esa-sceva/satcom-chunk-collection huggingface dataset:
# Intermitt Dynamic Compression Conters Anabolic Effects in Articular Cartilage
## 3 Results
### Long-Term Intermittent Compression Acts Synergistically with IGF-1  
We investigated the effect of long-term intermittent compression in explants treated with IGF-1. Explants were subjected to dynamic compression five times weekly. Naive explants were co-cultured as a control for the ability of compression to induce cartilage formation alone. Compression did not alter the metabolic activity in naive explants, or those treated with 100 ng/mL IGF-1 (Figure 3A,B). Compression of naive explants increased PRO-C2 compared to unloaded naive explants (Figure 3C). On day 21, PRO-C2 was 2.9-fold higher in compressed naive explants compared to unloaded naive explants (\(p\) < 0.0001) (Figure 3C,D). A significant separation between compressed IGF-1 treated explants versus unloaded IGF-1 explants was observed on day 21, where PRO-C2 was elevated 1.6-fold (\(p\) = 0.0215) (Figure 3C,D). On day 21, the absolute difference in PRO-C2 levels in compressed IGF-1 versus unloaded IGF-1 explants was 89 ng/mL; and 26 ng/mL in compressed naive versus unloaded naive explants. Thus, the compression-induced increase in PRO-C2 was 3.3-fold greater in IGF-1-treated explants than in naive explants, demonstrating a synergistic interaction (Figure 3D).  
Long-term intermittent compression resulted in significant FBN-C increases on day 21 of culture. Compression of naive explants increased FBN-C by 3.1-fold versus unloaded naive explants (\(p\) < 0.0001); and similarly, FBN-C was increased 2.5-fold in compressed IGF-1 versus unloaded IGF-1 explants (\(p\) = 0.0006). However, IGF-1 did not elevate FBN-C levels compared to culture media alone (Figure 3E,F).
## 3 Results and Discussion
### Quantitative Analysis of Soil Microstructure
#### 3.2.1 Equivalent Diameter of Pores  
Figure 6 shows the curve of the average equivalent diameter of pores under different consolidation times. The equivalent diameter decreased as the consolidation time continued to increase. This is because the deformation of the soil during the consolidation process was primarily caused by the combined influence of structural connection, structural units and pores. The existence of pores in soil samples, in particular, was the root cause of structural crushing and damage. Under the action of an external force, the connection of some soil particles was destroyed and created a structure of scattered debris. In addition, the large pores were extruded, and the area was reduced, after which the particles were cohesive, compressed and recombined with each other, resulting in a reduction in the particle spacing and a decrease in the average equivalent diameter of the pores.  
Figure 6: The curve of the average equivalent diameters of pores under different consolidation times.  
In addition, the change in the average equivalent diameter of pores throughout the consolidation process may be broadly divided into three stages, as shown in Figure 6: (1) At the initial stage of consolidation, with the alternate appearance of aggregation and damage of soil particles, the equivalent diameter decreased slightly. (2) At the middle stage of consolidation, the large pores were compressed sharply and filled with particles, the particles gradually approached, the structure seemed to be connected and the soil became more compact. (3) At the later stage of consolidation, as time passed, the adjustment of particle aggregation and dispersion in the soil slowed down, and the area of the pore approached a constant value. The rate at which the equivalent diameter decreased slowed, indicating that the consolidation process was nearly complete.  
It is worth noting that the equivalent diameter of the soil decreased quickly between 60 and 240 min, corresponding to the middle stage of consolidation, and the soil sample already belonged to the disturbed soil with poor structural strength. As a result, more attention should be paid to the control of soil deformation in practical engineering during the middle stage of piling.
# Multiparameter Fusion Decision Routing Algorithm for Energy-Constrained Wireless Sensor Networks
## 3 Proposed Routing Optimization Algorithm
### Principle and Implementation
#### 3.1.1 Establishment of the Forward Neighbor Information Table  
In the initial phase, the sink sends an initialization message to all of the nodes in the maximum power. After receiving this message, every node evaluates a similar distance to the sink, depending on the Received Signal Strength Indicator (RSSI). Then, the node sends the neighbor discovery message to nodes within the one-hop range in the default transmission power. The message consists of the node'sidentification, the distance to sink, the current residual energy, the cache queue size, etc. The node that received this message will measure the distance to its neighbor and establish forward neighbor set \(FN(i)\) according to Equations (1) and (2). Hence, the forward neighbor information table can be created.
