esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Optimization of the ASC Frame Crossbar  
Based on the results from the analysis of the current state, the shape design of the ASC crossbar was optimized. The modification that has been proposed on the frame is the addition of triangular and rectangular reinforcements to the welding points of the crossbars, which will absorb some quantity of the stress peak, and therefore drop the above concentration observed at the top of the hollow section joints (Figure 10).  
Figure 10: Implementation of crossbar reinforcement.  
Figure 9: Load characteristics at the node 46 439.  
The reinforcements were selected in each corner as double, placed on the edges of the hollow section and reinforced from the outside by a rectangular flat sheet. Only the reinforcements of the vertical hollow sections, to which the side members were attached, were designed as double triangles, to solely facilitate the assembly of the side member using bolts. Their thickness, after the previous analysis, was chosen to be the same as the thickness of the reinforced parts, i.e., 4 mm. Consequently, the arrangement of the crossbar welds was optimized so that the weld did not pass through the critical point with the maximum stress value (Figure 11).  
We carried out a geometrically and materially nonlinear analysis (GMNA) in the same way as in the original design of the ASC frame. The actual value of the highest von Mises stress did not exceed the strength, \(\sigma_{D}=105\) MPa. The distribution of the mechanical stress in the steel structure is shown in Figure 12.  
Figure 11: Implementation of crossbar reinforcement, — the original state, — a new state.  
Figure 12: Von Mises’s stress distribution in the optimized ASC crossbar.  
The value \(L_{Fst}\) of the GMNA analysis for the limit state of the newly proposed frame solution is 3.94. The strength evaluation, in terms of limit states, and the calculation of the permissible load was carried out according to Formula (2), \(L_{FD}=1.15\) (Figure 13). The permissible Load Factor is greater than 1, so the design of the ASC frame crossbar fits in terms of its strength. The maximum permissible load on the SC wheels can be 15% higher for compliance. The structure conforms to the limits of plasticity and stability, and the maximum allowable stress.
## 4 Numerical Simulation  
### Establishment of the Finite Element Model  
A 3D finite element model was established using the finite element software Midas GTS NX to simulate the construction process of the shield tunneling under-passing of the culvert. The grid covers an area that is six times the radius of the tunnel, according to geomechanical theory. Based on practical engineering, the stratum size of the model was set at 90 m in the X direction, 80 m in the Y direction, and 25 m in the Z direction. The tunnel overburden was 8 m in height; the soft-rock height ratio of the excavation face \(\beta\) was 0.5, and the distance between the outer edge of the left and the right tunnel was 5.5 m.  
The soil layer, the tunnel segments, the grouting layer, the pedestrian culvert (incl. the foundation and upper structure), the roadbed and pavement structure, the grouting reinforcement body of the MJS and the sleeve-valve pipe, the composite foundation of the jet grouting pile, and the raft with the planting bar needed to be considered in the model. Among them, the stratum based on the elasto-plastic deformation adopts the Mohr-Coulomb criterion. The reinforced body, the segments, and the other structures, considering only their elastic working, utilized the constitutive relationship of linear elasticity. According to the results [34] of the load test on the segments in the staggered joint assembly in the literature, the reduction in the circumferential and longitudinal stiffness had to be considered in the assembly of the shield segments. In this study, the reduction factor was taken as 0.7. The parameters of the stratum and main materials are listed in Table 1 (the definition of the \"equivalent layer\" shown in Table 1 is explained in Section 4.2). The groundwater level was set to 4.7 m below the surface. The overall model is shown in Figure 15.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name of Soil or Structure**} & **Modulus of** & **Poisson’s** & **Severe** & **Cohesive Force** & **Internal Friction** \\  & **Elasticity (MPa)** & **Ratio** & **(KN/m\({}^{3}\))** & **(kPa)** & **Angle (\({}^{\circ}\))** \\ \hline \(<\)1\(>\) Miscellaneous fill & 2.5 & 0.35 & 16.5 & 8 & 10 \\ \(<\)4-2B\(>\) Silty sand & 5 & 0.42 & 17 & 12.6 & 12.8 \\ \(<\)3-1\(>\) Silty-fine sand & 12 & 0.25 & 20.3 & 15 & 35 \\ \(<\)3-3\(>\) Gravelly sand & 40 & 0.30 & 19.6 & 10 & 30.5 \\ \(<\)9C-2\(>\) Slightly weathered limestone & 10,000 & 0.30 & 25 & 1500 & 55 \\ Grouting reinforcement & 2000 & 0.23 & 20 & - & - \\ material & 30,000 & 0.3 & 25 & - & - \\ Raft & 34,500 \(\times\) 0.7 & 0.2 & 25 & - & - \\ Segment & 34,500 \(\times\) 0.7 & 0.2 & 25 & - & - \\ Equivalent layer & 13 & 0.3 & 22.5 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stratigraphic and main material parameters.  
