esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Embedding Model for Knowledge Graph Entity Alignment Based on Graph Neural Networks
## 4 Experiments
### Ablation Study of PCE-HGTRA  
Tables 4 and 5 record the results of the ACE-HGTRA model in four different datasets for the ablation study. PCE and HGTRA refer to the property character embedding module and the heterogeneous graph transformer with relation awareness, respectively. The HGTRA module has made significant contributions to the model's performance improvement, indicating that processing the corresponding heterogeneous graphs inside the knowledge graph is of great help in achieving entity alignment tasks. This also reflects that the knowledge graph stores a large amount of heterogeneous information, and our approach can relatively well address such issues by using the model's HGTRA module. We also found that the property character embedding module played an important role in the model's final performance. This module can preliminarily reduce the heterogeneity of the knowledge graph through predicate alignment, providing a foundation for subsequent  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Datasets** & \multicolumn{4}{c}{**EN-FR-15K-V1**} & \multicolumn{4}{c}{**EN-FR-15K-V2**} \\ \hline
**Model** & **Hits@1** & **Hits@5** & **Hits@10** & **MR** & **Hits@1** & **Hits@5** & **Hits@10** & **MR** \\ \hline MTransE & 18.67 & 37.56 & 46.75 & 284.34 & 8.88 & 19.48 & 25.38 & 476.9 \\ JAPE & 20.94 & 41.65 & 51.28 & 196.16 & 20.17 & 38.97 & 47.73 & 141.81 \\ IPTransE & 23.09 & 46.56 & 57.63 & 454.66 & 34.08 & 62.73 & 74.47 & 39.65 \\ RDGCN & 20.12 & 33.28 & 38.27 & 1513.92 & 22.72 & 37.7 & 43.56 & 1209.87 \\ SEA & 28.64 & 53.63 & 64.91 & 319.98 & **35.27** & **64.35** & 76.4 & **32.81** \\ IMUSE & **54.20** & **69.00** & **75.11** & **121.81** & 34.97 & 60.08 & 70.99 & 34.11 \\ GCN-Align & 35.44 & 60.91 & 70.01 & 228.37 & 35.63 & 63.81 & **79.93** & 80.27 \\ PCE-HGTRA & 62.14 & 74.8 & 79.05 & 51.95 & 34.64 & 61.97 & 72.85 & 21.99 \\ Improv.best & 7.94 & 5.48 & 3.94 & 69.86 & - & - & - & 10.82 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results from the experiments conducted on EN-FR-15K-V1 and EN-FR-15K-V2 datasets.  
work. Furthermore, it can further enrich the available triplets by using transitivity rules. The processed relationship triplets can be transferred to the HGTRA module by the PC module, thereby improving the model's performance. In summary, our model has tried its best to reduce the heterogeneity of the knowledge graph. The experimental results show that the design of these two modules is relatively effective for entity alignment tasks.
## 2 Experimental Section
### Material Properties  
Various compressive strengths were selected depending on the applications of the structures. In South Korea, infrastructure projects, such as bridges, usually use concrete with a compressive strength of 40 MPa. To simulate the degradation in the strength of a concrete structure that requires strengthening, ready-mixed concrete with a design strength of 35 MPa was used to produce the beams. Three cylinders (100 mm diameter \(\times\) 200 mm height) were used for the compression test. The measured average compressive strength proved to be 30 MPa. For the internal steel rebar used in the beams, D10 (9.5 mm in diameter), D13 (12.7 mm in diameter), and D16 (15.9 mm in diameter) deformed bars were used. The average yield strength and ultimate strength of these bars are 450 and 620 MPa, respectively. The steel rebar mounted in the lifting hole had a diameter of 34 mm, a length of 400 mm, and an ultimate tensile strength of 828 kN. The steel tendon used for prestressing had a diameter of 12.7 mm and an ultimate tensile strength of 183.4 kN. The anchorages were manufactured using steel plates 12 to 20 mm thick with an average yield strength of 314 MPa and an average tensile strength of 530 MPa. Stud anchors with a diameter of 18 mm were used to install the anchorages, and the allowable tensile and shear strengths of these anchors were 27 and 40 kN, respectively. The [PERSON]'s modulus of steel materials used in this study was 200 GPa. A round plastic pipe with a 35 mm inner diameter and a 37 mm outer diameter was used to create a lifting hole in the concrete beam. The rupture strength of this pipe was 28 MPa. Epoxy resin with a tensile strength of 33.5 MPa and a bonding strength of 2.0 MPa was injected into the space between the anchorages and the concrete beams to provide greater adhesion. The above-mentioned material properties excluding the concrete were provided by the manufacturers.
