esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Input: Base Cation Weathering  
Base cation weathering depends on the types of soil minerals present and thus on the geology and nature of the soil parent material. The weathering rate is also very important and affected by a wide range of factors, including climate, soil wetness and pollutant deposition, as well as by land use and management. Forest practices such as cultivation and drainage can increase weathering rates, while fertiliser inputs represent direct nutrient additions that are more readily available for uptake.  
Site-specific soil mineral weathering rates were estimated by several methods: the PROFILE model and Zirconium Enrichment. The PROFILE model is a steady-state soil chemistry model [29] that includes process-oriented descriptions for chemical weathering of minerals, leaching and accumulation of dissolved chemical components, and solution equilibrium reactions. It was run with site-specific mineralogical input data; mineral surface areas were calculated using the empirical equation [28] and measured by the nitrogen BET (Brunaeur, Emmett and Teller) gas adsorption method [32; 33]. The site-specific soil mineralogy was measured by X-ray diffraction technique (XRD) and calculated both as a percentage of the total mineral mass and of the total mineral surface area (for details see [34; 35]. The zirconium enrichment technique generates individual base cation weathering rates for each horizon at each site. However, it is not universally applicable and should only be used where the soil is clearly derived from the underlying rock, or where the location of the parent material is known and can be directly sampled. Three Level II sites, Alice Holt, Savernake and Grizedale, were considered suitable for its application.
## 3 Results and Discussion
### Creep Assessment
#### 3.3.2. Creep Response--Statistical Analysis  
In order to further examine the significance of the loading speed and the preset depth on the basic mechanical properties measured in the present effort, four different techniques were adopted to ascertain the strength of each input parameter and their impact on the resulting values. **Method 1: ANOVA**  
The ANOVA analysis results presented in Table 4 delve into the relationship between various input and output variables by examining the Sum of Squares, Degrees of Freedom, F-statistic, and \(p\)-value. These metrics are crucial for determining the statistical significance of the impacts that each input variable has on the output variables. In this context, a \(p\)-value less than 0.05 generally signals a statistically significant effect. When assessing the strength of the correlations among the various input-output pairs within the dataset, the F-statistic and \(p\)-value are of particular importance. The F-statistic gauges the ratio of the variance that is explained by an input variable against the variance that remains unexplained, whereas the \(p\)-value calculates the probability of observing the gathered data if the input variable had no actual impact. A combination of a higher F-statistic and a lower \(p\)-value often points to a stronger correlation between the input and output variables. From the ANOVA, several input-output pairs emerge as having notably strong correlations, as evidenced by their high F-statistics and low \(p\)-values. Among these, the correlation between the Output \"Net Creep Depth (nm)\" and the Input \"Loading Speed (mN/s)\" stands out with the highest F-statistic of 52.91 and a \(p\)-value of 0.000768, making it the most significant correlation identified. Following closely is the correlation between the Output \"Net Creep Depth (nm)\" and the Input \"Creep duration (s)\", marked by an F-statistic of 21.26 and a \(p\)-value of 0.005783. Other significant correlations include the Output \"Critical Volume for Dislocation Nucleation\" with the Input \"Preset Depth (nm)\" featuring an F-statistic of 15.74 and a \(p\)-value of 0.010658; the Output \"Strain Rate Exponent\" with the Input \"Preset Depth (nm)\", an F-statistic of 13.56, and a \(p\)-value of 0.014264; and the Output
# Dimensional Errors Due to Overhanging Features in Laser Powder Bed Fusion Parts Made of Ti-6 Al-4V
## 2 Methodology
### Measurements  
In order to measure the dimensional error caused by the varying parameters in the down-facing area, the thickness of the overhang was measured, and the deviation of the thickness was compared with the CAD dimension to calculate the error. The methodology of the measurement is as follows.  
Using an optical microscope, images of the side view of all test pieces were captured. An image processing technique was developed and employed to measure the thickness of the overhanging surface. The image processing technique first works by gray scaling the image and applying a threshold, in order to detect the edges of the test piece. The program then scans the image both vertically and horizontally and extrapolates a straight line though the detected edge points. As the scale of the image is known, it is then possible to calculate the distance between the two straight lines, which gives the thickness of the overhanging surfaces of the parts. The measured thickness was then compared to the CAD design to determine the error in the printed part and the percentage of this error. Both sides of the part were measured to detect any differences in thickness within the same part. As seen in Figure 3, the dotted lines depict the detected edge of the part, which are used to measure the thickness. The obtained measurements were validated by comparing them with Vernier Calliper measurements.
