esa-sceva/satcom-chunk-collection huggingface dataset:
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 2 Phenology of Forests
### Vegetation Indices for Phenological Research in Woody Species
(2020) [87] used Sentinel-2 images to estimate foliar N content across the start and end of the growing season with a random forest classifier to establish a relationship between the bottom of atmosphere (BoA) reflectances from all Sentinel-2 bands (except bands 1, 9 and 10) and spatial variation of foliar N content. The study revealed that the variability of N concentration is smaller at the start of the season, resulting in high measurement accuracies at the start of the season. However, if it is possible to monitor N concentration in the leaves of deciduous trees, this would be very useful for tracking the progress of autumn leaf senescence, which is closely linked to N recycling. Such approaches require additional research matching Sentinel-2 imagery to N concentration in a wider range of forest vegetation types and tree species to determine the relationship to N recycling and other senescence parameters. Studies using hyperspectral data have indicated the improved performance of simulated Sentinel-2 bands in comparison to Worldview-2, RapidEye and Landsat-8 datasets for estimation of nitrogen and its effect on yields [89; 90]. However, a review of the literature points to the lack of usage of Sentinel-2 satellite data, which need to be explored further for their efficacy in estimating nitrogen concentration.
# Predicting Decompensation Risk in Intensive Care Unit Patients Using Machine Learning
## 2 Materials and Methods
### Modelling Methodology  
Our proposed decompensation risk prediction model implements a sequence-to-sequence approach that processes sequences of clinical characteristics within a 24-h input time window, predicting the risk of a decompensation event every hour during a 24-h forecast period. We define the decompensation event as a 2-class problem (i.e., decompensation, no-decompensation), and predicting the risk of a decompensation event as the problem of predicting the probability of such event to happen. A patient is coded as decompensating if they would be recorded as having died 24 h later. This definition accounts for the fact that a patient is at high risk of dying at any time after a decompensation event has started. This is a more conservative approach than the one used in [11]. Two DL architectures were considered in the development of the sequence-to-sequence risk prediction model: LSTM and hybrid CNN-LSTM. We propose a patient's decompensation score, which is defined as the area under the predicted sequence of decompensation risk within the forecast window. For each patient, the decompensation score is calculated every hour after the 24-h input window, using the 24-h forecast window. This sliding window continues to move until the patient is discharged from the ICU.  
In addition, we developed baseline models based on traditional ML algorithms such as LR, SVM, and RF, although their tasks were modified since they are not designed for handling time series. Therefore, instead of predicting a time series, they were implemented to predict one decompensation event within the forecast period. Therefore, the sole purpose of these baseline models is to establish the minimum performance level against which the DL algorithms should be evaluated.  
Numerical variables were standardised (i.e., mean-centred and scaled by the standard deviation), whilst one-hot encoding was applied to the categorical variables. The methodological approach used in this study is illustrated in Figure 1.  
Figure 1: Methodological approach implemented for this study. (**a**) Displays the input (e.g., vitals, lab tests, etc.) sequences that the DL models take and the output (decompensation risk) sequence that they forecast. A decompensation score is calculated as the area under the decompensation risk sequence (**b**).
## 4 Experimental Results and Discussions
### Discussions  
In the research of lightweight object detection network, previous works have achieved some remarkable success. With the rapid development of the Internet of Things (IoT), some efforts [49; 50; 51] have been made to reduce the need for storage space and computational complexity on edge devices by using the IoT and cloud-based services. These existing efforts send algorithms and databases to AWS services hosted in the cloud for saving storage space on the edge devices. However, these works also have the limitations of long data transmission time, slow data transmission speed, and high demand on the equipment's network condition. Compared with these works, the proposed SSD7-FFAM can overcome these limitations by directly deploying it to edge devices to realize real-time object detection. Similarly, SSD7-FFAM also has the limitation of insufficient processing power.  
Aiming at realizing real-time object detection on embedded devices, we reduced the number of convolutional layers of the original SSD. A novel method of feature fusion and attention mechanism is proposed to improve the detection accuracy. The experimental results comparatively demonstrate the compactness of SSD7-FFAM.  
