esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Mechanical Properties and Damage Constitutive Model of Water-Bearing Coal
## 4 Damage Constitutive Model
### Evolution Law of Mesoscopic Damage
#### 4.1.3 Evolution of Total Mesoscopic Damage  
The first damaged state of coal is defined as the state in which only the water-rock interaction takes place, while the second damaged state is described as the state in which coal is only loaded. The one-dimensional constitutive relations of coal under two damage states are as follows:  
\[\sigma_{\omega}=E_{0}(1-D_{1})\varepsilon_{\omega} \tag{9}\]  
\[\sigma=E_{\omega}(1-D_{2})\varepsilon \tag{10}\]  
In Equations (9) and (10), \(\sigma_{\omega}\) and \(\varepsilon_{\omega}\) are stress and strain caused by the water-rock interaction respectively; \(\sigma\) and \(\varepsilon\) are stress and strain caused by the load, respectively.  
According to [PERSON]'s equivalent strain hypothesis [40] and [PERSON] et al.'s generalized strain equivalence principle [41], the one-dimensional constitutive model of water-bearing coal under loading can be obtained by solving Equations (4), (9) and (10):  
\[\sigma=E_{0}(1-D)\varepsilon \tag{11}\]  
In Equation (11): \(D\) is the total damage of a coal sample caused by the water-rock interaction combined with load, and its expression is:  
\[D=D_{1}+D_{2}-D_{1}D_{2} \tag{12}\]  
It can be seen from Equation (12) that the dual impact of the water-rock interaction and load increase the overall mesoscopic damage of coal, but the total mesoscopic damage is not a simple superposition of two basic damages, and the coefficient of the coupling term \(D_{1}D_{2}\) is negative, indicating that the coupling effect reduces the total mesoscopic damage. We conclude that this may be because the mesoscopic damage caused by the water-rock interaction increases the mesoscopic defects in the coal mass, and, to a certain extent, increases the threshold of dislocation movement under load in the coal body. After loading, the micro-pores and micro-cracks in coal are sealed, and the mesoscopic damage caused by the water-rock interaction is reduced.  
From Equations (4), (8) and (12), the total mesoscopic damage evolution equation of water-bearing coal under loading can be obtained:  
\[D=1-\frac{E_{w}}{E_{0}}\left\{1-\exp\left[-\left(\frac{S}{n}\right)^{m}\right]\right\} \tag{13}\]  
According to Equation (13), when only the damage resulting from the water-rock interaction is considered, there is \(S=0\), and \(D=D_{1}\); when only the damage caused by loading is considered, there is \(E_{w}=E_{0}\), and \(D=D_{2}\).
## 4 Methods and Results
### \(\Delta\)logR Method  
#### 4.1.1 Traditional \(\Delta\)logR Method  
The TOC calculation formulas of source rock based on the traditional \(\Delta\)logR method are the empirical formulas proposed by [PERSON] et al. (Equations (1)-(3)). After combination, the complete TOC calculation formula of traditional \(\Delta\)logR method is:  
\[\text{TOC}=\text{lg}(\text{R}/R_{base})+K\cdot(\Delta\text{t}-\Delta t_{base} )\cdot 10^{2.297-0.1688\text{LCOM}} \tag{4}\]  
where \(R_{base}\) and \(\Delta t_{base}\) are baseline values of resistivity and interval transit time. K is a proportionality coefficient and LOM is maturity of organic matters.  
Baseline values are extremely subjective since they have to be determined manually. In addition, it is very difficult to calculate maturity accurately. Therefore, the traditional \(\Delta\)logR method has several disadvantages. Moreover, some parameters in the traditional \(\Delta\)logR method were gained by [PERSON] et al. according to practical experiences [5]. The traditional \(\Delta\)logR method is only applicable to formations with interval transit time of 260-460 \(\upmu\)s/m [24]. However, the interval transit time of practical formations in this study area was 130-220 \(\upmu\)s/m. Therefore, the formula of traditional \(\Delta\)logR method is not applicable to the study area.
