esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results  
### Accuracy of Cross-Calibration between MODIS and Sentinel-3 Products  
The most accurate calibration models homogenizing MODIS NDVI and LST products with Sentinel-3 counterparts are given in Table 2. The RF and kNN models were found to be the optimal for the cross-calibration of NDVI and LST, respectively.  
Table 2 reveals clear improvement in time series homogeneity between MODIS and Sentinel-3, which is confirmed by all three quality metrics. Yet, the impact of the remaining difference between Sentinel-3 and MODIS products on time series homogeneity is not easy to determine. This in turn may have further implications on the reliability of crop yield pre  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Response** & **Status** & **Predictors** & **MBE** & **RMSE** & **EF** \\
**Variable** & & & & & \\ \hline NDVI\({}_{\text{S-3}}\) & prior to calibration & – & 3.40 & 5.16 & 0.73 \\ NDVI\({}_{\text{S-3}}\) & calibrated by RF & NDVI\({}_{\text{MODIS}}\), DOY, GDD & 0.07 & 2.95 & 0.91 \\ LST\({}_{\text{S-3}}\) & prior to calibration & – & \(-\)7.16 & 7.83 & \(-\)0.65 \\ LST\({}_{\text{S-3}}\) & calibrated by kNN & LST\({}_{\text{MODIS}}\), DOY, GDD & 0.00 & 2.51 & 0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy of MODIS NDVI and LST cross-calibration to Sentinel-3 counterparts.  
dictors that are calculated as anomalies or standardized by extreme values (i.e., VCI, TCI). To compare the spurious temporal variability introduced by the cross-calibration with natural variability of MODIS-derived predictors (NDVI, LST), the differences between monthly cross-calibration RMSE and double monthly standard deviations of MODIS-derived predictors within the period 2000-2019 were computed and revealed for administrative units (Figure 6). The negative differences (marked with blue) indicate that the cross-calibration error is lower than the natural variability of a predictor expressed by the double standard deviation. Overall, such a situation for NDVI occurred in 78% of LAU units and for LST in 98% of LAU units. This implies that in a great majority of administrative units, the cross-calibration errors do not obscure the natural variability of the predictors. Thus, it can be concluded that the developed and validated cross-calibration models are sufficient to homogenize MODIS products with the Sentinel-3 counterparts and further to use the homogenized data records to predict crop yields.
## 2 Investigated Battery Module
### Objective Functions Investigated  
Temperature profiles of sixteen (16) cells from the battery module were monitored and recorded by the K-type thermocouples and the temperature DAQ as the output variables of the experiment. An average of 172 temperature data points (corresponds to 172 min) were generally recorded during the 0.5 C level charge experiment and 150 data points during the charging experiment with 0.75 C current rate. Generally, lower times of discharging period were observed for the 0.5 C and 0.75 C experiments.  
The temperature values recorded during each charge/discharge cycle experiment were stored in a generic created file by the T-DAQ which was retrieved for data processing for data analysis.  
For results analysis of the measured temperature profile of selected cells in the battery module, the nomenclature depicted in Figure 14 was adopted to address various individual cells (e.g., Cell#01, Cell#07, etc.), or a group of cells in a row as Row 1, Row 2, Row 3 and Row 4.
## 8 Future Prospects
Furthermore, the synthesis of functional ENFMs with selective adsorption capacity to pharmaceuticals in particular, in the existing system or with synergistic adsorption and reduction effects to accomplish pharmaceutical recovery, should be a focus of future work. Electrospun nanofibers are environmentally friendly, inexpensive, non-toxic, and reusable through desorption. Co-polymers such as poly (ethylene oxide) (PEO) and poly (vinyl alcohol) (PVA) can be used in the electrospinning process because they are biocompatible, easy to electrospun, and non-toxic. Even difficult-to-electrospin biopolymers such as lignin, chitosan, or cellulose can be electrospun using such polymers. Without a doubt, electrospun nanofibers with a highly available surface area represent a viable solution to pollutant adsorption because they provide a sufficient number of sorption sites. However, nano-meter-scale fibers are difficult to obtain from polymers using traditional manufacturing processes [168]. Electrospinning is still the method of choice for creating nano- or micron-size fibers from biopolymers. Regeneration of nanomaterials is required for cyclic applications in pharmaceutical wastewater treatment. A suitable adsorbent must have good desorption-adsorption performance in order to maximize efficiency and reduce costs. Fluent selection for improved desorption is primarily determined by the adsorption mechanism, adsorbent nature, and adsorbates. For eluting pharmaceutical compounds, the most commonly used eluent agent in desorption is an acidic or alkaline solution. Aside from these agents, thiourea HCl and EDTA have been reported as highly efficient eluents for removing pharmaceutical compounds from used nanomaterials. Systematic studies should be carried out in order to optimize various conditions such as desorption time, pH, recovery efficiency, eluent type, and so on.
