esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Potential of MODIS EVI Data to Track the Dynamics in WUE  
The strong correlations between time-series MODIS EVI data and ecosystem WUE implied the possibility to extrapolate tower-based measurements to large-scale regions, which can also avoid the uncertainties in the indirect WUE estimates from MODIS GPP and ET products. Then, an alternative method that solely relied on eight-day EVI data (greenness model-GR) was developed to monitor seasonal dynamics of ecosystem WUE for grassland (Equation (3)) and maize cropland (Equation (4)) in the dry areas, respectively. \(R^{2}\) and RMSE of the grassland GR model reached 0.92 and 0.19 g kg\({}^{-1}\), respectively; and for maize cropland, \(R^{2}\) and RMSE were 0.89 and 0.39 g kg\({}^{-1}\), respectively. Thus, both had high accuracy.  
\[\text{WUE} = 4.322\times\text{EVI}-0.559 \tag{3}\] \[\text{WUE} = 7.211\times\text{EVI}-0.652 \tag{4}\]  
However, the model's performance still needs to be independently validated for regional-scale applications. Seasonal patterns and the scatterplots between tower-based \(\text{WUE}_{\text{EC}}\) and MODIS estimates \(\text{WUE}_{\text{GR}}\) were exhibited in Figure 5. It proved the GR model can accurately simulate eight-day composite GPP variability for both grassland and cropland ecosystems, except for individual periods. Generally, \(\text{WUE}_{\text{GR}}\) agreed rather well with the in situ observations \(\text{WUE}_{\text{EC}}\), with the percentage deviation in annual mean values of 7.1% for the Arou site and 6.7% for the Daman site, respectively. The 1:1 line in the scatterplots also demonstrated that most points distributed close to the line. Meanwhile, a strong linear relationship was found between \(\text{WUE}_{\text{EC}}\) and \(\text{WUE}_{\text{GR}}\). \(R^{2}\) and RMSE reached up to 0.91 and 0.93, and 0.24 g kg\({}^{-1}\) and 0.27 g kg\({}^{-1}\) for the Arou grassland site and the Daman cropland site in 2015, respectively. Therefore, this simple empirical GR model by exclusive use of MODIS EVI data has great potential to quantify ecosystem-level WUE, which is also an important attempt for terrestrial ecosystems in the arid and semi-arid areas.  
Figure 4: A comparison of seasonal variations of ecosystem water use efficiency from flux tower measurements (\(\text{WUE}_{\text{EC}}\)) and the estimates from MODIS products (\(\text{WUE}_{\text{MOD}}\)) at the two flux sites Arou and Daman during the period of 2013 and 2014.
# Efficient Processing of \(k\)-Hop Reachability Queries on Directed Graphs
## 3 A Vertex-Cover-Based Approach
### Index Construction
#### 3.2.4 A Comparison of Computational Complexity  
We show the comparison of the algorithms for kHRQs on general directed graphs in Table 2 from which we know that the common problem of _kReach_ and _ESTI_ is that they both suffer from a higher cost for query answering. The reason for this is that, when answering a query, they both need to traverse the underlying graph to obtain the answer due to the fact that their index covers only part of the reachability information. As a comparison, both of our approaches, i.e., _VCRea_ and _VCRea_+, can obtain the query answer by comparing the vertex labels of the source and the target query vertices without graph traversal. The cost for this is affording more time to the index construction time even though our experimental results show that, for some datasets, our approaches need less index construction time and obtain a smaller index.
