esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
The longer cycling performance is shown in supporting information, remaining around 800 mAhg\({}^{-1}\) after 500 cycles. (Figure S4). The rate performance of Li\({}_{x}\)Si/Li\({}_{2}\)O-4 anode was shown in Figure 5b. It yields a reversible capacity of over 1500 mAhg\({}^{-1}\) at 0.05 C, around 1200 mAhg\({}^{-1}\) at 0.1 C and 800 mAhg\({}^{-1}\) at 0.2 C. At a high rate of as high as 1 C, the capacity is as low as 100 mAhg\({}^{-1}\). However, when the current rate reversed to 0.05 C, a high specific capacity have been recovered. As schemed in Figure 6, Li\({}_{x}\)Si nano-domains are homogeneously dispersed in robust Li\({}_{2}\)O matrix after the thermal prelithiation. For nano-sized SiO\({}_{2}\) particles, the Li\({}_{x}\)Si domain is small and each domain is covered by a Li\({}_{2}\)O layer. This Li\({}_{2}\)O layer, formed by trace air oxidization of surface Li\({}_{x}\)Si, is electrical insulating, which hinders the electrical conductivity between the Li\({}_{x}\)Si/Si nano-domains. After many electrochemical cycles, the electrical contact becomes even worse due to the SEI formation on these small domains. While for micron-sized particles, the Li\({}_{x}\)Si domains are relatively large. In addition, oxidized Li\({}_{2}\)O surface layer is mainly wrapped out of the whole microparticle, leaving Li\({}_{x}\)Si domains contacting with each other inside the particle. The Li\({}_{2}\)O outer layer can spatially limit the direct SEI formation on Li\({}_{x}\)Si/Si cores during the cycling. As a result, the Li\({}_{x}\)Si/Li\({}_{2}\)O from SiO\({}_{2}\) microparticles can achieve enhanced cycling stability in half cells. Therefore, SiO\({}_{2}\) microparticle should be a very promising anode material, considering its low cost and the high cycling stability. Figure 5: (**a**) Cycling performance of four Li\({}_{x}\)Si/Li\({}_{2}\)O samples at C/20 (1C = 1.96 A g\({}^{-1}\) Li\({}_{x}\)Si, the capacity is based on the total mass of Li\({}_{x}\)Si in the electrode). The green line is the Coulombic efficiency of Li\({}_{x}\)Si/Li\({}_{2}\)O-4 composite; (**b**) Rate stability of Li\({}_{x}\)Si/Li\({}_{2}\)O-4 composite.
# A Comparative Analysis of Anomaly Detection Methods in IoT Networks: An Experimental Study
## 2 Related Work in Anomaly Detection for IoT Networks
### Machine Learning Approaches for Anomaly Detection  
Anomaly detection in IoT networks is characterized by the following approaches depending on how the models are trained. Among them, the following approaches can be distinguished: supervised learning, unsupervised learning, and reinforcement learning, described in detail below.  
Supervised learning is an ML paradigm in which an algorithm is trained on a dataset of labeled examples to learn a mapping function. This function can then be used to predict outcomes for new, unseen data [10; 11].  
Unsupervised learning is an ML paradigm in which algorithms are trained on unlabeled data. This means that the data are not provided with class labels or target values. Unsupervised learning aims to find patterns in the data. The algorithms organize the data themselves by grouping similar observations and identifying anomalies. Unsupervised learning also involves self-organization, which means that the computer groups similar things together [10; 11].  
Reinforcement learning is an ML technique in which an agent, a decision maker, learns to interact with the environment in order to achieve a goal. The agent's actions are guided by a reward system, where positive outcomes reinforce beneficial behaviors and negative outcomes discourage harmful ones. Unlike supervised learning, which relies on labeled data, reinforcement learning agents learn through trial and error [11].
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 4 Conclusions  
### Theoretical Contributions  
This study makes some improvements in the methodological framework for the safety assessment of complex systems, and the theoretical contributions are as follows:  
* By integrating Bayesian network technology, fuzzy expert judgment, and Bayesian updating methods, the framework addresses data uncertainty challenges in safety assessments. Unlike existing approaches limited to unidirectional updates (e.g., backward diagnosis), this framework uniquely supports both forward dynamic prediction (updating UE probabilities with new RE data) and backward dynamic adaptation (adjusting RE probabilities based on observed incidents), enabling comprehensive risk-evolution analysis.
