esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Bayesian Identification  
### Prior Probability Distribution  
Assume that the functions \(\mathbf{g}_{r}\), \(r=1,\,\cdots,\,n_{\phi}\), are statistically independent, and the complex-valued vector \(\mathbf{H}\) follows a multivariate complex Gaussian distribution with zero-mean,  
\[p(\,\mathbf{H}|\,\boldsymbol{\theta})=\frac{1}{\pi^{W/2}\text{det}^{1/2} \Sigma}_{H}\times\exp\left(-\frac{1}{2}\mathbf{H}^{\mathbf{H}}\overset{-1}{ \sum}_{\mathbf{H}}\mathbf{H}\right) \tag{15}\]  
where \(W=2(n_{\phi}+1)F\), and the augmented covariance matrix reads  
\[\overset{\sum}{\sum}_{\mathbf{H}}=\begin{bmatrix}\mathbf{K}(\gamma_{0})& \mathbf{0}&\mathbf{0}&\mathbf{C}(\gamma_{0})&\mathbf{0}&\mathbf{0}\\ &\ddots&\mathbf{0}&\mathbf{0}&\ddots&\mathbf{0}\\ &&\mathbf{K}\Big{(}\gamma_{n_{\phi}}\Big{)}&\mathbf{0}&\mathbf{0}&\mathbf{C} \Big{(}\gamma_{n_{\phi}}\Big{)}\\ &&&\mathbf{K}^{*}(\gamma_{0})&\mathbf{0}&\mathbf{0}\\ \text{Conjugate}&\text{Sym.}&&\ddots&\mathbf{0}\\ &&&\mathbf{K}^{*}\Big{(}\gamma_{n_{\phi}}\Big{)}\end{bmatrix} \tag{16}\]  
In Equation (16), the dependence of \(\mathbf{K}\) and \(\mathbf{C}\) on \(\gamma_{r}\) is highlighted, and \(\gamma_{r}\) is the scaling coefficient of the kernel function which is attached to the function \(\mathbf{g}_{r}\). The kernel hyperparameters that describe \(\mathbf{H}\) are collected in the vector \(\boldsymbol{\theta}=\left[\alpha,\beta,\gamma_{0},\cdots,\gamma_{n_{\phi}}\right]\).
# Multi-Fusion Approach for Wood Microscopic Images Identification Based on Deep Transfer Learning
## 4 Experimental Results and Discussion
### Impact of Dataset Expansion on the Test Accuracy  
The dataset expansion method selected in this paper is the collaborative use of offline traditional image enhancement methods and online Mix-up data augmentation technology, which better enriches the diversity of original data samples and improves the problem of uneven distribution. The baseline method of experimental comparison is the model trained on the 608 samples before the improvement of the standard Faster RCNN detection method. The ablation method is the model of traditional data expansion and Mix-up data expansion, respectively, and the method used in this paper is in conjunction with the two expansion strategies. Table 1. shows the detection accuracy of each category after the standard Faster RCNN algorithm is trained on different wood microscopic image datasets. The last row in the Table 1 represents the average result of the detection accuracy of all categories (Original dataset, traditional data augmentation, Mix-up data augmentation, proposed approach).  
By observing the test results in the first three columns of Table 1, it can be seen that the expansion of the original dataset can bring a certain accuracy improvement. The traditional data expansion operation increased Average accuracy from 73.04% to 75.91%, and the Mix-up data expansion operation increased it to 76.44%, highlighted the importance of enriching the training set sample information. In addition, comparing the two data expansion methods, it can be concluded that the dataset processing method based on Mix-up can achieve better generalization of the test samples than the traditional method, and it obtained an increase of 0.53% on the basis.  
The reason is that the mix-up method refers to different data in a batch of training samples for fusion processing, and the resulting fusion image can retain the characteristics of the corresponding original image according to a certain proportion. Furthermore, such training data are very useful for training the network, so that in the process of optimization and learning, it pays more attention to discriminative features that help distinguish different categories, and better captures the edge information between features. However, traditional data expansion methods that focus on enriching the diversity of the sample itself, such as flipping, panning, zooming, etc., also have the effect of improving the robustness of the network, which can be found from the mAP improvement of 2.9% relative to the original data set. Therefore, this paper combines the two data expansion methods and introduces the mix-up data fusion strategy on the basis of traditional expansion operations to improve the learning efficiency of sample features and make the network more specific for the learning of similarity between classes and diversity within classes.
# Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
## 3 Results  
### Quantum Scheme and Probabilities for States  
Quantum operations, such as rotations, quantum gates, and measurements, are applied to qubits to change and study their states. After applying all the described steps, qubits are obtained in certain states, which are called eigenstates. To define these states, the final quantum circuit looks like this.  