## 2 Materials and Methods
### Adsorption Tests and Behavior of the Samples in Water  
Batch adsorption tests were carried out in 100 mL Erlenmeyer flasks at room temperature (20 \({}^{\circ}\)C) containing Pb within 10-500 mg\(\cdot\)L\({}^{-1}\) with an adsorbent dose of 0.5 g\(\cdot\)L\({}^{-1}\) and 200 rpm stirring. According to previous studies of lead adsorption, the pH was set between 5 and 6. For pH adjustment, dropwise addition of NaOH or HCl solutions (1 or 0.1 M) was carried out. The pH was monitored in a Crison BasiC 20 device. The lead adsorption isotherms were obtained after adsorbent-lead solution contact for 24 h, keeping constant the adsorbent dose. Aqueous samples were filtered with Nylon Filters (0.45 \(\upmu\)m), and the concentration of Pb in water was obtained by Atomic Absorbance (AA) spectrometry in a PerkinElmer(r) PinAAcle 500 Flame device. Adsorption experiments were carried out in triplicate, and the average value was represented. The relative standard deviation was lower than 5%.  
The pH at the point of zero charge (pH\({}_{\text{pzc}}\)) was determined by a standard acid-base titration method [38]. Briefly, solutions of 50 mL were prepared at different pH values between 2 and 11 by adding NaOH or HCl. Next, the solid was added at a dose of 0.5 g\(\cdot\)L\({}^{-1}\) and kept under stirring until reaching a constant pH value. The pH\({}_{\text{pzc}}\) was obtained as the intersection of the curve of final versus initial pH with the bisector. The metal leaching of the solids was carried out at 3 different pH values, i.e., 3, 7, and 11, at a dose of 0.5 g\(\cdot\)L\({}^{-1}\). The metal present in the aqueous solution after 24 h of contact was analyzed in an Inductively Coupled Plasma Mass Spectroscopy (ICP-MS) in a PerkinElmer(r) Optima8300 device, with samples filtered as mentioned above. The lixiviation degree of the whole metal content in the solid samples was determined by acid digestion according to standard EN 13656:2000. Moreover, the released Total Organic Carbon (TOC) and Inorganic Carbon (IC) in the water were analyzed in a Shimadzu(r) TOC-V\({}_{\text{CSH}}\) analyzer.
# Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
## 3 Results
### Vibration Signal  
The vibration signal of the fault motor was collected by the sensor, which, after conditioning, is shown in Figure 10.  
Then, the improved wavelet packet algorithm was used to decompose the vibration signal into four wavelet packet nodes: (2, 0), (2, 1), (2, 2) and (2, 3). The results of reconstructing these wavelet packet nodes are shown in Figure 11.  
Figure 11: _Cont_.  
Figure 9: Spectrum diagrams of the current signal: (**a**) spectrum diagram of the normal motor; and (**b**) spectrum diagram of the fault motor.  
To observe the frequency distribution, the reconstructed nodes were transformed by FFT and compared with the results of normal motor signals. As shown in Figure 12, the changes of the frequency at (2, 0) are obvious.  
Compared with the frequency spectrum of the normal motor, the amplitude of the fault motor in the fundamental frequency, \(2f_{\mathrm{s}}=100\) Hz, is obviously increased. In addition, the amplitudes of the harmonic components at the frequencies of \(3f_{\mathrm{s}}=150\) Hz, \(4f_{\mathrm{s}}=200\) Hz and \(8f_{\mathrm{s}}=400\) Hz increased.
# A Performance Test and Internal Flow Field Simulation of a Vortex Pump
## 4 Numerical Calculation of Pump's Internal Flow Field
### Boundary Conditions  
#### 4.3.1 Inlet Boundary Conditions  
The inlet of the vortex pump calculation area is an axial inlet. The inlet axial velocity value is given at the inlet pipe according to the flow rate. Assuming that the inlet has no rotation, the tangential velocity and the radial velocity are 0, and the turbulence is fully developed and evenly distributed in the import section. A is the area of the inlet of the vortex pump. The inlet axial velocity \(v_{in}\) is given at the inlet pipe according to the flow rate \(q_{v}\).  
\[v_{in}=\frac{q_{v}}{A} \tag{11}\]  
The turbulent kinetic energy and turbulent energy dissipation rate at the inlet are set according to the flow and inlet cross-sectional area. The formula is as follows:  
\[k_{in}=\frac{3}{2}(\overline{u}I)^{2} \tag{12}\]  
\(\overline{u}\) is the mean flow velocity, and here \(\overline{u}=v_{in}\).  
