esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Simulation of Seismic-Wave Propagation in Specific Layered Geological Structures
## 4 Methodological Extension  
### Explosion Response of Water and Layered Geological Structures Models  
#### 4.1.1 Numerical Modeling of Layered Geological Structures  
Figure 8 presents various numerical explosion models developed for certain geologies: water and layered geological structures. These models facilitate a comprehensive understanding of stress-wave propagation and attenuation patterns, as well as stress-state features, as these can be influenced by the specific geological medium. Four equivalent simulated explosions are considered, reflecting earthquake surface-wave magnitudes of 7.5. Based on Equation (1), these magnitudes correspond to the energy release equivalent to the detonation of 2,212,100 tons of TNT [29].  
Specifically, the numerical models for the various geologies are as follows:  
* Water Model (Figure 8a): This spatially axisymmetric model simulates the seismic-wave attenuation behavior of water at a 9 km depth across the given magnitudes. The model incorporates an air layer 1 km high, effectively representing a water body of 9 km depth and a 10 km radius.
* Three-Layered Medium (Figure 8b): This model explores responses in a tri-layered structure: an upper water layer 1 km deep, a middle granite layer 5 km deep, and a lower basalt layer 3 km deep. An air layer of 1 km is added above this structure. Specifically, the mechanical parameters for the various media are as follows:
* Air: Density of 1.29 kg/m\({}^{3}\) and \(V_{p}\) of 0.344 km/s [54].
* Water: Density of 1000 kg/m\({}^{3}\) and \(V_{p}\) of 1.46 km/s [54].
* Granite: Density of 2660 kg/m\({}^{3}\), \(V_{p}\) of 5.8 km/s, and \(V_{s}\) of 3.456 km/s [35; 36].
* Basalt: Density of 3300 kg/m\({}^{3}\), \(V_{p}\) of 6.03 km/s, and \(V_{s}\) of 3.73 km/s [35; 36].
# Methodology for the Assessment of Multi-Hazard Risk in Urban Homogeneous Zones
###### Abstract  
The multi-hazard risk assessment of urban areas represents a comprehensive approach that can be used to reduce, manage and overcome the risks arising from the combination of different natural hazards. This paper presents a methodology for multi-hazard risk assessment based on Spatial Multi-Criteria Decision Making. The PROMETHEE method was used to assess multi-hazard risks caused by seismic, flood and extreme sea waves impact. The methodology is applied for multi-hazard risk evaluation of the urban area of Kastel Kambelovac, located on the Croatian coast of the Adriatic Sea. The settlement is placed in a zone of high seismic risk with a large number of old stone historical buildings which are vulnerable to the earthquakes. Being located along the low-lying coast, this area is also threatened by floods due to climate change-induced sea level rises. Furthermore, the settlement is exposed to flooding caused by extreme sea waves generated by severe wind. In the present contribution, the multi-hazard risk is assessed for different scenarios and different levels, based on exposure and vulnerability for each of the natural hazards and the influence of additional criteria to the overall risk in homogenous zones. Single-risk analysis has shown that the seismic risk is dominant for the whole pilot area. The results of multi-hazard assessment have shown that in all combinations the highest risk is present in the historical part of Kastel Kambelovac. This is because the historical part is most exposed to sea floods and extreme waves, as well as due to the fact that a significant number of historical buildings is located in this area.  
