esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Turbulence Intensity  
The turbulence intensities were extracted based on the analytical framework and shown in Figures 1a and 2a respectively for investigated typhoons and hurricanes. The corresponding probability density functions (PDFs) are shown in Figures 1b and 2b. For typhoons, the average value of longitudinal turbulence intensities is 0.1952 and the standard deviation is 0.0032. For hurricanes, the average value of longitudinal turbulence intensities is 0.1906 and the standard deviation is 0.0022. The longitudinal turbulence intensity of these four typhoons presents slightly higher values in terms of both mean and standard deviation compared to those three hurricanes. This observation can be further demonstrated by comparing Figures 1b and 2b, where the probability distribution of the longitudinal turbulence intensities in both typhoons and hurricanes follows the normal distribution quite well. One can easily conclude that in these four typhoons the turbulence intensity has a larger value than that in those three hurricanes under the same probability of exceedance.  
In ASCE7-10, the longitudinal turbulence intensity is given by:  
\[I_{u}=c\Big{(}\frac{10}{z}\Big{)}^{\frac{1}{6}}, \tag{34}\]  
where \(c\) equals to 0.30, 0.20, and 0.15 for category B, C (corresponding to the standard terrain in this study), and D exposures, respectively. In AJJ-RLB-2004 code, the longitudinal turbulence intensity over flat terrain categories is given by:  
\[I_{u}=0.1\Big{(}\frac{z}{z_{G}}\Big{)}^{-\alpha-0.05}, \tag{35}\]  
in which \(\alpha\) and \(z_{G}\) are parameters reflecting the category of exposures. In category II exposure, which is the closest to the standard terrain in this study, \(\alpha\) and \(z_{G}\) are respectively 0.15 and 350. The longitudinal turbulence intensities obtained from ASCE7-10 and AIJ-RLB-2004 are 0.2000 and 0.2036, respectively, for the standard exposure. As depicted in Figures 1a and 2a, both ASCE7-10 and AIJ-RLB-2004 present a slightly higher estimation of longitudinal turbulence intensity for hurricanes and typhoons. Generally, the estimation of ASCE7-10 is relatively better compared to that of AIJ-RLB-2004.  
Figure 1: Turbulence intensities and their probability density functions (PDF) for typhoons.
# Hand Gestures in Virtual and Augmented 3D Environments for Down Syndrome Users
## 4 Results
### Analysis of Success Rate  
The first hypothesis to test is whether the task itself determines the success rate. In other words, we must establish whether there are tasks that are more difficult to perform due to their nature. Firstly a Kolmogorov-Smirnov test was performed to identify the distribution of the sample regarding the success rate. Both samples show normal distribution, so a parametric test is applied to find out the presence of significant differences between groups. To test hypothesis H\({}_{1A}\): The gesture has an effect on the success rate percentage of the task, the success rates of both groups were compared calculating successes/attempts and analysed using two sample Student's \(T\)-test. As Table 4 reveals, it is the tasks involving the gestures Pinch, Grab and drop, Pan and Tap that present significant difference between the two groups. Thus, as they prove to be more difficult for one group to perform than the other, these gestures should be avoided when trying to create inclusive technology. When analysing the mean of these activities, it is possible to see that the neurotypical participants had a much higher success rate than individuals with DS. The H\({}_{1A}\) hypothesis is accepted for the tasks: Stop, Take, Point and Swipe, because there is no significant difference in the success of execution by both experimental groups. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Action**} & \multirow{2}{*}{\begin{tabular}{c} **Stop** \\ **Vehicle** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Place** \\ **Marbles** \\ **in a Box** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Move** \\ **Coloured** \\ **Blocks** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Pick the** \\ **Apple from** \\ **the Tree** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Push Ball** \\ **Towards** \\ **Goal** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Search** \\ **for the** \\ **Cat** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Press a Button** \\ **to Move a** \\ **Character** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Move** \\ **Ball over** \\ **Obstacle** \\ \end{tabular} } \\ \hline
**Gesture** & \multirow{2}{*}{**Stop**} & \multirow{2}{*}{**Pinch**} & \multirow{2}{*}{
\begin{tabular}{c} **Grab and** \\ **drop** \\ \end{tabular} } & \multirow{2}{*}{**Take**} & \multirow{2}{*}{**Pan**} & \multirow{2}{*}{**Point**} & \multirow{2}{*}{**Top**} & \multirow{2}{*}{**Swipe**} \\ \hline Down & & & & & & & & \\ syndrome vs. & \(P=0.485\) & \(P=0.000\) & \(P=0.001\) & \(P=0.322\) & \(P=0.000\) & \(P=0.600\) & \(P=0.002\) & \(P=0.143\) \\ Neurotypical & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of tasks by success rate.
