esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Attenuation of RF Signal by Rain  
Theoretical models have been developed to predict impairment and simulate and evaluate performance of fade mitigation techniques. Signal attenuation due to rain increases in proportion to the rainfall intensity and is also related to signal frequency. Upon obtaining prediction of rain, the attenuation must be calculated. The best known and utilized models for prediction of rain attenuation are those of the International Telecommunication Union  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & \(\sigma^{2}_{R}\) & \(\sigma^{2}_{r}\) & \(m_{R}\) (mm/h) & \(m_{r}\) (mm/h) \\ \hline Southwest France & 2.38 & 1.95 & 0.63 & \(-1.43\) \\ \hline Ivory Coast & 108 & 1.74 & 4.81 & 0.7 \\ \hline Caribbean & 23 & 2.12 & 1.77 & \(-0.49\) \\ \hline French Guyana (equatorial coastal) & 0.42 & 1.85 & 0.24 & \(-2.35\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Typical parameters for various regions.  
Figure 1: Lognormal distribution for values of parameters in Table 1.  
Radiocommunication sector, detailed in Section 1. The ITU formula for specific attenuation of rain is given by a power law relation  
\[\gamma_{r}(r_{r})=kr_{r}{}^{\alpha} \tag{4}\]  
where \(\gamma_{r}\) is the specific attenuation (dB/km), \(r\) is the rain rate (mm/h), and \(k\) and \(\alpha\) are frequency dependent coefficients given specifically in the recommendation [17], where they are calculated for linear and circular polarization dependent on path geometry (path elevation angle and polarization tilt angle). This model is defined for frequencies ranging from 1 to 1000 GHz. All these model assumptions include stationarity of rainfall.  
At high frequencies, particle diameter is relatively large compared to wavelength. It was found [8] that rain attenuation increases in proportion to frequency for frequencies below approximately 50 GHz, but that there is a peak at around 100 GHz. At higher frequencies, the degree of attenuation remains constant or decreases. This work employed the ITU-R power law for calculation of attenuation. Other approaches based on Mie scattering found that the Weibull distribution [36] is suitable above 30 GHz, or later, that Best and P-S distributions are appropriate from 90 to 225 GHz while the ITU-R model and Mie scattering with Weibull are best at 313 and 355 GHz [8]. Table 2 shows parameters for the specific attenuation for various frequencies, as calculated with mathematics from the formula given in [17]. The computation is for circular polarization but can easily be utilized for specific linear polarization as well. Polarization tilt relative to horizontal and path elevation were taken as 45 degrees.
## 4 Results
### Machine Learning Analysis  
We also focused on identifying relations between gait features and a participant's group (PD and non-PD, EL, S) or the medication state (ON/OFF). Moreover, a machine learning analysis was performed based on the severity levels as denoted by the MDS-UPDRS Part III Total Score. The results of the classification studies using data derived from the Smart-Insole Gait Assessment Protocol for PD patients are presented in Table 8.