esa-sceva/satcom-chunk-collection huggingface dataset:
# Global Transcriptomic Response of _Staphylococcus aureus_ to Viulent Bacteriophage Infection
## 3 Results  
### Experimental Design of the Study  
To assess the bacterial transcriptional response to bacteriophage infection, the virulent bacteriophage vB_SauM-515A1 and the _S. aureus_ strain SA515 were chosen. The bacterium and the bacteriophage characteristics as well as the choice of time points for RNA-Seq are described in detail in our previous study [25].  
In brief, whole-transcriptome sequencing was performed at three time points (5 min, 15 min, and 30 min), representing different stages of the vB_SauM-515A1 phage lifecycle (Figure 1A). The first two time points corresponded to the host takeover and biosynthesis phases of the phage, while the selection of the last time point was associated with a shift from early gene expression to late genes, the completion of phage assembly, and its release from the host. Meanwhile, analysis of bacterial growth rate at these time points did not reveal that phage affects _S. aureus_ culture until the late stage of its life cycle; a reduction in the number of _S. aureus_ cells was observed only after 30 min of infection compared to non-infected controls (Figure 1B). However, the plating of infected cells showed a significant decrease in the number of CFUs throughout the entire infection period (Figure 1C).  
Figure 1: Analysis of the replication cycle of a bacteriophage and its effect on the bacterial growth. (**A**) The one-step growth curve of vB_SauM-515A1 bacteriophage, modified from [25]. (**B,C**) Growth inhibition curves of SA515 strain by the vB_SauM-515A1 phage. Phage-mediated lysis of bacteria was monitored by measuring the OD\({}_{600}\) values and colony-forming units/mL (CFU/mL) throughout the infection. The blue color corresponds to uninfected cells of SA515 strain, and yellow to phage-infected cells. Dotted lines indicate the three sampling time points selected for the transcriptomic analysis.  
Prior to the transcriptomic data analysis, whole-genome sequencing of the _S. aureus_ SA515 was carried out. The draft genome assembly of _S. aureus_ SA515 resulted in 51 contigs with a total length of 2,850,828 bp and an N50 value of 138,814 bp; the longest and the shortest contigs were 491,413 bp and 1210 bp, respectively. A total of 2658 CDSs, three types of rRNA, 53 tRNAs, one tmRNA, and two repeat regions were annotated (Table S1). Among the predicted CDSs, 2103 genes were assigned a putative function and 555 were annotated as hypothetical proteins.
# Upper-Bound Limit Analysis of Ultimate Pullout Capacity of Expanded Anchor Cable
## 5 Model Application  
In this section, the proposed model was adopted to analyze the influence of the key parameters on the ultimate pullout capacity of the expanded anchor cable. The influence of each variable on the ultimate lateral resistance, end resistance, and pullout capacity was  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**Lateral Resistance \(P_{1}\) (kN)**} & \multicolumn{3}{c}{**End Resistance \(P_{2}\) (kN)**} & \multicolumn{3}{c}{**Ultimate Pullout Capacity \(P\) (kN)**} \\ \cline{2-10}  & **Numerical** & **Theoretical** & **Error (\%)** & **Numerical** & **Theoretical** & **Error (\%)** & **Numerical** & **Theoretical** & **Error (\%)** \\ \hline M1 & 575.5 & 610.1 & 6.0 & 176.5 & 217.4 & 23.2 & 800 & 861.7 & 7.71 \\ M2 & 724.6 & 761.5 & 5.1 & 170.8 & 217.4 & 27.3 & 950 & 1019.3 & 7.29 \\ M3 & 608.9 & 580.8 & 4.6 & 146.7 & 144.9 & 1.2 & 830 & 782.9 & 5.67 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between ultimate pullout capacity according to numerical and theoretical solutions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & \multicolumn{3}{c}{**Lateral Resistance \(P_{1}\) (kN)**} & \multicolumn{3}{c}{**End Resistance \(P_{2}\) (kN)**} & \multicolumn{3}{c}{**Ultimate Pullout Capacity \(P\) (kN)**} \\ \cline{2-10}  & **Numerical** & **Zeng [18]** & **Error (\%)** & **Numerical** & **Zeng [18]** & **Error (\%)** & **Numerical** & **Zeng [18]** & **Error (\%)** \\ \hline M1 & 575.5 & 587.9 & 2.15 & 176.5 & 54.8 & 68.95 & 800 & 642.7 & 19.67 \\ M2 & 724.6 & 705.4 & 2.64 & 170.8 & 54.8 & 67.92 & 950 & 760.2 & 19.97 \\ M3 & 608.9 & 587.9 & 3.45 & 146.7 & 54.8 & 62.65 & 830 & 642.7 & 22.57 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between numerical solutions and theoretical solutions based on [18].  
