esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Inundation Modeling of Flooded Water in Coastal Cities
## 2 Methods  
### Applied Models  
To conduct the single and comprehensive inundation simulations, a two-dimensional hydrodynamic model, HDM-2D, was developed, and simulation results generated by this model were compared and analyzed using the commercial two-dimensional flow model FLUval Modeling ENgine (FLUMEN). The two models used in this study are explained below.  
HDM-2D is a two-dimensional flow analysis model that discretizes the shallow water equation using the Petrov-Galerkin stabilized scheme for numerical calculation. HDM-2D was first developed for a flow analysis, after which further studies were conducted to develop additional objectives and functions along with verifications of its applicability. The flow model features the following: stability assessment of riverfront facilities on inundated floodplains based on flow characteristics [17]; consideration of nonhydrostatic pressure distributions based on the total acceleration method [18]; prediction of river inundation or discontinuous shock flow by the SU/PG scheme [19]; interlayer connection analysis in underground spaces [20]; and inclusion of an externalflow source by the adaptive transfer method [21]. The preprocessing tools in the RAMS suite package created input conditions for geometrical mesh creation and initial/boundary conditions, while the postprocessing tools displayed the results of the flow velocity and water depth calculations [22]. The HDM-2D is a general purpose two-dimensional surface flow model that can simulate channel flows and urban inundation as described in Figure 1. In this study, the inundation analysis model was used to analyze and numerically predict complex flood situations with multiple sources of inundation, such as storm surges, river inundation, and inland submergence.  
The FLUMEN (FLUvial Modeling ENgine) model was developed by [PERSON] of Switzerland, and is now being used as an inundation analysis model in countries such as Switzerland, Germany and Austria. The FLUMEN model is a two-dimensional model that uses a depth-averaged shallow water equation with unstructured grids and is proficient in modeling hydraulically complex areas such as river meanders and confluence areas. This model is capable of using wet/dry techniques when modeling various types of topography, and can use both subcritical and supercritical flow modeling. The governing equations, numerical methods, main functions and applications are shown clearly in the FLUMEN manual [23] and in a study conducted by [PERSON] et al. [24].
## 3 The Proposed Algorithm
### Cluster-Head Election Strategy Based on Cost Function
The specific calculation method is as follows:  
\[C(i)=\left\{\begin{matrix}k_{1}\frac{dis(i)}{\underset{j=1}{\overset{N}{ \underset{j=1}{\overset{N}{\underset{dist}{\underset{j}{\underset{j}{ \underset{j}{\underset{j}{\underset{j}{\underset{\underset{j}{\underset{ \underset{j}{\  
In queuing theory, \(\lambda\) refers to the average number of packets arriving at the queuing system per unit of time, and \(\mu\) refers to the number of packets a router can process per unit of time. \(\rho\) indicates queuing strength, which meets \(\rho=\lambda/\mu\). To consider the service priority, we have Equation (8) according to the Pollaczek-Khintchine formula:  
\[L_{Q}=\frac{\rho^{2}}{2(1-\rho)}, \tag{8}\]  
in which \(L_{Q}\) is the average queue length. Based on the _Little_ theorem, the average queue time \(W_{Q}\) should be:  
\[W_{Q}=\frac{L_{Q}}{\lambda}=\frac{\rho R}{1-\rho}, \tag{9}\]  
where \(R\) is the average remaining time of packets under processing when the current packets arrive. Therefore, the average time in the queue satisfies Equation (10):  
\[W_{S}=W_{Q}+W_{B}=\frac{\rho R}{1-\rho}+\frac{1}{\mu}, \tag{10}\]  
in which \(W_{B}\) is the average process time for a packet. Assuming that the service priorities in the network are ranked from high to low as \(1,2,3,\ldots,i,i+1,i+2,\ldots,n\), when considering the service priority, the average queuing time of the service with priority \(i\) consists of three parts:  
1. The remaining time of the packet being processed;
2. The waiting time of the packets already in the queue with higher priorities;
3. The waiting time for newly arrived packets with higher priorities in the waiting process. Based on the analysis in ref. [20], \(W_{S}(i)\) satisfies:  
\[W_{S}(i)=W_{Q}(i)+W_{B}(i)=\frac{\sum\limits_{j=1}^{n}\rho_{j}R_{j}}{(1-\sum \limits_{j=1}^{i}\rho_{i})(1-\sum\limits_{j=1}^{i-1}\rho_{i})}+\frac{1}{\mu_{i}}. \tag{11}\]  
Therefore, the end-to-end delay of the service with \(i\)th priority is as follows:  
\[T_{avg}=W_{S}(i)_{1}+\sum\limits_{j=2}^{n}W_{S}(i)_{j}+\sum\limits_{j=1}^{n}t_ {trans}(i)_{j}, \tag{12}\]  
where \(t_{trans}(i)_{j}\) is the propagation delay of the hop \(j\), and \(W_{S}(i)_{j}\) is the average time in the queue of the hop \(j\). Note that each cluster of the in-vehicle network is composed of service nodes with the same priority. \(W_{S}(i)_{1}\) and \(W_{S}(i)_{j}\) should be calculated by Equations (10) and (11), respectively.
