esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### The Average Heating Rate at Monitoring Points
However, due to the fracture and the inherent heterogeneity of the granite, which create different effective heat transfer paths to the points, the times at which individual points reached their peak heating rates varied, albeit generally remaining within the range of 0.3 to 0.4 h. Comparing the heating rates of adjacent monitoring points on either side of the fracture in Figure 7c,d shows consistent temporal trends between neighboring points across different phases, despite differences in their peak heating rate magnitudes. This suggests that the fracture has a minor influence on the temperature change trend of adjacent points laterally across the fracture. Nevertheless, because the fracture significantly alters the heat transfer paths within the granite, the temperatures at laterally adjacent points across the fracture differ. The peak heating rate at a monitoring point reflects the relative speed of heat transfer in its vicinity, while the time taken to reach this peak rate is a critical parameter for defining the rapid heating phase. Consequently, studying the peak heating rate is significant for heat transfer analysis. Figure 8 presents the relationship curves between the peak heating rate and the \(d_{\rm h}\) for monitoring points on lines E-E, F-F, C-C, and D-D during the initial two hours of heating by the 80 \({}^{\circ}\)C source. Figure 8 shows that the peak heating rate decreases as \(d_{\rm h}\) increases, with the magnitude of this decrease also diminishing with greater distance. The peak heating rates for points located below monitoring line F-F range from 20 to 120 \({}^{\circ}\)C/h, significantly higher than the 10 to 20 \({}^{\circ}\)C/h observed for points above line F-F, indicating faster heat transfer and, consequently, higher temperatures in the lower region of the fractured granite compared to the upper region. Heat transfer along line F-F itself experiences less direct influence from \(d_{\rm h}\) and is primarily affected by the altered heat transfer paths due to the fracture, resulting in relatively small variations in the heating rates of its points, maintained within 15 to 30 \({}^{\circ}\)C/h. Furthermore, for points to the right of line F-F, the heating rate increases with proximity to the fracture, whereas for points to the left the heating rate increases with distance from the fracture; this phenomenon arises because heat converges towards the fracture side from below on the right but diverges away from the fracture side on the left. Within the sub-regions S\({}_{1}\) and S\({}_{2}\) formed by monitoring line E-E intersecting with lines C-C and D-D, respectively, monitoring points at equivalent \(d_{\rm h}\) exhibit essentially consistent heating rates and similar temperature changes (Figure 5).
## 3 Methodology
### Temperature-Sharpening Methods Based on Statistical Relationships
#### 3.2.1 The DisTrad Downscaling Method  
The DisTrad method is a statistical temperature-sharpening method that assumes that the negative correlation between NDVI and LST is invariant [1]. This method can combine high spatial resolution HJ-1B data and high temporal resolution MODIS data to estimate ET or LE on regional scales and over long periods [55]. The flow chart describing this method is shown in Figure 2 and is summarized as follows:  
1. The HJ-1B NDVI data with a resolution of 30 m were aggregated to the 990 m resolution of the NDVI data. We first selected a subset of pixels from the scene where NDVI is most uniform at a pixel resolution of 990 m. Using the original NDVI data (\(NDVI_{30}\)), the coefficient of variation (CV; the standard deviation divided by the mean) is calculated among the 33 \(\times\) 33 pixels (1089 pixels in total) that make up each 990 m NDVI pixel (\(NDVI_{990}\)).
2. Next, the \(NDVI_{990}\) is divided into several classes (\(0\leq NDVI_{990}<0.2\), \(0.2\leq NDVI_{990}<0.5\) and \(0.5\leq NDVI_{990}\)). A fraction (25%) of the pixels having the lowest CV were then selected from each class. Lower CV values correspond to more homogeneous land surface values.
3. The relationship between \(NDVI_{990}\) and \(T_{990}\) can be established using the selected pixels. Theoretically, \(T_{990}\) can be calculated by \(NDVI_{990}\) via regression (Equation (6)). \[\hat{T}_{990}(NDVI_{990})=a+b\times NDVI_{990}+c\times NDVI_{990}^{2}\] (6) where \(\hat{T}_{990}(NDVI_{990})\) represents the LST value estimated using \(NDVI\) on the 990 m scale.
