esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 5 ESDR: Event Segregation Based Delay Sensitive Routing
### Mobility of Mobile Sinks  
Courier nodes and AUV are employed for data gathering applications hence they are very crucial for underwater networks. Courier nodes can move vertically up and down and there is no facility of horizontal movement while AUV can be moved to any desired place with proper tour planning. However, there is no energy constraint on both of these devices. The mobility of courier nodes has not been exploited to its full potential in previous work. In most of the previous schemes, mobility of these nodes starts after certain percentage of death. Attaining the global knowledge of deaths and updating that to CNs itself has communication overhead. Additionally employing a resource in deep water and waiting for some certain level of calamity is not a wise option. Mobility of these nodes is needed to be exploited as per network requirements. So it is necessary to plan a better mobility pattern of CN to get full use of it. We have reconsidered the deployment and mobility of CNs. Moreover event segregation dictates and selects limited number of nodes to forward to CNs. Hence mobility of courier node is influenced by density of normal nodes and their rate of packet generation. In our proposed environment horizontal movement of CN is not required while vertical movement of each courier node is explained in two different ways. It is worthy to note that the mechanical aspect of the motion of mobile sink is beyond the scope of this work. Hence it is ignored that how and at what cost; these mobile devices move and what is the impact of underwater environment on this motion.
## 4 Classification of Routing Protocols in Wireless Sensor Networks
### Hierarchy Role of Nodes in the Network  
In the flat schemes, all sensor nodes participate with the same role in the routing procedures. On the other hand, the hierarchical routing protocols classify sensor nodes according to their functionalities [8]. The network is then divided into groups or clusters. A leader or a cluster head is selected in the group to coordinate the activities within the cluster and to communicate with nodes outside the own cluster. The differentiation of nodes can be static or dynamic.
# Pneumatically Actuated Microfluidic Platform for Reconstituting 3D Vascular Tissue Compression
## 2 Results and Discussion
### Finite Element Analysis Model of a Compressed Blood Vessel  
The deformation of the PDMS membrane and blood vessel were simulated using an FEA model to validate the experimental measurements and to analyze and predict the deformation of the PDMS membrane and blood vessel (Figure S1). FEA was used to model the deflection of the PDMS membrane via increasing the pressure of the pump channel (Figure 3A,B, Movie S1). As the pressure increased, contact occurred between the bottom surface of the PDMS membrane and the upper surface of the blood vessel (Figure 3C, Movie S2). Additional pressure resulted in an increased compressive force on the glass from the deflected membrane, forming a hard contact. These contacts were termed \"surface to surface\" contact conditions in ABAQUS simulations.  
As a proof of concept, we analyzed the stress and strain distribution along a blood vessel channel cross-section using FEA. The PDMS membrane deformation from the air pressure induced a corresponding blood vessel deformation due to the displacement. Stresses exerted upon the blood vessels within this model are expected to correspond to the biomechanical stresses associated with the blood vessel compression and failure, and as such, the stress distribution of stress within a given blood vessel is crucial to the understanding of stress-induced vessel failure.  
For the purpose of simulation simplicity, the pre-actuation gap between the PDMS membrane and the topmost layers of the vascular tissues was assumed to be 10 \(\upmu\)m. In this model, contact between the PDMS and upper surface of blood vessel occurred from 0.1 psi. As the pressure increased, the lower portions of the blood vessels made hard contact with the glass substrate. At 1.6 psi, the upper and lower blood vessels were compressed together in a self-contact region, after which, the self-contact region expanded in area with additional pressure (Figure 3D).  
The rate of stress increase from 0 psi to 1.0 psi was relatively small, but increased significantly after 1.5 psi. At the early stage of deformation, the topmost layers of the vascular tissue merely contacted the PDMS membrane. Additional pressure caused the PDMS to further deform and compress the tissues against the rigid glass substrate. The maximum observed stress level of 5.54 MPa occurred at 1.6 psi, after which, the self-contact area sustained the maximum stress level until 2.5 psi (Figure 3C,D).  