## 3 Results
### Model Reliability
#### 3.2.2 Equipment and Experimental Protocols  
The EMG data from six major lower limb muscles (rectus femoris, RF; vastus lateralis, VL; biceps femoris, BF; tibialis anterior, TA; gastrocnemius medialis, GM; and semitendinosus, ST) were collected during movements. The location of the electrodes was determined based on the specific anatomical landmarks system following the SENIAM guideline (Surface ElectroMyoGraphy for the Non-Invasive Assessment of Muscles is a European concerted action in the Biomedical Health and Research Program (BIOMED II) of the European Union) as shown in Figure 11. The data of five different movements with variable speed of the dominant lower limb (knee flexion, knee extension, ankle plantar flexion, ankle dorsiflexion, and straight-leg raising) were randomly selected for a total of 50 movements, and a total of 600 EMG trials were recorded in both systems from all EMG sensors.  
Figure 11: EMG electrodes placement.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Variables** & **Min-Max** & **Mean (SE)** \\ \hline Age (year) & 23-51 & 35.00 (2.80) \\ Height (cm) & 168–184 & 173.85 (1.50) \\ Weight (kg) & 60–96.80 & 79.73 (4.15) \\ ASIS distance (mm) & 205–264 & 234.40 (6.37) \\ R Leg length (mm) & 870–975 & 929.40 (10.56) \\ L Leg length (mm) & 870–970 & 929.20 (10.17) \\ R Knee width (mm) & 90–107 & 97.00 (1.71) \\ L Knee width (mm) & 86–107 & 95.90 (1.99) \\ R Ankle width (mm) & 64–82 & 72.70 (1.75) \\ L Ankle width (mm) & 64–80 & 71.90 (1.73) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Subjects’ demographic characteristics (\(n=10\)).
###### Abstract  
In this study, cellulose nanocrystals (CNs) were utilized to enhance the mechanical properties and sulfate corrosion resistance of engineered cementitious composites (ECCs). The results of compressive strength and uniaxial tensile tests demonstrated that the incorporation of CNS significantly improved the compressive strength, strain rate, tensile strength, and sulfate corrosion resistance of ECC specimens. Scanning electron microscopy (SEM) observations revealed that the addition of CNs facilitated the formation of increased amounts of ettrinigite and calcium silicate hydrate (C-S-H) in the matrix, enhancing the hydration degree of the cementitious system and increasing the overall density of the ECC structure. Molecular dynamics simulations were employed to investigate the interactions between CN, C-S-H, water molecules, and sulfate ions (SO4\({}^{2-}\)) while also calculating the kinetic parameters of atoms at the interface. These simulations provided insights into the microstructural strengthening mechanism of CNs in improving the sulfate corrosion resistance of ECCs. The results indicated that CNs adsorb onto C-S-H via Ca-O and H-O coordination, forming a protective layer that inhibits the penetration of SO4\({}^{2-}\) and water molecules into the C-S-H structure. Additionally, CNs form hydrogen bonds with SO4\({}^{2-}\) and water molecules, which restricts their diffusion and reduces their coordination with the C-S-H interface and the dissolution of SO4\({}^{2-}\) and water to the hydration product, thereby enhancing the sulfate corrosion resistance of ECCs.  
