esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Evaluation on the Perceptual Color Correction
#### 4.3.2 Recommend Parameters for Typical Lighting Conditions  
We explored subjective results with different perceptual parameters. Capture tongue images under different lighting conditions of the day, such as 9:00 a.m., 1:00 p.m. and 6:00 p.m. We calculated the variance data for each channel in the \(l\alpha\beta\) color space. In order to effectively adjust the tongue image and avoid bad adjustment results, we constrained the results of perceptual adjustment with the range of the tongue image set. The range of three parameters is estimated as shown in Table 10. The adjustment steps of user parameters are set at 0.01.  
The default values of the three user parameters are set at 1, which means that the PCC phase is turned off. We also calculated three recommended settings for three typical lighting conditions, viz. in the morning, noon, and evening, as shown in Table 11.  
To get more convincing results, we adjusted the tongue image captured in the imaging system with reference to Table 11. The results are shown in Figure 15. The tongue image (a) is the output of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{\(\mathbf{\lambda_{1}}\)} & \multicolumn{2}{c}{\(\mathbf{\lambda_{2}}\)} & \multicolumn{2}{c}{\(\mathbf{\lambda_{3}}\)} \\ \hline \(\mathbf{\lambda_{1 min}}\) & \(\mathbf{\lambda_{1 max}}\) & \(\mathbf{\lambda_{2 min}}\) & \(\mathbf{\lambda_{2 max}}\) & \(\mathbf{\lambda_{3 min}}\) & \(\mathbf{\lambda_{3 max}}\) \\ \hline
0.79 & 1.09 & 0.84 & 1.09 & 0.92 & 1.15 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The max and min value of the three parameters in adjustment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Morning**} & \multicolumn{2}{c}{**Noon**} & \multicolumn{2}{c}{**Evening**} \\ \hline \(\mathbf{\lambda_{1}}\) & \(\mathbf{\lambda_{2}}\) & \(\mathbf{\lambda_{3}}\) & \(\mathbf{\lambda_{1}}\) & \(\mathbf{\lambda_{2}}\) & \(\mathbf{\lambda_{3}}\) & \(\mathbf{\lambda_{1}}\) & \(\mathbf{\lambda_{2}}\) & \(\mathbf{\lambda_{3}}\) \\ \hline
0.89 & 0.98 & 1.02 & 0.94 & 1.00 & 1.08 & 0.91 & 0.93 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The recommend settings for the typical lighting conditions.  
Figure 14: The effects of the user parameters.  
our OCC network. Figure 15b-d are the results of the PCC phase by recommending parameters for the Morning, Noon, and Evening lighting conditions, respectively. Obviously, the morning tone is more natural. The tone of tongue image at noon becomes a little bit redder. The perceptual color of in the evening becomes a little pale due to the effects of daylight lamps.  
In Figure 16, we adjusted the image compared with the person who is captured in the imaging system, and we obtained a set of parameters. Then, we adopt the same parameter in another image shown in Figure 16b. As we can see, the corrected images remain coincident. Thus, the color adjustment scheme can get a good result in satisfying doctors' preferences for their diagnosis.
## 2 Experiments
### Proportional Error
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Proportional Value**} & \multicolumn{3}{c}{**Coordinate Difference**} \\ \cline{2-4}  & \(x\) & \(y\) & \(z\) \\ \hline \(x\)-axis, \(1.01\) & \(0.06\) & \(-0.10\) & \(-0.01\) \\ \(x\)-axis, \(0.99\) & \(-0.06\) & \(0.10\) & \(0.02\) \\ \(y\)-axis, \(1.01\) & \(0.15\) & \(-0.25\) & \(-0.03\) \\ \(y\)-axis, \(0.99\) & \(-0.14\) & \(0.24\) & \(0.04\) \\ \(z\)-axis, \(1.01\) & \(0.01\) & \(-0.01\) & \(-0.04\) \\ \(z\)-axis, \(0.99\) & \(-0.02\) & \(0.03\) & \(0.40\) \\ \(x\)- and \(y\)-axis, \(1.01\) & \(0.21\) & \(-0.35\) & \(-0.04\) \\ \(x\)- and \(y\)-axis, \(0.99\) & \(-0.20\) & \(0.35\) & \(0.05\) \\ \(x\)- and \(z\)-axis, \(1.01\) & \(0.06\) & \(-0.11\) & \(-0.05\) \\ \(x\)- and \(z\)-axis, \(0.99\) & \(-0.06\) & \(0.11\) & \(0.06\) \\ \(y\)- and \(z\)-axis, \(1.01\) & \(0.15\) & \(-0.25\) & \(-0.07\) \\ \(y\)- and \(z\)-axis, \(0.99\) & \(-0.14\) & \(0.25\) & \(0.08\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment results of proportional error simulation. (Unit: mm).
