esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Variation Trends of SOS and EOS  
Across the whole study region, a significant advancing trend of SOS was found over the 2000-2016 period in 2.8% pixels and only 0.4% of pixels presented a significant delaying trend (Figure 7a). For meadow steppe, typical steppe, and desert steppe, alone, we also found a higher number of pixels showing a significantly advancing trend (1.8%, 3.9%, 1.2%) compared to the number of pixels showing a significantly delaying trend (0.5%, 0.4%, 0%) (Figure 7c). For EOS, 1.8%/1.6% of pixels showed significant delaying/advancing trends over the whole study region (Figure 7b). In terms of different grassland types, significant advancing trends were detected in 1.7%, 0.8%, and 3.9% of pixels and significant delaying trends were identified in 1.9%, 2.2%, and 0.2% of pixels for meadow steppe, typical steppe, and desert steppe, respectively (Figure 7d).  
In order to reveal the overall changes of SOS/EOS in the three grassland types, we further compared the multi-year average SOS/EOS during 2000-2004 and that during 2011-2015 with a two-sample \(t\)-test in meadow steppe, typical steppe, and desert steppe, respectively. The results illustrated that SOS and EOS did not show a significant trend from 2000-2004 to 2011-2015 in meadow steppe (Figure 8). In typical steppe, SOS have advanced by 2.2 days, while EOS presented no significant changes from 2000-2004 to 2011-2015. In desert steppe, however, we identified a significant advancement for both of SOS and EOS from 2000-2004 to 2011-2015. In 2011-2015, SOS and EOS occurred earlier by 10.6 days and 6.8 days than that in 2001-2004, respectively.  
Figure 6: Response rates of SOS to pre-SOS temperature (**a**) and precipitation (**b**), and response rates of EOS to pre-EOS temperature (**c**) and precipitation (**d**). The different letters indicate there is a significant difference of responding rates of SOS/EOS to temperature/precipitation between the two grassland types at the level of 0.05, while the same letter denotes there is no significant difference of responding rates of SOS/EOS to temperature/precipitation between the two grassland types.  
Figure 8: Shifts of SOS (**a**) and EOS (**b**) from 2000–2004 to 2011–2015 in meadow steppe, typical steppe, and desert steppe. In the figure, the different letters denote there is a significant change of SOS/EOS from 2000–2004 to 2011–2015 at the level of 0.05, while the same letter denotes no significant change of SOS/EOS detected from 2000–2004 to 2011–2015.  
Figure 7: Spatial pattern of changing trends of SOS (**a**) and EOS (**b**). Frequency of changing trends of SOS (**c**) and EOS (**d**) in meadow steppe, typical steppe, and desert steppe. The vertical lines denote median values.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 2 The Reliability Evaluation of PR Data by Comparisons with Ground-Based Instruments
### Remarks on the Comparison Studies with Ground-Based Instruments  
It was shown from the results of these comparison studies that PR observations or estimates agreed well with those of reliable ground-based instruments on the whole, but the difference attracted more attention. Besides small convective rainfall systems, edges of precipitation, and rainfall areas distant from PR where nonhomogeneous beam filling was likely to occur, PR tended to give larger echo area and weaker echo intensity due to a coarse horizontal resolution, and give stronger echo intensity due to non-Rayleigh scattering. PR captured most of the rainfall amount though light rain and rain area might be overlooked because of the low detection sensitivity. It seems that PR tended to give underestimation of rainfall amount for convective and heavy precipitation and give overestimation for light precipitation because of the incomplete attenuation correction of PR products and the coarse temporal and horizontally spatial resolution of PR measurements.  
With new versions of PR products, further studies are necessary to quantitatively distinguish the roles of factors like grid and time resolution, rainfall types and amounts under different sampling time scales, the parameters in the \(Z\)-\(R\) relationship of PR algorithms, distribution density of rain gauges, etc. It had been noted that the peak of high correlation of PR and the rain gauge observations occurred for the near nadir cases and several minutes after the instantaneous PR observation because it took minutes for the raindrops to reach the ground surface [67].
# Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm
## 4 Benchmark Problems
### 15-Bar Truss Structure  
In this example, the sizing and layout optimization of a 15-bar planar truss structure is taken into consideration. The initial geometry of the structure is shown in Figure 5. A vertical load of 10 kips is applied at node 8. The stress limit is 25 ksi (172.369 MPa) in both tension and compression for all members. The material density is 0.1 lb./in\({}^{3}\).(2767.99 kg/m\({}^{3}\)), and the modulus of elasticity is 10,000 ksi (68,947.6 MPa). All detailed information regarding this structure is given in Table 1. The ISRES algorithm is once again employed to optimize the truss structure for the minimum weight while satisfying the given constraints. The discrete design variables, representing the cross-sectional areas of each member, are optimized to attain the objective of minimizing the truss weight.  
