esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis
### Analysis of Bridge Load Effect Considering Reinforcement Corrosion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Influence Factor** & & **Beam 1** & **Beam 2** & **Beam 3** & **Beam 4** & **Beam 5** \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Concrete \\ strength/MPa \\ \end{tabular} } & \multirow{2}{*}{Statistic} & Mean value & 29.7 & 32.5 & 32.7 & 33.1 & 30.8 \\  & & Standard deviation & 3.274 & 3.237 & 3.203 & 3.190 & 2.952 \\ \cline{2-7}  & \multirow{2}{*}{Bayesian estimation} & Mean value & 29.7 & 32.5 & 32.7 & 33.1 & 30.8 \\  & & Standard deviation & 2.353 & 2.351 & 2.349 & 2.348 & 2.335 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Concrete cover \\ thickness/mm \\ \end{tabular} } & \multirow{2}{*}{Statistic} & Mean value & 27.8 & 30.9 & 30.1 & 31.6 & 31.3 \\  & & Standard deviation & 1.050 & 1.307 & 1.086 & 1.406 & 1.295 \\ \cline{1-1} \cline{2-7}  & \multirow{2}{*}{Bayesian estimation} & Mean value & 27.8 & 30.9 & 30.1 & 31.6 & 31.3 \\ \cline{1-1}  & & Standard deviation & 1.228 & 1.238 & 1.229 & 1.243 & 1.237 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Bayesian estimation results of concrete strength and cover thickness of the simply supported T-beam bridge. further lead to the redistribution of the load effect. In the case of a vehicle loaded at one side, the load effect of Beam 1 decreases gradually with the increase in reinforcement corrosion. Moreover, part of the load effect is redistributed to the internal beams. The standard deviation of Beam 1 and Beam 2 does not change significantly during the first 10 years of service but increases with the increase in service time from the 10 th year and then stabilizes between 30 and 90 years of service. Furthermore, the measured data of  
Figure 5: Vehicle load arrangement: (a) load vehicle layout along the bridge and measuring point layout; (b) transverse arrangement of load vehicle; (c) field test. Figure 6: Time-dependent curves of mean value and standard deviation of LLD coefficients: (a) Beam 1; (b) Beam 2.  
the LLD coefficient are within the range of one standard deviation, which is close to the mean value of the LLD coefficient of the load test, which verifies the validity and accuracy of the calculation method of LLD considering the influence of reinforcement corrosion established in this paper. The results show that the structure is not affected by the external environment at the initial stage of service, for which the traditional calculation method can still be used for structural calculation and analysis. However, performing the calculation method without considering the influence of reinforcement corrosion will cause substantial errors with the increase in service time, whereas the method established in this paper fully considers the influence of reinforcement corrosion on structural stiffness and LLD.
## Nomenclature  
\begin{tabular}{l l} HEN & Heat exchanger network \\ MINLP & Mixed-integer nonlinear programming \\ GrA & Greedy algorithm \\ MILP & Mixed-integer linear programming \\ OCP & Optimal control problem \\ \(Q\) & Heat transfer rate [BTU/ h] \\ \(U\) & Overall heat transfer coefficient [BTU/ft2 h \({}^{\circ}\)F] \\ \(A\) & Heat transfer area [ft2] \\ \(\Delta T_{lm}\) & Logarithmic mean temperature difference [\({}^{\circ}\)F] \\ \(T_{h}^{in}\) & Inlet temperatures of hot streams [\({}^{\circ}\)F] \\ \(T_{h}^{out}\) & Outlet temperatures of hot