esa-sceva/satcom-chunk-collection huggingface dataset:
# Design, Modeling, and Simulation of a Wing Sail Land Yacht
###### Abstract  
Autonomous land yachts can play a major role in the context of environmental monitoring, namely, in open, flat, windy regions, such as iced planes or sandy shorelines. This work addresses the design, modeling, and simulation of a land yacht probe equipped with a rigid free-rotating wing sail and tail flap. The wing was designed with a symmetrical airfoil and dimensions to provide the necessary thrust to displace the vehicle. Specifically, it proposes a novel design and simulation method for free rotating wing sail autonomous land yachts. The simulation relies on the Gazebo simulator together with the Robotic Operating System (ROS) middleware. It uses a modified Gazebo aerodynamics plugin to generate the lift and drag forces and the yawing moment, two newly created plugins, one to act as a wind sensor and the other to set the wing flap angular position, and the 3D model of the land yacht created with Fusion 360. The wing sail aligns automatically to the wind direction and can be set to any given angle of attack, stabilizing after a few seconds. Finally, the obtained polar diagram characterizes the expected sailing performance of the land yacht. The described method can be adopted to evaluate different wing sail configurations, as well as control techniques, for autonomous land yachts.  
robotic sailing; design; modeling; simulation; wing sail; flap tail; land yacht +
Footnote †: journal: Applied Sciences  
1 ISEP/IPP--School of Engineering, Polytechnic of Porto, 4249-015 Porto, Portugal; [EMAIL_ADDRESS] (V.T.); [EMAIL_ADDRESS] (M.F.S.)
###### Abstract  
Power quality (PQ) is an increasing concern in the distribution networks of modern industrialized countries. The PQ monitoring activities of distribution system operators (DSO), and consequently the amount of PQ measurement data, continuously increase, and consequently new and automated tools are required for efficient PQ analysis. Time characteristics of PQ parameters (e.g., harmonics) usually show characteristic daily and weekly cycles, mainly caused by the usage behaviour of electric devices. In this paper, methods are proposed for the classification of harmonic emission profiles for typical consumer configurations in public low voltage (LV) networks using a binary decision tree in combination with support vector machines. The performance of the classification was evaluated based on 40 different measurement sites in German public LV grids. This method can support network operators in the identification of consumer configurations and the early detection of fundamental changes in harmonic emission behaviour. This enables, for example, assistance in resolving customer complaints or supporting network planning by managing PQ levels using typical harmonic emission profiles.  
Keywords:power system harmonics; harmonic current emission; consumer behavior; public low voltage network; time series; machine learning; classification; support vector machines +
Footnote †: journal: Applied Sciences
## 3 Materials and Methods
### Data Acquisition and Understanding  
As part of the TDSP methodology, the next step involves acquiring data. This entails gathering information from the greenhouse and analyzing it. In this stage, a weather station collected the data from a greenhouse with a curved roof. This type of greenhouse is for traditional use with no climate control and has natural ventilation, see Figure 2. The greenhouse has an area of 165 m\({}^{2}\), 27.5 m long, 6 m wide. This is located in South Mezquitera, Juchipila, Zacatecas, Mexico, with latitude and longitude (21.42624033959812, \(-\)103.10935313358475) and orientation 21 deg25'34.5'' N 103 deg06'33.8'' W.  
Inside and outside the greenhouse are nine sensors as a part of the Davis Vantage Pro 2 central weather system. Seven sensors outside the greenhouse measure temperature, humidity, solar radiation, barometric pressure, rainfall, wind speed, and direction. Inside the greenhouse, there are sensors for humidity and temperature. These sensors work together to create a uniform system within the greenhouse.  
Temperature Sensor: The Davis Vantage Pro 2 central weather system contains an SHT11, a digital, low power consumption, fully calibrated humidity, and temperature sensor Integrated Circuit. The sensor applies CMOSens technology that guarantees excellent reliability and long-term stability. Each one of the output signal sensors is delivered through a 14-bit analog-to-digital converter coupled to a serial interface circuit. The sensor operating temperature range is \(-40\,\mathrm{\SIUnitSymbolCelsius}\) to \(123\,\mathrm{\SIUnitSymbolCelsius}\) and a typical accuracy for the temperature sensor of \(\pm 0.4\,\mathrm{\SIUnitSymbolCelsius}\) and the humidity sensor of \(\pm 3\)%.  