## 4 Experimental Results and Analysis
### Road Data Extraction
#### 4.2.1 Traffic Density Detection
(**a**) Initial vehicles detection results with outlined bounding boxes and orientation arrows for a comprehensive overview. (**b**) Detailed analysis of vehicle orientation within bounding boxes.
## 4 Results  
### Zsd Estimation from In Situ Measurements  
We first calculated the band-equivalent AHSI reflectance using field-measured \(R_{rs}\). Following the steps in Section 3.3, the AHSI bands that have the most similar center wavelengths to the bands listed in Table 2 and Equation (3) were selected to calculate the \(Z_{sd}\). Then, the MAEs of the derived \(Z_{sd}\) were calculated, as well as the linear correlation analysis conducted between the estimated and measured \(Z_{sd}\) values for all sampling points. The estimated \(Z_{sd}\) values yielded MAE values of 0.35, 0.48, 0.69, and 0.42 m from SAMs based on \(QAA_{V5}\), \(QAA_{V6}\), \(QAA_{L09}\), and \(QAA_{M14}\), respectively. Figure 3 shows the estimation results with the in situ measured \(Z_{sd}\) values. Table 3 lists the MAEs and MREs for different \(Z_{sd}\) ranges.  
Figure 3: Estimated \(Z_{sd}\) from the AHSI band-equivalent in situ \(R_{rs}\) of 84 samples, based on semi-analytical methods using various QAA algorithms: (**a**)\(QAA_{V5}\), (**b**)\(QAA_{V6}\), (**c**) \(QAA_{L09}\), and (**d**)\(QAA_{M14}\).  
As shown in Figure 3, the \(Z_{sd}\) values retrieved with \(QAA_{V5}\) (Figure 3a) and \(QAA_{V6}\) (Figure 3b) show better consistency with the in situ measurements, as their paired data in the study regions predominantly fall along a 1:1 line, also yielding a higher \(R^{2}\) value (i.e., 0.9142 and 0.8057, respectively). The estimated \(Z_{sd}\) using the modified \(QAA_{L09}\) and \(QAA_{M14}\) are characterized by weaker correlations with the field-measured \(Z_{sd}\) (\(R^{2}\) of 0.7716 and 0.7497, respectively). In addition, as shown by Figure 3c,d, the semi-analytical methods with \(QAA_{L09}\) and \(QAA_{M14}\) tend to underestimate the \(Z_{sd}\) for relatively clear waters.  
Based on Table 3, we can observe the performance of the \(Z_{sd}\) estimations using the different QAA methods in various clarity ranges:  
(1) Overall, the \(QAA_{V5}\) and \(QAA_{M14}\) yield desirable estimations with both small MAEs and MREs for the \(Z_{sd}\) at all sampling sites. Specifically, \(QAA_{V5}\) yields better predictions in clearer waters (i.e., \(Z_{sd}\) > 3 m), whereas \(QAA_{M14}\) performs better for more turbid waters (\(Z_{sd}\) between 0.3 and 3 m). In addition, for a \(Z_{sd}\) range from 1-3 m, both the MAEs and MREs of the \(Z_{sd}\) estimated using the \(QAA_{V5}\) are slightly bigger than those of the \(Z_{sd}\) estimated from the \(QAA_{M14}\).  
(2) On the other hand, \(QAA_{V6}\) and \(QAA_{L09}\) have limitations for \(Z_{sd}\) retrieval using AHSI data. Their overall MAEs and MREs are higher than the \(Z_{sd}\) determined from two other algorithms. \(QAA_{V6}\) tends to better estimate the \(Z_{sd}\) for clearer waters (>3 m) and can induce larger errors in turbid areas (0.3-1 m). The opposite is true for \(Z_{sd}\) values derived using the \(QAA_{L09}\) method.  
Taking into account the MAE, MRE, and correlation from the estimated \(Z_{sd}\) values using the in situ dataset, we selected the \(QAA_{V5}\)- and \(QAA_{M14}\)-based semi-analytical methods for AHSI image testing.
