esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance and Challenges of Service-Oriented Architecture for Wireless Sensor Networks
## 10 Discussion  
A number of research studies attempted to achieve the role of Service-Oriented software designs for network embedded system, but they only considered the software engineering aspect of it. The underlying computational platforms, such as SANET, and their limitations have not been considered. For security, none of the proposed approaches provide a comprehensive framework for different services or data secure architecture. The main issues with those approaches relate to the lack of consideration for accuracy in the architecture and data/service aggregation. The middleware addresses the methods of publish/subscribe, virtual machine, database, and modular/macro programming. However, these solutions provide limited flexibility and interoperability based on interaction between end-users and high-level applications (clients). Most SOM architectures for WSNs are based on heterogeneous services. These services impact the response time and network efficiency. There are different mechanisms and protocols to improve the efficiency of the services as well as the response time. SOM architecture deals with massive amounts of messages and events from various services that share those messages and events between the components of the system. In this case, the system must have the reliability to guarantee that the messages run correctly. The event management technique is used to increase reliability and QoS in WSNs. The QoS has the capability to decrease faults in communication as well as congestion. The QoS mechanisms can be selected from the best available network according to the QoS requirements and contract negotiations based on SLA [36]. There are several SOA protocols used in various architecture such as SOAP, WSDL, and DPWS. These protocols have addressed many challenges such as performance, overhead, exchange data, and security. DPWS used XML for data representation which represents slight limitation on the performance. And increase overhead [95]. The web service has two types of protocol [96]: Simple Object Access Protocol (SOAP) and Representational State Transfer (REST). The REST is an architectural-style application that can access resources/data. The SOAP is an XML-based message protocol which can wrap the business logic. The REST is better throughout and its response time is faster than SOAP. SOAP is used for message communication over SOA [85]. The description and discovery services are a web service description language (WSDL) and universal description discovery and integration (UDDI) [85]. These protocols are based on XML to share data between various computing systems. In order to keep the overhead low, these services use HTTP instead of SOAP for its implementation. In addition, DPWS-based web service is used in the architecture for the cooperation, abstraction, and device orchestration of services. In [97], DPWS uses different web service protocols to enable data exchange between data centric WSNs and other IP networks [97]. This approach uses a Service-Oriented
# Application of Split Coordinate Channel Attention Embedding U2 Net in Salient Object Detection
## 1 Introduction
It accurately calculates the pixel loss of the object boundary, addressing the issues of missing edges and difficulty in distinguishing salient objects in cluttered backgrounds. The main contributions of this paper are as follows:  
1. This paper proposes an attention module called split coordinate channel attention (SCCA), which splits input channels to capture high-level semantic information and location information simultaneously. 2. This paper replaces the encoder and decoder of U2 Net [14] with SCCA and devises a novel loss function C2 loss to prioritize edges and objects. By implementing these improvement strategies, this paper presents SCCA-U2 Net for salient object detection. 3. SCCA-U2 Net achieves top-three results on four metrics in salient object detection tasks on the DUTS and HKU-IS datasets and outperforms nine other advanced attention modules. Ablation studies demonstrate the superiority of SCCA-U2 Net, and this paper provides visualizations of comparing detection results.
# Analysis of Vorticity and Velocity Fields of Jets from Gas Injector Using PIV
###### Abstract  
The present article offers a detailed analysis of helium jet velocity and vorticity intensity distribution using the particle image velocimetry (PIV) technique. A gaseous fuel injector featuring an interchangeable tip was implemented. The test campaign involved the use of three nozzle patterns characterized by different orifices shape and orientations. The helium was injected into a constant volume chamber (CVC) and the delivery pressure varied, as well as that inside the chamber, in order to obtain pressure ratios (PRs) ranging from 2 to 20. The synchronization system was set to record two consecutive frames at different time-instants after the start of energizing (aSOE). Green light from a dual cavity Nd:YAG laser was used for illumination and a 4-megapixel PIV-camera for image capture. Vegetable oil particles were seeded into the chamber to trace the helium jet structure and cross-correlation methodology employed to measure their instantaneous displacements. The role of orifices size and orientations has been deeply scrutinized and related to the morphological outcomes. The least-oriented nozzle (first) exhibited the highest values of jet penetration and well-defined vortex structures. In contrast, the more the orifices are oriented, the wider the regions interacting with surrounding environment. Specifically, geometry with smaller orifice sizes (third) returned an overall absence of localized significant vortex structures. This deficiency is counterbalanced by a large distribution of small vortices that were observed to replace the main rings for each condition examined.  
