esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Metamorphic Testing for Edge Detection in MRI Brain Diagnostics
## 2 Related Work
### Tumor Detection in MRI Brain Images
Results show that the improved algorithm is capable to extract the brain portion from MRI images. A Combination of K-Means and Fuzzy C-Means algorithm for the identification of Brain Tumor is proposed by [PERSON] et al. in [4]. They have used two filters for contrast adjustment, namely, a fast local Laplacian filter and a median filter. Similarly, two clustering algorithms (K-Means and Fuzzy C-Means) are used for the detection of brain tumor. Canny edge detection is used to detect the edges. The results are computed through confusion matrix. An improved algorithm for the detection of brain tumor is proposed by [PERSON] et al. [30]. The authors have used a median filter to remove noise from images. Then, image enhancement is performed by scaling the grey level of each pixel. Edges are detected through three traditional edge detection algorithms such as, Canny, Sobel, and Prewitt. Segmentation (thresholding segmentation technique) and clustering (K means clustering) is implemented for the detection of brain tumor. [PERSON] et al. proposed an algorithm for the detection of brain tumor in MRI images [31]. A median filter is used for noise removal. Contrast is enhanced through scaling the gray level of each pixel. Sobel and Canny edge detection algorithms are used for the detection of edges. Thresholding technique is used in segmentation process. Results are evaluated on the basis of confusion matrix.
## 2 Decomposition Methodology  
### Brief Introduction to VMD  
VMD is a self-adaptive and non-recursive signal decomposition method based on Wiener filtering, Hilbert transform, and frequency distortion [21]. The principle of the VMD algorithm is to decompose components of a signal into different adaptive bands for the development of a generalised Wiener filter. The input signal is decomposed into limited bandwidth modes, \(u_{k}\), with each sub-signal tightly focused around its own central frequency. The bandwidth of each mode can be evaluated by solving the constrained variational problem shown in Equation (1) [21].  
\[\begin{array}{c}\underset{\{u_{k}\},\{\omega_{k}\}}{\min}\left\{\sum_{k}\| \alpha_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\Big{]}exp^{-j \omega kt}\ \|_{2}^{2}\right\}\text{m}\\ s.t.\sum\limits_{k}u_{k}=f\end{array} \tag{1}\]  
where \(\delta\) is the Dirac distribution; \(k\) is the number of the mode. \(u_{k}\) are the set of all modes; and \(\omega_{k}\) is the centre frequency of each mode.  
In order to remove constraints on Equation (1), the constrained variational problem can be solved considering a Lagrangian multiplier \(\lambda(t)\) and a quadratic penalty term \(\alpha\), which results in Lagrangian \(\mathcal{L}\), as shown in Equation (2):  
\[\begin{array}{c}\mathcal{L}(\{u_{k}\},\{\omega_{k}\},\lambda)=\alpha\sum \limits_{k}\sum\limits_{k}\left\{\sum\limits_{k}\|\alpha_{t}\Big{[}\Big{(} \delta(t)+\frac{j}{\pi t}\Big{)}*u_{k}(t)\Big{]}exp^{-j\omega kt}\ \|_{2}^{2}\right\}+\|f(t)-\sum\limits_{k}u_{k}(t)\ \|_{2}^{2}\\ +\bigg{\langle}\lambda(t),f(t)-\sum\limits_{k}u_{k}(t)\bigg{\rangle}\end{array} \tag{2}\]  
In order to obtain an optimal solution of the constrained variational model, the Alternative Direction Method of Multipliers (ADMM) is applied to solve Equation (2). Hence,  
Figure 1: Flow chart indicating underside corrosion diagnosis method.  
