esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Analysis of Coverage Feedback in a Fuzzing Proprietary System
## 2 Background  
### Code Coverage and Fuzzing  
Code coverage encompasses various metrics, including function coverage, statement coverage, branch coverage, condition coverage, and line coverage [14]. These metrics differ in the choice of the unit for measuring coverage. For example, line coverage treats each line as a unit (based on source code), whereas branch or block coverage examines the number of executed branches or blocks in the assembly or binary level. In software testing, code coverage is measured via crafted test cases including data and code altogether (e.g., unit test) to find errors in the software development phase [15]. In fuzzing, code coverage is measured by feeding the target code a randomly generated (mutated) test input [16]. However, according to recent studies [8; 9], achieving 100% code coverage does not always guarantee testing quality nor the finding of bugs. In this paper, we figure out more details to this end, considering the system porting costs for applying coverage feedback fuzzing to closed-source systems.
## 2 Materials and Methods
### Data
#### 2.2.5 Forest Canopy Height Map  
Our goal is to compute the tree bias model for Costa Rica for the NASADEM model based on NASA's SRTM data collected in 2000. However, GFCH2019 provides the canopy heights for the year 2019. To account for some changes in vegetation between 2000 and 2019, we used an auxiliary, the 1 km mesh size Forest Canopy Height Map by [PERSON] et al., 2011 [6]. This model uses data from the GLAS radar aboard ICESat collected in May-June 2005; therefore, here, it is abbreviated as FCHM2005. The model was downloaded from [[https://landscape.jpl.nasa.gov/](https://landscape.jpl.nasa.gov/)]([https://landscape.jpl.nasa.gov/](https://landscape.jpl.nasa.gov/)) (accessed on 28 September 2020); it is shown in Figure 4b and its statistics in Figure 4d. The minimum and maximum canopy heights are limited to 5 and 40 m, respectively. It can be visually interpreted from the figure that the heights are overestimated when compared to the GFCH2019 model. This issue was mentioned in [6] when comparing FCHM2005 to another low-resolution canopy height model that uses [PERSON]'s mean tree height, not RH100, as the canopy height estimate. Estimation of the model's accuracy by [6] using validation at 66 sites provided an RMS of 6.1 m using all sites and an RMS of 4.4 m excluding seven outliers.
# Integrated Design Methodology of Automated Guided Vehicles Based on Swarm Robotics
## 3 Integrated Methodology for Designing AGVs  
AGVs can be considered modern mechatronic systems providing an increasing number of functionalities [35]. Our objective is to develop a design method allowing specialists from different engineering fields to combine their expertise to ensure functional, physical, and software integration using SysML and ROS. Indeed, today's need for AGVs development is a closer integration which encompasses various factors related to design practices and tools, design methods, and design team members and their interactions.  
The AGV design approach proposed in this article is based on swarm robotic concepts and it consists of two phases: a top-down phase from the requirements specification to functional and structural modeling using SysML; with a bottom-up phase for the integration of models and their implementation in ROS. The swarm designer begins by specifying the design requirements using SysML diagrams to describe the various swarm system needs. From these requirements, the designer identifies the different functions that build the collective swarm behaviors. SysML state-machine diagrams and activity diagrams are therefore used to model swarm behaviors. To ensure high-level traceability between requirements, behaviors, and functions, the designer uses the allocation matrices (Requirement-Behavior, Behavior-Function). These matrices trace the specified requirements with the functions that the AGV system must perform while respecting swarm behavior. Once the collective behaviors are modeled, the designer is interested in structural modeling. Using the SysML block definition diagram (BDD) and internal block diagram (IBD), the designer details the AGV structure by specifying the components that are able to ensure the previously modeled functions that the AGV must perform [36].  
To ensure code integration, the designer follows a bottom-up approach guided by the SysML model developed in the previous steps to implement the swarm behavior with ROS. At this level of design, the structure of the AGV system is modeled with a Unified Robot Description Format (URDF) file based on BDD and IBD SysML diagrams. In addition, the system environment is described in ROS by creating a world file based on a context BDD-SysML diagram. The final simulation of the swarm behavior of AGVs is performed with ROS/Gazebo to meet the requirements described with SysML requirement diagrams. Figure 2 represents the steps of the proposed integrated design methodology. These steps are detailed in the following sections.
###### Abstract  
High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed.  
remote sensing; non-destructive testing (NDT); transport infrastructure monitoring; infrastructure management systems (IMSs); Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR); data fusion and integration  
6
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 5 Discussion  
The L-band PALSAR-2 radar imagery provides a measurement suitable for direct comparison with the L-band GPS L1 band measurements of CYGNSS. However, our results are limited by the accuracy of the PALSAR-2 reference maps, which was previously determined to have an overall accuracy of 91.8% [10]. There may also be differences due to daily variability that is not captured by PALSAR-2 and that has been averaged by the CYGNSS measurements to match the 14-day cycle characteristic of the PALSAR-2 mosaics. In addition, the ancillary datasets have associated uncertainties that may also affect the final performance of the MDTR classification algorithm. The uncertainty of the ancillary datasets is assumed to be small compared to the uncertainties derived from the GNSS-R measurements.  
