esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Performance Prediction Model of Steel Bridge Deck Pavement System Based on XGBoost
## 3 Dataset Creation
### Orthogonal Test  
The orthogonal experimental design method is to select the most representative and comprehensive comparable samples from all the test samples and use these selected samples as the test protocol so that a more comprehensive test result can be achieved with as few trials as possible. The core of the orthogonal test design is the selection of a suitable orthogonal table. The usual notation of the orthogonal table is \(L_{n}(a^{b})\), where \(L\) represents the orthogonal table, \(n\) represents the number of experiments to be done, \(a\) represents the level of test factors, and \(b\) represents the maximum number of factors that can be arranged in this orthogonal table. The general steps of orthogonal test design are to first determine the appropriate level size of the test factor variables and factor variables, then select the appropriate orthogonal table and determine the test plan, and finally conduct the test according to the orthogonal table and record the test results.  
In this study, the orthogonal test design method is used to determine the numerical simulation test scheme, and the numerical simulation test results are used to establish the dataset of the mechanical performance indexes of the steel bridge deck pavement system. The test factors for the mechanical performance index of the steel deck pavement system were selected as follows: the elastic modulus of the upper layer of pavement \(E1\), the elastic modulus of the lower layer of pavement \(E2\), the thickness of the upper layer of pavement \(H1\), the thickness of the lower layer of pavement \(H2\), the thickness of steel bridge deck \(T\), and the spacing of cross-partition \(D\). The level range of the test factors was determined according to the Technical Specification for the Design and Construction of Highway Steel Bridge Deck Pavement (JTG/T3364-02-2019) and engineering experience [29]. The influencing factors and levels of the test were taken as shown in Table 3. There are six influencing factors of the test, two 9 levels, two 6 levels, one 5 level, and one 3 level. Therefore, the orthogonal table \(L_{81}(9^{6})\) was selected, and the proposed level method was used for the levels with a high number of factor levels. The experimental protocol was  
Figure 11: Maximum vertical displacement of pavement layer.  
designed according to the orthogonal table, the numerical simulation test was conducted according to the experimental protocol, and 81 sets of data were finally obtained.
# In Silico Investigation of the RBC Velocity Fluctuations in Ex Vivo Capillaries
## 2 Physical and Mathematical Description of the Problem
### Fluid Modeling  
It is well known that the velocity of RBCs inside the capillaries is in the range of 0.4-1.8 mm/s [23]. However, it is physiologically impossible to determine a single velocity that is valid for all capillaries. The velocity in each individual capillary is regu  
Figure 1: (**a**) Representation of the order of the capillaries starting from the arteriole. (**b**) Deviation in the average hydraulic diameter.  
lated by the downstream and upstream tube hematocrit due to partitioning and lingering events [8; 11; 24; 25]. This means that the inlet velocity (and consequently the flow rate) of the capillaries is inherently a transient phenomenon. For comparison purposes, we set the maximum average inlet velocity of the largest capillary (S4) to 1.5 mm/s, based on the maximum average velocity from the experimental study by [PERSON] et al. [23]. The average inlet velocities of the remaining capillaries are calculated by adapting an 'inverse' Hagen-Poiseuille relation, where velocity is assumed to be inversely proportional to the square of the average hydraulic diameter. This approach ensures that the flow rate is reduced when the average hydraulic diameter is lower, which is physiologically accurate.  
The flow field is solved using LBM (D3Q19) [26; 27] with the BGK operator [28] and the forcing term [29]. A fine lattice distance of \(\Delta x=0.25\) um is used for spatial discretization to accurately solve the flow field in narrow capillaries, and \(\tau\approx 1\) prevails for all simulations.
