esa-sceva/satcom-chunk-collection huggingface dataset:
# Chinese Fine-Grained Named Entity Recognition Based on BILTAR and GlobalPointer Modules
## 5 Conclusions and Future Work  
In fine-grained entity recognition tasks, it is difficult to distinguish between similar categories, and simple neural network layers cannot extract enough semantic features to distinguish between similar categories. This paper proposes the BILTAR deep semantic extraction module to extract more semantic information from the data, thereby improving the model's ability to distinguish between fine-grained categories. Sequence labeling tasks are insensitive to entity boundaries. Therefore, this paper uses the GlobalPointer module instead of the conditional random field to calculate the score of each possible entity boundary in each category, thereby fully considering all entity boundaries. The experiments show that the BILTAR module can improve the model's feature extraction ability, each sub-module of the BILTAR module can also individually improve the model's performance, the model's performance is slightly improved after the GlobalPointer module replaces the CRF layer, and the multi-head self-attention mechanism added to multiple positions can also improve the model's performance. Therefore, the experimental results fully prove the effectiveness of the multi-head self-attention mechanism, the BILTAR module, and the GlobalPointer module.  
The performance improvement of entity recognition model is of great significance to the development of the knowledge graph. The identified entities and entity categories can serve as knowledge for knowledge fusion, knowledge reasoning, and other downstream tasks of the knowledge graph. In each knowledge graph task, improving the entity recognition ability of the model can reduce the error propagation in the entity recognition stage and improve the realization effect of each downstream task. In view of the above situation, the entity recognition task is prospected as follows.  
In the future, improvements can be made to the BILTAR module by incorporating more complex network structures. The stronger semantic extraction module enables the model to extract deeper semantic features and improve the model performance. It is possible to optimize the GlobalPointer module to improve the model's ability to compute entity boundaries.  
Conceptualization, J.L.; Methods, J.L. and W.L.; Verification, J.L., Y.G. and J.G.; Formal analysis, X.Z.; Resources, J.L.; Data management, Y.G.; Writing\(-\)Original manuscript preparation, J.L.; Writing\(-\)Review and editing, W.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Scientific Research in Central Universities of North Minzu University (2021)CYJ12), the Ningxia Natural Science Foundation Project (2021 AAC03215), the National Natural Science Foundation of China (62066038, 61962001).  
The data presented in this study are available on request from the corresponding author. Because the data sets in this article were developed by individual institutions, the data are not publicly available.  
The authors declare no conflict of interest.
## 1 Introduction
Another reason is that beam steering is difficult to implement in present S-band tropo systems, which is mainly due to billboard-shaped passive reflector antennas that are very difficult to move owing to their large size (~20 m high) and enormous weight. The transmit and receive antenna beams in present tropo systems have fixed elevation angles, which are always looking at the same region of the troposphere, regardless of the scatterer density. Hence, propagation losses due to slow fading are considered inevitable in existing tropo systems and catered for in the link budget. However, in recent years, the possibility of tropo systems capable of operating in the C band [9] and Ku band [10] has been explored, which could result in small-sized, lightweight antennas. Trope systems with phased-array antennas that are capable of electronic beam forming have been suggested by [PERSON] et al. [11, 12], which could also be used for beam steering. These recent developments have made beam steering an achievable reality for tropo systems.
## 4 Proposed Estimation Scheme
### Estimation of Desired Power, CCI Power, and COR
#### 4.1.3 Estimation of Noise Power, CCI Power, and COR  
\(\eta_{I,B}\) are estimated from the various packets. Subsequently, the probability density function of \(\eta_{I,B}\) is obtained by histogram analysis. We assume that the access condition of the SC system is modeled as the independent random access for each packet access of LoRa. Consequently, the probability density function of \(\eta_{I,B}\) is modeled by the mixture probability of noise and noise plus CCI. In addition, the noise and CCI are modeled using the Gaussian random process. Based on these assumptions, the probability density function of \(\eta_{I,B}\) can be modeled as two Gaussian mixture probability density functions of noise and noise plus CCI [13]. We adopted the expectation-maximization (EM) algorithm [14] to estimate the two probability density functions and mixture rate. The EM algorithm is an iterative scheme suitable for estimating latent variables based on the maximum likelihood estimation method. The mean and deviation of the estimated probability density functions were also estimated, and the means are considered as the estimated average power of the noise \(\widehat{P_{n}}\) and noise plus CCI,\(\widehat{P_{n}}+\widehat{P_{I}}\). After subtracting the former from the latter, we estimated the estimated average power of the CCI, \(\widehat{P_{I}}\). The mixture rate estimated using the EM algorithm was equal to that of the SC system [13]. In addition, the variances of the noise power, \(\sigma_{\widehat{P_{n}}}^{2}\), and noise plus the CCI, \(\sigma_{\widehat{P_{I}}+\widehat{P_{n}}}^{2}\), were also estimated. After subtracting the former from the latter, we also estimated the variance of the CCI, \(\sigma_{\widehat{P_{I}}}^{2}\).  
