esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This approach fails to intrinsically examine the near-field propagation characteristics in the vicinity of the transport ship because it treats such a finite region near the ship as a point source, numerically. When establishing a safety strategy, it is essential to understand the HNS spill and propagation characteristics within one kilometer or less, especially in the early stages of the incident. Thus, the present study attempts to obtain useful information on HNS propagation near the spill location by using CFD, which is called \"near-field analysis\" in this study. The present study provides the two-dimensional CFD simulation results and makes a meta-model platform on the basis of the numerical predictions with the use of the response surface-regression method. The current simulation was conducted for a relatively high current velocity ranging from 0.5 to 1.5 m/s that was taken among various scenarios. This is because the highest risk scenario is generally considered in handling HNS accidents in the sea. Under this limitation, the two-dimensional analysis seems to be suitable for analyzing the propagation characteristics because of the very high Peclet number under which the convection effect becomes dominant. In fact, there may be many environmental factors in the case of a real HNS accident. Among them, the present study focuses on the influence of the current velocity on HNS propagation behavior using the commercial program, ANSYS FLUENT (V. 17.2). The HNS layer thickness, averaged HNS concentration at sea level, and average propagation velocity were predicted for different current velocities and HNS densities.
## 3 Results and Discussion
### Compressibility Curve  
The results will be presented in two stages. The first stage will cover the models under saturated conditions, including the HS_99% model. The second stage will analyze the unsaturated models, corresponding to HS_60% and HS_80%.  
It is observed that the suction obtained from Plaxis2D(r) shows a good fit with the curve in Figure 7, which was constructed based on the experimental model. This indicates that the numerical models can accurately represent the soil's ability to retain water. For the HS_99% model, expected values for fully saturated soil [31] are obtained, reaching full  
Figure 6: Experimental and fitted SWCC with suction obtained for each degree of saturation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **HS\_60\%** & \multicolumn{2}{c}{**SS\_60\%**} & \multicolumn{2}{c}{**Sr = 80\%**} \\ \hline
**Vertical Load [kPa]** & **Self** & **Suction Eff [kPa]** & **Self** & **Suction Eff [kPa]** & **Self** & **Suction Eff [kPa]** \\ \hline
10 & 60.84\% & 29.66 & 61.65\% & 31.00 & 81.44\% & 9.21 \\
25 & 65.15\% & 23.62 & 64.90\% & 23.99 & 89.77\% & 8.29 \\
50 & 69.98\% & 18.4 & 69.63\% & 18.80 & 98.65\% & 7.02 \\
100 & 79.01\% & 11.44 & 78.40\% & 11.91 & 100.00\% & 0.00 \\
200 & 88.23\% & 6.27 & 86.89\% & 7.05 & 100.00\% & 0.00 \\
400 & 100.00\% & 0.63 & 96.70\% & 1.81 & 100.00\% & 0.00 \\
800 & 100.00\% & 0 & 100.00\% & 0.00 & 100.00\% & 0.00 \\
1600 & 100.00\% & 0 & 100.00\% & 0.00 & 100.00\% & 0.00 \\
3200 & 100.00\% & 0 & 100.00\% & 0.00 & 100.00\% & 0.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evolution of saturation and effective suction in relation to the applied stress.  
saturation at 10 kPa and remaining so up to 3200 kPa, implying a value of 0 kPa throughout the entire effective suction load range.  
For all other cases shown in Table 5, the effective saturation remains similar to its initial degree of saturation when the first stress of 10 kPa is applied. As the vertical load increases, the effective saturation progressively rises, reaching 100% at 400, 800, and 1000 kPa for the HS_60%, SS_60%, and HS_80% cases, respectively. From these points onward, saturation remains constant.  
This behavior reflects the reduction in suction due to the compaction effect. At these moments, suction approaches values close to zero matric suction, indicating that the negative pore pressure decreases due to the reduction in air-filled voids and their replacement by water [32; 33; 34].
