esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Model Calibration  
All models required calibration, except for PCM. Site-specific data were used to calibrate them. Calibration of the VPM and MVPM models involves the estimations of \(\varepsilon_{\rm max}\), \(T_{\rm min}\), \(T_{\rm opt}\), and \(T_{\rm max}\). The values of \(T_{\rm min}\), \(T_{\rm opt}\), and \(T_{\rm max}\) vary among vegetation types [25]. The \(T_{\rm min}\), \(T_{\rm opt}\), and \(T_{\rm max}\) in the temperate steppe (NM and XL) were set to \(-2\), \(20\), and \(35\ ^{\circ}\)C, respectively [87], and those in the alpine meadow (GCT and SD) were set to \(0\), \(20\), and \(35\ ^{\circ}\)C, respectively [4]. The site-specific \(\varepsilon_{\rm max}\) is usually estimated by the nearly instantaneous (e.g., half-hourly scale) net ecosystem CO\({}_{2}\) exchange (NEE) and photosynthetic photon flux density (PPFD) during the peak growth stage [26; 88; 89]. We used \(\varepsilon_{\rm max}\) values of \(0.30\) g C mol\({}^{-1}\) PPFD, \(0.41\) g C mol\({}^{-1}\) PPFD, and \(0.38\) g C mol\({}^{-1}\) PPFD for the NM, GCT,  
Figure 2: MODIS evapotranspiration (ET) observations for the two temperate steppe sites (NM and XL) during the growing season from 2004 to 2011.  
and SD sites, respectively [4; 36]. To stay in accordance with the other sites, the \(\varepsilon_{\text{max}}\) value for the XL site was estimated by one week of half-hourly flux data during peak growth periods (16-22 July 2011). The one-week period was selected based on the maximum daily NEE during 2006-2011. The \(\varepsilon_{\text{max}}\) was calculated using the Michaelis-Menten function [89] as follows:  
\[NEE = \frac{\varepsilon_{max}\times PPFD\times GPP_{max}}{\varepsilon_{ max}\times PPFD+GPP_{max}}-R_{dark} \tag{11}\]  
where \(GPP_{\text{max}}\) is the maximum canopy CO\({}_{2}\) uptake rate at light saturation, and \(R_{\text{dark}}\) is the mitochondrial CO\({}_{2}\) respiration in the dark. \(\varepsilon_{\text{max}}\), \(GPP_{\text{max}}\), and \(R_{\text{dark}}\) are the fitted parameters with half-hourly observed NEE and PPFD data using nonlinear regression analysis (Figure 3). The \(\varepsilon_{\text{max}}\) was estimated to be 2.1 g CO\({}_{2}\) mol\({}^{-1}\) PPFD or 0.57 g C mol\({}^{-1}\) PPFD for the XL site.  
Calibration of the TG, GR, VI, and AVM models primarily involves the estimation of the scalar \(m\). In practice, they can be calibrated using a half-data method (with the remaining data for model validation) [28; 72] or an all-data method (all the data are used for both the model calibration and validation) [18; 90]. In this study, we used the all-data calibration since the half-data method may result in large uncertainties in the model performances if using short-term observations [18; 90]. For example, only two years of flux data are available for the NM site. In addition, the half-data method would make the LUE and statistical models noncomparable due to the different amounts of validation data.
# Winter Wheat Take-All Disease Index Estimation Model Based on Hyperspectral Data
## 2 Materials and Methods
### Modeling Method  
Partial least squares regression (PLSR) method produces calibration models, models based on multiple linear regression, combining principal component analysis (PCA) and canonical correlation analysis (CCA). PLSR model is suitable for a large number of variables, multiple correlations among variables and small amount of observed data [27], which solves the problems of small samples and multicollinearity often encountered in multiple linear regression analysis. PLSR has a good prediction effect in the case of small sample data and has widely been used in the estimation of crop disease degree [28; 29]. PLSR was used as the main modeling method in this study.
## 5 Results
### Parameter Group  
A parameter group includes a name, an optional IFC ID that can be used for mapping, and an optional description, along with a list of parameters and a mapping list for various simulation applications (Figure 12). The parameter group name is specific to the database (e.g., ASI_Raum), but it can store multiple mappings for IFC export (Figure 13). E.g., for Revit2 DALEC import to work correctly, the room parameters must be stored within the IfcPropertySet ASI_Raum, as Revit2 DALEC looks for this specific property set.  
Figure 11: Building element classes.  
