esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Postprocessing and Statistical Analyses of the Results  
All the quality of the simulation results will be checked to meet the quality criteria defined by the Pedestrian Human Model Certification [30]. The criteria include the initial penetration, total energy, hourglass energy, artificial energy, artificial mass increase and so on.  
Pelvic fracture can be investigated under various parametric responses, such as the peak impact force, peak strain, and peak stress [31]. We evaluated the pelvic fracture responses under different loading using qualitative and quantitative approaches. To qualitatively evaluate the possible injury regions, the effective strain was plotted using the fringe-range plot menu in LS-PREPOST 4.8 (LSTC, Livermore, CA, USA). The elements with effective strain values higher than 0.03 [32; 33] were highlighted in the fringe plots and compared among different loading conditions. Effective strain was selected because several studies were published to evaluate strain thresholds for pelvic fracture [17; 32; 33]. In the quantitative assessments, we extracted the maximum effective strain, Von-Mises stress, maximum principal stress of pelvic bones from 16 interesting sites, and the contact force between the pelvis and impactor/wheel, and the intercistal (IC) and the conjuata vera (CV) diameters. We investigated the criteria differences among different loading conditions. The statistical analyses of pairwise \(t\)-test, survival analysis principal components analysis and logistic regression and corresponding plots were performed using Jamovi 2 (jamovi.org), gggstatsplot [34] and Microsoft Office (Microsoft, Redmond, WA, USA), with \(p<0.05\) indicating statistical significance. We calculated the area under the receiver operating characteristic (ROC) to evaluate the prediction ability in discriminating the forensic injury manners of impact and run-over using Von-Mises stress, effective strain and maximum principal stress of all simulated loading conditions. The area under the ROC curve (AUC) determines the prediction power. The value of AUC greater than 0.9 shows the high predictive power [35].  
The 16 data extraction sites included the following parts: right hip joint (RH), right iliac crest (RIC), right ischial notch (RIN), right sacroiliac articular surface (RSAS), right sacrum (RS), top surface of the sacrum (TSA), right inferior pubic ramus (RIPR), right superior pubic ramus (RSPR), left superior pubic ramus (LSPR), left inferior pubic ramus (LIPR), pubic symphysis (PS), left hip joint (LH), left iliac crest (LIC), left ischial notch (LIN), left sacroiliac articular surface (LSAS), and left sacrum (LS). All the interesting sites are plotted in Figure 6.  
Figure 5: Pelvic run-over loading conditions. (**a**) Front side run-over by a wheel; (**b**) Back side run-over by a wheel.
## 4 Result Analysis
### Analysis of the Document Collection
#### 4.1.2 Document Collection Analysis  
Furthermore, the main characteristics of the 70 documents which were published during the period of 2013-2024 in 46 different sources (e.g., journals, conferences, edited books, etc.) are summarized in Table 1. The documents were written and published by 642 authors from 28 countries and had 3.99 co-authors on average. Of the 70 documents, only five documents (7.1%) were single-authored. An annual growth rate of 25.35% is observed, with documents being citable for 2.79 years on average and receiving 8.5 citations on average. Additionally, the number of documents which authors have contributed to was examined using [PERSON]'s law. Specifically, only one author (0.45) has contributed to six documents, while three authors have contributed to five documents. Most authors have contributed to one study (84.82). Given the specifications of the Bibliometrix tool, the authors' unique names were manually checked. Figure 5 depicts the related outcomes.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Description** & **Results** & **Description** & **Results** \\ \hline
**Main information about** & & **Document types** & \\
**data** & & & \\ Timespan & 2013:2024 & Journal article & 27 \\ Sources & 46 & Book chapter & 3 \\ Documents & 70 & Conference/Proceedings & 40 \\ Annual Growth Rate \% & 25.35 & & \\ Document Average Age & 2.79 & **Authors** & \\ Average Citations per & 8.5 & Authors & 642 \\ Document & & & \\ References & 2332 & Authors of single-authored & 32 \\
**Document contents** & & **Authors collaboration** & \\ Keywords Plus & 482 & Single-authored docs & 5 \\ Author’s Keywords & 198 & Co-authors per doc & 3.99 \\ \hline \end{tabular}
\end{table}
Table 1: Document collection information.  
Figure 5: Information regarding the document collection.
