esa-sceva/satcom-chunk-collection huggingface dataset:
# Intelligent Prediction of Ore Block Shapes Based on Novel View Synthesis Technology
## 3 Experimental Verification
### Evaluation Metrics  
In fields such as classification, regression, and QSAR model evaluation [40], metrics like RMS and MAE are commonly used to assess the accuracy of predicted values against actual values. However, in the domain of computer vision and generative models, in order to comprehensively evaluate the quality of the generated images, this study uses three evaluation metrics: peak signal-to-noise ratio (PSNR) [41], structural similarity index (SSIM), and learned perceptual image patch similarity (LPIPS) [42]. These metrics are widely used in image quality assessment. LPIPS is calculated using a deep learning model,  
Figure 4: Schematic diagram of two rendering sampling trajectories: (**a**) hemispherical uniform distribution sampling trajectory; (**b**) Archimedean spiral sampling trajectory.  
Figure 5: Partial dataset display of ore blocks.  
with lower values indicating better image generation quality. Generally, an LPIPS value below 0.3 indicates good overall image quality. However, for ore blocks with complex texture details, this value may be higher. SSIM considers the brightness, contrast, and structural information of an image and is used to measure the similarity between two images. The calculation formula has been explained in Formula (9). PSNR calculates the ratio of the peak signal power to the mean noise power. The calculation formula is as follows:  
\[PSNR=10\times\log_{10}(\frac{MAX_{I}^{2}}{MSE}) \tag{11}\]  
where \(MAX_{I}\) represents the maximum possible pixel value of the image, and MSE stands for the mean squared error. Higher values of SSIM and PSNR indicate better image generation quality.
# Data-Driven Identification of Crane Dynamics Using Regularized Genetic Programming
## 1 Introduction
The most popular regularization methods are based on easier-to-solve, convex relaxations of the \(l_{0}\) norm such as the \(l_{1}\) norm, e.g., Lasso or its hybrids introduced in the Elastic Net [32] and Fast Function Extraction (FFX) [33] algorithms. While most studies incorporate \(l_{1}\) and \(l_{2}\) regularization into GP, this paper presents the G3P-SR approach with the \(l_{0}\) norm for crane dynamic identification. To the best of the authors' knowledge, the combination of genetic programming and \(l_{0}\) regularization has been previously adapted only for a static regression problem [34], and other evolutionary sparse regression algorithms are based on a combination of the \(l_{1}\) and \(l_{2}\) penalties. An \(l_{0}\) pseudo-norm penalty is added to the objective function, and the subsequent sparse regression problem is solved by using the monotonically accelerated proximal gradient descent algorithm [35]. The sparse solution reduces the model complexity and decreases the probability of overfitting. The model structure optimization and prediction accuracy over a time-horizon are established as the main goals of identification. G3P-SR is employed to establish an overhead crane dynamic model and predict the sway angle. The proposed method is compared with the NARX-NN and TSF-ARX models, since these approaches are mostly reported in the literature for the data-driven modeling of underactuated crane dynamics. The MGGP method is also included in the comparison since only this method has recently addressed crane dynamics' identification [31]. A comparative study is performed in terms of the models' complexity, prediction accuracy, and sensitivity. The experimental data used for identification are obtained from a laboratory overhead crane for different operating conditions. The main contribution of this paper are as follows:  
1. A data-driven offline identification approach for underactuated crane dynamics is developed using an MGP approach called G3P-SR, which uses grammar-guided genetic programming to bias the search space to produce a fixed set of candidate functions for each individual in the population, while the model term selection as well as their coefficients are obtained via sparse \(l_{0}\) regression. 2. A comparative study is performed with an MGGP, an NARX-NN, and a TSF-ARX model in terms of model complexity, accuracy, uncertainty, and sensitivity. The rest of this paper is organized as follows. In Section 2, we introduce the proposed G3P-SR method as well as the methods used in our comparison during the identification of the overhead crane's dynamics, namely, the MGGP, NARX-NN, and linear parameter-varying TSF-ARX models. Section 3 presents the experimental setup of the laboratory overhead crane and provides a discussion of the identification results, including a sensitivity and uncertainty analysis, while the final remarks are made in Section 4.
