esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Services Provided by the Novel PHR Application for Emergency Situations: FirstER
#### 3.1.3 Developed System and Server of FirstER  
Based on privacy legislation, the medical cloud zone, an IaaS (Infrastructure as a Service) provided by Samsung Data System (Seoul, Korea), met the facilities and equipment standards necessary for the management and preservation of EMRs, which is a requirement for the remote storage of EMRs. Data transmission and reception between the hospitals and the MyData cloud servers were linked by RESTful API (Representational State Transfer Application Programming Interface), SSL (Secure Socket Layer, https), and JSON (JavaScript Object Notation, data format). (Figure 3).  
A patient sends subscriber information (patient hospital ID) to the hospital information system (HIS) through the private cloud platform when the patient agrees to subscribe to the mobile app and to the collection/use of their personal information. After validation of the subscriber information, the ED core data of the patient are extracted from the HIS server to the MyData-linked server in the hospital. Afterwards, when a patient requests \"download\", the core data of the patient are transferred from the connected server in the hospital to the MyData cloud platform.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Sample**} & \multicolumn{1}{c}{**Name of Laboratory Test**} \\ \hline Blood & White blood cell count \\ Blood & Hemoglobin \\ Blood & Platelet count \\ Blood & Absolute neutrophil count \\ Blood & Prothrombin time \\ Blood & Activated prothrombin time \\ Blood & Blood urea nitrogen \\ Blood & Creatinine \\ Blood & Total bilirubin \\ Blood & Gamma glutamyl transpeptidase \\ Blood & Alkaline phosphatase \\ Blood & Alanine transaminase \\ Blood & Aspartate transaminase \\ Blood & Amylase \\ Blood & Lipase \\ Blood & Sodium \\ Blood & Potassium \\ Blood & Uric acid \\ Blood & Hemoglobin A1c \\ Blood & Creatine kinase \\ Blood & Create kinase-myocardial band fraction \\ Blood & Troponin I \\ Blood & C-reactive protein \\ Blood & N-terminal pro-B-type natriuretic peptide \\ Blood & Brain natriuretic peptide \\ Urine & pH \\ Urine & Occult hematuria \\ Blood & ABO blood typing \\ Others & Influenza antigen immunoassay \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extraction and standardization of the most frequent laboratory examinations.
## 5 Discussion
### Challenges  
One major limitation of the proposed approach is that the accuracy of downscaled ET is greatly impacted by the accuracy of coarse-resolution ET. As many methods have been developed for improving MODIS ET [75; 76], better MODIS ET estimation is expected to increase the accuracy of the downscaled product. Another disadvantage of the approach is that downscaled ET can only be produced on cloudless image acquisition days. In areas with more frequent cloud cover, such as South Korea, the approach might not be able to describe the temporal trends of ET at higher spatial resolution. As Landsat 8 collects images in an 8-day offset from Landsat 7, our approach may provide ET at higher temporal resolution using the combination of the two datasets. In our future research, spatial-temporal fusion of Landsat data and MODIS ET will be explored to increase the temporal resolution of downscaled ET. The proposed approach may require post processing over the areas overlapped between adjacent Landsat scenes or the boundary areas of MODIS tiles where there could be spatial discrepancy of ET estimates. One possible solution would be to use merging algorithms typically used to mosaic images considering spatiotemporal variations of ET estimates. An alternative is to use fusion algorithms that are often used for downscaling such as Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM; [22; 77]) to mitigate the variations of ET estimates over such overlapped areas.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
###### Abstract  
The crucial need for perpetual monitoring of photovoltaic (PV) systems, particularly in remote areas where routine inspections are challenging, is of major importance. This paper introduces an advanced approach to optimizing the maximum power point while ensuring real-time PV error handling. The overarching problem of securing continuous monitoring of photovoltaic systems is highlighted, emphasizing the need for reliable performance, especially in remote and inaccessible locations. The proposed methodology employs an innovative genetic algorithm (GA) designed to optimize the maximum power point of photovoltaic systems. This approach takes into account critical PV parameters and constraints. The single-diode PV modeling process, based on environmental variables like outdoor temperature, illuminance, and irradiance, plays a pivotal role in the optimization process. To specifically address the challenge of perpetual monitoring, the paper introduces a technique for handling PV errors in real time using evolutionary-based optimization. The genetic algorithm is utilized to estimate the maximum power point, with the PV voltage and current calculated on the basis of simulated values. A meticulous comparison between the expected electrical output and the actual photovoltaic data is conducted to identify potential errors in the photovoltaic system. A user interface provides a dynamic display of the PV system's real-time status, generating alerts when abnormal PV values are detected. Rigorous testing under real-world conditions, incorporating PV-monitored values and outdoor environmental parameters, demonstrates the remarkable accuracy of the genetic algorithm, surpassing 98% in predicting PV current, voltage, and power. This establishes the proposed algorithm as a potent solution for ensuring the perpetual and secure monitoring of PV systems, particularly in remote and challenging environments.  