Figure 15: Structure diagram of 3D model.
## 7 Open Issues and Research Challenges
### Device Complexity  
6G is an intelligent network equipped with AI algorithms to enhance its performance, i.e., quality of service (QoS). For different users, differential QoS will be mapped to network units intelligently to map services with QoE. To manage the same, highly complex network virtualization needs to be deployed at core switches and routers. This increases the operational cost and leads to a higher device and network maintenance cost. It is an open challenge to make devices and network costs affordable to 6G users.
# Enhancing Space Management through Digital Twin: A Case Study of the Lazio Region Headquarters
###### Abstract  
Digital Twin is becoming an increasingly powerful resource in the field of building production, replacing traditional processes in the Architecture, Engineering, Construction and Operations sector. This study is concerned with the development of a DT, enabled by Building Information Modeling, artificial intelligence, machine learning, and the Internet of Things to implement space management strategies. It proposes an application case for the Lazio Region headquarters, which has partly adopted smart working typology post-COVID-19. The aim is to create an accurate digital replica of the building based on BIM, integrated with real-time data. This will help to improve the use of space, the management of resources, and the quality of services provided to the community. It also improves energy efficiency, reducing consumption by 530.40 MWh per year and reducing greenhouse gas emissions by 641.32 tons of CO\({}_{2}\) per year. The research proposes a holistic framework for the implementation of innovative solutions in the context of public infrastructure space management through the use of digital technology, facilitating the promotion of efficiency and sustainability in decision-making and operational processes through the application of a digital methodology.  
digital twin; Internet of Things; BIM; artificial intelligence; machine learning; space management +
Footnote †: journal: Applied sciences
# Sliding Mode Integral Separation PID Control for Hydrogen Fuel Cell Systems
## 5 Conclusions
\begin{tabular}{l l l l} \multicolumn{4}{l}{**Nomenclature**} \\ \(\mathrm{A}_{\mathrm{V,rp}}\) & valve effective flow area & \(T_{\mathrm{cp,out}}\) & air compressor's outlet temperature \\ \(\mathrm{C}_{\mathrm{P}}\) & air heat capacity on a mass basis & \(T_{\mathrm{rm}}\) & gas temperature in the return pipeline \\ \(\mathrm{C}_{\mathrm{V,rp}}\) & flow discharge factor & \(T_{\mathrm{st}}\) & the temperature of the stack \\ \(E_{\mathrm{nearest}}\) & nominal voltage output of battery thermody- & \(\mathrm{T}_{\mathrm{atm}}\) & ambient temperature \\ \(I_{\mathrm{st}}\) & load current & \(U_{\mathrm{out}}\) & stack output voltage \\ \(I_{\mathrm{cp}}\) & rotational inertia of the motor & \(U_{\mathrm{act}}\) & activation overpotential voltage drop \\ \(\mathrm{k}_{\mathrm{t}}\) & motor torque sensitivity coefficient & \(U_{\mathrm{ohm}}\) & resistive voltage drops \\ \(\mathrm{k}_{\mathrm{sp}}\) & outlet runner coefficient of supply pipeline & \(U_{\mathrm{con}}\) & concentration differential voltage drop \\ \(\mathrm{k}_{\mathrm{out,ca}}\) & orifice coefficient & \(V_{\mathrm{cm}}\) & control voltage of the motor \\ \(\mathrm{M}_{\mathrm{st}}\) & stack weight & \(V_{\mathrm{sm}}\) & piping aggregate volume \\ \(N_{\mathrm{cell}}\) & number of single cells & \(W_{\mathrm{cp}}\) & air compressor outlet flow rate \\ OER & oxygen excess ratio & \(W_{\mathrm{sm}}\) & gas mass flow rate in the supply pipeline \\ \(P_{\mathrm{out}}\) & stack output power & \(W_{\mathrm{out,ca}}\) & cathode outlet gas mass flow rate \\ \(P_{\mathrm{sm}}\) & supply pipeline gas pressure & \(W_{\mathrm{rm,out}}\) & return pipeline outlet flow \\ \(P_{\mathrm{atm}}\) & ambient pressure & \(W_{\mathrm{02,input}}\) & oxygen flow rate to the cathode of stack \\ \(P_{\mathrm{ca}}\) & cathode pressure in the stack & \(W_{\mathrm{02,react}}\) & oxygen flow rate consumed by electrochemical