esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Dimensionless Correlation of Frost Thickness  
An appropriate empirical correlation is helpful to understand and predict the relationship between frost characteristics and various parameters. Owing to the complexity of frosting where multiple parameters are changing, an empirical correlation that applies to most environments is hard to obtain. Compared with the correlations proposed by most other frost studies [25; 26; 27], the dimensionless correlation between frost thickness and working pressure around the cooling surface is proposed in this study. The dimensionless process takes place among the frost thickness, temperature of air, temperature of cooling surface, partial pressure of water vapor, and working pressure around the cooling surface, as shown in Table 3. The dimensionless correlation formula of the frost thickness is provided in Equation (2). The available range of frost thickness in the correlation equation is: \(T_{\rm a}\) = 15\(-\)25 \({}^{\circ}\)C, \(T_{\rm w}\) = \(-\)38\(\rightarrow\)\(-\)10 \({}^{\circ}\)C, partial pressure of water vapor \(P_{\rm v}\) = 350\(-\)1400 Pa, working pressure around the cooling surface \(P_{\rm a}\) = 0.15 \(\times\) 10\({}^{5}\)-2.00 \(\times\) 10\({}^{5}\) Pa.  
\[y*=8.180\times 10^{-17}\ T_{\rm a}*^{-6.115}\ T_{\rm w}*^{7.427}\ P_{\rm v}*^{0.519} \ P_{\rm a}*^{-0.326}\ Gr^{4.353}\ (F{{}^{0.38}}+1)^{1.558} \tag{2}\]  
The fit of the correlated data increases with test data as frost growth in this correlation. The overall error of the dimensionless frost thickness between the correlated data and the test data is shown in Figure 14. The overall error is within \(\pm\)25%. The errors in the experiment mostly come from photographing and parametric measurements. In the early frosting period, the small deviation produces a large relative error due to the small frost thickness (brown data), whereas the overall error is still within a reasonable range.  
Figure 13: (**a**) Frost thickness; (**b**) frost accumulation; (**c**) frost density with working pressure around cooling surface under different temperatures of cooling surfaces at 60 min.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Dimensionless Variable** & \(y^{\star}\) & \(T_{\rm a}*\) & \(T_{\rm w}*\) & \(P_{\rm v}*\) & \(P_{\rm a}*\) \\ \hline Normalized process & \(y/l\) & \(T_{\rm a}/273.15\) & \(T_{\rm w}/273.15\) & \(P_{\rm v}/101325\) & \(P_{\rm a}/101325\) \\ \hline \end{tabular}
\end{table}
Table 3: Dimensionless variables for correlation formula.
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 7 Conclusions  
In this paper, considering the stochastic network state and random data arrivals demand, we formulated the data transmission scheduling problem with a joint resource allocation and ISL assignment as a system utility optimization problem. We proposed the DTEM algorithm, based on the Lyapunov stability theory, to enhance system utility and guarantee system stability. To reduce computational complexity, we decompose the optimization problem into battery management optimization, data acquisition control optimization, and inter-satellite transmission optimization. Then, we solved the three subproblems to obtain the optimal energy harvesting rate, data acquisition rate, inter-satellite transmission rate, and ISL connection allocation for each time slot. Moreover, the system performance is analyzed comprehensively. Finally, extensive simulation results show the superiority of DTEM in improving system utility and maintaining long-term network stability and sustainability. Specifically, our proposed algorithm shows an improvement of 9.6% in system utility compared to the non-optimized transmission method and 20.3% compared to the non-optimized energy-management approach, respectively.  
Conceptualization, J.L. and Y.W.; methodology, Y.W. and L.G.; software, Y.W., L.L. and R.D.; formal analysis, J.L., L.L. and R.D.; writing--original draft preparation, Y.W.; writing--review and editing, J.L. and L.G.; supervision, L.L.; funding acquisition, J.L., Y.W. and L.G. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (Nos. 62102456, U2003208, 61972398, and 62202501), in part by the Foundation of Preview of Equipment (No. 6141A020227), in part by Theory and Method of Multi-Satellite Collaborative Application (No. 2019-JCJCJ-ZD-342-00), in part by the Project funded by China Postdoctoral Science Foundation (No. 2021 TQ0369), in part by a Project Supported by Scientific Research Fund of Hunan Provincial Education Department (No. 22B0922), in part by National Key R&D Program of China (No. 2021 YFB3900902), and in part by the Science and Technology Plan of Hunan (Nos. 2016 TP1003 and 2022)J40638).  
