esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Beam Focusing and Deflecting Characteristics of Liquid Crystal Optical Phased Array
## 3 Results
Lastly, and most importantly, we demonstrate the function of multi-beam fan-out with focusing of the LCOPA device. Since the designed composite phase distribution \(\varphi_{FL}(i)\) of the multi-beam fan-out with focusing can be calculated by Equation (2), a series of decent voltages on each electrode of the LCOPA device are loaded. We can input the focus length parameter in the working interface of focusing together with scanning angle parameters in the working interface of laser scanning. The experimental results are shown in Figure 9. It can be seen that the incidence light beam can be split into 2, 3, or 5 separate beams. In Figure 9a, the diffraction angles of the two fan-out beams are 1.8 mrad and \(-\)1.8 mrad, respectively. This experimental result is consistent with the simulation result of Figure 3. So, the combination function of multi-beam fan-out with focusing of the LCOPA device is effective. We can also achieve other arbitrary deflection of multiple beams within a small angular range by means of the GS algorithm superposed by the phase of lens focusing \(\varphi_{L}(i)\), which can be expressed by Equation (1). Figure 8: Experimental results of the focusing function, in which the receiver screen is 0.6 m away from the device, and the focal length of the light spot varies; (**a**) the focal length of the light spot is 0.6 m; (**b**) the focal length of the light spot is 1 m; and (**c**) the focal length of the light spot is 1.4 m.  
Figure 7: Simulation and experimental results for beam deflection of 4 mrad: (**a**) simulation of phase distribution for beam deflection of 4 mrad; (**b**) experimental results of beam deflection with 4 mrad, where the offset distance of far-field spot is about 8 mm (the distance between the LCOPA device and the viewing screen is about 2 m). In Figure 9, it can be seen that multi-beam fan-out with different angle compositions was demonstrated experimentally. Each of the fan-out beams has been focused in one dimension, which appears as an elongated oval shape. Furthermore, the focus length can be adjusted in real time by programmable driver circuits. This property may bring some unique advantages, such as multiple parallel outputting, dynamic control capability, no moving parts or less mechanical dependence, combining complex functions, and so on. These unique advantages may have potential application value for some special scenarios, including laser communications multi-user networking, ultra-short-pulse laser processing, etc.
# Prediction of Corroded Pipeline Failure Pressure Based on Empirical Knowledge and Machine Learning
## 3 Training Details
### Evaluation Metrics  
To comprehensively evaluate the performance of the proposed method in predicting the failure pressure of pipelines with corrosion defects, four widely recognized evaluation metrics were employed: the Mean absolute percentage error (MAPE), the root mean square error (RMSE), the mean absolute error (MAE), and the coefficient of determination (R\({}^{2}\)) [29; 30; 31]. These metrics collectively provide a detailed assessment of the accuracy, precision, and overall goodness-of-fit of the predictions. Specifically, MAPE measures the average magnitude of the prediction errors relative to the actual values, expressed as a percentage, which can be calculated as Equation (9).  
\[MAPE=\frac{1}{n}\sum\limits_{i=1}^{n}\left|\frac{y_{i}-\hat{y}_{i}}{y_{i}} \right|\times 100\% \tag{11}\]  
where \(y_{i}\) represents the truth failure pressure of \(i_{th}\) samples and \(\hat{y}_{i}\) is the corresponding predicted result. RMSE quantifies the square root of the average squared differences between predicted and actual values, highlighting the model's sensitivity to larger errors. RMSE is defined as Equation (10).  
\[RMSE=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{12}\]  
MAE provides a direct measure of the average absolute difference between predictions and actual values, indicating overall prediction accuracy. MAE can be calculated as Equation (11).  
\[MAE=\frac{1}{n}\sum\limits_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right| \tag{13}\]  
\(R^{2}\) reflects the proportion of variance in the actual data that is explained by the predicted values, with higher values (closer to 1) indicating a better model fit, which is defined as Equation (12).  
\[R^{2}=1-\frac{\sum\limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}}{\sum \limits_{i=1}^{n}\left(y_{i}-\hat{y}_{i}\right)^{2}} \tag{14}\]  
By utilizing these metrics, the evaluation offers a balanced and robust assessment of the proposed method's applicability and reliability in predicting pipeline failure pressures under diverse corrosion conditions.
