esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The main results of the study are as follows:  
1. A test bench was designed and assembled to study the patterns of heat and mass transfer in inclined mine workings with intense heat sources.
2. Experimental measurements of temperature and air flow velocity distributions in the test bench at two angles of inclination, different heat release powers, and varying air flow resistances validated the mathematical model of non-isothermal turbulent air flow in an inclined channel.
3. Multi-parameter modeling of air flow in an inclined channel was conducted at different heat release rates of the fire source, fan operating modes (pressure drops), and air resistances. The results were processed and generalized to build a linear regression model, which can determine the mass flow of air in an inclined channel under various parameters. This model was used to determine the dependence of the critical heat release rate on the aerodynamic parameters of real mine workings.
4. A weak dependence of the moment of reversing the air flow on the air resistance of the ventilation system surrounding the considered inclined channel when a heat source was revealed. The overturning of the air flow largely depended on the heat release rate of the fire and the initial pressure drop in the inclined part of the test bench.  
The main practical value of the results for mine ventilation is that the developed dependencies can be implemented in one-dimensional models of heat and mass transfer in mine ventilation networks and calculation methods. This will enable a more accurate consideration of the thermal effects of a fire on air flow parameters in complex mining systems without significantly increasing the computational complexity. However, further research is needed to analyze the influence of the channel inclination angle on the critical thermal power and to compare the experimental and theoretical results with known data from the literature.  
Conceptualization, L.L., M.S. and S.Z.; methodology, M.P., M.S. and S.Z.; software, M.P.; validation, M.P. and M.S.; formal analysis, M.P. and M.S.; investigation, M.P. and M.S.; resources, M.P. and S.Z.; data curation, L.L.; writing--original draft preparation, L.L. and M.S.; writing--review and editing, M.P. and S.Z.; visualization, M.P.; supervision, L.L. and S.Z.; project administration, L.L.; funding acquisition, L.L., M.P., M.S. and S.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The study was carried out with financial support from the Ministry of Science and Higher Education of the Russian Federation within the state assignment (projects No. 122030100425-6 and 124020500030-7).  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Thermal Runaway Diagnosis of Lithium-Ion Cells Using Data-Driven Method
## 3 Data-Driven Thermal Runaway Diagnosis
### Support Vector Machine  
SVM is a conventional ML technique that finds the hyperplane to maximize the margin between different classes [22]. The eligible hyperparameters of SVMs to optimize are box constraint, strategy, kernel function, feature scale, and polynomial order related to the hyperplane.  
Table 2 shows the range of these hyperparameters and selected values obtained by Bayesian optimization. A box constraint regulates the maximum penalty for violating data points of the margin to avoid overfitting. Increasing the box constraint reduces the number of hyperplanes at the cost of the training times. Two common strategies to apply  
\begin{table}
\begin{tabular}{l l l} \hline
**Data Type** & **Unit** & **Description** \\ \hline PostNegRateM & g & Mass of the negative side ejected mating. \\ \hline PostNegBoreM & g & Mass of the negative side ejected bore. \\ \hline PostNegCuM & g & Mass of the negative side copper mesh. \\ \hline BaselineEn & kJ & Released energy without corrections for heat and mass loss. \\ \hline CorrLossEn & kJ & Released energy corrected for heat loss. \\ \hline CorrEn & kJ & Released energy, corrected for both heat and mass loss. \\ \hline PosEn & kJ & Energy of unrecovered mass ejected through the positive side. \\ \hline NegEn & kJ & Energy of unrecovered mass ejected through the negative side. \\ \hline BodyEnP & \% & Percent of the released energy from the cell casing. \\ \hline PosEnP & \% & Percent of the released energy from the positive side. \\ \hline NegEnP & \% & Percent of the released energy from the negative side. \\ \hline Cause & - & Trigger mechanism to induce thermal runaway. \\ \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 1: Thermal runaway diagnosis framework using different ML techniques including SVM, NB, DTE, and MLP models where the optimal parameters are obtained by Bayesian optimization algorithm.  
binary classification algorithms for multi-classification problems are the one-vs-rest (OvR) and one-vs-one (OvO) schemes [22]. OvR divides a multi-class problem into one binary problem per class, while OvO converts it into binary problems per each pair of classes. Thus, for the given total number of unique class labels \(K\), the number of binary learners of OvR is \(K\), while the one of OvO is \(\frac{K(K-1)}{2}\), where it exhausts all combinations of class pair assignments.  