* A novel scenario-deduction model explicitly maps the causal chain from root events (e.g., X\({}_{4}\): procedural non-compliance) to critical events (CE: gas leakage) and outcome events (e.g., OE\({}_{6}\): long-duration jet fires). This model fills a gap in prior studies by systematically quantifying dynamic interactions among risk factors.
* The introduction of the RoV (ratio of variation) and hybrid importance measures (BM, RRW) establishes a quantifiable linkage between component-level failures and system-level risks, enhancing the precision of causal inference under uncertainty.
## 6 Conclusions  
This study proposed a 3D multi-scale grid search and waveform inversion-based location method (GSWI location) into mine MS location, and the main conclusions are made as follows: (1) The combination of reciprocity theorem and multi-scale grid generation and search strategy effectively reduces modeling computational cost and storage requirement of the 3D Green's function database.  
The establishment of the Green's function database is favorable to the multi-scale grid search, which is helpful to find the globally optimal source location that fits the observed waveforms best. (2) The SEM method can effectively realize precise waveform modeling in complex structures, and the frequency-dependent sensitivity kernels of travel time anomaly have more accurate spatial distribution and intensity characteristics than the ray tracing methods. (3) Both the proposed DD-SVD-FD wavelet and fractional-order Gaussian function can well estimate the source-time function to generate synthetic waveforms fitting the recording waveforms. The synthetic waveforms obtained by the DD-SVD-FD source wavelet taking advantage of recording waveforms have better fitting performance, and have better data adaptability and versatility. (4) The average location error of the 3D GSWI for eight blasting events is only 15.0 m, which is smaller than that of the 3D ray tracing location method (26.2 m) with the same 3D velocity model. This proves that the waveform inversion-based location method can better constrain source location in complex regions than the simplified ray tracing method only based on arrival time pickings. In the future, taking advantage of resolution improved 3D velocity model through full waveform inversion approach relying on numerical wave-equation-solver can further raise location accuracy.  
Methodology, Y.W.; software, Y.W.; resources, X.S.; writing--original draft preparation, X.S.; writing--review and editing, Z.W. and R.G. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 52004041 and Grant 41804045, the China Postdoctoral Science Foundation under Grant 2020T130757, the Basic Scientific Research Operating Expenses of Central Universities under Grant 2020 CDJQY-A047, and Chognqing Postdoctoral Science Foundation under Grant cstc2020 jcyj-bshX0106. The authors declare no conflict of interest.
# In-Home Evaluation of the Neo Care Artificial Intelligence Sound-Based Fall Detection System
## 4 Discussion
### Clinical Implications and Future Directions  
The study's findings hold implications for both developers of in-home falls monitoring systems and their potential users. Key to the system's success will be its ability to accurately detect falls while minimizing false positives. This highlights the ongoing need for refining the system's sound analysis algorithms to better distinguish between fall-related and everyday noises. Addressing design, responsiveness, and technical issues is also essential for increasing user satisfaction and trust. The Neo's design could be optimized for better handling of real-world complexities through personalized sound calibration tailored to individual home environments, multi-device integration for enhanced mobility coverage, and incorporation of visual/multi-modal alerts to improve accessibility. Empowering users with voice trigger capabilities and ensuring continuous operation during connectivity disruptions are also vital considerations. Ultimately, an adaptive, user-centric design approach that leverages machine learning and user feedback loops is crucial for refining the system's accuracy and robustness in complex residential settings. The incorporation of user-suggested features, such as personalized sound detection and visual cues for the hearing impaired, could significantly elevate the Neo's overall value and utility. This user-centric approach to system improvement aligns with the healthcare technology trend of prioritizing end-user needs, thereby enhancing outcomes and experiences.  
Future research should aim to improve the system's sensitivity and specificity by refining its sound analysis algorithms and adhering to user-centered design principles. Collaborative initiatives involving healthcare providers and end-users can inform the development of a falls monitoring solution that effectively balances accuracy, user-friendliness, and customization. As healthcare technology advances, systems like the Neo offer promising avenues for enhancing safety and independence for individuals at risk of falls, not just in home settings, but also potentially in institutional environments. Furthermore, the aim of this study was to assess the accuracy and responsiveness of the Neo in real-world settings. An in-depth analysis of the evaluation process would help identify potential biases and refine the process, making this an important topic for future research.