reactions \\ \(P_{\mathrm{pm}}\) & return pipeline gas pressure & \(\eta_{\mathrm{cp}}\) & efficiency of air compressor \\ \(\mathrm{Q}_{\mathrm{tot}}\) & heat from electrochemical reaction & \(\omega_{\mathrm{cp}}\) & motor speed \\ \(\mathrm{Q}_{\mathrm{cool}}\) & coolant cooling & \(\eta_{\mathrm{cm}}\) & motor efficiency \\ \(\mathrm{Q}_{\mathrm{gas}}\) & gas flow thermal exchange & \(\gamma\) & isentropic expansion factor \\ \(\mathrm{Q}_{\mathrm{amb}}\) & heat lost by the thermal radiation of stack & PEMFC & proton\(-\)exchange membrane fuel cell \\ \(\mathrm{R}_{\mathrm{cm}}\) & armature resistance & PID & proportional\(-\)integral\(-\)derivative \\ \(\mathrm{R}_{\mathrm{a}}\) & air gas constant & IS\(-\)PID & integral separation proportional\(-\)integral\(-\)derivative \\ \(\mathrm{R}\) & universal gas constant & SMC & sliding mode control \\ \(T_{\mathrm{sm}}\) & gas temperature of the supply pipeline & & SMC\(-\)IS\(-\)PID \\ \end{tabular}
# An Automated Framework for Streamlined CFD-Based Design and Optimization of Fixed-Wing UAV Wings
## 5 Framework Performance  
The performance evaluation of the proposed framework is conducted through a combination of validation studies and a large-scale development in order to acquire statistical insights across a large sample of the design space. Firstly, in Section 5.1, a grid independence study is conducted alongside the quantification of discretization errors in the generated grids. It must be noted at this point that the main goal of this work is to present and demonstrate the entire pipeline of a framework that leads from geometry to simulation. Validating the framework against experimental or flight-testing data is beyond the scope of this work, especially given the fact that the methods have been validated in previous studies in their non-automated form. Additionally, in Section 5.2, a validation case is selected to benchmark the framework's performance against the expertise of a highly skilled mechanical engineer specializing in CFD. This comparison focused on both time efficiency and the accuracy of the results.  
Following this, in Section 5.3, an extensive series of analyses were conducted, sampling 1165 wings from the predefined design space (Section 2). The purpose of these analyses is to assess the framework's performance across four primary aspects:  
1. sampling a range of wing configurations;
2. meeting boundary-layer resolution and mesh-quality thresholds (Section 4);
3. ensuring solver convergence and minimal iterative error;
4. achieving computational efficiency for large-scale design exploration.  
The findings indicate that the framework can handle a wide range of wing configurations without user intervention, while also providing detailed information on computational durations for geometry generation, mesh generation, solution setup, and solution execution. Additionally, the grids generated were evaluated using the quality metrics described in Section 4.  
All the analyses presented in this section for the framework's performance are conducted at the Aristotle University of Thessaloniki (AUTh) High Performance Computing (HPC) cluster, leveraging its advanced computational capabilities. This state-of-the-art system facilitated the efficient execution of the large-scale simulations required for validating the framework and analyzing the sampled wings. The HPC cluster specifications are detailed in Table 6.  
It should be noted that the definition of a \"highly skilled Mechanical Engineer specializing in CFD\" is inherently subjective. In this case, the expert is an engineer with over five years of experience in CFD, specifically for aircraft and UAV applications [39; 62; 63; 64]. The expert has served as the lead CFD engineer on multiple research and industrial projects, developing expertise in aerodynamic analysis, mesh optimization, and simulation validation. While this level of expertise provides a solid benchmark for comparison, it is acknowledged that individual skill sets and methodologies can vary significantly across practitioners, which may influence the comparison outcomes.
