esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Results
### Circular Dam Break Problem  
This problem is a numerical study of symmetrical flow due to a circular dam break. An interesting aspect of this example is that it is solved by a shock wave that propagates downstream by increasing water height. In contrast, a depression wave propagates upstream by decreasing the water height. Several authors have investigated numerical solutions to this problem. Their investigations resulted in numerous research reports in the literature, such as [30; 31; 32]. Here, we propose a channel of 10 m \(\times\) 10 m dimensions, with a flat, smooth bottom, enabling us to neglect the friction between the bottom and the water. The water is retained in a column 4 m in diameter placed at the center of the domain. This column is assumed to break suddenly at an initial time. The initial conditions of this problem are described as follows:  
\[q_{x}(x,y)=q_{y}(x,y)=0\quad\forall(x,y) \tag{25}\] \[h(x,y)=\left\{\begin{array}{ccc}1.0&if&(x^{2}+y^{2})\leq 4\\ 0.25&&\textit{otherwise}\end{array}\right. \tag{26}\]  
For boundary conditions, we assume that the basin is closed on all sides, and that the no-slip condition applies to all sides. The problem is solved at a density containing 4000 nodes.  
Figures 2 and 3 show the results obtained by the proposed method at different times and in different formats. A three-dimensional view and contour lines of water height are shown in Figure 2 for times \(t=0.3\) s, \(t=0.9\) s and \(t=3.6\) s at the left and right range, respectively. In practice, the propagation speed and the shape of the flood wave are accurately calculated in order to set up intervention plans. In this case, the calculations show that the wave front develops symmetrically and propagates towards the sides of the basin. The sections also show that the wave front is well captured. This method appears to reproduce the flow behavior correctly, and the results obtained are similar to those reported in the references mentioned above.  
In order to make a quantitative comparison, we compared the results obtained by the meshless method and those obtained by a mesh-based method, namely, second-order finite volumes. The method is a second-order augmented Riemann using the MUSCL scheme [33] with a mesh of 4000 nodes allowing for the generation of 15,678 volumes.  
Figure 3 shows a cross section of pollutant concentration at the water height \(y=5\) m in the instants \(t=0.3\) s and \(t=0.6\) s.  
Figure 1: Numerical solution of water height and depth of the lake at rest with an overview of the error.  
The numerical results obtained by our method are in reasonable agreement with those of previous studies. Our results are clearly seen to be close to the reference results, and to be comparable to those obtained by the finite volume method. Since meshless methods based on RBFs are easy to implement, they represent suitable approaches for the simulation of shallow water flows creating shock waves.  
Figure 2: Water level calculated after the dam break at times \(t=0.3\) s, \(0.9\) s and \(3.6\) s. On the right, a three-dimensional view, and on the left, the water level contours.
## 3 Results and Discussion
### Correlation between phytochemicalal contents and antioxidant Properties of Extract  
As shown in Table 4, significant positive correlations were observed between the studied phytochemicalal contents and antioxidant results, indicating that these compounds played a significant role in the antioxidant assays. Because the TFC and TTC values have higher R values (especially ABTS), this indicates that these two phytochemicals have greater antioxidant capacity than the phenolic compounds in the extracts.  
