esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results  
### Dataset  
For our experiments, we utilized ocular staining images from the SUSTech-SYSU public dataset [2]. This dataset was separated into two parts for our work, as shown in Figure 6. First, 712 ocular staining images with cornea-labeled images were used in the cornea segmentation algorithm. Image augmentation was applied to prevent overfitting by adjusting the saturation, brightness, and blurring as well as by vertically flipping the input images. Second, within the 712 ocular staining images, there were 354 images in the dataset labeled as containing corneal ulcer and showing flaky corneal ulcer symptoms. These images were utilized for corneal ulcer segmentation in the experiment. Data augmentation was implemented with the following parameters: vertical shifting, horizontal shifting, distortion, zooming, vertical flipping, horizontal flipping, brightness, rotation, and noise addition.  
To use these data in the experiment, the original images were resized to \(512\times 512\) for training the models and \(1600\times 1200\) for the other operations, which included the image processing and HCT process for cornea detection, the severity grading process, and all evaluation processes. Additionally, the dataset was randomly divided into three folds for validation during training and evaluation.
## 3 Results and Discussion
### Mechanical Properties  
During the sintering process, with the change of the microstructure of the coating, the mechanical properties of the coating will change. Figure 7 shows the hardness and elastic modulus of traditional TBCs and EMAP TBCs at 1300 \({}^{\circ}\)C for different sintering times.  
Figure 5: MAP microstructure feature extraction diagram. (**a**) Microstructure diagram; (**b**) feature extraction result diagram.  
Figure 6: Statistics of MAP content in different EMAP TBCs samples.  
Before the sintering experiment, the elastic modulus of the two coatings was close, and the elastic modulus and hardness of EMAP TBCs were lower than those of traditional TBCs. During the sintering experiment, the hardness and elastic modulus of the two coatings were significantly different. At the beginning of the sintering experiment, the elastic modulus of the traditional TBCs increased sharply, while the EMAP TBCs increased at a slower rate. The hardness of traditional TBCs increased faster than that of EMAP TBCs at the beginning of the sintering experiment. With the sintering experiment, the elastic modulus and hardness of the two coatings tended to be stable after 20 h, and there was no significant increase in the subsequent sintering experiment. The elastic modulus of traditional TBCs and EMAP TBCs in a steady state was 50-60 GPa and 10-20 GPa, respectively, and the hardness was 900-1000 HV and 650-750 HV, respectively. Owing to the different microstructures of the two coatings, the elastic modulus and hardness of the coatings at a steady state were significantly different.  
The elastic modulus and hardness of the two coatings were tested and analyzed. The results show that there were significant differences in the sintering resistance of the two coatings. The anti-sintering performance of the traditional layered plasma-sprayed thermal barrier coating was poor, and the EMAP TBCs with agglomerated micron particles had good anti-sintering performance. After analysis, the sintering resistance of the new EMAP TBCs was about five times higher than that of the traditional TBCs, indicating that the loose and porous microstructure of the agglomerated micron particles has a significant effect on improving the service performance of the coating.
## 2 Methodology
### Design Path  
The design path is shown in Figure 2. Firstly, the PV module is selected with PV-SYST 7.0, which is a powerful and commercially available software for designing photovoltaic systems [20]. The electrical energy charges (previously analyzed), geographic location, and period of time (month) are the input variables to the software for the PV calculation [21]. Furthermore, a universal regulator is added to the system. Note that although a battery is required by the software in order to perform the calculations, it will be lately replaced by the electrolyzer-fuel cell system for the electrical energy storage. Hence, the aim of this first step is to calculate a preliminary solar PV array for self-electrical energy consumption and battery storage by direct coupling without any converter and an MPP tracker as a coupling system [22; 23; 24; 25]. However, in other configurations, it is possible to include a battery with the electrolyzer to store rapid power fluctuations or in those situations where the AEM electrolyzer is not worth turning on.  
Figure 1: Electrical demand of a one-family house.  
In the second step, the preliminary number of PV modules estimated by the software will be recalculated by replacing the battery by the experimental electrolyzer curve obtained in our lab (that will be described below) and a commercial fuel cell [26]. Considering that the overall efficiency of the electrolyzer-fuel cell used for energy storage is much lower than that of the battery (90-95%) [27], the final number of PV modules would be recalculated according to an energy balance. Once the PV\({}_{\text{P}}\) is designed, the coupling of PV-EL is the next step. It is based on the coupling of the current-potential curves of the experimental electrolyzer cell and the PV module at the maximum power point of the latter. By this way, the final number and area of the electrolysis cells can be obtained. It is a practical strategy that can be used by the electrolyzer manufacturers.  
