esa-sceva/satcom-chunk-collection huggingface dataset:
# Feasibility of a Quasi-Static Approach in Assessing Side-Wind Hazards for Running Vehicles
## 3 Feasibility of the Quasi-Static Approach Under a Uniform Wind
### Comparison of Tire Forces Under Various Wind Speeds
The lateral displacement of the trailer body resulted in an additional rolling moment that reduced the vertical reaction force of the windward wheel, as illustrated in Figure 9. This additional moment was proportional to the lateral displacement, which is also highly dependent on wind speed. This effect of the addition of a rolling moment was not reflected in the derivation of the equilibrium equations for the quasi-static approach. This inference explains why the gap between the vertical reaction forces estimated by the two approaches increased with increases in the wind speed, as shown in Figure 7a. This issue was not induced in the van since the lateral displacement was limited, and because the center of gravity was relatively low. The inference was also confirmed by modifications of the trailer model with consideration of additional rolling moment.
# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 4 Experiments
### Implementation Details  
The framework incorporates the mask-conditioned feature fusion network, the mask-integrated multi-hypothesis tracking algorithm, and the occlusion-aware mask propagation network, which are trained, respectively, following the experimental configurations below. The training data for the mask-conditioned feature fusion network comes from the MOT17 and KITTI [42] datasets. Foreground extraction and related preprocessing are performed using the Mask R-CNN instance segmentation detection method. The method is pretrained on the COCO trainval35k dataset [22].  
The mask-integrated multi-hypothesis tracking algorithm is configured with the following parameters:  
* Short trajectory pair length: 3.
* Average confidence filtering threshold for short trajectory pairs: 0.7.
* Pruning backtracking layers: 5.
* Target disappearance window length: 20.  
The occlusion-aware mask propagation network uses a residual network [43] as the backbone of the encoder and is trained on the YouTube-VOS [36] and DAVIS-2017 [44] datasets.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Laser-Induced/Scribed Graphene ECG Electrodes
#### 2.4.3 Polyaziriidine-Encapsulated LIG ECG Electrode  
Although laser induction is an effective way to produce graphene electrodes, the realized LIG flakes are not well-interconnected due to the gap between the laser-scribed lines [61]. This results in uneven distribution of conductivity across the electrode, reducing overall conductivity. When used for daily ECG monitoring, cracks on the electrode surface will be further aggravated by movement, and the quality of the detected ECG signal will be reduced. To solve this problem, [PERSON] et al. proposed a polyaziriidine-encapsulated, 3D porous LIG electrode [65]. In this work, polyaziriidine was used as a linker inside the porous LIG to interconnect the graphene flakes. As shown in Figure 3b, the amine groups in polyaziriidine can covalently bind with many oxygen-containing groups of LIG, which ensures the LIG flakes are electrically connected under tension or bending. The ECG signals collected by the electrodes are of good quality both in static and dynamic conditions. Besides, due to the large specific surface area of 3D porous LIG (340 m\({}^{2}\)/g), the ECG electrode can achieve a signal-to-noise ratio comparable to conventional Ag/AgCl gel electrodes (13.5 dB) with only half of the area.
## 4 Regulation of Virulence Factors
### Hrp X/Y, HrpS and HrpL Cascade Leading to Activation of hrpT3 SS  
Two regulatory components, _hrpX_ and _hrpY_, of the _hrp_ system of _Erwinia amylovora_ have been identified [79]. The _hrpXY_ operon is expressed in rich media, but its transcription is increased threefold by low pH, nutrient, and temperature levels, conditions that mimic the plant apoplast. _hrpXY_ is autoregulated and directs the expression of _hrpL_, which, in turn, activates transcription of other loci in the _hrp_ gene cluster [80].  
The deduced amino-acid sequences of _hrpX_ and _hrpY_ are similar to bacterial two-component regulators including VsrA/VsrD of _Pseudomonas_ (_Ralstonia_) _solanacearum_, DegS/DegU of _Bacillus subtilis_, and UhpB/UhpA and NarX/NarP, NarL of _Escherichia coli_. The N-terminal signal-input domain of HrpX contains PAS domain repeats [79].  
