esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimentation
### Implementation Details  
As mentioned in Section 3.2, all videos from the training and testing datasets and their associated motion flow maps were divided into a specific number (\(N\)) of non-overlapping temporal segments, though the AUC of the proposed detector, DMRMs, is unaffected by utilizing multi-scaled overlapped temporal segments. In our experiments, we chose to divide them into 16 segments (\(N\) = 16). However, the ablation study (Section 4.4) demonstrated the effect of choosing a different number of segments \(N\). The input size for the 3D ResNet-34 was 112 \(\times\) 112 pixels; the results were similar for size 160 \(\times\) 160 (AUC = 68.54 on the ShanghaiTech Testing dataset). For training on the UCF-Crime dataset, 30 frames per second (fps) was chosen. Then, 60 randomly chosen videos (30 normal and 30 anomalous) were passed through the proposed framework to train it for the anomalous event detection task. However, for training on the ShanghaiTech dataset, 24 fps was chosen, and only 8 randomly chosen videos (four normal and 4 anomalous) were passed through the framework, DMRMs.  
Afterward, a 3D ResNet-34 [21] extracted 512 features from every 16 frames of the video segments/samples as well as 512 features from their respective motion flow maps, as shown in Figure 1. Here, 3\(-\)channels of motion flow maps are constructed by repeating 1\(-\)channel information to all 3\(-\)channels. These extracted features were fused in the data fusion block. Next, these integrated features were passed through a three-layer fully connected (FC) deep neural network, as shown in Figure 1 for the joint learning of these features. We have also used a 60% dropout among all 3 FC layers, similar to previous work [14].  
The proposed objective function described in Equation (17) using MRMs was used to train DMIL along with an Adagrad optimizer with an initial learning rate of 0.001 and 0.0001 for UCF-Crime and ShanghaiTech datasets, respectively. In the case of the ShanghaiTech dataset-all of the hyper-parameters \(\gamma_{1},\gamma_{2},\gamma_{3}\), and \(\gamma_{4}\) described in the proposed objective function in Equation (17) have values of \(8\times 10^{-5}\), \(8\times 10^{-5}\), \(8\times 10^{-5}\), and 0.01, respectively. Similarly in the case of UCF-Crime dataset, all of the hyper-parameters, \(\gamma_{1},\gamma_{2},\gamma_{3}\), and \(\gamma_{4}\) have values of \(8\times 10^{-5}\), \(8\times 10^{-4}\), \(8\times 10^{-4}\), and 0.01, respectively. These values achieved the best performance from DMRMs, as shown in Table 1. The proposed network was trained for \(15,000\) epochs for the UCF-Crime dataset and 5000 epochs for the ShanghaiTech dataset.  
For the anomalous event recognition task, we trained a 3D ResNet-34 on the UCF-Crime dataset using a 4-fold cross-validation strategy and using 50 videos from each category (normal and anomalous), dividing them into the ratio of \(75:25\) (see results in Section 4.4.4).
## 3 Experiments
### Comparative Experiments
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Row** & **Method** & **T(s)** & \
\begin{tabular}{c} **True Positive** \\ **(TP)** \\ \end{tabular} &
\begin{tabular}{c} **False** \\ **Negative (FP)** \\ \end{tabular} & **P(\%)** & **R(\%)** \\ \hline
1 & [PERSON] & 0.277 & 95 & 36 & 72.52 & 67.86 \\
2 & [PERSON] & 0.545 & 105 & 34 & 75.54 & 75.00 \\
3 & [PERSON] & 0.794 & 119 & 20 & 85.61 & 85.00 \\
4 & Histogram of Oriented Gradient (HOG) & 0.337 & 126 & 33 & 79.25 & 90.00 \\
5 & Fused Histogram of Oriented Gradient (FHOG) & 0.316 & 130 & 23 & 84.97 & 92.86 \\
6 & Gray Level Co-occurrence Matrix (GLCM) & 0.295 & 129 & 22 & 85.43 & 92.14 \\
7 & Local Binary Pattern (LBP) & 0.321 & 128 & 38 & 77.11 & 91.43 \\
8 & **Ours-S** & 0.333 & 135 & 18 & **88.24** & **96.43** \\
9 & Single Shot Multi-Box Detector (SSD300) & 0.712 & 133 & 8 & 94.33 & 95.00 \\
10 & You Only Look Once (YOLOv3) & 0.697 & 134 & 11 & 92.41 & 95.71 \\
11 & Region-based Fully Convolutional Network (R-FCN) & 1.351 & 132 & 7 & 94.96 & 94.29 \\
12 & Single Shot Refinement Detector (RefineDet321) & 0.987 & 135 & 5 & 96.43 & 96.43 \\
13 & Global Contrast-based Saliency Detection (RC) & 0.694 & 125 & 40 & 75.76 & 98.29 \\
14 & Contrast-based Filtering for Saliency Detection (SF) & 0.612 & 117 & 48 & 70.91 & 83.57 \\
15 & Boolean Map for Saliency Detection (BMS) & 0.797 & 131 & 20 & 86.75 & 93.57 \\
16 & Spectral Residual (SR) & 0.215 & 125 & 36 & 77.64 & 89.29 \\
