esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Background  
Renewable energy sources (RES) are rising rapidly in different countries, powered by the cost reduction of wind turbines and photovoltaic (PV) panels [1]. They are increasingly becoming the future's dominant energy source, but harvesting them requires an understanding of the mechanisms of their volatility and the ability to predict various environmental processes over a scale ranger [2]. Such understanding of the environment is the key to renewable energy processing, in particular solar and wind power. When the global population and industrialisation continue to rise exponentially, the fossil fuels used to generate electricity are also rapidly depleting [3]. The ongoing overuse of fossil fuels in the production of electricity continues to inflict environmental problems such as global warming. RESs are environmentally friendly and inexpensive [4].  
Solar energy is one of the most critical sources of renewable energy that can contribute to addressing current environmental and energy issues on the electrical grid [5]. It is widely regarded to be the world's best-growing energy industry [6]. Nonetheless, for proper and effective management of the electrical grid, the integration of solar energy onto the electrical grid requires detailed forecasting [7]. Policy-makers for power utilities face the challenge of balancing demand and electricity supply in a cost-effective way which also benefits future economic growth and environmental sustainability. Solar irradiance forecasting is critical for backup programming, strategic planning, short-term power procurement, shifting other energy sources, backup utilisation planning, and peak load demand [8]. It is relevant for various other activities along with photovoltaics (PVs), agriculture, medical study applications, and desalination by seawater [9]. The steadily increasing integration of solar systems around the world is an indication of the need for accurate knowledge and understanding of solar resources in the design of solar electric grids. Solar irradiance studies are therefore of great importance for the optimal project and power forecasting of PV grid-connected plants.  
A modelling framework to determine the statistical distribution of the daily total solar radiation at different locations is discussed in [10]. Researchers have since been paying attention to solar irradiance, using many modelling techniques. Solar irradiance forecasts consist primarily of physical models and data driven models [11; 12]. Physical models are based on numerical weather forecasts. The data driven models consist of statistical and machine learning models.
# Research on Fault Diagnosis Algorithm of Ship Electric Propulsion Motor
## 4 Experiments and Results
### Ablation Experiments
#### 4.2.3 The Effectiveness of the Fault Diagnosis Model Based on the Improved Bi-LSTM Neural Network  
In this paper, a Bi-LSTM neural network model based on WOA optimization is proposed to realize the fault diagnosis of inter-turn short circuits. By comparing the fault diagnosis results of the improved Bi-LSTM neural network with those of the unimproved Bi-LSTM neural network, the effectiveness of the improved algorithm is verified. It should be noted that the data preprocessing part adopts the improved VMD decomposition method, and the feature extraction part adopts the improved multi-scale feature extraction method.  
The number of hidden-layer nodes and learning rate in the Bi-LSTM prediction model are hyperparameters. During model training, the diagnosis accuracy and performance of the model can be improved by selecting the best set of hyperparameters through hyperparameter optimization. The WOA optimization algorithm is used to optimize the number of hidden layer nodes and the learning rate of the Bi-LSTM network to reduce the diagnostic error. The LSTM network has one hidden layer and one fully connected layer. The selection ranges of the number of hidden-layer nodes and the learning rate parameter are \([100,~{}300]\) and \([0.001,~{}1]\), respectively. The population and iteration times of the whale algorithm are 10 and 50, respectively.  
Figure 14: The 2D scatter plot using the improved multiscale feature extraction.  
