esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Swarm Formation  
In order to accelerate search and rescue operations, swarm formation is unavoidable. Many emergency applications require more than one UAV, such as those for highway road accidents, earthquakes, wild fires and floods. Consider a flood emergency scenario, since these are increasing due to environmental changes [28]. The search for humans during high water flow can be, more effectively carried out with the help of a swarm as compared to a single UAV. In addition, if one UAV carries one life jacket for a flood-flown person, there will be high significance of the UAV swarm for searching and rescuing. On the other hand, the swarm UAVs can share the search data (location and photos) with other UAVs for effective utilization of resource. Furthermore, a leading UAV be considered as a data acquisition terminal, and other UAVs can be assigned to drop the payloads. Consequently, the utilization of swarm of UAV during a hazard would be very effective for search-and-rescue applications.  
The formation of UAVs with five different techniques based on triangles is discussed. However, the triangular formation is considered the most efficient for aerodynamic applications [29]. For this purpose, \(N\) UAVs were selected (\(N=10\)), which were ultimately divided into further sub-clusters and unit swarms. From Figures 8-12, the mentioned types of triangular formation have been generated through Python code. According to Figure 8, the green dots represent the allocated space for the UAVs, whereas the random appointment of UAVs was taken for the worst case. The length of the UAV swarm is shown along the x-axis in Figure 8, and width along the y-axis.  
According to Figure 8, the equilateral-swarm mode was presented with \(N=10\) UAVs, wherein it can be observed that none of them crossed the boundary. It was considered that the UAVs were propagating along the direction of length. In order to avoid intra-swarm collision, very type of swarm formation was iterated multiple times to get a reasonable spatial distribution. Secondly, in isosceles-swarm formation proceeded with the starched top of the triangle. Thirdly, the obtuse-swarm data were generated from critically packed UAVs; most of them are seen in a linear arrangement and random uniform distribution. Furthermore, the acute- and right swarms are shown to have similar distributions.  
Figure 8: UAV equilateral swarm formation.  
Figure 10: UAV obtuse swarm formation.  
Figure 9: UAV isosceles swarm formation.
## 3 Results and Discussion
### Study of DFB for Indirect Gasification
#### 3.2.1 Hydrodynamic Properties of the DFB Cold Model
Figure 7: Relationship between gasification bed and combustion bed aeration amount on the circulation flux of the bed sand: (**a**) the Gs response (3D) surface, and (**b**) equivalent Gs (contour lines) under different aeration conditions. Moreover, the characteristics of DFB were further analyzed. The multivariate principle was implemented to analyze the point-to-point correlation between the measured pressure points of the dual fluidized-bed system in the cold model. By this analysis, the pressure interaction between the different points can be predicted, which can be used in the future as a reference for the operation of the double bed. If the value of correlation between two points was greater than 0.5, it displayed the change of pressures in the same direction; on the other hand, a value less than \(-0.5\) indicated that the pressure dynamic had different direction. The tested results of the correlation coefficients at the 13 pressure points for the aforementioned three values of \(u_{\text{c}}\) are plotted in Figure 8. The significant correlations are positively displayed by red mark, the negative is marked as blue region, and green shows the case with small correlation. The figure shows that there was a positive correlation between P4 and P5, that is, the pressure between the two points was more likely to rise or to fall at the same time. Conversely, there was a negative correlation between P1 and P2, P3 and P4, P3 and P5, P9 and P10, as well as P10 and P11.
## 5 DMME on an Application Example--\"Feed Axle\"
### Technical Understanding and Conceptualization  
#### 5.2.1 Determine Technical Objectives  
First, the technical use cases for condition-based maintenance were analyzed and evaluated. One of the most important maintenance tasks is the timely rehubrication of moving parts in the machine. For this reason, the project objective was specified for the development of a DM method for detecting marginal lubrication. To ensure a good dissemination, the condition monitoring system should be developed for one of the most common product types.  
The linear, servo-controlled axle (Figure 5) seems to be suitable and was defined as the target system. The system included the PLC, the drive controller and the data acquisition software \"IoT Gateway\" as interface to the drive controller and data acquisition software. Bosch Rexroth supplied all components, which was also a constraint of the project. In order to keep the costs for the proof of concept low, a small size was selected. Later, the DM method should be transferred to larger sizes. In this first concept, cyclic positioning (such as pick-and-place), is defined as the first investigated process.  
