esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning-Based Classification of Weld Surface Defects
## 2 CNN-Based Feature Extraction
### Feature Extraction Module  
The task of CNN is to automatically extract high-level features of the weld surface defects images. Inspired by LeNet-5 [32], a deep CNN architecture is constructed by overlaying multiple convolutional layers. A typical convolutional layer usually contains convolution, nonlinear function activation and pooling operations. Figure 2 shows the specific architecture of the CNN. The CNN mainly is made up of three convolutional layers, two pooling layers, one fully connected layer, and one softmax layer. The input of the CNN contains training and validation images with \(80\times 120\) (height \(\times\) width).  
Figure 1: The overview of proposed method.  
In our paper, the convolutional layers are named C1, C2, and C3, respectively, which consists of 4, 8, 16 filters. The size of corresponding filters is \(5\times 5\), \(3\times 3\), and \(2\times 2\). Let \(x^{l}\) denotes the out of convolutional layers, and it can be expressed as:  
\[x^{l}=f(b^{l}+\sum_{j}^{I}\sum_{i}^{I}w_{i,j}^{l}x_{i,j}^{l-1}) \tag{1}\]  
\[f(x)=ReLU(x)=\left\{\begin{array}{cc}x&x>0\\ 0&x\leq 0\end{array}\right. \tag{2}\]  
where \((J,I)\) denotes the size of the filters, \(J\) is the height of the filters, and \(I\) is the width of the filters. \(b^{l}\) denotes the bias of the convolutional layer. \(x^{l-1}\) denotes the output of the previous convolutional layer. \(w^{l}\) denotes the weight of convolutional layer. \(f(\cdot)\) is the nonlinear activation function. ReLU activation function is selected and is shown as Equation (2).  
Pooling operation [33] is another important component. The pooling layers are named S1 and S2. Two max-pooling layers with stride 2 are used to combine a \(2\times 2\) patch of the convolutional layers C2 and C3. Each pooling layer corresponds to the previous convolutional layer. The neurons in the pooling layer perform aggregation statistics on specific regions in the convolution layer to achieve the purpose of down-sampling for the input feature map. Common pooling operations include average pooling, maximum pooling, and random pooling. The maximum pooling used in this paper is expressed as follows:  
\[a_{j}=\underset{N\times N}{max}\Big{(}a_{i}^{N\times N}u(n,n)\Big{)} \tag{3}\]  
where \(u(n,n)\) is the window function, which is applied to calculate the maximum value of \(a_{j}\) in the neighborhood.  
After a series of convolution, activation, and pooling, the fully connected layer splices the feature that is extracted from convolutional layer C3 and max-pooling layer S2 into a 128-dimensional vector. The output of the fully connected layer can be obtained by:  
\[v^{m}=f\Big{(}w^{m}x^{m-1}+b^{m}\Big{)} \tag{4}\]  
where \(b^{m}\) denotes the bias of the fully connected layer. \(w^{m}\) denotes the weights of the fully connected layer. \(x^{m-1}\) denotes the output of the previous max-pooling layer. \(f(\cdot)\) is the ReLU activation function. The CNN is trained by the cross-entropy function. The details explain of CNN are given in Table 1.  
Figure 2: The architecture of convolutional neural network (CNN).
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 3 Synthetic Aperture Radar Satellite Missions
### Medium/Large Satellite Constellations
#### 3.1.1 SAR-Lupe  
SAR-Lupe is Germany's first military satellite system for global surveillance, delivering radar images for the German Armed Forces for at least ten years since its launch. The spacecraft design by the OHB-System AG utilized a parabolic reflector antenna instead of active beam-steering antennas, which led to major savings in the cost of instrument development [47, 48]. However, the size of a reflector antenna (3.3 m \(\times\) 2.7 m) poses a major volume constraint during satellite stowage and launch phases as shown in Figure 4, necessitating a dedicated launch for each satellite. The five identical SAR satellites were launched between 2006 and 2008, with individual launches separated by 4 to 8 months [49]. The five satellites are distributed in three orbital planes, marked with plane numbers and corresponding satellites in Figure 4 (right). Orbital planes 1 and 2 are separated by 64\({}^{\circ}\) in longitude, and orbital planes 2 and 3 are separated by 65\({}^{\circ}\). Satellite-pairs in orbital planes 1 and 3 have phase angles differing by 69\({}^{\circ}\). Together with a near-polar inclination (98.2\({}^{\circ}\)), this satellite arrangement provides the SAR-Lupe constellation with both global coverage and short response time.  
