esa-sceva/satcom-chunk-collection huggingface dataset:
# Bio-Inspired Aerodynamic Noise Control: A Bibliographic Review
## 1 Introduction
[PERSON] [20] compared the wing feathers of the owls and other non-silent flying birds, and suggested that the owl's wings have three distinctive and unique characteristics that can reduce noise (see Figure 1), namely, the serrated feathers on the leading edges, the fringes formed at the trailing edges, and the soft downy coating on the surface of wings and legs. In his opinion, it is these characteristics that make the owls \"silent flight\". Inspired by owls, researchers attempted to reduce aerodynamic noise by learning their noise reduction features from different viewpoints and developed a number of innovative noise reduction solutions, including serration-type noise reduction, fringe-type noise reduction, and porous material inspired noise reduction. Figure 1: Details of the three special feather adaptations of the barn owl (redrawn from [21]): **(A)** leading edge serrations; **(B)** trailing edge fringes; and **(C)** soft downy coating surface. Research into the bio-inspired aerodynamic noise control has continued for more than sixty years. The first boom seems to appear in the 1970s, while the second one is in the past twenty years (see Figure 2). This prolonged period of effort has achieved many fruitful results on the macroscopic and microscopic morphological characteristics of the owl wing feathers, as well as on the noise measurements of flying birds in the field or prepared wings in the wind tunnel. Meanwhile, several theoretical, numerical and experimental studies have been carried out in the past to explore the feasibility, parameter influence, aerodynamic effects and underlying mechanisms of the serration-type, fringe-type and porous material inspired noise reduction techniques. However, as far as the authors know, comparatively little work has been done to summarize the main findings and progresses in these areas. This motivates the present paper, which aims to go one step into that direction. In this bibliographic survey, the progresses and trends of the bio-inspired aerodynamic noise control are systematically reviewed, and some suggestions for future work are also discussed, in an effort to advance the state of the art in these important areas and try to focus future studies into the most promising directions or directions that have not yet been perfectly understood. It is worth pointing out that the selected literature in this paper is by no means complete due to the rapid development of these areas, but focuses on research efforts that may be relevant to the present review. The remainder of this paper is organized as follows: Section 2 briefly reviews the biological studies of the owl's silent flight. In Sections 3-6, the progress of four main bio-inspired aerodynamic noise control techniques, i.e., leading edge serrations, trailing edge serrations, fringe-type trailing edge extensions and porous material inspired noise reduction, are systematically reviewed. Finally, we provide some concluding remarks and suggestions for future work in Section 7.
# Comparison of Profit-Based Multi-Objective Approaches for Feature Selection in Credit Scoring
## 3 Methods
### Non-Dominated Genetic Algorithm (NSGA-II)  
NSGA-II is a popular multi-objective optimization algorithm that can applied to feature-selection problems [8]. The algorithm begins by evaluating the fitness of the initial population of potential solutions. From these, \"parent solutions\" are selected and crossed to generate \"child solutions\". Mutation may occur where some components of the solutions are randomly altered. If the stopping criteria are not met, a non-dominated sorting scheme is used to select the best solutions, with diversity maintained by calculating and maximizing the crowding distance between solutions. The result of this sorting process becomes the population for the next round of evaluation. In this manner, the population converges towards the set of overall best, non-dominated solutions known as the Pareto frontier. NSGA-II can be adapted to feature selection by denoting the individuals as different feature combinations and setting the number of features as an objective in addition to the main objective such as profit. The process is given in Algorithm 1.  
