esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Convolutional neural networks (CNNs) in deep learning have input pixel limitations, which leads to lost information regarding microcalcification when mammography images are compressed. Segmenting images into patches retains the original resolution when inputting them into the CNN and allows for identifying the location of calcification. This study aimed to develop a mammographic calcification method using deep learning by classifying the presence of calcification in the breast. Using publicly available data, 212 mammograms from 81 women were segmented into 224 \(\times\) 224-pixel patches, producing 15,049 patches. These were visually classified for calcification and divided into five subsets for training and evaluation using fivefold cross-validation, ensuring image consistency. ResNet18, ResNet50, and ResNet101 were used for training, each of which created a two-class calcification classifier. The ResNet18 classifier achieved an overall accuracy of 96.0%, mammogram accuracy of 95.8%, an area under the curve (AUC) of 0.96, and a processing time of 0.07 s. The results of ResNet50 indicated 96.4% overall accuracy, 96.3% mammogram accuracy, an AUC of 0.96, and a processing time of 0.14 s. The results of ResNet101 indicated 96.3% overall accuracy, 96.1% mammogram accuracy, an AUC of 0.96, and a processing time of 0.20 s. This developed method offers quick, accurate calcification classification and efficient visualization of calcification locations.  
deep learning; mammography; image classification +
Footnote †: journal:  
0000-0001-9  
using deep learning techniques. We base our classification on the presence or absence of calcification in mammography. Deep learning uses a neural network called CNN, which has a limitation on the number of pixels in its input. However, there are a large number of pixels included in mammography images, and compressing the images results in the loss of information of small lesions, such as microcalcifications [3]. If an entire image is input and classified based on the presence or absence of calcification, it becomes challenging to determine where calcification is located. There are no previous studies that analogize the location of calcification solely on the basis of image classification. In this study, the accuracy of the classifier as well as the accuracy and processing time for each mammography image were calculated and investigated. There are no studies that have been found that include these factors. Therefore, our objective is to divide the image into patches, enabling input into a CNN with the original resolution. This allows us to classify the presence or absence of calcification in each patch and display the integrated image to show the location of calcification in the original image.
# Target Localization Based on Bistatic T/R Pair Selection in GNSS-Based Multistatic Radar System
## 4 Simulation and Experimental Results
### Experimental Results of CHO Method  
We use the CHO method to select an optimal combination of 6 satellites from 12 visible satellites. The positioning results are shown in Figure 11(c) and Table 2.  
Figure 11: Comparison of theoretical accuracy before and after selecting.  
Figure 12: The positioning results before and after selecting: (**a**) The positioning result without selecting, (**b**) The positioning result of the CMF method, (**c**) The positioning result of the Convex Hull Optimization (CHO) method.  
Figure 10: Comparison of the measured Root Mean Square (RMS) before and after selection.  
In Figure 12, it is 'North Up', so East is to the right and the blue solid line is the AIS track used as a reference, and the red \"\(\times\)\" indicates all target positions obtained using a certain number of satellites, and the marking step is 1s.  
In contrast, the red \"\(\times\)\" marks are more condensed around the solid blue line in Figure 12c, and it shows that the positioning result obtained by CHO method is more accurate. In more detail, we can see Table 2. Under the same number of satellites (6 satellites), the measured RMS and the theoretical result of CHO method is reduced by 14.8632m and 13.9223m, respectively, and optimizations by CMF method are reduced by 8.3538m and 9.0115m, respectively. Moreover, before selecting satellites, the difference between the measured RMS and the theoretical result of 6 satellites is 10.4446m. After the CHO method selection, the difference between them is 9.4637m. It means the positioning accuracy has been improved. Finally, comparing Tables 2 and 3, we can see that the error of CHO method is even smaller than that of 7, 8, 9, and 10 satellites without selecting. Through comparison of various aspects, it is concluded that CHO method has good satellite selection capabilities.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 3 Simulation of Ship's Wake Bubble Laser Dual-Mode Detection Based on Monte Carlo  
This section is based on Monte Carlo simulation to analyze the backscattering characteristics of ship's wake bubbles and the imaging characteristics of laser illumination, verifying the effectiveness of the dual-mode fusion detection method.
