esa-sceva/satcom-chunk-collection huggingface dataset:
# Utilization of a Mobile Application for Motor Skill Evaluation in Children +
## 4 Discussion  
The study presents a mobile app for the analysis of fine motor development in children. The app proved to be useful in analyzing the fine motor performance during a specific finger task using a progressive trajectory and different speeds, detecting clear differences between movements of children from the first school years. In addition, it was easily managed by children and physiotherapists and showed a high level of satisfaction from all agents involved. The results show that the error obtained by the children decreases as age advances. The most notable differences were obtained between the first school years. Fundamentally, this decrease in error is associated with the neurodevelopmental and motor learning that children experience as they age [26]. The differences found between the first years show the importance of the early detection of any motor problem, because at those ages neuronal plasticity is higher and adaptation to stimuli and motor learning are more effective [27; 28]. When we compare the behavior of visual-motor coordination for each path (straight, parabola and zigzag paths), we see that the error increases as the execution speed increases, and is even greater when the target moves in the absence of the trajectory. These findings are directly related with the activation of brain areas related to imitation or replication of movements. When trying to follow the trajectory established by the ball, children respond better to stimuli presented at slow speeds, which allows them to adapt the movement at each moment of the trajectory more easily than at fast speeds [29; 30]. The hiding of the trajectory during the exercise requires that the child generates detailed reference copies (mental diagrams of the execution of a movement or task that allow one to anticipate it) of the exercise for its correct execution [31; 32]. For this reason, we emphasize the importance of the inclusion of this type of activity when detecting possible problems in visual-motor coordination and work to reinforce it. Finally, when we compared the levels or path types, we observed how the error increased as the children advanced in level. This was due to the incremental difficulty of the requested movements. Sudden direction changes require greater activation of the visual-motor coordination areas by the child, which translates into a significant increase in execution error at young ages where the neuromotor development of these areas is not completed. This effect will be even more accentuated in children with motor coordination problems [33]. Another point to take into account is handedness dominance. The development of dominance takes place throughout childhood, and several authors have even reflected how this specialization continues in adult life, finding stronger asymmetries in right-handed than left-handed people [34; 35]. In the present study, all participants were right hand dominant and the use of the dominant hand was controlled during all activity times by the evaluators. This control intended to eliminate any possible bias related to dominance,
## 3 Research Methodology  
The research methodology followed a three-stage process. Stage one involved identifying key factors through a literature review and expert interviews. Stage two focused on data collection and integration: experts evaluated factor relationships using linguistic variables, which were then converted into grey numbers. The Aczel-Alsina function was applied to synthesize these evaluations while addressing expert uncertainty and inconsistency. In stage three, the DEMATEL and ISM methods were employed to analyze the relationships between factors. Figure 1 illustrates this research process in a flow chart, and the notations used are illustrated in the Abbreviations Section.  
Figure 1: Research flow chart.
## 8 Blockchain in Autonomous Vehicles  
The potential of blockchain technology could revolutionize the operations and functionality of autonomous vehicles. As a distinctive form of distributed ledger technology (DLT), blockchain provides a platform for secure and transparent transactions and data exchanges, which are pivotal to autonomous vehicles. It strengthens security, maintains privacy, and builds trust among users, vehicles, and the various entities embedded in the transportation ecosystem. Moreover, it facilitates real-time data exchange, enhances the decision-making abilities of autonomous vehicles, and paves the way for innovative business models. With the integration of blockchain technology, a more efficient, interconnected, and intelligent transportation system can be envisaged.  
Blockchain technology can be used in autonomous vehicles in several ways:  
1. Enhanced situation awareness: Blockchain technology offers a secure and reliable mechanism for autonomous vehicles to share information, elevating their situational awareness and decision-making capabilities. It enables vehicles to exchange real-time data regarding traffic conditions, potential road hazards, and other relevant factors. This collected information is stored in the blockchain, which provides a transparent and tamper-resistant record [114].
