esa-sceva/satcom-chunk-collection huggingface dataset:
# A Tile-Based Multi-Core Hardware Architecture for Lossless Image Compression and Decompression
## 2 Compression and Decompression Algorithms Based on Hybrid Strategies
### Decompression Workflow  
In the case of decompression, the algorithm inversely applies the strategies to reconstruct the image data from the compressed state. The workflow, as depicted in Figure 2, ensures the restoration of the original image. During the decompression stage, the encoded data stream is meticulously parsed. This commences with the flag bit's interpretation, which dictates the ensuing decompression mode. Subsequently, the data volume that corresponds to the decompression mode is read. The pixel component, once decompressed, is accurately placed back within its designated position in the tile. This precise repositioning is critical for the integrity of the image reconstruction process. Upon the conclusion of the pixel components' decompression, the original image is meticulously reconstructed, reflecting the effectiveness of the algorithm in preserving the integrity of the image data. This complex process, governed by the flag bits and detailed computational methods, ensures the precision of the decompression, enabling the complete restoration of the image to its pristine condition. By harnessing the power of these three coding strategies within a block-based compression framework, the algorithm effectively reduces data redundancy, achieving substantial compression ratios without compromising on the quality of the decompressed images. This methodical approach enhances the efficiency of image compression and decompression, making it suitable for applications where storage or bandwidth conservation is critical.  
Figure 1: Process of the compression algorithm.
## 4 Methodology
### Network Architecture  
The policy network \(\pi_{\theta}\) takes the observation \(O_{i}^{t}\) as the input and outputs the action \(a_{i}^{t}\). Figure 1 shows the architecture of the policy network. There are four inputs to the policy network, and each one corresponds to one of the observation components \(O_{i}^{t}=\left[o_{e}^{t},o_{o}^{t},o_{l}^{t},o_{aux}^{t}\right]\), while each input has its branch of hidden layers through which it passes. The input \(o_{l}^{t}\) goes through the first branch, and it passes through a convolutional layer [20] that consists of 32 one-dimensional filters with a kernel size of 5 and a stride of 2, the second hidden layer in the branch is a another convolutional layer similar to the first one, except it has a kernel size of 3. The third hidden layer is a flatten layer followed by a 128 neuron fully-connected layer [20]. The second branch that takes input \(o_{o}^{t}\) passes it through a reshape layer that reshapes it into the shape \(\mathbb{R}^{N-1\times\left(q\times b\times N\right)}\), then it is passed through a 32 neuron fully-connected layer applied on the last dimension of the reshape layer output, and then, the output of the fully connected layer is flattened. The observation \(o_{e}^{t}\) goes through the third branch, which is just one flatten layer. The fourth branch has no hidden layers; it passes the \(o_{aux}^{t}\) input through to the next stage. The outputs of all the branches are then concatenated, then passed through a 256 neuron fully-connected layer. All the layers until now have ReLU activations [21]. The final layers are the two output layers, each corresponding to an action component \(a_{i}^{t}=\left[v_{i}^{t},v_{r}^{t}\right]\), and each layer has K neurons with softmax activations [22]. The components of the action \(a_{i}^{t}\) are then sampled from the two softmax distributions, as explained in Section 4.1.2.  
The value network \(V_{\phi}\) shown in Figure 2 shares the same architecture as the policy network except for the outputs. The output layer of the value network outputs the network's prediction of the value estimation, and it is a single neuron fully-connected layer with no activations. The value network has a separate set of weights with respect to the policy network.
## 2 Methodologies
### Mathematical Model of Real-Time Single-/Dual-Frequency PPP
#### 2.4.3 Parameter Modeling and Estimation  
According to the observation functions and state parameters mentioned above, the sequential least square algorithm is adopted. The corresponding expressions can be described by,  
\[\mathbf{V}_{k}=\mathbf{A}_{k}\mathbf{X}_{k}-\mathbf{L}_{k} \tag{19}\]  
where \(\mathbf{V}_{k}\), \(\mathbf{A}_{k}\), \(\mathbf{X}_{k}\), and \(\mathbf{L}_{k}\) denote the observation residual vector, the design coefficient matrix, the state vector, and the observation vector at time \(k\), respectively.  