4. The surface temperatures calculated using Equation (6) differ from the MODIS LST values (\(T_{990}\)) due to the influence of soil moisture and cannot be ignored. Based on the correlation between \(NDVI\) and LST is invariant, the simulated high-resolution LST data (\(\hat{T}_{30}(NDVI_{30})\)) can be calculated according to Equation (7) as follows: \[\hat{T}_{30}(NDVI_{30})=a+b\times NDVI_{30}+c\times NDVI_{30}^{2}+\Delta\hat{T} _{990}\] (7) where \(\Delta\hat{T}_{990}=T_{990}-\hat{T}_{990}\) is the spatial variability due to the effects of soil moisture.
## 4 Results and Discussion
### Simulation Result
#### 4.2.3 Analysis of Simulation Findings  
Figure 16a illustrates the differences in distance between interacting vehicle pairs in both horizontal and vertical directions. It can be observed that these distances were predominantly concentrated within the range of [\(-\)10, 10] meters, which is even more concentrated in a closer area than the original and generated scenarios.  
The distribution of MTTC values further indicated that most of these simulated scenarios are highly dangerous. As depicted in Figure 16b, instances where the MTTC was less than 2 s were more prevalent. Moments where the MTTC was less than 2 s or even less than 1 s signified situations requiring external intervention to prevent serious collisions. Particularly for the merging part of the roundabout, due to the green cover, the human driver finds it difficult to notice the oncoming traffic from the side, thus leading to aggressive behavior. These special scenarios that require human control or externalintervention are exactly the dangerous situations that need to be solved by the self-driving vehicles, and simulation testing with these dangerous scenarios can effectively verify the function of the self-driving vehicles.
## 3 On-Chip Circuit Implementation
### Linear Regulators  
The wirelessly transferred power is not very steady. The rectifier output voltage V\({}_{\rm DC}\) has ripples at 81.36 MHz, and fluctuates when there is relative movement between the coupling coils. Thus, a regulator is needed to regulate V\({}_{\rm DC}\) to a predefined constant voltage for the load. Two regulators are implemented in the implant unit as shown in Figure 7a,b, one for generating a 3 V supply for the electrical stimulation (ES) generator, and the other for generating a 1.2 V for the digital controller. The 3-V LDO is used to provide the stimulation current pulses that switch between zero and the maximum current during stimulation. A sensing metal-oxide-semiconductor field-effect transistor (MOSFET) M\({}_{\rm PS}\) is used to sense the output current, and then the bias current of the error amplifier is made adaptive to the output current with an additional bias current provided by M\({}_{\rm N7}\)[20]. The loop is stabilized by Miller compensation with a 10 pF MIM capacitor. The quiescent current of the 3-V LDO is less than 3 uA at light load, and is 21 uA when the load current is 1 mA. A 200 pF NMOS capacitor is connected to the output for PSR improvement.  
Figure 6: The two-branch self-biased symmetrically matched bandgap reference.  
The 1.2-V LDO shown in Figure 7b is a source follower that has low output impedance and fast response. This LDO consumes a quiescent current of 2 uA. A 2 pF decoupling capacitor is connected to V\({}_{\text{g2}}\) to improve its PSR performance.
