esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis and Characterization of Micro-Nano Pores in Coal Reservoirs of Different Coal Ranks
###### Abstract  
Coalbed methane represents a promising source of clean and efficient unconventional energy. The intricate network of micro-nano pores within coal serves as the primary adsorption space for gas, contributing to the complexity of gas migration channels. In this study, based on the box-counting method, three coal samples representing low, medium, and high ranks were subjected to high-precision micro-CT scanning and nano-CT scanning to generate three-dimensional (3D) pore network models using Avizo visualization software. This facilitated the accurate and quantitative characterization of the micro-nano pore structures within coal reservoirs. The results indicated that the face rate distribution range of each sample was large, indicating relatively strong heterogeneity in each sample. The volume fractal dimension of each sample, determined through micro-nano-CT scanning, was around 2.5, while the surface fractal dimension exhibited oscillatory characteristics with moderate uniformity. The pore equivalent radius and throat equivalent radius distributions were unimodal across all the samples, with the micro-CT scanning revealing a concentration primarily within the range of 100-400 \(\upmu\)m for the pore equivalent radius and within 200 \(\upmu\)m for the throat equivalent radius. Conversely, the nano-CT scanning exhibited concentrations primarily within the range of 500-2500 \(\upmu\)m for the pore equivalent radius and within 2000 \(\upmu\)m for the throat equivalent radius. The analysis of the 3D reconstruction structures indicated that the middle-rank coal exhibited more developed large-medium pores compared with the low-rank and high-rank coal, while the low-rank and high-rank coal exhibited relatively more micro-small pores. Furthermore, the low-rank coal exhibited the fewest number of pores but the largest average pore equivalent radius and throat radius. Additionally, the middle-high-rank coal exhibited a relatively larger number of pores. Despite the complex topological structures observed in each sample, a significant proportion indicated a coordination number of 0-20, indicating excellent connectivity within the coal samples. This study is conducive to the optimization of coaled methane surface development blocks and the formulation of reasonable development plans.  
pore structure; CT scan test; three-dimensional pore network modeling; coaled methane 2024  
[PERSON], [PERSON], [PERSON], Y. Analysis and Characterization of Micro-Nano Pores in Coal Reservoirs of Different Coal Ranks
# A Novel Batch Streaming Pipeline for Radar Emitter Classification
###### Abstract  
In electronic warfare, radar emitter classification plays a crucial role in identifying threats in complex radar signal environments. Traditionally, this has been achieved using heuristic-based methods and handcrafted features. However, these methods struggle to adapt to the complexities of modern combat environments and varying radar signal characteristics. To address these challenges, this paper introduces a novel batch streaming pipeline for radar emitter classification. Our pipeline consists of two key components: radar deinterleaving and radar pattern recognition. We leveraged the DBSCAN algorithm and an RNN encoder, which are relatively light and simple models, considering the limited hardware resource environment of a military weapon system. Although we chose to utilize lightweight machine learning and deep learning models, we designed our pipeline to perform optimally through hyperparameter optimization of each component. We demonstrate the effectiveness of our proposed model and pipeline through experimental validation and analysis. Overall, this paper provides background knowledge on each model, introduces the proposed pipeline, and presents experimental results.  
radar emitter classification; radar deinterleaving; radar pattern recognition; DBSCAN; RNN encoder; Bayesian optimization; electronic warfare Article
## 1 Introduction  
Target recognition has always been a research hotspot in the field of digital image processing. In the military field, it is widely applied in fields such as intelligence reconnaissance and precision guidance. In the civil field, it is commonly utilized in areas such as video surveillance and medical diagnosis [1]. Most of the current target recognition methods are based on visible light images. Visible light refers to light with wavelengths between 390 and 780 nm; the wavelength range of light is good, the detailed texture information of the scene can be obtained, the picture is clear, and the amount of information is rich, but it cannot recognize the target that is blocked and camouflaged [2], and it is easily affected by lighting, such as target recognition based on visible light images in the night and in rainy and foggy weather. The target recognition method based on infrared images can overcome the influence of illumination. Infrared light refers to the radiation electromagnetic wave with wavelengths between 760 nm and 1000 nm; infrared imaging reflects the difference in the target and background outward radiation energy and has a large distance and strong anti-interference ability. Even in smoke, rain and snowy weather, infrared image feature extraction and recognition can still be performed, but compared to visible light imaging, infrared imaging is not sensitive to brightness in a scene, resulting in lower contrast of the resulting image, poor resolution, and less detailed texture information [3]. In view of the advantages and disadvantages of visible light image recognition and infrared image recognition, this paper proposes a vehicle target recognition method based on visible and infrared image fusion using Bayesian inference, which makes full use of the advantages of the two to obtain images with rich spectral and textural information of visible light and thermal radiation information of infrared images [4], so as to improve the accuracy of vehicle target recognition under various weather conditions.