# Optimization Procedure for Computing Sampling Time for Induction Machine Parameter Estimation
## 4 Simulation and Experiment
### Testing Optimal Sampling Time on Parameter Estimation
It is very important to notice: the parameter estimation procedures using the optimal sampling time results in parameter identification with 800 data points, instead of original 12,000 for this specific case, thus significantly reducing the computing time of the optimization procedures. So even if the improvements of the optimal sampling time in the meaning of the residuals and parameter values can be relatively small, the computing and memory requirements benefits are evident. Figure 5: (**a**) Current residuals with original and optimal sampling time estimation; (**b**) Speed residuals with original and optimal sampling time estimation.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 2 Network Model and Problem Formulation
### Probing Model  
In this work, we use source-specific multicast (SSM) due to its effectiveness and efficiency for our purposes. In particular, the multicast probing technique inherently provides sufficient packet-level correlations, which are required to infer the topology and link performance parameters. When a node receives a given multicast probe packet, it forwards a copy of the packet to its children. Therefore, the multicast packets of the same probe observed at various terminal points experience the same network conditions in their shared paths (performance contribution from links in shared paths is identical). Another advantage of multicast probing is that it can scale better compared to its unicast counterpart, given that for the latter back-to-back or even larger stripes of closely spaced unicast packets are required to emulate a multicast probe.  
The main drawback of multicasting is that it is not widely deployed and readily available on the Internet. Nevertheless, as previously mentioned, the proposed algorithm can also function without any change or modification under striped unicast probing, albeit with an impact on the probing overhead scalability. The probes are only leveraged to estimate the distances between the terminal nodes, which are given as input to the algorithm. Back-to-back or even larger stripes of closely spaced unicast packets can be used to emulate a multicast probe. Of course, the emulated packet-level correlations achieved using unicast probes cannot be perfect. Therefore, multicast probing is considered the best-case experimental setting for our approach. For every probe traversing the tree, the state of each link of the routing tree is represented by the set of link state variables \(Y_{e},\forall e\in E\). Similarly, the set of outcome variables \(X_{k},\forall k\in V\) denotes the (random) outcome of the probe at each node. Clearly, the outcome at node \(k\) (i.e., \(X_{k}\)) is determined by the outcome at the node's parent (i.e., \(X_{f(k)}\)) and the state of the link \(e_{k}=(f(k),k)\) that connects them (i.e., \(Y_{e_{k}}\)).  
Figure 1: Example of corresponding physical and logical routing trees. (**a**) Physical routing tree. (**b**) Logical routing tree.
# Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model
## 3 Power Losses and Wind Farm Efficiency  
Like the wake decay effect, wind direction and wind farm layout importantly influence wind farm performance. Thus, changes in wind direction or wind farm layout cause changes in the interaction of wakes and the power output of a wind turbine. Wind turbine spacing, wind speed, ambient turbulence intensity, and atmospheric stability also affect power losses and wind farm efficiency. One analysis [25] revealed that, averaged over a wind speed from 5 m\(\cdot\)s\({}^{-1}\) to 15 m\(\cdot\)s\({}^{-1}\) and a turbine spacing of 5.8 to 20 rotor diameters, a 1D change in spacing causes a change in efficiency of 1.49% at Nysted and 1.06% at Horns Rev offshore wind farms, respectively. Furthermore, power losses that are caused by wakes depend strongly on wind speed. Efficiency is only 60% of that predicted using the free stream wind speed for directly-down-the-row wakes at low wind speeds but is larger than 97% thereof at wind speeds above 15 m\(\cdot\)s\({}^{-1}\). Analysis of the variation in efficiency with wind speed from 5 m\(\cdot\)s\({}^{-1}\) to 15 m\(\cdot\)s\({}^{-1}\) demonstrates an increase in efficiency of about 1.73% and 2.39% for every 1 m\(\cdot\)s\({}^{-1}\) increase in wind speed at Nysted and Horns Rev, respectively [25]. Wind farm efficiency increases with ambientturbulent intensity and atmospheric mixing tends to promote the recovery of wind turbine wakes, increasing wind farm efficiency.
## 3 Proposed Hybrid JADE-DO Algorithm
### Computational Complexity Analysis  
The computational complexity of the hybrid JADEDO algorithm can be analyzed by examining the operations performed in both the JADE (adaptive differential evolution) and dandelion optimizer (DO) phases. In each iteration, the algorithm executes a series of operations whose computational costs depend on the population size \(N\) and the dimensionality of the problem \(d\).  
