esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### AC W01-4 Injector  
Preliminary analysis of the flow measurement results indicated that they were characterized by non-constant variance (Tables 3-5). The variation of the volumetric flow rate (\(Q\)) as a function of the diameter of the outlet nozzle bore (\(d_{on}\)) indicated a nonlinear trend. For this reason, it was decided to use flow-characteristic matching through nonlinear regression [65; 67]. The study was conducted using the same test apparatus, so weighted fitting using the Levenberg-Marquardt method was possible [68; 69]. Minimization of the squared deviations in this method was conducted iteratively starting from the initial values of the assumed model.  
Figure 6: Example test results (**a**) and their statistical treatment (**b**) from the measurement of a Matrix 211.20 injector with a nozzle of 2.7 mm internal diameter.  
Analyzing the results of the AC W01-4 injector presented in Table 3, it was found that the values of \(Q\) mean and median are close to each other, which indicates the correctness of the inference. The values of standard deviation are within the limits of (0.119...0.150), which is sufficient to evaluate the shape of the flow characteristics. Positive kurtosis values indicate leptokurtic distribution. Asymmetry factor showed left and right skewness. The values of skewness, as well as kurtosis, are acceptable for further analysis [65]. Based on the average \(Q\) values, the flow characteristics of the W01-4 AC injector were determined (Figure 7a). The shape of the characteristics was fitted with a second-degree polynomial using the method described above. The identification of significant coefficients resulted in an equation in the form of Equation (1).  
\[Q=-1.377\,d_{on}^{2}+8.396\,d_{on}-7.447. \tag{1}\]  
At nine characteristic points, the root mean squared error was 0.292, with an adjusted \(R^{2}\) = 0.988. The determined coefficients of the polynomial were significant, \(p_{value}\) = 7.550 \(\times\) 10\({}^{-7}\). Figure 7a also shows the confidence intervals of the coefficient estimates in the nonlinear regression model. The sharpest points were observed at \(d_{on}\) of 1.9 mm and 2.3 mm. However, they are within the range defined by the confidence limits.  
Analyzing the standardized residuals at individual \(Q\) points (Figure 7b), a range of scatter (\(-1...1\)) is evident. The nature of the scatter is irregular and reaches maximum values at \(d_{on}\) of 1.9 and 2.3 mm. There was no correlation between the value of standardized residuals and the diameter of the outlet nozzle bore, which could suggest problems with the measurement range of the test equipment. The pressure and temperature fluctuations (Table 3 and Figure 7c) were negligible (\(p_{air}\) = 0.070 \(\times\) 10\({}^{5}\) Pa and \(T_{air}\) = (1.367 + 273.15) K), and thus should not affect the shape of the flow characteristics.
## 4 Error Simulation and Analysis  
### Cubic Prism Coordinate System Error  
As described in Section 3.1, in the process of establishing the cubic prism coordinate system, the measurement error accumulated between the actual and virtual points measured by the laser tracker affects the accuracy of the vector creation. This will result in the  
Figure 5: Datum alignment of the laser tracker and photogrammetry system.  
direction of the established vector not being parallel to the direction of the actual cubic prism normal vector, which in turn will affect the accuracy of the vector establishment of the cubic prism coordinate system. The point measurement error of a laser tracker falls into the category of random error and therefore the point measurement error can be described by a Gaussian normal distribution. The cubic mirror normal vector \(\overrightarrow{N}(e_{1},e_{2},e_{3})\) is related to the two-point linking vectors \(\overrightarrow{AB}(n_{1},n_{2},n_{3})\) by:  
\[\overrightarrow{N}(e_{1},e_{2},e_{3})=F_{Gauss}(\overrightarrow{AB}(n_{1},n _{2},n_{3})) \tag{12}\]  
We establish a spatial plane in the simulation software, set the incident ray length to 5 m, and use the cosine relationship to limit the incident ray angle to 60\({}^{\circ}\), 45\({}^{\circ}\), and 30\({}^{\circ}\), aimed at the space plane (simulating a cubic prism surface). The effect of the length of the reflected light on the accuracy of the coordinate system established by the cubic mirror is calculated for laser tracker and cubic prism positions (length of incident ray) of 1 m, 2 m, and 4 m according to (7)-(9). Figure 6a-c show the error curves established for the cubic prism vector for different incident ray length conditions. The horizontal coordinate is the length of the reflected ray, and the vertical coordinate is the angular error in the establishment of the coordinate system.  
