esa-sceva/satcom-chunk-collection huggingface dataset:
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Model Interpretability
#### 5.4.2 Existing Solutions  
XAI sheds light on the opacity of black-box models by unveiling crucial insights such as feature importance and correlations. By elucidating the decision-making process, users gain transparency into how, why, and when decisions are made. This empowerment allows users to evaluate both outcomes and input factors, thereby enhancing decision making. XAI techniques strike a balance between explainability and high accuracy, even in intricate models [115]. Commonly used XAI methods in practice include LIME, SHAP, Grad-CAM, and t-SNE. For instance, LIME (Local Interpretable Model-Agnostic Explanations) provides local approximations of the model's predictions, helping users understand which features most influenced a specific decision. SHAP (Shapley Additive Explanations) assigns an importance value to each feature, offering a clear view of feature contribution across the model. Grad-CAM (Gradient-Weighted Class Activation Mapping) highlights the regions of an image that the model focuses on when making a prediction, which is particularly useful in medical imaging for identifying areas of concern. t-SNE (t-Distributed Stochastic Neighbor Embedding) is a dimensionality reduction technique that helps visualize high-dimensional data, making it easier to identify patterns and clusters. In the medical field, visual descriptions are the most utilized explanation method, followed by numerical and rule-based descriptions. For example, in diagnosing diabetic retinopathy, Grad-CAM can be used to show which parts of an eye image were most influential in the diagnosis, thereby assisting doctors in validating the AI's findings. Another example is using SHAP values to interpret a predictive model for patient readmissions, allowing healthcare providers to understand which factors (e.g., age, previous hospital visits, specific health conditions) are most critical. Evaluation criteria for XAI methods typically focus on usability and reliability, assessing how user-friendly and accurate these methods are in providing explanations for their predictions [116]. Sometimes, the interpretability and explainability of AI algorithmscan be misunderstood, affecting how we evaluate and implement them. Interpretability involves understanding the inner workings of a system and predicting outcomes based on changes in input or parameters. In contrast, explainability focuses on providing clear justifications or reasoning for model decisions, helping users grasp the significance of system parameters [117].
## 1 Introduction
### Motivation  
Normal data are consistent with physical laws, like Kirchhoff's current and voltage laws, whereas the compromised data that are affected by a CCD assault are inconsistent with these laws. Therefore, normal and compromised data will have different distributions and will, therefore, tend to form different clusters. These clusters would be distinguishable in a feature space of suitable dimensions. This fundamental distinction inspires the distance-based anomaly detection schemes for the detection of CCD assaults. Unsupervised anomaly detection (AD) techniques are persuasive at differentiating between data that have different underlying distributions, particularly when the data are not labeled. Similarly, supervised anomaly detection techniques can be employed to detect the anomalies in the labeled data. Thousands of sensors or RTUs are employed in power grids, spanning over a vast geographical area. Practically, defense mechanism can be designed to protect a limited set of critical RTUs and corresponding measurement features (MFs). Therefore, in the context of SG cyber-security, the selection of distinctive features from the SE-MF dataset becomes a promising strategy to detect the CCD assault and tackle the curse of dimensionality [8].
# Intrusion Detection in IoT Using Deep Residual Networks with Attention Mechanisms
## 3 Proposed Model  
### Model Overview  
To achieve intrusion detection in the IoT environment, researchers have introduced convolutional neural networks [27]. However, CNNs are usually used for feature extraction in static environments and lack long-term relevance storage mechanisms, which are not suitable for modeling sequential data in IoT. Some researchers have applied RNNs to the field of IoT intrusion detection for the sequential relationship of traffic data in the IoT environment. RNNs integrate a temporal layer to capture sequential data, learning multifaceted changes through the hidden units of recurrent units. These hidden units are modified based on the data provided to the network and are continuously updated. RNNs are used for IoT security due to their efficient management of sequential data. The study of RNNs and their variants is important to improve the security of IoT systems, especially against time-series-based threats [14]. However, RNNs suffer from the gradient disappearance or explosion problem, where gradients can become too small or too large during training, leading to their unsatisfactory predictions for IoT intrusion detection.  
