esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Performance on Corel-1K Image Dataset
The image retrieval results for the semantic classes of Corel-1K image dataset are shown in Figures 11 and 12 (which reflects the reduction of the semantic gap). The image shown in the first row is the query image and the remaining 20 images are images retrieved by applying a similarity measure that is based on image classification score values. Here a classification label is used to determine the class of the image, while the similarity with-in the same class is calculated on the basis of similarity among classification scores of images of the same class from the test dataset. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Class Name/ Method** & **RGSIR** & **[PERSON] et al. [29]** & \begin{tabular}{c} **Level-1** \\ **RBF-NN**[9] \\ \end{tabular} & \begin{tabular}{c} **Visual Words** \\ **Integration** \\ **SIFT-SURF**[8] \\ \end{tabular} & \
\begin{tabular}{c} **SWBOF**[23] \\ \end{tabular} &
\begin{tabular}{c} **SIFT-LBP**[25] \\ \end{tabular} \\ \hline African People & 14.56 & 15.31 & 14.61 & 12.02 & 12.80 & 11.4 \\ Beach & 13.88 & 12.74 & 14.00 & 12.08 & 10.80 & 11.6 \\ Building & 13.24 & 13.81 & 15.35 & 13.93 & 10.30 & 8.6 \\ Bus & 19.43 & 17.54 & 18.45 & 18.73 & 18.80 & 18.6 \\ Dinosaur & 20.00 & 19.88 & 19.87 & 19.98 & 19.60 & 19.6 \\ Elephant & 16.16 & 18.21 & 16.28 & 14.15 & 15.60 & 11.6 \\ Flower & 18.92 & 18.34 & 16.68 & 17.67 & 14.20 & 16.6 \\ Horse & 18.16 & 19.08 & 16.56 & 16.55 & 18.60 & 13.6 \\ Mountain & 15.24 & 16.68 & 15.72 & 12.22 & 8.40 & 9.2 \\ Food & 17.20 & 13.16 & 16.54 & 13.02 & 10.00 & 10.6 \\
**Mean** & 16.68 & 16.48 & 16.41 & 15.03 & 13.94 & 13.14 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of recall when using Corel-1K image dataset. Figure 10: _P-R_ curve obtained using Corel-1K image benchmark. Figure 11 shows that, for a given query image, all images of the related semantic category are retrieved. In Figure 12 it can be seen that in a search based on a flower query image, an image from a different semantic category containing flowers is also displayed in the 3 rd row in addition to images from the flower image category. The experimental results demonstrate that the proposed approach achieves much higher performance compared to the state-of-the-art complementary approaches [9; 23; 25].
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Hyperspectral Imaging (HSI)
#### 4.3.4 Model Development  
The information desired from the sample cannot be directly obtained from a 3D hypercube [225]. Chemometric models are used to establish a correlation between the hyperspectral image and the desired properties [226]. Due to the huge amount of HSI data, a common approach is to use the mean spectrum of all the pixels within the ROI to build a model [227]. Generally, in HSI data analysis, there are two types of models: classification models and regression models [228]. Classification models are used to categorise targets into known or unknown discrete groups [112] and regression models establish a relationship between input data and target properties in a continuous range, enabling predictions within a range of values [222]. Finally, similar to vibrational spectroscopy, artificial intelligence (AI) and particularly deep learning algorithms have a substantial application in relation to hyperspectral data as well [229; 230; 231]. Through the application of advanced segmentation techniques in deep learning, combined with feature extraction and image processing, researchers have optimised food analysis, including red meat [232].
## 2 Materials and Experimental Preparation
### Materials for TiO\({}_{2}\) Photocatalytic Coating  
Besides nano-TiO\({}_{2}\), the materials for the preparation of nano-TiO\({}_{2}\) photocatalytic coatings include:  
* Dispersants: Two kinds of dispersants, S-5040 and sodium hexametaphosphate (SHP), were used. The detailed technical indicators, which were provided by the manufacturer Shandong Huali Bio-tech Co., Ltd. (Zibo, China), are shown in Table 3.
* Emulsifiers: Three kinds of representative emulsifiers--rapid-setting cationic emulsifier SY-QCE, medium-setting cationic emulsifier KZW-802, and slow-setting cationic emulsifier SY-CME--were chosen. The technical indicators, which were provided by the manufacturer Jiangsu Xinyue Asphalt Co., Ltd. (Zhenjiang, China), are shown in Table 4.
