esa-sceva/satcom-chunk-collection huggingface dataset:
# Developing a Cybersecurity Training Environment through the Integration of OpenAI and AWS
## 3 Results
### Security Validation Results
#### 3.4.2. Evaluation of DDoS Protection with AWS Shield  
The DDoS protection provided by AWS Shield was extensively tested. For this, DDoS attacks were simulated using stress testing tools, and the ability of AWS Shield to mitigate these attacks in real time was evaluated. The results indicated that AWS Shield was highly influential in mitigating DDoS attacks, maintaining the availability of services in the environment without significant interruptions.  
The analysis in Table 8 shows the results of testing the effectiveness of DDoS protection with AWS Shield. It reveals a high level of success in mitigating attacks of different intensities and durations. In the case of a high-intensity UDP amplification attack that lasted 1 h, the mitigation effectiveness reached 98%, indicating a robust response to a significant attack. For a moderate-intensity SYN Flood attack that spanned 2 h, mitigation effectiveness came to an impressive 99%, demonstrating an exceptional ability to protect against this threat. Finally, in a low-intensity DNS mirroring attack that lasted 30 min, the mitigation effectiveness was 100%, indicating complete and successful protection. These results confirm AWS Shield's effectiveness in protecting against various DDoS attacks, ensuring the availability and integrity of services in the cybersecurity environment.  
The security assessment results indicate that the measures implemented, such as AWS Security Group Management and the DDoS protection provided by AWS Shield, are effective and robust in protecting the cybersecurity environment against threats and attacks. The cybernetics findings support the security and integrity of the implemented cybersecurity training environment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type of Test** & **Detected Attempts** & **Blocked Attempts** & **Blocking Success (\%)** \\ \hline Unauthorized Access Attempts & 50 & 50 & 100 \\ Port Scanning & 30 & 30 & 100 \\ SQL Injection Attempts & 20 & 20 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Effectiveness of security group management.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type of DDoS Attack** & **Attack Intensity** & **Attack Duration** & **Mitigation Effectiveness (\%)** \\ \hline UDP Amplification Attack & High & 1 h & 98 \\ SYN Flood Attack & Moderate & 2 h & 99 \\ DNS Mirroring Attack & Low & 30 min & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effectiveness of DDoS protection with AWS Shield.
## 2 Uncertainty Processing Based on Clustering Technique
### Rough Fuzzy Clustering Analysis of Power Dataset  
Since the uncertainty level in different TNEP cases may vary significantly, it is important to adopt a flexible clustering method to ensure good clustering performance under various datasets.  
Traditional clustering methods, such as hard clustering, in which the membership degree of each data sample should only be 0 or 1, are not suitable for TNEP because the power data is inherently continuous. Although soft clustering can deal with continuous data by fuzzy membership degree, it also has the shortcoming of producing overlapped clusters.  
Considering the intrinsic characteristics of power dataset, this paper adopts a rough-fuzzy clustering method, which is based on minimizing the following objective function [16]:  
\[f=\sum_{j=1}^{h}\sum_{i=1}^{n}u_{ij}^{q}d(x_{i},\lambda_{j})^{q} \tag{4}\]  
where \(h\) is the cluster number; \(n\) is the sample number of the power dataset; \(u_{ij}\) is the membership degree of data sample \(x_{i}\) to cluster \(j\); \(q\) is the fuzzifier parameter set as 2; and \(d(x_{i}\lambda_{j})\) is the Euclidean distance between \(x_{i}\) and cluster center \(\lambda_{j}\), which represents the similarity degree between the power values of \(x_{i}\) and \(\lambda_{j}\).  
Unlike traditional clustering, the rough-fuzzy membership degree is applied in the adopted method to guide the clustering analysis. The corresponding membership degree matrix \(u\) is obtained by the following steps [17]:  
1. Calculate the \(n\times h\) dissimilarity matrix \(F\) by measuring the Euclidean distance between each data sample and each cluster center.
