esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of Diagnosis
### Dimensionality Reduction and Classification  
When applying the history of extracted features to data analysis, as shown in Figure 8b, the data history is represented in a 138-dimensional high-dimensional vector form.  
Therefore, the dimension reduction method by PCA was applied to the data of 138 extracted features to analyze the historical information of principle component 1 (PC1) and principal component 2 (PC2) that can represent high-dimensional data. Figure 9 shows the history of principle components reduced in dimensions by PCA for normal operation sensor data among the analyzed data. In the same process, data for each operating state were analyzed, and the time series information of the analyzed principal component is visualized in a two-dimensional plane based on principal component 1 and principal component 2. Figure 10 shows the status information of the visualized MAB equipment classified for each operation status. From this figure, it is possible to clearly distinguish the areas of the two-dimensional principal component values between each fault mode and the normal modes. This map is used as the basis for determining the failure of MAB equipment.  
Figure 8: Feature extraction for time interval and extracted features: (**a**) feature extraction; (**b**) part of the extracted features (136 EA).  
Figure 10: Operation status map of MAB by the PCA.  
Figure 9: Time series of principal components on the normal state: (**a**) history of PC1; (**b**) history of PC2.  
The analysis results in Figure 10 show that the normal state and filter plugging failure states have very similar patterns in the type of data. In addition, in case of turbine overheating, it is possible to check that the normal state is similar only to PC1. Other failures, such as oil level abnormal, fuel gas overheating, and steam turbine overheating can confirm that PC1 and PC2 are relatively low similarities.  
This result explains that, in the case of filter plugging failure, even in actual operation, it is difficult to determine the steady state and the fault condition in the sensor data. Therefore, to divide the boundaries of each operating state, especially to divide between normal state and filter plugging, the Naive Bayesian classifiers model was applied to establish the decision boundary for each operation state. The criteria for each established decision boundary allowed a probability model to be applied for the distribution of each cluster of data to represent each state as a probability distribution.
###### Abstract  
In this study, an ultra-broadband dielectric-resonator-based absorber for microwave absorption is numerically and experimentally investigated. The designed absorber is made of the carbon-loaded Acrylonitrile Butadiene Styrene (ABS) polymer and fabricated using the 3D printing technology based on fused deposition modeling with a quite low cost. Profiting from the fundamental dielectric resonator (DR) mode, the higher order DR mode and the grating mode of the dielectric resonator, the absorber shows an absorptivity higher than 90% over the whole ultra-broad operating band from 3.9 to 12 GHz. The relative bandwidth can reach over 100% and cover the whole C-band (4-8 GHz) and X-band (8-12 GHz). Utilizing the numerical simulation, we have discussed the working principle of the absorber in detail. What is more, the absorption performance under different incident angles is also simulated, and the results indicate that the absorber exhibits a high absorptivity at a wide angle of incidence. The advantages of low cost, ultra-broad operating band and a wide-angle feature make the absorber promising in the areas of microwave measurement, stealth technology and energy harvesting.  
Microwave absorption; dielectric resonator; 3D printing; Acrylonitrile Butadiene Styrene (ABS), ultra-broadband; periodical structure +
Footnote †: journal: materials
# Research on the Application Status of Machine Vision Technology in Furniture Manufacturing Process
## 2 Methods
### Quality Detecting
#### 3.2.4 Color Difference Detection  
Based on MV, fast, efficient, and low-error color difference detection can be realized [50; 51; 52]. The color difference detection based on MV is different from the above three aspects. It needs to use a color camera to collect color images. Commonly used detection methods need to pre-select multiple groups of boards with different color grades, extract the values of the color space (The color space models mainly include HSV, RGB, HSI, CHL, LAB, CMY, etc.), and perform training, testing, and verification. At the beginning of the application, image acquisition and pre-processing are executed. Then, locate the workpiece area, remove the interference of the background color to the detection, and extract the color features. At last, the classifier algorithm will be applied for color classification. Color difference detection involves deep learning algorithms and requires many data sets for training, testing, and verification. The more sufficient the data set, the more significant the grading effect will be. The color information the wood board, which captured by color camera, will be affected by the ambient light condition. In the factory actual application, the change of ambient light greatly interferes with the results of the color difference inspection for furniture. Placing a hood around the camera and lights can effectively reduce the impact of ambient light condition on the color information collection. In the actual application process, the combination of algorithms such as extreme learning machine (ELM), multi-layer perceptron (MLP), and gray wolf algorithm is more competent to achieve the optimal recognition effect [53; 54; 55].
