esa-sceva/satcom-chunk-collection huggingface dataset:
# 5D Gauss Map Perspective to Image Encryption with Transfer Learning Validation
## 3 Proposed Method
### Noise and Cropping Attack  
The cipher pictures can be corrupted by noise and cropping attacks when transmitted over the Internet or any other transmission method, making it impossible to extract the plain images. As a result, the performance of the proposed algorithm must be evaluated against various assaults to determine its effectiveness.  
Accordingly, [PERSON]'s \(256\times 256\) pixel cipher picture has been changed by adding Cropping (data loss 6.25%) and Salt-Pepper noise (noise density 0.005), respectively, as illustrated in Figure 11. However, because of the slight PSNR variation reported in Table 5, the results suggest that the assaults had little impact on the image and visual quality of the image.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Images** & **Horizontal** & **Vertical** & **Diagonal** & **Entropy** \\ \hline Proposed & 0.1100 & \(-\)0.0664 & \(-\)0.0752 & 7.9621 \\
[26] & \(-\)0.0414 & \(-\)0.0342 & 0.1083 & 7.4077 \\
[27] & 0.0039 & 0.0059 & \(-\)0.0050 & 7.9994 \\
[28] & 0.0008 & 0.0004 & 0.0020 & 7.9995 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation Coefficient and entropy comparison with state-of-the-art methods.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Images** & **NPCR (\%)** & **UACI (\%)** \\ \hline [PERSON] & 99.62 & 33.42 \\ [PERSON] & 99.59 & 33.35 \\ [PERSON] & 99.58 & 33.34 \\ [PERSON] & 99.57 & 33.33 \\ [PERSON] & 99.61 & 33.41 \\ Boat & 99.64 & 33.43 \\ \hline \hline \end{tabular}
\end{table}
Table 2: NPCR and UACI test results for different test images.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Images**} & \multicolumn{2}{c}{**Original**} & \multicolumn{2}{c}{**Encrypted**} \\ \cline{2-5}  & **CC (H)** & **CC (V)** & **CC (D)** & **CC (H)** & **CC (V)** & **CC (D)** \\ \hline Lena & 0.9502 & 0.9712 & 0.9282 & 0.1100 & \(-\)0.0664 & \(-\)0.0752 \\ Man & 0.9416 & 0.9630 & 0.9136 & 0.0843 & \(-\)0.0591 & \(-\)0.0682 \\ Peppers & 0.9682 & 0.9725 & 0.9425 & 0.0007 & 0.0155 & \(-\)0.0042 \\ Barbara & 0.8262 & 0.8942 & 0.8501 & 0.0061 & \(-\)0.0159 & \(-\)0.0123 \\ Baboon & 0.6940 & 0.6039 & 0.6027 & 0.0391 & 0.0131 & \(-\)0.0080 \\ Boat & 0.8864 & 0.9204 & 0.8400 & 0.1569 & \(-\)0.1549 & \(-\)0.1117 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Correlation Coefficient (CC) Horizontal (H), Vertical (V), and Diagonal (D) values for the input test images.
## 2 Theoretical Analysis of Moon-Based Earth Observational Angles  
The aim of this study was to analyze the angular combination for Moon-based Earth observations based on the analytical expression of lunar and solar orbits. In this section, we first introduce the orbital elements of the Sun and Moon. Subsequently, the three observational angles are defined and calculated according to the solar and lunar nadirs. Finally, the ACNI is proposed to evaluate the performance of the angular combination of the Earth observation platform.
## 3 Results
### Structural and Electronic Properties
#### 3.1.2 Mo Doping  
Concerning the doping of TiO\({}_{2}\) with Mo atoms, we show that Mo can be either an interstitial at 1.97 A from the nearest oxygen or a Ti substitutional at a 1.94 A distance from the nearest oxygen (Figure 1c,d, respectively).  