## 3 Mathematical Formulation
### Digital Twins
#### 3.1.4 Dt-Ias  
The DT-IAS is a forecasting algorithm that aims to predict and analyze the industry energy demand and is based on an autoregressive integrated moving average (ARIMA) model [39]. The ARIMA model integrates autoregressive and moving average elements, as well as differencing, to address non-stationarity in time series data. It is selected due to its high accuracy in modeling temporal dynamics, outperforming alternative approaches such as moving averages, multiple regression techniques, exponential smoothing, and neural networks [39].  
For that, the DT-IAS reads the historical data for the past days to then predict the next 24 h using the ARIMA model. Thus, the outputs of the DT-IAS are the industry consumption in real time, the consumption history, and the consumption forecast for the next 24 h. The output of the consumption prediction for the next 24 h will be needed as the input to the optimization algorithm of Sections 3.2.1 and 3.2.2.  
DT-IAS collects the measurements from sensors at consumption points using an API to extract the necessary information from the database. This data can be retrieved for periods of up to 1 week and longer, but for the case study, the past 7 days are considered. Total industry consumption aggregates multiple loads, including office electricity use, EV charging stations, and gas-powered compressors. While office consumption remains stable, charging stations and compressors create peak loads, introducing fluctuations that require management through energy storage and optimization strategies.
## 6 Implications, Limitations, and Future Work
### Limitations  
Despite the promising results presented in this study, several limitations must be acknowledged. Firstly, the evaluation datasets used in various tasks, particularly in image and audio data, were relatively small and not exhaustive. This limited sample size may not fully capture the model's performance across all potential scenarios. It may also result in some limitations in standardized comparisons among models. While we aimed for a comprehensive evaluation across data types and multimodal (breadth), the categories within each are not exhaustive (depth). For example, we did not evaluate image and audio generation as it was beyond the scope of this study.  
Moreover, qualitative or human judgment was not used as a criterion to assess performance. Incorporating human judgment is crucial for evaluating the practical usability and contextual accuracy of model outputs, as it provides insights that quantitative metrics alone may not reveal [64]. The model also exhibited inconsistencies in handling ambiguous or complex inputs, as seen in the varying accuracy rates across different tasks. Furthermore, the few-shot learning approach, although beneficial in some contexts, showed limitations in tasks with a high degree of variability, such as VQA. The potential for overfitting to specific examples in these cases remains a concern. Additionally, the lack of real-time and longitudinal data evaluation poses a constraint on understanding the model's adaptability and robustness over time. For example, evaluating the model's performance in real-time applications, such as continuously monitoring driver drowsiness or detecting suddenchanges in patient health through medical imaging, would provide valuable insights into its practical effectiveness and reliability under dynamic conditions.  
GPT-4o exhibits strong few-shot learning capabilities across various tasks, as evidenced by its performance in glaucoma detection. However, for tasks with high variability, such as VQA, the model's performance can be inconsistent. In some cases, providing just one example led to decreased accuracy, suggesting that few-shot learning may not always be beneficial in highly variable contexts. To enhance GPT-4o's adaptability and generalization, future research should focus on developing more sophisticated few-shot learning techniques that can better handle diverse and complex scenarios. This may include exploring dynamic prompt engineering, meta-learning approaches, or incorporating task-specific knowledge to guide the model's few-shot learning process more effectively.
## 5 Scalability
### Weak Scalability  
CHTKC and KMC3 show two opposite trends in the test of weak scalability when the dataset size and the number of threads increase proportionally (Figure 8 and Supplementary Table S9). The running time of KMC3 increases when the dataset size and thread number double simultaneously, indicating that KMC3 is not weakly scalable, so KMC3 cannot take advantage of the multi-core computing platform when processing large datasets. On the contrary, the running time of CHTKC decreases with the increase in dataset, indicating that CHTKC is weakly scalable, hence CHTKC is very suitable for k-mer frequency counting of super-scale datasets on high-performance computing platforms.