Observation of SAR-Lupe is made in the X band with a frequency of 9.65 GHz and a wavelength of 3.1 cm, capable of change detection based on SAR interferometry [50]. The stripmap (60 km \(\times\) 8 km) observation of SAR-Lupe provides nadir-looking scenes, whereas the spotlight (5.5 km \(\times\) 5.5 km) scenes require the orienting of a spacecraft towards a target area to increase integration time for 0.5 m resolution. When the image acquisition is complete, the spacecraft returns to its standby mode for recharging two 66 Ah lithium-ion batteries with solar panels. Amongst L, S, C, and X bands listed in Figure 5 used for radar sensors, the X band requires the smallest antenna size, which enabled the adoption of a parabolic antenna whose size would be unacceptable in other spectral bands [51]. It also led to a compact design of power systems such as solar arrays providing 550 W at the end of life (EOL), an order of magnitude smaller than the 7 kW power generation (EOL) by ALOS in Table 1. When it comes to onboard batteries, ALOS used nickel-cadmium batteries and SAR-Lupe used lithium-ion for the first time in SAR satellites. Although lithium-ion batteries do not have a \"memory effect\" suffered by nickel-cadmium batteries, they only became available after more lenient electric-power requirements owing to their higher costs. After all, SAR-Lupe is an interesting example featuring efficient onboard power management at a spacecraft level and a constellation architecture with multiple orbital planes at a higher level, both of which inspired later SAR constellations with small satellites.
###### Abstract  
Power curves, supplied by turbine manufacturers, are extensively used in condition monitoring, energy estimation, and improving operational efficiency. However, there is substantial uncertainty linked to power curve measurements as they usually take place only at hub height. Data-driven model accuracy is significantly affected by uncertainty. Therefore, an accurate estimation of uncertainty gives the confidence to wind farm operators for improving performance/condition monitoring and energy forecasting activities that are based on data-driven methods. The support vector machine (SVM) is a data-driven, machine learning approach, widely used in solving problems related to classification and regression. The uncertainty associated with models is quantified using confidence intervals (CIs), which are themselves estimated. This study proposes two approaches, namely, pointwise CIs and simultaneous CIs, to measure the uncertainty associated with an SVM-based power curve model. A radial basis function is taken as the kernel function to improve the accuracy of the SVM models. The proposed techniques are then verified by extensive 10 min average supervisory control and data acquisition (SCADA) data, obtained from pitch-controlled wind turbines. The results suggest that both proposed techniques are effective in measuring SVM power curve uncertainty, out of which, pointwise CIs are found to be the most accurate because they produce relatively smaller CIs. Thus, pointwise CIs have better ability to reject faulty data if fault detection algorithms were constructed based on SVM power curve and pointwise CIs. The full paper will explain the merits and demerits of the proposed research in detail and lay out a foundation regarding how this can be used for offshore wind turbine conditions and/or performance monitoring activities.  
wind turbines; power curves; SCADA datasets; condition monitoring; machine learning; support vector machines
## 2 Literature Review
[PERSON] [16] used FCM to partition the traffic state of highway network, used the LSTM algorithm to establish a traffic state prediction model, and finally used the K-means algorithm to build a traffic state classification model. The model performs well in evaluating and predicting the traffic state of highway networks and is consistent with the classification results of actual data. However, existing studies using only the fuzzy clustering method will ignore the effect of the magnitude of different factors during grading. All input data are treated as equally important, regardless of whether their dimensions are consistent or not. Specifically, if the value range of one attribute exceeds that of another, this attribute may dominate the clustering process, causing the clustering result to be biased towards this attribute. The Qinghai-Tibet Plateau boasts a unique environment, with the climate, topography, and geology along the four main highways to Tibet being notably complex and variable [17]. Consequently, traffic interruptions in this region exhibit distinct characteristics. However, current research falls short in analyzing the causes and characteristics of traffic interruptions specific to the Tibetan highway environment. A clear standard for grading traffic interruption states has yet to be established. As a result, research fails to provide a scientific foundation for subsequent rapid and precise safety precautions and emergency response measures. To address this situation, the work employed the entropy weight-TOPSIS algorithm [18] to quantify the state parameters of traffic disruptions on the four highways to Tibet and to mitigate the effects of the dimensions on each other. The algorithm considered the relative importance of indices through the entropy weight method. The entropy weight-TOPSIS method was used to assess the proximity of each scheme to the ideal solution. However, traffic interruption data predominantly comprised static historical data due to the limitations of sensing equipment along each highway. Thus, these data were characterized by a lack of systematization and fuzzy randomness. Consequently, the fuzzy C-means clustering algorithm (FCM) [19] was used to cluster quantified data. Then, a grading method was established for traffic interruption states in the extreme environmental conditions of the highways to Tibet.
