esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Solution Verification--Methodology  
The Fitness Function defined for the evolutionary algorithm used during refinement step cannot be used to measure the quality of the solution. The Fitness Function is used to heuristically compare solution candidates but does not have the information if the mechanical properties of the solution match ones of the input mesh. Solution verification is performed by computing the same measures for two objects--the structural optimization  
Figure 8: (**a**) Extracted features exported to CAD as cylinders. Partial connections between elements are visible. (**b**) The same features exported as cylinders with additional spheres at the ends. All elements are fully connected.  
result, which is also an input for the feature recognition system, and the reconstructed parametric solution, exported from the CAD system as an STL mesh.  
The first compared measure is the total mass of an object. Objects reconstructed in CAD should not have a bigger mass than the source objects, so the mass of the object after the structural optimization is compared with the mass of the reconstructed object exported from the CAD system. The mass is calculated using the density of the material and the volume of the mesh. The density is an input parameter of the optimization process and the volume is calculated using a volumetric mesh generator, which is part of the structural optimization system. The volume of all constituent mesh cells is summed to get the total mesh volume.  
The surface energy value, which is equalized during structural optimization, is selected in such a way that the von Mises stress does not exceed the critical value. Fulfilling the von Mises yield criterion guarantees structural integrity under the load of the optimization results. A second test is performed to prove that the von Mises stress in a reconstructed object does not exceed the maximum von Mises stress present in the structural optimization results. To obtain a stress distribution, the object is exported from CAD and loaded back into the structural optimization system. The exported surface mesh is first converted into a volumetric mesh and then into Finite Elements Method input data. A static elasticity calculation is performed using FrontISTR software [34; 35]. The maximal value of the von Mises stress is obtained and the stress distribution is analyzed. Together with the values of the von Mises stress, the displacement data were obtained. Using mesh displacements, the maximum deflection under load is calculated. Despite it providing no extra information compared to von Mises stress, the maximum deflection is a value often used in engineering analysis, so it is also presented. In case of multiple load scenarios, tests must be performed for each scenario independently.  
The presented solution verification has been performed for both cases described below.
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 2 Literature Review on Deep Anomaly Detection
### Evaluation Metrics  
In the literature on anomaly detection [11; 12], a prominent evaluation metric is the Receiver Operation Characteristic (ROC), which is obtained by gradually changing the threshold of the regularity score. The regularity score is used to judge whether the input frame is normal or abnormal by manually defining a threshold. The optimal value of this parameter is relevant, since a higher threshold leads to a higher false negative rate, while a lower one leads to a higher false negative rate. Then, the Area Under the Curve (AUC) is cumulated to a scalar for performance evaluation with a higher value indicating better performance. As AUC is the only metric that is present in every referenced work, it was leveraged for performance evaluation in this paper. To achieve a more detailed understanding of a certain model, precision, recall, true positive, and false alarm should also be considered. Although these metrics are not as popular as Frame AUC, their inclusion provides interesting indicators to estimate the potential success of a real-world application of a certain model (e.g., knowing if it is prone to false alarms).
## 3 Results  
### Storage Experiment 1--Microbiological and Metabolic Analysis  
#### 3.1.1 Total Microbial Count  
The impact of preservation on the total microbial load was assessed using a plate count method developed for the analysis of food and feed products [29]. The growth medium used was a non-selective rich medium that primarily enables the growth of bacteria. The algae concentrate initially contained \((2.4\ \pm\ 0.6)\times 10^{7}\) colony forming units (CFU)/mL. This value decreased during storage and was on average about one log unit lower after 14 days of storage for the samples to which no acetic acid was added (Figure 1a). Storage temperature had a significant impact on microbial numbers of these concentrates (\(p<0.001\)) and higher numbers were observed after 8 \({}^{\circ}\)C and 20 \({}^{\circ}\)C storage than after 4 \({}^{\circ}\)C (Figure 1b). The way of sealing the storage recipient, either with cotton plugs or a plastic cover (i.e., open or closed storage), had no significant effect (\(p\) = 0.59) but there was an interaction between the sealing type and storage time (\(p\) = 0.001) and between sealing type, storage time and storage temperature (\(p\) = 0.001, Figure 1c). The microbial count decreased in function of storage time in open tubes stored at 8 \({}^{\circ}\)C, but not in closed tubes. Yet, such changes were observed during 4 \({}^{\circ}\)C or 20 \({}^{\circ}\)C storage.  
