esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Basic Theory of the TPOD Method and the Rotor Model with Coupling Faults
### The Effects of Systematic Parameters
#### 3.2.1 The Cracked Depth  
The effect of the cracked depth variation on dynamical characteristics of the coupling fault is investigated in this section. As is shown in Figure 14a,b, they reflect the bifurcation diagrams of the coupling fault systems with the cracked depth \(a=0.6R\) and \(a=R\). Figure 15 shows the comparison of frequency-amplitude curves of two cracked depths. As the cracked depth decreases, the stiffness variation decreases correspondingly, so the first order critical speed of the coupling fault rotor slightly increases. The period-doubling bifurcation vanishes at the frequency of \(2\omega_{c}\), the 1/2 sub-harmonic vibration amplitude of the relevant amplitude-frequency characteristic decreases at the same time (Figure 15). Since the crack fault characteristic does not play the main role and even vanishes, the rub-impact fault emerges, the vibration amplitude suddenly increases at the frequency of \(3\omega_{c}\). This change demonstrates the crack is the main factor to generate 1/2 sub-harmonic vibration in the coupling system again, there will be more effect with the increase of the cracked depth. The rub-impact fault will result in the amplitude of the coupling fault peaking at the frequency of \(3\omega_{c}\), there is reversible inhibition of the crack to the amplitude aroused by the rub-impact. The restrain will be stronger as the cracked depth increases. When \(\omega\in(1100,1200)\), the cracked depth will have little impact to the  
Figure 13: The phase portraits of \(o_{1}\) when \(\omega=1020\) rad/s. (**a**) The oil-filmed system. (**b**) The cracked system. (**c**) The rub-impacted system. (**d**) The coupled system.  
amplitude based on the comparison of the amplitude-frequency curves (Figure 15). In comparison to the bifurcation diagrams (Figure 14), the complex motion state of the system changes.
# Review of Satellite Remote Sensing of Carbon Dioxide Inversion and Assimilation
## 2 CO2 Observation, Datasets, Global Related Research
### CO\({}_{2}\) Observation Database
#### 2.3.2 Anthropogenic Emissions Dataset
provides global monthly emissions information with spatial resolutions of 1 km \(\times\) 1 km and \(1^{\circ}\times 1^{\circ}\). The data can be seen from the website [[https://db.cger.nies.go.jp/](https://db.cger.nies.go.jp/)]([https://db.cger.nies.go.jp/](https://db.cger.nies.go.jp/)) (accessed on 30 July 2023). EDGAR Dataset  
EDGAR, published by the Joint Research Centre of the European Commission, provides independent estimates of global anthropogenic emissions and trends [39]. It relies primarily on population density data to map emissions spatially. The EDGAR methodology is bottom-up and sectoral, utilizing statistical estimates of CO2 emissions from various sectors, including industrial facilities. This approach considers factors such as population distribution and land use types to estimate emissions accurately. The data can be seen from the website [[https://edgar.jrc.ec.europa.eu/](https://edgar.jrc.ec.europa.eu/)]([https://edgar.jrc.ec.europa.eu/](https://edgar.jrc.ec.europa.eu/)) (accessed on 13 May 2023). MEIC (Multi-resolution Emission Inventory for China) is a detailed emission inventory for China, providing emissions data at various resolutions [40], including CO2 and other greenhouse gases. It integrates multiple data sources such as remote sensing data, ground observations, and statistical data to enhance the accuracy of emission estimates. The MEIC model categorizes emission sources into different types, such as energy, industry, and agriculture, offering detailed information on regional and temporal scales. This dataset is crucial for climate change research, policy-making, and environmental monitoring.
# Gait Recognition Algorithm of Coal Mine Personnel Based on LoRa
## 2 Materials and Methods
### System Architecture  
As shown in Figure 5, the proposed system consists of three modules: data collection, data preprocessing, and feature extraction and identity recognition.  
**Data collection:** In the process of data collection, a pair of LoRa transceivers was deployed. The LoRa node continuously transmits wireless signals, and the target walks in the sensing area. The LoRa gateway receives signals reflected by the direct path, the surrounding environment, and the moving target. By analyzing the received signals, we can obtain the target movement information.  
**Data preprocessing:** In the propagation process, signals are interfered with by other noises. Before the feature extraction of the received signals, it was necessary to select an appropriate filter for noise reduction of the received signals. In order to transform the received signal amplitudes in the dimensionless form for calculation and comparison, we normalized the amplitudes. According to the preliminary experiment, the received signals showed periodic variations during the round-trip walking of the target, and different targets showed different waveform characteristics. In this paper, the target walking period was determined based on the autocorrelation method, and the signals were divided to construct the training set and the test set according to the waveform periodicity.  