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Optimization of RIS Phase Shift Matrix  
RIS can control the channel conditions for eMBB and URLLC users to improve their performance. We propose three criteria to optimize the phase shift matrix to meet each mini-slot's existing eMBB and upcoming URLLC service requirements. However, the RIS phase shift matrix design may increase the latency. To resolve this issue, we pre-configure the RIS phase shift matrix. The optimal design of the three types of phase shift matrices is also executed in parallel in order not to generate additional delay effects.
## 6 Farber disease
### MALDI Non-Imaging MS Analysis of GSLs in Various Cell Cultures
#### 3.6.2 GSLs in Melanoma and Neuroblastoma Cell Lines  
Neuroblastoma and melanoma are two types of cancers arising from the neural crest cells, which give rise to the peripheral nervous system and melanocytes. Several studies have shown that the expression and composition of GSLs are altered in neuroblastoma and melanoma cells and that these changes may contribute to the development and progression of these cancers [163,276].  
GSLs are naturally present in neural tissues, and the identification of a specific pattern of GSLs composition associated with a malignant or aberrant evolution of these tissues can be a provoking challenge. MALDI MS is one of the tools used to elucidate the exact composition of such GSLs with good accuracy.  
Cavdarli et al. [265] performed MALDI MS on an MALDI-QIT-TOF (Shimadzu AXIMA Resonance) mass spectrometer, using THAP as a matrix solution, in negative ion mode.  
The focus of the study was on detecting native _O_-acetylated ganglioside species (their proportion calculated by the authors by integrating the intensity of individual signals on the mass spectra, was 10% in the LAN1 cell line, and 12% in the SKMel28 cell line), with the GSL profiles in both cell lines being complex and varied. In melanoma (SKMel28), various GSL species were identified, such as GM3, GM2, GM1, GD3, and GD2, but of important significance, native _O_-acetylated GD3 (OAc-GD3) was found. This is intriguing as _O_-acetylated gangliosides are often associated with malignancy, suggesting a potential role in the aggressive behavior of melanoma cells. The SKMel28 cell line exhibited a prevalence of GM3 and GD3 species.  
For the neuroblastoma cell line, LAN1, GM3, GM2, GM1, GD3, GD2, and LacNAcGM1 were identified. Interestingly, _O_-acetylated GD2 (OAc-GD2) was detected, indicating its potential involvement in neuroblastoma development. The specific GSL expression pattern differed from that of melanoma, emphasizing the cell-type-dependent nature of GSL expression in cancer.  
While these findings are promising and suggest the potential significance of _O_-acetylated gangliosides as tumor-specific antigens, further research is essential to validate their specificity across a broader range of tumor types.
## 3 System Performance Analysis
### PS Factor Optimization and IP Maximization  
This paper proposed a unity of IoT devices' bit-rate threshold as shown in (54) to ensure all IoT devices could decode their own message successfully; however, the eavesdropper \(E\) could also eavesdrop and detect IoT devices' messages. From expressions (45) and (52), the study analyzed that the OP and IP performance may be impacted by the factors, i.e., PA factors \(\alpha_{i}\), SNR \(\rho\), channel gain coefficients, data rate threshold \(\mathcal{R}\), and PS factor \(\lambda_{R_{r}}\), etc. Assuming PA factor \(\alpha_{i}\), channel gain coefficients and SNR \(\rho\) are fixed while a unify of IoT devices' bit-rate threshold is given by (54). Furthermore, the study optimizes PS factor \(\lambda_{R_{r}}\) for \(r=\{1,2\}\).  
