esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
The results of this experiment demonstrate that vapour-smoothed (VS) cold embossing is a viable manufacturing method for imprinting micro-scale details, such as Fresnel lens designs, onto 3D-printed ABS components. The replication of these details was achieved with a high degree of accuracy, where \(\mathrm{E_{t}}\) and \(\mathrm{E_{v}}\) parameters played a crucial role in ensuring fine-detail reproduction. A successful VS process was developed to enable cold embossing, and a set of \(\mathrm{E_{t}}\) values was established for optimal embossing. Shorter \(\mathrm{E_{t}}\) times hindered polymer flow, while excessively long times reduced replication quality.  
These findings confirm the initial hypothesis that cold embossing can be effectively achieved through this method, providing a viable alternative to HE, which requires significantly higher temperatures and processing times. This alternative approach offers potential benefits, including lower manufacturing costs and reduced energy consumption.  
Figure 11: Experiment 3 replication images for the 1 st and 48 th tests.  
Figure 12: Experiment 1 replication image using SEM.  
As a novel technique, this research could serve as a foundation for further studies on its feasibility in industrial applications.  
However, several limitations were identified. Firstly, the VS method employed in this experiment was adapted using repurposed equipment, introducing variability in the process that could affect results. The GU measurements in Figure 6 show a range of values obtained during testing, suggesting that a more tightly controlled process could improve accuracy and consistency. Additionally, when transferring VS parts from the vapour chamber to the embossing setup, timings were not measured, introducing the possibility that the ABS polymer began solidifying before embossing. Furthermore, the displacement of polymer during embossing was not measured, which could lead to inaccuracies in smaller components due to material overflow.  
Future work should investigate these factors further, including refining the VS process for greater consistency and assessing whether polymer overflow differs between cold and HE. Additionally, integrating robotic automation could enhance accuracy by ensuring a consistent transition time between VS and embossing, improving reproducibility and precision.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Effect of Error Sources
#### 4.2.2 Satellite Position Error  
The error of the satellite position computed based on TLE-SPG4 can be as high as 3 km, mostly in the tangent direction of the orbit. We study the influence of the satellite position error on the user position and attitude determination. This error is added on top of the baseline scenario that contains thermal noise. For this reason, each satellite position coordinate has a constant error added to it. Its value is randomly selected using a Gaussian distribution with a standard deviation given in Table 4. This table defines an intermediate case in which we used a typical value of the satellite position error, and a worst case in which the tangent error is increased to 3 km. It should be noted that the SPG4 model error is not Gaussian, and we assumed this distribution in order to have a random coverage of the uncertainty region of the satellite position error.  
Figure 12: Mean (\(\upmu\)) and standard deviation (\(\sigma\)) of (**a**) the position error; (**b**) the attitude error; (**c**) the receiver clock bias error; (**d**) the receiver clock drift error. Average over 200 simulations.  
The navigation errors are illustrated in Figure 13 with, for comparison, those related to thermal noise that we obtained in the previous paragraph. The 3D position error rises to 2.5 km for the intermediate case of the satellite position error and it extends to 4.4 km for the worst case. The intermediate and worst satellite position errors degrade the user clock bias estimation by a few hundred milliseconds, which explains the high receiver position error.  
The attitude estimation, however, is only degraded by 0.72\({}^{\circ}\) in the intermediate case of the satellite position error with regard to the noise only case. The attitude error degradation increases to 1.87\({}^{\circ}\) in the worst case. The relatively small impact of the satellite position error on the attitude determination is because in terms of the Doppler contribution, the attitude is inferred by the user acceleration variation (2), which remains small despite the satellite position error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Radial** & **Tangent** & **Normal** \\ \hline Intermediate case & 300 m & 1700 m & 150 m \\ \hline Worst case & 300 m & 3000 m & 150 m \\ \hline \hline \end{tabular}
\end{table}
Table 4: Standard deviation of the satellite position error.  