## 4 Flow Pattern Recognition Results and Discussion
### Network Training and Results  
The training model reads the classified data, and the optimal stream type classification model based on this experiment can be obtained by constantly updating parameters. In the parameter settings, the batch size is 16, with the learning rate set to 0.001, and the number of iterations at 100, while the cross-entropy loss function from Equation (10) is used as the training loss function. Some network parameters of feature fusion are set in Table 6.  
In this study, ECT image data sets with similar flow patterns are affected by each other. It is possible to compensate for the drawbacks of identical flow patterns, increase classification efficiency, and improve classification accuracy by extracting the vibration signal features of various flow patterns and combining them with the flow picture features. The feature fusion model's training loss and testing accuracy are displayed in Figure 17. The findings demonstrate that the model has a high degree of classification accuracy; within 100 iterations, the test accuracy can reach about 95.3%; and the loss of cross-entropy is reduced to 0.032.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{c}{**ECT Image**} & \multicolumn{2}{c}{**Signal**} \\ \hline Convolution kernel & \([3,3]\times 3\) & Convolution kernel & \([3,3]\times 2\) \\ Max pooling & \([2,2]\times 4\) & Adaptive max pooling & Outsize = 128 \\ Fully connected & \([4,256]\) & Fully connected & \([1,256]\) \\  & & Concatenate & \([4,256]\) \\ Add & & & \([4,256]\) \\ Fully connected & & & \([4,256]\) \\ Output & & & \(4\) \\ Epochs & & & \(100\) \\ Batch size & & & \(16\) \\ Learning rate & & & \(0.001\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Model parameter settings of feature fusion.  
Figure 16: Similar flow patterns of stratified flow and slug flow.  
The confusion matrix in Figure 18 demonstrates that the multi-sensor information fusion-based convolutional neural network model can reach 100% accuracy for annular flow. As show in Figure 18, 3 stratified flows were misclassified as slug flow, and 62 stratified flows were correctly identified. A total of 4 slug flows were misclassified as stratified flow, 2 were misclassified as bubbly flow, and 59 slug flows were correctly identified. In addition, 3 bubbly flows were misclassified as slug flows, while 62 bubbly flows were correctly identified.
# Experimental Test of Continuous Wave Frequency Diverse Array Doppler Radar +
## 2 Data Collection I: Far-Field Beampattern
### Temporal Periodicity of the Far-Field Pattern
#### 2.5.2 Far-Field Probe Collection  
Figure 3 shows the far-field measurements at a fixed location \((R_{0},\theta_{0})=(4.57\,\mathrm{m},0^{\circ})\) for linearly progressive frequency offset pairs \(\Delta f=10\,\mathrm{kHz}\) and 20 kHz, respectively. In Figure 3, it can be seen that doubling/halving the frequency offset between elements results in a halving/doubling of the period, with measured temporal periodicities \(T_{p}=1/\Delta f=99.28\,\mathrm{\SIUnitSymbolMicro s}\) and \(50.50\,\mathrm{\SIUnitSymbolMicro s}\) when \(\Delta f=10\,\mathrm{kHz}\) and 20 kHz, respectively.
