esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrogen from Depleted/Depleting Hydrocarbon Reservoirs: A Reservoir Engineering Perspective
## 4 Thermal H\({}_{2}\) Generation from Hydrocarbons: Theoretical Basis
### Autothermal Reforming  
Autothermal reforming includes the exothermic partial oxidation with O\({}_{2}\), which provides the energy needed for the endothermic steam reforming reactions. In essence, both steam and oxygen are introduced into the reformer, leading to the reforming and oxidation reactions simultaneously occurring to obtain a thermodynamically neutral reaction. As in steam reforming or partial oxidation, catalyst selection plays a crucial role in the overall performance, with nickel-based catalysts being most commonly used because of their effectiveness and low cost. Given the high thermal efficiency of this process, it requires lower energy than steam reforming or partial oxidation. A higher H\({}_{2}\) yield is produced in autothermal reforming than in partial oxidation; the yield is, however, lower in the steam reforming process. In thermal cracking or thermolysis, coke (solid hydrocarbon) and H\({}_{2}\) are also produced by the action of the heat excess.
## 3 Experiments Evaluation
### Algorithm Behavior on Simulated Data
#### 3.1.2 Recovery on CS Measurements  
In the second experiment, we investigate how CS measurement ratio affects the success of recovery. Here we fix the signal sparsity \(\rho_{s}\) as 5%, 10%, and 20%, respectively, and manually tune the CS measurement ratio from 10% to 80%. Figure 2 shows the fact that, given enough CS measurements, even a near sparse signal can be precisely estimated, for example a _100-sparse_ signal can be recovered with high quality from 350 CS measurements
# Analysis of Application Parameters of Hydraulic Slotting Technology in Jointed Coal Reservoirs
## 3 Optimization of Slotting Parameters
### Effect of Slotting Parameters on Slot Shape
#### 3.2.2 Effect of Transverse Slotting Speed on Slot Shape  
The transverse slotting speed is the rotation speed of the drill bit during water jet slotting. When the water jet pressure was 15 MPa, the speed became faster, and the slot became shallower. When the transverse speed was 0.09 m/s, the specimen was deeply slotted. When the transverse speed was 0.06 m/s, the specimen was slotted through. Moreover, the transverse speed had a minor effect on the slot width, which was approximately 15 mm. The slot depth \(h\) and traverse speed \(v\) are fitted in Figure 9. Within a certain range, the slot depth and exposed area decreased as the traverse speed increased, and the decay rate became progressively smaller.  
Figure 8: Relationship between slotting depth and water jet pressure.  
Figure 9: Relationship between slotting depth and slotting speed.  
Similarly, when the transverse speed was 0.06 m/s, the phenomenon of some coal fragments peeling off at the edge of the slot was observed. This means that the problems of stuck and collapsing boreholes were also induced when the transverse slotting speed decreased. Additionally, as the traverse speed decreased, the time of the water jet impacting the coal increased, and the superposition of the stress waves and the water cushion effect became more obvious. The coal at the edge of the slot received more energy, and the coal fragments promoted the peel-off. Therefore, there exists an appropriate transverse slotting speed value, which should also be selected according to the firmness coefficient of the coal seam.
# What's in Your Fruit Juice?--Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
## 2 Materials and Methods
### FT-IR Analysis  
The FJ samples underwent spectral analysis using a portable FT-IR spectrometer. The spectrometer was equipped with a dial-path transmittance accessory featuring a 50 \(\upmu\)L pathlength selection, a zinc selenide (ZnSe) crystal, and a deuterated triglycine sulfate (dTGS) detector. Samples' mid-infrared spectra were gathered at ambient temperature, spanning a range of 4000-700 cm\({}^{-1}\) with 4 cm\({}^{-1}\) resolution. To enhance the signal-to-noise ratio, 64 co-scans were co-added. To initiate the spectral measurement, a 75 \(\upmu\)L portion of the juice sample was directly deposited onto the opening of the dial path accessory. Before each spectral measurement, a background spectrum was acquired to mitigate potential environmental effects. Data collection was performed in duplicate, ensuring reliability and consistency. The spectral data, represented in absorbance, were examined using Resolutions Pro Software (Agilent, Santa Clara, CA, USA).
