esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of La-EPRC Particles for Static Adsorption of Phosphorus Removal  
#### 3.2.1 Adsorption Isotherms  
The Langmuir and Freundlich isotherm models were individually applied to fit the experimental data, as illustrated in Figure 8 and detailed in Table 4.  
Examining the data in Table 4 reveals that the correlation coefficients in the final fitting results of the Langmuir isothermal adsorption model exceed 0.99, while those of the [PERSON] isothermal adsorption model surpass 0.98. The fitting correlations for both models are notably high. Furthermore, the fit of the Langmuir isothermal adsorption model at different temperatures outperforms that of the Freundlich isothermal adsorption model. This suggests that the adsorption process of phosphate by La-EPRC material aligns more closely with the Langmuir isothermal adsorption model, indicating a predominant monomolecular layer adsorption mode for the particles.  
Analyzing the Q\({}_{\text{M}}\) values from the Langmuir model in the table reveals that the saturation adsorption amount of La-EPRC particles increases with temperature in the range of 15-35 \({}^{\circ}\)C. Parameter K\({}_{\text{L}}\) values in the table, all within the range of 0.1, indicate that La-EPRC material is favorable for the adsorption of phosphate.  
Regarding the Freundlich model, the parameter K\({}_{\text{F}}\), reflecting adsorption affinity, increases within the temperature range of 15-35 \({}^{\circ}\)C, suggesting that the primary adsorption mode of La-EPRC material is monomolecular layer adsorption. The values of 0.5 \(<\) 1/n \(<\) 2, remaining near 0.7 and decreasing with temperature, indicate that temperature continues to promote the adsorption reaction.  
The adsorption of phosphate by La-EPRC material conforms more closely to the Langmuir isothermal adsorption model. Consequently, the primary adsorption mechanism of this material is monolayer adsorption, with a maximum saturated adsorption capacity of 20.395 mg P/g. In contrast, the EPRC material prior to modification exhibits a maximum  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Temperature** (\({}^{\circ}\)C)} & \multicolumn{3}{c}{**Langmuir**} & \multicolumn{3}{c}{**Freundlich**} \\ \cline{2-7}  & **K\({}_{\text{L}}\) (L/mg)** & **Q\({}_{\text{M}}\) (mg/g)** & **R\({}^{2}\)** & **K\({}_{\text{F}}\)** & **1/n** & **R\({}^{2}\)** \\ \hline
15 & 0.00705 & 17.16754 & 0.99653 & 0.19981 & 0.7892 & 0.9882 \\
25 & 0.00895 & 18.36118 & 0.99637 & 0.29693 & 0.7481 & 0.98699 \\
35 & 0.01251 & 20.39518 & 0.99338 & 0.50447 & 0.6946 & 0.97572 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Adsorption isotherm fitting results.  
Figure 8: Results of adsorption isotherm fitting. (**a**) [PERSON] isotherm model fitting results; (**b**) Freundlich isotherm model fitting results.  
saturated adsorption capacity of 4.18 mg P/g [21], underscoring a substantial improvement in the adsorption capacity of the modified material.
# Numerical Study on Aerodynamic Noise Reduction of Pantograph
## 2 Geometry Description  
The research object is a Faiveley CX-PG pantograph, and the configuration is shown in Figure 1a. It can be classified into three areas: the panhead area, middle area, and bottom area. These three areas are identified by different colors in Figure 1a. The rods on the panhead and middle areas play an important part in generating the aerodynamic noise by inducing the separation flow and the vortex shedding. In order to study the capability of the sinusoidal wavy rod to reduce aerodynamic noise, the noise radiated from a pantograph of the wavy surface is simulated and analyzed. Figure 1b shows the configuration of the wavy rod model. The diameter of the wavy rod \(D\) is a function of the spanwise coordinate \(z\), and it is given by  
\[D(z)=D_{\text{ori}}-0.1D_{\text{ori}}\cdot\cos\left(2\pi\frac{z}{5D_{\text{ori }}}\right) \tag{1}\]  
where \(D_{\text{ori}}(z)\) is the diameter of the original model. According to the previous study [16], the optimum wavelength of about \(5D_{\text{ori}}\) can reduce the noise of the cylinder the most. Hence, the wavelength of \(5D_{\text{ori}}\) is applied in Equation (1). Moreover, the optimization design of the contact strip is considered to reduce the pantograph noise further. As shown in Figure 1c, the leading and trailing edges of the rectangular contact strip are modified into an equilateral triangle to weaken the vortex shedding.