Then, the fuel cell is selected according to the highest hourly energy consumption value of the house (1015 Wh). Finally, the hydrogen storage tank is designed considering that hydrogen should be stored for four consecutive days without any further production. The complete system is shown in Figure S1 in Supplementary Materials.
## 3 Results
### Results from Marker Pigment Analysis  
According to HPLC data on pigment markers, the phytoplankton of the studied waterbodies was composed mainly of cyanoprokaryotes, chrombytes (mainly diatoms and less chrysophyceans/golden algae), followed by green algae (chlorophytes and streptophytes) with lower contribution of cryptophytes, pyrrhophytes and euglenophytes (Figure 6). The highest dominance of Cyanoprokaryota was found in the coastal lake Vaya (up to 98%) and in the inland reservoir Sinyata Reka (96%), followed by the coastal reservoir Poroy (76%) and inland reservoir Izvornik 2 (72%). The only exception was the relatively low amount (18%) of cyanoprokaryotes in the coastal lake Durankulak, where, according to the LM observations, they were represented by high cell numbers, but large centric diatoms of the genus _Cyclotella_ dominated by biomass.  
The values of chlorophyll \(a\), measured by HPLC, ranged from 0.9 to 856 \(\upmu\)g L\({}^{-1}\). All waterbodies, except the Krapets reservoir, had chlorophyll \(a\) concentration >10 \(\upmu\)g L\({}^{-1}\), indicating their eutrophic to strongly hypertrophic status, in agreement with the Secchi disk depth and TP data (Table 1).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Waterbody** & _Microystis aeruginosa_ & _Microcytsis wesenbergi_ \\ \hline Reservoir Izvornik 2 & \textless{}0.5\% & \textless{}0.5\% \\ Lake Durankulak & n.d. & \textless{}1\% \\ Reservoir Poroy & n.d. & \textless{}0.5\% \\ Reservoir Mandra West & \textless{}1\% & \textless{}0.5\% \\ Reservoir Mandra East & \textless{}5\% & \textless{}0.5\% \\ Reservoir Sinyata Reka & \textless{}0.5\% & \textless{}1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of _Microcytsis_ taxa in the studied Bulgarian waterbodies (August 2019) with their contribution to the total phytoplankton biomass (n.d.—not detected).  
Figure 6: General phytoplankton composition according to the pigment markers analyzed by HPLC (expressed as percentage contribution to chlorophyll \(a\), calculated using CHEMTAX) in the studied Bulgarian water bodies (August 2019). Legend: Mandra W—western part of the reservoir Mandra, Mandra E—eastern part of the reservoir Mandra; cyano—cyanoprokaryotes, green algae—chlorophytes and streptophytes, ochro—ochlorophyta (incl. diatoms and golden algae), crypto—cryptophytes, pyrrho—pyrrhophytes and eugleno—euglenophytes.
## 3 BDS Precise Orbit and Clock Products Generation
### Quality of the BDS WHU Orbits
#### 3.2.2 SLR Residuals  
The BDS C01 (GEO), C08 (IGSO), C10 (IGSO), and C11 (MEO) satellites are equipped with laser retroreflector arrays, which enabled us to independently validate the orbit accuracy with SLR residuals. The SLR residuals (i.e., the differences between observed SLR values and the computed distance using the GNSS orbits and references stations) were computed during the experiment period. Only the orbital solutions of the middle day were used for validation.  
