esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### [PERSON]'s Regular Script Model Testing Results  
For Calligrapher [PERSON]'s regular script model, this paper collected one authentic work by [PERSON], which consists of 1015 characters (authentic work 1), and one imitation work by another calligrapher with 18 characters. Two works by different calligraphers were collected, one with 24 characters (other work 1) and the other with 28 characters (other work 2).  
In Table 2, Authentic 1 refers to the authentic calligraphy work by [PERSON] in regular script style. As a comparison, the imitation piece is a work by [PERSON] imitating the writing style of [PERSON], while the remaining two works are calligraphy works by other calligraphers.  
According to the test results from Table 2, all models except for MobileNetv3-L converge well during training. Among them, the improved SA-EfficientNet model achieves a similarity of 90.997% when identifying Authentic 1, which is a significant improvement compared to the 81.762% and 85.717% similarities achieved by EfficientNetv2-S and CBAM-EfficientNet, respectively. Furthermore, compared to InceptionResNetv2 and MobileNet, while exhibiting comparable performance in identifying Authentic 1, SA-EfficientNet has better discrimination ability when identifying imitations. When faced with imitation works, SA-EfficientNet only achieves a similarity of 5.556%, effectively distinguishing between  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Authentic 1** & **Authentic 2** & **Authentic 3** & **Imitation** & **Others** \\ \hline EfficientNetv2-S & 98.877\% & 93.911\% & 34.856\% & 4.003\% & 15.598\% \\ CBAM-EfficientNet & 98.633\% & 93.922\% & 35.900\% & 7.937\% & 13.557\% \\ SA-EfficientNet & 98.846\% & 95.611\% & 59.233\% & 17.265\% & 21.714\% \\ InceptionResNetv2 & 98.963\% & 90.910\% & 40.519\% & 0.153\% & 15.888\% \\ Inceptionv3 & 98.276\% & 90.918\% & 40.503\% & 5.587\% & 18.011\% \\ MobileNetv3-L & 98.278\% & 90.930\% & 41.143\% & 15.760\% & 21.630\% \\ ResNet50 & 97.131\% & 64.502\% & 23.942\% & 13.726\% & 38.338\% \\ MobileNet & 98.634\% & 90.914\% & 30.208\% & 1.961\% & 18.306\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calligrapher A’s regular script model testing results.  
authentic and imitation pieces, while InceptionResNetv2 and MobileNet achieve 10.999% and 16.674%, respectively.  
In conclusion, in the testing of Calligrapher B's regular script model, the improved CBAM-EfficientNet shows better discrimination ability than EfficientNetv2-S, while SA-EfficientNet further improves the discrimination performance based on the CBAM-EfficientNet network.
## 4 Conclusions  
The present study proposed a novel method to measure the temperature using the pNIPAM solution and SPRi technique, and the solid-liquid interface temperature of the droplet was predicted by calculating the heat conduction problem with the measured temperature using pNIPAM solution. The refractive index of the pNIPAM solution was measured through the angular modulation of the SPRi system, and the curve-fitted relations between the reflectance and temperature were obtained. We successfully measured the temperature distribution of the pNIPAM solution with a range of 24.3 \({}^{\circ}\)C \(<T<\) 29.3 \({}^{\circ}\)C using the SPRi technique. The temperature measurement method using the pNIPAM solution was evaluated by comparing the temperature results with the thermocouple data. It showed a good agreement with a small difference within the uncertainty range of the thermocouple. The present study also predicted the solid-liquid interface temperature of the droplet deposited on the gold film. The reflectance of the pNIPAM solution decreased after the deposition of the droplet, and it showed minimum values at the center of the droplet. The temperature distribution was also obtained using the curve fitted relations and reflectance. Based on the temperature of the pNIPAM solution, the one-dimensional heat conduction problem was calculated to estimate the solid-liquid interface temperature. The DI-water droplet had a lower temperature distribution at the center. It increased along the radial direction due to the decrease of thermal resistance corresponding to the droplet thickness. However, the temperature decreased near the contact line region owing to the maximum evaporative cooling. The variation of solid-liquid interface temperature increased with the surface temperature. Thus, the solid-liquid interface temperature should be considered to accurately predict the evaporation flux of the droplet for the high surface temperature.  