In addition to the results given, it should be noted that FFAM is a universal scheme that provides a general solution for designing and implementing a fast and high detection accuracy object detector. Except for remote sensing image detection, SSD7-FFAM also has the potential to be applied in other fields, including but not limited to autonomous driving, military object detection, and medical image detection.  
Although compared to other SSD-based lightweight object detection networks, our SSD7-FFAM can start from scratch. One of the limitations of SSD7-FFAM is its low accuracy on large-scale object detection datasets, which guides the direction of our future study.  
SSD7-FFAM only pays attention to the feature extraction part, and the prediction part is not optimized. In future work, we will focus on the overall optimization of SSD7-FFAM.
###### Abstract  
In a wind farm, the upstream wind turbine may cause power loss to the downstream wind turbines due to the wake effect. Meanwhile, the energy production is determined by the power generation and the lifetime of the wind turbine. In this paper, an optimal active power control method is proposed to maximize the energy production of wind farms by considering the wake effect and the lifetime of wind turbine. It starts with the analysis of the pitch angle curve and active power curve seen from the Maximum Power Point Tracking (MPPT) of individual wind turbines. Taking the wake effect into account, the pitch angle curve and active power curve are optimized with the aim of Maximum Power Generation (MPG) of the wind farm. Afterwards, considering the lifetime of wind turbines, a comparison is offered between the MPPT method and the MPG method for energy production using a simplified two-turbine wind farm as an example. Due to the small range of the effective wake area, it is found that the energy production is almost the same. Finally, the pitch angle curve and active power curve are optimized according to the Maximum Energy Production (MEP) of a wind farm. Upon considering and contrasting the MPPT method and the MEP method, it can be seen that the energy production of wind farms can be increased even in the case of there not being an effective wake area.  
maximum power point tracking; wake effect; energy production; lifetime; wind farm +
Footnote †: journal: Applied Sciences
# Simulation of the Fracturing Process of Inclusions Embedded in Rock Matrix under Compression
## 3 Fracturing Behavior of Multiple Inclusions
### Failure Process of Multiple Inclusions  
In rock strata in the field, there are always a large number of embedded inclusions with different sizes, shapes, and orientations. In this section, a 2D numerical model shown in Figure 13 is employed to investigate the fracturing behavior of multiple inclusions. In this model, a number of circular inclusions with different sizes are randomly distributed in the matrix. The model geometry and the boundary conditions are similar to that in Figure 2. The axial load is increased by 0.01 MPa/step and the other mechanical parameters are the same as those listed in Table 3. The inclusion is assumed to be heterogeneous with the homogeneity index of \(m=3.0\), while the matrix is relatively homogenous, \(m=20.0\).  
Figure 14a illustrates the fracturing process in detail, with the acoustic emission (AE) distribution. Moreover, Figure 14b is the fracturing process presented with maximum principal stress. Due to stress concentration, the fractures often initiate from the interface or near the cluster area with several inclusions adjacent to each other. With the increase in load,  
Figure 13: Sketch map of numerical model with multiple circular inclusions embedded in matrix under compressive condition established with RFPA. \(\sigma_{\text{(max)}r}\) is the load of 0.01 MPa/step applied in Y-direction of specimens. The zone with a yellow dotted rectangle (3/4 area of entire model) is split out to make analysis of fracturing mode and stress distribution.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Inclusion** & **Matrix** \\ \hline Homogeneity index (\(m\)) & 3 & 20 \\ Elastic modulus (\(E\))/GPa & 3, 5, 20, 50, 100 & 2, 5, 20, 100, 150 \\ Uniaxial compressive strength (\(f_{\text{c0}}\))/MPa & 50 & 100 \\ Tensile strength (\(f_{\text{fo}}\))/MPa & 2.5 & 10 \\ Poisson’s ratio (\(\
u\)) & 0.25 & 0.45 \\ Friction angle (\(\varphi\))/\({}^{\circ}\) & 30 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Physico-mechanical parameters employed in the models with multiple inclusions.  
the fractures are kept expanding in inclusions, and more and more inclusions are fractured. Finally, almost all of the inclusions are fractured with different extents of damage.  
