esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 New Sets of Benchmark Instances for the ACVRP  
### Existing Benchmark Instances  
To conveniently represent the sets of benchmark instances from previous studies, they are named after their authors. In this paper, they will be referred to as [PERSON] instances [18], [PERSON] instances [22], and [PERSON] instances [20]. Before looking into each set of instances in detail, the point is made that they were created in Western and Southern European countries. It would be both interesting and necessary to develop benchmark instances in Asian cities, including Seoul and Busan, with the aim of complementing the knowledge and experience that, until now, has been developed from European cities. Compared to Europe, Asian cities can have different population densities, road network systems, urban planning, or periods of development, resulting in distinguished urban structures.  
Fischetti instances are small in their number (the number of instances) and dimension (the number of nodes). The instance sets are derived from real-life pharmaceutical product delivery problems in downtown Bologna, Italy. There are eight instances in the set. Including 38 pharmacies, 32 herbalist shops, and a depot, the maximum dimension in the set is 71. The dimensional sizes in the set would not be sufficient for testing cutting-edge VRP solving algorithms. It is also noted that the instances were from the 1990s. The costs were obtained from solving multiple shortest path problems on the road network and not from map APIs.  
De Smet instances are not specifically designed for the ACVRP. After excluding instances for multi-depot or time window problems, five ACVRP instances are available. Each instance is provided with two matrix options, road distance and road time, which make a total of ten instances for the set. The dimensions range from 50 to 2750. The fact that the maximum dimension is very large makes these instances a viable alternative for testing state-of-the-art VRP algorithms. However, the customer nodes are not exactly from real-life problems. Administrative locations for cities, towns, and sub-towns are used as the node coordinates.  
Rodriguez instances are overwhelming in number. The set consists of 1350 instances. For each instance, two settings--the requested demand quantity from the customers and the vehicle capacity--are provided. This brings the aggregate number to 2/700. The number of customer nodes is set from small to large in size (from 50 to 500). The cost matrix is only in road distance. The customer locations are generated based on random, grid and radial distributions, as shown in Figure 2. Using geographic information system (GIS) functions,the generated locations are corrected to the nearest accessible location in the network. Even though the authors made significant efforts to include realistic perspectives, it is arguable that the locations are rather randomly placed, not exactly reflecting realistic circumstances.
# Corrosion-Fatigue Evaluation of Uncoated Weathering Steel Bridges
## 5 Analysis on Corrosion-Fatigue of Structural Steel for Bridges  
### Material  
Based on the proposed model, a case study was carried out to investigate the C-F process in steel bridges. The typical weathering steel HPS 70W [49] was selected while the normal carbon steel 14 MnNbq (named Q370q at the same time) was also investigated for comparison. The HPS 70W is a kind of high performance steel widely used in bridge engineering, which has higher strength, better corrosion resistance and better weldability compared to traditional weathering steel. The yield strength of the HPS 70W and the 14 MnNbq steel are 485 MPa and 370 MPa, respectively. The chemical compositions and mechanical properties [49; 50; 51; 52] are listed in Tables 4 and 5, respectively. It is worth stating that the fatigue limit \(\Delta K_{th}\) in Table 5 was the value for the long crack.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Element** & **HPS 70W** & **14 MnNbq** \\ \hline C & 0.11 max & 0.11–0.17 \\ Mn & 1.10–1.50 & 1.20–1.60 \\ P & 0.020 max & 0.025 max \\ S & 0.006 max & 0.015 max \\ Si & 0.30–0.50 & 0.20–0.60 \\ Cu & 0.25–0.40 & - \\ Ni & 0.25–0.40 & - \\ Cr & 0.45–0.70 & - \\ Mo & 0.02–0.08 & - \\ V & 0.04–0.08 & - \\ Al & 0.010–0.040 & - \\ N & 0.015 max & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition of the selected steel.  
Figure 6: C-F life of the selected precipitation hardening steel.  
As suggested by the NASGRO equation, instead of the original fatigue limit, a decreased fatigue value should be used in accordance with fracture toughness, to consider the behavior of small cracks. Table 5 shows the two parameters of the selected steel under different stress ratios.  
The parameters of the selected steel [12] are shown in Table 6.
