esa-sceva/satcom-chunk-collection huggingface dataset:
# Plant-Growth-Promoting Bacteria Mitigating Soil Salinity Stress in Plants
## 5 Role of PGPB in Alleviating Salinity Stress to Plants
### Phytohormone Signals
#### 5.1.2. Cytokines, Gibberellic Acid, and Abscisic Acid  
Both cytokins and gibberellic acid play important roles in plant physiological processes in the regulation of protein synthesis, seed germination and cell division and elongation, and chlorophyll accumulation, among others [11; 60; 70]. They are generated by PGPB but have not been studied in depth under salinity stress [58]. _Bacillus, Pseudomonas, Rhizobium, Azotobacter,_ and _Azospirillum_ are reported to synthesize cytokins and gibberellic acid and to promote plant growth [58; 71]. There is evidence that cytokin levels generated by PGPB are lower than those from phytopathogens; thus, the effect on plants is inhibitory in the case of the pathogens, while it is stimulatory in the case of PGPR [72]. [PERSON] (2009) reported wheat growth stimulation of about 50% under salinity stress conditions when _Pseudomonas_ strains, producing cytokins, were presented in the rhizosphere [27]. The role of gibberellins under salt stress is also associated with the mitigation of the harmful effects of salt shock by increasing water availability to plants [5; 73].  
Globally, wheat is one of the most important crops, and a lot of studies are directed to the use of PGPB to increase the yield of wheat and alleviate the environmental stresses to it [74; 75; 76; 77]. In one of them, four rhizobacteria that produced different phytohormones, such as IAA, gibberellic acid, cytokins, and abscisic acid, were isolated in in-vitro experiments under salt stress. _Bacillus_ sp., _Azospirillum brasilense, Azospirillum lipoferum,_ and _Pseudomonas stutzeri_, applied together as a consortium, improved plant biomass and relative water content significantly. At the same time, wheat plants exposed to salinity stress of 150 mM NaCl showed tolerance-reducing electrolyte leakage and increasing production of chlorophyll \(a\), \(b\), and total chlorophyll, and osmolytes such as soluble sugar, proline, amino acids, and antioxidant enzymes (superoxide dismutase, catalase, peroxidase) [74].  
A confirmation of the promotion effect that PGPB hormones render to plants is the application of _P. putida_ H-2-3 in soybean rhizosphere [78]. The strain ameliorated the stress effects, enhancing shoot length, fresh weight, and chlorophyll content of the plants. Plants under salinity stress shown a higher level of superoxide dismutase and total polyphenol, but, at the same time, the radicals' formation was decreased. Finally, _P. putida_ H-2-3 was able to induce tolerance to abiotic stress by a reduction of Na\({}^{+}\) in plants [78].
# Electrocardiographic Fragmented Activity (II):
## 4 Results
### Fragmentation and Fibrosis Detection Based on Non-Linear Models
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Classifier** & **Signal Selection** & **Input Space** & **Accuracy** & **Classifier** & **Signal Selection** & **Input Space** & **Accuracy** & **Classifier** & **Signal Selection** & **Input Space** & **Accuracy** \\ \hline C-SVM & Normalized QRS & Stats + PCA & 0.79 & C-SVM & Non-normalized QRS & Stats +3 PCA & 0.77 & **C-SVM** & **Normalized QRS** & **Sum + 8 Ld** & **0.91** \\ No-SVM & Non-normalized best & Stats + ICA & 0.78 & No-SVM & Non-normalized QRS & Stats +PCA & 0.78 & No-SVM & Normalized QRS & Sum + 8 Ld & 0.91 \\ NNN & Non-normalized QRS & Stats + PCA & 0.65 & KNN & Normalized QRS & Stats + PCA & 0.72 & NNN & Non-normalized QRS & Stats + 8 Ld & 0.79 \\ MLP & Non-normalized QRS & Stats + ICA & 0.78 & MLP & Non-normalized QRS & Stats + ICA & 0.78 & MLP & Non-normalized beat & Stats + 8 Ld & 0.78 \\ DT & Normalized QRS & Stats + S4L & 0.81 & DT & Non-normalized QRS & Stats + PCA & 0.78 & DT & Non-normalized QRS & Stats + 12 Ld & 0.79 \\
**NB** & **Non-normalized QRS** & **Stats +PCA** & **0.83** & **NB** & **Non-normalized QRS** & **Stats + PCA** & **0.83** & NB & Normalized QRS & Stats + S4L & 0.79 \\ \hline \end{tabular}
\end{table} 
Table 3: Accuracy for the best classification method of each proposed non-linear classifier applied over the fragmentation databases. (a) Strag-DB. (b) SWfrag-DB. As seen in Table 5 the non-linear methods outperformed the linear methods in detection of both fragmentation and fibrosis.
