esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Point Cloud Representation
#### 2.2.1 A Spherical Image  
Conversion of a point cloud to the form of a spherical image is the most frequently applied method, implemented in many commercial software tools [7; 8; 12; 35; 36; 37]. Raw data are used for processing, and generation of raster images with the maximum resolution does not require interpolation of the coordinate values for pixels.  
A point cloud acquired from terrestrial laser scanning is an arranged set of data. Two angles, the distance between the station and the measured object and the intensity of the laser beam reflectance, are recorded in the course of the measurements. A mathematical method of processing data from the polar form to the spherical image form has been described by Reference [38] (Figure 1).  
Figure 1: Relations between spherical coordinates and coordinates on spherical photographs, where O is the origin of the polar system, P is a point in the 3D space, P is the point projection on the sphere, \(\theta\) is the horizontal angle, \(\varphi\) is the vertical angle, and r is the radius of the sphere. (**a**) Graphical representation of the relations between polar coordinates measured by the terrestrial laser scanner and coordinates on the raster image in spherical projection [38], (**b**) formula which allows for recalculation of polar coordinates onto raster coordinates in spherical projection, and (**c**) formula that allows for recalculation of x,y raster coordinates in spherical projection onto polar coordinates.  
A spherical image for which the raster grey-level value assumes the laser beam reflectance intensity value is used, together with the map of depth (i.e., the distance to the analysed object), for TLS data orientation.
# Seismological and Engineering Demand Misfits for Evaluating Simulated Ground Motion Records
###### Abstract  
Simulated ground motions have recently gained more attention in seismology and earthquake engineering. Since different characteristics of waveforms are expected to influence alternative structural response parameters, evaluation of simulations, for key components of seismological and engineering points of view is necessary. When seismological aspect is of concern, consideration of a representative set of ground motion parameters is imperative. Besides, to test the applicability of simulations in earthquake engineering, structural demand parameters should simultaneously cover a descriptive set. Herein, simulations are evaluated through comparison of seismological against engineering misfits, individually defined in terms of log-scale misfit and goodness-of-fit score. For numerical investigations, stochastically simulated records of three earthquakes are considered: The 1992 Erzincan-Turkey, 1999 Duzce-Turkey and 2009 L'Aquila-Italy events. For misfit evaluation, seismological parameters include amplitude, duration and frequency content, while engineering parameters contain spectral acceleration, velocity and seismic input energy. Overall, the same trend between both misfits is observed. All misfits for Erzincan and Duzce located on basins are larger than those corresponding to L'Aquila mostly placed on stiff sites. The engineering misfits, particularly in terms of input energy measures, are larger than seismological misfits. In summary, the proposed misfit evaluation methodology seems useful to evaluate simulations for engineering practice.  
ground motions; stochastic finite-fault ground motion simulation technique; seismological measures; engineering demand parameters; log-scale misfits; goodness of fit score +
Footnote †: journal: Applied Sciences
# Processing of AZ91D Magnesium Alloy by Laser Powder Bed Fusion
## 3 Results and Discussion
### Future Outlook  
As the relative density results of _bulk samples III_ show a fairly even distribution of porosity over height, the mechanical properties exceed those achievable by the casting process, and the values are still comparable to recent studies, future research will be aimed at assessing the fatigue properties. Research has shown that some lightweight materials fabricated by the LPBF process can have good high-cycle fatigue properties [32]. The S-N curves show that they can withstand over \(5\times 10^{7}\) cycles without sample failure [33]. With optimization of the main process parameters such as laser power, laser speed, hatch distance and laser defocusing, control of the fatigue life can be reached [34]. Furthermore, the results of _bulk samples II_ suggest that the energy density should be optimized for each part geometry (thin, thick and overhanging) to achieve uniform microstructure and properties [35].  
Figure 13: SEM image of microstructure in longitudinal section.  
\begin{table}
\begin{tabular}{l c c c c} \hline  & **Mg** & **Al** & **Zn** & **Mn** \\ \hline
**Matrix \(\alpha\)-Mg** & 94.33 & 5.35 & 0.32 & - \\
**Eutectic-phase \(\beta\)-Mg\({}_{17}\)Al\({}_{12}\)** & 80.27 & 15.78 & 1.40 & - \\
**Precipitates rich in Mn** & 79.35 & 15.31 & 1.23 & 4.11 \\ \hline \end{tabular}
\end{table}
Table 8: Results of EDS chemical microanalysis of individual phases [wt.%].
