esa-sceva/satcom-chunk-collection huggingface dataset:
# Superpixel Segmentation Using Weighted Coplanar Feature Clustering on RGBD Images
## 3 Algorithm Framework
### Depth Impainting Using the Cross-Bilateral Filter  
Weighted coplanar feature clustering prefers to run on the hole-filling RGBD images rather than on raw data. Depth maps, as mentioned above, have shadows and holes in them. In many RGBD superpixel methods, such as VCCS, resulting supervoxels do not cover these holes since the clouds have no points in them. To generate complete 2D segmentation, these holes are filled using RGB superpixel algorithms, such as SLIC. However, the depth information has not been fully utilized, and the accuracy of the results has also been reduced. In contrast, WCFC fills in the depth map using a cross-bilateral filter in the beginning.  
The cross-bilateral filter is a nonlinear filter that smooths a signal while preserving strong edges. It smooths an image (\(I\)) while respecting the edges of another image (\(J\)). Here, we use the raw depth map as \(I\) and color image as \(J\). The filter output at each pixel is the weighted average of its neighbors. The weight assigned to each neighbor decreases with both the distance in the image plane (the spatial domain, \(S\)) and the distance along the intensity axis (the range domain, \(R\)) in image \(J\). Using a Gaussian \(G_{\sigma}\) as a decreasing function, the result (\(F\)) of the cross-bilateral filter is defined by:  
\[I_{\mathbf{P}}^{c}=\frac{1}{W_{\mathbf{P}}^{c}}\sum_{\mathbf{q}\in S}G_{\sigma _{s}}(\|\mathbf{p}-\mathbf{q}\|)G_{\sigma_{r}}(|J_{\mathbf{P}}-J_{\mathbf{q}}| )I_{\mathbf{q}}, \tag{1}\]  
\[W_{\mathbf{P}}^{c}=\sum_{\mathbf{q}\in S}G_{\sigma_{s}}(\|\mathbf{p}-\mathbf{ q}\|)G_{\sigma_{r}}(|J_{\mathbf{P}}-J_{\mathbf{q}}|), \tag{2}\]  
where parameter \(\sigma_{s}\) defines the size of the spatial neighborhood used to filter a pixel, and \(\sigma_{r}\) controls how much an adjacent pixel is downweighted because of the intensity difference. Parameter \(W_{\mathbf{P}}\) normalizes the sum of the weights.  
It is important to note that the fast version of the cross-bilateral filter is used, which meets the real-time requirements. Therefore, this will not become the bottleneck that limits the efficiency of our algorithm; it improves the performance of the algorithm in terms of quality.  
On the other hand, hole-filling RGBD images also have some flaws. The depth estimated by cross-bilateral filter is not always precise. In fact, the errors always form a rectangular box in the image edge. Fortunately, a large number of experiments show that WCFC could overcome this problem and behave robustly.
## 3 Application of Pavement Inspection Indicators to Maintenance Decision-Making  
### Developing Maintenance Decision-Making Methods for Provincial Highways  
This study focuses on pavement inspection indicators such as the crack rate,utting depth, and IRI, referencing maintenance decision strategies from various countries. The US MAP-21 classifies inspection values into three categories: \"Good\", \"Fair\", and \"Poor\", while Japan has six grading categories, broadly divided into \"Good Condition\", \"Requires Monitoring\", and \"Requires Maintenance\".  
Currently, provincial highway maintenance units classify maintenance strategies into \"Maintenance Sections\" and \"Observation Sections\". To allocate maintenance resources effectively, this study refers to the US and Japanese maintenance strategies, further classifying sections into \"Maintenance Sections\", \"Continuous Monitoring Sections\", and \"Good Condition Sections\". Considering the widespread application and reliability of the K-means clustering analysis method, this study adopts this method for grading pavement inspection indicators, classifying sections as \"Maintenance Sections\", \"Continuous Monitoring Sections\", and \"Good Condition Sections\" for the maintenance decision-making of Taiwan's provincial highways.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Inspection and Analysis** & **Human Resource** & **Total Cost** \\  & **Cost (USD/km)** & **Cost (USD/km)** & **(USD/km)** \\ \hline Traditional Manual & & & \\ Inspection & 35 & 188 & 223 \\ \hline Automated Pavement & & & \\ Inspection Vehicle & 85 & 47 & 132 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Inspection cost analysis.