**Feature extraction and identity recognition:** In the feature extraction stage, the autoencoder was trained to effectively reconstruct the preprocessed data so that it could extract the effective features of the gait behavior. The encoder in the trained autoencoder was stacked with the Softmax layer to generate a deep learning network, which output the individual identity corresponding to the gait behavior and realized single-target gait recognition based on LoRa.  
Figure 5: System architecture.
## 2 Methodology
### Artificial Neural Networks (ANNs)
#### 2.3.2 Recurrent Neural Network  
Another type of neural network is the recurrent neural network (RNN), which is designed for time series data [53]. These networks have loops so that they consider time dependencies between elements in time-series data. Unlike an MLNN, which takes all elements at once, elements in the time-series data are fed to an RNN sequentially. At each step, an inserted element is concatenated with the output of the previous step, and a new output is computed. This structure is illustrated in Figure 2. Given a time-series data set \(\left(\mathbf{x}^{0},\ \mathbf{x}^{1},\ \cdots,\ \mathbf{x}^{n}\right)\), the equations governing an RNN can be written as follows:  
\[\mathbf{y}^{t}=g\left(\mathbf{h}^{t}\right), \tag{8}\]  
\[\mathbf{h}^{t}=f\left(\mathbf{x}^{t},\mathbf{h}^{t-1}\right), \tag{9}\]  
where x, y, and h refer to the input, the output, and the hidden state at time step \(t\), respectively.  
Figure 1: Multi-layer neural network structure.  
While RNNs seem to be capable of capturing all dependencies, training an RNN can be unstable due to the problems of exploding gradients or vanishing gradients [49; 59]. These problems can be solved using a modification of RNNs, which is called the long short-term memory (LSTM) network [49; 59]. An LSTM network is a type of advanced recurrent neural network prediction model, which has achieved remarkable results in many research areas [8; 60]. It has a good structure for learning temporal patterns, which makes it useful for various tasks related to time-series analysis [61]. LSTMs have an advantage over conventional modeling using RNNs as they can much more efficiently learn long-term data through their memory cells and gates [62; 63]. An LSTM consists of a cell and three kinds of gates. The cell connects the first element to every element in the middle and to the final output. Three gates--forget gate, input gate, and output gate--contribute to updating the cell. The forget gate determines which information the cell forgets. The input gate determines which information the cell updates. Finally, the output gate results in an output at each step based on the input and the cell in the current step.
## 3 Results and Discussion
### Adsorption of OGNs on the MNPs
Detailed conditions were presented in Table 2. Different tendencies were observed for various MNPs. For MNP-Ac, the retention does not depend on the type of organic solvent, but its volume. The degree of adsorption is higher when 50% \(v/v\) addition of organic solvent is applied. Almost 90% of DNA-20 was retained when the sample was mixed with 50 \(\mu\)L of ACN. Similarly, in the case of MNP-Sul organic solvent type does not influence adsorption efficiency. It depends on the volume of organic solvent, but contrary to MNP-Ac, the higher retention was possible when 90% \(v/v\) addition was applied. Unfortunately, the highest possible degree of adsorbed OGN was nearly 65%. In the case of MNP-Mal, the DNA-20 adsorption depends both on the types of organic solvent and its volume. The highest retention was possible when the addition of 450 \(\mu\)L of ACE was applied; it was equal to 80%. Zwitterionic ligands of MNPs coatings strongly adsorb water by hydrogen bonding. In described conditions, when high organic solvent content is applied for adsorption, the water-rich layer on the adsorbent surface is created. The retention of polar analytes is predominantly caused by partition between this layer and the liquid phase with a higher content of organic solvent. Likewise, in the case of IE mode, other interactions are also possible. Among all tested modes and MNPs, the highest degree of adsorption was achieved for MNP-Ac, when the sample before the loading step was mixed with NH\({}_{4}\)OAc buffer at pH = 4.5. For these conditions, the impact of adsorption time on its effectiveness was investigated. The results of these investigations are presented in Figure 3. As can be seen, over 90% of OGN was retained on MNP-Ac after 5 min. Further extension of time only slightly increases the percentage of adsorbed DNA-20 (Figure 3). Relatively fast adsorption may relate to a strong electrostatic attraction between the positively charged polymer surface and negatively charged OGN molecules. The loading step time was appointed for 15 min, as a compromise between adsorption time and adsorption effectiveness. The sorption capacity of MNP-Ac for DNA-20 was measured. The measurements were performed by following the procedure described in Section 2.4 for different sample weights (0.52, 0.77, and 1.00 mg). A 50 \(\upmu\)L of 50 \(\upmu\)M DNA-20 standard solution was mixed with the same volume of 10 mM NH4 OAc (pH = 4.5), and then load into conditioned MPS1. After 15 min the supernatant was separated and analyzed. The process was carried out until OGN was detected in the supernatant. The adsorbent sorption capacity was calculated based on the amount of adsorbed OGN and was equal to 1.79 \(\pm\) 0.16 \(\upmu\)mol g\({}^{-1}\) (RSD = 8.8%). This value allows for estimating the adsorbent mass required for the extraction of OGNs. Consequently, 2.0 mg of adsorbent was used during further studies.
