esa-sceva/satcom-chunk-collection huggingface dataset:
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
## 4 Beyond 5G/6G Applications and Machine Learning
### Unsupervised Learning
#### 4.2.5 Security  
AI/ML technologies can also be considered in applications of authentication and access control to detect different kinds of attacks, such as jamming and malware attacks, Denial of Service (DoS) or Distributed DoS (DDoS) attacks. In IoT devices, it is important to address authentication and access control without leaking privacy-sensitive information such as localization. In [86], the authors use non-parametric Bayesian methods for IoT authentication, access control, malware detection, with satisfactory results. The authors in [87] propose a DRL based approach that detects various attacking possibilities through unsupervised learning to address the security issue, with result showing a 6 percent extra gain in accuracy. The authors in [88] propose an unsupervised Gaussian Mixture Model (GMM) approach for Physical Layer security, enhancing the performance of the model, whereas the authors in [89] used an unsupervised approach combining CNN and Stacked Encoders (SAE) for intrusion detection, achieving a precision of 98.44% black.
## 6 Conclusions  
This paper systematically analyzed the communication performance and security performance of the hybrid FSO/RF parallel transmission system with MRC technique, which was of great significance both in engineering practice and theoretical research. The channel model of the hybrid FSO/RF parallel transmission system over the Malaga turbulence and \(\alpha-\mu\) fading was established, and then, the PDF and CDF of the output SNR of the hybrid system were obtained. Finally, the BER, OP, ECC, SOP, and SPSC of the hybrid system were analyzed, and their new-closed mathematical expressions were derived.  
Through the simulation of the system, under the conditions of different modulation modes, different turbulence intensity, different RF fading parameters, and different pointing error parameters, the communication performance of the only FSO and the hybrid system, such as BER, OP, and ECC, were compared and analyzed; under the conditions of different turbulence intensity, different pointing error parameters, different target rates and different  
Figure 10: Under different turbulence conditions, the relationship between the SPSC of the hybrid system with RF and FSO link eavesdropping and the average SNR of the FSO channel.  
Figure 9: Under different \(\xi\) and \(\xi^{E}\) conditions, the relationship between the SOP of the hybrid system with RF and FSO link eavesdropping and the average SNR of the FSO channel.  
average SNR of the eavesdropper, the SOP and SPSC of the hybrid system with the FSO link eavesdropping and the RF link eavesdropping were compared and analyzed. Among the three kinds of subcarrier modulation provided in this paper, the CBPSK modulation had the best BER performance of the only FSO and the hybrid system; with the increase of turbulence intensity, the communication performance and security performance of the hybrid system decreased; the ECC of the hybrid system with MRC technique increased with the increase of \(\xi\); as the beam jitter of the hybrid system decreased (i.e., the value of \(\xi\) increased), the BER, OP, SOP, and SPSC performance of the hybrid system improved, while the security performance of hybrid system with the FSO link eavesdropping decreased with the increase of \(\xi^{E}\); increasing the turbulence intensity, increasing the SNR of the eavesdropper, increasing the target transmission rate, and reducing the beam jitter of the FSO eavesdropper (i.e., the increase of \(\xi^{E}\)) could increase the SOP of the hybrid system, on the contrary, the SPSC of the hybrid system would be decreased.  
Conceptualization, Y.W. and D.K.; methodology, Y.W. and M.J.; software, Y.W. and G.L.; validation, Y.W. and M.J.; formal analysis, Y.W. and M.J.; writing--original draft preparation, Y.W. and D.K.; writing--review and editing, Y.W. and D.K.; supervision, D.K. All authors have read and agreed to the published version of the manuscript. This research was funded by National Science Foundation of China (No. 62001333). Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest.
# The Mechanism of Tendentious Information Dissemination in Cyberspace
## 2 Theoretical Framework and Hypotheses
### Information Dissemination Analysis Based on the Theory of Planned Behavior
#### Impact of Perceived Behavior Control  
Perceived behavior control refers to an individual's cognitive perception of their own behavioral ability. In the context of tendentious information dissemination, PBC reflects the control of the information dissemination node on its own information dissemination ability, consciousness, and behavior. The potential influence of cognitive behavioral control is primarily reflected in two areas: the ability to control the level of communication technology and knowledge, and the individual's ability to think independently and make rational judgments in receiving and disseminating tendentious information. First, in the era of mass media, the application of cutting-edge technologies such as 5G, artificial intelligence, big data, and cloud platforms has resulted in new media making a qualitative leap in the means of communication technology. This has significantly improved the efficiency of information production and the timeliness of the dissemination, distribution, and feedback of all types of information during the process of information dissemination [13]. However, science and technology form a double-edged sword. The application of new technology involves hidden concerns regarding information security, such as information fraud and theft in the process of communication, and has higher ability and cognitive requirements for individuals involved in information dissemination [13]. According to [PERSON] et al., differences in self-control ability in network information dissemination could affect the intensity of social media usage and the pressure perception of information dissemination [14].  
