esa-sceva/satcom-chunk-collection huggingface dataset:
# An Inference Framework of Markov Logic Network for Link Prediction in Heterogeneous Networks
## 4 Proposed Method  
The inference framework for efficient LP tasks is depicted in Figure 2, comprising the following three components.  
* **MLN substructure construction** is proposed to construct MLN substructures to avoid the massive search space of the entire MLN, as presented in Section 4.1.
* **VEM-based inference** is proposed to calculate the joint distributions of nodes by extending the VEM with GCN, as presented in Section 4.2.
* **MLN-based LP** is proposed with the termination condition to fulfill LP by inferences in MLN, as presented in Section 4.3.  
Our method initiates by transforming knowledge graph triples into grounded predicates to construct the MLN structure. Subsequently, we partition this MLN structure into coherent substructures. Finally, we apply VEM-based inference independently on each substructure, enabling efficient LP tasks on KGs.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Notations** & **Descriptions** \\ \hline \(H=(\mathcal{R},\mathcal{V},\mathcal{A})\) & Heterogeneous network with entities \(\mathcal{V}\), relations \(\mathcal{R}\), and \\  & attributes \(\mathcal{A}\) \\ \(G=(V,E)\) & Graphical structure \(G\) with nodes \(V\) and links \(E\) \\ **w** & Weight parameters of formulas \\ \(M=(G,\mathbf{w})\) & MLN with distribution \(P_{\mathbf{w}}\) with respect to parameters \(\mathbf{w}\) over \(G\) \\ \(Z(\mathbf{w})\) & Partition function with \(\mathbf{w}\) \\ \(\mathcal{F}=\{f_{i}\mid 1\leq i\leq m\}\) & Set of \(m\) formulas \\ \(p(n)\) & Label distribution of node \(n\) \\ \(P(V)\) & Joint distribution of node labels in \(V\) \\ \(G=\{g^{i}|1\leq i\leq k\}\) & Set of \(k\) MLN substructures \\ \(N_{2}\) & 2-hop enclosing subgraph in \(H\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Notations and descriptions.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Abbreviation** & **Full Term** \\ \hline LP & Link Prediction \\ MLN & Markov Logic Network \\ SRL & Statistical Relational Learning \\ VEM & Variational Expectation Maximization \\ ELBO & Evidence Lower Bound \\ GCN & Graph Convolutional Network \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of abbreviations.  
Figure 2: Overview of the proposed framework.
## 4 Internationalization of Bioelectromagnetics Research
### Development of Methods to Assess Human Exposure to RF Radiation from Mobile Phones  
In 1996 the FCC adopted its first limits on SAR produced in the body by 'portable' RF emitting devices (which are intended to be used close to the body) [25]. This created the need for accurate and reproducible methods for SAR testing, which was addressed by the development of international standards for measurement of near-field exposures to mobile phones (IEEE 1528 series) and to hand-held and body-mounted wireless communications devices (IEC 62209), as well as by the development of sophisticated SAR testing equipment by IT'IS (Zurich). The International Electrotechnical Commission (IEC) has developed additional standards for RF exposure assessment, for example IEC 62232 for assessment of RF exposure from cellular base stations.
# Mapping Rice Fields in Urban Shanghai, Southeast China, Using Sentinel-1A and Landsat 8 Datasets
## Methodology
### Accuracy Assessment  
As stated in Section 2.4, 108 Rice points obtained from the 2016 field survey, 42 Water points and 50 points from each of the other land-cover types, derived from geo-referenced and high resolution Google Earth images, were used in a post-classification accuracy assessment. An error/confusion matrix was generated, and from which the producer's accuracy, user's accuracy, Kappa (statistic) index of agreement and overall classification accuracy of our mapping algorithm were calculated.  
The methodological approach adopted in this study is illustrated in the flow chart below (Figure 3).
