esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Validation with Observed FPAR  
To the south of Zhangye City, Gansu Province, large areas of maize have been planted. Its diurnal FPAR changes were observed by the SunScan canopy analysis system on 5 July 2012. Figure 6a shows the difference between observed diurnal FPAR and inversion diurnal FPAR. Both the observational and inversion curves show that FPAR is the highest in the morning and reaching a wave trough around noon. This occurs because the increase in the solar zenith angle makes the light path through the vegetation canopy longer, leading to an increase in FPAR. There are many reasons for uncertainty in the observation process, such as occlusion by leaves, meaning that the observed FPAR curve fluctuates significantly. The spatial resolution of the FPAR inversion is 1 km; the small changes at leaf scale cannot be reflected. The inversion is relatively stable. The observed daily average FPAR was 0.81, and the daily average inversed FPAR was 0.82. This shows that the DnD model can represent the diurnal changes in FPAR and that the daily average value is very close to the observed value. This paper also compared the instantaneous observed and inversed FPAR during July 2012 (Figure 6b). The RMSE was 0.03, and the \(R^{2}\) was 0.85.  
Figure 5: FPAR inversion: **(a)** direct FPAR at noon, 5 July 2012; **(b)** diffuse FPAR at noon, 5 July 2012; **(c)** total FPAR at noon, 5 July 2012; **(d)** daily average FPAR on 5 July 2012.
# A Classification Tree for Modeling Ground Fractures from Subsidence
## 1 Introduction
The study of the behaviors and properties of soils has been analyzed with artificial intelligence and machine learning very profusely in recent years, showing the enormous potential of this technology to solve many geotechnical problems [34; 35; 36; 37; 38; 39; 40; 41; 42]; however, few investigations with sufficient solidity can be found on ground fracturing and subsidence. Using machine learning in the simulation of fracturing from subsidence makes it possible to generate concrete information about the aspects in the environment (natural and anthropic) that activate or exacerbate the breaks,  
Figure 1: Collapse of the superficial layers of soil in southern Mexico City. (**a**) Exacerbated ruptures during the earthquake of 19 September 2017, (**b**) step-like displacements that have grown gradually for more than 10 years, and (**c**) cracks discovered during the construction of new houses. while the processes that extend from the depths of the exploited aquifer to the superficial layers stayed immersed in the data. This article presents a CART model (classification and regression tree) in classification mode that analyzes ground fracturing in a comprehensive way, eliminating subjective approaches and pointing out the spatial factors that most influence the adverse manifestations (cracks, fissures, and steps). The CART is trained with geotechnical and geological records that characterize the soils and rocks (composition and geometry), information on the buried infrastructure (pumping well location), and the arrangements of houses and streets in the most heavily fractured areas in Tlahuac. The tree makes it possible to define the areas that are most threatened by the phenomenon, the components (natural and anthropic) that exacerbate the manifestation, and the points on the surface that could potentially crack if their intrinsic characteristics and the water pumping patterns do not change. Its outstanding ability to predict breakpoints allows the use of machine learning to provide effective responses to city designers and government administrators for anticipating these kinds of events, work which is essential when the threatened areas have low resilience and a high level of poverty.
###### Abstract  
Accurate positioning and state estimation of surface vessels are prerequisites to achieving autonomous navigation. Recently, the rapid development of 3D LiDARs has promoted the autonomy of both land and aerial vehicles, which has aroused the interest of researchers in the maritime community accordingly. In this paper, the state estimation schemes based on 3D LiDAR scan matching are explored in depth. Firstly, the iterative closest point (ICP) and normal distribution transformation (NDT) algorithms and their variants are introduced in detail. Besides, ten representative registration algorithms are selected from the variants for comparative analysis. Two types of experiments are designed by utilizing the field test data of an ASV equipped with a 3D LiDAR. Both the accuracy and real-time performance of the selected algorithms are systemically analyzed based on the experimental results. It follows that ICP and Levenberg-Marquardt iterative closest point (LMICP) methods perform well on single-frame experiments, while the voxelized generalized iterative closest point (FastVGICP) and multi-threaded optimization generalized iterative closest point (FastGICP) methods have the best performance on continuous-frame experiments. However, all methods have lower accuracy during fast turning. Consequently, the limitations of current methods are discussed in detail, which provides insights for future exploration of accurate state estimation based on 3D LiDAR for ASVs.  