_hrpS_, located downstream of hrpXY, encodes a protein with homology to WtsA (HrpS) of _Erwinia_ (_Pantoea_) _stewartii_, HrpR and HrpS of _Pseudomonas syringae_, and other \(\sigma^{54}\)-dependent, enhancer binding proteins. Transcription of _hrpS_ also is induced under conditions that mimic the plant apoplast. However, _hrpS_ is not autoregulated, and its expression is not affected by _hrpXY_[79]. As a member of the NtrC family, HrpS is unusual in that it lacks a long N-terminal receiver domain [79].  
The structure of the input domain of _E. amylovora_ HrpX appears to be exceptional, compared with sensor proteins involved in other type III systems, which contain two transmembrane regions and a periplasmic domain [79]. Thus, at least two types of transmitter-receiver systems appear to have evolved for control of type III systems in response to environmental stimuli in hosts [79].
## 2 Materials and Methods
### Statistical Analysis  
A one-way repeated measures analysis of variance (ANOVA) and a Bonferroni correction for multiple pairwise comparisons were used. Sphericity was assessed using [PERSON]'s test. The Greenhouse-Geisser adjustment was made when the sphericity was violated. The effect size was determined using the partial eta-squared (\(\eta^{2}\)). According to [PERSON]'s guidelines, values of \(\eta^{2}\) were small at 0.01, medium at 0.06 and large at 0.14 [28]. Moreover, the statistical power was calculated. To analyze the absolute agreement between the two measurement tools, the intraclass correlation coefficient (ICC) with a 95% confidence interval (CI) related to the 2-way random single measures (2,1) was first used. Second, Bland-Altman plots showing systematic bias and the limit of agreement (LOA) for the two compared instruments were created [29]. Thirdly, the Pearson's product moment correlation coefficient (r) was calculated. The r was evaluated according to the following thresholds: <0.1 (trivial), 0.1-0.3 (small), 0.3-0.5 (moderate), 0.5-0.7 (large), 0.7-0.9 (very large) and 0.9-1.0 (almost perfect) [30]. Furthermore, a linear regression analysis was performed to assess the degree of relation between the two measurement tools. The absolute reliability of the measurement tools estimating the height jump for the 3 CMJ trials in each participant was examined using the ICC (2,1) [31], standard error of measurement (SEM) and the coefficient of variation (CV). The ICCs were interpreted based on the following scale: <0.5 (poor), 0.5-0.75 (moderate), 0.75-0.9 (good) and >0.9 (excellent) [32]. The SEM and CV were calculated using the following equations:  
\[\text{SEM}=\text{SD}\sqrt{1-\text{ICC}}, \tag{5}\]  
where SD--standard deviation for the average of the SDs from 3 trials,  
\[\text{CV}=\frac{\text{SEM}}{\text{Mean}}, \tag{6}\]  
where mean--the arithmetic average of the 3 trials. The significance level alpha was set at \(p<0.05\). The statistical analysis was performed in SPSS Statistics software for Windows, version 28.0 (Armonk, NY, USA: IBM Corp.).
## 4 Experimental Results
### Error Measurement Based on the number of APs (Base Stations)  
In order to test the influence of the number of APs in our proposal, we measured the error of the approach adding access point one by one in each location (in the same place of the 56 samples previously taken). In Figure 4 we can observe that the localization error tends to decrease exponentially (blue line with squares). Therefore, with higher number of APs we obtain lower error values in the sensor location estimation. This tendency is given because one of the methods used in our hybrid system is based on the triangulation method. This method uses the distance from the sensor to various access points based on RSS. Once the sensor obtains the value of at least three distances, between the sensor and the APs, the sensor estimates its position. Therefore, the more distances the sensor to different APs has, the higher the accuracy of the localization sensor will be, in other words, the error of location will be lower.  
Bearing in mind this tendency, we have estimated which function follows the error when the number of APs of the indoor environment varies. Equation (10) gives our estimation:  
\[\text{y}=2,8383\text{e}^{-0,255\text{x}} \tag{10}\]  
where \(x\) is the number of APs in the indoor environment. Equation (10) is shown in figure 4 with the black-thin line. We can see that it fits the error tend quite well.  