Data Availability Statement: Data in this study are available upon request by contacting the corresponding author.  
The authors of this current research declare that this study has no competing interest.  
Figure 12: The utility of RSNs versus \(\varphi_{\max}\).
# Review of Satellite Interferometry for Landslide Detection in Italy
## 6 How InSAR Products Have Been Used for Landslide Studies in Italy
### Landslide Mapping  
Where landslide inventories are not complete or outdated, InSAR can support the classical landslide mapping activities conducted by means of digital orthophotos or satellite optical images.  
DSGSD landslides are flank-scale phenomena with very slow annual deformation rates (usually below 1 cm/year [168]), whose contour is sometimes unknown or not included in landslide inventories. InSAR was used to support DSGSD mapping activities in the Lombardia [65; 66] and Valle d'Aosta [71; 72; 169] regions in the Alps, in Abruzzo [170] and the Marche [106] regions in the Central Apennines, and in the Palermo Province (Sicily [171]). In particular, the Valle d'Aosta DSGSD inventory was produced starting from ERS 1/2, and Envisat interferometric results obtained in the frame of the PST-A project [30]. DSGSD monographies connecting InSAR data and geomorphological features were the outputs of such activity [169].  
Slow-moving landslides along the Apennines were the target of several InSAR mapping applications. Going from north to south, some valuable examples come from the Provinces of Bologna and Modena (Emilia Romagna region [91]), the Maiella Massif [101; 102], the Daunia (Puglia region [172; 173]), the Northern Molise region [104; 174] and the Crati Valley (Calabria region [135]).  
Some authors used InSAR as a mapping tool in response to earthquakes, such as the 1997 Umbria-Marche event [175], the 2009 Aquila earthquake [100], and the 2016-2017 Central Italy sequence [97; 98; 99]. Other authors evaluated the presence of active landslides along main transportation routes, such as in the Isarco Valley (Trentino Alto Adige region [176]), or in the Cosenza and Crotone Provinces (Calabria region [136]).  
Recently, some authors proposed new techniques to semi-automatically extract clusters of moving points, focusing on active (and potentially unknown) landslides. These \"hot-spot\" approaches are useful for landslide mapping activities when the working scale requires the analysis of millions of measurement points. The concept has been developed by [PERSON] et al. [132] and further improved by [PERSON] et al. [177]. In Italy, the hot-spot mapping approach has been applied in the Valle d'Aosta [73; 74] and Tuscany [178; 179; 180] regions. More complex clustering approaches were proposed by [PERSON] in the Avellino (Campania region) area (CLARA, Clustering LARge Application algorithm [181]) and by [PERSON] et al. [35; 37; 182] for the Arno River Basin and the Volterra municipality (Tuscany region). The principal component analysis was applied by [PERSON] et al. [85] for landslide mapping in the alpine sector of the Piemonte region.
## 4 Power Amplifier Measurement Results  
This two-stage GaN MMIC PA is implemented in a 0.25 \(\upmu\)m gate length GaN/SiC HEMT process. Figure 12 demonstrates the final layout of the designed MMIC PA. Including the testing pads, the design occupies a size of 4.5 \(\times\) 2.6 mm\({}^{2}\).  
In order to verify this design, the developed GaN PA adopts a Class-AB bias point with a drain voltage of 28 V, a gate voltage of \(-\)2 V, and a total gate current of 954 mA. The electromagnetic simulated(EM) S-parameters of the layout are shown in Figure 13a. It can be seen that in the frequency range of 7-13 GHz, the amplifier achieves a small signal gain of 14.5-15.5 dB with a gain flatness of less than \(\pm\)1 dB, and the input and output return loss are better than 5 dB and 8 dB, respectively. Moreover, the simulated PAE and \(P_{out}\) results of the large signal are illustrated in Figure 13b. The designed MMIC PA achieves a PAE of 30-46% with an \(P_{out}\) of 40 dBm over the entire 7-13 GHz bandwidth.  
Figure 11: Return loss and insertion loss of the (**a**) ISMN; (**b**) IMN.  
Figure 10: The block diagram of the circuit structure: (**a**) ISMN; (**b**) IMN.  
In order to evaluate this MMIC PA, the performance comparison with the previously reported GaN PAs is presented in Table 3. It is obvious that the proposed PA exhibits quite a superior performance, including a high efficiency, high \(P_{out}\), and wide bandwidth in the MMIC power amplifiers.  
