esa-sceva/satcom-chunk-collection huggingface dataset:
# Dissolved Oxygen Modeling by a Bayesian-Optimized Explainable Artificial Intelligence Approach
## 3 Case Study  
Hydrological and water quality records on the Mississippi River are used to test the established models. The Mississippi River extends \(\sim\)3766 km from its source at Lake Itasca in Minnesota to its mouth at the Gulf of Mexico (Figure 3). It is one of the most important rivers in North America, serving as a crucial lifeline for numerous communities, ecosystems, and industries along its course [36]. The Mississippi River Basin, which spans 31 U.S. states and two Canadian provinces, drains \(\sim\)40% of the continental United States. Covering an area of 3.24 million km\({}^{2}\), it ranks as the third-largest drainage basin in the world and the seventh-largest in streamflow and sediment load [28,37]. The watershed encompasses a variety of landscapes, from the forested regions of the Upper Mississippi to the fertile agricultural plains of the Midwest and, ultimately, to the extensive deltaic wetlands of the Mississippi River Delta.  
From an ecological perspective, the Mississippi River and its associated wetlands create crucial habitats for various plant and animal species [36]. This river system supports numerous fish species, including those of significant economic value, such as catfish, bass, and paddlefish. Additionally, the river and its floodplains function as essential stopover sites for migratory birds, accommodating millions of waterfowl during their annual migrations [38]. Nonetheless, the Mississippi River faces considerable environmental challenges, including water pollution, habitat degradation, and the threat of invasive species [36]. Therefore, accurate DO modeling of this river is crucial for understanding and mitigating environmental issues. It ensures aquatic life's health and maintains the river's ecological balance.  
The data are collected at the Baton Rouge (30.45\({}^{\circ}\) N, 91.19\({}^{\circ}\) W) gauge station (Figure 3), spanning eight years from January 2016 to December 2023. Temperature \(T\), discharge \(Q\), gage height \(G\), pH, turbidity \(Tu\), and specific conductance \(Sc\) are considered the main influencing parameters of DO and are used to calibrate and validate the predictive models. The station employs standard hydrological and water quality monitoring protocols, following the established guidelines by the United States Geological Survey (USGS). The sampling techniques include automated in situ measurements and optical or membrane-based sen  
Figure 3: The studied Mississippi River reach and the monitoring station (red star). Source: Google Earth.  
sors. The daily average data are divided into training and testing sets with a ratio of 7:3. Table 1 presents the statistics of the dataset.
# Mobility Management of Unmanned Aerial Vehicles in Ultra-Dense Heterogeneous Networks
## 7 Future Research Directions
### Massive MIMO  
Massive MIMO will revolutionize the way UAVs are used in communication networks. Massive MIMO guarantees several factors, such as the UAVs' exceptional mobility. One scenario for massive MIMO in mobility is that a large number of antennas are used at the BS to serve multiple single-antenna terminals with very high capacity at the same time. As stated in [87], UAV deployment should not have major limits, which is what pilots prefer. Establishing enormous antennas for UAVs is a target that must be achieved to implement 5G connected UAVs in fully loaded networks without affecting performance for existing ground users. Several studies conducted on MIMO are cited here to provide researchers with the relevant knowledge [13; 14; 122; 123; 124; 125; 126; 127; 128].
# Seismic Effectiveness of Multiple Seismic Measures on a Continuous Gider Bridge
## 3 Establishment of a Finite Element Model of a Bridge
### Simulation of the Bridge and Seismic Measures
#### 3.2.2 Finite Element Model of Restrainer  
Many measures restrict the large displacement of bridges, such as restrainer and unseating prevention devices [14]. Restrainers can be used to restrict the displacement of the bearing within an allowable deformation; thus, such a device is selected to control large displacements between the superstructure and substructure of a bridge. The steel bar was selected as the restrainer in this study, and its structural diagram is shown in Figure 3a. The material of the steel restrainer bar has a yield strength of 335 MPa and the elastic modulus of 200 GPa. The restrainer details are described in [PERSON] et al. [12].  
