esa-sceva/satcom-chunk-collection huggingface dataset:
# A Method for Singular Points Detection Based on Faster-RCNN
## 3 Experimental Results
### Experimental Setup  
The experimental evaluation of the proposed method and its comparison with other algorithms are performed on FVC2002 (800 images) [24], NIST sd04 (4000 images) [25] and Ten-Finger Card dataset (62,655 images) from a laboratory database. Specifically, we randomly choose 80% data from Ten-Finger Card dataset as training data and the residual data are used for testing. The FVC2002 DB1 and NIST sd04 datasets are randomly sampled 50% for training and testing respectively. Meanwhile, we randomly selected 10% images from training data to generate the validation data to validate the performance of network during training. Obviously, singular points location and orientation (only up-core) of fingerprint images should be marked out artificially for training. However, it is extremely difficult to achieve because it is a time-consuming and labor-intensive work. Thus, we choose the singular points detection algorithm [7] to automatically extract the singular points information of Ten-Finger Card fingerprints, while the singular points of FVC2002 and NIST sd04 fingerprints are manually labeled beforehand as the ground truth labels. Besides, neither enhancement nor segmentation are carried out on all fingerprint images.  
In our works, the weights of each layer are initialized by drawing randomly from a Gaussian distribution with zero mean and standard deviation \(10^{-3}\) and biases are set to 0. A weight decay with the coefficient \(1.0\times 10^{-4}\) is used to prevent overfitting. The learning rate is initialized to 0.001 and changed with iteration steps by an exponent drop.  
The Tensorflow ([[https://www.tensorflow.org/](https://www.tensorflow.org/)]([https://www.tensorflow.org/](https://www.tensorflow.org/))) (version 1.4.1) [26] framework is used to implement the proposed method and the experiments are run on the machine with an Intel Core i7-6700 CPU at 3.4 GHz, 16 GB RAM, and a NVIDIA GeForce GTX 1060 GPU. Moreover, when testing the proposed method, we can even use a CPU to achieve the process.
###### Abstract  
As robotic systems become more prevalent in daily life and industrial environments, ensuring their reliability through autonomous self-diagnosis is becoming increasingly important. This study investigates whether acoustic sensing can serve as a viable foundation for such self-diagnostic systems by examining its effectiveness in localizing structural faults. This study focuses on developing a fault diagnosis framework for robots using acoustic sensing technology. The objective is to design a simple yet accurate system capable of identifying fault locations and types of robots based solely on sound data, without relying on traditional sensors or cameras. To achieve this, sweep signals were applied to a modular robot, and acoustic responses were collected under various structural configurations over five days. Frequency-domain features were extracted using the Fast Fourier Transform (FFT), and classification was performed using five machine learning models: Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), XGBoost, and Multi-Layer Perceptron (MLP). Among these, MLP achieved the highest accuracy (71.4%), followed by SVM (65.7%), LightGBM (62.9%), KNN (60%), XGBoost (57.1%), and RF (51.4%). These results demonstrate the feasibility of diagnosing structural changes in robots using acoustic sensing alone, even with a compact hardware setup and limited training data. These findings suggest that acoustic sensing can provide a practical and efficient approach for robot fault diagnosis, offering potential applications in environments where conventional diagnostic tools are impractical. The study highlights the advantages of incorporating acoustic sensing into fault diagnosis systems and underscores its potential for developing accessible and effective diagnostic solutions for robotics.  