## 2 Stress Distribution and Deformation Law
### Secondary Stress Field  
After the excavation of the tunnel, because the rock mass is released from the constraints at the excavation surface, the stress field balance of the original rock is destroyed, resulting in the displacement of the points around the tunnel, and stress redistribution. However, the stress redistribution is limited to the rock mass within a certain range around the tunnel, which is usually called the rock mass, and the stress state after redistribution is called the secondary stress state. Assuming that the rock is under elastic stress, the formula of stress distribution and displacement of rock mass of circular tunnel are deduced as follows [46; 47]:  
\[\sigma_{r}=\frac{\sigma_{y}}{2}\Big{[}(1-a^{2})(1+K_{0})+(1-4a^{2}+3a^{4})(1- K_{0})\cos 2\theta\Big{]} \tag{3}\]\[\sigma_{t}=\frac{\sigma_{y}}{2}[(1+a^{2})(1+K_{0})-(1+3a^{4})(1-K_{0})\cos 2\theta \tag{4}\]  
\[\tau_{rt}=-\frac{\sigma_{y}}{2}(1-K_{0})(1+2a^{2}-3a^{4})\sin 2\theta \tag{5}\]  
\[u_{a}=\frac{1+\mu}{2E}\sigma_{y}a[1+K_{0}-(3-4\mu)(1-K_{0})\cos 2\theta] \tag{6}\]  
where \(\sigma_{r}\) is radial stress (N/m\({}^{2}\)); \(\sigma_{t}\) is circumferential stress (N/m\({}^{2}\)); \(\tau_{rt}\) is shear stress (N/m\({}^{2}\)); \(u_{a}\) is displacement around the tunnel (m); \(a\) is the radius of tunnel (m); \(K_{0}\) is the lateral pressure coefficient (ratio of horizontal initial stress to vertical initial stress); \(E\) is the elastic modulus of rock mass (N/m\({}^{2}\)); \(\mu\) is Poisson's ratio of rock mass; \(\sigma_{y}\) is vertical initial stress (N/m\({}^{2}\)); and \(\theta\) is the angle between the position of the rock mass points in polar coordinates and the vertical direction (\({}^{\circ}\)).  
When the stress of the rock mass reaches a certain value, the yield of rock mass reaches a plastic state. Based on the Coulomb-Mohr criterion, the formulas for calculating the yield of rock mass under hydrostatic stress state are deduced as follows [46; 47]:  
\[r_{o}=a[\frac{2}{\xi+1}\cdot\frac{\sigma_{y}(\xi-1)+R_{b}}{R_{b}}]^{\frac{1}{ \xi-1}} \tag{7}\]  
\[\sigma_{rp}=\frac{R_{b}}{\xi-1}[(\frac{r}{a})^{\xi-1}-1] \tag{8}\]  
\[\sigma_{tp}=\frac{R_{b}}{\xi-1}[(\frac{r}{a})^{\xi-1}\xi-1] \tag{9}\]  
\[u_{a}=\frac{1}{2G}(\sigma_{y}-\sigma_{r0})\frac{r_{0}^{2}}{a} \tag{10}\]  
where \(r_{0}\) is the radius of plastic zone; \(\xi=\frac{1+\sin\varphi}{1-\cos\varphi}\); \(R_{b}=\frac{2\cos\varphi}{1-\sin\varphi}\); \(\sigma_{rp}\) is the radial stress in the plastic zone; \(\sigma_{tp}\) is the tangential stress in the plastic zone; \(c\) is the cohesion of the rock mass (N/m\({}^{2}\)); \(\varphi\) is the internal friction angle of the rock mass (\({}^{\circ}\)); \(r\) is the radius of the location of the rock mass points (m); \(\sigma_{r0}\) is the radial stress on the plastic boundary (N/m\({}^{2}\)); and \(G\) is shear modulus.
