esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The rapid development of 5G networks and the anticipation of 6G technologies have ushered in an era of highly customizable network environments facilitated by the innovative concept of network slicing. This technology allows the creation of multiple virtual networks on the same physical infrastructure, each optimized for specific service requirements. Despite its numerous benefits, network slicing introduces significant security vulnerabilities that must be addressed to prevent exploitation by increasingly sophisticated cyber threats. This review explores the application of cutting-edge technologies--Artificial Intelligence (AI), specifically Machine Learning (ML), Software-Defined Networking (SDN), and Network Functions Virtualization (NFV)--in crafting advanced security solutions tailored for network slicing. AI's predictive threat detection and automated response capabilities are analysed, highlighting its role in maintaining service integrity and resilience. Meanwhile, SDN and NFV are scrutinized for their ability to enforce flexible security policies and manage network functionalities dynamically, thereby enhancing the adaptability of security measures to meet evolving network demands. Thoroughly examining the current literature and industry practices, this paper identifies critical research gaps in security frameworks and proposes innovative solutions. We advocate for a holistic security strategy integrating ML, SDN, and NFV to enhance data confidentiality, integrity, and availability across network slices. The paper concludes with future research directions to develop robust, scalable, and efficient security frameworks capable of supporting the safe deployment of network slicing in next-generation networks.  
network security; SDN; NFV; ML; network slicing +
Footnote †: journal: _Future Internet_  
0000-0001-5699-2233-1800][PERSON]  
1 Department of Engineering, School of Sciences and Technology, Universidade de Tras-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal; [EMAIL_ADDRESS] (P.F.); [EMAIL_ADDRESS] (P.C.O.)  
2 Optre Solutions, Parque Tecnologic de Vigo, 35315 Vigo, Spain; [EMAIL_ADDRESS] (E.M.C.); [EMAIL_ADDRESS] (N.L.); [EMAIL_ADDRESS] (C.R.S)
## 4 Conclusions  
The contribution of current research is threefold. First, it substantiates location-aware beamforming for efficient maintenance of affordable level of interference in space-division multiple access scenarios, based on the state-of-the art analysis. Second, it presents and describes publicly released open-source simulator for signal-to-interference ratio calculation with two location-aware beamforming links. Third, it substantiates allowable UE density by the criterion of their terrestrial and angular separation. For considered urban micro-cell scenario it demonstrates insignificant SIR dependence on UE terrestrial separation and significant SIR dependence on UE angular separation. In particular, the width of the low SIR region decreases with the antenna array size increase, thus the width of this region can serve as a criterion for the minimum allowable UE angular separation in 5G UDN scenarios, when working with an array of given size. The matlab source code for presentedsimulation model can be downloaded at: [[https://github.com/grihafokin/LAB_link_level](https://github.com/grihafokin/LAB_link_level)]([https://github.com/grihafokin/LAB_link_level](https://github.com/grihafokin/LAB_link_level)) (accessed on 20 October 2022).  
Conceptualization, G.F.; methodology, G.F. and D.V.; software, D.V.; validation, D.V.; formal analysis, G.F.; investigation, G.F. and D.V.; resources, G.F.; data curation, G.F.; writing--original draft preparation, G.F.; writing--review and editing, G.F.; visualization, D.V.; supervision, G.F.; project administration, G.F.; funding acquisition, G.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Science Foundation Grant No. 22-29-00528, [[https://rscf.ru/project/22-29-00528/](https://rscf.ru/project/22-29-00528/)]([https://rscf.ru/project/22-29-00528/](https://rscf.ru/project/22-29-00528/)) (accessed on 8 November 2022).  
Not applicable.  
The authors declare no conflict of interest.
# Three-Dimensional Localization of Underwater Nodes Using Airborne Visible Light Beams
## 3 System Model and Analysis  
This section outlines the fundamental assumptions and offers background information necessary for calculating underwater light intensity for an airborne VLC beam. It also examines and derives the intensity distribution without considering depth information for the underwater node.