17 & Frequency-tuned method (FT) & 0.253 & 126 & 37 & 77.30 & 90.00 \\
18 & Maximum Symmetric Surround (MSS) & 0.390 & 131 & 24 & 84.52 & 93.57 \\
19 & **Ours-F** & 0.415 & 132 & 8 & **94.29** & **94.29** \\
20 & **Ours-Final** & 0.504 & 135 & 4 & **97.12** & **96.43** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the results of existing methods. Figure 9: F-curve and time consumption curve of all methods. around 0.5 seconds) is much smaller than those of CNN-based methods. All the experimental results indicate that the overall performance of this method is the best one.
## 5 Discussion
In particular, we discussed how confidence intervals can be exploited to obtain an informative overview on relations existing within models of a single configuration, providing the base for further exploration. **Assumptions and limitations. ** The proposed approach can only detect the statistical evidence of bias in cases where the data generation process is sufficiently biased to generate that evidence. This is unavoidable in any empirical approach. In cases where little data exist for the investigation, the statistical detection of small bias effects may fail, as evidence may be attributed to chance alone. In those cases, other approaches such as qualitative methods may be more appropriate. Similarly, while our approach proved to be less sensible to parameter tuning than rule mining, the impact of tuning can still be relevant when addressing datasets with severely underrepresented groups. In the absence of a ground-truth on discrimination levels and groups, our approach to generate a synthetic dataset aims to striking a balance between extreme cases, and considering general guidelines for discriminative actions (e.g., the four-fifths rule [20]) as guiding principles. Whereas a more thorough sensitive analysis for tuning parameters may provide additional estimates of method performance in more extreme cases, those estimates are likely to be misleading because of unknown confounding factors affecting the data. To showcase the applicability of our method, we therefore decided to not make any strong assumptions about the nature of our synthetic dataset. Another important observation concerns the presence of so-called \"proxy\" rules and \"redundant\" rules. The first ones are rules which, while not directly classifiable as biased decisions, actually lead to biased and unfair decisions [4, 21]. Redundant rules, instead, are rules that cover the same (or a similar) set of samples of one or more other rules in the dataset. The presence of these rules is typically due to the presence of some correlations within the features and can lead to misleading and/or unreliable outcomes. In our experiments, we assume that all the features relevant for the decisions are present in the dataset, and there is no strong spurious correlation among the features set. We plan to investigate these aspects in future work. Nevertheless, we would like to point out that some mitigation strategies can be applied. For example, proxy rules could be detected by interviewing, when possible, domain experts to understand the relevance and possible hidden use of the features of the dataset. On the other hand, to prevent the generation of redundant rules, one can apply, for example, feature selection approaches, or pruning the redundant rules using approaches such as [22].
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 3 Related Work
### Sunspot Identification and Tracking with OpenCV  
[PERSON] et al. [29] reported the development of a neural network for the identification and tracking of sunspots. It is split into two phases that consist of each of the goals of the neural network mentioned.  
The dataset consists of ten continuum images taken in February 2014. Due to the fact that the solar cycle was at its height for sunspot generation, this was determined to be the ideal moment. The images went through a pre-processing phase that consisted of the following:  
1. Filtering phase: This process consists of removing the noise from the image and detecting the solar disk, as well as its radius and center. It includes the creation of the mask that is used in the filtering process. With the use of some equations, it is possible to calculate the position angle, latitude and heliographic longitude.
2. Correlation phase: in this phase, magnetogram images are used to correlate both magnetogram and Michelson Doppler images (MDI) from the first stage. The method reached an accuracy of 88.4% in sunspot detection.
