esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
The authors in [25] proposed a mechanism for cluster formation based on QoS metrics for a wireless network. However, this approach is inefficient and stagnates in local issues, while there is an increase in scalability of the network. The authors in [26; 27] proposed an extended ZRP (Zone Routing Protocol) for the clustering-based network that selects a single cluster head with overlapping zones. Various security issues in web services including DDOS [28] are solved. A detailed survey regarding various QoS metrics in web services is discussed in [29; 30]. These algorithms selected a single header, resulting in a centralized cluster that might lead to single point of failure. From the existing research, we observed that an efficient cluster-head election should possess the following eminences in MANET:  
* Maximum utilization of a resource such as battery power;
* Header should be capable of withstanding any packet traffic;
* Load balancing;
* Lesser node mobility. Therefore, our proposed work considers the above-mentioned eminences along with QoS metrics for dynamic secured broker selection mechanism. In addition, a secured communication path is established between the broker and CHs to eradicate the intrusion.
## 4 Experiments and Analysis
### Data Representation Evaluation  
Our data representation model is implemented by replacing the text encoder module of the original CLIP model with BERT, while preserving the structure and parameters of the image encoder in the CLIP model. During the training of the representation model, we employed two methods. The first method involved keeping the parameters of the image encoder model unchanged and only adjusting the parameters of the BERT model to align with the image encoder parameters. The second method involved tuning the parameters of both the image encoder model and the BERT model simultaneously. The training loss curves for both methods are depicted in Figure 6.  
In Figure 6, the label \"NMV\" represents the scenario where no fine-tuning of the image encoder parameters was performed, while \"MV\" indicates the scenario where the image encoder parameters were fine-tuned. From the figure, it can be observed that both methods were able to converge the model parameters, and the NMV method achieved a lower loss value within the same number of training epochs. In the subsequent experiments, clustering analyses were conducted on the representations generated by the trained representation models obtained from both training methods. It was further observed that the clustering performance was better when the image encoder model was not fine-tuned.  
The original CLIP model is trained on a significantly larger dataset of over 400 million image-text pairs, which exceeds the scale of the experimental dataset used in this paper, which consists of 3.7 million samples. Fine-tuning the CLIP parameters on a small dataset may risk disrupting the already excellent parameters of the model. To mitigate this effect, this paper employs the cosine annealing algorithm to dynamically adjust the learning rate during the training of the representation model parameters. The specific learning rate adjustments can be observed in Figure 7.  
Figure 6: Representation model training loss.  
During the training process, the learning rate is kept relatively small, typically not exceeding \(5\times 10^{-5}\). This cautious approach is taken because the BERT model parameters also undergo transfer learning, utilizing pre-trained parameters from large text datasets. To ensure the stability and avoid rapid modification of the model parameters, a small learning rate is used.
## 4 Discussion
### Canopy Cover  
The canopy cover (CC), detected as LAI from the RS data, is one of the most important variables for crop models [60; 61] but is hard to determine in the field. According to field experiment results (Figure 4), there is a good relationship between the measured average LAI and rice age for the two experimental rice groups. This explains why the LAI value increases with rice age/growth stage. The leaf area of rice plants increases with growth, reaches a maximum around heading and decreases thereafter due to the death of lower leaves [73]. The presence of this relationship shows that the field data collection was highly accurate for crop parameter calibration.  
When LAI was converted to CC using Equation (3), the calibrated best K value for both experimental rice plots was 0.70, signifying that this is the optimal constant value to use when applying the LAI to the CC conversion formula for similar local rice cultivars. The maximum CC was set as a crop parameter along with other cultivar-specific parameters collected from the field (Table 2). The model was calibrated with this set of parameters, producing satisfactory simulated yields (Section 3.1.3). Consequently, the HJ1A/B images were selected to transform values to CC, using the regression equations (Figure 7) and polynomial formulae with higher R\({}^{2}\) and graph relationships closer to crop growth phenology [74]; therefore, these were proposed for the surface reflectance NDVI to CC conversion model. With the previously calibrated parameters (35 pixels in 15 plots), the CC values from HJ1A/B data were substituted and the yield was simulated in 126 test pixels in 25 plots for validation (Section 3.2). Furthermore, the maximum CC (CC\({}_{\rm x}\)) percentage was more constant in the plots transplanted by machine. The validated rice plots (group A) showed only slightly different CC\({}_{\rm x}\) values (average CC\({}_{\rm x}\) 79.06%, standard deviation 0.06), while the manually transplanted rice plots (group B) had fluctuated CC\({}_{\rm x}\); the average CC\({}_{\rm x}\) was 92.16% and the average standard deviation was 0.5. The LAI and CC were calculated using the surface reflectance NDVI of the HJ-1A/B images, producing significant crop phenology that could be applied in future crop models as well as the image classification for rice cultivation methods.