## 4 Conclusions
\begin{tabular}{l l} CubeSat & A class of miniaturized satellite based around a form factor consisting of \\  & 10 cm (3.9 in) cubes \\ EnMAP & Environmental Mapping and Analysis Program \\ FOV & Field of View \\ FTIR & Fourier Transform Infrared \\ GeoCarb & The Geostationary Carbon Cycle Observatory \\ GNSS & Global Navigation Satellite System \\ GOSAT & Greenhouse Gases Observing Satellite \\ GHGSat & Greenhouse Gas Satellite \\ HITRAN & High Resolution Transmission Database \\ IR & Infrared \\ IFOV & Instantaneous Field of View \\ LiFePO4 & Lithium-Iron-Phosphate Batteries \\ MicroCarb & Carbon Dioxide Monitoring Mission \\ MetOp & Meteorological Operational satellite \\ MPPT & Maximum Power Point Tracking \\ NEP & Noise Equivalent Power \\ NIR & Near Infrared \\ NDIR & Non-Dispersive Infrared \\ OCO & Orbiting Carbon Observatory \\ PRISMA & Precursor IperSpettrale della Missione Applicativa, Hyperspectral Precursor of the Application Mission \\ ppm & Parts Per Million \\ SNR & Signal-to-Noise Ratio \\ SWIR & Short-Wavelength Infrared \\ SSO & Sun-Synchronous Orbit \\ SHS & Spatial Heterodyne Spectroscopy \\ TANSO-FTS & Thermal and Near-Infrared Sensor for Carbon Observation Fourier-Transform Spectrometer \\ UHF & Ultra-High Frequency \\ \end{tabular}
## 2 Data and Methods
### Data Processing Method
#### 2.2.2 BEPS for the Calculations of NPP  
We used the BEPS model to evaluate the terrestrial \(NPP\) in Mongolia from 2003 to 2018. The model was initially built and validated for daily boreal ecosystems [38] and has been commonly used for calculating global and regional \(NPP\). Later, it was employed to monitor the dynamic changes in global and regional \(NPP\) of various vegetation types, such as shrubland, farmland, and grassland ecosystems [46]. The model computes \(NPP\) based on the difference between total carbon uptake from the air through photosynthesis (\(GPP\)) and the carbon lost due to respiration (\(R_{a}\)) [38; 47]. Equations (4)-(6) were used to run the NPP [48]. The model was operated on a daily step throughout the year from 2003 to 2018, pixel by pixel.  
\[NPP=GPP-R_{a} \tag{4}\]  
\[GPP=A_{canopy}L_{day}F_{GPP} \tag{5}\]  
\[R_{a}=R_{m}\ +R_{g} \tag{6}\]  
where \(NPP\) refers to net primary productivity (g C m\({}^{-2}\)), \(GPP\) (gross primary productivity; g C m\({}^{-2}\)) is the carbon flux during photosynthesis, \(A_{canopy}\) is the carbon assimilation rate, \(L_{day}\) is the day length, and \(F_{GPP}\) is the coefficient of conversion from umd m\({}^{-2}\) s\({}^{-2}\) to g C m\({}^{-2}\) day\({}^{-1}\). Daily \(R_{a}\) is the autotrophic respiration, while \(R_{m}\) and \(R_{g}\) are the maintenance respiration and growth respiration by all living parts including fine roots and leaves, respectively [49; 50].  
The assessment of drought-induced \(NPP\) anomalies was done by utilizing an anomaly index based on the growing seasonal \(NPP\) from 2003 to 2018 as defined in Equation (7) following [PERSON] et al. [51]:  
\[NPP_{anomaly}i=\frac{NPP_{\text{meani}}-\overline{NPP}}{NPP}\times 100 \tag{7}\]  
where \(NDVI_{anomaly}i\) is the \(NPP\) anomaly of an individual grid cell for a drought event, \(NPP_{\text{meani}}\) is the accumulated \(NPP\) value of the \(i\)th drought event, and \(\overline{NPP}\) is the long-term average growing seasonal value of \(NPP\) corresponding to the period of the \(i\)th drought. For example, if the drought occurred from May 2004 to September 2004, the \(NPP_{\text{meani}}\) represents the accumulated \(NPP\) value for this period, while the \(\overline{NPP}\) represents the mean of \(NPP\) values of the corresponding drought period (May-September across 2003-2018).
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 4 Performance Verification of Automatic Docking System for LNG Loading Boom
The monocular camera extracts the key features of the flange (such as the center and diameter) through instance segmentation, and calculates the 3D coordinate information of the flange based on geometric constraints. The ultrasonic sensor is used to obtain real-time feedback data on the vertical and horizontal distances between the 3D joint and the flange, which further  
Figure 18: LNG loading-arm automatic docking software information. supplements the image information that the monocular camera cannot cover in some positions. By comprehensively analyzing image information and distance feedback data, the system can adjust the pose of the 3D joint in real time, gradually approaching the target position and completing the docking. The coarse docking phase primarily relies on a single target position localization approach using the angle sensor feedback. Since this phase involves moving the loading arm to the target flange area, the system does not require precise positioning and can successfully approach the target. The system performs robustly under environmental conditions such as wind interference, and the angle sensor's feedback is sufficient to control the movement. In the fine docking phase, relying on a single localization method is insufficient to achieve precise docking. While stereo vision or LiDAR-based systems could provide more accurate depth information, they face significant challenges. Stereo vision often struggles with limited field of view and potential viewpoint obstructions during close-range localization. Additionally, LiDAR systems are expensive, require significant computational resources, and are less cost-effective compared to other solutions. In contrast, ultrasonic sensors provide high accuracy and excellent real-time performance, and they are cost-effective, making them ideal for fine docking stages. The ultrasonic sensor compensates for the areas where the monocular camera may lack depth information, especially in blind spots, and ensures reliable, accurate measurements. This fusion of data improves the overall positioning accuracy, particularly in close-range scenarios where the monocular camera's field of view is limited. The experimental results show that the system can maintain high docking accuracy under wind and rainy weather conditions, with docking errors controlled within 3 mm, meeting the engineering requirements for the automatic docking of the LNG loading arm.