To optimize the OP and IP performance, this study considers the OP performance at IoT relay \(R_{r}\) and OP performance at the farthest device \(D_{N}\) is approximate and IP performance at eavesdropper \(E\) is maximization. As a result, we exploits the optimal PS factor \(\lambda_{R_{r}}\) for EH at IoT relay \(R_{r}\) as follows:  
\[\lambda_{R_{r}}=1-\max\left\{1,\frac{\min_{n=\{N_{r},\ldots,1\}}\{\sigma_{R_{ r},D_{n}}\}}{\sigma_{S_{r},R_{r}}}\right\}. \tag{55}\]
# Optical OFDM for SiPM-Based Underwater Optical Wireless Communication Links
## Appendix A Calculating the Optimal Bias for DCO-OFDM  
The presented results for DCO-OFDM in the paper were mostly based on the definition of a clipping factor, for the selection of the DC bias. As explained in Section 5.5, the DC bias can be optimized in order to minimize the mean square error \(\mathcal{E}\) between the signals before (\(\tilde{x}_{n}\)) and after (\(\tilde{x}_{n}\)) double-side clipping, taking the LED DR into account. This appendix presents details on the optimal bias calculation.  
Assuming a large enough \(N\), the time-domain signal \(x_{n}\) can be modeled as a zero-mean independent identically distributed Gaussian random variable with the standard deviation \(\sigma_{x}\), based on the Central Limit theorem. Therefore, from Equation (12), \(\mathcal{E}\) can be expressed by [48]  
\[\mathcal{E} = N\left[V_{\min}^{2}\Phi(L)+\big{(}B_{\mathrm{\textsc{mc}}}^{2}+ \sigma_{\tilde{x}}^{2}\big{)}\big{(}\Phi(U)-\Phi(L)\big{)}\right. \tag{13}\] \[+2\,B_{\mathrm{\textsc{mc}}}\,\sigma_{\tilde{x}}\big{(}\Phi(L)- \[PERSON](U)\big{)}+\sigma_{\tilde{x}}^{2}\,L\big{(}\phi(L)-\phi(U)\big{)}\] \[+V_{\max}^{2}\,\big{(}1-\[PERSON](U)\big{)}+\sigma_{x}-2\,R_{\tilde{x}_ {n}\tilde{x}_{n}}\Big{]},\]  
where \(\sigma_{\tilde{x}}\) denotes the standard deviation of \(\tilde{x}=\alpha x_{n}\), and \(R_{\tilde{x}_{n}\tilde{x}_{n}}\) is the covariance of \(\tilde{x}_{n}\) and \(\tilde{x}_{n}\). Additionally,  
\[\left\{\begin{array}{ll}U&=(V_{\max}-B_{\mathrm{\textsc{mc}}})/\sigma_{ \tilde{x}}\\ L&=(V_{\min}-B_{\mathrm{\textsc{mc}}})/\sigma_{\tilde{x}}\\ \Phi(y)&=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^{y}\exp(-u^{2}/2)du\\ \phi(v)&=\frac{1}{\sqrt{2\pi}}\exp(-v^{2}/2).\end{array}\right. \tag{14}\]  
The optimal bias can be obtained by taking the partial derivation of Equation (13) with respect to \(B_{\mathrm{\textsc{mc}}}\) and setting the result to zero [48]:  
\[\frac{\partial\mathcal{E}}{\partial B_{\mathrm{\textsc{mc}}}}=2B_{\mathrm{ \textsc{mc}}}\big{(}\Phi(U)-\Phi(L)\big{)}+2\,\sigma_{x}\big{(}\phi(L)-\phi(U) \big{)}=0. \tag{15}\]  
Note that, in theory, solving Equation (15) may result in several local minima that should be between \(V_{\min}\) and \(V_{\max}\). The optimum \(B_{\mathrm{\textsc{mc}}}\) should be selected so as to result in the global minimum \(\mathcal{E}\) from Equation (13). Here, we calculate numerically the optimum bias from Equation (15) for given \(\alpha\), \(V_{\min}\), and \(V_{\max}\). In addition, according to the assumption of power-normalized \(x_{n}\), \(\sigma_{x}=1\), and consequently, \(\sigma_{\tilde{x}}=\alpha\).
