esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Module Combination for Underwater Image Enhancement
## 4 Experiment
### Experimental Details
#### 4.1.1 Comparative Methods  
We compare UMMC with seven UIE methods to validate our superior performance. The methods include an underwater image enhancement method based on an attention mechanism and adversarial autoencoder (UW-AAE) [30], an underwater image enhancement method by correcting color distortion, enhancing contrast, and enriching details (EUICCCLF) [31], a technique focused on minimizing color loss and enhancing local adaptive contrast (MLLE) [32], a method for optimizing contrast enhancement through piecewise color correction and dual prior (PCDE) [33], an underwater image enhancement convolutional neural network model based on underwater scene prior (UWCNN) [34], which we designate as Type I enhancement, an image enhancement technique that separates content and style (UIESS) [35] and a Bayesian Retinex algorithm for enhancing a single underwater image using multi-order gradient priors of reflectance and illumination (BRUIE) [36].
# CT-RPL: Cluster Tree Based Routing Protocol to Maximize the Lifetime of Internet of Things
## 3 Cluster Tree-Based Routing Protocol
### Cluster Formation  
Each node in the network calculates the distance using Euclidean distance [56]. The distance \"_dis_\" between two nodes is calculated by  
\[dis=\sqrt{(x_{j}-x_{i})^{2}+(y_{j}-y_{i})^{2}}, \tag{4}\]  
where \((x_{i},\ y_{i})\) and \((x_{j},\ y_{j})\) are coordinates of the network nodes \"_i_\" and \"_j_\".  
The distance between one node and all other nodes are calculated, and its matrix representation is given thus:  
\[dis\ =\left[\begin{array}{ccc}0&dis_{1,2}\ldots&dis_{1,n}\\ \vdots&0\ \ldots&\vdots\\ dis_{n,1}&dis_{n,2}&0\end{array}\right] \tag{5}\]  
The centroids are computed in the network nodes, and their calculation is given thus:  
\[centroid\Big{(}(x_{i}-y_{i}),\,(x_{j}-y_{j})\Big{)}=(\frac{x_{i}+x_{j}}{2}, \frac{y_{i}+y_{j}}{2}) \tag{6}\]  
Initially, the coordinates of the first node are known to be the centroid point. First, the CT-RPL measures the distance \"_dis_\" between two nodes and, also, finds out the centroid of both nodes. Later, it picks the next node's coordinates and the recently computed centroid point for calculating the new centroid point. If the centroid values are within the distance \"_dis_\" value, the node is added in the same cluster; otherwise, it will be added in the distinct cluster.  
The number of clusters is computed, and its calculation is given in Equation (7).  
\[C_{i}=\left\{\begin{array}{ccc}\textit{Same cluster},\ \textit{ centroid}(x_{i},\ y_{i})\ \pm\textit{dis}\\ \textit{Distinct cluster},\ \ \textit{otherwise}\end{array}\right. \tag{7}\]  
The total number of clusters \"C\" is calculated from Equation (8).  
\[C\ =\ \sum_{k=1}^{m}count(centroid\Big{(}x_{i},\ y_{i}\Big{)}) \tag{8}\]  
where \(m\) indicates the number of nodes in each cluster.
## 1 Introduction  
The Smart City (SC) paradigm helps renovate the traditional city concept. In fact, it is possible to realize and develop efficient demand-side strategies integrating the monitoring and automation featuresensured by intelligent devices and their communication apparatuses typically used in many applications. Within this concept, public lighting, being a great electrical energy consumer, has recently been attracting the interest of the research community. Scientists, combining the SC paradigm with alternative energies and new lighting technologies, are conceiving systems previously unimaginable, which can increase the efficiency obtaining considerable energy consumption savings and consequently money savings [1; 2; 3; 4].  
