esa-sceva/satcom-chunk-collection huggingface dataset:
# Virtual Sensor for Estimating the Strain-Hardening Rate of
## 2 Materials and Methods
### Methods  
#### 2.2.1 Multiple Linear Regression  
Next, the values of \(Rm\) obtained for each thickness reduction in every steel grade are plotted versus the thickness reduction to obtain the cold-rolling curves, as shown in Figure 1b. These curves are fitted using LR, and then the slope of each curve (coefficient \(a\)) is evaluated as an indicator of the strain-hardening rate for each steel grade. This slope is used as the output variable in the subsequent model to obtain the new parameter \(HRHS\) through regression.  
The LR is expressed by Equation (2):  
\[Rm=a\cdot x+b \tag{2}\]  
where \(b\) is the ordinate-intercept when \(x\) is equal 0, \(a\) is the slope of the line of the \(LR\), \(x\) is the independent variable (_red_, cold thickness reduction), and \(Rm\) is the dependent variable.  
While the MLR is expressed by Equation (3):  
\[HRHS=\sum w_{i}\cdot x_{i}+c \tag{3}\]  
where \(c\) is the ordinate-intercept of the hyperplane \(w_{i}\cdot x_{i}\), \(w_{i}\) are the estimated coefficients of the MLR model, \(x_{i}\) are the independent variables (concentration of alloying elements), and \(HRHS\) is the dependent variable. Matrix \(w_{i}\) values are estimated using the well-known least squares method with the training set of data.  
Therefore, the LR is the result of an experimental test battery which offers the estimation of the slope of each \(Rm\) curve of the several ASS that have been considered in the work. Meanwhile, the MLR gives an approximation of this slope (coefficient \(a\)) from the chemical composition through the parameter \(HRHS\) for modified or new ASS grades without the need for additional tests. So, this MLR model acts as a virtual sensor of the strain-hardening rate of the material.  
Finally, a database is constructed using the coefficients \(a\) (slopes of the \(Rm\)-cold thickness reduction curves obtained from LR) and the content of the alloying elements in each austenitic steel grade to develop a multiple linear regression model that correlates the strain-hardening rate with the chemical composition for the ASS family. The number of independent variables considered in this database is eleven, as described in Table 2.  
The LR database consists of 123 samples, representing combinations of different cold thickness reductions ranging from 0% to 70% for 19 sheets of 8 ASS grades. For MLR, the database comprises 19 cases, each corresponding to the coefficient \(a\) for one of the 19 sheets used in LR.  
Figure 1: (**a**) Engineering stress–strain flow curves of A2 with different thickness reduction levels; (**b**) \(Rm\) curves of the studied ASS.
# Dynamic Multiple Junction Selection Based Routing Protocol for VANETs in City Environment
## 1 Introduction
Considering only immediate junction at one time may result in routing the packets along the streets where there is no carrier or negligible connectivity resulting in an increased delay as well as packet loss. If the packets stay in a buffer for a long time then there is a possibility that they will be discarded due to the expiry of time-to-live. Consequently, this degrades the network performance. To the best of our knowledge, a protocol that is capable of establishing dynamic optimal routing path by considering multiple junctions/streets from source to destination based on shortest path and connectivity is missing in the existing literature. This paper presents a novel position based routing protocol for VANETs called Dynamic Multiple Junction Selection based Routing protocol (DMJSR). It is designed specifically for city scenarios. DMJSR takes into account vehicles speed, their directions and locations, and bi-directional double lanes road. It makes use of GPS and digital maps for getting the vehicle current location and the road information respectively. It considers multiple-neighbor junctions while making routing decisions which maximizes connectivity and enhances networks performance as compared to existing approaches. Moreover, it is also novel in its mechanisms as it combines multi-junction selection mechanism with position and direction based forwarding mechanism for forwarding of packets between the selected junctions. It establishes dynamic optimal connected routing paths, which enhances the performance in terms of packet delivery ratio, end-to-end delay, and routing overhead. It is vital to achieve enhanced performance in terms of the aforementioned parameters for providing safety services (like coordinated communication between two vehicles, real-time traffic management, cooperative message transfer, accidents warnings, road hazard control warnings, post-crash announcements, and traffic watchfulness), convenience services (like parking availability, electronic toll collections, and active prediction) and commercial oriented services (like distant vehicle diagnostics/personalization, downloading digital maps, internet access, advertisements, real-time video relay, etc.). The rest of the paper is organized as follows. Section 2 describes the existing routing techniques along with their limitations and elaborates the motivation of our work. Section 3 illustrates the proposed routing strategy. Section 4 presents the simulation results and analysis. The paper is concluded in Section 5.
