esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Data Analysis and Results
### Analysis of Model Fit Tests  
#### 5.3.1 Structural Validity  
The exploratory factor analysis results were used to construct a factor model for investigating factors influencing the propensity to use the cultural heritage education meta-universe system. The model's fit indices all satisfied the evaluation criteria after a validated factor analysis using AMOS 26.0, and the outcomes are displayed in Table 5.
## 3 Results  
### Comparison of the Three Models  
Three sets of models were trained by adjusting the parameters, from which a set of models with the best performance was selected. Figure 4 shows the Loss and MAE of the model validation set. During the training process, the CNN-LSTM hybrid model performed better than the CNN model and the ConvLSTM model after the epoch reached 250 times. The training effect of the CNN model is best when the epoch is less than 150 times, and the training effect of the CNN-LSTM model and ConvLSTM model is not much different. When the epoch is in the interval of 150 to 250 times, the CNN model is comparable to the CNN-LSTM model training effect.  
Table 1 shows the rice yield prediction performance of different models (CNN, ConvLSTM and CNN-LSTM). RMSE, MAE and correlation coefficient R were used as evaluation indicators between predicted and observed yields. The result shows that the best performing model architecture among the three models is the CNN-LSTM hybrid model. Compared with other trained models, the CNN-LSTM hybrid model has obvious advantages in rice yield prediction. Compared with the CNN model and the ConvLSTM model, the RMSE of the CNN-LSTM model was reduced by 20.4% and 46.5%, the MAE was reduced by 19.9% and 54.5%, and the correlation coefficient R was improved by 9.88% and 29.5%, respectively. The worst-performing model among the three models is the ConvLSTM model. Its performance is reflected in the evaluation metrics, which is clearly inferior to the other two models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Test RMSE** & **Test MAE** & **Test R** \\  & **(t)** & **(t)** & **-** \\ \hline CNN & 112,877 & 65,943 & 0.850 \\ ConvLSTM & 168,056 & 115,973 & 0.721 \\
**CNN-LSTM** & **89,878** & **52,802** & **0.934** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model performance of rice yield prediction measured by root mean square error RMSE, mean absolute error MAE and correlation coefficient R measures.  
Figure 4: Loss and MAE of the three model validation sets. (**a**) is the validation set Loss, (**b**) is the validation set MAE.  
In response to the study's objective (1), we conclude that the CNN-LSTM hybrid model is a more satisfactory model for rice yield prediction compared with the CNN and ConvLSTM models. The CNN-LSTM hybrid model exhibits better prediction performance in all three evaluation metrics.  
Figure 5 shows a comparison of the yield distribution map between the statistical data of rice production in Hubei Province and the predicted production at the county level.
## 3 Results and Discussions
### Optical Characterization
#### 3.2.1 Reflectance, Transmittance and Absorbance  
The transmittance and absorbance of the Un CG and CaF\({}_{2}\)-CG were measured using an ultraviolet-visible spectrophotometer (SCINCO S-3100). The reflectance can be calculated using the relation:  
\[\mathrm{R}(\lambda)=100-\mathrm{T}(\lambda)-\mathrm{A}(\lambda) \tag{1}\]  
where T is the transmittance, and A is the absorbance at \(\lambda\) wavelength.  
The spectral behaviors of transmittance and reflectance of the Un CG and CaF\({}_{2}\)-CG, obtained through simulations and experiments, are compared in Figure 2. The Un CG shows an average transmittance and reflectance of \(\sim\)91% and 8.1%, respectively, across the wide spectrum from \(\sim\)350 to 1100 nm (Figure 2a), whereas CaF\({}_{2}\)-CG shows improved an transmittance and reflectance of \(\sim\)95.7% and 4.3%, respectively, in the broad spectrum of \(\sim\) 350-1100 nm as shown in Figure 2b. The observed results are in good agreement with the results reported by [PERSON] et al. [29] and exhibit an improvement in the result, as discussed by [PERSON] et al. in their study on vacuum-evaporated CaF\({}_{2}\)[24]. The observed absorbance is the minimum (\(\sim\)0.015%) at 600 nm, which is quite low as expected.