Figure 10: Data structure and inheritance.  
Parameter  
A parameter is defined using the following attributes; the mandatory fields are marked with an *:  
* Value type
* Relevant from phase
* Building-Smart GUID  
* Data type  
* Parameter type *  
Parameters can belong to one or more parameter groups, where they are listed accordingly. Each parameter may also have mappings for BIM or simulation tools (see Figure 14). The library's parameter name is flexible, but mappings ensure that it is exported to the IFC with the correct name for the specific simulation tool. Use cases can be assigned to the parameter in line with ONORM A6241-2 [52]. Although phase and use case fields are currently optional, they will be used in future workflows to align parameters with the BIM model's phase and use case for improved clarity.  
Figure 12: Parameter groups (IfcPropertySets).  
Figure 13: Property set mapping.  
The \"dimensioning\" attribute links to internal Revit API dimensioning variables, while the \"data type\" maps to Revit's internal data types. The \"value type\" can either be free-form or a predefined list (e.g., Usage--Office | Meeting | Kitchen | Toilet, etc.). Similar to Revit, the \"parameter type\" can be set to either \"Instance\" (specific to a component) or \"Type\" (applies to all components of the same type). If a parameter lacks a mapping for the BIM tool, it is newly created; otherwise, the existing BIM property is adopted based on the mapping (see Figure 14, red box). Proper API naming alignment with the Revit API internal naming conventions is crucial.  
Note: Revit was the BIM tool used in this project, which explains the frequent references to Revit property mappings. While the Revit Workflow Manager plugin integrates and manages server-sourced properties, the underlying database structure remains tool-independent and can be linked to other BIM platforms in future developments.
## 2 Materials and Methods
### Climate, Evaportranspiration, and Soil Moisture Datasets  
We compiled the fifth-generation ECMWF (European Centre for Medium-Range Weather Forecasts) reanalysis assimilation data (ERA5) [57]. It is data assimilation that combines model data with observations from across the world into a globally complete and consistent dataset. ERA5 is a replacement for the ERA-Interim reanalysis and more relevant information can be found on the official website ([[https://www.ecmwf.int/](https://www.ecmwf.int/)]([https://www.ecmwf.int/](https://www.ecmwf.int/)), accessed on 6 February 2020). We obtained the monthly climatic datasets, including accumulated precipitation, and mean air temperature (2 m), which cover the period 1979-2019 and are at 0.25\({}^{\circ}\) spatial resolution. Based on the monthly data, we calculated the early growing season (May-June) precipitation (MJP), mid-growing season (July-August) temperature (JAT) and precipitation (JAP), growing season (May-September) temperature (GST) and precipitation (GSP). Moreover, we also selected the ERA5 datasets that contained monthly evapotranspiration (ET) and volumetric soil water/moisture (SM) in four soil layers (0-7 cm, 7-28 cm, 28-100 cm, and 100-289 cm) in this study.
###### Abstract  
The growing data demands are pushing researchers to pay more attention to spectrally efficient modulation formats. The four-dimensional (4D) signal constellation modulation format has been investigated for metro networks' applications to achieve better power efficiency. To cope with such modulation formats, the requirement of better digital signal processing (DSP) is also increasing rapidly. More complicated DSPs bring us extra costs; thus, the DSP-free coherent receivers are also investigated because of the high-power consumption of conventional DSP-based receivers, but the transceivers upgrading also results in extra costs. In this invited paper we implement a 4-dimentional modulation format based on Slepian sequences. We applied LDPC coding and experimentally investigated the BER performance in a two-dimensional (2D) 40 km fiber link transmission and demonstrate that being error free is possible without employing the complicated DSP. We compared our proposed modulation scheme with regular 16 QAM and found it outperforms 16 QAM with DSP over back-to-back transmission by 3.8 dB improvement in OSNR when BER = \(10^{-5}\), while over 40 km metro network communication link our proposed 4D modulation signals are still successfully transmitted, and the LDPC-coding still works properly with such a new transmission strategy. On the other hand, DSP-free transmission of LDPC-coded 16-QAM exhibits an early error floor phenomenon.  
