esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Hurricane Damage Prediction
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer (Type)** & **Output Shape** & **Param \#** \\ \hline input\_1 (InputLayer) & [(None, 128, 128, 3)] & 0 \\ block1\_conv1 (Conv2D) & (None, 128, 128, 64) & 1792 \\ block1\_conv2 (Conv2D) & (None, 128, 128, 64) & 36,928 \\ block1\_pool (MaxPooling2D) & (None, 64, 64, 64) & 0 \\ block2\_conv1 (Conv2D) & (None, 64, 64, 128) & 73,856 \\ block2\_conv2 (Conv2D) & (None, 64, 64, 128) & 147,584 \\ block2\_pool (MaxPooling2D) & (None, 32, 32, 128) & 0 \\ block3\_conv1 (Conv2D) & (None, 32, 32, 256) & 295,168 \\ block3\_conv2 (Conv2D) & (None, 32, 32, 256) & 590,080 \\ block3\_conv3 (Conv2D) & (None, 32, 32, 256) & 590,080 \\ block3\_conv4 (Conv2D) & (None, 32, 32, 256) & 590,080 \\ block3\_pool (MaxPooling2D) & (None, 16, 16, 256) & 0 \\ block4\_conv1 (Conv2D) & (None, 16, 16, 512) & 1,180,160 \\ block4\_conv2 (Conv2D) & (None, 16, 16, 512) & 2,359,808 \\ block4\_conv3 (Conv2D) & (None, 16, 16, 512) & 2,359,808 \\ block4\_conv4 (Conv2D) & (None, 16, 16, 512) & 2,359,808 \\ block4\_pool (MaxPooling2D) & (None, 8, 8, 512) & 0 \\ block5\_conv1 (Conv2D) & (None, 8, 8, 512) & 2,359,808 \\ block5\_conv2 (Conv2D) & (None, 8, 8, 512) & 2,359,808 \\ block5\_conv3 (Conv2D) & (None, 8, 8, 512) & 2,359,808 \\ block5\_conv4 (Conv2D) & (None, 8, 8, 512) & 2,359,808 \\ block5\_pool (MaxPooling2D) & (None, 4, 4, 512) & 0 \\ \hline Total params: 20,024,384 & & \\ Trainable params: 0 & & \\ Non-trainable params: 20,024,384 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Configuration of VGG 19 layers. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model: “Sequential”** & **Output Shape** & **Param \#** \\ \hline Vgg19 (Model) & (None, 4, 4, 512) & 20,024,384 \\ Global\_average\_pooling2d & G1(None, 512) & 0 \\ dense (Dense) & (None, 1) & 513 \\ \hline Total params: 20,024,384 & & \\ Trainable params: 513 & & \\ Non-trainable params: 20,024,384 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Trainable and non-trainable parameters. Model: “sequential”. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model: “Sequential”** & **Output Shape** & **Param \#** \\ \hline Vgg19 (Model) & (None, 4, 4, 512) & 20,024,384 \\ Global\_average\_pooling2d & G1(None, 512) & 0 \\ dense (Dense) & (None, 1) & 513 \\ \hline Total params: 20,024,384 & & \\ Trainable params: 7,079,937 & & \\ Non-trainable params: 12,944,960 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Configuration details and number of parameters. Model: “sequential”. Table 9 shows the comparative analysis of existing models and the proposed model. Fine-tuning VGG 19 layers gives the improved accuracy, and it outperforms the other existing techniques. Figure 19: Accuracy graph after fine-tuning the layers. Figure 18: Accuracy graph before fine-tuning.
# Sparse-View Artifact Correction of High-Pixel-Number Synchrotron Radiation CT
## 1 Introduction
However, the increase in the number of pixels means that more projection data are needed to satisfy the requirements of the Nyquist sampling theorem, which will enhance the advantage of sparse projection but also increase the difficulty of correcting sparse artifacts. In this paper, we present an artifact correction algorithm for high-pixel-number sparse-view CT. We performed CT imaging using a synchrotron radiation light source and acquired 1200 projections at 180 angles, using their CT reconstruction slices as labels. We obtained sparsely sampled projections and used their reconstructed CT slices as input to the neural network. This type of image post-processing strategy can effectively conserve computational resources. We cropped the input image into small patches and computed the loss of these patches with the corresponding labeled patches to optimize the network parameters. Additionally, we proposed constraints: the output patches of the network are reintegrated into full CT images, which are used to compute the structural similarity with the labeled CT slices to further optimize the network parameters. Experiments demonstrate that, compared with typical deep learning methods (FBPConvNet [15], DD-Net [19]), our proposed artifact correction method shows excellent results in both qualitative evaluation and quantitative metrics. We believe that this artifact correction method for high-pixel-number CT can be applied to other fields as well. The rest of this paper is organized as follows. First, Section 2 presents the details of our proposed method. Section 3 gives the experimental and research results. Finally, the paper is summarized.
