esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Modal Sonar Mapping of Offshore Cable Lines with an Autonomous Surface Vehicle
## 4 Experiments
### Experimental Setup  
The targeted survey area was the wave power plant testing site built by Korea Research Institute of Ships and Ocean Engineering (KRISO), located on the south-west coast of Jeju Island. The vehicle path used to survey the cable line is illustrated in Figure 8. The waypoints were selected based on the draft of the power cable lines provided by relevant authorities. The line had several segments, and waypoints were given for the joint of the segments, as well as the starting and end points. The vehicle travelled back and forth along the same waypoints.  
Low-level communication for thrust command, feedback, and battery management was performed through a microcontroller. Sensor acquisition and processing were implemented in a Linux PC running a robot operating system (ROS) [22], except for the raw sonar data processing software that ran on a Windows PC. The developed software components are shown in Figure 9. All sensor data were saved as automatically split files during the survey mission. The total size of the collected data was 47.5 GB for the survey path. The vehicle ran for approximately one and a half hours to complete this survey. The total travel distance was 2.56 km.  
The driver and data acquisition PCs were time-synchronized through the network time protocol server before the survey began. The heading angle of the AHRS was calibrated using an additional dual GPS antenna system connected to the AHRS. The initial extrinsic calibration parameters between the GPS antenna, AHRS, MBES, and SSS were obtained from a computer-aided design drawing of the vehicle body frame.  
Figure 7: SSS image generation with 3D projection of the intensity pixels onto the assumed underwater terrain.
# Development of Potato Mass Estimation System Based on Deep Learning
## 4 Conclusions  
1. A system was developed that carries out potato object tracking and counting by applying YOLOv5, one of the deep learning algorithms, and DeepSORT, one of the object tracking algorithms, to the real-time image obtained by a camera installed on top of the potato harvester sorting unit, and this system estimates the mass of a detected potato by calculating its volume using the major axis and minor axis lengths of its bounding box and then multiplying this by the density.
2. As a result of estimating the mass of individual potatoes using the developed system, R\({}^{2}\) was 0.9034 and the average error was found to be 13.09%. As a result of continuously putting in potatoes to match the actual harvesting speed, the potato object detection rate was found to be 95.2%, whereas the error of the total measured mass was 9%.
3. To improve the accuracy and reduce the error, a weighted value needs to be applied to the location of potato within the view angle of the camera. It is also necessary to improve the performance of the computer, and the phenomenon wherein potatoes are not counted due to frame drop must be prevented by widening the counting range to reduce the computation load. To reduce the error of the system, a regression model is required to be improved by dividing a dataset into calibration and validation.
4. The potato mass estimation system examined in the present study was developed and evaluated in a laboratory environment. The result of the evaluation indicates that the performance of the system and the algorithm used need to be improved. An additional study needs to be carried out in the future to apply an optimum algorithm and improve the problems found by conducting an actual field test, such as noise filtering caused by the vibration of the harvester and optimization of the integration time to improve the accuracy according to sunlight exposure.
5. An additional study needs to be carried out for the visualization of yield information within a lot by combining the yield information from the system and collecting GPS information to prepare a yield map, and it is judged that the yield monitoring of other underground crops such as onion would be possible by applying the same system used in the present study.  
Software, writing--original draft preparation, S.-H.J.; performed experiments and analysis, S.-P.M. and Y.-J.K.; writing--review and editing, S.-H.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Korea Rural Development Administration (RDA), research number PJ01569501.  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 5 Conclusions  
In this article, we present a novel tolerance-aided interference degradation algorithm tailored for power-constrained optical OFDM systems. At the receiver end, we introduce a decision tolerance and establish criteria to effectively mitigate both clipping distortion and additive noise. In the context of CEO-OFDM, this algorithm aids in determining whether the signals received in the first two signal frames, which are subsequently re-transmitted in the extra frame, are essential for data reconstruction. Furthermore, it enables compensation for clipping distortion. Our extensive analysis and numerical results showcased that, by judiciously selecting the decision tolerance, the bit error rate (BER) performance can be significantly enhanced by two orders of magnitude compared to conventional DCO-, ACO-, and CEO-OFDM techniques. Additionally, our proposed algorithm boasts a broader dynamic modulation index range, ensuring a BER below \(10^{-3}\).  
