esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Multilevel Communities: Scalable, Selective and Resolution-Limit-Free +
## 4 Discussion
### Computational Complexity of Our Approach  
Our method has exactly the same computational complexity as the Louvain algorithm.  
It has been reported that the Louvain algorithm has linear complexity on typical and sparse data [47], which has not been altered in this paper because we directly use the openly accessed code provided by the authors of the Louvain algorithm (see [PERSON]'s webpage: [[https://perso.uclouvain.be/vincent.blondel/research/louvain.html](https://perso.uclouvain.be/vincent.blondel/research/louvain.html)]([https://perso.uclouvain.be/vincent.blondel/research/louvain.html](https://perso.uclouvain.be/vincent.blondel/research/louvain.html)), accessed on 25 December 2022). Computational complexity that can be possibly introduced by our approach is mainly due to the calculations of multiple realizations.  
1. For each group of fixed parameters (\(\alpha\), \(\beta\)), we run the algorithm in 1000 realizations to make sure there are always some realizations converging to a best solution. In practice, this is usually not necessary; 100 realizations will be sufficient for most practical purposes.
2. For some networks whose fitness landscape is really complex, involving large numbers of local maxima [42], it is sometimes difficult to filter out unstable solutions within finite realizations. Then, a little trick may help to reduce the computational burden. We can run our computations in multiple batches. Each batch consists of a certain number of realizations, and produces an independent set of plateaus by the strategy proposed in Section 2.3. Next, we take an intersection over all sets of plateaus obtained in different batches: if at a certain resolution different batches yield different network divisions, this resolution will be considered irrelevant and knocked out from the plateaus in the final output. Namely, by such an intersection we are requiring not only \"one plateau one topology,\" but also the uniqueness of this topology over multiple batches of computations. We tested on all the synthetic and real-world networks studied in this paper. By 20 batches of 100 realizations of computations, we can efficiently remove unstable results that may sometimes require almost 10,000-20,000 realizations in a single batch to remove them. Yet, for most networks, running multiple batches is not necessary for practical purposes.
3. For each fixed \(\beta\), we vary \(\alpha\) from 0 to \(2\beta-1\) with a stepwise increment \(\Delta\alpha=0.01\), in order to search every inches of the resolution scales and discover all potential plateaus. In practice, to reduce the computational burden, we suggest to firstly use a relativelylarger value of increment \(\Delta\alpha\) for a global and coarse-grained search, and then use smaller values for detailed searches in the focused regions found in the global search.  
To summarize, with a classical Louvain algorithm, our method can be implemented efficiently on various classes of complex networks with acceptable computation time.
## 2 Related Works
### Location-Based Routing Algorithm  
The basic idea of location-based routing algorithms is to make routing decisions based on the geographical location information of nodes. Greedy Perimeter Stateless Routing (GPSR) is a typical location-based routing algorithm. In networks using GPSR, nodes do not need to maintain the topological information of the network. Instead, they only need to maintain the location information of neighboring nodes and the destination node to select the neighbor closest to the destination node as the next hop. [PERSON] improved GPSR based on Double Deep Q-Network (D-DQN) and tested the performance of the new algorithm and GPSR in mobile self-organizing networks. The results show that the delay of GPSR and its improved routing algorithm is up to 0.45 s, and the packet delivery rate can reach 95% at maximum [13].
## 4 Results
### Routing Performance Evaluation
#### 4.4.1 Impact of Link Lifetime Threshold
The preceding fact is the reason for obtaining a decreasing trend in PDR when the link lifetime threshold is increased beyond 1 s. Therefore, for the rest of this paper, unless otherwise specified, we set the link lifetime threshold at 0.2 s, since it is the minimum experimental link lifetime threshold value that achieves the highest PDR.
## 5 Nominal Simulation and Monte Carlo Shooting Simulation
### Compilation and Comparison of the Results of Three Transition Phase Designs
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Direct Switching** & **5 s Switching Mode** & **10 s Switching Mode** \\ \hline \(\theta\) (deg) & \(-\)83.7–82.7 & \(-\)49.6–54.8 & \(-\)1.6–3.1 \\ \(q\) (deg/s) & \(-\)237.9–318.1 & \(-\)62.9–9.2 & \(-\)3.5–7.6 \\ \(\delta_{e}\) (deg) & \(-\)0.1–9.5 & \(-\)0.1–9.5 & \(-\)0.1–9.5 \\ \(\Omega_{pitch}\) & \(-\)0.407–0.443 & \(-\)0.114–0.412 & \(-\)0.0741–0 \\ \(h\) (m) & 80.1–185.3 & 98.4–174.3 & 98.4–100.6 \\ \(v\) (m/s) & 11.21–42.55 & 11.15–18.92 & 11.94–19.82 \\ \(\delta_{T}\) & 0–1 & 0–0.912 & 0.280–0.599 \\ \(\Omega_{base}\) & 0.095–0.886 & 0.095–0.886 & 0.095–0.552 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the ranges of state variables in MC simulation. design with 5 s switching mode. For this type of UAV with inaccurate modeling, in order to achieve better robustness and safety, a 10 s switching mode is relatively more suitable.