Figure 12: Schematic diagram of the MMIC PA layout.  
Figure 13: Layout simulation results: (**a**) results for small signal; (**b**) results for large signal.
# A Multiscale Approach to the Numerical Simulation of the Solid Oxide Fuel Cell
## 4 Mathematical Model
### Transport Model
#### 4.2.1 Species Conservation  
The species transport is based on the Fick model of diffusion. In terms of the mass fractions, the species conservation equation is written as:  
\[\rho_{\text{mix}}\varepsilon\boldsymbol{v}\boldsymbol{\cdot}\boldsymbol{\
abla} \,\omega_{i}=\boldsymbol{\
abla}\boldsymbol{\cdot}\left(\rho_{\text{mix}}D_{i} ^{\text{eff}}\,\boldsymbol{\
abla}\,\omega_{i}\right)+\hat{m}_{i}M_{i}, \tag{12}\]  
where \(\omega_{i}\) is the mass fraction of the species \(i\), \(\rho_{\text{mix}}\) (kg m\({}^{-2}\)) is the density of the mixture of gases, \(\varepsilon\) is the porosity, \(\boldsymbol{v}\) (m s\({}^{-1}\)) is the velocity vector, \(D_{i}^{\text{eff}}\) (m\({}^{2}\) s\({}^{-1}\)) is the effective diffusion coefficient for species \(i\) and \(\hat{m}_{i}\) (mol m\({}^{-3}\) s\({}^{-1}\)) stands for the mass source. Utilizing the fact that, for the ideal gases, the mass fraction may be written as: \(\omega_{i}=\dfrac{p_{i}}{P}\dfrac{M_{i}}{M}\) and using the ideal gas law Equations (6) and (12) may be reformulated as:  
\[\dfrac{\varepsilon}{RT}\boldsymbol{v}\boldsymbol{\cdot}\boldsymbol{\
abla}\,p _{i}=\boldsymbol{\
abla}\boldsymbol{\cdot}\left(\dfrac{D_{i}^{\text{eff}}}{RT} \,\boldsymbol{\
abla}\,p_{i}\right)+\hat{m}_{i}. \tag{13}\]  
The effective diffusion coefficient is microstructure-dependent and may be computed as:  
\[D_{i}^{\text{eff}}=\dfrac{\varepsilon}{\tau_{\text{pore}}}D_{i}, \tag{14}\]  
where \(D_{i}\) (m\({}^{2}\) s\({}^{-1}\)) is the bulk diffusion coefficient of species \(i\), calculated with the Bosanquet formula [56]:  
\[D_{i}=\left(\dfrac{1}{D_{i,\text{mix}}}+\dfrac{1}{D_{i,\text{K}}}\right)^{-1}. \tag{15}\]  
Here, \(D_{i,\text{mix}}\) (m\({}^{2}\) s\({}^{-1}\)) is the diffusion coefficient of species \(i\) in the mixture of gases and it is calculated using modified Blanc's law [57; 58]:  
\[D_{i,\text{mix}}=\left(\sum_{\begin{subarray}{c}j=1\\ j\
eq i\end{subarray}}^{N}\dfrac{p_{j}}{PD_{ij}}\right)^{-1}, \tag{16}\]  
and the binary diffusion coefficient \(D_{ij}\) (m\({}^{2}\) s\({}^{-1}\)) is computed using the Fuller, Shettler and Giddings method for gas mixture at low pressure [58; 59]:  
\[D_{i,j}=\dfrac{0.00143T^{1.75}}{P\left(2\left(M_{i}^{-1}+M_{j}^{-1}\right)^{- 1}\right)^{1/2}\left(\Sigma_{i}^{1/3}+\Sigma_{j}^{1/3}\right)^{2}}. \tag{17}\]  
The atomic diffusion volumes \(\Sigma\) may be found in Ref. [58].  
In the Bosanquet equation, Equation (15), \(D_{i,\text{K}}\) (m\({}^{2}\) s\({}^{-1}\)) stands for the Knudsen diffusion coefficient [57]:  
\[D_{i,\text{K}}=\dfrac{2}{3}\left(\dfrac{8 RT}{\pi M_{i}}\right)^{1/2}\bar{r}_{ \text{pore}}, \tag{18}\]  
where \(\bar{r}_{\text{pore}}\) (m) is the average pore radius and it comes from the microstructure quantification (see Figure 3).  