# Performance Comparison among KN95-Certified Face Masks by Classical Techniques and Innovative Test
## 1 Introduction
In Europe, the European Committee for Standardization fixed the standards for respirators (type 1, 2, and 3, defined as FFP1, FFP2, and FFP3) with the EN 149-2001 regulation; in the US, the NIOSH defined with the 42 CFR84 regulation the N95 and N99 standards; while in China, the GB262-2006 regulation defined the KN95 standard. Properties and characteristics involve filter performance, test agent, flow rate, total inward leakage, inhalation resistance (max pressure drop) and exhalation resistance, and CO\({}_{2}\) clearance requirement [29,30]. In addition, certified standards were developed to fit people with facial features common in a specific country or region. As a result, some individuals with different facial features may not be able to achieve a satisfactory fit and this should be considered when selecting and using a respirator approved by other countries and region standards. In case of an emergency, such as a sudden manufacturing response, new, cheap, fast, and efficient tests that can give information on filtering performances are needed [28]. The main object of the present study is to apply a newly developed method [31], to evaluate the filtering efficacy of several disposable respirators, KN95-certified, available on the market during the first period of the pandemic. The method takes advantage of a fine nebulization of a methylene blue aqueous solution towards a head mannequin wearing a respirator. Within the developed technique, we preventively focused on the inhaled fraction which is the ability of masks to protect the individual from an unmasked subject. This procedure allowed us to obtain preliminary data on the filtration efficiency of each tested mask. Successively, results were compared with general physical-chemical characterization techniques such as scanning electron microscopy (SEM), light optical microscopy (LOM), contact angle measurements, bacterial filtration efficiency (BFE), and pressure decay.
###### Abstract  
Vegetation phenology reflects the response mechanisms in ecology and climate change, so it is important that the parameters used to study vegetation phenology are accurate. Previous studies mainly focused on phenological changes. However, because the extraction methods used in those investigations led to inconsistencies in setting vegetation phenological parameters, a more accurate approach needs to be developed. To resolve this issue, we select five methods to extract the start of the growing season (SOS) and the end of the growing season (EOS) from the normalised difference vegetation index (NDVIS) data. The five chosen methods are the second-order derivative method (Method 1), the first-order derivative method (Method 2), the 0.2 dynamic threshold method (Method 3), the 0.5 dynamic threshold method (Method 4), and the fixed threshold method (Method 5). Our study area is the Northern Hemisphere (above 30\({}^{\circ}\)N), and our study period is 1982 to 2015. After applying the five methods, we evaluate the consistency of the vegetation phenological parameters. The results show that (1) regardless of the method used, the average changes in phenological parameters are consistent; however, the SOS and EOS under Methods 1, 3 and 5 are up to 30 days earlier than those under Methods 2 and 4. (2) Under all five methods, the SOS trend mainly shows an advance, but the trend is substantially higher under Methods 1, 3 and 4 than under Methods 2 and 5 from 45\({}^{\circ}\)N to 60\({}^{\circ}\)N. The distribution of the EOS trend under different methods is consistent. (3) Under the tested extraction methods, the SOS trends of evergreen needleleaf forests (ENF) and mixed forests (MF) have significant differences (\(p<0.05\)), whereas, the EOS trend for different vegetation types is consistent. (4) By analysing the consistency of the phenological parameters between remote sensing data and ground data under different methods, we now know that Methods 3 and 4 are the most accurate for extracting the SOS and EOS, respectively. The above results can provide a reference for the accurate extraction of phenological parameters above 30\({}^{\circ}\)N.  
phenological parameters; consistency; evaluation; different extraction methods; above 30\({}^{\circ}\)N +
Footnote †: journal: remote sensing  
[PERSON], [PERSON], [PERSON], [PERSON], Y. Evaluating the Consistency of Vegetation Phenological Parameters in the Northern Hemisphere from 1982 to 2015. _Remote Sens._**2023**, _15_, 2599. [[https://doi.org/10.3390/rs15102559](https://doi.org/10.3390/rs15102559)]([https://doi.org/10.3390/rs15102559](https://doi.org/10.3390/rs15102559))
# Non-Ionizing Radiation Measurements for Trajectory Radars
###### Abstract  
This work presents a Non-Ionizing Radiation (NIR) measurement campaign and proposes a specific measurement method for trajectory radars. This kind of radar has a high gain narrow beam antenna and emits a high power signal. Power density measurements from a C-band trajectoryrader are carried out using bench equipment and a directional receiving antenna, instead of the commonly used isotropic probe. The measured power density levels are assessed for compliance test via comparison with the occupational and general public exposure limit levels of both the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and the Brazilian National Telecommunication Agency (Anatel). The limit for the occupational public is respected everywhere, evidencing the safe operation of the studied radar. However, the limit for the general public is exceeded at a point next to the radar's antenna, showing that preventive measures are needed.  