## 5 Conclusions  
In this paper, we propose a new approach to malicious certificate detection (Cert GCN). The certificate dataset is processed by using certificate attributes and certificate documents as nodes of the graph and using attribute co-occurrence information as edges between nodes. We construct a heterogeneous graph for the certificate corpus. Cert GCN transforms the certificate detection problem into a node classification problem. A classification model was constructed based on GCN. The experimental results show that Cert GCN outperforms Text GCN and several other machine learning algorithms in terms of accuracy. Using Cert GCN, we address the problem with machine learning algorithms being unable to handle unstructured data and must therefore rely on whether the features extracted by feature engineering can accurately represent the data.  
However, with the emergence of new malicious certificates, there is a growing problem of model degradation. There is still a space for improvement to enhance the accuracy of detection. The certificate dataset is relatively small and has data imbalance problems. Improvement of future work can: (1) address the problem of model degradation in malicious certificate detection by improving the model; (2) expand the dataset of benign and malicious certificates to alleviate the problem of low data size and data imbalance; (3) introduce other detection techniques of graph neural networks to improve the accuracy of malicious certificate detection.  
Figure 10: Accuracy of various algorithms in predicting malicious certificates.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Cert** & **Text** & **SVM** & **KNN** & **MLP** & **LSTM** & **NB** \\ \hline Accuracy & 97.41\% & 93.55\% & 96.53\% & 95.10\% & 93.21\% & 93.72\% & 85.42\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Accuracy of different models for malicious certificate detection.  
**Author Contributions:** Conceptualization, J.L. and N.L.; methodology, J.L. and N.L.; software, J.L.; validation, J.L., Y.C. and W.Y.; formal analysis, J.L.; investigation, J.L. and W.Y.; resources, J.L. and Y.C.; data curation, J.L. and Y.C.; writing--original draft preparation, J.L.; writing--review and editing, J.L., W.Y. and Y.C.; visualization, J.L.; supervision, Y.C.; project administration, N.L.; funding acquisition, N.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded in part by National Social Science Fund of China under Grant 20&ZD93, and as part of the Innovation Environment Construction Special 355 Project of Xinjiang Uygur Autonomous Region under Grant PT1811.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Conclusions  
A field vibration measurement system centered around an accelerometer was constructed, utilizing the tractor's power source for operation. The system is capable of wirelessly transmitting vibration data and effectively meets the field data collection requirements, with a sampling frequency of 2000 Hz. The vibrational frequencies of the seed drill during field operations are concentrated within the range of 0-25 Hz, with amplitudes primarily within 3 mm. Changes in the working speed of the seed drill do not affect the distribution of vibrational power across frequencies and mainly influence the amplitude of  
Figure 8: Sketch map of rapeseed movement in the test rig with different vibration frequencies (**a**) at 8 Hz, (**b**) at 10 Hz, and (**c**) at 12 Hz.  
the vibrational power. Amplitude has less of an impact on the seed metering performance of the double-row hole-type rapeseed seed metering device, whereas vibration frequency has a more significant effect. When the vibration frequency is between 0 and 10 Hz, the seed metering device's discharge rate remains stable between 7.6 and 8.2 g/min, with minimal influence from vibration. The coefficient of variation in seed metering uniformity is between 35.81% and 44.58%, indicating stable and favorable uniformity. When the vibration frequency is between 10 and 24 Hz, the seed metering device's discharge rate decreases rapidly, exhibiting a linear relationship with frequency change and a coefficient of determination \(R^{2}\) of 0.92376. The coefficient of variation in seed metering uniformity also rises quickly and shows a certain linear relationship with frequency change, with a coefficient of determination \(R^{2}\) of 0.87973. Theoretical analysis was conducted on the causes of seed ejection from the population, and we provide a formula to calculate the minimum frequency at which seed ejection from the hole-wheel surface occurs: \(f>\sqrt{\frac{g}{4\pi^{2}A}}\).  