The Gram matrix is computed using the kernel function in Lagrange dual formulation for nonlinear SVM model [22]. The kernel trick essentially generates the Gram matrix \(\mathbf{G}(\mathbf{x}_{i},\mathbf{x}_{j})\) instead of the inner product of the features \((\mathbf{x}_{i}^{\top}\mathbf{x}_{j})\), where \(\mathbf{x}_{i},\mathbf{x}_{j}\in\mathbb{R}^{p}\) represent the vectors of samples \(i\) and \(j\). We use different kernel functions: linear, Gaussian, and polynomial. The feature matrix is divided by the feature scaling parameter for computing the Gram matrix. In addition, the polynomial order is the order of the polynomial kernel function.
# Application of Graph Theory for Blockchain Technologies
## 5 Graph Theory Application in Blockchain Systems
### Evaluating Security and Privacy
#### 5.2.1 Topological Attacks  
Critical nodes whose failure fragments networks widely are attractive attack vectors. Graph metrics like betweenness centrality, bridges and articulation points detect such components [71].  
Figure 6: Topological Attack.  
**Assumption 5**.: _Graph Model: The blockchain network is modeled as an undirected, connected graph \(G=(V,E)\), where:_  
* \(V\) _represents the set of nodes (miners, validator pools)._
* \(E\) _represents connections between the nodes._
* _Critical Nodes: Nodes whose removal fragments the network topology into multiple disconnected components._
* _Articulation Points: Nodes whose removal increases the number of connected components in the graph._
* _Bridges: Edges whose removal disconnects the graph._  
**Application Statement 5**.: _Nodes with high betweenness centrality are critical articulation points or bridges in a blockchain network. Targeted removal of such nodes presents an effective topological attack vector to fragment the network._  
Proof.:
* Betweenness Centrality: The betweenness centrality \(C_{B}(v)\) of a node \(v\in V\) is defined as: \(C_{B}(v)=\sum\limits_{s\
eq v\
eq t\in V}\frac{\sigma_{st}(v)}{\sigma_{st}}\) Where \(\sigma_{st}\) is the total number of shortest paths from \(s\) to \(t\), and \(\sigma_{st}(v)\) is the number of those paths passing through node \(v\).
* Critical Node Identification: Nodes with higher \(C_{B}(v)\) values participate in more shortest paths in the network. The removal of such nodes leads to disconnecting components previously connected via them.
* Network Robustness: Analyzing the distribution of betweenness centrality scores over time provides insight into the emergence of critical nodes and quantifies network resilience against topological attacks.  
Figure 6 combines betweenness centrality, articulation point status, and degree, offering a comprehensive measure of a node's role in the network. Node Size and Color, reflect the importance score, larger and more intensely colored nodes indicating higher importance. Node Border Color, Articulation points are highlighted with a gold border, distinguishing them as nodes whose removal would significantly impact network connectivity. Edges with gray representing standard connections and blue highlighting bridges are crucial links whose removal would fragment the network.  
This proves that betweenness centrality can effectively identify critical components in blockchain networks that serve as attractive targets for malicious attacks aimed at fragmenting the network topology.
