esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Two-Way Satellite-Terrestrial Relay Network with SWIPT
## 5 Conclusions  
In this paper, we have investigated the performance of the two-way satellite-terrestrial relay network with SWIPT, where both SPNC and PNC protocols have been considered in the system. Firstly, we have derived exact average end-to-end throughput expressions of PNC and SND protocols in the SWIPT aided system. Secondly, we have derived the probability of single node detection occurrence of SPNC protocol in the SWIPT aided system. Thirdly, to get the energy efficiency performance of the SWIPT aided system, the energy harvesting at the user source node has been given. The analysis and simulation results show that: (i) the proposed SPNC protocol outperforms the conventional PNC protocol in the two-way satellite-terrestrial relay network with SWIPT in ILS, AS, and FHS Shadowed-Rician fading channels; (ii) as the channel state gets worse, SPNC protocol can achieve more performance improvement than PNC protocol; (iii) as the PS coefficient increases, the average end-to-end throughput performance increases progressively, and the average energy efficiency performance increases progressively within a certain range while decreasing in the others. The results suggest that if we want to have better comprehensive performance in the SWIPT aided system, we should trade-off between the average end-to-end throughput and the average energy efficiency.  
[PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
Figure 7: Average energy efficiency performance versus different PS coefficients \(\rho\) in PS aided two-way satellite-terrestrial relay network with ILS, AS, and FHS.  
**Funding:** This research was supported by the National Natural Science Foundation of China (Grant No. 62071146).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Factors Influencing U-WSN Design
### Architectures of U-WSN
#### 3.1.2 UAV-Sensor Link (U-S)  
In the link between UAV and sensor, the UAV usually acts as a mobile sink. The distance between the UAV and a sensor is ranging from several meters to hundred meters. In the case of close range, the U-S links could apply the technologies as in S-S link. The connection between UAV and sensors is able to use the IEEE 802.15.4 (ZigBee) and IEEE 802.11 ah (WiFi) [39] protocols. These protocols have the medium range that could achieve several hundred meters.
# Integrating Interpolation and Extrapolation: A Hybrid Predictive Framework for Supervised Learning
### Contributions and Organization  
To address the above research question, we establish a mathematical programming model to classify whether a new multivariate data point is interior or exterior to the known dataset. By solving the established optimization model, we obtain the defined centrality coefficient of the new data point. Accordingly, we propose a novel hybrid prediction framework that integrates both interpolation and extrapolation methods by taking advantage of the centrality coefficient. If the new observation is an interior point to the known dataset, we can use prediction methods with good interpolation abilities, such as \(k\)NN. Otherwise, we can use prediction methods with good extrapolation abilities, such as linear regression. Consequently, our hybrid prediction framework takes advantage of both interpolation and extrapolation abilities.  
Our framework distinguishes itself from the existing interpolation and extrapolation methods in several ways:  
1. It can handle both interior and exterior data points without prior knowledge or assumptions;
2. It flexibly selects the optimal prediction strategy by considering the centrality coefficient obtained from the optimization model;
3. It enhances the precision of predictions by harnessing the collective power of both interpolation and extrapolation abilities.  
As a practical application, we harness our framework to address the ship deficiency prediction problem using the port state control (PSC) inspection dataset for the port of Hong Kong. A comparative analysis against the simple uses of \(k\)NN and linear regression reveals that our model excels in specific scenarios. This paper, therefore, stands as a valuable addition to the literature, offering a refreshed and effective method that melts the advantages of both interpolation and extrapolation.  
The remainder of this paper is organized as follows. Section 2 presents our optimization model for classifying exterior or interior data points and describes our hybrid framework combining \(k\)NN and linear regression. Section 3 describes the numerical experiments within the considered case study, focusing on ship deficiency prediction. Section 4 concludes our paper and suggests future research directions.
