esa-sceva/satcom-chunk-collection huggingface dataset:
# Targeting Type II Toxin-Antitoxin Systems as Antibacterial Strategies
## 3 Strategies for the Artificial Activation of Toxin-Antitoxin Systems
### Direct Activation of TA Systems
#### 3.1.2 Inhibition of Antitoxin Translation  
Toxin release can also be caused by a reduction in the amount of antitoxin molecules in a bacterial cell. One way to achieve such an effect is by blocking antitoxin translation (Figure 2b) [20]. The toxin and antitoxin genes, co-transcribed as a single mRNA, possess separate Shine-Dalgarno sequences [62]. Therefore, inhibition of antitoxin translation should not influence the translation of the toxin protein. This strategy was tested by [PERSON] and co-workers who designed a peptide nucleic acid (PNA)-based treatment to inhibit translation of the antiotoxins of the _mazEF_ and _hipBA_ TA systems of _E. coli_[63].  
The sequence-specific antisense PNAs targeted either _mazE_ or _hipB_ antitoxin mRNAs and as a result lowered the cellular levels of these transcripts, which caused an effective inhibition of _E. coli_ growth [63]. Importantly, the PNA treatment did not change the relative levels of the _mazF_ or _hipA_ toxin mRNAs. The crucial role of the MazF and HipA toxins in the observed growth inhibition was confirmed by showing that _E. coli_ mutants lacking the genes encoding these proteins were \"resistant\" to treatment with the antitoxin-specific PNAs.  
Another PNA-based strategy, described in the same report, used PNA oligomers directed at the cellular target of the toxin, thus bypassing the involvement of the TA system. It was demonstrated that PNAs can mimic the action of the HipA toxin by silencing the _gltX_ gene encoding its cellular target, glutamyl-tRNA synthase [63]. For these experiments, the PNA oligomers were conjugated with a cell  
Figure 2: Proposed antibacterial strategies based on the direct activation of toxins of TA systems; (**a**) disruption of the protein toxin and antitoxin complex and/or prevention of protein complex formation; (**b**) inhibition of antitoxin translation (see text for details).  
penetrating peptide--(KFF)\({}_{3}\)K, which indicated the importance of employing an efficient carrier to introduce these antisense oligonucleotides into bacterial cells [63].  
The above results confirmed experimentally that TA systems are susceptible to sequence-specific antisense agents and provided a basis for their further exploitation in antimicrobial strategies. Importantly, PNA oligomers exhibit nuclease and protease resistance, high binding affinity to natural nucleic acids, and negligible toxicity to eukaryotic cells [64; 65].
## 7 Results
### Assessment of Maslach Burnout Inventory-Human Services Survey (MBI-HSS) Constructs
Next, a confirmatory factor analysis was conducted for the three factors MBI-HSS obtained from EFA of 300 sample. It is important to state that 588 sub-sample isolated earlier was used in this CFA. The results of CFA on the revised model are shown in Figure 1. Chi square value of 788 was significant with \(p\) value \(<\) 0.001. Investigation of other indices gave us RMSEA value of 0.06 (\(<\)0.08), GFI value of 0.91 (\(>\)0.90), CFI value of 0.911 (\(>\)0.90) and AGFI value of 0.89 (\(>\)0.8) for the new measurement model. This showed that revised three factor model fit adequately with data of healthcare professional in Saudi Arabia hospitals. In addition, composite reliability (CR) was calculated for each of the construct. Construct EE showed CR value of 0.898, construct PA showed CR value of 0.790, and construct DP exhibited CR value of 0.790, thus study concludes that the measurement model has good reliability (internal consistency). Study believes that the revised model (Figure 1) will be more appropriate in assessing the burnout syndrome of healthcare professional in hospital around Saudi Arabia, compared to the original measurement model. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**New Factor**} \\ \hline
**Item (Origin Factor)** & **EE** & **PA** & **DP** \\ \hline MBI 1 (EE) & 0.85 & & \\ MBI 2 (EE) & 0.88 & & \\ MBI 3 (EE) & 0.72 & & \\ MBI 4 (PA) & & 0.62 & \\ MBI 5 (DP) & & & 0.73 \\ MBI 6 (EE) & 0.56 & & \\ MBI 7 (PA) & & 0.75 & \\ MBI 8 (EE) & 0.83 & & \\ MBI 9 (PA) & & 0.70 & \\ MBI 10 (DP) & & & 0.65 \\ MBI 11 (DP) & 0.70 & & \\ MBI 12 (PA) & & 0.56 & \\ MBI 13 (EE) & 0.73 & & \\ MBI 14 (EE) & 0.72 & & \\ MBI 15 (DP) & & & 0.79 \\ MBI 16 (EE) & & & 0.69 \\ MBI 17 (PA) & & 0.57 & \\ MBI 18 (PA) & & & \\ MBI 19 (PA) & & 0.66 & \\ MBI 20 (EE) & 0.49 & & 0.46 \\ MBI 21 (PA) & & 0.71 & \\ MBI 22 (DP) & & & 0.55 \\ Variance explained \% & 33.47 & 14.11 & 8.71 \\ Eigen value & 7.36 & 3.10 & 1.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Factor loading of EFA (\(n=300\)).