* Asphalt: A kind of 70# penetration \"A\" grade asphalt binder was selected (Shell (China) Limited). The main physical properties of the asphalt binder were tested following the Chinese Standard Test Methods of Bitumen and Bituminous Mixtures for Highway Engineering (JTG E20-2011) [24] and met the requirements in the Chinese Specifications for Construction of Highway Asphalt Pavements (JTG F40-2004) [25], as shown in Table 5.
* In addition, CaCl\({}_{2}\) (Wuhan Fude Chemical Co., Ltd., Wuhan, China) and hydrochloric acid (Wuhan Fude Chemical Co., Ltd., Wuhan, China) were selected as additives.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emulsifiers** & **SY-QCE** & **KZW-802** & **SY-CME** \\ \hline Appearance (25 °C) & Light yellow liquid & Creamy white paste & Light yellow liquid \\ Purity of emulsifiers (\%) & \(\geq\)60 & \(\geq\)50 & \(\geq\)75 \\ pH value & 9-10 & 2-4 & 3-4 \\ Solubility & Easy to dissolve in water & Easy to dissolve in water & Easy to dissolve in water \\ Type of Emulsifiers & Rapid-setting & Medium-setting & Slow-setting \\ Adding content of emulsifiers (\%) & 0.5 & 0.8 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Technical indicators of emulsifiers.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Appearance** & **Specific Surface** & **pH** & **Particle Size** & **Loss on** & **Loss on** \\  & **Area (m2/g)** & **(nm)** & **Drying (\%)** & **Ignition (\%)** & **Purity (\%)** \\ \hline white powder & 80 & 5 & 20 & 0.2 & 0.25 & \(>\)99.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical indicators of anatase nano-TiO\({}_{2}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dispersants** & **Appearance** & **Solid Content** & **Viscosity** & **Boiling Point** & **Solubility** \\  & **(\%)** & **(Pa-s)** & **(\({}^{\circ}\)C)** & **Solubility** \\ \hline \multirow{2}{*}{S-5040} & Colorless, viscous, and & & & & \\  & transparent liquid & 40 & 400 & 110 & Extremely easy to \\ \hline \multirow{2}{*}{SHP} & Transparent glass flake & & & & \\  & or white powder & — & — & 1500 & Extremely easy to \\ \hline \hline \end{tabular}
\end{table}
Table 3: Technical indicators of dispersants.
# Air Traffic Trajectory Operation Mode Mining Based on Clustering
## 3 Clustering Analysis-Based Aircraft Operation Pattern Mining
### Trace Feature Compression
#### 3.1.2 ADMM-Based Model Solving Algorithm  
ADMM can decompose large-scale equality constrained optimization problems into multiple independent subproblems for distributed optimization. When optimizing a subproblem, the rest of the optimization function should remain unchanged. The optimization problem of OFAE can be divided into two parts: \(\parallel L-D_{\theta}(E_{\theta}(L))\parallel_{2}\) and \(\lambda\parallel S\parallel_{21}\).  
Since the former optimization solution is differentiable, when the deviation matrix remains unchanged, the classical back-propagation algorithm can be used to train it. The latter optimization is relatively complex, and the near-end gradient method can be used to solve the norm optimization problem.  
When dealing with the optimization of \(\min(\lambda h(x))\), for the function \(h(x)\) that is non-differentiable at the point \(x\), the near-end gradient method looks for an alternative point, \(u\), to make it as small as possible and close to the original non-differentiable point, \(x\). \(u\) is called the proximal operator, \(prox_{\lambda,h}(x)\), at \(x\). The proximal operator of L21-norm is:  
\[prox_{\lambda,\lambda_{2,1}}(s)_{i}=\begin{cases}s_{i}-\lambda\frac{s_{i}}{ \parallel s_{i}\parallel_{2}}&,\parallel s_{i}\parallel_{2}>\lambda\\ 0&,\parallel s_{i}\parallel_{2}\leq\lambda\end{cases} \tag{17}\]  
where \(s_{i}\) is the row vector of the \(i\) row of the matrix \(S\), and \(\lambda\) is the sparsity parameter.  
The near-end operator determines the next step of the iteration during L21-norm optimization, and finally obtains the track data after OFAE compression and depolarization, which provides a high-quality dataset with low dimension and low noise for the clustering algorithm. The complete model solving algorithm based on the ADMM framework is shown in Figure 7.
## 4 Performance Modeling of the IASF-IoT Framework
### Performance Evaluation and Implications
The projected added network latency of 0.05008 s is minimal and might not significantly affect the user experience or the timeliness of data transmission, as it remains well below the threshold of 0.5 s.
