esa-sceva/satcom-chunk-collection huggingface dataset:
# Priority-Based Machine-To-Machine Overlay Network over LTE for a Smart City
## 1 Introduction
The overlay network is based on the CSMA-CA mechanism similar to IEEE 802.15.4 [11] and allows M2M access to be completed without the four-way handshake. H2H traffic concurrently uses remaining resources and is coupled with M2M traffic through the SINR at the eNodeB. Further differentiation is possible using different and explicit power levels for M2M and H2H traffic [12]. However, this approach [10; 12] does not address two important aspects of M2M communications. First, differentiation among different traffic classes in the M2M overlay network is needed. Second, SINR coupling between M2M and H2H traffic classes has been considered only at the packet reception level, but not at the level of listening to the medium during the CSMA-CA backoff process. This could produce incorrect durations of the backoff process and a too conservative estimate of the congestion at the overlay network. In this work, we introduce priority differentiation in the overlay network by using the CSMA-CA similar to IEEE 802.15.6 [13] with modifications necessary to match the existing physical layer derived from PRACH. We model the medium access control algorithm including a model of imprecise listening outcome during the backoff process, which is shown to decrease the capacity of the overlay network to a non-negligible extent. We demonstrate the functionality and performance of our scheme using different schemes and bandwidths of PRACH, as well as PRACH design scenarios for micro- and macro-cells. Using an accurate characterization of noise and interference caused by other calls from the given cell, as well as from the surrounding cells (which is absent from other proposals), we show that the scheme is capable of achieving satisfactory performance, as well as sufficient differentiation between traffic classes. It is, thus, suitable for the massive Machine-Type Communications (mMTC) scenario--i.e., a large number of MTC devices with short messages and low arrival rates--which represents one of the major use cases for the development of 5G radio and network technology [14]. Furthermore, our scheme allows M2M terminals to actually transmit data during PRACH access, which in most cases should suffice given the short messages typical for M2M devices, whereas other schemes use random access to initiate a connection and send actual data only later, which increases message latency and leads to inefficient utilization of the available bandwidth. The rest of the paper is organized as follows: In Section 2, we present the PRACH architecture and random access procedure. In Section 3, we present the M2M overlay network with the physical and MAC layer, preceded by a brief overview of earlier work on such overlays using different WLAN technologies. In Section 4, we present the random access model for H2H and PM2M traffic. The analytical model of PM2M with backoff error is discussed in Section 5. Performance evaluation of H2H and PM2M traffic (with and without the backoff error) is shown in Section 6. Finally, Section 7 concludes the paper.
# Design of Typhoon Detection Downcast Device Based on Four-Arm Helical Antenna Structure
## 4 Analysis and Improvement
### Comparison with Existing Antennas  
One solution currently available on the market is InterMet's iMet-3050A portable antenna, which is also a helical antenna that works in the 403 MHz frequency band and can perform environmental detection. However, it is not very suitable for lightweight typhoon detection scenarios. It has a gain of 2 dB, weighs 2.2 kg, and is 35 cm long. The results of the present study exhibit not only the reduced length and volume of the antenna but also an increase in gain of 4 dB, making our sensor more suitable for typhoon detection environments.  
Other typhoon receiving antennas have been developed in recent years; for example, [PERSON] et al. proposed a compact conformal helical antenna designed for typhoon detection that features a good axial ratio and wide bandwidth characteristics [20]. Similarly, [PERSON], [PERSON] et al. developed another antenna designed for environmental sensing, emphasizing lightweight construction to enhance reliability [21]. However, despite their superior axial ratios and other parameters, these antennas cannot be effectively deployed as droppable mechanisms in typhoon detection scenarios due to their complex structures, large dimensions, and weight constraints.  
Compared to existing typhoon detection solutions and the typical application scenarios of quadrifilar helical antennas, our innovation lies in the following aspects. First, through mechanical structure design, we achieved maximum space utilization, using the quadrifilar helical antenna as a droppable device, thereby providing space for the radiosonde. Secondly, for airship detection scenarios, we reduced the device's weight, achieving better integration, miniaturization, and lightweight design. Thirdly, flexible materials were chosen to ensure that the antenna can still operate stably in harsh typhoon environments.