## 2 Related Work
### RSA and ECC Performance Comparisons  
Currently, RSA and ECC are not often used by IoT nodes due to their resource demanding requirements, although some researchers proposed resource-efficient hardware implementations of both [33] and evaluated their performance on resource-constrained devices. For example, in [34], the authors evaluated the performance of RSA and ECC on a smart card. Another example is described in [35], where an ECC versus RSA time performance comparison is carried out by using 8-bit CPUs. In this paper, the authors concluded that, for the tested key sizes, ECC outperforms RSA. It is also worth mentioning a report from ATMEL [36] that compares RSA and ECC for embedded systems and concludes that RSA is 10 times slower than ECC for a 128-bit security level while, for a 256-bit security level, ECC is 50-100 times faster.  
ECC and RSA have also been studied in terms of power consumption in different scenarios (e.g., [37; 38]), but such studies fall short in some aspects (e.g., security levels are not taken into account, the consumed energy is just estimated or outdated hardware platforms and insecure/deprecated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Security** & **Symmetric** & **RSA** & **ECDSA** \\
**Level** & **Key Algorithms** & **Key Size** & **Curve** \\ \hline
80 & 2 TDEA & 1024 bits & prime192v1 \\
112 & 3 TDEA & 2048 bits & secp224r1 \\
128 & AES-128 & 3072 bits & secp256r1 \\
192 & AES-192 & 7680 bits & secp384r1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparable strengths for symmetric, RSA and ECDSA ciphers [30].  
cipher suites are used). No paper providing a fair comparison of power consumption in real-world IoT devices with proper security levels, which is presented in Section 4, was found.
## 4 Discussion
### Limitations  
We acknowledge that the present study originates from a simple and straightforward hypothesis. This explains, but does not exempt, the absence of kinematic and EMG recordings. This limitation prevents framing the effect described in the context of the neuromechanical command for the control of the medio-lateral position of the CoP, and prevents a direct support to the assumption that the post-effect is constructed by a repetitive proprioceptive discharge from and by the activation of the muscles responsible for the active medio-lateral tilts of the body. In addition, we did not test a different distance between the feet, which might modify the postural responses compared to the pelvis-width condition [82; 83] or different feet orientation, known to modulate the APA preceding perturbations [56]. Furthermore, considering the potential application of this protocol for stroke patients exhibiting an asymmetric position of the CoP during stance, information on the modulation of the post-effect by the numerosity of the perturbation cycles or by the amplitude of the translation distance appears relevant.
## 3 Metasurfaces  
Metasurfaces are two-dimensional or surface counterparts of metamaterials. Just like metamaterials, it is possible to characterise their response through their electric and magnetic polarizabilities. They are also referred to in the literature as metafilms [14]. Metamaterials control the propagation of light due to their bespoke permittivity and permeability values; however, they still use the propagation effect to manipulate the electromagnetic waves. This can result in a complicated relatively bulky structure whereas metasurfaces try to manipulate the wave over a single extremely thin layer [15; 16]. The two-dimensional nature of metasurfaces, therefore makes them less bulky and offers the possibility of lower loss structures [1]. Due to their 3D nature, it is also difficult to fabricate metamaterials. Metasurfaces offer an extremely promising alternative. Due to their planar structure, metasurfaces can be easily fabricated using planar fabrication tools [17; 18]. The planar fabrication process is also very cost-effective in comparison to the manufacturing of the complex 3D metamaterials [19]. Metasurfaces, being two-dimensional materials, can, therefore, be easily integrated into other devices which can make them a salient feature for nanophotonic circuits; this property will also allow them to be a part of \"lab on chip\" photonics [20].  