# Efficient Melody Extraction Based on Extreme Learning Machine
## 1 Introduction
The audio signal is first processed through spectral transform and the instantaneous frequency method is adopted for frequency and amplitude correction. The salience function, based on bin salience mapping and harmonic weighting, is then constructed for determining the salience of frequencies among the pitch range. Afterwards, pitch contours are created based on peak filtering and peak streaming cascaded by contour characterization. Finally, spurious melodic trajectories are iteratively discarded based on some heuristic rules. This method tends to reserve singing contours, hence it works especially well on singing melody extraction. [PERSON] and [PERSON] proved that a single-hidden layer feedforward neural network (SLFN) with \(N\) hidden nodes and with almost any nonlinear activation function can exactly learn \(N\) distinct observations [18]. Later, [PERSON] et al. proved that the input weights and hidden layer biases of SLFN can be randomly assigned if the activation functions of the hidden layer are infinitely differentiable and proposed the extreme learning machine (ELM) [19]. The ELM works efficiently and effectively in both pattern classification and regression [19]. It does not need the slow gradient-based learning algorithms to train neural networks or the iterative parameter tuning. As a result, the ELM network training speed is much faster. Inspired by the fact that the ELM can learn non-linear features efficiently and effectively, an ELM-based melody extraction method is proposed in this work. The mixture signal is processed by an equal loudness filter to mimic the human auditory system and constant-Q transform (CQT) is employed to obtain multi-resolution spectral analysis. The CQT amplitude spectra of several adjacent frames are then concentrated to construct the input vectors for ELM. Next, the rough melodic pitches are estimated by the trained ELM network based on the training set. Finally, melodic pitch fine-tuning is carried out by searching the spectral peaks around the frame-wise rough pitches. The experimental results show that the proposed method achieves higher overall accuracies with fast speed, compared with some typical reference methods. The proposed method has some potential applications in other areas, such as fault diagnosis [20], biomedical signal processing [21], animal emotional recognition [22], and so on. The main contributions of this paper include: The melody extraction is formulated in the ELM framework, which can extract melody efficiently and effectively; the pitch estimation and voicing detection are conducted jointly, which reduces their mutual inhibition; and the melodic pitches are fine-tuned after coarse melody estimation, which reserves the tiny dynamics of melody. The rest of this paper is organized as follows. ELM is presented in Section 2. The ELM-based melody extraction method is elaborated in Section 3. The experimental results and discussions are provided in Section 4. Finally, the conclusions are drawn in Section 5.
## 4 Conclusions  
The results show for the first time that ion-plasma sputtering is an effective method for modifying or removing surface layers in copper selenide films. The ion energy and process duration are significant. At an ion energy of 25 eV, local masks are formed on the surface, leading to the formation of ensembles of cones. When the ion energy is increased to 200 eV at longer process times, these local masks are peeled off and no high cones are formed. The etching process in argon plasma at ion energies of 25-200 eV is accompanied by a transformation of the phase composition of the films by the Cu\({}_{2\times}\)Se-to-Cu\({}_{2}\)Se mechanism. This transformation is due to the specific behavior of the metal and chalcogen atoms during ion-plasma treatment.  
Figure 5: XRD patterns of the films after plasma treatment at ion energies of 25 and 200 eV. The bottom of the figure shows the ICDD card details for cubic phase Cu\({}_{1.78}\)Se (ICDD card 01-072-7490), cubic phase Cu\({}_{2}\)Se (ICDD card 01-088-2043), and monoclinic phase Cu\({}_{2}\)Se (ICDD card 00-058-0228).  
