esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Aiming at the accurate motion control of an AUH at low speed, this paper focuses on overshoot, steady-state accuracy, and the anti-interference of an AUH's heading and depth control, which is the basis for docking the AUH onto an SDS. Moreover, based on the data-driven principle, the LADRC-TD algorithm was adopted to resolve the problem in this paper's hydrodynamic modeling of the AUH. By adding differential tracking to the classical LADRC algorithm, the excellent anti-interference performance of classical LADRC can be obtained, and the overshoot of the system can be reduced. According to the simulations and pool experiments, the following conclusions are drawn:  
1. The LADRC-TD algorithm has the least overshoot, namely 20 cm and 3\({}^{\circ}\) in the depth control and heading control, respectively, which is less than PID and LADRC.
2. According to the simulations, the anti-interference of LADRC-TD is better than PID and nearly the same as LADRC.
3. The steady-state error of LADRC-TD is \(\pm\)21 cm and \(\pm\)2.5\({}^{\circ}\) in the depth control and heading control, respectively, which is lower than PID and the same as LADRC.  
As demonstrated, LADRC-TD has a better control effect than PID and LADRC. The next step is to solve the motion control problem of an AUH at 1 m/s. In practice, the pitch angle of the AUH undergoes significant changes at such speeds, which can lead to instability and negatively impact its cruising capabilities. Therefore, it is crucial to develop  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Depth Control** & **PID** & **LADRC** & **LADRC-TD** \\ \hline Overshoot & 43 cm & 41 cm & 20 cm \\ Anti-interference & worse & baseline & nearly \\ Steady-state error & \(\pm\)1.1 m & \(\pm\)21 cm & \(\pm\)21 cm \\ \hline
**Heading Control** & **PID** & **LADRC** & **LADRC-TD** \\ \hline Overshoot & 12\({}^{\circ}\) & 6\({}^{\circ}\) & 3\({}^{\circ}\) \\ Anti-interference & worse & baseline & nearly \\ Steady-state error & \(\pm\)3\({}^{\circ}\) & \(\pm\)2.5\({}^{\circ}\) & \(\pm\)2.5\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Result summary of each algorithm.  
effective motion control strategies to mitigate the adverse effects of pitch angle variations and ensure stable and efficient operation of the AUH at 1 m/s.  
Conceptualization, H.L., X.A. and Y.C.; methodology, H.L., X.A. and Y.C.; software, H.L., X.A. and R.F.; validation, H.L., X.A. and R.F.; formal analysis, H.L. and X.A.; investigation, R.F.; resources, Y.C.; data curation, H.L.; writing--original draft preparation, H.L.; writing--review and editing, X.A. and Y.C.; visualization, H.L.; supervision, R.F.; project administration, X.A. and Y.C. All authors have read and agreed to the published version of the manuscript.  
The study was supported financially by the National Natural Science Foundation of China (Grant No. 52001279), the National Key R & D Program of China (NO. 2017 YFC0306100).  
The authors would like to thank [PERSON] and other Ocean College people at Zhejiang University for their inspiration and helping experiments.  
The authors declare no conflict of interest.
# Historical Eco-Environmental Quality Mapping in China with Multi-Source Data Fusion
## 5 Conclusion  
In conclusion, this research paper aimed to address the lack of comprehensive studies on the evaluation of eco-environmental quality (EEQ) in China, particularly in terms of monitoring its spatial and temporal changes at a national scale. The study highlighted the limitations of existing evaluation methods based on statistical data and proposed a novel approach using remote sensing data to construct an EEQ evaluation system, called the modified remote sensing eco-environmental quality index (M-RSEQI). The main findings of this research provide valuable insights into the EEQ trends in China from 2000 to 2017. The results showed that the EEQ of most regions in China has been improving over the past 18 years, with 60.3% of the areas experiencing an increase in M-RSEQI. However, the overall EEQ of China remained low during this period. The study also analyzed the spatial  
Figure 15: Characteristics of future (2020–2100) spatial (**a–c**) and temporal changes (**d–f**) in EEQ in China.  