## 4 Finite Element Numerical Calculation
### Numerical Simulation Verification  
First, a numerical model was established according to the slope size in the lab-scale physical model test. Then, the results from the numerical simulation were compared with those from the laboratory model test. Due to the limitations in the laboratory model test, the bending moments of only five sections were tested along the pile. However, the bending moments of nine sections were extracted in the numerical simulation. The fifth load level was selected as the condition for verification during the analysis process, as shown in Figures 13 and 14. Although there were numerical differences in the test point along the pile between the laboratory model test and the numerical simulation, their distribution characteristics along the pile of the bending moment were basically the same. Figure 13 indicates that the extreme values of \(M_{x}\) appear at the pile top, and the bending moments at the pile end are also close to zero. In Figure 14, the values of \(M_{y}\) were basically negative, and the extreme value of \(M_{y}\) appeared at a certain distance from the pile top. However, as for the piles located at the arch foot, their extreme values still appeared at the pile top. The  
Figure 12: Mesh subdivision.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Unit Weight** & **Cohesion** & **Internal Friction** & **Poisson’s** & **Elasticity** & **Unit Type** & **Constitutive Model** \\  & **(k\(\mathbf{v}\))** & **(k\(\mathbf{p}\))** & **Angle (\(\mathbf{\dot{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{    \mathbf{ }}}}}}{}}}{{}}{}}}}}}\))** & **Ratio** & **Modulus \(E\) (MPa)** & **Unit Type** & **Constitutive Model** \\ \hline Soil & 20.8 & 42.5 & 30.05 & 0.32 & 16.38 & Solid & Elastic-plastic model \\ Crown Beam & 24 & — & — & 0.2 & \(3\times 10^{4}\) & solid & Elastic model \\ Pile & 24 & — & — & 0.2 & \(3\times 10^{4}\) & Solid & Elastic model \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters.  
values of \(M_{x}\) and \(M_{y}\) from the numerical simulation were consistent with the results from the laboratory model test; the distribution regularity of the pile was basically the same, and the difference in the extreme bending moments of each pile was within 8%.
## 2 Case Study of the Valencia Region
### Data Sources  
The information utilized to establish susceptibility factors and terrain features in this article was obtained from official national sources, as outlined below.  
* The data for the terrain slope gradient were obtained from the Spanish National Geographic Institute's digital elevation model (IGN-DEM) MDT25. The database was created from Lidar flight and has a resolution of 50 cm/pixel, with each pixel measuring 25 \(\times\) 25 m.
* The land use and land cover (LULC) information was sourced from the SIOSE (Spanish Land Use and Land Cover Information System) 2006 model (1:25,000 scale), from the Spanish National Geographic Institute (IGN). Since this model contains a high level of detail and complexity, a hierarchical model derived from the SIOSE was chosen as it is simpler to implement [24].
* Data on surface geology were obtained from the 1:50,000 scale national geological map created by the Spanish Geological and Mining Institute (IGME).
* Digital landslide inventory data were obtained from a 1:50,000 scale vector format landslide map created by the Department of Public Works of the Valencian Regional Government [25]. The map was constructed using 1:50,000 scale geological and geotechnical maps from the Spanish Geological and Mining Institute, as well as aerial photographs from that time. Additionally, information from specific publications and field surveys were also utilized to identify landslide events based on factors such as morphology, lithology, structure, slopes, and vegetation. The map includes all types of landslides represented as polygons and differentiates rock falls from other types of landslides, such as slides and flows, due to their higher frequency (accounting for 2/3 of all landslides).  
The European Landslide Susceptibility Map (ELSUS 1000) is of particular interest, and this is the only unofficial source. This map displays the likelihood of generic landslide occurrence at the European scale. ELSUS was created by the Joint Research Centre in 2012, using a 1 \(\times\) 1 km grid and five susceptibility categories (VH, H, M, L, VL) for different types of physiographic regions (coastal, flatland, and mountainous regions). Zone 5, classified as mountainous with arid climate, was chosen for comparison with the area studied in this study [23,26].  