# Data-Driven Network Anomaly Detection with Cyber Attack and Defense Visualization
## 4 Methodology and Implementation
### Established Connections (ECs)  
Because TCP defines ECs to be maintained until data exchange or transfer between a client and a host is complete, it is essential to obtain all existing connections in the network following an FR alert for a malicious host. To ensure that the malware is not replicated to other hosts, an aggregate of hosts in the network connected to the malicious host is obtained. As mentioned before, we induce random generated network malicious hosts into the network running the KDDCUP'99 attack data. Once we receive an alert from the FR part of FREC-NIDS, we need to check all hosts connected to the malicious client and flag them as unavailable. We assume that having infiltrated a network host, connections to the malicious host would be under the attacker's control and malware is replicated. To present a lucid picture of actualized threats in the realization of MO-2 and MO-3, our visual display system will output all affected connections as described in Section 4.9.  
We model the FREC-NIDS parameters in our simulated environment to visualize cyberattack effects through Algorithm 1 and Equation (21), as will be explained in Section 4.9. The relationship between our visualization parameters defines \(L=C\cup H\) as the set for the total number of network hosts, including all client nodes and active hosts, and as per Equation (5), then, the total number of hosts is given by \(S=m+k\) at a set of time intervals \(t=\{t_{1},t_{2},\dots,t_{n}\}\). For each \(t_{n}\), let \(G(t_{n})\) be an undirected graph with vertex set \(V=\{v_{1},v_{2},\dots,v_{m}\}\) as connections to other hosts, with edge set \(E(t_{n})\). The degree \(\delta_{(v)}\) of a vertex \(v\) is the number of edges incident to it. Malicious connections \(M_{c}\) in the EC part of FREC-NIDS are given by  
\[\gamma_{M_{c_{(t_{n})}}}=\sum_{i=0}^{S}\delta_{(v_{i})},\qquad\text{if $FR_{c}$ is malicious} \tag{15}\]  
where \(S\) denotes the number of nodes at time interval \(t_{n}\); with this, we calculate the network capacity as:  
\[\beta_{Y_{(t_{n})}}=\sum_{i=1}^{n}\Bigl{(}S^{i}\cdot\mathcal{O}_{v_{C_{k}}}^{ i}\Bigr{)} \tag{16}\]  
where \(\mathcal{O}_{v_{C_{k}}}\) denotes the total number of connections at time interval \(t_{n}\). Then, the network capacity helps obtain average network unavailability, the calculated comparison of all established malicious connections, to the network capacity at \(t_{n}\)  
This gives the security analyst all cyber-assets affected as the TCE%.  
\[TCE_{t_{n}}=\frac{\gamma_{M_{c}}}{\beta_{Y_{(t_{n})}}} \tag{17}\]
## 3 Results and Discussion
### Characterization of MIPs and NIPs  
#### 3.2.1 SEM Analysis  
The morphological structures of MIPs and NIPs prepared with different polymerization processes are shown in Figure 2. There were distinct differences between bulk polymerization and sol-gel surface layer imprinting. Both NIP1 (Figure 2A) and MIP1 (Figure 2B) were prepared by bulk polymerization. After the prepared MIPs and NIPs were grinded, heterogeneous particles were obtained with irregular shapes and different sizes. Compared to the surface morphology of NIP1 (Figure 2A1-A3), MIP1 (Figure 2B1-B3) was rough, loose, and porous. The imprinted recognition sites within cavities could serve as predetermined high affinity sites for the template. From Figure 2C,D, it was observed that by choosing the appropriate silica-gel support in terms of size and shape, the MIPs via surface layer imprinting were roughly spherical in shape. It was obvious that the morphologies of the MIP2 (Figure 2D) and the NIP2 (Figure 2C) were completely different from the morphology of the activated silica particles. Result showed that the surface of the silica gel was quite smooth, while the MIPs exhibited a highly rough outside polymeric surface, larger particle size, and a certain degree of agglomeration, indicating the formation of the imprinted layer on the surface of the silica support and the successful synthesis of the surface imprinted polymers. After the imprinting process (the support silica gel with the average particle size of approximately 500 nm), the average diameter of the MIP2 increased to approximately 900 nm, as shown in Figure 2D3, implying that a 200 nm thickness imprinting layer was formed on the surface of the silica gel. Similar surface morphologies were observed for MIPs and NIPs.