Conceptualization, M.W. and Z.W.; methodology, L.Z.; software, L.Z. and Y.L.; investigation, L.Z., Y.L., M.W. and Z.W.; resources, L.Z.; writing--original draft preparation, L.Z.; writing--review and editing, Z.W.; supervision, M.W.; project administration, Z.W.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Hunan Provincial Intelligent Agricultural Machinery Equipment Innovation Project in 2023 by Hunan Provincial Department of Agriculture and Rural Affairs, grant number 2023-8.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Author [PERSON] was employed by the company Zoomlion Neo Material Technology Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Minimum Audible Angle in 3 rd-Order Ambisonics in Horizontal Plane for Different Ambsonic Decoders
## 4 Selected Ambsonic Decoders
### Experimental Setup  
The focus of this study is on the accuracy of the sound field reproduction on a horizontal plane of the 3 rd-order ambisonic playback system, consisting of 16 Genelec 6010 loudspeakers on a surface of a sphere, as illustrated in Figure 6. The 3 rd order is the highest available resolution in the laboratory used for the evaluation and the most popular order to work with in music production at the moment of writing [28]. In the three-dimensional space, the operational system is a spherical coordinate system with a radius of \(r=1.5\,\)m, and azimuth and elevation angles, as specified in Table 1. In the AllRAD case, two additional imaginary loudspeakers were included for correct decoder calculations (yellow dots in Figure 6).  
Figure 5: Distribution of the objective measures for the combination of the investigated playback system and the AllRAD. Red dots mark the loudspeaker positions. Please note that the scales for \(\mathbf{r}_{V}\) angular error and energy are not equal to better visualise the differences in distributions.  
Figure 6: (**Left**) Visualisation of the loudspeaker layout in the AGH UST Laboratory of Auralisation rendered with the IEM AllRAD plugin. Yellow dots represent the imaginary loudspeakers. (**Right**) Photograph of the loudspeaker layout.  
The primary objective of this experimental series was to investigate the MAA values for three ambisonic decoders: the EPAD, AllRAD, and SAD. All listening tests were conducted at the Laboratory of Auralisation, AGH University of Science and Technology (UST), which is an acoustically treated room of dimensions 3.9 m \(\times\) 6.7 m \(\times\) 2.8 m (length, width, and height, respectively), with an average reverberation time of 0.15 s.
## 3 Results and Discussion  
### Proximate Composition Analysis  
Figure 1a,b summarise the proximate composition of both WFSP and WFS. The results of Figure 1a show that WFSP presented the maximum moisture of 5.57 \(\pm\) 0.22, ash of 1.79 \(\pm\) 0.28, crude fat of 0.86 \(\pm\) 0.41, crude protein of 5.56 \(\pm\) 0.99, and crude fibre of 2.06 \(\pm\) 0.17 and nitrogen-free extract (NFE) of 83.17 \(\pm\) 0.98 g/100 g DW, while WFP exhibited the highest moisture of 8.50 \(\pm\) 1.01, ash of 1.53 \(\pm\) 0.04, crude fat of 1.62 \(\pm\) 0.02, crude protein of 2.06 \(\pm\) 0.06, and crude fibre of 1.49 \(\pm\) 0.02 and nitrogen-free extract (NFE) of 80.71 \(\pm\) 0.91 g/100 g DW. Figure 1b reveals that WFSP and WFP exhibited the total energy of 362.73 \(\pm\) 2.49 and 337.21 \(\pm\) 2.15 kcal/100 g DW, respectively. Overall, it was observed that moisture and crude fat content were higher in WFP, while ash, crude protein, crude fibre, NFE, and total energy were higher in WFSP. The results were significant (\(p\) < 0.05) based on the Tukey HSD test. Our results are in co-relation with the findings of previously published data. [PERSON] et al. (2018) stated that the BARI SP4 Bangladesh variety of orange-fleshed sweet potato (OFSP) showed the highest moisture, protein, fat, crude fibre, ash, and carbohydrate content of about 70.95 \(\pm\) 0.70, 5.83 \(\pm\) 0.30, 0.30 \(\pm\) 0.25, 0.54 \(\pm\) 0.14, 1.29 \(\pm\) 0.06 and 21.10 \(\pm\) 0.93 g/100 g of FW, respectively. Similarly, [PERSON] et al. (2018) demonstrated that UMUSPO1 and UMUSPO2 varieties of Nigerian, orange-fleshed sweet potato showed the maximum moisture content of 85.12 \(\pm\) 0.08 and 84.71 \(\pm\) 0.05 g/ 100 g DW, ash content of 15.49 \(\pm\) 0.06 and 12.71 \(\pm\) 0.01 g/100 g DW, the crude fibre content of 4.50 \(\pm\) 0.02 and 4.90 \(\pm\) 0.03 g/100 g DW, the protein content of 30.07 \(\pm\) 0.09 and 25.00 \(\pm\) 0.08 g/100 g DW, fat content of 1.44 \(\pm\) 0.06 and 1.74 \(\pm\) 0.04 g/100 g DW, and total energy value of 327.24 \(\pm\) 0.05 and 338.26 \(\pm\) 0.07 kcal/100 g, respectively. [PERSON] and co-workers (2018) reported that Lady Rosetta and Lady Claire cultivars of potato peel powder showed the highest crude fat content of 2.09 \(\pm\) 0.01 and 1.27 \(\pm\) 0.38%, crude protein content of 11.17 \(\pm\) 0.03 and 12.44 \(\pm\) 0.09%, ash content of 7.24 \(\pm\) 0.02 and 4.83 \(\pm\) 0.13%, moisture of 6.98 \(\pm\) 0.05 and 77.38 \(\pm\) 0.65%, and total carbohydrate of 72.53 \(\pm\) 0.08 and 77.38 \(\pm\) 0.65%, respectively.