# Data Privacy in the Internet of Things: A Perspective of Personal Data Store-Based Approaches
## 2 Related Works
Table 1 compares prior works based on key privacy-related dimensions. The Lifecycle Phase indicates at which stage of the data lifecycle (e.g., generation, storage, sharing, or processing) the proposed solution operates. The User-Centric criterion reflects whether the approach prioritizes user agency, such as data ownership and consent mechanisms. Decentralized refers to whether data control and processing are distributed across multiple entities rather than centralized in service providers. GDPR Compliant evaluates whether the solution explicitly addresses or aligns with core requirements of the General Data \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Authors** & **Year** & **Domain** & **Cycle** &
\begin{tabular}{c} **User-** \\ **Centric** \\ \end{tabular} & **Decentralized** & **GDPR** & **Data Control** \\ \hline [PERSON] et al. [17] & 2020 & Federated Learning & DP & ✗ & Partial & ✓ & Low \\ \hline [PERSON] and [PERSON] [16] & 2020 & Privacy Law & DS, DP, DSR, Processing & ✓ & ✗ & ✓ & Medium \\ \hline [PERSON] et al. [15] & 2020 & Taxonomy & DS, DP, DSR & ✗ & ✗ & ✗ & Low \\ \hline [PERSON] et al. [14] & 2023 & Machine Learning & DP & ✗ & ✗ & Partial & Low \\ \hline [PERSON] and [PERSON] [18] & 2024 & General & DG, DS, DP, DSR & ✗ & ✗ & ✗ & Medium \\ \hline [PERSON] et al. [19] & 2024 & Federated Learning & DP & ✗ & Partial & ✓ & Low \\ \hline [PERSON] [20] & 2025 & General & DG, DS, DP, DSR & ✓ & ✗ & ✓ & Medium \\ \hline
**This study** & **2025** & **Personal Data Store** & **DS, DP, DSR** & ✓ & ✓ & ✓ & **High** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of existing privacy-preserving approaches in IoT. Protection Regulation. Finally, Control Granularity assesses the level of precision and flexibility users have over what data are shared, how, and with whom--ranging from limited control to fine-grained policies. While prior studies have explored different approaches to IoT data privacy, including federated learning, privacy-preserving machine learning, legal frameworks, and threat taxonomies, these works present limitations when offering an approach focusing on user-centric and decentralized data management mechanism solutions. In contrast, this study distinguishes itself by focusing on understanding potential privacy protection mechanisms through the perspectives of using Personal Data Stores.
###### Abstract
With the aim of improving the forecast result, the MET started to develop the Gridded Post-Processor (GRIDPP), which is a tool for post-processing gridded weather forecasts [6]. This tool can downscale the MEPS grid of 2.5 km to a 1 km grid, and also implements different improvements on many parameters, such as altitude corrections on the air temperature parameter, and bias correction using several measurements from sensors spread over Norway. It should be noted that the primary resolution of MEPS would already be considered great compared to other regional models, such as COSMO, which has a 7 km grid, and BRAMS with a 5 km grid, both used officially in Brazil [7; 8; 9]. Furthermore, the power of generalization of artificial neural networks is widely known. [PERSON] [10] has shown the use of deep learning in several parts of the meteorological process, mainly in post-processing, and described how this could largely decrease the computational cost of the process. [PERSON] [11] used an encoder-decoder-based convolutional model to perform post-processing in a gridded forecast. [PERSON] [12] also used a 2D convolutional model to post-process weather data in his Master's thesis. [PERSON] [13] introduced a model based on U-net for correction, named CU-net, to accomplish the model forecast correction task using weather variables from the European Centre of Medium-Range Weather Forecast Integrated Forecasting System--Global. [PERSON] [14] used data from weather stations in Germany to assist the post-processing. The neural network approach significantly outperformed the proposed benchmark, being computationally more accessible. The GRIDPP is the most used weather product in Norway, and the aim of this work is to use artificial neural networks to further improve its accuracy, especially for challengingscenarios--for example, where it may not achieve precise forecasts with under 3 K of error. Therefore, the main contributions of this work are highlighted as follows:  
1. To develop and test two architectures of neural networks for estimating the air temperature based on publicly available data;
2. To present a solution that performs better in 74.8% of the samples where the current post-processing solution achieves large errors;
3. To develop an air temperature historical database for training machine learning models. Only real data are used in this work. Therefore, the air temperatures measured by weather stations were chosen as the target of the model. With the aim of trying to predict the measured temperature, the data selected as features included latitude, longitude, date, time, elevation, the output from the MEPS and the output from the GRIDPP. Next, a section will be devoted to describing how the data were obtained and transformed, as well as the description of the neural network architectures used. After this, a section will discuss the benchmarks used and their performance during the training process. Finally, a discussion of the achieved results and how to access the data used in this work is presented in the following section.
## 4 Pre-Evaluation Based on the Spatio-Temporal Model
### Accessible Path Analysis Between Origin and Destination
#### 4.3.2 Primary Transfer Path Analysis  
For an OD without direct lines, its core is the 'transfer line' analysis of all downstream stops from the origin stop. First, we find all the passing lines of the origin and destination stops through stop names and walking distances. Second, we find all downstream stops for the origin stop from the line model according to the lines, stop order, and direction. Third, we obtain the transfer line collection \(L_{f}\) of all downstream stops according to the transfer model of each downstream stop. Finally, we compare the transfer line collection \(L_{f}\) and the stopping line collection \(L_{d}\) of the terminal stop; if \(L_{f}\cap L_{d}\
eq\varnothing\), it means there is a single transfer path between the origin and the destination. We record the starting and ending stop, direction, distance, travel time, and transfer walking distance (space) of the upstream and downstream lines.  