# RIS-Assisted Network Slicing Resource Optimization Algorithm for Coexistence of eMBB and URLLC
## 4 Problem Conversion and Solution
### Solution of eMBB Allocation Problem
#### 4.1.2 Stage 2: Joint Optimization of Power Allocation and RIS Phase Shift Matrix  
After RB assignment, problem \(P1\) is reduced to maximization of the variable \(P_{eb}\) and optimization of the RIS phase shift matrix, as follows:  
\[P1.1:\max_{P,\Phi_{e}}E\left(\sum_{e=1}^{E}\sum_{u\in U}Wlog_{2}\Bigg{(}1+ \frac{P_{eb}|G_{RIS,e}\Phi_{e}H_{gNB,RIS}+D_{gNB,e}\big{|}^{2}}{\sigma^{2}\Gamma }\Bigg{)}\right) \tag{25}\]  
\[s.t.0\leq P_{eb}\leq P_{eb}^{max},\forall e\in E,\forall b\in B \tag{26}\]  
\[|\Phi_{n}|\leq 1,0\leq\theta_{n}\leq 2\pi,\forall n \tag{27}\]Since the objective function of \(P1.1\) and its constraints are non-convex, the relaxation variable \(S\) is introduced to handle its non-convexity and satisfies the following equation:  
\[\frac{P_{eb}|G_{RIS\epsilon}\Phi_{\epsilon}H_{gNB,RIS}+D_{gNB,\epsilon}|^{2}}{ \sigma^{2}\Gamma^{\prime}}\geq S_{e,b},\forall e,b \tag{28}\]  
\[s.t.S=\left\{S_{e,b}=\frac{P_{eb}|G_{RIS\epsilon}\Phi_{\epsilon}H_{gNB,RIS}+D_{ gNB,\epsilon}|^{2}}{\sigma^{2}\Gamma^{\prime}},\forall e,b\right\} \tag{29}\]  
Thus, problem \(P1.1\) can be equivalently transformed into:  
\[\begin{array}{l}P1.2:\underset{P,\Phi,S}{\max}E\bigg{(}\sum\limits_{i=1}^{E }\sum\limits_{n\in U}Wlog_{2}(1+S_{e,b})\bigg{)}\\ s.t.0\leq P_{eb}\leq P_{eb}^{max},\forall e\in E,\forall b\in B\\ |\Phi_{n}|\leq 1,0\leq\theta_{n}\leq 2\pi,\forall n\\ S=\left\{S_{e,b}=\frac{P_{eb}|G_{RIS\epsilon}\Phi_{\epsilon}H_{gNB,RIS}+D_{ gNB,\epsilon}|^{2}}{\sigma^{2}\Gamma^{\prime}},\forall e,b\right\}\end{array} \tag{30}\]  
The nonconvex problem \(P1.2\) remains unsolvable because the variables \(P_{eb}\) and \(\Phi\) are coupled. To make the problem approachable, decompose it into two subproblems as follows:  
\[\begin{array}{l}P1.21:\underset{P,S}{\max}E\bigg{(}\sum\limits_{n=1}^{E} \sum\limits_{n\in U}Wlog_{2}(1+S_{e,b})\bigg{)}\\ s.t.0\leq P_{eb}\leq P_{eb}^{max},\forall e\in E,\forall b\in B\\ S=\left\{S_{e,b}=\frac{P_{eb}|G_{RIS\epsilon}\Phi_{\epsilon}H_{gNB,RIS}+D_{ gNB,\epsilon}|^{2}}{\sigma^{2}\Gamma^{\prime}},\forall e,b\right\}\end{array} \tag{31}\]  
\[\begin{array}{l}P1.22:Find\Phi_{\epsilon}\\ s.t.|\Phi_{n}|\leq 1,0\leq\theta_{n}\leq 2\pi,\forall n\\ S=\left\{S_{e,b}=\frac{P_{eb}|G_{RIS\epsilon}\Phi_{\epsilon}H_{gNB,RIS}+D_{ gNB,\epsilon}|^{2}}{\sigma^{2}\Gamma^{\prime}},\forall e,b\right\}\end{array} \tag{32}\]  
Problem \(P1.2.1\) is a convex optimization problem that convex optimization tools (e.g., CVX [22]) can efficiently solve to obtain an optimal solution. The problem \(P1.2.2\) will be solved in Section 4.2.1.
