esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Investigation of a Novel System for Combined Dew Point Cooling and Water Recovery
## 2 Materials and Methods
### Mathematical Model of the DPC-WR System
#### 2.2.1 Mathematical Model of the First Stage of the DPC-WR System  
The balance equation for sensible heat related to the air stream in the primary channels (primary air) in the 1 st stage is given with Equation (1). The governing equation describing sensible heat related to the primary air in the 1 st stage is described with Equation (2).  
\[\left(\frac{dY}{L_{Y}}\right)G_{1}c_{p1}\frac{dt_{1}}{dX}dX=-d\dot{Q}_{1}^{S} \tag{1}\]  
\[\frac{dt_{1}}{d\overline{X}}=\text{NTU}^{\prime\prime}{}_{1}\big{(}t_{p1}-t_{ 1}\big{)} \tag{2}\]  
The balance equation for sensible heat related to the air stream in the secondary channels (secondary air) in the 1 st stage is given with Equation (3). The governing equation describing sensible heat related to the secondary air in the 1 st stage is described with Equation (4). The balance equation for mass transfer related to the secondary air in the 1 st stage is given with Equation (5). The governing equation for mass transfer related to the secondary air in the 1 st stage is described with Equation (6).  
\[-\left(\frac{dY}{L_{Y}}\right)G_{2}c_{p2}\frac{dt_{2}}{dX}dX=-d\dot{Q}_{2}^{S} \tag{3}\]  
\[\frac{dt_{2}}{d\overline{X}}=-\text{NTU}^{\prime\prime}{}_{2}\big{(}t^{\prime }{}_{w}-t_{2}\big{)} \tag{4}\]  
\[-\left(\frac{dY}{L_{Y}}\right)G_{2}\frac{dx_{2}}{dX}dX=dM_{2} \tag{5}\]  
\[\frac{dx_{2}}{d\overline{X}}=-\text{NTU}^{\prime\prime}{}_{2}\bigg{(}\frac{1}{ \text{Le}_{2}}\bigg{)}\big{(}x^{\prime}{}_{w}-x_{2}\big{)} \tag{6}\]  
The balance equation for mass transfer related to the water stream in the secondary channels in the 1 st stage is given with Equation (7) and the governing equation is described with Equation (8).  
\[\left(\frac{dY}{L_{Y}}\right)\frac{dG_{w}}{dX}dX=-dM_{w} \tag{7}\]  
\[\frac{dG_{w}}{d\overline{X}}=G_{2}\text{NTU}^{\prime\prime}{}_{2}\bigg{(} \frac{1}{\text{Le}_{2}}\bigg{)}\big{(}x^{\prime}{}_{w}-x_{2}\big{)} \tag{8}\]  
The balance equation for total heat transfer (sensible and latent) related to the 1 st stage is given with Equation (9).  
\[\frac{dt_{w}}{d\overline{X}}=W_{1}/W_{w}\text{NTU}^{\prime\prime}{}_{1}\big{(} t_{p1}-t_{1}\big{)}+W_{2}/W_{w}\text{NTU}^{\prime\prime}{}_{2}\bigg{[}\big{(}t_{w}-t _{2}\big{)}+\bigg{(}\frac{r}{c_{p2}}\bigg{)}\big{(}x^{\prime}{}_{w}-x_{2}\big{)} \bigg{]} \tag{9}\]  
where: \(W_{1}/W_{w}=(G_{1}c_{p1})/(G_{w}c_{w})\); \(W_{2}/W_{w}=(G_{2}c_{p2})/(G_{w}c_{w})\).
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 4 Blood Pressure Measurement
### Regression Prediction Based on Machine Learning Algorithms  
The preprocessed feature set was divided into training and testing sets (in this study, the ratio of training set to testing set was 8:2). Machine learning regression models were used to train the feature set and perform regression prediction. Common machine learning regression algorithms include linear regression, ridge regression, support vector regression, random forest regression, and eXtreme Gradient Boosting (XGBoost) regression.
