esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In order to investigate the weathering characteristics of rocks in response to freeze-thaw conditions in northern latitudes, we analysed meteorological data from the Ny-Alesund region in Norway, and observed changes in the physical and mechanical properties of rocks of dolomite and quartzite. To assess the effects of freeze-thaw weathering on these rock properties, 900 cycles of long-term freeze-thaw tests were conducted for the sampled rocks in two locations. P-wave velocity, absorption, shore hardness, and the uniaxial compressive strength of the sampled rocks were measured at every 150 cycles in order to analyse physical and mechanical mediator variables of freeze-thaw weathering. It was found that an increasing number of freeze-thaw cycle on the sampled rocks decreases uniaxial compressive strength, shore hardness, and P-wave velocity and increases absorption.  
air temperature; freezing-thaw cycle; rock weathering; dolomite; quartzite; rock physical property +
Footnote †: journal: Applied sciences
## 2 Materials and Methods
### Sampling and Data Treatment  
In this study, physicochemical analyses, such as pH, temperature, and electrical conductivity (EC), were carried out in situ. The concentrations of major ions, i.e., chloride (Cl), calcium (Ca), magnesium (Mg), sodium (Na), potassium (K), sulfate (SO\({}_{4}\)), and bicarbonate (HCO\({}_{3}\)), were conducted in the Environment Science and Technology Research Laboratory. The PHREEQ geochemical program [32] was used to calculate the waterless speciation and the thermodynamic equilibrium conditions (mineral saturation indices) of the waters with respect to the major mineral phases present in the aquifer. The saturation index (\(SI\)) quantitatively describes the difference between water and balance with respect to dissolved minerals and is given by the equation below [33,34]:  
\[SI=log\,\left(\frac{IAP}{Kt}\right) \tag{1}\]  
where \(IAP\) is the ion exertion product and \(Kt\) is the equilibrium solubility constant.  
To study the hydrochemical characteristics of the Mornag shallow aquifer groundwater and identify the various types of water, Diagrammes software was used to develop the Piper diagram for Mornag groundwater corresponding to the years 2013, 2016, and 2019 [35].  
This software was also used to evaluate the quality of irrigation water in the Mornag study area by calculating the risk of alkalinity hazard (\(SAR\)) [36] and the percentage of sodium (%Na) [37].  
\[SAR=\left(\frac{Na^{+}}{\sqrt{\left(Ca^{2+}+Mg^{2+}\right)/2}}\right)\times 100 \text{~{}meq/L} \tag{2}\]  
\[Na\%=\frac{\left(Na^{+}+K^{+}\right)}{\left(Ca^{2+}+Mg^{2+}\right)+\left(Na^{ +}+K^{+}\right)}\times 100 \tag{3}\]  
Principal component analysis (PCA) was performed using XLSTAT software, which covered 9 variables (pH, TDS, Na\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), K\({}^{+}\), SO\({}_{4}\)\({}^{2-}\), Cl\({}^{-}\), and HCO\({}_{3}\)\({}^{-}\)) [38].  
Figure 3: Piezometric maps: (**a**) piezometric maps from 2008 [12]; (**b**) piezometric maps from 2018 [31].
## 6 Conclusions  
This study has applied TOPMODEL to two mountainous watersheds, namely, Dongkok and Ieemokjung watersheds, which are representative IHP watersheds in South Korea. This study investigated TOPMODEL runoff optimizations that depend on the resolution of the DEM grid cell size and analyzed the effects of DEM resolution using fixed input parameter sets. Further, this work  
Figure 14: Results obtained using the optimized input parameters depending the DEM resolution for all Rainfall-runoff events. (**a**) Dongkok watershed. (**b**) Ieemokjung watershed.  
