esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Data Source and Processing  
We obtained the global land cover maps of 2013 from the MODIS data products (MCD12Q1, [[http://modis-land.gsfc.nasa.gov/landcover.html/](http://modis-land.gsfc.nasa.gov/landcover.html/)]([http://modis-land.gsfc.nasa.gov/landcover.html/](http://modis-land.gsfc.nasa.gov/landcover.html/)), accessed on 24 February 2020) with the International Geosphere-biosphere Programme (IGBP) classification system. The IGBP classification system defines 17 classes of primary land cover types. In this study, classes 1 to 5 were reclassified as forest, classes 6 to 9 were reclassified as shrubland, classes 12 and 13 were reclassified as farmland, classes 15-16 were reclassified as water, and class 17 was reclassified as city (Table 1).  
The 0.05 degree monthly NDVI (normalized difference vegetation index) was offered from the Moderate Resolution Imaging Spectroradiometer (MODIS) data products MOD13C2 ([[http://ladsweb.nascom.nasa.gov/data/search.html](http://ladsweb.nascom.nasa.gov/data/search.html)]([http://ladsweb.nascom.nasa.gov/data/search.html](http://ladsweb.nascom.nasa.gov/data/search.html)), accessed on 4 March 2020). The 0.05 degree monthly NDII (normalized difference impervious index) was calculated by using a red band 1 from MOD13C2 and thermal infrared band 32 from MOD11C3. Both MOD13C2 and MOD11C3 image datasets were converted to Albers equal area conical projection and WGS-84 datum using the ArcGIS V9.3 software (ESRI, San Diego, CA, USA). To reduce the image noise from the atmospheric clouds, particles, shadows, etc., three-point smoothing was used to improve data quality.  
Figure 9: Elevation of the MP (**a**) and grassland types derived from MODIS land cover product (MCD12Q1, IGBP) by the year of 2013 (**b**).  
We obtained the monthly meteorological data from the gridded datasets of Climatic Research Unit (CRU) TS 3.22 ([[http://crudata.uea.ac.uk/cru/data](http://crudata.uea.ac.uk/cru/data)]([http://crudata.uea.ac.uk/cru/data](http://crudata.uea.ac.uk/cru/data)), accessed on 6 January 2019). These gridded datasets cover the global land surface (excluding Antarctica) at a 0.5\({}^{\circ}\) resolution and provide the best estimates for month-by-month variations in climate variables ([PERSON], 2018). No measurement value is missing in the datasets. The scPDSI datasets were provided by the CRU. The scPDSI uses \(-0.99\)-\(0.99\) as normal, and negative values indicate drought. Classification relevant to this research mainly includes extreme moist, heavy moist, moderate moist, slightly moist, slightly normal drought, moderate drought, heavy drought, and extreme drought (Table 2). In order to facilitate spatial statistics, meteorological data are resampled by ArcGIS 10.2 software with a resolution of 0.05 degree.
# Optimizing Foreign Language Learning in Virtual Reality:
## 2 Literature Review
### Previous Works on Technology-Assisted Language Learning  
Incorporating technology into language education is by no means a fresh endeavor. Over the years, technological advancements have persistently reshaped language teaching, introducing fresh tools and methods to enrich the educational journey [31].  
Computer-Assisted Language Learning (CALL): Introduced in the 1960s, CALL signified the initial notable fusion of technology into language education [32]. The primary versions of CALL were largely text-focused drills. However, as technology developed, these transformed to embrace multimedia features, interactive tasks, and virtual simulations [33]. Numerous studies have established that CALL can tailor learning experiences to individual preferences, enabling students to learn at a rhythm that suits them.  
Mobile-Assisted Language Learning (MALL): The proliferation of smartphones and tablets ushered in a new phase of language education [34]. MALL apps like Duolingo and Babbel employ game-like features to capture the users' interest. These platforms offer concise lessons, prompt feedback, and are tailored to cater to contemporary learners.  
Online Language Portals: Platforms like Coursera, Udemy, and iTalki have democratized access to language resources, featuring courses from global institutions and native speakers [35]. Such websites amalgamate video sessions, interactive tests, peer interactions, and on-the-spot feedback to craft a holistic learning ambiance.  
Virtual Worlds and MMORPGs: Virtual realms like Second Life and specific MMORPGs geared toward language learning offer learners' virtual spaces to apply language skills in lifelike situations [36]. They enable genuine interactions, allowing learners to hone their speaking, listening, and cultural comprehension.  
