esa-sceva/satcom-chunk-collection huggingface dataset:
# Approximate Image-Space Multidirectional Occlusion Shading Model for Direct Volume Rendering
## 3 Image Space Multidirectional Occlusion Shading Model
### Multiple Layers with Transparent Volume  
We often render volumetric datasets with several transparent and opaque layers according to important features. In previous studies, the occlusion shading was applied to the entire volume. However, it was still difficult to see the shading effects on the transparent layers. Since our method operated in the image-space, applying our model to multiple layers was not easy with only one depth value per pixel. However, it was possible to find multiple layers depending on a layer differentiation method since we were using ray casting for rendering. In this work, we differentiated layers according to the transfer function settings. Note that depth peeling could be used to find multiple layers for polygonal rendering. Depth values for the layers were stored in depth buffers. The corresponding opacity values were calculated on-the-fly using our opacity approximation approach introduced in Section 3.5. We stored four front-most layers in the depth buffer as RGBA components, but it could be extended easily for more layers using more buffers. The multiple layers are illustrated in Figure 7c.d. In Figure 7c, we applied partial occlusion for pixels (d3 in the figure) on layers whose opacities (\(\alpha\)s) were higher than \(\alpha_{th}\). For transparent pixels (d1 in the figure), we used local illumination only, such as Phong shading. The example shown in Figure 5f gave us an idea that our shading model was applied only on the opaque blue surface. We can see the shade through the transparent object (orange ball). The occlusion was computed using Equations (4) and (5).  
However, applying our shading model only on opaque layers might produce unnatural effects. Instead, we computed occlusions on all layers and blended them. Figure 7d, for example, shows three layers (d1, d2, d3). d1 is not occluded, whereas d2 and d3 are partially and fully occluded, respectively. Therefore, \(O_{cc1}\) for d1 is 0, \(O_{cc2}\) is between 0 and 1, and \(O_{cc3}\) is 1. Once occlusions for all layers were calculated, we simply applied the alpha blending technique for the final occlusion, \(O_{cc}\) from the front layer (d1) to the back layer (d3). One disadvantage was that the partial occlusion computation had to be repeated for the number of layers, but the overhead was not significant.
# An L-Slot Frequency Reconfigurable Antenna Based on MEMS Technology
## 2 Theory and Design
### Design of a Frequency Reconfigurable Antenna  
The structure of the coaxial fed L-slot frequency reconfigurable antenna based on RF MEMS switches is shown in Figure 3. The antenna is designed on a Rogers RT5880 substrate with a thickness of 0.254 mm (\(\varepsilon\), = 2.2). The radiation patch is located on the upper surface of the substrate. An L-slot is opened on the left and right sides of the patch, and a straight plate-type MEMS switch is loaded at the corner of the slot. Driving electrodes located on the same side apply an on-state voltage to the switch, and the frequency tunable function is implemented by controlling the on-off of the MEMS switch. The ground plane of the antenna is located on the lower surface of the substrate and uniformly covers the entire plane. The detailed dimensions of the antennas are given in Table 2.  
Figure 3: L-slot frequency reconfigurable antenna (**a**) Stereogram; (**b**) Front view; (**c**) Side view.  
Figure 2: Parameters of S for the RF MEMS switch (**a**) Isolation and insertion loss; (**b**) Return loss.  
On the one hand, the L-slot changes the current path on the surface of the radiation patch, and on the other hand, the addition of the RF MEMS switch further controls the current density at the slot. Both of them together modify the radiation properties of the antenna to enable four-frequency state switching. For the convenience of illustrating the working principle of the reconfigurable antenna, the surface current distributions of the antenna in the states without slot and switch, with slot but no switch, and with slot and switch are listed below, as shown in Figure 4. Figure shows that the current clusters flow around the edges of the patch in the absence of gaps and without switching and are denser on the left and right sides. The large amount of current inside the patch is uniformly shifted up and down. The current is transferred along the slot shape when there is a slot but no switching state, and then collects in large quantities at the end of the gap. With the slot and switch state (this state sets the switch to be entirely on), current flows along the slot inlet to the switch. A large amount of current is concentrated on the switch. Only a finite fraction of the current flows toward the end of the gap.
