esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Aromatic Estolide Esters from Biobased Resources by a Green Synthetic Approach
## 3 Results and Discussion
### Enzymatic Synthesis of Oligo (Ricinoleate) Aromatic Esters  
The enzymatic synthesis of ricinoleic acid-based aromatic oligoesters provides a sustainable and efficient approach to oligomeric products with unique properties. Using lipase as a biocatalyst, the synthesis process involves the esterification of ricinoleic acid under mild conditions, which not only preserves the integrity of the monomer structure but also minimizes the environmental impact compared to traditional chemical methods.  
In this study, three different aromatic compounds (caffeic acid, cinnamyl alcohol and synapic acid) were investigated as substrates for the synthesis of aromatic oligoesters of ricinoleic acid. The reaction conditions (biocatalyst, molar ratio, temperature, solvent, reaction time) were selected based on our previous results concerning oligoesters of hydroxy acids [20] and some preliminary experiments. These preliminary experiments evidenced that the immobilized lipase from _Pseudomonas stutzeri_, the most efficient for the oligomerization of ricinoleic acid [20], was not effective for the endapping esterification with an aromatic acid, unlike the immobilized lipase from _Candida antarctica_ B (Novozyme 435), which was consequently selected for these studies.  
A general reaction scheme is presented in Figure 1.  
The reactions were performed in toluene for 72 h, as described in the Methods Section. Monoesters of RCA with the aromatic alcohol or acid used in the reaction are also possible  
Figure 1: Reaction schemes for the enzymatic synthesis of estolide esters from ricinoleic acid and (**A**) cinnamyl alcohol, (**B**) sinapic acid or caffeic acid.  
to be synthesized, as well as homo-oligomers of RCA. The products were not purified for the removal of the RCA estolide.
# Evaluation of Positioning Accuracy of Smartphones under Different Canopy Openness
## 2 Materials and Methods
### Data Processing  
G138 BD exports waypoint data through GIS Office software and the original data of starfish IRTK2 is directly exported by the device to a CSV format record file on a PC through a USB data cable. RINEX data obtained by smartphones is processed by RTKLIB 2.4.2 software. The following settings need to be selected before using the RTKPOST module in RTKLIB to process original GNSS data: positioning mode: single point/static PPP, frequency: L1 + L5 + E5, filter type: combination, cut off height angle: 15, constellation: GPS + BDS + GLONASS + Galileo + QZSS, integer ambiguity solution: continuous signal processing. After processing the original observation data with RTKPOST, use RTKPLOT module to draw the image, which can visually compare the distance and direction of the coordinates of the observation value and the coordinates of the test point and provide basic error statistical information (mean value and standard deviation). HGO v2.0.4 software is used to convert geographical coordinates and projection coordinates in the experimental data.  
The root mean square horizontal error (\(RMS_{xy}\)), one of the most common accuracy measurement methods in geodesy, is used as the primary index to compare the positioning accuracy of devices.  
The first step is to calculate the root mean square X and Y coordinate errors:  
\[RMS_{x}=\sqrt{\frac{\sum_{i=1}^{n}\Delta X_{i}^{2}}{n}}\quad RMS_{y}=\sqrt{ \frac{\sum_{i=1}^{n}\Delta y_{i}^{2}}{n}}\]  
Figure 4: Test points openness and classification.  
where \(\Delta x_{i}\) and \(\Delta y_{i}\) are the average coordinate error, which is the difference between the measured value of GNSS and the actual value, and n is the number of epochs. Then calculate the root mean square horizontal error \(RMS_{xy}\) as follows:  
\[RMS_{xy}=\sqrt{\left(RMS_{x}^{2}+RMS_{y}^{2}\right)}\]  
Compare the error of the tested devices with the minimum value, maximum value, average value, and standard deviation (SD). The statistical software SPSS24 was used for the one-way analysis of variance (ANOVA) of least significant difference (LSD) post test to evaluate the differences between devices and different canopy openness. The mean value, standard deviation, maximum value, and minimum value of the position error obtained at different test points are used to evaluate the impact of different canopy openness on positioning accuracy. The design of the experimental method and the steps of data processing are well illustrated in Figure 5.