As can be seen from the simulation data, when the length of the reflected light is in the range 0.5-2 m, an increase in the angle of incidence has a more sensitive effect on the error established by the cubic prism vector.  
As the reflected ray grows as the position of the receiver changes, the laser tracker measurement error increases, but the error established by the cubic mirror vector gradually decreases and converges to approximately 2\({}^{\circ}\). In the experiments, this method was well validated by comparing the autocollimator measurement data.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Continuous IRP System with Different Levels of Adhesive Strength
#### 3.3.2 Debonding Modes of Adhesive Layer  
Figure 37 illustrates the shear stress distribution in the adhesive layer of a continuous IRP system subjected to a load of 71.2 kN (16 kip). The results indicate that the maximum shear stress observed in the adhesive layer is only 0.189 MPa (0.027 ksi), which is significantly lower than the lowest shear strength of 5 MPa (0.7 ksi) for the unprepared surface. As a result, no debonding is observed in the adhesive layer, even under the maximum load of 71.2 kN (16 kip) in the continuous IRP system.
## 3 Results
### Proactive System Based on Predictions
#### 3.2.2 Prediction Models Applying Meta-Classifiers  
After the previous work testing the performance of classical classifiers, we continued our research to analyze and understand the behavior of predictions when using meta-classifiers. In summary, the use of these algorithms aims to mitigate the issues related to testing and selecting classifiers for each target. Although they do not necessarily achieve better results, they stabilize the prediction processes. Next, we describe the measurements conducted in the prediction steps associated with each grouped dataset and both targets, namely elongation and porosity occurrence.
# Modeling the 2013 Zika Outbreak in French Polynesia: Intervention Strategies
## Methodology  
The system is divided into three phases: (1) the mosquito gonotrophic cycle, to determine the number of human biting female mosquitoes (only these mosquitoes play a part in the spread of the disease) (2) the Zika transmission phase, to study the outbreak dynamics and (3) the local transit network to simulate the human movement from one island to another. Phases 1 and 2 are a combination of three compartmental models governed by three sets of differential equations. The three models and their interaction with each other is shown in Figure 1. The phases are explained in detail below.
## 2 Computational Approach
### Density Functional Theory  
Density Functional Theory (DFT) is a powerful tool that has revealed that the electron density of a many-electron system can determine the properties of this system. In DFT, the many-body problem is transformed into a single-particle Kohn-Sham equation [91; 92]. DFT is exact in principle, but the actual form of the exchange-correlation energy term (E\({}_{\text{xc}}\)) is unknown. Hence, approximate functionals for E\({}_{\text{xc}}\) are usually used [92; 93]. Local density approximation (LDA) assumes that the E\({}_{\text{xc}}\) functional depends only on the value of local electronic density. In contrast, generalized gradient approximation (GGA) considers both  
Figure 5: Computational modeling approaches for graphyne analysis, including Density Functional Theory (DFT), GW approximation, tight-binding methods, and Nonequilibrium Green’s Function (NEGF) formalism.  
the electron density and its gradient. LDA and GGA are widely used to investigate the properties of materials. Still, they underestimate the band gap, resulting from the missing derivative discontinuity of total energy at integer particle numbers. Hybrid functionals, such as HSE [94], mix nonlocal Hartree-Fock exchange with LDA or GGA energy and provide better band gaps than LDA or GGA, but at a higher computational cost.