Figure 13: Influence of the satellite position error on (**a**) the position RMSE; (**b**) the attitude RMSE; (**c**) the receiver clock bias RMSE; (**d**) the receiver clock drift RMSE.
## 3 Results and Discussions
### Robocasting of Lattice, Dense and Bi-Layer Parts
#### 3.1.4 FGM Bi-Layer Parts  
Stereomicroscopy images of a representative bi-layer FGM sample from top and side views are presented in Figure 7. It can be seen that these samples were a regular combination of a dense part surrounded by four concentric rings with different diameters. The dense part and the rings were bonded together by radial filaments providing a well-suited firm structure. Open pores in the shape of isosceles trapezoids were created by the interception of the concentric rings and radial filaments of which sizes increased by distancing from the dense core. However, several detachments of the neighboring filaments in the porous section were observed. The presence of these flaws along with a similar problem in dense samples underlines the importance of considering the right amount of overlap between two adjacent rods in the slicing phase of the CAD designs, such that the rods squeeze into each other during consolidation, and the sintering shrinkage does not result in their disjunction.  
As listed in Table 2, the final open porosity of the bi-layer structures was \(26.8\pm 2.6\%\) on average measured by Archimedes' method. The solid parts were densified up to 96%TD (\(2.99\pm 0.01\) g/cm\({}^{3}\)). The open pores ranged from \(605\pm 55\)\(\mu\)m to \(1.04\pm 0.031\) mm measured for the largest edges of the trapezoids. These values demonstrate the extraordinary potentiality of the robocasting technique in fabricating porous structures with large tailored macroporosities which besides well densified strong solid parts, cannot be achieved by conventional ceramic manufacturing methods. Moreover, observation of the structure from the side view shows that during the printing of the outside ring the  
Figure 6: Low magnification SEM image of a dense sample illustrating open spaces between filaments.  
Figure 7: A representative bi-layer sample from the top (**A**) and side (**B**) views.  
filaments spanned the large gap between two radial supports equal to 1.28 mm without considerable deflection, proving the high strength and shape stability of the formulated paste in supporting its own and upper layers weights. This observation is noteworthy and can be the key parameter for printing more complex and stable structures with high accuracy.  
Comprising two filaments next to each other, the average thickness of the rods was \(619\pm 21\) um. Other physical characteristics of the sintered samples such as the diameters of the concentric rings and mean pores sizes are collected in Table 3. The pores sizes were considered equal to the diagonals of the trapezoids in the X-Y plane and the numbering under the pore sizes column presents their sequence from the core. Having the mean diameter of the dense core of \(5.48\pm 0.11\) mm and the overall diameter of the structure equal to \(15.49\pm 0.35\) mm, the dense core shared only 12.5% of the total volume of the structures.
## 2 Materials and Methods
### Pullout Test Devices
The displacements of the geogrid were measured using inextensible wires fixed to the geogrid. To reduce friction, the wires were protected by nylon tubes with an internal diameter of 2 mm, which were placed along the reinforcement length. Furthermore, two total pressure cells were installed 10 mm above the geogrid to evaluate the development of localized normal stresses. For the large-scale equipment, the displacements were recorded by five different tell-tales nominated T1, T2, T3, T4 and T5 and two linear variable differential transformers (LVDT) with a maximum capacity of 50 mm and denominated L1 and L2 (Figure 4a). All points T1 to T5 were spaced 45 mm longitudinally. On the other hand, for the small box, the geogrid displacements were measured by four different tell-tales nominated T1, T2, T3 and T4 (Figure 4b). For consistency, all points were spaced 45 mm in the longitudinal direction, similar to the large-scale equipment. Regarding the size of the geogrid specimens, a constant relationship was considered between the width and length of the specimens in the confined region for each piece of equipment. The ratio between the width and length in the confined region was limited by the size of the small-scale equipment. The dimensions were defined considering a distance of 20 mm between the side walls of the box and the geogrid, as well as a distance of 30 mm between the back wall and the reinforcement. Thus, the size of the geogrid specimen inside the small-scale equipment was defined as 215 mm \(\times\) 260 mm, resulting in a ratio of 1.21, as shown in Figure 4. Considering the same ratio of 1.21 for the large-scale equipment and following the ASTM D6706 [46] recommendation of a 150 mm minimum distance between  
Figure 3: Small-scale pullout test apparatus: (**a**) overall view; (**b**) inextensible wires over the GGR length. the geosynthetic and the box sidewalls, a length of 250 mm and a width of 300 mm were adopted, resulting in a similar confined length in both types of equipment.