The steel cable restrainers are modelled by using the tension-only bilinear elements. Considering that the initial design space of the restrainer needs to provide enough common deformation space for bearings, the initial design space \(o\) was selected to be the same as the displacement of bearings under a small earthquake, which is 38 mm, and the stiffness \(k\) was selected to be \(10^{4}\) kN/m. The restrainers were set on #1 and #5 piers [12; 35].  
The gap element in the finite element program was used to simulate the restrainer; hence, simultaneously considering the design parameters such as initial design space and stiffness, the finite element model of the device is as shown in Figure 3b. When the relative displacement between the superstructure and the substructures \(d\) on one side of the bridge exceeds the initial design space of the restrainer \(o\), the restrainer on this site will be activated:  
\(o\) is the initial design space of the restrainer,  
\(d\) is the relative displacement of the superstructure and substructures of the bridge,  
\(k\) is the stiffness of the restrainer,  
\(f\) is the force of the restrainer, the value of which can calculated according to Equation (9):  
Figure 3: Structural diagram and finite element model of restrainer: (**a**) structural diagram; (**b**) finite element model.  
\[f=\left\{\begin{array}{c}k(d+o)\\ 0\end{array}\left(\begin{array}{c}d+o\leq 0\\ d+o>0\end{array}\right).\right. \tag{9}\]
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 2 Structural Diversity and Roles of Rice Secondary Metabolites
### Terpenoids and Their Biological Functions
#### 2.3.3. Diterpenoids and Their Biological Functions
Momilactone B (**149**) exhibited greater growth inhibitory activity than momilactone A (**148**) [91]. Momilactone B (**149**) was preferentially secreted from the rice roots into the neighboring environment over the entire life cycle at phytotoxic levels. Momilaconte B (**149**) seems to account for the majority of rice allebotathy, while momilactone A (**148**) accumulates to higher levels in the plant upon infection. Interestingly, both momilactones A (**148**) and B (**149**) inhibited root and shoot growth of rice seedlings only at concentrations greater than 100 \(\upmu\)M and 300 \(\upmu\)M, respectively. Therefore, the ability of momilactones A (**148**) and B (**149**) to suppress the growth of rice seedlings was much lower than their effect on _E. crus-galli_ and _E. colonum_, with no visible damage to rice seedlings exerted by momilactones A (**148**) and B (**149**) at levels that were cytotoxic to other plant species [91]. Selective removal of the momilactones from the complex mixture of rice root exudates significantly reduced allebotathy, which demonstrated that momilactones served as allelochemicals [8,92]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Rice Part Used for Isolation** & **Biological Activity and Function** & **Ref.** \\ \hline Phytohormone gibberellins & & & \\ Gibberellin A (**145**) & Whole plant & Growth-promoting activity & [70] \\ Gibberellin A (**146**) & Whole plant & Growth-promoting activity & [70] \\ Gibberellin A (**147**) & Whole plant & Growth-promoting activity & [70] \\ \hline Pimaradiene-type diterpenoids & & & \\ Momilactone A (**148**) & Coleoptiles & & \\  & Bran & Growth inhibitory activity on rice roots & [90] \\  & & Inhibitory activities on seed germination & \\  & & and growth of barnyard grass & \\  & Root exudates & Allebotathy effect & [91] \\ Momilactone B (**149**) & Coleoptiles & Antifungal activity & [71] \\  & Seedings & Growth inhibitory activity on rice roots & [90,92] \\  & Seedings & Allebotathic effects & [40] \\  & & Bran & Inhibitory activities on seed germination & [93] \\  & & and growth of barnyard grass & \\  & Root exudates & Allebotathy effect & [91] \\  & & Weak growth inhibitory activity & [94] \\  & & Roots & - & [95] \\  & & - & [95] \\  & & - & [95] \\  & & Leaves & Weak antifungal activity & [72] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Diterpenoids and their biological activities. _Molecules_**2018**, _23_, 3098  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Rice Part Used for Isolation** & **Biological Activity and Function** & **Ref.