Keywords:engineered cementitious composite; cellulose nanocrystals; molecular dynamics; interfacial adsorption +
Footnote †: journal: Applied sciences
## 3 Results
#### 3.1.1 Simulation Experiment  
The system used in the simulation experiment is MEMPHIS, an airborne multi-baseline SAR system from Germany [26]. The relevant parameters of the system and simulation are shown in Table 1. It can be seen that the theoretical resolution of the simulation system in the elevation direction is about 42 m, the theoretical minimum mean square deviation in elevation direction is about 1.2 m, and the maximum unambiguous distance in height direction is 210 m [14]. Since the theoretical resolution of the elevation direction is 42 m, in order to verify its super-resolution performance, the height of the scatterers selected in the simulation experiment is 0 m and 20 m, the amplitude of the scatterers is the same, the SNR is set as 20 dB, and the number of views of the multi-look compressive sensing method is five. The simulation results are shown in Figure 2.  
It can be seen from Figure 2 that the scatterer height errors of the traditional L1 norm compressive sensing method are 7.4 m and 6.8 m, respectively, while the scatterer height errors of the multi-look compressive sensing method are \(-\)0.4 m and 0.6 m, respectively. It can be seen that the scatterer height values in layover areas solved by the multi-look compressive sensing method are more reliable and stable, and closer to the real value.  
\begin{table}
\begin{tabular}{c c} \hline
**Radar Parameters** & **MEMPHIS** \\ \hline Radar frequency (GHz) & 35 \\ Off-nadir angle \(\theta\) (\({}^{\circ}\)) & 60 \\ Baseline length (m) & 0, 0.055, 0.165, 0.275 \\ Baseline angle (\({}^{\circ}\)) & 60 \\ Slant range (m) & 1545 \\ SNR (dB) & 20 \\ \hline \end{tabular}
\end{table}
Table 1: System parameters of simulation data.  
Figure 2: Simulation results provided by two methods. (**a**) Traditional L1 norm compressive sensing method; (**b**) Multi-look compressive sensing method. The horizontal axes are heights in the elevation direction with the unit m, and the vertical axes are normalized amplitudes.
# Ontology Modeling Using Fractal and Fuzzy Concepts to Optimize Metadata Management
## 5 Conclusions
This integration would enable predictive analytics and decision making in highly dynamic environments, where both operational parameters and fuzzy thresholds may shift in real time. * Automated tool development: The development of specialized automation tools to facilitate the creation and OWL transformation of FFOM ontologies has the potential to reduce the implementation barriers experienced by practitioners with limited ontology expertise. Such tools could include user-friendly interfaces or plugins for platforms like Protege, facilitating rapid model generation and updates without the need for extensive manual configuration. * Performance benchmarking and systematic evaluation: Implementing a rigorous benchmarking methodology, including real-world datasets and high-frequency data streams, will help quantify FFOM's advantages in terms of scalability, efficiency, and uncertainty management. Key metrics could include structural modification overheads (when adding new processes), reasoning speed during incremental updates, fuzzy threshold detection accuracy, and SPARQL query performance. * Standardization and ongoing enhancement: By continuing to refine FFOM's core functionalities and aligning them with emerging industry standards, the framework can evolve into a pivotal data management tool that addresses the challenges of data complexity and interaction across diverse industrial contexts. As these refinements are developed, FFOM has the potential to revolutionize ontology-driven workflows, eventually becoming a vital solution for real-time, high-volume data environments. **Author Contributions:** S.K. : Conceptualization, methodology, data curation, writing--original draft, writing--review and editing, visualization. Y.Y.L. : validation, data curation, writing--original draft. K.R. : conceptualization, methodology, validation, investigation, writing--review and editing, supervision. All authors have read and agreed to the published version of the manuscript. **Funding:** Not applicable. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data are available on request due to restrictions. The data presented in this study are available on request from the corresponding author. The data are not publicly available because the data are also part of the authors' ongoing research. **Acknowledgments:** This work was supported by the project for Smart Manufacturing Innovation R&D funded by Korea Ministry of SMEs and Startups (No. RS-2022-00140411). **Conflicts of Interest:** The authors declare that there are no conflicts of interest.