## 3 Results and Discussion
### 2D GC TOF-MS Results
** & **Value** & **Name** & **Formula** & **Match** & **CAS \#** & **Molecular** \\  & & & & & **Factor** & **Weight** \\ \hline  & 14217646 & Aniline & C6H7N & 828 & 62-53-3 & 93 \\  & 5934255 & Amfatamine & C9H13N & 744 & 300-62-9 & 135 \\  & 7638604 & Benzaldehyde, 3-methyl- & C8H8O & 869 & 620-23-5 & 120 \\  & 5129038 & Cyclohexosiloxane, dodecamethyl- & C12H36O686 & 851 & 540-97-6 & 444 \\  & 54896184 & Quinoline, 2-methyl- & C10H9N & 873 & 91-63-4 & 143 \\  & 5541745 & Isoquinoline & C9H7N & 903 & 119-65-3 & 129 \\  & 23662350 & Formamide, N-phenyl- & C7H7 NO & 896 & 103-70-8 & 121 \\  & 1943815 & Glycoslamide, N-phenyl- & C9H9 NO2 & 842 & 32331-52-5 & 163 \\  & 3061044 & Acetamide, N-phenyl- & C8H9 NO & 889 & 103-84-4 & 135 \\  & 29294004 & Cyclohexptosislamide, tetradecamethyl- & C14H42O5737 & 881 & 107-50-6 & 518 \\  & 988329 & Cycl
# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
## 2 Related Work  
In recent years, researchers have shown an increasing interest in the acoustical-optical hybrid solutions for underwater communication [6]. In some cases, nodes and modems have been studied in research projects and developed within experimental AUV prototypes.
## 5 Conclusions  
In this study, the GPP estimated from EC flux measurements over rice-wheat-rotation cropland can represent the amount of carbon uptake by the main land cover type in the NYRD area. To obtain multiple samples for calibration of the MOD17A2H GPP product, a RF model for estimating GPP was designed by integrating multi-source satellite retrievals and in situ ground observations during the period of 2014-2018 over the rice-wheat double-cropping fields of eastern China. The RF model showed that multiple co-acting factors (NDVI, LAI, DSR, \(T_{\text{max}}\), and FPAR) modulate GPP dynamics. GPP\({}_{\text{RF}}\) performed well when compared with GPP\({}_{\text{PEC}}\), with a \(R^{2}\) of 0.99 and RMSE of 0.42 g C m\({}^{-2}\) d\({}^{-1}\), indicating these explanatory variables are reasonably representative and reliable for regional GPP upscaling. The regional upscaled cumulative seasonal GPP\({}_{\text{RF}}\) in rice paddies (924 g C m\({}^{-2}\)) was roughly two times higher than that in a wheat field (532 g C m\({}^{-2}\)) at the station scale, probably because of the much longer growing season and lower LAI of wheat. Compared with GPP\({}_{\text{PEC}}\), this indicates that GPP\({}_{\text{MOD}}\) underestimates GPP during the active crop growth stages but performs well during the crop rotation periods. Based on the upscaled results of GPP\({}_{\text{RF}}\) at the station scale, the functional relationship between GPP\({}_{\text{MOD}}\) and GPP\({}_{\text{RF}}\) at the grid scale was established to calibrate the GPP\({}_{\text{MOD}}\). The error range of \(\Delta\)GPP (GPP\({}_{\text{RF}}\) minus GPP\({}_{\text{MOD}}\)) was higher for rice paddies than for wheat fields, i.e., between 0.75 and 1.25 g C m\({}^{-2}\) d\({}^{-1}\) during the wheat growth seasons, between 2.5 and 3.25 g C m\({}^{-2}\) d\({}^{-1}\) during the rice growth seasons, along with an annual mean of 1.75-2 g C m\({}^{-2}\) d\({}^{-1}\). To sum up, the GPP in rice-wheat-rotation agroecosystems is considerably diverse and varies with the seasons. Our findings are potentially applicable in terms of the climate response of greenhouse gases over wide-scale cropland areas. Our research demonstrates that RF machine learning is a powerful and expedient modeling tool for estimating and even calibrating the MODIS GPP product. In future, it would be worthwhile using global FLUXNET data, multi-source satellite observations and machine learning methods to simulate the GPP in more ecosystem types (e.g., grassland and forests) and climate zones at large scales to fully understand the nature of global carbon dynamics.