# The Impact of Leveee Openings on Storm Surge: A Numerical Analysis in Coastal Louisiana
## 2 Storm Surge Model  
### Delft3D-FLOW  
The open-source process-based numerical model suite Delft3D by Deltares ([[https://oss.deltares.nl/web/delft3d/about/](https://oss.deltares.nl/web/delft3d/about/)]([https://oss.deltares.nl/web/delft3d/about/](https://oss.deltares.nl/web/delft3d/about/)), accessed on 23 October 2022), which has been used widely for storm surge studies [2; 5; 6; 7; 8; 9; 10; 11], was selected to develop a hydrodynamic model for the study of the effect of lever removals on storm surge in both Breton Sound and Barataria Basin. The Delft3D suite, consisting of multiple modules, can carry out simulations of flows, sediment transports, waves, water quality, morphological developments and ecology. Delft3D-FLOW is a multi-dimensional hydrodynamic (and transport) module which calculates non-steady flow and transport phenomena that result from tidal and meteorological phenomena forcing on a rectilinear or a curvilinear, boundary fitted grid. The features of Delft3D-FLOW for storm surge applications include boundary-fitted grid generation for complex coastlines and estuary geometries, nesting tools for local boundary conditions, embedded vegetation module for the impact of different marsh types and \"sub-grid\" treatments for hydraulic and coastal structures.  
Delft3D-FLOW solves the Navier-Stokes equations for an incompressible fluid, under the shallow water and the Boussinesq assumptions. The two-dimensional (2D) depth-averaged continuity equation is given by:  
\[\frac{\partial H}{\partial t}+\frac{\partial(UH)}{\partial x}+\frac{\partial (VH)}{\partial y}=Q \tag{1}\]  
where  
\(H=\zeta+h=\) total water depth, \(h=\) bathymmetric depth, \(\zeta=\) water level  
\(U\), \(V=\) depth-averaged velocities in x-, y- directions  
\(Q=\) contributions per unit area due to the discharge or withdrawal of water, precipitation and evaporation  
The 2D momentum equations in x- and y-directions are given by:  
\[\frac{\partial U}{\partial t}+U\frac{\partial U}{\partial x}+V\frac{\partial U }{\partial y}-fV=-g\frac{\partial\zeta}{\partial x}-\frac{1}{\rho_{0}}\frac{ \partial P}{\partial x}+\frac{\tau_{xx}}{H\rho_{0}}-\frac{\tau_{bx}}{H\rho_{0 }}-\frac{D_{x}}{H}+S_{x} \tag{2}\]  
\[\frac{\partial V}{\partial t}+U\frac{\partial V}{\partial x}+V\frac{\partial V }{\partial y}+fU=-g\frac{\partial\zeta}{\partial y}-\frac{1}{\rho_{0}}\frac{ \partial P}{\partial y}+\frac{\tau_{sy}}{H\rho_{0}}-\frac{\tau_{by}}{H\rho_{0 }}-\frac{D_{y}}{H}+S_{y} \tag{3}\]  
where  
\(f=\) Coriolis parameter  
\(g=\) acceleration due to gravity  
\(\rho_{0}=\) reference density of water  
\(P=\) atmospheric pressure at the sea surface  
\(\tau_{xx},\ \tau_{xx}=\) surface wind stresses in x-, y- directions  
\(\tau_{bx},\ \tau_{bx}=\) bottom friction stresses in x-, y- directions  
\(D_{x},\ D_{y}=\) momentum dispersions in x-, y- directions  
\(S_{x},\ S_{y}=\) contributions due to external sources or sinks of momentum (e.g., external forces by hydraulic structures, discharge or withdrawal of water) in x-, y- directions  
Refer to the Delf3D-FLOW User Manual [12] for details.
# A Validated Thermal Computational Fluid Dynamics Model of Wine Warming in a Glass
## 2 Problem Configuration and Experiments
### Average Wine and Water Temperatures  
For the first experiment, the thermometer sensor was removed from the glass immediately after pouring and measuring the initial liquid temperature. The experimental setup was not disturbed for 5, 10, 20, or 40 min, respectively. Afterwards, the liquid was mixed quickly in the glass with a plastic stirring device to achieve a uniform temperature distribution without introducing additional heat. The temperature was measured in the middle of the liquid. The result was taken as the average temperature of the water or wine in the glass.  
