esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Covariance Matrix-Based Statistical Beamforming (CMSB)  
The CMSB [39] is implemented by estimating adaptive weights using the ratio between the mean and standard deviation of a covariance matrix through rotary averaging and diagonal reducing. The normalized reciprocals of ASD from all imaging points are used to dynamically select the subarray length for each imaging point.  
The normalized reciprocals of the ASD from all imaging points are \(\mathbf{\sigma^{\prime}}=\sqrt{\Pi(1/\sigma)}\), where \(\sigma\) is the ASD from all imaging points, and \(\Pi(\cdot)\) is the normalization operation. The ASD at imaging point \(p\) is \(\sigma(p)=\sqrt{\frac{1}{N}\sum_{n=1}^{N}\left(y_{n}(p)-\bar{y}(p)\right)^{2}}\), where \(\bar{y}(p)=\frac{1}{N}\sum_{n=1}^{N}y_{n}(p)\).  
The selected subarray length \(L(p)\) for imaging point \(p\) is  
\[L(p)=\lfloor\sigma^{\prime}(p)\cdot L_{max}\rfloor, \tag{7}\]  
where \(\lfloor\cdot\rfloor\) is the rounding operation and \(L_{max}\) is the maximum subarray length. When \(L(p)\leq 2\), the dynamic subarray length \(L(p)\) is set to 2.  
The covariance matrix is then estimated based on \(L(p)\) as  
\[\mathbf{R}(p)=\frac{1}{N-L(p)+1}\sum_{l=1}^{N-L(p)+1}\hat{\mathbf{y}}_{l}(p)\hat{\mathbf{ y}}_{l}^{H}(p), \tag{8}\]  
where \(\hat{\mathbf{y}}_{l}(p)=[y_{l}(p),y_{l+1}(p),...,y_{l+L(p)-1}(p)]^{T}\).  
The rotary averaging and diagonal reducing are then applied on \(\mathbf{R}(p)\) using the following (9) and (10), respectively, to form a modified covariance matrix,  
\[\hat{\mathbf{R}}(p)=\frac{1}{4}(\mathbf{R}(p)+\mathbf{J}\mathbf{R}^{T}(p)+\mathbf{J}\mathbf{R}(p)\mathbf{ J}+\mathbf{R}^{T}(p)\mathbf{J}), \tag{9}\]  
\[\hat{\mathbf{R}}(p)=\hat{\mathbf{R}}(p)-\delta\hat{\mathbf{R}}(p)\cdot\mathbf{I}, \tag{10}\]  
where \(\mathbf{J}\) and \(\mathbf{I}\) are the reversal matrix and identity matrix of size \(L(p)\times L(p)\), respectively, and the constant \(\delta>0\) is the user-defined diagonal reducing factor.  
Then, the weight vector of CMSB is obtained by calculating the mean-to-standard-deviation ratio (MSR) of each row vector \(\tilde{\mathbf{R}}_{i}(p)\), where \(i\in[1,L(p)]\) is the row index. The adaptive weight is calculated as  
\[w_{i}(p)=\frac{\frac{1}{L(p)}\sum_{n=1}^{L(p)}\tilde{\mathbf{R}}_{i,n}(p)}{\sqrt{ \frac{1}{L(p)}\sum_{n=1}^{L(p)}(\tilde{\mathbf{R}}_{i,n}(p)-\frac{1}{N}\sum_{n=1}^ {L(p)}\tilde{\mathbf{R}}_{i,n}(p))^{2}}}, \tag{11}\]The output of CMSB estimated with the weight vector \(\mathbf{w}(p)=[w_{1}(p),w_{2}(p),...,w_{L(p)}(p)]^{T}\) is finally obtained as  
\[V_{\text{CMSB}}(p)=\frac{1}{N-L(p)+1}\sum_{l=1}^{N-L(p)+1}\frac{\mathbf{w}^{H}(p) \hat{y}_{l}(p)}{L(p)}. \tag{12}\]  
CMSB has demonstrated a notably improved resolution and contrast over DAS. Additionally, CMSB can enhance the border visualization in rat mammary tumor imaging and human heart imaging with reduced off-axis clutter and smoothed tissue texture [39].
