esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background  
### Marangoni Effect  
In a gas-liquid mass transfer process, the interface condition is important. Interfacial tension, molecular structure of the interface, intermolecular forces of combining gas and liquid molecules, and interface turbulence all have an important impact on the gas-liquid mass transfer process [29]. During gas-liquid mass transfer, the interfacial convection phenomenon is caused by the change in some physical properties of the liquid such as surface tension and density due to the mass transfer at the interface, which may lead to convective movement of the interfacial fluid [29; 30; 31]. Researchers attribute this to the Marangoni effect or Rayleigh effect caused by transmission, which in turn strengthens the transmission process [30; 32]. During interphase mass transfer, when the temperature or concentration in different areas on the surface are different, a surface tension gradient is generated, resulting in the movement of liquid on the surface layer and liquid under the interface. This interfacial convection phenomenon is known as the Marangoni effect. The Marangoni effect not only has various geometric patterns on the flow structure, but also enhances the heat and mass transfer processes. Marangoni convection can be characterized by the dimensionless number Ma, which is expressed as follows:  
\[\text{Ma}=\frac{\text{d}\Delta\sigma}{\text{D}\mu} \tag{1}\]  
where d is the characteristic length (m); \(\Delta\sigma\) is the difference in surface tension between the surface fluid and the main fluid (N m\({}^{-1}\)); D is the diffusion coefficient of the solute in the fluid (m\({}^{2}\) s\({}^{-1}\)); and \(\mu\) is the viscosity of the fluid (Pa s). In Equation (1), when Ma \(>\) 0 and exceeds a certain critical value, the surface tension gradient generated by the interface will cause Marangoni convection. In gas-liquid and liquid-liquid liquefaction processes, due to the changes in the physical and chemical properties of the system [33; 34], the heat and mass transfer processes cause interfacial instability, and the surface tension is a function of concentration and temperature, which causes the temperature and concentration gradient of the interface to lead to the surface tension gradient of the interface, resulting in Marangoni convection.
# Topology of the World Tourism Web
## 3 Results and Discussion
### Does the Tourism Network Behave as a Social or Technological Network?
#### 3.2.2 Pareto (20/80) Rule  
For the sake of thoroughness, we will not ignore the Pareto (20/80) rule, which is usually related to scale-free networks (as it might be present in the observed one). Top 20% of the countries (roughly 48 countries) obtain 42.336% of all or the arcs (measured by degrees), receive tourists from 56.31% countries, and emit tourists to 37.56% of all countries. In the case of a weighted network, top 20% of the countries comprise 85.57% of the travels in 2018 (measured by weighted degrees) and, similarly, cover 85.58% and 83.04% of the tourism market as recipients and emitters of tourists, respectively. When reading this, one must keep in mind that separate sorting of the countries was made with regard to what was measured, so the list of top 20% of the countries is not the same for each share.  
[PERSON] and [PERSON] [7] reveal a high degree of centralization of outbound tourism, and they identified the United States, Canada, France, Spain, and Malaysia as the core--a few countries that maintain a central position in the world tourism network. While our results also point out to countries with a higher level of centralization, we do not find evidence of a core of few countries that remain at the top when applying different centralization measures. Nevertheless, we find evidence of the 20/80 rule, where roughly 20% of the countries benefit from over an 80% share of the tourism market, and this is valid only for the weighted network.  
Figure 7: Degree distributions. (**a**) In-degree distribution, (**b**) out-degree distribution; (**c**) degree distribution, (**d**) weighted in-degree distribution, (**e**) weighted out-degree distribution, and (**f**) weighted degree distribution. Source: authors.