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Statistical Significance** & \multicolumn{6}{c}{\(p\)**-Values**} \\ \hline \hline \multirow{2}{*}{**Related Class**} & \multicolumn{3}{c}{**Medication State**} & \multicolumn{3}{c}{**MDS-UPDRS-Part III/**} \\  & & \multicolumn{3}{c}{**ON/OFF**} & \multicolumn{3}{c}{**Severity Levels**} \\ \hline \multirow{2}{*}{**Type of Test**} & **WST Slow** & **WST Non-** & **WST High** & **mTUG** & **WST Slow** & **WST Non-** & **WST High** & **mTUG** \\ \hline Left Step Duration (s) & **0.010** & 0.923 & 0.665 & 0.257 & 0.154 & 0.276 & 0.300 & 0.679 \\ Right Step Duration (s) & **0.006** & 0.923 & 0.302 & 0.929 & **0.049** & **0.016** & 0.783 & 0.614 \\ Step Duration (s) & **0.003** & 0.274 & 0.216 & 0.477 & 0.055 & 0.110 & 0.349 & 0.959 \\ Stride Duration (s) & **0.004** & 0.823 & 0.213 & 0.486 & 0.055 & **0.043** & 0.415 & 0.891 \\ Steps Number & **0.018** & 0.073 & **0.000** & **0.021** & **0.018** & **0.001** & 0.434 & 0.132 \\ Single Support Time (s) & **0.010** & 0.562 & **0.000** & 0.158 & **0.011** & **0.004** & 0.114 & 0.193 \\ Double Support Time (s) & 0.454 & 0.677 & **0.023** & 0.651 & 0.504 & 0.753 & 0.124 & 0.915 \\ Stance Time (s) & **0.007** & 0.829 & 0.894 & 0.943 & **0.048** & 0.149 & 0.711 & 0.660 \\ Swing Time (s) & **0.000** & **0.003** & **0.001** & **0.040** & **0.005** & **0.000** & **0.003** & 0.217 \\ Single Support (\%) & 0.074 & 0.757 & **0.003** & 0.328 & **0.061** & 0.447 & 0.593 & 0.254 \\ Double Support (\%) & 0.139 & 0.247 & **0.000** & 0.809 & 0.693 & 0.169 & **0.005** & 0.871 \\ Stance Phase (\%) & 0.458 & 0.289 & **0.013** & 0.290 & 0.611 & **0.006** & **0.041** & 0.811 \\ Swing Phase (\%) & 0.683 & 0.222 & **0.010** & 0.345 & 0.611 & **0.006** & **0.041** & 0.811 \\ Gait Velocity (m/s) & 0.792 & **0.026** & 0.152 & **0.003** & **0.010** & **0.004** & 0.334 & **0.036** \\ Step Length (m) & 0.136 & **0.043** & 0.056 & **0.009** & **0.031** & **0.026** & 0.679 & **0.082** \\ Stride Length (m) & 0.179 & **0.037** & 0.091 & **0.012** & **0.055** & **0.042** & 0.630 & **0.060** \\ Step Frequency (steps/min) & **0.020** & **0.000** & 0.053 & 0.926 & **0.020** & 0.543 & **0.068** & 0.906 \\ Walk Ratio (mm/step/min) & **0.008** & **0.042** & 0.150 & **0.032** & 0.337 & 0.382 & 0.846 & 0.262 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Features affected in a statistically significant manner (\(p<0.05\)), elaborating WST Slow-Normal-High and mTUG tests, relevant to the ON/OFF medication state and MDS-UPDRS-Part III ratings in relation to the severity levels.
# Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
###### Abstract  
A technology drift is currently taking place from traditional battery-powered sensor networks, which exhibit limited lifetime, to the new Energy-Harvesting Wireless Sensor Networks (EH-WSN), which open the way towards self-sustained operation. However, this emergent modality also brings up new challenges, especially due to the time-varying nature and unpredictability of ambient energy sources. Most proposals for implementing EH-WSN rely on heuristic approaches to redesign the duty-cycling mechanism at the MAC layer, with the ultimate goal of optimizing network performance while preserving self-sustained and continuous operation. In contrast to the common system-wide reduced duty cycle of battery-powered sensor networks, the duty cycle in EH-WSN is much larger and adapted to the energy harvesting rate and traffic load of each node in the network. In this paper, we focus on solar-based EH-WSN devoted to environmental monitoring. In contrast to current works, we follow an analytical approach, which results into closed-form expressions for the duty cycle and initial energy storage that guarantee self-sustained operation to any node in a solar-based EH-WSN. To center the analysis, we consider TinyOS sensor nodes, though we postulate that the essential components of the obtained formulation will contribute to further develop duty cycle adaptation schemes for TinyOS and other software platforms.  
