esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Results
#### 4.2.2 Usefulness  
(1) Explanation Generating  
Based on the explanation of PSO4 RNN, word clouds and word highlighting are used to visualize the explanation. As shown in Figure 10, a word cloud of the DLRV dataset is calculated from the word frequency under each word category. The font size of a word reflects the appearance frequency in this dataset.  
Here are the explanation examples in Figures 11 and 12. The explanation words are highlighted in the sentences of the DLRV and IMDB datasets. The scores under each word are the explanation importance calculated by Algorithm 2.  
In Figures 11 and 12, the gray words correspond to self-pointed words, which hardly affect sentiment classification. The blue words correspond to cross-state words, and they do not significantly affect sentiment classification. The red words correspond to cross-cluster words, which influence the sentiment classification. It is not difficult to see that the highlighted words conform to human cognition. That is, cross-cluster words can convey and influence the emotions of the sentence. Through experiments on other texts, we found that this rationality is very common, which can effectively promote human understanding of the cause of the decision making of an RNN. Compared to directly generating explanations on the RNN model, generating explanations by PSO4 RNN is faster  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Technique Type** & **State Numbers** \\ \hline BSL1 on GRU text sentiment analysis & 37 \\ BSL1 on LSTM text sentiment analysis & 43 \\ BSL2 on GRU text sentiment analysis & 25 \\ BSL2 on LSTM text sentiment analysis & 31 \\ PSO4 RNN on GRU text sentiment analysis & 22 \\ PSO4 RNN on LSTM text sentiment analysis & 28 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The number of states abstracted from different models.  
Figure 10: Word cloud of cross-cluster words, cross-state words, and self-pointed words in the DLRV dataset. Subfigure (**a**) corresponds to the word cloud of the cross-cluster words, Subfigure (**b**) corresponds to the word cloud of the cross-state words, and Subfigure (**c**) corresponds to the word cloud of the self-pointed words.  
and reduces the cost of accessing the model. In addition, visualization techniques, such as the word cloud and the highlight explanation, make the explanation intuitive.
# CGFL: A Robust Federated Learning Approach for Intrusion Detection Systems Based on Data Generation
## 5 Conclusions  
This paper presents a novel intrusion detection method for Industrial Control Systems utilizing Conditional Generative Federated Learning (CGFL). It addresses challenges like data imbalance, slow convergence, and model instability in Federated Learning by integrating CVAE-GAN data augmentation with geometric median aggregation. Experimental results show that CGFL achieves 92.19% accuracy on datasets such as CIC-IDS2017, outperforming traditional methods like FedAvg, FedProx, and FedOpt. It is particularly effective in identifying rare attack types such as R2L and U2R, with an F1 score of 0.98. Through data augmentation and geometric median aggregation, CGFL offers improved accuracy and stable convergence, as well as superior F1 scores and G-mean metrics compared to other methods. Additionally, CGFL enhances training efficiency and reduces communication overhead using strategies like sparse client sampling and warm-up rounds. Overall, CGFL demonstrates significant advantages in various performance metrics, especially for minority class attacks, highlighting its strong generalization capabilities.  
Conceptualization, S.F. and L.S.; methodology, S.F. and L.S.; software, S.F.; validation, S.F. and L.G.; formal analysis, S.F.; investigation, L.G.; resources, S.F.; data curation, S.F.; writing--original draft preparation, S.F.; writing--review and editing, L.S.; visualization, S.F.; supervision, S.F.; project administration, S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author. The data are not publicly accessible as a result of the security of passenger travel information.  
The authors all affirm that the research was conducted without any commercial or financial relationships that could be interpreted as a potential conflict of interest.
## 3 Result Analysis of the Proposed MPA
### Machine Learning-Based Resonance Frequency Prediction  
To achieve simplicity in the design of the antenna and predict the antenna performances, different ML and/or DL models are introduced. Using those models, the design and optimization of the antennas are much easier than with the traditional EM simulation tools. In this study, we are investigating and showing a wide variety of regression methods along with convolutional neural networks (CNN). All these algorithms are used to predict the resonance frequency of the proposed MPA. A brief description of the different models that are used for the prediction of resonance frequency is provided in the following subsection.
