esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Implementation of a Deep Learning Acceleration Core Architecture for Image Target Detection
## 4 Conclusions  
This paper proposes an FPGA-based deep learning acceleration core architecture for image target detection, which designs a parallel acceleration scheme to address the problem of arithmetic power, speed, and resource consistency. In this paper, the computational scheduling kernel is streamlined so that the computation unit can perform one parallel computation per clock without waiting for data pre-processing. In order to provide sufficient data access bandwidth for parallel computing units, this paper also designs and implements a three-level data cache architecture of off-chip storage, on-chip storage, and registers, which provides high bandwidth data streams for parallel computing units by slicing on-chip storage to avoid data stream operations affecting the computational efficiency of parallel acceleration cores. Using bus accessing and DSP resource optimization strategies improves bus bandwidth utilization and saves computational resources, reducing the DSP resource to half the original. This paper uses the HLS high-level synthesis tool for deep learning acceleration core development on FPGAs and achieves 14 FPS inference for the TinyYolo model with 5 GOPS computation using less than 25% of the FPGA resource. The acceleration core can run at 30% higher clock frequency, 2-4 times higher arithmetic power, and 28% more efficient DSP resource utilization than other methods. The limitation of this paper is that our proposed parallel acceleration algorithm is only suitable for CNN-based models, and further research on acceleration algorithms for RNN-based or GAN-based models should be conducted in the future.  
Conceptualization, X.Y. and C.Z.; methodology, W.F.; software, X.Y.; validation, X.Y., Z.Y. and Q.W.; formal analysis, C.Z.; investigation, X.Y.; resources, X.Y.; data curation, X.Y.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y. and C.Z.; visualization, X.Y.; supervision, C.Z.; project administration, W.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China under grant number 61901015.  
Not applicable.  
The authors acknowledge graduate student [PERSON] for his contribution to literature search and collation.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} FPGA & Field Programmable Gate Array \\ CNN & Convolutional Neural Networks \\ DSP & Digital Signal Processing \\ YOLO & You Only Look Once \\ Faster R-CNN & Faster Region Convolutional Neural Networks \\ SAR & Synthetic Aperture Radar \\ mAP & Mean of Average Precision \\ AP & Average Precision \\ AR & Average Recall \\ UAV & Unmanned Aerial Vehicle \\ RES-YOLO & Residual YOLO \\ GPU & Graphics Processing Unit \\ DSPs & Digital Signal Processors \\ NPUs & Neural Processor Units \\ FPS & frames per second \\ \end{tabular}
## 2 Function of Factsage Software
### Viscosity Module
Although the Viscosity model has been shown to predict viscosity with high accuracy [64], it still has certain limitations. [PERSON] et al. [65] investigated the viscosity of CaO-MgO-Al\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\) (CMAS) melt using real experiments and compared it with several viscosity prediction models. Ultimately, they found that among the three viscosity models tested, the Factsage model was the best in predicting the viscosity of molten CMAS. However, there are still certain drawbacks. For example, [PERSON] et al. [57] found that the model cannot account for water or rare earth substances present in the actual environment, and there may be significant errors when studying more complex systems. Therefore, in practical scenarios involving more complex systems, the model can be refined by consulting multiple viscosity models and integrating them with empirical data from real experiments. They observed discrepancies between the viscosity predictions made by the Factsage for industrial iron silicate slag with added alumina blends at temperatures between 1100 and 1300 \({}^{\circ}\)C, compared to actual measurements. When the temperature is above the liquid phase line, i.e., above 1150 \({}^{\circ}\)C, the viscosity calculated by Factsage is slightly higher than the actual measured value. This discrepancy can be attributed to the software's exclusion of minor oxides in the calculation process. However, below the liquid phase line temperature, solid particles begin to form, increasing the actual melt viscosity. Factsage does not take into account this solid-liquid phase coexistence, resulting in a predicted value that is significantly lower than the actual viscosity. By using the Einstein-Roscoe equation to adjust the predictions, they ensured that the variations in slag viscosity with temperature matched the corrected software predictions.
