esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[20] designed a sliding film integral controller to achieve decoupled control of the induction motor, although the motor model parameter mismatch problem is not considered, but the control of the existence of the jitter vibration phenomenon is, which can easily lead to the system's oscillation instability. The authors of [21] used a combination of neural networks and particle swarm optimization, a good solution to the model parameter mismatch problem, but the current intelligent algorithm design process is more complex, computationally large and difficult to ensure its convergence speed and stability. [PERSON] et al. [22] used a differential beat-free torque and chain control scheme to select the optimal voltage vector by determining the position of the torque angle but did not consider the effect of the voltage vector on the torque and chain. [PERSON], [PERSON] et al. [23] used a multiparameter planning approach to reduce the computation time but could not reduce the online computation as it could only be operated offline. [PERSON] [24] introduced a DTC controlled switching table to determine the sectors where the optimal vectors are located through torque and chain error variations to secondly reduce the number of voltage vectors that need to be computed, but it has a limited number of voltage vectors to choose from for each sector, which leads to large electromagnetic torque and chain pulsations. [PERSON], [PERSON] et al. in [25] proposed the idea of sorting, which transforms the performance of different control objectives into dimensionless sorted values, thus eliminating the weighting coefficients, but the optimal vectors cannot be selected when there are multiple identical sorted values. Ref. [26] used fuzzy control to rectify the weight coefficients, but the fuzzy rules were not verified in advance and the online calculation was more complicated. In this paper, we propose a weightless model predictive control method based on an ESO observer by designing an improved extended observer (ESO) to observe and compensate for the motor parameter perturbations in real time and reclassifying the voltage vector sector selection map, according to the torque error and stator chain error signals, using a new type of two-dimensional switching table to directly select three out of the eight voltage vectors to be evaluated, which reduces the amount of computation while also increasing the alternative voltage vectors, and finally, designing a cost function without considering the weight coefficients to reduce its computational complexity. The remaining work is arranged as follows. Section 2 introduces the mathematical model of the traditional MPTC; Section 3 proposes a weight-free MPTC based on an ESO observer; Section 4 builds a simulation model and compares it with both the traditional MPTC and the weighted MPTC with an ESO observer, followed by an analysis of the results; and Section 5 presents the conclusions.
## 3 Controller Design and Stability Analysis
### Simulation Results for HSTs without Actuator Faults  
To validate the feasibility of the proposed controller for HSTs under actuator fault-free condition, simulation for HSTs without actuator faults was carried out. The simulation results are shown in Figure 5. From Figure 5a,b, the proposed controller can ensure the position and velocity to track the desired trajectories accurately. Figure 5c shows that there is no obvious chattering in the actual actuator output and the actuator saturation limit is satisfied. Figure 5 demonstrates the feasibility and effectiveness of the proposed controller for HSTs despite in presence of model uncertainties, additional disturbance, and actuator saturation.  
Figure 5: Control performance for high-speed trains (HSTs) without actuator faults: (**a**) position tracking error, (**b**) velocity tracking error, (**c**) actual control force.
## 2 Materials and Methods
### Data
#### 2.2.5 Evaporative Stress (ES) and Root Zone Soil Moisture (RSM)  
The Global Land Evaporation Amsterdam Model (GLEAM) is designed to estimate the land surface evaporation and root zone soil moisture from remote sensing observations and reanalysis data [49; 50]. Specifically, the Priestley-Taylor equation is used to calculate potential evaporation within the GLEAM based on the near-surface temperature and net radiation, while the root zone soil moisture is obtained from a multilayer water balance driven by precipitation observations and updated with the microwave soil moisture estimates [50]. The actual evaporation is estimated by constraining potential evaporation with a multiplicative evaporative stress factor based on the root zone soil moisture and vegetation optical depth estimates. The GLEAM version 3.5a provides the global daily and monthly potential and actual evaporation, evaporative stress conditions, and the root zone soil moisture from 1980 to 2020 at a spatial resolution of 0.25\({}^{\circ}\) ([[http://www.gleam.eu](http://www.gleam.eu)]([http://www.gleam.eu](http://www.gleam.eu)), accessed on 20 May 2021). In this study, the GLEAM v3.5a root zone soil moisture and evaporative stress were used.  