# Intelligent Design of Pavement Concrete Based on RSM-NSGA-III-CRITIC-VIKOR
## 2 Materials and Methods
### Multi-Objective Optimization Framework
#### 2.2.1 RSM Model Design Method  
RSM was applied to analyze and optimize the non-linear interactions between W/C, sand ratio, and AEA. The strength of RSM lies in its ability to quantify both main effects and interaction effects between parameters through polynomial models, capturing complex relationships that linear models cannot represent. The Box-Behnken experimental design (BBD) was employed, with dry shrinkage, porosity, and R\({}_{\text{f}}\)/R\({}_{\text{c}}\) selected as response variables. BBD was selected for its efficiency, requiring fewer experimental runs than full factorial designs while maintaining the ability to estimate quadratic terms and interactions--critical for understanding the complex parameter relationships in concrete performance. A total of seventeen sample sets were generated using Design-Expert software, including twelve sets for model fitting and five sets for validation. The model's significance was evaluated through analysis of variance (ANOVA), and the coefficient of determination (\(R^{2}\)) and the adjusted coefficient of determination (\(R_{\text{p}}^{2}\)) were used to assess the model's fitting and predictive performance. Analytical methods followed the approach outlined in references [27; 28; 29; 30]. Factors and codings are shown in Table 2.
## 1 Background  
With the development of the times, online education has become the main mode of teaching. Limited by time, space, equipment, teachers, and other factors, traditional classroom teaching methods can no longer meet people's growing demand for knowledge. The introduction of virtual teachers solves this pain point problem [1]. The emergence of virtual teachers not only improves the efficiency and quality of teaching, but also provides students with more convenient and diversified learning ways, which has a far-reaching role in promoting the cause of education and teaching.  
As a new teaching assistant tool, the virtual teacher has important innovative significance in the field of educational technology. Virtual teachers integrate cutting-edge technologies, such as natural language processing, machine learning, and human-computer interaction, and provide new possibilities for teaching. Through the study of virtual teachers, the nature and law of teacher-student interaction in the education process can be deeply discussed [2]. As a human-computer interaction system, virtual teachers can help us better understand the mechanism of information transmission, cognitive inspiration, and emotional communication in teaching and provide theoretical support for improving teaching quality and educational experience. Therefore, the research of virtual teachers can not only help to improve the level of personalized teaching and promote the innovative development of educational technology, but also deepen our understanding of the education process and provide important reference and support for education reform and improving teaching effect [3].  
The importance of the virtual teacher lies not only in its technical innovation and teaching function, but also in the emotional value it can bring. An American study shows that when people express their thoughts through speech, 93% of the information is carried out through non-verbal behaviors and intonation and only 7% of the information is carried out through speech [4]. In addition to the usual oral feedback, teachers can also use non-verbal communication to influence students, and non-verbal expressions can better help virtual teachers convey information. Articulate and enthusiastic teachers are able to clearly convey suggestions for solving problems, while also having a noticeable incentive effect on student learning outcomes [5]. Therefore, based on the VOCA model, this paper will generate virtual teachers with rich head movements, expressions, and intelligent question-answering capabilities, aiming to behave like real people and provide a richer and more diverse teaching experience.