Utilizing the specified parameters, our algorithm, employing 64-QAM modulation, achieves a comparable best BER performance as DCO-, ACO-, and CEO-OFDM with 32-QAM. Consequently, it delivers a remarkable 20% higher data rate than the other aforementioned techniques. This substantial performance enhancement positions TAID as a promising solution for power-constrained optical OFDM systems, offering superior BER performance and data rate efficiency.  
Conceptualization, Y.G.; methodology, Y.G.; software, Y.G.; validation, Y.G.; formal analysis, Y.G. and J.L.; investigation, Y.G.; resources, Y.G.; data Y.G.; writing--original draft preparation, Y.G. and J.L.; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON]; project administration, J.L.; funding acquisition, [PERSON] and J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Young Scientists Fund of the National Science Foundation of China (NSFC) (Program No. 62001392), the Key Research and Development Program of Shaanxi (Program No. 2022 GY-097), the Scientific Research Plan of Education Department of Shaanxi Province (Grant No. 20K0873), Xi'an Science and Technology Association Youth Talent Lifting Plan (Grant No. 959202313081), Xi'an Science and Technology Plan (Grant No. 23 GXFW0079), and Xi'an University Three-Year Action Plan (Grant No. 2021 XDJH30).  
Not applicable.  
The study did not involve humans.  
Data Availability StatementData may be obtained from the authors upon request.  
The authors declare no conflict of interest.
## 4 Forecasting Methods
### GNN-Based Models  
In the field of MTSAD, capturing the complex relationships and dynamic changes in MTS is crucial. The MTAD-GAT [29] model learns complex dependencies on both the feature and temporal dimensions via a graph attention network [67] and achieves high performance in MTSAD through a joint optimization strategy. However, it may encounter issues with efficiency and scalability when dealing with large-scale datasets. GDN [30] leverages graph neural networks to learn a dependency graph among sensors and enhances the interpretability of anomaly detection through attention weights. However, it fails to fully capture the spatial and temporal correlations between sensors when processing sensordata. To address these issues, [PERSON] et al. [31] proposed DVGCRN, effectively models the spatial and temporal correlations of time series by combining an Embedding-Guided Probabilistic Generative Network(DEPN) with an adaptive variational graph convolutional recurrent network(GCRN), demonstrating superior performance in anomaly detection. FuSAGNet [32] integrates feature selection with GNNs to effectively identify and model the relationships between different time series features. The model constructs an inter-series adjacency matrix based on the latent feature representations generated by a sparse auto-encoder, enhancing its ability to detect anomalies. The DyGraphAD [33] framework strengthens the capture of short-term and long-term dependency relationships in time series through dynamic graph forecasting, thereby improving detection accuracy.
## 4 Proposed Model
### Data Preprocessing  
A data preprocessing step is important because the performance of a model not only depends on algorithms, but also on the quality of data. Generally, real time-series data is noisy, inconsistent, and incomplete (missing values), which increases the difficulty of mining useful information. The SGCC dataset contains missing and incorrect values due to various factors like breakdown of smart meter, unscheduled maintenance of data storage, and unreliable transmission measurement [14]. Consequently, to attain high performance in NTL detection, many preprocessing techniques have been used in the literature. For that reason, we perform preprocessing using imputation and normalization. To remove missing values, we use a simple imputation method. In this method, empty values are considered as Not a Number (NaN) and their forward and backward values are checked. If these values exist, NaN is replaced by the mean of these two numbers; otherwise, zero is replaced in all empty fields. We normalize the data by applying MinMax normalization, which normalize the data between 0 and 1. The formula of MinMax normalization is follows,  
\[n=\frac{(x-t_{min})}{t_{max}-t_{min}} \tag{1}\]  
where \(n\) represents newly generated values, \(x\) is the selected cell on which operations are performed, \(tmin\) is the minimum value of the column, and \(tmax\) is the maximum value of the column.  