non-ionizing radiation (NIR); power density; radar; trajectory +
Footnote †: journal: Sensors
# Interference Monitoring from Low Earth Orbit: The OPS-SAT Experiment +
## 2 Experiment Setup
### Norwegian Jammer Test Coordination Group  
Since 2022, the northern shore of Norway, specifically the island of Andoya, has become the world's largest testing ground for jamming and spoofing, thanks to the Jammertest event. This event is organized by the Norwegian Public Roads Administration (NPRA), Nkom, FFI, Norwegian Metrology Service (JV), and NOSA. Andoya's location is also somewhat remote, minimizing disruptions to air traffic and normal civilian activities. In this context, the JRC's team participated in the Jammertest 2023, held between 18 and 22 September, to assess the resilience of GNSS equipment against various types of attacks [6]. The team located on-site coordinated with Norwegian colleagues to set up a high-power jammer, implemented using a reduced configuration of the test setup known as \"The Hulk\". As depicted in Figure 2, this setup includes one USRP X410 SDR from Ettus Research, Austin, TX, USA. The exciter output signal is routed to a programmable step attenuator with an attenuation range of 95 dB in 0.25 dB steps. The output from the attenuators is then connected to a power amplifier. The antenna is a directional helical antenna with Right Hand Circular Polarized (RHCP) and 10 dB gain. A PC running Linux controls the high-power jammer, managing both exciters and the step attenuator. The high-power jammer is connected to Internet and synchronized with GPS time using Network Time Protocol (NTP). The chirp signal type was chosen as the most suitable candidate to be  
Figure 1: OPS-SAT operations: Norwegian passage scheduling and data recordings scheme representation including details on the position during the sample recordings used in the analysis.  
generated for the space experiment. The following characteristics have been considered: (i) carrier frequency (1575.42 MHz with a 250 kHz shift); (ii) bandwidth (+/\(-\)20 kHz); (iii) sweep time (1 ms); (iv) power (20 W). Figure 2 shows the Power Spectral Density (PSD) of the chirp signal before the transmission.
# Geometric Processing and Accuracy Verification of Zhuhai-1 Hyperspectral Satellites
## 3 Results and Discussion
### Geometric Calibration  
The 15 th band is selected as the reference, and the 2018-09-28-OHS-Hubei image and Hubei DOM image are matched using the automatic matching algorithm [16]. To reduce the influence of the random error of the attitude and orbit measurements on the geometric calibration, only 2102 matching control points are used for geometric calibration from 4000 to 6000 lines in the 2018-09-28-OHS-Hubei image. The distribution is shown in Figure 8.  
Figure 8: 2018-09-28-OHS-Hubei image control point distribution (4000–6000 lines).  
Figure 6: Henan regional control and verification data.  
Figure 7: Hebei regional control and verification data.  
Since the offset matrix mainly eliminates satellite attitude and orbit measurement system error, the camera creates system error, which cannot eliminate the internal orientation element error. Therefore, the positioning error after solving the offset matrix mainly reflects the internal orientation element error (camera distortion, etc.). As shown in Table 1 and Figure 9, due to satellite orbit focusing, band reconstruction, etc., the on-board real camera parameters vary significantly from the pre-launch laboratory measurement parameters. As shown in Table 3, the positioning error is still 36 pixels (about 360 m) after solving the offset matrix, but after further solving the camera distortion parameters, the positioning accuracy is increased to 0.5 pixels (5 m), which is equivalent to the control precision.  
The remaining 31 spectral bands are geometrically scaled using the 15 th spectral band after calibration as the reference spectral band. The calibration results are shown in Figure 10. It can be seen that the calibration accuracy of each spectrum band is 0.3-0.4 pixels.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Sample (Pixels)**} & \multicolumn{3}{c}{**Line (Pixels)**} & **Plane RMS** \\ \cline{2-7}  & **MAX** & **MIN** & **RMS** & **MAX** & **MIN** & **RMS** & **(Pixels)** \\ \hline
**a** & 37.34 & 6.12 & 19.44 & 59.10 & 12.67 & 30.79 & 36.42 \\ \hline
**b** & 1.34 & 0.00 & 0.38 & 1.22 & 0.00 & 0.32 & 0.50 \\ \hline \hline \end{tabular}  
* a indicates that only the positioning residual of the offset matrix is solved and b represents the positioning residual after the camera distortion is solved on the basis of a. RMS is root mean square.  