Data were collected from 12 July 2020, to 24 June 2021, with sampling at 5-min intervals. Information was not collected during two periods: 16 December 2020-3 January 2021, and  
Figure 1: Data science lifecycle for TDSP methodology.  
7 March 2021-21 March 2021, due to maintenance at the weather station and changes in the polyethylene plastic of the greenhouse, respectively. A total of 85,989 samples were obtained to train and test the prediction models. Figure 3 shows the data collected corresponding to the greenhouse internal temperature.  
During data collection, tomatoes were grown from July 2019 to January 2020, and bell peppers from February 2020 until the end of the data.  
The Table 1 shows the main variables obtained from the data collected.
# Reservation-Based 3D Intersection Traffic Control System for Autonomous Unmanned Aerial Vehicles
## 8 Simulation Results
### Evaluation of the Scheduling Sequence  
#### 8.3.1 Comparison of our Proposed System with FCFS Scheduling  
To show the performance of our proposed system to the total travel time of the UAVs in the intersection, we compared the performance of our scheduling scheme with First Come First Serve (FCFS) scheduling. In FCFS scheduling, the UAVs are scheduled in the order their requests are received by the intersection manager. This is different with our scheduling that uses genetic algorithm where the UAVs are scheduled based on the best sequence obtained using the objective function. We compared their performances through the average time in the system of the UAVs. We used mode 2 and the parameters in Tables 2 and 3 except for the number of generations, \(G\), used in genetic algorithm. We ran simulations on two values of \(G\), which are 50 and 80. As previously mentioned, the average time in the system of UAVs when they do not experience delay is 20 s when the parameters in Table 2 are used.  
The results of the simulations are shown in Figure 19. We can see that our scheduling scheme that uses genetic algorithm (GA) always performs better than FCFS. It also shows that the average time in system decreases when bigger value of \(G\) is used. However, there is no significant improvement in the performance when the value of \(G\) is higher than 80 using the simulation parameters in Tables 2 and 3, thus we only showed results when values of \(G\) are 50 and 80. Compared to FCFS, our scheduling scheme decreases the average time in the system by 27% when \(G\) = 80 and the arrival rate is 100 UAVs per direction.  
As shown in Figure 19, the increase in the average time in the system is higher in FCFS when arrival rate is higher than 70 UAVs per minute. A problem with FCFS is the intersection is not efficiently utilized when the traffic is heavy since the UAVs from different ways cross the intersection in the order of their arrivals. This causes more interruptions to the flow of traffic in the intersection and thus increases the average time spent of the UAVs in the system and decreases the throughput in the intersection. On the other hand, our scheduling scheme that uses genetic algorithm processes multiple UAVs in scheduling and it allows the UAVs to enter the intersection by group resulting to less interruptions to the paths of the UAVs.
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 2 Related Works
### Energy saving in an MEC System  
In line with the energy-saving objectives, studies pertaining to energy efficiency in an MEC system can be classified into two primary groups. The first group is dedicated to diminishing the energy consumption in end devices, while the second group is centered around addressing the energy consumption within an MEC system. User devices have the potential to conserve energy through the offloading of computing tasks to an MEC system. However, the task-offloading process itself entails the consumption of transmission power by the user device. Furthermore, task offloading can introduce an increase in task completion delay since the processed results are delivered to the user device after the task is sent to and processed within an MEC system. Consequently, various task-offloading decision methods are proposed to minimize the energy consumption of a user device. These methods take into account factors such as computing power, transmission power, and task completion delay [21; 22; 23].  