Figure 2: Proposed system model.
# Numerical Simulation Research on Plane Alignment Parameters of Desert Highway
## 3 Results and Analysis
### Analysis of the Results of the Angle Embankment Model
#### 3.1.3 Distribution Characteristics of Sand Accumulation on Embankment  
Figure 6 shows the cloud map of sand accumulation around the embankment at t = 60 s at different angles. When the angle \(\alpha\) = 90\({}^{\circ}\), the sand accumulation is mainly distributed at the toe of the windward slope of the embankment, the toe of the leeward slope, and the rear area away from the leeward slope. The distribution of sand accumulation is relatively uniform. Comparing Figure 6b,c, it can be seen that the sand accumulation at the starting position of the line is far less than that at the end of the line, and the smaller the angle is, the greater the difference in the amount of sand accumulation on both sides. It shows that, when there is an angle between the wind direction and the line, the line has a lateral dredging effect on the wind-sand flow, so that some sand particles change from the linear movement across the embankment to the lateral movement along the line direction, resulting in the accumulation of sand particles at the far end of the line.  
Figure 5: Wind-speed distribution at different heights from the top of the embankment. (**a**) Change of wind speed at 0.3 m from the top of the embankment. (**b**) Change of wind speed at 0.5 m from the top of the embankment.  
When there is an angle between the wind direction and the line direction, the wind has a certain ability to transport and divert the sand accumulation on the roadside slope: the smaller angle \(\alpha\) is between wind direction and the line, the stronger the transportation ability is and the less sand accumulation there is around the embankment. In the design process, the angle between the line and the main wind direction should be minimized.
## 4 Results
### Model Robustness  
Table 1 depicts the aggregated results over all three test scenarios and hyperparameter configurations. As this study aims to assess each model type's general performance, we choose the mean \(RMSE\) as a relevant metric for the model comparison. Its overall error rank determines the best model. All hyperparameter setups are sorted in ascending order and get a rank assigned. This procedure is repeated for the error metrics \(MAE\) and \(RMSE\)  
As the results ranking of the \(RMSE\) and \(MSE\) are identical, the latter will be disregarded in further analysis.  
Thus, the overall error rank is the sum of the individual error ranks of \(MAE\) and \(RMSE\). After calculating the ranks for the two metrics and ranking all the models according to this sum in ascending order, LSTMs are the best-performing models across the defined scenarios. It achieves an average \(RMSE\) of 79.33, with the best configuration \(RMSE\) of 59.92. The second best model type is a hybrid of CNN and LSTM. The best CNN-LSTM configuration achieves slightly better overall results across the scenarios than the LSTM. However, the improvement appears insignificant. The best LSTM model will be used for further analysis as its complexity is lower and has no performance loss over the CNN-LSTM model. The decision tree model has an \(RMSE\) of over 110 and is approximately 40% worse on average. The Naive forecaster, the baseline model of this study, is a good model on average. SVM, kNN, and decision trees are worse on average. However, all well-tuned machine learning models achieve better results than the Naive model.  
Figure 5 shows the error distributions across all hyperparameter setups and test scenarios. The decision tree stands out negatively as its mean, median, and total error deviation is higher than other models. SVMs are slightly better but also come with large deviations. The random forest has a relatively low deviation and is, therefore, a more robust model that is less sensitive to the choice of hyperparameters or the respective test scenario.  
\begin{table}
\begin{tabular}{r c c c c} \hline \hline  & \multicolumn{2}{c}{**Average**} & \multicolumn{2}{c}{**Best**} \\ \hline
**Model** & **RMSE** & **MAE** & **RMSE** & **MAE** \\ \hline
**LSTM** & **79.33** & **64.52** & **59.92** & **48.45** \\ CNN-LSTM & 80.52 & 65.16 & 59.15 & 46.18 \\ ARIMA & 90.67 & 75.80 & 81.93 & 68.21 \\ Random Forests & 94.54 & 81.24 & 73.20 & 59.73 \\ Gradient Boosted Trees & 95.65 & 80.70 & 72.83 & 61.10 \\ Naive & 102.07 & 79.90 & 102.07 & 79.90 \\ SVM & 104.45 & 90.42 & 64.06 & 52.41 \\ KNN & 106.97 & 88.82 & 83.50 & 68.98 \\ Decision Trees & 113.01 & 98.60 & 86.80 & 70.47 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Error metrics on average and of the best hyperparameter configurations.  