Quantum gates and operations applied to quantum qubits are used to construct all quantum circuits shown in Figures 2 and 5-7, which are written on IBM Quantum Lab. Once these quantum circuits are created, these circuits will run in quantum simulators.  
This quantum circuit performs a sequence of operations on \(N\times N\) qubits and one classical bit. Let us take a look at its overview:  
* A quantum circuit is created with \(N\times N\) qubits and one classical bit.
* The gates associated with matrix \(A\) are applied. Each non-zero element of matrix \(A\) leads to the application of the corresponding gates. If the element is on the main diagonal (\(i=j\)), the rz gate is used. Otherwise, when the element is off the main diagonal (\(i=j\)), the CRZ gate is used.
* The Hadamard h gate is applied to all qubits.
* QFT quantum Fourier transform is applied on all qubits.
* cswap gate is applied between certain qubits.
* gates rz and rx with defined angles are applied on each qubit.
* The last rz valve with an angle of 0 can be omitted because it is equivalent to the identity valve.
* The last qubit is measured, and the result is stored in the classic bit.  
In quantum computing, calculations are probabilistic in nature, and the results of an experiment can change from run to run. To obtain more accurate results, it is necessary to perform several repetitions of the experiment (measurements) and average the results. The \"shots\" parameter specifies the number of iterations to perform this calculation. In this case, the equations are run 8196 times to obtain reasonably accurate results. The more runs, the more accurate the results, but the more time it takes to calculate. For these calculations, a quantum simulator AerSimulator ([[https://qiskit.org/ecosystem/aer/stubs/qiskit_aerSimulator.html](https://qiskit.org/ecosystem/aer/stubs/qiskit_aerSimulator.html)]([https://qiskit.org/ecosystem/aer/stubs/qiskit_aerSimulator.html](https://qiskit.org/ecosystem/aer/stubs/qiskit_aerSimulator.html)), accessed on 12 September 2023) with 16 qubits is used. The results are shown in Figure 8.  
Then, applying the measurement to the last qubit, one obtains the result for the classical bit.
## 3 Results
### CaCO\({}_{3}\) Content and Carbonation Degree
\begin{table}
\begin{tabular}{c c c c} \hline \hline HVFA Mix & **0.04\% CO\({}_{2}\)** & **1\% CO\({}_{2}\)** & **10\% CO\({}_{2}\)** \\ \hline Experimental. A\({}_{\text{nat}}\) or A\({}_{\text{acc}}\) (mm/\(\surd\)weeks) & 2.12 (R\({}^{2}\): 0.92) & 7.54 (R\({}^{2}\): 0.92) & 13.94 (R\({}^{2}\): 0.95) \\ Experimental \({}^{\star}\)/estimated \({}^{\star}\)** A\({}_{\text{nat}}\) (mm/\(\surd\)years) & 15.29 \({}^{\star}\) & 10.89 \({}^{\star}\) & 6.35 \({}^{\star}\) \\ \hline
**CEM II/B-V 42.5 N Mix** & **0.04\% CO\({}_{2}\)** & **1\% CO\({}_{2}\)** & **10\% CO\({}_{2}\)** \\ \hline Experimental. A\({}_{\text{nat}}\) or A\({}_{\text{acc}}\) (mm/\(\surd\)weeks) & 0.43 (R\({}^{2}\): 0.99) & 2.02 (R\({}^{2}\): 1.00) & - \\ Experimental \({}^{\star}\)/estimated \({}^{\star}\)** A\({}_{\text{nat}}\) (mm/\(\surd\)years) & 3.10 \({}^{\star}\) & 2.91 \({}^{\star}\) & - \\ \hline
**CEM I 42.5 N Mix** & **0.04\% CO\({}_{2}\)** & **1\% CO\({}_{2}\)** & **10\% CO\({}_{2}\)** \\ \hline Experimental. A\({}_{\text{nat}}\) or A\({}_{\text{acc}}\) (mm/\(\surd\)weeks) & 0.32 (R\({}^{2}\): 0.95) & 1.48 (R\({}^{2}\): 0.95) & - \\ Experimental \({}^{\star}\)/estimated \({}^{\star}\)** A\({}_{\text{nat}}\) (mm/\(\surd\)years) & 2.31 \({}^{\star}\) & 2.13 \({}^{\star}\) & - \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental natural/accelerated carbonation rates and estimated natural carbonation rates from accelerated tests at 1% (and 10%) CO\({}_{2}\) using Equation (1) for the HVFA mortar mix, the CEM II/B-V 42.5 N mix and the CEM I 42.5 N mix. those of the HVFA mix. One would perhaps also attribute this to the higher carbonatable material content of the former mix. However, when considering these results alongside the carbonation degrees obtained for the reference mix consisting of Portland cement CEM I 42.5 N with a CaO content of no less than 63.12% (the highest value of the three considered binders), this statement does not seem to hold true. With carbonation degrees of 53% and 65% at 0.04% CO\({}_{2}\) and 1% CO\({}_{2}\), respectively (Figure 1f), they are the lowest when compared with the corresponding ones of the CEM II/B-V 42.5 N mix and the HVFA mix. Still, the CEM I 42.5 N reference mix has the highest carbonation induced CaCO\({}_{3}\) content of all three considered mixes after carbonation at 0.04% CO\({}_{2}\) and 1% CO\({}_{2}\) (Figure 1e). As stated earlier, this can most probably be explained by the high CaO content of the cement used (63.12%) and thus an overall high carbonatable material content of this mortar mix. Note that also for this Portland cement reference mix, the CaCO\({}_{3}\) content tends to increase with the applied CO\({}_{2}\) concentration from 59.4% m/m to 73.3% m/m. Figure 1: CaCO\({}_{3}\) content (% m/m relative to the binder) and carbonation degree a\({}_{c}\) (%) for the HVFA mortar (**a,b**), CEM II/B-V 42.5 N mortar (**c,d**), and CEM I 42.5 N mortar (**e,f**). Error bars correspond with the standard deviation on the mean (\(n=3\)).
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation  