Conceptualization, H.J.L., C.K.C. and S.H.L.; methodology H.J.L. and C.H.J.; investigation, D.Y.K.; visualization H.J.L.; formal analysis, H.J.L. and S.H.L.; writing--original draft preparation, H.J.L.; writing--review and editing, C.K.C. and S.H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No.NRF-2021R1A2C3014510 and No.NRF-2020R1A4A4078930).  
The authors declare no conflict of interest.
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
###### Abstract
Moreover, accelerometers used so far in gravity missions exhibit relative high noise at low frequency and are the next-largest error contributor after the aliasing effects. Quantum spaceborne gravimetry (QSG) measurement techniques hold the promise of substantially improving on current technologies since they can provide absolute measurements with impressive performance in terms of long-term stability. The key innovative technique, the cold-atom-interferometry-based quantum sensor, should exploit their full potential in space by the ability to achieve long interrogation times. This could fill the technological gap and help to increase the spatial and temporal resolution of mass transport products. Several space projects involving the operation of cold atom sensors have emerged over the past thirty years mostly for fundamental physics exploration [2; 3; 4; 5] such as for testing the weak equivalence principle [6; 7], detecting gravitational waves [8; 9] or dark matter [10], but also for studying their potential in the context of satellite geodesy [8; 11; 12; 13; 14; 15]. The first accomplishments of cold atom experiments in space occurred recently in 2017-2018 with the in-orbit operation of a cold atomic clock (CACES) on-board China's Tiangong-2 space laboratory [16] and the first realizations of a Bose-Einstein condensate (BEC) in space, on a sounding rocket with the MAIUS-1 project [17] and on the ISS with the CAL project [18]. Nevertheless, up to now, no demonstration of inertial measurements with a cold atom interferometer (CAI) has been yet carried out in space.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 2 Technology Advancements
### Ground-Based Receivers  
This category of technological advancements is devoted to ground-based sensors. Early in the 2010s, the GNSS-IR technique was conceived independently in [7,8]. Since then, geodetic GNSS receivers have been used to estimate soil moisture or vegetation properties. However, since commercial geodetic receivers are used for GNSS-IR, most advances have come from new algorithms [9] and not from new receivers or technologies. Despite that, it is worth mentioning that new applications have been derived using the GNSS-IR concept. Sea level was monitored using the GNSS-IR concept [10], lake ice thickness was retrieved for the first time in [11], and sea ice and snow thicknesses were also retrieved using multi-frequency observations and using a four-layer scattering model [12].  
The second technique covered in this section is a new technological approach that makes use of the scattered GNSS-R signals in order to generate a synthetic aperture radar (SAR)-like image [13]. The technology is based on ground-based GNSS-R receivers that accurately compute and compensate the delay and Doppler of the reflected signal of a satellite while it crosses the sky. A SAR-like processing operation can be performed, producing a 2D image of a wide area in front of the receiver [14].
# GPRS Sensor Node Battery Life Span Prediction Based on Received Signal Quality: Experimental Study
## 4 Data Analysis and Discussion  
The power consumption of the radio unit can be in different operating modes [8]: idle mode which is the mode where the device will be consuming very little current and this time the device does not send or receive any data, with one or two active modes depending on the work. The functional  
Figure 8: RSSI Acquisition LabVIEW graph.  
Figure 7: RSSI acquisition process.  
Figure 9: System schematic diagram.  
block diagram of sensor node is shown in Figure 10; from this, the total power consumed can be given by the formula below:  
\[P_{t}=P_{s}+P_{mcr}+P_{r} \tag{3}\]  
where \(P_{t}\): Is the total power consumed by the sensor node, \(P_{s}\) the total power consumed by the sensor(s), \(P_{mcr}\): The power consumed by the microcontroller unit and \(P_{r}\) the total power consumed by the radio unit.  
