esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion of the Nasal Cavity Three-Dimensional Structure Reconstruction System
### Results and Analysis of Nasal Acoustic Signal Preprocessing
In addition, the accuracy of the method at the minimum and maximum cross-sectional area points is improved by 44.1% and 224%, respectively. Comparison of the acoustic wave reflection method based on the long-source acoustic tube [28] with MRI and CT shows that the cross-sectional areas measured in the first 7 cm of the nasal cavity are in good agreement [27; 28]. However, the cross-sectional areas measured by the acoustic wave reflection method based on the long-source acoustic tube after 7 cm were significantly larger than those measured by CT and MRI. The nasal cavity three-dimensional structure reconstruction system based on the short-source acoustic tube of this research has a mean relative error of 3.3096% in the cross-sectional area of the first 13 cm of the simulated nasal cavity model, and the error of the volume from 0 to 10 cm with the simulated nasal cavity model is 2.5%, which greatly extends the measurement depth of the acoustic wave reflection method. In order to facilitate the observation of the local features of the reconstructed results, the depth-cross-sectional area curves of the straight tube model and the simulated nasal cavity model are visualized in a three-dimensional view with the elimination of the DC offset component and the moderate value of the constraint factor. The results of the three-dimensional structure reconstruction of the two tested models are shown in Figure 16. The three-dimensional structure reconstruction of the nasal cavity is displayed in different colors according to the size of the radius, and the reconstruction results are displayed more intuitively and stereoscopically. This reconstruction can not only provide a basis for further auxiliary diagnoses of the disease but also facilitate doctors in explaining the physiological structures and pathological characteristics to patients. Figure 15: Relative errors of key points in reconstructed results with a simulated nasal cavity model under different preprocessing methods.
## 1 Introduction
### Importance of Salt-Tolerant Plants  
Research has traditionally focused on glycopytes for neuroprotective and mental health benefits. However, salt-tolerant plants, which thrive in high salinity environments, are now gaining interest for their medicinal properties, including unique secondary metabolites that could offer new neuroprotection and mental health treatment strategies.  
Salt-tolerant plants, or halophytes, can thrive in highly saline (\(\geq\)200 mM sodium chloride) environments, such as salt marshes and coastal dunes. Salinity stress triggers the development of specialised adaptations, including the synthesis of osmolytes (proline, sugars), antioxidative enzymes (superoxide dismutases, catalases, peroxididases), or the synthesis of non-enzymatic antioxidants, crucial for the plant's defence mechanisms and survival [21]. Besides their role in plant welfare, these metabolites also have significant pharmacological potential, including various flavonoids, phenolic acids, and alkaloid derivatives, which are well-documented for their potent antioxidant, anti-inflammatory, and neuroprotective properties [22]. Moreover, with the progression of climate change leading to increased soil and water salinisation, these plants offer a sustainable agricultural alternative well-adapted to these changing conditions. As rising temperatures and altered precipitation patterns exacerbate soil salinity, salt-tolerant plants can be cultivated on lands that would otherwise be unsuitable for agriculture, thus helping to maintain food security and agricultural productivity in salt-affected regions [23].  
Despite the growing interest in these plants, information on their neuroprotective properties remains scattered across various studies. Therefore, this review will provide a detailed analysis of the ethnomedial uses, alongside their in vitro and in vivo protective effects for neurodegenerative diseases and mental disorders, and the bioactive metabolites of salt-tolerant plants. It aims to highlight their potential in traditional and herbal medicine for improving mental wellness and neurological health. By merging traditional knowledge with modern research, we can explore novel treatments leveraging the neuroprotective qualities of salt-tolerant plants, advancing therapeutic options for mental and neurological disorders.