## 3 Results and Discussion
### Numerical Modelling Analyses
Figure 7: Beam deflection subjected to tensile stress of **(a)** 1.8 MP, immediate reaction; **(b)** 1.8 MPa, deflection after 200 h; **(c)** 2.25 MPa, immediate reaction; **(d)** 2.25 MPa, deflection after 300 h; **(e)** 2.7 MPa, immediate reaction; **(f)** 2.7 MPa, after 500 h. (where red color represent max. defection and blue represents min. deflection). Figure 8: Total time-dependent displacement curves of tunnel walls gained using analytical and finite element methods according to various values of GSI (40, 50, 60). Figure 9: Vertical displacements of tunnel wall (**a**) at GSI 40, immediate reaction; (**b**) at GSI 40, after 200 h; (**c**) at GSI 40, after 500 h; (**d**) at GSI 50, immediate reaction; (**e**) at GSI 50, after 200 h; (**f**) at GSI 50, after 500 h; (**g**) at GSI 60, immediate reaction; (**h**) at GSI 60, after 200 h; and (**i**) at GSI 60, after 500 h. (heat map presents the displacement distribution).
###### Abstract  
In the Alpine environment, snow plays a key role in many processes involving ecosystems, biogeochemical cycles, and human wellbeing. Due to the inaccessibility of mountain areas and the high spatial and temporal heterogeneity of the snowpack, satellite spatio-temporal data without gaps offer a unique opportunity to monitor snow on a fine scale. In this study, we present a random forest approach within three different workflows to combine MODIS and Sentinel-2 snow products to retrieve daily gap-free snow cover maps at 20 m resolution. The three workflows differ in terms of the type of ingested snow products and, consequently, in the type of random forest used. The required inputs are the MODIS/Terra Snow Cover Daily L3 Global dataset at 500 m and the Sentinel-2 snow dataset at 20 m, automatically retrieved through the recently developed revised-Let It Snow workflow, from which the selected inputs are, alternatively, the Snow Cover Extent (SCE) map or the Normalized Difference Snow Index (NDSI) map, and a Digital Elevation Model (DEM) of consistent resolution with Sentinel-2 imagery. The algorithm is based on two steps, the first to fill the gaps of the MODIS snow dataset and the second to downscale the data and obtain the high resolution daily snow time series. The workflow is applied to a case study in Gran Paradiso National Park. The proposed study represents a first attempt to use the revised-Let It Snow with the purpose of extracting temporal parameters of snow. The validation was achieved by comparison with both an independent dataset of Sentinel-2 to assess the spatial accuracy, including the snowline elevation prediction, and the algorithm's performance through the different topographic conditions, and with _in-situ_ data collected by meteorological stations, to assess temporal accuracy, with a focus on seasonal snow phenology parameters. Results show that all of the approaches provide robust time series (overall accuracies of A1 = 93.4%, and A2 and A3 = 92.6% against Sentinel-2, and A1 = 93.1%, A2 = 93.7%, and A3 = 93.6% against weather stations), but the first approach requires about one fifth of the computational resources needed for the other two. The proposed workflow is fully automatic and requires input data that are readily and globally available, and promises to be easily reproducible in other study areas to obtain high-resolution daily time series, which is crucial for understanding snow-driven processes at a fine scale, such as vegetation dynamics after snowmelt.  
data fusion; gap-filling; MODIS; random forest; Sentinel-2; snow cover; snow cover duration 1  
[PERSON], [PERSON], [PERSON], C.
## 2 Materials and Methods
### Input Data
#### 2.3.2 Solar Irradiance  
The solar resource data used in this analysis provide global horizontal irradiance (GHI) along with the clearness index. The clearness index is the ratio of the measured solar radiation at the Earth's surface to the extraterrestrial solar radiation under clear-sky conditions, indicating atmospheric clarity and cloud cover. The annual average radiation is 4.43 kWh/m\({}^{2}\)/day, indicating a good potential for solar energy utilization. The clearness index varies throughout the year, with higher values in summer when daily radiation peaks at approximately 7 kWh/m\({}^{2}\)/day. Conversely, lower values are observed during winter, with daily radiation dropping to as low as 2 kWh/m\({}^{2}\)/day. This variability reflects seasonal changes and highlights the importance of storage or complementary energy systems to ensure consistent energy availability.