Target function is,  
\[\mathbf{V}_{k}^{T}\mathbf{P}_{k}\mathbf{V}_{k}+\mathbf{V}_{X,k}^{T}\mathbf{P}_{X,k-1}\mathbf{V}_{X,k}=\min \tag{20}\]  
where \(\mathbf{V}_{X,k}\) denotes the difference between the current state vector \(\mathbf{X}_{k}\) and its predicted vector \(\mathbf{\overline{X}}_{k}\); \(\mathbf{P}_{X,k-1}\) denotes the weight matrix of the predicted state vector. The equation can be calculated by,  
\[\mathbf{X}_{k}=\left(\mathbf{A}_{k}^{T}\mathbf{P}_{k}\mathbf{A}_{k}+\mathbf{P}_{X,k-1}\right)^{-1} \left(\mathbf{A}_{k}^{T}\mathbf{P}_{k}\mathbf{L}_{k}+\mathbf{P}_{X,k-1}\mathbf{\overline{X}}_{k}\right) \tag{21}\]  
\[\mathbf{P}_{X,k}^{-1}=\left(\mathbf{A}_{k}^{T}\mathbf{P}_{k}\mathbf{A}_{k}+\mathbf{P}_{X,k-1} \right)^{-1} \tag{22}\]  
The corresponding posterior covariance matrix of the state vector  
\[\mathbf{D}_{X,k-1}=\mathbf{P}_{X,k-1}^{-1}(\mathbf{V}_{k}^{T}\mathbf{P}_{k}\mathbf{V}_{k}+\mathbf{V}_{ X,k}^{T}\mathbf{P}_{X,k-1}\mathbf{V}_{X,k})/\left(n-m\right) \tag{23}\]  
where \(n\) and \(m\) represent the number of satellites obtained at \(k\) and \(k-1\) epochs, respectively.
# Image Inpainting Based on Multi-Patch Match with Adaptive Size
## 3 Proposed Approach
### Adaptive Target Patch Size  
[PERSON]'s algorithm utilizes a fixed patch size during the whole process. However, given an image, some areas contain rich textures and details, others may have little texture distribution. If a larger patch is used in rich-textured areas, stitching cracks are often easy to occur, thus losing the consistency of the texture structure. Therefore, the smaller patch should be used to achieve a smoother and natural texture propagation. Moreover, for those relatively flat regions, a larger patch is applied to prevent the staircase effect and speed up the filling process. It is more reasonable to apply different sizes of patch for different regions.  
Denote \(n\) as the number of the sizes of patch used in our work. For a certain target patch \(\Psi_{\hat{p}}\), its size \(w_{\Psi_{\hat{p}}}\times w_{\Psi_{\hat{p}}}\) is specified by the nonuniformity \(T(\hat{p})\), as the following equation:  
\[w_{\Psi_{\hat{p}}}=-2[nT(\hat{p})+1]+2n+3, \tag{9}\]  
where \(\lfloor\bullet\rfloor\) is downward rounding operator. The purpose of Equation (9) is to evenly divide the interval \([0,\,1]\) into \(n\) subintervals with the step of \(\frac{1}{n}\). Suppose \(T(\hat{p})\) falls in the \(k^{\text{th}}\) subinterval \([\frac{k-1}{n},\frac{k}{n}),\ (k=1,2,\ldots,n)\), as \(k\) decreases from \(n\) to \(1\), \(w_{\Psi_{\hat{p}}}\) increases from \(3\) to its maximum size \((2n+1)\) with the step of \(2\) (the size should be odd), namely, the patch size is inversely proportional to the nonuniformity. Note that \(n\) should not be too small, or the choices of patch will be too limited to adaptively fit the different situations; neither too large, otherwise the max size of patch will also grow too large. If we paste an oversized patch into the target region, it is more likely to cause the stitching inconsistency even if in flat areas. We empirically let \(n\) change along with the image size \(H\times W\) as \(n=\max([\frac{\min(H,W)}{100}],1)\), since we did not find the obvious evidence to show that there exists an optimal value of \(n\).