## 3 Simulated Result and Analysis
### Analysis of the Simulated Application Scenario  
In Figure 6, one representative application scenario, namely single-handed holding mode (SHM), is presented, and the impact of a hand phantom is studied. The corresponding simulated metrics of the antenna, including S-parameters, the transmission coefficient, the total efficiency, the SAR (specific absorption rate) field, and the 3D radiation pattern, are also shown. For SHM mode, Ant. 3, Ant. 4, Ant. 5, Ant. 6, and Ant. 7 are directly contacted with the hand issue. The 3D radiation pattern of the system and SAR field distribution in SHM mode are shown in Figure 6a,b, respectively. SAR is the power of the electromagnetic waves absorbed or consumed per unit time per unit mass of human tissue. Since smartphone antennas cannot avoid interference from hands, the effects of electromagnetic absorption of the human body can be studied through SAR. The greater the SAR value, the greater the thermal effect of electromagnetic waves on human tissue. The SAR values measured in Figure 6b comply with the Chinese standard SARYD/T 1644.2-2011 [28] body test. Because of this arrangement, the reflection coefficients of these elements deteriorated significantly in both bands, as plotted in Figure 7a. Notably, minimal variation in the transmission coefficient in SHM mode is depicted in Figure 7b. The realized peak gains of the proposed miniature eight-port antenna array, except for Ant. 1, all dropped by 1 dB to 3 dB in the desired bands, as shown in Figure 7c. Since the hand is a lossy medium, the realized peak gains of Ant. 8 also reduced by about 0.8 dB. The total efficiencies of the proposed miniature eight-port antenna array, except for Ant. 1 and Ant. 8, all dropped to between 20% and 40% in the wideband, as shown in Figure 7d. The efficiency of Ant. 5 also reduced to about 45% since the hand is a lossy medium and parts of the radiating power of the antenna element are assimilated by hand.  
Figure 5: Simulated current and electric field distribution of the proposed miniature eight-port antenna array. (**a**) Current distribution at 3.5 GHz. (**b**) Current distribution at 4.9 GHz. (**c**) Electric field at 3.5 GHz. (**d**) Electric field at 4.9 GHz.
# Noise Attenuation Based on Wave Vector Characteristics
## 3 Numerical Data Test  
### Numerical One-dimensioal Two-component Data  
As shown in Figure 4, the synthetic one-dimensional two-component (1D2C) records (Figure 4a) for the test are generated with the valid seismic signal at a 30 Hz dominant frequency (Figure 4b) added by 50% (noise to signal energy ratio) random noise (Figure 4c) and strong ground roll (Figure 4d). It is clear that the apparent periods of the seismic signal and the ground roll are about 40 and 100 ms, respectively. As shown in Figure 5, we are able to well separate the ground roll (Figure 5a) and valid signal with random noise (Figure 5b) using a 40-ms moving time window. As shown in Figure 5, the data at the start and end 20 ms cannot be filtered because they are located at the start and end borders of the moving time window. The polarization hodograms in Figure 6 show that median filtering effectively improves the ellipticity of the ground roll because the body wave and noise vectors are reduced. However, with median filtering, the separated ground roll--without adopting any scaling factor--presents a smaller elliptical polarization trajectory (Figure 6b). The time-variant scaling factors can improve the matching of the polarization trajectories between the separated ground roll and the true one (Figure 6c).  
Then, we perform median filtering to the ground-roll-attenuated data using a 5-ms moving window to separate the valid signal (Figure 7a) and the random noise (Figure 7b). Figure 8 shows that our noise attenuation method can well distinguish between the body-wave and random-noise polarizations and recover the linear polarization of the body wave. Comparing Figure 8b with Figure 8c, it is clear that adopting scaling factors can make our noise attenuation method more amplitude preserving.  
Figure 3: Moving windows used in median filtering to the wave vectors from shot three-component (3C) records. The red dots are seismic samples. The blue rectangles mark the moving windows for the median filtering performed over time. The green rectangle marks the moving windows for the median filtering performed over trace within the same receiving line.  
Figure 4: Synthetic two-component records. (**a**) Full two-component record; (**b**) the valid seismic signal at a 30 Hz dominant frequency; (**c**) the 50% random noise and (**d**) the ground roll.  
Figure 5: Results of ground roll separation. (**a**) Separated ground roll and (**b**) valid signal with random noise.  
Figure 6: Hodogram plots of two-component records. (**a**) Raw two-component records; (**b**) separated ground roll without adopting any scaling factor and (**c**) separated ground roll using scaling factors.
## 3 Application and Results
### Case Study VI: The MW12 Problem  
In this case study, the ALMO approach is applied to the MW12 problem, another constrained multi-objective optimization problem from the test suite introduced by [PERSON] and [PERSON] [28]. The MW12 problem is characterized by its highly complex Pareto front and constraint region, making it one of the most challenging problems in the suite. The mathematical formulation of the MW12 problem is as follows:  
\[\text{Minimize }f_{1}(\mathbf{x}) =x_{1}g_{1}(\mathbf{x}),\] (41) \[\text{Minimize }f_{2}(\mathbf  
to 0.004, demonstrating the need for higher \(\beta\) values if the constraints of the problem statement are difficult for the learner to understand.  