In the JADE phase, the key operations are mutation, crossover, and selection. Each of these operations involves processing all \(N\) individuals in the population, and for each individual, the computational cost is proportional to the dimensionality \(d\). Therefore, the computational complexity of the JADE phase per iteration is as follows:  
\[C_{\text{JADE}}=O(N\times d) \tag{30}\]  
Similarly, the DO phase consists of the rising, decline, and landing stages. Each stage also processes all \(N\) individuals, with operations proportional to \(d\) per individual. Hence, the computational complexity of the DO phase per iteration is as follows:  
\[C_{\text{DO}}=O(N\times d) \tag{31}\]  
Combining both phases, the total computational complexity per iteration of the hybrid JADEDO algorithm is as follows:  
\[C_{\text{Total}}=C_{\text{JADE}}+C_{\text{DO}}=O(N\times d)+O(N\times d)=O(N \times d) \tag{32}\]  
Since constants are ignored in Big-\(O\) notation, the combined complexity remains \(O(N\times d)\) per iteration.  
If the algorithm runs for \(T\) iterations, the total computational complexity is as follows:  
\[C_{\text{Total}}=T\times O(N\times d)=O(T\times N\times d) \tag{33}\]  
This indicates that the computational complexity of the hybrid JADEDO algorithm scales linearly with the population size \(N\), the problem dimensionality \(d\), and the number of iterations \(T\).  
While the hybrid approach introduces additional computational overhead compared to standalone methods, it enhances optimization performance, particularly in high-dimensional and multimodal problems. The increased computational cost is justified by the improved convergence speed and solution quality achieved through the effective combination of exploration and exploitation mechanisms in both the JADE and DO phases.
## 1 Introduction
To obtain the best results, the most suitable AI method needs to be identified. AI methods such as ANN, SVM, k-nearest neighbors (k-NN), extreme learning machine (ELM), and support vector regression (SVR) were compared in some previous studies [25; 26; 27; 28; 29]. The different methodologies that have been used and tested in previous research are summarized in Table 1.  
The literature review showed that most studies used wavelet transform to analyze signals in electrical systems due to its suitability for analyzing the transient state of the power system. However, past research has focused on fault analysis in either overhead transmission lines or underground cables. Fault analysis has not been performed in hybrid transmission lines. Thus, this paper aims to \begin{table}
\begin{tabular}{c c c} \hline \hline
**Proposed Method** & **Parameter for Decision** & **Reference** \\ \hline \multicolumn{3}{c}{Wavelet Transform (WT) and Algorithm} \\ \multicolumn{3}{c}{Energy} \\ Discrete wavelet transform (DWT) & Coefficients, & [6; 7; 8; 9] \\  & Power spectral density, & [4] \\  & Alienation coefficients & [5] \\  & WT and artificial intelligence (AI) & \\  & DWT and SVM & Coefficients, Spectral, Energy & [10; 12; 18; 19; 28] \\  & DWT and Fuzzy & Coefficients & [20] \\  & DWT and ANN & Coefficients, Energy, Spectral & [11; 13; 14; 15; 17; 25] \\  & DWT and HSA & Coefficients & [16] \\  & DWT and ELM & Coefficients & [26; 27] \\  & DWT and LDA & Coefficients & [21] \\  & Wavelet packet transform (WPT) and SVM & Coefficients & [22] \\  & WPT and ELM & Energy and entropy & [23] \\  & Empirical Mode Decomposition (EMD) and SVM & - & [24] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fault detection and classification method of past articles. present the performance of different AI methodologies in fault classifying in hybrid transmission lines. In previous studies [7; 8; 9], wavelet coefficient behavior to hybrid transmission lines was studied [7] and the algorithm for classification of faults using only DWT was designed [8]. In addition, different mother wavelets for classification were compared to identify the optimal mother wavelet using an algorithm [9]. The algorithms developed in previous studies [7; 8; 9] are applied to AI methodologies to improve their accuracy. Three types of AI methods (probabilistic neural networks (PNN), back-propagation neural networks (BPNN), and SVM) have been used and compared in terms of accuracy of classification results in order to select the suitable AI methodology to implement in protection systems. The overall process of the study is shown in Figure 1. First, the fault signal was simulated by ATPDraw. Next, the fault signals were analyzed using a WT. The maximum coefficient value was input into the AI. Finally, the AI methods were compared in terms of accuracy. The detailed process is explained in the next section.
