esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Forcing Method  
Forced mode assimilation, as a straightforward and simple DA approach, has been extensively adopted in crop growth monitoring [114]. Its fundamental principle involves directly utilizing RS observational data as external driving forces for CGMs, bypassing complex mathematical optimizations or probabilistic statistical treatments. Instead, it relies on the model's inherent dynamical mechanisms to respond to these external signals, aiming to align model outputs with actual observations [30], as shown in Figure 6. The assimilation model has the advantages of intuition, high efficiency, and no iteration, and plays an important role in the study of early crop DA. Its achievements are mainly focused on the accurate simulation of aboveground biomass and yield [115; 116; 117; 118].  
Simultaneously, these studies have vividly exposed the limitations of the Forced mode assimilation; for instance, it is highly sensitive to external driving factors but tends to overlook fluctuations in the model's internal states. Furthermore, it heavily depends on RS observational data and has limited capacity to deal with uncertainties in the model's internal states. These constraints are evident in the context of precision agriculture applications [119]. Thus, facing potential nonlinear and non-Gaussian processes in future large-scale and complex application scenarios, it is imperative to explore the integration and innovation of the forced mode assimilation with more advanced DA methods, such as variational assimilation and filtering assimilation. Through such integration, we can effectively enhance the accuracy and robustness of DA, thereby addressing the challenges in the agricultural domain in the future [120].  
Figure 6: The assimilation strategy framework of yield prediction is driven directly by assimilation model based on crop canopy temperature as input. Notes: DBA is dry biomass accumulation, kg ha\({}^{-1}\); FBA is fresh biomass accumulation, kg ha\({}^{-1}\); RDBA is relative DBA; RFBA is relative FBA [121].
# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 4 The Fundamentals of Clustering
### General Framework  
In a WSN, clustering aims to maximize the service life of the network, which is carried out in two major phases; namely, cluster formation and CH selection.
# Multi-Source Data Fusion-Based Grid-Level Load Forecasting
## 3 Results  
### Experimental Setup  
#### 3.1.1 Hardware and Software Configuration  
The computational experiments were conducted on a system with Intel (Shanghai, China) Core i7-12600K CPU, ASUS (Guangzhou, China) Dual GeForce RTX 4060 GPU, and 32 GB RAM. The software environment consisted of Python 3.10, PyTorch 2.0.0, CUDA 11.8, and cuDNN 8.10. This configuration provided sufficient computational capacity for the training and evaluation of our proposed peak-weighted loss models across multiple regions.
## 3 Results
### Input Data Intercomparison
#### 3.2.4 Soil Temperature (TS)  
Soil temperature (TS) in the upper soil layer (0-10 cm, Tables 9 and 10 and Supplementary Figures S6 and S7) were observed in CO and FB. Surprisingly, the worse result is the one of EXP1, which shows the largest difference in mean and median values, and the largest standard deviation due to the more extreme minima (underestimation by about 4 \({}^{\circ}\)C) and maxima (overestimation by about 5-7 \({}^{\circ}\)C), and consequent larger range. Simulations with GLDAS2.1 give similar outputs than EXP1, with lower maxima and similar minima, and a larger number of values in the lower half of the distribution. The initialization with GLDAS-2.0 seems to give the best results in both stations, with minima slightly lower than the observations. It is also noticeable that, for the TS variable, the differences between the two vegetation types are small.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Bias** & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP1a & 19.5 & 48.3 & 33.1 & 34.8 & 3.5 & 143.9 & 140.4 \\ EXP2a & \(-\)9.6 & 19.2 & 22.1 & 14.3 & \(-\)43.4 & 88.2 & 131.6 \\ EXP3a & \(-\)2.9 & 6.9 & 16.3 & 5.0 & \(-\)34.0 & 49.3 & 88.3 \\
