esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Implementing Models with the Geographic Automata Add-In: Two Case Studies
### Case Study 2: Comparing ML-CA Models of Urban Growth
#### 4.2.2 Model Implementation
Therefore, the tool inputs include the urban areas in the year 2000 as the initial raster layer, the real urban areas in the year 2010 as the reference layer, and first timestep outputted by the model as the simulated layer. To calculate metrics for the validation stage, the initial raster is maintained, while the reference layer is replaced with the layer depicting urban areas in the year 2020 \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Rule ID** & **Rule Type** & **Transition Rule Description** & **Model Types Using the Rule** \\ \hline Rule A & _Neighborhood-Based_ & \begin{tabular}{c} _IF_ there are 1 to 25 urban cells within the \\ 5 \(\times\) 5 Moore neighborhood of a non-urban \\ location, _THEN_ the central cell will become \\ urban with a probability of 60\%. \\ \end{tabular} & All \\ \hline Rule B & _Constraint_ & \begin{tabular}{c} _IF_ a cell potentially transitioning to urban is \\ located within a restricted area, _THEN_ the \\ cell is prevented from transitioning and will \\ maintain its previous state. \\ \end{tabular} & All \\ \hline Rule C & _Allocation and Quantity_ & \begin{tabular}{c} Cells potentially transitioning to urban must \\ be in a location where the suitability value is \\ at least 0.5. \\ \end{tabular} & \
\begin{tabular}{c} LR-CA, RF-CA, \\ andAutoML-CA\({}_{1}\) \\ \end{tabular} \\ \hline Rule D & _Allocation and Quantity_ &
\begin{tabular}{c} Only the 30,000 most suitable cells potentially \\ transitioning to urban are permitted to \\ become urban at the next iteration. \\ \end{tabular} & AutoML-CA\({}_{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Transition rule types, descriptions, and model type application. and the simulated layer is the second timestep generated by the model. Cells outside of the township boundary are excluded to ensure the model evaluation results are not obscured. The options selected in the _Model Evaluation_ tool were _Accuracy Measures, Kappa Statistics, Change Error Assessment_, and _Figure of Merit_ from the GUI options presented in Figure 7.
## 6 Result and Discussion
### Sensitivity Analysis  
The sensitivity results of the GPR model were examined using [PERSON] and [PERSON]'s approach for determining the impact of input variables on \(D_{H}\). This strategy, which has been used in a number of research [44; 45; 46; 47], is as follows:  
\[r_{ij}=\frac{\sum_{k=1}^{n}(x_{ik}\times x_{ok})}{\sqrt{\sum_{k=1}^{n}{x_{ik}}^ {2}\sum_{k=1}^{n}{x_{ok}}^{2}}} \tag{14}\]  
\(x_{ik}\) and \(x_{ok}\) are the actual and estimated variables, respectively, and \(n\) represents number of datasets (i.e., 198 data samples). For each input variable, the \(r_{ij}\) value varies from zero to one, with the greatest \(r_{ij}\) values indicating the most efficient output factor (i.e., \(D_{H}\)). Figure 3 displays the \(r_{ij}\) values for all input factors. The accumulative thickness of saturated layers with adjusted SPT number, \(T_{15}\) (\(r_{ij}=0.843\)) has the greatest effect on the \(D_{H}\). Furthermore, Table 3 shows that the accumulative thickness of saturated layers with adjusted SPT number, \(T_{15}\) has the highest \(\rho\) of 0.518 in all other parameters validating the sensitivity analysis results.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Indicators** & \(R^{2}\) & \(r\) & \(MAE\) **(m)** & \(RMSE\) **(m)** & \(RSR\) & \(NSE\) & **Reference** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} GPR \\ \end{tabular} } & Training model & 0.9402 & 0.9697 & 0.3403 & 0.5597 & 0.248 & 0.938 & Present study \\  & Testing model & 0.894 & 0.9455 & 0.544 & 0.8438 & 0.387 & 0.851 & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} EPR \\ \end{tabular} } & Training model & 0.913 & - & 0.537 & 1.003 & - & - & \\  & Testing model & 0.883 & - & 0.291 & 1.158 & - & - & \\ \hline \multirow{2}{*}{ANN} & Training model & 0.875 & - & 0.702 & 1.074 & - & - & \\  & Testing model & 0.872 & & 0.82 & 1.21 & - & - & \\ \hline \multirow{2}{*}{MLR} & Training model & 0.868 & - & 0.81 & 1.24 & - & - & \\  & Testing model & 0.875 & - & 0.43 & 1.196 & - & - & \\ \hline \hline \end{tabular} Note: - represents that this performance statistic is not included in the reference.  