## 3 Results and Discussions
### Ethidium Bromide Fluorescence Boost upon Binding to the DNA Contaminants  
The presence of DNA contaminants on the substrate surface was investigated using ethidium bromide's fluorescence enhancement upon binding to DNA as a \"proof-of-concept method\". Ethidium bromide, a cationic dye that interacts strongly and specifically with DNA and RNA, is widely used in spectrofluorimetric studies because of the striking fluorescence enhancement it displays upon binding. It is well known that fluorescence enhancement accompanies not only the intercalation of the dye into the double-helix conformation of the nucleic acids, but also the electrostatic binding of the same dye [40].  
As observed in Figure 8, the analyzed substrate materials, when excited with UV light, produce a slightly orange light, as evident for the composites based on PET, PP, and HDPE (M1\(4\), M6_4, and M11_4, respectively), suggesting that the presence of Al nanopowder obstructs the transparency of the materials and, therefore, their autofluorescence. After the treatment of the materials with the DNA and staining them with ethidium bromide solution, the dye molecules squeezed between the neighboring base pairs in a DNA double helix. Under UV light, the intercalated ethidium bromide in the DNA fluoresces, yielding a bright orange light, which is more prominent in the materials based on PET, PP, and HDPE (M1_4_DNA, M6_4_DNA, and M11_4_DNA, respectively), and slightly visible in the samples containing Al nanopowder (M2_4_DNA, M7_4_DNA, and M12_4_DNA). The reduction in the fluorescence for the materials based on Al nanopowder does not represent the absence of DNA contaminants on the substrate surface or the intercalated fluorescent ethidium bromide; rather, it denotes that since the fluorescence is measured from the bottom, the black surface inhibits the color. The intercalated fluorescent dye reagent for DNA staining in all substrate samples can be visualized from the picture of the samples on transparent slides (Figure 8B) with their fluorescent bottom.  
The same contaminated/stained substrate samples were also visualized individually on transparent slides using the fluorescence microscopy, and compared with the optical images of the same sections before the treatment. The results from Figure 9 show that the emitted fluorescence with an orange color is distinguishable on the supports' surfaces due to the presence of the nucleic acid's fluorescent tag.  
Figure 8: Ethidium bromide fluorescence boost upon binding to DNA before and after the treatment process (UVP GelSolo Transilluminator): (**A**) UV images of bare vs. DNA contaminated materials; (**B**) DNA contaminated materials visualized with their fluorescent bottom.  
Figure 9: Fluorescence and optical photomicrographs of the composite supports before and after the contamination and ethidium bromide staining process.  
The fluorescent intercalation complex of ethidium bromide with DNA on the recycled PET samples, presented as a proof-of-concept method for the first time in the literature, validates the results obtained from the ATR-FTIR spectra.
## 3 Results and Discussion
### Hardened State Properties
#### 3.4.2 Tensile Strength  
Figure 6a and Table 5 show the results of the splitting tensile strength test. It is evident that the tensile strength decreases with the increasing replacement of cement by FA and MIBA. The mixes with a higher percentage of MIBA incorporation, C10 FA20 BA and C20 FA40 BA, show a greater decrease in strength than the RC, with values at 91 days lower by 30% and 53%, respectively. The reasons for this decline in performance were the same as those discussed in the previous section. Figure 6b shows the relationship between compressive strength versus tensile strength in cylindrical specimens for all SCC at 28 and 91 days. Control mixes were found to be well within the confidence intervals for f\({}_{\text{ctk}}\) of conventional concrete despite the greater than expected standard deviation of the corresponding f\({}_{\text{cm}}\). However, as the MIBA content increased, for a given f\({}_{\text{cm}}\), there was an unexpected increase in f\({}_{\text{ctm}}\).  