## 4 Conclusions  
In summary, we reported the synthesis of magnetic mesoporous silica nanoparticles, followed by the modification with 2-diethyl aminoethyl methacrylate (DEAEMA) using surface-initiated ARGET atom transfer radical polymerization (ATRP). Furthermore, the polymer chains were quaternized using 2-iodoethanol to obtain a cationic polymer that would not be affected by the solution's pH. The synthesized materials were characterized using a variety of advanced techniques. The characterization results showed that Fe\({}_{3}\)O\({}_{4}\) nanoparticles were spherical in shape with particles sized ca. 29 nm. When the Fe\({}_{3}\)O\({}_{4}\) were coated with a mesoporous silica shell, the particle size was increased to 230 nm, confirming the successful loading of the silica shell. Finally, the materials were evaluated for the removal of methyl orange (MO) and sunset yellow (E110) dyes from an aqueous solution. The results showed that the Fe\({}_{3}\)O\({}_{4}\)@MSN-PDMAEMA sample exhibited a weak adsorption performance toward both MO and E110, compared with Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA. The maximum adsorption capacities of MO and E110 using Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA were 294 mg g\({}^{-1}\) and 194.8 mg g\({}^{-1}\), respectively. Thus, the high sorption ability, ease of applicability, abundance of the raw materials, and low price make Fe\({}_{3}\)O\({}_{4}\)@MSN-QPDMAEMA a promising adsorbent for the removal of both dyes from aqueous solutions.  
Conceptualization, A.A.A., H.M.A.-S., A.A.; methodology, K.M.A., A.M.B. and A.M.A.; software, S.A.B.; validation, K.M.A., A.M.B. and A.M.A.; formal analysis, K.M.A., A.M.B. and A.M.A.; investigation, K.M.A., A.M.B. and A.M.A.; data curation, K.M.A. and A.M.A.; writing--original draft preparation, K.M.A., A.M.B. and A.M.A.; writing--review and editing, K.M.A., A.M.B. and A.M.A.; visualization, H.M.A.-S.; supervision, K.M.A. and A.M.A.; project administration, K.M.A., A.M.B. and A.M.A.; funding acquisition, A.M.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through Research Group No. RG-1441-304.  
The authors declare no conflict of interest.
## 1 Introduction  
A cerebral aneurysm is an abnormal dilation of the artery caused by a weakness on the wall and is located on the subarachnoid space at the base of the brain. A reduction of the tunica media and middle muscular layer on the artery wall, combined with hemodynamic factors, lead to this process [1]. Wall shear stress (WSS), oscillatory shear index (OSI), and relative residence time (RRT) have been proposed as indicators of aneurysm rupture risk [2; 3; 4; 5].  
Fluid-structural simulations (FSI) using image-based models of cerebral aneurysms can help to better understand the vascular remodeling processes associated with aneurysm growth and its subsequent stabilization or rupture [6; 7].  
Risk factors include hypertensive states [8; 9]. Regarding these, FSI simulations performed under normal blood and high blood pressure conditions showed that WSS and mechanical stresses in the aneurysm wall were strongly affected by hypertension [10]. Within these states, a very important one corresponds to isolated systolic hypertension (ISH), which is the most common form of hypertension in elderly people (>65 years old) [11]. Moreover, the prevalence in young adults has been increasing over the past decade, due to the epidemic of overweight and obesity [12; 13; 14; 15]. No records of studies about ISH in cereb aneurysms were found, using computational simulations, CFD, FSI or similar techniques, so, investigate about the effect of this type of condition on the hemodynamics of an aneurysm would represent an advance in the understanding of the pathology.  
Therefore, the objective of this research is to report and study the hemodynamic effects of an isolated systolic hypertension condition on image-based cerebral aneurysms models from real patients, comparing it with a \"traditional\" hypertensive condition and a normal or normotensive pressure condition by carrying out FSI simulations.
## 4 Discussion
### Economic Feasibility  
According to priority in the present study, the economic feasibility of biostimulant applications in _Diplotaxis tenuifolia_ L. has been evaluated as a risk mitigation strategy against regulatory non-compliance rather than solely as a yield enhancement tool. Biostimulants can provide secondary benefits through a small increase in yield [49], but their primary economic value lies in their ability to reduce leaf nitrate concentrations by 10-33%, thereby lowering the probability of exceeding the EU regulatory thresholds [50; 51; 52].  
