esa-sceva/satcom-chunk-collection huggingface dataset:
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 4 Experiments
### Comparative Experiments
#### 4.3.2 VisA  
Further, to validate the generalizability and versatility of our method, we compared it with other SOTA methods [7, 8, 10, 11, 20, 36] on the VisA [3] dataset. The anomaly detection results for the VisA dataset are listed in Table 3. Experiments demonstrated that our proposed method performed competitively on the VisA dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **DAGAN** & **Skip-GANomaly** & **PaDiM** & **CFLOW** & **PatchCore** & **Ours** \\ \hline Bracket Black & 68.55 & 61.30 & 75.60 & 72.67 & 81.88 & **93.42** \\ \hline Bracket Brown & 77.07 & 62.14 & 85.40 & 88.84 & 78.43 & **93.14** \\ \hline Bracket White & 72.11 & 73.33 & 82.22 & 87.78 & 76.00 & **89.33** \\ \hline Connector & 99.76 & 73.62 & 91.67 & 94.76 & 96.67 & **100.00** \\ \hline Metal Plate & 85.43 & 73.24 & 56.30 & 99.51 & **100.00** & 99.57 \\ \hline Tubes & 31.93 & 46.42 & 57.51 & 73.14 & 59.73 & **94.16** \\ \hline Avg. & 72.48 & 64.84 & 74.78 & 86.12 & 82.12 & **94.94** \\ \hline \hline \end{tabular} Best results are highlighted in bold.  
\end{table}
Table 1: Comparison of image-level detection results (AUROC%) on the MPDD dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **DAGAN** & **Skip-GANomaly** & **PaDiM** & **CFLOW** & **PatchCore** & **Ours** \\ \hline Bracket Black & 89.73 & 88.96 & 94.23 & 96.88 & 98.41 & **98.97** \\ \hline Bracket Brown & 81.50 & 78.07 & 92.44 & **97.78** & 91.46 & 93.10 \\ \hline Bracket White & 70.63 & 78.81 & 98.11 & **98.61** & 97.44 & 97.82 \\ \hline Connector & 85.73 & 80.20 & 97.89 & 98.39 & 95.00 & **98.95** \\ \hline Metal Plate & 89.95 & 89.72 & 92.93 & 98.21 & 96.57 & **98.78** \\ \hline Tubes & 82.31 & 77.30 & 93.94 & 96.43 & 95.05 & **99.17** \\ \hline Avg. & 83.31 & 82.19 & 96.74 & 97.72 & 95.66 & **97.80** \\ \hline \hline \end{tabular} Best results are highlighted in bold.  
\end{table}
Table 2: Comparison of pixel-level detection results (AUROC%) on the MPDD dataset.
# IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction
## 3 Materials and Methods
### Hardware  
#### 3.2.1 Raspberry Pi  
Raspberry Pi 3 Model B: The Raspberry Pi (RPi) 3B [38] is a portable and powerful SBC (Single Board Computer), meaning that it runs a full operating system and has sufficient peripherals (memory, CPU, power regulation) to start execution without the addition of hardware. It has been proved to be an immediate access due to the low price of $35. By adding an SD storage, it is possible to quickly have a fully working computer running Raspbian, a Debian-based Linux operating system, which is free and optimized for the RPi hardware. The RPi has a built-in quad-core 1.2 GHz 64 bit CPU, which gives it very good computing and processing power. Therefore, in many of IoT applications, RPi has been deployed as an edge node, see for example [39; 40; 41; 42].  
Based on the processing power and computing characteristics of the RPi, this paper also uses the RPi as the edge computing device and the central node of the sensor network (as shown in Figure 2). The RPi periodically reads parameter data from various sensors based on the corresponding connection pins. After obtaining the monitoring data, the background process is awakened from the sleep state, actively runs the Kalman Filter algorithm, and calls the monitoring data to iteratively update it (see Section 4 for details). When the calculation is completed, the predicted data of various air pollutant concentrations at the next moment can be obtained. At this point, the RPi will upload the prediction results and monitoring data to the cloud through the Wi-Fi module, and the cloud stores the data.
