esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Proposed RSII Model
### The Fusion Module  
By jointly training the teacher module and the review semantic learning module, it was possible to generate the target review semantics effectively and also reasonably alleviate the problem of difficult target review acquisition. However, some target reviews still showed low credibility, meaning that they were inconsistent with the rating. If only the features are extracted from the target reviews to predict the rating, it is difficult to achieve significant results. Therefore, this paper introduced user and item ID information to mine the missing information from the target reviews, dynamically integrating the ID information and the target review semantics to enrich the prediction features. Figure 4 shows the structure of the fusion module.  
First, this paper embedded the user and item IDs into a dense space to get \(u_{i}^{1}\) and \(v_{j}^{1}\). In order to better capture the interaction information between the users and items, this paper mapped \(u_{i}^{1}\) and \(v_{j}^{1}\) into the same space to get the ID information \(O_{i,j}\). The formulation is as follows:  
\[u_{i}^{1}=W_{i}u_{i} \tag{21}\]  
\[v_{j}^{1}=W_{j}v_{j} \tag{22}\]  
\[O_{i,j}=u_{i}^{1}\oplus v_{j}^{1} \tag{23}\]  
where \(W_{i}\) and \(W_{m}\) denote the learnable matrices, \(W_{i}\in R^{n\times\frac{d}{2}}\), and \(W_{j}\in R^{m\times\frac{d}{2}}\).  
To compensate for the lack of review information, this article calculated the relative coefficient between the target review semantics and the ID information, and then combined them to enrich the prediction features of the target review semantics and ID information. Firstly, this paper put \(O_{i,j}\) and \(\hat{c}_{i,j}\) in the same space to get \(\overline{T}_{i,j}\). Then put \(\overline{T}_{i,j}\) into the heater to obtain the relative coefficient \(a_{i,j}\) between \(O_{i,j}\) and \(\hat{c}_{i,j}\). Finally, it formed a brand-new  
Figure 4: The structure of the fusion module.  
predictive feature, \(T_{i,j}\). Additionally, the fuser was a neural network based on MLP. The formulation is as follows:  
\[\overline{T}_{i,j}=O_{i,j}\oplus\hat{c}_{i,j} \tag{24}\]  
\[a_{i,j}=soft(f_{1}(\sigma(f_{0}(\overline{T}_{i,j})))) \tag{25}\]  
\[T_{i,j}=a_{i,j}[0]\times O_{i,j}\oplus a_{i,j}[1]\times\hat{c}_{i,j} \tag{26}\]  
where \(f_{0}\) represents a fully connected layer with inputs of size \(2d\) and outputs of size \(2\). \(f_{1}\) represents a fully connected layer with inputs of size \(2\) and outputs of size \(2\).
## 2 Related Work
### Topological Mapping and Localization  
Generally, the visual topological localization is cast as a matching process of image-to-node, determining the topological node corresponding to the position of the robot [30]. To this end, the measurement between the acquired image of the robot and topological nodes need to be determined. For instance, in Reference [31], a novel topological localization method adopting classification with reject option in omnidirectional images was proposed to increases the accuracy in all cases. The work [13] employed sharpness measure to select high-quality keyframes and used vector field consensus to retrieve the keyframe efficiently and robustly. In Reference [32], a novel topological localization method based on multiple observation fusion was proposed, regarding the corridors and the intersections as vertices and branches correspondingly to build the topological map. Concurrently, many works pay attention to the multiple view geometry validation technology to obtain the accurate pose of the robot for navigation. The work [33] proposed Topomap, a lightweight topological mapping and navigation system designed to extract free space from sparse visual features. This method can deal with noisy and sparse visual observation. The work [10] presented a robust topological navigation method for omnidirectional mobile robot. In the hierarchical localization process, the robot recognizes the coarse location via node recognition algorithm and computes the precise metric pose via five-point relative pose estimation algorithm.  
With respect to robot topological localization, the above research works are based on maximum likelihood scheme, matching the current view image of the robot with the reference images in topological node directly. This strategy may suffer from perceptual aliasing in an indoor environment with similar geometric structure and layouts, resulting in localization failure. To tackle above problem, many works adopt Bayesian filtering to generate a maximum posteriori probability, fusing the past estimation and current observation. The work in Reference [34] proposed a lightweight scene recognition approach to realize topological modeling via adaptive descriptors, using a hierarchical Bayesian modeling to perform online inference process of topological localization automatically. Reference [35] described a topological navigation system in large-scale and complex environment, utilizing the maximum posteriori scheme to estimate the probability of the current view image, in which each node in topological map was assigned the same probability in the initial localization phase. Then, the observation of the robot was utilized to estimate the probability of the robot location iteratively for global localization.
