esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Summary
### Problem Analysis and Prospects  
Although the algorithm shows promising results in identifying structural damage and has advantages over traditional methods, there are still some limitations. These mainly include the following aspects.  
The first is that this study is limited to damage identification only and does not complete the work of damage localization, which can be followed up by using some localization algorithms to locate the specific horizontal and vertical coordinates of the damage.  
The second is that if two defects are far away from each other on the image, it is difficult to accurately capture the information about both defects at the same time. To overcome this challenge, we can apply a period of continuous Lamb waves to obtain more information about defect detection. By continuous Lamb wave excitation, more information can be introduced in the image, including multiple angles and directions of excitation. This will make the information in the damaged region richer and more diversified, which will help improve the defect detection rate and accuracy. At the same time, the continuous excitation can cover a larger area, enabling a more comprehensive view of the anomalies in the image and avoiding missing more distant damage.  
Finally, we can further explore the algorithm regarding the choice of non-convex penalty functions. Different non-convex penalty functions may have an impact on the excellence of the algorithm, so comparative experiments can be conducted to try differentpenalty functions and observe their effects on damage recognition. Such research can help us to find a more suitable penalty function for the problem characteristics and further improve the accuracy and robustness of the algorithm.  
Conceptualization, D.L.; Methodology, Y.Z.; Software, Y.Z.; Validation, L.Y.; Formal analysis, Y.Z.; Resources, X.J.; Data curation, Y.Z.; Writing--original draft, A.L.; Visualization, G.S. 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 (Grant no. 51405409), Key research project of Jiangsu grant number [BE2016056], Primary Research and Development Plan of Jiangsu Province (Social Development) (BE2019649), State Key Laboratory of Acoustics, Chinese Academy of Sciences (SKLA201913), the Fundamental Research Funds for the Central Universities (2018B04514, B200202212), Technology Support Plan (Social Development) Project Foundation of Changzhou (Grant No. CE20215017), and China Scholarship Council (202006715050).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. (for legal/ethical reasons).  
The authors declare no conflict of interest.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 5 Tool Optimization
To this end, an additional option was added to the project creation window that, when selected, \"next\" and \"previous\" buttons would be added to the bottom of the project window, which would change the step presented to the user to the next or previous when clicked, as presented in Figure 16.  
Figure 17 presents the project creation window with the mentioned feature highlighted. Figure 16: Satellite project link budget step window. Figure 15: Invalid input error message. 6. To facilitate the project design for professional users, it was requested to reduce the number of outputs given by the software. In order to meet this need without removing the academic value of the project design, an additional project layout was created. In this project, the outputs considered unnecessary for professional use were hidden from the user, thus presenting fewer steps to complete the project design. For terrestrial projects, the following hidden steps were included:  
* Equipment reliability;
* Obstacle attenuation;
* Atmospheric attenuation;
* Rain attenuation;
* Unavailability distribution;
* Fast fading;
* Signal reflections;
* SNR in IPC;
* Uniform fading margin;
* Selective fading margin. For satellite projects, the following hidden steps were included:  
* Free-space attenuation;
* Atmospheric attenuation;
* Rain attenuation. To select this project layout, upon creating a project, the user is presented with the option to create a professional project, as seen in Figure 18.  
7. It was requested to create a mechanism that allowed the user to identify in which step it is currently being displayed, in relation to the total steps presented in the project design. To accomplish this goal, a slider was added to the top of the project window, indicating the steps that the current project has and the step currently being presented to the user. The slider also displays which step each element represents by hovering the mouse on each step number and allowing the user to select the said step, scrolling or skipping to the selected step in a normal project or with a step-by step-layout, respectively. The developed feature is presented in Figure 19.  
Figure 17: Step-by-step project layout option.
# Experimental and Theoretical Study on Operation Characteristics of an Oscillating Heat Pipe
## 4 Results and Discussion
### Temperature Variation  
Figure 4a shows the temperature oscillations of the OHP under a heating power of 25 W. Two periods can be defined: the initial start-up period and the stable oscillating period. The temperature oscillation differs in different periods. During the first period, no oscillation was observed. The temperatures increased significantly on both sides of the OHP. During the stable oscillating period, the amplitude of the temperature oscillation was small, and the frequency is high.  