# Analysis of Acoustic Flow Field Characteristics of Key Components of High-Speed Train Bogies
## 1 Introduction
### Calculation Current Status of Research on the Effect of Flow Field on Sound Field
[PERSON] [20] used computational fluid dynamics method to simulate the acoustic characteristics of a muffler and analyzed the spectral structure of the exhaust noise after installing a muffler in the vehicle so as to form a complete set of simulation and calculation methods of the sound quality of the exhaust noise of the vehicle. The State Key Laboratory of Internal Combustion Engine Reliability of Weichai Power Co., Ltd. [21] analyzed the transfer loss calculation method and characteristics of the perforated pipe muffler under non-uniform-flow conditions, applied CFD software (15.0) to calculate the flow field inside the muffler, then transferred the fluid properties to the LMS Virtual Lab acoustic finite element model through the way of mesh mapping, and chose different perforated impedance models to calculate the transfer loss. Different perforated impedance models were chosen to calculate the transmission loss of the muffler. [PERSON] [22] of the Hefei University of Technology applied three-dimensional numerical methods to analyze the effect of airflow velocity on the acoustic performance of cross-flow perforated pipe, and the results show that there is a positive correlation between airflow velocity and muffling volume, i.e., an increase in the velocity of the airflow will make the muffling volume increase. [PERSON] [23] investigated the effect of the flow field inside the antisocial muffler on the transmission loss using the principle of fluid dynamics and the acoustic finite element method, carried out numerical simulation experiments, and concluded that the calculation based on the analysis of the flow field to improve the efficiency of the noise reduction can be achieved by the optimization of the structure of the multi-objective. [PERSON]\)[24] used the finite element method to analyze the transmission loss of the muffler under three conditions: no-flow, uniform-flow, and non-uniform-flow. [PERSON] and [PERSON] [25,26] suggested that the boundary element method can be used to predict the propagation law of acoustic waves in mufflers, confirming that the method is able to predict the behavior of acoustic wave propagation in high frequencies where the plane wave theory fails, and that it can be used to simulate and analyze the mufflers with complex internal structures. In summary, the existence of flow field will have an impact on the sound field distribution. Therefore, this paper will introduce the flow field, study the intact field distribution characteristics of the key components of the bogie of high-speed trains under the influence of the flow field, and compare and analyze them. This will provide a new reference and choice for the subsequent acoustic signal acquisition of the fundamental components of the bogie.
## 5 Discussion
### Emerging Trends and Future Directions in Wearable Technology for VRU Safety
#### 5.2.1 Wearable Technology Integration with Connected Vehicles and Smart Infrastructure  
The collaborative fusion of wearable devices with connected vehicles and smart infrastructure will emerge as the foremost development trend in VRU safety technology, a trajectory already evident in the evolution of the \"#3 cyclist safety\" and \"#5 smart glasses\" clusters. Future research will investigate how to position wearable technology as a central access point, deeply integrating with smart city infrastructure and V2X technology to construct a user-centered, multi-layered safety protection network. Existing studies such as those by [PERSON] et al. [29] and [PERSON] et al. [47] have preliminarily verified the feasibility of this integration, demonstrating how wearable devices can overcome their inherent perceptual limitations through information sharing. In the future, with the proliferation of 5G/6G communication technologies and enhanced edge computing capabilities, wearable devices will transform from current isolated sensing systems into critical nexus points connecting users and environments within intelligent transportation ecosystems, enabling pre-emptive hazard prediction and collaborative avoidance.  
For future ecosystem integration research, we recommend focusing on developing lightweight communication protocols for vehicle-to-pedestrian communication that are specifically optimized for the power and computational constraints of wearable devices [87; 88], allowing users to receive danger warnings from vehicles beyond their line of sight and fundamentally expanding the safety warning range; and also researching real-time information exchange standards between wearable devices and traffic infrastructure to enhance user perception in high-risk areas. These research efforts will accelerate the transformation of wearable technology from independent protective devices to organic components of intelligent transportation systems, providing more comprehensive and proactive safety protection for VRUs.
