esa-sceva/satcom-chunk-collection huggingface dataset:
# Joint Channel Estimation Algorithm Based on DFT and DWT
## 5 Joint Channel Estimation Algorithm Based on DFT and DWT  
On the one hand, although the channel estimation algorithm based on segmented threshold DFT proposed in Section 3 can effectively reduce the channel estimation error under medium and low SNR conditions, due to the existence of virtual subcarriers, it still cannot eliminate the problem of an estimation performance floor under high SNR. On the other hand, the wavelet-denoising algorithm converts noisy signals to the wavelet domain for processing. Selecting the wavelet base that matches the signal and the appropriate decomposition times can separate the effective signal from the noise, which can effectively reduce the estimation performance floor of channel estimation under high SNR. Therefore, based on the estimation performance characteristics of the two algorithms in different SNR intervals, the channel estimation performance can be optimized across the entire SNR range by dynamically and reasonably selecting the two algorithms for channel estimation.  
To achieve a dynamic selection of better denoising algorithms from wavelet-denoising algorithms and segmented threshold DFT algorithms, it is necessary to find an effective selection decision metric. Obviously, the most direct decision metric is the channel estimation MSE of the two algorithms under given CIR conditions. According to the theoretical (approximate) MSE values based on the threshold DFT algorithm and the wavelet-denoising algorithm presented in Sections 3 and 4, the corresponding estimates can be obtained.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 3 Results and Analysis
### Case 2: \(u=x^{\prime}_{1}/x^{\prime}_{2}=1\)  
Figure 4 shows the distribution of the absolute value of the IA error with a change in the line damping parameter in the two quasi-uniform flow field regions when the ratio of the normalization coefficient is fixed at 1 and the IA ratio \(A_{1}/A_{2}\) is set at 1:0.2, 1:0.4, 1:0.6, and 1:0.8. Additionally, considering the physical meanings of the parameters, the temperature distribution is uniform, and there are only two quasi-uniform flow field regions with different static pressures or absorber concentrations. Different cloud images indicate the relative magnitude of the IA in the two quasi-uniform flow field regions. A comparison of the four cloud images in Figure 4 reveals that the area enclosed by the contour \(R=0.01\) decreases as the IA ratio of the two quasi-uniform flow field regions approaches 1. However, when the IA in one of the quasi-uniform flow field regions occupies most of the field, the nonuniformity of the flow field may still generate large errors (\(R>0.01\)).  
The error in most areas from the bottom left to the top right of the two graphs is less than 0.01. It is seen that the IA error is less than 0.01 when the changes in partial pressure and temperature are not drastic. However, the errors rise sharply as the gap in the line damping parameter between the two quasi-uniform flow field regions increases. It is considered that the changes in the static pressure and the component concentration have a stronger effect on the IA error.  
Both images have large amounts of black noise in the area \(R>0.01\). A point of black noise is surrounded by a contour line indicating that the error at this point is less than 0.01, 0.05, or 0.1; i.e., there are points with small errors in areas with large errors. Mathematically, the error distribution should be a smooth surface, and the noise may result from using the Voigt line shape approximated using a numerical algorithm for fitting. Refer to Section 3.4 for a detailed analysis of the effect of the fitting function.
# A Reflection-Type Dual-Band Phase Shifter with an Independently Tunable Phase
## 2 Design Method of Proposed Dual-Band Phase Shifter  
Figure 1 depicts the proposed structure of the dual-band tunable PS, which consists of a 3-dB hybrid coupler, where the coupled and through ports terminated in the same two reflection loads. Figure 1b shows the one-port reflection load (\(\Gamma_{\rm L}\)) that consists of a \(f_{\rm L}\) operating arm and a \(f_{\rm H}\) operating arm. Each arm has series transmission lines (TLs) with characteristic impedance (\(Z_{\rm Ho}\) or \(Z_{\rm Lo}\)) and electrical length (\(\theta_{\rm Ho}\) or \(\theta_{\rm Lo}\)), shunt open stubs with characteristic impedance (\(Z_{\rm H}\)s or \(Z_{\rm LS}\)) and electrical length (\(\theta_{\rm H}\)s or \(\theta_{\rm LS}\)), parasitic compensation element (\(L_{\rm c}\) and \(C_{\rm c}\)), and varactor diodes (\(C_{\rm v}\)). The series TL and shunt open stub is \(\lambda/4\) at \(f_{\rm L}\) in case of \(f_{\rm H}\) operating arm, and vice versa. The \(\lambda/4\) shunt open stub provides a short-circuited condition at node A/node B at the co-operatizing frequency.  
