esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 4 Applications of Microseismic Monitoring in Coal Mine
### Coal and Gas Outburst  
The generation and mechanism of coal and gas outbursts during deep coal mining are complicated, and a catastrophic disaster suddenly ejects a significant quantity of coal and gas to the excavation face, which destroys underground structures and threatens worker  
Figure 9: Sketch of water inrush from various water source locations in coal mines [19].  
safety [111]. Approximately 510 coal and gas outburst accidents occurred in China between 2001 and 2017, which resulted in 3576 deaths [112].  
The micro-fracture of coal/rock masses is the precursory characteristic of coal and gas outbursts; therefore, microseismic monitoring, which is capable of capturing the precursor, is extensively utilized to study and warn of coal and gas outburst disasters. [PERSON] et al. [113] studied the generation process and the mechanism of a coal and gas outburst in the Haizi coal mine by analyzing the spectral characteristics of seismic waveforms and the evolution of the event count and energy release and revealed that the hazard was caused by external shock wave disturbance and highly stressed igneous rock. [PERSON] et al. [114] employed seismic tomography and microseismic monitoring programs to assess the dynamic response of the coal seam of Coal Mine Velenje and found a direct relationship between microseismic energy and gas emissions, in which the gas emission rate tends to reach its peak with a sharp increase in microseismic energy. [PERSON] et al. [115] analyzed the seismic waveform, time-frequency spectrum, and fractal of microseismic energy to identify the differences between tremors and coal and gas outbursts in coal mines. They further divided the coal and outburst process into the pre-shock stage, the secondary shock stage, and the main shock stage. [PERSON] et al. [89] utilized the characteristic parameters of the frequency spectrum and microseismic waveform as inputs for the long short-term memory network to recognize the microseismicities generated in different operation stages in the heading face of the Xinyuan coal mine with a low loss value and high accuracy rate, and these events can be used to study the gas concentration process and accurately warn miners of the outbursts. [PERSON] et al. [116] divided the coal and gas outburst process into the preparation stage, the start stage, the development stage, and the stop stage based on the variation in cavity gas pressure in a simulation experiment (Figure 11), noting that coal and gas outbursts occur at stage III. Furthermore, the microseismic characteristics during the generation process of coal and gas outbursts were studied, which quantitatively describe the coal and gas outburst process and provide insight into warnings of coal and gas outbursts.  
Figure 10: The early warning system for coal mine water innush disaster, revised from [59].
# A Survey of Generative AI for Detecting Pedophilia Crimes
## 5 Benefits of Generative AI in Detecting Pedophilia
### Predicting Potential Offenders  
Generative AI has the potential to analyze online signals and identify high-level patterns and risk factors associated with harmful behaviors. This capability represents a strategic shift from reactive responses to proactive prevention. By processing large-scale datasets derived from past incidents, such as the abuse reports studied by [PERSON] et al., these models can support authorities in identifying offender tactics and the underlying dynamics of criminal activity. These data-driven insights are essential for developing more targeted and effective preventive strategies, including awareness campaigns tailored to specific threats. This contributes to safer digital environments for children [6].  
In a related study, [PERSON] et al. [26] proposed an LSTM-based model that was trained using chat logs and enriched with sentiment vectors. The system could anticipate predatory behavior by analyzing the emotional tone of conversations. This provided a predictive layer that was highly relevant for early intervention efforts.