###### Abstract  
Back focal plane (BFP) ellipsometry, which acquires the ellipsometric parameters of reflected light at different incident and azimuthal angles through a high-\(NA\) objective lens, has recently shown great potential in industrial film measurement. In on-line metrology cases for film manufacturing, the film vibration, which is caused by equipment vibrations or environmental disturbances, results in defocus blur and distortion of the received BFP images. Thus, subsequently extracted ellipsometric spectra and film parameters significantly deviate from the ground truth values. This paper proposes a cost-effective method for correcting vibration-induced BFP ellipsometric spectral errors. The method relies on an initial incident angle calibration of BFP radii at different defocus positions. Then, corresponding ellipsometric spectral errors are corrected by inserting a calibrated Jones compensation matrix into a system model. During measurement, the defocus position of the vibrational film is first determined. Then, BFP ellipsometric spectral errors, including incident angle mapping distortion and ellipsometric parameter variations, are corrected for a bias-free film analysis using the previous calibration results. Experimental results showed that this method significantly improved measurement accuracy without vibrational defocus compensation, from over 30 nm down to less than 1 nm.  
microellipsometry; back focal plane; antivibration measurement; thin film +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Defocus Effect Correction for Back Focal Plane Ellipsometry for Antivibration Measurement of Thin Films. _Appl. Sci._**2023**, _13_, 1738. [[https://doi.org/10.3390/app13031738](https://doi.org/10.3390/app13031738)]([https://doi.org/10.3390/app13031738](https://doi.org/10.3390/app13031738))
# Saliency Detection with Bilateral Absorbing Markov Chain Guided by Depth Information
## 2 Methodology
### High-Level Saliency Map Optimization via Depth Guidance
#### 2.4.2 Refinement via Depth Information  
Cellular automata mainly explores the neighborhood relationship between the nodes in the color feature space, but ignores the spatial position information in the scene. Therefore, we use depth cues to enhance and refine the salient regions and suppress the background regions. In this work, we design a depth selective refinement mechanism by a suppression-enhancement function pair: the suppression function is used to suppress the background, and then an enhancement function is used to emphasize the salient regions through high-confidence depth seeds.  
**Suppression function**: The regions far away from the camera have a higher probability of being the background and need to be suppressed. Therefore, we defined the suppression function as follows,  
\[S_{SF}(i)=\left\{\begin{array}{ll}S_{CA}(i),&\text{if $S_{CA}(i)>0.$7 and $S_{d}(i)>0.$5 and $S_{CA}(i)>(th_{CA}+0.1)$}\\ S_{CA}(i)\cdot S_{d}(i),&\text{if $S_{CA}(i)\leq th_{CA}$}\\ \sqrt{S_{CA}(i)\cdot S_{d}(i)},&\text{otherwise}\end{array}\right. \tag{35}\]  
where \(th_{CA}\) is the adaptive threshold of the saliency map \(S_{CA}\) obtained by Otsu [50] algorithm, and \(S_{d}(i)=norm(d_{i})\) is the depth prior. After filtering \(S_{SF}\) through the Otsu algorithm, the suppressed saliency map \(S_{1}\) is obtained.  
**Enhancement function**: Although the suppression function inhibits background information to a certain extent, it may lose some saliency information. The enhancement function can play a complementary role. First of all, we need to determine which depth information is reliable and needs to be retained. Here we combine three saliency maps to filter out the potential depth seed set \(\Omega_{D}\) with high confidence. The depth seeds are  
Figure 8: Visualization of the main saliency refinement process. (**a**) Original RGB images. (**b**) Original depth images. (**c**) Background prior probability maps combining color and depth cues. (**d**) Contrast prior probability maps combining color and depth cues. (**e**) Background-based saliency maps by absorbing Markov chain model. (**f**) Foreground-based saliency maps by absorbing Markov chain model. (**g**) Saliency maps optimized by cellular automata. (**h**) Saliency maps generate by the depth selective refinement mechanism based on suppression-enhancement function pair. (**i**) Ground truth.  
all salient in saliency maps of \(S_{fg}\), \(S_{bg}\) and \(S_{CA}\). The enhancement function is defined as follows,  
\[S_{EF}(i)=\left\{\begin{array}{ll}S_{1}(i)+0.2\cdot S_{d}(i),&S_{d}(i)\geq 0. 9,i\in\Omega_{D}\\ S_{1}(i)+0.05\cdot S_{d}(i),&S_{d}(i)<0.9,i\in\Omega_{D}\\ S_{1}(i),&\text{otherwise}\end{array}\right. \tag{36}\]  
After the suppression-enhancement function pair, we can get the final saliency map \(S_{EF}\), which is shown in Figure 8h.