**OBS** & **0.0** & **28.8** & **13.3** & **30.0** & \(-\)7.5 & **64.4** & **71.9** \\ EXP1b & 49.9 & 78.7 & 55.0 & **72.4** & **4.4** & 182.0 & 177.6 \\ EXP2b & 4.9 & 33.7 & 45.0 & 13.2 & \(-\)99.5 & 150.9 & 250.4 \\ EXP3b & \(-\)7.2 & 21.6 & 33.0 & 11.3 & \(-\)44.1 & 107.5 & 151.6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: SHF (W m\({}^{-2}\)) simulated and observed at the FB station (data number 323).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Bias** & **Mean** & **Standard Deviation** & **Median** & **Min** & **Max** & **Range** \\ \hline EXP1a & 6.8 & 74.4 & 57.2 & 44.5 & 8.9 & 189.3 & 180.4 \\ EXP2a & 28.2 & 95.8 & 50.7 & 96.1 & 6.1 & 204.6 & 198.5 \\ EXP3a & 20.7 & 88.3 & 28.6 & 89.3 & 17.2 & 154.3 & 137.1 \\
**OBS** & **0.0** & **67.6** & **24.4** & **70.1** & **11.1** & **124.9** & **113.8** \\ EXP1b & \(-\)13.5 & 54.1 & 52.5 & 29.6 & 8.7 & 226.0 & 217.3 \\ EXP2b & 22.6 & 90.2 & 59.0 & 77.3 & 7.6 & 296.0 & 288.4 \\ EXP3b & 13.1 & 80.7 & 38.0 & 82.3 & 19.1 & 186.6 & 167.5 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Latent heat flux (LHF) (W m\({}^{-2}\)) simulated and observed at the FB station (data number 268).  
In the case of TS, the obtained differences exceed the instrumental precision (which can be assumed as 0.1 \({}^{\circ}\)C). However, as it can be seen in Supplementary Figure S28, the simulations appear shifted with respect to observations, while fluctuations of daily values are captured, as testified by the high values of the correlation coefficient (Supplementary Table S19).
# A Hybrid Deep Learning-Based Load Forecasting Model for Logical Range
## 2 Materials and Methods
### Proposed Load Forecasting Methodology
#### 2.2.2 Temporal Feature Extraction  
The GRU is a recurrent neural network architecture that incorporates gating mechanisms to address the problems of vanishing and exploding gradients faced when using traditional RNNs [24].  
When processing time-series load data in the Logical Range, the GRU preserves temporal features through its hidden states. The GCSG model employs a two-layer stacked GRU to extract temporal features from \(x_{\mathbf{v}_{i}}\):  
1. Input vector \(\mathbf{x}_{i}^{\mathbf{v}}\) from dataset \(s_{i}^{\mathbf{v}}\) (where \(\mathbf{x}_{i}^{\mathbf{v}}=[x_{i-\mathbf{v}},x_{i-\mathbf{v}+1},\ldots,x_{i}]\)) with initialized zero hidden state into the first GRU layer.
2. Compute reset gate, update gate, candidate hidden state, and hidden state updates using \(\mathbf{x}_{i}^{\mathbf{v}}\) to obtain the hidden state vector \(\mathbf{u}_{i}\).
3. Process \(\mathbf{u}_{i}\) and \(\mathbf{x}_{i}^{\mathbf{v}}\) through the second GRU layer to generate the hidden state vector \(\mathbf{o}_{i}\).
4. Transform \(\mathbf{o}_{i}\) via a fully connected layer to derive the temporal features output vector \(\mathbf{k}_{i}\).
## 5 Discussion
### Uncertainties and Limitations  
Due to the limitations of the statistical models, there are still some uncertainties of this research. In this study, climate factors and land cover are considered to build the LUE-RE model; however, other factors like topographic characteristics, soil type, and human activities would also affect future GPP changes, which were not considered and may result in certain uncertainties in GPP predictions. Furthermore, with the continuous increase in elevated CO\({}_{2}\), the fertilization effect of CO\({}_{2}\) on vegetation may be weakened, and the response of GPP to the increase in CO\({}_{2}\) concentration shows a nonlinear near-saturation feature [47]. The positive impact of increasing CO\({}_{2}\) concentration on terrestrial carbon absorption may be reduced [48]. This indicates that the response of GPP to elevated CO\({}_{2}\) may decrease in the future, which is an issue that the LUE-RE model did not consider and may lead to overestimation of the simulated GPP. Research has shown that by the end of the 21 st century, the average temperature in tropical regions will exceed the optimal temperature, affecting vegetation photosynthesis. From our results, it can also be seen that as the future temperature increases, spring and autumn may have already reached the current summer temperature, leading to a change in the impact of temperature on GPP, which may also lead to a degree of overestimation of the simulated values. The above factors may lead to increasing uncertainties in the model at a larger time scale in the future.