## 3 Methodology
### Luminance and Point Cloud Mapping  
Texture mapping is a widely utilized technique for integrating image and point cloud data, leveraging grayscale or RGB values from images to enhance visual detail. This integration facilitates the detection of tiny surface defects with improved precision. As shown in Figure 7, we propose a Luminance and Point Cloud Mapping (LPM) method that incorporates image luminance to provide color information for point cloud data. This method comprises three key steps: length mapping, width mapping, and luminance mapping. The first two steps align the position and scale between the image and point cloud, while luminance mapping assigns color to the point cloud.  
The length (\(L_{p}\)) and width (\(W_{p}\)) of the point cloud can be calculated based on the intrinsic parameters of the laser scanner. Specifically, \(L_{p}\) is determined by the scanning frame \(S_{f}\) and scan interval \(S_{i}\), while \(W_{p}\) is determined by the number of points per frame \(N_{p}\) and the distance between points \(D_{p}\). Thus, \(L_{p}\) and \(W_{p}\) can be expressed as follows:  
\[L_{p}=S_{f}\times S_{i} \tag{2}\]  
\[W_{p}=N_{p}\times D_{p} \tag{3}\]  
With the length and width of the point cloud determined, aligning the image with the point cloud can be achieved by simply scaling the image dimensions by their respective factors. However, this process generates a significant number of virtual pixels without luminance values. To assign appropriate luminance values to these virtual pixels, bilinear interpolation was employed [73]. Assuming that (\(x_{1}\), \(y_{1}\), \(l_{11}\)), (\(x_{1}\), \(y_{2}\), \(l_{12}\)), (\(x_{2}\), \(y_{1}\), \(l_{21}\)), (\(x_{2}\), \(y_{2}\), \(l_{22}\)) are the four neighboring pixels surrounding the given point (\(x_{i}\), \(y_{j}\), \(l_{ij}\)), where \(x_{1}<x_{i}\) <\(x_{2}\), \(y_{1}<y_{j}\) <\(y_{2}\), with \(l_{11}\), \(l_{12}\), \(l_{21}\), and \(l_{22}\) representing their respective luminance values, the luminance value \(l_{ij}\) for the given point can be expressed as:  
\[l_{ij}=\frac{y_{2}-y}{y_{2}-y_{1}}\bigg{(}\frac{x_{2}-x}{x_{2}-x_{1}}l_{11}+ \frac{x-x_{1}}{x_{2}-x_{1}}l_{21}\bigg{)}+\frac{y-y_{1}}{y_{2}-y_{1}}\bigg{(} \frac{x_{2}-x}{x_{2}-x_{1}}l_{12}+\frac{x-x_{1}}{x_{2}-x_{1}}l_{22}\bigg{)} \tag{4}\]  
Figure 7: Illustration of luminance and point cloud mapping.  
Through the aforementioned steps, the LPM method effectively assigned appropriate luminance values to each point and achieved alignment between the image and point cloud, thereby enhancing the accuracy of defect detection.
## 2 Materials and Methods
### Methods  
#### 2.3.1 Vpd  
\(VPD\) (kPa) can be calculated by the following equation:  
\[VPD=e_{s}-e_{a} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type** & **Code** & **Factors** & **Unit** \\ \hline Climate & X1 & Temperature & \({}^{\circ}\)C \\  & X2 & Precipitation & mm \\  & X3 & Humidity index & / \\  & X4 & Vapor pressure deficit (VPD) & kPa \\  & X5 & Reference evapotranspiration (ETo) & mm \\  & X6 & Solar radiation & W m\({}^{-2}\) \\ Topography & X7 & Elevation & m \\  & X8 & Slope & \({}^{\circ}\) \\  & X9 & Aspect & / \\ Land use & X10 & Land use type & / \\ Geomorphology & X11 & Landform type & / \\ \hline \end{tabular}
\end{table}
Table 1: Potential driving factors for the changes of LAI and GPP in the MHRB.  
