esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of NVIS Channel for USN Protocol Definition in Antarctica
## 6 Conclusions  
In this article, we present a robust frame protocol for NVIS USN communications that performs better than the standards STANAG and MIL-STD-188-110D for distances between 0 and 250 km. To achieve these conclusions, we have taken measurement of an NVIS channel in two different scenarios: a single NVIS communication and an NVIS communication with the presence of groundwave. First, we analyzed the most critical values of the delay spread and Doppler spread scenarios. Then, we tested the robustness of QPSK and 4 FSK in terms of BER. Finally, we measured the Doppler shift caused by the use of low stability clocks on a low-cost system.  
Thereafter, we designed the NVIS signal frame protocol based on an OFDM. The use of an OFDM has two benefits: first, it is more robust in front of multipath signals due to the addition of a cyclic prefix. Second, the equalization in the frequency domain is much simpler. Also, the absence of ISI and the use of pilot subcarriers in the OFDM make the equalization of the signal easier. The simulations show a great improvement in comparison to the QPSK and 4 FSK modulations for both NVIS channel scenarios.  
The main drawback is that the use of an OFDM on the signal frame protocol decreases the bitrate in comparison to the standards STANAG and MIL-STD-188-110D. This is not critical for most USN requirements, especially for low bit rate remote sensors that do not have any other way to communicate.  
Funding acquisition, J.L.P.; Investigation, J.M.M. and J.P.; Methodology, J.L.P.; Project administration, D.B.; Software, J.M.M. and J.P.; Supervision, D.B.; Validation, J.M.M. and J.P.; Writing--Original draft, J.M.M. and J.P.; Writing--Review & editing, J.M.M., J.P., J.L.P. and D.B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry on Science, Innovation and University, the Investigation State Agency and the European Regional Development Fund (ERDF) under the grant number RTI2018-097066-B-I00 (MCIU/AEI/FEDER, UE) for the project \"NVIS SENSOR NETWORK FOR THE SOUTH SHETLAND ISLANDS ARCHIPELAGO\" (SHETLAND-NET).  
The authors declare no conflict of interest.
# Progress on Single-Feed Quality Wideband Linear Wire Array
## 8 Discussion and Future Directions  
### Conclusions and Discussion  
This paper provides a comprehensive review of the definition and development of QWL antennas, presenting for the first time an analytical expression for the input impedance of the WHEMS antenna, which acts as the driven element of the QWL antenna. Theoretical analysis shows that the WHEMS antenna possesses infinite impedance bandwidth. Furthermore, the WHEMS antenna can be effectively modeled as a three-dipole array, capable of delivering high side-lobe gain with a single element. Key advancements in the three-dipole array director, W-shaped reflector, and low-common-mode-current connectors are also discussed, highlighting their contributions to the performance improvements in QWL antennas.  
The QWL antenna stands out for its wide bandwidth, high gain, compact structure, and robust mechanical design, characterized by low wind resistance and lightweight construction. To achieve even higher gain, this paper introduces the implementation of a series-fed array for WHEMS antennas and decoupling methods to enhance the gain of the QWL series-fed array.  
Already adopted in the industry, the QWL antenna has demonstrated its versatility across a range of applications. Its single-feed linear array design has significantly impacted fields such as long-distance communication, radar systems, and wireless networks. The simple design and low cost of the QWL antenna also make it accessible to a wide audience, including amateur operators and hobbyists. The innovations and concepts discussed in this paper are expected to inspire further research and broaden the scope of applications for linear antennas in the future.
## 3 Results and Discussion
### Textural Analysis  
Texture is a key quality indicator in refrigerated products, facilitating practical assessment through structural analysis. Hardness reflects the force required to compress the sample to a specific degree, while elasticity measures the sample's ability to recover its shape after deformation. Additionally, texture reveals the sample's resistance to compression [63,64]. The texture parameters evaluated in the TPA test include hardness, chewiness, springiness, and cohesiveness [58]. Table 4 presents these parameters for pork loin after refrigerated storage and exposure to magnetic fields and pulsed light.  
