esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
Consider a connected network \(\mathcal{G}=(V,E)\) where \(V\) and \(E\) are the sets of vertices and edges of \(\mathcal{G}\), respectively. Suppose \(v_{0}\) is a seed vertex or a group of seed vertices, and we intend to find the local community originating from \(v_{0}\). For example, a web crawler starts from a list of seed web addresses, searches for hyperlinks in the current pages, and visits the newly discovered pages recursively. In a recommendation network, a single item can lead to a group of other topically related items through similar browsing and purchasing history. Several methods have been proposed to identify local communities that lack a global network structure [7; 8; 10; 12]. Among these works, [PERSON] proposed a very intuitive way of finding the local community in an iterative bottom-up procedure [10]. Let us denote the local community as \(\mathcal{C}\). Its neighbor set \(\mathcal{U}\) is defined to be those vertices that are not in \(\mathcal{C}\) but adjacent to at least one vertex in \(\mathcal{C}\). The boundary set \(\mathcal{B}\) of \(\mathcal{C}\) is defined to be those vertices in \(\mathcal{C}\) that have at least one neighbor in \(\mathcal{U}\). See Figure 1 for an illustration. A new vertex is added to the current local community one at a time such that the local modularity,  
\[R(\mathcal{C})=\frac{\text{the number of edges with both ends in }\mathcal{C}}{\text{the number of edges with one or more ends in }\mathcal{C}}\,, \tag{1}\]  
is increased the most. \(R(\mathcal{C})\) quantifies the proportion of internal edges of \(\mathcal{C}\), and thus can be considered an indicator of the bounding strength in \(\mathcal{C}\). Higher value of \(R(\mathcal{C})\) represents a more tightly bounded group of vertices, which has a higher chance of being a community. It is possible to use other local criteria, e.g., clustering coefficient [13], to define tightly connected local groups.  
This algorithm is deterministic in the sense that exactly the same result (local community) will be obtained if we rerun the algorithm starting from the same seed. Moreover, the result is qualitative since a given vertex will be either included in or excluded from  
Figure 1: In an intermediate step, the current local community \(\mathcal{C}\) consists of vertices 1 through 7, in which vertices 1 through 5 (red color) are boundary \(\mathcal{B}\). Vertices 6 and 7 (gray color) are the interior of \(\mathcal{C}\). The neighbor set \(\mathcal{U}\) includes vertex 8 through 14 (blue color). The remaining vertices in the network are in white.  
the detected local community. In fact, two different vertices in the local community might differ in a quantitative way such that one vertex is closely connected to the seed while the other one barely makes it. Therefore, a more natural and informative way is to gauge the likelihood that a given vertex should be in the local community. Every vertex will be marked by a numerical value such that a higher value implies a larger chance of being in the local community than a lower value.
## 7 RF-Based Localisation for Aerial and Ground Robots
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Refs** & **Year** & **Range-Based/ Fingerprinting** & **Distributed/ Centralised** & **Cooperative/ Non-Cooperative** & **Anchor-Based/ Anchor-Free** & **2D/3D** & **Experiment** & **Technique** & **Technology** \\ \hline
[45] & 2014 & fingerprinting & centralised & non-cooperative & anchor-free & 2D & Yes & PF & WLAN/RSS \\
[80] & 2010 & range-based & centralised & non-cooperative & anchor-based & 2D & Yes & UKF & -/RSS \\
[81] & 2020 & range-based & centralised & non-cooperative & anchor-based & 3D & Yes & UKF & UWB/TOA \\
[82] & 2019 & fingerprinting & centralised & non-cooperative & anchor-based & 2D & Yes & PF & ZigBee/RSS \\
[83] & 2021 & fingerprinting & centralised & non-cooperative & anchor-free & 2D & Yes & PF-ML & WiFi/RSS \\
[84] & 2018 & range-based & centralised & cooperative & anchor-based & 3D & No & EKF & WiFi-UWB/ RSS-TOA \\
[85] & 2008 & range-based & centralised & non-cooperative & anchor-based & 2D & No & EKF & -/TDOA \\
[86] & 2009 & range-based & centralised & non-cooperative & anchor-based & 2D & No & EKF & -/TDOA \\
[87] & 2018 & range-based & centralised & non-cooperative & anchor-based & 3D & Yes & LS & UWB/TOA \\
[88] & 2016 & range-based & centralised & non-cooperative & anchor-based & 2D & Yes & SRCKF & -/RSS \\
[89] & 2016 & range-based & distributed & cooperative & anchor-based & 2D & No & DEKF & -/AOA \\
[90] & 2020 & fingerprinting & centralised & non-cooperative & anchor-free & 2D & Yes & PF & -/RSS \\
[91] & 2021 & range-based & centralised & non-cooperative & anchor-based & 3D & No & ML & -/RSS \\
[92] & 2022 & range-based & distributed & cooperative & anchor-based & 3D & No & ML & -/RSS \\
[93] & 2021 & range-based & centralised & non-cooperative & anchor-based & 3D & Yes & EKF & UWB/TW-TOF \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons between existing works on RF-based localisation for UGVs and UAVs.
