esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Multi-Task Graph Isomorphism Network for Classification of Alzheimer's Disease
## 2 Related Work
### Application of Multi-Task Learning in AD Classification  
Multi-task learning is a machine learning paradigm that simultaneously learns multiple related tasks, improving each task's performance by sharing information and latent knowledge. Multi-task learning has been widely applied in neuroimaging analysis. For example, [PERSON] et al. [26] proposed a multi-modal Alzheimer's disease classification method, considering feature selection for each modality in medical images as a separate task, allowing information sharing across multiple tasks. This method achieved an accuracy rate of 92.51% in AD classification. [PERSON] et al. [27] introduced a multi-task learning algorithm based on Deep Belief Network and employed a multi-task feature selection method to select a feature set relevant to all tasks.This method shared weights across the AD vs. HC, pMCI vs. HC, and AD vs. sMCI tasks, resulting in accuracy rates exceeding 95% for all tasks. [PERSON] et al. [15] proposed a new GCN that uses demographic information as additional outputs, adding two structurally similar but parameter-different auxiliary networks to predict gender and age, sharing parameters with the main network. Ultimately, the classification results yielded an accuracy of 90.0%, a sensitivity of 91.7%, and a specificity of 88.6%. These studies show that multi-task learning can effectively utilize latent correlated information in neuroimaging data, thereby improving model performance. However, previous research has not adequately addressed the issue of balancing task weights during training. If weights are not allocated properly, some tasks may be over-optimized, while others may be negatively impacted.  
A novel DMT-GIN model has been proposed, effectively capturing local and global features of brain networks through graph-based layers and outperforming CNN-based methods. This architecture overcomes the limitations of CNNs in handling non-Euclidean data. In comparison to GNN-based methods, our DMT-GIN model incorporates the GIN model, addressing the limited representational capabilities of GNN-based methods and enhancing the model's ability to distinguish graph isomorphic structures. In contrast to other deep learning methods such as RNNs and autoencoders, our DMT-GIN model efficiently extracts local and global features from graph data. In the context of AD classification based on fMRI data, our dynamic multi-task learning strategy effectively resolves task conflicts during training and optimizes the balance of weights between tasks. This approach distinguishes our DMT-GIN model from traditional multi-task learning strategies and contributes to its superior performance in Alzheimer's disease classification.
## 2 Mechanistic Foundations of Skeletal Muscle Aging  
### Structural and Cellular Organization of Skeletal Muscle  
Skeletal muscle is a highly organized and adaptable tissue composed of multinucleated myofib, connective tissue, blood vessels, and resident stem and immune cells (Figure 2). Each myofiber is encased in a basal lamina and surrounded by a specialized niche containing satellite cells--the adult muscle stem cells that orchestrate repair and regeneration [18; 32; 33; 34; 35; 36]. These quiescent cells reside beneath the basal lamina and are activated in response to stress, injury, or exercise. Satellite cells are characterized by the expression of transcription factor Pax7 (paired box protein 7) and, upon activation, initiate a myogenic program involving _myogenic factor 5 (Myf5)_ and _myogenic differentiation 1 (MyoD)_[18; 36; 37; 38; 39; 40; 41]. They proliferate, differentiate into myoblasts, and ultimately fuse with existing fibers or form new fibers to restore tissue integrity. A subset of activated satellite cells self-renews to maintain the stem cell pool. This process is tightly regulated by both intrinsic factors (e.g., epigenetic status and cell polarity) and extrinsic cues from the extracellular matrix (ECM), immune cells, and systemic factors such as hormones and cytokines [18; 36; 37; 38; 39; 40; 41].  
Figure 2: Structural organization of skeletal muscle and localization of satellite cells. This diagram illustrates the anatomical relationship between bones, joints, and skeletal muscles, emphasizing the microarchitecture of muscle fibers. Within the enlarged view, muscle fibers are shown alongside capillary networks (blood vessels) and quiescent satellite cells situated between the basal lamina and sarcolema. Satellite cells are essential for muscle regeneration and respond to injury or metabolic stress by activating, proliferating, and differentiating into myogenic progenitors. This close spatial association with blood vessels ensures access to nutrients, oxygen, and signaling molecules, critical for regenerative responses in both physiological and pathological conditions such as aging and sarcopenia.