## 2 Related Work
### The Extended Kalman Filtering Algorithm  
The Kalman Filter (KF) is an optimal sequential state estimation algorithm for a linear state-space model in terms of the mean squared estimation error. When the state-space model is nonlinear, the KF is no longer applicable and nonlinear filters, especially the Extended Kalman Filter (EKF) [14; 15; 16], become necessary. For the EKF, the nonlinear Gaussian state-space model is linearized around the current best estimates of states and the KF algorithm is applied to the linearized models. The EKF algorithm is divided into two parts: time and observation updates. In the time update, the state estimation is predicted by the system model; for the observation update, the predicted state estimate is corrected using the observation based on the observation model. This corrected state estimation is called the filtered state estimate and it is then used for the next time update.  
The nonlinear state-space model can be described as:  
\[\begin{cases}X(k)=f[X(k-1)]+GV(k),\ V(k)\sim N(0,Q_{k})\\ Z(k)=h[X(k)]+W(k),\ W(k)\sim N(0,R_{k})\end{cases} \tag{12}\]  
Here, \(k=1,2,\cdots\) and \(X(k)\) and \(Z(k)\) are \(N\) and \(L\) dimensional random vectors called the state and the observation at \(k\). \(V(k)\) and \(W(k)\) are independent zero-mean white Gaussian noises which covariance matrices are \(Q(k)\) and \(R(k)\), respectively. These noises are also assumed as independent from \(X(0)\). G is the noise input matrix.  
The predicted state estimate and the covariance matrix are:  
\[\hat{X}(k|k-1)=f[\hat{X}(k-1)] \tag{13}\] \[P(k|k-1)=A(k-1)P(k-1|k-1)A(k-1)^{T}+GQ_{k}G^{T}. \tag{14}\]  
The filtered state estimate and the covariance matrix are:  
\[K(k)=\frac{P(k|k-1)H(k)^{T}}{H(k)P(k|k-1)H(k)^{T}+R(k)} \tag{15}\] \[\hat{X}(k)=\hat{X}(k|k-1)+K(k)[Z(k)-h[\hat{X}(k|k-1)]]\] (16) \[P(k)=[I-K(k)H(k)]P(k|k-1) \tag{17}\]  
Among them, \(A(k-1)\) and \(H(k)\) are Jacobian matrices of non-linear systems, which are derived from partial derivatives of \(f[X(k-1)]\) and \(h[X(k)]\), respectively.
## 4 Performance Evaluation
### Evaluation Time
The evaluation time of H-HHT is closely related to that of R-HHT, but it greatly depends on the final grid size, too. The grid search, performed in the second stage, can be considered as an augmentation of the target area for the R-HHT: if the R-HHT in the first phase provides a position so that the grid search area around this position contains the optimal position, then the H-HHT will find a solution close to the optimum. Increasing the grid size results in a larger search area, which leads to faster evaluation. However, the accuracy decreases with larger grid sizes, thus the grid size cannot be set arbitrarily high. The effect is very apparent in scenarios #5 and #6: here, only the final grid size changed, and the larger grid size resulted in shorter run times. Both grid sizes were below the CRLB, thus both solutions provided good accuracy. In the experiments, the H-HHT demonstrated a 10-100-fold improvement over the R-HHT. In most cases, the F-HHT significantly overperformed R-HHT and H-HHT in the experiments. Additionally, in the near-field scenarios, the F-HHT was faster than the LS, while in the far-field cases, the LS solution was more efficient.