The factors or criteria for this study have been selected according to existing research on ELSUS mapping [23,26] and are slope gradient, lithology, and LULC. This is considered an appropriate selection, as these three factors have proven to be more relevant in different  
Figure 1: Situation and distribution map of provinces in the Region of Valencia (Spain).  
geographical areas than other factors such as NVDI, slope aspect, annual rainfall, distance to rivers, distance to roads, or a stream power index [27; 28]. The weighting methodology used by ELSUS was initially used as a point of comparison, as described in Section 3.
# Mechanical Analysis of Ground Sleeve
## 3 Test Results
### Failure Mode  
Figure 9 illustrates the typical failure modes of the specimens. The typical failure modes for the grouted sleeve lapping connectors were bar tensile fracture and bar-grout slip. All of the grouted splice connectors failed by the mode of grout-sleeve slip.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & \(P_{\mathbf{n}}\) **(kN)** & \(f_{\mathbf{n}}\) **(MPa)** & **Failure Mode b ** & \(R_{\mathrm{s}}\) & **Strength Rating c ** \\ \hline  & 100-1 a & 77.14 & 303.14 & BGS & 0.76 & N \\  & 100-2 & 66.67 & 261.99 & BGS & 0.65 & N \\  & 100-3 & 88.17 & 346.49 & BGS & 0.87 & N \\  & 150-1 & 148.66 & 584.20 & BGS & 1.46 & Y \\  & 150-2 & 154.92 & 608.80 & BGS & 1.52 & Y \\  & 150-3 & 142.53 & 560.11 & BGS & 1.40 & Y \\ Grouted sleeve & 150-4 & 155.04 & 609.27 & BT & 1.52 & Y \\  & 200-1 & 145.61 & 572.21 & BGS & 1.43 & Y \\  & 200-2 & 154.37 & 606.64 & BT & 1.52 & Y \\ connector & 200-3 & 154.05 & 605.38 & BT & 1.51 & Y \\  & 250-  
The tensile capacity of the specimens was governed by the tensile capacity of the main bars, the bond capacity between the bar and the grout, and the bond capacity between the grout and the sleeve.  
For the grouted sleeve lapping connectors of 150-4, 200-2, 200-3, the 250 series, and the 300 series, the lap length was long enough to provide a sufficient bond stress between the bar and the grout. Thus, the bond capacity between the bar and the grout for these specimens was larger than the tensile capacity of the main bars, which results in the failure mode of the bar fracture. The grouted sleeve lapping connectors of the 100 series, 150-1, 150-2, 150-3, and 200-1 had a short lap length, and the ultimate bond stress between the bar and the grout was not sufficient to prevent the bar from slipping out of the grout. Thus, these specimens failed the bar-grout slip. The grout-sleeve slip did not occur for the grouted sleeve lapping connectors.  
For all of the grouted splice connectors, the ultimate bearing capacity depended on the bond strength between the grout and the sleeve. All of the specimens failed by the grout-sleeve slip as long as the load exceeded the ultimate bond between the grout and the sleeve.
###### Abstract  
Optical metrology is a key element for many areas of modern production. Preferably, measurements should take place within the production line (in-process) and keep pace with production speed, even if the parts have a complex geometry or are difficult to access. The challenge for modern optical in-process measurements is, therefore, how to simultaneously make optical metrology precise, fast, robust and capable of handling geometrical complexity. The potential of individual techniques to achieve these demands can be visualized by the tetrahedron of optical metrology. Depending on the application, techniques based on interferometry or geometrical optics may have to be preferred. The paper emphasizes complexity and robustness as prime areas of improvement. Concerning interferometric techniques, we report on fast acquisition as used in holography, tailoring of coherence properties and use of Multiple simultaneous Viewing direction holography (MultiView), self reference used in Computational Shear Interferometry (CoSI) and the simultaneous use of several light sources in Multiple Aperture Shear Interferometry (MArS) based on CoSI as these techniques have proven to be particularly effective. The use of advanced approaches based on CoSI requires a transition of the description of light from the use of the well-known wave field to the coherence function of light. Techniques based on geometric optics are generally comparatively robust against environmental disturbances, and Fringe Projection (FP) is shown to be especially useful in very demanding measurement conditions.  