# LightVSR: A Lightweight Video Super-Resolution Model with Multi-Scale Feature Aggregation
## 3 Feature Aggregation Module
### Head-Tail Convolution  
To design lightweight neural networks, one of the most common and efficient approaches is to reduce the number of convolutional layers, the number of convolutional kernels, or the size of the input data. However, such strategies often lead to a significant drop in model performance. To address this issue, we designed a novel head-tail convolution to perform efficient feature extraction while reducing computational overhead.  
The core idea of head-tail convolution is to segment the input feature tensor along the channel dimension into three distinct regions of the head, middle, and tail. Figure 2a presents the conventional convolutional approach. In contrast, as shown in Figure 2b, the head and tail sections of the feature tensor participate in standard convolution operations, whereas the middle section is left unchanged. Although no convolution operations are explicitly applied to the intermediate section, these regions are still effectively captured by the receptive fields of the head and tail convolution, which can significantly reduce redundant computations while ensuring critical features from the entire feature tensor are effectively represented.  
Formally, denote the input feature tensor as \(X=[X_{1},X_{2},\ldots,X_{N}]\), where \(N\) represents the tensor length. We partition \(X\) into head features, middle features and tail features as follows:  
\[X_{head}=[X_{1},X_{2},\ldots,X_{\alpha}] \tag{1}\]  
\[X_{mid}=[X_{\alpha+1},\ldots,X_{N-\beta}] \tag{2}\]  
\[X_{tail}=[X_{N-\beta+1},\ldots,X_{N-1},X_{N}] \tag{3}\]  
where \(\alpha\) and \(\beta\) denote the lengths of the head and tail segments, respectively. We then apply convolutional operations to the head and tail features:  
\[X^{\prime}_{head}=f_{conv}(X_{head}) \tag{4}\]  
\[X^{\prime}_{tail}=f_{conv}(X_{tail}) \tag{5}\]  
The middle features remain unchanged:  
\[X^{\prime}_{mid}=X_{mid} \tag{6}\]  
Finally, we concatenate above features to form the output feature vector:  
\[\mathcal{X}\prime=\left[X^{\prime}_{head},X^{\prime}_{mid},X^{\prime}_{tail}\right] \tag{7}\]  
To fully exploit head-tail convolution, we integrate it into a basic building block, called the HT-Block, as shown in Figure 3, in which the input tensor first applies head-tail convolution to extract features, then a GELU (Gaussian error linear unit) activation function layer is applied to accelerate training and improve generalization. Finally, a full \(3\times 3\) convolution layer is utilized to further fuse information of the head, middle, and  
Figure 2: (**a**) Principle of conventional convolution; (**b**) principle of head-tail convolution.  
tail from head-tail convolution. Note that the HT-Block is designed as a general-purpose module that can be seamlessly integrated into various network architectures.
###### Abstract  
The restoration and protection of wetlands are crucial in reducing greenhouse gas emissions. In this research, the SWAT model was modified to investigate and estimate the groundwater table, net ecosystem exchange (NEE), and soil respiration impact on carbon dioxide (CO\({}_{2}\)) emission in the cold regions in Alberta. There is a lack of a process-based model that accounts explicitly for the CO\({}_{2}\) emission and ecosystem exchange resulting from interactions between hydrological and biogeochemical processes. The SWAT model is modified to make unique contributions to wetlands by estimating CO\({}_{2}\) emissions, soil temperature, and soil respiration that account for the dynamics of water tables and the relationship between subsurface and surface water storage. The modified model results predicted daily NEE with a very good model fit resulting in an R\({}^{2}\) (Coefficient of determination), NSE (Nash-Sutcliffe Efficiency), PBIAS (percent bias), and RMSE (root mean square error) of 0.88, 0.72, 2.5, and 0.45 in the calibration period and 0.82, 0.67, \(-\)1.8, and 0.56 for the validation period, respectively. The prediction result indicated that the modified model performed well in predicting soil temperature, the groundwater table, and ecosystem respiration in the calibration and validation periods. In general, this study concluded that the modified model has the capability of representing the effects of water table dynamics on CO\({}_{2}\) emissions and NEE in cold wetlands.  