[PERSON] et al. [40] discovered an even stronger relationship between the TFC of honey and FRAP and DPPH, which were 0.888 and 0.899, respectively, compared to the TPC and DPPH, which was 0.597. However, [PERSON] et al. [41] found a stronger relationship between TPC and DPPH than TFC and DPPH, where R was 0.949. The terpene content correlated with the antioxidant activity of the extracts, particularly with ABTS. Terpenes, particularly  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameters** & **FRAP** & **ABTS** & **DPPH** & **TPC** & **TFC** & **TTC** \\ \hline FRAP & 1 & & & & & \\ ABTS & 0.54896 & 1 & & & & \\ DPPH & 0.333834 & 0.622806 & 1 & & & \\ TPC & 0.512248 & 0.965626 & 0.784889 & 1 & & \\ TFC & 0.623255 & 0.948893 & 0.762314 & 0.944358 & 1 & \\ TTC & 0.834865 & 0.917917 & 0.569816 & 0.881282 & 0.915261 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Correlation between phytochemicalal contents and antioxidant properties of extract.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Extracts** & **FRAP** & **ABTS** & **DPPH** \\
**(uM (Fe2+/g dw))** & **(mg TE/g dw)** & **(mg TE/g dw)** \\ \hline Water & 0.33 \(\pm\) 0.02 a & 0.97 \(\pm\) 0.05 a & 0.95 \(\pm\) 0.01 a \\ Ethanol & 1.22 \(\pm\) 0.02 b & 4.71 \(\pm\) 0.18 d & 4.46 \(\pm\) 0.02 e \\ Methanol & 1.22 \(\pm\) 0.01 b & 4.02 \(\pm\) 0.15 b & 3.93 \(\pm\) 0.06 d \\ Propanol & 1.25 \(\pm\) 0.02 c & 4.64 \(\pm\) 0.08 cd & 1.43 \(\pm\) 0.07 b \\ Acetone & 3.38 \(\pm\) 0.02 d & 4.41 \(\pm\) 0.21 c & 3.12 \(\pm\) 0.10 c \\ \hline \hline \end{tabular}
\end{table}
Table 3: Antioxidant properties of extracts. Different lower-case letters mean statistically different values (\(p=0.05\)).  
triterpenes and tetraterpenes (carotenoids), appear to be important compounds for humans in relation to ROS-mediated oxidative stress [42].  
Some structural characteristics of terpen explain their antioxidant properties. The oxygenated type with a phenolic structure and the hydrocarbon type with a methylene group in its structure has the highest antioxidant activity for the monoterpene group. The allylic alcohol types are the most active antioxidant compounds among sesquiterpene compounds. Triterpenes with dienone-phenol rearrangement have the highest antioxidant activity among the various triterpene structural types and carotenoids, which contain conjugated double bonds and, therefore, act as an extremely efficient antioxidant among tetraterpenes [43].
## 4 Design of Pit-Probing Control Based on Admittance Control Method  
### Admittance Control Method  
The core idea of admittance control is to take the deviation between the desired force and the foot-end touchdown force as the input to the impedance controller, which then generates a position correction amount. The target position of the foot-end is dynamically adjusted to achieve the force control effect. This control method is usually described by a second-order differential model:  
\[F_{r}-F_{gnd}=M_{d}(\ddot{x}_{d}-\ddot{x}_{a})+B_{d}(\ddot{x}_{d}-\ddot{x}_{a}) +K_{d}(x_{d}-x_{a}) \tag{18}\]  
where \(F_{gnd}\) is the foot-end touchdown force; \(F_{r}\) is the foot-end desired force; \(M_{d}\) is the inertia parameter of the impedance controller; \(B_{d}\) is the damping parameter; \(K_{d}\) is the stiffness parameter; \(x_{d}\), \(\dot{x}_{d}\), \(\ddot{x}_{d}\) are the desired position, velocity, and acceleration, respectively, during the motion of the single leg; and \(x_{a}\), \(\dot{x}_{a}\), \(\ddot{x}_{a}\) are the actual position, velocity, and acceleration, respectively, during the motion of the single leg. Define \(\Delta x\) as the displacement deviation at the foot-end, and \(\Delta x=x_{d}-x_{a}\); then, the impedance control equation can be simplified as follows:  
\[F_{r}-F_{gnd}=M_{d}\Delta\ddot{x}+B_{d}\Delta\dot{x}+K_{d}\Delta x \tag{19}\]  
The schematic based on the admittance control principle is shown in Figure 9. Where \(F_{gndx}\) is the horizontal force exerted by the external environment on the foot-end; \(F_{gndy}\) is the vertical force exerted by the external environment on the foot-end; \(\Delta x\) and \(\Delta y\) are the position correction amounts in the horizontal and vertical directions of the foot-end, respectively; and \(\Delta F=F_{r}-F_{gnd}\).  
Figure 8: Figure of the tripod gait.  
Figure 7: Schematic diagram of the tripod gait stepping-out scenarios.  
Although admittance control has a certain lag, it features simple calculations and does not require a complex dynamic model, and it offers relatively high precision in position control of the foot-end. In contrast, force-based impedance control has relatively lower control precision, but it can directly adjust the contact force, thus providing better compliance.