Augmented Reality (AR) and Language Learning: AR supplements the natural environment with computer-generated enhancements [37]. Tools like Google Translate's real-time camera translation illustrates the potential of AR in language learning. AR integrates language learning into daily routines by converting routine situations into learning moments.  
Virtual Reality (VR) in Language Education: The shift towards VR in language learning is the next logical step in this technological evolution [31]. VR's unparalleled immersive quality allows learners to engage with languages within contextually appropriate virtual scenarios. Preliminary studies into VR-supported language education are encouraging, indicating heightened learner enthusiasm, involvement, and recall [33].  
However, while the potential of technology in education is vast, it is crucial to perceive it as an enabler rather than an ultimate solution [31]. The success of any tech-driven language learning system is contingent on its judicious blend into a broader educational strategy. As VR starts to take a more central role in language teaching, comprehending its strengths and challenges, as seen through past technological evolutions, becomes crucial.
## 4 PFAC Calibration Results
### Sentinel-3B Cal/Val
#### 4.2.2 Sentinel-3B Cal/Val Results Over Sea Surface  
The CRS1 and RDK1 sea-surface Cal/Val sites provide ocean truth measurements for the calibration of the ascending Sentinel-3B Pass No. 14 and Pass No. 71, respectively. Figure 21 presents the calibration results of Sentinel-3B over sea surface as derived by these two Cal/Val sites.  
Figure 21: _Cont._  
Figure 20: The latest calibration results of Sentinel-3B using its descending Pass No. 335 at the CDN1 transponder Cal/Val site.  
The bias of Sentinel-3B for the sea-surface height is +4.00 mm (\(\pm\)7.50 mm, \(\pm\)32 mm FRM uncertainty) based on both ascending Pass No. 14 (B(S3B) = +4.55 mm, CRS1 Cal/Val) and Pass No. 71 (B(S3B) = +4.85 mm, RDK1 Cal/Val). As expected, the sign of the sea-surface height bias is opposite to the transponder range bias, while it appears that their magnitude agrees at the millimeter level.
# Outdoor Visible Light Communication in Intelligent Transportation Systems: Impact of Snow and Rain
## 2 System Model
### Atmospheric Turbulence Analysis
#### 2.4.1 Very Clear Weather Attenuation  
Attenuation under clear-air conditions is mainly the attenuation due to the absorption by gaseous molecules. Atmospheric absorption at specific optical wavelengths results from the interaction between photons and atoms or molecules of N\({}_{2}\), O\({}_{2}\), H\({}_{2}\), H\({}_{2}\)O, CO\({}_{2}\), etc., which leads to the absorption of the incident photon.  
In this paper, the operating wavelength is 505 nm, of the green LED, and the extinction coefficient \(\gamma(\lambda)\) is \(\gamma_{\text{very-clear}}(\lambda)\cong\) 0.911 dB/km [20].
# Error Detection for Arabic Text Using Neural Sequence Labeling
## 5 Results
### Increasing Corpus Size  
Since we were dealing with quite a small training corpus, it seemed that the size of the corpus would be a limiting factor. Therefore, we decided to manually augment our corpus (explained in Section 3.2) and use that to see the effects of increasing data. For training, we used the best configuration of each model (as in Table 6) in order to see the impact on performance when more data are used.  
Table 7 presents the results of this experiment. In comparison to Table 5, all three models actually performed significantly better when trained on more data, even though the augmented corpus was only 25.5% larger than the original. Additionally, with more data, LSTM outperformed SimpleRNN over all measures. This indicates that LSTM would handle larger data better. While BiLSTM achieved better recall when trained on the augmented corpus, it had the least recall in comparison to the other two, meaning it had given more false negatives. Moreover, the three neural networks outperformed the baseline in recall and F0.5.
## 8 Future Opportunities and Research Directions  
The future of RFEH promises to bring about transformative changes across diverse industries, from wireless communication and smart cities to biomedical applications and space technology. As emerging demands for autonomous, maintenance-free devices continue to grow, significant research opportunities are unfolding to advance RFEH technologies. Key areas for future development include innovations in materials, reconfigurable systems, integration with next-generation networks, AI-driven energy management, and strategies for enabling widespread deployment and commercialization.