Second, cognitive behavioral control is reflected in an individual's ability to think independently and make rational judgments in the process of receiving and disseminating tendentious information. With the increasing decentralization of various network platforms in new media, the discursive power of mainstream media on Internet platforms shows a trend toward equality. With anyone able to become an information publisher, mainstream discourse faces the risk of being fragmented, buried, and even deconstructed in the dissemination process [15]. Meanwhile, the increasing prominence of opinion leaders in the network has resulted in their discriminatory and biased views influencing public opinion. This presents greater challenges to ordinary users in the analysis and judgment of tendentious information. Advocating personal assumptions with subjective undertones, opinion leaders adeptly mislead the public's understanding of the truth, shape public opinion, and potentially cause social instability.  
Based on the foregoing analysis, we propose the following hypotheses:  
**H4(a):**: _PBC positively promotes SND._
**H4(b):**: _PBC negatively affects ATD._
**H4(c):**: _PBC negatively affects IDI._
**H4(d):**: _PBC negatively affects IDB._
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 2 Technical Background of NB-IoT
### The Internet of Things (IoT) and Industrial Internet of Things (IIoT) Technologies
#### 2.2.3. IIoT Challenges
104 & [5,6,38,6,39,70,71,72,73,74,75,76,77,78,79,79,80,78,79,81,70,79,82,79,83,84,85,86,87,88,89,80,80,81,82,83,84,85,86,87,88,89,80,82,84,86,88,89,80,83,85,87,88,82,85,89,80,84,86,88,89,81,82,84,86,87,88,89,82,85,86,89,80,84,87,88_Security:_ Data generated by IIoT are important assets to the industry management, and while lack of proper security of these data is yet to be provided, this provides the reason why some industries are yet to fully deploy IoT. Although some traditional security mechanisms are in use, these are not sufficient enough to protect the complexity of the IIoT systems. These mechanisms include secure protocols in [75], privacy assurance in [76], and lightweight cryptography, as described in [77].
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 3 The Locust Search (LS-II) Method
### Social Process  
The social process is a mechanism applied to improve the accuracy of the best candidate solutions identified from \(\mathbf{P}^{*}\) during the solitary process. During the social process, it is generated a subset of search agents \(\textbf{B}=\big{\{}\mathbf{b}_{1},\dots,\mathbf{b}_{q}\big{\}}\) that involves the best \(q\) elements of the population \(\mathbf{P}^{*}\). Then, for each candidate solution \(\mathbf{x}_{i}^{*}\in\textbf{B}\), a set \(\textbf{M}^{i}\) of \(a\) new random solutions \(\left(\textbf{M}^{i}=\big{\{}\mathbf{m}_{1}^{i},\dots,\mathbf{m}_{a}^{i} \big{\}}\right)\) are generated inside a limited subspace \(\textbf{C}_{i}\in\textbf{U}\). The subspace \(\mathbf{C}_{i}\) in which it is generated \(a\) new solutions around \(\mathbf{x}_{i}^{*}=\left[x_{i,l}^{*},\dots,x_{i,n}^{*}\right]\) presents the following limits:  
\[C_{i,d}^{lower}=x_{i,d}^{*}-r \tag{7}\]  
\[C_{i,d}^{upper}=x_{i,d}^{*}+r\]  
where \(C_{i,d}^{lower}\) and \(C_{i,d}^{upper}\) corresponds to the upper and lower limits of each subspace \(\mathbf{C}_{i}\) for the \(d\) decision variable (\(d\in[1,\dots,n]\)). On the other hand, the perturbation \(r\) is computed by the following expression:  
\[r=\frac{\sum_{d=1}^{n}(u_{d}-l_{d})}{n}\cdot\beta \tag{8}\]  
where \(l_{d}\) and \(u_{d}\) symbolize the lower and upper limits for the \(d\)-th decision variable, respectively. \(n\) is the total number of dimensions, while \(\beta\) corresponds to a scale element that regulates the limit size of \(\mathbf{C}_{i}\). The value of \(\beta\) is within the interval [0,1].  