investigated, and the values of the other parameters were based on those of case M1. When the anchorage segment diameter changed from 0.4 m to 0.8 m with a spacing of 0.1 m, the anchorage segment length changed from 6 m to 14 m with a spacing of 2 m, the free segment length changed from 6 m to 14 m with a spacing of 2 m, the inclination angle of the anchor cable change from 10\({}^{\circ}\) to 50\({}^{\circ}\) with a spacing of 10\({}^{\circ}\), and the buried depth of the anchor cable changed from 6 m to 10 m with a spacing of 1 m.
# Sustainable Building Tool by Energy Baseline: Case Study
## 4 Analysis and Results
### EnBL Time Series Models
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & \multicolumn{3}{c}{**Model 1**} & \multicolumn{3}{c}{**Model 2**} & \multicolumn{3}{c}{**Model 3**} & \multicolumn{3}{c}{**Model 4**} & \multicolumn{3}{c}{**Model 5**} & \multicolumn{3}{c}{**Model 6**} \\  & **Actual** & **Estimated** & **MAPE** & **Estimated** & **MAPE** & **Estimated** & **MAPE** & **Estimated** & **MAPE** & **Estimated** & **MAPE** & **Estimated** & **MAPE** & **Estimated** \\  & **Consumption** & **Consumption** & **MAPE** & **Consumption** & **MAPE** & **Consumption** & **MAPE** & **Estimated** & **MAPE** & **Estimated** & **MAPE** & **Estimated** & **MAPE** \\ \hline Jan & 8157 & 9369 & 14.9\% & 10,110 & 23.9\% & 9918 & 21.6\% & 15,699 & 92.5\% & 10,054 & 23.3\% & 10,179 & 24.8\% \\ Feb & 18,339 & 19,136 & 4.3\% & 19,050 & 3.9\% & 20,257 & 10.5\% & 15,381 & 16.1\% & 17,857 & 2.6\% & 17,857 & 2.6\% \\  & 19,271 & 18,186 & 5.6\% & 18,508 & 4.0\% & 19,252 & 0.1\% & 15,358 & 20.3\% & 19,770 & 2.6\% & 19,228 & 0.2\% \\ Apr & 15,445 & 18,346 & 18.8\% & 18,350 & 18.8\% & 19,422 & 25.7\% & 15,476 & 0.2\% & 16,129 & 4.4\% & 16,359 & 5.9\% \\ May & 18,876 & 18,855 & 0.1\% & 18,903 & 6.0\% & 19,940 & 5.7\% & 15,182 & 19.6\% & 19,770 & 4.7\% & 20,159 & 6.8\% \\ Jun & 12,135 & 8774 & 27.7\% & 9682 & 20.2\% & 9288 & 23.5\% & 15,236 & 25.6\% & 12,343 & 1.6\% & 11,467 & 3.8\% \\ Jul & 20,271 & 11,086 & 45.3\% & 11,980 & 40.9\% & 11,736 & 42.1\% & 15,376 & 24.1\% & 12,640 & 37.6\% & 12,152 & 40.1\% \\ Aug & 24,242 & 19,269 & 20.5\% & 19,940 & 21.0\% & 20,398 & 15,951 & 15,921 & 34.3\% & 17,459 & 28.0\% & 17,830 & 26.5\% \\ Sep & 22,723 & 18,946 & 18.6\% & 18,484 & 18.8\% & 19,581 & 13.8\% & 16,346 & 28.1\% & 16,896 & 25.6\% & 16,811 & 26.0\% \\ Oct & 21,390 & 16,742 & 21.7\% & 17,044 & 20.3\% & 17,723 & 17.1\% & 16,715 & 21.9\% & 17,459 & 18.4\% & 16,725 & 21.8\% \\ Nov & 17,109 & 13,979 & 18.3\% & 14,199 & 17.0\% & 14,799 & 13.5\% & 17,038 & 0.4\% & 16,896 & 1.2\% & 17,100 & 0.1\% \\ Dec & 10,418 & 7494 & 28.1\% & 8222 & 21.1\% & 7934 & 23.8\% & 17,281 & 65.9\% & 7909 & 24.1\% & 7920 & 24.0\% \\ \hline \multicolumn{1}{c}{Measurement Error} & \multicolumn{3}{c}{MAPE} & \multicolumn{3}{c}{MAPE} & \multicolumn{3}{c}{MAPE} & \multicolumn{3}{c}{MAPE} & \multicolumn{3}{c}{MAPE} \\ \multicolumn{1}{c}{Value} & \multicolumn{1}{c}{18,66\%} & \multicolumn{1}{c}{17,58\%} & \multicolumn{1}{c}{17,78\%} & \multicolumn{1}{c}{29,08\%} & \multicolumn{1}{c}{14,52\%} & \multicolumn{1}{c}{15,21\%} \\ \hline \end{tabular}
\end{table} 
Table 4: Mean Absolute Percentage Error (MAPE) for time series models.