## 3 Experimental Results
### High-Speed Calibration  
As is illustrated in Figure 3a, when the temperature of the extended blackbody is constant, the gray value is linear with the integration time. Additionally, the responsivity increases with the rising temperature of the blackbody. As is shown in Figure 3b, the gray value is linear to the radiance. Additionally, the responsivity increases when the integration time rises.  
The calibration equation can be obtained from Equation (2).  
\[y=t\times(341.65\times L(T)+1060.7)+137.5 \tag{9}\]  
The calibration results at integration times of 1500, 2000, and 3000 us were obtained by Equation (7), and then compared with the standard ones. The errors are shown in Figure 4. The temperatures of the blackbody vary from 20 to 100 \({}^{\circ}\)C at an interval of 20 \({}^{\circ}\)C. The maximum error between the actual calibration results and calculated results is 1.40% and the calibration error of the system is 0.63%, which demonstrates that the high-speed calibration method ensures the accuracy of the radiometric calibration. Therefore, the high-speed calibration method is valid for IR imaging systems to improve the efficiency of radiometric calibration.  
Figure 3: The result of high-speed calibration considering the integration time. (**a**) Output gray value as a function of the integration time; (**b**) Output gray value as a function of the integration time.
# Stability Evaluation of a Simplified Reclaimer Using Fluid-Structure Interaction
## 2 Materials and Methods
### Mathematical Model
#### 2.1.2 Numerical Model
The boundary conditions are represented in \[\frac{\partial}{\partial t}(\rho u)+\frac{\partial}{\partial x}(\rho uu)+ \frac{\partial}{\partial y}(\rho uv)+\frac{\partial}{\partial z}(\rho uv)=- \frac{\partial P}{\partial x}+\mu\left(\frac{\partial^{2}u}{\partial x^{2}} +\frac{\partial^{2}u}{\partial y^{2}}+\frac{\partial^{2}u}{\partial z^{2}} \right)+\frac{\partial}{\partial x}\left(\rho k\frac{\partial k}{\partial x }\right)-\rho\varepsilon+S_{Mx} \tag{4}\]1 To simulate the effect of the wind velocity, the equipment was placed inside an air box, with a prescribed value for the air velocity at the inlet. The prescribed value varied from 36 m/s to 132 m/s. The box dimensions are 115 m in height, 205 m in width, and 500 m in length, according to the requirements presented in [33], which established that it can extend from 10 to 15 times the equipment's largest dimension. The k-\(\varepsilon\) turbulence model was used as in reference [4]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Specific Mass** & **Young Modulus** & **Poisson** & **Dynamic Viscosity** \\  & **(kg/m3)** & **(N/m2)** & **Coefficient** & **(Pa.s)** \\ \hline Steel & 7850 & \(2.0\times 10^{11}\) & 0.3 & / \\ Air & 1.2 & / & / & \(17.2\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Materials properties. Figure 4: Complex and simplified models. The FSI model worked with a two-way approach, and the software used was solids4 FOAM developed by [PERSON] et al. [16], which uses an automatic remeshing procedure to represent the solid displacement. After a preliminary mesh test, a CFD mesh of about 350 thousand tetrahedral, pyramidal, and hexahedral elements was defined. The solid mesh had about 250 thousand tetrahedral and pyramidal elements. The mesh refinement is shown in Figure 6, with smaller elements next to the reclaimer, growing to the domain limits. The body was set up with all six degrees of freedom and solid-solid contact between the upper reclaimer part (in red in Figure 5) and the lower reclaimer part (in yellow in Figure 5). The lower part was fixed in the ground with all six degrees of freedom fixed. The problem was solved in unsteady conditions. For the static stabilization, the simulation was set up to run for 0.25 s with a wind velocity of 0 m/s and, subsequently, the prescribed velocity was applied.
