esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract
In [17; 18], artificial noise and relay-assisted cooperative technology were applied to NOMA networks with ST and MCC schemes, further enhancing the secrecy outage performance of the system. However, it is worth noting that [15; 16; 17; 18] were conducted solely in non-cognitive scenarios. In order to further improve the spectrum efficiency and secrecy outage performance of next-generation wireless networks, researchers have turned their attention to the power allocation (PA) problem. For instance, the authors proposed a power allocation algorithm for single-input-single-output (SISO) NOMA systems to enhance the secrecy sum rate of the secondary networks [19]. In [20], the authors examined the optimization problem of secrecy outage probability (SOP) for both primary users and random secondary users in a CR-NOMA network. In [21], a machine-learning algorithm was proposed to minimize the SOP of the secondary networks in cooperative CR-NOMA systems. Simulation results indicated the superiority of the proposed PA algorithm in terms of achievable SOP. The primary limitation of the aforementioned studies pertains to the analysis of secrecy performance in two-user networks implementing either NOMA or CR-NOMA schemes. Notably, the primary objective of NOMA technology is to ensure optimal performance while serving a maximum number of users, thereby not being recommended for use in two-user scenarios. However, the derivation of the mathematical model becomes increasingly complex as the number of users grows, which poses a significant challenge. Presently, investigations into the performance of three-user CR-NOMA networks remain in their nascent stages [22; 23]. The authors of the studies have demonstrated superior performance in connection outage probability and achievable capacity for such networks [23].
# A Review of Degradation and Life Prediction of Polyethylene
## 5 Prediction Techniques for Polyethylene Materials
### Arrhenius Equation
#### 5.2.2 The modified Arrhenius Equation  
The Arrhenius equation has undergone several revisions, and the updated equation now accounts for the impact of relative humidity on the rate of degradation (Equation (17)) [105; 106; 113; 114; 115]:  
\[\ln K(T)=\ln A-\frac{E_{a}}{RT}+B(\%RH) \tag{17}\]  
where \(lnK(T)\) is affected linearly by the humidity sensitivity component \(B\) at a constant temperature. In the examined range, it is presumed that the relative humidity has an impact only on the molecular mobility and not on the reaction route [106; 114]. The frequency of molecule collisions determines the degree and extent of a substance's migration within a material, which is known as molecular mobility [106]. Equation (18) can be used to get \(B\) from the intercept of the line to \(B\) at constant temperature, where \(lnA-E_{a}/RT\) is a constant term. Equation (18) can also be used to calculate \(B\) from the slope of \(lnK(T)\) to \(\%RH\):  
\[\ln K(T)=intercept+B(\%RH) \tag{18}\]  
Numerous investigations have conclusively demonstrated that some degradation processes can be adequately characterized by straightforward linear Arrhenius equations [116; 117; 118; 119]. As Arrhenius is dependable, time-efficient, and makes it simple to compute changes in aging performance, it is the approach of choice for life prediction in the majority of studies. On the basis of these examples, it can be demonstrated that the Arrhenius equation may be used to forecast the life of polymeric materials other than polyethylene.
###### Abstract  
The low rock breaking efficiency of conventional polycrystalline diamond compact (PDC) bits in hard abrasive formations prompts the development of PDC cutting elements from the planar structure to the non-planar structure. As an innovative non-planar cutter, the design and research of the three-ridged diamond element (3-RDE) cutter is still in its infancy, and its rock breaking mechanism and laws are not yet clear. In this paper, a three-dimensional (3D) finite element model of dynamic rock breaking with 3-RDE cutter has been established. The accuracy of the numerical model was verified by experimental data. Then, the difference of rock breaking mechanism between 3-RDE cutter and conventional cutter was studied. The effects of back-rake angle, cutting depth, rotational angle, and rock properties on rock breaking efficiency were also analyzed. The results show that, unlike the conventional PDC shear rock breaking cutter, the 3-RDE cutter breaks rock mainly by crushing and shearing, and the rock breaking efficiency is higher. A small back-rake angle and reasonable cutting depth contribute to improving the rock breaking efficiency; the existence of rotational angle is not conductive to the rock breaking. The field application shows that compared with the conventional cutter, the 3-RDE cutter is easier to penetrate into the formation, and is more stable with less torque required. The research results can be of benefit to the design and manufacture of 3-RDE PDC bits.  
