esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Phase Noise Crystal Oscillator with a Fast Start-Up Bandgap Reference for WLAN Applications
## 5 Conclusions  
This study presents a low phase noise crystal oscillator with 40 MHz frequency outputs, fabricated using a 55 nm CMOS process. The oscillator provides a low-noise reference clock source suitable for high-performance PLL and digital circuits. The design employs a self-biased low-voltage common-source common-gate current mirror with a start-up circuit to ensure proper and fast powering up across various process corners and temperatures. In addition, under the condition that the XO circuit can operate properly, the optimal R\({}_{3}\) resistor value is selected to reduce the phase noise. A low-pass filter is also incorporated into the LDO input to minimize the impact of power supply noise on crystal oscillator noise. The crystal oscillator achieves the 40 MHz output phase noise of \(-\)164.36 dBc/Hz at 100 kHz offset. Testing verifies normal start-up within 0.718 ms. According to the simulation and test results, the final FOM value calculated in this paper was 209.93 dBc/Hz at 100 kHz offset.  
Methodology, P.W., P.L., X.C., P.C. and J.Z.; software, P.W., P.L., P.C. and J.Z.; validation, P.W., P.L., P.C. and J.Z.; formal analysis, P.W., P.L., X.C. and J.Z.; investigation, P.W., P.L., X.C. and J.Z.; resources, P.W., P.L., P.C. and J.Z.; data curation, P.W., P.L., X.C. and J.Z.; Writing--original draft preparation, P.W. and P.L.; writing--review and editing, P.W., P.L., X.C. and P.C.; visualization, P.W.; supervision, P.W., P.L., P.C. and J.Z.; project administration, P.W., P.L., X.C., P.C. and J.Z.; funding acquisition, P.C. All authors have read and agreed to the published version of the manuscript.  
The project fund comes from Zhejiang CASEMIC Electronics Technology Co., Ltd.  
Not applicable.  
The data in this paper are all from simulation and test results of real circuits, and there is no data plagiarism or falsification. Due to project confidentiality requirements, we regret that we cannot release more details of the data at this time.  
The authors declare no conflict of interest.
# Investigation of Mixed-Mode I/II Fracture under Impact Loading Using Split-Hopkinson Pressure Bar
## 5 Conclusions  
With the proposed variable angle single cleavage semi-circle (VASCSC) specimen, a comprehensive method of investigation on mixed-mode fracture under impact loading was introduced with both experimental and numerical approaches applied. The mixed-mode I/II crack propagation behavior  
Figure 14: Crack propagation path and Von Mises contour plot of the specimen-30\({}^{\circ}\) in the numerical (AUTODYN) simualtion. (**a**) t = 235.39 \(\upmu\)s. (**b**) t = 262.42 \(\upmu\)s. (**c**) t = 296.61 \(\upmu\)s. (**d**) t = 338.91 \(\upmu\)s. (**e**) t = 356.57 \(\upmu\)s. (**f**) t = 403.16 \(\upmu\)s.  
Figure 13: Crack propagation velocity of the numerical (AUTODYN) and experimental result.  
and determination of initiation stress intensity factor were discussed and a numerical predict strategy was demonstrated. The conclusions are listed below.  
1. During the mixed-mode I/II crack propagation process, the deceleration of crack extending velocity would occur at the deflection area of the crack path, which is the flag of fracture mode transformation.
2. The variable angle single cleavage semi-circle (VASCSC) specimen is applicable to the investigation of mixed-mode fracture with SHPB device. It can not only be applied in crack propagation behavior study but also employed for determining dynamic initiation fracture toughness of brittle material with the mixed-mode fracture.
3. The AUTODYN can be applied in predicting crack propagation of mixed-mode I/II for brittle material under dynamic loading with guaranteed precision.  