# Measurement of Bridge Vibration by UAVs Combined with CNN and KLT Optical-Flow Method
## 2 Methods
### DIC Technology  
The principle of DIC method is shown in Figure 4: \(I_{2}(x,y)\) is the deformed image of the image \(I_{1}(x,y)\); \(S_{1}\) centered on \((x,y)\) and \(S_{2}\) centered on \((x^{\prime},y^{\prime})\) are two windows with the same size, \(S\), which are established at \(I_{1}(x,y)\) and \(I_{2}(x,y)\), respectively. Where \(x^{\prime}=x+\Delta x,y^{\prime}=y+\Delta y\). The correlation between two windows can be expressed as [33]:  
\[C(\Delta x,\Delta y)=\frac{\iint_{S}I_{1}(x,y)I_{2}(x+\Delta x,y+\Delta y)dxdy} {\sqrt{\iint_{S}I_{1}^{2}(x,y)dxdy}\cdot\sqrt{\iint_{S}I_{2}^{2}(x+\Delta x,y+ \Delta y)dxdy}}. \tag{9}\]  
The real displacement of \((x,y)\) between two images, \((\Delta x,\Delta y)\), can be obtained when the function value of \(C(\Delta x,\Delta y)\) is the maximum.  
A fixed-size window is used by both DIC and KLT optical-flow methods to search target points in the whole image range; however, the two methods are different in tracking target points. As shown in Equation (1), the target tracking of KLT optical-flow method is based on the residual error of a fixed-size window between two frames, while that of DIC is based on the correlation of a fixed-size window between two frames, as shown in Equation (9). Moreover, the implementation process of KLT optical-flow method is accompanied by  
Figure 4: Principle of DIC method.  
Figure 3: Image pyramid.  
the image-pyramid technology, which tracks the points at multiple levels of resolution of an image to optimize tracking effect to improve the tracking accuracy.
# Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications
## 2 Antenna System Structure  
The proposed antenna is designed on a \(100\:\mathrm{mm}\times 100\:\mathrm{mm}\) FR-4 substrate with a dielectric constant of 4.4, a loss tangent of 0.02, and a thickness of 1.52 mm. The proposed antenna system integrates a circularly polarized wideband antenna for sensing and two reconfigurable narrowband antennas for communication.
# You Only Attack Once: Single-Step DeepFool Algorithm
## 4 Experiments
### Experimental Results
#### 4.4.3 Performance Variation in YOAO and DeepFool Algorithms Under Different Parameters  
As shown in Figure 5, to investigate the impact of classifier accuracy on adversarial attack algorithms, experiments were conducted using ResNet and VGG networks with an accuracy of 60% as classifiers, comparing the performance of the DeepFool algorithm and the YOAO algorithm on the CIFAR-10 dataset. In this section, we employed the ResNet classifier with an accuracy of 95.45% to evaluate the attack success rates and efficiency of both the DeepFool and YOAO algorithms on the CIFAR-10 dataset. It was observed that, at a maximum iteration count (max-iter) of 1, the attack success rate of the YOAO algorithm dropped to 38%. This indicates that, on well-trained classifiers, the attack success rate of the YOAO algorithm may decrease. However, as the max-iter parameter is increased, the attack success rate for the YOAO algorithm also improves. Notably, under the same parameter settings, the attack success rate of the YOAO algorithm consistently surpasses that of the DeepFool algorithm. Additionally, the time required to generate a single adversarial sample was calculated for both algorithms, revealing that, even with high-accuracy classifiers, the YOAO algorithm requires significantly less time to produce a single adversarial sample compared to the DeepFool algorithm. This underscores the superior performance of the proposed YOAO algorithm.  