**Author Contributions:** Conceptualization, S.P.Z. and N.-H.K.; methodology, S.P.Z. and N.-H.K.; investigation, I.I.A., S.V.V., I.S.F., L.A.M., and N.-H.K.; data curation, I.I.A., S.V.V., and S.P.Z.; writing--original draft preparation, S.P.Z.; writing--review and editing, N.-H.K.; supervision, S.P.Z. and N.-H.K.; project administration, S.P.Z.; funding acquisition, S.P.Z. and N.-H.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was performed in the framework of the R&D initiative of Yaroslavl State University, project AAAAA16-116070610023-3. The investigation was partially supported by Program no. 0066-2019-0002 of the Ministry of Science and Higher Education of Russian Federation for Valiev Institute of Physics and Technology of RAS. This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20184010201650).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** SEM and XRD investigations were carried out at the Facilities Sharing Center \"Diagnostics of Micro- and Nanostructures\" with the support of the Ministry of Science and Higher Education of Russian Federation.  
**Conflicts of Interest:** The authors declare no conflict 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.
# A Novel Method for Risk Assessment and Simulation of Collision Avoidance for Vessels based on AIS
## 1 Introduction
This paper is organized as follows. Section 2 presents the necessary background and related work information regarding the risk assessment for maritime surveillance and the theory of a vessels' collision warning system. Section 3 introduces and describes the proposed ship encounters in relation to a collision at sea. Section 4 describes the collision avoidance algorithm. The results are discussed in Section 5. Finally, the conclusions and future work are given in Section 6.
# A Flexible Infrastructure-Sharing 5G Network Architecture Based on Network Slicing and Roaming
## 2 Background and Related Work
### Related Work
It uses a two-tier system, with edge data centers for low-latency services and small cell virtualization and a central data center for heavy processing to support public safety applications, offering priority to first responders and emergencies. Each supported public safety service was associated with a different network slice, and the project was validated using an LTE infrastructure. This paper's proposal aims at solving the 5G infrastructure sharing problem by using roaming and network slicing. Our neutral host architecture embraces two different types oftenants: an MNO type, which receives only the necessary NFs to authenticate and control its customer QoS; and a non-MNO type, which receives all NFs to provide a 5G service to its users. This approach keeps the visibility and trust of the SPs, isolates and separates traffic among SPs, and reduces the complexity and management by providing only the necessary NFs to each SP according to its needs and agreements.
## 4 Experimental Results  
This section provides a systematic analysis and evaluation of both the detection function and the image processing capability of the enhanced YOLOV9 across different conditions. The experimental outcomes of the proposed algorithm in varied adverse weather environments are consolidated and summarized. To validate the IDP-YOLOV9 structure in fog, rain, and snow scenarios, comparisons were made with existing defogging methods (AOD-NET [58], GridDehazeNet [41]), deraining methods (EfficientDeRain [59], ADMM-ResNet [60]), and desnowing methods (U-DenseNet [61], ALL In One [62]). Subsequently, comparisons were conducted with existing detection methods, including the Faster R-CNN, SSD, RetinaNet, YOLOV8, and YOLOV9 methods. Finally, a comprehensive comparison was made between the image processing algorithms, such as AOD-NET, GridDehazeNet, EfficientDeRain, ADMM-ResNet, and so on. The above-mentioned experiments were performed using a system featuring an NVIDIA GeForce GTX 4090 GPU. This training procedure integrated the YOLOV9 detection loss with the losses from the three routing attention layers, providing a holistic optimization approach for improved detection performance under various weather conditions.