## 4 Results on Australian HSR Risks  
### Survey Results  
#### 4.1.1 Survey Overview  
[PERSON] [55] notes that surveys should be open long enough to reach the target audience. However, they should not be left open for too long, as it introduces time-related biases in the research. [PERSON]'s publication found that the appropriate survey duration ranges from 3 to 8 weeks [55], thus supporting this research's 7-week period.  
Rapid changes characterise the Australian HSR domain due to continuous advancements in technology, evolving policies, and shifting public opinions. New information frequently comes to light, influencing stakeholder perspectives and the feasibility of such projects. Given this dynamic environment, a 3- to 8-week period proposed in [PERSON]'s research for conducting surveys is considered reasonable, further substantiated by a 7-week survey period requiring minimal resources.  
While 44 responses may seem limited given the broad societal implications of high-speed rail (HSR), this study specifically targets professionals within the infrastructure and construction industry, as they are directly involved in the planning, design, and delivery of such projects. The aim was not to capture the views of all stakeholder groups, but to explore industry perspectives on barriers to HSR implementation. From a statistical standpoint, the sample size exceeds the threshold at which the Central Limit Theorem (CLT) applies, supporting the reliability of observed trends within this group [56]. However, the findings should be interpreted within this scope, and future research could expand to include perspectives from policymakers, economists, and the general public to enrich the analysis.  
The demographic of the survey participants is shown in Figure 6 below. Nearly 40% of the respondents had over 21 years of industry experience, providing confidence in the insights gathered, reflecting the perspectives of seasoned professionals with extensive knowledge and expertise in the field. Most respondents had 0-5 years of industry experience, ensuring that the survey captures the mean sentiment from both ends and offers a well-rounded view of industry perspectives.  
The participants in this survey have worked on megaprojects across various industry sectors, as shown in Figure 6. Notably, 33% of respondents have experience specifically in rail sector megaprojects. Given the relevance of their expertise in HSR projects, this lends further validity to the survey results.
###### Abstract  
Production performance of heavy oil deposits in Xinjiang oilfield developed by vertical-well cyclic steam stimulation (CSS) is increasingly challenged by reservoir heterogeneity, which is comprised of original sediment heterogeneity and steam-induced heterogeneity. In order to understand the impacts of sedimental heterogeneity and high-speed steam injection to steam conformance, and strategies to maximize steam swept volume, a series of experiments were designed and implemented. Three-tube coreflooding experiments were performed to study the steam displacement dynamics under heterogeneous conditions, and a high-temperature plugging agent was developed. The coreflooding experiments indicate that the injection conformance deteriorates once the steam breakthrough occurs in a high-permeability tube, leaving the oil in the medium and low permeability tubes being surpassed. The optimized plugging agent could resist high temperatures over 260 \({}^{\circ}\)C and its compressive strength was 13.14 MPa, which is higher than maximal steam injection pressure. The plugging rate of high permeability core was greater than 99.5% at 220-280 \({}^{\circ}\)C with a breakthrough pressure gradient over 25 MPa/m. The field test validated its profile improvement feasibility with cyclic oil, 217.6% of the previous cycle. The plugging agent optimized in this study has significant potential for similar heterogeneous reservoirs.  
CSS; heavy oil; heterogeneity; steam conformance; plugging agent
## 3 Experimental Setup (Materials and Equipment)
### Materials  
Due to a non-disclosure agreement signed with the factory, it is not possible to publish the commercial names of the materials used, but the chemical and physical properties can be disclosed. This research investigated two grades of PC resins, referred to as R1 and R2 henceforth, each having a different melt flow index (MFI) value. R1 had an MFI of 25 and R2 had an MFI of 6.5 g/10 min. The resins were manufactured by General Electric (GE) and traded under the name of Lexan. Color pigments were in the form of powders; four different colored pigments, namely black, white, red and yellow, were used. In addition, three additives were also used, labeled as F1, F2 and F3. One was a stabilizer, another a light stabilizer and the third offered weather-resistant properties (see Table 3).
## 3 Results and Discussions  
### Factorial Experiment Design and Experimental Results  
The analysis is done on the design based on full-factorial DoE [42], which is a two-level factorial analysis, and all three factors are taken into consideration. The total number of runs performed is \(n\times L^{F}\) = 2 \(\times\) 23 = 16, in a random order, where \(n\) is the number of replicates, \(L\) is the number of levels and \(F\) is the number of factors. All eight combinations of factors \"A\", \"B\" and \"C\" are chosen as shown in Table 2.  