# Study on the Influence of Deep Soil Liquefaction on the Seismic Response of Subway Stations
###### Abstract  
Subway systems are a crucial component of urban public transportation, especially in terms of safety during seismic events. Soil liquefaction triggered by earthquakes is one of the key factors that can lead to underground structural damage. This study investigates the impact of deep soil liquefaction on the response of subway station structures during seismic activity, aiming to provide evidence and suggestions for earthquake-resistant measures in underground constructions. The advanced finite element software PLAXIS was utilized for dynamic numerical simulations. Non-linear dynamic analysis methods were employed to construct models of subway stations and the surrounding soil layers, including soil-structure interactions. The UBC3D-PLM liquefaction constitutive model was applied to describe the liquefaction behavior of soil layers, while the HS constitutive model was used to depict the dynamic characteristics of non-liquefied soil layers. The study examined the influence of deep soil liquefaction on the dynamic response of subway station structures under different seismic waves. The findings indicate that deep soil liquefaction significantly increases the vertical displacement and acceleration responses of subway stations compared to non-liquefied conditions. The liquefaction behavior of deep soil layers leads to increased horizontal effective stress on both sides of the structure, thereby increasing the horizontal deformation of the structure and posing a potential threat to the safety and functionality of subway stations. This research employed detailed numerical simulation methods, incorporating the non-linear characteristics of deep soil layer liquefaction, providing an analytical framework based on regulatory standards for evaluating the impact of deep soil liquefaction on the seismic responses of subway stations. Compared to traditional studies, this paper significantly enhances simulation precision and practical applicability. Results from this research indicate that deep soil layer liquefaction poses a non-negligible risk to the structural safety of subway stations during earthquakes. Therefore, the issue of deep soil liquefaction should receive increased attention in engineering design and construction, with effective prevention and mitigation measures being implemented.  
numerical simulation; deep liquefiable soil layer; subway station; acceleration; effective horizontal stress +
Footnote †: journal: Applied sciences  
[PERSON], [PERSON], [PERSON], Z.  
Study on the Influence of Deep Soil  
Liquefaction on the Seismic Response of Subway Stations  
_Appl. Sci._**2024**, _14_, 2307. [[https://doi.org/10.3390/appl4062307](https://doi.org/10.3390/appl4062307)]([https://doi.org/10.3390/appl4062307](https://doi.org/10.3390/appl4062307))
# Toward Low Time Fragmentation of Equipment: A Double-DQN Based TT&C Task Planning Approach
## 7 Conclusions  
In this paper, we proposed a low time fragmentation-oriented TT&C task-planning method to reduce the total time fragments of TT&C equipment and improve the response time of emergency tasks. Firstly, this paper established a mathematical model with the optimization goal of minimizing the total time fragments and maximizing the total revenue of TT&C tasks. Then, we transformed the problem of TT&C task planning into an independent set problem in the graph, and introduced DDQN in deep reinforcement learning as an evaluation strategy. Afterward, we designed a low time fragmentation-oriented TT&C task-planning method to improve the service capability of TT&C resources. The simulation results of multiple experimental scenarios showed that the LTFT&C algorithm is significantly better than GRASP and GA algorithms in terms of time fragmentation. Therefore, using the LTFT&C algorithm to solve the TT&C task-planning problem is reasonable and effective.  
Methodology, H.X. and R.L.; Validation, H.X.; Writing--original draft, H.X.; Writing--review & editing, R.L.; Supervision, R.L. All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Natural Science Foundation of Shaanxi Province (2023-JC-YB-556), in part by the Young Talent fund of University Association for Science and Technology in Shaanxi China (20200112), in part by the Key Research and Development Program in Shaanxi Province of China (2021 GY-006), and in part by the Postdoctoral Foundation in Shaanxi Province of China (2018 BSHEDZZ47).  
