esa-sceva/satcom-chunk-collection huggingface dataset:
# Antifungal Activity of Quaternary Pyridinium Salts Against _Fusarium culmorum_ in Wheat Seedlings
## 2 Materials and Methods
### Isolation and Identification of Fusarium calmorum  
The _F. culmorum_ was isolated from wheat grains, which showed disease symptoms. The procedure followed standard phytopathological methods described by [PERSON] et al. [45], [PERSON] [46], and [PERSON] [47]. The grains were rinsed under running water, disinfected in 96% ethanol for 30 s, rinsed with sterile distilled water, and dried on filter paper. The grains were then placed in Petri dishes on potato dextrose agar (PDA). The dishes were incubated in a climate chamber (Aralab, Blue Line, Portugal) at 22 \({}^{\circ}\)C with a 12 h light/12 h dark cycle. After the development of reproductive structures (mycelium and conidia), pure cultures were obtained by subculturing them in a fresh PDA medium.  
DNA Extraction, Amplification, and Sequencing  
DNA was extracted from the mycelium at the edges of 10-day-old fungal colonies grown on potato dextrose agar (PDA). Total DNA was isolated using the OmniPrep(tm) for Fungi PCR Kit (G-Biosciences Co., Overland, MO, USA). Polymerase chain reaction (PCR) was conducted using primers targeting the translation elongation factor 1-alpha (TEF-1\(\alpha\)) region: EF1 forward primer (ATG GGT AAG GAR GAC AAG AC) and EF2 reverse primer (GGA RGT ACC AGT SAT CAT G). The PCR mix comprised 12.5 \(\upmu\)L EmeraldAmp(tm) PCR ReadyMix, 0.5 \(\upmu\)L of each primer, 11 \(\upmu\)L of nuclease-free water, and 0.5 \(\upmu\)L of genomic DNA. The PCR amplification program for the TEF-1\(\alpha\) region is detailed in Table 2[28]. The PCRs were carried out using a MiniAmp Plus Thermal Cyler (Applied Biosystems, Foster City, CA, USA). The PCR products were visualized under UV light on a 1% agarose gel using the Kodak EDAS 290 system with a UV transilluminator (UVITEC). The PCR products were sequenced by Macrogen Europe (Amsterdam, The Netherlands). The sequences obtained were processed using DNA Dynamo v. 1.629 software (Blue Tractor Software, Bangor, North Wales, UK) and compared with sequences from the GenBank(tm) database. The sequence showed 99.97% similarity to _F. culmorum_ reference sequences in the NCBI database, confirming the isolate's identity.  
After the isolation and identification of _F. culmorum_, the pure culture was preserved by three methods: lyophilization (freeze-drying), storage at \(-80\)\({}^{\circ}\)C in 1.5 mL microtubes with a glycerol-water mixture, and storage at 4 \({}^{\circ}\)C in 10 mL tubes containing sterile distilled water. The culture maintained in sterile water is renewed every two years.
# Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning
## 2 Methods
### Detachment Seen in the Images  
In Figure 2 we may discover the following characteristics:  
* Overall growth of visible radiation during divertor detachment;
* Increased distance between the inner wall and the radiation belt (1);
* The strengthening and moving of a tail-like structure in the radiation belt (2).  
These observations reinforced the hypothesis that detachment could be detected using computer vision solutions.  
Our idea was to significantly reduce the amount of data to be processed by pre-selecting parts of the image where the effect of detachment is most pronounced. Following preliminary analyses, we decided to use as inputs up to 200 pixel intensity values along  
Figure 1: Schematics of the video diagnostics system at the W7-X and the EDICAM.  
Figure 2: Images of attached (**left**) and detached plasmas (**right**). The most distinctive differences are marked with red on the detached image: Increased distance between the inner wall and the radiation belt (1), a tail-like structure strengthening in the radiation belt (2).  