risk assessment; multi hazard; multi-hazard risk assessment; multi-criteria decision-making; GIS; PROMETHEE method +
Footnote †: journal: Medical Informatics  
1 Faculty of Civil Engineering, Architecture and Geodesy, University of Split, 21000 Split, Croatia1  
2
## 5 Experimental Results
### Performance Comparison between the Proposed Method and Baseline Approaches
#### 5.1.2 Embedding Capability in Different Embedding Capacities
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **Our Method** \\ \hline Lena & – & 51.79 & 55.90 & 60.32 & **57.15** \\ Baboon & – & – & 50.93 & – & **50.62** \\ Boat & 48.24 & 46.25 & 53.35 & – & **54.00** \\ Peppers & – & 48.66 & 54.53 & – & **54.11** \\ Airplane (F-16) & 48.10 & 54.47 & 57.87 & 61.43 & **57.18** \\ House & 48.35 & 52.80 & 61.09 & 62.25 & **60.68** \\ \hline Average & 48.23 & 50.79 & 55.61 & 61.33 & **55.62** \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Comparison of PSNR (dB) between the proposed method and the methods of [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [21], and [PERSON] et al. [5] for a capacity of 10,000 bits. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **[PERSON] et al.** & **Our Method** \\ \hline Lena & 47.81 & 55.14 & 59.50 & 54.14 & **60.59** \\ Baboon & 44.41 & 47.23 & 53.89 & – & **52.52** \\ Boat & 51.99 & 52.98 & 56.55 & – & **57.27** \\ Peppers & 44.08 & 53.96 & 57.82 & 61.52 & **57.00** \\ Airplane (F-16) & 54.31 & 57.34 & 60.50 & 64.29 & **59.86** \\ House & 53.98 & 56.60 & 64.53 & 65.28 & **63.48** \\ \hline Average & 49.43 & 53.88 & 58.80 & 63.81 & **58.45** \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of PSNR (dB) between the proposed method and the methods of [PERSON] et al. [46], [PERSON] et al. [47], [PERSON] et al. [21], and [PERSON] et al. [5] for a capacity of 5,000 bits. Figure 9: PSNR with different embedding methods and capacity ranges for the image Lena. According to the above results, when the bits of embedded secret data are few, the distortion of the image can be slightly reduced by embedding the secret in fewer octants. Thus, the reducing effect is limited. Conversely, when the bits of embedded secret data is larger, the better quality of the image can be kept by embedding secret in more octants of the 3D-PEH. It can be expected that the more bits of embedded secret data, the larger gap between different embedding methods occurs. Therefore, we consider embedding secret data in eight octants in the proposed method.
# Damage Detection Method Based on Continuous Wavelet Transformation of Lamb Wave Signals
The damage localization was improved by a time compensation in all the original output signals. First, the single and multiple damage cases were tested using the difference signals, and then the results were obtained for the same cases directly from the damaged signals, without the baseline. The organization of the article is as follows: Section 2 introduces the background and basic theory of the proposed method, Section 3 shows the implementation of the proposed method using difference signals, the baseline-free implementation is shown in Section 4, the influence of the environmental noise is presented in Section 5, experimental validation of the proposed method is presented in Section 6, a comprehensive discussion of results is presented in Section 7, and concluding remarks are summarized in Section 8.
# Wheel Flats in the Dynamic Behavior of Ballasted and Slab Railway Tracks
## 2 Dynamic Models for Coupled Vehicle-Track Systems
### Wheel Flat Modeling  
The most common defects of vehicle wheels are corrugation and flats. The first one is more or less a regular phenomena, and it may be characterized by sinusoidal expressions  
Figure 3: Quarter bogie model.  
Figure 2: Track model of continuous slab systems.  
or by Fourier series. With regard to the second defect, flats with no wear at the corner edges, i.e., a newly formed defect, consist of a geometric chord on the circumference of the wheel. Newly formed or fresh flats appear as a result of wheel-locking due to brake action.  
For flats with no wear or rounded corners, the depth of the defect \(d\) can be related to its length \(l\) and the wheel radius \(r\), as expressed by Equation (1). By solving this equation for \(d\), the flat depth can be evaluated by Equation (2).  
\[r^{2}=(r-d)^{2}+(\frac{l}{2})^{2} \tag{1}\]  
\[d=\frac{d^{2}}{2r}+\frac{l^{2}}{8r}=d\approx\frac{l^{2}}{8r} \tag{2}\]  
disregarding \(d^{2}\), since \(d\) is a very small value.  
For the geometrical definition of wheel flats with no wear, [PERSON] and [PERSON] [10] proposed Equation (3).  
\[x\approx d-\frac{1}{2r}(z-\frac{l}{2})^{2}\text{ if }0\leq z\leq l \tag{3}\]  
where \(r\) is the radius of the wheel; \(l\), the length of the flat; \(d\), the depth of the flat.  
However, in real situations, as the wheels of the train are subject to wear, the corners of the flat become rounded and the flat length tends to increase, forming a rounded flat.  
Usually, it is very difficult to characterize the real shape of a rounded flat. In the absence of real wheels' profiles, rounded corner flats can be defined by Equation (4) as proposed by [10].  
\[x=\begin{cases}\overline{a}z^{2}&\Leftarrow 0\leq z\leq z_{1}\\ d-\frac{1}{2r}(z-z_{2})^{2}&\Leftarrow z_{1}\leq z\leq z_{2}\end{cases} \tag{4}\]  
Wheel flats can be modeled by transforming the wheel defect into an equivalent and spaced rail defect, over which runs a perfect wheel. This approach has been used by several authors such as [10; 15; 21; 30].  