As the heating power increased from 25 to 62.5 W, the temperature oscillation presented a large fluctuation associated with the occasional large amplitude and low frequency at the high heating input, as shown in Figure 4b. This might occur due to the boiling phenomena. At the low heating input, the temperature of the heat pipe surface is slightly higher than the working fluid temperature, which forms nucleate boiling. The heat flux is small, so the amplitude of the temperature variation is small. As the heating input increases, boiling enters the transition boiling phase driven by a large temperature difference. It is an unstable boiling, which led to large amplitude and low frequency of the temperature oscillation. As the heating input increased up to 75 and 100 W, the heating input increases, and the boiling enters the film boiling. During this period, a thin layer of vapor that has low thermal conductivity insulates the surface. The temperature oscillation is moderate with small amplitude and high frequency, as shown in Figure 5a,c,e.  
Figure 4: Temperature variation of the OHP under heating power between 25 W and 62.5 W.
# Fault ride-Through Control Strategy for Variable Speed Pumped Storage Unit with Full-Size Converter
## 5 Simulation Verification
### Verification of the LVRT Strategy  
This section verifies that the proposed combination of the LVRT strategies can meet the fault ride-through requirements mentioned in Section 4.1. The system voltage signal command is shown in Figure 13.  
(1) In the fast speed mode  
Figure 14 shows the simulation curve for fast-speed mode under power generation conditions without any measures. The excessive energy on the rotor side causes the DC  
Figure 12: LVRT characteristics simulation. (**a**) Rotor side active power. (**b**) Rotor side reactive power. (**c**) Grid side active power. (**d**) Grid side reactive power. (**e**) Unit speed. (**f**) DC voltage.  
Figure 13: Grid voltage during the LVRT.  
voltage to rapidly increase, making it impossible for FSC to operate stably, resulting in the loss of stability of the unit. Figure 15 shows the effect of investing in the combination strategy. As shown in Figure 15c, the rotor speed increases even more, reaching 1.052 pu. The guide vane opening as shown in Figure 15f instantly increases to 0.84 pu, which reduces the power input on the machine side to a certain extent. The DC crowbar circuit is triggered, maintaining the DC bus voltage at 1.1 pu during the fault period, as shown in Figure 15d. The GSC outputs reactive power, and the unit remains stable. The DC voltage is within a safe range, and the LVRT control effect is good after the combination strategy is put into use.  
(2) In the fast power mode  
Similar to the fast-speed mode, low-grid voltage can cause unit instability. The unstable waveform is similar to Figure 14. Figure 16 shows the effects of applying the combination strategy. The introduction of the fast power reduction strategy causes rotor-side power to rapidly decrease to 0.78 pu. Due to the governor controlling the unit speed at this time, its adjustment speed is slow. Therefore, the power imbalance caused a significant increase in the unit speed. In addition, the GSC outputs reactive power to increase grid voltage, keeping the unit stable and the DC voltage within a safe range.  
Figure 14: Simulation results of deep voltage drop in power grid (fast speed mode). (**a**) Rotor side active power. (**b**) DC voltage.  
Figure 15: Simulation results of deep voltage drop in power grid (fast speed mode with LVRT strategy). (**a**) Rotor side power. (**b**) Grid side power. (**c**) Unit speed. (**d**) DC voltage. (**e**) Mechanical power. (**f**) Guide vane opening.  
In pumping condition  
Similar to power generation conditions, low-grid voltage can cause insufficient energy for the pump and instability of the unit. After adopting low-power protection measures, the water pump can be quickly shut down to ensure unit safety, as shown in Figures 17 and 18.
# Spatially-Precise Video Editing with Reference Imitation: A Region-Aware Cross-Modal Framework
## 4 Results  
### Experimental Setup  
This experiment aims to evaluate the effectiveness and performance of our pro-posed approach, which combines the image editing method based on the diffusion model with CoDeF, in the context of video editing. The key focus is on demonstrating how our method effectively propagates edits across video sequences while maintaining spatial and temporal coherence.  
