esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Profiling of Onion and Lemon Volatiles Using SPME/GC-MS and Multivariate Data Analysis
The figure highlights limonene and \(\delta\)-terpinene as the most discriminatory aroma compounds for the pickled lemon, whereas \(\beta\)-pinene and \(\alpha\)-citral were found distinctive for fresh lemon. Citral is a key flavor compound that accounts for lemon aroma [38]. Similarly, the OPLS-DA score plot of the pickled onion model (Figure 2C) discriminated between both sample groups, with 2-hexyl-5-methyl-3-(2\(H\))-furanone and 3,5-diethyl-1,2,4-trithiolane (identified as markers for pickled onion) being highly correlated to the variation of the pickled onion. 2-Hexyl-5-methyl-3(2\(H\))-furanone has previously been reported in \"shallot\" onion [39] as a decomposition fatty acid product derived from the onion lachryatory factor [40], suggesting that sulphur compounds also undergo such degradation during pickling. Similarly, 3,5-diethyl-1,2,4-trithiolane may occur due to the thermal decomposition of cysteine [41], as reported in heat-prepared _Allium_ essential oil [39]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Peak** & **rt (min)** & **KI** & **Name** & **Average \(\pm\) Std.** & **Average \(\pm\) Std.** \\ \hline
1 & 7.44 & 924 & 1-propenyl methyl disulphide & 2.91 \(\pm\) 2.19 & - \\
2 & 7.99 & 958 & Dimethyl trisulfide a  & 3.24 \(\pm\) 3.00 & 2.31 \(\pm\) 3.18 \\
3 & 9.79 & 1068 & 2-Acetylpyrrole & 0.26 \(\pm\) 0.15 & 0.00 \(\pm\) 0.00 \\
4 & 10.12 & 1089 & Unknown sulphur & 3.45 \(\pm\) 0.36 & 6.71 \(\pm\) 1.27 \\
5 & 10.31 & 1101 & Isopropyl \(\alpha\)-mercaptoethanolpropionate & 10.42 \(\pm\) 1.94 & 8.35 \(\pm\) 0.70 \\
6 & 10.89 & 1143 & Diethanol disulphide & 12.20 \(\pm\) 5.82 & 10.90 \(\pm\) 2.89 \\
7 & 10.99 & 1149 & Methyl pentyl disulfide & 0.24 \(\pm\) 0.10 & - \\
8 & 11.11 & 1157 & Allyl methyl trisulfide & 11.48 \(\pm\) 5.08 & 25.25 \(\pm\) 7.56 \\
9 & 11.83 & 1209 & \(\beta\)-Hydroxyethyl phenyl ether & 0.38 \(\pm\) 0.20 & - \\
10 & 11.88 & 1212 & Dimethyl tetrasulfide & 3.35 \(\pm\) 3.67 & - \\
11 & 12.16 & 1234 & 3-Isopropylbenzaldehydehyde & 0.26 \(\pm\) 0.05 & - \\
12 & 12.32 & 1246 & 4,7-Dimethylundecane & 0.20 \(\pm\) 0.10 & - \\
13 & 12.92 & 1292 & (Allylsulfanyl)acetonitrile & 2.72 \(\pm\) 3.67 & - \\
14 & 13.23 & 1316 & Dippropyl trisulfide & 4.44 \(\pm\) 1.26 & 1.28 \(\pm\) 2.21 \\
15 & 13.37 & 1328 & 3,5-Diethyl-1,2,4-trithiolane & 18.95 \(\pm\) 7.15 & 32.08 \(\pm\) 4.53 \\
16 & 13.42 & 1332 & Diallyl trisulfide isomer & 0.75 \(\pm\) 0.42 & - \\
17 & 14.43 & 1415 & 2-Hexyl-5-methyl-3(2\(H\))-furanone & 0.42 \(\pm\) 0.43 & 3.44 \(\pm\) 1.06 \\
18 & 15.41 & 1495 & Unknown hydrocarbon & 0.34 \(\pm\) 0.29 & - \\
19 & 16.76 & 1589 & 4,6-Diethyl-1,2,3,5-tetrathiolane & 10.03 \(\pm\) 9.17 & 9.69 \(\pm\) 1.13 \\
20 & 17.00 & 1605 & 2,4-Dimethyl-5,6-dithia-2,7-Nonadienal & 13.96 \(\pm\) 12.73 & - \\  & & & Total sulphur compounds \% & 98.14 & 96.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Relative percentiles of volatile components to total peak areas detected in fresh and pickled onion bulb using SPME/GC-MS measurements (\(n=3\)).