wireless sensor network; energy consumption model; duty cycle; low power listening; MAC; TinyOS; energy harvesting model; solar irradiance +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Characterization  
The UV-Vis spectrum was recorded on a UV-Vis-NIR spectrophotometer (UV-3600-DRS spectrophotometer, Shimadzu Corporation, Kyoto, Japan). Band gap energy was calculated from a Tauc plot from the data obtained from the UV-Visible absorption spectrum of the solid sample using a Shimadzu, Kyoto, Japan UV/Vis-3600, spectrophotometer in the range of 200-800 nm. The FTIR spectrum was recorded with a Perkin Elmer-USA in the wavenumber range of 4000-400 cm\({}^{-1}\). The powder X-ray diffraction patterns were recorded on Philips PW-3710 diffractometer using Cu-K\(\alpha\) radiation (\(\lambda\) = 1.54 A), a Cu-filter, 35 kV generator voltage, 30 mA current, and a proportional counter detector. The SEM micrographs of the samples were obtained using a scanning electron microscope (Model: JEOL/EO-JSM-6510, Tokyo, Japan). Transmission electron microscopy (TEM) images were obtained using a JEOL HRTEM, JEM-2100F-Tokyo, Japan transmission electron microscope and were analyzed at 200 kV. Diluted samples were placed on copper grids and analyzed at 200 kV of tension. The N\({}_{2}\) physisorption analysis of the sample was performed using a Micromeretics Tristar 2000 instrument. The sample was degassed under helium gas at 300 \({}^{\circ}\)C for 2 h prior to analysis. The photoluminescence (PL) spectra of the samples were measured using a fluorescence spectrophotometer (F-4500, Hitachi, Tokyo, Japan).
# Mechanism Study of Combustion Dynamics of GO@CL-20 Composite
## 3 Results and Discussion
### Reaction Path Analysis  
Given the significant presence and pivotal role of CL-20 and GO molecules in this type of propellant, their reaction paths and fluxes within the CL-20@GO composite at 2000 K and 10 bar nitrogen were modeled using the detailed kinetic mechanism implemented via CHEMKIN software. Specifically, the cracking reaction curves for both CL-20 and GO were plotted, along with the detailed kinetic mechanisms based on the preliminary construction of NEPE. The simulations captured the reaction paths and fluxes of CL-20 and GO within the composite under the specified conditions. Additionally, the respective cracking reactions, total reaction paths, and fluxes of CL-20 and GO were visualized. The visualization results are shown in Figures 11 and 12.  
Figure 11: Reaction paths and fluxes of CL-20.  
Figure 9: Sensitivity analysis results of CL-20 for 2000 K at 10 bar.  
Due to the high-temperature environment, the tendency of CL-20 molecular decomposition is more favorable towards ring-opening reactions. Therefore, as shown in Figure 11, the initial decomposition of CL-20 primarily involves ring-opening to form CL-20A, which also has the potential to break the N-NO\({}_{2}\) bond and directly generate H\({}_{2}\)CNNO\({}_{2}\) through self-opening. Subsequently, a series of successive N-NO\({}_{2}\) bond cleavage and ring-opening reactions occur. The resulting small molecule products are mainly NO\({}_{2}\), C\({}_{2}\)N\({}_{2}\), CO, H\({}_{2}\), HCN, and N\({}_{2}\), which are consistent with the high-temperature pyrolysis documented in the literature. On the other hand, the production of C\({}_{2}\)N\({}_{2}\) aligns with the significant amounts observed in the experiments, further supporting the proposed initial decomposition pathway of this species.  
In the initial stage of GO decomposition, a large number of hydrocarbon products are produced. From the basic mechanism, it decomposes in one step into functional products such as HCOOH and OH and hydrocarbon products such as C\({}_{2}\)H\({}_{2}\) and CO\({}_{2}\) and their derivatives. These small hydrocarbon products (e.g., C\({}_{2}\)H\({}_{3}\) and C\({}_{3}\)H\({}_{6}\)) can contribute to the initial decomposition of CL-20 accordingly. These findings are consistent with the existing literature. The reaction channels are in complete agreement with the descriptions in the relevant literature. The presence of these small hydrocarbon molecules and C\({}_{2}\)N\({}_{2}\) further corroborates the accuracy and reliability of the high-temperature pyrolysis pathways for CL-20 and GO@CL-20. This provides a solid foundation for future research into the combustion reaction kinetics of GO and CL-20.