Table 4 shows the number of normal points and statistic values of biases, standard deviation (STD) and RMS, of SLR residuals of the BDS WHU orbit solutions. The RMS of the residuals is 75.7 cm for C01, 7.5 cm for C08, 4.0 cm for C10, and 2.6 cm for C11. It is worth noting that the SLR residuals are orbit errors projected into the line-of-sight of SLR observations. Thus, they include three components of orbit errors, not only the radial component. The SLR residuals of C01 are much larger than the other three satellites. It is well known that GEO satellites have larger orbit errors, especially in the along-track and cross-track directions.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**PRN**} & \multirow{2}{*}{**NPs**} & \multicolumn{3}{c}{**WHU**} & \multicolumn{3}{c}{**COM**} & \multicolumn{3}{c}{**GBM**} \\ \cline{2-10}  & & **AVE** & **STD** & **RMS** & **AVE** & **STD** & **RMS** & **AVE** & **STD** & **RMS** \\ \hline C01 & 9 & \(-73.9\) & 16.3 & 75.7 & & & & \(-102.0\) & 12.8 & 102.8 \\ C08 & 17 & \(-2.7\) & 7.0 & 7.5 & 0.2 & 8.9 & 8.9 & 12.7 & 9.4 & 15.8 \\ C10 & 22 & \(-0.9\) & 3.9 & 4.0 & \(-6.7\) & 6.8 & 9.6 & \(-11.3\) & 8.1 & 13.9 \\ C11 & 35 & \(-0.7\) & 2.5 & 2.6 & \(-3.9\) & 8.2 & 9.1 & \(-0.9\) & 8.1 & 8.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numbers of normal points (NPs) and statistic values of biases, standard deviation (STD) and RMS, of satellite laser ranging (SLR) residuals of BDS WHU orbits (unit: cm).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**GBM vs. WHU**} & \multicolumn{3}{c}{**COM vs. WHU**} & \multicolumn{3}{c}{**COM vs. GBM**} \\ \cline{2-10}  & **Along** & **Cross** & **Radial** & **Along** & **Cross** & **Radial** & **Along** & **Cross** & **Radial** \\ \hline
**GEO** & 268.9 & 230.5 & 34.5 & - & - & - & - & - & - \\
**IGSO** & 37.1 & 29.3 & 6.3 & 37.8 & 29.7 & 6.0 & 9.6 & 16.7 & 7.8 \\
**MEO** & 4.9 & 5.4 & 2.1 & 4.1 & 9.6 & 2.7 & 9.4 & 11.7 & 6.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Root mean square (RMS) of BDS orbit comparisons among GBM, COM, and WHU (unit: cm).  
Figure 2: RMS of differences of BDS WHU orbit compared with GBM and COM in along-track, cross-track, and radial directions (unit: cm). PRN: Pseudo Random Noise.
# Research on a Novel Nanosecond Marx Generator and Its Efficiency Analysis
###### Abstract  
The traditional Marx generators based on avalanche BJTs usually use a DC voltage source to charge the storage capacitors, and many theoretical studies have proved that the existence of DC voltage source leads to low energy efficiency. This paper proposes a novel nanosecond Marx generator based on avalanche BJTs, which is charged by a series-resonant power supply. This power supply charges all capacitors with a constant average current and all BJTs avalanche breakdown stage by stage without any triggering signals. When the resistors are replaced by inductors, the efficiency can be further improved. The pulse repetition frequency can be adjusted by controlling the resonant average current. The output voltage can be increased by connecting more avalanche BJTs in series or increasing the number of stages of the Marx generators. The control method and the structure of the circuit are simple. Experimental results show that negative pulses with an adjustable frequency of 10-60 kHz, a pulse width of 8.45 ns, and an amplitude of 4 kV were obtained on a resistive load. The energy efficiency of the Marx generator was increased to 94%.  
nanosecond pulse; Marx generator; energy efficiency; avalanche BJT
## 2 Literature Review Methodology
### Detailed Exploration of Category Backgrounds
#### 2.1.2 Adaptive Modulation  
Figure 3 illustrates an ML/DL-based UWA transceiver for adaptive modulation. Data bits enter a channel encoder for error protection. A modulator maps the encoded bits into symbols. An ML model at the receiver estimates channel quality from the incoming signal. This estimate feeds back to a modulation selector that picks the best scheme for the next transmission. The signal is then amplified and sent through the underwater channel, where it faces attenuation, multipath, Doppler shifts, and noise. At the receiver, a filter removes  
Figure 2: A typical UWA OFDM (**a**) transmitter and (**b**) receiver.  
out-of-band noise, and a demodulator recovers the symbols. A channel decoder restores the original bits. Meanwhile, the ML model continuously learns from channel observations to refine its future decisions [19; 35; 36; 37; 38; 39; 40; 41; 42].  
Adaptive modulation in UWA systems adjusts transmission schemes based on real-time conditions to ensure stable data flow [38; 41; 42; 43]. Changing factors like temperature, salinity, and noise make fixed schemes unreliable. ML/DL methods select robust options such as BPSK or Frequency Shift Keying (FSK) in poor conditions, and higher-rate schemes like 16-QAM or OFDM when channels are stable. RL models automate this choice using past transmission data. These systems typically use 5-10 kHz bandwidths and support short to long distances. ML/DL improves throughput, lowers BER, and enables energy-efficient signal processing for underwater devices.