## 3 Results
### Model Validation  
The scatterplot between observed OCO-2 SIF and predicted SIF by the selected model (i.e., the model based on half of the training samples without land cover type) was shown in Figure 4. The plot density figure showed that our model estimated SIF fairly well (R\({}^{2}\) = 0.79, RMSE = 0.07 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)). The model slightly under- and overestimated values greater than 0.6 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\) and lower than 0.3 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\), respectively. We further validated the model for each biome, and the results of the model with land cover type were also provided for comparison purposes (Table 2). Our model had a high predictive performance for deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, and croplands (all with R\({}^{2}\) greater than 0.75), and a moderate performance for evergreen broadleaf forests (R\({}^{2}\) = 0.43, RMSE = 0.08 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)) and open shrulands (R\({}^{2}\) = 0.46, RMSE = 0.06 W m\({}^{-2}\) um\({}^{-1}\) sr\({}^{-1}\)). The model with land cover type had almost identical performance with our selected model in each biome (Table 2).  
Figure 3: The accumulated errors of predicted SIF by different models: (**a**) Using 250,000 training samples without land cover type, (**b**) using 250,000 training samples with land cover type, (**c**) using half of the training samples without land cover type, and (**d**) using half of the training samples with land cover type.
## 4 Simulation Analysis
### Simulation Results  
The simulation results are presented in Figures 14-17, in which Figures 14-17 show the responses in the generating operation mode, and Figures 18-21 show the responses in the pumping operation mode. It is seen in Figures 14 and 18 that, in the case of the FSG in Case 1, the grid frequency decreased greatly, and the power system became unstable. This was because the wind farm was decoupled from the grid according to the FRT grid code, as shown in Figure 13, and then the output from the wind farm was lost. In contrast to Case 1, in the case of the ASG in Case 2, it is observed in Figures 14 and 18 that the proposed control of the battery and ASG was successfully performed, and the grid frequency could be returned to the rated frequency in spite of the disconnection of the wind farm. Figures 15 and 19 show the output powers of the FSG (Case 1) and ASG (Case 2), and Figures 16 and 20 show the responses of the rotor speed of each generator, in which it is seen that the ASG, which was controlled based on the proposed method, controlled its output effectively by adjusting its rotor speed to maintain the stability of the system, and as a result the system could be stabilized, as can be seen in Figures 14 and 18. Figures 17 and 21 show responses of the battery output in Case 2 (positive value corresponds to discharge), in which it is seen that the battery also controlled its output based on the proposed method and contributed effectively to the stabilization of the system. It is seen in Figure 15, Figure 17, Figure 19, and Figure 21 that the battery increased its output to the maximum level (rated power) rapidly after the disconnection of the wind farm in Case 2 (Figures 17 and 21), and then the ASG increased its output in the generating operation mode (Figure 15) and decreased its pump load in the pumping operation mode (Figure 19) to bring back the system frequency to the rated frequency. The operation of the ASG followed the control action of the battery because the output of the ASG was limited by the rate limiter, as explained before. In this way, the ASG and the battery controlled each output cooperatively to stabilize the power system.  
Figure 14: System frequency (Generating Operation).  
Figure 13: Responses of wind farm (WF) voltage and fault ride-through (FRT) grid code.  
Figure 16: Generator rotor speed (Generating Operation).  
