esa-sceva/satcom-chunk-collection huggingface dataset:
# 4D Building Reconstruction with Machine Learning and Historical Maps
## 4 Methodology
### Heights Prediction Metrics
#### 4.4.2 Single-View Metrology from Historical Images  
Assessing the quality of the prediction of disappeared buildings is more complicated. If historical images are available, single-view metrology [60; 61] and fundamental invariants of projective geometry [62] can be used. An essential property of projective geometry is that some measures are invariant to projective transformations. The cross-ratio invariant and image vanishing points can be used for deriving distance measurements from a single image. Knowing one reference distance (\(H\)), the method (Figure 7) implies that the height of the camera (\(H_{C}\)) and any other distance (\(H_{U}\)) between two planes perpendicular to the reference direction (\(v_{3}\)) can be derived. Two points \(B\) and \(T\), lying on two planes \(P\) and \(P^{\prime}\) perpendicular to the reference direction \(v_{3}\), are represented in image space by points \(b\) and \(t\) lying on the two planes defined by the two vanishing points \(v_{1}\) and \(v_{2}\). The image point lies at the intersection of the line joining the corresponding points \(C\) (the camera centre) and \(C^{\prime}\) with the vanishing line \(l_{\Sigma 162}\). The point \(C\) lies on a plane at a distance \(H_{C}\) from the reference plane \(P\). Under this configuration, the image points \(b\), \(t\), \(c\), and \(v_{3}\) are aligned along the vertical reference direction, and therefore they define a cross-ratio. The ratio also holds in object space with points \(B\), \(T\), \(C^{\prime}\), and \(v_{3}\). Therefore, we can derive (6):  
\[\frac{H}{H_{C}}=1-\frac{d\ (t,c)d(b,v_{3})}{d\ (b,c)d(t,v_{3})} \tag{6}\]  
where \(d(a,b)\) is the Euclidean distance between points \(a\) and \(b\), measured in image space. Therefore, if we know a vertical reference height in the scene (e.g., a building, a person), we can derive a building's height and use this information to evaluate a prediction.
###### Abstract
The prime objective of the present study is to verify the accuracy of the single and dual-frequency low cost receiver in urban areas. The research seeks to determine if the low-cost GNSS receiver can be used in urban areas for geodetic and topographic applications. The present study improves understanding and provides results on accuracy and positioning estimates for different low-cost GNSS receivers. The study recommends performing many tests in different environmental conditions and locations worldwide to evaluate equipment for various applications.
## 4 Boundedness and Stability
### Set of Bounded Trajectories
The term \(\rho w_{i}\) traces a line segment from zero to \(w_{i}\) (blue line), while \(p_{i1}\) is a fixed point in the complex plane. The expression \(\left|\frac{p_{i1}-\rho w_{i}}{1-\rho w_{i}\overline{p_{i1}}}\right|\) represents the pseudo-hyperbolic distance between \(p_{i1}\) and any point along the segment defined by \(\rho w_{i}\). A key geometric property states that the maximum distance between a fixed point and a point on a line segment occurs at one of the endpoints of the segment (these are depicted as a red line segment). Therefore, the maximum in (31) is reached when \(\rho\) takes either of its extreme values, \(\rho=0\) or \(\rho=1\). As discussed in Section 3, the values of \(\frac{p_{11}-\rho w_{i}}{1-\rho w_{12}p_{11}}\) at \(\rho=0\) and \(\rho=1\) are \(p_{i1}\) and \(p_{i2}\) respectively:  
\[\left\|G(\rho)-G_{2}\right\|_{\infty}\leq f(G_{1},G_{2})=\max\{g_ {1}(G_{1},G_{2}),g_{2}(G_{1},G_{2})\} \tag{21}\]6  
It is worth noting that the result in (32) is connected to the choice of \(w_{i}=\frac{p_{i1}-p_{i2}}{1-p_{i2}p_{11}}\). \(w_{i}\) is a point with the property that the hyperbolic distance of \(w_{i}\) from the origin equals the hyperbolic distance between \(p_{i1}\) and \(p_{i2}\) and the hyperbolic distance of \(w_{i}\) from \(p_{i1}\) is equal to the hyperbolic distance between \(p_{i2}\) and the origin. The distance of \(w_{i}\) from the origin is a direct consequence of the definition of \(w_{i}\). After this, we can notice that the boundedness of the hyperbolic pole trajectory follows from the fact that the distance from a point to a line segment is maximal at one of the endpoints of the line segment see \[\left\|G(\rho)-G_{2}\right\|_{\infty}\leq f(G_{1},G_{2})=\max\{g_ {1}(G_{1},G_{2}),g_{2}(G_{1},G_{2})\} \tag{21}\]7  
