esa-sceva/satcom-chunk-collection huggingface dataset:
# Interoperability of Digital Tools for the Monitoring and Control of Construction Projects
## 4 Results and Discussion
### Validation of Digital Tools

Table 13: Frequencies obtained according to the criteria for FDCT. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**FDCT**} & \multirow{2}{*}{**Utility**} & \multicolumn{2}{c}{**Time**} & \multirow{2}{*}{**Accuracy**} & \multicolumn{2}{c}{**Level of**} & \multirow{2}{*}{**Training**} & \multirow{2}{*}{**Equipment**} \\ \cline{2-4}  & & **Efficiency** & & & **Automation** & & **TRA** \\ \cline{2-4}  & **UTI** & **EFI** & **ACC** & **AUT** & **TRA** & **EQUI** \\ \hline Ultrawide band (UWB) & 2 & 2 & 2 & 2 & 0 & 1 \\ \hline Laser scanner (LS) & 2 & 2 & 2 & 2 & 0 & 1 \\ \hline Robotics \(\textcircled{\textbackslash}\) & 1 & 2 & 2 & 2 & 1 & 1 \\ \hline Radio frequency identification & & & & & & \\ (RFID) & 1 & 1 & 2 & 2 & 0 & 1 \\ \hline Smartphones (SMT) & 0 & 1 & 1 & 0 & 1 & 2 \\ \hline Unmanned aerial vehicle (UAV) & 2 & 1 & 2 & 1 & 1 & 2 \\ \hline Wireless sensor networks (WSN) & 1 & 2 & 2 & 2 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Performance matrix of digital tools for in situ data acquisition. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**TC** & **Interoperability** & **Virtualization** & **Decentralization** & \
\begin{tabular}{c} **Real-Time** \\ **Capacity** \\ \end{tabular} &
\begin{tabular}{c} **Service-** \\ **Oriented** \\ \end{tabular} & **Flexibility** \\ \cline{2-6}  & **INT** & **VIR** & **DES** & **RTC** & **SO** & **FLE** \\ \hline Big data analysis (BDA) & 1 & 2 & NA & 2 & 2 & NA \\ \hline Mobile applications & 2 & 1 & 1 & 1 & 1 & 1 \\ (MAA) & 2 & 1 & 1 & 1 & 1 & 1 \\ \hline Machine learning (ML) & 1 & 2 & 2 & 1 & 1 & 1 \\ \hline Deep learning (DL) & 1 & 2 & 1 & 2 & 1 & 1 \\ \hline Automated regulation & & & & & & \\ checking and audits (ARCA) & 0 & 2 & 1 & 2 & 1 & 1 \\ \hline Edge computing (EC) & 1 & 2 & NA & 2 & 1 & NA \\ \hline Cloud computing (CC) & 1 & 1 & 2 & 2 & 2 & 2 \\ \hline Mobile computing (MC) & 1 & 1 & NA & 1 & 2 & 1 \\ \hline Common data & 1 & 1 & 2 & 2 & 2 & 2 \\ environment (CDE) & & & & & & \\ \hline Photogrammetry (PHT) & 2 & 2 & NA & 2 & 2 & 2 \\ \hline Digital twins (DT) & 2 & 2 & 1 & 1 & 2 & 1 \\ \hline Data sharing (DS) & 1 & 1 & 2 & 2 & 2 & 2 \\ \hline Internet of Things (IoT) & 2 & 2 & 1 & 2 & 2 & 2 \\ \hline Industrial Internet (II) & 1 & 2 & NA & 2 & 2 & NA \\ \hline Data mining (DM) & 1 & 1 & NA & 2 & 1 & NA \\ \hline Building information & 2 & 1 & 2 & 2 & 2 & 2 \\ model (BIM) & & & & & \\ \hline Simulations models and & & & & & \\ tools (SMT) & 2 & 1 & 1 & NA & 2 & 1 \\ \hline Augmented reality (AR) & 1 & 2 & 1 & 2 & 2 & 1 \\ \hline Mixed reality (MR) & 1 & 2 & 1 & 2 & 1 & 1 \\ \hline Virtual reality (VR) & 1 & 2 & 1 & 2 & 2 & 1 \\ \hline Social networks (SN) & 0 & 1 & 1 & 1 & 1 & 2 \\ \hline Geographic information & & & & & \\ system (GIS) & 1 & 2 & NA & 2 & 2 & 1 \\ \hline Embedded system (ES) & 1 & 2 & NA & 2 & 1 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Communication and collaboration technology tool performance matrix.