Figure 15: NSE (Nash–Sutcliffe efficiency) changes based on the resolution of the DEM grid cell size with constant parameter sets. (**a**) Dongkok watershed. (**b**) Ieemokjung watershed.  
combined the resolution of the DEM grid cell size and optimized input parameter sets and proposed the most reliable DEM grid-size resolution for TOPMODEL in the two mountainous watersheds. The optimized parameter results represent that an exponential storage parameter among calibrating input parameter is the most sensitive parameter based on TOPMODEL efficiencies.  
The estimated hydrographs obtained by TOPMODEL based on well-optimized parameters that depend on each resolution of the DEM grid cell size were very similar to the observed runoff, regardless of the DEM resolution (up to 300 m in this study), which demonstrated that DEM resolution does not affect the runoff estimation when the input parameters are well optimized. In addition, this study analyzed the DEM resolution effects by applying a fixed parameter set and found that the NSE of the estimated runoff in TOPMODEL decreased as the DEM resolution increased. The estimated runoff results depending on the different optimized input parameters in both the Dongkok and Ieemokjung watersheds were similar to the observed runoff data when the DEM resolution was less than about 40 m. The results of this study imply that reliable DEM grid size depending on experimental watersheds can be evaluated in TOPMODEL. However, these results have limitations in that the reliable DEM grid cell size was evaluated using only one watershed model and two study watersheds. For future studies, it is necessary to describe more about the relations among contributing area, input parameters, and DEM resolution with rainfall events, because those relations have not been clarified. Also, it is necessary to apply different scales of watersheds and watershed models to support the relationship between reliable DEM resolution and watershed characteristics in this study.  
Conceptualization, D.P. and K.J.; Funding acquisition, D.P.; Investigation, D.P., H.-J.F., J.-Z., S.-E.O. and K.J.; Methodology, D.P. and M.-J.U.; Writing--review & editing, D.P. and K.J.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2019R1A2C1007447) and was written as part of Konkuk University's research support program for its faculty on sabbatical leave in 2019.  
The authors declare no conflict of interest.
## 6 Discussion  
### Limitations of the Metaverse Classroom Model  
While the Metaverse classroom model presents a groundbreaking approach to virtual learning, it is not without limitations [92]. One primary challenge lies in the accessibility and resource requirements needed to implement the model effectively [54]. The infrastructure demands for running immersive simulations and 3D environments can be significant, often requiring high-performance hardware and stable high-speed internet connections [225]. This creates potential barriers for underresourced schools and communities, which may not have access to such technology [155]. Moreover, even when hardware is accessible, the varying levels of digital literacy among students and educators can impede effective use. The need for intensive teacher training to manage and optimize virtual classrooms poses another challenge, as it requires time, financial investment, and dedicated resources [16]. Another limitation is the psychological and physical impact of prolonged immersion in virtual environments, especially in young students [34]. Concerns about screen time, eye strain, and the effects of VR on cognitive and emotional development warrant careful consideration [161]. Although highly engaging, the immersive and gamified nature of the Metaverse classroom can lead to an overreliance on virtual interactions, potentially affecting students' abilities to function in non-digital social environments [248]. Furthermore, privacy and data security are critical issues; with student data collection to personalize experiences, robust measures must be taken to protect sensitive information [213]. The risk of data breaches, unauthorized access, and ethical considerations surrounding data usage represent significant limitations that require ongoing monitoring and stringent security protocols.