The investment becomes economically sustainable when the cost of biostimulant application is counterbalanced by the reduced risk of losing the total crop because of nitrate threshold violations. This is particularly valuable when nitrate levels are likely to approach the regulation limits [8] consequent to greenhouse growing in winter when nitrate accumulation is usually higher [49], even more upon intense nitrogen fertilization [53].  
However, the biostimulant employment may not be sustainable in extensive cultivation systems with naturally low nitrate levels [27], or low market value when the crop loss risk is minimal. The decision framework should consider the probability-weighted expected yield loss against the annual cost of biostimulant applications.
## 3 Results
### Modeling and Design of the Diffractive Optical Elements
#### 3.1.1 Green Lens Design  
First, the range of spatial frequencies expected to be recorded across the lens for a recording wavelength of 514 nm was calculated for a number of different off-axis angles (different central spatial frequencies). The calculated range of spatial frequencies for lenses having central spatial frequencies of 300, 400, 455 and 500 L/mm are shown in Figure 5a. Figure 5b shows the calculated Q values for the lowest spatial frequency occurring in each lens from Figure 5a for different thicknesses. It was decided to choose the lens with the lowest off-axis angle (and spatial frequency range) that would ensure volume Bragg grating behavior (Q > 10) across the whole lens aperture for 150 microns in thickness. This ensures that higher orders are negligible. A minimum of 140-150 micron-thick gratings were shown to be needed to avoid crosstalk in previous work [14]; thickness above 150 microns can be more challenging to prepare without compromising layer optical quality. The lens chosen was, therefore, Lens 3, with 150 um layer thickness and 455 L/mm central spatial frequency.
## 1 Introduction
Another area of interest, other than flow around streamlined aerofoils, is how synthetic jets can alter flow around bluff bodies such as large pylons, e.g., onshore wind turbines, tidal turbine mounts or the risers used for offshore platforms, deep-water wind turbines or bridge sections [19; 20; 21]. Through periodic vortex shedding, unwanted vibrations can be induced in the structure that can ultimately lead to failure. Synthetic jet actuators could be used to alter the wake produced by a structure and hence reduce the magnitude and/or frequency that a structure oscillates at, hence decreasing the chance of failure. When turbines are implemented in arrays, the wake from one device can affect turbines positioned in its wake, causing a reduction in efficiency [22]. Synthetic jet actuators could be used to alter these wakes and thus increase the efficiency of the array or at least decrease the turbulence intensity experienced by a structure downstream. Previous flow control work has predominantly focused on aerofoil geometries with air as the fluid. Recently, experimental investigations into active flow control for a circular cylinder in cross-flow have been conducted with water as the fluid, which have shown drag reductions of 20% to 29% [23; 24]. Numerical approaches using large eddy simulation (LES) for active drag control for a two-dimensional cylinder in cross-flow by jet actuation have achieved drag reductions of 13%, as reported by [PERSON] et al. [25]. The optimum location of the synthetic jet orifice has been shown to be at a position perpendicular to the freestream [25]. Higher order methods such as the spectral element method are typically applied to direct numerical simulation (DNS) but have also been applied to LES [26]. More recently, spectral element or _hp_ finite element methods have also been applied to more complex configurations [27; 28]. The use of such methods allows for more accurate simulation of complete internal flows such as those of micropumps [28; 29], which function similarly to synthetic jet actuators. In the present study, ANSYS Fluent will be used to conduct simulations. Currently, these higher order methods are unavailable for transient simulations in this commercial software package and therefore the unsteady Reynolds-averaged Navier-Stokes (URANS) computational fluid dynamics (CFD) approach with the transition shear stress transport (SST) turbulence model will be used instead, as described in more detail in Section 2. The numerical work presented here focuses on the effect of varying synthetic jet actuation frequency on drag and lift coefficients, pressure coefficient and the turbulence intensity in the wake of a circular cylinder in cross-flow.