## 3 Results  
### Hg, As and Sb in Waste Tops Soils  
The concentrations of As, Hg, and Sb at the sampling sites (<2 mm topsoil fraction) and the main descriptive statistics of the studied topsoils (e.g., number of observations, minimum, maximum, mean, median, standard deviation and skewness) are listed in Tables 3 and 4. Note that, in Table 4, unless Hg and As, Sb was only determined in 31 soil samples as four out of 35 had contents <2 mg kg\({}^{-1}\). The minimum and maximum contents of As, Hg, and Sb in the toposils were 10, 4 and <2 mg kg\({}^{-1}\) and 610, 1910 and 1610 mg kg\({}^{-1}\), respectively. Basically, the concentrations of Hg and Sb were largely variable since they were spanning between three and four orders of magnitude, respectively, while those of As were included within two orders of magnitude. This suggests that the three chalcophilic elements are heterogeneously distributed in the dumping area of Le Lame.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Solids Phases and Minerals** & **Reactions** & **Log k** & **References** \\ \hline Tripuhyite & FeSbO4 + H2O + 3H* = Fe3+ + Sb(OH)\({}^{0}\)\({}_{5}\) & \(-\)10.68 & [42] \\ Schafarzikite & FeSb2O4 + H2O + 2H* = Fe2+ 2 Sb(OH)\({}^{0}\)\({}_{3}\) & \(-\)12.21 & [42] \\ Bystromite & MgSb2O7 + 4H2O + 2H* = Mg2* + 2 Sb(OH)\({}^{0}\)\({}_{5}\) & \(-\)9.44 & [43] \\ Brizziite & NaSbO3 + 3H2O = Na\({}^{+}\) + Sb(OH)\({}^{-}\)\({}_{6}\) & \(-\)6.70 & [44] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **As** & **Hg** & **Sb** \\ \hline
**LM1** & 17.0 & 103.0 & \textless{}2 \\
**LM2** & 43.0 & 34.0 & 14.0 \\
**LM3** & 16.0 & 218.0 & \textless{}2 \\
**LM4** & 97.0 & 60.0 & 338.0 \\
**LM5** & 10.0 & 96.0 & \textless{}2 \\
**LM6** & 36.0 & 551.0 & 7.0 \\
**LM7** & 16.0 & 105.0 & 2.0 \\
**LM8** & 29.0 & 4.0 & \textless{}2 \\
**LM9** & 226 & 794.0 & 967.0 \\
**LM10** & 25.0 & 112.0 & 4.0 \\
**LM11** & 181.0 & 131.0 & 1419.0 \\
**LM12** & 25.0 & 31.0 & 4.0 \\
**LM13** & 132.0 & 1043.0 & 347.0 \\
**LM14** & 23.0 & 18.0 & 3.0 \\
**LM15** & 122.0 & 240.0 & 338.0 \\
**LM16** & 72.5 & 80.4 & 410.0 \\
**LM17** & 90.3 & 13.9 & 62.5 \\
**LM18** & 66.1 & 56.8 & 132.0 \\
**LM19** & 14.6 & 33.5 & \textless{}2 \\
**LM20** & 134.0 & 68.0 & 530.0 \\
**LM21** & 24.4 & 304.0 & 3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Concentrations of As, Hg, Sb (mg kg\({}^{-1}\)) in the studied toposils.
# Boiling Flow Pattern Identification Using a Self-Organizing Map
## 3 Results and Discussion
Figure 9: Results of SOM analysis for two-channel heat exchangers at \(G=61.65\) kg/m\({}^{2}\)s and \(P=75.6\) W: (**a**) the maximum, minimum, and average values of the pressure drop oscillations; (**b**) the average hit histogram of 10 results of \(SOM\) obtained from the \(RQA\) of pressure drop time series; (**c**) boiling flow pattern oscillations and heat exchangers at G = 38.8 kg/m\({}^{2}\)s and 55.8 W: (**d**) the maximum, minimum and average values of the pressure drop oscillations; (**e**) the average hit histogram of 10 results of \(SOM\) obtained from the \(RQA\) of the pressure drop time series; (**f**) boiling flow pattern oscillations. In the case presented in Figure 9, the hit histogram has five local maximums; therefore, it can be divided into five areas. Comparing Figure 9 with Figure 9 and the recorded video enables us to suppose that the following two-phase flow patterns were distinguished: _I_--liquid flow, _II_--liquid flow with small vapor bubbles, _III_--confined bubble flow, _IV_--wavy annular flow, and _V_--vapor slug flow. In the case presented in Figure 9 the hit histogram has three local maximums; therefore, it can be divided into three areas. Comparing Figure 9 with Figure 9 and the recorded video enables us to suppose that the following two-phase flow patterns were distinguished: _I_--bubble flow, _II_--slug flow, and _III_--wavy annular flow. The obtained results show that the proposed method (the combination of _RQA_ and _SOM_) can be used to analyze changes in two-phase flow patterns in a non-stationary boiling. However, it seems that the use of more complex algorithms of neural networks and their learning process can lead to the automation of the process of identifying boiling regimes in minichannel heat exchangers.