4D modulation; Slepian sequence; DSP-free; 16 QAM; metro network; 2D transmission +
Footnote †: journal: Sensors
## 3 Results and Discussion
### Adsorption Isotherms
#### 3.4.3 Adsorption Mechanism
Slightly less important parameters affecting dye retention efficiency will be the physical features of the anion exchangers such as grain size, uniformity coefficient and sphericity [75]. Taking into consideration assumptions of the Langmuir and Freundlich models, calculated isotherm parameters, the flat structure of direct dye and properties of the anion exchange resins it can be stated that the adsorption of DY50 on A21, A23 and A24 has a complex nature. The mechanism of dye binding by anion exchangers depends on the chemical structure of the dye, the type and number of chromophore and auxochrome groups, as well as the structure and type of adsorbents. For a more accurate analysis of DY50 retention by the A21, A23 and A24 anion exchangers there were recorded ATR-FT-IR. The spectra before and after DY50 sorption using the polystyrene (A21) and polyacrylic (A24) resins are shown in Figure 10. There were many bands characteristic for the anion exchangers matrix [76]. For example, the broad bands at 3374 cm\({}^{-1}\) and 3271 cm\({}^{-1}\) can be attributed to -OH stretching vibrations connected mainly with residual hydration water in A21 and A24, respectively. This band is particularly visible in the case of the polyacrylic anion exchanger, the matrix of which is more hydrophilic in comparison to the polystyrene anion exchanger, which is rather hydrophobic. There are low-intensity bands related to the stretching vibrations of the C-H in aromatic ring in the range 3100-3000 cm\({}^{-1}\). The bands at 2800-3000 cm\({}^{-1}\) refer to stretching vibrations of C-H in >CH- and -CH\({}_{2}\)-. The stretching vibrations C-C of benzene rings and waging or twist of methylene in amine functionalities can be found at 1420-1480 cm\({}^{-1}\) and 1250-1350 cm\({}^{-1}\), respectively. Vibrations at 1000-1100 cm\({}^{-1}\) can be attributed to C-N stretching vibrations in -NH-CH\({}_{3}\) as well as -CH\({}_{3}\) rocking in -N(CH\({}_{3}\))\({}_{2}\). Bands at 1085, 1145 and 1013 cm\({}^{-1}\) are confirmation of a new symmetric and asymmetric vibration of the sulfonic -SO\({}_{3}\)\({}^{-}\) and -S=O groups in the analyzed spectra of the anion exchangers after DY50 sorption.
# Knowledge Graph Extrapolation Network with Transductive Learning for Recommendation
## 4 The Proposed Method
### Prediction Layer  
The embedding representation of each layer contains the underlying latent feature of the item and the user, as well as higher-order connectivity information. It is useful for mining the user's underlying preferences. Hence, it is indispensable to aggregate information of each layer of user and item representation. There are three types of aggregators:  
* Sum aggregator, which sums embedding representations of each layer up before applying a non-linear transformation, as follows: \[f_{\text{sum}}=\sigma(\mathbf{W}\cdot\sum_{\mathbf{e}\in\phi}\mathbf{e}+ \mathbf{b}),\] (16) where \(\mathbf{W}\) and \(\mathbf{b}\) are the trainable weight and bias, \(\sigma\) is set as an activation function as Sigmoid. \(\phi\) denotes the representation of users or items at all layers. If the representations of all layers of the item need to be combined, \(\phi\) represents \(\phi_{v}\) and \(\mathbf{e}\) represents each of these representations in \(\phi_{v}\). The same goes for the user. The other two aggregators are also represented this way.
* Pool aggregator, which selects the maximum vector in the feature representations of all layers and uses a non-linear transformation as follows: \[f_{\text{pool}}=\sigma(\mathbf{W}\cdot\text{pool}_{\text{max}}(\phi)+\mathbf{ b}).\] (17)
* Concat aggregator, which concatenates representation vectors followed by a non-linear function as follows: \[f_{\text{concat}}=\sigma\Big{(}\mathbf{W}\cdot\Big{(}\mathbf{e}^{(1)}\| \ldots\|\mathbf{e}^{(k)}\|\ldots\|\mathbf{e}^{(|\phi|)}\Big{)}+\mathbf{b} \Big{)},\] (18)where \(|\phi|\) is the number of vectors in \(\phi\), \(\mathbf{e}^{(k)}\in\phi\), \(\parallel\) is the concatenation operation which means concatenate two vectors.  
The use of aggregators which combines the representation of different layers is able to capture higher-order structural information in the knowledge graph and enrich the potential representation of users and items. The effects of different aggregators are compared in Section 5.5.3.  
