esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
### Experiment 1 (Adhesive Contact)  
The elastomer used in the experiments formed strong adhesive contact with steel surfaces. First, strong homogeneous adhesive contact was created between the steel plate and the elastomer by the application of preliminary pressure. Then, the plate was pressed into the elastomer using an indenter, and after the maximum indentation depth was reached, the contact was unloaded. In this experiment, the adhesive contact was not destroyed at any time. Figure 6 shows the experimentally measured dependence of the normal force on the indenter displacement, which is almost reversible with only slight hysteresis at loading-unloading.  
Figure 6: (**Left panel**) Experimental dependence of the normal force \(F\) on the indentation depth \(d\). Loading—red curve; unloading—blue curve. (**Right panel**) Photographs of the contact region corresponding to points A–D on the dependence \(F(d)\) for the loading stage. Note that the edges of the plate remain in contact with the elastomer; due to this fact, all pictures look similar. Supplementary Video S1, which shows the whole experiment in detail, is available.  
At the beginning of loading, a nonlinear section \(F(d)\) was observed, which is associated with inhomogeneities of rubber and the practically inevitable small misalignment of the cylindrical indenter. The right panel in Figure 6 shows photographs of the contact area between the plate and the elastomer, corresponding to points A-D on the \(F(d)\) curve. Regardless of the applied force, the plate always remained in full contact with the elastomer (its edges do not rise). Photographs of the contact area for the unloading phase are not given, since they are not visually different from the photographs of the indentation phase.
# Durability Evaluation Study for Crumb Rubber-Asphalt Pavement
## 1 Introduction
### Effects of the Surface Characteristics of Rubber Crumbs  
Rubber powder is used to modify asphalt based on the fact that under the action of high temperature and shear, rubber powder absorbs lighter constituents from asphalt, which leads to an increased softening point and decreased ductility of asphalt. Moreover, the \"swelling effect\" occurs and deformations are induced in rubber particles due to swelling. The surface of the rubber powder is gradually softened. A three-dimensional continuous phase is formed into the asphalt matrix and the mechanical properties of binders are influenced significantly. The successful application of rubber powder-modified asphalt is attributed to the reaction of the above-mentioned mechanism [30; 31; 32]. However, in crumb rubber-asphalt pavement, rubber crumbs are incorporated as aggregates [33]. Since rubber crumbs are incorporated as aggregates, when the temperature of the rubber crumbs is low the contact time with the asphalt is short, and the swelling effect is not obvious; in addition, in this case, the adhesion of the asphalt to the rubber crumbs can also be compromised. Notably, many studies have also evaluated the adhesion of the asphalt to aggregate by analyzing the cohesion of bituminous binders [34]. For rubber powder modified asphalt, this research idea is correct and applicable. However, when the rubber crumbs are incorporated as aggregates into the asphalt mixture, the cohesion angle is no longer suitable for evaluating the adhesion of the asphalt to the rubber crumbs.  
Electron microscopy images of rubber crumbs at different magnifications (100\(\times\), 500\(\times\), 2000\(\times\)) (Figure 1) show their surface to be uneven with granular protrusions. The rubber crumbs have no sharp edges and are approximately circular and smooth. Therefore, the interface characteristics of rubber crumbs are less conducive to coating with an asphalt film than those of ordinary stone aggregates, and mechanical adhesion between the components will thus be inferior [35].  
Figure 1: _Cont._  
The interface between asphalt film and rubber crumb becomes the weakest interface for component adhesion in the mixture, and complex environmental and loading effects can easily damage this interface [34], resulting in peeling off of the road surface and damage in the internal structure. Furthermore, the high elasticity of the rubber crumbs makes it easy for them to deform and recover under load, which damages their bitumen coating. Under the action of hydrodynamic pressure, the bitumen film rapidly peels off and loses its adhesion effect, resulting in rapid devastation such as loose spalling.