## 2 Background  
Many researchers have described the concepts of M2M and IoT [3; 9]; while all definitions apply, the simplest way to outline IoT is the Internet of objects or the Internet of everything excluding humans. Devices such as smartphones, SIM-enabled sensors, smart meters, and IP cameras are all intelligently connected via the Internet to facilitate communications between systems, people, and devices, with the aim to improve a particular aspect of business or life [10]. Such devices use network infrastructures for  
Figure 1: Scheme of a simplified device analytics model.  
interconnections. Cellular networks with long-term evolution (LTE) and LTE-A (advanced) already support low power device interconnections; having more than 20 million connected devices entails an exponential growth of traffic in the network, which can result in high latency and network congestion.
# Bone Conduction Auditory Navigation Device for Blind People
## 5 Evaluation of the Proposed Navigation System  
### Experimental Scheme  
The navigation experiment was conducted at the Noda campus of the Tokyo University of Science, as shown in Figure 11. The route of navigation is from S to G in the figure. Note that there were no stairs or steps in order to prevent injury of the subjects. Four sighted males in their 20 s participated in the experiment. All four subjects were quite familiar with the route because all of the subjects were students of the Noda campus of the Tokyo University of Science. The subjects used an eye mask and a white cane, as shown in Figure 1a, to simulate a visually impaired person. To ensure safety, an assistant (person) closely followed the subject. Table 8 shows the three experimental conditions for the different navigation sounds. Condition 1 gives quite basic information concerning the direction in which the subject should walk when the subject arrived at each of the junctions from C1 to C8. Detailed voice guidance reproduced at each junction is shown in Table 9. As shown in this table, the voice indicating the direction stated, \"Please turn right in the 2/3/9/10 o'clock direction\", which is often used to guide visually impaired people. Conditions 2 and 3 provide additional information of binaural flashing sounds indicating the traveling directions of 60\({}^{\circ}\), 90\({}^{\circ}\), 270\({}^{\circ}\), and 300\({}^{\circ}\), as shown in Figure 4a, the sound localization performances of which were confirmed in Section 3.2. Note that the panned sound with a 20\({}^{\circ}\) swing angle was considered for the flashing sound. In addition, conditions 2 and 3 give voice information on obstacles, such as a walking person, a bicycle, or a chair, in front of the subject. In condition 3 only, a binaural warning sound indicating the obstacle direction was also reproduced.  
In the experiment for each condition, the subjects walked all routes one time without \"blindness\". Thereafter, as practice in conducting the experiment, the subjects also walked all routes one time with \"blindness\". Finally, as the main experiment, the subjects walked a route following the indicated information for each condition. Note that, in condition 1 only, the assistant guided the subject when walking in the wrong direction because the traveling direction of condition 1 is quite unclear due to the lack of binaural sounds.  
To avoid the effect of familiarity with the walking route in the experiment caused by the order of navigation for conditions 1, 2, and 3, the order of the three conditions was randomized for each of the four subjects, and the experiments using these three conditions were conducted after an interval of a few days between each condition.
# Practical Application of Mesh Opportunistic Networks
## 3 Materials and Methods
### Mesh Communication Devices  
In this experiment, 8 goTenna Mesh (GT01 to GT08) communication terminals ([[https://gotenna.com](https://gotenna.com)]([https://gotenna.com](https://gotenna.com)), accessed on 6 April 2023) were used, 6 of which were distributed to pairs of participants. The seventh device was carried by the coordinator and device number 8 was carried by one of RTVE's professional journalists, a controller of the communication flow and an external participant. The goTenna Mesh terminal used measures 12.7 \(\times\) 3.8 \(\times\) 1.27 cm, and it is basically a radio receiver and transmitter that emits on UHF frequencies in Europe, between 869,425-869,625 MHz (as opposed to 902-928 MHz in the US). This band, with 25 kHz channel widths, is dedicated to low-power applications and data in general, mobile, and broadcasting, according to the Radio Regulations of the International Telecommunication Union [28]. The terminal uses the mobile phone, to which it is paired by Bluetooth (versions 5.0 and 4.0+), as a screen interface for its dedicated app, and to receive and send conversations, locations, and information.  
