esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Conflict Prediction for Sharp Turns on Mountain Roads Based on Driver Behavior Patterns
## 2 Materials and Methods
### Data Collection Environment and Procedures
#### 2.1.4 Data Matching and Preprocessing  
To effectively integrate driving behavior data with traffic conflict data, a set of data-matching and preprocessing programs was developed. Firstly, the driving simulation test data and traffic conflict observation data were synchronized according to the time series, and the driving behavior sequence data 30 s before and after the occurrence of a conflict event were extracted to form matched pairs of driving behavior-traffic conflict data. Subsequently, the matched data were subjected to noise reduction [35; 36] and outlier handling [37; 38] to eliminate invalid or abnormal data caused by equipment malfunctions, signal loss, etc., ensuring the quality of the data. Then, statistical features were extracted from valid matched data, including mean speed, speed standard deviation, mean acceleration, acceleration standard deviation, and minimum headway distance as driving behavior indicators, as well as types and severity levels of traffic conflicts as safety indicators. Finally, the extracted driving behavior features and safety indicators were stored and labeled in a consistent format to create structured datasets for correlation analysis and predictive model construction. Through systematic data matching and preprocessing processes, we achieved effective integration of cross-domain datasets, laying a solid foundation for further exploration of underlying patterns in driving behavior and traffic conflicts based on high-quality datasets.  
Figure 3: Test instruments.
## 4 Results and Discussion
### Watermark Robustness
#### 4.2.1 Resistance to Geometric Attacks
The DTM-based feature vertex watermarks have a large capacity and are uniformly distributed, in these cropping patterns, at least one complete sub-block is included, therefore, the complete watermark information can be extracted. Non-feature vertex watermarking relies on Polylines where all non-feature vertices are located, under these six cropping patterns, the remaining parts are less than 50% of the original maps. Because the content is missing too much, it is not enough to extract an effective non-feature vertex watermark, so the \(BER\) values are also very high. This scheme combines the advantages of feature vertex watermarking and non-feature vertex watermarking, therefore, the complete watermark with a \(BER\) of 0 can be extracted in the six cropping patterns. The vertex-constrained blocking in the data preprocessing section contains at least one complete watermark information, the remaining blocks under these six cropping patterns are much larger than the smallest block under preprocessing. Therefore, the ability of this scheme to resist cropping attacks is more than this experiment in theory.
# Laser Lens Size Measurement Using Swept-Source Optical Coherence Tomography
## 2 Principle and Systems  
### Principle of OCT  
OCT generates images by measuring the intensity of the interference light between the backscattering light from the sample and the reference mirror. The interference spectrum detected by the single-point detector can be expressed as the function of the wave number \(k\)[23].  
\[I(k)=P_{r}+P_{s}\cdot\int_{-\infty}^{+\infty}r^{2}(z)dz+2\sqrt{P_{r}P_{s}}\int _{-\infty}^{+\infty}r(z)\Gamma(z)\cos[2k(t)z+\phi(z)]dz \tag{1}\]  
where \(P_{r}\) is the intensity of the light returned from the reference arm to the detector, \(P_{s}\) is the intensity of the light irradiated to the sample, \(k(t)=2\pi/\lambda(t)\) is the wave number changing with time, \(z\) is the sample depth coordinate, \(r(z)\) and \(\phi(z)\) representing the amplitude and phase of the reflection coefficient in the depth direction of the sample, respectively, \(\Gamma(z)\) is the coherence function of the light source. In Equation (1), the first two terms are noises. The last term is a superposition of cosine components of different frequencies containing sample axial depth information. It represents the interference spectrum between the reference arm and the different depths of the sample. According to Wiener-Khinchin theorem, the depth information of the object can be reconstructed by performing the inverse Fourier transform of the interference spectrum.
## 3 Simulation Experiment
### Comparison of Denoising Effects at Different Noise Levels  
To test the performance of the four methods at different noise levels, we added 10 sets of random noise at different levels to the pure signal, and the noise variance changed from 0.02 to 0.20. Signals with different input noise levels, denoised signals, and SNR are shown in Figure 9. Figure 9f shows the SNR at different noise levels and the SNR after denoising. The red line is the proposed method. It can be seen directly from Figure 9f that the red line corresponding to this method is at the top of all other lines, indicating that the denoising effect is the best. When faced with high SNR signals, the proposed method is equivalent to DWT denoising, but when faced with low SNR signals, the proposed method seems to perform better than other methods. This phenomenon indicates that the proposed method is more effective for signals with higher noise levels.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Category** & **B-SNR** & **A-SNR** & **SD** & \(CC\) \\ \hline EMD\_CS\_ST & 4.651 & 27.170 & 0.028 & 0.972 \\ DWT & 4.651 & 22.624 & 0.035 & 0.955 \\ EEMD & 4.651 & 21.285 & 0.038 & 0.945 \\ VMD\_DWT & 4.651 & 23.680 & 0.034 & 0.952 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four methods for comparative analysis of the denoise effect.