## 3 Methodology
### Implementation
#### 3.5.4 Baseline Method II: Window before and after  
The window before and after baseline uses the last measurement before and the first measurement after the flexibility activation. During the flexibility activation, linear interpolation is used to generate a baseline for the intermediate timesteps. Equations (4) and (5) describe the baselining procedure mathematically, in which \(t\) is the timestep, \(T_{act}\) is the list of activated timesteps, \(|T_{act}|\) the length of the activation period, \(t_{act}\) the first activated timestep, \(P_{t}^{meas}\) the power measurement at timestep \(t\), and \(P_{t_{act}-1}^{meas}\) the power measurement at timestep \(t_{act}-1\).  
Figure 3: Illustration of baseline I and its activation period. The dashed line reflects the baseline.  
The method is illustrated with the same example as in Section 3.5.3. Figure 4 visualises the baseline applied for method II, illustrating the newly introduced variable \(t_{act}\) (the first activated timestep, in this example 14). In this example, the baseline values for the timesteps in \(T_{act}\) are derived from a linear interpolation between the last step before (timestep \(t_{act}-1=13\)) and first step after activation (timestep \(t_{act}+|T_{act}|=17\)).  
\[\frac{\Delta P^{\textit{base}}}{\Delta t}=\frac{P^{\textit{meas}}_{\textit{ act}+|T_{act}|}-P^{\textit{meas}}_{\textit{act}-1}}{|T_{act}|+1} \tag{4}\]
## 5 Results
### MASW Seismic Technique  
MASW-type seismic profiles have been executed over the same position as the refraction ones to compare both interpretation models. In this case, the results are not in two dimensions (section-like) but a single dimension, thus obtaining only thicknesses of materials and shear wave velocities [40]. As a profile-style survey, the investigation involves all extensions where geophones are distributed, but the final values are assigned to the central point of each profile.  
The results, in the distribution of layers of geophysical levels, can be correlated to those observed in the interpretation of the previous profiles (refraction ones), but now obtaining the S-shear wave velocity distribution model.  
The results were represented in the same figure to show the comparison between both techniques: refraction and MASW (Figure 5). Here, the vs. average values obtained are a velocity of 142 m.s\({}^{-1}\) for the first layer, 318 m.s\({}^{-1}\) in the second, 581 m.s\({}^{-1}\) in the third, and 1020 m.s\({}^{-1}\) in the layer corresponding to the semi-infinite space, the last one. The correlation of these velocity levels with the area materials is the same as was described in the previous paragraph.  
The MASW results corroborate the identification of the geophysical model of the studied area by refraction technique with a little difference in thickness that can be observed in Figure 5 (as a consequence of the analysis way that uses the techniques). The relationship between \(V_{p}\) and vs. could define the dynamic elastic modulus and the impedance contrast in vs. velocities varies from 2.3 to 2.5 in the three analyzed profiles.
## 3 Fundamentals of CFD in the Context of Mechanical Processes
### Governing Equations
RANS models assume isotropic turbulence and often fail to capture transient, anisotropic eddies or flow instabilities critical to phase distribution and microstructure formation. In contrast, LES offers higher fidelity by resolving large-scale turbulent structures and accommodating localized flow features. However, LESrequires fine mesh resolution and significantly greater computational resources, which can be prohibitive for large-scale or multiphase food processes. Therefore, the choice between RANS and LES should balance accuracy needs with practical constraints, especially when modeling shear-sensitive or structurally evolving food materials [23]. Multiphase flow modeling is often essential in food processing, where systems commonly involve coexisting solid, liquid, and gas phases. The EE (Euler-Euler) approach treats each phase as a continuous, interpenetrating medium, making it suitable for dense suspensions such as doughs, batters, or gas-solid fluidized beds. The EL (Euler-Lagrange) model, in contrast, tracks discrete particles or droplets within a continuous fluid phase and is especially effective for simulating mixing, emulsification, or spray processes. The Volume of Fluid (VOF) method is used to capture sharp interfaces between immiscible fluids--such as oil-water systems or foam generation--making it valuable in modeling surface evolution and interfacial dynamics [23]. Each multiphase modeling strategy requires the accurate specification of interphase forces, including drag, lift, and virtual mass, along with closure relationships to describe momentum, heat, and mass transfer between phases. These parameters are often derived empirically and must be validated against experimental data for each specific food system to ensure model fidelity.