Figure 17: Battery output (Generating Operation).  
Figure 18: System frequency (Pumping Operation).  
Figure 15: ASG and fixed-rotor-speed pumping generator (FSG) output (Generating Operation).  
As a result, it was concluded that the proposed coordinated control of the battery and the ASG could maintain the stability of the grid system effectively during a disconnection of the WF due to a network fault.
## 4 Brillouin Optical Correlation Domain Analysis: BOCDA
### Overview of Performance Improvement Schemes for BOCDA
#### 4.3.8 Longitudinal Sweeping Scheme to Enhance the Speed for Entire Distribution Measurement  
The specific BOCDA feature is the random accessibility. However, some applications simply require a high-speed measurement of the entire distributed information. For this requirement, modified BOCDA schemes have been proposed. In these schemes, the correlation peak is swept at first along the fiber axis while keeping the frequency difference between the pump and probe; then, the frequency difference is changed. This sequence is repeated to have all of the BGS at any positions along the fiber [60]. In these schemes, the random accessibility cannot be realized, but the measurement speed that has the entire distribution can be enhanced. This scheme is called the longitudinal sweepingscheme. We have demonstrated a system to realize a high-speed measurement of the entire distribution with 12 traces/s [61].  
Another BOCDA system for this purpose has been demonstrated, in which the time-division pump-probe generation scheme is adopted. The point in the system is that the sinusoidal frequency modulation for the correlation peak generation is performed with different frequencies between the pump and the probe portion, by which the correlation peak can be automatically swept [62]. By additionally changing the lightwave frequency difference between the pump and the probe through simply changing the amplitude of the square-like LD injection current waveform, a longitudinal sweeping scheme can be realized. This scheme is called the \"differential frequency modulation\" scheme [62]. A high-speed measurement of the entire distributed information with 20 traces/s has been demonstrated [62]. This work was done with collaboration between our group and Professor [PERSON] of Chung Ang University, Korea.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 3 Single-Carrier Transmission Schemes with Dimming Control
### On-Off Keying (OOK)--Analogue Dimming
#### 3.1.1 PD Receivers  
The BER of the PIN PD detector of OOK scheme can be expressed as [41]  
\[{\rm BER}^{\rm ook}_{\rm PIN}=Q\left(\sqrt{\frac{\zeta^{2}\Re^{2}\gamma^{2} \overline{P}_{\rm TX}^{2}}{2K\tau^{0}R_{L}}}\right), \tag{9}\]  
where \(\Re\) refers to the responsivity of the photodiode, \(R_{b}\) denotes the bit rate, subject to the condition \(R_{b}\leq B_{\rm max}\), where \(B_{\rm max}\) represents the maximum electrical bandwidth that can be achieved by LED sources at the transmitter. Additionally, \(T^{0}\) indicates the absolute photodiode temperature, and \(R_{L}\) is the load resistor for the PIN receiver. It is noted that the effects of shot noise are disregarded, as thermal noise is the primary noise factor for the PIN PD receiver.  
The maximum achievable data rate at target BER, \(P_{\text{e}}^{\text{th}}\), is another metric that will be used, in this work, to compare different schemes. This metric is denoted by \(R_{\text{max}}^{\text{ook}}\) and is limited by the system's bandwidth \(B_{\text{max}}\) as  
\[R_{\text{max}}^{\text{ook}}=\min\left[\frac{\zeta^{2}3\mathbb{R}^{2}\gamma^{2} \mathbb{P}_{\text{TX}}^{2}}{[\frac{2\mathbb{K}^{T0}}{R_{L}}][Q^{-1}(P_{\text{e} }^{\text{th}})]^{2}},B_{\text{max}}\right], \tag{10}\]  
where \(Q^{-1}(.)\) denotes the inverse of \(Q\) function.
