esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Trees in Sand Dune Rehabilitation: Insights from Global Experiences
## 4 Discussion  
### Classical Review-Synthesis of Literature  
We found that the literature about the role of trees in the rehabilitation of sand dunes is extensive and uses a multidisciplinary approach. This emphasizes the complexity of this subject from both an ecological and societal perspective. For example, conference proceedings represent 7% of total publications, emphasizing the interdisciplinary approach and the concern raised by this subject in academic and professional institutions. This diversity also stems from the large range of research domains tackled, such as ecology, forestry, land management, and climate science.  
From a temporal perspective, we can see that these publications have increased during the past two decades, highlighting this trend in environmental research [108; 109; 110]. This increase can be correlated with global concerns about desertification, erosion, climate change, and biodiversity conservation. Our keyword analysis also highlights this trend, with terms such as \"restoration\", \"desertification\", and \"climate resilience\" being more used in these studies.  
From a geographical perspective, the largest number of contributions come from the USA, China, and Australia. Sand dunes are extensively present in these countries, leading to significant contributions in research focused on afforestation and land restoration. The results of these articles have been published in two journal categories: generalist environmental science journals (e.g., Catena, Forest Ecology and Management, Sustainability) and arid and coastal ecosystems journals (e.g., Journal of Arid Environments, Journal of Coastal Research).  
The reviewed literature has emphasized important insights, but we must also acknowledge methodological biases that can impact these findings. As such, the majority of studies are either ecological or technical, focusing on measurable biophysical results (vegetation growth rates, soil stabilization metrics). This reflects a possible publication bias towards quantifiable and tractable results. On the other hand, the socio-economic implications and the perspectives of local communities are harder to track and, as such, are underrepresented in these studies, although they directly impact the success and sustainability of rehabilitation projects. In addition, most of the studies come from certain geographic areas, showing a possible geographic bias that affects the impact of these findings on certain ecological and cultural contexts.  
In this context, it is important to recognize these constraints, especially in future studies. We need to include more interdisciplinary approaches that consider the ecological, social, economic, and political perspectives. Research that uses mixed methods, participatory approaches, and long-term monitoring can address these gaps and offer a more holistic understanding of the impact of trees on the rehabilitation of sand dunes.
# Implementation and Evaluation of Nodal Distribution and Movement in a 5G Mobile Network
## 2 Review of Simulators
### Ns3  
NS3 is a network simulator for discrete event simulation. It is free software under the GNU GPLv2 license. This tool is directed to research applications, as well as for educational purposes [11; 12]. NS3 has the flexibility and speed of simulation scenarios due to the using C ++ as a master programming language. At the same time, it supports the Python language, which gives a lower learning curve for new users using this simulator. Each of these programming languages allows describing the behavior of telecommunication systems.  
The NS3 gives the opportunity to developers to build models of any topology and complexity. Along with this, a simulator gives an opportunity to implement, modify and supplement both existing models and modules with the user's own developments. Disadvantages of the NS3 are missing of a fully supported IDE and also missing a built-in graphical interface. However, there are ready-made third-party implementations for visualizing the behavior of models.  
Models of wireless network types have been developed and built into NS3, which allow modeling with moving objects in three-dimensional space. Particular attention is given to the detailed use of the physical and link layer implementations of the OSI network model, which opens the way for many opportunities for research in this area.  
The development of the NS3 project does not stop. Updates with the correction of existing shortcomings and the implementation of new standards represented as separate modules or separate products are continuously released. There is also built-in support for distributed computing, and it continuous modernizes, which further accelerates the simulation process.  
From the point of information security view, the project developers focus on the reliability of data transmission, leaving these issues of secondary importance. However, the community supports both the implementation and method of combating attacks at the high layers of the OSI model. Most often, the simulator used is the general source of traffic generation and behavior of a node within the network. Specified attention to the implementation of the physical and link layers opens the way for opportunities in security research.