## 5 Applications in Control Theory
### Control of Chaos
#### 5.2.3 Identification of Chaotic Systems  
In [118; 119], Chen et al. studied a feed-forward artificial neural network in a Wiener-type cascade dynamic model in which a three-layer feed-forward artificial neural network is employed as the nonlinear static subsystem and a simple linear plant is used as the dynamic subsystem. They presented theoretical results showing that various chaotic systems can be well identified by these two basic structures by training the neural network and choosing an appropriate linear subsystem. They demonstrated the approximation capability of the proposed system through simulations, where it was used to identify (synchronize) the chaotic continuous-time Duffing oscillator and the discrete-time Henon and Lozi systems.
## 3 Results
### Accuracy Assessment for Calculated DSM of Aquatic Reed Beds  
The moving tilted plane interpolator (movingPlanes) in combination with a defined number of neighbours (16) allowed for the creation of raster files with a minimum grid size of 10 centimetres. After evaluating observations against modelled height, an accuracy assessment for flat structures showed a total RMSE of 0.1 m, while for vegetation height, it showed a RMSE of 0.8 m. The distribution of the residuals in both assessments was not significantly skewed, since the skewness factors for all 14 AOIs were lower than two times the standard error of the skewness (\(\text{SES}=\sqrt{6/\text{n}}\)). In relation to the vegetation height assessment, a perfect normal distribution resulted in residuals from AOI-7 and -12. Meanwhile, the lowest and greatest asymmetries found were in AOI-5 (skewness = 0.52) and AOI-11 (skewness = \(-\)0.47). Overall, 1279 independent observations were used to assess the vertical accuracy of vegetation heights in DSM. The correlation analysis between the observed and predicted relative stem height (RSH) revealed a low positive correlation (R\({}^{2}\) = 0.42).  
Figure 8: Classification of dense and sparse aquatic reed (AOI-6).
## 2 Basic Principles  
### The Empirical Optimal Envelope Method  
In the LMD method, the commonly used sliding average method can lead to distortion in constructing envelope lines, prompting researchers to delve into the issue and make some progress. For example, it employs cubic spline interpolation instead of sliding averages and combines extreme point fitting techniques from the EMD method to compute envelope lines, thereby obtaining local mean curves and envelope estimation curves [31].  
This method requires only one interpolation calculation to obtain the two required curve functions, avoiding the cumbersome process of sliding averaging and thus improving the computational efficiency of local mean decomposition in signal analysis. However, when applying cubic spline interpolation to analyze strongly non-stationary signals, issues such as \"over-enveloping\" and \"under-enveloping\" may arise, affecting the accuracy of signal analysis. To address this problem, scholars have proposed using monotone cubic Hermite interpolation as a replacement for cubic spline interpolation and have achieved some success. However, in practical applications, it has been found that for some vibration signals with strong non-stationary characteristics, using cubic spline interpolation or cubic Hermite interpolation to construct envelope lines may lead to distorted decomposition results [32].  
Therefore, this paper adopts an empirical optimal envelope method based on Hermite interpolation, which utilizes Hermite interpolation to obtain the optimal envelope curve of the signal, and subsequently obtains local mean functions and envelope estimation functions. The main steps include:  
1. First item; given a signal \(x(t)\), identify all of its local maxima and minima points. Use Hermite interpolation to interpolate the local maxima points of the signal to obtain the initial upper envelope curve \(c_{+0}(t)\). Similarly, obtain the initial lower envelope curve \(c_{-0}(t)\).
