esa-sceva/satcom-chunk-collection huggingface dataset:
# The Relationship between Training Load Measures and Next-Day Well-Being in Rugby Union Players
## 1 Introduction
Collectively, previous research suggests that following match-play and during periods of high-intensity training, both countermovement jump (CMJ) metrics and well-being questionnaires are sensitive to changes in players' fatigue. Importantly, descriptive trends do not identify the presence of a relationship between load and fatigue measures. Therefore, to better understand the suitability of commonly used TL metrics to influence well-being, the dose-response relationship needs to be assessed. During the competitive in-season, fatigue is typically managed to optimise players' match performance. Leading into a match, coaches typically look to taper training to allow players to recover and maximise match performance [16]. This 'tapering' process is achieved by altering several components such as training volume, intensity, and frequency [13] alongside the duration of the taper. Meta-analysis of tapering strategies has identified that reducing training volume was most sensitive to improving performance [13, 14]. Reductions of 41% to 60% in training volume over 8 to 14 days have demonstrated an average improvement of 1.96% in performance measures in individual sports (runners, cyclists, rowers, swimmers and triathletes) [13]. The challenge in team sports is the need to manage tapering for matches on a weekly basis, where an 8 to 14-day taper isn't practical. Consequently, within team sports there is a need to better understand players' response to daily training loads so that coaches can evaluate and adapt the subsequent days training to ensure players are sufficiently recovered to optimise their match performance that week. By identifying the relationship between a range of commonly used internal and external load measures and next day subjective well-being would help inform coaches on the changes in TL required to elicit a desired response in players' well-being. This 'tapering' process is achieved by reducing the TL in the final days before a match to allow players to recover from the higher TL experienced earlier in the training week. The challenge for coaches is to improve our understanding how much recovery a player needs to optimise their match-day performance. Therefore, understanding the relationship between measures of TL and next-day well-being during the competitive in-season can help improve coach's ability to better understand players recovery. Therefore, the aim of the study was to examine the changes in players' well-being following the previous days' training during an in-season period. If next-day wellbeing demonstrates a strong relationship with the previous days training coaches could use the information to decide if a player requires more recovery and thus adapt the subsequent training that day. On the other hand, if such relationships are absent it questions the use of both dose and response measures used to suitably inform such a process.
## 1 Introduction
* A dual-loop control framework that integrates kinematic and dynamic models in the inner and outer loops, respectively, is proposed. A decoupled control method including a yaw rate controller and a speed controller is utilized to achieve the tracking target of a reference path with a desired speed. * An RBF neural network is employed for yaw rate control to realize adaptability to longitudinal slipping and skidding caused by complex terrain. The remainder of this paper is organized as follows: In Section 2, a kinematic model of a skid-steer wheeled robot is formulated with the definition of position and orientation errors. Dynamic equations for the robot are also derived for path tracking controller design. Section 3 introduces the dual-loop control framework for path tracking. Section 4 describes simulation results by tracking a snaking and a DLC reference path under variations in slip ratios. Finally, the conclusion and future work of this paper are shown in Section 5.
## 1 Introduction
Traditional low-fidelity methods, such as blade element momentum theory (BEMT) and lifting-line theory, are unable to accurately capture these mechanisms, particularly under low Reynolds number conditions. Although these methods remain popular for preliminary design due to their computational efficiency, their predictive accuracy deteriorates rapidly in flow regimes dominated by viscous and unsteady effects. Consequently, high-fidelity Computational Fluid Dynamics (CFD) approaches--especially Reynolds-Averaged Navier-Stokes (RANS) solvers coupled with transition-sensitive models such as the \(\gamma\)-\(Re_{\theta}\) model--have been increasingly employed to model these flows with improved accuracy [44; 42]. Recent research has also integrated such solvers with shape optimization algorithms to develop propellers that actively control or delay laminar separation bubble formation, thereby enhancing aerodynamic efficiency over a wider range of operating conditions [45]. Several propeller designs tailored for various high-altitude systems have been reported in the literature. Each design is optimized to operate efficiently within specific altitude ranges and with different propulsion systems. [PERSON] provides an overview of existing propeller systems used in various HAPS configurations [27]. Some of the main HAPSs exploiting stratospheric propellers are the Condor aircraft [18]; Pathfinder aircraft [22; 46] shown in Figure 3; Grob Strato 2C aircraft [46; 47] shown in Figure 4; Grob EGRET aircraft [48]; Perseus A and B [21; 27] aircraft shown in Figure 5, Centurion and Helios aircraft [29; 46; 49] shown in Figure 6; and Theseus [50]. Although limited information is available regarding the design of propellers used in existing HAPSs, a review of their key characteristics is provided in Table 1[51]. Figure 4: Illustration of the Grob Strato 2C propellers (Grob-Werke GmbH & Co. KG, Mindelheim, Germany). Reprinted from ref. [46]. Figure 5: Illustration of the UAV Perseus B propeller (Aurora Flight Sciences Corporation, Manassas, VA, USA). Reprinted from ref. [46]. Figure 3: Illustration of the Pathfinder propeller (AeroVironment, Inc., Simi Valley, CA, USA). Reprinted from ref. [46]. This review aims to provide a comprehensive and critical overview of the state of the art in propulsion systems and propeller technologies for HAPSs. Special attention is given to the influence of stratospheric environmental conditions on propeller aerodynamic behavior and to the unique design challenges posed by low Reynolds number regimes. This paper systematically reviews the evolution of propulsion architectures for HAPSs, with emphasis on the modeling methodologies, ranging from low- to high-fidelity approaches, adopted in propeller design, as well as on recent advances in optimization strategies tailored to high-altitude platforms. Finally, it examines experimental methods used to validate propeller performance under the low-Reynolds-number conditions typical of HAPS operations.