## 3 Results and Discussion
### Fluctuations in the Lng  
Figure 10 shows the calculation results for the full tank, the depth of which is 41.6 m. The blue part represents the LNG fluid, which fluctuates under the influence of earthquakes. In Figures 11 and 12, the curves show the variation in wave height at the monitoring points on the LNG surface over time. From the graph, it can be discerned that, along the \(x\)-axis, under the influence of seismic activity, the farther it gets from the center, the more pronounced the wave amplitude becomes, signifying more intense liquid oscillations. It is worth noting that the wave height is highest at RP 0 (approximately 0.392 m for aboveground tanks and 0.284 m for underground tanks). The fluctuation amplitude of aboveground tanks is significantly greater than that of underground tanks. At RP 8, the peak wave height is slightly smaller than at RP 0. Along the \(y\)-axis, the temporal waveforms of the liquid level exhibits similarity, along with minimal disparity in wave height. At the same time, the fluctuation period of LNG in aboveground tanks is about 4.1 s, while that in underground tanks is about 1.9 s. This indicates that the constraint of soil can reduce the height and fluctuation period of LNG waves. Because excessive waves will impact the ceiling of the tank and cause adverse effects, underground full tanks are safer than aboveground full tanks.
## 2 Materials and Methods
### The Software Implemented  
For the processing of data obtained from TLS in line scan mode, a code has been developed in Matlab(r). Starting from the point cloud, the best fitting cubic spline is obtained in the case of unloaded structure. This spline represents the reference line for obtaining the displacement curves.  
In the case of static load, the same calculation is performed using the point cloud acquired by TLS during the permanence of the load on the structure. The spline extracted is corrected by imposing compliance with structural constraints. The difference between the obtained spline and the reference line gives the displacements due to the load.  
As for mobile loads, the following operations are performed: (1) the single line scans are obtained by detecting the negative increase of the TLS zenith angle; (2) for each line scan, the average of the first and the last point's timestamps is assumed as line time; (3) the best fitting cubic spline is calculated and corrected by imposing compliance with structural constraints; (4) the position of the mobile load is obtained using the previous and following GPS times with respect to the timestamp. The difference between the spline obtained for each line and the reference line gives the displacements due to the mobile load in the position obtained by GNSS receiver.  
If a FEM analysis is available, a comparison between the displacements obtained by TLS measurements and those expected in the design phase can be performed.  
The flowchart of the implemented software is shown in Figure 2.  
Figure 2: The Flowchart of the implemented software for structural analysis.