and temporal changes in the Yellow River Basin, Yangtze River Basin, and four major cities (Beijing, Shanghai, Guangzhou, and Nanjing). The findings revealed variations in the EEQ trends among these regions, with improvements observed in the Yangtze and Yellow River basins. Additionally, the study highlighted the influence of industrial structure and urbanization on the EEQ of Beijing, with a significant reduction followed by a stable trend. Guangzhou exhibited the best EEQ among the four cities, while Shanghai experienced continuous deterioration. Notably, Nanjing's EEQ showed steady improvement due to extensive efforts in water pollution prevention and control. Overall, this research contributes to the understanding of China's ecological environment and provides objective, scientific, and reliable data support for ecological protection and pollution prevention policies, thereby helping to mitigate potential ecological risks associated with future urbanization in China.  
Conceptualization, S.W.; Methodology, S.W.; Software, S.W., D.X. and C.Z.; Formal analysis, D.X.; Resources, S.W.; Data curation, S.W.; Writing--original draft, S.W.; Writing--review & editing, L.C., D.X. and C.Z.; Supervision, L.C.; Project administration, D.X.; Funding acquisition, L.C. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by the Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University (Grant No. 2022-KF-13).  
Not applicable.  
Data Availability Statement: Data sharing is not applicable to this article.  
The authors declare no conflict of interest.
# Numerical Study of Aircraft Wake Vortex Evolution under the Influence of Vertical Winds
## 1 Introduction
This study intends to enhance our understanding of wake vortex dynamics under varying vertical wind conditions, critical for improving safety and efficiency in aircraft operations. At present, researchers have made significant progress in studying the effects of typical atmospheric conditions such as crosswinds, ground effects, temperature stratification, and rainfall on the evolution and decay of wake vortices. However, more research on the impact of vertical wind environments still needs to be performed. Therefore, this study holds importance in enhancing the calculation and optimization of wake vortex separations under different meteorological conditions. The specific chapter arrangement for this article is as follows. The first chapter introduces the research background and significance of this article. It provides an overview of the research methods for wake vortices, their advantages and disadvantages, and summarizes the research progress and findings in wake vortex numerical simulations. The chapter also outlines the content covered in subsequent chapters. In the second chapter, we describe the whole process of numerical simulation on the Fluent platform. It begins by outlining the construction of simplified wing and fluid domain models. The chapter then delves into the structured grid division of the fluid domain, validates the grid independence of the numerical simulation, and determines the appropriate number of grids. It also specifies the N-S equations, turbulence models, and boundary conditions used in the numerical simulations. In the third chapter, we focus on analyzing the numerical simulation results. It starts by validating the accuracy of the numerical simulations by comparing the results with theoretical models and radar detection data. The chapter then examines the impact of wind direction and speed on wake vortex structure through iso-surface plots of vorticity. It proceeds to provide a quantitative analysis of wake vortex parameters such as vorticity, vortex trajectory, and axial and vertical velocity and describes the phenomenon of secondary vortices. In the fourth chapter, the main focus is on summarizing the primary research findings of this article.
## 3 The Q-Holonic-Based ARchitecture (QHAR)
### Representation of Entities and Actors by Q-Holons  
To implement the QHAR, it is necessary to identify the basic constituents of the system that will be represented by Q-Holons, to define the characteristics of each constituent, and to specify the operating rules of the system.  
With regard to the main production system studied, any entity that has its own identity, which exists as such and which can play at least one autonomous role in one of the production processes, can be represented by a Q-Holon. It can be a processing machine, a robot, a conveyor, an AGV, an information system, a raw or consumable material, or an operator. Two or more Q-Holons can be combined to form a process segment. Where relevant, the process segment can be represented as a single Q-holon, by combining the attributes and operations of the Q-Holons that compose it. The other actors of the value chain whose operation is not detailed (e.g., customer, supplier, subcontractor) can each be represented by a Q-Holon as well.  