Figure 4: Average location estimation error as a function of the number of APs.  
It should be noted, that when there are more than five APs the improvement appreciation in terms localization error is minimal when a new AP is added to the indoor environment.
## 3 AI-Based Identification and Authentication
### Enhanced Accuracy Through Deep Learning  
Deep learning algorithms, a subset of machine learning, are pivotal in improving the accuracy of biometric systems. These algorithms process vast amounts of data, learning intricate patterns and features critical for accurate biometric recognition. Convolutional neural networks (CNNs) have been extensively used for facial recognition, significantly improving the system's ability to correctly identify individuals even with variations in lighting, angles, and expressions [34]. This capability is crucial in industrial settings where environmental conditions can be unpredictable.  
Deep learning's effectiveness in enhancing biometric systems is demonstrated through various studies. For example, deep learning combined with serial fusion methods significantly improves authentication accuracy, achieving an AUC of 0.9996 [35]. Another study reviews over 120 works on biometric recognition using deep learning models, demonstrating substantial improvements in accuracy across face, fingerprint, and iris recognition [36].  
In industrial applications, a method combining machine vision and deep learning for quality control achieves high accuracy with limited computational time using low-cost hardware [37]. Additionally, a comparative study of single-mode, two-mode, and three-mode biometric systems using CNNs and genetic algorithms shows that three-mode systems significantly outperform others in terms of accuracy and robustness [38].  
At the same time, the behavioral biometrics leverages AI to analyze patterns in human activities such as typing, walking, and using devices, enhancing security by providing continuous and unobtrusive authentication. AI algorithms process vast amounts of behavioral data, identifying unique traits and detecting anomalies that suggest potential security threats. This technology is particularly effective because it adapts to changes in user behavior over time, improving accuracy and robustness against fraud. Recent studies highlight AI's role in refining these systems, showing significant improvements in both identification accuracy and user experience [39; 40; 41].
## 2 Global Open Earth Observations Satellite Missions for Monitoring Land Surface Phenology
### Landsat Program
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite** & **Launch Date** & **Resolution** & **Spectral Bands** & \
\begin{tabular}{c} **Temporal** \\ **Resolution** \\ \end{tabular} \\ \hline AVHRR & 13-Oct-1978 & 1.1–4.5 km & 5–6 & 1–2 days \\ \hline MODIS & 18-Dec-1999 & 250 m, 500 m, 1 km & 36 & 1–2 days \\ \hline Landsat & 23-Jul-1972 & 30 m (most bands) & 11 & 16 days \\ \hline Sentinel-2 & 23-Jun-2015 & 10–60 m & 13 &
\begin{tabular}{c} 5 days (dual \\ satellite) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Global open Earth observations satellite missions for monitoring land surface phenology.
# Capacity Analysis of Lattice Reduction Aided Equalizers for Massive MIMO Systems
###### Abstract  
Massive multi-input-multi-output (MIMO) systems are the future of the communication system. The proper design of the MIMO system needs an appropriate choice of detection algorithms. At the same time, Lattice reduction (LR)-aided equalizers have been well investigated for MIMO systems. Many studies have been carried out over the Korkine-Zolotareff (KZ) and Lenstra-Lenstra-Lovasz (LLL) algorithms. This paper presents an analysis of the channel capacity of the massive MIMO system. The mathematical calculations included in this paper correspond to the channel correlation effect on the channel capacity. Besides, the achievable gain over the linear receiver is also highlighted. In this study, all the calculations were further verified through the simulated results. The simulated results show the performance comparison between zero forcing (ZF), minimum mean squared error (MMSE), integer forcing (IF) receivers with log-likelihood ratio (LLR)-ZF, LLR-MMSE, KZ-ZF, and KZ-MMSE. The main objective of this work is to show that, when a lattice reduction algorithm is combined with the convention linear MIMO receiver, it improves the capacity tremendously. The same is proven here, as the KZ-MMSE receiver outperforms its counterparts in a significant margin.  
