esa-sceva/satcom-chunk-collection huggingface dataset:
# Course Recommendation Based on Enhancement of Meta-Path Embedding in Heterogeneous Graph
## 4 Proposed Approach for Course Recommendation
### Meta-Path Embedding Layer  
#### 4.1.1 Original Meta-Path Embedding  
This module is used to generate the embedding of nodes on meta-paths of the heterogeneous graph. For this task, the meta-paths of the heterogeneous graph are generally sampled first, then the nodes on the sampled meta-paths are embedded. This module uses a meta-path based Deep Walk (abbr. MDK) to generate node embedding on each meta-path. MDK combines the random walk and word2 vec algorithms. First, the heterogeneous graph is sampled and filtered for node sequences of various meta-paths, then the skip-gram algorithm in word2 vec is used to map the node sequences into low-dimensional space:  
\[R(n_{t+1}=y|n_{t}=x,P)=\left\{\begin{array}{ll}\frac{1}{|N_{A_{t+1}}(x)|^{ \prime}},&(x,y)\in E,\Phi(y)=A_{t+1}\\ 0,&(x,y)\in E,\Phi(y)\
eq A_{t+1}\\ 0,&(x,y)\
otin E\end{array}\right.\]  
where \(n_{t}\) represents the \(t\)-th node of the walk, \(x\) is a set of the node type which includes \(A_{t}\), \(|N_{A_{t+1}}(x)|\) represents the number of nodes with node type \(A_{t+1}\) among the neighbors of node \(x\), and \(R(n_{t+1}=y|n_{t}=x,P)\) represents the probability for a given node \(x\) of selecting a node \(y\) which represents the constraints of meta-path \(P\). This regular sampling can ensure uniform sampling of various heterogeneous types of nodes under the constraint of the meta-path \(P\) to obtain the node sequence \(X=[x_{1},x_{2},x_{3},...,x_{n}]\) of various meta-paths. Next, in order to improve the training efficiency of meta-path nodes, we filter \(X\), that is, we keep nodes of the same type as the starting node, map nodes of the same type to the same space, form a new homogeneous node sequence \(\hat{X}\), and then pass embedding methods to generate embedded representations of the filtered nodes.  
Next, the skip-gram algorithm is used to generate node embeddings of \(\hat{x}\). The skip-gram algorithm adopts the principle of similarity of adjacent nodes, uses the central node to predict the context node, and obtains the embedding of the target node after continuous iterative optimization. Skip-gram is a three-layer network structure consisting of an input layer, hidden layer, and output layer. It first generates a node sequence according to the size of the window, then takes the central node of the sequence as input and continuously maximizes the occurrence probability of its neighbor nodes, and finally obtains the embeddings of all nodes. The optimization objective of this algorithm is as follows:  
\[L=\underset{f}{max}\sum_{u\in V}logPr(N_{u}|f(u)), \tag{1}\]  
where \(f\) is a function that maps each node \(u\) to a \(d\)-dimensional space vector, e.g., \(f=V\to R^{d}\), \(u\) is the current node, \(N_{u}\) is the neighbor node of the \(u\) node obtained through the meta-path, and _Stochastic Gradient Descent_ is used to optimize L.
## 5 Simulation Results and Discussion  
To verify the proposed scheduling method, extensive simulations are performed based on an actual wind farm using MATLAB (R2011a) and Cplex (12.4) software. The data used in this study are obtained from the power generation data for a wind farm in Inner Mongolia, China, with an installed capacity of 50 MW. The sampling time of the MPC is 5 min. The simulations have a duration of 24 h and include a total of 288 test points in total. At each sampling point, the forecast power data of the wind farm is required, and these data can be obtained from the prediction software or algorithm [36]. In this paper, these data are obtained by adding random noise to the power data of a real wind farm. The parameters of pumped storage and flywheel storage are listed in Table 3.  