Our findings indicate that the combined application of magnetic field and pulsed light significantly affected (\(p<0.05\)) hardness in both cycles 1 and 2, as well as muscle rigidity after 10 days of refrigerated storage. Additionally, a statistically significant interaction effect (\(p<0.05\)) between storage duration and treatment type was observed for hardness in cycles 1 and 2. Significant differences (\(p<0.05\)) in chewiness were also identified between the control sample and the sample treated with magnetic field and pulsed light, as well as between samples treated with magnetic field and pulsed light over the full storage period and the control sample on days 7 and 10. No significant differences were observed for other texture parameters, including adhesiveness, cohesiveness, and resilience. The alterations in protein structures during the initial stages of cold storage were likely minimal, which explains the slight increase in meat hardness on the 1 st and 7 th days following treatment with pulsed light and magnetic field (although these changes were not statistically significant). However, after 10 days of refrigeration, more substantial changes in the protein structures of the meat were observed. As a result of the combined effects of pulsed light and magnetic field treatment along with the storage period, a decrease in meat hardness was noted (\(p<0.05\)). Consequently, the treatment led to a reduction in the meat's rigidity and chewiness (\(p<0.05\)). The literature lacks data on the impact of magnetic field and pulsed light on the texture of the _longissimus dorsi_ muscle. However, a study by [PERSON] et al. [23] on chicken breast chilling found that combining pulsed electric field with oscillating magnetic field effectively preserved better texture properties, particularly cutting force. [PERSON] et al. [24] examined the influence of a weak magnetic field on water retention, texture, and volatile compounds in pork and beef during frozen storage. They found that frozen pork exposed to a magnetic field exhibited higher elasticity and lower hardness compared to conventionally frozen meat. Conversely, [PERSON] et al. [43] reported that magnetic field application increased the hardness of pork tenderloin throughout the storage period. The lower values of certain texture parameters observed in meat exposed to magnetic fields and pulsed light under refrigerated conditions are likely due to alterations in protein structure and an accelerated rate of protein transformation.
# Localization of a BLE Device Based on Single-Device RSSI and DOA Measurements
## 7 Experimental Results
### Accuracy Assessment of the Denoising Algorithm and Measurement Method
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \begin{tabular}{c} **Measurement Location** \\ \end{tabular} & \begin{tabular}{c} **Actual** \\ **Heading \(\theta_{1}\), \(\theta_{2}\)** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Heading \(\theta\)** \\ \end{tabular} & \
\begin{tabular}{c} **Estimated** \\ **DOA \(\hat{\theta}\)** \\ \end{tabular} &
\begin{tabular}{c} **Error** \\ \(\theta_{c}\) \\ \end{tabular} \\ \hline \hline \multirow{2}{*}{1} & \(230^{\circ}\) & \(227.5^{\circ}\) & \(207.16^{\circ}\) & \(20.34^{\circ}\) \\ \hline \multirow{2}{*}{2} & \(325^{\circ}\) & \(327.5^{\circ}\) & \(301.76^{\circ}\) & \(25.74^{\circ}\) \\ \hline \multirow{2}{*}{3} & \(248^{\circ}\) & \(246^{\circ}\) & \(222.43^{\circ}\) & \(23.57^{\circ}\) \\ \hline \multirow{2}{*}{4} & \(247^{\circ}\) & \(248.5^{\circ}\) & \(241.83^{\circ}\) & \(6.67^{\circ}\) \\ \hline \multirow{2}{*}{5} & \(113^{\circ}\) & \(111.5^{\circ}\) & \(98.27^{\circ}\) & \(13.23^{\circ}\) \\ \hline \multirow{2}{*}{6} & \(70^{\circ}\) & \(65^{\circ}\) & \(166.53^{\circ}\) & \(101.53^{\circ}\) \\ \hline \multirow{2}{*}{7} & \(177^{\circ}\) & \(179.5^{\circ}\) & \(166.56^{\circ}\) & \(12.94^{\circ}\) \\ \hline \multirow{2}{*}{8} & \(208^{\circ}\) & \(209^{\circ}\) & \(182.84^{\circ}\) & \(26.16^{\circ}\) \\ \hline \multirow{2}{*}{9} & \(40^{\circ}\) & \(32^{\circ}\) & \(36^{\circ}\) & \(27.92^{\circ}\) & \(8.08^{\circ}\) \\ \hline \multirow{2}{*}{10} & \(250^{\circ}\) & \(247.5^{\circ}\) & \(252.35^{\circ}\) & \(4.85^{\circ}\) \\ \hline \multirow{2}{*}{11} & \(245^{\circ}\) & \(247.5^{\circ}\) & \(251.94^{\circ}\) & \(4.44^{\circ}\) \\ \hline \multirow{2}{*}{12} & \(250^{\circ}\) & \(252.5^{\circ}\) & \(292.56^{\circ}\) & \(40.06^{\circ}\) \\ \hline \multirow{2}{*}{13} & \(325^{\circ}\) & \(327.5^{\circ}\) & \(292.73^{\circ}\) & \(34.77^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results with differences between the actual heading and the estimated DOA.