This article shows how an isle of lamp posts, used to light a crossroad and placed in an area not reached by ADSL lines and 3G signals, can be much more efficiently managed by applying the smart city principles. The realized application changes the way of conceiving the isle: sensing the transit of a car approaching the crossroad and controlling it by an efficient management strategy, a typical power grid, like the isle of the lamp posts, is transformed in a smart city application.  
The goal is obtained combining an efficient management strategy with new technologies. The strategy divides the complex structure of the isle into layers which are linked by communication layers. Each layer uses innovative technologies to make possible the application of the smart city principles. For example the lamp post exploits some innovative technologies both for the supply (photovoltaic panel) as well as for the control (ZigBee network and RaspBerry-Pi). Moreover, to provide a robust system also able to face and overcome the absence of Asymmetric Digital Subscriber Line (ADSL) or 3 rd Generation (3G) communication, that in some parts of the city might not available, we used WiMAX technologies that allowed us to fully control the isle from a remote station.  
The choice to test our system in this application is strongly joined to the road safety problem. In fact, the lighting of crossroads is fundamental to ensure the good night vision of typically hazardous points of the road where fatal crashes often happen. Based on this application, an economical analysis has also been realized to show how after the breakeven point is achieved, the system is more convenient with respect to older technology.
## 3 Undifferentiated Cells (Stem Cells)
### Adult Stem Cells
#### 3.4.6 Synovium-Derived Mesenchymal Stem Cells  
##### 3.4.6.1 General Information  
The synovial membrane (synovium) is a specialized soft connective tissue that lines the inner surface of the cavities of joints, tendon sheaths, and bursae. The ability of synovium to heal itself after subtotal surgical synovectomy is considered a noteworthy sign indicating its high regenerative potential [342]. In this regard, [PERSON] et al. published the first report for the presence of a subpopulation of MSCs in human synovium (S-MSCs) in 2001 [343]. Since then, synovium has been identified as a promising source of MSCs for therapeutic applications such as musculoskeletal regeneration [344].  
Under proper environmental cues, these cells exhibit common features of MSCs such as differentiation into cell lineages such as myocytes, adipocytes, chondrocytes, and osteocytes in vitro [343]. In addition to humans, these cells have also been isolated from other species such as rats [345]. The isolation procedure of S-MSCs consists of a simple protocol in which harvested fresh synovial tissue is shattered to pieces (by shears) and then is digested using a collagenase solution of suitable concentration (Figure 10) [343; 346]. It has been previously proposed that S-MSCs have similar characteristics in comparison to BMSCs including the morphology, immune phenotype, colony frequency, and differentiation capacity [347; 348]. However, there is some evidence showing that S-MSCs have a higher colony-forming efficiency and proliferation rate in comparison to BM-MSCS [347].  
It has been previously shown that S-MSCs are good candidates for cartilage reconstruction since they express the hyaluron receptor (CD44) and uridine diphosphoglucose dehydrogenase (UDPGD), which is a vital enzyme for hyaluron synthesis [350]. Although in vitro cultured S-MSCs specifically tend to differentiate into chondrocytes, new attempts have been made to utilize them for bone regeneration strategies.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 5 Theoretical and Experimental Evaluation
### Complexity of Algorithm 3: Adding Redundancy to a DODAG  
This algorithm enhances redundancy in a DODAG by evaluating and adding new edges based on adjacency constraints.