The study did not require ethical approval.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Informed Consent Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The author declares no conflict of interest.
# A Review of Recent Progress of Carbon Capture, Utilization, and Storage (CCUS) in China
## 5 Progress of CO2 Storage in China
### Development Status of CO\({}_{2}\) Ocean Storage in China  
The ocean, which makes up over 71% of the planet's surface, has the largest potential capacity to store CO\({}_{2}\). However, CO\({}_{2}\) ocean storage is still subject to some uncertainties, such as the isolation prediction of CO\({}_{2}\) emitted to the seafloor, the risk of CO\({}_{2}\) leakage during storage in marine sedimentary strata, and the influence of CO\({}_{2}\) on pH value change of the seawater, which have limited the deployment of CO\({}_{2}\) ocean storage [94; 163]. Therefore, no CO\({}_{2}\) ocean storage project has been implemented in China before 2021.  
In recent years, China has strengthened the progressive exploration of marine geological conditions and continuously improved the CO\({}_{2}\) ocean storage technology. China's first off-shore large-scale CO\({}_{2}\) storage demonstration project has all manufacturing and installation completed in June 2022 [54] and began to officially launch [192]. Since 2021, CNOOC has been conducting research on critical technologies for the integration of geological reservoirs, drilling and completion, and engineering appropriate for CO\({}_{2}\) ocean storage, and has successfully developed a complete set of technology and equipment systems for CO\({}_{2}\) capture, treatment, injection, storage, and monitoring on off-shore platforms, filling the gap in China's CO\({}_{2}\) ocean storage technology [193]. The project is situated in the Enping 15-1 oilfield group in the Pearl River Estuary Basin. It is planned to permanently store 1.46 million tonnes of CO\({}_{2}\) in total at an injection rate of about 30 \(\times\) 10\({}^{4}\) tonnes/year [194]. In the project, the CO\({}_{2}\) associated with the oil field development is injected into the saline aquifer at a depth of 800 m on the seabed. CO\({}_{2}\) can be retained effectively and gas leakage can be prevented by making use of the saline aquifer's dome structure in conjunction with the muddy protective layer of sufficient thickness [194]. The successful implementation of this project will create new industries and new formats of CO\({}_{2}\) storage in China, showing important demonstration significance for the environmentally friendly development of offshore oil and gas resources [194].
## 3 Design of the Pressure Control Algorithm
### Design of the Adaptive Fuzzy PID Algorithm
#### 3.2.3 Adaptive Fuzzy PID Control Rules  
In the adaptive fuzzy PID controller, the three parameters work together to affect the system, which must be considered. The tuning requirements of \(\Delta K_{p}\), \(\Delta K_{i}\), and \(\Delta K_{d}\) under different deviations \(|e|\) and deviation change rate \(|ec|\) are as follows:  
1. [label=()]
2. When \(|e|\) is a large value, to speed up the response of the system, the \(\Delta K_{p}\) value should be larger; \(\Delta K_{i}\) is often set to 0 to avoid the differential oversaturation caused by the instantaneous increase of the \(|e|\) value
3. When \(|e|\) is a medium value, to reduce the overshoot of the system, the values of \(\Delta K_{p}\), and \(\Delta K_{i}\) should be smaller and the value of \(\Delta K_{d}\) should be appropriate to speed up the response of the system.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variables** & **Basic Domain** & **Quantization Domain** & **Quantization Factor** \\ \hline \(e\) & [-1.5 1.5] & {-3,–2,–1,0,1,2,3} & 2 \\ \hline \(ec\) & [-15 15] & {-3,–2,–1,0,1,2,3} & 0.2 \\ \hline \(\Delta Kp\) & [-0.5 0.5] & {-1,2/3,1/3, 0,1/3, 2/3,1} & 2 \\ \hline \(\Delta Ki\) & [-1 1] & {-1,2/3,1/3, 0,1/3, 2/3,1} & 1 \\ \hline \(\Delta Kd\) & [-0.02 0.02] & {-1,2/3,1/3, 0,1/3, 2/3,1} & 50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables and domain.  
