esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Contribution  
Section 5 provides a review of previous research linking MBSE and MDAO. However, existing approaches insufficiently address development data (e.g., models) reused from system development activities. As a result, existing potentials to reduce the initial implementation effort of MDAO remain untapped. Therefore, the following research question shall be answered in this paper:  
_How to link MDAO to MBSE system models taking advantage of existing models?_  
The following main contributions are made to answer the research question:  
* A novel approach (Section 2) is developed to link MDAO in MBSE system models. The novelty lies in reusing existing development data. For this, data from the system specification and models from design (Section 2.2.1) and test activities (Section 2.2.2) are reused. Utilizing an analogy (Section 2.2.3), design and test activities are mapped to system optimization (Section 2.3). Reusing development data from design and test activities reduces the initial effort of MDAO.
* For the first time, the MDAO problem is formalized in the MBSE system model as an optimization workflow. The purpose is to resolve the MDAO black box behavior and reduce the reconfiguration effort (Sections 3.1-3.3).  
The approach is applied to the optimal design of an electric coolant pump, and exemplary numerical results are presented (Section 3.4).  
Figure 1: (**Left**): MDAO in document-centered development. Scattered data and black box behavior. (**Right**): MDAO in model-based development. Single source of data and resolved black box behavior.
###### Abstract  
Jamming devices introduce unwanted signals into the network to disrupt primary communications. The effectiveness of these jamming signals mainly depends on the number and distribution of the jammers. The impact of clustered jamming has not been investigated previously for an unmanned aerial vehicle (UAV)-assisted cellular-vehicle-to-everything (C-V2X) communications by considering multiple roads in the given region. Also, exploiting three-dimensional (3D) beam-width variations for a millimeter waveband antenna in the presence of jamming for vehicular node (V-N) links has not been evaluated, which influences the UAV-assisted C-V2X system's performance. The novelty of this paper resides in analyzing the impact of clustered jamming for UAV-assisted C-V2X networks and quantifying the effects of fluctuating antenna 3D beam width on the V-N performance by exploiting millimeter waves. To this end, we derive the analytical expressions for coverage of a typical V-N linked with a line-of-sight (LOS) UAV, non-LOS UAV, macro base station (MBS), and recipient V-N for UAV-assisted C-V2X networks by exploiting beam-width variations in the presence of jammers. The results show network performance in terms of coverage and spectral efficiencies by setting V-Ns equal to 3 km\({}^{-2}\), MBSs equal to 3 km\({}^{-2}\), and UAVs equal to 6 km\({}^{-2}\). The findings indicate that the performance of millimeter waveband UAV-assisted C-V2X communications is decreased by introducing clustered jamming in the given region. Specifically, the coverage performance of the network decreases by 25.5% at \(-\)10 dB SIR threshold in the presence of clustered jammers. The performance further declines by increasing variations in the antenna 3D beam width. Therefore, network designers must focus on considering advanced counter-jamming techniques when jamming signals, along with the beam-width fluctuations, are anticipated in vehicular networks.  
Keywords:Poisson process; unmanned aerial vehicle; vehicle-to-everything; Matern cluster process; jamming +
Footnote †: journal: _Mathematics_
## 3 Case Study Analysis
### Effects of Failure Surface Width on Failure Probability  
To demonstrate the effect of failure surface width on failure probability, the PSO algorithm is used for calculations to obtain the \(P_{f}\) and \(F_{s}\) corresponding to different failure surface widths (B/H) under the constant slope height and slope angle. The soil parameters  
Figure 12: Variation curves of \(P_{f}\) and \(F_{s}\) with respect to \(L_{v}\) (different combinations of \(Cov_{c}\), \(Cov_{\varphi}\), and \(\rho(c,\,\varphi)\)).  
required for Case III refer to the case in the study by [PERSON] et al. (2013) [41]. The geometric model and calculation parameters for Case III are shown in Figure 13.  
