esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For DAS, a monochromatic laser light is tuned across an absorption line of the target gas, and the attenuation of the laser light by the absorption (transmission) is analyzed directly, which enables a calibration-free determination of the gas concentration in consideration of spectroscopic parameters from the HITRAN database [6]. In conventional WMS, the laser wavelength is relatively slowly scanned across the absorption feature in the range of 10 to 100 Hz and additionally modulated with a sine wave in the range of 1 to 100 kHz. This enables phase-sensitive detection of the transmission signal in its second harmonic (WMS-2f, lock-in detection). A calibration, a reference gas cell or a reference spectrum is generally used to determine the target gas concentration. Considering the limited frame rate of sensitive mid-infrared cameras and the potential of image processing methods, DAS was applied and investigated in this work. The goal was to show the spectral and spatiotemporal resolution of released methane in a controlled leak scenario at a small leakage rate of 0.25 mL/min in a laboratory setup. A gas concentration image was calculated using the Beer-Lambert law (BLL) and the HITRAN database in a calibration-free manner. A baseline-free DAS analysis approach--called DAS-2f--is introduced and applied with respect to the wavelength and intensity modulation of the laser, as well as the absorption line shape of the gas.
## 3 Case Studies
### Case Study 1: Monitoring Scenario (Social Distancing)
#### 3.1.2 Case Study 1: Algorithms  
Based on the above, a computer vision algorithm--the Algorithm 1--with the following characteristics can be used to derive the spatiotemporal distribution of the people in each monitored area of the park (see Figure 4a) from the dataset described above (i.e., video from Wi-Fi cameras) and is used to trigger the overcrowding alarm. The software was developed using the programming language Python.  
Figure 6: Two examples of a crowd management plan in case of an overcrowding alarm active at (a) node 4, and (b) nodes 2 and 5.  
Figure 7: Example of the application result of the Algorithm 1 (computer vision).  
In pursuing the implementation of person detection and tracking, we use a two-step procedure for (a) the detection of the position of the people in the video frame, carried out periodically due to the large computational cost and implemented with the built-in OpenCV algorithm HOG (Histogram of Oriented Gradients) + Linear SVM model [69], and (b) the tracking of the people's movement around the video frames, using the built-in OpenCV algorithm Discriminative Correlation Filter with Channel and Spatial Reliability (DCF-CSR) [70].  
For every camera installed in every single node, the output of the software task is the value of people actually in the area, with a refresh rate of 1 sec. The maximum number of people admitted in every single area is a priori known, so an alarm is raised if the number of people in the area is above the threshold. If the overcrowding alarm is detected by Algorithm 1, the Algorithm 3 is triggered to define a crowd management plan, as shown in the previous section. In this case, the \"digraph\" function [46] is automatically activated to derive the shortest path that the people in the overcrowded area must follow to leave the area.
## 2 Background and Related Work
### Evaluation Criteria for Forensic Explainability  
Recent research highlights the critical need for robust evaluation frameworks to determine the appropriateness of explainable artificial intelligence (XAI) techniques in digital forensic contexts. Unlike conventional validation metrics such as accuracy or precision, forensic applications require assessment criteria aligned with legal, procedural, and evidentiary standards. Key dimensions include the following: (i) the stability of explanations, ensuring reproducibility across datasets and perturbations; (ii) fidelity to the original model, measuring how accurately surrogate methods like SHAP or LIME replicate the behavior of the underlying classifier; (iii) causality and feature relevance, focusing on the causal, not merely correlative, importance of attributes; (iv) comprehensibility for non-experts, ensuring that outputs are intelligible to legal professionals and forensic analysts; and (v) conformance to legal standards, such as transparency, auditability, and verifiability, as defined by ISO/IEC 27037 or the Daubert standard. Although there is currently no universally accepted benchmarking protocol, recent studies have proposed comparative methodologies and case-based validation strategies to address these challenges in high-stakes domains [8]. These criteria must be interpreted within the broader ethical and legal frameworks that govern the deployment of AI in forensic settings.
