esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Application of Conventional ML Algorithms
### K-Nearest Neighbors (K-NN)  
K-nearest neighbor (K-NN) algorithm is a commonly used supervised learning method in machine learning, which is a nonparametric lazy learning method. The working principle is that, for a given test sample, the K-nearest training samples are found according to some distance metric, and the prediction is made based on the information of these training samples. The principle of K-NN algorithm is shown in Figure 6.  
To implement the K-NN algorithm, we first need to determine two parameters, the algorithm hyperparameter K and the distance metric of the model vector space.  
K is the only hyperparameter in the K-NN algorithm. The selection of the value of K has a great impact on the performance of the algorithm. When the K value is small, only the training samples that are similar to the input will play a role in the prediction result. At this time, the approximation error of the algorithm is relatively small. However, because the prediction results are sensitive to the neighboring points, when the neighboring points are noise points, the prediction will have a large deviation, resulting in an over-fitting phenomenon. Similarly, when the value of K is selected to be large, points that are far away will also have an impact on the prediction results. At this time, the algorithm has good robustness, but it is prone to under-fitting.  
The distance measure indicates how similar two points in the sample space are. Commonly used distance metrics are Manhattan distance, Euclidean distance, Chebyshev distance, Minkowski distance, cosine distance, etc.  
K-NN is widely used in the classification and recognition of GPR images. As early as 2000, K-NN algorithm has been applied to GPR image detection [17]. In 2007, [PERSON] et al. proposed a method consisting of EHD and a possibilistic K-NN classifier for landmine detection with GPR, as a possibilistic K-NN classifier can discriminate between neighbors with equal distance while standard K-NN, weighted K-NN, and fuzzy K-NN cannot [18]. In addition, it was presented in this work that possibilistic K-NN also performed better than crisp K-NN and fuzzy K-NN over ROC curves for the detection of landmines. In [13; 14], K-NN had better results than SVM overall, which was probably caused by the decision pattern by two classifiers, as K-NN decided according to the votes from nearest neighbors while SVM decided with consideration of the entire dataset. In 2019, [PERSON], [PERSON] et al. used moving average background subtraction (MA-BS), DC-offset removal, and the subtraction and weight (SaW) methods for data augmentation and K-NN for classification [19]. Another study in 2019, using features of row and column vectors to produce a twin gray statistics sequence (TGSS) as an input of classifiers, applied K-NN together with rotation forest, logistic regression (LR), and some other algorithms in the hyperbola classification [20].
## 3 Database of Water Content and Hyperspectral Information
### Data Acquisition Method
#### 3.1.4 Equipment of Drone Flight  
The drone used in the experiment was the Matrice 300 Pro (DJI, Shenzhen, China), which measures 810 mm wide, 670 mm long, and 430 mm high when its wings are fully spread. As shown in Figure 6a, the overall setup includes a hyperspectral camera, an auxiliary battery to supply power to the hyperspectral camera, and an GNSS sensor (TW4721) that can check real-time coordinates. The maximum flight time of the drone is 55 min with a payload of 0, and it can fly for up to 30 min when additional equipment is attached. This is a continuous shooting time, and since the drone's battery is removable, the flight time does not matter if landing and takeoff are performed repeatedly. When all equipment is installed, the drone weighs 7.6 kg. Detailed specifications of the equipment are listed in Table 1.  
Figure 5: Input coordinates: (**a**) Principal of hyperspectral camera; (**b**) Total coordinates.  
The main scanning methods used in hyperspectral cameras include static staring or spectral scanning, which captures the entire scene in a band-sequential format, and push-broom, which generates hyperspectral images using dynamic line-by-line scanning [25]. In this study, push-broom scanning, which acquires spectral images through an aircraft such as an aerial system, was applied to capture long-distance images using a UAV. In this method, as shown in Figure 6b, a hyperspectral camera (or sensor) is mounted on an aircraft (drone) using a slit perpendicular to the drone's forward flight direction. Shooting is performed with an overlapping range depending on the drone movement. The performance of push-broom scanning has been verified by various researchers [26, 27, 28, 29, 30] and is capable of providing a reasonable range of spatial resolution and high spectral resolution [31].