The aggregated vectors of the user and item were obtained in the previous step. Let \(\mathbf{e}_{u}\) denote the vector representation of the user. \(\mathbf{e}_{v}\) is set as the vector representation of the item. Finally, the inner product of the user representation and item representation is conducted to predict the probability of user preference for the item:  
\[\hat{y}_{uv}=\mathbf{e}_{u}^{\top}\mathbf{e}_{v}. \tag{19}\]
## 4 Sealing Evaluation of Coprocks in Three UGSs Planned or in Construction
### Verification of Evaluation Results  
Additionally, the maximum method is adopted to verify the accuracy of the evaluation results. In this study, the maximum value is 10. Taking the thickness of the caprock as an example, the thickness of caprocks in Zhujiadun gas field is the thickest among these three UGSs, and the score of caprock thickness in Zhujiadun gas field is 10, while the caprock thickness of Xing-9 is 400 m, and the score of Xing-9 is (400/500) \(\times\) 10 = 8. The assignment of values for other factors follows a similar method. According to the above analysis results, the score of the six evaluation factors on three UGS can also be obtained; the score criteria is based on optimum method as shown in Table 8.  
According to the ranking grades in Table 4, the sealing of caprocks is divided into four aspects: good, generally good, medium, and bad. The detailed values of these four  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Xu-2** & **Xing-9** & **Zhujiadun** \\ \hline A & 6 & 5 & 10 \\ B & 10 & 0.2 & 0.05 \\ C & 3 & 8 & 10 \\ D & 10 & 4 & 4 \\ E & 10 & 10 & 10 \\ F & 10 & 7 & 1 \\ \hline \end{tabular}
\end{table}
Table 8: The assignment of values for six parameters in three depleted gas fields designed for underground gas storage.  
Figure 6: Evaluation results of caprock’s sealing performance of three underground gas storage.  
levels are 10, 8, 7, and 5. The comprehensive value M is proposed to evaluate the sealing of caprocks; the detailed equation is as follows:  
\[M=\sum_{i=1}^{6}Z_{i}m_{i} \tag{14}\]  
where \(M\) is the comprehensive value, \(Z_{i}\) is the weight of six evaluation factors, and \(m_{i}\) is the score of the gas storage. The evaluation criterion of the comprehensive values are listed in Table 9.  
After the calculation, the final comprehensive values of Zhujiadun, Xing-9, and Xu-2 are 7.43, 6.71, and 6.67, respectively. From Table 9, we can see that the Zhujiadun caprocks show a good sealing ability, and it is suitable for the construction of underground gas storage. The evaluation result is in accordance with the actual engineering, which indicates that the evaluation method and comprehensive value is reasonable. From Table 9, the caprock sealing ability of Xu-2 and Xing-9 gas fields is medium, which requires further demonstration tests to analyze the feasibility of constructing the underground gas storage.  
From the evaluation of three gas fields, the Zhujiadun has the best sealing performance, Xu-2 has the worst sealing performance. These results are similar to the fuzzy comprehensive evaluation results, which indicates that the accuracy of fuzzy comprehensive evaluation can be acceptable.  
It should be noted that only six factors, such as breakthrough pressure, permeability, porosity, the thickness of caprock, brittleness, and crack development degree, are considered in this paper using the fuzzy comprehensive evaluation method. If more parameters, such as rock mechanical properties, are obtained, they can be also considered to update the evaluation models.
## 3 Deep-Learning-Based Address Verification Technique
### Deep Learning Model  
Figure 4 shows the generation of a deep learning model [38; 39; 40; 41]: word embeddings, LSTM layers, and dense layers.  
A text vectorization technique is used to vectorize address elements before using them in a deep learning model [39]. Text vectorization can be divided into two levels: word and character levels. In this study, the character level was selected because learning is conducted about the components of the address rather than the entire string constituting the address. Thus, address elements, including spelling errors, can be classified at the appropriate administrative district level according to the degree of similarity. In addition, even when an abbreviation or code is used, accuracy can be increased by including it in the training dataset.  
The Long Short-Term Memory (LSTM) layer is a special type of recurrent neural network that can learn order dependence in sequence prediction or classification problems [42; 41; 43]. In this research, the LSTM model consists of two LSTM layers with 128 hidden units in each LSTM cell.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Country** & **Level\_1** & **Iv1\_Division** & **Level\_2** & **Iv2\_Division** & **Level\_3** & **Iv3\_Division** \\ \hline Korea & Chungcheongbuk & Do & Cheongju & Shi & Seowon & Gu \\ Korea & Gyeonggi & Do & Anseong & Shi & Bogae & Myeon \\ \hline \hline \end{tabular}
\end{table}
Table 1: Administrative district sample dataset (Korea).  