###### Abstract  
Background: The objective of this case series was to evaluate the efficacy of miniscrews to upright the lower second molar as a result of early loss of the lower first molar in order to permit rehabilitation by means of an implant-supported single crown. Methods: The case series included ten patients who each received a miniscrew placed distal of the second molar in order to straighten the tooth; the prosthetic space gained (sufficient to allow implant placement in the dementulous space) and the change in angulation obtained were analyzed. The changes produced by miniscrews were evaluated in radiographs. Results: Statistical analysis identified significant improvements in angulation (\(p=0.005\)) and significant amounts of space gained (\(p=0.005\)) as well as a strong correlation between these two parameters (\(r=-0.93\); \(p<0.001\)). Conclusions: The use of miniscrews alone without the intervention of other orthodontic methods to straighten a mesialized lower second molar in order to replace a missing first molar with an implant significantly improves both the angulation of the molar and the prosthetic space available.  
miniscrews; reduced prosthetic space; dental implant; implant-supported single crown
## 3 Compressive Sensing Framework
### Reconstruction Methods
#### 3.1.1. Newton \(+\) Log-barrier  
The recovery procedure aims at minimizing the \(l_{1}\) norm of \(\mathbf{s}\) with quadratic constraints, i.e.,  
\[\hat{\mathbf{s}}=\arg\min_{\mathbf{s}}\|\mathbf{s}\|_{1}\qquad\quad\text{s.t.} \qquad\|\mathbf{y}-\mathbf{\Phi}\mathbf{\Psi}^{*}\hat{\mathbf{s}}\|_{2}\leq \mathcal{E}, \tag{4}\]  
in which \(\mathcal{E}\) is an empirically chosen parameter. This optimization is done by recasting the problem in Equation (4) as a _Second-Order Cone Program_ (SOCP) [30]. Then, a Newton algorithm with a logarithmic barrier (log-barrier) method [33] is used to solve the optimization problem in Equation (4). The recovery procedure is implemented in the L1-magic package [30]. In [34], authors consider the algorithm in L1-magic to reconstruct CS-coded environmental signal (temperature) in a WSN. The advantage of using the _Newton \(+\) log-barrier_ method is that it directly minimizes the L1-norm of the sparse signal, which is small with respect to their energy.
## 3 Data and Methods
### Model Optimization
#### 3.4.3 Genetic Algorithm Optimization for the Support Vector Regression Model  
The GA [32; 33] is an efficient stochastic search and optimization method based on natural genetic mechanisms and biological evolution. The GA exhibits a global optimization performance and can find the most appropriate values of the penalty and kernel function parameters for the SVM, by which the SVM can achieve the optimal configuration to accurately predict cementing quality.  
The optimization flow chart of the GA method is shown in Figure 5.  
Figure 5: GA flow chart.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Penalty** \\ **Parameter** \\ **C** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Kernel** \\ **Function** \\ **Parameter** \(g\)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Actual** \\ **Cementing** \\ **Quality** \\ **\end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **BOA-SVR** \\ **Predicted** \\ **Cementing** \\ **Quality** \\ **/\%** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Relevant** \\ **Error** \\ **\end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **RMSE** \\ **More** \\ **Time/s** \\ \end{tabular} } \\  & & & & & & & & \\ \hline \multirow{8}{*}{47.38} & \multirow{8}{*}{0.07635} & 14 & 13.37 & 13.52 & 0.0112 & \multirow{8}{*}{2.533} & \multirow{8}{*}{0.0800} & \multirow{8}{*}{19.8} \\  & & 16 & 19.34 & 20.51 & 0.0605 & & \\  & & 17 & 17.29 & 21.21 & 0.2267 & & \\  & & 20 & 22.31 & 22.49 & 0.0081 & & \\  & & 26 & 20.19 & 20.24 & 0.0025 & & \\  & & 42 & 28.34 & 24.83 & 0.1239 & & \\  & & 43 & 29.76 & 25.18 & 0.1539 & & \\  & & 44 & 21.39 & 22.53 & 0.0533 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: BOA results and model evaluation.  