## 3 Results and Discussion
There is a large difference in the lattice mismatch and thermal expansion between GaN and Si when compared to the GaN/sapphire template and sapphire. The calculated values of stress for GaN grown on different substrates are shown in Figure 1:0 The Raman spectra of the MGS sample is displayed for comparison, as shown in Figure 1:1 The GaN E\({}_{2}\) peak of MGS was evaluated at 570.2 cm\({}^{-1}\) with a compressive stress value of \(-\)0.548 GPa, which is larger than the compressive stress value of \(-\)0.468 for sample B. It can be concluded that the PLD growth method is beneficial for the release of stress in the films. Cross-sectional TEM images were used to investigate the GaN-on-Si meltback-etching reaction with PLD operating at a high temperature of 1000 \({}^{\circ}\)C. Previously, it was reported that the meltback-etching process caused by alloying reaction Ga with Si leads to a rough GaN surface and deep hollows in the Si substrate [39; 40]. Figure 8a,b shows the TEM images of the GaN films grown on Si(111) and Si(100), respectively. From Figure 8a,b, it can clearly be observed that no significant Ga-Si meltback occurred at the GaN/Si surface; this is likely because of the suppressed interaction between the GaN epitaxy films and the Si substrates developed through PLD. Figure 1:2 Residual stress and its corresponding E\({}_{2}\) Raman shift for samples MGS, A, B, C, and D.
## 3 Results and Discussion
The highest extraction efficiency obtained using 50% acetone and ethanol can be associated with the reduced activity of the polyphenol oxidase responsible for the oxidation of phenolic compounds [54]. Impact of Process Variables on the Extraction of L-Asorbic Acid (AA) from Rose Fruits (Rosa rugosa) Pomace  
Following the extraction procedure, the extract was separated in an Onyx Monolithic C18 column with a UV detector for the analysis of AA. Figure 1 shows an example chromatogram of the aqueous extract of rose fruits pomace. The quantification of AA was performed by external calibration. The equation obtained from the calibration curve of AA in the range of 0.5-40 mg/100 mL was y = 443,770x - 87,769 (r = 0.9999). LOD and LOQ were 0.14 mg/100 mL and 0.45 mg/100 mL, respectively. Recovery was tested at two fortification levels. The mean recovery percentages were in the range of 94.82%, 92.98%, 93.45%, 91.54%, 93.23%, 92.23%, and 91.77% for water, methanol, ethanol, acetone, 50% methanol, 50% ethanol, and 50% acetone extract from rose fruits pomace, respectively. The precision from six consecutive injections was satisfactory, with an RSD of 1.8%. All recovery and precision values were in the acceptable range. The validation of our HPLC method for the determination of L-ascorbic acid in various extracts prepared from rose fruits pomace is consistent with the validation of the HPLC method for the determination of vitamin C in fruits reported by [PERSON] et al. [38]. The type of solvent (water, ethanol, methanol, acetone, and their water mixtures) used, different temperatures (25 \({}^{\circ}\)C, 45 \({}^{\circ}\)C, and 65 \({}^{\circ}\)C), and extraction times (15, 30, 45, and 60 min) had a significant impact on the extraction efficiency of AA from rose fruits pomace (Tables 4 and 5). In the case of AA, temperature and solvent turned out to be insignificant among the interactive interactions (Table 6). Figure 1: Example chromatogram of the aqueous extract of rose fruits pomace. Asterisks indicate the peak of ascorbic acid. This extraction efficiency, depending on the solvent used, was influenced by the structure of vitamin C, which is a polar molecule, and its solubility increased with the polarity of the solvent [28]. In general, Vitamin C is a natural antioxidant that primarily exists in and is available in its reduced form as L-ascorbic acid and its oxidized form as L-dehydroascorbic acid [55]. Our research showed that the highest content of AA was in the water extract, followed by methanol, 50% acetone, 50% methanol, 50% ethanol, and acetone. These results are consistent with those reported by [PERSON] and [PERSON] [28], who found that the solubility of
# A Generative Adversarial Network-Based Fault Detection Approach for Photovoltaic Panel
## 4 Experiment
### Model Validation  
After the model training is completed, the model needs to be evaluated to ensure that efficient and accurate anomaly detection tasks are completed at minimum cost. Therefore, the model is evaluated from the following perspectives:  
1. We evaluate the likelihood of the original image and its reconstructed image of the generator. As shown in Figure 11, in the training process, as the number of iterations increases, the difference between the original image and the reconstructed image gradually shrinks. 2. We evaluate the data distribution comparison between the original image and its reconstructed image for the positive sample and negative sample, respectively. Since  
Figure 11: Original images and reconstructed images during training. (**a**) 10 iterations results; (**b**) 100 iterations results; (**c**) 200 iterations results.  
