esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Marine Predators Algorithm (MPA)
[0,1]; \(n\) is the swarm size; and _Iter_ and _Max_Iter_ represent the current and maximum number of iterations, respectively. (3) During the intermediate stage of the iteration, when the speeds of predators and prey are equal, the prey employs the Levy flight strategy for exploitation, while the predators utilize the Brownian motion strategy for exploration. Over time, the predators gradually transition from an exploration-focused approach to an exploitation-focused approach. The mathematical formulations for both exploitation and exploration are as follows:  
\[\left\{ \begin{array}{l}stepsize_{i}\ =R_{L}\otimes(Elite_{i}\ -R_{L} \otimes Prey_{i}\ )\\ Prey_{i}\ =Prey_{i}\ +0.5\times R\otimes stepsize_{i}\end{array} \right.\ \ \ \ i=1,2,3....,n/2.\ \frac{Max\_Iter}{3}<Iter<\frac{2 Max\_Iter}{3} \tag{5}\]  
\[\left\{ \begin{array}{l}stepsize_{i}\ =R_{B}\otimes(R_{B}\otimes Prey_{i}\ - Elite_{i}\ )\\ Prey_{i}\ =Elite_{i}+0.5\times CF\otimes stepsize_{i}\end{array} \right.\ \ \ i=n/2,...,n.\ \frac{Max\_Iter}{3}<Iter<\frac{2 Max\_Iter}{3} \tag{6}\]  
where \(R_{L}\) is a random vector that follows the Levy distribution; _CF_ is an adaptive parameter controlling the step length of the predator's movement; and the meanings of the other parameters are consistent with those described previously. (4) During the latter stages of the iterative process, when the movement speed of the predator lags behind that of the prey, the Marine Predator Algorithm (MPA) implements a development (exploitation) strategy. In this phase, the predator utilizes the Levy flight strategy. The mathematical formulation for this approach is presented below:  
\[\left\{ \begin{array}{l}stepsize_{i}\ =R_{L}\otimes(Elite_{i}\ -R_{L} \otimes Prey_{i}\ )\\ Prey_{i}\ =Prey_{i}\ +0.5\times R\otimes stepsize_{i}\end{array} \right.\ \ \ i=1,2,3....,n.\ Iter>\frac{2 Max\_Iter}{3} \tag{7}\]  
(5) To incorporate the FADs effect, this mechanism is designed to prevent stagnation at local optima. The equation for this approach is described as follows:  
\[ \begin{array}{l}Prey_{i}\ =\left\{\begin{array}{l}Prey_{i}\ +CF[X_{min}\ +R \otimes(X_{max}\ -X_{min}\ )]\otimes U,\ r\leq FADs\\ Prey_{i}\ +[FADs(1-r)+r](Prey_{r_{1}}\ -Prey_{r_{2}}),\ r>FADs\end{array}\right.\end{array}  \tag{8}\]  
Among them, _FADs_ denote the probability influencing the optimization process of the algorithm and are typically set to 0.2; \(U\) is a binary vector sequence randomly generated with elements of 0 or 1; \(R\) is a random number within the interval [0, 1]; and \(r_{1}\) and \(r_{2}\) are random indices of the prey matrix.
# The Potential Role of Timosaponin-AIII in Cancer Prevention and Treatment
## 4 The Anticancer Effects of TSAIII
### Anti-Angiogenesis  
Angiogenesis plays a key role in tumor growth and metastasis. The formation of new blood vessels can facilitate the transportation of oxygen and nutrients to the tumor cells, promoting the growth and metastasis of tumors. The angiogenic factors produced by tumor cells include the vascular endothelial growth factor (VEGF), basic fibroblast growth factor (bFGF), TNF\(\alpha\), and HIF\(\alpha\)[81]. It has been reported that TSAIII suppresses cell viability and exhibits cytotoxicity in human umbilical vein endothelial cells (HUVECs). In addition, TSAIII decreases VEGF-induced proliferation, migration, invasion, and tube formation in HUVECs by downregulated the VEGF/PI3K/ AKT/MAPK signaling pathway. Furthermore, TSAIII inhibits angiogenesis in the transgenic zebrafish line Tg (fli-1a: EGFP)y1, as shown by the decreased growth of intersegmental vessels (ISVs) and subintestinal vessels (SIVs) [82].  