# Design, Implementation, and Field Testing
## 2 Background and Related Work
It is also possible to use active learning [28] to improve the alert/alarm accuracy by actively taking the inputs from the alert/alarm recipient regarding their quality. In PACTS, we combine the rule-based and model-based approaches for alert generation. A unique characteristic of PACTS is that the target system and the intended recipient for alerts are the same user. The rule-based approach is used to inspect individual frames with respect to the predefined key posture specification, and the finite state machine is used to model the life-cycle of each non-compliant activity. The combining of these two approaches ensures the generation of one alert for each non-compliant activity. This study is a continuation of our project started in 2015 [29; 30]. Initially, the system was designed to work in a single room only. This limitation would prevent the system from being used in practice because a nursing assistant in a nursing home is often assigned to take care of patients in several different rooms. We later extended the original design so that systems deployed in multiple rooms could be federated together to enable the tracking a nursing assistant when she or he goes to different rooms to do her/his work [31]. In this paper, we provide greater technical details on the system design and present a more comprehensive analysis on the data obtained during the field study.
## 1 Introduction  
In response to the demand for increased production capacity in high-tech industries, machinery and equipment are often required to be in continuous operation, putting more focus on their reliability and stability. However, [PERSON] et al. [1] points out that equipment aging to a state of complete damage is a continuous process of change, and the losses are hard to predict if the machine fails and stops producing. Motor failures can be divided into electrical failure and mechanical failure; electrical failure consists of stator winding, non-uniform air gap, and broken bar failure, while mechanical failure consists of eccentricity, unbalance, and loss. Moreover, failures by components are classified as stator, rotor, bearing, or centering device-related.  
As the motor has many types of failures, diagnosis has become a popular issue. In recent years, much domestic and foreign literature has been published on failure diagnosis strategies, the aims of which are to increase motor life, reduce accidents, and save on maintenance costs. [PERSON] et al. [2] uses the characteristic frequency spectrum captured from wavelet transformation as the training data for the artificial neural network in order to identify bearing failures. In addition, [PERSON] et al. [3] infers the possibility of detecting stator failure through a fuzzy system set by way of expert experience. A further example is shown in Reference [4], using genetic algorithm to improve the Elman neural network's convergence speed in gearbox fault detection.  
It can be deduced from such literature that motor failure diagnosis technology is progressing, especially in the areas of artificial neural network, fuzzy theory, and wavelet transform, which are the most widely used [2; 3; 4; 5; 6; 7; 8; 9; 10]. The failures can be effectively diagnosed by relying on better network weights in the artificial neural network, in addition to the requirement for huge historical data. Furthermore, if the diagnosis of the failure types increases, the network distribution will be more complex, which will reduce the efficiency of diagnosis. Moreover, if the fuzzy rules and membership functions are only formulated by expert experience, the inference result will be unpersuasive.  
Above all, this paper proposes an effective rotor failure diagnosis method (Figure 1), which contains two core components: (1) use of electrical detection to collect motor current data and capture characteristics of normal and abnormal motors by fast Fourier transform, so as to construct the diagnosis database; and (2) putting the training samples into a genetic algorithm to search for the best membership functions, so as to promote the benefits of failure diagnosis.
# Performance Evaluation of Ground AR Anchor with WebXR Device API
## 3 Materials and Methods
### Experimental Method
#### 3.4.2 WebXR with Checked Pattern  
The method of obtaining the difference image from the captured image is explained in Equation (2). The image in which the actual model is used to obtain the difference image is denoted as \(R\), the image captured by the 3D model is denoted as \(V\), and the image of only the background is denoted as \(B\).  
\[\text{Difference image}\ =|(B-R)-(B-V)| \tag{2}\]  
Equation (3) is used for obtaining the MAE of the difference image. For each image, to obtain an average of the error between pixels, the absolute error between the measured value and the absolute value is obtained; all the obtained absolute errors are added. The average is calculated by dividing this by the number of absolute errors. \(x\) is the X-axis pixel coordinate of the image, \(y\) is the Y-axis pixel coordinate, \(W\) is the width, and \(H\) is the height.  