# Understanding Neutrophil Dynamics during COVID-19 Infection
## 4 Numerical Results
### Bifurcation Analysis  
The stability analysis shows bistable behavior between the no-inflammation and chronic-inflammation states when conditions Equations (11) and (14) hold. To determine the parameter space where bistability exists and its relationship with the estimated mild and severe parameters we constructed \(\alpha_{c}\) and \(\phi\) bifurcation diagrams. In both diagrams, we start with parameters and initial conditions estimated from severe cases and vary \(\alpha_{c}\) and \(\phi\), respectively. Figure 5 shows that, for low neutrophil recruitment rates \(\alpha_{c}<3.67\), only the no-inflammation equilibrium exists and is stable. This range includes the mild case \(\alpha_{c}=3.44\) value (see Figure 5, green line). For larger neutrophil recruitment rates \(\alpha_{c}>3.67\), we obtain bistability of the no-inflammation and chronic-inflammation equilibria. The range includes the severe case \(\alpha_{c}=4.28\) value (see Figure 5, red line). The dynamical transition happens at the bifurcation point \(\alpha_{c}^{critical}=3.67\). These results suggest that when starting with the severe outcome setup, we only need to lower neutrophil recruitment rates to speedup the resolution of inflammation, independently of the other biological processes.  
Conversely, Figure 6 shows that, for low macrophage phagocytosis rates \(\phi<0.047\), we obtain bistability of the no-inflammation and chronic-inflammation equilibria. For higher macrophage phagocytosis rates \(\phi>0.047\) we obtain that only the no-inflammation equilibrium exists and is stable. The transition happens at the bifurcation point \(\phi^{critical}=0.047\). Unlike the \(\alpha_{c}\) bifurcation diagram, however, we note that both the mild and severe \(\phi\) values are in the bistable region (see Figure 6, green and red lines) suggesting that the resolution of inflammation seen in the mild case is dependent on other parameters and/or initial conditions.  
Figure 4: **Long-term dynamics.** Dynamics of model Equation (3) over 100 days for parameters for (**A**) mild and (**B**) severe COVID-19 infections.  
Figure 5: **The \(\alpha_{c}\) bifurcation diagram.** Stable (solid black curves) and unstable (dashed black curves) population equilibria for varied \(\alpha_{c}\) for (**A**) total neutrophils \(\tilde{n}+\tilde{u}\), (**B**) IL-10 \(\tilde{g}\), and (**C**) NSR \((\tilde{n}+\tilde{u})/\tilde{e}\). The green and red lines are the \(\alpha_{c}\) values from the mild and severe cases, \(\alpha_{c}^{un}\) and \(\alpha_{c}^{s}\).
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### How Can ChatGPT Assist throughout Each Stage of the Nutrition Care Process?
#### 3.2.1 Nutrition Assessment  
The NCP begins with the collection and interpretation of pertinent information concerning an individual's current nutritional status, clinical history, physical condition, and other physiological and pathological factors. This initial assessment serves as a foundation for understanding the individual's specific nutritional requirements and identifying any existing or potential nutrition-related issues. The data obtained during this phase encompasses various elements, such as anthropometric measurements (e.g., height and weight), biochemical data, clinical observations, and dietary intake assessments (commonly referred  
Figure 2: The Nutrition Care Process and Model.  
to as the ABCD method). According to the guidelines provided by the American Society for Parenteral and Enteral Nutrition (ASPEN), a comprehensive nutritional assessment encompasses a thorough clinical examination (i.e., comprising a detailed history and physical examination), anthropometric measurements, diagnostic tests, and dietary assessments [51]. Some examples of how ChatGPT can assist in this phase are as follows.  
