esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Simulations of Other Crucial Elements in the Water Cycle
#### 3.2.3 Spatial Variation of the Cumulative Runoff  
The runoff of the fully coupled system is determined by both precipitation and soil moisture, as the variables transferred back to [PERSON] by the hydrological module of the fully coupled system include the water accumulation depth and soil moisture content of each soil layer. The spatial distributions of the 24 h cumulative runoff are shown in Figure 13, for which both WRF-only and the coupled WRF/WRF-Hydro were generally consistent with the rainfall, and high runoff values in places with high rainfall as well. The generated runoff of the storm center simulated by the two systems was higher than the other regions of the study catchments. Event 5 had the greatest rainfall accumulation, and the local maximum runoff was almost 80 mm, while Event 2 had the least cumulative rainfall, and the local runoff was just 8 mm. At the same time, the volume of the generated runoff was also subject to the change in the local soil moisture. With relatively dry soil conditions such as Event 4 and Event 6, it can be seen from Figure 12 that when the soil moisture was less than 0.26, the generated runoff was also relatively low. Besides this, the runoff changed from the WRF-only to the coupled WRF/WRF-Hydro, and Event 3, Event 4, and Event 6 in particular could reflect subtle differences similar to the rainfall.  
Figure 12: Spatial distributions of the hourly averaged soil moisture of the four soil layers: (**a**) WRF-only; (**b**) the fully coupled WRF/WRF-Hydro.
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 3 Theoretical Foundation of Resilience Curve and Its KPIs in This Domain
### Resilience Curve and Selected R-KPIs
#### 3.1.2 Minimum Performance Level  
The determination of the minimum performance level in critical infrastructure depends on the inherent design features of the system and the severity of the encountered disturbance. Monitoring this minimum performance level holds crucial significance as it ensures the preservation of essential services vital for the system's proper functioning. The minimum performance level is intricately defined by the indispensable thresholds associated with the societal needs for that particular service. In the event that the minimum performance of a critical infrastructure (CI) descends below this predefined level following a disturbance, it becomes imperative to reassess the response strategy to this risk. Such a proactive approach is essential for safeguarding the reliability and functionality of the critical infrastructure system, aligning its performance with the crucial needs of the society it serves. Continuous monitoring and adaptive strategies are thus indispensable to maintain the resilience and effectiveness of critical infrastructure in the face of dynamic challenges. Figure 2 shows the minimum performance level after disturbance.
# Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions
## 3 Results  
The potential of the proposed approach for detecting oil spills under severe observation conditions with a good trade-off between reliability and sensitivity has been tested by analyzing images acquired during the DWH accident (April-May 2010), where the oil presence has already been detected by other methodologies [5; 22; 23; 44; 45; 46]. It should be stressed that almost all the images are affected by sun glint. Hence, in the following section, we mostly focus on these specific conditions. Images were first investigated by implementing the standard RST-OIL configuration (i.e., using the reference fields computed as described in Section 3.1) and then the one specifically proposed in this paper (Section 3.2).
###### Abstract  
To comprehensively evaluate the seismic performance of offshore bridges, a seismic risk analysis of an example bridge was developed based on improved two-dimensional (2D) seismic fragility analysis. Taking a simply-supported beam bridge in an offshore tidal environment as an example, the adverse effects of chloride ion erosion are considered and the seismic response process of the example bridge is simulated using the Incremental Dynamic Analysis (IDA) method. The appropriate damage indexes are chosen for the plate rubber bearing and the pier, and the one-dimensional (1D) seismic fragility curves of single components and the entire bridge are obtained. The correlation coefficients of vulnerable components are quantitatively proposed based on the correlation analysis method, and the 2D seismic fragility curves of the entire bridge are achieved while accounting for seismic correlation between vulnerable components. The seismic risk probability of the entire bridge is finally determined after combining hazard analysis at the bridge site and seismic loss analysis. The results show that there is a significant correlation between vulnerable components and that 2D seismic fragility analysis based on the reliable correlation coefficients of vulnerable components can more comprehensively evaluate the seismic performance of the entire bridge. In the context of seismic disaster reduction research focused on slight and moderate damage caused by moderate-to-small ground motions, this research can provide scientific and technical support for seismic design and seismic risk assessment of offshore bridges.  
Keywords: offshore bridges; chloride ion erosion; seismic correlation between vulnerable components; 2D seismic fragility analysis; seismic risk analysis +
Footnote †: journal: Applied Sciences
# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 2 Related Work
### Correlation Filtering Algorithms  
The correlation filter tracking (CFT) algorithm is an online learning method that adapts to changes in the target by updating the model in time, thus guaranteeing tracking accuracy. Since [PERSON] et al. (2019) first introduced the CFT algorithm to the field of target tracking in 2010 by proposing the minimum output sum square error (MOSSE) tracking algorithm, the filtering algorithm has developed rapidly.  