## 6 Performance Comparisons
### Functionality Comparisons  
This subsection shows functionality comparisons among [PERSON] et al. (2018), [PERSON] et al. (2018), and the proposed TCVK scheme in Table 1. Providing secure communication protocols is a consistent design goal for researchers. In this article, we replace timestamp annotations with one-time random numbers. Our method avoids time inconsistencies and prevents most common malicious attacks.
# Coverage Path Planning with Semantic Segmentation for UAV in PV Plants
## 3 Results and Discussion
### Results of the CPP Method
#### 3.2.2 The Last Three Experiments with 3 DR Iris UAV
Finally, the graph in Figure 16c shows that the BECD method had the lowest energy consumption when the CPP width was in the range of 1 to 3 m, and the GBWC method had the lowest consumed energy when the CPP width was in the range of 3 to 8 m. The test was performed at CSUSL's PV plant. Summary Tables of the results of the metrics for each of the tests with the Typhoon UAV and 3 DR Iris UAV are shown. All files, and logs for the experiments are available on GitHub at [100].
## 2 Related Works
### Dependability Assessment
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Work** & **QoM** & **Occlusion** & **WSN Evaluation** & **VCF** & **Brief Description** \\ \hline [PERSON] et al. [12] and & X & – & – & – & – & Area coverage analysis aiming at full-view coverage, considering QoM by the facing angle of points of interest. \\ \hline [PERSON] et al. [14] & X & – & – & – & – & Automatic deployment of WVSN to maximize coverage and visual quality in indoor environments, considering perspective distortion of the acquired images. \\ \hline [PERSON] and [PERSON] [15] & X & – & – & – & UAV network for area coverage. Network parameters are carefully handled during redeployment to keep the coverage quality constant. \\ \hline [PERSON] et al. [6] & X & X & – & – & QoM-enhancing coverage scheme in a full area coverage scenario, considering the different area importance and the weighted quality of captured image. \\ \hline [PERSON] et al. [5] & X & X & – & – & – \\ \hline [PERSON] et al. [16] & X & X & – & – & Occlusion-aware area coverage with aerial sensing quality, providing satisfactory spatial resolution subject to the energy constraints of UAVs. \\ \hline [PERSON] et al. [17] & – & X & – & X & Selection of redundant visual nodes for enhanced resistance to visual failures, considering occlusion caused by obstacles modeled by lines. \\ \hline [PERSON] et al. [11] & – & X & – & X & Selection of faultless visual nodes based on the amount of monitored area, considering occlusion caused by mobile obstacles modeled by rectangles. \\ \hline [PERSON] and [PERSON] [18] & – & – & X & – & Petri net-based approach for modeling and analysis of dependability on IoT networks for disaster recovery. \\ \hline [PERSON] et al. [19] & – & – & X & – & Integrative methodology for dependability evaluation of industrial WSN, based on fault tree analysis, considering hardware and communication failures. \\ \hline [PERSON] et al. [20] & – & – & X & X & – & Integrative methodology for dependability evaluation of WVSN, based on fault tree analysis, considering hardware, communication and visual failures. \\ \hline [PERSON] et al. [10] & – & – & X & X & – & Automated integrative methodology for dependability evaluation of WVSN, based on fault tree analysis, considering hardware, communication and visual failures. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Visual coverage research in dependability of wireless sensor networks.
