esa-sceva/satcom-chunk-collection huggingface dataset:
# Theoretical Study on a Cable-Bracing Inerter System for Seismic Mitigation
###### Abstract  
In this paper, cables are proposed to connect the inerter and main frame for translation-to-rotation conversion, i.e., the cable-bracing inerter system (CBIS), with a magnified mass and enhanced damping effect. This novel configuration has the benefits of deformation relaxation at the connecting joints, easy installation, and an adaptive layout for nonconsecutive-story deployment. Dynamic motion equations were established for a single degree-of-freedom (SDOF) model equipped with a CBIS. The influence of dimensionless parameters, such as inertance-mass ratio, stiffness ratio and additional damping ratio on vibration mitigation were studied in terms of displacement response and force output. A single objective and multiple objective optimal design method were developed for a CBIS-equipped structure based on a performance-oriented design framework. Finally, the mitigation effect was illustrated and verified by a numerical simulation in a time-domain. The results showed that a CBIS is an effective structural response mitigation device used to mitigate the response of structural systems under earthquake excitation. Using the proposed optimization method, CBIS parameters can be effectively designed to satisfy the target vibration control level.  
passive vibration control; inerter system; cable bracing; parametric study; optimal design 2019
# Noise Modeling to Build Training Sets for Robust Speech Enhancement
## 5 Conclusions
### Limitations and Future Work  
In our experiments, we employ the \"GAN-train\" and \"GAN-test\" performance as the criteria to judge the effectiveness of the training sets preparation. However, this method indirectly verifies the NM-GAN performance. In the future, we will explore using methods that directly evaluate the quality of the generated noise.  
Second, we only used one SE model as a tool to verify our method's feasibility. Future work shall test applying the prepared datasets to more network structures. Furthermore, the number of noise samples used for NM-GAN may impact the experimental results, and therefore further in-depth research is required.  
Third, given more recent public corpora, such as MUSAN, we need to test different noise types when verifying the NM-GAN's performance.  
An additional limitation that should be considered is that noise is currently assumed to be additive. Beyond this, further research is still required regarding different speech synthesis methods. Although NM-GAN has a strong ability to learn noise distributions, if the synthesis method is not correct, our method's applicability in real situations is limited. Other noise and pure speech synthesis methods, such as parallel model combination (PMC), could be explored to investigate whether they provide additional advantages.  
Conceptualization, Y.W. ([PERSON]); Data curation, X.K.; Funding acquisition, H.Z.; Investigation, Z.W.; Methodology, Y.W. ([PERSON]); Project administration, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No: 62071057).  
Not applicable.  
Data Availability StatementData available in a publicly accessible repository that does not issue DOIs. Publicly available datasets (CSTR VCTK Corpus, reference number [25] and Demand database, reference number [26]) were analyzed in this study. The CSTR VCTK Corpus (accessed on 5 June 2019) can be found here: [[[https://datashare.ed.ac.uk/handle/10283/2651](https://datashare.ed.ac.uk/handle/10283/2651)]([https://datashare.ed.ac.uk/handle/10283/2651](https://datashare.ed.ac.uk/handle/10283/2651))], and the Demand database (accessed on 5 June 2019) can be found here: [[[https://zenodo.org/record/122712#.YgZJN3b5](https://zenodo.org/record/122712#.YgZJN3b5) BzR]([https://zenodo.org/record/122712#.YgZJN3b5](https://zenodo.org/record/122712#.YgZJN3b5) BzR)].  
The authors declare no conflict of interest.
## 4 Results
### Relationship between Potential Driving Factors and Forested Areas  
Among the ten selected variables, all except population density and vegetation index demonstrated a significant relationship with forested areas, contributing to a complex landscape. Rising temperatures and altered rainfall patterns are evident. Notably, the correlation between rainfall and both LPI and LSI (\(p<0.05\)) indicates adverse effects on  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Years**} & **Patch Density** & **Largest Patch** & **Aggregation** & **Landscape Shape** \\  & **(PD N/ha)** & **Index (LPI \%)** & **Index (AI \%)** & **Index (LSI Index)** \\ \hline
2005 & 2.00 & 3.00 & 89.00 & 111.00 \\ \hline
2010 & 4.00 & 1.00 & 83.00 & 147.00 \\ \hline
2015 & 3.00 & 6.00 & 89.00 & 116.00 \\ \hline
2020 & 1.00 & 3.00 & 91.00 & 73.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Changes in the overall landscape pattern index in the Thüs region from 2005 to 2020.  
