esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Aeroacoustic Model
### Engine Acoustic Source Model
#### 3.1.1 Aerodynamic Excitation Modelling  
The engine noise modeling has been performed by the tool PropNoise. The tool is a DLR in-house code developed at DLR's department of engine acoustics during the last fourteen years [9]. The acoustic modules of the tool use an analytical theory utilizing circumferential modes in frequency domain. Based on the known aerodynamic excitation, PropNoise allows the prediction of several tonal and broadband engine noise sources, such as self- or interaction noise. One of the most recent extensions of the code enable PropNoise to account for boundary layer rotor interaction noise [10]. The aerodynamic excitation can be estimated by the tool itself, using a mean-line approach or alternatively by aerodynamic measurement or high-fidelity CFD-RANS computations.  
In the present study, the RANS-informed operation mode of PropNoise, as shown in Figure 2 (CFD-informed PropNoise), is used. This ensures a highly accurate prediction of the RSI noise source and its underlying source mechanism. DLR's flow solver TRACE [11] was used to compute the turbulent flow within the turbomachinery. This step is given by the box \"3D RANS flow solution\" in Figure 2. Here, the compressible Navier-Stokes equations are solved in the relative system of the rotor and stator. This flow solver was specifically developed for calculating the flow in turbomachinery applications. In a next step, the mean and turbulent flow quantities, such as the rotor wakes, responsible for the RSI noise mechanism, as well as geometrical parameters, such as leading and trailing edge positions, are extracted (step \"Extraction of flow and geometry\" in Figure 2) and used to reconstruct the aerodynamic excitation of RSI noise at the stator vane leading edges (step \"Reconstruction of aerodyn. excitation\" in Figure 2). The process of extraction and reconstruction is done according to Jaron [12].  
Figure 2: PropNoise workflow for standalone and computational fluid dynamics (CFD)-informed modes.
# Recovery of Polyphenols from Agri-Food By-Products: The Olive Oil and Winery Industries Cases
## 3 Polyphenols from Wine Production Wastes: Source and Applications
### Applications of Polyphenols from the Wine Industry  
Winery waste extracts and grape by-products can be applied to foods, pharmaceuticals, or cosmetics, either in the form of liquid, concentrated, or powder extracts [8; 37; 38; 39].  
The polyphenolic content of grapes has attracted the interest of the pharmaceutical, cosmetic, and food industries as a profitable source of natural antioxidants [31]. Currently, there are cosmetics or food supplement products marketed with grape polyphenols [8]. [PERSON] et al. [39] demonstrated that red wine lees extracts exhibited the highest antioxidant capacity and inhibitory effect to enzymes enrolled in the skin aging process (elastase, collagenase, and tyrosinase), and the protection of human skin cells (keratinocytes and fibroblasts) against oxidative stress.  
In the health field, there are studies about the effect of polyphenols from natural sources in the prevention and treatment of cancer [8; 40; 41; 42]. For instance, [PERSON] et al. [40] provided evidence that grape seed extract has an anticancer effect in liver cancer by the inhibition of cell proliferation, induction of apoptosis, modulation of oxidative damage, and suppression of inflammatory response. Additionally, [PERSON] et al. [41] found that resveratrol, a stilbene present in grapes, inhibits the proliferation of human cervical carcinoma cells and elevates apoptosis. Therefore, resveratrol may be a promising inhibitor of human cervical cancer.  
Likewise, grape pomace extracts have been successfully incorporated into edible chitosan films, providing antioxidant properties and prolonging shelf life in foods, as shown by [PERSON] et al. [37]. They prepared chitosan films with grape seed extract and carvacrol microcapsules and tested their physicochemical properties and their effect on physico-chemical and microbiological parameters in refrigerated salmon. It was observed that the chitosan films increased the shelf-life of refrigerated salmon from 4-7 days of storage due to the antimicrobial effect of the natural agents. Additionally, [PERSON] et al. [43] developed active edible films with antiviral activity by adding grape seed extract as an active agent in alginate-lipid films. The films exhibited significant antiviral activity against murine norovirus and hepatitis A virus. Furthermore, the addition of grape seed extract exhibited strong antioxidant activity to the active films and can improve the food quality and safety.  