## 4 Simulation Analysis
### Correction Direction Misjudgment Rate  
Frequency estimation errors can be attributed to two main categories: one is the inaccurate estimation of frequency deviation, leading to deviations in interpolation positions; the other is the misjudgment of the correction direction, resulting in increased frequency estimation errors. To address this, a comparative analysis of the misjudgment of the correction direction for each algorithm under different SNR conditions is shown in Figure 6.  
As previously analyzed, the Rife algorithm tends to exhibit a relatively high misjudgment rate for the correction direction when the frequency point is near the quantized frequency, leading to increased frequency errors after deviation correction. To address this issue, the ASIQ-Rife algorithm reassesses the correction direction following signal frequency shifting, which reduces the misjudgment rate. The PAI-Rife algorithm enhances the accuracy by shifting the signal to the optimal estimation range, thereby improving the correction direction judgment; however, significant errors persist under low-SNR conditions. In contrast, the SP-Rife algorithm effectively mitigates the effects of spectral leakage and noise by refining the frequency resolution through spectrum refinement. Additionally, it enhances the accuracy of correction direction judgment using phase angle interpolation. Consequently, the SP-Rife algorithm achieves a misjudgment rate for the correction direction that is significantly closer to zero and much lower compared to both the Rife and ASIQ-Rife algorithms, especially in high-noise environments.  
Figure 6: Correction misjudgment rates of several Rife algorithms under different SNRs.
# Unsupervised Anomaly Detection for Time Series Data of Spacecraft Using Multi-Task Learning
## 2 Related Work  
### Spacecraft Anomaly Detection  
The existing autonomous anomaly detection methods for spacecraft can be grouped into three categories: model-based, signal processing-based, and knowledge-based methods [16]. Anomaly detection using expert system-based and machine learning methods has been applied to spacecraft such as Hayabusa, the Geotail Space Shuttle, and the International Space Station [5]. For remote sensing satellites, independent health management systems have been designed [17]; these systems can diagnose and isolate anomalies and achieve appropriate recovery, thereby effectively improving the spacecraft reliability.  
The random forest algorithm has been utilized for anomaly detection of spacecraft rolling bearings based on the frequency of the bearing fault signal [18]. Moreover, the hierarchical anomaly detection method has been proposed to identify failures in the attitude control system [19; 20]. A method based on morphological variational modal decomposition and the Jensen-Renyi divergence distance has been proposed to overcome the problem of weak anomaly identification for on-orbit spacecraft failures [21]; this approach has proven to be effective when applied to spacecraft reaction wheel data. Additionally, a model-based fault detection, isolation, and identification scheme has been proposed, which uses unscented Kalman filters to detect and isolate faults in control moment gyros to facilitate a satellite attitude control subsystem [22].  
Deep learning technology [23] has been successfully applied to various fields, and deep-learning-based anomaly detection in complex systems has received considerable attention [24]. Deep learning methods can mine hidden patterns in data and do not rely on expert knowledge. In the context of a spacecraft, a recurrent neural network (RNN)-based method has been developed to detect actuator failure in a satellite attitude control system [25]. Moreover, the effectiveness of the deep learning method for aircraft fault diagnosis has been verified through practical application to fault diagnosis and prediction of mechanical parts [26]. LSTM-based methods constitute a typical prediction-based approach, and the effectiveness of such a method for anomaly detection has been proven using  
Figure 1: Overall structure of proposed MTAD method.  
expert-labeled telemetry anomaly data from the Soil Moisture Active Passive satellite and Curiosity, the Mars Science Laboratory rover, with a complementary unsupervised and nonparametric anomaly thresholding approach and false-positive mitigation strategies also being proposed [6]. A causal network and feature attention-based LSTM method has also been used to detect anomalies in satellite telemetry data [27].