## 4 CFD Modeling  
The finite volume method was used in the Computational Fluid Dynamics (CFD) modeling to solve, within each cell in which the tunnel tube volume was discretized, the main governing equations (i.e., continuity, momentum, and energy equations), which are coupled with sub-models describing the physical phenomena (i.e., combustion, turbulence, and thermal radiation). The accuracy of CFD modeling results depends more especially on the fire scenario, physical models used, and mesh fineness. In our study, we used the FDS version 6.7.3, which is developed by the US. National Institute of Standards and Technology (NIST). It represents an open source CFD modeling, widely employed to simulate tunnel fire scenarios. The aforementioned physical phenomena implemented in FDS are, instead, discussed in depth in the FDS user's guide [1]. However, the main input data of FDS regard: tunnel geometry, position of emergency exits, location and geometry of burning vehicle as well as its HRR, pressure difference between portals, yields of combustion products, number and distance between the vehicles queued (computed by using hourly traffic volume at the time of fire).  
It is to be stressed that we preliminarily validated the FDS code by making a comparison between the simulation results with the experimental data of temperatures reported in [PERSON] et al. [21]; an error of no more than 5% proved a good level of conformity.  
After validating the FDS code, a grid sensitivity analysis was performed to define an appropriate mesh resolution. The results of this analysis, which are reported more in details in [PERSON] et al. [18], showed that a cell size smaller than 0.4 m did not provide substantial differences in temperature predictions. Therefore, the tunnel tube was divided into cubic cells of 0.4 m side. A total number of 1,095,255 cells was employed in our study.
## 3 Stratum-Foundation Pit-Station-Track Interaction Model
### Comparative Analysis of Monitoring Results and Numerical Simulation
#### 3.3.2 Comparison of the Deformation Results of the Main Structure of the Station  
The simulation and measured data of station vertical displacement monitoring points ZDH-1-ZDH-14 in the model after the completion of foundation pit excavation were selected for comparative analysis. Figure 23 shows that the simulation of the vertical displacement of the station structure is basically consistent with the measured deformation trend, and the position of the simulated maximum deformation value point is consistent with the position of the measured maximum deformation value point, both of which occur at measuring point ZDH-8 (i.e., the middle of the station). The three-dimensional simulation model of deep and large foundation pit excavation based on the composite support system established by the new foundation pit project can reflect the deformation law of adjacent existing subway stations under the influence of foundation pit construction and has a high reference value for similar projects in the future. The maximum deformation of the station vertical displacement simulation is 1.95 times the actual measured maximum deformation, mainly for the following reasons:  
(a) Because the construction quality of the complex supporting system of the foundation pit is high and the construction requirements of the deformation control of the adjacent station are stricter, the control of the adjacent station in the construction process of the foundation pit is better.  
(b) The model is calculated according to the most unfavorable factors and cannot overcome the time-space effect in the process of foundation pit excavation. The environment of the foundation pit construction site is complex, and the three-dimensional simulation cannot fully simulate the actual situation; thus, there is an idealized state.  
(c) There is a certain error between the hypothesized structure simulation and the real structure in the three-dimensional finite element, and the calculation process is not completely consistent with reality.  
Figure 22: Comparison between the simulation and actual measurement of the horizontal displacement of the retaining pile. **(a)** Comparison of ZQT-4 and ZQT-9 data; **(b)** comparison of ZQT-15 and ZQT-21 data.
## 5 Experiments
### Components Evaluation
#### 5.5.3 Importance of Embedding Model for HG without Manually Setting Meta-Paths  
A graph embedding model can efficiently convert graph data into low-dimensional vectors, supporting downstream tasks such as classification and visualization [48]. Our method adopts the embedding model based on three primary considerations. Firstly, the embedding model approach effectively preserves the complex topology of graph data [49]. This method involves analyzing the connections between nodes during the conversion of high-dimensional graph data into low-dimensional vectors. Secondly, the embedding models have lower computational and memory resource requirements. Traditional machine learning methods often entail the storage of a large number of feature vectors, which can significantly increase computation and memory demands, especially on large-scale datasets. In contrast, embedding models typically have fewer parameters and many of them support the mini-batch technique, which allows them to process large datasets efficiently under limited computational resources. Thirdly, the graph embedding model has been demonstrated to enhance performance across various tasks [50]. One reason for this improvement is the automatic learning of features. While the embedding model also involves feature generation for each component of the Android application, it distinguishes itself from traditional machine learning methods by eliminating the need for manual feature engineering. Instead, the embedding model enhances performance by learning how to represent the data optimally.  