# Reducing the Power Consumption of VR Displays with a Field Sequential Color LCD
## 6 Conclusions  
One of the biggest hurdles for immersive VR is screen-door effect, which arises from inadequate resolution density of the microdisplays. In other words, the pixel structure including black matrices may be noticeable because of the large magnification from the lens if the resolution density is not high enough. To solve this tough problem, we propose an FSC LCD to achieve 60 PPD and 100\({}^{\circ}\) FoV while maintaining a low power consumption for compact VR displays. To prove the concept, in the experiment we reported and evaluated a new high birefringence nematic LC mixture. Our simulation results show that the average GTG response time can be as fast as \(\sim\)1.5 ms to avoid color breakup. The FSC LCD also exhibits a high contrast ratio, superior color uniformity, and wide color gamut.  
Methodology, Z.Y.; experiments, Z.Y., Y.Q. and J.Z.; simulations, Z.Y. and Y.Q.; power consumption, Z.Y. and C.-L.L. ([PERSON]); writing--original draft preparation, Z.Y.; writing--review and editing, S.-T.W.; supervision, C.-L.L. ([PERSON]) and S.-T.W. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the Meta distinguished faculty award.  
Not applicable.  
The data presented in this study are available from the authors upon reasonable request.  
The authors would like to thank JNC for providing the LC mixture.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Rise Time (ms)**} \\ \cline{2-7}  & Gray level & 0 & 64 & 128 & 192 & 255 \\ \cline{2-7}  & 0 & \(\backslash\) & 10.18 & 6.66 & 4.62 & 1.50 \\ \cline{2-7} Decay time & 64 & 1.79 & \(\backslash\) & 5.23 & 3.81 & 1.15 \\ \cline{2-7} (ms) & 128 & 1.93 & 5.51 & \(\backslash\) & 3.65 & 1.02 \\ \cline{2-7}  & 192 & 2.08 & 5.36 & 4.65 & \(\backslash\) & 0.96 \\ \cline{2-7}  & 255 & 2.35 & 5.37 & 4.88 & 4.14 & \(\backslash\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulated response time of VA mode between different gray levels without overdrive and undershoot. The operating temperature is 50 \({}^{\circ}\)C.
## 2 3D LiDAR Fundamentals
### LiDAR Multiple Returns  
The LiDAR has multi-return properties that enable us to detect multi-depths in the same light direction. When a laser pulse is left away from the sensor, it gradually grows larger because of beam divergence. Any emitted laser pulse that encounters multiple reflection surfaces as it travels is split into many returns. A part of the light pulse will be reflected from the first surface and another part that has enough intensity to measure will be reflected from the surface behind it. This effect can occur multiple times in the same light direction. It is important to determine whether the measured value is part of the application or whether it should be discarded, e.g., due to a part falling across the field of vision. The receiver detector triggers when the incoming pulse reaches a set threshold, thus measuring the time of flight. The received laser pulse can be evaluated as measurement data in different manners, such as from the most significant return, from the first and last significant return, or from all returns which are above the threshold. For example, the Velodyne HDL-32E MBL records two return signals using the strongest and last signals.  
The measuring rate is 695,000 pts/s in the case of recording a single return signal, and 1,390,000 pts/s in the case of recording two return signals. The number of return signals to be recorded can be determined by the user. Many return signals increase the amount of measurement data and level of detail, but reduce the signal-to-noise ratio (SNR). On the other hand, it is useful to remove the errors caused by rain, snow, and dust in the FoV. Or, it is evaluated for height detection of trees, in aerial measurements [24]. Scanning LiDAR can use returns of up to five.
## 3 Simulation and Analysis
### Temporal Distributions of the Reflections  
Figures 11 and 12 show the mean revisit times for the specular reflections in each bin at different latitudes ranging from 40\({}^{\circ}\)S to 40\({}^{\circ}\)N during a complete 13-day cycle at the GPS L1 and L2 frequencies for the GPS-R and four-system GNSS-R cases, respectively. The mean revisit times for the GPS-R  
Figure 10: Average numbers of four-system GNSS-R specular reflection points in 1\({}^{\circ}\times 1^{\circ}\) grids at different longitudes during a complete 13-day cycle at the GPS L1 (**a**) and L2 (**b**) frequencies.  