# A Novel Bionic Structure Inspired by Lufta Sponge and Its Cushion Properties
## 3 Results and Discussion
### Comparison and Discussions  
The specific energy absorption values of the bionic structure are compared in Figure 16. The specific energy absorption value of the bionic structure is close to that of the luffa sponge. With the same specific energy absorption value, the bionic structure corresponds to a higher specific strength, but there is still a gap compared with foam-filled metal honeycomb and rubber lattice structures. The research in this paper is mainly based on a quasi-static compress. The velocity effect under dynamics needs further study. Many existing studies have shown that velocity has an impact on the mechanical properties of materials [15; 26]. In addition, TPU filament was used in this study, and its density was 1200 kg/m\({}^{3}\). The advantage is that the TPU material is a kind of hot-melt material, which is easy to process, has excellent resilience performance and can be reused. It can effectively reduce the single-use cost with more time. However, the high density limits its mechanical properties. TPU is not the most suitable material, and the properties of the material itself have a great influence on the mechanical properties. The lateral comparison of using other materials is the main content of the next stage in order to find the most suitable material. The bionic structure is similar to the tree structure, where the latter has been widely used in the building field [27]. However, this type of structure is rarely used in the design of cushioning, shock absorption and packaging structure. As an application example, we present a concept of transport pallet based on the luffa sponge bionic structure, as shown in Figure 17. The new pallet adopts the bionic structure parts instead of the traditional solid wood pad to bear the main load. Due to its good resilience, it can provide lasting protection for products, so it is greatly suitable for the transportation of precision instruments. The bionic structure has been greatly expanded due to the study of the luffa sponge, which is a potential research direction in the future.  
Figure 16: Comparison of _SEA_ and specific strength among bionic structures and the other materials [11; 28].
## 5 False Call Analysis  
The observed data aggregate the characteristics of the targets signature corrupted by aberrant signals generally referred to as noise. Classical POD methods usually measure noise as part of planned experimental measurements, however, that is absent in the current work. Noise, therefore, will be inferred from the observed data. Noise in this context refers to observed signals with no useful target characterization information. Therefore, observed data outside the prediction bounds will be interpreted as noise because the corresponding POD is zero. Still using data from Figure 10, the noisy data refer to the red circled data in Figure 10. The extracted noise and the corresponding noise parameters are shown in Figure 12.  
The false call or probability of false positive (PFP) can be calculated as the noise distribution above the selected threshold. The PFP is computed as:  
\[PFP=P(y_{noise}>y_{th}) \tag{9}\]  
A statistical \(\chi^{2}\) (Chi-squared) hypothesis test is undertaken to identify the nature of noise distribution. Various distributions are tested. The Gaussian distribution emerges as the most plausible. An analysis is carried out on the noisy data, and the mean \(\mu_{noise}\) and standard deviation \(\sigma_{noise}\) are calculated.  
Figure 11: POD curve, \(\alpha\): 90/95 POD value.  
For a Gaussian distribution, the probability of false call is computed as:  
\[PFP=\int_{y_{th}}^{\infty}\frac{1}{\sqrt{2\pi}\sigma_{noise}}e^{-\frac{(y-B_{ noise})^{2}}{2\sigma_{noise}^{2}}}dy. \tag{10}\]  
The distribution with regards to PFP is illustrated in Figure 13 (shaded red area relative to the selected decision threshold).  
From Figure 13, it becomes evident that for a selected decision threshold (DT), a corresponding unique FAR value exists; however, the detection probability varies relative to a parameter (here: contrast). This implies that the premise for the construction of the ROC/PR curve for applications requiring the incorporation of process parameter is deficient. This is because for a selected cut-off point there is not one FAR to one DR value but one FAR to many DR values. This consideration was not factored initially because the size of opposition objects/planes during WWII was irrelevant, but modern applications are concerned with how the characteristics of target change the probability of detecting it. Relative to this specific example, for a selected decision threshold of 3.5, the PFP value is 0.1606. To evaluate the PFP corresponding to all thresholds and the associated POD, a trade-off analysis is presented next.  
Figure 12: Gaussian noise parameters.  
Figure 13: PFP relative to decision threshold.
