esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Environmental Conditions  
In the Marche Region, excluding January, all months from February to July were characterized by lower-than-normal rainfall, causing signs of drought throughout the region. Spring 2021 was the second driest spring for the Marche Region since 1961 (www.assam.marche.it, accessed on 20 January 2023). Consequently, the meteorological conditions were not favorable to the development of fungal diseases, and fewer treatments were carried out compared to previous years. However, symptoms of GPM and GDM appeared on the bunches in mid/late July. On the other hand, around the end of the summer, some significant precipitations favored the development of GDM late infections. Data from the ASSAM weather station of Arcevia (AN) are reported in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Number of Droplets (in Percentage)**} \\ \hline
**Diameter Classes (mm)** & **L *** & **S *** & **VL *** & **VS *** \\ \hline
0.01 & 0 & 0 & 0 & 0 \\
0.05 & 3.9\% & 4.8\% & 3.6\% & 3.8\% \\
0.1 & 22.4\% & 23.6\% & 18.6\% & 17.3\% \\
0.2 & 26.7\% & 26.5\% & 25.2\% & 23.7\% \\
0.3 & 16.9\% & 17.9\% & 17.3\% & 17.0\% \\
0.4 & 10.3\% & 10.8\% & 11.3\% & 11.3\% \\
0.5 & 6.3\% & 6.0\% & 7.3\% & 7.5\% \\
0.6 & 4.1\% & 3.5\% & 4.8\% & 5.2\% \\
0.7 & 2.7\% & 2.1\% & 3.2\% & 3.8\% \\
0.8 & 1.8\% & 1.3\% & 2.2\% & 2.6\% \\
0.9 & 1.3\% & 0.9\% & 1.5\% & 1.9\% \\
1 & 0.9\% & 0.6\% & 1.1\% & 1.4\% \\ \textgreater{}1 & 2.7\% & 2.0\% & 3.8\% & 4.6\% \\ \hline \end{tabular}  
* L stands for LiDAR; S stands for Standard; VL stands for LiDAR gaps; VS stands for Standard gaps.  
\end{table}
Table 6: Summary table about the number of droplets per diameter range, expressed in percentages.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coverage Classes** & **Coverage Range (\%)** & \
\begin{tabular}{c} **Lidar Vines** \\ **Gaps Frequency** \\ \end{tabular} &
\begin{tabular}{c} **Standard Vines** \\ **Gaps Frequency** \\ \end{tabular} \\ \hline
1 & 0–5 & 3 & 0 \\
2 & 5–10 & 18 & 0 \\
3 & 10–15 & 36 & 12 \\
4 & 15–20 & 16 & 10 \\
5 & \textgreater{}20 & 17 & 25 \\ \hline \end{tabular}  
* L stands for LiDAR; S stands for Standard; VL stands for LiDAR gaps; VS stands for Standard gaps.  
\end{table}
Table 5: Summary table of the LiDAR and standard WSP samples placed in vine gaps.
## 1 Introduction
Due to the complexity of the physiological activities and pathological changes of the human heart, up to now, no wavelet function for all ECG signals or universal ECG model has been developed. Neural network learning methods need a large amount of data for training, but the target signals are difficult to obtain. Therefore, they are not suitable for ECG signal extraction. In addition to the above single-channel methods, multichannel methods can also be used to extract FECGs after transforming the single-channel signal to multichannel signals, such as pseudo-phase space reconstruction combined with the singular value decomposition (SVD) [21], principal component analysis (PCA) [22], and independent component analysis (ICA) extraction methods [23, 24]. Pseudo-phase space analysis is appropriate for stationary signals, but for nonstationary ECG signals, it reduces nonstationarity. Blind source separation methods such as ICA in pseudo-phase space are not effective and often fail to extract. In this paper, an instantaneous correlation coefficient and a simplified coherent averaging method for single-channel foetal ECG extraction are proposed. An instantaneous correlation coefficient method based on the ECG reference model is proposed to improve the accuracy of ECG R peak detection. The MECG and FECG mean waveforms are extracted step by step by the simplified coherent averaging method, and the nonlinear, nonstationary characteristics of the FECG are recovered from the residual signal by the threshold method. The validity of the proposed method was verified by experiments using synthetic signals and real ECG data from the database for the identification of systems (DaISy), abdominal and direct fetal ECG database (ADFECGDB), and non-invasive fetal ECG database (NIFECGDB).
