esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In conclusion, in our study we have shown how the size, the shape and the functionalization of pores in graphene membranes decisively affect the permeance of CO\({}_{2}\), H\({}_{2}\)S and CH\({}_{4}\) molecules. We found that circular pores of 5.90 A diameter can reject by steric hindrance CH\({}_{4}\) while ensuring the permeation of CO\({}_{2}\) and H\({}_{2}\)S. A membrane containing such pores can be effectively employed in the natural gas purification process. A pore can be selective also on the basis of the shape of the gas molecules. We proved that a pore matching the elongated size of CO\({}_{2}\) can filter out H\({}_{2}\)S. Consequently, a second membrane can be used to separate CO\({}_{2}\) from H\({}_{2}\)S, once all the CH\({}_{4}\) molecules have been removed. Finally, gas-pore interactions play a significant role. In particular, the hydroxyl functionalization may enhance the permeance of the membrane without sacrificing its selectivity as polar interactions drove both CO\({}_{2}\) and H\({}_{2}\)S toward the pore. In general, one must take into account all the highlighted factors in the design of a membrane process suitable for the species that one aims at separating. Based on the results of our simulations, we suggest that a natural gas separation process should initially consist of membranes with a relatively large pore size, such as pore P3. These would block all the large contaminants such as relatively long chain alkane, while allowing the passage of small molecules such as CH\({}_{4}\), CO\({}_{2}\) and H\({}_{2}\)S. Afterwards, the permeated gas would have to go through a second separation process with membranes containing P3 OH pores, which would retain all methane molecules, generating as permeate a mixture of CO\({}_{2}\) and H\({}_{2}\)S. The latter could then be further pushed through membranes having P2-like pores to effectively separate CO\({}_{2}\) from H\({}_{2}\)S. In summary, an appropriate multi-step separation process with nanoporos graphene membranes with different porosity has to be engineered to achieve high methane purity starting from natural gas.  
Conceptualization, F.R. and G.C.; Methodology, F.R. and G.C.; Investigation, G.T.; Writing--Original Draft Preparation, G.T.; Writing--Review & Editing, F.R. and G.C.; Funding Acquisition, G.C. This work is part of the \"DESAL\" project funded by Politecnico di Torino.  
We acknowledge the CINECA award under the ISCRA initiative and HPC@POLITO for the availability of high- performance computing resources and support. The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
Gpu Gas Permeation Units  
NEB Nudged Elastic Band
## 3 Case Analysis and Discussion
### Data Preprocessing  
#### 3.2.1 Data Extraction  
The NGSIM dataset contains a large amount of trajectory data. The parameters available are detection frame number, vehicle identification number, vehicle length, and the coordinate data of vehicles' front center. The list of raw data used in this study is shown in Table 1.
# Numerical Analysis of Tube Heat Exchanger with Trimmed Star-Shaped Fins
## 2 Materials and Methods
### Governing Equations  
The applied mathematical model consists of a set of differential equations and constitutive relations, together with boundary and initial conditions. Conservation law of mass:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho\,u_{j})}{\partial x_{j}}=0 \tag{1}\]  
Conservation low of momentum:  
\[\frac{\partial(\rho\,u_{j})}{\partial t}+\frac{\partial(\rho\,u_{j}\,u_{i})}{ \partial x_{j}}=\rho\,f_{i}+\frac{\partial\sigma_{ji}}{\partial x_{j}} \tag{2}\]  
Conservation low of energy:  
\[\frac{\partial(\rho\,e)}{\partial t}+\frac{\partial(\rho\,e\,u_{j})}{\partial x _{j}}=-\rho\,f_{i}\,u_{i}+\frac{\partial(\sigma_{ji}\,u_{i})}{\partial x_{j}}- \frac{\partial q_{j}}{\partial x_{j}} \tag{3}\]  
