esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Sports science and medicine (SSM) is integral to professional football clubs. The level below professional football in England, 'non-league football' (NLF), consists of full-time and part-time clubs. The existing literature has exclusively focused on SSM in professional football, with the resources and practices in NLF currently unknown. Therefore, this study explored the SSM resources and practices within NLF by investigating the perspectives of key stakeholders working within NLF coaching and SSM disciplines. Methods: Fifty participants (coaching practitioners [_n_ = 25] and SSM practitioners [_n_ = 25]) from NLF clubs completed an anonymous online survey comprising 31 multiple-choice and Likert-scale questions, alongside optional open-ended comments. Results: Support was mixed for SSM evidence-based practices across clubs in Tiers 5-10 within the National League System. The most common SSM resources were the training ground (_n_ = 39), resistance training equipment (_n_ = 15), and rehabilitation area (_n_ = 13). Fitness testing was frequent (86%) pre-season but rare end-of-season (8%). Workload monitoring primarily consisted of the session duration (80%) and time-motion data (36%). Performance analysis of competitive matches commonly used video (74%) or post-match technical analysis (40%). Injury monitoring generally occurred 'always' (44%) or'sometimes' (28%). Nutritional support on match days was mostly fluids (80%), with 'no support' reported most outside match days (54%). Conclusions: The SSM resources and practices vary considerably within NLF, influenced by individual club constraints and barriers, including financial support, access to facilities, and equipment availability. These findings may inform future SSM provisions in NLF to enhance team performances and player availability.  
sccerc; semi-professional; match analysis; injury prevention; applied practice 2020
# Finite Element Analysis of Proposed Self-Locking Joint for Modular Steel Structures
## 5 Parametric Study on the Proposed Connection
### Ductility and Deformation Capacity  
Seismic design codes provide a well understanding of ductility and deformation capacity of the joint and guides for the appropriateness for seismic application. A drift ratio of 0.04 radian is required for Special Moment Frame (SMF) while 0.02 radian for Intermediate Moment Frame (IMF) in AISC seismic provisions [33]. Table 7 presents the related capacities of joint produced by all developed models. It comprises \(M_{pi}\), which is defined as predicted moment capacity of joint which can be obtained by equation (14) [17]. \(M_{y}\) is defined as yield moment, which taken as two-thirds of maximum moment (\(M_{u}\)) [36], \(M_{0.04}\) is moment at 0.04 radian, the ratio between \(M_{0.04}/M_{u}\), the angular displacement  
Figure 10: (a) Effect of bolt pretension. (b) Effect of coefficient of friction \(\mu_{f}\).  
coefficient (\(\Delta_{d}\)), which is the ratio between maximum inter-story drift (\(\Theta_{y}\)) and yield inter-story drift ratio (\(\Theta_{u}\)).  
It can be concluded from results provided in Table 7 that all joints were able to accommodate an inter-story drift ratio of a minimum of 0.02 radian at yielding except joint Cp5. Moreover, all joints produced a good performance in respect to drift ratio at yield and can maintain more than 80% of the maximum bending resistance beyond a deformation of 0.04 radian, as required for SMF systems in AISC. The ductility factor having a value of 2.5 is considered good plastic deformation ability of modular steel building connection [22]. Table 7 summarized the ductility factor, \(\Delta\) of joints presented in this study. The maximum value of \(\Delta_{d}=3.5\) was observed for joint Cp5. The average ductility factor of all joints was 2.9, which showed a satisfactory deformation pattern required for seismic design.  
\[M_{pi}=M_{pcb}+M_{pfb} \tag{14}\]  
\[M_{p}=f_{y}*\mathrm{Z} \tag{15}\]  
where \(M_{pi}\) is the predicted bending resistance of the connection; \(M_{pcb}\) and \(M_{pfb}\) are the nominal moment capacity of the ceiling beam and floor beam, respectively, \(f_{y}\) is yield stress and \(Z\) is the plastic section modulus.