# Data-Driven Forecasting of Acute and Chronic Hepatitis B in Ukraine with Recurrent Neural Networks
## 2 Current Research Analysis
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Paper** & **Task** & **Method** & **Findings** \\ \hline [PERSON] and [PERSON]. [22] & To assess how media coverage and environmental noise influence long-term hepatitis B dynamics in China. & Develop a stochastic S-I\({}_{1}\)-I\({}_{2}\)-R adjusted in media-modulated, saturated incidence terms, prove analytical extinction and persistence criteria, and fit the system to 2005–2021 national case data. & Enhanced media responses and higher noise intensities can push the stochastic reproduction number below unity, leading to extinction; otherwise the disease persists around a stationary distribution that forecasts a long-term incidence plateau of 50–60 per 100,000. \\ \hline [PERSON] et al. [23] & To construct a time-series model to accurately predict monthly hepatitis B notifications across China for early-warning purposes. & Researchers fitted multiple seasonal ARIMA candidates to log-transformed 2013–2020 annual surveillance data, selected the optimal specification using BIC and residual diagnostics, and tested it on 2021 observations. & SARIMA(1,0),0)(0,1)\({}_{12}\) reproduced historical trends and kept 2021 forecasts within the 95\% prediction interval, confirming its suitability for short-range hepatitis B monitoring in China. \\ \hline [PERSON] et al. [24] & To determine whether two leading hepatitis B transmission models give concordant forecasts and to quantify how scaling infant and birth-dose vaccination affects future infections and deaths. & The paper populated deterministic models with identical country-specific data, simulated three vaccination-coverage scenarios from 2015 to 2099, and compared outputs for incident chronic infections and HBV-related mortality. & While baseline epidemics aligned, PRoGReSs predicted larger reductions from infant-series scale-up and Imperial from birth-dose expansion, exposing structural uncertainty that should inform vaccination policy deliberations. \\ \hline [PERSON] and [PERSON]. [25] & To investigate how combined white noise and regime-switching disturbances affect hepatitis B dynamics and to identify disease persistence or extinction conditions. & The paper formulated a stochastic S-I\({}_{1}\)-I\({}_{2}\)-R differential equation system with Markovian switching, proved well-posed and threshold theorems using Lyapunov functions and martingale techniques, and validated findings through numerical simulation. \\ \hline [PERSON] et al. [26] & To assess how alternative age-specific booster vaccination strategies could curb hepatitis B transmission in China. & A multi-group deterministic SEICR model was developed and calibrated with preferential mixing. Then sensitivity and scenario analyses were used to quantify the effects of varying contact patterns and vaccination rates. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)
## 3 Results and Discussion
### Statistical Results  
We divided the physiological indexes by aggregate size in the PC, SC, and LC treatment groups for PCA. To identify the main factor influencing the growth of the SC and LC groups, we evaluated the applicability of PCA, and the Kaiser-Meyer-Olkin (KMO) and Bartlett sphericity tests were administered. KMO is a measure of sampling adequacy that indicates the proportion of variance due to the underlying factors. Desired KMO values are typically greater than 0.5 [36]. In this study, the KMO test values were 0.608 (SC) and 0.512 (LC), showing that the data could be used for PCA and that PCA could result in a significant dimensionality reduction. [PERSON]'s test compares the correlation matrix with an identity matrix, evaluating the redundancy among the variables. In this study, the significance levels were reported to be 0.000 (SC) and 0.000 (LC), and all variables were significantly correlated. According to the correlation analysis, the unrelated variable in both groups of specimens, the compressive strength, was deleted. Figure 12 shows the average distribution of different weights allocated to the first and second components for all indicators with different sizes of PCs. The figure in the box shows the contribution of each index to the first and second principal components. According to Figure 12, the parameters REC, SP, MDA, and alkalinity had the largest weights and longest distances from the determination line and were therefore the most influential parameters for measuring plant growth stress; their contribution values to the first component were 0.9677, 0.9374, 0.9208, and 0.9157, followed by 0.8906 for Chl in the SC group. The void ratio shows the opposite tendency in the figure; this outcome and the results of the correlation analysis are similar. The parameters REC, SP, MDA, and Chl had the largest weights and longest distances from the determination line in Figures 13 and 14, illustrating that the cumulative contribution rate of the two principal components in each group was more than 80%, at 95.810% in the SC group and 93.678% in the LC group, indicating that choosing two components was feasible. In addition, the other three parameters with lower weights can be used to assist in plant growth prediction.