The description of the characteristics (attributes, operations) of each Q-holon depends on the nature of the entity/actor--which it represents--and the needs related to the operation of the system.  
Thanks to the Q-Holon material flow, the QHAR takes into account the physical flows between entities/actors. These physical flows are absent in other holonic architectures. The cyber dimension and data flow allow for specifying aspects related to interoperability between applications or software. The energy requirement and the possibility of energy recovery from certain entities can be modeled by the energy dimension and the energy flow. This is the case, for example, for certain resources (e.g., machines, AGVs) which consume and/or supply energy. Humans are an integral component of production systems, whose needs and services must be taken into account. A human can be equipped with accessories that can have cyber, physical, and energetic dimensions (e.g., augmented reality glasses, exoskeleton). The equipped operator, who can be qualified as an \"Augmented Worker\", can therefore be modelled using a Q-holon with the attributes and operations corresponding to these four dimensions. Ergonomic aspects can be taken into account, using attributes such as \"Recommended working environment conditions\", \"Extreme reach zones\", and \"Maximum capacity\".  
Below (see Figure 4) are some examples of Q-Holons corresponding to entities belonging to one of the three levels of the QHAR, or representing another actor of the value chain.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 7 Case Study
### Case Study Protocol
#### 7.1.3 Data Gathering  
Qualitative data is gathered from interviews and discussions with stakeholders and technology experts in the hospital's management, who are involved in the decision of adopting and using the e-health solutions. Two types of interviews were conducted, which are pre-implementation and post-implementation of the case study. The pre-implementation interviews' questions focused on extracting information about the e-health solution to be used to evaluate the sustainability model and assessment method. The post-implementation interviews aim to gather stakeholders' feedback on the evaluation's results and to compare their sustainability evaluation, if it exists, to the sustainability evaluation generated by our assessment method. The social dimension is expected to be evaluated by the intended users of the e-solutions; hence, an online survey was designed and distributed to the users.
## 1 Introduction
[24] focused on the shield-tunneling parameters as input values and constructed a set of intelligent methods for optimizing the shield-tunneling parameters and predicting the shield-tunneling attitude. [PERSON] et al. [25] employed PCA to analyze the correlated parameters and implemented the rolling window mode in the gated cycle unit. Thereafter, they established an intelligent framework for trajectory deviation prediction in the shield-tunneling process to predict the attitude deviation in real time. However, although studies have been conducted on shield-trajectory deviation prediction, two challenges (interrelated high-dimensional data and data noise processing) remain. Data in shield-acquisition systems are not independent of each other. Their quality is frequently affected by measurement sensor failures, and using these raw data can lead to the over-fitting of machine learning models. To solve these problems, a hybrid model of attitude trajectory deviation prediction for shield machines based on PCA and SVR is proposed herein. PCA was employed to reduce the dimension of input parameters and filter the raw data noise by preserving the principal components. Statistical learning and structural risk minimization were applied in SVR to forecast the target parameters [26]. Thereafter, the proposed method was employed in a super-large-diameter shield-tunnel project in Zhuhai, China. The results were compared with those of the classical SVR model, and the relationship between the tunneling, geometric, and geological parameters and the attitude trajectory parameters is discussed. The proposed attitude-prediction model provides a reference for similar projects in the future.
## 4 Results
### Training Results
#### 4.1.2 Performance of _NavACL-Q_ SAC with Pre-Trained Convolutional Encoders  
We are presenting the learning curves of the best DRL variant during the complete training episodes in Figure 5. The algorithm is run a total number of three times, with each run consisting of 0.25 M episodes. This approximately corresponds to the wall clock time of 28 h and 5 M frames, where the training is split across three GPUs (Quadro RTX 8000 48 GB) with the CPU Intel Xeon Gold 5217. Figure 5 outlines the episodic return and success probability of reaching underneath the dolly. It can be observed that the final success probability approached 1, i.e., the mobile robot succeeds mostly in navigation from arbitrary defined initial position domain as shown in Appendix C. The episodic return also converges to the final value stably, which can be interpreted as converging to a near-optimal policy. The variance of episodic return and success probability is small at the end of training, signifying the learned policy registers similar patterns among three runs. The robustness of our algorithm is therefore evident. Moreover, our trained DRL agent further exhibits the adaptability to a non-stationary environment. We show that the agent is even able to reach the goal dolly that changes its location during the navigation process. For instance, the dolly is originally placed left front to the AVG, and the agent steers towards the goal direction. Then the target dolly is shifted from the left front of the agent to its right front, the AVG drives first backwards and adjusts its orientation towards the goal direction and then advances to the goal. The video illustrations of leaned policy are available in our supplementary materials. The generalizability of the trained agent serves as a great advantage of DRL and will be further discussed in Section 5.1.
