esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Experiments
### Model Evaluations  
#### 3.5.1 Accuracy Test  
The information criterion and statistics of the SVR model for short-term traffic flow forecasting grounded in road association are shown in Tables 9 and 10:  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Variance Analysis**} \\ \hline
**Source** & **Degrees of Freedom** & **Square Sum** & **Mean Square** & **F-Value** & **Pr \textgreater{} F** \\ \hline Model & 15 & 136,171.667 & 67,417.894 & 90.8905 & 1 \\ Inaccuracies & 7 & 5208.3333 & 724.0476 & & \\ Corrected total & 23 & 1,493,000 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 10: SVR model variance and error table.  
Figure 15: Comparison of different models at 95% confidence intervals.  
Table 8 demonstrates the information criteria and statistics of the SVR short-term traffic flow prediction model. Specifically, it lists three different information criteria (AIC, BIC, and HQ) and their corresponding values, which are 6.0702, 7.7832, and 6.1134, respectively. The statistics of the model, including the results of the coefficient of determination (R\({}^{2}\)), the adjusted coefficient of determination (Adjusted R\({}^{2}\)), and the Durbin-Watson test (D-W test), are also given, which are 0.9167, 0.9189, and 1.8144, respectively. This indicates that the model has high goodness of fit and good autocorrelation.  
Table 9 provides the ANOVA and error estimates for the SVR model. By comparing the mean square error (MSE) of the model with the inaccuracies of the mean square error, it can be found that the MSE of the model is much smaller than the MSE due to inaccuracies, which implies that the model itself introduces less error, and most of the error comes from the inaccuracy of the data. In addition, degrees of freedom, square sum, F-value, and Pr > F-value are also listed in the table, which further support the stability and reliability of the model.  
In summary, these two tables together demonstrate that the SVR short-time traffic flow prediction model established based on road association has a good fitting effect and high accuracy, and can meet the needs of short-time traffic flow prediction.
# Random Noise Suppression Method of Micro-Seismic Data Based on CEEMDAN-FE-TFPF
## 3 Experiments and Results
### Quantitative Analysis of Synthetic Data Experimental Results  
Table 2 shows the Mean Square Error (MSE), SNR, and Peak Signal-to-Noise Ratio (PSNR) of the noisy data and denoising results obtained by four entropies as discriminant factors, respectively. Comparing the data in Table 2 shows that FE is better than the others in terms of MSE, SNR, and PSNR. Through the above analysis, FE as the discriminant factor for CEEMDAN-TFPF denoising method is the best choice.
## 5 Experimental Results
### The Datasets and the Evaluation Metrics  
#### 5.1.1 The Datasets  
ISPRS (WGII/4) 2D semantic segmentation benchmark dataset [39] was used for validating our methodology. This dataset is provided by the ISPRS 2D semantic labeling challenge which provides a whole platform for the evaluation of semantic segmentation algorithms in aerial imagery context. We used Potsdam and Vaihingen datasets that are freely accessible by the community. Every image is provided with DSM (digital surface model) data. However, we used only the image data because we are targeting the domain adaptation using image data only. The two datasets comprise of very high-resolution photos with a 5 cm per pixel for Potsdam photos and 9 cm per pixel for Vaihingen photos. This difference of resolution represents one of the domain shift factors between the two domains. The VHR (Very High Resolution) helps in minimizing the interclass variance and maximizing the intraclass variance through the provision of more details about the objects represented in the images.  
Every image in the two datasets is afforded with its semantic segmentation label that is categorized into six classes of ground objects: car, low vegetation, building, tree, clutter/background, and impervious surfaces. Clutter/background includes any ground object excluded from the five classes, while impervious surfaces show a paved area without any structure on it. In the Vaihingen dataset, there are 33 TOP images whose sizes are around \(2000\times 2000\) pixels. There are three channels in the TOP file: green, red, and the infrared bands. Out of the 33 TOP images, 27 were used for the training, and the remaining six were used for test. The Potsdam dataset contains 38 TOP images of size \(6000\times 6000\) pixels. These TOP files have three spectral channels: blue, green, and red. We subdivided these images into 32 TOP images for train, and the remaining 6 for test. For training of the segmentation model, the images were divided to squares of size \(512\times 512\) pixels used to feed the network. Samples from Vaihingen and Potsdam ISPRS datasets are shown in Figure 6.  