## 8 Conclusions  
A long-range remote monitoring system constituted by three parts: Remote Terminal Units (BTUs), Base Terminal Units (BTUs) and a Central Server (CS) has been developed. Field tests have shown a high reliability (97%) of the satellite network data transmission and in the LOS analysis in the 900 MHz wireless networks it was 99.8%. The WSN currently implemented in the region \"Los Altos de Chiapas\" reduces the need of satellite links for each RTU due to the use of 900 MHz RF transmitters in the ISM band intended for Industrial, Scientific and Medical applications and as a consequence the implementation cost is minimized. Although the monitoring system is currently focused on the measurement and transmission of climatologic variables, it is designed following the principle of modularity, so it is possible to change the network sensors and apply it in multiple applications in the industrial, scientific and medical sectors and generally speaking in any application where the remote monitoring represents some danger, or could be inaccessible or economically non-viable for the user.
## 3 Simulation Method  
### Numerical Setting and Governing Equations  
This paper presents a numerical modeling tool using commercial CFD software, STAR-CCM+(V12.02.010) (Siemens, Munich, Germany), to perform a fully coupled dynamic analysis of the DeepCwind semi-submersible floating platform with the NREL 5-MW baseline wind turbine model under combined wind-wave excitation conditions.  
This investigation used the unsteady incompressible Navier-Stokes equations, according to the first principles of the conservation of mass and momentum. To solve the pressure-velocity coupling, a semi-implicit method was used, which involved a predictor-corrector approach. Second-order upwind and central difference schemes were used for the convection terms and temporal time discretization, respectively. Additionally, the shear stress transport (SST) \(k\)-\(\omega\) turbulence model (Menter's Shear Stress Transport) is a robust two-equation, eddy-viscosity turbulence model used for many aerodynamic applications to resolve turbulent behavior in the fluid domain and was first introduced in 1995 by [PERSON] [19]. The model combines the \(k\)-\(\omega\) and \(k\)-\(e\) turbulence models; therefore, the \(k\)-\(\omega\) turbulence model can be used in the inner region of the boundary, and the \(k\)-\(e\) turbulence model can be used in free shear flow. [PERSON]'s SST turbulence model can be expressed as follows:  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial\big{(}\rho u_{j}k\big{)}}{ \partial x_{j}}=P-\beta^{*}\rho\omega k+\frac{\partial}{\partial x_{j}}\Bigg{[} (\mu+\sigma_{k}\mu_{t})\frac{\partial k}{\partial x_{j}}\Bigg{]} \tag{1}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial\big{(}\rho u_{j}\omega \big{)}}{\partial x_{j}}=\frac{\gamma}{v_{t}}P-\beta\rho\omega^{2}+\frac{ \partial}{\partial x_{j}}\Bigg{[}(\mu+\sigma_{\omega}\mu_{t})\frac{\partial \omega}{\partial x_{j}}\Bigg{]}+2(1-F_{1})\frac{\rho\sigma_{\omega 2}}{\omega} \frac{\partial k}{\partial x_{j}}\frac{\partial\omega}{\partial x_{j}} \tag{2}\]  
To obtain details of the free surface between air and water, the unsteady CFD method with the volume of fraction (VOF) approach coupled with the 6 DOF solver was used for the hydrodynamic analysis considering the surge, sway, heave, roll, pitch, and yaw motions of the platform.
## 2 Data and Methods  
### EUREC\({}^{4}\)A Campaign  
The EUREC\({}^{4}\)A campaign was a cloud- and climate-focused undertaking, seeking to measure microphysical properties of trade-wind cumuli as a function of the large-scale environment and provide benchmark data for future satellite and modelling efforts [23; 24]. Initially, a Caribbean-French-German partnership, the EUREC\({}^{4}\)A campaign, operated eastward of the island of Barbados in the lower Atlantic trades (Figure 2) and incorporated the Barbados Cloud Observatory (BCO), along with aircraft, research vessels, buoys and drifters. Surface level observations carried out by the research vessel Meteor and the BCO would serve to complement the airborne measurements within the 'EUREC\({}^{4}\)A-Circle' (Study area A east of Barbados where the HALO (High Altitude and Long Range Research Aircraft) would circle in range of the land-based radar PoldiRad (Polarization Diversity Doppler Radar), Figure 2) characterizing the atmospheric environment from the surface to provide full measurement coverage of the atmospheric column.  
Figure 1: The region of EUREC\({}^{4}\)A-OA/ATOMIC study. Saildrones focused on the region with submesoscale variability, North Brazil Current (NBC) region (located in the red box), where the NBC rings (red circle) can result in strong fronts and ocean barrier layers. The blue arrow shows the direction of water transport via the NBC towards the Subtropical Gyre.  
