esa-sceva/satcom-chunk-collection huggingface dataset:
# Vulnerability Management Models Using a Common Vulnerability Scoring System
## 5 Experimental Design
### Description Environment A  
In environment A, there were 23 servers, which were responsible for providing services such as www for the internal requirements of the company. These servers contained 36 vulnerabilities that were detected by the Nessus scanner. Additionally, the scanner reported 32 vulnerabilities of the info type. The scan was performed with no authorization, i.e., there was no logging into the scanned assets. Table 1 shows the number of operating systems recognized in environment A as detected by the Nessus scanner, which was able to detect only 13 operating systems. Figure 4 presents a vulnerability severity histogram according to CVSS 2.0 and 3.x after detection with the Nessus scanner. Table 2 shows the number of vulnerabilities detected by the Nessus scanner, and both standards are distinguished. The difference between the numbers indicated by the Nessus report and the numbers of vulnerabilities calculated according to the base score stems from the fact that Nessus groups some CVEs into one category. For example, the reported vulnerability \"134944-PHP 7.3.x < 7.3.16 Multiple Vulnerabilities\" had three CVEs assigned--namely, CVE-2020-7064, CVE-2020-7065, and CVE-2020-7066. The difference between the numbers of base CVSS 2.0 and base CVSS 3.x vulnerabilities was due to the lack of both assessments for some vulnerabilities [37; 38; 39]. Finally, it is noted that the scan was performed on 2 November 2021 and lasted 3 h.  
Considering the information received from the administrators, the developed assets contained the CIA flag configurations with the distribution shown in Table 3. Low, medium, and high CIA flags were set for only five resources, which corresponded to 21.74% of all resources in the tested environment. Thus, the CIA flag was \"Not Defined\" for 78.26% of the resources in environment A. The availability requirement flag was high for three network elements and medium for two network elements, corresponding, respectively, to 13.04% and 8.7%, while the integrity requirement flag was high for one network element and medium for one network element, which corresponded to 4.35%.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Operating System** & **Quantity** \\ \hline
1. & Other (the scanner was unable to recognize the system) & 10 \\
2. & Linux Kernel 2.6 & 7 \\
3. & CentOS Linux 7 Linux Kernel 3.10 & 3 \\
4. & Linux Kernel 4.15 on Ubuntu 18.04 (bionic) & 2 \\
5. & Darwin & 1 \\ \hline \multicolumn{3}{c}{In total} & 23 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Numbers and types of operating systems detected in environment A.  
Figure 4: Vulnerability severity histogram divided into the base CVSS 2.0 and 3.x scores for environment A.
# Analytical Solutions for the Propagation of UltraShort and UltraSharp Pulses in Dispersive Media
## 4 Gaussian Pulse
### Boosted Gaussian  
When the initial pulse is a boosted Gaussian,  
\[A(t,z=0)=A_{0}\exp\Bigl{(}-t^{2}/\theta^{2}+i\omega_{0}t\Bigr{)} \tag{15}\]  
then using Relation (6), the final (after a distance \(z\)) profile is  
\[A(t,z)=\frac{A_{0}\theta}{\sqrt{\theta^{2}-2i\beta_{2}z}}\exp\left(i\frac{ \beta_{2}}{2}\omega_{0}^{2}z+i\omega_{0}t\right)\exp\left(-\frac{(t+\beta_{2} \omega_{0}z)^{2}}{\theta^{2}-2i\beta_{2}z}\right) \tag{16}\]  
which means that beside the pulse spreading, the pulse's peak propagates with reciprocal \"velocity\" \(\beta_{2}\omega_{0}\). The larger the spectral shift \(\omega_{0}\), the larger the temporal shift of the pulse. The pulse's profile and its intensity's dynamics are presented in Figures 3 and 4, respectively.  
Figure 3: The real (upper panel) and imaginary (lower panel) components of the Gaussian pulse (Equation (16)). The dashed curve represents the initial profile (for \(\zeta=\beta_{2}z/\theta^{2}=0\)), while the solid curve represents its final shape (for \(\zeta=\beta_{2}z/\theta^{2}=2\)). In this example, the carrier frequency is \(\omega_{0}=-4/\theta\).  