# An Assessment Scheme for Road Network Capacity under Demand Uncertainty
## 4 Numerical Experiments
### Performance of the Sensitivity-Based Approximate Approach  
#### 4.6.1 Error Analysis  
For three different distributions, we randomly sampled 200 travel demand results. The comparison of the error of the exact solution was obtained by repeatedly solving the capacity model, and the estimated solution of the approximate approach is shown in Figure 14. The error of the two solutions under uniform distribution and normal distribution was within 5%. The error of the two solutions under exponential distribution has 199 results within 5%, and only one error value was greater than 5%, at 8.96%. The error analysis of the approximate solutions shows that the results of the SAB approximation approach for solving the network capacity values are reliable.  
Figure 12: (a) Link 10-16 sensitivity analysis results for travel requirements; (b) Link 16-10 sensitivity analysis results for travel requirements.  
Figure 13: The improvement in network capacity under critical O–D pairs.  
According to the principle of sensitivity analysis, the error is related to the nominal travel demand. When the nominal travel demand is the mean of the O-D demand distribution, the average error is minimum. The effectiveness of the sensitivity analysis algorithm is also related to the upper and lower bounds of the interval constraints. In this study, five different intervals were selected to validate the results of the model, which were \([0.5\bar{q}_{ij}.1.5\bar{q}_{ij}]\), \([0.4\bar{q}_{ij}.1.6\bar{q}_{ij}]\), \([0.3\bar{q}_{ij},2\bar{q}_{ij}]\), \([0.2\bar{q}_{ij},3\bar{q}_{ij}]\), and \([0.1\bar{q}_{ij},4\bar{q}_{ij}]\). \(\bar{q}_{ij}\) is the nominal value for travel demand. The success of the algorithm is judged by the result of the travel demand matrix multiplier. The success rate of the algorithm is calculated as [total number of the positive travel demand matrix multiplier/number of samples]. From the results, we can see that when the interval constraint is \([0.4\bar{q}_{ij},1.6\bar{q}_{ij}]\), the success rate of the sensitivity-based analysis algorithm is 100%, which shows that the algorithm is effective. However, the success rate of the algorithm will decrease as the travel demand range expands, and we think the algorithm begins to fail. Therefore, the application of the proposed method in the actual project needs to pre-assess the range of travel demand fluctuations.
## 5 Discussion  
The selection of the key period for the RS-WOFOST assimilation framework is very innovative. In this study, the optimal assimilation time points were determined using the Harris algorithm based on the LAI curves. It is very important for the RS-WOFOST assimilation framework applied at large scale, in monitoring the heavy metal stress in rice. This is because the more CCD images inserted, the longer the time that is required for the assimilation framework. However, the key period was determined based on the analysis of LAI, which was primarily applicable to the assimilation of LAI into WOFOST model. In application, considering the growing conditions and rice varieties, the key periods could also be suitable for rice which has similar growing conditions and the same rice varieties. The field measurement could also be conducted in the key period involved in this study. Considering the phenology of rice, the dates for the acquisition of the remote sensing images should be as close as possible to the four time points, but not confined to these time points. Meanwhile, further study is still needed to gain a better understanding of the role of each growth stage in stress monitoring when considering the phenology of rice [54; 55]. The key period in this study was determined based on the rice growth cycle (approximately 100 days) in the study areas. If the conditions are different, such as  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Period** & **Time Points** & **Efficiency/s** & **R2** & **MAE** \\ \hline I & 8 & 521 & 0.984 & 0.113 \\ II & 4 & 283 & 0.956 & 0.265 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of the accuracy and efficiency between the two assimilation key periods.  