2. Reputation management: Employing blockchain technology enables the development of a reputation system for autonomous vehicles in which reputation points are awarded for the sharing of accurate and beneficial data. This enables vehicles to actively contribute to the network and ensures the transmission of high-quality data [115].
3. Security for firmware updates: Blockchain can be utilized to secure firmware updates in autonomous vehicles, mitigating the risk of harmful attacks that could compromise the vehicle systems [116].
4. Liability attribution: In scenarios involving accidents with autonomous vehicles, blockchain technology can play a crucial role in accurately identifying the vehicle at fault. This assists in resolving disputes and ascertaining fair liability assignment [117].
5. Ride-hailing platforms: Blockchain technology is applicable in the creation of secure, decentralized ride-hailing platforms for autonomous vehicles. Such platforms provide a secure and transparent process for users to arrange rides and for vehicles to receive appropriate compensation for their services [118].
6. Internet of Vehicles: The implementation of blockchain can facilitate a secure, decentralized network of vehicles or devices, drawing parallels to the Internet of Things. This would enable vehicles to communicate amongst themselves, share data, and coordinate actions in a secure, transparent manner [119; 120].
# Cross-Attention U-Net for Elastic Wavefield Decomposition in Anisotropic Media
## 2 Theoretical Framework  
### Elastic Wave Vector Decomposition in Anisotropic Media  
Wave mode separation in elastic wavefields is commonly performed using Helmholtz decomposition, which allows the vector wavefield \(\mathbf{U}=(U_{x},U_{y},U_{z})\) to be expressed as the sum of P- and S-wave components:  
\[\mathbf{U}=\mathbf{U}_{P}+\mathbf{U}_{S}. \tag{1}\]  
The P-wave component satisfies \(\
abla\times\mathbf{U}_{P}=0\), while the S-wave component satisfies \(\
abla\cdot\mathbf{U}_{S}=0\)[3]. However, in anisotropic media, the polarization directions of qP and qS waves are not necessarily aligned with the wave vector \(\mathbf{k}\), rendering simple divergence and curl operators insufficient for mode separation [4]. Instead, the vector wavefield must be projected onto the polarization vectors of each wave mode [2,5].  
The polarization vectors \(\mathbf{p}_{qP}\), \(\mathbf{p}_{qSV}\), and \(\mathbf{p}_{qSH}\) are computed by solving the Christoffel equation:  
\[(\mathbf{C}-\rho V^{2}\mathbf{I})\mathbf{p}=0, \tag{2}\]  
where \(\mathbf{C}\) is the stiffness tensor, \(\rho\) is the mass density, \(V\) is the phase velocity of each wave mode, and \(\mathbf{p}\) is the polarization vector. To obtain a wave vector decomposition that is analogous to Helmholtz decomposition, the vector wavefield is first expressed in the wavenumber domain. Applying the Fourier transform to the vector wavefield U results in its representation in the wavenumber domain as \(\mathbf{\tilde{U}}(\mathbf{k})\). The P- and S-wave separation expressions in isotropic media can be reformulated as follows:  
\[\mathbf{\tilde{U}}_{P}(\mathbf{k})=\mathbf{k}\bigg{[}\frac{\mathbf{k}\cdot \mathbf{\tilde{U}}(\mathbf{k})}{\left|\mathbf{k}\right|^{2}}\bigg{]},\ \mathbf{\tilde{U}}_{S}(\mathbf{k})=-\mathbf{k}\times\Big{(}\mathbf{k}\times \mathbf{\tilde{U}}(\mathbf{k})\Big{)}. \tag{3}\]  
Since the wave vector \(\mathbf{k}\) does not necessarily align with the polarization directions in anisotropic media, it is replaced with the polarization vectors obtained from the Christoffel equation, yielding the anisotropic wave vector decomposition:  
\[\mathbf{\tilde{U}}_{qP}(\mathbf{k})=\mathbf{p}_{qP}(\mathbf{k}) \Big{[}\mathbf{p}_{qP}(\mathbf{k})\cdot\mathbf{\tilde{U}}(\mathbf{k})\Big{]}, \tag{4}\] \[\mathbf{\tilde{U}}_{qSV}(\mathbf{k})=\mathbf{p}_{qSV}(\mathbf{k}) \Big{[}\mathbf{p}_{qSV}(\mathbf{k})\cdot\mathbf{\tilde{U}}(\mathbf{k})\Big{]},\] \[\mathbf{\tilde{U}}_{qSH}(\mathbf{k})=\mathbf{p}_{qSH}(\mathbf{k}) \Big{[}\mathbf{p}_{qSH}(\mathbf{k})\cdot\mathbf{\tilde{U}}(\mathbf{k})\Big{]}.\]  
This formulation extends Helmholtz decomposition to anisotropic media by incorporating polarization vectors derived from the Christoffel equation, ensuring physically accurate separation of elastic wave modes.