## 2 Experimental Details
### Fluxes Preparation and A-TIG Welding Process  
#### 2.3.1 Nanoscale Fe2O3 Synthesis  
\(\alpha\)-Fe2O3 nanowires were synthesized via two steps [41; 42]. In brief, Fe-based complex precursor nanowires were prepared. 0.15 M FeCl3 aqueous solution was mixed with isopropanol, to which 3 mmol nitrilotriacetic acid (NTA) was added. After magnetic stirring, the mixture was transferred into a Teflon-lined autoclave and hydrothermally treated at 190 \({}^{\circ}\)C for 24 h. The resultant white sediment was collected by centrifugation, washed with deionized water and absolute ethanol twice, and dried at 60 \({}^{\circ}\)C. In the second step, the precursors were put in a crucible and annealed at 350 \({}^{\circ}\)C for 1 h. After that, the precursors were converted into \(\alpha\)-Fe2O3 nanowires. The width of \(\alpha\)-Fe2O3 nanowires was around 25 nm, and the length was around 2-3 \(\upmu\)m, as Figure 1a shows. Figure 1b shows XRD results of Fe2O3 nanowire. The peaks can be indexed by the anatase phase corresponding to the JCPDS card no. 39-0238.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Chemical Composition (Mass \%)**} \\ \hline
**C** & **Mn** & **P** & **S** & **Si** & **Cr** & **Cu** & **B** & **N** \\ \hline
0.35–0.55 & 22.5–25.5 & \(\leq\)0.03 & \(\leq\)0.01 & 0.1–0.5 & 0.3–0.4 & 0.3–0.7 & \(\leq\)0.005 & \(\leq\)0.05 \\ \hline \multicolumn{8}{c}{Tensile properties} \\ \hline  & Yield strength & \multicolumn{6}{c}{Tensile strength} & \multicolumn{6}{c}{Elongation} \\ \hline  & 480 Mpa & \multicolumn{6}{c}{850 Mpa} & \multicolumn{6}{c}{50\%} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of high manganese steel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Process Parameters** & **Value** \\ \hline Electrode type & 2\% thoriated (W with 2\% thorium) \\ Electrode diameter & 2.4 mm \\ Electrode angle & 45\({}^{\circ}\) \\ Welding current & 200 A \\ Welding speed & 120 mm/min; 60 mm/min \\ Arc length & 3 mm \\ Shielding gas/flow rate & 99.99\% Pure Argon/10 L min\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Welding parameters used for A-TIG.
## 3 Results and Discussion  
### Apparent Viscosity and Structural Recovery  
This article preliminarily explores the effect of adding CSM on the printing performance of the mixed materials. As shown in Figure 3, under a constant shear rate, the static yield stress of the mixed materials with different formulations initially increases and then decreases with the increasing amount of CSM. This may be due to the strong attraction of Na\({}^{+}\) in the CSM to water after it is added to the mixed material slurry, which reduces the free water in the slurry and increases the static yield stress. However, the excessive addition of CSM can occupy the geopolymer framework, leading to a decrease in static yield stress [3; 15]. According to the previous literature [16; 17], the static yield stress corresponds to the peak point of the curve, and the highest static yield stress can reach 1473.43 Pa. This indicates that the appropriate addition of CSM does not affect the constructability and printability of the mixed materials. It also lays the groundwork for further research on the effects of adding CSM to other properties. The subsequent experiments maintain a constant addition of 0.5% CSM.  
As depicted in Figure 4, the trend of apparent viscosity as the shear rate increases suggests a minimal alteration in the apparent viscosity of the composite material upon CSM addition, highlighting the consistent printability of the mixture formulations. This indicates that the moderate addition of CSM does not affect the constructability or printability of the mix. Subsequent experiments maintained a constant addition of 0.5% CSM.
# An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders
## 1 Introduction
### Multi-Objective Optimization  
In early days, due to the lack of methodologies, multi-objective optimization problems (MOOP) in general were solved by casting all objectives in one and solving it as a single objective problem ([PERSON] et al., 2017). Currently, MOOP are solved differently by optimizing a group of two or more fitness functions (usually in conflict ([PERSON] et al., 2017)), without prejudice among them, until no more improvements to the current solution set could be achieved. MOOP results are (usually) a set of optimal solutions that minimize all the objective functions at the same time, or either maximize all the functions or a combination of maximization and minimization of different functions ([PERSON] et al., 2017; [PERSON] et al., 2017). The set of optimal solutions is designated as Pareto front, on which a point \(x\in X\) is called Pareto optimal IFF there does not exist another point, \(x^{*}\in X\), such that \(F(x)\leq F(x^{*})\), and \(F_{i}(x)<F_{i}(x^{*})\) for at least one fitness function ([PERSON] et al., 2017). Also, on the Pareto front, all the solutions yield the same optimal value ([PERSON] et al., 2017).  
One way to tackle MOOP and obtain solutions that converge to a Pareto front is through the usage of evolutionary algorithms (EA), very popular among researchers because of its flexibility and easy adaptation to different problems. Also, as EA are population-based algorithms, they are well suited to be used to solve MOOP in form of multi-objective evolutionary algorithms (MOEA). Currently, the usage of MOEA can be found on multiple areas [21], integrating diverse selection and estimation mechanisms.  