# Review of Propulsion System Design Strategies for Unmanned Aerial Vehicles
## 3 Results  
### Literature Analysis  
Figures 1 and 2 depict the distribution of the papers among the proposed categories and their numerical evolution in time, respectively, in aggregate form. For the papers categorized within the functional design, sizing category, Table 2, synthesizes the results of the requirements' assessment. Figure 1: Distribution of the analyzed papers among the identified categories according to the proposed classification. Figure 2: For each category, the number of published papers over time is plotted in stacked style. In the black line, the cumulative function. Table 3-11 collect the results of the literature analysis, according to the taxonomy, which focuses on the papers' content. In those tables, data are presented by category and sorted by year (from newest to oldest) and, within each year, by the first author's surname (in alphabetic order), whereas Table 12 synthesizes all the results of the literature review at a glance. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Paper**} & \multicolumn{4}{c}{**Technical**} & \multirow{2}{*}{**Normative**} & \multirow{2}{*}{**Custom**} \\ \cline{2-2} \cline{5-6}  & **G** & **D** & & **PS** & **MRs** \\ \hline [PERSON] 2020 [11] & x & x & x & x \\ [PERSON] 2020 [12] & x & x & & \\ [PERSON] * 2020 [13] & x & x & x & x \\ [PERSON] 2020 [14] & x & x & x & x \\ [PERSON] 2018 [15] & x & x & & x \\ [PERSON] 2018 [10] & x & x & & x1 \\ [PERSON] 2017 [16] & & x & x & \\ [PERSON] 2017 [17] & x & x & & x \\ [PERSON] 2015 [9] & x & x & x & x \\ [PERSON] 2015 [18] & x & x & & x \\ [PERSON] 2012 [19] & x & x & x & \\ [PERSON] 2012 [20] & & x & x & \\ [PERSON] 2010 [5] & x & x & x & \\ [PERSON] 2010 [21] & & x & x & x \\ [PERSON] 2010 [22] & x & x & x & x2 \\ [PERSON] 2009 [23] & & x & x & \\ [PERSON] 2009 [24] & & x & x & \\ [PERSON] 2008 [8] & x & x & x & x1 \\ [PERSON] 2005 [25] & x & x & x & \\ \hline \hline \end{tabular}  
* **Legend**: **G**, Geometry; **D**, Dynamics; **PS**, Power Supply; **MRs**, Mission Requirements. **Notes: x1*
* Mission Requirements: operating altitude. x2 Aircraft size restricted to the Group 2 (small) UAS category: maximum gross mass. At take-off of 21 to 55 1 Bs, normal operating altitude below 3500 feet AGL. x1 Logistics and loitering missions requiring no more than 4 people, usable by non-professional operators. x11 Limited maintenance, limited noise.
* Data from paper abstract (full-text not available).  
\end{table} 
Table 2: Classification of the constraints analyzed in each paper within the functional design, sizing category, among technical, normative, and custom sets. For the technical constraints, the distinction among the four subsets of Geometry (G), Dynamics (D), Power Supply (PS), and Mission Requirements (MRs) is reported.