2. Sort the elements of the matrix \(F\) by row in ascending order and obtain a new dissimilarity matrix \(F^{\prime}\). Then modify the sorted matrix \(F^{\prime}\) as follows [17]: \[F(x_{i},\lambda_{j})=+\infty\;if\;\frac{Fr(x_{i},\lambda_{j})}{F(x_{i}, \lambda_{1})}>1.2\;(i\in[1,n],j\in[2,h])\] (5)
3. Convert the modified matrix \(F^{\prime}\) into the matrix \(F\) with the original order. Then calculate the membership degree matrix \(u\) by: \[u_{ij}=F(x_{i},\lambda_{j})-\frac{2}{q-1}/\sum_{k=1}^{h}F(x_{i},\lambda_{k})- \frac{2}{q-1}\] (6)  
As shown in Equations (5) and (6), the 0-1 membership degree is employed if the data sample is closed to the cluster center; otherwise the fuzzy membership degree is employed. On the basis of this mixed membership degree, the rough-fuzzy clustering method integrates the advantages of hard clustering and soft clustering [16].  
The value of cluster center \(\lambda_{j}\) is updated with the changes of membership degree matrix \(u\):  
\[\lambda_{j}=\sum_{i=1}^{n}u_{ij}^{q}x_{i}/\sum_{i=1}^{n}u_{ij}^{q}\;\;(i\in[1,n],j\in[1,h]) \tag{7}\]  
Through the above analysis, the mathematical formulation of the rough-fuzzy clustering analysis is represented by Equations (4)-(7). A differential evolution algorithm is adopted to solve the above optimization problem; the detailed solving process can be found in [18].
## 4 Results and Discussion  
### Prediction Model Selection  
The feature factors obtained after completing the data preprocessing were sequentially input into six machine-learning models. The initial hyperparameter adjustment was performed for the training set, followed by hyperparameter optimization through 5-fold cross validation. The accuracy assessment results are presented in Table 4 below. The result  
Figure 5: Example of 10-fold Cross-Validation Calculation.  
shows that the RF algorithm achieved the highest \(R^{2}\) value and the lowest _MAE/RMSE_ among the algorithms.  
The scatterplot of the actual versus predicted values obtained from the machine-learning model's training set through 5-fold cross validation is depicted in Figure 6. The distribution of points from RF in the scatterplot closely resembled a straight line, indicating a strong correlation between the predicted and actual values and high prediction accuracy. In contrast, for the other machine-learning prediction models, the scatterplot patterns revealed systematic deviations from the ideal straight line, suggesting potential issues of underfitting or overfitting.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & \(\mathbf{R^{2}}\) & **MAE (mm)** & **RMSE (mm)** \\ \hline RF & 0.9909 & 0.8581 & 1.8970 \\ CB & 0.9820 & 1.7913 & 2.6642 \\ ET & 0.9500 & 1.4448 & 4.3536 \\ GB & 0.8373 & 5.5417 & 8.0098 \\ DT & 0.9785 & 1.0482 & 2.9032 \\ MLP & 0.9512 & 2.5083 & 4.3831 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy assessment metrics for 5-fold cross validation for each machine-learning model training set.  
Figure 6: _Cont_.
# A Review on the Recovery and Separation of Gallium and Indium from Waste
## 3 Methods of Recovering Gallium and Indium from Secondary Sources
### Sorption and Ion Exchange
#### 3.4.3 Separation of Ga and In in an Acid Solution  
Indium and gallium can be separated from the solution of hydrochloric acid using the relationship between the occurrence of different forms of these metals and the acid concentration--in short, via the cation and anion exchange. In the solutions with a concentration of 0.1 M and 1.5 M HCl, the cationic forms In(H\({}_{2}\)O)\({}_{6}\)\({}^{3+}\) and Ga(H\({}_{2}\)O)\({}^{3+}\) dominate, respectively. In the case of indium, the anionic forms begin to dominate at a concentration of 1 M, and for gallium at a concentration of at least 2.5 M. Therefore, with appropriate changes in the acid concentration, it is possible to separate gallium from indium ions using first a strong acid cation exchanger (e.g., an ion exchanger with the trade name Dowex 50W X8), and then a base anion ion exchanger (e.g., an ion exchanger with the trade name Dowex 1 X8) [88].  