The recovery of polyphenols from waste, whatever their type, generally requires the implementation of extraction and purification processes, in addition to having an analytical methodology to control the processes and the quality of the products.
## 3 Application to Resistive Driven-Rod Method: Testing with Some Examples
### Synthetic Examples with No Internal Resistance
This effect is most obvious when there are large changes in resistivity between layers. The continuous red line in subfigure, labeled as E-Calculated 1, stands for the percentage of error \(\varepsilon_{R}=(R_{true}-R_{calc})/R_{true}\) in the calculation of the grounding resistance using the estimated soil model, which give rise to the above-mentioned curve labeled Calculated 1 in the main figure. If the grounding resistance is now measured every time 0.5 m of rod driven into the ground, new data have to be added to Table 2 obtaining Table 3.  
The minimization procedure applied on data of Table 3 results in a set of soil parameters \(\rho_{1}=49.34\,\Omega\text{m}\), \(\rho_{2}=217.28\,\Omega\text{m}\), \(\rho_{3}=10.73\,\Omega\text{m}\), h\({}_{1}=1.95\,\text{m}\), and h\({}_{2}=3.02\,\text{m}\), which give rise to the green dashed line labeled as Calculated 3 in Figure 5. Note that the calculated grounding resistance by the new optimum model is very close to that calculated by the true soil model since the values of the estimated soil parameters are very close to those of the actual soil. The continuous blue line, labeled as Calculated 2, stands for the grounding resistance profile from the improved resistivity model. The corresponding error \(\varepsilon_{R}=(R_{true}-R_{calc})/R_{true}\) is shown as E-Calculated 3 in the subfigure. As a conclusion, the larger the sample of grounding resistance data, the better the estimation of the soil model parameters. Figure 5: Simulated and calculated grounding resistance. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**L (m)** & 0.5 & 1 & 1.5 & 2 & 2.5 & 3 & 3.5 & 4 \\ \hline
**R (\(\Omega\))** & 71.11 & 41.63 & 30.51 & 25.10 & 24.01 & 22.78 & 21.66 & 20.53 \\ \hline
**L (m)** & 4.5 & 5 & 5.5 & 6 & 6.5 & 7 & 7.5 & 8.0 \\ \hline
**R (\(\Omega\))** & 19.46 & 18.12 & 8.21 & 5.79 & 4.55 & 3.77 & 3.24 & 2.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Buried rod length L (m) and the corresponding grounding resistance R (\(\Omega\)).
## Appendix A Definition of Mesh Faces  
Each triangular face of the mesh is defined by a triplet of vertex indices \((v_{1},v_{2},v_{
# Pseudo-3D Receiver Deghosting of Seismic Streamer Data Based on \(l_{1}\) Norm Sparse Inversion
## 5 Conclusions  
In this paper, we propose a novel pseudo-3D deghosting framework based on 3D sparse inversion to improve the effect of the conventional 2D deghosting method. The pseudo-3D data cube required by the framework is easily implemented from conventional 2D streamer data. To improve the inversion accuracy, the data coherence along the shot direction and the compressed sensing-based inversion method are introduced.  
The synthetic and field examples show that the main advantages of the proposed method are the frequency compensation and noise stability. Field noise cannot be avoided in marine seismic acquisition, and the processing process requires the amplitude-preservation of useful events. The algorithms with low noise sensitivity are more suitable for deghosting in industrial data processing.  
[PERSON] and [PERSON] conceived and designed the study; [PERSON] programmed the code of the deghosting algorithm; [PERSON] evaluated and interpreted the results; [PERSON] provided the idea; [PERSON] processed the synthetic and field datasets; and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This research was supported in part by the National Natural Science Foundation of China (No. 42074151 and No. 41374108) and Major Projects of the National Science and Technology of China (Grant No. 2016 ZX05026-002-003).  
The seismic data used in this paper are the property of Jilin University.  
We are grateful for the SPGL1 solver package provided by University of British Columbia, the Madagascar software package provided by University of Texas at Austin, and the open-source code.  
