esa-sceva/satcom-chunk-collection huggingface dataset:
# A Practical Approach to Deploying a Drone-Based Message Ferry in a Disaster Situation +
## 2 Overview of Locally Accessible Cloud System (LACS)  
In this section, we first describe an overview of LACS, its features and uses. Subsequently, we explain in detail the use of LACS in a disaster response to collect and deliver vital information.
# Optimization of Associative Knowledge Graph Using TF-IDF Based Ranking Score
## 2 Related Work
### Associative Knowledge-Graph Extraction Using Data Mining
Figure 6 represents the knowledge graph of association words. The result shows the associated-words knowledge graph on the basis of the association rule algorithm. A knowledge graph is extracted through a directed graph. A node represents a term. An edge represents the direction of the graph. A direction represents the association of words. The upper graph of Figure 6 shows the number of the generated rules visualized, which is about 2400. In addition, the lower part of the figure shows the related rules by visualizing traffic-related subgraphs out of 2400 rules. The reason for visualizing the subgraph is that there is a problem--rules generated in the entire graph are not easily visible, so a part of the overall association rule graph is enlarged and visualized. The bidirectional arrow in the generated rules means that both words have their association rules. Therefore, although the number of rules is 2400, the generated knowledge could be wider. Since a particular word has associations with multiple words, the rules and unnecessary knowledge caused by word duplication are included. Additionally, there is a limitation in obtaining knowledge due to the low efficiency of visualization. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lhs => Rhs** & **Support** & **Confidence** & **Count** \\ \hline local => news & 0.18575 & 0.58719 & 266 \\ news => local & 0.18575 & 0.34590 & 266 \\ time => local & 0.16829 & 0.62597 & 241 \\ local => time & 0.16829 & 0.53200 & 241 \\ time => news & 0.16689 & 0.62077 & 239 \\ news => time & 0.16689 & 0.31079 & 239 \\ relation => news & 0.14315 & 0.54376 & 205 \\ news => relation & 0.14315 & 0.26658 & 205 \\ seoul => news & 0.12220 & 0.72614 & 175 \\ news => seoul & 0.12220 & 0.22756 & 175 \\ police => news & 0.11592 & 0.55333 & 166 \\ news => police & 0.11592 & 0.21586 & 166 \\ communication => news & 0.11522 & 0.54635 & 165 \\ news => communication & 0.11522 & 0.21456 & 165 \\ reporter => news & 0.11451 & 0.58320 & 164 \\ news => reporter & 0.11451 & 0.49093 & 164 \\ traffic => speed & 0.11449 & 0.61239 & 160 \\ speed => traffic & 0.11449 & 0.41233 & 160 \\ distance => time & 0.11389 & 0.59234 & 159 \\ time => distance & 0.11389 & 0.31456 & 159 \\ gas-station => explosion & 0.10998 & 0.56133 & 155 \\ explosion => gas-station & 0.10998 & 0.32331 & 155 \\ expressway => speed & 0.10967 & 0.65333 & 152 \\ speed => expressway & 0.10967 & 0.45467 & 152 \\... &... &... &... \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Association Rules based on the Apriori Algorithm.
## 3 Results  
### Optimal Conditions for HPLC  
#### 3.1.1 Effect of Mobile Phase pH on Retention of OATD-02  
The influences of mobile phase on separations were investigated and summarized. OATD-02 has two pKa at approximately 1.6 and 7.1. The peak shapes were narrow and symmetrical when the mobile phase was comprised of acetonitrile and 10 mM sodium bicarbonate solution. Accordingly, different mobile phases were tested: ammonium for mate, sodium acetate with acetonitrile or methanol. The pH values of the aqueous buffers were measuring before mixing them with the organic modifier. The pH of the mobile phase had impact on resolution, selectivity and peak shapes of analyzed compounds. At a lower pH of approximately 6.3 in ammonium formate solution, basic analytes such as OATD-02 were protonated and eluted more quickly without sufficient resolution from others compounds. At a higher pH in sodium acetate of approximately 7.3, OATD-02 were more retained, because there were a mixture of neutral and protonated amine groups. Peak tailing was observed because the pKa of OATD-02 is similar to the pH of the buffer solution and the analyte eluted as both a charged and uncharged species. When the acetonitrile and sodium bicarbonate (10 mM) were used as mobile phases, a significant improvement in peak shape of OATD-02 was observed compared with the combination of acetonitrile and ammonium formate or sodium acetate as shown Figure 2 (A), (B), (C). The addition of sodium bicarbonate to the mobile phase was necessary to obtain the best separation and run time as shown in Figure 2 (C). At the highest pH in sodium bicarbonate, approximately 8.3, in OATD-02 there were mostly uncharged amine groups and all basic compounds were more retained without tailing peaks. Finally, acetonitrile and sodium bicarbonate (10 mM) were chosen as the optimal chromatographic mobile phase. Chromatographic analysis of the samples was performed within 23 min. As seen in Figure 2 (C), the impurities and OATD-02 in crude mixture were separated well with a resolution higher than 2.0 among each other.  
