esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Technical Discussions on Related Works
#### 2.1.2 WMAC Strategies with Energy Optimization Techniques
These works found the maximization of packet delivery rate through energy optimization models. [PERSON] et al. [50] intended an energy-efficient sensor management architecture for industrial applications. Conspicuously, industrial IoT systems are completely distributed and heterogeneous in nature. Hence, the provision of energy optimization procedures is complicated for different types of sensor nodes. Additionally, this technique implemented load balancing rules and MAC scheduling procedures to optimize the sensor node's energy in the distributed IoT platform to organize the industrial components. In this case, [PERSON] et al. [51] concentrated on node clustering techniques, multi-hop routing principles and bearable energy solutions for underwater sensor networks. The contributions of the above works mainly found MAC-based and channel-based energy leakages due to various circumstances. At the same time, the future findings regarding green-MAC (G-MAC) computing models and reactive scheduling models are expected as follows:  
* Adaptations of various WMAC principles are expected to save energy under tiny autonomous WSNs. * Distributed and heterogeneous WMAC principles are highly anticipated for IoT-based WSNs. * Multi-channel reactive scheduling models and control management principles are required for WSNs. * Routing and medium coordination solutions are needed to minimize the energy with a cross-layered design. Hence, the exhaustive literature search and contribution analysis provided future technical needs in order to improve the energy-efficient solutions for WMAC functions. To a certain extent, knowing the impacts of routing layer functions with energy-saving models is essential for confirming a better lifespan of WSNs.
## 3 Numerical Analysis
### Pressure Difference  
The injection pressure in both processes is set to 2300 kPa. In the production well, two well constraints are applied, namely the minimum pressure of 1300 kPa and the maximum steam production of 10 m\({}^{3}\)/day. In reality, the steam production rate is the dominant well constraint, and the production well pressure varies. The pressure difference is the difference between actual injection pressure and actual production pressure. The pressure difference between the injector and producer of the two processes is plotted in Figure 7.  
In the typical SAGD process, a relatively small pressure difference is retained between the injector and producer to avoid steam production. The pressure difference between the injector and producer in the SAGD process is only 30-50 kPa. As seen in Figure 7, the pressure difference is kept almost constant at approximately 30 kPa during the whole process. The main driving force in the SAGD process is gravity. However, the pressure difference in the bottom-up pressure drive varies significantly. At the start of the process, the pressure difference is up to 380 kPa. Such a large pressure difference is capable of displacing the oil between the injector and the producer. This is the reason that high oil production is observed in the first year of the process, as seen in Figure 5a. With the establishment of the steam chamber, the pressure difference is reduced to 70 kPa, and the oil production is reduced correspondingly. With continuous steam injection, the steam chamber encounters shale barriers. Although oil production in both processes is restricted by shale barriers, oil production in the bottom-up assisted pressure drive is still higher than that in the SAGD process between 350 and 1300 days. This is because the pressure difference  
Figure 6: Comparison of oil saturation at the end of the (**a**) SAGD process and (**b**) bottom-up assisted pressure drive process indicating that bottom-up assisted pressure drive decreases residual oil.  
of the bottom-up assisted pressure drive during this period is also higher. The pressure difference behaves as a driving force to displace the oil above the shale barriers. The pressure difference of the bottom-up assisted pressure drive process continues to decrease and is lower than that in the SAGD process at approximately 1300 days. Interestingly, the oil production rate is also below oil production in SAGD at this time. This observation confirms that the pressure drive is another driving mechanism in the bottom-up assisted pressure drive. Meanwhile, this indicates that bottom-up assisted pressure drive is able to improve oil production in oil sands reservoirs with multiple shale barriers.
## 1 Introduction
The performance study revealed the effectual outcomes of the FSSML-EDM model over the compared models under several dimensions. Our contributions are summarized as follows:  
* We propose a model comprising data preprocessing, CSO-FS, ELM classification, and AHB parameter;
* We designed a new CSO-FS technique to reduce the curse of the dimensionality problem and enhanced the classification performance;
* We employed the ELM classification model with the AHB-based parameter optimization technique for the EDM process;
* We validated the performance of the FSSML-EDM model using the benchmark dataset from the UCI repository.