H2 substitute; helium; mixing properties; PIV; optical investigation Article
# Efficient Cluster Tree Topology Operation and Routing for IEEE 802.15.4-Based Smart Grid Networks
## 3 Related Works
Therefore, in this study, a multi-channel cluster tree topology is proposed to prevent interference and conflict in smart grid applications. The proposed technique has a cluster tree structure. However, it can reduce routing routes using a new address allocation scheme and prevent congestion through alternative routes.
## 5 Performance Evaluation  
### Simulation Setup  
All the simulation results were obtained through MATLAB. Simulation parameters are summarized in Table 1. Two multicast channels, that is, BMCH and SMCH, suffer from the same path loss. Regarding Rayleigh fading on the BMCH and the SMCH, independent and identically distributed (i.i.d.) Rayleigh fading is assumed as the ideal case, which is reasonable when the coherence bandwidth is smaller than the system bandwidth in multipath fading environments. We also consider a case when the two channels are correlated. The performance was evaluated in terms of the bandwidth required to obtain \(R_{T}=1\) Mbps with an outage probability of 5%, where a user experiences an outage when failing to meet the target multicast rate.
## 4 Training
Figure 3 shows the dependence of the absolute error in determining the FBG central wavelength found according to norm (3) on the number of training process cycles. The dependence in Figure 3 is plotted on a semi-logarithmic scale for better visualization. The error value begins to slow after \(10^{6}\) training cycles and stabilizes at around 0.525 pm. Neural network training generates a \(10\times 19\) matrix of weighting coefficients for the associative layer \(\mathbf{w}_{A}\), a \(1\times 10\) matrix for the response layer \(\mathbf{w}_{R}\), a \(10\times 1\) matrix for neuron shift vectors of associative \(\mathbf{b}_{A}\) layers, and a \(1\times 1\) matrix for neuron shift vectors of response \(\mathbf{b}_{R}\) layers. The matrices of the weighting coefficients \(\mathbf{w}_{A}\), \(\mathbf{w}_{R}\), together with the shift vectors \(\mathbf{b}_{A}\) and \(\mathbf{b}_{R}\), make it possible to determine the FBG reflection spectrum central wavelength  
Figure 3: Absolute error in the FBG central wavelength determination depending on training cycles. according to the decisive rule, using the values of the \(19\times 1\) FBG reflection spectrum \(\mathbf{R}\) as input data,  
\[\overline{\lambda}=\mathbf{w}_{R}\times f(\mathbf{w}_{A}\times\mathbf{R}+ \mathbf{b}_{A},\mathbf{\alpha})+\mathbf{b}_{R}, \tag{6}\]  
and ensure that the FBG central wavelength value is restored from the spectral data (Figure 4) obtained under low-resolution conditions.
## 5 Results and Discussion
### Network Stabilization Period  
Figure 7 shows the network stability period for various network sizes. The proposed EACH-COA achieved a stable network state compared to EECHS-ARO, EECHS-ISSADE, and PDU-SLnO. The COA converges rapidly. The reduction in the energy consumption within the network nodes was attributed to the use of this technique. This approach is best suited for networks that exhibit high node density.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Number of** \\ **Rounds** \\ \end{tabular} } & \multicolumn{4}{c}{**Average Energy Consumption (Joule)**} \\ \cline{2-5}  & **EECHS-ARO** & **EECHS-ISSADE** & **PDU-SLnO** & **EACH-COA** \\ \hline
0 & 0 & 0 & 0 & 0 \\ \hline
500 & 0.17 & 500 & 0.17 & 500 \\ \hline
1000 & 0.18 & 1000 & 0.18 & 1000 \\ \hline
1500 & 0.19 & 1500 & 0.19 & 1500 \\ \hline
2000 & 0.39 & 2000 & 0.39 & 2000 \\ \hline
2500 & 0.44 & 2500 & 0.44 & 2500 \\ \hline
3000 & 0.55 & 3000 & 0.55 & 3000 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Average energy consumption vs. number of rounds.  