For the algae stored at 8 \({}^{\circ}\)C in open tubes, the effect of acetic acid addition (50 mM) was tested. The addition of the organic acid had a strong impact (\(p\) < 0.001) and this factor interacted with storage time (\(p\) < 0.001). The colony count was lower for acetic acid supplemented samples, especially after seven days of storage (Figure 1d).  
Figure 1: Total microbial load—storage experiment 1. (**a**) Average colony count after 0, 7 and 14 days of storage in algae samples without added acetic acid. (**b**) Effect of storage temperature for algae without acetic acid addition and stored for 7 or 14 days. (**c**) Average colony count of algae without acetic acid addition. (**d**) Effect of acetic acid addition for algae stored at 8 \({}^{\circ}\)C in open tubes. Lowercase letters (a,b,c) denote significant differences; values within one figure that are not labelled by the same letter are significantly different. Error bars represent standard deviations. CFU: colony forming units.
# Risk-Sensitive Rear-Wheel Steering Control Method Based on the Risk Potential Field
## 2 Design of Rear-Wheel Steering Control Based on the Risk Potential Field
### Optimization of the Reference Yaw Rate  
Equations (4-10) show the optimization algorithm for the reference yaw rate.  
The algorithm is expressed as  
\[r^{*}\ =\ r+\Delta{r_{p}}^{*} \tag{4}\]  
\[\Delta{r_{p}}^{*}\ =\ \underset{\Delta{r_{p}}(i)}{\text{argmin}}J \tag{5}\]  
with  
\[J\ =\ \underset{j\ =\ 1}{\overset{N}{\sum}}\Big{[}U_{risk}\big{(}X_{p}(i,j),Y_{p}(i,j)\big{)}+r_{y}\Delta{r_{p}}^{2}(i)\Big{]} \tag{6}\]  
subject to  
\[X_{p}\ =\ X+\int_{0}^{t_{p}}\big{(}V+a_{x}t_{p}\big{)}\cos\big{(}\psi+r_{p}t_{p} \big{)}dt_{p} \tag{7}\]  
Figure 1: Block diagram of the four-wheel steering (4 WS) control system.  
\[Y_{p}\ =\ Y+\int_{0}^{t_{p}}\big{(}V+a_{x}t_{p}\big{)}\sin\big{(}\psi+r_{p}t_{p} \big{)}dt_{p} \tag{8}\]  
\[\big{|}\Delta r_{p}\big{|}\leq 0.1 \tag{9}\]  
\[|V\times r|\leq 5.0 \tag{10}\]  
The reference yaw rate increment or decrement (\(\Delta r_{p}\)*) that minimizes the cost of the evaluation function (Equation (6)) is selected, and the reference yaw rate (\(r\)*) is obtained by adding \(\Delta r_{p}\)* to the existing yaw rate (\(r\)). This implies that the selected yaw rate must minimize the risk potential of the predictive position and achieve smooth vehicle yawing motion. The predicted vehicle positions \(X_{p}\) and \(Y_{p}\) are calculated by Equations (7) and (9). The constraints on the reference yaw rate increment or decrement candidate and the vehicle state (lateral acceleration) for this optimal control problem are Equations (9) and (10). This optimal control problem must be solved periodically to react to unforeseen changes in the environment, such as prediction errors of the dynamic obstacles. This receding horizon concept is generally used in model predictive control, and a solver such as the generalized minimal residual method (GMRES) can be applied [18; 19]. Figure 2 depicts the overview of the optimization of the reference yaw rate.  
Herein, \(i\) denotes the sampling frequency of the prediction time, \(j\) indicates the sampling frequency of the reference yaw rate increment or decrement candidate, \(N\) represents the prediction time horizon, \(M\) denotes the reference yaw rate candidate horizon, \(r_{y}\) indicates the weighting coefficient, \(\Delta r_{p}\) denotes the reference yaw rate increment or decrement candidate, \(t_{p}\) indicates the predicted time, and \(r_{p}\) denotes the reference yaw rate candidate. In this paper, the weighting coefficient is set as follows: \(r_{y}=70\).  
The constraint of the lateral displacement along the course of collision avoidance is a soft one. This may be violated as the optimization is trying to find the minimum risk path under the constrained reference yaw rate increment or decrement (the absolute value is limited to 0.1 rad/s).
## 6 Windowed RMS Analysis
### Comparative Discussion  
There are distinct differences in the windowed-RMS results from the three excitation frequencies. The longer wavelength of the 100 kHz excitation interacts differently with the defect when compared to the shorter wavelengths of the 250 kHz and 300 kHz excitations. The primary difference is that the 100 kHz Lamb wave does not transmit through the disbonded region like the higher frequency excitations. This is demonstrated by the low RMS values to the right of the disbonded region and the higher values to the left indicating that the wave is reflected instead of being transmitted through the disbonded region.  
