esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Investigation
### Signal Analysis
#### 2.4.2 Frequency Response Function (\(FRF\))  
The \(FRF\) between two signals \(x(t)\) and \(y(t)\) is defined as in Equation (4) (also known as \(FRF_{H1}\)[36]), where \(X(f)\) and \(Y(f)\) are \(x(t)\) and \(y(t)\) in the frequency domain. Again, in this case, \(x(t)\) is any of the sensors signal and \(y(t)\) the excitation signal sent to the PZT:  
\[FRF_{H1}\ =\ \frac{X(f)}{Y(f)}\frac{Y^{*}(f)}{Y^{*}(f)} \tag{4}\]  
A \(FRF\) constant along the frequency band studied means that the excitation and the response are totally proportional, whereas a non-constant \(FRF\) gives the response of the structure due to the excitation. To detect erosive cavitation, it is convenient to have a constant \(FRF\) along the frequency band, which means that the sensor is directly detecting the excitation and not the structural response due to that excitation.
# Performance Evaluation of Mobile RPL-Based IoT Networks under Hello Flood Attack
## 4 Related Works
The experiments were conducted using the Contiki Cooja simulation environment, focusing on network performance metrics such as power consumption and control message transmission. Results revealed significant alterations in control message dynamics, particularly with a notable increase in the number of Destination Advertisement (DIO) messages due to the presence of the Hello Flood attacker node. As a result, the total energy consumption increased significantly post-attack, with a significant portion attributed to the malicious node's reception of DIO messages. According to the previous analyses, we can conclude that all works tackle the Hello Flood attack only in a static environment without addressing its implications in mobile settings. This renders our work distinctive as it delves into the study and thorough analysis of the Hello Flood attack within a mobile environment, employing various group mobility models for a comprehensive examination.
# Performance Analysis of Software-Defined Networks to Mitigate Private VLAN Attacks
## 3 Related Work
To the best of the authors' knowledge, this is the first study that compares the performance of a conventional solution and an SDN-based solution to secure a corporate network where PVLANs are deployed as a network segmentation mechanism.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 3 Proposed Ship Classification Method
### Feature Extraction Based on Improved CNN
#### 3.1.1 Motivation for Development of Improved CNN
Figure 3: Classification accuracy (%) of each ship class corresponding to four cases listed in Table 1.  
accuracies of four ship classes were observed to be the highest in the third case, and these were closely followed by those corresponding to the second case. In view of these results, the third case was used to design the improved CNN-based method proposed in this study. Additionally, convolution kernel sizes of \(5\times 5\) and \(3\times 3\) were considered to make the extraction of ship features subtle and comprehensive. Figure 4 depicts the visualization of feature maps within different convolutional layers. There exists 32 feature maps in Figure 4a,c, whereas Figure 4b,d comprises of 64 feature maps. It is easy to conclude that convolution kernels could describe ship-image characteristics based on different aspects. The first and second convolutional layers mainly extracted features pertaining to the texture and detail of ship images. Moreover, these features were very close to the original image (i.e., container ship). All features extracted from these convolutional kernels could be combined to characterize the ships more comprehensively. The shallow networks contained more features and possessed the ability to extract key features (for example, the container feature extracted from the fifth feature map in Figure 4a). The third and fourth convolutional layers mainly extracted the contour, shape, and other strong features (e.g., container area). The greater the depth of these layers, the more abstract these features were observed to be. The said features could be regarded as a combination of features extracted within previous layers. Features obtained using different convolution kernels can be complementary with regard to the description of ship images. By combining the features extracted by the different convolution kernels, information concerning ship images can be accurately represented using the proposed method. Figure 4: Visualization of feature maps within different convolutional layers: (**a**) First; (**b**) second; (**c**) third; and (**d**) fourth.