Figure 6: Input and output variable membership function: (a) Membership function of the input variables \(e\), \(ec\); (b) membership function of the output variables \(\Delta K_{p}\), \(\Delta K_{i}\), and\(\Delta K_{d}\).  
3. When \(|e|\) is a small value, to keep the good steady-state performance of the system, \(\Delta K_{p}\) and \(\Delta K_{i}\) should be set as larger values, and the value of \(\Delta K_{d}\) is up to \(|ec|\). When \(|ec|\) is small, \(\Delta K_{d}\) takes a larger value. When \(|ec|\) is large, \(\Delta K_{d}\) should take a smaller value to avoid oscillation of the system.  
According to the tuning requirements above, the fuzzy control rules for PID control parameters were designed as follows:  
According to the adaptive fuzzy PID control rule established below in Table 2, the dynamic tunings of \(\Delta K_{p}\), \(\Delta K_{i}\), and \(\Delta K_{d}\) were obtained. According to the membership degree of each fuzzy subset and the fuzzy control model of each parameter, the fuzzy matrix table of PID parameters is designed by using fuzzy synthesis reasoning and the corrected parameters \(\Delta K_{p}\), \(\Delta K_{i}\), and \(\Delta K_{d}\) are found out. The proportional, integral, and differential parameters \(K_{p}\), \(K_{i}\) and \(K_{d}\) of PID controller are as follows:  
\[\left\{\begin{array}{l}K_{p}=K_{p0}+\Delta K_{p}\\ K_{i}=K_{i0}+\Delta K_{i}\\ K_{d}=K_{d0}+\Delta K_{d}\\ \end{array}\right. \tag{27}\]  
In conclusion, the adaptive fuzzy PID controller model established in this paper is shown in Figure 7.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 4 Reliability and Lifespan Improvement Methods for EMD Components
### Methods for Mitigating Stress Factors
Additionally, the approach requires precise knowledge of motor parameters, which may not always be available in practice. This limits its applicability in real-world systems where parameter uncertainty is common. Another approach for minimizing losses in IMD is presented in [113]. The authors introduce a controller for minimizing losses and torque ripple based on a loss minimization algorithm. One aspect of this work is the inclusion of leakage inductance in the loss model, which is often ignored in previous studies. The method seems more suitable for larger motors, but it has not been tested in dynamic conditions, where problems may occur in drives with rapid changes in speed and load profiles. In [114], the authors extended the lifespan and reduced the costs of PMSM drives through the optimization of the motor's design and DC-link voltage. According to the authors, the drawbacks of the proposed methods related to motor geometry are that a higher DC-link voltage requires more advanced power converters, while a lower DC-link voltage may reduce efficiency and degrade drive performance. To enhance the efficiency of electric vehicle drive systems, the authors of [115] propose a torque distribution strategy for electric vehicles with both front-wheel and rear-wheel drive. The proposed method significantly increases system efficiency compared to equal torque distribution. It is evident that, by using the proposed scheme, motor losses can be reduced, although this comes at the cost of requiring an additional clutch. In [116], the authors introduce a method for mitigating harmonics, inverter temperature, and losses in a PMSM drive system using an LC filter. The experimental results show a significant decrease in the THD of phase voltage and current, as well as the inverter's temperature compared to the case without the LC filter. The method is simple and effective, but it increases costs because of the need for an additional LC filter. Also, the filter's frequency characteristics need to be carefully considered, as poor tuning can reduce its effectiveness or cause other issues. Paper [117] investigates methods for mitigating voltage stress on stator windings caused by uneven voltage surges. The reliability of the system is improved by utilizing multilevel inverters and line inductance filters. Compared to two-level ASD, this method provides better voltage quality and reduces the risk of insulation damage. However, multilevel inverters require more complex control algorithms, and are more expensive, and line inductance filters can add extra losses. The methods for enhancing the reliability and lifespan of EMD components are summarized in Table 4.