the mode \(u_{k}\) and centre frequency \(\omega_{k}\) can be updated, as shown in Equations (3) and (4), respectively:  
\[u_{k}^{n+1}\overset{\circ\circ}{\leftarrow}\underset{u_{k}}{argmin}L(\left\{u_{ i<k}^{n+1}\right\},\left\{u_{i\geq k}^{n+1}\right\},\left\{\omega_{i}^{n+1} \right\},\lambda^{n}) \tag{3}\]  
\[\omega_{k}^{n+1}\overset{\circ\circ}{\leftarrow}\underset{u_{k}}{argmin}L( \left\{u_{i}^{n+1}\right\},\left\{u_{i<k}^{n+1}\right\},\left\{\omega_{i\geq k }^{n+1}\right\},\lambda^{n}) \tag{4}\]  
Decomposition to identify all sub-signals (IMFs) can be achieved through continuous iteration as presented in Equation (5):  
\[\overset{\wedge}{u}_{k}^{n+1}(\omega)=\frac{\overset{\wedge}{f}(\omega)- \sum_{i<k}\overset{\wedge}{u}_{i}^{n+1}(\omega)+\frac{\overset{\wedge}{\lambda ^{n}}(\omega)}{2}}{1+2\alpha\left(\omega-\omega_{k}^{n}\right)^{2}} \tag{5}\]
# An Improved Aeromagnetic Compensation Method Robust to Geomagnetic Gradient
## 4 Field Experiments and Data Analysis
### Compensation Flight and Level Flight  
The compensation flight for this experiment was carried out in the South China Sea at an altitude of more than 3000 m and a flight speed of 180 km/h. Five cycles of maneuvers consisting of about \(\pm 5^{\circ}\) rolls, \(\pm 5^{\circ}\) pitches, and \(\pm 10^{\circ}\) yaws are carried out in each heading. As it is shown in Figure 9, a calibration flight and a test flight were recorded, and these two flight paths were very similar. Usually, the test flight is conducted under the same magnetic background as the calibration flight to verify the performance of aeromagnetic compensation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Constellations & GPS, GLONASS, BeiDou, Galileo and QZSS & Operating temperature & \(-15\ ^{\circ}\)C to \(55\ ^{\circ}\)C \\ \hline Navigation update rate & 10 Hz & Input voltage & 12 to 24 V DC \\ \hline Horizontal position accuracy & 5 cm at 2 \(\sigma\) (95\%) & Power consumption & 4 W (12 V input) \\ \hline Elevation position accuracy & 12 cm at 2 \(\sigma\) (95\%) & Receiver size & 90 mm (H) \(\times\) 158 mm (W) \(\times\) 227 mm (D) \\ \hline Data ports and interfaces & 3 Serial Connectors (EIA-232/422) & Weight & 2.25 kg \\ \hline \hline \end{tabular}
\end{table}
Table 4: The technical specifications of the DGPS receiver.  
Figure 8: The block diagram of data recording and compensation system.  
The level flight path is shown in Figure 10. The level flight was about 30 km, the aim of which was to verify the best performance of aeromagnetic compensation.
## 2 Improved Adaptive VMD Algorithm
### IFOA-VMD Decomposition  
#### 2.2.1 Energy Factor  
Due to the complexity and variety of the measured signals, the difficulty and key to using the VMD algorithm is how to select the appropriate decomposition number K and the penalty parameter value \(\alpha\)[37]. When using VMD, the preset decomposition number K and the penalty parameter \(\alpha\) need to be selected. The preset decomposition number K and the penalty parameter \(\alpha\) will affect the decomposition result. The smaller the penalty parameter \(\alpha\) in the VMD algorithm, the larger the bandwidth of each IMF component obtained by the decomposition; the larger the \(\alpha\), the smaller the bandwidth. If the selected K value is too large or too small, it is easy to cause information loss or redundancy. Based on this, this paper proposes a new method of fusion of FOA and VMD, which can adaptively optimize the internal combination parameters \((K,\alpha)\) of VMD. However, before the parameter optimization, the fitness function needs to be determined. The selection principle of K is that the new central frequency maximum or minimum value no longer appears, thus ensuring that the VMD decomposition does not miss higher or lower center frequencies. Therefore, the IMF component energy growth factor can be used as the fitness function, and the energy growth factor e is defined as the ratio of the energy difference between the current decomposition component and the previous decomposition component to the original signal energy, which can be calculated by equation (7).  
\[e = \frac{\left\|\sum u_{k}-\sum u_{k-1}\right\|_{2}^{2}}{\left\|f \right\|_{2}^{2}} \tag{7}\]  
where \(\sum u_{k}\) is the energy of the current decomposition component, \(\sum u_{k-1}\) is the energy of the last decomposition component, and \(f\) is the original signal. In this paper, the energy growth factor is used to determine an indicator of the number of modes, and the energy growth factor threshold e is set to 0.01 [38]. At the same time, the maximum decomposition modulus is set to 10, and the maximum penalty parameter is set to 6000. In the process of decomposing the signal by the VMD, one of the above conditions is satisfied, and the decomposition is stopped. Therefore, the number of decomposition modes and the penalty parameters can be adaptively determined by seeking the minimum value of the energy growth factor, and the parameter optimization process of the VMD is intuitively described as solving the problem of minimization of the fitness function.