## 4 Proposed Architecture of Model-Based Predictive Control for Thermal Comfort in Building  
MPC is a simple and satisfyingly intuitive approach to constrained control that has been successfully applied in many areas over the last few decades. During each sampling interval, a finite horizon optimal control problem is formulated and solved over a finite prediction horizon. The result is a trajectory of inputs and states into the future satisfying the dynamics and constraints of the building while optimizing some given criteria. A convolutional MPC strategy consists of the following steps: first, a model is established to compute the predicted variable as the process output. Then, a cost function is defined and minimized in relation to the control signals. Finally, the first of these control signals is applied to the process. Figure 2 depicts the conceptual architecture of the proposed enhanced MPC system for thermal comfort control. The way that the thermal comfort issue is included in the control law is based on the PMV index. To reach this objective, the control system will follow a reference in order to reach an optimal indoor temperature that allows for obtaining a PMV index close to zero.  
More specially, the model is designed in a MISO (Multiple-Input/Single-Output) architecture of an indoor home coupled the home HVAC system. The manipulated values (i.e., air temperature,  
Figure 1: Predicted Mean Vote (PMV) and thermal sensation scale.  
relative humidity, air velocity, etc.) from the indoor environment are inputs of the control system and the control variable is provided to the HVAC device (e.g., fan). These input variables are used by the MLR predictor to estimate the PMV index. The optimizer sets the optimal inputs of the regression model depending on the cost function. The Fuzzy controller takes the estimated PMV index values, OT (outside temperature), and \(\Delta\)PMV (variation of PMV index) to compute the control signal by referring to the predefined control rules. OT allows the control system to identify and capture knowledge about the outdoor seasonality when optimizing the indoor comfort conditions. The control signal is then applied to the building HVAC system for regulating the cooling devices. These are the main points that make the thermal comfort control system presented in this paper different from the existing ones. The detail processes the PMV prediction and the fuzzy control based on PMV optimization will be described in the following subsections.
# A Novel Power-Saving Transmission Scheme for Multiple-Component-Carrier Cellular Systems
## 4 Proposed Scheme
### Session Admission Control (SAC)  
At the start of each round, the mechanism will check to determine whether or not incoming session requests from the RT or NRT processing queues should be allowed to proceed through the admission gate to join the scheduling queue to access the network. Define \(\vartheta(s)\) as the number of non-occupied sub-channels in the system during round \(s\). The key feature of the scheme's design is that if \((\vartheta(s)-|\Re_{\text{w}}(s)|)>~{}0\), the admission gate is opened to allow some type-\(m\) user requests to enter the scheduling queue based on the ratio of the type-\(m\) users' required data rate to the required data  
Figure 2: A conceptual flow chart of the proposed scheme.  
rate of all the users in the processing queues; otherwise, those session requests in both the RT and NRT processing queues are not allowed to enter the scheduling queue (_i.e._, the admission gate is kept closed). This design effectively ensures that both the data rate of the users in \(\Re_{\text{w}}(s)\) and the fairness indexes for the different types of users can be maintained as well as possible at certain levels.  
To facilitate description, define \(\Re_{\text{rw}}^{(m)}(s)\) as the set of type-\(m\) users in the processing queue until round \(s\), while also defining \(\tilde{r}_{\text{rw}\_\text{req}}^{(m)}(s)\) as the average required data rate of those type-\(m\) users in \(\Re_{\text{rw}}^{(m)}(s)\). In addition, let \(n^{(m)}(s)\) denote the number of type-\(m\) user requests in the respective processing queues that are permitted to proceed to the scheduling queue in round \(s\). The following pseudo code details the exact operation of this component:  
\[\begin{array}{rcl}\textbf{If}&((\theta(s)&-&|\Re_{\text{w}}(s)|)&>&0)\\ &&\\ &n^{(m)}(s)&=&\left[\begin{array}{ccc}(\phi(s)&-&|\Re_{\text{w}}(s)|)\frac{ \tilde{r}_{\text{rw}\_\text{req}}^{(m)}(s)\left|\Re_{\text{rw}}^{(m)}(s)\right| }{\sum\limits_{n=1}^{2}\tilde{r}_{\text{rw}\_\text{req}}^{(n)}(s)\left|\Re_{ \text{rw}}^{(m)}(s)\right|}\\ \end{array}\right],&m=1,&2;\\ &\textbf{Else}&n^{(m)}(s)&=&0,&m=1,&2.\end{array}\]
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 2 Materials and Methods
### Pol-SAR Features Extraction
#### 2.1.2 Co-Polarized Correlation Coefficient  
The hh and vv channels may have different echoes due to different scattering mechanisms. The co-polarized correlation coefficient can be used to describe the scattering characters of the illuminating area, and it is defined as Equation (8) with respect to the coherency matrix [20]:  
\[\rho_{\text{hh\_vv}}=\frac{\left|C_{13}\right|}{\sqrt{C_{11}C_{33}}}. \tag{8}\]  
The co-polarized correlation coefficient is sensitive to permittivity and surface roughness [21; 22; 23], which is proportional to water content when the roughness is in a certain range.