## 3 Proposed Method
### Autoencoder for Feature Extraction from the Voice Data  
Architecture of the Autoencoder  
Figure 3 and Table 2 show the structure of the autoencoder for feature extraction and details on the hyperparameters of the autoencoder, respectively. In this paper, an autoencoder is used to extract features from the voice data. In DeepDetection, this autoencoder is used for preprocessing of user authentication and deep voice detection. The structure of the autoencoder consists of a Conv1d layer, MaxPool1d layer, and MaxUnpool1d layer. The Conv1d layer is a convolutional layer for sequential data, extracting the features of the input data and making a feature map from them. MaxPool1d is a maxpooling layer for the sequential data which reduces the dimensions of the feature map and prevents overfitting. MaxUnpool1d is opposite of MaxPool1d, and it is used to restore the dimensions reduced through MaxPool1d's encoder in the decoder. The encoder consists of two Conv1d layers and one MaxPool1d layer. The input, hidden, and output channels of the Conv1d layers are 1, 16, and 8, respectively. The length of the data is reduced to 1000, 988, and 978 through the Conv1d layers. The output values of Conv1d go through the ReLU function, which is an activation function that adds nonlinearity, and then are used as the input values of the MaxPool1d layer. The kernel size and stride of MaxPool1d are 6, and the output value has a data length of 163. The decoder consists of two Conv1d layers and one MaxUnpool1d layer. The input is the same as the output of the encoder, and the shape of the decoder is identical to the encoder. Training was performed in 100 epochs, and we achieved an average loss of 0.00076 in classification for 20 users and the fake voice.
# Explainable Machine Learning for Mapping Rainfall-Induced Landslide Thresholds in Italy
## 1 Introduction
Building upon the work of these previous studies, this study used machine learning to extend empirical rainfall threshold models by incorporating multiple environmental factors that potentially influence rainfall thresholds and employed explainable methods to ensure model transparency. In this way, an explainable landslide prediction model was developed that improved prediction accuracy and provides a reference for establishing regional rainfall thresholds. Specifically, our study includes the following contributions:* We compared the predictive performance of empirical threshold models with four machine learning models on imbalanced datasets. The XGBoost model achieved optimal overall predictive performance and effectively balanced sensitivity and specificity;
* The SHAP methodology was employed to enhance the interpretability of the machine learning model, which helped clarify the model's decision-making process. The results indicate that hydrological factors, particularly total rainfall, play a central role in the modeling process. * Using the trained XGBoost model, we generated rainfall threshold maps for the Italian region under three different probability scenarios. * This study established an intuitive and practical modeling framework for regional rainfall threshold development, offering reference for subsequent studies.
# Cyclic Elasto-Plastic Behaviour of 51 CrV4 Steel for Railway Parabolic Leaf Spring Design
## 2 Cyclic Elasto-Plasticity for Steels
### Kinematic Hardening Model  
In several investigations of cyclic hardening behaviour of steels, the Chaboche hardening model has been used. Chaboche kinematic hardening was developed considering the [PERSON] plasticity criterion (Equation (1)). As an advantage, the Chaboche model can predict transient effects such as cyclic hardening or softening, ratcheting, mean stress relaxation, etc. The Chaboche kinematic hardening model is an improved model from the Amstrong and Frederick, AF, model, which itself is an improved model from the linear hardening rule [35], developed by [PERSON] [35], by introducing the concept of dynamic recovery [40]. In the Chaboche model [41], the back stress tensor, \(\mathbf{\alpha}\), is given by a sum of the \(i\)-th component of the back stress tensor, \(\mathbf{\alpha}_{i}\), such that  
\[\mathbf{\alpha}=\sum_{i=1}^{N}\mathbf{\alpha}_{i}, \tag{3}\]  
with the variation of each individual back stress tensor, \(\dot{\mathbf{\alpha}}_{i}\), given as  
\[\dot{\mathbf{\alpha}}_{i}=\frac{2}{3}C_{i}\dot{\epsilon}^{p}-\gamma_{i}\mathbf{\alpha }_{i}\dot{p}. \tag{4}\]In Equations (3) and (4), \(C_{i}\) and \(\gamma_{i}\) are fitting parameters, and \(\dot{p}\) represents the effective plastic strain rate, which is calculated as  
\[\dot{\textbf{p}}=\sqrt{\frac{2}{3}\dot{\textbf{$k$}}^{p}\colon\dot{\textbf{$k$}}^ {p}}. \tag{5}\]  
The Chaboche kinematic hardening model permits overcoming some constraints pointed out by the Prager and AF models, such as the difficulty in obtaining an accurate behaviour representation for large deformation regimes and the transition from elastic to plastic regimes [41]. Several investigations have proved that considering only three components can reach good predictions of ratcheting behaviour [41; 54].