The mass sources \(\hat{m}_{i}\) in Equation (13) describe the amount of species produced during the electrochemical reactions and they are calculated using [PERSON]'s laws of electrolysis. The summary of the mass sources is presented in Table 9.
# Examining the Link between Isokinetic Strength Metrics and Ball Speed in Women's Soccer
## 3 Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & \multicolumn{2}{c}{**Peak Torque**} & \multicolumn{2}{c}{**Average Torque**} \\  & \multicolumn{1}{c}{**X \(\pm\) SD**} & \multicolumn{1}{c}{**Range**} & \multicolumn{1}{c}{**X \(\pm\) SD**} & \multicolumn{1}{c}{**Range**} \\ \hline D-EXT (Abs) & 139.46 \(\pm\) 34.23 & 92–184 & 91.54 \(\pm\) 21.49 & 57–123 \\ D-EXT (\%BW) & 271.31 \(\pm\) 58.02 & 167–349 & 177.85 \(\pm\) 36.48 & 121–224 \\ D-FLX (Abs) & 68.92 \(\pm\) 17.63 & 39–103 & 51.62 \(\pm\) 14.68 & 29–79 \\ D-FLX (\%BW) & 135.69 \(\pm\) 35.70 & 72–191 & 101.00 \(\pm\) 28.85 & 53–147 \\ D-\%IPS & 50.77 \(\pm\) 13.26 & 37–86 & 57.08 \(\pm\) 14.75 & 42–97 \\ ND-EXT (Abs) & 141.54 \(\pm\) 40.18 & 69–199 & 91.92 \(\pm\) 24.78 & 49–131 \\ ND-EXT (\%BW) & 275.46 \(\pm\) 73.84 & 155–390 & 179.00 \(\pm\) 46.38 & 103–257 \\ ND-FLX (Abs) & 65.85 \(\pm\) 18.02 & 37–94 & 46.15 \(\pm\) 14.69 & 23–74 \\ ND-FLX (\%BW) & 128.77 \(\pm\) 35.84 & 66–185 & 90.31 \(\pm\) 29.08 & 42–136 \\ ND-\%IPS & 49.31 \(\pm\) 21.82 & 37–120 & 52.46 \(\pm\) 22.95 & 31–124 \\ \hline \hline \end{tabular}  
\(\times\): arithmetic mean; SD: standard deviation; D: dominant; ND: nondominant; EXT: extension; FLX: flexion.  
\end{table} 
Table 3: Eccentric contraction-derived FLX and EXT strengths of the players.
# Dynamic Analysis of Fuzzy Systems
## 4 Dynamic Analysis of Non-Linear Fuzzy Systems: Second-Order Model
### Performance of Fuzzy Second-Order Dynamical Systems  
Standard performance measures are usually defined in terms of the step response of a system, as shown in Figure 15. The speed response depends on the peak time (tp). The similarity between the transient response and the step input is measured by the per cent of overshoot (Mp). The transient response of the system can be described by two factors: the speed of the response and the peak time. The closeness of the response to the desired one is represented by the overshoot and settling time [40].  
In this case, explicit dynamic specification, as Mp and tp, cannot be obtained as in the first-order case. A significant number of simulations to obtain the relationship between Mp, tp and \(\zeta\) are performed. This will be applied to concentration and dilation, as seen previously. In Figures 16 and 17, the per cent overshoot is shown as a function of the relative damping ratio, \(\zeta\). In addition, Figures 16 and 17 present the per cent overshoot and peak time as a function of the relative damping ratio. The dotted line indicates the performance of the second-order linear system, while the solid one corresponds to the non-linear system. As shown in Figure 16, the segmented line shows the concentration of a fuzzy system, while in Figure 17, the dilation of a fuzzy system is shown. This analysis is valid for dynamical systems; undamped, underdamped and critically damped. In the case of overdamped, it has no peak time and per cent overshoot.  
Figure 15: Step response, with normalised time \(\omega_{n}t\), for K = 1 and \(\zeta=0.3\).