For the interstitial doping, the DOS in Figure 2c shows peaks in the bandgap at 0.4, 1.2, 1.4, and 1.75 eV. Moreover, the bandgap is decreased to 2.87 eV. Focusing on the PDOS (Figures 2c and 3c), we see that the bandgap available states are mainly created from the Ti-3d and O-2p orbitals. For the gap states at 0.4 eV, we observe a small contribution from the Mo-4d orbitals. We have also predicted that the Mo-4d orbitals create energy states near the conduction band edge. For the substitutional case, mid-gap states are formed at 1.6 eV (Figure 2d). Figure 3d shows that these gap states are created from the hybridization of Ti-3d with O-2p with a small contribution of Mo-4d. However, similarly to the interstitial doping, available states are formed near the conduction band mainly from Mo-4d and Ti-3d orbitals. For the substitutional case, however, our bandgap is reduced to 2.77 eV. The present results for the substitutional doping are consistent with previous theoretical studies [53], however, we could not find any relevant work for the interstitial  
\begin{table}
\begin{tabular}{c c c c} \hline  & **a (Å)** & **c (Å)** & **Volume (Å\({}^{3}\))** \\ \hline TiO\({}_{2}\) & 3.80 & 9.73 & 140.80 \\ \hline Cr\({}_{\text{Ti}}\):TiO\({}_{2}\) & 3.80 & 9.80 & 143.75 \\ \hline Cr\({}_{\text{Ti}}\):TiO\({}_{2}\) & 3.81 & 9.69 & 140.29 \\ \hline Mo\({}_{\text{Ti}}\):TiO\({}_{2}\) & 3.83 & 9.70 & 142.29 \\ \hline Mo\({}_{\text{Ti}}\):TiO\({}_{2}\) & 3.81 & 9.72 & 141.10 \\ \hline W\({}_{\text{Ti}}\):TiO\({}_{2}\) & 3.83 & 9.70 & 142.30 \\ \hline W\({}_{\text{Ti}}\):TiO\({}_{2}\) & 3.82 & 9.69 & 141.40 \\ \hline \end{tabular}
\end{table}
Table 1: Lattice parameters and cell volume for every doping case.  
case. [PERSON] et al. [54] and [PERSON] et al. [55] performed experiments regarding the Mo doped TiO\({}_{2}\) and estimated a bandgap ranging from 2.65 to 3.0 eV.  
Figure 2: _Cont._
## 3 Methodology
### Road Region Extraction
#### 3.1.2 Road Region Searching.  
According to the method mentioned above, the LiDAR points are segmented within categories. Because the 'other' category includes not only road boundary but also unmarked obstacles, the road boundary cannot be directly extracted on the basis of the points with the 'other' category.  
Considering the road region, including the road surface and obstacles, the road region can be extracted into the categories of 'ground', and obstacles into 'truck','vehicle', and 'pedestrian'. Since the semantic segmentation method may cause false segmentation, especially on rough road surfaces and on the edges for small obstacles such as pedestrians, as shown in Figure 4, parts of the pedestrian point clouds are false semantic segments into 'other' and 'ground'.  
Figure 3: Illustration of cloud-semantic segmentation.  
To address this problem, a grid map method was used to search road region point clouds, as shown in Figure 5. Because the point clouds had disorganized characteristics, it was difficult to extract the outer contour of the ground and obstacles. Therefore, semantic segmentation point clouds were projected onto a 2D grid map, and the grid map was searched to extract the outer contours.  
The pipeline for road region searching is shown in Figure 5.  
After the semantic segmentation of LiDAR points, as shown in Figure 5a, 'ground' is green, 'truck' is red, and 'other' is gray. Then, after projecting semantic segmentation point clouds onto a grid map with \(M\) rows and \(N\) columns, there were different categories with different colors, as shown in Figure 5b. For the left boundary, each column was searched from left to right. If one grid cell stored both 'ground' and 'other' category points, the 'other' category points were the left boundary points and the search was stopped in this row. For the right boundary, the same search strategy was used, with the search direction from right to left. The left and right boundaries are shown as red lines in Figure 5b. After searching for each row of the grid map, the points were extracted between the left and right boundaries for each row, which were points in the ROI, as shown in Figure 5c.  