Figure 2: A false-color presentation of the pulse’s intensity \(|A(t,z)|^{2}\)(Equation (14)) as a function of the normalized time \(\tau\equiv t/\theta\) and the normalized distance \(\zeta\equiv\beta_{2}z/\theta^{2}\).
## 2 Channel Model and Main Results
### Symbol Notations and Channel Model
In our model, the wiretap channel is assumed to be degraded from the main channel, _i.e._, \(X\to Y\to Z\) form a Markov chain. Then, we give the definition of \"achievable\" and \"sum secrecy capacity\" as follows. **Definition 1**: _A rate triple \((R_{1},R_{2},R_{e})\) is said to be achievable for the model in Figure 2 if there exists a channel encoder-decoder, such that:_  
\[\lim\limits_{N\rightarrow\infty}\frac{log\|\mathcal{M}_{1}\|}{N} =R_{1} \tag{1}\] \[\lim\limits_{N\rightarrow\infty}\frac{log\|\mathcal{M}_{2}\|}{N} =R_{2}\] (2) \[\lim\limits_{N\rightarrow\infty}\frac{H(M_{1},M_{2}|Z^{N})}{N} \geq R_{e}\] (3) \[P_{e1}\leq\epsilon,P_{e2}\leq\epsilon \tag{4}\]  
_where \(\epsilon\) is an arbitrary small positive real number, \((R_{1},R_{2})\) are the rates of the message \((M_{1},M_{2})\) and \(R_{e}\) is the rate of equivocation. The capacity-equivocation region is defined as the convex closure of all achievable rate triples \((R_{1},R_{2},R_{e})\)._  
**Definition 2**: _The sum secrecy capacity is the maximum rate at which the confidential message can be sent to the receiver in perfect secrecy. The sum secrecy capacity:_  
\[C_{s}=\max\limits_{(R_{1},R_{2},R_{e}=R_{1}+R_{2})\in\mathcal{R}}(R_{1}+R_{2}) \tag{5}\]  
_where \(\mathcal{R}\) is the capacity-equivocation region._  
Based on the definition in Equation (5), the sum secrecy capacity for the model in Figure 2 with noncausal action-dependent states is \(C_{sn}=\max\limits_{(R_{1},R_{2},R_{e}=R_{1}+R_{2})\in\mathcal{R}_{n}}\left(R_ {1}+R_{2}\right)\), where \(\mathcal{R}_{n}\) is the capacity-equivocation region of the noncausal case. Similarly, we can define the sum secrecy capacity of the causal case, \(C_{sc}\).
## 3 Results and Discussion
### Physical Asset Management  
Regarding the behaviour of the mean failure risk presented in Figure 5, this section confirms two aspects. First, it confirms that the algorithm has no regard for risk, showing an unclear behaviour or tendency towards minimising risk, which is the reason behind the difference of risk between the 7% and 8% scenarios, wherein there is an important increase in risk (8% tolerance) resulting from an initial decrease in risk (7% tolerance). Second, it confirms the impact on risk of the activities with higher periodicities, showing an important variation of almost 5% of failure risk for the strategy with 9% tolerance.
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## 8 Comparison between LoRaWAN and Slotted LoRaWAN  
A comparison between the standard LoRaWAN and the S-LoRaWAN is presented in this chapter. The same parameters of the radio configurations and the traffic injected are used for both protocols. In this test the channel traffic and the corresponding throughput are calculated considering only the payload time (\(T\)) as traffic generated, the following conditions applied in Equation (6). Moreover, with this approach the tolerance interval (\(T_{\text{b}}\)) inserted into the slot time is considered into the protocol overhead. The channel throughput in Table 3 describes the effective payload transmitted on the channel; this allows a more accurate comparison without considering the protocol overhead.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Test** & **Test 1** & **Test 2** & **Test 3** \\ \hline Test duration [s] & 10273 & 6977 & 3524 \\ Packets sent & 2708 & 3285 & 2894 \\ N of end nodes & 10 & 18 & 24 \\ TX period [s] & 15 & 15 & 13 \\ TX success rate [\%] & 69 & 55 & 43 \\ Traffic generated (G) & 0.264 & 0.471 & 0.773 \\ Channel throughput (S) & 0.184 & 0.275 & 0.332 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the results obtained from the tests with Slotted LoRaWAN.  
