esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Parameter Definition  
The aerodynamic drag contributes to energy consumption and has a minor impact on train safety. As shown in Figure 4, the main focus of this research was on the changing characteristics of lateral force (\(F_{y}\)), lift force (\(F_{z}\)), rolling moment (\(M_{x}\)), pitching moment (\(M_{y}\)), and yawing moment (\(M_{z}\)). All the time-varying values were monitored and saved when each time step ends. As shown in Figure 4, the origin of the moment coordinate system of each car was located at the center of gravity of the car, and the local coordinate system was also assigned the same motion as the train.  
The pressure distribution on the train surface was also employed to give a comparison of the flow pattern. For a more systematic comparison with existing works, these aerodynamic forces and moments were given as a dimensionless form below:  
\[c_{s}=\frac{F_{y}}{\frac{1}{2}\rho V^{2}S}, \tag{1}\]  
\[c_{l}=\frac{F_{z}}{\frac{1}{2}\rho V^{2}S}, \tag{2}\]  
\[c_{M_{x}}=\frac{M_{x}}{\frac{1}{2}\rho V^{2}SH}, \tag{3}\]  
\[c_{M_{y}}=\frac{M_{y}}{\frac{1}{2}\rho V^{2}SH}, \tag{4}\]  
Figure 4: The Parameter definition and the origin of the moment coordinate system.  
Figure 3: Computational mesh employed in the simulations: (**a**) front view of mesh density in the computational domain; (**b**) side view of mesh density in the computational domain; (**c**) mesh distribution on the surface of the nose; (**d**) the prism layer cells attaching to the train surface.  
\[c_{M_{z}} =\frac{M_{z}}{\frac{1}{2}\rho V^{2}SH}, \tag{5}\] \[c_{p} =\frac{P-P_{0}}{\frac{1}{2}\rho V^{2}}, \tag{6}\]  
where \(F_{y}\), \(F_{z}\), \(M_{x}\), \(M_{y}\), \(M_{z}\), and \(c_{p}\) are the side force, lift force, rolling moment, pitching moment, yawing moment, and pressure aerodynamic coefficients, respectively; \(S\) is the train's cross-sectional area; \(V\) is the resultant velocity in the domain; \(P_{0}\) is the ambient atmospheric pressure; \(P\) is the train surface pressure, and \(H\) is the height of the train.
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 5 Protocol and Interfaces
### Control Plane Interfaces
#### 5.2.2 Tier 1 Interfaces  
The Tier 1 BGP service sends BGP messages to other network controllers, while the control agent listens for service request for Transport Link maintenance (create, delete, get, modify). Additionally, the control plane application and KMS server reads the announced BGP messages, which means that the Tier 1 agents have three interfaces.  
* A BGP speaker service running on the network controller. The BGP messages consist of two new address families. The new address families are reflected by the announcement of the network controllers and the announcement of the Transport Links (Figure 17). The address families are defined as Network Controller routes (NCR) and Transport Link routes (TR). These BGP messages are distributed globally.
* A configuration interface to inject new Tier 1 routes. The Tier 1 control agent receives a \"create Transport Link\" message from the controller application, and it injects a Transport Link route into BGP.
* A Get-Capability interface, which transports the BGP table to a YAML format that consists of all Transport Links.  
Because of the tiered architecture of BGP announcements, the Tier 2 and the Tier 3 routes are automatically withdrawn if the Tier 1 Transport Link goes down. Hence, no further distribution of error handling messages is needed from the Tier 1 control agent.  
Figure 16: A Virtual Network Function provisioning message.
## 2 Materials and Methods
### Pull Stem Roll Design  
#### 2.4.1 Stem-Pulling Roller Diameter  
The size of stem-pulling roller directly affects the efficiency of stalk grabbing. In order to achieve effective grasping of corn stalks, it is necessary to ensure that the stem-pulling roller does not have too large grasping clearance to grab the stalk.  
In order to ensure that the stem-pulling roller grabs the corn stalk without grabbing the corn ear, its diameter should meet the following requirements:  
\[\frac{d_{j}-h}{1-\frac{1}{\sqrt{1+\mu_{j}^{2}}}}\leq D\leq\frac{d_{g}-h}{1- \frac{1}{\sqrt{1+\mu_{g}^{2}}}} \tag{7}\]  
In the formula, \(D\) is the diameter of stem-pulling roller, mm; \(d_{j}\) is the diameter of corn stalk, mm; \(d_{g}\) is the diameter of the large end of the ear, mm; \(h\) is the gap between two stem-pulling rollers, mm; \(\mu_{j}\) is the grabbing coefficient of the stem-pulling roller to the corn stalk; \(\mu_{j}\) is the grabbing coefficient of the stem-pulling roller to the corn ear. \(\mu_{j}=\mu_{g}=0.7\)-1.1.  