## 3 Results and Discussion
### Fracture Analysis  
The analysis of the fracture surfaces after tensile testing already reveals a macroscopic difference, as can be seen in Figure 12. The bright, as-sintered specimens in Figure 12a show very little plastic deformation at the fracture location. In contrast, the fracture surfaces of the dark HIP-compacted specimens in Figure 12a appear more ductile, as reflected by the rough surface and the 50% increase in fracture strain in the test results.  
The high-resolution analysis of the fracture surfaces in SEM shows for the fracture surface in the as-sintered condition (Figure 13a) parts of cleavage fracture, whereas after HIP (Figure 13c), an almost completely ductile honeycomb fracture is present. In both conditions, the oxide inclusions already detected in the cross-sections are found on the fracture surfaces (Figure 13b,d).  
Again, EDX analysis confirms that these are the previously identified Si-Mn-Al oxides. The EDX analysis shown in Figure 14 was performed on the fracture surface of a HIP post-densified specimen.  
While almost no oxide inclusions are seen in the cross-sections of the as-sintered specimens, they can be found on the fracture surface of these specimens. Although it seems that the inclusions before and after HIP treatment do not differ in size and shape, it appears that there are more inclusions on the fracture surfaces of the HIP specimens. However, this was not quantified and may also be due to the different fracture types; as in the more ductile fracture of the HIP specimens, inclusions are generally very well visible within the honeycombs.  
Figure 12: Macroscopic fracture appearance of tensile specimens in (**a**) the as-sintered and (**b**) the after HIP condition, illustrated by photo and exemplary secondary electron image.
# Effects of Visually Induced Self-Motion on Sound Localization Accuracy
## 4 Discussion
Nevertheless, our results indicate that although the difference was small, the accuracy of sound localization did not improve, but rather degraded. This study shows that sound localization is compromised by self-motion perception under section and that self-motion perception is induced only by visual, i.e., crossmodal information. This could mean that the perception \"I am moving\" affects sound localization, based on the sensory modality of the information input. Such a top-down effect is related to crossmodal effects or simultaneous multimodal stimulation [34, 35, 36, 37]. Such phenomena have been reported previously for visual-auditory [34, 35], visual-tactile [36], and visual-vestibular [37] interactions. For instance, it has been reported that adaptation to optic flow leads to subsequent nonvisual self-motion perception [37]. Our present study will help further the understanding regarding the influence of the visual, proprioceptive, and vestibular systems on sound localization. This study has several limitations. Our sample size was small, and the differences in JNDs between the three conditions were not large. We did not manipulate the strength of the self-motion and did not examine the relationship between sound localization accuracy and section strength. Future studies should consider addressing these questions. Nevertheless, our findings demonstrate that sound localization accuracy deteriorates while listeners perceive visually induced self-motion without physical movement. This observation may be helpful in designing efficient VR applications, including dynamic binaural displays, as fewer computational resources can be assigned while listeners are in motion or perceived self-motion. **Author Contributions:** All authors were involved in study design, data analysis, and data interpretation. [PERSON], K.M. and S.S. contributed data acquisition. A.H., S.S. and [PERSON] were involved in writing and funding acquisition. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Ministry of Education, Culture, Sports, Science, and Technology Grants-in-Aid for Scientific Research (A) (No. 16H01736) and (B) (No. 19H04145). This research was partly performed under the Cooperative Research Project Program of the Research Institute of Electrical Communication, Tohoku University (No. H29/A22 and R02/A32). **Institutional Review Board Statement:** The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the ethics committee of the Research Institute of Electrical Communication at Tohoku University. **Informed Consent Statement:** Written informed consent was obtained from all participants s involved in the study. **Data Availability Statement:** The data that support the findings of this study are available from the corresponding author upon reasonable request. **Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Empirical Results
### Spatial Distribution of GTFP Scores  
Mapped areas of the spatial distribution of the GTFP scores of 30 provinces are depicted in Figure 3.  
