esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Method of Seismic Signal Detection Using Waveform Features
## 4 Implementation
### Extracting Features
The test results for each feature are shown in Table 2. We can see that the features in this article follow the Gaussian distribution. In this paper, we use GP to construct the distribution of each feature. After processing of each feature, we can create a joint probability model containing multiple characteristics to distinguish seismic signals from noise. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{2}{c}{**Signal Train Set**} & \multicolumn{2}{c}{**Signal Test Set**} & \multicolumn{2}{c}{**Noise Test Set**} \\ \cline{2-6}  & **Mean** & **Standard Deviation** & **Mean** & **Standard Deviation** & **Mean** & **Standard Deviation** \\ \hline \(\alpha\) & 5.19 & 1.45 & 5.66 & 1.55 & 2.61 & 0.38 \\ \(\rho\) & 4.08 & 1.49 & 4.48 & 1.53 & 1.96 & 0.28 \\ \(\log(\gamma)\) & 3.95 & 2.61 & 4.51 & 2.60 & 0.04 & 0.69 \\ \(\log(\lambda)\) & 8.62 & 2.86 & 9.19 & 2.88 & 4.74 & 0.87 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical distribution of each waveform feature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \(\alpha\) & \(\rho\) & **log(\(\gamma\))** & **log(\(\lambda\))** \\ \hline \(p\)-value & 0.366 & 0.233 & 0.127 & 0.077 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The Kolmogorov–Smirnov (KS) test results of features. Figure 6: Distribution of the maximum value of the STA/LTA curve. From (**a**) to (**c**), the subgraphs are the train set data distribution, the seismic test set distribution and the noise test set distribution of the maximum feature. The red dotted line represents the statistical distribution, while the blue dotted line is distribution obtained by the Gaussian process using prior information.
## 5 Data Processing
### GPR Data Processing  
The noise and inappropriate reflections induced by the antenna ringing, fluctuations in energy coupling with the ground, and many reflections between the antenna and the ground surface were removed using REFLEXW software version 9.1 [71]. This technique improves the required reflections while also correcting horizontal and vertical resolution [72]. Static correction, band pass frequency filter, running average, background-removing filter, gain, and so on are some of the processing steps in REFLEXW, as stated in the program's documentation [73]. The radagram sections appear as a relation between the two-way travel times in a draped fashion and the offset distances. The radagram section that is function in time is converted into another section that is function in depth for interpretational purposes using the appropriate velocity. The velocity of the radar wave was calculated using both the reflected wave approach and the hyperbolic shape procedure in this study. The velocity which was used to convert the time into depth in this survey for all profiles was 0.107 m/ns.
# One-Shot Distributed Generalized Eigenvalue Problem (DGEP): Concept, Algorithm and Experiments +
## 5 Numerical Experiments  
In this section, numerical experiments on synthetic and real data are carried out to illustrate the effectiveness and accuracy of the proposed algorithms. In the GEP, Algorithm 1, as the first distributed algorithm for the GEP, is compared with the centered result. In the pursuit of the eigenvectors of the DGEP in the central server, SVD and the power method (PM) are carried out. They are denoted as DGEP-SVD and DGEP-PM in the following. In CCA, Algorithm 3 (denoted as DCCA-PM) and DGEP-PM are compared with the centered results. We only pursue the generalized eigenvector corresponding to the largest generalized eigenvalue for convenience in the maximum optimization of distributed GEP and distributed CCA, which is \(k=1\). All the experiments are performed on MATLAB R2019a on a computer with 6 Core 2.20 GHz CPUs and 8 GB RAM. The codes are available in [[https://github.com/kelenlv/one-shot-DGEP](https://github.com/kelenlv/one-shot-DGEP)]([https://github.com/kelenlv/one-shot-DGEP](https://github.com/kelenlv/one-shot-DGEP)) (accessed on 1 May 2022).