# Multi-Band Array Antenna Sharing a Common Aperture with Heterogeneous Array Elements
## 3 Verification of the Proposed Multi-Band Array Antenna
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref** &
\begin{tabular}{c} **Freq.** \\ **(GHz)** \\ \end{tabular} & **Area** & **Array** & **Application** & **Gain** & **SLL (dB)** \\ \hline
[10] & 21 & \(3.85\lambda\times 3.85\lambda\) & \(4\times 4\) &
\begin{tabular}{c} radar and \\ communication \\ \end{tabular} & \(17.8\) dBi & \(-16.2\) \\
[11] & 3.5 & \(2.3\lambda\times 1.17\lambda\) & \(1\times 4\) & 5G base stations & NA & NA \\
[12] & 5.75 & \(0.127\lambda\times 0.127\lambda\) & NA & NA & \(1.3\) dB increase & NA \\
[13] & 37 & \(10.48\lambda\times 10.48\lambda\) & \(8\times 8\) & Ku-band CubeSat & \(24.04\) dBic & NA \\
[14] & 26.5 & \(4.73\lambda\times 0.274\lambda\) & \(1\times 12\) & 5G Millimeter-wave & \(14.1\) dBi & \(-16.2\) \\
[15] & 1.96 & \(2.16\lambda\times 0.54\lambda\) & \(1\times 4\) & \
\begin{tabular}{c} Unmanned aerial \\ vehicle (UAV) \\ \end{tabular} & \(9.98\) dBi & \(-4\) \\ Proposed & 3.25 & \(1.6\lambda\times 1.6\lambda\) & \(3\times 3\) &
\begin{tabular}{c} 15.8 dBi \\ 23.24 dBi \\ \end{tabular} & \(-13.3\) \\ work & 9.5 & \(2.2\lambda\times 2.2\lambda\) & \(4\times 4\) & MFR radar & \(23.24\) dBi & \(-22\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the proposed array antenna and reference antennas. the blue and red lines denote the expanded array beam pattern before and after mounting on the ship mast, respectively. The bore-sight gain of array with ship mast is 35.6 dBi, which is about 0.8 dB higher than the bore-sight gain of array without ship mast. The side lobe level (SLL) for the array beam pattern is about \(-13.3\) dB. Figure 9c,d presents the simulated radiation patterns for the expanded \(4\times 4\) S-band array. The bore-sight gain of the array with the ship mast is 23.249 dBi, which is about 0.998 dB lower than the bore-sight gain of array without the ship mast. The SLL for the array beam pattern is about \(-22\) dB. Figure 8: The geometry of the \(12\times 12\) X-band array configuration. Figure 7: Simulated and measured steering array beam patterns at 9.5 GHz.
# Efficient and Accurate Hemorrhages Detection in Retinal Fundus Images Using Smart Window Features
## 2 Materials and Methods  
### Dataset Setup  
The proposed method was tested with two datasets (DIARETDB0 and DIARETDB1). The first dataset has 130 retinal fundus images, 110 images contain the signs of DR, and 20 normal images [23]. The second dataset has 89 fundus images, with 84 of these images having mild non-proliferative signs and 5 normal images [24]. The images in both datasets have the dimension of 1152 \(\times\) 1500 pixels and a 50-degree field of view digital fundus camera with various imaging settings.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 4 Result Discussion and Analysis
### Results and Discussion
#### 4.2.6 Relative Percentage Deviation (RPD)  
A relative percentage deviation represents the percentage deviation of a solution from the best one and can be defined by the following equation:  
\[RPD=\frac{|E-E_{best}|}{E_{best}}\times 100 \tag{23}\]  
where \(E\) is the energy required for the drone to follow the generated path from the starting point to the ending point by algorithms, and \(E_{best}\) is the energy required for the shortest path from the starting point to the ending point, regardless of obstacles. Results are obtained for 30 independent runs and RPD is calculated for all algorithms. We also apply a complete random design on the generated data. The aim is to show if these algorithms are significantly different at a significance level of 0.05. We test the normality of the data using the Anderson-Darling tool, and the results are illustrated in Figure 9.  
Figure 8: SLR Tukey pair-wise comparison.  
Figure 9: RPD normality test.  
