esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Implementation  
### Data Set  
#### 2.1.1 Training Data  
Figure 1 shows a will that contains the four characters used in the experiment. For the data, the four characters were selected based on the complexity according to the number of strokes among frequently used Korean characters. The characters used were '@', '@', '@', and '@', and the complexity decreased in order from the left.  
The experimenters who participated in the handwriting verification were 20 men and women, and each experimenter wrote the characters '@', '@', '@', and '@' 10 times each. Since 20 people wrote it down 10 times, there were 200 images for each character, and the data for 800 characters were collected. In the process of cutting out the character written in the manuscript, the size was normalized to 112 \(\times\) 112 pixels so that the aspect ratio of the original character was not affected.  
Through the data augmentation technique, 10% of the image size was applied for enlargement and reduction, \(\pm 5^{\circ}\) for rotation, and 3% for horizontal and vertical movements. This increased the number of images by nine times for the training and validation set and five times for the test set. Therefore, the number of images in the training, validation, and test sets consisted of 61, 29, and 50 images per person, respectively. Training was conducted using the Siamese network, and due to the nature of this network, data were input in pairs. The data were composed of combinations of pulling two of the images in an augmented set. The number of training, verification, and test sets (ratio) comprised 54,900 (70%), 12,180 (15%), and 12,250 (15%), respectively. The order of the combinations was decided at random, and because the number of combinations to be drawn from the handwriting of others was much larger than the number of combinations from the same person, the number of combinations to be drawn from the handwriting of others was matched to the number of combinations to be drawn with the same person.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 6 Security Analysis and Formal Verification
### Numerical Experiments and Validation
Furthermore, as the number of consensus increases and node positions change in real time, C-GPBFT can dynamically update the clustering based on the node distribution, allowing the consensus latency to remain stable. Figure 13 presents a comparison of throughput between the two algorithms. Since C-GPBFT has significantly lower consensus latency compared to PBFT, its throughput is considerably higher. Moreover, as the clusters are dynamically updated, the throughput exhibits only minor fluctuations across different consensus counts. This indicates that the proposed C-GPBFT algorithm has excellent adaptability to dynamic environments and holds promising practical potential in highly dynamic ATM scenarios.
## 4 CRISPR/Cas Mechanism
### Adaptation via Spacer Acquisition
#### 4.1.1 Cas Proteins Required for Adaptation  
In the _E. coli_ type I-E system, Cas1 and Cas2 are required for spacer incorporation from plasmids and phages [43,55]. These recent results are consistent with the dispensable role of Cas1 and Cas2 in crRNA biogenesis / targeting in type I-E [45], type II-A [59] and type III-A [60] systems. The type I-E data is of broad relevance because Cas1 and Cas2 are conserved across all CRISPR/Cas types [10,24]. It is interesting that an insertion mutation of _csn2_ inhibited spacer acquisition in the _S. thermophilus_ type II-A system, suggesting that, in addition to Cas1 and Cas2, other Cas proteins might play a role [26].  
Exactly how Cas1 and Cas2 function is unclear, but biochemical and structural studies offer some clues [61, 62, 63]. Cas1 is a metal-dependent endonuclease and the _Pseudomonas_ Cas1 cleaves dsDNA, generating \(\sim\)80 bp fragments [61]. The 80 bp dsDNA fragments generated from Cas1 are larger than expected for integration (\(\sim\)32-33 nt), indicating that other factors are involved. Cas1 dimers contain a stirrup-like structure and a positively charged surface that together might be involved in binding dsDNA [9]. Cas2 from _S. solfataricus_ cleaves ssRNA at U-rich regions _in vitro_[64], but ssRNA or ssDNA binding or cleavage was not detected for _Desulfovibrio vulgaris_ Cas2 [65]. Recently, the _Bacillus halodurans_ Cas2 dimer was shown to contain Mg\({}^{2+}\)-dependent endonuclease activity against dsDNA and generated 120 bp products [66]. Despite these studies the exact mechanistic roles of Cas1 and Cas2 in acquisition are unknown.  
There is increasing evidence that some Cas1 and Cas2 proteins may function together directly (e.g., via protein-protein interactions). For example, the type I-A system from _Thermoproteus tenax_ contains a Cas1-Cas2 fusion protein, which interacts with Cas4 and Cas1, in what the authors name Cascis (CRISPR associated complex for the integration of spacers) [67]. It is noteworthy that the type I-F Cas3 is a Cas2-Cas3 hybrid, containing an N-terminal Cas2-like domain fused to Cas3 [10,24].