By running the ISRES algorithm multiple times with carefully selected parameters and control settings, the optimization process converges to a set of optimal solutions for the truss sizing and layout. These solutions are analyzed and compared to evaluate their performance and efficiency.  
In the comparison phase, the results obtained from the ISRES algorithm are juxtaposed with those achieved using other optimization methods reported in the literature. The  
Figure 4: Stress constraint values with allowable limits for 10-bar structure.  
Figure 5: Schematic of 15-bar structure.  
comparison includes alternative evolutionary algorithms, gradient-based methods, and other metaheuristic approaches. The goal is to assess the ISRES algorithm's superiority or competitiveness in terms of solution quality and computational efficiency.  
Furthermore, the study examines the practicality of the optimized truss designs by conducting a finite element analysis to verify their structural integrity and stability. The optimized truss configurations are subjected to the applied load, and their response is evaluated for stress distributions, displacements, and other relevant structural properties.  
Through the comparison and verification processes, this study provides valuable insights into the capabilities and limitations of the ISRES algorithm in solving complex truss sizing and layout-optimization problems with multiple constraints. The findings offer engineers and designers valuable guidance in selecting appropriate optimization methods for similar structural design tasks in real-world engineering applications. The convergence graph and constraints graph for allowable stress, as presented in Figures 6 and 7, offer a detailed visual representation of the optimization outcomes for the 15-bar planar truss structure under consideration.
## 2 Background and Related Work
### Efficiency of Terrestrial Broadcasting Network Resources and Transmission Delay Issues  
The live broadcast scheme in Figure 1 displays the problem of the resource efficiency and transmission delay due to the _\"fixed resources\"_ and _\"variability of the DASH segment file size\"_ when a segment file is transmitted in the broadcasting network. Even though the DASH segment files have the same quality and playback time, the size of each DASH segment file is variable according to the media composition [15]. For example, 4K or 8K content has more data than the FHD content, so each DASH segment file has more variability than the FHD DASH segment file.  
The problem of resource efficiency in the live broadcast scheme in Figure 1 occurs in the following situations. For the DASH segment, File 1 starts transmission at time \(T_{i}\) and completes transmission before time \(T_{i+1}\). The display starts time of the DASH segment File 1 is \(T_{i+1}\), and assuming the playback time is \(\rho\), the display ends at time \(T_{i+2}\). Since the live  
Figure 1: Transmission schemes that transmit segment files of variable sizes in a broadcasting network with fixed resources. The file broadcast scheme successively transmits segment files. The live broadcast scheme periodically transmits segment files.  
broadcast scheme transmits periodically, segment File 2 starts transmission at time \(T_{i+1}\). Therefore, resource \(\alpha_{i}\) is wasted.  
The problem of transmission delay in the live broadcast scheme in Figure 1 occurs in the following situations. It is expected that DASH segment File 2 starts transmission at time \(T_{i+1}\) and transmission is complete before time \(T_{i+2}\). However, since because transmission is completed in time \((T_{i+2}+\beta_{i+1})\), the transmission delay of segment File 2 is as much as \(\beta_{i+1}\). Segment File 3 can be transmitted within time \(\rho\), but a delay occurs that is as much as \(\beta_{i+2}\) due to the influence of the segment File 2.  
Resource efficiency and the transmission delay occur due to the _fixed resources_ and _variability of the DASH segment file size_, which are characteristics of the terrestrial broadcasting network. Greater variability in the DASH segment file size results in greater resource efficiency and transmission delay problems.