modified SWAT; CO\({}_{2}\); soil respiration; groundwater table +
Footnote †: journal: Water
## 4 Control Design
### Positioning Experiment  
By setting a constant angle signal, we carried out the positioning experiment and obtained the positioning performance of the pneumatic rotary actuator. Figures 12 and 13 show the optimization process of the PD controller coefficients and the identification process of the friction parameters respectively. The positioning performances of the pneumatic rotary actuator under three control strategies are shown in Figure 14, including the angle contrast curve, the error contrast curve and the angular velocity contrast curve.  
As shown in Figure 14b, the maximum angular errors of four control strategies (traditional PD control, PD with friction compensation, DE-PD and GA-PD control) were \(0.225^{\circ}\), \(0.107^{\circ}\), \(0.003^{\circ}\) and \(0.075^{\circ}\) respectively. This result shows that the friction feedforward compensation based on evolutionary algorithm improved the positioning accuracy of the system, and that the DE-based case achieved higher accuracy. Figure 14c shows the velocity fluctuations of the pneumatic rotary actuator when input angle was a constant, and reveals that the pneumatic rotary actuator run smoothly when the  
Figure 12: Optimization process of \(k_{\mathrm{p}}\) and \(k_{\mathrm{d}}\) in the positioning experiment.  
Figure 13: Identification process of the friction parameters in the positioning experiment.  
system tended to a steady state. Also, as shown in Figures 9, 10, 12 and 13, the iterative processes of the algorithms tend to be stable, which ensures that the evolutionary algorithm-based case does not go unstable.
# Power Control Techniques for Interference Management-
## 4 Power Control Technique Evaluation
### Power Control Techniques: Case Studies
#### 4.2.2 Case Study 2: Interference Management in 5G and Beyond Networks  
A sudden increase in wireless data services has been observed over the past ten years. Data traffic has been growing at a high rate. Therefore, fifth generation (5G) and future mobile communication services should improve technologies to manage this level of growth, for instance, through the use of terahertz communications and the introduction of non-terrestrial networks. On the one hand, there is technological improvement, but on the other hand, there is an interference challenge that needs to be managed properly. To address this challenge, the authors provided a survey of interference management in 5G and beyond networks and discuss its future evolution.  
This survey examines cross-link, multi-user, and inter-cell interference management strategies. It also covers network densification, massive MIMO, and the use of millimetre-wave spectrum.  
The survey classifies and explains different types of interference. This is followed by presenting a taxonomy of existing interference management approaches. The survey explains interference measurement reports and signalling in 5G NR. The survey presents an in-depth literature review and technical discussion of appropriate management schemes for each type of interference identified. The survey concludes by discussing the main interference challenges encountered in future 6G networks and presenting insights on the suggested new interference management approaches, including useful guidelines for an AI-based solution.  
This review provides a first-hand guide to the industry in determining the most relevant technology for interference management. It also allows for the consideration of future challenges and research directions.  
Details of the case study are found in [151].
## Questions concerning the Relationship between CLIL and Digital/Computer Technology
### How Have New Digital Technologies Impacted CLIL Learning?
#### 2.3.4 Motivation and Confidence of Students  
Numerous studies demonstrate that students appreciate utilizing technology to study a foreign language and prefer it to more conventional techniques and resources [18]. As a result, incorporating TELL activities enables teachers to take advantage of students' interests and include fun activities in a CLIL class (e.g., adolescents enjoy working with their peers). Additionally, it implies that teachers can create personalized curricula employing teachings that reflect local culture and educational philosophy [2], which is also aligned with the components of designing bridging materials in CLIL: L1, local languages and culture, and multimodality, which play a significant part in CLIL [21].  