Figure 10: ROC curves of different CBAM embedding methods.  
only positive sample dataset \(X\) is fed into the generator \(G_{E-D}\) during the training process, \(G_{E-D}\) only learns by the data distribution \(P_{x}\) of the normal PV panel images while the data distribution \(P_{y}\) of the abnormal PV panel images is unknown. When a normal PV panel image \(x\) is put into the generator, the reconstructed image \(\widetilde{x}\) generated by the generator is equal to the input image \(x\) because it obeys the data distribution learned by the generator, then its data distribution \(P_{\widetilde{x}}\) is infinitely close to \(P_{x}\) as shown in Figure 12a. When an abnormal PV panel image \(y\) is put into the generator, \(G_{E-D}\) still encodes it into \(z_{y}\) and then decodes it into \(\widetilde{y}\) in a manner that obeys the data distribution \(P_{x}\). Due to the difference between \(P_{x}\) and \(P_{y}\), \(y\) and \(\widetilde{y}\) are different, and its data distribution \(P_{\widetilde{y}}\) is different from \(P_{y}\) as shown in Figure 12b.  
3. We evaluate the error between the latent space vectors of the original image and its reconstructed image. As shown in Figure 13a, the \(M(z,\widetilde{z})\) of the positive samples (in blue color) is distributed around 10, but the \(M(z,\widetilde{z})\) of the negative samples (in red color) is mainly distributed between 30 and 70. It can also be seen in Figure 13b. So, the \(M(z,\widetilde{z})\) of the positive samples and negative samples has a clear decision boundary.
## 5 Simulation of Coded Laser Pilots-Based HILPB WPT System
### Simulation Results
#### 5.2.3 Pairing Algorithm Based on the Best PTE Power  
Table 9 and Figure 16 are simulation results of the third pairing algorithm in which the PRX chose the PTX with the best PTE first. When the power provided by the first chosen PTX is insufficient, the PTX having the next best PTE is sequentially selected. The PTX selection process continues until all the necessary power is provided. In the first round, the PRX R1 selects PTX T2 as the transmitter, which has the best transmission rate among the PTXs. It then receives the power of 231.75 kW through the WPC (154, 13) that is more than the required power of 200 kW. In the second round, the PRX R2 selected PTX T3 and T4, which have the subsequent best transmission rates, among the remaining PTXs. PRX R2 received 318.1 kW power from PTX T3 and T4 simultaneously, which is greater than the required power of 300 kW. The PTX T3 used WPC (150, 326), and the PTX T4 used WPC (162, 279). In the third round, the PRX R3 received power from the unallocated PTX T1. It was able to receive 297.9 kW power, which is higher than the required power of 150 kW. With the algorithm that selects the PTX with the best PTE, the PTXs with the highest PTE are allocated first regardless of the power receiver's power requirement. As a result, the PRX R1 received 116 % of the required power, the PRX R2 received 106 %, and the PRX R3 received 197 % of the required power.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline  & & \multicolumn{3}{c}{**PRX-PTX Pairing Sequences**} \\  & & **First Round** & **Second Round** & **Third Round** \\ \hline  & R1 & 315.3 kW, 200 kW & 315.3 kW, 200 kW & 315.3 kW, 200 kW \\ PRX & R2 & - & 492.8 kW, 300 kW & 492.8 kW, 300 kW \\  & R3 & - & - & 0 kW, 150 kW \\ \hline  & T1 & - & (153, 103), 300 kW & (153, 103), 300 kW \\ PTX & T2 & - & (149, 56), 250 kW & (149, 56), 250 kW \\  & T3 & (161, 283), 200 kW & (161, 283), 200 kW & (161, 283), 200 kW \\  & T4 & (173, 153), 150 kW & (173, 153), 150 kW & (173, 153), 150 kW \\ \hline \hline \end{tabular}
\end{table}
Table 8: Results of WPC using the least power-based PTX selection algorithm.  
Figure 15: Results of power transmission path establishment.  
The PTE varies according to the distance and geometric position between the PTX and the PRX. According to their characteristics and arrangement, the pairing between the PTX and the PRX is different in the three algorithms. Among the three algorithms, only the third algorithm that selects the PTX with the best PTE first successfully provides the power required by all PRX. Since HILPB is heavily influenced by PTX characteristics (MTP, MTE), PRX characteristics (MPQ), and the geometric placement relationship between PTX and PRX, it is possible to use various allocation algorithms to create the PRX-PTX pairs appropriately. Besides, the battery's priority and charging time in the EV vary depending on the amount of remaining power, so it is more effective to allocate all the power needed to the priority battery by stopping all other receivers.