To set up the equivalent rail/track defect, which corresponds to the wheel flat, the Haversine function may be used. This function is expressed in Equation (5). In this equation, \(z\) and \(x\) correspond respectively to the vertical and the longitudinal direction of the track.  
\[z=\frac{d}{2}(1-\cos 2\pi\frac{x}{l}) \tag{5}\]
## 4 Discussion
### Analysis of Topics by Years and Geographical Area
Latin America reveals an exclusive interest in TECHINNOVATION in 2020, potentially reflecting a pivotal moment or event that focused attention on this area, though no research is recorded in other years. The Middle East presents a variety of interests in 2022, with an equal distribution among QUALITY, RESOURCE, and TECHINNOVATION, indicating a holistic approach to healthcare research that considers various aspects of AI use. This is represented by a distribution of 12.1% across different themes in 2022. North America, in 2022 and 2023, heavily focuses on TECHINNOVATION, highlighting how technological innovation has become a central pillar of research in the region, with interest remaining steady at 3.0% in 2022 and increasing to 9.1% in 2023. The aggregated \(\chi^{2}\) test analysis reveals a significant variation in AI research themes within healthcare management, with a \(\chi^{2}\) value of 44.9 and a \(p\)-value of 0.006, suggesting that changes over the years, such as the emergence of COVID-19 or technological advancements, have influenced research priorities. This statistical significance underscores the impact of global events and technological evolution on healthcare research interests, emphasizing the importance of temporal analysis to understand the dynamics driving focus towards specific research themes.
## 5 Experiments and Results
### Evaluation and Testing
Since no robot went towards the same goal position as the other robot, the reward function \(r^{0}\)--described in Equation (12)--had a positive value for both robots. As for the reward function \(r^{t}\)--described in Equation (13)--it had a very small negative value for Robot 0 as it took the robot 2.22 s to reach its goal position. On the other hand, Robot 1 had a slightly lower \(r^{t}\) reward as it took 16.93 s to reach its goal position. On the other hand, the episode that ended in a collision is shown in Figure 11. The collision that occurred in this episode is Category B. Since Robot 0 reached a goal position successfully, therefore its aggregate reward function showed positive values across its movement, and when the robot reached the goal position, a large terminal reward value was given to the robot. On the other hand, Robot 1 moved towards the same goal position as Robot 0 and collided with it. This behavior is reflected in the reward function as the reward function \(r^{\prime}\)--described in Equation (12)--took on a negative value once Robot 1 started to move towards the same goal position as Robot 0. Once the collision occurred, Robot 1 received a large negative terminal reward according to the reward \(r^{\prime\alpha}\)--described in Equation (10). The combination of all of the rewards led to the aggregate reward received by Robot 1 having a negative value for most of the time steps. Figure 10: Visualizing the rewards collected by each robot using our method in an episode that ended successfully. The trajectory of Robot 0 is shown in red, while the trajectory of Robot 1 is shown in green. Figure 9: The distribution of the reached goal positions in the environment done by each robot controlled by our method during testing. The environment spans the interval \([-2,2]\) for both the x and y axes. The black squares represent the starting positions of the robots.
## 2 Materials and Methods
### Synthesis of 2((5-(3,5-Dimethoxystyryl)) thiophen-2-yl)methylene-1H-indene-1,3(2H)-dione (DVB-T-ID)  
Intermediate **2** (0.642 g, 2.01 mmol), trans-2-(3,5-dimethoxyyphenyl) vinylboronic acid pinacol ester (0.875 g, 3.01 mmol), and a catalyst of 5 mol\(\%\) Pd(PPh3)4 were mixed in anhydrous toluene (25 mL), followed by the addition of 5 mL of 2 M potassium carbonate (K\({}_{2}\)CO\({}_{3}\)), and refluxed at 110 \({}^{\circ}\)C for 24 h. After refluxing, the reaction mixture was poured into deionized (DI) water and washing with brine, DI. The extraction of the resultant organic part was done using a dichloromethane (DCM) solvent, followed by drying over MgSO4. The washed organic phase was evaporated under reduced pressure using a rotary evaporator, and then further purified by a flash column using Diethyl ether-Hexane (1:3, _v_/_v_), dried in an oven and recrystallized twice with DCM-EtOH to obtain bright red crystals (yield 53.94%, 0.426g). 1 HNMR (500 MHz, (CD\({}_{3}\))\({}_{2}\)SO, ppm) \(\delta\): 8.13 (d, J = 4.05 Hz, 1H), 7.96 (s, 1H), 7.88 (m, 4H), 7.56 (d, J = 16.2 Hz, 1H), 7.42 (d, J = 3.35 Hz, 1H), 7.24 (d, J = 16.2 Hz, 1H), 6.84 (d, J = 2.25 Hz, 2H), 6.42 (t, J = 2.25 Hz, 1H), 3.75 (s, 6H). 1\({}^{3}\)CNMR (100 MHz, (CD\({}_{3}\))\({}_{2}\)SO, ppm) \(\delta\): 189.93, 189.44, 161.25, 155.55, 145.58, 141.97, 140.38, 138.54, 136.33, 136.06, 136.02, 135.91, 133.49, 128.43, 123.92, 123.24, 123.06, 122.58, 105.59, 101.87, 55.86. MS: m/z calc. for [C\({}_{28}\)H\({}_{24}\)O\({}_{2}\)S\({}_{3}\) + H]\({}^{+}\): 403.46. Found: 403.60.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 2 Asteria
### Telecommunication Requirements and Design  
ASTERIA was required to use the S-Band frequency as it was initially planned to use the Near Earth Network (NEN) stations as the ground station for this project. However, as the design progressed the team moved from the decision of using the NEN stations to use the 21 m antenna at Morehead State University (MSU) which is now part of the Deep Space Network (DSN) and also operating at S-Band.  
