esa-sceva/satcom-chunk-collection huggingface dataset:
# Maintenance Time Prediction for Predictive Maintenance of Ship Engines
## 2 Materials and Methods
### Experimental Equipment
The detailed specifications of the software, including version and licensing information, are presented in Table 3.  
In total, 42 types of numerical data verifying the engine status during four voyages (VOYAGE 21101, 21102, 21111, and 21112) were analyzed. A voyage is a period during which the ship departs from its home port, takes on cargo at ports of call and loading ports, and returns to the home port to discharge the cargo. Although the criteria for classifying voyages vary between shipping companies and ships, a voyage serves as a reference point for ship scheduling and management. The frequency of generator operation among the collected data was high, and the minimum operating load exceeded 25% due to the continuous equipment operation during normal navigation and berthing. Thus, only data where the engine load exceeded 25% (i.e., above 257.9 kW) were used in this study. Figure 1: Schematic diagram for data acquisition for experimental vessel and digital asset. \begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Description** \\ \hline Simulation software & AVL CRUISE TM M \\ Software version & R2021.1 \\ Developer & AVL List GmBH, Graz, AT \\ License type & Academic license \\ Simulation domain & 1D engine system modeling and dynamic performance \\ Host environment & Window 10 Pro 64-bit \\ CPU & Core i9–12900 K, Intel, Santa Clara, CA, USA \\ GPU & GeForce RTX 2070 SUPER (8 GB), NVIDIA, Santa Clara, CA, USA \\ Python version & 3.9.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Software and simulation environment specifications. The data of a voyage with anomalous symptoms (VOYAGE 21113) implemented through engine simulation using AVL CRUISE(tm) M in a previous study [4], which showed an increase in the exhaust gas temperature (the current criterion for maintenance decisions), were utilized as data with anomalous symptoms in this study.
# A Review of Treatment Techniques for Short-Chain Perfluoroalkyl Substances
## 3 Treatment Techniques for Short-Chain PFASs
### Other Techniques
#### 3.3.3 Membrane Separation  
In recent years, low carbon treatment of membrane separation techniques, including nanofiltration (NF) and reverse osmosis (RO), have made great advances. During the rejection in membrane processes, the molecular size and structure of PFASs were considered key factors. For instance, NF membranes could reject more than 96% of PFhxA (ug/L to mg/L) under neutral pH, whereas the rejection rate of shorter-chains PFASs about PFBS decreased to 69% attributable to the small molecular size [64]. Furthermore, charge, hydrophobicity, pH, and dipole moment might also affect the solute-membrane interactions and thus the rejection efficiency of PFAS. It was shown that pH reduction could increase the membrane rejection efficiency of PFhxA. Moreover, the presence of the ions generally suppressed the electrical repulsion, but the exclusion efficiency was enhanced with increasing ionic strength, which indicated that the exclusion of membrane size was the dominant factor. In the practical application, the rejection efficiency of short-chain PFASs would be easily influenced because of membrane contamination. In addition, membrane separation techniques could produce a high concentration of PFASs and still require subsequent treatment or disposal.
###### Abstract  
The prediction of chemical oxygen demand (COD) by ultraviolet-visible absorption spectrum is a common method. Many researchers use the absorbance at the characteristic wavelength to establish COD prediction models. However, selecting the characteristic wavelength is a problem. In this paper, the extreme values of absorption spectrum change rate, was proposed as a new characteristic parameter to determine the characteristic wavelengths. On this basis, a novel hybrid strategy for detecting COD in surface water was proposed. We first proposed to combine the first derivative method with the permutation entropy method (FDPE) to determine the characteristic wavelengths. Then we used partial least square (PLS) to establish a COD prediction model. Experimental results demonstrated the linear correlation coefficient (\(R^{2}\)) of the FDPE_PLS was above 0.99 without turbidity interference. Secondly, a dual-wavelength method (DWM) was proposed to determine the turbidity values. The DWM used slopes of absorbance values at 400 nm and 600 nm to predict the turbidity values. Compared with the single-wavelength method, the DWM improves the measurement accuracy of turbidity. Finally, a new turbidity compensation method was proposed to compensate for the interference in the first derivative spectrum. After compensation, FDPE_PLS can predict COD concentrations accurately, whose \(R^{2}\) was 0.99.  
