esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive BBU Migration Based on Deep Q-Learning for Cloud Radio Access Network
## 5 Simulation Results
### Comparative Study of the Proposed System with Some Statistical Methods  
Cloud-based BBU pools require smart approaches for the effective use of resources. With dynamically changing workloads and changing system states, the selection of an ideal node for VM migration and consolidation is a challenge. Techniques such as IQR, LR, and MAD use past information for deciding thresholds for utilizing resources, but DQN techniques use real-time feedback and learning for effective decision making. The following are the statistical methods that are compared with a proposed DQN.
# Dosimetric Optimization of a Laser-Driven Irradiation Facility Using the G4-ELIMED Application
## 3 Results  
Medical applications demand a high control of the beam characteristics. In this section, several clinically accepted parameters connected to the beam quality [33] were studied at the irradiation point to verify the capability of using the beamline to perform radiobiological experiments. The capability of the beamline to filter out unwanted carbon ions was additionally studied and discussed.
## 3 Review of Trajectory Planning
### Approaches Based on Learning by Demonstration (LbD)  
Learning by demonstration (LbD), also known as programming by demonstration (PbD), is sometimes employed in robotic programming for complex and nonstrict motion trajectories [52]. LbD consists of two stages (Figure 4): (i) a learning phase that first acquires behavior data and encodes them through a learning model, and (ii) a reproduction phase that uses appropriate control models to reproduce similar behavior [15; 53; 54]. Planning based on LbD is an alternative approach to the traditional approach to path planning and IK illustrated in the previous section since it either makes it possible to avoid motion planning in Cartesian space (as in [11]), or it helps to find a unique inverse kinematics solution (as in [18]). However, this approach requires learning the target joint configuration. For upper-limb exoskeletons, the generation of reference trajectories is grounded in learning models, the most common of which are neural networks (NNs) [8; 11; 54], hidden Markov models (HMM) [55], dynamic-motion-primitive (DMP) models [45], and Gaussian mixture models [14; 56]. Concretely, the idea behind LbD is to extract an adequate control law from demonstrations of human motion during ADL tasks.  
In the literature, many planning methods based on LbD were proposed for redundant and nonredundant exoskeleton robots [11; 14; 54; 55; 56; 57]. These methods usually utilize machine-learning methods (supervised and reinforcement learning [58]) to learn the IK of exoskeletons robots. For tasks with little to no interaction with the environment, such as writing, the observation of human motion (demonstration) makes it possible to train a movement model that can replicate the task. LbD is also used for complex tasks involving interactions with the environment, where the robot learns using HMMs. In [11], a motion-planning method based on LbD was adopted to generate reference trajectories in joint space. The authors used an NN to learn motion features (DMP) and the robot's inverse kinematics. Indeed, depending on the task and object position, an NN trained through the Levenberg-Marquart supervised learning algorithm can provide distinctive features (DMP parameters) and target joint positions. These DMP parameters are then processed to provide the exoskeleton with reference joint trajectories. In [54], three trained artificial NNs (ANNs) were combined in the form of a closed-loop model that generated elbow angles similar to experimentally recorded trajectories. [PERSON] et al. adopted a planning approach based on LbD [55]. From human demonstrations, they generated the desired joint trajectory by using a learning method based on an HMM [55]. A supervised artificial neural network was employed in [18] to conduct the motion-trajectory models from a set of training patterns collected through a motion capture system. Other studies employing the proposed planning method based on LbD were [45; 56], wherein a DMP and a Gaussian mixture model, respectively, were used to generate trajectory planning for reaching movements.
## 2 Related Work
### RESTful WS Semantic Annotation Alternatives  
As stated above, from the RESTful WS perspective, StoRHm v2 uses both hRESTS and MicroWSMO to achieve machine automation and semantic annotation respectively. These technologies are discussed in more detail in Section 3. Other available alternatives are discussed now:  
Figure 3: SBWS stack compared to the StoRHm stack for annotation of RESTful WS.  
Figure 2: SBWS stack compared to the StoRHm stack for annotation of SOAP WS.  