# A Usable Encryption Solution for File-Based Geospatial Data within a Database File System
## 6 Discussion
### GIS Security Model and Security Analysis
#### 6.1.4 Accountability  
Enterprise systems normally include mechanisms to track user actions on sensitive data. Since a DBFS uses the underlying DBMS, database triggers can be coded to execute procedures that track and log any access or changes made to spatial files using SELECT or UPDATE queries, respectively [85]. In such a manner, even the actions of the DBFS administrator can be tracked.
# Digital Twins Driven Supply Chain Visibility within Logistics: A New Paradigm for Future Logistics
## 4 Digital Twins  
[PERSON] first proposed the idea of Digital Twins at a University of Michigan presentation on Product Lifecycle Management in 2003 [64]. NASA created the first actionable Digital Twins in 2011 as a way to forecast aircraft structural performance by examining and designed to simulate them as virtual objects. NASA scientists later describedDigital Twins as \"an interactive multiphysics, multiscale, probabilistic simulation of a vehicle or device that uses the highest quality physical models, sensor update fleet history, and so on, to mirror the life of its flying twin [65]. A Digital Twins is a unique, virtual representation of a physical thing that monitors and simulates both the physical state and behavior of the thing [17]. The digital copy is continually connected to the physical object(s) and updates itself to reflect real-world changes. Applied to products, machines and even entire business ecosystems, Digital Twins can reveal insights from the past, optimize the present and even predict future performance [65].  
The report explains the concept and rise of Digital Twins and how it creates value [66]. The technology, which involves using digital models to better understand and manage physical assets, is already well established in some industries and has the potential to change logistics operations [18] significantly. For a graphic representation, see Figure 6.  
Suppose the number of possible Digital Twins implementations increases by the day. In that case, it becomes evident that there is no existing consensus in the scientific world on what constitutes a typical Digital Twins. It may entice an organization to enthusiastically mark its current 3D-modeling systems that include asset-tracking technology as a Digital Twins. This strategy avoids diluting and underselling the complexity and future advantages of a fully-fledged Digital Twins [18]. The following list of features is a suggested description of what makes a Digital Twins interested in furthering the consensus and acting as a foundation for future scholarly endeavours. These features are based on the personal study as well as findings from scholarly works by [PERSON] et al. [66], [PERSON] et al. [65], [PERSON] et al. [17], and [PERSON] et al. [18]. The below are the characteristics:  
* A Digital Twins is a virtual representation (or model) of a physical object or process.
* The Digital Twins is continuously updated with real-time data to reflect the physical object or process's current state and behavior.
* The Digital Twins can help visualize and analyse the physical object or process, and by use of machine learning, further optimizations and predictions can be made.  
These characteristics can be used to widely cover any possible Digital Twins implementations, from something as small as a portion of a manufacturing line to something as big as the entire city.
# Preliminary Insights into 3D Cheiloscopy for Forensic Applications: A Pilot Study
## 3 Results  
In this pilot study, although a quantitative wrinkle count was performed, the primary focus was on assessing the repeatability of the methodologies and the level of detail captured. Given the exploratory nature of the research, numerical data were not the main objective. Our analysis highlighted that the analog impressions were often incomplete to varying degrees, influenced by both the operator and the acquisition method (Figure 5). In contrast, the digital impressions consistently provided complete and analyzable data, regardless of the operator (Figure 6). This finding underscores the greater reliability and standardization offered by 3D cheiloscopy compared to traditional analog techniques.  
A significant difference emerged in terms of the homogeneity and level of detail between the analog lip prints and the digital scans. The analog impressions obtained by the two operators were not always legible and, when they were, sometimes displayed different morphologies due to variables such as the pressure applied by the adhesive tape or inconsistencies in lipstick application. Out of a total of 20 analog samples, only 12 were deemed usable, eight acquired by the expert and only four by the neophyte operator.  
The digital scans, however, showed significant uniformity across both the patients and the operators, with slight differences observed mainly in the areas outside the perimeter border of the vermilion. However, once these discrepant areas were eliminated, the resulting 3D models presented substantially overlapping morphologies. These data suggest an important greater repeatability in the collection of information using 3D technology compared to traditional methods.  
To assess potential inconsistencies, the analog data were subjected to a manual wrinkle count and classified according to the [PERSON] and Tsuchihashi classification. The same classification was applied to the digital scans, but with an additional step: alongside the manual wrinkle counting, a surface curvature analysis of the 3D meshes was performed (Figure 7). This analysis highlighted the main visible wrinkles, providing an additional dimension of detail that is difficult to achieve with traditional methods. The [PERSON] and Tsuchihashi classification was easier to apply in the digital models due to the higher level of detail available.  