## 4 Experiments Results
### Datasets
#### 4.1.3 Arabic Sentiment Tweets Dataset (ASTD)  
The ASTD dataset was gathered from Twitter and is separated into four categories: objective, subjective negative, subjective positive, and subjective mixed [64]. In our paper, of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multirow{2}{*}{**Parameters**} & **Values for Main-AHS Dataset** & **Values for Sub-AHS Dataset** & **Values for ASTD Dataset** \\ \hline \multirow{6}{*}{CNN} & Num\_filters & [256; 256; 512] & [128; 500] & [128; 256; 512] \\ \cline{2-5}  & Kernel\_size & [5; 4; 4] & [5; 5] & [2; 5; 5] \\ \cline{2-5}  & Pool\_Size & [2; 4] & [2; 4] & [2; 5] \\ \cline{2-5}  & Dense\_Unit & 500 & 300 & 400 \\ \cline{2-5}  & learning\_rate & 0.0012 & 0.0014 & 0.00304 \\ \hline \multirow{6}{*}{CNN-LSTM} & Num\_filters & 64 & 512 & 200 \\ \cline{2-5}  & Kernel\_size & 5 & 5 & 2 \\ \cline{2-5}  & Pool\_Size & 3 & 2 & 2 \\ \cline{2-5}  & LSTM\_Unit & 200 & 600 & 200 \\ \cline{2-5}  & Dense\_Unit & 450 & 680 & 600 \\ \cline{2-5}  & learning\_rate & 0.00525 & 0.00051 & 0.0007 \\ \hline \multirow{6}{*}{CNN-GRU} & Num\_filters & 64 & 64 & 128 \\ \cline{2-5}  & Kernel\_size & 3 & 5 & 5 \\ \cline{2-5}  & Pool\_Size & 5 & 3 & 4 \\ \cline{2-5}  & GRU\_Unit & 350 & 450 & 100 \\ \cline{2-5}  & Dense\_Unit & 200 & 870 & 950 \\ \cline{2-5}  & learning\_rate & 0.0054 & 0.00142 & 0.0013 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of parameters after applying DeepCNN, CNN-LSTM and CNN-GRU models with SkipGram.  
the selected tweets, 1642 were positive and 777 were negative; therefore, it is an unbalanced dataset. The total number of rows is 2419. It does not have missing values. The ASTD dataset was then divided into two parts: training and testing. The training set includes 1294 negative tweets and 641 positive tweets. The testing set includes 280 negative tweets and 136 positive tweets.
## 2 Materials and Methods
### Statistical Analysis  
The Shapiro-Wilk W test was used to check the data distribution. Due to the non-Gaussian distribution of most variables, the general participant characteristics were presented as median (25-75 th percentiles) or count and percentages. Wilcoxon rank-sum tests (continuous variables) and chi-square tests (categorical variables) were used for comparisons between individuals with CKD and those without CKD. Robust linear regression models, unadjusted and adjusted for age, gender, smoking status, drinking status, HTN, and DM status were used to assess the association between whole blood miRNAs and eGFR. The models used were as follows: Model 1: Crude; Model 2: Model 1 + age + gender; Model 3: Model 2 + smoking status + drinking status; Model 4: Model 3 + HTN status + DM status. Logistic regression models with a similar level of adjustment were used to analyze the ability of whole blood miRNAs to predict prevalent CKD. The area under the receiver operating characteristics (ROC) curve (AUC) was used to determine the discriminatory power of each miRNA ( hsa-miR-novel-chr1_36178, hsa-miR-novel-chr1_18383, hsa-miR-novel-chr2_55842, hsa-miR-novel-chr7_76196, hsa-miR-novel-chr5_67265, and hsa-miR-novel-chr13_13519), alone and as a collective, and alongside conventional risk factors to distinguish participants with CKD from those without. Statistical significance was defined as a \(p\)-value of <0.05.
# Cell Selection Technique for Millimeter-Wave Cellular Systems with Hybrid Beamforming
## 3 Proposed Cell Selection Technique
### Cell and Beam Synchronization Signal (CBSS)
The value of \(L\) must increase proportionally with respect to the STO, because of the time delay between the BS and MS. This can result in the reduction of the number of BIDs to \(\lfloor N_{z}/L\rfloor\).
# Monitoring Autonomous Mowers Operative Parameters on Low-Maintenance Warm-Season Turfgrass
## 5 Conclusions  
This trial highlights how different AMs and different moving schedules affect turf characteristics related to both qualitative and ground parameters. Both AMs showed satisfactory performance for residential turf, with slightly better qualitative results obtained in the plots moved by Automower\({}^{\circledS}\) 535 AWD.  
This moving method has been shown to ensure the quality of turfgrass even close to the dormancy period. Autonomous mowers are effective tools for obtaining reliable results for the maintenance of the height at a certain value. This trial demonstrated how autonomous mowers are a management solution that could enhance turfgrass quality, while also benefiting some species, without affecting the soil structure.  