GRDDL: GRDDL (Gleaning Resource Descriptions from Dialects of Languages) is a W3C standard that enables an author to markup an XHTML file with any microformat and specify the transformation (an XSL file) that extracts the RDF based on that microformat [12]. GRDDL uses the XHTML _profile_ attribute to specify that this XHTML page contains GRDDL annotations. GRDDL aware parsers will then look for the _rel_ and _src_ attributes on the _link_ element to locate the transformation document to apply.  
RDFa: RDFa (RDF in Attributes) is also a W3C standard [13]. Using both existing and new XHTML attributes, RDFa enables the insertion of RDF statements directly into an XHTML page. The values of these attributes can refer to concepts from _any_ independently defined vocabulary thereby giving RDFa great flexibility. Parsers can generate RDF files from RDFa-annotated XHTML. SA-REST (see Section 2.3) uses RDFa to annotate service descriptions with its vocabulary of terms [10].  
GRDDL and RDFa both have their advantages and disadvantages. The advantage of GRDDL is that it is less intrusive than RDFa _i.e._, GRDDL only requires two lines of code in the _head_ section of the XHTML document. However, two files must now be maintained: the annotated XHTML file and the transformation file. RDFa has the advantage of being a standardized microformat. In addition, RDFa is free to use any vocabulary of terms and requires maintenance of only one file: the annotated XHTML file.  
Essentially the decision is between microformats (hRESTS/MicroWSMO) and RDFa (SA-REST/RDFa). However, at the time StoRfm v2 was being developed, the dearth of tools supporting RDFa/SA-REST was instrumental. By contrast, a lightweight, browser-based suite of tools, under the umbrella term Core Dashboard was available for hRESTS/MicroWSMO annotation [14].
# A Machine-Learning-Based Software for the Simulation of Regional Characteristic Ground Motion
## 3 Results  
### Validation of Methods  
An earthquake of magnitude Ms6.0 occurred at 28.34\({}^{\circ}\) N 104.90\({}^{\circ}\) E, located in Changing County, Yibin City, southeastern Sichuan Province, China, on 17 June 2019. In this study, the ground-motion database was created by collecting the mainshock of the Changning earthquake and aftershock records of magnitude 4 or higher that occurred between June 17 and 24. The specific earthquake information is summarized in Table 1. The epicenter, station locations, and the intensity distribution of the mainshock in Changning are illustrated in Figure 2a; the epicenter and source mechanism of the selected main aftershock are illustrated in Figure 2b. The database contains 9 earthquakes with a total of 286 horizontally oriented seismic records, and raw ground-motion data are filtered and baseline adjusted [44]. The feasibility of the synthesis method is verified by simulating the Changning earthquake. The Changning earthquake was selected as the scenario, and the target response spectrum and duration were calculated to validate the method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Name**} & \multicolumn{1}{c}{**Date**} & \multicolumn{1}{c}{**Latitude**} & \multicolumn{1}{c}{**Longitude**} & \multicolumn{1}{c}{**Depth**} & \multicolumn{1}{c}{**Magnitude**} \\  & & & & **(km)** & **(Ms)** \\ \hline Mainshock & June 17 & 28.34\({}^{\circ}\) N & 104.9\({}^{\circ}\) E & 16 & 6 \\ Aftershock & June 17 & 28.43\({}^{\circ}\) N & 104.769\({}^{\circ}\) E & 16 & 5.1 \\ Aftershock & June 18 & 28.389\({}^{\circ}\) N & 104.849\({}^{\circ}\) E & 10 & 4.1 \\ Aftershock & June 18 & 28.389\({}^{\circ}\) N & 104.87\({}^{\circ}\) E & 10 & 4.2 \\ Aftershock & June 18 & 28.379\({}^{\circ}\) N & 104.87\({}^{\circ}\) E & 14 & 4.5 \\ Aftershock & June 18 & 28.37\({}^{\circ}\) N & 104.889\({}^{\circ}\) E & 17 & 5.3 \\ Aftershock & June 22 & 28.43\({}^{\circ}\) N & 104.769\({}^{\circ}\) E & 10 & 5.4 \\ Aftershock & June 23 & 28.389\({}^{\circ}\) N & 104.819\({}^{\circ}\) E & 14 & 4.6 \\ Aftershock & June 24 & 28.44\({}^{\circ}\) N & 104.8\({}^{\circ}\) E & 10 & 4.1 \\ \hline \hline \end{tabular} Note: Information on seismic events listed in the table comes from the China Earthquake Network Center (CENC).  