## 5 Discussions
### Limitations and Future Work  
Although this study presented a robust methodology that yielded promising results, it is essential to acknowledge certain limitations. This research relied primarily on data from the KNHANES dataset, which may not fully represent the global population. Future research should aim to validate the proposed model across diverse datasets and populations to ensure its generalizability. Additionally, exploring other advanced feature selection techniques and machine learning algorithms could further enhance model performance, a significant advancement in diabetes prediction and prevention, by demonstrating the efficacy of combining the Mahalanobis distance for outlier detection with machine learning techniques. The proposed approach improved the accuracy and reliability of predictive models and provided valuable insights into the factors influencing diabetes risk during the COVID-19 pandemic. These findings pave the way for the development of more effective public health strategies and predictive models for chronic disease management, ultimately contributing to better health outcomes and reduced disease burden.  
The practical implications of this study are as follows: Healthcare providers can leverage enhanced predictive models to more accurately identify at-risk individuals and implement early intervention strategies. Public health authorities can use these insights to design targeted prevention programs, particularly when considering the increased risk of diabetes observed during the pandemic. Moreover, this methodology can be adapted and applied to other chronic diseases, offering a versatile tool for improving health outcomes through advanced data analytics and machine learning. Future research should refine the model by integrating additional data sources, such as genetic information and real-time health monitoring data, to improve the prediction accuracy. Exploring deep learning techniques and ensemble methods can provide further enhancements. The development of user-friendly tools and applications incorporating these advanced predictive models could facilitate their adoption in clinical practice and public health policies. By addressing these areas, future studies can build on the foundation of this research, advancing the field of predictive modeling in healthcare and contributing to the overall goal of reducing the global burden of diabetes and other chronic diseases.
## 2 Materials and Methods  
### Plant Materials and Light Treatment  
Basil (_O. basilicum_ Linn. var. _pilosum_ (_Willd._) Benth.) seeds were obtained from the Zhejiang Academy of Agricultural Science (Hangzhou, China). The seeds were sown in plastic flower pots with nutrient soil and cultivated under a 12 h/12 h light photoperiod at 30 \({}^{\circ}\)C (day)/24 \({}^{\circ}\)C (night) in a greenhouse. After germination, the seedlings were watered with half-strength Hoagland nutrient solution every five days. For light treatment, the plants were divided into three groups after being sown in the soil and placed under monochromatic red (R, 661 nm), blue (B, 449 nm), and white (W, peaks at 445 and 560 nm) LED light. The photon flux density of light in each group was set to 300 \(\upmu\)mol photons m\({}^{-2}\) s\({}^{-1}\). After being cultivated for five weeks, leaves from each treatment sample were collected and stored for further analysis. In total, 40 seedlings in each group and three replicates were set.
# Unlocking Efficiency in Fine-Grained Compositional Image Synthesis: A Single-Generator Approach
## 4 Experiments and Results
### Results
#### 4.2.3 Noise and Latent Space Property  
As SSSGAN utilizes a shared local generator for generating all the parts, we can analyze the properties of the noise codes and latent codes directly, without considering the local generator. In particular, we can visualize the noise codes and latent codes through low-dimensional embeddings. Figure 7 presents the two-dimensional embedding results using the t-SNE algorithm [71]. It can be observed that the noise codes were not well-separated, with clusters formed by the background, neck and clothes being close together. In contrast, the latent codes were well-grouped and did not overlap. These results suggest that the latent space of SSSGAN is flatter (or more linear) than the noise space. Consequently, the t-SNE algorithm finds it easier to separate the parts in the latent space, compared to the noise space. Furthermore, we noticed that the noise codes for consecutive semantic parts, such as the eyes, eyebrows and mouth, were closely grouped. This finding indicates that the LSTM module of SSSGAN can capture the relationships between these parts well.  
Given that t-SNE is designed to capture local patterns within a distribution, we also examined the relationships between the noise codes and latent codes by measuring the pairwise cosine similarity. The results, illustrated in Figure 8, revealed a distinct pattern: the cosine similarity between parts was significantly stronger in the latent space, compared to the noise space. This suggests that the vectors representing the parts are more closely distributed, possibly forming a smoother manifold, in the latent space. In contrast, the distribution of part vectors in the noise space appeared to be less structured. Additionally, in the noise space, we observed slightly stronger cosine similarity between consecutive parts, indicating that the LSTM module can successfully capture the interdependence of adjacent parts, in line with our earlier analysis. Furthermore, in the latent space, we observed a stronger cosine similarity among facial components such as the eyes, eyebrows, mouth and nose. This observation indicates stronger correlations between these features, suggesting that they are distributed closer to each other in the manifold.  
