esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Scheme to Secure Device Layer Network
### Proposed Secure Communication Protocol
#### 3.2.1 Pre-Registration Phase  
A pre-shared key (PsK) used for registration of a device in Figure 5 can be generated by the M2M application server after verification of the nonexistence of the device in the blockchain. PsK is sent to the device via a secure channel, or automatically stored in the devices during the initial configuration of the device (prior to deployment) and communicated to the M2M application server via a secure channel.  
Figure 5: Pre-registration phase.  
The device ID is transferred via a secure channel to be appended in the private blockchain using the deployed smart contract in Figure 6. First it checks the existence of the device in the blockchain using the function IsRegistered() from the smart contract, after that the node can be appended to the blockchain only if it does not already exist by calling the function AddDevice().  
The registeration order must be validated by the network nodes through PBFT consensus, so that it can be stored in the private blockchain. It is assumed that base stations are already registered at the network level.  
Before starting the information exchange, the BS and end device must establish a session key (SK) to be used for symmetric encryption. An AES-128 bit is the most efficient block cipher (approved by the NIST) for confidentiality purpose in terms of security. However, its implementation on resource constrained devices is inappropriate due to its high execution cycles and memory consumption. Additionally, a lightweight derived variant of AES such as PRESENT [6] with shared key of 80 bits and block of 64 bits, presenting accepted performances, is suggested for ensuring confidentiality in our design.  
The session key is obtained from the base point of the ECC curve used by the ECDH key exchange protocol to be transferred to the device for secure storage. The session key has a validity period and can be regenerated for each new session. Table 1 presents the used parameters with their lengths in bits.  
The private keys DPrK and BSPrK are integers randomly chosen in the interval \([1,n-1]\), where n is the order of the curve. The public keys of the BS and device are correlated with their respective private keys.  
Since: DPbK = DPrK* G and BSPbK = BSPrK*G where G is the base point on the curve, the shared secret key is then given by Equation (1):  
Figure 6: Authentication scheme smart contract.  
\[\text{BSPrK}^{*}DPbK=\text{BSPrK}(DPrK^{*}G)=\text{DPrK}(\text{BSPrK}^{*}G)= \text{DprK}^{*}\text{BSPrK} \tag{1}\]  
When a wireless device tries to connect to the network for the first time, the actual registration phase will be launched. The device starts by scanning the environment and requests the connection to the nearest base station.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 4 Experimental Results and Comparaison
### Cross-Validation and Statistical Significance Analysis
#### 4.5.3 Statistical Significance Testing  
To mathematically validate if the observed enhancements in performance of TSA-GRU over MSA-GRU are statistically significant, we conducted a paired \(t\)-test on fold-wise accuracy scores for both models.  
The \(t\)-test found a t-statistic \(=20.1063\) and a \(p\)-value \(<0.0001\), meaning that the performance difference is statistically very significant (\(p\ll 0.05\)).  
These results support our assumption that the TSA-GRU's improved performance cannot be attributed to random factors and that the improvements come from its architectural changes. The summary of the statistical comparison is shown in Figure 17 for completeness.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Model** & **Mean Accuracy** & **Standard Deviation (SD)** & **95\% Confidence Interval (CI)** \\ \hline TSA-GRU & 95.63\% & \(\pm 0.0014\) & \(\pm 0.0013\) \\ MSA-GRU & 65.21\% & \(\pm 0.0346\) & \(\pm 0.0430\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confidence interval and variability analysis of models.  
Figure 16: Accuracy distribution across folds.
