esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
A review of the existing optoelectronic monitoring devices revealed that the design of optoelectronic detectors of the mine atmosphere does not sufficiently take into account the factor of external optical interference. This includes any extraneous source of thermal emission: a source of artificial lighting or enterprises. As a consequence, the optoelectronic detectors -based safety systems currently installed at mining sites are not able to ensure properly the detection of the ignition source in the presence of optical interference. Thus, it is necessary to determine the working spectral wavelength ranges from methane and coal dust explosions. The article presents the results of experimental research devoted to the methane-air mixture and coal dust explosion spectral analysis by means of the photoelectric method. The ignition of a methane-air mixture of stoichiometric concentration (9.5%) and coal dust of size characterized by the dispersion of 63-94 microns and concentration of 200 g/m\({}^{3}\) was carried out in a 20 L spherical chamber with an initial temperature in the setup of 18-22 \({}^{\circ}\)C at atmospheric pressure. Then, photometry of the explosion light flux was conducted on a photoelectric unit. Operating spectral wavelength ranges from methane and coal dust explosions were determined. For the methane-air mixture, it is advisable to use the spectral regions at the maximum emission of 390 and 900 nm. The spectrum section at the maximum emission of 620 nm was sufficient for dust-air mixture. It enabled us to select the wavelength ranges for automatic explosion suppression systems' launching references. This will exclude false triggering of the explosion suppression system from other radiation sources. The research results will help to improve the decision-making credibility of the device in its direct design. The results will be used in further research to design noise-resistant optical flame detection sensors with a high response rate.  
explosion; coal dust; methane; explosion suppression; spectral characteristics; explosion pressure; radiation intensity; free radicals +
Footnote †: journal: Applied Sciences  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))
## 3 Results and Discussion
### Stability Studies Using a Similarity Index  
The results of stability studies showed that the optimized brtutinib-loaded nanosponge (IBNS4) was stable after 26 weeks, as per ICH. To assess stability, optimized brtutinib-loaded nanosponges were kept for stability studies at accelerated conditions (40 \(\pm\) 2 \({}^{\circ}\)C, 75\({}^{\circ}\)RH). The in vitro release profile, %EE, and %DL of IBNS4 after stability studies (IBNS4-ST) showed no significant difference compared to IBNS4, as shown in Figure 9. The similarity index f2 value for both tests (IBNS4-ST) and reference (IBNS4) calculated was 51.57, within the range 50-100, indicating that the two release profiles are similar [56].  
The f2 value was calculated by taking the logarithmic reciprocal square root transformation of the sum of squared error, and is a measurement of the similarity in the percent (%) drug release between the test and reference curves.  
Figure 8: Cytotoxicity of pure Ibrutinib (ITB) and optimized brtutinib-loaded nanosponges (IBNS4) after 48 h of incubation with MCF-7 cells at different concentrations (0.78–100 \(\upmu\)g/mL) and control.  
Figure 9: Cumulative release profile after storage.
## 2 Results
### DEGs Obtained and qRT-PCR Validation  
In order to further explore the molecular mechanism of the two _A. arguta_ varieties of the AsA metabolism, RNAseq was carried out using the fruit at 84 DAF of Ana and Issai. The total cDNA library was prepared from the fruit of the two cultivars, repeated three times, and sequenced using an Illumina double-sequencing platform, resulting in 55,686,882, 53,457,420, and 56,151,694 raw reads in Issai and 57,088,922, 5,088,658, and 58,250,288 raw reads in Ana. After cleaning and quality checking, 53,933,324, 51,733,194, 54,239,872, 55,014,194, 48,602,224, and 56,524,894 clean reads were generated from the two cultivars' cDNA libraries, respectively (Supplementary Table S2). The percentage of Q20 between Issai and Ana is greater than 96%, the percentage of Q30 is greater than 91%,  
Figure 4: The content of AsA and DHA (**A**), and AsA/DHA ratio (**B**) at 84 DAF. The data are presented as mean \(\pm\) standard error from three biological replicate assays (**\(p<0.01\)). The error bars represent standard error of the means.  
Figure 5: Enzymatic activity of ascorbic acid biosynthesis pathway at 84 DAF. Data are presented as mean \(\pm\) standard error from three biological replicate assays (**\(p<0.01\)). The error bars represent standard error of the means.  
and the error rate of the 6 samples is 0.02%. The GC content between Issai and Ana was consistent, with Issai at 46.8% and Ana at 47% (Supplementary Table S2). This shows that the sequencing data can meet the requirements of subsequent analysis.  
