esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Application Example with SmartSync
### MBSE/Multi-Physics  
We needed to make a second comparison to assert the global consistency of our design with the Modelica/SysML comparison. The translation from Modelica to S2 ML was done following the process described in [21], with the difference that we considered Modelica's variables. Part of this translation can be found in Figure 15 with the translation of the motor class. Modelica classes were abstracted to S2 ML classes, with model instances being translated to blocks. Modelica variables were translated to S2 ML ports, and connect clauses were translated to connections.  
Once this translation was conducted, we compared the models with the SmartSync tool. A first iteration showed that the structure of the models is very different. For ease of comparison, we added drivetrain and cell blocks to the models, respectively containing the motors and propellers and the fuselage and wings. We then translated again and compared the models.  
After four comparison steps, we had three conclusions:  
* Most components of the drone are missing in the Modelica model. This can be explained by the fact that we are here only interested in the propulsion and aerodynamics of the drone; therefore, components related to other functions are not represented.  
Figure 15: A part of the Modelica model (**b**) and its translation in the S2 ML model (**a**).  
* In some specific cases, the Modelica representation is more detailed than the architecture model. This is the case of the motors since the SysML model only considers them through a black box view, whereas the Modelica view shows the inner parts of the motor for calculations.
* There is an inconsistency between the models. This inconsistency is the presence of the fuselage within the Modelica model, which is modeled for aerodynamics purposes, but unrepresented in the architecture model. This inconsistency is corrected by adding the fuselage to the architecture model.
# Chitosan Nanocomposite Coatings for Food, Paints, and Water Treatment Applications
## 2 Chitosan and its Properties
### Physico-Chemical Properties of Chitosan
#### 2.2.3 Solubility  
Chitosan is a semi-crystalline polymer due to the strong inter- and intra-molecular hydrogen bonds. Solubility plays a critical role in various applications of chitosan as it is readily soluble in dilute acidic solutions at pH 6.0, but insoluble in most organic solvents. The pKa value of primary amine groups of chitosan is ~6.3, and thus, under acidic conditions the amine groups are protonated, leading to the repulsion between positively charged macromolecular chains, which allows water molecules to diffuse in and solubilize the polymer. Chitosan precipitates from solutions at pH 6.0 or above, limiting the use of chitosan in basic conditions. Derivatives of chitosan such as acyl-chitosan [46], N-alkyl-chitosan [47], hydroxyalkyl-chitosan [48], PEG-chitosan [49], carboxymethyl chitosan acyl thiourea [50], and TEMPO-lacase oxidised chitosan [51] have been synthesized to improve the solubility in water over broader pH ranges. Water-soluble derivatives of chitosan have been reported to be effective in food, paints, and water treatment applications [52; 53; 54].
## 5 Results
### Spectral Unmixing Results
#### 5.2.1 Aster  
The abundance map of _rhydacites_ in ASTER showed a broad spatial distribution throughout the whole study area (Figure 5a). The highest abundance values were observed around Lofos Dome, at the central sector craters (Phlegethon, Mikros, and Megalos Polybotes craters), around the Nis-1  
Figure 5: Abundance maps of rhydacites (**a–c**), basaltic andesites (**d–f**), alluvial deposits (**g–i**), hydroxyl-bearing alteration (**j–l**), and sulfur (**m–o**). Left column corresponds to ASTER, middle column to Landsat-8/OLI, and right column to Sentinel-2 MSI (pixels with dense vegetation are masked). Abundance colors vary from low (blue) to high values (red) and correspond to positive abundance values (zero values have been excluded). Basemap: World Imagery of ArcGIS 10.4, Source: ESRI, DigitalGlobe, GeoEye, Earthstar Geographics, CNES/Airbus DS, USDA, USGS, AeroGRID, IGN, and the GIS User Community.  
and Nis-2 geothermal wells, inside of and at the south-southeastern rim of Stefanos crater and the southern and southeastern part of the study area. On the other hand, _basaltic andesites_ showed high abundances at few specific locations where hyrocalcites were present in low abundances (Figure 5d). The highest basaltic andesites abundances appeared only in a few pixels located inside Stefanos crater. A different pattern was observed for the _alluvial deposits_ (Figure 5g). In particular, high abundances of alluvial deposits were observed in the northeastern part of the study area and southwest of Stefanos crater. It is worth noting that in certain areas, such as southwest of Stefanos crater and northeast of the study area at Nis-1 and Nis-2 geothermal wells, both rhyodacites and alluvial deposits appeared in high abundances. Concerning the _hydroxyl-bearing alteration_, the corresponding abundance map (Figure 5j) showed a distinctive pattern in the central part of the study area, presenting three main centers: (1) Lofos dome and the central sector craters, (2) southeast of and inside Stefanos crater, and (3) the outside rims of Kaminakia craters. There was also a distinctive NE-oriented linear high-abundance pattern west of Stefanos crater that seemed to follow the main NE-SW striking fault system within the caldera. Finally, the spatial distribution of _sulfur_ (Figure 5m) generally followed the hydroxyl-bearing alteration pattern, with few local differentiations such as the inner part of Stefanos crater, Kaminakia craters, and the surrounding areas of the Nis-1 and Nis-2 geothermal wells.