The time period of SIF, P, T, RH, ES, and RSM we used in this study were from January 2001 to December 2020, and the 2001-2019 MCD12C1 land cover product was used. With regard to the spatial resolution, through the resample method which was 'nearest' in ArcGIS ([[https://desktop.arcgis.com/zh-cn/arcmap/10.6/map/main/mapping-and-visualization-in-arcgis-for-desktop.htm](https://desktop.arcgis.com/zh-cn/arcmap/10.6/map/main/mapping-and-visualization-in-arcgis-for-desktop.htm)]([https://desktop.arcgis.com/zh-cn/arcmap/10.6/map/main/mapping-and-visualization-in-arcgis-for-desktop.htm](https://desktop.arcgis.com/zh-cn/arcmap/10.6/map/main/mapping-and-visualization-in-arcgis-for-desktop.htm)), accessed on 1 June 2021) version 10.6, we resampled the GPM precipitation (0.1\({}^{\circ}\)) and the GOSIF (0.05\({}^{\circ}\)) to 0.25\({}^{\circ}\), that is, the same as the ERA5 and GLEAM datasets, which also meant the resolution of the pixel used in the next study and analysis was 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\).
## 2 Literature Review
### Problem Statement and Objective  
Despite recent advances in vision-based deep learning for excavator activity recognition, existing studies focus on single-source RGB video data captured by external cameras. These approaches are significantly hindered by obstructions, limited viewpoints, and sensitivity to lighting conditions, leading to reduced reliability in dynamic construction environments. While RGB video streams have been extensively used for excavator activity recognition, they inherently lack depth perception and spatial context, which are critical for accurately distinguishing between visually similar activities. A secondary source of visual data is required to enable uninterrupted streaming of excavators while working at the construction site, thereby enhancing the visual capabilities and improving the training process of the deep learning-based activity recognition model. The point cloud enables better depth perception and spatial context in three-dimensional space. However, point cloud data have not yet been fully leveraged for activity recognition tasks in earthmoving operations. Furthermore, no existing studies have effectively combined RGB and point cloud data using a unified deep learning framework to enhance the accuracy and robustness of excavator activity recognition.  
This research addresses these limitations by proposing a two-stream CNN-LSTM architecture that fuses synchronized RGB and point cloud data to extract complementary spatial and temporal features. The objectives of the proposed study are: (i) utilize point cloud as one of the input data streams to enhance the spatiotemporal features; (ii) develop a two-stream deep learning architecture (CNN-LSTM) that integrates RGB images and 3D point cloud data for robust recognition of excavator activities in dynamic construction environments; (iii) improve the accuracy of the activity classification of excavators using the two-stream CNN-LSTM deep learning model; (iv) evaluate the performance of the proposed two-stream CNN-LSTM model against single-stream models using RGB or point cloud data alone, in terms of accuracy, precision, recall, and F1-score. The proposed approach addresses the limitations of single-modality inputs and lays the groundwork for real-time monitoring, predictive analytics, and digital twin integration in earthmoving operations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
# Formulating Cybersecurity Requirements for Autonomous Ships Using the SQUARE Methodology
## 2 Background
### Artificial Intelligence Technology in Autonomous Ships
#### 2.2.2 Autonomous Navigation System  
The autonomous operation of ships involves various AI technologies. Additionally, the structure of the automatic navigation system can be divided into four technical areas: situational awareness and detection technology via sensor data collection and analysis for autonomous operation without the intervention of human decisions; technology for making judgments such as automatic navigation, collision avoidance, and efficient route planning; technology for controlling the ship's activities and taking action on the situations based on the judgment; infrastructure technology for operating autonomous ships from outside the ship and remote monitoring and controlling. Figure 3 depicts the structure of an autonomous navigation system [13].  
Most ship collisions are caused by human accidents; they pose a major threat at sea [14]. Ship collision avoidance is regulated by the 1972 Convention on the Prevention of Collisions at Sea (COLREGs). Autonomous ships can avoid collisions with advances in deep learning. Figure 4 is a schematic representation to illustrate the collision avoidance procedure [9]. Collision avoidance involves analyzing precollected data and real-time sensor data to find actions that can follow COLREGs. The AI uses this iterative process to detect objects and implement collision avoidance procedures.  
Additional decision-making support must also be possible for ship-to-ship situational awareness and collision avoidance. Research and development in deep and machine learning frameworks are needed to achieve the ship intelligence required for collision avoidance [15].  