# Mission Planning of UAVs and UGV for Building Inspection in Rural Area
## 3 Design of Planning Algorithm for Cooperative Inspection
### Mission Planning Algorithm Based on ACO-GA
#### 3.2.5 Computing Fitness Values and Updating Pheromones  
During each iteration, the aforementioned operations are performed. Subsequently, the fitness values of the newly generated population are computed. The best individual of each round is recorded, and its fitness value is output. Finally, the pheromones of the ant colony are updated according to the following formula:  
\[\tau_{i,j}(t+1)=(1-\rho)\cdot\tau_{i,j}(t)+\Delta\tau_{i,j} \tag{12}\]  
In Equation (12), \(\rho\) is the evaporation coefficient of the pheromone, and \(\Delta\tau\) represents the pheromone increment from task points \(i\) to \(j\) in this iteration, which is calculated as follows:  
\[\begin{array}{l}\Delta\tau=\sum\limits_{k=1}^{n_{NP}}\Delta\tau_{i,j}^{k}\\ \Delta\tau_{i,j}^{k}=\left\{\begin{array}{l}\frac{Q}{\aleph_{k}},\text{while ant }k\text{ passes }i,j\text{ in this iteration}\\ 0,\text{others}\end{array}\right.\end{array} \tag{13}\]  
In Equation (13), \(\Delta\tau_{i,j}^{k}\) refers to the amount of pheromone left by the ant \(k\) from task points \(i\) to \(j\) in this round of iteration, \(n_{NP}\) represents the population size, \(Q\) is the pheromone enhancement coefficient, and \(S_{k}\) is the fitness value of the ant \(k\) in this iteration.  
The detailed procedure of the complete ACO-GA algorithm for solving the mission planning problem is shown in Figure 6.  
Figure 6: The flow chart of the computation of ACO-GA.
# Aerodynamic Enhancement of Vertical-Axis Wind Turbines Using Plain and Serrated Gurney Flaps
## 3 Results and Discussion
### Effect of GFs on H-VAWT
#### 3.2.1 Power and Moment Coefficient  
Figure 13 shows the comparison of the power coefficient of VAWTs. Flaps with appropriate size can improve wind turbine performance at different tip speed ratios. When TSR \(>\) 3, the SGF rotor has a higher power coefficient than the PGF one, no matter the flap height. With H = 1.5%\(c\), the Cp of the prototype, PGF rotor, and SGF rotor are 0.365, 0.385, and 0.393, respectively, at TSR = 3.1. With H = 6%\(c\), the performance of the PGF rotor deteriorates obviously, and the performance of the SGF rotor surpasses the PGF one by  
Figure 12: Iso-surfaces of the Q criterion (Q = 2 \(\times\) 10\({}^{5}\) s\({}^{-2}\)) with contours of velocity magnitude (**a**) with PGF; (**b**) with SGF.  
13.9% at TSR = 2.62. However, both TSGF and TPGF have a lower power coefficient than the prototype when TSR > 2.62.  
It can also be observed that across the whole range of TSR, the \(Cp\), \(max\) happens at TSR = 2.62 for all of the rotors. The \(Cp\),\(max\) of the prototype is 0.375, the PGF rotor has a \(Cp\),\(max\) of 0.399 when the flap height is 1.5%, and the SGF one has a \(Cp\),\(max\) of 0.399 when the flap height is 3.0%.  
The angle of attack of the turbine blade varies along its azimuth position. For a specific tip speed ratio, the angle of attack can be calculated by the following formula:  
\[\alpha=\arctan\biggl{(}\frac{\sin\psi}{\lambda_{tsr}+\cos\psi}\biggr{)} \tag{7}\]  
where \(\alpha\) is the angle of attack of the rotor blade, \(\psi\) is the azimuth angle of the rotor blade, and \(\lambda_{tsr}\) is the tip speed ratio of the wind turbine.  
It is noted that smaller tip speed ratios correspond to the larger angle-of-attack range, indicating that the rotor blade is more susceptible to experiencing stall conditions, as shown in Figure 14.  
Figure 15 shows the varied moment coefficient along the azimuthal angle at four aforementioned tip speed ratios. At TSR = 1.62, and a round \(\psi=60^{\circ}\), the moment coefficient suddenly decreases due to dynamic stall, although the angle of attack does not reach the peak. Introducing Gurney flaps to augment the lift coefficient further advances the stall angle. This observation aligns with the findings outlined in Section 3.1.1 for the isolated  
Figure 14: Angle of attack versus azimuth position.  