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 5 Recent Developments and Accomplishments
### Pancreas
Human islets and endothelial cells cultured on human pancreatic scaffolds initiated insulin secretion and blood vessel formation, which indicates that the human ECM could provide an appropriate niche for generating a functional BAEP. [PERSON] et al. tested varying concentrations of detergents and their impact on the decellularization of rat pancreata [127]. Intact vasculature and significant reduction in immunogenic content was optimal using 0.05% SDS for 6 h for both cannulation of the common bile duct and aorta. Using the same decellularization protocol, [PERSON] et al., showed that acellular rat pancreatic scaffolds were recellularized in vivo post-implantation with de novo vascularization and the presence of infiltrated beta cells [128]. Overall, the development of acellular pancreatic scaffolds has shown great promise for supporting the survival and function of pancreatic islets across species, which could lead to improved outcomes for pancreatic cell transplantation. In addition to providing a biocompatible pancreas-like niche, next-generation therapies aim to address the shortage of donor islets and immune-rejection. Current strategies are exploring the use of stem cells to mass-produce functional beta cells, which could dramatically reduce or completely replace the need for donor pancreatic tissues. [PERSON] et al. developed a differentiation protocol to generate millions of glucose-responsive human beta cells from human pluripotent stem cells (hPSCs) [129]. This discovery was a major step forward in the effort to replace the need for donor pancreata for islet transplantation; however, these cells are not immune-protected. Therefore, in order to capitalize on this virtually unlimited source of beta cells, it is necessary to develop methods for immuno-protection. Several groups have postulated the use of inert biomaterials that could shield beta cells from immune cells, while allowing for glucose-sensing and insulin secretion. [PERSON] et al. reported successful application of polymeric encapsulation of human beta cells in diabetic, immune-competent mice [130]. Although encapsulation methods protect beta cells from immune cells and allow normal function, they still face several hurdles, including proximity to blood vessels and foreign body response to biomaterial implantation. However, recent advances in genetic engineering and immunology may lead to the generation of immune-privileged cells without the need for immunosuppression or encapsulation [131; 132].
## 4 Experimental Results
 \tag{6}\]  
As shown in the table, in case of the proposed method, precision and recall values have a value closer to one than the conventional method. Therefore, the proposed method could result higher classification performance than the conventional method. Figure 10: Comparison of confusion matrix #2. The additional experiment was conducted about the illumination change of the images when captured. One of common applications of the smart factory is AOI inspection. It is important to maintain a stable lighting source for AOI inspection. As per the experimental results, in case of the trained model using original images, the classification accuracy is 45.89%. The classification accuracy of the trained model by the conventional method is 43.67%, and that of the trained model by the proposed method is 82.89%. The experimental results show that the classification performance of the proposed method is higher than other trained models; namely, it represents that the proposed method could be robust against the illumination change.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 4 Trends and Prospects of Carbon Emission Research on Biomass Combustion
### Model Optimization and Introduction of New Algorithms  
(1) Model parameter optimization: It is acknowledged that traditional emission inventory models, such as the GFED and the GFAS, are not without uncertainty when applied globally. This is primarily due to the manner in which the model parameters are set. In recent years, researchers have reduced these uncertainties by incorporating additional measured data into the model calibration process and utilizing more sophisticated combustion efficiency and emission factor formulas. By constructing the CASA model, improving the fuel consumption parameter settings, and accurately estimating the net primary productivity of vegetation, [PERSON] et al. were able to reduce the emission estimation error of the GFED4 model by 15% [41]. (2) The introduction of new algorithms: The rapid development of big data and machine learning techniques has created new avenues for research on carbon emissions from biomass burning. The application of machine learning algorithms to historical fire data enables more precise forecasting of future fire occurrences and associated emissions. [PERSON] et al. implemented a deep learning model (YOLOv5n-CB) for real-time forest fire monitoring, improving detection accuracy and speed [42]. This supports the role of machine learning in enhancing fire detection and biomass burning emission tracking. [PERSON] et al. introduced the Fire Danger from Earth Observations (FDEO) system, which uses satellite data and machine learning to predict wildfire danger by up to two months in advance. This highlights how advanced algorithms improve fire prediction and emissions forecasting [43].