## 4 Results and Discussions
### Bayesian Logistic Ordinal Regressions
#### 4.2.1 Results and Analysis of Experiment 1
Compared to high traffic flow, medium traffic flow reduces drivers' cognitive load by 84%, while low traffic flow reduces it by 42%, indicating that lower-traffic environments are more likely to decrease cognitive load. In high traffic flow, the density and rate of information change significantly increase, demanding numerous and complex decisions from drivers within short timeframes, thereby heightening the burden on their cognitive systems. Conversely, in low-traffic flow, with fewer road users, the volume and complexity of information decrease, substantially reducing the information processing required by drivers and alleviating cognitive pressure. Blind Spot Imaging Design:  
The distance between the blind spot image and the steering wheel center is negatively correlated with the time buffer (mean = \(-\)0.03, 95% CI \(-\)0.04 to 0.02), suggesting that within a certain range, positioning the blind spot image closer to the steering wheel center is more likely to reduce drivers' cognitive load. Comparing the blind spot imaging designs of Vehicle Type 2 and Vehicle Type 3, Vehicle Type 2's design is more effective in reducing drivers' cognitive load (mean = 0.43 > 0, 95% CI 0.26 to 0.60). Vehicle Type 2 boasts a larger display area for blind spot images on the center console, closer to the driver, facilitating information capture. When comparing Vehicle Type 3 and Vehicle Type 1, Vehicle Type 3's design also outperforms that of Vehicle Type 1 when it comes to reducing drivers' cognitive load (mean = \(-\)0.41, 95% CI \(-\)0.60 to 0.22). Although Vehicle Type 3's blind spot images are farther from the driver, the larger display area and higher proportion of blind spot images on the center console likely outweigh this distance factor, resulting in a net reduction in cognitive load. In summary, among various vehicle blind spot image designs, those featuring larger display areas for blind spot images on the center console and a higher proportion of such images are more likely to reduce drivers' cognitive load.
# Bacterial Flagellar Filament: A Supramolecular Multifunctional Nanostructure
## 6 Filament-Host Interaction
### Secretion of Non-Flagellar Proteins during Host Invasion
For instance, FedB, Cial, and FspA1 proteins are secreted during hook biogenesis, but the level of secretion of these proteins is reduced once filament polymerization begins. In addition, FedB, Cial, and FspA1 are produced simultaneously with flagellin FlaA, but transcribed from different promoters and produced at lower levels than FlaA [198]. Thus, they may require a distinct mechanism to efficiently compete with abundant levels of flagellins for interactions with the flagellar T3 SS that result in secretion. Flagellins and other flagellar proteins require a specific motif within the N-terminus for T3 SS recognition [199; 152; 200]. Specific chaperones and intramolecular domains within non-flagellar exported proteins may assist these proteins to compete with flagellins for recognition and secretion by the T3 SS. These proteins may have adopted traits common to natural substrates such as flagellin proteins to be delivered via the same secretion machinery. Many interesting questions arise about the mechanisms by which these non-flagellar proteins, not involved in motility, are specifically and efficiently secreted by the flagellar organelle. For instance, it would be important to analyze whether the secretion of non-flagellar proteins is common among flagellated bacteria and whether there are specific chaperones in charge of their export.
# A Review of Experimental and Numerical Studies of Lithium Ion Battery Fires
## 3 LIB Chemistry
### Electrolyte  
The electrode materials, particularly the cathode, govern the energy and power density capacities. Electrolyte determines the power density (current), reliability, and safety of the battery (due to the interaction that it has with all other components) and the formation of the SEI. The requirement for the electrolyte to interact with both electrodes necessitates a certain level of chemical stability, thereby limiting the operational scope of the electrolyte material. The SEI whose formation and physical properties depend on the nature of the electrodes (particularly the anode) ensures the chemical compatibility [32].  
