esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
## 2 Principles of NMR Core Experiments
### T\({}_{1}\)-T\({}_{2}\) Measurement  
When pore fluid components in the core are complex, T\({}_{2}\) spectrum signals may overlap, making it difficult for one-dimensional NMR distribution to distinguish fluid signals. Therefore, two-dimensional NMR measurements (such as T\({}_{1}\)-T\({}_{2}\) distribution) are required. T\({}_{1}\)-T\({}_{2}\) two-dimensional NMR technology can simultaneously observe longitudinal relaxation time T\({}_{1}\) and transverse relaxation time T\({}_{2}\), thereby enhancing the accuracy of identifying and distinguishing pore fluid signals [15; 16]. The primary methods for T\({}_{1}\)-T\({}_{2}\) two-dimensional NMR measurements are the saturation recovery (SR-CPMG) and inversion recovery (IR-CPMG) methods. Both methods measure spin echo sequences under different waiting times, with the primary difference being the dynamic range of the magnetization vector in T\({}_{1}\) measurement. The SR method measures the dynamic range of the magnetization vector as \((0,M_{0})\) (\(M_{0}\) is the maximum net magnetization vector), while the IR method measures it as \((-M_{0},M_{0})\). The signal amplitude decay over time measured using the IR method follows Equation (5):  
\[b_{is}=\sum_{j=1}^{m}\sum_{r=1}^{p}f_{j,r}\bigg{(}1-2\cdot exp\bigg{(}-\frac{ T_{ws}}{T_{1r}}\bigg{)}\bigg{)}exp\bigg{(}-\frac{t_{i}}{T_{2j}}\bigg{)} \tag{5}\]  
where \(b_{is}\) is the echo amplitude at time \(t_{i}\) with a waiting time \(T_{ws}\); m is the number of transverse relaxation times \(T_{2j}\); \(p\) is the number of longitudinal relaxation times \(T_{1r}\); \(f_{j,r}\) is the distribution function for transverse relaxation time \(T_{2j}\) and longitudinal relaxation time \(T_{1r}\). By performing a two-dimensional joint inversion of this equation, we obtain \(f_{j,r}\) and thus the T\({}_{1}\)-T\({}_{2}\) two-dimensional distribution [15].
## 6 Results and Discussion
### Model Developed with Data Excluding Outliers  
The data used in this model are the data that excluded outliers among the data used in 6.1. A total of 46 data sets, approximately 3.5 % of the original total data sets, are treated as outliers. For training and testing of the model, 90% of the total data was used for training and 10% was used for testing. In addition, 90-10, 85-15, and 80-20 are the ratios of the most used training and testing data. When developing an ML model using average data, the number of data is reduced. Therefore, in order to secure as much of the training data as possible, a ratio of 90-10 was used.  
* Total data sets: 1252 data sets;
* Train data sets: 1127 data sets;
* Test data sets: 125 data sets.  
The four machine learning models (random forest, neural network, gradient boosting, and AdaBoost) were run, and the results of training and testing for each model are shown in Table 9a,b below. As shown in Table 9a, the gradient boosting model with training data provides the highest determination coefficient, \(R^{2}\) = 0.809, followed by \(R^{2}\) = 0.805 from the AdaBoost model, and then 0.795 from the random forest model. The neural network gave the lowest \(R^{2}\) value at 0.726. As shown in Table 9b, the gradient boosting model provides the highest determination coefficient, \(R^{2}\) = 0.803, followed by \(R^{2}\) = 0.794 from the AdaBoost model, and then 0.791 from the random forest model. The neural network gave the lowest \(R^{2}\) value at 0.726.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{(**a**)} \\ \hline
**Model** & **MSE** & **RMSE** & **MAE** & \(R^{2}\) \\ \hline Random forest & 0.351 & 0.592 & 0.411 & 0.768 \\ \hline Neural network & 0.551 & 0.742 & 0.547 & 0.635 \\ \hline Gradient boosting & 0.334 & 0.578 & 0.393 & 0.779 \\ \hline AdaBoost & 0.341 & 0.584 & 0.389 & 0.774 \\ \hline \multicolumn{3}{c}{(**b**)} \\ \hline
**Model** & **MSE** & **RMSE** & **MAE** & \(R^{2}\) \\ \hline Random forest & 0.312 & 0.559 & 0.402 & 0.740 \\ \hline Neural network & 0.416 & 0.645 & 0.461 & 0.655 \\ \hline Gradient boosting & 0.297 & 0.545 & 0.390 & 0.753 \\ \hline AdaBoost & 0.315 & 0.561 & 0.389 & 0.738 \\ \hline \hline \end{tabular} MSE: mean squared error, RMSE: root mean squared error, MAE: mean absolute error; \(R^{2}\): coefficient of determination.  
