esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Metamaterial Absorber Design  
### Unit Cell Design and Absorption Calculation  
In his section, we discuss the design of an MMA unit cell with an absorption mechanism. The square split-ring resonator (SSRR) achieves quad-band near-unity absorption peaks. FR4 substrate materials with 1.6 mm thickness were selected for the absorber design due to their low cost, zero water absorption, and versatility, making them commercially attractive. The dielectric constant, thermal conductivity, and loss tangent of the substrate are 4.3, 0.3 W/K/m, and 0.025, respectively. Copper was used for the patch and ground design, with an electrical conductivity (\(\rho\)) of 5.96 \(\times\) 10\({}^{7}\) S/m.  
Figure 1 shows a front view of the MMA unit cell with a sketch of all required dimensions. The MMA patch design consists of a Jerusalem cross, two square split rings, and four microstrip lines. The unit cell dimensions are 10 \(\times\) 10 \(\times\) 1.6 mm\({}^{3}\), and all the design parameters are recorded in Table 1. The proposed MMA was designed and simulated using the CST microwave studio [50], where the unit cell boundary conditions were applied along the Y- and X-axes, and electromagnetic waves were applied along the negative Z-axis. The absorption behavior \(A(\omega)\) was determined according to Equation (1) [27].  
\[A(\omega)=1-S_{11}^{2}-S_{21}^{2} \tag{1}\]  
where \(S_{11}\) and \(S_{21}\) are reflection and transmission coefficients, respectively, as shown in Figure 2, and four near-zero reflection coefficient (\(S_{11}\)) resonance peaks are achieved at 12.62 GHz, 14.12 GHz, 17.53 GHz, and 19.91 GHz. A copper ground of 0.035 mm thickness results in a zero transmission coefficient (\(S_{21}\)), which can be obtained by calculation of the skin depth [27].  
\[\text{Skin depth},\;\delta=\sqrt{\frac{\rho}{\pi f\mu}} \tag{2}\]  
where permeability (\(\mu\)) is 1, resistivity (\(\rho\)) is 1.72\(\Omega-m\), with lower frequency defined as \(f=12.62\)GHz. The skin depth becomes \(\delta=0.0065\) mm, which completely blocks the electromagnetic (EM) wave transmission through the MMA. Therefore, Equation (1) becomes:  
\[A(\omega)=1-S_{11}^{2} \tag{3}\]  
The peak 97% absorption at 12.62 GHz and 99% absorption at 14.12 GHz, 17.53 GHz, and 19.91 GHz were attained for the proposed MMA presented in Figure 2. The high-quality (\(Q\)) factor represents high sensitivity, where the \(Q\) factor is calculated by \(Q=\)_fc/FWHM_, where \(f_{\text{C}}\) is the center frequency, and _FWHM_ is the full wave of half maximum [51]. The \(Q\) factors of the proposed MMA at 12.62 GHz, 14.12 GHz, 17.53 GHz, and 19.91 GHz are 39.43, 34.43, 37.29, and 34.32, respectively.
## 1 Introduction
Recent studies reported on methods to reduce target registration error (TRE), which utilize mobile 3D scanners to collect point clouds across the whole head [4; 7]. However, scanners with guaranteed precision are generally very expensive [3]. In addition, since the device differs from that used for commercial image-guided surgery systems, an additional coordinate transformation process must be performed, which may result in accumulation of errors [19]. Therefore, the commercial image-guided surgery systems that are currently in use extract point clouds in patient space through contact with the surface of the face. Accordingly, in neurosurgery or head and neck surgery, clinicians perform surface registration by placing a probe on the patient's face as a convenient method of extracting the point cloud; several clinical case reports of the surgical effects of this method have been conducted [5; 20; 21; 22]. However, in all of these studies, only the original patient space coordinates obtained through facial surface tracing were used, and no attempts have been made to improve the point cloud in order to reduce the surface registration error. Therefore, the main purpose of this study is to propose a novel registration protocol that extracts the point cloud in patient space using the contact approach to increase the accuracy of surface registration of an image-guided surgery system. The proposed registration protocol consists of a two-step process: augmentation of the point clouds and creation of a proper point cloud. The secondary purpose of this study is to validate the proposed registration protocol using a hemisphere mathematical model and a plastic facial phantom.
