esa-sceva/satcom-chunk-collection huggingface dataset:
# VulEye: A Novel Graph Neural Network Vulnerability Detection Approach for PHP Application
## 3 Background
### Graph Neural Network  
**A Graphical Neural Network (GNN)** is a deep learning method for processing graph structure data. It has excellent performance in the spatial non-Euclidean data analysis domain, such as social networks [40], knowledge graphs [41], traffic networks [42], etc. A GNN uses a deep neural network to learn the graph's node and structural features. In a word, the primary process of the GNN model includes two procedures. One is propagation and the other is output. In the first procedure, the GNN model updates node representations over a timestep and handles the target output (such as the classifications of nodes) in the second procedure.  
A graph structure \(G=(V,E)\), where \(V\) represents a node-set, \(E\) represents a set composed of all edges, and \(h_{v}\in R^{d}\) is a state embedding for each node \(v\), which contains the feature of the neighborhood and itself. The state embedding \(h_{v}\) is a \(d\)-dimension vector of node \(v\). In the propagation procedure, the GNN projects the graph data into lower-dimension spaces to obtain node representations by iteration. It initializes the node representation \(h_{v}^{(l)}\) with the model input \(X_{v}\), then updates each node representation by a propagation formula (1), where \(l\) is the iteration number, which automatically increases between 0 and the maximum iteration number specified before the training step.  
\[h_{v}^{l}=f(X_{v}X_{CO_{v}}X_{NB_{v}}h_{NB_{v}}^{l-1}) \tag{1}\]\(X_{CO_{v}}\) is a feature vector of the edge-binding node \(v\), \(X_{NB_{v}}\) is feature of the neighboring vertex of node \(v\), and \(h_{NB_{v}}\) is the embedding matrix of the neighboring vertex of node \(v\). \(f(\cdot)\) is a projection function that maps input data into \(d\)-dimensional space. According to [PERSON]'s immobility theory [43], we can obtain a unique \(h_{v}\) solution for iterative updating as follows (2), where \(H\) and \(X\) are all linkages of \(h\) and \(x\).  
\[H_{v}^{l+1}=F(H^{l},X) \tag{2}\]  
Update the function and continuously use the hidden state of the neighbor node at the current time as part of the input to generate the hidden state of the center node the next time, until the next state of each node changes very little. So far, each node has \"known\" its neighbor's information. Finally, GNNs use an output function to adapt to different downstream tasks, which calculate the output by feeding the state \(h_{v}\) and feature \(x_{v}\), as follows (3).  
\[o_{v}=g(x_{v},h_{v}) \tag{3}\]
# A Novel Hybrid Ant Colony Optimization for a Multicast Routing Problem
## 4 ACO&CM Algorithm
### Pheromone Trail Updating  
In ACO, there are local and global updates in pheromone updating. Local pheromone trail updating is executed while constructing a multicast tree, and global pheromone updating is executed at the end of completing the multicast tree. The main purpose of local pheromone updating is to avoid producing very high pheromone links being selected by other ants. These links use more pheromone on the trail to make the ACO algorithm fall into a local optimum. The local pheromone trail is updated using the following formulation:  
\[\tau_{ij}=(1-\rho)\tau_{ij}+\Delta\tau \tag{10}\]  
where \(\rho\in[0,1]\) represents a pheromone evaporation parameter, \(\Delta\tau=\sum\limits_{k=1}^{m}\Delta\tau_{ij}^{k}\), \(\Delta\tau_{ij}^{k}\) is the incremental pheromone trail on the edge between \(i\) and \(j\) nodes, and \(m\) means the ant number. To avoid overuse of some nodes and hinder the ants from exploring new paths, we adjust parameter \(\rho\) by cloud model to avoid the ACO algorithm trapping into local optimum. The parameter \(\rho\) is performed using the following formula,  
\[\rho=\left\{\begin{array}{ll}k_{1}e^{\frac{-(x-E_{x})^{2}}{2\rho_{1}^{2}}}, \ \tau_{\min}<x<\tau_{\max}\\ \rho_{1},\ x>\tau_{\max}\\ \rho_{2},\ x<\tau_{\min}\end{array}\right. \tag{11}\]  
where \(\rho_{1}\), \(\rho_{2}\)\(\in\)[0, 1] are pheromone decay parameters, and \(k_{1}\) denotes a parameter. Other parameters refer to the description of formula (7).  