Indium, gallium, and zinc were separated from the solution obtained in the process of gallium-based liquid metal (G-LM) waste leaching with 3 M HNO\({}_{3}\) solution for 2 h at 40 \({}^{\circ}\)C. Under these optimal conditions, more than 99.99% of Ga, In, and Zn were leached and then adsorbed on a CL-P204 solvent-impregnated resin. The metals were then eluted using HCl solutions with varying pH levels. The results showed a recovery efficiency and purity of 96.01% and 99.49% for gallium, and 99.83% and 98.23% for indium, respectively. The leaching efficiency for tin was only 4.43%, and it was mainly found in the slag phase as \(\beta\)-H\({}_{2}\)SnO\({}_{3}\). The P204 solvent-impregnated resin used in the experiments can be recycled [52].  
On the other hand, when the solution obtained in the process of waste leaching contains sulfate ions, it was proposed to use HZ-803 resin impregnated with EHEHPA and D2 EHPA solvents to separate indium, gallium, and zinc. A solution with a pH of 1 was passed through a first column, where indium was completely separated from it. Then the pH of the solution was raised to 3 and it was directed to a second column, where gallium ions and some zinc ions were adsorbed. To completely separate Ga(III) from Zn(II), the solution after regeneration of the second column had to be passed through a third column [31].  
Figure 13: Structures of active resin groups before and after the attachment of the indium ion: TP 260 (**a**), TP 260-In (**b**), TP 208 (**c**), TP 208-In (**d**), IRA 743 (**e**), and IRA 743-In (**f**).
## 4 Case Example: Australian Public-Private Partnership (PPP) Schools  
The theoretical mathematical methodology involves two variables, i.e., the perceived service quality by the end-user \(P(t)\) and assessment of delivered service in contract \(A(t)\), where probability of failure is defined as the probability of \(P(t)\) being lower than \(A(t)\). The mathematical method in this reliability-based decision support framework could evaluate service performance and identify when the gap between \(P(t)\) and \(A(t)\) warrants reconsideration. Using the case study, the data of \(P(t)\) and \(A(t)\) of three PPP schools are statistically analyzed for performance assessment.  
The scope of services provided in these 25-year-long PPP schools included: the original design and construction of the buildings and associated amenities such as playgrounds and sporting grounds, provision of finance, ongoing facilities management, upgrades, any required and approved changes and a selection of services such as cleaning and maintaining. At the time of the study the schools had been operational for nine years. The actual payments (and any abatements) made by government over this period were collected along with details of minor works, variations and any modifications undertaken during this period. The end-user's perceived service quality is derived based on a series of semi-structured face-to-face interviews with the key end-users (the principal and senior teachers) within the schools, through a common list of questions. The interviews were conducted in accordance with The University of Melbourne ethics approval #1750880. This method provides an interactive dialogue with the respondents to tap into their front-line experience under a specially designed questionnaire.
## 4 Discussion
### Limitations and Future Perspectives  
In the present study, we used a single BCI class, the detection of elevated alpha activity, and implemented a cyclic menu to increase the number of available commands. This approach, while simple and intuitive, has a few limitations. Adding more items to the menu greatly increases waiting times, reducing the system's responsiveness. A possible solution would be to introduce another control signal that could, e.g., be used to swap between different menus or have it cycle through the available items. Doing so would increase the number of commands without extending the time spent waiting. Another option is to have said control signal cycle through the available targets instead, reducing idle times altogether. Future work could involve introducing EEG rhythm modulation, e.g., frontal theta, frontocentral alpha, and gamma power [53; 54; 55], during cognitive tasks, such as mental arithmetic tasks [56; 57; 58], to control the robot. Since this system uses only two electrodes, its possible to replace the EEG cap with a headband, an inconspicuous alternative that is faster and easier to mount for caretakers and relatives. Additionally, frontal modulated alpha could still be introduced.  
As mentioned in Section 2.1, three individuals who were initially screened for the study were excluded since the BCI was unable to detect adequate alpha. BCI-illiterate users who fail to achieve efficient BCI control are not uncommon; it is estimated that they make up about 15-30% of the population [59]. Further, it is well documented that both the amplitude and peak frequency of alpha rhythms vary greatly between individuals [60]. Future BCIs driven by alpha wave modulation could account for an individual's neuro-physiology by, for example, selecting subject-specific frequency bands, a method commonly used in MI-BCI [61; 62; 63] to optimize classification. Ultimately, users may also benefit from neurofeedback training protocols, such as the one proposed in [PERSON] et al. [64], which have been shown to successfully help increase alpha power.  