## 2 M&S Software Design for Multi-Radio Integrated Navigation System
### Design of a Message Broker-Based M&S Software for the Multi-Radio Integrated Navigation System
#### 2.3.3 Navigation Algorithm Module  
Figure 9 shows the navigation algorithm module block diagram. An integrated navigation algorithm was constructed from radio navigation algorithms. The navigation results could be obtained from the least squares method, the weighted least squares method, and the Kalman filtering method.
# A QoS Optimization Approach in Cognitive Body Area Networks for Healthcare Applications
## 6 Future Work  
The work presented in this paper can be seen as a starting point towards the research and development of C-BANs. This work can be extended toward more diverse scenarios and applications of WBANs. Considering the pace of development in modern technologies, it is expected that the size of the sensors will reduce dramatically. Hence, it will be possible in the future to carry or wear many more sensor nodes on the body. Such dense deployment of the sensor nodes in a WBAN will create new challenges in terms of spectrum management, e.g., interference, QoS, etc. Traditional approaches will no longer be very effective to encounter such challenges. The cognitive capabilities in the C-BANs will be suitable candidate to combat these challenges. Since most of the WBANs are proposed or designed for the ISM band and with growing diverse services in this unlicensed band, more cognitive functionalities will be required at various layers in the C-BANs.  
Interference is a major hindrance for the QoS of the WBANs. With the introduction of C-BANs, efficient and opportunistic spectrum management algorithms can be proposed. In this work, inter-gateway interference avoidance has been proposed through power control. In the future, inter-WBAN and beyond-WBAN interferences could be avoided with cognitive capabilities of future C-BANs.  
Channel modeling has been a tough subject for WBANs' researchers. Off-body and body-to-body channel modeling has been very little investigated in the literature. In this work, an off-body mathematical channel model has been proposed based on the body postures and shadowing resulting from various factors. A more comprehensive model of the off-body channel link based on the antenna design and position could be a future investigation task.  
Finally, the algorithm presented in this work (i.e., the RL-CAA) has been proposed for channel allocation and simulation results show it as a promising candidate for future C-BANs. However, hardware implementation and design considerations for resource constrained platforms (in particular their computational power and energy conservation issues) remain to be investigated; for this, further modifications and optimizations to the proposed approach would be needed to improve the matching between the algorithms and architecture.  
This work has been supported in part by TUT baseline project B38 and IT Academy stipend program. This project has also received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No. 668995. This material reflects only the authors' view and the EC Research Executive Agency is not responsible for any use that may be made of the information it contains.  