## 3 Results  
### Characterization of Sodium Alginate  
#### 3.1.1 Analysis of SEC-MALLS  
Sodium alginate from _S. duplicatum_ was characterized by SEC-MALLS to determine molecular weight and polydispersity index. The average molecular weight of alginate _S. duplicatum_ was determined via SEC-MALLS analysis (Figure 1a). Sodium alginate extracted from _S. duplicatum_ contains polysaccharides with weight-average molecular weight (M\({}_{\text{w}}\)) and number-average molecular weight (M\({}_{\text{n}}\)) of \(3.535\times 10^{4}\) g/mol and \(2.015\times 10^{4}\) g/mol, respectively. Molecular weight of commercial alginate products from brown seaweed about 0.32-\(4.0\times 10^{5}\) g/mol [32]. Alginate's dispersity (M\({}_{\text{w}}\)/M\({}_{\text{n}}\)) was 1.76, showing a good homogeneity of the polysaccharide alginate from _S. duplicatum_.
## 1 Introduction
While many studies apply variable selection to obtain the optimal variable combinations, little effort has been devoted to quantifying or visualizing the optimization of model performance after variable selection. Moreover, there is a paucity of studies adopting various modeling approaches to predict multiple soil properties. Haihun River is a tributary of Xiushui River in the Poyang Lake watershed, Yangtze River basin, China. The Haihun River sub-watershed is an important agricultural area in Jiangxi Province, with typical agro-ecosystems and agro-cultural heritage. Maintaining soil health in the sub-watershed is essential for regional agricultural production and ecological protection. In this study, the hypothesis proposed is that the integration of machine learning techniques and meticulous variable selection may significantly enhance the accuracy of soil mapping. We adopted two common models, RF and XGBoost, to predict the SOC, STP, and SAP in the Haihun River sub-watershed. The performance of the models with and without variable selection was compared to demonstrate the optimization of the prediction accuracy for different soil properties. The spatial distributions of the SOC, STP, and SAP in the study area were mapped using the optimal model with the combination of environmental covariates having the highest predictive ability. The results of this study could be useful for assessing soil health and formulating precise agricultural policies at the sub-watershed scale.
## 4 Conclusions  
We analyzed the collocated GPS and seismic data collected during the 2011 Tohoku-Oki (Japan) and the 2010 El Mayor-Cucapah (Mexico) earthquakes using a tightly-coupled integration. The integrated waveform takes the advantages of both sensors and is more precise and accurate than the GPS-only or seismic-only waveforms. The power spectral densities of integrated displacements follow the GPS-only spectrum at the low frequencies and the seismic-only spectrum at the high frequencies.  
The integrated displacements can provide the full spectrum of the seismic motion allowing the detection of P-wave arrivals and the estimation of permanent offsets. Small-scale features including P-waves are visible in the integrated displacement and velocity waveforms. The P-wave arrival can be picked up accurately and used for reliable determination of epicenter and origin time. Permanent offsets can also be extracted with high accuracy and used for reliable fault slip inversion and  
Figure 8: Fault slip inversion. (**a**) Inversion with permanent coseismic displacements obtained from real-time tightly-integrated solution; (**b**) Inversion with post-processed daily solution. From top to bottom are the inverted fault slip distributions, comparisons between the observed and the synthetic displacements on the horizontal components, and on the vertical components, respectively.  
magnitude estimation. These earthquake parameters are critical for earthquake/tsunami monitoring and early warning systems.  
The accelerometer data (200 Hz) was provided by the Southern California Seismic Network (SCSN) operated by the USGS (U.S. Geological Survey) and Caltech. The 5 Hz GPS data used to derive the displacement waveforms were provided by the Plate Boundary Observatory operated by UNAVCO for EarthScope. The K-NET and KiK-Net strong-motion data for the 2011 Tohoku earthquake were provided by the National ResearchInstitute for Earth Science and Disaster Prevention (NIED) of Japan. 1 Hz GEONET data was provided by the Geospatial Information Authority of Japan. Thanks also to the International GNSS Service (IGS) for providing GPS data of globally distributed reference stations. This study was supported by National Natural Science Foundation of China (Grant No. 41074024), National 973 Project China (grant No. 2013 CB733301), and the Surveying and Mapping Foundation Research Fund Program, National Administration of Surveying, Mapping and Geoinformation (12-02-010).  
The authors declare no conflict of interest.
# Planetary-Gearbox Fault Classification by Convolutional Neural Network and Recurrence Plot
## 4 CNN Combined with Angular-Domain RP for Fault Classification of Planetary Gearboxes  
In this study, a combination of a CNN and angular-domain RP for the fault classification of planetary gearboxes is proposed. The corresponding schematic is shown in Figure 1.  
The main steps of the proposed scheme are listed as follows:  
1. Vibration-signal acquisition and processing. Raw vibration of the planetary gearbox is picked up synchronously with the tacho plus trains of the reference shaft. Then, equiangle resampling is performed to convert the data series into those in the angular domain.  