Keywords:COD; first derivative; permutation entropy; partial least squares; turbidity compensation +
Footnote †: journal: Applied Physics Letters
## 4 Discussion  
### Historical and Future Hurricane Frequency, Intensity, and Landfall  
Our historical analysis highlights the Quintana Roo coastline as a TC hotspot where landing frequency is the highest followed by Yucatan and Campeche. A previous analysis of the periods 1851-2000 [94], 1951-2001 [95], 1970-2010 [31], and 1990-2011 [96] used similar NHC track and position records to analyze landfall patterns and observed this spatial trend separately, especially near Cancun [94]. Comparatively, the relative frequency in TC landings during the most recent period (2011 up to 2019) has not changed significantly, although long-term landings varied when the frequency analysis was performed on a decadal basis (Figure 4; Supplemental Materials, Figure S1).  
We analyzed 1894 TC tracks from 1851 to 2019 across the Atlantic basin, where only 151 (\(\sim\) 8% of the total) reached the YP. The most active landfall season was in 1931 when six TCs made landfall in the peninsula while four TCs landed in the seasons of 1933, 1942, 1971, 2005, and 2010 (Figure S1); in 2005, two events occurred in both July and October, respectively. After landfall, most TCs tend to move into the GOM, either weakening over central Mexico or the southern United States. Wilma was an extraordinary major hurricane, concerning the Atlantic basin activity, and comparable to other strong landfall events in other basins around the world [96; 97]. Wilma also leads a group of TCs with maximum intensity over the ocean as determined by satellite data [98]. If the period of record is narrowed to 1995-2005, Wilma is one of eight major hurricanes reaching maximum intensity over the western Caribbean, south of the Yucatan Channel, and east of Quintana Roo ([99]; Figure 3) (Table S1).  
Although it is hypothesized that storm frequency will increase overall in the Atlantic basin as a result of climate change [100; 101; 102], it is not clear how the interaction between climate variability and change will specifically affect regional storm landings' location, frequency, and strength across the Caribbean basin and the YP. These projections are needed to determine the regional, annual, and decadal impact on the hydrological cycle, particularly in the case of precipitation (e.g., [103]). This is the main source to recharge aquifers, supporting drinking water demands and rain-fed agriculture throughout the YP [104; 105]. These aquifers are widely distributed due to the YP karstic geology and influenced by different regional climates [54; 57].
# Linear Optics Calibration in a Storage Ring Based on Machine Learning
## 3 Results
### Sample Analysis  
We took a sample for analysis. The orbit response matrix is shown in Figure 6. Before the correction, the peak-peak values of \(\Delta R\) are 4.980 m/rad (x) and 4.260 m/rad (y), and the RMS values of \(\Delta R\) are 0.791 m/rad (x) and 0.506 m/rad (y). After the correction, the peak-peak value is reduced to 0.436 m/rad (x) and 0.388 m/rad (y), and the RMS value is reduced to 0.066 m/rad (x) and 0.061 m/rad (y). For our lattice, the designed working points are \(\
u_{x}\) = 22,222 and \(\
u_{y}\) = 12,153. Before the correction, the lattice with the field error has working points \(\
u_{x}\) = 22,263 and \(\
u_{y}\) = 12,143. After the correction, the working point is restored to \(\
u_{x}\) = 22,224 and \(\
u_{y}\) = 12,152. The peak-to-peak value and the standard deviation of \(\Delta R\) are significantly reduced after the correction. Beta-beating is shown in Figure 7. The figure shows that the beta-beating value decreases rapidly after the correction by machine-learning-based methods. The machine-learning-based method produces an accelerator model with beta-beating below the 1% level with accurate betatron tune correction. This result is similar to the result calculated by the traditional LOCO algorithm. Using the traditional LOCO algorithm, the beta-beating can reach about 1% after correction [13].  
Figure 6: The orbit response matrix \(\Delta R\). (**a**,**b**) are the x-direction, (**c**,**d**) are the y-direction, and (**a**,**c**) are before the correction, (**b**,**d**) are after the correction.  
To explore the influence of multiple iterations on the results, we iterated the algorithm five times to observe the changes in the results. In the five iterations, the peak-to-peak value and standard deviation variation in \(\Delta R\) are shown in Figure 8, where the ordinate is the logarithmic coordinate. The figure shows that the result after the first correction greatly improves, and the subsequent iteration slightly improves. Multiple iterations in practical operation require multiple measurements of the response matrices, which takes more time.