# Deep Learning-Based Community Detection Approach on Multimedia Social Networks
## 4 Experimental Evaluation
### Experimental Protocol  
Our evaluation aimed to investigate the effectiveness and efficiency of the proposed approach according to the following three criteria:  
* Time efficiency analysis of our approach on the artificial dataset, varying the matrix sparsity and number of nodes;
* Performance analysis during the training phase considering the _loss_ value and different optimization metrics;
* Effectiveness analysis of the proposed approach with respect to _DeepWalk_[28], _SDNE_[33], _LINE_[34], _GraphGAN_[29], _CANE_[30], and _DNNNC_[31].  
The proposed approach was evaluated on three different datasets: an artificial dataset, varying the number of nodes from 50,000 to 200,000 and the related sparsity degree between \(10^{-8}\) and \(10^{-3}\), BlogCatalog3 ([[http://datasets.syr.edu/datasets/BlogCatalog3.html](http://datasets.syr.edu/datasets/BlogCatalog3.html)]([http://datasets.syr.edu/datasets/BlogCatalog3.html](http://datasets.syr.edu/datasets/BlogCatalog3.html)), accessed on 10 October 2021), and Yahoo Flickr Creative Commons 100M (YCFMM100M) [35], a dataset containing about 100 million photos and videos extracted from Flickr. For the dataset YCFMM100M, images were filtered by tags in order to consider only the ones about cultural heritage, which were, successively, processed according to the methodology described in Section 3. The final dataset was composed of 138,100 nodes and 135,613 non-zero values, thus having a degree of sparsity equal to \(7.1\times 10^{-6}\) and only 71,605 values different from zero. Finally, we developed our framework on the Microsoft Azure ([[https://azure.microsoft.com/](https://azure.microsoft.com/)]([https://azure.microsoft.com/](https://azure.microsoft.com/)), accessed on 10 October 2021) cloud computing platform using a E2-64 v3 virtual machine equipped with a 2.3 GHz Intel XEON(r) E5-2673 v4 (Broadwell) processor and 32 GB of RAM.
# Detection of Bovine Mastitis in Raw Milk, Using a Low-Cost NIR Spectrometer and k-NN Algorithm
## 2 Materials and Methods  
### Data Acquisition  
Data acquisition is always the first step in any chemometric workflow because any data analyses will be unduly dependent on the quality and quantity of the collected data. In this particular case, acquisition was done in a semi-intensive milk production system under tropical weather conditions.  
Samples were obtained from 67 mixed-breed dairy cows that were randomly chosen from the population, composed of 2-to-6-year-old cows from 5 farms in the same area. From each chosen individual, a 50 mL sample of foremilk was obtained after discarding the first three milking streams from each quarter, unless they were blinded. Although some examples are from the same cow, the examples are considered different because it is not strange for a cow to have mastitis during one quarter while for the remaining 3 being perfectly healthy. The result was a dataset of 210 independently labeled samples of milk, which were collected in individual vessels.  
Immediately after their collection, the samples were scanned from above at an optical path length of 10 mm [26]. The scanner used to record was a \(\text{SCiO}^{\text{TM}}\) v2 NIR spectrometer, which is a low-cost scanner. This device can measure a reflectance spectrum in the wavelength between 740-1060 nm with a 1 nm resolution. Additionally, a 3D-printed holder was developed to ensure the same conditions between measurements by keeping the same position and distance to the sample from the NIR sensor. Moreover, this holder has a second function: isolating the sample from external light, which could have undesirable effects on measurements.  
Once the samples are scanned, the result is a signal stored in the cloud through a smartphone application [31], which can be downloaded to any device to proceed with the development of the chemometric models.  
Subsequently, the CMT test was performed with each sample according to the procedure described in [PERSON] et al. [32]. Each sample was therefore classified in one of the 4 possible categories on the basis of the result of the thickness of the gel formed by the mixture between the milk and CMT reagent. The possible classes were: 0, negative; 1, trace (+); 2, weak positive (++); 3, distinct positive (+++); and 4, (++++) strong positive.  
Once the dataset was captured, the interpretation of the CMT tests resulted in high discrepancies among the experts. They tended to disagree between degrees 1 and 2, and between degrees 3 and 4. Therefore, a second label was added to group the samples; cases with traces or weak positives were brought together under the label 'weak', and distinctive positives or strong positives under the label'strong' (Table 1). Additionally, Table 1 shows the distribution of instances per class and its interpretation. The prevalence of mastitis observed in this work is similar to the prevalence reported by other previous research work in this area [33].
