esa-sceva/satcom-chunk-collection huggingface dataset:
# Integration and Flight Test of a 7 kW Turboelectric Vertical Take-Off and Landing Unmanned Aircraft
## 3 Methodology  
[PERSON] et al. [18] previously laid out the rationale and analysis for the sizing and selection of the major components of the turboelectric propulsion and power system. The reader is directed to that previous work for details of the design, progressing logically from the lift rotors required for VTOL operation, propeller for horizontal flight, turbine engine for the main source of power, and then generator, battery, and electrical components for conditioning, distributing, and managing electrical current, and then, finally, the airflow required for cooling. This section compliments and completes that paper by detailing the avionics utilized and the final weight breakdowns of the flight-tested aircraft. Figure 3 shows the final power system diagram. For simplicity the diagram only shows two of the five distributed ESC and motor pairs.  
Figure 3 shows the hybrid system architecture depicted with each arrow representing a wire. The system utilizes a series hybrid configuration, which is to say the turbine engine creates no thrust for the aircraft [19]. All thrust for the aircraft is generated by the distributed motors. Power for those motors is taken from the turbogenerator and the batteries during peak load, and during cruise, the batteries are recharged by the excess power of the turbogenerator. The nomenclature for a turboelectric hybrid system can become confusing since series hybrid and parallel hybrid (utilizing the turbine engine to create a portion of thrust) architectures are both common configurations of turboelectric systems.
## 5 Conclusions  
In this study, a semi-submersible floating platform with different numbers of offset columns but with the same total weight based on the DeepCwind prototype is proposed. Importantly, by increasing the number of offset columns, the dynamic performance of surge, heave, and pitch motions improve, especially for the surge component. The following summarizes the detailed findings in this study. 1. Through the free-decay test, the in-house developed code is found to be satisfactorily accurate, which can be used for the calculations about the semi-submersible floating offshore wind turbines. 2. The free-decay test shows that increasing the number of offset columns can reduce the natural period in the surge mode, while the natural period of the heave and pitch modes do not change significantly. 3. The regular wave-load test shows that when the number of offset columns of the floating platform increases, the surge motion decreases, while the effects of the column numbers is negligible for the heave and pitch motions. When the number of offset columns is six, the decreasing in the surge motion is the most obvious, reaching nearly 80% that of the model with three offset columns. 4. For the irregular wave load, as the number of offset columns increases, the responses of surge, heave, and pitch decrease, and similar to the regular wave loads, the most obvious influence from the increased column number is in the surge direction. When the number of offset columns is six, the decreasing in the surge amplitudes is the most obvious, reaching nearly 70% of that of the model with three offset columns. Figure 14: Pitch response spectrum for the three platforms under wave loadings (**a**) LC4, (**b**) LC5, and (**c**) LC6. **Author Contributions:** Conceptualization, Z.L. and Y.F. ; methodology, Y.F. ; software, Y.F. ; validation, Y.F. ; writing--original draft preparation, Y.F. ; writing--review and editing, Z.L. and Y.F. ; supervision, Z.L. ; guidance, Z.L; coding, W.W and G.Q. **Funding:** The study was funded by National Key Research and Development Plan of China (2016 YFE0127900) and the National Natural Science Foundations of China (51608220). **Acknowledgments:** The corresponding author sincerely thanks Huazhong University of Science and Technology for the support to this research. **Conflicts of Interest:** The authors declare no conflict of interest. **Nomenclature**