The distribution of inclusions naturally formed in the matrix is random. It causes dense inclusion areas and sparse inclusion areas. Figure 15a,b shows the field observations and numerical simulation of inclusion fractures, respectively. It can be seen that the consecutive sub-parallel fractures mainly are in dense inclusion areas, while in sparse inclusion areas, non-consecutive fractures and debonding fractures can also be found. The numerical results agree well with the field observations as shown in Figure 15.  
Figure 14: Numerically simulated failure mode associated with both maximum principal stress field and AE distribution in the inclusions. (**a**) AE events; (**b**) maximum principal stress.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 1 Introduction
Then, the multi-objective evolutionary algorithm with classification-based preselection (CPS-MOEA) is employed to solve the optimization problem and its performance is compared with other algorithms. Finally, a comprehensive decision-making process is conducted using the fuzzy C-means clustering (FCM) and grey relational projection algorithm (GRP). The main contributions of this paper are as follows: a model is constructed for the mobile hybrid microgrid that accurately reflects the real-world situation. The microgrid considers renewable energy sources, energy storage systems, and diesel generators as backup power sources. To address the configuration optimization problem of the mobile microgrid system, we utilized a two-step approach. The first step focuses on the mobile microgrid system using a multi-objective evolutionary algorithm with classification based on preselection. This step takes into account the system's mobility and stability to obtain a series of Pareto optimal solutions. The experimental results demonstrate the effectiveness of CPS-MOEA in managing the complexities of the system's planning and configuration, highlighting a significant contribution to the field. The second step builds upon the first by considering decision-makers' preferences. It applies fuzzy C-means clustering to the Pareto optimal solutions obtained in the first step and ultimately selects the best compromise solution that best aligns with decision-makers' preferences using grey relational projection. The rest of this work is structured as follows:  
Section 2 presents the mathematical modeling of the components of the mobile photovoltaic-storage-diesel microgrid system. Section 3 presents the construction of the optimization model. Section 4 introduces the CPS-MOEA, FCM, and GRP. Section 5 analyzes the experimental results. Finally, Section 6 concludes the paper.
## 3 Framework Description
### Process Definition
* The tasks and subtasks (with their actions and goals) that comprise the industrial process. The handling of unpredicted situations that may arise during the process is modeled as a set of tasks and subtasks. * The localized message data containing the descriptions of all the tasks and subtasks. As can be seen in Figure 2, a task may depend on other tasks to start and serve as a logical container for the subtasks. For this reason the task will not have actions or explicit goals, the implicit goal of the task being that it finalize its subtasks. Additionally, the subtasks may depend on other subtasks to start. Another point of the model is to allow parallelism among tasks and subtasks, because tasks or subtasks that are not dependent on one another start in parallel. Every subtask has an action in which the actor that will be responsible for its execution is defined. This, together with the consideration of the actor's operations by the interface (which will be explained in Section 3.3), allows the framework to deal with simulations which have multiple instances of different types of actors. It is important to note that this is a base model and it can be extended during the design phase depending on the requirements of the simulation. The decision of using this model and no other models presented in the bibliography is based on its simplicity, and, therefore, the person in charge of modeling the process does not need to be an expert in a certain mathematical model/theory (e.g., petri nets) or a more formal modelling language (e.g., Business Process Execution Language (BPEL)), but may adapt the model to the theory, language or format of his or her expertise that he or she finds suitable to use for the industrial process to be defined. This decision is aimed at making the process definition more flexible and less time consuming. The data can be stored in any database structure and is processed later by the interface to generate the input needed by the process controller adapted to the simulator.