## 6 Experimental Data Results and Analysis
### Experimental Performance Metrics Analysis
This discrepancy may be attributed to the algorithm's extensive use of recursion and pointers, leading to a large number of recursion layers, thereby significantly increasing the running time. In contrast, the average recovery time for our algorithm is significantly better than BUTR and is comparable to BSTR, particularly at large scales. The experimental results show that the average recovery time of our algorithm is reduced by 98.60% compared with BUTR and by 15.87% compared with BSTR. Therefore, our algorithm demonstrates a superior time efficiency advantage. (4) Comprehensive Evaluation Metric (CEM)  
In this paper, we propose a comprehensive evaluation metric CEM in order to assess the overall performance of the algorithms. The comparative results of CEM are shown below:  
Figure 10 shows the comparison results of the CEM of three topology reconfiguration algorithms. As shown in the figure, the CEM of BUTR is significantly lower than the other two algorithms. In contrast, both our algorithm and BSTR achieve CEM values exceeding 90%, with our algorithm demonstrating a higher CEM. Additionally, the CEM of all three algorithms decreases gradually as the working size increases. In particular, when the working size is 4, the CEM of our algorithm is 1.94% higher than BSTR, and when the  
Figure 8: Core reuse rate of BUTR, BSTR, and ACTR in different sizes of topology networks. Figure 9: Normalized average recovery time of BUTR, BSTR, and ACTR in different sizes of topology networks. working size is 16, the CEM of our algorithm is 31.18% higher than BTTR. Furthermore, the average CEM of our algorithm is 29.80% higher than BTTR and 0.70% higher than BSTR. In summary, the combination of the above graphs and data demonstrates that the overall CEM of our algorithm is optimal and its overall performance is superior, which indicates that our adaptive core distribution optimization algorithm achieves a higher topology reconfiguration success rate, a core reuse rate, and a shorter average recovery time. Consequently, it is a robust and efficient solution for maintaining network integrity in the presence of core failures.
## 3 Solutions and Results
### Steady Weak Concentration Boundary Layer
#### 3.1.2 Exact Solution of the Momentum Boundary Layer Problem  
By substituting Equation (19) into Equation (21), one can obtain the following ordinary differential equation.  
\[\theta^{\prime\prime}+\bigg{(}\frac{3 HPr}{4R+3 HL}\bigg{)}\bigg{[}\big{(}Sw+ \frac{1}{m}\big{(}1-e^{-m\eta}\big{)})\theta^{\prime}-\big{(}e^{-m\eta}\big{)} \theta\bigg{]}=0, \tag{25}\]  
with boundary conditions that are given by the following equation.  
\[\theta(0)=1,\;\theta^{\prime}(\infty)=0. \tag{26}\]  
Equation (25) is a homogenous linear ordinary differential of the second order with a variable coefficient, which can be solved by introducing the change variable.  
\[\varepsilon=-\lambda\frac{e}{m^{2}}^{-m\eta}, \tag{27}\]  
where \(\lambda=\Big{(}\frac{3 HPr}{4R+3 HL}\Big{)}\). Equation (25) can then be written as below.  
\[\varepsilon\frac{d^{2}\theta}{d\varepsilon^{2}}+[1-\xi-\varepsilon]\frac{d \theta}{d\varepsilon}+\theta=0, \tag{28}\]  
where \(\xi=\frac{\lambda}{m^{2}}(1+m\;Sw)\), with the following boundary conditions.  
\[\theta\bigg{(}\frac{\lambda}{m^{2}}\bigg{)}=1\;\text{and}\;\theta^{\prime}\;( \infty)=0. \tag{29}\]  
Equation (28) is similar to [PERSON]'s differential equation, which has a Kummer confluent hypergeometric function, 1F1, as a solution.  
\[\theta(\varepsilon)=(-\frac{\varepsilon m^{2}}{\lambda})^{\frac{\xi}{2}}\frac {1\text{F1}(\xi-1;\xi+1;-\varepsilon)}{1\text{F1}(\xi-1;\xi+1;-\frac{\lambda} {m^{2}})}. \tag{30}\]  
The solution of Equation (30) in terms of the similarity variable \((\eta)\) can be found as described below.  
\[\theta(\eta)=\big{(}e^{-m\eta}\big{)}^{\xi}\frac{1\text{F1}(\xi-1;\xi+1;- \varepsilon)}{1\text{F1}\Big{(}\xi-1;\xi+1;-\frac{\lambda}{m^{2}}\Big{)}}. \tag{31}\]  
In addition, the surface temperature gradient is described below.  