Figure 1: Structure of the proposed dual-band PS: **(a)** Overall two-port circuit using a 3-dB hybrid coupler and reflection loads; **(b)** One-port reflection load.  
The series \(\lambda/4\) TL transforms the short-circuited condition into an open-circuited condition at the co-operating frequency. Accordingly, the \(f_{\rm L}\) and \(f_{\rm H}\) operating arms imitate an open-circuited condition at \(f_{\rm H}\) and \(f_{\rm L}\), respectively.
## 4 Result
### Quantitative Evaluation of Generated Images
#### 4.4.3 Effect of the Number of Expanded Images on Accuracy  
In order to determine the impact of the number of expanded images on the classification accuracy, T3 data augmentation method, which has the best performance in Section 4.4.2, is adopted to successively expand the images of the original datasets to test the accuracy. That is, on the basis of the original images, the expanded images and generated images by traditional data augmentation methods are added at the same time. The number of augmented images was gradually increased in steps of 100, 300, 500, 700, 1000, 1500, 2000, 2500, 3000, and 3500 to determine the optimal augmentation level. Figure 11 shows the experimental results.  
With the gradual increase in the expanded images, the accuracy of each classifier was enhanced with different extents. The accuracy of ResNet reached its highest value of 78.79% when the number of expanded images was 2000. When 2500 images were expanded, VGGNet and AlexNet achieved accuracies of 94.79% and 87.1%, respectively. And as the number of expansions continued to increase, the accuracy of each classifier did not improve further, but decreased. This decline could be attributed to the limited size of the original  
Figure 11: Accuracy of each classifier gradually increasing the number of images.  
Figure 10: Histogram of experimental results.  
datasets, which constrained the diversity of features learnable by the improved DCGAN. As the amount of generated data in the classifier increases, overfitting occurs, resulting in a decline in accuracy.
## 4 Standards Enabling CPES
### Opc UA (Iec 62541)
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline \multicolumn{2}{c}{**CPES Requirements**} \\ \multicolumn{2}{c}{**according to [4]**} \\ \hline \hline \multirow{4}{*}{(i) Reliability} & Robustness and reliability against system failures and changes in the environment are offered by OPC UA by the possibility of a distributed infrastructure of any number of OPC UA server clients. The standard ensures change security by using unique namespaces. \\  & OPC UA can continue to operate even in the event of a subsystem failure; the possibility of self-adjustment has to be implemented individually. A message broker can also be used instead of the client-server mechanism to decouple communication. The different server profiles and security policies allow an implementation close to the device even with limited hardware resources. The connection to the physical component is always system-dependent and is realized, e.g., via bridges to fieldbuses. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Cyber-physical energy systems (CPES) features and their coverage by OPC Unified Architecture (OPC UA). The high suitability of OPC UA for smart grids is also attested by works such as [58; 59], in which the standard is combined with semantic web services or information models for energy management. Existing implementations in limited field devices [60] and at chip level [61] show the applicability for CPES at the sensor level.
# Progress of the Computer-Generated Holography Based on Deep Learning
## 3 Network Structures
### U-Net  
U-Net was initially proposed to solve the problem of image recognition and segmentation. In this area, the fully convolutional network (FCN) was the first representative of pixel-level image classification and semantic-level image segmentation [41]. With the improvements and developments of FCN, [PERSON] et al. improved the network structure by expanding the capacity of the network decoder. A contracting path between the encoder and decoder was added to achieve more accurate pixel boundary positioning [22]. U-Net is a kind of FCN structure. Furthermore, [PERSON] et al. proposed V-Net in 2016 [42]. V-Net can be understood as a 3D version of U-Net, which is suitable for medical image segmentation of 3D structures. There are also some other U-Net-based networks. For example, [PERSON] et al. proposed a variant network by adding dense connection blocks, called DenseNet [43].  
U-Net is a U-shaped network structure that obtains context information and location information. It is a commonly used segmentation model, which is simple, efficient, easy to build, and can be trained with small datasets. The structure is shown in Figure 2. The input image is what we want to process, while the output map represents the recognition and segmentation of the input. The structure consists of a contraction down-sampling path for feature extraction (left) and an up-sampling expansion path for reconstruction (right).  