Figure 13: Comparison of power coefficient of VAWTs with and without GFs.  
airfoil. At TSR = 2.62, the turbines have positive moment coefficients for the baseline one, and the one which has flaps with heights of 1.5% and 3%c.
## 7 Analysis of Nonlinear Problems  
### Finite Element Model  
Figure 8 shows the 2D model of a liquefiable site, which is composed of a 15 m dense sand layer, a 15 m liquefiable loose sand layer, and an 8 m-dense sand embankment from bottom to top. The seawater has a depth of 2 m. The top surface of the model is set as a drainage boundary. To simplify the model, the horizontal and vertical displacements at the bottom of the model are fixed, and the horizontal displacements of the left and right side boundaries are also fixed.  
Figure 8: Computational model of 2D liquefiable site.  
Figure 7: Comparison of computation time.  
The Wolong station seismic record of the Wenchuan earthquake is selected as the input motion. It has a peak acceleration of 0.3 g. The acceleration time history curve is shown in Figure 9. Attention is fixed on the numerical results at 6.2 s, 16.6 s, and 30 s, respectively.  
The saturated soils are simulated using the quadrilateral plane strain element element quadUP [49,50]. The parameters of soil elements are listed in Table 2. Each node of the element has three DOFs, corresponding to horizontal displacement, vertical displacement, and pore pressure, respectively. In view of using a 2D plane strain model to simulate a 3D problem, the out of plane thickness of the model is set to be 1.0 m. The constitutive model of saturated soil chooses the PressureDependMutiYield02 (PDMY) command [51,52], with the material parameters [49] listed in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Loose Sand Layer** & **Dense Sand Layer** \\ \hline Element thickness & 1.0 m & 1.0 m \\ Vertical gravitational acceleration & \(-9.81\) m/s\({}^{2}\) & \(-9.81\) m/s\({}^{2}\) \\ Liquid-phase undrained bulk modulus & \(4.4\times 10^{6}\) kPa & \(7.3\times 10^{6}\) kPa \\ Horizontal permeability coefficient & \(1\times 10^{-4}\) m/s & \(1\times 10^{-5}\) m/s \\ Vertical permeability coefficient & \(1\times 10^{-4}\) m/s & \(1\times 10^{-5}\) m/s \\ \hline \hline \end{tabular}
\end{table}
Table 2: Soil element parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Loose Sand Layer** & **Dense Sand Layer** \\ \hline Density \(\rho\) & \(1.7\) ton/m\({}^{3}\) & \(1.9\) ton/m\({}^{3}\) \\ Reference shear modulus \(G_{r}\) & \(3.57\times 10^{4}\) kPa & \(2.59\times 10^{4}\) kPa \\ Reference bulk modulus \(B_{r}\) & \(8\times 10^{4}\) kPa & \(6\times 10^{4}\) kPa \\ Friction angle \(\phi\) & \(33.5\) & \(31\) \\ Octahedral peak shear strain \(\gamma_{\max}\) & \(0.1\) & \(0.1\) \\ Pressure coefficient \(n\) & \(0.5\) & \(0.5\) \\ Dilatancy angle \(\phi_{PT}\) & \(25.5\) & \(31\) \\ Shear contraction coefficient \(c_{1}\) & \(0.045\) & \(0.087\) \\ Shear contraction coefficient \(c_{3}\) & \(0.15\) & \(0.18\) \\ Dilatancy coefficient \(d_{1}\) & \(0.06\) & \(0.0\) \\ Dilatancy coefficient \(d_{3}\) & \(0.15\) & \(0.0\) \\ Number of yield surface & \(20\) & \(20\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Material parameters of the model.  
Figure 9: Acceleration time history curve of the Wolong station seismic record of Wenchuan earthquake.