# An Overview on the Applications of Matrix Theory in Wireless Communications and Signal Processing
## 4 Matrices in Image Processing  
In digital image processing, images are usually represented using matrices. For example, the gray-scale image \"Lena.tiff\" is illustrated in Figure 6(a), while its matrix representation is shown in Figure 6(b). A matrix element is called \"pixel\". Most digital images use integer numbers between 0 (to indicate black) and 255 (to indicate white) to represent the values of pixels, giving a total of \(2^{8}=256\) gray levels. Color images, in turn, can be represented by three matrices. These matrices specify the amount of red (R), green (G) and blue (B) present in the image. This representation system is known as the RGB system.  
In the following sections, two image compression methods are discussed, namely the block transform coding and wavelet representation, both of which decompose the image matrix into several submatrices and use the sparsity of these submatrices to compress the image.
# Numerical Study of a Polarization Selective Visual Optical Switch
## 4 Conclusions  
In brief, based on a kind of composite structure with different number of metal strips in the hollow circular hole, we can adjust the position of the transmission peak within the visible range and display the corresponding color by rotating the polarization direction of the incident light. At the same time, an optical switch characterized by color, making use of the shift of transmission peak position, is constructed, and the contrast ratio of the switch is more than 20 dB. In addition, the geometric parameters (including length and width) and material strip are studied. The results show that the contrast is higher when the material strip is Al, and the color brightness is stronger when the material is Si.  
Plasmonic nanostructures with resonant excitations allow the extreme confinement of incident light within nanoscale space to form an enhanced electromagnetic (EM) field. At the same time, the working  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline  & & **Al** & & & **Ni** & & & **Si** & \\ \hline Number of metal strips & 1 & 2 & 3 & 1 & 2 & 3 & 1 & 2 & 3 \\ \hline The working wavelength (nm) & 411 & 442 & 450 & 413 & 420 & 438 & 404 & 416 & 424 \\ \hline The contrast ratio (dB) & 8 & 13.8 & 20.3 & 2.6 & 4.3 & 6.5 & 1.5 & 2.3 & 2.6 \\ \hline \end{tabular}
\end{table}
Table 1: The contrast ratio and the working wavelength of different material strips.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & & **L** & & & & & **W** & \\ \hline Size (nm) & 60 & 70 & 80 & 90 & 100 & 10 & 15 & 20 & 25 & 30 \\ \hline The working wavelength (nm) & 426 & 428 & 437 & 446 & 453 & 453 & 479 & 502 & 577 & 698 \\ \hline The contrast ratio (dB) & 9 & 11.6 & 15.5 & 18.3 & 21.2 & 21.2 & 14.3 & 12.8 & 8.2 & 2.7 \\ \hline \end{tabular}
\end{table}
Table 2: The contrast ratio and the working wavelength of different geometric parameter when the metal strip number is 3.  
Figure 7: **(a–c)** The colors of the material in different cases; **(d)** CIE1931 color space inversion map distribution under different conditions.  
state of the device can be more intuitively described by the color of the material. The combination of structure color and device provides a new method for the design of visual plasmonic device, which enriches the application of metal structure in the field of optical imaging and micro-nano devices.  
Conceptualization, K.W. and J.Y.; data curation, K.W.; methodology, K.W.; software, K.W., Z.Z., X.J. and J.H.; project Administration, J.Y.; supervision, J.Y.; writing--original, [PERSON]; writing--review, [PERSON]; data analysis, [PERSON] All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (60907003, 61805278), the China Postdoctoral Science Foundation (2018M633704), the Foundation of NUDT (JC13-02-13, ZK17-03-01), the Hunan Provincial Natural Science Foundation of China (13J)[3001], and the Program for New Century Excellent Talents in University (NCET-12-0142).  
The authors declare no conflict of interest.
# An Environmental Pattern Recognition Method for Traditional Chinese Settlements Using Deep Learning
## 5 Experiments and Results
### Results of the Baselines  
To test the effect of training deep learning models on the TCS environmental pattern dataset, three CNNs are used as benchmarks, including AlexNet [26], ResNet50 [27], and DenseNet121 [28]. All models are trained on the training set with 200 epochs and a batch size of 32, using the Adam optimizer with an initial learning rate of 0.001 and a decay factor of 0.1, decaying at 50 and 100 epochs. The input images are remote sensing images of TCSs, and each image is resized to 256 \(\times\) 256 pixels. Standard data augmentation techniques are applied, including random resizing, cropping, and rotation. The accuracy rate is the average of the ratio of correctly recognized samples in each category to all samples in that category. The results are shown in Table 4, and the learning curves are shown in Figure 7.  