###### Abstract  
Applying Software Engineering (SE) processes is vital to critical and complex systems including security and networking systems. Nowadays, Wireless Sensor Networks (WSNs) and their applications are found in many military and civilian systems which make them attractive to security attackers. The increasing risks and system vulnerabilities of WSNs have encouraged researchers and developers to propose many security solutions including software-based Intrusion Detection Systems (IDSs). The main drawbacks of current IDSs are due to the lack of clear, structured software development processes. Unfortunately, a substantial gap has been observed between WSN and SE research communities. Integrating SE and WSNs is an emerging topic that will be expanded as technology evolves and spreads in all life aspects. Consequently, this paper highlighted the importance of Requirement Engineering, Software Design, and Testing when developing IDSs for WSNs. Three software IDS designs were proposed in this study: Scheduling, Broadcast, and Watchdog designs. The three designs were compared in terms of consumed energy and network lifetime. Although the same IDS approach was used, but, by highlighting the design phase and implementing different designs, the network lifetime was increased by 73.6% and the consumed energy was reduced by 20% in some of the designs. This is a clear indication of how following a proper SE process could affect the performance of the IDS in WSN. Moreover, conclusions were drawn in regard to applying software engineering processes to IDSs to deliver the required functionalities, with respect to operational constraints, with an improved performance, accuracy and reliability.  
software engineering; wireless sensor networks; WSN; requirement engineering; intrusion detection system; IDS; LEACH; packet dropping; network lifetime; energy; IoT +
Footnote †: journal: Sensors
## 4 Simulation Examples
### Simulation of High-Voltage Transmission Line without Load Being Switched-In Suddenly
Considering the boundary conditions of both ends of the transmission line, we obtained the following equations by combining Equations (16)-(18):  
\[K\dot{x}+\rho x+\omega(t)=0 \tag{19}\]  
where \(K,\rho\in R^{(2N+1)\times(2N+1)}\) is the constant coefficient matrix; \(\omega(t)\) is the input excitation source for the transmission line; and where:  
\[x=\Big{[}v_{0}\quad v_{1}\quad\cdots\quad v_{N}\quad i_{1}\quad\cdots\quad i_ {N}\Big{]}^{\rm T} \tag{20}\]  
\[\omega(t)=[-i_{0},0,\cdots,0,i_{N+1},0,\cdots,0]^{\rm T} \tag{21}\]  
\[K=\left[ \begin{array}{cccccccc}a_{4}&a_{1}&&&&0\\ a_{1}&a_{2}&\ddots&&&&\\ &\ddots&\ddots&\ddots&&&&\\ &&\ddots&a_{2}&a_{1}&&&&\\ &&&a_{1}&a_{4}&&&&\\ &&&&b_{3}&b_{1}&&\\ &&&&b_{1}&b_{2}&\ddots&\\ &&&&\ddots&\ddots&b_{1}\\ 0&&&&&&&b_{1}&b_{3}\end{array} \right] \tag{22}\]  
\[\rho=\left[ \begin{array}{cccccccc}\beta_{4}&\beta_{1}&&&&1&&&\\ \beta_{1}&\beta_{2}&\ddots&&-1&1&&\\ &\ddots&\ddots&\ddots&&\ddots&\\ &&\ddots&\beta_{2}&\beta_{1}&&&-1&1\\ &&&\beta_{1}&\beta_{4}&&&-1\\ -1&1&&&\phi_{3}&\phi_{1}&&\\ &-1&1&&&\phi_{1}&\phi_{2}&\ddots&\\ &&\ddots&\ddots&&\ddots&\ddots&\phi_{1}\\ &&&-1&1&&&\phi_{1}&\phi_{3}\end{array} \right] \tag{23}\]  
where  
\[a_{j}=c_{0}\cdot\Delta y\cdot\theta_{j},\,b_{j}=l_{0}\cdot\Delta y\cdot\theta_{ j},\,\,\beta_{j}=g_{0}\cdot\Delta y\cdot\theta_{j},\,\,\phi_{j}=r_{0}\cdot \Delta y\cdot\theta_{j},\,\,(j=1,2,3,4) \tag{24}\]The four-step L\({}_{2}\)MF method and the implicit trapezoidal integration method were used to solve Equation (19), and the integration step of the simulation test was \(h=1\)\(\mu\)s for both L\({}_{2}\)MF and the implicit trapezoidal integration method. Moreover, the transmission line was uniformly divided into \(N=50\) sections. The simulation results are shown in Figure 8, and Figure 9 is an enlargement of Figure 8.  