## 3 Case Study
### Validation of the Dynamic Nonlinear Model  
The numerical algorithm elaborated in Matlab/Simulink is presented in (Figure 7), where (a) represents the seismic and scale factor blocks, (b) is the integrator (ode23t-trapezoidal rule and backward differentiation formula with variable-step), (c) multiplies the state variable with the state matrix and feeds back the result, (d) multiplies the plastic displacement vector and feeds the result into (b), while (e) and (f) are the main routines that simulate the nonlinear behavior of the structure. This diagram describes the exact data flow of the numerical routine. In the first instance using blocks \((a)\), \((b)\), \((c)\), and \((d)\), information from previous step is fed into the integrator Equation (7) in order to determine the state vector. Further, the displacements are used in block \((f)\) to determine the trial plastic rotation increments (\(\mathbf{\Delta\theta_{\text{trial}}^{\prime\prime}}\)) and consequently the trial bending moments (\(\mathbf{m_{k+1}^{trial}}\)), as described by Equations (14) and (15). At the same time, the trial \(\mathbf{K_{d}}\) matrix is populated with slope values describing unloading Equation (13). Additionally, the axial force is established in order to determine columns bending moment capacity as described by [10]. Next the data is fed into the main nonlinear routine embedded in block \((e)\). In this block, using a _while loop_, the \(\mathbf{K_{d}}\) matrix and (\(\mathbf{\Delta\theta_{\text{trial}}^{\prime\prime}}\)) vector are updated to the actual values (Figure 1, Equations (16) and (17)) in order to determine the real bending moments (\(\mathbf{m_{k+1}}\)) Equations (10) and (11). Furthermore the final plastic rotation (_theta_\(\_p\)) and consequently the plastic displacement (\(x\_p\)) are determined. The interested reader will find in [10] more insights regarding the FAM 3D nonlinear routine in block \((e)\), especially for the return mapping algorithm used. Furthermore, before putting the algorithm to work, the performance of the routine was evaluated using a comparison of the dynamic nonlinear structural response obtained, with the one conducted on an equivalent model developed in a commercial software \(SAP2000\). It must be noted that \(SAP2000\) capabilities of modeling hysteresis behavior is limited to certain default options. Therefore, a similar model to Takeda was used for the FAM 3D validations. Beam (#125 and #651) and column (#1 and #2) bending moments together with the top story displacements time histories response are compared using as seismic input the Vrancea 1977 recordings scaled by a scale factor of 1.4 (Figure 8). The results shown in Figures 9-12 demonstrate very good agreement of the structural responses.  
Figure 6: The representation of columns capacity function.  
Figure 8: Vrancea 1977 earthquake recordings.  
Figure 7: FAM -based peak-oriented nonlinear dynamic model developed in Simulink.  
Figure 9: Top story displacements time history.  
Figure 11: Beam bending moments time history.  
Figure 12: Beam bending moment-plastic rotation response.  
Figure 10: Base column bending moments time history.
## 3 Apparent Reflectance Error Analysis and Its Predictive Power
### Spectral Error Analysis on Apparent Reflectance
Main findings for the O\({}_{2}\)-B region are summarized as follows:  
* The over-/under-estimation of the AOT causes an under-/over-estimation of \(\rho_{app(atm)}\) in SIF emitting surfaces, i.e., partially-mixed or dense vegetation, while the opposite effect is observed in non-SIF emitting surfaces, i.e., bare soil. * Errors in the the Angstrom parameter (\(\alpha\)) hardly lead to errors in \(\rho_{app(atm)}\). However, the spectral distortion appears to be driven by the underlying surface reflectance, the spectral distortions being more abrupt for the bare soil and the mixed vegetation and bare soil than in a full vegetation spectrum. * The asymmetry parameter (\(g\)) of the HG scattering phase function is clearly the driving parameter that causes the strongest distortion in the \(\rho_{app(atm)}\). Additionally, the spectral distortions mostly follow a similar spectral pattern regardless of the surface reflectance spectra. Figure 7: (**a**) bare soil, (**b**) mixed bare soil and vegetation, and (**c**) vegetation surface reflectance and fluorescence spectra simulated with the version 1.53 of SCOPE model. The corresponding biophysical parameters of Leaf Area Index (LAI) and Chlorophyll (_Chl_) content used in the simulations are detailed in Appendix C.  