Typically, the electrolytes used for LIBs are composed of a lithium salt (LiPF\({}_{6}\) most commonly) within a mixture of flammable organic carbonates. Ethylene carbonate (EC) is combined with dimethyl carbonate (DMC) or diethyl carbonate (DEC). The organic carbonates provide a combination of relatively high chemical stability, low electronic conductivity, low viscosity, and high ionic conductivity. EC is used as the electrolyte solvent in the majority of LIBs; once decomposed it forms a stable SEI on the anode during the first cycle of charging.  
Research on the thermal instability of electrolyte has been carried out since the inception of LIBs, and considerable examination into the development of safer electrolytes by providing more stable lithium salts [33; 34], which utilize nonflammable solvents [35; 36], electrolyte additives [35; 36], ionic liquid [37; 38; 39], aqueous electrolytes [40; 41], polymer electrolytes [42; 43; 44], and solid-state electrolytes [45; 46; 47; 48]. At present, the most viable solutions in providing a safer electrolyte center around the stability of lithium salts and solvents that are nonflammable [45].
## 5 Simulation Analysis
### Simulation Setup  
A layered randomized deployment is used in the simulation, in which the area is vertically divided by depth. Shallow layers have higher node density due to stronger disturbances and heavier communication loads, while deeper layers allow sparser deployment to balance energy efficiency and coverage. Node mobility follows an improved  
Figure 6: Packet reception and transmission process.  
model combining Ekman drift and two-dimensional random walk, with nodes moving horizontally at fixed depths and speeds decreasing with depth. Five surface buoys are fixed, the source node is placed in the deepest layer, and the relay nodes are randomly distributed across the layers. Routing is initiated by buoy broadcasts, and the source sends data every 40 s. To ensure practicality for resource-constrained underwater sensor nodes, the protocol limits per-node overhead by employing simple arithmetic for relay selection and adaptive messaging strategies. Nodes consume 0.1 W in reception, 1 mW in idle, and dynamically adjust transmission power between 1 and 10 W based on link conditions. Only selected relays initiate transmissions, with a maximum of three retransmission attempts if no ACK or forwarding behavior is detected, thus reducing unnecessary energy expenditure. The network lifetime is measured by the round in which the first node's energy drops below 0.1 J, and the simulation ends when 2000 rounds are completed or 70% of the nodes fail. The simulation settings are presented in Table 5.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 3 Principle of the Proposed DTDAS
### Dynamic Interaction Process
#### 3.5.2 Energy Consumption and Availability Prediction  
A lot of real-time data will be generated when the workshop execution system starts running. The knowledge system in the virtual space will analyze real-time data and historical data to establish a dynamic energy consumption model that can accurately describe the characteristics of AGV energy consumption. This model will be used to predict the battery SOC of the AGVs according to the latest scheduling plan and current AGV data. Therefore, the AGV's availability can be obtained by executing the current scheduling plan continuously. If the availability results show that the power of some AGVs according to the current scheduling plan is not enough to complete the transportation tasks, the charging scheduling should be triggered. Otherwise, the original scheduling plan will be maintained.
## 4 Conclusions  
FTIR spectra of the GO-Ag, GO-ZnO powders and silanized powders put in evidence the specific functional groups formed by the Ag or ZnO attached on the surface of the graphene sheets. The total mass losses of GO powders combined with Ag or ZnO, recorded after the TG/DTA analysis, are lower than those of GO powders. The decreases of the mass losses recorded after the silanization of the powder samples confirm that some of the functional groups with oxygen from the surface of the graphene sheets were functionalized with Ag, ZnO, and with APTES, respectively. Following the UV-VIS analysis, some peaks corresponding to the \(\pi\)-\(\pi\) * electronic transitions attributed to the aromatic C-C bonds and to the n-\(\pi\) * electronic transitions attributed to the C=O bonds were registered. SEM images confirm the presence of Ag nanoparticles on the graphene sheets, but also their overloading with zinc oxide in the case of the GO-Zn powder. The antibacterial effect of the silanized and non-silanized samples depends on the tested bacterial strains. The measured diameters of the inhibition zones around the tested powders ranged between 12 and 22 mm. Although the GO-Zn sample had the highest antibacterial effect against most of the bacterial strains tested, a satisfactory antibacterial effect was obtained for all functionalized, silanized or non-silanized powders. The inclusion of these powders as additives in hydraulic mortars can lead to an improvement in their quality.  