## 6 Energy Consumption Calculation  
To estimate the energy cost of SpikeCAN, we follow the methodology commonly adopted in recent spiking neural network studies. In conventional ANNs, most operations rely on MAC computations, which are energy-intensive. In contrast, SNNs primarily use AC operations in their internal layers, requiring MAC operations only in the first layer for input encoding.  
In SNNs, the effective number of floating-point operations (FLOPs) depends not only on the layer dimensions but also on the spike firing rate (SFR) and the number of simulation time steps (T), since spike-based operations are event-driven and occur only when spikes are present. Following the formulation in prior work [27], the computational cost of SNNs is split as:  
\[\text{First layer:}\quad\text{FLOPs}_{\text{first}}=\text{FLOPs }(\text{layer}_{1}) \tag{36}\] \[\text{Other layers:}\quad\text{FLOPs}_{\text{spiking}}=\text{SFR }\times T\times\text{FLOPs}(\text{layer}_{\text{otherwise}}) \tag{37}\]  
To convert FLOPs into energy consumption, we use standard energy coefficients: \(E_{\text{MAC}}=4.6\) pJ per MAC operation and \(E_{\text{AC}}=0.9\) pJ per AC operation. Using these values, the total energy consumption of SpikeCAN can be estimated as:  
\[E_{\text{SpikeCAN}}=E_{\text{MAC}}\cdot\text{FLOPs}(\text{layer}_{1})+\sum_{i =2}^{L}(E_{\text{AC}}\cdot\text{SFR}_{i}\cdot T\cdot\text{FLOPs}(\text{layer} _{i})), \tag{38}\]  
where \(L\) represents the total number of layers and \(\text{SFR}_{i}\) indicates the average spike firing rate in the \(i\)-th layer. Since spiking activity is sparse, the effective energy usage in deeper layers is typically much lower than in the first layer.  
This formula allows us to approximate the total energy usage of our model under a realistic SNN execution paradigm. All spike firing rates and FLOPs values are empirically computed based on simulation logs during evaluation.
## 3 Theoretical Background
### Graph Node Embedding
#### 3.2.2 GraphSAGE  
GraphSAGE creates embeddings for nodes using node features and the local structure of the graph [28]. Only a certain number of neighboring nodes are selected for a node. The features of the selected neighboring nodes are combined with a specific function (e.g., mean, maximum, LSTM). Embedding is created by combining the information from neighboring nodes with the target node's features. GraphSAGE uses aggregation functions to aggregate the information accumulated in the node's local neighborhood. The graphic illustration of the GraphSAGE sample and the aggregate approach is shown in Figure 2.  
Specifically, at each iteration or layer depth \(k\), each node \(v\) collects feature information from its \(N(v)\) neighbors. This collection process can be performed by different methods such as mean pooling, LSTM-based aggregation, or max pooling. The representation of each node \(v\) at depth \(k\) is expressed as \(h_{v}^{k}\) (Equation (6)) [28].  
\[h_{v}^{k}=\sigma\Big{(}W_{k}\cdot\text{CONCAT}\Big{(}h_{v}^{k-1},\text{AGGREGATE }_{k}\Big{(}\{h_{v}^{k-1},\forall_{u}\in N(v)\}\Big{)}\Big{)}\Big{)} \tag{6}\]  
where \(W_{k}\) is the trainable weight matrix in layer \(k\), \(\sigma\) is the non-linear activation function, \(h_{v}^{k-1}\) is the representation of the node features from the previous layer, and AGGREGATE is the aggregator function that collects information from neighbors of \(v\).
# Road to Efficiency: V2V Enabled Intelligent Transportation System
## 6 Open Research Challenges and Future Research Directions
### Edge Intelligence and Edge Computing  
With the proliferation of edge computing resources in vehicular networks, future research should explore the integration of edge intelligence and machine learning algorithms to enable real-time decision-making and data analytics at the network edge. This approach can enhance V2V communication by enabling dynamic resource allocation, adaptive routing, and context-aware data processing.