The values of \(I_{\text{2D}}/I_{\text{G}}\) and \(I_{\text{D}}/I_{\text{G}}\) peak intensity ratios (obtained from Raman characterization) for each of the eight sets of factor levels and each of the two replicates are collected, averaged and recorded in Table 2. Figure 1 shows the Raman Spectra for the experimental runs mentioned in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Controlling Factors**} & \multicolumn{3}{c}{**Responses**} \\ \cline{3-6}
**Runs’** & **Raman** & **Annealing** & **a-C Layer** & **Annealing** & \(I_{\text{2D}}/I_{\text{G}}\) **Peak** & \(I_{\text{D}}/I_{\text{G}}\) **Peak** \\
**Order** & **Spectrum in** & **Temperature** & **Thickness** & **Time** & **Intensity Ratio** & **Intensity Ratio** \\ \cline{3-6}  & & **A** & **B** & **C** & **(Response I)** & **(Response II)** \\ \hline
5 & (a) & 820 \({}^{\circ}\)C & 12 nm & 10 min & 0.300 & 0.600 \\
7 & (b) & 1020 \({}^{\circ}\)C & 12 nm & 10 min & 1.900 & 0.400 \\
1 & (c) & 820 \({}^{\circ}\)C & 36 nm & 10 min & 0.001 & 0.001 \\
6 & (d) & 1020 \({}^{\circ}\)C & 36 nm & 10 min & 0.001 & 1.000 \\
3 & (e) & 820 \({}^{\circ}\)C & 12 nm & 50 min & 0.434 & 0.434 \\
8 & (f) & 1020 \({}^{\circ}\)C & 12 nm & 50 min & 0.250 & 0.100 \\
4 & (g) & 820 \({}^{\circ}\)C & 36 nm & 50 min & 0.001 & 0.300 \\
2 & (h) & 1020 \({}^{\circ}\)C & 36 nm & 50 min & 0.890 & 0.464 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Full-factorial DoE analysis.  
Figure 1: Raman Spectra for all eight experimental runs (as mentioned in Table 2). The Raman spectrum from (**a**) to (**h**) corresponds to experimental Runs 5, 7, 1, 6, 3, 8, 4, 2, respectively.
## 3 Results
### Agreement of Tear Meniscus Height Measurements  
No significant differences were found for TMH between both devices for right and left eyes (\(p\leq 0.13\)). However, the TMH obtained with VX120+ was significantly higher in comparison with the one measured with the E300 system (\(p=0.037\)). The TMH values obtained from both devices are shown in Table 3.  
No significant correlations were found between TMH measured with E300 and VX120+ devices for right (Rho = 0.06; \(p=0.76\)), left (Rho = 0.32; \(p=0.09\)), and both eyes (Rho = 0.20; \(p=0.13\)).  
Figure 3 presents the Passing-Bablok regression analyses for the TMH outcomes between E300 and VX120+ devices for right, left, and both eyes. The intercept and slope (and 95% confidence interval) of Passing-Bablok regression for right eyes were 0.14 (\(-0.19\)/0.25) and 0.62 (0.24/0.78), respectively; for left eyes were 0.10 (\(-0.01\)/0.20) and 0.70 (0.39/1.12), respectively; and both eyes were 0.12 (\(-0.02\)/0.20) and 0.67 (0.40/1.18), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Eye** & **Coefficients** & **NIBUT E300 vs. First** & **NIBUT E300 vs.** \\  & & **NIBUT VX120+** & **NIBUT50\% VX120+** \\ \hline Right & Intercept (95\% CI) & \(-1.12\) (\(-12.89\)/\(2.88\)) & 4.85 (\(-2.21\)/\(7.85\)) \\ Slope (95\% CI) & 0.90 (\(-1.12\)/\(0.05\)) & 0.50 (0.11/1.15) \\ \hline Left & Intercept (95\% CI) & \(-0.36\) (\(-17.79\)/\(2.73\)) & 2.21 (\(-7.50\)/\(6.64\)) \\ Slope (95\% CI) & 0.48 (0.12/2.19) & 0.60 (0.16/1.50) \\ \hline Both & Intercept (95\% CI) & \(-1.16\) (\(-6.74\)/\(2.20\)) & 3.28 (0.53/5.16) \\ Slope (95\% CI) & 0.82 (0.22/1.38) & 0.55 (0.30/0.96) \\ \hline \hline \end{tabular}  
CI: confidence interval.  