The proposed system is contingent on the subject's ability to blink. According to the telephone survey of people with ALS (n = 61) by [PERSON] et al. [65], 20% of respondents reported having some difficulty blinking, while 7% reported having no control over eye blinking movements. For these users, self-regulated slow cortical potentials or sensorimotor rhythms could be used as an alternative to the alpha waves [66]. However, training of the user may be needed to achieve adequate BCI control; thus, alpha waves may be better suited for earlier stages of ALS where blinking is still possible, as it allows users' familiarization with the BCI and robot with an expected higher success rate. Nevertheless, tests with end-users are also needed to assess the robustness of the system.
## 4 Discussion
This offers a promising technique in different areas, mainly in critical care medicine, because the information obtained with this model will not only indicate the state of the patient during mechanical ventilation, but it will also help medical staff decide whether or not a patient needs treatment with mechanical ventilation, determine the right extubation moment and optimize the mechanical ventilator settings. However, for future work, a database of patients in ICU would be recorded to validate the model and its possible use in the daily routine of healthcare professionals. Design of the work, I.C.M. and A.M.H. ; the data acquisition, I.C.M. ; analysis, I.C.M., A.M.H., M.A.M. ; writing--original draft preparation, I.C.M. ; writing--review and editing, A.M.H., M.A.M., and I.C.M. ; final approval of the version to be published, A.M.H. and M.A.M. Research was partially supported by PGR-CODI-2015-7851 \"Analisis de la actividad muscular respiratoria en ventilacion mecanica no invasiva y su relacion con la configuracion del ventilador\". Grant by University of Antioquia (Medellin, Antioquia, Colombia). By FP44842-450-2018 \"Desarrollo de aplicaciones moviles para el aprendizaje del sistema respiratorio: Concepto sasicos, diagnostico de erfermedades, terapia y rehabilitation\". Grant by Sistema General de Regalias fondos de CTel de la Gobernacion de Antioquia, administrados a traves del patrimonia atomo tomod noacional de financial deinacion para la ciencia, la recnologia y la innovacion [PERSON]. And by the Spanish Ministry of Economy and Competitiveness- Spain project DPI2017-83989-R. The authors declare no conflict of interest.
# An Efficient and Secure Certificateless Aggregate Signature Scheme for Vehicular Ad hoc Networks
## 1 Introduction
In this paper, we propose a certificateless aggregate signature scheme based on hyperelliptic curve cryptography (HECC), which is the advanced version of Elliptic Curve Cryptography (ECC), using a key size of 80 bits, providing the same security benefits of ECC, but with less computational cost, communication overhead, and memory requirement. HECs are algebraic curves with the genus \(\tilde{g}\geq 1\); since the field of an HECC is a quadratic extension of the field of rational functions, we can say that it is the simplest field of algebraic functions, except the field of rational functions ([PERSON], 1996). HECC consists of the divisor \(\tilde{\mathcal{D}}\), the finite formal sum of points on a hyperelliptic curve. The divisor \(\tilde{\mathcal{D}}\) also forms an Abelian group known as the Jacobian group ([PERSON], 1996). Based on the above discussion, this research article contributes to the security of VANETs through the following key characteristics:  
* In this article, we propose an efficient certificateless aggregate signature scheme for the security and privacy protection of VANETs using hyperelliptic curve cryptography;
* The proposed scheme enables participating vehicles to share their identities with trusted authorities via an open channel without revealing their identities to unauthorized participants; as a result, sender and recipient anonymity will be ensured;
* In addition, this scheme will disclose the partial private key to participating devices via an open channel while keeping it concealed from other third parties;
* Finally, the noteworthy feature of the proposed scheme is its utilization of a hyperelliptic curve to generate and verify signatures with less computational and communication costs. The subsequent sections of this article are organized in the following manner: Section 2 discusses the literature review. The proposed scheme is described in Section 4, while Section 3 discusses the necessary preliminary steps. Section 5 deals with the security evaluation. An analysis of performance is covered in Section 6. Concluding remark on the proposed scheme is detailed in Section 7.