Finally, the new position of the search agent \(\mathbf{x}_{i}^{i+1}\) is obtained as the best element of the set integrated by the value of \(\mathbf{x}_{i}^{*}\) and all its \(a\) respective random solutions \(\left\{\mathbf{m}_{1}^{i},\dots,\mathbf{m}_{a}^{i}\right\}\). This task can be modeled by the following formulation:  
\[\mathbf{x}_{i}^{i+1}=best\left(\mathbf{x}_{i}^{*},\mathbf{m}_{1}^{i},\dots, \mathbf{m}_{a}^{i}\right) \tag{9}\]  
The complete search procedure of LS-II is an iterative process that starts with random initialization of the population \(\mathbf{P}(k)\) in the first iteration \((k=0)\). Then, it is applied the solitary operator over the current population \(\mathbf{P}(k)\). As a result, a temporary population \(\mathbf{P}^{*}\) is produced. Finally, the social operator is considered to generate the next population \(\mathbf{P}(k+1)\). This process is repeated until the number of iterations has been reached. Figure 1 shows the flowchart of the complete process.
# Full-Field Vibration Measurements by Using High-Speed Two-Dimensional Digital Image Correlation
## 2 Principle of the Method
### Estimate Both in-Plane and Out-of-Plane Displacements in Vibration Measurements  
In a 2D-DIC measurement, in-plane displacement fields, i.e., a \(U\)-displacement field and \(V\)-displacement field, estimated from the captured speckle patterns by using the 2D-DIC algorithm, as shown in Figure 1, can be represented as [13; 28]  
\[\left\{\begin{aligned} & U_{m}(x_{s},y_{s})=U_{a}(x_{s},y_{s})-\frac{x_{s}}{ \mathcal{V}}\cdot W(x_{s},y_{s})\\ & V_{m}(x_{s},y_{s})=V_{a}(x_{s},y_{s})-\frac{y_{s}}{\mathcal{V} }\cdot W(x_{s},y_{s})\end{aligned}\right., \tag{1}\]  
where \(U_{m}\) and \(V_{m}\) are the displacements measured by a 2D-DIC; \(U_{a}\) and \(V_{a}\) are the actual displacements, which are composed of the specimen deformation, rigid body translation, and rigid body rotation; \(Z\) is the object's distance as shown in Figure 2; \(W\) is the out-of-plane displacement; and \((x_{s},y_{s})\) is the spatial coordinate in the sensor plane. If neglecting the specimen's out-of-plane rotation, the corresponding in-plane strain field can be estimated by calculating the gradient of the measured displacements  
\[\left\{\begin{aligned} &\varepsilon_{xxm}(x_{s},y_{s})=\varepsilon_{xxa}(x_{s},y_{ s})-\frac{1}{\mathcal{V}}\cdot W(x_{s},y_{s})\\ &\varepsilon_{yym}(x_{s},y_{s})=\varepsilon_{yya}(x_{s},y_{s})- \frac{1}{\mathcal{V}}\cdot W(x_{s},y_{s})\end{aligned}\right., \tag{2}\]  
where \(\varepsilon_{xxm}\) and \(\varepsilon_{yym}\) are the measured in-plane strains; and \(\varepsilon_{xxa}\) and \(\varepsilon_{yya}\) are the actual in-plane strains. Since the specimen deformation is minimal in the vibration measurements, the actual strains \(\varepsilon_{xxa}\) and \(\varepsilon_{yya}\) can be neglected, and thus the measured strains \(\varepsilon_{xxm}\) and \(\varepsilon_{yym}\) can be considered entirely denoted by the out-of-plane motion-induced virtual strain, which is  
\[\left\{\begin{aligned} &\varepsilon_{xxm}(x_{s},y_{s})=-\frac{1}{ \mathcal{V}}\cdot W(x_{s},y_{s})\\ &\varepsilon_{yym}(x_{s},y_{s})=-\frac{1}{\mathcal{V}}\cdot W(x_ {s},y_{s})\end{aligned}\right... \tag{3}\]  
Figure 1: Two-dimensional digital image correlation: (**a**) system configuration, (**b**) in-plane displacement field calculation.  
Therefore, when measuring the full-field vibrations using an HS-2D-DIC, the out-of-plane displacement \(W\) can first be estimated from the measured in-plane strains \(\varepsilon_{xcm}\) and \(\varepsilon_{ycm}\) by using Equation (3). Then, the actual in-plane displacements \(U_{a}\) and \(V_{a}\) can be calculated based on the estimated out-of-plane motion \(W\) and the measured in-plane displacements \(U_{m}\) and \(V_{m}\) by using Equation (1).