## 3 Experimental Results
### The Refrigerant Pump  
The use of the plunger pump in the micro-scale ORC test bench is a double-edged sword. In the steam Rankine cycle, the power consumption of the pump is negligible compared to the power produced by the turbine and can be neglected in the calculation of the net system power [11]. Despite its importance, the real pump performances in the ORC system were reported in relatively few studies in the literature, as shown in Figure 3. It can be concluded from Figure 3 that the average efficiency of 35% is reasonable for pumps in ORC systems unlike numerical studies or engine design, in which the pump efficiency is taken as 65-85% [12]. The low pump performance is more critical in micro-scale ORC  
Figure 2: Range of the parameters constituting the experimental database: expander suction pressure vs flow rate (**a**), expander suction temperature vs flow rate (**b**), hot fluid inlet temperature to the evaporator vs flow rate (**c**), expander pressure ratio vs flow rate (**d**), expander pressure ratio vs expander shaft speed (**e**), hot fluid flow rate vs refrigerant flow rate in the evaporator (**f**), superheating degree vs refrigerant flow rate in the evaporator (**h**), and subcooling degree vs. refrigerant flow rate in the condenser (**i**).  
systems as even negative net power was reported by [PERSON] et al. [13]. The negative net power was observed in the experiments in this work as well.  
The advantage of the selected pump in this work is its ability to provide a wide range of pressure and flow rate, facilitating the study of the expander machine in its off-design conditions. However, considering the maximum pressure of the system, these conditions lead to a significant drop in its isentropic efficiency and the net system efficiency accordingly.  
This effect is observed in the experiments in this work as shown in Figure 4, in which the calculated isentropic efficiency of the pump is considerably lower than the nominal value of 85% stated by the manufacturer.  
Instead, the pump volumetric efficiency shows high values as presented in Figure 5. Despite being penalized in higher flow rates (and therefore higher rotational speeds) due to an increment in the internal leakage, the good volumetric efficiency shows the high quality of the pump sealings during the tests.  
Figure 4: The refrigerant pump isentropic efficiency with pressure ratio (PR) (**left**), and with flow rate (**right**).  
Figure 3: Reported pump maximum efficiency and hydraulic power in the literature up to 2017 based on data from [11].
## 2 Materials and Methods
### Identification of Trace Elements  
For trace element (TE) identification, each sample was heated to a high temperature of 400 \({}^{\circ}\)C for 2 h in a furnace to fully carbonize organic materials. Metal quantification in each sample was carried out using the aqua regia extraction technique, involving partial digestion with an HNO\({}_{3}\)-HCl mixture on 1 g of each sample at 90 \({}^{\circ}\)C for 2 h. After digestion, each sample was diluted with 100 mL of distilled water and filtered after 3 h [17; 18]. The acids and standards used in this study were provided by Merck (Darmstadt, Germany). TE concentration analysis was performed using a flame atomic absorption spectrometer (ContrAA 300, Analytic Jena, Jena, Germany), referred to as FAAS. Before the analysis of the samples, the FAAS was calibrated using standards from the same company. The data mean was determined using three different measures; to ensure accurate and precise measurements, we also analyzed certified reference material (CRM) OREAS 465 from Mantra Resources Nyota prospect in Tanzania, with results aligning with the CRM values. The trace elements studied include Ba, Br, Co, Cr, Cu, Fe, Ga, Mn, Nb, Ni, Pb, Sn, Ti, V, Y, Zn, and Zr. This approach is almost identical to the one used by [PERSON] et al. [19] in their study on Cu and Zn contamination in urban soils, where they emphasize the importance of a rigorous methodology to assess the health risks associated with soil contamination. The quantification limits for the analyzed elements are as follows: 0.5 ppm for Br, Cu, Ga, Nb, Ni, Sn, Y, Zn, and Zr; 0.6 ppm for Pb; 1 ppm for Cr, Fe, Mn, and V; 1.5 ppm for Ba; 2 ppm for Ti; and 3 ppm for Co.
# Estimating the Optimal Overall Slope Angle of Open-Pit Mines with Probabilistic Analysis
## 2 Methods of Slope Stability Analysis
### Numerical Modelling Set Up
#### 2.2.2 Mesh Sensitivity Analysis  
Conducting mesh sensitivity is highly recommended in finite elements to avoid the negative effects of very long/thin elements on analysis results. The aspect ratio of such thin elements is very high. The mesh quality option is a built-in feature in RS2D that allows users to identify and fix finite element mesh problems. Non-convergence of solution, terminated calculation, alerting or misconfigurations during computation, and extraordinary analysis results are examples of such issues [54; 55]. Therefore, a sensitivity analysis was conducted to determine the optimal mesh size. Such size determines the number of nodes, elements and their suitable lengths in order to obtain consistent results. This was done by constructing models with different mesh densities. Both vertical and horizontal displacements were monitored while solving for elastic equilibrium, as shown in Figure 3.  
