esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Droplets Size Evolution
#### 3.2.3 Droplets Deformation during Destabilization  
Basically, destabilization of emulsion involves at least three steps process, namely flocculation, coalescence, and finally separation of the oil and water phase. In this work droplets deformation was carried out intentionally by addition chemical agent, to speed up destabilization. The observation of droplets deformation as a function of chemical agent concentration which was introduced into the emulsion system is presented in Figure 6 and is summarized in Table 5.  
The DSC test shows the displacement T* to the higher temperatures from \(-\)35.6 to \(-\)30 \({}^{\circ}\)C for the sample C-2.5 and from \(-\)35.5 to \(-\)21 \({}^{\circ}\)C for the sample C-5 indicates that the population of the singles droplets has coalesced to form a large size due to the presence of the chemical agent. These results are in line with the microscopic observation where a bigger population of large droplets present in some part of the sample containing \(\leq\) 5% w/w of chemical agent compare to the one in 2.5% w/w. The droplet  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Storage, 54 \({}^{\circ}\)C**} \\ \cline{2-5}  & **0 Day** & **5 th Day** & **10 th Day** & **14 th Day** \\ \hline T* (\({}^{\circ}\)C) & \(-\)35.5 & \(-\)35.5 & \(-\)34.8 & \(-\)33.8 \\ D43 (\(\mu\)m) & 10 & 10 & 11 & 14 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Droplet shelf-life during storage.  
Figure 6: Observation droplet deformation during destabilization by; (**a**) DSC test; (**b**) microscopy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Chemical Agent (\% w/w)**} \\ \cline{2-5}  & **0** & **2.5** & **5** & **7.5** & **10** \\ \hline T* (\({}^{\circ}\)C) & \(-\)35.5 & \(-\)30 & \(-\)21 & \(-\)17 & \(-\)13 \\ D43 (\(\mu\)m) & 10 & 28 & 163 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 5: Droplets deformation during destabilization.  
size measurement is found to be about 28 \(\upmu\)m and 163 \(\upmu\)m for the samples, respectively C-2.5 and C-5. For the samples containing 7.5% and 10% w/w of chemical agent, the shape of the signals observed is totally changed (Figure 6a, C7.5 and C10) in a form like that of the aqueous solution. It implies that the emulsion has totally broken. These results were then confirmed using optical microscopy observation (Figure 6b, 4) were presents that dispersed droplets population in the emulsion system has disappeared.
## Results  
### Data Set  
Figure 1 shows a PRISMA diagram [20] of the procedure of data collection for the meta-analysis. As a result of searching articles, 1487 articles were detected in the various online databases, and those articles were screened and excluded the duplicated and inappropriate studies. Finally, 47 research articles with data expressed as mean and standard deviation or -error were collected and considered in the meta-analysis. The review articles, articles not related to TJP expression in intestinal tracts, and book chapters were excluded. Table 1 shows the properties of the collected research articles. The 47 included articles were published between 2007 and 2021. The animals used in the studies were mice, rats, rabbits, chicks, and turbots. Among these, most of the collected research (32 articles) was performed using mice, specifically strains C57 BL/6, Balb/c, Swiss Albino, and ICR. The dextran sodium sulfate (DSS) was the most commonly identified factor in intestinal epithelial cell loss in the research. Other factors were 2,4,6-trinitrobenzene sulfonic acid (TNBS), dinitrobenzene sulfonic acid (DNBS), acetic acid, ovalbumin, water avoidance stress (WAS), adenine, homocysteine, popsin-trypsin-digested gliadin (PTG), sodium fluoride, alcohol, aging, and microbes, including _S. typhimurium_, _E. coli_ and _A. hydrophilia_.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Authors** & \begin{tabular}{c} **Animal** \\ **(Train)** \\ \end{tabular} & \(\pi\) & \begin{tabular}{c} **Induced** \\ **Chemical** \end{tabular} & \begin{tabular}{c} **Treated** \\ **Time (d)** \\ \end{tabular} & **Probotics 2** & \begin{tabular}{c} **Administration** \\ **Form** \\ \end{tabular} & \
\begin{tabular}{c} **Analytical** \\ **Items** \end{tabular} & **TJP Evaluation 4** \\ \hline [PERSON] et al. [23] &
\begin{tabular}{c} Mouse \\ (C57 BL/6) \\ Mouse \\ (Balb/c) \\ (ICR) \\ \end{tabular} & 10 & DSS & 12 & Mixed culture & Gavage & Occladin, & Western blot \\  & & & & & _L. thummosus_ & & ZO-1 & Western blot \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Studies used in the data set and their information for the meta-analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
Figure 1: The PRISMA flow diagram of the procedure of data collection for the meta-analysis.