# Can Vegetation Removal Successfully Restore Coastal Dune Biodiversity?
## 1 Introduction
To mitigate the threat of dune fixation in NDNR, the Nizzanim Long Term Ecological Research (LTER) project implemented two restoration experiments involving perennial vegetation removal as a form of pulse disturbance. The goal of these trials was to restore the heterogeneity of dune states and increase \(\gamma\)-diversity by providing a suitable habitat for mobile dune species [69]. This is the largest landscape scale restoration program undertaken in Israel, and the long-term responses have been monitored as part of the LTER program since 2005. Parallel to the trials, off-road vehicles (ORV) have been utilizing several dunes in the reserve providing an opportunity to examine the effect of a continuous press disturbance on species composition. In this study, we investigate the effect the two controlled pulse removals and the un-controlled ORV press disturbance, in order to determine whether these different types of vegetation removal recreate suitable habitat for mobile dune species. Restoration can take several years, even decades, and can rarely precisely replicate an original status [2; 73]. Therefore, it was expected that by removing vegetation and creating disturbance, the dunes would become more suitable for mobile-dune obligate species, and less suitable for fixed-dune species. Employing the terminology of Lake [73], we used a M-RCI experimental design; comparing multiple repeated replicates in Reference (mobile), Control (un-treated) and Intervention (treated) dunes. We addressed the following questions:  
1. How do species compositions of different taxa respond to different removal treatments? 2. Can indicator species represent assemblage level responses? 3. Can the different forms of removal contribute to successful coastal dune management?
## 3G Deployment Stage
### Economic Sustainability  
EHAS and FITEL conducted a cost analysis of the TUCAN3G solution and proposed new business models based on the sharing of resources among different actors in the rural environments [12]. The results obtained from the pilot showed that the solution proposed by TUCAN3G was technically and economically viable in the medium term, since the reduction of costs achieved allowed for the maintenance of the network with the income of small communities [13]. However, these studies also showed that the income obtained was not sufficient to compensate the investment required in jungle environments, therefore it was still necessary to look for subsidies to scale this type of solution to other regions and ensure sustainability in the long term.
## 4 Automatic Classification of ECG Signal
### ECG Signal Denoising  
ECG signals are characterized as weak, low amplitude, low frequency, and random. They can easily be disturbed by noise, which may come from organisms, such as respiration and muscle tremors, or external interference due to poor contact. The three main noises of ECG signals are power frequency interference, EMG interference, and baseline drift.  
The Wavelet Transform (WT) can perform a time-frequency transform and is an ideal tool for signal analysis in these two domains. Thus, the denoising mean based on the WT is adopted, which is split into the following three steps:  
1. First, a wavelet basis function sym40 is selected [30]. Given that the noise and signal are mixed together, the WT is used to decompose the ECG signal, and the wavelet coefficients of each scale are obtained.
2. After the scale decomposition of the ECG signal by the wavelet transform, the wavelet coefficients with large amplitude are considered useful signals, and those with small amplitude are noise. On the basis of the frequency distribution of the ECG signal and mixed noise, the wavelet coefficients on each scale are threshold-processed, and those smaller than the threshold must be changed to zero or processed with the threshold function.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Case Record/Strip** & **ECG Data/Segment** & **Category** \\ \hline MIT-BIH Arrhythmia ECG & 43 & 92,192 & N, A, V, L, R \\ Dataset & EU ST-T ECG & & \\ Database—Double Lead & 81 & 56,443 & N, S, V, L \\ Sudden Cardiac Death & 12 & 244,176 & S, V, N \\ Dynamic ECG Database & 28 & 76,149 & N, V, J, L, B \\ \hline \hline \end{tabular}
\end{table}
Table 1: Detailed table of case data division.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Full Name** \\ \hline N & Normal beat \\ A & Fusion of ventricular and normal beat \\ V & Premature ventricular contraction \\ L & Left bundle branch block beat \\ R & Right bundle branch block beat \\ S & Supraventricular premature or ectopic beat (atrial or nodal) \\ J & Nodal (junctional) premature beat \\ B & Bundle branch block beat (unspecified) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Complete category name.  