# Lift-Thrust Integrated Ducted-Grid Fusion Configuration Design for a Ducted Fan Tail-Sitter UAV
## 5 Conclusions  
This paper proposes a new configuration of embedded grid channels for the improvement of a ducted fan, and presents design steps for the duct-grid fusion configuration. The results of the present study generally analyzed the aerodynamic characteristics of the ducted fan configurations before and after the improvement based on CFD methods and the influence of grid channel parameters was studied in detail. The main conclusions are as follows:  
1. The design of embedded grid channels on the duct wall can generate a significant level of flight lift without affecting the thrust and power characteristics of the original duct, which is expected to significantly reduce the wing area and thus make the overall shape of the aircraft more compact;
2. The grid embedded in the duct wall induces the expansion of the low-pressure zone at the lip, resulting in a difference in force components on both sides with and without the grid, and the lift also comes from the difference in force on both sides;
3. The design parameters of the duct-grid fusion configuration mainly include the distribution angle of the circular grid, the width of the grid channel, the number of circumferential grids, and the number of flow grids. Among them, the parameters that have the most significant impact on the performance of the entire improved design configuration are the distribution angle of the circular grid, the width of the grid channel, and the number of flow grids. The range of circular distribution angles should not be too large. In small angle ranges, lift increases with the expansion of the range. The width of the grid channel determines the impact effect on the airflow in the front and rear of the duct. If the grid width is too large, it will induce the development of the low-pressure area at the lip and also cause separation at the rear of the duct. When the number of flow grids increases, it has a favorable effect on the flow field before and after the duct, and can reduce the tip vortex leakage effect and improve the comprehensive aerodynamic characteristics.  
Conceptualization, L.L.; Validation, L.L. and B.M.; Formal analysis, L.L.; Investigation, B.M.; Writing--original draft, L.L.; Writing--review & editing, B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 2 Principle
### Principle of BiLSTM  
BiLSTM networks are an extension of Long Short-Term Memory (LSTM) networks. LSTM networks address the vanishing gradient problem encountered in traditional Recurrent Neural Networks (RNNs) by introducing memory cells (cell state) and three gates, the input gate, the forget gate, and the output gate, allowing the network to retain long-term dependencies. BiLSTM consists of two LSTM networks: one processes the sequence from the beginning to the end (forward direction), while the other processes the sequence from the end to the beginning (backward direction). The outputs of both LSTMs are then concatenated, providing a complete context for each time step in the sequence.  
The forget gate determines which information in the cell state should be discarded. It is a sigmoid layer that outputs a number between 0 and 1 for each number in the cell state \(C_{t-1}\). A value of 1 means \"completely keep this\", while a value of 0 means \"completely get rid of this [12]\":  
\[f_{t}=\sigma(W_{f}\cdot[h_{t-1},x_{t}]+b_{f}) \tag{1}\]  
The input gate decides which new information will be stored in the cell state. It is composed of a sigmoid layer and a tanh layer. The sigmoid layer decides which values to update, and the tanh layer creates a vector of new candidate values \(\tilde{C}_{t}\) that could be added to the cell state  
\[i_{t}=\sigma(W_{i}\cdot[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\tilde{C}_{t}=tanh(W_{\text{C}}\cdot[h_{t-1},x_{t}]+b_{\text{C}}) \tag{3}\]  
The output gate decides which parts of the cell state will be output. The output is based on the cell state, but it is a filtered version. First, a sigmoid layer decides which parts of the cell state are output. Then, the cell state is put through the tanh function and multiplied by the output of the sigmoid gate:  
\[o_{t}=\sigma(W_{o}\cdot[h_{t-1},x_{t}]+b_{o}) \tag{4}\]  
\[h_{t}=o_{i}\cdot tanh(C_{t}) \tag{5}\]  
By processing sequences in both forward and backward directions, BiLSTM networks can capture information from the entire sequence context, leading to a more comprehensive understanding of the data. This capability makes BiLSTM particularly effective for tasks requiring an understanding of temporal dependencies, such as VLC channel modeling for DCO-OFDM systems.