###### Abstract  
Coverage is a vital indicator which reflects the performance of directional sensor networks (DSNs). The random deployment of directional sensor nodes will lead to many covergae blind areas and overlapping areas. Besides, the premature death of nodes will also directly affect the service quality of network due to limited energy. To address these problems, this paper proposes a new area coverage and energy consumption optimization approach based on improved adaptive particle swarm optimization (IAPSO). For area coverage problem, we set up a multi-objective optimization model in order to improve coverage ratio and reduce redundancy ratio by sensing direction rotation. For energy consumption optimization, we make energy consumption evenly distribute on each sensor node by clustering network. We set up a cluster head selection optimization model which considers the total residual energy ratio and energy consumption balance degree of cluster head candidates. We also propose a cluster formation algorithm in which member nodes choose their cluster heads by weight function. We next utilize an IAPSO to solve two optimization models to achieve high coverage ratio, low redundancy ratio and energy consumption balance. Extensive simulation results demonstrate the our proposed approach performs better than other ones.  
directional sensor network; area coverage; cluster; particle swarm optimization; energy consumption balance +
Footnote †: journal: Sensors
# Marine Diesel Engine Fault Detection Based on Xilinx ZYNQ SoC
## 2 Methods
### Neural Network Related  
#### 2.4.1 Introduction to Convolutional Neural Networks  
A convolutional neural network (CNN) is a type of deep learning model specifically designed for image recognition and data classification. CNNs are capable of automatically learning and extracting features from images, thereby facilitating content recognition and classification.  
The main components of a CNN include the following layers:  
Figure 2: Physical diagram of the ZYNQ chip.  
Convolutional Layer: This is the core layer of the network, aimed at feature extraction. Each convolutional layer consists of multiple filters, each capable of learning different features. The primary method involves padding the input feature map so that the filter can perform convolution operations on the overlapping sections with the content. Each filter then combines to output a new feature map. The formula for the convolution operation is as follows:  
\[Y_{i,j,k}=\sum_{m=0}^{F-1}\sum_{n=0}^{F-1}\sum_{c=0}^{F_{n}-1}X[8i+m,8j+n,c]W[m,n,c,k]+b[k] \tag{1}\]  
\(i\) and \(j\) represent the spatial positions of the output feature map, \(k\) represents the channel of the output feature map, and \(F\) is the size of the filter. \(C_{in}\) represents the number of channels in the input feature map, s is the stride, p is the padding, \(X[i*8+m,j*8+n,c]\) represents a pixel in the input feature map, \(W[m,n,c,k]\) is the weight of the filter, and \(b[k]\) is the bias term.  
Pooling Layer: After the convolutional layer, the output image has a data volume similar to the input image, necessitating the use of a pooling layer to reduce the dimensions of the feature maps and decrease sensitivity to position. The main operations include max pooling and average pooling.  
Activation Function Layer: This layer introduces non-linear properties, enabling the CNN to learn more complex functional relationships. This paper utilizes the ReLU (Rectified Linear Unit) function, which allows for faster convergence during training. The formula for the ReLU function is as follows:  
\[f(x)=\max(0,x) \tag{2}\]  
\(x\) is the input value. The ReLU function sets all negative values to zero, while leaving positive values unchanged.  
Fully Connected Layer: This layer integrates the features extracted from the convolutional and pooling layers and outputs the final classification results and feature representations. In the fully connected layer, the output \(y_{i}\) of each neuron can be represented as follows:  
\[y_{i}=f(\sum_{j=1}^{n}W_{ij}x_{j}+b_{i}) \tag{3}\]  
\(n\) is the number of neurons in the previous layer, \(W_{ij}\) represents the weight connecting the \(j\) input neuron to the \(i\) output neuron, \(b_{i}\) is the bias of the \(i\) output neuron, and \(f\) is the activation function.