Figure 6: Tensile strength (f\({}_{\text{ctm,sp}}\)) versus substitution rate (**a**); relationship between tensile strength (f\({}_{\text{ctm}}\)) and compressive strength in cylindrical specimens (f\({}_{\text{cm}}\)) at 28 and 91 days (**b**).
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 3 Motivation, Background, and VDB Assessment
### Motivation  
Specifying the precise inventory is so crucial for assessing vulnerabilities. In other words, detecting vulnerabilities that may affect inventory products remains a complicated with a high incidence of false positives and negatives. Meanwhile, this operation requires two vectors, notably the specification of the installed products and their associated vulnerabilities. These relevant data are retrieved from the target system, the cybersecurity event management databases, websites, and other sources. As a result, the mapping process identifies the target products potentially affected by vulnerabilities. Unfortunately, automating this process faces multiple challenges [5, 10, 11]:  
* Various configuration systems impact product inventories and technical content of VDBs;
* Product properties, such as name, version, and edition, might change frequently affecting mapping with VDBs and inventory systems;
* Vulnerability databases that list the same product under different properties have inconsistent product names (character and semantics);
* Inconsistencies in vulnerability databases, including both structured and unstructured product names.
* Relevant insights may reveal CVE feeds without CPE entries;
* Some product vulnerabilities, including software, hardware, and operating systems are published without assigned CPE;
* Product identity is not unified across information systems and VDBs;
* Some CVE feeds contain CPE entries that are not in the CPE dictionary;
* The high rate of false positives and negatives in the vulnerability detection process.
## 2 _Aspergillus_ Is an Expression Host for Heterologous Protein Production
### A. _nidulans_  
_A. _nidulans_ has long been recognized as a model organism for eukaryotic research, owing to its well-characterized genetics, physiology, and amenability to molecular manipulation [44; 45]. Although \(A\). _nidulans_ is not a predominant industrial species, it serves as a promising microbial platform for industrial enzyme production. To date, \(A\). _nidulans_ has synthesized several industrial enzymes, including endoglucanase, xylanase, cellulases, \(\beta\)-glucosidaseases, lacases, and lipases [46]. Most of them were produced under submerged fermentation rather than solid-state fermentation due to the availability of fermentation parameters and reduced fermentation time of submerged fermentation [47]. In addition, \(A\)_nidulans_ is also a versatile fungal cell factory for the heterologous production of different carbohydrate active enzymes (CAzymes), which are industrially relevant biocatalysts responsible for the degradation of plant cell walls, with a promising result for the production of bio-based compounds from lignocellulosic feedstock, such as biofuel and nutraceuticals [48]. However, due to its relatively low protein secretion capacity, _A. nidulans_ has not been fully developed as a cell factory for heterologous protein expression. Therefore, [PERSON] et al. successfully developed a high-efficiency _A. nidulans_ strain (_\(\Delta\)agsB-derA_) by performing stepwise modification of mycelial morphology and protein secretory pathway to alleviate this limitation. Consequently, a higher yield of secretory and expression of human interleukin-6 (HuIL-6) was achieved by further disruption of extracellular proteases, indicating that _A. nidulans_ is a promising platform for efficient heterologous protein expression [49]. Very recently, [PERSON] et al. employed CRISPR/Cas9 technology to examine how deleting genes related to N-glycan assembly and endoplasmic reticulum protein quality control affects recombinant \(\beta\)-xylosidase secretion in _A. nidulans_. Their findings demonstrated that combined _algC_ and _algI_ deletion enhances the secretion and alters the secretome, which may be a promising strategy for boosting recombinant protein secretion [50].  