In this process, the proposed scheme can estimate the power of the desired signal, CCI, and COR.
###### Abstract  
A method of soil moisture and organic matter content detection based on hyperspectral technology is proposed. A total of 800 different soil samples and hyperspectral data were collected in the laboratory and from the field. A hyperspectral database was established. After wavelet denoising and principal component analysis (PCA) preprocessing, the convolutional neural network (CNN) module was first used to extract the wavelength features of the data. Then, the long- and short-memory neural network (LSTM) module was used to extract the feature bands and nearby hidden state vectors. At the same time, the genetic algorithm (GA) was used to optimize the hyperparametric weight and bias value of the LSTM training network. At the initial stage, the data were normalized, and all features were analyzed by grey correlation degree to extract important features and to reduce the computational complexity of the data. Then, the GA-optimized CNN-LSTM hybrid neural network (GA-CNN-LSTM) algorithm model proposed in this paper was used to predict soil moisture and organic matter. The prediction performance was compared with CNN, support vector regression (SVR), and CNN-LSTM hybrid neural network model without GA optimization. The GA-CNN-LSTM algorithm was superior to other models in all indicators. The highest accuracy rates of 94.5% and 92.9% were obtained for soil moisture and organic matter, respectively. This method can be applied to portable hyperspectralers and unmanned aerial vehicles to realize large-scale monitoring of moisture and organic matter distribution and to provide a basis for rational irrigation and fertilization in the future.  
hyperspectral; soil moisture; soil organic matter; genetic algorithm; long- and short-memory neural network
# An FDM-Based Dynamic Zoning Method for Disturbed Rock Masses above a Longwall Mining Panel
###### Abstract  
Underground longwall mining can seriously disturb the surrounding rock masses above the panel. A surface cracking zone, continuous deformation zone, fractured zone, and caved zone can be formed in the overlying strata (termed the \"four zones\"), which may further result in the spontaneous combustion of coal seams and water intrush. It is essential to study and predict the development characteristics of the four zones induced by longwall mining to guarantee mining safety. These four zones are developed during the mining process, and the mechanical properties in different regions correspondingly differ. Thus, the dynamic zoning characteristics of the disturbed rock masses should be considered in any simulations. In this paper, an FDM-based dynamic zoning method for disturbed rock masses above a longwall mining panel is proposed. This method is mainly composed of four stages: (1) establishing a simplified complete stress-strain curve; (2) determining the zoning criteria; (3) adaptively adjusting the mechanical parameters of the disturbed rock mass; and (4) numerically modeling the longwall mining based on the FDM. The proposed method was applied to a study site in the Taixi coal mine. The dynamic development process of the four zones induced by longwall mining was clearly observed in the modeling procedure. The numerical modeling results achieved in this work, including the periodical coal-seam roof caving and the dynamic development characteristics of the four zones, are consistent with the observed distribution and other studies. The heights of the caved and fractured zones are basically consistent with the empirical formula. Thus, the dynamic zoning method can analyze and predict the dynamic development characteristics of four regions.
## 5 Conclusions and Discussion  
Through field tests and numerical analysis, the surface settlement laws and prediction methods of a composite-stratum ultra-large-diameter shield tunnel were studied. The main conclusions are as follows:  
1. When the shield tunnel passes through the soil-rock composite stratum, there will be large and uneven settlement near the composite interface along the direction of the shield excavation. This range is about 3.1D in the soil strata and 1.9D in the rock strata.
2. The \(S_{\text{max}}\) of soil strata decreases as the distance to the composite interface decreases, reaching its minimum (46% of the maximum \(S_{\text{max}}\)) at the interface. Conversely, the \(S_{\text{max}}\) of rock strata increases, reaching its maximum (approximately 1.4 times the minimum \(S_{\text{max}}\)) at the interface. The \(i\) value of soil strata as the distance decreases, reaching its maximum (about 1.5 times the maximum \(i\)) at the interface. Conversely, the \(i\) value of rock strata increases, reaching its minimum (88% of the maximum  
Figure 14: Comparison graph of settlement curves among monitoring cross-sections in stratum 7.  