# Investigation of the Influence of Splitter Blades on the Resonance Conditions of Impellers
## 2 Theoretical Analysis
### Lumped Parameter Model of the Impeller with Splitter Blades
In addition, it is also found that the order of the \(h_{1}\)th harmonic is equal to the nodal diameter index of the mode, so that the order of the \(h_{2}\)th harmonic can be obtained by Equation (18) as well. \[h_{2}=N-n_{ND}, \tag{18}\]  
in which, \(n_{ND}\) denotes the nodal dimeter index.
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 4 Results and Discussion
### Network Lifetime  
Figure 18 demonstrates that the suggested method has its FND after 3650 rounds, whereas the M-GWO, ALOC, DHCO, and MT-MAC protocols have their first node deaths after 3000, 2500, 2200, and 2000 rounds, respectively. This demonstrates that the suggested method increases NLT.  
Figure 16: Performance Assessment of Buffer Occupancy.  
Figure 17: Jitter vs. Network Size.  
Figure 18: Network lifetime analyses.  
The proposed method evaluates throughput, PDR, residual energy, AE2E latency, throughput, PLR, energy consumption, and network lifetime. Furthermore, the simulation results of the FND, HND, and LND rounds are given, and their outcomes are compared with traditional clustering algorithms. The proposed algorithm increases network lifetime by incorporating packet routing strategies, CH selection, and energy-saving clustering. The number of packets transmitted to the BS accurately reflects the routing protocol's transmission performance. HEESR [11] has the limitation of simply considering network factors and assessing system performance. MT-MAC [13] provides the changes in network topology are not adequately examined for mobility consequences. The DHCO [23] method is suitable for WSN networks. ERRS [19] considers only mobility and scalability. In the ALOC [17] method, network design alterations could reduce the duration of clusters. Compared to the limits of these existing approaches, our proposed approach performs better than these traditional methods by resolving the limitations of individual techniques, and this comprehensive approach provides a more flexible and effective outcome for WBAN.  
The suggested ABBSOA-MSSOA architecture improves on MT-MAC, DHCO, ALOC, and M-GWO by providing quantitative performance metrics. Unlike the MT-MAC design, which has high packet loss and low throughput, the ABBSOA-MSSOA architecture has a packet delivery ratio (PDR) of 98.2%, indicating stable and efficient data transfer. ABBSOA-MSSOA claims 0.3-millisecond jitter and real-time data processing, unlike DHCO's static WSN setup. This is carried out via dynamic WBAN topology adaption. In contrast to ALOC, ABBSOA-MSSOA solves ALOC's energy inefficiency and shorter cluster lifetimes by using 42 mJ on average.  
ABBSOA-MSSOA's network lifespan extension is impressive, unlike MT-MAC, DHCO, and ALOC, which lasted 2000, 2200, and 2500 rounds; the first node death (FND) happens after 3650 rounds. The framework's dual-layer optimization reduces superfluous transmissions and evenly distributes the load among nodes, improving performance. This improves performance. The approach's 12.2-s cluster formation time and 600-s cluster longevity demonstrate its efficacy and stability. These findings show that the ABBSOA-MSSOA architecture for wireless body area networks (WBANs) is robust and scalable, outperforming conventional protocols.
# Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications
###### Abstract  
In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters.  