Comparing Figures 12-14, it can be seen that the improved VMD decomposition method, the improved multi-scale feature extraction method, and the improved Bi-LSTM fault diagnosis model have been greatly improved in all aspects. It can be seen from the whole training iteration process that the accuracy of the model tends to be stable after 300 iterations. The accuracy of fault identification is as high as 100%, the distance between classes becomes larger, the distance within classes becomes smaller, and it has high accuracy and robustness. The proposed fault diagnosis model based on multi-scale space can effectively classify PMSM inter-turn short circuit faults of different degrees.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 4 Results
### Statistical Tests  
The results of all DM tests are shown in Table 10. From the results, it can be seen that the full-based model is much lower than 0.05 relative to the other listed combinatorial  
Figure 8: MAPE reduction rate for PSO-GA approach relative to the mean approach for scallions.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Combined Strategy** & **Mean** & **Median** & **PSO-GA** \\ \hline Panel A: Homogeneous & & & \\ ARIMA-based & 2.5245 & 2.5103 & 2.1190 \\ LSTM-based & 3.6087 & 3.6555 & 3.3173 \\ GRU-based & 2.9431 & 3.0036 & 2.3952 \\ \hline Panel B: Heterogeneous & & & \\ VMD-based & 3.5940 & 2.8778 & 2.8794 \\ EMD-based & 2.7204 & 2.6548 & 2.3808 \\ SSA-based & 2.4552 & 2.3464 & 2.1190 \\ \hline Panel C: Semi-heterogeneous & & & \\ Full-based & 2.6724 & 2.6809 & 1.9209 \\ \hline Panel D: Benchmark combination model & & & \\ Single-based & 3.8139 & 3.8117 & 3.7718 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The MAPE error in combination models for scallion price forecasting.  
Figure 7: MAPE (%) of combination models for scallion price forecasting.  
models. In most cases, this indicates that the full-based model outperforms the other listed combinatorial models at the 95% confidence level. The main reason for this is that the full-based combination strategy increases the size of the model pool and maintains the model diversity within a certain range.
## 3 Results and Discussion
### Analysis of Performance of the Final ANN Model
#### 3.3.3 Sensitivity Analysis  
In this section, sensitivity analysis based on Individual Conditional Expectation (ICE) [98] is introduced, in order to explore the influence of each input variable on the output response of the ANN model. By definition, ICE is able to display how the output response changes when an input variable changes. Figure 12 shows the results of sensitivity analysis for each input variable, including the raw curve obtained by ICE, the most appropriate fit of the raw curve and the normalized area of the covered surface. It demonstrates that all input variables have an impact on the LBC of CFSS members, but to varying degrees and with differing effects. In terms of positive effect, the LBC of CFSS members increases with increasing \(f_{y}\) following a linear equation; \(f^{\prime}_{c}\), B and \(\delta\) following a nonlinear form. In terms of negative effect, the LBC of CFSS members decreases with increasing L, \(e_{t}\) and \(e_{b}\) following a nonlinear equation. In addition, it seems to have a minimum point in the graphs of \(e_{b}\) and \(e_{t}\). The descent stage allows confirming the negative effect of loading eccentricities on the LBC. However, the ascent stage should be checked in further studies, by investigating more configurations of loading eccentricities (see also Figure 2f,g). Regarding the normalized area of covered surface, B, \(f^{\prime}_{c}\), \(\delta\), \(f_{y}\), L, \(e_{b}\) and \(e_{t}\) exhibit 38.7%, 19.3%, 17.9%, 9.4%, 7.7%, 3.8% and 3.2%, respectively. This means that the most influential input variables are the geometric parameters of the steel tube and the compressive strength of the concrete core. These remarks are in close accordance with experimental studies in the literature [11; 15; 18; 99]. Moreover, the sensitivity analysis indicates that linear correlation is not enough to describe the relationship between the inputs and the LBC, especially in the case of geometric parameters of the cross section. From physical point of view, such observation is in good agreement with the literature, as the LBC depend on the cross-sectional areas of the steel tube (\(A_{s}=(B-2\delta)^{2}\)) and the concrete core (\(A_{c}=B^{2}-(B-2\delta)^{2}\)), respectively [100; 101]. Overall, without solving complex equations, an approach using an interpretable ANN model could indicate the relationships between input variables and output response in an efficient manner and avoiding excessive computational cost.  
Figure 11: Uncertainty analysis of the ANN model.  
Figure 12: Sensitivity analysis of (**a**) f\({}_{y}\), (**b**) f\({}^{\prime}\)\({}_{c}\), (**c**) B, (**d**) L, (**e**) \(\delta\), (**f**) e\({}_{t}\) and (**g**) e\({}_{b}\), on the output response from the ANN model.