\[\theta^{\prime(0)}=-(m\xi)+\bigg{(}\frac{\lambda(\xi-1)}{m(\xi+1)}\bigg{)} \frac{1\text{F1}\Big{(}\xi;\xi+2;-\frac{\lambda}{m^{2}}\Big{)}}{1\text{F1} \Big{(}\xi-1;\xi+1;-\frac{\lambda}{m^{2}}\Big{)}}. \tag{32}\]
## 3 Results and Discussion
### Gold Adsorption from Chloride Media
#### 3.3.4 Influence of the Initial Hydrochloric Acid Concentration  
The variation in adsorption, as a function of the initial acid concentration, at 1 g L\({}^{-1}\) carbon nanotubes dosage, has been studied when the aqueous phase contained 0.005 g L\({}^{-1}\) gold at different HCl concentrations. The results are shown in Table 7, in which a near quantitative gold adsorption occurs at every initial HCl concentration and 2 h of contact between the aqueous solution and the adsorbent; though at shorter contact times, i.e., 30 min, an order in the sequence 0.1 M > 1 M> 10 M HCl was obtained and this is probably due to the existence of the HAuCl\({}_{4}\) species in the aqueous phase at higher HCl concentrations, against the presence of AuCl\({}_{4}\)\({}^{-}\) species, at the more dilute hydrochloric acid solutions, which is more readily adsorbed than the former.
# Chord Recognition Based on Temporal Correlation Support Vector Machine
## 4 Automatic Chord Recognition
### Support Vector Machine Classification  
SVM is a popular machine learning method for classification, regression, and other learning tasks. LIBSVM is currently one of the most widely used SVM software packages. A classification task usually involves a training set where each instance contains the class labels and the features. The goal of SVM is to produce a model to predict the target labels of the test data given only the test data features.  
Many methods are available for multi-class SVM classification [47; 48]. LIBSVM uses the \"one-against-one\" approach for multiclass classification. The classification assumes the use of the radial basis function (RBF) kernel of the form \(K(x_{i},x_{j})=e^{-\gamma\,|\,1\,x_{i}-x_{j}\,|\,1^{2}}\). The two parameters of an RBF kernel, \(C\) and \(\gamma\), must be determined by a parameter search as the optimal values vary between tasks. We use the grid-search method to obtain the \(C\) and \(\gamma\) parameters.  
Once the \(C\) and \(\gamma\) parameters are set, the class label and probability information can be predicted. This section discusses the LIBSVM implementation for extending the SVM to output probability estimates. Given \(K\) chord classes, for any \(x\), the goal is to estimate \(p_{i}=P(y=i|x),\ i=1,...,K\).
## 4 Results and Discussion
### Product Structure and Results
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Connections RBF 3-8-1** & **Weight Values** & **Connections RBF 3-8-1** & **Weight Values** & **Connections RBF 3-8-1** & **Weight Values** \\ \hline \(X_{\text{1}}\)–hidden neuron 1 & 0.400000 & \(X_{\text{3}}\)–hidden neuron 5 & 1.000000 & Radial range hidden neuron 5 & 0.640312 \\ \hline \(X_{\text{2}}\)–hidden neuron 1 & 0.500000 & \(X_{\text{1}}\)–hidden neuron 6 & 0.600000 & Radial range hidden neuron 6 & 0.200000 \\ \hline \(X_{\text{3}}\)–hidden neuron 1 & 1.000000 & \(X_{\text{2}}\)–hidden neuron 6 & 0.500000 & Radial range hidden neuron 7 & 0.200000 \\ \hline \(X_{\text{1}}\)–hidden neuron 2 & 0.00000 & \(X_{\text{3}}\)–hidden neuron 6 & 1.000000 & Radial range hidden neuron 8 & 0.20000 \\ \hline \(X_{\text{2}}\)–hidden neuron 2 & 0.00000 & \(X_{\text{1}}\)–hidden neuron 7 & 0.400000 & Hidden neuron 1—\(y\) & 0.044928 \\ \hline \(X_{\text{3}}\)–hidden neuron 2 & 1.000000 & \(X_{\text{2}}\)–hidden neuron 7 & 0.00000 & Hidden neuron 2—\(y\) & \(-0.059589\) \\ \hline \(X_{\text{1}}\)–hidden neuron 3 & 0.200000 & \(X_{\text{3}}\)–hidden neuron 7 & 