## 3 Results and Discussion
### Volatile Fatty Acids (VFA) and Total Organic Carbon (TOC)  
Modelling VFA concentration was based on the spectral regions 944.2-694.0 cm\({}^{-1}\) and 1934.2-1685.8 cm\({}^{-1}\) (Table 1) which include v(C-C) skeletal stretches (860-840 cm\({}^{-1}\)) [43] and c(C=O) stretching vibrations (1725-1750 cm\({}^{-1}\)) [24; 43; 44], respectively. By applying mean centering alone as pre-processing, this model yielded very good cross-validation results in the VFA concentration range from 0.1 to 2.7 mmol C L\({}^{-1}\) (RMSECV = 0.18 mmol C L\({}^{-1}\), R\({}^{2}_{\text{CV}}\) = 95.4% and RPD\({}_{\text{CV}}\) = 4.7; Figure 3j). In contrast to the time-consuming analytical method used for assessing VFA concentration (HPLC), Raman spectroscopy has the potential to deliver much faster information about the reactor performance.  
Regarding the final model selected for predicting TOC concentration, the spectral regions used in the model span over a large range of the Raman spectrum (from 1500 to 600 cm\({}^{-1}\); Table 1). In fact, important regions for the vibrations associated with organic matter are expected to involve a wide spectral range, including aliphatic C-H stretching, vibrations related to carboxylic groups, aromatic groups, carboxylate groups and protein amide [37]. Although TOC measurements were only available for one of the studied cycles, a good correlation between the reference analytical data and the pre-processed (constant offset elimination) selected Raman spectral regions could be obtained by using five LVs, as indicated by the calibration results (R\({}^{2}\)Cal = 99.5%, RMSEC = 0.76 ppm, RPDCal = 13.6; Table 1). Moreover, the cross-validation was successful (R\({}^{2}\)CV = 96.7%; RPDCV = 5.5), the reference TOC profile within the 17.4-43.2 ppm range being very well predicted by the Raman-based PLS model (Figure 3g).  
Real-time information on the concentration of VFAs and TOC can help in preventing the presence of organic carbon during the light aerobic period of the SBR cycles. The presence of organic carbon during the light aerobic period promotes the growth of heterotrophic phototrophic purple bacteria and ordinary aerobic heterotrophs, which consequently, reduce the efficiency of the photo-BNR, since PAOs accumulate more P [7].
## 2 Methods  
### Network Structure  
Faster R-CNN is a two-stage target detection model for end-to-end training. It is mainly composed of a backbone network, a region proposal network (RPN), region of interest pooling (ROI Pooling), classification and regression. The backbone network is a basic CNN, in which the input image is processed by convolution, rectified linear unit (ReLU) activation, pooling, etc. Its output image with high-dimensional features is imported into the RPN and processed by a 3 \(\times\) 3 convolutional layer to generate the anchors with multiple size proportions. The two outputs of the classification layer and regression layer are obtained for every anchor.  
For the medical scene with densely distributed cells, there are many characteristics, such as a large number of cell samples, dense and disorderly distributions and largermorphological difference between adherent cells and single cells. Thus, the recognition problems of the conventional Faster R-CNN are as follows:  
1. The labeling results inevitably contain some non-target information due to the dense distributions of cells in the complex environment, which leads to an increased false detection rate.
2. The squeezing of cells may happen, resulting in irregular morphology. This increases the difficulty of feature extraction.
3. There are single red cells, agglomerated red cells, white blood cells or other impurities in an actual medical scene. These cause the problem of multi-scale recognition and reduce the range of receptive field.  
In the BDE_RCNN method, ResNet50 is selected to replace Vgg16 as the backbone network in the Faster R-CNN. Firstly, a feature pyramid network (FPN) [24] is added in the feature extraction network to make full use of the visual features extracted. Considering the dense distributions of cells, the FPN is integrated with a non-local module [25] to produce a BFP structure, in which the feature extraction capability is enhanced by correctly identifying multi-scale targets. Secondly, due to the diversity of irregular changes in cell morphology, it is difficult for the conventional convolutional networks to learn the multi-posture features caused by cell squeezing and deformation. The DCN is adopted to reform the C3 and C4 layers in the backbone feature extraction network, thus improving the feature extraction capability of the multi-posture features. Finally, the EPSA module is introduced to extract the multi-scale features of spatial information in each channel feature map by channel segmentations. It solves the loss problem of feature details caused by the different sizes of cells and the overlap of adherent cells. In addition, the resolution of the feature map decreases with the deepening of network depth in target detection of conventional networks, so the top-level feature map will lose the cell details. By using ROI Align in the improved network, instead of ROI Pooling, the problem of region mismatch caused by two quantization errors can be avoided in the pooling process. Figure 1 shows the structure of BDE_RCNN.