With the relevant data, we can obtain the following:  
\[(d_{j}-h)\leq D\leq(3\sim 3.5)(d_{g}-h) \tag{8}\]  
This is calculated as 89 mm \(\leq D\leq\) 98 mm  
Combined with the actual situation, it should be ensured that the stem-pulling roller can better grasp the stem without grasping the ear. In order to achieve the best drawing efficiency and the spatial arrangement of the header platform, the diameter of the stem-pulling roller is determined to be 90 mm.
# Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
## 2 Materials and Methods
### Materials
#### 2.2.3 Discharge Data  
The discharge observations from six gauges along the main stream of the Mekong River provided by the MRC were used for the calibration of the SWAT model and the evaluation of the precipitation products--i.e., Chiang Saen, Luang Prabang, Nong Khai, Mukhdan, Pakse, and Stung Treng. The catchment area and location are given in Table 2 and Figure 1.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 3 Dynamic Model of the Transmission System  
### Dynamic Model  
Taking the example of the differential PGT, the dynamic model, illustrated in Figure 3, is established taking into account parameter excitations, such as TMS, dynamic transmission errors, and support conditions, during the gear meshing process. The fixed-axes PGT follows a similar model, with only different subscripts. Each component in this model has four degrees of freedom. The variables \(xj\), \(yj\), and \(zj\) (\(j=a\), \(m\), \(dL\), \(dR\), \(m1\), \(\ldots\), \(mN\), \(s\), \(h\), \(rL\), \(rR\), \(p1\), \(\ldots\), \(pN\)) represent the translational displacements of the sun gear, carrier, ring gear, and each planet gear in the \(x\), \(y\), and \(z\) directions, respectively. The component torsional displacements around the z-axis are represented by \(\theta_{zj}\). The symbols \(k\), \(c\), and \(e\) in the figure stand for mesh stiffness, mesh damping, and transmission errors, respectively, with distinct components denoted by subscripts. The left and right gears are represented, respectively, by the subscripts \(L\) and \(R\). Bearing stiffness is represented by subscripts with \(b\). As an example, \(k_{\text{loss}}\) represents the support stiffness of the sun gear, \(s\), in the \(x\)-direction. Similarly, \(k_{\text{spill}}\) represents the mesh stiffness of the left-side gear pair between the sun gear, \(s\), and the planet gear, \(p\). Likewise, the remaining symbols utilized in the model adhere to the identical notation.  
Figure 1: Diagrammatic representation and model of the coaxial counter-rotating planetary transmission system.  
Figure 3: Dynamic model of the system.  
Figure 2: Diagram of the coaxial counter-rotating planetary transmission system represented by the Wolf–Arnaudov symbol.
## 7 Conclusions  
In this work, we mainly demonstrated the feasibility of orbit determination and gravity-field recovery using only inter-satellite range data in three steps. First, sub-problem 1, in which the gravity field is known a priori and the orbits of the chief and the deputy are autonomously determined, is studied. Second, sub-problem 2, in which the orbits of the chief and the deputy are known a priori and the gravity field is recovered, is studied. Then, the complete-inverse problem in which both the orbits of the chief and the deputy and the gravity field are determined is studied. Our study shows that the complete-inverse problem is invertible, but the convergence region is very narrow, i.e., an initial estimate close to the real one is necessary. Two improved algorithms are proposed to solve this problem: using multiple links by adding more deputies and adding the angle measurement data between the chief and deputies. By our calculation, both algorithms can increase the convergence region and improve the accuracy. Our studies find that the accuracy of the zonal \(C_{20}\) term is worse than the accuracy of the tesseral \(C_{22}\) term. In addition, we also analyzed the influence of different orbit types on the inter-satellite range data and the influence of different arc lengths on the orbit determination.  
We need to stress that this work is only a feasibility study. Our conclusions are based on some assumptions. For asteroids of hundreds of meters in size or smaller, it is possible that SRP is one order of magnitude larger than non-spherical gravity which may seriously challenge the assumptions in the current study. The study indicates that it is possible to solve the complete inverse problem using only the inter-satellite range data, but a good initial estimation is necessary and the accuracy of the results is limited. A more realistic mission scenario is to combine the accurate inter-satellite range data along with ground observation data to do the complete inverse problem. We are currently working on this project.  
Writing--original draft, H.L.; Writing--review & editing, X.H. All authors have read and agreed to the published version of the manuscript.  