To minimize the energy consumption of an MEC system while providing a reasonable quality of service to end users, various resource management methods have been  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Category** & **Main Goal** & **Architectural Property** \\ \hline RecGNNs (Recurrent GNNs) & Learn node representation & Recurrent architecture and message passing \\ \hline ConvGNNs (Convolutional GNNs) & Node/graph classification & Extend convolution to non-Euclidean data space \\ \hline GAEs (Graph AutoEncoders) & Node/graph encoding, graph generation & Autoencoder using graph convolution \\ \hline STGNNs (Spatial-Temporal GNNs) & Learn latent spatio-temporal pattern in a graph & Graph convolution, CNN, RNN \\ \hline \hline \end{tabular}
\end{table}
Table 1: Categories of GNNs.  
proposed [24]. Load-balancing methods among MECSs are proposed in [25; 26] to increase the resource efficiency of an MEC system while reducing the system cost. The authors in [27; 28] propose optimal computation resource allocation methods to reduce the energy consumption of an MECS while ensuring the service delay requirement in each MECS. Content caching methods are proposed in [29; 30] to accommodate the massive computation demands from users in an energy-efficient manner. Service placement schemes are proposed to reduce both service completion time and energy consumption by placing the services requested by users to their serving MECS [31; 32].  
However, these studies are primarily focused on designing the intended functionalities in an energy-efficient manner. In other words, they assume all MECSs are always in an active mode. However, servers consume a considerable amount of energy even in the idle state. Therefore, if a majority of MECS are underloaded, a potential strategy for decreasing the overall energy consumption of an MEC system is to activate only a subset of MECSs to handle tasks while putting the remaining MECSs in a sleep state.
# Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
## 4 Experiment
### Result and Analysis
#### 4.4.1 Main Results
Therefore, our model's results on the WebNLG dataset are close to those of the CasRel model. On the other hand, for the WebNLG dataset itself, the small amount of training data (5019) makes it particularly difficult to extract 246 predefined relations. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**NYT**} & \multicolumn{3}{c}{**WebNLG**} \\  & **Prec.** & **Rec.** & **F1** & **Prec.** & **Rec.** & **F1** \\ \hline NovelTagging & 62.4 & 31.7 & 42.0 & 52.5 & 19.3 & 28.3 \\ CopyRE\({}_{\text{OneDexoder}}\) & 61.0 & 56.6 & 58.7 & 37.7 & 36.4 & 37.1 \\ CopyRE\({}_{\text{MultiDexoder}}\) & 61.0 & 56.6 & 58.7 & 37.7 & 36.4 & 37.1 \\ GraphRel\({}_{1p}\) & 62.9 & 57.3 & 60.0 & 42.3 & 39.4 & 37.1 \\ GraphRel\({}_{2p}\) & 63.9 & 60.0 & 61.9 & 44.7 & 41.1 & 42.9 \\ OrderCopyRE & 77.9 & 67.2 & 72.1 & 63.3 & 59.9 & 61.6 \\ ETL-Span & 84.9 & 72.3 & 78.1 & 84.0 & 91.5 & 87.6 \\ WDec & **94.5** & 76.2 & 84.4 & - & - & - \\ CasRel & 89.7 & 89.5 & 89.6 & **93.4** & 90.1 & 91.8 \\ DualDec & 90.2 & 90.9 & 90.5 & 90.3 & 91.5 & 90.9 \\ RMAN & 87.1 & 83.8 & 85.4 & 83.6 & 85.3 & 84.5 \\ \hline SGNetw\({}_{\text{WG}}\) & 90.5 & 89.8 & 90.2 & 90.6 & 90.0 & 90.2 \\ SGNet & 91.2 & **91.4** & **91.3** & 91.8 & **91.9** & **91.9** \\ \hline \hline \end{tabular} Bold data in the table indicates the best results.  
\end{table} 
Table 2: Main results. Bold marks the highest score. SGNetw\({}_{\text{WG}}\) does not contain graph modules.
# Enhanced Tuna Detection and Automated Counting Method Utilizing Improved YOLOv7 and ByteTrack
## 3 Research Methods
### Tuna Target Tracking Counting Based on ByteTrack Algorithm  
#### 3.2.1 ByteTrack Algorithm  
The ByteTrack [26] multi-object tracking algorithm utilizes Kalman filtering to predict the trajectories from the current frame to the next frame. It then uses the intersection over union (IoU) between the predicted bounding boxes and the detected bounding boxes as the similarity measure for a two-step matching process. The matching is performed using the Hungarian algorithm. The detailed workflow of the ByteTrack multi-object tracking algorithm is illustrated in Figure 6. First, based on the detection results, all detected bounding boxes are divided into high-score detection boxes (D_high) and low-score detection boxes (D_low). In the first matching step, the high-score detection boxes are matched with the previous tracking trajectories. During the second tracking process, the low-score detection boxes are matched with the remaining unmatched tracking trajectories (T_remain). Tracking trajectories that remain unmatched after both steps are retained for 30 frames. Finally, new tracking trajectories are created for the high-score detection boxes (D_remain) that do not match any existing trajectories.  
