esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
## 4 Practical Implications
### Talent Identification  
The studies focused on talent identification in tennis revealed several practical implications for improving the process of identifying promising young athletes [38; 45; 48; 53]. The use of automated methods, particularly machine learning, has demonstrated higher accuracy of competitive performance in players younger than 16 years. These automated methods outperform coach-based evaluations, especially for female players when linear regression is applied. This suggests that coaches and talent scouts should integrate automated methods alongside their evaluations to enhance the accuracy of predicting young players' performance, thereby adopting a more data-driven approach.  
In identifying significant variables, both morphological and motor characteristics, such as body height (M-1) and acceleration (S-1), have been recognized as crucial by both coaches and automated methods. The advantage of body height in tennis is evident in its contribution to reaching higher contact points and performing various strokes, while acceleration significantly explains the variance in competitive performance. Thus, talent identification programs should prioritize the assessment and development of these key characteristics to ensure competitive readiness [45].  
[PERSON] et al. [48] emphasized that combining different statistical prediction methods, including non-linear methods, such as neural networks, with linear methods, yielded the highest prognostic validity. Despite this, some high-performance players may still elude prediction by any method. Therefore, talent identification programs should utilize a combination of linear and non-linear methods to improve prediction accuracy, offering a more comprehensive assessment of a player's potential.  
Participation in elite junior tournaments has been identified as crucial for future career development, with a significant proportion of participants achieving professional rankings [38]. Coaches should, therefore, encourage and facilitate participation in these tournaments, as they are pivotal for long-term development and success. Additionally,continuous data collection and model refinement are essential for improving predictive accuracy. Expanding the research scope to include more variables and a larger number of junior tournaments can enhance the predictive models. This approach will help identify the most critical variables for career prediction, ensuring models remain relevant and accurate over time [38].
# An OCARI-Based Wireless Sensor Network for Heat Measurements during Outdoor Fire Experiments
## 2 Wireless Sensors Networks (WSN) in the IEEE 802.15.4: Zigbee and OCARI Protocols
### General Information
#### 2.1.1 Related Works in Fire Studies  
Before introducing each stack in order to exhibit the advantages of OCARI vs. Zigbee, let us first observe that there is no previous attempt to perform continuous measurements through a WSN strategy in the frame of fire studies. Indeed, the present study aims to break the technological limitation of the incorrect timestamping of data transmitted for measurements versus a real scale fire using a WSN. Even if it is non-exhaustive, the list [7; 8; 9; 10; 11] illustrates that the literature only reports on wireless sensors systems for fire detection in the open or in harsh industrial environments. The continuous measurements of physical quantities (heat, air content of moisture) is rarely addressed with a wireless sensor network. The reasons must be explained in this paragraph. The IEEE 802.15.4 radio communication protocol for microcontroller-based networks can work according two modes: As presented in Figure 1, the nodes can either be set to work as a full function device (FFD), i.e., each node is able to route the digital information towards accessible neighbours, or they work as end-devices (reduced function device in Figure 1), just sending a message to a local FFD. The FFD mode is energy-consuming because routes are frequently recomputed by the stack for each network. A measurement protocol necessarily requires this mode because the digital information must continuously flow through the network despite a constantly possible breakdown of a node in the routing protocol. However, in an FFD configuration, this wireless measurement solution is energy-consuming, as more of the radio environment is polluted by external perturbations than in a fire. This is why in practical cases, and except our tests in Reference [6] with a Zigbee-based solution, there is no paper illustrating the use of IEEE 802.15.4 for measurements during fire experiments. In Reference [6], the energy consumption by node approaches 1 Ah/day, in a star topology with a Zigbee-based heat measurement system. That illustrates the IEEE 802.15.4 can only be set up as an FFD for short-time measurement campaigns (about 1 day) and that the long-term autonomy of the Zigbee network imposes an RFD parametrisation of the network. The list [7; 8; 9; 10; 11] reports on fire monitoring with a WSN solution where the protocol only sends radio packets when a fire causes the measurements to change over certain values [7] or for fire detection only [8; 9; 10; 11]: Both concern the RFD configuration of the node network contrary to ours in Reference [6].