Through the analysis of transcriptome sequencing results, 30 differential genes for ascorbate metabolic pathways were screened, including the key gene for the synthesis pathway of L-galactose metabolism: GME, GGP, GPP, GalDH, and GalLDH; MIOX in the inositol synthesis pathway; and ALDH, UGDH, GLCAK, etc.; which were the key genes for the regeneration pathway of AsA, MDHAR, AO, and APX. Among the 30 differentially expressed genes, 16 genes were down-regulated and 14 genes were up-regulated. We have chosen 14 expressive genes to perform fluorescent quantitative PCR (qPCR) verification. From the results of qPCR, 9 genes were up-regulated and 5 genes were down-regulated (Supplementary Figure S2), which is consistent with the sequencing results of the transcriptome. This shows that the results of the transcriptome of the hardy kiwifruit are accurate and reliable.
# An Anonymous Offline RFID Grouping-Proof Protocol
## 7 Security Analysis and Comparison
### Security Analysis
#### 7.1.5 Resist to Impersonation Attack  
The impersonation attack includes two methods: impersonate tag, and impersonate reader. In the first type, the adversary impersonates the tag, tries to cheat the reader to pass the grouping-proof verification, and further cheats the verifier. In the second type, the adversary impersonates the reader to collect the tag's information, or generates the valid grouping-proof without scanning to the real tag. The attack process is described as follows.  
* Impersonate tag There are two situations where the adversary impersonate a tag: (1) the adversary does not know any secret key, that means it cannot deduce legal \(T_{i}\). In this situation, the grouping-proof generated in presence of attack cannot pass the reader validation Equation (21). This attack can be detected before the proof is sent to the verifier, protecting the system from DoP attack. (2) The adversary gets the group's public key \(Y\). From \(Y\), the adversary can deduce the session key \(k_{i}^{t}\). Then the adversary can generate the grouping-proof that can satisfy Equations (21). However, due to the lack of tag \(Tag_{i}\)'s authentication secret key \(k_{i}\), to forge the legal \(m_{i}\) need solve the ECDLP described in Section II, thus the probability is negligible. So it is nearly impossible to pass the verifier validation. In conclusion, EAGP can resist the tag impersonation attack in both situations.
* Impersonate reader If the adversary impersonate the reader, it needs the group key \(y\) to generate \(s\), which is used by tag to authenticate the reader. Without the correct \(s\), the tag will abort the protocol, and the adversary cannot get any information about \(Tag_{i}\). From the above, it is difficult for the adversary to impersonate tag or reader. The EAGP can resist impersonation attack.
## 4 Evaluation Results
### Small-Scale Prosumer
Figure 9: Energy peak (**left**) and valley (**right**) cluster centers profiles for TC-S2 and TC-S3. For test case TC-S2, we used only energy features. The higher variability of live energy data streams and the size of the training data affect the accuracy of the energy prediction. The average SMAPE was 37.58%. Figure 11 shows the energy predicted values, considering the monitored energy data stream for 24 h.  
To improve the accuracy of the energy prediction on real-time data streams in test case TC-S3, we considered temperature as an additional feature. After we added the temperature sensor to the model training, the average SMAPE was 29.9%. Figure 12 shows the prediction improvement compared to Figure 11. The forecasted energy values follow with better accuracy the actual energy profile. As it can be seen, the use of temperature as a feature brings a significant improvement for the energy prediction values, with the SMAPE decreasing for the best predicted day from 14.9 down to 9.06.
## 5 Conclusions  
In this study, a Rotor67 bladed disk was developed. Combined with the resonant condition, the bladed disk's resonance condition has been detailed while considering both model frequency and nodal diameter number. Based on the calculations and analyses of three-dimensional unsteady rotor-stator interference flow fields, the induced flow excitation of tuned and mistuned stator cases towards bladed disks has been studied. The main conclusions are as follows:  
1. The excitation components of rotor-stator interference flow field on a bladed disk can be described by frequency components and circumferential wave components. The main frequency components can be written as \(f=k_{s}N_{s}\Omega\), whereas the main circumferential wave components can be written as \(n=\mid(k_{s}N_{s}-kN_{r})\mid\), among which \(k_{s}=1,2,3;k=0,1,2\). Components corresponding to \(k_{s}=1\) are the main excitation, both for frequency and circumferential wave components, and the amplitude decreases with the increase in \(k_{s}\).