\end{table}
Table 8: (**a**) Result of ML models with training original data. (**b**) Result of ML models testing original data.
## 4 Performance Analysis
### Outage Probability  
As another assessing metric, we also analyze how the proposed methods behave in terms of the outage probability. In this paper, we mainly focus on the outage probability of UTs located in the overlapping area.
## 2 Finite Element Analysis and Equation of Motion for Pipeline System
### Equation of Motion for Fluid-Structure Interaction  
#### 2.2.1 Equation of Fluid Motion  
The fluid motion in the pipeline is regarded as incompressible flow. The fluid continuity equation is from [33]:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial(\rho u_{i})}{\partial x_{i}}=0 \tag{1}\]  
The momentum equation is  
\[\frac{\partial}{\partial t}(\rho u_{i})+\frac{\partial}{\partial x_{i}}\big{(} \rho u_{i}u_{j}\big{)}=-\frac{\partial\rho}{\partial x_{i}}+\frac{\partial}{ \partial x_{i}}\Bigg{[}\mu\Bigg{(}\frac{\partial u_{i}}{\partial x_{j}}+\frac{ \partial u_{j}}{\partial x_{i}}\Bigg{)}-\frac{2}{3}\mu\delta_{ij}\frac{\partial u _{k}}{\partial x_{k}}\Bigg{]}+\frac{\partial}{\partial x_{j}}\Big{(}-\rho \overline{u^{\prime}_{i}u^{\prime}_{j}}\Big{)} \tag{2}\]  
where \(\rho\), \(\mu\), \(k\), \(u\), and \(\overline{u^{\prime}_{i}u^{\prime}_{j}}\) are the fluid density, dynamic viscosity of the fluid, turbulent kinetic energy, averaged velocity component, and Reynolds stress component, respectively. Herein, \(\delta\) is the \"kroneckerdelta\" symbol, and the subscript \(i\) and \(j\) represent the time. If the subscript \(i\) is equal to \(j\), \(\delta_{ij}\) is one, otherwise \(\delta_{ij}\) is zero.  
The standard \(k\)-\(\epsilon\) turbulent equation is used to express the fluid motion state in the pipeline, which is expressed as  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho k_{ i}x_{i})=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{i}}{ \sigma_{k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}+G_{b}-\rho _{\epsilon}-Y_{M} \tag{3}\]  
\[\frac{\partial}{\partial t}(\rho\epsilon)+\frac{\partial}{\partial x_{i}}( \rho\epsilon\mu_{i})=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac {\mu_{i}}{\sigma_{\epsilon}}\bigg{)}\frac{\partial\epsilon}{\partial x_{j}} \Bigg{]}+C_{1\epsilon}\frac{\epsilon}{k}(G_{k}+C_{1\epsilon}C_{k})-C_{2 \epsilon}\rho\frac{\epsilon^{2}}{k} \tag{4}\]  
where \(G_{k}\), \(G_{b}\), \(Y_{M}\), \(C^{*}{}_{1\epsilon}\), and \(C_{2\epsilon}\) are the terms for the generation of turbulent kinetic energy due to the mean velocity gradient turbulent dissipation rate, the generation of turbulent kinetic energy due to the influence of buoyancy, correction coefficient, empirical constant, Prandtl numbers corresponding to turbulent kinetic energy and dissipation rate, respectively. The subscripts \(k\), \(\epsilon\), and \(t\) represent turbulent kinetic energy, dissipation rate, and time, respectively.
