esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation and Results
### Discussion  
According to the aforementioned findings, topological invariants, such as K-Banhatti Sombor invariants, Contraharmonic-Quadratic invariants, Dharwad invariants, and their reduced forms, allow us to gather information about cloud networks in the form of algebraic structures and provide us with a mathematical technique to infer the hidden properties of various structures, such as the particular networks. The degree-based topological indices and the distance-based topological indices are the two primary classes of topological indices that clash, but in the present study, the networks were solved using the degree-based topological indices, and the best results are displayed in the graphs. Through the application of topological invariants, the research focuses on the topological characteristics and solutions of cloud networks for interconnection networks used on the internet, power-generating networks, chemical compounds, robotics, etc. The lower bounds, higher bounds, and the ability to predict the outcomes of the derived mathematical operations are among the topological traits. Although these solved solutions serve to model interconnection networks, engineers and product developers anticipate issues with items before creating or developing them. The efficiency, load balancing, and latency delay are also dependent on the topology of the network [41; 42]. This is the reason why the study solved the topology of a cloud network. The deduced mathematical results are used for the construction of the new, best characteristic cloud networks, including efficiency, load balancing, and less latency delay.  
In answer to the expected contribution, the study provides the mathematical results for the modeling of the cloud networks, developed the model in the Methodology section, and provided the graphical predicted results and the lower and upper bounds of the cloud graph. Equations (7)-(12) are the mathematical solutions of the cloud networks, with the help of topological invariants and graphical and mathematical results, providing the guidelines for the network engineers and architectural engineers during the modeling of the cloud network and all other networks in which the cloud network is used, and also during actual constructions of these networks.
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 3 Results
### Results for the Design Variants' Exploration  
This stage is related to a more detailed review of two parameters' influence--the air gap and the coil thickness--as it was planned in the scheme shown in Figure 3. Half of the models (II.1, II.2, and II.3) have an air gap of 50 mm, and the aim was to explore coil thickness's influence mainly by varying it to 200 mm, 250 mm, and 330 mm. Another three models (II.4, II.5, and II.6) explore the same thickness variations but at an increased air gap of 80 mm. The design model uses three rotor disks, and all simulations are run at an 18 MW mechanical power input.  
The results from these six separate simulations are presented similarly--by comparing power losses and efficiencies calculated for each variant. This comparison is shown in Figure 7.  
The results from this stage are commented upon as follows:  
* Increasing the coil thickness leads to a definitive negative effect. A minimal thickness of the coil should be searched for in the next stage to improve the design performance.
* An increased air gap of 80 mm has less if an effect, but it is also negative. A further decrease in the air gap is limited by design and manufacturing constraints related to the maintained reliability during operation.  
Figure 6: Power loss and efficiency for examined variants of stage I.  
Figure 7: Power loss and efficiency for examined variants of stage II.  
Further simulations are to be performed using a design with a minimal coil thickness and a minimal air gap.
## 2 Materials and Methods
### Features Evaluation
#### 2.3.1 Effects of Data Sampling  
To examine the impact of data sampling on the classification performances, we randomly selected one subset from CAD subjects and one subset from non-CAD subjects as the testing datasets, respectively. All the remaining subsets were used as the training datasets. This process was repeated 16 times for each algorithm. In this part, we only studied the classification performances of the polynomial-SVM algorithm as it showed good performances (see Results below).
## 2 Methods
### Mutation Interval Reconstruction Envelope  
#### 2.2.1 Demodulation Mutation Phenomenon  
In the LMD algorithm, each PF component is multiplied by an envelope estimation function \(a_{i}(t)\) and a pure FM function \(s_{i}(t)\). The demodulation of the pure FM function \(s_{i}(t)\) is obtained according to Formula (4) in step (2) after several sifting iterations. Under normal circumstances, the amplitudes of extreme points in function \(s_{i1}(t)\) are closer to 1 than those in the previous iteration. When all the absolute amplitudes of extreme points equal 1, a pure FM function \(s_{i}(t)\) is obtained. In the actual demodulation process, the local amplitudes of some extreme points in \(s_{i1}\) will mutate far away from 1 due to the error of the envelope estimation function \(a_{i1}(t)\), which leads to the non-convergence of the iteration process and the PF component mutation. We call this the demodulation mutation phenomenon.  