# Research on Building Target Detection Based on High-Resolution Optical Remote Sensing Imagery
## 2 Data and Methodology
### Methodology
#### 2.2.3 Pixel-Level Fusion and Interference Removal  
The building target detection results based on rectangular approximation and diffusion saliency have been obtained previously, and the two detection results are fused in order to improve the accuracy and recall. In the first two methods of building detection, the screening of detection results is more stringent. The method to fuse the detection results is shown in Equation (21).  
\[R=R_{rec}\cup R_{sal}, \tag{21}\]  
where \(R_{rec}\) represents the mask map generated by the rectangular approximation-based building target detection method and \(R_{sal}\) represents the mask map generated by the multi-feature saliency detection method.  
Most of the false alarms in the detected building targets are due to the fact that stacks, various types of ships and vessels in the port area have great similarity in shape and color to the building targets. In order to solve this problem, improve the accuracy of building target detection and reduce false alarms, this paper separates the ocean area from the land area. By observing the remote sensing images, it is found that the image brightness of the water area is lower than that of the land area, which can be used as the basis for segmentation. Use the image segmentation method of region growth to obtain the ocean area. First, the grayscale threshold of the image is calculated by using Otsu algorithm. Then, randomly select the seed points of the sea surface that satisfy Equation (22) [21].  
\[P(x,y)=\left\{\begin{array}{l}1\forall(x_{1},y_{1})\in U(x,y),I(x_{1},y_{1})< threshold\\ 0\exists(x_{1},y_{1})\in U(x,y),I(x_{1},y_{1})\geq threshold\end{array}\right., \tag{22}\]  
where \(U(x,y)\) represents the neighborhood of point \((x,y)\) and \(I\) represents the gray value of each pixel of the experimental image.  
Then the region growth is performed, the growth condition is \(I(x,y)<b\times mean(R(x,y))\), where \(b=1.2\), \(R(x,y)\) represents the part of the region growth that has been completed, and \(mean(.)\) is to calculate the mean value of the grown part.  
After obtaining the ocean area mask, the fused experimental results are combined with the land area mask by Equation (23) to obtain a mask map of the building targets in the land area. - means to take the opposite.  
\[R_{final}=R_{init}\cap(\sim R_{sea}). \tag{23}\]
## 2 Mathematical Modeling of Unmanned Surface Vehicle (USV)  
### Kinematics of USV  
Figure 1 and Table 1 summarize the coordinate system of the developed USV and its vector and scalar notations per the definitions provided by the Society of Naval Architects and Marine Engineers, respectively.  
The coordinate system of the USV employs earth- and body-fixed coordinates. However, because the earth- and body-fixed coordinates represent motion variables in different directions within the coordinate system, the expression of these motion variables based on the two coordinate systems must be transformed into a rotating coordinate system using rotation matrices.  
Equation (1) represents the transformation matrix for converting the linear velocity observed in the body-fixed coordinate system to the linear velocity in the north-east-down (NED) coordinate system.  
\[R_{b}^{n}=\begin{bmatrix}\cos\psi\!\cos\theta&\cos\psi\!\sin\theta\!\sin\phi- \sin\psi\!\cos\phi&\sin\psi\!\sin\phi+\cos\psi\!\sin\theta\!\cos\phi\\ \sin\psi\!\cos\theta&\cos\psi\!\cos\phi+\sin\psi\!\sin\theta\!\sin\phi&\sin\psi \!\sin\theta\!\cos\phi-\cos\psi\!\sin\phi\\ -\sin\theta&\cos\theta\!\sin\phi&\cos\theta\!\cos\phi\end{bmatrix}. \tag{1}\]  
Equation (2) represents the transformation matrix for converting the angular velocity observed in the body-fixed coordinate system into that of the NED coordinate system.  