2. Second item; utilize the upper envelope curves \(c_{+0}(t)\) and lower envelope curves \(c_{-0}(t)\) to calculate the upper distance function \(e_{+0}(t)\) and lower distance function \(e_{-0}(t)\) of the signal. \[\begin{cases}e_{+0}(t)=x(t)-c_{+0}(t)\\ e_{-0}(t)=c_{-0}(t)-x(t)\end{cases}\] (1)
3. Third item; employ Hermite interpolation to interpolate the sequences of local maxima points of the upper envelope curves \(e_{+0}(t)\) and lower envelope curves \(e_{-0}(t)\) separately, obtaining the first iteration upper envelope curves \(c_{+1}(t)\) and lower envelope curves \(c_{-1}(t)\).
4. Fourth item; repeat the second and third items until all of the local maxima points of the upper distance function \(e_{+n}(t)\) of the \(n\)th iteration upper envelope curve \(c_{+n}(t)\) equal zero, resulting in the empirical optimal upper envelope curve. Similarly, obtain the empirical optimal lower envelope curve \(c_{-n}(t)\).
5. Fifth item; compute the local mean function \(f_{11}(t)\) and the optimal envelope estimation curve \(g_{11}(t)\). \[\begin{cases}f_{11}(t)=\frac{c_{+n}(t)+c_{-n}(t)}{2}\\ g_{11}(t)=\frac{c_{+n}(t)-c_{-n}(t)}{2}\end{cases}\] (2)
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 3 Methodology
### Deep Learning Models
#### 3.5.1 Convolutional Neural Networks (CNNs)  
While CNNs are primarily known for their image-processing capabilities, they have also shown good performance on text classification tasks by capturing local n-grams and word patterns. CNNs use convolutional filters atop word embeddings to find important attributes essential to hate speech classification [43].
## 4 The Compressive Strength and Resistivity Prediction Model of the CLSM
### Relationship Between Compressive Strength and Electrical Resistivity  
Previous researchers have applied the resistivity of concrete to predict the compressive strength, and such studies often involve the development of a relationship between resistivity and compressive strength. Previous studies have concluded that there is a good correlation between resistivity and compressive strength for concrete with a specific water-binder ratio. However, the compressive strength of concrete increases linearly or nonlinearly with resistivity ([PERSON] et al., 2012 [19], [PERSON] and [PERSON], 2014 [20], [PERSON] et al., 2012 [21], [PERSON] and [PERSON], 2014 [26], and [PERSON] et al., 2014 [27]). Thus, the relationship between the surface resistivity and compressive strength may be related to the water-binder ratio of the concrete. Figure 14 shows the test results between the surface resistivity and the compressive strength of the CLSM when the water-binder ratio is 1.5 and at different DS contents. For the same DS content, the compressive strength \(f_{c}^{\prime}\), increases with the resistivity \(E_{r}\), and the increasing trend is a smoothed nonlinear function. Thus, in the process of establishing the predictive model, the study will ignore the influence of the water-binder ratio's change and use the hyperbolic function to simulate the relationship between the compressive strength and resistivity of the CLSM, as shown in the Equation (9), Where \(a_{er}\) and \(b_{er}\) are hyperbolic function coefficients of the relationship prediction model between the compressive strength and electrical resistivity versus the DS contents and shown in Table 6. \(E_{r}\) is the surface resistivity of the CLSM, \(f_{c0}^{\prime}\) is the initial compressive strength, and \(E_{r0}\) is the initial surface resistivity; \(f_{c0}^{\prime}\) and \(E_{r0}\) will be directly obtained from the test results. Under the same conditions, based on the assumption of a fixed water-binder ratio, the coefficient \(a_{er}\) shows a tendency of the secondary parabola to rise first and then decrease as the content of the DS increases. The coefficient \(b_{er}\) decreases linearly with the increment of the replacement content of the DS, as shown in Table 6.  
Figure 13: Characteristics of the coefficient \(b_{e}\) of the surface resistivity prediction model versus the DS content.