The geometric parameters are specified as follows: the slope height \(H\) = 5 m and the slope angle \(\beta\) = 26.5\({}^{\circ}\). The failure surface width parameter \(B/H\) varies within the range of [3.0, 7.0]. We consider four distinct combinations for the variation coefficients and cross-correlation coefficients in Figures 14 and 15. Additionally, there are three combinations for the horizontal and vertical correlation lengths: \(L_{h}\) = 10 m, \(L_{v}\) = 2 m; \(L_{h}\) = 10 m, \(L_{v}\) = 4 m; and \(L_{h}\) = 20 m, \(L_{v}\) = 2 m. When analyzing each combination of variation coefficients (\(Cov_{c}\), \(Cov_{\varphi}\)) and cross-correlation coefficient \(\rho(c,\,\varphi)\), the horizontal and vertical correlation lengths are held constant at (\(L_{h}\) = 10 m, \(L_{v}\) = 2 m). Conversely, when examining the combinations of horizontal and vertical correlation lengths, the variation coefficients (\(Cov_{c}\), \(Cov_{\varphi}\)) and cross-correlation coefficient \(\rho(c,\,\varphi)\) are fixed at \(Cov_{c}\)=0.3,  
Based on all combinations of variation coefficients (\(Cov_{c}\), \(Cov_{\varphi}\)), cross-correlation coefficient \(\rho(c\), \(\varphi)\), and horizontal and vertical correlation lengths (\(L_{h}\), \(L_{v}\)), the failure probability (\(P_{\rho}\)) increases as the ratio \(B/H\) grows, while the safety factor (\(F_{s}\)) correspondingly diminishes with the growth of \(B/H\).  
It can be found from the above parametric analysis of Case II that \(P_{f}\) exhibits a positive correlation with soil spatial variability parameters (e.g., \(Cov_{c}\), \(Cov_{\varphi}\), \(\rho(c\), \(\varphi)\), and \(B/H\)), while \(F_{s}\) demonstrates an inverse dependence. This pattern is consistent with established probabilistic geomechanics principles. [PERSON] and [PERSON] (1999) synthesized decades of research, concluding that parameter variability systematically undermines both reliability indices and nominal safety factors [42]. [PERSON] and [PERSON] (2004) demonstrated through MCS that spatial variability in shear strength parameters elevates \(P_{f}\) while reducing \(F_{s}\)[43]. [PERSON] (2013) further validated this relationship using stochastic finite element models, showing that variability in elastic modulus and friction angle amplifies instability risks [44].
# Forecasting the Distortion in Solar Radiation during Midday
## 2 Hypothesis and Methodology
### Mathematical Modeling  
The proposed approach is characterized by its simplicity. The mathematical modeling is straightforward. Assuming that the solar radiation at minute n is given by _Gn_, then the difference (D) between the solar radiation at minute n and the solar radiation at minute \(n+1\) during the early hours is given by Equation (1):  
\[D(G)=k(G_{n+1}-G_{n}), \tag{1}\]  
where \(k\) is constant to magnify the difference between the two values. This difference \(D\) will be investigated to extract more information about the behavior of the radiation in the future which is the midday hours. For clear sky conditions, the function \(D\) is always positive in the first half of the day time, i.e., until noon time.  
The following distortion index (_DI_) function is formulated using the difference function \(D\):  
\[DI=w_{1}*NV+w_{2}*ZV+w_{3}*A, \tag{2}\]  
where \(w_{1}\), \(w_{2}\), and \(w_{3}\) are weights that can be varied according to the readings, and coefficient \(NV\) is the number of events/times when the values of the difference \(D\) are negative. Coefficient \(NV\) quantifies the frequency of radiation decrements due to cloud passage. High values of \(NV\) mean high fluctuations in solar radiation. Coefficient \(ZV\) is the zero value of the function \(D\) which gives hints about the period of cloud coverage that causes a lack in solar radiation. In addition, coefficient \(A\) is the amplitude of \(D\) that is greater than a threshold value. \(A\) is related to the severity of the cloud coverage. High values of the coefficient \(A\) mean big variation in solar radiation and vice versa.  
For clear sky conditions, the \(DI\) will be zero. For days with clouds, the \(DI\) will be a positive number whose value is proportional to the condition of the clouds in terms of severity and frequency of cloud coverage.  
After calculating the objective function \(DI\), we assume that the solar radiation of the midday hours (hours 10 a.m. to 1 p.m.) will have similar trends to the solar radiation of the early hours of the day and, hence, we can interpret high values of \(DI\) as a high distortion/fluctuation of solar radiation during the midday time and the low values of the objective function \(DI\) as a low distortion/fluctuation.  
For the first stage of this research, the following ranges of the \(DI\) were considered:  
1--0-40 means low distortion in solar radiation.  
2--41-60 means medium distortion in solar radiation.  
3--More than 60 means high distortion in solar radiation.  
These ranges were selected after examining the data of the solar radiation.  
Using this methodology, we can predict the reliability and the fluctuation in the generation of solar PV plants during the midday hours.