# A Study of Model Iterations of Fitts' Law and Its Application to Human-Computer Interactions
## 2 Physiological Basis of Fitts' Law
### Various Formulations and Studies on Fitts' Law as It Relates to Human Motor Behavior  
The human body transmits signals in phases as people respond to tasks, so information is not processed instantly and actions take time to complete [8]. Longer distances impair the accuracy of faster actions, as this delay impacts both movement speed and accuracy. Based on these factors, the Fitts formula was created to forecast movement time. Fitts' law was first proposed fifty years ago, and since then, a great deal of study has been conducted on the physiological mechanisms behind movement, demonstrating the law's wide application. Fitts' law has so established itself as a common tool in the field of HCI studies [5].  
[PERSON] first proposed in 1899 that a basic component of athletic conduct is a trade-off between accuracy and speed [9]. Expanding on this idea, [PERSON] suggested that the average amount of information (measured in bits) needed to carry out a movement corresponds with the amount of time needed to finish a task. This theory affirms that the trade-off between speed and accuracy is a fundamental characteristic of human motor activity and highlights the crucial role that this trade-off plays in human motor behavior.
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 3 Parent and PHY Selection Heuristic  
In this section, a computationally efficient heuristic algorithm is proposed that allows TSCH slot bonding nodes to make a parent and PHY selection. The heuristic is a step towards a distributed selection mechanism that could be integrated into the RPL or any other routing protocol. First, it is re-iterated how parent nodes are normally selected in a single PHY network and afterwards the motivation for this heuristic is explained. Then, the heuristic itself is presented. As a conclusion, insight into how this heuristic could be integrated into RPL is provided.
# Development of Jasmine Rice Flour Properties as a Safe and Efficient Ingredient for Compact Powder
## 3 Results
### Testing of Satisfaction  
The analysis of satisfaction toward the developed product (F2) provided the highest satisfaction level overall (concealment, slipperiness, skin adhesion, color, spread, and skin-softening). Averages of satisfaction with the developed product and the commercial product were 4.22 and 3.79, respectively, as shown in Figure 7. The paired samples \(t\)-test (SPSS; Version 23.0) indicated a statistically significant difference in the satisfaction between the developed product and a commercial product (\(p<0.05\)) in terms of slipperiness, spread, and skin softening. When considering each aspect, the results indicated that the highest average value of the satisfaction was the spread (4.52), followed by the skin softening (4.40), slipperiness (4.24), skin adhesion (4.16), concealment (4.08), and color (3.92).