###### Abstract  
This paper developed a new method to easily record and automatically reproduce the 3D natural head position (NHP) of patients using a portable 3D scanner based on immediate calibration. We first optically scanned the patient's face using a portable 3D scanner, and the scanned model was easily aligned with the global horizon based on an immediate calibration procedure using a developed calibration plate. The 3D patient NHP Computed Tomography(CT) model was reproduced automatically by performing registration between the CT model and the optically scanned model in the NHP using a modified coherent point drift (CPD) algorithm. In a phantom experiment, we evaluated the developed method's accuracy using the error between the true and the calculated orientations in roll, pitch, and yaw directions. The mean difference was \(-0.05\pm 0.13^{\circ}\), \(0.08\pm 0.22^{\circ}\), and \(-0.05\pm 0.18^{\circ}\) in the roll, pitch, and yaw directions, respectively. The measured roll, pitch, and yaw directions were not significantly different from the true directions (\(p>0.05\)). The calibration procedure for aligning the scanner coordinate system was easy enough for an inexperienced user to operate, and the 3D NHP CT model could be reproduced automatically. The developed method could be used for diagnosing and treating orthognathic patients with facial asymmetry accurately and conveniently in dental clinics.  
natural head position (NHP); orthognathic surgery; optical 3D scan; coherent point drift (CPD) registration 2020 10 174 10.3390/app10010174
# Utilization of Waste Clay-Diatomite in the Production of Durable Mullite-Based Insulating Materials
###### Abstract  
Microstructural, mechanical and qualitative phase identification of durable mullite-based ceramics obtained by utilization of waste clay-diatomite has been studied. Mullite-based ceramics were fabricated using waste clay-diatomite from the Barosevac open-cast coal mine, Kolubara (Serbia). The raw material consists mainly of SiO\({}_{2}\) (70.5 wt%) and a moderately high content of Al\({}_{2}\)O\({}_{3}\) (13.8 wt%). In order to achieve the stoichiometric mullite composition (3 Al\({}_{2}\)O\({}_{3}\)-2 SiO\({}_{2}\)), the raw material was mixed with an appropriate amount of Al(NO\({}_{3}\))\({}_{9}\)-9H\({}_{2}\)O. After preparing the precursor powder, the green compacts were sintered at 1300, 1400 and 1500 \({}^{\circ}\)C for 2 h. During the process, rod-shaped mullite grains were formed, measuring approximately 5 \(\upmu\)m in length and a diameter of 500 nm (aspect ratio 10:1). The microstructure of the sample sintered at 1500 \({}^{\circ}\)C resulted in a well-developed, porous, nest-like morphology. According to the X-ray diffraction analysis, the sample at 1400 \({}^{\circ}\)C consisted of mullite, cristobalite and corundum phases, while the sample sintered at 1500 \({}^{\circ}\)C contained mullite (63.24 wt%) and an amorphous phase that reached 36.7 wt%. Both samples exhibited exceptional compressive strength--up to 188 MPa at 1400 \({}^{\circ}\)C. However, the decrease in compressive strength to 136 MPa at 1500 \({}^{\circ}\)C is attributed to changes in the phase composition, the disappearance of the corundum phase and alterations in the microstructure. This occurred despite an increase in bulk density to 2.36 g/cm\({}^{3}\) (approximately 82% of theoretical density) and a complete reduction in open porosity. The residual glassy phase (36.7 wt% at 1500 \({}^{\circ}\)C) is probably the key factor influencing the mechanical properties at room temperature in these ceramics produced from waste clay-diatomite. However, the excellent mechanical stability of the samples sintered at 1400 and 1500 \({}^{\circ}\)C, achieved without binders or additives and using mined diatomaceous earth, supports further research into mullite-based insulating materials. Mullite-based materials obtained from mining waste might be successfully used in the field of energy-efficient refractory materials and thermal insulators. for high-temperature applications  