# Multiple Network Fusion with Low-Rank Representation for Image-Based Age Estimation
## 4 Experimental Evaluation
### Comparison of Multi-Modal Fusion Methods  
We used different methods to integrate outputs of multiple neural networks. The following methods were used:  
* Low-Rank Representation (LRR): The proposed method using multiple neural network fusion and low-rank representation.
* Concatenating Different Features (CON): For CON, features from different modals are simply concatenated to construct long features. Principle Component Analysis [31] is then used for dimensionality reduction.
* Multiview Spectral Embedding (MSE) [32]: This method calculates a low-dimensional embedding. In this embedding, the distribution of each modal is sufficiently smooth. The complementary properties of different modals are then explored to obtain a fused representation.
* Multi-View Hypergraph Learning (MHL) [33]: In this method, hypergraph learning is combined with the patch alignment framwork [34]. A multi-view hypergraph Laplacian matrix is constructed, and fused features are computed by solving the standerdeigen-decomposition of the multi-view hypergraph Laplacian matrix.  
We computed the performance under different dimensions, and the results of different multi-modal fusion methods are shown in Figure 5. According to the figures, all these methods achieved optimal performance among \([400,600]\). However, optimal performance was not achieved on the same dimensionality. The proposed method uses LRR, and the best performance was achieved on 400 on IMDB and 500 on WIKI. Therefore, these settings were used in the other experiments.  
Figure 4: Different combinations of networks. (**a**) Results of IMDB; (**b**) Results of WIKI.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 3 Congenital Heart Disease
### Defects Amenable to Stenting
#### 3.2.1 Hypoplastic Left Heart Syndrome
Hybrid therapy has been able to replace the Stage I Norwood procedure since 1993 [43], especially in those patients considered at high risk for cardiopulmonary bypass or cardiac/total circulatory arrest [44]. This kind of approach integrates interventional cardiac catheterization with off-cardiopulmonary bypass, achieving the same goals of the standard Norwood stage in a less invasive manner. The delaying of cardiopulmonary bypass to the next surgical stages allows a more clinically stable situation to be reached and reduces the risk of abnormal neurocognitive development [39]. In the Hybrid stage, after a few days, a stent is placed in the ductus arteriosus through a mini-invasive transcatheter procedure to guarantee good systemic perfusion. Moreover, surgical bilateral banding of the pulmonary arteries is performed to reduce blood flow into the lungs and obtain a balanced systemic-to-pulmonary flow ratio. Pulmonary venous return is accomplished with balloon atrial septostomy, enlarging the opening between the two atria. The second stage is similar to the standard Glenn technique, plus the reconstruction of the neoaorta, adding the release of pulmonary bands and the removal of the ductus stent, while the third stage is exactly like the Fontan stage.
## 5 Image Processing
### Leaf Size  
Based on the pixels that compound the good leaves, which were obtained by applying the green condition explained in the previous stage (see Figure 10(b)), we have implemented an automatic estimation of an average leaf size in the image, since this result will be required in later stages. The algorithm is based on the idea that, as there are more leaves in the image, the different orientations and shapes leads to a higher variability of the intensity of neighbor pixels in the image.  