streams [\({}^{\circ}\)F] \\ \(T_{C}^{out}\) & Inlet temperatures of cold streams [\({}^{\circ}\)F] \\ \(T_{C}^{out}\) & Outlet temperatures of cold streams [\({}^{\circ}\)F] \\ \(F_{h}\) & Mass flow rate of hot streams [lb/h] \\ \(C_{ph}\) & Specific heat of hot streams [BTU/lb \({}^{\circ}\)F] \\ \(F_{c}\) & Mass flow rate of cold streams [lb/h] \\ \(C_{pc}\) & Specific heat of cold streams [BTU/lb \({}^{\circ}\)F] \\ \(U_{c}\) & Overall heat transfer coefficient at clean condition [BTU/ft2 h \({}^{\circ}\)F] \\ \(R_{f}\) & Fouling resistance [ft2 \({}^{\circ}\)F/BTU] \\ \(a\) & Fouling constant [ft2 \({}^{\circ}\)F/BTU] \\ \(t_{F}\) & Entire time horizon [month] \\ \(\Delta t^{CL}\) & Cleaning sub-period [month] \\ \(\Delta t^{OP}\) & Operating sub-period [month] \\ \(y_{np}\) & Binary control (decision) variable \\ MIOCP & Mixed integer optimal control problem \\ DAEs & Differential-algebraic system of equations \\ \(Q_{F}(t)\) & Extra energy consumption depending on the difference between the actual and the target temperature of the crude oil entering the furnace [MM BTU] \\ \(C_{E}\) & Cost of fuel [\(\pounds\)/MM BTU] \\ \(\eta_{f}\) & Furnace efficiency \\ \(C_{C}\) & Cleaning cost [\(\pounds\)/cleaning action] \\ \(N_{P}\) & Number of the discretized time periods \\ \(N_{E}\) & Number of heat exchangers \\ \(S_{MW}\) & Moving window size \\ CIT & Crude inlet temperature to furnace [\({}^{\circ}\)F] \\ SQP & Sequential Quadratic Programming \\ \(Obj_{opt}\) & Objective function value [k\(\pounds\)] \\ \end{tabular}
# 5G New Radio Open Radio Access Network Implementation in Brazil: Review and Cost Assessment
## 6 Open RAN Costs in Brazil
### Total Cost of Ownership (TCO)
#### 6.1.1 CAPEX Calculation  
To calculate the Open RAN CAPEX, we have  
\[\begin{split} CAPEX_{o-RAN}^{5G}=N_{DPC}.(C_{DPCbuild}+C_{DPC Equip}+C_{CW}^{DPC})+N_{site}.(C_{site}^{o-RAN}+C_{CW}^{site})\\ +C_{optic}+C_{soft}+C_{5 GC}+C_{5 GC}^{pool}+C_{CWcore}\end{split} \tag{1}\]  
where \(N_{DPC}=ceil(N_{site}/N_{site}^{DPC})\) is the rounding operation and \(N_{site}^{DPC}\) is the number of sites within a 15 km radius of a data center.  
To calculate the number of racks in a datacenter, \(N_{rack}^{DPC}\), we have  
\[N_{rack}^{DPC}=ceil\left(\frac{ceil(N_{site}^{DPC}*\frac{N_{RU}}{N_{RU}^{DI}*N_{ UDU-DU}})+ceil(N_{site}^{DPC}*\frac{N_{RU}}{N_{DU}^{RU}*N_{CUU-CU}})}{N_{ serv}^{rack}}\right) \tag{2}\]  
where \(N_{DU}^{RU}\) is the number of virtual DUs served per CU, \(N_{oCU\to DU}\) is the number of virtual DUs hosted in a Physical DU, \(N_{oCU\to CU}\) is the number of CUs hosted on a physical server CU, \(N_{serv}^{rack}\) is the maximum capacity supported by a physical server.  
For the cost of building a data center, we have  
\[C_{DPCbuild}=C_{rack}^{Tier}N_{rack}^{DPC}, \tag{3}\]  
where \(C_{rack}^{Tier}\) is the cost of building a data center in terms of racks for the required level of resilience.  
As for the total cost of equipment in the data center, we have the following:  
\[C_{DPC Equip}=TC_{DU}^{DPC}+TC_{CU}^{DPC}+C_{cool} \tag{4}\]  
where \(TC_{DU}^{DPC}\) is the total cost of DU servers installed in a data center and \(TC_{CU}^{DPC}\) is the total cost of CU servers installed in a data center. In this case, the total cost is the number of servers multiplied by the cost of implementing the unit plus the cost of the Ethernet interface.  
Included in the data center cost \(C_{CW}^{DPC}\) is the installation cost, which is 25.  