The main source of misclassification is the size of the scattering surface. In this study, we assumed that the size of the first Fresnel zone represents the area contributing to the received power. The less coherent the scattering, the bigger this area becomes. A deeper study is needed to look into a more adequate representation of the scattering zone size, and a more detailed analysis of what the reflected power properties are as a function of the scattering zone size and the elements within this zone. Better understanding is needed of the effect of having a small portion of standing water within a scattering area, and how the position of the standing water affects the resulting reflected power as a function of its proximity to the specular point. In this study, we observed that a small percentage of open water or flooded vegetation within the scattering area but away from the specular point, produced scattering properties closer to non-flooded conditions. In general, more studies are needed to understand the scattered power resulting from a non-homogeneous surface with variable scattering properties derived from differently located surface characteristics within the scattering surface.
## 4 Conclusions  
Analyzing the optimal period of crop irrigation in large areas is a complex problem. By examining the regional distribution differences of precipitation self-sufficiency rates during the growing period of winter wheat in Shaanxi Province, the regional variation of precipitation-meteorological yield integral regression function was studied and the effect of precipitation on winter wheat yield at different periods was discussed. Finally, the optimal irrigation period for winter wheat was determined. The main conclusions of this study are as follows:  
1. Compared with the optimal irrigation period determined by the previous small area experiment, the method proposed in this study has the advantages of small workload and strong timeliness. It can overcome the shortcomings of the previous test method that the result is not stable, due to the small test area and the lack of representativeness.
2. The irrigation periods of winter wheat in subregions I and II are the overwintering and jointing periods, the irrigation period in subregion III is in the middle of the overwintering period, and the irrigation period in subregion IV is the early overwintering period, which is basically consistent with irrigation habits of local farmers, and can be used to guide the irrigation of local winter wheat. It also demonstrates that the method proposed in this paper has important application value in determining the deficit irrigation period with small water amount, in a large region lacking in research materials.  
Conceptualization, Y.W., T.Y. and X.M.; Data curation, Y.W., R.Z. and X.M.; Methodology, Y.W., T.Y. and K.J.; Writing--original draft, Y.W.; Writing--review and editing, X.M.  
This research was funded by the Special Fund for Agro-scientific and Research in the Public Interest of China (201503124) and the National Natural Science Foundation of China (51279167).  
The authors declare no conflicts of interest.
# Hybrid Local and Global Deep-Learning Architecture for Salient-Object Detection
## 3 Proposed Approach
### Global Convolutional Block  
Enhancement in classification was achieved by using a global convolutional block (GCB). This helps to efficiently exploit accessible visual information in low-contrast images. The GCB seeks to extend the receptive region of feature maps. It also introduces dense connections between features and classifiers, which helps the model to identify the salient region with very little additional computational cost.  
As mentioned before, the GCB enhances the classification efficiency of proposed model by considering the dense connections between classifiers and feature maps, which enables the network to handle different types of transitions. GCB's large kernel is also useful in encoding more spatial information from feature maps, which increases precision in salient-object detection.  
The GCB has two subdivisions that consist of two convolutional blocks. The left block has \(7\times 1\) convolutional layer, after which comes another convolutional layer of \(1\times 7\). The right block has \(1\times 7\) convolution followed by \(7\times 1\) convolution. These two subdivisions are combined to allow for dense connections, which enhances the receptive field's validity. GCB's computational cost is fairly low, which makes it more practicable.
# Energy Efficient Routing Algorithm with Mobile Sink Support for Wireless Sensor Networks
## 7 Discussions and Open Research Issues  
### Uneven Energy Distribution Between Clusters  
The results show that our proposed algorithm had good performance in terms of its lifetime; however, we found a common phenomenon that the energy distribution between clusters was uneven. As is shown in the Figure 17, some clusters had no node alive, whereas the nodes in some clusters were all alive. The following reasons may cause the problem:  
* Sensors were deployed unevenly and each cluster could have different number of nodes.
* The average distance between nodes and the regional center in one cluster could be longer than that in other clusters.  
Figure 16: Energy consumption of different numbers of mobile sinks.  
Figure 17: Uneven energy distribution between clusters.  
Node deployment had a great influence on the performance of the network. The study of node deployment strategies has been a hot research issue in recent years since it can improve the performance of WSNs in the aspects of energy consumption, network lifetime, and coverage rate. Homogeneous deployment of sensor nodes, if possible, can eliminate the phenomenon mentioned above.