# Retinex-Based Relighting for Night Photography
## 3 Proposed Method  
This paper proposes an algorithm that partially relights nighttime skyline photographs and determines the positions and shapes of light sources. The proposed method has four steps: (1) extracting an illuminating saliency map, (2) sampling lighting points from the saliency map as a 2D probability distribution, (3) diffusing the lighting points for generating omnidirectional/directional lighting area, and (4) relighting based on Retinex enhancement. The following sections describe the detailed information for each step.
# Experimental Evaluation of the Packet Reception Performance of LoRa
###### Abstract  
LoRa technology is currently one of the most popular Internet of Things (IoT) technologies. A substantial number of LoRa devices have been applied in a wide variety of real-world scenarios, and developers can adjust the packet reception performance of LoRa through physical layer parameter configuration to meet the requirements. However, since the important details of the relationship between the physical layer parameters and the packet reception performance of LoRa remain unknown, it is a challenge to choose the appropriate parameter configuration to meet the requirements of the scenarios. Moreover, with the increase in application scenarios, the requirements for energy consumption become increasingly high. Therefore, it is also a challenge to know how to configure the parameters to maximize the energy efficiency while maintaining a high data rate. In this work, a complex evaluation experiment on the communication capability under a negative Signal to Noise Ratio is presented, and the specific details of the relationship between physical layer parameters and the packet reception performance of LoRa are clarified. Furthermore, we study the impact of the packet length on the packet reception performance of LoRa, and the experimental results show that when there is a large amount of data to be transmitted, it is better to choose long packets instead of short packets. Finally, considering the influence of physical layer parameters and the packet length on the packet reception performance of LoRa, the optimal parameter combination is explored, so as to propose a transmission scheme with a balanced reliability, delay, and energy consumption. This scheme is the first to consider the physical layer parameters and packet length together to study the communication transmission scheme, which reduces the communication time by 50% compared with the traditional transmission scheme and greatly reduces the energy consumption.  
IoT technology; LoRa; physical layer parameters; packet length; transmission scheme +
Footnote †: journal: Sensors
# Subcritical Water Extraction of Ursolic Acid from _Hedyotis diffusa_
## 3. Results and Discussion
### Comparison SWE with Other Extraction Methods  
The comparison among different extraction methods has been shown in Table 3. SWE of _H. diffusa_ presented the greatest extraction yield (5.94 \(\pm\) 0.03a mg/g) followed by UE (5.37 \(\pm\) 0.26b mg/g), HRE (4.66 \(\pm\) 0.1c mg/g), MAE (4.45 \(\pm\) 0.25c mg/g) and ME (3.49 \(\pm\) 0.2d mg/g) by one-way analysis of variance of the UA yields. UE and MAE have an advantage over SWE in terms of time consumption. However, volatile organic solvent consumption in extraction processes is huge. Furthermore, water in contrast to ethanol as the solvent is cost effective and also superior in terms of flammability and availability. The most important advantages of SWE over traditional extraction methods are shorter extraction time, lower costs of the extracting agent and an environmentally compatible technique because subcritical water at high temperature can modify the dielectric constant of water and accelerate the extraction process. Therefore, it is concluded that SWE is a satisfactory method for extraction of UA from _H. diffusa_. The moderately high temperature of the SWE process needed to increase the solubility of UA in water may be pinpointed as the only shortcoming of this extraction.
## 2 Materials and Methods
### Image Quality Enhancement Strategy
#### 2.2.1 Image Denoising and Sharpening Processing  
This study first analyzes the effectiveness for the testing of image denoising and sharpening processing. Unsharp masking (USM) is a commonly used technique to sharpen the edge of the image; it can improve the contrast of image edge detail. In this study, we subtract the Gaussian smoothed version from the original image and retain the value of the constant area in a weighted manner to realize the sharpening function, when \(X^{\prime}\) is the enhanced image, \(X\) is the original image, \(Y\) is the Gaussian smoothed image, and \(a\) and \(b\) are the weights of images, the formula is expressed as follows:  
\[X^{\prime}=a\cdot X+b\cdot Y \tag{1}\]  
We use the Gaussian Blur function to generate a Gaussian blurred image as an USM, and then we mix the original image with the blurred image in a ratio of 1.5: \(-0.5\) to make the blurred image perform the reverse operation. The unsharp mask and the original image are combined to produce an image that is more obvious than the original image edge, which strengthens the model's ability to detect the edges during training. The original image and the sharpened image are compared, as shown in Figure 4.  