## 3 Results

Table 4: Statistical significance of differences between the parasympathetic activity (RMSSD) values calculated from 3 min segments in different conditions: Relaxation (Rel), Exercise (Exe), and Recovery (Rec). Statistically significant values are in bold. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**BF**} \\ \hline  & **Exe1** & **Exe2** & **Exe3** & **Exe4** & **Exe5** & **Exe6** & **Rec1** & **Rec2** & **Rec3** & **Rec4** & **Rec5** \\ \hline Rel1 & **0.036** & **0.015** & **0.005** & **0.005** & **0.005** & **0.008** & 0.508 & 0.878 & 0.126 & **0.005** & 0.721 \\ Rel2 & **0.047** & **0.005** & **0.005** & **0.005** & **0.005** & **0.008** & 0.359 & 0.221 & **0.017** & **0.005** & 0.241 \\ Rel3 & **0.022** & **0.012** & **0.007** & **0.005** & **0.005** & **0.008** & 0.386 & 0.114 & **0.017** & **0.005** & 0.285 \\ Rel4 & **0.037** & **0.012** & **0.009** & **0.005** & **0.007** & **0.008** & 0.295 & 0.974 & **0.022** & **0.005** & 0.515 \\ Rel5 & **0.050** & **0.013** & **0.011** & **0.011** & **0.008** & **0.012** & 0.110 & 0.051 & **0.015** & **0.008** & 0.314 \\ \hline Exe1 & & & & & & & & & & & \\ Exe2 & & & & & & & & & & & & \\ Exe3 & & & & & & & & & & & & \\ Exe4 & & & & & & & & & & & & \\ Exe5 & & & & & & & & & & & & \\ Exe6 & & & & & & & & & & & & \\ \hline Rec1 & & & & & & & & & & & & \\ Rec2 & & & & & & & & & & & & \\ Rec3 & & & & & & & & & & & & \\ Rec4 & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical significance of differences between the breathing frequency (BF) values calculated from 3 min segments in different conditions: Relaxation (Rel), Exercise (Exe), and Recovery (Rec). Statistically significant values are in bold. In the first step of the examination of cardio-respiratory coupling, we were interested in the mutual behavior of HR and BF in different physiological conditions, and a statistically significant correlation was obtained only during exercise (Figure 4). Further, we analyzed the relationships between HR with RMSSD and BF with RCA under three conditions. We found a statistically significant correlation between RMSSD and HR only during relaxation. A low HR is related to high parasympathetic activity in untrained subjects, but this relationship ceased to exist at higher HR values and reduced RMSSD during recovery and exercise (Figure 5). Interestingly, a relationship between RMSSD and BF was found during exercise. A relationship between RCA and BF was not found in relaxation (\(p\) = 0.398). During exercise, there was weak positive correlation (\(r\) = 0.324, \(p\) = 0.013), and in recovery, a weak negative correlation (\(r\) = \(-\)0.34, \(p\) = 0.016).
###### Abstract  
Southern corn rust (SCR) is one of the most destructive foliar diseases in maize (_Zea mays_ L.), resulting in significant yield losses. Therefore, the continuous identification of disease-resistant germplasm and the deployment of resistant hybrids is essential for durably controlling SCR. The objective of this research was to identify and characterize resistance loci against SCR in maize to expand disease management strategies. Here, we identified a maize landrace with high resistance to SCR 'Silunuo' (SLN) approaching complete immunity. We backcrossed it with a susceptible inbred line, N531, to generate a stable SCR-resistant introgression line N531_R. By crossing it with F35 (a susceptible inbred line), we created a large F\({}_{2}\) segregating population and mapped a major SCR-resistant locus on chromosome 10, known as _RppSLN_. Based on the genome assembly and annotation, we found that _RppSLN_ harbors two NBS-LRR (nucleotide binding site-leucine-rich repeat) genes, namely _Zmays_10_GO00430 and _Zmays_10_GO00440. These NBS-LRR genes were significantly induced during artificial inoculation with _Puccinia polysora_, suggesting that they might be candidate genes collectively contributing to the resistance level at this locus. In conclusion, this study identified a major SCR resistance locus directly isolated from a landrace, providing valuable support and information for expanding new disease-resistant germplasms and promoting the utilization of landraces.  
maize (_Zea mays_ L.); southern corn rust; landrace; _RppSLN Article_
## 4 VRFormer
### Training and Validation Procedures
#### 4.7.1 Training Pipeline  
VRFormer is trained using a comprehensive dataset generated by VRDeepSafety. This dataset includes diverse accident scenarios, meticulously annotated data, and V2X communication records. Data preparation involves splitting the dataset into three subsets: 70% for training, used to optimize the model parameters, 15% for validation, used to tune hyperparameters and prevent overfitting, and 15% as a test set, used for the final evaluation. During preprocessing, sensor data (such as RGB images, LiDAR, and radar data) are normalized. In addition, point clouds are converted into BEV representations for spatial analysis, and temporal data are segmented into fixed-length intervals for LSTM training.  
Model initialization involves setting the weights of the CNN layers using Xavier initialization to enable efficient convergence. The hidden and cell states of the LSTM are initialized to zero to provide temporal consistency. Adaptive learning rates are used, beginning with a rate of 0.001 and decaying by a factor of 0.1 after every ten epochs, where no improvement in validation loss is observed. This adaptive approach ensures that the model can converge to an optimal solution efficiently.  