Figure 1: Combination of convolutional neural network (CNN) and recurrence plot (RP) in angular domain.  
2. Construction of training dataset. Vibration data series are first divided into segments. For a planetary gearbox with a fixed annulus gear, the transmission ratio of the planet carrier and sun gear can be expressed by \[i_{cs}=\frac{N_{s}}{N_{s}+N_{r}}\] (7) where \(N_{s}\), \(N_{r}\) are the tooth numbers of the sun and ring gear, respectively. For the minimal common complete integral period of the sun gear and planet carrier, the sun gear needs to rotate \(N_{s}+N_{r}\) circles, and the carrier rotates \(N_{s}\) circles. Therefore, to ensure each segment covering the meshing vibration of all teeth, the corresponding rotating circles of the sun gear should not be less than \(N_{s}+N_{r}\). We assumed that the rotating speed of the sun gear was \(n_{s}\), and the total time of the observed signal was \(T\). To save computation time, the resampled length data in the angular domain can be expressed by: \[L=\frac{n_{s}TN_{resample}}{60}\] (8) where \(N_{resample}\) is resampling points per revolution. Thus, the points of each segment equal to \((N_{s}+N_{r})N_{resample}\). Then, to a condition with three operating speeds, each segment was randomly selected to constitute the training sample.
3. Build CNN model and set parameters. The configuration of a 2D CNN was used. Several key parameters, such as learning rate, the size of convolutional kernels, number of iterations, and nodes of fully connected layers were set and are discussed in the next section.
4. Identification results and evaluation of CNN model.
# Team-Communication Modeling Framework for Multi-Unit Nuclear Power Plant Accident Analysis
## 2 Methods and Data
### Research Methods  
Upon scrutinizing the existing team-communication studies, we realized that there is a need to examine team communication in NPP from multiple perspectives. Thus, we decided to explore NPP team communication from three perspectives. The first one is the team-communication influencing factors of listing and categorization [29]. The second is the team-communication process, consisting of input, output, and communication functions, and the third is the team-communication outcome. To the best of our knowledge, our proposed team-communication analysis framework for NPP covering these three perspectives is the first of its kind. While including the three perspectives in our framework improves its comprehensiveness, we also realized that we need to expand our references into the general system models, especially the ones that consider human workers, their interaction, and work in a systemic approach.  
Various models to describe human workers and their interactions in sociotechnical systems have been proposed, such as the Input-Process-Output framework, Input-Mediator-Outcome input framework [19], the work-system framework [36], the systems-engineering initiative for patient safety model [37], and so on [38; 39]. Among these models, we are particularly interested in the systems-engineering initiative for patient safety (SEIPS) model, which was developed to understand the structure, processes, and outcomes in healthcare. SEIPS was upgraded to SEIPS 2.0 and SEIPS 3.0, and recently, a practice-oriented SEIPS model was also provided [40; 41; 42]. It became our reference model because of its comprehensiveness, steady improvement, and interest until recently. However, our proposed modeling framework refined the SEIPS model (hereafter the reference model) in several major ways, as presented in Section 3.4.
## 5 Experimental Setup and Measurements  
To verify the simulated EM shielding characteristics of the proposed FSS structure, a finite prototype is fabricated and tested. The fabricated FSS panel consists of 25 \(\times\) 33 unit cells with the overall dimensions of 243 mm \(\times\) 188 mm, as shown in Figure 8a. A free-space measurement setup is employed to validate the EM performance of the FSS shield, as illustrated in Figure 8b. The measurements are carried out in a microwave anechoic chamber. The measurement setup consists of a pair of horn antennas placed on both sides of the absorber screen. These antennas are connected to a vector network analyzer (VNA). Prior calibration is carried out without the FSS shield to calibrate the measurement setup. Then, the fabricated FSS panel is placed in between the transmitting and receiving horn antennas ensuring the far-field condition \(d\geq 2D^{2}/\lambda\), where \(D\) is the size of the horn antenna, and \(\lambda\) is the wavelength at the lowest working frequency. Moreover, the SE of the FSS is calculated based on the magnitude of the measured \(S_{21}\) in dB as follows:  
\[SE=-|S_{21}|\ \mathrm{dB}, \tag{11}\]
# Complementary Segmentation of Primary Video Objects with Reversible Flows
## 4 Efficient Temporal Propagation with Neighborhood Reversible Flow  
The per-frame initialization of foregroundness and backgroundness can only provide a location prediction of the primary objects and background distractors at the spatial domain. However, the concept of primary objects is defined from a more global spatiotemporal perspective, not only salient in the intra-frame but also consistent in the inter-frame and throughout the whole video. As mentioned earlier, the primary video object should be spatiotemporally consistent, i.e., the saliency foreground regions should not change dramatically along the time dimension. This implies that there still exists a large gap between the frame-based initialization results and the video-based primary objects. Therefore, we need to further infer the primary objects that consistently pop out in the whole video [15] according to the spatiotemporal correspondence of visual signals. In this process, two key challenges need to be addressed, including:  
1. How to find the most reliable correspondences between various (nearby or far-away) frames;
2. How to infer out the consistent primary objects based on spatiotemporal correspondences and the initialization results?  