## 3 Results
### DC/DC Converter Synchronous Boost Converter  
Taking into account the restrictions imposed on a hybrid generator, the topology of the DC/DC converter had to be as simple as possible and include the minimum number of magnetic components in order to reduce weight. For this reason, a boost converter was used. The conventional topology of a boost converter uses inductance, a MOSFET commutating in the range of kHz, a diode and a filtering capacitor. Increasing the commutation frequency decreases both the filtering capacity and inductance. However, very high frequencies complicate or prevent the design of the converter because of parasitic effects such as leakage reactance. Consequently, a value of 50 kHz was chosen in order to reach a balance between design complexity and weight.  
More sophisticated resonant topologies might seem to be an attractive solution, since it is possible to increase the frequency without compromising the efficiency. However, when operating with high levels of current, the capacitors must be able to withstand high voltages, which implies the use of bulky capacitors. A simple calculation can be made to show the problem. At an output current of 100 A, which implies values for the resonant current of around 150 A, a switching frequency of 100 kHz and a capacity of 100 nFm, the voltage in the resonant capacitor exceeds 2 kV.  
For a high-current output, such as 150 A, the presence of the diode produces the same undesirable effects described for the rectifier. Therefore, active rectifying was also applied to the converter, and the diode was replaced by three parallelized low-resistance MOSFETs. With this topology, first in simulation and then with a simple prototyped PCB, the reached efficiencies varied from 91% to 95% depending on the DC-bus voltage values or the ICU throttle. A study was carried out enforcing maximum throttle variation, and 91% efficiency was obtained when the ICU was running at idle RPM. An efficiency of 95% was reached for the output of 150 A, where the efficiency of the generator was at maximum.  
Although some PCBs have been dismounted and the rectifier sent for manufacturing, Figure 17 shows some PCBs and the SMD design sent for manufacturing. Figure 18 presents the first SMD prototype of the active rectifier.  
Figure 19 shows the result of a test in which the voltage control loop of the prototype converter was closed. The graph is presented to show that, even with an unfinished prototype, the DC bus presents a very low voltage ripple, at around 250 mV--0.5% of the maximum voltage.  
Figure 16: Advanced gate-driven signal for optimal MOSFET commutation.  
Figure 17: PCB test prototypes.  
Figure 18: SMD active rectifier: first SMD prototype.
## 3 Results  
This section systematically elucidates the performance optimization effects of non-smooth structures on ESPs across a wide operating range. First, parametric simulations (Section 3.1) reveal quantitative relationships between pit dimensions/locations and performance metrics. Subsequently, experimental data from additively manufactured prototypes (Section 3.2) validate the numerical model's reliability while providing in-depth analysis of key performance enhancement mechanisms.
## 4 Results
### Cavitation Collapse Events and Pressure Waves  
In Figure 17 we show a set of snapshots for a stage of the shedding cycle at which the cavity undergoes a rapid collapsing stage, and the associated instantaneous pressure distributions. The figure shows the case with the lower cavitation number, \(\sigma=0.53\). The cavity cloud has shed off at \(t_{0}\), and we observe an increase in pressure on the plane at mid-span, just below the cavity. As the collapse stage advances, the pressure values grow larger, and the pressure front starts to propagate in all directions quite rapidly. We note very high values for the \(C_{p}\) due to the major collapse events \(t_{0}+0.8\) ms. The maximum value recorded for the investigated plane was \(\approx\)3. After this, the pressure wave front grows and intensifies, and then also quickly dissipates. Next, we are left with small regions of negative pressure in the place of the shed cavity clouds at the two latest investigated time instants. Looking at the cavitator body, the pressure wave due to the major collapse at \(t_{0}+0.8\) ms reaches it a few tenths of ms later. The cavitator body on the upstream side of the wing shows areas of increased positive pressure, and the downstream side of it in turn has a momentarily lower negative pressure. At the moment of the collapse event, also high pressures are recorded on the suction side of the wing near the trailing edge, as the cavity collapses just above it, at the same time the pressure side of the wing is masked from this effect. The dynamics of the bulk cavitation on the wing, then again, appears to be unaffected by the pressure waves that result from the cavity collapse. The attached sheet cavitation continues to grow on to enter the shedding phase described in Section 4.3.3. Also, at subsequent time instants, the shed-off cavity continues to form the cavitating ring vortex. We assess this further in Section 4.5. The cavitating vortex due to the hub of the cavitator body also appears to be not affected by the collapse event.  
Figure 17: A sequence of snapshots visualizing a collapse event of the cavity. DDES with medium grid, \(\sigma=0.53\). Top frames show the left wing from the side and the top, and the bottom frame shows a cut plane at mid-span. The cavitator and the cut plane are colored by \(C_{p}\).
