esa-sceva/satcom-chunk-collection huggingface dataset:
# Hydrodynamic Modelling in a Mediterranean Coastal Lagoon--The Case of the Stagnone Lagoon, Marsala
## 2 Materials and Methods  
### Study Area  
The Stagnone is a coastal lagoon confined by Isola Grande Island, and the main water exchanges with the open sea occur through two different inlet mouths (Figure 1).  
The north mouth is 400 m wide, and it is characterized by shallow water, with an average depth of 0.30 m. The south mouth is circa 2900 m wide with a depth of about 1.0-1.50 m.  
In the north mouth, water exchange is reduced by the progressive sediment deposition and strong accumulation of dead seagrass leaves coming from the near sea region. This  
Figure 1: Study area.  
also produces an increase in the characteristic residence time of the particles inside the Stagnone, developing high water temperatures and salinity during the summer. For this reason, the ecological balance of the lagoon is seriously at risk, and a 10 m wide and 1 m deep channel was dredged on the west side of the north mouth inlet to increase water exchange and to allow the transit of small vessels. Because of effects of seasonal freshwater are negligible [4], the hydrodynamic mixing is predominantly influenced by sea-related phenomena and wind.  
Inside the lagoon, the mean water depth is less than one metre (circa 0.95 m), with a decreasing depth going northward. From a hydrodynamic point of view, the Stagnone can be divided into two different parts: the southern part (from the south mouth to Motia Island) is characterized by deeper water, and the northern part, on the other hand, is shallower with a water depth lower than 0.90 m and elevated residence times.  
In the southern part, the water elevation is strongly dependent on the offshore regions entering from the south mouth [2]. In the northern part, the effects produced by secondary phenomena are dominant, following what was found in [7].  
As demonstrated by [2], _Posidonia oceanica_ is the main biotic component in the Stagnone. It covers the central part of the lagoon area with atoll patterns (10-20 m in diameter) and reef formation (1-2 m wide). Where hydrodynamic conditions and sediment composition worsen for _P. oceanica_, the seagrass meadow is partially replaced by _Cymodocene nodosa_ and _Caulerpa prolifera_[4].  
A bathymetric survey was performed in 1999 and shows very shallow waters in the northern area of the lagoon, with an increasing depth moving southward. A sketch of the bathymetry is reported in Figure 2b, where it is possible to appreciate the deeper water in the southern region and shallower waters in the middle-north area.  
The submerged vegetation produces a loss of energy due to plant-induced forcing acting on the fluid, resulting in smaller wave heights [16]. It is important t12o underline, as found by [17; 12], that since in the northern part of the lagoon the water is shallower, the relative submergence of the vegetation is higher, inducing a higher damping effect for the hydrodynamic circulation.
## 1 Introduction
Geodetector's capacity to capture nonlinear and interactive effects among multiple factors offers a distinct advantage over conventional linear models, providing more nuanced insights into how variables such as soil type, elevation, cropland expansion, and tourism interact to shape ecosystem trajectories. Using Geodetector, [PERSON] et al. [41] identified land use type, landform type, and leaf area index as determinants of ecosystem service multifunctionality in China's coastal zones, whereas [PERSON] et al. [42] demonstrated that land use conversion type, mean annual precipitation, and soil type were the most significant factors influencing NDVI changes in the Heihe River Basin. These methodologies hold considerable potential for advancing conservation strategies that align with current conservation goals while addressing the developmental needs of national park systems. Therefore, understanding these changes and their driving forces is crucial for rationally defining the boundaries and functional zones of protected areas [43; 44; 45]. This study has two main objectives: (1) to elucidate the spatial and temporal dynamics of forest and grassland ecosystems within the Altai Mountains National Park Candidate Area, and (2) to propose a framework for the conservation and management of national parks that focuses on identifying key drivers of ecosystem change, informing targeted conservation strategies, and evaluating the effectiveness of protected areas, using the Altai Mountains National Park Candidate Area as a case study. The novelty of this study lies in its holistic evaluation of both forest and grassland ecosystems within an ecologically sensitive area that straddles international borders. In contrast to previous research that primarily focuses on a single ecosystem type or overlooks the complex interactions of multiple drivers, our study integrates spatial-temporal analyses, robust trend detection, and driver identification across forest and grassland systems. By examining how these ecosystems co-evolve, as well as how established protected areas influence their trajectories, this study not only addresses a critical knowledge gap for the Altai Mountains, but also provides a framework applicable to other mountain regions facing complex environmental and policy challenges globally.
