esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Discussion
### Experimental Results and Analyses  
#### 4.4.1 The Performance of the NER Model  
We first show the performance of the NER model. For the NLPCC2016 dataset, the results of the experiment are shown in Tables 4 and 5. According to the experimental results, the SoftLexicon method can improve the performance of the Bi-LSTM+CRF model in handling Chinese NER tasks. Since the dataset is dominated by single-entity queries, the entity recognition accuracy of the model is high, so the improvement brought by the SoftLexicon method is not dramatically evident in this work.However, this method does solve some entity recognition errors caused by word segmentation errors. For the test set data, there are a total of 151 entity errors in the recognition results of the Bi-LSTM+CRF model. By using the model combined with the SoftLexicon method, the number of incorrectly recognized entities due to word segmentation errors was reduced to 140. In this experiment, we take the basic character-level embedding Bi-LSTM+CRF model as the baseline. For the basic Bi-LSTM-CRF model, the model that uses character embedding generated by the SoftLexicon method performs better than the model that uses randomly generated character embedding or pre-trained character embedding. Lattice-LSTM, LGN and SoftLexicon all utilize lattice structure and lexicon features, and we regard them as a class of models. Among them, SoftLexicon(LSTM) achieved the highest F1 score of 93.26%. In addition, SoftLexicon only modifies the character representation layer of the model, while Lattice-LSTM modifies the LSTM layer of the model, and LGN adds an additional graph structure to the model. Therefore, SoftLexicon has stronger portability and higher efficiency. Among the models without BERT, the SoftLexicon+Bi-LSTM-CRF model has the best performance. The addition of the pre-trained model BERT significantly improved the performance of the NER model. In this work, even the baseline model Bi-LSTM-CRF achieves an F1 score of 96.84% after incorporating BERT. In addition, comparing BERT+BiLSTM-CRF and BERT+Bi-LSTM-Softmax, we can find that using CRF has better performance in sequence labeling. Compared with Softmax, CRF fully considers the connection between sequence contexts when labeling and adjusts the results through the learned constraints. For the NLPCC2016 dataset, the SoftLexicon+Bi-LSTM-CRF model combined with BERT has the best performance, with an F1 score of 97.06%. Then, we utilize the power grid dataset to conduct the experiment. In this experiment, we improve the SoftLexicon according to a specific application scenario. Considering that the source of the corpus of the matching lexicon used by the SoftLexicon method comes from the public domain, for the NER task in the power grid field, we utilize some
# Evaluation of Ionospheric Delay Effects on Multi-GNSS Positioning Performance
## 4 Results and Discussion
### Performance of Single-Frequency PPP  
Table 4 shows the parameter estimation method in single-frequency quad-constellation PPP models, where m, n, p and q denote the satellite number of the GPS, Beidou, GLONASS and Galileo, respectively. The method of eliminating ionospheric parameters is actually equivalent to estimating the parameters [28]. The variance-covariance matrix and the solution vector are identical though they may apply different algorithms [29]. Hence, the GRAPHIC and UC approach are equivalent provided the variance-covariance matrix is transformed according to the law of covariance propagation. The ionosphere-constrained approaches will produce different results for the priori ionospheric information provided by different models. Figure 4 shows the comparison of b2 single-frequency PPP with different schemes for GPS, GLONASS and GPS/Beidou/GLONASS/Galilee solutions at station MR01 on 2 September 2018. The value errors of respective solutions are shifted by 1 m in order to avoid overlapping. For the single-frequency PPP approach, the positioning filter is considered to have converged when the \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GRAPHIC** & **UC** & **Ionosphere Constraint** \\ \hline Observed quantity & m+n+p+q & 2m+2n+2p+2q & 3m+3n+3p+3q \\ Parameter number & m+n+p+q+8 & 2m+2n+2p+2q+8 & 2m+2n+2p+2q+8 \\ Redundancy & \(-8\) & \(-8\) & m+n+p+q\(-8\) \\ \multirow{2}{*}{Type of the parameters} & Position, receiver clock, & Position, receiver clock, & Position, receiver clock, \\  & ZWD, ambiguity & ZWD, slant ionospheric & ZWD, slant ionospheric & ZWD, slant ionospheric \\ \cline{2-4}  & delay, ambiguity & delay, ambiguity \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameter estimation method in single-frequency quad-constellation PPP schemes.
# Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data
## 4 Results
### Spatial Distribution of Wheat Sowing and Maturity Dates in South Central Shanxi Province  
Our results show that, in 2010, wheat sowing in south central Shanxi Province mainly occurred during 17 September and 10 October (Figure 4a). The sowing dates significantly varied with increasing elevation (y = \(-\)0.0106x + 271.1763, \(p\) < 0.05). In 2011, the wheat maturity phase mainly occurred from 2 June to 29 June. Lagged maturity dates significantly varied with increasing elevation (y = 0.0101x + 162.0040, \(p\) < 0.05) (Figure 4b). The spatial distributions of sowing and maturity dates generally exhibited opposite patterns, _i.e._, sowing occurred earlier and harvest occurred later at high elevation, whereas at low elevation, sowing occurred later and harvest earlier. In general, the spatial  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Reported** & **HJ Sowing** & **Difference** & **Reported** & **HJ Maturity** & **Difference** \\  & **Sowing** & **Date** & **(HJ-Reported)** & **Date** & **Date** & **(HJ-Reported)** \\ \hline Yanhu & 280 & 270 & \(-\)10 & 157 & 164 & 7 \\ Yaodu & 271 & 267 & \(-\)4 & 157 & 165 & 8 \\ Ruicheng & 290 & 275 & \(-\)15 & 163 & 170 & 7 \\ Wanrong & 268 & 269 & 1 & 163 & 169 & 6 \\ Jincheng & 264 & 267 & 3 & 164 & 170 & 6 \\ Fenyang & 279 & 271 & \(-\)8 & 177 & 172 & \(-\)5 \\ Jiexiu & 289 & 271 & \(-\)18 & 173 & 170 & \(-\)3 \\ Anze & 273 & 268 & \(-\)5 & 175 & 172 & \(-\)3 \\ Changzhi & 266 & 268 & 2 & 171 & 173 & 2 \\ Average & 276 & 270 & \(-\)6 & 167 & 169 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of sowing and maturity dates (DOY) estimated with HJ-1A/B satellite and ground-based data.  
patterns of sowing and maturity dates were related with elevation and latitude, suggesting that temperature is a key variable controlling this spatial pattern.
## 2 Simulation and Research Methods
### Thermal Comfort Model  
As argued above, PMV proposed by Professor [PERSON] (1970) is a more comprehensive index for evaluating indoor thermal comfort than air temperature ([PERSON], 1970). It combines four environmental variables (i.e., air velocity, air temperature, mean radiant temperature and relative humidity) and two individual variables (metabolic rate and clothing) to reflect the thermal equilibrium relationship between the human body and the environment. However, people in an uncomfortable indoor environment will adapt to the environment through certain behavioral adjustments, so the adaptive thermal comfort (APMV) theory is more applicable to the rural buildings in this study. Equation (1) shows the assessment of APMV based on the PMV indicator.  
\[\text{APMV}=\text{PMV}/(1+\lambda\cdot\text{PMV}) \tag{1}\]  
where \(\lambda\) is the adaptive coefficient ([PERSON] et al., 2018), when PMV \(\geq\) 0, \(\lambda\) = 0.24, and when PMV \(<\) 0, \(\lambda\) = \(-\)0.50.  
In the sleep state, clothing resistance consists of bed, mattress, bedding, pillow and so on, which can no longer be selected according to ASHRAE Standard 55-2017. Therefore, [PERSON], et al. proposed Equation (2) to describe the PMV in sleeping condition termed SMPV ([PERSON] et al., 2018).  