Next, the effect of varying \(\phi\) and \(\beta\) on the percentage of infeasible solutions in the final Pareto front is explored and shown in Figure 30. The results show that with \(\beta=0.6\), around 25% of the candidate solutions in the final Pareto front are infeasible, and when \(\beta=0.8\), this reduces to about 20%. This drops significantly, to below 5%, when \(\beta\) is increased to 0.9. Interestingly, the percentage of infeasible solutions in the MW12 problem is higher than that observed for MW9 (Section 3.5), likely because MW12 presents a more complex and difficult landscape for the active learner to model accurately.  
Figure 28: Case study VI: Pareto-front solution found using ALMO and two different configurations.  
Figure 27: Case study VI: Pareto-front solution found by NSGA-II without AL using _pymoo_ library.  
Figure 29: Case study VI: Pareto-front solution found using ALMO.  
In terms of constraint evaluation savings, the ALMO approach shows more modest savings for the MW12 problem compared to MW9. With \(\beta=0.8\), approximately 10% of the constraint evaluations are saved, whereas with \(\beta=0.9\), the savings drop to around 5%. The lower savings compared to MW9 suggest that the increased difficulty of MW12 requires more constraint evaluations to ensure feasible solutions, despite its improved IGD values.  
Finally, we investigate how the savings in constraint evaluations evolve over the course of the optimization process. Similar to the MW9 problem, the ALMO approach becomes more efficient as the optimization proceeds, achieving greater savings in its later stages. Figure 31 illustrates the percentage savings in constraint evaluations across five equal optimization intervals.
## 4 Metal-Oxide Nanoparticles (MONPs) in Water Treatment
### Copper Oxide Nanoparticles  
The application of Cu oxide nanoparticles is very successful in wastewater treatment due to several factors, such as their small size, low production costs, large surface area, and inherent synthesis availability [134]. Cupric oxide (CuO) and cuprous oxide (Cu\({}_{2}\)O) are the two basic copper oxides most frequently used in industrial applications. The nano form of these oxides functions as effective adsorbents in the field of wastewater [36]. CuO NPs have a remarkable maximum fluoride adsorption capacity of 3152 mg. This indicates that they have a considerable capacity to remove a substantial amount of fluoride from wastewater. CuO NPs exhibit endothermicity and spontaneity in their thermodynamic adsorption behavior, which highlights their applicability in wastewater treatment processes [36; 135]. Moreover, the inherent redox activity of Cu nanomaterials enables participation in chemical reactions that aid in removing or transforming pollutants [136].  
Recently, [PERSON] et al. [127] studied the efficiency of using biosynthesized CuO NPs in the process of treating tanning wastewater from the textile industry. Tanning wastewater exhibited a significant decrease in its physicochemical parameters when it was in an ideal setting. There was a significant decrease of 95.2%, 86.7%, 91.4%, 87.2%, and 97.2% in total suspended solids (TSSs), total dissolved solids (TDSs), chemical oxygen demand (COD), biological oxygen demand (BOD), and conductivity, respectively. The studies by [PERSON] et al. [128], [PERSON] et al. [129], and [PERSON] and [PERSON] [130] investigated the effectiveness of CuO nanoparticles for the removal of pollutants from wastewater (Table 5).
## 2 EMS Selection Methodology  
The proposed methodology follows the flowchart shown in Figure 1, proceeding with the following steps:  
* Step 1. Select EMS: Identify the EMS or multiple EMSs to be compared depending on the type of vehicle, its expected utilization, the features of its powertrain, and the objectives in terms of hydrogen and energy consumption, as well as fuel cell and battery life expectations.
* Step 2. Define scenarios: Specify the driving cycles for the FCEV that will serve as inputs to the AVL model in Step 4.
* Step 3. Parameterize models: Configure the model with data for the powertrain, such as the fuel cell, battery, and electric motor, as well as for the vehicle structure and the environment, to accurately represent the targeted FCEV utilizations.  
* Step 4. Simulate scenario and EMS: Run simulations for each scenario, where N equals the number of selected EMS options multiplied by the number of driving cycles. Each scenario consists of a unique combination of EMS and cycle.  
In addition to the methodology, this step includes the validation of the AVL model. The model is considered valid when the performance variables show an error of less than the selected tolerance between the results obtained from the MATLAB Simulink model and the AVL model.  
* Step 5. Evaluate results: Record the simulation results, including fuel cell and battery aging, as well as hydrogen and energy consumption, for scenario comparison.