Figure 6: Average energy consumption vs. number of network round.  
Table 6 shows the network stability period concerning different network nodes. The EACH-COA approach keeps the network more stable than the EECHS-ARO, EECHS-ISSADE, and PDU-SLno methods. It was discovered that the network is stabilized at the 1100 th round of EACH-COA with a 300-node network and uses less energy in the network nodes.
## 3 Results
### Biomass Yield  
The yields measured in all the treatments in this study were within the range reported for other studies of miscanthus in the US [22; 23; 46; 47]. Reports in the literature are not conclusive as to the net effects of N fertilizer application on biomass yields of miscanthus, suggesting an undetermined secondary control on the conversion of N inputs to aboveground biomass. Although some studies suggest that N fertilization could help arrest the long-term yield decline in miscanthus stands [22; 23], results of this study indicate that there was no effect of fertilization when applied in the 6 th growing season (an age when yield declines are sometimes observed). Biomass yields reviewed from prior literature generally varied depending on different factors such as geographical location, soil type, and management practices, but they all ranged from 10 to 30 Mg ha\({}^{-1}\) y\({}^{-1}\). The range for yield in this study, 16.7-25.4 Mg ha\({}^{-1}\) y\({}^{-1}\), is similar to prior literature.  
The mean biomass yields in the urea-treated and control plots were very similar (21 Mg ha\({}^{-1}\)), suggesting that the fertilization treatment did not affect biomass yield as reported by others [7; 17; 24]. The digestate treatment produced the lowest biomass yield (20.2 Mg ha\({}^{-1}\)). Previous studies suggest that there might be a delay in plant response to organic fertilizers (e.g., digestate) due to the slow release of organically bound nutrients [48]. [PERSON] et al. [48] argued that plants can take up 50-80% of mineral fertilizer, but the slow release of organically-bound N and the medium- and long-term N immobilization in soils causes a relatively small effect in the first year after application. Thus, multi-year comparative studies of fertilizer treatments could yield different results than those reported here.  
Field studies with the application of hydrochar to crops are scarce [3]. Results from a one-year field trial with hydrochar made from sugar beet pulp and brewers dregs (spent grain) applied at a dosage of 7.5 Mg ha\({}^{-1}\) increased the yield of winter wheat slightly, but decreased the yield at an application rate of 10 Mg ha\({}^{-1}\)[49]. This yield reduction was attributed to an immobilization of N caused by mineralization of the labile fraction in the hydrochar [49]. The hydrochar-treated plots in this study had the greatest biomass yield (23.5 Mg ha\({}^{-1}\)), though not significantly different from the other treatments.
# Timing and Frequency Synchronization Using CAZAC Sequences for OFDM Systems
## 6 Conclusions  
In this paper, first, a new preamble structure based on ZC sequences for OFDM systems was presented. On this basis, a novel timing synchronization algorithm--the CCPD algorithm--and its improved algorithm--the ACPD algorithm--were proposed. Next, the correlation peaks that were obtained during the timing synchronization were used for the frequency offset estimation. The frequency offset estimation algorithm adopted the quadratic interpolation algorithm, which was improved from the FFT algorithm.  
We simulated the performance of the proposed algorithms at different values of number \(m\) and length \(N\) of ZC sequences, and then we compared their performance with previous algorithms. The simulation results showed that the timing synchronization accuracy was contradictory to the frequency offset estimation range in the choice of parameters \(m\) and \(N\); therefore, we need to make a compromise. When the correct timing probability reached 100%, under the parameters of \(m=8\) and \(N=512\), the performance of the CCPD algorithm was 4 dB higher than that of [PERSON]'s algorithm, and the performance of the ACPD algorithm was 7 dB higher than that of [PERSON]'s algorithm. Under the same parameters, the quadratic interpolation algorithm also showed a great improvement in performance in both small and large frequency offsets, when compared with the FFT algorithm.  