# Simulating Fog and Edge Computing Scenarios: An Overview and Research Challenges
## 2 Fog and Edge Computing: Modelling and Simulation Challenges
### Scalability
DTS simulation has been used in the context of very large scale simulation of traditional and self* based cloud environments in supercomputing environments ([PERSON], 2018; [PERSON], 2018). DES and DTS approaches have both strong and weak points when compared to one another. DES is generally considered to be a suitable tool when applied to a problem requiring a more granular modelling approach which is more difficult to scale, whereas DTS typically enables the modelling of large scale systems with relatively less effort, but with the possibility of a higher degree of inaccuracy ([PERSON], 2018). Both paradigms can be applied to the fog and edge domain depending on the experimentation objectives. In terms of accuracy, there are challenges in relation to the validation of large scale systems as simulation represents an abstracted model of a real environment, hence it is imperative that the level of abstraction does not impede simulation result accuracy. The simulation validation process ensures that simulation experiments produce reliable estimations of system behaviour. Existing approaches suggest to validate simulation models with domain experts to ensure model face validity attributes and behaviour constraints ([PERSON], 2018). Furthermore, simulation results should be validated by comparing simulated results with monitored real system data e.g., by visually comparing simulated and monitored results plotted side by side ([PERSON], 2018) or statistically comparing data distribution e.g., using a t-test approach ([PERSON], 2018). Validating simulation models and results is not a new challenge, however applying validation techniques to fog and edge computing simulations can be challenging. Firstly, it is difficult to inspect the target environment due to size and complexity. Secondly, lack of access to real data impacts validation by comparison. Automatic or a semi-automatic validation methodologies capable of processing high volumes of data can potentially resolve or alleviate validation challenges by checking model data for consistency and result anomalies.
# Pulsed Thermography Applied to the Study of Cultural Heritage
## 3 Results
### Analysis of Bronzes  
From an artistic point of view, a very interesting application of pulsed IRT consists in the possibility to study historical bronzes produced by means of the lost wax method. According to this method, after casting, the bronze requires the repairing of casting faults--like cavities, porosities, and openings--and the cold working process, a finishing step where several tools, such as chisels, burins, and files, are used [60; 61]. Finally, the marks left by such tools are usually erased or concealed during the subsequent polishing of the surface and/or the patination [60]. However, these workings produce plastic deformations and inhomogeneities in the structure of the bronze and they can be identified by IRT [61], even after the final polishing.  
For opaque samples like bronzes, after the light pulse is absorbed at the sample surface, the heat diffuses towards the sample volume and in the case of a homogeneous material, it will give rise to a homogeneous evolution of the temperature distribution at the sample surface. On the other hand, as stated earlier on, the presence of inhomogeneities beneath the surface, like cavities, locally affects the heat propagation rate and it results in a localized temperature difference at the sample surface with respect to the surroundings (thermal contrast).  
Recently, the IRT approach has been applied to the study of the Capitoline She Wolf, one of the most important historical bronzes hosted in the _Capitoline Museums_ of Rome. In particular, the She Wolf mechanical and metallurgic repairs, decorative elements, and casting faults, such as cavities, have been detected in order to add information on the adopted casting method. Moreover, some geometrical information concerning the bronze thickness and the depth of the casting faults has been obtained by means of a series of quantitative studies. This requires prior knowledge of the thermal diffusivity of the constituting bronze, which is therefore measured by analyzing the time dependence of the pulsed IRT data obtained in different points of the bronze.  
Figure 2: Semi-logarithmic plot of the normalized transient temperature rise \(\Delta T(L,t)\) vs. time calculated for the same \(L\) and \(D\) values of Figure 1.
## 6 Conclusions  
In this study, the conventional triaxial test and the triaxial test under freeze-thaw cycles were carried out on the deep expansive clay samples, and the mechanical properties of the soil were analyzed. Based on the test, the damage classification and research methods were discussed, and the damage constitutive equation of the combined action of freeze-thaw cycles and loading was determined by establishing appropriate damage variables.  
The major conclusions of this study are as follows:  
1. In conventional triaxial test, the compressive strength of deep expansive clay gradually decreased with an increase in the water content. At this time, the stress-strain curve of clay under high confining pressure tended toward strain hardening, while low confining pressure shows strain softening.