Additionally, technology can also lessen learners' anxiety by fostering an accepting, autonomous learning environment [22]. For instance, [PERSON] [23] suggests that teachers consider using the proposed idea of CLILQuests for in-class or out-of-class work. As a result, TELL not only is an effective teaching strategy that can resolve the conflict between the lack of teaching time and the demand for students to learn additional material or consolidate their prior knowledge, but also creates an online interactive communication environment to provide learner-oriented opportunities to foster students' independence and relieve them of some of the pressures of traditional classroom learning.  
Moreover, intelligent tutor feedback is more efficient than conventional feedback. Intelligent tutor systems imitate a tutor by delivering direct, individualized teaching and/or feedback to a student. Typically, such a system consists of four components: an interface (platform), an expert model (domain of knowledge the student is intended to acquire), a student model (current state of student's knowledge), and a tutor model (which provides feedback and instruction based on the identified gaps between the student and the expert models) [11]. Therefore, technology can boost students' motivation by providing them a more prominent role, allowing for self-evaluation and feedback. However, even though most intelligent tutors' feedback is timely due to the development of network technology, this does not mean that the evaluation system of online education is complete. According to [PERSON] [23], they are awaiting actual implementation and classroom action research that can provide feedback on its validity and suggest the replacement and improvement of its defective parts.
## 5 Simulation Results and Analysis
### Simulation Analysis
#### 5.2.2 Effect of Data Scheduling Strategies
(**b**) The variation trends of the throughput under the Round-Robin strategy. (**c**) The variation trends of the throughput under the WCDS strategy. Figure 7: Data Transfer Completion Time under Different Scheduling Strategies.
## 5 Discussion and Implications
### Practical Implications  
The current work's findings highlight a set of helpful directions that managers and organizations may use to take the most significant advantage of employees' information security intentions and behaviors. The current findings also highlight the critical outcomes of information security intention, such as policy compliance, and that participation can be accomplished when marketing management is employed within organizations. As a result, organizations and stewardship need to create organizational marketing systems with a promoting-sharing policy to appeal to employees' daily involvement.  
This study also sheds light on ISA and how it is beneficial for organizations to reduce costs and achieve employees' security intentions. ISA is likely to take place in various occupations organized in routine work in shipping, such as information technology personnel who keep data safe, email receivers/senders, and other workers who access the Internet in the office. Similarly, this also applies to employees who frequently use the Internet for work and other branches or offices in charge of global regions. High levels of ISA can be established by regular training (e.g., email exercises, workshops, user manuals, or classes about security information), thus benefitting the protection of an organization's information system.  
The study further found that spam is the most popular computer virus reported by the respondents, followed by phishing, malware, and ransomware. This implies that many firms' systems cannot accurately identify different kinds of email. Despite information technology needing to be enhanced, organizational employees should notify their information technology personnel when receiving any suspicious email. Although it is not easy to find the most effective approach to avoid spam, its prevalence can be reduced using a firewall.