The main findings for the O\({}_{2}\)-A region are summarized as follows (see Figure 9):
* Spectral distortions in the O\({}_{2}\)-A caused by each aerosol optical property follow a similar distortion pattern regardless of the surface reflectance and the fluorescence emission. Due to the deepest absorption in the O\({}_{2}\)-A band, this region seems spectrally more sensitive to aerosol over-/under-estimation. * Although distortions on \(\rho_{app(atm)}\) produced by changes in the AOT and \(g\), which are the driving parameters, are approximately on the same order of magnitude, the spectral distortion pattern is slightly different. * As in the O\({}_{2}\)-B region, the over-/under-estimation of the \(\alpha\) parameter produces a weaker spectral distortion than those produced by the AOT and the \(g\) parameters. Note that the spectral distortion patterns observed in both O\({}_{2}\) regions are the result of: (1) the specific shape of the reflectance and the fluorescence emission spectra, and (2) the distortions in the atmospheric transfer functions. Figure 8: Apparent reflectance spectral distortion caused by an over-/under-estimation of the AOT, \(\alpha\) and \(g\) aerosol optical properties for three different surfaces at the O\({}_{2}\)–B absorption band at FLORIS spectral resolution. The dashed horizontal black line over the \(g\) parameter figures delimits the area where the \(\rho_{app}\) spectral distortions have been computed by extrapolating \(g\) values higher than 1.
## 2 Materials and Methods
### Adsorption Isotherm Study
#### 2.6.2 Theoretical Background of Isotherm Models  
The ability of the Freundlich model to fit the experimental data, via plotting a curve of \(\log q_{e}\) with respect to \(\log C_{e}\), was employed to generate a slope of n and the intercept value of \(K_{f}\). The Freundlich model can be easily linearized by plotting it in a logarithmic form [31]:  
\[\log q_{e}=\log K_{f}+1/n\,\log C_{e} \tag{3}\]  
Freundlich constants \(K_{f}\) and \(n\) were determined from the isotherm equation according to Equation (3).  
The Langmuir model is presented by mathematical expression [32] as the following equation:  
\[q_{e}=q_{\max}\;bC_{e}/(1+bC_{e}) \tag{4}\]  
where \(q_{\max}\) (mg g\({}^{-1}\)) is the maximum sorption capacity corresponding to the saturation capacity (representing the total binding sites of a fiber) and \(b\) (L mg\({}^{-1}\)) is a coefficient related to the affinity among the McNTs and dye ions.  
The linear relationship can be achieved from plotting curve \((1/q_{e})\) vs. \((1/C_{e})\):  
\[1/q_{e}=1/(bq_{max}\;C_{e})+1/q_{max} \tag{5}\]  
in which \(b\) and \(q_{\max}\) are determined from the slope and intercept, respectively.  
The Henderson and Halsey Isotherm Models  
These models are suitable for heteroporous solids and the multilayer sorption technique [33; 34]. The Halsey model was calculated using the following equation:  
\[\ln\;q_{e}=\frac{1}{n}\ln\;K+\frac{1}{n}\;\ln\;C_{e} \tag{6}\]  
where \(n\) and \(K\) are Halsey constants. Meanwhile, the Henderson model was obtained from the following equation:  
\[\ln[-\ln(1-C_{e})]=\ln K+(\frac{1}{n})\ln q_{e} \tag{7}\]  
while the Henderson constants are \(nh\) and \(Kh\).  
The Harkins-Jura Model  
This model explains multilayer sorption, in addition to the presence of heterogeneous pore scattering in a sorbent [34]. This model was obtained from the following equation:  
\[\ln\;q_{e}=\frac{1}{n}\ln\;K+\frac{1}{n}\;\ln\;C_{e} \tag{8}\]  
where the isotherm constants are \(AH\)] and \(BH\)].  