## 2 The Proposed Method
### Scenario-Based Noise Removal  
#### 2.2.1 WLS Algorithm  
The WLS [34] algorithm is a kind of edge-preserving filter. The purpose of this algorithm proposed in this paper is that the algorithm based on bilateral filtering cannot extract good details on a multi-scale level, and there may be artifacts, but the edge part is as good as possible. The goal of filtering is to get as close to the original image as possible, retain the edge information of the strong gradient to the greatest extent, and smooth the edge information of the small gradient. Assign the initial, undenoised image as \(X\), and the smoothed result image as \(Y\). The optimization target will be determined by Formula (2).  
\[\underset{Y}{\text{min}}\sum_{s}\left((Y_{s}-{X_{\ast}}^{2}+\lambda(\omega_{x, s}(\
abla Y_{x,\ast})^{2}+\omega_{y,s}(\
abla Y_{y,\ast})^{2})\right. \tag{2}\]  
\((i,j)\) represents the pixel position, and \(\
abla Y_{x,(i,j)}\), \(\
abla Y_{y,(i,j)}\) represents the horizontal and vertical gradient of the output image \(Y\) at \((i,j)\). Weights \(\omega_{x,(i,j)}\) and \(\omega_{y,(i,j)}\) are defined as follows:  
\[\omega_{x,s}=\left(\left|\
abla\updownarrow_{x,s}\right|^{\alpha}+\varepsilon \right)^{-1}\omega_{y,s}=\left(\left|\
abla\updownarrow_{y,s}\right|^{\alpha} +\varepsilon\right)^{-1} \tag{3}\]
# The NIRS Brain AnalyzIR Toolbox
## 5 Connectivity and Hyper-Scanning Modules
### Correlation Models
#### 5.1.1 Pre-Whitening  
Physiological noise in the fNIRS signals are temporally correlated (colored) noise. In this case, pre-whitening filter removes the autocorrelation and whiten the frequency content of the signal. A pre-whitening filter can be defined from an autoregressive model of the data. The effect of this approach on fNIRS signals has been presented in our previous work [16]. Once applied to the data, yields an uncorrelated innovations signal. The autoregressive model for a signal (\(Y\)) is defined as  
\[Y_{\{t\}}=\sum_{i=1}^{P}a_{i}\cdot Y_{\{t-i\}}+\epsilon_{\{t\}} \tag{32a}\] \[\epsilon_{\{t\}}\in N\Big{(}0,\sigma^{2}\Big{)} \tag{32b}\]  
where \(t\) indicates the sample point (time) and the set \(a_{i}\) is the autoregressive coefficients of the model, which need to be estimated. \(P\) is the model order, which can be selected using an information criteria such as BIC (Bayesian information criteria) [54]. Equations (32a) and (32b) says that the current sample point (\(Y_{\{t\}}\)) can be predicted based on the last several time-points in its history (\(a_{1}\cdot Y_{\{t-1\}}\dots\ a_{P}Y_{\{t-1\}}\)) and newly added information at that time point, which is called the innovations (\(\epsilon_{\{t\}}\)). The innovations can be thought of as the new information that is added to the total signal at each time point. The innovations time-course is a whitened signal with no autocorrelation representing the signal information added at each time point. The innovations signal can be estimated by first fitting the autoregressive coefficients of the model and using them to filter the original signal. Pre-whitening is applied to any two signals \(A\) and \(B\) to yield their respective whitened innovations models \(A_{w}\) and \(B_{\mathrm{w}}\). Instead of correlating the original signals, the two innovations (\(A_{w}\) and \(B_{\mathrm{w}}\)) are compared which estimates the correlation of the addition of only the new information being added to both signals at each time point. So-called Wiener causality models [55] are an implementation of this concept that specially look at the relationship of lagged cross terms in the innovations (e.g., the history of signal \(B\) predicts the current value of \(A\)). In the remainder of this section however, we focus on the zeroth lag correlation terms.  
The pre-whitening will remove the serially correlated errors between two signals for both no motion and motion affected confirming. After pre-whitening using an appropriate high model order, the autocorrelation of both motion-affected and -unaffected drops to random change within a single time point. The comparison of the analysis of original data, whitened signal, and autocorrelation have been examined in depth in [PERSON] et al. [18] (see Figure 2 in [18] for detail).
## 3 Results  
### Relationships of Vegetation Phenology, Greenness, and GPP with Climate Factors  
After analyzing the relationships between phenology metrics/greenness and climate factors, the results with coefficients of determination (R\({}^{2}\)) larger than 0.4 were selected and presented in Figure 3. The NDVI- (first column panels in Figure 3a) and EVI-derived (second column panels in Figure 3a) SOS advanced with increasing early season average Ta, demonstrating the role of higher spring temperature for greening-up of vegetation. The NDVI- and EVI-derived LOS was negatively correlated with the TGS and AGS average Ta with a larger R\({}^{2}\) in the former. The relationships between NDVI- or EVI-derived phenology metrics and climate factors were generally similar.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & Thermal GS total RF \\ \cline{2-3} NDVI/EVI sum & Thermal GS average Ta \\ \cline{2-3} Annual GS total RF \\ \cline{2-3} Annual GS average Ta \\ \hline \multirow{4}{*}{NDVI/EVI average} & Thermal GS total RF \\ \cline{2-3}  & Thermal GS average Ta \\ \cline{2-3}  & Thermal GS average Ta \\ \cline{2-3}  & Annual GS total RF \\ \cline{2-3}  & Annual GS average Ta \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 3: Simple linear regressions with coefficients of determination larger than 0.4 for (**a**) phenology metrics vs. climate, (**b**)greenness metrics vs. climate. The first, second, third, and fourth column panels are for NDVI-derived phenology metrics, EVI-derived phenology metrics, NDVI-based greenness, and EVI-based greenness, respectively, in the native pasture.  