The most important chromatographic parameters (area, high, efficiency expressed as number of theoretical plates, resolution, peak width and peak symmetry) obtained in different mobile phase compositions are summarized in Table 1. Regarding the peak resolution, number of theoretical plates and peak width, the best results for all peaks were obtained with the sodium bicarbonate in mobile phase.
# Experimental Investigation into the Number of Phases in Debris Flows
## 7 Conclusions  
The number of phases is a key consideration in the field of debris flow research and remains a matter of debate. Here, we experimentally investigated the number of phases in fully developed debris flows (i.e., the main bodies of debris flows) using six sediments with three maximum diameters of up to 30 mm from representative sandy and muddy debris-flow deposits in China. Fluid escape tests, displacement experiments, relative motion experiments, and rheometric tests were conducted using 12 slurries prepared with the sediments (two solid volume concentrations for each sediment). The results from the four types of experiments show that debris flows are close to one flow and far from two-phase flow.  
Under both gravity and artificial hydraulic and mechanical forcing, no relative motion of water or fluid composed of water and fine-grained particles and solids (phase separation) occurred in both the experimental debris flows and static slurries. Water or fluid capable of moving independently of (coarser-grained) solids does not exist or is negligible. This demonstrates that water and solid particles of all sizes in debris flows move together as a single fluid and are deposited en masse via freezing.  
The behavior of the experimental debris flows is fairly similar to that of Bingham materials, which is quite different from that of two-phase experimental hyperconcentrated flows. This shows that water and solids in debris flows move as a whole and that debris flows may be regarded as one-phase flow, considering that Bingham behavior is exhibited only by continuous, homogeneous, isotropic, and monophasic flows.  
In conclusion, debris flows behave more like Bingham flows, are far from biphasic flow, and should be treated as one-phase flow rather than two-phase flow. The two-phase hypothesis seems to arbitrarily complicate debris-flow problems and, in practice, leads to confusion between peculiar debris-flow phenomena and common hyperconcentrated flow processes in mountain basins.  
Clasts (solids) of any size, including those of meters, in the continuous grain-size spectrum of the sediment comprising a debris flow are the components of the flow. Monophasic debris flows can propagate alone and function as transporting medium by carrying blocks that are much coarser than the coarse end-member of their continuous gradation (transported object).  
Funding acquisition, Z.-M.X.; investigation, B.L.; writing--original draft, B.L.; writing--review and editing, Z.-M.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Nature Science Foundation of China (41931294) and the National Natural Science Foundation of China-Yunnan Joint Fund (U1502232, U1033601).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no competing interests.
## 2 Mobile M2M Literature Review
### M2M Traffic  
It is important to distinguish mobile M2M communications from mobile Human-based (H2H or H2M) communications. Small and infrequent data transmissions will be more common in M2M [17; 21], and thus the knowledge developed for Human-based traffic, which is mostly bursty (web browsing), bulky (file transfer), or constant or variable bit rate streams (VoIP or video) can be difficult to apply directly to M2M. [PERSON] _et al_. [17] mention that M2M and Human-based traffic differ further in traffic direction, since M2M traffic direction will be mainly uplink, while Human-based traffic is either balanced or mainly downlink. M2M applications will be duty-cycled and should have very short connection delay to guarantee fast access to the network when waken up, while Human-based applications tolerate longer connection delays but are very demanding once connections are established [17]. M2M applications might require very high priority with a detailed level of granularity due to the transmission of critical information, whereas priority for Human-based applications is mainly among applications for each user and not between different users [17]. Finally, M2M will have higher number of devices and may be required to operate for years or decades without maintenance, but users can recharge/replace batteries [17].