A recent review of evolutionary multi-objective methods can be found in [22], including some that became standards when dealing with MOOP, such as the NSGA-II algorithm. The work considers three types of MOEA: Pareto-based (such as NSGA-II [23]), indicator-based (such as SMS-EMOA [24]), and decomposition-based (such as MOEA/D [25]), exposing their advantages and disadvantages. Also, choosing the right method depends on the dimension of the objective space, how many solutions should be generated, the distribution of solutions, and an a priori knowledge about the location and shape of the Pareto front.  
Considering real problems of engineering, EA are currently being used for energy, electrical, structural and civil engineering, scheduling, transport, combinatorial optimization, and more, as described by [PERSON] et al. in [26]. However, these are just some examples, as many other problems that need to do multi-objective optimizations are using EA.
###### Abstract  
Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consists of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to liquefied natural gas (LNG) terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.  
combined simulation; power and gas interdependence; security of supply; transient gas simulation; scenario analysis; power system contingency
###### Abstract  
Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution's adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host's resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network's performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.  
digital watermark; neural networks; invisibility; robustness; digital images +
Footnote †: journal: Applied Physics Letters  
_Appl. Sci._**2020**, _10_, 6854; doi:10.3390/appl0196854
# Model Based Open-Loop Wind Farm Control Using Active Power for Power Increase and Load Reduction
## 4 Discussion
### Total Power  
Figures 10 and 11 show the power increase of the proposed wind farm control in percentage relative to the power of the baseline control for two different mean wind speeds, 8 m/s, and 10.5 m/s. Figure 10a,b presents 8 m/s, and Figure 11a,b presents 10.5 m/s. Two wind speeds were chosen for simulation because they correspond to two different wind turbine control regions. The former is the control region for maximum power coefficient and the latter is the control region for smooth transition from the maximum power coefficient control and the rated power control. Based on the figure, it can be found that the power increase of the proposed controller with respect to the power with the baseline controller decreases with increasing wind turbine spacing and increases with increasing control frequency. In addition, the power increase decreased when the ambient turbulent intensity increased. This is partly because more fluctuations in wind speed make the power commands to the wind turbines deviate more from the optimal values. In addition, this is because, with the higher turbulence intensity, the wake recovers more rapidly, and, therefore, the wake loss with the baseline controller is reduced and finally the effect of active induction control is reduced. The power increase was reduced when the wind speed is in the transition control region. When the turbine spacing reached 10 RD, no power increase was observed. It is because when the turbine spacing is 10 RD, the wake loss due to the upstream wind turbine is small. In addition, when the turbulence intensity was large such as 0.21, the power with the proposed control became even lower than the power with the baseline controller when the control frequency was 0.01 or 0.05. This is because the wind farm controller runs too slow to effectively track the high wind speed variation.  
However, when the control frequency was 0.1 Hz, although the ambient turbulence intensity was 0.21, the power with the proposed control became higher again than the power with the baseline control. When the turbine spacing was 7 RD the increase was 2.29%. In the previous study with an extremum seeking control [15], a similar decreasing trend in the power gain relative to the baseline control was observed with increasing ambient turbulent intensity, but, with the ambient turbulence  
Figure 9: Simulation Result for Load (**a**,**c**) wind turbine tower load variation in time domain when applying baseline controller and proposed controller; (**b**,**d**) wind turbine tower DEL variation when applying baseline controller and proposed controller.  
intensity of 0.21, the controller failed to distinguish the change in power by control from the change in power by turbulence, and the power gain became negative.
## 4 Results
### Model Performance Evaluation  
First, when using the Holdout test method, 80% of the course rating dataset was randomly used as the training dataset. The remaining 20% was used as the testing dataset [33].  
Second, the learner-based collaborative filtering algorithm (Learner-CF), the item-based collaborative filtering algorithm (Item-CF), and the proposed hybrid recommendation algorithm removing clustering (NoCluster-CF) were set as baseline algorithms. Then, the performance of the baseline algorithms was compared with the proposed hybrid recommendation algorithm (Cluster-CF).  