in-process measurement; in situ measurement; optical metrology; quality control; interferometry; fringe projection; computational shear interferometry; coherence function; structure function; additive manufacturing +
Footnote †: journal: Applied Sciences  
Article  
Optical In-Process Measurement: Concepts for Precise, Fast and Robust Optical Metrology for Complex Measurement Situations.
## 3 Results  
### Cross-Validation  
As shown in Figure 2, the \(R^{2}\) between the Landsat GPP estimated by the CASA model and GOSIF GPP reaches 0.9150, while the \(R^{2}\) between the Landsat GPP estimated by the CASA model and MODIS GPP reaches 0.8473. This indicates a high degree of similarity between the GPP estimated by the CASA model and the control groups, demonstrating the reliability and accuracy of the CASA model. The GOSIF GPP shows a higher average GPP, compared to the MODIS-LANDSAT GPP. In contrast, the MODIS GPP shows an underestimation of high values, leading to a lower average GPP relative to the LANDSAT GPP.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 4 Results
### Measurement Uncertainty Due to Uncertainty of Emissivity  
The use of grey body measurement principles under real conditions is currently the standard practice in the field of radiation thermometry. Since the grey body simplification of spectral homogeneity never applies under real conditions, the uncertainty of spectral emissivity must be accounted for in the uncertainty of non-contact temperature measurement.  
The highest measurement deviations are expected when the spectral parameter (emissivity) is at its highest deviation along the full (measurement) spectrum. Deviations of spectral emissivity in narrow spectral bands always produce a lesser or equal effect than deviations along the full spectrum (of measurement). Therefore, spectrally local uncertainties of the spectral emissivity are considered to be included in the uncertainty budget of the uncertainty interval of the total spectrum. Therefore, the calculation of the measurement of uncertainty interval is performed as the worst-case study, where the highest and the lowest expected deviation of the spectral emissivity (over the whole spectrum) define the uncertainty interval at the output of the measurement process.  
Figure 18 shows the uncertainty intervals of GBRT due to the uncertainty of emissivity. Although a (spectrally homogeneous) grey body was used in this figure, any case-specific (spectral) emissivity and the corresponding (spectral) uncertainty can be used for the uncertainty calculation. In such a case, the highest possible uncertainty should be used.  
Figure 17: The sum of effective atmospheric transmissivity and emissivity with (**a**) object temperature and (**b**) environmental temperature, assuming a thermally homogeneous environment. Multiple plots show the variation of (**a**) environmental temperature and (**b**) object temperature.  
Similarly, the ranges of relative uncertainty are shown in Figure 19. The relative uncertainty is expressed as the ratio between the total measurement uncertainty and the temperature difference between the object and the reflected environment.  
Figure 18: Measurement uncertainty intervals for grey body radiation thermometry of surfaces with different spectral emissivities and homogeneities. The uncertainty contributions are calculated at 25 \({}^{\circ}\)C environmental temperature and correspond to the probability of occurrence of the spectral emissivity within the specified uncertainty interval.
