esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Cell Proliferation and Morphology  
The NIH 3T3 cell line, which has been commonly used in research studies of tissue engineering-related scaffolds, was chosen to evaluate the biocompatibility of the 3D nanofibrous scaffolds due to its stability [39]. The cell viability and biocompatibility of the 3D nanofibrous scaffolds were evaluated by MTT after culturing for 1, 3, and 7 days (Figure 5). Cells proliferated significantly with time for all the groups. On days 1 and 3, no apparent difference in cell viability was observed for any of the scaffolds containing nanostructures. In contrast, pure chitosan/RHC scaffolds showed the lowest cell viability among the samples. This can be attributed to the smooth pore wall structures of these materials, since rough structures are found more suitable for the adhesion and proliferation of seeded cells [31]. The 1%-1 mL and 1%-0.5 mL scaffolds showed significantly higher cell viabilities than the chitosan/RHC nanofibrous membranes after 7 days. Both chitosan and RHC were known to be biocompatible and suitable for cell proliferation, and the combined porous scaffolds and nanofibers provided sufficient 3D spaces and topography signals for cell attachment and migration. However, cell viability decreased on the 2%-1 mL scaffolds after 7 days, which was possibly because the reinforcements on mechanical properties hindered the cellular growth, since the affinities of the cell to materials are dependent on mechanical strength [40].  
The morphology and migration of NIH 3T3 on 3D nanofibrous scaffolds were observed by fluorescence staining from the best performing group in MTT tests (1%-1 mL scaffolds). After the first day of culturing, cells distributed randomly on the surface of the scaffolds exhibited a green spindle-like shape around the pores (Figure 6A,D). After culturing for 3 and 7 days, well-spread cells with a typical fibrobl morphology were observed, and these cells covered the entire scaffold and infiltrated the pores (Figure 6B,C). During the culture period, only a few red round cells were observed, indicating that NIH 3T3 maintained high viability throughout the experiment. The vertical section pictures (Figure 6G-I) were allowed to directly monitor the states of the cells infiltrated into scaf  
Figure 4: Mechanical tests of the different scaffolds. (**A**) Stress–strain curves of the scaffolds; (**B**) compression modulus of 3D nanofibrous scaffolds. * \(p<0.05\) and *** \(p<0.001\), compared with pure chitosan/RHC scaffold (1%-0 mL), # \(p<0.05\). Bars represent mean \(\pm\) SD, number of repeats \(n=5\).  
folds. Cells were assembled on the surface of the scaffolds, and only a few cells were observed inside the scaffolds on day 1. Later, the infiltration distance increased with the culture time, and cells were distributed homogenously at the vertical section on day 7. This result suggests that the porous scaffolds with nanofibrous structures can prompt the proliferation and infiltration of the seeded cells.
## 2 Principles and Model Establishment
### Extinction Coefficient \(k\)  
Scatterers (rain, snow and other hydrometeors) are generally considered to be randomly distributed, and assume that there is no coherent phase relationship between the scattering fields of each single particle. Therefore, the volume absorption and scattering containing many particles can be calculated through incoherent scattering theory. According to the different attenuation effects of rainfall and snowfall, the attenuation coefficient \(k\) will be expressed by \(k_{r}\) and \(k_{s}\) respectively. For a given droplet size distribution \(p(r)\), where \(r\) is the droplet size, the rainfall extinction coefficient can be calculated [15] (pp. 318-326):  
\[k_{r}(x)=\frac{\lambda^{3}}{8\pi^{2}}\int_{0}^{\infty}\chi^{2}p(x)\varsigma_{ e}(x)\mathrm{d}\chi, \tag{13}\]  
\[\chi=\frac{2\pi r}{\lambda}, \tag{14}\]  
where \(\varsigma_{e}\) is the attenuation coefficient of Mie Scattering. Furthermore, the snowfall extinction coefficient can be calculated [15] (pp. 326-328):  
\[k_{s}(x)=4.34\times 10^{-4}\left[\frac{2\times 10^{-3}\pi^{5}}{3\lambda^{4} \rho_{s}{}^{2}}|K_{ds}|^{2}\sum_{i=1}^{N_{p}}d_{i}{}^{6}+\frac{\pi^{2}}{\lambda \rho_{s}}Im\{-K_{ds}\}\sum_{i=1}^{N_{p}}d_{i}{}^{3}\right], \tag{15}\]  
where \(\rho_{s}\) is the density of snowfall, \(K_{ds}\) is related to the refraction of dry snow, \(d_{i}\) is diameter of snowfall, \(N_{p}\) is the total number of droplets per unit volume.  
