esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of PM2.5 Concentration on the Basis of Multitemporal Spatial Scale Fusion
## 2 Materials and Methods
### Regional Space Station Representation and Data Conversion  
We developed a PM2.5 forecasting model utilizing a fusion of multiple time scales and regional space station representation and data conversion. Due to the uneven distribution of the 30 air quality monitoring stations in Jilin Province, the network is unable to use these irregular images as input.  
Considering [PERSON]'s first geographical law [3], the study area is segmented into discrete square or rectangular pixels arranged on a grid using a raster dataset to represent the location features of meteorological stations. A regional space station representation method is used to reorganize 30 air quality monitoring stations into a regular distribution and positional relationships (longitude and latitude) are used to adapt to the data input requirements of the ConvLSTM network, which is considered an approximate representation of the regional PM2.5 co. The codes in the blocks in the figure represent the numbering names of the 30 air quality monitoring stations. Figure 2 shows the reorganization and distribution of 30 air quality monitoring stations and their concentration distribution.  
In the process of data conversion, we first define a space-time matrix of pollution in a given area. In the matrix \(X_{t}\)\(R_{HW}\), H and W represent the proportional vertical and horizontal dimensions of the surveillance region, which can be thought of as the area PM2.5 approximate representation of the concentration distribution. We also represent the pollution map at time t by accommodating the positional relationship (longitude and latitude) data input requirements of the multi-branch network. It is assumed that the data at time t is \(X_{t}\in R_{HW}\) and the spatiotemporal series \(<\)\(X_{1}\), \(X_{2}\), \(\ldots>\in R_{5 PHW}\) includes  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Item** & **Description** \\ \hline Monitoring points & 30 \\ Collection time period & 1 January 2016–31 December 2021 \\ Resolution & 1 h per record \\ PM2.5 concentrations & Including g/m\({}^{3}\) \\ Longitude & Location information \\ Latitude & Location information \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data set.  
Figure 1: The study region along with the 30 stations for monitoring air quality.  
numerous continuous frames of pollution images; S is the temporal depth, and at time t, the forecasting model predicts the most probable future sequence of K steps <Xt+1,..., Xt+k>.
# Mobile Learning and Its Effect on Learning Outcomes and Critical Thinking: A Systematic Review
## 5 Discussion
### M-Learning and Critical Thinking  
The review carried out through this research found some significant trends around the generation of knowledge linking m-learning with critical thinking. Similarly to the previous case, most of the analyzed articles were contextualized on the Asian continent, reinforcing the idea that m-learning is a topic of great interest to local researchers.  
Overall, the articles support the idea that the use of educational applications and mobile devices can improve students' critical thinking skills. Specifically, it was found that strategies such as peer assessment [79], inquiry [9], concept mapping [1], gamification [83; 84], and social and cooperative interaction [56; 81; 82] assisted by mobile devicescontribute to significantly improve students' skills regarding their judgment, reasoning, comprehension, and evaluation, contributing to more meaningful learning.  
Similarly, using mobile devices improved students' self-regulation levels [32; 74]. For [PERSON] [27], self-regulation is one of the fundamental skills of critical thinking. The self-direction process involves active control by learners of their cognitive, motivational, and behavioral engagement in learning [88]. For example, it involves reconsidering the interpretation or judgment on a controversial topic, monitoring and modifying their motivational and affective states, managing time, setting learning goals, making plans, and selecting appropriate strategies [27; 74; 88]. In literature, various authors posit that m-learning and self-regulation enhance learning more effectively when intentionally integrated into the curriculum but they warned that, for proper implementation of the initiative, students must be guided by technology-savvy educators who provide appropriate support and scaffolding [89].  
At global levels, the articles in the sample highlight how their m-learning initiatives can contribute to the development of higher-order skills. However, one has to be cautious with the results as most of the papers used self-perception questionnaires, which, and as [PERSON] et al. [31] raised, is not necessarily a true indication of improvements as sometimes students overestimate the acquisition of critical thinking skills. This area is undoubtedly relevant and deserves further research.  
Finally, it is remarkable that the volume of scientific production relating m-learning to critical thinking is significantly lower than that relating m-learning to learning outcomes. This low number may be because higher-order thinking skills are more challenging to measure than classroom learning achievements [90].