## 3 An Overview of Existing Works  
Before focusing on our main analysis, we first go over the existing works that tackle the effect of channel correlations on the performance of AnF cooperative systems. A summary of channel correlation models considered in the existing literature is given in Table 2.
## 3 Particle Filter Frame
### Particle Filter Frame Algorithm  
The specific PFF algorithm is described as follows:  
1. Initialization. Sampling \(N\) particles \(\left\{x_{0}^{i}\right\}_{i=1}^{N}\) from the prior probability distribution function, and the initialization weight is \(\omega_{0}^{i}=1/N\) accordingly.
2. Sequential importance sampling (SIS). 1. Resample independently \(N\) times from the above discrete distribution. 2. The prior weights are used to update the new weights, as follow, \[\begin{array}{l}\omega_{k}^{i}=\omega_{k-1}^{i}p(y_{k}^{i}|x_{k}^{i})\\ =\omega_{k-1}^{i}\frac{p(y_{k}|x_{k}^{i})\,p(x_{k}^{i}|x_{k-1}^{i})}{p(x_{k}^ {i}|x_{k-1}^{i},y_{1:k})},i=1,2,3,\cdots,N\end{array}\] (11) where \(p(y_{k}|x_{k}^{i})\) is the likelihood function. 3. Normalize the importance weights, i.e., \(\widetilde{\omega}_{k}^{i}=\omega_{k}^{i}(\sum\limits_{i=1}^{N}\omega_{k}^{i}) ^{-1}\), where \(\widetilde{\omega}_{k}^{i}\) is the normalized weight of the \(i\)-th particle at time \(k\), and \(N\) the number of particles.
3. Resampling.  
After calculating the normalized weights, resampling is used to avoid the particle degeneracy phenomenon of the PFF algorithm by means of removing the small weighted particles and preserving the promising particles with the larger weight.  
The threshold of resampling is defined as effective sample \(N_{\text{eff}}\), if \(N_{\text{eff}}=\frac{N}{1+\text{var}(\omega_{k}^{i})}\approx\frac{1}{\sum \limits_{i=1}^{N}\left(\omega_{k}^{i}\right)^{2}}<N_{\text{threshold}}\), then generating a new set of particles \(\left\{x_{k}^{i,*}\right\}_{i=1}^{N}\) by resampling \(N\)-times from \(p(x_{k}|y_{1:k})=\sum\limits_{i=1}^{N}\widetilde{\omega}_{k}^{i}\delta(x_{k}- x_{k}^{i})\), and set the weights of all the new particles to \(1/N\) accordingly. Finally, for the sampled particles with the associated weights, the state vector \(\overset{\wedge}{x}_{k}\) can be estimated with \(\overset{\wedge}{x}_{k}=\sum\limits_{i=1}^{N}\widetilde{\omega}_{k}^{i}x_{k}^ {i}\). Figure 1 illustrates the filter evolution procedures of particles in the recursive processes, where the dash line represents the optimal solution.
## 1 Introduction  
Electrospun polycaprolactone (PCL) membranes have been broadly applied in biomedical engineering due to their excellent biocompatibility, high mechanical strength, and slow degradation rate [1; 2]. Common solvents used to dissolve PCL pellets for electrospinning include dimethylformamide, tetrahydrofuran, chloroform. In fact, these types of solvent are extensively employed because they are considered good solvents for obtaining a fine membrane morphology (bead-free and smooth fibers) and avoiding clogging at the needle tip, which is ideal for automatic operation and massive production [3]. However, these solvents are extremely devastating to human health and harmful to the living environment [4; 5]. Alternatively, relatively safe chemicals such as acetone (AC) and acetic acid (AA) were discovered to be applicable for electrospinning of PCL [6]. Unlike AC which can be used as an independent solvent for PCL, studies suggest using AA in combination with other solvents at appropriate ratios rather than in pure form to avoid non-fiber formation [7] or eliminate needle-tip clogging [8]. Furthermore, the use of AA in solvents to dissolve PCL can allow the co-electrospinning of PCL and natural polymers such as chitosan [9], gelatin [10], and collagen [11] using only one single homogenous electrospinning solution rather than two solutions, because these polymers are also easily dissolved in AA. Compared to the toxic solvents mentioned above, AC and AA are much less detrimental to human health, showing potential as alternative solvents in electrospinning. However, research on PCL membranes using solvents containing AC and AA and systematic comparisons between them are still sparse, and results are strongly dependable on each electrospinning apparatus design. In particular, no research has been done on cell-material interaction, which has impeded our understanding of their potential applications in various areas including the biomedical, clinical, and surgical fields.  