# Numerical Study on Cooling Performance of a Steam-Cooled Blade Based on Response Surface Method
## 3 Effect of Working Parameters on the Cooling Performance of the Blade
### Influence of Mainstream Inlet Temperature
The lowest dimensionless temperature distribution is found in the mid-chord region of the steam cooled blade tip, with a value of about 0.79. The highest dimensionless temperature distribution is found in the root of the steam-cooled blade at the trailing edge, with a value of up to about 0.92. From Figure 9a-c, it can also be seen that when the mainstream inlet temperature increases from 680 K to 710 K, the dimensionless temperature of the steam-cooled blade changes by about 0.01%, which indicates that the variation of the mainstream inlet temperature has almost no effect on the dimensionless temperature distribution on the surface of the steam-cooled blade. As can be obtained from Figure 9d, the influence of the mainstream inlet temperature on the average dimensionless temperature of both air-cooled and steam-cooled blade is not significant, but the steam-cooled blade has a lower average dimensionless temperature compared with the air-cooled blade at the same mainstream inlet temperature, and the former is about 2.16% to 2.23% lower than the latter, with an average of about 2.20% lower. Figure 8: Influence of mainstream inlet temperature on the distribution of cooling efficiency: (**a**) \(T_{\text{gi}}=680\) K; (**b**) \(T_{\text{gi}}=695\) K; (**c**) \(T_{\text{gi}}=710\) K; (**d**) Variation law of the mean value.
## 2 Material and Methods
### Exposure via Soil  
#### 2.3.1 Test Soil and Spiking Procedures  
The natural standard LUFA 2.2 soil (Speyer, Germany) was used for the tests, presenting the main characteristics: pH (0.01 M CaCl\({}_{2}\)) = 5.8, organic carbon = 1.71%, cation exchange capacity = 9.2 meg/100 g, maximum water holding capacity (WHC) = 44.8% and grain size distribution of 7.2% clay, 8% silt and 77.5% sand.  
The soil was dried (48 h; 60 \({}^{\circ}\)C) before use. The control soil was prepared by adding deionized water to adjust to the adequate moisture content (50% of the maximum WHC). For the enchytraeid reproduction test (ERT), soil spiking was performed using the following nominal concentrations: 0.015, 1.5, 15, 150, 300, 450, 600, 900, 1200 and 1500 mg NPIs/kg soil (Table 2).  
Due to the presence of tween 20 (0.1%) and NaN\({}_{3}\) (2 mM) on the NPIs dispersion, the effects of the combination of these two compounds were also assessed, adding the same volume of the dispersant  
\begin{table}
\begin{tabular}{c c} \hline \hline
**NPIs Nominal Concentration (mg/kg)** & **Number of Nanospheres/kg** \\ \hline
0.015 & 0.000036825 \(\times\) 10\({}^{16}\) \\
1.5 & 0.0036825 \(\times\) 10\({}^{16}\) \\
15 & 0.036825 \(\times\) 10\({}^{16}\) \\
150 & 0.36825 \(\times\) 10\({}^{16}\) \\
300 & 0.7365 \(\times\) 10\({}^{16}\) \\
450 & 1.10475 \(\times\) 10\({}^{16}\) \\
600 & 1.475 \(\times\) 10\({}^{16}\) \\
900 & 2.2095 \(\times\) 10\({}^{16}\) \\
1200 & 2.946 \(\times\) 10\({}^{16}\) \\
1500 & 3.6825 \(\times\) 10\({}^{16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Correspondence between the tested nominal concentrations of polystyrene nanoplastics (NPIs) and the number of particles.  
aqueous solution (0.1% tween 20 and 2 mM NaN\({}_{3}\)) as those used in the concentrations of NPIs. The tested concentrations of tween 20 and NaN\({}_{3}\) are presented on Table 3. Based on the results from ERT, for the avoidance test, soil spiking was performed using the following nominal concentrations: 0.015, 1.5, 150, 300, 450 and 600 mg NPIs/kg soil and a dispersant control (tween 20 + NaN\({}_{3}\)), adding the same volume of the dispersant aqueous solution as used with the highest concentration of NPIs (600 mg/kg). The NPIs dispersions or dispersant aqueous solutions were added to the premoistened soil (to which water was previous added considering the 50% of the maximum WHC) and mixed manually [23]. The replicates were mixed individually. Tests started 1 d after soil spiking.