## 3 Materials and Methods
### Optimized Network Input Size  
Most object detection methods are first pre-trained on ImageNet [33], after which the weight parameters obtained from the pre-training are used in formal experiments. Starting from AlexNet [34], most classifiers operate on input images smaller than \(256\times 256\). YOLOv1 [35] pre-trained the main part of the model on the ImageNet classification dataset using a network input size of 224 \(\times\) 224, and later increased the network input size to \(448\times 448\) for detecting objects. However, this approach may make it difficult for the network model to quickly adapt to the high size network input. In contrast, YOLOv2 [36] used an input network size of 416 \(\times\) 416. The network input size of 224 \(\times\) 224 is used in pre-training, and then the model is fine-tuned with 10 epochs using an input size of 448 \(\times\) 448 in order for the network model to work better on the higher network input size. In YOLOv3 [37] and YOLOv4 [38], network input sizes of 416 or 608 are mostly used for better detection of multiple small objects. YOLOv5 most often uses a network input size of 640, using three different scales of feature maps, namely 20, 40, and 80, to detect more fine-grained features of the object.  
Figure 3: Effective channel attention net.  
Due to the visual details of high-resolution images, the high image input size model performs better for object detection with low resolution. However, the low image input size model has better results for large object detection than the high-resolution model, because the backbone network captures more information in the whole image. Due to the design of the currently used FPN structure, the feature map will handle objects of corresponding sizes. Experimental results by [PERSON] et al. [39] show that small objects perform better with large input size, while large objects perform better with small input size. This phenomenon also shows, to some extent, that the divide-and-conquer strategy of the FPN structure used to assign superparameters is not an optimal solution.  
In the YOLO network, the process of downsampling goes through five iterations, so the network input size is generally set to the 5 th power of 2, which is a multiple of 32. Figure 4 shows a DCE_YOLOX network model with an input size of 1024 \(\times\) 1024.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 1 Introduction
### Contributions  
This paper proposes a novel security scheme for SCADA systems with the following contributions:  
* We propose an intrusion immune SCADA framework by incorporating quantum and post-quantum security scheme. We use the B92 protocol to generate a secret quantum key for encryption and SPHINCS-256, a preimage and collision-resistant algorithm to obtain a digital signature with a true random number generator.
* In general, SPHINCS-256 uses a Pseudo-Random Number Generator (PRNG) based on Chacha-12 to generate a secret key of the HORST tree. However, to increase the security of SPHINCS-256, we introduce QRNG to obtain a non-deterministic and truly random HORST secret key.
* In general, SPHINCS-256 sends a message, public key and digital signature over the public channel. In our proposed scheme, we replace the message with the cipher obtained in the encryption phase.
## Materials and Methods  
### The Contaminated Soil Used for the Experiment  
The selected soil samples were collected from the Moston Brook closed landfill site in the Greater Manchester region, northwestern England. Information about the sampling site was documented in Mukwaturi and Lin [24]. After collection, the soil samples were oven-dried at 40 \({}^{\circ}\)C for two days in the laboratory and then ground with a mortar and a pestle to pass through a 2 mm stainless steel sieve. This is done to achieve a very fine and homogenous sample prior to analysis. Samples were later stored in an air-tight re-sealable laboratory polythene bags for further use.  
Prior to the batch experiment, the soil was characterized and some major chemical characteristics of the soil samples are given in Table 1. The soils had a pH of 7.1 and an electrical conductivity (EC) value of 0.039 dS/m. The concentrations of Cd, Cu, Ni, Pb, and Zn exceeded the guideline values for soils with plant uptake [25; 26].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Experimental Soil** & **Guideline Value for Soil with Plant Uptake** \\ \hline pH & 7.1 & \(-\) \\ EC (dS/m) & 0.039 & \(-\) \\ Total As (mg/kg) & 29.6 & 43 \({}^{*}\) \\ Total Al (mg/kg) & 21,013 & \(-\) \\ Total Ca (mg/kg) & 32,166 & \(-\) \\ Total Cd (mg/kg) & 6.19 & 1.8 \({}^{*}\) \\ Total Cr (mg/kg) & 68.8 & 130 \({}^{*}\) \\ Total Cu (mg/kg) & 2768 & 200 \({}^{**}\) \\ Total Fe (mg/kg) & 28,131 & \(-\) \\ Total K (mg/kg) & 1859 & \(-\) \\ Total Mn (mg/kg) & 3865 & \(-\) \\ Total Na (mg/kg) & 959 & \(-\) \\ Total Ni (mg/kg) & 811 & 230 \({}^{*}\) \\ Total Pb (mg/kg) & 1498 & 450 \({}^{*}\) \\ Total Zn (mg/kg) & 1276 & 450 \({}^{**}\) \\ \hline \hline \end{tabular} \({}^{*}\)CLEA Soil Guideline Value [26], \({}^{**}\)EC Directive 86/278/EEC [25].  