\end{table}
Table 2: Passing–Bablok regression coefficients for the agreement of non-invasive tear break-up time (NIBUT) outcomes between Medmont E300 and Visionix VX120+.  
Figure 3: Passing–Bablok regression analysis for the agreement of the tear meniscus height (TMH) obtained from Medmont E300 and Visionix VX120+ devices comparing: (**a**) right eyes, (**b**) left eyes, and (**c**) both eyes.
# DPMS: Data-Driven Promotional Management System of Universities Using Deep Learning on Social Media
## 4 Experimental Results and Evaluation
### Confusion Matrix Analysis  
We have generated a confusion matrix from the prediction made by the BiLSTM network on testing data, which is illustrated in Figure 3. The variables in the testing dataset are 'Good', 'Average', 'Neutral', and 'Poor'. Each variable has 350 instances. The confusion matrix analysis for these four classes shows that the overall accuracy of the system is 98.66%.  
The analysis of the confusion matrix for the categories Good, Average, Neutral, and Poor reveals an impressively high-performing model, as indicated by a global accuracy of 98.66%. The values of different evaluation metrics are listed in Table 2. The individual class metrics further underscore this performance. The precision, which measures how many of the items are identified as belonging to a particular class actually do belong to that class, is highest for 'Average', at approximately 98.86%, followed closely by 'Poor', at 98.33%. 'Good' and 'Neutral' are also notably high, at approximately 97.49% and 97.79%, respectively. Recall, which assesses how many items of a particular class were correctly identified, is similarly high across all categories, peaking for 'Poor' at 99.04%. Specificity, indicating how well the model correctly identifies negative cases for each class, also maintains high rates--being above 99% for all classes. The F1-score, a balanced metric that considers both precision and recall, remains consistently high, ranging from approximately 97.66% for 'Good' to 98.68% for 'Poor'. These statistics collectively signify a robust model that accurately distinguishes between the Good, Average, Neutral, and Poor classes.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Class** & **Precision** & **Recall/Sensitivity** & **Specificity** & **F1-Score** \\ \hline Good & 0.9749 & 0.9784 & 0.9933 & 0.9766 \\ \hline Average & 0.9886 & 0.983 & 0.9958 & 0.9858 \\ \hline Neutral & 0.9779 & 0.9779 & 0.9943 & 0.9779 \\ \hline Poor & 0.9833 & 0.9904 & 0.9922 & 0.9868 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance evaluation metrics values for each class.  
Figure 3: The confusion matrix analysis to find TP, TN, FP, and FN.  
The analytical results of individual classes have been illustrated in Figure 4. From the performance visualization, it is evident that none of the values in the evaluation metrics are less than 97%. This proves the outstanding classification performance of the BiLSTM network developed in this research.
## 2 Design of the All-in-One Collimating Splitter  
### Concept of the All-in-One Collimating Splitter  
Figure 1 shows the concept of the all-in-one collimating splitter based on a single-mode fiber. In the traditional design shown in Figure 1a, the light output from the fiber should first be collimated by a collimator and then directed to the Dammann splitter to generate a beam array in the far field. The traditional optical system consists of discrete optical components with bulky size and heavy weight, which hinders their applications, such as face identification, with smart devices. Here, utilizing the flexibility to manipulate beams, an all-in-one collimating splitter based on metasurfaces is proposed, with a compact configuration and excellent optical performance, as shown in Figure 1b. The designed metasurface combines the functions of the collimator and the splitter and can be conveniently integrated on the end face of the single-mode fiber using methods like bonding [17; 38].