The finite volume method is commonly used to solve a defined mathematical model. The computational domain was meshed using ANSYS Meshing software, version 17.2. A hybrid mesh approach was used. This means that the larger part of the volume is meshed with a structured mesh, while the remaining volume is meshed with an unstructured mesh. Eight inflation layers were defined in the area of convective heat transfer from tubes and fins to the air. Figure 3 shows a 3D view of the reference geometry (a) and detail of the mesh (b).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & _Variable_ & **Unit** & **Value** \\ \hline Material & - & - & stainless steel \\ \hline  & \(d_{0}\) & mm & 20 \\  & - & - & staggered \\ Tube data & \(s_{t}\) & mm & 50 \\  & \(s_{l}\) & mm & 40 \\  & \(N_{1}\) & - & 5 \\ \hline Fin data & \(t_{f}\) & mm & 0.5 \\  & \(s_{f}\) & mm & 4.5 \\ \hline Trimming diameter & - & mm & 28, 31 and 34 \\ \hline \end{tabular}
\end{table}
Table 1: Data of the tube and the star-shaped fin.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Boundary Condition** & _Variable_ & **Unit** & **Value** \\ \hline Temperature of air at the inlet & \(T_{\text{in}}\) & K & 288 \\ \hline Air velocity at the inlet & \(u_{\text{in}}\) & m/s & 1, 2, 3, 5 and 7 \\ \hline Temperature of the tube internal wall & \(T_{\text{w}}\) & K & 353 \\ \hline Gauge air pressure at the outlet of the heat exchanger & \(p_{\text{out}}\) & Pa & 0 \\ \hline Condition at the air-side tube wall & & & Hydraulically smooth wall \\ \hline \end{tabular}
\end{table}
Table 2: Boundary conditions.  
Mesh independence analysis was performed, focused mainly on \(Nu\) and \(Eu\). The results are shown in Figures 4 and 5.  
For this analysis, we chose a 14.5 million volume mesh and the k-\(\omega\) SST turbulence model described in detail in [17].
## 5 Experiments
### Sensitivity Analysis
#### 5.3.1 Sensitivity Analysis on the Freight Rate Ratio
Consequently, enhancing Obj 1 requires selectively shedding these less profitable services, leading to a pronounced trade-off. However, as \(\alpha\) rises, the revenue per TEU increases, transforming previously unprofitable  
Figure 6: The Pareto frontiers with different freight rate ratios. transport demands into financially viable options. This improved baseline profitability means that operators can now pursue higher profit levels with smaller sacrifices in total transported volume or expand transport volume with less detriment to their profit. The frontier, therefore, not only moves to a more favorable position but its slope, representing the rate of substitution between the two objectives, becomes less steep, indicating that higher freight rates grant operators greater flexibility to enhance profitability with smaller volume compromises. To compare computational efficiency, Table 11 presents the required solution time across different \(\alpha\) for two key comparisons: the MIP model against the PRMIP model, and the augmented \(\epsilon\)-constraint method against the basic \(\epsilon\)-constraint method. Table 11 details the computational performance of the proposed models. Using the augmented \(\epsilon\)-constraint method, the average CPU time for the full MIP model is 9.2 s, whereas the PRMIP model, with its relaxed ship chartering variables, significantly reduces this computation time to an average of just 3.7 s. This efficiency is benchmarked against the standard basic \(\epsilon\)-constraint method, which required substantially more time on average: 155.4 s for the MIP and 58.6 s for the PRMIP on average. Consequently, the average solution time of the augmented method is merely 5.9% of the basic method's time for the MIP model, and 6.3% for the PRMIP model. This highlights a critical gain in computational performance by selecting the more advanced solution technique. In summary, these results confirm that the PRMIP model is significantly more computationally efficient than the MIP model without surrendering solution quality, which aligns with Theorem 3. Furthermore, the dramatic performance gain from using the augmented \(\epsilon\)-constraint method validates its suitability for solving large-scale, practical problems efficiently. Computational experiments conducted with real-world-inspired scenarios highlight the practical effectiveness of the proposed PRMIP model compared to the MIP model, as well as our augmented \(\epsilon\)-constraint method compared to the basic \(\epsilon\)-constraint method.