In this study, electrospun PCL membranes were fabricated using two different types of eco-friendly solvents containing AC and AA, whose effects on cell adhesion and proliferation were preliminarily examined in relation to membrane morphology and its wettability, chemical, and physical characteristics.
## 5 Simulations of UGW Testing
FBR-RC-SSP was applied to the above signals, the corresponding SNRs and DCRGs were calculated and the results are shown in Figure 14 and Table 2. It can be observed that FBR-RC-SSP-SPT show the worst processing effect. Although it can retain the defect and end-reflected echoes and the signal resolution is improved to a certain extent--where the average SNRG and DCRG are 6.12 dB and 5.49 dB, respectively--there are still coherent noises in the signals making it difficult to locate and quantify defects. The processing effects of FBR-RC-SSP-PT and PTM are similar: both defect and end-reflected echoes are clearly displayed with almost no coherent noise (the average SNRs are 13.19 dB and 14.22 dB, and the average DCRGs are 13.21 dB and 13.34 dB, respectively). Therefore, it is logical to choose PT and PTM as the signal recombination methods in UGW testing, which makes signal interpretation very convenient. The following sections focus on these two methods.
## 1 Introduction
With the development of modern measuring devices, we can observe a significant advent of powerful measuring devices enabling 3D analysis of components in real time [9], including outputs in a format that can be relatively quickly and easily compared with numerical modelling results. It should be noted that, in the case of numerical modelling, it is often an investigation of an ideal model, where the results depend on the input parameters defined by the designer. In the best case, information obtained from experimental measurements on real products is needed, otherwise tabular values are used. In some cases, the problem of defining the required parameters is mainly for modern (developed) composite materials, which mechanical properties are not known for all stress (loading) states or types of stress [10; 11]. The designer must always keep in mind that any small change (modification) of one component may adversely affect the functionality of another and thus endanger the safe and reliable operation of the entire mechanism. The presented paper describes the methodology of experimental verification of the occurrence of excessive deformations on the roof of a car, which were identified around the roof antenna. The assumption was that the main cause was the process of tightening the screw to fix the antenna. The proposed methodology was based on a deformation-stress analysis determined by numerical modelling. Subsequently, displacements around the antenna before and after its assembly were experimentally determined with a Leica 3D measuring scanning device. To validate the achieved results, a strain gauge method was used, which allowed us to measure strain in selected locations under operational loading. Based on the comparison of the obtained data, the influence of antenna assembly on the occurrence of excessive deformations was assessed.
# Improved Cell Allocation Strategies Using K-Means Clustering in Congested 6 TiSCH Environments
## 3 Background
### Routing Protocol for Low-Power and Lossy Networks (RPL)  
The Routing Protocol for Low-Power and Lossy Networks (RPL) is designed for resource-constrained environments such as wireless sensor network. RPL offers efficient and reliable routing by addressing challenges such as node mobility, dynamic topologies, and unpredictable conditions. It supports hierarchical structures and uses the Destination-Oriented Directed Acyclic Graph (DODAG) to establish optimal paths toward specific destinations while considering topology and delivery needs [30]. RPL also dynamically reorganizes paths in response to network changes, making it ideal for WSN and IoT applications thanks to its efficiency, reliability, and ability to manage control overhead and memory constraints [31].