Besides, the pheromone of global solution is updated according to global pheromone updating, which is intended to strengthen the neighborhood search of the optimum solution. The global pheromone updating can not only accelerate convergence by increasing differences between better and worse solutions, but also avoid the fast accumulation of pheromones on relatively optimal paths at an early stage. In the basic ACO algorithm, only the pheromone trails on the edges of the best solution have the chances to be updated using the following formula,  
\[\tau_{ij}=(1-\varphi)\cdot\tau_{ij}+\frac{\varphi}{Lbest} \tag{12}\]in which \(\varphi\in[0,1]\) is a decay parameter, \(L_{best}\) is the cost value of the best-so-far solution.
## 5 Conclusions  
In this study, we used pedestrian interaction state features, pedestrian 3D pose features, pedestrian velocity features, and pedestrian blind observation state features as input data and constructed an ff-LSTM to fuse the above features, providing a method for recognizing and predicting pedestrian crossing intentions when views are obstructed. We also verified the effectiveness of our given pedestrian interference judgment method by comparison experiments. In the experimental section, the advancement of our method was verified from three perspectives: input parameter selection, intention recognition algorithm, and intention prediction time range. In the input parameter selection and intention recognition algorithm experiments, the mean values of our method's four evaluation metrics (Recognition Accuracy, Recall rate, F1-score, and Precision) were 0.974, 0.975, 0.978, and 0.976, which are higher than other commonly used methods. In the intention prediction time range experiment, our method achieved 0.889, 0.888, 0.898, and 0.911 for the four evaluation metrics, even though 1 s advanced the independent variable. The limitations of our study are mainly the following:  
* As the system relies on vision sensors, we may not be able to make accurate decisions at night, in bad weather, fog, dust, or when the pedestrian is far from the camera.
* Only the case between two pedestrians is considered in the pedestrian interaction module, and the case of three or more people is not covered.
* We did not consider hazardous crossing intentions caused by the irrational behavior of pedestrians, e.g., when pedestrians intentionally cross the street with the intention of blackmailing or killing themselves after spotting vehicles in the blind spot.  
To address the above problems, subsequent studies can consider adding sensors such as radar, improving the model accordingly to reduce the impact of ambient lighting and camera hardware limitations on the recognition and prediction, and adding the group activity recognition (GAR) algorithm to achieve pedestrian behavior recognition for more than two people [48].  
As for the hazardous crossing problem caused by the irrational behavior of pedestrians, we can consider introducing the quantum-mechanics-based irrational driving behavior decision method in our previous study [49].  
Conceptualization, J.Z.; methodology, J.Z.; software, J.Z., X.B. and W.H.; validation, J.Z., X.B. and W.H.; formal analysis, J.Z.; investigation, J.Z.; data curation, J.Z. and W.H.; writing--original draft preparation, J.Z.; writing--review and editing, J.Z.; visualization, J.Z., X.B.; supervision, J.Z.; project administration, J.Z.; funding acquisition, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [52202501], [Natural Science Foundation of Shaanxi Province] grant number [2022]Q-546] and [Research Initiation Fund of Xi'an University of Technology] grant number [118-451119003].
## 3 Applying Bioinspired Networking
### Clustering  
The grouping of nodes into clusters has been a widely pursued strategy to achieve the network scalability objective. Clustering is an important technique which aims at generating the minimum set of clusters and hereby minimizing the transmission distances in the network. The introduction of clusters allows a hierarchical routing of traffic from the sources to the destinations, conserve bandwidth and provide fault tolerant and stable operation. Furthermore, a cluster topology might be useful in networks that mix limited devices and full-functional devices deployed as cluster-heads. However, in most sensor networks applications network clustering is not desirable due to the uneven draining of resources in the network.  