Figure 12: Summary of the results obtained from the tests with Slotted LoRaWAN.  
Referring to the traffic analysis, Table 3 shows the comparison between the S-LoRaWAN performed with the S-ALOHA MAC (Figure 13 point 1) and the LoRaWAN standard protocol (Figure 13 point 2). Interestingly, the generated traffic resulting from the two protocols is slightly different, this is partly due to the statistical evolution of transmissions; in fact, in the case of the LoRaWAN standard protocol, the overlapping of the frames is much more probable, with consequent recourse to the backoff algorithm and an average reduction in the number of packets transmitted. In the proposed results, the measured throughput of S-LoRaWAN and standard LoRaWAN is respectively 15% and 2.6%. Finally, the measured improvement is 5.8x. This result is higher than the theoretical 2x between S-ALOHA and P-ALOHA; indeed, it has to be considered that the maximum throughput is not placed at the same \(G\) (Figure 13), and with the parameters used in this test, the P-ALOHA channel is heavily crowded with a TX success rate of 7%.
## 7 Concluding Remarks  
The results of the investigation focused on using the ANN method to conduct the evaluation of the optimum gap size between adjacent buildings so as to prevent their collisions during seismic  
Figure 7: Examples of the results of the parametric analysis under various earthquakes scaled to different PGA values.  
excitations have been shown in the paper. In the study, two adjacent structures have been subjected to different earthquake records. Six lumped mass models of buildings with a different number of stories (from one to six) have been considered. The earthquake characteristics and the parameters of buildings have been defined as inputs in the ANN analysis.  
Using the ANN, the required seismic gap preventing bounding between adjacent structures has been firstly determined for specified structural arrangements and earthquake records. The obtained values have been extensively verified. The results of the verification analysis show that the seismic gaps are large enough to prevent structural collisions, and they are just appropriate for all considered structural arrangements and seismic excitations which confirms the accuracy of the method applied.  
In order to validate the method for other structural parameters, the study has been further extended for buildings with different values of height, mass, and stiffness of each story for both buildings. The results of the validation analysis indicate that the calculated seismic gaps using the ANN method are large enough to prevent structural collisions, and they are just appropriate for all other structural arrangements, seismic excitations, and structural parameters which validates the accuracy of the method applied for different cases and conditions.  
Finally, the parametric analysis has been conducted for various earthquakes scaled to different values of the PGA. The results of this analysis show that the increase in the PGA of earthquake records leads to a substantial, nearly uniform, increase in the required separation gap between structures, which mainly results from larger lateral structural displacements.  
The above conclusions clearly indicate that the ANN method can be successfully used to determine the minimal distance between two adjacent buildings preventing their collisions during different seismic excitations.  
Conceptualization, S.M.K., H.N. and S.M.N.R.; methodology, S.M.K., H.N., S.M.N.R., R.C.B., B.S. and R.J.; software, S.M.K. and S.M.N.R.; validation, S.M.K., H.N., S.M.N.R., R.C.B., B.S. and R.J.; formal analysis, S.M.K. and S.M.N.R.; investigation, S.M.K. and S.M.N.R.; writing--original draft preparation, S.M.K, H.N., S.M.N.R. and R.C.B.; writing--review and editing, B.S. and R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
In this paper, an accurate error compensation method based on geometric parameter correction and process optimization is proposed for the problem of coupling error in a heavy-load longitudinal and transversal swing table (HLTST) under space constraints, which makes it difficult to control the position efficiently and accurately. The key geometric parameters of pitch and roll layers are determined according to the machining process and assembly relationship, and the kinematic model is modified to effectively reduce the impact of contour error on the system's accuracy. A coupling error model is established and its transmission mechanism is analyzed to develop a positioning error compensation strategy. Numerical simulation is employed to examine the distribution law, sensitivity, and volatility of independent error and coupling error. This aids in optimizing the design of the table's machining process by balancing machining accuracy and economy. After the identification of the error parameters, the error compensation model is verified using the uniform design experimentation. The experimental results demonstrate 96.94% and 65.63% reductions in absolute average errors for the pitch and roll angles, respectively, especially when the maximum positioning error under the maximum load condition is controlled within \(\pm\)5%, which significantly enhances motion accuracy and robustness under complex working conditions. This provides theoretical support and practical guidance for real-world engineering applications.  
spatial constrained; heavy load; double swing table; coupling error; error compensation Article  
Constrained Space Coordinate Precision of a Heavy-Load Longitudinal and Transversal Swing Table. _Appl. Sci._**2025**, 15, 4693.  