# Soft Sets Extensions: Innovating Healthcare Claims Analysis
## 1 Introduction  
In the fast-evolving field of healthcare research [1; 2], the complexity of data, particularly within healthcare claims, mirrors the intricacies of biological systems. The ability to model and analyze this vast, multifaceted data is crucial for making informed decisions about patient care, diagnostics, and treatment pathways. Soft sets and their numerous extensions provide a valuable toolkit for addressing the uncertainty and variability prevalent in healthcare claims data, which encompasses details about treatments, providers, costs, and prescriptions [3].  
These mathematical constructs, first introduced by [PERSON] in 1999 [4], offer a flexible framework for tackling imprecision. Healthcare claims data, where uncertainty is intrinsic, benefits from soft set theory, which models this uncertainty more effectively than classical statistical methods [5; 6]. Since their inception, soft sets have evolved significantly. Extensions like HyperSoft Sets, introduced by [PERSON] in 2018 [7], and more recent advancements such as SuperHyperSoft Sets, IndetermSoft Sets, IndetermHyperSoft Sets, and TreeSoft Sets [8; 9; 10; 11] have been developed to address specific challenges in handling intricate relationships within healthcare data. Furthermore, the contributions of [PERSON] in 2010 with MultiSoft Sets have further enriched these mathematical tools [12].  
While the evolution of soft sets has been robust, with applications spreading across diverse fields, including bioinformatics, chemistry, and public health, healthcare remains a relatively underexplored area for these models. The paucity of studies applying soft set theory to healthcare claims data presents an opportunity for significant advancements.  
A legitimate question arises: _How can the application of soft set theory and its recent extensions in analyzing and modeling healthcare claims data contribute to improving diagnostics and personalized treatments?_  
Recent works have examined the fusion of soft set theory with fuzzy logic, yielding combinations like neutrosophic, picture fuzzy, and plithogenic soft sets, each contributing unique perspectives on handling uncertainty. TreeSoft Sets, for instance, offer promise for improving healthcare analytics in the era of Industry 4.0 [13], while IndetermSoft Sets are increasingly applied to real-world challenges in healthcare [14; 15]. However, further research is needed to explore how these methodologies can be combined with modern computational techniques to address complex real-world problems.  
Future research should focus on refining these applications and addressing existing limitations, ensuring that soft set methodologies can be fully leveraged to enhance healthcare decision-making and improve patient outcomes.
###### Abstract  
The IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) was proposed for various applications of IPv6 low power wireless networks. While RPL supports various routing metrics and is designed to be suitable for wireless sensor network environments, it does not consider the mobility of nodes. Therefore, there is a need for a method that is energy efficient and that provides stable and reliable data transmission by considering the mobility of nodes in RPL networks. This paper proposes an algorithm to support node mobility in RPL in an energy-efficient manner and describes its operating principle based on different scenarios. The proposed algorithm supports the mobility of nodes by dynamically adjusting the transmission interval of the messages that request the route based on the speed and direction of the motion of mobile nodes, as well as the costs between neighboring nodes. The performance of the proposed algorithm and previous algorithms for supporting node mobility were examined experimentally. From the experiment, it was observed that the proposed algorithm requires fewer messages per unit time for selecting a new parent node following the movement of a mobile node. Since fewer messages are used to select a parent node, the energy consumption is also less than that of previous algorithms.  