To overcome the tracking drift phenomenon in complex scenes due to changes in target scales, [PERSON] et al. (2017) proposed a spatially regularized discriminative correlation filtering (SRDCF) algorithm that introduces a spatially regularized component and uses regularized weight pairs to train the aforementioned objective function. Moreover, it accurately tracked the target by training two different filters for position and scale, respectively, which better handled the scale change problem.  
[PERSON] et al. (2018) introduced temporal regularization into the single-sample SRDCF algorithm and proposed the spatially temporal regular correlation filtering (STRCF) algorithm. The algorithm solves the system of equations using the alternating directional multiplier method (ADMM), which improves the tracking accuracy of the algorithm while also significantly increasing the tracking speed of the algorithm. Considering the change in target background over time, [PERSON] (2018) proposed the background-aware correlation filtering (BACF) algorithm, which dynamically models the foreground and background of the tracking target using HOG features over time to ensure higher accuracy and real-time performance.  
To suppress distortions, [PERSON] et al. (2018) proposed the correlation filtering algorithm (CFA). By extracting background samples as negative samples for the model training and detection phases and introducing a regularization term that limits the rate of change of the response map, CFA avoids variation in the response map and improves the robustness. The automatic spatiotemporal regularization tracker (AutoTrack) is also an improved target tracking algorithm used in UAV scenarios, and the proposed algorithm is proved to have very high robustness to complex and variable UAV scenarios through extensive comparison experiments.  
The CFA could ensure that the UAV tracking model is trained and updated online promptly to extract the effective features of the target, which significantly reduces the computational complexity and improves the target tracking in real-time. At the same time, it enables the UAV to achieve real-time tracking on a single CPU. However, it is difficult to effectively deal with fast scale changes, moving object occlusion, low resolution, small targets and other tracking problems with the correlation-based filtering algorithm. Moreover, the filtering structure element is not adaptive, which makes it difficult to accurately predict the position of the target.
# Experimental Evaluation of a LoRa Wildlife Monitoring Network in a Forest Vegetation Area
## 2 State-of-the-Art and Related Works
### Related Works
The authors were able to achieve a coverage range of 6 km in the urban and suburban scenarios under adverse scenarios, while achieving 18 km coverage in the open rural scenario. In [51], LoRa performance in a mountainous environment was demonstrated. Their results introduced the effect of high temperature, which deteriorates the signal, thus resulting in the reduction of the communication range by an order of magnitude, and the choice of antenna greatly impacted the quality of the signal. Authors in [52] demonstrated the behaviour of LoRa communication and its transmission range in dense urban and forest environments conducted in Brazil and Portugal. The longest range achieved in the dense-urban area was approximately 2.1 km, while results in the forest environments were inconclusive. Finally, [PERSON], [PERSON] et al. [53] presented a signal propagation study of LoRa in forest, urban and suburban vehicular environments. The authors also considered node mobility, and the results showed how LoRa signal stability greatly depends on the environment and how much more stable it is in suburban areas than in high density urban areas. In summary, whilst the surveyed studies are focusing on LPWAN performance for smart agriculture applications, and on the performance of LoRa in urban and suburban environments, more studies need to be carried out on hard natural environments such as dense forest environments. Such experimental deployment studies are needed to provide meaningful insight in providing a suitable propagation model for forest environments. In this study, we propose to explore the performance of a LoRa-based IoT network deployed in a forest vegetation area. The present work differs from the aforementioned literature, as it investigates the propagation of LoRa in the forest environments operating in the 433 MHz and 868 MHz frequency bands.
## 6 Simulation Analyses
### Flying Base Station Channel Capacity: \(\gamma\)  
The flying base station channel capacity increases with greater path loss exponent \(\gamma\). As the wireless communication channel experiences greater attenuation with distance (greater \(\gamma\)) and with the SNR\({}_{SU}\) = 10 dB fixed, flying base station channel capacity increases as shown in Figure 4. When the channel performance between the stationary base station and the user is fixed (i.e., SNR\({}_{SU}\) = 10 dB), the flying base station introduction increases the channel capacity even more with harsher channel conditions with greater attenuation (greater \(\gamma\)). While \(\gamma\) is usually known to reduce the channel performance because of the greater signal attenuation in communication, the flying base station serving as a relay between the stationary base station and the user provides greater channel capacity. As \(\gamma\) increases from 2 (closer to line-of-sight with no barrier) to 5 (many barriers with higher fading and attenuation), the flying base station channel capacity increases by 55.4%. Comparing the full-duplex (upper curve) and the half-duplex (below curve) in Figure 4, full-duplex capability increases the channel capacity by double, which is consistent with our analyses when the transmission power varies with \(\eta\) in Section 6.1. As \(\gamma\) increases from 2 to 5, the flying base station increase from 5.358 bps/Hz to 8.326 bps/Hz in full-duplex case and from 2.679 bps/Hz to 4.163 bps/Hz in half-duplex case.