# An Intersection Signal Control Mechanism Assisted by Vehicular Ad Hoc Networks
## 4 Simulation and Analysis
### Results and Analysis  
#### 4.3.1 Average Waiting Time of Vehicles  
Figure 5 shows the average waiting time of vehicles under different traffic flow environments at the intersection. From the three graphs, it can be clearly seen that the waiting time increased with the increasing traffic volume. Under the environment with the same even traffic flows (Figure 5a), when the value of traffic flows was small, due to the estimation for the arrival time of each far vehicle and then the raising of the precision level in decision-making, the waiting time in the fixed-FA and dynamic-FA was obviously smaller than that in the traditional timing scheme; when the number of vehicles increased,  
the results of three schemes tended to be the same (especially around 87 s at 350 veh/lane/h), which was because all arrival traffic flows became equal and saturated, and then the regulating effect of the dynamic signal control dropped gradually and even disappeared regardless of whether the far vehicles were taken into account. Under the environment of same uneven traffic flows (Figure 5b), with the aid of predicting the arriving time of each far vehicle, the signal decision system could make the corresponding adjustment in advance for these uneven traffic flows. Furthermore, compared with the other two fixed-phase signal control schemes (i.e., with four phases), in the dynamic-FA, there were 12 possible combination phases that could provide more available options and enhance the adjustment capability. As a result, the waiting time of vehicles was reduced by averages of 51.4% and 37.8% compared with the traditional timing and fixed-FA schemes, respectively. When the volumes of some individual traffic flows were relatively larger than others (i.e., two times larger), as shown in Figure 5c, the set of values of the waiting time was overall greater than that with the same even traffic flows and the same uneven traffic flows. Moreover, since the signal combination phase and the green light duration were dynamically determined according to the real-time situation, the large traffic flows could be easily favored in the decision-making. Furthermore, with the raising of the accuracy supported by the information of far vehicles, the performance of the dynamic-FA scheme was clearly better than the other two schemes. In addition, Table 2 provides the information of confidence intervals with a 95% confidence level for the calculated average waiting time of the fixed-FA and dynamic-FA schemes when the traffic flow was 250 veh/lane/h.
## 3 Micro-CT Image and Characterization of the Pore Structure
### Probabilistic and Quantitative Characterization Methods
#### 3.2.2 Local Volume Ratio and Shape Factor  
For the quantitative characterization of the foamed specimen, the concept of the local volume ratio [37] is adopted. In this method, the local volume ratio of a specific component is calculated using a kernel with a specific volume to compute local porosity (or volume). Figure 7 presents a schematic for investigating the local porosity. For example, Kernels 1 and 2 in Figure 7 have different local porosities because each kernel contains different numbers of pore (white) particles depending on its location. The measurement is proceeded by using the kernel along the whole model, and the local porosity distribution is calculated based on the number of pore particles inside the kernel volume at every location in the model. The local volume ratio has a value between zero and one, and then, the local volume ratio distribution for each specimen is described using the contour in 3D space. Like the local porosity, the local volume ratio of the cell (solid) can be obtained here using this method.  
Figure 6: Examples of the probability functions for the foamed specimen: (**a**) \(P_{pp}(r)\), (**b**) \(L_{p}(r)\) (note: the white regions are pores (\(p\)), and the gray regions represent solids; lines are shown as \(r_{i}\) with arbitrary lengths and directions).  
To describe the shape of each pore in the foamed concrete specimens, the sphericity (or isoperimetric shape factor) is also investigated. The sphericity, \(S_{p}\), is a parameter to describe the roundness of an object [38] and can be calculated as follows:  
\[S_{p}=6\sqrt{\pi}\frac{V}{\sqrt{A^{3}}} \tag{3}\]  
where \(V\) is volume and \(A\) is the surface area of the pore. Here, the sphericity of each pore in the foamed specimens is calculated from the segmented image by counting the voxel numbers of each pore particle, and the surface area is also measured from the boundary configuration of the pore. \(S_{p}\) has a value between zero and one, where the \(S_{p}\) of a sphere is one by definition. Using this index, the shape of pores in the specimen can be identified.