## 5 Interference Analysis
### LOS-LAPs Interference  
The LOS-LAPs interference at the recipient V-N can be expressed as \(L_{I_{X}}(s)\), wherein \(X\) is \(L\), which is produced by presuming that \(g_{L}\) implies Nakagami-\(m\) fading and is given as [16]  
\[L_{I_{X}}(s)=\mathbb{E}_{X_{L}}\Bigg{[}\prod_{i\in X_{L}}\Bigg{(}1+\frac{sP_{ L}\eta_{L}^{-1}\mathcal{G}_{t,i}\mathcal{G}_{r,i}\|z_{r,i}-z_{t,i}\|^{-\alpha_{r} }}{m_{L}}\Bigg{)}^{m_{L}}\Bigg{]}. \tag{37}\]  
Using coordinates that are polar and presuming the pgf of the point process, the equation is derived as  
\[L_{I_{L}}(s)=\exp\left(-2\pi\lambda_{L}\int_{h_{U}}^{\infty}\mathfrak{f} \mathfrak{p}_{L}\bigg{(}\sqrt{t^{2}-h_{U}^{2}}\bigg{)}\Bigg{(}1-\Bigg{(}1+ \frac{sP_{L}\eta_{L}^{-1}\mathcal{G}_{L,t}\mathcal{G}_{\mathbf{d},r}\|z_{ \mathbf{d}}-z_{t}\|^{-\alpha_{\mathbf{d}}}}{m_{L}}\Bigg{)}^{m_{L}}\Bigg{)}dt \right). \tag{38}\]
## 2 Materials and Methods
### Data Sources and Processing  
The Gaofen-2 satellite (GF-2) is characterized by high-speed positioning, sub-meter spatial resolution, and fast-maneuvering side-swing capability, which effectively improves the comprehensive observation capability of the satellite [22]. GF-2 plays a vital role in land resource surveys; exemplary urban management; mineral resource monitoring; forest resource surveys; desertification monitoring; geological disaster monitoring; flood  
Figure 1: Location map of the study area: (**a**) location map of Chuxiong Yi Autonomous Prefecture, (**b**) Location map of Lufeng County, (**c**) GF-2 image.  
monitoring; and post-disaster reconstruction. In this study, the high-resolution remote sensing image covering Lufeng County was acquired via GF-2 (download address: [[https://data.cresda.cn](https://data.cresda.cn)]([https://data.cresda.cn](https://data.cresda.cn)) (accessed on 14 April 2021)) on 13 March 2020, was utilized, and the specific parameters are shown in Table 1.  
In remote sensing image acquisition, satellite attitude, speed, altitude, atmospheric disturbance, and other factors cause geometric deformation and information errors in remote sensing images. Therefore, the remote sensing service platform ENVI5.3 was utilized to preprocess the GF-2 images of the study area. First, radiometric calibration and atmospheric correction were performed on the multispectral images; radiometric calibration was performed on the panchromatic images; and the digital number (DN) of the remote sensing image components was converted to radiance and reflectance. Then, the images were orthorectified using DEM data with a resolution of 12.5 m. The calibrated multispectral image was fused with the panchromatic image using the Nearest Neighbor Diffuse (NNDiffuse) panchromatic-sharpening fusion algorithm--recently proposed by the Rochester Institute of Technology (RIT)--resulting in suitable image fusion results [23]. Finally, the fused image is cropped; the preprocessed result is shown in Figure 1c.  
The Advanced Land Observing Satellite (ALOS), part of the Advanced Land Observing Satellite-1 project of the Japan Aerospace Exploration Agency (JAXA), offers valuable information for mapping; precise regional land-cover observation; disaster monitoring; and resource surveys. It particularly excels in digital elevation mapping [24]. This study utilizes the ALOS DEM acquired on 31 January 2009 at 15:45, with a spatial resolution of 12 m. The data can be accessed from the open-source website (download link: [[https://search.asf.alaska.edu](https://search.asf.alaska.edu)]([https://search.asf.alaska.edu](https://search.asf.alaska.edu)) (accessed on 29 May 2022)).