* Answer questions about specific nutrients, their functions, and dietary sources.
* Provide general information on dietary guidelines and recommendations.
* Assess individual nutrition needs based on factors such as age, sex, and activity level.
* Offer dietary assessment tools and calculators to estimate nutrient intake.
* Provide guidance on assessing body composition and interpreting results.
* Explain common nutrition-related health conditions or risk factors.
# Prediction of Curing Time/Shear Strength of Non-Conductive Adhesives Using a Neural Network Model
## 4 Conclusions  
In this study, a framework for verification and prediction of the curing time/the shear strength of NCA and optimization of the NCA formulation using the ANN method was established. The snap curing time and the shear strength of NCA based on resin, hardener, catalyst, and coupling agent were modeled by ANN. The following conclusions were derived:  
1. ANN models were constructed to predict the snap curing time and shear strength of NCA. The regression models indicated that the coefficients of determinations (R\({}^{2}\)) were 28.9-35.2% higher accuracy in the prediction than the results of the reference even though only 65 datasets and 44 datasets were used in the ANN model.
2. In verifying the accuracy of the trained ANN models, the predicted curing time and shear strength from the trained ANN models were compared and verified with third-party experimental data. The predicted curing times and shear strength had errors of 0.3-0.7 s and 5.66 MPa, respectively.
3. The curing time and the shear strength in accordance with the content of resin, hardener, and catalyst were predicted. As the content of catalysts and resins was increased, the curing time was decreased, and the shear strength was also increased. In the combination of DGEBA resin, HX-3941 catalyst, 80 phr MHHPA hardener, and 0.55 phr A-174 coupling agent, the optimized NCA formulation was suggested the range of 0.2137 \(\times\) (resin content in phr) + (catalyst content in phr) \(\geq\) 35.87 to meet mass production requirements.  
This study proposed a neural network framework to predict the curing time/the shear strength and optimize NCA formulation based on epoxy resins, anhydride hardeners, imidazole and amine catalysts, and silane coupling agent. In the future research, polyurethane and polyamide resins will be considered, and data mining will be implementing using matching hardeners, catalysts, and coupling agents. In addition, in order to generalize the developed neural network model, it is necessary to develop characteristic indices including chemical chain length and reaction that can characterize polymer components.  
Conceptualization, K.-E.M., S.Y. and C.K.; methodology, K.-E.M., J.-W.J. and J.-K.K.; investigation, K.-E.M. and J.-W.J.; writing--original draft preparation, K.-E.M.; writing--review and editing, C.K. and S.Y.; project administration, C.K. All authors have read and agreed to the published version of the manuscript.  
This study has been conducted with the support of the Korea Institute of Industrial Technology as \"Development of root technology of multi-product flexible production (KITECH-EO-22-0006)\".  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 4 Discussion and Conclusions
The Helmert approach is considered here as a strict solution with higher computational burden than the Forstner approach because of the necessity for least-squares adjustment of weighting coefficients rather than a straightforward formula. Secondly, we demonstrate ISL range bias estimation considering weighting method, number, and location of ground stations, but also the value of the ISL range bias. The results indicate that in applied settings, the number of stations is not critical in achieving a good accuracy of bias estimation (differences are usually \(<\)\(\pm\)25% of applied value). The differences between simulated and estimated bias are lower when VCE is used. This is especially clear for intra-plane schemes for which the observation geometry is insufficiently variable. Static connectivity schemes (i.e., intra-plane schemes) used in estimation without VCE has about 2-3 times greater differences from the simulated constant value. Finally, we focused on the impact of ground station sets on ISL range bias estimation and overall orbit determination accuracy to complete the research. These results agree with our previous analysis in terms of exploited number of stations with using ISL measurements in orbit determination but without using VCE as the primary weighting method [12; 34]. In the case of number and distribution of the stations, the number of GNSS measurements plays a vital role while exploiting the VCE. The RMS orbit errors show the possible improvement with the Forstner approach when the station distribution is poor compared to the Nominal weighting. The ISLs might have a larger impact on accuracy when the uniform station location cannot be used. In this case the use of VCE is the most justified to obtain better accuracy. For clock estimation, the choice of weighting methods is not as crucial as station distribution (as a consequence of the number of GNSS measurements) and contribution of ISLs. In general, the choice of the connectivity schemes is essential in the presented ISL simulations. Schemes created according to constellation geometry properties or routing schemes created in advance might be succeeded by schemes created by more sophisticated algorithms to optimize data transfer but require broadcast orbits. Thus, further investigation of this topic will be important in future ISL development. In conclusion, the relative weighting might be an additional value to include in processing schemes in orbit determination based on ISL and GNSS measurements. Here, we focused on relative weighting of measurements from two technologies to assess their impact on the combined solution. The results indicate the usefulness of VCE for orbit determination when ISL range bias estimation is assumed as a constant value per day per satellite. These findings may be helpful for future practical application in orbit determination with ISLs use.