## 4 Experimental Results and Discussion
### Assessment Methods
#### 4.1.2 Ssim  
The structural similarity index measure is an evaluation index used to compare the structural similarity between two images. By calculating the index SSIM, a difference between the output image and the undistorted image can be quantified to evaluate the quality of image recovery. That is, a larger SSIM represents better image quality, which can illustrate the minor differences between the output image and the undistorted image. By comparing the brightness, contrast ratio, and structure of two images, SSIM can be used to quantify the levels of similarity [60]. The computational formula of SSIM can be expressed as:  
\[SSIM(x,\,y)=l(x,\,y)^{\alpha}c(x,\,y)^{\beta}s(x,\,y)^{\gamma} \tag{13}\]  
where \(l(x,y)\) represents the brightness, \(c(x,y)\) represents the contrast ratio, \(s(x,y)\) represents the structure, \(x\) is the original image, and \(y\) is the denoised image.  
The structural factor accounts for the majority of the calculation. When \(\alpha=\beta=\gamma=1\) and \(c_{3}=c_{2}/2\), the computational formula of SSIM can also be expressed as:  
\[SSIM(x,\,y)=\frac{\big{(}2\mu_{x}\mu_{y}+C_{1}\big{)}\big{(}2\sigma_{x}\sigma _{y}+C_{2}\big{)}}{\big{(}\mu_{x}^{2}+\mu_{y}^{2}+C_{1}\big{)}\big{(}\sigma_{x }^{2}+\sigma_{y}{}^{2}+C_{2}\big{)}} \tag{14}\]where \(\mu\) is the corresponding mean value, \(C\) is a denominator adjustment parameter and \(\sigma^{2}\) is the corresponding variance.
# Advancing Data Privacy in Cloud Storage: A Novel Multi-Layer Encoding Framework
## 3 Privacy-Protecting Attributes Classification for the Storage of Cloud Data
### Design Management  
Design plays a crucial role as it enables the integration of enhanced functionalities within the operational system. With the increasing demand for secure and multifunctional privacy systems, experts strive to incorporate novel privacy design layers, techniques, and methodologies to create a more efficient and secure framework [7; 45; 46]. Within this category, five attributes, namely design features, cryptographic techniques, cloud-storage-structure, data auditing, and external assets, have been identified [47]. A one-time key, or one-time password (OTP), is a powerful encryption method used for securing data, especially in cloud environments. It ensures unparalleled security by using a unique, random key for each of the encryption sessions, which is never reused. This may eliminate repetition and may prevent attackers from identifying patterns or decrypting the message through analysis.  
To generate an OTP, the generated key must be truly random, match the message length, and be used only once. These keys can be generated using hardware-based or cryptographically secure random number generators.  
Example: Encrypting \"HELLO\" (converted to numbers: H = 7, E = 4, etc.) with a key like [3; 5; 8; 19; 21], we add each number mod 26 to get an encrypted result. The recipient will reverse the process using the same key.  
Since OTPs offer perfect secrecy when used correctly, they are especially suitable for protecting highly sensitive information transmitted through the cloud or stored in the cloud.  
The upcoming sections will comprehensively discuss these attributes, and Table 1 provides a summary of the attributes within the design-management category.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Privacy-Preserving Attribute** & **Study** & **Characteristics** \\ \hline Design features & [11; 14] & \(\bullet\) & An automatic blocker protocol is employed to authorize and validate external audits, ensuring their credibility and integrity. \\  & \(\bullet\) & OTP authentication is utilized to verify and authenticate cloud users, enhancing security. \\  & \(\bullet\) & The key generation and tag generation processes are carried out in distinct phases, ensuring the secure and efficient creation of cryptographic keys and identification tags. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Attributes of design management.