In the early works of U-net, the left half network consists of repeated \(3\times 3\) convolutions (unpadded convolutions). Each convolution operation is followed by a correction linear unit (ReLU) and a \(2\times 2\) maximum pool operation. At each down-sampling step, the number of feature channels is doubled. Each step in the up-sampling expansion path consists of a \(2\times 2\) up-convolution and two \(3\times 3\) convolutions followed by a ReLU. It halves the feature channel number and concatenates with the corresponding cropped feature map in the contraction path. Clipping is required because of the loss of border pixels in every convolution operation. The total number of convolution layers is 23 [22].  
U-Net has become the baseline for most semantic segmentation tasks, which has also inspired a large number of researchers to think about the U-shaped network. For multi-modal images, U-Net can extract the features of different modes. The images processed by U-Net have simple semantics and fixed structure, and fewer data are required for U-Net. However, when the image is complex, its performance is greatly reduced. Compared with many current networks, U-Net has fewer layers and fewer parameters, so it is easy to overfit the datasets during the training process. Changing the original simple structure into DenseNet can increase the hyperparameters in the network [43]. At the same time, using transfer learning and loading training weights can also improve network performance. Thus, U-Net was firstly considered for CGH calculation and gradually revised or replaced by other advanced networks, such as ResNet or GAN.
# Relation-Aware Graph Transformer for SQL-to-Text Generation
## 4 Experiments
### Main Results  
Table 5 shows the main results, including seq2 seq baselines, graph2 seq baselines and our model. Our model relation-aware graph transformer (RGT) outperforms all baseline models on both WikiSQL and Spider in both BLEU and NIST. Specifically, RGT outperforms the strongest baseline transformer with relative position by 1.17 BLEU and 0.23 NIST on Spider, 0.42 BLEU, and 0.1 NIST on WikiSQL, indicating the effectiveness of our model.  
We discover that the GCN does not perform well compared to other baselines. GCN only cares about the structure of the graph without considering any special relations between nodes. We also notice that the transformer with a relative position works well even it only considers the relative position relation. This finding encourages us to consider more relations than the structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**WikiSQL**} & \multicolumn{2}{c}{**Spider**} \\ \cline{2-5}  & **BLEU (\%)** & **NIST** & **BLEU (\%)** & **NIST** \\ \hline BiLSTM without copy & \(29.59\pm 0.81\) & \(7.45\pm 0.17\) & \(22.22\pm 0.43\) & \(4.64\pm 0.06\) \\ BiLSTM & \(30.45\pm 0.17\) & \(7.75\pm 0.03\) & \(26.09\pm 0.70\) & \(5.55\pm 0.14\) \\ Transformer-w/o position & \(25.74\pm 0.32\) & \(7.08\pm 0.09\) & \(23.06\pm 0.75\) & \(5.07\pm 0.18\) \\ Transformer-absolute position & \(29.98\pm 0.30\) & \(7.70\pm 0.05\) & \(25.85\pm 0.50\) & \(5.61\pm 0.06\) \\ Transformer-relative position (REL) & \(30.78\pm 0.42\) & \(7.76\pm 0.05\) & \(27.67\pm 0.60\) & \(5.71\pm 0.07\) \\ \hline TreeLSTM & \(29.18\pm 0.43\) & \(7.59\pm 0.09\) & \(24.15\pm 0.51\) & \(5.35\pm 0.10\) \\ Graph2 Seq [15] & \(28.70\) & \(7.34\) & - & - \\ GCN & \(28.86\pm 0.17\) & \(7.51\pm 0.04\) & \(24.65\pm 0.38\) & \(5.26\pm 0.05\) \\ GAT & \(29.58\pm 0.81\) & \(7.63\pm 0.12\) & \(25.94\pm 1.08\) & \(5.50\pm 0.09\) \\ \hline
**RGT** (ours) & \(\mathbf{31.20\pm 0.23}\) & \(\mathbf{7.86\pm 0.04}\) & \(\mathbf{28.84\pm 0.22}\) & \(\mathbf{5.94\pm 0.06}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Main results for all models. **Bold** means the best result.