All of the authors declare that the funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Inter-Subject MEG Decoding for Visual Information with Hybrid Gated Recurrent Network
## 3 Experiments
### MEG Signal Preprocessing  
According to the experiments of the public visual MEG decoding dataset [4; 8], the subject will stimulated by two different visual stimulations, face and scrambled face images. Sixteen subjects took part in the MEG decoding experiment. During the experiment, each subject received about 580 visual stimulations. The corresponding MEG signals were recorded by a MEG equipment with 306 channels. The provided MEG signals were downsampled to a rate of 250 Hz. Before each stimulation, the subject can rest 0.5 s and then receive a stimulation which will last one second. Firstly, the MEG signals evoked by each stimulation will be extracted. Secondly, each MEG signal was filtered by a [0.1, 20] Hz 4-order Butterworth filter. Lastly, each signal was normalized by subtracting its mean, and then dividing its standard deviation.  
Consequently, the MEG data of all 16 subjects will be applied for leave-one-out cross-validation (LOOCV) in the following experiment.
## 3 Results and Discussion
### SERS and Surface Morphology of AuNP-Decorated ZnO NRs by Changing the PLIP Conditions  
Finally, we evaluated the experimental conditions of fabricating AuNPs using the PLIP method, followed by the immersion process, to grow AuNPs on ZnO NRs. Figure 7a depicts the Raman spectra of R6G using AuNP-decorated ZnO NRs as the SERS substrate for AuNP fabricated for different laser irradiation times at a fixed immersion time of 30 min. The results indicate that a short laser irradiation time of 8 min is suitable for fabricating AuNP-decorated ZnO NR SERS substrate to support the LSPR mode and enhance the Raman signal. A longer laser irradiation time of 20 min had the best ability of the SERS effect with the highest Raman intensity for the same FWHM from the Raman peak. The sample fabricated with the laser irradiation time for 30 min was not favorable for the SERS effect, and the Raman signal decreased. Figure 7b illustrates the Raman signal of R6G using AuNP-decorated ZnO NRs as the SERS substrate for AuNPs fabricated at different immersion times at fixed laser irradiation time of 8 min. A suitable immersion time facilitates the growth of AuNPs, and an immersion time of 30 min was the most suitable. The SERS sample with a longer immersion time exhibited a decrease in the Raman signal. Figure 7c depicts the SEM images of AuNP-decorated ZnO NRs for AuNPs fabrication at an immersion time of 60 min. Nonhomogeneous and aggregated AuNPs growth on ZnO NRs could be the cause of the weak Raman signal. We tried to correlate the size, density and the degree of agglomeration of AuNPs, observed in the SEM images (Figures 6b and 7c), with the SERS enhancement and found out the size of AuNPs has to be in \(\sim\)50-nm-diameter, the density of AuNPs has to be less than 10 AuNPs per ZnO NR, and the AuNPs has to be isolated and independently grown on each ZnO NR without aggregation.  
Figure 6: (**a**) Raman signal of R6G at different concentrations using Au-decorated ZnO NRs as the SERS substrate where the ZnO NRs was fabricated using the unannealed seed layer; (**b**) corresponding SEM images of Au-decorated ZnO NRs of different magnification.
###### Abstract  
In hearing aid devices, speech enhancement techniques are a critical component to enable users with hearing loss to attain improved speech quality under noisy conditions. Recently, the deep denoising autoencoder (DDAE) was adopted successfully for recovering the desired speech from noisy observations. However, a single DDAE cannot extract contextual information sufficiently due to the poor generalization in an unknown signal-to-noise ratio (SNR), the local minima, and the fact that the enhanced output shows some residual noise and some level of discontinuity. In this paper, we propose a hybrid approach for hearing aid applications based on two stages: (1) the Wiener filter, which attenuates the noise component and generates a clean speech signal; (2) a composite of three DDAEs with different window lengths, each of which is specialized for a specific enhancement task. Two typical high-frequency hearing loss audiograms were used to test the performance of the approach: Audiogram 1 = (0, 0, 0, 60, 80, 90) and Audiogram 2 = (0, 15, 30, 60, 80, 85). The hearing-aid speech perception index, the hearing-aid speech quality index, and the perceptual evaluation of speech quality were used to evaluate the performance. The experimental results show that the proposed method achieved significantly better results compared with the Wiener filter or a single deep denoising autoencoder alone.  
