esa-sceva/satcom-chunk-collection huggingface dataset:
# Photonic Crystal-Based Sensors for Detecting Alcohol Concentration
## 2 Experimental Section
### Characterizations  
The average diameter of the PS microspheres was determined by atomic force microscopy (AFM, Digital Instruments, DI 3100, Bresso, Italy). The morphology of the PS opal and TiO\({}_{2}\) inverse opal arrays was examined by a field-emission scanning electron microscope (FE-SEM, JEOL JSM-6700F, Tokyo, Japan) operating at a 15 kV accelerating voltage under the secondary-electron image mode. The samples were coated with a thin layer of gold by vapor deposition using a vacuum sputter (Sputter JEOL JFC-1100C, Tokyo, Japan) prior to FE-SEM characterization. The X-ray diffraction (XRD) data of the TiO\({}_{2}\) samples sintered at different temperatures was obtained using a [PERSON], D8 DISCOVER XRD system (Billerica, Massachusetts, MA, USA). Each sample was scanned with Cu \(K_{\alpha}\) radiation (30 kV, 20 mA) at a continuous scan rate of 5\({}^{\circ}\)/min. The reflectance spectra can be observed by the variable angle multifunctional optical characteristic measuring system (MFS-630, Hong-Ming Technology Co., Ltd., New Taipei City, Taiwan) (see Figure 2) as the ethanol are dropped onto the opal and inverse opal films, respectively. The apparatus was equipped with a halogen light source (LSH-100, Taiwan Fiber Optics, Inc., Taichung, Taiwan) and a barium sulfate coated standard integrating sphere was placed inside the detector module. The MFS-630 is a high resolution spectrometer capable of measuring absorbance, transmittance, and reflectance of solid (film) samples in the visible range (from 400 to 800 nm). The spectra data were recorded by the optical fiber spectrometer (Ocean Optics USB4000, Dunedin, FL, USA). Sample and the MFS-630 apparatus were covered under an acrylic rectangular box to reduce the interference from environment during testing. The refractive indices of difference ethanol concentration solutions were measured by Abbe refractometer (MAOAN, WYA-2S, Jinan Mao An Instrument Co., Ltd., Jinan, China). The sample is placed between the illuminating and measuring prisms. The rotating knob is used to align the X mark with the shadow boundary on the telescope crosshairs, and the refractive index is recorded with four decimal places from the scale.  
Figure 1: Schematic diagram of the polystyrene (PS) opal and TiO\({}_{2}\) inverse opal sensor preparation.
## 4 Results and Discussion
### Comparison of the Analytical Performance of MP-AES, ICP-OES, and FAAS  
Three different biosludge samples and one sample of certified sewage sludge reference material were digested using the USEPA method 3051A and the resulting sample solutions were analyzed by MP-AES and ICP-OES, and flame-AAS. The analytical results are provided in Table 6 and the graphical representation of the same results is shown in Figure 1. The analysis showed that the FAAS failed to detect B, P, and Mo at low levels of concentrations. The detection limits of MP-AES are much better than those of FAAS, and are much closer to those obtained using ICP-OES, even in the determination of other elements such as Fe, Zn, Mn, and Cu. The one-way statistical analysis of variance (ANOVA) performed on the results obtained using MP-AES and ICP-OES yielded a \(p\)-value greater than 0.05, indicating that there were no statistically significant differences between the two techniques [21].