3-RDE cutter; numerical simulation; rock-breaking mechanism and laws; verification Article
## 5 Discussion
### Limitations  
This study presents several limitations that could impact the generalizability of the results. Firstly, the technical indicators and hyperparameters used were selected based on prior knowledge rather than a systematic optimization process. As a result, it is possible that alternative configurations could yield improved outcomes. The chosen set of indicators may not represent the optimal solution across all market conditions, potentially limiting the performance of the models in different environments.  
Moreover, the reliance on daily data based on market close and open prices may not fully capture the real-world execution prices faced by traders. Price fluctuations between the close and the following day's open can introduce discrepancies, leading to inaccuracies in the simulation. Although the inclusion of slippage and transaction costs mitigates this effect to some extent, alternative methods of preparing bar data, such as those proposed by [39], may offer a more accurate reflection of trading conditions.  
Additionally, this study assumes that financial instruments are both liquid and fractional, simplifying the complexities of actual trading environments. In reality, liquidity constraints and the need to trade in discrete units may affect portfolio adjustments, particularly in markets with limited liquidity or where fractional shares are not available. These assumptions may limit the applicability of the results to real-world scenarios, where trading constraints can significantly impact portfolio rebalancing and execution.
# Source Microphone Identification Using Swin Transformer
## 3 Method  
### Audio Forensic Dataset for Digital Multimedia Forensics (AF-DB) [41]  
The Audio Forensic Dataset for Digital Multimedia Forensics is a comprehensive collection of audio files that are specifically designed for the purpose of developing, training, and validating algorithms and tools in the field of digital multimedia forensics. This dataset is intended to provide researchers and practitioners with a diverse set of audio samples that can be used to investigate various aspects of audio forensics, such as audio tampering detection, speaker identification, and environment and recording device identification. The dataset includes audio samples recorded in six distinct settings (a soundproof room, classroom, lab, stairs, car parking lot, and garden). There are microphones from seven distinct brands totaling 22, which can be used to develop and evaluate algorithms for identifying the source microphone. The dataset consists of files that have a duration of about three minutes each. The initial minute of each recording is characterized by silence, while the remaining duration features a reading of a predefined set of sentences by the volunteers. The dataset includes 660 audio recordings that were obtained from a group of five volunteers. The volunteers were asked to read a specific set of sentences in several languages, such as English, Arabic, Indonesian, and Chinese, and their voices were recorded for the dataset [41]. Microphone models and quantities for the AF-DB are presented in Table 1.
# PLI-VINS: Visual-Inertial SLAM Based on Point-Line Feature Fusion in Indoor Environment
## 1 Introduction
[18] added surface features and coplanar constraints on the basis of PL-VIO to further model the environmental topology based on the 3D grid generated by point features. However, because the LSD [19] algorithm takes a long time to extract line features, it is difficult to run the algorithms combined with point and line features above in real time. [PERSON] et al. proposed PL-VINS [20], which adjusted the implicit parameters of the LSD algorithm and realized real-time operation of the LSD algorithm without affecting accuracy as much as possible. [PERSON] et al. proposed another VIO system PLF-VINS [21] that integrates point-line features in 2021. It introduces two methods of fusing point and line features. Firstly, the similarity of the relative positions of points and lines is calculated, and then the relationship between points and line features is judged by the position relation, and the parallel line relation is judged in this process. However, many SLAM systems based on multi-source information fusion still face many challenges in indoor environments. First of all, most point-line based visual SLAM systems do not make full use of line segment information and effectively combine with IMU. Second, the VIO system was unable to extract enough point features from an area with repeated textures. Although many of the above methods are committed to solving these challenges, methods such as [16; 17; 20] do not fully consider the influence of different types of line segments on camera pose estimation in indoor structural environments. In order to solve the above problems, we propose a visual-inertial SLAM system based on point-line feature fusion for indoor environments. The main contributions are as follows:  
* In order to effectively obtain the structural information of indoor environments and process the environment with repeated texture, an adaptive threshold line segment extraction algorithm is proposed on the premise of point-line feature fusion, which is used to process various redundant line segments in indoor environments to improve the quality of line features. * The point feature, line feature and IMU information are effectively fused in an optimization-based sliding window to achieve high precision pose estimation. Experiments on EuRoC datasets [22] show that the algorithm presented in this paper has better performance than optical flow-based VINS-Mono and point-line based PL-VINS. In the remainder of this article, the architecture of the proposed approach is described in Section 2. Sections 3 and 4 describe in detail the work of the line segment extraction algorithm proposed in this paper in indoor environments and the effective utilization of point and line features and IMU in sliding windows. Section 5 describes the experimental setup and the experimental results under a common data set. Finally, Section 6 provides concluding observations and describes future work.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 3 Experiments and Analysis
### Results  
The prediction results obtained for the dip angle and azimuth angles by the Pi-GRU model are presented in Figures 9 and 10, respectively. At this time, the MSE of the azimuth angle was 0.06, and the R2 was 0.93; the MSE of the dip angle was 0.01, and the R2 was 0.95. This provides strong evidence that the Pi-GRU model has high accuracy in azimuth angle and dip angle prediction tasks. The fitting effect between the predicted curve and the actual curve is excellent, and it can accurately reflect the actual change in the azimuth angle. In addition, to more intuitively display the trend of the actual wellbore trajectory and the predicted trajectory, Figure 11 shows a three-dimensional comparison diagram between these wellbore trajectories.  