Meta-paths define the complex semantic relationships between source nodes and target nodes, which is a common method to structure heterogeneous graphs. In the context of malware detection, an intuitive meta-path is denoted as App-X-App, where X signifies other node types excluding App. This implies that two applications are similar if they share the same entity, such as a permission.  
However, the experimental results of HAN and Metapath2 vec in Section 5.4 reveal that the schema of meta-paths like App-X-App is not efficient in modeling the structure of APK HGs. Despite the general efficacy of heterogeneous embedding models based on meta-paths, the identification of effective meta-paths within a specific domain is profoundly context-dependent.  
Thus, we employ an embedding generation model for HGs capable of autonomously learning meta-paths and adjusting their importance. This approach is adopted to optimize the model's performance and simplify its usability, as it allows the model to dynamically adapt and optimize the relevance of meta-paths without manual intervention.
## 4 Results and Discussions
### Emerging Digital Technologies
#### 4.1.1 Digital Twin
Compared to traditional manufacturing management tools, such as static CAD models and standard simulation platforms, DTs provide a more dynamic and interactive connectionbetween the physical and virtual environments. While legacy systems typically offer reactive insights based on historical or static data, DTs enable predictive modeling, real-time risk assessment, and continuous feedback loops. This makes them particularly valuable for proactive safety and operational monitoring. Figure 8 highlights the key insights related to DTs in terms of industrial applications, strategic benefits, enabling technologies, safety, and design. The influence of DT technology is categorized into four principal domains. First, technological enablers, which include IoT, AI, data analytics, simulation, and real-time data integration, form the foundation of DT systems. Secondly, industrial applications which encompass workflow optimization, predictive maintenance, quality assurance, system resilience, and adaptive manufacturing, demonstrating DT's role in enhancing operational processes. Thirdly, safety and design benefits are realized through real-time monitoring, hazard simulation, ergonomic adaptation, and virtual risk assessment, contributing to safer and more efficient work environments. Lastly, strategic benefits highlight the impact of DTs on improving efficiency, decision-making, collaboration, sustainability, and responsiveness to dynamic operational needs. This diagram illustrates how DT acts as a pivotal transformative technology driven by advanced digital tools to deliver substantial operational, safety, and strategic advantages across various industries. To conclude, DTs are not just digital copies but dynamic, self-updating systems that allow for real-time simulation, prediction, and intervention. They serve as a core platform for replicating real-time physical environments, enabling immersive simulations and predictive analyses for process monitoring, hazard identification, and ergonomics monitoring. This review found that DTs are central to enabling proactive safety management, validated across high link strength papers. DTs also serve as the backbone for integrating IoT and sensor data, making them essential for conducting predictive analytics. Their integration  
Figure 8: Key insights related to DTs. with IoT and AI lays the foundation for proactive, human-centric safety management in Industry 5.0. DTs have shifted OHS from static compliance-based models to continuous ergonomic optimization and incident forecasting. For example, [56] demonstrated how shipbuilding DTs reduce pre-construction hazards through airflow simulation and equipment layout optimization. However, the effective implementation of DTs depends on access to high-quality real-time data, specialized technical expertise, and seamless integration across platforms. These requirements can pose challenges, especially for smaller or less digitized organizations, such as SMEs.
# Detection and Monitoring of Fitting Progression on Gear Tooth Flank Using Deep Learning
## 1 Introduction
### Gear Failure  
There are many different types of gear teeth damage such as pitting, wear, scoring, and scuffing. Reasons for gear damage and gear failure can be inadequate lubrication, lubrication contamination, installation errors, overload, handling errors, etc. One of the most common types of damage of gear teeth surfaces is pitting. Pitting occurs due to repeated load and contact stress that exceeds surface fatigue strength of the gear material. Pitting can occur as soon as gears start to operate.  
Pitting is a result of rolling and sliding contact fatigue damage, which can occur at the scale of the nominal areas subject to this type of contact or at the scale of roughness. Pitting can be micropitting and macropitting. Micropitting manifests itself as roughness and it can be observed as changed color on some part of the gear tooth flank. Macropitting is the teeth surface damage that includes the appearance of pin-holes and extended flank spalling [25].  
In this paper, we will focus on macropitting. Pitting is divided into initial and destructive pitting. Initial pitting is caused by high local stress due to uneven surfaces on the gear teeth and it can occur without any further progression. In some cases, after the initial pitting occurs, wear may occur in further operation, which could decrease the size of pits or even lead to the disappearance of pitting traces on the teeth surface. Destructive or progressive pitting, as a result of surface overload, increases progressively in size and number of pits. The existence of pits that increase in their number and size decreases the operation effectiveness of the drive and increases noise. Therefore, detection and monitoring of pitting progression are crucial for understanding operating conditions and detecting damage causes.