## 3 Scheme Overview  
After gaining initial access to the target machine, data exfiltration and command execution are paramount. We exploit the generally unrestricted nature of outbound HTTP(S) traffic by leveraging a web browser as a covert channel. Our novel approach utilizes a browser extension with native messaging API capabilities, enabling the attacker's C2 server to communicate through the extension to a native application proxy. This proxy downloads and executes a DLL from a URL provided by the C2 server. This DLL handles command execution and data exfiltration, gaining access to the operating system (OS) and establishing persistence.  
We exploit the popularity of Chromium-based browsers (Chrome and MSEdge) for our data exfiltration and command execution strategy. As already mentioned, our approach relies on the native messaging API, which facilitates communication between browser extensions and native applications. A browser extension and associated native application are installed on the target system. The attacker's C2 server communicates with the extension over HTTP/3 (QUIC), relaying messages to the native application. The native application downloads DLLs hosted on the C2 server and uses reflective DLL loading to execute them in memory. This provides a mechanism for dynamic command execution.  
Figure 1 illustrates the communication flow between the attacker and the victim. A short description of each step is shown in the figure below.  
1. The attacker's C2 server waits for the browser extension on the victim's machine to initiate a connection.
2. Upon connection, the C2 server sends a URL to the extension.
3. The extension forwards the URL to the native application.
4. The native application downloads the DLL from the URL via HTTP/3, loads it into memory, and executes it.
5. The native application sends the execution result to the extension.
6. The extension relays the result back to the C2 server.
7. The attacker views the execution result.
# Bearing Capacities and Failure Behaviors of F-Type Socket Joint in Rectangular Pipe Jacking Tunnel
## 4 Parametric Analysis
### Analysis of Steel Ring Dimensions  
The effect of steel collar size on the rotational stiffness of the pipe joint is mainly in the concentrated steel collar length and thickness, and the lengths l = 360 mm, l = 340 mm, l = 320 mm, l = 300 mm, and l = 280 mm and t = 19 mm, t = 16 mm, t = 15 mm, t = 12 mm and t = 10 mm were selected here for comparative analysis [14]. Their M-\(\theta\) relationship curves under the influence of length and thickness are shown in Figures 21 and 22.  
Before the relative angle of joint \(\theta\) reaches 0.027 rad, the contact between the steel ring and the socket joint has not started, which is mainly manifested by the extrusion of concrete on both sides of the top joint and the \"head knocking phenomenon\". When the relative angle of the joint reaches 0.122 rad, the joint bending moment continues to increase, the angle remains constant, and the steel collar and the concrete of the socket pipe joint are mutually extruded and slide to the upper edge of the chamfer. After that, the joint bending moment continues to increase linearly with the relative angle of rotation. This is because the steel ring at the top plate and the concrete at the socket of the pipe joint are extruded from each other, and gradually slide to the upper edge of the chamfer from the middle area of the pipe joint, while the lower part of the steel ring and the lower part of the pipe joint are disconnected. At this time, the joint bending moment continue to increase, and the angle remains unchanged.  