# Experimental Study on Grouting Visualization of Cover Layer Based on Transparent Soil
## 3 Grouting Test  
### Grouting Pressure Setting  
In the cover layer, the grout is usually diffused and reinforced in various forms under different grouting pressures. In order to show a variety of grouting states, it is necessary to first determine the critical splitting stress, so as to facilitate the setting of grouting pressure in the test. Regarding the covering layer as a continuous medium, on the basis of continuum mechanics, we believe that the following: At the onset of grouting, the borehole wall is subjected to the applied grouting pressure. Once this pressure exceeds the initial in situ stress, splitting (fracturing) of the borehole wall occurs. Assuming the cover layer is a homogeneous rectangular prism of uniform thickness, and based on elastic theory, the initial stress around the borehole under an applied grouting pressure \(p\) can be expressed as ([PERSON] et al., 2008) [17]:  
\[\sigma_{\tau}=\frac{\sigma_{2}+\sigma_{3}}{2}(1-\eta^{2})+\frac{\sigma_{2}- \sigma_{3}}{2}\cos 2\theta(1-\eta^{2})(1-3\eta^{2})+p\eta^{2} \tag{1}\]  
\[\sigma_{\theta}=\frac{\sigma_{2}+\sigma_{3}}{2}(1+\eta^{2})-\frac{\sigma_{2}- \sigma_{3}}{2}\cos 2\theta(1+3\eta^{4})-p\eta^{2} \tag{2}\]  
\[\tau_{\tau\theta}=\frac{\sigma_{2}-\sigma_{3}}{2}\sin 2\theta(1-\eta^{2})(1+3 \eta^{4}) \tag{3}\]  
In the formula: \(\eta=a/r\), \(a\) is the drill hole radius, \(r\) is the slurry diffusion radius; \(\sigma_{r}\), \(\sigma_{\theta}\), and \(\tau_{\tau\theta}\) are the radial, circumferential, and tangential stresses of the slurry diffusion body, respectively; \(\sigma_{2}\) and \(\sigma_{3}\) are the second and third principal stresses, respectively; \(\theta\) is the clockwise deflection angle; and \(p\) is the grouting pressure. When shear failure occurs, according to the Mohr-Coulomb failure criterion, the critical splitting stress \(\sigma_{\theta}\) satisfies:  
\[\sigma_{\theta}=3\sigma_{3}-\sigma_{2}-p \tag{4}\]  
When the hoop stress \(\sigma_{\theta}\geq|-\sigma_{1}|\) (\(\sigma_{t}\), the tensile strength of the transparent soil) is applied, the cover layer at the borehole wall will experience cracking. Combined with the test parameters of the model box and the strength data of the transparent soil, the critical splitting stress in the grouting test is 9.37 kPa, which can be solved by substituting Equation (4). To prevent the grout from breaking through the transparent soil too quickly, a stepped grouting pressure is set up during the experiment to show changes in the grout diffusion pattern at each level until the grout breaks through the top surface of the transparent soil and stops grouting. Considering that the organic glass of the model box breaks when the pre-test grouting pressure reaches 300 kPa, the stepped grouting pressures are set at 50 kPa, 100 kPa, and 200 kPa. To form a high-quality scattering surface within the transparent soil and reduce interference from the grout diffusion process on laser imaging, this experiment chooses to drill horizontally. The mechanical concept diagram for the grouting test is shown in Figure 5.