Figure 7: Characteristics of forested areas’ dominant morphology evolution in 2005, 2010, 2015, and 2020, with PD = Patch density; LPI = Largest Patch Index; AI = Aggregation Index; and LSI = Landscape Shape Index.  
forested areas in the Thies region. Forest exploitation through agriculture significantly influences (\(p<0.05\)) and shapes forested areas in the Thies region. This influence is further highlighted by the relationship between LPI and slope (0.032493) with a \(p\)-value \(<0.05\), indicating a significant impact on forested area morphology. In addition, the results also indicate a significant correlation between AI (0.000837), LSI (0.00358), and slope, which emphasizes the influence of topography on forest fragmentation (Table 5).  
Agricultural production is closely tied to the expansion of cultivated land, as indicated by its nexus with AI (0.05894). Interestingly, despite significant population growth, forested areas in the Thies region have not been substantially impacted, contrasting with trends observed in East African nations where human activities have significantly altered natural landscapes. Thus, the analysis of the dominant forested areas' morphology in the Thies region reveals complex interactions between human activities, climate change, and socioeconomic factors. These findings underscore the importance of addressing these factors to ensure sustainable forest management, warranting attention from both the government and scholarly communities. In a nutshell, the results of this study partially disproved our initial hypotheses. Vegetation index, temperature, and population density did not influence forested areas in the Thies region. Moreover, this is a valuable scientific outcome, as it can refine understanding and lead to new hypotheses.
## 2 Related Works
### Human Keypoint Detection  
A 2D skeleton map of the human body is formed by obtaining the 2D coordinates of the human keypoints through the human keypoint detection algorithm and connecting them according to the structural features of the human skeleton, thus forming the structure of the skeleton map of the human body [12]. In deep learning, the human keypointdetection task is divided into single human keypoints detection [13; 14; 15] and multiple human keypoint detection [16; 17]. Currently, the most commonly used methods of human keypoint detection are based on deep learning. In deep learning-based methods, the regression method of human keypoints directly impacts the effectiveness of human keypoint detection. Two regression methods exist for human keypoint detection: coordinate regression and heat map regression.  
The first method that introduced deep neural networks (DNNs) to human keypoint detection in 2014 was DeepPose [18], which is a direct regression coordinate model that applies a cascaded CNN structure to regress the coordinate values of human skeleton points forcibly. This model, which mimics the detection of critical points on the face, does not apply to the detection of the human skeleton, as it changes constantly with movement and posture. To address the disadvantages of this model, in the same year, [PERSON] et al. [19] proposed a method called Joint-CNN-MRF, which combines a CNN with the Markov random field and innovatively uses a decoding heatmap to obtain the positions and categories of human keypoints. This achieves higher accuracy and proves that the heatmap-based approach performs better than the direct regression approach. However, in some cases, the single-person pose estimation algorithm may output vague or inaccurate pose estimation results due to the limitations of image quality, pose angle, or other factors.  
Furthermore, human keypoint detection methods can be classified into two types according to the architecture of the network: top-down and bottom-up human keypoint detection algorithms. In 2016, [PERSON] et al. [20] first proposed a top-down skeleton point detection algorithm that constructs a full connectivity graph based on the human body connectivity edges and then uses an integer linear program (ILP) to obtain the optimal connections. It can handle the case of the human body being occluded or truncated. Since then, due to the more rapid and mature development of human body detectors, most scholars have focused on the algorithmic innovation of a single human skeleton point, represented by SimpleBaseline and HRNet, in which SimpleBaseline constructs a simple and effective skeleton point detection model by adding an inverse convolution module at the end of ResNet. At the same time, HRNet designs an advanced feature extraction network based on parallel multi-resolution subnets, which can maintain high-resolution output and achieve ultra-high-accuracy skeleton point recognition. However, when human keypoints are obscured, the accuracy of human keypoint detection is reduced greatly.