## 2 Materials and Methods
### Experimental Design
#### 2.3.2 Experiment 1--Overall Performance Evaluation  
We initially evaluated the spatial overlap agreement of each approach relative to the ground truth by utilizing the Dice Similarity Coefficient (DSC) as a metric to select one IML-assisted and one manual segmentation from each rater. For glioblastoma, only the whole tumor (WT) area was used for these selections. The DSCs of the two sets were statistically compared. Additionally, the volumes calculated for IML and manual segmentations were quantitatively compared with the ground truth, by plotting volume pairs in scatterplots. Each pair comprise the volume of the approach and the volume of the ground truth for a particular case. Using regression, a line can be drawn from the IML-ground truth pairs and another one from the manual-ground truth ones. The closer these lines are to the middle line, that splits evenly the quadrant, the more similar the approach prediction volumes are to the ground truth ones and the closer to parallel the lines are, the more systematic were the potential errors. Furthermore, we estimated the Pearson's Correlation Coefficient [27] for each of the paired segmentations: (i) IML correlation to ground-truth and (ii) manual correlation to ground truth. The average active drawing time, i.e., time spent on inspecting images and drawing input annotations, was compared for each cohort between IML-assisted and manual segmentation.
# Improved Direct Linear Transformation for Parameter Decoupling in Camera Calibration
## 3 Improved Direct Linear Transformation
### Improved Direct Linear Transformation
Then, we have:  
\[(r_{11}a_{x}+r_{31}u_{0})\frac{t^{\prime}{}_{z2}-t^{\prime}{}_{z1}-dz_{1}}{dz_ {1}}-(t^{\prime}{}_{z1}\frac{a^{\prime\prime}{}_{x}-a^{\prime}{}_{x}}{dz_{1}}+ a^{\prime\prime}{}_{x})+a_{x}=0 \tag{40}\]  
A linear relationship exists between \((t^{\prime}{}_{z2}-t^{\prime}{}_{z1}-dz_{1})/dz_{1}\) and \(t^{\prime}{}_{z1}(a^{\prime\prime}{}_{x}-a^{\prime}{}_{x})/dz_{1}+a^{\prime \prime}{}_{x}\). By repeatedly moving the 3D template, \(a_{x}\) can be solved with a linear least-squares fit. With respect to \(t_{z1}\), from Equation (37), we have:  
\[\frac{\Delta a^{\prime}{}_{x}}{\Delta a^{\prime\prime}{}_{x}}=\frac{n(t^{ \prime}{}_{z_{1}}-t_{z_{1}})}{t^{\prime}{}_{z_{2}}-nt_{z_{1}}} \tag{41}\]  
Then,  
\[\Delta a^{\prime}{}_{x}t^{\prime}{}_{z_{2}}-n\Delta a^{\prime\prime}{}_{x}t^{ \prime}{}_{z_{1}}=tof IDLT are less than 10% of the error of DLT. The errors in \(t_{x}\) and \(t_{y}\) are less than 0.1 mm. For \(a_{x}\), \(a_{y}\) and \(t_{z}\), the errors of IDLT are less than 60% of the error of DLT. Figure 6: _Cont._  
Figure 7 shows the calibration result with noise and distortion. Gaussian noise with a 0 mean and a 0.1-pixel standard deviation is added to the projected image points. Image distortion comes from ideal image points \((u_{i},v_{i})\) and real image points \((u^{\prime}_{i},v^{\prime}_{i})\) with \(k\) = 2.915 \(\times\)\(10^{-9}\) - 2.915 \(\times\)\(10^{-8}\) (the image distortion is 1-10 pixels at (1400, 0) when \(u_{0}\) = 700). \[ \begin{array}{l}u^{\prime}_{i}=u_{i}+(u_{i}-u_{0})kr^{2}\\ v^{\prime}_{i}=v_{i}+(v_{i}-v_{0})kr^{2}\end{array}  \tag{45}\]  