\end{table}
Table 1: Selected Changing mainshock–aftershock events.
## 4 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & **NB** & **SVB** & **SVB** (**N B** and **SVB**) & **Vb6** (**Vb** and **FB**) & **F-Score** **for** **Vb6** \\ \hline \multirow{2}{*}{**Ceneralized Models**} & \multirow{2}{*}{**Data**} & **Work** & **S** & **P\({}^{*}\)** & **S** & **P\({}^{*}\)** & **S** & **P\({}^{*}\)** & **S** & **P\({}^{*}\)** \\ \cline{3-10}  & & Mar [10] & 0.90 & 0.99 & 0.83 & 0.34 & & 0.87 & 0.61 & 0.7171 \\  & & [PERSON] [13] & 87 & 99 & 0.79 & 0.385 & & 0.77 & 0.819 & 0.794 \\  & & Limeode [14] & & & 0.77 & 0.39 & & 0.81 & 0.87 & 0.8399 \\  & & [PERSON] [11] & & & 0.608 & 0.523 & & 0.815 & 0.631 & 0.7112 \\  & & **This work** & & & & **0.9996** & **0.9848** & **0.8039** & **0.9945** & **0.8891** \\ \hline \multirow{10}{*}{**Patient adaptive (no training data from the same patient)**} & \multirow{2}{*}{**MTBHI**} & Limeode [12] & 0.93 \(\pm\) 0 & 0.99 \(\pm\) 0 & 0.77 \(\pm\) 0 & 0.39 \(\pm\) 0 & & 0.82 \(\pm\) 0 & 0.70 \(\pm\) 0 & 0.7552 \\ \cline{3-10}  & & **This work** & & & & **0.9996** & **0.9848** & **0.8039** & **0.9945** & **0.8891** \\ \cline{1-1} \cline{3-10}  & & **MTBHI**-AR **common** & Al Rahhal [13] & & 0.115 & 0.132 & & 0.847 & 0.871 & 0.8588 \\ \cline{1-1}  & & **This work** & & & & **0.9928** & **0.9797** & **0.8309** & **0.9335** & **0.8792** \\ \cline{1-1} \cline{3-10}  & & **Limeode [12]** & 0.96 \(\pm\) 0 & 0.98 \(\pm\) 0 & 0.76 \(\pm\) 2 & 0.43 \(\pm\) 2 & & 0.80 \(\pm\) 2 & 0.82 \(\pm\) 3 & 0.8998 \\ \cline{1-1} \cline{3-10}  & & **Al Rahhal [13]** & & & 0.41 & 0.429 & & 0.91 & 0.795 & 0.8466 \\ \cline{1-1} \cline{3-10}  & & **HAMILTON** [10] & & & & & 0.928 & 0.795 & 0.8563 \\ \cline{1-1} \cline{3-10}  & & **This work** & & & & **0.9957** & **0.9865** & **0.8360** & **0.9428** & **0.8862** \\ \cline{1-1} \cline{3-10}  & & **Limeode [12]** & 0.94 \(\pm\) 0 & 0.97 \(\pm\) 0 & 0.47 \(\pm\) 3 & 0.50 \(\pm\) 1 & & 0.82 \(\pm\) 0 & 0.54 \(\pm\) 1 & 0.5511 \\ \cline{1-1} \cline{3-10}  & & **[PERSON] [13]** & & & 0.880 & 0.1431 & & 0.6519 & 0.931 & 0.1639 \\ \cline{1-1} \cline{3-10}  & & **This work** & & & & **0.9938** & **0.9941** & **0.9961** & **0.8930** & **0.8996** \\ \hline \multirow{10}{*}{**P Patient adaptive (no training data from the same patient)**} & \multirow{2}{*}{**MTBHI**} & Limeode [12] & 0.89 \(\pm\) 0 & 0.99 \(\pm\) 0 & 0.74 \(\pm\) 4 & 0.8 \(\pm\) 0 & & 0.88 \(\pm\) 0 & 0.96 \(\pm\) 1 & 0.9182 \\ \cline{1-1} \cline{3-10}  & & **Al Rahhal [13]** & & & & & 0.7511 & 0.3763 & 0.5013 \\ \cline{1-1} \cline{3-10}  & & **This work** & & & & **0.9946** & **0.9899** & **0.9220** & **0.9575** & **0.9394** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparing results with similar works.