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 4 Energy-Efficient Routing Protocol for Reliable Data Transmission in WBAN  
This paper proposes an energy efficient routing protocol for reliable data transmission in WBAN, which adopts two-hop or multi-hop communication to alleviate the energy consumption caused by direct communication. We also construct a multi-parameter maximum benefit function to dynamically select the next-hop node with good state and dynamically adjusts the weight of the maximum benefit function based on the priority of data. Reliable and efficient data transmission is achieved via dynamic routing adjustment in this paper. Following this, the construction of the maximum benefit function and the routing process are described in detail.
## 1 Introduction
[PERSON] et al. [24] used the relevance vector machine to improve the accuracy and reliability of a battery's SOH estimation. [PERSON] et al. [25] estimated the SOH based on ensemble learning by extracting six deterioration features from battery data. Furthermore, [PERSON] et al. [26] proposed an algorithm to estimate the SOH by combining a deep neural network and an autoencoder. [PERSON] et al. [27] estimated the SOH based on recurrent neural networks (RNNs) using the voltage and current data over time. In RNNs, the memory used to remember the sequences in long inputs becomes shorter, which may cause the vanishing and exploding gradient problem. In previous studies, general data preprocessing techniques (e.g., Normalization) and mathematical battery models that are generally applied to data preprocessing were used. These limit the SOH accuracy of LIB with nonlinearity. To preprocess the data with such nonlinearities, [PERSON] et al. [28] estimated the SOH by applying continuous wavelet transform (CWT) to the voltage profile of the battery. [PERSON] et al. [29] estimated SOH by applying discrete wavelet transform (DWT). The CWT facilitates the extraction of useful features related to battery deterioration by converting one-dimensional (time) battery data into two-dimensional (time-frequency domain) data. The DWT facilitates the extraction of electrochemical features from signals with a broad frequency range through multi-resolution analysis. In this study, we proposed a preprocessing method suitable for SOH estimation using both CWT and DWT. In the case of CWT, it was possible to increase the estimation accuracy. In the case of DWT, since a neural network was not used in existing papers, a SOH estimation method was proposed using LSTM. To confirm the usefulness of the proposed algorithm, the SOH estimation technique by normalization and SOH estimation results were compared. From this, it was found that the SOH estimation accuracy using the wavelet technology proposed in this study was high. It was confirmed that the proposed algorithm is more suitable for data with nonlinearity than the conventional preprocessing method. Furthermore, regarding the impacts of the specific wavelet-related methods, we found that it was more appropriate to apply the DWT methods to the LSTM than to the CNN and CWT. The remainder of this paper is organized as follows. Section 2 describes the data composition. Section 3 describes CWT and DWT, which are preprocessing methods, the CNN and LSTM methods, which are machine learning methods, were explained, as well as the neural network model structure for SOH estimation. Section 4 compares the SOH estimation performances of the LSTM and CNN models to verify the accuracy of the proposed model, the SOH estimation performance of the proposed method was examined, and its validity was determined.
# Assessment of Daytime HONO Emission Source from Asphal Surface to Urban Air
## 3 Results
### Vertical Profile of HONO above the Asphalt Surface
Figure 5 also depicts the simulated vertical profile of daytime HONO and the observed HONO of medians and percentiles (10 th, 25 th, 75 th, and 90 th) for the two field measurements in Seoul. While the simulated HONO shows a maximum of 0.13 ppbv at the ground level, it decreases slowly within the surface layer of a few tens of meters, which is obviously affected by well-mixed air characteristics under unstable weather conditions in the summer. This profile was computed with the assumption of steady-state HONO equilibrated only on the asphalt surface production. The fraction of asphalt paved roads is 13.7% of the total surface area in Seoul. The percentage is much higher, up to 50%, in the downtown area, and the unknown but significant amount of urban roof surface is also covered with asphalt shingles. For these reasons, the calculated vertical HONO profile cannot be overestimated by more than a factor of two. If we consider all of these uncertainty factors together, then we can conclude that asphalt-surface HONO formation may attribute up to 0.07 ppbv of HONO near the ground level, which is about 13% of daytime HONO in Seoul. The  
Figure 5: Estimated HONO concentration vertical diffusivity in red color and calculated vertical profile of daytime HONO in blue color compared with measured HONO of means (green triangle), medians and percentiles (10 th, 25 th, 75 th, and 90 th) for two field observations in Seoul, both with heights on logarithmic scale. results suggest that the majority of daytime HONO is formed by unknown sources such as aerosols, photoreactive minerals, ground sources with soils, or vegetated surfaces. It should be noted that HONO formation on the asphalt surface in this study was tested with precursor concentrations observed in the tunnel. In a real scenario, we can expect a much higher HONO formation rate on the asphalt road surface as it is directly exposed to vehicle exhaust containing ppm levels of NO\({}_{2}\) and hydrocarbons. Fixing all other factors, our data show that the HONO formation rate on the asphalt surface increases proportionally to the NO\({}_{2}\) concentration. This implies that HONO production on the asphalt surface may play a significant role in daytime HONO production in urban areas, especially where the real road surface is exposed to the immediate emission of NO\({}_{2}\) and hydrocarbons from vehicle exhaust. It is necessary to note that HONO formation on asphalt surfaces was not tested under real traffic conditions; thus, further detailed analysis must be performed considering traffic conditions. Additional efforts are needed to assess comprehensive mechanisms of surface HONO production on asphalt and its contributions to daytime HONO in urban areas in the future.
