esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Elman Neural Network for Bridge Condition Prediction
Then, the genetic algorithm code is written in MATLAB software 2022, and Equations (2) and (3) is expressed as Equations (2)-(6) with the function \(fun1\), which is then inserted into the genetic algorithm to perform the iterative operation. Since each unknown quantity in the function represents a transition probability matrix--for example, \(P(1)\) denotes the transition probability matrix from Condition 9 to Condition 8--the range of each unknown is between 0 and 1. \[fun1=Min{\sum_{t=1}^{N}}|A(t)\,-\,E(t)| \tag{6}\]  
The genetic algorithm determines that the results of the transition probability matrix for the first 6 years are: \(P(1)\) = 0.844, \(P(2)\) = 0.905, \(P(3)\) = 1, \(P(4)\) = 1, \(P(5)\) = 1, and \(P(6)\) = 0.998. Following the same methodology, the transition probability matrix is solved for the period of 1-60 years, and the transition probability matrix for the 60-year period is shown in Table 3.  
Figure 7: Selected prediction curve from Elman neural network. After obtaining the transition probability matrix, the condition rating of the bridge can be predicted using Equations (2)-(4). Taking an actual bridge as an example, it is selected for the long-term deterioration performance prediction of the bridge. The long-term performance prediction is based on the latest inspection records as the initial state vector, which is used to predict the bridge state level over the next 20 years. The bridge deterioration prediction model is validated by comparing the predicted values with the actual values, as shown in Table 4.  
In the real-world scenario, the bridge condition ratings are whole numbers. Therefore, the predicted data for the bridge condition levels from 1 to 20 years are rounded. The predicted values are then compared with the actual values, as depicted in Figure 9, which demonstrates that the prediction results align well with the actual values. Thus, a 60-year bridge condition rating for the case study is predicted. As illustrated in Figure 10, the 1-20 year condition levels from the bridge inspection reports are compared with the prediction results of the bridge condition level by the Markov chain model from 1 to 60 years, where the bridge condition prediction curve closely matches the actual bridge condition. This reflects the real deterioration performance of the bridge accurately. The bridge condition worsens over time, and the deterioration rate becomes increasingly slower, consistent with the actual deterioration performance of the bridge. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Bridge Age** & **Bridge Condition Level** & **Bridge Age** & **Bridge Condition Level** \\ \hline
1 & 8 & 11 & 7 \\
2 & 8 & 12 & 7 \\
3 & 8 & 13 & 7 \\
4 & 8 & 14 & 7 \\
5 & 8 & 15 & 7 \\
6 & 8 & 16 & 7 \\
7 & 8 & 17 & 7 \\
8 & 8 & 18 & 7 \\
9 & 8 & 19 & 7 \\
10 & 7 & 20 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Measured condition level of this case from 1 to 20 years. Figure 9: Comparison with predicted and detection values of bridge condition of the case from 1 to 20 years.
## 2 Related Work
### Summary of Existing Literature  
Based on a thorough examination of the literature, Table 1 compares the features of the most relevant research works to this proposed effort focusing on the mathematical modeling of smart farming systems as well as intrusion detection. It may be noted from the table that several studies address sensor-based smart farming frameworks, but most frameworks lack UAV integration to collect farming data. Similarly, there is limited research on intrusion detection in smart farming systems. The proposed framework is novel in that it combines aspects of smart farming, UAV based data collection, as well as proposes an advanced hyperparameter optimization-based intrusion detection system.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Authors** & **Env * S/R * Det** & **Int * Dt * Dt * Dt * Dt & **M L * Dt * Dt * Dt & **Intg * Dt * Dt * Dt * Dt & **Supported Application** & **UAV Data Collection** & **UAV Energy Model** & **U2U * Com * Dt \\ \hline [PERSON] [4] & R & \(\times\) & \(\times\) & \(\times\) & Remote Sensing & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [58] & S & \(\checkmark\) & \(\times\) & \(\times\) & Behavior Specification & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [17] & S & \(\times\) & \(\times\) & \(\times\) & IoT in Agriculture & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [20] & R & \(\times\) & \(\times\) & \(\times\) & Monitoring & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [59] & R & \(\checkmark\) & \(\checkmark\) & \(\times\) & Risk Mitigation & \(\times\) & \(\times\) & \(\times\) \\ \hline [PERSON] [60] & S & \(\times\) & \(\times\) & Fog & Farming Simulation Toolkit & \(\checkmark\) & \(\times\) & \(\checkmark\) \\ \hline [PERSON] [21] & R & \(\times\) & \(\times\) & \(\times\) & Connected Farms & \(\times\) & \(\times\) & \(\times\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of Related Work in Precision Agriculture and Intrusion Detection.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 4 Experiments and Results
### Results and Discussions
#### 4.2.6 Visualization Generated Data  
Synthetic samples were generated for the four intrusion classes in the NSLKDD dataset, with the exception of the \"Normal\" class. To ensure a balanced distribution of samples across all classes, the number of synthetic samples for each class was adjusted to correspond with the number of samples in the largest class. The model's performance and robustness are improved by this balancing procedure. Conversely, the IDS2017 dataset poses an entirely distinct obstacle. In this dataset, benign traffic accounts for 50% of the samples, which total 696,300 instances. Infiltration, a minor class, has as few as 29 samples. This substantial imbalance cannot be effectively resolved by merely increasing the minor class samples to match the majority class, as this would result in redundant and less diverse data, which could potentially impede the model's potential to accurately classify the majority class. Additionally, the training process would be considerably slowed by a substantial increase in the sample size. In order to rectify the class imbalance in the CIC-IDS2017 and CSE-CIC-IDS2018 datasets, we implemented a strategy that involved progressively increasing the number of samples in the minor classes to a predetermined level for each class. This method ensures that the class distribution is balanced without sacrificing the efficacy of training or the diversity of the data. Visual representations, such as infographics, can be used to demonstrate the effectiveness of this balancing strategy and the distribution of the samples.  