## 4 Discussion  
The results of this study revealed that some of the jump and speed performance parameters improved significantly in the SG. Specifically, the SG showed significant improvements in CMJ, PSJT, 10 m ST, and 20 m ST performance from pre- to post-intervention. Moreover, both the HG and the SG showed improvements in CoD performance. However, a more detailed analysis shows that only the HG improved in Avg Vel 1a, while Avg Vel 1b only improved in the SG. These findings suggest that sand could be an effective training surface to improve jumping, sprinting, and CoD performance. To examine the immediate influence of sand as a training surface, the acute effects of one training session on CMJ, LA, HR, and RPE were compared between the SG and the HG, the findings of which revealed an enhancement in CMJ performance, higher LA levels, and increased RPE values in the SG, indicating both an acute performance enhancement and heightened metabolic demands compared to training on a hard surface. These results align with previous research that has examined the metabolic responses to sand-based training [25; 43; 44]. The inherent instability and force-absorbing characteristics of sand-based training impose heightened stabilization demands during the support phase of the stride, accompanied by an elongated ground contact time and, thus, a prolonged time under tension of the muscles. Furthermore, the reduced efficiency of force transmission, manifested by the foot sliding during push-off, necessitates greater muscular exertion to achieve the desired movement patterns [25]. Therefore, an immediate enhancement in CMJ performance following sand training could be attributed to the heightened muscle activation induced by the interplay of the aforementioned factors, as this could elicit a potentiation effect. However, the required increase in muscular activation also leads to heightened metabolic demands, as indicated by elevated LA levels and an increased RPE. High-intensity training on sand thus leads to the accelerated depletion of phosphocreatine, consequently heightening the demand for glycogenolysis and glycolysis. If the resulting accumulation of LA becomes excessive, muscle acidosis could result, thus inhibiting contraction processes and decreasing performance [45]. It is generally accepted that training to improve speed must be carried out without a drop in performance over the course of the workout. As such, studies focused on sprinting and jumping have confirmed the importance of sufficient recovery to maintain running speed and jumping performance [46; 47]. However, based on the longitudinal improvements observed in performance throughout this study, it can be
# Dynamic Interaction Analysis of Long-Span Bridges Under Stochastic Traffic and Wind Loads
## 3 Nonlinear Dynamic Solution Methods for Large-Span Structures
### Large-Span Nonlinear Dynamics Solution  
This section outlines the steps involved in solving joint dynamics using numerical methods. We employed the Newmark-\(\beta\) method for time-step discretization and the Newton-Raphson method for an iterative solution of the resulting nonlinear equations. The steps are as follows:  
1. Initial conditions: The initial displacement \(X_{b}(0)\), initial velocity \(\dot{X}_{b}(0)\), and initial acceleration \(\ddot{X}_{b}(0)\) are given at the initial moment t = 0.
2. Time-step discretization (Newmark-\(\beta\) method): The kinetic equations are discretized within time step \(\Delta t\) using the Newmark-\(\beta\) method to obtain a system of nonlinear equations with respect to \(X_{b}(t+\Delta t)\). This method was chosen for its balance between computational efficiency and accuracy in handling dynamic systems.
3. Iterative solution (Newton-Raphson method): For each time step \(t+\Delta t\), the system of nonlinear equations is solved iteratively using the Newton-Raphson method: 1 Initial Value Setup: Set the initial value \(X_{b}^{(0)}=X_{b}(t)\). 2 Residual Calculation: Calculate the residual \(R(X_{b}^{(k)})\), which measures the difference between the system's current state and the desired state. 3 Jacobian Matrix Calculation: Calculate the Jacobi matrix \(\frac{\partial R}{\partial X_{b}}\) and update the displacement.  
The iterative update formula for the displacement is given in Equation (12). This formula is utilized to update the displacement at each iteration step.  
The Jacobian matrix is crucial for linearizing the nonlinear system and guiding the iterative process towards a solution.  
4. Convergence Check: Continue iterating until the convergence condition is satisfied, i.e., \(\left|R(X_{b}^{(k+1)})\right|<\varepsilon\), where \(\varepsilon\) is the preset convergence accuracy.
4. Update acceleration and velocity: After obtaining the converged displacement \(X_{b}(t-\Delta t)\), the acceleration and velocity are updated using the formulas of the Newmark-\(\beta\) method: \[\ddot{X}_{b}(t+\Delta t)=\frac{1}{\beta\Delta t^{2}}\bigg{(}X_{b}(t+\Delta t) -X_{b}(t)-\Delta t\dot{X}_{b}(t)-\frac{\Delta t^{2}}{2}(1-2\beta)\ddot{X}_{b} (t)\bigg{)}\] (13) \[\ddot{X}_{b}(t+\Delta t)=\dot{X}_{b}(t)+\Delta t[(