Additionally, the telecommunication system was required to be CCSDS (Consultative Committee for Space Data Systems) compatible and to deliver a data volume of approximately 1 Gb per day. This translated into a data rate requirement of approximately 1 Mbps considering the availability of the ground station and the average duration of the contacts (approximately 8 min, and 2 contacts per day). In terms of sizing, all the components needed to be compatible in mass and power with the typical CubeSat form factor.  
Given all the requirements and constraints, the system was designed as a full duplex S-band system (Figure 1) including one S-band transceiver assembly and two low gain antennas (LGA). The transceiver assembly includes a switch to select among two possible antennas, a diplexer to separate uplink and downlink paths, a solid state power amplifier (SSPA) for the downlink, and a low noise amplifier (LNA) for the uplink. The two antennas are identical S-band patch antennas which operate at both transmitting and receiving frequencies and are placed on opposite sides of the spacecraft to maximize coverage.  
The data rates used are 10 kbps and 1 Mbps in downlink, and 4 kbps and 32 kbps in uplink. The low data rates are designed for acquisition and safe modes, while the high data rates are for normal operations.
# Microbiome-Based Interventions for Food Safety and Environmental Health
## 2 Microbiome-Based Interventions for Ecosystem Sustainability  
Microbiomes are emerging as pivotal players in environmental health due to their abilities to enhance nutrient cycling, remediate pollutants, and maintain soil and water quality [6]. Advancements in long-read sequencing and AI are unlocking precise microbial insights, enabling targeted interventions like bioremediation of oil spills [27] and pathogen suppression in agricultural soils [28]. [PERSON] et al. [6] asserted how microbial interventions will be a vital step in ecological restoration, showing promising application of microbiomes in the bioremediation of pollutants in contaminated environments. One Health, which is an integrated approach to tackle the health of humans, animals, and their shared environment, reinforces microbiome strategies for sustainable development, as reported by [PERSON] et al. [29]. Microbial biotechnology tackles industrial pollutants, such as heavy metals in farmland, supporting safer food production [30]. According to the above studies, it can be argued that microbiomes are the top priority to enhance environmental resilience [31] and that they should be implemented in the strategy for tackling the global environmental challenge. Figure 2 illustrates microbiome-based interventions aimed at promoting ecosystem sustainability.  
Figure 1: Timeline of key advancements in microbiome research methodologies.  
To ensure the reliability and effectiveness of microbiome-based interventions, it is essential to evaluate their consistency and reproducibility across varying environmental conditions. Reproducibility is particularly critical for large-scale applications in agriculture, bioremediation, and public health. For instance, in a multi-site field trial, [PERSON] et al. [32] applied a synthetic microbial consortium to degraded farmland soil and observed a consistent 35% increase in crop yield over three consecutive growing seasons, highlighting the intervention's reproducibility. Similarly, a study by [PERSON] et al. [33] demonstrated the bioremediation potential of hydrocarbon-degrading bacteria, observing a total petroleum hydrocarbon reduction of 61% with bacterial augmentation, which improved to a 73% reduction when combined with nutrient amendments (nitrogen, potassium, and phosphorus). Statistical modeling, such as Bayesian hierarchical models, can further enhance reproducibility assessments by accounting for environmental variability, as shown in the study by [PERSON] et al. [34]. Their research demonstrated that Bayesian inference provides meaningful uncertainty estimations, especially under limited data conditions. These findings underscore the need for large-scale, multi-environment trials combined with robust statistical frameworks to validate the consistency of microbiome-based interventions.