# Reliable Augmentation and Precise Identification of EPG Waveforms Based on Multi-Criteria DCGAN
## 1 Introduction
In the EPG signals of psyllids, the E1 waveform appears infrequently, making it costly to obtain a sufficient dataset. Data augmentation techniques could be an effective solution to this problem. Generative adversarial networks (GANs) are unsupervised generative models initially developed to augment datasets in machine vision. These models aim to learn the distribution of real data and generate virtual data that resemble the real data [13; 14]. GANs have shown great potential in the field of one-dimensional signal research. [PERSON] introduced GANs for data augmentation, improving the classification accuracy of their support vector machine (SVM) model from 88.89% to 95.33% [15]. [PERSON] used a deep convolutional generative adversarial network (DCGAN) to generate an extended hyperspectral dataset of maize haploid seeds, achieving 100% classification accuracy when combined with a SVM model [16]. In a study on electrocardiography, the use of a deep learning-based atrial fibrillation (AF) signal classification method benefited more from data augmentation using GANs and GARCH than from oversampling. Ultimately, the accuracy improved by 3% after GAN augmentation [17]. These studies provide effective solutions to the time-consuming and labor-intensive problems of collecting sufficient sample data for EPG signals. However, the methods for assessing the reliability of generated data remain immature and require further research testing. Herein, this study aimed to generate and evaluate the difficult-to-collect E1 waveforms of psyllid EPG signals using a multi-criteria DCGAN and to identify the seven types of EPG signals with four machine learning algorithms (i.e., DT, SVM, KNN, and CNN). The main research tasks of this study were as follows: (1) The DCGAN network was used to generate psyllid E1 waveforms, with maximum mean discrepancy (MMD) employed to assess the similarity between generated and real data, singular value decomposition (SVD) to determine the optimal training iterations for DCGAN, and visual inspection to ensure the accuracy of the generated data. (2) Verification of the accuracy of the generated waveforms through visual inspection and comparison of the classification accuracy of DT, SVM, KNN, and CNN models before and after DCGAN optimization to validate the effectiveness of the DCGAN network. (3) Selection of the DCGAN-CNN framework to achieve efficient recognition of seven types of waveforms (i.e., A, B, C, PD, E1, E2, and G) in psyllid EPG signals, providing insights into the feeding behavior of psyllids. The proposed DCGAN sample augmentation strategy, based on parameter optimization and evaluation, combined with the CNN framework, can serve as a rapid, accurate, reliable, and stable method for EPG waveform classification and recognition. This study provides a technical approach for the automatic segmentation and waveform discrimination of actual EPG signals and offers a theoretical basis for research in insect virus transmission mechanisms and crop insect resistance mechanisms.
# Experimental Analysis of a Novel GFRP Stiffened Pipe
## 6 Conclusions  
The design methods of the pipe configuration are proposed to enhance the stiffness of GFRP pipe, based on its inherent merits, to obtain the material properties satisfying required performance. In this research, the pipe stiffness, deformation mechanism, and failure modes of pipes of different types were considered. Conclusions drawn are listed as follows:  
(1) Under almost the same fabrication and material cost conditions, the flatwise compression tests showed that, with the same diameter, the ring stiffness of the outer stiffened pipe was about 38.7% greater than that of the plain one; while the ring stiffness of the inner stiffened pipe was about 16.7% greater than that of the plain one. Therefore, the proposed stiffened pipe can effectively improve the ring stiffness.  
(2) As shown in Table 3, the average mass fractions of resin were calculated as 26.48%, 26.22%, 26.51%, and 27.95% for plain pipes of DN400, outer stiffened pipes of DN400, plain pipes of DN500, and inner stiffened pipes of DN500, respectively. The similar mass fractions of resin consumed by different types of pipe specimens indicate that the flowing resin in the stiffened pipes does not flow into empty spaces to cause additional usage of resin. Thus, if the same ring stiffness is considered, the proposed stiffened pipe allows low costs in batch production, which can be easily applied to engineering practice.  
(3) During the flatwise compressive tests, the tests for the outer stiffened pipes and the plain pipes were terminated when the applied displacement reached almost 30% diametric deflection, while those for the inner stiffened ones were at 24% diameter deflection. In general, damage to the pipe specimens first occurred at the point (e.g., the 0 degand 180 deg hoop directions) where the strain increased most quickly. The failure mode of the outer stiffenedpipes was \"ductile fracture\" with obvious precursors, which is different from the brittle failure of plain pipes and inner stiffened pipes.  