**Keywords:** mullite; mining waste; diatomaceous earth/diatomite; sintering; compressive strength; thermal insulators
# Mechanical Behavior of Hollow Corrugated Sandwich Cylinders Under Inner Pressure Loading
## 5 Results and Discussion
### Minimum Weight Analysis of Hollow Corrugated Cylinder Shell  
Figure 6 shows the dimensionless minimum weight as a function of the dimensionless loading parameter \(P_{i}/E\) for the hollow corrugated sandwich cylinder and the homogeneous panel cylinder. By analyzing the data in Figure 6, the following findings can be obtained:  
1. The minimum weight of the hollow homogeneous panel cylinder exhibits approximately linear growth with respect to the limit load, whereas the hollow corrugated sandwich cylinder undergoes a gradual change in weight;
2. Compared to the homogeneous panel cylinder, when the inner pressure is relatively low (\(P_{i}<1.57\times 10^{-5}\cdot E\)), a higher minimum weight is required for the hollow corrugated sandwich cylinder to withstand the same inner pressure load. However, as the limit inner pressure load exceeds this threshold, there is a notable improvement in structural efficiency, resulting in the hollow corrugated sandwich cylinder having a lighter minimum weight than the homogeneous panel cylinder for the same inner pressure load;
3. Furthermore, when the thickness of the hollow corrugated sandwich cylinder \(h_{c}\) is varied, it is observed that the parameter \(h_{c}\) significantly influences the emergence of the inflection point. At relatively small thicknesses (\(t_{i}/h_{c}>0.026\)), the structural efficiency of the hollow corrugated sandwich cylinder is minimal. However, as the thickness increases to a certain value (\(t_{i}/h_{c}<0.026\)), the structural efficiency of the hollow corrugated sandwich cylinder becomes notably superior to that of the homogeneous panel structure. Moreover, as the thickness of the corrugated core increases, the structural efficiency also increases.  
Consequently, it can be concluded that the structural efficiency of the hollow corrugated sandwich cylinder is significantly higher when the loading pressure is relatively high, and there may be an inflection point with the increase in the loading. Additionally, the optimal structural efficiency is closely related to the topological parameters of the corrugated sandwich structure. Subsequently, the effect of every topological parameter on the occurrence of the inflection will be discussed, respectively.  
Figure 5: Stress distribution of typical corrugated cylinder shell (all parameters are from Table 1).
# A New Approach to Defining Uncertainties for MODIS Land Surface Temperature
## 2 Framework
### Algorithm Review  
The standard MODIS retrieval algorithm is based on the generalized split-window (GSW) approach [37], similar to the split-window method [38] used for the Advanced Very High Resolution Radiometer (AVHRR) data. LST is estimated as a linear function of the clear-sky top of atmosphere (TOA) brightness temperatures (BTs) from bands 31 and 32 centred on 11 \(\upmu\)m and 12 \(\upmu\)m respectively. The typical form of the equation is:  
\[ST=\ \Big{(}A_{1}+A_{2}\frac{1-\varepsilon}{\varepsilon}+A_{3}\frac{\Delta \varepsilon}{\varepsilon^{2}}\Big{)}\frac{T_{11}+T_{12}}{2}+\Big{(}B_{1}+B_{2} \frac{1-\varepsilon}{\varepsilon}+B_{3}\frac{\Delta\varepsilon}{\varepsilon^{ 2}}\Big{)}\frac{T_{11}-T_{12}}{2}+C \tag{1}\]  
where \(T_{11}\) and \(T_{12}\) are the TOA-BTs for the 11 \(\upmu\)m and 12 \(\upmu\)m channels respectively (bands 31 and 32), \(\varepsilon\) is the mean land surface emissivity of the two channels, and \(\Delta\varepsilon\) the emissivity difference between the channels (\(\varepsilon_{11}-\varepsilon_{12}\)). In the operational MODIS Collection 5 [1] and Collection 6 [5] implementations of the algorithm the retrieval coefficients: \(A_{j}\), \(B_{j}\) (\(j=1,2,3\)) and \(C\) are dependent on water vapour and zenith view angle, such that the algorithm is dependent on a linearization of the TOA BTs with the surface temperature and the controlling factors are the surface emissivity, atmosphere and satellite viewing angle.  