particular lines in the images, in other words pixel intensity profiles. Five regions were identified on the image, where the radiation belt shows a similar behavior within the regions but behaves differently in each of them. Region A (located at the outer wall): changes in the plasma size corresponds to a significant movement of the radiation pattern. Region B (inner wall): here the distance between the plasma and the wall can clearly be seen, although the radiation pattern moves much less when the plasma size changes. Region C (above divertor baffle): here a tail-like structure changes its position and intensity significantly when the plasma goes to detachment. Region D (divertor): the image is often overexposed here, therefore we exclude it from the analysis. Region E (outer wall/ports): no light emission is detected here. To appropriately capture the changes of the plasma, a homogeneous background was needed behind the radiation belt and along the profile, so we had to place them in such areas of the examined parts of the images. We selected three profiles in all three useful regions, A, B and C, as shown in Figure 3, to be qualified during a pre-selection test. We found that these profiles describe the images well with regards to detachment. Figure 3 shows the characteristics of profile A in a detached and a non-detached plasma. The increase in overall visible radiation is seen, along with the maximum point of the graph shifting towards the confined region, thus the distance between the stellarator wall and the radiation belt is increasing.
## 3 Operation of the Proposed Converter
### Operation Principle of the SISO Modes  
During the SISO mode, as PV power is unavailable and consequently the switch S5 should be turned OFF, the battery supplies the load power through the battery stage, which represents a conventional buck converter, and by assuming that the SC voltage is at (Vsc\({}_{\rm max}>\) V\({}_{\rm O}\)), the output voltage can be defined as:  
\[\rm{V_{O}=\ d_{3}V_{\rm batt}} \tag{9}\]  
Accordingly, the maximum output voltage during this mode is limited to the battery voltage, compared to the previous modes of operation at which the maximum output voltage can be higher than the battery voltage. This requires the battery voltage to be designed at a voltage greater than the required output voltage to maintain the regulation of the output voltage. However, by operating the switch S5 during this mode with a duty cycle that is controlled by the output voltage control loop, the buck-boost operation of the proposed converter achieved in other modes of operation can be sustained, and regulation of the output voltage can be achieved with a battery voltage either higher or lower than the output voltage. Accordingly, to prevent the undesired conduction of the switch S3 through its body diode to maintain the controllability of battery charging current, the minimum duty cycle of the switch S5 should satisfy:  
\[\rm{d_{5\min}\geq 1-\frac{Vbatt_{min}}{V_{O}}} \tag{10}\]  
where Vbattmin is the minimum voltage of the battery at the lowest state of charge (SoC).  
Typical waveforms and current paths of the converter under this mode are shown in Figures 8 and 9. The output voltage during this mode can be driven by applying voltage balance on L2, same as in (5) and (6).  
Figure 8: Illustration of the current paths (black lines and gray lines indicate the activated and deactivated paths, respectively) during the different stages of operation of SISO mode: (**a**) Step I, (**b**) Step II, (**c**) Step III, and (**d**) Step IV.
# Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory
## 4 Instance Verification  
Taking an urban utility tunnel in Zhengzhou as an example, a questionnaire was developed after field research. Five senior industry experts were invited to score and assess the indicators of fire risk evaluation for the tunnel according to Tables 1 and 2. Their research fields include power system design, bridge and tunnel structure design, fire safety, etc. The quantitative results of the risk evaluation of each indicator are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Indicators** & **Expert 1** & **Expert 2** & **Expert 3** & **Expert 4** & **Expert 5** & **Average Value** \\ \hline Structural design \(C_{1}\) & 4 & 6 & 5 & 4 & 6 & 5.0 \\ Fire-resistance class \(C_{2}\) & 5 & 7 & 6 & 5 & 7 & 6.0 \\ Fire load \(C_{3}\) & 4 & 5 & 5 & 4 & 4 & 4.4 \\ Electrical equipment condition \(C_{4}\) & 5 & 6 & 4 & 3 & 5 & 4.6 \\ Fire zone setting \(C_{5}\) & 3 & 4 & 3 & 2 & 3 & 3.0 \\ Fire detection system \(C_{6}\) & 3 & 4 & 3 & 5 & 6 & 4.2 \\ Fire alarm system \(C_{7}\) & 4 & 5 & 5 & 4 & 3 & 4.2 \\ Fire-fighting equipment & 5 & 4 & 6 & 4 & 5 & 4.8 \\ configuration \(C_{8}\) & 5 & 6 & 7 & 5 & 4 & 5.4 \\ Water supply and fire extinguishing system \(C_{9}\) & 5 & 6 & 7 & 5 & 4 & 5.4 \\ Ventilation and smoke extraction & 3 & 2 & 2 & 3 & 2 & 2.4 \\ system \(C_{10}\) & 3 & 3 & 2 & 3 & 3 & 2.6 \\ Emergency lighting system \(C_{11}\) & 2 & 2 & 3 & 2 & 2 & 2.4 \\ Safe route planning \(C_{12}\) & 3 & 1 & 2 & 1 & 1 & 1.4 \\ Evacuation sign \(C_{13}\) & 2 & 4 & 5 & 4 & 3 & 3.8 \\ Personnel security awareness \(C_{15}\) & 3 & 2 & 2 & 3 & 2 & 2.4 \\ Personnel emergency response & 4 & 5 & 4 & 4 & 5 & 4.4 \\ capacity \(C_{16}\) & & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Risk evaluation value table.