# Machine Learning-Based Satellite Routing for SAGIN IoT Networks
## 1 Space-Air-Ground Integrated Network
### Architecture for Space-Air-Ground Integrated Network
#### 1.2.3 Advantages of SAGIN Architecture  
In SAGIN architecture, the three network layers can operate together and work independently. It integrates heterogeneous networks to build a layered broadband wireless network. The satellites may efficiently reach rural and distant regions, significantly lowering the cost of fifth generation (5G) ground networks. Meanwhile, it provides advantages in coverage, throughput, reliability, and flexibility, which provides it with broad application prospects in next-generation networks. In addition, SAGINs are of great importance in many situations such as communication, remote sensing, and navigation.  
Figure 1: An architecture for a space–air–ground integrated network.
## 3 Results and Discussion
### Response Analysis and Interpretation by Box-Behnken Design (BBD)  
The design matrix of the variables (height, X\({}_{1}\); concentration, X\({}_{2}\) and residence time, X\({}_{3}\)) in the uncoded and coded units by the Box-Behnken design (BBD) are shown in Table 7, along with the predicted and experimental values of the responses (adsorption capacity, Y). The response function with the determined coefficients for adsorption capacity of lime sand bricks towards Cu(II) is presented by Equation (8). \[ \begin{split}\text{Y}=0.1034-1.512\times 10^{-3}\text{A}+0.0194 \text{B}+0.0112\text{C}-1.825\times 10^{-3}\text{AB}-3.2\times 10^{-3}\text{AC}- \\ 5.225\times 10^{-3}\text{BC}-5.45\times 10^{-4}\text{A}^{2}-6.67 \times 10^{-3}\text{B}^{2}-0.0117\times\text{C}^{2}\end{split}  \tag{8}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{3}{c}{**Variables**} & \multicolumn{3}{c}{**Response (Y, mg/g)**} \\ \cline{2-6}  & \multirow{2}{*}{**Height (cm)**} & **Concentration** & **Residence** & **Experimental Adsorption** & **Predicted Adsorption** \\  & & **(mg/L)** & **Time (min)** & **Capacity** & **Capacity** \\ \hline
1 & 0 & 0 & 0 & 0.1034 & 0.1026 \\
2 & 0 & 0 & 0 & 0.1034 & 0.1031 \\
3 & 0 & 1 & 1 & 0.1209 & 0.1139 \\
4 & 1 & \(-1\) & 0 & 0.0840 & 0.0771 \\
5 & 0 & 1 & \(-1\) & 0.1009 & 0.0985 \\
6 & \(-1\) & \(-1\) & 0 & 0.0826 & 0.0765 \\
7 & 0 & 0 & 0 & 0.1034 & 0.1034 \\
8 & 0 & 0 & 0 & 0.1034 & 0.1034 \\
9 & 0 & \(-1\) & 1 & 0.0796 & 0.0820 \\
10 & \(-1\) & 0 & 1 & 0.1034 & 0.1071 \\
11 & 0 & \(-1\) & \(-1\) & 0.0387 & 0.0492 \\
12 & 1 & 1 & 0 & 0.1062 & 0.1123 \\
13 & \(-1\) & 0 & \(-1\) & 0.0827 & 0.0783 \\
14 & 1 & 0 & \(-1\) & 0.0853 & 0.0817 \\
15 & 1 & 0 & 1 & 0.0932 & 0.0976 \\
16 & \(-1\) & 1 & 0 & 0.1121 & 0.1190 \\
17 & 0 & 0 & 0 & 0.1034 & 0.1034 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The experimental design matrix and response based on the experimental runs by Box–Behnken Design (BBD). Figure 9: The breakthrough curve for varying residence time (min) at a bed depth of 20 cm and inlet Cu(II) concentration of 0.5 mg/L. The analysis of variance (ANOVA) tests were also conducted for each response and presented in Table 8. It is indicated that the predictability of the model is at a 90% confidence level. Response function predictions agreed well with the experimental data (\(r^{2}\) = 0.914). To evaluate the relationship between the predicted values from the model calculated by Equation (3) and the observed values, the data obtained was very close to linear, indicating that both values were accurate and reliable, as shown in Figure 10.  