# Survey on Routing Protocols for Vehicular Ad Hoc Networks Based on Multimetrics
## 4 Routing-Protocol Proposals Based on Metrics
### Multimetric-Routing Proposals
#### 4.2.9 Multimetric Opportunistic Routing (MMOR)  
In [39], MMOR was presented, which included multimetrics in the selection of the best opportunistic next forward. To choose the forwarding candidate, it takes into account the distance to the destination, the load of the node, moving direction, velocity, and density. These parameters are key metrics in this proposal when the candidate for the next hop is selected.  
After calculating each metric value, the opportunistic forward decision can be computed with these values, combined by Equation (11).  
\[\begin{array}{l}Opp(N_{i})=\Sigma_{i=1}^{5}U_{i}\end{array} \tag{11}\]  
The \(Opp(N_{i})\) decides each node's priority. The node with the highest value among the candidates is selected as the best opportunistic forwarding node.
## 3 Results and Discussion  
### Morphology  
The morphology via SEM is shown in Figure 1, illustrating the texture and inner structure of the SCL and Ca\({}^{2+}\) hydrogels of all groups. Specifically, it shows the morphology of hydrogels without cells: 1A for the SCL hydrogel and 1a for the Ca\({}^{2+}\) hydrogel; and the morphology of hydrogels with cells: 1B, 1C, 1D, and 1E SCL hydrogels, and 1b, 1c, 1d, and 1e for Ca\({}^{2+}\) hydrogels, where the alginate concentrations are 0.8%, 1.1%, 1.5%, and 2%, respectively.  
The SEM images show no major differences in the inner structure of both SCL hydrogels and Ca\({}^{2+}\) hydrogels, but reveal minor differences between the two types of hydrogels in terms of pore size and degree of compactness. Specifically, when studying the software image J, for the SCL and Ca\({}^{2+}\) hydrogels, the average areas of pore size were found to be 124 \(\upmu\)m\({}^{2}\) and 7.1 \(\upmu\)m\({}^{2}\), respectively, which indicates that the pore size of the SCL hydrogels is significantly larger than that of the Ca\({}^{2+}\) hydrogels; and the average number of pores in each type of hydrogel per 125 \(\upmu\)m\({}^{2}\) area was six and 89, respectively, indicating the large number of pores in Ca\({}^{2+}\) hydrogels. When considering the pore density, calculated as the overall area of pores (pore size multiplied by pores amount per 125 \(\upmu\)m\({}^{2}\)of each type of hydrogel, it was found to be slightly higher for the SCL hydrogels (744 um\({}^{2}\)) than for the Ca\({}^{2+}\) hydrogels (632 um\({}^{2}\)). The overall structure of the SCL hydrogels is a connective network, which contains fewer pores, but of a larger size, than the Ca\({}^{2+}\) hydrogels; while the Ca\({}^{2+}\) hydrogels reveal a more compact network structure.  
With respect to the ability of the two types of hydrogels to entrap cells, the SEM images show that the Ca\({}^{2+}\) hydrogels, with their larger number of pores, appear better able to encapsulate cells than the SCL hydrogels. Indeed, it was noted during the experimental process that large piles of cells were easier to find in the Ca\({}^{2+}\) hydrogels than in the SCL hydrogels. We attribute this to cells in the Ca\({}^{2+}\) hydrogels being able to survive and regenerate, due to proper growth signals secreted from adjacent groups of cells; cells in Ca\({}^{2+}\) hydrogels can build connections with each other more easily, due to the shorter distance between them.
# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 4 Evaluation and Discussions
### Forecasting Result and Discussion
As shown in Figure 12. In the periods of 2 nd to 5 th hour and the 25 th to 28 th hour, except LSTM algorithm, the predicted trends of other algorithms showed larger error with the actual power trend. Its RMSE value could reach 13.86%, which is higher than other seasons. In Figure 13 starting from the 13 th hour, the results predicted by other algorithms lag behind the actual ones. After the 30 th hour, the same error appears in the LSTM model. It can be seen that the prediction accuracy under ideal weather type is not high, because the diversity of power generation trends under this type is lacked in training set. It can be observed that LSTM model exhibits optimal forecasting in each season and shows the similarly good forecasting performance for non-ideal weather types compared to the ideal weather type. RMSE and MAPE values of forecasting are shown in Table 4. It shows that the RMSE values of forecasting using LSTM models in four seasons are respectively 5.34%, 9.57%, 13.86%, and 9.26%, while the lowest values using other algorithms are respectively 18.8%, 13.03%, 20.94%, and 17.74%. In addition, it demonstrates that the forecasting results in autumn are not particularly ideal with RMSE value of 13.86%. However, the LSTM model still shows the best performance compared with other algorithms. Figure 10: Short-term PV power forecasting in spring by various algorithms. (**a**): the general forecasting power trend of three days (**b**): the trend in the first 5 hours in (**a**) (**c**): the trend from 28 th to 32 th hour in (**a**).