# A Compact Size Antenna for Extended UWB with WLAN Notch Band Stub
###### Abstract
Having the advantage of high gain and two notch bands, the antenna still has some disadvantages, including its limited bandwidth of 4.8-10 GHz and its large physical size of 33 mm \(\times\) 33 mm, with an overall critical height of 7 mm. Last but not least, a pac-man-shaped printed UWB antenna was designed to achieve two distinct notch bands [29]. However, the notch band does not have the potential for practical usage due to poor comparison between simulated and measured results. Winding up the whole discussion, there is still a need for a UWB antenna that has notch band functionality without degrading the other design constraints like compact size, wideband, geometrically simple, high gain, and stable radiation patterns. Thus, an antenna having a wide operational bandwidth with a WLAN notch band characteristic is proposed in this paper. The organization of the remaining manuscript is as under. The design methodology of the proposed UWB antenna and respective results are presented in Section 2, while the performance parameters are analyzed in Section 3. Furthermore, Section 4 contains the comparison of the proposed work with the state-of-the-art, while the manuscript is concluded in Section 5, followed by references.
# Korean Sign Language Recognition Using Transformer-Based Deep Neural Network
## 4 Proposed Methodology
### Convolutional Layer-Based Transformer
#### 4.2.2 Lightweight Multihead Self Attention  
Self-attention (SA) is a popular, effective model in the neural network [23]. Generally, the input of the self-attention module can be written as \(X\in R^{n\times d}\), which is transformed into three matrices, namely query, key and value defined by \(Q\in R^{n\times d}\), \(K\in R^{n\times d}\), and \(V\in R^{n\times d}\) respectively. Here, several patches are represented by \(n=H\times W\). The dimension of the input, key and value can be written as \(d\), \(d_{k}\) and \(d_{v}\), respectively. The self-attention module can be written as the following Equation (2):  
\[SA=softmax(\frac{qk^{T}}{\sqrt{d_{k}}})\times v \tag{2}\]  
Here, SA is the self-attention, and \(q\), \(k\), \(v\), and \(d_{k}\) are the query, key, value and key dimension. To make self-attention lighter, we employed an element-wise convolutional neural network with stride \(k\) to reduce the dimension of key and value before the attention model [28]. Besides this, relative position bias B was added in each of the self-attention modules [81; 82]. We can rewrite Equation (2) for the lightweight self-attention (LSA) as follows:  
\[LSA=softmax(\frac{q\overline{k}^{T}}{\sqrt{d_{k}}}+B)\times\overline{v} \tag{3}\]  
Here, LSA is the lightweight self-attention, and \(q\), \(k\), \(v\), and \(B\) are the query, key, value, key dimension and relative position. \(B\) can be written as \(B\in R^{n\times\frac{n}{2}}\), which is randomly initialized and learnable. The relative position \(B\) can be transferred into other dimensions \(\overline{B}\) in bicubic interpolation with a different size \(m_{1}\times m_{2}\). The transformation equation can be written as \(\overline{B}=Bicubic(B)\). This procedure is excreted for one head, the same way it will be completed for \(h\) heads, defined as lightweight multi-head self-attention (LMHSA). An \(h\) number of lightweight attention modules are employed to produce attention features. Each individual head outputs a sequence of size \(n\times\frac{d}{h}\). These \(h\) sequences of attention feature are then concatenated into an \(n\times\frac{d}{h}\) sequence. Figure 4 shows the steps of the LMHSA. Because of the parallelization, we used the lightweight self-attention mechanism because of the computational complexity per layer and the minimum number of operations requiredin the sequence. Convolutional layer-based transformer block computation can be written as the following formula:  
\[\overline{X_{i}}=IM(X_{i-1}) \tag{4}\]  
\[\overline{\overline{X_{i}}}=LMHSA(LN(\overline{X_{i}})+\overline{X_{i}}) \tag{5}\]  
Here, \(\overline{X_{i}}\) and \(\overline{\overline{X_{i}}}\) represent the \(IM\) and LMHSA block feature for the individual module \(i\), consequently. Moreover, layer normalization is denoted by the term \(LN\).