At present, the most successful and most applied multi-sensor data fusion method is the federated Kalman filtering method. The federated filter was proposed by [PERSON] [28]. Because of its flexibility, a small amount of calculation, and good fault tolerance, it has been extensively studied. This method is very flexible for multi-source heterogeneous navigation systems and can support the introduction of new sensor types at any time. This article uses GNSS, INS, LEO, and 5G multi-source heterogeneous navigation information to perform integrated navigation and positioning. There are many types of sensors and rich application scenarios. Therefore, the federated Kalman filter meets the requirements of this article for algorithm functions. The basic algorithm framework of source integrated navigation is the result of this article.
# Novel Hand Gesture Alert System
## 2 Related Work
### Hand Gesture Recognition (Detection and Classification)  
Looking at the previous work, we can see that various studies are dealing differently with the classification and location issues related to gestures. Regarding the location of the hand, also known as the hand detection, the authors of [26; 27; 28] extract the hand from the body using depth indices and by setting a threshold, estimated at a specific moment. The authors of [29; 30] used skin color maps, and the authors of [31; 32] achieved better segmentation results using both depth thresholding and skin detection (using color). In terms of classification, several CNN-based approaches relied on hand-crafted features [33; 34], which can capture information about the silhouette, shape, and structure.  
In [35], the authors presented a 3D dynamic system that helps to recognize gestures using hand pose information. More precisely, the authors used the natural structure of the topology of the hand--called the skeletal data of the hand--to extract kinematic descriptors from the actual hand of the sequence of gestures. Using a Fisher kernel and a multilevel temporal pyramid, respectively, the descriptors were encoded in a temporal and statistical representation. Considering a feature vector calculated over the entire pre-segmented gesture, an improvement in the recognition can be achieved by associating a linear SVM classifier directly at the end (see Figure 2).  
In terms of social factors, while browsing the literature, the need to acquire statistical data appeared. Indeed, many databases are available in the United States and offer real visibility on the increasing crime rate in general and in relation to sexual assault. Based on the data collection in [33], Figure 3 presents a forecast that reveals the areas with a high risk of experiencing a significant amount of sexual assault in the United States over the next three years.  
Figure 3: An estimation of future sexual assault cases in the USA [33].  
Figure 2: Improvement in the recognition by adding a Support Vector Machine (SVM) as the final component.
## 4 Experiments
### Case Western Reserve University (CWRU) Bearing Experiment Data Analysis
#### 4.1.3 Comparative Experimental Analysis with Other Methods  
The proposed method in this paper was analyzed using the Case Western Reserve University bearing dataset, as shown in Table 6. The number of samples per run condition was 40,000, and there were 2000 training samples and 2000 test samples included in each state. This paper's module was also compared with three other methods, including stacked self-encoder (SDAE)+ joint geometrical and statistical alignment (JGSA) [30], deep domain confusion (DDC) [31], and a domain-adversarial convolutional neural network (DACNN) [32]. For the SDAE + JGSA method, there are 3000 samples for each operating condition, and every health state contains 300 samples. The algorithm first extracts the deep features of the image using the SDAE algorithm and then reduces the feature distribution differences using the JGSA algorithm.  