###### Abstract  
The Multi-View Stereo model (MVS), which utilizes 2D images from multiple perspectives for 3D reconstruction, is a crucial technique in the field of 3D vision. To address the poor correlation between 2D features and 3D space in existing MVS models, as well as the high sampling rate required for static sampling, we proposeU-ETMVSNet in this paper. Initially, we employ an integrated epipolar transformer module (ET) to establish 3D spatial correlations along epipolar lines, thereby enhancing the reliability of aggregated cost volumes. Subsequently, we devise a sampling module based on probability volume uncertainty to dynamically adjust the depth sampling range for the next stage. Finally, we utilize a multi-stage joint learning method based on multi-depth value classification to evaluate and optimize the model. Experimental results demonstrate that on the DTU dataset, our method achieves a relative performance improvement of 27.01% and 11.27% in terms of completeness error and overall error, respectively, compared to CasMVSNet, even at lower depth sampling rates. Moreover, our method exhibits excellent performance with a score of 58.60 on the Tanks &Temples dataset, highlighting its robustness and generalization capability.  
multi-view stereo; transformer; depth inference; differentiable homography warping; image processing; neural network +
Footnote †: journal: _Applied_  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON] U-ETMVSNet: Uncertainty-Epipolar Transformer Multi-View Stereo Network for Object Stereo Reconstruction. _Appl. Sci._**2024**, 14, 2223. [[https://doi.org/10.3300/app14062223](https://doi.org/10.3300/app14062223)]([https://doi.org/10.3300/app14062223](https://doi.org/10.3300/app14062223))  
Academic Editor: [PERSON]  
Received: 3 February 2024  
Revised: 23 February 2024  
Accepted: 27 February 2024  
Published: 7 March 2024
## 5 Lactic Acid Bacteria
### Antimicrobial Compounds Produced by LAB
#### 5.3.2 Organic Acids  
Organic acids are the main fermentation products of carbohydrate metabolism from LAB. Examples include lactic acid, acetic acid, propionic acid, succinic acid, 2-hydroxybenzoic, and vanillic acid [15]. These acids create an unfavorable environment which inhibits the growth of Gram-negative and Gram-positive bacteria, yeasts, and molds in many food products [81]. Their antimicrobial action primarily involves (1) disrupting the bacterial cell wall and cell membrane structure, (2) lowering intracellular pH; and (3) causing extracellular acidification [82].  
The best characterized and effective metabolites are lactic and acetic acid [15]. Acetic acid is known for its effectiveness against different pathogens, particularly fungi [17]. As fermentation time increases, lactic acid concentration rises, and an increased effect of the lactic acid antifungal activity was observed [83]. In addition, synergistic antimicrobial action between lactic and acetic acid can be suggested [16]. In their protonated form, these organic acids are bioactive at low pH [15].  