## 2 Research Methods
### Numerical Simulation
The surrounding rock samples and the triaxial testing equipment are shown in Figure 2.  
The typical curves showing the relationship between the axial and lateral stress difference (\(\sigma_{1}-\sigma_{3}\)) and axial strain obtained from the triaxial compression test of rock samples are shown in Figure 3. The best-fit relationship curves for \(\sigma_{1}\) and \(\sigma_{3}\) for each group of samples are shown in Figure 4.  
Figure 3: Axial and lateral stress difference (\(\sigma_{1}-\sigma_{3}\)) versus axial strain (\(\varepsilon 1\)) relationship curve. (a) Surrounding rock; (b) ore body group 1; (c) ore body group 2. Figure 2: (a) Rock surrounding samples; (b) testing equipment. Due to significant differences between the engineering mechanical properties of rock masses in the field and the results of laboratory mechanical tests, laboratory test results cannot be directly used for numerical simulation calculations and must first be subjected to reduction. The mechanical parameters are reduced using the following formula, with the reduction results shown in Table 3.  
\[\rho_{reduced}=\rho_{lab}(1-V_{v}) \tag{2}\]  
\[E_{reduced}=E_{lab}10^{\frac{GSI-100}{100}}(1-D) \tag{3}\]  
\[\mu_{reduced}=\mu_{lab}+0.05\bigg{(}1-\frac{GSI}{100}\bigg{)} \tag{4}\]  
Figure 2.0  
Figure 2.1  
In the aforementioned formula, \(V_{v}\) represents the porosity, \(GSI\) is the rock mass quality index, \(D\) is the disturbance factor caused by excavation or other engineering activities. Figure 2.2 Best-fit curve for the relationship between \(\sigma_{1}\) and \(\sigma_{3}\) in triaxial tests. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Material Type**} & \multicolumn{5}{c}{**Mechanical Parameters**} & \multicolumn{5}{c}{**Seepage Parameters**} \\ \cline{2-9}  & \multirow{2}{*}{\begin{tabular}{c} **Density \(\rho\)** \\ **(kg/m\({}^{3}\))** \\ \end{tabular} } & \begin{tabular}{c} **Elastic** \\ **Modulus E** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Poisson’s** \\ **Ratio \(\mu\)** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Internal** \\ **Ratio \(\mu\)** \\ **(GPa)** \\ \end{tabular} & \begin{tabular}{c} **Cohesion c** \\ **(MPa)** \\ \end{tabular} & \begin{tabular}{c} **Permeability** \\ **(MPa)** \\ **(MPa)** \\ \end{tabular} & \
\begin{tabular}{c} **Fluid** \\ **Coefficient** \\ **k (cm/s)** \\ \end{tabular} &
\begin{tabular}{c} **Porosity n** \\ **Er (GPa)** \\ \end{tabular} \\ \hline III & 2850 & 39.985 & 0.23 & 49.17\({}^{\circ}\) & 20.108 & \(1.09\times 10^{-6}\) & 0.25 & 2.01 \\ IV & 2530 & 21.991 & 0.25 & 38.80\({}^{\circ}\) & 10.44 & \(8.02\times 10^{-6}\) & 0.26 & 2.01 \\ V & 2110 & 7.33 & 0.27 & 28.54\({}^{\circ}\) & 5.036 & \(2.20\times 10^{-5}\) & 0.27 & 2.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Numerical model parameter values.