This experiment was performed with red wine and water for the initial temperatures of 8 \({}^{\circ}\)C and 12 \({}^{\circ}\)C. The white wine was measured for \(T_{0}=8\,^{\circ}\)C. The experiment was conducted five times for each period and liquid. Figure 2a,b present the mean temperature and the standard deviation of the measurements. After each period, the mean temperature is nearly the same for water, red, and white wine. The maximal difference is only about 0.2 \({}^{\circ}\)C between the mean temperature of the water and red wine for \(T_{0}=8\,^{\circ}\)C after 40 min. The results indicate that, apart from water, the additional components in wine do not significantly affect the warming process in the glass. The impact of the alcohol content seems to be negligible, considering the difference of 13.5 % alcohol between red wine and water. Furthermore, there is no notable difference between the red wine and white wine with 11.5 % alcohol in terms of warming at similar sugar and acidity levels. The investigated wines contain a moderate sugar content, typical of semi-dry wines [2]. Sugar is the only component that can vastly vary between dry and sweet wines, in quantity, relevant to the warming [21]. Therefore, the warming process could differ for wines with a significantly higher sugar content, which is not explored here. The standard deviations are relatively small. Hence, only the mean temperatures are included in the figures throughout this work.  
Figure 2: Average wine and water temperatures after specified warming periods at room temperature \(T_{\rm R}=23\,^{\circ}\)C.
## 2 Materials and Methods
### Methods  
#### 2.2.1 Surface Preparation and Characterization  
Before SH functionalization, the PES fabrics were cut from 10 cm \(\times\) 10 cm samples as received to 1 cm of diameter to be inserted in 24-well TCPS plates. In order to produce superhydrophobic (SHS) surfaces, different formulation were applied on fabrics by spray coating technique for few a seconds. The first formulation (SHS1) was prepared by the dispersion of fumed silica nanoparticles in a concentration range 0.5-2 g/L in fluoropolymer blend [17] with methoxy-nonafluorobutane as solvent, the superhydrophobic surface (SHS2) was obtained starting from dispersion obtained by mixing PS dissolved in EtOAc in a concentration range 0.5-2 g/L and PTFE powder (nominal diameter 1 micron) at fixed concentration of 40 g/L. The as-prepared coatings have been rinsed in water to assess the high water repellence and remove the eventual residuals that could affect the cell proliferation.  
Once dried, surface wettability was investigated by contact angle (CA) by drop shape method by ASTRAview tensiometer [18] allowing real time drop volume control up to tens of \(\upmu\)L for hysteresis studies (advancing-receding CA) and up to 15 frames/s of frame grabbing. Drops of about 5 \(\upmu\)L were deposited on coated and uncoated samples and contact angle was measured up to spreading equilibrium.  
SEM observations included morphology studies for both coated and uncoated fabric samples with energy-dispersive X-ray spectroscopy (EDS) were done. From EDS, elemental maps were acquired for following material distribution (C, F, O, and Si) along the fibers and to evaluate homogeneity distribution in the area under investigation.  
The surface structure of samples was investigated by 3D Confocal and Interferometric Profilometry (Sensor S-NEOX, Barcelona, Spain) in order to evaluate the roughness and to acquire confocal image. The profilometry was chosen to permit large surfaces scan, for its ease and fast non-destructive use. The profilometry surface characterization were performed according to the standard ISO 25178.
# Performance Analysis of Interferometer Algorithm under Phase Measurement Error
## 3 Uniform Circular Array (UCA)  
Consider the case of a uniform circular array (UCA) with M antenna elements. A signal is incident on the array and we are to estimate the azimuth angle and the elevation angle of the incident signal. Note that, for simultaneous estimation of the azimuth and elevation, the uniform linear array (ULA) cannot be adopted since ULA-based algorithm cannot uniquely estimate the azimuth and elevation due to the ambiguity pertinent to ULA structure.  
Our interest in this paper is to get an closed-form expression of the MSEs of the azimuth and the elevation for the UCA geometry. In the interferometer algorithm for AOA estimation, the cost function in (2) is evaluated as the azimuth and the elevation vary, and the maximum value of the cost function can be evaluated. The azimuth and the elevation at which the maximum of the cost function occurs are chosen to be the azimuth estimate and the elevation estimate.  
The UCA structure is adopted to simultaneously estimate the azimuth and the elevation of an incident signal, since the ULA structure is not able to simultaneously estimate the azimuth and the elevation of an incident signal due to an ambiguity problem.  