## 1 Introduction
Therefore, the present research encompasses a thorough examination that integrates multiple analytical techniques, including the utilization of scanning electron microscopy (SEM) in conjunction with energy-dispersive X-ray spectroscopy (EDX), Fourier transform infrared (FTIR) spectroscopy, and \(\upmu\)TBS testing along with microbial analysis. It was hypothesized that there would be no significant difference in the \(\upmu\)TBS of SE adhesive modified with ZrO\({}_{2}\)/Ag\({}_{3}\)PO\({}_{4}\) loaded in three different concentrations (0.15 wt%, 0.25 wt%, and 0.5 wt%) compared to unmodified SE adhesive. Furthermore, it was also anticipated that there would be no significant difference in the antibacterial efficacy or FTIR spectroscopy of ZrO\({}_{2}\)/Ag\({}_{3}\)PO\({}_{4}\) NP-modified SE adhesive compared to unmodified SE adhesive. Thus, this study aimed to evaluate the impact of ZrO\({}_{2}\)/Ag\({}_{3}\)PO\({}_{4}\) NPs in 0.15 wt%, 0.25 wt%, and 0.5 wt% added-in adhesive. Surface characterization, elemental analysis, survival rate assessment of \(S\). _mutans_, \(\upmu\)TBS, and the DC of composite bonded to CAD were determined.
## 3 Legislation Process and Contents of the E-Government Acts in Korea and the United States
### Main Content of the E-Government Act in Korea  
The E-Government Act of Korea, which was the first established in the world, not only involved the introduction and utilization of electronic documents but also caused substantial changes in administration via electronic civil service. Therefore, this section examines the content of the e-government law and changes in administrative behavior from the viewpoint of productivity, transparency, and democratization of government administration processes.  
The E-Government Act stipulates the purpose of this law as follows.  
_Article 1_ (_Purpose_)  
_The purpose of this Act is to facilitate the efficient realization of electronic government, enhance productivity, transparency and democracy in the public administration, and improve the quality of life of citizens by providing for fundamental principles, procedures, methods of promotion, and other relevant matters for the electronic processing of administrative affairs._  
In this way, the E-Government Act did not merely aim to reduce the cost of government operation (enhancing the internal productivity of the government) or to make it easier for the public to receive better government services at a lower cost. These were only the basics and the act had a more fundamental implementation purpose, which was the implementation of e-government to make it easier for the public to look at what is happening in the government in a convenient way (to improve transparency), to display the will of the people more conveniently and more effectively to the government, and to promote the power of the people as a sovereign to the government (to enhance democracy). Most importantly, the purpose of implementing e-government was ultimately to improve the quality of life of the people and promote national welfare in a knowledge- and information-based society. We have discussed the three aspects of e-government implementation mentioned in the act in more detail below.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 3 Results
### Analytical Modelling  
The maximum airflow pressures changed over time while the cross-section was approximately constant for both devices. The spatial and temporal distribution of the airflow pressures could be described by a Gaussian function, Equation (1). The curves fitted to the airflow pressure of both the CorvisST and ORA did not follow a normal distribution (\(p<0.050\)). Comparing the two main meridians of the spatial distribution (t = t[p\({}_{\rm max}\)]) across the horizontal p(x) and vertical p(y) meridians for both devices show minor differences relative to the average pressure at the respective measurement location:  
* CorvisST \(\Delta\)p((p\({}_{\rm x}-\)p\({}_{\rm y}\))/average(p\({}_{\rm x}\), p\({}_{\rm y}\))) = (0.6 \(\pm\) 0.45)%;
* ORA \(\Delta\)p((p\({}_{\rm x}-\)p\({}_{\rm y}\))/average(p\({}_{\rm x}\), p\({}_{\rm y}\))) = (-0.4 \(\pm\) 1.19)%.  
Thus, for analytical modelling purposes, both airflow pressure profiles are assumed to be symmetrical in the 3D space over time.  
\[\rm{P_{airflow}(t,a)}=p_{\rm max}(t)\cdot e^{-2\cdot\frac{(a-2p_{\rm max})}{ w_{\rm 2}^{2}}} \tag{1}\]  
Figure 9: Spatial distribution of the ORA maximal airflow pressure (n = 10): (a) 3D map of the spatial distribution. The bottom contour map shows the CoV of ten consecutive measurements at the respective points. Low CoV is denoted by the blue colour, high CoV denoted by red. (b) The relative pressure values were divided into deciles. The maximum pressure (90 to 100%) was detected centrally in an area of 1 mm\({}^{2}\). (c) The average spatial distribution profile highlights the distinct levels of pressures affecting the analysed area.  
\(\text{Pairflow}\) = Airflow pressure (mmHg)  
t = Time (ms)  
a = Spatial dimension (mm)  
P\({}_{\text{max}}\) = Maximum airflow pressure (mmHg)  
w = Width (unit of a)