Figure 4: The “Technical Implementation” task as part of the “Deployment” phase.  
As technical requirements were defined that no changes shall be made to the PLCs program and that only the internal sensor of the system may be used for DM.  
For the practical tests, a testbed is available in the laboratory. This testbed represents a sub-structure of a machine, but can map the essential characteristics. The technical goal was to prove the applicability of the DM method for the detection of abnormalities in the drive based on drive controller data. In the narrower sense, the marginal lubrication should be reliably identified. Reliably means that the lubrication condition must be clearly distinguishable from other system states. This is important in order to be able to assign the measures clearly and to generate an error-free message to the maintenance department for re-lubrication.  
Due to the objective of preparing a feasibility study, the user group was assigned to the development engineers involved. 6 months of project time were available. All requirements and boundary conditions were fixed in the project task.
## 3 Results and Discussion
### Kinetic Parameters of Sorption
#### 3.2.2 Pseudo Second-Order Model  
Figure 4 shows the linearized form of the pseudo-second order kinetic dependence for DY50 sorption from a solution of different initial concentrations on A21, A23 and A24. If the kinetics of the sorption process is described by means of the pseudo-second order model, the plot \(t/q_{t}\) vs. \(t\) gives a linear relationship from which \(q_{e}\) and \(k_{2}\) can be calculated and it is not necessary to know any parameter beforehand. In the case of the pseudo second order model, the \(k_{2}\) constant decreases as the concentration of dye in the aqueous solution increases. Based on the data presented in the Table 2 and Figures 3 and 4, it can be concluded that the PSO model received the best fit to the experimental data. This is indicated by not only high values of the determination coefficient \(R^{2}\) (0.999), but also by the values of \(q_{e,exp}\), which are very similar to those calculated from the PSO equation. Applicability of PSO model for description of experimental data in the DY50-cotton fiber [60] and DY50-natural clay and organolay adsorbents systems was described in paper by [PERSON] et al. [60] and [PERSON] [61] and is also summarized in Table 3. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & & \multicolumn{2}{c}{**PFO**} & \multicolumn{2}{c}{**PSO**} & \multicolumn{2}{c}{**ID**} \\ \cline{3-10}
**Resin** & \(\begin{pmatrix}C_{\theta}\\ \text{(mg/L)}\end{pmatrix}\) & \(\begin{pmatrix}q_{e,exp}\\ \text{(mg/g)}\end{pmatrix}\) & \(\begin{pmatrix}\mathbf{q_{e}}\\ \text{(1/min)}\end{pmatrix}\) & \(\begin{pmatrix}\mathbf{R^{2}}\\ \text{(mg/g)}\end{pmatrix}\) & \(\begin{pmatrix}\mathbf{q_{e}}\\ \text{(g/mg-min)}\end{pmatrix}\) & \(\mathbf{R^{2}}\) & \(\begin{pmatrix}\mathbf{k_{i}}\\ \text{(mg/g min}^{0.5}\end{pmatrix}\) & \(\mathbf{R^{2}}\) \\ \hline  & 100 & 10.0 & 2.38 & 0.0435 & 0.779 & 10.12 & 0.069 & 0.999 & 0.441 & 0.809 \\ A24 & 300 & 30.0 & 4.65 & 0.0334 & 0.715 & 30.28 & 0.0201 & 0.999 & 1.106 & 0.946 \\  & 500 & 50.0 & 9.19 & 0.0608 & 0.907 & 50.44 & 0.0128 & 0.999 & 1.732 & 0.924 \\ \hline  & 100 & 9.8 & 8.3 & 0.0163 & 0.915 & 10.3 & 0.0038 & 0.999 & 0.923 & 0.874 \\ A21 & 300 & 29.9 & 24.6 & 0.0297 & 0.991 & 31.9 & 0.0019 & 0.999 & 4.397 & 0.987 \\  & 500 & 49.9 & 37.5 & 0.0311 & 0.989 & 52.9 & 0.0015 & 0.999 & 5.612 & 0.874 \\ \hline  & 100 & 9.9 & 0.2 & 0.0137 & 0.257 & 9.9 & 1.1945 & 0.999 & 0.560 & 0.529 \\ A23 & 300 & 29.3 & 6.5 & 0.0219 & 0.917 & 29.3 & 0.0188 & 0.999 & 0.668 & 0.789 \\  & 500 & 42.9 & 21.5 & 0.0169 & 0.944 & 43.2 & 0.0032 & 0.999 & 3.184 & 0.998 \\ \hline \end{tabular}
\end{table} 
Table 2: The parameters determined from the pseudo-first order, pseudo-second order and intra-particle diffusion models in the DY50-A21, DY50-A23 and DY50-A24 adsorption systems. Figure 4: \(Cont.\)Figure 4: The \(t/q_{t}\) vs. \(t\) dependences determined from the linear form of the PSO model in (a) DY50—A24, (b) DY50—A24 and (c) DY50—A24 systems.