Figure 11: Spatial distribution of heavy metal stress level represented by continuous simulation of the WRT in study area B.  
the crop variety and region, the key period should be adjusted accordingly. In addition, the Harris detector method in this study is scale-dependent. In future work, an optimized multi-scale Harris method that can avoid the scale problems could be used for corner detection [56]. Also, a key period with equal intervals should be considered. However, this is only a suggestion as the result is unknown. On the contrary, the key period determined by the Harris algorithm based on the LAI curve resulted in a good outcome in that the key period improved the assimilation efficiency while maintaining its accuracy.  
As a result, the verification in study area B indicated that assimilation with the key period could significantly improve the efficiency because it guarantees the accuracy, and shortens the run time of model operation by more than 50%, which leads to a more efficient and reliable choice of useful imagery to continuously monitor heavy metal stress in the study area with the RS-WOFOST model.
## 5 Results and Discussion
### The Comparative Performance of PRDFS and BRDFS in Detecting Malicious Nodes
#### 5.3.2 Further Observations  
The following strategy differences between the TMS of PRDFS and that of BRDFS may explain their striking differences in \(\left(\overrightarrow{D(\mathbf{t})}\right)\) estimation accuracy in the face of an attack:  
* The TMS of PRDFS considers the following three factors in determining the trustworthiness of a sensor node at a time distant \(\mathbf{t}\): (1) the trustworthiness of the node, (2) the similarity of the sensor's reading with the other sensors' readings at the same time step, and (3) the similarity of the sensor's measurement at time step \(\mathbf{t}\) with its past measurements, but in contrast, the TMS of BRDFS uses only the first two factors.
* The TMS of BRDFS updates the reputation score of a node at the end of a time step \(\mathbf{t}\) after determining the most trustworthy measurement at that time step. In the presence of on-off attacks, the normal behavior of a node at the last few time steps does not guarantee normal behavior from the node at the next time step. For this reason, the TMS of PRDFS predicts the trust score of a node at the beginning of a time step \(\mathbf{t}\) (before determining the most trustworthy measurement at that time step) using GRNN, which is a powerful pattern recognition algorithm capable of learning local patterns.
* In BRDFS, the reputation score of a node at time step \(\mathbf{t}\) is a linear function of its long-term reputation and its intermediate reputation score computed at time step \(\mathbf{t}\). In contrast, the TMS of PRDFS uses a powerful fuzzy clustering-based non-linear algorithm (GRNN) to predict the trust score of a node at time step \(\mathbf{t}\) based on its past trustworthiness scores.
* The TMS of BRDFS sets the reputation score of a node at time step \(\mathbf{t}\) to the estimated expected (i.e., mean) reputation score of the node at time step \(\mathbf{t}\). In stealthy on-off attacks, where at each time step the malicious nodes randomly choose whether to act maliciously or not, the variance of their reputation is an important indicator of their conditions. Hence, the point estimate of the expected reputation score is not very reliable. To overcome this problem, the TMS of PRDFS sets the trust score of a node to the lower bound of the predicted distribution of the plausible trust scores of the node at time step \(\mathbf{t}\)  
The Comparative Performance of PRDFS and BRDFS in Terms of Demand Forecasting when No Attack Is Underway
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 2 Methods
### Dataset Generation and Automatic Annotation  
By the above method, we extract the individual signals at the receiving end for time-domain superposition, record the time and frequency parameters of each signal at the time of superposition, synthesize the composite signal with time-domain superposition, and then perform STFT transformation to obtain its two-dimensional time-frequency image. Based on the previously recorded time, center moment, and frequency parameters, we map them to the two-dimensional time-frequency image to obtain the coordinate information of the real prediction frame of each signal in the graph and then perform automatic labeling of the data.  
The block diagram of the procedure for dataset generation and automatic labeling is shown in Figure 4. It is worth mentioning that we simulate Rayleigh fading channels utilizing [PERSON]'s model [23]. Regarding the simulation of this model, all experiments in this paper use parameters with a superimposed sine wave number of 4 and a maximum Doppler shift of 100 Hz. By this method, we can simplify the dataset acquisition procedure and improve the efficiency of dataset acquisition without reducing the data validity.  