# Stability Analysis of GNSS Stations Affected by Samos Earthquake
## 4 Conclusions  
This study focuses on the coseismic displacements recorded at numerous stations during the Samos earthquake. According to the results, vertical offsets and horizontal displacements both contribute to the overall deformation pattern. Due to its proximity to the epicenter, the SAMO GNSS station experiences the largest displacement; however, other stations including CESM, MNTS, IZMI and IKAR also experience significant displacements. The S-transformation model with hypothesis testing is used to evaluate station stability. Unstable stations are identified through iterations, and they are excluded from subsequent analyses until no unstable station remains. Threshold analysis is also utilized and similar results are yielded. Additionally, the results obtained from different coordinate systems are compared and produce consistent outcomes, indicating that the results are independent from the selected coordinate system. The methods used in the study successfully identify stations significantly influenced by the earthquake, providing valuable insights for deformation analysis. Displacements obtained with geodetic methods may provide insights into the behavior of the underlying faults, the potential for seismic activities and the associated risks to nearby infrastructures. Additionally, the analysis used in this study may contribute to the development of more effective seismic hazard models and early warning systems, ultimately helping to improve resilience and preparedness in earthquake-prone regions. It should be highlighted that stations with significant displacements can be found by the analysis, but not their magnitudes. The S-transformation method proved successful in detecting unstable reference points and stations with significant coseismic offsets, allowing for accurate evaluation of the earthquake's effects on GNSS stations. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available in the article.
## 3 Results
### Experiment Settings
#### Model Parameter Configuration  
The model used in this study was implemented based on the PyTorch framework. In the Input Embedding layer, word embedding vectors were obtained by adding learnable word vectors and positional vectors, both with a dimension of 768. The maximum supported sequence length for the model was set to 512. The Feature Extraction layer consisted of 1 CNN layer and 3 stacked Attention layers. The CNN had parallel structures with kernel sizes of 1, 3, and 5, while the Attention layer had 4 attention heads. To reduce computational complexity, the word embedding vector dimension of 768 was reduced to 256 in both the CNN and Attention structures, and after the Feature Fusion layer, the extracted features from each layer were combined back to 768 dimensions. LayerNorm was applied for normalization after the embedding layer, CNN layer, multi-head attention layer, and linear transformation layer. Dropout with a rate of 0.5 was used for regularization. During the training phase, the Adam optimizer was used with an initial learning rate of \(1\times 10^{-3}\) and weight decay of \(1\times 10^{-4}\). The batch size was adjusted based on the dataset size and computer performance. The model was trained for 100 epochs. The activation function used in the model was the gelu linear activation function.