###### Abstract  
The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare.  
federated learning; resource orchestration; Internet of Medical Things; blockchain; scalability; resource efficiency
# Quantum Circuit Implementation of Multi-Dimensional Non-Linear Lattice Models
## 2 Non-Linear Lattice Modeling of Fluid Dynamics  
To highlight the challenges in creating quantum algorithms for fluid dynamics associated with non-linearity of the governing equations, it is convenient to limit the discussion to the incompressible flows of a Newtonian fluid in two-dimensional space. Then, the momentum conservation equations in the Navier-Stokes equations can be written in vector forms as,  
\[\frac{\partial}{\partial t}\bigg{(}\begin{array}{c}U\\ V\end{array}\bigg{)}+\frac{\partial}{\partial x}\bigg{(}\begin{array}{c}U^{2 }+P/\rho\\ UV\end{array}\bigg{)}+\frac{\partial}{\partial y}\bigg{(}\begin{array}{c}UV \\ V^{2}+P/\rho\end{array}\bigg{)}=\
u\bigg{[}\frac{\partial^{2}}{\partial x^{2}} \bigg{(}\begin{array}{c}U\\ V\end{array}\bigg{)}+\frac{\partial^{2}}{\partial y^{2}}\bigg{(}\begin{array}[] {c}U\\ V\end{array}\bigg{)}\bigg{]} \tag{1}\]  
where \(U\) and \(V\) are two Cartesian components of the velocity vector, and with \(P\), \(\rho\) and \(\
u\) representing the pressure, fluid density and kinematic viscosity, respectively. In terms of deriving effective quantum algorithms to numerically simulate flows governed by the Navier-Stokes equations, the non-linear convective terms on the left-hand side of Equation (1) form a key challenge. Mass conservation is enforced by the continuity equation  
\[\frac{\partial U}{\partial x}+\frac{\partial V}{\partial y}=0 \tag{2}\]  
that completes the Navier-Stokes equations for incompressible, iso-thermal flow. The spatial discretization of Equations (1) and (2) using finite-difference, finite-volume or finite-element approaches has been a topic of CFD research for many years. In the present work, an alternative approach to modeling fluid dynamics based on the Lattice Boltzmann Method (LBM) was selected for a number of reasons:  
* In the LBM, the non-linear convection terms shown in Equation (1) do not appear in this form. Instead, the convection-related part of the LBM approach becomes linear. However, the inherent non-linearity of modeling fluid dynamics manifests itself as non-linear terms in the collision step, as detailed later;
* Gradient calculations of \(U\), \(V\) and \(P\) are not used in the LBM. In case quantum-measurement-related noise is present in the hybrid classical-quantum algorithms, then using the LBM avoids increasing this noise level by computing gradients of this noise data;
* The LBM models convection by'streaming' components of a particle distribution on a regular lattice, as defined later in this section. This proves to be easier to implement than the numerical flux evaluation used in a direct discretization of Equations (1) and (2).
## 2 Materials
### Filler and Coupling Agent  
The filler is generaly an element added to the base material, most often for improvement of some its properties. More than 50 million tons of fillers are used annually in different application areas according to marketing study [30].  
Magnesium oxide (MgO) in the form of nanoparticles with an average size of 20 nm (NanoAmor) [31] has been chosen as filler, due to its chemical and physical stability, refractoriness and also due to its excellent electrical insulating properties [32]. Magnesium oxide occurs in nature as mineral periclase [33] in contact metamorphic rocks.  
Taking into account the preliminary measurements of the main electrical properties, which are deeply described in [32], the optimum weight ratio was set to 1%. Especially due to the values of dielectric strength and volume resistivity. The box plots with marked average values of investigated parameters are shown in Figure 5.  
For the improvement of the mixability between filler and matrix, the silane coupling agent \(\gamma\)-glycidoxypropytrimethoxysilane (Sigma-Aldrich) has been selected, hereinafter referred to as GLYMO. It is an epoxysilane coupling agent, specifically an organofunctional trialkoxysilane utilizing high reactivity between epoxy rings and amino groups [34]. The optimal amount of coupling agent depending on its and filler properties and it has been calculated to 18.12% of filler weight. The effect of addition of surface coupling agent is presented on selected dielectric parameters (Figure 6).  
As has been already mentioned, more detailed information about the MgO, its dielectric properties and possibilities of surface modification by \(\gamma\)-glycidoxypropytrimethoxysilane could be found in our previous studies [32; 35].  
Figure 5: Analysis of dielectric parameters of Epoxy/MgO composites (adapted from: [32]).