Pixel distribution is not balanced across the six classes. Some classes like Buildings are more redundant than other classes like cars. Each class percentage in proportion to the whole number of pixels in the dataset is represented in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline
**Category** & **Potsdam** & **Vaihingen** \\ \hline Buildings & 28.2\% & 26.9\% \\ Impervious Surfaces & 29.9\% & 29.3\% \\ Low vegetation & 20.9\% & 19.4\% \\ Trees & 14.4\% & 22.4\% \\ Clutter & 4.8\% & 0.7\% \\ Cars & 1.7\% & 1.3\% \\ \hline \end{tabular}
\end{table}
Table 1: The distribution of pixels among categories.
## 3 Non-Thermal Technologies in Winemaking Process
### High-Power Ultrasound (HPU) Effects on Varietal Aroma/Phenol Extraction and Wine Quality
Despite the associated benefits, there may be potential degradation of phenolic and aroma compounds during HPU treatment, probably as a result of the accompanying thermal effect (increased temperature) of ultrasound. Therefore, it is necessary to precisely optimize and model the HPU conditions in order to achieve the highest treatment efficiency and maintain or even improve the overall wine quality. It should also be emphasized that a comparison of reported data are difficult, as differences in ultrasound systems, process parameters, and grape varieties can significantly influence the final result. Finally, with Resolution OENO 616-2019 [77], the OIV has recognized and supported the use of ultrasound in the processing of crushed grapes (must), particularly with the aim of enhancing grape compound extraction during pre-fermentative maceration after de-stemming and crushing. However, the OIV resolution still does not propose the application of HPU for finished wines. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Sample** & **Processing Conditions** & **Key Outcomes** & **References** \\ \hline \multirow{8}{*}{Cabernet Sauvignon red wine/Grasevina white wine 12.7, 19.1, 25.4 mm; 25, 100\% immigration depth 2 cm)} & \multicolumn{1}{p{113.8 pt}}{_Red wine:_} \\  & & \(\bullet\) & Mild ultrasound conditions showed a more beneficial effect on wine chemical composition. \\  & & \(\bullet\) & Decreased phenolic content, lightness, and fatty acids in aged wines. \\  & & \(\bullet\) & No effect on tannins and greater part of color and aroma in aged wines. \\  & & \(\bullet\) & Reduction of SO\({}_{2}\) requirement. \\  & & \(\bullet\) & _White wine:_ \\  & & \(\bullet\) & No significant effect on color. \\  & & \(\bullet\) & Degradation of aroma compounds with higher bath temperature. \\  & & \(\bullet\) & Beneficial effect on phenolic and aroma composition with larger probe diameter and higher amplitude. \\  & & \(\bullet\) & Reduction in addition of SO\({}_{2}\). \\ \hline \multirow{3}{*}{Cabernet Sauvignon red wine 20–80 min} & \multicolumn{1}{p{113.8 pt}}{\(\bullet\)} & \(\bullet\) & Increased particle size distribution and rheological characteristics. \\  & & \(\bullet\) & Modification of wine sensory characteristics. \\  & & \(\bullet\) & Improved Tyndall effect of wine. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary on recent research outcomes for High-Power Ultrasound (HPU).