Owing to the influence of image distortion, the calibration errors of DLT are relatively large. However, the calibration errors of IDLT are relatively small. In particular, the errors of \(u_{0}\) and \(v_{0}\) are less than 0.7 pixels, whereas the maximum error of DLT is 44 pixels. The errors of \(a_{x}\) and \(a_{y}\) are less than 3. Because the pixels are square, the errors of \(a_{x}\) and \(a_{y}\) have the same form. The error in \(t_{z}\) is larger than that in \(t_{x}\), \(t_{y}\). The main reason is that \(t_{z}\) is larger than \(t_{x}\), \(t_{y}\). Figure 6: Calibration error analysis between direct linear translation (DLT) and improved DLT (IDLT) with noise: (**a**) error of \(u_{0}\); (**b**) error of \(v_{0}\); (**c**) error of \(t_{x}\); (**d**) error of \(t_{y}\); (**e**) error of \(a_{x}\); (**f**) error of \(t_{z}\) based on \(a_{x}\); (**g**) error of \(a_{y}\); (**h**) error of \(t_{z}\) based on \(a_{y}\). Figure 7: _Cont._
## 4 Simulation Results and Discussion
Compared to past results employing the LEM approach [91; 92; 93], when a non-associated flow rule is assumed, the critical slip surfaces appear to be closer to those obtained using LEM than those when associated flow rule is assumed. For both types of flow rule considered, the in situ stress due to gravity is \(2.62\times 10^{5}\) N/m\({}^{2}\). For the slope geometry considered, the maximum stress is found at the fixed base of the model. The maximum values for equivalent plastic strain and displacement magnitude correspond to the junction between the slope and the lower free part of the model.
## 3 Results
### The Effect of Drop Jump Height on PAPE at RT Conditions  
As shown in Table 3 and Figure 2a,b, under RT conditions, there was an interaction effect between drop jump height and resting time (\(F_{(6,66)}\) = 7.257, \(p\) = 0.002), indicating that drop jump height may significantly affect the induction and maintenance of PAPE.  
Figure 1: The experimental design.  
Compared with the pre-test, there was no significant difference in VJ performance at a 30 cm drop jump after 4 min (ES, 0.06, 95% CI (\(-\)1.18, 1.18), \(p\) = 1.000), 8 min (ES, 0.01, 95% CI (\(-\)1.01, 1.51), \(p\) = 1.000), and 12 min (ES, \(-\)0.09, 95% CI (\(-\)0.58, 2.25), \(p\) = 0.660). However, VJ performance after 4 min (60 cm, ES, 0.57, 95% CI CI (\(-\)4.78, \(-\)2.20), \(p\) < 0.001; 90 cm, ES, 0.37, 95% CI (\(-\)4.10, \(-\)0.90), \(p\) = 0.002) and 8 min (60 cm, ES, 0.55, 95% CI (\(-\)4.51, \(-\)1.99), \(p\) < 0.001; 90 cm, ES, 0.47, 95% CI (\(-\)5.75, \(-\)0.75), \(p\) = 0.009) of 60 cm and 90 cm drop jumps were significantly higher than the pre-test, and the PAPE effect was most significant at 4 min and 8 min, respectively. However, compared with the pre-test, there was no significant difference in VJ performance at 60 cm and 90 cm drop jumps after 12 min (60 cm, ES, 0.20, 95% CI (\(-\)1.70, \(-\)0.63), \(p\) = 0.160; 90 cm, ES, 0.19, 95% CI (\(-\)2.73, 0.23), \(p\) = 0.123), suggesting the disappearance of PAPE. In summary, under RT conditions, jump height had a significant effect on VJ performance, with the 60 cm and 90 cm drop jumps inducing PAPE, while the 30 cm drop jump did not induce PAPE.