## 3 Theoretical Framework of Compressed Sensing
### Sparse Representation of the Signal  
For a signal, if only a few of the elements are nonzero, it can be considered sparse. Generally, natural signals in the time domain are non-sparse but can be sparse in some transform domains. These signals require sparse representation. Signal's sparse representation is a concise representation; specifically, when the signal is projected into the orthogonal transform basis, most of the transformation coefficients indicate small absolute values and the acquired transformation vectors are sparse or approximately sparse. This is also the priori condition of compressed sensing, i.e., the signal must be represented in a sparse manner under certain transformations.  
A one-dimensional discrete time signal with a length of \(N\) can be expressed as the following linear combination of a set of orthogonal bases:  
\[\mathbf{x}=\sum_{i=1}^{N}s_{i}\psi_{i}\text{ or }\mathbf{x}=\mathbf{\
abla}\mathbf{s} \tag{1}\]  
where \(\mathbf{\
abla}=[\psi_{1},\psi_{2},\cdot\cdot\cdot,\psi_{N}]\) (\(\psi_{i}\) denotes the \(i\)-th column vector). Apparently, \(\mathbf{s}\) is an equivalent representation of \(x\). If s only includes few great coefficients, then x is compressible; if s only has \(K\) nonzero elements, then s can be regarded as a \(K\)-sparse representation of the signal \(x\)[14].  
In general applications, the transformation basis can be set as flexible according to signal characteristics. Some commonly used transformation bases include the discrete cosine transform (DCT) basis, the fast Fourier transform (FFT) basis, the discrete wavelet transform basis [15], the Curvelets basis [16], and the Gabor basis [17]. For signals that cannot be represented using orthogonal bases, sparse representation can be conducted by means of a redundant dictionary [18; 19].
## 2 Materials and Methods
### Image Features Extraction
#### 2.2.1 Histogram of Oriented Gradient HOG  
The histogram of oriented gradient (HOG) is a popular feature descriptor used for object detection and recognition in computer vision [15]. As illustrated in Figure 3, it is a  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class Data** & **Total Images** & **Training** & **Validation** & **Test** \\ \hline Red Braeburn & 579 & 405 & 87 & 87 \\ Fuji & 477 & 335 & 70 & 72 \\ Golden Reinders & 621 & 435 & 93 & 93 \\ Granny Smith & 590 & 413 & 89 & 88 \\ Kasel 37 & 525 & 368 & 78 & 79 \\ Mondial Gala & 477 & 334 & 71 & 72 \\ Red Chief & 612 & 429 & 91 & 92 \\ Scarlet Spur & 640 & 448 & 96 & 96 \\ Starkrimson & 618 & 433 & 92 & 93 \\ Starkspur Golden Delicious & 669 & 468 & 100 & 101 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image Dataset.  
Figure 2: Sample images of the ten apple varieties.  
technique for extracting features from an image by analyzing the distribution of gradient orientations in local image patches [30].  
In HOG feature extraction, the gradient magnitude and orientation for each pixel are computed using the Sobel operator or other edge detection algorithms, and the histogram distribution of oriented gradients of neighboring cells is normalized and concatenated into a single feature vector. In our case, 144 HOG features were extracted using MATLAB 2023a, \"extractHOGFeatures\" method with a filter cell size of 64 \(\times\) 64.