The radio unit power can also be detailed as follows:  
\[P_{r}=P_{i}+P_{a}+P_{t} \tag{4}\]  
where \(P_{i}\) is the power when the device is in idle condition, \(P_{a}\) active power, when is ready to transmit/receive data and \(P_{t}\) the tail power when the module is transmitting/receiving data.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 6 Simulation and Results Analysis  
### Network Simulation Model  
The simulation area is 100 m \(\times\) 100 m, consisting of 100 nodes, 30 of which are anchor nodes. All nodes have a communication radius of 20 m, and the degree of channel irregularity (DOI) is set to 0.05 [37]. Both the BGWO and GWO algorithms are set to a maximum of 50 iterations, with 30 grey wolves, and their initial positions are randomly generated. The relevant simulation parameters are provided in Table 1. In comparison to networks with uniformly distributed nodes, IoT networks with non-uniform topologies exhibit several rectangular or hole-like areas where node deployment is not possible. The simulation experiments are based on three classic non-uniform network topologies: C-type, \(O\)-type, and \(W\)-type, illustrated in Figure 3.  
Figure 2: Flowchart of the proposed localization algorithm.
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Analytical Methods
#### 4.2.4 Biochemical Assays  
MCs and NODs are potent inhibitors of protein phosphatases (PPs), and in addition to antibody screening, these toxins can also be detected and measured using a protein phosphatase inhibition assay (PPIA) [74; 100; 101; 102; 103]. This assay measures the rate of formation of \(p\)-nitrophenyl (pNP; yellow color) through hydrolysis of \(p\)-nitrophenol phosphate by PPs over time and measures all PP inhibitors present in a sample. The colorimetric PPIA has been optimized for MC and NOD detection in cyanobacteria extracts using 96-well plates and has shown acceptable correlation with HPLC data [104]. However, PPIA cannot distinguish co-occurring variants of MCs and cannot distinguish MCs from NODs. Therefore, results are often expressed as equivalent dosages relative to MC-LR, which is used as a reference standard. In addition, when analyzing bloom-containing water, interferences with unknown compounds can occur, leading to overestimation or underestimation of toxin concentration. Few studies have documented the use of PPIA for toxin measurement in tissue because of the complexity of tissue extracts and potential for interferences [64; 105]. Note that because PPIA detects only inhibitors of that enzyme, within the realm of cyanotoxins it is known to quantify only MCs and NODs and alternative analysis should be undertaken to detect other cyanotoxins if required.
###### Abstract  
Physics-informed DeepONet (PL_DeepONet) is utilized for the reconstruction task of structural displacement based on measured strain. For beam and plate structures, the PL_DeepONet is built by regularizing the strain-displacement relation and boundary conditions, referred to as geometric differential equations (GDEs) in this paper, and the training datasets are constructed by modeling strain functions with mean-zero Gaussian random fields. For the GDEs with more than one Neumann boundary condition, an algorithm is proposed to balance the interplay between different loss terms. The algorithm updates the weight of each loss term adaptively using the back-propagated gradient statistics during the training process. The trained network essentially serves as a solution operator of GDEs, which directly maps the strain function to the displacement function. We demonstrate the application of the proposed method in the displacement reconstruction of Euler-Bernoulli beams and Kirchhoff plates, without any paired strain-displacement observations. The PL_DeepONet exhibits remarkable precision in the displacement reconstruction, with the reconstructed results achieving a close proximity, surpassing 99%, to the finite element calculations.  
