esa-sceva/satcom-chunk-collection huggingface dataset:
# Sequential and Comprehensive Algorithm for Fault Detection in Semiconductor Sensors
###### Abstract
However, the semiconductor manufacturing process is highly sophisticated. A huge number of sensors should be installed for measuring the physical quantities relevant to wafers' status and the working environments (e.g., temperature, humidity, pressure, flow rate, and chemical gas flow) [17]. Although the model-based method has high accuracy, it is a time-consuming process to detect the adequate parameters in the complex semiconductor manufacturing processes. Additionally, the knowledge-based method often requires the skilled expertise and domain knowledge to make a relevant judgment on account of the correlation structure among faulty data. Therefore, the data-based method is more important to learn the process by monitoring historical data [18]. The statistical process control (SPC) has been employed to detect the wafer faults as a data-based method [19; 20; 21]. The SPC aims to illustrate the process variables and declare the abnormality based on the sensor data patterns. However, monitoring faults by the SPC is limited in inspecting the multiple variables. The multivariate analysis should be conducted for more effective fault detection at an early stage in the manufacturing process. For this analysis, the regression analysis and clustering method have widely been used in semiconductor manufacturing [22; 23; 24; 25]. As the fault detection using regression analysis, the support vector regression (SVR) method has previously been reported [26]. The SVR was derived from a support vector machine (SVM) by being combined with regression analysis. The SVM have been used for the data classification [27]. The SVR was asserted to have outperformed any other methods using the regression analysis [28; 29]. However, the SVR was only used for individual sensor inspection, not for figuring out the correlation between the two sensors. As a clustering method for the fault detection, the algorithm named dominant defective patterns finder (DDPfinder) has been proposed [25]. It clustered the patterns across the wafer and displayed wafer faults with Voronoi diagram based on the spatial dependencies. The DDPfinder showed excellent performance with respect to the accuracy, variance, and capability. Nevertheless, it has the complexity of the computational time due to spatial mapping of the faulty semiconductor chips on a wafer. In this paper, we proposed a sequential and comprehensive algorithm solution for detecting wafer faults. It could be comprehensively applied step by step from sensor stabilization to fault detection. Inspection methods used in this study involved the SPC, regression analysis, and clustering method. We focused on the classification to employ the most suitable inspections for each case. To reduce the computational time, we directly applied the clustering method to the data plane, not requiring mapping the data clustering on a wafer. Additionally, our algorithm divided the data plane into some regions through a Voronoi diagram, showing that the divided region could predict the faults of the semiconductor chips.
###### Abstract  
For quite a while, it has been evident that homogeneous network architectures, based on cells with a uniform radiation pattern, cannot fulfill the ever increasing demand of mobile users for capacity and service quality while still preserving spectrum and energy. However, only with the introduction of the Fourth Generation mobile communication networks to deal with the surging data traffic of multimedia applications, have smaller cells been widely used to break down service zone areas of macro base stations into multiple tiers, thus improving network performance, reducing traffic congestion, and enabling better management of spectrum and energy consumption in a macro network. In this paper, we present an analytical model for assessing the efficiency of bandwidth and energy usage, as well as of network deployment, taking into account overall network investment and maintenance costs. This paves the way to the improved planning of network coverage, and its capacity and reliability, thus preserving its spectrum and energy, as well as the environment. The analysis considers the downlink of an arbitrary heterogeneous cellular network by using tools of stochastic geometry that adopt the distribution of base stations in the form of a Poisson Point Process. The proposed analytical model is verified by the according software simulations using the ns-3 network simulator. The obtained results closely match the theoretically predicted values and boundaries, clearly indicating that, in all three analyzed aspects: spectral, energy, and deployment, the efficiency of small-cell networks was higher with respect to traditional large-cell networks and increased even further for heterogeneous (two-tier in our tests) networks.  
heterogeneous cellular network; spectral efficiency; deployment efficiency; Poisson Point Process; ns-3 2021  
1 Communications Regulatory Agency, 71000 Sarajevo, Bosnia and Herzegovina; [EMAIL_ADDRESS]  
2
# Medical Gesture Recognition Method Based on Improved Lightweight Network
## 4 Experiment
### Experimental Results and Analysis  
#### 4.3.1 Ablation Experiment  
To verify the network optimization effect of the proposed E-MobileNetv2 modules, we carried out ablation experiments and compared the accuracy of the algorithm and the number of model parameters under various structural combinations. In order to verify that the ECA module improves the model accuracy, we add the ECA module into the linear bottleneck structure with stride = 1. In order to verify that the improved activation function can improve the feature extraction ability of the model and thus improve the recognition accuracy, we replace all ReLU6 activation functions in the linear bottleneck structure with R6-SELU. In order to prove that reducing the number of parameters can improve the reasoning speed of the model, we set the hyper-parameters. The experimental results are shown in Table 3.  