low power and lossy networks; RPL; mobility; energy efficiency; solicitation transmission +
Footnote †: journal: Sensors
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 2 Materials and Methods
### Segmentation of Signals into Strides  
Human gait is characterized as \"the succession of phases separated by foot-strikes (the foot is in contact with the ground) and takeoffs (the foot leaves the ground)\" [39]. A stride is defined as a complete cycle of heel strike of a foot to the next heel strike of the same foot [40]. The low-level signals of IMUs are known to be noisy and suppression of noise is necessary in order to minimize the effect of the noise and correctly segment the raw signals into strides. To reduce the noise, we employed moving average filter [41; 42], which is a known method of noise suppression. A window size of nine samples was used to smooth the raw signal. The smoothed signal can be decomposed into single steps  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Accelerometer** & **Gyroscope** \\ \hline Axes & 3 & 3 \\ Noise & 0.0029 m/s\({}^{2}\)/\(\sqrt{\mathrm{Hz}}\) & 0.01 deg/s/\(\sqrt{\mathrm{Hz}}\) \\ Output Rate & 5 to 100 Hz & 5 to 100 Hz \\ Range & \(\pm\) 2 g, \(\pm\) 4 g, \(\pm\) 8 g\({}_{\mathrm{\varphi}}\)\(\pm\) 16 g & \(\pm\) 2000 deg/s \\ Resolution & 16 bits & 16 bits \\ \hline \hline \end{tabular}
\end{table}
Table 4: Technical specifications of MPU-6500 IMUs.  
Figure 2: Placement of sensors at different body parts: (**a**) chest (smart phone); and (**b**) lower back (smart phone).  
using local maxima (peaks) or local minima (valleys) [43; 44; 45]. We detected the local minima in \(y\)-axis of acceleration signal only as it is the gravitational axis. The same valleys were used to segment all of the 3D accelerations and 3D angular velocities. This technique assured that the length of the segmented step remained the same for all 6D components. For strides, we segmented the signal only when the same foot consecutively stuck the ground twice, i.e., two consecutive steps (Figure 3).
## 5 Research Gaps and Prospects
### High-Precision 3D Reconstruction and Dynamic Phenological Monitoring  
To achieve high-precision and dynamic perception of ecological environments, future UAV-RS research will increasingly advance toward 3D modeling and ecological process simulation. While traditional 2D imagery can capture land cover and changes, it often fails to adequately represent terrain, structural complexity, and ecological processes in regions with significant elevation variation, dense vegetation, or urban infrastructure. Consequently, researchers are actively exploring the integration of 3D reconstruction techniques with phenological modeling, promoting a transition in remote sensing from \"seeing\" to \"understanding deeply and comprehensively.\"  
(1) Hybrid Modeling by Integrating SfM and LiDAR.  
SfM offers low-cost and easily implemented 3D reconstruction in multi-view image scenarios, but its performance is limited by poor texture and variable lighting. By integrating lightweight LiDAR with point cloud and mesh data from SfM, researchers can improve the accuracy and robustness of 3D modeling through multi-scale ICP (Iterative Closest Point) algorithms and deep learning-based point cloud registration networks [143; 150]. This hybrid modeling approach is particularly effective for high-precision spatiotemporal monitoring of vegetation canopies, riverbank deformation, and urban structural changes.  
(2) Phenological Modeling and Ecological Response.  
Real-time and continuous phenological monitoring relies on high-frequency remote sensing data. UAV-RS enables the acquisition of high-resolution vegetation indices (e.g., NDVI, SAVI) and land surface temperature, which can be used to construct surface phenology time series. When combined with meteorological variables and soil moisture data, time-series clustering and change-point detection algorithms can be employed to identify plant growth phases, stress events, and ecosystem response mechanisms [232; 233]. This analytical framework is essential for assessing the impact of climate change on agricultural productivity and ecosystem health.
## 2 Materials and Methods  
The method used the SVM algorithm for satellite imagery classification. In addition to selected Sentinel-2 multispectral bands, vegetation indices were added to study their ability to increase classification accuracy (Figure 1) individually and as a group of indices.
## 4 Analysis of Internal Forces and Deformations in Circular Shaft Structures  
### Model Formulation and Assumptions  
The circular shaft is shown in Figure 9a. The inner and outer diameters of the shaft support structure are denoted as \(r\) and \(R\), respectively. It is subjected to internal pressure \(p_{1}\) and external pressure \(p_{2}\). To determine the distribution of internal forces and deformations, the following assumptions are made:  
(1) Cracking of the structure is ignored, and the stress and deformation of the supporting structure are assumed to be elastic.  