# A Novel Data-Aided Frame Synchronization Method Based on Hough Transform for Optical Communications
## 1 Introduction
In this paper, we propose a novel frame synchronization method based on Hough transform, in which synchronization is achieved through blindly recognizing the synchronization word. In our work, we build an interception matrix filled with intercepted bit stream. When frames are all aligned in the interception matrix, the columns of synchronization word appear as black and white stripes, if we consider the matrix as a binary image. Hence, Hough transform, which is a classic method for line detection in digital image processing, is applied in this work to detect the stripes in the interception matrix. Simulations are carried out in the optical QPSK transmission system, and the results show that the new method can efficiently recognize the synchronization word and attain frame synchronization. The proposed method is also compared with the AC-based algorithm and simulation results demonstrate that our HT-based algorithm performs better at high BER. The remainder of this paper is organized as follows. Section 2 briefly introduces the existing AC-based frame synchronization algorithm. In Section 3, principles of Hough transform are first introduced in detail and an example of its application is given. Then, the data-aided frame synchronization algorithm based on Hough transform is presented. Simulation experiments are carried out in Section 4, and the HT-based synchronization algorithm is compared with the AC-based algorithm. Finally, Section 5 provides the conclusion.
## 5 Experiments
### Analysis of the Impact of Environmental Factor Obstacle Density  
In this section, we analyze the impact of obstacle density on the algorithm's performance. It is important to note that obstacle density is independent of scenario size. Within our benchmark, the small-scale scenario presents the simplest scenario, while the medium-scale scenario poses the greatest challenge in terms of obstacle density, with the large-scale scenario falling somewhere in between.  
In the simplest scenario with low obstacle density, all algorithms, including FastSLAM, FastSLAM-PSO, FastSLAM-DE, and FastSLAM-MO-PSO, performed relatively well. However, as indicated in Section 5.2.1, FastSLAM-MO-PSO showed a slight edge in terms of mapping accuracy and trajectory tracking, which can be attributed to its ability to optimize particle distribution effectively in less complex environments. The medium obstacle density scenario, as in Section 5.2.3, posed a more significant challenge. Here, the performance gap between the algorithms became more pronounced. While FastSLAM struggled with increased noise and potential particle degeneration, FastSLAM-PSO and FastSLAM-DE showed improvements, but were still outperformed by FastSLAM-MO-PSO. The integration of MO-PSO allowed for better adaptation to the environment's complexities, as evidenced by the superior accuracy and robustness of FastSLAM-MO-PSO. In the high obstacle density scenario, as in Section 5.2.2, the differences in performance were most striking. FastSLAM-MO-PSO continued to demonstrate its superiority, maintaining a lower deviation from the true trajectory compared to the other algorithms. The challenges faced by FastSLAM and FastSLAM-DE in this scenario were more pronounced, with increased deviations and inaccuracies in mapping and localization. FastSLAM-MO-PSO's multi-objective optimization approach allowed it to navigate the dense environment more effectively, showcasing its adaptability and robustness.  
The comparative analysis of the algorithms across the three scenarios clearly indicates that FastSLAM-MO-PSO is the most robust and accurate SLAM solution under varying environmental conditions. As the obstacle density increases, the performance of traditional SLAM algorithms like FastSLAM declines, while FastSLAM-MO-PSO maintains its performance, demonstrating the benefits of multi-objective optimization in complex environments.
## 3 Methodology and Materials
### Dataset Used
#### 3.3.1 Scene Classification Dataset  
In our quest to advance scene recognition capabilities within our research, we leveraged the 'Scene Classification' dataset [28], an invaluable resource that significantly contributed to the diverse and comprehensive training and evaluation of our scene recognition model. This dataset is notable for its vast collection of approximately 25,000 images, which span a broad spectrum of natural scenes from various locations around the world. The primary objective of this dataset is to address the scene classification task, which involves the categorization of images into specific scene types based on their visual characteristics. In our specific study, the task involved a six-class classification problem, where each image was expected to be categorized into one of the following scene classes: Buildings, Forests, Mountains, Glacier, Street, or Sea.  