```
Initialize population, P\({}_{t}\), of size N \(\mathrm{Q}_{0}=\mathrm{\O}\) \(\mathrm{F}_{0}=\mathrm{Fitness\ evaluation\ of\ P}_{0}\) (\(\mathrm{F}_{1}\), \(\mathrm{F}_{2}\),... ) = Non dominated sorting of P\({}_{0}\) to establish rank  Determine crowding distance of (\(\mathrm{F}_{1}\), \(\mathrm{F}_{2}\),... ) whilestop criterion not satisfied do \(\mathrm{Q}_{t}=\mathrm{selection}\), crossover, mutation, recombination of P\({}_{t}\) \(\mathrm{R}_{t}=\mathrm{Q}_{t}\cup\mathrm{P}_{t}\) \(\mathrm{F}_{t}=\mathrm{Fitness\_Evaluation}(\mathrm{R}_{t})\) (\(\mathrm{F}_{1}\), \(\mathrm{F}_{2}\),... ) = Non dominated sorting of \(\mathrm{R}_{t}\) to establish rank  Determine crowding distance \(\mathrm{P}_{t+1}=\mathrm{select\ new\ population\ of\ size\ N\ based\ on\ rank\ and\ highest\ crowding\ distance}\) endwhile
```  
**Algorithm 1** Pseudo code for NSGA-II. Algorithm for NSGA-II
## 9 Topology Optimization Results
### L-Bracket: Stress Minimization Results  
Defining a stress-minimization problem statement may also lead to a transition from an angular to a curved geometry. This is particularly evident in the solution obtained by the SLSQP algorithm, shown in Figure 6C. However, this transition did not occur with the method available in ABAQUS(r). As a result, the SLSQP and Trust-constr algorithms converged to solutions with maximum stresses of 315 MPa and 330 MPa, respectively, representing a 35% reduction when compared to the 518 MPa installed in the solution obtained with ABAQUS(r).  
The data obtained with the SLSQP indicated an unstable behavior with large variability in the objective function. To improve readability, only the cumulative best solution obtained with this method was included in the plots referring to this problem statement. This variability indicates that the SLSQP was more likely to perform larger density redistributions, which may be a result of the strategy adopted by the SciPy implementation of this algorithm, and a result of the influence of their internal parameters, which largely affect the solutions obtained.  
Similar to the previous problem statement, the p-norm exponential factor had a significant influence on the result obtained, especially in the use of elements with intermediate densities. Also, although the MMA algorithm was removing material from the stress concentration area, further research should be performed to find appropriate tolerance parameters.
## 2 Materials and Methods
### Adhesion Test Design  
#### 2.3.1 Test Materials and Equipment  
(1) Geotextile material: For the slurry adhesion test, a cost-effective white non-woven geotextile (polyppropylene, PP) with a tensile strength of 7.5 kN/m and an O\({}_{95}\) of 0.06 mm was used. To align with practical applications and provide reference for future projects, polyester geotextiles with specifications of 300 g/m\({}^{2}\), 400 g/m\({}^{2}\), and 500 g/m\({}^{2}\) were selected for subsequent tests. All geotextiles were filament non-woven types.  
(2) Test material: The sand from the Tarim River bank, primarily composed of silt and fine sand, was collected from the K135 water conveyance embankment. The particle size ranged from 0.075 mm to 0.25 mm, characterized by small and uniformly distributed particles. Detailed properties are listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Soil Quality**} & **Moisture Content \%** & **Natural Gravity \%** & **Internal Friction \%** & **Cohesion \%** & & & **Grain Content\%** & \\  & **Content \%** & **Gravity \%** & **Angle \%** & **C/(Kpa)** & **>2** & **2-0.5** & **0.5–0.25** & **0.25–0.075** & **\(\leq\)0.075** \\ \hline Tarim River sand & 0.25 & 1.82 & 23 & 4.3 & 0 & 0.11 & 0.07 & 78.07 & 21.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical property indicators of sand on the bBanks of Tahe River.  
Figure 3: Geotextile testing samples.  
Figure 2: The geotextile samples for SEM analysis under three working conditions of Tarim River geotextile bag reventent (from left to right: brand-new geotextile; aged geotextile from the top of the revetment slope; geotextile covered in mud).  
(3) Test equipment: The equipment included an air compressor with a power rating of 3000 kW, an exhaust capacity of 270 m\({}^{3}\)/min, and a pressure of 0.8 MPa, as well as a 1500 W \(\times\) 2 \(-\) 50 L air compressor. A TL-6C model slurry spray gun with a 1 cm nozzle was used for the spraying process.