a 2025 15 6564 +
Footnote †: journal: Applied Sciences  
Article
# Characterizing Growing Season Length of Subtropical Coniferous Forests with a Phenological Model
## 3 Results
### Ecosystem-Scale Phenological Characteristics  
Prescribed fire and weather events (meteorological drought and flood) affected ecosystem-scale phenological processes and productivity [40; 49], especially summer phenology (Table 2; Appendix B, Figure A2) because of the variability of water availability. To develop a more thorough understanding of the phenological patterns of the study sites, we investigated SOS, EOS and LOS patterns and characteristics at an inter-annual scale (Figure 6; Appendix B, Table A1 and Figure A2). Although the three sites had different soil water availability [42], multivariate tests indicated there were no significant differences in SOS, EOS, and LOS by site (\(p>0.12\)) (Appendix B, Table A2). However, the mesic site had the longest average LOS (320 d), and the xeric site had the shortest average LOS (300 d) (Appendix B, Table A1).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Year/Site** & **Mesic** & **Intermediate** & **Xeric** \\ \hline
2009 * & Normal & Normal & Normal \\
2010 & AR (Water stress) & AR (Water stress) & AR (Water stress) \\
2011 * & AR (Water stress) & AR (Water stress) & AR (Water stress) \\
2012 & AR (Uneven spring precipitation) & Normal & Normal \\
2013 * & Normal & Normal & Normal \\
2014 & AR (Flood) & AR (Flood) & AR (Flood) \\
2015 * & AR (Water stress) & AR (Water stress) & AR (Water stress) \\
2016 & Normal & Normal & AR (Water stress) \\
2017 * & Normal & Normal & Normal \\ \hline \end{tabular}  
* Anomalous response: AR;
* fire year.  
\end{table}
Table 2: Description of the application of the phenology model for the three EC sites at the JCI, Newton, GA, USA.  
Figure 6: Relationship between (**a**) LOS and SOS, (**b**) LOS and EOS, and (**c**) EOS and SOS by site (N = 27). SOS = start of growing season, EOS = end of growing season, LOS = length of growing season.  
The relationship between SOS and LOS at all sites was significant and negative (Figure 6; \(p<0.01\)); earlier onset of SOS was associated with a significant extension in LOS. The relationship between EOS and LOS was significant and positive for all sites (\(p<0.01\)), with a delay of EOS associated with a significant extension in LOS. On the other hand, there was no significant relationship between SOS and EOS; that is, SOS and EOS jointly control LOS across the sites, but SOS and EOS were independent of each other, with their own response mechanisms to environmental and human activities.
# Gear Fault Detection Method Based on Convex Hull Clustering of Autoencoder's Latent Space
## 2 Materials and Methods
### Principal Component Analysis  
Principal component analysis (PCA) is a method used for dimensionality reduction in multi-featured data. It relies on the sequence of linear transformations of input variables. As a result, one obtains a set of new features, being linear combinations of the input, which maximizes the explanation of the dataset variance. Therefore, the first PCA variable explains the most variance, and hence the following subsequent features less. In our case, the PCA fit was performed on the healthy gear training dataset. All data were projected into a two-component space, as presented in Figure 8.  
Figure 7: Distribution of selected parameters of vibrational signal: (**a**) root mean square value; (**b**) maximum normalized amplitude over 900 Hz; (**c**) variance; (**d**) skewness.  
The data formed five clusters for normal operation and four clusters for the failure state. Each cluster corresponded to the different measurement series, conducted at different times and states of gear wear. As can be noted, there was no clear separation between the points representing the measurements of the operating and failing gears. First, two principal components explained no more than 61.3% and 9.9% of the data variance. This led us to the conclusion that more robust, nonlinear treatment was needed.
# Hyperspectral Imaging for Detecting Plastic Debris on Shoreline Sands to Support Recycling
## 1 Introduction
Building upon our previous research [36], which focused on polymer identification of microplastics in controlled laboratory conditions, this study extends the application of HSI to real-world field scenarios. By addressing complex substrates containing mixtures of plastic, sand, and other materials, this research bridges the gap between laboratory-based methodologies and practical field applications. To address these goals, three classification models were then developed following a cascade detection approach: the first model was created to differentiate plastics from other materials in sand samples, the second one to identify plastic particles in the sands, and the third to determine the polymer composition of each detected plastic particle. By simulating realistic shoreline conditions, the proposed approach incorporates a comprehensive classification pipeline capable of distinguishing plastic particles from mixed substrates, followed by the accurate determination of their polymer types. The presented HSI approach can be seen as a reference for the development of custom-novel classification systems that could be applied in situ for monitoring the presence of plastic waste on beaches, ultimately leading to its removal, which is crucial for effective recycling. Moreover, if fully implemented, the proposed NIR-HSI approach could be developed and deployed on mobile systems, such as Unmanned Ground Vehicles (UGVs) or Unmanned Aerial Vehicles (UAVs). These mobile platforms would enable comprehensive, large-scale monitoring of plastic waste along shorelines, significantly enhancing the speed and efficiency of data collection while facilitating real-time decision-making in debris management and recovery operations.