All three sets of RMS plots indicate that the defect in the disbonded region is not a perfect square as was intended. Results at all three excitation frequencies suggest that there is a discrepancy in the defect allowing the Lamb wave to transmit better through the lower part of the disbonded region. This is seen in the RMS plots for the 100 kHz excitation however it is clearer on the higher frequency plots which also suggest that there are other inconsistencies present in the disbonded region.  
There is a difference in the amplitudes between the 100 kHz vibrometry data and the higher frequencies shown by the differing values of the colour bars. This can be attributed to the higher frequencies attenuating more, thus the Lamb wave energy decreases significantly in the higher frequency plots.
# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 4 Experiment
### Subjective Effect Analysis  
In addition to the objective metrics, this paper also shows the subjective effect of segmenting some images in the Herlev dataset. As shown in Figure 8, the results were obtained from segmenting some images in the Herlev dataset using the U-Net and GDLAU-Net networks, where GDLAU-Net represents the network based on the U-Net framework after embedding the GDLA Block. The first four rows are normal cervical cells, and the last five rows are abnormal cervical cells. As shown in Figure 8, the GDLAU-Net network has better segmentation performance than U-Net for both normal and abnormal cells. From 1, 2, 3, and 8 in Figure 8, it can be seen that U-Net is susceptible to noise and background color, leading to multiple and wrong detections due to the lack of global contextual semantic information. The GDLAU-Net has excellent robustness for this. From 4 in Figure 8, it can be seen that GDLAU-Net is better for the segmentation of small targets, compared to U-Net. From 5 in Figure 8, it can be seen that GDLAU-Net is better than U-Net for the segmentation of cells with blurred boundary contours after expansion. From 6 and 7 in Figure 8, it can be seen that GDLAU-Net is finer for cell nucleus boundary segmentation compared with U-Net, due to the full extraction and utilization of spatial information. From 8 and 9 in Figure 8, it can be seen that GDLAU-Net can better capture the global information dependence and achieve the closure and uniqueness of cell nuclei segmentation.  
\begin{table}
\begin{tabular}{c c c c c} \hline SA & CA & GD & Nuclei & Cytoplasm \\ \hline  & & & \(0.875\pm 0.12\) & \(0.764\pm 0.14\) \\ ✓ & & & \(0.872\pm 0.08\) & \(0.763\pm 0.10\) \\  & ✓ & & \(0.887\pm 0.10\) & \(0.774\pm 0.10\) \\  & ✓ & ✓ & \(0.894\pm 0.07\) & \(0.781\pm 0.08\) \\ ✓ & ✓ & & \(0.885\pm 0.12\) & \(0.770\pm 0.11\) \\  & ✓ & ✓ & \(0.904\pm 0.10\) & \(0.786\pm 0.15\) \\ ✓ & ✓ & ✓ & \(0.907\pm 0.12\) & \(0.787\pm 0.13\) \\ ✓ & ✓ & ✓ & \(0.913\pm 0.09\) & \(0.796\pm 0.11\) \\ \hline \end{tabular}
\end{table}
Table 4: Disassembling GDLA module. The effects of the three components of the GDLA network on the network performance. The values are in the format of \(\mu\pm\sigma\).  
Figure 7: Visual results of disassembling the GDLA module.
# Orientation and Exposure Efficiency of a Solar Tracking Surface in Clear Sky
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\tau\), Hour & **0** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **8.075** & \(\Sigma\) \\ \hline \multicolumn{10}{c}{Winter solstice \(G_{sat}\) = 1412 W/m\({}^{2}\)} \\ \(\theta_{z}\) & 73.45 & 74.64 & 78.12 & 83.56 & 90.00 & – & – & – & – & – & – \\ \(cos\theta_{z}\) & 0.2849 & 0.2648 & 0.2059 & 0.1122 & 0.0000 & – & – & – & – & – & – \\ \(z=124\) m & 3.4151 & 3.6659 & 4.6615 & 8.0113 & 19.42 & – & – & – & – & – & – \\ \(\delta_{R}\) (\(m\)) & 0.0873 & 0.0852 & 0.0783 & 0.0635 & 0.0409 & – & – & – & – & – & – \\ \(G_{b}\) (\(T_{L}\) = 2) & 842 & 822 & 750 & 585 & 357 & – & – & – & – & 5846 \\ \(G_{b}\) (\(T_{L}\) = 3) & 651 & 627 & 547 & 376 & 179 & – & – & – & – & 4281 \\ \(G_{b}\)(\(T_{L}\) = 3.5) & 569 & 548 & 467 & 302 & 127 & – & – & – & – & 3667 \\ \hline \hline \end{tabular} Note: the values by Italic type refer to the moment of rise/set of the Sun, which is equal to 3.92 h after full sun in the day of winter solstice.  