# Estimating Global Gross Primary Production Using an Improved MODIS Leaf Area Index Dataset
## 5 Conclusions
The land type datasets used in this study are MCD12Q1.006 land cover data ([[https://doi.org/10.5067/MODIS/MCD12Q1.006](https://doi.org/10.5067/MODIS/MCD12Q1.006)]([https://doi.org/10.5067/MODIS/MCD12Q1.006](https://doi.org/10.5067/MODIS/MCD12Q1.006)) (accessed on 8 October 2020)), ERA-Interim meteorological data ([[https://www.ecmwf.int/en/forecasts/datasets/browse-reanalysis-datasets](https://www.ecmwf.int/en/forecasts/datasets/browse-reanalysis-datasets)]([https://www.ecmwf.int/en/forecasts/datasets/browse-reanalysis-datasets](https://www.ecmwf.int/en/forecasts/datasets/browse-reanalysis-datasets)) (accessed on 12 October 2021)), the FLUXNET2015 dataset ([[https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)]([https://fluxnet.org/data/fluxnet2015-dataset/](https://fluxnet.org/data/fluxnet2015-dataset/)) (accessed on 16 August 2021)), MODIS GPP products ([[https://doi.org/10.5067/MODIS/MODI7A2H.006](https://doi.org/10.5067/MODIS/MODI7A2H.006)]([https://doi.org/10.5067/MODIS/MODI7A2H.006](https://doi.org/10.5067/MODIS/MODI7A2H.006)) (accessed on 4 December 2019)), BEPS GPP products ([[https://doi.org/10.12199/nesdc.ecodb.2016](https://doi.org/10.12199/nesdc.ecodb.2016) YFA0600200.02.001]([https://doi.org/10.12199/nesdc.ecodb.2016](https://doi.org/10.12199/nesdc.ecodb.2016) YFA0600200.02.001) (accessed on 4 December 2023)), VPM GPP products ([[https://doi.org/10.1594/PANGAEA.87958](https://doi.org/10.1594/PANGAEA.87958)]([https://doi.org/10.1594/PANGAEA.87958](https://doi.org/10.1594/PANGAEA.87958)) (accessed on 16 August 2023)), Google Earth Engine surface solar radiation downwards products ([[https://doi.org/10.24381/cds.68d2](https://doi.org/10.24381/cds.68d2) bb30]([https://doi.org/10.24381/cds.68d2](https://doi.org/10.24381/cds.68d2) bb30) (accessed on 16 May 2023)), vegetation CI dataset products ([[https://doi.org/10.12199/nesdc.ecodb.2016](https://doi.org/10.12199/nesdc.ecodb.2016) YFA0600200.01.003]([https://doi.org/10.12199/nesdc.ecodb.2016](https://doi.org/10.12199/nesdc.ecodb.2016) YFA0600200.01.003) (accessed on 16 August 2021)), and the C3 and C4 vegetation dataset products ([[https://doi.org/10.1029/2001](https://doi.org/10.1029/2001) GB001807]([https://doi.org/10.1029/2001](https://doi.org/10.1029/2001) GB001807) (accessed on 16 August 2021)).
## 2 Architectures of the Modified Deep Neural Networks (DNNs)
### Sub-Networks of Inception-v3  
Inception-v3 is a classical deep network. Its architecture is shown in Figure 2. The network is composed of 11 Inception modules of five kinds in total. Each module is designed by experts with convolutional layer, activation layer, pooling layer and batch normalization layer. In the Inception-v3 model, these modules are concatenated to achieve maximal feature extraction.  
Inception modules adopt the idea of multi-scale. Each module has multiple branches with different sizes of kernels (1 \(\times\) 1, 3 \(\times\) 3, 5 \(\times\) 5 and 7 \(\times\) 7). These filters extract and concatenate different scale of feature maps and send the combination to the next stage; 1 \(\times\) 1 convolutions in each inception module is used for dimensionality reduction before applying computationally expensive 3 \(\times\) 3 and 5 \(\times\) 5 convolutions. Factorization of 5 \(\times\) 5, 7 \(\times\) 7 convolution into smaller convolutions (3 \(\times\) 3) or asymmetric convolutions (1 \(\times\) 7, 7 \(\times\) 1) reduces the number of DNN parameters.  