# A Performance-Based Airspace Model for Unmanned Aircraft Systems Traffic Management
## 3 Urban Airspace Model
### Occupancy Grid  
To facilitate ATM/UTM system processing and enhanced capability to balance airspace demand and capacity dynamically, the occupancy grid concept is proposed to approximate the volumetric demand. A grid of elementary cells is constructed for the entire urban region adopting the models in Section 3.2. The CNS protection volumes around each active aircraft are then superimposed to the grid as illustrated in Figure 9. An elementary cell is designated as occupied when it contains at least one point of the CNS protection volume bounding each aircraft. Each occupied cell, therefore, is a result of the demand placed on the airspace by virtue of the CNS performance. For visualisation purposes, occupied and free elementary cells are designated different colours (red and grey respectively).  
The occupancy grid approach allows for a rapid assessment of the occupied space against the overall free space. A count performed over the cells supports the calculation of the theoretical remaining capacity to service the airspace with the given CNS infrastructure.
## 5 Some Applications of Antiresonance Assignment through Active Control
### Antiresonance Assignment in Buildings and Civil Structures  
In civil engineering active and semi-active TMDs have been widely employed over the last decades. Several literature reviews list the papers; hence here just some samples are provided.  
Several studies have been performed, both through simulations and experimentations. For example, active TMDs have been developed to control buildings. In [92] an active tuned mass damper which exploits acceleration, velocity and displacement feedback isdeveloped to control 1-DOF systems. The technique is applied by simulating the response of a 10-Story Three-Bay Steel Building excited through the acceleration record in the 1940s during the Ground EI Centro earthquake. The control of floor vibrations is tackled in [93] through the design and simulation of a SATMD with variable damping obtained through piezoelectric friction dampers which ensure a quick response if compared with the traditionally employed hydraulic fluid dampers. The same problem is analyzed in [94] where the mass variability due to the human occupants is considered. A semiactive magnetorheological pendulum TMD is designed and experimental tests on a laboratory testbed are carried out. Several practical applications of ATMD and SATMD for vibration control of buildings in Japan are provided in [95].  
Active structural vibration control for seismic protection in nuclear power plants is considered in [96]. State-feedback control is employed to damp the resonances of the closed-loop system and further, anti-resonances of the structure are selected in such a way that those neutralize some of the main resonances of the bedrock, numerical simulations are performed to validate the effectiveness of the proposed approach.  
Some applications in the control of railway vehicles have been developed inspired by the seminal work on VATMD proposed in [44]. For example, in [97] the VATMD is exploited to suppress the self-excited vibration that arises between the girder and magnetic levitation vehicles by performing an active antiresonance assignment through an electromagnetic control force; numerical simulations corroborate the effectiveness of the technique. The semi-active TMD using a magneto-rheological fluid damper for controlling the vibration of railway vehicles is proposed in [98] with an experimental application.  
Employing TMDs for antiresonance assignment has been attractive for controlling wind turbines vibration too, wither through semi-active [99] or active [100; 101; 102] TMDs. An experimental application of semi-active control of a wind turbine is provided in [103] through a laboratory testbed. A review of structural control for wind turbines is proposed in [104].
## 1 Introduction
### Biomass Retrieval--Optimal Spectral Bands and Indices Selection  
Traditionally, biomass is estimated based on allometric equations or empirical relationships between the satellite data and the biomass measured in situ. However, these two approaches are species- and location-specific and therefore cannot be extrapolated to other species and locations. More complex approaches have been introduced such as Artificial Neural Networks [54], physically-based reflectance models (e.g., PARAS model by [44]), Support Vector Machines [55], Support Vector Regression [35], Random Forests [19; 37], and Sparse Partial Least Squares Regression [21]. Nevertheless, it is important to note that [PERSON] et al. [56] claimed that methodological complexity does not lead to improved precision; as a matter of fact, they suggested that a way to improve precision is by integrating uncertainty estimates.
# Real-Time SLAM and Faster Object Detection on a Wheeled Lifting Robot with Mobile-ROS Interaction
## 4 Experiments
### Comparative Analysis  
In this section, comparison experiments were conducted to verify the efficiency and performance of the proposed YOLOv8-R. Specifically, YOLOv8-R was compared with different sizes of YOLOv8 models. As shown in Table 2, the experimental results indicate that YOLOv8-R not only achieves comparable performance but also the fastest object detection speed among the five models. Furthermore, the visualization results in real scenarios can be seen in Figure 5.  