###### Abstract
superciliosus_ feed on roots, tunneling galleries, and causing irreversible damage, which leads to plant death and even the death of the entire orchard [23]. There has been no effective control against the larva because of the gallery's cryptic location inside the root. So far, using EPNs has been proposed as a promising management tool due to their ability to search for and find the host [24]. In recent years, the native EPN species _Steinernema australe_ (Panagro-laimorpha: Steinenematidae) was evaluated against larvae of _A. superciliosus_ with 100% and 72% efficacy under laboratory and field conditions, respectively [25]. Later, [PERSON] et al. [26] evaluated the same isolate of _S. australe_ against _Aegorhinus nodipennis_ (Coleopterara: Curculionidae) in blueberry and sarsaparilla orchards showing 68% and 65% mortality, respectively. [PERSON] et al. [27] reported _Steinernema australe_ as a new species of Steinernematidae isolated from a soil sample collected from Magdalena Island in the Chilean Patagonian region. This study provided baseline data on the biology and ecology of this species, highlighting its relatively rapid lifecycle and ability to infest a host at relatively low temperatures [28], two aspects of great interest considering its potential to be used in pest management. _S. australe_ is well-adapted to the territory where _A. superciliosus_ is present; however, little is known regarding plant-inset-EPN interactions considering the root blueberry model of study. In this study, we hypothesized that _S. australe_ would be attracted to the volatiles released by damaged blueberry roots when _A. superciliosus_ larvae attack them. To test this hypothesis, we (1) identified the volatiles emitted from roots of _V. corymbosum_ uninfested and infested with _A. superciliosus_ larvae and (2) determined the chemotaxis of the _S. australe_ infective juveniles (IJs) in response to the volatiles emitted by uninfested and damaged roots.
## 2 Proposed Spectrum-Sharing Scheme  
### System Model  
We consider a Cognitive Radio-based heterogeneous Wireless Sensor Network (CRHWSN) with N sensor nodes. The sensor nodes act as unlicensed users, purchasing idle spectra from M licensed users (Figure 1) without cooperation. Licensed user \(i\), which has a certain amount of idle bandwidth, denoted by \(W_{i}\), can decide its spectrum price independently. Depending on the heterogeneous nature of the WSN, sensors are deposed under various environments and with discrepant hardware devices. Therefore, unlicensed users enjoy unequal SNR for one licensed user, and each unlicensed user has unequal SNR for different licensed users. With spectrum demand \(D_{j}\), unlicensed user \(j\) needs to decide which licensed user should be chosen, based on spectrum price and SNR. In this system model, all licensed users are non-cooperative and cannot achieve a coalition, which also applies to all unlicensed users.  
Figure 1: System model.
## 5 Numerical Results  
In this section, we evaluate the joint design of UAV altitude and energy beamforming along with our proposed methods. The simulation settings were similar to those in [8; 9; 10; 11; 12; 13; 14], and the detailed parameters used to obtain our numerical results are listed in Table 1. Presented in Sections 5.1 and 5.2 are the performance evaluations for single- and multiple-EHD scenarios, respectively.
# The Bearing Surface Defect Detection Method Combining Magnetic Particle Testing and Deep Learning
## 7 Conclusions  
This paper proposes a method for bearing image defect detection that combines magnetic particle inspection with deep learning. Initially, the magnetic particle inspection method is employed to reveal defects on the bearing surface that are not easily observable through visual inspection, displaying them in the form of magnetic patterns. Subsequently, the diversity of defect samples is enhanced using a method based on dynamic thresholds and generative adversarial networks (GANs), thereby expanding the original defect sample dataset.  
