esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This work showcases the performance of the W-DSPEB approach to satellite-derived bathymetry based on wave kinematics using deep convolutional neural networks and Sentinel-2 satellite imagery. In a direct comparison, W-DSPEB is shown to be competitive with a state-of-the-art physics-based SDB method, S2 Shores, achieving RMSE performance of 3-5 m over areas reaching 40 m depths.  
The wider applicability of wave kinematics-based approaches for SDB is demonstrated through a comparison with C-DSPEB, a color-based variant of DSPEB, showing a promising direction towards a more global application of wave kinematics-based SDB.  
The use of composite bathymetry profiles over estimates from single dates is discussed and in shown to improve the test RMSE performance of all methods included in this study by \(\simeq\)50%.  
Finally, considering the impressive capabilities deep learning has recently demonstrated in image processing and model inversion applications, we strongly believe that the H-DSPEB model architecture presented in Appendix E is a strong motivation for further exploration in deep learning-based methods for satellite-derived bathymetry.  
Conceptualization, M.A.N., G.T., R.A., E.W.J.B. and D.G.W.; data curation, M.A.N., Y.E.B. and G.T.; formal analysis, M.A.N., Y.E.B., G.T., R.A., E.W.J.B. and D.G.W.; funding acquisition, R.A., R.B. and D.G.W.; investigation, M.A.N., Y.E.B. and G.T.; methodology, M.A.N., R.A., E.W.J.B., R.B., J.-M.D. and D.G.W.; project administration, M.A.N., R.A. and D.G.W.; resources, R.A., R.B. and D.G.W.; software, M.A.N., Y.E.B. and G.T.; supervision, M.A.N., R.A., E.W.J.B., R.B., J.-M.D. and D.G.W.; validation, R.A., E.W.J.B., R.B., J.-M.D. and D.G.W.; visualization, M.A.N.; writing--original draft, M.A.N. and D.G.W.; writing--review and editing, M.A.N., R.A. and D.G.W. All authors have read and agreed to the published version of the manuscript.  
M.A.N. was partly funded by the STAE Foundation (Sciences et Technologies pour l'Aeronautique et l'Espace) and is currently supported by the CNRS (Centre National de la Recherche Scientifique). Y.E.B. was funded by the IRD institute (Institut de Recherche pour le Developpement) and is currently supported by the Computer Science Research Institute of Toulouse (IRIT). G.T. was partly funded by the STAE Foundation and is currently funded by the IRD institute. All experiments were conducted on the CNES-HPC cluster HAL, granted as part of [PERSON]'s thesis contract.
## 3 Results and Discussion  
### Membrane Characterization  
#### 3.1.1 Scanning Electron Microscopy (SEM)  
Figure 2 shows the morphology of the membranes observed by SEM. The top and bottom images of all membranes revealed a smooth and homogeneous surface without any apparent porosity. The cross section showed a homogeneous, dense structure without any porosity. This could be explained by the solvent evaporation techniques, which usually lead to a dense membrane structure. An unexpected result was the brightness of the cross-section of the membranes probably due to the presence of Aliquat-336 in its liquid form. This is in contrast with some results in the literature where some dark regions, referring to the polymer matrix, are distinguished in the membrane matrix [40].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Names** & **Low Level** & **High Level** \\ \hline
[Cr(V)] (ppm) & A & 10 & 50 \\ Temperature (\({}^{\circ}\)C) & B & 20 & 50 \\
[Aliquat-336] (\%) & C & 50 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Feed phase factors and fields of study.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **Names** & **Low Level** & **High Level** \\ \hline Temperature (\({}^{\circ}\)C) & A & 20 & 50 \\ Aliquat-336 (\%) & B & 50 & 100 \\
[NaCl] (M) & C & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Receiving phase factors and fields of study.  
Figure 2: Scanning electron microscopy (SEM) images of the prepared membranes: (**A**) top surface, (**B**) bottom surface, and (**C**) cross-section.
## 2 Monitoring Protocols and Methods
### Other Field Measurements  
The field measurements were completed by measuring local temperature and pressure in the atmosphere using a Kestrel handheld monitor, by measuring soil temperature at 15 cm-depth with a Pt100 thermocouple (Ecoscan, EUTECH) and by evaluating the soil-water content at the same depth. This water content is expressed as the ratio of the mass of wet soil to that of dry soil. Drying was done at 65 \({}^{\circ}\)C, below the normalized temperature (105 \({}^{\circ}\)C) required by the gravimetric method, but the water loss was regularly checked over a much longer period (minimum 3 days vs. 24 hours for the gravimetric method) and the soil was sieved so that confidence was good for this evaluation of the water content.  