# Neural Graph Similarity Computation with Contrastive Learning
## 4 Experiments and Results
### Experimental Settings
#### 4.1.3 Parameter Settings  
Regarding the graph similarity prediction network, we use three convolutional layers and use ReLU as the activation function \(\sigma\). The dimension of node embeddings generated by all layers is set to 100. For attention-based pooling, \(\sigma_{p}\) is set to the sigmoid function, and the dimension of graph-level embedding is set to 100. _MLP_ consists of four fully connected layers with ReLU activation function, which reduces the dimensions from 600 to 200, 200 to 100, 100 to 50 and 50 to 1, which is then processed by a sigmoid function to predict the similarity score. As to the contrastive learning module, we set \(p_{r}\), \(p_{e}\) and \(p_{m}\) to 0.1 and randomly choose one of the three methods when augmenting the original graph. \(\overline{MLP}\) consists of two fully connected layers with ReLU activation function, where the dimension of embeddings is kept as 300. \(\beta\) is set to 0.5. We set the batch size to 1024 and the learning rate to 0.001. All the hyperparameters are tuned on the validation set. The experiments are conducted on a personal computer with the Ubuntu system, an Intel Core i7-4790 CPU, an NVIDIA GeForce GTX TITAN X GPU and 32 GB memory.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Contents** & **\#Graphs** & **\#Pairs** & **\#A. N.** & **\# A. E.** & **Label** \\ \hline AIDS & Molecular Compounds & 700 & 490 K & 8.9 & 8.8 & Yes \\ LINUX & Program Dependence & 1000 & 1 M & 7.53 & 6.94 & No \\ IMDB & Film Star Ego-Networks & 1500 & 2.25 M & 13 & 65.94 & No \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dataset statistics. # denotes the number of objects. A. N. and A. E. represent the average number of nodes and edges, respectively. Label denotes whether the graphs are labeled.  
It is noteworthy that, since the nodes in AIDS are labeled, we use one-hot embeddings as the initial node embeddings. For LINUX and IMDB, where nodes are unlabeled, we use a constant encoding scheme [7].
## 3 Results
### Results of the Supervised Method
#### 3.2.1 Selection of Features  
The dataset contained collected raw data, and extracting additional features from the data could improve the prediction model. Table 1 describes the extracted features used in this study. First and second-order difference (FD, SD) captured temporal variations, while the moving average (MA\({}_{1}\), MA\({}_{2}\)) took into account the time delay between parameters.  
Having these features available in the dataset did not necessarily improve the model, while it increased the computational time. Therefore, an iterative study to pick the right features for every set of predictors was performed. For every set of predictors, the Random Forest regression algorithm was applied with different combinations of features FD, SD, MA\({}_{1}\), MA\({}_{2}\), hour, weekday and month, including all of them, some or none. The combination which had the lowest error was chosen as the final. In Table 2, chosen combinations can be seen, where the letter in subscript rotates from which parameter the feature was calculated (L--lighting, E--equipment, W--water, T--total).
## 3 Security Aspects Regarding Wireless Body Area Framework
### Data Transmission  
Once authentication between the different entities of the WBAN has been established, communication and data transmission can proceed securely. This involves employing robust encryption, integrity, and message authentication mechanisms.  
Traditional encryption algorithms like Advanced Encryption Standard (AES), AES-GCM (Galois/Counter Mode), AES-CCM (Counter with CBC-MAC), Hash-Based Message Authentication Code (HMAC), and Cipher-based Message Authentication Code (CMAC) have been widely used for WBAN communication. These algorithms provide strong encryption and message authentication to protect against unauthorized access and data tampering [6; 11].