# Thermo-Regulated Cotton: Enhanced Insulation through PVA Nanofiber-Coated PCM Microcapsules
###### Abstract  
The innovative integration of phase change materials (PCMs) into textiles through microencapsulation presents a transformative approach to developing thermally regulated fabrics. This study explores the synthesis and characterization of microcapsules containing a coconut oil core and an ethylcellulose shell, and their application on cotton fabrics coated with polyvinyl alcohol (PVA) nanofibers. The dual-layer system involving microcapsules and nanofibers is designed to enhance the thermal insulation properties of textiles by regulating heat through the absorption and release of thermal energy. The microencapsulation of PCMs allows for the effective incorporation of these materials into textiles without altering the fabric's inherent properties. In this study, the coconut oil serves as the PCM, known for its suitable phase change temperature range, while ethylcellulose provides a robust shell, enhancing the microcapsules' structural integrity. The application of a PVA nanofibers layer not only strengthens the thermal regulation properties but also protects the microcapsules from release while the fabric is manipulated, thereby prolonging the functional life of the fabric. Comprehensive testing, including scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR), confirms the successful application and durability of the microcapsules on the textiles. Thermal imaging studies demonstrate the fabric's enhanced capability to maintain a consistent temperature, highlighting the potential of this technology in applications ranging from smart clothing to energy-efficient building materials or automotive isolation. The integration of PCMs in textiles via microencapsulation and nanofiber technology marks a significant advancement in textile engineering, offering new opportunities for the development of smart and sustainable materials. The study demonstrates the promising potential of integrating PCMs into textiles using microencapsulation and nanofiber technologies. Despite the initially modest insulation improvements, the methodology provides a robust foundation for further research and development.  
ccoonut oil; ethylcellulose; sustainability; textile; shell; cone; FTIR; SEM +
Footnote †: journal: Applied Sciences
# Implementing Many-Lights Rendering with IES-Based Lights
## 1 Introduction
In this paper, we analyze the state of the art of Many-Lights techniques for real-time rendering, and we investigate the feasibility of the integration of photometric data in the process of the generation of the VPLs. We base the definition of the light sources and the creation of the VPLs on photometric data provided in the standard IES format proposed by the Illuminating Engineering Society. In particular, our work extends the _Reflective Shadow Maps_ technique [8] so that any kind of light source described through a well-formed IES file can be used to illuminate a virtual environment. The technique generates an \"enhanced\" version of the original Reflective Shadow Map, associated to an IES-based light source. During the rendering step, each pixel of the map can be viewed as a VPL and used to compute its contribution to the overall lighting. The proposed approach, as in the original work [8], focuses on diffuse reflections. Following the standard Many-Lights approach, gathering and normalizing the illumination contributions from \(N\) samples offers a reasonable approximation of color bleeding effects in the final rendering. To our knowledge, there are no other examples of works that integrate a photometric description of light sources into a Many-Lights rendering technique. While IES-based lights are currently supported by several game engines, they are actually used to \"weight\" only the intensity of the emission component (thus mainly affecting the direct illumination component), and they are not actively considered in the computation of GI effects. This paper is structured as follows. In Section 2, we provide an overview of GI models based on Many-Lights techniques. An extensive explanation is reserved for the Reflective Shadow Maps [8] technique, which our work is based on. In Section 3, we describe the photometric data that are contained in IES files, as defined by the Illuminating Engineering Society. Section 4 describes our proposed technique and how photometric data can be used to characterize a light source in a real-time rendering pipeline. In Section 5, we present the results of the proposed method on two test scenes. Finally, Section 6 draws conclusions from the results and presents future work.
# Receiver-Initiated Handshaking MAC Based on Traffic Estimation for Underwater Sensor Networks +
## 2 Applications, Motivations, and Challenges  
Receiver-initiated-based MACs have several advantages over sender-initiated-based handshaking protocols in data gathering applications of UWSNs, as will be discussed in this section. We also describe the major challenges in designing an efficient receiver-initiated-based MAC protocol.