The grid map search method is illustrated in Algorithm 1.
## 4 Discussion
### Limitations and Future Considerations  
Limitation of this study were that only English studies were included and most of them were of moderate methodological quality. Additionally, six studies [24; 26; 27; 31; 32; 33], which relied solely on subjective findings (such as the ICIQ-SF questionnaire) for the evaluation of incontinence, were included. Furthermore, not many RCTs examining the long-term effect, as well as many studies assessing treatment satisfaction, were found. Lastly, the examination of the variables was based on their statistical significance and not on their clinical significance. In addition, the included RCTs showed heterogeny between the type of PFM exercises and follow-up periods. However, this was the case across published papers and clinical practice with PFM exercises. Lastly, a meta-analysis would have been useful, but due to the limited number of studies and the heterogeneity of the outcome measures used, this was not implemented.  
In future, more qualitative methodology studies should be carried out examining the satisfaction of therapy. Knowledge whether the intervention gives better satisfaction to the participants would help in the choice of treatment. Additionally, more studies should evaluate the effect in women with UUI, which was the least researched patient subgroup. Finally, it would be useful to investigate the comparison of the effectiveness of different BF types.
# Identification of Hybrid Okra Seeds Based on Near-Infrared Hyperspectral Imaging Technology
## 3 Results and Discussion
### Classification Results and Analysis by the Discrimination Models Based on the Full Spectrum  
Discrimination models which could classify the hybrid okra seeds were built based on the full spectrum. Firstly, PLS-DA and SVM were used to establish the discrimination models based on the full spectrum, and the accuracy of the classification recognition was used as an evaluation index of the model performance. As shown in Table 1, the classification ability of the SVM model, whose classification accuracy rate of the modeling set and the prediction set reached 99.31% and 93.62%, respectively, was obviously higher than that of the PLS-DA model. The correct recognition rate of the modeling set and the prediction set of the PLS-DA model reached 83.36% and 82.59%, respectively, which was also available for classification. [PERSON] et al. (2018) built SVM models to identify the variety of single rice seed [18]. Their accuracy of the training set and the test set reached 86.9% and 84.0%, respectively, which was not as good as our results. [PERSON] et al. (2015) compared the classification results of waxy corn seeds while using the SVM model and the PLS-DA model [15]. They also found that the performance of SVM is better than PLS-DA on most types of selected input  
Figure 2: The three-dimensional (3D) principal component (PC) score plot of three different varieties of okra seeds.  
datasets. When comparing the classification results of the two discriminative methods, the differences may be due to the fact that the SVM model uses a radial basis function (RBF) as a kernel function, and it performs a grid search within the optimization range to obtain a global optimal parameter combination [26; 27]. PLS-DA establishes a linear discriminant model, while the SVM algorithm establishes a non-linear model that can fully utilize the spectral information between different types and establish a classification model [24]. Therefore, the classification effect is significantly better than PLS-DA.