This paper studies intrusion detection models based on the combination of deep learning and attention mechanisms [28] for intrusion detection from IoT traffic data. For the characteristics of data in IoT, this paper combines CNN and LSTM units, redesigns and improves the ResNet architecture [29], and introduces an attention mechanism for the extracted high-dimensional features to give weight information to different features to avoid the problem of failing to express important information due to the excessive dimensionality of the features.  
The ResNet model proposed in this paper consists of three main pairs of modules. Each module pair contains a residual module and a ResBlock-CBAM module. A jump connection is used between each two module pairs, and after the summation operation is performed on the output of the last module pair, the output of the module pair is classified by the classification layer, which finally constitutes the main structure of the deep residual network model used in this paper. The overall architecture of the model is shown in Figure 1. This network model overcomes the disadvantages of traditional deep learning, such as gradient disappearance and gradient explosion, and has a strong generalization capability.  
Figure 1: Model architecture diagram.
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 2 Methodology
### Multi-Predictor Ensemble  
RL is a kind of interactive learning, which makes the agent learn by interacting with the environment [37]. The agent can learn the optimal strategy and adjust it automatically according to the objective, which makes RL maximize the return or achieve a fixed objective. RL has been utilized in many fields, such as autonomous driving, game confrontation, integration optimization, and so on [38; 39].  
The SARSA, proposed by [PERSON] and [PERSON] [40], has strong scalability and excellent convergence. Hence, we used the SARSA to combine three neural networks in the paper. The parameters of the SARSA are defined as follows:  
State: The state of each neural network weight is regarded as the state matrix \(S\).  
\[S=[w_{1},w_{2},w_{3}] \tag{11}\]  
where \(w_{1}\) represents the weight of TCN, \(w_{2}\) represents the weight of GRU, and \(w_{3}\) represents the weights of the ESN.  
Action: Action matrix \(a\) represents the action to change the weight of each network.  
\[a=[\Delta w_{1},\Delta w_{2},\Delta w_{3}] \tag{12}\]  
where \(\Delta w_{m}\) represents the change of weights for different neural networks.  
The SARSA is a deterministic strategy. To better balance \"exploration\" and \"utilization\", and make the model can search for more potential actions, the SARSA introduces the \(\varepsilon-greedy\) policy to select the action. The definition of the \(\varepsilon-greedy\) policy is as follows:  
\[a_{m}=\left\{\begin{aligned} &\textit{Based on }\max Q\ (\textit{probability of }1-\varepsilon)\\ &\textit{Random }(\textit{probability of }\varepsilon)\end{aligned}\right. \tag{13}\]  
\[\varepsilon\in(0,1) \tag{14}\]  
where \(\varepsilon\) is exploration probability.  
Reward: The model acquires reward \(R\) by calculating loss function \(L\).  
\[L=\sqrt{(\sum_{t=1}^{N}\left[Y(t)-\hat{Y}(t)\right]^{2})/N} \tag{15}\]  
\[R=\left\{\begin{aligned} &+1+L_{t}-L_{t+1}(L_{t+1}<L_{t})\\ &-1+L_{t}-L_{t+1}(L_{t+1}>L_{t})\end{aligned}\right. \tag{16}\]  
where \(Y(t)\) represents the raw crude oil price data, \(\hat{Y}(t)\) indicates the forecasting value.  
Action value function Q: The iterative equation of value function is expressed as follows:  
\[Q_{n+1}(S_{n},a_{n})=Q_{n}(S_{n},a_{n})+\eta_{n}(R(S_{n},a_{n})+\gamma Q_{n}(S _{n+1},a_{n+1})-Q_{n}(S_{n},a_{n})) \tag{17}\]  
where \(\eta\) indicates learning rate, \(\gamma\) indicates discount coefficient.  
The solving processes of the ensemble module are described in Algorithm 1.
# Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway
## 3 Preliminaries of RaPP
### Results
Unfortunately, this high efficiency was not observed for the other Kaggle and UCI datasets in Table 2. In any case, this is especially important when concept drift is a possibility [20], as the ability to adapt quickly depends on training efficiency. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \# Hidden & \multicolumn{3}{c}{**Standard Autoencoders**} & \multicolumn{3}{c}{**Extended Autoencoders**} \\ \cline{2-7}
**Layers** & **AE** & **VAE** & **AAE** & **XAE** & **XVAE** & **XAAE** \\ \hline
2 & 0.896 & 0.882 & 0.896 & 0.892 & 0.892 & 0.900 \\  & (0.012) & (0.012) & (0.012) & (0.007) & (0.011) & (0.012) \\
4 & 0.889 & 0.903 & 0.889 & 0.892 & 0.908 & 0.895 \\  & (0.009) & (0.013) & (0.08) & (0.007) & (0.006) & (0.006) \\
6 & 0.907 & 0.908 & 0.900 & 0.910 & 0.907 & 0.906 \\  & (0.010) & (0.008) & (0.010) & (0.010) & (0.011) & (0.006) \\
8 & 0.899 & 0.902 & 0.896 & 0.899 & 0.900 & 0.897 \\  & (0.012) & (0.012) & (0.011) & (0.011) & (0.013) & (0.015) \\
10 & 0.902 & 0.901 & 0.896 & 0.890 & 0.896 & 0.894 \\  & (0.012) & (0.016) & (0.013) & (0.015) & (0.017) & (0.010) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AUROC with different number of hidden layers on MI-V dataset. The number of in parenthesis indicates the standard deviation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \# Hidden & \multicolumn{3}{c}{**Standard Autoencoders**} & \multicolumn{3}{c}{**Extended Autoencoders**} \\ \cline{2-7}
**Layers** & **AE** & **VAE** & **AAE** & **XAE** & **XVAE** & **XAAE** \\ \hline
2 & 0.820 & 0.696 & 0.820 & 0.835 & 0.744 & 0.820 \\  & (0.010) & (0.011) & (0.013) & (0.012) & (0.020) & (0.021) \\
4 & 0.829 & 0.762 & 0.819 & 0.831 & 0.743 & 0.821 \\  & (0.010) & (0.016) & (0.014) & (0.013) & (0.024) & (0.012) \\
6 & 0.771 & 0.734 & 0.774 & 0.773 & 0.745 & 0.787 \\  & (0.032) & (0.036) & (0.047) & (0.024) & (0.043) & (0.022) \\
8 & 0.673 & 0.701 & 0.686 & 0.639 & 0.725 & 0.693 \\  & (0.019) & (0.030) & (0.077) & (0.065) & (0.056) & (0.035) \\
10 & 0.643 & 0.655 & 0.639 & 0.637 & 0.653 & 0.646 \\  & (0.012) & (0.016) & (0.013) & (0.015) & (0.017) & (0.010) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: AUROC with different number of hidden layers on MI-F dataset. The number of in parenthesis indicates the standard deviation. Additionally, we found that the performance of the extended autoencoders tended to decrease as the number of epochs increased. This issue may be caused by difficulties associated with GAN training. Mode collapse [21] is a well-known problem that occurs when the generator learns to map several different input values to the same output point. In our case, the decoder was trained to reconstruct only images that were capable of deceiving the discriminator. As a future work, other improved techniques for training GANs [22] can be applied to prevent mode collapse in image datasets.
## 5 Conclusions  
This research has used computational intelligence to investigate the FOS in measurement cases under different conditions. For model design and simulation, this study was implemented based on six parameters, namely, the unit weight of slope material (\(\gamma\)), cohesion (\(c\)) and angle of internal friction (\(\phi\)), average angle of slope (\(\beta\)), shear strength parameter, pore water pressure coefficient (\(ru\)), and the slope height (\(H\)). These data were reviewed in the first stage, and initial analyses were performed on them. Then, these data were used for modeling into a set of basic SVR models to simulate their behavior to evaluate the FOS parameter. Different models were designed, and the best ones were selected according to performance and flexibility for different conditions. Among the various SVR models, the RBF model was developed with a lower error rate (RMSE = 0.076 and MAE = 0.046) and higher accuracy (\(R^{2}\) = 0.947) in evaluating the FOS parameter. This basic model was used as a function by two optimization models WOA and HHO. Different optimization steps of the parameters of these two algorithms were performed to find different optimization conditions. Finally, both models came to close results with different conditions. The developed model was also successfully validated using new data with nine data samples. The results showed that combining a simulation model with optimization models can provide a new solution for the optimal design of important and sensitive projects. This research can be used as a new approach for FOS analysis.  