displacement reconstruction; physics-informed DeepONet; geometric differential equations; beam and plate structure +
Footnote †: journal:  
Applied Sciences
# Mycoremediation as a Potentially Promising Technology: Current Status and Prospects--A Review
## 6 Mechanisms of Mycoremediation
### Role of Fungal Enzymes in Mycoremediation  
Fungal enzymes can be used for production of organic materials, hydrolysis of lignin-related compounds, decolourising dyes and textile inks, wastewater treatment, lacase-based biosensors, pulp bleaching, and wood pulping [202; 203]. Organic pollutants such as trinitrotoluene (TNT), PAHs, PCBs, phenols, and dyes can be broken down using white-rot fungi [204; 205]. Reduced solids levels and pathogen burden can be achieved using fungal enzymes, including lacases, cellulases, peroxidases, xylanases, and proteases (Table 3). These enzymes are also used to help deflocculate the sludge more efficiently. Enzymes are often used in the mycoremediation of various pollutants, hydrolases, and oxidoreductaseues. Due to an aerobic flooded fermentation process, lignin peroxidase (an extracellular enzyme) can be released from fungi or be present in the aqueous phase [206]. Extracellular enzymes generally accelerate the breakdown of pollutants and can be applied successfully in industrial settings to treat biodegradable and organic waste [207]. Table 7 provides an overview of the most widely used fungal enzymes, the target chemical, and their potential use in bioremediation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fungal Species** & **Enzymes Involved** & **Compound Degraded** & **Remarks** & **Ref.** \\ \hline _Fusarium oxysporum_ & Endoglucanase & Transform silver & \
\begin{tabular}{c} Grows on arid, temperate, and \\ tundra soils. \\ Typically grows on \\ decaying wood. \\ \end{tabular} & [208] \\ _Bjerkandera adusta_ & Lignin peroxidases & Xenobiotic compounds &
\begin{tabular}{c} Typically grows on \\ decaying wood. \\ \end{tabular} & [209] \\ \hline \end{tabular}
\end{table}
Table 7: Some widely used enzymes for mycoremediation produced by fungal species and targeted compounds.
## 2 Simulation and Experimental Procedure
### Gain Degradation Measurements  
In order to evaluate the gain degradation under irradiation, the EDFA was also irradiated with the same X-ray machine and irradiation conditions. The setup used for the gain measurement, reported in Figure 3, is as follows: 1999 CHP pump diode at 980 nm by 3S Photonics, a 1550 nm laser diode with an isolator and a WDM combiner from IDIL Fibres Optiques (Lannion, France).  
The amplified signal is acquired by the YOKOGAWA OSA. For the measurements, a 4.3 m-long EDF was used, since this length allows the maximum gain to be achieved with a 50 mW pump at 980 nm and a 1 \(\mu\)W signal at 1550 nm. Also, in this case, the temperature of the experiment is 25 \({}^{\circ}\)C.
# Synchronous Grouting Analysis of Shield Tunneling through High Water Pressure Fault Fracture Zone
## 4 Analysis of Synchronous Grouting Parameters  
According to existing research, the main influencing factors of synchronous grouting construction in shield tunneling include synchronous grouting pressure, grouting volume, etc. The focus is on considering the impact of synchronous grouting influencing factors on the stress, deformation, and pore water pressure changes of rock and soil in the fractured zone during the construction process.  
In practical engineering, the synchronous grouting pressure at the tail of the shield is continuously adjusted based on monitoring data, geological changes, and control indicators. In the slurry shield tunneling project that crosses the Shiziyang Water Transmission Tunnel, the mud pressure at the top of the excavation surface is between 3.5 Bar and 4.5 Bar, and the selected mud pressure at the excavation surface is 400 kPa. The model is analyzed based on synchronous grouting pressures of 350 kPa, 400 kPa, 450 kPa, and 500 kPa, while the other parameters remain unchanged; the grouting filling coefficients were set at 100%, 110%, 120% m, 130%, and 140% for analysis, while the other parameters remained unchanged; selected water cover depths of 25 m, 30 m, 35 m, and 40 m for analysis.
## 3 Results
### KI for Three-Band Parameters  
The 3D KI slice maps of the ideal damage level sequence \(0,0,\cdots,0,1,1,\cdots,1,2,2,\cdots,\)\(2,3,3,\cdots,3,4,4,\cdots 4\) and damage level sequence \(g_{1},g_{2},\cdots,g_{m}\) using three-band RISIs, which consisted of RIDSI, RIRSI, RPSI in ascending order, are shown in Figures 3-5.  
Figure 2: The contour map of KI for the two-band combinations (**a**) RDSI, (**b**) RNDSI, (**c**) RRSI.  