## 3 Construction of Mathematical Model
### Dynamical Constraint Equations for Non-Cooperative Targets in Cislunar Space
For \(k\) non-cooperative targets in cislunar space, the state vector containing information such as velocity and position can be expressed as:  
\[\textbf{S}=[x_{l},y_{l},z_{l},\overset{d}{x_{l}},\overset{d}{y_{l}},\overset{d }{z_{l}},\overset{d}{x_{l}},\overset{dd}{y_{l}},\overset{dd}{z_{l}}]^{T},l=1,2,\ldots,k \tag{5}\]  
where \(x_{l},y_{l},z_{l},\overset{d}{x_{l}},\overset{d}{y_{l}},\overset{d}{z_{l}}, \overset{dd}{x_{l}},\overset{dd}{y_{l}},\overset{dd}{z_{l}}\) are the \(x,y,z\)-axis position coordinates, velocity components, and acceleration components of the \(l\)th cislunar space non-cooperative targets, respectively. \(\overset{dd}{x_{l}},\overset{dd}{y_{l}},\overset{dd}{z_{l}}\) satisfies:  
\[\left\{ \begin{array}{l}\overset{dd}{x_{l}}=\frac{\partial p_{l}}{\partial x_ {l}}+2\overset{dd}{y_{l}}\\ \overset{dd}{y_{l}}=\frac{\partial p_{l}}{\partial y_{l}}-2\overset{dd}{x_{l} }\\ \overset{dd}{z_{l}}=\frac{\partial p_{l}}{\partial z_{l}}\\ \end{array} \right. \tag{6}\]  
where \(p_{l}\) is the potential function that satisfies:  
\[p_{l}=\frac{{x_{l}}^{2}+{y_{l}}^{2}}{2}+\frac{1-\lambda}{r_{es}}+\frac{\lambda }{r_{ms}} \tag{7}\]  
From (1) to (7), the dynamical constraint equations of non-cooperative spacecraft in cislunar space can be finally expressed as:  
\[\left\{ \begin{array}{l}\textbf{S}=[x_{l},y_{l},z_{l},\overset{d}{x_{l}}, \overset{d}{y_{l}},\overset{d}{z_{l}},\overset{dd}{x_{l}},\overset{dd}{y_{l} },\overset{dd}{z_{l}}]^{T},l=1,2,\ldots,k\\ \overset{dd}{x_{l}}=x_{l}-\{(1-\lambda)(x_{l}+\lambda)[(x_{l}+\lambda)^{2}+{y _{l}}^{2}+{z_{l}}^{2}]^{-\frac{3}{2}}\}\\ +\lambda(x_{l}+1-\lambda)[(x_{l}+1-\lambda)^{2}+{y_{l}}^{2}+{z_{l}}^{2}]^{- \frac{3}{2}}\}+2\overset{d}{y_{l}}\\ \overset{dd}{y_{l}}=y_{l}-y_{l}\{(1-\lambda)[(x_{l}+\lambda)^{2}+{y_{l}}^{2}+ {z_{l}}^{2}]^{-\frac{3}{2}}+\lambda[(x_{l}+1-\lambda)^{2}+{y_{l}}^{2}+{z_{l}} ^{2}]^{-\frac{3}{2}}\}-2\overset{d}{x_{l}}\\ \overset{dd}{z_{l}}=-{z_{l}}\{(1-\lambda)[(x_{l}+\lambda)^{2}+{y_{l}}^{2}+{z _{l}}^{2}]^{-\frac{3}{2}}+\lambda[(x_{l}+1-\lambda)^{2}+{y_{l}}^{2}+{z_{l}}^{2} ]^{-\frac{3}{2}}\}\\ \end{array} \right. \tag{8}\]
# Optimal Prediction of Individual Vessel Trajectories Based on Sparse Gaussian Processes
## 5 Experiments
### Comparison of Hyperparameter-Tuning Algorithms  
The SGPR model was developed using the proposed GA for hyperparameter optimization. The results were then compared with those obtained from the Bayesian optimization (BO) algorithm [44]. The GA's parameter settings are provided in Table 4.  
In Table 4, \(P_{size}\) is the population size, \(G_{max}\) is the maximum number of generations, \(T_{max}\) is the maximum number of stall generations, \(N_{vars}\) is the number of variables, \(F_{stall}\) is the criterion for determining the stall generation. As a termination condition, the algorithm continues until the number of generations reaches \(G_{max}\) or the objective function value over \(T_{max}\) generations is less than or equal to \(F_{stall}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(P_{size}\) & 200 \\ \(G_{max}\) & 100 \\ \(T_{max}\) & 20 \\ \(N_{vars}\) & 5 \\ \(F_{stall}\) & \(10^{-7}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of genetic algorithm for SGPR.  