[[https://doi.org/10.3390/appl5094693](https://doi.org/10.3390/appl5094693)]([https://doi.org/10.3390/appl5094693](https://doi.org/10.3390/appl5094693))
# Augmented Reality's Impact in Industry--A Scoping Review
## 4 Discussion
### RQ4--What Type of Environment-Detection System Do the Developed Applications Use?  
The choice of the detection system depends on the specific objectives of the application, aiming to provide the most optimal solution [86]. More than half of the analyzed studies employed either marker recognition or surface-detection methods. This finding helps establish these methods as standard practices in the development of AR applications for Industry X.0.  
The marker-based system is the most commonly used and one of the simplest to implement [60]. It requires only an RGB camera and a recognizable image to establish the relationship between the real and virtual worlds [87]. Surface detection also facilitates this relationship but relies on the quality of the camera and its depth sensors for accurate results [87]. Another frequently used system is Object Recognition, which pairs a real object with its 3D counterpart to implement the AR system. This allows for a 360-degree view of the 3D object within the real environment, providing multiple perspectives of the detected surroundings [77]. Environment detection has been utilized for confined indoor spaces, enabling the AR system to scale to an entire room and provide relevant information to the user [88]. The study employing face tracking aimed to create filters that place virtual elements on the user's face, though it did not explore industrial applications such as emotion detection [89] or gesture-based interaction [90].  
Also, advanced detection technologies not only support seamless human-machine collaboration but also enable tailored solutions that prioritize user engagement, comfort, and ethical practices [91]. These systems highlight the evolving role of AR as a bridge between operational excellence and the human-focused priorities of modern industrial ecosystems [92].  
The choice of execution hardware is not related to the recognition system used. Any execution hardware can be compatible with any of these recognition systems, though the quality of performance varies depending on the specific device [93]. There is no noticeable trend favoring one type of execution hardware over another within the recognition systems reviewed in this study.
## 5 Conclusions
* **Generalizability:** Investigation with more EEG datasets is required to establish generalizability of the workflow. **Author Contributions:** Conceptualisation, V.P. ; methodology, software, validation, V.P. and R.N.M. ; visualisation, formal analysis, investigation, V.P., R.N.M. and P.K.M. ; writing--original draft preparation, V.P. and R.N.M. ; writing--review and editing, V.P., R.N.M., and P.K.M. ; supervision, P.K.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** EEG data related to cognitive states and mental load were used to investigate the method. The dataset is available at [[https://github.com/BCI-HCI-IITKGP/Cognitive-Mental-workload-EEG-Data](https://github.com/BCI-HCI-IITKGP/Cognitive-Mental-workload-EEG-Data)]([https://github.com/BCI-HCI-IITKGP/Cognitive-Mental-workload-EEG-Data](https://github.com/BCI-HCI-IITKGP/Cognitive-Mental-workload-EEG-Data)) accessed on 6 February 2025. The CHB-MIT Seizure EEG dataset is available at [[https://archive.physionet.org/physiobank/database/chbmit/](https://archive.physionet.org/physiobank/database/chbmit/)]([https://archive.physionet.org/physiobank/database/chbmit/](https://archive.physionet.org/physiobank/database/chbmit/)) accessed on 6 February 2025. **Acknowledgments:** The authors acknowledge the anonymous reviewers whose constructive comments helped identify the gaps in presentation and descriptions in the original manuscript, leading to this final version. The authors also acknowledge the use of _ChatGPT_ and _Grammarily_ tools in refining the clarity and coherence of certain sections of the manuscript. The final content was reviewed and edited by the authors to ensure accuracy and alignment with the study's objectives. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} MDPI & Multidisciplinary Digital Publishing Institute \\ DOAJ & Directory of open-access journals \\ GKKE & Graph Koopman kernel embedding \\ EEG & Electroencephalography \\ SNR & Signal-to-noise ratio \\ DMD & Dynamic Mode Decomposition \\ EMD & Empirical Mode Decomposition \\ LSTM & Long Short-Term Memory \\ MLP & Multilayer Perceptron \\ SVD & Singular Value Decomposition \\ CSL & Cost-sensitive learning \\ CNN & Convolutional Neural Networks \\ ROC & Receiver-operating characteristic curve \\ kernel CCA & Kernel canonical correlation analysis \\ mPLV & Mean-phase locking value \\ WL & Weisfeiler-Lehman Kernel \\ SD & Spectral decomposition kernel \\ RW & Random Walk kernel \\ DT & Decision Tree Classifier \\ RF & Random Forest Classifier \\ SVC & Support Vector Classifier \\ SVM & Support Vector Machine \\ AUC & Area under the curve \\ fMRI & Functional magnetic resonance imaging \\ SSE & Sum of squared error \\ BCI & Brain-computer interface \\ EDF & European Data Format \\ \end{tabular}