3D LiDAR; ASV; scan matching; registration; state estimation +
Footnote †: journal: Marine Science and Engineering  
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Footnote 0: _J. Mar. Sci. Eng._**2023**, _11_, 840. [[https://doi.org/10.3390/jmse11040840](https://doi.org/10.3390/jmse11040840)]([https://doi.org/10.3390/jmse11040840](https://doi.org/10.3390/jmse11040840))
# On-Line Partial Discharge Monitoring System for Switchegears Based on the Detection of UHF Signals
## 1 Introduction  
High voltage switchegears are very important electrical equipment in power systems. Long-term operation of switchegears will increase the risk of insulation deterioration [1; 2; 3]. According to statistics, nearly 50% of switchgear accidents are caused by insulation deterioration [4; 5]. Therefore, online partial discharge (PD) diagnosis and early warning of insulation failures in high-voltage switchegears are key to maintaining electrical safety [6; 7].  
When the insulation of the switchgear is aging, a partial discharge phenomenon will occur. At present, the main detection methods for PD phenomena are the pulse current method, radio frequency method, ultrasonic method, ultra-high frequency (UHF) method and so on [8; 9; 10]. The UHF method is mainly aimed at the electromagnetic radiation signals with a frequency of 300 MHz\(\sim\)3 GHz [11; 12], which are generated by the partial discharge pulse. The UHF method can effectively avoid invalid electromagnetic waves such as GSM signals and corona signals in the switchegears. The UHF PD detection method has advantages in interference immunity, detection sensitivity and bandwidth of response frequency [13]. However, high requirements are needed for data measurement, and the sampling frequency should be higher than GHz, which is difficult and costly.  
In this paper, an on-line monitoring system was designed based on the UHF method and Internet of Thing (IoT) technology. To detect the UHF signal, the corresponding conditioning circuit and data collector are designed. The main component of the signal conditioning circuit is a logarithmic detector circuit, which is used to detect the envelope information of the UHF signal. This envelope signal reduces the sampling frequency requirement of the data collector while retaining the main characteristics of the UHF discharge signal (amplitude, phase and number). Test results show that the designed data collector can easily detect the conditioned discharge signal with a sampling rate of only MHz level, which greatly reduces the cost. This monitoring system adopts browser/server architecture and IoT technology. When the monitoring system is working, the communication module uploads the collected data to the cloud, and the real-time data and historical data of theswitchgear can be viewed on the PC webpage. At the same time, it is of great significance to improve the management efficiency and human resources.
# Uncertainty Analysis of Multisource Land Cover Products in China
## 3 Results
### Resolution Uncertainty  
The uncertainty among multisource land cover products decreased as the resolution became coarser. Although the uncertainty decreased, the coarser resolution inevitably led to a decrease in the classification accuracy of land cover products. Precision and consistency were two irreconcilable contradictions. The average and root mean square error of the percentage of land cover types were calculated, and the results are shown in Figure 8.  
As shown in the figure, the overall consistency of quantitative VCF and ESA CCI products was the best, indicating that the uncertainty of VCF and ESA CCI was the smallest. As the resolution decreased, the difference in each product decreased rapidly at first and then slowly, and the consistency increased rapidly at first and then slowly. In the research area, the resolution was reduced, and the consistency was enhanced. The resolution of 0.1\({}^{\circ}\) was the inflection point of uncertainty and accuracy. Before 0.1\({}^{\circ}\), the uncertainty of multisource land cover products decreased significantly as the resolution decreased, and after 0.1\({}^{\circ}\), the uncertainty decreased slowly as the resolution decreased.  
Figure 7: The spatiotemporal difference in grass and crop coverage changes of the products. Green indicates increase, blue indicates loss, and the color shading indicates the degree.
## 4 Experiments and Analysis
### Ablation Experiments  
In contrast to existing methodologies, this paper introduces two novel modules: the enhanced feature pyramid network and the gradient density object detection classification loss function. To meticulously assess the actual efficacy of these modules in enhancing the performance of the baseline method in real-world scenarios, this study undertakes ablation experiments. The comparative reference point for these evaluations is the baseline method derived from the official implementation of Sparse R-CNN.  
The results in Table 1 highlight the enhancements achieved by incorporating the enhanced feature pyramid network and gradient density object detection classification loss function modules into the initial baseline method, Sparse R-CNN. The integration of the enhanced feature pyramid network module contributes to a 0.4% increase in average precision compared to the original Sparse R-CNN baseline. This improvement is consistent across various object scales, encompassing large, medium, and small objects. By replacing focal loss with the gradient density loss function in the baseline approach, no substantial changes are observed in the overall metrics, yet the average precision experiences a slight elevation of 0.1%. Notably, when both the enhanced feature pyramid network module and gradient density loss function are simultaneously applied to the baseline, the average precision advances by 1.1%. This translates to a 2.4% boost in precision for small-scale objects, and 0.9% and 0.3% enhancements for medium and large objects, respectively.  
The adoption of the enhanced feature pyramid network module brings about a marked improvement in feature quality, positively impacting detection performance across various object scales. Furthermore, the incorporation of the gradient density loss function during training effectively reduces the model's susceptibility to challenging small objects, resulting in enhanced detection accuracy for such objects, without compromising the performance for medium and large objects. In terms of computational efficiency, both Improvement 1 and Improvement 2 had a minor effect on the training and inference speeds of the model. Specifically, the model's inference rate slightly decreased by 1.1 images per second, and the training time per iteration saw a marginal increase of 0.0159 s.