The GA-SVR code is run in Python, and the optimization results and model evaluation are shown in Table 4, from which it is evident that the prediction accuracy of the SVR model constructed after GA optimization is higher than that of SVR, GS-SVR, and BOA-SVR. The MSE is 7.30%, and the _RMSE_ is 2.318, which suggests that the optimization effect is the best.
## 2 Related Work
### Service Composition Based on Machine Learning
[45] introduced a DRL-based service composition solution, PPDRL, which uses pre-training policies and maximum likelihood estimation for adaptive and large-scale service composition. [PERSON] et al. [46] proposed a cloud manufacturing service composition model involving logistics based on deep deterministic policy gradient, which solved the optimal service composition solution through repeated training and learning. [PERSON] et al. [47] constructed tasks and services as graphs, used Graph Neural Networks (GNN) to mine potential correlations, predicted the probability of each service being used to construct the solution corresponding to the task, built the initial service solution based on Petri nets and reinforcement learning, and used the whale optimization algorithm (WOA) to fine-tune the initial solution for obtaining high-quality solutions. [PERSON] et al. [48] proposed a multi-strategy deep reinforcement learning algorithm that combines a basic DQN algorithm, dueling architecture, a double estimator, and a prioritized-replay mechanism. In addition, strategies, such as instantaneous reward strategy, greedy strategy, and heuristic strategy, are added to the algorithm. DRL-based methods can handle complex and large-scale service composition problems, learn and adapt to different environments, and have good transferability. Compared with the works mentioned above, our method has better adaptability than heuristic algorithms and does not require tedious design. Moreover, compared to existing deep reinforcement learning methods, we greatly improved the training efficiency of our method by using the skyline algorithm and achieved a trade-off between energy consumption and QoS. Our method also performs well when facing large-scale problems. In addition, existing service composition methods based on deep reinforcement learning assume a single-cloud environment while we consider a multi-cloud environment.
# Developing a Decision-Making Framework to Improve Healthcare Service Quality during a Pandemic
## 2 Literature Review
### Research Related to ICU Service Quality Improvement  
Improving ICU service quality is a crucial task in any hospital that requires input from multiple stakeholders, and such decisions have become especially important during the COVID-19 pandemic. [PERSON] [24] suggested the use of multiple lean tools, improvement science, behavioural science, and other techniques to improve the quality of patient service in response to the COVID-19 pandemic. Similarly, in their study of the Cleveland Clinic (Ohio), [PERSON] et al. [25] suggested that the management team could improve patient care by implementing managerial changes in response to changes in the pandemic situation.  
Researchers have been studying ICU service improvement since well before the COVID-19 pandemic struck. In one such study, [PERSON] et al. [26] implemented a series of managerial changes to redesign the operation of an ICU at an Australian hospital, including appointing a lead nurse to discharge patients from the ICU and developing a patient handover sheet containing the patient's critical medical information. Their findings showed that these changes reduced the ICU patient discharge delay time from 4.6 to 3.2 h, while not affecting patient mortality rate [26]. In another study, [PERSON] et al. [27] addressed the negative impact of using mechanical ventilators on ICU patients for prolonged periods by adding a supportive rehabilitation team to the ICU's operations. Their findings showed that these changes improved ICU mobility and reduced both the number of ventilator days per patient and the mortality rate [27]. This sub-section clearly shows that ICUs are constantly in need of service improvements, and that these improvements require input from professionals from numerous different disciplines. A structured MCDM framework is one of the most effective tools for arriving at sustainable conclusions in group decision-making processes. In the next subsection, we identify the main inputs used in our model.
## 3 Results
### Off-Target Drift Losses  
#### 3.4.1 Ground Run-Off and Drift Losses  
There was no significant difference in deposition for sub-tree run-off obtained from modified irrigation micro-emitter i.e., T1 (1720.6 \(\pm\) 289.3 ng cm\({}^{-2}\)) and hollow cone nozzle treatment i.e., T2 (1785.3 \(\pm\) 435.6 ng cm\({}^{-2}\)) (Table 6). Moreover, the percent of applied active ingredient lost underneath the free for T1 (45.3%) was marginally lower than T2 (47.4%). The analysis of coverage samplers exhibited similar results. The treatment T1 and T2 had a coverage of 36.9 \(\pm\) 5.7% and 35.3 \(\pm\) 7.6%, respectively.  