(4) The proposed stiffened pipe can effectively enhance the cross-section shear strength of the interface between the FRP pipe and the concrete filler. This can provide effective solutions for applications in engineering components, such as anti-corrosion columns and beams, rock-socketed pile casing in offshore engineering, etc.  
Conceptualization, L.K. and Q.S.; methodology, Q.S.; experiment and data analysis, B.Z.; writing--original draft preparation, L.K.; writing--review and editing, B.Z.; funding acquisition, B.Z. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 11602204).  
All data, models, or code generated or used during the study are available from the corresponding author by request.  
We acknowledge the support of [PERSON] for her help with experiments and data analysis.  
The authors declare no conflict of interest.
# Investigation of Mixed-Mode I/II Fracture under Impact Loading Using Split-Hopkinson Pressure Bar
## 3 Dynamic Initiation Stress Intensity Factor
### Determination of Dynamic Initiation Stress Intensity Factor  
To calculate the Dynamic Initiation Stress Intensity Factor, the numerical samples which have the same dimension as the experimental specimens were established in the ABAQUS, and each assembled with the same material parameters given in Table 1. The crack tips were well-meshed with six-node triangular elements (CPS 6), while eight-node quadrilateral elements (CPS 8) were applied in the rest of the area shown in Figure 9.  
The numerical model was composed of 47,668 elements. The dynamic impact loading obtained in the experiment, shown in Figure 4, was applied in the simulation. For CPS 6 element, the \(r_{\rm OA}\) is equal to a quarter of \(r_{\rm OB}\). Based on the fracture mechanics, the displacemen-time data of points A  
Figure 8: Verification of the ABAQUS. (**a**) The geometry of [PERSON]’s model. (**b**) Comparison between [PERSON]’s model and ABAQUS.  
Figure 9: A numerical model of the specimen in ABAQUS.  
and B was used to compute dynamic stress intensity factors. The relationship can be expressed by Equation (3) [8].  
\[K_{\rm I}(t)=\frac{E_{d}}{24(1-{v_{d}}^{2})}\sqrt{\frac{2\pi}{r_{OA}}}[8u_{A}(t)- u_{B}(t)], \tag{3}\]  
where \(E_{d}\) denotes the dynamic elastic modulus, \(v_{d}\) denotes [PERSON]'s ratio. \(u_{A}(t)\) and \(u_{B}(t)\) are the historical displacement of point A and B, respectively.  
The DSIF versus time were extracted from ABAQUS, and the DSIF curves of the specimen-0\({}^{\circ}\) and specimen-30\({}^{\circ}\) are illustrated in Figure 10. With the initiation fracture time obtained by the CPG in the experiment, the DISIF could be determined sophisticatedly. The negative period of the K\({}_{\rm I}\) curve means the crack is under compressive condition, while the positive indicates tensile status. However, when it comes to K\({}_{\rm II}\), the positive and negative of K\({}_{\rm II}\) can only denote the moving direction of the crack surface. It is distinctive that the specimen-0\({}^{\circ}\) is the mode I initiation due to the value of K\({}_{\rm II}\) is constant 0 MPa\(\cdot\)m\({}^{1/2}\), and the DISIF of the specimen-0\({}^{\circ}\) is 5.57 MPa\(\cdot\)m\({}^{1/2}\), less than the maximum DSIF 8.33 MPa\(\cdot\)m\({}^{1/2}\). However, for specimen-30\({}^{\circ}\) in Figure 10b the dynamic stress intensity factor curve of K\({}_{\rm II}\) varies with the time, which indicates that the specimen-30\({}^{\circ}\) is mixed-mode I/II crack. Based on the experimental results, the initiation time of the specimen-30\({}^{\circ}\) is 223.32 \(\upmu\)s and the initiation of crack could be dominated by K\({}_{\rm I}\) = 4.35 MPa\(\cdot\)m\({}^{1/2}\) and K\({}_{\rm II}\) = 2.95 MPa\(\cdot\)m\({}^{1/2}\), which is the initiation fracture toughness.
# Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
## 1 Introduction
The field of view of the all-sky ground-based cloud images is generally 180 deg, which can capture most of the sky, as shown in Figure 1. The collected cloud images have clear textures and rich structural features, which are suitable for PV power stations of almost all sizes. However, there are still few studies on this type of cloud images, and the amount of data in public datasets is also small. In some existing studies, the accuracy of classification is low, which cannot well meet the application in PV power generation. In response to the above problems, we made a ground-based cloud images dataset with a larger amount of data and proposed a deep learning-based ground-based cloud image classification method. The main contributions are shown as follows:  
(1) By collecting historical cloud images data published by the National Renewable Energy Laboratory (NREL) on the US Measurement and Instrument Data Center (MIDC) website, a ground-based cloud images dataset NRELCD (NREL Cloud Dataset) is constructed. The dataset contains 15,450 cloud images and is divided into 7 categories. (2) A novel ground-based cloud images classification method by improved combined neural network is proposed; overlap pooling kernels are used in the sub-network to improve the effect of eliminating information redundancy and reduce the risk of overfitting. The improved channel attention module ECA-WS is introduced after the pooling layer, which further enhances the sub-network's ability to express channel characteristics. The synchronization of parameter optimization among sub-networks is realized by improving the sub-networks. The decision fusion algorithm is used to weight the output of the two sub-networks in the combined network to improve the classification accuracy significantly. The rest of this paper is organized as follows: Section 2 briefly introduces some related work; Section 3 describes a novel ground-based cloud images classification method based on improved combined neural network; and Section 4 presents experimental results and some discussion. At the end of this paper, some remarking points are given in Section 5.
## 4 Experimental Verification and Analysis
### Multi-View Data Acquisition  
During the experiment, each monitoring direction of the three-direction sensor was regarded as a view. Therefore, five sensors obtained nine data views. The time-domain waveform of the vibration signal of the planetary gear all teeth wear simultaneously collected by five different acceleration sensors at nine views when the rotation speed was 1500 rpm is shown in Figure 5. As can be seen from the figure, the time-domain waveforms of the first, fourth and seventh views were very similar in time-domain waveforms, indicating that the information from different views had a certain consistency and redundancy. However, the second, fifth and seventh views show relatively large differences in amplitude and time-domain waveform, indicating that there were certain differences and complementarities between the information of different views. Therefore, it can be predicted that the amplitudes of the vibration signals monitored from different views were different, and the information contained in these vibration signals was different. Combining multi-view data can provide more information for fault diagnosis.
# Performance Analysis of NR-DCSK Based Copper Cable Model for G.fast Communication
## 6 Conclusions  
This paper presents NR-DCSK system as a robust modulation to improve the traditional DCSK over an impulsive noise copper channel for G.fast communication. Impulsive noise is analyzed using the Middleton Class A model. The simulation results confirm the BER theory and demonstrate its accuracy. Various parameters are tested on the performance of the system, including the spread factor \(\beta\), the impulsive noise density \(\gamma\), and the number of pieces \(P\). The results show that the DCSK system is a robust modulation in copper channel and the NR-DCSK system improves the system performance by increasing \(P\), while maintaining the energy and spectral efficiency of the conventional system. In addition, increasing \(\gamma\) impacts the performance of the system and increases the severity of the channel. This paper is the first study of DCSK over copper channels and demonstrates the robustness of DCSK modulation for this channel type. As a future work, channel coding can be implemented with this system to meet the G.fast communication and compared with the traditional G.fast systems.  
Conceptualization, A.J.A.-A. and F.S.H.; methodology, A.J.A.-A. and F.S.H.; software, A.J.A.-A. and F.S.H.; validation, A.K.H.A.-A., A.J.A.-A. and F.S.H.; formal analysis, A.J.A.-A. and F.S.H.; investigation, A.K.H.A.-A.; resources, A.K.H.A.-A.; data curation, A.J.A.-A. and F.S.H.; writing--original draft preparation, A.K.H.A.-A., A.J.A.-A. and F.S.H.; writing--review and editing, A.J.A.-A. and F.S.H.; visualization, A.J.A.- and F.S.H.; supervision, F.S.H.; project administration, A.K.H.A.-A.; funding acquisition, A.K.H.A.-A., A.J.A.-A. and F.S.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 8: BER performance of the LDPC-NR-DCSK versus LDPC coded system in [16].