It is required to test the significance and adequacy of the model through the analysis of variance. The F-value indicates that the factors adequately explain the variation in the data about its mean, and the estimated factor effects are real. The corresponding analysis of variance (ANOVA) is presented in Table 8. Concentration (\(X_{2}\)) and residence time (\(X_{3}\)) in the model have significant effects on the amount of adsorption since the p-value was less than 0.05.
# Wearable Flexible Antenna for UWB On-Body and Implant Communications
## 4 Results
### Power Flow Analysis at Different Frequencies for Layer Model  
Next, we study in-body propagation from the proposed flexible antenna at the selected frequencies using the notion of power flow. Power flow represents the directional energy flux (the energy transfer per unit area per unit time) of an electromagnetic field. The total electromagnetic power flowing through a given surface is represented by the flux of the Poynting vector through it. In this case, the power flow values are selected to be expressed as decibels. Power flow values are normalized so that 0 dB is the maximum, which means the value at the transmitting antenna. The plotted dB range is \(-\)65-0 dB which is chosen to illustrate coverage according to the receiver's sensitivity, 65 dB [60].  
Figure 11a-g presents the power flow variation from the flexible antenna (ranging from 0 to \(-\)65 dB) on a 2D vertical cross-cut plane of the tissue layer model at 2.45, 3.1, 4.1, 5.8, 7, 9, and 10.6 GHz, respectively. All these frequency ranges are presented to enable a comparison in the power flow and especially in the propagation depth at different frequency ranges targeted for medical communications.  
As is noted, the small intestine layer is reached easily up to a frequency of 3.1 GHz since the propagation loss is more moderate at lower frequencies. However, as the frequency increases, power loss increases affecting straightly to the propagation depth. The outer corner of the small intestine layer with the selected layer model thicknesses can be achieved up to 7 GHz, but at 9 GHz the small intestine area is not reachable. However, at the higher frequencies the flexible antenna could be used for the applications where the requirements for the propagation depth are minor. Next, the simulated power flows are studied with the voxel models.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Frequency [GHz]** & **2.45** & **3.1** & **4.0** & **5.8** & **7.0** & **9.0** & **10.6** \\ \hline Simulated, a-s = 0 mm [dB] & \(-\)15.5 & \(-\)16.5 & \(-\)17.2 & \(-\)17.1 & \(-\)16.0 & \(-\)17.0 & \(-\)17.5 \\ Simulated, a-s = 3.2 mm [dB] & \(-\)10.5 & \(-\)12.5 & \(-\)11.0 & \(-\)7.5 & \(-\)7.0 & \(-\)4.2 & \(-\)4.5 \\ Measured [dB] & \(-\)16.6 & \(-\)14.8 & \(-\)11.0 & \(-\)6.5 & \(-\)7.1 & \(-\)5.9 & \(-\)6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Measured and simulated total efficiencies at different antenna-skin distances.
