esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Analysis of Hybrid Optical-Acoustic AUV Swarms for Marine Monitoring
###### Abstract  
Autonomous Underwater Vehicles (AUVs) are assuming an important role in the monitoring and mapping of marine ecosystems, especially for their ability to explore harsh environments. AUV swarm can collect data operating autonomously for long periods enabling new applications in this field. However, the mission duration is usually limited also by the high power consumption required for acoustic transmissions. A new generation of devices complements the acoustic modem with an optical modem that can provide a communication channel with higher capacity and lower power consumption with respect to the acoustic channel. However, the optical link that uses the visible light is very sensitive to the water turbidity that can strongly limit the link coverage. In this paper, we evaluate the networking performances of the Venus vessel, a real AUV prototype equipped with an acoustical modem and an optical modem. The presented analysis aims to evaluate key system parameters allowing to select the best way to set up network communications according to the surrounding conditions (e.g., quality of water) and to the application requirements. Simulation results account for the case of ports or basins, where the water quality is poor and the use of the optical modem is strongly limited by distance. We evaluate system performance in terms of transmission delay in the network and we also provide a power-capacity trade-off.  
underwater; hybrid optical-acoustic communication; AUV +
Footnote †: journal: Future Internet
## 2 Numerical Model and Methods
### Boundary Conditions  
The reactant is the stoichiometric mixture of CH\({}_{4}\) and air. The desired mass flow rate of CH\({}_{4}\) (\(\dot{m}_{\text{CH4}}\)) and air (\(\dot{m}_{\text{air}}\)) is satisfied by mass flow inlet boundary condition applied to CH\({}_{4}\) and air inlets. Pressure on the outlet (\(p_{\text{b}}\)) is set by pressure outlet boundary condition. The inner wall, outer wall and inlet wall are all set as no slip adiabatic wall boundary. Specific parameters of boundaries are listed in Table 2, \(\dot{m}_{\text{total}}\) denotes the total mass flow rate in RDC, \(T_{\text{total}}\) denotes total temperature.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & \(\dot{m}_{\text{CH4}}\)/**(kg/s)** & \(\dot{m}_{\text{air}}\)/**(kg/s)** & \(\dot{m}_{\text{total}}\)/**(kg/s)** & \(\dot{T}_{\text{total}}\)/**K** & \(p_{\text{b}}\)/**MPa** \\ \hline
1 & 0.7 & 12.01 & 12.71 & 300 & 0.1 \\
2 & 0.5 & 8.58 & 9.08 & 300 & 0.1 \\
3 & 0.1 & 1.72 & 1.82 & 300 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Boundary condition parameters on various cases.  
Figure 3: Comparison of simulated and literature values.
## 3 Preliminary Investigations in a Virtual Environment
### Experimental Results and Conclusions  
Table 1 shows how the recognition performance was affected by different simulated conditions. In the case of a single training image per painting, the recognition performance was considerably affected by reduced lighting, but other factors such as distance, occlusions, viewing angle and wall texture also affected the recognition performance. In the case of using multiple images per painting for training (Experiment 2), the recognition results  
Figure 4: Typical examples of simulated training images for a particular painting.  
improved considerably. The only cases where a noticeable performance deterioration was observed were the cases of a highly reduced lighting (this is an extreme case that is not likely to be encountered in a real gallery) and the addition of multiple occlusions. As shown in Table 1, when using multiple images per painting in the training set, a significant performance improvement was also recorded in the case where the camera was moved randomly in the virtual space, capturing images displaying combined modes of variation in terms of distance, viewing angle and occlusions.  
The results provided a useful insight into issues related to the efficiency of artwork recognition. The most important conclusion was the need for using multiple images per painting during the training stage, rather than relying on a single image per painting. Furthermore, museum curators that may adopt such technologies should ensure that a plain wall texture is used, a strong ambient lighting exists in all areas, and limit the number of visitors per room to avoid excessive occlusions.  