In general, there is a set of curves that satisfy the condition that one of the endpoints is the furthest point from a given point in the plane. For example, see \[\left\|G(\rho)-G_{2}\right\|_{\infty}\leq f(G_{1},G_{2})=\max\{g_ {1}(G_{1},G_{2}),g_{2}(G_{1},G_{2})\} \tag{21}\]8 The furthest point on the line segment \(\overline{w_{i}O}\) from \(p_{i1}\) is \(w_{i}\) in this example. The bound is determined by the furthest point, so every curve that starts from the origin \(O\) and ends at \(w_{i}\) with no point on that curve having a distance from \(p_{i1}\) which is greater than \(d_{h}(p_{i1},w_{i})\) gives the same bound. The geometric constraint is that any candidate curve must lie in the hyperbolic circle around \(p_{i1}\) with radius \(d_{h}(p_{i1},w_{i})\) and must start from the origin and end at \(w_{i}\). It is obvious that the actual pole trajectory is obtained after the chosen \(w_{i}O\) trajectory inside the boundary circle is transformed by Equation (20). \[\left\|G(\rho)-G_{2}\right\|_{\infty}\leq f(G_{1},G_{2})=\max\{g_ {1}(G_{1},G_{2}),g_{2}(G_{1},G_{2})\} \tag{21}\]9 Geometric representation of the derivation of the upper bound.
# Prediction of Tunnel Face Stability Using a Naive Bayes Classifier
## 3 Application Example
The total number of 3D simulations may reach up to several thousand because each case involves several or even many tries to determine the safety factor. This means that it is extremely difficult to obtain 3D safety factors for constructing a naive Bayes classifier. Thus, this paper utilizes 2D upper bound analysis with associated flow rule to complete the task of collecting training samples. In fact, the 2D failure mechanism of tunnel face stability has been developed and used in the existing literature to obtain approximate solutions [27, 43]. Moreover, twenty cases with different values of features are used to perform both 2D (automatically) and 3D (manually) strength reduction analyses. The comparison results shown in Figure 3 imply that 2D simulations may give relatively good and conservative approximations of 3D safety factors. Figure 3: _Cont_. Figure 2: Numerical model and boundary conditions. Note that the main purpose of strength reduction analysis is to figure out whether or not a tunnel face is stable, rather than to assess the degree of safety (or stability). This means that only a few samples with 2D safety factors varying between 0.8 to 1.0 (based on Figure 3) may cause adverse effects on subsequent prediction accuracy of the classifier since these samples may be stable in 3D simulations but will be misclassified into the unstable category according to the simulated 2D safety factors. In most cases, this type of approximation error will not cause any prediction error as long as the 2D and 3D safety factors represent the same state of stability (i.e., both stable or both unstable). However, 3D simulations are still expected in the near future to obtain a large number of high-accuracy safety factors, if strength reduction analyses of various cases can be run programmatically in OptumG3. For the cases with \(C~{}=~{}5\) m and \(D~{}=~{}5\) m, the simulated safety factors were collected and listed in Table 1, in which the safety factors greater than 1.0 were indicated with red dashed rectangles. As mentioned above, the 25 samples can be classified into 7 stable samples and 18 unstable samples according to the simulated safety factors. Similarly, the other 725 strength reduction analyses were performed, and the collected safety factors are plotted versus the reference numbers and shown in Figure 4, in which a red dashed line with \(F_{s}~{}=~{}1\) was added to classified the collected samples into two categories, i.e., 45 stable samples and 705 unstable samples. Thus, the prior probabilities of the stable and unstable states are available:  
\[P(\text{Stable})~{}=~{}\frac{45}{750}~{}=~{}0.06; \tag{13}\]  
\[P(\text{Unstable})~{}=~{}\frac{750-45}{750}~{}=~{}0.94. \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(c~{}=~{}5\) & \(\boldsymbol{\varphi~{}=~{}5^{\circ}}\) & \(\boldsymbol{\varphi~{}=~{}10^{\circ}}\) &
# Shear Resistance Evolution of Geogrid Reinforced Expansive Soil Under Freeze-Thaw Cycles
## 4 Conclusions  
In this study, the mechanical behaviors of GRES were investigated through a series of large-scale triaxial tests, and the effects of the confining pressures (100 kPa, 200 kPa and 300 kPa), geogrid reinforced layers (0-, 1-, 3-, and 5-layer geogrid arrangements) and number of F-T cycles (0, 1, 3, 6, and 10) on the shear strength parameters were evaluated and discussed. Through systematic investigation of the laboratory measurements, key outcomes emerge as follows:  
1. The dilatancy deformation of the expanded soils was effectively limited by geogrids, which was significantly smaller compared to the plain expanded soil. The geogrid reinforced impact zone appeared to overlap with the increase in geogrid layers when the geogrid layers exceeded three layers.