## 2 Diversity of LPBSs and NRPSs from _Bacillus_ and _Pseudomonas_
### Pseudomonas
#### 2.2.4. Viscosin and Massetolide Synthetases  
Viscosin and massetolide are structurally related lipononapeptides produced by _P. fluorescens_ SBW25 and _P. fluorescens_ SS101, respectively [46, 48]. Viscosin has significant surfactant activity by reducing surface tension of water to 28 mN/m with a CMC of 10-15 mg/L and forms stable emulsions [46, 103]. It also inhibits migration of a metastatic prostate cancer cell line without visible toxicity [103]. In addition, viscosoin and massetolide are required for biofilm formation and swarming motility of _Pseudomonas_ cells [46, 48]. Viscosin/massetolide is synthesized by NRPS systems that are encoded by three large ORFs, termed _viscA/massA_, _viscB/massB_, and _viscC/massC_. The _viscA/massA_ gene is not clustered with the latter genes, but is located at a different locus of the _Pseudomonas_ genome. The distance between _viscA/massA_ and the latter genes is more than 1.5 MB. Analysis of the amino acid sequences revealed two modules in ViscA/MassA, four modules in ViscB/MassB, and three modules in ViscC/MassC. Each module bears A-, T-, and C-domains like other NRPSs. However, none of the five d-amino acid-incorporating modules possesses a cognate E-domain, but contains a C/E domain similar to Arf. Tandem Te-domains were also identified in the last ViscC/MassC module and are likely to be functional for the biosynthesis of both lipopeptides as was shown for the two Te-domains in Arf [101]. Similar to other NRPSs involved in lipopeptide biosynthesis, the _N_-terminal C-domain in the first module is highly similar to the _N_-acyl domain and is presumably involved in _N_-acylation of the first amino acid.
# Three-Dimensional Hyperbolic Mesh Generation Method Based on the Neural Network
## 5 Conclusions  
Aiming at the issue of insufficient control of the boundaries in the traditional hyperbolic mesh generation process, which limits the applicability of the method in generating meshes for flow field simulation, this paper proposes a method that uses PINNs with output range constraints to solve the governing equations for hyperbolic mesh generation, achieving the neural network-based hyperbolic mesh generation of three-dimensional surface and solid models. The main conclusions are as follows:  
1. Adding output range constraints to PINNs can effectively limit the feasible solution space of neural networks, enabling the solution approximation of the governing equations in different models driven solely by the boundary data.
2. The hyperbolic grid generation method based on neural networks can effectively solve the problem of insufficient boundary control existing in traditional methods, and simultaneously generate high-quality mesh. Considering the presented model in this paper, the minimum equialge skewness and maximum deviation angle are used as mesh quality evaluation metrics. The mesh quality of the terrain surface generated by the proposed method improved by 14.9% and 50.9%, respectively, while the quality of the terrain solid model improved by 59.2% and 52.6%, respectively.  
Therefore, this study provides a novel strategy for hyperbolic mesh generation in flow field simulation procedures.  
Conceptualization, H.Y. and W.J.; Methodology, H.Y.; Validation, H.Y.; Writing--original draft preparation, H.Y.; Writing--review and editing, W.J.; Visualization, K.X.; Supervision, W.J.; Funding acquisition, Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Grants-in-Aid from the National Natural Science Foundation of China, grant number 11972239; Fundamental Research Funds for the Central Universities, grant number 2023 SCU12101; and Sichuan Province Major Science and Technology Special Project, grant number 2022 ZDZX0011.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.
# Causal Inference for Modality Debiasing in Multimodal Emotion Recognition
## 5 Experiments
### Ablation Studies  
We conducted ablation studies on the IEMOCAP [31] dataset, carrying out comprehensive experiments to assess the necessity of each component in CausalMER.  
**Importance of Components in CausalMER.** To assess the effectiveness of our model design, we removed specific components responsible for handling the direct effects of text, audio, and video modalities, as well as the proposed loss function. The results are shown in Table 6. We find that removing the direct effect of each single modality leads to consistent improvement (Rows 1-5).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Normalized Modality Contribution**} \\ \cline{2-5}  & **Text** & **Audio** & **Video** & **std** \\ \hline MulT † [20] & 0.287 & 0.304 & 0.409 & 0.066 \\ + CausalMER & 0.330 & 0.355 & 0.315 & **0.020** \\ \hline PMR † [23] & 0.393 & 0.307 & 0.300 & 0.051 \\ + CausalMER & 0.323 & 0.346 & 0.332 & **0.012** \\ \hline DMD † [24] & 0.349 & 0.316 & 0.336 & **0.017** \\ + CausalMER & 0.377 & 0.304 & 0.319 & 0.038 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Modality contribution in CMU-MOSEI dataset. Normalized Modality Contributions with minimal values are highlighted in bold. † means our reimplementation.  