During the training process, we set the initial learning rate to 0.001 with a background loss weight of 0.003. The model is trained for 10,000 iterations in total. To ensure stable convergence, we implement a multi-stage learning rate decay schedule, with rate adjustments at steps 2500, 5000, and 7500. Model checkpoints are saved every 2000 steps for model selection and evaluation purposes.  
We conducted the experiments on a high-performance computing platform equipped with a single NVIDIA A100 GPU, utilizing a CUDA-accelerated environ-ment. This robust infrastructure ensures the capability to handle large-scale video data and perform complex computations required by our model.  
In validating our approach, we employed a video dataset designed for diversity and representativeness, encompassing a broad spectrum of real-world scenarios. This intentional variety ensures experimental outcomes reflect generalized performance across applications rather than overfitting to niche domains. The evaluation dataset comprises video samples with varied motion dynamics and visual aesthetics. Clip lengths range from 40 to 120 frames, and editing tasks span color adjustments, texture synthesis, and object manipulations. These examples were curated to thoroughly assess the model's capacity to maintain temporal coherence across diverse editing requirements.Through appropriate tuning of training parameters, the duration of optimization can be flexibly adjusted within a range of 3 to 20 min.  
This study utilizes publicly available datasets under appropriate licenses, ensuring compliance with data privacy regulations. No personally identifiable information (PII) is processed throughout the research. For qualitative assessments involving human contributions, informed consent was obtained where applicable. Our work adheres to ethical guidelines, emphasizing responsible AI practices and measures to prevent misuse.
## Materials and Methods
### Experimental Data Acquisitions for Doppler Application  
The feasibility of the 4-D platform implementation was tested on a blood vessel mimicking phantom [30] for 3-D Doppler applications. The phantom is made in polyvinyl alcohol (PVA), silica and distilled water (10%, 1% and 89% in weight, respectively), ensuring elasticity (from the PVA) and a good visualization (from the silica) in US images, with 5 cycles of freeze-thaw. The length of the phantom was around 8 cm, while the phantom thickness and internal diameter were 2 mm and 8 mm, respectively. A circulating liquid (water and corn flour) obtained by using a setup with an external pump mimicked the blood flow inside the vessel. The role of the corn flour dispersed in the water increased the blood backscattering in order to quantify the flow. The resulting phantom is close to biological tissues in terms of acoustic and mechanical characteristics [31] and the fluid is in accordance with the standard physical and acoustic properties of blood [32].  
The phantom was immersed in static water for acoustic coupling, and the US cardiac probe described in Section 2.1 was placed a few centimeters above. Different set-ups between the vessel mimicking phantom and the US probe were investigated: US probe parallel to the tube phantom (\(\theta=0\)) and tilted at an angle of 12\({}^{\circ}\).  
The acquisitions for 3-D Power Doppler evaluation were performed using the full active 1024 matrix array of the US probe, with a single PW transmitted. The volumes were acquired at a high frame-rate with 2000 Hz for each configuration.
# Exploring Channel Properties to Improve Singing Voice Detection with Convolutional Neural Networks
## 3 Proposed CNN Architecture
### The Improvements  
Three improvements were made to the baseline architecture.  
First, the spectrograms were normalized by the precomputed min and max matrices before they entered the CNN. The min and max matrices were calculated in terms of image intensity, based on all the images generated from the spectrograms of the training, validation, and test datasets. Conventionally, we obtain the min and max scalars for normalization instead of their matrix counterparts; for example, for all the images \(m_{k}=[v_{ij}^{k}],k\in[1,K],i\in[1,I],j\in[1,J]\), where \(v_{ij,i}\) is the intensity at the position \((i,j)\). Conventionally, we also obtain the min scalar value \(p=\min\limits_{k\in[1,K],i\in[1,I],k\in[1,I]}(v_{ij}^{k})\) for min-max normalization; however, in this study we obtain the min matrix \(\mathrm{P}=[p_{ij}]=[\min\limits_{k\in[1,K]}(v_{ij}^{k})]\)  
Figure 8: The baseline CNN architecture.  