2. Referring to the Rotor67 bladed disk in this study, \(N_{s}=14\) was chosen as the structural period number for stators; flow excitation here will not cause resonance. Through numerical analysis, it was shown that this method can determine the rationality of the stator-structural period without the need for performing numerical simulations of three-dimensional unsteady flow induced by rotor/stator interactions.
3. Compared with tuned stators, mistuned stators break up the wake energy by changing the topology of the blade. Therefore, the maximum amplitude of the wake excitation component decreases; however, the number of excitation components increases.
4. Two types of mistuned stators were studied: asymmetric mistuned and sectional non-uniform mistuned stators. Asymmetric mistuned stators decrease the excitation amplitude, although the components may induce resonance increases which renders them unsuitable for application. Sectional non-uniform mistuned stators can significantly reduce the amplitude of excitation without introducing too many new components, which shows potential in suppressing vibrations.  
Conceptualization, H.M., Z.Z. and L.L.; methodology, H.M. and L.L.; software, H.M.; validation, H.M., L.L. and Z.Z.; formal analysis, H.M.; investigation, H.M. and Z.Z.; resources, H.M. and L.L.; data curation, Z.Z.; writing--original draft preparation, H.M.; writing--review and editing, H.M. and L.L.; visualization, H.M.; supervision, H.M.; project administration, H.M. and L.L.; funding acquisition, H.M. and L.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank the guidance and kind advice of [PERSON].  
The authors declare no conflict of interest.
## 5 Current Status and Limitations of Data Interoperability with Landsat and Sentinel
### Harmonized Landsat/Sentinel-2 (HLS) Project
The HLS project has encountered several other challenges in generating harmonized products. These include:  
* Sensor misregistration: as noted above, Landsat 8 ground control differs from Sentinel-2. To minimize image-to-image variability when stacked as time series, HLS has implemented an approach whereby each Landsat image is co-registered to a \"master\" Sentinel-2a image using image cross-correlation techniques [30]. * Cloud masking: in the absence of a thermal channel, it has proven difficult to generate reliable cloud masks for Sentinel-2 imagery. Currently, the HLS project uses the output of the LaSRC cloud mask combined with the Boston University F  
Figure 12: Seasonal phenology (greening) for natural grassland (blue line) and irrigated alfalfa fields (red line) near Cheyenne Wyoming observed from Harmonized Landsat/Sentinel-2 data products. The high temporal density of observations allows individual moving events to be detected within alfalfa fields. * Processing baselines: in 2017 USGS moved from the earlier L1T product to the \"Collection 1\" products, and announced plans to make analysis ready data (ARD) the default product by the launch of Landsat 9 in 2020. Similarly, ESA evolved the L1C baseline processing over several iterations during the early phases of the Sentinel-2 mission, including changes to the product filename and file structure. While welcome, these changes have made it difficult to standardize and synchronize the HLS processing. In addition, ESA has not yet implemented a \"collection\" model that includes archive-scale reprocessing. To date, HLS has generated products covering some 9.1 million km\({}^{2}\), or about 7% of the global land area. The project is planning to release a wall-to-wall North America product, with <5 day latency, during calendar year 2018. Additional details on the HLS processing and current status of the product suite can be found on the project web site: [[https://hls.gsfc.nasa.gov](https://hls.gsfc.nasa.gov)]([https://hls.gsfc.nasa.gov](https://hls.gsfc.nasa.gov)).
# Label-Guided Data Augmentation for Chinese Named Entity Recognition
## 6 Results and Discussion
### Ablation Study  
To address **RQ3**, we test the performance effects of removing each of the three components of the LGDA model--data augmentation module, label semantic fusion module, and loss function optimization. Table 7 presents the results of the ablation study. It is evident that removing each module leads to a decline in performance to varying degrees, indicating that all three modules play an indispensable role in enhancing the model's entity recognition capabilities. Specifically, on the MSRA dataset, removing the data augmentation module results in the most severe model degradation, with decreases of 0.49%, 0.93%, and 0.05% in the F1, P, and R metrics, respectively. This suggests that applying data augmentation to named entity recognition in low-resource scenarios is effective and plays the most significant role in improving recognition accuracy. On the OntoNotes 4.0 (Chinese) dataset, the removal of the label semantic fusion module has the greatest impact, with the F1 metric dropping by 1.36%, indicating that label semantic knowledge can enhance the model's ability to distinguish positive samples. The role of loss function optimization is primarily reflected in the enhancement in the P metric; by adaptively tuning the weights, the model can learn from both positive and negative samples sufficiently, achieving improved recognition precision. Without this module, the P metric would decrease by 1.98% and 0.53% on the OntoNotes 4.0 (Chinese) and MSRA datasets, respectively.  