# A Survey of Resource Allocation Techniques for Cellular Network's Operation in the Unlicensed Band
## 4 Channel Selection for LTE Operation in the Unlicensed Band
The joint user association, channel selection, and load balancing for LTE-U has been formulated as a non-cooperative game in [50] for the fully distributed scenario with the carrier aggregation mode. Furthermore, a distributed machine learning algorithm based on the echo state networks has been proposed in [50] for channel selection. In [50], it has been shown that the proposed algorithm converges to a mixed strategy Nash equilibrium and performs better than the existing Q-learning based schemes for distributed channel selection. Inter-RAT coordination for spectrum sharing between multiple operators using both LTE-U and Wi-Fi have been modeled as a repeated game in [51]. A geographical clustering-based approach for the LTE-U SCs and the Wi-Fi APs has been used for coordination. The coordinating SCs use a decision tree learning based channel selection scheme in [51]. Furthermore, an incentive mechanism has been proposed to motivate operators to willing to share spectrum resources. In [52], a deep learning approach has been proposed for modeling the resource allocation problem of LAA-LBT SCs with dynamic channel selection, carrier aggregation, and fractional spectrum access while guaranteeing fairness with existing Wi-Fi networks. Figure 4: Different modes of operation for the LTE in the unlicensed band. Note that channel selection should not be confused with dynamic frequency selection (DFS). The DFS is a regulatory requirement to select a channel on a relatively slow time scale in comparison to channel selection to avoid co-channel interference to existing systems like RADAR [43]. A key issue in LTE in unlicensed band deployment with various channel access mechanisms and channel selection schemes are the fair co-existence of LTE-U devices with other devices in the unlicensed bands. Thus, in the next section, we study the impact of LTE's operation in the unlicensed band on the fairness of the system.
# Smart Infrastructure and Additive Manufacturing: Synergies, Advantages, and Limitations
## 4 Benefits of Integrating 3D Printing with Smart Infrastructure
### Tailored Components for Specific Applications
Apart from the factor of time, 3D printing significantly reduces construction costs by minimizing material waste, labor expenses, and logistical challenges. Traditional construction often results in excess material waste due to cutting, shaping, and unused raw materials, all of which contribute to higher costs and environmental burdens. Additive manufacturing, however, uses only the exact amount of material needed, leading to cost savings of up to 30-60% on raw materials [148]. Furthermore, labor costs are dramatically reduced since automated 3D printers require fewer workers to oversee the process compared to traditional construction teams [149]. This is particularly impactful in regions facing skilled labor shortages or high labor costs, where 3D printing offers a viable alternative that reduces dependence on manual workforce availability [150]. Additionally, on-site 3D printing eliminates the need for extensive transportation of prefabricated components, cutting down fuel costs and reducing carbon emissions [151]. The ability to manufacture structures directly at the point of use not only enhances cost efficiency but also improves accessibility to remote or underserved areas, where traditional construction would otherwise be economically unfeasible [152]. With its combination of speed, material efficiency, and reduced labor demands, 3D printing is immensely aiding in cost-effective smart infrastructure development [153; 154]. Beyond efficiency, cost-effectiveness, and sustainability, 3D printing technology significantly enhances the resilience, adaptability, and intelligence of smart infrastructure. The ability to rapidly manufacture and deploy structures on demand makes 3D printing an essential tool for disaster recovery and emergency response, providing resilient solutions in post-disaster scenarios where traditional construction methods may be slow or impractical. Additionally, the adaptability of 3D printing allows for the creation of modular and reconfigurable structures that can be easily modified or expanded in response to evolving urban demands. This flexibility is particularly valuable in rapidly urbanizing areas, where infrastructure needs can change unpredictably. Furthermore, the integration of 3D printing with digital technologies such as artificial intelligence (AI), the Internet of Things (IoT), and real-time monitoring systems enhances the intelligence of built environments. Smart materials and embedded sensors within 3D-printed components enable predictive maintenance, structural health monitoring, and autonomous self-repair, ensuring that infrastructure remains functional and safe over time. These capabilities position 3D printing as a transformative technology for the development of future-proof, resilient, and intelligent urban systems.