During tuna longline fishing, branch lines with hooks need to be detached from the main line. When a tuna is caught on a branch line hook, workers hang the branch line on the rail next to the water gate. Subsequently, each tuna is individually hooked and brought onto the deck through the water gate using hooks. When the tuna is hauled from the water surface onto the deck, an ID is assigned to the detected tuna. A number in the upper-left corner of each bounding box is used to identify the unique target ID. In subsequent frames, if the ID does not switch, the tracking is considered effective until the tuna disappears from the field of view. The results of target tracking are shown in Figure 7, where the upper-left corner of the tuna detection box displays the species and count of the tuna.
# Neural Network System for Predicting Anomalous Data in Applied Sensor Systems
## 3 Results
### Evaluation of the Effectiveness of the Results Obtained
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Actual/Predicted** & **Predicted Anomaly** & **Predicted Normal** \\ \hline Actual Anomaly & \(TP\) & \(FN\) \\ Actual Normal & \(FP\) & \(TN\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The confusion matrix (author’s research). A comparative analysis (Table 4) was conducted between the developed neural network system (Figure 1), a neural network system based on an autoassociative neural network (autoencoder) created by the same research team [2], and a hybrid mathematical model utilizing Runge-Kutta optimization and Bayesian hyperparameter optimization [31], according to the quality metrics presented in Table 2.  
As shown in Table 4, the quality metric values for the developed neural network system (Figure 1) and the neural network system based on the autoassociative neural network (autoencoder) [2] are approximately equal. This indicates that both systems are interchangeable and can serve as alternatives to one another. The quality metrics for the developed neural network system (Figure 1) are, on average, up to 20% higher than those of the hybrid mathematical model utilizing Runge-Kutta and Bayesian hyperparameter optimization. For ROC analysis [57; 58] across the three approaches (the developed neural network system, the neural network system based on an autoassociative neural network (autoencoder) [2], and the hybrid mathematical model utilizing Runge-Kutta optimization and Bayesian hyperparameter optimization [31]), true positive and false positive rates were calculated for each category and technique, followed by the creation of the respective ROC curves. This procedure involved defining a binary classification for each category, isolating it from the others. The ROC analysis results are presented in Table 5.  
Figure 12 distinctly illustrates the variation in the regions beneath the AUC curve for the developed neural network system. Based on the obtained AUC-ROC metric values, it is noted that the developed neural network system (Figure 1) and the system based on an autoassociative neural network (autoencoder) [2] deliver high accuracy with a minimal false alarm rate. The use of the model employing Runge-Kutta and Bayesian hyperparameter optimization [31] results in lower accuracy and the highest number of false alarms.
# Explainable Ensemble Learning Model for Residual Strength Forecasting of Defective Pipelines
## 3 Case Study
### Robustness Analysis of the BO-XGBoost Model  
After developing and evaluating the predictive model for the remaining strength of defective pipelines using datasets, it is necessary to conduct a robustness analysis. This analysis provides additional insights into the model's adaptability and effectiveness to varying data conditions. The robustness of the BO-XGBoost model can be assessed by introducing varying levels of white noise into the input data distribution and observing the changes in R\({}^{2}\) values. The point at which the R\({}^{2}\) value trend begins to decline after adding extra noise identifies the model's noise tolerance threshold. At this threshold, the model with the highest R\({}^{2}\) value is considered the most resilient [44]. The model with the lowest slope of accuracy degradation is regarded as the most robust in terms of prediction performance.  