## 4 Experimental Results
### Evaluation Protocols
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Parameters** & **ANN** & **MLR** & **SVM** & **LSTM** & **GP** & **GFSDP** \\ \hline Input dimension & 25 & 25 & 25 & 25 & 25 & 13 to 16 \\ Output dimension & 1 & 1 & 1 & 1 & 1 & 1 \\ Optimizer & Bayes & Bayes & Bayes & Bayes & Bayes \\ Activation & Relu & - & - & Relu & - & - \\ Hidden unit on the layers & - & - & & 200 to 300 & - & - \\ Iterations & - & - & - & 1000 & - & - \\ Fully connected layer & - & - & - & 50 & - & - \\ Dropout & - & - & - & 50\% & - & - \\ Max epoch & - & - & - & 200 & - & - \\ Gradient threshold & - & - & - & 1 & - & - \\ Epsilon & - & - & 0.5 & 1.00 e-08 & - & - \\ Weight threshold & - & - & - & - & - & 1 to 3 \\ Shrinkage factor & - & - & 0.05–0.1 & - & - & - \\ Subsampling factor & - & - & 0.1–0.5 & - & - & - \\ Kernel function & - & - & Gauss. & - & SE & SE \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the parameters of the proposed GFSDP and conventional algorithms, where SE is a squared exponential kernel for the GP algorithm.
## 4 System Identification Methods
### Iterative Least Squares (ILS)  
Iterative least squares is a system identification method developed by [PERSON] and [PERSON] [32] to extract flutter derivatives from free vibration tests. The fundamental idea behind this method is to obtain system matrix \(A\) using a state-space model from noise-corrupted time histories. Considering the two-degree-of-freedom system, the state-space model can be expressed as  
\[\dot{X}=AX\]  
Matrix \(A\) is a square matrix of the order \(2n\times 2n\), where \(n\) is the number of degrees of freedom in the system. Matrix \(A\) can be identified if the acceleration, velocity, and displacement time histories are recorded for \(n\) degrees of freedom. The ILS method is preferred over other system identification methods due to its simplicity, as it does not require time shift parameters and directly identifies the system matrix without solving eigenvalue problems, reducing mathematical complexities. A flowchart of the ILS algorithm is shown in Figure 3.  
Figure 2: Typical decaying response, where \(\hat{x}\) is the maximum amplitude, and \(x_{n}\) and \(x_{n+j}\) are successive peak amplitudes.
# Review of Task-Scheduling Methods for Heterogeneous Chips
## 5 Research Status of Experiment
### Evaluation Metric
* Online adaptation: Online adaptability evaluates the ability of a heterogeneous chip to dynamically adapt to task changes during operation, especially the response ability during task arrival, system load, or resource status changes [22]. Common indicators include the task migration time, scheduling delay, and throughput [58]. Throughput refers to the number of tasks that the scheduling algorithm performs in a unit of time. Online adaptability of the algorithm can be evaluated by observing the change in throughput with increasing task volume [54]. * Load balancing: Load balancing refers to the reasonable allocation of workload among the processing units of the heterogeneous chip to ensure that no single processing unit becomes a performance bottleneck due to overload while avoiding idle processing units. One of the commonly used evaluation indicators is the relaxation degree, which is called Slack. Slack reflects the size of the time window for absorbing the calculation delay provided by the scheduling results of the algorithm without increasing the makespan [59]. There is a conflict between the Slack measures and the SLR measures, and a lower SLR generally implies lower Slack. The formula of Slack is defined as follows: \[Slack=\frac{\sum_{i=1}^{n}makespan-Inter(v_{i})-Exit(v_{i})}{n},\] (6)where n is the number of task nodes, indicating the longest path length from the entry node to the task node. \(\sum_{i=1}^{n}\mathit{makespan}-\mathit{Inter}(v_{i})-\mathit{Exit}(v_{i})\) represents the length of the longest path from the task node \(v_{i}\) to the termination node.
## 6 Simulation and Ocean Experiments  
This paper proposes a biomimetic modulation method using real dolphin whistles with link information and a detection scheme using the D-LSTM for the UWA channel distorted whistles. The performance comparisons were focused on two aspects: the first one is the whistle-by-whistle detection capability of the proposed machine learning networks. For the whistle-by-whistle detection comparisons, the BERs of the DAG-net1 and the DAG-net2, and the conventional ML detection, and the conventional DNN were compared. The second one is the detection performance by the additional link information. The BER performances of the D-LSTM1 and the D-LSTM2 were compared with those of the DAG-net1 and the DAG-net2. These comparisons were tested in computer simulations and practical ocean experiments.  
The communication parameters used for the comparisons were \(M_{\text{opt}}=3\), \(K_{\text{opt}}=8\), \(\alpha=2\), \(K_{\text{tot}}=32\) which are the same as in Section 5. For fair comparisons, the same DoM was kept for all algorithms, i.e., all 704 whistles were utilized for the transmissions. The learning processes of all algorithms were executed for one million whistle spectrograms in the computer simulation and the learning results were also used for the practical ocean experiments.