## 6 Conclusions  
In recent years, space IoT systems represented by satellite-UAV collaborative networks have been developing rapidly. These networks are highly dynamic, with changing distances and visibility among satellites over time. To optimize their topology more efficiently and reliably, a secure algorithm combining graph partitioning and simulated annealing has been proposed. The innovation of this work is that a new optimization path is provided. Instead of opting for neighborhood solution generation and estimation function computation to optimize the problem solution, the number of subsequent iterations is reduced, and the algorithm's efficiency is improved by constructing a smart initial solution. Introducing time slices to account for the network's dynamic nature speeds up problem-solving. This algorithm offers improved convergence and performance compared with traditional methods. In addition to serving as a solution for topology optimization, the proposed algorithm introduces a new way of thinking, enabling the handling of larger satellite-UAV collaborative IoT systems.  
Conceptualization, M.Z. and L.L.; methodology, M.Z. and L.L.; software, M.Z. and L.L.; validation, P.Y., Z.T. and Y.F.; formal analysis, P.Y.; investigation, Z.T. and Y.F.; resources, S.Z.; data curation, P.Y.; writing--original draft preparation, M.Z. and L.L.; writing--review and editing, M.Z.; visualization, L.L.; supervision, S.Z.; project administration, P.Y.; funding acquisition, S.Z. and P.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by 1. National Natural Science Foundation of China, grant number 62272089. 2. General Program of Science Funding Projects of Sichuan Province, grant number 2022 YFG0207. 3. Open project of Intelligent Terminal Key Laboratory of Sichuan Province, grant number SCITLAB-20006. 4. Science and Technology Department of Sichuan Province, grant number 2023 YFG0155. 5. Ministry of Industry and Information Technology of the People's Republic of China, grant number TC2108048.  
Data are contained within the article.  