Keywords:hearing aids; deep denoising autoencoder; speech enhancement; Wiener filter; noise reduction +
Footnote †: journal:
## 6 Performance Analysis
### Delay  
The network delay is defined as the total time taken for the information to travel from the source device to the destination device. It is to be noted that in our proposed modified superframe transmission, failure devices use separate retransmission period. Let \(D_{j}\) be the delay that the packet is transmitted successfully at time \(j+1\). Then,  
\[D_{j}=T_{s}+jT_{R}+\sum\limits_{i=0}^{j}D_{b} \tag{32}\]  
where \(D_{b}\) is the delay that the ZigBee device found the channel idle during \(maxMacBackoff\) number of backoffs, \(T_{s}\) is the average time of successful transmission and \(T_{R}\) is the allocated retransmission duration due to transmission failure. Let \(B_{k}\) be the event that the channel access is successfully at the \((k+1)\)-th time provided the channel access is failed for \(k\)-th times. Let \(B\) be the event that the channel access is successful within \(maxMacBackoff\). Hence, the expected delay for backoff during one transmission attempt is:  
\[D_{b}=\sum_{k=0}^{m}k\frac{p_{backoff}^{k}(1-P_{backoff})}{1-P_{backoff}^{m+1} }(\frac{CW_{i}-1}{2}) \tag{33}\]  
A device has to wait until the next superframe starts, if the packet is generated outside of the contention period. The packet arrival of ZigBee devices is assumed to follow the Poisson distribution with average packet arrival rate \(\lambda_{z}\). Then, the waiting time is:  
\[W_{time}=\int_{0}^{BI}(BI-x)\lambda_{z}e^{-\lambda_{z}x}dx. \tag{34}\]  
Propagation delay \(P_{delay}\) is the amount of time it takes for one bit to travel through a one-hop destination. Let \(E_{j}\) be the event that the packet is transmitted successfully at time \(j+1\) provided the packet has been failed for \(j\) times. Let \(E\) be the event that the packet is transmitted successfully within \(maxFrameRetries\). Considering the \(maxFrameRetries=r\), the conditional probability can be derived as follows.  
\[Pr(E_{j}/E)=\frac{P_{S_{j+1}}\prod\limits_{k=1}^{j}(1-P_{S_{k}})}{1-\prod \limits_{k=1}^{r}(1-P_{S_{k}})}. \tag{35}\]  
Hence, the delay can be derived as follows.  
\[D=\sum_{j=0}^{r}Pr(E_{j}/E)D_{j}+W_{time}+P_{delay}. \tag{36}\]
# MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
## 3 System Model
### Device Selection Based on Traffic Congestion Control with Back-Pressure Strategy  
The queue length (QL) value describes the number of backlog packets at the device buffer. Since devices are mobile and free to move in any direction, the value of QL frequently changes in a small duration of time. The queue length of devices could be obtained from the queue length model in bytes. The devices which have a lower QL are assumed to be higher priority for selecting the optimal path. Overall, this device selection \(DS_{QL(i,j)}(t)\) metric has been implemented for traffic congestion control based on QL in the D2D communication-based IoT 5G network using back-pressure calculated as follows:  
\[DS_{QL(i,j)}(t)=\max_{f_{C}\
eq i,j}\{Q_{i}^{fc}(t)-[1-\frac{QL_{j}(t)}{QL_{j} ^{\max}(t)}]\times Q_{j}^{fc}(t)\} \tag{7}\]  
where \(QL_{j}(t)\) denotes the number of bytes in device \(j\), and \(QL_{j}^{\max}(t)\) is the maximum number of the queue length size in devices.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
## 2 Related Work
### Vehicle-Infrastructure Cooperative Perception  
Vehicle-infrastructure cooperative perception aims to enhance the perception capabilities of autonomous vehicles by leveraging shared information from infrastructure. This area has recently gained significant attention. Based on the type of information exchanged between vehicles and infrastructure, vehicle-infrastructure cooperation can be categorized into three primary methods: early cooperation, late cooperation, and intermediate cooperation [19].  