[PERSON] et al. [84] inoculated six strains of _Escherichia coli_ on pea sprouts and then exposed to 0.2 mol/L of ascorbic acid, citric acid, and malic acid for 10 min. This exposition led to cell membrane damage and reduced cell proliferation. [PERSON] et al. [85] found that lactic acid was more effective towards bacteria, especially Gram-positive bacteria. Moreover, as the concentration of lactic acid increases, the efficacy of inhabitation against pathogens also increases. Another study conducted by [PERSON] et al. [86] reported propionic and acetic acids, lactic and acetic acids, and butyric acid as the primarily antifungal compounds in dairy fermentates from _Acidipropionibacterium junesenii_, _Lactobacillus rhamnosus_, and _Mucor lanccolatus_ strains. Other organic acids produced by _Bifidobacterium adolescentis_ inhibited _Penicillium expansum_, a mycotoxin (patulin) producing fungus [87]. It is relevant to highlight that _Penicillium_ and _Mucor_ are the main genera involved in dairy product spoilage, including yogurt [15; 88]. The studies prove the excellent antimicrobial activity of organic acids, which enhances the use of LAB as a clean label strategy for developing healthier and additive-free food products. In this sense, using organic acids can contribute to extending yogurt's shelf life and ensure the food quality within the clean label concept.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Triple Colocation Validation
#### 3.2.2 CYGNSS Storm-Specific Data Products
The somewhat higher CYGNSS errors are in part offset by the more frequent revisit rate of its constellation of satellites (~3 times per day for CYGNSS vs. once every 2-3 days for ASCAT-B and SMAP). The higher revisit rate is also a result of the use of GNSS-R, which lowers the per-satellite cost by approximately two orders of magnitude [6]. A direct scatterplot comparison between CYGNSS and ASCAT-B R34 products is shown in Figure 14 for all quadrants of all the storms included in the matchup dataset. ASCAT-B is chosen as the comparison reference because it has the best (lowest) RMSE measure of performance. The overall bias between the ASCAT-B and CYGNSS R34 products (mean difference across all matchup samples) is only ~2 km. The figure partitions the matchup samples by both storm intensity and quadrant (colors and symbols). The figure demonstrates that CYGNSS R34 performance is fairly consistent as a function of intensity and also between quadrants. Figure 13: RMSD and RMSE performance for MRG and NOAA products.
# Evaluation of Thorax Diameter Changes through Trunk Morphology and Different Running Intensities
## 1 Introduction
Recently, a 12-marker model has shown equivalent results to the spirometry technique when analysing breathing patterns, showing that models based on a reduced number of markers facilitate the test and offer satisfactory results [31]. Moreover, morphology has been observed to affect respiratory functioning [32]. Studies in anthropology show that the morphology of the thorax varies from one type of hominid to another depending on their lifestyle demands, concluding that running performance also depends on trunk shape, which encompasses important aspects such as the width of the pelvis, trunk flexion angle, lumbar lordosis, hip flexion, and breathing mechanics [32; 33]. Following these findings, more knowledge about the influence of thorax morphology is needed as a way to have clues about the sporting predisposition of the subjects. The analysis of breathing in sports also suffers from a lack of information about the change of the thorax volume throughout exercise. Such information would improve our knowledge about human body responses to physical activity and how morphology affects their predisposition to obtain a better performance during running. Despite the validation of the OEP technique as a reliable method for analysing kinematic variables, the principal cross-sections of the thorax have not been observed during exercise at different intensities, nor has there been an analysis of how the morphology of each subject may modify those variations. The objective of the present study was to analyse the variation of the thorax cross-sections during running exercises at different intensities and the influence of thorax morphology. The hypotheses were (1) that the increase of the cross-sections of the thorax would be greater when the exercise intensity increases and (2) that flatter trunks would experience a greater change in breathing patterns.
## 2 Guidance Law Formulation
### Pure Pursuit and LOS Guidance Algorithm  
The combination of pure pursuit and line of sight-based path following (PLOS) is a geometric technique for generating guidance law. The pure pursuit guidance law drives the UAV to the next waypoint, while the LOS guidance law steers the vehicle to toward the LOS. The guidance law of the pure pursuit is given in Equation (2), where \(k_{1}\) is a positive constant gain empirically determined such that the UAV path is smoothly parallel to the LOS path.  
\[\psi_{p}=k_{1}\left(\theta-\psi\right) \tag{2}\]  
Another LOS guidance law is given in Equation (3), where \(k_{2}\) is a constant selected to attract the UAV to be on the desired path.  
\[\psi_{LOS}=k_{2}d \tag{3}\]  
The PLOS guidance command for straight-line path following is a combination of both guidance laws in Equation (2), which is outlined in the following stepwise Pseudo code.  
1. Initialize the waypoints vector \(\to W_{i}=(x_{i},y_{i},h_{i})\), \(W_{i+1}=(x_{i+1},y_{i+1},h_{i+1})\), UAV current position \(p=(x_{p},y_{p},h_{p})\), and the path parameter \(\delta\).
2. Calculate the LOS angle \(\rightarrow\theta=atan2((y_{i+1}-y_{i}),(x_{i+1}-x_{i}))\).
3. Calculate the distance between the current position \(p\) and the next waypoint \(Wp_{i}\to R_{u}=\sqrt{(x_{p}-x_{i})^{2}+(y_{p}-y_{i})^{2}}\).
4. Calculate the cross-track error distance \(\to d=R_{u}\sin(\theta-\theta_{u})\).
5. Calculate the current miss distance \(S\) according to desired waypoint radius \(r\to S=\sqrt{(y_{i+1}-y_{i})^{2}+(x_{i+1}-x_{i})^{2}}\leq r\).  