## 4 Discussion
For instance, wearable devices can enable real-time monitoring and adjustments to therapeutic exercises, while virtual reality (VR) can offer immersive rehabilitation experiences that enhance motor skills and cognitive function more engagingly [14; 18; 49; 50]. Adopting these technologies more widely could help alleviate the burden on healthcare systems by providing more accessible and efficient care options [44]. Therefore, physiotherapists in Greece must stay updated on technological advancements to deliver high-quality services, with the primary goal of benefiting and satisfying their patients. This aligns with the European Physiotherapy Guidelines [9], which emphasize continuing professional education and the acquisition of new knowledge as essential for ensuring professional development and enhancing the quality of care provided to patients with Parkinson's Disease [7]. Ultimately, it is noteworthy that a limitation of this study could be the use of the online questionnaire, which requires the ability to use and access modern communication technologies [29]. At the same time, the geographical distribution of participants/students in the study was not recorded. Additionally, an important limitation was the small sample size out of a total of 9238 physiotherapists licensed to practice in Greece. However, there are no official data in Greece on the exact number of physiotherapists whose main scientific occupation is neurological or geriatric physiotherapy. In future studies, it would be beneficial to investigate further the perspectives of physiotherapists concerning the barriers they encounter in communication with other professionals, the utilization of assessment tools, and the adoption of modern technology across various countries. In Greece, it is essential to document these perspectives in both mainland and remote regions, as the barriers may differ. Additionally, documenting the primary areas of clinical practice for physiotherapists in Greece would be advantageous. This information could support the execution of targeted research across various scientific domains of physiotherapy.
## 6 Results and Discussion
However, in the best fuzzy solution this percentage rises to \(3.5\%\). Most instances show this steady growth when increasing the uncertainty level, which confirms that fuzzy scenarios have a higher uncertainty level when compared with deterministic and stochastic scenarios. Finally, the average safety stock increases when both variability and uncertainty levels rise, since more protection against uncertainty is necessary in both cases. Results corresponding to our best deterministic solution in Table 3 were yielded assuming that the realized demand is deterministic. Hence, an additional experiment has been performed, in which this solution (called henceforth OBD) is tested in a hybrid fuzzy-stochastic environment, using \(0\%\) of safety stock protection against uncertainty. Figure 6 compares this solution's results with our best-found hybrid fuzzy-stochastic solution (OBF) in terms of failure costs. Results for 12 Akca's instances are depicted for each demand variability scenario. Extreme points in dashed lines indicate the average cost for each set of data. As expected, average failure costs show an increasing trend when the variability grows, regardless of the type of solution. Conversely, Figure 6 shows that OBF outperforms OBD when tested under uncertainty conditions. This fact demonstrates the quality of our fuzzy simheuristic approach, especially in scenarios where the demand variability is high. Table 4 compares two types of hybrid fuzzy-stochastic solutions. Firstly, we show our best solution with a single facility size alternative given by the original Akca's instances  
Figure 6: Failure costs of our best deterministic and our best hybrid solutions. i.e., the solution is not flexible since only one size is available to select. Secondly, we show our best flexible solution, which corresponds to our best hybrid solution in Table 3. When comparing the total costs of both types of solutions, the negative gap obtained for all instances and under all variability levels shows the advantages of considering facility sizing decisions. For example, we reach a maximum absolute gap of 7.71% in total cost savings for a single instance. In average, both opening and routing costs decrease whenever alternative depot sizes are available. Nevertheless, each instance shows different results regarding OC and RC. The most evident case is that in which opening costs decrease. Clearly, this is a direct result of having smaller facility size alternatives. Without loss of generality, all examples below take as reference the high variability scenario. For example, the instance _Cr30x5b-3_ has a total demand of 1620. Both flexible and nonflexible approaches design the same routes and yield equal routing costs. Nevertheless, the nonflexible approach locates two depots of size 1000 each. Conversely, our flexible approach locates one depot of size 1000 and one depot of size 750. Hence, the nonflexible solution assigns an extra capacity that is not necessary under the problem's current conditions.