Although the application of heterologous protein expression using _A. niger_, _A. nidulans_, and _A. oryzae_ as hosts has reached a relatively mature stage, in-depth studies on the regulation of gene expression and protein secretory pathways are still required to further optimize expression platforms, enhance protein yields, and ultimately establish highly efficient expression systems for heterologous proteins [20; 51; 52].
## 3 Results and Discussion  
The physicochemical properties of BiPbSrCaCuO superconductor ceramics are given in Table 1. The calculated EGS4 and XCOM mass attenuation coefficients u/\(\rho\) (cm\({}^{2}\) g\({}^{-1}\)) of the samples at different energies of 59.5, 122, 383, 511, 662, 1274, and 1332 keV are given in Table 2. Theoretically, for each sample investigated, using WinXcom and EGS4 code, the mass absorption coefficient values obtained at all energies were found to be close to each other. The values of the half value layer (HVL), mean free path (MFP), effective atomic number (Zeff), and radiation protection efficiency (RPE) of the BiPbSrCaCuO superconductor ceramics are given in Tables 3-6, respectively. The change graphs of the BiPbSrCaCuO superconductor samples' HVL and MFP values versus photon energy, variation in energy with half value layer (HVL), and mean free path (MFP) for given the superconductors were plotted in the 1 keV-100 MeV energy range and shown in Figures 2 and 3, respectively. From these figures, it is seen that both the half value layer (HVL) and the mean free path (MFP) values for the superconductors examined increase with the increasing photon energy. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Compound**} & \multicolumn{1}{c}{**O**} & \multicolumn{1}{c}{**Ca**} & \multicolumn{1}{c}{**Cu**} & \multicolumn{1}{c}{**Sr**} & \multicolumn{1}{c}{**Pb**} & \multicolumn{1}{c}{**Bi**} & \multicolumn{1}{c}{**Ag**} \\ \hline Bi\({}_{1,64}\)Pb\({}_{0,36}\)Sr\({}_{2}\)Ca\({}_{2}\)Cu\({}_{3}\)O\({}_{10}\) & 15.63 & 7.83 & 18.63 & 17.12 & 7.29 & 33.49 & & \\ Bi\({}_{1,66}\)Pb\({}_{0,36}\)Ag\({}_{0,04}\)Sr\({}_{2}\)Ca\({}_{2}\)Cu\({}_{3}\)O\({}_{10}\) & 15.70 & 7.86 & 18.7 & 17.19 & 7.32 & 32.80 & 0.42 & \\ Bi\({}_{1,56}\)Pb\({}_{0,36}\)Ag\({}_{0,08}\)Sr\({}_{2}\)Ca\({}_{2}\)Cu\({}_{3}\)O\({}_{10}\) & 15.76 & 7.90 & 18.78 & 17.26 & 7.35 & 32.11 & 0.85 & \\ Bi\({}_{1,52}\)Pb\({}_{0,36}\)Ag\({}_{0,12}\)Sr\({}_{2}\)Ca\({}_{2}\)Cu\({}_{3}\)O\({}_{10}\) & 15.82 & 7.93 & 18.85 & 17.33 & 7.38 & 31.41 & 1.28 & \\ Bi\({}_{1,48}\)Pb\({}_{0,36}\)Ag\({}_{0,16}\)Sr\({}_{2}\)Ca\({}_{2}\)Cu\({}_{3}\)O\({}_{10}\) & 15.89 & 7.96 & 18.93 & 17.40 & 7.41 & 30.71 & 1.71 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Chemical composition (weight fraction %) of Ag-dopant BiPbSrCaCuO ceramic superconducting samples. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Elements and Compounds**} & \multicolumn{1}{c}{**59.54 keV**} & \multicolumn{1}{c}{**122 keV**} & \multicolumn{3}{c}{**383 keV**} & \multicolumn{3}{c}{**511 keV**} & \multicolumn{3}{c}{**662 keV**} & \multicolumn{3}{c}{**1274 keV**} & \multicolumn{3}{c}{**1332 keV**} \\ \cline{2-13}  & **EGS4** & **XCOM** & **EGS4** & **XCOM** & **EGS4** & **XCOM** & **EGS4** & **XCOM** & **EGS4** & **XCOM** & **EGS4** & **XCOM** & **EGS4** & **XCOM** \\ \hline \({}^{8}\)O & 0.195 & 0.192 & 0.148 & 0.145 & 0.102 & 0.097 & 0.087 & 0.086 & 0.075 & 0.077 & 0.056 & 0.056 & 0.058 & 0.055 \\ \({}^{20}\)Ca & 0.679 & 0.670 & 0.201 & 0.202 & 0.106 & 0.100 & 0.086 & 0.088 & 0.080 &