Taking the fault signal of the reciprocating compressor in an oversized bearing clearance state which is shown in Figure 5a as an example, the CSILMD decomposition process is performed to analyze the demodulation mutation phenomenon and its reason. The upper and lower envelopes of the signal shown in Figure 5b are constructed using CSI, and some intervals that occur demodulation mutation phenomenon are enlarged in Figure 5c. It is found that the upper and lower envelopes shown in Figure 5d intersected in these intervals, and we defined these intervals as mutation intervals. In the mutation interval, the upper envelope and lower envelope in CSILMD are nearly equal around the intersection point, resulting in \(a_{i1}(t)\) being equal or extremely close to 0. At this time, the value of this point is substituted into Formula (4) for demodulation, which causes a huge difference between the resulting signal \(h_{i1}(t)\) and the envelope estimation function \(a_{i1}(t)\), and thus the calculation result of the function \(s_{i1}(t)\) does not converge, which results in a demodulation mutation phenomenon. As shown in Figure 5e, two abrupt distortions appear in the mutation intervals of A and B.  
Figure 5: Demodulation mutation phenomenon.(**a**) Original signal (**b**) Envelope curve (**c**) Cross interval (**d**) Parts A and B (**e**) Mutation.
## 3 Results and Discussion

Table 2: The apparent first-order rate constant k of photocatalytic degradation and correlation coefficient, R\({}^{2}\). Figure 9: Experimental and simulated spectra of DMPO spin adducts generated by Y-CeO\({}_{2}\) photocatalyst suspension in DMSO after 25 min visible irradiation. RhB degradation was obtained with Vit C, indicating that \(\bullet\)O\({}_{2}^{-}\) radicals are the main species involved in RhB photodegradation. Based on the above studies, the photocatalytic mechanisms of Y-, Gd- and Eu-doped CeO\({}_{2}\) nanoparticles are illustrated in Figure 11. The doping of CeO\({}_{2}\) results in a higher concentration of oxygen vacancies and additional doping transition levels. When the doped CeO\({}_{2}\) is irradiated with visible light, the photogenerated electrons will move in the conduction band and the holes will be generated in the valence band. Part of the electrons will be trapped on the doping levels, limiting the recombination of the photoinduced electron and hole pairs [69]. Therefore, the electrons from the conduction band will interact with oxygen-producing superoxide radicals and the holes will interact with H\({}_{2}\)O, generating hydroxyl radicals. Both generated radical species will participate in a redox reaction, being capable of degrading the RhB molecule. From scavenger experiments it can be found that \(\bullet\)O\({}_{2}^{-}\) radicals are the main species required to degrade RhB dye; indeed, \(\bullet\)O\({}_{2}^{-}\) radicals are generated by the sample, but hydroxyl radicals are the majority, which explains the not-very-high photocatalytic performances of the samples. The HPLC method has been used as a complementary method to determine the concentration in RhB of the solutions formed after the UV photodegradation process. The obtained concentration values for all samples are summarized in Table 3 and the chromatograms are presented in Figure S1--Supplementary Material. Figure 11: Degradation mechanism of Y-doped CeO\({}_{2}\) nanoparticles. Figure 10: Scavenger experiment results of Y-CeO\({}_{2}\) nanoparticles for RhB degradation (control experiment = no scavenger). \begin{table}
\begin{tabular}{c c c c c c} \hline
**Catalyst** & **No Catalyst** & **CeO\({}_{2}\)** & **CeO\({}_{2}\):Eu** & **CeO\({}_{2}\):Y** & **CeO\({}_{2}\):Gd** \\ \hline Rhodamine B [mg/L] & 4.640 & 2.440 & 1.386 & 0.700 & 0.430 \\ \hline \end{tabular}
\end{table} 
Table 3: HPLC-FD concentrations of RhB before and after photodegradation with CeO\({}_{2}\)-doped catalysts (CeO\({}_{2}\):Eu, CeO\({}_{2}\):Gd, CeO\({}_{2}\):Y). The obtained results suggest a decreasing tendency for RhB concentration after the photodegradation of the pollutant solution with CeO\({}_{2}\)-doped catalysts (CeO\({}_{2}\) > CeO\({}_{2}\):Eu > CeO\({}_{2}\):Y > CeO\({}_{2}\):Gd), sustaining the previous results obtained using UV-Vis spectroscopy.