# Query-Based Cascade Instance Segmentation Network for Remote Sensing Image Processing
## 2 Methods
### Multistage Cascade Task Head
#### 2.4.2 Dynamic Mask Head  
As shown in Figure 6, similar to the dynamic box detection head, we simply changed to a more fine-grained mask segmentation after completing the instance interaction. Notably, compared to the detection task, the segmentation task requires more accurate pixel-location information. In the segmentation phase, the RoI is obtained through the RoI Align [21] operation and cropped into a feature map with a size of 14 \(\times\) 14. Specifically, the output feature after the instance interaction is completed is subjected to four 3 \(\times\) 3 convolution blocks to obtain richer semantic features, which are returned to the original image size by an upsampling operation (Transposed convolution [46]). In addition, we perform a 1 \(\times\) 1 convolution on the output of the feature map before upsampling. This step ensures its alignment with the next stage and connects it to the next mask prediction through elementwise summation. The aim is to enhance the flow of mask information between the different stages. Eventually, the predicted mask information is matched with the real label for loss calculation.  
Figure 7 shows the diagram of the multistage cascade task head. Assuming that the EFE module outputs a multilevel feature map of \(F_{i}\), \(i=1\), 2, 3, 4, all object queries are  
Figure 5: The internal structure of the dynamic box head.  
Figure 6: The internal structure of the dynamic mask head.  
pooled within the input feature map \(F_{i}\) by RoI pooling, and the detection process can be represented as:  
\[R_{box}=P_{box}(F_{i},Q_{o}) \tag{3}\]  
\[Q_{f+1},T_{box}=Dynamic_{box}(R_{box},Q_{f}) \tag{4}\]  
\[R_{mask}=A_{box}(F_{i},T_{box}) \tag{5}\]  
\[T_{mask}=Dynamic_{mask}(R_{mask},Q_{f+1})+M_{i} \tag{6}\]  
where \(P_{box}\) and \(A_{box}\) denote RoI pooling and RoI Align, respectively. \(R_{box}\) and \(R_{mask}\) denote the RoI after RoI Pooling and RoI Align, respectively. \(T_{mask}\) and \(T_{box}\) denote the prediction mask and box prediction, respectively. \(Q_{f}\) denotes the feature query and \(Q_{o}\) denotes the object query. \(M_{i}\) denotes that the mask information of the current stage is passed to the next layer.  
This design mode aims to ensure that the query feature of the next stage is closely related to the current stage. In this way, the model is able to gradually integrate the information from different layers to improve the understanding and expression of the goal. Unlike an ordinary cascade head, our task head can dynamically improve the quality of the mask with the flow of information and generate the final mask without any postprocessing.
## 3 Results  
### Initial Feature Selection Results and Analysis  
The classification method of random forest was employed in this study to obtain the accuracy curve and feature importance ranking as the number of features varied. The accuracy curve, depicted in Figure 6, illustrates a noticeable upward trend until the number of feature variables reaches 16. Beyond this point, the curve exhibits oscillations, leading to the selection of 17 features as the initial set. The 17 initial features include NDWI_mean, NDWI_max, NDBI_mean, NDTI_max, BSI_stdDev, SAVI_max, NDVI_stdDev, NDVI_max and Band 2, Band 3, Band 4, Band 5, Band 6, Band 7, Band 8, Band 11, and Band 12 in Table 1. Among these, \"mean\" denotes the average value, \"max\" represents the maximum value, and \"stdDev\" indicates the standard deviation. Terrain factors consist of slope, aspect, and elevation.  
Table 4 displays the optimized feature extraction results. It is evident that incorporating terrain and texture features resulted in a slight accuracy improvement, ranging from 0.1% to 0.3%. Moreover, the addition of morphological features yielded a more pronounced enhancement. Morphological features demonstrate their effectiveness in improving classification accuracy in this experiment.
## 7 Experimental Analysis
### Comparison with Industry  
Figures 24 and 25 show \(E_{op}\) and \(C_{max}\) of the industry and the B&B algorithm. The term \"Algorithm\" in these graphs represents the results of the B&B algorithm. The term \"Industry\" represents the actual data of the production company. The industry results shown in Table 19 are obtained considering the average monthly production load in kilograms of tortillas per year. The results of the B&B algorithm are obtained for a different number of machines per stage. Table 20 shows the same results as in Table 19 in percentages.  
Figure 24 shows \(C_{max}\) considering working hours. We see that the results of the algorithm are significantly improved compared with industry. For two machines per stage, the difference is almost a halving of \(C_{max}\) from 186 to 96.52 h. In the case of four machines, it remains below half (93 and 43.82 h). In the case of six machines, the results are better by almost three times with regard to \(C_{max}\) (62 h versus 25.57 h).  