Conceptualization, F.W., Z.Z., and M.W.; methodology, F.W., Z.Z., and M.W.; software, F.W. and H.Q.; validation, Z.Z. and M.W.; formal analysis, F.W.; investigation, F.W.; resources, Z.Z. and M.W.; data curation, F.W. and P.Y.; writing--original draft preparation, F.W.; writing--review and editing, F.W., Z.Z., M.W., and R.L.; visualization, F.W. and L.Z.; supervision, Z.Z. and M.W.; funding acquisition, Z.Z., M.W. and R.L.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number 11702181, 11672194), the open fund of Shock and Vibration of Engineering Materials and Structures Key Laboratory of Sichuan Province (18 yfjk02), the Fundamental Research Funds for the Central Universities, the Sichuan Science and Technology Program (Grant Numbers 2019 YFG0047).  
The authors declare no conflict of interest.
# Optimization of Multi-Day Flexible EMU Routing Plan for High-Speed Rail Networks
###### Abstract  
With the continuous expansion and increasing operational complexity of high-speed railway networks, there is a growing need for more flexible and efficient EMU (Electric Multiple Unit) routing strategies. To address these challenges, in this paper, we propose a multi-day flexible circulation model that minimizes total connection time and deadheading mileage. A multi-commodity network flow model is formulated, incorporating constraints such as first-level maintenance intervals, storage capacity, train coupling/decoupling operations, and train types, with across-day consistency. To solve this complex model efficiently, a heuristic decomposition algorithm is designed to separate the problem into daily service chain generation and EMU assignment. A real-world case study in the Beijing-Baotou high-speed corridor demonstrates the effectiveness of the proposed approach. Compared to a fixed strategy, the flexible strategy reduces EMU usage by one unit, lowers deadheading mileage by up to 16.4%, and improves maintenance workload balance. These results highlight the practical value of flexible EMU deployment for large-scale, multi-day railway operations.  
high-speed railway network; multi-day EMU routing plan optimization; EMU connection network; two-stage heuristic algorithm +
Footnote †: journal: Applied sciences
# The Multistate Reliability Assessment of Ship Hull Girders
###### Abstract  
Ships are designed to withstand various types of hull structure damage, including corrosion, fatigue, damage, crack, fouling, etc., throughout their projected life cycle of 25 years. In this study, we used a database of 25 different bulk carriers aged from five to twenty-five years, consisting of a total of 1920 thickness measurements of girder plate damage across 110 fuel tanks. Thickness measurements of longitudinal girder plate were conducted by certified technicians and approved company. Ultrasound thickness gauging equipment was used to collect data in accordance with the developed methodology and gauging scheme. Based on the classification societies' rules, the values of the reduction in steel plate thickness due to corrosion over time fall into three categories: acceptable corrosion, substantial corrosion, and extensive corrosion. While classification societies prescribe permissible thickness reductions between 15 and 30%, in this study, the authors considered the excessive corrosion values to be above 20% reduction in initial thickness. Measurements indicating more than 20% reduction were classified as failures, necessitating the replacement of the corroded surfaces. After applying the multistate approach to the reliability analysis of longitudinal girder plates and improving reliability after reaching the critical state, the results show that usability dropped significantly between ten and fifteen years of service for upper girder plating and between twenty and twenty-five years of service for lower girder plates. These findings highlight the crucial impact of gauging location on reliability analysis.  
corrosion; failure; girder plate; fuel oil tanks; reliability; risk Article
# Improvement of [PERSON]'s Stripe Noise Suppression Method for Side-Scan Sonar Images
## 2 Methods
### Improved Algorithm for Stripe Noise Suppression in Side-Scan Sonar Images  
#### 2.2.1 Improvement of Priority Function  
The Criminisi algorithm defines a priority function as the product of the confidence term \(C(p)\) and the data term \(D(p)\). Multiplication is sensitive to factors approaching zero, while addition is more stable. Therefore, we propose an enhanced priority function expressed as a weighted sum of the confidence and data terms, aiming to more accurately determine the patching order. Specifically, (1) dynamic weights are introduced into the priority computation, utilizing the Laplacian operator to assess local texture complexity dynamically and adjust the weights of the confidence and data terms adaptively based on the current patch block's texture complexity, and (2) the data term calculation incorporates the Sobel operator to compute gradients, improving edge information capture. This calculation is selectively applied only in boundary regions requiring patching rather than recalculating gradients for the entire image in each patching step.  