The negative index of the metamaterials is due to the resonance of the individual meta-atoms. This property makes the metamaterials inherently dispersive, thus the electromagnetic properties of such materials are highly sensitive to the changes in the operating frequency, thus making such materials bandwidth limited. It has been shown in [21] that by using extremely thin metasurfaces with deep sub-wavelength notches in a two-layered fishnet structure, the dispersion characteristics can be engineered. This technique was then used to make a broadband metasurface filter. The (in-band) transmission and (out of band) rejection was achieved by respectively matching and mismatching the impedance of this metasurface (to the free space). The dispersion characteristics were controlled by tailoring the primary (and secondary) magnetic resonances, and the plasma wavelengths for permittivity. Both these properties (of the metasurface) were highly dependent on the design of the sub-wavelength deep notches. The design was optimized by the help of a genetic algorithm. This broadband metasurface also had a very low insertion loss in the transmission band [21]. Due to the variety of advantages offered by metasurfaces over metamaterials, the scientific community has shown a keen recent interest in this area. This has led to rapid development in the underlying physics which govern the behaviour of metasurfaces and their potential applications.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 4 Artificial Intelligence in Production, the Fermentation Process, and Bottling Quality Control
### Predictive Control of Fermentation
#### 4.2.3 Quality Assurance  
AI systems enhance quality assurance by providing consistent monitoring and control throughout the fermentation process. By predicting potential deviations and automatically adjusting parameters, AI ensures that each batch of wine meets the established quality standards. This reduces variability and enhances the overall reliability of the winemaking process. AI-driven quality assurance systems can also detect, and correct issues related to off-flavors or other sensory defects. For example, AI can identify conditions that could lead to the production of unwanted compounds such as hydrogen sulfide and take preemptive actions to mitigate these risks. This capability helps maintain the desired flavor profile and quality of the wine.  
Several studies have demonstrated that the integration of advanced sensor technology with AI has the potential to enhance the ability to monitor and control fermentation processes in real-time that can affect wine quality. For example, the study by [PERSON] et al. utilized ATR-MIR spectroscopy combined with multivariate analysis to control the alcoholic fermentation process and detect wine fermentation problems early on. This approach allows winemakers to maintain the desired sensory attributes by monitoring the volatile compounds that contribute to the wine's aroma and flavor profile. When this data is fed into AI systems, the algorithms can predict potential issues and recommend adjustments to maintain optimal conditions, ensuring consistent quality [88].  
Similarly, the research by [PERSON] et al. demonstrated the use of a bienzymatic biosensor to monitor glucose and ethanol levels during wine fermentation. The real-time data provided by such sensors are critical for AI systems to make immediate adjustments to the fermentation parameters. By continuously analyzing fermentation kinetics, AI systems can determine the precise timing and quantity of nutrient additions needed to support yeast activity, ensuring a robust fermentation process. This level of control minimizes the risk of stuck fermentations and other common issues, thereby enhancing the quality and consistency of the final product [78].  
Furthermore, AI-driven quality assurance systems can use data from advanced sensors to develop more accurate predictive models. These models can forecast potential deviations from the desired fermentation trajectory, allowing for preemptive adjustments. For example, the study by [PERSON] et al. utilized PTR-ToF-MS for online monitoring of volatile organic compounds during alcoholic fermentation, providing insights into the impact of different yeast strains on wine quality. AI systems can leverage this detailed chemical analysis to predict and control the fermentation process more precisely, ensuring that each batch meets high-quality standards [89].
## 1 Introduction  
In the semi-arid and arid areas of North Africa, groundwater resources play an extremely important role in socio-economic development, restoring natural ecosystems, and mitigating climate change [1, 2]. Under these climatic conditions, the southern regions of the Mediterranean (from Morocco to Egypt) are distinguished by relatively limited groundwater resources and an uneven distribution in space and time. Despite the excessive use of these resources, the demand for water will continue to rise as a result of population growth, the expansion of irrigated areas, industry, and tourism. It is crucial to consider the limitations of the current synergies between protecting coastal areas, combating climate change, anthropization, and adapting to its effects. In addition, improving water governance is, without doubt, a key challenge for future regional progress. In fact, the chemical ion content resulting from the water-mineral interaction of the surrounding rock, or from anthropogenic activities, may directly impact water quality [3, 4, 5, 6, 7, 8]. The resultant physicochemical composition of the groundwater can have a significant impact on human health, quality of life, and cultural growth. Indeed, human activities related to industrialization, agricultural development, and urbanization have introduced high levels of chemical elements into water resources, causing a deterioration in water quality [9, 10, 11].  