Figure 5: Performance deviation as a mean of model robustness.
## 4 Experiments and Results
### Experimental Results
#### 4.3.1 Prior Decoder Training Results  
Figure 7 shows the prior decoder training experiment results, which involved 8000 steps. The MSE loss decreased rapidly from 0.14 to 0.02. Subsequently, the loss continued to decrease at a slower pace, eventually converging to approximately 0.001.  
Figure 8 shows the test results of the prior decoder experiment and the reference for comparison. Figure 8a shows the Mel spectrograms assembled from the patch output reconstructed by the decoder, while Figure 8b shows the original Mel spectrograms used for comparison with the reconstructed version; subtle local differences can be observed in the areas marked with red boxes. Interestingly, the Mel spectrograms assembled from the reconstructed patches in Figure 8a were almost indistinguishable from the original Mel spectrograms in Figure 8b, demonstrating that the prior decoder could effectively reconstruct the Mel spectrograms from the low-dimensional hidden states.
## 1 Introduction
[PERSON] [20] adopted orbit filtering to exclude cycles without VTWs. [PERSON] [21] proposed a geometric method based on 2D maps. Coarse and precise search concepts enhance speed and offer insights for further research. Previous research mainly addressed point targets, while computing for regional targets poses challenges due to irregular polygons. Various methods have emerged for regional targets like boundary intersection, boundary discretization, and grid matching. [PERSON] et al. [22] used boundary intersection and coarse/precise search, while [PERSON] [23] and [PERSON] [24] solved the intersection problem using geometric methods. [PERSON] et al. [25] suggested a method combining coarse calculation with a precise binary search based on boundary discretization. [PERSON] et al. [26] proposed a grid method, discretizing regional targets into a grid and traversing to identify covered grid moments. [PERSON] et al. [27] introduced a method based on map segmentation, traversing sub-points within a specific range by segmenting maps and trajectories. However, for large-scale payload systems in mission planning, the VTW is repeatedly invoked, and the emergence of regional targets also poses a challenge. Therefore, there is a need to enhance the speed and accuracy of existing methods. Efficient VTW calculation with minimal computational consumption and high accuracy is crucial. This paper introduces the Triple Time Interval Hybridization Strategy (TTIHS) for rapid VTW calculation of regional targets. The TTIHS integrates orbit filtering, large-step angle calculation, and short interval binary search strategies, employing different strategies in various intervals to enhance computational speed. In addition to the coarse and precise search phases in previous algorithms [25], the TTIHS introduces an orbit filtering phase before the coarse search, mitigating the time-consuming iteration of calculating visibility for each position in global time, a drawback of previous algorithms. After filtering, a large-step angle calculation strategy is employed within the filtered interval and a short interval binary strategy rapidly calculates the start-end times of the VTW. The challenges in developing this algorithm are twofold. One is determining the time range for the orbit filtering, which is addressed by introducing the concept of an extended rectangle based on the four-boundary range. The other is parameter selection, where an optimized analysis of the time step is conducted to establish the relationship between computational accuracy and the step. The paper makes three key contributions: proposing the TTIHS framework, introducing an orbital filtering method using extended rectangles, and conducting a comparative analysis of algorithm performance with parameter optimization for the time step. Its structure consists of a first part introducing the study's significance and research status, a second part presenting the preliminary work, a third part detailing exposition of the TTIHS model, a fourth part discussing the outcomes and results analysis, and a fifth part providing conclusions and potential avenues for future research.