2. Friction between the expansive soil and the geogrid and the geogrid-embedded locking effect indirectly provided additional pressure, limited shear deformation. With the increase in reinforced layers, the stress-strain curve changed from strain-softening to strain-hardening type.
3. The elastic modulus first decreased and then tended to stabilize with the increase in F-T cycles; the decrease was obvious after 1-3 F-T cycles and tended to stabilize to reach dynamic equilibrium after 6 F-T cycles. After the six F-T cycles, the elastic modulus decreased by about 35.7%, 23.3%, 18.3%, and 23.5% for 0-, 1-, 3-, and 5-layer GRES.
4. The friction angle and cohesion decreased with the increment of F-T cycles and increased with the increment of geogrid layers. After the six F-T cycles, the cohesion decreased by about 24.5%, 21%, 14.3%, 14.5% for 0-, 1-, 3-, and 5-layer GRES, and 7.6%, 5.9%, 4.5%, and 5.1% of the friction angle.
5. A modified Duncan-Zhang model with the confining pressure, the F-T cycles, and the geogrid layers was proposed; the predicted values agreed with the measured values by more than 90%, which can be used as a prediction formula for the stress-strain characteristics of GRES under freeze-thaw cycling conditions. The research results can provide important theoretical support for the practical engineering design of GRES in cold regions.  
Conceptualization, Z.Y.; data curation, writing--original draft, J.L.; data curation, project administration, R.Z.; supervision, W.S.; project administration, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 42372327, and the National Natural Science Foundation of China, grant number 42177153.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Author [PERSON] was employed by the company BOSTD Geosynthetics Qingdao. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 5 Features and Component Design of the Proposed Converter  
In the development of the 325 W prototype for the converter, key components include capacitors, inductors, a gate drive circuit, power MOSFETs, and power diodes. The design of these components is crucial to validate the anticipated high voltage gain. Specifically, the proposed converter requires four small-value inductors and five small-value capacitors, with detailed parameter values available in Table 1. This table outlines essential prototype design parameters, forming a critical foundation for the successful implementation and verification of the converter's performance.  
The table above clearly indicates that the values of the inductors are exceptionally small, attributed to the utilization of a high switching frequency. Additionally, the internal resistance of the inductors is remarkably low, signifying a reduction in power losses for the proposed converter. To design the inductors for the proposed converter, Equation (40) guides the determination of the values for L\({}_{1}\) and L\({}_{2}\), while Equation (41) allows the calculation of the critical values for inductors L\({}_{3}\) and L\({}_{4}\). Equations (42) to (45) provide the values for C\({}_{1}\), C\({}_{2}\), C\({}_{3}\), C\({}_{4}\), and C\({}_{5}\), respectively. Examining the inductor specifications from Table 1, it is evident that the internal resistance of all inductors is minimal, attributed to the use of flat wire inductors with a ferrite core sized at (2.8 cm \(\times\) 2.75 cm \(\times\) 2.2 cm).  