## 5 Experiments
### Sensitivity Analysis
#### 5.4.2 Results and Discussion  
The results of the experiment are shown in Table 12, which indicate that the impact of these hyperparameters on performance varies considerably. The number of candidate actions had minimal influence; ODR and makespan remained relatively stable across different values, suggesting strong robustness under the default setting of \(N_{\text{candidates}}\) = 3, 5, 7. In contrast, both \(T_{\text{KL}}^{\text{target}}\) and \(\epsilon_{\text{clip}}\) significantly affected performance. Increasing \(T_{\text{KL}}^{\text{target}}\) from 0.1 to 0.3 resulted in a substantial rise in ODR--particularly in MK03, where ODR rose from 0% to 32.1%--indicating that an excessively high KL target can destabilize policy updates. Similarly, smaller values of \(\epsilon_{\text{clip}}\) (e.g., 0.1) slowed convergence and introduced greater variability, whereas larger values (e.g., 0.3) yielded better stability and convergence speed.  
Based on these findings, we tested a combined setting using the most favorable individual values: \(N_{\text{candidates}}=5\), \(T_{\text{KL}}^{\text{target}}=0.1\), and \(\epsilon_{\text{clip}}\) = 0.3 with a clipping range of [0.2, 0.4]. The results are shown in Table 13. Compared to the original setting (\(T_{\text{KL}}^{\text{target}}\) = 0.3, \(\epsilon_{\text{clip}}\) = 0.2), performance improvements were marginal. For instance, in MK03, ODR slightly increased from 0.0% to 0.1%, while MK04 remained unchanged. This suggests that, although individ  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Value** & **Mean** & **MK03** & & & **MK04** & \\  & **Value** & **Mean** & **Std** & **ODR** & **Mean** & **Std** & **ODR** \\ \hline \multirow{3}{*}{Num candidates} & 3 & 347.6 & 42.4 & 2.6\% & 193.0 & 39.7 & 1.6\% \\  & 5 & 340.2 & 40.1 & 0.4\% & 190.6 & 33.1 & 0.4\% \\  & 7 & 338.9 & 43.9 & 0.0\% & 189.9 & 34.4 & 0.0\% \\ \hline \multirow{3}{*}{Target kl} & 0.1 & 303.2 & 32.7 & 0.0\% & 156.5 & 25.0 & 0.0\% \\  & 0.2 & 333.1 & 48.8 & 9.9\% & 200.5 & 42.3 & 28.1\% \\  & 0.3 & 400.4 & 52.9 & 32.1\% & 225.4 & 38.9 & 44.0\% \\ \hline \multirow{3}{*}{Clip epsilon} & 0.1 & 442.6 & 78.9 & 49.9\% & 270.5 & 46.9 & 80.1\% \\  & 0.2 & 298.8 & 26.5 & 1.2\% & 161.7 & 43.1 & 7.7\% \\ \cline{1-1}  & 0.3 & 295.2 & 29.1 & 0.0\% & 150.2 & 16.2 & 0.0\% \\ \hline \end{tabular}
\end{table}
Table 12: Parameter sensitivity analysis.  
ual parameters may influence performance, the algorithm remains stable under reasonable parameter configurations, demonstrating strong robustness.
# Low-Loss Active Grid Impedance Cancellation in Grid-Connected Inverters with LCL Filter
## 3 System Control and Modelling with the Proposed Compensator
### Modeling Analysis of the Active Compensator  
#### 3.2.1 Calculation of the feedforwarding voltage, \(\mathrm{u_{o,A}}\)  
Considering the effect of digital delay, \(u_{o,A}\) in Figure 5b is calculated as  
\[\Delta U_{o,A}(s)=e^{-1.5 sT_{S}}\ \Delta U_{\mathrm{pcc}}(s)\cong e^{-1.5 sT_{S}} \ \Delta U_{\mathrm{pcc,h}}(s) \tag{5}\]  
where the line-frequency component in \(u_{o,A}\) is neglected for simplifying the following combination of \(u_{o,A}\) and \(u_{o,B}\). Such simplification does not affect the stability analysis because line-frequency component stays away from the control bandwidth of the GC-VSI.
Although existing studies of LUCC on ESs have gradually increased, most of them are conducted on one-time cross-sections or interval time cross-sections, that is, from static perspectives. They ignored the impacts of extended periods of LUCC on ES, which may result in wrong guiding policies. Moreover, it is difficult to explain the driving mechanisms of ES when the historical changes in driving forces were not considered. In addition, how landscape patterns affect ecosystems was rarely explored. Different types of LUCC significantly impacted the quality and quantity of ecosystem services driven by human activities. Through rational land resource use and management [29], the structure and function of the ecosystem can be maintained in a healthy and balanced state. Additionally, such management can provide stable, balanced, and abundant natural resources for the sustainable development of human society, economy, and agriculture, thus maintaining the long-term coordinated development of natural-social-economic relations [30]. The Dongting Lake connects the Yangtze River, serves as an important water storage site and provides the ecosystem services of the Yangtze River basin [31]. Recently, the over-exploitation of ecosystems and natural resources has led to serious ecological and environmental implications in the lake region, such as the degradation of wetlands, forests, soil, and water conservation functions. Consequently, exploring the impact of LUCC on ES would contribute to a comprehensive understanding of the evolution of ecosystem functions in the Dongting Lake region. In this study, we aim to explore the spatiotemporal evolution characteristics of LUCC, landscape patterns, and ES in the lake region from 1988-2019, and to analyze the synergies and trade-offs among and the impacts of LUCC and landscape patterns on ES.