Figure 9: Average numbers of GPS-R specular reflection points in 1\({}^{\circ}\times 1^{\circ}\) grids at different latitudes during a complete 13-day cycle at the GPS L1 (**a**) and L2 (**b**) frequencies.  
reflections in the low latitude regions ranging from 40\({}^{\circ}\)N to 40\({}^{\circ}\)S are 8.2 h and 14.4 h at the GPS L1 and L2 frequencies, respectively. When the reflected signals from the four-system GNSS are available, these values fall to 2.9 h and 4.9 h, respectively. Shorter revisit times can help provide more frequent investigations of cyclones. The revisit time is short at approximately \(\pm\)35\({}^{\circ}\) because of the spatial distribution of the specular reflections caused by the LEO GNSS-R observatory's orbit inclination, as shown in Figures 9 and 10. In the lower latitude areas, the mean revisit time are longer than those at the higher latitude areas. In addition, 87% of hurricanes form no farther away than 20\({}^{\circ}\) north or south of the Equator [42; 43]. In future GNSS-R missions, one or two of the LEO micro-satellites should therefore be designed to be in orbits with an inclination of 20\({}^{\circ}\).  
In addition, to improve the accuracy of hurricane intensity forecasting, reduction of the revisit time for the reflections in the hurricane inner core is crucial [44]. Higher temporal resolutions can also help to improve the modeling of ocean tides and marine geoids when the GNSS-R mission is used in ocean altimetry. Adequate simulations and analyses represent the most direct and effective ways to obtain the revisit time characteristics required for future GNSS-R missions.
# A Flexible Localization Method of Multi-Constrained Free-Form Surface Based on the Profile Curves
## 3 The Localization Problem of the Near-Net Shape Blade Surface
### Establishment of the Localization Optimization Model
\(\mathbf{n}_{p,k}\) and \(\mathbf{n}_{q,k}\) are the unit normal of the scatter point. \(k\) is the index of the profile curves and \(p,q\) are the index of the scatter points on the profile curve. There is no accurate expression of the tolerance boundaries. The tolerance boundaries of the profile curves are defined by deviating from the nominal profile curves along the normal vector. The expressions of profile tolerance boundaries are represented by  
\[\mathbf{C}_{i,k}(t)=\sum_{i=0}^{n}N_{i,3}(t)V_{i}+\frac{T}{2}\mathbf{n}(t) \tag{5}\]  
\[\mathbf{C}_{i,k}(t)=\sum_{i=0}^{n}N_{i,3}(t)V_{i}-\frac{T}{2}\mathbf{n}(t) \tag{6}\]  
Figure 4: A profile curve of the blade and a non-uniform tolerance zone. where \(\mathbf{n}(t)\) is the unit normal of a given point on the designed profile curve. The profile curves \(S(t)\) of the blade surface are represented by cubic B-spline curves. They are represented by \(S(t)=\left\{s^{0}(t),s^{1}(t)\cdots s^{l}(t)\right\}\). The to-be-cut blade surface is represented by \(r(u,v)=f[S(t),i]\) where \(f\) is the lofting operator in terms of profile curves and \(i\) is the index of the profile curves. According to the affine invariance property of B-spline curves, the transformation of the control points has same effect as the transformation of the profile curves. The formula of each transformed profile curve is  
\[s_{j}(t)=\sum_{i=0}^{n}\mathbf{R}(\alpha)\cdot\mathbf{R}(\beta)\cdot\mathbf{ R}(\gamma)\cdot\mathbf{T}(\Delta x,\Delta y,\Delta z)\cdot N_{i,3}\cdot V_{i,j} \tag{7}\]  
\[\text{where }\mathbf{R}(\alpha)=\left[ \begin{array}{cccc}\cos\alpha&-\sin \alpha&0&0\\ \sin\alpha&\cos\alpha&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array} \right]\text{ and }\mathbf{T}(\Delta x,\Delta y)=\left[ \begin{array}{cccc}1&0&0&\Delta x\\ 0&1&0&\Delta y\\ 0&0&1&0\\ 0&0&0&1\end{array} \right].\]  
\(\mathbf{R}(\alpha)\), \(\mathbf{R}(\beta)\), and \(\mathbf{R}(\gamma)\) are the rotation matrices and \(\mathbf{T}(\Delta x,\Delta y,\Delta z)\) is the translation matrix. \(\alpha\) is the rotation angle around the X axis, \(\beta\) is the rotation angle around the Y axis, and \(\gamma\) is the rotation angle around the Z axis. \(\Delta x\), \(\Delta y\), and \(\Delta z\) are the translation values of the profile curve along the X, Y, Z directions, respectively.