# A Comprehensive Survey on the Societal Aspects of Smart Cities
## 2 Smart Cities Dimensions
### Smart Living
Telework or working from home is something that has been planned for and implemented for some decades already, although it was not the norm for the majority of the workforce [146; 147]. However, due to the COVID-19 pandemic, telework has surged to prominence almost overnight and has become an established normal work option to some extent [148]. Although telework has proven to be a means for work to be able to continue even during a global pandemic and as a way to minimize the spread of the disease, its sudden leap in relevance means that it must be tested against regular working conditions to evaluate its positive and negative aspects (e.g., efficiency, burnout rates, job satisfaction, promotion prospects, or work-life balance) [149]. Additionally, as there is more to life than just work it is necessary to consider how people have access to entertainment and cultural venues. Smart cities have focused on creating green spaces that are mobility-friendly, enabling all its citizens to experience them, as well as areas for sport and exercise [150]. Cultural projects have left the enclosure of physical buildings, like museums, to be integrated into the city itself for all to experience. Additionally, collections of artifacts and books have been digitized and made accessible, virtually, so that anyone can explore them. These initiatives promote integration, increase citizens' well-being, and provide a common cultural identity, which increases social cohesion [151; 152]. Moreover, these initiatives also have the side effect of making a city more attractive to tourists, with sport and cultural festivals and initiatives being of interest to people seeking to experience other culture and locations. Therefore, when handling tourism a smart city might implement measures to manage tourists, which allow both them and the city's population to enjoy themselves. Having accessible digital resources that promote lesser-known locations, inform peak times and capacity, and other resources allows for tourists to better manage their time and reduce their impact on the city. Additionally, cities can employ systems to fight illegal activities, such as illegal apartment rentals. These measures help combat overtourism and allow for a more sustainable tourism industry [153; 154]. Finally, security and safety are factors that impact a citizen's quality of life in significant ways, and any improvement can have a major impact not only on quality of life but also, and more importantly, on lives saved. Data collection and analysis can be used to create real-time and historical crime maps, improving the efficiency of emergency services responses and allowing for a more efficient use of resources. Early warning systems can drastically reduce mortality and secondary damages during disasters. Additionally, a city sensor network, coupled with home security systems, can detect and inform authorities about crimes in real-time.
## 2 Methodology
### The True OSR and OLR Fluxes  
To quantify the introduced error by certain set of spatial resolution and temporal sampling scheme, here we use the CERES SYN ED4.1A dataset to construct the true OSR and OLR fluxes. Such data is chosen due to its high spatial-temporal resolution (up to 1\({}^{\circ}\) longitude \(\times\) 1\({}^{\circ}\) latitude of spatial resolution and hourly temporal resolution) and the acceptable accuracy (uncertainties of OSR and OLR are less than 8 W/m\({}^{2}\) and 3 W/m\({}^{2}\), respectively) [15; 16].  
We utilized the global map of the TOA OLR and OSR fluxes of March 2019 with spatial resolution of 0.1\({}^{\circ}\) latitude \(\times\) 0.1\({}^{\circ}\) longitude (10 km at equator) and temporal resolution of 1-min to serve as the true OLR and OSR fluxes. The data of March 2019 is chosen since it can make a good assessment of the measurement error at Earth's polar regions (both the Arctic and Antarctic regions have 12 h in daytime at March). These 0.1\({}^{\circ}\) longitude \(\times\) 0.1\({}^{\circ}\) latitude-1 min OSR and OLR fluxes maps are derived through two steps: 1. spatially interpolating the 1\({}^{\circ}\) longitude \(\times\) 1\({}^{\circ}\) latitude global TOA OSR and OLR fluxes data from CERES SYN-1 dataset with the cubic spline interpolation; 2. temporally interpolating the data derived from step 1 with a combination of half-sine and linear interpolations. This is due to the fact that both the TOA OSR and OLR fluxes follow a sine/cosine pattern over the daytime and that the OSR stays at 0 W/m\({}^{2}\) during night (whereas the OLR would keep a linear variation during the night-time) [17]. Figure 2 shows the original hourly CERES SYN flux data (discretized points) and the interpolated TOA OSR and OLR fluxes (solid lines) of a certain place (20\({}^{\circ}\) E, 30\({}^{\circ}\) S) on Earth. The 1-min true OSR flux is derived by half-sine-interpolation at daytime (0 W/m\({}^{2}\) at night) (Figure 2a);whereas the 1-min true OLR flux is derived by half-sine interpolation during the day and linear interpolation at night of the hourly data (Figure 2b). Notably, this half-sine fit method performs well over land where there is an evident diurnal cycle of both OSR and OLR fluxes. However, this may not work well over regions where the OSR and OLR diurnal cycle is not obvious, such as ocean. This may introduce certain uncertainty to the results.