# Mechanisms of Sweep on the Performance of Transonic Centrifugal Compressor Impellers
## Numerical Method and Validation
### Grid Independence Study  
The accuracy of CFD results is associated with the grid size; therefore, the grid independence is studied before large amounts of simulations are carried out. Four different types of mesh within the impeller are investigated, whose number of nodes vary from 1.0 million up to 2.6 million. The resulting performance of all meshes at design speed is plotted in Figure 5a. It is illustrated that the CFD simulation produces consistent results when the mesh nodes surpass 1.5 million. In Figure 5b, the spanwise distribution of static pressure is compared at impeller exit, where all the cases share the same mass flow rate at the peak efficiency condition. The pressure distribution is reproduced for a mesh size of 2.3 million nodes and above, where the maximum difference of static pressure is only 0.03% of the backpressure. Hence, the numerical method that adopts a mesh with 2.3 million nodes is independent of the mesh resolution.
# GRACE/GFO and Swarm Observation Analysis of the 2023-2024 Extreme Drought in the Amazon River Basin
## 1 Introduction
Based on this, the main objectives and scopes of this study were as follows: (1) to integrate multi-source satellite gravity observation data to examine the spatiotemporal evolution characteristics of the 2023-2024 extreme drought in the ARB; (2) to quantify the terrestrial water storage (TWS) deficit during this drought and evaluate its impact; (3) to analyze the propagation path of the drought through the terrestrial water cycle by studying changes in TWS components; and (4) to investigate the impacts of atmospheric vertical movement and water vapor transport on drought events, as well as their connection with ENSO events, through vertical velocity and its anomalies, and vertically integrated water vapor flux and its divergence derived from wind speed and relative humidity data.
# New Approaches to AI Methods for Screening Cardiomegaly on Chest Radiographs
## 3 Results  
### Segmentation Process Using Different Networks  
Three different networks were at first used for comparison. The best results were observed for U-net, followed by SwinUNETR and UNETR. Using U-net for the training set, the Dice coefficient for the lung was 0.984 \(\pm\) 0.003 (min. 0.977), while for the heart it was 0.983 \(\pm\) 0.004 (min. 0.972). For the testing set, the Dice coefficient for the lung was 0.970 \(\pm\) 0.012 (min. 0.926), while for the heart it was 0.950 \(\pm\) 0.021 (min. 0.871). The Dice coefficients for the lung and heart using U-net for training and testing sets are shown in Appendix A Figure A1. Using SwinUNETR for the testing set, the Dice coefficient for the lung was 0.953 \(\pm\) 0.042 (min. 0.699), and for the heart, it was 0.908 \(\pm\) 0.059 (min. 0.665). Using UNETR for the testing set, the Dice coefficient for the lung was 0.896 \(\pm\) 0.077 (min. 0.585), and for the heart, it was 0.877 \(\pm\) 0.059 (min. 0.728).  
Besides the Dice coefficient, we have calculated the robust Hausdorff distance for the 95% quantile using the surface-distance package ([[https://github.com/google-deepmind/surface-distance](https://github.com/google-deepmind/surface-distance)]([https://github.com/google-deepmind/surface-distance](https://github.com/google-deepmind/surface-distance)); accessed on 28 November 2024). The robust Hausdorff distance for U-net test segmentations was 0.4 \(\pm\) 0.8 pixels (maximum 4 pixels) for the lungs and 3.9 \(\pm\) 8.1 (maximum 52 pixels) for the hearts. The robust Hausdorff distance for SwinUNETR test segmentations was 3.4 \(\pm\) 12.4 pixels (maximum 87.6 pixels) for the lungs and 15.8 \(\pm\) 19.6 (maximum 78.5 pixels) for the hearts. The robust Hausdorff distance for UNETR test segmentations was 28.5 \(\pm\) 34.5 pixels (maximum 112.5 pixels) for the lungs and 23.1 \(\pm\) 24.8 (maximum 127.9 pixels) for the hearts. Based on these results, we will only use the segmentations returned by U-net described below.  