## 6 Directional Scattering Characteristics: Forward and Backscattering Conditions
### The Asymmetry Factor
For instance, the asymmetry factor of a small sphere (\(x=0.5\)) is illustrated in Figure 9, where the real and imaginary values of the complex \(a_{1}\) and \(b_{1}\) terms are also plotted. It is clear that a maximum forward scattering occurs for the case where the imaginary terms are equal implying a zero phase difference between the coefficients. Similarly, the maximum backscattering happens at the position where the imaginary parts are equal but with different signs. Interestingly, the lineshape of the \(b_{1}\) coefficient exhibits a Lorentzian-like distribution, as seen in Figure 9. The asymmetry factor exhibits a Fano-like pattern, occurring by the interference of \(a_{1}\) and \(b_{1}\) distributions [21].
### Method for Assessing Earthquake Fatalities Based on Machine Learning
XGBoost can automatically handle feature selection and missing values, effectively modeling complex nonlinear relationships among variables such as magnitude, depth, population density, building structure, and terrain. Additionally, it offers high training efficiency, ease of parameter tuning, and the ability to output clear feature importance rankings, significantly improving the model's interpretability and practicality. Furthermore, XGBoost's mechanisms for automatically handling missing values and feature selection make it particularly suitable for modeling environments with multisource, heterogeneous, and noisy data, demonstrating strong adaptability and robustness in small-sample, high-dimensional, and nonlinear problems. Ultimately, this paper constructs an integrated modeling framework based on PCA dimensionality reduction, WOA parameter optimization, and XGBoost prediction. This approach not only leverages XGBoost's local fitting capability for modeling complex nonlinear relationships, but also combines WOA's global optimization characteristics with PCA's data simplification ability, demonstrating higher prediction accuracy and stability in real-world earthquake fatality prediction tasks. Experimental results show that this method outperformed traditional models in terms of generalization performance and robustness, providing strong support for earthquake disaster risk assessment and emergency response.
## 2 Materials and Methods
### Data Reduction Equations
#### 2.2.1 Dehumidification Index  
The characterization of the dehumidification system was examined by calculating the dehumidification index for adsorption and desorption process. To characterize the dehumidification index, firstly the calculation of adsorption and desorption amount of the desiccant was examined. The amount was defined as the water content removal that can be considered as the difference between the humidity ratio inlet and humidity ratio outlet. The following expressions can calculate the water content removal for each process:  
\[d_{ads}=\ X_{ads,in}-\ X_{ads,out},\ \text{for adsorption} \tag{2}\]  
\[d_{des}=\ X_{des,in}-\ X_{des,out},\ \text{for desorption} \tag{3}\]  
where \(d\) is the moisture removal capacity (kg/kg), \(X_{in}\) is the humidity ratio of the inlet air (kg/kg), and \(X_{out}\) is the humidity ratio of the outlet air (kg/kg), whereas _ads_ and _des_ indicate the adsorption and desorption processes. In the case of the desorption process, because the hot air brought about water vapor during the running of the system, the outlet humidity was thus higher than in the inlet, and the moisture removal became negative.  
During the adsorption process, the average dehumidification index (\(D_{i}\)) was defined as:  
\[D_{i}=\frac{1}{t}\int\limits_{0}^{t}d_{ads}dt \tag{4}\]  
where \(t\) is the dehumidification time (s).
## 3 Results and Discussion
It can be observed that the \(\kappa_{\rm L}\) of 1.4%TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) sample was significantly reduced before 450 K. The higher \(\kappa\) of 1.4%TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) in the temperature of 450-700 K is mainly due to the high \(\kappa_{\rm e}\), which is closely related with the distribution of nano-TiO\({}_{2}\) phase in the internal grain. The expected decrease of \(\kappa_{\rm L}\) due to the addition of nano-TiO\({}_{2}\) is not evident in other TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites, which is possibly attributed to the distribution of nano-TiO\({}_{2}\) in the grain boundaries. However, the \(\kappa_{\rm L}\) of 1.4%TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) sample is 1.43 W/mK at room temperature, which is now the lowest value of lattice conductivity for Cu\({}_{2}\)SnSe\({}_{3}\) sample and obviously lower than the previous studies. [PERSON] et al. reported a lattice thermal conductivity of about 2.25 W/mK at room temperature for Ga-doped Cu\({}_{2}\)SnSe\({}_{3}\) sample fabricated by hot pressing sintering [10]. Compared  
Figure 8: Temperature dependence of power factor (PF) for the TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites. Figure 7: Temperature dependence of Seebeck coefficient for the TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites. with the Ga- or In-doped Cu\({}_{2}\)SnSe\({}_{3}\) sample, the lower \(\kappa_{\rm L}\) of TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) sample in this study should be related with the distribution of TiO\({}_{2}\). Figure 10 shows the temperature dependence of the dimensionless figure of merit \(ZT\) of TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites. It can be seen that the \(ZT\) value of 1.4%TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composite was not improved due to its higher \(\kappa\). The maximum \(ZT\) value for 1.0%TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composite is 0.30 at 700 K, which is 17% higher than that of Cu\({}_{2}\)SnSe\({}_{3}\) matrix. Figure 10: Temperature dependence of \(ZT\) for TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites. Figure 9: (**a**) Temperature dependence of total thermal conductivity and (**b**) lattice thermal conductivity for the TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites.