The parameters of the hybrid storage systems are taken from our previous study [37]. In [33], we considered the minimized investment and the maximized revenue of hybrid storage systems. A wind farm power dataset covering a one-year timespan is used to obtain the optimal storage.  
The aim of this research was to present two MPC models to manage a wind farm and a hybrid storage system. In Section 5.1, the simulation results of the single-level MPC are compared with those of the two-level MPC.
# Integrating Biofeedback in Dynamic Biomechanical Gait Training for Chronic Ankle Instability
## 2 Methods
### Gait Analysis  
Kinematic and kinetic data were acquired using an eight-camera motion capture system (VICON Motion Systems, Inc., Lake Forest, CA, USA) operating at a sampling frequency of 100 Hz, whereas ground reaction force data were recorded at 2000 Hz with a force plate (ORG-6 AMTI, Watertown, MA, USA). Three-dimensional joint kinematics were analyzed using the VICON Plug-In Gait model (Oxford Metrics, London, UK). The participants wore standardized running shoes (FTY no. CLU 60001, Aididas, Herzogenau-rach, Germany) and completed walking and jogging tasks at controlled speeds of 1.34 m/s and 2.68 m/s, respectively, with timing measured by a running timer (TAKEI TKK 1274,  
\begin{table}
\begin{tabular}{c c c} \hline
**Group** & **Feedback (n = 15)** & **No Feedback (n = 15)** \\ \hline Sex (M:F) & 8:7 & 9:6 \\ Age (year) & 30.6 \(\pm\) 4.08 & 30.48 \(\pm\) 4.39 \\ Height (cm) & 170.49 \(\pm\) 11.09 & 171.63 \(\pm\) 9.9 \\ Weight (Kg) & 65.8 \(\pm\) 15.27 & 74.76 \(\pm\) 17.81 \\ AII (No.Yes) & 6.13 (2.26) & 6.47 (2.26) \\ FAAM-ADL (\%) & 91.9 (0.06) & 88.25 (0.11) \\ FAAM-Sport (\%) & 78.75 (0.15) & 77.5 (0.17) \\ No. of sprains & 6.87 (3.68) & 8.33 (2.77) \\ Last sprain (months) & 5.07 (3.59) & 5.53 (4.78) \\ First sprain (years) & 16.93 (6.51) & 13.2 (5) \\ STJ AoR-sagittal (\({}^{\circ}\)) & 36.44 (4.35) & 34.57 (2.84) \\ STJ AoR-transverse (\({}^{\circ}\)) & 18.07 (3.84) & 16.74 (4.21) \\ Navicular drop (cm) & 0.64 (0.3) & 0.66 (0.33) \\ Standing rearfoot angle (\({}^{\circ}\)) & \(-\)1.15 (2.49) & \(-\)1 (3.61) \\ \hline \end{tabular} Abbreviations: M: Male, F: Female, FAAM-ADL: Foot and Ankle Ability Measure-Activities of Daily Living, STI: AoR: Subtalar joint Axis of Rotation.  
\end{table}
Table 1: Demographic characteristics for each group (mean \(\pm\) SD).
## 4 Performance Evaluation
### Trace Characterisation  
In order to characterise mobility and determine the structure of the contacts, we analysed the trace used in the experiments. From this study, we could determine some social patterns of the trace that are useful to understand the performance of the Friendly-Sharing approach.  
Figure 5 shows the contact graph for different time intervals, starting from the first 3 h of the trace up to 24 h. Each node is labelled by a number and the diameter of each circle is proportional to the number of contacts. Node groups are represented by different colours and were computed using the Fruchterman-Reingold algorithm [46]. We can see that the social interrelation given by the dynamics of the contacts numbers increases according to the simulation time, while the number of communities decreases: on the first 3 h of simulations, the number of communities are 12, for 6 h and 12 h, we have eight communities, and, finally, after 24 h, we only have six communities. This means that the number of \"communities\" slowly decreases, thus contributing to message diffusion and all nodes eventually getting connected. This implies increasing the message TTL to 12 h or 24 h in order to produce a better delivery rate.  