## 3 Results
### Effects of the Level of Data Augmentation by AAFT  
Effect on training loss: the rANOVA result indicated that there was a significant main effect of the levels of data augmentation by AAFT on training loss, \(F(4,18.26)=85.93\), \(p<0.001\). The pairwise comparison revealed that AAFT(1) vs. AAFT(2) yields mean(standard error) = 0.10(0.08), \(p=1.000\), AAFT(1) vs. AAFT(3), 0.70(0.11), \(p=0.001\), AAFT(1) vs. AAFT(4), 1.14(0.08), \(p<0.001\), and AAFT(1) vs. AAFT(5), 1.89(0.09), \(p<0.001\), indicating a significant difference between AAFT(1) and the other levels of data augmentation except AAFT(2). This means that the data augmentation had an effect on the training loss when greater or equal than triple. In addition, the comparisons of AAFT(2) vs. other greater levels showed significant differences as AAFT(2) vs. AAFT(3), 0.60(0.13), \(p=0.010\), AFFT(2) vs. AAFT(4), 1.04(1.22), \(p<0.001\), and AFFT(2) vs. AFFT(5), 1.79(1.30), \(p<0.001\), implying that the training loss significantly decreased if the level of data augmentation was greater or equal than triple. Between AFFT(3) and AFFT(4), no significant differences were found. AFFT(5) showed significance difference against all the other levels.  
Effect on test accuracy: Please note that test accuracy was presented in terms of percentile in Table 2; therefore, 100% corresponds to 1.00 from now on. Analysis on test accuracy yielded a significant main effect for the level of data augmentation by AAFT on test accuracy as well, \(F(4,0.31)=18.50,\ p<0.001\). Pairwise comparisons revealed that AAFT(1) vs. AAFT(2), \(-0.06(0.04),\ p=1.000\), AAFT(1) vs. AAFT(3), \(-0.16(0.40),\ p=0.017\), AAFT(1) vs. AAFT(4), \(-0.19(0.03),\ p=0.002\), and AAFT(1) vs. AAFT(5), \(-0.25(0.03),\ p<0.001\), suggesting that the test accuracy increased significantly when the level of data augmentation was greater or equal than triple compared to AAFT(1). There were no significant differences among the test accuracy under AAFT(2), AAFT(3), AAFT(4). In contrast, AAFT(5) indicates significant difference over AAFT(1), 0.25(0.03), \(p<0.001\), AAFT(2), 0.19(0.04), \(p=0.004\), AAFT(3) 0.10(0.02), \(p=0.032\), except AAFT(4). This implies the test accuracy improved significantly as data were augmented by AAFT.
# A Carrier Estimation Method Based on MLE and KF for Weak GNSS Signals
## 6 Conclusions  
A low-complexity MLE-based carrier tracking loop is presented in this paper. It is designed to work in some low dynamic and weak signal circumstances such as indoor pedestrian and urban vehicle navigation. Compared with the conventional MLE-based methods, this method is operated based on the coherent integration results instead of the sampling data, which reduces the computation burden significantly. The MLE discriminator is designed and its performance is improved by an adaptive KF. Compared with the conventional two-order FLL assisted three-order PLL, its sensitivity has an improvement of 3 dB and 1 dB in pedestrian-level and vehicle-level dynamic circumstances, respectively. It also shows higher accuracy and lower bit error rate than the conventional loop. It is suitable for indoor pedestrian and urban vehicle navigation.  