## 4 Experimental Demonstration
### Test Scheme  
Since the HUST utility bearing dataset is a newly available dataset, we needed to let the model perform preliminary experiments on the CWRU rolling bearing dataset, which is Experiment I. Then, we used the HUST bearing dataset to investigate whether our model has high accuracy in facing different types of mixed bearing failures, which is Experiment II. The third experiment is the noise immunity experiment. The following is the reason why we performed these three experiments.  
* In the initial validation of the model performance using the CWRU dataset, we needed to perform data enhancement on the CWRU dataset due to its lesser amount of data than that of other datasets. We used sliding window sampling here to increase each sample type to 800. To verify the model's training effect on different bearings under complex working conditions, we divided the data into 12 classes. Subsequently, it was necessary to juxtapose various models to exemplify the model's efficiency and include ablation experiments to corroborate the indispensability of each segment within the model.
* The HUST dataset was sufficiently sampled, and no data enhancement was performed. Here, the data were divided into seven categories in total, and for the sake of experimental rigor, we set all other variables as the same. Finally, we also compared five models to prove that the model has made some modest contributions to the academic community in the face of the fault recognition rate of five types of bearings.
* In actual operational settings, the model for diagnosing faults within rotating machinery has to confront the noise produced by the mutual oscillations and abrasion amongst the machine's components. Such conditions make the vibration signals detected by the sensors susceptible to noise pollution, resulting in a blurring of the fault details contained within these signals. According to the survey, the noise of the diagnostic signal is generally additive white Gaussian noise [36]. Therefore, in this  
Figure 8: Illustrations of defects on actual test bearings: **(a)** inner crack; **(b)** outer crack; **(c)** ball crack; **(d)** inner and outer cracks; **(e)** inner and ball cracks; **(f)** outer and ball cracks. The red circle is the fault location of the bearing.  
paper, different levels of additive white Gaussian noise were added to the test set's signals to test the trained model's anti-noise performance.
## 3 Results
### Parameters of the Mixture Biogas and Hydrogen
#### 3.2.2 Change in Density and Volume of the Gas Mixture  
The graph in Figure 8 shows how a decrease in the density of the gas mixture leads to an increase in the mass proportion of hydrogen.  
In view of the aforementioned difference in energy density, an increase in the mass proportion of hydrogen results in a significant increase in the volume of the gas mixture. Figure 9 shows changes in the volume of the gas mixture as a function of the percentage of hydrogen per 1 kg of the mixture. The graph shows that the gas mixture containing 40% hydrogen has almost four times the volume of biogas alone. Therefore, difficulties arise in storing this type of gas mixture, since the storage system needs to be equipped with fittings comprising compressors and tanks designed for storage under increased pressure.  
The cost of producing hydrogen using renewable energy depends on the source of energy. The Hydrogen Europe report (2019) states that the cost of producing hydrogen by using photovoltaic farms ranges from 3.54 to 6.53 euro/kg, while using wind turbines it ranges from 4.72 to 6.41 euro/kg [11].
###### Abstract  
Solar panels are critical for renewable electricity generation, yet defects significantly reduce power output and risk grid instability, necessitating reliable AI-driven defect detection. We propose the SPHERE (Solar Panel Hidden-Defect Evaluation for Renewable Energy) method for such cases. This study compares deep learning models for classifying solar panel images (broken, clean, and dirty) using a novel, proprietary dataset of 6079 images augmented to enhance performance. The following three models were evaluated: YOLOv8-m, YOLOv9-e, and a custom CNN with 9-fold cross-validation. Pre-trained models (e.g., VGG16 and ResNet) were assessed but outperformed by YOLO variants. Metrics included accuracy, precision-recall, F1-score, sensitivity, and specificity. YOLOv8-m achieved the highest accuracy (97.26%) and specificity (95.94%) with 100% sensitivity, excelling in defect identification. YOLOv9-e showed slightly lower accuracy (95.18%) but maintained high sensitivity. The CNN model demonstrated robust generalization (92.86% accuracy) via cross-validation, though it underperformed relative to YOLO architectures. Results highlight YOLO-based models' superiority, particularly YOLOv8-m, in balancing precision and robustness for this classification task. This study underscores the potential of YOLO frameworks in automated solar panel inspection systems, offering enhanced maintenance and grid stability reliability. This contributes to advancing AI applications in renewable energy infrastructure, ensuring efficient defect detection and sustained power output. The dataset's novelty and the models' comparative analysis provide a foundation for future research in autonomous maintenance solutions.  