DC-DC converters; prognostic analysis; multi-valued neuron neural network; support vector machine; Zeta converter +
Footnote †: journal: _Algorithms_
# Fine-Grained Boundary Conditions in Field-Based Routing
## 6 Performance Evaluation
### Review of ALFA
Furthermore, ALFA combines the characteristics of both geographic routing and back-pressure routing. If \(\eta\) is small, ALFA behaves as a geographic routing protocol, while for large \(\eta\) values, it operates as a back-pressure routing protocol. In this sense, ALFA is a hybrid routing protocol capable of adapting its behavior based on network conditions.
## 3 Results
### Analysis of the LEP Event
Calculated maximum electron density values (for NAA signal Ne\({}_{\text{e\,pert}}\) (86.5 km) = 3.65 \(\times\) 10\({}^{7}\) m\({}^{-3}\) and Ne\({}_{\text{e\,unpert}}\) (86 km) = 3.39 \(\times\) 10\({}^{7}\) m\({}^{-3}\) and for GQD signal Ne\({}_{\text{e\,pert}}\) (86.5 km) = 3.76 \(\times\) 10\({}^{7}\) m\({}^{-3}\) and Ne\({}_{\text{e\,unpert}}\) (85.8 km) = 3.39 \(\times\) 10\({}^{7}\) m\({}^{-3}\)) are in agreement with [48] who reported N\({}_{\text{e\,unpert}}\) (87 km) = 3.14 \(\times\) 10\({}^{7}\) m\({}^{-3}\) and Ne\({}_{\text{e\,pert}}\) (85.3 km) = 3.67 \(\times\) 10\({}^{7}\) m\({}^{-3}\), while [54] reported higher values and a steeper slope (variation of Ne\({}_{\text{e\,pert}}\) (84 km) from ambient Ne\({}_{\text{e\,unpert}}\) (84 km) reported 1.519 \(\times\) 10\({}^{8}\) and 0.607 \(\times\) 10\({}^{8}\) m\({}^{-3}\), respectively). The errors, introduced by the technique used, place the uncertainty of the results between 10% and 25% (see, e.g., [23]). More precisely we estimate that the total error, i.e., noise error and the calibration error is \(\sim\)5% in amplitude and phase delay, which is in agreement with [58]. Moreover, the absolute amplitude variation between the recorded values and the signal amplitude and phase values acquired using LWPC is usually 10-20% which gives uncertainty of the results between 10% and 25%. Electron densities and ionospheric parameters obtained by different models and techniques [23; 30; 47; 59] vary by about one order of magnitude (factor 10). Electron density ratios related to flare lxmax given in [60] are within one order of magnitude and for unperturbed flare conditions given in [61] are smaller. Results obtained by VLF technique and LWPCV2.1 software are satisfactory for conducted qualitative analysis presented in this work. By NAA signal trace modelling, it was found that ionospheric irregularity related to LIE outreach from 4920 km to 5800 km when looking along the GCP path from transmitter to receiver, thus, LIE extent is 880 km, with corresponding coordinates (0.7\({}^{\circ}\) E, 51.5\({}^{\circ}\) N) and (12\({}^{\circ}\) E, 48.3\({}^{\circ}\) N). By GQD signal trace modelling, ionospheric irregularity outreach was found from 780 km to 1440 km when looking along the GCP path from transmitter to receiver, thus, LIE extent is 660 km, with corresponding coordinates (7.4\({}^{\circ}\) E, 51.4\({}^{\circ}\) N) and (14.9\({}^{\circ}\) E, 48\({}^{\circ}\) N). Modelled LIE extent for both cases is in agreement with [54; 57] who reported precipitation patches estimated as at least 1500 \(\times\) 600 km and spatial extent of disturbance as 728 km, respectively. Assumed geographical position of modelled irregularity in NAA and GQD waveguides, i.e., electron density increases in D-region due to energetic electron precipitation (see Figure 9), is presented by a blue oval in Figure 11, indicating that energetic electron precipitation took place over central Europe. Figure 11: Geographical position of modelled LIE (blue oval) related to energetic electron precipitation on 16 November 2004.