## 2 Methods
### Bayesian Methods for Uncertainty Quantification in Deep Neural Networks  
#### 2.1.1 Bayesian Methods and Deep Ensembles  
We denote as \(y\in Y\) the space of the predictions and \(x\in X\) the inputs. In Bayesian approaches for uncertainty quantification in CNNs, instead of obtaining a single set of weights \(\theta\) for the CNN by optimizing a given loss function, the aim is to obtain a posterior distribution of weights, \(p(\theta|D)\), given the training data \(D=(X,Y)\)[18]. In the following, we give an outline of deep ensembles interpreted as a Bayesian method. Given the posterior distribution, a predictive distribution can be obtained by marginalization over the weights:  
\[p(y|x,D)=\int p(y|x,\theta)p(\theta|D)d\theta.\]Since the posterior distribution, \(p(\theta|D)\), is intractable, it is common to either approximate it with a distribution or apply sampling methods [13]. Deep ensembles, as introduced in [10], can be interpreted as a sampling method [18]. The approach consists in training a number \(N\in\mathbb{N}\) of ensemble members that are CNNs, \(f_{\theta_{i}}:X\to Y\). Due to the non-convexity of the optimization problem for obtaining the weights and the stochastic gradient descent methods, this results in \(N\) different sets of weights, \((\theta_{i}(D))_{i=1}^{N}\). Then, the posterior distribution [18] is assumed to be approximated by  
\[p(\theta|D)\approx\frac{1}{N}\sum_{i=1}^{N}\delta(\theta=\theta_{i}). \tag{1}\]  
The resulting prediction of the deep ensemble (DE) is the mean of the predictions,  
\[\hat{y}_{\text{DE}}(x)=\frac{1}{N}\sum_{i=1}^{N}f_{\theta_{i}}(x) \tag{2}\]  
and the epistemic uncertainty is given by the variance of the predictions,  
\[\partial_{\text{DE}}(x)^{2}=\frac{1}{N}\sum_{i=1}^{N}f_{\theta_{i}}(x)^{2}- \hat{y}_{\text{DE}}(x)^{2}. \tag{3}\]
## 2 Materials and Methods
### Validation
#### 2.6.2 Airborne Lidar Data  
Many airborne discrete return lidar surveys have been acquired over NSW for government use, covering over 46,000 km\({}^{2}\) or 6% of the state (Figure 3D). The surveys were not randomly distributed, as many lidar surveys were over rivers, wetlands and urban areas, where high resolution elevation models were required for flood modelling. A stratified sample of tiles was taken, where a lidar tile was randomly sampled from each of the 17 vegetation formation classes (K
# Plant-Growth-Promoting Bacteria Mitigating Soil Salinity Stress in Plants
## 5 Role of PGPB in Alleviating Salinity Stress to Plants
### Plant Ion Homeostasis  
The excess of Na\({}^{+}\) results in an imbalance of nutrients due to the elevated uptake of this element, so the K\({}^{+}\)/Na\({}^{+}\) ratio decreases, which reduces plant growth in this circumstance. Some plants have mechanisms to compensate for this imbalance by controlling the Na\({}^{+}\) influx through the roots and the xylem flux back [136]. Halotolerant PGPB increase K\({}^{+}\) uptake by upregulating one K+ transporter and decreasing the accumulation of Na\({}^{+}\) in leaves; thus, the K\({}^{+}\)/Na\({}^{+}\) ratio increases. _Azotobacter_ populations were able to decrease Na\({}^{+}\) uptake and to stimulate the K\({}^{+}\) influx, which resulted in increased chlorophyll content and plant stress response mechanisms [113]. Other studies have reported that the inoculation of PGPB (_P. fluorescens_ biotype F, _P. fluorescens_ CECT 378T, _B. tequilensis, B. argabhattai, Providencia sturatti, Pantoea agglomerans_, or _Arthobacter_ sp.) in plant rhizosphere under salinity stress resulted in an increased K\({}^{+}\)/Na\({}^{+}\) ratio [4; 76; 128; 129]. All PGPB presented various PGP traits, the combined action of which leads to the amelioration of salinity stress to plants.