## 4 Interval Improved Fuzzy Partitions Fuzzy C-Means Clustering Algorithm
### Interval Improved Fuzzy Partitions Fuzzy C-Means under Euclidean Distance
The prototype \(\overline{\delta}_{i}=(\overline{\delta}_{i}^{j},\,\,\ldots,\,\,\overline{ \delta}_{i}^{p})\) of the \(i\)th cluster, which minimizes the objective function \(J_{ifpfcme}\), has the bounds of the interval \(\overline{g}_{i}^{j}=\left[\overline{a}_{i^{\prime}}^{j},\,\overline{\beta}_{ i}^{j}\right]\)\((j=1,\,\,\ldots,\,p)\). Hence, the updated formula for the prototype \(\overline{\delta}_{i}^{j}\) accords to the following:  
\[\frac{\partial L_{ifpfcme}}{\partial\overline{\epsilon}_{i}^{j}}=0\Leftrightarrow 2 \sum_{k=1}^{n}\overline{u}_{ik}^{2}\left(a_{k}^{j}-\overline{\epsilon}_{i}^{j} \right)=0\,\,and\,\,\frac{\partial L_{ifpfcme}}{\partial\overline{\beta}_{i}^{ j}}=0\to 2\sum_{k=1}^{n}\overline{u}_{ik}^{2}\left(b_{k}^{j}-\overline{\beta}_ {i}^{j}\right)=0, \tag{16}\]  
for \(i=1,\,\,\ldots,\,c\) and \(j=1,\,\,\ldots,\,p\). Thus, Equation (16) can be rewritten as:  
\[\overline{\epsilon}_{i}^{j}=\frac{\sum\limits_{k=1}^{n}\overline{u}_{ik}^{2}a_ {k}^{j}}{\sum\limits_{k=1}^{n}\overline{u}_{ik}^{2}}and\overline{\beta}_{i}^{ j}=\frac{\sum\limits_{k=1}^{n}\overline{u}_{ik}^{2}b_{k}^{j}}{\sum\limits_{k=1}^{n} \overline{u}_{ik}^{2}}. \tag{17}\]In Equation (8), adding a second term evaluates to \(-e_{k}/4\). When membership degrees become fuzzier, the second term increases as a'reward' for membership degrees near 0 and 1, aiming to enforce a crisper assignment. The adoption of this concept accelerates the convergence of IIFPFCME in this study. By introducing the second term, as membership degrees become fuzzier, its increase can minimize Equation (8). This effectively rewards the degree of membership, resulting in clearer divisions.
# A Distributed Approach to the Evasion Problem
## 2 Materials and Methods  
### Previous Algorithm for the Evasion Problem  
#### 2.1.1 Network Assumptions and Problem Formulation  
Following the definitions in [2], we assume that the target is constrained in an area of which the boundary is monitored by static boundary sensors. Each sensor has a unique ID and is able to establish a bidirectional communication link with neighbors within a certain range. The entire network forms a single connected component, such that every node is able to communicate with every other node via certain message propagation protocols.  
Let \(\mathcal{R}\in R^{d}\) be a bounded region homeomorphic to a \(d\)-dimensional ball, where \(d\geq 2\). Both a finite set of sensors \(S\) and the target \(E\) are moving within the region \(\mathcal{R}\). Each mobile sensor \(v\in S\) is initialized with location \(v(0)\), with that \(v(t)\) denotes the location of sensor \(v\) at time \(t\), and the covered region is denoted as the open ball \(B_{v(t)}=\{y\in\mathcal{R}|\ ||v(t)-y||<\alpha\}\). Thus, the evasion path problem can be described as \"Can we determine the existence of an evasion path for the target given certain motion models for the sensors \(S\) and target \(E\)?\".
## 3 Field Experiments
### Experiment Environment  
The experimental field was at Ogawa-cho, Hiki-gun, Saitama, Japan. The experimental field is shown in Figure 4. This rural area with little pedestrian traffic was selected for safe flights. Professional pilots were hired to manage and operate the UAVs in the demonstration experiment. Due to the limitation of our developed hardware, only throughput was measured in the experiments to evaluate the communication quality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Multi-Copter** & **Fixed Wing** \\ \hline Fly time & Short & Long \\ \hline Onboard weight & Light & Heavy \\ \hline Vertical take off & Support & Not Supported \\ \hline Air stop & Support & Not Supported \\ \hline Flight altitude & Low & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of two types of UAV features.  
Figure 3: Architecture of multi-copter UAV.  