Also, we add \(C_{site}^{o-RAN}\) to the cost of the Open RAN site, which is  
\[C_{site}^{O_{RAN}}=N_{RU}.C_{RU}+C_{mast} \tag{5}\]  
with the number of RUs added to the radio cost plus tower construction. On the same o-RAN site, we have the labor cost calculated:  
\[C_{CW}^{DPC}=0.2.(N_{RU}.C_{RU}) \tag{6}\]  
where \(C_{soft}\) is the cost of purchasing the software:  
\[C_{soft}=N_{site}.N_{RU}.C_{soft}^{RU}+N_{DPC}.(N_{DU}^{DPC}.C_{soft}^{DU}+N_{ CU}^{DPC}.C_{soft}^{CU})+C_{5 GCSoft} \tag{7}\]where \(C_{soft}^{RU}\) is the cost of the software of a RU, \(C_{soft}^{DU}\) is the cost of the software of a DU, \(C_{soft}^{CU}\) is the cost of the software of a CU, and \(C_{5 GCSoft}\) is the cost of the 5G core software.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 2 Related Knowledge
### Elliptic Curve Public Key Cryptography  
Elliptic curve discrete logarithm problem (ECDLP) means that \(x\) is determined by a given \(P\) and \(Q\), that is to say, if \(P\in E(F_{P})\) is the set, then \(\exists x\in N^{*}\) makes \(Q=xP\) true. The elliptic curve discrete logarithm problem is more difficult to deal with than the discrete logarithm problem on the finite field, and the security is also higher.  
Suppose that \(E\) is an elliptic curve and \(G\) is a point on \(E\). If \(\exists n\in N^{*}\) exists, \(nG=\mathrm{O}\) is established, then \(n\) is said to be the order of point \(G\), where \(O\) is the infinity point.  
Implementation of elliptic curve EIGamal (EC- EG):  
Key generation: Select the base field \(F_{p}\), the elliptic curve \(E_{p}\), and encode the plaintext information \(m\) to the point \(M(m)\) on the curve. Select the generator \(G\) (base point) of \(E_{p}\) as the public parameter. The user selects the private key \(sk\in F_{P}(sk<n)\) and the public key is \(pk=skG\).  
Figure 1: Distribution of points on an elliptic curve.  
In cryptography, describe an elliptic curve on an \(F_{p}\), commonly used to six parameters: \(T=(p,a,b,G,n,h)\) (\(p\), \(a\) and \(b\) are used to determine an elliptic curve, \(h\) is the integer part of the number \(\#E(F_{p})\) of all points on the elliptic curve divided by \(n\)). The choice of these parameters directly affects the security of encryption. The parameter values generally require the following conditions to be met:  
1. \(p\) is of course larger and safer, but the larger is \(p\), the slower is the calculation speed. \(p\) is about 200 bits to meet general safety requirements:
2. \(p\
eq n\times h\);
3. \(p^{t}\
eq 1\,(\bmod n),1\leq t<20\);
4. \(4a^{3}+27b^{2}\
eq 0\,(\bmod p)\);
5. \(n\) is a prime number; and
6. \(h\leq 4\). Encryption: [PERSON] selects a random integer \(r(r<n)\) and calculates the ciphertext as  
\[C(m)=(C_{1},C_{2})=(rG,M(m)+rpk) \tag{5}\]  
Decryption: [PERSON] calculates \(M(m)=C_{2}-skC_{1}\) to complete the decryption.  
Homomorphic addition: After encoding the plaintext \((m_{1},m_{2})\) to the two points \((M(m_{1}),M(m_{2}))\) on \(E_{p}\), the ciphertext is calculated according to Equation (6) using random integer \((r_{1},r_{2})\).  
\[\begin{split} C(m_{1})&=(C_{11},C_{21})=(r_{1}G,M(m _{1})+r_{1}pk)\\ C(m_{2})&=(C_{12},C_{22})=(r_{2}G,M(m_{2})+r_{2}pk) \end{split} \tag{6}\]  
According to \(pk=skG\) in Key generation, the homomorphic addition can be expressed as  
\[\begin{split} C(m_{1})+C(m_{2})=& C_{21}+C_{22}-skC _{11}-skC_{12}\\ =& M(m_{1})+r_{1}pk+M(m_{2})+r_{2}pk-r_{1}skG-r_{2}skG \\ =& M(m_{1})+M(m_{2})\end{split} \tag{7}\]  
That is, the sum of the two ciphertexts is equal to the sum of the plaintexts after decryption.