# Impact of Direction Parameter in Performance of Modified AODV in VANET
## 3 Simulation Environment  
We assessed the performance of the proposed routing protocols in OMNeT++ and used MATLAB(r) to analyze the simulation data. Recently, the combination of simulation of urban mobility (SUMO) and OMNeT++ has been used to build the network communication in a VANET. We combined the network with a traffic simulator named SUMO, which is used to reproduce the network protocols in the VANET. The mobility of each node in this simulation environment is controlled by SUMO, and the position is forwarded periodically to the network simulator.  
In a VANET, the outcomes of a simulation are not entirely reliable and mostly guided by the mobility model in use. In this context, [PERSON] et al. [17] suggested the necessity for a robust simulation environment for VANETs. This led to the advancement of Veins (vehicles in network simulation) [18]. Veins is an open-source simulation environment that uses two simulators: (1) SUMO for conducting microscopic road traffic simulation and (2) OMNeT++ for conducting the network simulation, as shown in Figure 3.  
The road map for VANET simulation is imported from openstreetmap.org [19] by manually choosing the required region or area and exporting to the.osm option to download a map.osm file. The resultant file is an XML file with an OpenStreetMap-defined region. We used Java Open Street Map Editor (JOSM) to edit the map.osm file manually. Apart from the road network, the imported and edited map.osm also includes other information, such as rivers, walkways, power distribution lines, bicycle routes, parks, and other features that are outside the scope of this study. A brief description of the simulation tools used in this research is given below:  
Figure 3: Objective Modular Network NESTbed in C++ (OMNeT++) with simulation of urban mobility (SUMO) [16].
## 4 Parameter Tuning Results
### Classification and Regression Trees--Accuracy versus Max Tree Depth  
As mentioned in Section 3, the CART model uses split conditions that are applied to the root node, and the decision procedure is repeated sequentially for each subroot node. The term max depth refers to the maximum depth to which the tree can grow. The larger the tree is allowed to grow further into the data, the more intricate the model becomes. As stated in [41,42], small trees are preferred as they are simple to print and display to subject matter experts. They also have a low likelihood of overfitting the dataset. A range of max depth values from 1 to 10 was chosen as our initial observation parameter to study the behaviour of the CART model. Based on our observation, our CART subcarrier prediction model in Figure 7 depicts an exponential increase in accuracy for a max depth range of 1 to 3. It starts to plateau at 100% accuracy as the max depth increases. The results indicate that the CART model prefers trees with a small number of nodes. Although increasing the max depth yields high accuracy cross-validation results, it overfits the training data and fails to capture important patterns as expected. A similar result is shown in [43] where the CART model easily overfits. Overcomplex trees may be created with low predictive ability. However, the decision tree's ability to detect patterns and interactions in the training data may be compromised if the max depth is too low. Consequently, the testing error also increases. Therefore, achieving an optimum depth value between the two extremes of being either too high or too low is a challenge.