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 7 Applications and Future Directions
### Future Research Directions
Enhanced audio quality assessment: Future work should incorporate objective audio quality metrics such as PESQ (Perceptual Evaluation of Speech Quality) and STOI (Short-Time Objective Intelligibility) to provide more robust and statistically significant quality assessments. Additionally, expanding subjective evaluations to include larger sample sizes would strengthen the statistical validity of perceptual quality assessments.
###### Abstract  
Lightweight speech denoising models have made remarkable progress in improving both speech quality and computational efficiency. However, most models rely on long temporal windows as input, limiting their applicability in low-latency, real-time scenarios on edge devices. To address this challenge, we propose a lightweight hybrid module, Temporal Statistics Enhancement, Squeeze-and-Excitation-based Dual Convolutional Attention, and Band-wise Attention (TSE, SDCA, BA) Module. The TSE module enhances single-frame spectral features by concatenating statistical descriptors--mean, standard deviation, maximum, and minimum--thereby capturing richer local information without relying on temporal context. The SDCA and BA module integrates a simplified residual structure and channel attention, while the BA component further strengthens the representation of critical frequency bands through band-wise partitioning and differentiated weighting. The proposed model requires only 0.22 million multiply-accumulate operations (MMACs) and contains a total of 112.3 K parameters, making it well suited for low-latency, real-time speech enhancement applications. Experimental results demonstrate that among lightweight models with fewer than 200K parameters, the proposed approach outperforms most existing methods in both denoising performance and computational efficiency, significantly reducing processing overhead. Furthermore, real-device deployment on an improved hearing aid confirms an inference latency as low as 2 milliseconds, validating its practical potential for real-time edge applications.  
2020 IEEEexample:BSTcontrol
## 2 Data and Methods
### Statistical Methods  
In this study, we used two measures to illustrate the carbon-water relationship on a short-term time scale. To be specific, we focused on the relationship between daytime GPP and ET on a daily and hourly scale. We used WUE to discover the carbon-water relationship on a daily scale, while using the correlation coefficient to represent the coupling of carbon and water on a sub-daily scale.  
WUE indicates the fixed carbon amount by vegetation with the consumption of per unit amount of water, which reflects the balance between carbon absorption and water supply for vegetation. The daytime WUE (hereafter referred to as WUE\({}_{\mathrm{dt}}\)) is calculated by the ratio of daytime GPP to ET at a daily time step. To characterize the GPP-ET relationship at the sub-daily step, we used the correlation coefficient (\(r\)) between hourly GPP and ET: \(r\)(GPP, ET). The correlation strength can represent how tightly the variations in hourly GPP and ET are correlated. We only considered cases for positive \(r\), which was 98% of the available cases at Dinghushan and 93% of the available cases at Qianyanzhou.  
The response of the carbon-water relationship to meteorological factors was analyzed using a ridge regression model, which can reduce the influence of collinearity among meteorological factors [25; 26; 27]. All meteorological variables were standardized before conducting the regression; thus, the regression coefficient between WUE and each of the meteorological variables can describe the relative importance of that variable to the variation in WUE, and so it was for \(r\)(GPP, ET).
## 3 Preliminaries and Motivations
### Problems Definition
#### 3.2.3 Hybrid-Overhead Problem in Hybrid-MOPs Mode  
Because the two MOPs cannot coexist or interoperate with each other, there are three options allowed: only non-storing, only storing, and no downward routing at all. These options are decided by the LBR setting. All other RPL nodes just accept and obey LBR's rule. Here, the two scenarios are met: (a) if the downward routing mode is set in a RPL instance, it cannot be changed in the future, and the whole network uses the same mode; therefore, two RPL instances cannot communicate if they adopt different MOPs; (b) furthermore, if a new node uses a different MOP, it cannot join the existing RPL instance.  