Therefore, to ensure water safety in dry conditions, water contamination must be prevented and mitigated. In this regard, we are interested in the spatiotemporal evolution of the groundwater mineralization of the Mornag shallow aquifer. The study area of Mornag, which is located 20 km south of the city of Tunis with a total area of approximately 370 km\({}^{2}\), has a Mediterranean climate characterized by irregular rainfall and hot temperatures [2; 12]. The average monthly temperature is around 22 \({}^{\circ}\)C, with annual precipitation ranging from 292 to 800 mm/year. In recent years, extensive irrigation and industrial development have taken place in this semi-arid region, contributing not only to increased agricultural production and economic growth but also unfortunately to a decline in the water table and groundwater quality degradation by the return flow of irrigation water and the recharge caused by wastewater [2].  
Understanding the spatiotemporal variations in the mineralization and the major sources of pollution is an effective means of mitigating and controlling the continued deterioration in groundwater quality. In fact, such monitoring can help create databases whose analysis can be used as a decision-making tool for the sustainable management of water resources.  
In the present study, which uses a combination of physicochemical data and water quality indices, a special emphasis is placed on the monitoring and evaluation of spatial and temporal changes in groundwater quality tracked for three years (2013, 2016, and 2019) and on the identification of sources of salinization of groundwater.
## 5 Conclusions  
In this paper, an energy release rate-based fatigue crack propagation equation was derived and used to describe the FCP rate of bridge steel Q345 qD using the experimental data obtained from various stress ratio tests. Then, a user subroutine based on the XFEM that considers the influence of the stress ratio was defined and the effects of mesh size were analyzed. Finally, the effects of the stress ratio were discussed and the adaptability of the equation was verified by comparing the values obtained from the equation with the experimental results. The main conclusions of this research are the following:  
* A fatigue crack propagation equation based on the energy release rate considering the influence of the stress ratio was derived and named the da/dN-\(\Delta\)G-R equation.
* Three material parameters in the da/dN-\(\Delta\)G-R equation were determined with a 95% guarantee rate based on the standard compact tensile tests of steel Q345 qD under different stress ratios. These parameters are log(C) = \(-\)10.71, \(m\) = 2.780, and \(\gamma\) = 0.957, respectively.  
Figure 12: Comparison of experimental and simulation results under different stress ratios. (**a**) \(R\) = 0.1; (**b**) \(R\) = 0.3; (**c**) \(R\) = 0.5; (**d**) \(R\) = 0.7.  
* A fatigue crack propagation subroutine using the da/dN-\(\Delta\)G-R equation based on the XFEM was defined. In the numerical simulation, a CT specimen mesh size of 1 mm showed better accuracy and efficiency than other mesh sizes.
* The effects of the stress ratio on fatigue crack propagation were discussed. The results showed that under the same energy release rate range, the fatigue crack growth rate decreases as the stress ratio increases.
* Under the same loading amplitude and number of cycles, the fatigue life decreases as the stress ratio increases. The numerical results considering the influence of stress ratio using the da/dN-\(\Delta\)G-R equation based on the XFEM were close to the test results, while the results without considering the stress ratio based on the Paris equation were inaccurate.
# An Assessment of CyGNSS v3.0 Level 1 Observables over the Ocean
## 3 Results  
### Temporal Stability  
The temporal stability of NBRCS from the CyGNSS v3.0 Sandbox 210 dataset was assessed at average surface wind speed conditions (U10 ~7 m/s). Results presented in Figure 2 show remarkable temporal stability (within ~0.2 dB) across the entire time-series for all receivers, apart from an initial phase seen in FM05 only. An ~0.2 dB magnitude deviation would be expected to lead to minimal biases in wind speed at average conditions, although it may become more significant in high wind speeds [21]. Calibration between the individual receivers also appears to be excellent, with a variability between CyGNSS units within a range of less than 0.1 dB.  
Figure 1: Temporal availability of CyGNSS records by flight model (FM01-FM08).