The \(p\)-value is less than \(0.005<\alpha\), which indicates that the data are not normal. Hence, we apply a non-parametric test, Kruskal-Wallis, with significance level of 0.05. We also compare the 95% confidence intervals for all algorithms. Results are plotted in Figure 10 for all UAVs. The CIs illustrate that IPSO shows the smallest RPD, which provides more accurate and diversity results. Thus, there is a difference between algorithms on RPD. This can be confirmed by applying the one-way ANOVA test to validate the results for different algorithms, and applying the Kruskal-Wallis test under two hypotheses. The null hypothesis assumes that there is no significant difference among the four algorithms, while the alternative hypothesis assumes that at least one algorithm differs from the others. The test statistic has a \(p\)-value of zero for all UAVs, indicating that the null hypothesis is rejected at \(\alpha\) level higher than 0.05, in favor of the alternative hypothesis. Hence, there is a significant difference among algorithms on the RPD.  
To compare different algorithms, we employ the Tukey-Kramer pair-wise test. The results of different confidence intervals (CIs) of the statistical test are illustrated in Figure 11. Taking UAV1 as an example, the different CIs of IPSO-PSO, IPSO-ABC, and PSO-ABC include zero, indicating that there is no significant difference between them. However, the different CIs of GA-PSO, GA-IPSO, and GA-ABC do not include zero, indicating a significant difference between GA and the other algorithms.  
Figure 11: RPD Tukey pair-wise comparison.  
Figure 10: RPD confidence interval.  
It is evident that IPSO yields lower average RPD values than the other competitors for all UAVs, followed by PSO and ABC, while GA exhibits the highest average RPD values. Although IPSO and PSO closely compete in UAV1, IPSO generally demonstrates superior results.
## 4 Numerical Results
### Real-Scale Rural Network  
The real-scale rural test network is the regional road network of Campania, an Italian Region with more than 5.8 million inhabitants. The network, as represented in Figure 7, has 764 road links, representing about 8700 km of roads, 262 road nodes and 91 centroids.  
We tested the proposed approach considering the OD matrix in the morning peak-hour, where the total trips are 165,328 veh/h; we also tested values of demand between 0.6 and 2.0 times the actual value, producing an average saturation degree between 0.4 and 1.25 (see Table 8). Moreover, in this case, we use the Multinomial Logit route choice model with the implicit enumeration of paths [27].  
On this network, we tested the proposed algorithm with the same values of parameter \(\eta\) previously adopted, assuming a coefficient of variation equal to 0.3. Moreover, in these cases, the proposed algorithm was able to reduce the iterations for convergence with some values of \(\eta\) lower than 1 (MSA). The best performances were obtained with values between 0.3 and 0.6 (see Table 9); the reductions in the number of iterations reached \(-69\%\) with respect to the traditional version of the MSA algorithm. In Figure 8, the results of Table 9 are illustrated.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Matrix** & **0.6** \(d\) & **0.8** \(d\) & **1.0** \(d\) & **1.2** \(d\) & **1.4** \(d\) & **1.6** \(d\) & **1.8** \(d\) & **2.0** \(d\) \\ \hline Av. Sat. Degree & 0.40 & 0.53 & 0.65 & 0.77 & 0.90 & 1.01 & 1.13 & 1.25 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average saturation degrees on the rural real-scale network.  
Figure 6: Results on the urban real-scale network (\(C_{v}=0.05\)).  