## 1 Introduction
### Applications of the Time-Domain Brillouin Scattering for Evaluation of Bismuth Ferrite Thin Films
A surface roughness of less than 20 nm was revealed by the atomic force microscope (AFM). The refractive index of tetragonal film was up to 10% less than the rhombohedral, demonstrating the importance of its measurement for the reliable determination of sound velocities and elastic moduli. In particular, the sound velocities of rhombohedral and tetragonal BFO films on the LAO substrates can be estimated as \(\
u_{S}\cong 3430\) m/s and \(\
u_{S}\cong 4050\) m/s, respectively. TDBS was applied for the first time for the characterization of the doping-induced structural transition from the rhombohedral to the orthorhombic phase of BFO in Ref. [34]. Bi\({}_{1-x}\)Dy\({}_{x}\)FeO\({}_{3}\) (BDFO) epitaxial films with x = 0 to 0.125 were grown on single crystal STO substrates by pulsed laser ablation of stoichiometric ceramic targets. The orientation of the STO substrates were 4\({}^{\circ}\) off the (001) plane along the \(<\)110\(>\) direction to ensure a ferroelectric single-domain state in the BDFO films. All films had a thickness of about 120 nm. The measured BFs at x = 0%, 5%, 10%, and 12.5% were approximately 53, 64, 60, and 49 GHz, respectively, clearly indicating the maximum for the Dy substitution of around 5-10%. This maximum correlates with the maxima of the piezoelectric coefficient and dielectric constant reported at these doping concentrations earlier [11] and is thus a direct indication of the phase transition revealed by TDBS. Unfortunately, measurements of the refractive indices were not performed and the anomalies of the sound velocity and of the refractive index across the phase transition were not disentangled [34].
# Performance Analysis of the Pilot and Data Component of a CSS Signal for LEO-PNT +
## 4 Performance Analysis
### BER Analysis  
The evaluation of the BER for the two considered modulations is shown in the right plot of Figure 3. In both cases, the design of the chirp period is given by the sensitivity analysis as given in Figure 2. First, for both modulations, for a given CN0 value, we obtain an improvement of the BER when increasing the chirp period, \(T_{\text{c}}\). Note from Figure 2 that \(T_{\text{c}}\) increases as the bandwidth value is reduced. Second, analogously, the narrower the bandwidth value, the better BER we obtain at a given CN0 value. Finally, from the results of the right plot of Figure 3 for a given bandwidth and CN0 value, we see an improvement of FSK-CSS with respect to BOK-CSS in terms of BER. The improvement varies with the bandwidth value, but we obtain values ranging from 2 to 7 orders of magnitude. The results provided in this section for the data component are very interesting for a practical implementation of a PNT signal. This is so for both FSK- and BOK-CSS signals. The reason is that they provide a good demodulation performance in comparison with current GNSS signals. Actually, the BER values obtained in this section could be modified in order to reach higher values by using smaller \(T_{\text{c}}\) values and then providing higher bit rates than in the current configuration. Unfortunately, by doing so, we would incur a modification of the sensitivity performance to a poorer level. Finally, we have not considered in this section the application of channel coding techniques that may allow better demodulation performance, and thus a better achievable bit rate for a given CN0 value.
## 4 Conclusions  
In this study, the coagulation efficiency of the PMC-based coagulant and the algae, geosmin, and 2-MIB removal efficiencies of the combined application of this coagulant with UV-based AOPs were investigated. The coagulation efficiency of the PMC-based coagulant was high in both the lab-scale and pilot-scale tests. In the lab-scale test, the removal efficiency of PMC showed 98%  
Figure 10: Degradation of geosmin and 2-MIB by UV, UV/H\({}_{2}\)O\({}_{2}\), and UV/Cl\({}_{2}\) processes in the pilot-scale test (PMC dose: 20 mg/L; UV dose: 871.8 mJ/cm\({}^{2}\); Cl\({}_{2}\) dose: 2, 5, and 10 mg/L; H\({}_{2}\)O\({}_{2}\) dose: 2, 5, and 10 mg/L).  