# A Novel DV-HOP and APIT Localization Algorithm with BAT-SA Algorithm
## 3 Principle of APIT Algorithm  
Due to the fixed nature of sensor nodes in WSN, which prevents random mobility, PIT studies have been demonstrated to be inapplicable in a variety of practical applications. As a result, the problems in PIT are generally overcome by the APIT algorithm, in which there is no change in the network sensor point. It can be observed that anchor nodes are received from the RSST, and every final node action is described with a link tothe sensor nodes and is compared with the standard initial algorithm of the APIT. All the nodes are present together in the interior of the design, which is compared with the target node to obtain the range of communication. Figure 2 demonstrates the position of the target node andwhether the node is present on the interior or exterior of the triangle zone. No neighbor of the target node is located nearer or further away from any of the triangle's three anchor nodes. The target nodes are always present in the interior of the triangle, and the rest of the nodes, like the anchor, are locatedclose to the nodes. Target nodes and anchor nodes are also included in the neighbor nodes. The target node moves to the location of a nearby node, let us say node 4, and we can assume that since it is equallyclose to the anchor nodes, it is outside the triangulation area [15; 16; 17].
## 7 Improvement of Modulation Bandwidth
The measured 3-dB modulation bandwidth of the two devices with and without Ag NPs at an injection current density of 57 A/cm\({}^{2}\) are 15.1 MHz and 29.8 MHz, respectively, as shown in Figure 18c. The lower modulation bandwidth is restricted by the carrier recombination lifetime. However, the modulation bandwidth for the LED with Ag NPs is found to be two times higher that without Ag NPs, suggesting that the spontaneous emission rate can be significantly improved by QW-SP coupling, thereby decreasing the carrier recombination lifetime. The modulation bandwidths of the two devices as a function of the injected current density are illustrated in Figure 18d, showing that the bandwidth increases with an increase in the injected current density. The modulation bandwidth  
Figure 17: Schematic flow of GaN-based nanorod LED fabrication with Ag nanoparticles. [39] Figures reproduced with permission from Optical Society of America. ratio of the LED with Ag NPs to the LED without Ag NPs is shown in Figure 18d, and it is found to be increasing from 1.1 to 1.9 with increasing current density, which may be due to increasing IQE. The modulation bandwidth ratio is related to the IQE as \(M=(F-1)\eta_{int}+1\), where \(M\) is the modulation bandwidth ratio, \(F\) is the Purcell factor, and \(\eta_{int}\) is the IQE. Therefore, LEDs with higher IQE will have a higher modulation bandwidth by enhancing the radiative recombination rate. Since QW-SP coupling is found to increase the IQE for the corresponding LEDs, QW-SP coupling can effectively improve the modulation bandwidth.
# An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
## 1 Introduction
(1) It should be noted that taxi demand volume data on weekdays or weekends in the taxi GPS datasets share periodic similarity spatiotemporally. Taking this into consideration, the matrix-based method is utilized to visualize this data pattern instead of the conventional vector-based method. (2) For the FCM method, it is widely used to deal with the clustering problem with incomplete data [17; 18]. Specifically, the FCM method is capable of generating satisfactory estimation results by analyzing multiple dimensional datasets. However, these clustering results are sensitive to the parameter of the membership degree and the number of cluster centroids. Therefore, in our study, a GA is incorporated into the FCM to optimize the above two critical parameters. The rest of this paper is organized as follows: Section 2 reviews the related studies in recent years. Section 3 presents the integrated methods for estimating missing data. Section 4 demonstrates an experimental test of taxi GPS data in Manhattan, New York City, in 2013, and provides comparisons and analyses of the results. Finally, Section 5 summarizes some findings and proposes future works.