Thanks to all the authors in the references, it is because of the excellent scientific research you have provided that we have been able to constantly access the most cutting-edge hitting information and learn a great deal of wealth of knowledge and have been able to successfully complete this paper. In addition, the authors are grateful to the editor and anonymous reviewers for their suggestions for improving the quality of the paper.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
## 3 Results
### The Experimental Preparation  
#### 3.1.1 Datasets  
In this study, comprehensive experiments are conducted on nine publicly available benchmark datasets to evaluate the effectiveness of the proposed feature selection method. Prior to the experiments, detailed descriptions of the parameter settings and evaluation metrics are provided. For the benchmark datasets--which include diverse data types such as facial images, object images, and gene expression data--200 samples were randomly selected from each digit class in the MNIST dataset [26]. MNIST is a large-scale dataset of handwritten digits widely used for image classification and machine learning benchmarking. It contains 70,000 grayscale images of size 28 \(\times\) 28 pixels, divided into 60,000 training and 10,000 testing samples, covering digits from 0 to 9. All datasets used in this study are publicly available benchmark datasets, which can be accessed through the scikit-feature selection library [6]. Table 1 summarizes the detailed specifications of all datasets used.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**ID** & **Datasets** & **Instances** & **Features** & **Classes** & **Data Types** & **Description** \\ \hline
1 & Isolet [6] & 1560 & 617 & 26 & Image, text & UCI dataset \\
2 & Mnist [26] & 2000 & 784 & 10 & Image, text & Handwritten digits, 28 \(\times\) 28 pixels \\
3 & Yale (32\({}^{\circ}\)32) [6] & 165 & 1024 & 15 & Image, Face & Yale face database, 32\({}^{\circ}\)32 grayscale \\
4 & ORL (32\({}^{\circ}\)32) [6] & 400 & 1024 & 40 & Image, Face & AT\&T face dataset \\
5 & COIL20 [6] & 1440 & 1024 & 20 & Image, Object & Columbia object images \\
6 & WarpAR10P [6] & 130 & 2400 & 10 & Image, Face & Cropped face images under different conditions \\
7 & WarpPIE10P [6] & 210 & 2420 & 10 & Image, Face & PIE face dataset, cropped \\
8 & Colon [6] & 62 & 2000 & 2 & Gene expression, Biology & Tumor vs. normal colon tissue \\
9 & TOX-171 [6] & 171 & 5748 & 4 & Gene expression, Biology & Toxicogenomic gene \\  & & & & & & expression data \\ \hline \end{tabular}
\end{table}
Table 1: Details of the datasets.
###### Abstract  
This paper presents a novel fast analysis of wave speed (FAWS) algorithm from the waveforms recorded by a random-spaced geophone array based on a compressive sensing (CS) platform. Rayleigh-type seismic surface wave testing is excited by a hammer source and conducted to develop the phase velocity characteristics of the subsoil layers in Shenyang Metro line 9. Data are filtered by a bandpass filter bank to pursue the dispersive profiles of phase velocity at various frequencies. The Rayleigh-type surface-wave dispersion curve for the soil layers at each frequency is conducted by the \(\ell_{1}\)-norm minimization algorithm of CS theory. The traditional frequency-wavenumber transform technique and in-site downhole observation are employed as the comparison of the proposed technique. The experimental results indicate the proposed FAWS algorithm has a good agreement with both the results of conventional even-spaced geophone array and the in-site measurements, which provides an effective and efficient way for accurate non-destructive evaluation of the surface wave dispersion curve of the soil.  
Rayleigh wave; surface wave; dispersion curve; compressive sensing; soil Article
## 4 Proposed Deep Learning Based Cooperative Communication Channel Model for WUSNs
### DL Based Routing Model in WUSN  
DL can maximize the utility by determining the optimum route through ongoing testing with the environment. DL combines a hierarchical machine learning model with reinforcement systems [38]. It is necessary to train variables using the current values of reinforcement learning and to replace the reinforcement learning's Q value table with a neural network [39]. Depending on the type of Cooperative communication channel construction and the benefits of DL in terms of evaluation and decision, the proposed route planning strategy is based on labeled data to meet the Q-value table of DL. The DL-based Routing model is depicted in Figure 2. The control layer mainly includes data collection module, data processing module, routing decision module and processing module. Data collection and processing are done before, it is given to the routing decision format in order to get undergo flow table processing module. Here, the deep learning actually refers to the decision making process which following the routing decision format.  
The essential aspects of the central controller in the overall system design are information collection domain, information analysis, routing choice, and route table information processing. The information collection domain collects a piece of data for data transfer to reduce the dimensionality before route table information processing in the traffic method. Finally, the path discovery decision module includes a traffic method-based method [40; 41]. A DL neural network is used in the routing selection module to acquire sufficient knowledge from the information collection domain and receive environmental monitoring attributes using DL neural networks [29].  
The trained neural network uses classification methods to accomplish the decision making task. It may turn the current circumstances into tasks using suitable procedures and evaluate various moving platforms [42]. Finally, the Q-value tables created by supervised learning can be linked into key entities to act as the foundation for routing decisions. In this work, a multi-Cooperative communication channel path routing scheme using deep r training is built successfully:  
Multi-Cooperative communication channel path routing Algorithm 1 based on Deep Learning:  
Figure 2: DL based Routing model.  