\[\varepsilon_{in}=C_{\mu}^{\frac{3}{2}}\frac{k_{in}^{\frac{3}{2}}}{l} \tag{13}\]  
where \(I\) is turbulence intensity  
\[I=u^{\prime}/\overline{u}=0.16\big{(}Re_{D_{H}}\big{)}^{-\frac{1}{8}} \tag{14}\]  
From the above formula, \(u^{\prime}\) is the root-mean-square of the turbulent velocity fluctuations, \(\overline{u}\) is the the mean velocity, and \(l\) is the inlet's length, \(Re_{D_{H}}\) is the Reynolds number based on \(D_{H}\), and \(D_{H}\) represents the hydraulic diameter,  
\[l=0.07L \tag{15}\]  
where \(C_{\mu}\) is 0.09, and \(L\) is the associated length. For the full development of the turbulence, desirable \(L\) is equal to the hydraulicic diameter. Here, \(L\) is equal to the inlet round pipe diameter.  
Specific boundary conditions: velocity vector (0 m/s, 0 m/s, 3.11 m/s), turbulent kinetic energy \(k_{in}=0.021\) m\({}^{2}/s^{2}\), and turbulent kinetic energy dissipation \(\varepsilon_{in}=0.22\) m\({}^{2}/s^{3}\).
# Graphene-Based Functional Hybrid Membranes for Antimicrobial Applications: A Review
## 5 Antimicrobial Applications
### Antifungal Applications
\begin{table}
\begin{tabular}{l l l l} \hline
**GMBs** & **Pathogens** & **Comments** & **Ref.** \\ \hline GNPs & _Streptococcus mutans_ & Inhibit adhesion and growth & [108] \\ GO-RO & _Escherichia coli_ & Reduced water permeability, reduced salt & [109] \\  & _Escherichia coli_ & selectivity, reduced pollution & \\ rGO-Zno-PES & _Serratia marcescens_ & Inhibit biofilm growth & [110] \\  & _Bacillus subtilis_ & & \\ QGO & _Escherichia coli_ & Hydrophilic, antibacterial and mechanical properties are significantly enhanced & [111] \\ GO-AG-PES & _Escherichia coli_ & Superior antibacterial effect & [112] \\  & _Escherichia coli_ & Good antibacterial activity, long duration & [113] \\  & _Escherichia coli_ & Significant bactericidal power & [114] \\  & _Staphylococcus aureus_ & Significant bactericidal power & [115] \\ fG-PP & _SARS-CoV-2_ & Good inhibition effect & [115] \\ GO-textile & _HIV-6A_ & Prevent cell infection, has antiviral properties & [116] \\  & _Pseudomonas_ & Inactivation of a large number of bacteria & \\ LIG-PI & _aeruginosa_ & and viruses & [117] \\  & _T4 virus_ & Virus particles are inactivated, slowing infection & \\ Cu-Gr & _Influenza virus_ & Virus particles are inactivated, slowing infection & [118] \\ PLA-CNC/GR & _Aspergillus niger_ & High antifungal activity & [119] \\ CS/alginate/GO/clotrimazole & _Oral candidiasis_ & GO can be used as a functional excipient for delivery of antifungal drugs. & [120] \\ PVDF/G & _Curvuria_ & High inhibition rate & [121] \\ GO/AgNP & _graminearum_ & High antibacterial activity and inhibit pathogen infection & [122] \\ \hline \end{tabular}
\end{table} 
Table 1: Antimicrobial materials, activity, and application of graphene-based membranes. Figure 8: Schematic diagram of the preparation of graphene-AgNPs nanocomposites and their antifungal properties in vitro and in vivo. (Printed image with permission from Ref. [122], copyright 2016, American Chemistry Society).
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 3 Reconstruction Algorithm  
A large number of works were done to study the recovery algorithm of compressed sensing. These studies focused on the stable construction, low calculation, and reconstructed accuracy of signals, especially with small measurements. This section introduces reconstruction algorithms such as the convex-optimization, greedy, and Bayesian algorithms.