2. In the triaxial shear test under freeze-thaw cycles, the growth rate of stress with strain gradually decreased as the number of freeze-thaw cycles increased. Moreover, the ultimate peak stress also decreased as a result of the freeze-thaw cycles. Under different freeze-thaw cycles, the stress-strain curves of the triaxial tests all showed strain hardening; as the number of freeze-thaw cycles increased, the cohesion tended to decrease, while the internal friction angle tended to increase.
3. Based on [PERSON]'s strain equivalence hypothesis and the Druck-Prager damage criterion, the parameters of the damage constitutive equation are calculated and determined, and the calculated data are substituted into the final damage constitutive equation for verification. The fitting degree between the calculated strength value and the theoretical strength value is as high as 99%.
4. The damage constitutive equation can reasonably predict the damage evolution of soil under the combined action of loading and freeze-thaw cycles. This study can serve as an available reference for well wall construction and disaster prediction in deep coal mining.  
Conceptualization, Z.Z. and S.C.; methodology, Z.Z. and S.C.; validation, Z.Z.; formal analysis, Z.Z.; investigation, S.C.; resources, B.L.; data curation, Z.Z. and S.C.; writing--original draft preparation, Z.Z. and S.C.; writing--review and editing, Z.Z.; visualization, Z.Z.; supervision, Z.Z.; project administration, B.L.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the University Doctoral Program Special Project, grant number 200803610004. The study was supported by the Natural Science Foundation of Anhui Province, grant number 2008085 ME143.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 3 Machine Learning Algorithms
### Kernel-Based Algorithms
#### 3.2.2 Gaussian Process Regression  
Gaussian process (GP) regression is based on Gaussian probability distribution and can be incorporated for non-linear data analysis problems. It utilizes input data as evidence to predict an unknown function [38]. It originates from Bayesian probability theory, where the search methodology involves centering around a prior function in the beginning, and a posterior function, after evidence has been extracted from the process, and is closely connected to other regression techniques.  
A GP model, before conditioning on data, is unanimously specified by its mean and covariance functions (e.g., the kernel) [39]. Covariance employs the assumptions about the function to learn and it is the main ingredient to guide the GP predictor. Popular kernels incorporated are the squared exponential, the linear, and the radial basis function (RBF).
# Investigations of the Windage Losses of a High-Speed Shrouded Gear via the Lattice Boltzmann Method
## 2 CFD Methodology
### Geometry and Numerical Setup  
A cylindrical spur gear whose main parameters as [PERSON] et al. [30] was chosen to produce a direct comparison between the numerical and experimental results. Table 1 enumerates some essential characteristics of the spur gear. To investigate spur gear's windage losses with various shrouded sizes matching [PERSON]'s experiments, the air is  
Figure 1: Velocity directions available from a lattice node in a D3Q27 model. The numbers 0–26 represent different velocity directions.  
assumed to be at normal temperatures and pressures, whose density is 1.225 kg/m\({}^{3}\) and viscosity is 1.7894 \(\times\) 10\({}^{-5}\) Pa-s. The rotating speed of the gear is changed from around 2500 rpm to about 15,000 rpm (the tangential velocity value of the gear is about 259.34 m/s), reflecting the typical working conditions of high-speed aeronautic gears. This speed value is varied later in this study. The air is incompressible when the Mach number (the proportion of linear velocity to sound speed) is less than 0.3 at the rotational speed of approximately 6300 rpm. On the other hand, at speeds greater than 6300 rpm, the air becomes compressible. To capture the flow characteristics near the gear wall and limit the number of lattice points influencing the computational efficiency, the near static wall refinement algorithm is adopted to highly refine the small regions near the spur gear, as shown in Figure 2.