First, the image is divided in non-overlapping squared blocks of pixels, and only blocks that all their pixels accomplish with the green condition are used. To reduce different lighting conditions of each image, a normalized difference index (NDI) that uses green and red components of the RGB values of the pixels is used [51]. NDI follows Equation (7):  
\[\textit{NDI}=\frac{green-red}{green+red} \tag{7}\]  
For each block, the mean of this value for all its pixels is calculated, and then the difference of this value with the mean of adjacent blocks. Finally, the global \"variability\" of the image is given by the mean of these differences.  
Figure 11: Colors included imposing the thresholds to HSV values (saturation is 1).  
Figure 12: **(a)** Pixels remaining after applying the green condition. **(b)** After applying the second condition.  
To test the validity of this method, several images have been processed, which have different leaf sizes since they are taken from different distances to the trees. An estimation of an average leaf size has been manually made for each one, and these values have been compared with the distance obtained after processing the images. Figure 13 shows this comparison, with the values of the manually-assigned leaf size in the x-axis, and the difference value in the y-axis.  
There is a decreasing trend of the difference value as the leaf area increases, which confirms that this difference is higher for small sizes. However, there is a great deviation for any particular image. There are several factors that can affect this relation, such as the lighting conditions, as said. At the same time, the leaf area assigned to each image is done manually, and so it is subjective to the operator. Also, we have assumed that one image have the same size for all its leaves, but this is not always true, since depending on the perspective, there can be leaves of very different sizes.  
Taking into consideration these results, we have defined roughly three range of variability to be assigned to three leaf sizes, small, medium and large. The parameters that have to be adjusted depending on this value will be set to the same value for each range.
# Investigation of Fluid-Structure Interaction Induced Bending for Elastic Flaps in a Cross Flow
## 3 Results and Discussions  
### 5 mm Thick Flap  
A comparison between FSI and the experimental bending of a flap at different Reynolds numbers is expressed in Figure 6a-c. For maximum flow velocity of v = 0.5 m/sec (Re = 12) the flap bending is near to 1/2 of the flap length that is, y/L \(\approx\) 0.5, for both FSI and experimental results. The simulated bending line represents the deflection of the flap in terms of x and y coordinates after reaching a steady-state. The flap bending increases with an increase in Reynolds number. However, a non-linear connection between flap deflection and Reynolds number is identified. Computational results can be taken for the whole flap length, though for the simplicity of measurements, five displacement points are measured along the length of the flap in experiments. In simulations, the x-coordinate bends to a maximum of x = 0.079 m and x = 0.076 m for leading edge and trailing edge respectively while in experiments, it is measured to be x = 0.070 m for leading edge and x = 0.067 m for the trailing edge. The results from experiments and simulations show the same bending behavior and are in good agreement. For the highest velocity, bending curves for both leading and trailing edge are shown in the Figure 6a,b, whereas the remaining results are focused only on leading edge of the flap.  
Time-dependent total deformation curve of an elastic flap is processed by ANSYS-Transient Structural. Total deformation consists of directional deformations, d, in all three directions that is, X, Y and Z for a given flap. Resultant deformation varies with time from smallest to highest Reynolds number till it reaches its maximum value and then becomes constant. The deformation curves for all four Reynolds numbers at a physical flow time of 2 s after reaching steady-state can be seen in Figure 7 while Figure 8 indicates the flap visualization versus time till reaching steady-state for a Reynolds number of 9. The deformation is influenced by the magnitude of inflow velocity that is, maximum structural deformation occurs at the tip of the flap and has a least value for Reynolds 3 and increases by 0.0449 m reaching the peak value of 0.0982 m for a Reynolds number of 12. Besides, less  
Figure 5: (**a**) Flap positioning at different clamping angles angle 0°-90°; (**b**) Inclined angle.  
computational time is required by the flap for the higher velocity flow in converging to the steady-state value. Thus the FSI model consists of the observations for the maximum deflections offered by the flap.  
Figure 6: Flap bending; (**a**) Re = 12 leading edge; (**b**) Re = 12 trailing edge; (**c**) Leading edge as a function of Reynolds number, thickness 5 mm.