\[T_{\theta}=\begin{bmatrix}1&\tan\theta\!\sin\phi&\tan\theta\!\cos\phi\\ 0&\cos\phi&-\sin\phi\\ 0&\sin\phi\!\cos\theta&\cos\phi\!\cos\theta\end{bmatrix}. \tag{2}\]  
Hence, the kinematic matrix representation of the USV is expressed in the form of Equation (3).  
\[\begin{bmatrix}p_{b/n}^{n}\\ \dot{\theta}_{nb}\end{bmatrix}=\begin{bmatrix}R_{b}^{n}(\theta_{nb})&0_{3 \times 3}\\ 0_{3\times 3}&T_{\theta}(\theta_{nb})\end{bmatrix}\begin{bmatrix}v_{b/n}^{b}\\ \omega_{b/n}^{b}\end{bmatrix}. \tag{3}\]  
In this context, \(p_{b/n}^{n}\) represents the position of the USV expressed in NED coordinates from the perspective of the earth-fixed coordinate system, where \(\theta_{nb}\) signifies the Euler angles representing the orientation between the earth-fixed and body-fixed coordinates. \(v_{b/n}^{b}\) denotes the linear velocity of the USV in the body-fixed coordinate system with respect to the earth-fixed coordinate system. \(\omega_{b/n}^{b}\) represents the angular velocity of the USV in the body-fixed coordinate system relative to the earth-fixed coordinate system. Detailed definitions of the parameters and conditions for Equations (1)-(3) are available in [9].
# A Review on 5G Sub-6 GHz Base Station Antenna Design Challenges
###### Abstract
The mMIMO in sub-6 GHz is also used by the operators for outdoor coverage due to low propagation and penetration losses at these frequencies. The challenges at mm-wave antennas have significant differences to the sub-6 GHz BSAs. Within the scope of this discussion, we present the challenges associated with the BSAs in sub-6 GHz and below, as these BSAs are the most widely used antennas by the operators to date. The mMIMO aims to control the Signal-to-Noise Ratio (SNR) to each user by forming beams to each user unlike single-user MIMO and Multi-User MIMO in LTE and LTE-A. This requires a two-dimensional antenna array with control on the amplitude and phase to steer the beam in azimuth and elevation. The conventional beamforming antenna arrays require the element spacing to be 0.5\(\lambda\) to reduce the grating lobes. In contrast, the MIMO demands more spacing between the arrays to increase spatial diversity at least 0.7\(\lambda\) or more. In practice, a compromise is made, and a 0.65\(\lambda\) column spacing is used in most of the mMIMO antennas. The need to form the beam requires a phase and amplitude control at each radiating element, which can be done using either via analog beamforming architecture, digital beamforming architecture, or a hybrid beamforming architecture. The analog and digital beamforming architectures that are the most commonly used are shown in Figure 5. Analog beamforming has a significantly lower power consumption than the digital beamforming technique due to a lack of active components. However, in practice, analog beamforming cannot provide true mMIMO capability because the beams formed by analog beamforming are either multiple fixed beams or steer at a much slower rate compared to the digitally formed beams. In practice, analog beamforming is achieved by either a beamforming network such as Butler Matrix [7] or using a lens [8]. Digital beamforming is the preferred architecture in Sub-6 GHz mMIMO antennas as most of the beamformers can provide IF outputs up to 6 GHz. Hybrid beamforming architecture is mostly used in mm-wave 5G realizations in order to achieve power savings and reduce complexity in the designs. More details on the current sub-6 GHz adapted beamforming technologies and the challenges are discussed in Section 3.3. Figure 4: Low band to high band 1:4 antenna array configuration.