# Container Volume Prediction Using Time-Series Decomposition with a Long Short-Term Memory Models
## 4 Experiments and Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hyper-Parameter** & **Trend** & **Residual** \\ \hline Recurrent layer & 3 LSTM layer & 3 LSTM layer \\ Hidden units & 200 of each layer & 200 of each layer \\ Gate activation & Tanh & Tanh \\ Batch Normalization & - & Each layer \\ Dropout & 20\% of each layer & 20\% of each layer \\ Wrapper layer & 1 Time Distributed layer & 1 Time Distributed layer \\ Hidden units & \# of prediction steps & \# of prediction steps \\ Activation & Sigmoid & Sigmoid \\ Loss function & MSE & MSE \\ Optimizer & Adam & Adam \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model configuration. Figure 8: Trend prediction (x: data, y: TEU). Residuals in container volume imply an increase in variability seen in the data problem; a decrease in residual prediction errors leads to a good prediction performance of container volume. In Figure 9, the residual prediction also shows reasonable results for six months, except for the last month. Predictive results of trends and residuals show the characteristics of time-series decomposition. For October 2020, the predicted value of the trend was lower than the actual value, whereas the predicted value of the residuals was higher than the actual value. Owing to the additive characteristics in the additive model, the container volume shows that the error of each element is mitigated when the time-series element predictions are added at the end. The total prediction results are shown in Figure 10.  
To compare the performance of the proposed model with the traditional model, experiments were conducted using various deep learning models, such as univariate LSTM, bidirectional LSTM, multivariate LSTM, GRU, and bidirectional GRUs. We compared the  
Figure 10: Multivariate LSTM prediction with time-series decomposition (x: date, y: TEU). Figure 9: Residual prediction (x: data, y: TEU). performance of the proposed model with the existing deep learning models and SARIMA-based prediction model, which is a traditional statistical methodology. As the RMSE and MAPE evaluation metrics show that the error of the TD (Time-series Decomposition) model is the smallest, we can see that our method can bring the most effective forecasting performance among various prediction methods. In addition, by comparing the results of the unidirectional and bidirectional LSTM model, we can tell that bidirectional LSTM can perform better and unidirectional LSTM, which is denoted as univariate LSTM in Table 6. Furthermore, we could analyze the improvement of forecasting accuracy by importing external variables, which are manufacturing production index, industrial product index, import-export price, import-export weight, and consumer price index as a result of multivariate LSTM.
## 3 Results
### Driving Mechanisms and Relative Contributions to NPP Variability  
#### 3.5.1 Driving Mechanisms of NPP Variability  
The area within the basin where NPP has improved due to the joint influence of human activities and climate change accounts for 65.39% (Figure 10). It is primarily located in the Jialing River Basin, upper reaches, Hanjiang River Basin, and downstream regions. The area solely improved by human activities accounts for 10.42%, mainly scattered in the southern parts of the Jialing River Basin, southern parts of the Hanjiang River Basin,  
Figure 8: Spatial distribution of climate factor trends from 2003 to 2020.  
southwestern parts of the Hanjiang River Basin, and the junction of the Jialing River Basin. The area solely improved by climate change accounts for 6.31%, with a relatively scattered distribution, scattered in the central parts of the Jinsha River Basin and the central parts of the Min River Basin. The area degraded due to the joint influence of human activities and climate change accounts for 8.76%, with similarly scattered distribution, mainly scattered in the southern parts of the Poyang Lake Basin, southeastern parts of the Dongting Lake Basin, and the central parts of the Wujiang River and Jinsha River Basin. The regions degraded solely by human activities and solely by climate change account for 5.15% and 3.37%, respectively. Overall, it can be observed that, under the joint influence of climate and humans, NPP exhibits an overall positive development trend.  
Figure 10: Spatial distribution of NPP change drivers.  
Figure 9: The 2003–2020 partial correlation coefficient between NPP and temperature.