## 7 Experimental Result
The isolation forest and feed-forward neural network (FFNN) exhibit inferior F1 scores, precision, and recall performance. The isolation forest algorithm achieves an F1 score of 0.68, which suggests difficulties in reliably detecting anomalies, although it achieves a moderate precision of 0.76. Similarly, the FFNN demonstrates an F1 score of 0.53, indicating constraints in its capacity to effectively generalize to unfamiliar data or accurately differentiate anomalies from usual occurrences. Figure 4: Voltage magnitude before attack. Figure 5: The voltage magnitude curve after the attack. The autoencoder, logistic regression, and the Support Vector Machine (SVM) perform similarly in all evaluation metrics. The autoencoder demonstrates a notable F1 score of 0.95, signifying its efficacy in detecting anomalies while upholding a high level of precision and recall. Logistic regression and the SVM exhibit high levels of robustness in correctly detecting anomalies, as evidenced by their F1 scores of 0.98. Nevertheless, logistic regression exhibits marginally inferior model performance compared to the SVM, indicating that it has possible difficulties in managing intricate anomaly patterns. In the ensemble approach, the choice between using a decision tree and logistic regression relies on their performance in evaluation metrics, which are shown in Table 3.  
An ROC (AUC value) closer to 1 means the model can discriminate between positive and negative instances, with a high actual positive rate and a low false positive rate across various threshold values. In practical terms, an AUC of 1, as shown in Table 3, means the model has a 100% chance of correctly identifying positive and negative instances. The graphical representation of Table 3 is presented in Figure 7, which shows the performance metrics for the algorithms used in the detection of false data injection attacks. The ensemble approach (a combination of decision tree and logistic regression) demonstrates outstanding performance in all measures: the F1 score, precision, and recall are 1, whereas the Kappa score and model accuracy are 0.99. These statistics clearly show that the ensemble approach can detect the anomaly. Figure 6: A snapshot (output figure from Python interface) of the Gp3 Ph (grid power three-phase) curve before and after the attack. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SL No.** & **Algorithm/Technique** & **F1 Score** & **Precision** & **Recall** & **Model Accuracy** & **ROC** & **Kappa Score** \\ \hline
1 & Random Forest & 0.97 & 0.97 & 0.97 & 0.96 & 0.98 & 0.93 \\ \hline
2 & Isolation Forest & 0.68 & 0.76 & 0.70 & 0.69 & 0.70 & 0.39 \\ \hline
3 & SVM & 0.98 & 0.98 & 0.98 & 0.98 & 0.98 & 0.93 \\ \hline
4 & FFNN (Feed-Forward Neural network) & 0.53 & 0.71 & 0.59 & 0.59 & 0.64 & 0.18 \\ \hline
5 & Autoencoder & 0.95 & 0.95 & 0.95 & 0.94 & 0.95 & 0.89 \\ \hline
6 & Logistic Regression & 0.98 & 0.98 & 0.98 & 0.98 & 0.92 & 0.93 \\ \hline
7 & Decision Tree & 0.99 & 0.99 & 0.98 & 0.99 & 0.98 & 0.99 \\ \hline
8 & Ensemble Approach & 1 & 1 & 1 & 0.99 & 1 & 0.99 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics.
## 3 Results and Discussion
### Morphological Characterization (TEM, SEM-EDS)  
The morphological features of CeO\({}_{2}\), ZnO and CeO\({}_{2}\)/ZnO mixed oxides were characterized by transmission electron microscopy analysis (TEM). Pure ZnO (Figure 3a) displays an irregular shape (50-100 nm), while bare CeO\({}_{2}\) (Figure 3b) exhibits a distinct rod-like morphology (50-200 nm in length). In Figure 3c-e, the CeO\({}_{2}\)/ZnO mixed oxides are presented. It is evident that the mixed oxides exhibit the rod-like morphology of CeO\({}_{2}\) nanorods, while separated ZnO particles of irregular morphology are also detected. These findings are in accordance with XRD results, implying the formation of distinct oxide phases.  