This paper did not consider the effect of phase offset on the performance of the proposed algorithm, because we have not yet proposed a more efficient phase offset estimation algorithm. Furthermore, the frequency offset estimation algorithm proposed in this paper had the limitation of estimation range. These are the problems that need to be solved in the future.  
Conceptualization, G.P., R.L. and Z.H.; methodology, G.P. and R.L.; software, G.P., R.L. and Y.H.; validation, G.P., R.L. and Y.H.; formal analysis, G.P., R.L. and Z.H.; investigation, G.P. and Z.H.; resources, G.P., Y.H. and Z.H.; data curation, G.P. and R.L.; writing--original draft preparation, G.P., R.L. and Y.H.; writing--review and editing, G.P., Y.H. and Z.H.; visualization, G.P. and R.L.; supervision, Y.H. and Z.H.; project administration, G.P., Y.H. and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 15: The normalized MSE comparison of the FFT algorithm and the quadratic interpolation algorithm when \(m=8\) and \(N=512\) with CFOs of 4 KHz and 20 KHz.
# 3D Shape Generation via Variational Autoencoder with
###### Abstract  
3D shape generation is widely applied in various industries to create, visualize, and analyse complex data, designs, and simulations. Typically, 3D shape generation uses a large dataset of 3D shapes as the input. This paper proposes a variational autoencoder with a signed distance function relativistic average generative adversarial network, referred to as 3D-VAE-SDFRaGAN, for 3D shape generation from 2D input images. Both the generative adversarial network (GAN) and variational autoencoder (VAE) algorithms are typical algorithms used to generate realistic 3D shapes. However, it is very challenging to train a stable 3D shape generation model using VAE-GAN. This paper proposes an efficient approach to stabilize the training process of VAE-GAN to generate high-quality 3D shapes. A 3D mesh-based shape is first generated using a 3D signed distance function representation by feeding a single 2D image into a 3D-VAE-SDFRaGAN network. The signed distance function is used to maintain inside-outside information in the implicit surface representation. In addition, a relativistic average discriminator loss function is employed as the training loss function. The polygon mesh surfaces are then produced via the marching cubes algorithm. The proposed 3D-VAE-SDFRaGAN is evaluated with the ShapeNet dataset. The experimental results indicate a notable enhancement in the qualitative performance, as evidenced by the visual comparison of the generated samples, as well as the quantitative performance evaluation using the chamfer distance metric. The proposed approach achieves an average chamfer distance score of 0.578, demonstrating superior performance compared to existing state-of-the-art models.  
3D shape generation; variational autoencoder; generative adversarial network; signed distance function; relativistic average
# Strength Characteristics of Biochar-Amended Clay Covered Soil Mixed with Methane-Oxidizing Bacteria
## 2 Materials and Methods  
### Test Materials  
Table 1 shows the basic physical indexes of the clay used in the test. According to the plasticity chart for fine-grained soil, our test soil is low-liquid limit clay.  
The biochar was obtained by pyrolysis of rice straw under anoxic conditions at 500 \({}^{\circ}\)C. Table 1 shows the basic physicochemical properties of the biochar. Its ash content was 18.8%, based on the ASTM D 1762-84 standard test method [23]. To obtain the mass loss rate, 20 g of biochar was placed in a muffle furnace at a temperature of 800 \({}^{\circ}\)C for 4 h, and the quality loss was obtained.  
The biochar was then passed through a 74 um sieve to obtain a sample with a particle size <74 um. Figure 1 shows the particle gradation curve of the soil and biochar. The part with a particle size >74 um was measured by the sieving method, and the part with a particle size <74 um was measured using a laser particle size analyzer.  
During high-temperature pyrolysis, the skeleton structure of the original biomass was not damaged. Figure 2a shows the scanning electron microscope results of the biochar. From the microscopic morphological characteristics of the rice straw biochar samples, the porosity of the internal structure of the biochar and the morphology of the pore structure can be seen.  