## 3 Results
### One-Way Analysis of Variance (ANOVA)  
To compare accident type with each other and their effects for incidence and mortality rates were evaluated during the analysis. For this purpose, the ANOVA test was applied and results were compared with each other in this study as shown in Tables 4 and 5. The results indicated a momentous difference in IRs on accident type at the p \(<\) 0.001 level. The post-hoc comparison based on accident types (F = 87.566, p \(<\) 0.001) of the construction worker's fatality was significantly different with each other in each year during the analysis period. In addition, it was found that the IRs of \"Slip, trip or fall on same level\" (mean = 1.004 and standard deviation (SD) = 0.0489) were indicated the significant effect with than all other accident type during the analysis. On the other hand, the IRs of \"fall of person from height\" (mean = 0.468, SD = 0.126) indicated the significant result with all other accident types expect \"Striking against fixed or stationary object\" throughout the study period. During the analysis, all other accident types have shown mixed trends in the comparison study. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Accidents Type (F = 87.566, p \(<\) 0.001)**} \\ \cline{2-11}  & **Accident Type** & \begin{tabular}{c} **Mean** \\ **(SD\(\pm\))** \\ \end{tabular} & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** \\ \hline \multirow{2}{*}{**1**} & \multirow{2}{*}{\begin{tabular}{c} **Fall of person** \\ **from height** \\ \end{tabular} } & 0.468 & & & & & & & & & & & & \\  & (0.126) & & & & & & & & & & & & \\ \hline \multirow{2}{*}{**2**} & \multirow{2}{*}{\begin{tabular}{c} **Slip, trip or fall** \\ **on same level** \\ \end{tabular} } & 1.004 & *** & & & *** & *** & *** & *** & *** & *** & *** & *** \\  & & (0.049) & & & & & & & & & & & \\ \hline \multirow{3}{*}{**3**} & \multirow{2}{*}{\
\begin{tabular}{c} **Striking against** \\ **fixed or stationary** \\ **object** \\ \end{tabular} } & 0.409 & & & & & & & & & & & & & \\  & (0.038) & & & & & & & & & & & \\ \hline \multirow{3}{*}{**4**} & \multirow{2}{*}{
\begin{tabular}{c} **Striking against** \\ **or Struck by** \\ **moving object** \\ \end{tabular} } & 0.683 & & & *** & *** & *** & *** & ** & ** & ** & ** & ** & ** & ** & ** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: One-way analysis of variance (ANOVA) results based on incidence rates (IRs). Figure 2: Mean of incidence rates (IRs) and mortality rates (MRs) of each accident type during the whole analysis period. (“Others: includes all accidents types other than mentioned above in occupational safety and health statics, Labour department of Hong Kong). Similarity, to compare the results of accident type with each other correspond to MRs were evaluated with the help of the one-way ANOVA test. Through the post hoc comparison, the results confirm the \"fall of person from height\" type have higher mean value (mean 11.959) than all other accident type in this study (Table 5). In addition, the \"fall of person from height\" (mean =11.959,
## 3 Results
### Acquisition of Backscatter Images and Co-Registration  
Although satellites revisit the same area along nominally identical orbits, perfect spatial overlap between SAR acquisitions cannot be achieved due to slight differences in spatial and temporal baselines. These differences result in pixel misalignment, which must be corrected through precise co-registration, particularly in PS-InSAR processing where millimetric accuracy is required. In this process, backscatter intensity maps were employed to ensure robust image alignment. One SAR image was designated as the master, and the remaining 28 images served as slave scenes. These were geometrically aligned to the master image using automated matching algorithms, resulting in a stack of co-registered Single Look Complex (SLC) images. The resulting backscatter images are expressed in grayscale, where brighter areas indicate stronger radar reflectivity and darker areas represent weaker reflectivity. The reflectivity of objects varies depending on the SAR satellite's operating frequency band. Typically, steel structures and concrete buildings exhibit a high backscatter intensity, whereas agricultural fields, bare land, mountainous areas, and waterfront zones display a low backscatter intensity. Asphalt surfaces show high reflectivity in the X-band but lower reflectivity in the L-band and C-band.  
Figure 4 presents the spatial overview of all 29 co-registered SAR acquisitions along the Honam High-Speed Railway corridor. High backscatter zones align with station buildings, bridge decks, and tunnel portals, confirming their suitability as persistent scatterer candidates.  
Figure 4: Co-registered images and backscatter images (29 scenes).
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
## 4 Application and Results
### Multivariate Representation  
Following expert validation, a marginal distribution of the cost and schedule-risk impact based on expert knowledge was determined, as shown in Figure 12. The parameters of the final cost marginal distribution were lower limit = $10,000, upper limit = $40,000, shape parameter \(\alpha\) = 2.7, and \(\beta\) = 12.745; and the parameters of the final schedule marginal distribution were lower limit = 1 day, upper limit = 10 days, shape parameter \(\alpha\) = 2.26, and \(\beta\) = 1.13. Membership functions provided in Tables 2 and 3 were used to derive the final marginal distributions. The steps of deriving the marginal distribution of the cost-risk impact are detailed in Supplement SB. To represent the cost and schedule-risk impact of the risk factor using multivariate distribution, an expert was asked to subjectively evaluate the correlation as either weak, moderate, or strong. A strong correlation between cost and schedule impact was evaluated, and the correlation matrix in Equation (14) was consequently used in the bivariate distribution.  
Figure 12: Final marginal distribution of the (**a**) cost impact and (**b**) schedule impact.
### Motivation and Original Contributions  
The most discussed advantage of FSO communication is that it allows huge bandwidths and data capacity, as demand is always growing in both the industrial and commercial sectors. However, eavesdropping attacks are possible when the transmission distance is large. For this reason, securing FSO links is essential to preserve the security of data and help with the adoption of new FSO technologies. Therefore, the main original contributions of this study are  
* Implement a secure FSO link using the NaCl library to generate encryption keys in software using GNU Radio Companion (GRC) 3.7.13.5 and investigate its performance in simulation. In particular, encryption algorithm (Xsalsa20) is used due to its high time complexity. Hence, it provides sufficient data security against adversary UAV that has limited computing power.