\[\text{SPMV}=0.0998\Big{\{}40-\frac{1}{\text{R}_{\text{c}}}\Big{[}\Big{(}34.6- \frac{\text{h}\text{f}_{\text{c}}+\text{h}\text{t}_{\text{c}}}{\text{h}_{ \text{c}}+\text{h}_{\text{c}}}\Big{)}+0.3762(5.52-\text{P}_{\text{a}})\Big{]} \Big{\}}-0.0998[0.056(34-\text{t}_{\text{a}})-0.692(5.87-\text{P}_{\text{a}})] \tag{2}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Enclosure** & **Thickness and Material of Each** & **Heat Transfer Coefficient** \\
**Structure** & **Layer from Outside to Inside** & **K (W/(m\({}^{2}\)K))** \\ \hline \multirow{4}{*}{Exterior wall} & Pre-retrofit: 20 mm Cement mortar + 240 mm red brick + 20 mm Mixed mortar & 1.79 \\ \cline{2-3}  & Post-retrofit: 20 mm Cement mortar + 240 mm red brick + 46 mm Polystyrene foam (EPS) + 20 mm Mixed mortar & 0.65 \\ \hline \multirow{2}{*}{Roof} & 20 mm Reinforced concrete + 43 mm Polystyrene foam (EPS) + 20 mm Cement mortar & 0.50 \\ \hline Exterior window (south) & Hollow glass casement window (6 + 6A + 6) & 2.80 \\ \hline Exterior window (others) & Hollow glass casement window (6 + 12A + 6) & 2.50 \\ \hline Exterior doors & wooden exterior door & 2.50 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Heat transfer coefficient settings of the simulation model.  
where \(\rm R_{t}\) is the total clothing resistance, clo; \(\rm h_{r}\) is the radiation heat transfer coefficient, \(\rm W/(m^{2}\cdot K)\); \(\rm h_{c}\) is convective heat transfer coefficient, \(\rm W/(m^{2}\cdot K)\); \(\rm\bar{t}_{r}\) is the mean radiant temperature, \({}^{\circ}\)C; \(\rm t_{a}\) is the air temperature, \({}^{\circ}\)C; and \(\rm P_{a}\) is indoor air water vapor partial pressure, kPa.
# UPC: An Open Word-Sense Annotated Parallel Corpora for Machine Translation Study
###### Abstract  
Machine translation (MT) has recently attracted much research on various advanced techniques (i.e., statistical-based and deep learning-based) and achieved great results for popular languages. However, the research on it involving low-resource languages such as Korean often suffer from the lack of openly available bilingual language resources. In this research, we built the open extensive parallel corpora for training MT models, named Ulsan parallel corpora (UPC). Currently, UPC contains two parallel corpora consisting of Korean-English and Korean-Vietnamese datasets. The Korean-English dataset has over 969 thousand sentence pairs, and the Korean-Vietnamese parallel corpus consists of over 412 thousand sentence pairs. Furthermore, the high rate of homographs of Korean causes an ambiguous word issue in MT. To address this problem, we developed a powerful word-sense annotation system based on a combination of sub-word conditional probability and knowledge-based methods, named UTagger. We applied UTagger to UPC and used these corpora to train both statistical-based and deep learning-based neural MT systems. The experimental results demonstrated that using UPC, high-quality MT systems (in terms of the Bi-Lingual Evaluation Understudy (BLEU) and Translation Error Rate (TER) score) can be built. Both UPC and UTagger are available for free download and usage.  
comparative corpus linguistics; Korean-English parallel corpus; word-sense disambiguation; neural machine translation; statistical machine translation +
Footnote †: journal: Applied
###### Abstract
As will be explained in more detail later in the paper, the contributions selected for analysis were grouped using an NLP technique, highlighting the main themes, and also making it possible to understand which contributions were most representative of a thematic area, without any claim of systematicity. NLP techniques allow the automatic analysis, processing, and synthesis of natural language, here used to present original works of scientific researches or studies. Through this phase of pre-analysis of the papers, they are categorized in topics for a more conscious reading of the contributions, with a significant indication of the most representative papers for each identified topic. Similarly, the identification of any overlapping thematic areas facilitates a \"multiple\" and parallel reading of the contributions, making it possible to identify the links between thematic areas. It also speeds up the analysis of the literature because only contributions with high values of belonging to a topic can be carefully considered. Finally, the analysis is more objective, because manual keyword labeling and subsequent categorization could be extremely subjective. The approach selected to implement NLP analysis is Latent Dirichlet Allocation (LDA) [16; 17]. The latter identifies topics and assigns every paper to one or more topic according to probabilistic logic models, thus providing more realistic and elucidative results. The literature proposes some applications of the text mining concept to support literature analysis [18; 19]. As far as the authors are aware, however, there are no contributions on the application of NLP, specifically LDA, to support literature reviews on the topic specifically under consideration. Consequently, the fruitful applicability of the LDA and its output are considered interesting contribution of this paper. The sections of this paper are organized as follows. Section 2 will present the methodology followed, both from the point of view of structuring the literature review and from the point of view of applying the LDA algorithm. Section 3 will present the evidence extracted from the literature, detailing the different topics identified. Finally, the discussion and conclusions section is presented, highlighting the extracted contents, their possible application in the maintenance field, and possible future developments of the research.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline
**Study** & **Application Scenario** & **Technique** & **Limitations** \\ \hline \multirow{4}{*}{[PERSON] et al. [21]} & Carrier-based aircraft maintenance support operation scheduling & Particle Swarm Optimization (IPSO), Resource-Constrained Multi-Project Scheduling Model & Prone to local optima, fails to consider obstacle interference on support paths \\ \hline \multirow{4}{*}{[PERSON] et al. [22]} & Carrier-based aircraft group combat mission planning & Multi-layer encoded genetic algorithm, convex optimization trajectory planning & High computational complexity, lacks optimization of mission planning and resource scheduling \\ \hline \multirow{4}{*}{[PERSON] et al. [23]} & Carrier-based aircraft ammunition support task allocation and transportation scheduling & Improved genetic algorithm, safe A\({}^{*}\) path planning, greedy algorithm & Serial use of multiple algorithms leads to error accumulation; greedy algorithm ignores dynamic changes in task priorities \\ \hline \multirow{2}{*}{[PERSON] et al. [24]} & Carrier-based aircraft landing scheduling & Improved firefly algorithm & Low optimization efficiency for high-dimensional discrete decision variables \\ \hline \multirow{2}{*}{[PERSON] et al. [25]} & Optimization of route selection and fleet allocation & MILP-based integration of route selection and fleet allocation problems & Fails to consider real-world constraints such as airport capacity limits and flight schedule coordination \\ \hline \end{tabular}
\end{table} 
Table 1: The main research topics and methods of carrier aircraft job scheduling at present. in-the-loop verification. This architecture establishes a collaborative digital twin-physical device platform, enabling immersive visualization of full-process carrier aircraft operations and dynamic spatiotemporal evolution characterization of the aircraft carrier deck. The structure of this paper is as follows: first, an overview of the current research on visual simulation systems for carrier aircraft job scheduling is provided. The second section presents the architectural design of the immersive simulation platform proposed in this study. The third chapter details the key technologies underlying the simulation platform and the process of constructing the immersive real-time simulation system. In the fourth chapter, the scheduling algorithm model used in the experimental cases is introduced. The fifth chapter presents a case analysis of the experimental platform, including the results of the scheduling algorithm and the simulation outcomes from the platform, as well as the development of the virtual-real simulation demonstration platform based on dynamic capture, with the corresponding case results. Finally, the paper concludes with a summary of the research findings and an outlook on future work.
# Numerical Simulation of Tunnel Face Support Pressure in Layered Soft Ground
## 4 Conclusions  
This paper has presented a series of three-dimensional finite element simulations using MIDAS GTS NX software, which were conducted to investigate the excavation face support pressure in layered ground. This study has considered the influence of different shear strength parameters and formation boundaries on the support pressure of excavation  
Figure 12: The relationship between the change in soil shear strength parameters and the ultimate support pressure ([13; 24; 32]): (**a**) the influence of lower friction angle change, (**b**) the influence of upper friction angle change, (**c**) the influence of lower cohesive change, (**d**) the influence of upper cohesive change.  
faces in composite strata, resulting in a modified formula for calculating this pressure. Based on the numerical simulation results, we draw the following conclusions:  
1. In layered ground, when the ground boundary line is higher than the axis plane, the lower soil layer has a greater influence on the support pressure of the excavation face, and the farther away from the axis, the greater the influence. When the formation boundary line is lower than the axis plane, the influence of the upper soil on the support pressure of the excavation face is greater, and the farther away from the axis, the greater the influence.
2. The soil in front of the tunnel excavation and above it plays a decisive role in setting the support pressure of the excavation face, while the soil at the bottom of the tunnel has no effect on it.
3. The influence of tunnel excavation on the strata is mainly concentrated on the soil directly in front of the excavation face and the soil above it. The larger the shear strength parameter, the smaller the displacement, and the maximum displacement point moves in the direction of the smaller shear strength parameter, while instability failure is generally controlled by weaker soil.