# Enhanced Effect of Phytoextraction on Arsenic-Contaminated Soil by Microbial Reduction
## 3 Results and Discussion
### Mechanism of Bio-Reductive Enhanced Phytoremediation  
The toxicity and bioavailability of arsenic in soil are determined by its chemical fractionation and state, with the mobility of arsenic closely linked to the geochemical cycling processes of sulfur and iron. Heavy metals in soil can be categorized into acid-soluble, reducible, oxidizable and residual fractions. The acid-soluble fraction exhibits greater mobility, while the reducible and oxidizable fractions are relatively stable but prone to transformation through redox reactions [48,49]. To address this, artificially cultured iron-sulfur-reducing microorganisms were utilized to promote the reduction of sulfate and trivalent iron, consequently reducing the reducible fraction in soil. This process leads to the release of arsenic bound to iron oxides or adsorbed on their surfaces, allowing it to enter the pore water as free arsenic fractions that can be absorbed by _Peteris vittata_ (Figure 10).  
In anaerobic environments, organic carbon sources serve as energy sources for heterotrophic bacteria. Small molecules, such as lactic acid and acetic acid produced during the fermentation of plant straw, are oxidized and decomposed by iron-sulfur-reducing bacteria. The resulting electrons are transferred to electron acceptors such as sulfate and Fe(III). Arsenic(V) can also serve as a direct electron acceptor or be reduced by S\({}^{2-}\) and Fe\({}^{2+}\). Additionally, the organic matter derived from fermented plants provides nutrients for the growth of _Peteris vittata_, promoting increased biomass [50]. The uptake of pentavalent arsenic by _Peteris vittata_ proceeds through the phosphate system, and thus there is competition between arsenate and phosphate, but the uptake of arsenite proceeds through an independent system. As(III) content can be increased through direct reduction by organic acids as well as indirect reduction by S\({}^{2-}\) and Fe\({}^{2+}\) produced by iron-sulfur-reducing bacteria's metabolism. This increase in As(III) content facilitates the uptake and translocation of arsenic by the root system of _Peteris vittata_.  
Figure 9: Effects of iron–sulfur-reducing bacteria on the phylum (**a**) and genus (**b**) composition of the bacterial community in rhizosphere soil cultivating _P. vittata_.
## 4 Discussion
The implementation of IMU-based analysis provides several advantages over traditional assessment methods, as highlighted by recent technological developments [17, 18]. The high sampling frequency and multi-axial movement detection capabilities allow for more precise quantification of performance parameters, representing a significant advancement from traditional video analysis methods [15, 31]. Our approach of combining velocity-time profiles, distribution histograms, polynomial fitting, and space-time relationships provides a comprehensive framework for performance analysis that captures both macro and micro aspects of technique execution. Several limitations should be considered when interpreting these results. First, the relatively small sample size (\(n=12\)), although typical for elite sports research, may limit the generalizability of findings. Second, environmental conditions, while controlled, could still influence performance parameters. Third, our original intention to integrate the algorithm directly into the inertial device for true real-time implementation was not realized due to changes in the commercial partnership that supported the technology development; however, the current implementation demonstrates robust phase detection with minimal processing delay. Fourth, the current algorithm relies solely on GPS velocity data, which may be subject to accuracy limitations and potential signal degradation in certain environments. GPS measurement errors could affect the precision of phase detection, particularly when velocity changes are subtle. Additionally, the algorithm does not yet incorporate data from the available inertial sensors (accelerometers, gyroscopes), which could potentially enhance phase detection accuracy by capturing stroke-specific movement patterns that complement velocity data. Future research could integrate multiple sensor inputs to increase robustness against GPS signal variations and provide redundancy in measurement, as well as expanding the sample size, incorporating different skill levels, and investigating the algorithm's applicability across various environmental conditions. Additionally, longitudinal studies examining the relationship between these performance metrics and competition outcomes would provide valuable insights into the predictive validity of the analysis system. The integration of machine learning approaches could further enhance the algorithm's capabilities in pattern recognition and performance prediction.