Figure 4: The impact of the maximum iteration parameter (max-iter) on the original DeepFool algorithm and the YOAO algorithm is illustrated in the four subfigures. (**a,b**) demonstrate the speed of generating a single adversarial sample at different maximum iteration values, while (**c,d**) show the success rate of the algorithm’s attacks under varying maximum iteration settings. In our experiments, we employed two classifiers, ResNet18 and VGG16.
# Deep Learning Framework for Advanced De-Identification of Protected Health Information
## 4 Results and Discussion
### CRF Model
As a result, the model's overall performance across PHI categories is uneven, showing marked accuracy for frequent labels but decreased precision and recall for less common, more complex labels. This highlights a limitation in the model's generalizability, emphasizing the need for more balanced dataset representation to improve classification across all PHI categories. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{4}{c}{**B-Tag**} & \multicolumn{4}{c}{**I-Tag**} \\ \cline{2-9}  & **Precision** & **Recall** & **F1-Score** & **Support** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Age & 0.56 & 0.48 & 0.52 & 764 & 0 & 0 & 0 & 1 \\ \hline City & 0.76 & 0.51 & 0.61 & 260 & 0.83 & 0.36 & 0.50 & 81 \\ \hline Country & 0.68 & 0.11 & 0.19 & 117 & 0 & 0 & 0 & 13 \\ \hline Date & 0.99 & 0.98 & 0.98 & 4980 & 0.95 & 0.88 & 0.91 & 509 \\ \hline Device & 0 & 0 & 0 & 8 & 0 & 0 & 0 & 2 \\ \hline Doctor & 0.65 & 0.57 & 0.60 & 1912 & 0.73 & 0.74 & 0.74 & 1350 \\ \hline Email & 0 & 0 & 0 & 1 & & & & \\ \hline FAX & 0 & 0 & 0 & 2 & 0 & 0 & 0 & 1 \\ \hline Hospital & 0.87 & 0.59 & 0.71 & 875 & 0.93 & 0.78 & 0.85 & 700 \\ \hline ID\_Num & 0.98 & 0.76 & 0.86 & 195 & 1 & 0.57 & 0.73 & 14 \\ \hline Location Other & 0 & 0 & 0 & 13 & 0 & 0 & 0 & 7 \\ \hline Medical Record & 0.97 & 0.96 & 0.96 & 422 & 0 & 0 & 0 & 22 \\ \hline Organization & 0.5 & 0.07 & 0.13 & 82 & 0.44 & 0.18 & 0.26 & 61 \\ \hline Patient & 0.53 & 0.25 & 0.34 & 879 & 0.54 & 0.30 & 0.38 & 479 \\ \hline Phone & 0.96 & 0.93 & 0.94 & 215 & 0.96 & 0.94 & 0.95 & 48 \\ \hline Profession & 0.59 & 0.21 & 0.31 & 179 & 0.77 & 0.38 & 0.51 & 156 \\ \hline State & 0.91 & 0.76 & 0.83 & 190 & 0 & 0 & 0 & 15 \\ \hline Street & 0.98 & 0.92 & 0.95 & 136 & 0.91 & 0.92 & 0.92 & 280 \\ \hline Username & 0.99 & 0.90 & 0.94 & 92 & & & & \\ \hline ZIP & 0.99 & 0.96 & 0.97 & 140 & & & & \\ \hline Other “O” & & & & 0.99 & 1 & 0.99 & 301,937 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of B-tag and I-tag Results using the CRF model.