Figure 25 shows \(E_{op}\) according to the processing time of machines (Table 3). We see that the B&B algorithm significantly improves upon the results obtained in industry. For two machines per stage, the \(E_{op}\) objective is reduced to almost half, and for four and six machines, the \(E_{op}\) objective of our algorithm is reduced to less than half.  
Table 20 shows the percentage of degradation of the results of industry and those selected from the Pareto front obtained from the bi-objective GA compared with results of the B&B algorithm.  
We observe that the degradation of \(E_{op}\) and \(C_{max}\) observed in industry are closer to or higher than 50%. Comparing B&B with our bi-objective GA, we observe that our results are less than 1.53% worse for \(E_{op}\) and 0.54% for \(C_{max}\). Due to the fact that the B&B algorithm finds the global optimum  
2. We proposed a bi-objective solution to solve a hybrid flow shop with unrelated machines, setup time, and work in progress buffers. It is based on the known NSGA-II genetic algorithm;
3. We calibrated our algorithm using a statistical analysis of multifactorial variance. The ANOVA technique is used to understand the impact of different factors on the solution quality. A branch and bound algorithm was used to assert the obtained performance.
4. We provided a comprehensive experimental analysis of the proposed solution based on data from a tortilla production company in Mexico.
5. We demonstrated that our solutions are not more than 3.5 percent off the global optimum for both criteria. They save more than 48 percent of production time and 47 percent of energy consumption compared with the actual production plan followed by the company.  
All authors contributed to the analysis of the problem, designing algorithms, performing the experiments, analysis of data, and writing the paper.  
This work is partially supported by Russian Foundation for Basic Research (RFBR), project No. 18-07-01224-a.  
The authors declare no conflict of interest.
## 4 Simulations and Results  
Since in these networks nodes numbers that form the network are unpredictable, the protocol scalability is one of the main issues to consider. Therefore, it is essential to evaluate the impact of increasing the nodes number in the network in distinct parameters such as latency in address assignment, the overhead because of control traffic or delay in the synchronization. Besides the nodes number in the network, it is necessary to take into account the frequency of the input and output nodes in the network. When a node leaves the network, the free address tables free in the network have been updated. If this is not done quickly, the network nodes cannot deal with requests for new entries in _the network to interpret_ that the network does not have free addresses.  
To evaluate the D2 HCP protocol performance has been used Network Simulator Network Simulator 3 (NS-3) [6]. Different scenarios of MANET networks were simulated to evaluate performance under different circumstances.
# Multi-Purpose Ontology-Based Visualization of Spatio-Temporal Data: A Case Study on Silk Heritage
###### Abstract  
Due to the increasing use of data analytics, information visualization is getting more and more important. However, as data get more complex, so does visualization, often leading to ad hoc and cumbersome solutions. A recent alternative is the use of the so-called knowledge-assisted visualization tools. In this paper, we present ST Maps (Spatio-Temporal Maps), a multipurpose knowledge-assisted ontology-based visualization tool of spatio-temporal data. ST Maps has been (originally) designed to show, by means of an interactive map, the content of the SILKNOW project, a European research project on silk heritage. It is entirely based on ontology support, as it gets the source data from an ontology and uses also another ontology to define how data should be visualized. ST Maps provides some unique features. First, it is a multi-platform application. It can work embedded in an HTML page and can also work as a standalone application over several computer architectures. Second, it can be used for multiple purposes by just changing its configuration files and/or the ontologies on which it works. As ST Maps relies on visualizing spatio-temporal data provided by an ontology, the tool could be used to visualize the results of any domain (in other cultural and non-cultural contexts), provided that its datasets contain spatio-temporal information. The visualization mechanisms can also be changed by changing the visualization ontology. Third, it provides different solutions to show spatio-temporal data, and also deals with uncertain and missing information. ST Maps has been tested to browse silk-related objects, discovering some interesting relationships between different objects, showing the versatility and power of the different visualization tools proposed in this paper. To the best of our knowledge, this is also the first ontology-based visualization tool applied to silk-related heritage.  
Keywords:visualization; ontology; spatio-temporal data; maps; cultural heritage +
Footnote †: journal: Applied Sciences