# E-Sail Three-Dimensional Interplanetary Transfer with Fixed Pitch Angle
## 2 Sail Pitch Angle Constrained Thrust Model of the E-Sail
Consequently, it is not physically possible to perform a transfer between two heliocentric (Keplerian) orbits that do not share the same value of the semilatus rectum. On the other hand, the case where \(\alpha_{n}\) has a very small value (or a value near to \(90\deg\)) corresponds to a scenario where the transversal component of the propulsive acceleration \(a_{\rm TN}\) is very small, and this leads to a large flight time in heliocentric scenarios where the semilatus rectum of the target orbit is very different from that of the parking orbit, as in the typical case of an interplanetary transfer. In all other cases, the time variation of the two control terms \(\{\tau,\,\delta\}\) can be obtained by optimizing an assigned performance index--as, for example, the total flight time--in a given mission scenario, in order to ensure the transfer of the E-sail propelled spacecraft between two given heliocentric orbits. This aspect is briefly illustrated in the next section.
###### Abstract  
This paper presents a numerical investigation of the R-curve effect in delamination propagation in composite materials. The R-curve effect refers to the phenomenon whereby resistance to crack propagation increases with the advancement of the delamination, due to toughening mechanisms, such as fiber bridging. Numerical models often neglect this effect assuming a constant value of the fracture toughness. A numerical approach based on cohesive elements and on the superposition of two bilinear traction-separation laws is adopted here to accurately predict the R-curve effect in skin-doubler composite specimens subjected to three-point bending tests. The carbon-epoxy material presents two different sensitivities to the fiber bridging phenomenon resulting in two different R-curves. Comparisons with literature experimental data, in terms of load and delaminated area vs. applied displacement, and ultrasonic C-scan images show the effectiveness of the adopted approach in simulating the R-curve effect. The predicted numerical stiffness aligns with the experimental scatter, although the maximum load is slightly underestimated by approximately 15% compared with the average experimental results. The numerical model accurately predict the R-curve effect observed in the experimental data, demonstrating a 31% increase in the maximum load for the material configuration exhibiting greater sensitivity to fiber bridging.  
composite materials; crack propagation; finite element analysis; cohesive elements; R-curve +
Footnote †: journal: Applied  
1
Footnote 1: institutetext: Department of Aerospace Engineering, Universidad Carlos III de Madrid, 28911 Leganes, Madrid, Spain; [EMAIL_ADDRESS]
## 1 Introduction  
Rapid increase of urbanization and high-intensity rainfall due to climate change cause an increase in flood damages in urban areas. Seoul is the capital city of South Korea and damages from a big flood originated due to intense rainfall during 26-29 July 2011. In this flood event, the maximum rainfall intensity was recorded as 86 mm/h in the Seocho-dong and 113 mm/h in the Namhyeon-dong. To prevent severe damages in the future, it is necessary to study vulnerabilities from flood events in the Sadang areas. However, essential data to analyze flood patterns such as flow velocity and inundation depth was not measured in the urban areas. Numerical models were used to reproduce the flood events [1], but it is difficult to do a model validation due to the lack of measurements. Thus, experimental studies are required to reproduce the flood events and provide data for model validations.  
Studies to reproduce artificial rainfall have been conducted using a rainfall simulator. [PERSON] and [PERSON] [2] developed a rainfall simulator generated by using seven nozzles arranged in a row, and flood runoff experiments were conducted in a model of a river basin. The rainfall simulator was used for field studies to investigate soil crusting and erosion, and portable rainfall simulators were designed to move the device in the study area [3; 4; 5]. Thus, the devices were limited to generating a rainfall intensity of around 100 mm/h. After that, a rainfall simulator that can reproduce a 180 mm/h rainfall intensity was designed to conduct soil erosion studies [6]. This rainfall simulator also can be used to investigate the transport of non-point sources induced by the urban runoff [7]. For urban -flood experiments, [PERSON] et al. [8] adopted a rainfall simulator to investigate the wind-driven rainfall effects to the distribution of flood runoff in a scale model of high-rise buildings. From previous studies, a rainfall simulator is useful to demonstrate the rainfall-runoff process in scale models and field studies. Thus, in this study, a rainfall simulator was designed to demonstrate urban inundation, and a case study was conducted in the Sadang area.  