ECA indicates the embedding of the ECA module in a linear bottleneck; R-S indicates adding the R6-SELU activation function into the network model; H-p indicates setting the hyper-parameters. The analysis of the ablation experiment results shows that the recognition accuracy of the model is increased by 2.19% after the addition of ECA, and the introduced parameters can almost be ignored. The R6-SELU activation function is  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **ECA** & **R6-SELU** & **Hyper-Parameters** & **mAp/\%** & **Param/M** \\ \hline \multirow{4}{*}{Mobilenetv2} & - & - & - & 93.65 & 3.34 \\  & ✓ & - & - & 95.84 & 3.4 \\ \cline{1-1}  & - & ✓ & - & 94.79 & 3.34 \\ \cline{1-1}  & - & - & ✓ & 91.13 & 1.6 \\ \hline E-Mobilenetv2 & ✓ & ✓ & ✓ & 96.82 & 2.17 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of ablation experiment.  
Figure 9: Part of the Jester dataset.  
introduced to enhance the extraction of negative-valued features, and the recognition accuracy of the model is increased by 1.14%, without increasing the parameters of the model. Finally, for the setting of hyper-parameters, although the recognition accuracy is reduced by 2.52%, the number of parameters is reduced by half, which improves the recognition speed. It can be seen that the proposed network improves the accuracy by 3.17% and reduces the number of parameters by about 30% compared with the unimproved network. Each additional module contributes to the model identification accuracy and parameter reduction.
# Error-Correction Coding Using Polynomial Residue Number System
## 3 Theoretical Foundations of Building Redundant Codes of Polynomial Residue Number System
### Codes of Polynomial Residue Number System with Two Control Bases
The orthogonal bases of PRNS can be calculated as follows:_  
\[B_{1}(z)=z^{14}+z^{13}+z^{12}+z^{11}+z^{10}+z^{9}+z^{8}+z^{7}+z^{6}+z^{5}+z^{4 }+z^{3}+z^{2}+z+1\]  
\[B_{2}(z)=z^{14}+z^{13}+z^{11}+z^{10}+z^{8}+z^{7}+z^{5}+z^{4}+z^{2}+z\]  
\[B_{3}(z)=z^{14}+z^{13}+z^{12}+z^{11}+z^{9}+z^{8}+z^{7}+z^{6}+z^{4}+z^{3}+z^{2}+z\]  
\[B_{4}(z)=z^{14}+z^{13}+z^{12}+z^{11}+z^{9}+z^{7}+z^{6}+z^{3}\]  
\[B_{5}(z)=z^{12}+z^{9}+z^{8}+z^{6}+z^{4}+z^{3}+z^{2}+z\]  
_The bases can then be divided into the operating range until the residue is obtained, thusly:_  
\[L_{1}(z)=z^{7}+z^{4}+z^{2}+z;\ \stackrel{{\cdot}}{{B}}_{1}(z)=z^{6}+z^{ 4}+z^{3}+z^{2}+1\]  
\[L_{2}(z)=z^{7}+z^{5}+z^{2}+z+1;\ \stackrel{{\cdot}}{{B}}_{2}(z)=z^{6}+z ^{5}+z+1\]  
\[L_{3}(z)=z^{7}+z^{4}+z^{3}+z+1;\ \stackrel{{\cdot}}{{B}}_{3}(z)=z^{5}+z ^{4}+z^{3}+z^{2}+z+1\]  
\[L_{4}(z)=z^{7}+z^{4}+z^{3}\]  
\[L_{5}(z)=z^{5}+z^{4}+z\]  
_Calculate the polynomial interval of the code combination of PRNS. Now, obtain the value of the rank of the polynomial in non-redundant PRNS:_  
\[B_{2}(z)=z^{14}+z^{13}+z^{11}+z^{10}+z^{8}+z^{7}+z^{5}+z^{4}+z^{2}+z\]0  
_This value is substituted in_  
\[B_{2}(z)=z^{14}+z^{13}+z^{11}+z^{10}+z^{8}+z^{7}+z^{5}+z^{4}+z^{2}+z\]1  
\[B_{2}(z)=z^{14}+z^{13}+z^{11}+z^{10}+z^{8}+z^{7}+z^{5}+z^{4}+z^{2}+z\]2  
\[B_{2}(z)=z^{14}+z^{13}+z^{11}+z^{10}+z^{8}+z^{7}+z^{5}+z^{4}+z^{2}+z\]3  
_._  