# Consistency Analysis and Accuracy Assessment of Eight Global Forest Datasets over Myanmar
###### Abstract
There must be some discrepancies among these datasets due to their different production purposes, satellite data sources, classification systems, and methods [24; 25; 26], which make it difficult for end-users to select an appropriate dataset. To help end-users to select their specific forest datasets, this study aims to evaluate the accuracy, consistency, and discrepancies of eight forest datasets in Myanmar (i.e., Hansen2010, CCI-LC2015, FROM-GLC2015/2017, FROM-GLC10, GLC-FCS2015/2020, and GlobeLand30-2020). Specifically, (1) we provide accuracy assessment by comparison with sample points collected from multi-temporal high-resolution images on Google Earth (GE); (2) we compare the area and spatial agreement among different datasets; (3) we explore the driving factor within the two recognized impacting factors (i.e., terrain and climate) over the discrepant areas. The following section describes global land cover datasets and ancillary data for assessment, together with the overall methodology. Section 3 presents the results of the study in terms of the accuracy and comparison of forest categories in different datasets, with the limiting factors. Section 4 discusses the reasons for the discrepancy between datasets and the future directions for improvement. Finally, Section 5 summarizes the research significance and findings of the paper, as well as the challenges and suggestions for future forest mapping.
# Design Approach for Composite Pavement Structure Incorporating Reflective Crack Considerations
## 2 Enhancing the Design Methodology of Current Composite
### Design Process of Composite Pavement Structure  
The design process for a new composite pavement structure can be systematically conducted by following the outlined steps, as illustrated in Figure 2:  
1. Systematic analysis of collected traffic parameters: Thoroughly examine the gathered traffic data and calculate the cumulative number of actions for the design lane's standard load.
2. Preliminary design of pavement structure: Determine the materials and thickness for each layer of the road surface. Additionally, establish measures to prevent reflection cracks, including the selection of appropriate materials, their locations, and the desired thickness.
3. Calculation of bending tensile stress at the bottom of the slab: Refer to the relevant specifications to assess whether the load stress and temperature stress are within the permissible limits of concrete's flexural and tensile strength.
4. Verification of maximum tensile stress at the bottom of the asphalt layer: Calculate the temperature stress of the asphalt layer, considering a combination of temperature warping stress and stress relaxation-induced temperature shrinkage stress. Also, evaluate the maximum load stress at the critical point of the asphalt layer structure. If the sum of these stresses is below or near the allowable tensile stress at the bottom of the asphalt layer, the initially selected thickness can be considered as the calculated thickness for the asphalt layer.
5. If it is not possible to satisfy both steps (3) and (4) simultaneously, it is necessary to reevaluate and adjust the thickness of the cement board and asphalt layer. Repeat the above steps until all the requirements are met.  
During the milling and paving process of the composite pavement, the thickness design of the asphalt layer can be adjusted based on the actual engineering conditions, while still adhering to the principles depicted in Figure 2.
# CubeSat Imaging Payload Design for Environmental Monitoring of Greenland
## 2 Materials and Methods
### Hardware Requirements  
To determine the most suitable camera for integration into the payload, we established specific preliminary requirements recognizing the pivotal role of the camera sensor as the core component. Our selection process was then guided by these criteria to ensure seamless interaction and optimal performance within the CubeSat system. Table 1 shows the most relevant requirements for selecting the suitable camera. Initially, we based our search on using the same camera model for both wide-field and telephoto cameras, but with different lens pairings. At the preliminary design stage, we did not calculate specific lens-sensor pairings.  
The image processor unit (IPU) controls the cameras and collects and processes the images [19]. Then, the IPU is designed to process the image using ML to determine if the image contains relevant information, and then to send them to the radio for transmission to the ground station when the satellite passes over Denmark. Therefore, its dimensions, ML capability, and power consumption are some of the main requirements shown by Table 2. The requirements provide insights into selecting suitable IPU boards afterwards.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ID** & **Requirement** & **Specification** & **Priority** \\ \hline Recam\_1 & Sensor size & 4512 \(\times\) 4512 px & High \\ \hline Recam\_2 & sensor aspect ratio & (1:1) & High \\ \hline Recam\_3 & Pixel pitch & \(\leq\)2.7 μm & High \\ \hline Recam\_4 & ADC bit depth & 10 or 12 bits & Low \\ \hline Recam\_5 & Color & RGB & High \\ \hline Recam\_6 & spectral range & 300 to 1100 nm & High \\ \hline Recam\_7 & Resolution & 20.4 or 16.2 mega pixels & High \\ \hline Recam\_8 & Shutter & GS global shutter & High \\ \hline Recam\_9 & Exposure modes & auto or manual & High \\ \hline Recam\_10 & Processing features & binning, cropping & High \\ \hline Recam\_11 & Operating temperature range & \(-20\geq+65\,^{\circ}\)C & High \\ \hline Recam\_12 & Documentation & ruggedness vibration and thermals & High \\ \hline Recam\_13 & Driver compatibility & linux & High \\ \hline Recam\_14 & Lens compatibility & C-Mount & High \\ \hline Recam\_15 & Lens mount face Interface & 17.2 mm & High \\ \hline Recam\_16 & Communication & USB2 OR USB3 and CAN & High \\ \hline Recam\_17 & Camera control & GPIO or CAN & High \\ \hline Recam\_18 & Power draw & \(<\)3.7 W & High \\ \hline Recam\_19 & Sleep modes & \(<\)1 W & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Camera requirements.