\end{table} 
Table 3: _Cont._that small deviations can be explained by local factors of impact on air transparency and random actinometrical criteria when assessing results of measuring and referring to the conditions of clear sky. Hence, energy efficiency of the tracking surface is reasonably calculated at a minimum level of air turbidity \(T_{LK}=2\), which meets the requirement of maximum liquidation of the impact of diffusive fluxes on the measuring results. Thus, the seasonal dependencies, presented in Figure 6, can be considered as close as theoretically possible and can be used later for assessment of energy efficiency of the tracking surface. Theoretically, they also correspond to the maximum possible energy efficiency of a continuous tracking of the Sun by means of a double-axis rotating tracker. Comparison of the daily data of solar energy registered at the meteorological stations \"Kovel\" and \"Boryspil\" for the fluxes at different levels of air turbidity are presented in Table 4. The values in bold type present the most appropriate values of seasonal natural energy exposures, meeting the conditions of a clear-sky model. Hence, daily solar energy can be considered as the additional integral characteristics of the efficiency of the tracking surface, along with hourly ones. It is clear that hourly and daily energy data, which correspond to the maximum transparency of air \(T_{LK}=2\) when one can ignore the diffusive impact on first approaching, are the most reliable and precise characteristics of the energy efficiency of the tracking surface.
## 5 Conclusions  
This paper gave an overview of the work conducted in the frame of the AQUASENSE project concerning the design, development, and analysis of a communication system. These communication systems are evaluated through computer simulations using SimuLTE for simulating an LTE network infrastructure connecting the WSN with a remote server, where data are collected. In this frame, two network topologies are studied: (i) network A, which represents a multi-cell network, where multiple sensors are associated with different base stations, sending water measurements to the server, and (ii) network B, which is again a multi-cell network, but this time multiple sensors are associated to local aggregators, which collect and aggregate the measurements and send them to the remote server through LTE base stations. For these topologies, the end-to-end delay and the goodput are evaluated as representative performance indices in two conditions: (i) periodic monitoring, where the data are transmitted to the server at larger intervals; and (ii) alarm monitoring, where the data are transmitted more often. The performance of the two scenarios described above demonstrates that when we use the aggregators to collect the data from the sensors, the network's QoS is higher. In particular, an intelligent implementation of how the aggregators provide the information to the server in normal and alarm conditions dramatically helps achieve efficient monitoring and accurate pollution event detection.  
Conceptualization, S.B. and S.T.; methodology, S.T.; software, S.B.; validation, S.B. and S.T.; formal analysis, S.T.; investigation, S.T.; resources, S.B.; data curation, S.B.; writing--original draft preparation, S.B.; writing--review and editing, S.T.; visualization, S.T.; supervision, S.T.; project administration, S.T.; funding acquisition, S.T. All authors have read and agreed to the published version of the manuscript.  
The European Commission supported the work through AQUASENSE (H2020-MSCA-ITN-2018-813680).  
Not applicable.  
Not new data were created or analyzed in this study. Data are contained within the article.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(E\) & \(S\) & **Mean E2E Delay [ms]** \\ \hline
2 & \((5+5)\) & 18.10 \\
3 & \((3+3+4)\) & 13.68 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Network A: Mean E2E delay for periodic case as compared to the number of eNBs and different distribution of sensors.
# Local Convergence Analysis of an Eighth Order Scheme Using Hypothesis Only on the First Derivative
## 1 Introduction
Hence, the results in [13] cannot be applied to show the convergence of method Equation (2) or its special cases requiring hypotheses on the fifth derivative of function \(F\) or higher. Notice that, in particular there is a plethora of iterative methods for approximating solutions of nonlinear equations [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. These results show that initial guesses should be close to the required root for the convergence of the corresponding methods. However, how close should an initial guess should be for the convergence of the corresponding method to be possible? These local results give no information on the radius of the ball convergence for the corresponding method. The same technique can be used for other methods. In the present study, we expand the applicability of method given by Equation (2) using only hypotheses on the first order derivative of function \(F\). We also propose the computable radii of convergence and error bounds based on the Lipschitz constants. We further present the range of initial guesses \(x^{*}\) that tell us how close the initial guesses should be required to guarantee the convergence of the method Equation (2). This problem was not addressed in [13]. The advantages of our approach are similar to the ones already mentioned for the method described by Equation (2). The rest of the paper is organised as follows: in Section 2, we present the local convergence analysis of scheme Equation (2). Section 3 is devoted to the numerical examples which demonstrate our theoretical results. Finally, the conclusion is given in Section 4.