# Delving into Causal Discovery in Health-Related Quality of Life Questionnaires
###### Abstract  
Questionnaires on health-related quality of life (HRQoL) play a crucial role in managing patients by revealing insights into physical, psychological, lifestyle, and social factors affecting well-being. A methodological aspect that has not been adequately explored yet, and is of considerable potential, is causal discovery. This study explored causal discovery techniques within HRQoL, assessed various considerations for reliable estimation, and proposed means for interpreting outcomes. Five causal structure learning algorithms were employed to examine different aspects in structure estimation based on simulated data derived from HRQoL-related directed acyclic graphs. The performance of the algorithms was assessed based on various measures related to the differences between the true and estimated structures. Moreover, the Resource Description Framework was adopted to represent the responses to the HRQoL questionnaires and the detected cause-effect relationships among the questions, resulting in semantic knowledge graphs which are structured representations of interconnected information. It was found that the structure estimation was impacted negatively by the structure's complexity and favorably by increasing the sample size. The performance of the algorithms over increasing sample size exhibited a similar pattern, with distinct differences being observed for small samples. This study illustrates the dynamics of causal discovery in HRQoL-related research, highlights aspects that should be addressed in estimation, and fosters the shareability and interoperability of the output based on globally established standards. Thus, it provides critical insights in this context, further promoting the critical role of HRQoL questionnaires in advancing patient-centered care and management.  
causal discovery; directed acyclic graph; health-related quality of life; resource description framework; semantic knowledge graphs 2020 IEEE/RSJ International Conference on Intelligent Systems (IEEE), pp. 1-10, 2018.
# Additive Manufacturing in Underwater Applications
###### Abstract  
Additive manufacturing (AM), commonly named 3D printing, is a promising technology for many applications. It is the most viable option for widespread use in automated construction processes, especially for harsh environments such as underwater. Some contemporary applications of this technology have been tested in underwater environments, but there are still a number of problems to be solved. This study focuses on the current development of 3D printing technology for underwater applications, including the required improvements in the technology itself, as well as new materials. Information about underwater applications involving part fabrication via AM is also provided. The article is based on a literature review that is supplemented by case studies of practical applications. The main findings show that the usage of additive manufacturing in underwater applications can bring a number of advantages--for instance, increasing work safety, limiting the environmental burden, and high efficiency. Currently, only a few prototype applications for this technology have been developed. However, underwater additive manufacturing is a promising tool to develop new, effective applications on a larger scale. The technology itself, as well as the materials used, still require development and optimization.  
additive manufacturing; 3D printing; underwater; marine application +
Footnote †: journal: Applied Sciences
## 4 Improved Tight Combination Model
### IMU Location Calculation  
The measured values of IMU are \(w\) and \(a\). When calculating the position, velocity, and quaternion, the IMU measurements need to be integrated by time. From moment \(i=0\) to \(k\), the measured value of IMU was integrated, and the expression of PVQ obtained is shown in Equation (16), where \(g\) is gravity acceleration of one point on earth, and \(g=9.8\) m/\(s^{2}\).  
\[\begin{split} p_{k+1}&=p_{k}+T_{i->j}v_{k}+\iint \limits_{t\in[i,j]}(a_{k}-g)dt^{2}\\ v_{k+1}&=v_{k}+\int\limits_{t\in[i,j]}(a_{k}-g)dt \end{split} \tag{16}\]
## 5 Numerical Simulations
### Comparison Study  
To demonstrate the superiority of the predefined-time control strategy proposed in this study, this subsection presents a comparative analysis against the conventional fixed-time control law [25].  