Mid-row ground drift deposition data collected at 1.5, 4.5 and 7.5 m downwind indicate that mean ground deposition for modified irrigation micro-emitter treatment, i.e., T1 (121.8 \(\pm\) 43.4 ng cm\({}^{-2}\)), was numerically lower than hollow cone nozzle SSCDS, i.e., T2 (447.4 \(\pm\) 190.9 ng cm\({}^{-2}\)) (Table 7). However, the difference among them were non-significant. Additionally, the percent of applied active ingredient lost to the ground drift for T1 (3.2 %) was considerably lower than T2 (20.8%). Furthermore, T1 had significantly lower mid-row ground deposition (364.4 \(\pm\) 85.3 ng cm\({}^{-2}\)) at 1.5 m downwind distance compared to T2 (1306.9 \(\pm\) 465.3 ng cm\({}^{-2}\)) (Figure 8a). The measured mid-row ground deposition at 4.5 and 7.5 m downwind for treatment T1 was also lower than T2; however, the difference was not significant. Similar trends were observed for the analysis of coverage samplers. The mean coverage corresponding to T1 (4.8 \(\pm\) 1.7%) was lower than T2 (20.5 \(\pm\) 6.2%); however, the difference was not significant (Table 7). Likewise, the coverage observed at  
Figure 7: Mean spray coverage assessed at different (**a**) canopy zones and (**b**) leaf surfaces for modified irrigation micro-emitter (T1) and hollow cone nozzle (T2) treatments. The lowercase letters above individual bar plots indicate the significance of mean differences in transformed mean at 5% level and associated error bars indicate standard error; non-transformed coverage (%) values are presented.  
1.5 m downwind distance for treatment T1 (14.3 \(\pm\) 3.3%) was significantly lower than T2 (61.0 \(\pm\) 8.0%) (Figure 8b).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Off-Target Loss** & **Treatment** & \
\begin{tabular}{c} **Deposition (ng cm\({}^{-2}\)) * \\ **[Run-Off (\%)]** \\ \end{tabular} & **Coverage (\%) * \\ \hline Run-off & T1 &
\begin{tabular}{c} 1720.6 \(\pm\) 289.3  \\ \end{tabular}
\end{table}
Table 6: Mean sub-tree run-off evaluated for tested treatments.  
Figure 8: Mean mid-row ground (**a**) deposition and (**b**) coverage assessed at 1.5 m, 4.5 m and 7.5 m downwind for tested SSCDS treatments (i.e., T1 and T2). The lowercase letters above the line markers indicate the significant differences in transformed mean at \(\alpha\) = 0.05 and associated error bars indicate standard error; presented values are non-transformed mid-row ground deposition (ng cm\({}^{-2}\)) and coverage (%).
## 1 Introduction
### Motivation and Potential Contributions  
Due to the large size of satellites and user requirements, and complex constraints of ISRPTWC in the low-orbit mega constellation, few researchers have proposed a nicely exact solution algorithm. Meanwhile, exact solution methods are considered to handle only small-scale problems in general combinatorial optimization problems, so many researchers use meta-heuristic methods, routing strategies, and routing protocols to solve inter-satellite routing problems. However, these methods have certain drawbacks, such as oversimplification of the problem and poor theoretical interpretation of algorithms. Therefore, we hope to propose an exact solution algorithm to solve the ISRPTWC for the low-orbit mega constellation in a reasonable time.  
We aim to merge pioneering work in several different directions. Specifically, the main contributions are summarized as follows. (1) We present the ISRPTWC for a low-orbit mega constellation, develop an integer linear programming model, and further construct a time-discrete multi-commodity flow model. (2) Time-space state networks are constructed for the ISRPTWC, and the single-task routing problem is efficiently solved using a dynamic programming method in the context of time-space state networks. (3) The extended Lagrangian relaxation framework is proposed to solve multi-task ISRPTWC, and a novel feasible solution construction method is constructed to obtain a tight upper bound.  
Finally, we further design cases to verify the computational efficiency and solution quality of the proposed algorithm.  