# Enhanced Spatial Stream of Two-Stream Network Using Optical Flow for Human Action Recognition
## 3 Proposed Methodology
### Spatial Stream  
For some actions, a single frame from the whole video can be enough to recognize the actions correctly. This can be true mostly for actions that involve human-object interactions such as playing a guitar, discus throwing, hammering, or basketball. As in these action videos, recognizing an object correctly in a frame can lead to recognizing the associated action successfully. Keeping this hypothesis in mind, the spatial stream can simply be called a modified version of the image classification stream, which takes a single RGB image as input for action recognition.  
As discussed earlier, the spatial stream is in fact an image recognition architecture. Keeping this analysis in mind, we use advanced pretrained models like ImageNet to our advantage and train the classification part only instead of training the original CNN model from scratch. Then, we fine-tune them on our dataset (UCF101) using a transfer learning technique [31] to form an enhanced spatial stream by adding a fully connected layer at the end and running the training session for a few iterations. The proposed architecture of our enhanced stream is shown in Figure 2, where the feature extraction part of the pre-trained model is merged with the classification part of the original architecture from a two-stream network [13].  
ImageNet [30] is a well-known dataset that has been extensively used in computer vision since its publication in 2015. The dataset contains over 100,000 classes and has been made available for educational and non-commercial research purposes. Several research teams, including Google, Nvidia, etc., have trained their models on this dataset and have made their trained models available for research purposes. Table 3 contains different  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Activity** & **Details** \\ \hline Actions & 101 \\ Clips & 13,320 \\ Groups per Action & 25 \\ Clips per Group & 4–7 \\ Total Duration & 1600 min \\ Min. Clip Length & 1.06 s \\ Resolution & 320 \(\times\) 240 \\ Max. Clip Length & 71.04 s \\ Frame Rate & 25 fps \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of UCF101 dataset.  
Figure 2: Enhanced model of spatial stream.  
models trained on ImageNet with the details of the number of layers in the model and the number of parameters being used in the specific model.  
In our experiments, we have have found quite a few similarities between the ImageNet and UCF101 datasets. As an example, UCF101 dataset has a class \"WalkingWithDog\" involving dogs, while the ImageNet also has examples containing dogs, such as \"Maltese-Dog\". Because of the similarities in the types of task between the two datasets, we used pre-trained models on ImageNet for our problem and tailored them to fit our problem by using transfer learning techniques. The different pre-trained models listed in Table 3 will be evaluated individually by training the classification layers only. The best-performing model will be selected and further fine-tuned to check for any further improvement in the performance.
# Radar Target Localization with Multipath Exploitation in Dense Clutter Environments
## 5 Conclusions  
In this work, we presented a target localization method in multipath scenarios with high clutter. Multipath propagation is predicted by the ray-tracing technique based on prior knowledge of the urban environment. The proposed method determines the multipath propagation model by the likelihood function of multipath measurement and the predicted multipath with respect to all possible association hypotheses between them. Approximation of the proposed likelihood function is derived by excluding impossible hypotheses with a gating threshold. Then, accurate target location is obtained by an NLS optimization based on the estimated multipath propagation model. Simulation results showed that the proposed method provides robust and accurate estimation of target location in a high clutter environment. The promotion of the proposed multi-measurement likelihood function to the classic single-measurement tracking problem can be an interesting and practical topic. Particularly, considering the association explosion in the multi-target multi-measurement localization problem, integration of localization and the tracking process is expected to provide a possible solution in future work.  