# Advances in Finite Element Modeling of Fatigue Crack Propagation
###### Abstract  
Fatigue crack propagation is a critical phenomenon that affects the structural integrity and lifetime of various engineering components. Over the years, finite element modeling (FEM) has emerged as a powerful tool for studying fatigue crack propagation and predicting crack growth behavior. This study offers a thorough overview of recent advancements in finite element modeling (FEM) of fatigue crack propagation. It highlights cutting-edge techniques, methodologies, and developments, focusing on their strengths and limitations. Key topics include crack initiation and propagation modeling, the fundamentals of finite element modeling, and advanced techniques specifically for fatigue crack propagation. This study discusses the latest developments in FEM, including the Extended Finite Element Method, Cohesive Zone Modeling, Virtual Crack Closure Technique, Adaptive Mesh Refinement, Dual Boundary Element Method, Phase Field Modeling, Multi-Scale Modeling, Probabilistic Approaches, and Moving Mesh Techniques. Challenges in FEM are also addressed, such as computational complexity, material characterization, meshing issues, and model validation. Additionally, the article underscores the successful application of FEM in various industries, including aerospace, automotive, civil engineering, and biomechanics.  
fatigue crack growth; advances in finite element method; computational efficiency; mesh technique; structural integrity +
Footnote †: journal: Applied  
**Citation:** [PERSON]; [PERSON] [PERSON] Advances in Finite Element Modeling of Fatigue Crack Propagation. _Appl. Sci._**2024**, 14, 9297. [[https://doi.org/10.3390/appl4209297](https://doi.org/10.3390/appl4209297)]([https://doi.org/10.3390/appl4209297](https://doi.org/10.3390/appl4209297))
# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 4 Experimental Results and Discussion
### Comparison of Algorithm Performance and Complexity  
In order to further verify the detection advantages of the proposed method, this paper conducts comparison and verification with mainstream lightweight models, including the Yolov5s model, Yolov7-tiny [30], and the latest YOLOv8n that incorporate different lightweight networks and attention mechanisms. The details are shown in Table 6. Compared with the other five models, the mAP0.5 values of the method proposed in this article increased by 0.008, 0.014, 0.020, 0.024, and 0.003, respectively, and the P values increased by 0.001, 0.005, 0.035, 0.042, and 0.019, respectively. This shows that the model proposed in this study is highly accurate in identifying ship multi-scale targets. The Yolov5s model integrated with the ShuffleNetv2 network and LSKblock [31] attention mechanism has a smaller GFLOPs value. However, the mAP0.5 value, detection time, and parameter amount of this model are relatively high. The Yolov8n model has a smaller parameter amount and detection time, which is slightly different from the parameter amount and detection time of the model proposed in this article. However, the model proposed in this article has more advantages in target detection accuracy, computational complexity, and mAP0.5 value. In general, the multi-scale ship target recognition model proposed in this article is lightweight and reaches a level comparable to the latest algorithm (Yolov8n) in terms of detection time. It is superior to the latest algorithms in terms of detection capabilities and is more suitable for multi-scale ship target detection tasks in different scenarios.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithms** & **Parameter** & **GFLOPs** & **Precision** & **mAP0.5** &
\begin{tabular}{c} **Intel(R) Core(TM)** \\ **i7-12700H (ms/Image)** \\ \end{tabular} \\ \hline Yolov5s\_GhostNetV2\_CA & 5.35 & 7.8 & 0.832 & 0.817 & 109.9 \\ Yolov5s\_ShuffleNetv2\_LSKblock & 5.97 & 3.2 & 0.828 & 0.811 & 63.6 \\ Yolov5s\_MobileNetV3 & 5.41 & 9.6 & 0.798 & 0.805 & 91.2 \\ Yolov7-tiny & 6.02 & 13.2 & 0.791 & 0.801 & 78.7 \\ YOLOv8n & 3.01 & 8.2 & 0.814 & 0.822 & 56.1 \\ YOLOv5s\_ESGW & 4.02 & 5 & 0.833 & 0.825 & 60.0 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison with mainstream lightweight methods.