The structure and processing of the datasets can be better understood through the visual representation of the generated samples for each class. Figure 11 illustrates the distribution of samples across various classes following the implementation of our balancing strategy.  
By visualizing the data distribution and balancing the sample sizes, we enhance the overall performance of our models on these datasets and ensure a more robust training process.  
Figure 11: Visualization results of synthesized data.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{5}{c}{**Test Accuracy/\%**} \\ \cline{2-6}  & \(K=100\) & \(K=150\) & \(K=200\) & \(K=250\) & \(K=300\) \\ \hline FedAvg & 94.13 & 85.82 & 78.90 & 77.56 & 77.51 \\ \hline FedProx & 94.15 & 84.97 & 78.92 & 77.28 & 77.49 \\ \hline FedOpt & 92.55 & 84.27 & 76.32 & 74.55 & 75.43 \\ \hline CGFL & **96.89** & **87.59** & **80.05** & **78.57** & **78.92** \\ \hline \hline \end{tabular}
\end{table}
Table 8: The impact of the number of clients on performance.
# Design, Modeling, and Analysis of IEEE Std 80 Earth Grid Design Refinement Methods Using ETAP
## 2 Literature Review
### Earth Grid Design Considerations for Microgrids  
As civilization has advanced, urban areas have developed and industrial areas have significantly grown as well, whereas the electricity generation and distribution infrastructure has not been expanded linearly to meet the electricity demand. This expansion and growth of the consumers has presented developments in the form of integrating external power sources of AC and DC microgrids that aid in the electricity demand to the existing power system grid [19]. In his study on microgrids, [PERSON] defined microgrids as a composite network of cluster loads, energy storage systems, and distributed generation units in a local distribution network [20].  
Since the microgrids are interfaced with existing power systems and have critical equipment such as transformers, the return path for fault currents is the earth mass; for this reason, the microgrids are not exempted from the design and operation compliance requirements of earth grid designs. The safety and operational requirements ofzare applicable for microgrids, as well as all the necessary measures as prescribed in a traditional substation, and earth grid design guidelines must be adhered to as there is a risk of exposure to dangerous elevated voltages [21; 22].  
Various earthing methods are applicable for microgrids depending on the type of system and configuration used in the respective AC and DC systems. For AC grids, the earthing systems used are the terra-neutral (TN), terra-terra (TT), isolated terra (IT), and high-resistance grounding (HRG) [23]. The TN system is further divided into different grounds, i.e., TN-S, TN-C, and TN-C-S, with S denoting separate and C denoting combined [23].  
Studies conducted by [2,23] highlight the importance of AC power system grid earthing configurations integrated with a DC microgrid system, where the different studies show that high neutral voltage fluctuations result in AC grid TN networks with an isolated earthing configuration in the microgrid's DC bus due to its common mode voltage generated by converters [23]. For this reason, amongst others, high-resistance grounding (HRG) is highly recommended instead of isolated grounding configurations [2]. In addition, it is critical that, when designing the earth grid for microgrids, the inherent fault level of the power system as well as the fault level generated by the microgrids is considered to ensure the adequate sizing of earth grid elements such as conductors, earth electrodes, connection bars, and risers.
# Measurement-Device-Independent Two-Party Cryptography with Error Estimation
## 6 Conclusions  
In this paper, we proposed several two-party cryptographic protocols based on joint measurement and error estimation, including WSE, 1-2 ROT, and other protocols, and demonstrated their security.  