## 3 Results
### Results: Part B
#### 3.2.4 Part B, Phase 4
Here, macro- and micro-average AUC are the same (the former computes the metric independently for each class, and then, takes the overall average, while the latter aggregates contributions from all classes). Figure 7: Phase 2 – Model 4 of 21 models: 80 pps, Alpha, Slots 1 vs. 2–5 vs. 6–8 (pre-stim-post). For this model, the train-to-test ratio was again 9:1, with _kappa_ = 0.906, area under the ROC curve (AUC) = 0.943 and F-measure = 0.938. Figure 8: Phase 3 –Model 10 of 12 models: 2.5 pps, post-stimulation (Slots 6–8), comparing five classes (EEG Delta, Theta, Alpha, Beta and Gamma bands). For this model, the train-to-test ratio was approximately 5:1, with _kappa_ = 0.718, area under the ROC curve (AUC) = 0.786 and F-measure = 0.777. Figure 9: Phase 4 –Model 7 of 12 models: 10 pps, stimulation (Slots 2–5), comparing five classes (EEG Delta, Theta, Alpha, Beta and Gamma bands). For this model, the train-to-test ratio was 4:1, with _kappa_ = 0.990, area under the ROC curve (AUC) = 0.998 and F-measure = 0.992. Each confusion matrix evaluates the performance of a classification algorithm, with each row representing instances in a predicted class, and each column instances in an actual class. The entry in the top-left corner represents the number of instances that were correctly predicted to be in the first class, and so forth. The receiver operating characteristic (ROC) curves show the performance of binary classifiers as their discrimination thresholds are varied. The ROC curve plots the true-positive rate (also called sensitivity or recall) on the y-axis and the false-positive rate (\(1-\)specificity) on the x-axis. A classifier with a higher true-positive rate and a lower false-positive rate will be higher and further to the left on the curve. The area under the curve (AUC) is a measure of the classifier's performance (the AUC ranges in value from 0 to 1, with a higher value indicating a better classifier).
## 3 Results and Discussion
### The effect of the compounds on cell viability
The treatment of HUVEC cells with Ga-5 HF-DPCNS2 for 48h did not influence apoptosis, regardless of the concentration used. The 24 h treatment with Ga-5 HF-DPCNS1 did not influence the apoptosis of LoVo cells, regardless of the concentration used (Figure 12a). Similar results were obtained when LoVo cells were treated for 48 h with Ga-5 HF (Figure 12b). The compounds Al-5 HF and Al-5 HF-DPCNS1, at 5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL and 0.5 \(\mathrm{\SIUnitSymbolMicro g}\)/mL, increased the percentage of apoptosis by two times and three times, when the LoVo tumor cells were treated for 24 h.  
Figure 11: Effect of the analyzed compounds on the apoptotic process of the HUVEC normal cells after (**a**) 24 h of treatment and (**b**) 48 h of treatment. Figure 12: Effect of the analyzed compounds on the apoptotic process of the LoVo colon cancer cells after (**a**) 24 h of treatment and (**b**) 48 h of treatment. The results obtained after the treatment of LoVo tumor cells with the studied compounds, both at 24 and 48 h, are compared with those obtained in the HUVEC cell line. At 24 h, there are no significant differences in the apoptotic process compared to the normal cell line at the concentration of 0.5 \(\upmu\)g/mL. Instead, the more extended treatment, up to 48 h, at the concentration of 0.5 \(\upmu\)g/mL, affects the apoptotic process more significantly, inducing a 1.5-fold increase compared to 24 h.  
The treatment of SKOV-3 tumor cells with the compound Al-5 HF for 24 h did not affect the apoptotic process regardless of concentration. Similar results were obtained when SKOV-3 cells were treated for 24 h with Al-5 HF-DPCNS2 and Ga-5 HF-DPCNS2 at a concentration of 5 \(\upmu\)g/mL. The SKOV-3 tumor cells were treated with compounds Al-5 HF-DPCNS2 and Ga-5 HF-DPCNS2, at a concentration of 0.5 \(\upmu\)g/mL, which induced a rise in apoptosis two times versus the control at 24 h treatment (Figure 13a). Treating SKOV-3 tumor cells with the compounds Al-5 HF, Ga-5 HF-DPCNS2, and Ga-5 HF-PMDACD1 for 48h did not affect the apoptotic process regardless of concentration (Figure 13b). The results obtained following the treatment of SKOV-3 tumor cells with the studied compounds at 24 and 48 h are compared with those obtained in the HUVEC cell line. At 24 h, significant differences in the apoptotic process are obtained compared to the normal cell line at the two concentrations used. A more extended treatment, up to 48 h, at both concentrations induces a decrease in the apoptotic percentage compared to HUVEC cells, except for the compounds Al-5 HF-DPCNS1 and Ga-5 HF-DPCNS1.