# A Novel UWB Positioning Method Based on a Maximum-Correntropy Unscented Kalman Filter
## 4 Results and Analysis
### Test Verification
Meanwhile, compared with those of the LSM, the mean standard deviations of the method were reduced by 74.85% and 67.03%, respectively. Compared with UKF method, the average standard deviations decreased by 63.14% and 54.51%, respectively. The MCUKF method had high robustness. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **X-Direction Error (m)** & **Y-Direction Error (m)** \\ \hline LSM & 5.1034 & 1.8855 \\ UKF & 3.9037 & 1.4531 \\ MCUKF & 1.6075 & 0.7472 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical results of mean standard deviation. Figure 8: Distribution curve of standard deviation of positioning results. In order to further illustrate the effectiveness of the proposed method, a dynamic UWB positioning test was designed. The moving area of the trolley was 14 m \(\times\) 5 m, including acceleration, deceleration, straight lines and turning. Figure 11 shows the positioning-result error curves of the three methods under dynamic conditions. The circle symbol represents the least squares method. The blue line represents the UKF method. The red line represents the MCUKF method proposed in this paper. This figure shows that the positioning result of the least squares method had large fluctuations due to external interference. Meanwhile, compared with the LSM, the positioning result of the UKF method was relative, but still displayed fluctuation. The MCUKF method proposed in this paper had the best robustness and high positioning accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **X-Direction Error (m)** & **Y-Direction Error (m)** \\ \hline LSM & 6.603 & 2.281 \\ UKF & 4.506 & 1.653 \\ MCUKF & 1.661 & 0.752 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical results of mean standard deviation for different methods. Figure 10: Distribution curve of standard deviation under interference. Figure 9: Test chart of UWB positioning results under static conditions.
# Covert Channel Based on Quasi-Orthogonal Coding
###### Abstract  
The paper presents a method of creating a hidden channel using a signals' superposition. According to this idea, a transmitter simultaneously sends overt and covert (secret) signals, whereby the overt signal is a carrier for the covert one. Due to the need to ensure a low probability of detection for covert communication, the covert signal should have low power. This implies a number of problems relating to its correct reception. This is similar to non-orthogonal multiple access (NOMA) systems, where the collective signal is a superposition of signals with different powers dedicated to different users. In this case, the successive interference cancellation (SIC) process is used in the receiver for the separation of the component signals. SIC requires accurate channel estimation. Even a small channel estimation error causes a significant increase in bit error rate (BER), performance degradation, or connection loss for covert transmission. This is due to the residual signal, i.e., the remnant of the cover signal after an imperfect SIC operation. The paper proposes a method of transforming (i.e., encoding) the applied hidden signal in such a way that the residual signal in the receiver is quasi-orthogonal to the hidden signal. The proposed model is based on appropriate sorting and, compared to methods with fixed constellation points, provides the covert channel with a low BER while maintaining high protection against detection as measured by the Kolmogorov-Smirnov distance. The proposed solution was tested using the USRP-2920 software-defined radio platform.  
s +
Footnote †: journal: Electronics  
c 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
###### Abstract  
Reduced deployment and calibration requirements are key for scalable and cost-effective indoor positioning systems. In this work, we propose a low-complexity, weak calibration procedure for an indoor positioning system based on infrastructure lighting and a positioning-sensitive detector. The proposed calibration relies on genetic algorithms to obtain the relevant system parameters in the real positioning environment without a priori information, and requires a low number of simple measurements. The achievable performance of the proposal was assessed by direct comparison with a formal offline calibration method requiring complex dedicated infrastructure and instruments. The comparative error assessment showed that the maximum accuracy reduction compared to the significantly more costly formal calibration was below 25 mm, and the overall absolute positioning error was smaller than 35 mm with orientation errors of around 0.25\({}^{\circ}\). The performance achieved with the proposed weak calibration procedure is sufficient for many indoor positioning applications and largely reduces the cost and complexity of setting up the positioning system in real environments.  