The differences between Figures 1 and 3 show the changes in the GTFP scores of each province for the four time intervals (2002-2006, 2007-2010, 2011-2014, and 2015-2018). From 2002 to 2006, among the 30 provinces, 17 provinces have GTFP growth, and are mainly located in northern China, whereas the GTFP scores of the remaining 13 provinces are less than 1, implying a decline in GTFP. Most of the 13 provinces are underdeveloped  
Figure 3: Spatial distribution of GTFP growth rates by province from 2003 to 2018.  
western ones. During the period, Guangdong, Shanghai, Beijing, and Tianjin have the highest average annual growth rate of GTPP. Later, from 2007 to 2010, most provinces with the highest GTPP growth are concentrated in the economically developed eastern region. In addition, three provinces in northwestern China, Qinghai, Ningxia, and Xinjiang, witness high GTPP growth during this time. From 2011 to 2014, in general, southern provinces have high GTPP growth, whereas the GTFP scores in northern provinces decrease, except for Jilin, Beijing, Shandong, and Shaanxi provinces. From 2015 to 2018, most provinces in China have rapid GTFP growth, with the exception of Hebei, Heilongjiang, Gansu, and Xinjiang; the implication here is that China's overall GTFP has greatly improved during the period. Notably, Beijing, Shanghai, Jiangsu, and Zhejiang witness the highest GTFP growths.  
To better understand the overall changes in the GTFP scores of the 30 provinces from 2002 to 2018, we have mapped the spatial distribution of annual change rates in Figure 4.  
Figure 4 shows that provinces with the lowest GTFP growth rates are mainly in the underdeveloped northern region, including the northeastern provinces, Inner Mongolia, Hebei, Shanxi, and Gansu, and one western province (Guangxi). The GTFP in these provinces has likely decreased from 2002 to 2018, indicating that they suffered from low economic growth rates as well as a reduction in GTFP. In contrast, provinces with the  
Figure 4: Spatial distribution of GTFP change rates by province.  
highest growth rates, Beijing and Shanghai, are the two most developed municipalities. The GTFP of six provinces (Tianjin, Jiangsu, Zhejiang, Shaanxi, Chongqing, and Guizhou) also increases rapidly across the study period. Finally, we observed that provinces with a GTFP lower than 1.000 were mostly located in central China. From the above analysis, we can conclude that the changes in GTFP growth of 30 provinces are not randomly distributed but instead present as strong spatial clustering. The spatial spillovers of GTFP should therefore be incorporated into the model. Otherwise, biased conclusions are likely to result.
# TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image
## 3 Results
### Comparative Efficiency Analysis  
To assess the efficiency of our method relative to the original SinGAN model, we conducted experiments where the model processed input images of varying dimensions: \(175\times 240\), \(350\times 400\), and \(500\times 500\). The SinGAN model's architecture, dictated by a scale factor parameter set by default to 0.75, generated 8, 11, and 12 scales for the respective image dimensions. At each scale of the pyramid, the training process ran for 1000 iterations. In all three experimental setups, topology loss was deliberately excluded during the first four scales to allow the model to form basic topological features without constraints. It was then introduced at finer scales (5-12) to refine the details while preserving structural integrity. All other configurations and parameters remained consistent with those detailed in Section 1.1. The experiment involved running the model on each image size ten times. Table 4 outlines the average recorded execution times for each configuration. On average, our TopoSinGAN model demonstrated an approximate 9.41%, 7.29%, and 5.98% increase in training time compared to the baseline SinGAN model for 175 \(\times\) 240, 350 \(\times\) 400, and 500 \(\times\) 500 dimensions, respectively.
## 3 Mathematical (CFD) Model of the Vertilated Solar Wall
### Governing Equations  
The conservation equations for the fluid in the air cavities are written as:  
* Mass conservation equation: Continuity equation \[\frac{\partial\rho}{\partial t}+\
abla(\overset{\rightarrow}{\rho}\overset{ \rightarrow}{V})=0\] (7) where \(\overset{\rightarrow}{V}=u\overset{\rightarrow}{i}+v\overset{ \rightarrow}{j}\) is the fluid velocity.