## 2 Autonomous Driving Control Algorithm
### GNSS-RTK/Motion Sensor Integrated Positioning Algorithm
\right]_{\text{\emph{GNSS}}}, \tag{3}\]  
where the subscripts _INS_ and _GNSS_ denote the value estimates from _INS_ mechanization and the data acquisition of the GNSS-RTK module, respectively; \(\varphi\), \(\lambda\), and \(h\) are the latitude, longitude, and ellipsoidal height, respectively; \(v_{n}\), \(v_{e}\), and \(v_{d}\) are the north velocity, east velocity, and down velocity, respectively; \(C_{b}^{n}\) is the direction cosine matrix from the body frame to the navigation frame; and \(V_{\text{\emph{GNSS}}}\) is the speed over the ground provided by the GNSS-RTK module. Figure 2: Block diagram of inertial navigation system (_INS_) mechanization (modified from [18]). The design matrix for _GNSS_ (\(H_{\text{GNSS}}\)) is expressed as:  
\[H_{\text{GNSS}}=\left[ \begin{array}{cccc}I_{3\times 3}&0_{3\times 3}&0_{3 \times 3}&0_{3\times 6}\\ 0_{3\times 3}&-\left(v_{GNSS}^{\text{\text{\tiny{H}}}}\times\right)&0_{3\times 3}&0_{3 \times 6}\end{array} \right], \tag{4}\]  
where \(I_{3\times 3}\) is the \(3\times 3\) identity matrix, \(O_{n\times m}\) is \(n\times m\) zero matrix, and \(v_{GNSS}^{\text{\text{\tiny{H}}}}\) is the _GNSS_ derived velocities expressed in the navigation frame. The measurement vector (\(z_{MAG}\)) and the design matrix (\(H_{MAG}\)) based on yaw measurement calculated by using a magnetometer measurement are written as:  
\[z_{MAG}=\psi_{INS}-\psi_{MAG}, \tag{5}\]  
\[H_{MAG}=\left[ \begin{array}{cccc}0_{1\times 3}&0_{1\times 5}&1&0_{1\times 6} \end{array} \right], \tag{6}\]  
where the subscript _MAG_ denotes the value calculated by using the magnetometers' measurement, and \(\psi\) is the yaw angle.
# The eIDAS Regulation: A Survey of Technological Trends for European Electronic Identity Schemes
## 2 Methodology
### Data Source  
Our analysis for the selected eID schemes relies on the information collected from the official documentation and official websites of the considered eID schemes [9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]. Furthermore, we support our analysis with eID scheme-related technical reports and the related OpenID Connect specifications and security best current practices, namely, [3; 6; 26; 27; 28; 29; 30; 31; 32; 33]. We present the list of considered eID schemes and their eID means in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MS** & **Ref** & **eID Scheme** & **ID** & **eID Means** \\ \hline BE & [9] & Belgian eID Scheme FAS/eCards & 1 & Belgian Citizen or \\  & [10] & Belgian eID Scheme FAS/itsme & 2 & Foreigner eCard \\ \hline CZ & [11] & National identification scheme of the & 3 & CZ eID Card \\  & [12] & National identification scheme of the & 4 & MojelD \\  & [34] & Czech Republic/MojelD & & \\  & [34] & National identification scheme of the & 5 & MEG \\ \hline DK & [13] & NemID & 6 & NemID \\ \hline DE & [14] & German eID based on Extended & 7 & ID/RP/EEA Card \\  & & Access Control & & \\ \hline EE & [6; 29] & Estonian eID Scheme/eCards & 8 &
\begin{tabular}{c} ID/RP/Diplomatic/e- \\ residency \\ Card \\ Dig
## 2 Materials and Methods
### Data
#### 2.2.2 Eddy Covariance Flux Data  
For the ground-truth data at the Gwangneung forest, we accumulated the measurements from an open-path EC and the meteorological system at 40 m above ground. In the EC system, vertical wind speed (\(\omega\)) was observed by a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Logan, UT, USA), and the CO\({}_{2}\) concentration (pc) was measured by an open-path infrared gas analyzer (LI-7500, LI-COR Environmental, Lincoln, NE, USA). These data were collected by a data logger (CR3000, Campbell Scientific, Logan, UT, USA) at the frequency of 10 Hz. Then, the half-hourly vertical CO\({}_{2}\) flux, which is equal to NEE, was calculated from the covariance between the fluctuations in \(\omega\) and \(\rho\)c. To derive the NEE from the 10 Hz raw data, we carried out the preprocessing such as air density correction and spike detection. Further, quality control, gap-filling, and nighttime CO\({}_{2}\) flux correction were also conducted according to the standard data-processing protocol of KoFlux [28; 29]. The forest GPP was calculated by adding the ecosystem respiration (R\({}_{\rm eco}\)) to the NEE. Given that the R\({}_{\rm eco}\) is mainly governed by the thermal regime, the R\({}_{\rm eco}\) was estimated using the relationship between the temperature and the CO\({}_{2}\) efflux rate derived from nighttime EC measurements. Such efforts should have improved the quality of the GPP data. In addition, meteorological measurements, such as solar radiation, photosynthetically active radiation, net radiation, air temperature, humidity, and precipitation, were sampled every second, averaged over 30 min, and logged in another data logger (CR3000, Campbell Scientific, Logan, UT, USA). They were used as the ancillary variables for the nighttime correction and gap-filling of NEE, and the estimation of R\({}_{\rm eco}\) and GPP.