Targeted improvement of the original YOLOv5 network: firstly, integrating the CA mechanism into the original backbone network; secondly, adding the ASFF (adaptive spatial feature fusion) module to the neck network; and lastly, replacing the original GIoU loss function with the WIoU loss function. Through designed ablation experiments, this paper validates the effectiveness of the proposed data augmentation method and the three enhancements on the original network.  
The proposed data augmentation method enhances the diversity of defect samples, with the improved network focusing more on the foreground regions in the images. This approach avoids feature inconsistencies across different levels and concentrates the bounding box loss function on regressing common-quality anchor boxes, thereby increasing the model's detection accuracy, reducing the likelihood of false negatives, and improving the production efficiency of bearings.  
Conceptualization, L.L. and Z.L.; methodology, L.L., Z.L. and H.Z.; software, Z.L.; validation, H.Z.; formal analysis, H.Z.; investigation, H.Z.; resources, L.L.; data curation, Z.L.; writing--original draft preparation, L.L.; writing--review and editing, L.L. and Z.L.; visualization, H.Z.; supervision, L.X.; project administration, L.X. and J.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics
## 5 Performance Evaluation and Results
### Performance Metrics  
The proposed scheme was evaluated using two popular metrics for measuring the performance of authentication systems: \(F_{1}\) score and Half of Total Error Rate (HTER). Here, the patterns from the authorized user or client were treated as negatives, while the patterns from the unauthorized users or impostors were positives. The two metrics were calculated from the four elements of the confusion matrix, true positives (\(TP\)), false positives (\(FP\)), true negatives (\(TN\)), and false negatives (\(FN\)). \(TP\) and \(TN\) are the numbers of correctly classified patterns from the impostors and client, respectively. \(FP\) and \(FN\) are the numbers of misclassified patterns from the client and impostors, respectively.  
\(F_{1}\) score is a widely used metric for evaluating the performance of classifiers which is defined as the harmonic mean of precision and recall, as shown in Equation (16).  
\[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{16}\]  
\[Precision=\frac{TP}{TP+FP} \tag{17}\]  
\[Recall=\frac{TP}{TP+FN} \tag{18}\]  
Figure 7: sEMG signal segment divided into a series of overlapping analysis windows.  
HTER has been used for evaluating the performance EEG-based authentication systems [24; 40]. HTER is defined as the average of the False Acceptance Rate (FAR) and False Rejection Rate (FRR):  
\[HTER=\frac{FAR+FRR}{2} \tag{19}\]  
FAR is the ratio of the number of false acceptances to the number of attempts from the impostors. FRR is the ratio of the number of false rejections to the number of attempts from the client.  
\[FAR=\frac{FN}{TP+FN} \tag{20}\]  
\[FRR=\frac{FP}{TN+FP} \tag{21}\]
## 2 Methodology
### Data Preprocessing
#### 2.2.1 Redundant Data Removal  
During trajectory data collection and transmission, equipment or network issues often generate numerous duplicate records. These redundant data not only occupy storage space but also affect the accuracy of statistical analysis. This study designs a deduplication method based on multi-dimensional features. The deduplication algorithm considers the combined uniqueness of vehicle ID (vid), timestamp (time), and coordinate position (xcoord, ycoord). The implementation utilizes Python's pandas library, first sorting the data according to these key fields, then using the drop_duplicates() function to remove completely identical records. This method ensures both deduplication accuracy and high processing efficiency.  
The original trajectory data contained 2\(A\)87,406 records. After redundant data removal, 1,721,189 records remained, with 766,217 redundant records eliminated, accounting for 30.80% of the original data volume. This proportion indicates severe redundancy issues in the original data, validating the necessity of deduplication processing.  
Figure 1: Workflow of fundamental data analysis.