Atmospheric parameters allow normalization of the measured gas flux. Comparison of the soil and atmospheric temperatures gives information on the thermal influence prevailing at the time of sampling. Coupled with knowledge on water content in the upper soil horizons, it provides insights into the influences of external parameters on gas emission from soil [49] and thus on gas flux from soil to atmosphere.  
Finally, a Barasol probe (Algade, France) was used to get temperature and pressure data on an hourly basis (precision: \(\pm\)0.1 \({}^{\circ}\)C and \(\pm\)1 mbar respectively). These data are used to provide local control on temperature and pressure changes and as a basis of comparison with meteorological data obtained from Pau-Uzein airport (about 10 km away). The probe was set at 60 cm-depth in the soil at monitoring point 24. The probe was installed after drilling a hole with a hand auger, and was laterally protected by a PVC pipe; it was installed so that its end was in contact with bare soil at the hole bottom. On surface, the PVC pipe was protected by a GoreTex\(\otimes\)membrane and then covered by a thin layer of soil.
## 3 Materials and Methods
### Multiphase Flow Modelling in Naturally Fractured Tight Reservoir  
The multiphase flow within the naturally fractured tight reservoir sample has been modelled using the Volume of Fluid (VOF) method. The VOF model is a free-surface modelling technique that can locate and monitor fluid-fluid interfaces, with the present one occurring between oil and water. The saturation (\(S\)) of each fluid phase is determined in the control volumes; the sum of \(S\) for all fluid phases present is equal to 1. When a control volume is filled with oil, the saturation of oil is 1 (i.e., \(S_{o}=1\)), while the saturation of water is 0 (i.e., \(S_{w}=0\)). Thus, if a control volume contains a mixture of both oil and water, \(S_{o}\) and \(S_{w}\) are between 0 and 1, and \(S_{m}\
eq 0\).  
Both the rock matrix and the fracture zone have been modelled as porous media. In the real world, a porous material consists of many tortuous routes (or pores) with varying flow velocity vectors. When a porous media is numerically modelled, as in this study, no such routes exist. This is introduced indirectly in the modelling by defining the porosity (\(\varphi\)) and permeability (\(k\)) of the material. The resulting mass conservation equation can thus be written as [41]:  
\[\frac{\partial}{\partial t}\big{(}\varphi\;S_{\beta}\;\rho_{\beta}\big{)}+ \
abla\cdot\left(\varphi\;S_{\beta}\;\rho_{\beta}\;\overset{\rightarrow}{U_{ \beta}}\right)+\frac{\mu}{k}\;\overset{\rightarrow}{U_{\beta}}=0 \tag{1}\]  
where \(\beta\) represents the phase. The third term in the equation is the source term representing viscous losses due to Darcy effects. The same viscous loss term is added to the momentum conservation equation as well.  
The momentum conservation equation is given as:  
\[\left(\frac{\varphi}{k_{f}}+(\alpha_{B}-\varphi)\frac{1-\varphi}{K_{d}} \right)\frac{\partial(S_{\beta}\rho_{\beta}\rho_{\beta})}{\partial t}-\
abla \cdot\left(-\frac{k_{x}K_{\beta}\;\rho_{\beta}}{\mu_{\beta}}(\
abla P_{\beta }-\rho_{\beta}\
abla D)\right)=\rho_{\beta}\alpha_{B}\left(\frac{\partial \epsilon_{\text{\tiny{grid}}}}{\partial t}\right) \tag{2}\]  
The mixture density (\(\rho_{m}\)) and the mixture dynamic viscosity (\(\mu_{m}\)) are given as [41]:  
\[\rho_{m} =S_{w}\;\rho_{w}+(1-S_{w})\;\rho_{o} \tag{3}\] \[\mu_{m} =S_{w}\;\mu_{w}+(1-S_{w})\;\mu_{o} \tag{4}\]
## 1 Introduction
We performed empirical investigations using synthetic data and common open datasets to assess the robustness of the proposed protocol against various types of poisoning attacks. The outcomes of these experiments provided a basis for comparative analysis, allowing us to evaluate the efficacy of our proposed scheme compared with conventional approaches such as PrivKV and PrivKVM. This examination aims to elucidate the resilience and performance of the protocol under varying conditions, thereby providing valuable insights into the discourse on privacy-preserving data collection methodologies. Our contributions are as follows. * Novel LDP Algorithm: We introduce a novel LDP algorithm that is designed to enhance robustness against specific types of poisoning attacks. This newly proposed algorithm leverages an iterative process involving Bayesian posterior probabilities, thereby improving the accuracy of the estimates while maintaining resilience against the impact of poisoning data. * Experimental Validation: The proposed protocol was empirically validated through a series of experiments employing both synthetic and publicly available open datasets. The experimental results demonstrate the robustness of the proposed algorithm. Comparative analyses reveal superior performance in terms of the estimation accuracy and resistance to poisoning attacks compared with the PrivKV protocol. These findings substantiate the efficacy and practical applicability of the proposed method in the context of privacy-preserving data collection frameworks. The preliminary version of this work was presented at Modeling Decisions for Artificial Intelligence (MDAI) 2023 [9]. This paper includes three additional points: related works and their comparison, experimental results and costs with OT protocol, and experimental results using synthetic data.