## 2 Results
### Hypothesis of P as Mutator, Evaluating Using Screen for Auxotrophy
All stab plates were incubated for 48 h. Potential auxotrophs were picked from the RM control plate and streaked onto MM for auxotrophy confirmation. Stabs for RK\({}^{-}\) mutants from Y836 were also made to MM+His+biotin plates, which allowed us to demonstrate that the drop in RK\({}^{-}\) frequency on MM relative to RM was not frequently linked to loss of the _bio_ operon introduced by the _bio_275 addition (Figure 1A), which if deleted along with the \(\lambda\) fragment would confer a Bio\({}^{-}\) phenotype. In summary, among CFU arising at 30 \({}^{\circ}\)C or 42 \({}^{\circ}\)C from strains without the (_cIII-ren_)\(\lambda\) fragment, or where the fragment was blocked for \(\lambda\) gene expression by pCI\({}^{+}\), less than 0.01% were auxotrophs. In contrast, for strains with the (_cIII-ren_)\(\lambda\) fragment that were derepressed for \(\lambda\) gene expression, about 3.4% of the CFU forming at 42 \({}^{\circ}\)C were auxotrophs. No auxotrophs were observed among the stabbed CFU when the Y836 strain was engineered to contain a partial gene replacement substituting Kan\({}^{\mathrm{R}}\) within \(P\), or if \(P\) was inactivated as in the RK\({}^{-}\) mutant 566a with a spontaneous IS2 insertion in \(P\). The observation that cell growth, with concomitant \(\lambda\) gene \(P\) expression, elevates the frequency of CFU acquiring an auxotrophic phenotype suggests the hypothesis that \(P\) expression confers a mutator phenotype in growing cells. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Strains** & **Assayed CFU \({}^{\mathrm{a}}\)** & **Auxotrophs/CFU \({}^{\mathrm{a}}\) (Ts CFU) \({}^{\mathrm{a}}\)** \\ \hline
**Nonlysogens or lysogens with noninduced prophage** & & \\ CFU from strains spread on RM, 30 \({}^{\circ}\)C & & \\
594 & 551 & 0 \\
594 & 413 & 0 \\ Y836 & 2520 & 0 \\ Y836 & 1179 & 0 \\
594: (_cIII-cl857-O\({}^{+}\)-P\({}^{+}\)-ren_)\(\lambda\) & 347 & 0 \\ Y836 RK\({}^{-}\) Bib11t O\({}^{+}\) P: Kan \({}^{\mathrm{c}}\) & 324 & 0 \\ Y836 RK\({}^{-}\) 566a O\({}^{+}\) P:S2 \({}^{\mathrm{d}}\) & 280 & 0 \\ Y836 RK\({}^{-}\) O208b \(P^{+}\) \({}^{\mathrm{e}}\) & 322 & 0 \\ Y836 RK\({}^{-}\) O223a \(P^{+}\)\({}^{\mathrm{f}}\) & 223 & 0 \\ Y836 RK\({}^{-}\) 534c O\({}^{+}\)\(P^{+}\) \(\delta\) & 167 & 0 \\ CFU from strains spread on RM, 42 \({}^{\circ}\)C & & \\
594 & 260 & 0 \\
594 & 919 & 1 \({}^{\mathrm{h}}\) \\ Y836 & [pCI] & 88 & 0 \\ Y836 & _dna_BgrpD55 & [pCI] & 100 \\
594: (_cIII-cl857-O\({}^{+}\)-P\({}^{+}\)-ren_)\(\lambda\) [pCI] & 110 & 0 \\ Total & 7803 & 1 \\ \hline
**RK\({}^{-}\) CFU isolated from Induced RK\({}^{+}\) strains (each expressing \(P\) at 42 \({}^{\circ}\)C)** & & \\ Y836 & 904 & 94 (79) \\ Y836 & 433 & 14 (11) \\ Y836 & 259 & 36 (36) \\ Y836 & 100 & 8 (8) \\
594: (_cIII-cl857-O\({}^{+}\)-P\({}^{+}\)-ren_)\(\lambda\) RK\({}^{+}\) & 427 & 57 (57) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stab assay screen for auxotrophic colony forming units (CFU).
# Short-Term Load Forecasting Based on the Analysis of User Electricity Behavior
## 2 Analysis of User Electricity Behavior
The adopted normalization is the most used extremum method during load clustering, as follows:  
\[x_{ij}^{\prime}=\frac{x_{ij}}{\max x_{i}} \tag{1}\]  
Figure 3: Load curves of a domestic user from 19 July 2009 to 25 July 2009. (**a**) The user load curve on 20 July; (**b**) The user load curve on 21 July; (**c**) The user load curve on 22 July; (**d**) The user load curve on 23 July; (**e**) The user load curve on 24 July; (**f**) The user load curve on 25 July; (**g**) The user load curve on 19 July. where \(x_{ij}\) denotes the load data of user \(i\) at the \(j_{th}\) moment, \(\max x_{i}\) denotes the maximum typical day load data of some date type, and \(x^{\prime}_{ij}\) is the normalized data. The normalized load curve of a user is shown in Figure 4, and the load peak, valley values, and the variations of the user load in different types of dates can be clearly seen. These data are clustered after they are normalized. In order to ensure that the algorithm does not fall into the local optimum, the experiment conducts iterative operations 500 times, and the chosen data set is respectively classified into 1-6 clusters. Because the ultimate objective is to conduct load forecasting, the study of the relationship between the cluster number and forecasting accuracy is required, and the appropriate cluster number depends on the forecasting accuracy.