# A Choreography-Based and Collaborative Road Mobility System for L'Aquila City
## 4 Case Study
### Congestion and Accidents  
exploits a traffic congestion monitoring system using real traffic data. The system consists of a vehicle detector (VD) sensors subsystem and a control center subsystem. The VD sensor subsystem is used to collect traffic data and send it to the control center.  
These services are REST [57] services and hence they are constrained to a client-server architectural style that is not suitable for the event-driven communication required by the CRMS use case. Thus, for each service, we developed a new service that implements the publish-subscribe pattern through the Message Queue Telemetry Transport (MQTT) ([[http://mqtt.org](http://mqtt.org)]([http://mqtt.org](http://mqtt.org))). In particular, each newly developed service interacts with the related service and, whenever new information is available, it is sent to the corresponding subscribers. Moreover, as a route provider (Route Information participant) for the Eco Driving Planning choreography, we have chosen Google Maps ([[https://cloud.google.com/maps-platform/](https://cloud.google.com/maps-platform/)]([https://cloud.google.com/maps-platform/](https://cloud.google.com/maps-platform/))). Starting from these services, and from specification of the Eco Driving Planning and Smart Data Collection choreographies (see previous section), our synthesis method produces the related choreography-based systems, whose architectures are shown in Figures 7 and 8.  
The system that realizes the Eco Driving Planning choreography (Figure 7) involves Coordination Delegates (CDs) for the following participants: Driver, Eco Route Planner, Traffic Information and Environmental Information. Each CD, except for the CD related to the driver client application, is connected with the web services implementing the business logic of the related participant. In particular, as explained in Section 4, the CDs and the web services for the participants Traffic Information and Environmental Information are connected in a bi-directional way to realize the event-based communication required when new information is available. Moreover, a binding component (BC with a black label) is needed for the driver and route information services. The latter also needs an adapter (with a dark blue label).  
Figure 7: ECO Driving Planning architecture.  
Figure 8 shows the software architecture of the system that realizes the Smart Data Collector choreography. It contains CDs for the following participants: Viability Control Center, Smart Data Collector, Environmental Information, Weather, Air Quality, Roads Status, Traffic Information, Congestion and Accidents. In particular, the CDs corresponding to the participants Smart Data Collector and Environmental Information are connected with the web services containing the related business logic. As said before, the accidents, congestion, weather, air quality and roads status services are MQTT services and hence they need a binding component. Moreover, weather, air quality and roads status also require an adapter.
# Improved U2 Net-Based Surface Defect Detection Method for Blister Tablets
## 4 Proposed Methods
### Segmentation Threshold Determination Based on Local Mean and OTSU Method  
The improved model outputs a prediction map, which requires an artificial setting of a segmentation threshold according to which the prediction map is binarized, so this step is very important for the final output. In order to ensure the accuracy of the overall results, the thresholds were determined adaptively using the local mean method [25] and the OTSU [26] method.  
The OTSU method is also known as maximum inter-class variance thresholding segmentation algorithm. The local mean method takes the local mean as the threshold value of the pixel and binarizes each pixel; if the pixel value is greater than the local mean, it is set as foreground; otherwise, it is set as background. The local mean method deals with local information in the image and is able to deal with images with uneven illumination, while the OTSU method deals with global information and is able to deal with images with high overall contrast.  
In Equation (9) is the computational principle of the OTSU method; \(t\) is the maximum threshold of inter-class variance, \(\omega_{0}(t)\) and \(\omega_{1}(t)\) are the proportion of pixels on both sides of the threshold \(t\), and \(\mu_{0}(t)\) and \(\mu_{1}(t)\) are the mean values of
## 3 Bias Impact Analysis
### Optimal Flight Line Configuration for Calibration  
Based on the above discussion, the following comments can be made regarding an optimal flight configuration for calibration:  
* The lever arm \(\Delta X\) can be estimated using opposite flight lines while scanning vertical planar features parallel to the flight direction.
* The lever arm \(\Delta Y\) can be estimated using opposite flight lines while scanning vertical planar features perpendicular to the flight direction.  
* The lever arm \(\Delta Z\) for a given spinning multi-beam laser scanner can be estimated only using vertical control, which can be in the form of horizontal planar patches.
* The boresight pitch \(\Delta\omega\) can be estimated using opposite flight lines along with another flight line at a different height while scanning horizontal planar features and vertical planar features perpendicular to the flight direction. Another alternative for having a flight line at different flying height is to have vertical planar features whose extent in the vertical direction is significant w.r.t. the flying height or having vertical planar patches at different heights.