The terminal was initially developed by [PERSON] following the loss of communication networks on the east coast of the United States after Hurricane Sandy in October 2012. He founded goTenna Inc. (Brooklyn, NY, USA) in November of that same year with his sister [PERSON] and after successive improvements [29; 30], they marketed the goTenna Mesh emitter and receiver device that connects via Bluetooth to a mobile phone that it uses as a screen. This device uses a dedicated app on the phone to send and consult text and locations, without the need for a central network, telephone towers, or Wi-Fi networks. All similar devices within a given range reinforce the decentralized network and they enhance the extension of the mesh network formed in this way. In 2019, the venture capital company Founders Fund considered that mesh communication systems were in an initial phase of market penetration and that other companies, such as Harris Corp. or Motorola, had neglected these developments. They provided $24 million to goTenna Inc. to diversify its growth into military areas, the public sector, and the consumer market [31; 32]. In addition, the 2019 World Economic Forum selected goTenna as one of the world's 56 most innovative startup tech companies [33].  
Note that goTenna is a commercial company and there are other tools and projects that could be good alternatives to the one used here (such as [[https://briarproject.org/](https://briarproject.org/)]([https://briarproject.org/](https://briarproject.org/)), accessed on 6 April 2023 or [[http://laboratoirehubertcurien.fr/c3](http://laboratoirehubertcurien.fr/c3) po-anr/index.html]([http://laboratoirehubertcurien.fr/c3](http://laboratoirehubertcurien.fr/c3) po-anr/index.html), accessed on 6 April 2023).
# Co-Seismic Magnetic Field Perturbations Detected by Swarm Three-Satellite Constellation
## 1 Introduction  
This paper aimed to search for perturbations of the geomagnetic field in the ionosphere possibly produced by the occurrence of an earthquake in the lithosphere. The possibility that the ionosphere could be perturbed just a few minutes after the occurrence of a medium (Mw6+) and large (Mw7+) earthquakes is well known. For example, [PERSON] and [PERSON], 1974 [1] presented a model for the generation, propagation and response of the ionosphere to acoustic gravity waves. They also provided experimental data by supporting their model. [PERSON], 1982 [2] theorized a model to describe the acoustic gravity waves induced by submarine earthquakes. Submarine earthquakes deserve special attention due to their potential to generate tsunamis. [PERSON] et al., 2006 [3] observed the impact of the acoustic waves by the Global Positioning System (GPS) network 660-670 s (-11 min) after the arrival time of Rayleigh waves at the ground for the Mw7.9 November 3 rd, 2002, Alaska earthquake. [PERSON] et al., 2012 [4] reported not only ionospheric Doppler caused by the Mw9.0 Tohoku 2011 earthquake, buteven geomagnetic disturbances from three different ground observatories. They carefully checked that the disturbances recorded on the ground were not caused by the ground shake due to the earthquake but as a magnetic effect. [PERSON] et al., 2015 [PERSON] et al., 2015 (2015) presented a general review about the acoustic gravity waves produced by earthquakes and gravity waves generated by tsunamis, in particular focusing on the alteration of plasma density and velocity in ionosphere retrievable by the Global Navigation Satellite System (GNSS) global network sensors. Furthermore, they presented also a detailed analysis of the Mw7.8 Wrenchuan 2008 and Mw9.0 Tohoku 2011 earthquakes.  
Many authors (2015; 2015; 2015; 2015) analyzed the Total Electron Content (TEC) variations from ground GNSS receivers and Global Ionospheric Map (GIM) and detected the impact of the acoustic gravity wave in the ionosphere associated with the occurrence of earthquakes. The analysis of TEC data has also shown to be a useful tool to detect the main earthquake parameters (epicentre and magnitude) using fast analysis techniques (2015). [PERSON] et al., 2004 [PERSON] et al.  
produce a disturbance in the ionosphere because of their significant released energy. The number of the selected events was 1830 out of 2437 downloaded earthquakes, represented by a red star/dot in Figure 1. In particular, in Figure 1b it is possible to note the exclusion of the deepest events. The selected earthquakes are a sub-set in which it is more reliable to search for effects induced in the ionosphere, but future works can also investigate other events or subsets.