## 4 Discussion
### Andrographis paniculata and Thepesia Populnea  
_Andrographis paniculata_ Nees. is known as the \"king of bitters\", a member of an Acanthaceae family, and is native to India and Sri Lanka. In Ayurveda, the roots and leaves are used for medicinal purposes [58]. _A. paniculata_ is a key ingredient of many herbal formulations for the treatment of liver disease, hepatitis, diabetes, cancer, etc. [59; 60]. A previous study by [PERSON] et al. [61] demonstrated Andrographide to downregulate EGFR expressed in epidermoid carcinoma (A-431) cells. Andrographolide and Neoandrographolide from _A. paniculata_ showed a protective effect against tert-butyl hydroperoxide intoxication-induced hepatotoxicity in mice [62; 63] and protective activity against galactosamine-induced hepatotoxicity in rats [64]. The crude extract of _A. paniculata_ showed potent inhibitory activity against the HBV surface antigen [65]. Dehydroandrographolide and Andrographolide showed potent inhibitory activity against HBV DNA replication [66]. Neoandrographolide is a potent anti-inflammatory compound that acts via inhibition of iNOS and COX-2 expression through inhibiting p38 MAPK activation [67]. It also suppresses NO production in LPS-activated macrophages [65].  
_Thesepsia populnea_ Soland ex. Correa belongs to the family Malvaccae. It is mainly found in tropical regions and the coastal forests of India. Ayurvedic physicians use bark decoction for the treatment of skin and liver diseases. The bark and flower possess hepatoprotective, antioxidant, anti-inflammatory, memory-improving, and cholesterol-lowering activities, and have been reported for the management of Alzheimer's disease [68]. In the current study, we identified Gossypetin, Herbacetin, and Kaempfer-7-Glucoside flavonoids from _T. populnea_ flower as potent hepatoprotective hits against HBV-induced HCC. This supports earlier reports of these compounds as potent antiviral flavonoids [49; 69; 70]. [PERSON] et al. also demonstrated that methanolic flower extracts of _T. populnea_ have a strong antiviral potency and possess nontoxic properties [71].  
The idea of modulating multiple proteins by a single molecule has triggered researchers' interest in finding the first strike on specific targets. Phytocompounds from _A. paniculata_ and _T. populnea_ have also been shown to have antiviral and hepatoprotective properties in the past. As a result, the current research discovered 11 compounds from _A. paniculata_ and 3 from _T. populnea_ that may have effectiveness against hepatocellular carcinoma caused by the HBV virus, which has further prompted us to investigate the binding affinity and intermolecular interactions of these compounds with active site residues of EGFR, a well-known cancer therapeutic target. The identified small molecules possess druggability and a non-toxic profile, which ensures safety and efficacy.
###### Abstract  
Robust wireless communication networks are a cornerstone of the modern world, allowing data to be transferred quickly and reliably. Establishing such a network at sea, a Maritime Internet of Things (MIoT), would enhance services related to safety and security at sea, environmental protection, and research. However, given the remote and harsh nature of the sea, installing robust wireless communication networks with adequate data rates and low cost is a difficult endeavor. This paper reviews recent MIoT systems developed and deployed by researchers and engineers over the past few years. It contains an analysis of short-range and long-range over-the-air radio-frequency wireless communication protocols and the synergy between these two in the pursuit of an MIoT. The goal of this paper is to serve as a go-to guide for engineers and researchers that need to implement a wireless sensor network at sea. The selection criterion for the papers included in this review was that the implemented wireless communication networks were tested in a real-world scenario.  
wireless communications; Maritime Internet of Things; environmental monitoring; wireless sensor networks +
Footnote †: journal: Medical Informatics
## 2 Data Preparation
### Data processing for drones  
Step 2-1: Noise data processing  
The data of unstable trajectory points existing in the process of collecting data during the initial flight of the drone is cleaned up. The cleaned drone trajectory data are divided into equal interval data according to the interval of the above-mentioned time series data for manned aircraft.  
Step 2-2: Coordinate system transformation  
The drone trajectory data processed in Step 2-1 are presented in the form of longitude and latitude in the World Geodetic System 1984 (WGS-84) Coordinate System. The longitude and latitude in the coordinate system are converted to the vertical and horizontal values in the ECEF Cartesian coordinate system.  