\end{table}
Table 5: Performance statistics of GPR model in comparison with extra available models in literature.
# Dynamic Ensemble Selection for EEG Signal Classification in Distributed Data Environments
## 3 Datasets and Experimental Results
For this purpose, we selected the approach described in [25]. EEGNet is a lightweight convolutional neural network specifically designed for EEG signal analysis. Its architecture uses depthwise and separable convolutions, which allows it to efficiently capture both spatial and temporal features from EEG data while keeping the model compact and computationally efficient. EEGNet is known for its ability to generalize well across various EEG paradigms, such as P300, ERN, MRCP, and SMR, without the need for extensive task-specific adjustments. It also performs strongly when training data are limited and offers interpretable results, as the features it learns often correspond to known EEG patterns. Specifically, we used EEGNet-SSVEP, as it achieved the best performance on the analyzed datasets. The obtained results are presented in Table 7. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Data Set** & **Method/Model** & **Prec.** & **Recall** & **F1 (Weig.)** & **F1 (Macro)** & **BAacc** & **Acc** \\ \hline EEG Epilepsy & RF (200,gini) & 0.946 & 0.933 & 0.932 & 0.932 & 0.933 & 0.933 \\  & kNN (4) & 0.782 & 0.74 & 0.716 & 0.716 & 0.74 & 0.74 \\  & AdaBoost (20) & 0.953 & 0.947 & 0.946 & 0.946 & 0.947 & 0.947 \\  & Gradient Boosting (50) & 0.946 & 0.933 & 0.932 & 0.932 & 0.933 & 0.933 \\  & Logistic Regression & 0.888 & 0.867 & 0.869 & 0.869 & 0.867 & 0.867 \\ \hline BCI Competition IV Dataset 1 & RF (20,gini) & 0.53 & 0.529 & 0.527 & 0.527 & 0.529 & 0.529 \\  & kNN (2) & 0.485 & 0.492 & 0.442 & 0.442 & 0.492 & 0.492 \\  & AdaBoost (10) & 0.606 & 0.604 & 0.603 & 0.603 & 0.604 & 0.604 \\  & Gradient Boosting (20) & 0.597 & 0.597 & 0.597 & 0.597 & 0.597 & 0.597 \\  & Logistic Regression & 0.542 & 0.541 & 0.541 & 0.541 & 0.541 & 0.541 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Results of precision (Prec. ), recall, F-measure (F-m.), balanced accuracy (_bacc_), and classification accuracy (_acc_) in the case where the distributed environments are not applied. RF is the abbreviation Random Forest and kNN stands for k-Nearest Neighbors. (Blue text color represent best result). \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Data Set** & **Prec.** & **Recall** & **F1 (Weig.)** & **F1 (Macro)** & **BAacc** & **Acc** \\ \hline EEG Epilepsy & 0.836 & 0.767 & 0.737 & 0.737 & 0.767 & 0.767 \\ \hline BCI Competition IV Dataset 1 & 0.516 & 0.516 & 0.513 & 0.513 & 0.516 & 0.516 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of precision (Prec. ), recall, F-measure (F-m.), balanced accuracy (_bacc_), and classification accuracy (_acc_) for deep learning EEGNet-SSVEP model commonly used in EEG classification. As shown, the results obtained using a dedicated neural network are inferior to those achieved in the distributed environment.
## 5 Vulnerabilities of SDN to LDDoS Attacks
### Deep Learning-Based LDDoS Detection
#### 6.2.2 Limitation of Deep Learning Based LDDoS Attack Detection  
This section provides an overview of the limitations of LDDoS detection mechanisms based on deep learning. According to our findings, the number of LDDoS detection mechanisms based on deep learning is lower than those based on classification. This could be due to the fact that classification-based LDDoS detection mechanisms have reasonable detection accuracy with lower computational complexity. Table 4 represents a summary of analysis methods used by existing work and their shortcomings.  