# Enhancing Wind Energy Harvesting Using Passive Turbulence Control Devices
## 4 Effect of the Coupling Strength
### Effect of the Coupling Strength on Displacement  
The response displacement of the GEH-PTC with three coupling strengths is shown in Figure 8. It is found that the response displacement increases along with increasing coupling strength and wind speed. The maximum displacement the GEH-PTC with the weak, critical, and strong couplings reached 6 mm, 10 mm, and 13.5 mm, respectively. Meanwhile, the corresponding wind speed is 1.824 m/s, 2.372 m/s, 2.646 m/s, respectively. Compared with the weak coupling, the displacement in the strong coupling case increases by 125%. However, increases in coupling strength makes the response displacement curve of the GEH-PTC with the optimal load appear as a 'depression' under each wind speed, which is obvious in the case of strong coupling. For the critical coupling (Figure 8b) and the strong coupling (Figure 8c), the response displacement curve even breaks when the wind speed is 1.687 m/s and 2.098 m/s due to the'sag' phenomenon. At this time, the load resistance ranges are 2-4 M\(\Omega\) and 0.5-2 M\(\Omega\), respectively. Galloping phenomenon does not occur when the load resistance is in these ranges. Therefore, it is called a'suppressed load'. When the load resistance in the three coupling cases are 1.1 M\(\Omega\), 3.0 M\(\Omega\), and 1.1 M\(\Omega\), respectively, the galloping suppression is the most obvious. On the contrary, this value is shown as the optimal load in Figure 6a,b.  
In Figure 9, the response displacement of the GEH-PTC with three coupling strengths are compared. The load resistances in each case are 0.5 M\(\Omega\), 0.7 M\(\Omega\), 1.1 M\(\Omega\), 2.0 M\(\Omega\), and 4.0 M\(\Omega\). The response displacement in the three cases also increases with increasing wind speed. When the wind speed is 2.6 m/s and the load resistance is 0.5 M\(\Omega\), the maximum displacement reaches 8 mm, 11 mm, and 14 mm for the weak, critical, and strong coupling cases, respectively. For a specific coupling strength, different resistance values correspond to different critical wind speeds \(U_{cr}\). High load resistance leads to a high critical wind speed. However, the displacement corresponding to high resistance values will obviously decrease. For the critical (Figure 9a), weak (Figure 9b), and strong coupling (Figure 9c) cases, the critical wind speed range is respectively 1.92-1.97 m/s, 1.94-2.04 m/s, and 1.76-1.86 m/s. The necessary condition for achieving galloping vibration is that the incoming wind speed is higher than the critical value.
## 2 Materials and Methods
### Planning System Overview  
The AP system used in this study is the Experience-Driven plan Generation Engine by Velindre Cancer Centre (EdgeVcc). It is a PGAP system built on a PBAIO framework with a Pareto navigation calibration interface. It is written in Python version 2.7 and implemented in the RayStation TPS using its native scripting functionality. What follows is an overview of the system, focusing on the definition and calibration the AP protocols that define the  
Figure 1: An outline of how the KBP-RBP process defined in this work (**bottom**) differs from the more classic site-specific methods (**top**). Classic approaches define a single site-specific template applied to all treatment cases. In this work, a ML approach is defined that achieves a patient-specific planning template based on patient anatomy.  
balancing of competing trade-offs during plan generation. A full description is provided by [PERSON] et al. (2019) [16].  