Such theoretical method of calculating the extinction coefficient of rainfall and snowfall are widely proved effective and many scholars have calculated the values under a wide range of frequencies [16; 17; 18]. However, due to its complexity, this method is difficult to be directly applied to a certain algorithm. To simplify the acquisition of the extinction coefficients, [PERSON] [19] proposed a linear relationship between the extinction coefficients and the precipitation:  
\[k(x)=a\cdot R(x), \tag{16}\]  
where \(R\) is the precipitation in mm/h, \(a\) is an empirical value. Later, a logarithmic relationship between the extinction coefficients and the precipitation is proposed and used [20; 21; 22]:  
\[k(x)=a\cdot R(x)^{b}, \tag{17}\]  
where \(a\) and \(b\) are both empirical coefficients. Scholars have conducted extensive research on the empirical coefficients of Equations (16) and (17) [23; 24; 25; 26; 27; 28]. Furthermore, their root mean square (rms) errors are listed in Table 1. It is obvious that the rms errors of the linear model are slightly larger than those of the logarithmic model. However, using the logarithmic model will largely increase the complexity of the method introduced in this paper and the rms error of the linear model is acceptable, so in this paper, the linear model is adopted. The values calculated by [PERSON] [15; 21] are adopted. For rain, \(a=3.349\times 10^{-3}\); for snow, \(a=2.229\times 10^{-3}\).
## 4 Results and Discussion
### Creation of Design Alternatives Based on Variations in the Horizontal and Vertical Ratios  
Third, we tested the addition of another criterion to the generative design algorithm, namely the horizontal and vertical lengths of the building. By determining the sum of these lengths in each direction and expressing them as a ratio, this criterion can help distinguish buildings that are elongated in the north-south direction from those that extend in the east-west direction. Figure 11 shows the results for high and low aspect ratios, and illustrates how this attribute can affect the building orientation and form. When a building is elongated in the east-west direction, more of its facades face south. This orientation can be advantageous, particularly in terms of sustainability, as it maximizes the exposure to solar radiation (a key factor for passive solar heating) and encourages the formation of wind paths in the north-south direction for natural ventilation. Alternatively, a building elongated in the north-south direction has more elevations facing east and west. These directions receive relatively less solar radiation than the southern direction, which may affect the energy efficiency of the building. This approach can provide insights into the impact of building orientation on energy efficiency and environmental comfort, enabling architects and designers to make more informed decisions regarding building layouts and forms.
# Are Green Buildings an Indicator of Sustainable Development?
## 3 Results and Discussion
### GBRS and GHG Emissions  
The relationship between the amount of GBRS certifications per year (dependent variable) and GHG emissions from buildings (independent variable) is studied through simple linear regression with data taken from Eurostat Database and the GBRS Public Directories.  
The ten points in Figure 12 (blue dots) represent the years between 2010 and 2019. The trendline is linear with an equation of \(\mathrm{y}=-0.1399\mathrm{x}+1810.1\), a \(p\)-value = 0.0006 and an \(\mathrm{R}^{2}=0.7917\). However, if we do not consider 2014 (highlighted with a red triangle  
Figure 11: Relationship between GBRS and GDP per capita in the EU (2010–2013 and 2015–2019).  
Figure 10: Relationship between GBRS and GDP per capita in the EU (2010–2019).  
in Figure 13 and characterised by extreme climate conditions with significantly fewer heating degree days compared to other years), the chart assumes a more linear equation \(\mathrm{y}=-0.1391\mathrm{x}+1819.6\) with more accurate \(p\)-value = 0.0002 and \(\mathrm{R}^{2}\) = 0.8825.  
According to the assumptions stated in the Methodology, \(p\)-value and \(\mathrm{R}^{2}\) are consistent, and the equation correlates the two variables in a very accurate way with a strong significance.  
As expected, more GBRS certifications are related to fewer GHG emissions due to the nature of green buildings that comprise the most innovative technologies in the construction sector in order to cut GHG emissions during the entire lifecycle. The figures presented quantify in detail, which is already illustrated in the literature [23,24].