## 2 Related Works
### Mel Spectrograms  
The complexity of the human body in expressing emotions means that detailed and sophisticated methods are necessary to extract most of the information. In the case of analyzing speech emotions, the acoustic features of the utterance are of great importance. Mel spectrograms are spectral representations of sounds that use the Mel scale, a nonlinear frequency scale that better corresponds to the perception of sounds by the human ear. Creating a Mel spectrogram involves transforming the audio signal into a frequency spectrum using a Fourier transform, and then converting this spectrum to the Mel scale, which allows for a better representation of the acoustic experience. Mel spectrograms represent the intensity of sound at different frequencies over time, which allows for the identification of subtle differences in tone, pitch, and timbre of the voice that may indicate different emotions. Images prepared in this way allow for the use of advanced machine learning algorithms, especially CNNs (convolutional neural networks), in search of appropriate patterns. Additionally, they allow for preliminary analysis of the input data and noise reduction. Thanks to this, spectrograms are often used in many studies for the analysis of emotions [13], acoustic scene classification [14], automatic speech recognition [15] or even in medical problems for [PERSON]'s dementia recognition [16]. Examples of Mel spectrograms are presented in Figure 1.
## 1 Introduction
In addition, self-triggering control can determine the precise time of the next triggering moment [29; 30], thus making it a popular choice for reducing communication costs and computational costs in system control applications [31]. Based on the above issues and research, this study proposes a distributed control strategy based on multiagent self-triggered model predictive control (ST-MPC), aiming at the supply and demand balance problem under the supply and demand relationship of microgrids, a model predictive control method based on hydrogen-based microgrids is designed. While ensuring that batteries and hydrogen storage tanks are always kept in a safe and sustainable state, the scheduling of each microgrid in the power grid is realized, the demand side control of the hydrogen energy microgrid system is realized, and the purpose of increasing the service life of the storage equipment is achieved. On the other hand, the control algorithm proposed in this paper targets the difference in electricity demand between the energy supply side of photovoltaic (PV)+Wind and the demand side, which can better schedule the microgrid in the power grid to achieve peak shaving and valley filling, thereby making up for these differences, and combining the event triggering algorithm with the MPC algorithm, thereby saving considerable communication resources and control resources in microgrid regulation. The main contributions of this paper can be summarized as follows:  
(1) The control method proposed in this article considers the conversion and storage of electricity and hydrogen, which can achieve peak shaving and valley filling in the supply and demand balance of microgrids. (2) The control method proposed in this article considers state constraints on the battery and hydrogen storage tanks, which can improve the lifespan of storage devices. (3) The ST-MPC algorithm proposed in this paper effectively reduces the computational burden associated with solving optimization problems.
## 1 Introduction
### Organization  
The rest of the paper is organized as follows. In Section 2, we detail system and channel models, and describe conventional, transfer, and meta-learning concepts. In Section 3, we develop solutions for single-antenna frequency-flat channels. In Section 4, multi-antenna frequency-selective channels are considered, and we propose LSTD-based linear prediction schemes. Numerical results are presented in Section 5, and conclusions are presented in Section 6. Notation: In this paper, \((\cdot)^{\top}\) denotes the transposition; \((\cdot)^{\dagger}\) the Hermitian transposition, \((\cdot)_{F}\) the Frobenius norm, \(|\cdot|\) the absolute value, \(||\cdot||\) the Euclidean norm, \(\text{vec}(\cdot)\) the  
Figure 2: Illustration of the considered transfer and meta-learning methods. With access to pilots from previously received frames, transfer learning and meta-learning aim at obtaining the hyperparameters \(\mathcal{V}\) to be used for channel prediction in a new frame.  
vectorization operator that stacks the columns of a matrix into a column vector, \([\cdot]_{i}\) the \(i\)-th element of the vector, and \(I_{S}\) the \(S\times S\) identity matrix for some integer \(S\).
Consequently, it is evident that there is still a need to develop robust three-dimensional models capable of estimating the kinematics and kinetics of human gait under the effects of S2 HI that allow further evaluation of anthropic loads and their implementation in HSI studies in lateral and vertical directions, which is a topic that is little addressed in the literature. To address these gaps, this paper proposes a novel coupled HSI predictive model composed of a complete three-dimensional biomechanical model of the human body and a pedestrian bridge represented as a simply supported Euler-Bernoulli beam. The main novelties and contributions of this work are as follows: (1) The incorporation of anthropic loads defined by a realistic three-dimensional human gait model as a source of excitation for HSI, based on inverse dynamics, and able to represent the changes in gait resulting from surface vibrations (S2 HI). (2) A simplified method is proposed to evaluate the three-dimensional HSI that considers the changes in the dynamic properties of the structure under anthropic excitation under HSI effects. A description and formulation of the three-dimensional human gait model are presented in Section 2, followed by the proposed structural system in Section 3. Section 4 describes the simplified HSI formulation, and Section 5 presents the optimization scheme for the coupled HSI based on the minimization of the total mechanical energy of the pedestrian moving through the structure. Finally, Section 6 presents and discusses the results.