photovoltaic parameters; PV error handling; evolutionary algorithm; PV monitoring 2023  
A Note: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
A Note: [[https://www.mdpi.com/journals/pdf/10.3390/applsci](https://www.mdpi.com/journals/pdf/10.3390/applsci)]([https://www.mdpi.com/journals/pdf/10.3390/applsci](https://www.mdpi.com/journals/pdf/10.3390/applsci))
# New Methods of Series Expansions between Three Anomalies
## 2 Materials and Methods
### Symbolic Expression for Extreme Value of Differences among Three Anomalies
#### 2.6.3 Symbolic Expression for Extreme Value of Difference between True and Elliptic Anomalies  
From Formulas (5), (7), and (8), the derivative of the difference between the true anomaly \(f\) and the mean anomaly \(M\) with respect to the elliptic anomaly \(E\), the mean anomaly \(M\), and the true anomaly \(f\) can be obtained:  
\[\frac{d(f-M)}{dE}=\frac{\sqrt{1-e^{2}}}{1-e\cos E}+e\cos E-1 \tag{45}\]  
\[\frac{d(f-M)}{dM}=\frac{\sqrt{1-e^{2}}}{\left(1-e\cos E\right)^{2}}-1 \tag{46}\]  
\[\frac{d(f-M)}{df}=1-\left(1-e\cos E\right)\frac{\sqrt{1-e^{2}}}{1+e\cos f} \tag{47}\]  
By making the above three equations equal to zero and the second derivative not zero, we can obtain the following:  
\[\cos E=\frac{1-\left(1-e^{2}\right)^{1/4}}{e} \tag{48}\]  
\[\cos f=\frac{-1+\left(1-e^{2}\right)^{3/4}}{e} \tag{49}\]  
Furthermore, we can obtain the extreme points of three types of anomalies:  
\[E=\pm\arccos\frac{1-\left(1-e^{2}\right)^{1/4}}{e} \tag{50}\]\[f=\pm\arccos\frac{-1+\left(1-e^{2}\right)^{3/4}}{e} \tag{51}\]  
\[M=\mp\sqrt{e^{2}-\left(1-\left(1-e^{2}\right)^{1/4}\right)^{2}}\pm\arccos\frac{1- \left(1-e^{2}\right)^{1/4}}{e} \tag{52}\]  
The extreme value is as follows:  
\[f-M=\pm\arccos\frac{-1+\left(1-e^{2}\right)^{3/4}}{e}\pm\sqrt{e^{2}-\left(1- \left(1-e^{2}\right)^{1/4}\right)^{2}}\mp\arccos\frac{1-\left(1-e^{2}\right)^{ 1/4}}{e} \tag{53}\]  
The extreme value expression of the differences among the three anomalies obtained above is too complex to have an intuitive understanding of their size relationship. Therefore, the above expression is expressed as a power series with respect to eccentricity \(e\), taken to the 6 th order, as shown in the table below.  
The following conclusions can be drawn from Table 3:  
1. The absolute values of the extreme values of the difference among the three anomalies increase with the increase in eccentricity.
2. The absolute values of the extreme values of the difference \(E-M\) between the elliptic anomaly and the mean anomaly, the difference \(f-E\) between the true anomaly and the elliptic anomaly, and the difference \(f-M\) between the true anomaly and the mean anomaly gradually increase.