Finally, the meshes obtained from optical scans were aligned using MEDIT DESIGN software to conduct a surface deviation analysis, evaluating similarities and differences between the various samples [13,14]. Each scan, after noise removal, contained approximately 795,000 triangles. The surface deviation analysis showed an average similarity of 70% between scans of the same participant, regardless of the operator. Additionally, the data presented a standard deviation of 0.229 mm and a variance of 0.052 mm.  
In our study, we focused on a descriptive analysis of the data to evaluate the consistency and reliability of the two operators (an expert and a novice) in counting lip wrinkles using both the analog and digital methods:
## 5 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Logit Regression**} & \multicolumn{2}{c}{**Poisson Regression with Robust Standard Errors**} \\ \cline{2-5}  & **(a)** & **(b)** & **(c)** & **(d)** & **(e)** & **(f)** \\ \hline \multirow{2}{*}{_scalar_} & \multicolumn{2}{c}{0.521 ***} & \multicolumn{2}{c}{0.321 ***} \\  & & (0.187) & & (0.115) \\ \hline \multirow{2}{*}{_indic\_lib\_char\_cause\_of\_prgr_} & \multicolumn{2}{c}{0.428 ***} & \multicolumn{2}{c}{0.273 **} \\  & & (0.191) & & (0.122) \\ \hline \multirow{2}{*}{_accpt\_or\_d\_d\_d\_d\_b\_brle_} & \multicolumn{2}{c}{0.0696} & \multicolumn{2}{c}{0.0389} \\  & & (0.256) & & (0.146) \\ \hline \multirow{2}{*}{_only\_strong\_com4\_cl\_acc\_in_} & \multicolumn{2}{c}{0.116} & \multicolumn{2}{c}{0.0535} \\  & & (0.175) & & (0.100) \\ \hline \multirow{2}{*}{_communist\_past\_migr\_cause_} & \multicolumn{2}{c}{0.155} & \multicolumn{2}{c}{0.0914} \\  & & (0.169) & & (0.102) \\ \hline \multirow{2}{*}{_injust\_int\_acc\_migr\_cause_} & \multicolumn{2}{c}{0.16} & \multicolumn{2}{c}{0.0855} \\  & & (0.193) & & (0.110) \\ \hline \multirow{2}{*}{_deprad\_work\_ethic\_migr\_cause_} & \multicolumn{2}{c}{0.254} & \multicolumn{2}{c}{0.158} \\  & & (0.223) & & (0.130) \\ \hline \multirow{2}{*}{_excessive\_brun\_currency\_migr\_cause_} & \multicolumn{2}{c}{0.111} & \multicolumn{2}{c}{0.0677} \\  & & (0.182) & & (0.110) \\ \hline \multirow{2}{*}{CONSTANT} & \(\begin{matrix}-1.890\\ **\end{matrix}\) & \(\begin{matrix}-1.746\\ **\end{matrix}\) & \(\begin{matrix}-2.161\\ **\end{matrix}\) & \(\begin{matrix}-2.031\\ **\end{matrix}\) & \(\begin{matrix}-1.939\\ (0.157)\end{matrix}\) & \(\begin{matrix}-2.207\\ **\end{matrix}\) \\ \hline \multirow{2}{*}{_Observations_} & 1100 & 1098 & 1098 & 1100 & 1098 & 1098 \\ \hline \multirow{2}{*}{_LR chi2(df)1 Wald chi2(df)_} & 205.68 & 206.25 & 232.34 & 212.34 & 214.97 & 274.93 \\ \hline \multirow{2}{*}{_df_} & 1 & 7 & 18 & 1 & 7 & 18 \\ \hline \multirow{2}{*}{_Prob \_> chi2_} & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\ \hline \multirow{2}{*}{_Pseudo R square_} & 0.1697 & 0.1703 & 0.1918 & 0.1117 & 0.112 & 0.1244 \\ \hline \multirow{2}{*}{_AUC(ROC)_} & 0.7271 & 0.7356 & 0.7816 & N/A & N/A & N/A \\ \hline \end{tabular} Source: Own calculations for all specifications/scenarios progressively, including the core variable—scenario (a)/(d) and the background controls—scenarios (b) and (c)/(e) and (f). Note: Standard errors in parentheses. *, **, ***, indicate significance at 10%, 5%, and 1%, respectively.  