## 3 Results
### Experimental Validation of Predicted Peptide  
To examine if the insertion of the leucine in TKKTLRT results in more binding coverage, we synthesized both peptides, i.e., TKKTLRT and TKKLTLRT, with a biotin tag. The biotinylated peptides were next mixed with Cd/ZnS quantum dots (Q-dots) coated with streptavidin with a red emission maximum of \(\sim\)655 nm. High binding affinity of streptavidin for biotin results in the formation of very stable peptide-coated Q-dots.  
We incubated the labeled peptides with type I collagen and washed the samples. As a negative control, collagen was incubated with streptavidin quantum dots without biotinylated peptides. Confocal microscopic images of the peptide-coated Q-dots and control samples are provided in Figure 8. Q-dot fluorescence was captured as a Z-stack with an excitation wavelength of 405 nm and emission wavelength of 655 nm (643 nm to 670 nm excitation range was evaluated). Images were processed using ImageJ 1.53 t. The bar plot in Figure 8 displays the averaged pixel intensities of the labeled images, with 255 being the maximum brightness for a pixel. The Q-dots showed some degree of non-specific binding affinity to collagen. However, among the two peptides tested, the predicted peptide TKKLTLRT showed higher coverage and the largest sum of pixel brightness as shown in the Figure 8 bar plot.  
Building upon the hydropathy interface model we described, we hypothesize that these collagen interfaces are highly related to the binding sites of collagen of the leucine-rich repeat motifs. Previous studies have shown that TKKTLRT binds to type I collagen. Using our design, we increased binding of the peptide to collagen and validated it by streptavidin-coated quantum dots. The increase in collagen binding is likely related to the collagenase peptide SQNPVQP binding sites, for which TKKTLRT shares hydropathic features. The binding of the TKKLTLRT peptide indicates the possible union between SQNPVQP-related binding sites and the LRR binding sites. The engineered TKKLTLRT peptide is predicted to have stable binding at these sites under a wider variety of pH conditions than the LRR-10 peptide at its binding sites. This prediction is limited to single binding sites. Further studies are required to determine if the cooperative effect of multiple binding sites from LRR-10 favor the LRR-10 binding over the TKKLTLRT peptide on collagen.
# Navigation Scenario Permutation Model for Training of
## 4 Discussion
### Permutation Method of Navigation Scenario Generation  
The permutation of the prepared values could result in massive outcomes of navigation scenarios that are practical and applicable for actual nautical chart information. As Figure 9a shows, the sample of proposed methods was practical, and none of the permutation results was impractical as in Figure 9b. The practical navigation scenarios that we are focused on were the usual and general navigation situations to train the remote operators of the ship's handling. In this perspective, the multiple large altering navigation situations in Figure 9b was considered impractical. The generation of navigation route was already conducted in other researches to design the ship's route for coastal seas [17] or ship's route design between two ports using data-driven approaches [18]. However, those researches were concentrated on the larger scale of navigation situations across the ports, not on the single altering scale. On the contrary, we used the distribution of plenteous values of the mariner's decision in the ship's handling on a relatively small scale. In addition, the massive generation of navigation scenarios is hard to be obtained manually, and if the general combination method is applied to the navigation elements, the outcome will include massive impractical navigation scenarios as well. Upon the proposed permutation methods, all of the outcomes are various, practical, and suitable for navigation training.
# Transmit and Receive Diversity in MIMO Quantum Communication for High-Fidelity Video Transmission
## Abbreviations  
ARQAutomatic Repeat RequestAVCAdvanced Video CodingAV1 AOMediaVideo1 CSIChannel State InformationFECForward Error CorrectionGOPGroup of PicturesHDHigh-DefinitionHEVCHigh Efficiency Video CodingLDPCLow-Density Parity-CheckMIMOMulti-Input Multi-OutputMISOMulti-Input Single-OutputPSNRPeak Signal-to-Noise RatioQECQuantum Error CorrectionQKDQuantum Key DistributionSISpatial InformationSIMSingle-Input Multi-OutputSNRSignal-to-Noise RatioSUMStructural Similarity IndexMeasureUHDUltra-High-DefinitionVMAFVideo Multi-Method Assessment FusionVP9 Video Processing9 VVCVersatile Video CodingTIemporal Information
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 1 Introduction
In the previous research [7], simulation was performed with V2 Verifier, but as mentioned above, Crystals-Dilithium cannot satisfy the correctness in the WAVE protocol. Therefore, in this paper, we propose a methodology to overcome these limitations and simulate our methodology. Finally, we focused on the on-board unit (OBU), which is the terminal platform in the autonomous driving environment. Most of the simulation research conducted thus far has examined CPU architectures. Currently, one mainly uses embedded-based OBUs as terminal platforms for V2V environments; therefore, simulation in that environment is essential. Since the embedded environment is mainly limited by performance and memory size, it is effective to use the optimization code for each OBU architecture.