Calculate the desired guidance law \(\rightarrow\phi_{c}=k_{1}\left(\theta-\psi\right)+k_{2}d\), which represents the desired bank angle.
## 2 State of the Art
### Identified Research Gap and Motivation  
Despite numerous publications on each individual technology, truly holistic evaluations of FDM and SLA remain limited. In particular:  
* Many comparative studies focus on one performance metric (e.g., tensile strength or dimensional accuracy) rather than capturing multiple factors (surface quality, mechanical properties, cost, energy use, environmental concerns) for the same geometry and build conditions.
* Post-processing steps, which can significantly impact surface finish, dimensional accuracy, and total manufacturing time, are often inconsistently reported or omitted altogether.  
Environmental burdens, such as recyclability or hazardous material handling, receive insufficient attention, even though they can be decisive in institutional or industrial settings.
* Tribological properties and the role of surface measurement techniques in AM characterization remain underexplored, despite their importance for functional applications requiring high-precision surfaces.  
Accordingly, there is a need for an integrated analysis that simultaneously measures and compares FDM vs. SLA under controlled experiments, bridging technical, economic, and environmental perspectives. By conducting a direct, multi-criteria comparison of these two prominent AM processes, we aim to offer a more robust basis for selecting the most appropriate technology in prototyping, functional testing, or low-volume production scenarios.
## 4 Conclusions  
This article points out that the flow-induced vibration in the DCM cooling system is mainly caused by the liquid nitrogen pulsation supplied by the pump, and the oscillation and impact caused by the two-phase flow of gas and liquid. To better understand fluid-induced vibration phenomena, this paper conducted a modal analysis of the DCM, coupled numerical simulation of fluid-thermal-solid, and flow field analysis of liquid nitrogen phase transition. The temperature distribution and coolant flow state during the operation of the DCM at the Shanghai Synchrotron Radiation Source beamline were successfully simulated, and the vibration response of the crystal's second crystal under various conditions was ultimately determined. Based on the results of numerical simulations, valuable recommendations are provided for designing and optimizing the DCM cooling system.  
1. The fundamental cause of the DCM vibrations induced by pulsating excitation is attributed to pipe resistance. In the design of the cooling system structure, measures can be taken to reduce pipeline resistance or flow velocity, such as choosing smooth pipes, increasing pipe diameter, reducing pipe length, and increasing the curvature of bends.
2. There is a linear relationship between the amplitude of velocity pulsation and the amplitude of DCM vibration. It is necessary to design a flow pulsation attenuator to weaken the amplitude of velocity pulsation fundamentally.
3. Elevating the modal frequency of the pipeline and ensuring that the excitation frequency is between the low first and second modal frequencies can reduce the amplification factors of low-order modal responses. When the frequency of the velocity pulsation is high, the pressure pulsation caused by the rapid conversion of energy has a significant impact.
4. When liquid nitrogen boils under high thermal load conditions, the generated two-phase gas-liquid flow will significantly affect the system's vibration. Adopting a high-pressure cooling system and utilizing large-radius bends in the piping is recommended to mitigate the adverse effects caused by slug flow.  
Conceptualization, X.G. and A.L.; methodology, A.L.; software, A.L. and K.C.; validation, X.G. and Q.L.; formal analysis, X.G.; investigation, Y.B.; resources, X.G. and Q.L.; data curation, S.L.; writing--original draft preparation, A.L. and W.Z.; writing--review and editing, X.G. and Y.B.; supervision, X.G.; project administration, X.G. and Q.L.; funding acquisition, X.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 62375261, 61974142) and National Key Research and Development Program of China (No. 2023 YFA1608603).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Thanks to [PERSON], a researcher in the Beamline Equipment Research Group, for his assistance in data checking and manuscript revision for this paper.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Validation of VMFDI  
To assess the performance of the VMFDI, it was compared with established drought indices such as the SPEI and GRACE-SDI, using observed drought events from 2003 to 2020. Drought events were identified using the SPEI values (SPE101, SPE103, and SPEI12) and GRACE-SDI values, with a threshold of \(-0.5\) for drought detection [18; 44; 58]. For the VMFDI, drought events were identified based on the multiyear average VMFDI values (\(\text{VMFDI}_{\text{mean}}\)) for the corresponding month. The details of the drought classification are shown in Table 2.  