MIMO; ZF; MMSE; LLR-ZF; LLR-MMSE; KZ-ZF; KZ-MMSE; capacity +
Footnote †: journal: Information Information
## 2 Related Work
### Graph Neural Network Methods
#### 2.2.1 Homogeneous Graph Methods  
Models like GCN [51] effectively capture the local structure and feature representations of nodes through semi-supervised learning, widely applied in link prediction within social networks. However, they incur significant computational overhead in large-scale graphs and fail to fully account for heterogeneous information. The Light Graph Convolutional Network (LightGCN) [52] simplifies the neighbor aggregation operation in the GCN model, improving training efficiency and reducing computational complexity, but it is less effective than more complex GNN models at capturing intricate interactions. GraphSAGE generates low-dimensional embedding representations to predict unobserved connections by sampling and aggregating neighborhood features, reducing computation in large-scale graphs but potentially overlooking some low-frequency and important adjacency information. The Multiscale Spatio Graph Convolution Network (MSGCN) [53] integrates multiple social relationships to generate personalized friend recommendations, capturing complex user behavior, but still suffers from data sparsity. The graph autoencoder proposed by [PERSON] et al. [54] generates latent features via convolutional layers, suitable for structured data but less effective in large-scale graphs. [PERSON] et al. [23] mitigated this issue by using neighborhood sampling methods. [PERSON] et al. introduced GAT, which weighs the importance of adjacent nodes using attention mechanisms, overcoming the limitations of the traditional GCN in aggregating neighbor information.
## 4 Sub-Optimal STMD with Clipped Viscous Damping-Stiffness Control Force  
In accordance with the terminology used in the literature on semi-active control, see, for example, [45], the term \"sub-optimal\" or \"clipped-optimal\" STMD refers to a type of STMD where a semi-active damper is used to realize, to the extent that it is possible, an optimal active control strategy.  
It is evident that implementing the active control force (12) in an STMD using a semi-active damper requires the clipping of the non-dissipative forces. This limitation inherently results in inferior performance compared to an ATMD equipped with an active actuator. Despite this, no modifications to the control force are introduced in the sub-optimal STMD system to compensate for the adverse effect of clipping.  
The sub-optimal STMD is separately depicted in Figure 1b to distinguish it from the optimized STMD examined later in this paper. Both STMDs, shown in Figure 1b,c, have the same structure. They consist of mass \(m_{2}\), the same passive spring \(k_{2 dh}\), as in [PERSON]'s TMD, and a semi-active damper to apply a semi-active force. However, the semi-active force is calibrated differently in each of the two STMD concepts.  
In this section, an analysis of the sub-optimal STMD is provided, with a focus on its clipped control force. Examples of force characteristics are described, and a clipping coefficient is introduced to analytically quantify the degree of clipping. The increase in energy dissipation due to the clipping is also calculated. Most notably, the Krylov-Bogoliubov method of equivalent linearization is employed to derive an equivalent model for a clipped viscous-damping control force. This model is useful for demonstrating the influence of force clipping on both the resulting damping and stiffness. These analyses are essential for understanding the limitations of the sub-optimal STMD and for its subsequent optimization.
## 5 Conclusions  
1. Based on strapdown inertial navigation technology, we analyzed the movement characteristics of the shearer on the working face, and we used inertial navigation instruments such as gyroscopes and accelerometers and the dead reckoning principle to propose a shearer positioning system on a fully mechanized working face;
2. In the shearer positioning experiments, the positioning system can accurately reflect the trajectory of the shearer running, including straight line running, inclined-sumping running, and straight line running after sumping. The experiments also show that the shearer positioning system based on SINS is indeed a very accurate shearer positioning technology;
3. To improve the positioning accuracy of the shearer positioning system and meet long-term positioning requirements, high-precision instruments and better data processing methods, including high-efficiency filtering technology and map-matching algorithms, can be used or an odometer can be used to correct the navigation data.
