasks at every quality level. This means that the tasks assignment is independent of the quality of the task. However, for the DYMO-Auction (Figure 8), the deviation of the average task quality per robot is small. This is because the tasks' quality is one of the objectives to be optimized.  
Figure 13: Average number of tasks executed per robot using FL-MTSP.
# Partial Order Rank Features in Colour Space
## 1 Introduction  
It is, at first sight, peculiar that one of the most robust tools for image description, namely rank features, have only seen limited application to colour images. The problem is, of course, that while they are very effective at dealing with noise, rank features run afoul of the main theoretical difficulty associated with colour spaces--that is the absence of a natural order.  
In recent years there has been a revival of interest in ranking of colour pixels. Notably, [PERSON] et al. [PERSON] et al. (2018) published an extensive comparative study in the use of total orders as rank features for texture recognition. However, interest has been keenest in the field of colour morphology, where several solutions have been proposed--from adaptive orders that work around the 'false colour problem' to the natural mathematical structure for ordering higher-dimensional sets--that is partial orders [PERSON] et al. (2018); [PERSON] et al. (2019); [PERSON] et al. (2019); [PERSON] et al. (2019).  
In partially-ordered sets we simply admit that there will be couples of elements incomparable to each other. Partial orders are therefore particularly suitable for dealing with colour spaces, where statements like \"yellow is greater than green\" make little or no sense at all.  
The objective of this work is to introduce a novel category of rank features based on partial orders. In the remainder, after providing some background on partial orders (Section 2), we detail the ways in which rank features can be defined (Section 2.5) and extend a classical descriptor (the Texture Spectrum) to work with partial orders (Section 3.1). We demonstrate the feasibility of the method through a set of experiment on 10 datasets of colour texture images (Section 3.2) and show that partial orders in colour space can outperform grey-scale total ordering (Section 4).
## 6 Static State-Feedback Synthesis
### Problem Solution
\begin{split} x_{i}=x_{1,i}+x_{2,i}&=\left( \mathbb{I}_{n}+\sum_{k=0}^{q-1}\bar{N}^{k}\bar{A}_{\rm C2}\bar{A}_{\rm C1}^{k} \right)x_{1,i}\\ &=\left(\mathbb{I}_{n}+\sum_{k=0}^{q-1}\bar{N}^{k}\bar{A}_{\rm C 2}\bar{A}_{\rm C1}^{k}\right)\Phi_{i}^{(\bar{A}_{\rm C1})}\bar{E}\bar{E}^{D}x _{0}.\end{split}  \tag{75}\]  
Note that  
\[x_{i}=\left(\mathbb{I}_{n}+\sum_{k=0}^{q-1}\bar{N}^{k}\bar{A}_{\rm C2}\bar{F}_ {\rm C}^{k}\right)\Phi_{i}^{(\bar{F}_{\rm C})}\bar{E}\bar{E}^{D}x_{0}=\left( \mathbb{I}_{n}+\sum_{k=0}^{q-1}\bar{N}^{k}\bar{A}_{\rm C2}\bar{A}_{\rm C1}^{k }\right)\[PERSON]_{i}^{(\bar{A}_{\rm C1})}\bar{E}\bar{E}^{D}x_{0}, \tag{76}\]  
since \(\Phi_{i}^{(\bar{F}_{\rm C})}\bar{E}\bar{E}^{D}=\Phi_{i}^{(\bar{A}_{\rm C1})} \bar{E}\bar{E}^{D}\) and \(\bar{F}_{\rm C}^{k}\bar{E}\bar{E}^{D}=\bar{A}_{\rm C1}^{k}\bar{E}\bar{E}^{D}\). The matrix \(\Phi_{i}^{(\bar{F}_{\rm C})}\) is given by (49) for \(\bar{F}_{\rm C}\) defined by (72). It is easy to show [31] that the norm of (76) can be expressed by  
\[\|x_{i}\|\leq\left\|\Phi_{i}^{(\bar{A}_{\rm C1})}\right\|\left\|\bar{E}\bar{E} ^{D}x_{0}\right\| \tag{77}\]  
or  
\[\|x_{i}\|\leq\left\|\sum_{k=0}^{q-1}\bar{N}^{k}\bar{A}_{\rm C2}\bar{A}_{\rm C 1}^{k}\right\|\left\|\Phi_{i}^{(\bar{A}_{\rm C1})}\right\|\left\|\bar{E}\bar{E }^{D}x_{0}\right\|. \tag{78}\]  
If the condition (73) is satisfied, then from (74), (77) and (78) it follows that the norm of the state vector decreases monotonically and the CL-DDFL system is superstable. Observe that the term \(\bar{G}(\mathbb{I}_{n}-\bar{E}\bar{E}^{D})\), which eliminates insignificant elements that may occur in the matrix \(\bar{A}_{\rm C1}\), does not change the solution \(x_{i}\) and the choice of the matrix \(\bar{G}\) is arbitrary. **Theorem 11**. : _The DDFL system (1) with the SSF (60) such that \(\bar{K}x_{2,i}=0\) is superstable if there exists an arbitrary matrix \(G\) such that:_the condition (57) is satisfied for the norm of the matrix \(\bar{F}_{C}\) defined by (72);
2. the inequality (73) is true. Proof. : The proof follows immediately from Theorems 9 and 10.
# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
## 5 Conclusions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Region** & **Station** & **Thiessen** & **Station** & **Thiessen** & **Station** & **Thiessen** \\
**Index** & **Index** & **Weight** & **Index** & **Weight** & **Index** & **Weight** \\ \hline \multirow{4}{*}{GW} & 90 & 0.04 & 95 & 0.06 & 100 & 0.16 \\  & 101 & 0.10 & 105 & 0.06 & 114 & 0.09 \\  & 130 & 0.01 & 211 & 0.15 & 212 & 0.11 \\  & 216 & 0.14 & 221 & 0.08 & 271 & 0.01 \\ \hline \multirow{4}{*}{GG} & 95 & 0.19 & 101 & 0.04 & 108 & 0.15 \\  & 112 & 0.02 & 114 & 0.01 & 119 & 0.18 \\  & 201 & 0.06 & 202 & 0.15 & 203 & 0.15 \\  & 212 & 0.01 & 232 & 0.04 & & \\ \hline \multirow{4}{*}{GN} & 152 & 0.02 & 155 & 0.09 & 159 & 0.03 \\  & 162 & 0.04 & 192 & 0.12 & 248 & 0.02 \\  & 279 & 0.10 & 284 & 0.11 & 285 & 0.12 \\  & 288 & 0.15 & 289 & 0.12 & 294 & 0.04 \\  & 295 & 0.05 & & & & \\ \hline \multirow{4}{*}{GB} & 130 & 0.04 & 135 & 0.10 & 136 & 0.08 \\  & 138 & 0.08 & 143 & 0.07 & 152 & 0.03 \\  & 216 & 0.01 & 226 & 0.01 & 271 & 0.07 \\  & 272 & 0.06 & 273 & 0.09 & 277 & 0.09 \\  & 278 & 0.11 & 281 & 0.10 & 284 & 0.01 \\  & 285 & 0.02 & 288 & 0.02 & & \\ \hline GJ & 156 & 1.00 & & & & \\ \hline DG & 143 & 0.93 & 285 & 0.07 & & \\ \hline DJ & 133 & 0.95 & 238 & 0.05 & & \\ \hline BS & 152 & 0.10 & 159 & 0.90 & & \\ \hline SE & 108 & 1.00 & & & & \\ \hline SJ & 131 & 0.44 & 133 & 0.34 & 232 & 0.22 \\ \hline US & 152 & 0.98 & 288 & 0.02 & & \\ \hline IC & 112 & 0.55 & 201 & 0.45 & & \\ \hline \multirow{4}{*}{JN} & 156 & 0.17 & 165 & 0.14 & 168 & 0.06 \\  & 170 & 0.06 & 192 & 0.01 & 245 & 0.03 \\  & 247 & 0.05 & 256 & 0.17 & 260 & 0.13 \\  & 261 & 0.09 & 262 & 0.09 & 295 & 0.01 \\ \hline \multirow{4}{*}{JB} & 135 & 0.01 & 140 & 0.08 & 146 & 0.17 \\  & 156 & 0.01 & 236 & 0.01 & 238 & 0.10 \\  & 243 & 0.10 & 244 & 0.09 & 245 & 0.16 \\  & 247 & 0.11 & 248 & 0.13 & 284 & 0.02 \\ \hline \multirow{4}{*}{JJ} & 184 & 0.30 & 185 & 0.19 & 188 & 0.26 \\  & 189 & 0.25 & & & & \\ \hline \multirow{4}{*}{CN} & 129 & 0.29 & 131 & 0.01 & 133 & 0.05 \\  & 140 & 0.04 & 232 & 0.20 & 235 & 0.14 \\  & 236 & 0.19 & 238 & 0.08 & & \\ \hline \multirow{4}{*}{CB} & 114 & 0.01 & 127 & 0.26 & 131 & 0.17 \\  & 133 & 0.02 & 135 & 0.08 & 203 & 0.04 \\  & 221 & 0.13 & 226 & 0.16 & 232 & 0.02 \\  & 238 & 0.05 & 272 & 0.04 & 273 & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Thiessen weight of rainfall observation stations for municipality.