Route planning is one of the main parameters in autonomous ship systems. The purpose of route planning is to arrive at the destination with the optimal distance and time. Currently, methods such as neural networks and fuzzy and genetic algorithms are being studied for route planning in the fields of unmanned cars, mobile robots, and drones [9]. Traditional path-planning algorithms recycle historical data, resulting in poor algorithm accuracy and inefficient actual paths. As a result, research is being conducted on the routing models for autonomous unmanned ships, and improved route planning algorithms and models are under development [16; 17].  
Figure 3: Autonomous navigation system (ANS) architecture.
## 2 Materials and Methods
### Statistical Analysis  
Data were analyzed using R statistical software (version 2.4.2, R Core Team, Vienna, Austria). Normality distribution was evaluated using the Shapiro-Wilk normality test; the Bartlett test was employed for homoskedasticity. A three-way ANOVA was performed to assess the effects of cover crop type (CC), technical itinerary (TI), year, and the interactions between these factors on the gross salable production (GSP) and gross income (GI). The post-hoc LSD (Least Significant Difference) test at 0.05 probability was carried out through the package \"agricolae\" when necessary.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Machines** & **Values (km/h)** \\ \hline Combined cultivator & 4.82–7.03 \\ Rotary narrow & 3.20–4.73 \\ Mulcher & 4.91–6.57 \\ Roller crimper & 5.61–8.18 \\ Flaming machine & 2.43–3.58 \\ Transplanter & 1.67–2.43 \\ Sprayer & 3.64–5.31 \\ Precision weeder & 2.81–4.16 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range values of working speed measured during the trial.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Fuel Consumption (kg/ha)** & **Field Time (h/ha)** & **Labor Cost (EUR/ha)** \\ \hline
2020 & RT + C & 202.75 & 146.31 & 2926.26 \\
2020 & RT + noC & 39.20 & 92.79 & 1855.79 \\
2020 & NT + C & 52.29 & 143.31 & 2866.25 \\
2020 & NT + noC & 22.10 & 69.51 & 1390.14 \\
2021 & RT + noC & 127.67 & 209.62 & 4434.94 \\
2021 & RT + noC & 29.59 & 74.54 & 1490.75 \\
2021 & NT + C & 107.16 & 217.93 & 4358.59 \\
2021 & NT + noC & 31.06 & 69.38 & 1387.69 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average fuel consumption values, field time, and labor costs per hectare of each itinerary tested.
## 5 Simulation Results and Analysis
### Comparison of the DRFTM Performance in Different Attack Modes  
To validate the performance of the DRFTM model under single-mode and hybrid attacks, we compare the DRFTM with three established trust models (ARTMM, STMS, and TUMRL). In a single-mode attack, only one mode of attack occurs at the same time, i.e., DDoS attack, selective forwarding attack, data contamination attack, and on-off attack. A hybrid attack occurs when all four attack modes occur at the same time.
## 3 Model and Scenario Selection  
### Geometric Information for the Boiler Model  
In the context of traditional, simple temperature fields generated using MATLAB R2024b, including single-peaked, double-peaked, triple-peaked, symmetric, and asymmetric temperature distributions, extensive research has been conducted to validate various temperature measurement methods. However, these measurement and reconstruction methods often fail to accurately reflect the details of the temperature distribution for complex temperature fields inside a four-corner tangentially fired boiler furnace, leading to a significant number of errors. Therefore, Fluent is utilized in this study to examine the furnace of a four-corner tangential swirl burner and validate the accuracy of the Tucker decomposition-based 3D temperature field reconstruction algorithm.  
As shown in Figure 2, the furnace model exhibits specific dimensions. The height of the furnace is 44 m, while its width and depth are 13.26 m and 12.65 m, respectively. The furnace is scaled down to a ratio of 13:1. Moreover, the height and width of the primary and tertiary air nozzles are 45.38 mm and 44.62 mm, respectively. In addition, the height and width of the secondary air nozzles are 25.08 mm and 44.62 mm, respectively.  
The burners are arranged starting at 0.563 m above the bottom of the furnace, and they are divided into two groups. The first group of burners consists of five layers, arranged from bottom to top, as follows: secondary air, primary air, secondary air, primary air, and secondary air. The second group of burners starts at a height of 0.931 m above the  
Figure 2: Schematic diagram of the boiler combustion system and cross-section selection.  
bottom of the furnace and comprises six layers, arranged from bottom to top, as follows: secondary air, primary air, secondary air, tertiary air, tertiary air, and secondary air. The angles between the burners and the walls are 42.3\({}^{\circ}\) and 45\({}^{\circ}\), respectively.  