## 4 Discussion
### DEMs Evaluation
Gesch 2018 [44] included global DEMs SRTM, ASTER GDEM, ALOS AW3D30, TanDEM-X (not the publicly available version with 12 m mesh size), NASADEM, and MERIT for assessments of Sea Level Rise (SLR) and coastal flooding exposure in 17 11\({}^{\circ}\)\(\times\) 11\({}^{\circ}\) test tiles. The reported accuracy was the best for TanDEM-X, followed by NASADEM and ALOS AW3D30: the corresponding mean/RMS/mean absolute errors were 0.43/1.69/1.07 m, 1.40/3.10/1.40 m, and 1.72/3.12/1.72 m, respectively. The MERIT DEM, a 3\({}^{\prime\prime}\) DTM, overall showed good performance, especially in terms of mean/median error (e.g., \(-0.7/0.0\) m with LVIS 2019 and \(-0.9/-1.4\) m with the levelling dataset). The shapes of the difference distributions for CRDTM2020 and the MERIT DEM (Figure 15a,b), which are nearly symmetrical and centred near zero, demonstrate the success in the elimination of VB for both DTMs. However, due to a larger mesh size and the inferiority of the canopy height model available at the time of its computation, CRDTM2020 outperforms it. The issue with the height reference for the BEST DEM unfortunately resulted in reduced performance over non-vegetated areas. CRDTM2020 performs better than other DEMs with respect to the majority of statistical metrics. Additionally, from the histograms of the differences between CRDTM2020 and the validation datasets (Figure 15), it can be observed that the differences follow a normal distribution with a high central peak around zero, demonstrating the success in the elimination of VB. In this way, for Costa Rica, the local bare-earth CRDTM2020 is superior to any publicly available global DEM model.
# Chinese Named Entity Recognition Based on Boundary Enhancement with Multi-Class Information
## 4 Experiment
### Result  
After training the BEMCI model and the baseline models on the training sets of the six datasets, they were tested using the respective test sets to evaluate their performance and analyze the results. The recognition results for the RESUME and MSRA datasets are shown in Table 4. The results for the WEIBO and AISHELL datasets are shown in Table 5. The results for the CLUENER and BOSON datasets are shown in Table 6. The bold part represents the optimal experimental results.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**RESUME (\%)**} & \multicolumn{3}{c}{**MSRA (\%)**} \\ \cline{2-7}  & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Char-Based & 92.71 & 92.44 & 92.57 & 90.75 & 86.82 & 88.74 \\ Word-Based & 92.82 & 92.60 & 92.70 & 92.62 & 86.91 & 89.67 \\ BiLSTM+CRF & 92.58 & 94.41 & 93.49 & 95.91 & 86.65 & 91.04 \\ IDCNN+CRF & 91.63 & 91.86 & 91.74 & 94.93 & 87.19 & 90.89 \\ Lattice LSTM+CRF & 94.81 & 94.11 & 94.46 & 93.57 & 92.79 & 93.18 \\ SoftLexicon & 94.65 & 94.72 & 94.69 & 94.61 & 92.59 & 93.58 \\ MECT & 94.63 & 95.26 & 94.94 & 94.29 & 93.79 & 94.04 \\ KVMN & 95.46 & 95.52 & 95.49 & 95.30 & 94.92 & **95.11** \\ BEMCI & 95.45 & 95.76 & **95.60** & 94.87 & 95.19 & 95.03 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The result of RESUME and MSRA.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameters** & **Values** \\ \hline Batch Size & 16 \\ Epochs & 100 \\ Optimizer & Adam \\ Radical CNN Kernels Size & 3 \\ Pinyin CNN Kernels Size & 2 \\ Head Num & 12 \\ d\({}_{\text{head}}\) & 16 \\ Encoder Layer & 2 \\ Learning Rate & 0.0001 \\ Radical Dropout & [0.1–0.4] \\ Dropout Probability & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Model hyperparameter settings.  