To establish a consistent and standardized approach to dataset preparation, we meticulously selected and worked with 500 images from each of the six scene classes, resulting in a total of 3000 images for our study. These images were strategically partitioned into two essential subsets: training and testing. Approximately 80% of the images were dedicated to the training subset, allowing our model to learn and extract vital scene features. The remaining 20% of the images constituted the testing subset, serving as a critical benchmark to rigorously evaluate the model's scene recognition proficiency and generalization capabilities. In alignment with best practices for image dataset management, all images within the 'Scene Classification' dataset were uniformly resized to a resolution of 200 \(\times\) 200 pixels. This resizing procedure not only facilitated computational efficiency but also ensured consistent image dimensions across the dataset, promoting a fair and equitable evaluation of the model's performance. The ground truth labeling of images in this dataset, which entails accurately associating each image with its respective scene class, was undertaken with utmost precision. This process ensured the reliability and trustworthiness of the scene labels, a critical factor underpinning the success of our scene recognition model. Figure 3 shows examples from this dataset.
###### Abstract
[19] added an attention mechanism to the ST-GCN, and adopted a two-stream adaptive GCN (2s-AGCN) to capture richer action features. However, the two-stream framework inevitably increased the computing load of the network. [PERSON] et al. [20] proposed a multiscale spatial-temporal GCN (MST-GCN) designed to capture the relationship between short- and long-range joints while enriching the perceptual field of the model in time and space. Similarly, [PERSON] et al. [21] proposed a Shift Graph Convolution Network (Shift-GCN) by adding the shift operation to graph convolution, which not only expanded the perceptual field of the model, but also greatly reduced the computational complexity of the algorithm. [PERSON] et al. [22] combined multiscale graph deconvolution with G3D, a unified spatial-temporal graph convolutional operator, into a deconvolution-unification GCN. Their network was shown to promote the direct exchange of action information across space and time and to learn features effectively. [PERSON] et al. [23] created a residual GCN (ResGCN), which replaced the fusion of multiple models with early fusion between the inputs of multiple branches to minimize the computational resources required. All these studies considered the recognition of whole-body actions, without focusing on lower limb gait. Thus, these methods cannot be directly applied to recognize abnormal gaits. Moreover, most of the existing GCNs require a long period of training, because multiple streams are fused in the later stage of modeling, and the sub-models of different streams need to be trained separately before fusion is performed. Aiming to address these problems, this paper proposes a method of abnormal gait recognition based on a multi-branch, spatial-temporal, joint attention graph convolution network (STJA-GCN). In order to enhance the extraction of key features in walking gait, the connection between human skeleton joints is simplified, and the multi-branch joint motion features are fused in the early stage to form an end-to-end single-stream model architecture, which not only preserves the multiple motion characteristics of the human skeleton, but also avoids the complex calculation of multi-stream model fusion.
## 2 The Proposed Method  
### Noise Analysis  
Due to the influence of manufacturing technology, the non-uniform noise in the space dimensional push sweep hyperspectral imaging system consists of two parts: one is the non-uniform fringe noise caused by the different photoelectric response rate of the detector; the other part is due to the shared amplifier and the stripe non-uniformity caused by the photoelectric conversion process [31; 32; 33]. We define the noise caused by different detector responses as the instrument fixed-mode noise (\(\varepsilon_{1}\)); the other part of the non-uniform fringe noise is defined as random noise (\(\varepsilon_{2}\)). Suppose that the measured image data containing noise is \(\gamma\); then, the relationship between \(\gamma\) and clean image \(\chi\) is shown in Formula (1). Therefore, the key to eliminating non-uniform noise is to separate \(\chi\) from \(\gamma\) as completely as possible, so as to completely eliminate the error caused by the non-uniformity of the sensor, and obtain the accurate absorption reflectivity value of the brain tumor sample tissue:  
\[\gamma_{(i,j)}=\chi_{(i,j)}+\varepsilon_{1(i,j)}+\varepsilon_{2(i,j)} \tag{1}\]  
Aiming at the above noise sources, this paper proposes a method that can simultaneously remove the fixed-mode noise and random signal noise of the tumor hyperspectral image instrument. The removal of the instrument's fixed-mode noise is suggested using an enhanced reference dark background imaging correction methodology. This technique can more precisely separate the instrument's fixed-mode fringe noise and retrieve the reference image's edge. For the random noise generated during signal conversion and processing, we propose a weighted least squares method with embedded bilateral filtering. This method can reduce noise while retaining more image information compared to other image filtering denoising techniques. In order to generate a clear and noise-free hyperspectral brain tumor image, our method's particular approach is to smooth and filter the image with the BLF-WLS algorithm, which retains more details. Then, utilizing the smooth image, we subtract the fringe component present in the dark background image. Figure 1 shows the flow of the suggested method.  