* The boresight roll \(\Delta\phi\) can be estimated using opposite flight lines along with another flight line at a different height while scanning horizontal planar features and vertical planar features parallel to the flight direction. Another alternative for having a flight line at different flying height is to have vertical planar features with significant height w.r.t. the flying height or having vertical planar patches at different heights. Additionally, increasing the lateral distance between the tracks and between horizontal patches and the tracks would improve the boresight roll estimation.
* The boresight heading \(\Delta\kappa\) can be estimated by scanning vertical planes from two flight lines in the same direction with a significant lateral separation between them. This configuration would eliminate any discrepancies caused by lever-arm components.
## 4 Experiment and Result Analysis
### Definition of Evaluation Indicators  
In the stage of data analysis, six indexes were used to evaluate the model effect, including accuracy rate (P), recall rate (R), mAP_0.5, mAP_0.5:0.95, training time and single image test time. Accuracy rate, recall rate and mAP (mean average precision) are all metrics that assess the accuracy of the model's detections and their formulas are shown below. Training time serves as a measure of the detection model's training efficiency, while single image test time represents a measure of the speed of image processing by the object detection network.  
\[P=\frac{TP}{TP+FP} \tag{12}\]  
\[R=\frac{TP}{TP+FN} \tag{13}\]  
\[AP=\int_{0}^{1}P(R) \tag{14}\]  
\[mAP=\frac{1}{c}\sum_{j=1}^{c}AP_{j} \tag{15}\]  
Here are the definitions: \(P\) represents accuracy, \(R\) stands for recall rate, \(TP\) is the number of true positives (positive samples correctly determined), \(FP\) is the number of false positives (samples incorrectly judged as positive), \(FN\) is the number of false negatives (samples incorrectly judged as negative), \(AP\) represents the average accuracy across all classes, and \(c\) denotes the total number of classifications.
# Data-Driven Packet Loss Estimation for Node Healthy Sensing in Decentralized Cluster
## 3 System Overview and Problem Formulation  
### System Overview  
In this section, we give an overview of our system of sensing healthy status of nodes in decentralized cluster based on Akka. Figure 1 shows the system overview of decentralized cluster services such as Akka cluster. Specifically, this system manages its members by maintaining a globally consistent member state table. To keep globally consistent, each node uses the Gossip Protocol ([PERSON] et al., 2016) to repeatedly replicate its state to a randomly selected neighbor. To detect and handle node failure, each node in the cluster implements a heartbeat based failure detector to detect the reachability to some node in its neighbor. If one detects a failure, it will mark this node in its own member state table and try to gossip it to the rest of nodes in the cluster. Then a temporarily selected leader will handle this issue. We next make an expression of these processes in detail.
# Multiclass Segmentation of Concrete Surface Damages Using U-Net and DeepLabV3+
## 2 Materials and Methods
### Evaluation Metrics  
For the classification task, there are four commonly used evaluation indexes, namely true positive (TP), true negative (TN), false positive (FP), and false negative (FN). In the multiclass segmentation problem, the evaluation index for each class is calculated individually, meaning that, when a specific class is evaluated, it considers other classes as the foreground. For example, when the parameters are calculated for cracks, TP means that the truth is crack and the prediction is also crack, TN means that the truth is non-crack and the prediction is also non-crack, FP means that the truth is non-crack while the prediction is crack, and FN means that the truth is crack while the prediction is non-crack.  
In this segmentation task, there is class imbalance due to the domination of the background area. Therefore, evaluation metrics used for measuring the models' performance cannot favor the dominant class. In this research, accuracy could not be used as the main metric, instead Dice coefficient and intersection over union (IoU) were used for the comparison purpose. Dice coefficient and IoU are defined in Equations (2) and (3), respectively.  
\[\mathrm{Dice\ coefficient}=\frac{1}{S}\sum_{s=1}^{S}\sum_{k=0}^{K}2\frac{p_{s}^{ k}g_{s}^{k}+\epsilon}{p_{s}^{k}+g_{s}^{k}+\epsilon} \tag{2}\]  
\[\mathrm{IoU}=\frac{1}{S}\sum_{s=1}^{S}\sum_{k=0}^{K}\frac{p_{s}^{k}g_{s}^{k}+ \epsilon}{p_{s}^{k}+g_{s}^{k}-(p_{s}^{k}g_{s}^{k})+\epsilon} \tag{3}\]  
where \(S\) is the total number of samples, \(K\) is the total number of classes, \(p_{s}^{k}\) is the prediction on a specific sample \(s\) and class \(k\), \(g_{s}^{k}\) is the corresponding grountruth, \(w_{k}\) is the class rebalancing weight that needs to be set manually, and \(\epsilon\) is a small constant to prevent zero division.  
Figure 11: (**a**) Regular dropout channel deactivation; (**b**) spatial dropout channel deactivation.