The formula for saturation vapor pressure (\(e_{s}\), kPa) can be expressed as follows [38]:  
\[e_{s}=\frac{e^{\circ}(T_{\max})+e^{\circ}(T_{\min})}{2} \tag{2}\]  
\[e^{\circ}(T)=0.6108\exp\left[\frac{17.27T}{T+237.3}\right] \tag{3}\]  
\(e^{\circ}(T)\) is saturation vapor pressure at the air temperature \(T\) (kPa), \(T_{\max}\) and \(T_{\min}\) are the daily maximum air temperature and daily minimum air temperature, respectively.  
The formula for actual vapor pressure (\(e_{a}\), kPa) is as follows [39]:  
\[e_{a}=\frac{q\times p}{0.622+q}\times 0.1 \tag{4}\]  
\(q\) is specific humidity (g g\({}^{-1}\)), \(p\) is air pressure (hPa).
# Development and Prospect of UAV-Based Aerial Electrostatic Spray Technology in China
## 2 Early Exploration of Aerial Electrostatic Spray Technology in China
### Research on Mechanism of Influencing Factors  
The settling process of charged droplets is restricted by environmental factors, controllable factors, and target parameters during spraying operation, thus affecting the operation quality of the electrostatic spraying system. Among these influencing factors, environmental factors include temperature, humidity, wind speed, soil, etc.; controllable parameters include charging voltage, spraying flow, spraying pressure, flight altitude, flight speed, etc.; target parameters include crop objects, target morphology, leaf inclination, and insect pest types and habits, and so on [21].  
[PERSON] et al. [26] studied the influence of different crosswinds wind speed conditions and electrostatic voltage on charging characteristics through indoor simulation of natural wind and constant wind environment, providing a basis for strengthening the anti-drift ability of charged spray droplets in the settling process. [PERSON] et al. [27] analyzed the characteristics of the electrostatic field induced by the ring electrode with the help of Fluent software (Version 6.3). It was found that the higher charging voltage or smaller electrode spacing (when the distance between the induction electrode ring and the nozzle was less than 10 mm) could effectively improve the charge effect and spray quality.
## 3 Results  
Classification models developed with gut microbiota data determined by 16S metagenomics showed sensitivity of 0.94 (0.007) and specificity of 0.77 (0.180) in the training cohort, and sensitivity of 0.92 and specificity of 0.58 in the validation cohort. Subsequently, quantification of 11 bacterial taxa that had the most impact on the classification models including microbiome data determined by 16S metagenomics was performed by quantitative PCR in non-diabetic patients at baseline and in those who developed T2 DM after a median follow-up of 5 years. For modelling, a Random Forest model approach was chosen by dividing the cohort into a training and a validation sub-cohort. The training cohort underwent class rebalancing due to class disproportionality using SMOTE-TOMEK. For the selection of suitable model hyperparameters, a 100-stage Bayesian optimisation was performed with the aim of maximising the average sensitivity resulting from a cross-validation with a fold of 5. The models were built with these optimised hyperparameters and trained on the training cohort. Subsequently tested in the validation cohort. The data were used as input for the generation of classification models which showed the following results. a.1) Clinical variables in training cohort: sensitivity of 0.56 (0.44-0.68) and specificity of 0.90 (0.86-0.94). a.2) Clinical variables in validation cohort: sensitivity of 0.47 (0.24-0.70) and specificity of 0.86 (0.78-0.94). b.1) Clinical variables combined with the 10 bacterial taxa in training cohort: sensitivity of 0.86 (0.77-0.95) and specificity of 0.87 (0.83-0.92). b.2) Clinical variables combined with the 11 bacterial taxa in validation cohort: sensitivity of 0.63 (0.41-0.85) and specificity of 0.71 (0.61-0.82). Compared to classification models that include the entire gut microbiome determined by 165 metagenomics, the reduction in model performance is commensurate with the information obtained by measuring only 11 bacterial taxa.  