After removing some deep Inception modules and the classification layers (global average pooling layer and last dense layer with 1000 outputs), we add a set of classification layers (a global average pooling layer, a dense layer with 3 or 4 outputs in our experiments) adapted to our datasets to the new network graph. Sub-networks are named after their last depth concatenation layers; whose names are consistent with that in the Keras implementation (the python deep learning library) [32]. For example, \"mixed10\" represents the sub-network removing no module; \"Mixed9\" represents the sub-network removing Inception E2 module; \"Mixed8\" represents the sub-network removing Inception E1, E2 modules; \"Mixed7\" represents the sub-network removing Inception D, E1, E2 modules; \"Mixed6\" represents the sub-network removing Inception C4, D, E1, E2 modules. The number of parameters in Inception-v3 sub-networks is listed in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Inception-v3** & **Total** & **Trainable** & **Non-Trainable** \\ \hline Mixed6 & 6,834,468 & 6,819,492 & 14,976 \\ Mixed7 & 8,978,340 & 8,959,524 & 18,816 \\ Mixed8 & 10,679,972 & 10,658,596 & 21,376 \\ Mixed9 & 15,730,916 & 15,703,012 & 27,904 \\ Mixed10 & 21,810,980 & 21,776,548 & 34,432 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of parameters in Inception-v3 sub-networks.
## 3 Results and Analysis
### Error Compensation Experiments
These results confirm the model's robustness in maintaining positioning accuracy improvements across different operational conditions.
## 3 Numerical Simulation and Analysis
### Effect of Wettability on Cavitation Inception  
The effect of wettability of the surface on the cavitation inception is investigated by changing wettability with setting the negative pressure in the liquid domain. The parameters of the EOS are the same as the parameters in Section 3.1. The cavitation inception is produced by setting the negative pressure at the computational domain. The negative pressure is used to simulate the conditions of cavitation such as the process of an internal tear. The negative pressure can be acquired by reducing the initial density of the equilibrium state. Subsequently, the paper discusses the time of cavitation generation under different negative pressure conditions. Thus, the paper studies these cases where the negative pressure is set as \(-6.02\), \(-5.69\), \(-5.36\), \(-5.02\), \(-4.69\) and \(-4.36\), respectively.  
The simulation results about the effect of the hydrophobicity on the cavitation inception are shown in Figure 5. It shows that the time of cavitation inception is short with the enhancement of the hydrophobicity of the surface. It means the hydrophobicity can accelerate the cavitation inception. There is no cavitation inception when the surface is hydrophilic in our simulation results. These simulation results verify the conclusion of Reference [11] from the perspective of numerical analysis.  
Figure 4: The comparison about evolutions of the bubble between numerical and the experimental results (**(a)** experimental results: \(\lambda_{0}\approx 1.3,R_{0}=2.50\) mm; **(b)** LBM simulation: \(\lambda_{0}=1.3,\ R_{0}=80\), \(\Delta p=18.334\) ).  
Figure 5: The effect of the hydrophobicity on the cavitation inception.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
## 5 Proposed Algorithm
### Localization Optimization Using GWO  
To enhance the accuracy of the original DV-Hop algorithm, GWO is employed during the localization phase to replace the least squares method for determining the unknown node's position. Using the proposed anchor node selection mechanism, the grey wolf's position vector with the minimum fitness value selects the corresponding anchor nodes (with a value of \"1\") to participate in the localization, while those with a value of \"0\" are excluded. In the GWO algorithm, to solve the distance equations formed by the dominant anchor nodes, the position vector of each grey wolf is represented as \([x,y]\), indicating the potential coordinates of the unknown node. The initial position coordinates of each grey wolf are randomly generated within the range \([0,100]\) and updated in each iteration using Equation (16).  