Conceptualization, R.D. and Z.W.; data curation, R.D.; formal analysis, R.D.; funding acquisition, L.J.; investigation, R.D.; methodology, R.D.; project administration, L.J.; resources, Z.W.; software, R.D.; supervision, Z.W.; validation, R.D.; visualization, R.D.; writing--original draft, R.D.; writing--review and editing, S.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Defense Basic Research program of China grant number WDZC20195500208.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Transcription Factors That Mediate Drought Tolerance and Flowering Time  
Several genes (342 contigs and 50 sequences without annotations) could not be mapped to GO terms, even after Blast analysis. All DEGs were further analyzed using a plant transcription factor database (PlantTFDB v. 4.0) to predict their function. Transcription factor motifs were identified in a total of 689 genes, 45 of which were identified as transcription factors. Of these transcription factors, _bHLH_, _C2H2_, _C3H_, _NAC_, _MYB_, _WRKY_, _ERF_, and _bZIP_ are the most frequently reported to have roles in development and the drought stress response of various plants. These TFs are known to be affected by drought and may also affect flowering time (Figure 3) [15; 17; 32; 68; 69; 70; 71].  
Our results included many transcription factors, such as _GATA_, _Dof_, _CO_, and _MADS_ families, that are known to be associated with flowering time (Table S5). Recently, a study on the expression pattern of each transcription factor in B73 was carried out, and a total of five hub genes (GRMZM2G113779, _SPL_; GRMZM2G387528, _bHLH_; GRMZM2G017349, _bHLH_; GRMZM2G011357, _C2H2_; GRMZM2G025642, _NAC_) were identified. Among the five hub genes, three genes (GRMZM2G113779, GRMZM2G387528 and GRMZM2G011357) were expressed in the leaves of B73. Our results confirmed the expression of three hub genes (GRMZM2G161673, homolog of GRMZM2G113779; contig1816, homolog of GRMZM2G387528; GRMZM2G011357) under drought conditions. GRMZM2G113779 is a hub gene that encodes _SPL_. The _SPL_ family has been reported to contain major plant-specific TFs involved in flower development [72; 73; 74]. Interestingly, our results showed that the identified gene was downregulated under drought conditions. Other hub genes that are associated with drought tolerance were upregulated under drought conditions.
# Research on X-ray Diagnosis Model of Musculoskeletal Diseases Based on Deep Learning
## 4 Experiments and Results
### Recall  
Recall is one of the indicators used to measure the performance of a classification model, which refers to the ability of a classifier to correctly identify all positive samples. The recall rates of various models are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**TP** & **FN** & & & & & \\ \hline
**FP** & **TN** & & & & & \\ \hline XR\_ELBOW & 218 & 17 & 204 & 31 & 211 & 24 \\  & 73 & 157 & 47 & 183 & 41 & 189 \\ \hline XR\_FINGER & 178 & 36 & 176 & 38 & 191 & 23 \\  & 82 & 165 & 70 & 177 & 71 & 176 \\ \hline XR\_FOREARM & 132 & 18 & 139 & 11 & 141 & 9 \\  & 51 & 100 & 48 & 103 & 43 & 108 \\ \hline XR\_HAND & 240 & 31 & 245 & 26 & 252 & 19 \\  & 91 & 98 & 86 & 103 & 78 & 111 \\ \hline XR\_HUMERUS & 121 & 27 & 120 & 28 & 128 & 20 \\  & 21 & 119 & 22 & 118 & 16 & 124 \\ \hline XR\_SHOULDER & 224 & 61 & 217 & 68 & 227 & 58 \\  & 67 & 211 & 76 & 202 & 58 & 220 \\ \hline XR\_WRIST & 336 & 28 & 328 & 36 & 330 & 34 \\  & 88 & 207 & 74 & 221 & 69 & 226 \\ \hline \end{tabular}
\end{table}
Table 2: Classification results of each model.  
\begin{table}
\begin{tabular}{c c c} \hline  & _Resnet50_ & _Densenet169_ & _HRD-Model_ \\ \hline XR\_ELBOW & 0.7491 & 0.8127 & 0.8394 \\ XR\_FINGER & 0.6846 & 0.7154 & 0.7318 \\ XR\_FOREARM & 0.7213 & 0.7433 & 0.7637 \\ XR\_HAND & 0.7251 & 0.7472 & 0.7652 \\ XR\_HUERUS & 0.8521 & 0.8451 & 0.8881 \\ XR\_SHOULDER & 0.7698 & 0.7406 & 0.7993 \\ XR\_WRIST & 0.7925 & 0.8159 & 0.8234 \\ \hline \end{tabular}
\end{table}
Table 3: The precision ratio of each model.  