## 3 Results
### The Fixation Duration at the Four Proficiency Levels  
The fixation duration for system-malfunction and system-limit failures at four proficiency levels is shown in Table 3. For facilitating visual observation, Figure 4 illustrates the data of fixation duration and SD from Table 3. \begin{table}
\begin{tabular}{c c c c} \hline
**Proficiency Level** & **Failure Type** & **Pupil Diameter** & **SD** \\ \hline
1. Inexperienced & Malfun & 4.661 & 0.774 \\  & Limit & 4.776 & 0.637 \\
2. Low Proficiency & Malfun & 4.736 & 0.624 \\  & Limit & 4.823 & 0.682 \\
3. Moderate Proficiency & Malfun & 4.731 & 0.663 \\  & Limit & 4.737 & 0.595 \\
4. High Proficiency & Malfun & 4.758 & 0.752 \\  & Limit & 4.801 & 0.648 \\ \hline \end{tabular}
\end{table} 
Table 2: The pupil diameter for system-malfunction and system-limit failures at four proficiency levels. The statistical analysis results indicated that at various proficiency levels, the impact of system-malfunction and system-limit failures on fixation duration was not significant (\(p\) = 0.163 \(>\) 0.05, F = 2.006). In terms of numerical values, the ratio of fixation duration between system-malfunction and system-limit failure, at proficiency levels 1 (inexperienced) to 4 (highly proficient), was 1.087, 1.270, 1.147, and 1.036, respectively. Across all proficiency levels, the fixation duration for system-malfunction failure was consistently higher than that for system-limit failure, although the difference was not statistically significant. In the case of system-malfunction failures, proficiency level had a significant impact on the fixation duration required to resolve the failures (\(p\) < 0.05). As proficiency level increased, the fixation duration for resolving the failures significantly decreased. Specifically, at proficiency level 1, the fixation duration was significantly different from level 2 (\(p\) < 0.05), level 3 (\(p\) < 0.001), and level 4 (\(p\) < 0.001). The ratio of fixation duration between lower proficiency level and adjacent higher proficiency level showed a monotonic increase: 1.068, 1.077, 1.086. Fixation duration could reflect the difficulty of extracting information to some extent. The decrease in average fixation duration with increasing proficiency level suggested that operators faced reduced difficulty in extracting information. In the case of system-limit failures, there was no consistent decreasing trend in fixation duration for resolving the failures as proficiency level increased. Instead, there was a significant decrease followed by a slight increase. The ratio of fixation duration between lower proficiency level and adjacent higher proficiency level was 1.249, 0.972, 0.981, respectively.
# Numerical Simulation-Based Study on the Arching Effect in Subsequent Backfill
###### Abstract  
To explore the influence of the arching effect on stress distribution in jointed backfill structures, this study employs three-dimensional numerical modeling to systematically analyze the mechanical behavior of backfill materials. A finite-difference approach was adopted to establish a representative stope model incorporating interface elements to simulate rock-backfill interactions. The methodology involved parametric studies examining key material properties (internal friction angle, cohesion, elastic modulus, Poisson's ratio) and geometric configurations, with boundary conditions derived from typical mining scenarios. The results demonstrate that stress distribution follows nonlinear relationships with all investigated parameters. Increasing the internal friction angle and the cohesion reduce internal stresses, though the arch effect exhibits a distinct upper limit. Mechanical properties significantly influence stress transfer characteristics, with the elastic modulus governing stiffness response and the Poisson's ratio affecting lateral stress development. Geometric parameters control the spatial extent of arching, with larger dimensions modifying the stress redistribution pattern. This research quantitatively establishes the operational limits of arching in backfill structures, providing crucial thresholds to prevent stability risks from overestimating its benefits. The findings offer practical guidelines for optimizing backfill design in deep mining and paste filling applications, contributing both technical solutions for mine safety and fundamental insights for geomechanical theory. The developed methodology serves as a robust framework for future studies on complex backfill behavior under various loading conditions.  
Article  
3
## 3 Results  
### Agreement and Correlation between V1 and VM  
The agreement rate, gross error rate, and correlation coefficient between V1 and VM for each visibility meter are listed in Table 4. It can be seen that the agreement rate between V1 and VM was high for all three sets of visibility meters, reaching approximately 90%. The V1 of these instruments was highly correlated with VM, with correlation coefficients exceeding 0.8. The correlation coefficient between the V1 of LT and VM was significantly higher than that between the V1 of FD and VM; however, the gross error rate for LT was also considerably higher than that for FD, which may have been associated with the significantly higher standard deviation of the difference between V1 and VM for FD than that for LT.