3. The low- and high-frequency coefficients after wavelet scale decomposition are processed separately, and then the signal is reconstructed. As shown in Figure 5.  
By comparing the images before and after denoising, the wavelet adaptive threshold denoising clearly removes most of the interference in the original signal and solves the problem of its baseline drift.
# Transformer-Based Explainable Model for Breast Cancer Lesion Segmentation
## 4 Results
### Model Evaluation and Complexity Analysis  
Figure 4A shows the parameter complexity comparison among different models. The number of parameters is a key indicator of model complexity. A higher parameter count generally indicates stronger representational capacity, but it also requires more computational resources and data for training and inference. This necessitates greater GPU memory during training and more memory to store parameters during inference. As shown in Figure 4A, the TEBLS model has the fewest parameters, with a parameter count of 13,416,834. Compared to Swin-Unet++, the TEBLS model reduces the parameter count by approximately 0.144 M, and compared to Swin-Unet, it reduces the count by about 0.106 M. This demonstrates that the proposed TEBLS model is a lightweight image segmentation model.  
In Figure 4B, we compare the inference time of different models. Inference time measures the duration from when the model receives input data to when it generates output results. This metric evaluates the efficiency of a model in processing input data for real-world applications, with shorter times indicating higher efficiency. The inference time for TEBLS was 0.0069 s, compared to 0.0078 s for Swin-Unet++ and 0.0089 s for Swin-Unet. Although the reduction in inference time (0.0009-0.002 s) may seem marginal, experiments conducted on 574 images using an NVIDIA GeForce GTX 1650 processor demonstrated a significant reduction in the overall experimental runtime. Figure 4D illustrates the segmentation performance comparison among the models. TEBLS achieved the best segmentation performance, accurately capturing lesion regions with clear details at image edges and within lesion areas. To gain a deeper understanding of the model's performance under various conditions, Figure 4C presents the confusion matrix of the TEBLS model with the test set, which shows that the model achieved a sensitivity of 0.7602 with the test set.  
Figure 4: A performance comparison of different models in terms of parameter complexity, inference time, and segmentation accuracy. (**A**) A parameter count comparison shows that TEBLS has the fewest parameters, highlighting its lightweight nature. (**B**) An inference time comparison, demonstrating that TEBLS was the most efficient model, with faster processing compared to Swin-Unet++ and Swin-Unet. (**C**) A confusion matrix showing the results of the TEBLS model using the test set shows that the model’s sensitivity was 0.7602. (**D**) Segmentation performance, where TEBLS outperformed other models by accurately capturing lesion regions with clear details at image edges and within lesion areas.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 2 WPT System Coupling Circuit Model  
### Coupled Circuit Model  
This paper adopts a series-series (SS) compensation topology with a simple structure, a straightforward analysis, and a good power transmission effect at a short distance. Figure 1 shows the coupled circuit model. In the figure, \(L_{1}\) and \(L_{2}\) are the self-inductance of the transmitting and the receiving coils, \(C_{1}\) and \(C_{2}\) are the compensation capacitors of the transmitting and receiving terminals, \(R_{1}\) and \(R_{2}\) are the internal resistances of the two coils, \(M\) is the mutual inductance of the two coils, and \(R_{\text{L}}\) is the load resistance.  