## Materials and Methods
### Evaluation of the Binding Parameters of 2-FAL on MIP  
#### 2.7.1 Binding Parameters by Batch Equilibration  
The binding parameters are usually evaluated from the adsorption isotherms by applying some adsorption models in order to fit the experimental data. One of the most commonly applied model is the Langmuir isotherm [29; 30] which relies on the assumption that binding takes place by combination of the substrate A with receptor sites in the solid (R), according to the following equilibrium:  
\[A+R\leftrightarrow AR\quad K_{A}=\frac{[RA]}{[R][A]} \tag{1}\]  
R represents the receptor sites for A, obtained by the molecular imprinting method with a given affinity and at concentration cR. [R] is the concentration of the free sites and [RA] is the concentration of the sites occupied by the target substance A. These concentrations are referred to the polymer phase (in mmol/gMIP).  
The Langmuir sorption isotherm is:  
\[[RA]=\frac{c_{R}K_{A}[A]}{1+K_{A}[A]} \tag{2}\]  
Sometimes the Langmuir-Freundlich equation is used to account for the possible heterogeneity of the binding sites, and in this case an exponential m, the heterogeneity index, is introduced to account for the heterogeneity [29; 30]. The parameters of Equation (2), included the heterogeneity index, can be determined by non-linear regression, available in the common statistical packages. In the present study the Hill equation from the software OriginPro 2016 b9.3.226 has been used.  
Alternatively, the relation can be expressed in the Scatchard linear format, which has been widely employed for the evaluation of the parameters:  
\[\frac{[RA]}{[A]}=-K_{A}[A]+K_{A}c_{R} \tag{3}\]  
This model well fit the saturation phenomena which are always observed when a sufficiently high amount of A is adsorbed on the MIP i.e., when [A] >> 1/K\({}_{A}\), and [RA] = c\({}_{R}\). Equation (2) and the derived Equation (3) hold when only one kind of interaction sites is present in MIP, however different kinds of sites with different affinities for A can be present in the non-covalent MIPs [20], due to their inherent heterogeneity. The presence of multiple classes of sites is indicated by a curvature of the Scatchard plot. In this case, when for example two kinds of sites are present, the bi-Langmuir model can be applied [29; 30] considering at the same time the sites with higher association constant (K\({}_{A2}\)) and site concentration c\({}_{R2}\), and those with lower association constant (K\({}_{A1}\)) and site concentration c\({}_{R1}\). The corresponding relationship is  
\[[RA]=\frac{c_{R2}K_{A2}[A]}{1+K_{A2}[A]}+\frac{c_{R1}K_{A1}[A]}{1+K_{A1}[A]} \tag{4}\]  
In this case the Scatchard plot (Equation (3)) should be composed of two straight lines at the extreme concentrations. Usually not more than two different kinds of sites are detectable, so a bi-Langmuir model is sufficient for the sorption characterization in a given concentration range. The adsorption on the corresponding NIP can be examined by the same model.
## 3 Results and Discussion
### Assessing the Environmental Ecological Risks of the Surface Soil  
To assess the potential impact of a given PTE in the soil on ecosystems, (\(E_{r}^{i}\)) was used in this study. The PTEs in the surface soil of the research area did not demonstrate a risk degree higher than \"moderate risk\" according to the average (mean) and median values \(E_{r}^{i}\) obtained at all sampling sites. While Cd and Hg posed higher ecological risks when compared to the other PTEs by the upper quarter values, Cd and Hg showed examined  
Figure 4: The distribution of counter maps of the geo-accumulation index (I\({}_{\text{geo}}\)) determined by inverse distance-weighted (IDW) interpolation.  
Figure 5: The distribution of counter maps of the contamination factor (C\({}_{i}\)) determined by inverse distance-weighted (IDW) interpolation.  
values of 64.36 and 61.17, respectively, due to the higher toxic response factor. Furthermore, the highest \(E_{r}^{i}\) values of Hg (2426.50), Cd (1229.28), As (485.25), and Pb (343.60) were examined, due to their greater toxic response factors of 40, 30, 10, and 5 when compared to Cu (five), Ni (five), Cr (two), and Zn (one), respectively. The environmental ecological risks of all of the studied PTEs can be ranked as: Cd > Hg > As \(\approx\) Pb > Cr = Ni = Zn. Overall, 0.64% (10 sites) of As, 16.07% (253 sites) of Cd, 16.39% (258 sites) of Hg, and 0.51% (eight sites) of Pb were shown to have \(E_{r}^{i}\) values\(>\) 80, and 7.05% (11 sites) of all of the studied PTEs with PERI values > 300 were at or above the level of \"considerable protentional ecological risk\".  