## 3 Methodology
### Feature Extraction and Selection
#### 3.2.1 Harmonic-Percussive Source Separation  
As mentioned in the Introduction, the PCG consists of a beat rhythm with intensity variation. Therefore, extracting separate features (chroma, MFCC, etc.) based on HPSS will provide a better encoding of unsegmented audio data, potentially increasing the effectiveness of the neural network training.  
The HPSS technique is particularly useful in audio signal processing, where it can enhance the analysis of audio elements by isolating the tonal and rhythmic aspects of the audio. The HPSS process involves the following steps: First, the audio signal is transformed into the time-frequency domain using a STFT. Then, the spectrogram is subjected to median filtering along the time axis to emphasize percussive components and the frequency axis to emphasize harmonic components. Finally, binary masks are created based on the filtered spectrograms to separate the harmonic and percussive components, which are then reconstructed into the time domain using an inverse STFT, displayed in Figures 6 and 7. For this analysis, the margin value of 4 is used to separate harmonic and percussive components. This process is carried out using the Librosa library. The margin parameter in HPSS is used to control the sensitivity of the separation process. The margin parameter simply defines a threshold that controls how aggressively the filtering should separate the components.  
A larger margin value makes the separation more tolerant, while a smaller margin value makes it more stringent. This choice of margin value in this analysis was guided by the need to achieve a more balanced separation between the harmonic and percussive elements of the PCG signals. Therefore, it was selected through an iterative process based on preliminary experiments and performance observations. PCGs typically contain both periodic heart sounds (S1, S2) and transient noise components (e.g., murmurs, clicks, etc.). Using a higher margin value ensures that subtle percussive elements, which could be indicative of pathological conditions, were adequately captured without overshadowing the harmonic components that represent the primary heart sounds.  
HPSS will be effective because the harmonics will provide the pitch in audio, whereas percussive components will provide beats with localization in time. It provides an improved estimation of tempo and chroma features. Therefore, this hypothesis is based on the assumption that the features extracted from harmonic components, which detect the abnormalities in pitch, offering a better approach to detecting abnormal heart conditions in an individual patient.
## 4 Results
### Model Comparison and Validation  
In order to evaluate the effectiveness of the above model for LSM, a series of indicators and the testing set data were devoted in the article. The results (Table 4 and Figure 12) indicate that all models produced great fitting and prediction performance.  
The PPV (0.877), NPV (0.831), and sensitivity (0.871) values of the SSL-DNN model were all the highest, indicating that the model had the best ability to distinguish landslide pixels from other pixels in the region. The DNN model with a value of specificity (0.857) was superior to other models. As far as the ACC index is concerned, it indicates the accuracy of the whole model, and the SSL-DNN model  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Models** & **TP** & **TN** & **FP** & **FN** & **PPV** & **NPV** & **Sensitivity** & **Specificity** & **ACC** & **AUC** \\ \hline DNN & 52 & 54 & 13 & 11 & 0.800 & 0.831 & 0.806 & 0.857 & 0.815 & 0.857 \\ SSL-DNN & 57 & 54 & 8 & 11 & 0.877 & 0.831 & 0.871 & 0.794 & 0.854 & 0.898 \\ SVM & 54 & 52 & 13 & 11 & 0.831 & 0.800 & 0.825 & 0.776 & 0.815 & 0.852 \\ LR & 50 & 48 & 15 & 17 & 0.769 & 0.738 & 0.762 & 0.716 & 0.754 & 0.780 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of the supervised and semi-supervised learning models.  
Figure 11: Comparison of the distribution using DNN, SSL-DNN, SVM, and LR models. (**a**) Total area covered by each susceptibility map; (**b**) Landslide density of each susceptibility class.  
Figure 12: ROC curves of the predictive rate.  
(0.846) was considered to be the most accurate model. The ROC curve and AUC value reflect the predictive power of the LSMs. The SSL-DNN model had the best predictive performance (0.898), and the distinction between the DNN and SVM models (0.857 and 0.852) was not large, but were significantly better than the LR model (0.780). Overall, semi-supervised learning achieved better results than supervised learning.  