Figure 3: Image (**a**) is the original time-frequency image, and image (**b**) is the normalized image.
## 1 Introduction  
Successful endodontic treatment depends on major factors, such as the anatomy and morphology of the root canal system, the disinfection of the root canal, and a good sealing. Among these factors, the root canal system is considered the first issue which should be known precisely due to the complexity of its anatomy and morphology. This system varies among populations even in individuals in the same population, the variations in root canal morphology are still a major issue for endodontists [1; 2; 3]. Knowledge of root canal morphology is important for clinicians to master therapeutic techniques and clinical outcomes will be improved. Knowing the root canal system means to know the number of roots and canals, the canals configuration and cross-sectional shape.  
The root canals could have various cross-sectional shapes, including round, oval, long oval, ribbon ones C-shaped, conical-pyramidal, eight-shaped, trapezoidal, or drop-shaped [4; 5]. In the literature, the authors have defined the canal shapes in the coronal third of the root canal, as round, oval, round oval,ribbon, irregular and C-shaped canals [6; 7; 8]. Ribbon canals (that contain isthmus between the canals) were classified by many authors such as [PERSON] and [PERSON] [9], [PERSON] and [PERSON] [10], and [PERSON] [11]. Several studies analyzed the presence of isthmus between canals especially in the molar region [12; 13; 14; 15]. Some authors have defined the ribbon shapes (with or without isthmus) in the apical third and it was observed that the root canal shape in the apical third could differ from the other thirds. The root canal changes its shape during its course to apex, and this affects the canal instrumentation. In addition, the variations in the geometry of the cross-sectional root canal shape before forming and cleaning have a great influence on the changes that occur during root canal preparation and depend on the preparation methods. The preparation and treatment of the apical third of the root canal and apical foramen has been the subject of numerous studies [16; 17; 18].  
In most of the studies, authors have used micro- computed tomography to study the morphology of the root canal system, this method is effective and helpful especially when studying anatomical structures [19]. Also, another technique is used to study the anatomical and morphological structures of the root canal system is cone-beam computed tomography (CBCT) which induces high-resolution, three-dimensional scans [20].  
Till now, the root canal shape was studied in one third, coronal or apical, but this shape changes and this change is so significant in endodontic treatment, that is why the aim of this research was to study the cross sectional root canal shape and its changes in three thirds (coronal, middle, apical) in different age groups using cone-beam computed tomography scanning and present a new classification of root canal shape changes.
# A Blockchain-Based Privacy-Preserving and Fair Data Transaction Model in IoT
## 7 Experiments
### Experimental Setup  
To assess the MDLDP algorithm, experiments were conducted on the real-world dataset \"Air Quality\" [40]. The dataset consists of 9358 instances recorded by five metal oxide chemical sensor arrays embedded in air quality chemical multisensory devices. These devices are located on the ground in a heavily polluted area of Italy, with a recorded duration from March 2004 to February 2005. These data, provided by certified reference analyzers from the same region, represent hourly average concentrations of ground-level pollutants such as carbon monoxide, non-methane hydrocarbons, benzene, total nitrogen oxides, and nitrogen dioxide. For comparison, this article randomly selected 500 data from non-methane hydrocarbons, except for missing values. This article uses the MATLAB platform for simulation experiments. All experimental programs are written in the MATLAB R2018b platform using the MATLAB language, and the experimental hardware environment is Intel (R) Core (TM)i3-7100U, equipped with a CPU @ 2.40 GHz and 8 GB RAM, running on the Windows 10 operating system. The equipment comes from Qingdao, China, and the manufacturer is Lenovo Company.  
This article uses privacy protection degree \(\alpha\) to evaluate the privacy protection effect of algorithm MDLDP. \(G_{j}\) represents the perturbed dataset obtained by the data provider after the \(j\)-th perturbed of the original dataset \(D\). \(L\) represents the privacy dataset of the data provider, which is a part of the original dataset \(D\). \(\Delta\left(L,G_{j}\right)\) represents the number of perturbed data in the privacy dataset \(L\) after the \(j\)-th perturbed, \(l\) represents the amount of data in dataset \(L\). \(\frac{\Delta\left(L,G_{j}\right)}{l}\) represents the proportion of perturbed data in the privacy dataset \(L\), which is the degree of protection for the privacy dataset. According to the MDLDP algorithm, the data provider needs to perform \(h\) random perturbations on the dataset and select the result with the maximum degree of protection. In addition, due to the randomness of differential privacy, w privacy protection degrees will be generated in the experiment, and their average value will be taken as the final result.  