Contour maps determined by IDW interpolation were produced to discover the spatial distributions of the possible ecological risk of these studied PTEs (Figure 6). The distributions of As, Cd, Hg, and Pb, which had \(E_{r}^{i}\) values > 80 and PERI values > 300, were compatible with the distribution of Hg and Cu-Pb-Zn deposits in the study area. The high PTE concentrations and their associated ecological risks in the soils surrounding Hg and Cu-Pb-Zn deposits have also been found by other researchers [5; 52].  
Figure 6: The distribution of counter maps of the potential ecological risk (\(E_{r}^{i}\)) of a particular PTE and potential ecological risk index (PERI) of all of the studied PTEs determined by inverse distance weighted (IDW) interpolation.
## 5 Local Pre-Rerouting Algorithm
### Alternate and Detour Path Calculations  
Under such a design, we can obtain the service distribution of the links affected by sun outage, and at the same time update the link load in the limited area based on the original paths of these services, and obtain the link load in the limited area after removing the link failure traffic. Assume that the updated link load is \(l_{e}^{new}\), the service set in the link that is about to fail is \(D\) and path \(p\) is the path of service \(d\) before sun outage occurs; \(y_{e}^{p}\) represents whether path \(p\) passes through link \(e\),  
\[y_{e}^{p}=\left\{\begin{array}{ll}1,&\text{ }path\ p\text{ }paths\text{ } through\ link\ e\\ 0,&\text{ }path\ p\text{ }doesn't\text{ }path\text{ }through\ link\ e \\ \end{array}\right.. \tag{11}\]  
\[l_{e}^{new}=l_{e}-\sum_{d\in D}y_{e}^{p}w^{d} \tag{12}\]  
Next, we design three different paths for each source and destination node pair. The first path is the shortest path. The calculation method for this path is to use the Dijkstra algorithm to calculate the shortest path after removing the link that is about to fail in the original topology. In the figure this is shown as s-5-2-3-4-d. The second path is the second shortest path. In the topology where sun outage occurs, the second shortest path is calculated through [PERSON]'s algorithm. In the figure, it is s-5-6-3-4-d. The third path is the detour path. In the topology where sun outage occurs, the links with updated loads greater than \(\alpha\) times the average load in the limited area are removed. The loads \(l_{e^{*}}^{new}\) of these links \(e^{*}\) satisfies  
\[l_{e^{*}}^{new}>\alpha(\sum_{e\in E}l_{e}^{new})/k \tag{13}\]  
Here, \(E\) represents the set of links in the limited area, and \(k\) represents the total number of links in the limited area. After removing these links, the Dijkstra algorithm is used to calculate the detour path; as shown in the figure: s-7-8-9-6-3-4-d.
# New Multi-View Feature Learning Method for Accurate Antifungal Peptide Detection
###### Abstract  
Antimicrobial resistance, particularly the emergence of resistant strains in fungal pathogens, has become a pressing global health concern. Antifungal peptides (AFPs) have shown great potential as a promising alternative therapeutic strategy due to their inherent antimicrobial properties and potential application in combating fungal infections. However, the identification of antifungal peptides using experimental approaches is time-consuming and costly. Hence, there is a demand to propose fast and accurate computational approaches to identifying AFPs. This paper introduces a novel multi-view feature learning (MVFL) model, called AFP-MVFL, for accurate AFP identification, utilizing multi-view feature learning. By integrating the sequential and physicochemical properties of amino acids and employing a multi-view approach, the AFP-MVFL model significantly enhances prediction accuracy. It achieves 97.9%, 98.4%, 0.98, and 0.96 in terms of accuracy, precision, F1 score, and Matthews correlation coefficient (MCC), respectively, outperforming previous studies found in the literature.  
AFP-MVFL; peptide; multi-view feature learning; ifFeature; random forest
## 3 **Introduction**
While some microstructural investigations in the literature have utilized 3D X-ray Computed Tomography (XCT) to capture the complex morphology of the intermetallics, these investigations have predominantly relied on relatively simple parameters such as particle volume, equivalent diameter, sphericity, aspect ratio, and Feret diameter [30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41] to quantify the complex intermetallic particle morphology and the emergent microstructure. While useful, these traditional morphological parameters often fall short in capturing the complex spatial distribution of interconnected intermetallic particles in 3D. Furthermore, even though the literature indicates that the MgZn phase enhances cathodic efficiency when present in an interconnected structure, the literature lacks detailed information on the spatial distribution and morphological changes the MgZn phase undergoes in response to compositional variations and solidification conditions. Therefore, in this work, we study the impact of the composition and solidification conditions on the microstructure of Mg-Zn-Si alloys. We propose a pair of novel morphological parameters that are extremely well-suited to the quantitative characterization of the MgZn phase, thereby addressing significant gaps in the literature in the design of biocompatible alloys. The 3D morphological investigation is complemented with compositional data obtained from SEM-EDS analysis on four distinct samples of Mg-Si-Zn alloys, each with different compositions and cooling rates.