As shown in Figures 8 and 9, the calculation results of the four-step L\({}_{2}\)MF method were basically consistent with the second-order the implicit trapezoidal integration method. In the moment of the circuit closing, the implicit trapezoidal integration method had a continuous numerical oscillation due to the lack of L-stability. On the contrary, the four-step L\({}_{2}\)MF method was L-stable, and therefore had strong damping property, which effectively inhibited the numerical oscillations. Figure 8: Terminal voltage of the transmission line calculated by the four-step L\({}_{2}\)MF method and the implicit trapezoidal integration method. Figure 7: A piecewise model of a transmission line. Figure 9: An enlargement of Figure 8.
## 1 Introduction
Compared with the standard CKF, the proposed filter is more flexible because the accuracy can be further improved with the increase of CL polynomial order. Also, computational cost increases linearly with CL polynomial order, which is superior to other high-degree versions of the CKF. The proposed filter can potentially be used in many applications such as air traffic control, remote sensing, ballistic missile guidance, and computer vision with good filtering accuracy. The rest of this paper is organized as follows. Section 2 describes the system model and Bayesian nonlinear filter framework. The orthogonal simplex Chebyshev-Laguerre cubature rule is presented in Section 3. Section 4 yields the performance comparison of the proposed algorithm with the conventional algorithms. Conclusions are drawn in Section 5.
## 4 Experimental Tests
### Influencing Factors of AR Placement Methods  
In an AR scene, the quality of placement is dependent on many factors that were tested using both marker-based and marker-less approaches.  
* Marker-based methods Several scientific works have studied the factors influencing the AR placement in marker-based methods. They identified the following factors (Figure 11): the quality of markers, the maturity of the  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of reference points** & 24 points (12 indoor and 12 outdoor) \\ \hline
**Mobile phone** & Mid-range phone (“Huawei P20 Pro”) \\
**Operator** & The same operator for topographic surveying of virtual and real points. \\
**Lighting Conditions** & Same lighting conditions (in the morning for all tests) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test parameters.  
Figure 9: Types of markers used outdoors.  
AR SDKs, the complexity of the virtual models [60], as well as the distance of the virtual object from the marker, the size and the spatial disposition of markers [13]. Practical tests on site were conducted to determine the extent to which these factors influence the AR placement of a 3D model in terms of accuracy and model stability.  
\(\bullet\) Marker-less methods  
Marker-less placement methods are affected by a single factor, which is the quality of localization [60]. Thus, developers can use better location technology and better hardware to improve the stability and placement accuracy of the virtual model. In our case, we used the Trimble SiteVision system, which improves the accuracy value obtained by the method based on the inertial sensors of the smartphone.
###### Abstract  
Multimedia security has received much attention recently because of the rapid transmission of elements such as text, images, audio, video, software, animation and games. Security is becoming especially critical for content owners concerned about the illegal usage of their original products. Encryption and watermarking are two methodologies for digital applications. Spatial domain and frequency domain watermarking algorithms give very promising results in embedding binary images into the cover images. This paper proposed a new method of semi-blind watermarking technique. The digital images are divided into 4 \(\times\) 4 blocks and converted into discrete Wavelet transformations (DWTs). The binary image is embedded into each block using the flexible scaling factor method. Experimental results show that the proposed method has higher peak signal to noise ratio (PSNR) and similarity ratio (SR) values compared to the standard Wavelet transformation and block-based Wavelet algorithms. The results prove that the proposed hybrid algorithm is more effective, robust, secure and resistant than DWT and block-based DWT algorithms.  
m +
Footnote †: journal: electronics  
attacks. Robust watermarking is generally used in copyright authentication due to its ability to resist most attacks [6]. Methods used for extracting watermarks are classified into two techniques, namely blind and semi-blind watermarking. A technique is called blind watermarking if only the secret key is required for extracting the watermark. The technique is called semi-blind watermarking if both the secret key and the watermarked image are used for extracting the watermark [7; 8].  
The remainder of the paper is organized as follows: Section 2 presents background reading and related work. To provide a deeper understanding of this area, Section 3 introduces methodologies. Section 4 introduces the proposed methodology. Section 5 presents the experimental results. Finally, the paper concludes with Section 6 focusing on the contribution along with a future recommendation. The next section discusses the related work.