In Table 4, according to the experimental results on the RESUME dataset, BEMCI improves the F1-score by 0.11%, compared with the optimal baseline KVMN. The experimental results on the MSRA dataset show that the F1-score of BEMCI is slightly less than that of the optimal baseline KVMN.  
In Table 5, according to the experimental results on the WEIBO NE, NM, and ALL dataset, BEMCI improves the F1-score by 0.03%, 5.24%, and 2.81%, respectively, compared with MECT. However, it is not better than KVMN. Although the features of the WEIBO dataset lead to a poor recognition effect, the F1-score of BEMCI on the WEIBO dataset is also significantly improved. The experimental results on the AISHELL dataset show that BEMCI improves the F1-score by 0.51% compared with KVMN.  
In Table 6, according to the experimental results on the CLUSTER dataset, BEMCI improves the F1-score by 1.11%, compared with KVMN. The experimental results on the BOSON dataset show that BEMCI improves the F1-score by 2.14%, compared with KVMN.
# Wavelet Decomposition Prediction for Digital Predistortion of Wideband Power Amplifiers
## 2 System Model and Problem Description  
### System Model  
WDP initially captures the input and output signals of the PA and employs a neural network to accurately model its nonlinear behavior. In this stage, the model's output remains nonlinear. Once the PA nonlinear model is trained, its parameters are frozen, and it is cascaded with the DPD model. By training this cascaded system to linearize its output, the DPD model is indirectly optimized.
## 2 Materials and Methods
### Comparison of Extraction Solvents and Partitioning Conditions  
A water saturated sample (5 g) was spiked with 50 \(\upmu\)L of a 10 mg/L standard solution, resulting in a pesticide concentration of 0.1 mg/kg within the sample. Subsequently, three different solvents were used for extraction: MeCN, MeCN/EA (1:1, \(\
u/\
u\)), and EA, with 10 mL of each solvent added, respectively. The extracted solution was then partitioned using either QuECheRS Original salts (4 g of MgSO\({}_{4}\) and 1 g of NaCl) or EN 15662 sults (4 g of MgSO\({}_{4}\), 1 g of NaCl, 1 g of Na\({}_{3}\)Citr-2H\({}_{2}\)O, and 0.5 of Na\({}_{2}\)HCitr-1.5H\({}_{2}\)O). Without further purification, 300 \(\upmu\)L of the organic solvent phase was taken and mixed with 300 \(\upmu\)L of MeCN for matrix-matching. The final solution (1 \(\upmu\)L) was injected into the mass spectrometer, and the signal (area) of the chromatographic peak was compared to the signal intensity of a matrix-matched standard with the corresponding concentration to calculate the recovery rate (\(n=3\)). Statistical analysis of the recovery rates was conducted using the R software (version 4.3.1) to determine significance among the tested groups. For the extraction/partitioning combinations that exhibited the best recovery rates, extraction efficiencies were further assessed using solvents supplemented with 0.1% FA.
## 4 Response Reconstruction for an Actual Continuous Bridge
### Data Analysis
#### 4.3.2 Case Configuration  
To validate the proposed method, monitoring data from two non-consecutive days, one in August and the other in October, were utilized for analysis. The temperature profiles for these days were distinct and did not overlap. For each day, a 30 min monitoring period was chosen for examination. As previously mentioned, the traffic conditions were not specified, and time-variant factors such as traffic and temperature were not set as input  
Figure 11: Scatter plots of high-frequency strain of strain gauges between three different cross-sections.  
Figure 10: Scatter plots of high-frequency strain at the same cross-sections 02 and 03, respectively.  
information for the training of the BP-ANN. The validation was conducted through two designed cases, as detailed below.  
Case-1: Validation of the proposed method was conducted using monitoring data from August. A BP-ANN was constructed using the monitoring record. The data were partitioned, with 20% allocated for network training and the remaining 80% dedicated to verifying the model's predictive accuracy. To reconstruct the strain readings at location 04-S01, input data from strain gauges 02-S01 and 03-S01 were used.  