_As the polynomial interval is equal to zero, it means that the code combination of PRNS does not contain an error._  
_Let the error occur on the base \(p_{1}(z)\) and the error depth be \(\Delta d_{1}(z)=1\). Then, the code combination has the form_  
\[ \begin{array}{l}D^{*}(z)=(1^{*},\ z,\ z^{3}+z^{2}+1,\ 1,\ z^{2}+z)=\\ =z^{14}+z^{13}+z^{12}+z^{11}+z^{10}+z^{9}+z^{8}+z^{7}+z^{6}+z^{3}+z^{2}.\end{array} \]  
_Using Expression (56), we can find the polynomial interval:_  
\[S(z)=\left[\frac{z^{14}+z^{13}+z^{12}+z^{11}+z^{10}+z^{9}+z^{8}+z^{7}+z^{6}+z ^{3}+z^{2}}{z^{8}+z^{7}+z^{5}+z^{4}+z^{3}+z+1}\right]=z^{7}+z^{4}+z^{2}+z.\]We now use the developed algorithm. First, we obtain a rank of the polynomial in non-redundant PRNS, \(\vec{k}(z)=z\), that we substitute in  
\[S(z)=\left|\sum\limits_{i=1}^{5}d_{i}(z)L_{i}(z)+\vec{k}(z)\right| _{W_{k}(z)}=\left|(z^{7}+z^{4}+z^{2}+z)+z(z^{7}+z^{5}+z^{2}+z+1)+\right.\] \[\left.+(z^{3}+z^{2}+1)(z^{7}+z^{4}+z^{3}+z+1)+(z^{7}+z^{4}+z^{3})+ (z^{2}+z)(z^{5}+z^{4}+z)+z\right|_{W_{k}}=\] \[=(z^{10}+z^{9}+z^{8}+z^{7}+z^{6}+1)\text{mod}z^{8}+z^{7}+z^{5}+z^{4 }+z^{3}+z+1=z^{7}+z^{4}+z^{2}+z.\]  
The obtained results coincide, which indicates the possibility of using redundant codes of polynomial residue number system to build fault-tolerant high-speed computing systems for digital signal processing and cryptographic protection of information operating in Galois fields.
## 2 Background
### Test Problems
#### 2.4.3 Lcs  
The LCS problem [14] is closely related to the subgraph isomorphism problem. In the LCS problem, we are given graphs \(G_{1}=(V_{1},E_{1})\) and \(G_{2}=(V_{2},E_{2})\), with vertex sets \(V_{1}\) and \(V_{2}\), and edge sets \(E_{1}\) and \(E_{2}\). In the optimization variant of the problem, we must find the graph \(G_{3}=(V_{3},E_{3})\), such that \(G_{3}\) is isomorphic to a subgraph of \(G_{1}\) and \(G_{3}\) is isomorphic to a subgraph of \(G_{2}\), and such that the cardinality of edge set \(|E_{3}|\) is maximized. This problem is NP-hard. The NP-complete decision variant of the problem asks whether there exists such a graph \(G_{3}\) such that \(|E_{3}|\geq K\) for some threshold \(K\).  
If \(|V_{1}|=|V_{2}|\), then a solution is represented by a permutation of \(\{0,1,\ldots,|V_{1}|-1\}\), and more generally the solution is represented by the first \(\min(|V_{1}|,|V_{2}|)\) integers of a permutation of \(\{0,1,\ldots,\max(|V_{1}|,|V_{2}|)-1\}\). Without loss of generality, assume that \(|V_{1}|\leq|V_{2}|\). For permutation \(p\) of integers \(\{0,1,\ldots,|V_{2}|-1\}\), the number of edges \(|E_{3}|\) in the common subgraph implied by such a permutation is then computed as  
\[|E_{3}|=\sum_{(u,v)\in E_{1}}\begin{cases}1&\text{if }(p(u),p(v))\in E_{2} \\ 0&\text{if }(p(u),p(v))\
otin E_{2},\end{cases} \tag{9}\]  
where \(p(u)\) means the element in position \(u\) of permutation \(p\). This is most efficiently implemented if the smaller graph, \(G_{1}\), is represented with adjacency lists or by a simple list of ordered pairs as the edge set; and if the larger graph \(G_{2}\) is represented with an adjacency matrix to enable constant time checks for existence of edges.  