VRFormer's multi-task loss function is designed to balance multiple objectives, ensuring that the model learns to predict object classification, motion trajectories, and accident risk within VRDeepSafety. This function combines four distinct loss components: cross-entropy loss, Smooth L1 loss, mean squared error (MSE), and Binary Cross-Entropy (BCE). Each component contributes to a specific task in the overall prediction framework. The loss function is expressed as the weighted sum of these individual components, as shown by the following equation:  
\[L_{\text{total}}\,=\lambda_{1}L_{CE}+\lambda_{2}L_{SmoothL}+\lambda_{3}L_{MSE}+ \lambda_{4}L_{BCE} \tag{31}\]  
where \(L_{\text{CE}}\) is the cross-entropy loss applied for object classification tasks, \(L_{\text{SmoothL1}}\,\) is the smooth L1 loss, applied for bounding box regression, \(L_{\text{MSE}}\) is the mean squared error loss, applied for motion prediction (trajectory prediction), and \(L_{\text{BCE}}\) is the binary cross-entropy loss, applied for accident classification.  
Each loss component is multiplied by a weighting factor denoted by \(\lambda_{1},\lambda_{2},\lambda_{3}\) and \(\lambda_{4}\), which is used to control the relative importance of each task. These weights are chosen to balance the model's ability to handle multiple tasks effectively.
# Measurement System for Finger Skin Displacement on a Textured Surface Using Index Matching
## 3 Measurement System
### Software  
Figure 7 shows the flow of the image-processing algorithm. This software enables the tracking of the marker position applied to the fingertip. The template-matching algorithm (matchTemplate function, use TM_CCOEFF_NORMED as a similarity calculation method) from the OpenCV library ([[http://opencv.org](http://opencv.org)]([http://opencv.org](http://opencv.org))) was used for position analysis.  
The algorithm proceeds as follows. First, the two image templates, the finger template, and the marker template are manually cut out from the skin displacement images. The finger template includes each marker point and a frame surrounding the point. The marker template contains a single point marker extracted from the entire fingertip. Then, the initial position of each marker is extracted from the first frame. For this, template matching using marker templates is applied. In this case, the region having a similarity of 0.65 or more is used as a marker. If a non-marker is tracked, it is removed manually during the subsequent analysis process. Then, the image of the initial position of each marker is saved as a template for each marker. For each frame, we extract the finger position using the finger template and estimate the displacement of the marker. A Gaussian filter (window-size: 11 pixels) is applied to the frame as a preprocessor. The template matching for each marker is performed by cropping a region with a 5-pixel offset to the marker region in the previous frame from the new frame. This is done to improve the computational speed and prevent the tracking of different markers. The centroid position is calculated from the similarity results of template matching. This result is considered to be an estimated marker position. This makes it possible to calculate the displacement from the previous frame with sub-pixel accuracy. This process is applied to the video (Supplementary Materials Videos S1 and S2). The measured results are output in the form of x-axis displacement (in the slope direction) and y-axis displacement (perpendicular to the slope direction). The movement of each marker is visualized using arrows.
# Clinical, Research, and Educational Applications of ChatGPT in Dentistry: A Narrative Review
## 4 Discussion
### Clinical Applications
#### 4.2.4 Prosthodontics  
[PERSON] et al. evaluated the performance of ChatGPT in generating answers about removable dental prostheses (RDPs) and tooth-supported fixed dental prostheses (FDPs). Technical questions were formulated regarding the various stages of prosthesis fabrication, including impressions, vertical dimension, trial phases, occlusal registration, and cementation. The answers showed a reliability of 25.6%, demonstrating that currently, ChatGPT has a limited ability to generate answers related to RDPs and tooth-supported FDPs [62]. The prosthodontist cannot rely on the chatbot to obtain information on correctly making a prosthesis yet.