During global optimization with GWO, Equation (22) is used as the fitness function to evaluate the quality of the grey wolf's position. The smaller the value of \(fitness\_gwo\), the closer the grey wolf's position to the true location of the unknown node. Once the predefined number of iterations is reached, the position of the grey wolf with the minimum fitness value is selected as the final estimated coordinates of the unknown node.  
\[fitness\_gwo(\mathbf{X})=\frac{\sum\limits_{i=1}^{K}{{(\sqrt{{{(x_{i}-x_{u})}^ {2}+{(y_{i}-y_{u})}^{2}}}-d_{ui})}^{2}}}{K\cdot R} \tag{22}\]  
where \(K\) represents the number of dominant anchor nodes selected using the BGWO anchor node selection mechanism.  
Due to the inevitable errors in the estimated distances from unknown nodes to anchor nodes, when using the least squares method for multilateration localization, according to Equation (7), the accuracy of the least squares solution depends on the estimation accuracy of \(d_{uNa}\). If the estimation deviation of \(d_{uNa}\) is large, the deviation generated by the least squares calculation will also increase accordingly. In contrast, when using the GWO algorithm for multilateration localization, Equation (22) is used as the fitness function value. This algorithm directly minimizes the distance measurement error. The minimum value obtained from the algorithm optimization is the localization coordinate of the unknown node. This method not only reduces the sensitivity to distance estimation errors but also avoids the dependence on the initial iteration values, demonstrating unique advantages.
## 4 GPSR Routing Mechanism  
Several proposals have been studied in the literature [51; 52; 53; 54; 55] in order to improve geographical routing protocols by establishing stable and faster routing paths towards the destination in various network environments in MANETs and VANETs/FANETs. Most of these protocols were assigned to improve routing to select the best candidate nodes toward the destination.  
In particular, GPSR routing protocol is one of the most classic location-based routing protocols; where each node is equipped with GPS unit. This will allow each node to know its location and the locations of its neighbors in addition to the location of the destination. A source node estimates the destination's location in the packet header and chooses the next hop that is the closest neighbor to the destination. It is based on calculating the optimal path to the target using an algorithm to transmit the packet until it reaches the destination [23]. However, it is called a stateless protocol in routing since each node in the network has only information about its neighbors and is unaware of the entire network nodes. The GPSR protocol uses two forwarding strategies, as shown in Figure 2, the greedy forwarding mode and the perimeter mode. Based on the state of network nodes; the greedy forwarding (GF) scheme chooses the closest neighbor to the destination in order to forward the packet. Otherwise, if no such neighbor is available closer to the destination (i.e., the void area), the perimeter routing strategy is used, where the source will consider the red-shaded area with no nodes to be a void area. Then, the source will route the packet around the void area by employing the right-hand rule to forward the packet. To accomplish this, the network nodes broadcast beacons on a regular basis. Each node sends a beacon to the broadcast MAC address, comprising its own identification number (ID/IP address) and position [52; 53].
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 5 Deformation Monitoring Visualization Platform
### Intelligent Vision Module  
The intelligent vision module encompasses several key functionalities and Figure 14 shows the interface of this module, including video monitoring (Figure 15b), remote lighting control, enhanced virtual environments (Figure 15a), and crack extraction (Figure 15c). To address the lack of dynamic updates in the 3D model, the module integrates real-time video data to restore the real scene. High-resolution vision sensors can accurately capture real-time images of deformation points and their surrounding areas, enabling managers to track local deformation processes and correlate them with GNSS data and meteorological data for analysis. This integration reveals interaction relationships and provides insights into equipment conditions. In cases of equipment damage, the module helps determine whether the cause is natural or human-induced.  
By visualizing alarm data, the intelligent vision module records specific physical processes and actual scenes, providing managers with a richer information dimension to understand the underlying causes of data fluctuation. The nighttime light control and vision sensor enable 24 h remote real-time monitoring of the target area (Figure 15b). Additionally, edge detection is applied to monitoring images to extract crack parameter information (Figure 15c).  