In this study, the research objectives can be summarized as: (1) design of rainfall simulator and validation of uniformity of artificial rainfall distributions; (2) demonstration of flood events in the Sadang area and analysis of flood characteristics from runoff velocity and inundation depth; (3) analysis of vulnerabilities of human activities during flood events in the Sadang area. The rainfall simulator was designed to reproduce the rainfall intensity exceeding the maximum rainfall intensity of the Sadang area for considering extreme cases. The spatial variability of rainfall was examined from the uniformity tests of nozzles. After finishing the validations, flood experiments were conducted in the scaled urban model, and the flood patterns were analyzed according to the rainfall intensity. From the measurements of the runoff velocity and the inundation depth, the flood vulnerabilities were investigated from the moment and the friction instabilities.
# Analysis of Precise Orbit Determination for the HY2D Satellite Using Onboard GPS/BDS Observations
## 3 POD Method and Strategies  
This Section mainly describes the observation and dynamic model used in the POD processing of HY2D. Meanwhile, we also present the detailed dynamic and observation model in the POD for HY2D.
## 3 Results and Discussion
### Adsorbent Characterisation
#### 3.1.3 Determination of pH of the Point of Zero Charge (pH\({}_{\text{PZC}}\))  
The effect of pH on the adsorption can be described on the basis of the point zero charge (pH\({}_{\text{PZC}}\)). The pH\({}_{\text{PZC}}\) is the point where the net charge of the adsorbent surface is zero. The pH\({}_{\text{PZC}}\) value allows to identify an increase of acidity or basicity after activation treatments. The results of the pH\({}_{\text{PZC}}\) measurement for ACSCGs obtained by the salt addition method are presented in the graph in Figure 4. The ACSCGs has a pH\({}_{\text{PZC}}\) value of 8.2, which shows that this carbon has a slightly basic nature. A relatively similar value was obtained in the study of [PERSON] et al., for activated carbon from corncobs activated with KOH [43]. Activated carbon is an amphoteric material, whose surface charge depends on the pH of the solution. As an example, the ACSCGs sample exhibited a pH\({}_{\text{PZC}}\) of 8.2, which will give its surface a negative charge for solutions of pH above 8.2 and a positive charge for solutions of pH below 8.2.
## 3 Results and Discussions
### DPPH Assay Analysis  
The DPPH assay measures the ability of an antioxidant to inhibit the free radicals DPPH and could be referred to as mg of raw sample, necessary to inhibit the radical. Our results, showed in Figure 2, are expressed as IC\({}_{50}\), indicating which concentration of each extract, it is necessary to inhibit the 50% of the radical) [35]). For this reason, lower is the value, higher is the antioxidant power.  
The extracts in Ethanol, N-hexane and SFE showed the highest antioxidant activity (p \(<\) 0.05), attested by the lower values of IC\({}_{50}\) (1.73 \(\pm\) 0.11, 1.97 \(\pm\) 0.90 and 1.22 \(\pm\) 0.03 mg/mL) (Figure 2). Similar results were obtained from [PERSON] et al. [10] that, in methanol extracts of _M. nodiforum_, found an IC\({}_{50}\) equal to 0.11 mg/mL [10].  