1.000000 & Hidden neuron 3—\(y\) & \(-0.006650\) \\ \hline \(X_{\text{2}}\)–hidden neuron 3 & 0.00000 & \(X_{\text{1}}\)–hidden neuron 8 & 0.400000 & Hidden neuron 4—\(y\) & 0.074476 \\ \hline \(X_{\text{3}}\)–hidden neuron 3 & 1.000000 & \(X_{\text{2}}\)–hidden neuron 8 & 0.500000 & Hidden neuron 5—\(y\) & 0.254939 \\ \hline \(X_{\text{1}}\)–hidden neuron 4 & 0.600000 & \(X_{\text{3}}\)–hidden neuron 8 & 1.000000 & Hidden neuron 6—\(y\) & \(-0.094136\) \\ \hline \(X_{\text{2}}\)–hidden neuron 4 & 0.500000 & Radial range hidden neuron 1 & 0.200000 & Hidden neuron 7—\(y\) & 0.006649 \\ \hline \(X_{\text{3}}\)–hidden neuron 4 & 1.000000 & Radial range hidden neuron 2 & 0.200000 & Hidden neuron 8—\(y\) & \(-0.045479\) \\ \hline \(X_{\text{1}}\)–hidden neuron 5 & 1.000000 & Radial range hidden neuron 3 & 0.200000 & Data offset—\(y\) & 0.541008 \\ \hline \(X_{\text{2}}\)–hidden neuron 5 & 1.000000 & Radial range hidden neuron 4 & 0.200000 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Neural network weights for prediction of assembly time and network parameters that were found best for prediction of assembly time. Figure 6: Changes in the value of network learning errors depending on the number of learning cycles.
## 3 Findings and Discussion
### Evaluation of Alternatives for Airport Transfer Center Selection
#### 3.3.2 Evaluation of Alternative Airport Terminals According to ELECTRE Method
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **CR11** & **CR12** & **CR13** & **CR14** & **CR15** & **CR16** & **CR17** \\ \hline
**AP1** & 0.4865 & 0.5175 & 0.6364 & 0.5101 & 0.5859 & 0.5582 & 0.5013 \\
**AP2** & 0.6255 & 0.5914 & 0.5569 & 0.5738 & 0.5859 & 0.5582 & 0.5729 \\
**AP3** & 0.2085 & 0.3696 & 0.3182 & 0.3825 & 0.2604 & 0.1861 & 0.2148 \\
**AP4** & 0.1390 & 0.2218 & 0.1591 & 0.2550 & 0.1953 & 0.3101 & 0.2148 \\
**AP5** & 0.5560 & 0.4435 & 0.3978 & 0.4463 & 0.4557 & 0.4961 & 0.5729 \\ \hline
**Weights** & **0.406** & **0.167** & **0.112** & **0.079** & **0.122** & **0.078** & **0.037** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Decision matrix created for the evaluated airports. In the last step, net concordance and discordance indexes were calculated to establish the ranking of alternatives, from the most favorable to the least favorable, according to these index values. Table 13 presents the net concordance (CP) and discordance (DP) values, as well as the ranking of alternative airports based on the ELECTRE method. Based on the ELECTRE method, the prioritization is as follows: Antalya Airport is ranked first, followed by Cukurova Airport (to be built in Tarsus district of Mersin province) in second place, Mugla Airport in third place, Sivas Nuri Demirag Airport in fourth place and finally Erzurum Airport in fifth place.
# Promising Approaches for Heat Utilization in Agricultural Machinery Engines
## 2 Presentation of the Main Material
### Research Results
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Heat Recovery System**} & \multirow{2}{*}{**Power Increase, \%**} & **Reduction in Specific Fuel Consumption, \%** & \multirow{2}{*}{**Type of Equipment**} \\ \hline Mechanical turbocompound & * & 4.8 & Tractors of traction class at least 5 \\  & & & and combines with a harvester \\ Electric turbocompound & * & 6.9 & capacity of more than 11 kg/s \\ \hline Rankine cycle on water & 5.4 & 6...9 & \\ The Rankine cycle on the refrigerant & 11.4 & 9...11 & All types of equipment \\ Water vapor injection into TK ** & 3.8 & 1.9 & \\ \hline Circulating air cooling ** & 17 & 14.6 & Harvesters \\ \hline Thermoelectric generator & 0.3 & 0.3 & All types of equipment \\ \hline \hline \end{tabular} Note. * See Figure 2, ** According to the author’s calculations.  