As the results in Table 6 show, the first two methods utilize SDAE and CNN to join the relevant joint geometrical and statistical alignment (JGSA) and domain confusion loss function to align the distribution differences. The highest diagnostic accuracy can be 98.3%. Both the DACNN and the present method introduce the adversarial mechanism, thus realizing the extraction of domain-invariant features. However, compared to the DACNN, the present study effectively improved the model to extract deeper features. As expressed in the table, each task of this paper's method had a better performance than the DACNN method, which shows that the introduction of the CRU in the domain-adversarial network had a significant effect on increasing the performance of diagnosing bearing faults.
## 7 Defining the Workspace of a Serial Manipulator  
Consider a serial three-link robot (Figure 10). The gripper position is completely determined by the vector of link lengths \(s=\left(s^{(1)},\dots,s^{(k)}\right)\) and by the vector of angles \(\theta=\left(\theta^{(1)},\dots,\theta^{(k)}\right)\) between the corresponding links. The task of determining the workspace for this work is considered in other works [24; 25]. Here is a brief description of the proposed solution.  
Figure 10: Angles and links defining the configuration of the robotic arm.  
Figure 9: Serial manipulator areas—(**a**) workspace, (**b**) technological area.  
The permissible set of angles and lengths of links is a box  
\[\mathsf{X}=\left[\underline{\underline{s}^{(1)}},\overline{s^{(1)}}\right]\times \ldots\times\left[\underline{s}^{(k)},\overline{s^{(k)}}\right]\times\left[ \underline{\theta}^{(1)},\overline{\theta^{(1)}}\right]\times\ldots\times \left[\underline{\theta}^{(k)},\overline{\theta^{(k)}}\right] \tag{53}\]  
where \(\left[\underline{s}^{(i)},\overline{s^{(i)}}\right]\) and \(\left[\underline{\theta}^{(i)},\overline{\theta^{(i)}}\right]\) are given ranges of possible values of the link length \(s_{i}\) and angle \(\theta_{i}\), respectively.  
The workspace of the robot manipulator is the image \(Y=F(X)\) of the admissible set \(X\), where \(F(s,\theta)=\left(f^{(1)}(s,\theta),f^{(2)}(s,\theta)\right)\) is given by formulas  
\[f^{(1)}(s,\theta)=\sum_{i=1}^{k}s^{(i)}.\cos\left(\sum_{j=1}^{i}\theta^{(j)}\right) \tag{54}\]  
\[f^{(2)}(s,\theta)=\sum_{i=1}^{k}s^{(i)}.\sin\left(\sum_{j=1}^{i}\theta^{(j)}\right) \tag{55}\]  
During the operation of the algorithm for determining the workspace of the serial mechanism, a list of points from the set \(\mathsf{X}\) is maintained such that its image \(\mathsf{F}\), which is determined using (54) and (55), contains a finite set of pairwise incomparable points, which after the completion of the algorithm will be an \(\varepsilon\)-approximation of the boundary of the set \(\mathsf{Y}\). An algorithm for constructing an \(\varepsilon\)-Pareto set with guaranteed accuracy \(\varepsilon\) is given in [26].  
In the current work, the covering set has been transformed into an ordered set of integers, similarly to the transformation of the covering set of a tripod. In this case, the vector has a two-level structure, since the workspace of a serial robot is two-dimensional.  
Figure 10(a) shows the simulation results for the following dimensions \(s_{1}=400\), \(s_{2}=265\), \(s_{3}=35\), ranges of angles: \(\theta_{1}\in\left[\frac{\pi}{6},\frac{\pi}{3}\right],\ \theta_{2}\in\left[\frac{\pi}{6},\frac{\pi}{3}\right],\ \theta_{3}\in \left[-\pi,\pi\right]\).  
The technological area was similarly defined for a serial manipulator. The results are shown in Figure 10(b).