Figure 1: Total phenolic content (mg GAE/g) in different extracts of dried leaves of _M. nodiforum_. Lowercase letters (a–e) indicate significant differences between different samples (\(p<\) 0.05)  
Figure 2: The 1,1-Diphenyl-2-picrylhydrazzyl (DPPH) radical scavenging activity (IC 50, mg/mL) in different extracts of dried leaves of _M. nodiforum_. Lowercase letters (a–c) indicate significant differences between different solvents (\(p<\) 0.05).  
We experienced a limitation in comparing our data with literature referred to this species, as both solvents and systems of extraction are different, determining variations in yield and quality of the extracts.  
In the congener species, _M. edule_, [PERSON] et al. [46] showed that the stronger antiradical activity was present in the extract obtained with the most polar solvents, such as 20% and 40% aqueous alcohol. According to several studies, increasing the polarity of the solvent, with the addition of water, the antioxidant activity of the resulting extracts improved [46; 47; 48].  
Our results (Figure 2) showed that SFE extracts have the same antioxidant capacity of ethanolic extracts. The effectiveness of the SFE technique for the extraction of bioactive antioxidant components has been evaluated in other vegetable sources such as the plant marigold [7], grape (_Vitis labrusca_ B.) peel [49], and bee pollen [50], where a high scavenging activity was attested.  
Although the SFE extract showed, among the others, some of the lower yield (p \(<\) 0.05) (Table 1), its high scavenging activity, combined with the sustainability of the technique, that avoid the use of toxic solvents, suggests the importance of this eco-friendly technique for the sustainable production of _M. nodifornum_ extracts.
## 1 Introduction
However, optical sensor data only capture the energy reflected from the top of the canopy and are affected by clouds, whereas radar parameters can capture information below the canopy top and are sensitive to canopy geometry and moisture, which can be used to assist in crop yield prediction [14]. Recent studies, such as those conducted by [PERSON] et al. [23], [PERSON] et al. [17], and [PERSON] et al. [24], have highlighted the potential of SAR images to improve crop yield prediction performance when integrated with optical data. [PERSON] et al. [23] achieved better soybean yield prediction performance at the field level by integrating optical vegetation indices and SAR images (r\({}^{2}\) = 0.85) compared to using only optical indices (r\({}^{2}\) = 0.65). Despite advancements in integrating optical and radar remote sensing for soybean yield prediction, a substantial gap remains in exploring different processing techniques and incorporating SAR vegetation indices for field-level predictions. Identifying the most effective input data can boost predictive performance and refine remote sensing-generated models. Additionally, determining optimal processing approaches for SAR images is crucial for enhancing efficiency and accuracy, as these images require complex processing and involve high computational costs. By finding more effective methods, we can not only improve prediction accuracy and increase reliability for data users but also make better use of available resources. Therefore, the objectives of this study were: (1) to explore the correlation of different SAR variables with soybean yield using different types of speckle noise filters at different stages of crop development; and (2) to test whether the use of these SAR variables improves soybean yield prediction performance compared to the exclusive use of optical remote sensing using EVI.
## 3 QoE-Aware Geographic Routing Protocol for Video Streaming over VANETs
### Mobility Metrics
#### 3.2.1 Position Prediction  
The proposed scheme assumes that vehicles broadcast periodically their information using beacons. Hence, each vehicle build and maintain updated its neighbours' table. To increase the possibility to select the best relay vehicle, GeoQoE-Vanet uses the predicted positions of neighbours based on their last known positions, velocities, and moving directions.  
The predicted position of a vehicle \(u\) which has a position \((x,y)\), absolute velocity \(s\), and direction \(\theta\) at time \(t\) can be computed using Formula (1):  
\[\left\{\begin{array}{rcl}x_{p}&=&x+s\cos(\theta)\Delta t\\ y_{p}&=&y+s\sin(\theta)\Delta t\end{array}\right. \tag{1}\]  
where \(x_{p}\), \(y_{p}\) represent the coordinates of the vehicle's predicted position. Time interval \(\Delta t\) is calculated by \(\Delta t=t_{cur}-t\), where \(t_{cur}\) is the current time and \(t\) is the time of the past position.