# High-Resolution Through-the-Wall Radar Imaging with Exploitation of Target Structure
## 2 Signal Model
### Vectorized Measurements  
For brief notation, the measurements in (1) can be transformed into a single column vector \(\mathbf{y}\) by stacking all \(MN\) received echoes, i.e., \(\mathbf{y}=[y(1,1),\cdots,y(1,N),\cdots y(M,N)]^{T}\in\mathcal{C}^{MN}\). Therefore, (1) can be rewritten as  
\[\mathbf{y}=\mathbf{\Phi}\mathbf{w}, \tag{2}\]  
where \(\mathbf{w}\in\mathcal{C}^{N_{x}N_{y}}\) denotes the target reflectivity vector and \(\mathbf{\Phi}\in\mathcal{C}^{MN\times N_{y}N_{y}}\) denotes the sensing matrix. The \((i,l)\)th element of the sensing matrix can be represented as  
\[[\mathbf{\Phi}]_{il}=\exp(-j2\pi f_{m}\tau_{ln}), \tag{3}\]  
where \(m=\lceil i/M\rceil\) denotes the modulus after \(i\) over \(M\), while \(n=\lfloor i/N\rfloor\) denotes the remainder after \(i\) over \(N\) for \(i\in\{1,\cdots,MN\}\).
# Digital Twin Framework for Road Infrastructure Management
## 4 Digital Twin Framework for Road Infrastructure Management
### Proposed Digital Twin Framework for Road Infrastructure Management
#### 4.3.2 Data Processing, Integration, and Storage Layer  
The data processing, integration, and storage layer handles the optimal processing of data by performing data conversion and integration, data extraction and fusion, preliminary analytics, and storage of multisource data received from the data acquisition layer within a cloud-based database environment.  
The acquired raw data are processed using various techniques, such as noise removal, anomaly detection, data cleaning, and normalization. ML techniques can be employed to assess data quality and accuracy, ensuring that the acquired data are reliable and free from anomalies and faults [37]. The robustness of algorithms used in these techniques is crucial for keeping smooth performance and functionality, irrespective of data volume or unexpected changes, such as incorrect inputs and data errors. Additionally, employing other relevant intelligent algorithms to analyze and explore data features is crucial in the whole framework when implemented.  
Furthermore, data collected from real-time sensor-based monitoring, manual inspections, construction, and O&M activities--often stored in heterogeneous formats such as databases, documents, files, spreadsheets, and images [44]--must be transformed into standardized formats or data structures (e.g., ontologies) to enhance modeling and analytics and support further data manipulation.  
The semantic-ontology-based approach is the most widely adopted method for modeling and storing asset data. These data structures are typically developed using the Web Ontology Language, which supports data description, integration, and extraction, thereby facilitating subsequent modeling, reasoning, and analysis. Moreover, semantic fusion facilitates the integration of heterogeneous data sources, yielding more comprehensive and accurate information to support decision-making during the O&M phase [44].  
Figure 7: Components of the data acquisition layer.