Examples of segmentation using manual measurements and automatic models are shown in Appendix A Figures A2 and A3.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Infrastructure-Based ITS Sensing
#### 3.2.3 Traffic Anomaly Detection  
Another topic in infrastructure-based sensing is traffic anomaly detection. As the name suggests, traffic anomaly refers to those abnormal incidents in an ITS. They rarely occur, and examples include vehicle breakdown, collision, near-crash, wrong-way driving, and so forth. Two major challenges in traffic anomaly detection are (1) the lack of sufficient anomaly data for algorithm development and (2) the wide variety of anomalies that lacks a clear definition. Anomalies detection is achieved mainly using surveillance cameras given the requirement for rich information, though time series data is also feasible in some relatively simple anomaly detection tasks [112]. Traffic anomaly detection can be divided into three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning methods are useful when the number of classes is clearly defined and training data is large enough to make the model statistically significant; but supervised learning requires manual labeling and needs both data and labor, and they cannot detect unforeseen anomalies [113; 114; 115]. Unsupervised learning has no requirement for labeling data and is more generalizable to the unforeseen anomaly as long as sufficient normal data is given; however, anomaly detection will be hard when the data nature changes over time (e.g., if a surveillance camera keeps changing angle and direction) [116]. [PERSON] et al. [117] designed an unsupervised method based on multi-granularity tracking, and their method won first place in the 2020 AI City Challenge. Semi-supervised learning needs only weak labels. [PERSON] et al. [118] proposed a semi-supervised model for freeway traffic trajectory classification using YOLO, SORT, and maximum-likelihood-based Contrastive Pessimistic Likelihood Estimation (CPLE). This model detects anomalies based on trajectories and improves the accuracy by 14%. [PERSON] et al. [119] considered videos as bags and video segments as instances in multiple instance learning and automatically learned an anomaly ranking model with weakly labeled data. Lately, traffic anomaly detection has been advanced not only by the design of new learning methods but also by object tracking methods. It is interesting to see that, in the 2021 AI City Challenge, all top-ranking methods somewhat made contributions to the tracking part [120; 121; 122].
# Studies on a Piezoelectric Cylindrical Transducer for Borehole Dipole Acoustic Measurements
## 2 Principles and Numerical Analyses
### Input Electrical Conductance in Water  
The numerical simulation result of the conductance curve of the transducer in water is shown in Figure 6. There are four resonance peaks in the curve. The frequencies of the four resonance peaks are 497 Hz, 980 Hz, 3698 Hz and 5000 Hz, respectively, which is similar to the situation in air. The conductance values at the four resonant frequencies are 57, 582, 922, and 830 uS, respectively. The conductance amplitudes at 980 Hz, 3698 Hz and 5000 Hz are far greater than that at 497 Hz, which shows that the latter three modes are more easily excited than the first one. This is because when the transducer is immersed in water, the three bender bars are still in air in the chamber formed by cylindrical shell and endcaps.
## 8 Biosynthetic Gene Clusters (BGCs): What Is the Function of BGCs in Phage?  
Bacteria can produce diverse metabolites, which are a rich source of industrially relevant natural products. The synthetic pathway of these metabolites is encoded by groups of genes called biosynthetic gene clusters (BGCs). BGCs can affect the interaction and competence among microorganisms. Further, BGCs are extremely important for the mining of new bioactivities [123]. In a recent study, the structure and function of phage-encoded BGCs were investigated [123]. pBGCs are rare, and most of them reside within temperate phages, which infect commensal or pathogenic bacterial hosts. The majority of pBGCs were found to encode for bacteriocins [123], which have been shown to provide a clear competitive fitness advantage for the infected bacterium. Through genetic and genomic comparisons, researchers revealed a strong association between the pBGC type and phage host range, which demonstrated bacteriocins are encoded in temperate phages of a few commensal bacterial genera, and lysogenic conversion also provides an evolutionary benefit to the phage itself [123].  
In our review, we also investigated the presence of BGCs in the 153 publicly accessible cyanophage genomes using antiSMASH. However, no BGCs in the 153 cyanophage genomes were predicted--an unsurprising revelation due to the documented rarity of pBGCs. A previous study utilized 10,062 high-quality phage genomes, which were available in the PATRIC database, and detected the presence of pBGCs in 69 genomes. Furthermore, 15,184 high-quality bacterial genomes available from NCBI were screened by ProphET to extract prophage regions and were subsequently screened by antiSMASH for the detection of pBGCs. This revealed the presence of only 307 pBGC regions [123]. However, with the increased discovery and genomic sequencing of cyanophages, it is likely that more pBGCs will be discovered. Understanding the natural function and distribution of pBGCs may provide insight into the mechanisms through which cyanophages infect, control, and utilize the host.