For example, assuming that 'Route L' in Figure 4 above is the adjusted line and S is the associated line, the passenger travel between the intersecting downstream lines, such as stops \(S_{1}\) and \(S_{2}\) in the associated lines, is not affected by the adjustment of Route L, and the paths of this type of OD do not need to be calculated.  
To further improve computational efficiency, only paths between OD stops within Class 1-3 (in Chapter 3) or OD stops between Class 1-3 and Class 4 are considered. This reduces unnecessary calculations and storage for paths between less relevant stop pairs, particularly those within Class 4 stops. This selective calculation helps streamline the overall analysis and ensures faster processing of bus network adjustments.
## 4 Results and Discussion  
### Attenuation Results  
First, recorded amplitude values of each sensor at all 16 nodes from four different artificial sources were obtained. Then, by stacking amplitude values of all nodes and interpolating them; 3D volumetric contour maps were created for all sensors and all artificial sources. Concrete heterogeneity was chosen as the experimental variable and the presence of fibers increased the concrete heterogeneity. To examine the effect of this situation on attenuation, 3D-contour maps showing the distribution of elastic waves of 24 V recorded by the sensor S6 are presented in Figure 5. Each contour map has a color scale from 40 dB to 80 dB. This artificial source, corresponding to 108 dB, was recorded at a maximum of 80 dB in REF, while it decreased to 60 dB (S05) and 40 dB (S10) in steel fiber-added specimens. In polyamide and polypropylene fiber-reinforced specimens, it increased up to 90 dB in some places. This situation may be due to the higher density due to over-close aggregates in that region. In this way, waves passing through that path can have higher velocity, as if they were passing through a much more solid area, and therefore attenuation may remain at low levels. All these show that the fiber added to the mixture increases the heterogeneity of the concrete and causes more attenuation of the signals. The type and amount of fiber are also effective in attenuation. With the amount of fiber increases, heterogeneity increases and thus, attenuation increase.
## 2 Materials and Methods
### Mathematical Model
#### 2.2.1 General Equations  
**Wave equation:**  
\[\
abla\times\mu_{r}^{-1}(\
abla\times\mathrm{E})-k_{0}^{2}(\epsilon_{r}-i\alpha/( \omega\epsilon_{0}))\mathrm{E}=0, \tag{1}\]  
where \(\mu_{r}\) is the relative permeability; \(\mathrm{E}\) is the electric field; \(k_{0}\) is the wave number in free space; \(\epsilon_{r}\) and \(\epsilon_{0}\) are the relative and free space permittivity, respectively; \(\sigma\) is the electrical conductivity; and \(\omega\) is the angular frequency.  
For the electric field displacement description, the dielectric loss model was used:  
\[\epsilon_{r}=\epsilon^{\prime}-i\epsilon^{\prime\prime}, \tag{2}\]  
where \(\epsilon^{\prime}\) and \(\epsilon^{\prime\prime}\) are real and imaginary parts of permittivity.  
**Heat transfer:**  
\[\
abla\mathrm{q}=-\kappa\
abla T=Q_{e}, \tag{3}\]  
where \(\mathrm{q}\) is the heat flux and \(Q_{e}\) is the electromagnetic heat source, \(T\) is the temperature, \(\kappa\)--thermal conductivity.  
Electromagnetic heat source or electromagnetic loss density includes resistive and magnetic losses:  
\[Q_{e}=Q_{rh}+Q_{ml}=\frac{1}{2}Re(\mathrm{J}\cdot\mathrm{E}^{*})+\frac{1}{2} Re(i\omega\mathrm{B}\cdot\mathrm{H}^{*}), \tag{4}\]  
where \(\mathrm{J}\) is the electric current density, \(\mathrm{E}\) is the electric field strength, \(\mathrm{B}\) is the magnetic induction, and \(\mathrm{H}\) is the magnetic field strength.
