esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Surface Water and Groundwater Interactions
#### 4.2.3 The Behavior of the Interactions in the Upstream and Downstream Fans  
The PPGB is a relatively large area with high variations of altitude and slope and a complex river network. The natural processes such as tectonic deformation, surficial deposition, and human activities have led to different behaviors of SGIs in the upstream and downstream fan areas in the PPGB. Figure 14 shows the average volumetric flow rates for the water cycle components obtained from the GSFLOW model. In the study, we had defined the upstream fan in the upstream area of the confluence point (i.e., the Li-lin Bridge) of the Kaoping River in the PPGB. However, the remanding area in the PPGB was defined as the downstream fan area that includes the downstream area of the Li-lin Bridge station and covers the Tungkang and Linien Rivers in the PPGB. We are interested in understanding the behavior of SGIs and quantifying the water cycle that had been influenced by the natural processes and human activities.  
Figure 13: Temporal variations of the volumetric flow rates near the interface of the soil zones in the PPGB.  
The water budgets in Figure 14 indicate that the average volumetric flow rates in the downstream fan area are generally higher than those in the upstream fan area. In the upstream fan area, only the average flow rate of the soil zone to the unsaturated zone is higher than that in the downstream fan area. The observations of the precipitation from 2015 to 2017 show that the upstream fan received relatively less average precipitation as compared to the data obtained from the downstream fan area (see Figure 14). However, the relatively high permeability of topsoil in the upstream fan can lead to the high flux of the vertical flow from the soil zones to the unsaturated zones. In Figure 14, the averaged water fluxes accumulate the fluxes from all computational cells in the simulation. By counting the water fluxes for the unit area in the upstream or downstream fans, we obtained relatively substantial interactions of the water cycle in the upstream fan area. Such interactions are very obviously near streams and indicate high impacts between groundwater and surface water systems in the upstream fan area.
# Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic
## 2 Related Work
### Anomaly-Based Detection Techniques  
This type of detection techniques relies on the monitored environment's baseline typical behavior profile [18]. This usual baseline is then utilized to compare the actions of the system at any given time. Any deviations from the authorized threshold are recorded by using an alarm, but no classification for the sort of attack detected is provided. There have also been attempts to use behavioral detection models based on ML models that learn normal and attack events; however, establishing normal profiles is preferable to learning normal and attack events, which cannot include new attack events in real world networks [19]. Anomaly-based detection approaches are more effective in discovering novel attacks compared to signature-based detection techniques. MI algorithms are used in anomaly-based detection strategies to create a baseline normal profile of monitored systems. Due to the significant computing resources required to train and test ML algorithms, their implementation in resource and energy constrained IoT environments remains a challenge.  
This research, Ref. [20] proposed a lightweight IDS scheme for IoT. There are two levels to this scheme: training and evaluation. The technique is trained to make the system lightweight using features derived from the packet inter-arrival time of the received data during the training phase. The scheme uses the support vector machine (SVM) classifier to detect an intrusion or abnormal traffic during the evaluation stage. In terms of detection speed and classification accuracy, the lightweight IDS method performs effectively.  
This research, Ref. [21] proposed a real-time scheme to detect wormhole attack in RPL-based IoT. It detects malicious users and nodes using routing information and Received Signal Strength Indicator (RSSI). In both centralized and dispersed installations, the real-time IDS systems are examined. It achieves a detection rate of 90%.
# Mapping Subtidal Marine Forests in the Mediterranean Sea Using Copernicus Contributing Mission
## 3 Results
### Fraction Mapping of the Target Habitats  
The spectral unmixing analysis produced fractional cover maps for each habitat class, with values scaled between 0 and 1 at the pixel level. Below we provide the fraction map for _Cystoseira_ s.l. (Figure 12), while the other habitats can be found in File S2.
## 3 Results
### Characterization of the Materials
#### 3.1.3 XRD Patterns  
Figure 3 displays XRD diffractograms for pure and composite samples. The XRD pattern of the LAC sample is characteristic of an amorphous activated carbon [16]. Considering the activation temperature (500 \({}^{\circ}\)C), graphite formation is not expected. Regarding P25, as expected, the characteristic peaks associated to anatase and rutile are observed ((anatase (*) at 20 = 25.4\({}^{\circ}\) (110), 38.1\({}^{\circ}\) (004), 48.2\({}^{\circ}\) (200), 54.3\({}^{\circ}\) (105), 55.3\({}^{\circ}\) (211) and 63.1\({}^{\circ}\) (204) and rutile (*) at 20 = 27.2\({}^{\circ}\) (110), 35.8\({}^{\circ}\) (101) and 40.9\({}^{\circ}\) (111)). For the composite materials, the XRD patterns are similar to that of P25 TiO\({}_{2}\), indicating that the incorporation of LAC did not affect the crystallinity of titania.