## 2 Materials and Methods
### Data Import Process Description
This table includes fields for recording essential information such as timestamps, geographical coordinates, magnitude, region, precipitation, surface pressure, air temperature, and air relative humidity. The table contains a single record for each day within the monitored timeframe. When no earthquake has occurred in the Vrancea region, the value in the Magnitude column is set to zero. Precipitation, surface pressure, air temperature, and air relative humidity values correspond to the averages from the multiple values provided by the API for the same day. Storing the data in this format allows for multiple analyses and simulations, enabling the creation and evaluation of various models. Additionally, if multiple earthquakes occur on a single day, only the earthquake with the highest magnitude is retained in the dataset. This decision is informed by the understanding that earthquakes can generally produce aftershocks. Retaining only the highest value magnitude event for that day allows for a more accurate representation of seismic activity while minimizing potential redundancy in the data. The database data import scripts were implemented in C#, utilizing the HttpClient class for making HTTP requests. This allows seismic and meteorological data to be retrieved from the respective APIs. The USGS API gathers earthquake records, while the Open Meteo API accesses historical weather parameters. The implementation involves sending asynchronous requests to these APIs to fetch data in a structured format, which is then processed and stored in the database. Figure 1 displays daily a sample of all records for the Vrancea region, encompassing key seismic and meteorological parameters. The Magnitude column registers the earthquake's magnitude, measured using the Richter scale. The Temperature column includes the values expressed in \({}^{\circ}\)C of the air temperature parameter, the Pressure column contains the level described in hPa of the surface pressure parameter, the Precipitation column comprises the values expressed in mm of the precipitation parameter, and the Humidity column incorporates the level expressed in % of the air relative humidity. Figure 1: Daily seismic and meteorological data storage example for the Vrancea region from 8 September to 13 September 2002. The database contains 8766 records, which serve as a dataset for training the predictive model. This volume of data enhances the model's ability to identify patterns and correlations between seismic activity and meteorological conditions. By incorporating a wide range of historical earthquake and weather data, the model can better predict potential seismic events in the Vrancea region based on variations in weather parameters.
## 3 Experimental Verification  
This section presents the modeling results of MulSPIDeepONet. We verify the effectiveness of the proposed method by solving three different PDE systems and compare the results with those from the baseline NN model. To ensure fairness, all networks in the following examples have a similar number of trainable parameters or weights, facilitating a more accurate comparison of the generalization performance of the model. Additionally, fixed random seeds are used to minimize the effects of randomness.  
The baseline model in this paper is the classic PIDeepONet architecture. All test results are obtained using an NVIDIA 3060 Ti GPU based on the open-source Jax deep learning framework, and the ground truth results of the PDEs are obtained by analytical numerical simulations. To comprehensively evaluate the fitting ability of the constructed model in solving PDEs, we select several performance indicators. These indicators are the mean absolute error (MAE), relative mean absolute error (RMAE), relative root mean square error (rRMSE), and L2 norm error:  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\Big{|}y_{i}^{true}-y_{i}^{predict}\Big{|}, \tag{14}\]  
\[RMAE=\frac{1}{n}\sum\limits_{i=1}^{n}\Big{|}\frac{y_{i}^{true}-y_{i}^{predict }}{y_{i}^{true}}\Big{|}, \tag{15}\]  
\[rRMSE=\frac{\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\Big{(}y_{i}^{true}-y_{i}^{ predict}\Big{)}^{2}}}{y^{average}}, \tag{16}\]  
\[L2\;norm\;\text{error}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\Big{(}y_{i}^{ true}-y_{i}^{predict}\Big{)}^{2}}, \tag{17}\]  
where \(n\) is the number of cells in the computational grid.
## 2 Materials and Methods
### qPCR Analysis of XO Gene Expression  
qPCR analysis was carried out using Bio-Rad CFX96 Real-Time PCR, version 3.1 to determine the expression level of XO after drug administration. Three rats with serum uric acid (SUA) levels close to the mean value in each group (day 7) were selected. Total RNA was extracted from their livers using a NucleoSpin RNA kit (Macherey-Nagel GmbH & Co. KG, Duren, Germany). Next, RNA quality and quantity were determined by performing agarose gel electrophoresis and Nanodrop spectrophotometry. One-step Real-Time SYBR Green qPCR was performed. The primers (forward primer: 5\({}^{\prime}\)-GCATGCCAGACCATACTGA-3\({}^{\prime}\); and the reverse primer: 5\({}^{\prime}\)-AAATCCAGTTGCGGACA-3\({}^{\prime}\)) for rat xanthine dehydrogenase (XDH; Gene ID: 497811, mRNA sequence: NM_017154) were purchased from IDT Singapore Pte Ltd., Singapore. The housekeeping gene, \(\beta\)-actin for rat (mRNA sequence: NM_031144) with primer sequences of 5\({}^{\prime}\)-ATGTGATCGGAGAC TCCCGAGA-3\({}^{\prime}\) (forward) and 5\({}^{\prime}\)-CAGCTCATAGCTCTTCCA-3\({}^{\prime}\) (reverse) was purchased from MYTACG Bioscience Enterprise, Kuala Lumpur, Malaysia.