The findings of this preliminary investigation were used to guide the further development of the proposed artwork recognition app, as described in subsequent sections.
## 2 Materials and Methods
### Experimental Design
Participants were instructed to jump with their hands on their hips without bending their legs, trying to minimize the ground contact time and to maximize the flight time. A metronome set at 100 rpm was connected to a loudspeaker to determine the jumping rhythm (JBL(r) Flip 4, Harman International Industries, Stamford, CT, USA). Jumps were recorded at 240 fps using an action camera in linear mode (GoPro(r) Hero 9 Black, GoPro Inc., San Mateo, CA, USA). Although the runners were already familiar with the test, 10 jumps were recorded, with the 5 intermediate jumps analyzed using the Kinovea software (v. 0.9.5). The vertical stiffness of runners before and after each running time trial was calculated from contact and flight times normalized by body mass at that specific moment [35]. **Blood lactate. ** Blood lactate concentrations were measured before (pre-) and after the exercise (post-) from the fingertips of the non-dominant hand (Lactate Scout v. 4.0, EKF Diagnostics, Barleben, Sajonia-Anhalt, Germany). After disinfecting and drying the fingertip, a puncture was made with a lancet ([PERSON], Arkray Inc., Kyoto, Japan). A small amount of blood was collected (0.2 \(\mu\)L), and the lactate analyzer measured the blood lactate concentration [39; 40]. These values provide a better characterization of the exercise intensity of the warm-up protocols and the 2 running time trials. **Comfort measurements. ** The participants were asked to complete a comfort test for both pair of shoes [41]. Runners rated each comfort feature by marking a vertical line on a visual analogue scale of 100 mm, where the left end represented the worst possible score (e.g., 'not comfortable at all') and the right end represented the best possible score (e.g.,'most comfortable shoe imaginable'). The evaluated comfort characteristics were as follows: 1. Overall comfort (i.e., overall feeling of the shoe); 2. Heel cushioning (i.e., firmness of the insole in the heel area); 3. Forefoot cushioning (i.e., firmness of the insole in the forefoot area); 4. Shoe stability (i.e., control of foot position provided by the shoe); 5. Forefoot flexibility (i.e., the capacity of the shoe to flex in the forefoot area); 6. Shoe grip (i.e., grip of the shoe on the ground) [41; 42; 43]. As previously described, the runners were familiarized with this scale on the 2 nd day. **Rating of Perceived Exertion. ** RPE was measured using [PERSON]'s CR-10 scale as described in previous studies [33; 34]. Runners verbally indicated their perceived effort, which was recorded in a form where 0 represented total rest and 10 represented maximum effort. Runners were already familiarized with this scale on the 1 st and 2 nd days and during the training sessions performed during this period.
# Prediction of Self-Healing of Engineered Cementitious Composite Using Machine Learning Approaches
## 5 Results and Discussion
### Prediction Performance Comparison
#### 5.2.1 Comparison of the MAE
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline \hline \multirow{2}{*}{**Benchmark**} & \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**\(\mathbf{R^{2}}\)**} & \multirow{2}{*}{**Benchmark**} & \multirow{2}{*}{**Model**} & **MAE** & **RMSE** & \multirow{2}{*}{**\(\mathbf{R^{2}}\)**} \\ \cline{5-8} \cline{8-10}  & & & \multicolumn{2}{c}{**Dev (\%)**} & & & & & \multicolumn{2}{c}{**_Dev (\%)_} \\ \hline LR & Ada\_LR & \(-\)4.6 & \(-\)3.6 & 0.8 & LR & Bag\_LR & 0.0 & 0.1 & 0.0 \\ BPNN & Ada\_BPNN & \(-\)2.4 & \(-\)1.2 & 0.1 & BPNN & Bag\_BPNN & \(-\)4.3 & \(-\)2.7 & 0.2 \\ CART & Ada\_CART & \(-\)2.3 & \(-\)5.2 & 1.2 & CART & Bag\_CART & \(-\)4.9 & \(-\)6.6 & 1.6 \\ SVR & Ada\_SVR & \(-\)3.5 & \(-\)3.6 & 1.1 & SVR & Bag\_SVR & 0.1 & \(-\)0.1 & 0.0 \\ \hline Ada\_LR & Stack\_LR & \(-\)17.8 & \(-\)17.3 & 4.3 & Bag\_LR & Stack\_LR & \(-\)21.5 & \(-\)20.4 & 5.