# Microbiological Safety and Health Properties of Markleted Fermented Root Vegetables
## 3 Results and Discussion
### Total Antioxidant Capacity
#### 3.2.2 Cuprac Assay Results  
Antioxidants capable of reducing Cu\({}^{2+}\) are relevant to the CUPRAC assay, with phenols, tocopherols, and vitamin C being the primary compounds in vegetables [75]. However, the first two compounds mentioned are hydrophobic, while in CUPRAC, hydrophilic antioxidants are major reagents. Therefore, ascorbic acid may be an essential compound in this assay [76]. However, betalains present in beetroot are also hydrophilic compounds. Presumably, due to their high amount in the mentioned vegetable, the results obtained in the CUPRAC assay were the highest for beetroot (2.89-4.09 mmol TE/100 g f.w. In contrast, carrot exhibited the lowest TAC values (0.56-1.02 mmol TE/100 g f.w.), which corresponds with its low vitamin C content (5.9 mg/100 g f.w.) [72]. Celeriac and radish displayed intermediate TAC values in the CUPRAC assay, with results ranging from 0.94 to 2.14 mmol TE/100 g f.w. for celeriac and 1.06 to 1.21 mmol TE/100 g f.w. for radish. Vitamin C content in these vegetables was 8 mg/100 g f.w. in celeriac and 14.8 mg/100 g f.w. in radish [77], both of which are higher than the amount found in carrot. Celeriac, in particular, is rich in quercetin, which has a high molar absorptivity in the CUPRAC assay [78], potentially contributing to the higher average TAC observed for celeriac compared to radish.  
The TAC values obtained for the fermented samples were lower than those observed for fresh roots. In this study, the TAC determined by the CUPRAC assay ranged from 1.89 to 4.09 mmol TE/100 g f.w., while literature values for fresh beetroot ranged from 1.52 to 6.87 mmol TE/100 g f.w. The lower results for fermented vegetables may be attributed to a reduction in vitamin C and betalain content during fermentation [4]. The CUPRACassay results for betetroot cultivated in Poland showed a value of 2.74 mmol TE/100 g fresh weight (f.w.) [28]. Depending on the specific pickled product, this value may be either lower or higher. It is worth noting that the characteristics of the raw material are a critical determinant of antioxidant properties [61].  
Statistically significant differences were observed between beetroot and carrot, as well as between white radish and beetroot, consistent with the results from the DPPH assay (\(p\) = 0.000).
## 4 Simulation Results
### Performance of H-Bridge VSI  
The H-bridge VSI is controlled by PLL, PR, SPWM pulses, and PI controller to produce a constant grid voltage and frequency (250 V and 50 Hz, respectively).  
Figure 14a shows the steady-state response of the grid voltage (249.5 V) and current (10.48 A) waveforms of the proposed algorithm under uniform weather condition at a power of 1497 W. Note that the waveforms are in phase and no ripple components appeared in them. Figure 14b illustrates the current and voltage waveforms from the unity power factor operation of the utility grid at shading pattern 1. The peak value of the current injected into the grid decreases, maintaining the same phase and unity power factor. Thus, ripple-less grid voltage and current are measured
## 3 Conception of the Bio-Implantable Customized Multi-Sensor
### The Electronic Design
In our case, for a three electrode case, a low-voltage low-power CMOS potentiostat amplifier was implemented for an amperometric measurement. These voltage levels could be applied by the potentiostat amplifier to the three electrode biosensor, defined by: (a) the working electrode (W), which serves as a surface on which the electrochemical reaction takes place and will be functionalized by the lab-on-a-chip module depicted in Figure 6 and Figure 7; (b) the reference electrode (R), which measures the potential at the W electrode; and (c) the auxiliary or counter electrode (A/C), which supplies the current required for the electrochemical reaction at the W electrode. A single potentiostat amplifier occupies an area of 327 \(\upmu\)m \(\times\) 260 \(\upmu\)m, and has an average power consumption of 51.2 \(\upmu\)W, which is smaller than Paglinawan [108] which has an area of 0.16 mm\({}^{2}\), and a power dissipation of 600 \(\upmu\)W, or Ahmadi and Jullien [109] which has a power dissipation greater than 150 \(\upmu\)W. Its open-loop gain is 60 dB at low frequencies, and 50 dB1 kHz. The current that is generated in the amperometric sensor, which is proportional to the electrochemical reaction that is generated at the working electrode, is measured by a transimpedance amplifier (TIA). Its input resistance of the design is 1 G\(\Omega\)\(\bar{\textit{o}}\)DC, allowing a current detection up to 1 nA. The current-to-voltage conversion is defined as V\({}_{\mathrm{TIA}}\) = -I\({}_{\mathrm{W}}\) R\({}_{\mathrm{TRANS}}\), where I\({}_{\mathrm{W}}\) is the current through the working electrode and R\({}_{\mathrm{TRANS}}\) is the externally selected gain resistance. A second gain stage based on an inverter configuration follows the TIA and adapts the voltage values for the next stage, defining the Sensor Conditioning module. The measured signal is forwarded to the _Data and Modulation Processing_ modules. In this case a simple absence/presence detector is defined in the Chanel Sensor Module. The detection is based on the conception of an event-detector and the True/False detector works as an alarm: when the analyzed concentration level exceeds, under or over, a threshold value or the system detects the alarm condition, then the modulation process is activated to send the information to the external reader using a backscattering method through the inductive link, which can be AC or DC modulation.