## 2 Image Analysis Method
### Improvement of Image Analysis Method
#### 2.2.2 Change in the Aggregate Segmentation Procedure Algorithm  
The algorithm for aggregate segmentation was also improved, in addition to the change in the background color for capturing the images of gravels. Figure 3 shows the previous algorithm for distinguishing aggregates from the background in order [11]. First, the image of the aggregates is captured with a digital camera (Figure 3a). Noise is removed by applying pyramid mean shift filtering to the original image (Figure 3b). The image is then converted to gray-scale, and the threshold is applied to obtain a black and white image of gravels (Figure 3c). Lastly, the watershed algorithm [25] is applied to detect the coordinates of the gravel image (Figure 3d).  
The aggregate segmentation procedure of the improved algorithm is shown in Figure 4. The HSV color model is used in the improved algorithm. The RGB image of the aggregates is converted to an HSV image. An appropriate threshold value is set for the S-channel (Saturation) in the converted color model to obtain a binary image of gravel aggregates, and then the boundaries of each gravel aggregate are obtained.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Scale** & **Shape Characterization** & **Shape Index in This Research** \\ \hline Large & Morphology & Roundness, Aspect ratio \\ \hline Intermediate & Roundness texture & Sphericity \\ \hline Small & Surface texture & Angularity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shape indices according to scale.  
Figure 2: Scale-dependent particle shape characterization [24].
# Model Test of Dynamic Response of Living Poles Slope Under Train Loads
## 2 Model Test of the Living Poles Slope Under the Action of Train Vibration
### The Layout Scheme of Monitoring Point  
To study the dynamic response characteristics of embankment slopes and live stumps under different working conditions, a dynamic response monitoring system was arranged in the model. To monitor the variation law of soil pressure response with depth, the PV1-PV5 were the miniature earth pressure cells buried at 0.14 m, 0.39 m, 0.64 m, 0.89 m, and 1.14 m, respectively. To monitor the horizontal displacement of the slope, five displacement meters (D1-D5) were arranged on the central axis of the slope with a spacing of 0.3 m, as shown in Figure 4. Living poles with 3 \(\times\) 3 dimensions were implanted in the slope, and strain gauges were pasted on the three living poles on the central axis. The three living poles were numbered from top to bottom as A to C. Seven strain measuring points were arranged on the primary root of poles A to C, and Z1 to Z7 was 4 cm, 9 cm, 12 cm, 15 cm, 18 cm, 22 cm, and 25 cm away from the top of the primary root for measuring the bending strain of primary root. Four strain measurement points, Aa1-Aa4 (X-directional growth) and Ab1-Ab4 (X-directional growth), were arranged on each of the two main lateral roots  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Bural Depth/m** & **Moisture Content/\%** & **Dry Density/g\(\cdot\)cm\({}^{-3}\)** & **Compacting Factor/K** \\ \hline \(-\)0.45 & 12.3 & 1.79 & 0.96 \\ \(-\)0.65 & 11.9 & 1.78 & 0.95 \\ \(-\)0.85 & 12.5 & 1.78 & 0.95 \\ \(-\)1.05 & 13.1 & 1.85 & 0.98 \\ \(-\)1.25 & 12.8 & 1.76 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Relevant physical property indexes of embankment body.  
Figure 3: Construction of model box and layout of energy absorbing foam board: (**a**) construction of model box; (**b**) energy absorbing foam board.  
on the vertical slope of pole A. The distance of Aa1\(\sim\)Aa4 from the primary root was marked in Figure 5 as 2.5 cm, 4.5 cm, 6.5 cm, and 8.5 cm, respectively, for measuring the axial tensile and compressive strains of the lateral roots. Pole B and pole C were arranged with the same strain measuring points as pole A.
# Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN
## 5 Experiments
### Experimental Results
#### 5.2.2 Performance Evaluation on Recognition and Verification Tasks
Six images from each subject were randomly divided as a gallery set; the remaining images were used as a probe set. The division process was repeated three times. For the verification, we randomly selected 600 reference-query pairs as \"same\" and another 600 pairs as \"not same\". This selection process was also repeated three times. Table 3 presents that our network achieved the highest average Rank-1 and Rank-5 recognition accuracies with 91.28 \(\pm\) 1.2% and 98.59 \(\pm\) 0.4%, respectively. The second best was achieved by multi-fusion CNN with 90.75 \(\pm\) 1.0% and 97.44 \(\pm\) 0.3% as Rank-1 and Rank-5 accuracies, respectively. Besides, Figure 6(a) also illustrates the CMC curve and shows that our network achieved the best performance of recognition for all ranks. For the verification, Table 4 reveals that our network achieved the lowest EER with 3.41 \(\pm\) 1.8% and AUC was 0.9938. This is concrete evidence to demonstrate that the proposed network can verify the unconstrained periocular robustly. Figure 6(b) shows that our network outperformed most of the benchmark networks and achieved the highest recall rate against all other approaches. Figure 6: Performances of recognition and verification tasks on CASIA-iris distance database. The figures are the best to view in colour. Figure 7: Performances of recognition and verification tasks on UBIPr distance database. The figures are the best to view in colour. Evaluation on Ethnic-Ocular Database  
We present the experimental results in Table 3 by following the recognition protocol mentioned in Section 4.3. To evaluate the performance of the proposed approach, we compared our results with seven benchmark approaches (see Table 3). For the results of recognition, our network achieved 84.79 \(\pm\) 1.9% and 94.23 \(\pm\) 1.3% as Rank-1 and Rank-5 accuracies, respectively. Figure 8a illustrates the CMC curve of the proposed network, showing that the proposed method outperformed other benchmark methods from Rank-1 to Rank-10 recognition accuracies. The results indicate that the late-fusion layers are capable of correlating the RGB image and OCLBCP descriptor. Table 4 also shows that the proposed network achieved the lowest EER accuracy with 6.63 \(\pm\) 1.5% for verification. Figure 8b illustrates the ROC curve, showing that our network outperformed all benchmark networks. The results prove that our approach can learn new features from the late-fusion layers in order to transfer knowledge between the networks to perform better performance of recognition.
# Potential Role of Plant Extracts and Phytochemicals Against Foodborne Pathogens
## 4 Plant Extracts and Phytochemicals against Viruses Causing Foodborne Diseases
### Hepatitis-A Virus  
The hepatitis A virus (HAV), a picornavirus is a widespread cause of hepatitis and is transmitted via oral intake of contaminated food, water and blood transfusion [195]. Medicinal plants including _Alnus japonica_ (Thunb.) Steud. have revealed significant virucidal activity against HAV and at a concentration of 50-\(\mu\)g/mL has reduced HAV titer by 3.43 \(\pm\) 0.24 logs. Similarly, _Artemisia annua_ L., _Allium sativum_ L., _Allium fistulosum_ L. and _Agrimonia pilosa_ Ledeb. extracts exhibited 2.33 \(\pm\) 0.43, 2.10 \(\pm\) 0.41, 2.07 \(\pm\) 0.60 and 2.03 \(\pm\) 0.26-log reductions, respectively [196]. It has been observed that green tea extract has a strong anti-HAV activity with a direct effect on the viral particles. Supplementing tap water with the extract with continuous shaking every 15 min for 1 h caused a significant reduction in the percentage of HAV plaque counts. There is a strong recommendation of supplementing water with a small concentration of green tea extract that does not cause an observed change in its color and taste for 1 h with persistent shaking before usage in endemic areas for HAV infections [197].
# An OCARI-Based Wireless Sensor Network for Heat Measurements during Outdoor Fire Experiments
## 3 Experimental Protocol
### Data Acquisition Systems
#### 3.2.2 OCARI-Based Network for Heat Measurements in Fire Experiments  
The wireless platform runs on the OCARI software prototype for wireless communication based on the Adwave ADWRF24-LRS technology (Figure 4). It generates a digitised data flow from the measurement area to the network coordinator (CPAN).  