## 3 Models for Gerotor Pumps
### Validation of Gerotor Pump Template  
In order to validate the gerotor pump template in Simcenter Amesim, various pumps were tested on the test bench at different operating points by measuring their volumetric and mechanical performance and comparing this data with the simulations. The following Figure 7 shows an excerpt from the validation runs. A gerotor pump was measured and simulated at temperatures ranging from 30 \({}^{\circ}\)C to 110 \({}^{\circ}\)C oil temperature, and system pressures of 5 bar and in a speed range of 1000-5000 rpm.  
The top row of plots in Figure 7 shows both the measured and the calculated volumetric flow rates of the oil pump. The influence of the increasing temperature and therefore decreasing viscosity of the fluid, as well as the varying pump gap sizes on the maximum achievable volumetric flow rate of the gerotor pump with the same boundary conditions can be clearly seen.  
The bottom row of plots in Figure 7 shows the overall efficiency (\(\eta_{t}\)) of the pump. The overall efficiency can be calculated from the ratio of hydraulic to mechanical power. The corresponding correlation is shown in Equation (6).  
There \(q_{v_{s}}\) refers to the leakage flow, \(\Delta p\) to the operating pressure difference, \(n\) to the pump speed, \(T_{s}\) to the torque losses and \(P_{m}\) to the drive power [40].  
\[\eta_{t}=\frac{q_{v_{s}}\Delta p+2\pi nT_{s}}{P_{m}} \tag{6}\]  
In contrast to the very accurate flow rate prediction and the precise prediction of the overall efficiency of the pump at higher oil temperatures, a significant deviation at 30 \({}^{\circ}\)C is noticeable. At low speeds, the efficiency prediction still matches accurately, but as the speed increases, the simulated efficiency deviates increasingly from the real measured overall pump efficiency. A more detailed analysis showed that the loss torques (\(T_{s}\)) from Equation (6) from the simulation were too high. The speed-dependent deviation can be seen in detail in Figure 8.  
Due to the accurate correlation between the measured data and the simulation data in terms of flow rate and overall efficiency of the pump, it is possible to formulate the  
Figure 8: Deviation between measurement and simulation at 30 \({}^{\circ}\)C in the efficiency prediction.  
Figure 7: Validation of the friction template at different operating points for volumetric flow and overall efficiency prediction.  
hypothesis that an unknown physical phenomenon occurs at low temperatures, which has not been sufficiently considered or not considered at all. Consequently, Section 3.3 deals exclusively with the phenomenon at lower temperatures down to \(-40\)\({}^{\circ}\)C and a novel approach of modeling the viscous friction heating.
# Region Pixel Voting Network (RPVNet) for 6D Pose Estimation from Monocular Image
## 5 Conclusions  
To accurately estimate target object's 6D-pose from a single image, a novel two-stage deep learning based method, RPVNet, is proposed. Instead of using the whole features from backbone, RPVNet uses local features to predict the object's direction maps, which is our main contribution. After aligning the direction maps with original image, pixel voting is conducted through RANSAC to generate keypoints. Finally, the 6D-pose is recovered by solving PnP. In the experiment, RPVNet achieved 86.1% and 43.7% accuracy on LINEMOD and Occlusion LINEMOD, respectively. Compared with the state-of-the-art (86.4% and 40.8%, respectively), RPVNet has a estimation accuracy close to the top level and is more robust in the occluded scenes. In addition, RPVNet has higher accuracy when estimating small-sized objects in the occluded scenes. As part of future work, we plan to study pose estimation method for scenes with a severe lack of object pixels. The possibility of restoring object pixels using adversarial generation methods is being considered.  
Funding acquisition, C.L. and Q.C.; Methodology, F.X.; Project administration, Q.C.; Resources, Q.C.; Software, F.X.; Supervision, C.L. and Q.C.; Validation, F.X.; Visualization, F.X.; and Writing--original draft, F.X. All authors have read and agreed to the published version of the manuscript.  