Table 2 shows the mmWave devices' specifications used in our demonstration experiments. In addition, a photograph of the inside of the mmWave device and numerically analyzed antenna details are shown in Figure 5. The reasons for selecting this device were that it is small and lightweight enough to be mounted on a UAV and is capable of 100 m communications. The device comprises a small PC, an array, and a lens antenna [47]. The mmWave wireless communication device was equipped with Ubuntu, and throughputs were measured using the iperf3 command. The measured values were evaluated at MAC layer.  
The demonstration experiment used two \"Matrice 600 Pro\" UAVs manufactured by DJI. They can carry a maximum of 6 kg of load, and their specifications are shown in Table 3[60]. The screen on the transmitter side of the drone displays a map and air information such as the drone's position, altitude, and remaining battery power. The flight time of the UAV used in the experiment was about 20 min per flight, prioritizing safety. Therefore, it was only possible to acquire experimental data for limited scenarios.  
The UAV equipped with mmWave interfaces and the on-board mmWave extension equipment are shown in Figures 6 and 7, respectively. Another item was used in the experiment. Others used are shown in Table 4. A large pocket warmer was inserted to keep the temperature at about 20 \({}^{\circ}\)C to prevent the battery from deteriorating in performance.  
Figure 5: (**a**) Photograph of the antenna used in the experiment. (**b**) Numerical analysis of the directivity [47].  
\begin{table}
\begin{tabular}{c c} \hline
**Category** & **Specification** \\ \hline Size & 1668 mm \(\times\) 1518 mm \(\times\) 727 mm \\ \hline Weight & 9.5 kg \\ \hline Max Takeoff Weight & 15.5 kg \\ \hline Hovering Time & 32 min \\ \hline Max Wind Resistance & 25\({}^{\circ}\) \\ \hline Max Speed & 40 km/h, 65 km/h (Windless) \\ \hline Battery Capacity & 4500 mAh \\ \hline Battery Voltage & 22.2 V \\ \hline Number of Batteries & 6 \\ \hline Max Transmission Distance & 3.5 km \\ \hline \end{tabular}
\end{table}
Table 3: Matrice 600 pro specifications.  
Figure 6: UAV with millimeter-wave device.
# BeiDou Geostationary Satellite Code Bias Modeling Using Fengyun-3C Onboard Measurements
## 2 Fengyun-3C Onboard Data Quality Analysis  
The GNOS receiver on the Fengyun-3C satellite is used for the GNSS radio occultation mission of China. The receiver has 3 antennas: a positioning antenna, a rising occultation antenna, and a setting occultation antenna [3, 4]. With the background described in Introduction, we confined our study to navigation-related measurements from the positioning antenna, which is a wide-beam, hemispherical coverage, low-gain antenna mounted on the top surface of the Fengyun-3C satellite. The measurement types include L1 C/A-code phase, L1 carrier phase, L2 P-code phase, and L2 carrier phase for GPS, and B1I code phase, B1 carrier phase, B2I code phase, and B2 carrier phase for BDS. The results given in the study are based on a 26-day data set collected from the positioning antenna during DOY 062 to DOY 087 in 2015; the sampling interval is 1 HZ.
## 2 Materials and Methods  
### Investigated Microplastic Samples  
The seven samples selected for this exploratory work were obtained from the sample bank of the UCA Marine Litter Lab ([[https://www.marinelitterlab.eu/](https://www.marinelitterlab.eu/)]([https://www.marinelitterlab.eu/](https://www.marinelitterlab.eu/))). In total, 981 particles were analyzed by acquiring SWIR-HSI images (Table 1). All selected samples were collected with surface-trawling plankton nets (i.e., neuston nets) and all corresponded to areas previously characterized as hot-spots of floating microplastic pollution (Figure 1) [45; 46; 47]. In addition, the sample selection covered a wide diversity of microplastic forms, potential pollution sources and hydrodynamic conditions, so we expected to find diversity in the plastic polymer composition of the samples as well. A sample (E4 Sarmiento) corresponded to the great accumulation of microplastics of the North Atlantic Ocean, one of the five large convergence zones of floating plastic described for the open ocean [49]. Three samples (A06 NS, A27 NS, A34 NS) were taken in three distant areas of the Mediterranean Sea, a semi-enclosed sea known for its high concentration of microplastics, comparable to the great accumulation zones in the open ocean [50]. The Mediterranean Sea, however, shows a close connection to land-based sources of plastic pollution. The Mediterranean, however, shows a closer connection to land-based sources of plastic pollution. Another sample (A02 DS) was in the Strait of Gibraltar, the only connection of the Mediterranean with the open ocean. This site is affected by strong water currents, which should also influence the composition of microplastics. Finally, two samples (ETO-16 and NST-41) were taken from Biscay Bay, regarded as a dead-end for floating plastics and an area with intense fishing activity [47; 51].  