COX-2 can bind to thromboxane A2 (TXA2), which is highly overexpressed in vascularized solid tumors and promotes angiogenesis and metastasis by activating the PI3K/AKT signaling pathway [83]. Platelets comprise various angiogenesis-stimulating factors, and platelet activation can be associated with tumor metastasis [84]. It has been reported that TSAIII is a selective inhibitor of the TXA2 receptor and suppresses the overproduction of TXA2. The inhibition of TXA2 by TSAIII could lead to the attenuation of platelet aggregation and activation [85].
# Methane Detection Based on Improved Chicken Algorithm Optimization Support Vector Machine
## 5 Results and Analysis
### Prediction Results  
In our experiment, there were 40 concentrations (1000 ppm-40,000 ppm) of methane. We randomly split the dataset into 80% training and 20% test sets. In other words, 32 samples were selected for training the classifiers, while the rest of the samples were used to test the model. The training set and testing set were randomly selected from the whole dataset. We repeated the train-test procedure five times with four models (ICSO-SVM, CSO-SVM, GA-SVM, and PSO-SVM), and calculated the mean value. The predicted results of the four models are shown in Table 3.  
In order to analyze the performances of four models clearly, we calculated the relative error percentages of the four models, as shown in Figure 10.  
As shown in Figure 10, the fluctuations of the ICSO-SVM and CSO-SVM relative error lines are stable, while the GA-SVM and PSO-SVM relative error lines are volatile. The maximum relative error percentage of the ICSO-SVM model was 4%, which is obviously lower than the other three models.  
Figure 8: The fitness curve of genetic algorithm.  
Figure 9: The fitness curve of particle swarm optimization algorithm.  
To eliminate bias in the test results, we repeated this train-test procedure 50 times with different random splits. We then averaged the recovery of each test to get the recovery rate and the mean squared error for each model.  
The recovery rate can be calculated with Equation (23), and the mean squared error with Equation (22). The recovery rates for 50 repetitions of the four models are shown in Figures 11-14. The recovery rates and the mean squared errors of ICSO-SVM, CSO-SVM, GA-SVM, and PSO-SVM models are shown in Table 4.  
It can be seen from Figures 11-14 that the recovery rate of the ICSO-SVM model remained stable within the values of [90, 110], the CSO-SVM and GA-SVM models within [80, 120], and the PSO-SVM  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **ICSO-SVM** & **CSO-SVM** & **GA-SVM** & **PSO-SVM** \\ \hline Average recovery rate/\% & 101.23 & 103.15 & 113.58 & 125.61 \\ Average mean squared error & \(1.12\times 10^{-5}\) & \(1.23\times 10^{-5}\) & \(3.56\times 10^{-5}\) & \(3.22\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: The recovery rates and the mean squared errors of four models.  
Figure 14: The recovery rate of PSO-SVM.  
Figure 12: The recovery rate of CSO-SVM.  
Figure 13: The recovery rate of GA-SVM.  
model within [75; 120]. The results of the stability study showed that the ICSO-SVM model has better stability. From Table 4, the four models could be indexed on their average recovery rate, as follows: ICSO-SVM \(>\) CSO-SVM \(>\) GA-SVM \(>\) PSO-SVM. The four models could also be indexed on their average mean squared error, as follows: ICSO-SVM \(>\) CSO-SVM \(>\) PSO-SVM \(>\) GA-SVM. The results from the experiments indicate that the ICSO-SVM has the best prediction performance.
# Improving the Robustness of AI-Based Malware Detection Using Adversarial Machine Learning
## 7 Results  
### Results Obtained before Performing Adversarial Attacks  
The authors compared the results obtained before performing the FGSM attack by training the MaleVis Dataset on the different classification models, i.e., random forest (machine learning), CNN (deep learning), and EfficientNetB0 (transfer learning). The confusion matrix is also given below for each of the classifiers used in work. In these matrices, we can see the number of images getting properly classified and the number of misclassified images for each class.  
1. Random Forest: The results obtained after the dataset training show that the random forest classifier was able to achieve an accuracy of 93%. The confusion matrix below shows that the classifier could classify the images correctly for classes like Vissel, where a total number of 71 images were given to the model. It was able to classify them with 100% accuracy, as no image was misclassified into any other class. Along with Vissel, the images belonging to the Amonetize, Hlux, Neoreklami, Regrun, Sality, and VBA classes were also classified with 100% accuracy. Dinwood and Adposhel followed this with only one misclassified image, whereas images from the Neshta class were highly misclassified. Figure 12 shows the confusion matrix for CNN.