## 3 Results and Discussion
### Synthetic Data
#### 3.2.3 Percent Root Mean Square Difference  
To measure the distortion between the original and synthetic signals, we defined the Percent Root Mean Square Difference (PRD) as in Equation (12):  
\[PRD=\sqrt{100\frac{\sum_{i=1}^{n}(Ori_{i}-Syn_{i})^{2}}{\sum_{i=1}^{n}(Ori_{i}) ^{2}}}. \tag{12}\]
# Conflict Resolution in Mechatronic Collaborative Design Using Category Theory
## 3 State-of-the-Art
### Category Theory (CT)
#### 3.3.1 Category Theory Basic Concepts  
The fundamental concepts of CT are presented as follows:  
A category consists in a set of objects X, Y, Z, etc. and a set of arrows or morphisms f, g, h, etc. Each morphism f has a domain (dom) and a co-domain (cod) f: X \(\rightarrow\) Y, where X = dom(f) and Y = cod(f) [33]. Given two arrows f: X \(\rightarrow\) Y and g: Y \(\rightarrow\) Z, there is a given morphism g \(\ominus\) f: X \(\rightarrow\) Z called the composition of g with f (see Figure 1a). Moreover, for each object X, there is a morphism 1\({}_{\text{X}}\): X \(\rightarrow\) X, called the identity morphism with respect to the property \(f\odot 1_{X}=f=1_{Y}\odot f\). Whenever the composition is defined, it has to be associative: \(h\odot(g\odot f)=(h\odot g)\odot f\), for all f: \(X\to Y\), \(g\): \(Y\to Z\) and \(h\): \(X\to Z\).  
A functor F: C \(\Longrightarrow\) D between two categories C and D is a mapping of objects to objects and morphisms to morphisms.  
An object is called Initial object I if for any object X in a category C there is exactly one morphism from I to X.  
An object is called Terminal object T if for any object X in a category C there is exactly one morphism from X to T.  
A Comma category is a construction in CT that involves two functors with the same co-domain. This concept allows the source and target object to vary over the image of distinct functors [33]. Let A, B, and C be categories and let F: A \(\Longrightarrow\) C and G: B \(\Longrightarrow\) C, be functors with the same co-domain. A comma category ca be formed as follows (Figure 1b): the objects are all triples (X, Y, h) where X is an object in A, Y an object in B and h: F(A) \(\rightarrow\) G(B) a morphism in C. The morphisms from (X, Y, h) to (X\({}^{\prime}\), Y\({}^{\prime}\) and h\({}^{\prime}\)) are all pairs (\(\alpha\),\(\beta\)), where: \(X\to X^{\prime}\) and \(\beta\):\(Y\to Y^{\prime}\) are morphisms in A and B respectively, for which the square commutes, that is \(G(\beta)\odot h=h^{\prime}\odot F(\alpha)\). The composition of pairs is done componentwise [33].  
In a category C a Pullback of arrows f and g with cod(f) = cod(g) consists of the arrows P\({}_{1}\) and P\({}_{2}\) such that \(f\odot P_{1}=g\odot P_{2}\). Given any arrow w\({}_{1}\): \(W\to X\) and w\({}_{2}\): \(W\to Y\) with f \(\odot w_{1}=g\odot w_{2}\), there exists a unique u: w \(\to P\) with w\({}_{1}=P_{1}\odot u\) and w\({}_{2}=P_{2}\odot u\) (see Figure 1c). The dual of a pullback can be defined as a Pushout structure.