## 5 Prediction Models
### Fuzzy Logic (FL)  
Fuzzy logic (FL), as introduced in Section 2, provides a framework for reasoning with imprecise or uncertain information. Unlike traditional Boolean logic, FL allows for degrees of membership, enabling the representation of linguistic variables and the creation of rule-based systems that mimic human decision-making. In this study, we leveraged FL to predict the compressive strength of UHPC based on the mix design parameters. The FL model used four input variables to predict the compressive strength values (7 and 28 days) from experimental tests. Developed with the FL toolbox in MATLAB vR2024a, it utilized a rule-based if-then form for prediction. Since there are no mathematical equations in the FL model, the rule based on the elements expressed in the if-then form was used for prediction. [PERSON]'s inference system uses fuzzy sets as the result of the law, and the output of each law is nonlinear and fuzzy. The inputs for the system are categorized into four distinct groups: cement, silica fume, water, and NSF. These inputs were fuzzified into four gradations: low (L), medium (M), high (H), and very high (VH). The outputs are divided into two groups: 7-day compressive strength and 28-day compressive strength. Table 8 shows the details of the inputs and outputs of the FL model. Triangular membership functions, chosen for their simplicity, were constructed based on experience. The triangular shape can be considered the easiest shape to implement compared to others [22]. Figure 6 shows a schematic view of the FL model for this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Input** & **Fuzzy MF** & **Range** \\ \hline \multirow{4}{*}{Cement (kg/m\({}^{3}\))} & “L” (Low) & 635–732 \\ \cline{2-3}  & “M” (Medium) & 686–824 \\ \cline{2-3}  & “H” (High) & 778–890 \\ \hline \multirow{4}{*}{Silica fume (kg/m\({}^{3}\))} & “L” (Low) & 140–174 \\ \cline{2-3}  & “M” (Medium) & 159–212 \\ \cline{2-3}  & “H” (High) & 193–225 \\ \hline \multirow{4}{*}{Water (kg/m\({}^{3}\))} & “L” (Low) & 140–174 \\ \cline{2-3}  & “M” (Medium) & 159–212 \\ \cline{2-3}  & “H” (High) & 193–225 \\ \hline \multirow{4}{*}{NSF (kg/m\({}^{3}\))} & “L” (Low) & 4.5–7.1 \\ \cline{2-3}  & “M” (Medium) & 6.6–8.4 \\ \cline{2-3}  & “H” (High) & 7.8–9.5 \\ \hline \multirow{2}{*}{**Output**} & **Fuzzy MF** & **Range** \\ \hline \multirow{4}{*}{Compressive strength (MPa)—7 Days} & “L” (Low) & 45–60 \\ \cline{2-3}  & “M” (Medium) & 57–68 \\ \cline{2-3}  & “H” (High) & 65–77 \\ \cline{2-3}  & “V.H” (Very High) & 74–90 \\ \hline \multirow{4}{*}{Compressive strength (MPa)—28 Days} & “L” (Low) & 110–123 \\ \cline{2-3}  & “M” (Medium) & 120–132 \\ \cline{1-1} \cline{2-3}  & “H” (High) & 128–141 \\ \cline{1-1} \cline{2-3}  & “V.H” (Very High) & 136–150 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Range of input and output values and corresponding linguistic gradation.
# An Effective Blockchain-Based Defense Model for Organizations against Vishing Attacks
## 3 Preventing Social Engineering Attacks Using Security Principle
### How Security Principles prevents Social Engineering Attacks
#### 3.3.1 Split Knowledge Effectiveness Analyis  
The proposed model is designated for corporations and organizations in a working environment. It is not to protect individuals from SE attacks outside their job.  
As we mentioned in Section 4.1, data within the organization are supposed to be classified according to the organization sector (governmental or private). So, the [recipient], who will be the first person in contact with a possible SE hacker, knows only public information. He or she does not have secret information to disclose. Smishing, for instance, uses SMS to contact the victim. If the victim (recipient) does not know the private information since the knowledge is split and classified, he or she will redirect the request through the blockchain network to another background verifier. Mostly, the second verifier will discover that the SMS is a kind of smishing and notify the recipient. The goal is to deal with the problem of single-point vulnerability presented by the recipient, but through split knowledge, the problem will be handled by more than one person who does not witness the psychological circumstances. Therefore, the secret information will not be disclosed since any transaction must be endorsed by several verifiers determined by the organization. Even, if one verifier is tricked, the transaction will be accepted only if all the verifiers endorse it. In addition, it will not be a summative gathering of the information by the hacker since the transaction atomic can only be accepted after all defined organization peers endorse it.  
Similarly, split knowledge will protect corporations against all SE attacks classified on its category.