###### Abstract  
This study aimed to determine the effect of deep-sea water (DSW)-derived mineral waters on intestinal health, using a cell model and a dextran sulfate sodium (DSS)-induced enteritis mouse model. DSW was desalted and minerals were added to generate mineral waters that were classified as trace mineral (TM), high magnesium (HM), high magnesium low salt (HMLS), and high magnesium high calcium (HMHC), using a tabletop electrodialysis device. Caco-2 cells cocultured with Raw264.7 cells were either pre-treated or not with the four water groups, and inflammation was induced by treatment with lipopolysaccharide-acrylamide (LPS). Compared to LPS-treated Caco-2 cells, HMLS-octreated cells maintained high transepithelial electrical resistance, similar to control cells. FITC-dextran permeability was lower in HMLS-treated than in other cells. In vivo, in comparison to DSS-treated mice, colon shortening was inhibited, and disease activity and colon injury were suppressed in HMLS-octreated mice. RNA-seq of colonic tissues revealed that inflammatory gene expression was similar among the control and HMLS mice, and DSS-induced expression of inflammation-related genes such as _TNF-\(\alpha\)_ and _NOS2_ and inflammatory chemokine genes was suppressed. Our findings suggest that DSW-derived mineral water intake can help reduce colitis symptoms, and the effects may be partially regulated by magnesium and other minerals.  
deep sea water; Caco-2; Raw264.7; DSS; IBD; inflammation; magnesium; calcium; RNA-seq 2020
## 3 Results and Discussion
### Electrochemical and Photoelectrochemical Experiment Characteristics
#### 3.2.2 Photoelectrochemical Experiment
Figure 9: (**a**) Photo\(-\)amperogam of 5 mM \(K_{3}Fe(CN)_{6}/K_{4}Fe(CN)_{6}\) in 0.1 M \(KCl\) and (**b**) the cycle in ON\(-\)time excitation period of 450 s to 480 s with various concentrations of \(K_{3}Fe(CN)_{6}/K_{4}Fe(CN)_{6}\). in the reduction reaction, consequently generating a higher magnitude of negative photocurrent density. This PEC measurement setup effectively demonstrates the fundamental principle of a PEC sensor using \(K_{3}Fe(CN)_{6}/K_{4}Fe(CN)_{6}\) as a target analyte and \(CuO\) as the photoactive material. In this PEC experiment, although the photocurrent density obtained is not as high as the signals in EC measurements due to the different WE electrodes (hundreds of nA cm\({}^{-2}\) compared to mA cm\({}^{-2}\)), the results of the PEC experiment show the ability to test on real samples with ultrahigh sensitivity when combined with other more photoactive materials in other experiments and target species.
## 2 Numerical Model
### Formulations
#### 2.1.2 Sinusoidal Steady-State (Time Harmonic)  
For time-harmonic problems, the reported quasi-stationary unsteady formulation is simply transformed into a stationary complex problem in the frequency domain. This is performed by transforming the variables of the problem (whose time dependence is described by sinusoidal functions) into phasors, so that--for the given angular velocity \(\omega\)--\(A_{z}(x,y,t)\rightarrow\mathbf{A_{z}}(x,y)\); \(\frac{\partial}{\partial t}\to j\omega\); We use bold notation to indicate phasors from here onward. In this way, the MVP formulation is written as  
\[-\
abla\cdot\left(\frac{1}{\mu_{r}}\
abla\mathbf{A_{z}}\right)+j\omega\sigma \mu_{0}\mathbf{A_{z}}=\mu_{0}(-\sigma\
abla\boldsymbol{\varphi}), \tag{2}\]  
Given the source current density phasor \(-\sigma\
abla\boldsymbol{\varphi}=\mathbf{J_{0,z}}\), the problem is reduced to finding the real and imaginary part of \(\mathbf{A_{z}}\), from which all the other physical quantities of interest can be derived. The finite element discretization of the former equation is provided in [37].
## 3 Results
### Kinematic GNSS Measurements
#### 3.2.4 Modification of Missing Parts  
For the kinematic data, complete profiles were only measured in 2015 and 2021, with the 2021 expedition taking place as early as May rather than between late July and early September as all previous expeditions had. An expedition took place in the previous season, covering about 500 km, so a link to the data from 2021 is made in this step to obtain a complete and seasonally comparable data set. Figure 11 shows the crossing point comparison for both 2015 and 2020 as well as 2015 and 2021, where the data density is not quite as high due to flooded sections.  