## 2 Synthetic Aperture Radar Interferometric Processing
### Acquisition of SAR Images
#### 2.1.1 Acquiring SAR Images by Repeat-pass  
The term \"repeat-pass\" in this paper means to pass the same area repeatedly. In this approach, only one antenna is used and the SAR system acquires SAR data sets by passing the same area twice, covering it with a slightly different viewing geometry. The imaging geometry of repeat-pass SAR interferometry is illustrated in Figure 2. From the figure, it is noticed that, on the first pass, the radar wave is transmitted from antenna \(s_{1}\), and after interaction with the terrain, the backscattered return is also recorded by antenna \(s_{1}\). The signal is then processed to a complex SAR image, so does \(s_{2}\) on the second pass. The signal is processed to another complex SAR image. These SAR images are called the master image and the slave image, respectively, as shown later.  
This approach is most suited to spaceborne systems. This is because the precise location of the flight path is required, and satellites typically have much more precise and stable orbital paths in the absence of the atmosphere than acircrafts. Normally, the repeat-pass approach is adopted to obtain SAR images to generate a DEM [44, 45, 46, 47]. It is also used to do surface deformation measurements [3-5].
## 4 Experiment II: Listener-Preferred \(\text{SBR}\) for Speech-Background Mixing
### Procedure and Listeners  
The playback and adjustment of foreground-background mixing level were controlled by a MATLAB GUI. The listener auditioned the scenes in the same room and using the same equipment as per Expt. I. While the foreground speech was presented diotically to the listener, the background sound was played stereophonically. The task for listeners was to choose an appropriate mixing level for the speech content and the background sound; the chosen mixing level for each scenario should result in the speech content being intelligible enough and the background sound providing good atmosphere to the scene. No reference scene was provided as a guide for either 'well' or 'badly' mixed content. The listener was allowed to adjust the master volume to a comfortable level at the beginning of the experiment. The mixing level was adjusted by only altering the background sound level using a vertical fader. Any changes to the mixing level took effect straight after the fader was adjusted while the audio was playing. The listener could listen to each excerpt as many times as necessary. A 'Play' and a 'Stop' button were provided for the listener in the programme to start and cease playing at anytime. The programme also informed the listener which sounds were the foreground and which were background sounds in the current scene. This ensured that the listener attended to the correct channel, especially in the 'News broadcasting' scene in which the background sound was also speech. Once a mixing level was chosen, pressing the 'Next' button started the next excerpt. The presentation order of the 20 excerpts was randomised for each listener.  
Thirty native British English speakers (ages: 18-35 year, mean age: 25 year) with normal hearing participated in this experiment. Nine participants were undergraduates or graduates studying acoustics at the University of Salford, or sound professionals.
## 5 Experimental Part
### Fuzzy Logic Algorithm Results
The rule base of the fuzzy logic algorithm was structured in such a way that the control system's output parameters contributed to reducing the impact of damage on the transmission while ensuring the mechanical system's functionality. This ensures minimal wear on mechanical parts and, in turn, does not interrupt the execution of operational tasks, allowing the completion of the work cycle. Moreover, this control method is easily adjustable, making the entire system much more flexible and adaptive. The modeling results for the diagnosis are illustrated in Figure 18a,b. The diagnostic modeling results indicate the likelihood of damage in the transmission. Thus, the closer the output parameter is to the corresponding value for the presence of the corresponding frequency in the vibrational spectrum of the signal (\(-1\) for overtension fault and \(1\) for worm heating fault), the more likely the presence of damage. By comparing the diagnostic patterns of damage with real signals, it is possible to identify the presence of damage even with a limited amount of data, relying solely on the frequency and amplitude of vibrations. It is important to note that values from the vibration sensor's input signal should be considered within specific frequency ranges. Therefore, in the presence of the necessary frequencies in the signal spectrum, the onset of damage can be identified even with the slightest amplitude. This diagnostic approach significantly reduces the frequency of repairs, meaning equipment maintenance will occur as needed, reducing overall maintenance costs. Similar to the control system, this diagnostic system is easily adaptable to various types of damage, making it a versatile solution for signal processing and machine learning.