# Magnetic Nanofluid Droplet Impact on an AAO Surface with a Magnetic Field
###### Abstract  
This paper presents an experimental study on the impact of magnetic nanofluid droplets on aluminum sheet surfaces subjected to a magnetic field. A magnetic nanofluid was prepared by synthesizing Fe\({}_{3}\)O\({}_{4}\) nanoparticles and coating amounts of oleic acid surfactant in deionized water. The wettability of an alumina sheet was first changed by using a phosphoric acid (H\({}_{3}\)PO\({}_{4}\)) solution to perform the first anodic oxidation process. A contact angle meter and a high-speed camera were then, respectively, used to capture the static contact angle of magnetic nanofluid droplets and their dynamic characteristics during impact on the surface with/without anodic oxidation process. The results of the static contact angle showed that a more hydrophilic surface could be obtained after the sheet was processed. The dynamic images showed that the processed surface exhibited a slightly greater degree of adhesion between the liquid and solid without a magnetic field. The effect of AAO surface topography can be significant under the action of an external magnetic field.  
magnetic fluids; droplet impact dynamics; dynamic magnetowetting; anodic aluminum oxide surfaces +
Footnote †: journal: Applied Physics Letters B
# An Improved Anti-Interference Precoding of Large-Scale Fading System Based on Channel Inversion
## 2 System Model
### Channel Model Settings  
In a cellular network OFDM-based system, one block group allocates all orthogonal subcarrier channels, and the other block group reuses subcarrier channels. For a cell in a cellular network, which uses an OFDM subcarrier channel, the channel factor is expressed as a correlation operation between a large-scale attenuation factor and a small-scale attenuation factor, which is expressed as in Figure 1.  
Cell \(j\) indicates the \(j\)-th cell in the area group, and the number of antennas of a cell base station is M; cell \(l\) indicates the \(l\)-th cell in the area group, and the number of cell users is \(K\). The channel factor between the \(m\)-th antenna of a base station in the \(j\)-th cell and the \(k\)-th user in the \(l\)-th cell can be expressed as:  
\[g_{mj}^{kl}=\sqrt{g_{j}^{kl}}h_{mj}^{kl} \tag{1}\]  
where \(h_{mj}^{kl}\) denotes a fast-fading factor which is a small-scale fading factor. It is mainly caused by a multipath propagation and modeled as \(i.i.d.\)\(CN(0,1)\) random variables.  
Figure 1: Cell \(j\) antenna and cell \(I\) user channel factors.  
Furthermore, \(\beta_{j}^{kl}\) denotes a slow fading factor which is a large-scale fading factor, mainly caused by the building blocking. We model the large-scale fading coefficient \(\beta_{j}^{kl}\) as a log-normal distribution based on the Urban Macro model. The specific value of \(\beta_{j}^{kl}\) is a positive real number, which is determined by shadowing and distance between the corresponding user and base station and it is given by:  
\[10 log_{10}\beta_{j}^{kl}=-139.5-35*d_{j}^{kl}+\psi \tag{2}\]  
where \(\psi\) denotes the shadow fading coefficient with _i.i.d._\(N\Big{(}0,\sigma_{\psi}^{2}\Big{)}\) distribution and \(d_{j}^{kl}\) denotes the distance, measured in kilometer, between the \(j\)-th BS and the \(k\)-th user in the \(l\)-th cell.