\[\mathbf{\kappa}_{c}=\Lambda^{-1}\Big{(}-\mathbf{K}_{1}\text{tanh}(\mathbf{z}_{1})-\mathbf{K}_{2} diag\Big{(}|\mathbf{z}_{1}|^{2\beta-1}\Big{)}\text{sgn}(\mathbf{z}_{1})-\mathbf{v}_{1}^{*}+ \hat{\mathbf{J}}_{1}^{T}\hat{\mathbf{\eta}}_{g}-\dot{\mathbf{M}}\Big{)} \tag{70}\]  
\[\mathbf{\tau}_{c}=m\Big{(}-\Lambda^{T}\mathbf{z}_{1}-\sigma\text{tanh}(\mathbf{z}_{2})-\mathbf{ K}_{3}\text{tanh}(\mathbf{z}_{2})-\mathbf{K}_{4}|\mathbf{z}_{2}|^{2\beta-1}\text{sgn}(\mathbf{z}_{2}) \Big{)} \tag{71}\]  
\[\sigma=\hat{l}_{c0}+\hat{l}_{c1}\parallel\mathbf{v}\parallel+\hat{l}_{c2} \parallel\mathbf{v}\parallel^{2} \tag{72}\]  
\[\hat{\hat{l}}_{ci}=\gamma_{i}\|\mathbf{z}_{2}\|\|\mathbf{v}\|^{i}-C_{\text{min}}2\beta \hat{\gamma}_{i}^{2-\beta}\hat{l}_{ci}^{1-\beta}-C_{\text{min}}2\beta\gamma_{ i}^{2+\beta}\hat{l}_{ci}^{1+\beta},i=1,2,3 \tag{73}\]  
The definitions of all symbols in the equations remained consistent with those in the proposed control law, with the subscript \"\(c\)\" denoting the comparison scheme. The simulation was conducted under the same conditions outlined in Section 3.1, and the results are presented in Figure 9. The figure illustrates that while the actual trajectory of the comparison scheme can follow the desired trajectory, the proposed control strategy demonstrated a faster convergence, reaching a small neighborhood of zero within 10 s.  
Furthermore, the adoption of the RBF neural network in the proposed approach effectively approximated the time-varying error, thereby reducing the impact of perturbations in the steady state. In contrast, the error comparison graphs in the cross-swing and pendant-swing directions revealed an overshooting phenomenon in the comparison scheme, underscoring the advantages of the proposed method in terms of trajectory tracking accuracy and response speed.  
This section compares the radial basis function neural network (RBFNN) with the feedforward neural network (FNN). To control for variables, both networks were configured with consistent parameters and evaluated under the conditions outlined in Section 3.1. In Figure 10. The results demonstrate that both the proposed RBFNN and the FNN were able to effectively compensate for errors after 5 s. However, the proposed RBFNN provided more efficient compensation when there were significant changes in the error, whereas the FNN exhibited suboptimal compensation at the inflection points. Although the error compensation signal proposed in this study showed some oscillations at the inflection  
Figure 9: Tracking error of the comparison scheme and the scheme.  
points, the amplitude of these oscillations remained within the permissible range of the controller, making it reasonable for practical applications. Therefore, the RBFNN-based error compensation scheme proposed in this paper showed a clear advantage over the FNN.
## 3 Results and Discussion  
A total of 445 studies were identified through the systematic database search (Figure 1). After removing 33 duplicates, 412 studies remained for title and abstract screening, resulting in the exclusion of 397 studies based on relevance and methodological criteria. Following a full-text evaluation of 15 studies, 10 studies met the eligibility criteria and were included in the final analysis.  
The selected studies investigated various badminton shoe modifications (Table 2), with a focus on forefoot bending stiffness (n = 2), torsional stiffness (n = 1), lateral-wedge hardness (n = 1), insole hardness (n = 1), shoe sole design (n = 2), midsole hardness (n = 1), and shoe heel design (n = 2). The biomechanical assessments were conducted using badminton-specific movement tasks, including lunges (n = 8), high clear (n = 3), 45-degree sidestep cutting (n = 1), consecutive vertical jumps (n = 1), cross-step (n = 1), sidestep (n = 1), and takeoff and landing (n = 1).  
A total of 127 participants were included across the studies, consisting of 112 males and 15 participants with unspecified gender. The mean age was 23 \(\pm\) 5 years (range: 18-28 years), with all participants classified as competitive-level athletes. Reported shoe sizes primarily included US 9.0 (n = 3) and US 8.5 (n = 2), while five studies did not specify shoe size.  
Participants with a history of lower extremity injuries were excluded based on different timeframes specified in the studies. One study excluded participants with injuries within the past 12 months, five studies excluded those with injuries within the past 6 months, and four studies did not specify the injury exclusion period.