# Progress in Construction Robot Path-Planning Algorithms: Review
## 2 Environmental Modeling and Path Quality Assessment Standards
### Environmental Modeling
#### 2.1.3 Geometric Characteristic Method (GCM)  
GCM is a technique that enables robots to derive abstract geometric features from the observed environment, including elements such as lines, arcs, and circles, to characterize the surroundings. This methodology results in a relatively compact representation, facilitating the process of position estimation. Nonetheless, the challenge of reliably extracting stable features from raw data is considerable, which limits the applicability of this approach primarily to structured operational environments.
# Remote Radio Frequency Sensing Based on 5G New Radio Positioning Reference Signals
## 7 Future Work  
Based on the findings of this study, several directions for future research and development can be identified. Comprehensive field tests should be conducted to validate the simulation results in real-world environments. This would help to assess practical challenges and refine the PRS configurations for optimal performance under various conditions. Enhanced signal processing algorithms must be developed and integrated to improve the accuracy and reliability of radar sensing. This includes exploring machine learning techniques for better interpretation of the radar data. Investigating the impact of different environmental factors, such as weather conditions and urban infrastructure, on the performance of PRS-based radar sensing is essential. These future directions aim to extend the applicability and efficiency of PRS-based radar sensing, contributing to the advancement of ISAC systems in mobile networks.  
Methodology, T.P.Z.; Software, M.B. and T.P.Z.; Validation, T.P.Z.; Formal analysis, T.P.Z.; Investigation, M.B. and T.P.Z.; Writing--original draft, M.B. and T.P.Z.; Writing--review & editing, T.P.Z.; Supervision, T.P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Polish Ministry of Science and Higher Education with the subvention funds of the Faculty of Computer Science, Electronics and Telecommunications of AGH University of Krakow.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Optimization of the Microwave Vacuum Drying Technology
#### 3.3.2 Construction of the Regression Model and Variance Analysis  
The quadratic multinomial regression models of the material surface temperature, vacuum degree, and microwave power with the soluble solid content, chewiness, and Vc content were constructed. The coding spatial regression equation after insignificant factors were eliminated as follows:  
\[\text{Y}_{1}=388.13\text{x}_{2}+0.169\text{x}_{3}-0.188\text{x}_{1}{}^{2}-524.6,\]  
\[\text{Y}_{2}=6.67-0.919\text{x}_{1}+0.1\text{x}_{3}-768.19\text{x}_{2}{}^{2},\]  
\[\text{Y}_{3}=262.7\text{x}_{2}+0.223\text{x}_{3}-0.0008\text{x}_{1}\text{x}_{3 }-0.057\text{x}_{1}{}^{2}-0.00026\text{x}_{3}{}^{2}-208.7,\]  
where Y\({}_{1}\), Y\({}_{2}\), and Y\({}_{3}\) are the soluble solid content, chewiness, and Vc content, respectively, and x\({}_{1}\), x\({}_{2}\), and x\({}_{3}\) are the material surface temperature, vacuum degree, and microwave power (code values), respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Level**} & \multicolumn{2}{c}{**Material Surface**} & \multicolumn{1}{c}{**Vacuum Degree**} & \multicolumn{1}{c}{**Microwave Power**} \\  & **Temperature/\({}^{\circ}\)C** & **/MPa** & **/W** \\ \hline
1.682 & 58.0 & 0.10 & 430 \\
1 & 56.0 & 0.09 & 400 \\
0 & 52.0 & 0.07 & 350 \\ \(-1\) & 48.0 & 0.05 & 300 \\ \(-1.682\) & 45.0 & 0.04 & 260 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The test-factor level codes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{2}{c}{**Material**} & \multirow{2}{*}{**Vacuum**} & \multirow{2}{*}{**Microwave**} & \multirow{2}{*}{**Soluble**} & \multirow{2}{*}{**Chewiness**} & \multicolumn{1}{c}{**Vc mg.**} \\  & **Surface** & & & & & **(100 g)\({}^{-1}\)** \\ \hline  
\hline \hline \end{tabular}
\end{table}
Table 12: The technological parameter test design and results of the microwave vacuum drying of the mulberry.  