# Thermal and Energy-Efficiency Assessment of Hybrid CLT-glass Facade Elements
## 6 Thermal Numerical Analyses of the Hybrid CLT-glass Component Level
### FE Temperature Factor for the Hybrid CLT-glass Facade Element  
The temperature factor was ultimately calculated at the component level based on Equation (1) and the FE numerical predictions for the system of Figure 13, as:  
\[f_{Rsi,FE}=\frac{T_{si,FE}-T_{out,FE}}{T_{int,FE}-T_{out,FE}}=0.613. \tag{7}\]  
This provides a promising value compared to the minimum requirements reported in Section 2 and further enforces the potential of the hybrid CLT-glass solution. Moreover, Equation (7) still represents the average value calculated from an ideal \(\Delta T\) = 20 \({}^{\circ}\)C thermal scenario (see Section 6.2).  
Accordingly, even more accurate calculations for \(f_{Rsi}\) were carried out (still at the component level only) by taking advantage of the meteorological records from the Live-Lab mock-up facility, assuming the average outdoor temperatures in Tables 3 and 5 as the reference input for a set of additional FE numerical calculations.  
In doing so, the FE assembly of Figure 13 was thus subjected to 12 different thermal scenarios, taking care of possible variations in the thermo-physical properties of the materials in use (air cavity included). The typical FE result is presented in Figure 16 (a detailed example for January temperatures), where the sensitivity of the \(f_{Rsi,FE}\) values from Equation (7) is shown. For evidence, we present the effects of the local details of the CLT-glass facade element on the thermo-physical performance indicators, with maximum variations close to the CLT frame and in the region of the corner joints. Even under various temperature boundaries (see Table 9) stable performance was also observed for different months of the year, further enforcing the potential of the facade solution, as well as the need for more extended studies.  
Figure 16: Numerical calculation of the temperature factor \(f_{Rsi,FE}\) for the CLT–glass facade element (ABAQUS, with legend values in \({}^{\circ}\)C, with the calculation example referring to January temperatures).
# On the Estimation of the Moving Mass of a TMD Installed on a Lively Structure
## 4 Validation Example
### Novel Procedure  
Finally, with the SDOF system installed on the lively structure, a force is applied on its moving mass, so the FRFs required to compute the left hand side of Equation (9) can be estimated. Similarly to Section 4.1, an impact hammer is used to apply the force, so the amount of moving mass is not modified during the test. The applied force is synchronously measured at 400 S/s, together with both acceleration responses (moving mass, \(\dot{x}_{i}(t)\), and structure, \(\ddot{x}_{s}(t)\)), from which the relative acceleration \(\ddot{\xi}(t)\) can be computed. The time signals are divided into segments encompassing one impact each by means of a rectangular window of length 32,768 points. The resulting averaged accelerometers are shown in Figure 6, where phase shifts of \(2\pi\) are also present due to noise and estimation errors.  
By using these two accelerometers and the transmissibility function computed in Section 4.2 as scenario S0, the left hand side of Equation (9) can be calculated. Note that all the FRFs in Equation (9) are defined as receptances, but they can be transformed into accelerometers by multiplying them by \(-\omega^{2}\) (second derivative in the frequency domain). As a result, the expression in the right hand side becomes the SDOF accelerance, \(a(\omega)\), shown in Equation (10). Figure 7 shows the obtained curve associated to the left hand side of Equation (9), together with the accelerance estimated in Section 4.1, by isolating the SDOF system from the structure. As can be seen, there exists a high correspondence between both curves, so the left hand side of Equation (9) effectively accounts for the SDOF FRF, as expected. The dotted line in Figure 7 shows the fitting performed to the calculated curve and the resulting properties are summarized in the last row of Table 1 together with the relative error. As can be seen, a high correspondence exists between the three methods, although the novel methodology leads to a better estimate of the moving mass value in comparison with both versions of the mass modification technique.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & \(\Delta m_{i}\) **(kg)** & \(f_{ti}\) **(Hz)** & \(\xi_{ti}\) **(\%)** & \(m_{ti}\) **(kg)** \\ \hline S0 & 0 & 2.34 & 2.01 & - \\ S1 & 2.02 & 2.28 & 1.86 & 36.15 \\ S2 & 4.04 & 2.21 & 1.71 & 34.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Estimated natural frequencies and damping ratios obtained from fitting the transmissibility function in each scenario.  