# LTM-LoRaWAN: A Multi-Hop Communication System for LoRaWAN
## 4 Performance Evaluation
### Result and Discussion
In addition, the higher number of lost packets due to arriving below sensitivity in the baseline system can be justified by the use of a higher spreading factor to reach the gateway. Nonetheless, the arriving signals are weak due to the long distance and consequently regarded below the sensitivity of the receiver. In order to improve traffic reliability, the proposed system allows data transmission using more than one hop for out-of-range devices. Thus, the impact of the developed mechanism on power consumption is investigated. Figure 10 shows the average daily energy usage for every single end device. The plotted result expresses the consumption calculated based on the radio spent time in a particular state. A LoRa device switches between three different states: receive, sleep, and transmit. As the figure illustrates, the proposed system consumed more power in contrast to the baseline. This is due to the fact the proposed system mechanism involves the relaying of out-of-range devices' traffic, which consumes additional energy compared to the baseline, where devices are restricted to one-hop transmission. However, this can be seen as a cost for providing data reliability, especially for those devices that experience network isolation. Therefore, the proposed system is more favorable for such cases despite the higher power consumption. The figure also shows that the consumed power by the proposed system decreases inversely with the size of the network. The increase in the number of end devices means an increase in the opportunity of finding an end device transmitting a packet and, hence, decreasing the listening time for an out-of-range device trying to transmit. As a consequence, the average power consumption decreases as the number of end devices increases. Figure 10: Average daily power consumption for an end device. Figure 9: Received packets below gateway’s sensitivity.
## 3 Methods
### Time Series-Based Methods
#### 3.3.4 Multi-Layer Perceptron  
An MLP maps the input time series data \(\mathbf{X}\) to an output \(\mathbf{Y}\) through a series of transformations and non-linear operations. For a single neuron \(j\) in a hidden layer, the weighted sum of inputs is expressed as \(z_{j}=\sum_{i=1}^{n}\ w_{ji}\cdot x_{i}+b_{j}\), where \(z_{j}\) is the weighted sum of inputs for neuron \(j,w_{ji}\) is the weight between input \(i\) and neuron \(j,x_{i}\) is the input value from the previous layer, \(b_{j}\) is the bias term for neuron \(j\), and \(n\) is the number of inputs. The weighted sum \(z_{j}\) is then passed through an activation function \(f(\cdot)\) to introduce non-linearity, producing the neuron's output \(a_{j}=f\big{(}z_{j}\big{)}\)[32].  
Common activation functions used in MLPs include the sigmoid, hyperbolic tangent (tanh), and Rectified Linear Unit (ReLU). The sigmoid function is defined as \(f(z)=\frac{1}{1+e^{-z}}\), the hyperbolic tangent as \(f(z)=\tanh(z)=\frac{e^{z}-e^{-z}}{e^{z}+e^{-z}}\), and the ReLU function as \(f(z)=\max(0,z)\). These functions enable the MLP to learn and represent complex patterns in the input data by adding non-linearity to the network [33].  
The final prediction of the MLP, denoted as \(\tilde{y}\), is computed in the output layer by taking the activations from the last hidden layer and applying a linear transformation: \(\tilde{y}=\sum_{j=1}^{m}\ w_{oj}\). \(a_{j}^{(L)}+b_{o}\), where \(w_{oj}\) is the weight connecting neuron \(j\) in the last hidden layer to the output layer, \(b_{o}\) is the bias term for the output neuron, \(a_{j}^{(L)}\) is the activation of the \(j\)-th neuron in the last hidden layer, and \(m\) is the number of neurons in the last hidden layer [34].  