Figure 7: Noise code and latent code visualization using the t-SNE algorithm [71].
# Fine-Scale Population Estimation Based on Building Classifications: A Case Study in Wuhan
## 2 Materials and Methods
### Classification of Urban Buildings
#### 2.2.2 Classification of Buildings with the _Tf-_idf_ Method
Building classification data are important inputs for population estimation in the proposed method. After classifying the buildings, residential houses that are closely related to the population distribution can be extracted. Statistical data related to residential properties are important parameters for population estimation in our method.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 2 Distinctive Characteristics of Tumor Microenvironment and Heterogeneity of EPR
### Ligand-Receptor Pairs
#### 2.2.2 Indirect Cell Communication  
Signaling by Extracellular Vesicles  
Extracellular vesicles (EVs) are lipid bilayer particles, naturally released from cells carrying signaling-active molecules such as DNA/RNA, proteins, lipids, and metabolites. Different types of extracellular vesicles are released under both normal and pathological conditions (e.g., cancer) and alter the metabolism of the recipient cell [44; 66].  
EVs are categorized based on size as apoptotic bodies (500-2000 nm), microvesicles (50-1000 nm), and exosomes (40-120 nm). apoptotic bodies are released during apoptosis and may carry fragments of genomic DNA. Microvesicles are excreted from the plasma membrane, while exosomes are released from the multivesicular bodies (MVBs) [44; 67; 68].  
Tumor-associated EVs (TEVs) released by tumor cells and tumor microenvironment cells circulate in body fluids and induce phenotypic changes in target cells locally and distantly [66]. They allow communication between tumor cells and the microenvironment, influencing processes involved in tumor initiation, progression, and metastasis, such as angiogenesis and resistance to cell death [66; 68]. For example, fibroblasts secrete exosomes to deliver nutrients to malignant cells, whereas cancer cells use EVs to communicate changes in their microenvironment [44; 68].  
Signaling by Cytokines, Chemokines, and Growth Factors  
Cytokines, chemokines, and growth factors contribute to cross-communication between cancer and non-cancer cells [44].  
Cytokines are usually polypeptides or glycoproteins with relatively low molecular weight (6-70 kDa), and they regulate the functions, differentiation, proliferation, and apoptosis of target cells. They act through receptors to activate various intracellular signaling pathways for growth, metastasis, homeostasis, and the inhibition of tumor progression [44; 69]. Cytokines include interferons (IFNs), the tumor necrosis factor (TNF) superfamily, chemokines, and growth factors [69].  
Chemokines, a large family of 45-50 proteins (8-12 kDa), activate intracellular signaling pathways through receptors (GPCRs) that interact with G-proteins. They are classified into four classes (CXC, CC, XC, CX3C) based on the location of cysteine residues, with corresponding receptors CCR, CXCR, XCR, and CX3 CR [70,71]. Chemokines modulate tumor immune infiltration and act directly on neoplastic, fibroblast, or endothelial cells to regulate processes that contribute to tumor growth through various mechanisms, such as growth activity, angiogenesis, and tumor spread and metastasis. They also play a critical role in leukocyte migration, cancer-associated inflammation, and macrophage recruitment to provide key growth, trophic, and angiogenic factors [44,70,71].