It can be seen in Table 13 that, after the variance analysis of the test results, the regression equation models of the soluble solid content, chewiness, and Vc content were extremely significant in the significance test results (_p_ < 0.01), and the lack-of-fit test results were also found to be insignificant (_p_ > 0.05). This indicates that the regression model had a good fitting degree in the test range.
# Highly Reliable Fuzzy-Logic-Assisted AODV Routing Algorithm for Mobile Ad Hoc Networks
## 3 Proposed Fuzzy-Logic-Assisted AODV Routing Protocol
### Defuzzification  
Defuzzification is to convert the fuzzy output language obtained by the fuzzy processing into an accurate output language. This paper uses the center of gravity (COG) [23] method to achieve defuzzification. The main theory of COG is to output the center of gravity of the membership function curve and the enclosed area of the abscissa as the final output value of the fuzzy control as  
\[v_{0}=\frac{\int_{v}v\mu_{v}(v)dv}{\int_{v}\mu_{v}(v)dv}. \tag{4}\]  
The COG method has smoother output inference control. Even in response to a small change in the input signal, the output will change. In the paper, we select the output reliability level REL to comprehensively evaluate the three parameters of the node, and set the six fuzzy languages Excellent, Good, Acceptable, Not acceptable, Bad, and Terrible as the membership function of REL output, as shown in Figure 6. For example, when the fuzzy language calculated and output according to the input fuzzy processing part is [Not Acceptable:0.5, Acceptable:0.5], the REL value of the output result of defuzzification according to the COG is 0.5.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rules** & **ND** & **NS** & **NRE** & **REL** \\ \hline
1 & LOW & HIGH & HIGH & Excellent \\
2 & LOW & HIGH & MEDIUM & Good \\
3 & LOW & HIGH & LOW & Not Acceptable \\
4 & LOW & MEDIUM & HIGH & Good \\
5 & LOW & MEDIUM & MEDIUM & Acceptable \\
6 & LOW & MEDIUM & LOW & Bad \\
7 & LOW & LOW & HIGH & Not Acceptable \\
8 & LOW & LOW & MEDIUM & Bad \\
9 & LOW & LOW & LOW & Terrible \\
10 & MEDIUM & HIGH & HIGH & Good \\
11 & MEDIUM & HIGH & MEDIUM & Acceptable \\
12 & MEDIUM & HIGH & LOW & Bad \\
13 & MEDIUM & MEDIUM & HIGH & Acceptable \\
14 & MEDIUM & MEDIUM & MEDIUM & Not Acceptable \\
15 & MEDIUM & MEDIUM & LOW & Bad \\
16 & MEDIUM & LOW & HIGH & Not Acceptable \\
17 & MEDIUM & LOW & MEDIUM & Bad \\
18 & MEDIUM & LOW & LOW & Terrible \\
19 & HIGH & HIGH & HIGH & Acceptable \\
20 & HIGH & HIGH & MEDIUM & Not Acceptable \\
21 & HIGH & HIGH & LOW & Terrible \\
22 & HIGH & MEDIUM & HIGH & Not Acceptable \\
23 & HIGH & MEDIUM & MEDIUM & Bad \\
24 & HIGH & MEDIUM & LOW & Terrible \\
25 & HIGH & LOW & HIGH & Bad \\
26 & HIGH & LOW & MEDIUM & Terrible \\
27 & HIGH & LOW & LOW & Terrible \\ \hline \hline \end{tabular}
\end{table}
Table 1: Fuzzy rules.