\end{table}
Table 1: Major physical and chemical characteristics of the soil materials used in this study.
###### Abstract  
When machine learning supports decision-making in safety-critical systems, it is important to verify and understand the reasons why a particular output is produced. Although feature importance calculation approaches assist in interpretation, there is a lack of consensus regarding how features' importance is quantified, which makes the explanations offered for the outcomes mostly unreliable. A possible solution to address the lack of agreement is to combine the results from multiple feature importance quantifiers to reduce the variance in estimates and to improve the quality of explanations. Our hypothesis is that this leads to more robust and trustworthy explanations of the contribution of each feature to machine learning predictions. To test this hypothesis, we propose an extensible model-agnostic framework divided in four main parts: (i) traditional data pre-processing and preparation for predictive machine learning models, (ii) predictive machine learning, (iii) feature importance quantification, and (iv) feature importance decision fusion using an ensemble strategy. Our approach is tested on synthetic data, where the ground truth is known. We compare different fusion approaches and their results for both training and test sets. We also investigate how different characteristics within the datasets affect the quality of the feature importance ensembles studied. The results show that, overall, our feature importance ensemble framework produces 15% less feature importance errors compared with existing methods. Additionally, the results reveal that different levels of noise in the datasets do not affect the feature importance ensembles' ability to accurately quantify feature importance, whereas the feature importance quantification error increases with the number of features and number of orthogonal informative features. We also discuss the implications of our findings on the quality of explanations provided to safety-critical systems.  
accontability; data fusion; deep learning; ensemble feature importance; explainable artificial intelligence; interpretability; machine learning; responsible artificial intelligence +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Fatty Acid Composition
In contrary, statistical differences (\(p<0.05\)) are seen in n-6 PUFAs according to one-way ANOVA (all samples) but the two-way ANOVA (only enzymatically extracted samples) does not show significant differences between the enzymes or hydrolysis times. The FAs contributing to the biggest differences between the different enzymatic extractions were myristoleic acid (14:1 (n-5)) and palmitoleic acid (16:1 (n-7)), which were significantly different between the enzymes and hydrolysis times. According to the fatty acid composition, the n-6/n-3 ratio in Baltic herring oil is 3.6/1 for whole fish and 2.9/1 for by-products. It is generally accepted that humans have evolved on a diet with an n-6/n-3 ratio of \(\sim\)1. However, the Western diet has changed drastically over the last 100 years, and nowadays people are getting more n-6 FAs from their diets, typically from vegetable oils. The n-6/n-3 ratio in the Western diet is approx. 15/1-16.7/1. Higher ratios of n-6/n-3 FAs in the diet have been linked to a myriad of health-related issues, such as cardiovascular disease, cancer and inflammatory and autoimmune diseases. Individuals with diets rich in fish or supplementation with DHA and EPA have been associated with reductions in cardiovascular diseases and related mortality compared to those who do not consume fish [32; 33]. Based on the fatty acid composition, oils from Baltic herring fish and by-products can be considered as a good source of n-3 PUFAs for human consumption. It is important to optimize the enzymatic extraction processes to minimize the loss of n-3 PUFAs caused by oxidation. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Fatty Acid** & **Enzyme** & **2-Way ANOVA** & \multicolumn{3}{c}{**Solvent**} & \multicolumn{3}{c}{**Aclasse**} & \multicolumn{3}{c}{**Neutrelase**} & \multicolumn{3}{c}{**Protancy**} \\ \cline{3-11}
**Fatty Acid** & **Enzyme** & **Time** & \(\mathbf{E\times T}\) & **Extraction** &