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Compact Powder**} & \multicolumn{3}{c}{**Before Applying the Compact Powder**} & \multicolumn{3}{c}{**After Applying the Compact Powder**} & \multicolumn{3}{c}{**Difference in Values (After-Before Applying the Powder)**} \\ \cline{2-9}  & **L\({}^{*}\)** & **a\({}^{*}\)** & **b\({}^{*}\)** & **L\({}^{*}\)** & **a\({}^{*}\)** & **b\({}^{*}\)** & **L\({}^{*}\)** & **a\({}^{*}\)** & **b\({}^{*}\)** \\ \hline The talcum-compact powder (F1) & 45.88 \(\pm\) 0.81 & 8.62 \(\pm\) 0.10 & 11.56 \(\pm\) 1.06 & 62.81 \(\pm\) 0.87 & 7.36 \(\pm\) 0.38 & 13.87 \(\pm\) 1.29 & 16.93 \(\pm\) 1.50 & \(-\)1.26 \(\pm\) 0.29 & 2.31 \(\pm\) 1.93 \\ \hline The modified rice flour compact powder (F2) & 47.97 \(\pm\) 1.61 & 8.89 \(\pm\) 0.25 & 10.47 \(\pm\) 0.59 & 58.46 \(\pm\) 2.07 & 7.56 \(\pm\) 0.22 & 12.69 \(\pm\) 0.60 & 10.49 \(\pm\) 0.64 & \(-\)1.33 \(\pm\) 0.26 & 2.21 \(\pm\) 1.05 \\ \hline Commercial product & 43.18 \(\pm\) 1.80 & 8.45 \(\pm\) 0.17 & 9.47 \(\pm\) 0.71 & 60.30 \(\pm\) 3.27 & 7.20 \(\pm\) 0.14 & 14.01 \(\pm\) 2.06 & 17.11 \(\pm\) 5.86 & \(-\)1.25 \(\pm\) 0.04 & 4.53 \(\pm\) 3.12 \\ \hline \hline \end{tabular} Note: Number of experiments: 3: Data given in mean \(\pm\) SD.  
\end{table}
Table 2: Mean of color values (color’s lightness (L’), red/green intensity (a\({}^{*}\)), yellow/blue intensity (b\({}^{*}\))), before and after applying the compact powders, and the difference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Wavelength (nm)**} & **Talcum Compact** & **Modified Rice flour** & **Commercial** \\  & **Powder (F1)** & **Replaced Talcum (F2)** & **Compact Powder** \\ \hline
290 & 1.140 & 1.143 & 1.117 \\
295 & 0.618 & 0.621 & 0.608 \\
300 & 0.374 & 0.377 & 0.368 \\
305 & 0.311 & 0.314 & 0.306 \\
310 & 0.283 & 0.285 & 0.278 \\
315 & 0.263 & 0.266 & 0.260 \\
320 & 0.235 & 0.244 & 0.235 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Absorbance of each compact powders used in the calculation of SPF data.
## 4 Data Aggregation
### Spatial
Using the aggregation model in Equation (20), the optimal cluster radius is different for different regions of the network fields, and this is the reason that the paper proposes to divide the network into unbalanced clusters according to the optimal cluster radius calculated for each region. The simulation results presented by the paper show some improvements in terms of average energy consumption in network _versus_ different cluster numbers of equal-size cluster protocols.
## 3 Results and Discussion
### Effect of Adsorption Conditions
#### 3.3.4 Adsorption Isotherms  
Four adsorption isothermal models, the linear/nonlinear Langmuir and Freundlich equations, were used to study the adsorption of MB by CPP200 (Figure S2, Table S2). The higher coefficient of determination (\(R^{2}\)) showed that the linear Langmuir model was more suitable for describing the adsorption process than the others. As initial MB concentration was increased to higher values (\(\geq\)50 mg/L), the adsorption capacity (\(q_{e}\)) of CPP200 remained nearly unchanged at a certain temperature, indicating that monolayer adsorption behavior at the CPP200 interface occurred, which was consistent with the Langmuir isothermal model. We could propose that MB was mainly adsorbed on the adsorbent surface in the form of single-layer coverage, but there was no migration of MB onto the CPP200 surface plane [44].