## 3 Results
### Results of the Studied Parameters  
Table 1 presents the results of the studied parameters, and Table 2 summarizes the significant effects of the floral and geographical origins on the studied analytes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **N** & **Mean** & **Std. Deviation** & **Minimum** & **Maximum** \\ \hline \multirow{6}{*}{Moisture \%} & Ziziphus Asir & 4 & 17.2500 & 1.84842 & 14.50 & 18.50 \\ \cline{2-6}  & Ziziphus Jazan & 2 & 17.0500 & 0.07071 & 17.00 & 17.10 \\ \cline{2-6}  & Acacia Asir & 21 & 16.5814 & 1.89471 & 13.95 & 20.25 \\ \cline{2-6}  & Acacia Jazz & 4 & 14.7125 & 0.41105 & 14.25 & 15.15 \\ \cline{2-6}  & Polyfloral Asir & 12 & 17.6750 & 1.50038 & 15.40 & 19.60 \\ \cline{2-6}  & polyfloral Jazan & 15 & 17.2400 & 1.92977 & 14.20 & 19.50 \\ \cline{2-6}  & Total & 58 & 16.9114 & 1.82670 & 13.95 & 20.25 \\ \hline \multirow{6}{*}{Sugars \%} & Ziziphus Asir & 4 & 80.4375 & 1.78390 & 79.00 & 83.00 \\ \cline{2-6}  & Ziziphus Jazan & 2 & 81.3750 & 0.17678 & 81.25 & 81.50 \\ \cline{2-6}  & Acacia Asir & 21 & 81.9643 & 2.25450 & 77.50 & 84.50 \\ \cline{2-6}  & Acacia Jazz & 4 & 83.7125 & 0.40285 & 83.25 & 84.05 \\ \cline{2-6}  & Polyfloral Asir & 12 & 80.8250 & 1.99437 & 78.50 & 84.00 \\ \cline{2-6}  & polyfloral Jazan & 15 & 81.1633 & 1.96891 & 78.60 & 84.40 \\ \cline{2-6}  & Total & 58 & 81.5164 & 2.07556 & 77.50 & 84.50 \\ \hline \multirow{6}{*}{Conductivity \(\mu\)S/cm} & Ziziphus Asir & 4 & 651.2500 & 412.88366 & 35.00 & 910.00 \\ \cline{2-6}  & Ziziphus Jazan & 2 & 595.0000 & 304.05592 & 380.00 & 810.00 \\ \cline{2-6}  & Acacia Asir & 21 & 622.3810 & 324.79078 & 170.00 & 1390.00 \\ \cline{2-6}  & Acacia Jazz & 4 & 475.0000 & 233.02360 & 130.00 & 630.00 \\ \cline{2-6}  & Polyfloral Asir & 12 & 766.6667 & 423.52060 & 210.00 & 1420.00 \\ \cline{2-6}  & polyfloral Jazan & 15 & 486.0000 & 136.84193 & 160.00 & 720.00 \\ \cline{2-6}  & Total & 58 & 607.8448 & 316.78361 & 35.00 & 1420.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The results of the studied parameters.  
Figure 2: Representative pollens for the floral origins of the studied honey samples. The studied honey samples were of three floral origins: Acacia (**A**), Ziziphus (**B**) and polyfloral (**C**).  
The floral origin in the Jazan region significantly affected the moisture, sugars, pH and the glucose oxidase while the geographical origin significantly affected the sugars, pH, GOx and CAT.
## 2 Calculation of Distortional Buckling Loads
### Simplified Method  
Standard approximate mechanical models for the calculation of the distortional buckling load invariably rely on the analysis of the part of the cross-section undergoing buckling. While early models consider the whole flange and lip [3], the simplified method of EN 1993-1-3 instead considers a reduced section (the \"stiffener\") that also accounts for local buckling. The \"strut\" cross-section, therefore, involves the effective part of the lip and of the adjacent flange. The calculation procedure is covered well in several textbooks (e.g., [2; 23]), and therefore will not be explained in detail here. It is nevertheless worth recalling that the procedure can be either \"direct\" (without iterations) or \"iterative\".  
The iterative method is rather straightforward for axial force and major axis bending, and detailed examples concerning lipped channels can be found in [23]. However, the procedure can be quite involved for minor axis bending, and the only example in [23] has fully effective walls due to local buckling, hence no iterations are carried out. The procedure adopted in this paper carries out iterations until the convergence of the effective section, in accordance with Figure 2.  