## 5 Conclusions  
The performance of high frequency communication antennas for the sub-THz frequency range deteriorates rapidly by impedance mismatch. We designed a new structure in which a superior impedance matching can be obtained by placing the Josephson junction detector remote from the antenna center. Simulations in the range of 130 to 170 GHz for optimal configuration parameters demonstrate a high radiation gain and a low reflection coefficient, S\({}_{11}\).  
Based on these simulations, we fabricated two bow-tie antennas with different matching strips widths: 4.6 \(\upmu\)m and 5.4 \(\upmu\)m. The measured performances of these antennas at 60 K, with and without a silicon lens, were analyzed. The experimental results agree extremely well with the simulation results. The high peaks in the various dI/dV characteristics, as well as the measured values of the NEP and \(\eta(V)\), are a clear indication of the high sensitivity and frequency selectivity of the proposed detection systems. We have shown that this structure has the potential of improving significantly the detection system efficiency at the THz range.  
The massive increase in use of communication networks, such as in the Fifth Generation (5G) technologies, leads to an ever-increasing demand for higher frequency communication ranges. New technologies must be implemented to enable handling the required communication capacity. Solving the problem of impedance mismatch between the HTSC detectors and the antenna presented in this work, is a significant step in the right direction.  
Conceptualization, E.H.; methodology, E.H.; electromagnetic simulations were performed by, E.H., M.M. and D.V.; resources, E.H., S.E.S. and E.F.; writing--original draft preparation, E.H.; writing--review and editing, S.E.S. and E.F.; experiments were performed by, E.H., A.G.M. and E.F.; data analysis, E.H. and A.G.M.; funding acquisition, S.E.S. and E.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 4 Discussion  
This section begins with providing the interpretation of our results, focusing on how various movement parameters influenced the fall risk. Then, we contextualize our findings with previous studies to present the validity and relevance of our study.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{4}{c}{**Confidence Interval of the Coefficients**} \\ \hline Variable & Lower Back & Torso & Head \\ \hline \multicolumn{4}{l}{**Static Tasks**} \\ Area under the curve (m/s) & [\(-\)0.57, 0.07] & [\(-\)0.56, 0.10] & [\(-\)0.17, 0.31] \\ Jerk X (m/s\({}^{3}\)) & [\(-\)0.21, 0.26] & [\(-\)0.37, 0.08] & [\(-\)0.17, 0.04] \\ Jerk Y (m/s\({}^{3}\)) & [\(-\)0.13, 0.14] & [\(-\)0.43, 0.04] & [\(-\)0.12, 0.12] \\ Jerk Z (m/s\({}^{3}\)) & [\(-\)0.18, 0.25] & [\(-\)0.15, 0.08] & [\(-\)0.11, 0.10] \\ Mean acceleration Y (m/s\({}^{2}\)) & [\(-\)0.18, 0.05] & [\(-\)0.11, 0.15] & **[0.17, 0.51]** \\ Mean acceleration Z (m/s\({}^{2}\)) & [\(-\)0.19, 0.05] & [\(-\)0.18, 0.08] & **[\(-\)0.30, \(-\)0.06]** \\ RMS angular velocity (rad/s) & [\(-\)0.13, 0.09] & [\(-\)0.22, 0.04] & [\(-\)0.16, 0.15] \\ Total pathway length (m) & **[0.001, 0.61]** & **[0.001, 0.65]** & [\(-\)0.19, 0.20] \\ Volume (m\({}^{3}\)) & **[\(-\)0.24, \(-\)0.01]** & [\(-\)0.11, 0.12] & [\(-\)0.18, 0.13] \\ \hline \multicolumn{4}{l}{**Dynamic Tasks**} \\ Area (m/s) & [\(-\)0.28, 0.10] & [\(-\)0.17, 0.12] & [\(-\)0.22, 0.15] \\ Jerk X (m/s\({}^{3}\)) & [\(-\)0.10, 0.11] & **[0.06, 0.33]** & [\(-\)0.19, 0.08] \\ Jerk Y (m/s\({}^{3}\)) & [\(-\)0.13, 0.10] & [\(-\)0.12, 0.12] & [\(-\)0.06, 0.22] \\ Jerk Z (m/s\({}^{3}\)) & [\(-\)0.12, 0.10] & [\(-\)0.16, 0.07] & [\(-\)0.03, 0.23] \\ Mean acceleration Y (m/s\({}^{2}\)) & [\(-\)0.07, 0.14] & **[\(-\)0.23, \(-\)0.01]** & [\(-\)0.03, 0.31] \\ Mean acceleration Z (m/s\({}^{2}\)) & [\(-\)0.09, 0.12] & [\(-\)0.15, 0.07] & **[0.06, 0.38]** \\ RMS angular velocity (rad/s) & [\(-\)0.13, 0.07] & [\(-\)0.16, 0.07] & [\(-\)0.04, 0.22] \\ Total pathway length (m) & [\(-\)0.09, 0.28] & [\(-\)0.08, 0.16] & [\(-\)0.08, 0.21] \\ Volume (m\({}^{3}\)) & [\(-\)0.06, 0.15] & **[0.03, 0.28]** & [\(-\)0.10, 0.19] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confidence intervals of the balance predictors by sensor location and task type.