# Extended Autoencoder for Novelty Detection with Reconstruction along Projection Pathway
## 2 Related Work  
With the advent of deep learning, deep autoencoder-based novelty detection algorithms have been widely researched. Under normal operation, a deep autoencoder uses the reconstruction error to measure the novelty score; that is, the autoencoder is trained to minimize the reconstruction error given the data samples. In doing so, the autoencoder learns a mapping function that reconstructs the normal data with a small reconstruction error. However, the ability to accurately reconstruct the novelty samples is limited, as the novelty samples do not resemble the data used for training. Therefore, novelty samples are detected according to a high reconstruction error. If the novelties seem to come from a specific distribution, generative models such as variational autoencoders (VAE) [9] are suitable for detecting novelties. Because VAEs encode the input as a distribution over the latent space instead of as a single point, a VAE can outperform an ordinary autoencoder in novelty detection based on reconstruction error [10].  
Adversarial autoencoders (AAEs) can also be used for novelty detection. The advantage of AAE over VAE is that adversarial training drives the encoding distribution to match the arbitrary prior distribution. Due to this advantage, there are many applications of AAE for novelty detection. For example, to improve the interpretability of detected anomalies, a mixture of Gaussians is chosen as an arbitrary prior distribution [11]. A holistic view of the latent space that resides within the Gaussian mixture partitions the latent space into semantic regions. One-class novelty detection using generative adversarial networks (OCGAN) [12] uses adversarial training to ensure that the latent space resembles a uniform distribution; in this bounded latent space, OCGAN finds informative negative samples to improve novelty detection. Additionally, generative probabilistic novelty detection (GPND) [13] improves novelty detection by making the computation of the novelty probability feasible. GPND introduces adversarial training to match the output of the decoder with the real data distribution. This adversarial training reduces blurriness and adds additional detail to the generated images. From this perspective, adversarial training can be introduced to conventional autoencoders, making them more suitable for RaPP applications.  
Figure 2: t-SNE (Stochastic Neighbor Embedding) visualization of the values of latent variables in the standard autoencoder (**left**) and the extended autoencoder (**right**) with normal samples. The digit 8 in MNIST (Modified National Institute of Standards and Technology) dataset is fed as a normal data to train the autoencoders. The values of latent variables for original input data and reconstructed ones are represented as black and gray points, respectively.
# Approximate Networking for Universal Internet Access
## 1 Introduction
### Why Adopt Approximate Networking?
#### 1.3.3. The Pareto Principle (80-20 Law): The Power of \"Good Enough\"  
To help manage the approximate networking trade-offs, it is instructive to remember the _Pareto principle_, alternatively called the _80-20_ rule [21], which states roughly that 20% of the factors result in 80% of the overall effect. This principle has big implications for approximate networking since this allows us to provide adequate fidelity to ideal networking by only focusing on the most important 20% of the effects. The key challenge in approximate networking then becomes the task of separating the all-important, essential, non-trivial factors from the trivial factors, which may be omitted or approximated. In this regard, we can leverage previous human-computer-interaction (HCI) research that has shown that human quality of service (QoS) perception can be flawed (e.g., relatively fast service may be judged to be unacceptable if the service is not predictable, visually appealing and  
Figure 2: Ensuring Global Access to the Internet for All (GAIA) requires provisioning ’good enough’ quality of service (QoS) that accommodates the diversity of applications requirements, device capabilities, user profile and requirements.  
reliable [22]) in choosing the precise approximate networking trade-off to adopt such that the users perceive the least inconvenience.
# A Reconfigurable Framework for Hybrid Quantum-Classical Computing
## 2 Background and Related Work
### Quantum Machine Learning  
Quantum machine learning (QML) [20] is an interdisciplinary field that merges the principles of quantum mechanics with machine learning techniques to enhance data processing and analysis capabilities. This approach has the potential to outperform classical ML algorithms by utilizing unique properties such as superposition and entanglement. QML in the NISQ era usually incorporates a hybrid approach, integrating quantum computing circuits with traditional machine learning methodologies [21]. This integration has attracted significant research attention as it promises considerable improvements in speed and efficiency for complex computational tasks [22]. A variety of QML algorithms have been proposed to capitalize on quantum computing advantages. The variational quantum classifier (VQC) [23] and quantum support vector machines (QSVM) [24] are notable examples. The VQC algorithm employs parameterized quantum circuits that are trained using classical optimization loops. The circuit parameters are trained using traditional approaches such as stochastic gradient descent, adaptive moment estimation (ADAM), and constrained optimization by linear approximation (COBYLA), significantly enhancing performance for specific tasks [20]. Hybrid QML algorithms have the potential to process large datasets more effectively than their classical counterparts, which is crucial in high-dimensional applications [25].