2. Convolutional Neural Network (CNN): Convolutional neural network gave an accuracy of 92.3% after training the images from the MaleVis Dataset. The confusion matrix shows that the images belonging to Amonetize, Fasong, HackKMS, Hlux, InstallCore, Regrun, Stantinko, Sarasite, VBA, and Viseal were correctly classified by the CNN classifier. Compared to random forest, CNN was able to give 100% accuracy to more classes. The Neshta class of the malware family had the least accuracy. Out of the total 88 images fed into the classifier, only 52 were classified correctly as Neshta, whereas 9 images were classified as benign images and 8 were classified into Sality class. After Neshta, the Expiro class had the least accuracy of 72.22%. Figure 13 shows  
Figure 11: Schematic representation of adversarial defense performed.  
the confusion matrix for CNN model and Figure 14 displays the training and testing loss curves.
3. EfficientNet B0: The model developed using EfficientNet-B0 was able to achieve an accuracy of 93.7%. The classes with 100% accuracy were HackKMS, InstallCore, Multiplug, Regrun, Sarnasite, VBA, VBKrypt, and Vilsel. Similar to random forest and CNN, this classifier also performed poorly for Neshta with an accuracy of 64.94%. Figure 15 shows the confusion matrix for the random forest model, and Figure 16 displays the training and testing loss curves.  
Figure 16: Training and training loss of EfficientNet B0.  
Figure 14: Training and testing loss of CNN.  
Figure 15: Confusion matrix for EfficientNet B0.
# A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment
## 5 Conclusions and Future Work
### Recommendations for Future Research  
The success of this research project has left some interesting avenues open for exploration. The case study conducted with the Estates and Sustainability departments at Leeds Beckett University concluded with a strong desire by the participants to extend the investigation by considering an important aspect of big data which had previously been left outside of scope: its variety. The complexity and multidimensionality of building data has, in fact, been identified in recent research as one of the biggest challenges to big data technology adoption within the sector [107]. A new research project aimed at investigating the appropriateness of the MC-BDP reference architecture for use-cases where the variety of the data is paramount was created as the case study concluded.  
Tasks for future works investigating aspects of MC-BDP include:  
* Investigating how MC-BDP handles batch processing. Comparing [108]'s approach of using a stream engine to process both batches and streams with an alternative approach based on the Lambda architecture.
* Following a trend observed in [109]'s research on container co-location, observing how MC-BDP performs with jobs of different characteristics. Memory-intensive, network-intensive, and disk I/O-intensive jobs could be created and compared to the CPU-intensive job utilised in the experiments.
* Integrating recent research on scheduling algorithms such as RTSATD, which uses task duplication to optimise the performance of big data stream processing across different cloud regions [52], or [110]'s algorithm developed to minimise data transfers over the network into MC-BDP's orchestration layer.
* Adding a cost perspective to MC-BDP's evaluation by integrating fine-grained billing information obtained from cloud providers. This is in line with [45]'s and [106]'s research. It is believed that understanding non-functional requirements from a cost as well as performance perspective would be advantageous to budget-constrained organisations.
* Extending MC-BDP's evaluation to include other domain-specific industry case studies.
* Conducting additional case studies with other types of SMEODs to validate the proposition that MC-BDP is beneficial to them.
* Strengthening the statistical significance of the quantitative results obtained by widening the scale of the experiments, using more than two commercial cloud providers and a greater number of virtual machines and containers and extending the experiments in duration and volume of incoming data.  
As other relevant research is added to the fields of big data, cloud computing, and virtualisation, new hypotheses and unexplored questions shall become apparent, impelling further investigation and solidifying this study's position as a mature contribution to the field.
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
## 2 Prediction of Tool Wear Based on Simulation
### Experimental Verification of Prediction of Micro-Milling Tool Wear  
Experiments of micro-milling aluminum alloy LF 21 are conducted on a micro-milling experimental platform built by our research group earlier (see Figure 9). The spindle speed is between 40,000 rpm and 140,000 rpm. The absolute positioning accuracy of the machine is 1 \(\upmu\)m and the repeated positioning accuracy is 0.2 \(\upmu\)m. The micro-milling tool used in the experiment is MX230, a two-edge micro-milling tool produced by NS Tool Co., Ltd., Tokyo, Japan, with a diameter of 0.3 mm. The workpiece material is aluminum alloy LF 21, and we selected the VHX-600E super depth of field microscope from KEYENGE to measure the diameter of the micro-milling cutter. The cutting forces corresponding to each set of test parameters are measured using a KISTLER 9256C1 precision 3D force measuring stage. The force meter has three channels, Ex, Fy and Fz, to measure the cutting force in X, Y and Z directions, respectively. Its measurement threshold is only 2 mN, and the measurement range is 250 N. The sampling frequency is 3000 Hz, and the cutting force amplitude can be clearly visualized in the DynoWare software that comes with the charge amplifier KISTLER Type 5080. The comparison of experimental and simulation results of the reduction rate of the tool diameter is shown in Table 5.  