In time series forecasting, the input data \(\mathbf{X}\) typically consist of lagged observations, such as \(\left\{x_{t-1},x_{t-2},\ldots,x_{t-p}\right\}\), where \(p\) represents the number of lagged time steps. This structure enables the MLP to capture temporal dependencies and predict future values based on historical data. The network's weights and biases are optimized using algorithms like gradient descent, which minimize a loss function such as the Mean Squared Error (MSE), defined as MSE = \(\frac{1}{N}\sum_{i=1}^{N}\left(y_{i}-\widehat{y}_{i}\right)^{2}\), where \(y_{i}\) is the actual target value at time step \(i,\widehat{y}_{i}\) is the predicted value by the MLP, and \(N\) is the total number of observations [35].
## 2 Materials and Methods
### Model Calibration and Validation
#### 2.4.2 Statistical Performance Indices  
This study used five objective functions--coefficient of regression (R\({}^{2}\)), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), Kling-Gupta equation (KGE), and the ratio of root mean square error to standard deviation (RSR)--to evaluate the statistical performance of the SWAT model. R\({}^{2}\) is the measurement of correlation between simulated and observed values, which is known as the coefficient of determination. The range of R\({}^{2}\) varies from 0 to 1, where a value approaching 1 means better performance. The Nash-Sutcliffe equation (NSE) is a normalized, dimensionless statistic that defines the relative magnitude of residual variance, compared to the measured data variance [55]. The NSE value ranges from \(-\infty\) to 1, with an optimal value of 1 [56]. The KGE is the Kling-Gupta efficiency between observed and simulated values, with the treatment of missing values. This goodness of fit was developed in [57] to provide a decomposition of the NSE that facilitates the analysis of relative importance, such as correlation, bias, and variability, in the context of hydrological modeling. PBIAS (percent bias) calculates the average tendency of simulated values to be larger or smaller than the observed values [56]. A smaller magnitude of PBIAS indicates better model performance with an optimal value of zero. A positive PBIAS value indicates underestimation of the model, while negative values represent overestimation. RSR is the ratio of the root mean square error (RMSE) to the standard deviation. The values of the RSR range from 0 to \(\infty\), where a lower RSR value indicates a lower RMSE value and a better model simulation performance [58]. A threshold of 0.5 is satisfactory for R\({}^{2}\) and NSE [59]. The P-factor and R-factor were also used to evaluate model performance. A P-factor close to 1 and R-factor close to zero indicates a good fit. The P-factor is the percentage of measured data covered by 95 PPU, while the R-factor is the ratio of the average of the band to the measurement of the standard deviation of measured variables, which varies from 0 to infinity; an R-factor value less than 1 indicates better performance [23]. A threshold for passing the sensitivity check (\(p\)-value < 0.05) could play a significant role in hydrological simulations [60].  
The statistical performance parameters of the five global climate models (historical rainfall data), compared with the observed rainfall data, are shown in Table 4.
# Robust JND-Guided Video Watermarking via Adaptive Block Selection and Temporal Redundancy
## 2 Related Works  
### Overview of the Original Method  
In our previous work [25], we proposed a computationally efficient and perceptually guided video watermarking method operating in the spatial domain. The method was designed to improve robustness while maintaining imperceptibility and low processing cost, which are three competing objectives in watermarking research.  
Three core contributions were central to the method:  
* Spatial-domain DCT-based embedding: Rather than computing the full DCT/IDCT transform on every frame, we developed a method for estimating and modifying selected DCT coefficients directly through spatial operations, dramatically reducing computational burden.
* Visual attention-modulated JND profile: Watermark energy was determined by combining JND thresholds with saliency analysis in a perceptual model that was computed on keyframes. This allowed stronger embedding in visually insensitive regions while preserving perceptual transparency [16].
* Scene-based watermark modulation: The video was segmented into scenes, each represented by a keyframe [26]. Watermark parameters were calculated only on these keyframes and propagated through the rest of the scene using motion tracking and saliency consistency, reducing redundant computation.  
The method was validated across video sequences of varying resolution and complexity, achieving high imperceptibility and robustness with a processing time reduction of over 80% compared to frame-by-frame transform-domain approaches. However, the original method relied on frame-specific, non-redundant insertion and lacked advanced block selection and encoding schemes to handle harsh or compound attack scenarios.  