## 2 Materials and Methods
### Data
#### 2.2.3. Vegetation Cover Analysis  
The presence of vegetation has an important role in reducing wind erosion [37]. The vegetation cover for the month with the most severe wind erosion (March) was calculated based on remote sensing data. The data for the vegetation cover were obtained using the normalized difference vegetation index (NDVI), calculated as:  
\[NDVI=\frac{(\text{NIR}-\text{R})}{(\text{NIR}+\text{R})},\]  
where R is the red spectral reflectance and NIR is the near infrared spectral reflectance.
# Eccentric Wear Mechanism and Centralizer Layout Design in 3D Curved Wellbores
## 5 Conclusions  
1. To solve the problems of SRS being prone to breakage and deflection in 3D curved wells, the mechanically coupled mathematical model of SRS in 3D curved wells is established. The central difference method is applied to solve the problem. In the solution procedure, the micro SRS element is taken as the force unit, while the influence of the elastic force, inertia force, and friction are included. The well trajectory, rod string structure, and tubing are treated as the external conditions.
2. The motion state and 3D stress-strain distribution of each point on SRS, as well as the location of the centraliser arrangement, are obtained by the proposed new dynamics model.
3. In the DaQing oilfield, the mechanically coupled mathematical model was applied to design the centraliser arrangement for two oil wells, achieving significant improvements in production performance. For the A1# oil well, the rod diameter was reduced from 22 mm to 19 mm, the maximum load decreased by 11.4% (from 27.67 kN to 24.52 kN), and the minimum load decreased by 9.3% (from 15.15 kN to 13.74 kN). The system efficiency increased by 25% (from 16% to 20%), and the pumping efficiency  
Figure 9: Design results of SRS along well depth (A2# well). (**a**) Maximum load distribution along well depth; (**b**) minimum load distribution along well depth; (**c**) lateral force distribution of SRS; (**d**) number of centralisers distributed along well depth.  
remained stable. For the A2# oil well, the pumping efficiency increased by 47.9% (from 39.8% to 58.9%), the system efficiency improved by 4.8% (from 33.4% to 35%), and the submergence depth increased by 860% (from 30.45 m to 292.33 m). This method, proposed in the study, overcomes the limitations of traditional approaches under complex well conditions through precise mathematical modelling and optimised design, significantly enhancing system efficiency, oil extraction efficiency, and production stability.
* The practical oil well application shows that the arrangement of centralisers in the well section where the rod and tube deviation wear can improve the stability of the sucker rod pumping system, reduce the load of the suspension point, and improve the system efficiency.  
Conceptualization, B.G. and Z.C.; methodology, Z.F.; software, H.Y.; validation, B.G. and Z.F.; formal analysis, H.Y.; investigation, resources, Z.F.; data curation, B.G.; writing--original draft preparation, B.G.; writing--review and editing, Z.F. All authors have read and agreed to the published version of the manuscript.  
The project was funded by Science and Technology Plan of the Market Supervision Administration of Wenzhou (No. 2023011).  
Not applicable.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
The authors declare no conflicts of interest.