In addition, the difference in the performance results between the LSM models should be statistically evaluated. Wilcoxon signed-rank test results are given based on the paired model (Table 5), and the requirements of \(p<0.05\) and \(|z|>1.96\) were met. This indicates that there are significant differences between each pair of models, which are statistically significant.
# A Novel QoS-Aware ARQ Scheme for Multi-User Transmissions in IEEE802.11 ax WLANs
###### Abstract  
The latest IEEE 802.11 ax protocol has been launched to provide efficient services by adopting multi-user (MU) transmission technology. However, the MU transmissions in the aggregation-enabled wireless local area networks (WLANs) face two drawbacks when adopting the existing automatic repeat request (ARQ) schemes. (1) The failed packets caused by the channel noise can block the submission of subsequent packets and the transmission of queued ones. (2) When the lengths of aggregate media access control protocol data units (A-MPDU) transmitted by different users are varied, dummy bits should be added to the shorter frames to align the transmission duration. These drawbacks degrade the quality of service (QoS) performances, such as throughput, latency, and packet loss rate. In this paper, a novel QoS-aware backup padding ARQ (BP-ARQ) scheme for MU transmissions in the IEEE802.11 ax WLANs is proposed to address these problems. The proposed scheme utilizes backups of selected packets instead of dummy bits to align the duration and to supress the influence of channel noise. An optimization problem that aims to improve the blocking problem of the failed packets is derived to determine the selection of packets. The promotion of the proposed scheme is well demonstrated by the simulations in NS-3.  
Keywords:IEEE802.11 ax; multi-user transmission; automatic repeat request (ARQ); A-MPDU; throughput; latency +
Footnote †: journal: Electronics Electronics
# The Prediction Performance Analysis of the Lasso Model with Convex Non-Convex Sparse Regularization
## 1 Introduction
### Notation  
Throughout this paper, the \(\ell_{1}\), \(\ell_{2}\), \(\ell_{q}\) and \(\ell_{\infty}\) norms of \(\beta\in\mathbb{R}^{p}\) are defined \(\|\beta\|_{1}=\Sigma_{i}^{p}|\beta_{i}|\), \(\|\beta\|_{2}=(\Sigma_{i}^{p}|\beta_{i}|^{2})^{\frac{1}{2}}\), \(\|\beta\|_{q}=(\Sigma_{i}^{p}|\beta_{i}|^{q})^{\frac{1}{q}}\) and \(\|\beta\|_{\infty}=\max_{i\in p}|\beta_{i}|\); meanwhile, \(\|\beta\|_{0}\) represents the number of non-zero elements in \(\beta\) and \(\mathrm{Supp}(\beta)\) is the support of \(\beta\). For any given set \(T\), we use \(T^{c}\) and \(|T|\) to respectively represent its complement \(p\backslash T\) and cardinality \(|T|\). For matrix \(A\), we denote its maximum eigenvalue and minimum eigenvalue as \(\sigma_{max}\) and \(\sigma_{min}\), respectively. The transpose and the pseudo-inverse of a matrix \(X\) are defined as \(X^{\top}\) and \(X^{\dagger}\) respectively. For any given subset \(T\) of \(p\) in a matrix \(X\in\mathbb{R}^{n\times p}\), we denote \(X_{T}\) as the resulting matrix obtained by excluding all columns belonging to the complement of \(T\) from \(X\). For the design matrix \(X\), we denote the orthogonal projection onto \(X_{T}\) by \(\Pi_{T}\). In addition, for the sake of convenience, we denote the prediction loss \(\frac{1}{n}\|X(\hat{\beta}-\beta^{*})\|_{2}^{2}\) of two vectors \(\hat{\beta}\), \(\beta^{*}\in\mathbb{R}^{p}\) by \(\ell_{n}(\hat{\beta},\beta^{*})\).