## 3 Materials and Methods
### Dataset Cleaning, Preprocessing, and Splitting
#### 3.1.1 Dataset Preprocessing  
To ensure the dataset was suitable for predictive modeling and fine-tuning, we employed a structured, multi-step preprocessing approach. Each phase of the preparation process was designed to maximize the data's relevance and effectiveness for our classification task.  
1. Column Removal: We removed the \"Unnamed: 0\" column, which was deemed irrelevant to the analysis and redundant.
2. Empty Row Removal: We performed a thorough check for missing values across the \"Title\", \"Text\", and \"Label\" columns. Any rows containing missing values were removed to maintain the integrity of the data.
3. Column Merging: The \"Title\" and \"Text\" columns were combined into a new consolidated column, named \"Text\", to provide the model with a unified input that included both the article headline and content.
4. Label Standardization: The \"label\" column was standardized and renamed as \"Label\" for consistency across the dataset and to align with our modeling pipeline.
5. Text Length Restriction: We set a maximum length of 2560 characters for the \"Text\" column. This length was chosen to balance sufficient contextual information for training (particularly for CNN and BERT models) while maintaining memory and processing efficiency. After this truncation, the dataset contained 7573 entries labeled as 1 (real) and 7313 entries labeled as 0 (fake).
6. Data Standardization: Following the truncation, we standardized the dataset to ensure consistency and facilitate model convergence. After this step, we re-checked for any empty rows that might have been introduced and removed them, leaving a final dataset of 7568 real (1) and 7313 fake (0).
7. Balanced Sampling: To address potential class imbalances, we applied stratified sampling to select 5000 entries, with 2500 entries from each class (fake and real). This step ensured that the dataset was balanced, which is essential for training effective classification models.
8. ID Addition: A unique identifier (ID) was assigned to each entry to assist with tracking and error handling during the modeling process.
## 3 Results and Discussion
### Fruit Quality Parameter Analysis
#### 3.3.2 The Firmness Change of Peaches and Nectarines  
The firmness of nectarines and peaches fruit largely decreased during the experimental chain. There were large differences in the fruit softening rates between peaches and nectarines, as well between fruits grown under red net as compared to control fruit. Moreover, the firmness decline rate for nectarines from control trees and nectarines grown under red net were 31.2% and 42.6%, respectively, which is larger than the firmness decline rate of peaches grown under red net (22.07%) and peaches from control (12.71%) trees. The firmness decline rate of fruit grown under red net is higher than in control fruits. This is especially pronounced in nectarines whose softening rate for fruits grown under the red nets was almost double the softening rate of control fruits. In the fresh peach industry, mechanical properties of the fruit flesh and particularly the softening speed are the most limiting factors of fruit quality along the commercial chain [3]. The results of this study show that fruit softening is not only cultivar-dependent [3; 41], but is also significantly affected by the red netting since fruits grown under red net softened more rapidly than control fruit. Red netting increases firmness in nectarines [42], which is contrary to the results of our study since no significant differences were found in fruit firmness as affected by red netting. This might be a consequence of some other environmental preharvest factors or cultivar differences. Increased CO\({}_{2}\) concentration during cold chain (Figure 6) was always below 5% and could significantly decrease softening [43].