All the results obtained could be the starting point for future possible longer trials regarding turfgrass maintenance:  
* enhancement of AMs positioning accuracy testing and comparing different positioning systems;
* the selection of local species suitable for turfgrass moved by AMs;
* further tests may be conducted at different times of the year (ideally in the late spring) to confirm any results from the instrumental analysis that are more visible and realistic;
* more extended studies on biological parameters through different vegetation indices;
* testing other responses to the same trampling levels using autonomous mowers with a systematic pattern, which may produce more accurate trampling values.  
Conceptualization, M.F., M.V., S.M. and V.F.; methodology, M.F., V.F., S.M.L. and S.M.; validation, A.P., M.R., L.C. and G.S; formal analysis, V.F. and S.M.L.; investigation, G.S., S.M.L. and L.C.; resources, M.F.; data curation, S.M.L. and M.S.; writing--original draft preparation, S.M.L. and M.S.; writing--review and editing, S.M.L., M.S. and C.F.; visualization, C.F.; supervision, L.G., L.C. and M.S.; project administration, M.F.; funding acquisition, M.F. and M.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors sincerely thank Husqvarna AB, Sweden, for providing the machines and [PERSON] and [PERSON] from DAFE for their technical assistance.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Application Analysis of Risk Assessment System  
In order to verify the efficiency of the RAS in the application process, the time used by the RAS was recorded in the risk evaluation of toxic gas leakages, and it was compared with the time used by the existing method in the steel plant, as shown in Table 7.  
The RAS takes a longer time for experts to score than the SCM because experts need to log in to the system, enter the scoring results, and submit them, while the safety checklist method only needs to record the results on the checklist when scoring. Generally speaking, the number of personnel required in the expert scoring process is similar to whether RAS or SCM is needed because the risk indicators of toxic gas leakage in steel manufacturing plants are spread at various points. A sufficient number of expert scores can ensure the accuracy of the evaluation results. In the RAS, the time cost for scoring, calculation, and risk assessment are much less than in the SCM. The reason is that the calculation can be done quickly on the server. The advantage of the RAS is that it can accurately evaluate the risk of toxic gas leakage in steel plants and then present the risk of each indicator through objective calculation in a brief period, which can provide a reference for safety management departments to respond more quickly.  
RAS completed the risk assessment process in 3765.38 seconds, and the time cost of RAS was nearly half of the existing method adopted in the steel plant. On this basis, the risk analysis and evaluation results of toxic gas leakage through the RAS can clarify the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Time Cost (s)**} & \multirow{2}{*}{**Time Reduced (\%)**} \\ \cline{2-2} \cline{4-6}  & **On-Site Scoring** & **Data Collection** & **Calculation** & **Evaluation** & **Total** \\ \hline RAS & 3765 & 0.08 & 0.2 & 0.1 & 3765.38 & 40.00 \\ \hline SCM & 3524 & 612 & 1843 & 297 & 6276 & - \\ \hline \hline \end{tabular}
\end{table}
Table 7: Time cost of the risk assessment system and safety checklist method.  
Figure 8: Evaluation analysis of the RAS and SCM in terms of the results for: (**a**) excellent; (**b**) good; and (**c**) moderate.  
risk level of each second-level index, which is conducive to optimizing the distribution of safety management resources, reducing safety management costs, and improving safety management efficiency.
## 2 Materials and Methods
### Research Method  
Based on decomposed sliding mesh technology [32], and to better meet the requirements of numerical methods for grid size, a scale model (\(1/10\)) was utilized for calculation and analysis. Because the moving state was studied in the current work and under crosswinds, there is an unsteady flow around the train, so a turbulence model should be used. Although large eddy simulation (LES) and detached eddy simulation (DES) can show more detailed flow structures, they require considerable computational resources and are time-consuming and costly. However, for research on engineering applications, the unsteady Reynolds-averaged Navier-Stokes (URANS) method is also feasible for reliable aerodynamic forces and average flow fields [33]. In previous studies [34; 35], the URANS method and different turbulence models were used for similar research geometry, such as shear stress transport (SST) \(k\)-\(\omega\) turbulence models, renormalisation group (RNG) \(k\)-\(\epsilon\) turbulence, the realisable \(k\)-\(\epsilon\) turbulence model, etc. Not only does the SST \(k\)-\(\omega\) method save computational cost, but it also has a higher predicting ability with respect to forces and the flow field around the train [36; 37]. Therefore, a URANS method based on the SST \(k\)-\(\omega\) method [38] was used in the present study. The time step was set to 0.0001 s to make the Courant-Friedrichs-Lewy (CFL) less than 1 in most cells around the train. Simulations were conducted using Fluent 18.1, which is based on the finite volume method (FVM), to discretise the governing equations. A second-order upwind scheme was set for the convection and diffusion terms, and a second-order implicit scheme was set for the time derivative. The pressure-velocity coupling and solution procedures were based on the Semi-Implicit Method for Pressure-Linked Equations Consistent (SIMPLEC) algorithm.  