Figure 7: Rural real-scale network.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Iterations Needed for Convergence**} \\ \hline
**Matrix** & **0.6**_d_ & **0.8**_d_ & **1.0**_d_ & **1.2**_d_ & **1.4**_d_ & **1.6**_d_ & **1.8**_d_ & **2.0**_d_ \\ \hline \(\eta=0.3\) & **7** & **10** & 12 & 15 & 19 & 26 & 34 & 44 \\ \(\eta=0.2\) & 9 & 12 & 16 & 21 & 27 & 36 & 48 & 62 \\ \(\eta=0.1\) & 13 & 19 & 26 & 35 & 47 & 63 & 87 & 115 \\ \(\eta=0.05\) & 19 & 31 & 45 & 61 & 84 & 117 & 163 & 218 \\ \(\eta=0.01\) & 60 & 114 & \(>\)999 & 258 & 366 & 525 & 747 & \(>\)999 \\ \hline \multicolumn{6}{c}{**Percentage Variations with Respect to MSA**} \\ \hline
**Matrix** & **0.6**_d_ & **0.8**_d_ & **1.0**_d_ & **1.2**_d_ & **1.4**_d_ & **1.6**_d_ & **1.8**_d_ & **2.0**_d_ \\ \hline \(\eta=1\)(MSA) & - & - & - & - & - & - & - \\ \(\eta=0.9\) & \(-16\%\) & \(23\%\) & \(-23\%\) & \(-24\%\) & \(-25\%\) & \(-30\%\) & \(-27\%\) & \(-27\%\) \\ \(\eta=0.8\) & \(-26\%\) & \(-8\%\) & \(-31\%\) & \(-41\%\) & \(-42\%\) & \(-48\%\
# Comparison of Disturbance Compensators for a Discrete-Time System with Parameter Uncertainty
## 4 Comparison and Results
### Principles for Compensators' Comparison  
Concerning the comparative evaluation of different methods, one key point is to select the appropriate principles to evaluate the performance level provided by each of the considered methods.  
Here, a direct principle of compensation performance evaluation appears to be the compensation error of the parameter uncertainty \(\Delta\Phi x(k)\), which can be calculated as \(\overline{u}_{di}=\Delta\Phi x(k)-u_{di}(k),\,i=1,\,2,\,\ldots,\,6\). This directly reflects the compensator's ability to observe and compensate the parameter uncertainty.  
Another principle is the width of the switching function \(s(k)\) in the steady state which, of course, reflects the performance of the compensator and, also, influences the final control accuracy of the closed-loop system.  
Regarding a fair comparison, every disturbance compensator is combined with the same reaching law, Equation (16), to construct the discrete-time sliding mode controller (DSMC), i.e., \(u_{i}(k)\), \(i=1,\,2\),..., \(6\), and the DSMC system. The corresponding DSMC controllers and the closed-loop performance analyses can be found in [6, 7, 11, 12, 13, 15], respectively. To better evaluate and compare the compensators' performance, a DSMC controller without a disturbance compensator, i.e., \(u_{7}(k)\) in [16] by [PERSON], is formulated based on the reaching law, Equation (16). Hence, the following seven DSMC controllers are adopted in simulation:  
\[\begin{array}{llllll}\text{ DSMC 1 }u_{1}(k):&\text{NSDE }u_{41}(k)&+&\text{reaching law (16);}\\ \text{ DSMC 2 }u_{2}(k):&\text{OSDE }u_{42}(k)&+&\text{reaching law (16);}\\ \text{ DSMC 3 }u_{3}(k):&\text{TSDE }u_{43}(k)&+&\text{reaching law (16);}\\ \text{ DSMC 4 }u_{4}(k):&\text{SDC }u_{44}(k)&+&\text{reaching law (16);}\\ \text{ DSMC 5 }u_{5}(k):&\text{DDC }u_{45}(k)&+&\text{reaching law (16);}\\ \text{ DSMC 6 }u_{6}(k):&\text{DTDO }u_{46}(k)&+&\text{reaching law (16);}\\ \text{ DSMC 7 }u_{7}(k):&+&\text{reaching law (16).}\end{array}\]  
A same sampling time is a basic condition for a fair comparison, which is selected as 0.01 s.  
The control parameters \(q=0.5\), \(\xi=0.01\) and \(C=\) [5, 1] in these controllers are tuned by a trial-and-error method to ensure stable closed-loop systems. The additional parameters \(g=0.5\) in \(u_{5}(k)\) and \(\Lambda=\text{diag}\{0.4,\,0.4\}\) in \(u_{6}(k)\) are adjusted using repetitive simulations to achieve optimal results. Moreover, we pick \(N=5\) in \(u_{1}(k)\).
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
## 3 Results: Telemonitoring System
### Mobile-Web-Application Focused on the Patient
#### 3.2.1 Glucose Record  
In the glucose registration module, the patient enters the results of his glucose levels; if the results are outside the ranges established by physicians, alerts are triggered with messages that recommend to the patient a set of preliminary actions that should be taken. Both messages and ranges for alerts were defined by medical specialists (see Figure 36). This is one of the main services, because the data that are recorded are an essential factor for the monitoring and treatment of patients to detect timely and avoid risky situations.  
This section also has a history of laboratory results (see Figure 37), with the objective that the patient observes the progress of the treatment.