In any case, LDDoS detection mechanisms based on deep learning perform better than classification-based LDDoS. This is because learning-based LDDoS detection approaches have a more profound ability to extract traffic than classification-based techniques. Therefore, it can improve detection accuracy in identifying LDDoS attacks and obfuscation.  
Various drawbacks or limits must be addressed when utilizing deep Learning to identify LDDoS. The first is that a deep learning model's training period might be exceedingly long. As a result of the lengthy training period, the deep learning unit may have significant hardware needs. The second barrier is that several parameters must be given when developing a deep learning module. Using CNN as an example, the user should pick the optimal or sub-optimal parameters, such as the number of layers, neurons, filters, epochs, learning rate, objective function, and the weight configuration.  
Analyzing the work in [82], we note that it has an apparent shortcoming in the use of test parameters since the evaluation of the detection framework was performed with offline datasets in a small SDN topology. There is also a limitation in traffic diversity as the regular traffic flows are limited to UDP and HTTP, and LDDoS attack traffic flows are HTTP only. Moreover, since the work uses CNN-LSTM based on deep learning, the training performance requires a long time when we compare it with classification-based methods. This is because the hybrid CNN-LSTM model requires a 3D data format as input [119], so the dataset needs to be transformed accordingly.  
In [84], the authors improved the LDoS attack detection method by developing a sailfish algorithm to enhance the parameters of the CNN-GRU characteristic. Although the model performed well compared to traditional machine learning algorithms, It requires a lot of training time and lacks real-world implementation. Furthermore, it is not intended for online attack detection in live SDNs since it must identify traffic flows that can be split across different capture time frames. Despite the limitations of the deep learning-based LDDoS detection approaches, we nevertheless recommend employing deep learning to construct an LDDoS attack detection mechanism.
## 2 Mathematical Formulation of OPF Problem
### Inequality Constraints in OPF  
For finding a solution to the OPF problem, there are several inequality constraints that should be satisfied. OPF inequality constraints are the generator voltages, real and reactive power limits, load bus voltages limits, and line capacity limits.  
Generator inequality constraints are expressed as follow:  
\[P_{TGi}^{\min}\leq P_{TGi}\leq P_{TGi}^{\max}\ \ \ i=1\ldots\ldots\rm{N_{TG}} \tag{15}\]  
\[P_{WGj}^{\min}\leq P_{WGj}\leq P_{WGj}^{\max}\ \ \ j=1\ldots\ldots\rm{N_{WG}} \tag{16}\]  
\[P_{SGk}^{\min}\leq P_{SGk}\leq P_{SGk}^{\max}\ \ k=1\ldots\ldots\rm{N_{SG}} \tag{17}\]  
\[Q_{TGi}^{\min}\leq Q_{TGi}\leq Q_{TGi}^{\max}\ \ i=1\ldots\ldots\rm{N_{TG}} \tag{18}\]  
\[Q_{WGj}^{\min}\leq Q_{WGj}\leq Q_{WGj}^{\max}\ \ \ j=1\ldots\ldots\rm{N_{WG}} \tag{19}\]  
\[Q_{SGk}^{\min}\leq Q_{SGk}\leq Q_{SGk}^{\max}\ \ k=1\ldots\ldots\rm{N_{SG}} \tag{20}\]  
\[V_{Gi}^{\min}\leq V_{Gi}\leq V_{Gi}^{\max}\ \ \ i=1\ldots\ldots\rm{N_{G}} \tag{21}\]  
Line and load bus inequality constraints:  
\[V_{Lm}^{\min}\leq V_{Lm}\leq V_{Lm}^{\max}\ \ m=1\ldots\ldots\rm{N_{l}} \tag{22}\]  
\[S_{\rm{In}}\leq S_{\rm{In}}^{\max}\ \ \ n=1\ldots\ldots\rm{N_{l}} \tag{23}\]
# Hermite Quartic Splines for Smoothing and Sampling a Roughing Curvilinear Spiral Toolpath
## 4 Case Study 1--BIG Pocket
### Building and Evaluation of Curvilinear Spiral  
Finally, each revolving of curvilinear spiral is built by linear interpolation between two points of the same index (i.e., the same normalized curvilinear abscissa) of two consecutive structure curves. Figure 10b shows the curvilinear spiral built with our method.  