Step 2-3: Dimensional processing and sample segmentation  
The drone trajectory data in Step 2-2 are normalized to eliminate the influence of dimension. The normalized trajectory data are divided into fixed-length time series slices using a sliding time window, and a sliced sample set containing the input and output data in the trajectory prediction model is generated. Each slice sample set includes nine  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Timestamp** & **Longitude** & **Latitude** & **Altitude** & **x-Speed** & **y-Speed** & **z-Speed** & **Heading** & **Pitch** & **Roll Angle** \\
**(ms)** & **(°)** & **(°)** & **(m)** & **(m/s)** & **(m/s)** & **(m/s)** & **Angle (°)** & **Angle (°)** & **(°)** \\ \hline
6600 & 107.828147 & 35.7985822 & 4.4 & \(-\)0.447 & \(-\)0.224 & \(-\)4.921 & 144.5 & 0.6 & 0.1 \\
6700 & 107.828147 & 35.79858204 & 4.6 & \  
variables: latitude, longitude, altitude, speed in the horizontal axis, speed in the vertical axis, vertical speed, heading angle, pitch angle, and roll angle.  
The protection zone of manned aircraft  
Conflicts between drones and manned aircraft mostly occur in the take-off and landing stages of manned aircraft. Considering that the flight height of light rotor drones is usually in the range of 0-300 m, the flight height of 0-300 m (meters) is divided into height intervals of 100 m.  
The flight speed of the manned aircraft in the take-off phase and the landing phase changes rapidly with the altitude. Based on the divided flight altitude intervals, the separation distance for the safe operation of manned aircraft is set according to the average aircraft speed at the corresponding altitude intervals. The protected area of the manned aircraft is a cylinder with the predicted position of the manned aircraft as the center. For the altitude of 0-100 m, the horizontal distance from the boundary to the center is 609.6 m, and the vertical distance is 76.2 m. In the altitude range of 100-200 m and 200-300 m, the separation distance for the safe operation of the drone and manned aircraft in the horizontal and vertical directions is expanded in proportion to the average speed of manned aircraft. Therefore, the protected areas for manned aircraft in the three altitude intervals can be obtained.
## 3 Methodology
### Heavily Weighted Cross-Entropy Focal Loss Function
#### 3.3.3 CEFL Loss Function  
The FL function only reduces the amplitude of losses for the categories with small sample sizes. It does not make a loss allocation of these samples. Therefore, we propose a re-weighted CEFL loss function to control the weights of CE loss and FL loss, which is defined as:  
\[\mathrm{L_{CEFL}}=\sum_{i=1}^{C}-(1-\mathrm{\widehat{y}_{i}})\mathrm{log\, \mathrm{\widehat{y}_{i}}}-\mathrm{y_{i}}(1-\mathrm{\widehat{y}_{i}})^{\gamma }\mathrm{log\,\mathrm{\widehat{y}_{i}}} \tag{7}\]  
In Equation (7), the weights of \(\mathrm{\widehat{y}_{i}}\) and \(1-\mathrm{\widehat{y}_{i}}\) are assigned to the CE loss and FL loss, respectively. When the sample prediction probability is small (\(\mathrm{\widehat{y}_{i}}<0.5\)), the CEFL loss training is closer to the CE loss; conversely, when \(\mathrm{\widehat{y}_{i}}>0.5\), the CEFL loss training is closer to the FL loss, which means that for samples of the majority categories, less loss is allocated by FL loss, and for the other categories, more loss is allocated by CE loss [20]. Therefore, the polarization trend of well-classified and poorly classified samples is conducive to reducing the loss in minority samples and increasing the loss in majority samples, making model training pay more attention to minority samples.  
To enhance the classification performance of the loss function, we introduce a reconstructed weight function for CEFL loss, and the calculation formula is shown in Equation (8). On this basis, a weight function is added to CEFL loss to enhance the allocation performance of CEFL loss. Considering the performance difference caused by the imbalance in sample number, the re-weighted CEFL loss can widen the distance between classes to a certain extent. It increases the effective loss gap of the majority and minority samples. It is calculated as:  
\[\mathrm{L_{CEFL}}=\sum_{i=1}^{C}(-(1-\widehat{y}_{i})\mathrm{log}\,\widehat{y }_{i}-y_{i}(1-\widehat{y}_{i})^{\gamma}\mathrm{log}\,\widehat{y}_{i})\mathrm{ f}(\lambda,\mathrm{N_{i}},\mathrm{N_{max}}) \tag{8}\]  
For imbalanced datasets, the re-weighted CEFL loss can widen the inter-class distance, which increases the effective loss gap between the majority and minority samples.