## 2 Materials and Methods
### Numerical Simulation
#### 2.2.1 Domain and Boundary Conditions  
As is shown in Figure 4a, the investigated tower crane is inside the cuboid filled with air. The geometrical dimensions of the cuboid are as follows: 230 m \(\times\) 230 m and \(H\) = 90 m. Such dimensions are sufficiently large to avoid the impact of the boundary conditions on the results, meaning the aerodynamic forces and moments acting on the crane. One of the vertical walls of the cuboid plays the role of the inlet, and the opposite one is considered  
Figure 3: Details of the simplified geometry of the top-slewing tower crane: (**a**) part of the crane jb with counterweight ballast, operator’s cabin, and jib; (**b**) base of the studied crane with central ballast; (**c**) truss structure of the jib; and (**d**) truss structure of the tower.  
a pressure outlet. The airstream, which goes through the inlet, and the wind are formed according to the following power laws [7], namely:  
\[V(z)=V_{40}\left(\frac{z}{z_{40}}\right)^{\alpha}, \tag{1}\]  
where \(V_{40}\) is wind speed measured at height \(z_{40}\). The value of the \(V_{40}=15\) m/s at height \(z_{40}=40\) m in all performed simulations is assumed. Exponent \(\alpha\) depends on the terrain, namely, 0.16, 0.28, and 0.4 for open, village, and urban terrain, respectively. The applied wind profiles as a function of space coordinate z are depicted in Figure 4b. The wind profiles are prescribed using an appropriate user-defined function (UDF). The intensity of the turbulence is assumed to be equal to 9% with the arbitrarily chosen length scale \(L_{turb}=3.5\) m.  
The reference values of the wind speed, necessary for aerodynamic force and moment coefficient computations, are determined according to the following expression:  
\[V_{ref}=\frac{\int_{0}^{z_{g}}V(z)dz}{z_{ref}}, \tag{2}\]  
where \(V(z)\) is an appropriate wind profile described by Equation (1). It seems that the reference wind speed, computed according to integral (2), better corresponds with the assumed wind profiles. Reference wind speeds determined using Formula (2) are as follows: \(V_{ref}=12.924\) m/s, 11.716 m/s, and 10.713 m/s for open, village, and urban terrain, respectively.  
The top surface and both sides of the domain are movable with identical wind profiles as defined for the inlet. Finally, the ground and the tower crane are assumed to be stationary boundary conditions.