Conceptualization, W.W.; methodology, W.W. and J.Z.; software, W.W.; validation, W.W. and J.Z.; formal analysis, X.L. and W.W.; investigation, J.L. and L.L.; resources, Y.Z.; data curation, J.L.; writing--original draft preparation, W.W. and J.L.; writing--review and editing, X.L. and J.Z.; visualization, W.W. and J.Z.; supervision, X.L. and J.L.; project administration, J.L. and Y.Z.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Innovation--Driven Project of Central South University (2020 CX040).  
Not applicable.  
All data generated or analysed during this study are included in this published article.  
The authors confirm that this article content has no conflict of interest.
## 3 Construction of GM-BP Prediction Model
### Model Construction  
The industry's prediction model of international competitiveness based on GM-BP neural network mainly includes the process of quantity sample, the design of network structure, the training and inspection of the network, and the prediction of competitiveness trend.  
Step 1: Data sample processing.  
The extreme value method is used to normalize the data to ensure consistency.  
\[Y_{ij}=\frac{X_{ij}-\min(X_{i})}{\max(X_{i})-\min(X_{i})} \tag{12}\]  
Here, \(X_{ij}\) is the original value, \(Y_{ij}\) is the normalized value, \(X_{i}=\{X_{1},X_{2},\ldots,X_{j}\}\).  
Step 2: Network structure design.  
The network structure design mainly includes determining the number of network layers and setting the network nodes. The topological structure of the BP neural network model is the input layer, hidden layer, and output layer. Existing studies have proved that the performance of the three-layer BP neural network model with only one hidden layer is better than that with multiple hidden layers. When building the model, the three-layer BP structure with only one hidden layer can be established first. The number of network nodes in the input and output layers is determined by the variables selected during model construction and the number of prediction objects. The number of hidden layer nodes is essential to the neural network's performance, and the number of hidden layer nodes can be determined by empirical formulas [38].  
\[L=\sqrt{M+N}+a \tag{13}\]  
Here, \(L\) represents the number of hidden layer nodes, \(M\) is the number of input layer nodes, \(N\) is the number of output layer nodes, and a constant from 1 to 10.  
Step 3: Sample training and testing.  
This model is simulated and trained by MATLAB, mainly using RPROP and Symmetrical Sigmoid functions. Among them, the network training function selects RPROP, and the transfer function of the hidden layer and the output layer selects Symmetrical Sigmoid. The RPROP algorithm is one of the most effective training algorithms for supervised feed forward neural networks. Its advantage is that it does not require parameter settings before use, and there is no need to determine the learning rate, momentum value, or update constantly. The Symmetrical Sigmoid function is characterized by being anti-symmetric, zero center, and differentiable. Its flatter shape and slower descending derivation indicate that it can learn more effectively.  
The collected samples are divided into two parts: one part is the training sample, and the other part is the test sample. All samples are trained and tested. The construction of the BP neural network is completed through the forward operation output of all samples and the reverse modification of weights to reduce the error until the accuracy requirements are met.  
Step 4: Competitiveness prediction.  
The input layer takes the grey pre-processing value of the RCA index obtained by the GM (1,1) model as the input data, uses the established BP neural network model to calculate, and outputs the predicted value of the international competitiveness of China's computer information service industry.
## 4 Results and Validation
### Validation and Comparision of Results  
Validation based on VHR images, field photos, paddy field POIs, street views, backscatter coefficients, and VI time profiles indicate that the produced paddy rice map in 2019 from the HJH plain had an overall accuracy of 95.4%. The paddy rice category had a producer accuracy (PA) and user accuracy (UA) of 93.7% and 92.6%, respectively. The comparison between three pixel-based paddy rice maps from MODIS, Landsat-8, Sentinel-2, and our PODS-derived paddy rice map showed that all products had a high spatial consistency in the HJH Plain (Figure 10). The Landsat 8-based results had the second highest amount of details, but the Sentinel-2-based results had the most details, excluding roads between paddy rice fields. However, the pixel-based Sentinel-2 rice map suffered from the salt-and-pepper effect across the region, especially in built-up areas, because building shadows were often misclassified as paddy fields due to their spectral similarity, which leads to noteworthy commission errors. Their accuracy assessment metrics are in Table 3.