This work is supported by national Natural Science Foundation of China (No. 12233003) and Space Debris and Near-Earth Asteroid Defense Research Project (KJSP2020020205) of China.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Conclusions
\begin{tabular}{l l}
**Nomenclature** & **Description** \\ \((X,Y,Z)\) & The global coordinate system \\ \((x,y,z)\) & The local coordinate system \\ \((\xi,\eta,\xi)\) & The natural coordinate system \\  & Element nodal displacement variables in the global \\ \(\mathbf{u}_{G}\) & coordinate system \\ \(\mathbf{u}_{L}\) & Element nodal displacement variables in the local coordinate system \\  & Translational displacement of element node i in the global \\ \(\mathbf{d}_{i}\) & coordinate system \\  & Translational displacement of element node i in the local \\ \(\mathbf{t}_{i}\) & coordinate system \\  & Vectorial rotational variables of node i in the global \\ \(\mathbf{n}_{gi}\) & coordinate system \\ \(\mathbf{\phi}_{i}\) & Vectorial rotational variables of node i in the local coordinate system \\ \(\mathbf{p}_{i}\) & Nodal normal vector of node \(i\) at the mid-surface \\ \(U_{i},V_{i},W_{i}\) & Three components of the translational displacement of node i along \\  & the global coordinate system \\ \(u_{i},v_{i},w_{i}\) & Three components of the translational displacement of node i along \\  & the local coordinate system \\  & The two minor components of the mid-surface normal vector along \\ \(p_{i,n},p_{i,m}\) & the three axes of the global coordinate system \\ \(r_{ix},r_{iy}\) & The components of the mid-surface normal vector along the \(x\)-axis \\ \(x_{i},y_{i},z_{i}\) & Local coordinates of node i in the deformed configuration \\ \(r_{ix0},r_{iy0},r_{iz0}\) & The three components of the mid-surface normal vector of node i along the local coordinate axes in the initial configuration \\  & Unit vectors of the three coordinate axes of the local coordinate \\ \(\mathbf{e}_{x0},\mathbf{e}_{y0},\mathbf{e}_{z0}\) & system in the initial configuration \\  & Vectors of diagonals 13 and 24 of the quadrilateral in \\ \(\mathbf{e}_{130},\mathbf{e}_{240}\) & the initial configuration \\ \(\mathbf{X}_{i0}\) & Vector from the origin O of the global coordinate system to the corner \\  & node of the quadrilateral \\ \(\mathbf{e}_{x},\mathbf{e}_{y},\mathbf{e}_{z}\) & Unit direction vectors of the three coordinate axes of the local \\ \(\mathbf{e}_{13},\mathbf{e}_{24}\) & coordinate system in the deformed configuration \\ \(N_{i}(\xi,\eta)\) & Vectors of diagonals 13 and 24 of the quadrilateral in \\ \((\xi_{i},\eta_{i})\) & the Lagrangian interpolation functions of node \(i\) \\ \(\mathbf{\overline{p}}_{i0}\) & Coordinate values of node i mapped to the natural coordinate system \\ \(\mathbf{\xi},\ \mathbf{e}_{m},\ \mathbf{\gamma},\ z_{i}\mathbf{X}\) & \\ \(\Pi\) & Potential energy functional \\ \(\mathbf{D}_{1},\mathbf{D}_{2}\) & The elastic-moduli matrices \\ \end{tabular}
## 4 Discussion
### The Stress Comparison between the Linear and Non-Linear Thermal Buckling Response  
In order to better obtain the weak part of the blade under thermal shock load, the first-order linear and nonlinear thermal buckling competing and axial stress distribution are compared, as shown in the Figure 15.  
Figure 14: The radial distribution buckling deformation along the blade. (**a**) The radial distribution along the blade; (**b**) the axial distribution along the blade.  
From Figure 15, the radial distribution of linear thermal buckling stress of the blade is corresponding to the distribution of thermal buckling deformation of Figure 14. The stress at 15-30 mm away from the blade root is in the shape of a peak, with a relatively concentrated distribution. The maximum stress at 20 mm from the blade root is 83.81 MPa. In the range of about 20 mm at the blade tip, the stress decreases slightly to 32.31 MPa at the blade tip, and the stress is small relative to the blade root. The nonlinear buckling stress of the blade is approximately symmetrically distributed along the radial direction, and the maximum stress at the blade root and tip is 72.59 MPa, which is 13.39% lower than the maximum stress of linear buckling.  