Although this proposal extends our previous work [25], it introduces several critical enhancements that restructure the original method. First, the current method employs a more sophisticated perceptual modeling framework by combining multiscale detail analysis, saliency detection, and semantic masking, as opposed to the earlier gradient-based texture selection. Second, rather than uniformly embedding watermark bits, the proposed scheme includes a block selection strategy based on perceptual scores to prioritize visually insensitive yet structurally robust regions. Third, while both methods operate in the spatial domain, the current technique incorporates an adaptive JND-based modulation that adjusts embedding strength based on local perceptual thresholds. Fourth, although DCT estimation was previously applied, the present method generalizes this approach with flexible coefficient targeting and a formalized QIM embedding mechanism. Finally, redundancy is no longer limited to static repetition; instead, it is systematically distributed across multiple frames using keyframe selection and majority decoding. These differences not only improve robustness under signal and geometric attacks, but also ensure greater imperceptibility and processing efficiency, as demonstrated in Sections 4 and 5.
# Parametric Analysis for 3D Modeling of Consolidation-Induced Solute Transport Using OpenFOAM
## 2 Theoretical Model
### Model Description  
This study uses a three-dimensional model with dimensions of 40 m (length) \(\times\) 40 m (width) \(\times\) 8 m (height), with a center hole measuring 20 m \(\times\) 20 m \(\times\) 3 m, as shown in Figure 1(a). The external load is applied only at the bottom of the center pond, gradually increasing to 400 kPa over the first 2 years and then remaining constant until the 20 th year. This setup simulates the loading process in actual engineering projects and allows the study of the impact of the load on consolidation and contaminant migration. In the model, the \(x\) direction is positive to the right, the \(y\) direction is positive to the back, and the \(z\) direction is positive upward.  
In a landfill system, the bottom of the pit is typically lined with a geomembrane; therefore, the model assumes that the bottom surface of the pit is impermeable. The external load is applied through the displacement boundary using the overall force balance equation to simulate real engineering conditions. Volatile organic compounds (VOCs) are assumed to penetrate the geomembrane in gaseous form. Both the side and bottom boundaries restrict soil movement and solute transport. Although the bottom allows water to drain freely, the side walls are impermeable to water flow. The boundary conditions (BC) and the initial conditions (IC) are summarized in Table 1.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Boundary Index** & **BC of \(p\)** & **BC of \(\mathbf{u_{s}}\)** & **BC of \(\mathbf{\epsilon_{f}}\)** \\ \hline Top & \(\frac{\partial p}{\partial x}=0\) & \(\mathbf{n}\cdot\sigma^{\prime}=-\mathbf{Q}(\mathbf{t})+p\mathbf{n}\) & \(\frac{\partial\epsilon_{f}}{\partial x}=\frac{D_{c}}{m\partial D_{m}}(c_{f}- c_{0})\) \\ Bottom & \(p=0\) & \(\mathbf{u_{s}}=0\) & \(\frac{\partial c_{f}}{\partial x}=\frac{\partial p}{\partial y}=0\) & \(\mathbf{u_{s}}=0\) & \(\frac{\partial c_{f}}{\partial x}=\frac{\partial c_{f}}{\partial y}=0\) \\ Internal sides & \(\frac{\partial p}{\partial x}=\frac{\partial p}{\partial y}=0\) & free surface & \(\frac{\partial c_{f}}{\partial x}=\frac{\partial c_{f}}{\partial y}=0\) \\ Initial conditions & \(p=0\) & \(\mathbf{u_{s}}=0\) & \(c_{f}=0\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Boundary conditions (BCs) and initial conditions (ICs) setup for the 3D model.  
Figure 2: Cross-section and load pattern.
## 2 Transcriptional Regulation in Integrons
### The Role of Pc Promoters on Gene Cassette Transcription
#### 2.1.4 Pc Promoter in Chromosomal Integrons  
The cassette promoter of CIs has been characterized in some species, being that of _V. cholerae_ IntVchA CI studied in more detail [22; 57]. The Pc sequence of IntVchA (-35 CTGATT (16 bp) -10 TAGTAT) was determined, and it was shown that this promoter was responsible for transcription of the first cassettes in this CI. Several in vitro approaches demonstrated that the mRNA corresponding to the first cassette in IntVchA started in the vicinity of this Pc promoter, which is located 65 bp upstream of the _attIA_ recombination crossover point [22], as found for class 2 integrons [48; 49]. Moreover, the genomic analysis revealed that the _attI_ and Pc sequences were shared between _V. cholerae_ and _V. mimicus_ CIs [22], suggesting that both species present similar transcriptional mechanisms.  