# Surrogate Based Optimization of Aerodynamic Noise for Streamlined Shape of High Speed Trains
## 4 Optimization Strategy  
### Construction of Cross-Validation Based Kriging Model  
Considering the huge computational cost in the aerodynamic optimization, especially in the shape optimization of high-speed trains, a sequential optimization method based on minimizing the response surface criteria has been adopted in the present paper to reduce the training points. The Kriging surrogate model is an interpolation technique based on statistical theory [26; 27; 28; 29]. This model takes full account of the relevant characteristics of the variable space, containing the regression part and the nonparametric part. The Kriging model is obtained based on the cross-validation algorithm [30]. The use of cross-validation algorithm could prolong the time of the training process. However, compared to the massive cost of CFD and CAA calculation for each sampling point, this time cost could be ignorable. A basic Kriging model could be firstly constructed from the initial 20 training points. Based on this model, the Pareto set could be obtained. Two typical points of the Pareto set could be chosen for CFD/CAA validation. If the optimization accuracy could not meet the requirement, these two points then would be added to the initial training points to build a more accurate Kriging model. If the optimization accuracy meets the design requirement, the Kriging model is finally constructed. During each iteration process, the training points are divided into groups randomly, and each group has two training points.
# Classification of OCT Images of the Human Eye Using Mobile Devices
## 2 Materials and Methods  
### Dataset  
The OCT2023 dataset contains optical coherence tomography (OCT) tomograms categorized into five classes: healthy eyes (NORMAL) and four classes representing pathological changes: choroidal neovascularization (CNV), diabetic macular edema (DME), presence of drusen associated with dry AMD (DRUSEN), and vitreomacular traction (VMT). The dataset currently includes 4467 images in JPEG and BMP formats. All B-scans underwent ophthalmological review and classification verification. Figure 1 illustrates examples of images from each class.  
The scans in the dataset were made using 3 different devices: Avanti RTvue XR (Optovue Inc., Fremont, CA, USA), Copernicus HR (Optopol Technology, Zawiercie, Poland), Spectralis (Heidelberg Engineering, Inc., Heidelberg, Germany). The distribution of images among the classes and devices is listed in Table 1.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Device Name** & **CNV** & **DME** & **DRUSEN** & **NORMAL** & **VMT** & **Total** \\ \hline Avanti RTvue XR & 0 & 0 & 54 & 514 & 860 & 1428 \\ Copernicus HR & 0 & 0 & 0 & 112 & 84 & 196 \\ Heidelberg Spectralis & 891 & 858 & 801 & 293 & 0 & 2843 \\ \hline Total & 891 & 858 & 855 & 919 & 944 & 4467 \\ \hline \end{tabular}
\end{table}
Table 1: Distribution of images between the devices and categories.  
Figure 1: Examples of OCT image classes.  
The images in the dataset have varying dimensions, as listed in Table 2. As can be seen, the OCT2023 dataset contains many large images. The utilized neural network architectures require \(224\times 224\) pixel input images. Therefore, the preprocessing of images requires scaling. Additionally, as the OCT images are single-channel gray-scale matrices, they were converted to 3-channel images to match the input expected by neural networks.  
The database was randomly divided into 3 parts: training set (80%), validation set (10%) and test set (10%). To maintain consistency of results for all tested neural networks, the data division was the same for all experiments (i.e., a one-time initial random division of the dataset).
## 5 Result and Discussion
The findings suggest that the hybrid approach has the potential to enhance the data transmission speed in various scenarios by reducing network latencies when compared to the other methods. Figure 6 displays the system overhead (%) for three scenarios: black hole attack, gray hole attack, and no attack. The data were analyzed using three methodologies: Watchdog, DSR, and the proposed hybrid approach. System overhead is the additional resources, such as computational power or bandwidth, utilized in a detection system. Lower values indicate a greater degree of system effectiveness. Watchdog experiences an overhead of 18% when dealing with a black hole attack. DSR reduces the overhead to 15%, and the hybrid approach further minimizes it to 10%. In the scenario of a gray hole attack,  
Figure 4: Packet delivery ratio (PDR) among Watchdog, DSR, and our hybrid approach. Watchdog experiences a 20% overhead, which is then decreased to 16% with the assistance of DSR. The hybrid approach manages to achieve the lowest overhead of 9%. In the absence of attacks, the no-attack scenario demonstrated decreased overhead when all three methodologies were employed. The overhead of Watchdog was 5%, DSR had an overhead of 4%, and the hybrid approach had the lowest overhead of 3%. The outcomes highlight the efficiency of the hybrid approach in using fewer additional resources compared to the Watchdog and DSR methods in different scenarios, indicating its potential to create a more resource-effective detection system. The comprehensive analysis in Table 1 clearly demonstrates that the hybrid detection approach excels across all evaluation metrics. It not only improves detection accuracy and reduces false positives but also enhances packet delivery ratios, minimizes network latency, and reduces system overhead. These improvements underscore the hybrid approach's potential to provide a robust and reliable security solution for MANETs, effectively mitigating the impacts of black hole and gray hole attacks. Figure 5: Network latency among Watchdog, DSR, and our hybrid approach. Figure 6: System overhead among Watchdog, DSR, and our hybrid approach.