4. A modified formula for calculating the support pressure of an excavation face in layered ground has been given.  
Conceptualization, B.X. and X.L.; methodology, B.X. and X.L.; validation, B.X. and L.H.; investigation, B.X. and L.H.; resources, B.X.; data curation, B.X. and L.H.; writing--original draft, B.X.; writing--review and editing, X.L. and L.H.; visualization, L.H.; project administration, B.X. and X.L.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Thanks are due to [PERSON] for his help with resources and supervision of this article.  
The authors declare no conflicts of interest.
# A Resilient LoRa-Based Solution to Support Pervasive Sensing
## 5 Results
### LoRa Tests
#### 5.2.3 Other LoRa Tests  
The greater transmission range achieved by the higher spreading factors comes at a cost: an increased time required to send messages. Figure 8 shows how sending times increase along with the spreading factor. This test was performed with messages of 244 bytes (20 bytes for the header and 224 bytes for the payload).  
With spreading factor 7, sending the message takes about 200 ms, while sending the same message with spreading factor 12 requires almost 2900 ms. The increase of almost 14 times between 7 and 12 highlights the main drawback of using a high spreading factor. Having longer sending times means that fewer messages can be sent overall, partially because the channel is busy for longer amounts of time, and also since LoRa uses unlicensed frequencies, it has a 1% duty cycle (at least in Europe, where the test was performed).  
The increased sending times also reflect on the connection's throughput between the nodes. Since the throughput is calculated by the formula \(\frac{B_{sent}}{T}\), where \(B_{sent}\) is the total amount of bytes sent during a \(T\) time window. If \(B_{sent}\) stays the same and \(T\) increases, the resulting throughput will obviously decrease. Figure 9 shows the effect of higher spreading factors on the throughput.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration** & **PDR** & **RSSI (db)** & **SNR** \\ \hline Ground floor-7 th floor (SF 7) & 93\% & \([-96;\,-84]\) & [1; 4] \\ Ground floor-5 th floor (SF 7) & 100\% & \([-85;\,-76]\) & [3; 6] \\ Ground floor-7 th floor (SF 10) & 100\% & \([-80;\,-72]\) & [5; 6] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results obtained from the indoor LoRa tests.  
Figure 8: Time required to send a message as a function of spreading factor.  
Thanks to the data in Figures 8 and 9, it becomes clear that using the highest spreading factor is not always the best solution and a compromise between range (and PDR) and throughput has to be made.
## 4 PCI Prediction Model Based on the ISTD-DisNet Outputs
### Model Verification  
To further evaluate the generalization ability of the PCI prediction model proposed in this article, the developed model was deployed for computation on 6 road sections with different road conditions, with a total of 89 100 m evaluation units (46 asphalt, 43 cement). Firstly, the PCI score of each evaluation unit was obtained by manual visual discrimination, and the total time was recorded. Secondly, all the images were analyzed using ISTD-DisNet to calculate the distress \(PDR\) for each evaluation unit. Finally, the extracted \(PDR\) features were used as the input values, and a nonlinear regression model (Figure 13) was used to calculate the PCI score of each evaluation unit and count the total computation time. Figure 14 shows the PCI calculation results of the two different methods. As can be seen from Figure 14, the PCI values calculated for each evaluation unit based on the distress \(PDR\) are in good agreement with the results of the corresponding manual visual discrimination. The maximum difference between the two methods is 7.33 and 8.93 for the asphalt and concrete pavements, respectively, and the mean absolute values of the differences are 2.48 and 3.92, respectively. It can be observed that the PCI obtained from the manual visual discrimination is lower than that calculated from the \(PDR\)-based PCI for nearly 80% of the evaluation units, which is because the training dataset used in this study did not contain all the distress types, such as the less common oiling, loosening, and exposed aggregate types. In addition, the efficiency of the PCI prediction model proposed in this article is about 24 times higher than that of manual visual discrimination. In summary, the computational model proposed in this article can realize the rapid calculation and ranking of pavement damage conditions, which has potential value in engineering applications.  
Figure 12: PCI prediction model based on a linear fitting algorithm: (**a**) asphalt; and (**b**) cement concrete.