* Momentum conservation equation: Navier Stokes equation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \(\xi_{v1}\) & \(\xi_{v2}\) & \(\xi_{m}\) \\ \hline Correlations & 0.0164 & 0.1532 & 0.6139 \\ \hline WINDOW 7 & 0.017 & 0.1468 & 0.6171 \\ \hline Error (\%) & 3.71 & 4.38 & 0.43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison between the values of the solar radiation absorption coefficients for the two glazings and the solar wall calculated by the correlations and those obtained with the WINDOW software.  
This equation (derived from [PERSON]'s second law) states that the variation in a fluid cell's momentum is equal to the total of all forces acting on it.  
\[\frac{\partial\overset{\rightarrow}{V}}{\partial t}+(\overset{\rightarrow}{V} \cdot\overset{\rightarrow}{grad})\overset{\rightarrow}{V}=-\frac{1}{\rho} \overset{\rightarrow}{grad}P+\
u\Delta\overset{\rightarrow}{V}+\overset{ \rightarrow}{g} \tag{8}\]  
* Energy conservation equation According to the first law of thermodynamics, energy can be neither created nor destroyed. \[\rho C_{p}(\overset{\rightarrow}{V}\cdot\overset{\rightarrow}{grad}T)= \lambda\Delta T\] (9)  
The air is considered a Newtonian fluid with thermophysical properties \(\lambda\), \(\rho\), \(\mu\), and \(C_{p}\) dependent on temperature (K), see Table 5.
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
## 3 Social-Aware-Based Resource Allocation  
The social-aware-based NOMA-enhanced D2D resource allocation proposed in this paper includes three steps: (1) form D2D clusters according to certain rules, (2) allocate channels for D2D clusters, and (3) optimize transmit power for D2D users in D2D clusters. The formation of clusters mainly considers the user's content requirements, physical location, and social relationship with other users. When D2D clusters are formed, the many-to-one matching game is utilized to allocate channels for D2D users. After converting the nonconvex optimization problem to a convex problem via SCA, GA is performed to solve the D2D transmit power optimization.
# The ConTAD Project: Detection of Multi-Container Loss for Safety of Navigation +
## 4 Closing the Technological Gap: The ConTAD Approach
### Power Management  
Regular tracking units send position updates once or twice a day. Their energy efficiency allows for multiple years of operation without battery changes. It can be assumed that a tracking unit with container loss detection must possess similar properties to be accepted on the market while still being able to perform the previously described operations in the event of container loss. To achieve this, it is necessary to consider resource sharing  
Figure 3: Communications overview for the ConTAD project.  
in the mobile ad hoc network, as outlined in Section 4.5. Tracking units with sufficient remaining battery capacity should perform power-intensive tasks, such as external communication. Moreover, the potential use of energy-harvesting methods, such as solar cells, should be explored.
## 4 Results of Probabilistic Stability and Performance Assessment
### Inverter Power Level Impact on System Stability and Performance
#### 4.2.2 Damping Ratio Analysis  
As shown in Figure 8b, the grid inductance uncertainties significantly affected the damping ratio in the weak grid and had the least affect in the solid one. For the weak grids, the minimum damping ratio was highly influenced by the fast current control loop (CC). Therefore, by increasing the inverter power level, the SCR that was seen by the current controller under the same grid inductance reduced; thus, the damping ratio improved. The fast dynamic parts, such as the highpass filter (HPF) and control system delay, also contributed to the minimum damping ratio for the strong grids.
# Camera Geolocation Using Digital Elevation Models in Hilly Area
###### Abstract  
The geolocation of skyline provides an important application in unmanned vehicles, unmanned aerial vehicles, and other fields. However, the existing methods are not effective in hilly areas. In this paper, we analyze the difficulties to locate in hilly areas and propose a new geolocation method. According to the vegetation in hilly area, two new skyline features, enhanced angle chain code and lapel point, are proposed. In order to deal with the skyline being close to the camera, we also propose a matching method which incorporates skyline distance heatmap and skyline pyramid. The experimental results show that the proposed method is highly effective in hilly area and has a robust performance against noise and rotation effects.  
enhanced angle chain code; lapel point; distance heatmap; skyline pyramid Article