## 2 Theoretical Analysis of the Model  
As the optical beam propagates through the atmospheric channel, the geometrical losses, the scattering, the absorption, and the turbulence are the most significant attenuation parameters.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 3 Experimental Results of RF Wireless Power Transmission
### Dual-Band (2.4 and 5.8 GHz) RF WPT Experiment
#### 3.2.2 WPT Experiment (4): With Two Rx Antennas Operating at Two Different Frequencies  
The dual-band RF WPT experiment shown in Figure 1d was carried out. Here, dual beams of 2.482 GHz and 5.73 GHz were formed individually and transmitted to two receivers (Rx(1) and Rx(2)) located at different positions, as described in Figure 20; Rx(1) is a 2.4 GHz Rx antenna, shown in Figure 2a, and exists at \(x^{\prime}=0.6\) m, \(y^{\prime}=0\), and \(z^{\prime}=2.5\) m, including the Fresnel region of the 2.4 GHz Tx thinned array. Rx(2) is the 5.8 GHz Rx antenna shown in Figure 2b and is at \(x^{\prime}=0,~{}y^{\prime}=0.6\) m, and \(z^{\prime}=2.5\) m in the Fresnel region of the 5.8 GHz Tx thinned array. In this experiment, 2.482 GHz wireless power of 40 dBm and 5.73 GHz wireless power of 40 dBm were radiated separately in the directions of Rx(1) and Rx(2), respectively. When the 2.482 and 5.73 GHz RF power was transmitted with focused beams, the power levels received through Rx(1) and Rx(2) were measured by spectrum analyzers. The measured results of the received power at 2.482 and 5.73 GHz were 15.57 dBm and 10.58 dBm, respectively (see Table 5). Accordingly, the proposed dual-band RF WPT system allows wireless power transfers to two receivers at different positions by forming dual beam with different and seperate operating frequencies.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Received**} & \multicolumn{4}{c}{**Rx: Dual-Band Antenna**} \\ \cline{2-5}  & \multicolumn{2}{c}{**2.482 GHz**} & \multicolumn{2}{c}{**5.73 GHz**} \\ \cline{2-5}  & **Calculated** & **Measured** & **Calculated** & **Measured** \\ \hline \multirow{2}{*}{**Beam-forming** **(in-phase)**} & \multirow{2}{*}{23.13 dBm} & \multirow{2}{*}{22.3 dBm} & \multirow{2}{*}{12.92 dBm} & \multirow{2}{*}{12.6 dBm} \\  & & & & \\ \end{tabular}
\end{table}
Table 4: Experimental results of the received power when transmitting dual-band power to a dual-band Rx antenna.  
Figure 20: Dual-band RF WPT experiment set-up for transmitting 2.482 and 5.73 GHz RF power separately to two received antennas.
## 5 Discussions and Future Directions  
### Progress for ANN in CM  
During the last two decades, there has been a growing interest in ANN in CM. Compared with the research between 1989 to 2000 [17], some progress has been revealed after conducting a comprehensive literature review on ANN in CM as shown in Table 7.  
New types of ANN (RBFNN, GRNN, PNN, CNN, etc.) have been developed and gradually applied to the CM field. CNN has solved image classification in the field of CM to a great extent, such as the applications of safety guardrail detection in 2D images and automatic detection of building damage. In addition, ANN is no longer considered as an isolated algorithm. Recent research is not only on ANN itself but also on integrating with other computing paradigms and more adaptive system development., e.g., fuzzy neural inference system and frameworks for facility operations and management. Similarly, in addition to previous research interests such as cost and scheduling [17], the research field of ANN in CM have been expanded more widely to meet challenging practical needs. In recent years, as shown in Table 4, ANN has been used to predict safe work behavior, manage energy consumption, detect building damage, and optimize the bid selection policy.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Applications Fields**} & \multicolumn{6}{c}{**Project Level**} \\ \cline{2-7}  & **Planning and** & **Bidding** & **Construction** &
\begin{tabular}{c} **Operation** \\ **Maintenance** \\ \end{tabular} & **Enterprise Level** & **Industry Level** \\ \hline Cost & 16 & 3 & 3 & 0 & 1 & 3 \\ Performance & 2 & 0 & 9 & 0 & 5 & 2 \\ Safety & 0 & 0 & 13 & 0 & 0 & 0 \\ Quality & 0 & 0 & 2 & 7 & 0 & 0 \\ Resource & 1 & 0 & 5 & 1 & 1 & 1 \\ Procurement & 0 & 4 & 0 & 0 & 0 & 1 \\ Risk management & 0 & 0 & 3 & 0 & 4 & 0 \\ Contract & 0 & 0 & 6 & 0 & 0 & 0 \\ Schedule & 1 & 0 & 5 & 0 & 0 & 0 \\ Environment and sustainability & 1 & 0 & 0 & 3 & 0 & 0 \\ Other & 4 & 1 & 1 & 1 & 0 & 2 \\ \hline Total & 25 & 8 & 47 & 12 & 11 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Stages and levels.  