To improve video surveillance quality at night, this study developed a dual-protocol illumination control framework leveraging WebSocket and TCP communication. A WebSocket server establishes persistent full-duplex connections with web clients, enabling the real-time bidirectional transmission of lighting commands and status feedback. Concurrently, a TCP server interfaces with switching registers through dedicated TCP clients, ensuring the reliable delivery of control signals. The protocol stack implements hexadecimal command conversion (e.g., 0X11\(\rightarrow\)17) through buffer-based data packaging, where Boolean control inputs (1/0) are translated into device-specific byte streams. Hardware actuation is achieved via electromechanical relays in switching registers, where coil energization (command = 1) closes circuit contacts to activate supplemental lighting, while de-energization (command = 0) terminates the power supply. System configuration involves predefined network parameters (IP/port) for switching register connectivity. Figure 16 shows the flowchart of the remote lighting control.  
Figure 14: Screenshots of the statement downloads module.  
Figure 16: Remote light control process.  
Figure 15: Intelligent vision module: (**a**) enhanced virtual environment, (**b**) video monitoring and remote light control, (**c**) information on crack parameters.
## 2 Materials and Methods
### Hybrid Adaptive PID Controller Design
#### 2.3.1 PID Controller Structure  
The enhanced PID controller implements a modified structure that incorporates adaptive elements while maintaining the fundamental PID framework. The controller transfer function is expressed as follows:  
\[\text{C(s)}=\text{Kp}+\text{Ki}/\text{s}+\text{Kds} \tag{14}\]  
where  
Kp is the proportional gain  
Ki is the integral gain  
Kd is the derivative gain  
s is the Laplace operator  
The controller parameters are dynamically adjusted through a hybrid optimization process that combines the following:  
* LSTM-based prediction of optimal parameters;
* Genetic Algorithm optimization for global parameter search;
* Real-time adaptation based on system performance.
## 3 Survey Results
### Lifetime and Energy

Table 4: _Cont._Although energy harvesting is envisioned as the only way for sustainable sensing systems [58], power sources other than batteries or static power networks are rarely used (5% of analyzed deployments). Harvesting module support at the operating system level is, therefore, not an essential part of deployments, until today. However, harvesting popularity may increase in future deployments, and support for it at the OS level could be a valuable research direction. More than 80% of deployments have powered motes present in the network: at least one node has an increased energy budget. Usually, these motes are capable of running at 100% duty cycle, without sleep mode activation. _Design rule 7_: Powered mote availability should be considered when designing a default networking protocol library.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 4 4G and NSA-Based 5G V2X Services  
The evolution of V2X from 4G (LTE) to 5G includes an important period where both technologies have been playing a hybrid role aiming to achieve support for V2X services. In terms of architecture and technology, NSA-based 5G technology is backward-compatible with 4G technology. Therefore, we refer to NSA-based 5G as 5G in this section. In what follows next, we focus on analyzing the system and network support for 3 GPP's 4G and NSA-based 5G V2X services.
# Artificial Neural Network Assisted Error Correction for MLC NAND Flash Memory
## 2 Channel Model
### Cci  
Because of the parasitic capacitance-coupling effect among adjacent cells in flash memory, the threshold voltage of the victim cell increases as the threshold voltage of an adjacent cell increases. The immediate adjacent cells are the major noise source of the CCI. We consider an all bit-line structure. As shown in Figure 1, when the (\(k\)+1)-th wordline (WL) has been programmed, the cell on the \(k\)-th WL can be programmed. Hence, the victim cell is influenced by three immediate adjacent cells. The threshold-voltage shift of the victim cell can be modeled as a linear combination of the threshold voltage changes of those immediate adjacent cells. We can estimate the threshold-voltage shift caused by CCI as  
\[\triangle V_{victim}=\sum_{n}(\triangle V_{t}^{(n)}\cdot\gamma^{(n)}), \tag{5}\]  
where \(\triangle V_{t}^{(n)}\) is the change of an immediate adjacent cell, which is programmed after the victim cell and \(\gamma^{(n)}\) represents the coupling ratio. We assume the vertical and the diagonal coupling ratio are \(\gamma_{y}\) and \(\gamma_{xy}\), respectively. According to the cell-to-cell coupling strength factor \(s\), we can set \(\gamma_{y}=0.08s\) and \(\gamma_{xy}=0.006s\)[12].