Funding: This research was funded by the National Natural Science Foundation of China (61673300, U1713211) and the Science and technology development fund of Pudong New Area (PKX2019-R18). Conflicts of Interest: The authors declare no conflict of interest.
# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 3 Proposed Methodology
### Text Aware Non-Maximal Suppression
The images for the resizing operation are selected with a probability of 0.2. * Next, the images are randomly rotated by an angle within [\(-45^{\circ}\), \(45^{\circ}\)]. * Additionally, some images are sampled for random flip and crop within the scale of [0.5, 1]. The cropped image segments are resized to \(224\times 224\). The probability of 0.2 is used in the sampling step. * Finally, the augmented examples having text instances smaller than half of the smallest text instances in the original dataset are filtered out. **Computing infrastructure:** All simulations discussed in this paper were performed on NVIDIA Quadro P5000 GPU workstations with 32 GB RAM. ### Baseline Evaluation

The baseline evaluation of the **MobileTDNet** architecture is performed without text attention blocks. The results are presented in Table 2. The traditional evaluation metrics of precision, recall, and F-score are used for bench marking. For the _MSRA-TD500_ dataset, the evaluation protocol proposed in [69] is followed. The evaluation on _ICDAR_ challenge datasets was performed in the manner specified in the challenge specification. Our sub-optimal implementation **MobileTDNet** took an average of 0.266 s to process the given input image (after required resizing). The analysis includes the computation time for the non-maximal suppression procedure. Figure 3 shows the detection results for some difficult cases, which illustrate the detection challenges because of all variations in style, scale, and script. As observed, **MobileTDNet** efficiently captures text attributes in input images at all scales and in varying styles. It is remarkable that the **MobileTDNet** scene text detector, without an attention mechanism, performs comparable to many prominent scene text detectors (presented later in Table 3). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Precision** & **Recall** & **F-Score** \\ \hline _ICDAR2013_ & 0.930 & 0.845 & 0.885 \\ \hline _ICDAR2015_ & 0.882 & 0.826 & 0.853 \\ \hline _MSRA-TD500_ & 0.820 & 0.784 & 0.801 \\ \hline _COCOText_ & 0.622 & 0.592 & 0.606 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: **MobileTDNet** baseline detection results.
## 3 Results and Discussion
### Frost Resistance  
The results of the HPC internal frost resistance after 56 days of curing are presented in Table 8. It can be stated that all the designed non-air-entrained concretes met the requirements of maximum average mass loss (5%) and the maximum reduction of compressive strength (20%) after 150 cycles of freezing and thawing. No cracks were found on the surfaces of the samples. The C55_SFA15_SF3 concrete with quaternary binder mix combination showed the best performance with the lowest value of mass loss and fully maintained compressive strength after testing. The reason for the high observed internal frost resistance in the designed low-clinker HPC was considered to be the formation of a dense binder (cement-GGBFS-FA-SF) matrix with low porosity and high homogeneity.
# A Single RF Emitter-Based Indoor Navigation Method for Autonomous Service Robots
## 6 Mapping and Path Planning  
### Simultaneous Localisation and Mapping (SLAM)  
In order to enable long-term decisions and allow for smarter path planning with obstacles, we use a SLAM module to map the environment by processing the odometry data and laser range finding provided by the Pioneer robot. By comparing the distance to detected objects at multiple locations using the odometry information to approximate the distance between scan locations, obstacles can be found and tracked in the environment.  
Simultaneous Localisation and Mapping (SLAM) has been an active research area for many years, with significant progress made towards universally applicable solutions [3]. Since our focus is on ascertaining the direction of the goal destination based on a single emitter and not on localisation or mapping of the robot itself, we used a third party tool, GMapping, for the SLAM module. More information about the approach used in GMapping can be found in [39].  
Gmapping produces an array that discretises the world into grid cells. Each grid cell is then filled with 100, 0, or \(-1\) representing occupied, empty, and unknown respectively. These are populated within Gmapping based on information gained from sensor data. In our implementation, each of these grid cells represents 400 cm\({}^{2}\) of real-world space (20 cm on each side). A visualisation of the occupancy grid is shown below in Figure 10.  