###### Abstract  
Current automatic shadow compensation methods often suffer because their contrast improvement processes are not self-adaptive and, consequently, the results they produce do not adequately represent the real objects. The study presented in this paper designed a new automatic shadow compensation framework based on improvements to the Wallis principle, which included an intensity coefficient and a stretching coefficient to enhance contrast and brightness more efficiently. An automatic parameter calculation strategy also is a part of this framework, which is based on searching for and matching similar feature points around shadow boundaries. Finally, a final compensation combination strategy combines the regional compensation with the local window compensation of the pixels in each shadow to improve the shaded information in a balanced way. All these strategies in our method work together to provide a better measurement for customizing suitable compensation depending on the condition of each region and pixel. The intensity component \(I\) also is automatically strengthened through the customized compensation model. Color correction is executed in a way that avoids the color bias caused by over-compensated component values, thereby better reflecting shaded information. Images with clouds shadows and ground objects shadows were utilized to test our method and six other state-of-the-art methods. The comparison results indicate that our method compensated for shaded information more effectively, accurately, and evenly than the other methods for customizing suitable models for each shadow and pixel with reasonable time-cost. Its brightness, contrast, and object color in shaded areas were approximately equalized with non-shaded regions to present a shadow-free image.  
automatic shadow compensation; high-resolution remote sensing image; Wallis filter; compensation combination; shadows of the cloud and ground object +
Footnote †: journal: Applied Sciences  
0000-0001-5000-0001-5000][PERSON]  
0000-0001-5000-0001-5000][PERSON]  
0000-0001-5000-0001-5000][PERSON]  
0000-0001-5000-0001-5000][PERSON]
## 3 APD Output Models
### APD Detection Process and Noise Model  
The photon absorption process in an APD during a PPM time slot follows Poisson statistics, where the absorbed photon count \(n\) has a mean value \(\bar{n}=P_{r}\eta_{a}T_{s}/\hbar v\). Here, \(P_{r}\) denotes the optical power derived in (1). The parameters \(\eta_{a}\), \(T_{s}\), and \(v\) are the APD quantum efficiency, PPM time slot width, and optical frequency, respectively. The resultant electron count \(m\), governed by the McIntyre distribution, arises from stochastic avalanche multiplication processes. Therefore, the PDF of electron generation is obtained by convolution of the Poisson and McIntyre PDFs, as established in [48].  
The statistical fluctuation in \(m\), arising from both the photon absorption randomness and the avalanche multiplication uncertainty, constitutes the fundamental mechanism of the shot noise in APD-based receivers. The shot noise magnitude depends intrinsically on the quantum efficiency and multiplication statistics governed by the McIntyre model. Beyond fundamental shot noise, the output current of APD exhibits two critical noise components: thermal noise and dark current noise [30]. The thermal noise, originating from electron thermal agitation in the front-end load resistor, follows additive white Gaussian statistics with power spectral density \(\sigma_{th}^{2}=4\kappa_{C}T_{e}B_{e}T_{s}^{2}/R_{L}\), where \(T_{e}\), \(B_{e}\), and \(R_{L}\) are the APD temperature, bandwidth, and load resistance, respectively. Dark current noise consists of surface leakage current and bulk current, obeying Gaussian statistics with mean \(I_{D}T_{s}\) and variance \(\sigma_{D}^{2}=2 qI_{D}B_{e}T_{s}^{2}\). The composite noise current \(i_{td}\) comprising both dark current and thermal noise components follows a composite Gaussian distribution expressed as  
\[\phi\Big{(}i_{td}|\mu_{td},\sigma_{td}^{2}\Big{)}=\frac{1}{\sqrt{2\pi}\sigma_{ td}}\exp\left(-\frac{(i_{td}-\mu_{td})^{2}}{2\sigma_{td}^{2}}\right), \tag{18}\]  
where \(\mu_{td}=I_{D}T_{s}\) and \(\sigma_{td}^{2}=\sigma_{th}^{2}+\sigma_{D}^{2}\).  