## 4 Discussion
Additional input from occupants such as user characteristics (e.g., age, gender, light sensitivity), user behavior, and user preferences can be collected to analyze the effects of the built environment on occupants in greater depth. Logging this feedback data and storing it in a database can provide a set of valuable evidence from the instantaneous feedback of occupants, which in turn can help with better prediction of human comfort and improvement of lighting design. For example, in an innovative study conducted by [PERSON] et al. [26], along with lighting simulation tools, a humanoid robot was used to attract the attention of the occupant about their real environmental situation and provide them with personalized suggestions to improve their well-being. If a building is responsive to the requirements and behavior of occupants and organizations, either via a POE and/or via continuous monitoring, it can become a truly intelligent building. Conducting metadata analysis on all data collected from evidence and designing building interfaces or (self-)learning systems based on this evidence-based knowledge would make the interaction of occupants with buildings more convenient. Regarding the third research question related to the usefulness of a POE with an increased frequency, the results show that in June, people were generally more satisfied with the lit environment compared to answers given in the original study performed in October. As daylight levels are higher in June, it seems that having more daylight has a positive impact on user satisfaction in general. The importance of daylight on user satisfaction was also shown by other researchers, e.g., [27; 28; 29; 30]. For example, a study was conducted by [PERSON], [PERSON], [PERSON], and [PERSON] [28] that measured physical data and surveys to assess occupants' subjective visual comfort. The results of their survey study (\(N=1068\)) showed that occupants who were more pleased with (their access to) daylight were also more likely to have a higher level of satisfaction. This finding also indicates that, depending on the time of year, a single POE study can overestimate or underestimate different lighting quality metrics. The paired sample \(t\)-test analysis shows a significant difference for the daylight 'Natural' variable and a marginally significant difference for the daylight 'A-Glare sun' and 'Light desk' variables, which means at least for these variables, it is worth running a POE at least twice at different times of the year.
# Global Transcriptomic Response of _Staphylococcus aureus_ to Viulent Bacteriophage Infection
## 3 Results
### Slutoff of Host Macromolecular Synthesis in the Early Stages of Infection  
To gain further insight into the bacterial response to infection, DEGs at 5 min were used for functional enrichment analysis. GO enrichment analysis classified these genes into 53 enriched GO terms (\(p_{\text{adj.}}\leq\) 0.05), among which, 49 terms corresponded to biological processes (BP), one to molecular functions (MF), and three to cellular components (CC) (Figure 2).  
Most of the genes in the BP category were downregulated (78.5%) and were mainly attributed to amino acid and nucleoside metabolic processes. Of them, genes related to nucleoside metabolic processes were assigned to purine and pyrimidine metabolism; two genes (_pyrB_ and _pyrC_) of the _pyrABCDER_ operon and six genes (_purE_, _purK_, _purC_, _purS_, _purQ_, and _purL_) of the _purEKCSQLFMNHD_ operon were downregulated upon bacteriophage infection. In addition, an alternative substrate biosynthesis pathway for purine metabolism was also affected at the level of histidine biosynthesis: products of the downregulated genes _hisH_ and _hisF_ are responsible for the formation of the 5-aminoimidazole-4-carboxamide ribonucleotide, which is required for further purine synthesis.  
Among the genes involved in amino acid metabolism, the most significant changes in expression levels were observed for the genes involved in amino acid catabolism of histidine, proline, alanine, threonine, serine, and arginine. In the case of histidine, in addition to the biosynthesis genes described above, the expression of the genes involved in its degradation with the formation of glutamate (_hutG_ and _hutU_) was also reduced. In the case of arginine metabolism, changes in the expression levels of genes involved in the catabolism (_arcA_, _arcB_, _arcD_) of this amino acid and those determining its biosynthesis (_argF_) were discovered. Proline catabolism disruption was associated with a decrease in the _fadM_ expression level, the product of which converts proline to delta-1-pyrroline-5-carboxylate. This is the primary reaction of the L-glutamate synthesis from L-proline. The change in alanine catabolism during phage infection is associated with the downregulation of the alanine dehydrogenase gene (_ald1_), which is responsible for the synthesis of ammonia and pyruvate from L-alanine. Serine and threonine catabolism was affected by reduced expression of _alsS_ (acetolactate synthase), _tdcB_ (L-threonine dehydatase catabolic _TdcB_), and _sdaAA_ (L-serine dehydatase, alpha chain). At the same time, it should be noted that a reduced expression of these genes may decrease the biosynthesis level of branched-chain amino acids.