The grid cell size is selected based upon a trade-off of having more information to navigate with against a higher computational load. Since each grid cell can only be empty or occupied, if the grid size is too large, a valid path may not be recognised, depending on the alignment of the grid. An example of this is shown in Figure 11, where the left image shows a situation where the robot, represented by the arrow, will determine that there is a valid path between the two obstacles. The right image, however, will register all of the grid cells in the row above the robot as occupied, and the robot will not believe there is a valid path. Therefore, it was necessary for the size of each cell to be slightly smaller than what the robot can drive through.  
This can be resolved by decreasing the size of the grid cell so that it was smaller than the width of the robot (40 cm in diameter). This reduces the number of false negatives where the robot does not  
Figure 11: Example of how a valid path can be interpreted as both valid and invalid.  
Figure 10: An example occupancy grid output visualisation.  
recognise a valid path, but introduces false positives where the robot will recognise empty space that it cannot actually fit through. This is accounted for separately by the path planning module with local obstacle avoidance.
## 2 Biosensor Geometry and Theory
### Governing Equations
The binding reaction at the sensitive surface is symbolized according to:  
\[C+\theta\rightleftharpoons C_{s}\]  
where \(\theta\) and \(C_{s}\) are the surface concentration (mol/m\({}^{2}\)) of the active sites and the antigen-antibody complexes, respectively. The rate constants related to adsorption and desorption are \(k_{1}\) and \(k_{2}\), respectively. The rate equation governing the time evolution of antigen-antibody complexes \(C_{s}\) at the sensor surface is given by the following expression:  
\[\frac{\partial C_{s}}{\partial t}=k_{1}C(\theta_{0}-C_{s})-k_{2}C_{s} \tag{4}\]  
where \(\theta_{0}\) denotes the total number of active sites accessible on the sensor surface. In order to solve the above governing equations (Equations (1)-(4)), we used the boundary conditions presented in Table 3.  
In the above conditions (Table 3), different meanings may be assigned to the word insulation owing to the considered equation. In the matter of the boundary conditions related to the energy equation, this word means thermal insulation, that is, the conduction heat flux through the considered surface is equal to zero. Concerning the Poisson equation, electrical insulation is used at all the boundaries except the electrodes. In other words, at these boundaries, the electrical field is equal to zero to insure its continuity. Concerning the analyte transport equation, the word impermeable means that there is no diffusion through the walls (except the reaction surface). The numerical method used to discretize the above equations and perform the simulations is the finite element method. Table 4 illustrates the values of the physicochemical parameters used for this numerical study. These values are taken from the literature [12; 23; 25]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Electric Field** & **Temperature** & **Velocity** & **Analyte** \\ \hline Inlet & Insulation & \(\overrightarrow{n}\cdot(k\
abla T)=q_{01}\) & Parabolic profile & \(C=C_{0}\) \\ Outlet & Insulation & \(\overrightarrow{n}\cdot(k\
abla T)=q_{02}\) & No viscous stress & \(\overrightarrow{n}\cdot(D\
abla C)=0\) \\ Walls (except reaction surface and electrodes) & Insulation & Insulation & No-slip & Impermeable \\ Reaction surface & Insulation & Insulation & No-slip & \(D\
abla C=\frac{\partial C}{\partial t}\) \\ Electrodes & \(\pm V_{\mathit{rsm}}\) & \(T=T_{0}\) & No-slip & Impermeable \\ \hline \hline \end{tabular} \(\overrightarrow{n}\) is the unit normal vector to the surface, \(q_{01}=k(T_{in}-T_{0})\) and \(q_{02}=k(T_{out}-T_{0})\) are the convective heat fluxes at the inlet and outlet. Initially, the concentrations of analyte \(C\) and complex at the \(C_{s}\) reaction surface were set to zero.  
\end{table} 
Table 3: Boundary conditions used in this simulation.