Gaussian white noise at different levels was generated using the probability density function \(P_{x}(x)\) defined in Equation (1), where \(x\) represents the variable, \(\mu\) represents the mean, and \(s\) represents the standard deviation. By adjusting the value \"\(s\)\" while keeping the mean (\(\mu\)) at zero [45,46].  
\[P_{x}=\frac{1}{s\sqrt{2\pi}}e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}} \tag{1}\]  
Figure 13 shows the R\({}^{2}\) values predicted by different models as the level of noise changes, the horizontal coordinate represents the noise level, and the vertical coordinate represents the R\({}^{2}\) predicted by the model after adding noise. For the dataset with added noise, the R\({}^{2}\) value of the BO-XGBoost model decreased the least. In contrast, the R\({}^{2}\) values of GBRT, RT, LightGBM, and MLP decreased significantly, and the R\({}^{2}\) value of the SVM model exhibited the most significant decline. When the S value exceeds 0.25, the R\({}^{2}\) decline rate of the BO-XGBoost model becomes more pronounced, indicating that its noise tolerance threshold is approximately 25%.  
Figure 12: The predicting results of six models.
## 4 Experiment and analysis
### Simulation Result Analysis
#### 4.2.2 Comparison Result Analysis between Algorithms under Different Number of Nodes
The reason why the overhead on average of the Epidemic algorithm has been increasing is mainly because the algorithm uses the message flooding mechanism. When the number of nodes increases, the replication of messages in the network increases sharply, and the destination node cannot quickly obtain messages, which results in a larger average network overhead. Figure 9: Average end-to-end delay with different number of nodes. The ICMT algorithm utilizes the cooperation mechanism between neighbor nodes and establishes a method of identifying nodes by relying on the probability of encounter. Therefore, when the number of nodes increases from 100 to 700, the average cost of the algorithm shows a certain downward trend. the average network overhead from MSNN reduces by only 13% compared with those from EIMST. To be specific, the EIMST algorithm uses community division and information management to perform an efficient data transmission process. In the same way, the mobile similarity and social similarity between nodes are also used in the MSSM algorithm, which is mainly based on the moving trajectories of nodes, the number of common neighbor nodes and their social attributes. Besides, communities share some parts of transmission missions, nodes carrying messages may reduce overhead. Therefore, when the number of nodes increases, the average cost of the algorithm gradually decreases from 224 to 86. In conclusion, The MSSM algorithm outperforms other algorithms in terms of network overhead when the number of nodes changes.
## 4 Discussion
### SMAP/CYGNSS TB Uncertainties  
Multiple uncertainties exist related to the SMAP/CYGNSS TB algorithm. Some of these uncertainties are due to the difficulties of collocating SMAP and CYGNSS data, and others are inherent in CYGNSS data and the TB algorithm.
# Comparing the Measured and Thermodynamically Predicted AFm Phases in a Hydrating Cement
## 3 Experimental Results
### Heat of Hydration Analysis  
The isothermal calorimetry heat of hydration results (Figure 5) from 45 min onwards show the start of the acceleration period at approximately 3 h. The measurements both show three separate peaks of heat evolution. The previous work [17; 18] relates the second peak to sulphates previously bound to the C-S-H dissolving into solution and producing a further release of heat. The third peak at two days is thought to be due to the later precipitation of AFm phases.
# Evaluation of CYGNSS Observations for Snow Properties, a Case Study in Tibetan Plateau, China
## 2 Method  
### Data Description  
The Tibetan Plateau is the highest plateau in the world, located between 26\({}^{\circ}\)N to 39\({}^{\circ}\)47\({}^{\prime}\)N latitude and 73\({}^{\circ}\)19\({}^{\prime}\)E to 104\({}^{\circ}\)47\({}^{\prime}\)E longitude. The topography is complex, and it has large altitude differences in various regions (Figure 1). The average annual temperature decreases from 20 \({}^{\circ}\)C in the southeast to below \(-\)6 \({}^{\circ}\)C in the northwest. In this study, we employed five public datasets to investigate the potential of CYGNSS for the study of snow cover on the Tibetan Plateau: (1) CYGNSS Level-1 (L1) data [24], (2) the Terra and Aqua combined Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover ClimateModeling Grid (CMG) (MCD12C1) version 6 data (0.05 \({}^{\circ}\)C) [25], (3) long-time series dataset of snow depth in China (1979-2019) [26], (4) Soil Moisture Active and Passive (SMAP) L3 radiometer global daily 36 km EASE-grid soil moisture, version 7 [27], (5) European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) soil temperature data [28].