Therefore, this study improves the priority as follows:  
\[D(p)=\frac{\left|\
abla I_{p}^{\perp}\cdot n_{p}\right|}{\alpha}\]  
where  
\[\
abla I_{p}^{\perp}=\left(\mathsf{g}_{x},\mathsf{g}_{y}\right)^{\perp}=\left( \phi_{p},h,\phi_{p},h^{\prime}\right)\]  
Here, the Sobel operator uses a \(3\times 3\) template, where \(\phi_{p}\) denotes the \(3\times 3\) template centered at the boundary point, \(h\) represents the Sobel operator in the horizontal direction, and \(h^{\prime}\) represents the Sobel operator in the vertical direction.  
The priority function \(P(p)\) is defined as follows:  
\[P(p)=\mathrm{wC}(p)+D(p)\]  
where \(w\) is a dynamic weight determined by the local texture complexity, calculated as follows:  
The Laplacian operator \(L\) is applied to the local area of the side-scan sonar image to obtain the Laplacian image:  
\[laplacian=L(patch)\]  
The average absolute value of the Laplacian image is calculated to determine the texture complexity:  
\[complexity=\frac{1}{N}\sum_{i=1}^{N}\left|laplacian(i)\right|\]  
where \(N\) is the number of pixels in the local area, and \(laplacian(i)\) denotes the value of the i-th pixel in the Laplacian image.  
The weight \(w\) is dynamically adjusted based on the texture complexity as follows:  
\[w=1.0+\frac{complexity}{100}\]  
The advantages of the defined priority function are as follows: Firstly, the original product is changed to addition in order to avoid ignoring the influence of the confidence terms and data items when one of them is 0. Secondly, the Sobel operator is used to calculate the gradient in the calculation of the data items, which can better repair the edge features. Finally, dynamic weights are introduced into the data items through texture complexity, which can better reflect the priority in the more complex texture areas.
# Effective Detection of Malicious Uniform Resource Locator (URLs) Using Deep-Learning Techniques
## 4 Result and Discussion
### Dataset Validation
#### 4.2.1 Log Transformation  
In data analysis, log transformation is a powerful technique used to stabilize variance, normalize distributions, and enhance linearity. In our case, as observed, the dataset exhibits right skewness with a skewness value of 0.338. As shown in Figure 6, we present the implementation of the log transformation to address this skewness. The left plot displays the original URL length distribution (right-skewed) and the right plot displays the log-transformed URL length distribution (more normalized). This shows how log transformation helps in stabilizing variance and reducing skewness.
# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
## 3 Materials and Methods
### Methods
#### 3.2.3 Adaptive Wiener Filter  
The adaptive Wiener filter alters the filter output based on the image's local variance. The final objective is to reduce the mean square error between the restored and original images. The adaptive Wiener filter impressively suppresses the internal noise of the sensors, which are perturbed due to the physical limitations of sensors, as compared to other filters. The adaptive Wiener filter also improves the SNR of an image and hence removes the degradation and illumination problem. The adaptive Wiener filtering particularly restores the image's edges and high-frequency regions. The selection of the optimal kernel size is always inevitable for proper implementation of the adaptive Wiener filter in a CWD system. Therefore, different combinations of kernel sizes were used, such as 1, 2, 3, 4, and 5. Of these, a kernel size of 5 shows better results. Consider the filtering of images distorted by signal-perturbed noise, then the degradation of the images can be described by Equation (10):  
\[y(i,j)=x(i,j)h(i,j)+n(i,j) \tag{10}\]  
where \(y(i,j)\) represents the noisy image, \(x(i,j)\) is the noise-free image, \(n(i,j)\) is the noise owing to the limits of the physical setup, and \(h(i,j)\) is the operator matrix of linear spatial distortions with kernel elements of the point spread function (PSF). The linear operator \(h(i,j)\) is frequently spatially invariant, therefore the distortions can be represented by a convolution operator. The adaptive Wiener filter is then used to solve problem mentioned in the above Equation (10). The working of the adaptive Wiener filter can be realized in the frequency domain as Fourier transform and inverse Fourier transform operations, as expressed in Equation (11):  
\[\mathbf{\hat{s}}=\mathcal{F}^{-1}\bigg{\{}F\{\mathbf{u}\}\frac{\mathrm{H}^{* }(\omega)}{|\mathrm{H}(\omega)|^{2}+P_{n}(\omega)/P_{s}(\omega)}\bigg{\}} \tag{11}\]  
where \(\mathrm{H}(\omega)\) is the Fourier transformation of \(\mathrm{PSF}\,\mathrm{h}(x,y),P_{n}(\omega)\) is the spectral density of the noise, and \(P_{s}(\omega)\) is the spectral density of the signal; \((\cdot)^{*}\) denotes complex conjugation.  