of the high initial algae concentration (97.1 mg/m\({}^{3}\)) and more than 94% of the turbidity removal. The geosmin and 2-MIB removal by the PMC coagulation process was found to be 45% and 37%, respectively. However, the level of residual T&O compound was above the threshold. In the pilot-scale test, coagulation process using PMC showed 64% and 96% of the average turbidity and chlorophyll-a removal efficiencies, respectively. For the PMC-based coagulation combined with UV/H\({}_{2}\)O\({}_{2}\) and UV/Cl\({}_{2}\) processes, the change in geosmin and 2-MIB concentration by UV direct photolysis was not significant, but it was significantly decreased after the injection of H\({}_{2}\)O\({}_{2}\). Complete degradation of geosmin and 2-MIB was achieved when the H\({}_{2}\)O\({}_{2}\) (oxidant) dose was increased to 10 mg/L. The UV/H\({}_{2}\)O\({}_{2}\) process was superior to the UV/Cl\({}_{2}\) process in both the lab-scale and pilot-scale experiments. It was also observed that water quality parameters such as turbidity reduced the efficiency of the oxidation process. Thus, it was concluded that combining the PMC-based coagulant with UV/H\({}_{2}\)O\({}_{2}\) processes is an efficient coagulation process for the removal of T&O compounds and the producers of such compounds (algae). Further research is needed to optimize the combination of AOP with different coagulation options for different water matrices. A cost-comparison analysis with other coagulants is also required.  
The following are available online at [[http://www.mdpi.com/2076-3417/8/9/1502/s1](http://www.mdpi.com/2076-3417/8/9/1502/s1)]([http://www.mdpi.com/2076-3417/8/9/1502/s1](http://www.mdpi.com/2076-3417/8/9/1502/s1)), Figure S1: Determination of scavenging factor for the water sample before and after PMC treatment used in collimated beam work: \(R_{OH,IIV}\) variation with the initial H\({}_{2}\)O\({}_{2}\) concentration.  
Y.W.A. (data curation, writing, original draft); H.K. (data curation, methodology, review and editing); M.K. (formal analysis, writing, review and editing); D.L. (data curation, review and editing); K.K. (data curation, review and editing); Y.J. (writing, review and editing); Y.A. (review and editing): J.W.K. (supervision, Project administration, writing, review and editing).  
This research was funded by the Korea Ministry of Environment (MOE) as \"A real scale project on algae monitoring and removal-utilization technology\" (2014001800001).  
The authors declare no conflicts of interest.
# Autonomous Detection of _Spodoptera frugiperda_ by Feeding Symptoms Directly from UAV RGB Imagery
## 3 Results
### Evaluation Parameters  
Model performance was assessed using six parameters: Accuracy, Sensitivity, Specificity, Precision, F1 Score, and Kappa.  
Accuracy is used as the main method to calculate the accuracy of a model, and the classification ability of the model is represented by the proportion of the correct number of samples in the total number of samples. The specific formula is as follows, and in the formula, T (True) and F (False) represent whether the prediction is correct, and P (Positive) and N (Negative) represent the category of the model prediction; _TP_ and _TN_ is the sum of True Predictions, and _FP_ and _FN_ are the opposite:  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
Sensitivity represents the model's recognition ability for positive samples, consisting of _TP_ and _FN_:  
\[Sensitivity=\frac{TP}{TP+FN} \tag{2}\]  
Specificity is defined to show true negative assessment ability, consisting of _TN_ and _FP_:  
\[Specificity=\frac{TN}{TN+FP} \tag{3}\]  
Precision shows the accuracy of all model-identified positive samples, consisting of _TN_ and _FP_:  
\[\text{Precision }=\frac{TP}{TP+FP} \tag{4}\]  
F1 Score is an aggregative indicator based on the harmonic mean of precision and recall.  
\[\text{F1 score }=\frac{2*\text{Precision }*Sensitivity}{\text{Precision }+Sensitivity} \tag{5}\]  
Kappa coefficient is a consistency test confusion matrix-based indicator with values between \(-1\) and \(1\); closer to \(1\) indicates the overall effect of the classification. In the formula, \(\text{a}_{\text{i}}\) and \(\text{b}_{\text{i}}\) represent the true number and the predicted number of the i category, respectively, and the sum means the number of all data.  
\[\text{Kappa }=\frac{p_{0}-p_{1}}{1-p_{1}}\]  
\[p_{0}=Accuracy \tag{6}\]  
\[p_{1}=\frac{a_{1}\times b_{1}+a_{3}\times b_{3}+a_{3}\times b_{3}}{sum^{2}}\]  
Figure 7: Illustration of the process used in this study.