The products resulting from the implementation in this study are known as the ESA DUE GlobTemperature ([[http://www.globtemperature.info/](http://www.globtemperature.info/)]([http://www.globtemperature.info/](http://www.globtemperature.info/))) MODIS products: GT_MOD_2P and GT_MYD_2P for Terra-MODIS and Aqua-MODIS, respectively. In our implementation, this functional form of the algorithm in Equation (1) is also used for both instruments and we generate retrieval coefficients categorised into 13 classes of satellite viewing angle (in steps of 5\({}^{\circ}\)) and 8 classes of water vapour (in steps of 7.5 kg m\({}^{-2}\)). These retrieval coefficients are derived by minimizing the l\({}_{2}\)-norm of the model fitting uncertainty for each viewing angle-water vapour category. When applying the algorithm to MODIS TOA BTs at each pixel, the coefficients are interpolated to the actual satellite zenith angle and water vapour for the observation.
## 4 Discussion
### Limitations  
The life cycle of an ML model--from problem definition to deployment--requires rigor at every step. For automated BA estimation, CNNs have shown outstanding accuracy (MAE = 0.59 years, 7.08 mos.) in [PERSON] et al., (2019) [24], but they also face technical challenges such as image preprocessing, context integration, and standardized X-rays to ensuring that models can be generalized and compared across different populations. Further, in data-scarce clinical settings, [PERSON] et al., (2019) [26] proposed multi-scale data fusion framework by fusing CNN features with NSCT at multiple scales, markedly improving accuracy over conventional CNN spatial domain images, highlighting its potentiality in situations with limited annotated data.  
However, high costs of AI-based models limit widespread adoption. Commercial systems like _BoneXpert(r)_ are precise but expensive. In contrast, open-sources such as ResNet-50 and VGG-19 offer a cheaper alternative. In fact, [PERSON] et al., (2023) achieved a MAE of 0.32 years (\(\approx\)3.85 mos.) on the RSNA dataset using divide-and-conquer and Lightweight CNN architecture [97]. To remove manual annotation entirely, [PERSON] et al., (2023) [111] proposed a two-stage, annotation-free DL pipeline that uses attention (CBAM) to localize bone regions and adds gender as an auxiliary input, reaching MAE of 0.45 years (\(\approx\)5.45 mos.) on RSNA and 0.28 years (\(\approx\)3.34 mos.) on a private set, offering an efficient and interpretable option for clinical use.
# Experimental Study on Renewable Porous Carbon Dioxide Adsorbent Materials for Space Shuttles
## 2 Experimental Section
### Effective Thermal Conductivity of the Solid Amine  
#### 3.4.1 The Effect of Pressure  
Figure 5 shows the effective thermal conductivity of the solid amine at different pressures (0-10 bar) and ambient temperatures. The effective thermal conductivity of the solid amine in the N\({}_{2}\) atmosphere increased in an 'S' shape, which is consistent with ref. [3]. The effective thermal conductivity of the solid amine included gaseous thermal conductivity, adsorbed thermal conductivity, and solid thermal conductivity at low temperature [3].  