###### Abstract  
As shallow coal resources in China become increasingly depleted, deep coal mining in complex geological areas has become an inevitable trend. However, the technical challenges associated with deep mining are becoming more significant, particularly the issues related to mine water hazards. This study utilized hydrogeological data from the III3 Mining Area in the Zhuxianzhuang Coal Mine, Anhui Province, and employed GMS (Groundwater Modeling System) software to construct a numerical karst water flow model under deep mining conditions. By simulating variations in the flow field, the study verified the drainage potential of the limestone water at the base of Seam 10 and assessed the water conductivity and connectivity of the F22 fault. The following conclusions were obtained: The simulation effectively captured the formation process of the karst water drawdown cone in the study area. The observed water level variations in different monitoring wells aligned well with the engineering reality after validation. The limestone water at the base of Seam 10 in the III3 Mining Area exhibited good transmissivity, weak recharge, and high drainage potential. Although the F22 fault is a normal fault with a maximum displacement of 550 m, offsetting formations from Seam 3 to the Ordovician limestone, its connectivity and water conductivity are poor, exhibiting significant water-blocking properties. The specific capacity (q) ranges from 1.40 \(\times\) 10\({}^{-4}\) to 3.26 \(\times\) 10\({}^{-3}\) m\({}^{3}\)/(s-m), and the hydraulic conductivity (K) ranges from 2.10 \(\times\) 10\({}^{-5}\) to 6.80 \(\times\) 10\({}^{-5}\). Under deep coal mining conditions, the extraction of coal disturbs the underlying limestone, generally resulting in an increase in its permeability coefficient compared to pre-mining conditions. The permeability coefficient (K) from the measured data before mining impact ranged from 0.000067 to 0.0022, while the simulated values after mining impact ranged from 0.0021 to 0.09. Additionally, mining activities affect the hydraulic head, flow rate, and flow paths of the karst water; the floor karst water is easily drainable, effectively reducing water pressure and the intrush coefficient, thus lowering water hazard risks. Although the mining area is affected by the large F22 fault, its water-resisting properties under sufficient drainage conditions prevent direct connectivity between the coal seam and the aquifer, avoiding water hazards. As global coal resources continue to be exploited, deep mining will inevitably become a common trend in coal extraction worldwide. This study develops a hydrogeological model tailored to deep mining under fault conditions, offering a solid theoretical foundation and practical reference for the prevention and management of mine water hazards on a global scale. This advancement contributes to the development of sustainable mining practices across the global industry.  
deep coal mining; GMS; numerical simulation; limestone water; hydrodynamic simulation; mine water hazard
## 5 Discussion
### Computational Cost  
All computations to solve the optimization problem were performed in Matlab R2023b on a PC with a 12 th Gen Intel Core i9-12900H processor (2.50 GHz), 64 GB of RAM, and a dedicated 12 GB GPU. The algorithm used a population size of 500. The most computationally demanding controllability metrics EV\({}_{\rm 2 min}\) and EV\({}_{\rm min}\) required, respectively, up to 2663 s (44 min and 23 s) and 1578 s (26 min and 18 s) to converge to the Pareto front and identify optimal solutions. The large population size and high number of generations help explore the solution space thoroughly, but they also make the simulations more time-consuming. Thus, the stability of the optimization algorithm is questionable when using metrics EV\({}_{\rm 2 min}\) and EV\({}_{\rm min}\).  
Using metrics EV\({}_{\rm max}\), TR, and DET is not so computationally demanding. Therefore, five independent optimization repetitions were conducted for each metric to evaluate the stability and variability of the optimization algorithm. The computational performance results are presented in Table 3. The fastest computation time was observed for the TR metric with 381.40 s (approximately 6 min and 21 s), and the lowest mean computation time (434.71 s) and a relatively modest standard deviation (69.99 s). EV\({}_{\rm max}\) required a mean computation time of 503.11 s, while DET required the highest average computation time (845.84 s), with the largest standard deviation (234.50 s), indicating greater computational effort and variability. The hypervolume measure was used to evaluate the quality of the obtained Pareto fronts. All objective function values were normalized within the (0, 1) \(\times\) (0, 1) range, and a fixed reference point at (1.2, 1.2) located outside the feasible objective space was used across all evaluations. TR showed the lowest hypervolume standard deviation (0.0063). The knee points, representing compromised solutions on each Pareto front, are also presented for comparison. EV\({}_{\text{max}}\) achieved knee points with relatively low variation in objective values, with standard deviations of 0.0286 for \(J_{\text{1 Knee}}\), and 0.0337 for \(J_{\text{2 Knee}}\). TR and DET exhibited a higher standard deviation in \(J_{\text{1 Knee}}\) (0.0587, and 0.0546, respectively). Despite this variability, all three metrics show knee points within a similar objective region.  