Figure 6 presents the average node speed at each hour of the movement trace, clearly showing that nodes have very dissimilar movement patterns. Some nodes move frequently and with considerable speed, while other nodes are quite static and move with lower speed. This effect could be explained by the students place of residence, e.g., some sleep close to the campus (or even inside) while others have to travel further away to their homes.  
Figure 4: Modifications to the ONE simulator code (original figure from [16]).  
Although nodes without movement in a large period of time could be considered not relevant for message interchanges, Figure 7 shows exactly the opposite. In this plot, the \(x\)-axis shows the average speed computed as in Figure 6, while the \(y\)-axis shows how much time a node is in contact during an hour. For example, a node that has an average speed of 2 m/s and a total contact duration of 1 h across all contacts will be plotted in position \((2,1)\). As a node could be contacted by more than one node at the same time, the \(y\)-axis extends away from 1. This inverse correlation between movements and contacts is easily explained by the students' typical behaviour: they stay together in a class for a bounded amount of time, they move a small distance to the next class, and so on. Even while not attending classes, they spend time with other students in the cafeteria, library, or other campus services.  
Figure 5: Contact graph for different time intervals of the trace: (a) 3 h; (b) 6 h; (c) 12 h and (d) 24 h.  
Figure 6: Average node speed at each hour of the movement trace.
# Occlusion Avoidance in a Simulated Environment Using Reinforcement Learning
## 5 Architecture and Training
### Model Architecture  
Since our goal with the CNN-based method was to predict the outputs for the images rendered in the environment, we designed a multi-head structure that, once properly trained using multi-task learning, would be capable of producing the occlusion masks, as well as the prediction for the environment gradients showing the optimal camera movement derived via differential rendering. The three parts of the architecture are:  
1. a shared convolutional encoder,
2. a convolutional decoder, responsible for predicting the occlusion maps, and
3. a dense head predicting the gradient directions.  
Our architecture is designed as a U-Net-like [37] structure, containing both an encoder and decoder subsystem Both the encoder and decoder subsystems are designed as U-Netlike structures. The resulting architecture makes use of 5 downscaling and upscaling blocks, each with 2 consecutive, \(3\times 3\) convolutional layers, followed by ReLU and BatchNorm. For downscaling and upscaling, strided and transposed convolution blocks were used, respectively. The initial channel count of 8 was doubled on every downscaling step, and skip connections between the encoder and decoder networks were utilized at every level. We designed and trained numerous different variations of these parts. We analyzed the impact of having residual connections in our convolutional blocks between the two parts in the R-UNet variant, and then introduced depth-wise separable convolution blocks in the SR-UNet version. We also tested the effect of atrous convolutions using the SAR-UNet variant. The proposed architecture is visualized in Figure 2.