## 5 Bioactivity of Secondary Metabolites in the _Leontopodium_ Taxa
### Angiogenic and Cardioprotective Bioactivity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bioactivity** & \begin{tabular}{c} **Taxon/Etract/** \\ **Bioactive** \\ **Compound(s)** \\ \end{tabular} & **Experimental Model** & **Effects/Applied Tests** & **Ref.** \\ \hline \multirow{9}{*}{\
\begin{tabular}{c} Angiogenic \\ \end{tabular} } & & mice & - & inhibited proliferation of \\  & & HUVEC lines & vascular smooth muscle \\  & & in vivo rat model with MI & cells, \\  & & chicken chorioallantoic & - & inhibited intimal \\  & & membrane assay & hyperplasia. & [89] \\ \cline{2-5}  & & Rabbit and human plasma & - & angiogenic activity \\  & & - & inhibited CETP & [108] \\ \hline \multirow{9}{*}{
\begin{tabular}{c} Cardioprotective \\ activity \\ \end{tabular} } & & & - & inhibited \\  & & 5-metoxicelogin & Hyperlipidaemic & 3-hydroxi-methyl-Gulary-CoA-reductase, \\  & & ApoE-/-mice & - & decreased cholesterol \\  & & & level. & \\ \cline{2-5}  & & & - & promoted activity \\  & & THP-1-derived macrophages & cholesterol efflux, \\  & cell culture & - & upregulated ABCA1 & \\  & & and ABCG1. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Angiogenic and cardioprotective activity of leoligin and 5-metoxicelogin isolated in _L. alipinum_ roots. in stent drug-releasing experiments [115], as a continuation of its pharmaceutical potential in cardiovascular diseases.
# Cooperative Three-View Imaging Optical Coherence Tomography for Intraoperative Vascular Evaluation
###### Abstract  
Real-time intraoperative optical coherence tomography (OCT) imaging of blood vessels after anastomosis operation can provide important information the vessel, such as patency, flow speed, and thrombosis morphology. Due to the strong scattering and absorption effect of blood, normal OCT imaging suffers from the problem of incomplete cross-sectional view of the vessel under investigation when the diameter is large. In this work, we present a novel cooperative three-view imaging spectral domain optical coherence tomography system for intraoperative exposed vascular imaging. Two more side views (left view and right view) were realized through a customized sample arm optical design and corresponding mechanical design and fabrication, which could generate cross-sectional images from three circumferential view directions to achieve a larger synthetic field of view (FOV). For each view, the imaging depth was 6.7 mm (in air) and the lateral scanning range was designed to be 3 mm. Therefore, a shared synthetic rectangle FOV of 3 mm \(\times\) 3 mm was achieved through cooperative three view scanning. This multi-view imaging method can meet the circumferential imaging demands of vessels with an outer diameter less than 3 mm. Both phantom tube and rat vessel imaging confirmed the increased system FOV performance. We believe the intraoperative application of this cooperative three-imaging optical coherence tomography for objective vascular anastomosis evaluation can benefit patient outcomes in the future.  
optical coherence tomography; imaging system; intraoperative evaluation; vascular anastomosis +
Footnote †: journal: Applied  
12 August 2018; Accepted: 26 August 2018; Published: 4 September 2018
# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 5 Proposed Method
### Robustness and Sensitivity Analysis  
Variations significantly influence the robustness of the 3D SMPL-X human pose model in key parameters, including pose (\(\theta\)), body shape (\(\beta\)), translation (\(t\)), and rotation (\(R\)). A sensitivity analysis was performed by perturbing these parameters within a range of \(\pm 20\%\) to assess their impact on the reconstruction error (see Figure 6a). The pose parameter is highly sensitive to perturbations, leading to substantial variations in the reconstruction error. A strong correlation between pose (\(\theta\)) perturbations and reconstruction error is observed, underscoring its dominant influence. Deviations in pose angles significantly degrade model accuracy. Body shape (\(\beta\)) variations introduce moderate fluctuations in the reconstruction error, suggesting that morphological changes impact reconstruction but are less critical than pose perturbations. Similarly, rotational perturbations introduce measurable errors, particularly at extreme perturbation levels. However, they remain more stable compared to pose variations, with both parameters exhibiting weaker correlations with error, indicating relatively lower sensitivity (see Figure 6b). Among all the factors evaluated, the translation parameter \(t\) demonstrates the lowest sensitivity, suggesting that minor global shifts exert minimal influence on the precision of the reconstructed output. The weak correlation between translation perturbations (\(t\)) and the reconstruction error further reinforces the robustness of the model to slight positional variations (see Figure 6b).