\end{table} 
Table 2: _Cont._
## 2 Materials and Methods
### Sample Processing
#### 2.5.3 FIB/SEM (Focused Ion Beam/Scanning Electron Microscope)  
Microstructural characterization was carried out using FEI Versa 3D SEM (Waltham, MA, USA). Through the use of the low vacuum (LV) mode in SEM, surface imaging was performed on the sample surface, which was not coated with a conductive layer. SEM measurements were performed at a water vapor pressure of 70 Pa and an accelerating voltage of 20 kV using a secondary electron (SE) detector.
## 4 Thermal and Structural Analysis and Design Improvements of the BC Region
### The First Update (v1) of the BC Region Design
#### 4.1.2 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Level A**} \\ \hline
**Path** & \(\mathbf{P_{m}/S_{m}}\) & \(\mathbf{(P_{m}+P_{b})/(K_{eff}\cdot S_{m})}\) & \(\mathbf{(P_{m}+Q_{m})/S_{em}}\) & \(\mathbf{(P_{m}+P_{b}+Q+F)/S_{ct}}\) \\ \hline Cap1 & 0.075 & 0.145 & 0.834 & 0.177 \\ Cap2 & 0.064 & 0.088 & 0.554 & 0.101 \\ Cap3 & 0.068 & 0.127 & 0.590 & 0.112 \\ AB\_Cap1 & 0.063 & 0.073 & 1.184 & 0.672 \\ AB\_Cap2 & 0.073 & 0.061 & 1.084 & 0.698 \\ AB\_A1 & 0.098 & 0.072 & 0.459 & 0.742 \\ AB\_A2 & 0.100 & 0.076 & 1.003 & 0.598 \\ AB\_B1 & 0.143 & 0.100 & 0.403 & 0.817 \\ AB\_B2 & 0.114 & 0.087 & 1.075 & 0.569 \\ AB\_C1 & 0.085 & 0.071 & 0.966 & 0.741 \\ AB\_C2 & 0.061 & 0.046 & 1.259 & 0.589 \\ CD\_A1 & 0.064 & 0.060 & 0.213 & 0.148 \\ CD\_A2 & 0.108 & 0.073 & 0.537 & 0.169 \\ CD\_B1 & 0.033 & 0.055 & 0.361 & 0.168 \\ CD\_B2 & 0.105 & 0.074 & 0.439 & 0.127 \\ CD\_C1 & 0.153 & 0.160 & 0.431 & 0.188 \\ CD\_C2 & 0.075 & 0.069 & 0.508 & 0.181 \\ EF\_A1 & 0.056 & 0.051 & 0.121 & 0.810 \\ EF\_A2 & 0.070 & 0.069 & 0.244 & 0.647 \\ EF\_B1 & 0.035 & 0.053 & 0.162 & 0.833 \\ EF\_B2 & 0.063 & 0.061 & 0.227 & 0.632 \\ EF\_C1 & 0.089 & 0.167 & 0.363 & 0.794 \\ EF\_C2 & 0.064 & 0.054 & 0.323 & 0.613 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RCC-MRx Level A criteria verification within FW and Cap. BC region v1. Figure 18: Selected paths within SPs. BC region v1—bottom view. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Level D**} \\ \hline
**Path** & \(\mathbf{P_{m}/S_{m}}\) & \(\mathbf{(P_{m}+P_{b})/(K_{eff}\cdot S_{m})}\) & \(\mathbf{(P_{m}+Q_{m})/S_{em}}\) & \(\mathbf{(P_{m}+P_{b}+Q+F)/S_{ct}}\) \\ \hline Cap1 & 0.253 & 0.486 & 0.536 & 0.141 \\ Cap2 & 0.204 & 0.440 & 0.319 & 0.100 \\ Cap3 & 0.312 & 0.490 & 0.383 & 0.107 \\ AB\_Cap1 & 0.443 & 0.703 & 0.846 & 0.435 \\ AB\_Cap2 & 0.242 & 0.292 & 0.718 & 0.446 \\ AB\_A1 & 0.473 & 0.405 & 0.206 & 0.581 \\ AB\_A2 & 0.439 & 0.370 & 0.396 & 0.419 \\ AB\_B1 & 0.518 & 0.383 & 0.222 & 0.640 \\ AB\_B2 & 0.436 & 0.352 & 0.460 & 0.398 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RCC-MRx Level D criteria verification within FW and Cap. BC region v1. Therefore, as already achieved in the TC region design [4], the thickness of both the vertical and horizontal SPs of the last cell has been increased, moving to 14 and 12 mm, respectively.