## 8 Future Research  
LED color detection is a challenge in visual-MIMO systems. In a past paper [27], we used regression analysis in LED color detection for both high and low environmental light intensities,  
Figure 14: Example of V2X communication [23].  
Figure 13: A smart module consisting of strapped cuff and zipper slider fasteners based on a color-independent visual-MIMO platform [26].  
representing a simple machine-learning approach. In this strategy, we first detected each LED image from the whole LED array image. We then manually extracted some features for training the LED images to establish a multiple linear regression model. Finally, we obtained the multiple linear regression model to predict the color of the LED images. Using our trained multiple linear regression model, the color closeness accuracy was close to 90% for high and low environmental light intensities. For this work, we designed an experimental setup with a smartphone camera as a receiver, a light meter (CL-200A) for measuring environmental light intensity, and two tripods to fix the smartphone and transmitter in front of two light sources in a dark black room. The distance between the transmitter and smartphone was 30 cm. The details of the experimental setup and measurements are described in our previous work [27]. Based on this research [27], it is clear that machine learning is a promising approach for the visual-MIMO system. In the near future, this machine-learning approach can be used to predict LED color from LED images based on the distance and angle between the transmitter and receiver for different environmental light intensities. We also intend to measure and analyze the communication performance of this approach.  
Visual-MIMO is the part of the VLC system. We have given some examples of visual-MIMO communication systems. Other researchers [28; 29; 30] focus on the sustainability of the indoor VLC in heterogeneous network. They pointed out technical challenges like handover, resource management, interference minimization, and optimization of channel capacity in this field. These topics are prominent to establish a complete network using VLC combining with an RF network. In the future, we will survey the suitability of our visual-MIMO scheme in indoor applications by considering above mentioned challenges.
# Homogeneous Data Normalization and Deep Learning: A Case Study in Human Activity Classification
## 3 Results
### Normalized Data with RobustScaler  
Thirdly, RobustScaler was also used to normalize the data acquired from the accelerometer. Table 5 presents the confusion matrix related to the experiments performed with the accelerometer sensor included in the dataset used. It was verified that the most correctly identified activities are walking upstairs and standing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-6}  & & **Walking** & **Walking** & **S**anding** & **Walking** \\ \cline{2-6}  & **walking upstairs** & 107 & 33 & 4 & 0 \\ \cline{2-6}
**Actual Class** & **walking downstairs** & 89 & 47 & 8 & 0 \\ \cline{2-6}  & **standing** & 7 & 3 & 6 & 12 \\ \cline{2-6}  & **walking** & 1 & 2 & 4 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix for normalized gyroscope data with MinMaxScaler.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Predicted Class**} \\ \cline{3-6}  & & **Walking** & **Walking** & **S**anding** & **Walking** \\ \hline \multirow{4}{*}{**Actual Class**} & **walking upstairs** & 42 & 153 & 2 & 4 \\ \cline{2-6}  & **walking downstairs** & 50 & 139 & 12 & 1 \\ \cline{2-6}  & **standing** & 2 & 8 & 22 & 6 \\ \cline{2-6}  & **walking** & 2 & 4 & 14 & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix for normalized accelerometer data with MinMaxScaler.  
Next, the classification results of the data acquired from the accelerometer sensor after the application of RobustScaler were analyzed, verifying that the DNN method implemented reported an accuracy of 49.79%, a precision of 52.16%, a recall value of 54.43%, and an F1-Score of 53.27%.  
RobustScaler was used to normalize the data acquired from the gyroscope data related to the different human activities. Table 6 presents the confusion matrix related to the experiments performed with the gyroscope sensor included in the dataset used. It was also verified that the most correctly identified activity is walking upstairs.  
Further, the classification results of the data acquired from the gyroscope sensor after the application of RobustScaler were analyzed, verifying that the DNN method implemented reported an accuracy of 50.87%, a precision of 48.22%, a recall value of 39.01%, and an F1-Score of 43.13%.