# Effect of Cu, Cr, S Doped TiO\({}_{2}\) for Transparent Plastic Bar Reinforced Concrete
###### Abstract  
In this study, after firing and powdering Cu, Cr, and S with NP-400 TiO\({}_{2}\), an NOx\({}_{x}\) removal rate test was performed according to the ISO test method to analyze the photocatalytic reactivity in visible light. The distribution of the photocatalyst and visible light reactivity on the surface of the test specimen were confirmed through SEM (Scanning electron microscope), EDS (Energy dispersive spectroscopy), XRD (X-ray diffraction), UV-visible absorption spectrum and energy band gap tests. The flowability of UHPC (Ultra high performance concrete) slightly decreased due to the increase in the photocatalyst mixing rate, but both J-Ring and L-Box test results showed that there is no problem when concrete is placed. As a result of SEM and EDS tests, good microstructure and peak values were confirmed in the test specimens doped with Cu, and as a result of the XRD test, anatase and rutile peaks were confirmed in the Cu, Cr, and S specimens. In the UV-visible absorption spectrum analysis, it was confirmed that only the specimen doped with Cu maintains a high absorption power of 0.8 up to 700 nm, and the inherent band gaps are reduced to 2.9 eV, thereby increasing the possibility of reaction in visible light. Finally, as a result of the NOx removal test by the ISO test method, about 15.8% was removed for 5 h in the specimen doped with Cu, and the removal efficiency is estimated to be possible up to about 25% when applied with the TPBRC (transparent plastic bar reinforced concrete).  
Keywords:TiO\({}_{2}\); NOx; TPBRC; energy band gap; calcination +
Footnote †: journal: Applied Physics Letters B
# The Recent Advancement in Unmanned Aerial Vehicle Tracking Antenna: A Review
## 3 Antenna Overview
### Aperture Antenna  
Aperture antennas are one of the most popular type of antennas; they emit electromagnetic waves through an opening also referred to as an aperture. The transmitted and received electromagnetic waves are obtained from waveguide into space. The directivity of this type of antenna is low and, as such, they are preferred design in frequencies, such as microwave frequency and UHF. Some examples are:
## 4 Experimental Results
### Evaluation Indicators
#### 4.3.1 mAP  
1. Classification target division result;  
The test result will divide the classification target into true positives (TP), false positives (FP), false negatives (FN), and true negatives (TN). TP is the number of true targets that are considered correct targets. FP is the number of false targets considered correct targets. FN is the number of true targets considered false targets. FP is the number of false targets considered false targets.  
2. Precision and Recall;  
Precision and Recall are defined according to the target classification results, as shown in Equations (5) and (6), respectively. From Equation (5), we can see that precision is the proportion of samples that the classifier considers to be positive and is indeed positive to all the samples that the classifier considers to be positive. Recall is the proportion of all actual positive samples considered to be positive from Equation (6).  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}), \tag{5}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}), \tag{6}\]  
3. Average precision (\(AP\)) and mean average precision (m\(AP\));  
\(AP\) is single-class average precision as shown in Equation (7). m\(AP\) is the average accuracy of all test images for all categories as shown in Equation (8). In Equation (7), \(N\) is the total number of single-class targets. In Equation (8), \(C\) is the total category of all detected objects.  
\[AP_{i}=\sum_{1}^{N}Precision/N, \tag{7}\]  
\[mAP=\sum_{1}^{C}AP_{i}/C, \tag{8}\]  
At present, there are two commonly used m\(AP\)s: [EMAIL_ADDRESS] and [EMAIL_ADDRESS]. [EMAIL_ADDRESS] is the AP when the threshold of intersection and union (IOU) of each category is set to 0.5, and then m\(AP\) values are calculated for all categories. [EMAIL_ADDRESS]:0.95 is the AP when the threshold range of IOU of each category is set to 0.5-0.95 and the step size is 0.5, and then the m\(AP\) values are calculated for all categories. [EMAIL_ADDRESS] is chosen in this experiment.
## 4 Results
### Prediction Performance Results  
The precision, recall and F1-score performance metrics produced by the XGBoost algorithm based on the test dataset are summarized in Table 6. The overall accuracy of the model was 90.18%. As expected, the metrics of the non-landslide class exhibited higher performance. The ROC and the precision-recall curves given in Figure 13 show very high AUC (=0.96) and average precision (AP = 0.94) performances. The normalized confusion matrix presented in Figure 14 show that the false classifications were around 10% for both classes.