## 4 Experiments and Discussion
### Case Study  
In this section, a case study is presented to illustrate the capability of the model to transfer knowledge from the source to the target language. As shown in Table 7, the experimental results indicate that, by integrating a heterogeneous graph with contrastive learning, the XCLHG model effectively captures both syntactic and semantic structural information across texts in different languages.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multicolumn{1}{c}{**XLM-R**} & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Full Model** \\ \hline POS & & & ✓ & ✓ & ✓ & ✓ & ✓ & ✓ \\ dependency & & & ✓ & ✓ & ✓ & ✓ & ✓ & \\ translation & ✓ & ✓ & & ✓ & ✓ & ✓ & ✓ & ✓ \\ similarity & ✓ & ✓ & ✓ & & ✓ & ✓ & ✓ & \\ TL-CL & ✓ & ✓ & & ✓ & & ✓ & & \\ LL-CL & ✓ & ✓ & & ✓ & & & ✓ & \\ \hline DE & 90.97 & 91.93 & 92.37 & 91.22 & 92.34 & 91.95 & 92.58 & 92.39 & 92.91 \\ FR & 89.15 & 92.05 & 92.11 & 91.70 & 92.57 & 92.46 & 92.99 & 92.70 & 93.13 \\ JA & 86.95 & 88.12 & 88.92 & 87.42 & 89.02 & 88.78 & 89.63 & 89.24 & 89.93 \\ \hline \hline \end{tabular}  
* DE represents German, FR represents French and JA represents Japanese.  
\end{table}
Table 6: Ablation study on Amazon Review dataset.  
Three distinct languages are selected for analysis. The models yield correct predictions for both German and Spanish texts; however, for longer Russian texts, the CLHG method produces prediction errors. This can be attributed to the tendency of longer texts to lose structured information, thereby weakening the dependency relationships. To address this challenge, the proposed model employs a heterogeneous graph structure to fuse syntactic and semantic information, while also incorporating a multi-view contrastive learning strategy. This strategy both rectifies translation errors and pulls together samples within the same category, thereby mitigating the loss of dependencies in long texts and enhancing the overall accuracy and robustness of the model in CLTC tasks.
## 2 Materials and Methods
### LDAS-Monde
#### 2.1.1 ISBA Land Surface Model  
In this paper, the CO\({}_{2}\)-responsive [19; 42; 43] version of ISBA is used as well as the itsmulti-layer soil water and heat transfer [44; 45] version. The soil is discretized with 14 layers spanning a 12 m depth. The lower boundary of each layer is 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1.0, 1.5, 2.0, 3.0, 5.0, 8.0, and 12 m deep (see Figure 1 of [45]). In this configuration, ISBA describes the water and carbon fluxes on a subdaily frequency, plant growth and crucial variables of vegetation components, such as LAI, and above ground-biomass.  
In the ISBA model, the evolution of vegetation variables is controlled by photosynthesis, which enables vegetation growth resulting from the CO\({}_{2}\) uptake. A minimum LAI threshold is prescribed as 1 m\({}^{2}\cdot\)m\({}^{-2}\) for coniferous forest or 0.3 m\({}^{2}\cdot\)m\({}^{-2}\) for other vegetation types. Conversely, a lack of photosynthesis triggers higher mortality rates. The carbon uptake related to photosynthesis represents the gross primary production (GPP) while the CO\({}_{2}\) released by the soil-plant system constitutes the ecosystem respiration (RECO). The difference between these two quantities corresponds to the net ecosystem CO\({}_{2}\) exchange (NEE).  
In the model version used in this study, ISBA parameters are prescribed for 12 generic land surface types, which consist of (i) nine plant functional types (needle leaf trees, evergreen broadleaf trees, deciduous broadleef trees, C3 crops, C4 crops, C4 irrigated crops, herbaceous, tropical herbaceous and wetlands), (ii) bare soil, (iii) rocks, and (iv) permanent snow and ice surfaces. Those parameters are derived from the ECOCLIMAP land cover database [46].