* Demonstrate a laboratory-based experiment of the secure FSO link using optics and software-defined radio (SDR) transceiver and compare results to the simulation.  
The rest of the paper is organized as follows. The proposed system model is described in Section 3. Secure FSO link performance in GNU Radio simulation is discussed in Section 4. The experimental demonstration of the link is given in Section 5. Finally, conclusions are provided in Section 6.
## 3 Results  
### VIs for Estimatin of Yield Parameters  
PCA was used to determine the two VIs most related to total fruit weight (kg-tree\({}^{-1}\)) and average fruit size (g). Non-linear regression was applied using the selected top two VIs to total fruit weight and average fruit size to determine the VI with the highest R-squared value for each block across all years and for all blocks in all years. The results from this analysis are presented in Table 2.  
RENDVI1 was identified as the VI with the highest R-squared for all blocks using all years of data, with an R-squared value of 0.29 for both total fruit yield (kg-tree\({}^{-1}\)) and average fruit size (g) per tree. In general, higher coefficients of determination were produced when each block was analysed separately using the VI identified with non-linear regression, except for Block 2 total fruit weight with RENDVI2 (R\({}^{2}\) = 0.25) and Block 4 average fruit size with Yellow SAVI (R\({}^{2}\) = 0.19).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Blocks**} & \multicolumn{3}{c}{**Total Fruit Weight (kg-tree\({}^{-1}\))**} & \multicolumn{3}{c}{**Average Fruit Size (g) Per Tree**} \\ \cline{2-7}  & **PCA** & **Non-Linear Regression** & **R\({}^{2}\)** & **PCA** & **Non-Linear Regression** & **R\({}^{2}\)** \\ \hline All blocks for 3 years & RENDVI1, RENDVI2 & RENDVI1 & 0.29 & RENDVI1, RENDVI2 & RENDVI1 & 0.29 \\ block 1 (3 years) & RENDVI1, NiCNDV1 & NiCNDV1 & 0.48 & RENDVI1, Ni/ RENDVIVI & RENDVI1 & 0.39 \\ Block 2 (3 years) & RENDVI1, RENDVI2 & RENDVI2 & 0.25 & RENDVI1, RENDVI2 & RENDVI1 & 0.43 \\ Block 3 (2 years) & CB SiPL, Yellow SAVI & CB SiPL & 0.40 & RENDVI2, CB Sipl & RENDVI2 & 0.28 \\ Block 4 (2 years) & SPIP, Yellow SAVI & SPIP & 0.43 & Yellow SAVI, RDVI & Yellow SAVI & 0.19 \\ Block 5 (2 years) & RENDVI2, RENDVI1 & RENDVI2 & 0.49 & RENDVI2, RENDVI1 & RENDVI2 & 0.40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Best vegetation indices (VIs) for all blocks and for each block for average fruit size (g) per tree and total fruit weight (kg-tree\({}^{-1}\)) using principal component analysis (PCA) and non-linear regression.  
Figure 3: Process of deriving parameter specific map from WorldView-3 (WV3) imagery using the algorithm developed from the 18 sampled trees per block.
###### Abstract  
In order to effectively identify complex power quality disturbances, a power quality disturbance classification method based on empirical wavelet transform and a multi-layer perceptron extreme learning machine (ELM) is proposed. The model uses the discrete wavelet transform (DWT) multi-resolution method to extract classification features. Combined with hierarchical ELM (H-ELM) characteristics, the particle swarm optimization (PSO) single-object feature selection method is used to select the optimal feature set. The hidden layer of the H-ELM classifier in the model is trained by forward training. Once the previous layer is established, the weight of the current layer can be fixed without fine-tuning. Therefore, the training speed can be accelerated, the recognition accuracy is almost independent of the parameter adjustment, and the model has strong robustness. In order to solve the problem of data imbalance in the actual power system, a data enhancement method is proposed to reduce the impact of data imbalance and enhance the generalization performance of the network. The simulation results showed that the proposed method can identify 16 disturbances efficiently and accurately under different noise conditions, and the robustness of the proposed method is verified by the measured data.  
classification; extreme learning machine; feature extraction; optimal feature selection; power quality +
Footnote †: journal: Applied Sciences