## 2 Forward Problem  
In this section, we describe the mathematical formulation of the diffusion approximation (DA).  
Let \(\Omega\subset R^{n}\), \(n=2,3\) be our domain of interest, and \(\partial\Omega\) the boundary of \(\Omega\). Then, the DA inside the domain \(\Omega\) satisfies the partial differential equation  
\[-\
abla[.D(r)\
abla\Phi(r)]+\mu_{a}(r)\Phi(r)=0\qquad r\in\Omega \tag{1}\]  
with the Robin-boundary condition  
\[\Phi(r)+2a\;D(r)\frac{\partial\Phi(r)}{\partial\hat{n}}=S(r)\qquad r\in \partial\Omega \tag{2}\]  
where \(\Phi(r)\) is the photon density, and \(D(r)\) is the diffusion coefficient defined by \(D(r)=\frac{1}{3(\mu_{a}+\mu_{s}^{\prime})}\). \(a\) is the Fresnel reflection coefficient, which depends on the mismatch between the refractive indices, \(\mu_{a}\) and \(\mu_{s}\) the absorption and scattering coefficient, respectively, and \(\mu_{s}^{\prime}\) the reduced scattering coefficient expressed as \(\mu_{s}^{\prime}=(1-g)\mu_{s}\), where \(g\) is the anisotropic factor. \(S(r)\) describes the boundary condition for the incoming radiation and \(\hat{n}\) is the outward normal vector to \(\Omega\).  
We assume that the medium is highly scattering such that \(\mu_{a}\ll\mu_{s}\).The forward model (1) and (2) is solved by using the finite element method as described in [20].
# Dynamic Bending Behaviour of Sandwich Structures for Marine Applications
## 4 Results and Discussion
### Seawater Effects on Multiple Low-Velocity Impact Bending Performance of FRPSS
#### 4.3.2. Energy-Time Multiple Impact Diagram  
For the energy absorption analysis of FRPSS subjected to multiple impacts (Figure 9) unlike the impact response for single fracture impact, at lower energy levels, the effect of seawater absorption is more significant. In a similar trend to the load performance of the specimens under multiple impacts, GSV experienced the largest drop in energy absorption after with a range of 51.5-58.8%, while GSH and GSP specimens showed a relatively lower trend after exposure. This further confirms the weakening of adhesive layers in terms of plasticization and deterioration of interface cohesion between the constituents of the FRPSS, which is then transmitted in the form of damage in the samples.  
However, there was an increase in the absorbed energy after exposure at the second impact in GSP (9.1%) and the fifth impact of GSH (42.9%). These values could be regarded as outliers and occur because of stabilisation issues after the impact of the hammer (GSP) or because of inherent impurities from manufacturing imperfections (GSH). Overall, results indicate that seawater exposure has an adverse effect on FRPSS samples subjected to multiple impacts at energy levels below. The details of the variation in the energy absorption performance of the samples with the number of impacts are described in Table 5.  
Figure 8. Impact force results of LVI—multiple impacts.  
Figure 9. Impact energy results of LVI—multiple impacts.
## 4 Experimental Setup
### Experiment Boundary Conditions
#### 4.2.5 Instrumentation Error and Calibration  
The pressure sensors (HPT300-S) and thermocouples (K-Type) were all acquired from the supplier as per stated precision standards. Before the experiment, all the possible sources of errors associated with the instrumentation were outlined for calibration and data acquisition errors. For pressure sensors, hysteresis and non-uniformity estimation activities were carried out, and all sensors were calibrated, using a precision electronic calibrator as a reference. All errors that occurred during calibration were reduced by performing the calibration under test conditions. For thermocouples, two methods: the stirred bath method and the block calibrator method, were employed for calibration.