## 2 Data and Methodology  
### JPL Development Ephemeris  
The Development Ephemeris project was initiated by NASA's Jet Propulsion Laboratory (JPL) during the 1960s. This ephemeris stands as the most widely utilized resource within the international astronomical community, providing comprehensive data on celestial positions, velocities, accelerations, lunar libration, and nutation. These data are expressed through Chebyshev polynomial coefficients. Moreover, a dedicated computer program is available for acquiring Cartesian coordinates in the J2000 geocentric inertial system for the nine planets within the solar system, incorporating lunar chapter and libration motions [28]. In this study, we employ the commonly used DE 430 ephemeris from JPL's collection to elucidate the relative motions of Earth, the Moon, and the L1 point. The positions and velocities of these entities are transformed into Cartesian coordinates [29; 30]. Access to both the data and the relevant processing program is provided through the JPL website ([[ftp://ssd.jpl.nasa.gov/pub/eph/planets/](ftp://ssd.jpl.nasa.gov/pub/eph/planets/)]([ftp://ssd.jpl.nasa.gov/pub/eph/planets/](ftp://ssd.jpl.nasa.gov/pub/eph/planets/)), accessed on 10 January 2021).
## Materials and Methods
### Firefly Algorithm and Its Improvements
#### 2.1.2 Improved Firefly Algorithm  
In order to solve the problem of early convergence and local extremum, this paper proposes an improved firefly algorithm (IFA). The main idea of the improved strategy is as follows:  
1. Firefly's update efficiency is improved. When the step size is updated, a fixed distribution step size is used in the classical algorithm. The updated strategy is consistent at different stages of the algorithm. Therefore, it was decided to introduce a step size range to limit the uniform distribution, so that the step size range will decrease as the number of updates increase. In the early stage, the firefly update step size is larger and rapid iterations are performed. As the iterations increase, the firefly update step size becomes smaller, which enhances the local search capability, and finally achieves the purpose of optimizing the algorithm. The updated step method of IFA is as follows: \[s(t)=s^{t}\cdot\varepsilon^{t}\cdot\left(\cos\left(\frac{\pi t}{t_{max}} \right)+1\right),\] (4)
2. The self-search ability of the local optimal firefly is improved. Because all firefly individuals only fly to fireflies which are brighter than themselves, the brightest individual in the group does not move, but there is no guarantee that the individual is in the optimal position. In response to such problems, the firefly in the optimal position is randomly searched in an adaptive circle centered on itself and the radius gradually decreases as the number of iterations increases. If the brightness of the firefly is brighter in the new location, the coordinates of the firefly will be updated, otherwise it is not updated, Therefore, the self-search ability of fireflies to the surrounding environment has been improved, and fireflies can jump out from the local optimal position. The optimal firefly self-search update formula is as follows: \[x_{i}^{t+1}=x_{i}^{t}+^{t}\cdot\cos\left(\frac{\pi t}{2t_{max}}\right)\cdot \varepsilon^{t},\] (5) where the \({}^{t}\cdot\cos\left(\frac{\pi t}{2t_{max}}\right)\) represents the self-searching range of the optimal firefly at the \(t\)-th iteration. The optimized firefly algorithm implementation process is shown Figure 1.