To address these two challenges, we propose a neighborhood reversible flow algorithm to find and propagate a neighborhood reversible subset from the inter-frames. Details of our solutions will be discussed in the following part of this section.
## 5 Discussion
Using the PSO algorithm, [PERSON] et al. (2018) assimilated the combined EVI \(\times\) RVI data from the HJ-1A/B + RADARSAT-2 images into the AquaCrop for yield estimation of the winter wheat, and the RMSE of the estimated yield with the measured data was 0.81 t/ha, which was comparable with our result. With an ensemble Kalman filter (EnKF) algorithm, [PERSON] et al. (2018) compared the accuracies of five assimilation schemes, which assimilated the LAI and soil moisture at different wheat growth stages into the CERES-wheat for the yield estimation. The results showed that the assimilation of LAI at the jointing and heading-filling stages into CERES-wheat obtained the higher accuracy, with a RMSE of 548.97 kg/ha, which was better than our estimated result ([PERSON] et al., 2018). Adding the GLDAS/Noah-derived surface incoming solar radiation as an input, MODIS-based LAI was also assimilated using a sequential update algorithm into the Soil Water Atmosphere Plant model, and the results of the yield-estimation were improved by 14-26%, with the absolute errors of <7%, which was better than our result ([PERSON] et al., 2018). [PERSON] et al. (2018) assimilated the LAI from the TM and MODIS data into the WOFOST model for the winter wheat yield estimation, and the error for yield-estimation was <8%, which was better than our result as well. However, the assimilation algorithms were time consuming and needed much more inputs than the ACPM model (e.g., meteorological data and soil data). Furthermore, the ACPM model was easy to operate and the corresponding satellite images were accessible. Thus, the model could be expected to provide reliable information on the winter wheat shortfalls and surplus. The yield estimation was performed at the harvest, whereas the forecasts were performed several weeks before the harvest ([PERSON] et al., 2018). However, the wheat GPP accumulation was vulnerable to environmental stress during the period from the anthesis to the ripening stage. Our estimation was conducted at the beginning of the harvest of the winter wheat in Hengshui City, and could be an indication of the winter wheat shortfalls and surplus, prior to the statistical data that were reported by the government. In addition, if the interannual time series PAR data from 2000 to 2014 could be obtained in the future, the effect of the interannual climate change on the yield could be considered, and our method could be modified for a forecasting yield.
# A Traffic-Based Method to Predict and Map Urban Air Quality
## 3 Results and Discussion
### Models and Prediction Accuracy
#### 3.3.2 PM\({}_{2.5}\) Prediction from Traffic Only  
Four models were built from the four different days of collected data. Figure 7 shows a good consistency between these models. All of them tend to classify a traffic type 1 (fast or completely stopped) as a low source of contamination. On the contrary, a traffic type 2 (significant reduction of the vehicle flow) is always identified as a high source of pollution. The value of the split is slightly different from one day to another except for 3 and 18 June, 2019 (b--broken line in Figure 7), where the threshold is always equal to 1.2. The lower value obtained for 30 May, 2019 (a--solid line in Figure 7) could be explained by the fact that during that day, secondary streets were considered. On 12 June, 2019 (c--dashed line in Figure 7), highly variable concentrations of PM\({}_{2.5}\) were registered because of variable meteorological conditions (e.g., cloudy weather and thus variations in temperature and humidity, Figure A1g, Appendix A). Considering these limitations, our results suggest that model b (b--broken line, see Figure 7) is the most representative of the city of Quito during the morning rush hours (worst air quality conditions). The assessment of each model supports this finding. The best performance is obtained for 3 June, 2019 (71% of accuracy), which outperforms 30 May, 2019 (66%), 12 June, 2019 (64%), and 18 June, 2019 (61%). The relatively lower accuracy of this latter model can be explained by the fact that on 18 June, 2019 weather conditions changed to typical of the dry season (i.e., warm temperatures that cause changes in wind speed, Figure A1h, Appendix A). This tends to increase the PM\({}_{2.5}\) concentrations in the street canyons, due to dust resuspension caused by an increased ventilation, which also might reduce the pollution (noise due to ventilation of the anthropogenic PM and suspension of the natural PM) [39; 40].