Figure 8: Relationship between tool wear depth and the reduction rate of the tool diameter.  
Figure 9: The micro-milling experimental platform.  
As can be seen from Table 5, the simulated reduction rate of the tool diameter is basically consistent with the experimental measured value, and the average relative error of prediction is 17.77%. The validity of the prediction of tool wear is verified.
## 3 Creep Experimental Results
### Long-Term Strength Characteristics  
[PERSON] et al. [29] defined the long-term strength (\(\sigma_{L}\)) of rock as the stress at which microcracks begin to expand. Investigating the \(\sigma_{L}\) of carbonaceous slate can provide valuable insights into the long-term sustainability of tunnels.  
The tangent method of steady creep rate [30] was utilized to calculate the \(\sigma_{L}\) of carbonaceous slate. Taking \(\beta=75^{\circ}\) as an example, a fitted curve was generated to represent the relationship between steady-state creep rate and loading ratio, as illustrated in Figure 7. Furthermore, two lines tangent to the curve (AB and BC in Figure 7) were created, and the stress corresponding to the intersection point (point B) was defined as \(\sigma_{L}\).  
Figure 5: Typical stages of creep curve under load [28].  
Figure 6: Volumetric strain–time curves of carbonaceous slate under multilevel stress with different foliation angles.  
Figure 8 presents the \(\sigma_{L}\) and \(\overline{\sigma}_{UCS}\) (average uniaxial compressive strength) samples with different foliation angles. The experimental data depicted in Figure 8 are exhibited in Table 1. The curves representing the relationship between long-term strength and foliation angles, \(\beta\), as well as average uniaxial strength and foliation angles, \(\beta\), displayed a U-shaped pattern, which also occurs in layered rock salt, sandstone, and shale. At \(\beta\) = 90\({}^{\circ}\), both \(\sigma_{L}\) and \(\overline{\sigma}_{UCS}\) reached their maximum values, measuring 75.79 MPa and 80.91 MPa, respectively. The lowest value of \(\sigma_{L}\), measuring 37.37 MPa, was observed at \(\beta\) = 15\({}^{\circ}\), while the lowest value of \(\overline{\sigma}_{UCS}\), measuring 40.76 MPa, was detected at \(\beta\) = 45\({}^{\circ}\).  
Additionally, \(\overline{\sigma}_{UCS}\) is greater than \(\sigma_{L}\). This is due to the fact that the rock accumulates more damage during the creep process [31]. As the foliation angle increased, the ratio of \(\sigma_{L}\) to \(\overline{\sigma}_{UCS}\) exhibited a monotonically increasing trend. Furthermore, the \(\sigma_{L}\) of carbonaceous slate varied between 69.97% and 96.56% of the \(\overline{\sigma}_{UCS}\), expressed as \(\sigma_{L}\) = 70.02-93.67% \(\overline{\sigma}_{UCS}\).
## 4 Experiments
### Implementation Details  
We leverage the TensorFlow framework to implement the SkipGT model. All experiments are conducted on a server equipped with an NVIDIA A100 GPU. The Adam optimizer is consistently employed for all gradient-based optimization tasks. An embedding size of 128 is uniformly applied to items across all models, including the initial embedding layer.  
We conduct a grid search for the learning rate and batch size, evaluating values within the ranges {0.0001, 0.001,..., 0.1} and [32, 64,..., 512], respectively. With regards to hyperparameter fine-tuning, the hidden size is explored within the range {32, 64,..., 512}, attention distance [8, 1, 12], smoothing factor {0.25, 0.2, 0.2}, and the dropout ratio is investigated across {0.0, 0.1,..., 0.9}. For the transformer module specifically, the number of multi-head attention units is selected from {1, 2,..., 16}, and the number of encoder-decoder stacks is explored within {1, 2,..., 16}. It is important to note that default hyperparameters are utilized for all baseline methods.  