# Iterative Refinement of Uniformly Focused Image Set for Accurate Depth from Focus
## 3 Experimental Results
### Comparison with the State-of-the-Art
In the evaluation on mobile dataset [18] shown in Figure 20, we use three test-samples and compare our work with state-of-the-art methods, such as DFM [18] and CFM [38]. The first row shows _\"plants\"_ sample with 30 frames, second: _\"fruits\"_ sample with 23 frames, and third row shows _\"books\"_ sample with 14 frames. Our method finds more accurate depth than prior works, especially around the edges on all samples. On the _\"books\"_ sample, DFM [18] failed to provide clean depth on background because of textureless region (background has black color and no texture), and CFM [38] provided a noisy result, while the proposed method provided clean depth even at textureless (background) regions. These results prove that the proposed method can provide a clean and accurate edge, even on textureless regions. Experimental evaluations show that our method gets improved depth estimation results over existing methods. PAD [39] method is less robust to textureless regions and suffers from texture-copy artifacts. DDFFNet [22] method fails to predict correct depth, providing depth is very blurry, and it has a limitation with image numbers in stack. VAR [1] can keep the overall structure of the scene but it cannot provide clean edges. RDF [19] and F-RDF [20] methods have limitations; their methods cannot provide good result if the image contains a region that is never in focus by a wide margin. The proposed method generates a depth using information extracted in both spatial and frequency domains. Fourier domain works more efficiently on low-textured, and spatial domain has better performance with textured areas. Figure 5b shows that our focus measure is more robust than state-of-the-art methods. PAD [39] and F-RDF [20] provided high noisy depth (initial depth), and VAR [1] provided a less noisy depth result, while proposed focus measure provide cleaner depth result (however, it needs post-processing). Figure 17: Qualitative evaluation on Lytro dataset [49] with recent (state-of-the-art). The proposed method provide more accurate depth than other methods. (**A**)—beers (6 frames), (**B**)—cake (12), (**C**)—cocktails (7), (**D**)—desert (12), (**E**)—love-locks (12), (**F**)—street (5), (**G**)—wooden-house (5). Figure 19: Qualitative evaluation on LFSD dataset [28] with recent (state-of-the-art) methods. The proposed method can provide depth with detail information even at complex scene. Note that depth image for CFM [38] method was cropped from the original paper.
## 5 Discussion
### RQ 3. How Are IoT and AR Used in MI Systems?  
Through our analysis, we determined that IoT devices are used either as identifiers for the placement of AR content or as devices that an agent can use to control the MI system. However, sensing and data collection over the network is the main function of using IoT devices. Therefore, all of the reviewed studies used IoT devices to collect data from their surroundings and then send them to the server for further analysis and interaction. The IoT devices used in the reviewed studies collected and communicated various data, such as the power state, energy consumption, motor speed, temperature, humidity, and relative position. Surprisingly, IoT devices were rarely used in the reviewed studies to collect an agent's biosignals, only appearing in one study [37]. Unlike an input source for the input modality discussed in RQ 2, biosignals can be collected by IoT devices and used to understand the state of an agent.  
We found that the reviewed MI systems used AR either as a data presentation tool to deliver data to an agent or as an interface to allow an agent to control IoT devices and other devices (e.g., lights in miniature building structures [42]). AR was used not only to visualize data received from IoT devices, but also to present pre-stored data, such as in AR navigation, to a destination represented by 3D arrows that are spatially pre-positioned in a registered environment [34; 45]. Of the reviewed studies, those that implemented the navigation feature were restricted to indoor environments; thus, the development of MI systems for navigation in outdoor contexts with the use of AR and IoT remains an open challenge. Exploring the effects of AR, IoT, and multimodality on the QoE of outdoor navigation may be an important aspect because outdoor navigation is not a novel research topic [62].