Each OCARI network node consists of a radio communication device based on an Atmel microcontroller SAM3. Each radio device is coupled with an acquisition interface in which two analogue transducers are connected through a 12-bit ADC on which a real-time circuit (RCT) operates.  
Figure 4: ADWAVE Node (**Top**): Analogue-to-digital converter (ADC) input for the heat fluxmeter (0–10 mV) and thermocouple (0–40 mV). (**Bottom**): Comparison of the Zigbee solution (**left**) and the OCARI solution (**right**).  
The RCT is integrated to generate a timestamp at the moment of analogue-to-digital conversion. This type of a timer circuit device is energy-consuming, although the ability of OCARI stack to promote very low energy consumption solutions increases due to reasonable RTCs. In the active mode, the RTC circuit uses 0.0825 W, whereas consumption decreases from three orders of magnitude in the power-down period of each cycle. This leads to an overall cost of an OCARI solution, which scales 0.4 W as the maximum energy consumption for the expected and extremely accurate timestamping of the data. The first input reads the signal from heat fluxmeters, whereas the second input is plugged into the range from the thermocouple (Figure 5).  
The wireless platform is therefore based on the OCARI 2015 release and was developed in cooperation with Adwave. It simultaneously records air temperature and radiative flux in a fire. A few adjustments were made on the nodes connected to the measurement interfaces: The analogue heat sensors must be conditioned for being plugged into OCARI hardware. For the sake of readability, details of the conditioning are reported in Appendix B.
# Assessment of Hull and Propeller Degradation Due to Biofouling Using Tree-Based Models
## 5 Assessment of the Impact of Biofouling and Maintenance Optimization
### Estimation of Added Fuel Cost  
For maintenance optimization, the added fuel cost due to biofouling must be evaluated. The methods described above provide an estimate of the power penalty caused by biofouling growth over a certain period after hull cleaning and propeller polishing. To calculate the consequent increase in FOC (dFOC), the approximate values of specific fuel oil consumption (SFOC) at different loads were used based on ME's shop tests, where no degradation of the performance of the main engine was considered. Since the power values were known, the dFOC was calculated as follows:  
\[dFOC=P_{f}\cdot SFOC\left(P_{f}\right)-P_{i}\cdot SFOC(P_{i}) \tag{20}\]  
where \(P_{i}\) and \(P_{f}\) are the initial and final SHP values respectively, and \(SFOC(P_{i})\) and \(SFOC\left(P_{f}\right)\) are the corresponding SFOC values. Based on the operator's data, the average fuel cost per ton during the examined period was 556 $/ton, so the added fuel cost per day (dCost) was derived from the following equation:  
\[dCost=dFOC\cdot FuelCost \tag{21}\]  
For instance, by applying this reasoning to the power values predicted with Model-2 for each loading condition and for different ship speed scenarios, the added fuel cost per day due to biofouling that accumulated during the corresponding period was estimated and is reported in Table 16. Hence, the added fuel cost when the vessel operated under the conditions assumed in Model-2 was estimated to be on average equal to $2228 per day. Considering that hull cleaning and propeller polishing for a Kamsarmax vessel ranges between $15,000 and $18,000, where the higher cost corresponds to the laden condition, it is concluded that, on average, in eight days of fouled operation, the accumulated additional fuel cost equaled the maintenance cost. Furthermore, the mean predicted power at each loading condition was linearly extrapolated using the straight-line fit to the points in Tables 14 and 15 to predict the power for an assumed value of the Days feature equal to 400. Thus, based on the behavior of the vessel in the past, on Day 400, an average added fuel cost of $2719 per day was predicted. This information can be used by the shipping company to decide the right time to perform the next maintenance action, although other factors may influence this decision, such as the ship's operation schedule.  
Figure 17: Mean predicted SHP values on the synthetic laden and ballast datasets in terms of days, plotted with respective linear fits.