## 2 Methods
### Satellite Sensor Data Processing  
Six Landsat 8 images were obtained between April 2013 and March 2014 (path 21, row 45). All images were pre-processed using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) algorithm in ENVI 5.0. The January 2014 image, which corresponds to the month where field data were obtained, was employed to compute eight broad band VIs: NDVI green, WDRVI green, NDVI, EV1z, WDRVI, CI green, SR, and EV1i (Table 1). These VIs produced a large correlation with CC from previous analyses. The correlation between CC at the ESU level and Landsat 8 VIs was examined. The index that provided the largest correlation between the Landsat 8 images and CC measured at the ground was then used to produce the CC maps. Non-mangrove pixels were masked out using the land cover map of the Yucatan Peninsula [74].
# Si Photonics for Practical LiDAR Solutions
## 3 Si Photonics for LiDAR
### LiDAR System Design Considerations
#### 3.2.1 Eye Safety  
Eye safety must be kept in mind when choosing a wavelength, optical power and beam shape. Eye safety ultimately limits the range of most civilian LiDAR systems can operate. International standard International Electrotechnical Commission (IEC) 60825 exists for regulating laser safety and is generally in consensus with other related standards such as American National Standards Institute (ANSI) Z136. Most LiDAR systems for automotive and consumer applications adopt an infrared laser as the light source complying with IEC 60825 Class 1 as they are used in an environment where human eyes are exposed. Infrared lasers are particularly hazardous, since body's protective glare aversion response, or \"blink reflex,\" is triggered only by visible light.  
The two groups of wavelengths that are commonly used for automotive or consumer LiDAR are the 800-1000 nm range and the 1300-1600 nm range, within the range of near infrared (NIR) and short-wave infrared (SWIR). The former group takes advantage of Si photo sensors and camera optics while the latter group leverages components and technologies of fiber optic telecom industry. Light in the former group can cause retinal damage at relatively low energy as an eye is transparent and well focuses light at these wavelengths. Light in the latter group of wavelengths is largely absorbed in the crystalline lens (1300-1400 nm) or the vitreous body (1400-1600 nm) of an eye so more energy is allowed. IEC 60825 defines Class 1 lasers as optical energy below the maximum permissible exposure (MPE) for a certain type of a laser operating at a certain wavelength with certain exposure time. The MPE of some commonly used laser wavelengths at different exposure time is calculated and shown in Figure 1.  
The MPE is a few orders of magnitude higher at 1550 nm than at 800-1000 nm range therefore 1550 nm is sometimes called \"eye-safe\" wavelength. The MPE at 1300 nm range generally falls between above two groups below 0.1 s exposure and surpasses other wavelengths when approaching continuous wave. Long range LiDAR systems using continuous wave, e.g., FMCW, tend to adopt \"eye-safe\" long wavelength like 1550 nm while those using pulsed light, e.g., direct ToF, mostly adopt short wavelengths for other merits such as availability of light sources and detectors.