\[\lambda_{eff}=\frac{\lambda_{wv0}}{1+2\beta Kn}+\frac{\lambda_{c0}(1+a_{\lambda c }(T-T_{0}))\sqrt[3]{(1-\varphi)}}{\mu}+\frac{\lambda_{w0}(1+a_{\lambda w}(T-T _{0}))\sqrt[3]{q(1-\varphi)\frac{\rho_{z}}{\rho_{w}}}}{\mu} \tag{3}\]  
From Equation (3), it can be noted that filling gas can greatly influence the effective thermal conductivity of a solid amine. This can be explained by the Knudsen number (\(Kn\)), which is defined by the mean free path of the gas molecules divided by the pore size and adsorbed N\({}_{2}\). The pore size of the solid amine is calcul  
Figure 4: Amount and isosteric heat of water adsorption in the solid amine. (**a**) Isotherm adsorption in the solid amine. (**b**) Heat of water adsorption of the solid amine at 30 \({}^{\circ}\)C.  
Figure 5: Effective thermal conductivity at different pressures.  
functional theory (DFT) in combination with Barrett-Joyner-Halenda (BJH) provided by the manufacturer, as shown in Figure 6. It can be noted that the solid amine contains wide mesopores of 4 nm (check from fitting, especially for BJH). At low pressure (0-0.0001 bar), the variation mainly comes from the gaseous thermal conductivity because, at this region, \(Kn\) > 0.01, the collision between gas molecules increases with increasing gas pressure, and the exchange of energy between gas molecules is directly proportional to the number of gas molecules. At medium pressure (0.0001-0.01 bar), the increase in effective thermal conductivity comes from the gaseous thermal conductivity and adsorbed N\({}_{2}\). At this region, the adsorbed N\({}_{2}\) increases fast with the increase in pressure, which makes a contribution to the effective thermal conductivity of the solid amine. At high pressure (0.01-5 bar), adsorbed N\({}_{2}\) reaches saturation and the stagnant gas can be considered as a continuum with a thermal conductivity independent of gas pressure, which results in the saturation trend of the effective thermal conductivity.
## 4 Artificial Measurements
### State Measurement Equations  
Artificial measurements were derived using Equation (2). These equations were rearranged to generate measurements for each state using the previous time-step values and current and voltage measurements. The state measurement equations are presented below.  
\[\hat{U}_{pa,k+1}=OCV(SOC_{k})-U_{L,k+1}-R_{0,k}I_{s,k}-U_{pc,k} \tag{44}\]  
\[\hat{U}_{pc,k+1}=OCV(SOC)-U_{L,k+1}-R_{o,k}I_{s,k}-U_{pa,k} \tag{45}\]  
\[S\hat{O}C_{k+1}=OCV^{-1}\Big{(}U_{L,k+1}+U_{pa,k}+U_{pc,k}+R_{o,k}I_{s,k}\Big{)} \tag{46}\]  
where \(\hat{U}_{pa}\), \(\hat{U}_{pc}\), and \(S\hat{O}C\) are the measurements for each state of the battery model and \(OCV^{-1}()\) is the inverse function of \(OCV(SOC)\).
## 1 Introduction  
As a non-destructive testing method, acoustic emission (AE, i.e., stress wave emission) has been widely applied in concrete, rock, wood, ceramic, and so on. It comes from the transient elastic waves generated by the rapid release of energy in the local stress concentration region inside a stressed material [1]. In the field of geotechnical engineering, AE sensors can help to detect the micro noises released from particle interactions, such as particle crushing and/or sliding. Due to such particle interactions, shearing resistance is mobilized in sandy soils. Especially in dense sands, shear strains are easier to be localized into concentrated shear zones to form shear bands, such as inclined column chains of particles [2]. Thus, a growing number of researchers have been exploring the potential of applying the AE technique to locate shear bands in sandy soils [3, 4].  
Regarding the AE source location technique, it has been successfully conducted on concrete [5, 6, 7, 8]. On the other hand, in the field of geotechnical engineering, the AE source location technique can be traced back to landslide noise research [9]. Experiments were successfully conducted on small slopes of partially saturated sands to determine the precise origin of the noises from small-scale landslides using four conventional seismic geophones.  