\[\alpha_{k}=\underset{j=1,2,\ldots,h}{max}\frac{\Delta\left(L,G_{j}\right)}{l}, \tag{4}\]  
\[\alpha=\frac{1}{w}\sum_{k=1}^{w}\alpha_{k}. \tag{5}\]  
In this paper, we use the commonly used method of mean square error (\(MSE\)) to evaluate the data availability of the algorithm, such as [41]. Among them, \(\left(a_{1},a_{2},\cdots,a_{n}\right)\) denotes the real data in the Original dataset \(D\), \(\left(a_{1}^{\prime},a_{2}^{\prime},\cdots,a_{n}^{\prime}\right)\) represents the data in the perturbed dataset \(G\).  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}\left(a_{i}^{\prime}-a_{i}\right)^{2}. \tag{6}\]
# An Efficient Data Retrieval Parallel Reeb Graph Algorithm
## 4 Reeb Graph Sequential Algorithm on Triangulated Surfaces
### Surfaces with Boundaries  
In the case when the surface \(M\) has a boundary, we modify the previous algorithm, as follows. In this case, \(f^{-1}(\partial I)=f^{-1}(a)\cup f^{-1}(b)\) is not empty and consists of a finite collection of simply closed curves. We treat each connected component of \(f^{-1}(a)\) as a minimal point, and we treat the boundary \(f^{-1}(b)\) as a maximum point. More precisely, the following modifications are added to the previous algorithm from Section 4.2.  
* In step (2), each connected component in \(f^{-1}(\partial I)\) is considered a vertex in the Reeb graph vertex set.
* In step (3), for each boundary component in \(f^{-1}(a)\), we pick an arbitrary vertex on the boundary and initiate an ascending path that starts from that vertex.
* In step (4), if an ascending path starting at a vertex \(v\) reaches a boundary vertex \(w\) in one of the connected components, say \(Bndry_{w}\), of \(f^{-1}(b)\), then we insert an edge in the Reeb graph \(R(M,f)\) between the vertex \(v\) and vertex in \(R(M,f)\) that corresponds to \(Bndry_{w}\).  
We denote the subcomplex obtained from \(M\) using the previous algorithm by \(X_{M,f}\). In other words, \(X_{M,f}\) consists of the critical sets \(C_{p}\) for all critical points \(p\), as well as the ascending paths we initiated at the saddle, minimum, or boundary vertices. When \(M\) and \(f\) are clear from the context, we will denote \(X_{M,f}\) simply by \(X\).
# Design of an Intelligent Tutoring System to Create a Personalized Study Plan Using Expert Systems
## 4 Proposed Model
### Application of Module M1A
#### 4.1.6 Assessing the Didactic Test Using LFLC 2000  
The final phase before creating an individual study plan is to process the didactic test. The foundation lies in the use of an expert system and a knowledge base containing IF-THEN rules. Our knowledge base includes 135 rules. The software to process the test results is called LFLC 2000, where the rules were also created. Below are a few examples of linguistic rules that are stored in our database:  
1. IF (V1 is medium) and (V2 is small) and (V3 is very big) and (V4 is big) THEN (V5 is very big);
2. IF (V1 is small) and (V2 is medium) and (V3 is medium) and (V4 is big) THEN (V5 is big);
3. IF (V1 is big) and (V2 is small) and (V3 is big) and (V4 is big) THEN (V5 is medium);
4. IF (V1 is medium) and (V2 is medium) and (V3 is small) and (V4 is medium) THEN (V5 is small);
5. IF (V1 is big) and (V2 is big) and (V3 is small) and (V4 is medium) THEN (V5 is ex_small).  
Before the test is processed, all answers from the test are clustered into categories (according to a given topic included in the test). The answers and their individual attributes  
Figure 6: Graphic representation of the output value set V5.  
for the input variables V1-V4 are gathered, processed in LFLC 2000, and the results in the form of the output variable V5 are stored in base B\({}_{\text{MIA}}\).