###### Abstract  
The accurate extraction of a coastline is necessary for various studies of coastal processes, as well as for the management and protection of coastal areas. Very high-resolution satellite imagery has great potential for coastline extraction; however, noises in spectral data can cause significant errors. Here, we present a newly developed _Coastal Extraction Tool_ (CET) that overcomes such errors and allows accurate and time-efficient automated coastline extraction based on a combination of _WorldView-2_ (WV-2) multispectral imagery and stereo-pair-derived digital surface model (DSM). Coastline extraction is performed and tested on the Iz-Rava island group, situated within the Northern Dalmatian archipelago (Croatia). Extracted coastlines were compared to (a) coastlines extracted from state topographic map (1:25,000), and (b) coastline extracted by another available tool. The accuracy of the extracted coastline was validated with centimeter accuracy reference data acquired using a UAV system (_Matrice 600 Pro Pro + MicaSense RaEdge-MX_). Within the study area, two small islets were detected that have not been mapped during the earlier coastline mapping efforts. CET proved to be a highly accurate coastline mapping technique that successfully overcomes spectral-induced errors. In future research, we are planning to integrate data obtained by UAVs infrared thermography (IRT) and in situ sensors, measuring sea and land surface temperatures (SST and LST), into the CET, given that this has shown promising results. Considering its accuracy and ease of use, we suggest that CET can be applied for automated coastline extraction in other large and indented coastal areas. Additionally, we suggest that CET could be applied in longitudinal geomorphological coastal erosion studies for the automated detection of spatio-temporal coastline displacement.  
Keywords: coastline extraction tool (CET); WorldView; multispectral data; thermal imagery +
Footnote †: journal: Applicid  
Article  
Automated Coastline Extraction Using the Very High Resolution WorldView (WV) Satellite Imagery and Developed Coastline Extraction Tool (CET). _Appl. Sci._**2021**, _11_, 9482. [[https://doi.org/10.3390/appl11209482](https://doi.org/10.3390/appl11209482)]([https://doi.org/10.3390/appl11209482](https://doi.org/10.3390/appl11209482)) +  
Footnote †: journal: Applicid
# Parent and PHY Selection in Slot Bonding IEEE 802.15.4e TSCH Networks
## 5 Evaluation
### Testbed Results  
Here, the performance of the heuristic was evaluated on the testbed. First, an appropriate \(\delta\) value was selected for the heuristic by comparing theoretical PDR values using the monitored reliability data. Afterwards, the PHY allocations for the different slotframe lengths were observed. Finally, the performance of the heuristic (with the selected \(\delta\)) was compared to that of the near-optimal GA scheduler using our proposed slot bonding implementation on the testbed.
## 5 Summary and Outlook  
We have reviewed and discussed three strategies for enzyme immobilization that are achieved simultaneously during the synthesis of nanocarriers. These strategies include organic-inorganic hybrid nano-flowers (HNFs), metal-organic frameworks (MOFs), and conductive polymers (CPs) as nanocarriers for enzyme immobilization. These three strategies avoid cumbersome preparation methods, maintain mild preparation conditions, and simplify the preparation process and workflow for enzyme immobilization. However, these three enzyme immobilization methods still have certain issues. For instance, HNFs exhibit poor stability. Although some studies have combined them with other immobilization methods or incorporated additional substances during the preparation process to regulate biomimetic mineralization, not all enzymes are suitable for this approach, limiting its applicability. Future efforts should focus on enzyme modification or changing the anions used in HNF preparation to expand the range of enzymes suitable for HNF preparation. MOFs, based on coordination bonding for precipitation, suffer from inadequate stability due to their single coordination bond. Moreover, the precipitation of MOFs not only requires consideration of enzyme electronegativity but also the strength of coordination interactions between central metal ions and organic ligands, which can affect MOFs' stability. Future directions should involve finding new co-precipitants to increase coordination interactions and promote MOF nucleation, thereby enhancing their stability. One-step in situ enzyme immobilization in a conductive polymer electropolymerization strategy based on physical embedding within polymers may significantly impact enzyme activity. Future research should focus on expanding the types of conductive polymers to improve enzyme stability. Currently, there are only these three strategies mentioned in this paper for achieving enzyme immobilization at the nanoscale during carrier synthesis, but they have shown significant potential. Future efforts should aim to improve the technical aspects of these strategies while also exploring and developing new immobilization strategies. This approach has the potential to leverage the advantages of simplicity, mild conditions, and other benefits, ultimately achieving the goal of green, clean, and rapid enzyme immobilization.  