A comparison of filtered and original images is shown in Figure 7. Figure 7a-d depict the ground truth, whereas Figure 7e-h show the filter-enhanced images. Wiener-filtered images have a much higher SNR and eliminate image inhomogeneity. The pictures obtained after implementing the Wiener filter are smooth images.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 2 Materials and Methods
### Measurement-Model Link
#### 2.1.1 The Case Where the Displacement, Velocity, and Acceleration are Measured
\[\widetilde{N}_{d}=\sum_{i}^{m_{d}}\widetilde{N}_{ei} \tag{15}\]  
Matrices \(\widetilde{N}_{v}\in\mathbb{R}^{m_{v}\times n_{d}}\) (\(m_{v}\) = the number of velocity sensors) and \(\widetilde{N}_{a}\in\mathbb{R}^{m_{a}\times n_{d}}\) (\(m_{a}\) = the number of acceleration sensors) can be obtained using the same method for the velocity and the acceleration, respectively. The displacement \(d_{s}\in\mathbb{R}^{m_{d}}\), velocity \(v_{s}\in\mathbb{R}^{m_{v}}\), and acceleration \(a_{s}\in\mathbb{R}^{m_{a}}\)velocity, and acceleration at the measurement location corresponding to eigenvector \(\phi_{i}\) can be defined in terms of the transformation matrices \(\widetilde{N}_{d}\), \(\widetilde{N}_{v}\), and \(\widetilde{N}_{a}\), as shown in Equations (19)-(21). \[d_{s}=\widetilde{N}_{d}\hat{d}=\widetilde{N}_{d}\sum_{i}^{n_{d}}\phi_{i}q_{di}= \sum_{i}^{n_{d}}\phi_{dsi}q_{di} \tag{16}\]  
\[v_{s}=\widetilde{N}_{v}\hat{v}=\widetilde{N}_{v}\sum_{i}^{n_{d}}\phi_{i}q_{vi}= \sum_{i}^{n_{d}}\phi_{psi}q_{vi} \tag{17}\]  
\[a_{s}=\widetilde{N}_{a}\hat{d}=\widetilde{N}_{a}\sum_{i}^{n_{d}}\phi_{i}q_{ai}= \sum_{i}^{n_{d}}\phi_{dsi}q_{ai} \tag{18}\]  
where  
\[\phi_{dsi}=\widetilde{N}_{d}\phi_{i} \tag{19}\]  
\[\phi_{psi}=\widetilde{N}_{v}\phi_{i} \tag{20}\]  
\[\phi_{ssi}=\widetilde{N}_{a}\phi_{i} \tag{21}\]
###### Abstract  
This article reviews the pollution status of anticancer drugs present in the Yodo River basin located in the Kansai district of Japan, covering both the soluble and insoluble (adsorbed on the river sediments and suspended solids) levels. Procedures ranging from sampling in the field and instrumental analytical methods to the data processing for mass balance estimation of the target basin are also described. All anticancer drugs concerned with this article were detected in sewage and river waters, where the presence of bicalutamide (BLT) was identified at considerably high concentrations (maximum 254 ng/L in the main stream, 151 ng/L in tributaries, and 1032 ng/L in sewage treatment plant (STP) effluents). In addition, sorption distribution coefficient (logK\({}_{\text{d}}\)) values showed a tendency to become higher in the silty sediments at Suita Bridge than in the sandy sediments at Hirakata Bridge; these trends were supported by the results of the laboratory-scale sorption experiment. STPs were concluded to be the main sources of the anticancer drug load in the river, and a mass flux evaluation revealed that the effect of attenuation in the river environment was small. The effectiveness of ozonation in the sewage treatment process for removal of these anticancer drugs was further confirmed. The present article should be of value for facilitating the environmental risk assessment of a wide range of drugs in a broader geographical area.  