\(i\in[1,I],j\in[1,J]\). This technique eliminated the effects of the variation in the scale in terms of intensity. We considered this to be more precise in terms of the overall spectrogram.  
Second, a Batch Normalization layer was added after every convolution layer. The Batch Normalization layer can reduce the internal covariate shift of the network and increase the speed of training [32].  
Third, we employed weighted binary cross entropy as the loss function. This is weighted because the overall duration of the singing voice is usually longer than that of non-singing in popular songs, and the application of SVD usually concerns popular music. We set the weight vector as the proportion of the number of voiced and unvoiced samples in the training, validation, and test datasets. The loss function can be formulated as follows:  
\[l=-\frac{1}{N}{\sum_{i}{w_{i}[y_{i}\log x_{i}+(1-y_{i})\log(1-x_{i})]}} \tag{12}\]  
where \(i\in[1,N]\), \(N\) is the number of samples, \(x_{i}\) is the probability of voice, and \(y_{i}\) is the sample label.
## 2 Proposed Method
### Interest Point Detection and Description
The voxel's identification number and the positions (X, Y, and Z coordinates) of 3D points can be accessed from the matched interest points in the data structure for 2D-3D matching. Figure 4: Illustration of the deep learning network architecture used in this study. Separate convolutional neural networks were used for voxel region detection and interest point detection/description. Figure 3: Overview of the training dataset architecture. Once matches have been made between 2D and 3D data, a PnP solver included inside the RANSAC loop estimates the camera pose. Here, in addition to the labeled regions, we consider the center of the labeled regions as a potential point of interest, if the number of interest points detected in the voxel regions is less than a given threshold (typically, four points are necessary for the PnP algorithm). Our use of the convex hull algorithm ensures that labeled regions are polygonal. As such, extrapolation errors are somewhat avoided with this procedure, since the centroids of the labeled regions are within these polygons. The 3D position of the centroid is determined as the closest 3D point to the centroid obtained by averaging the inlier 3D points in the voxel.
# On Beamforming with the Single-Sideband Transform
###### Abstract  
In this paper, we examine the use of the Single-Sideband Transform (SSBT) for convolutive beamformers. We explore its unique properties and implications for beamformer design. Our study sheds light on the tradeoffs involved in using the SSBT in beamforming applications, offering insights into both its strengths and limitations. Despite the advantage of having real-valued coefficients, we show that the convolution handling of the transform presents challenges that impact fundamental beamforming principles. When compared to the Short-Time Fourier Transform (STFT), the SSBT displays lower robustness, especially in scenarios involving mismatch and modeling noise. Notably, we establish a direct equivalence between the SSBT and STFT when using identical transform parameters, enabling their seamless interchangability and joint use in time-frequency signal enhancements. We validate our theoretical findings through realistic simulations using the Minimum-Power Distortion-less Response beamformer. These simulations illustrate that although the STFT performs marginally better than the SSBT under optimal conditions, it outperforms significantly in non-ideal scenarios.  
single-sideband transform; time-frequency transform; beamforming; array signal processing; signal enhancement +
Footnote †: journal: _Applied_
# Robust Miner Detection in Challenging Underground Environments: An Improved YOLOv11 Approach
## 4 Conclusions  
This study presents an innovative construction of a dataset for miner detection in underground coal mines, capturing complex environments including low-light conditions, partial strong light interference, and occlusion. The Efficient Channel Attention (ECA) mechanism was incorporated into YOLOv11, enhancing the model's ability to focus on salient features and thereby substantially improving detection accuracy under challenging conditions. Additionally, a weighted Complete Intersection over Union (CIoU) and an adaptive confidence loss function were introduced, which enhanced the model's focus on uncertain target regions, particularly in low-light and occluded environments, thereby improving its robustness. Comprehensive experimental comparisons with multiple improved algorithms and state-of-the-art detection models demonstrated that the proposed method achieved significant improvements in both detection performance and robustness in miner detection tasks, providing critical technical support and reference for ensuring coal miner safety and advancing intelligent mine management. In the future, the model will be extended to detect additional targets in underground coal mines, such as grippers, drill chucks, safety helmets, drill rods, etc., with the goal of enabling real-time identification of potential hazards and issuing alerts. This will significantly enhance the practical applicability and value of the model.  