Using Gambit, the computational domain is meshed with a total of 642,096 grid cells. In this case, the obtained results across all operating conditions demonstrate repeatability and consistency. Hence, the multi-layer 2D temperature distribution can be regarded as one representation of the 3D temperature field. Therefore, at different heights above the upper secondary air, sectional temperature distributions are selected--namely at 1 m, 1.15 m, 1.3 m, and 1.45 m--to serve as the data sources for the 3D temperature field. The section heights are shown in Figure 2, and the temperature distribution data are calculated under various boundary conditions.
## 5 Results
### Abramov Glacier (Kyrgyzstan) SLA Results  
Comparing SLA values obtained by ASMAG with those derived manually [59] for Abramov Glacier in Kyrgyzstan revealed accuracies of nearly 90% (Figure 9). The only outlier in Figure 9a (red arrow) is due to clouds on the tongue of the glacier, which was classified as snow covering more than five elevation bins. Figure 9b,c show that ASMAG sometimes includes more SC than the manual digitization. This might be related to difficulties in distinguishing firm from snow but also can arise if the manual classification is inaccurate. A very good agreement of the snowlines is shown in Figure 9d.  
Figure 8: ELA measured versus the max SLA for years with at least two Landsat images. (**a**) Regression analysis for Hintereisferner, (**b**) for Kesselwandferner and (**c**) for Vernagtferner.  
Figure 7: SLA values for all glaciers (grey lines connecting elevation values for each glacier) for the full study period. Coloured points represent the maximum SLA for HEF, KWF and VNF in the respective season. The equilibrium line altitude (ELA) values derived in the field for the three glaciers are shown as lines.
## 4 Problem Formulation
### Microgrid with ESS  
ESS is one of the important components of the WT/PV/DE/ESS microgrid. The operation strategy of ESS has a great impact on the economic operation of the microgrid. When the load demand of the microgrid system is small or the electricity prices are low, the ESS operates in the charging mode. When the load demand is high or the electricity prices are expensive, the ESS operates in the discharging mode. Additionally, ESS can smooth the fluctuation of wind speed and solar radiation and enhance the load availability [29; 30]. When the microgrid has access to ESS, the operation cost can be expressed as:  
\[min\quad F1=\sum_{s=1}^{S}\pi(s)\sum_{t=1}^{T}(C_{WT}(t,s)+C_{PV}(t,s)+C_{DE}(t,s)+C_{ESS}(t,s)+C_{GRID}(t,s)), \tag{17}\]  
\[C_{ESS}(t,s)=k_{ESS}(P_{ch}(t,s)+P_{dis}(t,s)), \tag{18}\]  
where \(C_{ESS}(t,s)\) is the operation cost of ESS; \(k_{ESS}\) is the operation and maintenance cost coefficient of ESS, yuan/kW; \(P_{ch}(t,s)\) is the charging power of ESS, kW; \(P_{dis}(t,s)\) is the discharging power of ESS, kW.  
The model of pollutant treatment cost is the same as Formula (6). The microgrid system should satisfy the following constraints:  
\[P_{LOAD}(t,s)+P_{ch}(t,s)=P_{WT}(t,s)+P_{PV}(t,s)+P_{DE}(t,s)+P_{dis}(t,s)+P_{ GRID}(t,s)\quad\forall t,\forall s, \tag{19}\]  
\[0\leq P_{ch}\leq u_{ch}(t,s)P_{ch,max}\quad\forall t,\forall s, \tag{20}\]  
\[0\leq P_{dis}\leq u_{dis}(t,s)P_{dis,max}\quad\forall t,\forall s, \tag{21}\]  
\[u_{ch}(t,s)\in\{0,1\}\quad\forall t,\forall s, \tag{22}\]  
\[u_{dis}(t,s)\in\{0,1\}\quad\forall t,\forall s, \tag{23}\]  
\[u_{ch}(t,s)+u_{dis}(t,s)\leq 1\quad\forall t,\forall s, \tag{24}\]  
\[SOC_{min}\leq SOC_{t}(t,s)\leq SOC_{max}\quad\forall t,\forall s, \tag{25}\]  
\[SOC_{T}=SOC_{0}\quad\forall s, \tag{26}\]  
where \(P_{ch,max}\) is the maximum charging power of ESS, kW; \(P_{dis,max}\) is the maximum discharging power of ESS, kW; \(u_{ch}(t,s)\) equals 1 if the battery is charging during period t and 0, otherwise; \(u_{dis}(t,s)\) equals 1 if the battery is discharging during period t and 0, otherwise; \(SOC_{min}\) is the minimum SOC of ESS; \(SOC_{max}\) is the maximum SOC of ESS; \(SOC_{T}\) is the SOC of ESS at the end of the day; \(SOC_{0}\) is the SOC of ESS at the beginning of the day.  