## 2 Numerical Approach
### Optimization  
Once a surrogate model is constructed, the optimal values of the input design parameters should be explored to find an optimal design. For the optimization algorithm, a genetic algorithm that searches through the optimal solutions heuristically without derivative information is used [43]. The advantage of using a genetic algorithm compared with using other gradient-based optimization methods such as the steepest descent method and conjugate gradient method is its capability to find a globally (rather than locally) optimal solution. This allows for the improvement ratio to be achieved from the optimal solution using the genetic algorithm better than the one from gradient-based optimization methods [44]. The population size and mutation rate of the genetic algorithm used in the current paper are set to 50 and 0.01, respectively. The genetic algorithm is terminated if the average change in the fitness value is less than \(10^{-6}\) or if the number of iterations exceeds 1000.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Neurons** & **Learning Rate** & \(E\) \\ \hline
16 & \(10^{-2}\) & 0.286 \\
64 & \(10^{-1}\) & 0.232 \\
64 & \(10^{-2}\) & 0.225 \\
64 & \(10^{-3}\) & 0.266 \\
256 & \(10^{-2}\) & 0.227 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The influence of the number of neurons and learning rate on artificial neural network (ANN)-computed surrogate models’ prediction accuracy. The O-DLB-4-3 case is used for the prediction accuracy test.
## 3 Results
### Seamless Deployment  
The results demonstrate JAVIS' ability to seamlessly adapt to varying operational requirements through the three recommended modality combinations. Each of them is allowed to upgrade from their current stage in a prompt manner by running a few commands in order to change modality, as shown in Appendix A (Figure A1).  
* **JAVIS Tailoring (Development JAVIS + Versatile Mode):** In our case, the initial tailoring phase was performed on a Windows 10 workstation located in an external network environment. This setup provided convenient access to the latest libraries and enabled rapid experimentation with new large language models (LLMs). By coupling JAVIS development with the versatile mode, updates and tests were easily implemented, ensuring that LLM evaluation and refinements could be conducted with minimal effort.
* **Personal/Team Serving (Development/Production JAVIS + Versatile Mode):** Once tailored, JAVIS was shipped to a small group of users running Ubuntu 18.04. Here, the production variant was combined with the versatile mode to allow straightforward  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Environment** & **GPU Configuration** & **GPU Driver** & **CUDA Version** & **Process Time (s)** & **Process Result** & **Additional Requirements** \\ \hline Windows 11 & x1 RTX 3060 12 GB & 561.09 & 12.6 & 37 & Successful & No changes needed \\ Ubuntu 18.04 & x4 A6000 50 GB & 535.54.03 & 12.2 & 180 & Successful & Container Toolkit + Docker \\ Ubuntu 24.04 & x1 RTX 3050 8 GB & 535.183.01 & 12.2 & 32 & Successful & No changes needed \\ Ubuntu 24.04 & x1 RTX 3050 8 GB & 550.127.05 & 12.4 & 112 & Successful & Container Toolkit \\ \hline \end{tabular}
\end{table}
Table 3: System setup and compatibility across different environments.  
manual adjustments, such as the adjustment of GPU allocation and switching of LLMs as needed. Despite multiple users accessing the system, the resource overhead remained stable. Even a single-instance model using the versatile mode proved sufficient to support a modest yet efficient team workload.
* **Hospital/Institutional Serving (Production JAVIS + Scaling Mode):** To meet broader institutional demands, the same Ubuntu 18.04 configuration was used to allow connection to additional departments. JAVIS remained in production mode, and by leveraging the container in scaling mode, it was possible to autoscale system resources dynamically. Granting access to more departments or accommodating higher usage no longer required complex reconfiguration. Instead, a few simple adjustments, such as changing containers ensured seamless expansion to meet the institution's growing needs without incurring any noticeable performance overhead.  
In summary, these results confirm the effectiveness of JAVIS' modular and flexible deployment approach. With minimal changes, the system is scaled smoothly from an individual workstation testing environment to serving multiple team members to institution-wide access, demonstrating its robust adaptability and efficiency in real-world scenarios.
## 1 Introduction
In order to determine the altimeter's absolute bias, the ellipsoidal altitude of the lake along the altimeter track is measured by two means--using the altimeter and using the GPS survey from a boat cruising, along the satellite tracks. The absolute bias is simply the averaged difference between both sets of measurements. In Section 2, altimetry data processing for lake level calculation and the new experimental design for C/V over Lake Issykkul are described. The main results on the atmospheric correction, first, then on the biases of the respective Sentinel-3A and Jason-3, from the last two field works, are given in Section 3. Conclusion and future perspectives are then proposed in Section 4.