## 2 Materials and Methods
### Summarizing the Articles  
Once reviewed, the selected articles were summarized into several tables, including one for cases series in which only PRP was used; one for case series in which PRP was used in combination with MSCs or other treatments; one for randomized clinical trials in which PRP was compared to saline or combined with MSCs, BMMCs or physical therapy; one for experimental studies in which PRP was evaluated alone; and one for experimental studies in which PRP was evaluated in combination with MSCs. The tables contained the following details: study (authors), study design, results, adverse effects, length of follow-up, and outcomes. The analysis included an evaluation of outcomes over the short, medium, and long terms, such as the ability of treated horses to return to sport or work, and the use of paraclinical data like ultrasonographic and magnetic resonance image findings or plasma or urine biomarker concentrations, among others. The clinical outcomes were categorized as positive, negative, or neutral. To enhance the clinical relevance of this review, the findings were discussed in the context of their practical implications for veterinary practitioners. For example, the efficacy of PRP in reducing the recovery time and improving functional outcomes in sport and working horses was highlighted as a key consideration for veterinarians managing equine tendon and ligament injuries.
# Semi-Supervised Learning Matting Algorithm Based on Semantic Consistency of Trimaps
## 3 Methodology
### Weakly Supervised Branch  
In the weakly supervised training branch, each unlabeled image \(I_{un}\) is provided with two trimaps of different granularity, denoted as \(T_{fine}\) and \(T_{coarse}\). Two training samples are generated by concatenating the original image \(I_{un}\) and the two trimaps separately and then feeding them into the network. Additionally, side outputs are also produced in the last two stages of the decoder, where downsampling strides are 4 and 8. The side outputs obtained from \(T_{fine}\) are denoted as \(l_{fine}^{4}\) and \(l_{fine}^{8}\), while those obtained from \(T_{coarse}\) are denoted as \(l_{coarse}^{4}\) and \(l_{coarse}^{8}\).  
Notably, there is no labeled alpha matte as ground truth to supervise the predictions in the weakly supervised branch. Previous experiments have shown that the trimap with a finer granularity produces better alpha predictions than the trimap with a coarser granularity. Moreover, the trimaps of different labeling granularities have inherent strong semantic consistency correlations. Therefore, we use the prediction results of the \(T_{fine}\) sample as the supervised ground truth for the \(T_{coarse}\) sample, and lead the network to learn in the direction from \(T_{coarse}\) to \(T_{fine}\).  
To achieve directional learning from \(T_{coarse}\) to \(T_{fine}\), the weakly supervised branch is trained by the following steps:  
* During the forward propagation of \(T_{fine}\), the network is converted to evaluation mode using the eval() function, not keeping track of gradients, retaining the intermediate features and final prediction results for consistency constraints;
* During the forward propagation of \(T_{coarse}\), the network is switched back to regular training mode. Loss is then calculated on the intermediate features and prediction results between \(T_{coarse}\) and \(T_{fine}\).  
Specifically, the weakly supervised loss \(Loss_{wk}\) includes the loss between alpha predictions \(Pred_{fine}\) and \(Pred_{coarse}\), the loss between \(l_{fine}^{4}\) and \(l_{coarse}^{4}\), and the loss between \(l_{fine}^{8}\) and \(l_{coarse}^{8}\). The trimaps with different granularity can be regarded as different perturbed samples of the unlabeled image with inherent semantic correlations. The consistency constraints between different perturbed samples help the network learn the distribution information of the unlabeled data and strengthen the semantic feature representation ability.
# Research Progress of Visual Inspection Technology of Steel Products--A Review
## 4 Detection Methods
### Statistics
#### 4.1.2 Co-Occurrence Matrix  
The spatial gray level co-occurrence matrix was first proposed by [PERSON] [58], and it is a popular texture analysis method belonging to second-order statistics, which is defined by the joint probability density of two positional pixels. Texture features derived from the co-occurrence matrix (energy, entropy, contrast, uniformity, deficit moment, and correlation) have been used in various surface  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Method** & **Strengths** & **Weaknesses** \\ \hline \multirow{3}{*}{Mathematical morphology} & Computational simplicity. & Morphological operations are only \\ \cline{2-3}  & Geometric representation of texture images. & implemented on non-periodic \\ \cline{2-3}  & Highly suitable for random or natural textures. & steel defects. \\ \hline \multirow{3}{*}{Co-occurrence matrix} & Extracting spatial relationship of pixels with & Difficult to judge the optimal \\  & different statistical computations. & displacement vector. \\ \cline{2-3}  & High accuracy rate. & Low detection rate (50–70\%) for \\ \hline \multirow{3}{*}{Histogram properties} & Translation and rotation invariance. & irregular textures. \\ \cline{2-3}  & Calculation is simple. & Sensitive to noise. \\ \hline \multirow{3}{*}{Local binary pattern} & Calculation is simple. & \\ \cline{2-3}  & Recognition ability is strong. & Too dependent on the gray value \\ \cline{1-1} \cline{2-3}  & Rotation invariance and gray invariance. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Strengths and weaknesses of statistical detection methods for steel defects.  
defects detection methods. The detection and classification of defects can be realized by extracting the spatial features of a gray-level co-occurrence matrix (GLCM) in combination with a classifier Yu [32].