The methanotrophs used in this study were screened from the overburdened landfill soil. Through a series of bacterial screening operations, such as enrichment, streaking, and purification, we obtained a methane oxidizing bacterium, identified as _Methylobacterium extorquens_, which belongs to the genus Methylobacter. Scanning electron microscopy of the biochar-clay mixture with methanotrophs revealed its morphology, as shown in Figure 2b.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Clay**} \\ \hline Liquid limit (LL/\%) & 35.98 \\ Plastic limit (PL/\%) & 22.20 \\ Plastic index (PI) & 13.78 \\ \hline Max. dry density (g/cm\({}^{3}\)) & 1.65 \\ Optimum water content \(w_{\text{opt}}\) (\%) & 22.50 \\ Specific gravity & 2.67 \\ pH & 7.70 \\ CEC (cmol kg\({}^{-1}\)) & 10.70 \\ \hline \multicolumn{1}{c}{**Biochar**} \\ \hline Specific gravity & 1.99 \\ Specific surface area (SSA m\({}^{2}\)/g) & 385.60 \\ pH & 10.00 \\ Ash content (\%) & 18.80 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Basic physical and chemical indexes of clay and biochar.  
Figure 1: Grain\(-\)size distributions of the clay and biochar.
## 3 Results and Discussion
### Analysis of the Orthogonal Experiment Results
#### 3.1.2 Range Analysis of the Erosion Volume  
The range analysis of erosion volume is illustrated in Table 5. As shown in the table, \(R_{V}>R_{D}>R_{L}\). Therefore, with regard to the erosion volume, the factors are arranged as follows from primary to secondary: Jet velocity, nozzle diameter, and standoff distance. Meanwhile, the erosion volume of the seventh test of Table 3 is the largest, which is 6.0329 cm\({}^{3}\), and thus the work parameter combination of the seventh test is optimal, namely, \(V3L1D3\). The corresponding jet velocity, standoff distance and nozzle diameter are 150 m\(\cdot\)s\({}^{-1}\), 0.5 cm and 2.5 mm, respectively.  
Similarly, the factors and levels of the orthogonal experiment were used as the abscissa, while \(\overline{K_{ij}}\) was used as the ordinate to draw a trend chart of the erosion volume variation of HBS. Figure 6 presents the trend chart. As shown in Figure 6, the erosion volume of HBS reaches the maximum when jet velocity, standoff distance, and nozzle diameter are 150 m\(\cdot\)s\({}^{-1}\), 0.5 cm and 2.5 mm, respectively, thus the optimal work parameter combination is still \(V3L1D3\). Moreover, the three main points are shown in Figure 6. First, the erosion volume increases rapidly as the jet velocity increases. Second, an increase in standoff distance corresponds to a decrease in the erosion volume of HBS. Third, when the nozzle diameter increases, the erosion volume initially increases slowly and then increases rapidly.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Values**} & \multicolumn{3}{c}{**Factors**} & \multirow{2}{*}{**Empty**} \\ \cline{2-2} \cline{4-5}  & \(V\) (m/s) & \(L\) (cm) & \(D\) (mm) & \\ \hline \(K_{1}\) & 1.083 & 8.276 & 3.403 & 6.384 \\ \(K_{2}\) & 5.710 & 5.558 & 5.291 & 7.241 \\ \(K_{3}\) & 11.276 & 4.235 & 9.375 & 4.443 \\ \(\overline{K_{1}}\) & 0.361 & 2.759 & 1.134 & 2.218 \\ \(\overline{K_{2}}\) & 1.903 & 1.853 & 1.764 & 2.414 \\ \(\overline{K_{3}}\) & 3.759 & 1.412 & 3.125 & 1.481 \\ \(R_{i}\) & 3.398 & 1.347 & 1.991 & 0.933 \\ \hline \multicolumn{5}{c}{Significance} & \multicolumn{3}{c}{\(V>D>L\)} \\ Optimal parameter combination & \multicolumn{3}{c}{\(V3L\). \(1D3\)} \\ \hline \end{tabular} \(K_{ij}\) is the sum of the test values corresponding to the \(j\) level of the \(i\) factor, and \(\overline{K_{ij}}\) is the average value of \(K_{ij}\). \(V\), \(L\) and \(D\) represents jet velocity, standoff distance and nozzle diameter, respectively.  
\end{table}
Table 5: Range analysis table of the erosion volume.  
Figure 6: Trend chart of the erosion volume variation of HBS. **(a)** Water jet velocity; **(b)** standoff distance; **(c)** nozzle diameter.