## 3 Results and Discussion
### Source Model
In addition, the finding that the maximum slip was located approximately at the position with longitude of 120.4\({}^{\circ}\) E and attitude of 23\({}^{\circ}\) N was consistent with the inverted source model [2; 3; 4; 9]. Figure 5 demonstrates the slip amplitude distribution on the fault. Figure 4: Slip distribution on the indicated fault derived using the source inversion technique. The maximum slip value was approximately 2.4 m. The star represents the epicenter of the Meinong earthquake. From the inverted model results, the rupture process can be modeled as a large asperity with an area of approximately 15 \(\times\) 15 km\({}^{2}\), and the maximum slip was 2 m. The asperity located below the hypocenter and the high slip area was at a depth of approximately 14-18 km, suggesting that the main rupture consisted of strike slips and down-dip slips propagating from the hypocenter in the northwest direction.
## 3 Spatial-Temporal Dependency Modeling
### The Graph Convolutional Neural Network  
The convolution network witnessed a great success in the image recognition for its ability to extract the spatial feature and many research studies use it for the spatial dependency modeling of the wind farms. However, this kind of method needs to arrange the wind farms in a specific way and ignore the geographic location relationship among the wind farms. To deal with this case, it is necessary to expand the spatial dependency modeling method and develop the convolutional neural network for the graph data. There has been some research [26] about using the graph neural network for the short-term windspeed prediction and its effectiveness has been verified. We can also use the graph convolutional neural network for the spatial feature extraction in the ultra-short-term wind farm cluster power prediction.  
There are two methods to develop the graph neural network, namely, the spatial domain method and spectral domain method. However, the spectral method is based on graph Fourier transformation and has a relatively solid theoretical foundation that is more suitable for the wind power prediction.  
We use the first order approximation of Chebyshev spectral filter brought out by [PERSON] in the graph convolution layer [27,28] and the propagation mode between layers are as follows:  
\[X^{l+1}=\sigma\Big{(}\hat{D}^{-\frac{1}{2}}\hat{A}\hat{D}^{-\frac{1}{2}}X^{(l) }W^{(l)}\Big{)} \tag{7}\]  
\[\hat{A}=A+I_{n} \tag{8}\]  
In this equation, \(A\in R^{n\  
\[X_{mnp}^{(l)}(i,j)=\sigma\left(\sum_{s=1}^{N}\left(\sum_{k=1}^{n}\frac{1}{\sqrt{ \hat{D}(i,i)\hat{D}(k,k)}}\hat{A}(i,k)V_{t+s}(k)\right)W(s,j)\right) \tag{10}\]  
In the equation above, \(X_{p}^{(l)}(i,j)\) and \(X_{mnp}^{(l)}(i,j)\) are the spatiotemporal feature of the historical wind power and NWP, where \(i=1,2,\ldots,n\) and \(i=1,2,\ldots,d\). It can be noticed that the spatial correlation is adjusted by the \(\hat{A}\) and the temporal correlation is mapped by the \(W\).  
In this way, the temporal feature of wind farms is utilized in the network. Through this design, we can get the graph neural network that is suitable for our wind power prediction and we call it Graph Convolution Module. The structure of the graph convolutional network can be represented as Figure 3.  
Graph convolution can extend the convolution from the traditional Euclidean distance space to the general graph data by carrying out the convolution in the spectral domain. In practice, we should decide the depth of the GCN and the adjacent matrix. In our wind power prediction problem, we use two layers of GCN for the spatial dependency modeling considering too many layers will lead to the feature embedding indistinguishably, although it can increase the size of receptive filed [27].
## 2 Materials and Methods
### Data Acquisition  
The data mainly include climate and remote sensing datasets (Table 1). Rainfall data were obtained from Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data [38]. SPEI data were sourced from the SPEI database ([[https://spei.csic.es/](https://spei.csic.es/)]([https://spei.csic.es/](https://spei.csic.es/)), accessed on 14 September 2023) [39]. The vegetation types and their distribution of Guilin were extracted from the previous study [40].  