Figure 8: Orthogonal test results.  
Figure 9: Predicted azimuth results with Pi-GRU model.  
In this study, we constructed a prediction model to predict the azimuth angle and dip angle and further analyzed the prediction errors of the model. To visually display the distribution of the prediction errors, we plotted the histogram of the azimuth angle and dip angle prediction errors, as shown in Figure 12.  
From the left chart, it can be seen that the prediction error of the azimuth angle is mainly concentrated within the range of \(-0.4^{\circ}\) to \(0.2^{\circ}\). The average error is \(-0.20^{\circ}\), which  
Figure 11: Three-dimensional wellbore trajectory comparison.  
Figure 10: Predicted dip angle results with Pi-GRU model.  
indicates that the model tends to underestimate the actual azimuth angle value. Although there is a certain systematic deviation, the error distribution is relatively concentrated, indicating that the model has good stability in predicting the azimuth angle. Moreover, the narrow error range also indicates that in most cases, the model can provide predicted results that are close to the actual values.  
The distribution of dip angle prediction errors is more concentrated, with most of the errors falling within the range of \(-0.2^{\circ}\) to \(0.2^{\circ}\). A significant peak appeared near \(0.0^{\circ}\) in the error value, indicating that the model's predictions were most concentrated within this error range. The average error was \(-0.01^{\circ}\), suggesting that the model has high accuracy and stability in predicting the dip angle.
## 3 Results
### Effect of Salt Stress on the Transcriptome of A.__mongholicus__Leaves__  
#### 3.3.1 Transcriptome Sequencing Analysis  
Transcriptome sequencing of four biological replicates of leaf samples subjected to the treatments DT-CK, DT-Treat, LQ-CK, and LQ-Treat was conducted. The twelve samples generated a total of 80.63 Gb of clean data, with each sample yielding at least 6 Gb. The GC content of all samples exceeded 42%, and the percentage of Q30 bases was at least 94%, indicating high-quality bases suitable for subsequent research.  
Figure 2: _Cont._  
Figure 2: Cont.  
Using the screening criteria for DEGs ( 1 log\({}_{2}\)Fold Change \(\mid\)\(\geq\) 1 and FDR \(<\) 0.05), this study identified 3237 downregulated and 3363 upregulated genes in DT-Treat_vs_DT-CK, as well as 5693 downregulated and 3562 upregulated genes in LQ-Treat_vs_LQ-CK. Comparison of the differentially expressed genes in DT-Treat_vs_DT-CK and LQ-Treat_vs_LQ-CK revealed that more genes were upregulated in the LQ, suggesting that 'LQ' exhibited a stronger response to salt stress. Among the differentially expressed genes analyzed across subgroups, 166 genes were shared in all combinations. 'LQ' exhibited 3510 unique differentially expressed genes after salt stress treatment, whereas 'DT' exhibited 1632. Additionally, 2515 differentially expressed genes were identified between 'LQ' and 'DT' under salt stress conditions. Future analyses will focus on these three categories of genes (Figure 3).