Figure 20: Relationship of joint rotation angle and moment under different section sizes.  
In addition, the joint bending moment and rotational stiffness were minimized at a length of 280 mm and a thickness of 10 mm, and maximized at a length of 360 mm and a thickness of 19 mm. By fitting the above data, the results show that increasing the length of the ring or the thickness of the ring can increase the rotational stiffness, with an average increase of 16% for each 20 mm increase in the length of the ring and 5% for each 1 mm increase in the thickness of the ring.
# Accuracy of Code GNSS Receivers under Various Conditions
## 4 Results
### Line  
The calculation result of survey points from the surveying instrument was uploaded into QGIS 3.16.14 software and a straight line was fitted (Figure 8). As a result of the interpretation of the accuracy of the measurement points, the straight line was fitted based on the precision solutions.  
After exporting the reference points, we proceeded to determine the NEU components deviations analogously to the point measurement. Figure 9 shows the deviations in the NE components for each of the five receivers. As a result of the analysis, it was noticed that for the N component, the best accuracy was achieved by the G_2 receiver using the GPS+GLOASS mode. These values were within a 10 m range. For the G_1 receiver, the accuracy did not exceed 11 m. It was observed that smartphone X_1 lost the signal after one minute of measurement and recorded the coordinates that were saved last until the end of the test. Phone X_3 achieved deviations within 19 m, while smartphone X_2 achieved deviations up to 17 m. When measuring a straight line in motion, it was noted that the Garmin receivers produced more consistent results relative to each other than when measuring a point. The graphs were very close to each other. After in-depth analysis,  
Figure 6: Plot of NE component deviations, point measurement.  
Figure 7: Plot of U-component deviations of point measurements with code receivers.  
we observed weaker signal receptions for all five receivers at 12:22:32, where error values increased several times for the NE components. For the deviations in the E component of the linear measurement, smaller discrepancies in deviation values were observed than for the N component. The best accuracy was determined by smartphone X_3, whose values did not exceed 5 m. Phone X_2 obtained deviations in the E component in the range of 7 m. Both the Garmin G_1 and G_2 receivers obtained values within 7 m. The values of the E component deviations of the handheld receivers were very similar to each other. In the case of X_2 and X_3 telephones, the similarity of the graphs could be noticed, but divergences appeared at the end of the measurement.  
Figure 8: Points from the RTK measurement (**dark blue**) and the fitted reference line (**yellow**).  
Figure 9: Plot of NE component deviations of linear measurement with code receivers.  
When analyzing the U-component deviation plot of the linear measurement (Figure 10), large error values were noted for receiver G_2. The loss of the signal near 12:22 resulted in the registration of several times larger errors for both devices.  
In the case of the G_1 instrument, the error values were within 12 m, while the deviation range of the G_2 receiver was from 28 to 55 m. When determining height on the move, better positioning capability was guaranteed by the receiver using the GPS system (G_1) compared to the receiver using the GPS+GLONASS mode (G_2), which was characterized by a systematic shift.
# Spatial Multiplexing Holography for Multi-User Visible Light Communication
## 3 Simulation Results
### Simulation Results for Power Allocation  
Following the methodology outlined in Section 2.1, the SMH for a five-user VLC system is successfully generated. The convergence line of GA during optimization is illustrated in Figure 5b. The population size is set to 100, with a migration factor of 0.5. The normalized central power for each user, as depicted in the bar chart of Figure 5a, demonstrates the efficacy of the SMH in terms of power distribution.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**User**} & \multirow{2}{*}{**Depth (m)**} & \multicolumn{2}{c}{**X-Y**} & **Power** & **Power** & **OAM** \\  & & **Coordinate (m)** & **Allocation** & **Coverage** & **Nodes** \\ \hline
1 & 0.6 & (0.032,0.032) & 0.2 & 0.2 & 1 \\
2 & 0.65 & (0.035,0.035) & 0.4 & 0.4 & 2 \\
3 & 0.7 & (0.035,0.044) & 0.6 & 0.6 & 3 \\
4 & 0.75 & (0.014,0.044) & 0.8 & 0.8 & 4 \\
5 & 0.8 & (0.037,0.057) & 1 & 1 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Target location in simulation.  