# Local Differential Privacy Image Generation Using Flow-Based Deep Generative Models
###### Abstract  
Diagnostic radiologists need artificial intelligence (AI) for medical imaging, but access to medical images required for training in AI has become increasingly restrictive. To release and use medical images, we need an algorithm that can simultaneously protect privacy and preserve pathologies in medical images. To address this, we introduce DP-GLOW, a hybrid that combines the local differential privacy (LDP) algorithm with GLOW, one of the flow-based deep generative models. By applying a GLOW model, we disentangle the pixelwise correlation of images, which makes it difficult to protect privacy with straightforward LDP algorithms for images. Specifically, we map images to the latent vector of the GLOW model, where each element follows an independent normal distribution. We then apply the Laplace mechanism to this latent vector to achieve \(\epsilon\)-LDP, which is one of the LDP algorithms. Moreover, we applied DP-GLOW to chest X-ray images to generate LDP images while preserving pathologies. The \(\epsilon\)-LDP-processed chest X-ray images obtained with DP-GLOW indicate that we have obtained a powerful tool for releasing and using medical images for training AI.  
differential privacy; deep generative models; medical images; privacy protection; database; image obfuscation +
Footnote †: journal: Medical Image Generation  
use the processed images without limiting the purpose of use. At the same time, LDP can retain valuable information in the original image, e.g., lung opacity suggesting pneumonia in chest X-ray (CXR) images.  
Because CXR images are the most representative medical images, in this study, we adopt CXR images from the Radiological Society of North America (RSNA) dataset. However, we can extend the proposed method to natural images and medical images of arbitrary modality and dimensions.  
To summarize, our contributions are as follows:  
1. We adopt GLOW to disentangle image pixels and realize the \(\epsilon\)-LDP algorithm for images (DP-GLOW).
2. We generate \(\epsilon\)-LDP-processed CXR images.
3. We evaluate the usefulness of \(\epsilon\)-LDP-processed CXR images using a pneumonia detection model.
4. We visually confirm how the proposed method obscures identities in medical images.
# Bandwidth Density Analysis of Coded Free-Space Optical Interconnects
## 4 Bandwidth Density of Uncoded FSOI System  
Herein, we demonstrate how the FSOI systems' design parameters affect the bandwidth density (BD) without the use of coding. Particularly, we illustrate the channel spacing (\(\Delta\)) and signal-to-noise ratio (SNR) effects on the bandwidth density under the operation of the fundamental-order Gaussian mode of the laser beam and the investigated higher-order modes. For all simulation results provided in this manuscript, we set the design parameters to be as follows: transmitted power per channel (\(P_{t}\)) of 50 \(\upmu\)W, operating wavelength (\(\lambda\)) of 0.85 \(\upmu\)m, interconnect distance (\(D\)) of 20 mm, VCSEL diameter (X) of 0.03 mm, noise equivalent power (NEP) of 0.1 nW/\(\sqrt{\text{Hz}}\), and PD diameter (Y) of 0.8 of the channel spacing (\(\Delta\)) distance. It should be mentioned that the used design parameter values represent typical approximate values for the operation of VCSELs and photodetectors at room temperature [1; 5; 33].  
In Figure 2, we illustrate the effect of \(\Delta\) on the BD using the TEM\({}_{00}\), TEM\({}_{01}\), TEM\({}_{10}\), and TEM\({}_{20}\) operating modes. These results were generated using a SNR of 10 dB. It is clear that each considered operating mode of the laser beam has an optimal channel spacing (\(\Delta_{opt}\)) value. For the fundamental-order Gaussian mode (TEM\({}_{00}\)), the \(\Delta_{opt}\) that achieves the maximum bandwidth density is 0.4 mm. At relatively small values of \(\Delta\) (<2.6 \(\times\) 10\({}^{-4}\) m),the BD of the system is quite small in comparison to the optimal BD; hence, it is not depicted on Figure 2. For TEM\({}_{01}\), TEM\({}_{10}\), and TEM\({}_{20}\) modes, the \(\Delta_{opts}\) are 0.54 mm, 0.66 mm, and 0.9 mm, respectively.  
Figure 3 shows a relationship between the BD and the SNR of the studied operating modes. Herein, we consider the \(\Delta\) of each mode of the FSOI system equal to its \(\Delta_{opts}\) shown in Figure 2. One can clearly see from the results that the BD is strongly correlated to the utilized SNR, and the relationship can be estimated as BD \(\propto 1/\text{SNR}^{2}\). This suggests the employment of coding in order to enhance the BD, as demonstrated in the following section.  
Figure 3: Bandwidth density versus SNR in dB.  
Figure 2: Bandwidth density of the FSOI system.