## 3 Wind Turbine Blade Fault Dataset and Fault Diagnosis Model Training
### Model Hyperparameter Setting  
Hyperparameter setting in deep learning is an important part of optimizing model performance. The performance of the AdaBoost weak classifier directly affects the performance of the final strong classifier. Too many BiLSTM neurons will cause the model to overfit, and too few will cause the model's performance to deteriorate. Keeping the input size unchanged, the loss function obtained by changing the number of neurons is shown in Figure 7. Observing Figure 7a, when the number of neurons is set to 128, although the training set has good convergence, the test set has an overfitting problem; increasing the number of neurons to 256 as shown in Figure 7b, the overfitting problem of the test set is more serious; reducing the number of neurons to 32 as shown in Figure 7c, it is found that the overfitting problem of the test set has improved a little, but the loss function value  
Figure 6: Training process.  
remains in a larger range, and the convergence speed is slow; further increasing the number of neurons to 64 as shown in Figure 7d, it is found that the overfitting problem of the test set has also been well handled, and the loss function value can be maintained in a smaller range. After adjustment, the BiLSTM network input size is set to 64, including 2 hidden layers, each with 64 neurons. The parameters of the model after adjustment are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline Learning rate & 0.00002 \\ Batch\_size & 8 \\ Number of training rounds & 500 \\ Number of convolution kernels & 1408 \\ Number of convolution kernels & \(32,3,5,7\) \\ Number of BiLSTM neurons & 64 \\ Number of BiLSTM layers & 2 \\ Activation function & ReLU \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hyperparameter settings.  
Figure 7: (**a**) Loss function for 128 neurons; (**b**) loss function for 256 neurons; (**c**) loss function for 32 neurons; (**d**) loss function for 64 neurons.
## 2 Models and Methods
### DP Algorithm with Emission Constraints
Thus, at the \(j\)-\(k\) grid point, an action can be selected from the 3316 action discrete points to allow Equation (11) to hold. After completing the selection of the optimal action at the \(j\)-\(k\) grid points, \(J_{\rm fuel}(j,k)\), \(J_{\rm CO}(j,k)\), \(J_{\rm PN}(j,k)\), and \(J_{\rm NOx}(j,k)\), as well as \(\rm n_{\rm e}\) and \(T_{\rm e}\) corresponding to the optimization-seeking action, are stored at the \(j\)-\(k\) grid point. Figure 7 shows the data flow of the action optimization-seeking at the \(j\)-\(k\) grid point. Subsequently, the action optimization at the next SOC grid point in the same time step continues until the optimization search process at all SOC grid points is completed. After that, the optimization process in the kth time step is repeated in the \((k-1)_{\text{th}}\) time step, thus completing the action selection at all the SOC grid points in the whole time control domain and ending the DP inverse solution. Next, in the DP forward solver solution, based on the initial battery SOC at the 1 st time step, which is given as the initial condition of the problem, \(n_{\text{e}}\) and \(T_{\text{e}}\) at this moment are obtained by interpolating the action information stored on the state-time grid points to calculate the engine fuel consumption and output power. Then, SOC at the 2 nd time step is obtained by the state transfer equation (Equation (12)), and the action variables at the 2 nd time step are solved by interpolating the stored information at the state-time grid points, the change rate of the engine speed is calculated, and the interpolated-startup-corrected emission models obtain the CO, PN, and NO\({}_{\text{x}}\) emissions. The above process continues in a loop until the computation of states and actions on all time steps in the control time domain is completed in the time-forward direction. In this process, the EREV energy management strategies constrained by real driving emissions and aimed at minimizing fuel consumption in the control time domain are investigated.