## 5 Conclusions
**Real-Time Deployment**  
Implementation of these models in real-world clinical scenarios and settings is a crucial next step. Further research in this field should focus on an optimized model with efficiency high enough to ensure low-latency prediction times. Model compression and quantization strategies in the edge computing field should be considered to unlock further capabilities for resource-constrained devices that are commonly used in healthcare. Validation of the model's performance in clinical data and trials with real data from patients is essential to ensure the effectiveness and the reliability of the model. Collaboration with healthcare institutes and experts is mandatory to test and refine the usability of this method in real-time prediction of aggressive episodes, validating its usability in existing care protocols. The successful deployment of real-time aggression detection models could revolutionize patient monitoring, offering a proactive approach to managing aggression-related incidents in dementia care. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15115823/s1](https://www.mdpi.com/article/10.3390/app15115823/s1)]([https://www.mdpi.com/article/10.3390/app15115823/s1](https://www.mdpi.com/article/10.3390/app15115823/s1)). Figure S1. Aggressive image dataset sample; Figure S2. Non-aggressive image dataset sample; Table S1. Demonstration of file data structure; Table S2. Sample from the visual_detection_results.csv file after visual model testing; Table S3. Sample from the audio_detection_results.csv file after audio model testing; Table S4. Sample from merged_results.csv file; Table S5. Top 15 Features Selected from Random Forest During Early Fusion; Table S6. Top 15 Features Selected from Random Forest During Late Fusion. Conceptualization, I.G. ; Methodology, I.G. and R.F.S. ; Software, I.G. ; Validation, I.G. ; Formal analysis, I.G. ; Investigation, I.G. ; Resources, I.G. ; Data curation, I.G. ; Writing--original draft, I.G. ; Writing--review & editing, I.G., R.F.S., N.K., A.V. and I.V. ; Visualization, I.G. ; Supervision, I.G. and M.S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original data presented in the study are openly available at [[https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset](https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset)]([https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset](https://www.kaggle.com/datasets/fangfangz/audio-based-violence-detection-dataset)) (accessed on 15 January 2025) and at [[https://virtualhumans.mpi-inf.mpg.de/3](https://virtualhumans.mpi-inf.mpg.de/3) DPW/]([https://virtualhumans.mpi-inf.mpg.de/3](https://virtualhumans.mpi-inf.mpg.de/3) DPW/) (accessed on 1 October 2024). The authors declare no conflict of interest.
# Frequency Response Analysis of Perforated Shells with Uncertain Materials and Damage
## 2 Preliminaries
### Perforated Domains
#### 2.2.3 Layer Chart over Representative Cells  
Even though the shell problems are elliptic partial differential equations, the solutions may exhibit local features, formally boundary layers, with characteristic length scales much larger than the chosen cell size \(e\). Therefore, it is necessary to review the possible boundary layers that may occur in different scenarios.  
For our discussion, it is useful to define the concept of boundary layer generators first (see [17]). The layer generators are independent of the length scale of the problem under consideration.  
**Definition 2** (Layer Generator).: _The subset of the domain from which the boundary layer decays exponentially, is called the layer generator. Formally, the layer generator is of measure zero._  
In perforated structures, the hole boundaries are natural layer generators. The boundary layers are not concentrated only around the holes but can emanate along the characteristics of the shell surface. Elliptic, parabolic, and hyperbolic structures each possess a distinctive set of layer deformations. The layer structure is classically assumed to be an exponential solution to the homogeneous Euler equations of the shell problem. In Pitkaranta, Matache, and Schwab [17], it is shown using the Ansatz  
Figure 4: Damage Model. For both regular and quasi-random penetration patterns, damage has been added with uniform global intensity. The regular grid is a \(20\times 20\), and the Halton one is \(H(5,23)\) with \(400\) points. Low intensity level for damaged holes is \(10/400\) and high \(100/400\).  
\[\mathbf{u}(s,r)=\mathbf{U}e^{\lambda s}e^{\mathrm{i}kr} \tag{24}\]  
that solutions with \(\mathrm{Re}\lambda<0\) such that the characteristic lengths \(L=1/\mathrm{Re}\lambda\to 0\) are of the form \(L\sim d^{1/n}\), where \(n\in\{1,2,3,4\}\). Here, \(s\) is the coordinate orthogonal to the layer generator and \(r\) the one along it. From these, the layer with \(n=2\) is present in all geometries, whereas layers with \(n=3\) and \(n=4\) are present only in hyperbolic and parabolic geometries, respectively, and thus play a role here as well. The case \(n=1\), i.e., the shortest one arises from a shear deformation and can be captured by the shallow shell formulation [17], if necessary. In the 3D visualizations for the very thin case (Figures 4(b) and 4(b)), distinct features of the solution, that is, the boundary layers, are clearly visible. In Figure 7, the layer chart is given both for the parabolic and hyperbolic representative cells, with the exception of the shortest one, which is present on every boundary.  
Figure 5: Parabolic shell. Regular \(8\times 8\) pattern. The smaller thickness is chosen to conform with a natural characteristic length scale \(\sqrt[4]{1}\).