## 3 The Radiative Transfer Equation
### RTE Analytical Solutions with Simplification
#### 3.3.2 Single-Scattering Approximation
Spatially Varying Scattering Coefficient: Instead of using a constant value for the scattering coefficient, you can define a function that varies with \(z\) or spatial position \(z\). This can better represent the spatial variations in scattering properties within the medium. 2. Wavelength-Dependent Scattering: We need to consider incorporating wavelength-dependent scattering coefficients if the application involves multiple wavelengths or spectral bands. We can use empirical data or models to define the scattering coefficient as a function of wavelength. 3. Scattering Anisotropy: The \(\widehat{\beta}_{HG}(\theta)\) phase function incorporates anisotropic scattering, where the scattered radiation is preferentially emitted in specific directions. We can adjust the \(S(z,\,z^{\prime})\) to incorporate this anisotropy by considering the angular dependence of scattering. 4. Physical Models for Scattering: Instead of using a simple constant value, consider using physical models for scattering based on the properties of the medium. For example, Mie theory or Rayleigh scattering models can be used for specific types of scattering particles (e.g., aerosols and water droplets). 5. Experimental Data or Measurements: We can use experimental data or measurements of scattering properties in similar media to define a more realistic scattering kernel. This can provide more accurate representations of scattering behaviours in a given application scenario.
# Gaze-Dependent Image Re-Ranking Technique for Enhancing Content-Based Image Retrieval
## 3 Gaze-Dependent Image Re-Ranking
### Re-Ranking Based on Vision Information  
After the above processing, the proposed method converts coordinate-format gaze trace data into text-format semantic information using image captions, which is used for a re-ranking that matches user preferences. To select the feedback information for re-ranking, obtaining many feedbacks from users contributes to reflecting their preferences. Conversely, too much feedback increases the burden for the user and the computational complexity of the model from a practical perspective. Considering the above factors, in this experiment, the proposed method uses the gaze traces corresponding to a specific number of images (the top \(a\) images) from the initial retrieval results as feedback from the user. The re-ranking process is as follows. First, the top \(a\) images from the initial retrieval results and their corresponding gaze traces are input into the CGT model to generate the same number of gaze-dependent image captions. By inputting the generated captions into the CLIP model, we can obtain the feature vectors \(\mathbf{v}_{1}\), \(\mathbf{v}_{2}\), \(\mathbf{v}_{3}\),..., \(\mathbf{v}_{a}\) representing user preference information. Moreover, we consider the query image feature during the re-ranking process to prevent accuracy degradation caused by the absence of its information. Finally, \(\mathbf{v}_{\text{r}}\) used for re-ranking can be obtained by averaging the feature vector \(\mathbf{v}_{\text{q}}\) of the query image with \(a\) feature vectors (\(\mathbf{v}_{1}\), \(\mathbf{v}_{2}\), \(\mathbf{v}_{3}\),..., \(\mathbf{v}_{a}\)) extracted from the gaze-dependent image captions by the following equation:  
\[\mathbf{v}_{\text{r}}=\frac{\mathbf{v}_{\text{q}}+\sum_{k=1}^{a}\mathbf{v}_{k}}{a+1}. \tag{8}\]  
Because the major components that constitute the feature vector \(v_{\text{r}}\) represent user preference information based on the gaze information, using \(v_{\text{r}}\) for re-ranking makes it possible to obtain the result ranking those images that reflect the user's preferences with a higher rank.