During the training of the model, the measure of the cost function (MSE) is reduced until it reaches values close to 150 months squared, or a 12-month difference, which represents an 80% accuracy compared to the cut-off value of 60 months at which the patient is considered not to develop diabetes. Additionally, if we make the prediction in months for each patient and compare it with the actual measurement of the patient themselves, the mean deviation is 3.5 months, which is 95.2% more accurate than the above-mentioned cut-off value. This 15% increase between these two measures of accuracy is due to the small size of the training set of patients who did develop diabetes (64 out of 294 subjects), despite the effort to use data augmentation techniques to prevent overfitting and the programmatic search of a considerably large hyperparameter space.  
This predictive model works as a computer application in a web environment (Figure S10 and Video SI); the only requirement for the user would be a computer with an internet connection.
## 1 Introduction
In relation to shale samples, QEMSCAN mineral quantification analysis technology can acquire mineral composition and distribution of sample surfaces through scanning, and then use the obtained element spectrum in each pixel on surface images for categorization [12; 13; 14]. However, when researching shale minerals, the resolution of QEMSCAN scanned images is typically insufficient (generally > 1 \(\upmu\)m), thereby leading to some minerals showing complex morphology, relatively low particle size, and low accuracy in the mineral contact boundary recognition. This is particularly observed in illite, chlorite, and other clay minerals and affects the accuracy of the shale mineral analysis and results [15]. In addition, both nationally and internationally, there have rarely been reports of mineral correction based on QEMSCAN identification results. MAPS high-resolution large-scale backscatter scanning electron microscopy technology is a type of imaging based on the backscattered electron (BSE) technique. The highest resolution of this technique reaches several nanometres and can complete nano-porosity classification and observe mineralogy. Simultaneously, this technique uses pyramid image processing to splice and overlay many scanned images of the sample surface area, thereby facilitating the observation of multiple layers and strata of rocks with a large field of view. Currently, increasingly more microscopic studies on dense cores, such as shale, are using this technology [16]. Rock petrographic analysis, such as microscopic observation, is very time-consuming and requires a long period of training and practice in order to achieve eligibility for use. The newest XRD, FESEM, FIB-SEM, XRF, QEMSCAN and MPAS are usually separately equipped with data processing software systems, making them relatively easy to use. Regardless of the sample preparation process, the measurement response times (per unit volume or area) of XRD, FESEM and XRF are relatively shorter, while FIB-SEM, QEMSCAN and MPAS have relatively longer measurement response times because of their high resolution. Due to their different use purposes, it is difficult to compare the training level requirements. However, there is reason to believe that, as computer-assisted technology continues to improve, these devices will become less difficult to operate. In the event the high-resolution and large-field features of MAPS technology can correct QEMSCAN results, the shortcomings of QEMSCAN technology in image resolution can be overcome. Mineral identification accuracy can be improved and there can be a quantitative characterization of minerals, organic matter, and pores on a 2D shale surface. Thus, through a series of image processing methods, this study utilized the MAPS image scanning technique to correct QEMSCAN results, to realize an improvement in the quantitative analysis and refined characterization of shale minerals.
## 2 Related Work
To obtain novelty and mark a noticeable shift from existing work, six objectives were pursued:  
* Investigate more empirical propagation models that suit the nature of low-altitude drones. * Optimize an empirical propagation model that offers advantages (e.g., accuracy, suite different environment, strong received signal strength (RSS) connectivity, and wide coverage). * Built a drone with onboard equipment (e.g., thermal FLIR lepton camera, 5G MIMO antenna, weather station, and GPS sensors). * Identify the optimum wireless communication configuration of the drone (e.g., drone altitude, covered footprint, weather conditions (temperature, wind speed), longitude, and latitude). These parameters were linked to the optimized model to ensure cohesion and accurate results. * Perform a hardware calibration before the implementation of the proposed design for testing and verification in a real scenario. * Validate the results against other well-known modified empirical models for validation using RMSE.