## 3 Key Technologies of UAV-Assisted SAGINs
### Deployment of UAVs
#### 3.5.1 Coverage  
UAV deployment can be divided into two types: aerial base station and communication relay deployment. The first type provides stable connections over large areas, offering services to regions without network access. The second type enhances end-to-end connection quality, enabling long-distance communication, and is commonly used in scenarios requiring low-latency end-to-end communication, such as military operations or emergency rescue missions. In SAGINs, UAVs serve as aerial base stations to enhance ground user coverage and provide high-probability LoS transmission, which has lower signal attenuation and noise, significantly improving communication quality. In addition, the number of UAVs directly affects coverage. A smaller number may fail to provide large-scale stable coverage, while excessive numbers increase collaboration complexity and resource waste. [PERSON] et al. [127] proposed a method to remove redundant UAVs, achieving comprehensive network coverage with minimal UAV usage while ensuring QoS and network stability. Moreover, coverage is influenced by multiple factors such as UAV position and altitude, antenna power, and beam configurations. UAV antennas generally fall into three types: omnidirectional, directional, and adjustable. Omnidirectional antennas offer broad coverage but experience significant signal attenuation with distance. To improve their communication range, UAVs can employ directional antennas with more focused signals. However, the radiation pattern of directional antennas is irregular. [PERSON] et al. [128] proposed an elliptical clustering (EC) algorithm based on analyzing actual beam direction patterns to maximize user coverage probability for UAV base stations. [PERSON] et al. [129] devised a deployment method for UAVs equipped with directional antennas, and [PERSON]
## 4 Discussion
### Applicability of Site Selection Method for Large Yellow Croaker (SSM-LYC)  
The SSM-LYC represents a multi-source data-based methodology tailored for the assessment of prospective cage aquaculture sites. The SSM-LYC method is delineated through a sequence of steps. Determining the template of YLC-A is the first step in SSM-LYC; therefore, the applicability of the template is also an important support for the reasonableness and applicability of SSM-YLC. Initially, a rational template for the LargeYellow Croaker Aquaculture Area (LYC-A) is constructed based on the various factors influencing it, which serves as our reference sequence. The comparative sequence is then derived from the environmental conditions of potential LYC-A sites and organized in the same order as the reference sequence. Given that the units of different factors vary, dimensionless data processing is applied to both the reference and comparative sequences. Subsequently, a correlation analysis is conducted on the two sequences, employing the grey relational degree calculation formula from grey system theory, chosen for its suitability in multi-factor correlation analysis and predictive evaluation [30].  
Upon elucidating the SSM-LYC method, it becomes evident that the determination of the reference and comparative sequences significantly impacts the site selection evaluation for LYC-A. Consequently, this study integrates multi-source data to ascertain these sequences, encompassing high-resolution satellite imagery, reanalysis data, measurements, and numerical simulation data. The SSM-LYC method is suitable for class II waters (the optical properties are influenced by phytoplankton and their detritus, inorganic suspended particles, and yellow substances, also known as dissolved organic matter (DOM)) in the world.  
The integration of high-resolution satellite imagery, oceanic reanalysis data, in situ measurement data, and numerical simulation data within the site selection evaluation for LYC-A demonstrates a synergistic approach that underscores the rationality and superiority of our methodology. High-resolution satellite imagery offers detailed observations of the surface characteristics, enhancing the precision of environmental assessments. Oceanic reanalysis data provide long-term climatic trends, enabling a deeper understanding of environmental dynamics [17; 49]. In situ measurement data serve as a crucial validation tool, ensuring the reliability of our findings by corroborating remote sensing and reanalysis data. Numerical simulation data, on the other hand, offer predictive capabilities, allowing for the anticipation of future environmental changes and potential risks [17; 38]. This multifaceted approach not only ensures a comprehensive and scientifically rigorous assessment but also optimizes cost-efficiency and timeliness, providing decision-makers with robust support for informed and timely decision-making in aquaculture site selection.