Figure 13: Comparison graph of settlement curves among monitoring cross-sections in stratum 6.  
_i_) at the interface. The composite stratum has a greater influence on \(S_{\max}\) than \(i\), particularly in soil strata.
3. When predicting the surface settlement of the composite-stratum settlement variation intervals, considering the Peck correction formula for the distance from the monitoring section to the composite interface is more accurate than the Peck empirical formula.  
It should be noted that the Peck formula is significantly influenced by engineering conditions. The Peck correction formula provided in this paper is only applicable to the prediction of surface settlement for the Wuhan He'ping Avenue South Extension Tunnel, and it has certain limitations. For the prediction of surface settlement in other regions' shield tunnel projects, it is necessary to readjust the correction parameters based on field measurements. In future studies, a data-driven approach can be explored to improve the adaptability of settlement prediction by leveraging big data.  
Conceptualization, J.L.; Writing--original draft, A.L.; Writing--review & editing, H.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The participant's personal identification information used in the study did not include personal information. Ethical review and approval were not required for the study.  
Data Availability Statement: Data sharing is not applicable.  
The authors declare no conflict of interest.
## 5 Simulation Verification  
In order to verify the effectiveness of the theoretical analysis, a 30 kW grid-connected inverter simulation model with classical vector current control was established in Matlab/Simulink. The control method is shown in Figure 8. To avoid the influence of high-frequency harmonics, the average model of the inverter was used. Firstly, an inductive grid case (\(R_{g}/X_{g}=0.3\)) with low SCRs was carried out. Then, a resistive grid case (\(R_{g}/X_{g}=7\)) with SCR = 10 was performed. The key parameters of the grid-connected inverter are listed in Table 1. Moreover, the  
The simulation results of the inductive grid case are shown in Figure 9, focusing on the power reference \(P_{nfp}\), the output active power of inverter \(P_{ninv}\), and the transferred active power to the grid \(P_{grid}\). Figure 10 shows that as the power reference increases, the current amplitude \(I_{inv}\) increases. When \(I_{inv}\) reaches 1 pu, \(i_{d}\) is limited and \(P_{inv}\) is also limited. In Figure 10a, the PCC voltage is controlled as 1 pu. It can be seen that \(i_{d}\) is quite large but \(i_{d}\) is small, so that the output active power of the inverter \(P_{inv}\) is limited (as low as 0.2 pu), and the grid power \(P_{grid}\) is only 0.1 pu. In contrast, as shown in Figure 10b, when the PCC voltage is controlled as 1.05 pu, \(i_{d}\) is not so small. Thus, the output active power of the inverter \(P_{inv}\) is 0.7 pu and the grid power \(P_{grid}\) is only 0.6 pu. These simulation results are in accordance with the maximum power operating points for \(R_{g}/X_{g}=7\) and SCR = 10, as shown in Figure 7b.  
It can be seen in Figures 9 and 10 that there is a steady-state error on the power \(P_{inv}\), when the current reaches the limiting value. In fact, this steady-state error is equal to (1-\(P_{inv(pu)}\)). According to the expression of \(P_{inv(pu)}\) in Equation (21), the steady-state error can be calculated accurately. The power steady-state errors in different cases are compared in Table 3. It can be seen that for the inductive grid case, the rated PCC voltage has a relatively small power steady-state error. As the R/X ratio increases, a higher PCC voltage has a smaller power steady-state error, which can also be observed from Figure 6d. Moreover, when \(V_{pcc(pu)}\) is equal to 1, the impact of the R/X ratio and the SCR on \(P_{inv(pu)}\) are shown in Figure 5. It can be seen in Figure 5 that there is a maximum power point on  
Figure 9: Simulation results of the inductive grid case with \(R_{g}/X_{g}=0.3\) and low SCRs. (**a**) SCR = 1; (**b**) SCR = 2.  
each constant SCR curve. When R/X ratios are smaller than the R/X ratios at the maximum power points, \(P_{im(pu)}\) increases as SCR increases. However, when R/X ratios are larger than the R/X ratios at the maximum power points, \(P_{inv(pu)}\) decreases as SCR increases. Even so, the R/X ratio at each maximum power point is not the optimal one because the optimal R/X ratio should be determined by \(P_{grid(pu)}\).