Several authors report on the use of PSO for network clustering: [PERSON], [PERSON] and [PERSON] [34] where the first to use a PSO algorithm for the clustering of nodes in a network. [PERSON] and [PERSON] [18] used a PSO-based clustering algorithm with an enhanced search ability. [PERSON], [PERSON] and [PERSON] [33] presented an energy-aware clustering for wireless sensor networks using PSO. The performance of the protocol was compared with conventional cluster-based protocols developed for wireless sensor networks, e.g., LEACH (Low-Energy Adaptive Clustering Hierarchy) and variants hereof. [PERSON], [PERSON], and [PERSON] [32] reported on a number of extension of the PSO algorithm by introducing clustering in a wireless sensor network to reduce the total communication distance of the network and hereby decreasing the energy cost.  
[PERSON] and [PERSON] [39] used bioinspired intercellular communication to achieve a compact cluster via a lateral induction model in a purely distributed and energy-efficient manner. Initially, the sensor nodes collaborate to construct a functional cluster via lateral induction followed by an lateral inhibition phase. Once clusters have been formed, a competitive scenario between nodes is created where nodescompete on which to be active or which nodes to go to sleep. Eventually, one of the active nodes becomes cluster-head as a result of competition.  
Clustering has been recognized as an effective mechanism for topology control in large-scale wireless sensor networks. Inspired by the collective behavior of small animals, self-organizing clustering algorithms for large-scale networks can be developed. The algorithm does not require prior knowledge of node locations, time synchronization or other network characteristics. By using simple rules most of the nodes in a network can determine their role as either cluster head or cluster member based on purely local decisions and by using only a limited amount of broadcast messages in the network.
## 3 Materials and Methodology
### Finding the Predicted Target Module
Reset gate (\(r_{\rm t}\)): Controls how much of the past information to forget: \[r_{\rm t}=\sigma(W_{\rm r}x_{\rm t}+U_{\rm r}h_{\rm t-1}+b_{\rm r})\] (12) * Candidate hidden state (\(\overset{\sim}{h}_{\rm t}\)): Computes the new information to be added: \[\overset{\sim}{h}_{\rm t}=\tanh(W_{\rm h}x_{\rm t}+U_{\rm h}(r_{\rm t}\odot h _{\rm t-1})+b_{\rm h})\] (13) * Final hidden state (\(h_{\rm t}\)): Combines the previous hidden state and the candidate hidden state using the update gate: \[h_{\rm t}=z_{\rm t}\odot h_{\rm t-1}+(1-z_{\rm t})\odot\overset{\sim}{h}_{\rm t}\] (14) * Output prediction (\(\hat{y}_{\rm t}\)): The output is computed as follows: \[\hat{y}_{\rm t}=W_{\rm o}h_{\rm t}+b_{\rm o}\] (15) * Output: Predicted relevance score for each module. Step 3: Combine predictions (weighted ensemble). * The predictions from XGBoost and GRUs are combined using a weighted ensemble approach: \[\hat{\gamma}=\alpha\cdot\hat{\gamma}_{\text{XGBoost}}+(1-\alpha)\cdot\hat{\gamma}_{ \text{GRU}}\]  
\(\alpha\): Weight assigned to explicit signals (e.g., 0.6). * Final output (\(\hat{\gamma}\)): The module with the highest \(\hat{\gamma}\) score is selected as the predicted target module.
## 2 Materials and Methods
### Prediction Models
#### 2.2.4 Results Verification and Performance Analysis  
After the three models were trained with the two feature matrices created previously and the first results were obtained, cross-validation and principal component analysis were used with the aim of checking and improving these results.  
In K-iteration cross-validation, the sample data are divided into K subsets. One subset is used as test data, and the rest (K\(-1\)) are used as training data. This process is repeated for K iterations with each of the possible subsets of test data. Finally, the arithmetic mean of the results of each iteration is used to obtain the result [30]. In the case of this project, K = 10 was used.  
Additionally, principal component analysis (PCA) is a method that makes it possible to reduce the dimensionality of the data. In other words, the aim is to reduce the number of variables until only those representing the most significant variance are left. In this case, the number of components necessary to represent 95% of the variance of the dataset was calculated.  
Therefore, each of the three models was trained (i) without cross-validation and PCA using the two feature matrices, (ii) after performing cross-validation with the matrices, and (iii) after performing PCA with the matrices.  
Finally, different metrics and methods were used to analyze the performance of each of these implementations. Each of these methods is detailed below.  