## 4 Results
### Characterization of C2C Certified Regarding ISO Typology
#### 4.1.1 Communication Characteristics  
The \"Communication\" category consists of criteria related to the awarding format of an eco-label, the type and multiplicity of covered aspects (environmental and other), as well as the end-user focus. Ultimately, an eco-label affiliation to any of the three ISO typologies is evaluated. In the case of C2C Certified, the program does not fully conform to Type I (ISO 14024) or Type III (ISO 14025), although many similarities to Type I are pointed out hereafter when presenting the results of the characterization analysis. C2C Certified applies a rating awarding approach consisting of five levels, from Basic to Platinum. This is considered advantageous in comparison to a binary pass-fail approach, since the label allows for the differentiation and ranking of the performance of the different certified products (the aspects of comparability in this relation are further described in Section 4.1.5). However, it is important to highlight that although a ban of the use of certain toxic substances and materials is ensured, only a rudimental material and energy inventory of the product is made at the first level of certification (i.e., Basic). This level is intended to show that the company has started out \"on the path to certification\" [20] (p. 11). The Basic certification is provisional, and the product must undergo further higher certification no later than two years after the Basic achievement, as it would otherwise be delisted from the program. Basic-level certification cannot be used as a mark on the product, but only in marketing materials [22]. This change in the latest version of the Product Standard v3.1 [20] reduces the risk of accusations of greenwashing. In contrast, Type I eco-labels are usually awarded for real product performance. This is assured by ISO 14024's requirement for market analysis, as part of the feasibility study on the development of criteria for a new product group (discussed further in Section 4.1.2). Type III declarations, on the other hand, assure the robust communication of products' LCA profiles. Nevertheless, these products can be completely imaginary, and are either still in the research and development (R&D) phase or configured to answer certain application needs, without having been put into application. C2C Certified is a multi-attribute system that covers both environmental and social performance aspects in their certification criteria. The intended end-user focus of C2C Certified is not explicitly stated. According to [PERSON] and [PERSON] [9], certification applies to both B2B and B2C, depending on the nature of the certified products. In practice, mostly B2B applications have lately been observed;
# Robust Phase Estimation of Gaussian States in the Presence of Outlier Quantum States
## 5 Phase Estimation of Noisy Coherent State
### Numerical Simulation  
We will now describe procedures of our numerical simulation. We set the true coherent state as \(\alpha=10+4i\). The true phase value is \(\theta=\arctan(0.4)\simeq 0.3805\). We randomly generate \(n\) quantum gaussian states, which is a convex mixture of the ideal coherent state and outlier quantum states (either a single outlier quantum state case (29) or a distributed outlier quantum state case (30)). We perform a random homodyne measurements at \(\phi=0,\pi/2\) with equal probability. From measurement outcomes \(\mathbf{x}\) at \(\phi=0\), we apply an M-estimator to estimate \(\hat{\alpha}_{R}(\mathbf{x})\). The imaginary part will be estimated by homodyne measurement at \(\phi=\pi/2\). We repeat the iterative algorithm of Section 2.3 to obtain estimates. We stop iteration when the difference between two successive estimates is below a predetermined threshold value. We then apply the formula (37) to obtain an estimate \(\hat{\theta}_{n}(\mathbf{x})\). In our simulation, we change the sample size \(n\) for a given contamination parameter \(\varepsilon\). We compare two types of M-estimators (bisquare and gamma) together with the sample mean and the median. (The sample mean for the ideal coherent state corresponds to the MLE in our setting).
# Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent
## 4 Results
### XOR Gate Dataset
#### 4.1.2 Floating Point Hardware  
To analyze the hardware implementation, requirements such as execution time and use of available hardware are of great importance. In the case of FPGAs the resources are measured through the use of LUTs, Registers, Slices and Digital Signal Processors (DSPs) units, to name a few. Regarding the floating point implementation made for the XOR gate dataset, the resource consumption can be observed on Table 1.  
It can be seen that with 16 kernels the design occupies approximately 50% of the LUTs available in the FPGA. As for the execution time, shown on Table 2 it can be noted that the FPGA implementation has a much shorter execution time than that presented in software. This increase in execution speed provides a significant increase in the number of samples that can be analyzed per second, as shown on the Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Kernels** & **Registers** & **LUTs** & **Slices** & **DSP48** \\ \hline
2 & 269 & 9135 & 3078 & 69 \\
4 & 270 & 14,540 & 4654 & 102 \\
8 & 470 & 27,614 & 9742 & 186 \\
16 & 870 & 53,679 & 18,036 & 354 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resource utilization of the XOR gate dataset—floating point.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number of Kernels** & **Software** & **Floating Point Hardware** & **Speedup** \\ \hline
2 & 0.518 ms & 94.285 ns & 5462 \\
4 & 0.515 ms & 120.701 ns & 4266 \\
8 & 0.513 ms & 235,276 ns & 2180 \\
16 & 0.508 ms & 375.323 ns & 4266 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Execution time, \(t_{s}\), of the XOR gate dataset—floating point.  