If the array has identical responses to two different sets of DOA's, then the ambiguity problem is said to arise. For the ULA, actually, there are infinitely many different sets of DOA's resulting in identical responses, which makes the ULA inappropriate for simultaneous estimation of azimuth and elevation. On the other hand, for the UCA, no two different sets of DOA's results in identical responses. Therefore, ambiguity problem does not occur for the UCA, which makes the UCA appropriate for simultaneous estimation of azimuth and elevation.
## 3 Results and Discussion
### Lubrication Performance in SSCs of Different Machine Sizes
#### 3.3.2 Water Film Stiffness  
Total torque \(T_{\text{t}}\) is calculated in the range of \(-\delta_{\text{lim}}\) to \(\delta_{\text{lim}}\) under the gate-rotor rotating angle \(\varphi_{\text{gr0}}\) when one of the teeth just closes the screw groove. The numerical results are shown in Figure 17.  
Figure 16: The total torque of a whole period in different machines.  
It is found that \(T_{\rm t}\) increases with \(\delta\) increasing, which is consistent with that found in Figure 12. It can also be observed that the machine of big discharge capacity has the larger variation range of \(T_{\rm t}\). Taking MCT for example, the variation range of \(T_{\rm t}\) are \(-\)3.22\(\sim\)0.82 N\(\cdot\)m, \(-\)3.99\(-\)3.14 N\(\cdot\)m, \(-\)4.26\(\sim\)13.41 N\(\cdot\)m for 3 m\({}^{3}\cdot\)min\({}^{-1}\), 6 m\({}^{3}\cdot\)min\({}^{-1}\) and 12 m\({}^{3}\cdot\)min\({}^{-1}\) machines, respectively. It implies that for the machine with big size, the self-regulating system is easier to be constituted. It is mainly due to the higher relative velocity and larger hydrodynamic water film in the SSC of big size.  
The SCT can establish the self-regulating system only in 12 m\({}^{3}\cdot\)min\({}^{-1}\) SSC. The SSLT can build the self-regulating system in both 6 m\({}^{3}\cdot\)min\({}^{-1}\) and 12 m\({}^{3}\cdot\)min\({}^{-1}\) SSC. The MCT can form this in all the three  
Figure 17: The total torque at various deflecting angles in different machines.  
SSCs. By derivation of the \(T_{\rm t}\) curves in Figure 17, the curves of the water film stiffness \(S\) are obtained and shown in Figure 18.  
It is found that the water film stiffness firstly decreases and then increases as \(\delta\) increases. The curve shape helps to prevent contact since the stiffness is big at both ends but small in the middle. The water film stiffness \(S\) at \(\delta\)=\(-\delta_{\rm lim}\) (trailing side) is often larger than that at \(\delta\) = \(+\delta_{\rm lim}\) (leading side). This is consistent with the phenomenon that wear at leading side is more serious than that at trailing side.  
Figure 18: The water film stiffness in different machines.  
For any MPP, the water film stiffness increases with the discharge capacity increasing. This implies that the relative velocity has significant impact on the water film stiffness. The water film stiffness is not in a linear relation with the discharge capacity. Taking the MCT as an example, the water film stiffness \(S(\delta\)=\(\delta_{\text{lim}})\) of the 6 m\({}^{3}\)-min\({}^{-1}\) and 12 m\({}^{3}\)-min\({}^{-1}\) machines raises by 160.84% and 695.01% compared to the 3 m\({}^{3}\)-min\({}^{-1}\) machine.
## 3 Results
### Accuracy and Uncertainty Analysis
#### 3.1.1 Uncertainty of Gray Body Assumption  
The gray body assumption postulates that the ratio of the values of the emissivity at small intervals of wavelengths \(\lambda_{1}\) and \(\lambda_{2}\), with small intervals, is equal to \(1\).  
\[U_{\varepsilon_{R}}=\Delta\varepsilon_{R}\bigg{|}\frac{1}{F\varepsilon_{R}} \bigg{|}, \tag{10}\]  
The uncertainties are then computed as a function of the different emissivity ratios \(\varepsilon_{R}\), and an uncertainty of \(\Delta\varepsilon_{R}=1\%\) on the emissivity ratio is chosen.
## 4 Results and Discussion  
### Characterisation of Membranes  
#### 4.1.1 Morphology of Unmodified and Modified Membranes  
The surface morphology and roughness can influence liquid spreading and also further influence the fouling resistance of membranes. The surface morphology of both the unmodified and modified membranes is shown in Figure 3. It can be observed that the commercial TFC membrane has the smoothest surface. All of the modifications considered in this study can thus increase the roughness of the surfaces.  