# A Study on the Improvement of Filter Performance to Remove Indoor Air Pollution
## 2 Simulation Method and Result
### Coanda Effect Simulation Result  
In the simulation, a multiphase flow was performed to express both particles and air at the same time. The total inflowing particles are collected by the filter by spraying 10 k particles from the inlet, and collected by the filter, and the uncollected particles are discharged to the outlet. The filter's dust-collection efficiency equation is shown in Equation (1).  
\[\text{Efficiency}(\%)=\frac{\text{Trap particle}}{\text{Total particle}} \tag{1}\]  
Table 2 shows the filter dust collection efficiency, pressure loss, and maximum speed values according to the shape. In the case of the filter to which the Coanda effect is applied, the efficiency improvement of 7-15% compared to the original model can be confirmed. As a result of the analysis, the dust collection efficiency was high in the order of original \(<\) bottom \(<\) slide model. When the pressure loss of the filter is less than 3.5 mmAq,  
Figure 1: Physical and electrical filter simulation flow.  
When the particles move along the streamline in the filter, they are collected by the filter through the inertia of the particles. This action is called inertial collision, and it can be expressed, as in Equation (2), by the Stokes number as the ratio of the particle stopping distance to the filter diameter [17; 18; 19; 20; 21].  
\[S_{tk}=\frac{\tau U_{0}}{d} \tag{2}\]  
Here, \(\tau\) is the relaxation time until the particle leaves the streamline and returns to the streamline again, which is calculated by Equation (3) [17; 18; 19; 20; 21].  
\[\tau=\frac{1}{18}\;\frac{d^{2}\rho_{P}}{\mu} \tag{3}\]  
\(U_{0}\) is the initial velocity of the particle, and \(\tau U_{0}\) is the shortest distance the particle has moved during the relaxation time. The larger \(S_{tk}\) means that there are many colliding particles, and the larger this is, the higher the dust-collection efficiency of the filter.  
Figure 4 shows the pressure distributions and velocity vectors of the three proposed filters. It is possible to visually confirm that low pressure is formed, and the back flow occurs near the tap to apply the Coanda effect. Figure 5 shows the dust-collection efficiency according to the particle size.  
In the case of the Slide model, the dust-collection efficiency was increased by about 15% compared to the original model, and the Coanda effect helped to improve the efficiency. However, a filter with an efficiency of 30% has a low dust-collection efficiency when used as an indoor kitchen hood, and cannot collect particles of 3-7 \(\upmu\)m in size, so it is ineffective in practice.  
Therefore, to increase the collection efficiency of the filter, as suggested from the results of the simulation and experiment, a sub-filter was proposed as a turbulence generator to increase the flow velocity and contact time of the filter, and the simulation was conducted.  
Figure 4: _Cont._  
Figure 4: (**a**) Original; (**b**) Bottom; and (**c**) Slide model pressure and vector contours.  
Figure 5: Efficiency by particle size in Coanda-applied filter.
# Steganalysis of Context-Aware Image Steganography Techniques Using Convolutional Neural Network
## 4 Conclusions  
In the proposed scheme, two non-trainable convolutional layers were used to enhance the performance. The high-boost filter was utilized in the first non-trainable layer to enhance the statistical information. The SRM filters were considered in the second non-trainable layer to fetch additional information. The bottleneck approach was followed in the network design to improve the performance. The clipped ReLU layer was taken instead of the simple ReLU layer to customize the thresholding. Two skip connections were also used for additional information from different connections. A single pooling layer was used at the end of the network to avoid information loss between the layers. The SVM classifier was applied instead of the softmax classifier to obtain better results than previous methods. In the experimental analysis, the proposed scheme was found to be the most effective for three context-aware steganography techniques on different payloads. The detection accuracy of the proposed scheme was better than the four existing techniques in most cases.  