Figure 6: Confusion matrix of the XOR training, software implementation.  
As for the correctness of the implementation in hardware, it can be seen through Figure 7 that the implementation converges in few iterations. Being the vertical axis is the value of the instantaneous quadratic error and the horizontal the number of iterations.
###### Abstract  
Maritime relay communication has emerged as a critical application scenario for non-terrestrial networks (NTNs), providing beyond-line-of-sight (BLOS) connectivity for offshore terminals. Unlike terrestrial environments, the complex marine propagation conditions lead to signal instability. To enhance the robustness of maritime two-way relay networks (TWRNs), we propose a novel physical-layer network coding (PNC) scheme based on block Markov superposition transmission (BMST). The proposed scheme introduces a novel co-design framework that achieves dual breakthroughs: (1) robust error correction via BMST's spatially coupled coding architecture and (2) spectral efficiency maximization through PNC's spatial-domain signal superposition. Moreover, we develop a decoding-computing (DC) algorithm that sequentially performs iterative decoding followed by computing. Compared to the computing-decoding (CD) algorithm, the proposed DC algorithm mitigates useful information loss at relay nodes, achieving a 2.9 dB coding gain at a bit error rate (BER) of \(10^{-5}\). Owing to the DC algorithm's dual-layer decoding architecture, we can further improve the overall system performance through targeted optimization of either the code rate or memory size for communication sides with poor channel conditions, yielding an extra 0.2 dB gain at a BER of \(10^{-5}\) compared to non-optimized configurations. The simulation results demonstrate that the proposed scheme significantly enhances maritime relay communication performance under harsh oceanic channel conditions while providing actionable insights for optimizing next-generation maritime communication system designs.  
1  
D 2025  
1  
[PERSON] and [PERSON]  
15 April 2025  
9 May 2025  
Accepted: 12 May 2025  
Published: 15 May 2025  
**Citation:** [PERSON]; [PERSON].  
Dual-Domain Superposition for  
Maritime Relay Communications: A Flexible-Coded Transmission Design Towards Spectrum-Reliability Synergy.  
_Electroins_ **2025**, **14**, **2019**. [[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).
## 4 Results and Discussion
### AI-Based Models Employed for Prediction Tasks  
Figures 9 and 10 and Tables 5 and 6 compare the performance of machine learning (ML) and deep learning (DL) models studied for predicting hourly PM\({}_{2.5}\) and PM\({}_{10}\) concentrations across training and testing phases. For PM\({}_{2.5}\), the top-performing models during training are GBoost, XGBoost, and GAM, respectively, all achieving R\({}^{2}\) values close to 1, indicating near-perfect model fitting and strong alignment with the training data. However, this performance does not generalize equally well to unseen data, as observed in the testing phase, where the best-performing models shift to xGBoost, RF, and TreeBag, respectively, achieving R\({}^{2}\) values of up to approximately 0.92. A similar trend is observed for PM\({}_{10}\) predictions, with GBoost, XGBoost, and GAM once again showing dominant performance during training, reflected by R\({}^{2}\) values near 1. However, their performance drops in the testing phase, where RF, TreeBag, and xGBoost, respectively, outperform others, although with slightly lower predictive accuracy--R\({}^{2}\) values remain below 0.83, suggesting increased variability and potential overfitting during the training phase. To further refine model inputs and improve generalization, the top-performing models from the testing phase--xGBoost for PM\({}_{2.5}\) and RF for PM\({}_{10}\)--are subsequently selected for a detailed statistical evaluation of the most effective feature selection techniques. This step aims to identify the optimal set of predictors that enhance model robustness and accuracy while reducing redundancy in the input space. Using these best-performing models ensures that the feature selection evaluation is aligned with models that have demonstrated the strongest generalization capabilities under realistic prediction conditions. Figure 8: Pearson correlation and \(p\)-values for PM\({}_{2.5}\) and PM\({}_{10}\) with SO\({}_{2}\) and meteorological variables for the entire period. Figure 10: A comparison of ML and DL for predicting PM\({}_{10}\) concentrations in the training and testing stages. Figure 9: A comparison of ML and DL for predicting PM\({}_{2.5}\) concentrations in the training and testing stages.