## 3 Materials and Methods
### Evaluation
#### 3.3.1 Evaluation Metrics  
The Dice coefficient (Equation (8)) is the most commonly used metric in medical image segmentation, and it evaluates an overlap between GT and prediction. True-positives (TP) are voxels that were segmented positive and are also positive on the GT. False-positive (FP) and false-negative (FN) are voxels segmented as positive and negative on the prediction when they have opposite values on the GT.  
\[Dice=\frac{2*TP}{2*TP+FP+FN} \tag{8}\]  
We also measured the sensitivity (Equation (9)) and precision (Equation (10)) of the method applied to the liver and tumors. Sensitivity measures the percentage of true-positive voxels compared to positive samples annotated on the GT. At the same time, precision characterizes a correlation between correctly found true-positive samples to all positive samples that were predicted.  
\[Sensitivity=\frac{TP}{TP+FN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
Due to the small number of voxels that tumors generally contain, Dice measurements may not always be reliable. Therefore, the total number of found and missed tumors per volume was measured in addition to the metrics above. Using the same approach that was taken in a Computer Tomography (CT) challenge for liver tumor segmentation, tumors were considered to be found if at least 50% of the tumor voxels were detected [23].
## 3 Results
### Evaluation Metrics  
We evaluated the model using four metrics: Intersection over Union (IoU), Recall, Precision, and F1-Score. IoU measures the overlap between predictions and ground truth, while Recall quantifies the proportion of true positives correctly identified, and Precision assesses the accuracy of positive predictions, and the F1-Score, which is the harmonic mean of Precision and Recall, provides a balanced performance assessment. For single-category assessments, we simplified the evaluation by focusing on IoU and Recall, with IoU reflecting overall performance and Recall focusing on road continuity and completeness.  
The evaluation matrices are formulated as follows, where TP, FP, and FN represent true positives, false positives, and false negatives, respectively:  
\[\text{IOU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{2}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{4}\]  
\[\text{F1-Score}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{ Precision}+\text{Recall}} \tag{5}\]
## 3 Tests and Results
### Kitsuine Backplane Verification
#### 3.2.2 On-Orbit Results
Figure 14 shows that the on-orbit power consumption of the main bus and the SPATIUM-II was comparable to the ground data. The average power consumption per orbit of SPATIUM-II was 65 mW, while that of the main bus was 16 mW. The number of voltage regulators explains the difference in power consumption between the main bus and SPATIUM-II. The main bus has a 1.8 V regulator, whereas SPATIUM-II has 3.3 V and 1.8 V regulators. We can recall that CPLD uses 1.8 V as the supply for its LVCMOS and 3.3 V as the supply for the output logic voltage. Since SPATIUM-II does not have a 3.3 V power line, it uses the 5 V power line and first converts it to 3.3 V, then 1.8 V. The 1.8 V and 3.3 V regulators have standby currents of 4 mA and 8 mA, respectively. In Figure 15, four temperature profiles of the CPLDs per one orbit in different Sun beta angles were plotted. All graphs show that the main bus CPLD (CPLD1) temperature was at least 3 \({}^{\circ}\)C higher than that of the SPATIUM-II CPLD. This observation was the same as the TVT result, where the temperature difference was attributed to the main bus CPLD being placed close to the battery. The direct relation of the Sun beta angle to the device temperature was also evident in all four graphs. Figure 14: Ground data vs. on-orbit data power consumption for 2U main bus (**left**) and SPATIUM-II (**right**). Figure 15: On-orbit CPLD temperature profile at different Sun beta angles.