solar panel classification; deep learning; YOLOv8; YOLOv9; convolutional neural networks; k-fold cross-validation +
Footnote †: journal: Applied sciences
# Enhancing Beamforming Efficiency Utilizing Taguchi Optimization and Neural Network Acceleration
## 3 Taguchi Method
### Amplitude Synthesis of Antenna Array Radiation Pattern under Constraints  
In this type of synthesis, constraints are imposed on the levels of sidelobes and the locations of nulls in the radiation pattern, which the beam should exhibit towards interfering signals. In this initial synthesis type, the Taguchi optimization procedure is applied to three linear antenna arrays, each with a different number of sources (10, 16, and 24), in order to minimize sidelobes.  
Figure 2: General arrangement of a linear antenna array ([PERSON], Imperial College London).
# Objective Prediction of Human Visual Acuity Using Image Quality Metrics
###### Abstract  
This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from a set of natural images. The amount of distortion added by the eye to the natural image was quantified using different image processing metrics, and the correlation between the result of each metric and subjective visual acuity was assessed. The correlation obtained for a model based on a linear combination of the normalized mean square error metric and the feature similarity index metric was very good. It was concluded that the proposed method could be an objective way to determine subjects' monocular and uncorrected decimal visual acuity with low uncertainty.  
visual acuity; aberration; image quality assessment Article
## 3 Fault Classification  
The behavior of coefficients can be used to design an algorithm for fault classification [7]. AI methods are important for data classification because they can increase accuracy and can rapidly analyze data. AI methods were used herein for fault classification and compared. ANNs are used in data classification and data forecasting. Probabilistic neural networks (PNNs) can be applied for data classification. Back-propagation neural networks (BPNNs) are popular ANNs because they can be used to solve numerous problems. In addition, the SVM is a popular choice for data classification. In this study, we used three methods to classify types of faults to compare accuracy. The data were divided into three parts (total 1080 data): 50% of the data were used for training (540 data), 25% of the data were used for validation (270 data), and finally, 25% of the data were used for the case study (270 data). The data detected the maximum first peak coefficients of phase ABC and zero sequence (Z).  
Figure 4: Signals from the wavelet transform (WT) in the case of phase A, short circuit to ground fault at 30% of the length of the transmission line: (**a**) sending end; (**b**) receiving end.  
The normalized values (0 to 1) of coefficients of phase ABC and zero sequence were input into the algorithm for classification.
## 1 Introduction
In this paper, we study a general problem of CIM, _Budgeted Competitive Influence Maximization_ (BCIM), which takes into account both arbitrary cost and time constraints for CIM problem. To model this problem, we first introduce Time constraint Competitive Linear Threshold (TCLT) to capture competitive influence progress within time constraint by extending Competitive Linear Threshold [21; 22]. Under TCLT model, the main challenges of BCIM lie in following aspects. Firstly, BCIM problem is NP-hard and it is #P-Hard [23] to calculate the objective function. Moreover, we point out that the objective function is neither submodular nor supermodular. Thus, it makes BCIM difficult to be solved using greedy-based algorithms, as well as methods for influence maximization. To address the above challenges, in this article, we present SPBA, an efficient randomized algorithm based on polling method and Sandwich Approximation framework [16]. Our main contributions are summarized as follows:  
* We formulate Time constraint Competitive Linear Threshold (TCLT) model by extending Competitive Linear Threshold model in [21; 22] to simulate competitive influence within time constraint \(\tau\). Given two competitors \(A\) and \(B\) who need to advertise their productions on OSNs, assume that we know nodes that are activated by \(B\) (\(B\)-seed set). Given the limited budget \(L\), heterogeneous cost of each node to active by \(A\) (i.e., each node has a cost to add it into \(A\)-seedset), and the time constraint \(\tau\), we study BCIM problem, which aims to seek \(A\)-seed set nodes within limited budget \(L\) and time constraint \(\tau\) to maximize nodes influenced by \(A\) under TCLT model. We then show that BCIM is NP-hard and the objective function is neither _submodular_ nor _supermodular_. * We propose SPBA, an efficient randomized algorithm based on Sandwich approximation and polling method. We first design _upper_ bound and _lower_ bound submodular functions of the objective function and develop a polling-based approximation algorithm to find the solution of bound functions that guarantees approximation ratio of \((1-1/\sqrt{e}-\epsilon)\) with high probability. Based on that, the Sandwich framework approximation in [16] is applied to give a data-dependent approximation factor. * We conducted extensive experiments on various real social networks. The experiments suggest that SPBA provides significantly higher quality solutions than existing methods including baseline algorithms and influence maximization algorithms. Furthermore, we also demonstrate that our algorithm can scale to million-scale networks within about 1.5 min. Organization. The rest of paper is organized as follows. The related work is presented in Section 2 and the preliminaries for Competitive Linear Threshold model and Competitive Influence Maximization are introduced in Section 3. We introduce our propagation model, problem definition and its properties in Section 4. Section 5 presents our proposed algorithms. The experiments are shown in Section 6. Finally, we give some tasks for future work and conclusion in Section 7.