The distortional buckling load is obtained using Equation (1), assuming that the stiffener is a strut undergoing flexural buckling, hence (see [8]) \(D_{d}=0\) (no plate-like torsion), \(X_{d}=1\) and \(C_{d}=EI_{s}\) (bending stiffness of the strut about its central axis). The transverse bending stiffness \(B_{s}\) is designated as \(K_{s}\), and is calculated as explained below. The buckling stress in the stiffener is therefore given by  
\[\sigma_{d,s}=\frac{2\sqrt{EI_{s}K_{s}}}{A_{s}}. \tag{3}\]  
For illustrative purposes, Figure 3 concerns the uniform compression case, and shows (i) the GBT distortional deformation mode in-plane (left) and out-of-plane (center) displacements, as well as the associated transverse bending moment diagram (right), and (ii) the EN 1993-1-3 simplified method effective section for the calculation of the stiffener (left), together with the assumed in-plane/out-of-plane displacements of the stiffener, and the model used to obtain the transverse bending stiffness \(K_{s}\) (right), which consists of lateral forces acting at the stiffener centroid, causing the bending moment diagram shown. Clearly, the displacements and transverse bending of the two approaches agree reasonably well in this case, but it is remarked that the full GBT numerical method includes more deformation modes in the analysis (namely local-plate modes) and, more importantly, the definition of the stiffener cross-section in the simplified method depends on effective widths for local buckling--no such effect is considered in the numerical method. In fact, it is remarked that, since the effective widths depend on the basic yield stress \(f_{yb}\), in the simplified method, the distortional buckling stress becomes dependent on this parameter.  
Figure 2: Converged effective section for the application of the iterative method for minor axis bending.
# Automatic Selective Encryption of DICOM Images
## 2 The DICOM Image Encryption: Motivations and Limitations
### Limitations  
Generally speaking, DICOM images contain sensitive information about patients and are usually saved for a long time, so they should be stored safely and far from any malicious threats. Additionally, the images might need to be exchanged with different medical entities over public networks. However, there are no existing recommendations on how to secure the DICOM pixel data, while the only existing recommendations are intended to anonymize (de-identification) the patient information that is found in the header data of the DICOM images to preserve the privacy of the patient data. Hence, it is preferable to use quite accepted and adopted encryption standards to secure medical image databases such as 3 DES and AES. These algorithms' processing time is not short, and the size of the medical images is large, so the encryption time of medical images may take a long processing time. Accordingly, encryption mechanisms should be developed to reduce the processing time while maintaining security robustness [20].  
For instance, if the physician would like to secure the pixel data of 100,000 DICOM images (with 2760 \(\times\) 1200 size and 16-bit depth) before sharing it with another hospital, she/he might use the Naive encryption method to encrypt the pixel data of the DICOM frames using conventional (sophisticated) and adopted encryption algorithms by the DICOM standard, such as AES. In this case, the processing time of the encryption process will reach around 4 h, which is a lack of efficiency and a delay for the limited time of physicians. This represents a challenge to be implemented in real life, and therefore, the DICOM standard left this problem open for research, leaving the decision for the researchers on how to secure the DICOM standard.  
Selective encryption mechanisms are promising methods that can overcome traditional encryption limitations. Since selective encryption mechanisms aim to reduce the sizes of the regions, which are subjected to sophisticated encryption algorithms such as AES, consequently, time and cost will be reduced. The main gap in selective image encryption is the classification (segmentation) of images into significant and insignificant regions. In the literature, most of the reported research works proposed a manual selection of regions which is impractical and added extra burden on the medical processing time. Moreover, within the author's knowledge, none of the reported research works has utilized the medical images' statistical properties to efficiently select regions for selective encryption. As image segmentation can be performed in the frequency or spatial domain, most of these research works segmented medical images into regions in the frequency domain. In this case, frequency-segmentation algorithms depend on image features that exist in the frequency domain; hence, additional computations are required to extract features prior to the segmentation process.
# Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
## 4 Dimensionality Reduction  
In this section of the study, dimensionality reduction techniques, PCA and LDA, were applied to the dataset. PCA was applied to the dataset to reduce its dimensionality by identifying the most important and best-contributing features. PCA can be used as an exploratory data analysis technique, with PC1 describing the highest variance in the RTA data. Four datasets were used to construct Figure 4: mean, median, k-NN, and MICE. Missing data methods were applied to the original data to handle the missingness. For the four augmented datasets, three principal components (PCs) and linear discriminants (LDs) were used during the experiments discussed in Section 5 and in designing the 3D graphs in Figure 5. LDA mainly considers the response/state variable chosen by the classifier. Linear discriminant analysis was used to reduce the different feature sets and predict RTA states by using different features in this paper. Overall, the PC results captured the following percentages for the datasets:* (1) Mean missing data method: PC1--20%, PC2--17%, and PC3--13%, which explained 50% of the overall dataset;
* (2) Median method: PC1--20%, PC2--16%, and PC3--14%, which explained 50%;
* (3) k-NN method: PC1--27%, PC2--17%, and PC3--14%, which explained 58%;
* (4) MICE method: PC1--30%, PC2--23%, and PC3--14%, which explained 67% (of the overall dataset).  
Figure 4: The proportions of variance explained for the RTA dataset.  
Figure 5: Three-dimensional principle component plots using the four missing data methods: (**a**) mean, (**b**) median, (**c**) k-NN, and (**d**) MICE.  
The MICE dataset explained more of the PCs when compared with the other PCs. In this study, the main idea behind PCA was to identify the correlation between the RTA features.  
Figure 5 shows the 3D scatter plots for PC1, PC2, and PC3 components against four events to better understand the data distribution. The distributions of PC1, PC2, and PC3 are represented by the colours yellow, blue and viridis, respectively. Figure 5 presents the data variance distribution for the RTA dataset.  
Figure 5a shows that PC2 was distributed more than PC1 and PC3. On the other hand, some outliers were observed from PC3. Outliers can affect the final results of the analysis of the study.  
Figure 5b shows that PC1 and PC2 contributed most of the variance in the data distribution, when compared with PC3. Some outliers are observed from PC2, which were moving towards PC3 using the median RTA dataset.  
Figure 5c shows that PC1 and PC3 were moving more towards PC3, which appeared to be overlapping with PC2. It can also be observed that PC1 was moving away from PC2 and PC3, with some outliers from PC1. The figure shows some correlation between PC2 and PC3.  
In Figure 5d, it can be observed that PC2 contributed more to the data points when compared with the other PCs. PC1 shows fewer data points moving towards PC3.
## 4 ME-SSA Receiver Design  
In this section, first a general overview of the ME-SSA receiver is provided in Section 4.1. Then, Sections 4.2 and 4.3 focus on the innovative blocks with respect to the conventional E-SSA receiver, i.e., the advanced PS, to dealing with impairments typical of non-geostationary (GEO) satellite scenarios, and the burst demodulator based on the MSD approximation of the linear MMSE detector, to increase the overall throughput performance, respectively.
# Effect of NS-DBD Actuator Parameters on the Aerodynamic Performance of a Flap Lifting Device
###### Abstract  
The flap lift device is an important part of the conventional configuration of aircrafts and has an important impact on the aerodynamic performance. In this paper, a high-efficiency, simple, and energy-saving nanosecond dielectric barrier discharge (DBD) plasma actuator is placed in the vicinity of the flap lift device to improve the aerodynamic performance of the flap by controlling the flow field. The two-dimensional airfoil GAW-1 and its 29% flap were selected as the research objects, and the nanosecond (NS) DBD actuators were fixed at different locations near the deflection angle of the 10\({}^{\circ}\)flap. The excitation frequency, pulse width, and energy density parameters of the pulse discharge were adjusted, and then, the effects of parameter changes on aerodynamic characteristics of the airfoil were studied by numerical simulation. The simulation results show that adjusting the excitation frequency on the aerodynamic drag is weak and that the effect on the aerodynamic lift is obvious. The increase of the discharge pulse width will have a more significant effect on the flow field, i.e., a proper increase of the discharge pulse width can achieve better drag reduction, and increase lift after a stall at a high angle of attack. Although the increase of discharge energy density can strengthen the pulse perturbation effect on the flow field, it also contributes to some adverse effects and has no obvious optimization effect on the control efficiency of lift increase and drag reduction.  
high lift system; dielectric barrier discharge; plasma; nanosecond pulse; aerodynamic performance