# Constructing a Microstrip Phase Shifter with Low Phase Error Using a Y-Resonator
## 3 Analysis and Design
\tag{16}\]  
The ABCD matrix of the reference line is brought into Equation (6) to obtain the insertion phase shift of the reference line and, hence, the phase shift value of the phase shifter. The phase response for different phase shift values is shown in Figure 5. The phase shifter proposed in this paper achieves wide broadband and low phase deviation for different values of phase shift. It can be seen from the figure that the phase deviation increases gradually as the phase shift value increases from \(90^{\circ}\) to \(225^{\circ}\). When the phase shift value is \(90^{\circ}\), the fractional bandwidth can reach \(60.8\%\), and its phase deviation is  
Figure 4: Phase shift response between main and reference lines. less than 1.2\({}^{\circ}\). A wide bandwidth can be realized with a smaller phase deviation and a simpler structure. In general, the phase response of a wideband phase shifter is highly sensitive to the coupling distance. Figure 6 illustrates the relationship between coupling distance and phase response, indicating that as the coupling distance decreases, both the phase shift bandwidth and phase shift deviation reduce. Conversely, increasing the coupling distance leads to a broader phase shift bandwidth but also increases the phase shift deviation. Specifically, when the coupling distance is set to S = 0.20 mm, the phase shift bandwidth becomes too small. At S = 0.25 mm, while the phase shift bandwidth increases, the phase shift deviation exceeds 2\({}^{\circ}\), which does not align with the design objectives. After carefully balancing the phase shift bandwidth and deviation, the optimal coupling distance is determined to be S = 0.22 mm. Figure 5: Phase response for four different phase shift values. Figure 6: Phase shift response for different coupling distances.
## 6 Analysis of the eLPRT Protocol
### Fine Grained Allocation of Slots  
The GTS scheme provides 16 slots, so the slot duration (\(T_{slot}\)) for a superframe period of 100 ms is 6.25 ms. Since the transmission time of the data packet (\(T_{data}\)) is 1.472 ms, the efficiency in the utilization of the allocated time is only 23.6%. The eLPRT provides 500 slots, which means that \(T_{slot}=0.2\) ms for the same superframe period. The number of slots required to accommodate the data packet (\(N_{s}\)) is 8, according to Equation (3); therefore, the allocated time is 1.6 ms, which means that the efficiency in this case is 92%, which represents a large improvement over the GTS scheme:  
\[N_{s}=ceil\left(T_{data}/T_{slot}\right) \tag{3}\]  
The minimum duration of the CAP specified by the IEEE 802.15.4 standard for the 2.4 GHz band is 7.04 ms, while the time required to transmit a maximum length beacon is 4.26 ms and therefore the maximum duration of the CFP (\(CFP_{max}\)), obtained from the subtraction of these two values from the superframe period, is 88.7 ms. If we assume that one additional slot is used as guard time between transmissions of the sensor nodes, each data packet allocates nine slots, resulting in an allocated time of 1.8 ms. In this case, for an error-free channel, the eLPRT protocol is able to support 49 nodes with 100% delivery ratio, which is almost twice the number of nodes supported by unslotted CSMA/CA mechanism in the best case (Figure 4). The maximum number of nodes supported by GTS scheme, if the limitation of seven nodes were removed, would be 14.
# Integration of BIM and GIS for the Digitization of the Built Environment
## 4 Results and Discussion
Nevertheless, in order to attain an equivalent level of detail between the two systems, a standardized mapping model could be devised that translates BIM LODs into GIS equivalents, and vice versa. The model should be based on a comprehensive analysis of the characteristics and requirements of both systems. [PERSON] et al. highlight the potential of graph-based technologies, in particular the Resource Description Framework (RDF) and the Labelled Property Graph (LPG), byundertaking a systematic investigation of these technologies to determine the most suitable for LOD conversion between BIM and GIS [82]. The choice between RDF and LPG depends on the specific use case, as these technologies perform differently in different scenarios. For example, LPG outperforms RDF in the context of linked data for smart homes, while RDF performs better when searching for the glycan substructure [82]. A further obstacle to the integration of the two standards is the use of divergent coordinate systems. In the context of BIM, absolute coordinates have limited relevance, as they are based on the World Coordinate System, a local rectangular coordinate system specific to drawing, without correlation to the global spatial reference system typical of GIS. The latter employs the Geographic Coordinate System (GCS) to represent cities, regions and the world in general. Each object is positioned by means of latitude, longitude and elevation, or by means of coordinated Universal Transverse Mercator (UTM) such as Northing, Easting and height. To resolve this discrepancy, [PERSON] et al. proposed a matrix transformation [83]. The IFC contains a greater number of defined classes than CityGML. Despite the assistance of the application domain extension (ADE), CityGML is unable to provide the requisite additions to guarantee a correct transformation of all objects and classes from the IFC. To illustrate, when considering the CityGML class \"Building Installation\", which encompasses columns, stairs and beams, each object of this class must be associated with the appropriate IFC class during the transformation to IFC [84]. The semantic distinction between IFC and GIS represents a final challenge. Whereas BIM places particular emphasis on the relationships between objects, GIS does not necessarily adopt the same approach, which can give rise to difficulties in integrating the two standards. One proposed solution to this discrepancy is matrix transformation. The semantic differences between the two standards present a significant challenge. BIM places a strong emphasis on the relationships between objects, whereas GIS does not consider these parameters. One potential solution to this problem is semantic mapping, which aims to ensure proper integration between the two standards by addressing the semantic differences between them.