## 6 Discussion  
We suggest that our method allows complex RSBD in NRT to be dealt with. The SAT-Hadoop-Processor acquires data from multiple series of satellites and sensors automatically. It rapidly pre-processes data, keeps only relevant datasets, and finally serializes the refined output in CSV or stream, which could be consumed directly by other third-party applications or integrated into Hadoop for extra massive calculation and analysis.  
We also incorporated cloud and parallel technologies to optimize the execution time by maximizing the hardware capacities. Accordingly, the developed software reduced the total execution time by 20-fold. We also integrated the ingested RSBD in Hadoop and made it compatible with HDFS, HBase, and Hive to facilitate storage and processing using MR and Spark's pioneer tools.  
The preliminary experimental results show the significant performance of the SAT-Hadoop-Processor as a promising prototype for RSBD processing. We can conclude that we successfully contributed to NRT RS data pre-processing and integration. The SAT-Hadoop-Processor is flexible software supporting several RS data input formats. It can also be customized with many EO applications, notably AP supervision, natural hazard detection [41], etc.
## 2 HSM for Base Station PA
### HSM Topology  
Figure 2 illustrates the topology of the proposed HSM, which consists of a wideband LA and a high-efficiency Switching Amplifier (SA).  
Figure 3 illustrates the operation of HSM. In the simulation shown in Figure 3, the PA was modeled as an equivalent resistance of 8 \(\Omega\) for simplicity. To simplify the PA model, a decoupling capacitor (\(\mathrm{C_{PA}}\)) of 200 pF and an equivalent resistance (\(\mathrm{R_{PA}}\)) of 8 \(\Omega\) were used. Although the PAs exhibit nonlinear impedance characteristics, such complexity is impractical for circuit-level simulations. Thus, a simplified resistive model was adopted. The selected resistance value was based on the target PA, TGA2307-SM, which specifies \(\mathrm{V_{SM,rms}}=17\) V, \(\mathrm{I_{SM,rms}}\)\(=1.25\) A and \(\mathrm{P_{SM,avg}}=21.25\) W, corresponding to \(\mathrm{R_{PA}}=\frac{17\) V }{1.25\) A \(=13.6\)\(\Omega\). To ensure sufficient drive strength and test under more demanding conditions, a lower resistance of 8 \(\Omega\) was chosen.  
The LA operates as a voltage source, generating an output voltage (\(\mathrm{V_{SM,out}}\)), proportional to the input RF envelope signal (\(\mathrm{V_{env}}\)) with a system gain of 15. The sensing circuit determines whether LA generates sourcing or sinking current into the PA. This block generates two sensing currents through \(\mathrm{V_{b,p}}\) and \(\mathrm{V_{b,n}}\), which flow through sensing resistors to produce the corresponding voltages, \(\mathrm{V_{sen,p}}\) and \(\mathrm{V_{sen,n}}\). These voltages serve as inputs to a hysteretic comparator, which then drives the SA. Based on the comparator output, \(V_{drive}\) controls the high- and low-side switches to regulate the LX node voltage and generate the SA current (\(\mathrm{I_{buck}}\)). In (5), the total output current of the supply modulator (\(\mathrm{I_{SM,out}}\)  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\mathrm{P_{SM,out}}\) & \(\mathrm{V_{SM,out}}\) & \(\mathrm{I_{SM,out}}\) \\ \hline \(\mathrm{P_{SM,max}}=112\) W & \(\mathrm{V_{SM,max}}=28\) V & \(\mathrm{I_{SM,max}}=4\) A \\ \(\mathrm{P_{SM,avg}}=21.25\) W & \(\mathrm{V_{SM,rms}}=17\) V & \(\mathrm{I_{SM,rms}}=1.25\) A \\ \(\mathrm{P_{SM,min}}=3\) W & \(\mathrm{V_{SM,min}}=6\) V & \(\mathrm{I_{SM,min}}=0.5\) A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Target SM output power, voltage, and current performance.  
Figure 2: HSM topology.  
consists of the sum of the \(\rm I_{buck}\) and the LA current (\(\rm I_{linear}\)). The \(I_{buck}\) primarily supplies the low-frequency, near-DC current, while the \(\rm I_{linear}\) handles the high-frequency AC current.  
\[\rm I_{SM,out}\ =\ \rm I_{linear}+I_{buck} \tag{5}\]