Each author discussed the details of the manuscript. [PERSON] designed and wrote the manuscript. [PERSON] implemented the proposed technique and provided the experimental results. [PERSON] reviewed and revised the article. K.-H.J. drafted and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Brain Pool program funded by the Ministry of Science and ICT through the National Research Foundation of Korea (2019H1D3A1A01101687) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R11A3049788).  
Not applicable.  
The datasets used in this paper are publicly available and their links are provided in the reference section.  
We thank the anonymous reviewers for their valuable suggestions that improved the quality of this article.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Steganography Technique/**} & \multicolumn{3}{c}{**HILL**} & \multicolumn{3}{c}{**S-UNIWARD**} & \multicolumn{3}{c}{**WOW**} \\ \cline{2-10}  & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** & **0.2** & **0.3** & **0.4** \\ \hline SRNet & 64.44 & 70.03 & 72.92 & 74.31 & 81.29 & 84.66 & 78.28 & 84.37 & 87.97 \\ \hline Ye-Net & 59.62 & 64.84 & 69.81 & 65.72 & 74.08 & 82.25 & 72.46 & 77.50 & 83.66 \\ \hline Yedroudj-Net & 60.94 & 66.93 & 70.17 & 67.03 & 75.40 & 80.02 & 74.25 & 82.66 & 86.85 \\ \hline Zhu-Net & 66.82 & 72.45 & 78.97 & 80.57 & 85.28 & 88.45 & 81.86 & 90.37 & 92.31 \\ \hline Proposed Method & 72.24 & 76.92 & 82.28 & 82.16 & 87.23 & 91.37 & 83.32 & 89.27 & 93.16 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance assessment while considering Case III.
# Prediction Method of Underwater Acoustic Transmission Loss Based on Deep Belief Net Neural Network
## 1 Introduction
DBN is composed of several layers of restricted Boltzmann machine (RBM) and a layer of back propagation (BP) neural network [13; 14; 15]. It not only has a good classification effect but also has an efficient feature extraction ability. We propose a prediction method of underwater acoustic transmission loss based on deep learning. Based on the structure of the DBN network, according to the marine environment and signal characteristics, we designed a deep belief neural network model (DBN) and generated a training dataset to train the DBN neural network model and evaluate the accuracy of the model. The new method could accurately adapt the underwater acoustic transmission loss model and accurately predict the underwater acoustic transmission loss in the absence of prior knowledge, multiple frequency bands, and complex marine environmental conditions. In order to verify the practicability of the underwater acoustic transmission loss prediction method proposed in this paper, we used sea test data in a certain sea area in May 2020 to calculate the measured underwater acoustic transmission loss. We compared and analyzed the measured underwater acoustic transmission loss with the underwater acoustic transmission loss predicted by the new method. The results show that the RMSE error of the predicted transmission loss and the sea trial transmission loss was stable below 6.5 dB, which was lower than the transmission loss prediction error predicted by the classical method, which verifies the reliability and accuracy of the new method.
# Fracturing Fluids and Their Application in the Republic of Croatia
## 5 Fracturing Fluids--Composition and Additives
### Classification of Fracturing Fluids
#### 5.1.7 Emulsion-Based Fracturing Fluids
An ultra-high temperature hydraulic fracturing fluid developed by [PERSON] and [PERSON] (2016) consists of tap water, a synthetic polyacrylamide copolymer (gelling agent), a zirconium complex (metal crosslinker), a multi-functional environmentally friendly additive (temperature stabilizer, pH adjusting agent and a crosslinking delay agent), and oxidizing breaker. The fluid shows stability up to 230 \({}^{\circ}\)C, delayed crosslinking for use in applications with up to a 5-min pipe time, controlled breaking over the range of 180 \({}^{\circ}\)C to 230 \({}^{\circ}\)C, excellent performance at low polymer concentrations (<0.5%), and has acceptable fluid loss characteristics. The volume of filtrate, examined using a static filter press, is 40 mL at 0.4% polymer, 6.89 MPa, and 170 \({}^{\circ}\)C [25]. Low viscous frac fluids based on a synthetic polymer--polyacrylamideamide--with only \(\sim\)30 mPa-s at 511 s\({}^{-1}\) was implemented with high operational success in 2019 on sandstone fracturing in Russia. It demonstrated high transport efficiency to carry and place a ceramic proppant at moderate rates (4-4.5 m\({}^{3}\)/min), both in combination with crosslinked gel as well as a single fracturing fluid [64]. An engineered fracturing fluid (EFF) that consists of a water-soluble associative polymer has been extensively tested by [PERSON] et al. (2020). The associative polymer used in this study was synthesized by controlled micellar polymerization using macromolecular design via the interchange of xanthates (MADIX) technology. The results show that the fluid exhibits outstanding properties and benefits to the transport of different types of proppants (specific gravity up to 3.25) into hydraulic, and natural fractures effectively without relying on high fluid viscosity. Unique fluid behavior is achieved via a novel elastic and a network of packed structures from associative polymer fluid, having proper proppant suspension, effectively placed at low viscosity, decreased fresh water requirement, low injection pressures, with no settling and up to 98% retained conductivities. This novel fluid could potentially replace conventional crosslinked, linear gels, and friction reducer systems for a variety of hydraulic fracturing applications [43].