## Materials and Methods
### Materials and Models
#### 2.1.1 Polyurethane Foam and Its Theoretical Sound Absorption Model
For a certain polyurethane foam sample with a thickness of \(d_{pf}\), its sound absorption coefficients in the frequency range \([f_{\text{min}},f_{\text{max}}]\) could be calculated by changing the value of the frequency \(f\) in the given range. From Equations (1)-(7), it could be found that except for static flow resistivity \(\sigma\) and porosity \(\phi\), the other parameters were constants or selected values in the given range. Thus, it was feasible to identify these two acoustic characteristic parameters through measuring the actual sound absorption coefficient \(\alpha_{a}\) of the polyurethane foam with the corresponding thickness \(d_{pf}\) and corresponding frequency \(f\). \[\omega=2\pi f \tag{5}\]  
\[K(\omega)=\gamma P_{0}\Bigg{[}\gamma-(\gamma-1)\Bigg{(}1-N_{u}\Bigg{(}j\frac {8\omega p_{0}P_{r}}{\sigma\phi}+N_{u}\Bigg{)}^{-1}\Bigg{)}\Bigg{]}^{-1} \tag{6}\]  
\[\rho(\omega)=\rho_{0}\Bigg{[}1+\Bigg{(}3^{2}+\frac{4\omega p_{0}}{\sigma\phi} \Bigg{)}^{-0.5}-j\frac{\sigma\phi}{\omega\rho_{0}}\Bigg{(}1+\frac{\omega p_{0} }{4\sigma\phi}\Bigg{)}^{0.5}\Bigg{]} \tag{7}\]Relative to the porous metal, the polyurethane foam had the advantages of higher porosity and larger static flow resistivity, which was favorable to obtain a better sound absorption performance. However, it is generally known that the porous material was not good at absorbing the noise in the low-frequency range, no matter the porous metal or the polyurethane foam. Therefore, the composite sound absorbing structure was essential to improve sound absorption performance of polyurethane foam in the low-frequency range.
# A Real-Time Hybrid Approach to Combat In-Browser Cryptojacking Malware
###### Abstract  
Cryptojacking is a type of computer piracy in which a hacker uses a victim's computer resources, without their knowledge or consent, to mine for cryptocurrency. This is made possible by new memory-based cryptomining techniques and the growth of new web technologies such as WebAssembly, allowing mining to occur within a browser. Most of the research in the field of cryptojacking has focused on detection methods rather than prevention methods. Some of the detection methods proposed in the literature include using static and dynamic features of in-browser cryptojacking malware, along with machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and others. However, these methods can be effective in detecting known cryptojacking malware, but they may not be able to detect new or unknown variants. The existing prevention methods are shown to be effective only against web-assembly (WASM)-based cryptojacking malware and cannot handle mining service-providing scripts that use non-WASM modules. This paper proposes a novel hybrid approach for detecting and preventing web-based cryptojacking. The proposed approach performs the real-time detection and prevention of in-browser cryptojacking malware, using the blacklisting technique and statistical code analysis to identify unique features of non-WASM cryptojacking malware. The experimental results show positive performances in the ease of use and efficiency, with the detection accuracy improved from 97% to 99.6%. Moreover, the time required to prevent already known malware in real time can be decreased by 99.8%.  
in-browser cryptojacking; cryptomining; Monero; cryptojacking detection; cryptojacking prevention; WASM +
Footnote †: journal: Applied Sciences
## 3 Results
### Confirmation of Z-Tyr-Ala-CHN\({}_{2}\) Activity in a Viral RNA Yield Assay  
To confirm the antiviral activity of Z-Tyr-Ala-CHN\({}_{2}\) against SARS-CoV-2, we tested its effect on viral RNA yield. To this end, RT-qPCR was performed at 48 hpi on supernatant of A549-hACE2 cells infected with SARS-CoV-2. Remdesivir and the MP\({}^{\rm{prot}}\) inhibitor PF-00835231 were included as reference compounds since both have a well-described mechanism of action and are known to reduce SARS-CoV-2 RNA levels [43; 44]. Z-Tyr-Ala-CHN\({}_{2}\) did not show cytotoxicity and reduced SARS-CoV-2 RNA (\(\sim\)4-log reduction) in the supernatant, similarly to remdesivir (\(\sim\)4.5-log reduction) and PF-00835231 (\(\sim\)4.8-log reduction) (Figure 2). The potency of the three compounds was also comparable, with an EC\({}_{50}\) of 0.32, 0.41, and 0.53 uM for Z-Tyr-Ala-CHN\({}_{2}\), remdesivir, and PF-00835231, respectively.  
