esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Evaluating the Performance of the Different Classifiers  
To evaluate the performance of the classifiers, the receiver operating characteristic (ROC) and the area under the curve [31] as well as the criteria for the confusion matrix were used (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Classifier**} & **Num. of** & **Number of** & **Task-Propagation** & **Back-Propagation** & **Back-Propagation** \\  & **Layers** & **Neurons** & **Neurons** & **Network Training** & **Weight/Bias** \\  & & & & **Function** & **Learning Function** \\ \hline \multirow{2}{*}{ANN-BA} & \multirow{2}{*}{3} & First layer: 9 & First layer: radbas & \multirow{2}{*}{learnlv1} & \multirow{2}{*}{traingda} \\  & & Second layer: 17 & Second layer: radbas & & \\  & & Third layer: 13 & Third layer: radbas & & \\ \hline \multirow{2}{*}{ANN-BBO} & \multirow{2}{*}{3} & First layer: 5 & First layer: tansig & \multirow{2}{*}{learnk} & \multirow{2}{*}{trainoss} \\  & & Second layer: 14 & Second layer: radbas & & \\  & & Third layer: 18 & Third layer: satlin & & \\ \hline \multirow{2}{*}{ANN-FA} & \multirow{2}{*}{3} & First layer: 7 & First layer: logsig & \multirow{2}{*}{learnhd} & \multirow{2}{*}{trains} \\  & & Second layer: 12 & Second layer: satlin & & \\ \end{tabular}
\end{table}
Table 2: The best structure of hidden layers of the artificial neural network (ANN) adjusted by bees algorithm (BA), biogeography-based optimization (BBO) and firefly algorithm (FA).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Description**} & **Formula** \\ \hline Percent of the correct samples that have been & \(\text{Recall}=\frac{TP}{TP+FN}\times 100\) \\ correctly identified & & \(\text{Recall}=\frac{TP+TN}{TP+FN+TP+FN}\times 100\) \\ Total percentage of the correct system responses & \(\text{Accuracy}=\frac{TP+TN}{TP+FN+TP+FN}\times 100\) \\ Total percentage of the correct & \(\text{Specificity}=\frac{TP+TP}{TP+TP}\times 100\) \\ system responses & \(\text{Precision}=\frac{TP+TN}{TP+FN+TP+FN}\times 100\) \\  & \(\text{F\_measure}=\frac{2\text{Recall}+Precision}{Recall+Precision}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Description of the classifiers used in performance evaluation.  
Here, the positive class is the fruit (the object of interest) and the negative class is the background. Therefore, TP is equal to the number of samples of plum fruit that are correctly classified; TN is the number of samples of the background class that are correctly classified; FN is the number of fruit pixels misclassified as background; and FP is the number of background pixels misclassified as fruit. It has to be noted that some measures should not be analyzed by themselves. For example, a naive system that always says true would have a recall of 100%, while a system that always says false would have a specificity of 100%.
###### Abstract  
Flying base stations, also known as aerial base stations, provide wireless connectivity to the user and utilize their aerial mobility to improve communication performance. Flying base stations depend on traditional stationary terrestrial base stations for connectivity, as stationary base stations act as the gateway to the backhaul /cloud via a wired connection. We introduce the flying base station channel capacity to build on the Shannon channel capacity, which quantifies the upper-bound limit of the rate at which information can be reliably transmitted using the communication channel regardless of the modulation and coding techniques used. The flying base station's channel capacity assumes aerial mobility and ideal positioning for maximum channel capacity. Therefore, the channel capacity limit holds for any digital and signal processing technique used and for any location or positioning of the flying base station. Because of its inherent reliance on the stationary terrestrial base station, the flying base station channel capacity depends on the stationary base station's parameters, such as its location and SNR performance to the user, in contrast to previous research, which focused on the link between the user and the flying base station without the stationary base station. For example, the beneficial region (where there is a positive flying base station capacity gain) depends on the stationary base station's power and channel SNR in addition to the flying base station's own transmission power and whether it has full duplex vs. half-duplex capability. We jointly study the mobility and the wireless communications of the flying base station to analyze its position, channel capacity, and beneficialness over the stationary terrestrial base station (capacity gain). As communication protocols and implementations for flying base stations undergo development for next-generation wireless networking, we focus on information-theoretical analyses and channel capacity to inform future research and development in flying base station networking.  