By integrating the McIntyre-Poisson convolution framework for photon-generated electron statistics [48] with the Gaussian noise component (18), the comprehensive APD output current model is derived as  
\[p_{m}(i)=\sum_{m=0}^{\infty}\sum_{n=1}^{m}\left(\frac{(\bar{n})^{n}\Gamma \big{(}c_{ka}m/k_{a}+1\big{)}(c_{C}-c_{G}k_{a})^{m-n}}{\sqrt{2\pi}\sigma_{m}m (n-1)!(m-n)!}\frac{(1/G+k_{a}c_{G})^{n+c_{ka}m}}{\Gamma(c_{ka}m+n+1)}\exp\left( -\bar{n}-\frac{(i-\mu_{m})^{2}}{2\sigma_{m}^{2}}\right)\right), \tag{19}\]  
where \(G\) and \(k_{a}\) are the APD gain and the APD ionization ratio, respectively. In addition, \(c_{ka}=k_{a}/(1-k_{a})\), \(c_{g}=(G-1)/G\), \(\sigma_{m}^{2}=\sigma_{td}^{2}\), and \(\mu_{m}=mq_{e}+\mu_{td}\) with \(q_{e}\) being the electron charge.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 3 From S-MIM to F-SIM
### F-SIM Physical Layer
For what concerns the satellite return link transponder nonlinearity effects, they are normally negligible as the transponder operates in the multi-carrier mode with many co-frequency spread spectrum carriers on each F-SIM dedicated frequency slot, and multiple frequency slots dedicated to F-SIM or other services. In this case, the transponder high-power amplifier (HPA) is operated in a moderately linear mode (e.g., >4 dB of output back-off) to minimize the HPA's intermodulation effects as it is common practice in any multi-carrier satellite transponder.
## 5 Conclusions  
Recently, the DNN-based AMC scheme has been studied as a method to improve jamming performance. However, research on the features used as the input data is insufficient and most studies aim at improving the calculation and the performance of the algorithm. In this paper, we propose an efficient feature-extraction method for the DNN-based AMC, we analyze feature used as input data, and we select an effective feature through the proposed method. From the results, it can be established that even if the same amount of data is used, the difference in classification rate performance according to each feature is large and the task of extracting efficient features is important. The optimal activity of selecting input data will be to find the optimal feature group by comparing the performance according to the combination of all the features. However, this is a difficult method to actually perform because it requires a large amount of calculation. Therefore, it is necessary to analyze features such as conventional techniques and proposed methods that use mutual information and correlation between data. It is expected that the AMC with high classification performance can be realized with a small computation effort by extracting the efficient feature values using the proposed method. Thus, we conclude that the proposed method can be considered a method to improve the performance of the AMC for military communication systems, AMC-based jamming systems, and the automatic coding and modulation for commercial wireless communication systems.  
S.H.L. contributed to this work in experiment planning, experiment measurements, data analysis and manuscript preparation. K.-Y.K. manuscript preparation, data analysis. Y.S. contributed in experiment planning, data analysis, manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2019-2018-0-01424) supervised by the IITP (Institute for Information and communications Technology Promotion).  
The authors declare no conflict of interest.  
Figure 6: Classification performance when only the features of the top group of each method are used as the input data.
# Spatial Resolutions of On-Axis and Off-Axis Transmission Kikuchi Diffraction Methods
###### Abstract  
Spatial resolution is one of the key factors in orientation microscopy, as it determines the accuracy of grain size investigation and phase identification. We determined the spatial resolutions of on-axis and off-axis transmission Kikuchi diffraction (TKD) methods by calculating correlation coefficients using only the effective parts of on-axis and off-axis transmission Kikuchi patterns. During the calculation, we used average filtering to evaluate the spatial resolution more accurately. The spatial resolutions of both on-axis and off-axis TKD methods were determined in the same scanning electron microscope at different accelerating voltages and specimen thicknesses. The spatial resolution of the on-axis TKD was higher than that of the off-axis TKD at the same parameters. Furthermore, with an increase in accelerating voltage or a decrease in specimen thickness, the spatial resolutions of the two configurations could be significantly improved, from tens of nanometers to below 10 nm. At a voltage of 30 kV and sample thickness of 74 nm, both on-axis and off-axis TKD methods exhibited the highest resolutions of 6.2 and 9.7 nm, respectively.  
transmission Kikuchi diffraction (TKD); transmission electron back-scatter diffraction (t-EBSD); on-axis detector; spatial resolution +
Footnote †: journal: Applied Physics Letters  
18 September 2019; Accepted: 19 October 2019; Published: 23 October 2019
## 4 Results and Discussion
### Characteristics of In Situ Stress Direction  
The interactions among the Indian Ocean Plate, the Pacific Plate, the Philippine Sea Plate, and the Eurasian Plate, along with the regional blocks within the continental plate, govern the crustal stress field and tectonic movement patterns of the lithosphere in and around the Chinese mainland [28; 29; 30; 31]. A key characteristic of the contemporary tectonic stress field of the Tibetan Plateau in China is NNE compression, which aligns with the subduction direction of the adjacent plates, primarily influenced by the subduction and extrusion of the Indian Plate towards Eurasia [32].  