This work was supported by the AeroSpace T.T.&.C. Innovation Program (201515A).  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
**Conflicts of Interest:** The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
## 1 Introduction and Background
### Approaches to the Problem, Actual Tests of Stability, and the Need for a New Test Facility Concept
Hence, the only possibility of overcoming the existing test systems and their limitations is to develop a novel experimental facility able to evaluate the static and dynamic stability of machines by means of new and unconventional tests.
Combining Signal Features of Ground-Penetrating Radar to Classify Moisture Damage in Layered Building Floors  
[PERSON]  
1 Bundesansatalt fur Materialforschung und-Prufung (BAM), 12205 Berlin, Germany;  
The sensitivity of GPR for water has already been proven in many publications, especially in geophysics [3; 4]. However, in civil engineering (CE), GPR is also increasingly being applied for non-destructive moisture measurements on building materials like asphalt, concrete, and screed [5; 6; 7; 8; 9; 10]. Here, various methods have already been established. However, their adequate use and suitability highly depends on the particular case. Due to numerous possible uncertainties, like the given structure, installed materials, and layer thicknesses, interpreting GPR results is not straightforward and requires the expertise of trained personnel. These uncertainties often influence the same signal features that are used for moisture measurements (see Section 1.1). Here, relying on only one feature, as it is done in most of the related publications [11], can lead to high uncertainty.  
In contrast, this work pursues the strategy of combining different features, which allows the use of multivariate data analysis. It aims to achieve an automated classification of three scenarios: (1) the dry state, (2) damaged insulation, and (3) a damaged screed, all of them on unknown floor constructions. This is accomplished by a machine learning approach trained with novel radargram features that consider the spatial continuity of the present damage. The features are extracted from an experimentally measured data set, including varying materials and layer thicknesses. Before discussing the methodology in Section 2, a short introduction to moisture measurements with GPR is given.
[19] combined Ansys and FLAC3D to carry out numerical simulation, and judged that the effect of three factors on the stability of the cavity based on the results of linear fitting was cavity burial depth \(>\) pressure interval \(>\) pressure difference. [PERSON] et al. [20] used FLAC3D 3.00 software to analyse the long-term operational stability of energy storage cavities in order to discuss the possibility of the safe operation of CAES reservoirs under Chinese domestic stratigraphic conditions. [PERSON] et al. [21] conducted permeability tests based on interface-bearing salt rocks from domestic salt mines, and numerical simulations based on the test results on the stability and confinement of natural gas reservoirs containing entrapped underground salt caverns. [PERSON] et al. [22] conducted a series of indoor tests, theoretical and numerical simulations, and discussions to simulate hydrogen leakage from cave chambers under different conditions using COMSOL Multiphysics 6.0 software, and analysed the leakage range, pore pressure, and the amount of hydrogen leakage. [PERSON] et al. [23] combined the stability characteristics of salt caverns and gave five key scientific and technological issues facing the deep large-scale salt cavern energy storage technology in China. [PERSON] et al. [24] explored the effects of different gas frequencies on the stability of horizontal salt caverns under different geological conditions. They found that shallow salt caverns are suitable for compressed air energy storage, medium and deep salt caverns are suitable for natural gas energy storage, and deep salt caverns are suitable for helium energy storage. [PERSON] et al. [25] discussed the suitability of underground hydrogen storage facilities in layered salt caverns based on a geographic information system (GIS), taking into account the results of previous studies on the storage capacity of rock salt deposits. This paper presents a study of the salt cavern gas storage reservoir in the Yexian area of the Pingdingshan Mountain. The research employs a comprehensive approach combining theoretical analysis and numerical simulations to investigate the impact of varying numbers of interlayers on the long-term stability of the gas storage reservoir. The objective is to address the research gap concerning mudstone interlayers and to provide theoretical support and numerical simulation analyses for the safe operation of gas storage reservoirs.