## 4 Results and Discussion
### PTE and LOP of WPT System
Figure 7: Transmission characteristics of the system under different misalignment states with deflection angles ranging from 0\({}^{\circ}\) to 90\({}^{\circ}\): (**a**) Lateral misalignment of 4 cm; (**b**) Lateral misalignment of 8 cm. Compared with PTE, the LOP of the system shows a different variation curve. In the range of the azimuthal deviation angle of 0\({}^{\circ}-80^{\circ}\), the LOP of the HTS-WPT and Cu-WPT systems remain almost constant, exhibiting a period of power plateau until a sharp drop occurs at 90\({}^{\circ}\). For a lateral misalignment distance of 8 cm, it can be observed that both the PTE and LOP of the HTS-WPT and Cu-WPT systems exhibit a monotonically increasing trend with increasing coil azimuthal deflection angles. At 0\({}^{\circ}\) azimuthal deflection angle, the PTE and LOP of the HTS-WPT system are the smallest, with values of 31.3% and 14.5 W, respectively. As the azimuthal deflection angle increases, both the PTE and LOP of the HTS-WPT and Cu-WPT systems show a peak at 90\({}^{\circ}\) when there is a lateral misalignment distance of 8 cm. Thus, it can be concluded that in practical misalignment scenarios of the HTS-WPT system, the efficiency can be significantly enhanced by increasing the azimuthal deflection angle of the system when the coil experiences moderate lateral misalignment. The power output of the system remains stable for a wide range of deflection angles less than 80\({}^{\circ}\). For larger lateral misalignment distances, increasing the azimuthal deflection angle of the system can simultaneously improve both the transmission efficiency and load power. These findings suggest that this characteristic of the HTS-WPT system is highly valuable.
## 2 System Design and Its Components
### Concept of Multi-Cloud Service Based on ODC  
The multi-cloud service proposed in this study is based on installing and operating private clouds within organizations, which are then integrated with external public cloud services to utilize the data. The cloud application model proposed in this study is a model that integrates a private cloud for commercial satellite images and a public cloud service for processing free satellite images. Private clouds can manage original data to maintain data security and can be expanded internally for various research purposes. Public clouds can include SaaS that allows anyone to access and perform basic processing on data prior to purchase. While these services can be applied to private clouds, public clouds have the access and expansion, making them easy to apply and expand services quickly.  
Therefore, the multi-cloud model proposed in this study includes processing systems for providing data on the private cloud, as shown in Figure 1, and additional services that internal users can use for various processing in the future. As an example of the model that processes ARD data, including the surface reflectance SaaS designed and constructed in [42], this study added a flow that can be connected to public cloud services. In particular, we propose a model that can efficiently add and analyze multiple satellites in large time series form, even in the internal cloud environment, by adding the ODC platform, which can manage and visualize diverse data. To index data in ODC, the satellite sensor model indexing process needs to be added.