## Materials and Methods
### Methodology
#### 2.3.3 Hydrological Simulation Scenarios
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & **Period** & **Time** & **Precipitation** & **Parameters** \\ \hline \multirow{6}{*}{I} & Calibration & 2006–2010 & Gauge-observed & Para1 \\ \cline{2-5}  & & & Gauge-observed & \\  & & & 7 Satellite & \\  & & & 2 Reanalysis & \\  & & & 2 Merged & \\ \hline \multirow{6}{*}{Calibration} & & & TMPA-3B42V7 & Para2 \\  & & & TMPA-3B42 RT & Para3 \\  & & & IMERG-cal & Para4 \\  & & & IMERG-uncal & Para5 \\  & & & GSMaP & Para6 \\  & & & ERA5 & Para7 \\  & & & ERA-Interim & Para8 \\  & & & Average Rain 1 & Para9 \\ \cline{2-5}  & & & & Average Rain 2 & Para10 \\ \hline \multirow{6}{*}{Validation} & & & TMPA-3B42V7 & Para2 \\  & & & TMPA-3B42 RT & Para3 \\  & & & IMERG-cal & Para4 \\  & & & IMERG-uncal & Para5 \\  & & & GSMaP & Para6 \\  & & & ERA5 & Para7 \\  & & & ERA-Interim & Para8 \\  & & & Average Rain 1 & Para9 \\  & & & Average Rain 2 & Para10 \\ \hline \hline \end{tabular}  
* Notes: Para1 denotes the parameter sets calibrated using data from CGDPA from 2006 to 2010. Para2 to Para9 are calibrated from 2011 to 2018 using data from TMPA-3B42V7, TMPA-3B42 RT, IMERG-cal, IMERG-uncal, GSMaP, ERA5, ERA-Interim, and two merged precipitation products, respectively.  
\end{table} 
Table 3: Precipitation data and simulation parameters for each period for Scenarios I–II *. Figure 2: Flowchart of Scenarios I–III used in this study. See Table 3 for definitions of other scenarios. IMERG-cal, IMERG-uncal, ERA5, ERA-Interim, GSMaP, and two merged precipitation products, respectively. Then, the different parameter sets of each product were used to simulate the streamflow obtained with their corresponding precipitation forcing during the validation period of 2011-2018. * Scenario III (manually controlled ensemble simulation)  
To further improve the streamflow simulations after Scenario II, we manually compared two merging schemes--precipitation input merging and streamflow output merging--using the arithmetic mean method. In this scenario, the new streamflow series were respectively derived from two arithmetic means of three selected streamflow simulations driven by each individual precipitation dataset (TMPA-3B42 RT, IMERG-uncal, and ERA5) as well as another three (TMPA-3B42V7, IMERG-cal, and GSMaP).
## Reliability of Wireless Sensor Networks  
**[PERSON], [PERSON] * and [PERSON]**  
Centro de Informatica, Universidade Federal de Pernambuco, Recife, PE, 50740-560, Brazil;  
E-Mails: [EMAIL_ADDRESS] (A.D.); [EMAIL_ADDRESS] (P.M.)  
* Author to whom correspondence should be addressed: E-Mail: [EMAIL_ADDRESS];  
Tel.: +55-81-2126-8430; Fax: +55-81-2126-8438.  
_Received: 2 May 2014; in revised form: 2 August 2014 / Accepted: 11 August 2014 / Published: 25 August 2014_  
**Abstract:** Wireless Sensor Networks (WSNs) consist of hundreds or thousands of sensor nodes with limited processing, storage, and battery capabilities. There are several strategies to reduce the power consumption of WSN nodes (by increasing the network lifetime) and increase the reliability of the network (by improving the WSN Quality of Service). However, there is an inherent conflict between power consumption and reliability: an increase in reliability usually leads to an increase in power consumption. For example, routing algorithms can send the same packet though different paths (multipath strategy), which it is important for reliability, but they significantly increase the WSN power consumption. In this context, this paper proposes a model for evaluating the reliability of WSNs considering the battery level as a key factor. Moreover, this model is based on routing algorithms used by WSNs. In order to evaluate the proposed models, three scenarios were considered to show the impact of the power consumption on the reliability of WSNs.  