Elemental analysis was indicatively carried out by scanning electron microscopy analysis along with energy dispersive X-ray spectrometry (SEM-EDS) over the CeO\({}_{2}\)/ZnO-0.2 sample. The SEM image along with the corresponding elemental analysis is shown in Figure 4. SEM-EDS analysis implies a Zn:Ce atomic ratio of ca. 0.19, being in good agreement with the nominal Zn/Ce ratio. The latter implies a uniform distribution of CeO\({}_{2}\) and ZnO phases on the entire material.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 5 Security and Performance Analysis
### Security Analysis
#### 5.1.5 Further Considerations on Known Attacks
\"P\" means possible to defend the attack if the location verification can be integrated with an appropriate technique or affected by the attack, but can mitigate its effect. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scheme** & **Range-Change** & **Impersonation** & **Wormhole** & **Sybil** \\ \hline LAD [12] & P & P & P & P \\ PLV [13] & P & P & P & Y \\ Phantom [14] & Y & P & Y & Y \\ Lightweight [15] & P & P & N & P \\ Ours & P & Y & N & Y \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Consideration of defense against some known attacks. LAD, location anomaly detection; PLV, Probabilistic Location Verification; P, possible.
# One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion
## 5 Conclusions  
In order to realize accurate and real-time maize leaf disease detection, based on deep learning technology, this paper proposes an MFF-CNN based on multi-scale feature fusion for maize leaf disease detection.  
We conducted experiments under the complex conditions of combined overlapping occlusion, sparse target distribution, and similar textures of the diseased areas and backgrounds. The results show that the proposed method obtains the best detection performance compared with the maize disease algorithms of Faster R-CNN, CenterNet, YoLov5s, and DETR.  
**Author Contributions:** Data curation, S.S. and C.Z.; Funding acquisition, Y.L. and G.Y.; Investigation, C.Z.; Methodology, Y.L.; Supervision, Q.Y.; Validation, S.S. and Q.Y.; Writing--original draft, Y.L.; Writing--review & editing, G.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Natural Science Foundation of Fujian Province under Grant 2021J01865, the Open Project Fund of Fujian Shipping Research Institute (No. HHXY2020014), and The Education and Scientific Research Project for Middle-Aged and Young Teachers of Fujian Province (No. JAT210678 and No. JT180877).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Experimental Results
### Evaluation Metrics  
To evaluate individual methods and setups, two complementary metrics are employed that are suitable for the segmentation task [24]. Although the method operates in 2D projections, all metrics are measured on the final unprojected segmentation masks in 3D.  
Let \(\hat{\mathcal{V}}\) be a point cloud of ground-truth boundary vertices, and \(\mathcal{V}\) be a point cloud of prediction boundary vertices. Let \(d\) be the Euclidean distance in \(\mathbb{R}^{3}\). We define the Hausdorff distance \(d_{H}\) as our boundary metric, and it is calculated as  
\[d_{H}(\hat{\mathcal{V}},\mathcal{V})=\max\{\max_{x\in\hat{\mathcal{V}}}\min_{ y\in\mathcal{V}}d(x,y),\max_{y\in\hat{\mathcal{V}}}\min_{x\in\hat{\mathcal{V}}}d(x, y)\}. \tag{4}\]  
\(d_{H95}\) is its 95 th percentile. This boundary metric detects artifacts in segmentated masks, such as missing details or narrow protruding parts.  
We choose Weighted Intersection over Union as the overlap metric. In contrast to the standard IoU computed over meshes, W-IoU takes into account that different triangle sizes contribute differently to the final value:  
\[\textit{W-IoU}=\frac{\sum\limits_{f\in\mathcal{F}_{\cap}}A_{f}}{\sum\limits_{ f\in\mathcal{F}_{\cup}}A_{f}}, \tag{5}\]  
where \(A_{f}\) is area of face \(f\), \(\hat{\mathcal{F}}\) and \(\mathcal{F}\) are faces of ground-truth and prediction masks, \(\mathcal{F}_{\cap}=\hat{\mathcal{F}}\cap\mathcal{F}\), and \(\mathcal{F}_{\cup}=\hat{\mathcal{F}}\cup\mathcal{F}\). _W-IoU_ detects errors such as under/oversegmentation, or shift errors. We consider an overlap metric that does not weight values by the polygon area inaccurate.
# Biosignals Monitoring of First Responders for Cognitive Load Estimation in Real-Time Operation
## 5 Results  
### In-Lab Tests  
At the end of the exercise, three NASA-TLX and a temporal series of six periods together with the exercise performance were recorded for every FR. Figure 10 shows an example of the HR and RR temporal series for the complete experiment, where six monitored (blue) and five rest (yellow) periods are presented.  