As can be seen from the learning curve in Figure 7, all three CNNs struggle with severe overfitting. They achieved almost perfect performance on the training set, while performing considerably worse on the validation set. For example, the ResNet50 model achieved 100% accuracy on the training set, but only 63.75% accuracy on the validation set and even lower accuracy on the two test sets, with an average accuracy of only 48.46%. In the loss curve of the ResNet50 model, the loss on the training set keeps decreasing, though it continues increasing on the validation set after the oscillation. A similar overfitting phenomenon is observed in the AlexNet and DenseNet121 models. This indicates that overfitting is a significant problem when training neural networks with small-sized unbalanced datasets, particularly when dealing with complex input data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Acc1 (\%)** & **Acc2 (\%)** & **mAcc (\%)** \\ \hline AlexNet & 42.52 & 47.26 & 44.89 \\ ResNet50 & 46.24 & 50.68 & 48.46 \\ DenseNet121 & **53.76** & **57.53** & **55.65** \\ \hline \hline \end{tabular} Note: Acc1 refers to the recognition accuracy of the test set constructed from TCS data in the Shaanxi Province, while the data in Acc2 are from the Anhui Province; mAcc is the average of the accuracies of the two areas.  
\end{table}
Table 4: Performance of three CNNs on the test set. The best metrics are highlighted in bold font.
## 2 Background and Theory
### Classification Algorithms
#### 2.3.2 GSVM  
A variant of the standard support vector machine, termed the GSVM, is a support vector machine using the Gaussian radial basis function (RBF) as a kernel [54]. This model serves as the second supervised learning model in this research. By integrating the Gaussian kernel--also referred to as the radial basis function--into the SVM framework, GSVM efficiently handles data that cannot be separated linearly (a frequent scenario in EEG signal analysis). The mathematical definition of the RBF Gaussian kernel is shown in Equation (25):  
\[\mathrm{K}\left(\mathrm{x},\mathrm{x}^{\prime}\right)=\exp\left(-\gamma\left| \mathrm{x}-\mathrm{x}^{\prime}\,\right|^{2}\right) \tag{25}\]  
In this context, K represents the radial basis function (RBF) kernel, which is used to map data into a higher-dimensional space. The variables \(\mathrm{x}\) and \(\mathrm{x}^{\prime}\) belong to the input space, and \(\gamma\) serves as an adjustable parameter that influences the impact of individual training points on the decision boundary.  
In the kernel-induced feature space, the SVM decision function can be written as in Equation (26):  
\[f(x)=\sum_{i=1}^{N}\alpha_{i}y_{i}K(x,x_{i})+b \tag{26}\]  
In this context, \(N\) signifies the total count of support vectors, \(\alpha_{i}\) represents the Lagrange multipliers obtained from the SVM optimization process, \(y_{i}\) refers to the training data class labels, and \(b\) is the bias term adjusting the decision boundary's position.
## 3 Proposed LI2A Scheme
### Key Agreement  
The key agreement phase is carried out between \(C_{i}\) and \(IIoT_{j}\), and after the two authenticated entities receive the initialized online information from \(TA\), the registration stages are explained as follows:  
1. Both \(C_{i}\) and \(IIoT_{j}\) compute \(K_{s}=h(\tau\|ID_{i}\|ID_{j}\|K_{m}\|r_{0})\) and store it in the temper proof memory.
2. \(C_{i}\) computes \(m_{1}=K_{s}\oplus ID_{i}\) and \(m_{2}=K_{s}\oplus ID_{j}\).
3. \(C_{i}\) generates \(m_{3}=(m_{1},m_{2},T_{m_{3}},h(T_{m_{3}}\|K_{s}))\) and sends it to \(IIoT_{j}\). Hashing \(T_{m_{3}}\) with \(K_{s}\) ensures its integrity.
4. Once \(IIoT_{j}\) receives \(m_{3}\), it computes the following: 1. \(ID_{i}=(m_{1}\oplus K_{s})\); 2. \(ID_{j}=(m_{2}\oplus K_{s})\); 3. \(T_{i}=h(T_{m_{3}}\|K_{s})\).
5. \(IIoT_{j}\) compares the calculated \(ID_{i}\) and \(ID_{j}\) to its stored communication pairs and compares the computed \(T_{i}\) to the received \(T_{m_{3}}\). If the results match, it confirms the calculated \(K_{s}\) and sends a key acknowledgment message \(m_{4}=(T_{ACK},h(T_{ACK}\|K_{s}))\) to \(C_{i}\).