3. When the eccentricity is small, the extreme absolute value of the difference \(E-M\) between the elliptic anomaly and the mean anomaly, as well as the extreme value of the difference \(f-E\) between the true anomaly and the elliptic anomaly, is approximately equal.
4. The absolute value of the extreme value of the difference \(f-M\) between the true anomaly and the mean anomaly is approximately twice as large as the absolute value of the extreme value of the difference \(E-M\) between the elliptic anomaly and the mean anomaly, and the absolute value of the extreme value of the difference \(f-E\) between the true anomaly and the elliptic anomaly.
## 4 Conclusions  
These experimental steels were designed based on (in wt.%) 0.04C-0.2 Si-1.0 Mn-0.06 Nb with 1 wt.%, 2.5 wt.%, and 4 wt.% Cr and processed through laboratory hot rolling followed by direct quenching. The effect of the Cr content on the continuous cooling transformation (CCT) diagram and deformation continuous cooling transformation (DCCT) diagram was studied. A Gleeble 3800 thermomechanical simulator was employed to determine the dilatation curves for the CCT and DCCT diagrams. In addition, the effect of the Cr content on the final microstructure and mechanical properties of the hot-rolled and direct-quenched materials was studied. The following conclusions can be drawn.  
1. In the case of non-strained austenite, the microstructure consists mainly of bainitic ferrite at high CRs, a mixture of bainitic ferrite with granular bainite at intermediate CRs, and polygonal ferrite with granular bainite at the lowest CRs. Increasing the Cr content and CRs refined the microstructure significantly. In addition, increasing the Cr content led to an increase in the hardenability, i.e., a decrease in transformation temperatures, as well as the promotion of bainite at the expense of the high-temperature transformation products like polygonal ferrite and granular bainite.
2. In the low-Cr steel, deformation of austenite below its recrystallization temperature has a large effect on the final microstructural constituents, changing the microstructure completely to ferrite at all the investigated CRs and causing a large drop in hardness relative to the non-deformed case. However, this effect decreased with increasing the Cr content.
3. To get a fully bainitic microstructure in the case of deformed austenite, the Cr content should be in the range of 2.5 wt.%-4 wt.%, and the CR should be higher than 20 \({}^{\circ}\)C/s.
4. In the case of the 12 mm thick hot-rolled and direct-quenched steel plates, the microstructure consisted mainly of different types of bainite, i.e., bainitic ferrite, granular bainite, and coalesced bainite. A significant amount of polygonal ferrite only appears at the lowest Cr level, i.e., 1 wt.%. Increasing the Cr content led to an increase in the hardness owing to an enhanced hardenability.
5. The targeted 700 MPa yield strength with good ductility and toughness were achieved in the hot-rolled and direct-quenched plates with Cr content in the range of 2.5 wt.%-4 wt.%, but not achieved with the lowest Cr content (1 wt.%) due to the presence of a significant fraction of polygonal ferrite, which increased the impact toughness and elongation at the expense of the yield strength.  
Writing--review and editing, M.A., T.N., A.K., J.H. and D.P.; supervision, D.P. and J.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by CBMM (Companhia Brasileira de Metalurgia e Mineracao).  
The authors declare no conflict of interest.