The Moderate Resolution Imaging Spectroradiometer (MODIS) employs advanced land surface data assimilation systems and modeling techniques to simulate satellite and ground-based observational data products, thereby generating precise surface flux data, including high-resolution Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and other indices, which play vital role in the Earth's climate and environment research. It is renowned for its high temporal resolution, multispectral capabilities, moderate spatial resolution, extensive data record, and wide range of applications. Since its inception in 2000, MODIS has provided over 20 years of continuous data. Specifically, NDVI, LST, gross primary productivity (GPP), and leaf area index (LAI) data from 2000 to 2022 are sourced from MOD13Q1 with a spatial resolution of 0.25 km, MOD11A2 with a spatial resolution of 1 km, MOD17A2H with a resolution of 0.5 km, and MOD15A2H with a resolution of 0.5 km, respectively. These datasets were downloaded from ([[https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)]([https://search.earthdata.nasa.gov/search](https://search.earthdata.nasa.gov/search)), accessed on 13 October 2023). The spatial resolution of these datasets was resampled to a uniform 1-km spatial resolution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Name**} & **Spatial** & **Temporal** & \multirow{2}{*}{**Period**} & \multirow{2}{*}{**Sources**} \\  & & **Resolution** & **Resolution** & & \\ \hline \multirow{3}{*}{Climate data} & Precipitation & 5.57 km & Daily & 2000–2022 & CHIRPS \\ \cline{2-6}  & SPEI & 0.50\({}^{\circ}\) & Monthly & 2000–2022 & Global SPEI \\ \cline{2-6}  & NDVI & 0.25 km & Monthly & 2000–2022 & MOD13Q1 \\ \cline{2-6} Remote sensing & LST & 1 km & Monthly & 2000–2022 & MOD11A2 \\ \cline{2-6}  & GPP & 0.50 km & Monthly & 2020–2022 & MOD17A2H \\ \cline{2-6}  & LAI & 0.50 km & Monthly & 2020–2022 & MOD15A2H \\ \hline \hline \end{tabular} Source: Field data collected by the authors, 2024.  
\end{table}
Table 1: Dataset summary.  
Figure 1: Guilin karst and non-karst area distribution patterns.
## 5 Experiments
### Experimental Result
Figure 7: Averaged performance for UDVRP200, which contains learning curves and average cost for PGDD (called DD), attention model (AM), deep reinforcement Learning (DRL), deep reinforcement-learning algorithm for path-planning (DRLP), deep q-learning (DQL), and pointer network (PN) with different baselines. Among them, each color corresponds to a type of method, and different wire styles represent different baselines or different improvement points. The following four conclusions can be drawn from the experimental results. * Small-Scale Superiority: PGDD achieves near-perfect success (98.7%) in 100-node tasks by balancing cost and satisfaction. * Path Smoothness: Measured by steering angle change rate \(\frac{\Delta\delta}{\Delta I}\). PGDD reduces this rate by 15% compared to baselines. * Scalability: Maintains >92% success rate in 400-node scenarios despite increased complexity. * Robustness: Fuzzy time-window penalties reduce late deliveries by 15.2% compared to fixed penalties.
## 2 Materials and Methods
### Diclofenac Sodium Release Studies  
The in vitro release of the drug from the synthesized MIL-88A materials was conducted using cellulose dialysis tubing (Sigma-Aldrich, Darmstadt, Germany). Before the liberation process, 25 mg of MIL-88A materials were added into the diclofenac solution (5 mg of DS in 3 mL aqueous solution) and agitated for 2 h. In the next step, the solution was evaporated overnight in an oven set at 60 \({}^{\circ}\)C. The release studies were performed in a phosphate buffer of pH 6.8, representing intestinal conditions and maintained at 37.0 \({}^{\circ}\)C \(\pm\) 0.5 \({}^{\circ}\)C. The system was stirred at a speed of 200 rpm. In order to prepare the dissolution medium, 100 mL of 0.1 M KH\({}_{2}\)PO\({}_{4}\) was mixed with 44.8 mL of 0.1 M NaOH and 55.2 mL of distilled water. At the interval of 15 min in the first 3 h and 30 min in the next 2 h, 3 mL of the mixture was collected, and the amount of released diclofenac sodium was assessed using a UV-Vis spectrophotometer at \(\lambda\) = 276 nm. Measurements were also conducted for the interval of 24 h and 42 h. Subsequently, the cumulative percentage of the released drug was calculated. The liberation results were correlated to five mathematical models to describe the release mechanism: zero-order (% DS release vs. time; Equation (4)), first-order (log of % DS release vs. time; Equation (5)), [PERSON]'s model (% DS release vs. square root of time; Equation (6)), Hixson-Crowell (cube root of % DS remaining vs. time; Equation (7)), and Korsmeyer-Peppas model (log of % DS vs. log time; Equation (8)) [41]. Experimental data were analyzed using OriginPro 2023 [42].  