Figure 3: Individual behavior sequences of test dataset and occurrence distribution of collision accidents.  
For the BO, the expected improvement (EI) [44] was adopted as the acquisition function, and the maximum number of objective function evaluations was set to 50. For the objective functions of the GA and BO, the RMSE was used equally for the validation dataset. Table 5 presents the hyperparameters optimized by GA and BO for the SGPR model. According to Table 5, the number of inducing points and the approximation method showed the same results for the BO and GA, and the SoD method showed excellent performance, except for the \(x_{OUT}\) dataset. The optimization results for the inducing points using the GA are illustrated in Figure 4. Figure 4a,b show the 64 inducing points for \(x_{
## 3 Results
### Aortic Wall Characterisation
#### 3.5.3 Non-Invasive Estimation of Wall Material Properties  
When the interest of the simulation requires modelling of the aortic wall, a suitable wall constitutive model needs to be chosen and calibrated. However, patient-specific material properties are not directly available and are very challenging to estimate, and since the numerical results are highly dependent on these parameters, an accurate estimate is vital to produce accurate results. Estimating wall properties in a non-invasive way is still a challenge and further developments are needed. Our research gathered eight works where methodologies to estimate in vivo or ex vivo mechanical properties were presented. [PERSON] et al. [91] implemented a code in MatLab that adjusted the parameters of a bidimensional Fung strain energy constitutive model to experimental stress-strain data, obtained from peel and direct tension tests performed on human ascending aorta and aortic sinus samples. [PERSON] et al. [92] presented a novel method to estimate aortic wall compliance. This method resorted to uncoupled FSI simulations to estimate PWV. The cardiac circle was divided into five time steps, and for each one, the following steps were executed and repeated until no significant differences were found: (i) the governing equations of the fluid domain are solved considering steady flow; (ii) the results of these simulations are updated on the solid domain; (iii) CFD simulations are newly performed with an updated computational domain according to the previous structural analysis. The results suggested close agreement in the estimated PWV, according to what was expected in this particular case (552 cm s\({}^{-1}\). Therefore, the authors affirmed that this methodology could be a promising metric to assess if the used material parameters are adequate and may also be used to inversely estimate wall compliance. The group of [PERSON] et al. [93] also developed a method to non-invasively estimate wall properties. In their first work [93], the multi-resolution direct search approach was presented, which aimed to efficiently solve optimisation problems. Multi-resolution direct search was used to estimate a set of constitutive parameters (Holzapfel-Gasser-Ogden model) that best represents the patient-specific structural behaviour by minimising the node to node differences between estimated (via CSM simulations) and synthetic \"real\" geometries. In the second work [94], the multi-resolution direct search approach was combined with CT data and the preliminary results were relatively close to experimental stress-strain curves. [PERSON] et al. [95] estimated local aortic stiffness from CT scans and found higher stiffness in ATAA when compared to healthy subjects. The inverse methodology applied in this work also allowed the authors to identify regional gradients of material properties that greatly impacted the numerical results.
## 3 Results
### Forecast of GD Levels  
For the DE levels across 30 provinces in China from 2022 to 2023, univariate time-series forecasting models were deployed, yielding the predicted values. The graphical representation (Figure 8) illustrates the trend in DE levels from 2020 to 2023. The results indicated a dip in the level for 2021 compared to 2020, followed by a consistent recovery in 2022 and 2023.  
Following the steps of the RBF kernel \(\varepsilon\)-SVR prediction model, the sample data underwent normalization. From this normalized dataset, a training set of six years' data was randomly selected, with the remaining data comprising the test set. The initial value of the parameter \(\varepsilon\) was set to 0.01. Grid search and 5-fold cross-validation were employed to  
Figure 7: Impulse response function in the western region. (**a**) Impulse response function of DE to DE. (**b**) Impulse response function of DE to GD. (**c**) Impulse response function of GD to DE. (**d**) Impulse response function of GD to GD.  
optimize the penalty parameter \(C\) and the kernel function parameter \(\gamma\). Both \(C\) and \(\gamma\) were varied within the range of \([2^{-10},2^{10}]\), with a grid width of 0.2. The system searched for a set of hyperparameters that minimized the training error within the training set.  