* Step 6. Repeat Steps 4 and 5 until all scenarios have been simulated.
* Step 7. Technical decision: Analyze and compare the scenarios, minimizing the key objective variables.
* Step 8. Economic evaluation (beyond the scope of this work): Assess the scenarios from an economic perspective.
# Matchability and Uncertainty-Aware Iterative Disparity Refinement for Stereo Matching
###### Abstract  
After significant progress in stereo matching, the pursuit of robust and efficient ill-posed-region disparity refinement methods remains challenging. To further improve the performance of disparity refinement, in this paper, we propose the matchability and uncertainty-aware iterative disparity refinement neural network. Firstly, a new matchability and uncertainty decoder (MUD) is proposed to decode the matchability mask and disparity uncertainties, which are used to evaluate the reliability of feature matching and estimated disparity, thereby reducing the susceptibility to mismatched pixels. Then, based on the proposed MUD, we present two modules: the uncertainty-preferred disparity field initialization (UFI) and the masked hidden state global aggregation (MGA) modules. In the UFI, a multi-disparity window scan-and-select method is employed to provide a further initialized disparity field and more accurate initial disparity. In the MGA, the adaptive masked disparity field hidden state is globally aggregated to extend the propagation range per iteration, improving the refinement efficiency. Finally, the experimental results on public datasets show that the proposed model achieves a reduction up to 17.9% in disparity average error and 16.9% in occluded outlier proportion, respectively, demonstrating its more practical handling of ill-posed regions.  
Keywords:stereo matching; disparity refinement; convolutional neural networks; deep learning +
Footnote †: journal: Medical Imaging
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC-AES Hybrid PKE Protocol
#### 5.2.2 Key Confirmation  
Key confirmation is performed as described for the PQC standalone PKE protocol (Section 5.1.2) to prevent MITM attacks.
## 3 Results and Discussions
### Hypothesis on the Formation of CuO in Different Forms  
The hydrothermal synthesis of \(CuO\) nanostructures with diverse morphologies (NPs, NRs, and NSs) is a complex process. The presence of precipitated copper hydroxynitrate (\(Cu_{2}(OH)_{3}NO_{3}\)), a basic copper salt [47], significantly influences the process. Figure 6 shows the XRD pattern of a sample after 15 h of hydrothermal anealing stage, which is a point where the process was not completed. This pattern reveals the presence of \(Cu_{2}(OH)_{3}NO_{3}\) alongside \(CuO\) at various controlled pH levels. The \(Cu_{2}(OH)_{3}NO_{3}\) diffraction peaks are observed at 29.55\({}^{\circ}\), 31.94\({}^{\circ}\), 42.12\({}^{\circ}\), and 54.1\({}^{\circ}\), which are corresponding with the JCPDS card File No. 77-0148.  
The intensity of the \(Cu_{2}(OH)_{3}NO_{3}\) peak at 29.55\({}^{\circ}\) in the XRD pattern (Figure 6) appears to be proportional to the initial precursor solution's pH level. This suggests that the amount of \(Cu_{2}(OH)_{3}NO_{3}\) produced during the incomplete hydrothermal process depends on the pH. Furthermore, \(Cu_{2}(OH)_{3}NO_{3}\) can thermally decompose into \(CuO\) (Equation (1)) [48]. These indications show the significant influence of this basic salt on the morphology of \(CuO\) in which the produced \(Cu_{2}(OH)_{3}NO_{3}\) quantity directly affects the morphology of the final \(CuO\) sample.  
\[Cu_{2}(OH)_{3}NO_{3(\mathrm{s})}\to CuO_{(\mathrm{s})}+ HNO_{3(\mathrm{g})}+H_{2}O_{(\mathrm{g})} \tag{1}\]
# Scale Reduction Techniques for Computing Maximum Induced Bicliques
## 2 Integer Programming Formulations  
In this section, we provide integer programming (IP) formulations for the problems of interest. Consider a simple, undirected graph \(G=(V,E)\). Let \(x_{ij}\) be a binary decision variable indicating whether a vertex \(i\in V\) belongs to either of the two parts \(j\in\{1,2\}\). Then the MB problem can be formulated as follows:  
Maximize \[\sum_{i\in V}(x_{i1}+x_{i2})\] (1) subject to: \[x_{i1}+x_{i2}\leq 1 \forall i\in V\] (2) \[\sum_{i\in V}x_{ij}\geq 1 \forall j\in\{1,2\}\] (3) \[x_{ij}+x_{kj}\leq 1 \forall\{i,k\}\in E,\ j\in\{1,2\}\] (4) \[x_{i1}+x_{k2}\leq 1 \forall\{i,k\}\
otin E\] (5) \[x_{ij}\in\{0,1\} \forall i\in V,\ j\in\{1,2\}\] (6)  
In this formulation, constraints (2) make sure that each vertex belongs to at most one part, and constraints (3) rule out solutions with empty parts, which correspond to independent sets. Constraints (4) ensure that each of the two parts forms an independent set, and constraints (5) guarantees that non-adjacent vertices cannot belong to different parts. Considering an instance with \(n=|V|\) vertices and \(m=|E|\) edges, the above formulation has \(2n\) binary variables and \(n^{2}+2\) constraints.  