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 4 Experimental Verification and Analysis
### Experiment II  
#### 4.2.1 Description of the Dataset  
In order to further verify the applicability of the proposed model, the proposed method is applied to an actual damage dataset. The rolling bearing dataset used in Experiment II comes from Paderborn University (PU) and includes two types of bearing failure data: simulated machining damage data and actual bearing damage data [31].  
The experiment utilizes real damage data from ball bearings collected during accelerated life tests. The PU accelerated life test rig is depicted in Figure 7, comprising a bearing housing and an electric motor that drives a shaft containing four test bearings. In the PU dataset, the speed of the drive system, load torque, and radial load vary, and corresponding bearing vibration data are collected under four different operating conditions. The specific parameters for each condition are detailed in Table 4. In addition, the data collected under each condition include 13 different fault categories, and Table 5 provides information on the actual failure categories in the accelerated life tests conducted in this dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Working Condition** & **0** & **1** & **2** & **3** \\ \hline Load Torque (N/m) & 0.7 & 0.7 & 0.1 & 0.7 \\ Radial Force (N) & 1000 & 1000 & 1000 & 400 \\ Speed (rpm) & 1500 & 900 & 1500 & 1500 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Working condition information from PU dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Rearing Code** & **Damage** & **Combination** & **Bearing Element** & \multicolumn{2}{c}{**Characteristic**} & \multicolumn{1}{c}{**Label**} \\ \hline KA04 & fatigue: pitting & S & OR & single point & 0 \\ KA15 & plastic deform: indentations & S & OR & single point & 1 \\ KA16 & fatigue: pitting & R & OR & single point & 2 \\ KA22 & fatigue: pitting & S & S & single point & 3 \\ KA30 & plastic deform: indentations & R & OR & distributed & 4 \\ KB23 & fatigue: pitting & M & IR(+OR) & single point & 5 \\ KB24 & fatigue: pitting & M & IR(+OR) & distributed & 6 \\ KB27 & plastic deform: indentations & M & OR+IR & distributed & 7 \\ KI14 & fatigue: pitting & M & IR & single point & 8 \\ KI16 & fatigue: pitting & S & IR & single point & 9 \\ KI17 & fatigue: pitting & R & IR & single point & 10 \\ KI18 & fatigue: pitting & S & IR & single point & 11 \\ KI21 & fatigue: pitting & S & IR & single point & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Real damage bearing fault category in PU dataset.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Tasks** & **Baseline** & **MMD** & **MKMMD** & **CORAL** & **DANN** & **ADDA** & **MPGCTN** \\ \hline \(T_{0-1}\) & \(96.52\pm 1.34\) & \(95.78\pm 0.59\) & \(96.56\pm 1.86\) & \(95.43\pm 1.49\) & \(97.97\pm 1.59\) & \(98.21\pm 1.22\) & \(\mathbf{99.86\pm 0.10}\) \\ \(T_{0-2}\) & \(92.08\pm 1.13\) & \(94.35\pm 2.34\) & \(95.54\pm 1.18\) & \(89.83\pm 2.76\) & \(94.29\pm 1.35\)
# Evolution of Access Network Sharing and Its Role in 5G Networks
## 3 Sharing of Optical Access Networks  
Because fiber optic technology has large bandwidth capacity and low signal attenuation, it has largely superseded copper transmission in telecommunications networks. The only exception is in the network access, where short sections of copper lines are still used. Copper wire is used, in conjunction with fiber distribution, to deliver higher bandwidth DSL such as VDSL and G.FAST. Despite the cost of delivering new fiber to residential premises, an increasing number of operators are investing in FTTH deployment, in order to provide a future-proof mechanism to deliver higher capacity to residential and  
Figure 2: NGA Bitstream architecture.  
business clients. Indeed, optical access networks, in their different forms, are today the technology of choice for operators upgrading their network, offering high-speed broadband to both residential and enterprise users. Because optical access networks were deployed after the de-regulation of telecoms networks, line unbundling regulations do not necessarily apply, and different regions have adopted completely different approaches. The US, Europe and Japan are an example where different regulatory choices have led to different levels of FTTH deployment, with Europe and the US having experienced a much lower coverage than Japan [13].  