* Accuracy and Recall: Accuracy allows calculating the proportion of the correctly labeled data, while recall allows seeing the proportion of positive values identified [31; 32].
* F1 Score: The F1 score combines the contributions of the accuracy and recall of the model. A high F1 shows a good performance of the model. If the accuracy is high, F1 will be higher; on the other hand, if there are many false negatives, F1 will be low [32].  
* ROC Curve: This allows plotting the true positive rate and false positive rate at different classification thresholds. One method used to evaluate the ROC is to look at the area under the curve (AUC), which shows how well the models can discern between the two classes (1 or 0). The higher the AUC, the better the performance [33].
* Confusion Matrix: This method can identify (i) the number of true positives (positive observations that were predicted to be positive), (ii) true negatives (negative observations that were predicted to be negative), (iii) false positives (negative observations that were predicted to be positive), and (iv) false negatives (positive observations that were predicted to be negative) [34].
## 4 Performance Comparison Analysis  
Moreover, a comparison of the performance is presented for the proposed meta-learning and DWT with other diabetes detection and glucose level monitoring approaches using different wearable sensors and machine learning techniques, as described in Table 1. Various techniques for chronic diseases such as diabetes prediction and glucose level detection have been proposed in previous studies [10; 34; 35; 36; 37], highlighting the importance of these areas in healthcare. In the comparison, previously studied work that focuses on wearable sensors is considered for chronic disease prediction such as diabetes, and glucose level prediction.  
Based on the result shown in Table 1, diabetes prediction techniques such as IGRNet, deep neural network (DNN), exponential GPR and conventional neural network (CNN), and logistic regression (LR) have lower performances because they either did not consider the denoising technique to improve data quality and improve the model performance or did not consider fast-learning algorithms.  
As shown in Table 1, the proposed meta-learning model integrated with the DWT denoising technique achieves higher accuracy compared to others. Thus, the demonstrated result proves the proposed model has the capacity to accurately predict diabetes, improve sensing accuracy, predict r-peaks, and monitor glucose levels in smart healthcare systems. The performance of our proposed meta-learning model in conjunction with DWT data denoising techniques shows an accuracy of 96.2%, an RMSE of 0.010798, and an AUC of 99%. The accuracy of the model without pre-training is 81%, and the accuracy without DWT is 80%. Hence, the RMSE of our meta-learning model with DWT is lower, and the  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method and Reference** & **Accuracy** \\ \hline IGRNet [10] & 78.1\% \\ DNN [34] & 94.53 \\ Exponential GPR and CNN [35] & 94\% \\ Logistic Regression [36] & 92.2\% \\ SVM [37] & 89\% \\ Without pre-train (in this paper) & 81\% \\ Meta-learning without DWT denoising (in this paper) & 85\% \\
**Meta-learning with DWT denoising (Proposed)** & **96.2\%** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison table of the proposed method and previously studied approach for diabetes prediction.  
Figure 8: Performance of different models.  
accuracy and AUC are higher than those of the models without pre-training and without DWT. Hence, our proposed model not only outperforms the baseline models but also surpasses the performance of previously studied techniques. Therefore, the proposed model achieves better performance for diabetes detection in smart healthcare systems.
###### Abstract  
This article presents a novel methodology to assess the financial risk to crops in highly weather-volatile regions. We use data-driven methodologies that use singular value decomposition techniques in a low-income economy. The risk measure is first derived by applying data-driven frameworks, a Principal Component Analysis (PCA), and Singular Spectrum Analysis (SSA) to productive coffee crops in Colombia (163 weather stations) during 2010-2019. The objective is to understand the future implications that index insurance tools will have on strategic economic crops in the country. The first stage includes the identification of the PCA components at the country level. The risk measure, payouts-in-exceedance ratio, or POER, is derived from an analysis of the most volatile-weather-producing regions. It is obtained from a linear index insurance model applied to the extracted singular-decomposed tendencies through SSA on first-component data. The financial risk measure due to weather volatilities serves to predict the future implications of the payouts-in-exceedance in both seasons--wet and dry. The results show that the first PCA component contributes to forty percent of the total variance. The seasonal forecast analysis for the next 24 months shows increasing additional payouts (PO), especially during the wet season. This is caused by the increasing average precipitation tendency component with POERs of 18 and 60 percent in the first and second years. The findings provide important insights into designing agricultural hedging insurance instruments in low-income economies that are reliant on the export of strategic crops, as is the case of Colombian coffee.  