* **Conclusion 3:** In low-resource scenarios, the data augmentation module contributes the most to improving model performance; for scenarios with fewer entity types, the label semantic fusion module of the proposed model plays the most significant role in enhancing recognition effectiveness.
## 3 Results and Discussion
### Comparison with Previous Studies
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Period** & **NPP (Pg C yr\({}^{-1}\))** & **Reference** \\ \hline TEM4 & modern & 3.65 & [66] \\ LUE model & 1992–1993 & 2.65 & [17] \\ CASA & 1997 & 1.95 & [15] \\ CASA & 1982–1999 & 1.80 & [16] \\ CASA & 1982–1999 & 1.44 & [14] \\ CEVSA, GLO-PEN & 1981–1998 & 3.05 & [19] \\ CEVSA & 1981–1998 & 3.09 & [23] \\ LPJ & 1961–2000 & 4.64 & [20] \\ Remote Sensing model & 1989–1993 & 3.12 & [65] \\ BEPS & 2001 & 2.24 & [21] \\ CEVSA,CASA et al. & 1980–2005 & 2.86 & [67] \\ AVIM2 & 1981–2000 & 2.94 & [68] \\ LPJ & 1981–1998 & 3.11 & [69] \\ BEPS & 2000–2010 & 2.74 & [24] \\ CASA & 1980–2010 & 2.54 & [25] \\ Statistical model & 2000–2010 & 3.89 & [13] \\ IBIS & 1960–2016 & 2.46 & [26] \\ CASA & 2015 & 2.93 & [18] \\ LPJ & 1961–2016 & 3.10 & This Study \\ \hline \hline \end{tabular}
\end{table} 
Table 3: National total NPP in China in previous and current studies.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 3 Calibration of GNSS-R Land Reflections
### Level 1 Land Calibration Results  
The specified land calibration algorithms were tested using 12 days of CYGNSS data. Two regions were analyzed at finer resolution to better illustrate the differences in NBRCS and reflectivity over known wetlands and regions of higher likelihood of coherent reflection: the southern U.S. and Gulf of Mexico region and the greater Indian Peninsula. Maps of the mean surface reflectivity, mean NBRCS, and coherence flag are shown in Figure 6. Regions of high coherent reflection correlate well with the regions of high reflectivity and NBRCS and with known regions having a high prevalence of inland water bodies.  
Figure 6: _Cont._
## 3 Thermodynamic Analysis
### Exergy Destruction  
Usually, the calculation of exergy destruction is the main purpose of exergy analysis of a system, because it causes this factor causes wastes of resources in thermal-chemical systems and in exergy analysis, methods for reducing these losses are usually explored and presented. Exergy balances can be used to determine the type and magnitude of energy loss in a component, as well as suggest ways to use fuel sources more efficiently. For a system in uniform state the exergy balance is written as follows [28]:  
\[\dot{Ex}_{i}+\dot{Ex}_{Q}=\dot{Ex}_{e}+\dot{Ex}_{w}+\dot{Ex}_{D} \tag{6}\]  
As \(\dot{Ex}_{e}\) and \(\dot{Ex}_{i}\) respectively the exergy flow rate of the system outlet and input to the system, \(\dot{Ex}_{Q}\) the exergy rate corresponding to heat transfer, \(\dot{Ex}_{w}\) the correspondingenergy rate of work transfer and \(\dot{E}x_{D}\) is equal to the exergy degradation rate. For each of the components of the system we have the following equations [28]:  
\[\dot{E}x_{i}=\dot{m}_{i}ex_{i} \tag{7}\]  
\[\dot{E}x_{Q}=\dot{Q}_{i}\left(1-\frac{T_{0}}{T_{i}}\right) \tag{8}\]  
\[\dot{E}x_{e}=\dot{m}_{e}ex_{e} \tag{9}\]  
\[\dot{E}x_{w}=\dot{W} \tag{10}\]  
\[\dot{E}x_{D}=T_{0}\dot{\hat{S}}_{gen} \tag{11}\]  
\[ex=ex_{ph}+ex_{ch} \tag{12}\]  
where \(T_{0}\) is the dead state temperature and \(\dot{S}_{gen}\) is the entropy generated by the irreversibilities in the system. In addition, \(ex_{ph}\) and \(ex_{ch}\) are physical and chemical exergies, respectively.  