**Keywords:** wireless sensor network; reliability; power consumption; reliability block diagram
# 4D Printing of Multicomponent Shape-Memory Polymer Formulations
## 4 Vat-Photopolymerization Based 4D Printing
### Stereolithography (SLA) of trSMP  
In stereolithography (SLA), a spot laser induces localized photopolymerization or crosslinking, enabling layer-by-layer fabrication of the final product [69]. The layer height of standard SLA printers ranges from 12 to 150 um, with the printing speed ranging from 10 to 20 mm/h. Like DLP, the platform in SLA printers is immersed in the resin and moves parallel to the \(z\)-axis, while the laser beam traces the part cross-section and cures the area according to the CAD model. SLA is used to print high-resolution 3D structures with excellent surface finish [72; 130]. There are only a few reports concerning the SLA printing of trSMP networks using photosensitive resins. For example, a _tert_-butyl acrylate-co-diethylene glycol) diacrylate (tBA-co-DEGDA) network with shape memory properties was successfully printed by SLA [113]. The wt.% of DEGDA crosslinker and photo-initiators in the photo-sensitive resin was varied to tailor the \(T_{g}\) of the resulting networks. The printed structure showed 100% recovery and stability of its shape-memory properties even after 22 thermo-mechanical cycles. [PERSON] et al. reported the stereolithographic printing of acetylated epoxidized soybean oil to produce smart biomedical scaffolds [131]. Shape memory tests confirmed that the scaffolds could fix a temporary shape at \(-18\,^{\circ}\)C and fully recover their original shapes at human body temperature (37 \({}^{\circ}\)C). This indicated the potential of these biobased printed structures for 4D printing. In another study, a photopolymer composed of polyurethane acrylate, epoxy acrylate, isobornyl acrylate, and radical photointiator was synthesized and used to print 3D objects via the SLA technique. The printed objects showed high mechanical strength and toughness along with excellent shape memory performance. Nevertheless, none of these reports deals with multicomponent aspects of SMP, which is the major focus of this review.
# Technologies Trend towards 5G Network for Smart Health-Care Using IoT: A Review
## 1 Introduction
Second is the energy consumption due to the nature of IoT-based applications that are dependent on wireless sensor networks (e.g., the minimum required battery life is 10 years for specific situations [27]). The studies on the deployment and commercialization of 5G network started in 2014 and are expected to completed by 2020 [28]. Besides network densification and support for a massive number of IoT devices, 5G networks are expected to provide a higher data rate. 5G networks are being designed to be flexible and versatile to support new applications, which not only require high data rate, but also other requirements, including massive connectivity, dense deployment, reliability, low-latency, high energy efficiency, and long-range communication to support IoT-based smart health-care applications.
# Inactivation of Contaminated Fungi in Rice Grains by Dielectric Heating
## 2 Materials and Methods
### Experimental Desing for Dielectric Heating System  
#### 2.3.1 Dielectric Heating System Setting  
The dielectric heating system (9 kW, 40.68 MHz) was used in this study. This system mainly consisted of two parallel electrode plates and a frequency oscillator as shown in Figure 3a. The upper and bottom electrode plate size is 52 cm \(\times\) 52 cm. The position of the upper electrode can be adjusted to achieve a different electric field intensity while the bottom electrode plate is fixed for placing the samples, the central position of the bottom electrode provides a better heating rate with a more uniform temperature distribution [42]. The sterile Petri dish containing the rice sample was placed at the center of the bottom electrode, as shown in Figure 3b.  
Figure 2: Measurement equipment (1. Laboratory jack, 2. Sample, 3. Open-ended coaxial probe, 4. Coaxial cable, 5.VNA, 6. Computer with software and 7. Measurement setup).  
For dielectric heating, the power conversion in a material depends on the operating frequency, dielectric loss factor and the electric field density inside the material. The power dissipated per unit volume in the dielectric material can be expressed [43] as Equation (1):  
\[P=fE^{2}\varepsilon^{\prime\prime}55.63\times 10^{-12} \tag{1}\]  
where \(P\) is the power dissipated per unit volume (W/m\({}^{3}\)), \(f\) is the frequency (Hz), \(E\) is the rms electric field intensity (V/m) and \(\varepsilon^{\prime\prime}\) is dielectric loss factor of the material being exposed to the alternating electric fields.
## 3 Non-catalytic Biodiesel Synthesis by Supercritical Fluid Technology
### Single Step SCFs Processes for Non-catalytic Biodiesel Synthesis
#### 3.2.1 Supercritical Alcohol
An additional advantage of the use of supercritical alcohols in biodiesel synthesis is the high tolerance towards possible feedstock contaminants, mainly FFAs and water. In supercritical alcohols, esterification of the FFAs to the corresponding fatty acid alkyl esters takes place simultaneously to oil transesterification, yielding higher amounts of biodiesel. Thus, low cost feedstock, usually containing high portions of FFA/oil, can be used in the process without any additional pretreatment. As regards the water content, transesterification reactions catalyzed under acid or alkaline conditions are highly sensitive to the presence of water as hydrolysis of the corresponding esters are favoured in the presence of water. In contrast, the non-catalytic supercritical process can be performed even with a high water contents [62].