## AI-Enabled 6G Network Challenges
### Radio Resource Management  
A few 6G applications that will necessitate an evolution of present RAN techniques in order to satisfy the key performance indicators (KPIs) required for 6G networks are unmanned aerial vehicles, multi-dimensional sensing services, smart factories, and cities. The majority of Dynamic Radio Resource Management (RRM) systems are dynamic and flexible. These characteristics suggest that radio resources may be distributed flexibly, depending on traffic demands, channel circumstances, and user QoS needs, enabling the optimization of network performance [33].
## 2 Results and Discussion
### Phenolic Compounds Profile and Quantification
However, the higher temperature used in CET could explain the significant higher extraction yields reported to the phloridzin, phloretin, (+)-catechin and (--)-epicatechin compared to MAE extract. According to a previous work that observes the impact of different extraction parameters on extraction yields of the main phenolic compounds of apple skins, only the temperature (an increase from 25 to 50 \({}^{\circ}\)C) was a significant positive effect for the extraction of (--)-epicatechin, procyanidin B2, phloridzin and their derivatives [35]. Thus, we may assume that MAE is generally more efficient in phenolics extraction than CET in this type of fruits. Furthermore, MAE consumes less energy and time for the extraction of these compounds. UAE and CE, in agreement with previous results of TPC, showed a smaller number of compounds proving that these methodologies are less efficient for phenolic compounds extraction in apples. The hydroxycinnamic acids are the main class of compounds recovered with these extraction methodologies, being the chlorogenic acid the higher compound (0.11 \(\pm\) 0.01 mg/g DE) recovered without significant differences between CE and UAE (\(p\) < 0.05). The (--)-epicatechin and (--)-epicatechin-3-gallate were the only two flavonoids recovered with these methodologies without significant difference (\(p\) > 0.05). Additionally, the HPLC-DAD profile of extracts obtained by UAE showed a potential degradation of some BCs by decreasing compounds intensity. This fact could be explained by the hot spots generated during bubble collapse that do not cause heat damage to thermal sensitive compounds. However, it should be noted that ultrasonic irradiation for periods longer than about 5 min at high ultrasonic power may generate heat, and this can cause some degradation in phenolic compounds. [PERSON] et al. [34] proved that, even under temperature control, prolonged sonication could lead to some degradation of the
# Surface Gloss, Radiopacity and Shear Bond Strength of Contemporary Universal Composite Resins
## 3 Results
### SBS Measurements  
Table 4 summarizes SBS values (the mean \(\pm\) SD) of the tested CRS to enamel end dentin in MPa. There were significant differences among the CRs in terms of SBS to enamel (\(p\) < 0.001) and dentin (\(p\) = 0.002). Additionally, there was a significant difference between the SBS of CRs to enamel and dentin in all groups (<0.001). The highest SBS to enamel was seen in GA, EA and F followed by NS and CD. The highest SBS to dentin was seen as F, GA, EA and CD > NS.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Materials**} & \multicolumn{3}{c}{**After Polishing**} & \multicolumn{3}{c}{**After Darkening**} & \multirow{2}{*}{\(p\) \({}^{*}\)} \\ \cline{2-2} \cline{5-8}  & **Mean \(\pm\) SD** & **Median** & **Mean Rank** & & **Mean \(\pm\) SD** & **Median** & **Mean Rank** & \\ \hline GA & 82.94 \(\pm\) 1.32 & 83.00 & 54.50 \({}^{\text{a}}\) & 77.50 \(\pm\) 1.44 & 73.33 & 53.38 \({}^{\text{a}}\) & _0.002_ \\ \hline CD & 69.22 \(\pm\) 5.22 & 69.50 & 21.92 \({}^{\text{bc}}\) & 59.61 \(\pm\) 4.78 & 57.33 & 20.96 \({}^{\text{bc}}\) & _0.002_ \\ \hline NS & 73.19 \(\pm\) 2.61 & 73.17 & 33.83 \({}^{\text{b}}\) & 68.94 \(\pm\) 4.46 & 67.00 & 35.71 \({}^{\text{ab}}\) & _0.023_ \\ \hline F & 73.08 \(\pm\) 1.64 & 72.83 & 33.21 \({}^{\text{b}}\) & 67.50 \(\pm\) 7.62 & 68.00 & 34.58 \({}^{\text{ab}}\) & _0.031_ \\ \hline EA & 65.28 \(\pm\) 1.91 & 66.17 & 9.75 \({}^{\text{c}}\) & 50.83 \(\pm\) 4.50 & 51.50 & 7.88 \({}^{\text{c}}\) & _0.005_ \\ \hline \(p\)\({}^{*}\) & \multicolumn{3}{c}{\textless{0.001}} & \multicolumn{3}{c}{\textless{0.001}} & \multicolumn{3}{c}{\textless{0.001}} \\ \hline \end{tabular}  
* Kruskal–Wallis H test result; (a, b, c) imply significant difference among CRs by [PERSON]’s test (\(p\) < 0.05). \({}^{*}\) [PERSON] signed rank test results.  