To develop an IP formulation for the MEB problem, we introduce \(m\) new binary variables \(z_{ik},\{i,k\}\in E,i<k\) indicating whether edge \(\{i,k\}\) is included in the optimal solution. We have:  
Maximize \[\sum_{\{i,k\}\in E}z_{ik}\] (7) subject to: (2)-(5) \[z_{ik}\leq x_{i1}+x_{i2},\ z_{ik}\leq x_{k1}+x_{k2} \forall\{i,k\}\in E,k>i\] (8) \[x_{ij},z_{ik}\in\{0,1\} \forall i\in V,j\in\{1,2\},\{i,k\}\in E,k>i\] (9)  
In this model, constraints (8) ensure that an edge is in the solution only if its incident vertices are selected to be in the solution. Note that the integrality of \(z_{ik}\) can be relaxed due to the fact that these variables are bounded by \(x_{ij}\) variables and the objective function is maximization. This formulation has \(2n+m\) variables and \(n^{2}+2m+2\) constraints.  
The proposed IP formulations can be employed in off-the-shelf solvers to find optimal solutions of the problems of interest. However, the practical applicability of this approach is limited to small-scale instances, unless the formulations are strengthened based on their detailed polyhedral study. In the next section, we propose an alternative global optimization approach that employs effective scale reduction techniques to solve large-scale real-life instances.
# Bitumen and Bitumen Modification: A Review on Latest Advances
## 1 Introduction
### Bitumen Chemistry  
From a chemical point of view, bitumen is defined as a viscous viscoelastic liquid (at room temperature) consisting essentially of hydrocarbons and their derivatives, which is totally soluble in toluene, substantially non-volatile, and softens gradually when heated [7]. It comprises a very great number of molecular species that vary widely in polarity and molecular weight [8; 9]. Elemental analysis show that bitumen composition is primarily determined by its crude oil source and it is difficult to give a specific geographical generalization [10; 11] (many suppliers also mix bitumen from different sources as well). This has been shown in a wide research by SHRP (Strategic Highway Research: Special Report) [12]. Based on this report, the main constituents of bitumen are carbon, which varies from 80 to 88 wt% and hydrogen ranging from 8 to 11 wt%. In addition, Heteroatoms and transition metal atoms (principally vanadium and nickel) are generally presents: sulfur (0 to 9 wt%), nitrogen (0 to 2 wt%), oxygen (0 to 2%), vanadium up to 2000 ppm, and nickel up to 200 ppm [10; 13; 14].  
From a molecular point of view, the main compounds of the polar heteroatoms above are sulphides, thiols and sulfoxides, ketones, phenols and carboxylic acids, pyrrolic and pyridinic compounds, and most metals form complexes such as metalloporphyrins [14]. Molecular weight distribution analysis shows that bitumen is a complex mixture of about 300 to 2000 chemical compounds (medium value 500-700) making a complete chemical characterization very difficult. For this reason, bitumen is generally fractionated by simpler methodologies, which allow two principal constituents to be identified:  
* Asphaltenes
* Maltenes (also called petrolenes)  
Maltenes are then classified into saturate, aromatic, and resin, which together with asphaltene are known as the bitumen SARA (Saturate, Aromatic, Resin, Asphaltene) fraction. The relative abundance of the SARA fractions allows the bitumen chemical composition to be related with its internal structure and some of its macroscopic properties [15]. However, it should be noted that changes in experimental conditions (especially eluent nature) significantly affect the proportion of every bitumen fraction [10; 16]. It is, therefore, important to specify the experimental setup condition for comparing the various chemical compositions of bitumen, even though they show some common features and overall properties that remain substantially unchanged.