## 3 Results and Discussion
### Time History of Displacements for All Three Risers
For velocities equal to 1.22 m/s, the maximum ranges of displacements in cross-flow direction/outside diameter (Ay/D) of risers 1, 2 and 3 were 0.005, 0.014 and 0.024 at \(-\)6 m, 0.006, 0.017 and 0.026 at \(-\)12.5 m and 0.003, 0.0075 and 0.012 at \(-\)19 m, respectively. For velocities equal to 2.13 m/s, maximum ranges of displacements in cross-flow direction/outside diameter (Ay/D) of risers 1, 2 and 3 were 0.018, 0.035 and 0.059 at \(-\)6 m, 0.022, 0.043 and 0.073 at \(-\)12.5 m and 0.009, 0.018 and 0.033 at \(-\)19 m, respectively. To summarize: (1) In the cases with the same current velocity, the vibration range of riser 3 was the largest, followed by riser 2, and riser 1 was the smallest for all locations of the three risers. (2) With the increase of current velocity, the Ay/D rose for all three risers. Take riser 3 at 12.5 m for an example, the Ay/D(s) was about 9 times (1.22 m/s) and 24 times (2.13 m/s) of that at 0.36m/s. (3) the end support conditions affected the displacement significantly, i.e., the simple support lead to larger vibration. Using riser 3 at 2.13 m/s as an example, the Ay/D(s) were about 0.059 at \(-\)6 m (6 m to the simple support end) and 0.033 at \(-\)19 m (6 m to the fixed support end). Figure 7: Time history of displacements in cross flow direction (Y) at three typical locations.
## 3 Methodology
### Data Pre-Processing
#### 3.3.4 Oversampling  
Advances in data science have led to the development of techniques to deal with class imbalance, such as data augmentation, over-sampling, under-sampling and ensemble methods. Two basic approaches can address the challenges of using imbalanced datasets: algorithm-level and data-level strategies [31]. Data-level methods, such as the SMOTE technique, entail modifying the class distribution of imbalanced data through under-sampling, over-sampling, or a mix of the two [32]. Oversampling is the suggested strategy for highly imbalanced datasets with few minority samples [16]. These techniques could, however, result in overfitting or the loss of valuable traits in majority classes. Algorithm-level approaches, such as bagging, boosting, or hybrid ensemble methods, entail modifying the classifier to imbalanced data. These approaches have improved the accuracy of decision tree models and other classifiers [47].  
The dataset displays a high-class imbalance for the feature'results groupings'. Therefore, the performance of the classification model may improve if the imbalanced dataset is handled correctly [33]. The model can then correlate to the majority class and ignore the minority class. After splitting the dataset into training and testing sets using a 75:25 ratio, the distribution of classes within the target variable was as follows:'may continue studies'--17,771, 'no/slow progress'--2112, 'obtained qualification'--1320, 'no re-admission'--655, 'no result'--10. It's important to note that employing the target variable with such imbalanced class proportions may introduce a bias toward the largest class, 'May continue studies'. The evaluation metrics may also be biased and may fail to consider the minority class. This study used the SMOTE technique from the imbalanced-learn library (imblearn)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Type** & **Classes/Value Range** \\ \hline Previous activity & Categorical & 6 (employed, unemployed, grade 12, college, university, national service) \\ Module name & Categorical & 92 (list of modules) \\ Module mark & Numerical & 0–100 \\ Mathematics & Numerical & 0–100 \\ Physical Sciences & Numerical & 0–100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._to address the class imbalance challenges. After this, the classes were balanced, as shown in Table 4 below.
# Spatial Distribution of Forest Fire Emissions: A Case Study in Three Mexican Exoregions
## 2 Materials and Methods
### Variables for Estimating Emissions  
The selection of satellite and cartographic products for calculating the emissions was based on the factors proposed by [PERSON] (1980) and on Equation (1) ([PERSON] and [PERSON], 1980):  
\[M_{i,j,k}=\ BL_{i,j,m}*BE_{i,j,m}*BS_{i,j}*E_{k}*10^{-15} \tag{1}\]  
where \(M_{i,j,k}\) is the amount of gas \(k\) released for a specific area (with the coordinates \(i,j\)) in Tg, \(BL_{i,j,m}\) is the biomass load for the same area (g m\({}^{-2}\)) (assuming the area has a homogeneous cover of fuel of vegetation type m), \(BE_{i,j,m}\) is the burning efficiency (0-1, dimensionless) of the fuel/vegetation type m, \(BS_{i,j}\) is the burned surface of the same area (m\({}^{2}\)), and \(E_{k}\) (Emission Factor) is the amount of trace gas \(k\) released per unit of dry matter (g kg\({}^{-1}\) of biomass). The data used for each parameter are described below.