Figure 4: Creation of a deep-learning-based address learning model for address validation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Region Name** & **Label** \\ \hline Korea & country \\ Chungcheongbuk & level\_1 \\ Cheongju & level\_2 \\ Seowon & level\_3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Sample training dataset.  
The dense layer applies the softmax activation function to divide the classification results into four different classes: country level, region level 1, region level 2, and region level 3. For example, if \"Korea\" is input into the LSTM model, \"Country level\" is output from the softmax function.
# CycleDiffusion: Voice Conversion Using Cycle-Consistent Diffusion Models
## 4 Experiments
### Subjective Evaluation  
Subjective evaluations of sound quality and similarity for the baseline DiffVC and the proposed CycleDiffusion were carried out using the pretrained MOSNet [37], which predicts the human mean opinion score (MOS) rating. The MOS scale ranges from 0 to 5, with 5 indicating the most natural speech and 0 indicating the least natural speech. MOSNet is designed to simulate a human evaluator conducting MOS assessments on input speech samples. Table 4 summarizes the mean MOS values and the corresponding 95% confidence intervals produced by MOSNet for the converted speech outputs generated by DiffVC and CycleDiffusion.  
The proposed method surpasses the baseline system in both intra-gender and inter-gender conversion scenarios. It is especially effective in enhancing the quality of voices that already perform well with the existing method (MOS of 3.5 or higher). For example, in the case of F1\(\rightarrow\)M2 conversion, the MOS value increased by approximately 0.49 (from 3.76 to 4.25), while in the case of M2\(\rightarrow\)F2 conversion, it improved by 0.27 (from 3.93 to 4.20). In some cases where the existing method struggled, that is, with MOS values of 3.2 or lower, such as in the case of M1\(\rightarrow\)F1 conversion, it decreased slightly by 0.05 (from 3.18 to 3.13). Despite these few less favorable cases of minor decreases, the average MOS values showed a significant improvement of approximately 5.7% (from 3.50 to 3.70), demonstrating that the voices generated by the proposed method sound more natural. It is worth mentioning that, in addition to the improvement in the average MOS values, the confidence interval decreased by 32.1% on average (from 0.06 to 0.04), indicating less variance in voice conversion performance among different speakers.
## 3 Materials and Methods
### Anomaly Detection
#### 3.4.3 Evaluation  
In order to evaluate the performance of the developed anomaly detection framework, three different metrics were computed, namely the Image Distance, Anomaly Score, and Z-distance. The corresponding definition is described below:  
* Image Distance: consists of the Mean Squared Error (MSE) between the real image and the fake image (i.e., image generated by GAN);
* Anomaly Score [14]: is the deviation between the query images and corresponding reconstructions, which is given by: \(A(x)=A_{R}(x)+k\times A_{D}(x)\), where x is a new image, \(A_{R}(x)\) is the image distance, \(A_{D}(x)\) is the MSE between the features extracted from the real and the fake images, and \(k\) is a weighting factor, which, in this work, was set to one;
* Z-distance: corresponds to the MSE between the real and the fake latent spaces.
# Chinese Event Detection without Triggers Based on Dual Attention
## 4 Experiment
### Experiment Results Analysis
#### 4.2.2 Dual Attention Fusion Ratio Analysis  
EDWTDA's core components are local and global attention. Local attention captures key semantic information in sentences and simulates hidden event trigger words by giving higher attention scores to keywords. However, it does not avoid the ambiguity of trigger words. Global attention resolves the issue of trigger word ambiguity and aids in determining event types by mining the rich semantics in documents, while it dilutes the fraction of attention words and is vulnerable to noisy data in documents. Local and global attention alone cannot solve all the problems. It is quite critical to set an appropriate dual attention fusion ratio. In this paper, we observe the trend of the F1-score of EDWTDA with the value of \(\lambda\), and take the highest F1-score as the value of \(\lambda\). Figure 5 shows that the curve overall presents a trend of first rising and then falling and reaches its peaks at 0.44 (i.e., the final value of \(\lambda\)), with a difference of about 1.5% between the two boundary points. Given the information mentioned above, it is clear that gradually increasing the proportion of local attention can help the model focus on the key words in the sentence and improve model classification accuracy. When the ratio reaches 0.44, the local attention enhancement capacity reaches its peak, after which it only weakens the ability to focus global attention.