## 3 Results
### The Effect of Dietary Chlorella and Spirulina Powder on Egg Yolk Color in Fresh and Boiled Eggs  
Table 8 presents the effects of chlorella and spirulina powder dietary inclusion on egg yolk color in fresh and boiled eggs. The treatment factor (CON, CV2%, SP2%) exhibited a significant effect on the L*, a*, and b* parameters. The \(p\)-values registered for all three parameters indicated a highly significant difference (\(p<0.0001\)) between the treatments. The boiling time factor (fresh yolk, 10, 20, and 30 min.) also had a highly significant effect (\(p<0.0001\)) on the L*, a*, and b* parameters, which indicated that the boiling time had a significant impact on the egg yolk color. The interaction between treatment and boiling time showed highly significant (\(p<0.0001\)) effects on the a* and b* parameters. However, the interaction effect was not statistically significant for the L* parameter (\(p=0.293\)). The L* parameter represents the lightness of the color, and therefore higher values indicate lighter or brighter colors, while lower values indicate darker colors. Comparing the values across different treatments and boiling times, it can be observed that as the boiling time increased, the L* values showed a tendency to increase as well. This suggests that the egg yolks become lighter in color as they are boiled for a longer duration. The darkest color values were noticed for SP2%, followed by CV2% (\(p<0.0001\)). The a* parameter values (red-green color axis, positive values indicate redness, negative values indicate greenness) varied across different treatments and boiling times. The highest value for a* parameters was noticed on SP2%, followed by CV2% (\(p<0.0001\)), compared to CON. However, there was no consistent trend concerning boiling time. The interaction effect between treatment and boiling time was highly significant (\(p<0.0001\)), indicating that the combination of treatment and boiling time influenced the red-green color component. Positive values indicate more yellowish, while negative values indicate more blueness. Similar to the a* parameter, the b* values also varied across treatments and boiling time. The b* parameter (yellow-blue color) registered the highest value on CV2%, followed by SP2% (\(p<0.0001\)), compared to CON. The interaction effect between treatment and boiling time was highly significant (\(p<0.0001\)), suggesting that the combination of treatment and boiling time affects the yellow-blue color component.
## 3 Structures of Microgrids
### Operation Modes of a Microgrid  
A microgrid enters islanding mode when it cuts off from the grid. In this scenario, a voltage control mode is applied by the Distributed Generator (DG) sources in order to maintain the voltage of the adjacent loads constant [42]. A point of common coupling (PCC) connects the microgrid to the grid. It acts as an interface for the exchange of electrical energy [43]. At this stage, the microgrid needs to satisfy the set interface specifications, like those found in the IEEE Standard 1547 series [44]. In grid-connected mode, the microgrid operates in accordance with distribution network regulations independently of the main power system [45]. Whether the microgrid is in connected or grid-islanded mode, it is still subject to uncertainties and imbalances that need to be managed in order to maintain the microgrid's proper operation (Figure 15).  
Figure 14: DC microgrid topologies, prepared and adapted by the authors from [41].
###### Abstract  
The mining face under the close coal seam group is affected by the superposition of the concentrated stress of the overlying residual diagonally intersecting coal pillar and the mining stress, which can easily cause the instability and damage of the section coal pillars during the process of mining back to the downward face. Additionally, the traditional methods of monitoring such as numerical simulation, drilling peeping, and acoustic emission fail to realize the real-time and accurate deformation monitoring of the internal deformation of the section coal pillars. The introduction of the drill-hole-implanted fiber-optic grating monitoring method can realize real-time deformation monitoring for the whole area inside the coal pillar, which solves the short board problem of coal pillar deformation monitoring. However, fiber-optic monitoring is easily disturbed by the external environment, which is especially sensitive to the background noise of the complex underground mining environment. Therefore, taking the live chicken and rabbit well of Shaanxi Daliuta Coal Mine as the engineering background, the ensemble empirical modal decomposition (EEMD) is introduced for primary noise reduction and signal reconstruction by the threshold determination (DE) algorithm, and then the singular matrix decomposition (SVD) is introduced for secondary noise reduction. Finally, a machine learning algorithm is combined with the noise reduction algorithm for the prediction of the fiber grating strain signals of coal pillar in a zone, and DBO-LSTM-BP is constructed as the prediction model. The experimental results demonstrate that compared with the other two noise reduction prediction models, the SNR of the EEMD-DE-SVD-DBO-LSTM-BP model is improved by 0.8-2.3 dB on average, and the prediction accuracy is in the range of 88-99%, which realizes the over-advanced prediction of the deformation state of the coal column in the section.  
legacy obliquely intersecting coal pillar; fiber grating; noise reduction; deformation prediction; EEMD; singular value decomposition