## 2 Materials and Methods
### Texture Extraction and \(V^{25}_{cmax,0}\) Models
#### 2.4.2 GLCM-Based Texture Features  
GLCM is a square matrix that can examine the spatial distribution of the gray levels in the image. It describes how frequently a combination of pixels is present in an image in a given direction and distance. For a given image \(I\) with a size of \(K\times K\) and maximum gray value of \(G\), the element of a GLCM for a displacement vector \(d(dx,\,dy)\) is defined as follows:  
\[P(m,n)=\sum\limits_{i=1}^{K}\sum\limits_{j=1}^{K}\left\{\begin{array}{ll}1, \text{ if }I(i,j)=m\text{ and }I(i+d_{x},j+d_{y})=n\\ 0,\text{ otherwise}\end{array}\right\} \tag{18}\]  
\[P(m,n)=\frac{P(n,m)}{\sum\limits_{i=0}^{G}\sum\limits_{j=0}^{G}P(i,j)} \tag{19}\]  
where \(dx=dcos\theta\); \(dy=dsin\theta\); \(d\) and \(\theta\) are the predefined distance and direction, respectively. The GLCM is typically interpreted using texture indicators. Six GLCM-based texture features (Table 2) were extracted from UAV images.  
The computation of GLCM is influenced by the pixel pair distance offsets and pixel pair angles. We pre-specified a series of distance offsets (1,2,4,8,16) and angles (0, \(\pi/4\), \(\pi/2\), \(3\pi/4\)). Then, 20 GLCMs, each representing a unique combination of distance offsets and angles, were employed to derive texture samples. Finally, the mean of 20 texture samples was set as the texture feature of the image.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Texture** & **Formula**
## 3 Finite Element Analysis Considering Support Stiffness and Axial Force
### Influence of Axial Force on Stiffness Matrix  
It is assumed that the axial force is not affected by the transverse displacement (Figure 4). Under the action of an axial force, the transverse displacement of the left node 1 is \(w_{1}(t)=u\), and that of the right node 2 is \(w_{2}(t)=u+du\). Therefore, the length of the shaft element \(e\) after deformation is  
\[\begin{split}& dx^{\prime}=\sqrt{dx^{2}+du^{2}}\\ &=dx\sqrt{1+\left(\frac{du}{dx}\right)^{2}}\approx dx+\frac{1}{2} \left(\frac{du}{dx}\right)^{2}\cdot dx,\end{split} \tag{16}\]  
and the additional strain cause by the axial force on the element is  
\[\varepsilon_{x^{\prime}}=\frac{1}{2}\left(\frac{du}{dx}\right)^{2}=\frac{1}{ 2}\left(\frac{\partial w(x,t)}{\partial x}\right)^{2}, \tag{17}\]  
where \(\partial w(x,t)/\partial x\) is the derivative of the transverse displacement curve \(w(x,t)\) with respect to \(x\) without considering the influence of the axial force.  
The additional stress caused by the axial force is denoted \(\sigma_{x}\). When the axial force corresponds to the pressure acting on the shaft, it should be negative:  
\[\sigma_{x}=-P/A. \tag{18}\]  
Figure 4: Deformation of shaft element under axial force \(P\).  