# Robot Navigation Based on Human Trajectory Prediction and Multiple Travel Modes
## 2 Methods
### Trajectory Detection and Optimization Based on Predicted Pedestrians' Positions
#### 2.2.3 The Overall Steps of the Trajectory Planning  
In the global path planning, the predicted pedestrians' occupied zones at \(t+0.2\) s and \(t+0.5\) s should not be penetrated in the detection area of the traffic state (DATS). The global planner is assumed to have properly arranged and ordered intermediate goals for the online trajectory planner. The steps of local trajectory planning are as follows:  
1. Sample a specified number of waypoints, \(\zeta_{i}\), that do not intersect with any predicted pedestrians' occupied zones at \(t+0.2\) s and \(t+0.5\) s and obstacle regions by the probabilistic roadmaps (PRM) approach for faster computation [20].  
Figure 4: Calculation of the trajectory chasing cost in \(G_{f}(p_{i})\) for \(p_{i}\) in different velocity ranges: (a) (1.2 m/s, 3 m/s); (b) [0.5 m/s, 1.2 m/s].  
Construct an exploration graph, \(G=\{V,\,E\}\), in which \(V\) is the set of vertices that include the robot's current position, \(z_{\text{s}}\); goal point, \(z_{\text{g}}\); and waypoints, \(\zeta_{i}\). \(E\) is the set of forward-directed edges that connect waypoint seeds with the orientation close to the direction from \(z_{\text{s}}\) to \(z_{\text{g}}\), and do not intersect with any predicted occupied zones and obstacle regions [15].
3. Based on the resulting graph, \(G\), extract each simple path from \(z_{\text{s}}\) to \(z_{\text{g}}\) by utilizing a depth-first search.
4. Identify the relevant topologies implicated by the predicted pedestrians' occupied zones and obstacle regions and calculate the \(H\)-signature for each resulting path (\(H\)-signature for trajectories of the same topology are equal). Only one initial trajectory is reserved for each topology and other trajectories are filtered out.
5. Optimize these initial trajectories in parallel by the TEB approach [14], and sort them based on costs from low to high.
6. Exclude the trajectories that penetrate groups detected by a modified Density-based spatial clustering of applications with noise (DBSCAN) method [21].
7. Judge the chasing costs of trajectories according to the method described in Section 2.2.2. If there is a feasible trajectory, the DATS is in the travelable state, and the first feasible trajectory found is the global optimal trajectory. If there is no feasible trajectory found, that current traffic state is no longer considered to be in the travelable state, and check if there is a low-speed-follow target and safe space ahead.
## 5 Simulation Experiments
### Parameter Impact in the DE Algorithm  
The parameters in the DE needing control include swarm scale _NP_, variation factor and crossover factor. Among them, \(F\) and _CR_ have comparatively greater impact on the algorithm. In order to discuss the impact of those two key parameters on the algorithm, suppose that _NP_ = 50 and remains unchanged. Select _CR_ = 0.2, 0.4, 0.6, 0.8 and and F = 0.1, 0.2, 0.3 for crossover experiments, totally in 12 groups to record the evolution generations of the algorithm convergence. The convergence criteria is the absolutely dominant position of some element in the experiment vectors (90% of the total amount). Each group is experimented for five times, and the average values of the evolution generations of the convergence are selected. The experiment results are shown in Table 1.  
The analysis of the simulation experiments shows that the impact of crossover factor and variation factor on the evolution process is interrelated. When the _CR_ value increases gradually and \(F\) is selected differently from small to large values, the evolution speed differs more and more. Smaller F tends to premature while bigger F is in a slower evolution. Overall, as the crossover factor CR increases and the value of the variation factor F decreases, the evolution speed increases gradually. However, as the crossover factor CR the increases, the evolution process becomes more and more sensitive to the value of the variation factor F. Although the larger value of CR has a faster evolution speed, the probability of a local optimal solution and premature is also large. After repeated experiments, the authors propose the parameters CR = 0.6\(\sim\)0.8 and F = 0.2\(\sim\)0.3 for the conditions in the experiments.