If a hybrid-MOP is not permitted, the previous two cases cannot be overcome. There are related works to meet those cases by: (1) interoperation with two MOPs; however, each node's self MOP cannot change; (2) the other methods to switch between two MOPs based on some condition; however, there are time and resource overheads for switching MOPs, which leads to a decrease in network performance. In this article, we define it as the hybrid-overhead problem.
# Multi-Objective Ship Route Optimisation Using Estimation of Distribution Algorithm
## 4 Proposed Solution
### The Search Algorithm  
The proposed multi-objective EDA-based algorithm with DP local search is presented in Algorithm 1. The input data include \(G_{3}^{(\text{AB})}\) (initial search space with the prior distributions of the path-generating Markov chain pgmc), \(\mathbf{v}_{t}\) (tidal stream velocity vector field), sim (ship simulator), \(N_{eda}\) (predefined number of steps of the EDA), \(N_{p}\) (predefined minimum number of new Pareto set candidates at each EDA step), and \(P_{G_{max}}\) (predefined maximum number of draws--path generations--at each EDA step). The output of the algorithm is an approximation of the set of Pareto optimal control input sequences (\(\overline{\mathcal{PS}}\)). The algorithm starts from initialising the set of Pareto optimal control input sequences (\(\overline{\mathcal{PS}}=\emptyset\)), the set of paths obtained from the path generator (\(\mathcal{S}_{ocp}\)), and the path generator pgmc (lines 1-3). The rest of the algorithm (lines 4-16) is iterated \(N_{eda}\) times. Firstly, a new path is generated and added to the set of paths obtained from the path generator (lines 8-9). Next, dp-based pruned local search is performed \"around\" the generated path, and the resulting best paths found are added to the set of new Pareto set candidates \(\mathcal{PS}_{\text{\tiny{comb}}}\) (line 10), and the draws counter \(p_{g}\) is incremented (line 11). The entire process is repeated until the minimum number of new candidates for the Pareto set has not been found and the maximum number of draws (generations of new paths) has not been reached (line 7). Subsequently, the approximation of Pareto set \(\overline{\mathcal{PS}}\) is updated using the set of new Pareto set candidates \(\mathcal{PS}_{\text{\tiny{comb}}}\). Finally, the distributions of the path generator (pgmc) are updated (lines 13-16). If the number drawn with a uniform distribution on the interval (0,1) is greater than \(T_{d}\) (see Figure 3), where:  
\[T_{d}(s,s_{\text{\tiny{MAX}}})=\exp\biggl{(}-5\,\frac{s-1}{s_{\text{\tiny{ MAX}}}-1}\biggr{)},\ s\in\{1,2,\ldots,s_{\text{\tiny{MAX}}}\} \tag{6}\]then the distributions of the path generator pgmc are updated for all elements in the Pareto set \(\widetilde{\mathcal{PS}}\) (line 14). Otherwise, the distributions of the path generator are updated only for new elements of the Pareto set \(\mathcal{PS}_{\text{\tiny{max}}}\) (line 16). This is to prevent premature convergence. ```
Input: \(\bullet\)\(G_{3}^{\text{\tiny{(AB)}}}\): initial search space with start point \(A\), target point \(B\), and the prior distributionsof the path-generating Markov chain pgmc (see Sections 4.2 and 4.1) \(\bullet\)\(\mathbf{v}_{t}\): tidal stream velocity vector field (see Equation (A3)), \(\bullet\) sim: ship simulator (see Appendix A), \(\bullet\)\(N_{\text{\tiny{eda}}}\): (predefined) number of steps of the EDA, \(\bullet\)\(N_{p}\): (predefined) min. number of new Pareto set candidates at each EDA step, \(\bullet\)\(P_{G_{max}}\): (predefined) max. number of draws (path generations) at each EDA step.
## 5 Simulation Results  
### The Pareto Solution Set Based on NSGA-III  
The simulation results of two IEEE standard bus systems show the efficiency of the OPP method. Appendix A shows the topology of the IEEE 33-bus system. The bus number is 1-33, and s1-s37 is the switch number. Two topologies are considered temporarily; switches 7, 9 and 14 are opened; and switches 33, 34 and 35 are closed to form the second  
Figure 3: The proposed OPP method flow chart.  
topology. The existing ZIBs are injection measurements of buses 5, 6 and 21. Power flow measurements are branches 2-19 and 28-29. Bus 2 has been pre-placed with a D-PMU.  