According to Equations (17) and (18) the additional deformation energy caused by the axial force is  
\[\begin{array}{l}V_{P}=\frac{1}{2}\iint\sigma_{x}\Big{(}\frac{du}{dx}\Big{)}^{2} dV=\frac{1}{2}\int_{0}^{l}P\Big{(}\frac{du}{dx}\Big{)}^{2}dx\\ =\frac{1}{2}\int_{0}^{l}P\Big{(}\frac{\partial w(x,t)}{\partial x}\Big{)}^{2} dx=\frac{1}{2}\mathbf{w}(\mathbf{t})^{T}\cdot\mathbf{K}\mathbf{p}\cdot\mathbf{w}(\mathbf{t}),\end{array} \tag{19}\]  
where \(\mathbf{K}\mathbf{p}\) denotes an additional stiffness matrix; it may be found by inserting Equation (7) into Equation (19):  
\[\mathbf{K}\mathbf{p}=-\frac{P}{30l}\begin{bmatrix}36&3l&-36&3l\\ 3l&4l^{2}&-3l&-l^{2}\\ -36&-3l&36&-3l\\ 3l&-l^{2}&-3l&4l^{2}\end{bmatrix}. \tag{20}\]  
Combing the stiffness matrix of the uniform shaft, the stiffness matrix of the tailstock radial bearing, and the additional stiffness matrix of the shaft under axial force, the total stiffness matrix of the transversely vibrating element of the shaft can be obtained:  
\[\mathbf{K}=\mathbf{K}\mathbf{s}+\mathbf{K}\mathbf{t}+\mathbf{K}\mathbf{p}. \tag{21}\]  
For an Euler-Bernoulli shaft, the critical axial load of a slender compression bar can be calculated using [PERSON]'s formula:  
\[P_{cri}=\frac{\pi^{2}EI}{(\mu l)^{2}}, \tag{22}\]  
where \(\mu\) is the length coefficient of the slender compression bar.
# Hybrid Multi-Granularity Approach for Few-Shot Image Retrieval with Weak Features
## 2 Related Studies
[26] proposed a deep hashing method that learns compact binary codes through multi-layer non-linear transformations. DeepBit [27] achieves efficient visual object matching by learning compact binary descriptors, but its binarization overlooks data distribution, leading to significant quantization loss. To address this, [PERSON] et al. [28] introduced the Deep Binary Descriptor with Multi-Quantization (DBD-MQ) method, which synchronously optimizes parameters and binarization function maps via K-AutoEncoders (KAEs) to reduce quantization loss. [PERSON] et al. [29] proposed Unsupervised Triplet Hashing (UTH), leveraging triplets of anchor images, rotated images, and random images to learn binary codes. [PERSON] et al. [30] presented Unsupervised Similarity-Adaptive Deep Hashing (SADH), which updates similarity graphs and optimizes binary codes. [PERSON] et al. [31] introduced a semantic part-based weighting aggregation. [PERSON] et al. [32] successfully applied similarity-based unsupervised deep transfer learning to remote sensing image retrieval. [PERSON] et al. [33] proposed the Self-Supervised Product Quantization (SPQ) method, which trains in a self-supervised manner. [PERSON] et al. [34] developed Hashing with Contrastive Information Bottleneck (CIBHash), combining contrastive learning with deep binary hashing. [PERSON] et al. [35] proposed Contrastive Quantization with Code Memory (MeCoQ), employing contrastive learning to acquire unsupervised binary descriptors. [PERSON] et al. [36] introduced Hierarchical Hyperbolic Product Quantization (HiHPQ), focusing on the multi-layer semantic similarities in images. In summary, image retrieval has achieved remarkable progress driven by deep learning models, but challenges persist in few-shot and weak-feature scenarios. Supervised methods rely on large-scale labeled data, limiting their applicability in data-scarce environments. Unsupervised methods partially address the lack of labels but still exhibit limited performance in weak-feature and complex-background retrieval. In light of the limitations of the related work discussed earlier, this paper proposes a multi-granularity retrieval algorithm based on an unsupervised image augmentation network. By incorporating the Omni-Dimensional Dynamic Convolution module and the Bi-Level Routing Attention mechanism, this method integrates multi-scale feature extraction and a key area that focuses into the model architecture, thereby achieving end-to-end efficient feature learning.