\begin{table}
\begin{tabular}{c c} \hline
**Classification** & **Information** \\ \hline Measurement Tool & Google Development Tool “inspect” \\ \hline Browser & Google “Chrome”, version 84.0.4147.89 \\ \hline
3D File Format & \(\cdot\)glb \\ \hline Model & Samsung galaxy Note 20 Ultra (SM-N986) \\ \hline Processor & Qualcomm snapdragon 865+ \\ \hline RAM & 12 GB \\ \hline ROM & 256 GB \\ \hline Device & Display & \(3088\times 1440\) (496 ppi) \\  & Dynamic AMOLED 2X \\ \hline \multirow{3}{*}{Sensor} & Acceleration \\  & Gyro \\ \cline{1-1}  & Light Proximity \\ \hline Network & 5 G Network \\ \hline OS & Android 10 \\ \hline \end{tabular}
\end{table}
Table 1: Technical basics for experiment.  
In addition, the pixel values of the \(x\) and \(y\) coordinates of the image where the real object is captured are \(R(x,y)\) and pixel values of the \(x\) and \(y\) coordinates of the image where the 3D model is captured are \(V(x,y)\); only the background is captured. The pixel value of the image's \(x\) and \(y\) coordinates is \(B(x,y)\). At this time, \(R(x,y)\) is called \"real value\", \(V(x,y)\) is called \"measured value\".  
\[\text{Mean of Difference image}=\frac{1}{W\times H}\sum_{x=1}^{W}\sum_{y=1}^{H} \lvert(B(x,y)-R(x,y))-(B(x,y)-V(x,y))\rvert \tag{3}\]  
Equation (4) is used to determine the Intersection over Union (IoU) of the union image and the intersection image. If the number of intersection pixels and the number of union pixels are the same, they are considered to match. The union image is extracted by removing the background image from the real model image. The difference image of the real model image from the real model image is added to the difference image of the 3D model image from the real model image. The resultant value is calculated from the union image to obtain the intersection image; the match rate is then calculated as follows. The image in which the real model and the 3D model overlap is referred to as \((R,V)\), background image as \((B)\), 3D model image as \((V)\), and actual model image as \((R)\).  
\[\text{Match rate}=\frac{\{(R,V)-(B)\}-\left[\{(R,V)-(R)\}+\{(R,V)-(V)\}\right]} {\{(R,V)-(B)\}} \tag{4}\]
# An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning +
## 2 Materials and Methods  
### Photorefractive Effect  
#### 2.1.1 Background  
The photorefractive effect is a nonlinear optical effect that converts an optical intensity variation to a refractive index variation (Figure 3) [10]. The underlying mechanism is an electrical charge redistribution inside the photorefractive crystal. A photorefractive crystal hosts deep charge traps, from which charge carriers are removed via photoexcitation. Photoexcitation occurs favorably in areas of high optical intensity, and recombination occurs favorably in dark areas. The resulting spatial charge redistribution gives rise to a static local electric field profile, which yields an index of refraction variation via the linear electro-optic effect. In the absence of light, the generated space-charge modification will persist at a time scale depending on the material's dark conductivity.  
At optical frequencies, intensity variations with a non-zero time average can be generated by the interference of two monochromatic beams (Figure 3a). The resulting intensity pattern is sinusoidal with a wave vector equal to the wave vector difference of the interfering beams (Figure 3b). Inside a photorefractive crystal, consequently a sinusoidal refractive index grating is formed that acts as a Bragg reflector (Figure 3e,f). The Bragg condition is met precisely along the respective intersecting beam direction.
## 5 Analysis of Factors Influencing the Digital Service Trade Network of RCEP Member Countries
### Empirical Result Analysis  
Using UCINET 6.0 software, the relationship between various influencing factors and the overall digital services trade of RCEP member countries was examined. Table 9 shows the empirical analysis results of the unweighted trade network and weighted trade network for two node years in 2006 and 2021. The regression results of the trade network for each segmented industry are shown in Table 10.  