From the comparison of axial stress distribution, the linear buckling stress mainly occurs in the range of 5-15 mm from the blade edge along the axial direction, and is concentrated on one side of the blade. The nonlinear buckling stress distribution is still symmetrical, and reaches the maximum at both blade tips. In other words, the nonlinear buckling results show that the blade tips on both sides bear large stress and are prone to failure.  
Meanwhile, the buckling instability of the blade calculated in this paper has the same distribution characteristics as the thermal fracture of the existing thin-walled blade. It is noteworthy that the blade tip is the most vulnerable part to generate buckling instability in both linear and nonlinear analysis. Therefore, more attention should be paid to the structural design and verification of the blade.
## 5 Discussion and Future Works  
As for future work, we believe that our study highlights some directions in this area. On one hand, for fully autonomous vehicles, the reliable and secure perception, functioning, and operation are the top requirement and concern for the CAV system. On the other hand, with the evolvement of vehicular connectivity and the occurrence of IoV (Internet of Vehicles), cloud can be integrated in vehicle connectivity and fully exploited to defend against cyber-attacks under the CAV environment [111; 112].
# Digital Twin for Supply Chain Coordination in Modular Construction
## 4 Case Study
### Project Description  
The case project was a 6-story apartment modular construction project that was erected over 8 calendar days with 80 modules [47]. The modules were manufactured and delivered from the factory to the construction site located near downtown Seattle area (Washington state, USA) by truck in 90% completed condition. The distance between the factory and the site was 156 miles. Trucks did not go through an intermediate warehouse or separate retailer. The Department of Transportation required pilot vehicles, which escort trucks with oversized loads, along its journey and requested limited speed. It took 80 trips to transport 80 modules and single-trip permits were required every time a module left the factory. Delivery trucks with modules were 14 feet wide, 50 feet long, 13 feet 6 inches high, and weighed about 20 tons. All of this information was stored in the building information model. The shortest delivery routes were searched for based on a vehicle routing application. Once a module was delivered on-site, it was assembled by a mobile crane and plumbing, electrical, HVAC, and mechanical systems were completed by the assembly crews. Since the site was in an urban area without enough stockyard space, the modules had to be delivered 'just-in-time' via consideration of previous history data on assembly and delivery times. Moreover, in the urban area, there were various logistics risks that imposed constraints on the routes heavy trucks could take, so it was a good project to achieve the purpose of the case study.
## 4 Performance Evaluation
### Impact of MEC Server's Computing Capability  
Figure 6 investigates the impact of the MEC server's computational capability on the user access probability and the delay violation probability of the accessed users under the proposed multiuser access control algorithm. As observed from the figure, the delay violation probability for the accessed users significantly decreases as the computing capability of the MEC server increases. Moreover, if the MEC server's computing capability is weak, the number of accessed users in the system tends to saturate quickly when the number of requesting users, \(I\), is relatively small. For instance, when the MEC server's computing capability is 0.8 Tbps, the delay violation probability approaches the threshold as early as \(I\) equal 4.  
Figure 5: The user access probability and the delay violation probability of the accessed users under different video caching times.  
Figure 6: The user access probability and the delay violation probability of the accessed users under different MEC computing capabilities.
## 4 Working of Modified Multi-Dimensional Collaborative Wrapped BiLSTM in Inventory Prediction  
The augmented dataset forms the input for the modified multi-dimensional collaboratively wrapped BiLSTM in inventory prediction. The overall structure of the MMCW-BiLSTM model is made up of several sub-components that function together to analyze the input information and deliver accurate predictions of future inventory levels. First, the model takes input data, extracts their attributes, and then uses ARIMA to calculate the time series. These time series data provide various statistical features, such as rolling mean, expanding mean, exponential smoothing, and auto-correlation features. Ultimately, the MMCW-BiLSTM model augments these extracted features with dataset features. The architecture model consists of three BiLSTM layers, followed by two additional BiLSTM layers. The collaborative attention layer, the multi-dimensional attention layer, and the full attention layer all received their output from the previous two BiLSTM layers. Subsequently, a Taylor series transformation is then used in order to identify higher-order feature interactions in a more effective manner and improve the model's capacity to identify complex underlying patterns within the data set. Finally, the dense layers are used for further transformations and dimensionality reduction to make a final forecast for the inventory levels in the future. This systematic interaction of components helps the MMCW-BiLSTM model to learn from augmented datasets and generate accurate inventory forecasts.