\[L_{1}=\frac{VgDTs}{\Delta iL_{1}}=L_{2} \tag{40}\]  
\[L_{3 Crit}=\frac{2 VgDTs(1-D)}{Vo(1+D)}=L_{4 Crit} \tag{41}\]  
\[C_{1}=\frac{4V_{o}(D+1)}{\Delta vc_{1}R_{L}fs(1-D)} \tag{42}\]  
\[C_{2}=\frac{2 Vo(D+1)}{\Delta vc_{2}fsR_{L}(1-D)}=C_{3} \tag{43}\]  
\[C_{4}=\frac{2V_{o}D(D+1)}{\Delta Vc_{4}.FsR_{L}(1-D)} \tag{44}\]  
\[C_{5}=\frac{V_{o}D}{\Delta V_{o}FsR_{L}} \tag{45}\]  
\begin{table}
\begin{tabular}{c c} \hline SiC MOSFETs & 650 V 45 A Sw\({}_{1}\) \\  & 1200 V, 35 A Sw\({}_{2}\) \\ \hline SiC Schottky diodes & 650 V 40 A \\ \hline L\({}_{1}\) = L\({}_{2}\) & 150 uH, 3.3 m\(\Omega\) \\ \hline L\({}_{3}\) = L\({}_{4}\) & 100 uH, 2.9 m\(\Omega\) \\ \hline \end{tabular}
\end{table}
Table 1: Prototype component design for the proposed converter.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 2 Previous Studies
### Issues with Previous Studies
#### 2.2.1 (A) Issues with Route Calculations  
[PERSON] et al. determined the route by minimizing the travel distance from the current position to the destination port under the constraints of the topographical information and congestion status of vessels at each candidate transit site. However, the future weather conditions are not considered in determining the route.  
Generally, the captain decides the route on the basis of future weather conditions to ensure the safety of the vessel. [PERSON] et al. [21] used operational data to analyze the winter Pacific route from Japan to the United States. The eastbound voyage from Japan to the United States generally passes through the center of the Pacific Ocean to take advantage of tailwinds and the following sea from low-pressure systems. On the other hand, because the wind/head sea in the westbound voyage from the United States to Japan is opposite to that of the eastbound voyage, a vessel will take a northerly route through the vicinity of the Aleutian Islands or a southerly one instead of going through the center of the Pacific Ocean. As the method proposed by [PERSON] et al. does not consider the influence of these weather conditions, the same route is calculated for both east- and westbound voyages. Such deviations from the actual route can reduce the prediction accuracy of the arrival time.  
Other studies mentioned in Section 2.1 also are not enough to predict the arrival time accurately. The study by [PERSON] et al. [10] predicted the flight arrival flow at an airport. However, it did not estimate the route and arrival time for each aircraft. The study by [PERSON] et al. [11] predicted each aircraft's route to a destination airport. However, it focused on short-distance routes with a linear system model; therefore, long-distance routes from departure to destination may not be estimated accurately. Although future weather conditions have been considered in other routing studies [14; 15], they used the Dijkstra method or the A* algorithm to determine the shortest route to the destination port. However, as [PERSON] et al. have shown, the shortest route is not always taken in practice because of safety considerations. Therefore, these methods may produce deviations from the actual route to routes that are significantly affected by weather. Note as well that the studies by [PERSON] et al. [16], [PERSON] et al. [17], and [PERSON] et al. [18] based on SVR and neural networks did not consider future weather conditions. Moreover, the study by [PERSON] et al. [20] used the equations of vessel motion to calculate the ship performance and voyage route. However, the equations involve some parameters such as the viscous resistance that must be tuned in water tank experiments for each vessel model. As water tank experiments are not always representative of actual marine environments, it is difficult to determine parameters suitable for them.
# Farm Monitoring System with Drones and Optical Camera Communication
## 5 Experimental Results
### Experimental Condition
#### 5.1.3 Coding and Modulation  
In this experiment, the sensor data collected at the sensor nodes were encoded and modulated as optical using On-Off Keying (OOK). More specifically, the sensor data were converted to binary. The bit sequence was then converted into a data signal using 3b4b coding. The LED panels mounted on the sensor nodes were used as transmitters. A signal sent from a data panel carried one bit with OOK. In other words, the one bit was modulated as the on-off signals of the blue color.  
Figure 10: Sensor node taken from drone.  
Figure 9: Sensor node.