## 4 A Construction Based on the Discrete-Logarithm Problem
Thus, it also holds that \(g^{a^{\prime}_{i}}\bmod p=h\), i.e., the adversary \(A\) is able to compute the discrete logarithm of \(h\) with respect to \(g\). It is easy to see that the view of adversary ACT-STAT, when executed as a subroutine by \(A\), is the same as in the impersonation experiment \(\textbf{Imp}_{\texttt{ACT-STAT},\Pi}(1^{\texttt{T}})\). As the advantage of adversary ACT-STAT in the impersonation experiment \(\textbf{Imp}_{\texttt{ACT-STAT},\Pi}(1^{\texttt{T}})\) is non-negligible, it can be concluded that the advantage of \(A\) in the discrete-logarithm experiment \(\textbf{DLog}_{A,\mathcal{G}}(1^{\texttt{T}})\) is non-negligible. Performance AnalysisIn this section, we analyze the DLP-EAC with respect to memory and computational requirements. It is easy to see that the private information for the prover consists of \(\ell\cdot\log q\) bits, since \(priv\) contains \(\ell\) values which are randomly chosen in \(Z_{q}\). In order to decrease the size of the private key in the DLP-EAC, we could use the same idea behind the LiS1 scheme: we chose at random an initial value \(r_{0}\) in \(Z_{q}\) and then compute all values \(r_{1},\ldots,r_{\ell}\) by using a universal hash function \(\mathcal{UHF}\) with a random key \(k\), i.e., \(r_{i}=\mathcal{UHF}(k,r_{i-1})\) for \(1\leq i\leq\ell\). With such a modification, the signer is only required to store the initial value \(r_{0}\), along with the key \(k\) for the universal hash function. In particular, if we instantiate the universal hash function with the multiply modular scheme in [27], the size of the private key for the signer is equal to \(3\cdot\log q\) bits, since the key \(k\) consists of two values \(k_{0}\) and \(k_{1}\) chosen in \(Z_{q}\). Moreover, to compute each \(r_{i}=\mathcal{UHF}(k,r_{i-1})\), where \(r_{i}=k_{0}\cdot r_{i-1}+k_{1}(\bmod q)\) for \(1\leq i\leq\ell\), the prover only needs to perform one modular addition and one modular multiplication. As regards as the public information, its size equals \(\ell\cdot\log p\) bits, since \(pub\) contains \(\ell\) values in \(Z^{*}_{p}\). As performed in the LiS1 scheme, we could use a Bloom filter [28] to efficiently represent the set of values in the public key, in order to reduce its size. In particular, if we use the Bloom filter construction proposed by [PERSON] et al. [29] to represent the set of \(\ell\) values \(g^{r_{i}}\bmod p\), for \(i=1,\ldots,\ell\), the size of the public key reduces to \(1.44\epsilon\ell\) bits, where \(\epsilon\) denotes the probability that a false positive occurs while testing the membership of an element to the Bloom filter.