## 4 Discussion
### Limitations and Future Works  
FEM analysis studies involving muscles and ligaments are lacking compared to bones. It is more accurate to assume the muscle is a hyper-elastic body while for the limited range of motions, the linear material assumption also provides the reasonable accuracy [30]; therefore, it is frequently used in previous works. However, the behavior of nonlinear models is largely different from that of linear models when the deformation of the model is large. Therefore, the results of the current study would not be directly applicable for the model with large motions.  
In this study, the calculation was also simplified with the assumption that the contact between different parts were correctly attached under frictionless contact conditions. For a more realistic interpretation, we should consider more realistic contact conditions with the experimentally obtained friction coefficients. Moreover, the effects of the intra-abdominal pressure or volume of the muscles, other surrounding tissues were not investigated.  
Furthermore, the assessment of the passive support process was performed without taking into account the line of action based on the point of contact, as the direction of the supporting force would vary depending on the shape in which the muscle was compressed. In addition, applying anisotropic properties with different physical properties according to the direction of the muscle will result in a more precise model, which may better express the direction of the passive bearing force. The inclusion of the line of action and anisotropic properties in the passive support of muscles will be an essential part of future research. Moreover, there is a difference in the volume of muscle in the supine and standing positions, and there may be an effect, and the differences in the size of the muscles are more prominent from person to person.  
This study only considered the effects of additional stiffness from the muscles, not the mass of the muscles. Research on the effects of the muscles in the spinal dynamic analysis is needed. Lastly, the future studies with experimental validations would be of great value.
# Wall Structure Geometry Verification Using TLS Data and BIM Model
## 2 Building Structures' Geometry Verification
### Plane Segmentation from Point Clouds
#### 2.2.1 Normal Vector-Based Filtration  
The plane segmentation procedure, depicted in the previous section, uses distance-based filtering for inlier identification as the distance threshold is needed for the definition of the close surroundings of the wall. In practice, points of the objects close to the wall are also considered as inliers, if only the distance-based filtering is executed. However, these points are not lying on the surface of the verified wall (plane) as is shown in Figure 6.  
After the initial plane segmentation, normal vectors at each point of the resulting subset of the point cloud are estimated using small local planes (calculated from the \(k\)-nearest neighbors). The selected number of \(k\)-nearest points depends on the point cloud density and noise. The value of \(k\) is set globally according to the average density of the point cloud. The calculated normal vectors are multiplied by the normal vector of the respective planes obtained after segmentation (9), and then the angle between them is quantified.  
\[\cos(\alpha)=\Delta_{\text{norm}}=\text{n}_{\text{PoC}}\cdot\text{n}_{\text{ point}}, \tag{9}\]  
where \(\text{n}_{\text{point}}\) is the normal vector of the local plane calculated from \(k\)-nearest neighbors, \(\text{n}_{\text{PoC}}\) is the normal vector of the corresponding regression plane of the point cloud and \(\alpha\) is the angle that the two vectors make at each other.  