# Design and Stability Analysis of Six-Degree-of-Freedom Hydro-Pneumatic Spring Wheel-Leg
## 3 Theoretical Analysis of Hydro-Pneumatic Spring
### Mathematical Derivation of Hydro-Pneumatic Spring
#### 3.2.3 Mathematical Model of Pipeline  
After passing through the damping valve system, the hydraulic fluid enters the accumulator through the pipeline, where there is pressure loss. The pressure loss mainly depends on factors such as the internal diameter, length of the pipeline, and flow rate of the fluid. Assuming that the pressure of the fluid after passing through the pipeline is \(P_{3}\), according to the fluid flow theory for long slender pipelines [27], we have  
\[P_{3}-P_{2}=\lambda\rho\frac{l}{2D_{l}}\dot{x}^{2}sign(\dot{x}). \tag{5}\]  
where \(\lambda\) represents the resistance coefficient along the path, \(l\) represents the length of the pipeline, and \(D_{l}\) represents the diameter of the pipeline. The calculation method for the resistance coefficient along the path \(\lambda\) is as follows [28]:  
\[\lambda=\left\{\begin{array}{ll}\frac{75}{R_{e}}&R_{e}\leq 2320\\ 0.0025R_{e}^{\frac{1}{2}}&2320<R_{e}\leq 4000\\ \frac{0.3164}{R_{0}^{0.28}}&4000<R_{e}\leq 10^{5}\\ 0.0032+\frac{0.221}{R_{0}^{0.289}}&10^{5}<R_{e}\leq 10^{6}\end{array}\right. \tag{6}\]  
where \(R_{e}\) represents the Reynolds number, and its calculation formula is  
\[R_{e}=\left|\frac{D_{l}\cdot v}{\mu}\right| \tag{7}\]  
where \(v\) represents the average flow velocity of the oil in the pipeline, and \(\mu\) represents the viscosity of the oil.
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 2 Methodology  
### CFD Analysis  
CFD analysis is a numerical technique used to simulate and analyze the behavior of fluid flow and related physical phenomena using computational methods and algorithms (e.g., [PERSON] et al. [16] and [PERSON] [17]). It involves the discretization of the governing equations of fluid mechanics, such as the Navier-Stokes equations, into a set of algebraic equations that can be solved numerically using numerical simulation software ANSYS V23 R2.  
The exhaust manifold analysis process involves several critical steps, as outlined in Figure 1 (e.g., [PERSON] et al. [18] and [PERSON] et al. [19]). The first stage is preprocessing, wherein we create a geometric model of the manifold and gather relevant information such as dimensions, material properties, and operating conditions. Next, we mesh the model into a finite number of elements and refine the mesh to enhance its accuracy in specific areas of interest.  
Figure 1: Flowchart of modeling.  
Our second step is the solver, whereby we solve the primary equations of fluid mechanics using numerical algorithms, considering the turbulence model used.  
Finally, postprocessing entails visualizing and interpreting the CFD analysis results, including extracting relevant information like drag and lift coefficients, pressure distribution, and velocity profiles. To choose the best model, it is crucial to complete these steps with all the turbulence models examined to compare velocity distribution, pressure, turbulent kinetic energy, and temperature predictions.
# Enhanced Conjunction Assessment in LEO: A Hybrid Monte Carlo and Spline-Based Method Using TLE Data
## 2 Materials and Methods
### Probabilistic Conjunction Screening Using Monte Carlo Simulation
This representation expresses the relative position uncertainty as a probability density function (PDF) defined by a mean vector and a covariance matrix. The formulation captures the spread and orientation of the uncertainty region and serves as the basis for collision probability estimation in the following expressions. \[p(\mathbf{r})=\underset{k=1}{\overset{k}{\sum}}\,\omega_{k}\cdot N(\mathbf{r} ;\mu_{k},\Sigma_{k}) \tag{3}\]  
Figure 2: Monte Carlo sampling for collision estimation. where \(\omega_{k}\) is the weight of the \(k\)-th Gaussian component, satisfying \(\sum_{k=1}^{K}\omega_{k}=1\), and \(\mu_{k}\) is the mean position vector of the \(k\)-th component \( \begin{bmatrix}x_{k}&y_{k}&z_{k}\end{bmatrix} ^{T}\). The covariance matrix (\(\Sigma_{k}\)) of the \(k\)-th component describes the spread and orientation of uncertainty:  
\[\sum_{k} \begin{bmatrix}\sigma_{xx}^{2}&\sigma_{xy}&\sigma_{xz}\\ \sigma_{yx}&\sigma_{yy}^{2}&\sigma_{yz}\\ \sigma_{zx}&\sigma_{zy}&\sigma_{zz}^{2}\end{bmatrix}  \tag{4}\]  
\[N\Big{(}\mathbf{r},\mathbf{\mu}_{k},\sum_{k}\Big{)} \tag{5}\]  
\[N\Big{(}\mathbf{r},\mathbf{\mu}_{k},\sum_{k}\Big{)}=\frac{1}{(2\pi)^{2/3}| \sum_{k}|^{1/2}}\exp\left(-\frac{1}{2}(\mathbf{r}-\mu_{k})^{T}\sum_{k}^{-1}( \mathbf{r}-\mu_{k})\right) \tag{6}\]  
This process identifies time intervals and spatial regions of elevated conjunction risk. These results define the region of interest to be used in the next phase for refined time of closest approach estimation. In the Monte Carlo method, uncertainty is modeled using a multivariate normal distribution. Samples are generated in the 3D relative position space at the estimated time of closest approach, using a Gaussian distribution with a defined mean vector and covariance matrix. The sampling is performed using the numpy.random.multivariate_normal function, and the resulting vectors represent the relative positions of the secondary object with respect to the primary in the local Radial-Transverse-Normal (RTN) frame. The covariance matrix is assumed based on plausible uncertainty magnitudes, either isotropic or anisotropic, depending on the case. Velocity components and full 12-dimensional orbital states are not sampled explicitly. Instead, the analysis focuses on the positional uncertainty at the time of closest approach. The TCA itself is computed once using a multi-stage refinement on the nominal trajectories and is then held fixed across all samples. Each sample is evaluated at this common reference time to compute the Euclidean separation. This simplified 3D approach reflects common practices when full state covariance is not available and allows for efficient estimation of collision probability from TLE data.