Assessment of Broad-Spectrum Antiviral Activity of Z-Tyr-Ala-CHN\({}_{2}\) against SARS-CoV-1 and HCoV-229E  
Next, the antiviral activity of Z-Tyr-Ala-CHN\({}_{2}\) was assessed against the closely related beta coronavirus SARS-CoV-1 and the alpha coronavirus HCoV-229E. In these assays, viral infection was monitored using intracellular staining of spike or dsRNA. Z-Tyr-Ala-CHN\({}_{2}\) showed activity against SARS-CoV-1 and HCoV-229E (Figure 3), with EC\({}_{50}\) of  
Figure 1: **(A)** Structure of Z-Tyr-Ala-CHN\({}_{2}\). **(B)** Antiviral activity of Z-Tyr-Ala-CHN\({}_{2}\) against SARS-CoV-2 from the primary screen performed in VeroE6\(\leftarrow\)GFP. The SpotTotalAreaCh2 of the green fluorescent protein (GFP) signal was used to quantify CPE reduction. Toxicity was determined on the same cell line and was quantified using ATPaseTilte. The results represent data from two to three independent experiments. CPE, cytopathic effect; CC\({}_{50}\), half-maximal cytotoxic concentration; EC\({}_{50}\), half-maximal effective concentration; eGFP; enhanced GFP; GFP, green fluorescent protein; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2.  
Figure 2: Comparison of viral RNA reduction in the supernatant of SARS-CoV-2-infected A549-hACE2 cells using Z-Tyr-Ala-CHN\({}_{2}\) (\(n=2\)), remdesivir (\(n=3\)), and PF-00835231 (\(n=3\)). Toxicity was determined using ATPaseTilte on uninfected A549-hACE2 cells and is based on two independent experiments. No significant differences (\(p<0.05\)) in compound potency and log drop were observed using [PERSON]’s unpaired t testing (assuming normality of data).  
0.050 and 0.069 uM, respectively, which suggests the compound is a broad-spectrum coronavirus antiviral.
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 5 Experiments in the Linux Shell Environment
### Linux Shell Function Execution Times
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Server** & **Backend** & **All-Reduce Time (s)** & **cudaMemcpy (H to D) Time (s)** & **cudaMemcpy (D to H) time (s)** & **Total Latency (s)** \\ \hline \multirow{4}{*}{Bare metal} & MPICH & 2.483 (64\%) & 0.639 (16\%) & 0.755 (20\%) & 3.877 \\  & OpenMPI & 1.903 (58\%) & 0.638 (19\%) & 0.755 (23\%) & 3.296 \\  & CUDA-aware MPI & 3.226 (100\%) & - & - & 3.226 \\  & NCCL & 2.285 (100\%) & - & - & 2.285 \\ \hline \multirow{4}{*}{Singularity} & MPICH & 1.579 (53\%) & 0.642 (22\%) & 0.756 (25\%) & 2.977 \\  & OpenMPI & 1.574 (53\%) & 0.637 (22\%) & 0.744 (25\%) & 2.955 \\  & CUDA-aware MPI & 2.952 (100\%) & - & - & 2.952 \\  & NCCL & 2.066 (100\%) & - & - & 2.066 \\ \hline \multirow{4}{*}{Single Docker} & MPICH & 1.893 (57\%) & 0.647 (20\%) & 0.770 (23\%) & 3.310 \\  & OpenMPI & 1.870 (57\%) & 0.639 (20\%) & 0.762 (23\%) & 3.271 \\  & CUDA-aware MPI & 3.242 (100\%) & - & - & 3.242 \\  & NCCL & 2.106 (100\%) & - & - & 2.106 \\ \hline \multirow{4}{*}{Cross Docker} & MPICH & 2.222 (62\%) & 0.643 (18\%) & 0.744 (27\%) & 3.600 \\  & OpenMPI & 2.396 (62\%) & 0.628 (16\%) & 0.841 (27\%) & 3.888 \\ \cline{1-1}  & CUDA-aware MPI & 3.610 (100\%) & - & - & 3.610 \\ \cline{1-1}  & NCCL & 2.200 (100\%) & - & - & 2.200 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Linux shell function execution times (all-reduce). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Server** & **Backend** & **Gather (Allgather) Time (s)** & **cudaMemcpy Time (s)** & **MPI Send Time (s)** & **Total Latency (s)** \\ \hline \multirow{4}{*}{Bare metal} & MPICH & 1.806 (57\%) & 0.758 (30\%) & - & 2.564 \\  & OpenMPI & 1.641 (57\%) & 0.748 (33\%) & - & 2.389 \\  & CUDA-aware MPI & 2.225 (00\%) & - & - & 2.225 \\  & NCCL & 0.446 (13\%) & 1.782 (22\%) & 1.220 (35\%) & 3.448 \\ \hline \multirow{4}{*}{Singularity} & MPICH & 1.825 (17\%) & 0.755 (29\%) & - & 2.580 \\  & OpenMPI & 1.574 (61\%) & 0.749 (39\%) & - & 2.563 \\  & CUDA-aware MPI & 2.565 (100\%) & - & - & 2.565 \\  & NCCL & 0.429 (12\%) & 1.782 (25\%) & 1.236 (36\%) & 3.447 \\ \hline \multirow{4}{*}{Single Docker} & MPICH & 1.679 (69\%) & 0.749 (31\%) & - & 2.428 \\  & OpenMPI & 1.685 (69\%) & 0.746 (31\%) & - & 2.431 \\  & CUDA-aware MPI & 1.920 (10\%) & - & - & 1.920 \\  & NCCL & 0.416 (12\%) & 1.811 (52\%) & 1.286 (36\%) & 3.513 \\ \hline \multirow{4}{*}{Cross Docker} & MPICH & 1.664 (65\%) & 0.751 (33\%) & - & 2.415 \\  & OpenMPI & 1.598 (65\%) & 0.747 (35\%) & - & 2.345 \\ \cline{1-1}  & CUDA-aware MPI & 1.973 (100\%) & - & - & 1.973 \\ \cline{1-1}  & NCCL & 1.925 (37\%) & 1.886 (37\%) & 1.324 (26\%) & 5.135 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Linux shell function execution times (gather).
# A New Method for Fatigue Evaluation of Titanium Alloy Welded Structures
## 4 Proposition of Notch Equivalent Stress Correction Factor of Titanium Alloy Welded Structures
### Correction of the Calculation Formula of the Notch Equivalent Stress  
In order to make the fatigue life predicted by the notch stress equivalent value method closer to the real situation, combined with the above fatigue test results, the correction notch equivalent stress \(\sigma_{\text{as}}\) is introduced, and its calculation formula is as follows:  
\[\sigma_{\text{as}}=f_{\text{as}}\cdot\sigma_{y} \tag{12}\]  
In order to ensure the accuracy of the fitting curve, the new three-parameter S-N curve is combined with the notch equivalent stress. Through the method of proposing the correction factor described in Section 2.3.2 of this study, the notch equivalent stress of titanium alloy welded joints under different stress ranges and the test life determined by the new three-parameter power function model are substituted into Equation (9). A total of 19 groups of notch equivalent stress correction factors can be obtained, as shown in Table 8.  
The average value \(\overline{f_{\text{as}}}\) is calculated by Equation (10) to be 0.85105. Finally, the calculation formula for the correction notch equivalent stress for commonly used titanium alloy welded joints is obtained as Equation (13).  
\[\sigma_{\text{as}}=\frac{0.85105\cdot\sigma_{\text{n}}\cdot(as+x_{\text{as}})}{ \left[\left[2^{\frac{1}{2\cdot p}}\cdot a^{(\frac{1}{p}-1)}\cdot x_{\text{as}} \right]^{q}+x_{\text{as}  
As can be seen in Figure 8, the titanium alloy has excellent material properties and relatively good welding performance, and the establishment of the S-N curve refers to the British BS7608 standard. Therefore, under the same stress range, the fatigue life of titanium alloy welded joints predicted by the notch stress equivalent value method is smaller than the test life. The notch stress equivalent value method of correction is based on the test data. Therefore, the fatigue life of the titanium alloy welded structure predicted by the notch stress equivalent value method of correction is closer to the actual life, which improves the accuracy of life evaluation.