Next, we validate the image data with the sharpening processing. The sharpening method is from the Non-local Means Denoising algorithm [12], which is an image noise reduction algorithm. When compared with the local algorithms such as the Gaussian Blur and anisotropic diffusion, the Non-local Means Denoising algorithm only uses the range near the target pixel to smoothen the image and remove the image noise. In the local averaging algorithm, each target pixel is defined as a block of a specified size, and all pixels in the entire image are weighted according to the similarity between the surrounding block  
Figure 4: (**a**) Original image. (**b**) Improved image using Gaussian Blur.  
Figure 3: Flowchart of the study design.  
of the pixel and the target pixel block. Similar block images are averaged, so that the processed image has less noise, and the retained image texture is more evident. Although this denoising method will take more time, the effect of denoising is better, thereby obtaining the edge details of the image with smaller loss. In the set block size, this paper uses 16 \(\times\) 16 as the average pixel range size, as shown in Figure 5.
# Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development
## 3 Materials and Methods
### Methods
#### 3.1.6 Convolutional Neural Networks  
Convolutional neural networks (CNN)s, proposed in [55], are feedforward NNs that make use of the mathematical operator called _convolution_ to extract new feature maps from data. They work by multiplying subsets of the input data matrix by another matrix, called a filter, to extract useful latent features. CNNs typically process multidimensional arrays, and the number of dimensions to which the convolution operator is applied distinguishes 1D, 2D, and 3D convolutional networks.  
The main characteristics that differentiate CNNs from the traditional fully connected ones are basically twofold [32]. The first is _local connectivity_, where nodes are only connected to a smaller region of the input, and each component of the feature maps is obtained by processing only a subset of the input data. The second is _weights sharing_, where each member of the kernel is used at every position of the input, meaning that all the nodes in the output detect exactly the same pattern.  
Let \(\mathbf{x}^{(t)}\in\mathbb{R}^{d}\), \(0\leq t\leq T\) be a multivariate time series of input variables. A 1D-CNN layer with filters \(W^{(j)}=(\mathbf{w}_{j,1},\mathbf{w}_{j,2},\dots,\mathbf{w}_{j,m})\in\mathbb{R}^{d\times m}\) and biases \(w_{j,0}\in\mathbb{R}\) for \(j=1,\dots,q\) extracts a new feature map \(\{z_{s,j}(\mathbf{x})\}_{0\leq s\leq T+1-m,1\leq j\leq q}\) from the input data, where each component is a nonlinear function of some input data and is given by  
\[z_{s,j}(\mathbf{x})=\phi\bigg{(}w_{j,0}+(W^{(j)}*\mathbf{x})_{s}\bigg{)}=\phi\Bigg{(}w _{j,0}+\sum_{l=1}^{m}\langle\mathbf{w}_{jl},\mathbf{x}^{(m+s-l)}\rangle\Bigg{)}.\]  
where \(*\) indicates the convolution operator and \(\langle\cdot,\cdot\rangle\) denotes the scalar product in \(\mathbb{R}^{d}\). It must be remarked that the index \(j\) refers to the different filters. Each filter \(W^{(j)}\) produces a set of new features. The second index \(s\) refers to the different features extracted from a given filter.
## 3 Results and Discussion
### BLUF Modules in Association with the Effector Domains
#### 3.3.12. AraC Domain  
The AraC protein (inducer/activator) regulates the _ara_BAD operon in _E. coli_, which is responsible for encoding structural components for the arabinose metabolism [128]. X-ray crystallization and NMR studies demonstrated that the AraC is a dimeric protein composed of two helix-turn-helix DNA-binding motifs [129]. AraC uses arabinose as a substrate, and the induction of the _ara_BAD operon depends on the concentration of extracellular arabinose (>10\({}^{-7}\) M) as well as on the rate of the arabinose uptake and catabolism [128].
## 4 Discussion
### Sample Size and Basis of Calculation  
The difference in prevalence values across studies can also be attributed to sample size and whether the analysis was tooth-based or patient-based. For instance,  
* [PERSON] et al., (2022), in their CBCT study on 1715 Chinese patients, reported a patient-based prevalence of 8.4% and a tooth-based prevalence of 4.5% for lateral incisors [23].
* [PERSON] et al. (2023) found a patient-based prevalence of 6.3% and a tooth-based prevalence of 1.3% in a study of 509 patients [24].
* [PERSON] et al. conducted a CBCT-based evaluation of 993 maxillary anterior teeth in 191 patients, reporting an overall prevalence of palatoradicular grooves (PGs) in 0.9% of teeth and 4.18% of patients [31].