Figure 2: The pixel intensity of the transverse direction in the central channel and PDMS membrane finite element analysis. (**A**) The change of gray value by the membrane deflection at six different pressures. The central channel was filled with FITC-dextran solution. Scale bar: 100 \(\upmu\)m. (**B**) Gray value plot of the central channel.
## 4 Discussion
A Brazilian study on the use of lateritic materials as a structural layer presents tests on clay soils (plasticity index from 22 to 46) and concludes that it was possible to consider them as structural materials for the rehabilitation of road infrastructure when the modified Proctor compaction energy is used ([PERSON] et al., 2018 [10]). Similarly, in Senegal, for the construction of \"in situ soil or reworked soil\" platforms supporting pavement layers, the use of the modified Proctor energy is recommended [50]. The multifactorial analysis (chemical, microscopic, and mineralogical) allowed us to make certain assumptions. The X-ray fluorescence results and the microanalysis of theEDX spectrum gave different percentages of the major elements. This difference is due to the precision of the analysis. Indeed, during the acquisition of a spectrum, the EDX may give differences percentages at different spots for an inhomogeneous surface. To complete these semi-quantitative evaluations of chemical elements, an infrared spectrum of each soil was carried out. This allowed us to appreciate the clay mineral group of each soil. The infrared analysis shows the presence of kaolinite, goethite, and hematite in these three soils (Figure 13A-C) but only gibbsite in Cabale and Morne Coco (Figure 13B,C). This presence indicates a more important alteration of the Cabale and Morne Coco soils corroborating the percentages obtained by the X-ray microanalyses (EDX and X-ray fluorescence)  
Some geotechnical properties are therefore partially explained by the amount of iron oxide such as goethite. Indeed, Morne Coco and Cabale show the highest values in unconfined and modified compression, which could be due to both the base saturation and clay content. Another hypothesis, supported by [PERSON] [51], would be that the aluminium and iron oxides would reinforce the soil structure, but their role is not clearly understood. [PERSON] et al. [52] demonstrated that the addition of iron oxide nanoparticles in a clay soil increases the strength parameters. As demonstrated by [PERSON] et al. [53], this double analysis highlighted some relationships between the micro and macroscopic analyses. However, it is only possible to have a trend by studying and comparing several grain sizes.
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 3 Data Sources
### Three-Dimensional Data  
The outdoor 3D data were acquired through tilt photography during regular UAV aerial surveys. In this study, a DJI M300 RTK UAV equipped with a Zenith P1 sensor was used to acquire the tilt photography data of the study area following a well-shaped flight route. The acquired data, including multi-view image data, feature spatial data, and texture details, were then used to create multi-temporal 3D models using ConTextCapture.  
The indoor 3D model was constructed using point cloud data, such as those of the indoor space and monitoring equipment, acquired by the Trimble SX10 high-precision la-ser scanner [31]. After investigating the layout and structure of the sites with potential deformation, the laser point cloud data of 30 sites were collected. We selected the indoor mode and a near-field distance range \(<\) 10 m. The resolution was set to 11 MPIs, with a  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Measuring Range** & **Unit** & **Accuracy** \\ \hline Temperature & \(-\)40\(-\)60 & \({}^{\circ}\)C & \(\pm\)0.5 (25 \({}^{\circ}\)C) \\ Humidity & 0\(-\)100 & \%RH & \(\pm\)3\% (60\% RH, 25 \({}^{\circ}\)C) \\ Wind speed & 0\(-\)60 & m/s & 0.2 \(\pm\) 0.02 V (0\(-\)30 m/s, 25 \({}^{\circ}\)C) \\ Wind direction & 0\(-\)360 & \({}^{\circ}\) & \(\pm\)2 \\ Rainfall & 024 & mm & \(\leq\)FS \(\pm\) 5\% \\ Atmospheric pressure & 30\(-\)110 & kPa & \(\pm\)0.1 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of meteorological sensors.  
vertical range of \(-60^{\circ}\) to \(90^{\circ}\) and a horizontal range of \(0^{\circ}\) to \(360^{\circ}\). The data were imported into Trimble Realworks for denoising and segmentation, and SketchUp was utilized to construct the indoor 3 DRS model.