Figure 6: Accelerances associated to the relative coordinate (blue) and the structure coordinate (red) under a force applied on the moving mass.
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 3 Methods for Industrial Data Distribution
### Building Blocks
#### 3.1.2 RPL (IETF RFC 6550)  
The RPL routing framework is suitable for networks composed of many embedded devices with limited power, memory and processing resources and is able to meet the requirements of a wide range of monitoring and industrial applications. It is widely considered an important networking solution for various industrial sectors. There are two RPL modes: one is called non-storing mode and the other is called storing mode [31]. In non-storing mode, only the routing tree root (in our case the CNC) collects and maintains topology information of the whole network. In storing mode, every node keeps a complete list of routing entries for nodes in its sub-routing trees. Consequently, there is a trade-off between storing and non-storing mode of operation in terms of computing and communication resources. For example, storing mode requires more memory in intermediate nodes while non-storing mode causes data packets to increase in size, thus consuming more transmission power and bandwidth.
## 3 Results and Discussion
### Molecular Docking Analysis between Ligands (ACA, MAY, and LUT) with Maltase-Glucoamylase (MGA)
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ligand** &
\begin{tabular}{c} **Binding Energy** \\ **(kcal/mol)** \\ \end{tabular} & **Active Site** & *** Experimental** \\ \hline \multirow{4}{*}{ACA} & \multirow{4}{*}{\(-7.8\)} & Asp203, Thr205, Tyr299, Asp327, & Asp203, Tyr299, Asp327, \\  & & Asp443, Met444, Arg526, Asp542, & Trp441, Asp443, Arg526, \\  & & Trp539, Th544, Phe575, Ala576, & Asp542, Asp571, Phe575, \\  & & His600 & His600, Tyr 605 \\ \cline{1-1}  & & Arg202, Thr204, Thr205, Tyr299, \\  & & Asp327, Trp406, Lys480, Asp542 \\ \cline{1-1}  & & Tyr299, Ile364, Trp406, Asp443, \\ \cline{1-1}  & & Arg526, Phe575, Gln603 \\ \hline \hline \end{tabular}  
* [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Mol Biol. 2008, 375, 782-792.  
\end{table} 
Table 1: Analysis of molecular interactions between the maltase-glucoamylase (NtMGAM PDB ID: 2 MQJ) and acarbose (ACA), maysin (MAY), and luteolin (LUT) provided by molecular docking simulations. The binding energy analysis performed in this section for each coupling between the ligands (ACA, MAY, and LUT) and the macromolecule (NtMGAM) gives valuable information for predicting the affinity with the protein. This analysis also identifies which amino acid of the NtMGAM is in contact with the docked ligands in the active site. Moreover, the interaction between the docked ligands and the amino acids of the active site of the NtMGAM can be predicted by the charge transfer descriptor, and finally, the analysis of the hydrogen bonds allows for determining which of the studied ligands present the best absorption and permeability properties with the NtMGAM. The behavior of the hydrogen bonds predicted here was further analyzed with MD simulations. Figure 4: Amino acids of the active site of the maltase-glucoamylase (NtMGAM), with **(A)** acarbose (ACA), **(B)** maysin (MAY), and **(C)** luteolin (LUT), and the calculated binding energy values. Figure 5: Hydrogen bonds and \(\pi\)-\(\pi\) interaction between the active site of maltase-glucoamylase and **(A)** acarbose (ACA), **(B)** maysin (MAY), and **(C)** luteolin (LUT).
## 2 Regulatory Framework and Guidelines
### Green Public Procurement  
Green Public Procurement integrates requirements and criteria in order to achieve value for money in the whole lifecycle of a project while supporting resource protection [13] and reducing environmental impacts [14]. Since public organizations and institutions yield a purchasing power of 15 to 30% of the national gross domestic product [10], GPP is supported and promoted at the international level as a means of driving the market towards innovative and sustainable practices [15].  
At the European level, the Directive 2014/24/EU introduced the possibility of including tender clauses related to environment protection, the minimization of negative impacts, and waste management by promoting and monitoring the implementation of GPP [16; 17]. In addition to that, the Directive 2014/24/EU emphasizes the critical role of public procurement in reaching a smart, sustainable, and inclusive growth [17].  