The model is configured with a maximum input sequence length of 32 and a hidden dimension of 128. While default hyperparameters are employed for all baseline methods, it is worth mentioning that the NARM method [26] utilizes 128-dimensional item embeddings, an initial learning rate of 0.001, and a fixed mini-batch size of 512. An early stopping strategy is implemented during the testing phase. Specifically, the training process terminates and outputs the recommendation results if no improvement in P@20 or MRR@20 metrics is observed on the validation dataset for 20 consecutive epochs.  
The optimal hyperparameter settings identified through experimentation on the three datasets are as follows: layer numbers {4, 5, 6, 7}, learning rate {0.0001, 0.0005, 0.001, 0.005}, and hidden layer {64, 128, 256, 512}.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 5 Performance Evaluation
### Results and Analysis
#### 5.2.4 Impact of Maximum Travel Distance  
Figure 8 reveals the impact of the maximum allowable travel distance on coverage. When the maximum allowable travel distance reflexes, the coverage gets increased. That is because it prevents the gateways from moving too far from their original positions, and that will constrain the deployment. Therefore, when it relaxes, the coverage will increase.
## 2 Related Works
[18] proposed an MC method based on pretrained fine-tuned CNN models (AlexNet, DenseNet-201, DarkNet-53, ResNet-50, Inception- V3, VGG16, MobileNet-V2, and Places365-GoogleNet). The proposed algorithm experimented on Malimg dataset. [PERSON] et al. [19] proposed an MC method based on combining two models of deep CNN, GoogleNet, and ResNet models. [PERSON] et al. [20] proposed an MC method based on combining three models of deep CNN, AlexNet, VGG, GoogleNet, and ResNet. [PERSON] et al. [2] proposed an MC method based on DenseNet model. The proposed algorithm experimented on Malimg dataset. In the classification scenario, a fully connected softmax layer is applied. In other cases, the CNN could be used to extract features when machine learning methods are being used to classify malware. [PERSON] et al. [21] proposed an MC method based on deploying a sequential multilayered Random Forest ensemble technique. Following that, four distinct machine learning (ML) approaches were used: Random forests (RF), Xgboost, Extra trees classifier (ETC), and Logistic regression (LR). [PERSON] et al. [22] proposed an MC method based similarities among the malware images. In the classification scenario, k-Nearest Neighbor (KNN) is the classifier that have been applied. [PERSON] and [PERSON] [12] proposed an MC method based on CNN features. CNN-SVM, GRU-SVM, and MLP-SVM models are used to classify each malware family. [PERSON] et al. [23] proposed an MC method based on spatial attention and convolutional neural network (SACNN) and deep learning frameworks (VGG19 model). The proposed algorithm experimented on the Malimg dataset. In the classification scenario, a fully connected softmax layer was applied. [PERSON] and [PERSON] [24] proposed an MC method based on an updated ResNet50 model. The proposed algorithm experimented on the Malimg dataset. In the classification scenario, a fully connected softmax layer was applied. [PERSON] et al. [25] proposed an MC method based on structural entropy and deep CNNs. The proposed algorithm experimented on the Malimg dataset. In the classification scenario, SVM is the classifier applied. Most previously mentioned MC techniques have a low classification rate attributable to the following factors. First, it takes a highly specialized level of domain knowledge to find and produce accurate features. Second, makes it challenging to classify and correctly identify malware. Therefore, the proposed malware classification method will be employed to address the aforementioned issues.
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Communication Overhead
#### 5.4.1 Cost for Admin-to-Client Communication  
Admin sends \(A_{i}\) and \(S_{i}\) to a client. In addition, it publishes \(g_{pk}\) as \((g_{1},g_{2},h,u,v,w)\) in the network. Suppose each group key has a size of 171 bits, and we have six parameters in \(g_{pk}\); hence, the size of \(g_{pk}\) will be 171 \(\times\) 6 = 1026 bits, and the secret keys used to secure the message should be of sufficient length to prevent it from being brute-forced by the server. Suppose the size is 1024 bits. Therefore, \(A_{i}\) and \(S_{i}\) will be 1024 bits each, and the total message size sent by the admin to a client will be 3074 bits. However, this is shared only once during the entire session. Therefore, the cost of communication for t iterations will be \(((m\times 2050)+(n\times 3074))/t\), where \(m\) is the total number of clients and \(n\) is the number of clients participating in the communication.  
Figure 7: Comparison of computation cost of algorithms for a varying number of iterations in an FL session (\(n=50,100,150,1000\)).