# Production of Porous Ceramic Materials from Spent Fluorescent Lamps
## 3 Results and Discussion
### Activation of Coarse Powders (SFL > 20 \(\mu\)m)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Element**} & \multirow{2}{*}{**GF-1**} & \multirow{2}{*}{**GF-2**} & \multirow{2}{*}{**GF-3**} & \multirow{2}{*}{**GF-4**} & \multicolumn{3}{c}{**Limit Values (mg/kg)**} \\ \cline{5-8}  & & & & & & **Rexcycled** & **Inert** & **Non-Hazardous** \\ \hline Ba & 8.5 & 5.5 & 2.8 & 1.3 & \textless{}20 & \textless{}20 & 20–100 \\ Cr & 0.11 & 0.27 & 0.05 & 0.08 & \textless{}0.3 & \textless{}0.5 & 0.5–10 \\ Mo & 0.06 & 0.06 & 0.05 & 0.08 & \textless{}0.5 & \textless{}0.5 & 0.5–10 \\ V & 0.002 & 0.01 & 0.02 & 0.04 & \textless{}1 & \textless{}0.5 & 0.5–10 \\ Cu & 0.00199 & \textless{}0.00131 & \textless{}0.00131 & 0.00141 & \textless{}1 & \textless{}2 & 2–50 \\ Ni & \textless{}6.6 \(\times 10^{-4}\) & \textless{}6.6 \(\times 10^{-4}\) & \textless{}6.6 \(\times 10^{-4}\) & \textless{}6.6 \(\times 10^{-4}\) & \textless{}0.4 & \textless{}0.4 & 0.4–10 \\ Co & \textless{}0.3 \(\times 10^{-4}\) & \textless{}0.3 \(\times 10^{-4}\) & \textless{}0.3 \(\times 10^{-4}\) & \textless{}0.3 \(\times 10^{-4}\) & \textless{}1 & & \\ W & 3.41 & 8.75 & 0.94 & 1.38 & \textless{}1.5 & & & \\ Hg & 0.004 & 0.010 & 0.001 & 0.002 & \textless{}0.04 & \textless{}0.01 & 0.01–0.2 \\ Cd & \textless{}0.3 \(\times 10^{-4}\) & \textless{}0.3 \(\times 10^{-4}\) & \textless{}0.3 \(\times 10^{-4}\) & \textless{}0.4 \(\times 10^{-4}\) & \textless{}0.04 & 0.04–0.1 \\ Y & \textless{}1.1 \(\times 10^{-4}\) & \textless{}2.7 \(\times 10^{-4}\) & \(3.3\times 10^{-4}\) & \(3.9\times 10^{-4}\) & & \textless{}0.01 & \\ Eu & \(9.4\times 10^{-4}\) & \(6.0\times 10^{-4}\) & \(3.2\times 10^{-4}\) & \(1.8\times 10^{-4}\) & & \textless{}0.01 & \\ La & \(1.0\times 10^{-4}\) & \(0.6\times 10^{-4}\) & \(1.5\times 10^{-4}\) & \(1.2\times 10^{-4}\) & & \textless{}0.01 & \\ Nb & \(0.5\times 10^{-4}\) & \(1.2\times 10^{-4}\) & \(0.1\times 10^{-4}\) & \(0.2\times 10^{-4}\) & & \textless{}0.01 & \\ Gd & \(0.4\times 10^{-4}\) & \(0.3\times 10^{-4}\) & \(0.8\times 10^{-4}\) & \(1.1\times 10^{-4}\) & & \textless{}0.01 & \\ Sm & \(0.3\times 10^{-4}\) & \(0.2\times 10^{-4}\) & \(0.1\times 10^{-4}\) & \(0.1\times 10^{-4}\) & & \textless{}0.01 & \\ Ce & \textless{}0.1 \(\times 10^{-4}\) & \textless{}0.1 \(\times 10^{-4}\) & \(0.7\times 10^{-4}\) & \(1.2\times 10^{-4}\) & & \textless{}0.01 & \\ U & \textless{}0.1 \(\times 10^{-4}\) & \textless{}0.1 \(\times 10^{-4}\) & \(0.1\times 10^{-4}\) & \(0.1\times 10^{-4}\) & & \textless{}0.01 & \\ Tb & \textless{}0.2 \(\times 10^{-4}\) & \textless{}0.2 \(\times 10^{-4}\) & \(0.2\times 10^{-4}\) & \(0.4\times 10^{-4}\) & & \textless{}0.01 & \\ Cl’ & 28.6 & 10.4 & 20.2 & 18.0 & 800 & 800 & 15,000 \\ F- & 1.42 & 2.2 & 3.7 & 0.58 & & 10 & 150 \\ SO42- & 9.6 & 5.7 & 12.1 & 8.4 & 2500 & 1500 & 20,000 \\ pH & 12.68 & 12.18 & 11.81 & 8.55 & & & \\ Conduct. & 876 & 482 & 274 & 395 & & & \\ \hline \end{tabular}
\end{table} 