The relations used in the first and second law analysis of the system components in the third configuration are outlined in Table 2. These relations are also used for similar components in the first and second configurations.
## 2 Materials and Methods
### Determination of the Optical Properties  
The optical properties of samples were characterized using a Cary 7000 UV-Vis-NIR Universal Measurement Spectrophotometer (Agilent Inc., Santa Clara, CA, USA). The samples were measured at room temperature in the 350-2500 nm spectral range with an increment of 4 nm. Transmittance and reflectance were measured using an integrating sphere module. The absorptance was calculated assuming the sum of transmittance, reflectance, and absorptance was 100%.
## 3 Experimental Results
### Optimisation of the Yb-Doped Fiber Amplifier Output Power  
Figure 3 presents the measured output-power characteristics of the single-frequency fiber amplifier. The amplifier was tested with different fiber lengths to identify the length that strikes a suitable compromise between the slope efficiency (which improves with increasing length) and the SBS power threshold (which decreases, i.e., degrades, with increasing length).  
Figure 2: The output power of the wavelength-stabilized pump-diode module operating in the CW (average power) and QCW modes (peak power). Inset: Electrical and optical pulse shape of the pump-diode module.  
Figure 3a shows the measured dependence of the output power at 1064 nm on the launched pump power for CW and QCW pumping, each for a different fiber length. For a length of 3 m (a small-signal pump absorption of -21 dB), in both the CW and QCW modes the SBS threshold occurred at a relatively low launched pump power of 38 W. The maximum output power in the CW (average) and QCW (peak power) modes was the same, and both were limited to 25 W by SBS. The slope efficiency was the same (61%). For the 1.5 m length, the output power in the CW mode was limited to 27.6 W by the available pump power, while in the QCW mode, the output peak power was limited by SBS to a value of 42.8 W. Further shortening of the fiber is not a viable option as the slope efficiency would be too low and the increase in output power would be insignificant. The stability of the output power of the fiber amplifier was measured in the CW mode over a period of 1 h. Power fluctuations of less than 0.1% were observed, and no long-term power degradation was noticed.  
Figure 3b shows the measured optical spectrum in the forward direction under QCW pumping of a 1.5-m fiber at the maximum output peak power of 42.8 W. The SNR exceeded 55 dB, and the power in the amplified spontaneous emission signal was found to be negligible.
## 5 Experiments
### Results
#### 5.5.3 Quantitative Evaluation of Framework Variants  
To further investigate the effects of hyper parameters, we have tested the framework under different configurations by changing the number of CNN and LSTM layers. The following variants of the proposed framework were tested: CNN(1)-LSTM(1), CNN(1)-LSTM(2), CNN(1)-LSTM(3), CNN(2)-LSTM(1), CNN(2)-LSTM(2), CNN(3)-LSTM(3), CNN(3)-LSTM(1), CNN(3)-LSTM(2), CNN(3)-LSTM(3), numbers in parenthesis representing the number of layers. Only OAHII1 measure is considered for this experiment. Data are preprocessed using the window size T = 12 and with different sliding step sizes s = 1, 2, 3, 4, 6, 12. The quantitative evaluation results of framework variants are given in Table 4.  
The best OAHIII measure for each framework variant is given in blue. The overall results show that for the given problem domain the network is not very sensitive to the number of layers and increasing the number of layers does not improve the performance significantly. The boxplot of each framework variant is given in Figure 9. According to the mean score of each model variant represented by the blue line in the box plot, the accuracy (OAHIII1 measure) does not deviate much between different models with different network parameters. The average accuracy calculated over all framework variants with different step sizes is 0.836 and more than 10% better than the next best scoring algorithm, which is IsolationForest according to the quantitative evaluation results given in  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**MeasuresModels** & **IF** & **LOF** & **LDBSCAN** & **ST-BDBCAN** & **HDLF** & **HDLF** & **HDLF** \\  & & & & & **(T = 3)** & **(T = 6)** & **(T = 12)** \\ \hline OAHII1 & 0.722 & 0.386 & 0.681 & 0.595 & 0.810 & 0.823 & 0.866 \\ \hline OAHII2 & 0.741 & 0.327 & 0.662 & 0.664 & 0.791 & 0.822 & 0.856 \\ \hline OAHII3 & 0.660 & 0.210 & 0.484 & 0.632 & 0.741 & 0.770 & 0.843 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative evaluation of algorithms.  
Table 3. The core architecture of the network and the way we use the spatial and temporal context prove to be a powerful approach in spatio-temporal anomaly detection problem.