# An Enhanced Precoder for Multi User Multiple-Input Multiple-Output Downlink Systems
## 5 Simulation Results
### Computational Complexity Analysis
\begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Number of Multiplications** \\ \hline \(\mathbf{H}_{i}=\mathbf{U}_{i}\boldsymbol{\Sigma}_{i}\mathbf{V}_{i}^{H}\) & \(4K\left(4N_{T}^{2}N_{R_{i}}+8N_{T}N_{R_{i}}^{2}+9N_{R_{i}}^{3}\right)\) \\ (Equation (21)) & \\ \(\mathbf{\bar{T}}_{i}=\mathbf{\bar{U}}_{i}\boldsymbol{\bar{\Sigma}}_{i}\bar{ \mathbf{V}}_{i}^{H}\) & \(16K\left(\ \ \ (K-1)^{2}N_{T}r_{i}^{2}+2(K-1)^{3}r_{i}^{3}\ \ \right)\) \\ (Equation (26)) & \\ \(\mathbf{H}_{i}\mathbf{F}_{\text{MU},i}=\mathbf{\bar{U}}_{i}\boldsymbol{\Sigma}_{i }\left[\ \ \mathbf{\bar{V}}_{i}^{(1)}\ \ \ \mathbf{\bar{V}}_{i}^{(0)}\ \ \ \right]^{H}\) & \(16K\left(N_{T}r_{i}^{2}+2r_{i}^{3}\right)\) \\ (Equation (27)) & \\ **Total Complexity** & \(16K\left(N_{T}^{2}N_{R_{i}}+2N_{T}N_{R_{i}}^{2}+(K^{2}-2K+2)\ N_{T}r_{i}^{2}+ \frac{9}{4}N_{R_{i}}^{3}+2\left(K^{3}-3K^{2}+3K\right)r_{i}^{3}\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Computational complexity for MGBD precoder. \begin{table}
\begin{tabular}{c c} \hline \hline
**Operation** & **Number of Multiplications** \\ \hline \(\mathbf{\bar{H}}_{i}=\mathbf{\bar{U}}_{i}\boldsymbol{\bar{\Sigma}}_{i}\Big{[}\ \ \mathbf{\bar{V}}_{i}^{(1)}\ \ \mathbf{\bar{V}}_{i}^{(0)}\ \ \Big{]}^{H}\) & \(16K\left(\ \ (K-1)^{2}N_{T}N_{R_{i}}^{2}+2(K-1)^{3}N_{R_{i}}^{3}\ \ \right)\) \\ (Equation (13)) & \\ \(\mathbf{H}_{i}\mathbf{F}_{\text{MU},i}^{\text{RBD}}=\mathbf{\bar{U}}_{i}^{ \text{RBDD}}\boldsymbol{\bar{\Sigma}}_{i}^{\text{RBD}}\ \boldsymbol{\bar{\mathbf{V}}}_{i}^{\text{RBD}(1)}\ \ \mathbf{\bar{V}}_{i}^{\text{RBD}(0)}\ \ \Big{]}^{H}\) & \(16K\left(N_{T}N_{R_{i}}^{2}+2N_{R_{i}}^{3}\right)\) \\ (Equation (14)) & \\ **Total Complexity** & \(16K\left(\ \ \left(K^{2}-2K+2\right)N_{T}N_{R_{i}}^{2}+2\left(K^{3}-3K^{2}+3K\right)N_{R_{ i}}^{3}\ \ \right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Computational complexity for a regularized block diagonalization (RBD) precoder. Figure 9: The comparison for computational complexity when each of 3 UEs has 3 receive antennas and BS has 9 transmit antennas.
# A Comparison of Different Topic Modeling Methods through a Real Case Study of Italian Customer Care
## 1 Introduction
### Literature Review  
In [7], the authors focused on a probabilistic model based on the LDA to automatically cluster dialogues between customers and agents of customer service in the Italian natural language. Examples of a comparison study of different TM algorithms, such as the probabilistic LDA model and the non-probabilistic model as NMF, are reported in the literature [2].  
Authors in [8] proposed an analysis of the evolution of unsupervised TM algorithms starting from the first models dating back to the 90s, arriving at a comparison with recent models. A total of 40 TM models were considered in the comparison study. Amongst these, LDA and Dirichlet Multinomial Mixture (DMM) were discussed in detail. The DMM [9] is a model based on the Dirichlet distribution, with a multivariate generalization of the beta distribution. Similarly, the CTM [10] is a variant of LDA and differs from it in the statistical distribution. In particular, the authors replaced the Dirichlet distribution with a normal distribution and added a covariance matrix between the topics to model the correlation.  