Conceptualization, D.P. ([PERSON]) and M.M.; validation, M.P. and S.V.; investigation, M.F., I.P., C.S., R.C. and S.C.; visualization, G.F.; writing--original draft preparation, D.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by a grant of MEN-UEFISCDI, Project PN-III-P2-2.1-PED-2019-3739.  
The authors declare no conflict of interest.
# Development and Evaluation of Training Scenarios for the Use of Immersive Assistance Systems
## 3 Methodology
### Experimental Setup  
The experiment was conducted in a laboratory (8.32 m \(\times\) 5.5 m). Figure 2 shows the setup schematically. At the beginning of this study, the participants were invited into the laboratory and sat down at the table (bottom right), where they answered the questionnaires on a laptop. In a subsequent relaxation phase, the physiological baselines were recorded. The live stream of HL2 was transmitted to the monitor via a Microsoft Miracast HDMI stick. The theoretical training session was spent on the left-hand side of the desk. There, the participant was either trained through e-learning (text and picture (TP), annotated video (AV)) or through personal training (PT). Within PT, the participant could follow both the supervisor acting and the AR stream on the screen. Participant questions were answered directly by the supervisor.  
For subtask 11/15, two reference dimensions, 169 mm and 1077 mm, which were marked on the whiteboard using a marker pen, had to be measured via a 'hand-ray' measurement application (HRMA). The HRMA allowed the subject to interact directly with both hands. As an extension of their two forearms, a raycast was immersively projected from each of the participant's hands against the wall (blue). The collision of the hand array with the wall was augmented as a cursor using a 2D ring. The next largest references (1342 mm and 2462 mm) were measured for subtask 12/15. For subtask 14/15, all two-point dimensions, including 8319 mm (from wall to wall), had to be measured using the 'point-to-point' measurement application (PTPMA). With this feature, measurement points can be arbitrarily translated and positioned as virtual assets (small cubes) according to the MRTK interaction paradigm [2]. A virtual measuring tape is annotated between the small cubes (see measuring tape 2462 mm in Figure 2).  
Subtask 13/15 involves measuring the flight case by precisely scaling and positioning the virtual 'bounding box' (blue) measurement application (BBMA) over the flight case as an enveloping object using the interaction paradigm of the MRTK [2]. The Cartesian dimensions are shown by the blue 'bounding box'.