# Performance Evaluation of Reinforced Recycled Aggregate Concrete Columns under Cyclic Loadings
## 3 Parametric Investigation  
A parametric study was conducted so as to extrapolate the existing experimental outcomes for evaluating the seismic response of RRC columns. For convenience, the extended numerical models were established and discussed on the basis of the typical test columns in Yang (2014) (i.e., NAC-0.30-60, RAC50-0.30-60, and RAC100-0.30-60; see Table 1). The numerical calculation was terminated once the lateral strength dropped to 70% of the peak strength in the descending branch, which was consistent with the loading protocol used in (2014).  
Four key variables were considered and varied in the parametric study: (1) the RCA percentage (0% \(\leq r\leq\) 100%), (2) the yield strength of longitudinal steel reinforcement (300 MPa \(\leq\)\(f_{\rm y}\)\(\leq\) 500 MPa), (3) the area ratio of the steel (1.57% \(\leq\)\(\rho_{\rm s}\)\(\leq\) 6.28%), and (4) bi-directional cyclic loading represented by the loading angle \(\alpha\) (\(0^{\circ}\)\(\leq\)\(\alpha\)\(\leq\) 45\({}^{\circ}\)).  
The related properties for the numerical analyses were selected as (basically in accordance with (2014)): (1) \(b=h=240\) mm, \(c=25\) mm, and \(L=1000\) mm; (2) \(f_{\rm y}=549\) MPa, \(f_{\rm y,v}=389\) MPa, and \(E_{\rm s}=241\) GPa; (3) \(d=12\) mm, \(d_{\rm hoop}=6\) mm, and \(S=60\) mm; and (4) axial load ratio \(n=0.3\).  
Note that the concrete mixing method was crucial to the mechanical properties of RAC. In view of this, two widely-accepted mixing methods--the additional water method (AWM) and the equivalent total water method (ETWM)--were considered in this numerical study. For each case studied, the following values of RCA replacement percentage were used: \(r=0\%\), 50%, or 100%, which corresponded, respectively, to the RAC's target strength\(f_{\rm c,0\%}=33.52\) MPa,\(f_{\rm c,50\%}=28.88\) MPa, or \(f_{\rm c,100\%}=28.0\) MPa, according to [PERSON] (2014).  
It should be highlighted that the pre-saturation method may lead to inferior properties of RAC as compared to the AWM as a result of the bleeding effect. However, there are still different opinions. [PERSON] et al. (2016) have demonstrated that concrete mixes using the pre-saturation method exhibited slightly worse fresh and hardened state behaviors than mixes made with the AWM; however, the mechanical behavior differences observed were generally small and sometimes inconclusive. [PERSON] (2014) have further clarified that this indeed depended on the water absorption of RCA: when the water absorption was low, both methods negatively affected concrete compressive strength, whereas when water absorption was high, compressive strength was not affected and both methods can be accurately used. The current authors also found that pre-saturation was, in general, similar to the AWM (2015). Therefore, regarding the AWM and pre-saturation methods, only the former was chosen to compare with the ETWM in this study.
## 5 Discussion  
### Overfitting Index (OFI) Assessment of the Models  
Overfitting is a common ML issue where the algorithm prioritizes noise in the dataset, distorting relevant relationships. To address this, an overfitting index (OFI) is used to quantify the models' ability to generalize. Lower OFI values indicate more generalizable models, while higher values indicate overfitting to the training subsets. The OFI helps assess a model's ability to identify true underlying trends, and guides optimization efforts to produce trained models that perform well on unseen data [37].  
Table 5 lists the calculated OFI values for developed models to predict the FV. Compression of the OFI calculated clearly shows that integration of the ML models, RBFNN and MELM, with GO technique considerably improves the generalizability of the developed models. The hybrid RBFNN-GO model generated the lowest OFI value of 0.0327 when applied to the studied FV datasets (Table 5), reflecting its enhanced generalizability.