Table 3: The results of the leaching tests on the developed foams.
# Recent Trends and Progress in Corrosion Inhibitors and Electrochemical Evaluation
## 3 Types of Corrosion Inhibitors
### Composite-Based Corrosion Inhibitors
#### 3.4.1 Biopolymer-Based Corrosion Inhibitors  
Organic materials known as biopolymers are made by the cells of both plants and animals. Due to their natural (animal and plant) origins, the usage of biopolymers is acknowledged as an environmentally friendly substitute for a variety of biological and industrial applications. Biopolymers, in contrast to synthetic polymers, are also biodegradable and non-bioaccumulative. Polysaccharides (starch, cellulose, chitosan, etc.), polypeptides, nucleic acids (RNA and DNA), natural rubber, lignin, and others are examples of common biopolymers. The use of eco-friendly biopolymers would be favored due to rising ecological awareness and environmental restrictions. It has been extensively demonstrated that chitosan has an anticorrosive impact on metals and alloys, such as copper, as well as aluminum and its alloys [62; 63].  
Corrosion inhibitors based on chitosan are regarded as environmentally beneficial due to their natural origin. In general, chitosan that has been modified with different organic compounds performs better in inhibiting corrosion than pure chitosan [3]. [PERSON] et al. [64] developed a CI using a biodegradable polymer that is synthesized from oligosaccharides, chitosan, and an epoxy glucose derivative. This property will optimize chitosan's corrosion inhibition by increasing the number of oxygen atoms while also retaining its ecological properties. Chitosan was employed by [PERSON] et al. [65] to generate a nitrogen-doped graphitized carbon layer for AA 6061\({}^{\prime}\)s corrosion prevention. This resulted in high corrosion resistance with an efficiency of 99.91 \(\pm\) 0.10% in the acidic condition of 0.5 M H\({}_{2}\)SO\({}_{4}\) and 2 ppm HF. [PERSON] et al. [66] used chitosan-copper oxide (CHT-CuO) by changing the amount of chitosan (0.5, 1.0, and 2.0 g) as a CI for X60 carbon steel in an acidic media of 5 wt% HCl. The resulting corrosion IE was CHT\({}_{1.0}\)-CuO (90.35%) > CHT\({}_{0.5}\)-CuO (90.16%) > CHT\({}_{2.0}\)-CuO (89.52%).