Compared with the protocol mentioned in [2; 17; 28; 29], our protocols discarded the assumption that the attacker's storage device was defective, but instead employed a combination of joint measurement and error estimation to limit the quantum storage of the attacker. Our protocols had no assumptions, were more secure, and had wider applicability. The two basic two-party cryptographic protocols mentioned in this paper could easily be extended to other two-party encryption protocols, such as 1-2 OT and quantum identification protocols.  
We eliminated the assumption that the attack was bounded by the attacker's technology, and employed the technique of joint measurement and error estimation to improve two basic quantum two-party cryptographic protocols. We demonstrated that our improved protocols offered stronger security and is applicable to many specific quantum two-party cryptographic protocols such as BC and PID.  
Inspired by [30; 31], we learned that quantum coherence plays an important role in quantum key distribution and quantum random number generation, and this might also be used to improve our work. Future work will also begin with this aspect.  
Conceptualization: Z.Z.; methodology: Z.Z.; validation: Q.G.; formal analysis: Q.G. and Z.Z.; writing--original draft preparation: Z.Z.; writing--review and editing: Q.G., C.G.; project administration: D.J.; funding acquisition: L.C. All authors have read and agreed to the published version of the manuscript. This research is supported by the National Natural Science Foundation of China (No. 61771236), National Key Research and Development Program of China (No. 2017 YFA0303700), Major Program of National Natural Science Foundation of China (No. 11690030,11690032), and Excellence Research Program of Nanjing University. The study claims no conflict of interest.
## 2 Materials and Methods
### Data Normalization and Feature Selection  
#### 2.5.1 Normalization  
The procedure of feature extraction is performed for all the mammograms, creating a matrix that contains all of them: the type of enhancement, the type of thresholding and an index for each image if it contains a malignant or a benign mass. If the mass is malignant, then the index is \"1\", and if the mass is benign, then the index is \"0\".  
The normalization method is a necessary procedure and must be performed before the data are inserted into the classifier because the range of the features' values may vary significantly. For example, one feature may have values ranging from 0 to 1, while another feature may have values from 0 to 5000. A normalization method maps all features to the same range, either from 0 to 1 or from \(-1\) to 1. The value of 1 maps the maximum feature value, while 0 or \(-1\) maps the minimum value. MATLAB's command \"normalize\" was used to map all features' values from 0 to 1 [37].  
The normalization can also be performed with the following function [46]:  
\[x^{\prime}_{i}=\frac{x_{i}-x_{min}}{x_{max}-x_{min}} \tag{11}\]  
where \(x_{min}\) and \(x_{max}\) are the minimum and maximum value of a feature, respectively; \(x_{i}\) is the \(i\)-th value from the total values that were extracted from the images; and \(x^{\prime}_{i}\) is the new normalized value.
# The Use of Radio and Telemedicine by TMAS Centers in
## 3 Results
### TMAS Centers and Means Communication  
Maritime emergencies, illnesses, accidents, and other incidents that require medical advice, based on the captain's decision, are managed by TMAS centers. Providing seafarers with health care is an essential part of their mission. TMAS centers in Europe conducted the majority of studies, while only one study was conducted in the US (Table 2). It appears that the Italian TMAS center, the International Radio Medical Center (C.I.R.M.), is one of the most populated in Europe. Additionally, seafarers contact these centers by telephone or by email to transfer their medical data, unlike RMD and CCMM, which use radios.
## 5 Basic Experiments
### Influence of \(\alpha\)  
In the following, we analyze the influence of the parameter \(\alpha\), which is used in the loss function as defined in Equation (8). This parameter can be interpreted as the margin between \(d(\mathbf{x}^{\alpha},\mathbf{x}^{\mathrm{P}})\) and \(d(\mathbf{x}^{\alpha},\mathbf{x}^{\mathrm{n}})\). Figure 4 shows the evaluation results (MAP) for various \(\alpha\) values. For this experiment, we only used the dimensionalities of \(K=6\) and \(K=12\) as examples to illustrate general tendencies. For a given \(\alpha\) and \(K\), 25 neural networks were trained for 10 epochs with different random initializations. Then, they were used for dimensionality reduction in the retrieval scenario, resulting in 25 MAP values. From these, we computed the mean (\(\mu\)) and the standard deviation (\(\sigma\)). The solid lines show \(\mu\) and the light areas show \(\pm\sigma\) around \(\mu\). For both \(K=6\) and \(K=12\), we see similar trends: \(\alpha=0\) achieves rather bad results. In this case, no margin is enforced, and the loss is zero as soon as \(d(\mathbf{x}^{\alpha},\mathbf{x}^{\mathrm{P}})\leq d(\mathbf{x}^{\alpha},\mathbf{x}^{\mathrm{ n}})\). However, increasing \(\alpha\) only slightly leads to a clear improvement in MAP. Any \(\alpha\) in the range of \([0.1,1.7]\) produces results of similar quality. For \(\alpha>1.7\), the results strongly decrease. This can be explained as follows: Only a small positive margin is needed for retrieving the correct versions. Using a large margin brings no benefit for retrieval, but makes the training much harder. In summary, for \(0.1\leq\alpha\leq 1.7\), we see a stable overall behavior of the results with a small standard deviation, showing robustness to the initialization used.