# Performance of Millimeter Wave Dense Cellular Network Using Stretched Exponential Path Loss Model
## 3 SINR Coverage Probability and Area Spectral Efficiency
### SINR Coverage Probability
#### 3.1.2 \(\zeta=2\)  
The NLOS coverage probability takes its simplest form for \(\zeta=2\), which corresponds to \(p=0\), and it is given as:  
\[P_{c,NLOS}(T)=\exp\left(-\sum\
olimits_{k=1}^{4}b_{k}\frac{\pi\,\lambda}{ \kappa}\ ln\big{(}1+Ta_{\mathbb{P}_{k}}\big{)}\right) \tag{15}\]  
Proof.: This follows from simplifying (12).  
For theoretical insights on the effect of increasing BS density on the downlink coverage performance of mmwave network, we consider the simplified expression obtained for \(\zeta=2\). The SINR coverage probability is a decreasing exponential function of BS density and approaches zero when \(\lambda\rightarrow\infty\). This is due to the increased LOS interference in a dense network.
## 3 Experiment and Analysis  
In the training and testing process of OD-MF and MS-ST, we used a GTX1080 TI GPU with 11 GB of video memory, 32 GB of memory, and an i78700k, 6-core, 12-thread CPU in the experiment. The deep learning framework used for training was pytorch 1.7.0, and the python version was 3.6. In order to simulate the express parcel sorting scene in the real scene and verify the correctness and effectiveness of VS-MF, an experimental platform for robot sorting based on RGB-D vision was built. It mainly includes a UR-5 6-DOF robot, a RGB-D camera and a sucker actuator, as shown in Figure 8. The sorting objects were box-shaped express parcels of different sizes. The parcels were randomly stacked in the view range of the RGB-D camera.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology
### Fitting into a DTMC Model
#### 3.4.3 DTMC Modelling Steps
An example of the function is displayed in Figure 11 below:  
Subsequently, for both scenarios, transition matrices were defined by employing the maximum likelihood estimator (MLE) [17] of the \(p_{ij}\) entry, where \(n_{ij}\) stands for the number of pairs (\(X_{t}=s_{i}\), \(X_{t+1}=s_{j}\)) found in the whole dataset. The calculation for each \(p_{ij}\) is presented by the equation below [17]:  
\[\hat{p}_{ij}{}^{MLE}=\frac{n_{ij}}{\sum_{tt=1}^{k}n_{it}} \tag{19}\]  
The standard errors of the \(\hat{p}_{ij}{}^{MLE}\) values [17] were then generated by:  
\[SE_{ij}=\frac{\hat{p}_{ij}{}^{MLE}}{\sqrt{n_{ij}}} \tag{20}\]  
Figure 11: Example of state-change pair generation (from sequential events to transition pairs). Figure 4:0 Initial State Distribution–“content viewing” screen. The transition matrix generation was implemented in Java by exploiting the features of hash maps and two-dimensional arrays. Subsequently, the transition matrices were exported into.csv files by the Java function and later were used in either MATLAB or R Studio to create DTMC models, implemented by the functions we have demonstrated in Section 3.4.2. For example, in the 'content viewing' scenario, R studio was used to read the.csv file of the created transition matrix. Using the \"markovchain\" package, a DTMC object was generated based on the transition matrix. We also acquired crucial Markov Chain properties, such as the classification of states. Next, the'steadyStates()' R function was used to compute the steady-state vector (see Equation (7)). Using the function'meanFirstPassageTime()', a matrix of mean first passage times between state \(s_{i}\) and \(s_{j}\) (let \(s_{i}\) be the initial state) was also generated. Equation (13) was then used to construct the'most likely trajectory,' which illustrated the course of the most likely state at each stage of the Markov process. In this instance, it depicts the most prominent way in which participants travel their eyes across the screen.
# Machine-Learning-Based Side-Channel Evaluation of Elliptic-Curve Cryptographic FPGA Processor
## 3 Design and Implementation of Elliptic-Curve Cryptosystem F256 on FPGA
### Elliptic-Curve Point Doubling--ECPD  
Point doubling uses three modular multiplier units to calculate (2)-(5) in parallel. Ten modular multiplications are done in five stages that reduce the point-doubling calculation time to \(5(n+3)+4\) clock cycles.  
For curve SECP256K1, as a = 0, the logic can be reduced. Using just one modular reduction unit, ECPD can be performed at 7 logic levels or 7(n + 3) + 2 clock cycles by the optimized-area ECPD. Figure 2 shows the data-flow diagram of the ECPD doubling with and without optimized area.