# Automated Vulnerability Detection in Source Code Using Minimum Intermediate Representation Learning
## 5 Experiments and Results
### Experimental Setup  
The dataset used in this paper comes from the Software Assurance Reference Dataset (SARD) [38] and the National Vulnerability Database (NVD) [39]. SARD comes from the Software Assurance Metrics And Tool Evaluation (SAMATE) project of the National Institute of Standards and Technology  
Figure 6: The overview of the representation learning stage. In the Embedding layer, the weights come from the pre-training stage. Three convolutional neural networks constitute the Convolutional layer. In this paper, the filter size of each network is \(h_{1},h_{2},h_{3}\) respectively. The outputs of the Convolutional layer are concatenated through the Concatenate layer. A global-max-pooling operation is conducted to find the max value in every column. Lastly, two dense layers are added, and the activation function is softmax.  
(NIST). SARD aims to serve as a standard dataset to evaluate the effectiveness of vulnerability detection tools with known software security errors and fixes for them. The samples in the dataset come in two forms: artificially designed vulnerabilities, and vulnerabilities found in software products. The NVD is the U.S. government repository that collects vulnerabilities and their patches in software products. We choose the buffer overflow vulnerabilities (CWE-119) and resource management error vulnerabilities (CWE-399) on SARD and NVD as the samples for learning vulnerable programming patterns. Other samples in C language on SARD were selected to extend the dataset for pre-training. Statistics on training data and pre-training data are summarized in Table 1. These datasets have been preliminarily processed by [25]. We set train data:test data = 7:3, and used 10-fold cross validation to choose super parameters. In our experiment, buffer overflow vulnerability was selected as the main detection object (except Section 5.5). This is because the buffer overflow vulnerability is the most significant vulnerability [35]. Therefore, the detection of this type of vulnerability has practical significance. In addition, abundant samples of the buffer overflow vulnerability are available. It should be noted that our approach also applies to other vulnerabilities caused by the misuse of data processing and control logic.  
The neural networks are implemented by Keras (version 2.2.5). The Random Forest, Gradient Boosting Decision Tree, SVM, Logistic Regression and Naive Bayesian algorithm were provided by the Scikit-learn (version 0.21.3). The distributed embedding is implemented by Word2 vec provided by genism (version 3.0.1). Our algorithm was run on Google Colaboratory with Tesla T4 GPU.  
In the representation learning stage, the parameters we used are shown in Table 2.
## 4 Experiment and Results
### Dataset Descriptions  
We conducted abundant experiments on five polyp segmentation datasets, as shown in Table 1. The first four were standard review datasets, and the last one was the largest publicly available challenging dataset in recent times. The training set consisted of 900 images selected from the subclass of Kvasir [38] and 550 images from CVC-ClinicDB [39], totaling 1450 samples. The testing set included samples from all five datasets:  
1. The complete ETIS-LaribPolypDB [40] dataset consists of 196 samples. The main characteristic of polyps in the ETIS-LaribPolypDB dataset is their small size, resulting in a notable imbalance between polyps and the background.
2. The remaining 62 images from CVC-ClinicDB. In the CVC-ClinicDB dataset, polyps vary in color, size, position, and texture.
3. The entire CVC-ColonDB [41] dataset includes 380 samples. In the CVC-ColonDB dataset, polyps are frequently occluded and indistinct.
4. Sixty images from the CVC-T dataset (CVC-T is derived from the 912 images originally found in EndoScene [42], with 612 images subtracted from CVC-ClinicDB). In the CVC-T dataset, polyps appear uniform and smooth, yet they display a significant similarity in color, texture, and other characteristics to surrounding normal tissues.
5. The rest of the 100 images came from Kvasir. In the Kvasir dataset, polyps are large but irregularly shaped.  
It is worth noting that the testing set was divided into two parts: one for evaluating the model's learning ability and the other for assessing its generalization ability. The learning ability was evaluated using the remaining images from the training set, while the generalization ability was assessed using data that was not seen during training.