## 4 Discussion
### Response of Backscattering Coefficients in Phenology Retrieval
Range overlap appeared during the late vegetative and early reproductive phase. As the water content of rice plants continuously fell and rice heads gradually became important scattering elements in the late reproductive phase, backscatter properties exhibited a notable change. There are still several issues waiting to be addressed in further research and development. First, since phenology evolution is a continuous process, dividing phenology into discrete intervals makes it hard to decide whether the plants are in one phase or another during the transition between phases. For example, ground observation indicated that rice plants at #19 sample site on 2 July were at the vegetative phase (stage 47), whereas the retrieval algorithm misclassified them as reproductive plants (stages 50-69). In the BBCH scale, an error between stage 47 and 50 is minimal (3 over 100), but in discrete intervals, it means an error between two phases (1 over 4). A possible alternative to avoid this problem consists of dividing phenology into more phases or retrieving phenology in a continuous range. This brings up another issue, that is, more ground observation and SAR data are needed. Rice plants grow rapidly during the bimestrial season. The phenological phase (e.g., transplanting and reproductive phase) lasts about two weeks on average and phase transition usually takes place in a short interval. The 24-day revisit period of RADARSAT-2 is relatively long for monitoring growth changes timely. To overcome this problem, different spaceborne C-band SAR sensors (e.g., Sentinel-1 and Gaofen-3) can be combined to increase the temporal resolution of SAR observations.
## 3 Results
### Comparison with In Situ Measurements from Weather Stations

Table 5: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE) calculated comparing snow seasonal parameters (First Snow Day, FSD, Last Snow Day, LSD, and Snow Cover Duration, SCD) of overall predicted snow cover data and weather stations. Figure 9: Accuracy metrics for Snow and No-Snow classes of the classification’s performances against weather stations. F1 = F1-score, OA = Overall Accuracy. A1 = blue line, A2 = red line, A3 = yellow line. continuous ground truth data and, moreover, results show a good predictive ability of seasonal snow parameters.
# Seismic Retrofit of Steel Truss Bridge Using Buckling Restrained Damper
## 2 Analysis Model
### Safety Evaluation of the Truss Bridge Members  
The existing bridge was initially designed according to the old design codes considering the moderate earthquake before the 1995 Hyogoken-Nanbu earthquake, and has not been seismically retrofitted so far. The seismic behavior of the bridge is evaluated by the acceleration wave of the Niigata Chuetsu earthquake mentioned above and the criteria of the current Japanese design codes [15; 17]. To this end, a nonlinear time history analysis is carried out. It is important to note that the nonlinear time history analysis has the potential to provide much better information for predicting the amount of damage, and hence for evaluating any risk due to earthquake, and the nonlinear time history analysis is also recommended in evaluating the dynamic response of structures to Level 2 earthquakes [17].  
The seismic behavior only in the longitudinal direction of the bridge is considered herein, and the analysis is therefore arranged with two steps, considering the material and geometrical non-linearity of the bridge members. The safety of the bridge members is examined by the following two conditions [15]:  
* when the normal stress of the member is positive (in tension): \[R_{t}\ =\ \frac{\sigma_{idm}}{\sigma_{i_{c}\ yd}}<1.0\] (1)
* when the normal stress of the member is negative (compression): \[R_{c}\ =\ \frac{\sigma_{idm}}{\sigma_{i_{c}\ rd}}<1.0\] (2)  
Figure 3: Niigata Chuetsu earthquake wave.  
where \(R_{c}\) and \(R_{t}\) are the demand-capacity ratio, and \(\sigma_{itm}\) is the axial stress value of the \(i\)-th member, obtained from the dynamic time history response (i.e., Figure 4). \(\sigma_{i,~{}nt}\) and \(\sigma_{i,~{}y}\) are the design local buckling stress of the member and the material yield stress of the member, respectively.  
The member that satisfies the above two equations is safe; otherwise, the member is considered damaged.  