With the development of advanced AE sensors in recent years, [PERSON] et al. [10] utilized these new AE sensors in small-scale pile model tests. They were the first ones to use an AE source location testing approach based on the principle of time difference of arrival (TDOA) and proved its feasibility to visualize the particle breakage behavior in dry sands. [PERSON] et al. [11] improved and successfully applied the developed AE source location technique on saturated silica sand in triaxial compression tests and visualized the initiation and evolution of shear bands. In addition, [PERSON] et al. [12] applied the improved method on saturated dense coral sand under different constant loading rates, aiming at locating shear bands in such soils.  
Although AE source location is achieved in the laboratory tests, due to some complex reasons, such as wave propagation and attenuation through the media, the accuracy needs to be confirmed. Thus, in this study, the accuracy of the improved 3D AE source location technique is verified by conducting pencil lead break (PLB) tests on a cylindrical metal specimen. Additionally, the effect of outside noises is also evaluated.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### AI-Based Insight and Predictive Analytics for Educators  
3.5.1. Introduction to AI-Based Insight and Predictive Analytics for Educators  
AI-based insight and predictive analytics involve the use of artificial intelligence algorithms and machine learning models to analyze educational data, extract meaningful insights, and predict future trends or learner outcomes [79]. These tools empower educators by providing data-driven decision support, enabling them to identify at-risk students, personalize instruction, and improve curriculum design. The integration of multimodal large language models (MLLMs) enhances these capabilities by processing and interpreting data from multiple modalities--such as text, images, audio, and video--offering a more comprehensive understanding of learner behaviors and needs [12].  
Traditional educational analytics often rely on structured data and predefined metrics, which may not capture the full spectrum of learner interactions and experiences. MLLMs can analyze unstructured data, such as discussion forum posts, assignment submissions, speech recordings, and facial expressions, providing deeper insights into student engagement, comprehension, and emotional states [6]. This multimodal approach enables educators to make more informed decisions and interventions to enhance learning outcomes.  
3.5.2. Technology Used: MLLMs in AI-Based Insight and Predictive Analytics  
The deployment of MLLMs in educational analytics involves several key technologies. Multimodal data analysis allows models like GPT-4 [9] and CLIP [14] to process and interpret data from various modalities, enabling the extraction of rich features and patterns from diverse data sources. Advanced language models perform natural language processing (NLP) and understanding (NLU) on textual data from student interactions--such as discussion forums, essays, and feedback--to identify topics, sentiments, and comprehension levels [19].  
In addition, MLLMs with computer vision capabilities analyze images and videos, such as classroom recordings or student-submitted media, to assess engagement, participation, and emotional states through facial expression recognition [80]. Tools like OpenFace provide robust frameworks for facial behavior analysis, enabling educators to gauge student emotions and engagement. Speech and audio processing models, such as Wav2 Vec 2.0 [65], transcribe and analyze audio data, offering insights from student presentations, oral exams, or verbal feedback.  
Furthermore, MLLMs contribute features to predictive models that forecast student performance, dropout risks, and learning trajectories based on historical and real-time data [81]. Techniques for integrating data from multiple modalities enhance the robustness and accuracy of predictive analytics [6].
## 3 Results and Discussion
### Thermal Characteristics of Packages Under Voltage Stress
Based on the above results, the changes in electrical characteristics due to voltage stress are expected to demonstrate a similar trend with thermal performance characterization. This would imply that the immersion cooling environment is more effective at suppressing the thermally induced performance degradation of semiconductor devices under elevated voltage stress conditions. To ensure consistency between measurement methods, additional temperature measurements were conducted under air conditions using a contact-based thermometer (Figure S1A). The results showed good agreement with infrared measurements at 2.5 V and 2.8 V, with differences within approximately 10% (Figure S2A,B). At higher voltages, stable contact could not be maintained because of thermal degradation of the attachment tape (Figure S1B).