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
###### Abstract  
Retinal vessel segmentation is critical for diagnosing related diseases in the medical field. However, the complex structure and variable size and shape of retinal vessels make segmentation challenging. To enhance feature extraction capabilities in existing algorithms, we propose PAM-UNet, a U-shaped network architecture incorporating a novel Plenary Attention Mechanism (PAM). In the BottleNeck stage of the network, PAM identifies key channels and embeds positional information, allowing spatial features within significant channels to receive more focus. We also propose a new regularization method, DropBlock_Diagonal, which discards diagonal regions of the feature map to prevent overfitting and enhance vessel feature learning. Within the decoder stage of the network, features from each stage are merged to enhance the segmentation accuracy of the final vessel. Experimental validation on two retinal image datasets, DRIVE and CHASE_DB1, shows that PAM-UNet achieves 97.15%, 83.16%, 98.45%, 83.15%, 98.66% and 97.64%, 85.82%, 98.46%, 82.56%, 98.95% on Acc, Se, Sp, F1, AUC, respectively, outperforming UNet and most other retinal vessel segmentation algorithms.  
deep learning; retinal vessel segmentation; plenary attention mechanism; regularization 2024
# Detection of Sleep Apnea from Electrocardiogram and Pulse Oximetry Signals Using Random Forest
## 5 Discussion
### Comparison with Other Related Works  
We compared the performance of our method with other studies. Table 6 summarizes the results of our work and related work on per-segment SA detection. As shown in Table 6, some studies such as [12; 31; 36] used ECG signals, where [36] used an autoregressive model and a spectral autocorrelation function to extract features from ECG segments with an accuracy of up to 93.9%. In addition, among the studies using SpO2 [25; 37], the best performance was reported by [25]. According to Table 6, our proposed approach provided higher per-segment classification accuracy than other studies. Moreover, [38] also used a combination of ECG and SpO2 signals and extracted 39 features. However, this study achieved 97.5% accuracy using only 12 features, which reduces the complexity of the model while improving the accuracy.
# Acoustic Improvement of Stator-Rotor Interaction with Nonuniform Trailing Edge Blowing
## 5 Experimental Results
### Acoustic Results
#### 5.2.2 Noise Transmission in the Backward Direction  
Figure 9 displays the results corresponding to those in Figure 8, but in this case for backward noise transmission. The noise levels that radiated backwards and forwards were measured simultaneously. The overall trends displayed by the results for the noise that radiated backwards mirrored those for forward radiation, in particular for the second-order BPF. However, noise reductions of the first-order BPF are not significant.  
Figure 8: SPL of the first and second-order BPF.  
BPF displayed a more complex scenario, where many acoustic modes revealed a decrease of the SPL with NTEB. With the maximum NTEB flow, the SPL reduction is just above 2.3 dB at mode (\(-1\)) of the first-order BPF, while the reduction is more than 10 dB at both maximum acoustic modes (\(-2\) and \(+4\)) of the second-order BPF.
## 3 Signal Quality Index Algorithm  
In the study, an SQI algorithm was developed to find out whether a PPG pulse was high quality or not. The flowchart of the proposed algorithm consists of several process steps, including the digital filtering, the Gaussian filtering, the segmentation, the pre-process, the parameter extraction, the post-process, and the SQI calculation, as shown in Figure 2. Our designed ICG device was described in reference [4], in which analog ICG and PPG signals all were digitalized with a 500 Hz sampling frequency. The PPG signal would be filtered to remove the wandering noise of the baseline and the high-frequency noise using a second-order Butterworth bandpass filter in which the lower and upper cutoff frequencies were 0.2 Hz and 10 Hz, respectively. A Gaussian filter was adopted smoothing the PPG signal. The smoothed PPG signal without the motion artifact is similar to a sinusoidal signal. Thus, we segmented the PPG pulse according to the sinusoidal signal. The rigorously corrupted PPG pulses would be detected and deleted in the pre-process procedure. Four parameters were extracted from the smoothed PPG pulse, which were used to determine the level of SQI for each PPG pulse with the SoNFIN algorithm. The levels were high, middle, and low.  
Figure 1: Four signals include impedance cardiography (ICG), differential ICG, photoplethysmography (PPG), and differential PPG. The left ventricular ejection time (LVET) is defined from the differential PPG signal.