## 5 Simulation and Discussion
### Reconstruction Performances under Varied Noise Conditions  
In accordance with the CACTI Imaging Model, the imaging performance is significantly affected by noise. Therefore, the comparison is made under different noise conditions in this section. Figure 9 shows the performances of different algorithms with the white Gaussian noise variances 0.01 and 0.05.  
As shown in Figure 9, the performances of all reconstructive algorithms decrease significantly in term of visual quality, although the PSNR still maintains a high value. The PSNR of reconstructed results for the traffic dataset with noises is shown in Figure 10. The average PSNR value of GAP + b method is decreases nearly 2 dB, compared with the results without noise. The average PSNR value of GAP + b method decreases nearly 2 dB, compared with the results without noise. Note that reconstructed frames by GAP + b look better but still with more blurring. Note that reconstructed frames by GAP + b looks better but still with more blurring. Because of its TV regulation reconstruction, the GAP-acc + TV algorithm can suppress the noise and thus results in smoother local details, but with the obvious missing texture. Compared with other algorithms, the proposed approach can suppress noise and motion blur and provide richer texture information, which is verified by the PSNR curve in Figure 10.  
Figure 10: Performance comparison of CACTI reconstruction algorithms under noise conditions: (**a**) PSNR for reconstructed frame of Traffic dataset under different reconstructed ratio; (**b**) reconstruction error for reconstructed frame of Traffic dataset under different reconstructed ratio.  
Figure 9: Comparison of reconstruction results under noise conditions (variances = 0.01): (**a**) frame reconstructed by GAP-acc + TV algorithm (variances = 0.01, \(N_{f}=8\)); (**b**) frame reconstructed by GAP + b algorithm (variances = 0.01, \(N_{f}=8\)); (**c**) frame reconstructed by proposed algorithm (variances = 0.01, \(N_{f}=8\)); (**d**) frame reconstructed by GAP-acc + TV algorithm (variances = 0.05, \(N_{f}=24\)); (**e**) frame reconstructed by GAP + b algorithm(variances = 0.05, \(N_{f}=24\)); (**f**) frame reconstructed by proposed algorithm (variances = 0.05, \(N_{f}=24\)); (**g**) the residual images of (**a**); (**h**) the residual images of (**b**); (**i**) the residual images of (**c**); (**j**) the residual images of (**d**); (**k**) the residual images of (**e**); (**l**) the residual images of (**f**).  
The performance of GAP-b drops significantly with the noises. From Figure 11, the proposed algorithm performs significantly better than GAP + b and GAP-acc + TV in terms of PSNR when the compression ratio is less than 24 under noise condition. It is robust enough to be applied for real world video CS in term of both noise and high compression ratio.  
Figure 11: _Cont._
## 3 Results and Discussions
### PTP-1B Activity Assay  
The total triterpen periods were tested, using different concentrations, for PTP-1B activity before and after purification. Table 3 shows that the degree of purification of triterpenos influences the final result. Both the fractions have shown activity against the PTP-1B, but the purified fraction of triterpenos showed significant inhibitory activities with IC\({}_{50}\) values of 0.47 \(\upmu\)g/mL. Among the two isolated triterpenoids, oleanolic acid showed potent activity against the PTP-1B with an IC\({}_{50}\) value of 0.97 and unsolic acid with IC\({}_{50}\) = 1.13 \(\upmu\)g/mL. These results confirmed total triterpenoids (after purification) as the best PTP-1B inhibitor in comparison to other tested compounds. The dose-response curves for inhibition of PTP-1B enzyme of the screened compounds are shown in Figure 6.  
Figure 5: Dose response curves for inhibition of three human breast cancer cells; SK-Br-3 (**A**), T47D (**B**) and MCF-7 (**C**).  
Figure 6: Dose response curve of inhibition of PTP-1B enzyme for total triterpenoids fraction before purification (**A**), total triterpenoids fraction after purification (**B**), unsolic acid (**C**), and oleanolic acid (**D**).