Keywords:index insurance; coffee production; low-income economies; Principal Component Analysis (PCA); Singular Spectrum Analysis (SSA); payouts in exceedance (PoE); financial risk measure; Colombia +
Footnote †: journal: _sciences_
## 2 Methodology
### F1-Score, Precision and Recall  
To evaluate the performance of LLMs in extracting patient characteristics, we employ the BERTScore methodology. BERTScore provides a comprehensive evaluation by comparing the tokens of the ground truth data \((x)\) with the LLM's output \((\hat{x})\). This comparison is performed using a pre-trained BERT model, which generates contextualized embeddings for each token [28].  
The BERTScore calculation involves a greedy matching process to maximize the similarity score between the tokens of the ground truth and the LLM's output. For each token in the ground truth, the most similar token in the LLM's output is identified, and vice versa. This matching process allows for the computation of precision and recall scores.  
Recall (\(R_{BERT}\)) measures the proportion of tokens in the ground truth that are correctly captured by the LLM's output. It is calculated as follows:  
\[R_{BERT}=\frac{1}{|x|}\sum_{x_{i}\in x}\max_{\hat{x}_{j}\in\hat{x}_{i}}x_{i}^{ \top}\hat{x}_{j} \tag{4}\]  
where \(x_{i}\) represents a token in the ground truth, \(\hat{x}_{j}\) represents a token in the LLM's output, and \(x_{i}^{\top}\hat{x}_{j}\) denotes the STS between their respective embeddings.  
Precision (\(P_{BERT}\)) measures the proportion of tokens in the LLM's output that are relevant to the ground truth [36]. It is calculated as follows:  
\[P_{BERT}=\frac{1}{|\hat{x}|}\sum_{\hat{x}_{j}\in\hat{x}}\max x_{i}\in xx_{i}^{ \top}\hat{x}_{j} \tag{5}\]  
The F1-score (\(F_{BERT}\)) is the harmonic mean of precision and recall, providing a balanced measure of the LLM's performance [36]. It is calculated as follows:  
\[F_{BERT}=2\cdot\frac{P_{BERT}\cdot R_{BERT}}{P_{BERT}+R_{BERT}} \tag{6}\]  
In our study, we calculate the F1-score, precision, and recall by applying the BERTScore methodology to the LLM's output and the corresponding ground truth data. Instead of using the original BERT model, we employ the T5 model [37], which has been specifically designed for text-to-text tasks and has shown superior performance in various natural language processing applications [28].
# Metal-Organic Frameworks Characterization via Inverse Pulse Gas Chromatography
## 1 Introduction
Separation, for example, can be driven by the difference in affinities and interactions between the MOF framework and the targeted probe molecules, where the structure and chemical functionality of the MOF pores can be varied, for example, via the organic linker length, functionalization, or integration of coordinatively unsaturated metal sites [35; 36; 37]. MOFs have demonstrated outstanding performances as separating agents either as adsorbents or membranes as a result of their well-defined chemical and structural properties in terms of pore size, functionality, and rigidity/flexibility, which correlate with the targeted adsorbates' intrinsic properties like functionality, size, shape, etc. Porous materials like MOFs that possess essential features for the selective separation of targeted analytes over others represent a potential alternative to energy-intensive techniques like distillation. An adsorption-based process can afford separation via thermodynamics, where the MOF acts as an adsorbent and shows a comparatively high affinity for a particular molecule due to the enhanced interactions between the adsorbates and the MOF material. Due to their unique surface properties and separation abilities, much research is focused on using MOFs in applications like sensing, catalysis, or as separating agents for carbon-capture, hydrocarbons separation, xenon/krypton, and many other essential separations. This study sums up past work concerning the utilization of IGC as a characterization tool to investigate the physicochemical features of MOFs and highlights IGC as a useful, revolutionary, and almost undiscovered technique for investigating and predicting the adsorption properties of MOFs.