## 2 Methodology
### Principal Component Analysis (PCA)  
Principal component analysis (PCA) is a linear transformation technique used to reduce the dimensionality of multivariate data. It aims to capture the maximum variance within a dataset while minimizing the loss of information. By transforming the original features into linearly uncorrelated principal components, PCA provides a succinct representation of the data.  
The mathematical formula for PCA is:  
\[\mathrm{X}=\mathrm{USV}^{\prime} \tag{2}\]  
where \(\mathrm{X}\) is the centered observation matrix, \(\mathrm{U}\) is the matrix of scores, \(\mathrm{S}\) is the diagonal matrix of singular values, and \(\mathrm{V}\) is the matrix of loadings.
## 2 Mathematical Model for a Small Wind Turbine System
### Wind Power Model  
For a small wind turbine system, the power absorbed by the blades is determined by wind speed, blade parameters, and wind power coefficient. According to [PERSON] [9], the effective power input for the proposed dual input transmission mechanism can expressed as  
\[P_{in}=\frac{1}{2}\pi R^{2}\rho V_{w}{}^{3}C_{p} \tag{1}\]  
where \(P_{in}\) is the power input (W), \(R\) the radius of the blade (m), \(\rho\) the air density (kg/m\({}^{3}\)), and \(V_{w}\) is the speed of the wind through the blades (m/s). The wind power coefficient \(C_{p}\) is a function of the blade angle and tip speed ratio (TSR), which is defined as the speed of the blade tip divided by the wind speed. For a given wind speed, the value of \(C_{p}\) determines the absorption efficiency of wind power for the wind turbine system. The empirical formula for \(C_{p}\) can be written as  
\[C_{p}(\beta,\lambda_{i})=0.22\cdot\left(\frac{116}{\lambda_{i}}-0.4\beta-5.0 \right)\cdot e^{(\frac{-12.5}{\lambda_{i}})} \tag{2}\]  
and  
\[\lambda_{i}=\frac{1}{\left(\frac{1}{\lambda+0.08\beta}-\frac{0.035}{\beta^{3} +1}\right)} \tag{3}\]  
where \(\beta\) is the blade angle and \(\lambda\) is TSR. Figure 1 shows the wind power coefficient variation for different blade angles. For a given blade angle, there exists a TSR at which the wind power coefficient \(C_{p}\) reaches its maximum value.  
For \(\beta=0^{0}\), Figure 2 shows the variations of the wind power coefficient and power input for different wind speeds. The power input and the power coefficient are seen to reach their maximum values at different wind speeds.  
Figure 1: Wind power coefficient variation for different blade angles.
# A Key Node Mining Method Based on K-Shell and Neighborhood Information
## 4 Experiments and Results
### Performance of KCH
#### 4.3.3 Correlation Analysis
The results displayed in the correlation matrix reveal that in most networks, the Kendall coefficient between KCH and KS exceeds 0.75, indicating a high degree of correlation. However, considering the superior performance of KCH in networks, as discussed earlier, it can be inferred that the KCH method is more adept at utilizing network information to mine the importance of nodes compared to the KS method while also addressing the monotonicity issues of the KS method. Figure 3: Subfigures (**a–d**) represent rank distribution graphs of the ranking results of different centrality metrics on Dolphins, USpowerGrid, WikiVote, and HepPh networks. The horizontal axis of the graph represents the nodes’ ranks, and the vertical axis represents the number of nodes with the same rank.