\[F_{t} =k_{0}t_{e} \tag{4}\] \[F_{t} =1-e^{-kt}\] (5) \[F_{t} =k_{H}\sqrt{t}\] (6) \[\sqrt[3]{F_{0}}-\sqrt[3]{F_{t}} =k_{HC}t\] (7) \[F_{t} =kt^{n} \tag{8}\]  
\(F_{t}\) is the fraction of diclofenac sodium release in time; \(F_{0}\) represents the initial quantity of diclofenac sodium incorporated into the nanocarrier \(k_{0}\), \(k_{t}\), \(k_{H}\), \(k_{HC}\) and \(k\) are the release constants of particular kinetic models; and \(n\) is the diffusion exponent.
## 3 Results and Discussion
### Comparison of VOCs  
The comparison of VOCs of SJ, SW and DL is shown in Figure 3. PCA analysis showed that the variance explained by the first principal component (PC1) and second  
Figure 2: Comparison of the physicochemical indices (**A**) and antioxidant activities (**B**) of SJ, SW and DL (\(n=3\)). Error bars represent the standard deviation (SD). a and b represent significant difference.  
principal component (PC2) reached 75.7%, thus strongly reflecting the differences between samples [27]. The three groups of samples were distinctly separated from each other (Figure 3A), indicating significant differences in VOCs among the three groups. A total of 133 VOCs were detected by GC-MS, with 76, 79 and 99 identified in SJ, SW and DL, respectively (Figure 3B). Fermentation significantly increased the total content of VOCs, especially the ester components (Figure 3C). Esters were the most abundant components in all three groups. Their contents were increased about three to four fold by fermentation, followed by alcohols and carbonyl compounds (aldehydes and ketones). [PERSON] [28] also reported that esters were increased by 13.03% in SW after fermentation and dominant (87.37%) in flavour compounds, which improved the fruit and ester aroma of SW and promoted flavour maturation.  
Six types of flavour compounds were detected, only in SJ, and disappeared after fermentation; these compounds were \(\beta\)-copaene (t1), \(\alpha\)-murolene (t3), oxaloacetic acid (ac6), \(n\)-decanoic acid (ac8), tridecane (alk3), and 5-tetradecene (alk7) (Figure 3B). Fermentation produced 10 new VOCs, including benzyl alcohol (al16), 1,1-dimethyl-cyclopropane (alk2),  
Figure 3: Comparison of VOCs of SJ, SW and DL by GC-MS (\(n=3\)). (**A**) PCA analysis, (**B**) Venn diagram, (**C**) bar chart for comparison of various types of VOCs. Error bars represent the standard deviation (SD). a, b and c represent significant difference.  
3-ethyl-tridecane (alk5), styrene (alk4), 1-heptanol (al6), and 3-ethoxy-1-propanol (al5). SJ and SW shared 69 VOCs. Distillation promoted the output of a large number of VOCs in SW and 47 new flavour compounds were formed. Benzyl alcohol (al16), 1,1-dimethyl-cyclopropane (alk2), 3-ethyl-tridecane (alk5), styrene (alk4) and 2,3-butanediol (alk8) were only present in SW, but were not detected after distillation.