**Author Contributions:** This research work has been conducted through the collective efforts of both authors. [PERSON] has prepared the manuscript based on the suggestions, guidance, and feedback of [PERSON].  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Optimal Coordination of Over-Current Relays in Microgrids Using Unsupervised Learning Techniques
## 5 Tests and Results  
The proposed approach was tested with a benchmark IEC microgrid that integrates DG. Figure 8 depicts the test microgrid and its parameters can be consulted in [32]. Also, the characteristics of the protection scheme can be found in [14]. Table 2 presents the 16 operative scenarios (OSs) taken into account for this microgrid. Such OSs are generated by considering different generation and topological conditions. Note that the topology of the microgrid is modified while considering the operational states of switches CB-1 and CB-2; apart from this, the microgrid is able to operate connected or disconnected from the main network (grid-connected and islanded modes).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**OS** & **Grid** & **CB-1** & **CB-2** & **DG1** & **DG2** & **DG3** & **DG4** \\ \hline OS1 & on & open & open & off & off & off & off \\ OS2 & on & open & open & on & on & on & on \\ OS3 & on & open & open & on & on & off & off \\ OS4 & off & open & open & on & on & on & on \\ OS5 & on & close & close & off & off & off & off \\ OS6 & on & close & close & on & on & on & on \\ OS7 & on & close & close & on & on & off & off \\ OS8 & off & close & close & on & on & on & on \\ OS9 & on & close & open & off & off & off & off \\ OS10 & on & close & open & on & on & on & on \\ OS11 & on & close & open & on & on & off & off \\ OS12 & off & close & open & on & on & on & on \\ OS13 & on & open & close & off & off & off & off \\ OS14 & on & open & close & on & on & on & on \\ OS15 & on & open & close & on & on & off & off \\ OS16 & off & open & close & on & on & on & on \\ \hline \hline \end{tabular}
\end{table}
Table 2: Microgrid operational scenarios.  
Figure 7: Illustrative example of the heuristic adjustment.  
For the sake of clarity, all of the relays were labeled with numbers ranging from one to 15, preceded by the letter R located, as illustrated in Figure 8. Standard IEEE 242  recommends that the coordination time between main relay and backup relays _CT1_ should be equal to or greater than 0.2 s [33]. In this case, a _CT1_ of 0.3 s is considered for ensuring a greater margin of safety. A standard inverse IEC characteristic curve was adopted for the operating characteristic of the DOCRs, with parameters A and B of 0.14 and 0.02, respectively. This type of curve was adopted, since it is the most widely used in protection coordination schemes; nonetheless, any other characteristic curve may be used. It was also assumed that the relays of the microgrid are able to configure a maximum of four setting groups. The test system was implemented in DIgSILENT Power Factory and several faults were considered in the lines of the test network.
## 3 The Role of Energy Storage in Micro-Grids
### Advanced Battery Management Systems
The battery ageing is a complex process that depends on the utilization of the battery in term of operating temperatures, load profiles, and the number of charge/discharge cycles. It typically results in the progressive fading of the maximum capacity of the battery and the increase of the series resistance, which determine the increased internal losses and reduced power capability. The knowledge of the battery health status, together with the expected operational profile, may lead the BMS to estimate the Residual Useful Life of the battery.
## 4 Results
### Validation Study  
The Dittus-Boelter correlation [59] provides the Nusselt number as a function of the Re and Pr according to Equation (7):  
\[\text{Nu}=0.023\;\text{Re}^{0.8}\;\text{Pr}^{0.4} \tag{7}\]  
Additionally, the Seban-Shimazaki correlation [59] (Equation (8)) is employed to compare with the simulation outcomes. A high correlation is suggested for \(\text{Pe}_{\text{D}}\geq 100\). The outcomes of the validation study are demonstrated in Figure 5. Therefore, the available work presents good accuracy. Additionally, to validate the present numerical model with the results of the performance of a real PTC system, the experimental results of [PERSON] et al. [60] are used, and the comparison is presented in Figure 6. It can be concluded that present numerical method has good capacity for modeling the fluid and thermal characteristics in a PTC system. The uncertainty in measurement of the experimental parameters of the [PERSON] et al. work [60] was as follows. The uncertainty in HTF velocity measurement was \(\pm 0.05\text{m/s}\) for HTF velocity up to \(0.28\;\text{m/s}\) and \(\pm 0.2\;\text{m/s}\) for HTF velocity between \(0.28\) and \(0.32\;\text{m/s}\). The uncertainty of HTF temperature was \(\pm 0.3^{\circ}\text{C}\) in this study.  
\[\text{Nu}=5+0.025\;\text{Pe}_{\text{D}}^{0.8} \tag{8}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Nusselt Number Error** & **Grid Number** \\ \hline
17.2\% & 65421 \\
9.04\% & 121470 \\
2.13\% & 164957 \\
1.98\% & 235712 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Outcomes of the grid independence check.  