The packet loss of traffic frequency is defined as the ratio of the total amount of bandwidth lost by all traffic to the total amount of bandwidth requested by all traffic.  
\[\text{Loss rate}=\sum\_\text{iBw loss(x)}/\sum\_\text{iBw (x)} \tag{8}\]  
Bw shows the bandwidth of the xth traffic. xth traffic is the formula for specifying traffic bandwidth loss.  
\[\text{Bw}^{\text{x}}_{\text{loss}}=\text{Bw}_{\text{x min}}\text{Bw}\text{R}^{ \text{x}}_{\text{bw}} \tag{9}\]  
\(\text{R}^{\text{x}}_{\text{bw}}\) includes the overall link throughput of the xth traffic's forwarded pathway; \(\text{min}\text{Bw}\text{R}^{\text{x}}_{\text{bw}}\) is the route's minimal connection frequency.
## 3 Results
### Preparation of Test Data
#### 3.1.3 Data Partitioning for CNN YOLO Model  
The dataset generated for the CNN model was partitioned into training, validation, and test sets in the proportions of 70% (227 images), 15% (49 images), and 15% (49 images) (in accordance with generally accepted practices [40; 41; 42; 43] in ML, allowing an effective assessment of the model's ability to generalize). Random partitioning of the data, with the granularity of the random generator controlled, provided repeatable results and an equal distribution of classes in each partitioned set, giving high confidence in avoiding class bias.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 7 Heuristic Solution: FastTs
### Complexity Analysis of FastTs  
The complexity of FastTs is related to the number of combinations that must be analyzed, which is calculated as the sum of the combinations of all the partitions. The first steps performed by the heuristic (i.e., variations per service and combinations) define its complexity and as a function of \(N\) the growth rate of FastTs is given by Equation (32). Moreover, if all of the partitions have the same size, the growth rate can be expressed, as shown in Equation (33).  
\[f(N)= N+(2\times N\times Ts+N)+\sum_{i=1}^{numPart}(2\times Ts+1)^{ lenPart_{i}} \tag{32}\] \[f(N)= N+(2\times N\times Ts+N)+numPart\times(2\times Ts+1)^{\frac{N}{ numPart}} \tag{33}\]  
Thus, discarding the low-order terms in Equation (33) the complexity of FastTs is \(\mathcal{O}(2^{\frac{N}{numPart}})\), i.e., the heuristic strategy presents a non-linear growth rate; however, it is much smaller than OptTs. In this context, in order to assess the difference between the exact and heuristic solutions in terms of the number of combinations and, subsequently, in terms of complexity and execution time, the relative gain parameter (\(G_{T}\)) has been defined, as indicated in Equation (34).  
\[G_{T}=\frac{(2\times Ts+1)^{N}}{\sum_{i=1}^{numPart}(2\times Ts+1)^{lenPart_{i}}} \tag{34}\]  
The \(G_{T}\) parameter only considers the most dominant terms of Equations (30) and (32) and allows an estimate of how many times less-complex or how many times faster the heuristic solution is, as compared with the optimal. Figure 10 shows growth rate for OptTs and FastTs, considering for this latter \(numPart=2\) and \(numPart=3\) partitions. This example reveals a considerable reduction in the size of the problem (for \(N>2\), because no partition is performed when \(N=1\)) when compared to OptTs. For example, for \(N=10\) services the exact algorithm produces exactly 282.475.249 combinations, while, with \(numPart=2\), this number is reduced to 33,614 combinations (\(G_{T}\leavevmode\
obreak\ \simeq\leavevmode\
obreak\ 8403\)), and with \(numPart\leavevmode\
obreak\ =\leavevmode\
obreak\ 3\) the number decreases down to 3087 combinations (\(G_{T}\leavevmode\
obreak\ \simeq\leavevmode\
obreak\ 91,504\)). Assuming a running time of 100 [hours] for OptTs, FastTs is able to solve the problem (suboptimal solution) in approximately 43 [seconds] if the \(numPart=2\), and in roughly 4 [seconds] if the \(numPart=3\). The reduction in complexity would not only be reflected in a shorter execution time, but also in a reduction of the computational requirements (processing and memory). Thus, FastTs could also be deployed at the edge of the network or near to the end user (i.e., at fog computing level).