Early cooperation involves the direct sharing of raw data, such as transmitting raw sensor data from infrastructure to vehicles. However, due to the substantial volume of raw sensor data, this approach demands significant communication bandwidth, making it impractical for real-world applications. For instance, [PERSON] [20] introduced the first early cooperative strategy based on LiDAR point clouds, which involved sharing raw LiDAR data for cooperative perception.  
Late cooperation, on the other hand, focuses on results-level integration, where detection results are exchanged between vehicles and infrastructure. This method requires minimal communication bandwidth and features low coupling between the perception systems of vehicles and infrastructure, making it the most widely adopted approach currently. However, late fusion heavily relies on the accuracy of both vehicles' and infrastructure's perception systems. It is susceptible to noise interference, which can lead to false positives and negatives, ultimately diminishing perception performance. For example, [PERSON] et al. [21] proposed a two-stage Kalman filtering scheme designed for late cooperation with infrastructure; meanwhile, [PERSON] et al. [22] explored lane marking detection by integrating vehicle and infrastructure sensors, employing Dempster-Shafer theory to address uncertainties in the perception process.  
Feature-level cooperation, as an intermediate strategy, offers a more effective solution. This method involves sharing features between vehicles and infrastructure, which can reduce communication costs while maintaining high detection accuracy. Currently, this approach is a leading research direction. However, a notable challenge persists: the domain gap between features extracted by vehicle and infrastructure sensors. Effectively fusingthese features is essential for advancing vehicle-infrastructure cooperative perception. In contrast to various solutions proposed in previous studies [14; 15; 16; 17], we advocate for a multi-scale dynamic fusion strategy that facilitates dynamic interactions between vehicle and infrastructure features across different scales. This approach enables more comprehensive and effective feature fusion, enhancing the overall performance of cooperative perception systems.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 3 Methodology
### Architecture of a 1-D CNN Model  
Figure 4a illustrates the architecture of a 1-D CNN classification model designed to detect foundation damage. The 1-D CNN model trained using time-history data receives \(1\times 5000\) input data, while the 1-D CNN model trained using SVD data receives \(1\times 2048\) input data. The 1-D CNN model extracts essential features of vibration data (i.e., concatenated time-history and SVD responses) through multiple CNN layers before providing classification results for the level of foundation damage. The configuration of the 1-D CNN layers is empirically chosen for the specific data characteristics, as detailed in Table 1. The CNN model includes three convolution layers, three max-pool layers, three ReLU layers, one flatten layer, and three dense layers. Convolutional layers are responsible for feature extraction from the input signals. Max-pooling layers then down-sample these features to reduce computational load and mitigate overfitting risks. ReLU layers introduce non-linearity to the network by converting all negative values to zero. The flatten layer reshapes the extracted features into a format suitable for the dense layers. Each node in a dense layer is connected to every node in the previous layer, with each connection associated with a learnable weight parameter. The CNN models are trained with 40 epochs, a learning rate of 0.001, and a batch size of 1. During training, the model minimizes the difference between the predicted and actual labels using the categorical cross-entropy loss function. The accuracy metric is utilized to evaluate the classification performance of the trained model.  
Figure 4b shows the data preparation and training procedure for a caisson-foundation system. The time-history responses obtained under foundation damage cases are concatenated and are input into the CNN model. The time-history responses are used to extract the SVD responses. The extracted responses are used for the CNN model trained using SVD data. The prediction results of two CNN models are integrated for damage identification of the caisson-foundation system.  
Robustness of the model can be achieved by ensuring the consistency and sensitivity of the training data. To ensure data consistency, experimental variations such as impact load magnitude, temperature fluctuations, and ambient noise are carefully controlled in lab-scale conditions. To achieve the high sensitivity of the training data, the training data are focused on regions susceptible to damage variations (i.e., here, 1000 data points surrounding the impact event were selected for the time-history analysis, and \(2^{11}\) = 2048 data points for the SVD analysis).  
To enhance the model's performance and prevent overfitting issue, data shuffling and early-stopping techniques are implemented [23]. Data shuffling helps to prevent the model from learning the order of the data, and the early-stopping halts training when the model's performance on the validation set begins to degrade.