## Materials and Methods
### Satellite Datasets
#### 2.2.3 Satellite Image Calibration  
The objective of SAR calibration is to provide imagery in which the pixel values can be directly related to the radar backscatter of the scene. The PALSAR-2 image that was used in this study was built on a 16-bit data type and all pixels have digital numbers (DN) ranging from 0 to 65,535. These DNs however do not represent the radar signal of features or objects on the ground. Therefore, the DNs have to be converted to backscatter (i.e., the returned radar signals) known as Normalized Radar Cross Section (NRCS) and represented as \(\sigma^{0}\) in decibels (dB). The equation that was used for the calculation of NRCS for PALSAR are slightly different from other sensors in that the usual sine term has already been included in the DN values. Thus, for the products stored at Level 1.5 and above, the equation for NRSC of any of the polarization component can be obtained by the following formula with single calibration factor (CF), which can be expressed as follows [29].  
\[\sigma^{0}_{dB}=10\cdot log_{10}\Big{(}DN^{2}\Big{)}-83 \tag{1}\]  
The Sentinel-1A product uses radiometric calibration look-up table (LUT) to do the calibration. This was performed on Sentinel Application Platform (SNAP) tool, a software that was designed specifically for Sentinel-1 products processing and it available for free at [[http://step.esa.int/main/download/](http://step.esa.int/main/download/)]([http://step.esa.int/main/download/](http://step.esa.int/main/download/)). The essential conversion of amplitude to \(DN\) and from \(DN\) to sigma nought were done automatically on SNAP and once the sigma nought values was obtained, the computation of backscatter (\(\sigma^{0}_{dB}\)) can be performed as  
\[\sigma^{0}_{dB}=10\cdot log_{10}\sigma^{0} \tag{2}\]
## Result
### Short-Term Prediction of GPP by ARIMA Model  
Time series analysis provides an effective tool for GPP modeling. In this study, the ARIMA model was used to simulate and predict GPP based on the monthly dataset during 1982-2015. Figure 7 shows the comparison between the annual average of reference GPP and the ARIMA-predicted GPP in 2016. The spatial pattern of the predicted value is basically consistent with that of the reference values, especially in the northern regions such as the SRB, the HRB and the YERB. Areas with a large degree of uncertainty were mainly located in the YRB and the SEB. Overall, the model has a strong capacity for predicting short-term GPP time series.  
Figure 8 exhibits the accuracy of the prediction results. In general, the ARIMA model achieved good results in most parts of China. The national averaged values of RMSE, MAE, rRMSE, and rMAE were 0.571 g C/m\({}^{2}\)/d, 0.401 g C/m\({}^{2}\)/d, 24.8%, and 16.5%, respectively. Regionally, the ARIMA model showed relatively stable results for the northern basins such as the SRB, the HRB, the CB and the YERB (the averaged values of RMSE and MAE in these four basins were 0.383 g C/m\({}^{2}\)/d and 0.248 g C/m\({}^{2}\)/d, respectively), whereas its performance for southern basins with high GPP values such as the YRB and the PRB tended to be more volatile (the average values of RMSE and MAE in these two basins were 0.822 g C/m\({}^{2}\)/d and 0.606 g C/m\({}^{2}\)/d, respectively). This is probably related to the high correlation between low accuracies and high GPP values.  
Figure 7: The annual mean of the (**a**) reference GPP value and (**b**) predicted GPP value from the ARIMA model in 2016. The nan value is displayed in white.  
Earlier studies have demonstrated that the availability of data greatly affects the prediction ability of the ARIMA model [72]. The uncertainty of the ARIMA model regarding the data value range and data availability is shown in Figure 9. It is observed that pixels with poor availability are mainly distributed in the CB and the SWB, and the GPP values of these two regions are relatively low. The results indeed show that the model capability is closely related to data availability. The more available the data are, the better the forecast result will be, and vice versa. Moreover, the ability of the ARIMA model is noticeably affected by GPP values (Figure 9a,b). The curves of Figure 9c,d both show an upward trend in the availability of 100%. This is because the pixels with availability below 100% have lower GPP values than those with almost 100% data coverage, leading to their comparatively high accuracy. In other words, despite some places with poor data quality, the precision of GPP prediction is relatively high because of the low GPP values. All in all, the precision of the model is affected by the value and quality of the data together [73].  
Figure 8: Spatial patterns of RMSE, MAE, rRMSE, and rMAE computed by the 12-month reference GPP values and predicted values using the ARIMA model in 2016. The nan value is displayed in white.