Locally, the feedrate is simulated with VPOp. Two turns of the curvilinear spiral are chosen in three zones of the pocket (internal, intermediate, and external). The modeled machine tool is a _Mikron UCP710_. Its characteristics are reported in Table 2[34].  
For each zone, three fitting methods are tested (RAW = no fit, FIR = moving average, HQS = Hermite quartic spline interpolation), and the toolpath length and the milling time are collected. The average feed rate is calculated. The programmed feed rate (\(V_{m}\)) is fixed at 10,000 [mm/min]. Data are summarized in Table 3, and Figure 11 shows the velocity profile as a function of the curvilinear abscissa for the three intermediate toolpaths (black = RAW, blue = HQS, red = FIR). Figure 12 links the velocity profile with the positions of the tool along the curvilinear spiral turns. Slowdowns for points A, B, C, E, F, G are due to the \"high\" curvature of the toolpath. D and H are, in fact, the beginnings of a curvilinear spiral turn, and there, as already explained, the \(C^{2}\) connectivity is lost.  
The results in Table 3 demonstrate that the method proposed in the paper, based on the reconstruction of isothermal curves with a small number of Hermite quartic spline patches, reduces the simulated milling time by 10% to 20% in each zone of the pocket, compared to the RAW toolpath. Although the FIR filter is selected for comparison, the HQS method offers an advantage: it maintains control over the distance between the RAW isotherm and the fitted one, a feature not preserved by the FIR method.  
The histogram in Figure 13 and the statistical indicators in Table 4 confirm the improvement of the toolpath: small curvatures are more populated, and both Cut95 and Cut99 values are significantly lower for the HQS method. Moreover, this new method eliminates the need for a Fresnel vector to generate the toolpath. Consequently, the limitation of using the [PERSON]'s method exclusively for 'globally convex pockets' is no longer necessary. Therefore, the HQS method allows the creation of curvilinear toolpaths similar to those by [PERSON] and [PERSON] but applicable to non-convex cavities as demonstrated in the second case study.  
Figure 12: Positions of the feed rate slowdowns along the path (to be linked with Figure 11).  
Figure 13: BIG pocket—histogram of \(|Curvature|\) for RAW and HQS entire curvilinear spiral.
## 3 Methodology
### Model Development  
In this study, a deep learning-based weed detection model is selected by comparing two single-stage object detection models. The performance of the selected DL models,  
Figure 4: The dataset is represented in a bar graph, where the frequency of each class is depicted. The graph reveals that okra has the most number of samples, while smallweed and sponge gourd have a lower count of instances.  
Figure 5: An annotated sample image using an open source tool LabellImg.  
integrated with different feature extractors, is analyzed on the images obtained after preprocessing. The total dataset consisted of 9000 images of which 1500 were chosen at random as the test set, 1500 for validation, and the remaining 6000 were used for training, respectively.  
Object detection modeling is accomplished by combining localization and classification. Finding the precise location of one or more items in an image and drawing a bounding box around them is the process of localization. Image classification is the process of giving an object a label or class. Previously, object detection was performed in two stages, making the detection process difficult. In two-stage object detection, regions of interest (RoIs) were classified and regressed in two different stages. However, with recent advancements, a single-stage detector instantly classifies and regresses the anchor boxes by eliminating the RoI extraction step. YOLO5, a one-stage object detection model, is used in this study. The performance of YOLOv5 in detection and classification is then compared to that of another one-stage object detection model, SSD-Resnet. To perform real-time object detection, YOLOv5 required the least amount of inference time. Therefore, it is a suitable model for the development of a lightweight weed detection system to be mounted on a laser-based weeding robot. The section that follows provides an overview of the YOLOv5 working mechanism and its suitability for detecting and classifying weeds and crops in real-time complex agricultural environments.