## 9 Conclusions and Future Works  
In this paper, we have provided an extensive literature review of various existing schemes that exploit sink mobility to alleviate the energy-hole problem and thus improve network lifetimes. Based on sink mobility patterns, we have classified the existing mobile sink-based data collection/dissemination schemes into path constrained, path unconstrained, and controlled sink mobility schemes. It is observed that most of the existing schemes exploit sink mobility to prolong the network lifetime while few focus on data delivery latency and reliability (successful packet delivery)issues which are caused due to the dynamic topology of sink mobility. Furthermore; it is observed that the existing schemes mitigate data delivery latency by either: (1) employing multiple mobile sinks covering various segments of sensor field simultaneously; or (2) explicitly controlling sink mobility (speed and direction) to reduce multi-hop communication in data delivery; or (3) deploying super-nodes which are rich in energy and/or storage-capacity to work as access-points to the mobile sinks. We argue that the use of multiple mobile sinks for reducing data delivery latency requires tight collaboration among the multiple mobile sinks and violates the intended low-cost nature of WSNs. Similarly, explicitly controlling the sink mobility so that the sink visits nearly every single sensor node and harvests data directly in less time may not be a viable option in many harsh environments such as battlefields or forests. Moreover, deterministic deployment of super-nodes to work as access-points for mobile sinks is against the basic theme of WSNs (low-cost and self-organized nature). In addition, neighbor nodes of these super-nodes may potentially suffer from more energy consumption compared to distant nodes thus resulting in energy-holes in the network. Some of the existing solutions are very promising for reducing data delivery latency, but are applicable only in query-driven applications. An ideal and generalized solution in case of mWSNs should cover data dissemination both in query-driven and event-driven (reactive) modes. As part of future work, we plan to incorporate cluster- based data dissemination together with mobility-aware duty-cycle mechanisms not only to further optimize the trade-off between energy consumption and data delivery latency, but to improve packet delivery ratios at the same time. We are interested in introducing sink mobility prediction in data routing and then integrating the routing and MAC mechanisms. Our approach will be based on sink mobility prediction and dynamic scheduling so the duty-cycles and time-slots of the nodes would be adjusted in accordance with the data delivery path learned from mobility prediction algorithms.  
The research is supported by Ministry of Education Malaysia (MOE) and conducted in collaboration with Research Management Center (RMC) at Universiti Teknologi Malaysia (UTM) under VOT NUMBER: Q.J130000.2528.03H19.
# LSTM-DQN-APF Path Planning Algorithm Empowered by Twins in Complex Scenarios
## 3 Method
### Reward Function Design  
In the training process of DQN, the agent evaluates the quality of actions through the reward function. The improved reward function combines goal rewards, exploration rewards, and heuristic rewards, working in deep synergy with the enhanced gravity-repulsion potential field function to collectively strengthen the decision-making ability of DQN-APF. This reward function not only encourages the agent to explore unvisited or less-visited states but also guides the agent towards the target point through heuristic rewards, avoiding the risk of getting stuck in obstacles by blindly pursuing rewards. The exploration reward provides feedback by calculating the difference in visit counts, while the heuristic reward is based on the change in the actual path length. By calculating the deviation between the drone's actual flight path and the theoretical optimal path, as well as considering factors such as energy consumption during the flight, the agent can quantify the effectiveness of obstacle-avoidance actions and assign corresponding reward values. The reward function table is shown in Table 1.  
\[R=C1\times r1+C2\times r2+C3\times r3+C4\times\left(\frac{1}{\max(N(s),\varepsilon )}-\frac{1}{\max(N(s^{\prime}),\varepsilon)}\right)+C5\times\left(D(s,g)-D \big{(}s^{\prime},g\big{)}\right)+C6\times F(s) \tag{12}\]  
\[F(s)=k_{rep}(\frac{1}{d(x,x_{t})}-\frac{1}{d_{0}})\frac{\
abla d(x,x_{t})}{d^{2 }(x,x_{t})}d^{\alpha}(x,x_{g}) \tag{13}\]  
where C1, C2, C3, C4, C5 are weights. n(s) is the number of visits to state s, N(s\({}^{\prime}\)) is the number of visits to state s, D(s, g) is the distance from the current state to the goal point, and D(s\({}^{\prime}\), g) is the distance from the next state to the goal point. Positive rewards are given when the distance to the goal point is closer and no rewards are given when the distance is farther.
### Needs of ISM, WI-FI, and Wireless Lan Applications
#### 1.1.3 Wireless LAN Connection  
The 0.5 GHz and 1 GHz bandwidths allow wireless LANs, including Wi-Fi networks, to use a variety of frequencies for varied deployment scenarios. The 0.5 GHz bandwidth makes indoor coverage and less data-intensive applications possible. In busy places like offices and public spaces, the 5 GHz band's 1 GHz bandwidth provides more channels and less interference for high-density deployments.