mobile network; 5G; 6G; flying base station; aerial base station; wireless communication; information theory; channel capacity +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
## 5 Validation of the Proposed Method on Weak Signal Detection
### Weak Electrical Signal Detection
The proposed method uses CIM to judge the similarity of the signals, which to some extent compensates for the problem that the difference spectrum is only selected based on the SV value. Because it can be seen from CIM that when the number of SV increases, the fluctuation of CIM also tends to be stable, the proposed method is still to make a certain selection in the area of large SV. However, this choice is not only based on the amplitude of the original spectrum. Sidebands of 7500 Hz have been taken into consideration. If  
Figure 11: Frequency spectrum of the two methods. Figure 10: The difference spectrum and CIM. (**a**) Difference spectrum. (**b**) CIM value. the differential spectrum method chooses more SCs, it may be able to choose these two components. However, many other components will be introduced which will reduce denoising performance. Finally, CCES is given for the two reconstructed signals in Figure 12. There are more spectral lines than the frequency spectrum in Figure 7:0 The red line is the CCES of SVD-CIM and the blue line is the CCES of SVD-3. Display in Figure 12 is better than the traditional frequency spectrum in Figure 7:1 Additionally, enlarging the 0\(\sim\)200 Hz spectrum, it can be seen that the peak of the blue line in this range is at 31.25 Hz, while the peak of the red line is at 93.75 Hz, which is the target frequency. The above phenomenon can be more obvious in Figure 12b. This experiment illustrates two aspects: (1) For periodic modulation signals, CCES is more expressive than frequency spectrum, which is reflected in the richness of the spectral lines and details. (2) For SVD, CIM selection has more advantages than difference spectrum selection, which is reflected in the target signal detection seen in Figure 12b. The CCES of SVD-3 also has a peak at 93.57 Hz, but the highest peak of the blue line is at 31.25 Hz, and the amplitude of 93.57 Hz of the blue line is smaller than that of the red line.
## 1 Introduction
To balance the network load and increase the network lifetime, this paper presents an equal probability method. In the route tree establishment process, the network adjusts the number of each route tree's sub-node according to the equal probability method to equilibrate the probability of each route tree. Therefore, the network can use each node equally and avoid some nodes exhausting their energy prematurely. The network lifetime would be extended by using this method. Furthermore, we introduce the network coding theory and combine it into the TSBR algorithm. In the traditional network communication, the forward method is store-and-forward, whereby intermediate nodes only forward packets and do not process anything. Based on this, the authors of [6] proposed a new forwarding method and named it network coding theory. Network coding is considered as a generalization of conventional store-and-forward routing techniques and it was originally proposed in order to achieve multicast data delivery at the maximum data transfer rate in single-source multicast networks. This feature had a great impact on the research field of information theory and research on network coding was first activated in the information theory community. Network coding is a new forward method that takes route and coding into consideration. In network coding theory, an intermediate node can process the received packets with linear or non-linear transforms [7] and then forward the processed packets. The basic idea of network coding is that each node processes the data which are received from each channel with a linear non-linear transform and forward it to the next hop. Based on the maximal flow-minimal cut theorem in graph theory [8], sender and receiver of data communication cannot communicate with the rate greater than maximal flow (or minimal cut). If we use traditional multi-broadcast-route method, it cannot achieve the maximal flow. [PERSON] and co-workers used a butterfly network as the instance of their research [9], and figured out that the use of network coding can achieve the maximum flow rate and improve communication efficiency. Currently, network coding theory is widely used in wireless networks, P2P network systems, distributed file storage, and network security. However, because of the particular situation of underwater networks, network coding theory needs a broader systemic study [10].