## 5 Experimental Evaluation
### Experimental Settings
Since computing exact solutions for such graphs is infeasible and no suitable dataset with graphs and corresponding ground truth exists in the literature, we constructed the observed values for training using state-of-the-art heuristics. Specifically, we employed both FastMinTC+ and Inner, the best-performing heuristics across different network configurations (FastMinTC+ generally excels on higher-density instances, while Inner is often superior for lower-density instances). For each graph, we generated solutions using both heuristics, selecting the one yielding the best results in terms of sum-span. Although it is not possible to train the network on optimal solutions, this approach enables the model to learn to approximate the best solutions regardless of the network's characteristics, thus taking the best of both the state-of-the-art heuristics. Provided that the training is executed correctly, the resulting model should, barring errors, produce coverage results as close as possible to those achieved by the best-performing state-of-the-art heuristics. To ensure that, by the end of the training phase, the network had effectively learned to construct meaningful coverage, we tested its ability to replicate the coverage generated by the heuristics. We conducted this evaluation with the understanding that the output produced by the network might neither be minimal nor entirely valid, aiming instead to refine the result further using the proposed iterative algorithm. Thus, we measured the performance using the classical metrics for classification problems: _accuracy_ which measures how often the model correctly predicts the outcome, _precision_ which measures how often the model correctly predicts the positive class, and _recall_ which measures how often the model correctly identifies positive instances from all the actual positive samples in the dataset. In this case, however, since the output of the network is an interval for each node, we convert the interval to a sequence of 0 and 1 for each timestamp for each node, which indicates weather the node is active in that timestamp or not, and we measure the problem as a binary classification problem. For this evaluation, we used a synthetically generated test dataset of 500 graphs similar to the training and validation dataset. Counting each node for each timestamp in this dataset results in a test case of more than \(500\times 10^{6}\) nodes. The results are reported in Table 2. Considering the size of the support, the training results, with an overall accuracy of 72.13%, while not extremely high, represent strong performances given the scale of the test cases. These results also indicate both the stability and sufficient generalization of the model, highlighting its robustness across a substantial dataset.
# Corrosion Behavior of Corrosion-Resistant Spring Steel Used in High Speed Railway
## 3 Results and Discussion
### Ac Impedance Spectroscopy  
Figure 8 shows the impedance spectra of the rusty samples with 60 Si2 Mn-A and 60 Si2 Mn-B at 24 h and 360 h and the corresponding Bode diagram. The impedance spectra of rusty samples include high-frequency compression capacitive reactance arc and low-frequency diffusion arc. The radius of the capacitive reactance arc reflects the difficulty of charge transfer. In the same period, the radius of 60 Si2 Mn-B is larger than the 60 Si2 Mn-A. With the increase of corrosion time, the radius also increases, indicating that 60 Si2 Mn-B has good corrosion resistance. In its corresponding Bode diagram (a), on the left side of the intersection with the vertical axis is the sum of Faraday resistance, solution resistance, rust resistance, and charge-transfer resistance between layers and solution; the general solution resistance in the same electrolyte solution is under little change, so the intersection-point height can reflect the rust layer resistance between the solution and relative size. It can be seen that 60 Si2 Mn-B after 360 h of corrosion due to the density of the rust layer has good value maximum.  
Z view software and [PERSON] model of rust layer under wet atmosphere corrosion [30] were used to fit the experimental measurement data combined with the equivalent circuit shown in Figure 9. Figure 10 and Table 4 show the fitted curve and measured curve of 60 Si2 Mn-B after 360 h of corrosion. It can be seen from the figure that the coincidence degree of the two curves is relatively high, and the error between the fitting data and the measured data is relatively small. The equivalent circuit can reasonably explain the electrochemical corrosion phenomenon. Table 5 shows the fitting data of ac impedance spectrum after the corrosion of the experimental steel for 24 h and 360 h. The impedance of the rust layer of the two kinds of steel is greater than 24 h at the corrosion of 360 h. The impedance of 60 Si2 Mn-B rust layer under the same corrosion period is larger than the 60 Si2 Mn-A, and the impedance of charge transfer also has the same trend. This is because the 60 Si2 Mn-B rust layer is relatively dense, and the oxides formed by corrosion-resistant elements in the rust layer are cationic selective, making it more difficult for ions in the reaction to diffuse, increasing the reaction resistance of corrosion electrochemical and increasing the impedance value.  
Figure 8: The Nyquist curve and Bode curve of the rusty samples at different corrosion time: (**a**) Nyquist plots; (**b**) Bode plots.  
Figure 9: Equivalent circuit of the rusty samples in 0.01 mol/L NaHSO3.