# Effect of Ultrasonic Treatment on Taste and Flavor Quality of Japonic Rice
## 3 Results
### GC-MS Data Analysis
#### 3.5.2 Aroma Characteristics
Additionally, ketones were generated through the oxidative breakdown of unsaturated fatty acids and the degradation and synthesis of amino acids [28]. The non-aromatic saturated ketone geranyl acetone was consistently detected in cooked Japonica rice samples under all treatment conditions, contributing a floral aroma. Geranyl acetone positively influenced the aroma of cooked rice [23]. As ultrasound treatment time increased, the concentration of geranyl acetone significantly increased, thereby enhancing the role of ketones in the overall aroma profile of the cooked rice. In addition to the volatile compounds mentioned above, ultrasound treatment cooked Japonica rice contained esters and phenolic compounds, which contributed to the aroma complexity of cooked Japonica rice. Esters are produced through the esterification of carboxylic acids and alcohols. Phenols are redox products that are decarboxylated by phenolic acids during heating [29]. Phenols, such as 2-methoxy-4-vinylphenol, are mainly associated with smoking, 2-methoxy-4-vinylphenol, a derivative of guaiaicol, contributes to an unpleasant nutty, pungent, and clove-like odor [30]. The main increase in volatiles after ultrasound treatment was mainly in aldehydes and heterocycles compared to traditional cooked rice. Ultrasound treatment is different from the high hydrostatic pressure (HHP), the high hydrostatic pressure had a stabilizing effect on the low-molecular-weight volatiles, resulting in an increase in the alcohol, ketone, ester, and olefin content of the rice, but a decrease in the heterocyclic, alkane, and aromatic content [29]. Ultrasound treatment maintained the alcohol, ketone, and ester contents while increasing the aldehyde and heterocyclic contents. The increase in phenolic concentration caused by ultrasound treatment could have an adverse effect on the aroma of the cooked rice, further requiring validation screening. The concentrations of the remaining phenolic and ester compounds remained unchanged after different ultrasound treatments, which ensured the complexity of the cooked rice aroma after ultrasound treatment.
# A Multi-Year Study on Rice Morphological Parameter Estimation with X-Band Pulsar Data
## 1 Introduction  
Rice is the main source of food and income for several highly populated countries. The increasing population and limited arable lands bring out the need for higher yields that depends on the development of better management practices. The traditional method monitoring by visual inspection is not possible for kilometer-square areas. For such large scales, remote sensing based methods are good alternatives. Among different data sources, information provided by Synthetic Aperture Radar (SAR) is advantageous with its sensitivity to geometric and dielectric properties of the objects and its availability in all light and weather conditions. Therefore, SAR is a valuable tool for monitoring the rice fields [1].  
Understanding the phenological evolution of rice fields is important to develop effective management strategies. In the literature, several SAR data based algorithms have been developed to investigate the phenological evolution of rice fields including their canopy height, growth stage, leaf area index and yields. In SAR based rice monitoring, one way of monitoring the phenological cycle is the use of different SAR data analysis techniques including temporal trend analysis [2; 3], interferometric analysis [4; 5] and polarimetric interferometry analysis [6; 7]. The other approach employs EM models to simulate the polarimetric parameters from plant properties [5; 8; 9].  
This letter presents the inversion results of the metamodel-driven EM model for the estimations of stalk height, leaf length, and leaf width parameters from copolar X-band TerraSAR-X time series data, by following the methodology given in Figure 1, detailed and presented in [10] for a single year and growth stage parameter, specifically BBCH. Unlike [10], this study focuses on the feasibility of the proposed approach for morphology parameter estimation under different conditions. The chosen EM model [11] uses the biophysical properties of rice plants to simulate the backscattering intensities (\(\bar{\sigma}^{o}\)) with Monte-Carlo simulations. The computation costs related to the multi-dimensional algorithm and the Monte-Carlo simulations were reduced by introducing metamodels, which mimics the EM model after trained for once. The stability of the inversion was tested over a dataset of three different cultivation periods (2013-2015), which includes data from rice fields under different agricultural and environmental conditions.  
This paper has four sections. It starts with the proposed methodology in Section 2 by providing the EM model, its metamodel, and the inversion procedure. Sections 3 and 4 present the SAR and ground data followed by the inversion analysis results. The overall summary is provided in Section 5.