Case-2: In the second case, a new series of data was generated by merging the monitoring data from both August and October. Following this, a new BP-ANN was trained. A total of 33% of the monitoring data were employed for training purposes. Specifically, the 20 min monitoring data from August were used to develop the neural network, and the remaining 40 min of data, combined monitoring data from both August and October, were then used to evaluate the accuracy of the developed network.  
As previously stated in Section 2.1, once a transfer matrix has been successfully simulated using a BP-ANN, it becomes suitable for long-term monitoring applications, even if it was initially derived from short-term monitoring data. Accordingly, in Cases 1 and 2, the duration of the test data was designed to exceed the length of the data employed in the construction of the BP-ANN. In Case 2, a transfer matrix was established using less than 1% of the total data, proving its applicability throughout the entire monitoring period, spanning from August to October. Moreover, the exact traffic conditions and temperatures remained unknown and were unlikely to be identical in both the training and test datasets. This setup aimed to confirm that the BP-ANN's performance remained consistent and was not affected by the variability in traffic conditions and temperature fluctuations.
## 1 Introduction
Various nonlinear methods are used to analyze RR interval dynamics, such as power-law analysis, geometrical analysis, and phase space reconstruction (PSR). Since HRV depends on too many parameters, analyzing it accurately is very complicated; however, phase space reconstruction has a great advantage compared to other methods, as it can be performed when a parameter is recorded for a sufficient time interval. When HRV is reconstructed in the phase space, various nonlinear indices, such as the largest Lyapunov exponent, correlation dimension, and approximate entropy, can be calculated from the generated attractor. These nonlinear parameters can provide information about variations in the system's complexity, which cannot be obtained using linear methods. A few studies have used PSR for analysis in different states [21; 22]. While there are limited studies that have used linear analysis methods to investigate the effects of energy drinks on the cardiovascular system [23; 24], those using nonlinear analysis methods are even more scarce. In the literature, [PERSON] and [PERSON] [25] reported that energy drinks increased the complexity of the cardiovascular system, examining the electrocardiogram signals recorded from young adults using phase space reconstruction. Additionally, [PERSON] et al. [26] and [PERSON] et al. [27] investigated the nonlinear dynamics of HRV and blood pressure after caffeine ingestion, respectively, while [PERSON] et al. [26] and [PERSON] et al. [27] only investigated the approximate entropy when conducting nonlinear analysis of HRV and blood pressure. However, to obtain more comprehensive information about nonlinear dynamics, it is necessary to examine more varied nonlinear parameters. In the literature, no study was found in which the effects of energy drink components on the cardiovascular system were examined using both linear and nonlinear analysis methods; this study, which aims to do so, will be the first of its kind. In addition, by analyzing more nonlinear parameters such as the largest Lyapunov exponent, correlation dimension, and Hurst exponent, we aim to obtain more information about the dynamics of the cardiovascular system under the influence of caffeine and taurine. In this way, information that cannot be obtained using linear methods will be obtained using comprehensive nonlinear analysis. From another perspective, we aim to obtain more detailed and comprehensive information about the possible effects of caffeine and taurine through the dynamic and unpredictable nature of the system and about how safe these ingredients are for young adults.
## 3 Design and Implementation of the Multi-Attribute PLA Scheme for LoRaWAN
### PLA Scheme Implementation  
The proposed PLA scheme authenticates devices using three physical-layer attributes: RSSI, BL, and altitude. Each attribute serves as a unique identifier, creating a PLA mechanism that is difficult for attackers to replicate. The RSSI values are analyzed to detect deviations outside expected ranges, indicating potential spoofing. For instance, a sudden shift from weak to strong RSSI could suggest an illegitimate source. The gradual depletion of BL is expected during normal operation. Sudden drops may indicate tampering or sensor compromise. Static sensors maintain consistent altitudes, and significant deviations suggest physical relocation or replacement. If any of the attributes fall outside their defined thresholds, the authentication will fail, ensuring that only devices meeting all criteria are successfully authenticated. This multi-attribute approach ensures effective authentication against spoofing attacks, enhancing security in vulnerable environments.  
Figure 2: LoRaWAN Architecture for the proposed forest fire detection use case.