\end{table}
Table 2: Mean, SD, and median of gloss values (gloss units/GU) of the tested CRs.  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**CR**} & **Radiopacity** & **Equivalent Al Thickness (mm)** \\  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \hline GA & 90.80 \(\pm\) 4.44 \({}^{\text{a}}\) & 4.83 \(\pm\) 0.37 \\ CD & 126.72 \(\pm\) 5.26 \({}^{\text{b}}\) & 6.65 \(\pm\) 0.41 \\ NS & 123.22 \(\pm\) 3.25 \({}^{\text{b}}\) & 6.48 \(\pm\) 0.42 \\ F & 104.79 \(\pm\) 6.37 \({}^{\text{c}}\) & 5.55 \(\pm\) 0.39 \\ EA & 69.19 \(\pm\) 4.02 \({}^{\text{d}}\) & 3.63 \(\pm\) 0.35 \\ Enamel & 63.17 \(\pm\) 1.50 \({}^{\text{d}}\) & 3.29 \(\pm\) 0.46 \\ Dentin & 39.72 \(\pm\) 1.13 \({}^{\text{e}}\) & 1.96 \(\pm\) 0.41 \\ \hline \end{tabular}  
* One-way ANOVA result (\(p\) < 0.05). Different lowercase letters imply significant difference among the composites by [PERSON]’s HSD test (\(p\) < 0.05).  
\end{table}
Table 3: Radiopacity and equivalent thickness (mm Al) of CRs and enamel and dentin.
## 3 Our Advanced Proposed Methodology
### Summary of Data Vectorization  
In our previous work [8], we developed a systematic approach to transform spatially sensed data into supervectors (SVs) for machine learning (ML) training. The purpose of this vectorization was to capture both the static and dynamic states of environmental changes, enabling more effective classification and prediction in the context of forest fire scenarios.  
Initially, the dataset was organized such that each row, referred to as a _static vector_, represented a single timestamp's readings from 15 physical sensors in a wireless sensor network (WSN). These readings included attributes like temperature (Temp), light (L), smoke (Sm), and sound (Sn), which were spatially arranged according to sensor positions. Each static vector encapsulated the _fire's static state_ at a specific time.  
To capture the _dynamics of the fire environment_ over time, we concatenated a sequence of static vectors across a fixed time period to form a supervector. Each supervector, therefore, contained data from multiple timestamps, providing a comprehensive snapshot of environmental changes. For example, concatenating vectors \(V_{1},V_{2},\ldots,V_{n}\), recorded at timestamps \(T_{1},T_{2},\ldots,T_{n}\), resulted in a supervector \(SV_{1}\), where \(n\) was the number of static vectors combined. In our experiments, we chose \(n=10\), meaning each SV spanned 10 time slots, ensuring uniformity across all supervectors.  
These SVs were then labeled according to predefined forest fire scenarios, such as _\"No fire\"_, _\"Smoke\"_, or _\"Fire Increasing\"_, with labels corresponding to camera-recorded field data. For simplicity and computational efficiency, numeric identifiers were assigned to scenarios, ranging from 1 (_No fire_) to 12 (_Rain started, and Fire stopped_).  
This structured dataset of labeled supervectors was used to train an artificial neural network (ANN) model, specifically a multi-layer perceptron (MLP), for scenario classification. By grouping 10 consecutive static vectors into an SV, the model captured the temporal dynamics of fire development, improving its predictive capabilities. The resulting SV dataset was stored in CSV format for further ML experimentation.  
Our vectorization approach ensured robust ML model training, enabling early and accurate detection of forest fire scenarios. Adjustments to the size of the SVs (e.g., reducing \(n\)) could resolve data overlaps between scenarios, albeit requiring retraining of the model with the updated dataset.