Among the most recent models, it is worth mentioning the Embedded Topic Model (ETM) [11], in which words and topics are both represented by vectors in an embedding space. In this case, the generative process is similar to that of the LDA, in which each document has topics structured according to a probabilistic distribution. In [11], the authors also proposed a discussion about the current state of the art regarding TM, introducing their views on the criteria that researchers should adopt to select a TM suitable for their needs.  
In [12], the authors proposed a comparison study of different TM methods applied to multiple datasets in the English language. In [12], two evaluation metrics were used, i.e., Topic Coherence and Topic Diversity. Topic Coherence was analyzed through normalized pointwise mutual information [13] and external word embeddings topic coherence [14].  
For a more in-depth literature review on TM, the reader is referred to recent surveys [15; 16; 17]. In particular, in [17], the authors used a holistic approach to survey TM. They discussed four categories of TM (i.e., algebraic, fuzzy, probabilistic, and neural), presented the wide variety of available models with a structured viewpoint, analyzed these models' characteristics and limitations, and discuss their proper use cases. Further aspects, which are illustrated in the survey study [17], relate to the criteria for appropriate evaluation of topic models, to the applications with some popular software tools that provide an implementation of some models, and to the available datasets and benchmarks.
## 4 Discussion
Concordantly, _IncF_ (3/27) and _IncFIA_ (5/27) plasmids were identified among our collection, and both co-occurred in five isolates, indicating the possible localization of the _blaCTX-M-15_ gene on one of these plasmids. The concomitant transfer of the _blaCTX-M-15_ and/or _IncFIA_ plasmids for these strains supported this hypothesis. However, further experiments (S1-PFGE hybridization) are needed to assess the exact genetic localization of all _bla_ genes. Both integrins belonging to classes 1 and 2, as well as the occurrence of these plasmids, might explain the MDRs of our isolates, as reported previously [31; 32]. A significant proportion of our isolates were observed to belong to phylogroups D and B2. This finding concurred with other studies; moreover, most of the ESBL-producing _E. coli_ are considered to be the main leading causes of extra-intestinal infections. Furthermore, three CTX-15-producing _E. coli_ strains positive for the B2 phylogeny belonged to the ST131 clone. The ST131 clone, known as the major pandemic clone, controlled the worldwide dissemination of the \(\beta\)-lactamase CTX-15 type [35; 37]. This clone has been also reported previously in KSA [7]. Genetic relatedness assessed by PFGE revealed 21 pulsotypes, where P1 encompassed three isolates, while pulsotypes P8, P16, and P19 contained two isolates. The clonally related isolates were all of pulsotype B2, and all, except two of pulsotype P16, harbored identical \(\beta\)-lactamases. However, antimicrobial susceptibility, PMQR genes, integrons, plasmids, and virulence genes contents were not mainly identical within the related isolates. Taken together, despite the reduced isolate number, our study showed the dominance of the CTX-15 enzyme among _E. coli_ in KSA, and the occurrence of some clonal isolates spreading within patients from different regions. ST131 was not the main clone in our isolates; other lineages or plasmids contributed significantly to the spread of the CMY-42 enzyme.
# Design of Low-Power ECG Sampling and Compression Circuit
## 4 Results and Discussion  
### Algorithm Simulation  
The CS algorithm is simulated to evaluate the reconstruction performance of our circuit. The experimental data are obtained from the MIT-BIH database [20], and ten Records of men and women of different ages are selected for verification. The MIT-BIH database is chosen among the Internet-available ones as it is the most widely used in the scientific literature. The sampling frequency of the ECG signal in the database is 360 Hz, and a 10 kHz frequency is used to resample. The measurement matrix is binary, and the dimension \(M\) of the compressed data is 50. The reconstruction performance under various CFs is evaluated to improve the reliability of experimental results. The original data are 12 bits, where the low 4 bits are calculated approximately. The compressed data and the accurate accumulation sum of the high 8 bits are set to 24 and 16 bits, respectively, to prevent overflow.  
Figure 7 reveals that the PRDs of ten ECG Records reconstructed data using two different calculation methods are less than 2%, indicating that the reconstruction performance is very good. The PRD of accurate calculation is slightly lower than that of approximate calculation. Although the approximate calculation sacrifices a little reconstruction quality, it is acceptable.