Based on the recall metrics, HRD outperformed ResNet50 and DenseNet169 in most categories.
# Improved Probability Prediction Method Research for Photovoltaic Power Output
## 5 Conclusions  
In this paper, photovoltaic power generation prediction was deeply studied. Improved sparse Gaussian process and improved least squares support vector machine were proposed, and the accurate results of photovoltaic power generation probability prediction were obtained. Through analysis, we found that the prediction performance of each model was different, and a combined probability prediction model with different interval weights is proposed to make the best of the characteristics of each model. Finally, the simulation results showed that compared with single prediction models, the combined probability prediction model could predict photovoltaic power generation more accurately. Moreover, whether on sunny days or rainy days, high probability prediction accuracy could be guaranteed for different months. Therefore, the model has high stability and practicability.  
**Author Contributions:** Methodology, Q.L. and W.Z.; Supervision, Z.C.; Writing--original draft, Q.L.; Writing--review & editing, W.Z.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61873180.  
**Acknowledgments:** All the authors thank the International Institute of Electrical and Electronics Engineers Energy Forecasting Working Group for providing the experimental data.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Month**} & \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Sunny**} & \multicolumn{6}{c}{**Rainy**} \\ \cline{3-10}  & & **RMSE** & **PINAW** & **PICP** & **CRPS** & **RMSE** & **PINAW** & **PICP** & **CRPS** \\ \hline \multirow{4}{*}{May} & IMSPGP & 0.0674 & 0.3160 & 0.9762 & 0.0348 & 0.0856 & 0.3158 & 0.9792 & 0.0431 \\  & QRNN & 0.0785 & 0.3352 & 0.9345 & 0.0417 & 0.1038 & 0.3108 & 0.9688 & 0.0402 \\  & IMLSSVM & 0.0961 & 0.2590 & 0.9881 & 0.0326 & 0.1271 & 0.3151 & 0.9688 & 0.0416 \\  & Group & 0.0706 & 0.2842 & 0.9762 & 0.0306 & 0.0801 & 0.3129 & 0.9896 & 0.0383 \\  & IMSPGP & 0.1077 & 0.3391 & 0.9389 & 0.0538 & 0.1011 & 0.3087 & 0.9583 & 0.0542 \\  & QRNN & 0.1087 & 0.3698 & 0.9111 & 0.0560 & 0.1250 & 0.3367 & 0.9444 & 0.0560 \\  & IMLSSVM & 0.1393 & 0.4084 & 0.9611 & 0.0552 & 0.1201 & 0.3861 & 0.9583 & 0.0630 \\  & Group & 0.1079 & 0.3405 & 0.9433 & 0.0513 & 0.0988 & 0.3189 & 0.9861 & 0.0480 \\  & IMSPGP & 0.0779 & 0.3630 & 0.9703 & 0.0450 & 0.1053 & 0.3085 & 0.9375 & 0.0570 \\  & QRNN & 0.0937 & 0.3388 & 0.9322 & 0.0491 & 0.1094 & 0.3549 & 0.9375 & 0.0555 \\  & IMLSSVM & 0.0993 & 0.3339 & 0.9915 & 0.0413 & 0.1210 & 0.3833 & 0.1000 & 0.0487 \\  & Group & 0.0808 & 0.3319 & 0.9873 & 0.0397 & 0.0805 & 0.3442 & 0.9792 & 0.0399 \\  & IMSPGP & 0.0899 & 0.3241 & 0.9512 & 0.0436 & 0.0876 & 0.3312 & 0.9577 & 0.0489 \\  & QRNN & 0.0967 & 0.3350 & 0.9329 & 0.0472 & 0.0865 & 0.2940 & 0.9155 & 0.0430 \\  & IMLSSVM & 0.1142 & 0.3168 & 0.9817 & 0.0423 & 0.0895 & 0.3393 & 0.9718 & 0.0431 \\  & Group & 0.0885 & 0.3489 & 0.9878 & 0.0403 & 0.0819 & 0.3420 & 0.9859 & 0.0372 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Evaluation indices of different models during four months.