# FocusedDropout for Convolutional Neural Network
## 5 Experimental Section
### Evaluation of FocusedDropout  
#### 5.2.1 CIFAR10 and CIFAR100  
The CIFAR10 [19] dataset consists of 60,000 32\(\times\)32 color images of 10 classes, each with 6000 images, including 50,00 training images and 10,00 test images. We adopt ResNet-20, ResNet-56, ResNet-110 [4], VGGNet-19 [21], DenseNet-100 [20], and Wide ResNet-28 [22] as the baselines to evaluate FocusedDropout's generalization for different structures of networks. We set \(\gamma\) to a random value between 0.3 and 0.6, \(participation\_rate=0.1\). As shown in Table 1, FocusedDropout achieves better improvement than other regularization methods in all networks. Generalization is an essential property of regularization methods. Experiments show that our method is suitable for networks with different layers, channels, and parameter spaces.  
The CIFAR100 [19] dataset has the same number of images but 100 classes, which means the training of CIFAR100 is harder than CIFAR10 because it has more image types and less training data for each kind. Results are summarized in Table 2. FocusedDropout still performs better than other dropout methods, and for ResNet-110, the promotion can even exceed 1.5%. Validation accuracy and training loss comparison of ResNet-110 on CIFAR100 can be seen in Figure 4, which shows that random MWD and FocusedDropout can effectively prevent overfitting.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **ResNet-20** & **ResNet-56** & **ResNet-110** & **VggNet-19** & **DenseNet-100** & **WRN-28** \\ \hline Baseline & 91.48 \(\pm\) 0.21 & 93.84 \(\pm\) 0.23 & 94.32 \(\pm\) 0.16 & 93.55 \(\pm\) 0.24 & 95.30 \(\pm\) 0.12 & 96.16 \(\pm\) 0.06 \\ Dropout & 91.51 \(\pm\) 0.31 & 93.74 \(\pm\) 0.11 & 94.52 \(\pm\) 0.23 & 93.14 \(\pm\) 0.30 & 95.32 \(\pm\) 0.08 & 96.07 \(\pm\) 0.04 \\ SpatialDropout & 91.98 \(\pm\) 0.13 & 94.28 \(\pm\) 0.20 & 94.76 \(\pm\) 0.15 & 93.19 \(\pm\) 0.12 & 95.40 \(\pm\) 0.16 & 96.13 \(\pm\) 0.02 \\ DropBlock & 92.15 \(\pm\) 0.14 & 94.01 \(\pm\) 0.08 & 94.92 \(\pm\) 0.07 & 93.50 \(\pm\) 0.04 & 95.40 \(\pm\) 0.31 & 96.21 \(\pm\) 0.03 \\ \hline FocusedDropout & **92.08 \(\pm\) 0.17** & **94.67 \(\pm\) 0.07** & **95.24 \(\pm\) 0.15** & **93.74 \(\pm\) 0.19** & **95.57 \(\pm\) 0.16** & **96.48 \(\pm\) 0.13** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Test accuracy (%) on CIFAR10 dataset using CNN architectures of ResNet-20, ResNet-50, ResNet-110,VggNet-19, Densenet-100,Wide ResNet-28 (Top accuracy is in bold). We report the average over 3 runs.  
Figure 4: Comparison of the validation accuracy curves and training loss curves of ResNet-110 on CIFAR100. The performance of the training networks with FocusedDropout and random Magnify Weight Decay (MWD) are worse than the baseline before epoch 150, but when the learning rate decays, the networks’ performance is much better than the original model.
# Aquasafe: A Remote Sensing, Web-Based Platform for the Support of Precision Fish Farming
## 2 Materials and Methods
### Data
#### 2.2.1 Satellite and CMEMS Data
Ocean color data are produced by the imagery acquired through the OLCI sensor with a spatial resolution of 300 m. OLCI's products can be used to retrieve surface-level chlorophyll-a concentration, total suspended matter concentration, the light attenuation coefficient, and other EOVs. The satellites are also equipped with the SLSTR sensor, which is used for the SST estimation. The resolution of the datasets varies between the sensors with OLCI having 300 m spatial resolution products and SLSTR 1000 m. The footprint of the imagery ensures the coverage of large marine areas which in favorable days can contain the whole of the Greek seas. Furthermore, the temporal coverage of the mission is estimated between 1-2 days, enabling continuous monitoring of specific and wide marine regions. The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic biogeochemical and physical information on the marine environment. Within the context of our study, we utilized Level-4, daily, gap-free satellite observations for chlorophyll-a concentration and sea surface temperature from multi-platform observations. We used CMEMS data to describe environmental conditions and investigate their correlation with dissolved oxygen concentration (DO). By understanding this relationship, _in-situ_ observations of DO were used with our previously published work [19] to train a machine learning regression model.