## 4 Simulation Studies
### Simulation Studies and Discussion
#### 4.2.4 Results and Finding
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hour** & **Import** & **Export** \\ \hline Petroleum & 0 & 171,897 \\ Liquid gas & 1055.61 & 0 \\ Fuel oil & 0 & 8857.3 \\ Gas oil & 0 & 1952.09 \\ Kerosene & 0 & 127.814 \\ Gasoline & 8777.53 & 0 \\ Plane fuel & 1263.726 & 0 \\ Natural gas & 2964.269 & 0 \\ Coke gas & 0 & 39.7234 \\ Coal & 390.384 & 0 \\ \hline \end{tabular}
\end{table} 
Table 4: Import and export of carriers (BOE). \begin{table}
\begin{tabular}{c|c c c c c c c c c c} \hline \hline
**Unit 1** & **Unit 2** & **Unit 3** & **Hour** & **Unit 4** & **Unit 5** & **Unit 6** & **Unit 7** & **Unit 8** & **Unit 9** & **Unit 10** \\ \hline
1 & 455 & 245.8952 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
2 & 455 & 295.9591 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\
3 & 455 & 376.087 & 20 & 0 & 0 & 0 & 0 & 0 & 0 \\
4 & 455  
iteration. As shown in this figure, FOA converged in iteration number 115 and provided the optimal solution of 9.0922 million dollars. Based on the outputs presented from the implementation of the EC, as well as the mentioned topics, the following findings are identified. * EC is a constrained optimization problem that can be solved using various methods, especially optimization algorithms. * The goal in EC is determination of the most appropriate pattern of using energy carriers to supply energy demand, wherein first technical problems and then economic issues are adequately addressed. * In the EC study, various sectors of energy consumption including transportation, agriculture, industrial, residential, commercial, and public sectors are considered. * EC is a more accurate and in-depth study than UC, as UC and ED are part of the outputs of energy commitment. * EC study provides more details and information about the grid than UC studies. * UC focuses only on the operation of electrical energy demand, while the EC also considers other energy carriers. * By implementing the EC on an energy grid, important outputs are obtained, including the need to differentiate energy carriers, ED and UC information, and the import and export of energy carriers. * FOA algorithm has a good performance in solving the EC problem by presenting a smooth convergent curve and achieving the optimal solution in the appropriate number of iterations.
## 4 Discussion
### Performance Comparison of Resonant and Anti-Resonant States  
Both the resistive loss coefficient and the effective electromechanical coupling coefficient play a significant role in determining if VEH systems exhibit anti-resonance. As seen in Figure 3, \(\eta\) and \(\xi_{E}\) define a critical value of \(Q\), at which the power reaches a theoretical limit and an anti-resonant peak starts to emerge. For systems with equivalent mechanical properties, the difference in \(\eta\) and \(\xi_{E}\) can thus determine if they are on opposite sides of this critical value of \(Q\) (or equivalently, \(k_{crit}\)).  
Assuming we can design an arbitrary EMEH or PEH to operate either at the resonant or the anti-resonant state, the two VEH systems will benefit differently from operating under either condition. Using the results of Section 3, we can compare these states regarding the key performance parameters: output voltage, power and efficiency.  
In general, a large output voltage is beneficial due to the substantial voltage drop in typical voltage rectification circuits. Due to the series nature of the EMEH circuit model, a large output voltage is obtained when \(\xi_{C}\) is large and \(\xi_{E}\) is small, while for a PEH, both should be small. From the results in Figures 5 and 6, we can see that a system run at resonance can in general achieve large values of optimal \(\xi_{C}\) at high \(k\) values. On the contrary, values of optimal \(\xi_{C}\ll 1\) at resonance require large values of \(\eta\) and \(k\ll 1\) (see Figure 6). Only at anti-resonance (see Figure 9) can we achieve an optimal \(\xi_{C}\ll 1\) at small values of \(\eta\). In both cases (PEH/EMEH), an increasingly beneficial value of optimal \(\xi_{C}\), with regard to output voltage, is achieved at increasing \(k\).  
In a similar sense, the power characteristics at anti-resonance (Figure 10) also favor the PEH over the EMEH as the power rapidly declines at increasing \(\xi_{E}\) and \(k\), while it is close to its theoretical maximum for \(\xi_{E}\ll 1\), regardless of \(k\).  
Assuming load and excitation frequency remain optimized by load power, both VEH systems have a maximum efficiency of 50% at resonance (see Figure 12). At anti-resonance, the efficiency is above 50% (if \(\xi_{E}>0\)) and increases along the boundary of \(k_{crit}\), in the direction of larger \(k\) and \(\xi_{E}\) (see Figure 13). It may be beneficial to operate in this region if the source power is small, as the load power cannot be larger than efficiency times source power. If we instead assume an arbitrary \(\gamma\) and a load optimized by efficiency, a system with a larger \(\eta\) will have a larger maximum efficiency, although for \(\gamma\ll 1\), the difference will be very small.