Keywords: indoor positioning system (IPS); visible light positioning (VLP); position sensitive detector (PSD); soft/weak calibration; genetic algorithms +
Footnote †: journal: _Sensors_
## 3 New Data Structure
### Coding Schemes
#### 3.1.2 Exception Code  
For this code we categorize the blocks into 3 classes (see also [16] for the a generalization of this idea called _alphabet partitioning_): frequent blocks, rare blocks and ultra rare blocks. For the first two classes we choose parameters \(b_{\text{freq}}\) and \(b_{\text{rare}}\), defining how many bits we use to encode their blocks. Ultra rare blocks are stored verbatim, so they still need their original size \(b_{s}\). We define the most frequent \(2^{b_{\text{freq}}}\) blocks to be frequent blocks. Rare blocks are the most frequent \(2^{b_{\text{rare}}}\) blocks that are not yet classified as frequent. All other blocks are classified ultra rare.  
We maintain simple arrays for each of those classes of blocks, encoding all blocks of that class in the order of their occurrence. In order to access a block we need to find out what class it belongs to and how many blocks of that class occur before. For this we maintain a bit vector _freq_ with one bit per block. We mark frequent blocks with a 1 and prepare it for rank queries using an appropriate rank dictionary. Those rank queries tell us how many frequent blocks occur before a desired block, so it is already sufficient for accessing frequent blocks. For non-frequent blocks subtracting the number of previous frequent blocks from its index we obtain its position in a conceptional list of non-frequent blocks. With this we can proceed exactly the same way as above: maintaining a bit vector _rare_ with one bit per non-frequent block marking rare blocks with a 1 and preparing it for rank queries. To access a non-frequent block we need to rank it both in _freq_ and in _rare_. Figure 1 illustrates the data structures.  
The Exception Code uses more space than the Canonical Code for the encoded blocks, and it is not clear at all if it achieves \(k\)th order empirical entropy in theory. However, the former only requires two rank structures on _freq_ and _rare_ to enable random access. As they are built upon bit vectors with only one bit per block, they require at most \(\frac{n}{b}(1+o(1))\) bits for blocks of size \(b\), and our hope is that this is small compared to the beginnings vector or even the two-level pointer structure required for the Canonical Code structure. In terms of execution time we need little more than one subordinate rank query for frequent blocks, and two for non-frequent blocks, hopefully resulting in faster average query times.  
Figure 1: Illustration to the Exception Code. Shaded blocks denote 1’s, and empty blocks 0’s.
###### Abstract
proposed a RepVGG convolutional neural network, which consists of 3*3 convolutional layers and ReLU. The decoupling of RepVGG is achieved by the structural reparameterization technique. Since it does not concern about the number of parameters, RepVGG is more parameter efficient than ResNets, showing the power of RepVGG compared to models such as EfficientNet and RegNet [19]. Swin Transformer, a brand-new visual transformer, was suggested by [PERSON] et al. This study suggests a hierarchical Transformer whose computation of its representation uses shifted windows, which improves the flexibility of the algorithm and as well as reduces the algorithm's computation, and greatly improves its efficiency, and its effectiveness on the COCO testdev target detection task further demonstrates the effectiveness of the Swin Transformer [20]. Solving OT problems using the Sinkhorn-Knopp method results in 25% more training time, according to [PERSON]'s new high-performance YOLO algorithm, YOLOX. As a consequence, they reduce the complexity of the Sinkhorn-Knopp algorithm to a dynamic top-k strategy, commonly known as SimOTA, a technique for positive and negative sample matching. This paper introduces a formula of cost, which calculates its minimum cost in the labeled region, making a better balance of algorithm accuracy and speed [21]. By applying the multi-headed self-attentive module and position coding in [22] and [23] to the algorithm of computer vision, the C3 TR module emerges. The C3 TR module is obtained by replacing the original Bottleneck with the TransformerBlock module based on the C3 structure. In order to improve the detection effect of occluded targets, the C3 TR module with TransformerBlock is used to replace the BottleneckCSP module. In this paper, YOLOv5s is improved to better apply YOLOv5s to wildlife target detection. This paper first introduces RepVGG lightweight network structure in the backbone to enhance the feature extraction capability of animal targets in the natural environment dataset of this paper, which enhances the model's adaptability and detection speed. This improves the detection accuracy and speed of the YOLOv5s algorithm. Secondly, some C3 modules of the backbone and head are replaced by Swin Transformer modules, which use sliding windows to divide the feature maps and improve the computational speed and efficiency at the cost of minimizing the perceptual field loss. The C3 TR module is then used to enlarge the feature map's perceptual field in place of the head's C3 module. Then,SimOTA is employed as a technique for matching positive and negative samples, and a cost function is added to reduce loss during training. As a result, the model's computation is simplified, the training period is shortened, and the YOLOv5s network's detection speed is increased. Depending on the experimental findings, the model has increased detection accuracy and speed, making it more suitable for use on edge devices and capable of fully detecting and identifying wild animals in their natural habitat.