# Evolutionary Algorithm to Optimize Process Parameters of AI/Steel Magnetic Pulse Welding
## 2 Materials and Methods  
### Design of the Experiment  
The process parameters, such as peak current, gap between workpieces, and frequency, were selected to investigate their effects on the maximum load and weld length. An easy way to estimate the RSM, central composite design (CCD), was employed, and a total of 20 welding conditions were designed to develop the prediction model. The total number of experiments for this study was 20, with three factors (n = 3) and five coded levels. The calculation for this number is based on the expression: (=8: factor points) + 2n (23 = 6 axial points) + 6 (center points: six replications) [1]. Table 1 lists the design parameters and their corresponding five levels. At the low level (\(-\)1), peak current, gap between workpiece, and frequency were set to 350 kA, 0.7 mm, and 17 kHz; at the high level (+1), these were set to 450 kA, 1.0 mm, and 20 kHz, respectively, as shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Symbol**} & \multicolumn{5}{c}{**Level**} \\ \cline{3-8}  & & & \(-\)**2** & \(-\)**1** & **0** & **1** & **+2** \\ \hline Peak current & kA & C & 316 & 350 & 400 & 450 & 484 \\ Gap between workpieces & mm & G & 0.2 & 0.4 & 0.7 & 1.0 & 1.2 \\ Frequency & kHz & F & 16.0 & 17 & 18.5 & 20 & 21.0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Process parameters and their levels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{3}{c}{**Input Parameters**} & \multirow{2}{*}{**Run**} & \multicolumn{3}{c}{**Input Parameters**} \\ \cline{2-2} \cline{5-6}  & & **C** & **G** & **F** & **C** & **G** & **F** \\ \hline
1 & 400 & 0.7 & 18.5 & 11 & 400 & 0.7 & 18.5 \\
2 & 400 & 0.7 & 18.5 & 12 & 450 & 1.0 & 20.0 \\
3 & 400 & 0.2 & 18.5 & 13 & 350 & 1.0 & 20.0 \\
4 & 450 & 0.4 & 17.0 & 14 & 4
## 3 Two Plasmon Detectors  
The detector performance is improved by increasing the input and output resistance of the detector device as shown in the theoretical analysis results of Section 2. However, the input resistance cannot be increased due to impedance matching to transfer all terahertz signals to the detector input. The output resistance cannot also be increased because the output signals are difficult to acquire. Therefore, the output node of the detector device requires a buffer amplifier to collect the signals detected, as opposed to increasing the output resistance.  
Figure 4 shows two THz detectors as a proposed method to compare the performance of the detector when a buffer amplifier is added to the detector core to improve detector performance. The power delivered to the detector input is determined by the antenna area irrespective of the number of feeding lines in the antenna. The detectors in the proposed method use a differential antenna to remove harmonics generated at the detector and ignore the transmission line connected to the gate-to-source voltage, which is considered to be a parasitic antenna. The gate nodes of the detector cores M1 and M2 are connected with the integrated antenna by using the transmission lines on Metal 4 of the fabrication process. The interconnection from the antenna to the inputs of the detector cores is designed using 3-D electromagnetic-wave simulation. Figure 4a shows a differential detector structure operating at 200 GHz including the detector core and differential antenna with the output of the detector core connected to the external low noise voltage amplifier. On the other hand, Figure 4b consists of the same differential detector as in Figure 4a, but includes a preamplifier that operates in the sub-threshold region with a unity gain [27]. The preamplifier output is increased by the high transconductance of the preamplifier. The noise characteristic is also reduced by combining the outputs of the detector core. The outputs of the detectors in Figure 4 are compared by using the SR560 low noise voltage amplifier equipment.  
The on-chip portions of the two THz detectors in Figure 4 are fabricated with a TSMC 0.25 \(\upmu\)m mixed-signal CMOS process technology with one poly and five metal layers as shown in Figure 5. The chip area of the preamplifier with a voltage gain of 1 is 89.2 \(\upmu\)m \(\times\) 53.2 \(\upmu\)m. The total area of the two detector circuits is 1.41 mm \(\times\) 1.04 mm including the differential antenna and bias circuits that denotes the low drop-out (LDO) regulator and current reference circuit. The difference between Figure 5a,b is the presence or absence of a preamplifier, and all the remaining circuits are identical. The differential antenna integrated into the detector is designed and simulated using the three-dimensional electromagnetic wave simulation program. The simulation results obtained the S parameter of \(-\)31.5 dB at 200 GHz, an antenna bandwidth of 30 GHz and directivity of 4.9 dBi.