## 5 Sensitivity and Comparison Analysis  
### Sensitivity Analysis  
#### 5.1.1 Indicators' Weights  
To evaluate the sensitivity of the indicators' weights to the benchmarking results, firstly, the indicators are classified into three groups as shown in Table 7. Then, different weighting scenarios were suggested as shown in Figure 5. The scenarios 1, 2, and 3 focus on the indicators in technology-related, governance-related, and human-related groups, respectively. The benchmarking results are recalculated for the scenarios with a fixed loss attenuation coefficient \((\mu=60)\) as illustrated in Figure 6.  
Figure 5 indicates that the best-performing nations are Singapore, Thailand, and Vietnam, while the lowest-performing countries are the Philippines, Myanmar, and Laos, regardless of the weighting situations. The rankings of the remaining nations have changed. Cambodia is placed fourth in the baseline scenario and seventh in scenarios 1 and 2. This demonstrates that a focus on criteria relating to technology and government management would boost Cambodia's standing within the ASEAN region.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Group** & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** \\ \hline Technology-related & X & X & & X & & X & \\ Governance-related & & & X & & X & & X \\ Human-related & & & & X & & X & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Indicator classification.  
Figure 5: Indicator weighting scenarios.
## 3 SOM and WSN
### Implemented SOM Algorithm  
Our SOM algorithm is implemented in C++ and consists of the following steps [32]:  
1. The size of the grid is established, by which the number of the cluster is also known; the centre of each cluster, _i.e._, grid node, is implemented as a collection whose size is not fixed, _i.e._, it can be changed on the fly, and whose elements are the \(n\)-grams defined in the previous text with assigned occurrence or frequency. Then, the number of iterations (\(N\)) is established. Afterwards, each cluster centre is initialized randomly.
2. A random instance from the training data is chosen, \(v(t)\).
3. The cluster centre closest to the chosen instance is established and named BMU - Best Matching Unit.
4. The BMU radius is calculated according to the Formula 1: \[\sigma(t)=\sigma_{0}\exp(-\frac{t}{\lambda}),t=1,2,3...\] (1) where \(\sigma_{0}\) stands for initial radius, \(\lambda\) is the time constant, and t is the current iteration. At the beginning, \(\sigma_{0}\), is quite high, it almost covers the grid, but it decreases during the time (with the time constant \(\lambda\)).
5. Every node, _i.e._, cluster centre that resides in \(\rho\)-neighborhood of BMU is adjusted in the following way: * If an n-gram of the instance \(v(t)\) exists in the node, its value (either occurrence of frequency) \(\phi(x)\) is modified according to the Formula 2: \[\phi(t+1)=\phi(t)+\Theta(t)L(t)(\phi(t)-v(t))\] (2) where \(L(t)\) stands for the learning rate that also decreases during the time according to the Formula 3: \[L(t)=L_{0}\exp(-\frac{t}{\lambda}),t=1,2,3...\] (3)while \(\Theta(t)\) introduces influence of the distance between a particular node and the BMU and is calculated according to the Formula 4: \[\Theta(t)=\exp(-\frac{dist^{2}}{2\sigma^{2}(t)}),t=1,2,3...\] (4) where _dist_ stands for the distance between the node and the BMU and \(\rho(t)\) is the radius function defined in step 4. * If an n-gram of the instance v(t) does not exist in the cluster centre, the n-gram is added to the centre.
6. Steps 2-5 are repeated until all the instances from the training set are chosen.
7. Steps 2-6 are repeated N times.  
The principal idea of the algorithm consists in the following: the neighborhood area of the node that corresponds to the current input is adjusted so that all the nodes from the established neighborhood become closer to the input. The change that is introduced in each iteration and the neighborhood that is getting updated are reducing with time, as each node, which at the same time represents a classifier, becomes more stable.