6. Once \(C_{i}\) receives \(m_{4}\), it computes \(T_{i}=h(T_{ACK}\|K_{s})\) and compares the result to \(T_{ACK}\). If the result matches, this confirms that \(K_{s}\) is correct and synced.
7. \(C_{i}\) will then choose a random time period for the timer \(\Delta T\), which represents the expiration time for \(K_{s}\). Once \(\Delta T\) expires, the forward secrecy stage begins.  
Figure 2: Online registration phase.  
Figure 3 demonstrates the key agreement phase.
# Sorption of Eu (III) onto Nano-Sized H-Titanates of Different Structures
###### Abstract  
Hydrogen titanates (H-titanates) of different nanostructures (nanotubes, nanowires, nanosheets) have been synthesized by hydrothermal methods. The europium (III) sorption from aqueous solutions onto nano-sized H-titanates was studied as a function of contact time, pH values, and initial Eu (III) concentration in batch experiments. Reversibility of adsorption of europium has been investigated as well. Nano-sized H-titanates can be used for tri-valent f-elements removal in polluted water treatment due to fast and efficient sorption of Eu (III).  
H-titanates; europium; radionuclides removal; sorption; water treatment Article
## 3 Proposed Hybrid JADE-DO Algorithm
### Mathematical Formulation of the Hybrid JADE-DO Method
#### 3.2.3 Dandelion Optimizer Integration  
After the JADE procedure, the dandelion optimizer (DO) is applied to the population via three stages: rising, decline, and landing. The DO stages promote enhanced exploration and controlled convergence.  
Rising stage  
The population is updated using a mechanism inspired by dandelion seed dispersal:  
\[P(i,:)=P(i,:)+\alpha\,v_{x}\,v_{y}\,(\lambda,0,1)\,\big{(}\text{Best}-P(i,:)\big{)}, \tag{21}\]  
where \(\alpha\) is a scaling factor, \(v_{x}\) and \(v_{y}\) are velocity components, and \((\lambda,0,1)\) is the value of a log-normal PDF at \(\lambda\). The vector Best is the best individual found so far.  
Decline stage  
Each individual converges toward the population mean according to the following:  
\[P(i,j)=P(i,j)-\beta(i,j)\,\alpha\,\Big{(}P_{\text{mean}}(j)-\beta(i,j)\, \alpha\,P(i,j)\Big{)}, \tag{22}\]  
where \(\beta(i,j)\) is a uniformly distributed random factor within \([0,1]\), and \(P_{\text{mean}}(j)\) is the \(j\)-th component of the population mean.  
Landing stage  
A final update step based on a Levy flight refines the population:  
\[P(i,j)=\text{Elite}(j)+\text{Step\_length}(i,j)\,\alpha\,\Big{(}\text{Elite}( j)-P(i,j)\,\frac{2l}{\text{Max\_iter}}\Big{)}, \tag{23}\]  
where Elite(\(j\)) is the \(j\)-th component of the best solution found so far. The term Step_length(\(i,j\)) is a Levy-distributed step size, and \(l\) denotes the current iteration number.
## 6 Results of the Calibration Process
### Results of Calibration for the SAC305 Material  
The material parameters for the SAC305 material are shown in Table 6. The table shows the initial and final values of the parameters. The procedure to determine the initial parameter values was described in Section 4.3, and the procedure to identify the final parameter values was described in Section 5.3. At the end of the table, the values of the total objective function for the initial and final values of the parameters are given.  
It can be seen from Table 6 that during the solution there is a significant change in the parameter values. When comparing, for example, the value of \(\sigma_{y}\) with the value in Table 3 or the value of \(E\) published in [22], the initial value matches more than the final value. The smallest changes occurred in the parameters of the [PERSON] model. The total objective function value also shows a significant improvement in the final parameters compared to the initial ones.  
The prediction of tensile tests of the SAC305 alloy using FEA can be seen in Figure 16a-c. The prediction of SAC305 creep tests is shown in Figure 16d.  
Figure 15: Typical convergence for objective function.  
The largest differences in the tensile test predictions are in the first part of the curve, around 20-25 MPa, where the simulation shows a much sharper transition than the experiment. The best agreement was achieved between the FEM simulation and the experiment data for the tensile test of 5 mm/min and for the creep at the lowest stress of 25.05 MPa.