With this PGAP system, plan generation is dependent upon a base site-specific 'AutoPlan protocol' containing a set of PGs which define the plan. The AutoPlan protocol requires PGs be divided into three priority levels: P1, P2 and P3. Primary normal tissue PGs (P1) are the highest priority and ensure necessary sparing to tissue at increased risk of unacceptable toxicity when the dose received exceeds a certain level (e.g., serial organs such as the spinal cord). Target PGs (P2) ensure target volume dose objectives are met including PTV coverage and hot spots. All other planning objectives are known as trade-off PGs (P3). Each PG is assigned a numeric weighting factor that the PBAIO AP solution will use to determine prioritisation of each objective during plan generation. Weighting factors are determined in one of two ways. Prioritisation of P1 and P2 are well defined for all patients and sites and are managed by algorithms where PGs are assigned a fixed weight, with P2 ROIs compromised in favour of P1 via ROI retraction to manage conflicts. Appropriate balancing of P3 PGs is not as well defined and requires calibration to derive suitable weighting factors.  
Calibration is performed using the Pareto navigation interface. This allows for the exploration of different P3 trade-off options and is equivalent to an a posteriori MCO planning methodology. For calibration, a set of plans with differing P3 weighting factors is generated using the PBAIO framework. A qualified professional navigates the different options to select the optimum balancing of P3 weighting factors for a given patient. This process is performed using a sliding interface that uses linear interpolation of neighbouring Pareto plans to enable information in the TPS to update in real-time including dose-volume histograms (DVHs), numerical information related to dose and 3D dose maps on CT scans. The associated P3 weighting factors of the chosen distribution are then stored in the AutoPlan protocol. In this study, these represent the gold standard set of PBAIO calibration parameters for the given patient and are used as the PBAIO input to generate MCO\({}_{\text{gs}}\).
# Dynamic Routings in Satellite Networks: An Overview
## 3 System Model and Characteristics
### Key Challenges for Satellite Network Routing
On the one hand, due to fading signal and ground obscuration, the satellite and user can only communicate at a high elevation. Since the LEO satellite has a large orbital altitude and pitch angle, the area covered on the surface is somewhat limited. On the other hand, the LEO satellite moves very fast, causing its coverage on the surface to change quickly as well. Consequently, the user terminal will be switched between LEO satellites, i.e., user-to-sat switching. Similarly, a multi-connector link generally passes through more than one ISL. The rapid movement of satellites cancause the failure of one or more ISLs in high latitudes. Under such a situation, a satellite-to-satellite switch, or sat-to-sat switching, occurs. For time-varying satellite networks, none of the switches can cause the established routes not to work correctly. Re-routing mechanisms increase network latency and cannot meet QoS latency requirements, causing communication disruptions. Unbalanced Load Traffic: Geographical conditions, satellite motion, and the Earth's rotation characterize the time-varying nature and uneven distribution of load traffic in satellite networks. A single satellite can cover a small populated area, such as the Arctic, and a densely populated area, such as a developed country. As the satellite is constantly in motion, the number of terminal users and the amount of load traffic within the network are changing. Since there is not enough resource space for up/down link (UDL) routing between satellite users, variations in such load traffic may affect some switching strategies in the network. For one, even if the ISL has sufficient resource capacity at the time of establishment, the change in link load traffic can cause network congestion, preventing the link from switching. In addition, even under the same user load traffic conditions, each LEO satellite and ISL may have different load traffic, causing link switching to fail. The blocked link prevents QoS routing, affecting satellite network communication.
## 2 Material and Method
### Finite Element Analysis  
The numerical analyses were conducted using the ANSYS Mechanical 2023 R1 Implicit Solver (Ansys, Inc., Canonsburg, PA, USA). In each of the nine FE simulations applied according to the Taguchi experimental design, the optimum autofrettage pressure calculated with Equation (15) was considered. For the analyses, the Poisson's ratio of the materials was taken as 0.3. The analyses were performed under the assumption that the materials exhibit bilinear kinematic hardening behavior. The finite element analysis was carried out as 2D Plain Strain.  
Mesh convergence was ensured by using mesh sizes of 10, 5, 4, 2, and 1 mm. When the analysis results were compared using mesh sizes of 2 mm and 1 mm, it was observed that the results varied by less than 2%. Therefore, to expedite the solution in the analyses, the general mesh size of the model given in Figure 3a was selected as 2 mm. MultiZone Quad4 elements were used in the finite element model to better conform to the geometry, ensure uniform distribution of the lateral loads, require fewer elements, and obtain more accurate results. Additionally, \"Share Topology\" was used between meshes to ensure that the neighboring elements share nodes, aiming for more accurate results. The generated finite element model consists of 4161 elements and 4380 nodes.  