Figure 1: Spatial dimensions push sweep hyperspectral imaging system noise removal method.
## 3 Proposed DACR Model
### Matching Function Learning  
Matching function learning-based CF methods not only include the matching function learning part but also have the representation learning part. As the initial representations of users and items given by \(v_{u}^{U}\) and \(v_{i}^{I}\), respectively, have very high dimensionality and sparseness, it is difficult for the model to explore the matching function. Hence, collaborative filtering methods based on matching function learning generally apply linear embedding layers to obtain low-dimensional latent representations for users-items, which is more effective in learning the matching function.  
The attention matching function learning module of the DACR model combines the MLP and the attention layer and takes the interaction matrix \(Y\) as input. Assuming that the dimension of the encoder of the attention layer is \(h\), we apply the attention layer to the initial representations \(Y_{u^{+}}\in R^{1\times N}\) and \(Y_{s_{i}}\in R^{M\times 1}\) to calculate the decoding vector as shown in Equation (4):  
\[\begin{split} p_{u}&=P^{T}Y_{u*}\\ q_{i}&=Q^{T}Y_{sj}\\ a_{0}&=\begin{bmatrix}p_{u}\\ q_{i}\end{bmatrix}\\ \alpha&=\delta(W^{T}a_{0}+b)\\ v_{d}&=\alpha\odot a_{0}\end{split} \tag{4}\]  
where \(p\) and \(Q\) denote the parameter matrix of the linear embedding layer, \(p_{u}\) and \(q_{i}\) denote the low-dimensional latent representation vectors for users and items, respectively. Overall, the derivation of this part can be defined as Equation (5):  
Figure 2: The framework of DACR.  
\[a_{0}^{\prime}=\begin{bmatrix}a_{0}\\ \bar{v}_{d}\end{bmatrix} \tag{5}\] \[a_{1}=\theta(W_{1}^{T}a_{0}^{\prime}+b_{1})\] \[a_{2}=\theta(W_{2}^{T}a_{1}+b_{2})\] \[\qquad\qquad\cdots\cdots\] \[a_{Y}^{ml}=a_{Y}=\theta(W_{Y}^{T}a_{Y-1}+b_{Y})\] \[\hat{y}_{ui}=\sigma(W_{out}^{T}a_{Y}^{ml})\]  
where \(a_{0}^{\prime}\) denotes the input of the MLP layer, \(W_{x}\), \(b_{x}\), \(a_{x}\) denote the weight matrix, bias vector, and output of the MLP layer, respectively, and \(a_{Y}^{ml}\) denotes the prediction vector. In summary, the matching function learning module implements the representation learning functions \(f(\cdot)\) and \(g(\cdot)\) through the linear embedding layer and learns the latent representation vectors \(p_{u}\) and \(q_{i}\) for the user and item, respectively. We concatenate the latent representation vectors of users and items and then input the aggregated vectors into the attention layer. Next, the output prediction vector is passed through the fully connected layer to calculate the matching scores. This process is realized by Equations (4) and (5), that is, the right dashed box in Figure 2.