Funding acquisition, D.L.; Methodology, Y.L.; Project administration, Y.L. and H.W.; Software, H.Y. and H.W.; Supervision, D.L.; Validation, Y.L. and H.W. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (Grant: 23 KJA520013) and the Xuzhou Science and Technology Plan Project (Grant: KC22305).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data used in this article are publicly available. The source code supporting the findings of this study is openly available at GitHub via [[https://github.com/whdcumt/CoalMineWorker](https://github.com/whdcumt/CoalMineWorker)]([https://github.com/whdcumt/CoalMineWorker](https://github.com/whdcumt/CoalMineWorker)), under the MIT license, accessed on 8 December 2024.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
YOLO & You Only Look Once  
ECA & Efficient Channel Attention  
CA & Channel Attention  
GAM & Global Attention Mechanism  
CBAM & Convolutional Block Attention Module  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Precision** & **Recall** & **mAP\({}_{\text{50}}\)** \\ \hline YOLOv11 & 0.914 & 0.925 & 0.938 \\ YOLOv11\_ECA & 0.930 & 0.922 & 0.941 \\ YOLOv11\_WCIoU-ACLoss & 0.915 & 0.934 & 0.954 \\ YOLOv11\_ECA\_ & & & \\ WCIoU-ACLoss (Ours) & 0.932 & 0.934 & 0.958 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation Study.
# Adaptive Downward/Upward Routing Protocol for Mobile-Sensor Networks
## 4 Simulation Environment and Performance Evaluation
### Simulation Results
#### 4.2.3 Dropped Packets Ratio  
The number of dropped packets is the number of packets that are dropped after exceeding the maximum number of retransmission attempts. Dropped-packets ratio is the ratio between the number of dropped packets and the number of generated packets. Figure 8 shows the dropped-packets ratio for different traffic patterns. Due to the fact that Flooding directly broadcasts packets, there are no retransmissions and thus no dropped packets. Hence, in Figure 8 we only show a comparison between ADUP and AODV.  
Figure 7(a) shows that ADUP outperforms AODV in terms of dropped packets ratio for upstream traffic. This is mainly due to the fact that link quality monitoring and rank updating helps ADUP detect movement in advance and select next-hop nodes with good link quality. This increases the success  
Figure 7: Average end-to-end delay.  
rate of sending a packet and reduces the number of dropped packets. Results also show that dropped packets ratio of ADUP and AODV for upstream traffic first increases with 40 nodes and then decreases with 60 nodes. When the number of nodes increase from 20 to 40, there is more upstream traffic, which causes more collisions and retransmissions. Thus, the dropped-packets ratio increases with 40 nodes. However, when the number of nodes continues to increase, network density becomes higher. A node would have more available next-hops to select from. This helps nodes to select next-hops with better link quality, which results in fewer dropped packets.  
Figure 7(b) shows that ADUP also outperforms AODV in terms of dropped packets for downstream traffic, and the ratio of ADUP even decreases when the number of nodes increases. Benefiting from upstream traffic and route building in downward routing, ADUP helps the sink node build downward routes to reach any node in the network. When the number of nodes increases, the density of nodes increases. Next-hop nodes with better link quality would be used by the sink, and this would enhance the path quality between the sink and the destinations. This also helps reducing the number of dropped packets. However, unlike ADUP, the dropped-packets ratio of AODV for downstream traffic increases slightly when the number of nodes increases. This is mainly due to the fact that AODV is a reactive protocol and it builds routes only when there is data to be transmitted. This process delays transmission, which results in AODV not being able to update topology in a timely manner. This causes packet loss due to inappropriate next-hop selection.