Related to the subjective perception of the NASA-TLX, a similar pattern is followed by the 80 users. All users had a low CL on Screen 1 and a higher CL on Screen 5 (see Figure 11). However, no significant CL increase is found from Screen 5 to Screen 9. Moreover, it is quite clear that there is a big dispersion (e.g., up to 75 points out of 100 for doctors on Screen 5) in the subjective perception of the exercise, which will clearly impact the modeling of generic CL models during future FRs' operation.  
Figure 8: Tilted bus visual test images.  
Figure 9: Narrow tunnel for practice scenario.  
From the objective measurements, all the biosignals acquired are compared and correlated with the NASA-TLX and test performance. From the biosignals values, we processed HR, GSR, wrist temperature, respiration rate, facial temperature, EMG activation, and eye gaze speed. For every screen, the framework extracts the maximum, average, and peak-to-peak values of all biosignals' time series. No specific information is found in the maximum and peak-to-peak values at this stage of the validation, so average values are presented hereafter. Moreover, facial temperature and EMG activation did not show any relationship with either NASA-TLX or performance of the game.  
Several average tendencies have been found between the increase in complexity of the task and the biosignals acquired; between them, eye gaze speed and respiration rate vs. NASA-TLX and performance stand out. These relationships are shown in Figure 12, where the respiration rate and eye gaze speed increase in most of the groups while the divided attention test increases in complexity (see Table 3). However, these tendencies do not provide statistical differences (\(p>0.05\)) in any FRs group or biosignals, either grouped or divided by profession.
## 1 Introduction
This manuscript introduces a modified Elias gamma scheme, which helps us to embed additional bits during the encoding process. * An experimental study of the proposed scheme is carried out on natural images and medical images. The results show that the proposed system performs better than the well-known existing schemes.
## 1 Introduction
under different strength criteria for wall design, suggesting the use of criteria that reflect the actual failure envelope of concrete. [PERSON] et al. [26] studied the concrete strength in composite wall design, showing that the compressive strength of concrete walls under triaxial compression increases by 2.53 to 2.72 times. These studies provide a solid foundation for understanding the strength of shaft wall concrete. When casting concrete in complex water-rich environments, traditional methods in pure water environments often suffer from inadequate water-blocking effects and the separation of cement and aggregate under washing [27]. As a result, researchers have investigated concrete formulations to create materials that do not separate when in contact with water [28; 29]. [PERSON] et al. [30] introduced a concrete additive containing an ether-type polymer compound that significantly enhances the concrete's resistance to water flow erosion and prevents decomposition in pure water environments, known as underwater anti-dispersive concrete [30]. Subsequently, China developed UWB-type anti-dispersive agents (polpropylene-based concrete anti-dispersive agents) and SCR-type anti-dispersive agents [8]. [PERSON] et al. [31] studied the anti-dispersive properties, flowability, and mechanical performance of underwater anti-dispersive concrete with UWB anti-dispersive agents, slag, and fly ash as adjustment parameters, finding that adding fly ash and slag powder improves anti-dispersive properties and mechanical performance but reduces flowability. [PERSON] et al. [32] investigated the effects of anti-dispersive agents and mineral admixtures on the strength of anti-dispersive concrete in pure water environments, finding that the optimal strength was achieved with 2.5% anti-dispersive agents, 5% silica fume, and 20% viscosity-reducing agents. These results provide a good foundation for the study of anti-dispersive concrete formulation and strength assurance in pure water environments. [PERSON] et al. [33] optimized the mix proportion of high-performance concrete by using range analysis and the comprehensive balance method based on orthogonal testing factors including fly ash, slag powder, and metakaolin and pointed out the best mix proportion. Currently, with the increasing need for deep vertical shaft construction, the in situ concrete wall method in a slurry environment has gained broader attention due to its high borehole utilization, good integrity, and high degree of automation, offering promising application prospects [34]. However, research on the strength characteristics of in situ concrete walls in slurry environments is scarce and unsystematic, which does not effectively support the promotion and utilization of this method in industrial sites. This study prepares slurry and underwater anti-dispersive mortar (as a concrete substitute), conducts casting experiments on mortar specimens in dry, pure water, and slurry environments, and investigates their compressive, tensile, shear strengths, and microscopic observations.