The total power generation of WT, PV, DE, ESS and the main grid must be equal to the total load demand of the microgrid system at any time, which is constrained by Formula (19). For ESS, constraints (20) and (21) are the maximum charging/discharging power limits. These two states are mutually exclusive, which is ensured by Formula (22). The state of charge (SOC) of ESS is defined in Formula (4) and the SOC of ESS is limited by Formula (25). Furthermore, Formulas (14)-(16) should be satisfied as well.
# Vehicle Cooperative Network Model Based on Hypergraph in Vehicular Fog Computing
## 4 Description of Vehicle Assistance Network Evolution Model Based on Hypergraph
(3) Network connections  
In vehicle cooperative network, vehicles that can cooperate are geographically adjacent, and vehicles meet at a certain time. The more times a node works effectively, the higher the probability that it can collaborate with other nodes, to complete a cooperative task (forming a super-edge). The greater the number of vehicle cooperation, the higher the probability that it can cooperate with other vehicles, to complete a cooperative task. Therefore, when a vehicle cooperative task is added, the newly added vehicle in the cooperation task and the vehicle, i, that has previously joined the network firstly encounter the node with higher activity with probability \(f_{i}^{1}\), and the probability of selecting the vehicle and connecting \(f_{i}^{1}\) is approximated as the effective activity of vehicle, i, the expression is as follows:\[f_{i}^{1}=\frac{d_{H}(i)+\alpha}{\sum_{j}\left(d_{H}(j)+\alpha\right)} \tag{3}\]  
where \(f_{i}^{1}\) is the probability that the vehicle, i, is about to join a task by the super-degree; \(d_{H}(i)\) is the super-degree of the vehicle \(i\), the number of tasks added; \(\alpha\) represents the harmonic parameter; and \(\sum_{j}d_{H}(j)\) is the sum of all vehicles in the entire network, the sum of the number of tasks participating in all vehicles. Considering the particularity of the VANETs, when the vehicles need to cooperate, they must also consider the communication range. Therefore, the probability of performing the preferential connection should also consider that some will be randomly connected, so the probability of the vehicle, \(i\), being connected in the network is as follows:  
\[f_{i}^{2}=\varphi\frac{d_{H}(i)+\alpha}{\sum_{j}\left(d_{H}(j)+\alpha\right)} +(1-\varphi)\frac{1}{n(t)} \tag{4}\]  
where \(n(t)\) is the total number of nodes in the network at time, \(t\). The probability that the vehicle, \(i\), in the network is selected to be connected is \(f_{i}^{2}\) and on the right side of the equal sign in Equation (4) indicates that the greater the vehicle super-degree is, the greater the probability of being connected by the new super-edge. The second right side of the equal sign in Equation (4) represents the probability of a random connection.
# Zernike Coefficient Prediction Technique for Interference Based on Generation Adversarial Network
## 2 Methods
### Generative Adversarial Network (GAN)
#### 2.2.1 The Generator Network  
The Generator network is a Convolutional Neural Network (CNN) with the U-Net structure. As shown in Figure 2, the Generator consists of seven down-sampling layers of CNN and seven up-sampling layers of CNN. The Generator's input is the logarithmic divided fringes, and the output is the fake image for predicting the Zernike coefficients. As the down-sampling layers are used to extract the features from the input, the data are reconstructed by up-sampling layers to increase the data size for output. In the down-sampling layers, one layer comprises two 2 \(\times\) 2 convolutional layers, two normalization layers, and one 2 \(\times\) 2 max-pooling layer. In the up-sampling layers, one layer consists of one 2 \(\times\) 2 convolution transpose layer, two 2 \(\times\) 2 convolutional layers, two normalization layers, and one 2 \(\times\) 2 max-pooling layer in which the convolution transpose layer concatenates with the corresponded layer of the down-sampling structure. Moreover, the down-sampling network uses the hyperbolic tangent (tanh) activation function, and the up-sampling form uses the parametric rectified linear unit (PReLU) activation function. The normalization layers of the Generator network are Instance Normalization.