## 3 Discussion
Consequently, the role of Fis in regulating the expression of other _lap_ genes or the peptide-dependent regulation needs to be further investigated. The LapA vWFa domain of _P. fluorescens_ has been previously described as essential for hydrophilic surface binding [10; 32]. Our research has shown the importance of poly-L-lysine, a hydrophilic positively charged biomolecule for _P. putida_'s LapA vWFa-domain (Figure 7). Notably, hydrophilic Na-cellulose and PGA, which are negatively charged biopolymers, have no direct relationship to biofilm enhancement via vWFa (Figure 4F,G). Referring to our previous study, neither uncharged poly-DL-alanine nor negatively charged poly-L-glutamate had a biofilm-enhancing effect on _P. putida_ F15 [19]. However, the positively charged spermine enhanced _P. putida_ F15 biofilm similarly to poly-L-lysine [19]. Moreover, the F15-Awwf\({}^{-}\) strain entirely or largely lost the positive effect of the peptides on biofilm formation (Figure 7C). Therefore, the surface's hydrophilicity and positive charge appear to be necessary for the binding LapA by the vWFa domain to the surface. However, we cannot say whether vWFa is specific for a particular sequence or whether it binds to a surface only by its electrostatic forces, which is an issue for future research to explore. However, how precisely LapA binds to the surface has not been elucidated. One explanation may be the importance of other LapA domains for surface binding. For example, the F15 AlapA and F15-Awwf\({}^{-}\) biofilms in the medium M9-0.2 CAA+pK form biofilm differently (Figures 2 and 7C). The complete absence of LapA eliminates the Fisher enhancing effect for biofilm, whereas LapA without the vWFa domain is significantly reduced but does not entirely abolish it. Thus, binding through another LapA domain may be necessary. However, it may simply be an electrostatic effect appearing in excess of adhesin and biopolymer. Due to the increased amount of Fis in _P. putida_ F15, the cell surface remains hydrophilic, the amount of LapA increases, and the amount of LapF decreases drastically (Figure 3) [2]. The resulting biofilm formation may be promoted through non-specific binding between the positively charged poly-lysine and LapA. In conclusion, this research was initiated because we had previously determined that LapA, extracellular peptides, and increased Fis in the cell promoted _P. putida_ biofilm formation. The question was whether these factors are interrelated. The LapA domain of vWFa was found to be essential for biofilm enhancement when peptides were added to the medium. It was particularly evident in _P. putida_ with an increased amount of Fis. This so-called biofilm-enhancing Fis effect wholly or partially depends on the LapA vWFa domain. In addition, it seems that the most crucial regulatory step in the _lapA_ expression and maintenance of LapA on the cell surface is the post-transcriptional and not the transcriptional step.
# STAVOS: A Medaka Larval Cardiac Video Segmentation Method Based on Deep Learning
## 3 Methods  
### Spatiotemporal Attention Mechanism  
Depending on the guidance provided for the target object, VOS can be classified into subclasses such as unsupervised VOS, reference VOS, and semi-supervised VOS. Since the cardiac pulsation of medaka fish causes surrounding areas to pulsate collectively, resulting in the non-uniqueness of moving objects in the video, we can only employ semi-supervised VOS. The primary distinction between semi-supervised VOS and unsupervised VOS lies in the fact that semi-supervised VOS requires additional dense annotation information for one frame in the video sequence. In this paper, we define the frame requiring this additional annotation as the guiding frame. Traditional semi-supervised VOS approaches commonly designate the first frame as the guiding frame. Some studies [17] have demonstrated that choosing the first frame as the guiding frame may not necessarily be the optimal choice. To address the question of which frame is the optimal choice for the guiding frame, we propose a method based on spatiotemporal attention mechanisms.  