The Smith model is appropriate for heteroporous solids and multilayer adsorption. This model is usually obtained from the following equation:  
\[q_{e}=W_{bS}-W_{S}\ln\left(1-C_{e}\right) \tag{9}\]  
where the Smith model parameters are \(W_{bS}\) and \(W_{s}\).  
The Tempkin isotherm model assumes that the heat of the sorption of each particle in the layer declines with coverage because of adsorbate-adsorbent interactions; moreover, the sorption process is described through a uniform distribution of the binding energies, up to some higher binding energy [35]. The Tempkin model was calculated from the following equation:  
\[q_{e}=B\ln A+B\ln C_{e} \tag{10}\]  
where \(A\) is the constant of equilibrium binding (L g\({}^{-1}\)) associated with a higher binding energy, \(b\) (J mol\({}^{-1}\)) is a constant corresponding to the heat of sorption, and \(B=\) (RT b\({}^{-1}\)) (J mol\({}^{-1}\)) is the Tempkin constant and is related to the heat of adsorption and the gas constant (R = 8.314 J mol\({}^{-1}\) K\({}^{-1}\)).
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
###### Abstract  
The purpose of this study is to enhance the performance of Multistream Fast Transmission Control Protocol (TCP) keeping in view the recent web-based applications that are being deployed on long-range, high-speed, and high-bandwidth networks. To achieve the objective of the research study, a congestion control after fast-recovery module for congestion control scheme of Multistream Fast TCP is proposed. The module optimized the performance of the protocol by reducing the time that is required to consume the available bandwidth after a fast-recovery phase. The module is designed after studying additive-increase, multiplicative-decrease and rate-based congestion window management schemes of related transport protocols. The module adjusts the congestion window on receipt of each individual acknowledgment instead of each round trip time after the fast-recovery phase until it consumes vacant bandwidth of the network link. The module is implemented by using Network Simulator 2. Convergence time, throughput, fairness index, and goodput are the parameters used to assess the performance of proposed module. The results indicate that Enhanced Multistream Fast TCP with congestion control after fast recovery recovers its congestion window in a shorter time period as compared to multistream Fast TCP, Fast TCP, TCP New Reno, and Stream Control Transmission Protocol. Consequently, Enhanced Multistream Fast TCP consumes the available network bandwidth in lesser time and increases the throughput and goodput. The proposed module enhanced the performance of the transport layer protocol. Our findings demonstrate the performance impact in the form of a decrease in the convergence time to consume the available network bandwidth and the increase in the throughput and the goodput.  
delay-based congestion control; Fast TCP; fast recovery; loss-based congestion control Article
## 4 Analysis and Results  
### Typical Subsequent-Leader Electric Field Waveform  
We first use the model described in Section 3 for computing the electric field waveform at an observation point P located 250 m above ground level for a typical subsequent leader with the following parameters: \(I_{p}=12\) kA, \(v=1\times 10^{7}\) m/s, \(H=5\) km. Our model prediction is shown in Figure 5.  
Note that the electric field in Figure 5 rapidly rises as the leader tip approaches the observation point, peaks when the leader tip passes through the observation point, and then begins to decline once the observation point is inside the leader channel. The electric field peak is about 2.3 MV/m and the half-peak width is 0.14 \(\upmu\)s.  
Modeling results presented in this subsection confirm that the electric field peak produced by a subsequent leader can briefly reach some megavolts per meter, which may be sufficient to cause runaway electron avalanches in warm (reduced air density) channel left behind by the preceding return stroke. In Section 4.2 below, we will perform a sensitivity analysis and show that the electric field peak associated with a subsequent leader can be as high as 20 MV/m, certainly enough to overcome the friction force (see the red curve in Figure 1) and to produce energetic radiation in a warm channel.  
Figure 5: The variation of the electric field at a point located 250 m above ground as the descending leader passes through it, computed using the formulation presented in Section 3 of this paper. The field peak at \(t=0\) corresponds to the leader tip being at the observation point, the part of the field waveform at \(t<0\) corresponds to the tip being above the observation point, and the part at \(t>0\) corresponds to the tip being below the observation point. Full time scale is 2.5 \(\upmu\)s.