A correlation analysis was conducted to examine the relationship between the VMFDI and other variables (VPD, SM, ET, PRE, SIF, NDVI, WUE, GPP, and LAI) to evaluate the sensitivity of the VMFDI compared to other drought indices. The correlation coefficient (Equation (3)) was calculated to quantify the strength of the relationships, with higher correlations suggesting greater sensitivity.  
\[\text{R}=\frac{\sum\limits_{\text{i}=1}^{\text{n}}\left(\text{X}_{\text{i}}- \overline{\text{X}}\right)\left(\text{Y}_{\text{i}}-\overline{\text{Y}} \right)}{\sqrt{\sum\limits_{\text{i}=1}^{\text{n}}\left(\text{X}_{\text{i}}- \overline{\text{X}}\right)^{2}}\sqrt{\sum\limits_{\text{i}=1}^{\text{n}}\left( \text{Y}_{\text{i}}-\overline{\text{Y}}\right)^{2}}} \tag{3}\]  
where \(\text{X}_{\text{i}}\) and \(\text{Y}_{\text{i}}\) are the values of the corresponding variables at time \(\text{i}\); \(\overline{\text{X}}\) and \(\overline{\text{Y}}\) are their mean values.
# Enhanced Feature Selection via Hierarchical Concept Modeling
## 3 The Proposed Models for Feature Selection
The paired design helps manage individual differences, minimizing the impact of confounding variables and optimizing data economy by leveraging the paired nature of observations. Furthermore, it serves as an efficient method for testing differences between two related conditions or measurements, offering a straightforward and practical means for comparing means within the same group. We performed feature selection based on a hierarchical structure as follows. On level \(l\) of hierarchical structure, for any \(l\in\{1,2,\ldots,k\}\), we defined the total accuracy as follows. \[TotalAcc_{l}=\sum_{i=1}^{n}\frac{Acc_{l,i}}{n} \tag{1}\]  
where \(Acc_{l,i}\in\{0,1\}\) is the \(i\)th accuracy on level \(l\) of the decision tree, and the values 1 and 0 mean that prediction of \(Acc_{l,i}\) is true or false, respectively. To find the set of chosen levels, we denoted by \(SoCL\) where level 0 is definitely chosen. Next, we consider sequentially the next level of decision tree by using the two-samples \(z\) test between total accuracy on level \(l\) and \(l-1\) defined as  
\[Z_{l,l-1} =\frac{TotalAcc_{l}-TotalAcc_{l-1}}{\sqrt{\hat{p}\hat{q}}\Big{(} \frac{1}{n}+\frac{1}{n}\Big{)}}\] \[=\sqrt{\frac{n}{2\hat{p}\hat{q}}}(TotalAcc_{l}-TotalAcc_{l-1}) \tag{2}\]  
where \(\hat{p}=\frac{\sum_{i=1}^{n}Acc_{l,i}+\sum_{i=1}^{n}Acc_{l-1,i}}{n+n}\) and \(\hat{q}=1-\hat{p}\) for any \(l\in\{1,2,\ldots,k\}\). The level \(l\) will be added to set \(SoCL\) depending on the level of significance \(\alpha\). This means we use \(\alpha\) to consider the significant difference between total accuracy of level \(l\) and \(l-1\). If the total accuracy of level \(l\) is significantly different from the total accuracy of level \(l-1\), we will add \(l\) into the set \(SoCL\). Otherwise, we do not add \(l\) into the set \(SoCL\) and stop the level finding process. Table 1 shows some values of the significance level \(\alpha\) and their corresponding confidence intervals for \(z\). For example, when we consider the level of significance \(\alpha\)=0.01, if \(|Z_{(l,l-1)}|\leq 2.576\), then we add \(l\) to the set \(SoCL\). Otherwise, \(l\) is not added to set \(SoCL\), and we stop the process. We denote the set of chosen attributes by \(SoCA\). If \(m\) is the last level added to the set \(SoCL\), we have set \(SoCL=\{0,1,2,\ldots,m\}\) and set \(SoCA=\bigcup_{i=0}^{m}Att_{i}\), where \(Attr_{i}\) is the set of all attributes on level \(i\). We present the proposed feature selection algorithm using FCA and decision tree, presented as Algorithm 1. \begin{table}
\begin{tabular}{l c c c} \hline \hline \(\alpha\) & **0.01** & **0.05** & **0.1** \\ \hline \(z\) & \(-2.576\leq z\leq 2.576\) & \(-1.96\leq Z\leq 1.96\) & \(-1.645\leq z\leq 1.645\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The mapping from \(\alpha\) to confidence interval for \(z\).