The numerical results are shown in Table 2. The maximum and minimum stress values, shown in Table 2 are determined based on the comparison among the stress time history responses in each damaged member.  
Figure 5 shows the deformed shapes of the truss bridge during the large earthquake. As can be seen in Figure 5b,c, the damaged members are also highlighted in black (see also the node numbers in Figure 2).
## 4 Control System Design
### Surge Speed Controller  
When time \(t\rightarrow\infty\), the objective of the speed is \(\lim\limits_{t\rightarrow\infty}U\to U_{d}\). Since \(U=\sqrt{u^{2}+v^{2}}\), we get that \(u\rightarrow\sqrt{U_{d}^{2}-v^{2}}\). Hence, we define a desired surge speed as \(u_{d}\rightarrow\sqrt{U_{d}^{2}-v^{2}}\), which is valid when assuming \(U_{d}\geq|v|\) at all times. This assumption is highly realistic since in practice, \(|v|\) is just a small fraction of \(U\). Then, the objective of the surge speed controller becomes \(\lim\limits_{t\rightarrow\infty}u\to u_{d}\). Define a surge speed tracking error \(u_{e}\) as:  
\[u_{e}=u-u_{d} \tag{45}\]  
Taking the time derivative of (45) along (9) yields:  
\[\dot{u}_{e}=f_{u}+\frac{1}{m_{11}}\tau_{u}+\frac{1}{m_{11}}b_{u}-\dot{u}_{d} \tag{46}\]  
Equally, the neural network minimum parameter learning method is used to approximate \(f_{u}+\frac{1}{m_{11}}b_{u}\). Its approximation error is \(\varepsilon_{u}\) and \(|\varepsilon_{u}|\leq\varepsilon_{u},\varepsilon_{u}>0\).  
The corresponding control law is chosen as:  
\[\tau_{0u}=-k_{ue}u_{e}+m_{11}u_{d}-\frac{1}{2}u_{e}\phi_{u}h^{T}h \tag{47}\]  
where \(k_{ue}\) is a positive design parameter.  
\[\dot{e}_{u}=\left\{\begin{array}{ll}-K_{eu}e_{u}-\frac{|u_{e}\Delta\tau_{u}|+0.5\Delta\tau_{u}^{2}}{|\dot{e}_{u}^{2}|}\cdot e_{u}+\Delta\tau_{u},|e_{u}|\geq \xi_{u}\\ 0,&|e_{u}|<\xi_{u}\end{array}\right. \tag{48}\]  
Then, the ultimate control law for \(u\) is selected as:  
\[\tau_{u}=\left\{\begin{array}{ll}\tau_{u\max},&\quad if\ \tau_{0u}>\tau_{u\max} \\ \tau_{0u},&\quad if\ \tau_{u\min}\leq\tau_{0u}\leq\tau_{u\max}\\ \tau_{u\min},&\quad if\ \tau_{0u}<\tau_{u\min}\end{array}\right. \tag{49}\]  
where the meaning of each symbol has the same meaning as those defined in (33) and (34).  
The adaptive law of the estimation function is:  
\[\dot{\phi}_{u}{=}\frac{1}{2}\gamma_{u}u_{e}^{2}h^{T}h-\kappa_{u}\gamma_{u} \dot{\phi}_{u} \tag{50}\]  
where \(\gamma_{u}\) and \(\kappa_{u}\) are two positive design parameters.  
In order to facilitate the understanding of the full paper, the block diagram of the padded propulsion USV path following is shown in Figure 3.  
As shown in Figure 3, the \"modeling\" of padded propulsion (PP) unmanned surface vehicle is the basis of the path-following system. The reason why \"modeling\" and \"desired path\" are connected by dotted lines is that the problem of model identification is not considered in this paper. Then, the \"desired path\" is converted into target heading by \"LOS guidance\". Finally, the path following of the padded propulsion unmanned surface vehicle is implemented by designing the \"yaw rate controller\" and \"surge speed controller\" respectively.