The remainder of this paper is structured as follows. Section 2 presents the ISRPTWC description and mathematical formulation. In Section 3, the Lagrangian relaxation solution framework is present, and the lower and upper bound of the ISRPTWC solution methods is proposed. Some solution space reduction strategies for handling large-scale multi-task ISRPTWC are presented in Section 4. Then, Section 5 provides experimental results of the illustrative and actual cases. Finally, we provide our conclusions in Section 6.
## 4 Displacement Prediction
Considering Table 9 and Figure 24, the shape factor had an insignificant role in the prediction, while Figure 24c represented that the bolt distance effect as an additional parameter is able to make a distraction in the prediction process. On the contrary, length has a good effect on the precision of the displacement prediction that is shown in Figure 24d,e. Figure 25 reveals the tolerance diagram based on the prediction and measured results of the models in the testing and training phase. As can be realised, both of the MPF with five input models are capable of accurately predicting most of the test samples. As shown in Table 10 and Figure 25, the best performance parameters for the MPF neural network are RMSE = 0.001, r = 1.000, R\({}^{2}\) = 0.999, NS = 1.000, MAE = 0.0, WI = 1.000. Higher values of R\({}^{2}\) show a more suitable regression diagram. Besides, for NS and MAE, smaller results and for WI, larger results indicate better performance. Figure 26 reveals the error histogram of the MPF model in the best prediction with 45 iterations and 250 populations utilising five inputs, where the convergence of the error in both the testing and training phases indicates the same pattern with an acceptable range. Figure 24: Regression of the training (above charts) and testing (below charts) phase results with measured values of displacement for (**a**) one input, (**b**) two inputs, (**c**) three inputs, (**d**) four inputs, (**e**) five inputs. Figure 26: Error histograms for displacement prediction by the MPF model with five inputs: (**above**) training phase, and (**below**) testing phase. Figure 25: Tolerance diagram of the displacement prediction corresponding to the MPF model with five inputs: (**above**) training phase, and (**below**) testing phase.
## 1 Introduction  
Many uniaxial and triaxial tests have shown that the failure mode of soil not only comprises shear failure but also tensile failure. Under the action of unidirectional stress, the soil mass produces transverse deformations, such as splitting or bulging failure. This deformation is caused by the lateral tension stress in the soil body; that is, the axial stress in the soil body appears as a stress conversion phenomenon, in which part of the axial stress converted into lateral stress is applied to the soil body [1,2]. [PERSON] [3] observed that in conventional triaxial compression tests, loose sand deforms mainly via drum deformation, while dense sand fails due to the shear plane. Since then, an increasing number of tests have demonstrated that the failure modes of undisturbed and remodeled soils under low confining pressures are significantly different from those under high confining pressures. Undisturbed soils fail due to shear planes at low confining pressures and deform via tensile deformation at high confining pressures [4,5]. The soil of different materials also exhibits different deformations under the same conditions [6]. Different stress conditions and different materials may lead to changes in failure forms, which may indicate different stress conversion coefficients under different conditions.  
The theory of tensile stress control for material failure was first put forward by [PERSON] [7]. However, when [PERSON]'s theory came out, people tended to regard the failure mode of the soil as a shear failure. Regardless of the type of failure mode that the soil material produces, it is assumed that the tensile strength of the soil is very small or almost zero, and the shear failure mode was adopted for analysis. The fact or possibility that tensile failure occurs earlier than shear failure is generally considered in the process of investigating whether the soil material experiences shear failure. It also makes the existing equation of the Mohr envelope insufficient regarding its fit to the Mohr circle under a stretched state. Both the parabolic envelope and the hyperbolic envelope are simply fitted, and the failure is still attributed to the relationship between normal stress and shear stress. Therefore, it is of great significance to explore tensile failure and stress transfer in soil materials for the development of strength theory.  
The study of the stress conversion coefficient can better determine the Mohr envelope equation of rock and soil masses and more accurately predict the strength of rock and soil masses; this can serve practical engineering. However, how does the stress transfer phenomenon occur, and how does the stress transfer coefficient change? Based on the uniaxial compression test and equivalent tensile test of large samples combined with microscopic analysis, the mechanisms, influencing factors, and measuring methods of the stress conversion of soil under compression are explored in this paper. This provides an important foundation for a better judgment of the soil's failure mode and for improvements in the soil's strength criterion.