# Antioxidant Activity of Sulfate Metabolites of Chlorogenic Acid
###### Abstract  
This study aimed to determine the antioxidant properties of the sulfate monoesters of ferulic, caffeic, dihydroferulic and dihydrocaffeic acids, the main metabolites of chlorogenic acids. These compounds are not commercially available, so they were synthesized in the laboratory. The LC-MS/MS analysis allowed for the full characterization of these derivatives, which has made them reliable standards for further research. Purified metabolites including ferulic acid-4-O-sulfate, caffeic acid-4-O-sulfate and caffeic acid-3-O-sulfate, dihydrocaffeic acid-4-O-sulfate and caffeic acid-3-O-sulfate were examined for their antioxidant capacities and compared to their precursor compounds using Folin-Ciocalteu, CUPRAC (cupric ion--reducing) and DPPH* (2,2-diphenyl-1-picrylhydrazyl) methods. This study shows that hydrogenation of caffeic and ferulic acids into dihydroferulic acids has a positive influence on their reducing properties. Moreover, all synthesized sulfate monoesters exhibited very weak antioxidant properties compared to precursor compounds. The presented results show that the transformation of phenolic acids via sulfation leads to the inhibition of antioxidant properties due to the blockage of hydroxyl groups.  
phenolic acids; sulfate metabolites; antioxidant activity; LC-MS/MS
# Experimental Validation of Ellipsoidal Techniques for State Estimation in Marine Applications
## 3 Ellipsoidal State Estimation Procedure
In addition to the dynamic system model (21), the measured system outputs \(\mathbf{\mathrm{y}}_{\mathrm{m},k}=\mathbf{\mathrm{y}}_{\mathrm{m}}(t_{k})\) are described by an ellipsoidal uncertainty representation in the form  
\[(\mathbf{\mathrm{C}}\cdot\mathbf{\mathrm{x}}_{k+1}-\mathbf{\mathrm{y}}_{\mathrm{m},k+1})^{ T}\cdot\mathbf{\mathrm{Q}}_{\mathrm{m}}^{-1}\cdot(\mathbf{\mathrm{C}}\cdot\mathbf{\mathrm{x}}_{k +1}-\mathbf{\mathrm{y}}_{\mathrm{m},k+1})\leq 1\enspace, \tag{23}\]  
where \(\mathbf{\mathrm{Q}}_{\mathrm{m}}\) is the shape matrix defining outer bounds for the measurement uncertainty. To make the ellipsoidal set intersection operator introduced in [4] applicable for the state estimation task, the expression (23) is reformulated equivalently in terms of the inequality constraint  
\[\left(\mathbf{\mathrm{x}}_{k+1}-\mathbf{\mathrm{y}}_{\mathrm{m},k+1}^{\prime}\right)^ {T}\cdot\mathbf{\mathrm{P}}_{\mathrm{m}}^{\prime}\cdot\left(\mathbf{\mathrm{x}}_{k+1} -\mathbf{\mathrm{y}}_{\mathrm{m},k+1}^{\prime}\right)\leq 1\enspace. \tag{24}\]  
In this inequality (24), the matrix \(\mathbf{\mathrm{P}}_{\mathrm{m}}^{\prime}\) is a purely diagonal matrix if the system's output matrix \(\mathbf{\mathrm{C}}\) in (23) represents a direct measurement of selected components of the state vector \(\mathbf{\mathrm{x}}_{k+1}\). If a smaller number of outputs than state variables are available in this case, the matrix \(\mathbf{\mathrm{P}}_{\mathrm{m}}^{\prime}\) is not invertible and represents a degenerate ellipsoid in the \(n\)-dimensional state space; see also [4; 12]. Using this notation, entries in the vector \(\mathbf{\mathrm{y}}_{\mathrm{m},k+1}^{\prime}\) that correspond to non-measured components of the state vector at the time instant \(k+1\) are set to the associated ellipsoid midpoint \(\mathbf{\mu}_{k+1}\) obtained from the state prediction, while all other components correspond to the point-valued measured data \(\mathbf{\mathrm{y}}_{\mathrm{m},k+1}\).