Conceptualization, P.L. and X.H.; writing--original draft preparation, X.H.; writing--review and editing, P.L.; project administration, X.C.; funding acquisition, X.C. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Key Research and Development Program of China (2021 YFC2101000). The authors declare no conflicts of interest.
## 4 Two Improved Decision-Level Fusion Criterions for MUD
### The Veto Logic Decision-Level Fusion Criterion (VL-DFC)  
Another DFC introduced into the field of MUD in this paper is the veto logic (called VL-DFC). The veto logic implies that all sub-optimal detectors (all members) have to agree with the final decision that the chairman makes. If anyone of them dissents, the final decision should be abolished.  
In this method, the voting bit is defined at first:  
\[u_{m}=f(u_{1m},u_{2m},\cdots,u_{m})=\begin{cases}+1,&\text{if}\sum\limits_{i=1}^{n }u_{m}=+n\\ -1,&\text{if}\sum\limits_{i=1}^{n}u_{m}=-n\\ \times,&\text{otherwise}\end{cases} \tag{30}\]  
where \(m=1\), \(2\), \(\ldots\), \(K\) and \(K\) is the number of users. Besides, the symbol \"\(\times\)\" means this bit cannot be determined between \(+1\) and \(-1\) in this veto logic, while the term \(\sum\limits_{i=1}^{K}u_{m}=+n\) (or \(\sum\limits_{i=1}^{n}u_{m}=-n\) ) means that \(+1\) (or \(-1\)) is the unanimous agreement reached by all sub-optimal detectors.  
Through Equation (30), the \(K\)-dimensional voting vector is \(\mathbf{U}=(u_{1}\), \(u_{2}\), \(\ldots\), \(u_{K})^{T}\), and the number of undetermined bits in it is \(L\) (\(L<K\)).  
It is evident that there are \(2^{\text{L}}\) likely solutions that the chairman should make a further decision. Here, we construct a simplified solution space, which consists of all these likely solutions, for the chairman to make his final decision. The approving degree function (Equation (27)) in CA-DFC is also applied to select the best one as the output of VL-DFC.  
To summarize, this VL-DFC can be represented by the following steps:  
1. Calculate the \(K\)-dimensional voting vector \(\mathbf{U}\) by Equation (30);
2. Construct a simplified L-dimensional solution space based on \(\mathbf{U}\), and L is the number of undetermined bits in \(\mathbf{U}\);
3. Compare the \(2^{\text{L}}\) likely solutions in this space, and consider the solution that has the largest value of Equation (27) as the final decision made by the chairman.  
Note that, the CA-DFC is a special case of VL-DFC, for the final decision in CA-DFC is bound to be involved in the simplified solution space of VL-DFC. Consequently, the BER performance of VL-DFC is better than that of CA-DFC, that is, \(P_{ecm\_VL}<P_{ecm\_CA}\). Whereas, the performance improved by VL-DFC, compared with CA-DFC, is at the cost of its computational complexity increased. The detailed discussion about this is located in the following section.
# An Ultrasonic Guided Wave Mode Selection and Excitation Method in Rail Defect Detection
###### Abstract  
Different guided wave mode has different sensitivity to the defects of rail head, rail web and rail base in the detection of rail defects using ultrasonic guided wave. A novel guided wave mode selection and excitation method is proposed, which is effective for detection and positioning of the three parts of rail defects. Firstly, the mode shape data in a CHN60 rail is obtained at the frequency of 35 kHz based on SAFE method. The guided wave modes are selected, combining the strain energy distribution diagrams with the phase velocity dispersion curves of modes, which are sensitive to the defects of the rail head, rail web and rail base. Then, the optimal excitation direction and excitation node of the modes are calculated with the mode shape matrix. Phase control and time delay technology are employed to achieve the expected modes enhancement and interferential modes suppression. Finally, ANSYS is used to excite the specific modes and detect defects in different rail parts to validate the proposed methods. The results show that the expected modes are well acquired. The selected specific modes are sensitive to the defects of different positions and the positioning error is small enough for the maintenance staff to accept.  
ultrasonic guided waves; SAFE; rail defect detection; mode excitation +
Footnote †: journal: Applied Physics Letters