# Boating Tourism and Fishing Interactions: A Social Network Analysis Using AIS Data
###### Abstract  
Boating tourism in coastal-maritime areas often overlaps spatially and temporally with other economic activities, such as fishing, leading to complex interactions. These interactions can create opportunities for positive cooperation or generate conflicts that pressure natural resources and stakeholders. The aim of this study is to show whether or not there is evidence of interactions between fishing (\(\mathrm{n_{f}=43}\)) and tourism/recreation (\(\mathrm{n_{t}=65}\)) vessels. This study focuses on the interaction between maritime tourism activities and fishing in southern Portugal, using a social network analysis (SNA) approach based on automatic identification system (AIS) data to evaluate spatial and temporal patterns. The findings reveal that tourism activities dominate zones closer to the coast, with intermediate areas serving as shared spaces where interactions between vessel activities are more likely to occur. There was evidence of occasional interactions between a few recreational and fishing vessels (two passengers and three seiners), but the inferences from the results are insufficient to demonstrate how beneficial they are for both activities.  
automatic identification system (AIS); common pool resources (CPRs); maritime tourism; social network analysis (SNA) 2025  
27 March 202512  
As presented in the previous paragraphs, although there are some studies in the literature that focus on the different activities carried out by boat and that explore the coastal zone, little is still known about the interaction between the different activities. The reasons for this lack of studies could be diverse, including the complexity of collecting and analysing data assertively.  
This study aims to contribute to the analysis of the complex network of interaction between maritime tourism and fishing activities through AIS data via a social network analysis. Additional tools such as the Sankey diagram and the dendrogram are also used to strengthen the SNA and to facilitate the understanding of trends in the analysed data.
# A Trust-Based Predictive Model for Mobile Ad Hoc Network in Internet of Things +
## 4 Simulation
### Simulation Parameters
Figure 6: Packet drop rate vs. % of malicious nodes. From Figure 6, we can see that PDropR increases with the increased number of malicious nodes in the network. In CBRP, PDropR increases from 0.41 to 0.89 when the number of malicious nodes increases from about 5% to 25%. It is evident that the proposed protocol outperforms the protocol proposed by [PERSON] et al. and CBRP. This is due to the fact that our proposed protocol can predict the trust value of a node at least one step ahead and provides trustworthy routes between source and destination. Figure 7 shows the impact of malicious node behavior on average PDeIR for CBRP, the protocol proposed by [PERSON] et al., and our proposed protocol. All the protocols give around 93% PDeIR when none of the nodes are malicious. Due to the absence of security mechanisms in CBRP, the PDeIR reduces to 19% in the presence of 25% malicious nodes. Though [PERSON] et al. consider the security aspects (trust) while setting up the route, the proposed protocol outperforms the other two protocols and gives around 50% PDR even when 25% of the nodes behave maliciously. From Figure 8, we can observe that all the protocols give good throughput when none of the nodes are behaving maliciously. It can also be seen that the proposed protocol outperforms the other protocols in the presence of malicious nodes. In the presence of 25% malicious nodes, the proposed scheme gives throughput around 2600 bps whereas CBRP and the protocol proposed by [PERSON] et al. offer throughput around 800 bps and 2300 bps, respectively.
## 7 Differentiation between Switching and Faulty Transients
### Effect of Feeder Tripping and Reclosing  
The switch between nodes 671 and 692 was opened at the fourth cycle and re-closed at the sixth cycle to realize the event of feeder tripping and re-closing. The results for a period of twelve cycles to analyse the performance of algorithm are detailed in Figure 13. It can be inferred from Figure 13a that the current waveform deviates from its sinusoidal nature at the moment of feeder tripping as well as re-closing by a small disturbance. It can be observed from Figure 13b that the WD-index has small magnitude peaks at the moment of feeder tripping and re-closing. Figure 13c indicates that the ALN-index has high magnitude peaks at the time of tripping and re-closing of the feeder. Figure 13d indicates that FI has relatively high magnitudes at the time of feeder tripping and re-closing. However, the peak magnitude of this FI is lower than the threshold value. Hence, the proposed algorithm effectively discriminates the switching transients due the tripping and re-closing of the feeder from faulty events.