# Learning Optimal Time Series Combination and Pre-Processing by Smart Joins
## 4 Experimental Setup
### Experiments on Synthetic Data  
The experiments on synthetic data are carried out on the \(x,y,z\) 3D curve generated in time \(t\) by a Lorenz system (originally a simplified model for atmospheric convection) [9].  
\[\begin{split}\frac{dx}{dt}&=\sigma(y-x)\\ \frac{dy}{dt}&=x(\rho-z)-y\\ \frac{dz}{dt}&=xy-\beta z\end{split} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & \(NaEI(\hat{y})\) & \(MissEI(y,\hat{y})\) & \(DelEI(y,\hat{y})\) & \(DeIT(y,\hat{y})\) & \(AntEI(y,\hat{y})\) & \(AntT(y,\hat{y})\) & \(Diff(y,\hat{y})\) \\ \hline left & 0.882 & 0.818 & 0.0 & 0.0 & 0.0 & 0.0 & 1.0 \\ nearest & 0.0 & 0.0 & 0.471 & 0.031 & 0.412 & 0.031 & 0.045 \\ forward & 0.0 & 0.091 & 0.0 & 0.0 & 0.882 & 0.094 & 0.092 \\ backward & 0.0 & 0.091 & 0.882 & 0.094 & 0.0 & 0.0 & 0.084 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Error values for different join methods in the application example.  
Figure 4: Application example results for different joining methods. (**a**) Left join. (**b**) Nearest join. (**c**) Forward join. (**d**) Backward join.  
with parameters \(\sigma=10,\rho=28\) and \(\beta=8/3\) and initial conditions \(x(0)=y(0)=z(0)=1\) and \(t\in[0,40]\). The time sampling interval selected for the time series was 0.1 time units.  
The simulated data can be observed in Figure 5a. To apply the smart join methodology only dimension \(x\) was used. The time series is shown in Figure 5b.  
In order to generate a distorted version of this time series in a controlled manner, some distortion methods were applied, inspired by from the work of [PERSON] and [PERSON] [10], which will be described later in the section.  
This controlled experiment setup was used to demonstrate how errors change depending on the join method and on the type of distortion that is applied to each time window. The distortions have been selected in order to represent usual problems such as missing data or delays in receiving data points.  
The series was divided into four parts of equal size. In the first part (\(t\in(0,10]\)) the time series remains unaltered. In the range \(t\in(10,20]\), 20% randomly selected data points were removed. This distortion can be seen in Figure 6a. In the remaining part of the time series, 20% of data were shifted forward (in \(t\in(20,30]\)) or backward (in \(t\in(30,40]\)). The shifted quantity was selected by a random uniform variable, guaranteeing that data points cannot be disordered. In other words, the maximum possible shifted quantity was set by the sampling frequency value of the original simulation data. The distortion effect generated in the time series can be observed in Figure 6b.  
The difference between modified and original data can be seen in Figure 7.  
Figure 5: Lorenz system result data. (**a**) Three-dimensional data. (**b**) First dimension time series.  
Figure 6: Zoomed distortions of Lorenz first dimension. (**a**) Removed data. (**b**) Shifted data.
## 3 Steel Truss Experiment
### Signal Recognition and Analysis  
#### 3.3.1 Dataset Construction  
In this experiment, sound signals from a total of eight conditions, including loose bolt damage and environmental noise mentioned above, were collected. For each condition, typical signal lengths totaling 10 s were collected. After framing, 780 frames of signals were obtained. The first 400 frames of signals with relatively high short-time energy were selected as sample data. The sample set was divided into training and validation sets using a random split ratio of 7.3. Therefore, the total number of samples for the eight conditions is 3200, with 2240 samples in the training set and 960 samples in the validation set. The eight operating conditions are identified by the numbers 1-8, as shown in Table 2.  
Following the analysis process outlined above, the collected samples were first analyzed in the time domain to obtain the short-time energy temporal features of each frame signal. Then, MFCC analysis was performed with 24 Mel filters, resulting in 24 MFCC features for each frame signal. Furthermore, wavelet packet analysis was conducted using the db10 wavelet basis function with a decomposition level of 4, resulting in 24 = 16 wavelet packet energy features. The MFCC and WPES features were concatenated to obtain the MFCC-WPES combined features. Therefore, the original feature set contains a total of 24 + 16 = 40 features. The corresponding input dataset for the classifier is a 3200 \(\times\) 40 matrix.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 3 Experiments
### Parameter Settings  
In the experiments, the thresholds used for the keypoint detection are set to 0.01 and 0.02, respectively. In the process of descriptor construction, to obtain more information, the circle radius of the support region is empirically set to \(30s_{j}\). Settings of the parameters in the two comparison algorithms are the same as described by their authors' instructions. At the same time, in order to obtain a similar number of keypoints, we fine-turned the thresholds of the keypoint detection.
## 4 Expected Area-Based Real-Time Routing (EAR2)
### Calculation of Data Delivery Speed
Second, if the sink moves toward \(p2\), which is the farthest point from the source, the time to deliver the data by flooding increases so the mobile sink may not receive the data within the desired time deadline. Third, because the data forwarding path to a single flooding point is a single path, if data loss on the path happens, then the mobile sink cannot receive data [35; 36]. Therefore, in the next section (Section 5), we exploit multiple flooding points in EAR2 to overcome the weaknesses of single-point flooding.