## 3 Composite Types
### Composite nanoparticles  
Composite nanoparticles as core/shell nanoparticles or surface modified nanoparticles may be considered as a special type of nanocomposites. When these nanoparticles contain an inorganic core and an organic shell one may speak about hybrid nanoparticles. In this case the inorganic core may be a metal or a metal oxide, and the organic shell either a polymerized monomer, a chromophore, a detergent or surfactant, carbon, or some organic molecule. Particles of this type, consisting of a metal-oxide core and a polymerizable organic shell, were reported in the mid and late-90s by [PERSON] [39; 40; 41], synthesized in a microwave plasma reactor by gas phase synthesis. This concept allows the design of new functional materials with novel or modified magnetic, optical, electronic or biological properties. Figure 4: TEM micrograph of \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\)/polymer core/shell nanoparticles. Figure 3: Transmission electron microscopy (TEM) micrographs of Aerosil® R8200, dispersed in a methylmethacrylate (MMA) using a high speed stirrer (left) or a high pressure homogenizer (right), after solidification to polymethylmethacrylate (PMMA). Figure 4 visualizes exemplarily inorganic/organic core/shell nanoparticles by a transmission electron micrograph of polymer-coated \(\gamma\)-Fe\({}_{2}\)O\({}_{3}\) nanoparticles. In this case, the core is around 8 nm in diameter; the polymer coating is around 2 nm in thickness. In the last decade, a broad portfolio of nanocomposite particles with different functional properties have been developed as shown in Table 1, depending on the inorganic core and the organic shell. Many research groups worldwide are involved in this field. [PERSON] and [PERSON], for example.described the preparation of Al\({}_{2}\)O\({}_{3}\)/PAA core/shell nanocomposites by a controlled polymer bridging, using commercial Al\({}_{2}\)O\({}_{3}\) nanoparticles [42]. A polystyrene-based coating was used for SiO\({}_{2}\) nanoparticles [43] and TiO\({}_{2}\) nanoparticles [44], respectively. Acrylate-based nanoparticle composites were reported from ZnO [45], TiO\({}_{2}\)[46, 47] and SiO\({}_{2}\)[48]. Al\({}_{2}\)O\({}_{3}\) nanoparticles were coated with pyrrole [49], or _in situ_ with polyethylene [50]. [PERSON] _et al._ reported about ZrO\({}_{2}\) nanoparticles, coated with quasi-polyethylene [51]. Various oxide nanoparticles were coated with acrylic based monomers and
## 4 Results and Discussions
### Results of Feature Selection Method  
Features were selected to represent social lifestyle factors and those linked to depression diagnosis. From an initial set of 110 features, the IFEA method identified the most relevant ones. Three feature selection methods were compared: IFEA, GA, and MRMR (Table 3). By varying the number of features in each method, the optimal result was achieved with 62 features (Table 4). The general category encompasses personal details like gender, age, education, and marital status. This analysis reveals how different feature sets impact accuracy by adding or removing specific characteristics.
## 3 Results and Discussion
### Discussion
Figure 11 shows also that EV/CS ratio figures were spread over a broader range and were also higher on average for Florence, when compared to Bruxelles. This again was related to the higher number of PHEV expected to be circulating in Bruxelles, given the fact that their lower energy requests were optimally fulfilled by the CS of the lowest power level, which were also the one with the lowest EV/CS ratio. The higher variability of the xEV fleet size forecast for Florence probably affected also the variability of EV/CS ratio for this area. Global use ratio of infrastructure:  
An important parameter for business and profitability evaluations is the global usage ratio \(u\), which is defined within this model using the following equation:  
\[u_{y}=\frac{\sum_{k}[(E_{TOT})_{k}]_{y}}{\sum_{k}\left(k_{MIN}\right)_{y}*h_{k }*\left(P_{k}\right)_{y}}. \tag{21}\]  
Figure 12 shows that the global use ratio was quite low and rather constant during the whole period; the results were in line with other researches findings, such as [20; 22; 56]. Figure 11: Publicly accessible charging infrastructure electric vehicle (EV)/CS ratio for Florence (light blue) and Bruxelles (light green). Impact of infrastructure on private and public parking:  
The impact on public stalls \(I_{y}\) for a given area is defined as following:  
\[I_{y}=\frac{CS_{y}}{\frac{xEV_{y}}{TOT_{y}}*PP}. \tag{22}\]  
The resulting value was low and the trend decreasing in time, as highlighted by Figure 13; this was the effect of the high value of EV/CS ratio, that also increased in the second part of the period given the higher share of medium and fast CS. Figure 12: Publicly accessible charging infrastructure utilization rate for Florence (light blue) and Bruxelles (light green). Figure 13: Public stall usage ratio by publicly accessible charging infrastructure for Florence (light blue) and Bruxelles (light green). The impact on the private infrastructure was higher, given the fact that the probability of an xEV owner being also a parking owner was twice as high than with an ICEV owner; anyway, the result was still compatible with the existing situation. As summarized in the following conclusions, the analysis carried out in this research work clearly identified elements impacting on policy at different governance levels. The central government layer, in charge of accelerating the transition to a cleaner transport system and implementing the EU Directives, will have to allocate the financial resources in the best possible way to allow local governments to maximize the fast deployment of charging infrastructure \"on the ground\". In this respect, the analysis here carried out about the optimal ratio between slow and medium CS to fast CS is a key element: results of our analysis did not seem to fully support the widely adopted idea (at the higher centralized policy level) of favoring fast CS versus slow and medium ones, in regards to cost optimization.