The NSGA-III algorithm is applied to solve the proposed model. In detail, the number of iterations is assigned to 1000. The number of individuals in the group is set to 100. The initial probabilities of the operation mutation and crossover operation are assigned to 0.4 and 0.8, respectively. In this paper, the NSGA-III algorithm is programmed with MATLAB software. The presented model is tested on a computer with an intel core i3 CPU and 2.0 GB of RAM.  
The Pareto set obtained by the proposed method is shown in Figure 4. The optimal solution can be selected according to the needs. For example, for any given NMR and ANOBC, the solution with the smallest number of D-PMUs can be found from the solution set in Figure 4. In order to facilitate comparison, the \(N_{\text{D-PMU}}\) is taken as the primary goal, and the remaining goals are selected by the TOPSIS method. The compromised optimal solution is the point marked by the red star in Figure 4.  
According to Figure 4, considering SCADA, ZIB and two topologies, the compromised optimal solution of OPP requires 11 D-PMUs. Through the D-PMU placement order determination method in Section 4, the order of placing D-PMUs is given, which can maximize the state estimation accuracy of each installation stage. The D-PMU placement order according to the sequential calculation method is: 3, 26, 14, 9, 17, 20, 29, 32, 11, 2, 24.  
Figure 5 shows the application result of the proposed method for the IEEE 33-bus system. The D-PMU of bus 2 can observe bus 1, 2, 3 and 19; the D-PMU of bus 3 can observe bus 4 and 23; and the D-PMU of bus 26 can observe bus 6, 26 and 27; bus 9, 11, 14 and 17 can observe bus 8 to 18; the D-PMU of bus 20 can observe bus 20 and 21; the D-PMU of bus 24 can observe bus 23 and 25; and the D-PMU of bus 29 and 32 can observe bus 28 to 33. Because bus 4 and 6 are observed, and because bus 5 has injection power measurement equipment, bus 5 is observed. Similarly, 7 and 22 are observable based on the D-PMUs of bus 5, 6 and 26 and of bus 20 and 21, respectively. So far, the whole topology is considerable. Similar to the analysis of the second topology after switching, the observability of the whole topology can also be proved. Therefore, the whole network's observability under multiple topologies is realized.  
Figure 4: The Pareto set and the compromised optimal solution.
## 4 Discussion  
The detection method developed in this work has been shown to work well with these simulated data sets and outperformed the event-domain and the frequency-based methods used for performance comparisons. As expected, the undetectable targets had lower frequencies or were closer to the center of the overwhelming source, both due to undersampling caused by the overwhelming source reducing target event generation. Additionally, with stronger overwhelming source powers, the optimal number of PCs to subtract increased and the detection ratio (generally) decreased.  
Verification tests showed that using both ON and OFF events in the FFT calculation added noise and reduced detection confidence through higher false-alarm ratios. Further, a poor temporal bin size choice was shown to lead to higher false-alarms or low detection.  
Because the events of combined signals cannot be attributed to a specific source, calculating simple detection limit metrics (e.g., a signal-to-overwhelming source event rate ratio) in the event space is not trivial. Detection limits could potentially be empirically estimated as a metric of apparent power on the sensor by taking into account several entangled factors: background radiance (a brighter background will generate a larger initial photocurrent), target and overwhelming source radiance ranges (a high minimum radiance or a smaller range will generate less events) and frequencies (lower frequencies may be undersampled), length of the recording (longer recordings allow for more opportunity to accurately sample the targets), and distance of the target from the overwhelming source (effects produced by the overwhelming source weaken with distance from its center). Future work will include an investigation of detection limits.  
Other methods adapted from techniques used in hyperspectral remote sensing will be examined as comparisons to determine this method's success. Future testing scenarios includes sample of constrained randomized scenes to generate ROC curves for performance metrics as well as simulated moving targets. Additionally, laboratory scene recreation and data collection using a physical EVS is planned as validation for the method.  
Improvements to the event binning such as overlapping bins, kernel smoothing, and adaptive binning will be explored to improve frequency resolution by smoothing event distributions and reduce binning artifacts [45, 46, 47]. Other FFT methods--such as non-uniform FFTs and short-time FFTs--as well as independent component analysis (in place of PCA), will be examined as potential improvements [48, 49, 50]. These approaches aim to address the current limitations in low-frequency detection and background separation near the center of the overwhelming source by accepting the non-uniformity of the EVS output, adjusting for apparent time-varying frequencies caused by the overwhelming source, and more robustly accounting for the combination of signals.