Rigid (thick-walled) fragments are by far the predominant form of microplastic particles on the ocean surface. However, the share of other forms of plastic (e.g., film-type, foam, pellets, granules or lines) may increase considerably in areas more closely linked to land-based sources of pollution or areas of intense fishing activity, such as the Mediterranean or the Bay of Biscay, respectively.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding
### Damson
#### Measurement Setup  
DAMSON setup is an oblique channel sounding system, which has been developed by the UK Defence Research Agency (DRA), made of commercially available equipment such us HF communication receivers and transmitters, computers, and so forth, and make an extensive use of digital signal processing (DSP) techniques to measure channel parameters using different waveforms [34; 35]. This system was designed with a high precision GPS (Global Positioning System) to synchronise in time the receiver and transmitter with an accuracy better than 10 \(\upmu\)s, which this synchronizations is mandatory to obtain a measurement of the time of flight of the received signal. A block diagram of the transmitter and receiver setup is depicted in Figure 5a,b.  
The main parts and features of this transmitter platform are the following:  
* An IBM compatible personal computer (PC) which is used to carry out control functions.
* Configurable parameters such as frequency and transmit bandwidth are controlled by the PC and communicated to other modules using a serial communication interface (RS232).
* A DSP card plugged into the PC to perform both the real time processing and the digital to analogue conversion required to generate waveforms.
* An HF single-sideband (SSB) to modulate the output base band signal of the DSP card. The HF driver has a high stability frequency reference better than 1 ppm and a configurable SSB bandwidth up to 12 kHz.
* The HF output fed a wide band linear Power Amplifier, which feeds the antenna system with a maximum power between 500 and 1000 W.
* A GPS receiver plugged into the PC is used to obtain time synchronization between the transmitter and the receiver.  
Figure 5: (**a**) Complete block diagram of the DAMSON transmitter and (**b**) Complete block diagram of the DAMSON receiver.  
The main parts and features of this receiver platform are the following:  
* An IBM compatible personal computer (PC) which is used to carry out control functions, data storage, data analysis and display.
* Configurable parameters such as frequency and transmit bandwidth are controlled by the PC and communicated to other modules using a serial communication interface (RS232).
* A DSP card plugged into the PC to perform both sampling the received signal with an on board analog to digital converter (ADC), and the real time processing to detect and process received signal.
* An HF single-sideband (SSB) to demodulate the received signal obtaining a base band signal to feed the DSP card. The HF driver has a high stability frequency reference better than 1 ppm. The HF SSB can operate in automatic gain control (AGC) mode or in a manual (controlled by the PC) gain mode.
* A GPS receiver plugged into the PC is used to obtain time synchronization between the transmitter and the receiver.
# A Numerical Study of the Performance of Point Absorber Wave Energy Converters
## 3 Results and Discussion  
This study is based on work performed in [17], where the authors considered the same model and validated their numerical results against experimental data. This validated case is thus taken as the base case here. Looking at the results, more parameters affecting power absorption and efficiency are identified, such as the influence of stiffness and wave height [11] on the half-submerged buoy, as highlighted by the authors.  
An optimal float shape for a heaving point absorber was also identified via a CFD investigation in [17]. Among the three basic shapes tested (rectangular, hemispherical, and trapezoidal), the rectangular float displayed more vertical displacement, followed by the trapezoidal and hemispherical floats. The rectangular shape was therefore established as one good option for wave energy converters based on pitch motion. The trapezoidal geometry can be optimized further by adding a fin in its bottom region, minimizing the drag and enhancing the lift.  
The parameters mentioned above are also key for submerged buoys. The influence of the submergence depth, WEC shape, wave height, and spring constant are thus discussed in the following sections.  
Information on submerged WECs in water is, however, now required to enhance energy generation. For this purpose, WECs are considered with a density of 1000 kg/m\({}^{3}\) and a weight of 6 kg, keeping the volume constant at 0.006 m\({}^{3}\). Figure 2 displays the series of floating and submerged WECs at different submergence depths: \(d_{s0}=0\) m, \(d_{s1}=0.1\) m, and \(d_{s2}=0.2\) m. A rectangular box (W1), a circular cylinder (W2), and a trapezoidal fin (W3) are the shapes of the wave energy converter investigated.