# EBIC Imaging of Conductive Paths Formed in Graphene Oxide as a Result of Resistive Switching
## 1 Introduction
The EBIC contrast of the conductive path in GO was assumed to be formed as a result of the collection of excess carriers by a conductive path acting as a nanometer size Schottky barrier collector [21]. The possibility to visualize conductive paths in the RRAM insulator materials calls for additional investigation of the mechanism of conductive paths' image formation in EBIC mode. The goal of the present work is to study the mechanisms of EBIC imaging of the conductive paths formed in Al-gated graphene-oxide-based MIS structures. Our choice of graphene oxide is motivated by its promising perspectives for non-volatile memory applications [30; 31]. Owing to its complex structure, graphene oxide demonstrates multiple transitions from a high to a low resistance state [21; 32]. This makes GO a perfect material for investigation of the conductive channels by the EBIC method. Determination of the mechanism of EBIC imaging of the conductive channels can help better understand the mechanism of their formation during resistive switching. The results of the experimental and theoretical study presented in this work have validated an assumption that the mechanism of the conductive path image formation in the Al/GO/Si structures is based on the collection of the excess minority carriers by Schottky-like barrier nanocontacts at the interface with n-type Si. EBIC detection of the minority carrier transport through the insulator to the top electrode provides strong evidence of a close link between the conductive paths and the observed EBIC images.
## 3 Results and Discussion
### Small Impulse Excitation Force  
In the research presented, the bearing placed on the rotating rotor shaft was hit twice. The value of the excitation force has not been accurately measured; but, in the first case, it was approximately 100 N. The displacement of the point, located (on the surface of the sleeve) very close to the foils supporting the bearing, was measured.  
The displacement in the vertical direction of the point illustrated in Figure 8 is shown in Figure 9. The figure shows that the displacement lasted about 0.05 s. The unit of the axis of ordinates is pixels because the points in the image are tracked with such accuracy. To estimate the correlation between pixels from the high-speed camera and the displacement expressed in micrometres, we used the recordings of known geometry of the bearing. We did this calculation using the same positions of the camera and bearing as those used in the following measurements. We know how large the maximum displacement of the bearing sleeve was when it was pushed to one side with a great force (because this is how we assembled the bearing). We recorded this movement using a high-speed camera. The next calculation was easy, we divided known recorded displacement (analysed by the TEMA Motion software) by a known distance. We assumed that the displacement was linear, which means that each pixel recorded by the high-speed camera represents the same distance of the movement. According to rough calculations, one pixel corresponds to a displacement of 7.4 \(\upmu\)m. The displacement recorded by a high-speed camera was 15 pixels (111 \(\upmu\)m). The figure also shows that after being subjected to an excitation force, the bearing operated in a different position than it had before--the entire sleeve moved one pixel down, that is to say about 7.4 \(\upmu\)m.  
Figure 8: Point on the surface of the sleeve, which was used to perform a displacement analysis when a small excitation force was applied to the bearing (indicated by green arrow).
## Acknowledgments  
This work was supported in part by the National Natural Science Foundation of China (No. 81100520), the Science and Technology Development Plans of Shandong Province (No. 2012 GGE27073) and Independent Innovation Foundation of Shandong University, IIFSDU, (No. 2012 JC015 and 2012 DX001).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **With Color Adjustment** & **Without Color Adjustment** \\ \hline A1 & 94.6\% & 90.2\% \\ A2 & 95.1\% & 89.4\% \\ D & 19.13 & 29.43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance evaluation with and without color adjustment.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Methods** & **Neutrophil** & **Lymphocyte** & **Monocyte** & **Eosinophil** & **Basophil** & **Average** \\ \hline Threshold-based method [6] & 88.7\% & 90.1\% & 92.3\% & 87.2\% & 83.9\% & 89.3\% \\ Learning-based method [9] & 94.4\% & 95.6\% & 97.3\% & 87.2\% & 85.0\% & 93.3\% \\ Active-contour-based method [7] & 91.9\% & 94.6\% & 94.5\% & 88.3\% & 86.2\% & 92.2\% \\ Metaheuristic-based method [13] & 94.4\% & 93.6\% & 96.2 & 93.8\% & 88.5\% & 93.5\% \\ Saliency-based method [14] & 95.2\% & 98.1\% & 95.6\% & 94.5\% & 85.1\% & 94.1\% \\ Proposed method & 97.6\% & 97.0\% & 97.8\% & 89.4\% & 89.7\% & 95.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of each types of WBC separately.