Figure 3: Measured stress value vs. depth at the Kunlun Mountain pass.  
Before and after the earthquake, the direction of in situ stress changes, gradually adjusting to align with the pre-earthquake in situ stress orientation. Previous studies indicate that the average direction of maximum principal stress in this region was NE51.5\({}^{\circ}\) prior to the earthquake, shifting to 30.5\({}^{\circ}\) post event [3; 33]. Figure 4 illustrates the directional characteristics of maximum horizontal principal stress in this area, derived from in situ stress tests conducted using hydraulic fracturing methods across five boreholes. The results predominantly fall within the range of NE20\({}^{\circ}\)-NE54\({}^{\circ}\), with an average value of NE33\({}^{\circ}\). When compared to earlier research findings, it is evident that the current direction of maximum horizontal principal stress closely resembles that observed after the earthquake. This suggests that while there is ongoing recovery within the current stress field, it has not yet returned to its pre-earthquake levels.
## 3 Results  
Results of the reliability analysis for pedal and knee loading are presented in Tables 1 and 2, respectively. The tables show the max and min loading and corresponding crank angle, which is defined as the duration of max and min loading (in degrees). Knee loads are expressed in respect to calf ACS. Peak medial knee force and peak medial pedal force exhibited negative ICC and Cronbach's alpha values. This occurred due to relatively small between-subject variation compared to within-subject variation. All other variables exhibited Cronbach's alpha >0.7, indicating high internal consistency. Furthermore, high ICC values were also recorded (>0.735) on all peak loads and crank angles. There were no significant \(p\)-values from ANOVA except for minimum normal force and a crank angle of minimum tangential force. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Mean Difference \(\pm\) SD** & **Cronbach’s Alpha** & **ICC** & \(p\)**-Value** \\ \hline Tx—inversion/eversion torque & & & & \\ max (Nm) & 0.1 \(\pm\) 0.1 & 0.958 & 0.963 & 0.639 \\ min (Nm) & 0.0 \(\pm\) 0.0 & 0.991 & 0.985 & 0.052 \\ max (deg) & 10.1 \(\pm\) 14.2 & 0.846 & 0.818 & 0.187 \\ min (deg) & 12.6 \(\pm\) 13.6 & 0.923 & 0.880 & 0.058 \\ Tz—internal/external rotation & & & & \\ torque & & & & \\ max (Nm) & 0.3 \(\pm\) 0.2 & 0.891 & 0.906 & 0.973 \\ min (Nm) & 0.3 \(\pm\) 0.3 & 0.700 & 0.735 & 0.938 \\ max (deg) & 5.4 \(\pm\) 8.5 & 0.843 & 0.817 & 0.171 \\ min (deg) & 20.3 \(\pm\) 32.3 & 0.791 & 0.776 & 0.271 \\ Fx—tangential force & & & & \\ max (N) & 5.5 \(\pm\) 3.0 & 0.971 & 0.975 & 0.802 \\ min (N) & 4.4 \(\pm\) 4.0 & 0.987 & 0.989 & 0.789 \\ max (deg) & 23.7 \(\pm\) 28.5 & 0.781 & 0.784 & 0.393 \\ min (deg) & 4.1 \(\pm\) 2.2 & 0.997 & 0.937 & 0.010 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reliability results for pedal forces and torques. All loads and corresponding crank angles had high internal consistency with Cronbach's alpha exceeding >0.714. Furthermore, high ICC values were also recorded (>0.740) on all loads and corresponding crank angles. There were no significant \(p\)-values from the \(t\)-test except for a crank angle of anterior force and axial loads (minimum and maximum). Figure 4 illustrates average pedal forces (A) and torques (B) of all seven participants. The solid line (Figure 4A) shows tangential backward and forward pedal force with its