The video exhibits dynamic variations, with each frame containing varying levels of informational value, influencing the overall segmentation performance of the entire video sequence to different extents. The objective of VOS is to segment specific dynamic objects within the video, with the primary challenge residing in the precise segmentation of target edges. Guided by the concept of the spatiotemporal attention mechanism, we concentrate solely on the target edge region across the entire spatial extent of the image. Define the mean gradient of the target edge region in the image as the spatial feature value. The larger the mean gradient, the more pronounced the edges of the image. Throughout the temporal sequence of the entire video, our attention is specifically directed towards the frame with the highest spatial feature value. Ultimately, frames derived from this spatiotemporal attention mechanism, as per this ideology, are termed spatiotemporal attention frames (STA frames). Due to their possession of the highest spatiotemporal feature values, we designate the STA frame as the guiding frame for the semi-supervised VOS proposed in this paper. This choice strengthens the model's comprehension of edge features, thereby enhancing the segmentation performance of the model.
# Fault Detection of Stator Inter-Turn Short-Circuit in PMSM on Stator Current and Vibration Signal
## 4 Discussion  
With the fault characteristics extracted from current signal and vibration signal analyzed synthetically, the fault of stator inter-turn short circuit of PMSM can be judged accurately. However, after extracting fault features, human beings are still necessary to identify them. Artificial intelligence technology needs to be used in the research of motor fault diagnosis in the future.
## 1 Introduction
Sometimes, the plants do not show any symptoms of disease (asymptomatic) in the aboveground plant region [23; 25]. The roots cannot support the aboveground plant regions and they start to die. Hence, there will be less photosynthetic activities in the plants and the plant slowly starts to die. A study [26] on symptomatic and asymptomatic effects of this disease has been conducted in 15 plant species. There are many existing methods [27; 28] to identify the presence of Cylindrocarbon destructans, but these methods are destructive in nature. The root rot disease has been identified from the plant leaves using hyperspectral leaf images and machine learning techniques [25]. The root images are analyzed using feature extraction methods [29] and deep learning techniques [30] to find the root rot disease in Lentil. In order to obtain the root images, the plants are removed from the pots and the root images are captured. Hence, non-destructive identification of root rot disease is necessary to prevent a reduction in crop yield. In order to develop a low-cost, high-throughput, non-destructive, image-based analysis model, RGB images can be used. It is easy to collect numerous RGB images at a low cost and in a short time. Furthermore, various analyses are possible with these numerous data. The visible information from the collected RGB images helps in identifying the state and morphological changes in the plants through RGB-image-based plant phenotyping. Nowadays, deep learning models are widely used to solve various complex real-time problems. Furthermore, their usage is increasing in solving agriculture-related problems. Among them are deep-learning-based plant phenotyping methods. The deep learning models can be applied to the collected data to perform various analyses. In the proposed method, various deep learning models are applied to identify biotically stressed (root rot) Korean ginseng plants based on RGB plant images. Furthermore, a new simple linear deep learning model is proposed. The proposed method involves three steps: (i) a region growing method for plant region segmentation, (ii) noise removal, and (iii) deep learning models for identifying biotically stressed (root rot) ginseng plants. In the first phase, the plant region is segmented from the raw RGB images using the seeded region growing method. Then, in the second step, the noise regions are removed from the segmented plant region. Finally, these noise-free segmented plant images are given as input to the proposed deep learning model to identify root-rot-diseased ginseng plants. The main advantages of the proposed method are that it (i) is non-destructive, (ii) is high throughput, (iii) does not require ground-truth images, (iv) is robust, and (v) is cost effective. The structure of the remaining portion of this paper is as follows: Section 2 describes the materials and methods. Section 3 explains the results and provides a discussion, and finally, conclusions are given in Section 4.
## 5 Discussion
### CNN and Other Classifiers  
As presented in Section 5.1, there are many studies related somehow to the one we present now. We considered it appropriate to compare some of the classifiers those works present that are not CNN. To compare the obtained results from the CNN with other classifiers, we used support vector machines (SVM) and [PERSON]'s discriminant analysis (FDA) for all of the available trial lengths. Details for how we implemented these two classifiers can be found in the Appendix. Table 5 shows the comparison between the accuracy, precision and recall results obtained from the SVM, FDA and the CNN model that achieved the highest accuracy rate in four different trial intervals: 200, 300, 400 and 500 ms.  
The results from CNN with the highest accuracy compared to those of the SVM or the FDA are clearly higher, and this difference decreases if we take into consideration the lowest accuracy obtained by one of our CNN models, which is 0.783, with a precision of 1.0 and a recall of 0.618 for the 200-ms trial interval.