## 4 Discussion
Therefore, to clarify the effect of this stimulation method on walking stability in the elderly, further studies should to be conducted using a randomized controlled trial with a larger sample size, including older adults. Second, a previous study reported that there are gender differences in biomechanical gait parameters [32]. Therefore, only male participants were considered in this study to eliminate the effects of gender differences. To elucidate the gender-specific effect, further studies are necessary to examine the effect of this stimulation method on walking in female subjects during walking. Third, the stimulation amplitude was set to a suprathreshold level and was adjusted to be as weak as possible by tuning it for each subject. The differences in the magnitude and frequency of vibration, including supra- and subthreshold values, affected the stride-to-stride fluctuations in the stride length and interval [10]. In addition, no consensus was reached on the effect of the differences between vibrating devices or the duration and magnitude of the stimulation on gait parameters [33]. In particular, older adults have an age-related decline in the sensorimotor and/or cognitive function [6]. In a future study, we intend to investigate the optimal setting of the vibration applied to the nail of the hallux considering the age of the subjects. Furthermore, in this study, the effectiveness of the proposed method could not be compared with that of the conventional method that stimulated the plantar surface because the subjects may feel uncomfortable while attaching the vibrator to the plantar skin and walking barefoot. In the future, we will consider fabricating shoes for the experiments to avoid discomfort and to compare the effects of both the proposed and the conventional methods. Finally, the lateral control of walking was achieved using hip abductor muscles [34]. Analysis of the kinetics and muscle activity of this muscle may be useful to verify the effect of the vibration stimulation on the lateral displacement of the CoM.
## 2 Method
### Evaluator: Filter  
The filter evaluator searches for checkerboard patterns in the geometry and outputs a scalar value \(F\in[0,1]\) that points to the amount and extent of checkerboard patterns detected. These checkerboard patterns consist of alternating high and low density values of the geometry. They are undesirable because they do not reflect the optimal material distribution and are difficult to transfer to real parts. These checkerboard patterns exist due to bad numerical modeling [30].  
Several solutions for the checkerboard problem were developed in the framework of conventional topology optimization [31]. In this work, a new strategy was chosen, which allows for inclusion of the checkerboard filter into the quality function. In the present approach, checkerboard patterns are admitted but detected and penalized accordingly. Since the type of implementation is fundamentally different, it is not possible to compare the conventional filter method with the filter evaluator. With the matrix  
\[\mathbf{H}=\frac{1}{4}\begin{bmatrix}1&-1&1\\ -1&0&-1\\ 1&-1&1\end{bmatrix}=(h_{ij})\in\mathbb{R}^{3\times 3} \tag{22}\]  
the following two-dimensional convolution operation (discrete convolution) is performed:  
\[\mathbf{V}(\mathbf{x})=|\mathcal{R}_{2d}(\mathbf{x})*\mathbf{H}|=|\mathbf{X}_ {\mathbf{M}}*\mathbf{H}|=(v_{ij}) \tag{23}\]  
In detail, the convolution operation is carried out as follows:  
\[\begin{array}{l}v_{pq}=\sum_{i=1}^{3}\sum_{j=1}^{3}\left|x_{\text{M}_{(p+i- 1)(q+j-1)}}h_{ij}\right|\\ \text{with }p,q\in\{1,2,\dots,d-2\}.\end{array} \tag{24}\]  
The convolution matrix \(\mathbf{V}\) is visualized in Figure 7 for an exemplary case.  
The matrix \(\mathbf{V}\) has high values in areas where the geometry has checkerboard patterns. A first indicator can be computed as the mean value of the convolution matrix:  
\[\bar{v}=\frac{1}{(d-2)^{2}}\sum_{i=1}^{d-2}\sum_{j=1}^{d-2}v_{ij}. \tag{25}\]  
Figure 7: (**Left**): sample geometry. (**Right**): intermediate result (convolutional matrix \(\mathbf{V}\)) in the checkerboard pattern search.  
This indicator would already be sufficient to exclude geometries with checkerboard patterns but also penalizes good geometries without recognizable checkerboard patterns. Therefore, an improved indicator is formed on the basis of the mean value and with the help of the \(e\)-function, which is less sensitive to small mean values but nevertheless results in a corresponding penalization for large checkerboard patterns:  
\[F=f_{\text{eva,F}}(\mathbf{x})=\frac{e^{\text{g-F}_{\text{k}}}-1}{e^{\text{F} _{\text{k}}}-1} \tag{26}\]  
where the parameter \(F_{\text{k}}\) controls the shape of the \(F\)-function (see Figure 8).