EUREC4A's aim eventually expanded to include the examination of how air-sea interaction is influenced by mesoscale eddies, sub-mesoscale fronts and filaments in the vicinity of the NBC [23]. Collaboration between the Ocean Atmosphere component of EUREC4A (EUREC4A-OA) and the ATOMIC campaign led to the inclusion of three additional research vessels (R/Vs): L'Attalante, Maria S. Merian and Ronald H. Brown. These vessels were fitted with instrumentation to measure both the atmosphere and the upper kilometer of the subsurface water column. Measurements from autonomous vehicles including ocean gliders, drifters, floats and saildrones were used to supplement measurements directly from the research vessels. More details of the motivations, the wide range of measurement platforms and instrumentations involved in the EUREC4A campaign can be read at [PERSON] et al. [23].
## 3 Design of IPFC Controller
### Control System Structure  
The improved PFC controller proposed in this paper is applied to the outlet temperature control system of the heat exchanger, and its structure is shown in Figure 3.  
The manipulated variable is the fluid flow rate \(MV=G_{2}\), the controlled variable is the exchanger output temperature \(CV=T_{1o}\). The heat exchanger model \(G(s)\) in the figure is shown in Equation (14). The prediction model \(G_{m}\) is chosen as \(G_{m}(s)=\frac{1.9}{106s^{2}+26s+1}e^{-5.2s}\).  
The set value is the step signal, and the unit step function is selected as the basis function. The control quantity of the IPFC controller at any given time \(k\) can be obtained as:  
\[\begin{array}{l}u(k+i)=f_{k}{}^{T}(0)\mu=1\times k\frac{\big{(}g^{T}d+d^{T}g \big{)}}{g^{T}g} \tag{54}\]At this time, we can control the output of the IPFC controller by adjusting the three parameters \(k_{p}\), \(k_{i}\), and \(k_{d}\).  
Meanwhile, in the IPFC control in this paper, to adjust signals more conveniently, constraint conditions are considered for IPFC, and the upper limit \(u_{up}\) and lower limit \(u_{low}\) are added to the calculated control variables [30]:  
\[u_{low}\leq u(k)\leq u_{up} \tag{55}\]  
Then, the restricted manipulation variable \(u_{lim}(k)\) is:  
\[u_{lim}(k)=\left\{\begin{array}{ll}u_{up}&u(k)>u_{up}\\ u(k)&u_{low}\leq u(k)\leq u_{up}\\ u_{low}&u(k)<u_{low}\end{array}\right. \tag{56}\]  
At this point, the finite control variables will act as input signals to the model in the controller, as shown in Figure 4. The IPFC module and the limiting module constitute the IPFC controller, and the processing module refers to the heat exchanger system model. In the subsequent simulation in this paper, the limit of \(u_{lim}(k)\) was between \(\pm 3\).
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 3 Results
### Experiment on rEl-World Image Sequence
#### 3.3.2 Experimental Results
As illustrated in Figure 13e,f, the badly reconstructed lines due to mismatches can be rejected by setting a threshold on reprojection error(here we set \(\tau_{rep}=2\) pixels, few lines are left if we set the same threshold on reprojection error as Section 3.2). The proposed uncertainty \begin{table}
\begin{tabular}{c c c c c} \hline
**Line Segment** & \(\Delta\theta\) **(rad)** & \(\Delta\phi\) **(rad)** & \(\Delta m_{l}\) & \(\Delta\)**(rad)** \\ \hline A & 0.089 & 0.012 & 21.11 & 0.047 \\ B & 0.084 & 0.010 & 21.06 & 0.047 \\ C & 0.526 & 0.158 & 159.21 & 0.674 \\ D & 0.123 & 0.013 & 25.17 & 0.045 \\ \hline \end{tabular}
\end{table} 
Table 2: The 95% confidence interval length of each component for line segment A, B, C, and D.  