Figure 4: Considered grid for proposed geometry as PTC.
# Quantum Computing for Healthcare: A Review
## 6 Security of Quantum Computing for Healthcare
### Quantum Key Distribution  
Frequent recycling of strong cryptographic keys in healthcare IoT devices and terminals that are placed in public spaces plays an important factor in mitigating the increasing number of medical data breach incidents plaguing the healthcare system. Quantum key distribution (QKD) is a protocol that is used to authorize two components by distributing a mutually agreed key to ensure secure transmission. QKD protocol uses certain quantum laws (which are generally based on complex characteristics of quantum computing) to detect information extraction attacks. Specifically, QKD leverages the footprints left when an adversary attempts to steal the information for attack detection. QKD allows the generation of arbitrarily long keys, and it will stop the keys generation process if an attack is detected. The first QKD technique, known as BB84, was proposed by [PERSON] [129], and it is the most widely used method in theoretical research on quantum computing. QKD has enormous potential in helping to overcome key management and distribution limitations in classical algorithms. [PERSON] et al. [130] presented the proof of the BB84 technique by relating the security to the entanglement purification protocol and the quantum error correction code. [PERSON] et al. [2] suggest utilizing QKD using an enhanced version of the BB84 protocol for sharing keys between communicating entities in the remote health monitoring of patients using wireless body sensor networks. Their results demonstrate that the approach helps secure the sensed data being transmitted across the sensor network to the physician from attacks. [PERSON] et al. [123] designed an optimized QKD technique for heterogeneous medical devices. The communication is set up using quantum channels between authorized parties, and the key server distributes the encryption key in terms of qubits over the quantum channel. In the literature, substantial research has been conducted using the QKD security protocol, and several novel improvements in the quantum computing security paradigm using QKD protocol have been made so far.
## 3 Results and Discussion  
### Physico-Chemical Characterization of SeNPs  
The appearance of red colour (Figure 1a) suggest the formation of elemental nano-selenium, which has been confirmed by us through AFM and DLS.  
As evidenced by AFM image (Figure 1b), regular, spherical shape, Se nanoparticles were obtained by eco-friendly synthesis. The DLS measurements (Figure 1c) revealed two maximum size distributions: first one with average size of 90 nm and lower concentration, and second one, with average size of about 400 nm and higher concentration. Even SeNPs tends to aggregate into larger size spheres (including at room temperature) due to their high surface to volume ratio, we were able to identify single nanoparticles, well distributed on the mica-support surface, without any aggregation. Moreover, the size of SeNPs obtained by DLS assay was in line with the observed AFM details. The zeta potential measurements (Figure 1d) indicates \(-\)26.6 mV, which is considered an indicative for good stability, according to the literature [38]. It is well known that the criteria of stability of NPs are measured when the values of zeta potential ranged from higher than +20 mV to lower than \(-\)20 mV [39].  
Different microorganisms are able to reduce inorganic selenium into red elemental SeNPs in different size with an unique structured nanospheres with regular and uniform size [20; 22; 39]. According to previous our work [20] selenium in the LSeNPS is manly (>95%) in a form of nanoselenium and the rest (<5%) is organic selenium.  
[PERSON] et al. [40], demonstrated that probiotic _L. casei_ 393 is able to transform sodium selenite to SeNPs under anaerobic conditions which is considered to be one of the mechanism of Se detoxification. Three different ways were proposed to explain the bioconversion of Se from +4 to 0 oxidation state [40]: (i) the periplasmic nitrite reductase (nitrite reductase, sulfite reductase, and GSH reductase); (ii) redox precipitation of both elemental sulfur and elemental Se; and (iii) a glutathione reductase catalyzes the reaction of GSH with Se +4 to produce GS-Se-SG, further generate GS-Se.