Concerning the Italian regulatory framework, the Legislative Decree 50/2016 was a turning point for national procurement regulations, making GPP mandatory in public tenders and introducing the Most Economically Advantageous Tender (MEAT) approach, which aims to achieve value for money on a basis of the lifecycle of a construction project [18]. However, only around 20% of the Italian Provinces have adopted GPP including sustainability criteria so far, while around 50% of municipalities have very poor knowledge of GPP. The main causes, especially when considering smaller municipalities, are the lack of staff training and competences regarding GPP, and issues in drafting tender documents that include sustainability criteria [11].  
Concerning GPP implementation, the presented research aims to:  
Figure 1: Regulatory levels.  
provide Public Clients a method for GPP and environmentally sustainable criteria integration that can be further applied and adapted by municipalities;
* test the effectiveness of the proposed methodology through the presented case study.
# A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks
## 2 Related Works
Although network coding can improve the transmission reliability greatly, it also brings heavy redundant transmission problem. In [16], the authors propose a network coding based hybrid ARQ protocol (NCHARQ) which combines network coding and hybrid automatic repeat request (ARQ) techniques in underwater data transmissions. This protocol reduces the redundant transmissions significantly with the help of channel quality feedbacks and transmission rate control. However, these protocols mainly deal with transmission problems in transport layer, which do not consider the routing protocol in network layer. The relays are supposed to be chosen by a perfect method beforehand and this assumption restricts the practical application of network coding in UWSNs.
# Optimization of a Permanent Magnet Synchronous Motor for e-Mobility Using Metamodels
## 3 Design Optimization
### Optimization Results  
The CPU of the workstation used to derive the optimal design was an Intel Xeon W-2235 processor with 64 GB RAM. The best metamodels of the three output variables as selected by the RMSE test were combined with hybrid metaheuristic algorithm (HMA) and STDQAO, respectively. HMA is a kind of evolutionary algorithm, and STDQAO is a kind of gradient-based optimization algorithm. In general, evolutionary algorithms show better optimization results than gradient-based algorithms. However, in the case of this optimization, when STDQAO was applied, better results were shown than when HMA was applied, as shown in Table 4. The torque ripple of the optimal model was improved by approximately 1.63% compared with that of the initial model. In addition, it was confirmed that the average torque and efficiency of the optimal model were similar to those of the initial model. Figure 6 shows the torque waveforms of the initial and optimal models. The optimal design based on the metamodel generated predictive values of the design variables; therefore, an FEA was performed in this study to verify the adequacy of the design. As observed from the data presented in Table 4, the optimal design and FEA values of the variables are almost identical.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Items** & **Unit** & **Initial** & \begin{tabular}{c} **Optimal (HMA)** \\ **Predicted** \\ \end{tabular} & \begin{tabular}{c} **Optimal (STDQAO)** \\ **FEA** \\ \end{tabular} \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Design \\ variables \\ \end{tabular} } & Notch width (X1) & mm & 1.504 & 1.655 & & 1.504 \\  & Notch depth (X2) & mm & 0.398 & 0.420 & & 0.398 \\  & Barrier length (X3) & mm & 0.072 & 0.072 & & 0.072 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Design \\ results \\ \end{tabular} } & Torque ripple & \% & 4.835 & 4.816 & 5.092 & 4.835 & 5.032 \\  & Average torque & N-m & 25.278 & 25.251 & 25.253 & 25.278 & 25.284 \\ \cline{1-1}  & Efficiency & \% & 90.118 & 90.111 & 90.113 & 90.118 & 90.120 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimization results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Metamodel**} & \multicolumn{3}{c}{**RMSE Test Value**} \\ \cline{2-4}  & **Torque Ripple** & **Average Torque** & **Efficiency** \\ \hline EDT (Hybrid) & 0.861980 & 0.108295 & 0.029970 \\ Kriging & 0.444338 & 0.081922 & 0.027080 \\ MLP & 0.653252 & 0.063183 & 2.723662 \\ PR (Forward Step.) & 0.695273 & 0.029480 & 0.012375 \\ RBF (Int.) & 1.381964 & 0.062220 & 0.019989 \\ RBF (Reg.) & 0.778125 & 0.092219 & 0.241133 \\ \hline \hline \end{tabular}
\end{table}
Table 3: RMSE test results of the metamodels for different output variables.