In order to perform the normal vector-based filtration, an angle threshold has to be defined. This threshold defines the maximum deviation of the local normal from the normal vector of the previously estimated regression plane. The recommended value of maximum deviation, based on our experience, is a small value between \(2^{\circ}\) and \(4^{\circ}\). The choice of the threshold depends on the inspected wall. The proposed values mean deviations along 1 m of the structure 35 mm for \(2^{\circ}\) (in an extreme case it can be caused by nonflatness of the wall) and 70 mm for \(4^{\circ}\) (means that the subset of the point does not belong to a flat wall). This step removes the points from the segmented cloud that belong to the boundaries and curved parts of structural elements not directly related to the wall plane, e.g., the planes of the floor, ceiling, door frame, etc. (Figure 7).  
However, after normal vector-based filtration, some points are still included in the inliers, while these are not belonging to the wall plane, e.g., points on doors, electrical sockets, paintings on the wall, etc. Therefore, a novel filtration approach based on evolving curves was proposed.  
Figure 6: Segmented point cloud—only distance-based filtering.
## 3 Results and Discussion
### Parameter Screening with Plackett-Burman (PBD) and RSM Modeling
#### 3.1.4 FWL and RWL Stability Factor Response  
Three-dimensional and perturbation response graphs from the BBD-RSM show how input parameters affect output responses. The charts show how input factors affect output. Each model shows input parameters on the Y and X axes and the output (safety factor) on the Z. Figure 4 shows the interactions among FWL parameters, (a) \(c\) vs. \(H\), (b) \(\phi\) vs. \(H\), (c) \(\rho_{s}\) vs. \(H\), (d) \(\phi\) vs. \(c\), (e) \(\rho_{s}\) vs. \(c\), and (f) \(\rho_{s}\) vs. \(\phi\). The a-f signified how the combined variables influenced the factor of safety. It can be seen from the plots that factor of safety (FS) increases with increase in \(\phi\) and \(c\), and decreases with increase in \(\rho_{s}\) and \(H\). The model's 3D contours for AC, BC, AD, and CD are relatively flat, suggesting little interaction between these factors. This shows that these two elements' magnitudes affect the response behavior less. The vivid blue area of the plot denotes a region with minimal influence on the factor of safety, while the reddish-orange area indicates a region of optimal influence. Also the contour plots show response values in blue, green, and red classes. The colors indicate low, moderate, and very ideal interaction [41].  
Figure 5 depicts the interactions among RWL parameters in 3D for (a) \(\phi\) vs. \(c\), (b) \(\rho_{s}\) vs. \(c\), and (c) \(\rho_{s}\) vs. \(\phi\), while (d) \(\phi\) vs. \(c\), (e) \(\rho_{s}\) vs. \(c\), and (f) \(\rho_{s}\) vs. \(\phi\) represents the 2D. The a-f signified how the combined variables influenced the factor of safety for both 3D and 2D surfaces. From the 2D plots, it is shown that the FS is maximized when \(\phi\) = 37.4 \({}^{\circ}\)C, \(c\) = 15.4 Kpa, and \(\rho_{s}\) = 1710 kg/m\({}^{3}\). Both 2D and 3D response surface diagnostic plots show sloping flat and color margin contour profiles, indicating a strong joint interaction between independent variables [53]. The charts show that each of these factors significantly affects safety. The safety factor increases as unit weight decreases by 10-20%. An increase in the input variables cohesion and internal friction angle, is positively correlated with the stability factor. The BBD-RSM perturbation analysis shows an inverse relationship between unit weight and the two remaining input variables (cohesion and internal friction angle). The flatness of the 3D surface suggests that changes in one parameter and its effects on the  
Figure 4: Three-dimensional response surface plots displaying the influence of all parameters on FWL coefficient.  
factor of safety are relatively constant across the range of values of the second parameter, and vice versa, while the overall effects of these parameters are significant.