# Real-Time Stress Field Prediction of Umbilical Based on PyEf-U-Net Convolutional Neural Network
## 1 Introduction
The network can only predict the load-displacement curves of large-diameter umbilical in the plastic stage with data from small-diameter umbilicals, and the accuracy of the network decreases with the reduction of input data. Although the above research has brought about the prediction of the mechanical properties of umbilical, the network structure is relatively simple. Aiming to avoid the time and economic cost consumption caused by traditional numerical simulation methods, a novel deep convolutional neural network PyEf-U-Net is proposed. Adding the pyramidal convolution (Pyra-Con) module in the network transition layer, which employs multiple convolutions of different sizes for feature extraction of the feature map, which fuses multi-scale features. Additionally, adding the efficient channel attention module (EffCA) module after the low-level feature map, avoids the inefficiency of dimensionality reduction operations for channel attention prediction, while capturing feature information across channel interactions in an extremely lightweight manner. The results demonstrate that the PyEf-U-Net not only has fast convergence characteristics and well-generalized capability but also can achieve real-time accurate prediction of the mechanical properties distribution of umbilical.
## 4 Experimental Results and Analysis  
### Experimental Datasets  
The experiments were conducted on the Aalto [22], UNSW [23], and LSIF datasets [24], obtained from IoT device traffic data during device installation, user interaction, and idle states, respectively. As Siamese networks rely on the relative relationships between samples rather than absolute labels, only a small amount of data is required for effective learning. Thus, up to 50 payload samples were randomly selected from each IoT device to construct a set of PNSPs. The specific number of samples for each device is shown in Table 2. To ensure fairness, all subsequent comparison experiments considered 50 samples for each IoT device. In addition, in order to facilitate the unknown IoT device identification experiments, three IoT devices were randomly excluded as unknown IoT devices from the training set beforehand.
# Performance Evaluation of TCP BBRv3 in Networks with Multiple Round Trip Times
## 4 Simulation Framework  
### Details of ns-3 Implementation  
The full source code of the modified ns-3 simulation environment is available online [23]. The ns-3 was forked from the main branch of ns-3-dev. We adopted BBRv2 implementation from the repository ([[https://github.com/lanigan23/BBRv2-Eval-ns-3](https://github.com/lanigan23/BBRv2-Eval-ns-3)]([https://github.com/lanigan23/BBRv2-Eval-ns-3](https://github.com/lanigan23/BBRv2-Eval-ns-3)) (accessed on 6 May 2024) built on top of [PERSON]'s BBR adaption [24]. All simulation scripts used to produce the results are available online. The BBRv2 and BBRv3 implementations are based on the Linux kernel code from the official repository on GitHub: BBRv2 ([[https://github.com/google/bbr](https://github.com/google/bbr)]([https://github.com/google/bbr](https://github.com/google/bbr)) (accessed on 6 May 2024) and BBRv3 ([[https://github.com/google/bbr/blob/v3](https://github.com/google/bbr/blob/v3)]([https://github.com/google/bbr/blob/v3](https://github.com/google/bbr/blob/v3)) (accessed on 6 May 2024).  
It was also decided to address reported bugs that were found to be incompatible with the Linux implementation, thus causing BBR to inaccurately estimate the delivery rate in certain scenarios. The initial fix involved modifying the delivery rate update mechanism as described here ([[https://tinyurl.com/yeymtvcx](https://tinyurl.com/yeymtvcx)]([https://tinyurl.com/yeymtvcx](https://tinyurl.com/yeymtvcx)) (accessed on 6 May 2024). Furthermore, adjustments were made to the implementations of BBRv2 and BBRv3, thus incorporating conditions to verify rate samples in alignment with those in Linux kernel. Additionally, variables were introduced to enable the implementation of short-term and long-term inflight and bandwidth estimates, thus enhancing the original implementation, which solely relies on the windowed maximum filter of the delivery rate.  
Limitations: The simulation model always entails simplifications compared to the actual implementation. In the Linux implementation, Core TCP stack informs us that the given skb was just marked lost and triggers function, which adapts the inflight_HI event that cannot be replicated in ns-3. Consequently, upper limits may be set too optimistically in some cases, thus leading to delayed responses to link degradation induced by either another stream or a change in conditions. Moreover, ECN support is not implemented in the current version.