Figure 5: (**a**) Simulation results for power allocation; (**b**) Convergence line of GA; (**c**) Simulation results for power coverage under MF method; (**d**) Simulation results for power coverage under SMH.  
When compared to the multiplexing holography (MPH) method, which is detailed in Ref. [15], the power allocation achieved by the SMH is notably closer to the intended targets. The Mean Square Error (MSE) between the actual and targeted power distributions for the SMH is only \(4.37\times 10^{-4}\), representing a substantial improvement over the MSE of 0.2149 observed with the MPH approach for the same five users. This discrepancy is attributed to the expansion of the light field in the MPH method. Consequently, the SMH method exhibits an improvement of 99.8% in power allocation accuracy compared to the MPH, highlighting its superior performance in maintaining the integrity of power distribution within VLC systems.
## 1 Introduction
In parallel with global warming, climatic extremes occurred much more frequently in recent years in China, for example, the low temperature freezing in early 2008 [27], severe national droughts in 2011[28] and six consecutive years of spring droughts in Yunnan province since 2009 and so on. These extremes might be related to EI Nino-Southern Oscillation (ENSO) [29]. The ENSO is a coupled ocean-atmosphere phenomenon and characterized by irregular fluctuations between warming and cooling of sea surface temperature (SST) in tropical eastern Pacific (EP) or central pacific (CP) every 2-7 years. The warming and cooling anomalies are known as EI Nino and La Nina events, respectively. ENSO is a major driver for interannual and seasonal anomalies of regional and even global climates [29; 30]. A number of studies have indicated that precipitation and temperature anomalies caused by the ENSO have large impacts on NPP of terrestrial ecosystems [9; 31; 32; 33; 34]. For instance, the transition from EI Nino to La Nina during 1997-1998 caused a reduction of terrestrial NPP in eastern Africa related to decreased precipitation [31]. The prevalent La Nina since 2010 caused up to six consecutive seasons of increased precipitation in Australia, which resulted in a remarkable increase in terrestrial ecosystem carbon sequestration in 2011 [34]. The global gross primary productivity (GPP) changed concurrently with ENSO, and GPP anomalies were more strongly associated with the ENSO than La Nina [9]. However,the influence of ENSO on terrestrial NPP in China has not been well investigated. Analyzing the linkage between total NPP of terrestrial ecosystems in China and ENSO was also one of the objectives of this study. CO\({}_{2}\) fertilization induces global warming and concurrently enhances vegetation growth. For example, 12 free air CO\({}_{2}\) enrichment (FACE) experiments in different global regions indicated that CO\({}_{2}\) increases of 50% would cause NPP increases of 12% [35]. However, CO\({}_{2}\) fertilization effect may be limited by various factors, such as water availability [36]. Positive effects of CO\({}_{2}\) fertilization in arid and temperate grassland ecosystems appeared in sufficient water conditions [37]. Furthermore, effects of CO\({}_{2}\) fertilization was influenced by the precipitation at different times of year. Increasing spring precipitation would promote the effects of CO\({}_{2}\) fertilization, while increasing non-spring precipitation would inhibit this effect [38]. At the same time, warmer climatic conditions resulting from increasing CO\({}_{2}\) may increase vegetation moisture stress [9] and attenuate the direct positive effects of CO\({}_{2}\) increase on NPP, especially in semi-arid ecosystem [37]. Compared with the averaged total NPP from 2000-2012, climate change alone simulated by the LP) model would reduce global terrestrial NPP by 12.5% and 7.5% under A2 and RCP85 scenarios during the period of 2091-2100, respectively [39]. The extent to which CO\({}_{2}\) fertilization and climate change affect terrestrial NPP in China is unclear; examining this question is another objective of this study.