The predictive performance was gauged by the correlation coefficients between the fitted values and actual values (Figure 9). For the training set, over 56.7% of R\({}^{2}\) values surpassed 0.9, with the remainder exceeding 0.7. In the test set, 73.3% of the R\({}^{2}\) values surpassed 0.8, except for one coefficient at 0.679, with others ranging from 0.7 to 0.8. The training and test sets exhibited a good level of fit.  
The GD levels for the 30 provinces were individually forecasted, yielding the predicted values for each province in 2022 and 2023. The changes in the GD levels from 2021 to 2023 are presented in Figure 10. The results revealed that 22 provinces showed a declining trend  
Figure 8: Change in the 2020–2023 provincial DE level.  
Figure 9: Results of the linear regression correlation coefficients between the training set and test set in each province.  
in 2022, followed by a rebound in 2023, accounting for 73.3% of the total. Eight provinces experienced a continuous upward trend in the GD levels from 2022 to 2023. Notably, Henan Province displayed a decreasing trend in the GD levels from 2021 to 2023.
## 4 System Simulation and Performance Analysis
### Performance Degradation Analysis
#### 4.2.3 Effect of Valve Leakage  
In addition to the system components' characteristic parameter change, the compressor valve's working state also has a certain influence on the capacity regulating effect.  
Figure 13 displays the pressure in the cylinder with different suction valve leakage diameters when the compressor was fully loaded. As can be seen from the figure, the expansion process decreased and the suction valve plate was opened in advance caused by the leakage of the suction valve. A larger leakage resulted in the expansion process to occur earlier and consequently introduced a larger loss of discharge efficiency. The compression process increased, and the discharge process decreased due to the leakage of high-pressure gas from the cylinder, which is equivalent to the reduction of the actual capacity of the compressor. When the diameter of the leakage orifice of the suction valve is increased from 3 to 7 mm, the angle at the end of the expansion process is advanced by 3\({}^{\circ}\), and the angle at the beginning of the exhaust process is delayed by 5\({}^{\circ}\).  
Under 40% load regulation condition, the comparison of the pressure curves in the leakage holes of the suction valves with different diameters is shown in Figure 14. The angle at the end of the expansion process is advanced from 35\({}^{\circ}\) to 25\({}^{\circ}\), and the angle at the beginning of the exhaust process is delayed from 315\({}^{\circ}\) to 327\({}^{\circ}\) when the diameter of the leakage hole of the suction valve changed from 0 to 10 mm. In order to further analyze the influence of the leakage on capacity regulation, the changing trend of the ratio between the area of the indicator diagram with different leakage diameters and the area of the indicator diagram without leakage at full load is shown in Figure 15. It can be seen that the capacity control effect was larger than the design value caused by the suction valve leakage.  
It can be concluded from the above analysis that the reset spring of the unloader, the performance parameters of solenoid valve, and valve leakage will all cause system regulation performance degradation and decrease the regulation accuracy. It also indicates that the same control signal will generate different regulation results if the operation parameters are different. To address this problem, it is necessary to evaluate and optimize the system performance in real time to ensure the accuracy and performance of the system.  
Figure 13: The cylinder pressure with different diameter leakage hole under full load condition.
# Robust IoT Activity Recognition via Stochastic and Deep Learning
## 6 Experimental Results and Analysis
### Preprocessing and Filter Parameter Optimization  
The collected sensor data underwent rigorous preprocessing through filtering and normalization to improve data quality. A critical advancement in our methodology is the systematic optimization of filter parameters through sensitivity analysis. We evaluated multiple combinations of Kalman filter parameters (process noise \(Q\in[10^{-6},10^{-3}]\) and measurement noise \(R\in[10^{-3},1]\)), revealing that optimal noise reduction occurred at \(Q=10^{-5}\) and \(R=10^{-2}\). This configuration significantly reduced random noise while preserving essential signal characteristics.  
Figure 7 illustrates Fourier transform plots of the raw, low-pass filtered, high-pass filtered, and Kalman filtered accelerometer data along the x, y, and z axes during running. The optimized Kalman filter effectively attenuated high-frequency noise components while maintaining the fundamental frequency characteristics of the activity signal, demonstrating superior performance compared to traditional low-pass and high-pass filtering approaches.  
Further sensitivity analysis on window segmentation length indicated that 30-sample windows provide the optimal balance between temporal resolution and feature stability. Shorter windows failed to capture complete activity cycles, while longer windows introduced class boundary confusion when activities transitioned. This systematic approach to parameter optimization represents a significant methodological contribution of our work.