## 2 Structure of the Monitoring and Control System  
### System Structure  
The structure of this monitoring and control system is shown in Figure 1. There are four main sections. Firstly, the CO\({}_{2}\) concentration sensor node based on NDIR technology with anti-condensation prevention and wireless communication is used to measure the required environmental factors and transmit data. Secondly, the established wireless sensor network realizes the function of data transmission and smart regulation of transmission power. Thirdly, the interactive software platform based on LabVIEW achieves signal processing and synchronization with the remote terminals. Finally, the control node fertilizes a suitable amount of CO\({}_{2}\) in the greenhouse following the transmitted control parameters by the software platform on the terminal. These four sections establish a closed-loop monitoring and control system to realize CO\({}_{2}\) measurement and fertilization.  
Figure 1: Structure of the CO\({}_{2}\) monitoring and control system based on wireless network in a greenhouse environment.
# A Novel Intrusion Detection Approach Using Machine Learning Ensemble for IoT Environments
## 5 Conclusions  
The research work conducted used an experimental study on an anomaly-based intrusion detection system that is suitable for providing network security against DoS attacks to an IoT-enabled framework. The performance evaluation was done using two machine learning classification algorithms: random forest and gradient boosting machines. The hyperparameter optimization technique was used to find the best optimal parameters for the classifiers to train the models and compare their performances, respectively. The valuated performance of both the classifiers was measured in terms of accuracy, sensitivity, specificity, rate of false positives, and the area under the receiver operating characteristic curve. Benchmarking of both the classifiers was performed on the CSE-CIC-IDS-2018 dataset. The performance results indicate that the gradient boosting machine ensemble classifier performs best for binary classification, yielding a very attractive performance accuracy of 98.27%. Whereas, even on the validation and test sets, the classification of intrusion types was incorrectly falling for malicious network traffic. Additionally, the  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Feature Selection** & **Accuracy Rate (\%)** & **Significance Test Results** \\ \hline Border/router node & & & \\ convection [15] & No & & \\ \hline GBM [17; 18] & No & 93.64 & No \\ \hline Random tree [18] & Yes & 95.23 & Yes \\ \hline
**Proposed (RF + GBM)** & **Yes** & **98.27** & **Yes** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison results of proposed model with other models.  
Figure 11: Average response times of classifiers.  
results show the trade-off between prominent metrics and response time, implying that both can be used for building IoT-specific anomaly-based intrusion detection systems.  
All authors contributed equally to this work. All authors have read and agree to the published version of the manuscript.  
This work was not funded by any agency or organization.  
Not applicable.  
The dataset presented and during this work were collected by the Canadian Institute of Cyberspace (CIC) on Amazon's AWS LAN network. The data is publicly available in the 'CSE-CIC-IDS2018-V2 Dataset' at the link: [[https://registry.opendata.aws/CSE-CIC-IDS2018-V2/](https://registry.opendata.aws/CSE-CIC-IDS2018-V2/)]([https://registry.opendata.aws/CSE-CIC-IDS2018-V2/](https://registry.opendata.aws/CSE-CIC-IDS2018-V2/)), accessed on 15 March 2021.  
The authors declare that they have no conflict of interest.
# Unveiling the Landscape of Operating System Vulnerabilities
## 5 Findings and Discussion
### Severity Analysis Results  
We demonstrate the CVSS score analysis as a stacked bar chart for all the 10 OS categories under study, as shown in Figure 7. This chart is based on the CVSS score reported in our OS_CAT dataset. All the OS categories contain the majority of the CVSS scores above 4. The quite critical observation is that all our top 10 vulnerable OS categories have the majority of vulnerabilities with CVSS scores in the ranges 7-8 and 9-10. This indicates a heightened level of severity and underscores the urgent need for proactive measures to address these vulnerabilities and enhance the security of these OSs. It is crucial to prioritize the identification and patching of vulnerabilities within these score ranges to mitigate potential risks and safeguard the integrity and functionality of these vulnerable systems.  