## 1 Introduction
The purpose of the current paper is to compare the prediction accuracy of the surrogate models computed by using the RSM and ANN in predicting the aerodynamic performance of a wind turbine airfoil and to figure out how the accuracy of a surrogate model affects the performance improvement ratio of a wind turbine airfoil if the wind turbine airfoil is optimized with the surrogate models that have a different prediction accuracy. The remainder of the present paper is structured as follows: The background knowledge needed to understand this study is presented in Section 2. The settings for the prediction accuracy comparison between the surrogate models computed from the RSM and ANN are described in Section 3. The results of the prediction accuracy tests and optimization are explained in Sections 4 and 5, respectively. Finally, the paper is concluded in Section 6.
# Impacts of Integration of Wind Farms on Power System Transient Stability
## 3 Dynamic Model of DFIG and Wind Turbine
### Model of Drive Train and Wind Turbine  
Generally, the rotor is considered to be two concentrated masses, one is the mass of generator and the other is mass of the turbine. Both are connected together with a specific damping and stiffness system value, which could be as described in (14) and (15) [21].  
\[\frac{\mathrm{d}\theta_{\mathrm{tw}}}{\mathrm{d}t}=\omega_{\mathrm{b}}( \omega_{\mathrm{t}}-\omega_{\mathrm{r}}) \tag{14}\]  
\[\frac{\mathrm{d}\omega_{\mathrm{t}}}{\mathrm{d}t}=\frac{1}{2H_{\mathrm{t}}} \bigg{[}\frac{P_{m}}{\omega_{\mathrm{t}}}-K_{\mathrm{tw}}\theta_{\mathrm{tw} }-D_{\mathrm{tw}}(\omega_{\mathrm{t}}-\omega_{\mathrm{r}})\bigg{]} \tag{15}\]  
where \(\omega_{\mathrm{t}}\), \(\omega_{\mathrm{r}}\) and \(\omega_{\mathrm{b}}\) are the turbine speed, rotor speed and system base speed, respectively; \(\theta_{\mathrm{tw}}\) (rad) is the shaft twist angle; \(K_{\mathrm{tw}}\) (p.u./rad) and \(D_{\mathrm{tw}}\) are the shaft stiffness and mechanical damping coefficients; \(H_{\mathrm{t}}\) (s) is wind turbine inertia constant; and \(P_{\mathrm{m}}\) is the mechanical power extracted from the wind.
## 4 Network Layer: The Main Building Block of Fixed Networks
### Five Generations of Backbone Networks: From PDH to OTN/SDN
#### 4.2.1 Intro on the Evolution of Backbone Networks over the Past 50 Years
The SDN framework can be used to simplify the routing process in (G)MPLS networks, for example, to provide load-balancing of MPLS tunnels by better tracking traffic variations and improving the routing of MPLS LSP tunnels. By adding the actual optical-layer routing information to the IP/MPLS-layer, the SDN framework allows us to find and implement the OSPF loop-free alternate (LFA) and fast reroute (FRR) paths faster and more efficiently. A fast lightpath provisioning (or rerouting) mechanism can be applied using more sophisticated or open network management interfaces to the ROADMs, such as a REST interface with an information model based on the Yang protocol, by reusing the DWDM-layer transponders on an existing lightpath or by using preinstalled spare transponders. Based on SDON concepts, another approach called self-optimizing optical networks (SOON) has been proposed, which combines Machine Learning (ML) technology with SDON to increased optical network intelligence [75]. SOON's architecture consists of a data layer, a model layer and a policy layer. Each network element, known as a self-optimizing network element (SNE), communicates with the model layer through a management and data interface (MDI). In parallel with SDN developments, the network functions virtualization (NFV) concept that leverages IT virtualization technologies to virtualize entire classes of network functionalities into building blocks that may connect, or \"chain\", together to create and  
Figure 27: Elastic (flexible) optical networks [74]. deliver end-to-end communication services, came along. In this context, virtual network functions (VNFs) are chained together, instead of physical network functions (PNFs). Through replacing PNFs with VNFs, networks may be greatly softwarized by running VNFs as program entities on commercial off-the-shelf (COTS) servers (virtual machines; VMs). This \"network functionality cloudification\" eliminates the need for costly application-specific hardware, which is difficult to manage, leads to \"vendor lock-in\" situations and is highly rigid/centralized. VNFs, can be deployed almost anywhere in the network, and an operator can negotiate a favorable pricing strategy with the VNF provider, giving the opportunity to build and exploit VNF instances whenever they are needed. VNFs can be generated in data centers, at the network's edge (e.g., in central offices), or at the network core as middle boxes.