## 3 Results and Discussion
### Proximate Composition
Several studies have also reported no differences in fibre content between cultivated and wild WEM, and WEM also recorded the highest fibre content than other edible species [41; 45; 49]. In addition, the non-digestible polysaccharide of edible mushrooms is an alternative source of dietary fibre that remains underutilised compared to fruits, vegetables, and legumes [34]. Some but not all proximate compositions of WEM powder in the current study are comparable with other findings of cultivated WEM. The slight variation may be due to factors such as strain, the development stages of the fruiting body, nutrient distribution within the fruiting body, post-harvest handling and environmental conditions [47]. However, ensuring the stability of nutritional constituents is critical to incorporate WEM powder in food products [52]. A previous study by [PERSON] et al. found that the 5% of WEM incorporated in instant noodles was the best formulation among five modified formulas ranging from 5% to 25% [25]. The sensory evaluation of the 5% formulation recorded the highest score for colour, aroma, taste, texture, and overall acceptance, and there was no significant difference in any of the sensory attributes when compared with the control. This would be a good indication for the product to be accepted and its suitability for consumption. Table 1 shows the proximate composition of instant noodles incorporated with WEM and without WEM powder (control). The results showed a significant (\(p<0.05\)) reduction of carbohydrates, increased ash and crude fibre, and no significant differences in moisture, protein, and fat content between the control instant noodles incorporated with 5% WEM. Specifically, the moisture content of both noodles was higher than previous findings (\(<\)5%), but the values remained within the recommended shelf-life stability (\(<\)12%) [53]. Furthermore, the current protein value of noodles did not improve with WEM inclusion, thus, contradicting past studies where the addition of mushroom powder had significantly increased the protein content of the noodles [13; 14; 15]. This finding might be attributed to the lower protein content of WEM compared to other edible mushrooms. Meanwhile, the significant increase of ash content in the noodles incorporated with 5% WEM powder indicated that this ingredient is a good source of minerals, as observed in other studies [40; 46]. In addition, the current study demonstrated a significant (\(p<0.05\)) increase in crude fibre content in WEM noodles, due to the presence of natural fibre in the mushroom [13; 14; 15]. Consequently, the addition of WEM powder significantly reduced the carbohydrate content. Nevertheless, according to [PERSON] et al., long storage periods can reduce the fibre content and increase the carbohydrate content caused by the degradation of complex polysaccharides into simple sugar [53].
## 3 Results
### Summary of the Models' Performance
The testing process for all three models can be considered successful and their diagnostic ability confirmed, and they can be efficiently utilized for research and scientific purposes. The primary goal was to develop a tool that supports radiologists and improves their efficiency. For this purpose, the custom CNN-based model was the best-suited model from the three discussed models. The U-Net-based model underwent testing with a lung CT slice that was not included in its training data. To ensure compatibility with the model input, the image dimensions were adjusted to fit the default matrix. Furthermore, the image was converted into grayscale. The model output was an infection mask that was presented as a heatmap and overlaid on a segmented lung image. As illustrated in Figure 10, the heatmap captured all the regions impacted by lung tissue degeneration effectively. The intensity of the color in the heatmap correlated with the severity of the observed damage. Thus, the model provided accurate information about the location and intensity of the detected pathologies. \begin{table}
\begin{tabular}{c c|c c} \hline \hline \multirow{2}{*}{**Classification Quality Measures**} & \multicolumn{3}{c}{**Models**} \\ \cline{2-4}  & **VGG-19-Based** & **InceptionResNetV2-Based** & **Custom CNN Model** \\ \hline Accuracy & 0.75 & 0.97 & 0.98 \\ Sensitivity & 0.71 & 0.94 & 0.97 \\ Specificity & 0.79 & 0.98 & 0.99 \\ Precision & 0.77 & 0.97 & 0.99 \\ Negative predictive value & 0.73 & 0.97 & 0.97 \\ False-negative rate & 0.29 & 0.97 & 0.03 \\ False-positive rate & 0.21 & 0.06 & 0.01 \\ False discovery rate & 0.23 & 0.02 & 0.01 \\ False omission rate & 0.27 & 0.03 & 0.03 \\ Threat score & 0.59 & 0.03 & 0.96 \\ F1 score & 0.74 & 0.91 & 0.98 \\ Positive likelihood ratio & 2.43 & 0.97 & 34.37 \\ Negative likelihood ratio & 0.37 & 15.75 & 0.03 \\ Balanced accuracy & 0.75 & 0.06 & 0.98 \\ Prevalence threshold & 0.35 & 0.11 & 0.10 \\ Log loss score & 0.20 & 0.01 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the statistical measures associated with the quality and efficiency of the classification task undertaken by the three tested models. Color coding: green—best outcome, yellow—medium outcome, red—worst outcome. Figure 10: The testing outcome for the U-Net-based model in the form of an infection heatmap. It was produced using (**A**) a CT slice with visible lung tissue degeneration caused by COVID-19 infection. The next steps were (**B**) lung segmentation and (**C**) infection mask extraction. (**D**) The extracted mask in the form of a heatmap was superimposed on the segmented lung area to visualize where the infection focus points were localized.