Third, according to the top 20 nearest neighbors and the top 10 recommended courses from the previous sensitivity analysis, the precision rate, recall rate, and F1 score of the four algorithms were compared and analyzed, as shown in Figure 4. Compared with the Learn-CF, Item-CF, and NoCluster-CF, the precision and recall rates of Cluster-CF proposed in this study improved. Specifically, the F1 score (comprehensive evaluation index) was increased by 12%, 17%, and 7%, respectively.  
Figure 4: Comparative analysis of algorithm performance.  
Figure 3: Sensitivity analysis of the number of recommended courses.  
Although the F1 score in the Cluster-CF improved by only less than 10%, its runtime efficiency improved by nearly 50% compared to NoCluster-CF. This shows that Cluster-CF is superior to the baseline recommendation algorithms with better runtime efficiency. This is because relatively homogenous learners belonging to the similar learning groups can be summarized by a single cluster representative, which enables data reduction.
## 2 Materials and Methods
### Orbit Estimation with an Unscented Batch Filtering Method
#### 2.2.2 Orbit Estimation Based on an Unscented Batch Filtering Method
The process continues until the maximum number of iterations is reached, or the absolute value of the difference between the root mean square (RMS) values of the measurement residuals from two consecutive iterations is smaller than a predefined convergence criterion, designated as \(\varepsilon\)  
\[\text{RMS}_{\text{new}}=\frac{\Delta\widetilde{\mathbf{z}}_{k}^{T}\widetilde{ \mathbf{R}}_{k}^{-1}\Delta\widetilde{\mathbf{z}}_{k}}{N},\ \bigg{|}\frac{\text{RMS}_{\text{new}}-\text{RMS}_{\text{ old}}}{\text{RMS}_{\text{old}}}\bigg{|}<\varepsilon. \tag{20}\]  
Since the OD approach presented in this section adopts a batch processing scheme to estimate the satellite's augmented state vector at a specific epoch, the iterative procedure does not involve propagating state covariances to other measurement epochs. As a result, this method avoids potential issues commonly encountered in sequential estimators, such as instability due to covariance propagation. Upon the completion of each iteration, the initial state covariance is reinitialized. The flowchart of the OD procedure based on an unscented batch filtering method is shown in Figure 2.
## 4 Discussion  
Because of the complicated structure and relatively low magnitude of vertical air motion in convective clouds, direct measurement of accurate vertical air velocity with high resolution is difficult and still lacking. In some previous studies, observations from in-situ research aircraft were used to validate radar retrieval as a feasible solution [22; 26]. However, this approach may be not work on the TP due to bad flight conditions in synoptic storms and in the presence of stronger convective cells, and also due to the low availability of airports. In this paper, the validations for radar retrievals were preliminarily performed from two aspects. First, characteristics of the other radar measurements from two cases were analyzed to qualitatively infer the reliability of the derived vertical air velocities; such as, positive mean Doppler velocities corresponding to strong updraft regions. A feature of the alternating variation of vertical air motion across the small-scale cumuli was similar to previous studies [25; 35], and a melting layer was found when downdrafts predominated. These features support the use of radar retrievals for this application. Second, a surface retrieval from an optical disdrometer was formed and compared to the radar retrievals. While certain biases remain, the comparison reveals a similar trend in the vertical air velocities from the two instruments.  
In the future, measurements or retrievals from other equipment (such as, wind profile radar) could be compared with cloud radar retrievals under some special conditions. However, this is just an initial idea, and further research is required.
## 3 Single Channel Modulated Wideband converter  
### Structure Description  
The structure of SCMWC is shown in Figure 2. It consists of a mixer, a low-pass filter and a low-rate ADC. As shown in **a**, the input signal \(x(t)\) is firstly multiplied by the mixing periodic sequences \(p_{i}(t)\), which is described in **b** (left drawing). Then it is filtered by the low-pass filter \(h(t)\) with cutoff frequency \(1/2T_{s}\) shown in **b** (right drawing). Finally the output signal is sampled at rate \(f_{s}=1/T_{s}\). The \(p_{i}(t)\) is a piecewise constant function that alternates between the levels \(\pm 1\) for each of \(M\) equal time intervals. Its period is \(mT_{p}\) and there are \(m\) different sequences with the same time length \(T_{p}(1/T_{p}\geq B)\), which have no correlation with each other. Now we define \(\textbf{{T}}_{i}=\left[(i-1)T_{p}+nmT_{p},iT_{p}+nmT_{p}\right]\) (\(n\in\)\(\mathbb{N}\)), and it is obvious that the \(p_{i}(t)\) in the interval \(\textbf{{T}}_{i}\) is \(T_{p}\)-periodic.