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance deviation of ensemble models from benchmark models on the self-healing of ECC. Figure 11: Comparison of the observed crack widths of ECC after self-healing with the crack widths predicted by the individual models. Figure 12: Cont. It is known that a smaller crack width is favorable for autogenous healing in concrete [76,77] as small cracks consume less repair products to complete self-healing [78]. However, a larger crack width will not heal completely or just heal partially. As shown in Figures 11b, 12b, 13b and 14b, the variations between the observed and predicted results for the LR, bag_LR, and Ada_LR models increased with the increase of the crack width. For a crack width below 20 \(\upmu\)m, the MAE values were less than 1.5, which was much lower than those for crack widths between 20 \(\upmu\)m and 60 \(\upmu\)m (i.e., 6.23) and between 60 \(\upmu\)m and 100 \(\upmu\)m (around 10). Similar trends were observed in other models, but with smaller variations. Specifically, for a crack width over 100 \(\upmu\)m, the LR, bag_LR, and Ada_LR models showed much higher variations. Their MAE values were over 20 and higher than other ML models with the MAE less than 10.
###### Abstract
According to the International Agency for Research on Cancer (IARC), safrole is classified in group 2B--possibly carcinogenic to humans [38]. Furthermore, the acute toxicity of safrole to rodents was already established. The lethal doses to kill 50% of populations of rats and mice (LD\({}_{50}\)) were 1950 and 2350 mg kg\({}^{-1}\) of body weight, respectively [39]. The Codex Alimentarius Commission and the Brazilian Health Regulatory Agency (ANVISA) limit the maximum concentration of safrole in foods and beverages to 1 mg kg\({}^{-1}\). The exceptions include alcoholic beverages with up to 25% and those with more than 25% alcohol (by volume), whose thresholds are 2 mg kg\({}^{-1}\) and 5 mg kg\({}^{-1}\), respectively. The limit is also raised to 15 mg kg\({}^{-1}\) in foods containing mice and nutmeg [40; 41]. The criteria for using _P. higpidinervum_ essential oil (PHEO) in stored-grain pest control must consider its post-treatment persistence in the grain mass. Analyses of insecticide persistence are critical to ensuring food security and determining the grace period [14]. Nonetheless, it is remarkable that only a few studies have approached this topic for botanical insecticides applied to stored grains. Therefore, it is necessary to develop analytical methods that quantify lingering volatile compounds of EOs in agricultural products to ensure a safe application. The solid-phase microextraction technique (SPME) is widely employed in residue analysis, mostly because it performs both extraction and pre-concentration of analytes simultaneously [14; 42; 43]. The SPME fiber can be used either in the headspace (HS-SPME) or directly immersed in the sample (DI-SPME). However, HS-SPME is more suitable for volatile analytes or highly-complex samples [44]. Some parameters can affect the extraction efficiency, such as the type of fiber coating, the addition of salt to the solution, agitation speed, temperature, time to reach equilibrium in the headspace, fiber exposure time (adsorption of volatile compounds by the fiber), and desorption time (in the chromatograph injector) [45]. Even though several works have investigated the toxicity of EOs to different pests of stored products, it is of great importance to carry out more detailed research on the persistence of these compounds in foodstuff in order to guarantee its safe use. Thus, this study aimed to optimize and validate a method using HS-SPME and gas chromatography with a flame ionization detector (GC/FID) for extracting and analyzing safrole residue in cowpea beans. It also sought to evaluate the persistence of safrole residue in cowpea beans treated with PHEO, either by contact or fumigation, throughout storage.