Regarding the method integrated with ANN, researchers integrate GA, FL and wavelets with ANN more often before 2000, while nearest neighbor, decision tree, case-based reasoning, support vector machine, regression analysis, and deep learning methods have become the mainstream between 2000 and 2020. Emerging technologies, e.g., computer vision, smart sensors, and smartphone applications, have been actively used to assist ANN in solving complex CM problems. [PERSON] et al. [82] proposed an early warning system for working in hot and humid environments, integrating smart sensor technology, location tracking technology, and information communication technology.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 3 Energy-Efficiency Model of the Electric Drive
### Electric Drive Combinations  
To increase the energy efficiency of the VSDP, we propose five alternative combinations of motors and converters. These have been selected to mechanically match two centrifugal pumps (15 kW and 18.5 kW), which are described later in Section 5. For each pump, we compared at least two motor efficiency classes: the standard IE3 class and the new high-efficiency IE5 class. As the widespread introduction of the IE5 class is still in its infancy, manufacturers have not yet offered a motor for each power level. We have therefore selected two high-efficiency motors for the 18.5 kW pump, namely the IE4 class 18.5 kW and the IE5 class 22 kW.  
Table 2 lists the motors with their type, size, rated data, and efficiency class. The first two entries are used for a 15 kW pump, the remaining three for an 18.5 kW pump. It is important to note that all high-efficiency motors (IE4 and IE5) are synchronous reluctance motors (SRM). A suitable converter is selected for each motor based on the required current under the rated operating conditions (Table 3). Both the motors and the converters are from the same manufacturer (ABB, Zurich, Switzerland). In addition, the last row in both tables represents an equivalent motor and converter to the existing drive configuration (Table 1).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Designation** & **Frame Size** & \begin{tabular}{c} **Motor** \\ **Type** \\ \end{tabular} & \begin{tabular}{c} **Power** \\ **(kW)** \\ \end{tabular} & \begin{tabular}{c} **Current** \\ **(A)** \\ \end{tabular} & \begin{tabular}{c} **Speed** \\ **(rpm)** \\ \end{tabular} & \begin{tabular}{c} **Torque** \\ **(Nm)** \\ \end{tabular} & \
\begin{tabular}{c} **Temp. Rise** \\ **(K)** \\ \end{tabular} &
\begin{tabular}{c} **Efficiency** \\ **Class** \\ \end{tabular} \\ \hline
15 kW IE3 & 160 M & IM & 15 & 28.0 & 1467 & 97.6 & 51 & IE3 \\
15 kW IE5 & 160 M & SRM & 15 & 33.7 & 1500 & 95 & 46 & IE5 \\
18.5 kW IE3 & 160 M & IM & 18.5 & 35.1 & 1473 & 120 & 57 & IE3 \\
18.5 kW IE4 & 180 M & SRM & 18.5 & 42 & 1500 & 118 & 54 & IE4 \\
22 kW IE5 & 200 M & SRM & 22 & 49.1 & 1500 & 140 & 30 & IE5 \\
30 kW IE1 & 200 M & IM & 30 & 57.3 & 1475 & 198 & 78 & IE1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected motors.
## 1 Introduction
Finally, the actually measured load data is applied to verify the prediction effectiveness and prediction effect of the model under different weather conditions. The phase space is a tool to feature a dynamic system that is reconstructed from a univariate or multivariate time series [23], which is widely used in forecasting models. The DNN is the development of the traditional ANN and suitable for net load forecasting because the nonlinear fitting ability is strengthened [24]. The high accuracy in forecasting the net load under high PV penetration rate and different weather conditions is verified using real data. The contribution of this paper can be summarized as follows:  
* The bus load prediction model is established considering distributed PV power supply. The prediction results are necessary guidance for the power grid dispatching, which is conducive to the improvement of PV consumption. * The phase space reconstruction is used to process bus load data, and one-dimensional time series data are inversely constructed into the phase space structure of the original system, which can better describe the dynamic characteristics and adapt to the strong fluctuation of the bus load. * Levenberg-Marquardt back propagation (LMBP) algorithm is used to train DNN, which accelerates the training speed. Compared with the single hidden layer neural network, DNN can fit the historical data better and significantly improve the accuracy of ultrashort-term load forecasting.