## 3 Results and Discussion
### Field Emission Scanning Electron Microscopy Analysis  
Field emission scanning electron microscopy (FESEM)-EDS was conducted to examine the surface morphology and distribution of elements. The FESEM images of all the three samples are presented in Figure 4a-c. The surface of all the three samples was rough, folded and wrinkled, indicating that GO was reduced to rGO. This observation agreed with the XRD pattern shown in Figure 1a [18; 26]. A few nanoscale iron particles were detected on the rGO surface in the micrographs. The lack of iron particles was probably due to the confinement of nanoparticles in the rGO interlayer or the lack of nitrogen-anchoring sites on rGO. The element mapping of 20% Fe/N-rGO via EDS is shown in the circulated area in Figure 4c and the distributions of Fe, C and O are illustrated in Figure 4d. The respective  
\begin{table}
\begin{tabular}{c c c c} \hline
**Name of Samples** & **wt.\% of C** & **wt.\% of O** & **wt.\% of Fe** \\ \hline
10\% Fe/N-rGO & 71.22 & 24.37 & 4.41 \\
15\% Fe/N-rGO & 86.85 & 11.50 & 2.10 \\
20\% Fe/N-rGO & 76.65 & 13.43 & 9.92 \\ \hline \end{tabular}
\end{table}
Table 1: Energy dispersive spectroscopy (EDS) results presenting the elemental composition of X Fe/N-rGO (X = 10%, 15% and 20%).  
Figure 3: Fourier transform infrared (FTIR) spectra of 10% Fe/N-rGO, 15% Fe/N-rGO and 20% Fe/N-rGO.  
semiquantitative element distributions of all the samples are summarized in Table 1 to estimate the Fe loading on rGO. Approximately 50% of the designed Fe in all the samples except in 15% Fe/N-rGO was embedded onto rGO upon irradiation.
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 4 Optimization for Tandem Blades
### Optimization for a 2D Tandem Blade Model
#### 4.1.2 Numerical Simulation Results and Analyses  
Figure 17 presents the comparisons of tandem blade performances (incidence--total pressure loss and incidence--diffusion factor characteristics) between the baseline and optimized blades, with an inflow Mach number of 0.8. The total pressure loss at the DE point decreases by about 6%, and the incidence range extends \(2^{\circ}\) while maintaining the aerodynamic load.  
The performance improvement under large negative-incidence conditions illustrates the growth in flow capacity due to the well-designed FB camber line. Consequently, there is an obvious shock wave passing through the baseline FB passage (Figure 18a), which shows that it is operating in a deep choke status with an incidence of \(-5^{\circ}\), while the optimized blade is still operating in an ideal status with the same incidence condition (Figure 19a). Compared with the baseline, the optimized blade has a slightly increased AB suction spike, especially for \(-5^{\circ}\) and \(0^{\circ}\) incidence conditions, and it provides stronger dynamic pressure to prevent the separation near the AB trailing edge.  
Figure 16: Comparison of blade geometry.  
Figure 17: Comparison of baseline and optimized blade performances. (**a**) Incidence—loss coefficient; (**b**) incidence—diffusion factor.  
As shown in Figure 20, the isentropic Mach number distributions reveal that the optimized blade achieves a typical CD airfoil shape in three operating conditions.  
According to the FB distributions at the DE point, there are more radical accelerations before the 0.1 normalized chord length, and there is a subsequent continuous deceleration on the suction side, while flow velocity is kept nearly constant on the pressure side. The overall FB load distribution moves forward; consequently, the suction spike Mach number increases from 1.0 to 1.05 at the DE point, which is still well within the acceptable range. We emphasize that the FB forward-load should be carefully controlled to avoid a strong normal shock on the suction side, especially when the inflow Mach number is higher than 0.8.  
On the other hand, the most significant difference in AB is the modification of local incidence conditions compared to the baseline. Although FB incidences change in a wide range, the optimized AB has a typical distribution of a CD airfoil near the \(0^{\circ}\) incidence condition. The modification for AB incidences is good for reducing total pressure loss under the whole operating conditions.  
Figure 19: Optimized blade Mach contours in typical operating conditions. (**a**) \(i=-5^{\circ}\); (**b**) \(i=-1^{\circ}\); (**c**) \(i=+5^{\circ}\).  
Figure 20: Comparison of the baseline and optimized blade surface isentropic Mach numbers. (**a**) \(i=-5^{\circ}\); (**b**) \(i=-1^{\circ}\); (**c**) \(i=+5^{\circ}\).