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 3 Structural Damage Identification Methods
### Revised Counter-Propagation Network (RCPN)  
In view of the above defects of the CPN network model in Section 2, namely, when the two training data values of the input layer are similar, the network connection weight vector may be adjusted twice in each learning, resulting in the failure of the final network  
Figure 2: Structural damage detection method.  
training. Reference [36] revised the CPN network model. The improvement process can be summarized as follows: during network training, the samples are trained one by one, that is, a sample is trained first. When the network output error of the sample is less than the set error or reaches the maximum number of iterations, the network training is terminated, and then the next sample is trained until the training of all samples is finished.  
After improvement, the defect caused by one connection weight vector being adjusted according to multiple input vectors in the CPN model can be avoided, and each input sample can be successfully trained, and the minimum distance between the connection weight vector of the competing winning neuron and the input mode can be guaranteed. In addition, the number of neurons in the competition layer of the RCPN model is less, and some of them are even equal to the number of samples. Therefore, the structure of the CPN network model is greatly simplified, thus shortening the time of network training and testing [36]. Therefore, in this study, based on the RCPN network model combined with fractal and data fusion, a damage detection model based on decision-level fusion was proposed.
## 3 Methodology
### OPC UA Communication  
The configuration of the OPC UA protocol is carried out using TIA Portal software where the following steps are performed: OPC UA server activation, IP configuration, communication port configuration, DB block creation, OPC UA server interface creation, sampling, publication times configuration, and finally, security configuration.  
It should be considered that the DB blocks are used to store the information of the DT components, so all of the variables to be used in the DT should be created and one should make sure to activate the accessibility to them to guarantee the communication between the different environments. In addition, when selecting the server interface, the variables to be transferred to the client must be selected and an ID node will be observed for each enabled variable; these variables will be used for communication from Node-RED. Figure 5 shows the OPC UA communication diagram used in the proposed DT architecture.  
The OPC UA protocol is used because it allows for bidirectional communication between different platforms and levels of the automation pyramid, in addition to communicating with devices from different manufacturers. The client-server architecture is used because it is compatible with the OPC UA communication framework and implements a security layer with authentication, authorization, encryption, and data integrity. As field devices become more powerful in terms of CPU and memory performance, there is a trend to incorporate OPC UA server functionality within field devices to provide direct data access to external OPC UA clients [34].  
Figure 4: Device and network topology of the DT.  
Figure 5: OPC UA communication diagram with Node-RED.
## 2 Materials and Methods
### Rice Identification on the Basis of the Time-Series Sentinel-1A SAR  
On the basis of the comprehensive analysis of VV, VH, VV/VH, coherence coefficient, and other parameters, the features with great difference among different ground objects were considered. In addition, the features were used as the basic features of rice identification so as to rapidly extract the rice-planting areas. Classification was made based on the VV and VH polarization on 12 June, VV polarization on 11 August, and VV/VH value in two months. In addition, the results are shown in R (12/06 VV), G (11/08 VV), and B (12/06 VH)) in three channels (Figure 18). In Figure 18, rice is colored red and dark brown, water is colored black, artificial buildings are colored yellow and white, and vegetation is colored dark green. Therefore, the color can be used to easily distinguish rice from other ground objects.