The size of the computational domain and corresponding boundary conditions are illustrated in Figure 4. For the fixed domain, symmetry wall boundary conditions were set for the faces of ABCD, EFGH, and DCGH, with no convective flux across a symmetry plane; therefore, the normal velocity component at the symmetry plane is zero. To reproduce the crosswind effect, a velocity-inlet boundary condition was used on the BFGC face. In addition, a zero-pressure outlet was set for the AEHD face to simulate the real atmospheric environment. Finally, no-slip walls were used for the ground, windbreak, and train surfaces. In the sliding zone, the IJKL and MNOP faces were zero-pressure outlets, and the other faces were interface boundary conditions.  
Figure 4: Computational domain and boundary conditions.
# Detecting Malware C&C Communication Traffic Using Artificial Intelligence Techniques
## 4 Research Methodology  
This research paper aims to classify C&C network traffic flows as belonging to Zeus, which indicates that the C&C network traffic is malicious. The high-level activities include the following steps:  
* Obtain pcap samples of the Zeus banking malware and benign traffic.
* Extract features from the pcap samples.
* Train and test the algorithms with the data.
* Compare and discuss the results. Bot samples are collected as pcap files, and these pcap files are made up of network flows. A flow is defined as a sequence of packets flowing between a source and a destination host. Each flow is referred to as an ML sample, and the features are extracted from these samples. For this research, supervised ML algorithms are chosen, as these algorithms are well-suited for solving predication and classification problems such as the one being researched in this paper [56]. This paper analyzes three supervised ML algorithms, which are the decision tree (DT), random forest (RF) and K-Nearest Neighbor (KNN) ML algorithms, and examines an ensemble approach. The approach and methodology are explained in the next few sections.
# Underwater Acoustic Signal LOFAR Spectrogram Denoising Based on Enhanced Simulation
## 3 Experiments and Analysis
### Measuring Metrics  
To quantify the line spectrum enhancement effect of the denoising method, signal-to-noise ratio (SNR) is used to measure the image quality of the LOFAR spectrogram, and the SNR gain evaluates the effectiveness of the convolutional denoising model proposed in this paper. For the set of pixels belonging to the collected LOFAR spectrogram \(Y(T,F)\), denoted as \(S\in\mathbb{R}^{(T\times F)}\), the set of pixels corresponding to the target signal is \(S_{t}\), and the set of pixels corresponding to non-target signal is \(S_{n}\), such that \(S=S_{t}\cup S_{n}\) and \(S_{t}\cap S_{n}=\emptyset\). In the calculation of SNR in Equation (6), since the powers of target signal and noise are not able to be calculated for real measured data, the average value of the non-target signal pixels is approximated as the noise signal power, while the difference between the average pixel value of the target signal region and the noise signal power is approximated as the target signal power. Therefore, the SNR based on the LOFAR spectrogram can be approximate by:  
\[\text{SNR}=10\log_{10}\left(\frac{\frac{1}{|S_{t}|}\sum_{(t,f)\in S_{t}}Y^{2}( t,f)-\frac{1}{|S_{n}|}\sum_{(t,f)\in S_{n}}Y^{2}(t,f)}{\frac{1}{|S_{n}|}\sum_{(t,f) \in S_{n}}Y^{2}(t,f)}\right) \tag{13}\]  
The signal-to-noise ratio of model input is denoted as \(SNR_{in}\) and the signal-to-noise ratio of model output is denoted as \(SNR_{out}\). The signal-to-noise ratio gain \(G\) of the convolutional denoising model can be defined as:  
\[G=SNR_{out}-SNR_{in} \tag{14}\]  
In addition, the line spectrum recall is used as an evaluation metric for the model to determine whether the model suppresses the target line spectrum during the denoising process, which may lead to missed detections in subsequent analysis and recognition. It is defined as:  
\[Recall=N_{correct}/N_{all} \tag{15}\]  
where \(N_{correct}\) represents the number of line spectrum peaks after denoising that match the true line spectrum frequencies, and \(N_{all}\) represents the total number of true line spectrum. The true line spectrum frequencies are known conditions in the simulated data, while in the measured data, they are considered to be manually annotated. The two metrics exhibit a non-strict negative correlation. As the signal-to-noise ratio gain increases, the likelihood of suppressing the target line spectrum increases, while the likelihood of detection of line spectrum decreases. Therefore, both metrics are used together to evaluate the performance of proposed method.