# Iteratively Refined Multi-Channel Speech Separation
###### Abstract  
The combination of neural networks and beamforming has proven very effective in multi-channel speech separation, but its performance faces a challenge in complex environments. In this paper, an iteratively refined multi-channel speech separation method is proposed to meet this challenge. The proposed method is composed of initial separation and iterative separation. In the initial separation, a time-frequency domain dual-path recurrent neural network (TFDPRNN), minimum variance distortionless response (MVDR) beamformer, and post-separation are cascaded to obtain the first additional input in the iterative separation process. In iterative separation, the MVDR beamformer and post-separation are iteratively used, where the output of the MVDR beamformer is used as an additional input to the post-separation network and the final output comes from the post-separation module. This iteration of the beamformer and post-separation is fully employed for promoting their optimization, which ultimately improves the overall performance. Experiments on the spatialized version of the WSJ0-2 mix corpus showed that our proposed method achieved a signal-to-distortion ratio (SDR) improvement of 24.17 dB, which was significantly better than the current popular methods. In addition, the method also achieved an SDR of 20.2 dB on joint separation and dereverberation tasks. These results indicate our method's effectiveness and significance in the multi-channel speech separation field.  
Keywords:speech separation; microphone array; minimum variance distortionless response (MVDR); beamforming; iterative separation +
Footnote †: journal: _Applied Sciences_
# Speech Inpainting Based on Multi-Layer Long Short-Term Memory Networks
## 3 Proposed Speech Inpainting Methods and Model Architecture
### Datasets  
The datasets used in this research work were constructed from four widely used publicly accessible speech datasets: the LJ Speech dataset [38], the Hi-Fi multi-speaker dataset [39], the RyanSpeech dataset [40], and the LibriSpeech dataset [41]. Two categories of eight speech datasets were built for training and testing the proposed models. The first category contains four single-speaker datasets: one from the LJSpeech dataset, one from the RyanSpeech dataset, and two from the LibriSpeech dataset. The second category contains four multi-speaker datasets: one from the Hi-Fi multi-speaker dataset and three from the LibriSpeech dataset. The details of the datasets constructed in this research are shown in Table 1:  
* Single-speaker dataset: The first two datasets were built directly from the original LJSpeech and RyanSpeech datasets. The last two datasets were built from the LibriSpeech dataset, specifically from the \"train-clean-100\" folder, which contains speech data from multiple male and female speakers. The third dataset uses \"folder 26\" and the fourth uses \"folder 32\". In this paper, these four datasets are referred to as LJSpeech, RyanSpeech, Libri_26, and Libri_32. Each dataset collates 400-second-long female or male speech samples at a 16 kHz sampling rate and contains one speaker.
* Multi-speaker dataset: The first dataset was built from the original Hi-Fi dataset, and the others were built from the LibriSpeech dataset. Each dataset contains 10 distinct speakers from both genders, collating 400-second-long speech samples at a 16 kHz sampling rate. These four datasets are referred to as HIFI, LibriM1, LibriM2, and LibriM3 in this paper.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 2 Methods
### HRU-Net
#### 2.3.3 Network Structure  
The multi-feature fusion module was integrated into the standard U-Net architecture to create a multi-scale fusion network--the HRU-Net. The HRU-Net structure is depicted in Figure 3.