## 5 Conclusions  
Due to the low resolution of SIF data, its application in small-scale agricultural monitoring is challenging. This study applied a downscaling approach, utilizing the Random Forest (RF) model with MODIS products (b1, b2, b3, b4, NDVI, EVI, NIRv, OSAVI, SAVI, LST, LAI, and FPAR) as the driving variables. The model achieved high accuracy for the period from October 2018 to May 2019, with R\({}^{2}\) = 0.931, RMSE = 0.052 mW/m\({}^{2}\)/nm/sr, and MAE = 0.031 mW/m\({}^{2}\)/nm/sr. From October 2019 to May 2020, the model's accuracy remained high, with R\({}^{2}\) = 0.926, RMSE = 0.058 mW/m\({}^{2}\)/nm/sr, and MAE = 0.034 mW/m\({}^{2}\)/nm/sr. The 0.05\({}^{\circ}\) resolution eSIF products were downscaled to 1 km resolution, and validation using time series data confirmed that the downscaled SIF maintained a high accuracy (R\({}^{2}\) > 0.8). Comparison with TanSIF and GOSIF products showed strong correlations (R\({}^{2}\) > 0.8), and the data distribution trend was consistent with GOSIF-GPP, demonstrating a strong SIF-GPP linear relationship.  
This study demonstrates the successful downscaling of SIF products for improved crop growth monitoring in precision agriculture. It provides valuable data and methodologies for future research in remote sensing applications. Future studies may explore further enhancements in model accuracy and the integration of higher-resolution datasets to extend the applicability of downscaled SIF in ecological and agricultural monitoring.  
**Author Contributions:** Conceptualization, J.F. and G.C.; Software, J.F., Z.L., and J.W.; Validation, J.F. and G.C.; Writing--original draft, J.F.; Formal analysis, X.L. and Z.L.; methodology, J.F.; Supervision, X.L.; Project administration, X.L.; Funding acquisition, X.L.; Writing--review and editing, X.L.; Investigation, G.C.; Visualization, J.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Key Research and Development Plan, grant number 2016 YFC0803103.  
**Data Availability Statement:** The code used in this study is available by contacting the corresponding author.  
**Acknowledgments:** We thank the scientists of the TROPOMI Enhanced eSIF, MODIS product for providing the data products. We thank the scholars of TanSIF, GOSIF, and GOSIF GPP for providing the datasets that enabled the validation of this study. Thanks to GLASS LAI remote sensing product for providing crop distribution point datasets. We also thank the anonymous reviewers for their constructive comments on the manuscript.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 2 Materials and Methods
### Ex Vivo Experiment with Fuzzy Logic Control  
In order to maintain the target sample at a constant temperature during the HIFU treatment to destroy the tumor, a fuzzy logic controller was applied to the microcontroller. The range of temperature errors was acceptable at around \(\pm 4\)\({}^{\circ}\)C; therefore, a \"good enough\" solution like the fuzzy controller fit this requirement. With a classic feedback scheme, the exact temperature provided by the thermocouple must then be converted into the fuzzy value that comprises the variables of the predecessor in the rule base [37]. Likewise, the fuzzy values inferred from the rules must be transformed into commands for use in the voltage regulator. A general fuzzy system was introduced to control the target temperature as shown in Figure 4; the fuzzy set, fuzzy state, and the fuzzy rule are demonstrated in Table 1.  
Following the datasheet of the step-down switching regulator (LT1074, Analog Devices, Northwood, MA, USA), both positive and negative output voltage could be adjusted by changing the resistance between switch voltage and feedback pins. By default, the output voltage was set at \(\pm 21\) V with the value of resistors at 18.8 k\(\Omega\) which is connected to a parallel group of fixed potentiometers. Each individual could be activated to change the total resistance and create the required bipolar output voltage. The output voltages \(\pm 18\) V, \(\pm 15\) V, \(\pm 12\) V, \(\pm 9\) V, \(\pm 6\) V were chosen to be the outputs of the fuzzy logic controller corresponding to \"heat fast\", \"heat\", \"remain\", \"cool\", or \"cool fast\" the target sample.  
Figure 3: Ex vivo experiment for validating HIFU system performance: (**a**) acoustic pressure measurement (**b**) maximum temperature achievement and fuzzy logic controller testing.  