\end{table} 
Table 1: Potential change in technical parameters of engines under different heat recovery schemes and possible use. Based on the above, the following classification system of heat recovery systems is proposed. 1. According to the source of secondary heat: coolant; engine oil; heat engine parts; exhaust gases. 2. According to the type of energy resource that can be obtained: hot water; coupling; mechanical energy; electrical energy. 3. Use of the obtained energy resources: hot water (heating system, power supply system, including heating of both liquid and gaseous fuels); injection at the input of a centrifugal compressor; heat accumulator; steam: steam engine/turbine; injection into the turbocharger turbine; electric energy (on-board electrical system power supply, traction electric motor power supply, fuel heating); mechanical energy (transmission to the crankshaft of the engine, fan of the cooling system). 4. By types of recycling systems cycles: open (injection of superheated water into the compressor, injection of steam into the turbine); closed (Rankin cycle). 5. By type of working fluid in utilization cycles: water; liquids with a low boiling point. 6. By type of disposal: external; internal. 7. According to the purpose of the disposal system: improvement in environmental indicators; improvement in economic indicators of the engine; ensuring the needs of external consumers. 8. According to the thermal potential of the secondary coolant: high temperature (ex-haust gases); low temperature (coolant, compressed air, engine oil). The classification by type of disposal (external and internal) requires some explanation. External utilization uses diesel heat to meet the needs of consumers not related to the engine (interior heating system, air conditioning system, etc.). Internal recycling is aimed at increasing the performance and environmental and economic indicators of the engine itself and related systems. The given scheme does not consider in detail the classification of gas turbine supercharging systems, as it is given in many scientific works and training manuals.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 6 Complexity Analysis  
By considering that the number of users in the scenario is \(U\), the computational complexity of the Gale-Shapley algorithm depends on the number of iterations until convergence. It is computed as \(O\bigg{(}\left(\frac{U}{2}\right)^{2}\bigg{)}\). The complexity of the Hungarian algorithm is calculated as the number of iterations needed to find the optimal pairs, which is \(O\bigg{(}\left(\frac{U}{2}\right)^{3}\bigg{)}\). The complexity of correlation calculations for the NLoS scenario is \(O\left(U^{2}\,A\right)\) since the numerator requires \((A)\) complex multiplications and the denominator has neglected computational complexity for norm calculations and single values multiplication. The process is repeated \((U^{2})\) times. The power solution is solved based on the SCP approach, using Algorithm 2, which converges in \((J_{2})\) iterations. The transformation process performed in Algorithm 1, namely the bisection method, requires \((J_{1})\) iterations. Since our model contains \((U)\) users, the computational complexity of power solution using algorithm-1 and algorithm-2 is \(O\left(J_{1}J_{2}U\right)\). Hence, the computational complexity of our solution is  
\[Complexity\ of(correlation-SCP)=O\left(U^{2}A+J_{1}J_{2}U\right) \tag{30}\]  
\[Complexity\ of(GSalgorithm-SCP)=O\left(\left(\frac{U}{2}\right)^{2}+J_{1}J_{2}U\right) \tag{31}\]  
\[Complexity\ of(Hungarianalgorithm-SCP)=O\left(\left(\frac{U}{2}\right)^{3}+J_{1}J_{2 }U\right) \tag{32}\]  
compared to the computational complexity of NOMA-SCP-GA after applying in our problem  
\[Complexity\ of(NOMA-SCP-GA)=O\left(\left(\frac{U}{2}\right)^{2}+J_{3}J_{4}U\right) \tag{33}\]where \(J_{3}\)_and_\(J_{4}\) are numbers of iterations till convergence according to [42]. The computational complexity of joint user-RB PA utilized to solve our problem in our network model is given as  
\[Complexity\ of(Joint\ user-RB\ PA)=O\left(\left(\frac{U}{2}\right)^{3}+J_{5}J_{6}J _{7}U^{2}\right) \tag{34}\]  
where \(J_{5},\ J_{6},\ and\ J_{7}\) are numbers of iterations till convergence according to [39].