## 2 Related Work
### RS-HLAR Using Inertial Sensors  
Inertial sensors are held in high esteem when it comes to human locomotion activity recognition. As modern smartphones have very useful inertial sensors such as accelerometers, gyroscopes, and magnetometers embedded in them, RS-HLAR also benefits from them. [PERSON] et al. [33] extracted hand-crafted features from smartphone-embedded gyroscope and accelerometer and selected the best ones among them using neighborhood component analysis. Then, they used a dense neural network for the classification of locomotion activities. Using smartphone accelerometer data, [PERSON] et al. [34] extracted deep features from temporal and spatial domains. Their locomotion activity classification system, namely C4M4 BL, consisted of four convolutional layers, four max-pooling layers, and one bidirectional long short-term-memory (LSTM) layer. [PERSON] et al. [35] compared the performance of different kernels of a support vector machine (SVM) in recognizing the locomotion activities such as climbing stairs, coming down the stairs, walking, and standing. They used a smartphone-embedded accelerometer, gyroscope, and magnetometer to extract various frequency- and time-domain features. After that, they employed a multiclass SVM in a one vs. all mode. Their 10-fold cross-validated results acknowledge the credibility of their system. In [36], the authors created a one-dimensional magnitude vector by using instantaneous values of a tri-axial smartphone accelerometer. After that, the magnitude vector was forwarded to a one-dimensional convolutional neural network (CNN) that auto-generated features and then performed the classification. [PERSON] et al. used the inertial sensors of a smartphone to classify human locomotion activities such as jumping, walking, standing, jogging, etc. Initially, they split the activities into static and dynamic categories using a cross-correlation-based template matching approach. Then, they separately extracted time- and frequency-domain features for each branch (static and dynamic). Following this, for the data optimization, a vector-quantization-based codebook was generated. For dynamic activities, they employed a multiclass SVM classification model, while for static activities, they used a Gaussian naive Bayes (GNB) classification algorithm [37].  
Another approach that was followed in [38] used a hybrid CNN-LSTM model in which they introduced an attention mechanism. The smartphone sensors used in this system were accelerometers, gyroscopes, and magnetometers. In another work, a model that combines a deep bidirectional long short-term memory (DBLSTM) and CNN is proposed. While using an abstract approach, a DBLSTM serializes the smartphone accelerometer and gyroscope data and generates a bidirectional output vector. As the DBLSTM model is not good for feature extraction, the feature extraction task is assigned to a CNN. Finally, a SoftMax function is implemented in the final layer of the network to perform the classification of the activities [39].
## 4 Proposed mmWave Assisted CR-IoV
### Comparative Analysis
#### 5.3.5 Analysis of Detection Accuracy  
Accuracy is defined as the ability of the proposed algorithm to classify the attackers correctly. It is measured in terms of the number of correct predictions and the total number  
Figure 14: Analysis of BER.  
Figure 13: Delay with respect to the number of attackersof predictions. Here, we have compared the accuracy of the proposed detection methods with the base algorithms.  
In Figure 15, SSDF attack detection accuracy is measured and compared with base algorithms such as SVM-Linear Kernel, SVM-Polynomial Kernel and SVM-RBF Kernel. The proposed work uses a hybrid kernel that fuses all three kernels to separate the hyperplane. Here, we can see that the SVM-RBF kernel and polynomial kernel show better performance when compared to the linear kernel. With RBF and polynomial kernels, we can expect accuracy up to 84% when the number of iterations is increased. Over iteration, the linear kernel only achieves 75% of accuracy which is 20% lower than the proposed work. The major reason for this reduction is that kernel selection is the most important process in SVM classification. When random kernels are used, then the accuracy level will be degraded. Thus, the existing kernels are unsuitable for detecting SSDF attacks accurately. In contrast, we have presented a hybrid kernel which is the fusion of all three kernels. By utilizing the advantages of all three kernels, we have achieved an accuracy of up to 99%. From this analysis, it is clear that the SVM classifier highly depends upon kernel function and the optimal kernel function provides better results.  
In Figure 16, we have compared the PUE attack detection accuracy of the proposed work with conventional Direction of Arrival (DoA), Time of Arrival (ToA) and Time Difference of Arrival (TDoA) approaches. All existing works adopt the general model for localizing signal sources in the network, while the proposed FA-TDoA uses the FA algorithm for localization. Furthermore, we have employed the conventional Energy Detection (ED) method for existing localization approaches and E2D for the proposed TDoA approach. The analysis shows that the fusion of E2D, FA and TDoA detects PUE attacks with 99% of accuracy. The main reason for this accuracy level is we first optimized the sensing method even for a noisy environment. Thus, the sensing report is probably accurate. With the accurate sensing report, we perform PUE detection by using FA-TDoA, which accurately finds the location. When compared to DoA, ToA and TDoA, the proposed FA-TDoA performs well since it uses a proper coordinate system modeled by FA instead of using general models as in existing works.  
From the analysis, it is clear that the proposed attack detection algorithms work well in the mmWave-CR-IoV environment.  
Figure 15: Detection accuracy analysis (SSDF attack).