# The Progress in Treatment of \(\beta\)-Thalassemia and Research on Animal Models of the Disease
## 4 Animal Models of \(\beta\)-Thalassemia
### Rabbit Models  
Rabbits serve as an important model for studying various human diseases due to their unique physiological and genetic characteristics. Compared to mice, rabbits exhibit distinct anatomical features that more closely resemble human structures [53]. Notably, the \(\beta\)-globin gene cluster in rabbits is similar to that in humans and includes three functional globin genes: the embryonic \(\varepsilon\)-globin gene (HBE), the fetal \(\gamma\)-globin gene (HBG), and the adult \(\beta\)-globin gene (HBB). These similarities make rabbits particularly valuable for studying human \(\beta\)-thalassemia, as they can provide insights into the disease mechanisms that are more relevant to human physiology [53].  
[PERSON] et al. [54] made significant advancements in the field by establishing the first \(\beta\)-thalassemia rabbit model using CRISPR/Cas9-mediated insertion/deletion mutations. They meticulously described the characteristic phenotypes associated with \(\beta\)-thalassemia in the HBB2-targeted rabbit model. Analysis of peripheral blood smears showed that both HBB2-targeted F0 and F1 rabbits exhibited anisocytosis, dysmorphic erythrocytes, and target cells, mirroring the erythrocyte morphology seen in human patients with \(\beta\)-thalassemia. Furthermore, hematological assessments indicated that the heterozygous F1 rabbits displayed classic features of \(\beta\)-thalassemia, while the chimeric F0 rabbits showed only mild anemia. Interestingly, histological analysis revealed that, unlike most non-transfused \(\beta\)-thalassemia patients, HBB2-targeted rabbits exhibited spontaneous iron deposition in the early spleens [54]. This finding could significantly enhance our understanding of the pathophysiology of iron overload in various organs and offer valuable insights for developing drug-based therapies.
## 5 Pre-Test and Pre-Test Data Analysis
### Pre-Test Subjects' Demographic Data  
This study recruited 14 males and 17 females at the university participated in the pre-test of the VRET prototype. Pre-testing subjects' demographic data including gender, height, weight, age, and the years of driving experience are presented in Table 1. 14 males with a mean height of 172.5 cm (SD = 5.21 cm) and a mean weight of 64.29 kg (SD = 7.61 kg), 17 males with a mean height of 160.43 cm (SD = 4.75 cm) and a mean weight of 52.71 kg (SD = 5.44 kg). All the subjects with a mean age of 22.39 years (SD = 0.95 year) and the mean duration of driving experience is 2.10 years (SD = 1.13 years).
## 5 Simulation and Analysis
The extension of cyclic prefix will improve the equalization effect and reduce this error, but the cyclic prefix will further waste the throughput of underwater acoustic communication. This algorithm overcomes the dependence on cyclic prefix. The symbol error ratio curve of two algorithms for Channel 2 is given in Figure 6. As revealed, the existing algorithm may improve the symbol error ratio to some extent but limitedly, so that the symbol error ratio does not satisfy the requirements of reliable communication. A longer cyclic prefix must be added for this purpose. However, the proposed frequency domain decision feedback adaptive equalization algorithm can effectively overcome the dependence of the OFDM communication system on longer cyclic prefixes [35]. Additionally, it can greatly improve the symbol error ratio of the OFDM communication system. When the signal-noise ratio is large, the proposed equalization  
Figure 5: Error curve of compensation for Channel 2 with two frequency domain equalization algorithms. algorithm can keep a low symbol error rate, which guarantees the reliable operation of the communication system. If only a few cyclic prefixes are added (but not added in this simulation), the OFDM underwater acoustic communication system will achieve better bandwidth utilization. Moreover, such advantageous utilization will become more noticeable with the increase of multipath time delay of an underwater acoustic channel. This will then guarantee the high-speed data transmission of the communication system. As proved in the simulation, the simplified algorithm achieves a symbol error ratio similar to that of the original algorithm. Furthermore, the simplified algorithm can significantly reduce the number of complex multiplications during each iteration, which greatly lowers the system requirements for hardware. Hence, it is more beneficial for practical applications.