## 2 Materials and Methods
### Statistical Analysis  
The parameters used in this study were firstly tested for the normality (Shapiro-Wilk test) at the group level to ensure that they fulfilled the assumption required by the statistical analysis. If the parameter followed a normal distribution, the paired \(t\)-test would be applied. Otherwise, the Wilcoxon signed-rank test would be used. Paired \(t\)-test was used to compare the mean values of TOI and the normalized energy of \(\Delta\)AtHb signals between the pre- and post-exercise phases. The iTOI and itHb signals were divided into two equal parts. The root-mean-square (RMS) values of each half-segment were calculated and compared by the Wilcoxon signed-rank test (Figure 3). Statistical analysis was conducted by the software IBM SPSS Statistics for Windows version 25 (IBM Corp., Armonk, NY, USA). Significant differences are defined as * \(p\) \(<\) 0.05, ** \(p\) \(<\) 0.01, and *** \(p\) \(<\) 0.001.  
Figure 2: **(A)** One \(\Delta\)AtHb signal recorded during a 30-min rest phase and filtered by 6 th-order Butterworth filter. **(B)** The wavelet amplitude of the same \(\Delta\)AtHb signal in time-frequency domain. **(C)** The time-averaged wavelet amplitude of the same signal with the frequency band divided into 6 frequency intervals: I. 0.6–2 Hz, cardiac activity; II: 0.145–0.6 Hz, respiration; III: 0.052–0.145 Hz, myogenic activity; IV: 0.021–0.052 Hz, neurogenic activity; V: 0.0095–0.021 Hz, endothelial-related metabolic activity and VI: 0.005–0.0095 Hz, endothelial activity.
## 4 Conclusions  
In the present paper, a technique for calculating the three main solidification parameters for a two-dimensional columnar crystal growth during complete penetration laser beam keyhole welding of thin steel sheets was developed. Based on the obtained results, the following conclusions were drawn:  
* The Lame curves approximation was successfully utilized for the reconstruction of the rear weld pool boundary and thus for the analysis of the crystallization parameters. It was shown that the reconstruction of the weld pool boundary with Lame curves provides higher accuracy and flexibility compared to results obtained with elliptical functions.
* General analytical expressions for the main solidification parameters, namely the crystal axis and the corresponding growth rate and cross-sectional area of the crystal were derived.
* Dimensionless analysis of the influence of the size and shape of the rear weld pool boundary on the crystallization parameters was provided. It was shown that sharpened rear weld pool boundaries lead to a higher solidification rate and vice versa. Thus, critical welding parameters can be estimated and adapted accordingly to improve the welding process.
* The derived equations and herewith obtained results were validated and verified by comparing them to known theoretical solutions and experimental measurements, showing a very good correlation.  
Figure 6: Calculated dimensionless crystallization parameters for different shapes of the dimensionless rear part of the weld pool boundary, \(\Lambda=\frac{L_{r}}{\Phi}\), and m = n = 2: (**a**) crystal axis \(\xi\), (**b**) growth rate \(\rho\), and (**c**) cross-sectional area \(\omega\).  
**Author Contributions:** Conceptualization, A.A., V.K., X.M., M.B. and M.R.; Methodology, A.A. and V.K.; Software, A.A.; Formal analysis, A.A. and V.K.; Investigation, A.A. and X.M.; Writing - original draft, A.A. and V.K.; Writing - review & editing, A.A., V.K., X.M., M.B. and M.R.; Project administration, A.A. and M.B.; Funding acquisition, A.A., M.B. and M.R. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) project No. 411393804 (BA 5555/5-2) and No. 416014189 (BA 5555/6-1).  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks
## 4 Models
### Consumption Network Model  
The Consumption Network Model is responsible for evaluating the power consumption of the network [16] as defined in activity _Evaluate Power Consumption Models_ (see Figure 3). This model is defined by composing three models: two models that express the power consumption of the network and link layers (protocol stack); and an Environment Model, which is responsible for forwarding the packets among the sensors nodes.  