# Size and Shape Optimization of a Guyed Mast Structure under Wind, Ice and Seismic Loading
## 3 Load Analysis
### Dead Loads  
The structure is made of steel S355, whose mechanical stress-strain behaviour is depicted in Figure 3, and the characteristics are listed here: \(f_{us}=510\) MPa, \(f_{ys}=355\) MPa, \(E_{s}=210\),000 MPa, which are the ultimate and yielding stresses and Young's modulus, respectively.  
The cables are made of galvanized steel consisting of 6 strands (216 wires) with an independent metal core (49 wires). The main characteristics are illustrated in Table 1.  
The structure investigated consists of a few types of elements, as indicated in Table 2. Dead loads are calculated from the weight per unit length of each member.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Steel Ropes (Cables)** & \\ \hline Model & \(6\times 36\)WS + IWRC/265 wires \\ \hline Construction pattern & \(6\times(14+(7+7)+7+1)+(7\times 7)\) \\ \hline Winding direction & \multicolumn{2}{l}{right cross} \\ Material & & galvanized steel \\ \hline Resistance & \multicolumn{2}{l}{1170 N/mm\({}^{2}\)–180 kg/mm\({}^{2}\)} \\ \hline Cable diameter & Weight & Area & Wire diameter & Load to failure \\ \hline
[mm] & [kg/m] & [mm\({}^{2}\)] & [mm] & [kN] \\ \hline
16 & 1.36 & 173.25 & 0.91 & 161 \\
18 & 1.67 & 212.74 & 1.03 & 204 \\
20 & 2.02 & 257.32 & 1.14 & 252 \\
22 & 2.41 & 307.01 & 1.26 & 305 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical specifications of the steel ropes.  
Figure 3: Mechanical stress–strain behaviour of steel S355 implemented in SAP2000.  
The non-structural dead loads originate from the wiring weight and the steel ladder for inspection and maintenance. This load results in 0.3 kN/m. Antennas and parabolas represent the weight of the equipment. Two groups of three antennas are located at 26.00 and 29.25 m in height, with a 120\({}^{\circ}\) in mutual spacing. The first one is the model AOC4518R7v06 produced by Huawei(r). The second one is the model 6888670N manufactured by Amphenol(r). Finally, there are three parabolas located at 23.15 m height, spaced 120\({}^{\circ}\) apart from each other, 30 cm in diameter. Tables 3 and 4 detail the weight of the equipment and the non-structural dead loads.
## 4 Validation and Error Analysis of Extracting Transmission Characteristics  
### Experiment Validation  
The setup of the validation experiment for the extraction of transmission characteristics from the near-field emission is shown in Figure 6. The probe was secured to the bracket, which is right above the printed trace and perpendicular to the ground. Arbitrary waveform generators (AWG) were connected to the ends of the printed traces for the signal injection, and the other ends of the traces were connected to 50 \(\Omega\) terminals to guarantee steady signal transmission, while the output of the probe was connected to an SA.  
The proposed method was validated using two types of circuits: one has only one trace with different discontinuities, which was first measured to verify the accuracy of the method, and the other one has many traces fabricated on PCBs, and it was used to validate the effectiveness of complicated structures with the interference signal.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Items**} & **Preset Values** & **Extracted Results** & **Extracted Results** \\  & **(\%)** & **for 1 st–13 th (\%)** & **for 8 th–20 th (\%)** \\ \hline \(D_{1}\) & 2.00 & 1.94 & 2.01 \\ \(D_{2}\) & 30.00 & 30.08 & 30.05 \\ \(D_{3}\) & 3.00 & 2.95 & 2.98 \\ \hline Error & \(D_{1}\) & 3.0 & 0.5 \\ (\%) & \(D_{2}\) & 0.27 & 0.17 \\ \(D_{3}\) & 1.7 & 0.67 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Extracted results from 1 st to 13 th order or 8 th to 20 th order harmonic amplitudes.  
Figure 6: The setup for the transmission characteristics extraction measurement.