(2) Three-dimensional effects are ignored, and the support structure is modeled as a thick-walled cylinder.  
Under the above assumptions, the stress and deformation problem in the shaft support structure is analyzed in the polar coordinate system using elasticity theory. The analytical model is shown in Figure 9b, with boundary conditions satisfying Equation (32).  
\[\left\{\begin{array}{l}\sigma_{\rho}\left|{}_{\rho=r}\right.=-q_{1}\\ \sigma_{\rho}\left|{}_{\rho=R}\right.=-q_{2}\\ \tau_{\rho\varphi}\left|{}_{\rho=r}\right.=0\\ \tau_{\rho\varphi}\left|{}_{\rho=R}\right.=0\end{array}\right. \tag{32}\]  
where \(\sigma_{\rho}\) is the radial stress; \(\sigma_{\varphi}\) is the circumferential stress; \(\tau_{\rho\varphi}\) is the shear stress; \(q_{1}\) and \(q_{2}\) are the internal and external pressures, respectively.
# The Boundary Element Method in Acoustics: A Survey
## 4 The Standard Boundary Element Method in Acoustics
### The Exterior Acoustic Problem
#### 4.3.1 The Integral Equation Formulations of the Exterior Helmholtz Equation and Their Properties  
The direct boundary element reformulation of the Helmholtz equation follows the format for Laplace's Equation (30) and is as follows:  
\[\left\{M_{k}\varphi\right\}_{S}(\mathbf{p})-\left[L_{k}v\right]_{S}(\mathbf{p})= \left\{\begin{array}{c}0\text{ if }\mathbf{p}\in D\\ \varphi(\mathbf{p})\text{ if }\mathbf{p}\in E\\ \frac{1}{2}\varphi(\mathbf{p})\text{ if }\mathbf{p}\in S\end{array}\right.. \tag{70}\]The indirect formulation is  
\[\varphi(\mathbf{p})=\{L_{k}\sigma\}_{S}(\mathbf{p})\ \ \ (\mathbf{p}\in E\cup S), \tag{71}\]  
\[v(\mathbf{p})=\left\{M_{k}^{t}-\frac{1}{2}I\right\}_{\Gamma}\circ\left(\mathbf{p};n_{p} \right)\ \ (\mathbf{p}\in S). \tag{72}\]  
However, the \(L_{k}\), \(M_{k}-\frac{1}{2}\ I\) and \(M_{k}^{t}-\frac{1}{2}I\) operators are degenerate at the eigenvalues of the interior Dirichlet problem (see Section 4.2.1). The issues in using the boundary integral equations in (70)-(72) as general purpose exterior acoustic/Helmholtz equation solvers has been known for over 50 years. The wavenumbers or frequencies in which these boundary integral equations are unsuitable are often termed the characteristic or irregular wavenumbers or frequencies in the literature. These characteristic wavenumbers are physical in the interior problem, but they are unphysical in the exterior problem in which they are not a property of the Helmholtz equation model but are manifest in the boundary integral equations. In Section 3.4.2 the effects of the non-uniqueness of the solution of the interior Laplace problem with a Neumann boundary condition were discussed. The boundary integral Equations (70)-(72) have a similar property at the characteristic wavenumbers, and this is also often termed the non-uniqueness problem.  
Although it is 'unlikely' in practice that the value of wavenumber \(k\) is equal to a characteristic wavenumber \(k\)', it is shown in Amini and Kirkup [230] that the numerical error as a consequence of being 'close' to a characteristic wavenumber is \(O(\frac{1}{k-k^{t}})\). Hence, one technique of resolving the problem is to use finer meshes in the neighbourhood of the characteristic wavenumber in order to offset this error [231]. However, this strategy is likely to be prohibitive, requiring the overhead of creating a range of meshes and increased computational cost. The values of the characteristic wavenumbers are also generally unknown, although they can be found (as discussed in Section 4.2), but at a substantial computational cost. It is also found that the characteristic wavenumbers cluster more and more as the frequency increases. In conclusion, therefore, the boundary integral equations are--in practice--only useful for frequencies reasonably below a conservatively estimated first characteristic wavenumber, severely restricting the methods to the low frequency range in practice.