# Urban Traffic Signal Control under Mixed Traffic Flows: Literature Review
## 4 Conventional Traffic Signal Control Strategies
### Signal Program Parameters  
Traffic signal parameters define how an intersection can be controlled regarding traffic flow throughput. Depending on the parameter setup, the intersection can operate in various traffic conditions. Each signal program is defined with the following parameters [17; 22]:  
* Green time: The time duration in seconds, during which a given traffic movement at signalized intersection proceeds at a saturation flow rate.
* Cycle length: The amount of time it takes a signal to complete the signal cycle.
* Phase sequence: The sequence in which the signal program phases occur throughout a signal cycle.
* Change interval: Also known as a Clearance interval, a short time period to provide clearance before the green time for conflicting traffic movements.
* Offset: The relationship in time between the beginning of the signal cycle between two or more consecutive intersections.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 4 Discussion  
This research was successful in developing the necessary hardware and software for automated identification and treatment of submerged aquatic weeds. A small fleet (two watercraft) with the required systems and capabilities (self-navigation, hydroacoustic data collection, and herbicide dispersal) were developed over three prototype generations. _Hydrilla_, being a very invasive weed species with severe impacts, was chosen as the targeted aquatic weed. The deep learning model was later extended to multiple species, namely _Hydrilla_, _Cabomba_, and Coontail (four classes in total, including \"Other\"). High classification accuracy of 99.06% on both the validation and test sets indicate excellent generalization. Training on four classes with a higher volume of data improved the algorithm accuracy as compared to training on two classes. Geotagged information from classified images was reliably extracted for treatment of the target areas with herbicides.  
While the integration of autonomous scanning, identification, and treatment of invasive plant species was to some extent limited by software and hardware capabilities, the current practice of manually scanning and treating entire water bodies can be eliminated using this approach. The technologies detailed here clearly have the potential to reduce the cost and increase the effectiveness of aquatic plant management.
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 2 Methods
### Supervised Classifications
#### 2.5.3 Maximum Likelihood Classification (ML)  
ML is a kind of classical classifier that is widely used in a variety of remote sensing applications. Based on training data, the maximum likelihood method selects the set of values of the model parameters that maximizes the likelihood function [37].
# The Construction of a Digital Agricultural GIS Application Suite
## 2 Design Considerations and Methods
### Design Considerations
#### 2.1.2 Principles of GIS Technology Guiding  
Spatial-temporal object GIS, knowledge GIS, and computational GIS represent the cutting-edge trends in GIS technology development [29]. They drive the collaborative paradigm of the \"data knowledge model\" within the context of geographic big data. The technological orientation principle necessitates the creation of a digital agricultural GIS application suite that not only fully realizes the fundamental capabilities of GIS but also adaptively integrates emerging GIS technologies to foster industry-wide technical advancement.  
Spatial data management, spatial analysis, and visualization are considered the foundational functions of GIS. These functions support basic agricultural information management, enable crop distribution analysis and yield prediction, and facilitate the production of  
Figure 1: Digital agricultural GIS application suite design concept.  
agricultural thematic maps. Consequently, they enhance the information level of digital agriculture and contribute to making agricultural management decisions more scientific.  
The new data model, grounded in spatial-temporal object GIS, is capable of comprehensively describing, managing, analyzing, and visualizing the multifaceted characteristics of digital agricultural entities. These characteristics include spatial forms, composition structures, association relationships, cognitive abilities, and behavioral patterns [30].  
Knowledge-based GIS focuses on understanding spatial data and generating knowledge. By integrating multi-source data and domain knowledge such as agricultural situation perception, agricultural geographic information, and scientific issues related to digital agriculture in the digital agriculture research area, knowledge GIS can build a knowledge map oriented to digital agriculture. Knowledge GIS can mine the spatial-temporal interaction between agricultural entities and the correlation between agricultural resources and management and provide higher-level insights [31; 32].  
Computational GIS focuses on the calculation and simulation of complex spatial processes and models, which is of great significance to the analysis of complex geographical phenomena [33; 34]. At the algorithmic level, computational GIS can efficiently process large-scale and multi-dimensional agricultural data and become an important means to expand the comprehensive analysis ability of digital agriculture by inverting the past, simulating the process, revealing the law, and predicting the future.