## 5 Results and Discussion
### Comparison of Mass Flux in Various Flow Regimes  
The relationships between mass flux under various flow regimes and reservoir pressures are shown in Figure 6. For the slip flow regime, mass flux decreased and then increased with decreasing reservoir pressure, with a minimum mass flux occurring at a reservoir pressure of 10 MPa. This was due to the fact that the effective pore diameter size decreased when stress-sensitivity reduces mass flux in the slip flow regime (a \"negative effect\"). Conversely, effective pore diameter size increased when the desorption of adsorbed gas enhanced mass flux in the slip flow regime (a \"positive effect\"). At reservoir pressures of \(<\)10 MPa, the negative effect had a greater impact on the slip flow regime than the positive effect, while the reverse was the case at reservoir pressures of \(>\)10 MPa. Mass fluxes in the continuum regime, Knudsen diffusion and surface diffusion decreased with decreasing reservoir pressure. For the continuum regime, the effective stress increased with decreasing reservoir pressure, leading to changes in both matrix and hydraulic fracture permeability, which in turn lead to a reduction in mass flux. For surface diffusion, the concentration gradient on the pore walls decreased with decreasing reservoir pressure, resulting in a weakening of the driving force of surface diffusion [3].
## 4 Results
### Metal Contamination Based on SQG (TEL-PEL)  
Sediment concentrations of Cr, Ni, Cu, Zn, and Pb were evaluated against Threshold Effect Level (TEL) and Probable Effect Level (PEL) from [PERSON] et al. [53] (Table 7). Ni levels predominantly ranged from moderate to high-risk categories, as most samples exceeded the TEL (20.9 mg/kg) and some surpassed the PEL (36 mg/kg). Cu was found at concentrations  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Er\({}_{\text{Cr}}\)** & **Er\({}_{\text{Ni}}\)** & **Er\({}_{\text{Cu}}\)** & **Er\({}_{\text{Zn}}\)** & **Er\({}_{\text{Pb}}\)** & **RI** \\ \hline K1 & _2.46_ & _4.24_ & _9.41_ & _0.72_ & _0.72_ & _17.56_ \\ K2 & _2.48_ & _3.99_ & _6.96_ & _0.64_ & _1.00_ & _15.07_ \\ K3 & _2.61_ & _4.35_ & _9.93_ & _0.85_ & _1.07_ & _18.81_ \\ K4 & _3.72_ & _6.54_ & _38.57_ & _6.55_ & _1.90_ & _57.28_ \\ K5 & _3.30_ & _4.49_ & _25.89_ & _4.91_ & _1.46_ & _40.05_ \\ K6 & _2.43_ & _4.55_ & _28.21_ & _5.42_ & _1.33_ & _41.95_ \\ K7 & _2.48_ & _4.83_ & _28.21_ & _4.58_ & _1.25_ & _41.35_ \\ K8 & _25.07_ & _3.77_ & _4.55_ & _1.45_ & _1.96_ & _36.79_ \\ K9 & _2.30_ & _5.21_ & _23.93_ & _5.00_ & _1.17_ & _37.62_ \\ K10 & _2.35_ & _5.20_ & _23.57_ & _5.01_ & _1.20_ & _37.33_ \\ K11 & _4.33_ & _4.30_ & _5.77_ & _1.01_ & _1.53_ & _16.94_ \\ K12 & _2.72_ & _3.86_ & _6.95_ & _1.11_ & _1.23_ & _15.86_ \\ K13 & _3.59_ & _3.90_ & _6.55_ & _1.10_ & _1.39_ & _16.53_ \\ K14 & _4.04_ & _4.57_ & _8.62_ & _1.69_ & _2.17_ & _21.10_ \\ K15 & _5.35_ & _5.29_ & _10.09_ & _2.00_ & _2.10_ & _24.82_ \\ \hline \hline \end{tabular}
\end{table}
Table 6: Ecological Risk Factor (Er) and Risk Index (RI) of selected metals in stream sediments (italic values indicate low risk (Er \(<\) 40 or RI \(<\) 150); regular values indicate moderate risk (Er = 40–79 or RI = 150–299)).  
Figure 5: The bar chart shows the PLI for each sediment sample (the horizontal dashed line at PLI = 1 indicates the threshold between unpolluted (\(<\)1) and polluted (\(>\)1) conditions. All samples except K15 fall below this threshold, indicating low contamination levels.  
above both TEL (18.7 mg/kg) and PEL (34 mg/kg) in nearly all samples, with notably elevated values in samples K4 to K7. Zn concentrations exhibited a mixed pattern; while many samples remained below the TEL value (124 mg/kg), several samples, such as K4, K5, K6, K7, K9, and K10, exceeded this threshold, and some even approached or exceeded the PEL (271 mg/kg). Pb concentrations were generally low across all samples, remaining well below TEL (30.2 mg/kg) and PEL (112 mg/kg) values, indicating minimal Pb-related ecological concern in the studied sediments.