# Enhancing Compressive Strength of Reticulated Porous Alumina by Optimizing Processing Conditions
## 2 Materials and Methods  
The average particle size of the \(\alpha\)-alumina was varied using three types of commercial alumina raw materials: with average particle sizes of (1) \(<\)50 nm (\"fine\", Sigma-Aldrich, St. Louis, MO, USA), (2) 0.26 \(\upmu\)m (\"intermediate\", AKP-30, Sumitomo Chemical, Tokyo, Japan), and (3) 2-4 \(\upmu\)m (\"coarse\", AM-210, Sumitomo Chemical, Japan).  
Commercial polyurethane foam (SKB Tech, Ulsan, Korea) with a pore density of 45 PPI was used as a sacrificial template. The dimensions of the polyurethane foam used were 100 mm \(\times\) 100 mm \(\times\) 20 mm. An alumina slurry consisting of 150-200 g of alumina (fine, intermediate, and coarse), 100 mL of distilled water as a solvent, 0-15 g of polyvinyl alcohol (PVA) as an organic binder (PVA 500, Junsei Chemical, Tokyo, Japan), and 0-1 g of dispersant (DARVAN C-N, Vanderbilt Minerals, USA or Dolapix CE 64, Zschimmer & Schwarz GmbH Co., Burgstadt, Germany) was used to coat the polyurethane foam.  
Prior to coating the polyurethane foam, the alumina slurry was mixed uniformly by ball-milling with alumina balls for 4 h. The viscosity of the alumina slurry was measured using a rotational rheometer (Discovery HR-1, TA Instruments, New Castle, DE, USA) at 25 \({}^{\circ}\)C.  
The reticulated porous alumina specimens were fabricated using the conventional replica method. A sacrificial polymer template (polyurethane foam) was fully soaked in the prepared alumina slurry until its strut walls were completely coated with alumina particles. The alumina particle-impregnated polyurethane foam was then squeezed to remove excess alumina slurry and to maintain a thin alumina layer over the struts of the polyurethane foam, before being completely dried for 24 h at 25 \({}^{\circ}\)C. Following this, the dried specimens were heat-treated at 400 \({}^{\circ}\)C for 1 h to burn off the binder and the polyurethane foam and finally sintered at 1600 \({}^{\circ}\)C for 1 h using a box furnace (AJ-SKE1-7, Ajeon Heating Industrial, Namyangju-si, Korea). After sintering, the polyurethane foam coated with alumina slurry was transformed into reticulated porous alumina.  
After preparing the reticulated porous alumina, the pore structure of specimens was characterized using scanning electron microscopy (SEM, JSM-5800, JEOL, Tokyo, Japan) and micro-computed tomography (\(\mu\)-CT, XT H 160, voxel size = 3 \(\mu\)m, Nikon, Tokyo, Japan).  
The pore size distribution of the specimens was also analyzed using mercury porosinometry (Autopore IV 9510, Micromerities, Norcross, GA, USA). After machining samples with dimensions of 20 mm \(\times\) 20 mm \(\times\) 20 mm, the compressive strength of the specimens was measured with a fixture using an Instron 4206 testing system (Instron, Norwood, MA, USA).