Prior approaches to the LCS problem include GA [20], hill-climbing [56], and heuristic approaches [57; 58; 59]. There are applications of the LCS problem in computer-aided engineering [56], software engineering [59], protein molecule comparisons [58], integrated circuit design [57; 60], natural language processing [61], and cybersecurity [62], among others.
# Multi-Source Image Fusion Based Regional Classification Method for Apple Diseases and Pests
## 1 Introduction
Most existing research establishes diagnostic methods based on the correlation between a single data source image and crop diseases and pests. However, it is difficult to comprehensively capture the growth status of vegetation solely through RGB images. At the same time, analyzing apple biomass solely through VIs may ignore the appearance and texture changes in the pathological process, resulting in the low accuracy of disease and insect pest diagnosis. Therefore, it is urgent to diagnose apple diseases and pests by combining multi-source image data. This study reports work to solve the problems of low diagnostic accuracy and poor stability of the existing single-source image-based disease and pest regional classification methods, focusing on the following issues:  
* Firstly, the visible light and multispectral image data of an apple orchard canopy are collected by UAV, and the multi-source multi-label apple canopy image dataset of apple diseases and pests is constructed, which made up for the shortage of multi-label classification training data in large-scale orchards. * Secondly, a VI selection method based on the saliency attention module is proposed. Aiming at the habitat information of apple plants, a feature selection method is used to weight the selected 22 vegetation indices in order to improve the accuracy of regional multi-classification. * Finally, a multi-label classification model called AMMFNet, based on the joint prediction of RGB and multispectral images, is constructed. It can effectively utilize the complementary features of RGB images and VIs so that the model can automatically pay attention to the features related to diseases and pests and reduce the impact of redundancy in multi-source data on prediction performance. In summary, this paper aims to design a deep learning model based on multi-source image fusion by combining visible light and multispectral image data. This model aims to realize multi-label classification of apple disease and insect pest regional images. It further seeks to provide technical support for the fast and efficient coarse-grained positioning of apple disease and insect pest canopy areas in order to provide new ideas and methods for the healthy development of the apple industry. The structure of this paper is as follows: Section 2 introduces the detailed structure of AMMFNet. Section 3 presents the experiment results and corresponding analysis. Finally, Section 4 summarizes the research findings and outlines the corresponding research contributions.
# Millimeter-Wave Smart Antenna Solutions for URLLC in Industry 4.0 and Beyond
## 2 Advantages and Challenges of mmWave Industry 4.0 and beyond Communication  
### Advantages of mmWave Communication  
#### 2.1.1 Large Available Bandwidth  
The large available bandwidth of 60 GHz unlicensed band unfolds many applications for Industry 4.0 and beyond. Some of the sophisticated smart industrial applications include vision-guided robots, ultrahigh definition video and imaging for remote visual monitoring, smart safety instrumented systems, intelligent logistics, and high precision image-guided automated assembly, to name a few [45; 46]. The capacity to achieve URLLC in factory automation scenarios allows smart robots and machines to work alongside humans or collaborate on a similar goal. However, with the potential use of mmWave high throughput communication, a seamless adaptive behavior can be employed in the  
Figure 3: Organization of this review paper.  
machines/equipment to make them capable of detecting adjacent individuals or objects and reacting appropriately, such as by modifying their movements, slowing their operating rate, or even halt completely.
## 4 Results
### Fracture Network Modelling  
An image analysis of the core photographs revealed \(\sim\)650 individual fractures. This dataset was evaluated to obtain reliable geometric parameters of the area along the well. The spatial distribution of the fractures indicated that the entire 30-m-long section was intensively deformed, with varying fractured intervals. In certain locations, the neighbouring fractures were only a few centimetres away from each other, while the largest spacing was \(\sim\)40 cm (Figure 9a). The variation in the P10 values also indicated a slightly heterogeneous fracture density undergoing alterations between 16 and 30 m\({}^{-1}\). This index, along with P10-log, revealed three depth intervals with particularly large values (>25 m\({}^{-1}\)): \(\sim\)2052, 2060, and 2070 m (Figure 9b). As the fractal dimension (\(D\)) accumulates information for 6-m-long sections, \(D\)-log exhibited an even smoother curve. Its values varied between 1.07 and 1.50, again with three extremes at depths of \(\sim\)2052, 2065, and 2075 m. While the spacing and P10 had no meaning inside the \(\sim\)5-m-long brecciated interval at \(\sim\)2075 m, the \(D\)-value could be computed using the neighbouring data (Figure 9c).  