## 3 Paradigm Shift to U-Learning
### U-Learning  
With the advancement of wireless networking and sensor technology, a new computing model known as ubiquitous learning (U-learning) has emerged. U-learning is a new learning paradigm in a ubiquitous computing environment that uses various small electronic devices such as smart cards, handheld terminals, sensor network nodes, smart mobile phones, and radio frequency identification (RFIDs). The concept of ubiquitous learning has been recommended in academia due to the widespread availability of computing technology and its penetration in the teaching field. In addition, these tools enhance context understanding and make it easy to interact between real and digital learning resources, all while offering individualized learning opportunities [34]. U-learning enables learners to learn the right thing in any place at any time. For this type of learning to be successful, it is essential that the appropriate technology is in place to ensure quick and easy connection. According to [35], this technology should include a reliable internet connection, a secure network, and the appropriate software and hardware to support learning activities. Additionally, the technology should be user-friendly so that learners can easily access the educational content and engage with it.  
U-learning has been described as an integration of E-learning and M-learning paradigms since it creates an environment where students may access information, interact, and participate in learning activities without regard for time or space. Generally, there were three waves of the computing revolution that effect U-learning:  
1. Ubiquitous computing makes computers more persuasive, convenient, and less expensive, and therefore information is much more accessible by wireless communication.
2. Based on the technique of ubiquitous computing, ubiquitous learning is an everyday learning environment enabled in our daily lives by mobile and embedded computers and wireless networks.
3. Ubiquitous learning could be appropriate to the learner's needs, preferences, cognitive characteristics, and interests by using both virtue and electronic resources in the learner's own context or environment [36].  
Figure 1 shows the paradigm-shifting of learning.
## 1 Introduction
With respect to a graphical network, the tasks that can be accomplished are node classification, link prediction, graph classification, graph clustering, etc. In this article, we consider the task of node classification to predict the label of a node in the network based on the aggregation of the information from its neighbor. The work presented in this article is an enhancement to the traditional GCN-based node classification in which the training dataset is chosen based on graphical properties of the node: (i) clustering coefficient and (ii) betweenness centrality. The proposed method, named Bet-GAT, accomplishes the task of node classification using the Graph Attention Model, which improves the classification accuracy of the traditional Graph Attention Model. Further, the proposed method attains improved results with a training dataset of around 20% only, which is appreciable as feature availability for all the entities in a real-world network are a big challenge; hence, models need to work with a limited training dataset. There are a number of studies based on GCN models to perform effective classification of nodes on different benchmark datasets. In this paper, we propose a network-centrality-based approach to select training set nodes for GCN and GAT models. The idea of GCN revolves around edge-connectivity-based information propagation and property transfer from node to node. Effective information propagation occurs in the network when important nodes are selected based on their structural position. We exploited this idea to select the training set such that nodes with high network centrality are selected [11]. Our results analysis of different benchmark networks shows that the validation and test accuracy improved significantly for these networks. The simulative analysis was performedfor different centrality measures (degree, closeness, eigenvector, etc.). However, the best results are obtained by selecting the training sample _betweenness centrality_. This idea helps in accurate weight learning and in building the attention model. The results are promising for both binary and multiclass node classification tasks. The comparative analysis of the results with the other current methods proves the superiority of the proposed method. The rest of the paper is organized as follows: Section 1 gives a brief introduction about GCN, its applications, and the contributions of the paper. Section 2 discusses in detail the modeling dynamics of GCN and GAT models. We also present the state of the art in graph theoretic models for deep learning, focusing on methods based on convolutional operations. Section 3 discusses how network centrality is helpful in improving the performance of GCN-based classification tasks. The section also defines the problem formulation, experimental setup, and algorithmic procedure and proposed framework. Section 4 is the results and analysis section. This section highlights the behavioral aspects of the proposed model. We also present the performance evaluation of the proposed method by comparing our results with the other prominent current methods. Section 5 summarizes our main contributions.