Figure 2: Model setup shows geometry, boundary conditions (BCs) and meshing before excavation (e.g., slope angle = 500).  
Several factors influence selecting mesh size or density. For instance, dense mesh provides a better representation of high-stress gradients; the accuracy of the results is proportional to element length, and if different mesh sizes are needed, then the more gradual change from dense to coarse should be employed. As shown in Figure 3, the model ran with 200 nodes when the external boundary reached a plateau.
# A Review on the Use of Microalgae for Sustainable Aquaculture
## 3 Microalgae-Assisted Aquaculture
### Technologies for Biomass Production  
To improve the biomass productivity and increase the economic performance, previous studies devoted a lot of effects into the technologies for biomass production in wastewater, particularly wastewater pretreatment and construction of algal-bacterial consortia, which have been proven to play an important role in aquaculture wastewater remediation. The existence of solid organics and the unbalanced nutrients profile are two main problems that should be addressed by wastewater pretreatment.
## 4 Conclusions  
This paper has investigated means of correcting secondary spectrum in meter-class correctors for large telescopes. The new type of corrector presented here is the only known type of meter-class corrector that is capable of simultaneously satisfying requirements of:  
* Near-perfect secondary spectrum correction;
* Requiring only glasses that are readily available at large diameters;
* Near-zero deviation of image and pupil.  
As with the old joke regarding optics fabrication, \"good, cheap and on-time, pick any two... \", the currently known types of corrector described in the introduction are each only capable of simultaneously achieving two of these three goals.  
The R-ADC types can achieve near perfect secondary spectrum correction with stable pupils and images, but require special glasses, or crystals, to achieve this performance. Such materials are not available for larger diameter correctors, such as the metre-class correctors that might be required for ELT-GLAO systems of the future. Residuals of the secondary spectrum using the available materials for a 1.5 m corrector of this type are shown in Figure 6.  
In principle, the L-ADC types can achieve good secondary spectrum correction and can be made from glass types available in large diameters, but have the chronic problem of deviating image and pupil.  
The CL-ADC systems are a relatively new concept, and improved designs for GLAO systems may well be possible, but the indications from the system considered in Section 2 are that residual secondary spectrum is not able to be controlled to the same degree as it is with the RoL-ADC system.  
When we consider that modern GLAO systems are pushing boundaries both upwards in field size and downwards in wavelength [14], and also the general demand for increasing the bandwidth of observations, it seems likely that it will prove useful for optical designers of C-ADC systems in these cases to consider the benefits offered by the RoL-ADC, i.e., performance and material availability at large diameters, against the \"costs\" of additional mechanical complexity.