anticancer drugs; urban river basin in Japan; river water; river sediment; sorption distribution coefficient (logK\({}_{\text{d}}\)); sewage treatment plants (STPs); ozonation Article
# Truncated Newton Kernel Ridge Regression for Prediction of Porosity in Additive Manufactured SS316L
## 3 Applied Regression Models and Modelling
### Support Vector Regression (\(Svr\))  
\(SVM\) is a promising statistical learning technique based on the principle of structural risk minimization. This ML method is less sensitive to the dimensionality of the input and is more likely to achieve a lower generalization error of the regression model [2]. \(SVM\) is well known in classification problems. It is also applied to regression problems, in which case it is referred to as \(SVR\). In brief, \(SVR\) finds an appropriate hyperplane to fit the data and provides flexibility in defining how much error is acceptable in the model.  
The regression hyperplane is determined by maximizing the distances between nearby data points, known as support vectors, in a non-parametric regression model. The nonlinear \(SVR\) formulation is obtained by considering kernel functions employed to map inputs into high-dimensional feature spaces [41]. The goal of \(SVR\) is to search for a fitting function \(f(\mathbf{x})=\{\mathbf{w},\ \mathbf{x}\}+b\), where \(\mathbf{w}\) is the weight vector, and \(b\) is the constant (bias), which has a deviation of less than a small value \(\varepsilon\) from the target (\(y_{i}\)) acquired for the related training data set (\(x_{i}\)) [16].  
The objective function and constraints of \(SVR\) are given in Equations (12) and (13), respectively, as follows [42]:  
\[\text{Minimize}:\ \frac{1}{2}\ \parallel\mathbf{w}\parallel^{2}+C\ \sum_{i=1}^{n}( \xi_{i}+\xi_{i}{}^{*}) \tag{12}\]\[s.t\left\{\begin{array}{c}y_{i}-f(x_{i})\leq\varepsilon+\xi_{i}\\ f(x_{i})-y_{i}\leq\varepsilon+\xi_{i}\\ \xi_{i},\xi_{i}{}^{*}\geq 0,\ i=1,2,\ldots,n\end{array}\right. \tag{13}\]  
where, \(\xi_{i}\) and \(\xi_{i}{}^{*}\) are slack variables, and \(C\) is a regularization parameter that adds a penalty for each misclassified data point.  
\(SVR\) simultaneously minimizes both the coefficient sizes and the prediction errors. The error term is included in the constraints, where the error is set as less than or equal to a specified margin, called the maximum error, \(\varepsilon\)[12]. The maximum error, \(\varepsilon\), has to be properly tuned to obtain the desired accuracy of the model. Further, with the introduction of the Lagrange multipliers \(\mathbf{\kappa}\) and \(\mathbf{\kappa}^{*}\), and the application of the radial basis kernel function to the dot product of the input vectors, the following formula can be obtained:  
\[f(x)=\sum_{i=1}^{n}(\mathbf{\alpha}_{i}-\mathbf{\alpha}_{i}^{*})k\big{(}\mathbf{x}_{i},\ \mathbf{x}_{j}\big{)}+b \tag{14}\]  
The radial base function kernel is introduced in Equation (6). The parameters \(\varepsilon\) and \(\gamma\) of the \(SVR\) model are user-defined.