Thus, sharing of optical access networks is today not common across the world. In places where sharing is in place, the approach has typically been limited either to fiber unbundling, that is, where the optical access is point-to-point fiber, or to higher layer NGA bitstream. In NG-PON2, where multiple wavelengths are available, it is possible, in principle, to separate OLOs by wavelength. However, there are still a number of technical issues. One issue relates to the ownership of the Optical Line Terminations (OLTs), as multiple wavelengths could interfere in certain cases, if not controlled by the same system. Another issue is that the allocation of a PON wavelength to one OLO is static and inefficient, preventing capacity unused by one PON to be used by other operators. Furthermore, NG-PON2 currently defines only 8 wavelengths for Time Division Multiplexing (TDM) access and the technology is not yet widespread, due to the high cost of the end-users tunable Optical Network Terminals (ONTs). More dynamic techniques have been discussed, for example in [14; 15], and could be made technically feasible with the recent development of SDN and NFV in the central office [16]. In the rest of this section, we briefly introduce some of the prevailing optical access technologies and review their major challenges with respect to multi-tenancy.
## 2 Data and Methodology
### Methodology
#### 2.3.2 Vegetation Water Content Estimation  
The VWC is the most vital parameter in the WCM. To remove the effect of vegetation on SSM estimation, the VWC needs to be calculated. Published studies [26; 36; 37] have indicated the spectral indices calculated from remote sensing images can be used for VWC estimation. The spectral indices commonly include NDVI, NDWI, and MCARI. The calculation formulas are as follows:  
\[\text{NDVI}=\frac{R_{nir}-R_{red}}{R_{nir}+R_{red}}=\frac{R_{i8}-R_{b4}}{R_{i8} +R_{b4}} \tag{6}\]  
\[\text{NDWI}=\frac{R_{nir}-R_{swir}}{R_{nir}+R_{swir}}=\frac{R_{i8}-R_{b11}}{R_ {i8}+R_{b11}} \tag{7}\]  
\[\text{MCARI}=\Big{[}\Big{(}R_{veg-red}-R_{red}\Big{)}-0.2\Big{(}R_{veg-red}-R_ {green}\Big{)}\Big{]}\times\frac{R_{veg-red}}{R_{red}}=[(R_{b5}-R_{b4})-0.2(R _{b5}-R_{b3})]\times\frac{R_{b5}}{R_{b4}} \tag{8}\]  
where \(R_{nir}\) is the reflectance of the near-infrared band, \(R_{red}\) is the reflectance of the red band, \(R_{blue}\) is the reflectance of the blue band, \(R_{swir}\) is the reflectance of the shortwave infrared band, and \(R_{bx}\) is the reflectance of the \(x\)-band of the S2 satellite.  