# Low-Light Mine Image Enhancement Algorithm Based on Improved Retinex
## 4 Experiment and Analysis
Average gradient \[AG=\frac{\sum_{i=1}^{M}\sum_{j=1}^{N}\sqrt{\frac{(\frac{\partial f}{\partial x })^{2}+(\frac{\partial f}{\partial y})^{2}}{2}}}{MN},\] (22) where \(\frac{\partial f}{\partial x}\) and \(\frac{\partial f}{\partial y}\) are the gradients of the image in both the horizontal and vertical directions. AG assesses the level of fluctuation in pixel values across the image, and a higher AG indicates the presence of more edges and details. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Image 2** & **Mean** & **SD** & **IE** & **AG** & **PSNR** & **SSIM** \\ \hline Original & 34.6971 & 35.0741 & 6.2112 & 6.4716 & - & - \\ SSR & 73.5088 & 36.0588 & 7.2658 & 9.6218 & 10.6560 & 0.6096 \\ MSR & 86.5889 & 38.3281 & 7.2851 & 10.7908 & 11.4824 & 0.6263 \\ Ref. [6] & 89.7056 & 40.1476 & 7.3253 & 11.7218 & 11.4985 & 0.6463 \\ Ours & 110.5771 & 44.4188 & 7.4199 & 12.9321 & 13.0189 & 0.7835 \\ \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of different algorithms for Image 3. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Image 1** & **Mean** & **SD** & **IE** & **AG** & **PSNR** & **SSIM** \\ \hline Original & 39.1851 & 47.3219 & 6.5405 & 9.8552 & - & - \\ SSR & 54.7889 & 55.1060 & 7.2588 & 11.9823 & 10.2724 & 0.6128 \\ MSR & 62.5176 & 58.3479 & 7.2629 & 15.1629 & 10.6629 & 0.6515 \\ Ref. [6] & 67.3683 & 60.4638 & 7.2838 & 15.9618 & 12.1916 & 0.6587 \\ Ours & 98.7722 & 73.1727 & 7.6195 & 19.6732 & 14.4768 & 0.7539 \\ \hline \end{tabular}
\end{table} 
Table 2: Evaluation results of different algorithms for Image 1. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Image 3** & **Mean** & **SD** & **IE** & **AG** & **PSNR** & **SSIM** \\ \hline Original & 76.8960 & 34.8910 & 7.2031 & 9.7077 & - & - \\ SSR & 98.9119 & 43.6048 & 7.2848 & 10.9226 & 12.0918 & 0.6156 \\ MSR & 117.0049 & 44.2004 & 7.3035 & 11.5720 & 12.2162 & 0.6397 \\ Ref. [6] & 124.9763 & 45.6719 & 7.3149 & 11.1557 & 14.8119 & 0.7322 \\ Ours & 161.9878 & 46.8571 & 7.4639 & 12.3482 & 15.9980 & 0.7718 \\ \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of different algorithms for Image 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Image 1** & **Mean** & **SD** & **IE** & **AG** & **PSNR** & **SSIM** \\ \hline Original & 39.1851 & 47.3219 & 6.5405 & 9.8552 & - & - \\ SSR & 54.7889 & 55.1060 & 7.2588 & 11.9823 & 10.2724 & 0.6128 \\ MSR & 62.5176 & 58.3479 & 7.2629 & 15.1629 & 10.6629 & 0.6515 \\ Ref. [6] & 67.3683 & 60.4638 & 7.2838 & 15.9618 & 12.1916 & 0.6587 \\ Ours & 98.7722 & 73.1727 & 7.6195 & 19.6732 & 14.4768 & 0.7539 \\ \hline \end{tabular}
\end{table} 
Table 3: Evaluation results of different algorithms for Image 2.
## 2 Methodology: Computation of the Altimetry Performance
### Basic Instrument Performance
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & & \multicolumn{3}{c}{\(\sigma_{\text{h}}\oplus\sigma_{\text{i}}=0^{\circ}\)} & \multicolumn{3}{c}{\(\sigma_{\text{h}}\oplus\sigma_{\text{i}}=35^{\circ}\)} \\ \cline{3-7}
**Altimetry Precision [cm\({}_{\text{rms}}\)]** & & \multicolumn{2}{c}{\(\mathbf{T_{coh}=1\text{ ms}}\), \(\mathbf{N_{i}}\) = 14,500} & \multicolumn{3}{c}{\(\mathbf{T_{coh}=1\text{ ms}}\), \(\mathbf{N_{i}}\) = 14,500} \\ \cline{3-7}  & & \(\mathbf{P_{T,min}}\) & \(\mathbf{P_{T,typ}}\) & \(\mathbf{P_{T,max}}\) & \(\mathbf{P_{T,min}}\) & \(\mathbf{P_{T,typ}}\) & \(\mathbf{P_{T,max}}\) \\ \hline Level-1 & L5 & 49.8 & 29.7 & 15.5 & 95.3 & 56.0 & 28.5 \\ Lower band (L5 + E5) & E5 & 10.1 & 8.3 & 7.2 & 19.1 & 15.5 & 13.3 \\ Level-1 & L1 & 22.5 & 16.4 & 12.9 & 51.2 & 37.2 & 27.6 \\ Higher band (L1 + E1) & E1 & 15.3 & 12.8 & 11.3 & 33.6 & 26.6 & 22.4 \\ Level-2 & L1\&L5 & 49.2 & 30.5 & 18.1 & 97.4 & 60.5 & 35.7 \\ (LB + HB + iono correct.) & E1\&E5 & 16.5 & 13.7 & 12.3 & 34.8 & 27.7 & 23.4 \\ \hline \end{tabular}
\end{table} 
Table 1: Altimetry rms error estimated by combining L1/E1 and L5/E5 bands to correct for the ionospheric delay at nadir and at an incidence angle of 35\({}^{\circ}\) (swath edge) (modified from [6]). LB = 1186.6 MHz and LH = 1575.42 MHz. Requirement is for \(\sigma_{\text{h}}<30\) cm\({}_{\text{rms}}\) over 100 km dwell line.