# Prediction of Manufacturing Quality of Holes Based on a BP Neural Network
## 3 BP Neural Network Prediction for the Tear Number of Hole
### Establishment of Neural Network Model  
An 8-14-1 three-layer topology was used to construct a neural network model for predicting the drilling quality, as shown in Figure 6. The topology of the BP neural network model usually includes an input layer, hidden layer, and output layer [17]. The input layer mainly receives external data and information, the hidden layer is calculated using various functional relationships, and the output layer is mainly used to carry out the calculation conclusions and give prediction results.
# Implementing Deep Convolutional Neural Networks for QR Code-Based Printed Source Identification
## 4 Experimental Results
### Customized Fine-Tuned Residual Model Structure  
In this study, a deep learning neural network with residual connections is proposed (as shown in Figure 8) and trained on both BW and color QR code image blocks. Residual connections are a common feature of convolutional neural network architecture because they can improve the gradient flow through the network and enable the training of deeper networks.  
Figure 8: Network architecture of the customized residual network. The dotted shortcuts increase dimensions. Kernel input of the first layer can be modified into [\(7\times 7\)], [\(5\times 5\)], or [\(3\times 3\)]. Refer to Table 9 for the residual model’s architecture.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Model Name** & **Optimizer** & **Accuracy** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** \\ \hline \multirow{3}{*}{ResNet50} & SGDM & 0.9566 & 0.999 & 1 & 0.979 & 0.999 & 0.979 & 0.957 & 0.995 & 0.730 \\ \cline{2-11}  & Adam & 0.9584 & 1 & 1 & 1 & 0.951 & 0.751 & 0.999 & 1 & 0.967 \\ \cline{2-11}  & RMSprop & 0.9628 & 0.999 & 1 & 1 & 1 & 0.861 & 0.997 & 1 & 0.832 \\ \hline \multirow{3}{*}{VGG16} & SGDM & 0.9977 & 1 & 1 & 1 & 1 & 0.995 & 1 & 0.997 & 0.977 \\ \cline{2-11}  & Adam & 0.125 & – & – & – & – & – & – & – & – \\ \cline{2-11}  & RMSprop & 0.125 & – & – & – & – & – & – & – \\ \hline \multicolumn{11}{c}{Printer Identification Results} & 0.977 & 0.999 & 0.898 & 0.986 & 0.850 & 0.865 & 0.953 & 0.789 \\ \hline \multicolumn{11}{c}{(”-” means data can be ignored).} \\ \end{tabular}
\end{table}
Table 8: _Cont._For many applications, a network that consists of a simple sequence of layers is sufficient. However, some applications require networks with a more complex graph structure, in which inputs come from multiple layers and outputs go to multiple layers. These types of networks are often referred to as directed acyclic graph (DAG) networks.  
A residual network is a type of DAG network that includes residual (or shortcut) connections that bypass the main layers of the network. These residual connections allow the parameter gradients to more easily propagate from the output layer of the network to the earlier ones and result in deeper training, which improves accuracy for challenging tasks, such as identifying intrinsic signatures, which often look identical in most datasets. The overall structure of our residual model is shown in Figure 8, and its architecture is detailed in Table 9.