# Enhanced Drug Screening Efficacy in Zebrafish Using a Highly Oxygen-Permeable Culture Plate
## 2 Materials and Methods
### Zebrafish Embryo Culture on Plates
#### 2.3.1 Impact of Fish Density and Oxygen Conditions on Development in 24-Well Plates  
At 6 hpf, embryos that appeared morphologically normal were chosen using a stereomicroscope according to standard criteria, which included a symmetrical blastoderm, an intact yolk, and no visible abnormalities. These embryos were utilized in all experiments to ensure consistency and reproducibility. Embryos were placed in each well at densities of 1, 5, 10, 20, 30, and 50 fish per well per milliliter of medium. To assess how the oxygen permeability of the InnoCell(tm) plate influences zebrafish development, two distinct conditions were established: one with a standard plate lid, referred to as the \"No sealing\" condition,and the other with plate seals to prevent oxygen exchange from the top of the well, termed the \"Sealing\" condition. Embryos were incubated at 28 \({}^{\circ}\)C for 72 h.
## 4 Discussion
### Effect of MPs on Fish Growth  
It is generally accepted that MPs would decrease fish body length or weight, since the ingestion of MPs might increase the energy burden on organisms and decrease energy reserves through the catalysis of lipids [35]. Specifically, the MPs-treated fish possibly have to redirect energy used for growth toward other important functions, such as systemic removal and detoxification of the MPs in the body [21]. Energy is also required to cope with other stresses, such as inflammation and a compromised endocrine system, burdened liver detox function and aberrant food absorption.  
The hypothesis stating that exposure to virgin MPs adversely effects fish growth was not supported by the data in our study. After four weeks, the treatment fish did not show lower growth in length or body weight compared to controls. Growth index is one of the most important parameters that is measured in aquaculture. While the results presented here may provide an optimistic outlook for the aquaculture industry, laboratory results may not reflect the true situation in aquaculture where there are many other environmental factors (i.e., antibiotics, heavy metals and hormones). Although there are papers where microplastics did not show any effect on fish growth [36; 37], and there are also a lot of studies showing retarded growth of fish [21; 38]. This topic very much depends on fish species, microplastic shape and size, behavior and feeding habits of fish, etc. Further research is needed on the combined toxicity of MPs and other environmental contaminants.
## 6 Experimental Examples  
In this section, we aim to validate the proposed augmented method using experimental results. To this end, a load identification experiment for a simply supported bilateral beam is carried out  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Force**} & \multirow{2}{*}{**SNR**} & \multicolumn{2}{c}{**Tikhonov + L Curve**} & \multicolumn{2}{c}{**Tikhonov + GCV**} & \multicolumn{2}{c}{**Augmented Tikhonov**} \\ \cline{3-8}  & & \(\mathbf{r}\) & \(\mathbf{RE}\) & \(\mathbf{r}\) & \(\mathbf{RE}\) & \(\mathbf{r}\) & \(\mathbf{RE}\) \\ \hline sinusoidal & 40 & 69.91\% & 54.04\% & 68.05\% & 12.07\% & 99.72\% & 7.45\% \\ load & 30 & 98.18\% & 26.81\% & 87.32\% & 20.40\% & 99.20\% & 12.66\% \\ \hline Impact & 40 & 97.49\% & 16.04\% & 96.26\% & 16.04\% & 99.44\% & 10.33\% \\ load & 30 & 97.36\% & 16.67\% & 95.97\% & 21.91\% & 98.86\% & 14.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Force identification effect of cantilever.  