## 1 Introduction
The structural damping and tension variations were also significant to the VIV of composite risers [13], i.e., higher tension and damping could reduce VIV-induced fatigue [13; 17]. [PERSON] et al. [18] found that the maximum VIV stresses of a composite riser were much smaller than those of a steel riser, indicating the better fatigue lives of composite risers. Similarly, [PERSON] et al. [25] and [PERSON] et al. [24] conducted comprehensive studies on both local and global scales on composite and steel risers using a finite element method and a larger safety margin for composite risers were observed. Although a composite riser without VIV suppression had a moderate VIV-induced fatigue damage, adding strikes and buoyancy modules to a composite riser could lead to even better VIV responses [13; 24]. In addition, the design of a composite riser (thick composite tube) can be optimized to specific requirements by modifying some of its design variables, such as fiber and matrix combinations, fiber orientations, thicknesses of the liner and composite lamina, and stacking sequence, to enhance the benefits offered by FRP composites risers using different methods, e.g., finite element analysis [22; 26; 27; 28; 29], simple-input displacement-based method [30; 31] and optimization algorithms [21; 32; 33; 34]. Besides all these research studies, joint industry projects emerged several decades ago, and Table 1 summarizes some of them. From all these research studies and joint industry projects, the feasibility, advantages and effectiveness of using FRP composites for risers have been proven, although no offshore riser currently being employed is made entirely of composites, except the demonstration composite drilling riser joint (a tube segment) installed in the field on the Heidrun TLP [38]. In this paper, three different risers with large length to diameter ratio, the X80 steel riser (riser 1), AS4-epoxy composites with titanium liner with geometries of (liner/90/(0/90)\({}_{10}\)) (riser 2) and (liner/0\({}_{3}\)/(+53, \(-53\))\({}_{5}\)/90\({}_{4}\)) (riser 3) are selected to study their VIV characteristics. The CFD simulation with coupled fluid-structure interaction was achieved using ANSYS Workbench 17.1. Based on the combination of different risers, tension forces, buoyancies, gravities and current conditions, nine study cases were considered. The VIV responses under different situations of all these three risers were obtained, including the natural frequencies, global displacements, global stresses and the stress distributions in every layers of both FRP composite risers.
## 3 Results and Discussion
### Conformation Evolution of Molecular Chains  
Figure 4 shows the conformation evolution process of PE molecular chains. As can be seen from Figure 4b, a certain degree of relaxation occurs in the PE molecular chains of the sheared composite system during the initial period after the cessation of shear. However, due to the adsorption effect of CNTs [28], relaxation of the molecular chains is inhibited, which stabilizes the conformation of the molecular chains. The principal axis of the molecular chains is still basically parallel to the \(Z\)-axis. Over time, and with decreasing temperature, the CNT induces PE molecular chains to adjust conformation along its axial direction, to increase the orderliness of their arrangement. As shown in Figure 4c, the sheared pure PE system, with the same extension degree as the PE chains in the sheared composite system, undergoes a significant conformation change after the cessation of shear. Without the restriction of the CNT, PE molecular chains relax considerably at a high temperature and rapidly evolve from orientation state to winding state. For the static composite system, as shown in Figure 4a, PE molecular chains are mostly distributed around the CNT in a disordered state at the beginning, and the number of molecular chains is large. Hence, the molecular chain segments are hindered by the surrounding molecular chain segments in the process of the CNT-inducing conformation ordering. Furthermore, for locations where the arrangement of molecular chains around the CNT is slightly sparse, the space for conformation adjustment is a little larger, and the molecular chain has different degrees of folding arrangement. However, for locations where the molecular chains are more densely arranged, the space for conformation adjustment is smaller, and the regularity is even worse.  
Figure 4: The conformation evolution process of polyethylene (PE) molecular chains. (**a**) The static composite system; (**b**) The sheared composite system; (**c**) The sheared pure PE system.