The closely comparable mAP values across these models can be attributed to the robust performance of the YOLO series and the simplicity of the dataset we employed. Within the vision subsystem, our foremost objective is to achieve the fastest possible detection speeds while ensuring reliable object detection performance. To achieve this, a powerful lightweight module, i.e., Star Block, is employed to extract features from images more efficiently. Meanwhile, pruning techniques are utilized to effectively remove non-essential parameters, thereby retaining only those that are crucial for maintaining the integrity of the detection performance. The integration of the pruning model with the lightweight module utilizes fewer parameters compared to the original versions. In simpler scenarios, this configuration not only ensures the maintenance of high object detection performance but, most importantly, significantly increases the speed of detection. Consequently, robust real-time object detection can be achieved on edge devices with limited computational resources while maintaining high object detection performance. Notably, the main contribution of this section is the construction of such a scenario to verify the effectiveness of the proposed AMSeWL-R system, which can later be extended to more complex scenarios.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **[EMAIL_ADDRESS]/\% \(\uparrow\)** & **[EMAIL_ADDRESS]:0.95/\% \(\uparrow\)** & **Parameters/M \(\downarrow\)** & **FLOPS/B \(\downarrow\)** & **FPS \(\uparrow\)** \\ \hline YOLOv8n & 0.960 & 0.841 & 3.0 & 8.1 & 283.0 \\ YOLOv8s & 0.965 & 0.842 & 11.1 & 28.4 & 115.3 \\ YOLOv8m & 0.971 & 0.847 & 25.8 & 78.7 & 38.3 \\ YOLOv8l & **0.980** & **0.861** & 43.5 & 164.8 & 26.4 \\ \hline
**YOLOv8-R (Ours)** & 0.973 & 0.821 & **0.1** & **1.0** & **639.8** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of the proposed YOLOv8-R compared to four different sizes of YOLOv8 on the collected test dataset. The up arrow (\(\uparrow\)) indicates that higher values are better, and the down arrow (\(\downarrow\)) indicates that lower values are better.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 2 NTF-Based Digital Twin Ecosystem in Aviation Industry
### Structure of NTF-Based Digital Twin Ecosystem
The decentralized nature of blockchain also enhances data security and resilience, which are crucial factors in the highly regulated aviation industry. Smart contracts, self-executing code on the blockchain, play a pivotal role in automating various processes within the ecosystem. These contracts can trigger maintenance alerts based on usage metrics, update certification statuses, manage access rights to technical  
Figure 1: Structure of the aviation digital twin ecosystem based on NFTs. data, and even facilitate ownership or lease transfers. By automating these processes, smart contracts reduce human error, increase efficiency, and ensure compliance with predefined rules and regulations. The rich, real-time data provided by this DTE enables advanced analytics and predictive maintenance capabilities. By analyzing the vast amount of data collected across multiple aircraft and components, airlines and maintenance providers can identify patterns, predict potential issues before they occur, and optimize their operations. This data-driven approach not only enhances safety but also has the potential to significantly reduce maintenance costs and aircraft downtime. Regulatory compliance, a critical aspect of aviation, is streamlined within this ecosystem. Regulatory authorities can have direct, permissioned access to relevant NFT data, allowing for more efficient audits and inspections. The immutable nature of blockchain ensures that all records are tamper-proof, providing a reliable audit trail. Compliance checks can be automated through smart contracts, alerting stakeholders to any deviations from regulatory requirements. As aircraft and components reach the end of their operational life, the DTE continues to provide value. The complete history contained within the NFTs facilitates efficient and compliant decommissioning processes. Recycling and disposal activities are recorded, ensuring adherence to environmental regulations. Even after a physical asset is retired, its digital twin remains as a permanent, accessible record.