Due to the complexity of creating a generic model to express the power consumption of different WSN communication protocols, a template was proposed instead (see Figure 6). This template includes basic elements used to specify several communication protocols. It defines that any protocol model should contain two places (_UpperSend_ and _UpperReceive_) to interact with the upper layer and a transition _LowerLayer_ that acts as a bridge to the lower layer.  
For example, in a network layer protocol, the places _UpperSend_ and _UpperReceive_ are connected to the upper layer via places _in_ and _out_ in the Consumption Scheduler Model (Figure 5), respectively; and a transition (_LowerLayer_) represents the lower layer connecting the places _LowerSend_ and _LowerReceive_ with the places _UpperSend_ and _UpperReceive_ in the link layer model, respectively. We should put transitions and places between place _UpperSend_ and transition _LowerLayer_ to represent actions to send a packet. Additionally, we can put transitions and places between place _UpperReceive_ and transition _LowerLayer_ to represent actions to receive a packet.  
In this paper, we use the energy math model named _First Order Radio Model_[17]. This model calculates the power consumption of the network and has been widely adopted in WSNs. The model calculates the power consumption for sending and receiving packets. In practice, the power consumption of the packet processing is ignored because the sensor node consumes more power in packet transmission than in its processing.  
Due to these characteristics, the power consumption of the Network model is calculated in the link layer, because it sends and receives packets to/from the environment. For example, the template proposed in Figure 6 shows two functions, namely _powerTX()_ and _powerRX()_, to calculate the power consumption for sending and receiving packets, as defined in _First Order Radio Model_.  
The Environment Model is the propagation model, which is responsible for modelling everything in the middle between the sender and receiver sensor nodes. It identifies the connections between the  
Figure 6: Template of protocol models.  
Figure 7: Environment Model.  
sensor nodes and forwards the packets from a sensor node (source) to another (target). In Figure 7, places _send_ and _receive_ represent the time that a node sends and receives a packet, respectively. The transition _env_ models everything between the sensor nodes, e.g., free space, indoor, outdoor.
###### Abstract  
Plasma-activated water (PAW) has emerged as a platform for sterilizing fungal pathogens. In this study, we investigated the influence of PAW on black melanized spores of _Aspergillus brasiliensis_ to explore the mechanism of fungal spore inactivation. PAW was prepared by activating deionized water with a nonthermal atmospheric pressure air plasma jet (soft plasma jet). The concentrations of \(H_{2}O_{2}\) and \(NO_{x}\) in the PAW treated by the soft plasma jet for 3 min were 50 \(\upmu\)M and 1.8 mM, respectively, and the pH of the PAW was 3.10. The reactive oxygen and nitrogen species (RONS) in the PAW increased with longer plasma activation time. After being treated for 30 min in the PAW with a plasma activation time of 3 min, the spore viability dramatically dropped to 15%. The viabilities of 0.3% \(H_{2}O_{2}\)- and 0.3% \(H\)NO\({}_{3}\)-treated spores were 22% and 42%, respectively. The breakage of the spore cell wall by the PAW was revealed in scanning electron microscope images and flow cytometry measurements. Disruption of cell wall integrity provides a path for intracellular components to escape and RONS of the PAW can attack intracellular components directly. Degradation of high molecular genomic DNA was also observed by agarose gel electrophoresis. These results suggest that long-lived reactive species generated in the PAW play an important role in the inactivation of melanized fungal spores. Consequently, PAW produced by a soft plasma jet can be applied to sterilize bioprotective walled fungal spores in a relatively large volume.  
Aspergillus brasiliensis; plasma-activated water; cell wall integrity; DNA degradation; fungal spore inactivation; reactive oxygen species; reactive nitrogen species
# Deep Learning-Based Flap Detection System Using Thermographic Images in Plastic Surgery
## 2 Materials and Methods
### Performance Metrics  
To test the performance of our model, we use the following performance metrics: sensitivity (SE), specificity (SP), accuracy (AC) and F1-score (F1). These metrics are computed based on the subsequent indicators: true positive (TP), true negative (TN), false positive (FP), and false negative (FN).  