# The Suitability of PlanetScope Imagery for Mapping Rubber Plantations
## 2 Materials and Methods
### Feature Extraction  
In this study, a total of 50 feature types were obtained from multitemporal PlanetScope images. These features can be categorized into four types: spectral features (8 features), index features (33 features), the first principal component (PCA1) (1 feature), and textural features (8 features). Since two classification approaches, namely, pixel-based and object  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Training Samples** & **Validation Samples** & **Total** \\ \hline Water & 53 & 26 & 79 \\ Building & 71 & 35 & 106 \\ Rubber & 167 & 83 & 250 \\ Farmland & 125 & 62 & 187 \\ Forest & 110 & 55 & 165 \\ Total & 526 & 261 & 787 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The samples used in this study.  
-based classification methods, were used in this study, these features were calculated based on pixel information and object information, respectively.
# Gene Networks and Pathways Involved in _Escherichia coli_ Response to Multiple Stressors
## 3 Results
### Identification of Genes Affected by Multiple Stressors  
Next, we examined the common DEGs when _E. coli_ K-12 MG1655 were subjected to different stressors. The overlap between transcriptional responses to multiple stressors could provide better understanding of general stress response and suggest targets for decreased tolerance towards groups of stressors.  
We found a significant overlap of up- and downregulated genes within the five stressors. Within our list of upregulated genes, we identified 683 genes that are common between antibiotic treatment and oxidative stress, 431 between antibiotic treatment and heat stress, 436 between antibiotic treatment and cold stress, and 120 between nitrosative and antibiotic  
Figure 2: Heatmap and hierarchical clustering of transcriptomic data for all studies for (**A**) cold stress, (**B**) Antibiotics, (**C**) oxidative and nitrosative, and (**D**) heat stress. Only top 250 up- or downregulated genes are included. Shades of orange and blue denote high and low relative expression, respectively.  
treatment stress. Regarding downregulated genes, we identified 620 common genes between antibiotic treatment and oxidative stress, 466 between antibiotic treatment and heat stress, 430 between antibiotic treatment and cold stress, and 124 common downregulated genes between the nitrosative and antibiotic treatment stress (Figure 3A,B).  
To this end, we sought to analyze the functional potential of the DEGs shared by different stressors to unveil the functions and pathways playing pivotal roles in stress response. Identification of key functional categories is particularly important for understanding _E. coli_ physiology, mechanism of adaptation to different ecological niches, and pathogenicity.
# Experimental Study on Deformation Monitoring of Bored Pile Based on BOTDR
## 3 Bored Pile Field Test
### Monitoring the Deformation of Pile
(**a**) Fixing optical fiber; (**b**) U-shaped wiring; (**c**) Field loading; (**d**) BOTDR instrument.
# A Machine Learning Approach for the Prediction of Thermostable \(\beta\)-Glucosidases
## 2 Related Works  
A systematic literature review was performed to detect a dataset of \(\beta\)-glucosidase enzymes resistant to glucose inhibition [25]. Later, residues related to glucose tolerance were detected, and multiple sequence analyses were used to detect other structures that share these characteristics [26]. Molecular dynamics studies demonstrated the importance of residues D238, Y180, W350, and F349 in a mechanism for glucose release in glucose-tolerant \(\beta\)-glucosidaseases, termed the \"slingshot mechanism\" [27]. Furthermore, it was demonstrated that the conformational flexibility of \(\beta\)-glucosidase regions was related to enzymes more resistant to glucose inhibition [28].  
Then, graph-based signatures were used together with a simple model based on the K-nearest neighbors (KNN) algorithm to detect a structural signature of glucose tolerance [29]. Structural signatures are mathematical representations of three-dimensional structures of proteins and other macromolecules capable of characterizing similarities and differences in vector space [30,31].  
A literature review pointed out several factors that could be related to the thermostability of glycoside hydrolase enzymes [15]. These characteristics can be linked to intramolecular contacts, such as hydrophobic effect [32], hydrogen bonds and other electrostatic interactions [33], and aromatic stacking [34], among others [35]. Also, a machine learning model was used to detect thermostable enzymes [36]. However, this model was sequence-based and did not consider three-dimensional structures.  
Moreover, a previous study demonstrated the importance of point mutations for the thermostabilizing mechanisms of GH1 \(\beta\)-glucosidaseases [17]. However, the study did not use a machine learning analysis to detect similar patterns in other structures. To the best of our knowledge, the use of structural signatures to detect thermostability patterns in \(\beta\)-glucosidase enzymes has not been found in the literature.