Among the three types of reconstructed three-dimensional SIs, the maximum KI value was the maximum value of 1, and the number of band combinations whose kappa value reached 1 were 3162, 46,471, and 42,130 for RIDSI, RPSI, and RIRSI, respectively. The contour maps in Figures 3-5 show that RIDSI had more relatively greater KI values than RPSI and RIRSI. According to the contour maps of RIDSI, RPSI, and RIRSI, the blue regions had weaker correlation with DI, especially for RIDSI and RIRSI. According to Figure 4, it  
Figure 4: The 3D KI slice map of the ideal sequence and damage level sequence on RIRSI. (**a**) Vertical slice, (**b**) horizontal slice, (**c**) best slice.  
Figure 5: The 3D KI slice map of the ideal sequence and damage level sequence on RPSI. (**a**) Vertical slice, (**b**) horizontal slice, (**c**) best slice.  
Figure 3: The 3D KI slice map of the ideal sequence and damage level sequence on RIDSI. (**a**) Vertical slice, (**b**) horizontal slice, (**c**) best slice.  
was obvious that the spectrum in the red edge and NIR region had stronger correlation with DI for RIRSI. For multiple band combinations whose KI achieved the maximum value, according to Equation (10), RIDSI (R1051, R1049, R747), RPSI (R519, R438, R706) and RIRSI (R732, R878, R712) were taken as the sensitive spectral index band combinations for RIDSI, RPSI, and RIRSI, consisting of 747 nm for RIDSI, 706 nm for RPSI, 732 nm, and 712 nm for RIRSI in the red edge region. This shows that the red edge region had strong correlations to the cotton aphids. Moreover, RIRSI (R732, R878, R712) yielded the best results for both KI and the inter- and intra-ratio \(\eta\).  
Among the six types of pest and disease specific indices tested, RIRSI (R732, R878, R712) had the highest KI and \(\eta\) followed by RRSI (R656, R643). Therefore, RIRSI (R732, R878, R712) were selected as the sensitive spectral index band combinations.
# Studying the Cable Loss Effect on the Seismic Behavior of Cable-Stayed Bridge
## 2 Three-Dimensional FE Model of the Cable-Stayed Bridge  
### Objective Bridge  
In order to conduct this study, the cable-stayed bridge part of the Seohae Grand Bridge, which was completed in 2000 in Korea, was selected as the target bridge. The bridge is a 3-span cable-stayed bridge with a total length of 990.0 m (60.0 + 200.0 + 470.0 + 200.0 + 60.0) and a main span of 470.0 m. The superstructure consists of a steel -concrete composite section with a width of 34 m, and the bridge has two H-shaped pylons measuring 182 m in height. The cable arrangement was a semi-sharp type, with 32 cables on one side of the tower and 72 cables on the left and right sides, leading to a total of 144 cables as shown in the Figure 2.  
Figure 1: Recent natural and man-made disasters on cable-supported bridges: (**a**) Seohae Bridge (Korea, 2015); (**b**) Gwangan Bridge (Korea, 2019); and (**c**) Ulsan Bridge (Korea, 2019).
## 4 Optimal Parameters for Maximum Power Operation in Inductive and Resistive Grids
If the penetration level is increased from 20% to 40%, the rated power \(S_{N}\) will be doubled, and the SCR will become 3/2. As shown in Figure 7a, when the \(R_{g}/X_{g}\) is 0.3, an SCR higher than 2 is preferable. Thus, the grid impedance \(|Z_{g}|\) should be reduced to 3/4. For example, increasing the voltage level can reduce grid impedance. Moreover, since the low inertia in wind generation systems may cause frequency instability issues, grid-forming control techniques on inverters are recommended to support the grid frequency. Moreover, energy storage systems (ESSs) can also be used to smooth the wind energy and realize an uninterrupted supply of power. Figure 7: Curves for maximum \(P_{grid(pu)}\) with different \(R_{g}/X_{g}\) ratios, SCRs, and PCC voltages. (**a**) Inductive grid impedance case; (**b**) resistive grid impedance case.