Sensitivity, also known as recall, indicates the proportion of actual positive samples that are correctly identified by the model. It reflects the ability of the model to recognize positive samples and is calculated according to the following formula:  
\[SE=\frac{TP}{TP+FN}. \tag{6}\]  
Specificity measures the proportion of actual negative samples that are correctly identified by the model and thus indicates its ability to identify negative samples. It is calculated using the following formula:  
\[SP=\frac{TN}{TN+FP}. \tag{7}\]  
The accuracy rate evaluates the correctness of the pixel classification and is obtained using the following formula:  
\[AC=\frac{TP+TN}{TP+TN+FP+FN}. \tag{8}\]  
F1 is used to measure the performance of a binary classification model by considering both precision and recall. It is the harmonic mean of precision and recall, where precision is the ratio of correctly predicted positive observations to the total predicted positive observations (\(\text{TP}/(\text{TP}+\text{FP})\)), and recall is equivalent to sensitivity. The F1-score is calculated using the following formula:  
\[F1=2\times\frac{\text{precision}\times\text{recall}}{\text{precision}+\text{ recall}}=\frac{2 TP}{2 TP+FP+FN}. \tag{9}\]In addition, receiver operating characteristic (ROC) curves and precision recall (PR) curves are used to visually compare the performance of the individual networks. The area under the curve (AUC) (i.e., a single number summarizing the overall performance of a classifier) for both the ROC curve and the PR curve of each network are also computed to provide a further quantitative measure of performance.
# A Robust Security Architecture for SDN-Based 5G Networks
## Robust Security Architecture of SDN-Based 5G Networks  
### Basic Idea  
Our basic idea is to add authentication that can verify whether the message is from an attacker or a legal user in the communication channel. The concept underlying our security authentication is synchronizing secret. In our scheme, all of the 5G devices will maintain a secret key, which is a value generated by the encryption algorithm. These keys will also be recorded in a back end system. During the communication process, the back end system will check whether the device has the same secret as the its records. Since the attacker can not obtain this secret in advance, its requirement will not pass the authentication process, namely, we have successfully found an SDN security attack. Moreover, as shown in Table 1, the security attacks will occur in two different scenes: the first type occurs in the communication between the SDN control in the control plane and the terminal devices in the data plane, the other type occurs between different terminal devices. In light of this, we propose two different authentication schemes to respectively handle the security problem in the two scenes. In the following, we will give our 5G SDN architecture in details.
###### Abstract  
The rapid expansion of satellite networks has enabled the widespread deployment of satellite-based services across various sectors. However, these networks often prioritize cost-effectiveness over security considerations, leading to inherent architectural vulnerabilities. The complex architecture, comprising heterogeneous devices, operating systems, and communication protocols, exacerbates security risks and broadens the attack surface. Attackers can exploit these threat vectors to compromise system availability and data integrity. Prior research has primarily focused on specific security improvements, providing limited comprehensive evaluations and systematic threat modeling. This study proposes a systematic approach for modeling and analyzing satellite network security. A security framework originally developed for traditional networks has been adapted for satellite environments. Utilizing the Threat Vector-Hierarchical Attack Representation Model (TV-HARM), this study comprehensively models and analyzes threat vectors and network security. Key attack paths are identified, and vulnerabilities are quantitatively assessed using three refined security metrics. The experimental results reveal residual threats despite existing security measures, underscoring the need for robust defense strategies. This study presents a systematic framework for evaluating satellite network security, demonstrating the applicability and effectiveness of the proposed methodologies. These findings contribute to enhanced threat mitigation strategies and the overall improvement of satellite network security by addressing critical vulnerabilities.  
s +
Footnote †: journal: Applied sciences  
article networks; threat modeling; security analysis; graphical security models; satellite cybersecurity; satellite systems