# Spatiotemporal Evolution and Impact Mechanisms of Gross Primary Productivity in Tropics
## 4 Results
### Relative Contribution of Climate Change and Land Cover Change to Vegetation GPP Change  
A contribution rate analysis (Figure 8) revealed that deciduous coniferous forest, tree mixed, and urban land types in the tropics contributed more to the change in total vegetation GPP than did climate change. The remaining land types used climate change as the dominant factor affecting local vegetation GPP. Among the land cover types, the urban area exhibited the highest contribution of land cover change to the change in total GPP, reaching 90%. The deciduous coniferous forest area also exhibited a notable contribution of 61.25%. Conversely, the tree mixed area exhibited a relatively modest contribution of 49.99%, with a 3.67% increase due to land cover change compared to climate change. Upon examination of the contribution rates of the two factors, it can be observed that overall climate change was significantly more influential than land cover change. In most areas, climate change contributed more than 70% to total vegetation GPP change. The land types with the highest contribution rates of climate change to GPP change were wetland and cropland, accounting for 95.12% and 89.42%, respectively. Additionally, water, snow and ice, and bare areas exhibited higher contribution rates to climate change, at 99.96%, 99.7%, and 85.96%, respectively. However, the local vegetation GPP production was extremely low, rendering the results inconclusive. In summary, climate change is the most significant factor influencing vegetation GPP in the tropics.
## 4 Comparative Analysis of Test Results  
### Numerical Simulation Reliability Analysis  
Figure 14 illustrates the outcomes of the numerical simulation alongside the conventional triaxial test. The numerical simulation effectively captures the pore pressure data, with the curves aligning closely, and it provides a more accurate prediction of the peak value in the stress-strain curve. The stress-strain curves obtained by numerical simulation are very close to the test data at the beginning and the end of loading, which are slightly smaller than the conventional test values. However, there is a significant difference with the conventional triaxial test data in the middle part of the curve. The numerical simulation curves rapidly approach the peak values when the specimens are subjected to smaller  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** & **Confining Pressure/kPa** & \(\mathbf{e_{0}}\) & \(\mathbf{a_{0}/kPa}\) \\ \hline M & 0.5810 & \(\mathrm{k}/(\mathrm{m}\cdot\mathrm{s}^{-1})\) & \(5\times 10^{-8}\) & 100 & 1.133 & 50 \\ \hline \(\lambda\) & 0.150 & \(\gamma/(\mathrm{k}\mathrm{N}\cdot\mathrm{m}^{-3})\) & 17.5 & 200 & 1.045 & 100 \\ \hline \(\kappa\) & 0.022 & \(\beta\) & 1 & 300 & 0.940 & 150 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Modified Cambridge model calculation parameters.  
Figure 13: Model deformation diagram.  
axial strains. In contrast, the conventional triaxial test slows the curve significantly before reaching the maximum bias stress. [PERSON] [31] described that the same problem exists existed in modeling the stress-strain characteristics of Hong Kong marine sediments.  
Figure 14: Numerical simulation and conventional triaxial test results. (**a**) Stress–strain curves and (**b**) pore pressure and axial strain.  
Taking the conventional triaxial test bias stress as the true value, it can be seen from Table 8 that the relative error of the bias stress decreases with the increase in the confining pressure, with an average absolute error of 0.45 kPa and an average relative error of only 0.21%, which is negligible. The finite element simulation results are consistent with the actual test results and can match well through the stress-strain curve and the pore pressure curve. The stress-strain curve also indicated that the deformation of the soft clay is in the form of cylinder deformation, which is consistent with the actual situation, indicating that the modified Cam-Clay model in the numerical simulation of ABAUQS adopted in this paper is reliable.