Figure 8: Vertical variation in the _F(1-2)_ values along the A-1 well. The dashed line separates the undeformed host rock from the faulty rocks (Figure 4a). Red bars indicate the possibly deformed intervals.  
As numerous fractures were found to not terminate inside the bore core, only \(\sim\)550 fractures yielded reliable data length. Their measured values varied between 3 and 25 cm with a modus of 9.5 cm. The tight correlation (r = 0.96) between log\(L\) and logN(\(L\)) revealed that the data followed a power-law distribution. The length exponent was computed as the slope of the regression line on this plot, resulting in E = \(-\)4.78 (Figure 10).  
Figure 10: Fracture length distribution measured along the A-1 well. Inset: the log\(L\) - logN(\(L\)) plot of the histogram for estimating the length exponent E.  
Figure 9: Fracture density logs along the A-1 well. (**a**) Spacing log. (**b**) P10 parameter log. (**c**) Fractal dimension (\(D\)) log. For definitions of the parameters, please refer to the text.  
All 10 independent near-well models showed similar results, indicating two intensively fractured zones at \(\sim\)2065 and 2075 m, where individual fractures formed a communicating network (Figure 11). Some simulations indicated that these zones were interconnected, while others exhibited a compartmentalised network of two entirely isolated sub-systems (Figure 11). Based on the consistent result of all models, the zone at \(\sim\)2052 m comprised individual fractures, which probably do not define a mutually interconnected network.
# Interpretability Study of Gradient Information in Individual Travel Prediction
## 4 Experiment  
We have designed a series of experiments to explore the interpretability of gradient information across different time ranges by constructing models with varying sequence lengths. These models are based on the same network architecture, but the change in input sequence length allows us to analyze the temporal dependencies of key features in travel patterns and their impact on prediction results.  
First, we build a travel prediction model based on an embedding layer, temporal processing layer, and attention layer as the platform for our interpretability study (Section 4.1). Next, we construct a mask matrix based on gradient information to quantify feature importance (Section 4.2) and introduce metrics used to assess the impact of the mask operation on model prediction behavior (Section 4.3). Finally, we investigate the specific contribution of features with different gradient magnitudes to the model's prediction through feature importance evaluation (Section 4.4). These experimental designs and evaluation metrics will help us systematically validate the interpretability of gradient information in the travel prediction model and lay the foundation for subsequent experimental analysis.
## 5 Experiments and Results
### Hardware Resource Utilization  
Through the synthesis and implementation steps in Vivado 2018.3, the resource utilization of the data controller in the FPGA can be obtained. The results are shown in Table 4:  
The table shows the utilization of the on-chip resources of LUT, the register, RAM and DSP. The total number of available resources is the total number of these resources in Xilinx XC7 VX690T FPGA, and the resource utilization (%) in the table is the utilization number for each resource divided by the total number of resources. It can be seen that the controller only occupies a small amount of LUT and storage resources. Since the address generation process is optimized in the controller, the address update can be completed only by judging, shifting, adding and subtracting, without using the DSP resources on the FPGA chip, so the utilization rate of DSP is 0. More FPGA computing resources are used for the system's computing engine, which can further improve the system's computing capabilities.
## 4 Results and Discussion
### Statistical t-Test  
The statistical \(t\)-Test has also been performed to show the significance of the proposed approach. In the null hypothesis of the \(t\)-test, \(H_{o}\) shows that the accuracy difference of methods is not significant while alternate hypothesis \(H_{a}\) shows that the accuracy difference is significant. We have performed a \(t\)-test of the proposed model and the second-best performing model on each dataset. At first, the test is performed on KDD CUP 99 dataset on MLP and RFMLP which shows a 9.22158 value for test statistics and 0.001349 \(p\)-value. It concludes that the proposed model has improved the performance. Secondly, the test is performed on the NSL-KDD dataset on MLP and RFMLP which shows a 4.9265 value for test statistics and 0.008014 \(p\)-value. It also proves that the proposed model has improved the performance. Finally, the test is performed on the STIN dataset on RF and RFMLP which shows a 7.10539 value for test statistics and 0.002868 \(p\)-value. Results prove that the difference is statistically significant with \(p<0.05\) for all three datasets. The proposed model obtained the highest mean rank for accuracy.