## 2 Molecular Mechanisms of Antimicrobial Agents Recalcitrance in Clinically Important Biofilms
### Biofilm Matrix  
Many authors presumed that the EPS matrix would hamper the penetration of antimicrobials within the biofilm depth. However, restricted antimicrobial diffusion seems to be of minor importance in determining recalcitrance, since even those antibiotics exhibiting easy penetrability into biofilms are still ineffective in killing biofilm-associated bacterial cells [15]. The EPS matrix is not just a simple physical barrier against penetration, but rather its chemical constituents play much more complex roles in recalcitrance development.
# Security Vulnerabilities in LPWANs--An Attack Vector Analysis for the IoT Ecosystem
## 4 Attack Vectors in the IoT Ecosystem
Figure 12: Definition of Attack Vectors in Low Power Wide Area Networks (LPWAN)-based Internet of Things (IoT) applications. \begin{table}
\begin{tabular}{c l l l l} \hline \hline
**Attack Vector** & **Description** & **Attack** & **Attack Type (Table 3)** & **References** \\ \hline \multirow{3}{*}{\#0} & An attack that forces a change in the physical environment. & \multirow{3}{*}{Physical-related (Section 3.4.1)} & \multirow{3}{*}{Physical} & \multirow{3}{*}{[43,47,62,64,72]} \\  & Can consist of physical environment manipulation to produce & & & \\  & malicious sensor readings that may wrongly trigger a system & & & \\  & malfunction. & & & \\ \hline \multirow{3}{*}{\#1} & An attack that has compromised a sensor (or actuator). & \multirow{3}{*}{Wormhole (Section 3.4.5)} & \multirow{3}{*}{Software} & \multirow{3}{*}{[56,62,80,83,85]} \\  & Can consist of the injection of false sensor signals, & & & \\ \cline{1-1}  & causing the control logic of the system to act on malicious data. & & & \\ \hline \multirow{3}{*}{\#2} & An attack that has compromised the wireless communications & \multirow{3}{*}{Jamming (Section 3.4.3)} & \multirow{3}{*}{Network} & \multirow{3}{*}{[43,47,64,76,80]} \\  & Can consist of eavesdropping the connections secretly, & & & \\ \cline{1-1}  & between the target devices to collect information. & & & \\ \hline \multirow{3}{*}{\#3} & An attack that has compromised the LPWAN gateway. & \multirow{3}{*}{Physical-related (Section 3.4.1)} & \multirow{3}{*}{Physical} & \multirow{3}{*}{[43,47,62,64,72]} \\  & Can consist of any kind of capture attack (Sniffing) & & & \\ \cline{1-1}  & or even physical attacks, this can block the communications & & & \\ \cline{1-1}  & between the devices and the rest of the network. & & & \\ \hline \multirow{3}{*}{\#4} & An attack that has compromised the Backhaul communications & \multirow{3}{*}{Replay (Section 3.4.4)} & \multirow{3}{*}{Network} & \multirow{3}{*}{[43,62,64,69,71,72,80,81]} \\  & between the gateway and the LPWAN server. & & & \\ \cline{1-1}  & Can consist of delaying the communications or, for instance, & & & \\ \cline{1-1}  & Mild (Man-in-the-Middle) attacks where the malicious agent & & & \\ \cline{1-1}  & could modify the communications transmitted. & & & \\ \hline \multirow{3}{*}{\#5} & An attack that has compromised the LPWAN server. & \multirow{3}{*}{Bit Flipping (Section 3.4.2)} & \multirow{3}{*}{Data Privacy} \\  & Can consist of multiple service requests (DoS), overwhelming & & & \\ \cline{1-1}  & the server resources and leading to server malfunction. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Attack Vectors and their characterization according to Figure 12.