## 2 Materials and Methods
### Motivation  
The main physical effect of a structural damage is to give rise to local signal scattering in the readout signal. In this application the distributed fiber optic sensor measures residual strain; this means that the initial state (pre-impact), whatever it is, is set as reference for the current (after impact) measure. The readouts are provided in matrix form, whose rows are timestamps and whose columns are the sensors readouts. If the current structural condition is unchanged (with respect to the initial reference state) sensors readouts do not modify their values. On the contrary, if a gradient exists, this is due to an unexpected structural deformation visible both in time for each single sensor evolution (matrix columns) then in spatially adjacent sensors (matrix rows).  
The readouts locations geometrically laying outside an expiring distance (about 2 cm) [6] from the damage, provide a quite unchanged strain value. For this reason, most of the SHM methodologies use the approach based on the difference between a baseline set of measurements and the current set, the so called \"differential strain approach\". But this difference does not compensate for the background random-like noise. Indeed, to overcome the difficult interpretation of the results due to the signal-to-noise ratio, which sometimes hide too small deviations, an effective data filtering post process technique is needed [29; 30]. In this scope, the cross-correlation function is herein applied in order to highlight minor changes owing to structural failure from backscattering noise with a general improvement of the signal quality. The cross-correlation filters portions of the signal slowly variable (or identical) whose characteristics are defined by a null value.  
Figure 1: Methodology flowchart.
## 4 Investigations on the Propulsion Subsystem
### Prime Mover and Fuel  
The mechanical means of powering the propulsion is provided broadly by two-stroke engines. These engines have higher efficiencies and can be coupled directly with the propellers when compared with four-stroke engines.--The four-stroke engines have higher power densities and lower height requirements, though they will need a reduction gear to reduce the high speed of rotation before coupling with the propeller [33].--These engines mainly use diesel currently, but a development towards the use of alternative fuel is being made where propulsion powered by hydrogen, methanol, or ammonia is used. In a SailREC System, these are the very fuels that could be produced and would be used in the engines. With higher demands for more efficient and cleaner ships, hybrid technology (i.e., additional use of batteries) is also being adopted and integrated into the marine industry. The use of battery technology is shown to reduce the annual operational cost of ships as compared with diesel-mechanical propulsion systems [34]. This is--apart from the target of environmental friendliness and carbon neutrality--a sound argument for designing a propulsion system which is powered electrically rather than just chemically.  
A possible electric propulsion architecture that can be used in a SailREC System is shown in Figure 6. The wind turbine feeds the power bus with energy, which is used by the electrolyser and in the conversion processes (Haber reactor or methanol synthesis reactor). The energy will also be used by other subsystems and auxiliary systems. Part of the energy is stored in the electrical form in batteries, which can be used to feed the bus. The bus can also be energised by using the stored fuel through a chemical to power conversion (X2P) when required. The whole control of power on the bus should be monitored by a power management system. The prime movers for the propellers are electric motors, and they source power from the bus. The SailREC System will require a battery (or other storage technology) to conserve energy to supply power for conversion processes that would need power when the wind turbine is not generating (enough) power, as the processes may not be flexible enough to be turned up and down depending on the fluctuating supply of power. The power bus can be based on AC (alternating current) or DC (direct current); currently, AC-based applications are more prevalent for marine applications. The study by [PERSON] et al. [35] has shown benefits in the use of DC-based systems--in both electrical performance and fuel efficiency--in a diesel-electric marine vessel. The challenge would be the safety of the system, as designing protection devices for DC applications is of increased difficulty.
## 7 Applications
### Environmental Analysis
demonstrated the use of a self-supported TF-SPME device composed of a zeolitic imidazolate framework, which extracts the organophosphorus pesticide ethion with subsequent TD for the analysis of environmental water samples [63]. In other respects, there are examples of more conventional TF-SPME being used in conjunction with LD for environmental analysis [64], ranging from the analysis of fluorinated benzoic acids in aqueous samples [21] to the analysis of polychlorinated biphenyls (PCBs) by direct application of a TF-SPME device into fish tissue [65].