# Vehicle Routing Problem with Drones Considering Time Windows and Dynamic Demand
## 5 Computational Experiments
### Algorithm Performance Test
#### 5.1.1 Comparison of the Results of Different Algorithms
 & **T\_C** & **EM** & **GA** & **SA** & **CHOOA** & **SACHOA** & **GAPEM** & **GAPEM** & **GAPSA** & **GAPSHOA** \\ \hline R101\_10 & 10 & 7 & 250.2162 & 250.2162 & 250.2162 & 250.2162 & 250.2162 & 0.00 & 0.00 & 0.00 & 0.00 \\ R102\_10 & 10 & 7 & 244.9569 & 244.9569 & 244.9569 & 244.9569 & 244.9569 & 0.00 & 0.00 & 0.00 \\ R201\_10 & 10 & 7 & 232.2306 & 232.2306 & 232.2306 & 232.2306 & 232.2306 & 0.00 & 0.00 & 0.00 & 0.00 \\ C101\_10 & 10 & 7 & 97.6206 & 97.6206 & 97.6206 & 97.6206 & 97.6206 & 0.00 & 0.00 & 0.00 & 0.00 \\ C102\_10 & 10 & 7 & 61.7037 & 61.7037 & 61.7037 & 61.7037 & 61.7037 & 61.7037 & 0.00 & 0.00 & 0.00 & 0.00 \\ C201\_10 & 10 & 7 & 163.0913 & 163.0913 & 163.0913 & 163.0913 & 163.0913 & 163.0913 & 0.00 & 0.00 & 0.00 \\ RC101\_10 & 10 & 7 & 180.8669 & 180.8669 & 180.8669 & 180.8669 & 180.8669 & 0.00 & 0.00 & 0.00 & 0.00 \\ RC102\_10 & 10 & 10 & 151.4601 & 151.4601 & 151.4601 & 151.4601 & 151.4601 & 0.00 & 0.00 & 0.00 & 0.00 \\ RC103\_10 & 10 & 7 & 149.4657 & 194.657 & 149.4657 & 149.4657 & 149.4657 & 0.00 & 0.00 & 0.00 & 0.00 \\ RC104\_10 & 10 & 7 & 152.6316 & 152.6316 & 152.6316 & 152.6316 & 152.6316 & 0.00 & 0.00 & 0.00 & 0.00 \\ RC105\_10 & 10 & 7 & 153.3902 & 153.3902 & 153.3902 & 153.3902 & 153.3902 & 0.00 & 0.00 & 0.00 & 0.00 \\ RC201\_10 & 10 & 7 & 152.7721 & 154.2408 & 152.7721 & 152.7721 & 152.7721 & 152.7721 & 0.00 & -0.95 & 0.00 & 0.00 \\ R101\_25 & 25 & 19 & - & 584.857 & 575.5174 & 575.7988 & 527.6039 & - & \(-\)9.79 & \(-\)8.33 & \(-\)8.37 \\ R102\_25 & 25 & 22 & - & 514.6691 & 513.6467 & 489.5837 & 487.1129 & - & \(-\)5.35 & \(-\)5.17 & \(-\)0.50 \\ R201\_25 & 25 & 13 & - & 478.3015 & 476.4763 & 486.1999 & 476.4763 & - & \(-\)0.38 & 0.00 & \(-\)2.00 \\ C101\_25 & 25 & 19 & - & 327.1768 & 423.8668 & 390.68 & 333.757 & - & \(-\)9.25 & \(-\)20.32 & \(-\)13.55 \\ C102\_25 & 25 & 22 & - & 327.8577 & 279.9872 & 280.0399 & 278.1451 & - & \(-\)3.37 & \(-\)0.66 & \(-\)0.68 \\ C201\_25 & 25 & 13 & - & 374.1189 & 353.567 & 345.433 & 335.7829 & - & \(-\)10.25 & \(-\)5.03 & \(-\)2.79 \\ RC101\_25 & 25 & 19 & - & 360.2397 & 361.6697 & 37.8553 & 349.6057 & - & \(-\)2.95 & \(-\)3.34 & \(-\)6.98 \\ RC102\_25 & 25 & 22 & - & 344.6897 & 344.6897 & 340.357 optimal solution; second, the quality of the optimal solution it finds is relatively higher, which means that the SACHOA algorithm has higher applicability and accuracy for solving the VRPDTW problem.