## 2 Research Conditions and Methods
### Optimization Methods  
#### 2.3.1 Decision Optimization Method  
In the rural road network maintenance decision-making process, limited budget, complexity, insufficient program efficiency, and low optimization efficiency are common problems ([PERSON] [20] et al. 2021). This paper introduces an improved heuristic optimization algorithm to solve these issues. The method is based on changes in pavement performance after maintenance measures are implemented, aiming to ensure optimal decision-making benefits throughout the life cycle ([PERSON] [21] et al. 2018). By using a set decision-making model and benefit model, this approach determines the combination of maintenance measures that provides the most long-term benefits for each road section, optimizing a decision-making scheme strategy with the best benefits quickly, as shown in Figure 8. The solution process involves (1) determining the maintenance decision plan for each individual maintenance section within the road network one by one, (2)-(3) combining all maintenance road section decision plans to achieve all possible combinations of programs for the entire road network. (4) The optimization process is based on annual budget cost and a pavement performance-based benefit model as the main optimization link. (5) It calculates and determines the benefits and costs of each maintenance measure based on the benefits model, and (6) conducts a comparative analysis of the cumulative benefits of all strategy schemes through optimization algorithms to determine the best maintenance strategy.
## 3 Results and Validation
### Temporal Perspectives
Considering the absence of early HR images before 1986 and the uncertainty in the visual interpretation of change years, a confidence interval of \(\pm\)2 years was employed for the comparison. This comparison revealed a 77.78% consistency between the detected change years and the actual years from high and medium-resolution images (where four fifths of the samples had detected change years matching the visually interpreted change years). The deviation in years is depicted in Figure 20, with negative values on the x-axis indicating that the detected change years precede the actual change years, while positive values indicate the opposite. Figure 19: Visual comparison of the detected change years (the first column) with the images from Google Earth and Landsat (the second and third column). The highlighted areas with blue shapes were the change regions. (**a**) Change from unused land to water body, (**b**) the conversion from unused land to urban area, (**c**) change from unused land to cropland. Figure 20: Deviation of detected change years from the change samples.
## 5 Applications of Thermal Images to Future Space Missions
### Deployable Satellite or Rover Navigation  
An impact experiment using the Small Carry-on Impactor (SCI) took place to expose the subsurface materials by forming a 10 m-sized artificial impact crater [29; 30] on 5 April 2019 and collect the ejected materials on the asteroid surface that were sedimented around the artificial impact crater during the  
Figure 6: Close-up thermal image taken by TIR on 25 October 2018, during the descent operation of TD1-R3 Campaign. The width of this image is 6 m across. The Target Marker (TM, in the red circle) of the diameter of 10 cm was clearly detected in the thermal image as a “Cold Sphere” beside a 1 m-sized rugged boulder. This thermal image shows the surface is covered with several tens-of-centimeter-to meter-sized boulders larger than the TM and not covered with fine or small grained regolith materials. A very cold boulder (in yellow) is seen at the bottom-right in this thermal image. (Image: hyb2_tir_20181025_024712_12_fit, ©JAXA).  
second touchdown operation. Thermal images have successfully tracked the SCI moving from the spacecraft with the background against the asteroid surface after it was released at altitude of 500 m (see Figure 7). TIR detected a nutation of the SCI, and it was found to be sufficiently small to keep the impact point on Ryugu within a few tens of meters beside the target position on Ryugu. The optical camera was also used but it could not image the SCI and the asteroid surface at the same time without overexposure because of too higher reflectance of SCI compared to the low reflectance of the surface of the C-type asteroid. Thermal imaging has an advantage of simultaneous imaging of the SCI and the asteroid surface without any change of exposure time. This capability is useful and applicable to take an image of deployable small satellites in view of planetary surfaces, or to navigate the rovers moving on planetary surfaces.