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
###### Abstract  
The subpixel dynamic imaging technique of a beam splitter is one of the most effective super-resolution imaging methods. Aiming to create a linear time delay integration charge coupled device (TDI-CCD) subpixel imaging system based on the optical assembly method, its modulation transfer function (MTF) is analyzed based on the spatial over-sampling theory. Firstly, Fourier transformation of the sampling point is used to describe the frequency domain characteristics of TDI-CCD, which transform a unit cell of the spatial sampling lattice into a bandwidth cell in the spatial-frequency domain. Considering the effects of velocity mismatch and misalignment, the best subpixel staggering position of the linear TDI-CCD pair is given. Moreover, according to the analysis of the MTF of super-resolution reconstruction results from multiple subpixel images with random spatial offsets, the condition of sampling in the limitation of the enhancement of MTF is obtained. The numerical simulation and real experimental analysis reveal results that are consistent with the theoretical model.  
subpixel imaging; super-resolution reconstruction; Fourier optics; MTF 14 July 2017; accepted: 30 August 2017; Published: 5 September 2017
# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 5 Conclusions  
This paper addresses the challenge of distinguishing between thin clouds and the unclear boundaries of surfaces in complex scenes, proposing an effective cloud detection method named CloudformerV3 tailored for high-resolution remote sensing image data. By introducing a multi-scale adapter into the backbone, this method accomplishes dual objectives: injecting prior information from remote sensing images into the backbone to enhance its suitability for remote sensing image cloud detection tasks, and enabling multi-scale  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{**Evaluation Metrics**} & \multirow{2}{*}{**Inference Time**} \\ \cline{2-2} \cline{4-5}  & **MIoU (\%) \(\uparrow\)** & **MAcc (\%) \(\uparrow\)** & **PAcc (\%) \(\uparrow\)** & **(ms) \(\downarrow\)** \\ \hline SwinTransformer-UpperNet & 90.47 & 93.37 & 94.12 & 12.07 \\ Mask2 former & 90.89 & 94.69 & 94.89 & 13.84 \\ Segformer & 90.65 & 94.92 & 95.61 & 8.40 \\ Lawin transformer & 90.73 & 94.55 & 95.67 & 10.92 \\ GCDB-UNet & 89.45 & 93.62 & 94.08 & 7.83 \\ ViT Adapter-UpperNet & 91.85 & 95.20 & 96.11 & 18.87 \\ Cloudformer & 91.78 & 94.49 & 95.07 & 11.20 \\ CloudformerV2 & 92.52 & 95.66 & 96.75 & 14.29 \\
**CloudformerV3** & **92.89** & **96.04** & **97.12** & **15.54** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of performance with state-of-the-art methods. Indicators with \(\uparrow\) prefer higher values, while those with \(\downarrow\) favor lower values.  
Figure 9: Versus different methods using the GF1_WHU dataset. (**a**) Image. (**b**) CloudformerV3. (**c**) CloudformerV2. (**d**) Cloudformer. (**e**) Lawin transformer. (**f**) Segformer.  
feature extraction in collaboration with the backbone to capture richer image information. Additionally, the decoder employs multi-level large window attention, facilitating the establishment of connections between feature maps from different levels. In the data preprocessing stage, dark channel and bright channel prior information is incorporated, enabling the model to acquire more comprehensive prior knowledge. This approach offers a strategy to integrate multi-channel remote sensing image prior information into a model pretrained on natural images. Compared to existing advanced models, CloudformerV3 exhibits superior performance on the GF1_WHU dataset, boasting higher accuracy and effectively enhancing its ability to detect thin clouds and cloud edges. These findings validate for effective cloud detection of CloudformerV3 in optical remote sensing image.