# Dynamic Investigation of a Solar-Driven Brayton Cycle with Supercritical CO2
###### Abstract  
The exploitation of solar irradiation is a critical weapon for facing the energy crisis and critical environmental problems. One of the most emerging solar technologies is the use of solar towers (or central receiver systems) coupled with high-performance thermodynamic cycles. In this direction, the present investigation examines a solar tower coupled to a closed-loop Brayton cycle which operates with supercritical CO2 (sCO2) as the working medium. The system also includes a storage system with two molten salt tanks for enabling proper thermal storage. The sCO2 is an efficient fluid that presents significant advancements, mainly reduced compression work when it is compressed close to the critical point region. The novelty of the present work is based on the detailed dynamic investigation of the studied configuration for the year period using adjustable time step and its sizing for achieving a continuous operation, something that makes possible the establishment of this renewable technology as a reliable one. The analysis is conducted with a developed model in the Modelica programming language by also using the Dymola solver. According to the simulation results, the yearly solar thermal efficiency is 50.7%, the yearly thermodynamic cycle efficiency is 42.9% and the yearly total system efficiency is 18.0%.  
solar tower; Modelica; Brayton cycle; dynamic analysis; power production
## 4 Intelligent Identification and Information Extraction of Rock Discontinuities
### Rock Discontinuity Surface Parameter Extraction  
(1) Spacing  
The rock discontinuity spacing is an important indicator to measure rock development and is used to reflect the integrity of rock masses [24]. The International Society of Rock Mechanics (ISRM) defines the discontinuity spacing as the vertical distance between two fissures on the outcrop. Therefore, the discontinuity set extractor (DSE) based on Matlab [22] was used to extract the spacing information for each set of rock discontinuities after clustering. The spacing measured in DSE takes into account two cases of rock discontinuities in the same set, fully continuous and not fully continuous, and is calculated by measuring the orthogonal distance between a given cluster and the nearest-neighbor cluster.  
\[D_{ij}=-\frac{A_{ij}}{n}\sum_{k=1}^{n}x_{ij}^{k}--\frac{B_{ij}}{n}\sum_{k=1}^{ n}y_{ij}^{k}--\frac{C_{ij}}{n}\sum_{k=1}^{n}z_{ij}^{k} \tag{13}\]  
where \((x_{ij}^{k},y_{ij}^{k},z_{ij}^{k})\) are the coordinates of the \(k\) point in a cluster, and \(A_{ij},B_{ij},C_{ij}\) are its normal vectors.  
(2) Continuity  
ISRM states that the continuity of a rock mass is the extent or size of the area along the planar discontinuity, and in two dimensions, the trace length is often used to characterize the continuity of a rock plane [25]. Thus, we calculated the continuity of the structural plane by the Matlab-based DSE program. It can measure the true continuity by calculating the trace length of each cluster's strike and dip [1]. For the clusters after clustering, a coordinate system is established with the dip direction as the \(x\)-axis and the strike direction as the \(y\)-axis, and each cluster of the original coordinate system (the origin of the coordinate system is the center of mass) is transformed with the following transformation matrix.  
\[R=\begin{bmatrix}\cos\beta\sin\alpha&-\cos\alpha&\sin\beta\sin\alpha\\ \cos\beta\cos\alpha&\sin\alpha&\sin\beta\cos\alpha\\ -\sin\beta&0&\cos\beta\end{bmatrix} \tag{14}\]  
where \(\alpha,\beta\) are the dip direction and dip angle of the discontinuous sets, respectively, \(O-XYZ\) is the original coordinate system, and \(O^{\prime}-X^{\prime}Y^{\prime}Z^{\prime}\) is the transformed coordinate system. The ductility is extracted in the directions of dip \(O^{\prime}X^{\prime}\) and strike \(O^{\prime}Y^{\prime}\). \(O^{\prime}Z^{\prime}\) is orthogonal to the plane \(O^{\prime}X^{\prime}Y^{\prime}\), and its normal vector is that after the plane is clustered (Figure 13). Let the number of the discontinuous set be \(i\) and the number of the cluster be \(j\); the point set \(x_{(i,j)}\) can be obtained. If \(x^{\prime}_{(i,j)}\) is the local coordinate of \(x_{(i,j)}\), the equation of the extension length in the inclination direction is  
\[L=\max(x^{\prime}_{(i,j)})-\min(x^{\prime}_{(i,j)}) \tag{15}\]