Although active in several distinct environmental conditions and growth phases, the IntVchA Pc presented maximal levels of induction at the end of the exponential phase in rich media, and under higher temperatures (42 \({}^{\circ}\)C) and salinity, which mimicked the environmental conditions faced by _V. cholerae_ within the human host. After the intestine invasion, _V. cholerae_ produces the cholera toxin that ultimately leads to the increase of salinity concentrations, coinciding with the highest expression of Pc proximal cassettes, which allows the bacteria to adapt to the challenger environment where they will be released. Moreover, it was demonstrated that this Pc promoter was activated by the global regulator cAMP-CRP complex. It directly binds in a CRP box located in _attI_A 41 bp upstream from cassette array transcription start site, overlapping Pc -35 region. The cAMP-CRP regulator complex also has an important role in the _V. cholerae_ survival, indicating that the expression of gene cassettes in CIs is tightly linked to conditions requiring adaptation, increasing the chances of bacterial survival in new challenger environments [22].  
Different from the Pc promoter of the IntVchA integron, the cassette promoter found in the CI of the environmental _Pseudomonas stutzeri_ species (IntIPstQ) was located within its integrase gene. This Pc promoter (-35 TTGAGC (16 bp) -10 TCTGAT) was found 25 bp downstream the _intI_ coding gene and it showed to be active in driving transcription of a resistance gene cassette [57].
## 3 Materials and Methods
### Maturity Status Estimator  
#### 3.6.1 Artificial Neural Network (ANN)  
Artificial neural networks (ANN) are mathematical models that emulate the functioning of biological neural networks. This was used in this study for its structural simplicity, learning skills, and its application for the approach, classification, and pattern recognition. Figure 7 presents the ANN architecture that was used to estimate the \({}^{\circ}\) Brix content of the fifty samples presented in Figure 1. Its architecture consisted of an input layer, three hidden layers, and an output layer. The input layer is used to present the training and test patterns that correspond to the GAROI, YAROI, OAROI, and RAROI regions of interest. The hidden first layer used radial base-type activation functions, each of its neurons calculating the similarity between the input and its training set. The second hidden layer adds the values of each neuron from the first hidden layer that are multiplied by their weight associated with each neuron to obtain the class that the sample belongs to. The third layer of neurons employs neurons with sigmoidal-type firing functions where the sample classification information and the four regions of interest were weighted. The output layer is of the linear type, allowing the Brix content to be estimated using the weights associated with the neurons of the third hidden layer.  
One of the ANNs most used in classification tasks is radial basis function ANN (RBF-ANN), as shown in Figure 8. Its architecture uses an input layer, hidden layers, and output layer. The input layer is used to present the training and test patterns. The hidden layer is made up of radial (Gaussian) functions that are completely interconnected between all its nodes with the input layer, which are activated by this function. The output layer is activated by the linear functions that determine the classification and is interconnected with all the nodes of the hidden layer.  
The proposed RBFNN classification models were developed with Matlab's Deep Learning Toolbox. The main difference between them is the number of neurons that the hidden layer contains, as shown in Table 2. The inputs used by each classifier are the interest regions with shades of green, yellow, orange, and red and the outputs are the four classes. The model design used 70% of the data for training, 20% for validation, and the rest for the testing stage.  
Figure 8: RBF-ANN maturity classifier.  
Figure 7: Brix prediction Artificial neural networks (ANN) model.  
Together, three models of two-layer feed-forward network with sigmoid hidden neurons and linear output neurons (Fitnet) are proposed. Figure 9 presents the architecture of the implemented model to predict the \({}^{\circ}\) Brix content of bell peppers. Three models were proposed where their main difference is the number of neurons in the hidden layer, as shown in Table 3; the precision of each model to predict the \({}^{\circ}\) Brix was different.