From the fitting degree of the model, in the overall trade network and the trade networks of segmented industries, the adjusted coefficient values of the unweighted trade network for both node years are better than those of the weighted trade network, indicating that for these variables, their impact on trade links is more significant and important than their impact on trade value. The regression results of the overall and segmented industries' trade networks are analyzed below. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Index** & \multicolumn{2}{c}{**Weighted Trade Network**} & \multicolumn{2}{c}{**Unweighted Trade Network**} \\ \hline  & **2006** & **2021** & **2006** & **2021** \\ \hline \multirow{3}{*}{Constant} & 0.42562 *** & 0.31259 *** & 0.39917 *** & 0.27406 *** \\  & (0.0000) & (0.0000) & (0.0000) & (0.0000) \\ \cline{1-1}  & 0.10199 & 0.14928 & \(-\)0.17209 ** & \(-\)0.23271 *** \\ \cline{1-1}  & (0.0052) & (0.0087) & (0.0078) & (0.0009) \\ \cline{1-1}  & \(-\)0.13392 * & \(-\)0.10611 ** & \(-\)0.31804 ** & \(-\)0.33712 ** \\ \cline{1-1}  & (0.0346) & (0.0081) & (0.0088) & (0.0067) \\ \cline{1-1}  & \(-\)0.17701 ** & \(-\)0.26565 ** & \(-\)0.16734 ** & \(-\)0.13901 *** \\ \cline{1-1}  & (0.0387) & (0.0137) & (0.0032) & (0.0009) \\ \cline{1-1}  & 0.19311 ** & 0.25770 *** & 0.20534 *** & 0.21497 *** \\ \cline{1-1}  & (0.0002) & (0.0004) & (0.0001) & (0.0002) \\ \cline{1-1}  & \(-\)0.01646 * & \(-\)0.08230 * & \(-\)0.07832 * & \(-\)0.03433 ** \\ \cline{1-1}  & (0.0149) & (0.0511) & (0.0455) & (0.0632) \\ \cline{1-1} Adjusted R\({}^{2}\) & 0.191 & 0.082 & 0.238 & 0.276 \\ \hline \hline \end{tabular} Note.: *, **, and *** represent significant at the level of 10%, 5%, and 1%, respectively. Data source: compiled by UCINET 6.0 software.  
\end{table} 
Table 9: QAP Regression Results of the Overall Digital Services Trade Network of RCEP Member Countries in 2006 and 2021.
# FrameAugment: A Simple Data Augmentation Method for Encoder-Decoder Speech Recognition
## 1 Introduction  
Deep learning [1] has recently shown remarkable performance in many fields including speech recognition. However, the performance of the deep learning network architecture is greatly affected by the amount of training data. Therefore, researchers have investigated increasing the effective size of training data by utilizing unlabeled data for learning [2; 3; 4; 5], or perturbing labeled data and augmenting it to training data [6; 7].  
As up-to-date speech recognizers have begun to be changed from a hybrid network architecture to a deep learning network architecture [8], much effort has been made to increase the amount of training data effectively. Due to such effort, several data augmentation techniques have been proposed. For example, the speed perturbation [9] technique complements training data by using the data obtained to resample the labeled data at several different speeds. The SpecAugment [10] technique adds speech data obtained by randomly overlaying a spectrogram with binary masks. The SpecSwap [11] technique obtains new speech data by switching two parts of the spectrogram.  
We propose a data augmentation method that randomly changes the speed of randomly chosen sections of an utterance. Compared with the speed perturbation technique that changes the speed of the entire utterance at a single fixed speed, the proposed method changes the speed of an utterance at a different speed for every section, and thus, can give more speed variability to the augmented speech data. The speed perturbation technique operates in the waveform domain, whereas the proposed method works in the spectrogram domain. The sections where the speed is changed are randomly selected in a similar manner to the SpecAugment technique, and the speed of the selected sections is changed through linear interpolation. We validate the effectiveness of the proposed method reliably using the WSJ database [12] and LibriSpeech [13] 100 h database.  
In Section 2, we describe the network architecture used as a baseline speech recognizer, and review the previous data augmentation methods. In Section 3, we describe the proposed method. In Section 4, we show the experimental results using the WSJ and LibriSpeech databases, optimize hyperparameters, and provide the analysis results and discussion. In Section 5, conclusions are drawn.