The chicken breast tissues were also used to check the degree of thermal damage of the proposed HIFU design. The experimental setup was mostly the same as in the last section but this time, the focal point of the transducer would be justified on the middle plane of the tissue. The experiment duration was stretched to various periods of times (60, 120, 180, 240, and 300 s) for evaluating the extent of thermal coagulation. After the treatment, each sample was removed, and a cross-section was made in the longitudinal direction at the treated area. Later, every sample was captured by a digital camera, and the region of treatment was defined and consciously calculated five times (N = 5) in terms of the area in ImageJ (ImageJ 1.53a, National Institute of the Health, Bethesda, MD, USA).
## Appendix A  
The gyratory compact records specimen density variation and shear resistance continuously in relation to a given starting point. These compaction and resistive effort curves allow to evaluate mix resistance respectively to densification and to traffic loads calculating specific densification indexes [60; 61; 67; 68; 69].  
These curves can be divided into two zones, each one related to different asphalt mix performances (Figure A1). Furthermore, densification indexes (Compaction Densification Index, CDI, and Traffic Densification Index, TDI) are related to the energy required for reducing the air voids or changing volume of the asphalt mixture and can be calculated by integration [18; 70].  
As regards the resistive effort curves, two other indexes can be defined: the Compaction Force Index, CFI, and the Traffic Force Index, TFI (Figure A1); both of them are related to mix stability and they are used to describe mix resistance to distortion (shear resistance). The resistive effort \(w\) is calculated as follows:  
\[w=\frac{4 Pe\theta}{Ah}\]  
where \(e\) (m) is the eccentricity of the resultant force, \(p\) is the magnitude of the resultant force (kN), \(\theta\) is the angle of tilting (rad), and \(A\) (m\({}^{2}\)) and \(h\) (m) represent the area and the height of the specimen, respectively.  
The parameter \(w\) has a unit of stress (kPa) and it represents the work done by the compactor per unit volume per gyration, assuming the material perfectly viscous or plastic [71]. The area between the function %Gmm(\(n\)), ranging from Nini (10 gyrations) to N92, corresponds to the 92% of maximum theoretical density: it can be defined as the \"construction effort\". The 92% Gmm, which is the target density at the end of construction, represents the compatibility of the mix in laying operations until it reaches a value of air voids of around 8% [19; 69; 72].  
The area under the densification curve between N92 and a number of gyrations corresponding to 98% Gmm is associated to mix performance under traffic loading: the critical density of 98% representsthe traffic post-compaction action during pavement service life until a final value of air voids of about 2% [71].  
Mixtures with higher values of CDI/CFI show worse workability because they are difficult to compact, whereas higher values of TDI/TFI characterize asphalts with a better stability under traffic loads [68].  
In this study, these indexes were calculated for the two mixes to obtain more detailed information on how the aged bitumen can modify asphalt mix workability and compatibility at different number of gyrations.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 4 Machine Learning Approach  
We followed an agile CRISP-DM method to find the best models with the best hyper parameters, by starting with simple models and then gradually increasing the number of features and the model's complexity. The key components of CRISP DM-based agile holistic machine learning approach include the following steps:  
Figure 1: The operational EGNOS Performance Prediction system uses trained GIVEI and UDREI prediction models in combination with preprocessed GNSS and (space)weather data to produce predicted UDREI and GIVEI timeseries. These are then translated into the user-level performance indicators such as protection levels and EGNOS availability for the user’s operations.  
The _Domain and Data Understanding_ step involves gaining understanding of the EG-NOS performance prediction problem. See also Section 5.1 below;
2. The _Data Collection and Preprocessing_ step gathers relevant data for the problem domain and preparing it for use in a machine learning model by cleaning the data and transforming them into a suitable format, see also Section 5.1;
3. _Feature Engineering_ is then performed by selecting relevant input features that are expected to help the machine learning model make accurate predictions. See also Section 5.2;
4. _Model Selection and Training_ involves selecting an appropriate machine learning algorithm and optimizing its parameters to achieve the best performance on the training data. More information is found in Section 5.3;
5. _Evaluation_: once a model has been trained, it needs to be evaluated on a separate testing set to determine its performance. More information is found in Section 5.4;
6. _Deployment_: the trained model is finally deployed in a production environment and monitored to ensure that it continues to perform well over time.