Figure 27: Identification result of impact load at SNR 30 dB on cantilever: (**a**) Tikhonov method; (**b**) the proposed method.  
Figure 31: \(\beta_{2}\) ignition value selection: (**a**) sinusoidal load; (**b**) triangle wave load.  
Figure 32: Identification result of sinusoidal load.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Force** & **Correlation Coefficient (_r_)** & **Relative Error (_RE_)** \\ \hline Sinusoidal load & 99.42\% & 8.07\% \\ Triangle wave load & 99.55\% & 9.83\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Evaluation of identification results of experiment.  
Figure 33: Identification result of triangle wave load.  
The results in this section show experimentally that the proposed method can accurately identify the external loads applied on the considered beam. Figure 29 shows that the sinusoidal load is accurately recovered. Both the actual load period and amplitude match those obtained with the proposed approach with high accuracy. The zero initial stage and the initial change in the load amplitude for the first few cycles are all accurately reflected in the load identification. A similar observation can also be made for the triangular wave load identification. The load period, amplitude, and initial changes are all recovered with good accuracy, as can be seen in Figure 30. The correlation coefficients, for both types of considered loads, are above 99%. Similarly, the relative error is around 10% for both loads.
## 4 Experimental Results and Analyses
### Experimental Comparison of Regression Models  
We extracted the number of pixels of the height of the detection frame and used the support vector regression (SVR) method for regression. In the regression problem, our independent variable was the number of pixels in the detection frame, which was usually around 350,000, and the dependent variable was the length of the tibia in millimeters, which was usually around 120 mm.  
In Table 8, we compare the evaluation indicators of nine regression models: Gradient-Boosting Regressor, Ridge Regression, KNeighbors Regressor, RandomForest Regressor, LASSO Regression, Decision Tree Regression, ElasticNet Regression, XGBoost Regression, and SVR. It was observed from the regression data that the independent variables were all relatively large numbers, while the dependent variables were relatively small. This indicated that our data involved a high-dimensional input space. SVR proved to be more adaptable in a high-dimensional space and handled large independent variables effectively. Simultaneously, the kernel technique of the SVR model was better adapted to nonlinear relationships. Furthermore, SVR was more robust to outliers and noise, which is crucial with high-dimensional data. We analyzed the KNeighbors Regressor method and found that the computational cost of this method was too high when processing high-dimensional data, and it was abnormally sensitive to noise and outlier values. Two methods, Gradient-Boosting Regressor and Decision Tree Regression, were sensitive to noise and easily caused overfitting.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & \multicolumn{2}{c}{**VOC2012 Dataset**} & \multicolumn{2}{c}{**COCO2016 Dataset**} & \multicolumn{2}{c}{**Tibia Dataset**} \\ \hline  & \(mAP_{\text{b.s}}\) & \(mAP_{\text{b.s}}\) & \(mAP_{\text{b.s}}\) & \(mAP_{\text{b.s}}\) & \(mAP_{\text{b.s}}\) & \(mAP_{\text{b.s}}\)50.95 \\  & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline YOLOv8-s & 62.0 & 44.9 & 57.1 & 41.4 & 99.5 & 84.7 \\ \hline Tibia-YOLO & 62.6 & 45.9 & 58.3 & 42.6 & 99.5 & 90.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance of Tibia-YOLO on three datasets, and the comparison with the basic YOLOv8-s as the baseline.  
Figure 8: Comparison of the test results between the YOLOv8-s (baseline) and Tibia-YOLO models on three datasets.