# Uniform Pressing Mechanism in Large-Area Roll-to-Roll Nanoimprint Lithography Process
## 3 Experiment for Uniform Pressure Distribution  
### Improving Uniformity Using the Center Roller  
It was confirmed by conducting an experiment whether the individual backup roller applied to the large-area R2R uniform pressing system can affect the pressure distribution  
Figure 5: Large-area R2R uniform pressing system capable of independent pressing: (**a**) Design of large-area R2R uniform pressing system; (**b**) Appearance of large-area R2R uniform pressing system.  
Figure 6: Configuration of the control system for large-area R2R uniform pressing system.  
at the contact area during the actual pressing process. The experimental conditions were that the sum of the loads applied during pressing was 400 kgf, which is the sum of the load applied by the pneumatic cylinder installed at both ends of the imprinting roller and the load applied by the servo motor of the backup roller. The experiment was conducted using only one backup roller in the center of the roller span under the condition that the total sum of the loads did not change.  
Figure 7 shows the results of the uniform pressure test using the backup roller. Figure 7a shows the result of applying loads of 170 kgf to both ends of the imprinting roller and 60 kgf to the center backup roller. In Figure 7b, 140 kgf was applied to both ends of the imprinting roller, and 120 kgf was applied to the center backup roller; in Figure 7c, 100 kgf was applied to both ends of the imprinting roller, and 200 kgf was applied to the center backup roller. It can be seen that the center backup roller can improve the pressure distribution uniformity on the contact area when it is actually pressed.  
It can be seen from Table 1 that, for various reasons such as skin deflection, waviness error and misalignment of the roller, it is difficult to press and obtain uniform pressure on the contact area with only the one center backup roller. As shown in Figure 5, even when a backup roller is installed in the center of the roller and pressure is applied, asymmetric non-uniform pressure distribution occurs in the contact area. This is caused by other factors such as the deformation of the roller skin, waviness error, misalignment of the rollers, etc. In consideration of this, the proposed system has five backup rollers and two actuators at both ends for an individual pressing mechanism in which more backup rollers are driven systematically. Because of the more number of rollers, numerous roller pressing force combinations exist for uniform pressure distribution. Since it would take a lot of time to test this large number of cases only by experiment, we intend to use machine  
Figure 7: Pressure distribution along roller using center backup roller pressing: (**a**) Left force 170 kgf, right force 170 kgf and center force 60 kgf; (**b**) Left force 130 kgf, right force 130 kgf and center force 140 kgf; (**c**) Left force 100 kgf, right force 100 kgf and center force 200 kgf.  
learning techniques to derive the optimal uniform pressing conditions based on the results of pressing experiments.
# Design and Performance Evaluation of eLoran Monitoring System
## 6 Conclusions  
In this paper, the components of the eLoran system are presented, focusing on the working principle and realization path of the monitoring system. And the performance of the monitoring system is verified on the BPL system.  
Kalman filter prediction is introduced into the eLoran monitoring system and the difference between the designed prediction value and the actual value is used to determine the state of the transmitter and broadcasting station. The eLoran monitoring system can monitor the status of the eLoran system to ensure the quality of its time service.  
Due to the weak correlation between the next moment value of the time difference data and the previous moment data, the prediction can be utilized with less historical data, resulting in a large error between the predicted value and the actual value. The next step is for the authors to try to accumulate more time difference data and to analyze the possibility of applying machine learning to the prediction of the time difference data in order to provide a better monitoring service.  
Conceptualization, C.Y. and S.L.; investigation, C.Y.; resources, S.L.; data curation, X.G. and Z.H.; writing--review and editing, C.Y.; funding acquisition, C.Y. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Nanjing comparison chart of predicted value and actual value.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Serial Number** & **Location** & **STD of Actual TOA** & **STD of Predicted TOA** \\ \hline
1 & Lanzhou & 16.2 ns & 14.5 ns \\
2 & Chengdu & 65.4 ns & 54.7 ns \\
3 & Wuhan & 66.6 ns & 61.8 ns \\
4 & Nanjing & 70.1 ns & 58.2 ns \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison chart of predicted TOA and actual TOA.  
**Funding:** This research was funded by the Youth Innovation Promotion Association, Chinese Academy of Sciences, grant number: 2021409.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the eLoran monitoring system still being under construction.  
**Conflicts of Interest:** The authors declare no conflicts of interest.