# Adapting Probabilistic Flooding in Energy Harvesting Wireless Sensor Networks
## 1 Introduction
The main enhancement presented here is that the previous model is considered along with a solar radiation dataset obtained from the NASA Langley Research Center (LaRC) POWER Project [6] and also new simulations have been performed in a more realistic environment in order to compare the proposed information dissemination mechanism against PF. Still, it is shown that the proposed mechanism outperforms PF regarding coverage at the expense of increased termination time. Past related work regarding probabilistic information dissemination and energy harvesting is presented in Section 2. Section 3 presents the proposed Markov chain model which is analyzed and simplified in Section 5. The proposed RPF mechanism is presented in Section 4 and evaluated later in Section 6. Finally, the conclusions are drawn in Section 7.
# A Phase Bias Compensation Method for Anti-Interference Antenna Arrays in RTK Positioning
## 2 Materials and Methods
### The Bias Compensation Method for Anti-Interference Algorithm
#### 2.5.2 Bias Compensation at Anti-Interference Output Signal  
To improve the phase bias compensation accuracy, we adopt phase bias compensation after the anti-interference algorithm to achieve bias compensation. The steps are as follows.  
Step 1. Obtaining the signal after the SAP algorithm based on (31);  
Step 2. Multiplying the reciprocal of \(\mathbf{w}^{\mathbf{H}}\mathbf{a}(\theta_{s},\varphi_{s})\) as in (32).  
\[\mathrm{Y}_{b}(n)=\mathbf{X}_{b}(n)\frac{1}{\mathbf{w}_{opt}^{\mathbf{H}} \mathbf{a}(\theta_{s},\varphi_{s})}=\mathbf{w}_{opt}^{\mathbf{H}}\mathbf{a}( \theta_{s},\varphi_{s})\mathbf{X}_{s}(n)\cdot\frac{1}{\mathbf{w}_{opt}^{ \mathbf{H}}\mathbf{a}(\theta_{s},\varphi_{s})} \tag{32}\]  
\(\mathbf{X}_{b}(n)\) is the output signal after the SAP algorithm.  
Obviously, it is feasible in theory, and the method is simple. The structure is shown in Figure 6. At the receiver end, each channel eliminates the bias for the specific direction signal.
# UV3D: Underwater Video Stream 3D Reconstruction Based on Efficient Global SFM
## 3 SFM Preliminaries
### Extract Key Frames
In this paper, we use the OTSU algorithm [31] to obtain an adaptive threshold to split the dendrogram, which is often used to determine the threshold during image binarization. The basic idea of the OTSU algorithm is to divide the samples into two categories based on the threshold, whereby the greater the variance between the two categories, the better the classification. First, we calculate the interclass variance with all sample values as the threshold and take the value corresponding to the maximum variance as the optimal threshold. **Step 5:** Split the dendrogram by \(t\) with the fcluster function in SciPy and extract clustering centers \(F_{key}=\{F_{1},F_{2},\ldots,F_{k}\}\) as the output key frames.
###### Abstract  
Estimation of vital signs using image processing techniques have already been proved to have a potential for supporting remote medical diagnostics and replacing traditional measurements that usually require special hardware and electrodes placed on a body. In this paper, we further extend studies on contactless Respiratory Rate (RR) estimation from extremely low resolution thermal imagery by enhancing acquired sequences using Deep Neural Networks (DNN). To perform extensive benchmark evaluation, we acquired two thermal datasets using FLIR(r) cameras with a spatial resolution of 80 \(\times\) 60 and 320 \(\times\) 240 from 71 volunteers in total. In-depth analysis of the proposed Convolutional-based Super Resolution model showed that for images downscaled with a factor of 2 and then super-resolved using Deep Learning (DL) can lead to better RR estimation accuracy than from original high-resolution sequences. In addition, if an estimator based on a dominating peak in the frequency domain is used, SR can outperform original data for a down-scale factor of 4 and images as small as 20 \(\times\) 15 pixels. Our study also showed that RR estimation accuracy is better for super-resolved data than for images with color changes magnified using algorithms previously applied in the literature for enhancing vital signs patterns.  
Keywords:respiratory rate; remote medical diagnostics; vital signs estimation; super resolution; deep learning; convolutional neural networks +
Footnote †: journal: Applied