# Photoacoustic Imaging in Oxygen Detection
## 1 Introduction
On the application level, the UI signal originates from the acoustic impedance mismatch between different tissues, while the PA signal comes from the preferential light absorption spectra of tissues. Therefore, PAI provides better tissue differentiating capability [15]. Since its development, PAI has been regarded as a scalable imaging tool to help understand life activities from both macroscopic and microscopic fields of view. On a coarse scale, photoacoustic computed tomography (PACT) can image at several-centimeter-depth with an acoustic resolution at a seconds-to-minutes 3D imaging speed. In a fine scale, acoustic-resolution photoacoustic microscope (AR-PAM) can provide up to 10 mm imaging depth with acoustic resolution by focused transducers, which can be on the order of tens of micrometers with a MHz frequency transducer. In the highest resolution regime, optical-resolution photoacoustic microscope (OR-PAM) with tightly focused laser illumination can provide sub-wavelength resolution whilst the imaging depth is within a few hundred micrometers. As oxygen distribution in the body holds great heterogeneity, such as between arteries and veins, among tissues, cells, and even organelles, the adjustable imaging depth and resolution of a single modality can greatly facilitate understanding oxygen uptaking and metabolism in different scales and finally drawing a whole picture. In this review, we briefly discuss the application of photoacoustic imaging in both sO\({}_{2}\) and pO\({}_{2}\) detection, including the state-of-the-art as well as their potentials and limitations.
## 2 Results and Discussion
### Electrochemical Corrosion Measurement
#### 2.3.2 Morphological Studies  
The cross-sectional images of before (top) and after (bottom) the corrosion test in the 3.5 wt% NaCl solution for three days are shown in Figure 5. The cross-sectional image of WPVU\(-\)g\(-\)BTA\(-\)A in shows that the corroded layer was uniform and without pits. The image of WPVU\(-\)g\(-\)BTA\(-\)B in also shows a rough corroded layer with a few pits, while the WPVU\(-\)g\(-\)BTA\(-\)C coating shows a relatively less rough surface. The specimens with higher cross-linking density showed complex corrosion morphologies.  
Figure 4: Tafel plots for raw CRS electrode and CRS electrode coated with WPU\(-\)g\(-\)BTA\(-\)A, WPU\(-\)g\(-\)BTA\(-\)B, and WPU\(-\)g\(-\)BTA\(-\)C in 3.5 wt% NaCl(aq) for 3 days of immersion.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Code** & **Ecorr (V)** & **icorr (uA.cm\({}^{-2}\))** & **Rp (kf)** & **P\({}_{\rm EF}\) \%** \\ \hline CRS & \(-\)0.634 & 4.98 & 2.587 & - \\ \hline WPU-g-BTA-A & \(-\)0.633 & 4.17 & 3.065 & 16.16 \\ \hline WPU-g-BTA-B & \(-\)0.576 & 4.08 & 2.349 & 18.13 \\ \hline WPU-g-BTA-C & \(-\)0.550 & 3.41 & 3.127 & 31.56 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Long term anti-corrosion study for raw CRS and CRS electrode coated with WPU-g-BTA-A, WPU-g-BTA-B, and WPU-g-BTA-C in 3.5 wt% NaCl (aq) at 298 K.  
SEM images of freshly polished surface (top) as well as specimens retrieved from 3.5 wt% NaCl solution (bottom) after 3 days of immersion are shown in Figure 6. The freshly polished WPU\(-\)g\(-\)BTA coatings showed a relatively smooth surface as shown in Figure 6,b, while the longest chain extender C due to high crosslinking density strengthened the coating in WPU\(-\)g\(-\)BTA\(-\)C film showed a coarse surface, as shown in Figure 6. The WPU\(-\)g\(-\)BTA specimens retrieved from corrosive media showed a rough surface with corrosion products. The WPU\(-\)g\(-\)BTA\(-\)C coating with high crosslinking density seemed compact, tight, and wrapped around the corrosion products, which is beneficial to shield them from corrosive medium.
# Improvement of ID3 Algorithm Based on Simplified Information Entropy and Coordination Degree
## Improved ID3 Algorithm
### The Solution of Simplification of Information Gain
#### 3.1.1 The Derivation Process of Simplifying Information Entropy
 \tag{11}\]  
Therefore, Equation (8) can be rewritten as:  
\[ \begin{split} Gain(D,a_{i})&==\frac{2 pn}{p+n}-\sum \limits_{i=1}^{V}\frac{2p_{i}n_{i}}{p_{i}+n_{i}}.\end{split}  \tag{12}\]  
Hence, Equation (12) can be used to calculate the information gain of every attribute and the attribute that has the maximal information gain will be selected as the node of a decision tree. The new information gain expression in Equation (12) that only includes addition, subtraction, multiplication, and division greatly reduces the difficulty in computation and increases the data-handling capacity.