It was demonstrated that the relationship between the VWC and vegetation indices is linear, one-variable quadratic, or exponential [38; 39]. Considering that the study area is flat and the influence of soil roughness is not taken into account, the land cover is mainly farmland, which is similar to the research conditions carried out by [PERSON] et al. [40] in Spain and the UK. Therefore, the VWC can be expressed as follows:  
\[M_{v}=a\cdot\text{VI}^{2}+b\cdot\text{VI}+c \tag{9}\]
## 2 Related Works
It also detects glissades as separate event types. However, glissades are detected based on duration only, which may lead to short saccades being classified as glissades or long glissades being classified as saccades. The algorithm can identify fixations, saccades, and glissades only. It does not consider smooth pursuits. Threshold-based event detection methods require several threshold parameters, and finding the optimal values can be challenging. Machine learning and deep learning approaches can address these problems by applying end-to-end, fully automated event detection without human intervention or manually setting parameters like thresholds. These approaches are becoming popular in eye movement event detection. For instance, Ref. [30] proposed a fully automated eye movement event classification using a random forest classifier. The method classifies the input data into fixations, saccades, and PSOs. According to the authors, the machine learning approach outperforms other event detection methods. However, this method does not consider the important event type (smooth pursuit). The other fully automated event detection method was proposed in [22]. It is based on a deep convolutional neural network that predicts probabilities for each sample to belong to a fixation, saccade, or smooth pursuit based on a sequence of gaze samples. The approach uses the signal's frequency to classify the data into event types. The method classifies fixations, saccades, and smooth pursuits only. The comparison is done with velocity and dispersion threshold algorithms only. Ref. [21] showed another example of applying deep learning to event detection. The proposed network combines a 1D convolutional network with a BLSTM layer. It classifies raw eye movement data into fixations, saccades, and smooth pursuits. Individual feature sets used for the model are raw xy coordinates, speed, acceleration, and direction. According to the authors, the combination of direction and speed provided good results. Acceleration as an additional feature did not improve the average event classification, probably because it cannot be used to distinguish smooth pursuits from fixations. The algorithm identifies fixations, saccades, and smooth pursuits. It does not consider PSO event types. Another deep learning approach in [31] is proposed for online event classification for fixations, saccades, and smooth pursuits. In summary, there have been many attempts to utilize machine learning and deep learning for eye movement event detection. However, to the authors' knowledge, none of them attempted to combine the four eye movement features used in this work or to develop a model that simultaneously classifies eye movement data into four event types, i.e., fixations, saccades, smooth pursuits, and post-saccadic oscillations.
## 4 Experimental Results and Discussion
### SFO Compensation Performance in Different Underwater Environments  
Broken surface waves and rain produce air bubbles in the ocean. To evaluate the influence of air bubbles on the compensation performance of UWOC, we put the oxygen pump in the water tank to generate the bubbles and disturbances. The physical device diagram is shown in Figure 7. The exhaust volume is set to 16 L/min and the experiments are carried out under a 1 m tap water link. The EVM performance after SFO compensation is shown in Table 2. The results show that the EVM penalties are similar to the cases without bubbles. Compared with the SFO-free case, the EVM at 200 ppm has no loss after compensation. The EVM penalty is 2 dB in the presence of 1000 ppm SFO. It can prove that the air bubbles have little impact on SFO compensation performance, which shows the proposed SFO estimation and compensation method is robust.  
Figure 6: EVM versus sampling clock frequency offset. Subfigures show the 16-QAM constellations (**a–d**) without SFO compensation; (**e–h**) with SFO compensation.  
Light propagation undergoes serious reduction due to the absorption and scattering in seawater [33]. Therefore, we add sea salt in the 0.5 m link to assess the system performance. Since a longer communication distance always means a lower received optical power and SNR, we move the condenser lens away from the direction perpendicular to the incident light to reduce the SNR of the receiver, which can show the potential of the UWOC system at low SNR or long-distance transmission. The BER performance over the different salinity of water is presented in Figure 8 and the corresponding receiving optical power (ROP) is listed in Table 3. It is worth noting that the transform model of BER and SNR/EVM based on Gaussian noise is still applicable in the underwater channel [11]. It can be found that the concentrations of sea salt-added water are dominated for the signal degradation when the SFO is 0 ppm. With the increase in salinity, the signal attenuation is more serious due to the scattering effect, which results in the deterioration of BER performance. In the presence of SFO, the main reason for the deterioration in system performance becomes the SFO-induced phase rotation. The BER is approximately 0.4 without SFO compensation. With the help of the proposed SFO compensation method, even if the SFO is up to 1000 ppm, the BER performance has been greatly improved and is very close to the ideal case without SFO. The constellation diagrams corresponding to 1000 ppm, 200 ppm and 0 ppm are inserted in Figure 8a-c, respectively. It can be seen from the constellation diagrams that up to 1000 ppm SFO can be well-compensated with slight BER penalty.
## 2 Methods  
General description of the application is described in Figure 1. The user defines the input study, which contains the full set of MRI images of a single test. As an output, the application displays only the images that are relevant to the diagnostic process. The MR images within a selected study initially pass through two filters:  
* File type selection filter which removes all images of irrelevant MRI types
* Range filter and image alignment which removes images with irrelevant brain regions  
The remaining files (5-8%) continue to a preprocessing stage, which performs skull stripping.  
The next step, detection of MS lesions, employs four separate processing procedures: Solidity Detection and Object Size, Boundaries Detection, Object Shape Detection and Grayscale Range Detection.