# Unified Ciphertext-Policy Weighted Attribute-Based Encryption for Sharing Data in Cloud Computing
## 6 Security Analysis
### Discussion  
In UCP-WABE, CSP cannot know the details of the data because the data are stored in an encrypted form. The data are encrypted under a weighted access policy, so only the consumers who possess a set of weighted attributes satisfying the policy can decrypt the ciphertext. Hence, UCP-WABE achieves a fine-grained access control, and the confidentiality of the data can be guaranteed against unauthorized consumers. A data provider constructs the weighted access policy according to the data consumers' attributes instead of the data consumers' detailed information. The threshold gates are also used to construct the weighted access policy. Therefore, the providers are able to construct sophisticated weighted access policies, and there is no limit to the number of threshold gates and attributes. Thus, UCP-WABE can support an extremely large number of weighted access policies, and UCP-WABE can implement flexible access control.
# Corrosion Behavior of Stainless Steel in Seawater in the Presence of Sulfide
## 4 Conclusions  
In the present work, the effect of temperature and concentration of sulfide ions on the corrosion behavior of AISI 304L and AISI 316L stainless steels in seawater was studied. The results can be summarized as follows.  
The corrosion resistance of 304L and 316L stainless steels at \(E_{\mathrm{OC}}\) is due to the formation of a natural surface film. It was found that the stability (resistance, compactness, and thickness) of the surface film decreases with increasing temperature and sulfide ions. In addition, sulfurization of the surface film most likely occurs in the presence of sulfide ions.  
An increase in seawater temperature and pollutant concentration has a negative effect on the corrosion stability of stainless steel; therefore, the corrosion potential shifts toward the cathodic side, and the corrosion current increases. In the passive potential region,  
Figure 14: Topographic view of the segment of the corroded AISI 304L surface after potentiodynamic polarization measurement in seawater at 298 K with (**a**) 10 ppm of S\({}^{2-}\) and (**b**) 40 ppm of S\({}^{2-}\). The depth and width of pits are determined along the red lines.  
the passivation current increases, the depassivation potential decreases, and the passive potential region narrows.  
The occurrence of pitting corrosion on the surface of stainless steel was confirmed with non-contact 3D profilometer analysis. Fewer large pits (depth 80-100 \(\upmu\)m and width 100 \(\upmu\)m) are formed on the surface of AISI 304L steel, while a larger number of smaller pits (depth 40-50 \(\upmu\)m and width 50 \(\upmu\)m) are formed on the surface of AISI 316L steel. With increasing temperature and sulfide ion concentration, the width, depth, and density of the pits increase in both steel samples.  
In the observed temperature range (from 288 to 308 K) and sulfide ion concentration (up to 40 ppm), the AISI 316L steels exhibit higher corrosion resistance. The influence of sulfide ions on steel corrosion is more pronounced than the influence of temperature.  
Conceptualization, S.G. and L.V.; methodology, S.G. and L.V.; software, S.G., J.K. and A.N.; validation, S.G. and E.E.O.; formal analysis, S.G., J.K. and A.N.; investigation, A.M., L.V., S.G. and A.N.; resources, A.M., L.V., S.G. and J.K.; data curation, L.V., J.K. and A.N.; writing--original draft preparation, S.G., L.V., A.M., E.E.O. and J.K.; writing--review and editing, S.G., L.V., E.E.O. and A.N.; visualization, S.G., J.K. and A.N.; supervision, S.G.; project administration, S.G.; funding acquisition, S.G. and L.V. 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 conflict of interest.