Unlike previous studies [10, 11, 13], the proposed CausalMER can identify and remove biases across more than three modalities by using three learnable parameters: \(c_{t},c_{a},\) and \(c_{o}\). The proposed loss \(\mathcal{L}_{kl_{hal}}\) is included to balance the influence of the \(TE\) and \(NDEs\) during training; otherwise, the model may risk overemphasizing certain terms. Eliminating this loss results in a 1.4% decrease in average accuracy and a 1.2% drop in average F1 score (Row 2), indicating that the loss function effectively promotes balanced learning between the \(TE\) and each \(NDE\) as intended.  
CausalMER effectively balances the effects of the text, audio, and video modalities. To accomplish this, we introduced three modality-specific \(NDEs\): \(NDE_{t}\), \(NDE_{a}\), and \(NDE_{o}\). Removing any of these \(NDEs\) resulted in performance degradation across all instances (Rows 3-5). This finding suggests that multimodal models are not biased toward a single modality but instead exhibit multiple coexisting modality biases. By successfully mitigating these biases, CausalMER achieved improved performance.  
**Different Fusion Strategies.** Following previous studies [9, 13, 14], we applied several fusion strategies in Equation (12). Table 7 shows that MASK [14] (Row 1) significantly outperforms other fusion strategies (Rows 2-4), suggesting that this fusion approach effectively mitigates bias across terms in CausalMER. Future research could further explore fusion methods best suited for CausalMER.
## 4 Conclusions  
The microstructure of the laser remelted FeCrCoNiTiAl\({}_{0.6}\) high entropy alloy HVOF coating and the wear behavior have been investigated in this study. The following conclusions can be made:  
1. The as-sprayed FeCrCoNiTiAl\({}_{0.6}\) HVOF coating exhibited two separate BCC phases and aluminum oxide precipitated after laser remelting process. The laser remelted HVOF coating possessed a compact microstructure and the pre-existing pores and micro-cracks were remarkably eliminated.
2. The laser remelted FeCrCoNiTiAl\({}_{0.6}\) HVOF coating had a higher microhardness of \(914\pm 17\) HV\({}_{0.1}\) and better wear resistance than HVOF coating.
3. The major wear mechanism of the FeCrCoNiTiAl\({}_{0.6}\) HVOF coating and the laser remelted HVOF coating is abrasion. Fatigue wear participated in the HVOF coating.  
Methodology, L.C. and D.H.; software, L.L.; validation, B.H., Z.G. and L.Z.; formal analysis, Z.T.; investigation, L.C. and X.W.; data curation, L.C.; writing--original draft preparation, L.C.; writing--review and editing, Z.Z.; supervision, D.H. and Z.Z.; funding acquisition, L.C. and Z.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2017 YFB0306100; the Academy of Sciences Project of Guangdong Province, grant number 2016 GDASRC-0105; the GDAS' Project of Science and Technology Development, grant number 2021 GDASYL-20210103092, 2017 GDASCX-0115 and 2018 GDASCX-0115. The APC was funded by the Academy of Sciences Project of Guangdong Province.  
The authors declare no conflict of interest.  
Figure 8: Cross-section views of friction samples of the HVOF coating and the laser remelted HVOF coating.
## 2 Materials and Methods
### Characterization Methods
#### 2.3.3 Electrical Conductivity (EC) and pH Measurements  
To ensure optimal cultivation conditions, the pH and electrical conductivity (EC) of the cultures were monitored daily. The pH of the liquid media was measured directly using a pH-GL21 meter (Crison Instruments, Barcelona, Spain), with a resolution of 0.01 pH, calibrated with two buffer solutions (pH 4.01 and 7.01, HANNA Instruments, Leighton Buzzard, UK). EC was directly assessed with an Orion Star(tm) A212 electrical conductivity meter (Thermo Fisher Scientific, Waltham, MA, USA), with an accuracy of 0.5% of reading, calibrated with an EC 1413 \(\upmu\)S/cm conductivity standard solution (HANNA instruments).