The proposed approach is not suitable for real-time applications because of the long computation times. However, it is feasible for offline planning and long-term studies where detailed analysis and solution quality are more important than speed.
## 5 Results and Analysis
### Structural Model
For a relationship to be statistically significant, zero should not fall within the confidence interval range. This implies that the interval between the lower and upper bounds should exclude zero. Based on data presented in Table 6, the confidence interval condition is only applied to the relationship between self-directed learning and student satisfaction. With the lower and upper bounds at 0.185 and 0.476, respectively, this suggests that the relationship between these two variables is statistically significant. Across all other relationships, the confidence intervals encompassed zero between their upper and lower bounds, indicating that these relationships are not statistically significant. Including both positively and negatively phrased questions in the updated question-naire enabled a more effective assessment of participant consistency. We utilized [PERSON]'s alpha to evaluate internal consistency, and the results indicated strong reliability across all dimensions. Furthermore, no significant response bias was detected between the positive and negative items, confirming that the randomization of item order successfully minimized any potential response patterns. To evaluate the model's predictive power, the R\({}^{2}\) value was used. R\({}^{2}\) is a statistical metric that shows the extent to which variation in the dependent variable is explained by the independent variables. The acceptable threshold for R\({}^{2}\) is at least 0.10, with the condition that some hypotheses must be statistically significant. The R\({}^{2}\) value for the model was 0.18, indicating that information quality, self-directed learning, service quality, student-student interaction, and system quality collectively explained 18% of the variation in student satisfaction, surpassing the acceptable level.
## 2 Materials and Methods
### Data Processing of the EC-Based Fluxes  
As an indispensable part of the global-scale long-term network of micrometeorological flux observations, ChinaFLUX addressed to reveal the variability in land-atmosphere carbon and water fluxes, as well as the underlying mechanisms controlling it [38; 39]. In this study, the EC-based flux measurements between 2003 and 2010 at the two subtropical forest flux sites (QYZ and DHS) were used for analysis. At these flux sites, the eddy covariance system together with an automatic meteorological station (AWS) were mounted and continuously observe the site-scale net carbon exchanges (NEE) and the related meteorological data. These synchronously observed climate data consisted of near-surface air temperature (T\({}_{\text{a}}\)), solar radiation (R\({}_{\text{g}}\)), soil temperature and water content (T\({}_{\text{s}}\) and SWC), relative humidity, vapor pressure deficit (VPD) and the amount of precipitation (P). The open-path EC system contains two sensors including a fast response infrared CO\({}_{2}\)/H\({}_{2}\)O gas analyser (Li-7500, LI-COR Biosciences, NE, USA) and a 3-D sonic anemometer (CSAT3, Campbell Scientific, UT, USA). The raw data are sampled at the frequency of 10 Hz by the data logger (CR5000, Campbell Scientific, UT, USA). Then, the procedures including spike detection and despiking, two-dimensional coordinate rotation, time delay removal of H\({}_{2}\)O and CO\({}_{2}\), virtual temperature correction, density effects (WPL correction) and frequency response corrections were completed to produce a half-hour flux dataset [40; 41]. More details about the processing procedures can be seen in [PERSON] et al. [42].  
Furthermore, owing to the power failure, instrument malfunctions, and severe weather conditions, a few flux observations were lost. These gaps were interpolated with a standardized algorithm developed by ChinaFlux [40; 41]. Meanwhile, these site-level NEE measurements were separated into GPP and R\({}_{\text{e}}\) using a short-term temperature dependent method [15; 43]. The R-based software (REddyProc, MPI, Jena, Germany) developed by the Max Planck Institute (MPI) for Biogeochemistry was applied to complete the gap-filling and flux partitioning procedures [44]. Finally, the half-hourly GPP data were processed into the daily time scale and averaged over each 8-day period to match the composites of satellite-based SIF and GPP products.