Figure 12: The number of lines passed reprojection error rejection and the number of lines passed reprojection error rejection but have large uncertainties at different reprojection error thresholds. analysis originates from two-view triangulation and holds the assumption that line matches are correct, thus it is not robust to mismatches. By integrating the reprojection error and uncertainty, both inaccurate lines due to mismatches and unreliable lines from insufficient effective ego-motion can be rejected. As showed in Figure 13 h,i, a 3D line map consistent to the scene structure is obtained. By comparing Figure 13d,g, we can figure out which lines are rejected by the uncertainty rejection. The common characteristic of these lines is that they are nearly coplanar with camera movements, which results in estimations with large uncertainties. In existing line reconstruction work, the reprojection error has always been the golden rule to ensure accuracy. however, the reprojection error is an indirect reflection of the accuracy in the image plane and fails in two-view triangulation. In the proposed uncertainty analysis, the accuracy is directly evaluated by the positional and orientational uncertainty of the line in 3D. Map culling can be robust to degenerate cases by adding the proposed uncertainty analysis.
## 4 Results and Discussion
### Effect of Water Vapor  
In order to study the performance of the Roots pump working with saturated wet hydrogen, the effect of water vapor on volumetric efficiency and isentropic efficiency under different suction temperature are conducted numerically. As indicated in Figure 6a, the volumetric efficiency increases almost linearly with an increase of the suction temperature, which is also an indication of the water vapor content. Furthermore, it shows that volumetric efficiency is improved greatly at a lower rotation speed and larger pressure difference with an increase of suction temperature. These results can be explained by the different properties of the mixture under various temperatures. In the CFD simulation, the dynamic viscosity and molar mass are considered for the working fluids. The dynamic viscosity of water vapor is close to that of hydrogen, but its molar mass is much higher. With the increase of suction temperature, the water vapor accounts for more of the mixture, since the saturation pressure goes up. At the same time, the molar mass of the mixture increases rapidly, while the dynamic viscosity increases slightly. Therefore, it could be deduced that the molar mass has a dominant effect on the leakage, and the water vapor helps to improve the volumetric efficiency.  
A similar pattern to volumetric efficiency can also be found in isentropic efficiency between suction temperatures in Figure 6b. The difference is that the change of the isentropic efficiency with the temperature is slightly smaller than that of the volumetric efficiency. For example, when the pressure difference is 15 KPa and the rotation speed is 9000 rpm, the volumetric efficiency of the Roots pump is improved by 9% from 50% to 59%, while the isentropic efficiency is improved by 6% from 47% to 53% with the increasing of suction temperature from 60 \({}^{\circ}\)C to 80 \({}^{\circ}\)C. Similarly, this could also be caused by reduced leakage, which means reduced internal energy loss.  
Figure 5: Simulation model validation.
## 3 Materials and Methods
### Antidiabetic Activity: PTP-1B Enzymatic Assay  
The _P. papularia_ essential oil was screened for PTP-1B inhibition using pNPP (_p_-nitrophenyl phosphate disodium salt) as the substrate. Both the essential oil sample and the enzyme were pre-incubated at room temperature for 5 min before use. A buffer solution (178 \(\mu\)L of 20 mM HEPES, 150 mM NaCl, and 1 mM EDTA) was added to each well of a 96-well plate. The PTP-1B protein solution (1 \(\mu\)L at a concentration 0.115 mg/mL) was added to the buffer solution, and then 1 \(\mu\)L of the test solution and the positive control solution were added. The pNPP substrate (20 \(\mu\)L of 35 mM) was added and mixed for 10 min. The plate was incubated for 30 min in the dark, and the reaction then terminated by adding 10 \(\mu\)L of 3 M NaOH. The absorbance was then determined at 405 nm wavelength using a Spectra Max MD5 plate reader (Molecular Devices, USA). The system without the enzyme solution was used as a blank. Inhibition (%) = [(OD\({}_{405}-\)OD\({}_{405}\) blank)/OD\({}_{405}\) blank] \(\times\) 100. The IC\({}_{50}\) was calculated from the percent inhibition values.
## 3 Results and Discussion
### Assessment of Building Base Shear and Displacement (Drift)
#### 3.1.1 Newcastle--Low-Risk Seismic Area  
Newcastle, characterized as a low seismic risk area with minimal historical earthquake occurrences and a low hazard factor of 0.11, exhibits relatively low seismic forces, as depicted in Figure 19a,b which showcase the shear force and building drift variations, respectively. In this low-risk region, the base shear is limited to approximately 200 kN, with a building drift of around 20 mm. Consequently, the feasibility of employing SIM panels for multi-story buildings in Newcastle appears limited. However, for structures with an importance level of 4 and soft soil conditions (e.g., Case 6), the use of SIM panels may be considered. Nevertheless, the recommendation is to continue using traditional masonry panels for multi-story infill panels in this region.