As a result of the mesh generation process, a minimum element quality of 0.87987 and an average element quality of 0.95181 were obtained. When the aspect ratio was examined, the maximum value was found to be 1.6301, and the average value was 1.3082. The data related to the element quality and aspect ratio values obtained from the analysis support the accuracy and stability of the solution.  
Figure 3: (**a**) Schematic representation of the material removed region for internal machining operation (**b**) Contour plots of the equivalent stress for the FEM results of the internal machining (**c**) Schematic representation of the material removed region for the external machining operation (**d**) Contour plots of the equivalent stress for the FEM results of the external machining.  
No supports were used during the analysis, and the \"weak springs\" feature was activated to conduct the simulations. The numerical analyses related to the machining were performed using the \"Element Kill\" and \"Element Birth\" commands of ANSYS APDL. In this context, a material removal of 5 mm from the inner and outer surfaces of the cylindrical model, as shown in Figure 3b, and the application of a pressure of 400 MPa to the inner surface of the part, as shown in Figure 4, were conducted for numerical analysis.
# Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection
## 2 Related Works  
### Underwater Object Detection Methods  
Underwater object detection is crucial for marine exploration, ecological protection, and underwater engineering, but implementing these technologies faces several challenges, such as poor visibility, complex lighting conditions, the impact of water currents, and acoustic disturbances. In terms of technology, image preprocessing is first performed, including color correction and image enhancement to tackle issues like color distortion and blurring, which are unique to underwater environments. Next, traditional detection methods such as background subtraction and thresholding are applied, which are suitable for simple scenarios [30]. In advanced detection technologies, YOLO (You Only Look Once) [31] offers an efficient balance between time and accuracy, making it suitable for detecting fast-moving objects; SSD (Single Shot MultiBox Detector) [32] performs better than YOLO in detecting small objects; Faster R-CNN [33] provides high-precision recognition in complex environments; and Mask R-CNN [7] focuses on high-precision object segmentation, which is particularly useful for environmental and biodiversity research. These technologies collectively advance underwater detection capabilities, helping to address the unique challenges of underwater environments.  
The URPC 2019 dataset is specifically designed for underwater robot professional competitions, used to evaluate the performance of underwater object detection algorithms. This dataset is not only used for competitions but also widely applied in technology development for marine resource surveys and underwater facility maintenance. [PERSON] [34] conducted comparisons on this dataset among multiple advanced underwater object detection models, including Faster R-CNN (VGG16), SSD (VGG16) [34], FCOS (ResNet50) [35], Faster R-CNN (ResNet50), Free-Anchor (ResNet50) [36], EfficientDet [37], CenterNet (ResNet50) [38], YOLOv8n, DDA + YOLOv7-ACmix [39], and YOLOv8-MU, with the results listed in Table 1. For a more intuitive comparison of our model with others, the main metric used is _[EMAIL_ADDRESS]. The refined URPC 2019 dataset contains 4757 images, while the URPC 2019 dataset excluding aquatic plants contains 4707 images. Despite the difference in the total number of images, the YOLO models still demonstrate superior detection accuracy with fewer images, further highlighting the model's advantages in detection precision.
## 3 Results
### Melting Characteristics  
Table 1 shows the experimental data obtained during DSC (differential scanning calorimetry) analysis concerning the melting characteristics of the enzymatically interesterified oil blends and crude plant oils. It was noticed that tested oil samples were characterized by various melting profiles with different numbers of endothermic peaks.  
Among the tested enzymatically interesterified oil blends, only sample A25B75 indicated different melting characteristics behavior. The DSC curve of this oil blend, combined with the highest proportion of coconut oil, had only one endothermic peak at a temperature range of \(-13.10\,^{\circ}\)C to \(25.5\,^{\circ}\)C. According to the results of the melting profiles of the tested crude oils, tomato seed oil showed different thermal melting behavior in comparison to coconut oil.