## 4 Conclusions  
This study quantified the influence of turbulence closure models in simulating curvature-driven flow separation and resulting non-Fickian mixing behaviors in meandering open channels across a wide range of sinuosity. To achieve this task, the 3D RANS equations integrated with \(k-\varepsilon\), \(k-\omega\) and SST \(k-\omega\) turbulence models were used for flow simulations, and the 3D LPT model was adopted for solute transport simulations. The major findings from this work can be summarized as:  
* The strong transverse gradients of mean velocity simulated with increasing sinuosity induce the flow separation along the outer bank for channel sinuosity 1.57 and 1.90 because of the adverse pressure gradients. The size of the flow separation increases as sinuosity increases.
* The onset and size of the flow separation are significantly affected by the turbulence models. Notably, the \(k-\varepsilon\) model fails to predict the emergence of the flow separation or unpredicts its reattachment length and width by underestimating the velocity gradients.
* The flow separation with vigorous recirculating flows acts as a storage (trapping) zone of solute particles, and the trapping effects increase solute residence times. Here, the \(k-\varepsilon\) model underestimates the power-law tailing of BTCs since it undervalues the effects of the separated flow recirculation compared to the other turbulence models.
* The SST \(k-\omega\) model yields heavier-tailed BTCs characterized by flatter power-law slopes and larger truncation times as it reproduces larger and faster recirculating flows than the \(k-\varepsilon\) and \(k-\omega\) models.  
Consequently, the present work elucidated that the turbulence models substantially impact the 3D solute transport simulations since these turbulence closure schemes determine the onset and extent of the separated flow recirculation identified as the surface storage zones. Even if the 3D model is effective in explicitly understanding the interaction between solute dispersion and hydrodynamic effects, this approach may not be appropriate for water quality management in rivers and streams because of the high computational demand. Therefore, the next stage of this study will parameterize the transient storage model, which is the 1D upscaled model extensively used for simulating the non-Fickian transport in rivers and streams [63], using the simulation results of this work and predict the heavy-tailed BTCs induced by the meander-driven flow separation.  
Conceptualization and visualization, J.S.K.; Simulations, J.S.K. and D.B.; writing--original draft preparation, J.S.K. and I.P.; writing--review and editing, J.S.K. and I.P.; funding acquisition, I.P. All authors have read and agreed to the published version of the manuscript.  
This study was supported by the Research Program funded by the SeoulTech (Seoul National University of Science and Technology).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### THPF and Effectiveness Analysis: Best Performance Case  
To evaluate and compare the performances of the different cases, Table 2 shows the values of the main variables that indicate the performance of each case when the inlet air velocity is 1 m/s. Case 3.1 achieves the highest heat transfer and is therefore the one with the highest thermal effectiveness. Moreover, it has the disadvantage of having a relatively high head loss compared with the other cases, although not the highest. When performing the evaluation in terms of THPF, it is again observed that Case 3.1 performs best; i.e., in spite of the relatively high pressure drop, thanks to its higher heat transfer, globally, it is the case that presents the best ratio between the evacuated heat and the energy consumed by the fans.  
Figure 9 shows the effectiveness and THPF of each case. The overall trend observed in the cases studied is that the higher the thermal effectiveness, the higher the THPF. However, they do not adhere to a perfectly defined relationship, as can be seen by comparing Cases 1.3, 1.4, 3.1, and 3.2, since they show a very similar thermal effectiveness but a THPF that clearly establishes that Case 3.1 presents the best performance.  
This study has shown that Case 3.1 has the best performance from both the thermal and the thermo-hydraulic performance points of view. However, in situations where this does not occur, either the case with the best thermo-hydraulic performance is chosen, or a compromise solution is chosen.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Q** & **\(\Delta\)P** \\  & **(W)** & **(Pa)** \\ \hline
0 & 1243.90 & 9.43 \\
1.1 & 1324.38