# Effect of Data Augmentation on Deep-Learning-Based Segmentation of Long-Axis Cine-MRI
## 3 Results
### Influence of Data Augmentation
In general, data augmentation proved to be beneficial across the two orientations, although with different best-performing configurations. The experiments _ENet + Stage 1 Aug_ and _ENet + FullAug_ were associated with the best metric results over the four-chamber and two-chamber sets, respectively. Their corresponding results are reported in Tables 1, 4 and 6. More-robust metric scores were observed across all classes, as seen in Figure 4. The average markers in red show a general increase in Dice on both the ED and ES frames. Moreover, the computed Hausdorff scores display a general decrease in correlation as the Dice increases. The performance improvement is more visible over the four-chamber dataset, with higher differences in the Dice and Hausdorff scores, as can be inferred from Table 1. On the other hand, this increase is less significant for the two-chamber dataset, with a less consistent difference. This behavior is not surprising, in part due to the segmentation task being easier due to a lower number of classes and the segmentation performances already being convincing without data augmentation. Details regarding the latter observation are provided in Section 4.2. With both datasets, the Dice score and Hausdorff distance show a reduction in the amount of outliers and in the standard deviation for both the ED and ES phase when data augmentation is applied.
# Linear Method for Diagnosis of Inter-Turn Short Circuits in 3-Phase Induction Motors
## 4 Turn-to-Turn Short and Inter Turn Short Circuit
### Short Circuit Current According to ITSC  
Unlike the turn-to-turn short explained above, an ITSC is a phenomenon of a short circuit between two adjacent turns and has a very small \(N_{S}\). The \(R_{S}\) maintains a very large value due to insulation resistance and shows exponential attenuation, decreasing rapidly over time. Attenuation indicates the occurrence of a turn-to-turn short. Thus, major accidents can be prevented if the ITSC can be detected before the exponential attenuation appears, and this requires further research.  
In Equation (13), \(1-(N_{S}/N_{T})\) can be considered to be equal to 1 because \(N_{S}\) is very small. \(I_{S}\) can also be ignored by insulation resistance. Hence, the same result as the magnetic flux linkage quantity in steady state, \(\lambda_{T}=L_{T}I_{d}\), is obtained. In other words, in the case of an ITSC, the sizes of pulsation by losses such as iron loss and copper loss are similar in steady state.  
However, the current \(\lambda_{ITSC}\) according to the total flux of ITSC, \(\lambda_{ITSC}\), can be derived as follows by Equation (14):  
\[I_{ITSC}=\frac{\lambda_{ITSC}}{(L_{T}-L_{ITSC})}=\frac{(N_{T}-N_{ITSC})\times( \phi_{T}-\phi_{ITSC})}{(L_{T}-L_{ITSC})} \tag{14}\]  
where \(I_{ITSC}\) is the phase current, \(L_{ITSC}\) is the loss inductance, and \(N_{ITSC}\) is the loss winding number. Because the total flux changes have no big difference, the change of flux according to the ITSC, \(\phi_{ITSC}\), can be considered to be equal to zero. When an ITSC occurs, both \(L_{ITSC}\) and \(N_{ITSC}\) show decreasing tendencies. The relationship between these two elements is similar to a ring-shaped solenoid and thus can be determined by the following equation:  
\[L_{ITSC}=\frac{N_{ITSC}\times\phi_{T}}{I_{ITSC}}=\frac{\mu SN_{ITSC}^{2}}{l} \tag{15}\]  
where \(\mu\) is the conductor dielectric constant, \(S\) is the cross section area of the stator core, and \(l\) is the average length of the coil. From this, we can see that \(L_{ITSC}\propto N_{ITSC}^{2}\).  
In other words, in Equation (14), even if the magnetic inductance of \(L_{T}-L_{ITSC}\) decreases, \(\lambda_{ITSC}\) decreases at a doubled rate. Therefore, the phase current \(I_{ITSC}\) according to the ITSC shows a decreasing tendency in general. However, in the case of a turn-to-turn short, the phase current increases again as it is combined with the short circuit current \(I_{S}\).