Continuing this analysis, Figure 8 depicts the CVSS scores for our top 10 most recent vulnerable OSs with specific versions. We present the following observations and pattern analysis from the figure:  
\begin{table}
\begin{tabular}{c l l c c} \hline \hline
**Rank** & **Operating System** & **Vendor** & **Release Date** & **\#Vulnerabilities** \\ \hline
1 & Windows 10 & Microsoft & 29 July 2015 & 8659 \\
2 & Windows 11 & Microsoft & 5 October 2021 & 1293 \\
3 & Android 11 & Google & 8 September 2020 & 1283 \\
4 & Debian Linux 10 & Debian & 6 July 2019 & 1206 \\
5 & Android 12 & Google & 4 October 2021 & 1148 \\
6 & Debian Linux 11 & Debian & 14 August 2021 & 871 \\
7 & Windows Server 2012 & Microsoft & 25 November 2013 & 753 \\
8 & Fedora 37 & Fedora Project & 15 November 2022 & 333 \\
9 & Android 13 & Google & 15 August 2022 & 328 \\
10 & HarmonyOS 2 & Huawei & 2 June 2021 & 323 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Vulnerability count (#) of OS versions that are still maintained by their respective vendors, during 2021-2022, using our OS_VER dataset.  
Figure 7: CVSS scores for the top 10 OS categories using our OS_CAT dataset.  
* For Windows 10, 5046 vulnerability reports indicate CVSS scores between 7 and 8, and 1169 vulnerability reports indicate CVSS scores between 9 and 10. Similarly, for Windows 11, 759 vulnerability reports correspond to CVSS group 7-8, and 192 vulnerability reports correspond to CVSS group 9-10.
* For Android 11 and 12, the majority of CVSS scores lie in groups 5-6 and 7-8. The same pattern is also seen in Android 13; however, the amount is quite lower in number as compared to these two versions.
* Debian Linux 10 and 11 exhibit a similar pattern of CVSS groups, with group 7-8 being the maximum.
* All the top 10 OS versions have the majority of CVSS scores above 5. This indicates the critical potential for exploitation and greater security risks in recent times.
* HarmonyOS 2 has most of its vulnerability reports incorporating 7-8 CVSS scores.
## 3 Results
### Permeable Coefficient  
The permeability coefficient is a comprehensive index of the permeability of a dam and is generally related to the particle size, shape, and porosity of the dam material [41; 42]. As shown in Figure 6, there is a significant decrease in the permeability coefficient of the dams at the beginning of the experiments and it varies with time [43; 44]. The permeability coefficient of 1# dam is always at a low level, indicating that the sediment deposition inside 1# dam is serious. At the end of the experiments, the permeability coefficient of 1-3# dam is 0.0408-0.0715, 0.0505-0.0830, and 0.0540-0.1031 m/s (D = 3-5 mm) and 0.0166-0.0361, 0.0221-0.0419, and 0.0309-0.0462 m/s (D = 1-2 mm), respectively. When sediment-laden flow passes through a cascade permeable dam, it will remain in the dam body for a certain period of time, and the sediment particles carried by flow will result in pore blockage of different degrees in the dams [45]. At the end of the experiments, the permeability coefficient of 1-3# dam with large and small particle size decreases by 37-51%, 21-39%, and 10-28%, and 40-56%, 23-41%, and 10-32%, respectively, indicating a gradual reduction in dam blockage along the flow direction.  
Figure 6: Variation curve of the permeable coefficient of 1-3# dam body.  
For permeable dams with large particle sizes, the variation amplitude in the permeability coefficient of 1-3# dam decreases monotonically with increasing discharge at different sediment concentrations. Under the different discharges, the variation amplitude in the permeability coefficient of 1# dam increases monotonically with increasing sediment concentration, while 2# and 3# dam decrease monotonically. After a period of operation, more and more sediment accumulates inside the dams, and the sediment is compacted under the hydraulic action, causing a porosity decrease in the permeable dam body.  
For the small particle size and at lower discharge, the variation amplitude of the permeability coefficient of 1# dam increases monotonically with increasing sediment concentration. At 1.0 g/L, the variation amplitude of the permeability coefficient of 1-3# dam increases monotonically with increasing discharge overall. Under the different discharge, the decreasing rate of the permeability coefficient of 1# and 2# dam with small particle size increases monotonically with increasing sediment concentration, which is consistent with the study of [PERSON] et al. about the physical clogging of porous media with the variation pattern of permeability [46].