\end{table}
Table 3: Accuracy evaluation of 15-band calibration (pixels).  
Figure 9: 2018-09-28-OHS-Hubei image calibration residual diagram.
## 2 Methods and Materials
### Moran Test  
Spatial autocorrelation is defined as follows: \"Spatial autocorrelation refers to the fact that the value of a variable at one point in space is related to the value of that same variable in a nearby location\" [28; 33]. The spatial information concept has long been used in image de-noising and image quality estimation with different format types [25; 26; 27; 28]. To determine the spatial autocorrelation, [PERSON] introduced \(AC\) to measure the degree of spatial autocorrelation in a data area [33; 34]. \(AC\) is shown in the following equation, and its range is between \(-1\) and 1:  
\[AC=\frac{N\sum_{j=1}^{r\times c}\sum_{i=1}^{r\times c}\delta_{ij}(x_{i}-\bar{x })\big{(}x_{j}-\bar{x}\big{)}}{S_{0}\sum_{i=1}^{r\times c}(x_{i}-\bar{x})^{2}} \tag{1}\]  
where \(x_{i}\) is the gray level for pixel \(i\), \(\bar{x}\) is the mean window gray level, \(S_{0}\) = 2(2 \(mn-m\) - \(n\)), \(m\) and \(n\) are the number of rows and columns in the window, \(N\) is the total number of pixels in the window, and \(\delta_{\bar{y}}\) = 1 if pixel \(i\) and \(j\) are adjacent and 0 otherwise. A larger \(AC\) value means a greater correlation between the pixels and that the area is not likely noise. When the size of \(N\) is large enough (>25), the variable \(AC\) follows a normal distribution with the mean and variance given by  
\[a=-1/(N-1) \tag{2}\]  
and  
\[\sigma^{2}=\frac{N\big{[}\big{(}N^{2}-3N+3\big{)}S_{1}-NS_{2}+3S_{0}^{2}\big{]} -K\big{[}N(N-1)S_{1}-2 NS_{2}+6S_{0}^{2}\big{]}}{(N-1)(N-2)(N-3)S_{0}^{2}}-a^{2} \tag{3}\]where \(K=N\sum(x_{i}-\bar{x})^{4}/\left[\sum(x_{i}-\bar{x})^{2}\right]^{2}\), \(S_{1}=2S_{0}\), and \(S_{2}=8(8 mn-7m-7n+4)\). We can use the standardized normal statistic  
\[Z=\frac{AC-a}{\sigma} \tag{4}\]  
to determine the features of an image.  
A higher \(Z\) will lead to null hypothesis rejection, making the image noisy. This means that more structured information exists in this area and random noise is less likely in the image [12]. Based on this premise, the Moran \(Z\) value has successfully been applied to present the image spatial features [18; 19; 25; 26; 27; 35]. The Moran statistics used in this work divide the noisy areas in the RE.
## 3 Results and Discussion
Analyzing the entire 1-year dataset, ERA5 and WRF yielded mean RMSE values of 1.27 kg/m\({}^{2}\) and 1.34 kg/m\({}^{2}\), mean BIAS values of \(-\)0.03 kg/m\({}^{2}\) and 0.17 kg/m\({}^{2}\), and mean correlation coefficients of 0.958 and  
Figure 8: Hovmöller diagram of water vapor density profiles every 30 min. **(a)** WRF; **(b)** ERA5; and **(c)** TOMO solution. Averaged over the tomographic domain. 0.953, respectively. These results show that the tomographic inversion is well within the observed range and captures most of the observed vertical water vapor density variability in different synoptic and seasonal time scales. We employed the NNLS algorithm to derive the tomographic solution, avoiding the occurrence of negative values that can arise at higher altitudes when using the initially proposed pseudoinverse (pinv). Compared to the previous method, it takes on average 20-30% longer to find a positive solution. In our case study, inverting a tomographic domain with dimensions of 10 \(\times\) 7 \(\times\) 20 voxels requires approximately 20 s (CPU i7 13 th with 32 GB RAM). As anticipated, significant differences between the two methods manifest at upper levels; however, they have a residual impact on the integrated water vapor values. Our hypothesis attributes the negative values to a poorly conditioned system at higher altitudes. Adding more satellites (from other constellations) may obviate the need to apply the NNLS algorithm, requiring confirmation through future studies.