## 1 Introduction
Most current research has mainly focused on PLA-based nanofibrous textiles, exploring various modifications and potential applications. A significant emphasis has been placed on improving the hydrophilicity and antibacterial properties of these materials, with the aim of developing advanced functional and protective clothing. This focus reflects the growing demand for high-performance textiles that can offer enhanced protection and comfort in various settings, from healthcare to industrial environments. To address the limitations of existing approaches and further advance the field, this research proposes an innovative strategy that evaluates both pre- and post-electrospinning techniques. This approach aims to synergistically enhance the functionality of electrospun nanofibrous textiles, particularly for antibacterial applications. This integration approach could lead to the development of more effective and versatile antibacterial textiles, opening up new possibilities for applications in medical textiles, protective gear, and other areas where bacterial resistance is crucial. This study incorporated silver nanoparticles (AgNPs) into both hydrophobic poly (lactic acid) (PLA) and modified hydrophilic PLA fabrics using pre-and post-electrospinning methods via pre-solution blending and post-solution casting methods. To increase the hydrophilicity and water stability, a PEG-PPG-PEG tri-block copolymer was employed, while AgNPs served as antibacterial agents in the textile matrix. Various tests were conducted to confirm the changes in the morphology, surface wettability, chemical composition, and thermal stability of the PLA composite nanofibrous textiles. Additional tests ensured the structural and mechanical properties. The antibacterial efficacy of the PLA-basedfabrics was also assessed. The results indicated that enhancing the hydrophilic properties of PLA nanofibers improved their water penetration efficiency, suggesting that the modified PLA could be a promising candidate for antibacterial applications. Combining electrospinningning with nanofiber modification techniques may lead to a rise in the use of PLA-based textiles in drug delivery, scaffolding, disease management, medical devices, and pharmaceutical applications.
# Design of the CIEMAT Corrosion Loop for Liquid Metal Experiments
## 3 CiCLo-C Loop: Requirements and Design
### Test Section  
In order to simulate the most realistic environment in terms of geometry, the TS was designed as a cylindrical tube, 176 mm long. All TS components follow this cylindrical configuration, as shown in Figure 10.  
Inside the main pipe, 10 samples in the form of rings and an extra piece to adapt the flow from the main pipe of the loop to the TS caudal, 32.8 \(\times\) 8.5 \(\times\) 16 mm\({}^{3}\), are foreseen. Liquid metal enters from the top of the TS and flows down with a laminar flow rate. According to this direction, the first piece has a different shape in order to adapt the caudal smoothly from 32 mm outside of the TS to 15.8 mm inside the TS. The lower diameter of the TS assures the desired PbLi velocity by maintaining the rest of the circuit at a lower velocity, as can be seen in Table 4.  
Figure 10: Tubular test section with cylindrical EUROFER samples located inside a 316 L pipe.  
Figure 9: Dependence of heat capacity of PbLi on temperature.  
At the end of the TS, an extra piece is installed to contain the samples in the appropriate position even if the flow is swift.  
This configuration of the test section provides an important new characteristic to the corrosion samples analyzed: the flow regime of the lead lithium in contact with the EUROFER will be uniform, as in the case of breeder blanket channels in a fusion reactor.  