## 2 Materials and Methods
### Target Polyphenols' LC-MS/MS Characterization  
The quantification of the polyphenols in the extracts and in the digestates was performed via LC-MS/MS using a Thermo Scientific (San Jose, CA, USA) instrument based on a TSQ Quantum UltraTM triple quadrupole mass spectrometer equipped with a heated electrospray ionization (HESI) source, and an Accela Open autosampler with a 20 \(\upmu\)L loop. The optimal instrumental conditions were previously optimized by [PERSON] et al. [22] to obtain the best chromatographic separation of the target polyphenols. The chromatographic separation was performed employing a Kinetex C18 column (100 mm 2.1 mm 100 A) obtained from Phenomenex (Torrance, CA, USA). The mobile phase was composed of water (A) and methanol (B), both with 0.1% formic acid. The chromatographic gradient was set at 5% B,reaching 90% B in 11 min and kept constant for 3 min. The initial conditions were achieved in 6 min. The injection volume was 10 \(\mu\)L, with a flow rate of 0.2 mL min\({}^{-1}\). The column temperature was set at 50 \({}^{\circ}\)C. Compound identification and detection were performed via selected reaction monitoring (SRM) working simultaneously in both negative and positive modes, monitoring two or three MS/MS transitions for each compound. Other HESI source parameters were the spray voltage: 3000 V, vaporizer temperature: 350 \({}^{\circ}\)C, sheath gas pressure: 35 au (arbitrary units), ion sweep and auxiliar gas pressure: 0 and 10 au, respectively, and the capillary temperature: 320 \({}^{\circ}\)C. The system was operated by Xcalibur 2.2. and Trace Finder 3.1. software. External calibration was used for the quantification of polyphenols. Linearity was evaluated at a wide range of concentrations from 0.01 to 10 \(\mu\)g mL\({}^{-1}\) (8 levels and 3 replicates per level), employing standard solutions prepared in water/methanol (50:50 \(v/v\)). The obtained coefficients of determination (R\({}^{2}\)) were, in all cases, higher than 0.9900. Three replicates of each sample were processed for analysis.
# Pulmonary Fissure Detection in 3D CT Images Using a Multiple Section Model
## 4 Experimental Results
### Quantitative Evaluation  
Our scheme was validated in the LOLA11 dataset including 55 CT scans. The box-plots of the indices (\(FDR\), \(FNR\) and \(F_{1}\)) corresponding to the proposed method (\(n=5\)), the proposed method (\(n=4\)), ODoS filter (od) [14], DoS filter (d) [13] and fissureness filter (f) [7] are displayed in Figure 9. The median values of the box-plots were 0.8916, 0.8812, 0.8768, 0.8331, 0.8181, 0.0846, 0.1157, 0.0654, 0.1020, 0.0981, 0.1172, 0.1139, 0.1524, 0.2418 and 0.2635. It can be seen that the proposed method gained a higher median \(F_{1}\)-score than the compared methods [7; 13; 14]. In other words, our scheme outperformed the compared methods [7; 13; 14] in pulmonary fissure segmentation. The good performance of our scheme is demonstrated in Figures 8 and 9 due to the lower \(FNR\) value, which means that the completeness of the pulmonary fissure detection improved with our scheme.  
Furthermore, to compare the computation efficiency of different methods, we tested them on a typical \(400\times 512\times 512\) size CT image. As shown in Table 1, the original fissureness filter took the least amount of time due to its simple implementation. The proposed method required the second shortest time, but had much better performance than the similar DoS and ODoS methods.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(400\times 512\times 512\) & Proposed method & ODoS & DoS & Fissureness \\ \hline Runtime & 1410s & 1390s & 1470s & 600s \\ \hline \hline \end{tabular}
\end{table}
Table 1: The runtime of the proposed method, ODoS method, DoS method and fissureness method for a typical \(400\times 512\times 512\) size image.  
Figure 8: 3D fissure segmentation results from: (**a**) the proposed method, (**b**) the ODoS filter, (**c**) the DoS filter, and (**d**) the fissureness filter. Here, the cubes at the lower right corner indicate the view direction of the visualization in a standard anatomical coordinate system.
## 2 Materials and Methods
### Statistical Analysis  
A database was created and processed using a statistical software package Jamovi 2.6.24. The qualitative variable gender is presented as frequencies (f) and percentages (%), while the quantitative variables--age, initial and final strength of the dominant and non-dominant sides, and initial and final vertical jump height--are reported as mean values and standard deviations (\(\pm\)).  