# Sensitivity Analysis for Microscopic Crowd Simulation
## 1 Introduction
### Parameter Studies in Crowd Simulations  
Independent of the model choice, microscopic crowd simulations have a medium-sized (\(\sim\)10-100) list of input parameters. The first step towards reliable simulations is a careful verification and validation of the models. In the case of our simulation framework, Vadere, verification is ensured by a continuous integration setup with unit tests. The interested reader is referred to [13]. The validity of the models, especially the Optimal Steps Model and its extensions, in comparison to empirical data, has been investigated in several studies [11; 12; 14; 15]. In addition, Vadere simulations are regularly tested against the RiMEA guidelines [16] with integration tests as part of the continuous integration setup.  
Once the model has been verified and validated as far as possible, the quality and validity of the simulation outcome depends strongly on the input parameter choice. While the input parameters for crowd simulations are chosen with great care, there are still uncertainties due to limited measurement accuracy, non-physical parameters, or simply a lack of knowledge (epistemic uncertainties).  
Sensitivity analyses reveal the magnitude of the impact of single input parameters. They also indicate in which areas experiments and observations are necessary to identify the range of those parameters that greatly contribute to the output variance [17]. This setting is often referred to as Factor Prioritization.  
Furthermore, sensitivity indices often are a starting point for model reduction by identifying non-influential parameters or an inactive subspace in the input parameter space. One straight-forward approach for model simplification is factor fixing. If a parameter has no impact on the quantity of interest over the chosen range and it is not involved in interaction terms, the parameter can be fixed at an arbitrary value within its range [18]. Some methods also provide surrogate models that are based on the important parameters or directions that can be used for faster evaluation [19]. In crowd simulation, this is of particular interest for large scenarios as well as for real-time predictions.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 5 Results and Discussion
### Prediction Performance Comparison
#### 5.2.2 Comparison of the RMSE  
A box plot, as shown in Figure 15, was created to show the distribution of the RMSE results of each ML model based on the ten-fold cross-validation. The RMSE values were calculated based on the differences between the predicted and observed crack widths. The box plot is a statistical tool that is used to depict numerical data through their quartiles including the maximum, minimum, and median values of a dataset [79,80]. The medium value is shown as the red line within the box. The interquartile range (IQR) in each box covers 50% (the lower 25% to the upper 75% quartiles) of the RMSE data point, while the whiskers drawn up and down to the maximum and minimum values represent 1.5-times the IQR from the RMSE data. All other points out of the whiskers range are outliers and shown as red dots. A mean value of the RMSE equal to zero would indicate that the predictions perfectly fit the observed data. However, this is almost never achieved in practice [81]. In general, the lower the RMSE value, the better the prediction performance of a model is.  
Assessment of the box plot revealed that the stack_LR model outperformed all other models because of its shortest IQR length and smallest RMSE values, as shown in Figure 5. In contrast, the LR and bag_LR models had the longest IQR length and largest RMSE values, thereby suggesting that the LR model and its ensemble methods have low accuracy. Among the individual models, the BPNN had the lowest RMSE, while SVR had the shortest IQR length, but with three outliers (out of ten data points). In general, the BPNN gave the most stable performance, showing reasonable low RMSE values with a short IQR length.  
Figure 12: Comparison of the observed crack widths of ECC after self-healing with the crack widths predicted by the AdaBoost ensemble models.  
Figure 14: Comparison of the observed crack widths of ECC after self-healing with the crack widths predicted by the stacking ensemble models.  
Figure 13: Comparison of the observed crack widths of ECC after self-healing with the crack widths predicted by the bagging ensemble models.