Considering that the extreme conditions that this new loop can achieve are also the most corrosive ones, the test section has been designed to ensure them only in this part, leaving the rest of the experiment to work in milder conditions.  
In Table 4, the main operative parameters in the TS and in the rest of the loop are summarized.  
As previously mentioned, the temperature in the TS can achieve 550 \({}^{\circ}\)C and a PbLi flow of 1 m/s. The experiment stops after running for 250, 500, 1000, 2000, and 4000 h in order to obtain the corrosion rate at different exposure times and evaluate the cinematic rules of the corrosion rate. When the pump stops running, the loop is drained by gravity to the main tank of the loop, where PbLi keeps its temperature over the melting point. Once the loop is totally drained, the test section is opened and manipulated in a glove box under an argon atmosphere. The loop is turned on again without changing the experimental conditions, and the dynamic of the flow does not change. The test section is reinstalled, and the loop is cleaned with argon to eliminate possible oxygen and filled up again.  
On the other side, there is the possibility of introducing samples with different geometries from an additional port located just before the TS just described, in the elbow. In this way, it is possible to analyze samples with different geometries to test additional effects, such as hardening or embrittlement.
## 2 Materials and Methods
### Protein Expression and Purification  
_E. coli_ BL21(DE3) transformed with pET32a-_FPPS_ were inoculated in 50 mL LB medium containing 50 \(\upmu\)g/mL Amp at 37 \({}^{\circ}\)C for 12 h, and empty pET-32a (+) was used as a control. The cells were inoculated in LB medium at a ratio of 1:25, 1 mM isopropyl \(\upbeta\)-D-thiogalactoside (IPTG) was added and the cells were cultured at 37 \({}^{\circ}\)C for 1-2 h. Proteinexpression was induced at 20 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, 26 \({}^{\circ}\)C and 28 \({}^{\circ}\)C. The cells were cultured for about 4-5 h and centrifuged at 13,523\(\times\)\(g\) for 1 min. The cells were washed three times with PBS; then, they were fully suspended with 1.5 mL lysis buffer (50 mM NaH\({}_{2}\)PO\({}_{4}\), 300 mM NaCl, 10 mM Imidazole, pH 8.0), and 1 mM phenylmethylsulfonyl fluoride (PMSF, Yuanye Bio-Technology, Shanghai, China) was added immediately.  
The treated cells were ultrasonically broken for 20 min in 6 s\({}^{-1}\) pulses with 4 s between pulses on ice (the power was selected at a level slightly below the bubble generation of the solution [40%]). The cell lysate was centrifuged at 14,000\(\times\)\(g\) for 30 min at 4 \({}^{\circ}\)C, the supernatant was collected in a new tube, the precipitate was fully resuspended with the lysis buffer equal to the supernatant, and the supernatant and the precipitate were both analyzed by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE, 12% separate gel, 5% concentrated gel) with Coomassieie Brilliant Blue staining for their expression. Lightly stained for 1 h with a solution of 25% isopropanol and 10% acetic acid containing 0.1% (\(w/v\)) Coomassieie Brilliant Blue R250, they were then decolored with a solution of 10% acetic acid and 5% anhydrous ethanol until the electrophoresis band was clear.  
Recombinant protein pET32a-FPPS was purified using a 1 mL HisTrap HP column (Cytiva, Marlborough, MA, USA), and the supernatant obtained was filtered through a 0.22 \(\upmu\)m membrane and added to a nickel column to collect the effluent. The wash buffer (50 mM NaH\({}_{2}\)PO\({}_{4}\), 300 mM NaCl, 20 mM Imidazole, pH 8.0) was filtered through a 0.22 \(\upmu\)m filter membrane and added to a nickel column; the enzyme run was performed five times using the Ni column by reloading the effluent, and the effluent was collected separately. The elution buffer (50 mM NaH\({}_{2}\)PO\({}_{4}\), 300 mM NaCl, 500 mM Imidazole, pH 8.0) was used in the same way as the wash buffer, and the purification of the target protein was analyzed by SDS-PAGE.