The NDVI sum was positively correlated with total RF for both TGS and AGS (R\({}^{2}\) = 0.38 and 0.52, respectively), while it was negatively correlated with TGS and AGS average Ta (R\({}^{2}\) = 0.51 and 0.42, respectively) (first column panels in Figure 3b). These relationships indicated that rainfall was a limiting factor while temperature was a stressor, especially during the TGS. The relationships between EVI sum and climate factors also showed similar trends (second column panels in Figure 3b). The VI sum vs. AGS total RF had the largest R\({}^{2}\) values (0.52 for NDVI and 0.53 for EVI), indicating the importance of RF in the influencing of vegetation phenology and productivity.  
TGS sum of GPP was positively correlated with total RF during TGS/AGS and negatively correlated with TGS/AGS average Ta (Figure 4). As in the VI vs. climate relationships (Figure 3), RF provided stronger explanatory power than did Ta. GPP showed large variations in both NP and IP (Figure 5). The GPP in 2011 had the lowest values due to severe drought. After IP was homogeneously covered by old world bluestem, GPP in both NP and IP was very similar for the following two years (2009 and 2010). The GPP decreased more in IP than NP during the continuous droughts in 2011 and 2012.  
Figure 4: Relationships between thermal growing season sum of gross primary production (GPP) and climate in the native pasture.
# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 3 Choice of System Components and Programming
### Citizen Weather Observer Networks
#### 3.3.1 Automatic Weather Stations
Thermistors, thermopars, and RTD probes are used to measure the temperature, and for the relative humidity, a condenser-type hygrometer [84]. In most AWSs, the measuring range for the temperature goes from \(-\)40 to 60 \({}^{\circ}\)C or 65 \({}^{\circ}\)C, except for the Bloomsky Weather Station model, which has a smaller range (from \(-\)20 to 55 \({}^{\circ}\)C). The accuracy is variable,from \(\pm\)0.3 to \(\pm\)2 \({}^{\circ}\)C, depending on the device. As for humidity, the range is from 0% to 100%, with an approximate accuracy of \(\pm\)2%. The data registered by the sensors are automatically acquired by the CPU for processing later and preparing dispatch via Wi-Fi using different transmission frequencies. The characteristics and functions of the CPU vary depending on the model used and are not clearly specified. However, many of their functions can be programmed in the factory, such as raw data conversion, local data storage, message coding, and transmission. In all cases, it is necessary to carry out an initialization of the equipment through the internet, using the company's own app. It is thus possible to adjust the configuration of the AWSs, such as the periodicity of the data collection (sampling), which by default is one minute. Some stations, such as the RainWise MK4-C Compact Cellular Weather Station, automatically average the data in order to provide values at intervals of 15 min. The data quality control is performed from the centralized system, using validation algorithms to ensure the quality of the data with quality verification rules and to identify discrepancies and errors on the basis of the shared reviews, which compare the data with that collected by adjacent AWSs [85]. For most AWSs, it is possible to incorporate an additional visualization console to view the data locally. There is no record of them performing manual entry of observations or data reduction functions. As for the peripheral equipment, these devices are powered by solar energy, including capacitors. They also have long-life backup batteries to ensure the electrical supply. The equipment used to guarantee accuracy in the timing of the registers from the AWSs is unknown, as is whether the test equipment is incorporated to supervise the performance of the components. (b) Auxiliary elements  
The Nt AWSs does not possess forced ventilation but can have an optional radiation screen with apertures to ventilate naturally. This white screen is made of materials resistant to solar radiation. The sensor of the WU AWSs comes from the factory with solar radiation protection (and protection from other phenomena) incorporated. Therefore, it is not necessary to include any auxiliary elements to ensure correct functioning. In addition, the AcuRite 5-in-1 Weather Environment System model [86] has an internal suction ventilator powered by solar panels.