The mechanism of each modification has been reported [28; 30; 31; 32]. The dopamine coating formed loose and relatively regular rough structures with nanoscale narrow valleys, as shown in Figure 3b. When the polydopamine layer made further contact with TiO\({}_{2}\) nanoparticles in suspension, nanoparticles filled all of the nanoscale valleys in the polydopamine surface, and the polydopamine had a chance to grow around the nanoparticles. Consequently, nanosized papillae surface structures are formed (Figure 3c), which is more uniform and flat than the structures shown in Figure 3b.  
Compared with the surface morphology of the self-assembly binding method (Figure 3d), the polydopamine layer maintains a relatively uniform, identical, and complete coverage of TiO\({}_{2}\) nanoparticles on the membrane surfaces (Figure 3c). Generally, when the pDA-modified membrane makes contact with the TiO\({}_{2}\) suspension, the catechol/quinone groups on the polydopamine surface immediately attract the TiO\({}_{2}\) nanoparticles. Strong coordination bonds between the membranes and the nanoparticles keep TiO\({}_{2}\) nanoparticles in their initial position and prevent the nanoparticles from forming big clusters. Since the aqueous environment is basic, the residual dopamine monomers have a chance to polymerize between the nanoparticles and the membrane surface. As the immersion time increases, the oligomers gradually link with each other and grow around the nanoparticles, and the final structure is formed, as shown in Figure 3c. In the self-assembly method, when the membrane surface makes contact with the TiO\({}_{2}\) suspension, the coordination of Ti\({}^{4+}\) to the electronegative group  
Figure 3: SEM (scanning electron microscopy) images displaying the unmodified and modified membranes: (**a**) unmodified commercial TFC (thin film composite) membranes; (**b**) TFC membranes modified by polydopamine for 30 min; (**c**) TFC membranes modified by TiO\({}_{2}\) nanoparticles (polydopamine method); (**d**) TFC membranes modified by TiO\({}_{2}\) nanoparticles (self-assembly method).  
(i.e., oxygen), or the hydrogen bond between carbonyl group and the surface hydroxyl group of TiO\({}_{2}\) keeps an uniform layer of TiO\({}_{2}\) nanoparticles on the surface in the initial stage. However, as the deposition time increases, the uniform TiO\({}_{2}\) nanoparticle layer tends to aggregate. Irregular, big TiO\({}_{2}\) clusters are formed, and the membrane surface is covered incompletely (Figure 3d).
## 1 Introduction  
The increasing number of vehicles on the road has resulted in a rise in accidents and traffic congestion, causing significant economic losses and safety concerns. As of December 2023, the number of licensed vehicles in New Zealand is estimated to be around 4.5 million [1]. Traffic-related issues such as congestion, jams, and poor road conditions contribute to the constant threat of accidents. Vehicular ad hoc networks (VANETs) offer a potential solution for improving road safety and addressing these challenges. VANETs utilize wireless communication technology, such as Wi-Fi, to enable vehicles to communicate with each other in a point-to-point manner. Researchers have been actively working on enhancing road safety through advancements in vehicular technology and wireless communication. These VANETs have great potential to transform future vehicle applications and allow them to actively participate in communication, reducing accidents and improving the overall road conditions [2]. Road safety and efficiency can be achieved by enabling vehicle-to-vehicle (V2V) communication. The idea is to monitor the vehicle speeds and mitigate accident risks through various warning messages [3]. VANETs facilitate the exchange of safety-related information, such as traffic updates and collision warnings, to enhance road safety. The main challenges in VANET operation are due to dynamic network topology. However, IEEE 802.11-based VANETs are gaining popularity because of their cost-effectiveness and standards [4]. However, issues such as performance degradation, online management, radio channel utilization, high mobility, environmental conditions,security, and privacy are still open challenges that need to be overcome before the practical deployment of VANETs.  
The main objective of this study was to conduct a feasibility study for the deployment of a low-cost IEEE 802.11-based (Wi-Fi) VANET in the parking lot of the WestCity Auckland shopping mall. We aim to evaluate the system performance with respect to the received signal strengths (RSS) and response times. To fulfil this objective, a field trial measurement was conducted using 802.11n cards. We use simulation software such as the OPNET Modeler to generalize the research findings and to validate the system performance.  
This study provides an insight into the deployment of a low-cost Wi-Fi-based VANET in the parking lot of a large shopping mall in Auckland City. We also outline a system performance testing environment, collecting and analyzing data with theoretical radio propagation models to achieve an accurate and realistic RSSI and transmission time. The research questions/challenges and research contributions are discussed next.