# Influence of Electrohydrodynamics on the Drying Characteristics and Volatile Components of Ginger
## 3 Results and Discussion
### Rehydration and Shrinkage  
The higher the rehydration rate, the better the quality of ginger dried products, as this causes the least damage to the structure. Figure 3a shows the effect of different voltage levels on the hydration ability of ginger. Figure 3a shows that the rehydration rate varies at different voltage levels, with the control group having the lowest rehydration rate, while the rehydration ability is improved after electrohydrodynamic drying (EHD). The best rehydration ability is achieved at a voltage of 30 kV. Internal structural damage reduces rehydration capacity [31]. Thus, EHD drying does not destroy the internal structure of ginger. From Figure 3b, it can be observed that, as the voltage increases, the shrinkage rate of ginger remains basically unchanged. During the drying process of ginger, the shrinkage rate gradually increases due to volume loss. At the initial stage of drying, the volume of ginger samples was reduced by 15% due to the removal of free water. In the middle drying stage, the charged particles caused cell shrinkage and the deformation of the capillary structure, which further increased the volume shrinkage. At the end of drying, the volume of the sample was reduced by >80%. Therefore, there is a significant relationship between  
Figure 2: Drying characteristics of ginger chart. (**a**): Time-dependent graph of moisture content ratio; (**b**): Drying rate versus time at different voltages; (**c**): Variation of average drying time and average drying rate of ginger at different voltages; (**d**): Effective water diffusion coefficient diagram of ginger. Values followed by the same letter are not significantly different at a statistical significance level of \(p<0.05\).  
the shrinkage volume of the material and its moisture content. During the microwave infrared synergistic drying process of ginger, [PERSON] et al. [32] also found a similar relationship between material shrinkage and moisture content. The main reasons for volume shrinkage during food drying are structural damage and the removal of moisture. The high rehydration rate and essentially constant shrinkage rate suggest that electrohydrodynamics are well-suited to drying ginger.
## 2 Materials and Methods
### Procedure for the Simulation Process (Workflow)  
The workflow diagram in Figure 9 illustrates the entire simulation process, encompassing tasks, such as data collection, building information modeling (BIM), gbXML file exportation, building energy modeling (BEM), model manipulation, PCM incorporation, simulation, and post-processing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Location**} & \multicolumn{4}{c}{**Average Temperature [\({}^{\circ}\)C]**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Summer**} & \multicolumn{2}{c}{**Winter**} \\ \cline{2-5}  & \multicolumn{2}{c}{**June to September**} & \multicolumn{2}{c}{**December to March**} \\ \cline{2-5}  & **Daytime** & **Nighttime** & **Daytime** & **Nighttime** \\ \hline Amman & 33 & 21 & 17 & 6 \\ \hline Irbid & 32 & 20 & 15 & 5 \\ \hline Aqaba & 38 & 25 & 24 & 14 \\ \hline Oradea & 24 & 15 & 2.5 & \(-\)3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average daytime and nighttime ambient temperature for the selected locations [53].  
Figure 9: Flowchart of the adopted methodology for the simulation process.  
A two-story residential building model in Jordan was created using Autodesk Revit and DesignBuilder software, transitioning from a 2D Cad file to a 3D building model in Autodesk Revit. DesignBuilder, chosen for its comprehensive material library, particularly for PCMs, supplements Autodesk Revit's absence of predefined PCM options. DesignBuilder, a Building Energy Performance Simulation (BEPS) tool, offers a user-friendly interface, a diverse range of building components and construction materials, and integrates with EnergyPlus simulation tool for energy simulations, daylighting analysis, and thermal load calculations. DesignBuilder's compatibility with industry standards and software, such as Autodesk Revit (v23.0.11.19), streamlines the workflow, facilitating automatic data export through Green Building XML (gbXML) files. The gbXML file, an open-source schema, contains crucial building information.