Normality was confirmed via Kolmogorov-Smirnov tests, justifying parametric testing. An independent samples \(l\)-test was used to compare mean values between the control and experimental groups at both baseline and post-intervention phases. A paired samples \(t\)-test was used to assess within-group differences (pre- and post-intervention) for both experimental and control groups.  
To analyze the effects of the intervention on absolute strength (dominant and non-dominant) and vertical jump, a 2-way mixed ANOVA assessed intervention effects with the following details: within-subject factor: time (pre/post); between-subject factors: group (EG/CG) and sport (6 disciplines); interactions tested: time \(\times\) group (TG) and time \(\times\) sport \(\times\) group (TSG); and effect sizes: \(\mathrm{np}^{2}\); \(p<0.05\).
# Efficient Transcoding and Encryption for Live 360 CCTV System
## 1 Introduction
The conceptual architecture of the proposed system including various CCTV cameras is illustrated in Figure 1.  
The rest of the paper is organized as follows: Section 2 describes related works that were considered for proposed system. Section 3 addresses the challenges in real-time CCTV system with or without 360 cameras, and presents the proposed method. Section 4 shows the implemented demonstration and the performance evaluation. Finally, Section 5 presents conclusions about the proposed scheme and future work.
# A Multidimensional Analysis of Released COVID-19 Location-Based Mobile Applications
## 4 The Framework of Mapping and Evaluating COVID-19 LBMAs
### Technology Aspects of COVID-19 LBMAs
#### 4.2.3 Tracing Protocols  
Decentralized privacy-preserving proximity tracing (DP-3T) is a protocol created for Bluetooth technology that ensures that individuals' exchanged data remain anonymous. Its purpose is to create a protocol that will help in reducing the spread of COVID-19 by notifying people with digital contact tracing of potential exposure to the virus. This feature aims to break the chain of transmission of COVID-19 [3]. The European Union (EU) created the Pan-European Privacy-Preserving Proximity Tracing (PEPP-PT/PEPP) that enables the utilization of LBMA by keeping the private information of end-users safe. It was created specifically for the COVID-19 pandemic inside Europe as an alternative to the decentralized privacy-preserving proximity tracing (DP-3T) protocol, and both are linked to the utilization of Bluetooth. This protocol is considered centralized, which may compromise the privacy of the smart device user. However, it cannot prevent the user from using a pseudonym instead of their real name. In case of an emergency, such as potential infection, the user could be reidentified in order close contacts can be alerted for potential transmission of COVID-19 [3].
## 3 Review of Indoor Navigation System Using Unity and a Smartphone (INSUS)
### INSUS Overview  
_INSUS_ has been designed and implemented on _Android_ and _iOS_ platforms [36]. It shows navigation guides on the smartphone display. It integrates \(AR\) technology, the 3D virtual environment, the _SLAM_ algorithm, and the _path-planning_ algorithm in the _Unity game engine_ environment. Figure 1 shows the _INSUS_ overview.  
The system consists of the modules for _Input_, _Unity game engine_, the _SEMAR_ server, and _Output_. _Input_ considers the \(QR\) code reader and other necessary input components for navigation. _Unity game engine_ provides services for real-time navigation processes through the _SLAM_ algorithm, including _path-planning_ and the estimation of user positions. The _SEMAR_ server stores all the data related to navigation and user identification. _Output_ displays the 3D arrow as a navigation guide for users.
## 7 Case Study
### Effectiveness of the Proposed Sensor Deployment Scheme
#### 7.3.1 Simulation Error Analysis  
To verify the reliability of our simulation, the lifetime of a WSN under our hexagonal deployment scheme is simulated with only one sensor per grid to explain the associated error of our Monte Carlo method. The simulation parameters are taken from Table 11 and the optimization results of uniform sensor deployment under retransmission are shown in Table 11, where \(d=58.97\) m and \(L=9\). The simulation is repeated for 1000 times. The error of lifetimes of the analytical solution and the simulation result is 5.5 s, and the error of the corresponding success transmission rates is 0.0000117.