## 3 Results and Discussion
### Model Parameter Fitting
We compared the model predictions with the degradation data from the literature [11; 25]. The model was implemented with the parameters mentioned in Table 3. The accuracy of the model in predicting degradation was verified in different energy regions as well as at different temperatures. The results are shown in Figure 11.
## 2 Calculation Model of Drainage Radius of Single-Layer Gas Well
### Sensitivity Analysis  
From Equation (14), it can be seen that the parameters influencing the calculation results of drainage radius primarily include the change rate of the pseudo-bottomhole flowing pressure (\(\alpha\)), natural gas properties (such as \(\mu_{\rm g}C_{\rm g}\) and \(\mu_{\rm wf}C_{\rm wf}\)), coefficient of productivity equation (\(A_{j}\)) and change rate of the gas rate (\(\beta\)). Bottomhole flowing pressure during gas well production is relatively easy to obtain and has high accuracy. Therefore, the values of \(\alpha\) and \(\mu_{\rm wf}C_{\rm wf}\) are relatively accurate. However, \(A_{j}\) is determined by formation coefficient (\(Kln\)) and skin factor (\(S\)). Thus, the parameters affecting the calculation results of drainage radius are primarily natural gas properties (\(\mu_{\rm g}C_{\rm g}\)), formation coefficient (\(Kln\)), skin factor (\(S\)) and change rate in the gas rate (\(\beta\)).  
Figure 8: Gas rates and bottomhole flowing pressure in a commingled gas reservoir model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Production Time** & **Production Data** & **Layer 1** & **Layer 2** & **Layer 3** \\ \hline
300 d & \(q_{\rm ssf}\) (m\({}^{3}\)/d) & 13,216.9 & 20,740.4 & 66,042.7 \\  & \(P_{\rm wf}\) (MPa) & & 10.9988 & \\ \hline
600 d & \(q_{\rm ssf}\) (m\({}^{3}\)/d) & 11,181.7 & 21,973.9 & 66,844.4 \\  & \(P_{\rm wf}\) (MPa) & & 7.1236 & \\ \hline \hline \(r_{\rm ej\_calculated}\) (m) & 295.7 & 492.3 & 716.3 \\ \hline Relative Error (\%) & 1.43 & 1.54 & 2.33 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Calculation results of drainage radius for three layers.  
In the actual production process of gas wells, there is a certain uncertainty in the values of each parameter. In order to analyze the impact of the uncertainty of each parameter on drainage radius, a sensitivity analysis of various parameters is conducted. Figure 6 illustrates the impact of four parameters on the drainage radius within different ranges of variation. From Figure 9, it can be seen that natural gas properties has the greatest impact on calculation results of drainage radius, while formation coefficient, skin factor and change rate in the gas rate have relatively small effects. Therefore, the values of natural gas properties are the most important.
# CFD and Energy Loss Model Analysis of High-Speed Centrifugal Pump with Low Specific Speed
## 2 CFD Simulations and Energy Loss Model  
### Structural Parameters of Pump  
A new type of oil-free high-speed centrifugal pump with the low specific speed has been developed in our previous studies [21; 22]. The design parameters of the centrifugal water pump are flow rate \(Q=3\) m\({}^{3}\cdot\)h\({}^{-1}\), head \(H=30\) m, and rotational speed \(n=7500\) rpm. The structural parameters of the hydraulic components are shown in Table 1.  
The image of the pump is shown in Figure 1a. Referring to the physical pump, a three-dimensional model of the low specific speed high-speed centrifugal pump was established, and the general assembly drawing of the pump is shown in Figure 1b. The pump consists of dynamic parts and static parts. The dynamic parts contain the inducer, the impeller, the hydrodynamic bearings, and the main shaft, while the rest are the static parts, including the inlet and outlet cover plate, the vaned diffuser, the pump shell, the motor stator, and other supporting parts. The space between the dynamic and the static parts is the flow channel. As the red line in Figure 1b shows, the liquid flows into the impeller after being pressurized in advance in the inducer (A to B), which improves the cavitation performance. In the impeller, the energy of the motor is transformed into the power of the liquid (B to C), and the energy is transformed into the pressure energy when the liquid flows through the vaned diffuser (C to D) and the vaneless diffuser (D to E). A small stream of liquid flows through the internal flow channel to lubricate the bearing (C to F). The liquid flows through the motor channel cooling motor (E to G), then the liquid flows out of the outlet (G to H).