The equivalent impedance in the transmitter loop, the equivalent impedance in the receiver loop, and the total input impedance of the system are as follows:  
\[\left\{\begin{array}{l}Z_{1}=R_{1}+j\omega L_{1}+\frac{1}{j\omega\mathbb{C}_{1}} \\ Z_{2}=R_{2}+R_{L}+j\omega L_{2}+\frac{1}{j\omega\mathbb{C}_{2}}\\ Z_{\text{in}}=Z_{1}+\frac{(\omega M)^{2}}{Z_{2}}\end{array}\right. \tag{1}\]  
Writing the KVL equation based on the circuit model in Figure 1 yields:  
\[\left\{\begin{array}{l}Z_{1}I_{1}-j\omega MI_{2}=U_{\text{in}}\\ j\omega MI_{1}-I_{2}Z_{2}=0\end{array}\right. \tag{2}\]  
From the equation, the current in the two coil loops is:  
\[\left\{\begin{array}{l}I_{1}=\frac{Z_{2}U_{\text{in}}}{Z_{1}Z_{2}+\omega^{2} M^{2}}\\ I_{2}=\frac{j\omega M\text{U}_{\text{in}}}{Z_{1}Z_{2}+\omega^{2}M^{2}}\end{array}\right. \tag{3}\]  
The output power \(P_{\text{out}}\) obtained from Equation (3) is:  
\[P_{\text{out}}=|I_{2}|^{2}R_{\text{L}}=\frac{(\omega M)^{2}R_{\text{L}}U_{ \text{in}}}{\left|Z_{1}Z_{2}+(\omega M)^{2}\right|^{2}} \tag{4}\]  
The transmission efficiency \(\eta\) is:  
\[\eta=\frac{(\omega M)^{2}R_{\text{L}}}{\left|Z_{2}[Z_{1}Z_{2}+(\omega M)^{2}] \right|}\times 100\% \tag{5}\]  
The resonating state frequency of the system under ideal conditions is:  
\[f_{0}=\frac{1}{2\pi\sqrt{L_{1}\mathbb{C}_{1}}}=\frac{1}{2\pi\sqrt{L_{2}\mathbb{ C}_{2}}} \tag{6}\]  
In this case, the total input equivalent impedance \(Z_{\text{in}}\) of the system is the minimum, the voltage and current phase angle \(\varphi\) of the transmitter is 0, and the power efficiency transmission is the maximum.  
The phase difference \(\varphi\) calculated from the input impedance \(Z_{\text{in}}\) of the system is:  
\[\varphi=\arctan\left[\frac{-\omega^{2}M^{2}X_{2}+X_{1}\left[\left(R_{2}+R_{ \text{L}}\right)^{2}+X_{2}^{2}\right]}{\omega^{2}M^{2}(R_{2}+R_{\text{L}})+R_{ 1}\left[\left(R_{2}+R_{\text{L}}\right)^{2}+X_{2}^{2}\right]}\right] \tag{7}\]  
Figure 1: SS-type topology coupled circuit model.  
In the above equation, \(X_{1}=\omega L_{1}-1/\omega C_{1}\); \(X_{2}=\omega L_{2}-1/\omega C_{2}\).
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## 4 Negotiation-Free Encryption Method  
In this section, we describe how the NFEM can be implemented. The whole process includes three stages: key generation, data encryption transmission and decryption authentication. The key negotiation is no longer needed, and we use the permission, which is used to acquire the state of the vehicle to control the encryption and decryption. In the communication process, the communicated vehicles interact with vehicle cloud by LTE channel only once respectively, saving a lot of repeated  
Figure 1: The system model.  
interaction steps. LTE is very beneficial for delay-sensitive vehicular applications, which can provide a round-trip time theoretically lower than 10 ms, and transfer latency in the radio access up to 100 ms.
## 1 Introduction
However, from the commercial point of view, a sensor device system should be simple, low cost, and rapid with self-operating artificial intelligence meant for an easy and efficient screening method [29]. There is a growing need for such an alternative device to selectively measure and evaluate the quality parameters of grated Parmigiano Reggiano cheese. Solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) yields information about the identification and concentration of volatile compounds present in a cheese sample without pre-treatment, also about the relationship between different chemical classes of the volatiles and how they present the perceived sensory attributes. The identification of unique markers help in resolving the authenticity issues of the PDO cheeses [30]. Moreover, SPME-GC-MS coupled with an electronic nose analysis can provide a better understanding of the distribution of cheese volatile compounds into classes and track the cheese quality [31]. In the present study, a novel nanowire gas sensor device Small Sensors System (S3) (SENSOR Laboratory, Brescia, Italy) housing six metal oxide semiconductor gas sensors was used to evaluate and characterize the volatile organic compounds and the quality of Italian Parmigiano Reggiano cheese. The S3 system was applied directly on grated cheese samples at different ripening times. The correlation between S3 and SPME-GC-MS analysis helped to identify the specific volatile markers of this type of cheese that could be useful to distinguish Parmigiano Reggiano from other cheese varieties and to monitor its adulteration.