## 4 Results and Discussion
### Computational Efficiency Analysis  
To assess the practical feasibility of the proposed localization framework, we conducted a detailed evaluation of its runtime performance on a standard non-GPU-enabled system. The implementation was executed on a laptop equipped with an Intel Core i7-7500U CPU @ 1.80 GHz, 16 GB RAM, and running Windows 11 Home (64-bit).  
Unlike image-based localization approaches, our system operates on LiDAR point cloud frames, where each frame corresponds to a 3D scan obtained from a Velodyne VLP-16 sensor. The average computation time for processing a single point cloud frame during the online testing phase was measured to be approximately 41 milliseconds, enabling a processing rate of around 24 frames per second (FPS). This confirms the potential of our framework for near real-time autonomous vehicle localization tasks.  
To ensure computational efficiency, the following optimizations were applied:  
* Cluster Matching Optimization: Only the most relevant clusters are matched using Bhattacharyya distance, significantly reducing the number of comparisons during track association.
* Selective Kalman Updates: Kalman filter prediction and update steps are performed exclusively for those clusters actively involved in interactions, avoiding unnecessary computations.
* Efficient MJPF Design: The Markov Jump Particle Filter marginalizes linear components within the state space, which reduces the number of particles and accelerates inference.  
In terms of memory scalability, the interaction dictionaries grow linearly with the number of observed tracks and frames in a given environment. Each dictionary entry encodes one frame-level interaction, making the size of the dictionary proportional to the temporal span and the number of interacting static objects. For example, in our current setup, combining six track dictionaries results in a total memory footprint of less than 15 MB. During testing, only the relevant time slices are queried from these pre-indexed dictionaries, ensuring low-latency access. Thus, even when scaled to more complex scenes  
Figure 19: Box plot of discrete anomalies.  
with dozens of static landmarks, the structure remains computationally efficient. This is due to the selective querying and use of spatial hashing for dictionary access, supporting real-time performance in extended urban driving scenarios.  
These design strategies ensure that the proposed approach remains computationally feasible even on resource-constrained hardware, and can be potentially extended to embedded systems with comparable performance capabilities.
# AR in the Architecture Domain: State of the Art
## 5 AR System
### Devices
#### 5.1.6 Social Acceptance  
Some elements--such as device intrusion, privacy, and security--influence AR systems' social acceptance and diffusion [99]. Device intrusion is perceived as a device's physical presence and visibility within everyday life, affecting both the aesthetic and functional aspects. The miniaturization of display devices and an increasingly interactive and natural interface may reduce this factor. Early applications with backpack laptops and HMDs induced significant device intrusion. Device size reduction and performance advances have partially mitigated this problem, leaving some areas for improvement. On the one hand, the aesthetic design of some smart glasses (i.e., Microsoft HoloLens, Google Glass) remains quite invasive, and not very \"similar\" to standard glasses. On the other hand, gesture optimization reduces the pointing activity of cameras and physical user discomfort [109].  
The privacy problem is felt in both the private and public spheres. The data ownership of recorded images, videos, and sounds saved in the cloud is mainly related to social networks. The use of an OST-HMD that registers information in space introduces a severe privacy issue. For example, the movement \"Stop the Cyborgs\" has tried to convince people to ban Google Glass in their premises, while many companies have banned Google Glass in their offices.  
Finally, the last aspect is related to caution. The use of visors that superimpose additional information over the real world can expand the knowledge of the environment. However, the presence of \"external\" elements in the scene catalyzes the gaze, reducing attention to the real world. This translates into a safety problem when the device is in motion, especially in vehicles. Hence, the design of the positioning of the supplementary information on the visor concerning the real scene becomes crucial to preserve attention. These aspects take on a different role in the educational domain; there, the acceptance is based on what users expect from this technology. A central role must balance the technological aspect with the pedagogical ones, offering AR applications that guarantee continuous engagement during the interaction, self-learning capacity, and parental involvement. It is also important to keep in mind the context or educational conditions of the students and the AR platform's typology [110].