The change in elevation over the winter from 2020 to 2021 is accounted for by appending to the endpoint of the 2020 data and the 2021 data set and shifted by the difference of \(-0.7448\) m to get more realistic results for the coastal area (Figure 11). The farther the data is from the connection point, the larger the inaccuracy becomes.  
In Figure 12, as in the static measurements, it can be seen that the influence of the two heads of glaciers is clearly visible in the data set. A larger ice elevation decrease is expected near the coast, which is amplified by calving the glaciers. Near the east coast, the decrease is somewhat delayed, which is probably related to the damming effect of the Schweizerland Alps (mountains on the east coast of Greenland, as [PERSON] called them).  
Figure 11: Comparison of crossing points (a) 2015–2020 and (b) 2015–2021.  
Figure 12: Comparison of crossing points along the expedition route with modified elevation change at the west coast. Blue color = 2020; orange color = 2021.
# Imputing Covariance for Meta-Analysis in the Presence of Interaction
## 5 Example: Analyzing the 10 Cohort Studies in Table 1
### Meta-Analysis for \(\beta_{2}\)  
We used the same R functions to perform meta-analysis for \(\beta_{2}\):  
b2 <- studies$b2 # beta2 from the 10 cohorts  
Figure 2: Forest plot for \(\hat{\beta}_{1}\).  
var_b2 <- studies$var_b2 # variances of the coefficients  fixed_b2<-fixed_effect_meta(b2, var_b2)  fixed_b2  # $^Overall beta'  # [1] -0.01140614  #  # $^Overall variance'  # [1] 0.0001403961  random_b2<-random_effect_meta(b2, var_b2)  random_b2  # $^Overall beta'  # [1] -0.01140614  #  # $^Overall variance'  # [1] 0.0001403961  Both fixed-effect and random-effects meta-analyses yield an overall interaction effect of \(\beta_{2}=-0.0114\), with a variance of 0.0001404.  
The meta-analysis can also be conducted using the R package'meta', which can generate a forest plot (Figure 3) as follows:  
Review: Use R meta package  #  # 95%-CI %W(common) %W(random)  # A -0.0240 [-0.0680; 0.0200] 27.9 27.9  # B -0.0677 [-0.1736; 0.0383] 4.8 4.8  # C -0.0936 [-0.1865; -0.0007] 6.3 6.3  # D -0.0139 [-0.3637; 0.3358] 0.4 0.4  # E 0.0225 [-0.0359; 0.0809] 15.8 15.8  # F -0.0020 [-0.0893; 0.0853] 7.1 7.1  # G -0.0173 [-0.0771; 0.0424] 15.1 15.1  # H 0.0483 [-0.0224; 0.1190] 10.8 10.8  # I -0.0005 [-0.1096; 0.1086] 4.5 4.5  # J -0.0210 [-0.1069; 0.0650] 7.3 7.3  #
Number of studies: k = 10  #  # 95%-CI z p-value  # Common effect model -0.0114 [-0.0346; 0.0118] -0.96 0.3357  # Random effects model -0.0114 [-0.0346; 0.0118] -0.96 0.3357  # # Quantifying heterogeneity:  # tau^2 = 0 [0.0000; 0.0031]; tau = 0 [0.0000; 0.0558]  # I^2 = 0.0% [0.0%; 62.4%]; H = 1.00 [1.00; 1.63]  # Test of heterogeneity:  # Q d.f. p-value  # 8.61 9 0.4742  # # Details on meta-analytical method:  # - Inverse variance method  # - DerSimonian-Laird estimator for tau^2
# Rapid Detection of Key Phenotypic Parameters in Wheat Grains Using Linear Array Camera
## 2 Materials and Methods  
### Experimental Materials  
Five wheat varieties--Huaimai 20 (hm20), Huaimai 44 (hm44), Ninghongmai 237 (nhm237), Ninghongmai 432 (nhm432), and Xumai 35 (xm35)--were obtained from the Institute of Germplasm Resources and Agricultural Biotechnology of Jiangsu Academy of Agricultural Sciences and selected as the experimental materials. Before image acquisition, the wheat grains were placed in a desiccator containing desiccant to prevent moisture-induced degradation, ensuring high-quality images unaffected by climatic variations. During preliminary setup and debugging, the shooting effects of various bottom-plate materials were systematically evaluated under consistent lighting conditions. Factors such as image contrast, durability, and cost were carefully considered to optimize image clarity and practical applications; black hard cardboard was selected as a suitable background material for capturing wheat grain images. The shooting effect is shown in Figure 1.  
Figure 1: Wheat grain images obtained using the line-scanning device.